diff --git a/.github/workflows/package-install.yml b/.github/workflows/package-install.yml index 96964c660..63150c0e7 100644 --- a/.github/workflows/package-install.yml +++ b/.github/workflows/package-install.yml @@ -26,7 +26,7 @@ jobs: curl -LsSf https://astral.sh/uv/install.sh | sh echo "$HOME/.cargo/bin" >> "$GITHUB_PATH" - - name: Build wheel + - name: Build managed-runtime wheel run: python scripts/build_package.py --wheel - name: Smoke test base wheel import @@ -42,7 +42,7 @@ jobs: cd "$project_dir" uv init --name art-base-install-smoke --python 3.12 --bare uv add "openpipe-art @ file://${wheel_path}" - uv run python -c "import importlib.util; assert importlib.util.find_spec('numpy') is None; assert importlib.util.find_spec('torch') is None; import art; from art.pipeline_trainer import PipelineTrainer; print(art.__name__, PipelineTrainer.__name__)" + uv run python -c "import importlib.util; assert importlib.util.find_spec('numpy') is not None; assert importlib.util.find_spec('torch') is None; import art; from art.pipeline_trainer import PipelineTrainer; print(art.__name__, PipelineTrainer.__name__)" uv add "weave==0.52.41" uv run python -c "from weave.trace.settings import override_settings; import art" @@ -58,5 +58,79 @@ jobs: project_dir="$(mktemp -d)" cd "$project_dir" uv init --name art-install-smoke --python 3.12 --bare - uv add "openpipe-art[backend] @ file://${wheel_path}" + uv add --index https://download.pytorch.org/whl/cu128 \ + --index-strategy unsafe-best-match \ + "openpipe-art[backend] @ file://${wheel_path}" uv sync + + - name: Smoke test distributed wheel surface + env: + ART_VLLM_RUNTIME_CACHE_DIR: ${{ runner.temp }}/art-vllm-runtime-cache + run: | + wheel_path="$(python - <<'PY' + from pathlib import Path + + print(next(Path("dist").glob("openpipe_art-*.whl")).resolve()) + PY + )" + + project_dir="$(mktemp -d)" + cd "$project_dir" + uv init --name art-distributed-install-smoke --python 3.12 --bare + uv venv --python 3.12 + uv pip install --python .venv/bin/python \ + --index-url https://download.pytorch.org/whl/cpu \ + "torch==2.11.0" + uv pip install --python .venv/bin/python \ + "openpipe-art @ file://${wheel_path}" \ + 'aiohttp>=3.13.0' 'msgspec>=0.21.0' 'torchmonarch==0.6.0' \ + 'transformers>=5.2.0,<=5.12.1' + uv pip install --python .venv/bin/python --no-deps \ + "openpipe-art[distributed,megatron] @ file://${wheel_path}" + + .venv/bin/python - <<'PY' + import sys + from importlib.metadata import metadata + from pathlib import Path + + import art + import art.distributed as distributed + + assert Path(art.__file__).resolve().is_relative_to(Path(sys.prefix)) + assert "art.distributed.art_runtime" not in sys.modules + from art.distributed import ( # noqa: E402 + ArtRuntime, + ClusterSpec, + HostSpec, + InstalledAsyncCallable, + NcclTransportSpec, + PackingRequest, + compile_topology, + ) + + assert all( + value is not None + for value in ( + ArtRuntime, + ClusterSpec, + HostSpec, + InstalledAsyncCallable, + NcclTransportSpec, + PackingRequest, + compile_topology, + ) + ) + assert "monarch" not in sys.modules + assert "PackingRequest" in distributed.__all__ + assert "NcclTransportSpec" in distributed.__all__ + assert {"distributed", "megatron"} <= set( + metadata("openpipe-art").get_all("Provides-Extra") or () + ) + PY + + PYTHONPATH="$GITHUB_WORKSPACE/examples/multinode" timeout 150s \ + .venv/bin/art-monarch local \ + --program program:main \ + --port 0 \ + --startup-timeout 90 + test ! -e "$ART_VLLM_RUNTIME_CACHE_DIR" diff --git a/.github/workflows/trainer-rank-gpu.yml b/.github/workflows/trainer-rank-gpu.yml index bac8bab20..47b268da0 100644 --- a/.github/workflows/trainer-rank-gpu.yml +++ b/.github/workflows/trainer-rank-gpu.yml @@ -45,7 +45,7 @@ jobs: git cat-file -e "${BASE_SHA}^{commit}" 2>/dev/null \ || git fetch --no-tags origin "${BASE_SHA}" - pattern='^src/art/megatron/(_hybrid_ep/|hybrid_ep_setup\.py|setup\.sh|prefix_tree(_packing|_state)?\.py|context_parallel/|flex_attn/|gdn/|lora\.py|megatron_patches\.py|training/(finalize_grads|microbatches)\.py)' + pattern='^src/art/(trainer_rank/|megatron/(_hybrid_ep/|hybrid_ep_setup\.py|setup\.sh|prefix_tree(_packing|_state)?\.py|context_parallel/|flex_attn/|gdn/|lora\.py|megatron_patches\.py|training/(finalize_grads|microbatches)\.py))' changed="$(git diff --name-only "${BASE_SHA}...${HEAD_SHA}")" critical="$(printf '%s\n' "${changed}" | grep -E "${pattern}" || true)" if [ -n "${critical}" ]; then diff --git a/dev/sft/sft-from-file.py b/dev/sft/sft-from-file.py index deed4595c..d549588f3 100644 --- a/dev/sft/sft-from-file.py +++ b/dev/sft/sft-from-file.py @@ -9,8 +9,10 @@ async def main(): - backend = MegatronBackend() - + art.init_megatron_runtime_config( + topology=art.MegatronTopologyConfig(), + packed_sequence_length=4096, + ) model_name = "run-" + "".join( random.choices("abcdefghijklmnopqrstuvwxyz0123456789", k=8) ) @@ -20,14 +22,14 @@ async def main(): project="sft-from-file", base_model="Qwen/Qwen3.6-35B-A3B", ) - await model.register(backend) - - await train_sft_from_file( - model=model, - file_path="dev/sft/dataset.jsonl", - epochs=1, - peak_lr=2e-4, - ) + async with MegatronBackend() as backend: + await model.register(backend) + await train_sft_from_file( + model=model, + file_path="dev/sft/dataset.jsonl", + epochs=1, + peak_lr=2e-4, + ) print("Training complete!") diff --git a/dev/sft/sft-warmup.py b/dev/sft/sft-warmup.py index b14a3d056..a8719f2b4 100644 --- a/dev/sft/sft-warmup.py +++ b/dev/sft/sft-warmup.py @@ -43,7 +43,10 @@ async def rl_rollout(model: art.TrainableModel, prompt: str) -> art.Trajectory: async def main(): load_dotenv() - backend = MegatronBackend() + art.init_megatron_runtime_config( + topology=art.MegatronTopologyConfig(), + packed_sequence_length=int(os.environ.get("PACKED_SEQUENCE_LENGTH", "4096")), + ) model_name = "sft-warmup-" + "".join( random.choices("abcdefghijklmnopqrstuvwxyz0123456789", k=8) ) @@ -53,75 +56,76 @@ async def main(): project="sft-warmup", base_model="Qwen/Qwen2.5-7B-Instruct", ) - await model.register(backend) - - # ======================================================================== - # Phase 1: SFT - # ======================================================================== - print("\n[Phase 1] SFT training...") - for chunk in create_sft_dataset_iterator( - SFT_TRAJECTORIES, batch_size=1, peak_lr=1e-5 - ): - await model.train_sft(chunk.trajectories, chunk.config) - print("SFT phase 1 complete.") - - # ======================================================================== - # Phase 2: RL (GRPO) - # ======================================================================== - print("\n[Phase 2] RL training...") - prompt = "respond with yes, no, or maybe" - - for i in range(10): - print(f" RL step {i + 1}") - train_groups = await art.gather_trajectory_groups( - [ - art.TrajectoryGroup(rl_rollout(model, prompt) for _ in range(6)) - for _ in range(12) - ] - ) - await model.train(train_groups) - print("RL phase 2 complete.") - - # ======================================================================== - # Phase 3: SFT again - # ======================================================================== - print("\n[Phase 3] SFT training again...") - for chunk in create_sft_dataset_iterator( - SFT_TRAJECTORIES, batch_size=1, peak_lr=1e-5 - ): - await model.train_sft(chunk.trajectories, chunk.config) - print("SFT phase 3 complete.") - - # ======================================================================== - # Phase 4: RL (GRPO) again - # ======================================================================== - print("\n[Phase 4] RL training...") - prompt = "respond with yes, no, or maybe" - - for i in range(10): - print(f" RL step {i + 1}") - train_groups = await art.gather_trajectory_groups( - [ - art.TrajectoryGroup(rl_rollout(model, prompt) for _ in range(6)) - for _ in range(12) - ] + async with MegatronBackend() as backend: + await model.register(backend) + + # ======================================================================== + # Phase 1: SFT + # ======================================================================== + print("\n[Phase 1] SFT training...") + for chunk in create_sft_dataset_iterator( + SFT_TRAJECTORIES, batch_size=1, peak_lr=1e-5 + ): + await model.train_sft(chunk.trajectories, chunk.config) + print("SFT phase 1 complete.") + + # ======================================================================== + # Phase 2: RL (GRPO) + # ======================================================================== + print("\n[Phase 2] RL training...") + prompt = "respond with yes, no, or maybe" + + for i in range(10): + print(f" RL step {i + 1}") + train_groups = await art.gather_trajectory_groups( + [ + art.TrajectoryGroup(rl_rollout(model, prompt) for _ in range(6)) + for _ in range(12) + ] + ) + await model.train(train_groups) + print("RL phase 2 complete.") + + # ======================================================================== + # Phase 3: SFT again + # ======================================================================== + print("\n[Phase 3] SFT training again...") + for chunk in create_sft_dataset_iterator( + SFT_TRAJECTORIES, batch_size=1, peak_lr=1e-5 + ): + await model.train_sft(chunk.trajectories, chunk.config) + print("SFT phase 3 complete.") + + # ======================================================================== + # Phase 4: RL (GRPO) again + # ======================================================================== + print("\n[Phase 4] RL training...") + prompt = "respond with yes, no, or maybe" + + for i in range(10): + print(f" RL step {i + 1}") + train_groups = await art.gather_trajectory_groups( + [ + art.TrajectoryGroup(rl_rollout(model, prompt) for _ in range(6)) + for _ in range(12) + ] + ) + await model.train(train_groups) + print("RL phase 4 complete.") + + # ======================================================================== + # Test: Check model output + # ======================================================================== + print("\n[Test] Model output after training:") + client = model.openai_client() + completion = await client.chat.completions.create( + messages=[{"role": "user", "content": "respond with yes, no, or maybe"}], + model=model.get_inference_name(), + max_tokens=10, ) - await model.train(train_groups) - print("RL phase 4 complete.") - - # ======================================================================== - # Test: Check model output - # ======================================================================== - print("\n[Test] Model output after training:") - client = model.openai_client() - completion = await client.chat.completions.create( - messages=[{"role": "user", "content": "respond with yes, no, or maybe"}], - model=model.get_inference_name(), - max_tokens=10, - ) - print(f"Response: {completion.choices[0].message.content}") + print(f"Response: {completion.choices[0].message.content}") - print("\nAll phases complete!") + print("\nAll phases complete!") if __name__ == "__main__": diff --git a/dev/trainer_rank_checkpoint_acceptance.py b/dev/trainer_rank_checkpoint_acceptance.py new file mode 100644 index 000000000..41257fed0 --- /dev/null +++ b/dev/trainer_rank_checkpoint_acceptance.py @@ -0,0 +1,107 @@ +"""Exercise canonical TrainerRank checkpoints under ``torchrun``. + +The driver runs this module repeatedly with different rank counts. ``step-save`` +loads a LoRA/checkpoint, applies one deterministic optimizer step, and saves a +canonical checkpoint. ``step-export`` applies the same step and exports LoRA +weights for cross-topology comparison. +""" + +from __future__ import annotations + +import argparse +import asyncio +import hashlib +import json +import os +from pathlib import Path +import time + +import torch +import torch.distributed as dist + +from art.trainer_rank import AdamParams, TrainerRank + + +def _args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument("operation", choices=("step-save", "step-export", "load")) + parser.add_argument("--source", required=True) + parser.add_argument("--output") + parser.add_argument("--model", default="Qwen/Qwen3.5-4B") + parser.add_argument("--layers", type=int, default=1) + parser.add_argument("--grad", type=float, default=1e-4) + parser.add_argument("--output-json") + return parser.parse_args() + + +def _digest(path: Path) -> str: + digest = hashlib.sha256() + for item in sorted(path.rglob("*")): + if item.is_file(): + digest.update(item.relative_to(path).as_posix().encode()) + digest.update(item.read_bytes()) + return digest.hexdigest() + + +async def _load(trainer: TrainerRank, source: str) -> None: + await trainer.load_checkpoint(source) + + +def main() -> None: + args = _args() + os.environ.setdefault("ART_MEGATRON_TENSOR_MODEL_PARALLEL_SIZE", "1") + os.environ.setdefault("ART_MEGATRON_CONTEXT_PARALLEL_SIZE", "1") + os.environ.setdefault("ART_MEGATRON_PIPELINE_MODEL_PARALLEL_SIZE", "1") + torch.cuda.set_device(int(os.environ["LOCAL_RANK"])) + dist.init_process_group("nccl") + rank = dist.get_rank() + started = time.perf_counter() + try: + from art.megatron import train as megatron_train + + runtime = megatron_train.build_training_runtime( + model_identifier=args.model, + provider_configure=lambda provider: setattr( + provider, "num_layers", args.layers + ), + print_env=rank == 0, + ) + trainer = TrainerRank(runtime) + asyncio.run(_load(trainer, args.source)) + loaded = time.perf_counter() + if args.operation != "load": + slot = trainer._checkpoint_slots[args.source] + for parameter in slot.params: + parameter.grad = torch.full_like(parameter, args.grad) + metrics = trainer.optim_step( + params=AdamParams(learning_rate=3e-4, grad_clip_norm=0), + scale_grads=1 / dist.get_world_size(), + ) + if metrics["update_successful"] != 1: + raise RuntimeError(f"optimizer step failed: {metrics}") + if args.output is None: + raise ValueError("--output is required") + if args.operation == "step-save": + trainer.save_checkpoint(args.output) + else: + trainer.export_lora(args.output) + dist.barrier() + if rank == 0: + output = None if args.output is None else Path(args.output) + payload = { + "world_size": dist.get_world_size(), + "load_seconds": loaded - started, + "total_seconds": time.perf_counter() - started, + "output_digest": None if output is None else _digest(output), + "peak_gpu_bytes": torch.cuda.max_memory_allocated(), + } + encoded = json.dumps(payload, sort_keys=True) + print(encoded, flush=True) + if args.output_json: + Path(args.output_json).write_text(encoded + "\n") + finally: + dist.destroy_process_group() + + +if __name__ == "__main__": + main() diff --git a/dev/yes-no-maybe-megatron.py b/dev/yes-no-maybe-megatron.py index 9a85ff518..5bce36b96 100644 --- a/dev/yes-no-maybe-megatron.py +++ b/dev/yes-no-maybe-megatron.py @@ -198,7 +198,10 @@ async def main() -> None: ) ) - backend = MegatronBackend() + art.init_megatron_runtime_config( + topology=art.MegatronTopologyConfig(), + packed_sequence_length=packed_sequence_length, + ) model = art.TrainableModel( run_name=model_name, name=model_name, @@ -214,7 +217,7 @@ async def main() -> None: prompts = prompts[: int(os.environ.get("PROMPTS_LIMIT", str(len(prompts))))] eval_prompts = prompts[: int(os.environ.get("EVAL_PROMPTS", "24"))] - try: + async with MegatronBackend() as backend: print(json.dumps({"event": "register_start"}), flush=True) await model.register(backend) print( @@ -294,7 +297,6 @@ async def main() -> None: model, train_groups, learning_rate=learning_rate, - packed_sequence_length=packed_sequence_length, ) print( json.dumps( @@ -327,8 +329,6 @@ async def main() -> None: ), flush=True, ) - finally: - await backend.close() if __name__ == "__main__": diff --git a/dev/yes_no_maybe_trainability.py b/dev/yes_no_maybe_trainability.py index 019e34603..d86f73b8c 100644 --- a/dev/yes_no_maybe_trainability.py +++ b/dev/yes_no_maybe_trainability.py @@ -247,7 +247,7 @@ def make_backend( if backend_name == "local": return LocalBackend(path=art_path, in_process=in_process) if backend_name == "megatron": - return MegatronBackend(path=art_path, in_process=in_process) + return MegatronBackend(path=art_path) raise ValueError(f"Unsupported BACKEND={backend_name!r}") diff --git a/docs/docs.json b/docs/docs.json index 2b99e176e..741dadbb1 100644 --- a/docs/docs.json +++ b/docs/docs.json @@ -47,6 +47,7 @@ "getting-started/about", "getting-started/quick-start", "getting-started/installation-setup", + "getting-started/multi-node", "getting-started/notebooks", "getting-started/faq" ] diff --git a/docs/getting-started/multi-node.mdx b/docs/getting-started/multi-node.mdx new file mode 100644 index 000000000..69e801fce --- /dev/null +++ b/docs/getting-started/multi-node.mdx @@ -0,0 +1,277 @@ +--- +title: "Multi-node deployment" +sidebarTitle: "Multi-node deployment" +icon: "network-wired" +--- + +ART's distributed runtime consumes a Monarch host mesh. SkyPilot can provision +that mesh, but it is a deployment tool rather than an ART dependency. The ART +process does not launch another SkyPilot cluster from inside its allocation. + +## Controller program + +The bootstrap accepts an import path to a top-level async function. SkyPilot +runs the bootstrap on every node, but only rank 0 imports its module and invokes +the controller. `examples/multinode/program.py` is a complete CPU-runnable +Yes/No/Maybe smoke: + +```python +import asyncio +import os +import socket +from typing import Any + +import art +from art.distributed import ( + ArtRuntime, + ClusterSpec, + HostSpec, + InstalledAsyncCallable, + compile_topology, +) + +REWARDS = {"yes": 0.5, "no": 0.75, "maybe": 1.0} + + +async def rollout( + _model: art.TrainableModel, answer: str, _config: None +) -> art.Trajectory: + messages: art.MessagesAndChoices = [ + {"role": "user", "content": f"Respond with {answer}."}, + {"role": "assistant", "content": answer}, + ] + return art.Trajectory( + messages_and_choices=messages, + reward=REWARDS[answer], + metadata={ + "answer": answer, + "hostname": socket.gethostname(), + "process_id": os.getpid(), + }, + ) + + +async def main(hosts: Any) -> None: + host_count = int(hosts.region.slice().sizes[0]) + host_ids = tuple(f"host{rank}" for rank in range(host_count)) + runtime = await ArtRuntime.start( + hosts, + compile_topology( + cluster=ClusterSpec( + hosts=tuple( + HostSpec( + host_id=host_id, + node_rank=rank, + worker_address=f"attached://{rank}", + cpu_slots=1, + ) + for rank, host_id in enumerate(host_ids) + ), + controller_host_id=host_ids[0], + startup_timeout_s=90, + rpc_timeout_s=30, + ) + ), + ) + try: + workers = tuple(range(host_count)) + executor = runtime.rollout_executor( + InstalledAsyncCallable.from_callable(rollout), + target_workers=host_count, + ) + executor.set_workers(workers) + model = art.TrainableModel( + name="multinode-smoke", project="art", base_model="not-loaded" + ) + trajectories = [] + for answer in REWARDS: + trajectories.extend( + await asyncio.gather( + *( + executor.run(worker, rollout, model, answer, None) + for worker in workers + ) + ) + ) + answers = [ + str(trajectory.metadata["answer"]) for trajectory in trajectories + ] + expected = [answer for answer in REWARDS for _ in workers] + placements = { + (trajectory.metadata["hostname"], trajectory.metadata["process_id"]) + for trajectory in trajectories + } + if answers != expected or len(placements) != host_count: + raise RuntimeError( + f"distributed rollout mismatch: {answers=}, {placements=}" + ) + print(f"ART_MULTINODE_SMOKE_PASS hosts={host_count} answers={answers}") + finally: + await runtime.close() +``` + +The controller runs once, while the top-level `rollout` runs in one process on +each host. Both must be installed or synchronized at the same import paths on +every node; ART sends verified import references and never ships opaque +closures. The dummy `TrainableModel` is serialized for the rollout contract but +never loaded, so this validates the package, host admission, process placement, +public trajectory types, and cleanup without a GPU or inference server. + +The distributed service APIs are opt-in. Existing single-node programs continue +to construct and use `LocalBackend` exactly as before: + +```python +from art.local import LocalBackend + +backend = LocalBackend() +``` + +## SkyPilot + +Start with `examples/multinode/skypilot.yaml`. It is an intentionally CPU-only +two-node smoke that runs all three bounded rollouts on each host without +reserving training GPUs or provisioning the managed vLLM runtime. + +For source-based GPU training, replace its resources and setup with the desired +topology and both locked environments: + +```yaml +resources: + accelerators: H200:8 + +setup: | + set -euo pipefail + uv sync --frozen --no-dev --extra distributed --extra megatron + uv sync --project vllm_runtime --frozen --no-dev + +run: | + set -euo pipefail + export NCCL_NET=IB + exec .venv/bin/art-monarch skypilot \ + --program your_package.train:main +``` + +`set -euo pipefail` is required because SkyPilot runs multiline setup under +Bash without enabling fail-fast behavior. A source-based GPU run needs the root +and `vllm_runtime` locked projects at the same revision on every node. The CPU +example only syncs the root `distributed` extra. + +Any GPU workload spanning hosts must set one explicit NCCL network contract in +its `ClusterSpec`, for example +`nccl_transport=NcclTransportSpec(net_name="IB")`, and set `NCCL_NET` to that +exact registered name on every node. `IB` covers built-in InfiniBand/RoCE; +external network plugins use their registered NCCL name. Before model +allocation, ART runs a small collective in both the trainer and managed-vLLM +environments and requires each rank to report that exact selected module. It +never retries with Socket. Deployment qualification remains responsible for +all-GPU bandwidth, GPU Direct RDMA, HCA, and GID validation. + +If `ART_VLLM_RUNTIME_BIN` is set, it must point directly to a standard +`.venv/bin/art-vllm-runtime-server` executable. ART derives the matching Python, +runtime root, environment, and working directory from that path so the preflight +cannot certify a different runtime. Arbitrary command wrappers fail closed. + +For a published release wheel, use a separate setup rather than synchronizing +the source runtime: + +```yaml +setup: | + set -euo pipefail + uv venv --python 3.12 + uv pip install --python .venv/bin/python \ + "openpipe-art[distributed,megatron]==VERSION" +``` + +Release wheels use a content-addressed managed runtime bundle. Only wheels +built with `scripts/build_package.py` contain that bundle; a generic `uv build` +wheel does not. Every node still needs the external `uv` command and either +first-use network access to populate the runtime cache or a preinstalled +runtime selected with `ART_VLLM_RUNTIME_BIN`. + +Launch it from the project root: + +```fish +sky launch -c art-multinode examples/multinode/skypilot.yaml +``` + +One task rank runs on each allocated node. Every rank owns one Monarch worker +subprocess; rank 0 also attaches the host mesh and runs the controller program. +Rank-0 program completion or failure closes the lifecycle sockets and releases +the peer task ranks. No manual SSH or per-node command is required. + +Each task invocation owns fresh worker loops and terminates them after the host +mesh shuts down. A later `sky exec` starts new loops; ART does not reattach a +second controller to completed workers. + +Ctrl-C disconnects SkyPilot log streaming; it does not stop the remote job. +Check the queue and cancel explicitly when needed: + +```fish +sky queue art-multinode +sky cancel art-multinode JOB_ID +``` + +Only after the previous job is terminal, reuse an existing cluster without +rerunning setup: + +```fish +sky exec art-multinode examples/multinode/skypilot.yaml +``` + +`sky exec` synchronizes the workdir before scheduling, so running it while the +previous job is live can change files under that job. Use `sky launch` instead +when setup, mounts, the image, SkyPilot config, a wheel, `pyproject.toml`, or a +lockfile changed. Setting `num_nodes: 1` uses the same controller on one node. + +For a local process that explicitly wants the same Monarch service APIs, ART +can own one loopback worker directly: + +```fish +env PYTHONPATH=(pwd)/examples/multinode .venv/bin/art-monarch local \ + --program program:main \ + --port 0 \ + --startup-timeout 90 +``` + +Port `0` selects a fresh loopback port. `ArtRuntime.start_local(...)` is the +equivalent library API. It accepts the same one-host compiled topology used by +multi-node code and owns the worker for the runtime lifetime. + +SkyPilot provides `SKYPILOT_NODE_RANK`, `SKYPILOT_NODE_IPS`, and +`SKYPILOT_NUM_NODES`; ART validates and translates them internally. Port +`22222` is the Monarch worker port and `22223` is its job-lifecycle port. Pass +`--port N` to reserve `N` and `N + 1` instead. These ports must be reachable +between allocated nodes but must not be publicly exposed: ART's pinned Monarch +0.5 runtime currently uses unauthenticated `trust_all_connections` transport. + +## Existing SSH hosts + +For preallocated machines, start and own all workers from one controller +command: + +```fish +.venv/bin/art-monarch ssh \ + --host gpu-a=10.0.0.10 \ + --host gpu-b=10.0.0.11 \ + --python /shared/project/.venv/bin/python \ + --program your_package.train:main +``` + +Each value is `SSH_TARGET=WORKER_HOST`. Omit `=WORKER_HOST` when the SSH target +is also the private address to which Monarch should bind. The controller must +have both passwordless SSH access to every `SSH_TARGET` and a direct trusted +private or VPN route to every `WORKER_HOST:N`. SSH options such as `ProxyJump` +or `--ssh-arg=-F` affect only launch and stop commands; they do not tunnel +Monarch traffic. SSH mode uses only worker port `N`, not SkyPilot's lifecycle +port `N + 1`. + +The selected Python executable and user code must exist at the same paths on +every host. ART uses non-interactive SSH, verifies that each launch-specific +worker PID owns its listener, and monitors each foreground SSH process for the +controller lifetime. A pre-existing listener is a hard error rather than a +worker to reattach. SIGTERM and SIGHUP trigger bounded remote cleanup before the +controller exits. + +The lower-level `worker` and `controller` subcommands remain available for +schedulers or process supervisors that own worker lifecycle themselves. Those +supervisors must replace worker loops before a subsequent controller attach. diff --git a/examples/multinode/README.md b/examples/multinode/README.md new file mode 100644 index 000000000..fea594815 --- /dev/null +++ b/examples/multinode/README.md @@ -0,0 +1,52 @@ +# ART multi-node smoke + +`program.py` is a bounded CPU example using only public ART APIs. Its top-level +controller admits the attached hosts, then its top-level rollout returns one +synthetic Yes/No/Maybe `Trajectory` per host for each answer. It never loads a +model or starts Megatron or vLLM. + +Run the same controller on one local Monarch worker from the project root: + +```fish +env PYTHONPATH=(pwd)/examples/multinode .venv/bin/art-monarch local \ + --program program:main \ + --port 0 \ + --startup-timeout 90 +``` + +Or let SkyPilot run it on every node in one allocation: + +```fish +sky launch -c art-multinode examples/multinode/skypilot.yaml +``` + +SkyPilot synchronizes `workdir` and runs `setup` on every node before starting +the same `run` command on every node. ART starts one Monarch worker per node and +calls `program:main` only on rank 0. User controllers and rollouts must remain +importable at the same paths on every node; ART sends import references rather +than pickled closures. + +Edit the accelerator and setup commands for your infrastructure. The example +assumes `uv` is installed in the image and installs ART's `distributed` extra +from the synchronized source checkout. GPU training also needs the `megatron` +extra and the locked `vllm_runtime` project; release wheels instead carry the +managed vLLM runtime bundle. Use `sky launch` after changing setup, and reuse an +unchanged cluster without rerunning setup with: + +```fish +sky exec art-multinode examples/multinode/skypilot.yaml +``` + +GPU workloads spanning hosts must also set `NCCL_NET` on every node and provide +the same exact registered name through `ClusterSpec.nccl_transport`. ART proves +that selected module before trainer or vLLM model allocation and never falls +back to Socket. `ART_VLLM_RUNTIME_BIN`, when set, must point directly to a +standard `.venv/bin/art-vllm-runtime-server`; arbitrary wrappers fail closed. + +Each invocation terminates every worker loop before the task exits. Reusing the +cluster starts fresh loops; Monarch 0.6 worker addresses are generation-owned and +completed loops are not reattached. + +Setting `num_nodes: 1` exercises the same API on one node. Do not expose the +default private ports `22222` and `22223`; pinned Monarch 0.6 does not +authenticate its transport. diff --git a/examples/multinode/program.py b/examples/multinode/program.py new file mode 100644 index 000000000..06f2dd7e6 --- /dev/null +++ b/examples/multinode/program.py @@ -0,0 +1,98 @@ +from __future__ import annotations + +import asyncio +import os +import socket +from typing import Any + +import art +from art.distributed import ( + ArtRuntime, + ClusterSpec, + HostSpec, + InstalledAsyncCallable, + compile_topology, +) + +REWARDS = {"yes": 0.5, "no": 0.75, "maybe": 1.0} + + +async def rollout( + _model: art.TrainableModel, answer: str, _config: None +) -> art.Trajectory: + messages: art.MessagesAndChoices = [ + {"role": "user", "content": f"Respond with {answer}."}, + {"role": "assistant", "content": answer}, + ] + return art.Trajectory( + messages_and_choices=messages, + reward=REWARDS[answer], + metadata={ + "answer": answer, + "hostname": socket.gethostname(), + "process_id": os.getpid(), + }, + ) + + +async def main(hosts: Any) -> None: + host_count = int(hosts.region.slice().sizes[0]) + host_ids = tuple(f"host{rank}" for rank in range(host_count)) + runtime = await ArtRuntime.start( + hosts, + compile_topology( + cluster=ClusterSpec( + hosts=tuple( + HostSpec( + host_id=host_id, + node_rank=rank, + worker_address=f"attached://{rank}", + cpu_slots=1, + ) + for rank, host_id in enumerate(host_ids) + ), + controller_host_id=host_ids[0], + startup_timeout_s=90, + rpc_timeout_s=30, + ) + ), + ) + try: + workers = tuple(range(host_count)) + executor = runtime.rollout_executor( + InstalledAsyncCallable.from_callable(rollout), + target_workers=host_count, + ) + executor.set_workers(workers) + model = art.TrainableModel( + name="multinode-smoke", + project="art", + base_model="not-loaded", + run_name="multinode-smoke", + ) + trajectories: list[art.Trajectory] = [] + for answer in REWARDS: + trajectories.extend( + await asyncio.gather( + *( + executor.run(worker, rollout, model, answer, None) + for worker in workers + ) + ) + ) + answers = [str(trajectory.metadata["answer"]) for trajectory in trajectories] + expected = [answer for answer in REWARDS for _ in workers] + placements = { + (trajectory.metadata["hostname"], trajectory.metadata["process_id"]) + for trajectory in trajectories + } + if answers != expected or len(placements) != host_count: + raise RuntimeError( + f"distributed rollout mismatch: answers={answers}, placements={placements}" + ) + print( + f"ART_MULTINODE_SMOKE_PASS hosts={host_count} answers={answers}", + flush=True, + ) + finally: + await runtime.close() diff --git a/examples/multinode/skypilot.yaml b/examples/multinode/skypilot.yaml new file mode 100644 index 000000000..543c8d3c2 --- /dev/null +++ b/examples/multinode/skypilot.yaml @@ -0,0 +1,23 @@ +name: art-multinode + +num_nodes: 2 + +workdir: . + +resources: + cpus: 4+ + memory: 8+ + +envs: + PYTHONUTF8: "1" + +setup: | + set -euo pipefail + uv sync --frozen --no-dev --extra distributed + +run: | + set -euo pipefail + export PYTHONPATH="$PWD/examples/multinode${PYTHONPATH:+:$PYTHONPATH}" + exec .venv/bin/art-monarch skypilot \ + --program program:main \ + --startup-timeout 90 diff --git a/progress_log.md b/progress_log.md new file mode 100644 index 000000000..ec80dcf90 --- /dev/null +++ b/progress_log.md @@ -0,0 +1,133 @@ +# ART Multi-Node Progress Log + +## Current State + +- Objective: complete the Monarch hard cutover acceptance from one clean source commit: all ten handlers must pass their complete no-sensitivity workflow, and the strict two-node GLM-5.2 E1 shape must complete 20 policy updates with comparable performance and clean teardown. +- Completed: the one-host and multi-host typed runtimes, async packing/publication/durability, current model-handler support, B300 throughput contracts, and the broader multi-node performance work are implemented. Measurement contract v12 aggregates the complete contiguous stable-setting interval and six exact E2E/isolated replay pairs. All ten clean B300 calibrations from `1860748a1` pass every substantive gate; the installed identities are their only derived source change. Three fresh Qwen 3.5 MoE trainability replicates pass at policies 19, 28, and 23, motivating a bounded 40-policy cap without changing its accuracy target or optimization. +- Validation: clean workflow/config tests pass `94/94`, tuner tests pass `5/5`, and all commit hooks pass. The ten-handler calibration retains `0.997-1.004` paired-core/isolated parity, pressure `0.510-0.765`, and trainer underfeed at most `0.00171`. DSV4 completes in `1870.4 s`, confirming the scoped 40-minute supervisor budget. The failed E1 diagnostic reached seven updates with zero inference preemptions before exposing the now-fixed `0 ready / 6 packing / capacity 6` queue state. +- Active: validate a generated-token parity determinism correction, then restart every handler's complete no-sensitivity workflow from one immutable source across all three nodes. +- Risk: serial seeded GPT-OSS requests both passed but still produced different continuation sets, proving seed plus fixed request shape is insufficient. Correctness, freshness, throughput floors, activation-tail limits, trainability accuracy, model logits, and optimization settings remain unchanged. +- Next: prove singleton-allowlisted generated rollouts produce identical GPT-OSS inputs in two clean runs, complete all ten workflows from the resulting commit, then reproduce the strict 20-update two-node E1 run. +- Authoritative artifacts: final v12 calibration generation `scratch/full_workflow_cutover/throughput_calibration/frozen_1860748a18_*_authoritative_v12_timeout_r1`; trainability replicates `scratch/full_workflow_cutover/full_gate/focused_b1172de7e_qwen3_5_moe_length_{head,worker1,worker2}`; failed E1 diagnostic `scratch/b300/results/b300_glm52_e1_policy_kv_b845dd7a5_profile`; historical accepted comparator `scratch/b300/results/post_main_merge_e1_two_node_20_final3_profile`. + +## Prior State (2026-08-09) + +- Objective: hard-cut ordinary single-node Megatron/PipelineTrainer execution onto the one-host form of the typed multi-node runtime, remove the superseded filesystem/polling internals, and qualify every model-support handler with the complete workflow plus an autotuned end-to-end throughput stage. +- Baseline: integrated branch `austin/monarch_multinode_training` at fast-metrics candidate `9a7f91d42`, based on current `origin/main` through merge base `901a9e261`. +- Completed: the multi-node control/data plane, async packing and typed publication, B300/H200 topology work, current GLM-5.2 full workflow, the one-host typed-runtime cutover's physical lifecycle proof, canonical Llama trainability and restored canonical inherited workflow, complete Qwen 3 dense workflow excluding sensitivity, Qwen 3 MoE correctness and train/inference parity, Qwen 3.5 TP2/EP2 correctness, matched isolated/E2E throughput proofs for Llama, Qwen 3 dense, Qwen 3 MoE, both Gemma 4 handlers, GLM-5.2, GPT-OSS, and DSV4, removal of upstreamed or obsolete runtime shims, success-aware oracle artifact cleanup, exact GPU/MIG identity propagation, loaded non-streaming response materialization offload, bounded autotuner packing search with measured trial-quality evidence, the 753-test tracked Megatron unit corpus, authoritative trainer-to-serving activation timestamps, one shared autotuner capacity budget across ready, packing, and packed trajectory groups, exact full-cycle final-window throughput accounting, bulk/event-driven trajectory queue admission without producer polling, exact mid-prefill policy provenance through transactional multi-rank LoRA updates, cancellation-safe service/runtime teardown, no-yield publication reservations, truthful partial-versus-complete workflow reports, content-addressed workflow fixtures, source/dependency/accelerator-bound calibration fingerprints, immediate fatal vLLM startup detection, carried async stale-discard accounting, six-slot packed Qwen 3 expert publication, and a strict vLLM 0.25.1 DSV4 patch contract with obsolete upstreamed shims removed. +- Active: all ten B300 throughput geometries have completed from `9a7f91d42`. The matrix exposed two harness-only perturbations before the authoritative calibration: an arbitrary 64-worker ceiling underfed fast inference handlers, and final matched-input serialization competed with serving activation. Those are being removed along with one unstable Gemma 4 MoE autotuner window. +- Blocker/risk: no external blocker. Exact H200 stage artifacts do not exist for every handler, so final H200 gates must be explicitly identified as conservative historical extrapolations rather than measured calibrations. +- Next: validate and commit the final harness correction, recalibrate all ten handlers from that immutable commit, record B300 and estimated H200 floors, then run every handler's complete workflow excluding sensitivity followed by the final two-node reproduction. +- Authoritative state: candidate `9a7f91d42` passes all hooks plus the clean workflow/autotuner contract (`64` passed). Its ten-handler B300 artifacts are under `scratch/full_workflow_cutover/throughput_calibration/frozen_9a7f91d425_*`; every completed handler preserves isolated/matched trainer parity. The artifacts are intentionally geometry evidence rather than final floors because the harness correction changes runtime provenance. + +## Work Blocks + +| UTC | Elapsed | Outcome | Evidence / next action | +| --- | ---: | --- | --- | +| 2026-08-11 23:23 | 67h33 | Rejected serialization as the complete parity fix. Two clean four-GPU GPT-OSS runs both passed (`KL 0.0018357/0.0018396`, MAPE `2.625%/2.359%`) but canonical continuation hashes still differed. vLLM's V1 sampler computes returned raw logprobs before applying `allowed_token_ids`; the harness now assigns each rollout a distinct tokenizer-derived singleton allowlist, requires all 16 returned IDs to match it, and preserves the real decode path, raw top-k logits, route capture, temperature, seeds, and branch geometry. The 122-test contract probe also found one stale DSV4 assertion; it now matches the established B300 TP2/EP4/DP2 four-trainer-GPU resource. | Commit and repeat the two-host GPT-OSS stage. Require exact continuation hashes plus unchanged numerical gates. | +| 2026-08-11 23:07 | 67h17 | Fenced the first full-workflow generation after GPT-OSS exposed nondeterministic parity inputs rather than a justified threshold change. Its passing and failing runs used byte-identical configs and seven identical continuation sequences, but one seeded sequence diverged after token 11; KL moved from `0.0029306` to `0.0030418`. The harness had issued all eight sampled requests concurrently. It now preserves temperature, unique seeds, and divergent branches while issuing requests serially, with an invariant proving one active request. | Commit for the clean-tree artifact guard, then run GPT-OSS parity twice and require identical logical continuation sets before restarting the full gate. | +| 2026-08-11 22:19 | 66h29 | Recalibrated all ten handlers from clean source `1860748a1` across six NUMA-local four-GPU lanes. Every run failed only its superseded fingerprint; paired-core/isolated ratios are `0.997-1.004`, pressure is `0.510-0.765`, underfeed is at most `0.00171`, and all fixed floors, freshness tails, policy-age windows, and workload-validity checks pass. DSV4 completed in `1870.4 s`, beyond the old generic 30-minute limit but within its scoped 40-minute budget, at `7.76k` isolated and `7.69k` full-cycle tok/s. | Install exactly the ten measured identities, commit the frozen source, and run the complete no-sensitivity workflow matrix. | +| 2026-08-11 21:39 | 65h49 | Completed nine of ten authoritative v12 calibrations with only fingerprint mismatches. DSV4 executed the intended six capture and replay samples but the generic workflow supervisor killed the stage at `1811 s`, just beyond its old `1800 s` budget, before result publication. Scoped only DSV4 E2E throughput to `2400 s`. Separately, three fresh Qwen 3.5 MoE length runs passed at policies `19`, `28`, and `23`; historical same-code runs include best errors `1.5625` and `3.15625` at the 30-policy cap while fp32 oracle, packing invariance, and train/inference parity pass identically. Raised only its early-stopped maximum to 40; the `1.5` target and all training/sampling settings remain unchanged. | Commit this bounded policy correction, run clean focused tests, and recalibrate all ten handlers because `workflow.py` is deliberately fingerprinted. | +| 2026-08-11 21:00 | 65h10 | Six paired samples crossed a previously hidden harness boundary: capture-only steps formed a complete four-step autotuner window after the declared `max_steps`, so the collector saw a final decision through step 39 instead of the measured step 35 and failed before writing metrics. This is not model behavior. Contract v12 explicitly limits decision evidence to windows ending at or before `max_steps`; capture batches still execute the frozen measured setting and retain exact paired fingerprints, but can no longer redefine the measured tuning interval. Added a post-boundary decision to the end-to-end collector test. | Commit and clean-tree validate the boundary, then relaunch the final matrix from the new immutable source. | +| 2026-08-11 20:46 | 64h56 | Rejected another retry-driven performance gate after GPT-OSS exposed the residual two-sample variance. One paired E2E sample had a backward-only `181 ms` GPU tail (`1.149 s` versus the normal `0.968 s`); the other pair, stable E2E core, and isolated replay remained near `90.4-90.5k tok/s`. Two of the latest twenty clean GPT calibrations show this same `~0.94` paired ratio while eighteen cluster at `0.99-1.01`. Contract v11 therefore averages six exact E2E/isolated pairs, which adds roughly twelve seconds but tolerates one transient without widening the unchanged `0.95` parity floor. Hard freshness and activation-tail gates remain per-window/per-event. Three v10 handlers had otherwise passed before the generation was fenced. | Commit and clean-tree test v11, then restart the final matrix. Do not accept or install any v10 fingerprint. | +| 2026-08-11 20:28 | 64h38 | The first contract-v10 calibrations exposed one deterministic collector defect: the stable-setting suffix correctly selected more than two windows, but a stale tuple unpack still required exactly two. Fenced every obsolete queued and active calibration by explicit owner PID, preserved normal harness cleanup, and extended the full measurement test from two to three windows rather than masking the bug in helper-only coverage. The old DSV4 v9 probe completed within the 30-minute stage budget; its rejection was calibration pressure/fingerprint evidence, not timeout. | Commit the one-line collector correction plus regression fixture, run the clean-tree gates, and restart all ten calibrations from the new immutable commit. | +| 2026-08-11 20:11 | 64h21 | Replaced final-pair-only load/timing evidence with the maximal contiguous post-warmup suffix that physically executed the frozen setting. Gemma 4 MoE ran one unchanged setting for eight windows whose pressure ranged `0.458-0.552`; the arbitrary final pair failed at `0.470`, while ratio-of-sums over all same-setting evidence is `0.50517`. GLM-5.2 likewise preserved `16.6k tok/s` trainer-core parity across seven unchanged windows but one transient final-window queue wait made the final-pair estimator report `12.9%` underfeed. Per-window policy age/variance, ordered input fingerprints, hard activation tails, floors, and thresholds remain unchanged. | Bump the measurement contract, commit, run the clean workflow unit gate, then restart all ten calibrations from the immutable source. | +| 2026-08-11 19:55 | 64h05 | Stopped the superseded calibration matrix after GPT-OSS exposed two harness assumptions. Its final fixed step coincided with a valid `38 -> 34` worker decision, so decision-object equality rejected healthy windows; its two natural captured batches also differed by 31 nonpadding tokens. The corrected contract freezes the actual setting for the final two windows and validates every runtime history row plus both captures, while the tuner continues recording counterfactual decisions. Ordered per-sample workloads may differ, but each trajectory, packed tensor set, and workload must match its corresponding isolated replay exactly. | No threshold changed. Direct measurement/freeze contracts, tuner `5/5`, Ruff, `ty`, format, and diff checks pass. Commit for the required clean-tree integration gate, then restart all ten calibrations. | +| 2026-08-11 19:38 | 63h48 | Audited the throughput gate's non-correctness failures rather than widening thresholds. Stable vLLM load now sums waiting-capacity and running request-seconds across both contiguous windows; trainer underfeed is recomputed once from all eight rows. The noisy matched-core ratio now measures two distinct natural batches on both E2E and isolated paths with ordered trajectory/packed fingerprints and explicit failure-path lease cleanup. Correctness, throughput floors, freshness limits, and activation-tail limits are unchanged. | Focused tuner `5/5`, direct throughput contract, Ruff, format, and diff checks pass. Commit the source so the clean-tree integration gate can run, then launch the authoritative ten-handler calibration. | +| 2026-08-11 19:20 | 63h30 | Completed the first post-fix ten-handler B300 calibration matrix and isolated measurement noise from real imbalance. Gemma 4 MoE had one `0.4954` pressure window but `0.5989` in the adjacent window with all correctness, freshness, and throughput evidence healthy; stable load is now duration-weighted across both contiguous windows. GLM-5.2's old one-row geometry was genuinely underloaded at `0.416/0.394`; changing from 20 groups x 640 completion to 16 x 1024 preserved `16.6k` isolated trainer tok/s and raised pressure to `0.801/0.981`, with `1.4e-5` trainer underfeed and matched-core parity. | Commit the measurement/workload correction, run focused clean-tree validation, then recalibrate all ten fingerprints from the immutable commit. Artifacts `scratch/full_workflow_cutover/throughput_calibration/frozen_1ae05bd905_*`. | +| 2026-08-11 18:49 | 62h59 | Integrated `ed6f55568` and `4d3fcaf15`. vLLM preemption now rebases mixed-policy block hashes to the current policy while preserving old shared cache entries; unsupported connector/multimodal rollback paths fail before worker mutation. Trajectory admission now treats packing/packed leases as future capacity releasers instead of poisoning a pending minimum acquisition. | Focused validation: policy `17/17`, queue `5/5`, service `12/12` plus 2 capability skips, pipeline lifecycle `5/5`, Ruff, `ty`, lock, and diff checks. Freeze source, recalibrate ten B300 fingerprints, then launch the one-commit workflow matrix and strict E1. | +| 2026-08-09 05:42 | 25h52 | Committed and qualified the fast-metrics transport correction as `9a7f91d42`, then completed all ten interim B300 calibrations. The managed HTTP pool plus `TCP_NODELAY` removed the deterministic 42 ms response floor: 200 loaded requests issued every 5 ms completed without error at 1.09 ms median, and probed versus clean Qwen 3 dense runs differed by less than 1% in every trainer throughput measure. Across the matrix, matched-core throughput remains at isolated parity. Four fast-inference workloads nevertheless stopped at the harness's explicit 64-worker ceiling, and the only large activation tails align with the final test-only matched-input serialization. Gemma 4 MoE's last autotuner adjustment left only one stable trailing window. | Metrics proof `scratch/full_workflow_cutover/throughput_calibration/frozen_9a7f91d425_qwen3_dense_austin-b300-41f759d5-head_metricsproof`; matrix `scratch/full_workflow_cutover/throughput_calibration/frozen_9a7f91d425_*`. Remove the harness ceiling, serialize only after activation tasks settle, give Gemma 4 MoE one extra window, then freeze and rerun the authoritative matrix. | +| 2026-08-09 05:12 | 25h22 | Completed six clean B300 calibrations from `3f1ee7a8c` and a live fast-metrics root-cause study. Qwen 3.5 MoE, Gemma 4 MoE, GLM-5.2, DSV4, Llama, and Qwen 3 dense all retained isolated/matched parity; their pressure results selected bounded one-row workload adjustments and lower DSV4 initial concurrency. Loaded `/art/metrics` had a `~42 ms` persistent-response floor despite a scalar-only snapshot. Independent Uvicorn probes proved vLLM's pre-bound listener omitted `TCP_NODELAY`: the same nonempty JSON endpoint measured `41.88 ms` median without it versus `0.92 ms` median and `1.22 ms` p95 with it. ART also constructed a fresh `AsyncClient` on every poll. | Interim artifacts `scratch/full_workflow_cutover/throughput_calibration/frozen_3f1ee7a8ce_*`. Set `TCP_NODELAY` before vLLM accepts connections, reuse the model's managed OpenAI pool with a one-second/no-retry request, restore 250 ms calibration polling, then rerun all ten handlers from the new commit. | +| 2026-08-09 04:37 | 24h47 | Completed a clean ten-handler B300 calibration matrix and bounded geometry study across all 24 GPUs. Every handler retained matched isolated/E2E trainer throughput. Rejected three superficially longer-completion probes because they spilled into two packed rows; selected one-row geometries at 96-99% utilization instead. Root-caused Qwen 3 MoE's isolated mismatch to the first direct-path transition sample only (`2.70-2.82 s` versus stable `2.27-2.29 s`), DSV4's `2.64 s` activation tail to synchronous capture serialization/write blocking the event loop around policies 31/32, and Llama telemetry failures to a 250 ms poll cadence whose request latency exceeded the 500 ms sample hold. | Clean artifacts `scratch/full_workflow_cutover/throughput_calibration/frozen_bdcc06981f_*`; use one unmeasured direct-path transition, thread-offloaded capture bytes, a 500 ms metrics cadence with the unchanged 65% coverage gate, and recalibrate from a new immutable commit. | +| 2026-08-09 03:36 | 23h46 | Committed the source-freeze candidate as `19f552de1` with Ruff, format, repository hook-scoped `ty`, and lock synchronization passing. The clean aggregate integration gate passed `316` cases and skipped `9` unavailable capabilities; its sole failure was a lifecycle test launcher that imported all of `art.distributed.monarch_bootstrap` in a fresh child merely to read a constant. The measured cold import took `28.06 s`, exceeding the test's `10 s` process budget before worker creation. Embedding the already-loaded constant from the parent preserves the exact legacy worker program and reduces the test to `1.78 s`. | Commit the test-only harness correction, then use that immutable commit for B300 calibration. | +| 2026-08-09 03:19 | 23h29 | Finished the Qwen 3 publication design gate and source-freeze static audit. vLLM 0.25.1 cannot consume ART's six independent gate/up/down packed tensors directly, and its built-in four-tensor 3D format is not lossless because it assumes shared gate/up A weights. Instead, the handler declares that conversion of publisher-produced tensors is view-only: ART stages six packed tensors per layer and creates canonical per-expert views on pinned CPU storage. The production-shape probe improves snapshot launch from `85 ms` median/`406 ms` p95 to `14-15 ms` median/p95 without changing transport or disk formats. All six exact packed-expert cases, including dynamic slots, preserve tensor values and byte-identical safetensors. Repository-wide Ruff/format, `git diff --check`, and scoped `ty` over 102 changed Python files pass. | Probe `scratch/full_workflow_cutover/probes/qwen3_snapshot_shape_benchmark.py`; exact codec gate `6 passed, 21 deselected`. Complete the compact CPU/lifecycle preflight, then commit the immutable source candidate before authoritative integration tests and calibration. | +| 2026-08-09 03:04 | 23h14 | Closed all four release-audit blockers with physical and failure-injection evidence. Positive trajectory minimums are acquired atomically rather than holding partial leases; unsatisfiable group/record/byte capacity cycles fail with a sticky typed error; normal put/get waits are event-driven. Local Monarch workers install `PR_SET_PDEATHSIG(SIGKILL)` in the child, verify the parent-death race, and leave exact identity metadata for pidfd-based orphan reconciliation. Trainer, model-service, ProcMesh, and backend owners retain failed resources until a retry succeeds. Each backend-owned local model runtime now reserves globally unique loopback API/rendezvous ports, explicit ports cannot collide, and reservations release only after successful runtime close. The combined lifecycle/endpoint file passes `12/12` applicable cases with no worker left behind. Separately, the exact 16-layer Qwen 3 shape showed the 12,288-view GPU repack at `85 ms` median and `406 ms` p95 versus `36-37 ms` for generic underlying-storage staging; alias-aware staging preserves all durable per-expert keys and passes deterministic view/value checks. | Queue proof: `scratch/full_workflow_cutover/test_trajectory_queue_bulk.py` plus `scratch/test_direct_owner_packing.py`, `11/11`. Lifecycle proof: `tests/integration/megatron/runtime_isolation/test_service_runtime_boundary.py`, `12 passed`, `2` capability skips. Snapshot probe: `scratch/full_workflow_cutover/probes/qwen3_snapshot_shape_benchmark.py`. Finish the packed-vs-fused design audit, then rerun broad preflight against the settled source. | +| 2026-08-09 02:45 | 22h55 | Finished the dependency and source-provenance audit while focused ownership fixes run. The ART Megatron and vLLM runtimes both use Transformers 5.12.1. vLLM now resolves NumPy 2.3.5 and OpenAI 2.53.0, `uv pip check` passes for all 198 vLLM-runtime packages, its plugin-free 0.25.1 API server imports without ART patches, and the obsolete OpenAI `NamespaceTool` import shim is removed. Calibration fingerprints cover all ART/runtime/workflow/build source and lockfiles but deliberately exclude the threshold resource table, allowing measured floors to be recorded after a source-frozen calibration. A release audit additionally found fixed local service ports, partial-lease capacity deadlock, controller-orphaned workers, and cleanup paths that discarded ownership after failed teardown; focused agents are implementing real failure-path tests rather than masking existing run-owned orphans manually. | Dependency files: `vllm_runtime/{pyproject.toml,uv.lock}` and `vllm_runtime/src/art_vllm_runtime/patches.py`. Source contract: `tests/integration/megatron/model_support/workflow_throughput.py`. Preserve the existing root-environment SkyPilot/Click and excluded NVML package warnings as separate environment-contract work; they are not vLLM-runtime conflicts. Review the two focused patches, then implement unique local endpoint ownership. | +| 2026-08-09 02:17 | 22h27 | Completed the broad dirty-source preflight and corrected the remaining Qwen calibration model. Roughly 750 unit tests and the focused model-support, serialization, optimizer, runtime-isolation, workflow, vLLM, Ruff, `ty`, and diff gates pass. Qwen 3 MoE repeats at `56.0k` isolated and matched-core tok/s, but maximum serving activation remains `2.12-2.16 s`. This one-host path is explicit local tmpfs transport, not NIXL: immutable CPU LoRA tensors are written to `/dev/shm`, then vLLM loads them in place. Qwen 3's thousands of tensor keys incur one unbuffered syscall each, while Qwen 3.5's 132 packed tensors stage in `8-15 ms`; bounded `writev` is therefore the compact root-cause fix. Qwen 3.5 prefix caching is effective (`65-78%` hit rates), but MoE `c48/c24/c16` remain inference-heavy and dense `c24` creates two approximately half-full packed rows. | Qwen 3 repeat: `scratch/full_workflow_cutover/throughput_calibration/qwen3_moe_l16_g32_p3884_c48_w64_n64_b65k_s31_repeat_36/`. Qwen 3.5 `c16` has `4.15-4.27` pressure and `0.21-0.34` underfeed; dense and MoE `c8` one-row probes are active. Finish those without source churn, then implement and microbenchmark vectored safetensors output before the final Qwen 3 repeat. | +| 2026-08-09 01:14 | 21h24 | Finished the bounded packing-search study, the DSV4 balance point, and the final lifecycle/workflow/vLLM preflight hardening. The retained 64-trial packing budget is evidence-based: relative target steps and a 20% range sharply reduce actual pack calls while the real zero-spill input still needs 45 evaluated candidates for the first certifiable optimum. DSV4 at 768 completion tokens retained `7.762k` matched E2E core versus `7.762k` isolated tok/s, `7.317k` full-cycle tok/s, `0.973-0.981` inference pressure, and `0.055/0.050` underfeed, while its remaining `2.294 s` train gap is attributable to lifecycle work rather than trainer compute. Publication reservation now happens before any yield, failed teardown remains retryable, architecture failures terminate dependent stages truthfully, and workflow fixtures verify every generated byte against exact source identity. The vLLM integration now rejects anything except 0.25.1's exact signatures, delegates unchanged upstream behavior, patches all three current DSV4 attention implementations, and removes the old reload-shadow, Marlin clamp, module fallback, and Qwen3-VL compatibility shims. | Evidence: DSV4 artifact `scratch/full_workflow_cutover/throughput_calibration/dsv4_l8_g8_p12800_c768_w32_n64_b131k_s31_29/`; packing study `scratch/full_workflow_cutover/packing_trial_study/`; dirty-source workflow `59/59`, vLLM DSV4 `14/14`, lifecycle/integrity `9/9`, scoped Ruff/`ty`, and clean diff check. Run the broader focused suite, then calibrate the two MoE handlers without further source churn. | +| 2026-08-08 23:34 | 19h44 | Closed the pre-calibration lifecycle, workflow, and publication contracts. Publication waiters reserve generations synchronously before yielding; canceled Local/Megatron close completes service shutdown before owned runtime teardown; partial workflows are passed only when their selected stages pass but remain explicitly incomplete; full correctness always runs every objective and configured topology; subprocess stages share one fixture; stage timeouts fail with retained logs; and calibration fingerprints now cover ART, runtime, fixture generation, workflow code, and both lockfiles. vLLM startup tails fatal EngineCore markers rather than waiting five minutes behind a live API wrapper. Qwen 3 expert LoRA uses six packed gate/up/down A/B slots and expands only at the stock-vLLM boundary. A disposable two-item prepared-queue proof confirms wholesale stale discards, preselection discards, zero-variance discards, and dequeued denominators carry exactly once into the next train row. | Dirty-tree contracts: workflow `59/59`, runtime launcher `16/16`, packed Qwen/Qwen3.5/DSV publication `6/6`, lifecycle focused tests, Ruff, and stale probe `1/1`. Qwen 3.5 128K analysis shows `61.3/64` running sequences, `172.7` waiting, `0.93%` KV use, and only `51%` of required prompt/decode service; probe `max_num_seqs=128` before workload retuning. | +| 2026-08-08 23:08 | 19h18 | Completed the DSV4 128K-vLLM-budget control and isolated Qwen 3's publication overhead. DSV4 retained `7.82k` matched E2E core versus `7.96k` isolated tok/s (`98.21%`) with only `2.34%` trainer capacity unused, but saturated inference (`0.978-0.983` pressure) left `5.676 s` mean trainer idle and reduced full-cycle throughput to `6.21k tok/s`; the 800-token completion geometry is therefore inference-heavy rather than a trainer regression. Qwen 3's independent gate/up/down LoRA currently expands to `12,288` expert keys for a 36.91 MB payload, while Qwen 3.5 carries a larger payload through 64 packed blocks; declaring six existing `expert_rows` slots reduces collective metadata 64x without changing parameterization or collective ordering. | DSV4 artifact `scratch/full_workflow_cutover/throughput_calibration/dsv4_l8_g8_p12800_c800_w32_n64_s31_vllm128k_17/`, fingerprint `de7061c7a1f4effbc5cc9eded44ed4cc00c8081da5470b6c227a55d1e086b4ea`. Qwen evidence and exact path are recorded in the prior Qwen/Qwen3.5 artifacts; target warm snapshot launch is below 150 ms. Close the lifecycle/workflow audit findings before choosing the next DSV completion point. | +| 2026-08-08 22:43 | 18h53 | Closed permanent ephemeral-LoRA lifetime coverage against vLLM 0.25.1's real LRU manager/cache semantics, then separated two failed MoE calibration points. Qwen 3 kept the packed queue full and retained `56.04k` matched-core versus `56.01k` isolated tok/s, but synchronous per-expert LoRA collection/conversion made snapshot launch vary from `0.3-1.36 s`, reduced full-cycle throughput to `34.57k tok/s`, and produced a `1.421 s` mean train gap. Qwen 3.5's packed LoRA path launched in about `18 ms` and retained `57.50k` matched-core versus `58.79k` isolated, but vLLM's 32K batch cap exposed only about 25K newly executed prompt tok/s after its 75% cache-hit rate, leaving the trainer idle `2.392 s` per batch. | Qwen artifacts `scratch/full_workflow_cutover/throughput_calibration/{qwen3_moe_l16_g32_p3884_c48_w64_n64_s31_async_final_16,qwen35_moe_l16_g32_p3839_c48_w64_n64_s31_async_final_16}/`. The workflow now gives vLLM the same 128K token budget as Megatron; Qwen 3.5, DSV4, and a fresh Qwen 3 dense control are repeating across all three hosts. Investigate the existing packed-expert abstraction for Qwen 3 rather than accepting a model-specific slow path. | +| 2026-08-08 22:20 | 18h30 | The recurring real-trainer proof validates deferred immutable snapshots across more than twenty live policy publications and multiple durability boundaries. Warm LoRA-only snapshot launch is `2.6-2.7 ms`, optimizer-save launch is `6.0-7.2 ms`, pool waits are `5-8 us`, maximum serving activation lag is `0.738 s`, and durable optimizer lag resets after completed persistence. Matched E2E core training retained `196,423` of `197,436 tok/s` isolated (`99.49%`); the deliberately shallow two-layer geometry was correctly rejected as a release calibration because inference overfed it and the trailing windows had `0.116-0.219` trainer underfeed. | Artifact `scratch/full_workflow_cutover/throughput_calibration/qwen3_dense_l2_async_snapshot_15/`, fingerprint `d494dc05cf0a0974562887c135843e7a7714bbfca77c63ec9a7accd5ab117e60`. This closes the async snapshot performance/durability gate; strengthen permanent LoRA-cache lifetime coverage, then resume balanced model geometries. | +| 2026-08-08 22:08 | 18h18 | The real Qwen 3.5 MoE lifetime smoke survived every in-flight policy replacement and activation; it stopped only because its deliberately short 11-step budget could not produce two trailing unchanged autotuner windows. Deferred CPU snapshots now preserve the same immutable-generation contract without blocking the trainer on D2H: LoRA uses its existing snapshot-owned flat, optimizer leaves read live state on one persistent side stream, and a barrier fences only the next optimizer mutation after allowing the following forward/backward to overlap. Cold reload/replacement paths synchronize explicitly, publisher threads resolve events before transport or persistence, and the metric is now honestly named `snapshot_launch_s`. | Lifetime smoke: `scratch/full_workflow_cutover/throughput_calibration/qwen35_moe_l16_g32_p3839_c48_w64_n64_s11_14_lifetime_smoke/`; every runtime update returned HTTP 200 and no deleted-path reload occurred. `scratch/full_workflow_cutover/test_deferred_snapshot.py` proves forward-like CUDA work completes while D2H remains pending, the mutation fence waits, and persisted values stay pre-mutation. Scoped Ruff, compile, diff, and CPU-copy checks pass; integration pytest remains intentionally gated on the final clean commit. Run one recurring real trainer proof before calibration. | +| 2026-08-08 21:45 | 17h55 | Root-caused and closed an ephemeral-adapter lifetime defect reproduced by both Qwen 3.5 MoE at step 4 and DSV4 at step 23. The in-flight update correctly loaded from the temporary transfer path, but then copied that `load_inplace=True` mutation request into every scheduler request; after ART released the transfer lease, a later `set_active_loras` re-entered vLLM's loader and failed on the deleted `adapter_config.json`. Mutation requests now exist only for the paused collective load, scheduler requests are normalized to `load_inplace=False`, and every worker pins the active slot after bootstrap declaration and after each update so exact/eval adapters cannot evict it. | Four installed-vLLM 0.25.1 policy tests pass, including real request rekeying, partial-update quarantine, launch declaration, scheduler normalization, and both worker pin paths. Failed run evidence: `scratch/full_workflow_cutover/throughput_calibration/qwen35_moe_l16_g32_p3839_c48_w64_n64_s31_12_launch_declared/` and `scratch/full_workflow_cutover/throughput_calibration/dsv4_l8_g8_p12800_c768_w24_n64_s31_06_launch_declared/`. Run a short live Qwen 3.5 loop beyond step 4 before performance work. | +| 2026-08-08 21:20 | 17h30 | Root-caused a model-independent HTTP 500 in all three balanced repeats: launch-time vLLM loaded the mutable `:active` adapter as a plain `LoRARequest`, so every worker retained sequence zero and the new admission guard correctly rejected the first request before token execution. The launch contract now carries an explicit initial policy version; after vLLM loads the adapter but before HTTP serving, ART declares `(policy_version, update_seq=1)` through a typed engine collective, verifies every worker loaded the same slot/path, publishes that identity to request admission, and seeds sequence two for the first update. The guard remains strict and no path/version inference or adapter reload was introduced. | Isolated declaration test passes. A real Qwen 3 MoE TP2/EP2 launch carried `--initial-policy-version=0`; all wrapped chat requests returned HTTP 200 where the prior runs returned 500, and explicit process-group teardown left no GPU process. Runtime log: `/tmp/art-vllm/throughput-qwen3_moe-844c98ea/0/local/logs/vllm-runtime.log`. Restart the three calibration points. | +| 2026-08-08 21:02 | 17h12 | Closed the final policy-provenance and one-host cutover audit findings before restarting calibration. Mutable LoRA identity now includes a monotonic update sequence, request cache keys encode policy history across same-version reloads, every worker acknowledges the declared identity, cancellation releases admission, and partial rank mutation poisons the slot instead of resuming mixed weights. Protocol-native exchange spans feed authoritative trainer age counts and exact in-flight mode rejects missing, malformed, incomplete, or future spans. The managed one-host and multi-host paths share one RL/SFT train-job lifecycle; default managed serving uses the compiled endpoint port; nested durability work remains owned through close. Gemma 4 MoE's corrected one-row point passed at `45.25k` isolated, `45.20k` matched core, `42.17k` full-cycle tok/s, `0.197 s` mean gap, and `1.524 s` activation lag. Qwen 3 and Qwen 3.5 depth repeats were invalidated by concurrent partial-source edits, not model behavior; DSV4 completed production FP4/hash-MoE at about `8.1k` tok/s but changed workers at the final tuner boundary, so it is repeating from the inferred 24-worker balance point. | Current-contract gates: 10/10 release blockers, 12/12 immutable-generation contracts, 5/5 trainer span cases, 4/4 installed-vLLM policy cases, Ruff, `ty`, and diff checks. Gemma evidence: `scratch/full_workflow_cutover/throughput_calibration/gemma4_moe_l12_g32_p3640_c80_w64_n64_s31_07_one_row/verified_measurements.json`. Relaunch all three points on isolated hosts, then freeze resource contracts. | +| 2026-08-08 20:21 | 16h31 | Closed two invalid calibration assumptions before setting floors. Qwen 3.5 MoE's 300-second startup failure came from compiling FlashInfer's unused fused all-reduce/RMS extension even though custom all-reduce was disabled; disabling that pass starts cleanly and produces `113.23k` isolated versus `112.96k` matched-core tok/s, but the 48-token point remains rejected at `0.112-0.132` trainer underfeed. Gemma 4 MoE's nominal one-row point physically produced `134,877` tokens and two steps, leaving `48.5%` capacity unused; a prompt derived from the measured excess is repeating at 3,640 tokens. DSV4 now derives its three hash layers from the canonical layer list and initializes dummy-only hash routes deterministically without replacement; a constructor-level probe proves range, distinctness, token/layer variation, and non-dummy preservation. Its first production-FP4 launch correctly rejected the stale forced Triton backend because that MXFP4 kernel cannot run SiLU; the same workload is repeating with vLLM's LoRA-aware backend oracle. | Qwen evidence: `scratch/full_workflow_cutover/throughput_calibration/qwen35_moe_l8_g32_p3839_c48_w64_n64_s31_08_no_ar_fusion/`. Gemma evidence: `scratch/full_workflow_cutover/throughput_calibration/gemma4_moe_l12_g32_p3775_c80_w64_n64_s31_06_one_row/`. DSV probe: `scratch/full_workflow_cutover/probes/dsv4_dummy_hash_routes.py`; failed explicit-backend run: `scratch/full_workflow_cutover/throughput_calibration/dsv4_l8_g8_p12800_c768_w16_n64_s31_03_fp4_hash_routes/`. | +| 2026-08-08 19:47 | 15h57 | Closed the independent throughput-contract findings before final calibration. Current-schema focused collection passes; fractional runtime counters now fail instead of rounding; nominal, unused, dummy, executed, loss-bearing, and accepted counts reconcile per step; future policy spans fail before backend training; policy-age limits are fingerprinted explicitly; and activation lag is schema-bounded to two seconds. vLLM telemetry now uses bounded left-hold intervals and requires 65% successful coverage, so one tail sample cannot admit a window. DSV4 no longer replaces its production hash-MoE prefix with ordinary MoE. Failed workflow stages prune only large runtime directories while retaining reports, and process-group teardown checks descendants even after the leader exits. | Focused contracts: `scratch/full_workflow_cutover/test_future_policy_provenance.py` and `scratch/full_workflow_cutover/test_vllm_pressure_telemetry_coverage.py`; direct measurement/reducer tests, scoped Ruff, `ty`, and diff checks pass. Old balanced points selected the final geometries but cannot set exact fingerprints. Three fresh B300 repeats are active across the three hosts. | +| 2026-08-08 19:29 | 15h39 | Closed Gemma 4 MoE's two compatibility gaps and root-caused a fast-step publication race. The throughput fixture now supplies Gemma's required prenorm tensors and index, ART normalizes its canonical `top_k_experts` alias at the vLLM route boundary, and the real run completed at `46.93k` isolated, `46.79k` matched core, `44.42k` full-cycle tok/s with a `0.139 s` mean gap and `1.578 s` maximum activation lag; only `0.45-0.47` inference pressure missed. Qwen 3.5 MoE then showed step 3 rank records being requested only after step 2 durability finished, by which time step 4 had expired the unclaimed generation. Each generation now starts its rank-record waiter before awaiting prior durability, preserving ordered commits without blocking training. | Gemma evidence: `scratch/full_workflow_cutover/throughput_calibration/gemma4_moe_l12_g32_p3839_c64_w64_n64_s31_03_route_alias/`. Qwen failure evidence: `scratch/full_workflow_cutover/throughput_calibration/qwen35_moe_l8_g32_p3839_c64_w64_n64_s31_fixed_queue_04/`, first exception `trainer has no publication step-00000003-*`. The expanded lifecycle proof passes in `scratch/full_workflow_cutover/test_monarch_publication_lifecycle.py`; scoped Ruff and `ty` pass. Exact Qwen and rate-balanced Gemma repeats are active. | +| 2026-08-08 18:42 | 14h52 | Closed two lifecycle/audit gaps and rejected two misleading qualification paths. Typed publication outcomes and replay state are now bounded across recurring jobs, failures, canceled/concurrent waiters, and graceful shutdown; review is tightening the final late-wait/idempotent-retry contract before acceptance. The workflow runtime audit attributes `1,960 s` of a `2,505 s` Qwen run to train/inference mismatch, correctness, and length trainability, with a defensible `300-415 s` target from exact adapter materialization, removal of nested pytest, startup overlap, and eliminating redundant offload/postprocessing. DSV4's 640-token point remained trainer-saturated but left vLLM almost idle (`0.04-0.06` pressure), while GPT-OSS's three failed retries were run at TP2 inference rather than the previously passing TP1 topology. A mistakenly included sensitivity stage was stopped and its orphaned process group exposed a workflow signal-cleanup defect. | Publication stress proof: `scratch/full_workflow_cutover/test_monarch_publication_lifecycle.py`. DSV evidence: `scratch/full_workflow_cutover/throughput_calibration/dsv4_l8_g8_p13452_c640_w32_n64_s31_native_random_11_balanced/`. GPT TP1 is rerunning on worker2; Qwen 3.5 dense exact fixed-queue comparison is active on head GPUs 4-7. Implement process-group teardown in the test harness, retain only evidence-backed runtime optimizations, and never retry completed numerical assertion failures. | +| 2026-08-08 18:24 | 14h34 | Root-caused short-step trainer underfeed to queue-control overhead rather than insufficient rollout work. A full 32-group queue retained 28 source leases for the prepared batch and then issued one actor RPC per take/release while 64 blocked producers polled `enqueue` every 50 ms; Qwen 3.5 dense therefore spent `1.4-2.0 s` selecting each next batch despite saturated vLLM. Queue take/release is now typed and bulk, producers await an explicit capacity-generation event without polling, and trainer admission synchronously releases source leases before forward/backward while retaining the immutable packed-tensor lease. Gemma 4 dense independently closed at `26.28k` isolated, `26.16k` matched core, `25.64k` full-cycle tok/s, pressure `0.514`, underfeed `0.000051`, `0.095 s` mean train gap, and `0.855 s` maximum activation lag. | Disposable concurrency proof `scratch/full_workflow_cutover/test_trajectory_queue_bulk.py` verifies one blocked enqueue, one bulk take, and one bulk release; scoped Ruff, `ty`, compile, and diff checks pass. Gemma evidence: `scratch/full_workflow_cutover/throughput_calibration/gemma4_dense_l12_g32_p3839_c64_w64_n64_s31_native_random_02/verified_measurements.json`. Rebuild HybridEP once for the changed source, then repeat Qwen 3.5 dense/MoE against the fixed queue. | +| 2026-08-08 18:08 | 14h18 | Closed DSV4's first valid production-width B300 throughput proof and the complete Qwen 3 dense preflight. DSV4 sustains `7.86k` isolated, `8.07k` matched E2E core, and `7.77k` full-cycle logical tok/s with `0.304 s` mean train gap and `1.244 s` maximum activation lag; its `0.111` vLLM pressure correctly rejects that prefill-heavy geometry. Qwen 3 dense passed every executed inherited stage, including all RL/SFT correctness topologies and native LoRA, with sensitivity excluded and throughput calibrated separately. | DSV4 evidence: `scratch/full_workflow_cutover/throughput_calibration/dsv4_l8_g8_p15500_c128_w32_n64_s31_native_random_10_preimport_alloc/verified_measurements.json`. Qwen report: `scratch/full_workflow_cutover/final_preflight/qwen3_dense_non_throughput_01_retry.json`. A DSV4 point with the same `16,012` physical tokens/group but `640` completion tokens is active; Qwen 3 MoE now owns the released worker2 GPU quartet. | +| 2026-08-08 17:56 | 14h06 | Isolated two vLLM-0.25 handler regressions without weakening timeouts. GPT-OSS native LoRA was incorrectly forced onto fused Triton MXFP4, which vLLM rejects because that kernel cannot host adapters; engine selection is mode-aware again (`triton_unfused` for LoRA, fused `triton` for merged weights). Qwen 3.5's Blackwell `auto` choice sent the first active-LoRA 32K prefill to FlashInfer, occupied both TP2 GPUs for the exact 300-second executor budget, and died; the identical CuTeDSL prefill completed at about `73.7k` prompt tok/s. | Focused GPT train/inference parity is rerunning on worker1 GPUs 4-7. Qwen 3.5 CuTeDSL E2E calibration is active on worker2 GPUs 4-7, and the ART-owned vLLM boundary now redirects only Blackwell `auto -> flashinfer` GDN prefill to CuTeDSL while preserving explicit choices and Hopper behavior. DSV4's allocator-corrected run is at step 16/31 with recurring `~32-33 s` steps and no first-backward OOM. | +| 2026-08-08 17:43 | 13h53 | Fixed the DSV4 first-backward allocator failure at the physical interpreter boundary. The prior retries set expandable segments only after Monarch actors had imported Torch, leaving about 56 GiB reserved but unusable before a 16 GiB allocation. Fresh worker bootstrap now exports both allocator aliases before any actor import, and dedicated vLLM subprocesses remove only the incompatible expandable-segments option while preserving unrelated allocator settings. Direct `/proc` evidence confirms the DSV worker and actor started with both aliases while the vLLM child has neither. Llama has passed merged serving, all ten RL/SFT correctness variants, chat, and packing; Qwen 3 dense has passed HF parity, LoRA coverage, train/inference mismatch, and merged serving. | Fresh DSV run `scratch/full_workflow_cutover/throughput_calibration/dsv4_l8_g8_p15500_c128_w32_n64_s31_native_random_10_preimport_alloc/` is the decisive first-backward validation. Qwen 3.5 dense retry is spending its cold first 32K-token prefill in both vLLM workers at 100% SM; the 60-second shared-memory warning is compile backpressure, not a failed health/metrics endpoint. Preserve cold-start evidence and require reliable completion rather than masking it. | +| 2026-08-08 17:25 | 13h35 | Closed three cutover defects before the final gate. Local topology compilation had captured Megatron's resolved checkpoint path before the backend restored the provider model identity, causing managed vLLM to treat a LoRA-only directory as its base model; the provider identity now precedes compilation, and a focused live Llama launch physically shows the HF model in `--model` and checkpoint `0000` only in `--lora-path`. DSV4 random mode exposed custom `torch.empty` parameters that pretrained/oracle paths overwrite but native random initialization did not; initialization now matches Transformers' canonical contract: attention sinks, HC residual bases, and compressor position biases are zero; HC function weights use the configured initializer; HC scales are one. CUDA 13 setup now overlays the exact CUTLASS DSL wheel into the vLLM purelib instead of uninstalling shared namespace packages, and the installed Torch 2.11/CUDA 13/vLLM 0.25.1/B300 stack validates cleanly. Removed impossible cutover branches, silent SFT token-count coercion, a dead executor protocol, and an accidentally exported topology compiler. | Focused topology/Ruff checks pass. Live Llama train/inference parity is running on GPUs 4-7 with the corrected launch. DSV4 balanced throughput is running on GPUs 0-3 past vLLM startup. Valid Qwen 3 MoE release point: `scratch/full_workflow_cutover/throughput_calibration/qwen3_moe_l8_g32_p3884_c48_w64_n64_s31_native_random_unfused_06/verified_measurements.json`, with pressure `0.701`, underfeed `0.0186`, `0.838 s` recurring gap, and `1.965 s` maximum activation lag. | +| 2026-08-08 16:40 | 12h50 | Closed four valid ordinary-actor B300 calibration points. Llama's 52-worker geometry sustains `61.21k` isolated, `61.08k` matched core, `53.15k` full-cycle, `14.95k` accepted tok/s, pressure `0.756`, `0.122 s` mean backend gap, and `1.860 s` maximum activation lag. GLM-5.2 sustains `16.72k`/`16.73k` isolated/matched core with pressure `0.617`, `0.106 s` gap, and `1.177 s` activation lag. Qwen 3 dense remains `51.95k`/`51.76k`; GPT-OSS remains `90.29k`/`90.79k`. Qwen 3 MoE at 64-token completions has `110.34k`/`110.72k` matched parity and healthy pressure/underfeed, but one stable-window activation takes `2.289 s`; a 48-token point directly tests request-quantum latency. DSV4's first retry proved that Transformers derives its compression pattern through `layer_types`, then reached vLLM and exposed unsupported `hash_moe` validation. The throughput fixture now uses ART's existing all-MoE serving subset with DSV4's FP8/eager/Triton settings while retaining production width and sliding/CSA/HCA layers. | Evidence: `scratch/full_workflow_cutover/throughput_calibration/{llama_l16_g24_p3922_c384_w52_n64_s31_native_random_06,glm52_l12_g32_p3836_c64_w64_n64_s31_native_random_04,qwen3_dense_l8_g32_p3839_c64_w64_n64_s31_native_random_02,gpt_oss_l4_g32_p3839_c64_w64_n48_s31_native_random_03,qwen3_moe_l8_g32_p3836_c64_w64_n64_s31_native_random_unfused_05}/verified_measurements.json`. Cutover/workflow/autotuner contracts pass 70/70; focused DSV4/throughput contracts pass 16/16; Ruff and diff checks pass. DSV4 and Qwen 3 MoE repeats are active. | +| 2026-08-08 16:20 | 12h30 | Closed the typed random-initialization propagation defect and obtained the first valid ordinary-actor throughput proof. `get_model_config()` had omitted `megatron_model_initialization`, so the earlier Llama run silently retained Bridge's checkpoint hooks and produced zero gradients. The field now reaches `TrainerRuntimeSpec`; random mode rejects non-Bridge hooks, removes every Bridge load hook, and restores Megatron parameter initialization. GPT-OSS then trained with nonzero loss/gradients through the shipped `MegatronBackend` and `MonarchTrainerActor`, matching `90.79k` E2E core against `90.29k` isolated with a `0.137 s` recurring gap and `0.926 s` maximum activation lag. | Valid proof: `scratch/full_workflow_cutover/throughput_calibration/gpt_oss_l4_g32_p3839_c64_w64_n48_s31_native_random_03/report.json`. Pressure `1.055` is the only geometry issue, so a 48-worker repeat is active. The earlier Llama artifacts remain useful only as performance measurements, not as native-random lifecycle proof; a corrected repeat is active. Qwen 3 MoE startup was separately isolated: plain NCCL still enabled vLLM's FlashInfer all-reduce/RMS compiler pass, while disabling that pass completed cold startup in 180 s including FlashInfer sampling compilation and graph capture; the handler-scoped E2E repeat is active. | +| 2026-08-08 15:55 | 12h05 | Removed the throughput stage's test-only trainer subclass and introduced typed Megatron random initialization. A 16-layer production-width Llama CP2 run completed all 31 online steps plus exact matched isolated replay: `61.46k` isolated, `61.84k` matched E2E core, and `55.20k` full-cycle logical tok/s; matched fingerprints, zero packed-queue starvation, `0.258 s` mean recurring train gap, and `1.715 s` maximum activation lag all passed. Subsequent audit showed this run predated the `get_model_config()` propagation fix and therefore retained the checkpoint path; its zero gradients disqualify it as a random-initialization lifecycle proof. | Performance-only evidence: `scratch/full_workflow_cutover/throughput_calibration/llama_l16_g24_p3922_c384_w48_n64_s31_native_random_02/report.json`. Runtime isolation passes `4 passed, 2 skipped`. An idle-host Qwen-MoE launch spent the full six-minute startup budget in vLLM initialization; focused startup isolation continued rather than increasing the timeout. | +| 2026-08-08 15:40 | 11h50 | Closed the remaining single-node semantic-floor findings before final qualification. Throughput fixture failure is now contained to the throughput stage rather than suppressing inherited stages; canonical stages no longer alias generated fixtures; exact adapter references retain an in-flight policy until the final release and then unload it; and concurrent one-host trainers retain their rendezvous store through mesh startup. The bounded autotuner search remains statistically exact: relative candidate spacing and early stopping remove redundant packing estimates without weakening the 3% spill-risk contract. | Focused fixture-containment and exact-adapter lifecycle probes pass, canonical Llama HF parity passes, and GPT-OSS physically passes the ordinary typed runtime. The throughput stage is being cut from a patched trainer subclass to a typed native Megatron random-initialization mode, leaving the complete shipped actor/runtime path under test. | +| 2026-08-08 15:25 | 11h35 | Restored inherited workflow semantics and closed a concurrent typed-runtime startup race. Inherited stages again use each handler's canonical provider and no generated fixture; the reduced production-width fixture is isolated to the new throughput stage. GPT-OSS and Qwen 3.5 no longer replace established resource cases. Two simultaneous one-host Monarch trainers exposed that upstream `get_host_port()` releases its socket before ranks create the rendezvous store; a trainer-owned SPMD actor now keeps an ephemeral `TCPStore` alive for the ProcMesh lifetime and clients use the agent store. GPT-OSS then completed physically with isolated/E2E core parity (`87.16k`/`87.32k` logical tok/s), pressure `0.578`, trainer underfeed `0.048`, and mean backend gap `0.189 s`. | Canonical Llama HF parity passed on the actual provider in `scratch/full_workflow_cutover/canonical_restore/llama_hf_parity.json`. GPT evidence: `scratch/full_workflow_cutover/throughput_calibration/gpt_oss_l4_g32_p3768_c96_w64_n64_s31_03/report.json`; its only throughput failure is the intentional missing-calibration gate. Focused TCPStore probe, 12/12 bounded-packing tests, Ruff, `ty`, and diff checks pass. Qwen 3 MoE remains active. | +| 2026-08-08 14:56 | 11h06 | Separated exact matched trainer parity from aggregate online convergence and narrowed balanced workloads without weakening the gate. The 64-group Qwen 3 MoE point preserves exact final-batch parity (`130.54k` E2E versus `129.94k` isolated), but pressure `1.157` and underfeed `0.112` reject it. The 48-group point reduced underfeed to `0.014`, but pressure remained `0.951` and a new 43-group final shape paid a one-time `10.4 s` CUDA graph warmup before immediate same-shape repeats returned to `~1.0 s`. Llama's 24-group, 450-completion geometry executes `131,064` logical tokens with `60.93k` aggregate core tok/s against `60.93k` isolated, `0.071` underfeed, and a `0.672 s` mean non-forward/backward gap; only inference pressure/freshness remains high. | Artifacts: `scratch/full_workflow_cutover/throughput_calibration/{qwen3_moe_l8_g64_p1757_c72_w64_n64_s15_01,qwen3_moe_l8_g48_p2343_c96_w64_n64_s15_01,llama_l16_g24_p3658_c450_w64_n64_s15_01}/verified_measurements.json`. Release repeats keep the Qwen/Llama batch geometries, reduce initial workers to 48, and run 31 steps so final evidence covers repeated stable shapes. A 40-group Qwen bracket and GPT-OSS calibration remain active. | +| 2026-08-08 14:30 | 10h40 | Proved the typed-publication cutover physically and tightened the throughput release metric. A real one-host CP2 trainer plus TP2 vLLM run repeatedly sent rank publication records over Monarch, overlapped serving activation and durable commit, closed without marker files, survivors, or GPU processes, and exercised actor-close-dependent publication draining. Throughput `mean_train_gap_s` now measures all recurring critical-path time not spent in forward/backward, including in-call snapshot/preparation work; Python time between `backend.train()` calls remains a separate diagnostic. Removed Qwen 3.5's obsolete weighted-SwiGLU workaround after it restored TP2/EP2 from 149/197 checks and `11.6%` layer-gradient MAPE to 197/197 and `0.000413%`, then deleted the dead workaround and superseded packed-batch protocol. | Physical cutover: `scratch/full_workflow_cutover/throughput_calibration/qwen3_dense_typed_publication_live_g32_s11_01/verified_measurements.json`; no `.trainer_publications` artifacts. The corrected gap contracts pass 3/3, runtime/publication contracts pass 5/5, and Ruff/`ty`/diff checks pass. GLM exact-boundary evidence: `scratch/full_workflow_cutover/throughput_calibration/glm52_l12_g32_p3836_c64_w64_s31_diag_03/verified_measurements.json` (`16.85k` isolated/core, `16.49k` full-cycle, pressure `0.549`, underfeed `0.000035`). Qwen-MoE and completion-heavy Llama balance probes are active. | +| 2026-08-08 14:05 | 10h15 | Removed the last filesystem-polled trainer-publication control plane. Every rank now returns a typed immutable publication record over the job's Monarch channel; the controller validates complete rank coverage and atomically commits optimizer generations, while serving transport remains concurrent with persistence. Graceful service shutdown now flushes rank publisher pools and drains publication events concurrently rather than deadlocking in sequential teardown; publication drain and shutdown share one bounded budget. Deleted the stale marker-path helper and aligned the outer service timeout with owned teardown. | Typed publication contracts pass 5/5, including a regression where publication can finish only after trainer close begins; Ruff, `ty`, and diff checks pass. The dirty-tree import-isolation gate remains to be rerun through its existing focused harness. Qwen3-MoE's 16-group geometry was rejected because it spills to two rows with 47% unused capacity; GLM's exact-boundary rerun trained cleanly but the matched-input identity gate correctly rejected a replay mismatch, now under focused diagnosis. | +| 2026-08-08 13:42 | 9h52 | Replaced the throughput gate's noisy single-window endpoint with sustained final-state evidence without changing any acceptance threshold. Measurements now span the final two contiguous unchanged autotuner windows, use all eight exact trainer cycles and runtime token rows, and duration-weight pressure/underfeed across both windows; convergence still requires at least two stable windows. The first 12-layer GLM point is balanced (`16.64k` isolated, `16.88k` E2E core, `16.27k` full-cycle logical tok/s, pressure `0.514`, trainer underfeed `0.000066`). Llama-16 retained isolated/E2E core parity (`42.11k`) but is correctly rejected because stale-work reductions never converge and inference pressure stays low. | Throughput contracts pass 15/15 with Ruff/diff checks. Evidence: `scratch/full_workflow_cutover/throughput_calibration/{glm52_l12_g32_p3716_c64_w64_s31_01,llama_l16_g12_p8252_c667_w64_s31_01}/verified_measurements.json`. GLM is rerunning just below the one-row spill boundary at prompt 3836; Qwen3-MoE is testing 16 larger groups at exact 128K. DSV4 passed correctness, chat, LoRA, packing, both serving stages, and yes/no; its root-caused count-aware length fixture is in final live qualification. | +| 2026-08-08 13:32 | 9h42 | Root-caused and fixed GPT-OSS's first post-publication inference death below ART. Policy-1 publication and eight one-token requests succeeded; the next cached multi-token decode entered vLLM's vendored Triton-kernels 3.5.1 persistent MXFP4 `matmul_ogs` and made an illegal SM103 memory access despite valid route metadata. The identical update/request sequence passes with upstream Triton-kernels 3.6.0. CUDA 13 now pins exact commit `7c56a5e40f7fd928dfd5c72902d5def0097db73a`, while CUDA 12 retains vLLM's existing faster vendored path. | Production-runtime proof: `scratch/full_workflow_cutover/gpt_oss/triton36_production_validation/{probe_exact.log,vllm_runtime_exact.log}`; preserved 3.5.1 failure: `legacy35_managed_failure.log`. All requests, repeated updates, and final health pass; HF parity passes 39/39, six RL/SFT correctness topologies pass, native LoRA serves steps 0 and 1, the exact dependency source is locked, and Ruff/diff checks pass. Remaining gate is the complete GPT-OSS workflow. | +| 2026-08-08 13:25 | 9h35 | Closed the physical B300 packing-overlap proof and corrected a stale Hopper-only runtime mutation. Qwen 3 dense's definitive exact-128K run achieved `51.05k` isolated, `50.99k` E2E core, and `48.93k` full-cycle logical tok/s; packing/fetch remained off the critical path (`0.22 ms` packed-queue wait), pressure was `0.560`, trainer underfeed `0.000081`, mean train gap `67.9 ms`, and maximum serving activation lag `0.557 s`. Clean trainer workers no longer replace setup's `TORCH_CUDA_ARCH_LIST` with `9.0`; runtime leaves explicit setup identity untouched and otherwise lets bound-GPU admission/JIT code select the hardware target. | Qwen evidence: `scratch/full_workflow_cutover/throughput_calibration/qwen3_dense_l8_definitive_g32_p3839_c64_w64_s31_01/verified_measurements.json`. Runtime-environment probes preserve explicit `10.3` or absence without initializing CUDA; admission independently derives `10.3`, and installed/expected HybridEP identity is exactly `1.2.1.post1+art.3a66004d9140951c`. Corrected-metric Llama-16 and architecture-fixed Qwen3-MoE runs are active on separate four-GPU workers. | +| 2026-08-08 13:09 | 9h19 | Closed the autotuner packing-search side task and corrected the throughput release denominator. Candidate batch sizes now cover only a centered `+/-20%` range with relative granularity (step 2 at target 24), safe projections stop after the first statistically sufficient zero-spill sample, unsafe projections stop as soon as the full trial ceiling cannot make them safe, and measured spills propagate monotonically across skipped sizes. The configured 64-trial ceiling remains: under the production Beta(1,8), 80%-confidence, 3%-spill contract, 16/24/32 clean trials have upper bounds `6.486%/4.905%/3.944%`; the first certifiable count is 45. Final-window E2E throughput now spans the preceding trainer completion through the final completion, accounting for exactly four train-plus-gap cycles rather than four trains and three gaps. A production Qwen run then exposed and fixed the original-trajectory history key (`train/prefix_tree/logical_tokens`); failed calibration runs now prune heavy runtime state in the scratch harness. | Packing evidence: `scratch/autotuner_packing/`; realistic target-24 safe cases require 45 estimates instead of 240-320, while observed-spill target-48 cases stop in as few as four total estimates. Packing/tuner tests pass 12/12 and throughput contracts pass 14/14. Qwen's model path itself sustained about `51k` logical tok/s on an exact 128K row with `0.26-0.35 ms` packed-queue waits; the definitive corrected-metric repeat is active alongside Llama-16 and Qwen-MoE calibration. | +| 2026-08-08 12:25 | 8h35 | Closed two throughput-stage correctness gaps and found a real data-plane overlap defect. The service now records exact trainer-completion and first successful serving-activation times per policy; the stage verifies finite ordered timing, exact train-call counts, complete runtime-plan token identities, a complete final tuner window, and captures the actual final prepared training batch. A Qwen 3 dense smoke then showed `0.43-1.18 s` packed-get waits tracking `0.42-1.16 s` preparation despite `0.65-0.67 s` warm training: source trajectory leases occupied the one-batch shared queue capacity until forward/backward ended. Packed-batch acceptance now validates provenance, detaches and consumes source selections immediately, and retains only the packed tensor lease through training. Capture likewise materializes full source trajectories from the prepared selections rather than serializing message-free summary groups. | Pre-fix artifact: `scratch/full_workflow_cutover/throughput_calibration/qwen3_dense_contract_smoke_capacity_v2_20260808T1215Z/`; its 11 online steps completed but isolated replay correctly rejected empty captured messages. Focused source-consumption/capture tests pass 2/2; workflow contracts pass 61/61 and the combined workflow-runtime suite passes 88/88. Next run repeats the exact Qwen point on current source and must show both valid replay and successor packing overlapping forward/backward. | +| 2026-08-08 12:00 | 8h10 | Established the useful 32-group throughput geometry and rejected short-window calibration noise rather than encoding it into thresholds. Qwen 3 dense executes an exactly full 128K packed batch at `198.84k` isolated and `200.83k` E2E logical tok/s, with `0.27 s` mean train gap and `0.00022` trainer underfeed; its remaining pressure miss (`0.240`) is being tested by reducing vLLM sequence admission from 64 to 32 without changing workload. Llama's same 32-group point reproduces `116-117k` trainer throughput but its final-window underfeed varied from `0.205` to `0.446`, proving 19 steps are insufficient for a stable autotuner verdict. Focused handler work also surfaced real remaining defects: GPT-OSS serving exits after its first update, DSV4 does not behaviorally respond despite 20 published steps, and Qwen 3.5 TP2 gradients differ by up to `11.6%` layer MAP. | Qwen artifact: `scratch/full_workflow_cutover/throughput_calibration/qwen3_dense_l2_p3839_c64_g32_w32_n64_s19_01/verified_measurements.json`. Rejected Llama repeats: `scratch/full_workflow_cutover/throughput_calibration/llama_l8_p309{4,5}_c250_g32_w32_s19_0{1,2}/verified_measurements.json`. Exact GPT-OSS, DSV4, and Qwen 3.5 root-cause probes remain assigned; next source edit adds authoritative activation times, exact final-window enforcement, complete runtime-plan fingerprints, and final-step matched capture. | +| 2026-08-08 11:20 | 7h30 | Closed dense correctness artifact lifetime end to end and established the first healthy balanced-throughput point. Qwen 3 dense now passes all five RL and five SFT topology comparisons from one grouped worker run; successful consumers prune tensor payloads, while worker, comparison, unexpected-signal, and shared-reference failures retain diagnostics. Eight focused lifecycle cases pass. The 8-layer Llama `12.35k + 4x1k` workload achieves `66.95k` isolated and `67.08k` E2E logical tok/s (`1.002x`), `11.92k` accepted tok/s, `0.000027` trainer underfeed, `0.731 s` mean non-train step time, and `1.827 s` max policy activation lag. | Qwen report: `scratch/full_workflow_cutover/qwen3/full_workflows/qwen3_dense_correctness_direct_v4/stage_result.json`. Llama report: `scratch/full_workflow_cutover/throughput_calibration/llama_l8_p12350_c1000_w32_s11_01/verified_measurements.json`. Its zero pressure came from `max_num_seqs=64`; a dedicated saturated probe at that capacity sustains `20.55k` generated tok/s with mean pressure `0.61-0.76`, and the `32`-capacity comparison is active to find the smallest scheduler capacity that preserves peak throughput. | +| 2026-08-08 11:10 | 7h20 | Closed a correctness-workflow artifact-lifetime regression while continuing balanced-throughput calibration. The grouped dense worker correctly emitted both RL and SFT results, but the RL suite pruned each paired SFT trace before the SFT suite consumed it; paired tensors can now remain leased through the second comparison while successful RL tensors still prune immediately. The first 8-layer Llama `9k + 4x1.6k` point retained isolated/E2E parity (`68.29k`/`68.35k` logical tok/s) but was not balanced: stale work drove workers `24 -> 12`, final trainer underfeed was `0.489`, and vLLM pressure was zero. | Three paired-artifact lifecycle tests pass and Qwen dense correctness is rerunning on worker2 GPUs 0-3. The next Llama point increases prompt-dominant inference work; if useful vLLM concurrency saturates below the configured 64 sequences, calibration will verify that reducing scheduler capacity preserves peak inference throughput before using pressure as a gate. Broad Ruff and diff checks pass. | +| 2026-08-08 10:50 | 7h00 | Bounded the autotuner's prefix-packing projection cost and closed two release-path audits. Packing candidates now cover only a centered 20% radius with relative granularity (step 2 at target 24), inherit observed spills across skipped counts, and stop when a candidate is already provably safe or unsafe. The 64-trial ceiling remains because 16/24/32 zero-spill samples cannot certify the configured 3% risk at 80% confidence; a clean candidate certifies after 45. vLLM now builds and pre-encodes final non-streaming chat responses off the API loop while preserving the real response object and binary route metadata. GPU and MIG UUIDs remain exact identities through local topology compilation, host admission, clean Monarch process activation, NCCL preflight, and vLLM launch. | Packing probes and five tests: `scratch/autotuner_packing/`; target-24 projection work falls from 240-320 estimates to 45 in the safe case and target-48 recorded spill history needs no new estimates for rejected candidates. Loaded vLLM proof: `scratch/full_workflow_cutover/workflow/vllm_response_offload_head_05/report.json`; 24 x 4 x 900-token responses in 5.939 s, exact tokens/logprobs/policy spans/routes, zero control timeouts. Exact UUID live admission and 4 focused placement tests pass; physical MIG execution remains unavailable. Qwen correctness was relaunched with an absolute scratch shim path; Llama/Qwen throughput calibration and H200 evidence extraction are assigned. | +| 2026-08-08 10:25 | 6h35 | Closed the remaining release audit contracts and proved two opaque handler failures to their exact tensor/process boundaries. Adapter-transfer release now bounds every host RPC and remains cancellation-safe; explicit one-host serving ports are applied without bind-release probing; terminal PipelineTrainer completion still waits for serving plus durability. Qwen 3.5's prior timeout was controller/host timeout disagreement plus a test-only logical-GPU mapping error, not model startup. DSV4 trained all 88 adapter tensors and published/activated every tested generation, but Megatron restarted RNG per split parameter while vLLM restarted per packed parameter, so KV/compressor/expert base weights differed before LoRA application. | Release blocker tests pass 36/36 with Ruff and diff checks. Qwen direct startup reached ready in 164.5 s; GPT-OSS train/inference parity passes. DSV4 evidence: `scratch/full_workflow_cutover/dsv4/{dsv4_dummy_base_packing_proof.json,length_trainability_scaled_fixture_v5_tensor_proof.json,length_trainability_scaled_fixture_v5_adapter_response.json}`. The test-only vLLM initializer now restarts RNG at canonical packed boundaries; one learning rerun is active. A final audit caught that UUID/MIG masks cannot map to compact ordinals after the clean worker clears `CUDA_VISIBLE_DEVICES`; that correction is assigned separately before release acceptance. | +| 2026-08-08 10:13 | 6h23 | Closed the critical terminal-publication gap found by an independent cutover review. Async prepared steps still leave serving publication and persistence off the per-step GPU path, but `PipelineTrainer.train()` now invokes a real Megatron terminal hook after every successful session and does not return until the final learner generation is both active for serving and durably committed. Final publication metrics are forwarded to pipeline attachments. This covers max-step, exhausted-input, and clean external-stop termination without passing Megatron-only kwargs to generic backends. | A held finalizer proves the pipeline cannot return early; the concrete Megatron hook finalizes the current learner step. Current CPU gates: workflow contracts 61/61; PipelineTrainer contracts 61/61 with 6 capability skips; runtime/publication contracts 63/63 with 2 capability skips; Ruff, ty, and diff checks pass. Remaining review findings for bounded transfer cleanup, ports, and UUID/MIG visibility are assigned with disjoint ownership. | +| 2026-08-08 10:03 | 6h13 | Automated success-only cleanup for the recurring large workflow artifacts without weakening failure evidence. After successful length, yes/no, or E2E throughput stages, the parent workflow now removes only closed runtime state directories (`checkpoints`, optimizer generations, trajectories, publication scratch, and runtime leases), while preserving reports, logs, matched packed inputs, fingerprints, and all artifacts from failed stages. Cleanup byte/directory counts are recorded in stage metrics and cleanup failure fails the stage. Separately, a direct Qwen 3.5 MoE vLLM member probe loaded the exact workflow adapter and became ready in 164.5 s, proving the prior 451 s workflow failures are not a model-startup limit. | Focused cleanup contract passes 2/2; Ruff, ty, and diff checks pass. Qwen evidence: `scratch/full_workflow_cutover/qwen35/startup_probe/direct_member_warm_v2/report.json`. Next: compare the successful member argv/environment/readiness with the workflow launch, finish active GPT-OSS/Gemma runs, and trace DSV4 gradients/adapter deltas/publication rather than rerunning the failed 20-step loop. | +| 2026-08-08 09:47 | 5h57 | Completed the remaining Llama legacy qualification and root-caused the cross-handler Gemma first-step optimizer failure. Llama length trainability, yes/no trainability, and native vLLM LoRA all pass on current source. Gemma's provider has `art_flex_head_dims_by_window` keyed by both `None` and `int`; runtime identity used recursive JSON `sort_keys=True`, which attempted to order those unlike key types before optimizer selection. Mixed-key mappings now use a deterministic typed canonical representation while ordinary provider dictionaries retain their previous digest. Sensitivity no longer implicitly keeps every successful topology trace; the explicit diagnostic override remains. | Llama evidence: `scratch/full_workflow_cutover/llama3/qualification_report.md`. The exact Gemma provider now fingerprints successfully; the focused digest probe preserves the legacy ordinary-dict hash and is insertion-order invariant for the mixed mapping. Optimizer contracts pass 5/5, Ruff and ty pass. Next: rerun Gemma trainability, finish shared replicated-KV QKV LoRA consolidation, and qualify the balanced throughput workload. | +| 2026-08-08 09:25 | 5h35 | Accepted success-aware oracle cleanup and completed the tracked unit gate. MoE capture tensors are pruned only after the persisted routing bundle and manifest reload consistently; candidates are pruned only after their expected signal is established; shared references and packed inputs survive every unexpected failure and are removed only after all consumers succeed. The installed Megatron environment passed 753 tracked unit tests with 12 capability skips in 62.11 s after excluding three temporary cutover audit modules. | Oracle lifecycle invariants pass 51/51 with Ruff and diff checks. The two residual tracked failures import unchanged optional Tinker package initialization without installing the mutually exclusive Tinker extra; `origin/main` has the same eager import and this is not a cutover regression. The temporary audit modules will be removed before the final commit rather than weakening accepted serving-readiness semantics. | +| 2026-08-08 09:07 | 5h17 | Proved matched trainer throughput on the 8-layer Llama workload and completed the vLLM 0.25 compatibility-shim audit. Llama sustained `53.835k` E2E versus `54.082k` isolated logical tok/s (`0.9954x`) with identical runtime, trajectory, packed-input, and workload fingerprints, `0.054 ms` mean packed-queue wait, and `0.000021` trainer underfeed. Its `0.0` final vLLM pressure is genuine underload: 16 rollout requests do not approach the reduced 8-layer engine's capacity, so the workload must increase inference work rather than relax the `0.5` gate. vLLM 0.25.1 now natively handles guarded disconnects, pickled `ncclUniqueId`, skipped GLM IndexShare construction, and GLM indexer RoPE; those four monkey patches and their obsolete tests were removed while still-missing OpenAI, Gemma, and GLM LoRA metadata shims remain. | Llama evidence: `scratch/full_workflow_cutover/workflow/llama_calibration_l8_c768_02/report.artifacts/20260808T085305Z_811877_21ef0817/e2e_throughput/throughput_measurements.json`. Fresh runtime CLI import succeeds and runtime isolation passes 9/9 after the shim removal. Qwen's parallel 8-layer run was rejected because isolated/E2E packed fingerprints differed; the workflow owner is correcting deterministic matched-input capture before reuse. The artifact audit found 1.85-1.88 GB oracle traces and recommends success-aware immediate pruning while retaining all diagnostics on unexpected failures. | +| 2026-08-08 08:50 | 5h00 | Accepted the final one-host runtime release audit and established matched isolated/E2E throughput baselines. Cancellation-safe transfer ownership, trainer invalidation, skipped-step durability, direct-SFT serving readiness, external-adapter unload, and failure-resilient teardown pass 7/7 focused tests plus Ruff and ty. Qwen3 dense sustained `115.1k` E2E versus `116.0k` isolated logical tok/s; Llama at four layers sustained `111.9k` versus `111.6k`, with sub-millisecond packed-queue waits and sub-second policy activation. Both 128/256-token completion workloads were intentionally too inference-light (`pressure=0.053/0.000`), so 8-layer/768-token balance points are active. | Runtime evidence: `scratch/test_cutover_release_blockers.py`. Throughput artifacts: `scratch/full_workflow_cutover/workflow/{qwen3_dense_e2e_b300_worker2_baseline_01,llama_calibration_l4_c256_04}/`. An initial concurrent calibration pair accidentally re-execed the Qwen wrapper as Llama and reset its mask, causing both launches to overlap GPUs 0-3 and fail vLLM startup; those runs are rejected, the wrapper now preserves model identity and GPUs 4-7, and clean `_02` runs own disjoint partitions. | +| 2026-08-08 08:29 | 4h39 | Completed the full included GLM-5.2 workflow and removed the cross-handler train/inference adapter-cache race exposed by parallel qualification. GLM now uses the smallest two-period IndexShare coverage depth (10 layers; 12 after PP/VPP divisibility), and all non-sensitivity, non-throughput stages pass, including six RL/SFT correctness topologies with CP2/EP2/PP2/VPP2. Default reusable adapter caches are now namespaced by base model, so one handler's bounded pruning cannot delete another handler's live adapter. | Consolidated GLM evidence: `scratch/full_workflow_cutover/glm52/preflight_report.md`; correctness report: `scratch/full_workflow_cutover/glm52/correctness_after_indexshare_fix.report.json`. The cache namespace probe passes 2/2, and Ruff plus ty pass. The Llama E2E balance matrix now owns worker2 GPUs 0-3. | +| 2026-08-08 08:21 | 4h31 | Finished the bounded autotuner prefix-packing search and retained the spill-risk contract. Candidate targets now cover only the centered +/-20% interval at fixed relative granularity, historical spills propagate across skipped counts, and each candidate stops as soon as it is either certifiably safe or incapable of becoming safe within the configured cap. With the default 3% risk and 80% posterior-confidence contract, zero-spill candidates certify after 45 estimates; 16/24/32 trials cannot certify even zero spills, so reducing the statistical cap to those values would force undersized batches. | Two production-tokenized trajectory replays agree: a safe target-24 upper candidate fell from 320 estimates to 45, while an oversized target-48 search fell from 448 estimates to four one-spill rejections. Focused autotuner tests pass 10/10; Ruff, ty, and diff checks pass. Evidence: `scratch/autotuner_packing/{current_search.json,optimized_search_safe_stop.json,optimized_search_safe_stop_critical_path.json}`. | +| 2026-08-08 08:11 | 4h21 | Completed the first full Llama online throughput phase and root-caused its isolated-replay rejection. Warm CP2 training sustained about `235k` non-padding logical tok/s at `0.54 s` per 128,112-token step with 2.26% unused capacity and no dummy work; however the final autotuner window was inference-under/trainer-under (`vLLM pressure=0.123`, trainer underfeed `0.150`) and stale groups forced workers from 16 to 8. A CPU replay proved that process-global trajectory/row shuffles changed `tokens`, `logprobs`, and `advantages` for identical bundles; deterministic local pseudo-shuffles make every packed tensor byte-identical. | Online artifact: `scratch/full_workflow_cutover/workflow/llama_e2e_b300_worker2_smoke_10/`. Repeatability probe: `scratch/full_workflow_cutover/workflow/probe_packing_repeatability.py`; uncontrolled packing changed three tensors before the fix and zero afterward. Twenty-six focused preprocessing tests, fingerprint mutation test, Ruff, and diff checks pass. The corrected four-GPU rerun is active. A separate release audit found public-SFT readiness, skipped-step durability/atomicity, failed receive cleanup, and shutdown cleanup blockers; the audit agent owns those disjoint fixes. | +| 2026-08-08 07:38 | 3h48 | Proved that ordinary trainability remains real pretrained-model learning after the typed-runtime cutover. The pinned Llama-3.2-1B-Instruct stage passed its unchanged `5.0/1.5` error gates, improving mean absolute token error from `29.8333` to `1.25` at policy step 12 in `277.52 s`. The first correctly placed B300 throughput smoke brought up vLLM and both trainer ranks and completed two updates; its sole terminal defect was Pydantic attempting to JSON-serialize `array.array` fields while fingerprinting the captured packed batch. | Llama report: `scratch/full_workflow_cutover/llama3/length_trainability_canonical.json`. The packed-input fingerprint now hashes stable shape metadata, raw token-array bytes, and SHM tensor bytes directly; its focused mutation test is in place. Worker-two GPUs 4-7 are reserved for the warm-cache rerun, while newly free partitions are being assigned to the remaining handlers. | +| 2026-08-08 07:29 | 3h39 | Closed two workflow-design ambiguities before spending more GPU time. Controller-facing backend GPU IDs are now logical within `CUDA_VISIBLE_DEVICES`; the one-host topology compiler performs the single translation to host-physical IDs after a clean Monarch worker drops the inherited mask. Only the direct train/inference subprocess harness translates its default role slots before launch. Separately, the failed Llama length gate proved that random compact weights are not a pretrained-behavior fixture: ordinary trainability stages use pinned canonical weights, while infeasible GLM-5.2 and DSV4 runs use explicit validated reduced contracts rather than downloading full public checkpoints. | Three local-runtime namespace tests and the backend logical-to-physical workflow contract pass. The prior accepted GLM workflow is reproduced by `scratch/main_merge_validation/run_glm52_workflow.sh`: reduced 12-layer weights plus exact allowed-token controls passed length `6.5 -> 1.5` and yes/no `0.5 -> 1.0` without changing cases or thresholds. The first canonical Llama length-only rerun is active on head GPUs 0-1; Qwen train/inference is restarting under the nonzero `4,5,6,7` mask. | +| 2026-08-08 07:05 | 3h15 | Completed the Llama train/inference fixture correction and advanced its full dirty-tree workflow through HF parity, LoRA coverage, train/inference parity, and merged serving. The tokenizer-compatible compact fixture now preserves the canonical 128,256-token vocabulary and rejects out-of-range representative IDs before CUDA launch. Correctness also derives the least common multiple required by the selected PP/VPP topologies and rounds only the compact layer count, so the mandatory PP2/VPP2 case is legal without changing any topology or threshold. | Llama train/inference parity passed on the first corrected run: logprob MAPE `0.0150623%` and top-20 KL `6.89e-6`; full-run artifacts are under `scratch/full_workflow_cutover/llama3/full_no_sensitivity_dirty.artifacts/20260808T064801Z_2281534_85a6c874/`. The grouped RL/SFT correctness stage is active and its completed topology is well below the existing fp32 thresholds. Qwen correctness grouping reduced dense wall time from `1059.91 s` to `481.10 s` with exact report parity; MoE launches remain separate because route-replay lifetime is a real process boundary. | +| 2026-08-08 06:35 | 2h45 | Reviewed and accepted the runtime audit fixes: prepared batches now validate the complete effective packing configuration, failed or cancelled trainer jobs invalidate resident state, exact-eval adapter leases are transactional, and first-service construction is serialized. Root-caused the Llama train/inference crash to the compact fixture rather than CUDA or the typed runtime: its production tokenizer emitted IDs through 128,009 into an 8,192-entry compact embedding; 482 of 1,024 packed positions were out of range, exactly matching the embedding gather assertion. | Post-audit gates pass 40 runtime tests with 6 capability skips and 98 workflow/runtime contracts with 2 skips; Ruff and ty pass. Failure artifact: `tests/integration/megatron/train_inf_mismatch/artifacts/tests_integration_megatron_train_inf_mismatch_test_live_real_path_output_parity.py_test_real_path_train_inf_mismatch_live/983f01816e8f/20260808T060245Z_2018281_ae7c2e15/`. Preserve production vocabulary in compact fixtures and add a CPU token-range preflight before the one focused rerun. | +| 2026-08-08 06:22 | 2h32 | Bounded prefix-packing estimation without weakening its spill-risk contract. The tuner now searches a centered +/-20% target window at relative granularity, probes the upper bound before binary search, carries historical spills across skipped group counts, and stops a Monte Carlo candidate once even all-clean remaining trials cannot make it admissible. | Replayed 280 production-tokenized group shapes from a recent B300 multi-turn/tool workload. At the 64-trial maximum, target 24 initially fell from 320 to 64 packing estimates and target 48 from 448 to 4; wall speedups were 2.64x and 6.47x on this short-record replay and should approach call-count speedups on the reported 9 s estimate-heavy case. Trial budgets 16/24/32 cannot certify the default 3% target even with zero spills (posterior upper bounds 6.49%/4.91%/3.94%), so the 64-trial maximum remains; the 08:21 refinement also stops safe candidates after 45 clean trials. Ten focused tests, Ruff, and ty pass; artifacts are under `scratch/autotuner_packing/`. | +| 2026-08-08 05:47 | 1h57 | Completed the CPU/contract side of the one-host hard cutover and exercised the real managed serving path on B300. Live Llama gates found two exact cutover/test regressions: direct workflow probes omitted the new managed API key, and merged launch/restart incorrectly dropped the adapter bootstrap path that the vLLM runtime requires before typed merged synchronization. Both are corrected; merged serving now passes with the expected model ID and zero reload warnings. | CPU evidence: 51 workflow-contract tests, 27 focused runtime tests with 6 optional skips, Ruff, lock, and diff checks. Live merged report: `scratch/full_workflow_cutover/llama3/merged_vllm_serving_cutover_fixed.json`. Native LoRA advanced past authentication and now uses a real typed no-op policy generation instead of fabricating a copied checkpoint; its rerun and Llama length trainability are active. | +| 2026-08-08 05:25 | 1h35 | The one-host runtime boundary now passes 46 focused unit checks plus 31 integration contract checks (6 capability-dependent skips), covering typed RL/SFT payloads, PipelineTrainer packing/metrics, runtime isolation, optimizer generations, output-parity invariants, and merged export. Gemma 4 was root-caused as a workflow-fixture regression: the current and main handlers pass unchanged HF and packing tests with pinned canonical weights, while the compact random post-norm fixture amplifies shape-level GEMM differences into invalid oracle failures. | Runtime checks used the scratch-only dirty-validation shim; the clean final gate will not. Gemma evidence and the stage-aware canonical-weight fixture design are in `scratch/full_workflow_cutover/gemma4/root_cause_report.md`. Keep compact fixtures for other stages, but use the pinned canonical snapshot for Gemma HF parity and packing without changing cases, precision, or thresholds. | +| 2026-08-08 05:10 | 1h20 | The one-host hard cutover has removed the legacy Megatron service, filesystem job client/poller, and SFT directory handoff. Its new local-runtime, RL/SFT payload, adapter-transport, queue, and PipelineTrainer contracts pass 45 focused tests. Qwen 3 dense and MoE now pass HF parity (16/16 and 26/26), LoRA coverage, and all four packing-invariance scenarios on the current tree. | Cutover tests: `tests/unit/test_local_megatron_cutover.py`, `tests/unit/test_local_rollout_data_plane.py`, and focused PipelineTrainer suites. Qwen evidence: `scratch/full_workflow_cutover/qwen3/runs/`. Remaining CPU failures are isolated to the in-progress workflow fixture/throughput integration and two test-harness issues already assigned; no broad GPU gate yet. | +| 2026-08-08 04:49 | 0h59 | Closed the Qwen 3.5 HF-parity regression at its execution-model boundary. HF now evaluates recurrent GDN inputs along true root-to-leaf prefix-tree paths, counts each packed loss token once while retaining descendant gradients through shared prefixes, and reassembles MoE routes by packed-token identity. Dense passed 55/55 checks, MoE passed 71/71, and a Llama standard-attention control passed 13/13 without handler, fixture, precision, topology, case, or threshold changes. | Evidence and root-cause report: `scratch/full_workflow_cutover/qwen35/report.md`. Final wrapper reruns remain after fixture/runtime changes stabilize. Cutover audit also caught and assigned an unset-revision bug before live validation: local vLLM launch must preserve `None`, not request a literal `default` revision. | +| 2026-08-08 04:30 | 0h40 | Isolated two real oracle issues without weakening tests. Qwen 3.5 HF parity was flattening recurrent GDN sibling state while Megatron followed prefix-tree ancestry; a root-to-leaf HF prototype restores dense and MoE parity. Gemma 4 tied-embedding HF and packing failures remain under focused investigation. Confirmed that Qwen 3 dense length trainability currently loads the full 32B checkpoint, establishing a concrete workflow-runtime target rather than a model regression. | Qwen analysis and proofs are under `scratch/full_workflow_cutover/qwen35/`; Gemma probes are under `scratch/full_workflow_cutover/gemma4/`. Keep canonical model identity separate from compact correctness and production-width reduced-layer throughput fixtures; do not run the full matrix until these focused gates and the local runtime cutover pass. | +| 2026-08-08 04:17 | 0h27 | Established focused dirty-tree validation without weakening production artifact pinning, passed Qwen 3 dense packing invariance on four B300s, and proved the prior DSV4 merged-serving failure is already fixed at clean `983f01816`. The DSV4 four-layer TP2/EP2 Megatron plus TP2/EP2 vLLM fixture exercised DeepGEMM packed E8M0 scales, served successfully, and emitted zero reload warnings. | Scratch-only `sitecustomize.py` records dirty state for iteration; the final gate will omit it. DSV4 evidence is under `scratch/full_workflow_cutover/dsv4/`; Qwen report is `scratch/full_workflow_cutover/baseline/qwen3_dense_packing.json`. Continue skipped-stage baselines while the one-host runtime and workflow changes are isolated. | +| 2026-08-08 03:50 | 0h00 | Started the single-node hard cutover and complete handler-workflow qualification. Confirmed a clean source tree, 24 idle B300 GPUs across `10.0.1.86`, `10.0.1.192`, and `10.0.0.72`, and that the prior handler run skipped train/inference mismatch, correctness, length trainability, and other full-workflow stages. | Preserve existing test semantics, use focused stage iteration, calibrate throughput from isolated and healthy E2E evidence, checkpoint uncommitted diffs under `scratch/`, and run each full workflow only as the final gate. | +| 2026-07-15 07:46 | 0h00 | Binding 48-hour multi-node goal started; waiting for the GLM/E2E handoff signal at 30-minute intervals. | No project work performed before the signal. | +| 2026-07-15 08:17 | 0h31 | Observed `scratch/start_multinode` and created the implementation worktree from the finalized GLM snapshot. | Clean source `austin/glm52_cp` at `bc39c128`; created `base/austin/glm52_cp` and `austin/monarch_multinode_training`. Next: current-code/API audit and design freeze. | +| 2026-07-15 08:34 | 0h48 | Audited the current E2E runtime and pinned distributed APIs. Chose direct Monarch 0.2 actor-per-rank orchestration, a run-scoped typed trainer boundary, host-local shared packed-batch leases, and one MCore schedule adapter for RL/SFT/reference execution. Rejected TorchStore as an early, version-coupled dependency and rejected nested multi-node `torchrun`. | Research notes in `scratch/multinode_research/{monarch,art_runtime,contracts_data_plane,pp_vpp}.md`. Remaining gates: pinned vLLM replica/router details and exact 2x8 H200 matrix. | +| 2026-07-15 08:46 | 1h00 | Froze and committed all research gates and shared topology schemas. A real local Monarch test attached two worker loops, spawned two processes per host, and observed ranks `(global, local, world) = (0,0,4), (1,1,4), (2,0,4), (3,1,4)`. Found that Monarch 0.2 resolves a uv-venv interpreter symlink when spawning ProcMesh children; explicit child `PYTHONPATH` propagation made the smoke pass. | Commits `2bae190d`, tracker `61e2894` and `fa42db5`; probe `scratch/monarch_local_smoke.py`. Four isolated coding agents active. Require a pre-CUDA child import/build probe in production bootstrap. | +| 2026-07-15 08:52 | 1h06 | Passed a real cross-host Monarch smoke on the idle `austin-art0`/`austin-art1` 8xH200 pods: attached both worker loops, spawned one process per host, configured torch-elastic, and returned ranks 0/1 with world size 2. No GPU process was created. | Probe `scratch/monarch_remote_controller_smoke.py`. Required `enable_transport("tcp")` before every Monarch API; raw TCP liveness probes are invalid because the worker port accepts root clients. Actor health endpoints are mandatory. | +| 2026-07-15 08:59 | 1h13 | Passed a real cross-host Monarch RDMA ownership probe: one source actor exposed a 128 MiB CPU buffer, both hosts read it concurrently with exact SHA-256 equality, and the owning actor released the handle cleanly. | Probe `scratch/monarch_remote_rdma_smoke.py`. Use actor-owned `RDMABuffer` handles for one transfer per trainer host, POSIX shared memory for host-local rank fanout, and owner-actor release. Root-created or root-dropped handles are invalid in pinned Monarch 0.2. | +| 2026-07-15 09:09 | 1h23 | Integrated native vLLM replica management and the bounded prefix-aware router, installed the pinned vLLM 0.23 runtime, validated its real native-multiprocessing parser, and passed the focused router suite. | Commit `db7abd06`; 8 focused tests passed on the integrated branch. Next: live 2-host PP=2 serve smoke before GLM scale-up. | +| 2026-07-15 09:11 | 1h25 | Passed the two-node NCCL preflight on GPU 7: NCCL selected `NET/IB/.../GDRDMA` in both directions, discovered all eight 400 Gb/s HCAs, and sustained 43.908 GB/s algorithm payload on repeated 256 MiB all-reduces. | Probe `scratch/nccl_multinode_preflight.py`. Preserve the validated IB/GDR environment and reject socket fallback for all model tests. | +| 2026-07-15 09:12 | 1h26 | Passed native vLLM 0.23 PP2 serving across both hosts on one H200 each. Llama-3.2-1B loaded as leader/headless members, `/art/state` reported `nnodes=2`, and deterministic chat returned exactly `YES` with ART token/policy metadata. | Probe `scratch/run_vllm_pp2_smoke.sh`. Startup took about 126 s and used NCCL IB/GDRDMA. Parent cancellation orphaned EngineCore/PP workers, establishing the required managed-process-group teardown fix. | +| 2026-07-15 09:20 | 1h34 | Integrated the leased batch plane and typed warm-trainer branches, then hardened distributed rollouts: explicit Monarch TCP/PYTHONPATH bootstrap, valid stable actor names, inference-only model snapshots, per-host/version client reuse, and raw metric-delta return. | Commits `283303ec`, `27b20be8`, `daf34750`, `55b8ce6f`, `ef4fc826`, `6605aa89`; focused pipeline/runtime tests and repository pre-commit passed. Next: remove duplicate batch refs and use the proven actor-owned RDMA path. | +| 2026-07-15 09:31 | 1h45 | Unified trainer jobs on the canonical leased RDMA batch contract and integrated one MCore PP/VPP schedule path with LoRA publication and TrainerRank support. Real two-GPU synthetic PP2 tests passed for VP1 and VP2 after integration. | Commits `68bd51ab`, `20bad91f`; focused unit suite and both `pp2_schedule_smoke.py` modes passed. Remaining research gate: remove the temporary PP incompatibility with activation recomputation plus MoE routing replay before GLM validation. | +| 2026-07-15 10:17 | 2h31 | Moved trajectory serialization and Megatron prefix-tree packing into a host actor, preserved Choice/token metadata and autotuner group-shape feedback, then transferred one immutable host-owned lease by RDMA. Fixed the packaged worker no-op, eager-import contamination, uv child import path, Monarch tuple return handling, and a concurrent package-import deadlock. | Commit `c58ffdc3`; synthetic 45 MiB transfer passed exact destination-host tensor checks; real GLM tokenizer packing produced a 46,080-byte batch with two trainable tokens and exact nonzero advantages on node 1. Ruff, ty, pre-commit, lazy public exports, and five focused tests passed. Next: cohesive runtime ownership and backend cutover. | +| 2026-07-15 10:53 | 3h07 | Wired `MegatronBackend(runtime=...)` to host-owned packing and a run-scoped warm Monarch trainer while leaving `MegatronBackend()` unchanged. Added gang-managed multi-node/multi-replica vLLM, streaming bounded routing, atomic policy commits, in-flight update quarantine, exact-policy leases, and aggregate replica metrics. | Commit `ee6fcedc`; live two-upstream gateway and five contract/runtime tests passed. Full ruff, ty, lock, format, and commit hooks passed. Next: live two-rank typed trainer startup/job. | +| 2026-07-15 12:04 | 4h18 | Completed a real ART `backend.train()` optimizer step on two hosts and eight H200s (four ranks/host) with reduced-depth, production-width GLM-5.2 at CP4/EP4. Host packing, RDMA fanout, typed trainer execution, checkpoint publication, and returned metrics all passed. | Checkpoint `scratch/live_distributed_trainer_state_glm52/multinode/models/glm52-multinode-smoke/checkpoints/0001`; loss `2.0995e-5`, grad norm `8.3363e-5`, schedule wall `50.39 s`, peak/rank `53,897,090,048` bytes, full backend step `185.67 s`. Fixed Monarch identifier length, uv virtualenv/CUDA-library inheritance, typed config conversion, HybridEP validation, registered model resolution, and Megatron scalar-int token accounting in commits through `6c676535`. The debug smoke accidentally uploaded W&B run `glm52-multinode-smoke`; force W&B disabled for subsequent probes. | +| 2026-07-15 12:06 | 4h20 | Corrected healthy warm-trainer teardown: both actor close and proc stop can complete as internally cancelled Monarch futures, while caller cancellation must still propagate. | Commit `57b5d5e8`; three focused cancellation tests, ruff, ty, and commit hooks passed. Next: live close validation as part of multi-node inference/combined-runtime work. | +| 2026-07-15 12:21 | 4h35 | Passed GLM-5.2 inference on 16 H200s with one native vLLM TP8/PP2/EP replica. This was initially believed to use the reduced snapshot; later physical load evidence proved that this run resolved the public full checkpoint instead. | Startup `285.08 s`; runtime reported `world_size=16`, `max_model_len=122880`, `max_num_batched_tokens=16384`, no preemptions, and 3,072 cached of 6,198 prompt tokens on the repeated-prefix probe (49.56% token hit rate). Fixed managed-runtime CUDA/Python isolation and failed-start cleanup in commits through `4e4374b3`. | +| 2026-07-15 12:49 | 5h03 | Replayed eight identical 24,776-token prefixes on TP4, TP8/PP1, and TP8/PP2. Every topology computed 3,272 prompt tokens, reused 21,504, reached an 86.7937% prefix-hit rate, and had zero preemptions. Independent TP8/PP1 greedy continuations diverged, proving generated-token equality is not a valid PP attribution surface for this MoE runtime. | Artifacts `scratch/glm_inference_{tp4,tp8,tp8_pp2}.json`; fixed teacher-forced prompt scoring added for deterministic PP parity. Warm TP8/PP1 startup fell to `114.13 s`. | +| 2026-07-15 13:02 | 5h16 | Landed the provider-neutral owned worker lifecycle, secure allowlisted gateway, vLLM-0.23-compatible prefix/load router, domain-preserving remote error envelope, and bounded Monarch trainer cancellation. A live bad-lease call preserved actor PID/health and raised the typed lease error. | Commits `04c886b5`, `78fcb979`, `18af2b59`, `4fa473fe`, `98a8be7d`, and `072664a3`. Router/gateway suite passed 8 tests at `0.821 ms/decision`; cancellation suite passed 10 tests. Prefix routing is not complete until runtime KV-event subscribers and exact request block hashes feed this core. | +| 2026-07-15 13:35 | 5h49 | Made LoRA publication transactional down to every vLLM engine worker and pinned model plus tokenizer revisions in the physical launch. The parity audit found that environment-dependent resolution had compared a 171.25 GiB reduced snapshot against the 1,403.19 GiB public full model; explicit shared paths now remove that ambiguity. | Commits `5ded7aa2`, `86c07a32`, `910b8fc9`, and `bd38f62a`; 17 controller tests and 8 runtime endpoint tests passed. Live TP8 exercised the exact endpoint and correctly failed closed on one strict tuple/list wire mismatch, now fixed. Fresh same-snapshot parity is running with warm compile artifacts. | +| 2026-07-15 13:46 | 6h00 | Connected exact vLLM KV-cache events to deterministic request-block hashing and replica routing. Replaced a custom MessagePack parser with direct `msgspec`, matched vLLM 0.23's exact hash and ART policy-salt contracts, enforced replay monotonicity, and rejected unsupported decode-context-parallel routing rather than silently misrouting. | Commits `3fdbd562` and `9a7ab65a`; 14 focused tests plus real installed-vLLM event-codec/hash parity passed. CPU cost: `9.11 us` per three-event decode, `721.89 us` per 8,192-token hash, and `0.802 ms` per four-replica routing decision with 7,680 hashes each. Next: prove affinity with live two-replica publishers. | +| 2026-07-15 13:55 | 6h09 | Made optimizer checkpoints generation-atomic and the trainer-to-serving transaction crash-consistent. Each rank now publishes an fsynced shard, rank 0 commits a manifest plus generation pointer, and serving failure preserves the already-durable trained checkpoint while quarantining failed replica publication. Distributed close and stalled collective paths are bounded and propagate failures. | Commits `62dcb22e`, `f2f2d403`, `ba1a1d6d`, and `8e01a86e`; 27 focused timeout/transaction tests passed. Strict hard cutover deliberately rejects legacy optimizer directories. | +| 2026-07-15 14:02 | 6h16 | Simplified the Megatron PP/VPP adapter and exposed `grad_accumulation_sequences` through the public backend train call so PP2/VPP can receive enough microbatches without generalized-RL work. Synthetic two-GPU PP2 VP1/VP2 and the 426-test pipeline suite pass. | Commits `e71cbfcc` and `5aea181a`. Research matrix is PP2/VPP1 and PP2/VPP2 at 122,880 packed tokens, followed by lower packed length only if it increases end-to-end token throughput. Host-admission implementation is under line-count and repeated-fingerprinting review before landing. | +| 2026-07-15 14:28 | 6h42 | Hardened distributed rollout placement and code identity, validated the two-node SkyPilot task shape, and secured ART-managed distributed vLLM by default. Scale-down now preserves draining workers while remapping survivors, installed rollout callables are checked once against a source SHA-256 on every worker, and a backend-lifetime random key protects both replicas and the non-loopback gateway without changing explicit or external-runtime credentials. | Commits `532bfaa1`, `bd389b38`, `2407b541`, and `b8ed1ca3`; focused rollout/auth tests, ruff, and ty pass. `sky launch -y --dryrun` resolved two 8xH200 Kubernetes nodes. Next: land host admission and corrected optimizer generations, then run the owned two-host CPU rollout as the physical-launch acceptance test. | +| 2026-07-15 14:56 | 7h10 | Landed typed host admission and completed the physical launch acceptance. Startup now caches exact ART/package/environment fingerprints, validates physical host/GPU identity and shared-root create/read/rename/delete/fsync semantics, and bounds every startup/teardown phase. The first real SSH smoke exposed that disconnecting local SSH clients could orphan remote fish sessions; run-scoped PID/address/cmdline validation now terminates remote process groups explicitly. | Commits `c47cd77c` and `63908e61`; 26 admission/close tests and 7 bootstrap lifecycle tests pass. Real `art-monarch ssh` admitted both hosts and executed CPU rollouts in PIDs `383947` and `1330761` on the two distinct pod hostnames, then left no Monarch, host-service, or rollout processes. Next: finish optimizer-generation audit and autotuner capacity wiring before fresh GPU runs. | +| 2026-07-15 16:18 | 8h32 | Completed rollout-capacity/autotuner integration and the second optimizer durability audit. The service now owns a typed model transaction lease through distributed staging, canonical adapter publication, and optimizer completion; adapter acknowledgments, generation readers, RL/SFT retention, pointer-swap retries, and serving digests fail closed without repeated producer hashing. Cross-host admission also verifies shared `flock` coherence before launch. | Commits `509fde50`, `29696ce5`, `ad560f86`, `9ed14528`, and `820c9ab6`; 59 durability/runtime tests, 36 LocalBackend tests, Ruff, formatting, and `ty` pass. A repeated owned SSH acceptance ran four CPU workers across PIDs `633665`, `633825`, `1559212`, and `1559373` on both hosts and left no worker processes. Next: fresh isolated-root GPU matrix. | +| 2026-07-15 17:19 | 9h33 | Passed fresh same-checkpoint GLM inference on TP8/PP1 and two-node TP8/PP2, then proved live two-replica prefix affinity. The first affinity run exposed that vLLM overrides the requested 16-token hash granularity with a 64-token unitary cache; ART now obtains the effective post-profile size through a typed EngineCore capability instead of dual-hashing requests. | Commit `df3b9f78`; TP8/PP2 startup `195.29 s`, world size 16, zero preemptions, and the same 86.7937% prefix-hit accounting as PP1. The affinity repeat routed both 8,192-token requests to one TP4 replica, reused 8,128 tokens, and left the other replica at zero prompt tokens. ART/runtime suites passed 73 focused tests plus Ruff, formatting, and `ty`. TP8/PP1 repeats are bitwise exact; PP1-versus-PP2 actual-token logprob MAPE is 1.215% on the seeded random BF16 model, with route-sensitive rank churn retained as a fidelity research item rather than hidden by the 3% forward tolerance. | +| 2026-07-15 18:04 | 10h18 | Upgraded the control plane to Monarch 0.5 and repeated the physical two-host CPU acceptance without leaked workers, shutdown panic, or inherited-environment logging. Made serving capability discovery and LoRA state DP-complete across every vLLM engine core. The first combined trainer/inference launch then rejected CP4 because GLM DSA does not support CP and exposed a second, independent local-checkpoint support-identity bug after retargeting to efficient intra-node TP4 plus cross-node PP2. | Commits `aa86a26e` and `05228350`; 31 serving and 24 lifecycle tests passed. Root cause: the reduced physical path selected `default_dense`, leaving Bridge's experimental `dsa` dispatcher to reject `dsa`; canonical support identity is now being separated from the physical checkpoint before resuming the PP2/VPP matrix. | +| 2026-07-15 18:54 | 11h08 | Separated canonical model-support identity from physical checkpoint location and proved reduced GLM PP2 provider/identity-LoRA construction. The fresh combined TP4/PP2 VPP1 run brought up eight inference and eight trainer GPUs, but 122,880 tokens at CP1 spent 35:37 in PP0 CPU graph/plan construction without a first GPU kernel and was boundedly cancelled. | Commit `b8c9f1b9`; 12 runtime and 21 provider tests passed. All owned GPU/worker processes were removed, but close surfaced a false-negative Monarch deadline plus propagated `CancelledError`. VPP2 research shows the legal `[2,4,4,2]` layout improves estimated trainer-stage memory from about 57/110 GiB to 75/93 GiB and ideal two-microbatch bubble from 33.3% to 20%. Next: lower packed-length matrix and cancellation cleanup repair. | +| 2026-07-15 19:48 | 12h02 | Proved the controller and full reduced-GLM identity-LoRA path remain CUDA-free, then isolated the lower-length trainer startup from inference. TP4/PP2 at 30,720 tokens loaded both stages at about 67/116 GiB per rank and entered concurrent first-step Dynamo/Inductor graph capture on all eight ranks; no GPU kernel has launched yet, so the run is being preserved for cold/warm evidence rather than repeatedly cancelled. | Commits `77467429` and preceding `05228350`; controller import plus 174 MiB identity-LoRA generation left CUDA uninitialized. The resilience audit found a partial-rank completion acceptance bug, no real member-kill proof yet, and an incomplete explicit one-host runtime path. An interrupted 30,720-token attempt required exact-PID termination of two run-owned Monarch workers, so lifecycle repair is a release gate. | +| 2026-07-15 20:05 | 12h19 | Closed two lifecycle defects and root-caused both the long cold start and the host actor's 520 MiB GPU-0 context. Full trainer-rank quorum is now required, model-service close is one shielded task, and managed vLLM explicitly restores assigned devices. The 27:43 EP1 compile probe was stopped after recognizing that TP4/EP4/PP2 is valid and keeps both expert and tensor collectives within each four-GPU host stage. | Commits `9253d336`, `469e783c`, and `b0f13baf`; 27 focused lifecycle/transaction tests passed. Monarch RDMA registers CPU buffers through rdmaxcel, whose driver path calls `cudaFree(0)`; CPU host/rollout actors must mask CUDA while vLLM children restore placement. Compile audit found fresh-process warm disk caches do not remove the synchronous frontend delay, making warm rank-process reuse the correct default. One SIGINT removed every owned GPU, trainer, actor, and worker process on both hosts despite the old imported service surfacing its known cancellation group. | diff --git a/pyproject.toml b/pyproject.toml index bb243c176..f84e1b6a0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -17,11 +17,27 @@ dependencies = [ "polars>=1.26.0", "tblib>=3.0.0", "nest-asyncio>=1.6.0", + "numpy<2; python_version < '3.13'", "setproctitle>=1.3.6", ] [project.optional-dependencies] plotting = ["matplotlib>=3.10.1", "seaborn>=0.13.2"] +distributed = [ + "aiohttp>=3.13.0", + "msgspec>=0.21.0", + "torch==2.11.0+cu128 ; sys_platform == 'linux' or sys_platform == 'win32'", + "torch==2.11.0 ; sys_platform == 'darwin'", + "torchmonarch==0.6.0", + "transformers>=5.2.0,<=5.12.1", +] +distributed-cu130 = [ + "aiohttp>=3.13.0", + "msgspec>=0.21.0", + "torch==2.11.0+cu130 ; sys_platform == 'linux'", + "torchmonarch==0.6.0", + "transformers>=5.2.0,<=5.12.1", +] backend = [ "peft>=0.14.0", @@ -29,7 +45,31 @@ backend = [ "bitsandbytes>=0.45.2,!=0.50.0", "unsloth==2026.3.3", "unsloth-zoo==2026.3.1", - "torch==2.11.0", + "torch==2.11.0+cu128 ; sys_platform == 'linux' or sys_platform == 'win32'", + "torch==2.11.0 ; sys_platform == 'darwin'", + "torchao==0.16.0", + "accelerate==1.7.0", + "awscli>=1.38.1", + "setuptools>=78.1.0", + "wandb==0.28.0", + "transformers==5.2.0", + "duckdb>=1.0.0", + "pyarrow>=15.0.0", + "trl==0.20.0", + "nbclient>=0.10.1", + "pytest>=8.4.1", + "nbmake>=1.5.5", + "gql>=4.0.0", + "nvidia-cudnn-frontend<1.21 ; sys_platform == 'linux'", + "nvidia-resiliency-ext<0.5 ; sys_platform == 'linux'", +] +backend-cu130 = [ + "peft>=0.14.0", + "hf-xet>=1.1.0", + "bitsandbytes>=0.45.2,!=0.50.0", + "unsloth==2026.3.3", + "unsloth-zoo==2026.3.1", + "torch==2.11.0+cu130 ; sys_platform == 'linux'", "torchao==0.16.0", "accelerate==1.7.0", "awscli>=1.38.1", @@ -44,17 +84,22 @@ backend = [ "nbmake>=1.5.5", "gql>=4.0.0", "nvidia-cudnn-frontend<1.21 ; sys_platform == 'linux'", + "nvidia-nccl-cu13==2.28.9 ; sys_platform == 'linux'", "nvidia-resiliency-ext<0.5 ; sys_platform == 'linux'", ] megatron = [ + "aiohttp>=3.13.0", + "msgspec>=0.21.0", "numpy<2", - "torch==2.11.0", - "torchvision==0.26.0", + "torch==2.11.0+cu128 ; sys_platform == 'linux' or sys_platform == 'win32'", + "torch==2.11.0 ; sys_platform == 'darwin'", + "torchvision==0.26.0+cu128 ; sys_platform == 'linux' or sys_platform == 'win32'", + "torchvision==0.26.0 ; sys_platform == 'darwin'", "flash-attn-4==4.0.0b5", "flashinfer-cubin==0.6.8.post1", "flashinfer-python==0.6.8.post1", "ninja>=1.11.1", - "quack-kernels==0.3.7", + "quack-kernels==0.3.9", "apex", "transformer-engine==2.11.0", "transformer-engine-cu12==2.11.0", @@ -63,6 +108,7 @@ megatron = [ "pybind11>=2.13.6", "setuptools>=78.1.0", "megatron-bridge==0.4.0rc0", + "torchmonarch==0.6.0", "nvidia-cuda-cccl-cu12==12.9.27 ; sys_platform == 'linux'", "tilelang==0.1.10 ; sys_platform == 'linux' and platform_machine == 'x86_64'", "causal-conv1d==1.6.1 ; sys_platform == 'linux' and platform_machine == 'x86_64' and python_full_version < '3.12'", @@ -74,6 +120,35 @@ megatron = [ "scipy>=1.17.0", "ml-dtypes>=0.5.0 ; python_full_version < '3.13'", ] +megatron-cu130 = [ + "aiohttp>=3.13.0", + "msgspec>=0.21.0", + "numpy<2", + "torch==2.11.0+cu130 ; sys_platform == 'linux'", + "torchvision==0.26.0+cu130 ; sys_platform == 'linux'", + "flash-attn-4==4.0.0b5", + "flashinfer-cubin==0.6.8.post1", + "flashinfer-python==0.6.8.post1", + "ninja>=1.11.1", + "quack-kernels==0.3.9", + "apex", + "transformer-engine==2.14.1", + "transformer-engine-cu13==2.14.1", + "transformer-engine-torch==2.14.1", + "megatron-core==0.17.0", + "pybind11>=2.13.6", + "setuptools>=78.1.0", + "megatron-bridge==0.4.0rc0", + "torchmonarch==0.6.0", + "tilelang==0.1.10 ; sys_platform == 'linux' and platform_machine == 'x86_64'", + "nvidia-ml-py==13.580.82", + "nvidia-nccl-cu13==2.28.9 ; sys_platform == 'linux'", + "nvidia-modelopt>=0.42.0a0 ; sys_platform != 'darwin'", + "nvidia-resiliency-ext<0.5 ; sys_platform == 'linux'", + "transformers==5.12.1", + "scipy>=1.17.0", + "ml-dtypes>=0.5.0 ; python_full_version < '3.13'", +] langgraph = [ "langchain-core>=0.3.51", @@ -90,7 +165,8 @@ tinker = [ "protobuf>=6.31.1", "tinker-cookbook>=0.5.2,<0.6", "tinker>=0.23.4,<0.24", - "torch==2.11.0", + "torch==2.11.0+cu128 ; sys_platform == 'linux' or sys_platform == 'win32'", + "torch==2.11.0 ; sys_platform == 'darwin'", "transformers>=5.2.0,<=5.5.3", "uvicorn>=0.35.0", "datrie>=0.8.3", @@ -98,6 +174,7 @@ tinker = [ [project.scripts] art = "art.cli:app" +art-monarch = "art.distributed.monarch_bootstrap:main" [build-system] requires = ["hatchling"] @@ -173,19 +250,70 @@ conflicts = [ { extra = "tinker" }, { extra = "megatron" }, ], + [ + { extra = "distributed" }, + { extra = "distributed-cu130" }, + ], + [ + { extra = "distributed" }, + { extra = "backend-cu130" }, + ], + [ + { extra = "distributed" }, + { extra = "megatron-cu130" }, + ], + [ + { extra = "distributed-cu130" }, + { extra = "backend" }, + ], + [ + { extra = "distributed-cu130" }, + { extra = "megatron" }, + ], + [ + { extra = "backend" }, + { extra = "backend-cu130" }, + ], + [ + { extra = "backend" }, + { extra = "megatron-cu130" }, + ], + [ + { extra = "backend-cu130" }, + { extra = "megatron" }, + ], + [ + { extra = "backend-cu130" }, + { extra = "megatron-cu130" }, + ], + [ + { extra = "megatron" }, + { extra = "megatron-cu130" }, + ], + [ + { extra = "tinker" }, + { extra = "backend-cu130" }, + ], + [ + { extra = "tinker" }, + { extra = "distributed-cu130" }, + ], + [ + { extra = "tinker" }, + { extra = "megatron-cu130" }, + ], ] override-dependencies = [ "click==8.2.0", + "flashinfer-python==0.6.8.post1", "megatron-core==0.17.0", "numpy<2", + "nvidia-cublas==13.2.2.2 ; sys_platform == 'linux'", "nvidia-resiliency-ext<0.5", - "quack-kernels==0.3.7", - "transformer-engine==2.11.0", - "torch==2.11.0", - "torchvision==0.26.0", + "quack-kernels==0.3.9", ] exclude-dependencies = ["pynvml", "emerging-optimizers", "causal-conv1d", "mamba-ssm"] -no-build-isolation-package = ["apex", "transformer-engine", "transformer-engine-cu12", "transformer-engine-torch", "megatron-bridge", "nv-grouped-gemm"] +no-build-isolation-package = ["apex", "transformer-engine", "transformer-engine-cu12", "transformer-engine-cu13", "transformer-engine-torch", "megatron-bridge", "nv-grouped-gemm"] [tool.uv.extra-build-dependencies] apex = ["torch>=2.11.0"] @@ -194,7 +322,7 @@ nv-grouped-gemm = ["torch>=2.11.0"] transformer-engine-torch = ["torch>=2.11.0"] [tool.uv.extra-build-variables] -apex = { APEX_CPP_EXT = "1", APEX_CUDA_EXT = "1", APEX_FAST_LAYER_NORM = "1", APEX_PARALLEL_BUILD = "16", NVCC_APPEND_FLAGS = "--threads 4" } +apex = { APEX_CPP_EXT = "1", APEX_PARALLEL_BUILD = "16", NVCC_APPEND_FLAGS = "--threads 4" } transformer-engine-torch = { NVTE_NO_LOCAL_VERSION = "1" } [[tool.uv.dependency-metadata]] @@ -309,8 +437,10 @@ allowed-unresolved-imports = [ "einops.**", "fla.**", "megatron.**", + "monarch.**", "quack.**", "safetensors.**", + "scipy.**", "transformer_engine.**", "triton.**", ] @@ -335,15 +465,33 @@ dev = [ ] [tool.uv.sources] -torch = [{ index = "pytorch-cu128", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }] -torchvision = [{ index = "pytorch-cu128", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }] +torch = [ + { index = "pytorch-cu128", extra = "distributed", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { index = "pytorch-cu128", extra = "backend", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { index = "pytorch-cu128", extra = "megatron", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { index = "pytorch-cu128", extra = "tinker", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { index = "pytorch-cu130", extra = "distributed-cu130", marker = "sys_platform == 'linux'" }, + { index = "pytorch-cu130", extra = "backend-cu130", marker = "sys_platform == 'linux'" }, + { index = "pytorch-cu130", extra = "megatron-cu130", marker = "sys_platform == 'linux'" }, +] +torchvision = [ + { index = "pytorch-cu128", extra = "megatron", marker = "sys_platform == 'linux' or sys_platform == 'win32'" }, + { index = "pytorch-cu130", extra = "megatron-cu130", marker = "sys_platform == 'linux'" }, +] apex = { git = "https://github.com/NVIDIA/apex.git", rev = "25.09" } flash-attn-4 = { url = "https://files.pythonhosted.org/packages/24/f7/01ee2576ce41f9884d291ee21861ef194afc0b2b1ce3bd175fc7a6e1b133/flash_attn_4-4.0.0b5-py3-none-any.whl" } megatron-bridge = { git = "https://github.com/NVIDIA-NeMo/Megatron-Bridge.git", rev = "e1a207ac757e5d0ed94d8ffbe1cbd28e81d8c084" } panza = { git = "https://github.com/corbt/panza.git" } -transformer-engine-torch = { git = "https://github.com/NVIDIA/TransformerEngine.git", rev = "v2.11", subdirectory = "transformer_engine/pytorch" } +transformer-engine-torch = [ + { git = "https://github.com/NVIDIA/TransformerEngine.git", rev = "v2.11", subdirectory = "transformer_engine/pytorch", extra = "megatron" }, +] [[tool.uv.index]] name = "pytorch-cu128" url = "https://download.pytorch.org/whl/cu128" explicit = true + +[[tool.uv.index]] +name = "pytorch-cu130" +url = "https://download.pytorch.org/whl/cu130" +explicit = true diff --git a/scratch/glm52_fixture_manifest_20260716.json b/scratch/glm52_fixture_manifest_20260716.json new file mode 100644 index 000000000..b701ee1f3 --- /dev/null +++ b/scratch/glm52_fixture_manifest_20260716.json @@ -0,0 +1,18 @@ +{ + "algorithm": "sha256(sorted(relative_file_sha256_manifest))", + "bytes": 183895569805, + "file_count": 30, + "fixture": "/mnt/ws_pvc/ws/projects/worktrees/art/monarch_multinode_training/scratch/glm52_e2e_hf_home/hub/models--zai-org--GLM-5.2/snapshots/e2e_12l_full_dims_serialized", + "manifest_sha256": "26fcd5b38da2a44fdd88cdecd31e7986ea129b3d04068435288ec3add665f737", + "state": { + "bytes": 183873818112, + "layers": 12, + "shards": 24, + "tensors": 7102, + "values": 91936906752 + }, + "validation": { + "10.0.7.93": "native PP1 physical load and LoRA registration", + "10.0.9.49": "full manifest, exact serialized shape audit, and native PP0 physical load" + } +} diff --git a/scratch/live_vllm_member_recovery.py b/scratch/live_vllm_member_recovery.py new file mode 100644 index 000000000..26d3cdaf7 --- /dev/null +++ b/scratch/live_vllm_member_recovery.py @@ -0,0 +1,204 @@ +import asyncio +import json +import os +from pathlib import Path +import time +from typing import Any, cast +import uuid + +import httpx + +import art +from art import dev +from art.distributed import ( + ArtRuntime, + ClusterSpec, + EndpointSpec, + HostSpec, + ModelServiceMemberSpec, + ModelServiceSpec, + NcclTransportSpec, + VllmParallelSpec, + compile_topology, +) +from art.distributed.monarch_bootstrap import attach_controller +from art.megatron import MegatronBackend +from art.megatron.distributed_service import DistributedMegatronService + +REDUCED_MODEL = "/mnt/ws_pvc/ws/projects/worktrees/art/glm52_cp/scratch/glm52_e2e_hf_home/hub/models--zai-org--GLM-5.2/snapshots/e2e_12l_full_dims" +MODEL_NAME = "glm52-vllm-recovery" +HOSTS = ("10.0.9.49", "10.0.7.93") +MONARCH_PORT = int(os.environ.get("ART_MONARCH_PORT", "22242")) +SERVICE_PORT = int(os.environ.get("ART_SERVICE_PORT", "18114")) +ROOT = Path( + os.environ.get( + "ART_RECOVERY_ROOT", Path(__file__).parent / "live_vllm_recovery_state" + ) +) +READY = Path( + os.environ.get( + "ART_RECOVERY_READY", Path(__file__).parent / "vllm_recovery_ready.json" + ) +) +OUTPUT = Path( + os.environ.get( + "ART_RECOVERY_OUTPUT", Path(__file__).parent / "vllm_recovery_result.json" + ) +) + + +def _cluster() -> ClusterSpec: + return ClusterSpec( + hosts=tuple( + HostSpec( + host_id=f"host{rank}", + node_rank=rank, + worker_address=f"tcp://{host}:{MONARCH_PORT}", + cpu_slots=8, + gpu_ids=tuple(range(4, 8)), + ) + for rank, host in enumerate(HOSTS) + ), + controller_host_id="host0", + artifact_root=str(ROOT), + nccl_transport=NcclTransportSpec(net_name="IB"), + startup_timeout_s=1800, + rpc_timeout_s=300, + ) + + +def _service() -> ModelServiceSpec: + return ModelServiceSpec( + name=MODEL_NAME, + members=tuple( + ModelServiceMemberSpec( + member_id=f"node{rank}", + host_id=f"host{rank}", + node_rank=rank, + gpu_ids=tuple(range(4, 8)), + ) + for rank in range(2) + ), + leader_endpoint=EndpointSpec(host=HOSTS[0], port=SERVICE_PORT), + rendezvous=EndpointSpec(host=HOSTS[0], port=SERVICE_PORT + 11510), + base_model=REDUCED_MODEL, + model_revision="local-e2e-12l-full-dims", + runtime_fingerprint="glm52-e2e-12l-vllm-recovery", + parallel=VllmParallelSpec( + tp=4, + pp=2, + enable_expert_parallel=True, + ), + update_mode="lora", + ) + + +async def _completion(client: httpx.AsyncClient, base_url: str, model: str) -> Any: + response = await client.post( + f"{base_url}/v1/chat/completions", + json={ + "model": model, + "messages": [{"role": "user", "content": "Return one token."}], + "temperature": 0, + "seed": 314159, + "max_tokens": 1, + "logprobs": True, + }, + ) + response.raise_for_status() + return response.json() + + +async def main(hosts: Any | None = None) -> None: + os.environ.pop("WANDB_API_KEY", None) + ROOT.mkdir(parents=True, exist_ok=True) + READY.unlink(missing_ok=True) + OUTPUT.unlink(missing_ok=True) + owns_host_mesh = hosts is None + if hosts is None: + hosts = await attach_controller( + [f"tcp://{host}:{MONARCH_PORT}" for host in HOSTS], + name=f"glm52_vllm_recovery_{uuid.uuid4().hex}", + ) + runtime = await ArtRuntime.start( + hosts, + compile_topology(cluster=_cluster(), model_services=(_service(),)), + owns_host_mesh=owns_host_mesh, + ) + backend = MegatronBackend( + runtime=runtime, path=str(ROOT), enable_expert_replay=False + ) + model = art.TrainableModel( + name=MODEL_NAME, + project="multinode", + base_model=REDUCED_MODEL, + _internal_config=cast( + dev.InternalModelConfig, + { + "allow_unvalidated_arch": True, + "init_args": {"max_seq_length": 122880}, + "engine_args": { + "distributed_executor_backend": "mp", + "max_model_len": 122880, + "gpu_memory_utilization": 0.75, + "enable_prefix_caching": True, + "max_num_batched_tokens": 16384, + "max_num_seqs": 64, + }, + }, + ), + ) + try: + await model.register(backend) + service = cast(DistributedMegatronService, backend._services[MODEL_NAME]) + manager = runtime.model_service(MODEL_NAME) + initial = manager.state + base_url = str(model.inference_base_url).removesuffix("/v1") + inference_name = model.inference_model_name + assert inference_name is not None + headers = {"Authorization": f"Bearer {model.inference_api_key}"} + async with httpx.AsyncClient(timeout=300, headers=headers) as client: + before = await _completion(client, base_url, inference_name) + READY.write_text( + json.dumps( + { + "service_name": MODEL_NAME, + "generation": initial.generation, + "members": [ + member.model_dump(mode="json") for member in initial.members + ], + }, + indent=2, + ) + ) + deadline = time.monotonic() + 1200 + while manager.state.generation == initial.generation or ( + manager.state.phase != "ready" + ): + if time.monotonic() >= deadline: + raise TimeoutError( + f"model service did not recover: {manager.state.model_dump()}" + ) + await asyncio.sleep(0.25) + while service._recovery_tasks: + if time.monotonic() >= deadline: + raise TimeoutError("model-service recovery task did not finish") + await asyncio.sleep(0.25) + after = await _completion(client, base_url, inference_name) + state = (await client.get(f"{base_url}/art/state")).json() + result = { + "before": before, + "after": after, + "initial_generation": initial.generation, + "recovered_generation": manager.state.generation, + "state": state, + } + OUTPUT.write_text(json.dumps(result, indent=2)) + print("VLLM_MEMBER_RECOVERY_PASS", OUTPUT, manager.state.generation) + finally: + await backend.close() + await runtime.close() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/scratch/pipeline_trainer_single_deployment_20260716e.json b/scratch/pipeline_trainer_single_deployment_20260716e.json new file mode 100644 index 000000000..08b1318d5 --- /dev/null +++ b/scratch/pipeline_trainer_single_deployment_20260716e.json @@ -0,0 +1,757 @@ +{ + "data_plane": { + "host0": { + "batches": 0, + "capacity_bytes": 2147483648, + "copied_bytes": 317030400, + "copy_count": 33, + "created_bytes": 317030400, + "leases": 0, + "peak_bytes": 52838400, + "reserved_bytes": 0, + "transmitted_bytes": 158515200, + "used_bytes": 0 + }, + "host1": { + "batches": 0, + "capacity_bytes": 2147483648, + "copied_bytes": 317030400, + "copy_count": 33, + "created_bytes": 317030400, + "leases": 0, + "peak_bytes": 52838400, + "reserved_bytes": 0, + "transmitted_bytes": 158515200, + "used_bytes": 0 + } + }, + "elapsed_s": 492.514533970505, + "inference": { + "engine_count": 1, + "generation": 0, + "last_update_unix_s": 1784246898.4318142, + "metrics": { + "external_prefix_cache_hit_rate": 0.0, + "external_prefix_cache_hits_total": 0.0, + "external_prefix_cache_queries_total": 0.0, + "generation_tokens_total": 544.0, + "kv_cache_usage_perc": 0.0, + "max_model_len": 122880.0, + "max_num_batched_tokens": 16384.0, + "max_num_scheduled_tokens": 16384.0, + "max_num_seqs": 64.0, + "num_preempted_reqs_total": 0.0, + "num_requests_running": 0.0, + "num_requests_waiting": 0.0, + "num_requests_waiting_capacity": 0.0, + "num_requests_waiting_deferred": 0.0, + "policy_cache_salted_lora_requests_total": 44.0, + "policy_cache_started_waiting_requests_skipped_total": 0.0, + "policy_cache_unsalted_lora_requests_total": 0.0, + "policy_cache_waiting_requests_updated_total": 0.0, + "prefix_cache_hit_rate": 0.6570567344137046, + "prefix_cache_hits_total": 1144832.0, + "prefix_cache_queries_total": 1742364.0, + "prompt_tokens_cached_total": 1144832.0, + "prompt_tokens_computed_total": 597532.0, + "prompt_tokens_external_kv_transfer_total": 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+ +import pytest + +from art.local import backend as backend_module +from art.local.backend import LocalBackend + + +def _runtime_metrics_module(monkeypatch: pytest.MonkeyPatch) -> ModuleType: + loggers = ModuleType("vllm.v1.metrics.loggers") + setattr(loggers, "StatLoggerBase", object) + for name in ("vllm", "vllm.v1", "vllm.v1.metrics"): + monkeypatch.setitem(sys.modules, name, ModuleType(name)) + monkeypatch.setitem(sys.modules, loggers.__name__, loggers) + path = Path(__file__).parents[1] / "vllm_runtime/src/art_vllm_runtime/metrics.py" + spec = importlib.util.spec_from_file_location("test_art_vllm_metrics", path) + assert spec is not None and spec.loader is not None + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) + return module + + +def test_runtime_world_size_includes_data_parallel_workers( + monkeypatch: pytest.MonkeyPatch, +) -> None: + state = _runtime_metrics_module(monkeypatch)._ArtRuntimeMetricsState() + state.configure( + SimpleNamespace( + scheduler_config=SimpleNamespace( + max_num_seqs=8, + max_num_batched_tokens=1024, + max_num_scheduled_tokens=1024, + ), + model_config=SimpleNamespace(max_model_len=4096), + parallel_config=SimpleNamespace(world_size=8, world_size_across_dp=16), + ), + engine_idx=0, + ) + + assert state.snapshot()["metrics"]["world_size"] == 16.0 + + +def _metrics( + *, prompt: float, generation: float, queries: float, hits: float +) -> dict[str, float]: + return { + "prompt_tokens_total": prompt, + "generation_tokens_total": generation, + "prefix_cache_queries_total": queries, + "prefix_cache_hits_total": hits, + "num_preempted_reqs_total": 1.0, + "num_requests_running": 1.0, + "num_requests_waiting": 2.0, + "num_requests_waiting_capacity": 1.0, + "kv_cache_usage_perc": 0.25, + "max_num_seqs": 8.0, + "max_num_batched_tokens": 1024.0, + "max_num_scheduled_tokens": 1024.0, + "max_model_len": 8192.0, + "world_size": 16.0, + } + + +@pytest.mark.asyncio +async def test_backend_reads_leader_metrics_and_fences_counter_generations( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + payloads = iter( + [ + { + "process_uuid": "leader-a", + "generation": 0, + "metrics": _metrics(prompt=100, generation=50, queries=20, hits=10), + }, + { + "process_uuid": "leader-a", + "generation": 0, + "metrics": _metrics(prompt=200, generation=100, queries=40, hits=20), + }, + { + "process_uuid": "leader-b", + "generation": 1, + "metrics": _metrics( + prompt=10_000, generation=5_000, queries=2_000, hits=1_000 + ), + }, + { + "process_uuid": "leader-b", + "generation": 1, + "metrics": _metrics( + prompt=10_100, generation=5_050, queries=2_020, hits=1_010 + ), + }, + ] + ) + requests: list[tuple[str, dict[str, str] | None]] = [] + + class Response: + def __init__(self, payload: dict[str, Any]) -> None: + self._payload = payload + + def raise_for_status(self) -> None: + return None + + def json(self) -> dict[str, Any]: + return self._payload + + class Client: + def __init__(self, **_kwargs: Any) -> None: + pass + + async def __aenter__(self) -> Client: + return self + + async def __aexit__(self, *_args: Any) -> None: + return None + + async def get(self, url: str, *, headers: dict[str, str] | None) -> Response: + requests.append((url, headers)) + return Response(next(payloads)) + + times = iter((0.0, 10.0, 20.0, 30.0)) + monkeypatch.setattr(backend_module.httpx, "AsyncClient", Client) + monkeypatch.setattr( + backend_module, "time", SimpleNamespace(monotonic=lambda: next(times)) + ) + backend = LocalBackend(path=str(tmp_path)) + model: Any = SimpleNamespace( + name="test-model", + inference_base_url="http://leader.test/v1", + inference_api_key="secret", + _serving_capabilities=SimpleNamespace(require=lambda *_args, **_kwargs: None), + ) + + first = await backend.collect_train_step_vllm_metrics(model) + second = await backend.collect_train_step_vllm_metrics(model) + restarted = await backend.collect_train_step_vllm_metrics(model) + recovered = await backend.collect_train_step_vllm_metrics(model) + + assert "vllm/prompt_tok_per_s" not in first + assert second["vllm/prompt_tok_per_s"] == 10.0 + assert second["vllm/completion_tok_per_s"] == 5.0 + assert "vllm/prompt_tok_per_s" not in restarted + assert restarted["vllm/prefix_cache_hit_rate"] == 0.5 + assert recovered["vllm/prompt_tok_per_s"] == 10.0 + assert recovered["vllm/completion_tok_per_s"] == 5.0 + assert recovered["vllm/world_size"] == 16.0 + assert set(backend._vllm_metric_snapshots) == {("test-model", "leader-b", 1)} + assert ( + requests + == [ + ( + "http://leader.test/art/metrics", + {"Authorization": "Bearer secret"}, + ) + ] + * 4 + ) diff --git a/scratch/test_distributed_package_api.py b/scratch/test_distributed_package_api.py new file mode 100644 index 000000000..9cdc8aa32 --- /dev/null +++ b/scratch/test_distributed_package_api.py @@ -0,0 +1,108 @@ +from __future__ import annotations + +import os +from pathlib import Path +import subprocess +import sys + +import art +from art.distributed import NcclTransportSpec, PackingRequest + +EXAMPLE_DIR = Path(__file__).parents[1] / "examples" / "multinode" + + +def test_packing_request_from_public_groups() -> None: + model = art.TrainableModel( + name="packing-public-api", project="test", base_model="not-loaded" + ) + group = art.TrajectoryGroup( + [ + art.Trajectory( + messages_and_choices=[ + {"role": "user", "content": "Respond with maybe."}, + {"role": "assistant", "content": "maybe"}, + ], + reward=1.0, + initial_policy_version=3, + ) + ], + metadata={"split": "smoke"}, + ) + + request = PackingRequest.from_groups( + model, + [group], + packed_sequence_length=128, + allow_training_without_logprobs=True, + group_ids=("maybe",), + min_source_version=3, + max_source_version=3, + ) + + assert request.model.build().base_model == "not-loaded" + assert request.trajectory_groups[0].build().model_dump(mode="json") == ( + group.model_dump(mode="json") + ) + assert request.group_ids == ("maybe",) + assert request.min_source_version == request.max_source_version == 3 + + +def test_distributed_package_import_is_lazy() -> None: + subprocess.run( + [ + sys.executable, + "-c", + """ +import sys +import art.distributed as distributed +assert "art.distributed.art_runtime" not in sys.modules +from art.distributed import ( + ArtRuntime, ClusterSpec, NcclTransportSpec, PackingRequest, compile_topology, +) +assert all(value is not None for value in ( + ArtRuntime, ClusterSpec, NcclTransportSpec, PackingRequest, compile_topology +)) +assert "monarch" not in sys.modules +assert "PackingRequest" in distributed.__all__ +assert "NcclTransportSpec" in distributed.__all__ +""", + ], + check=True, + ) + + +def test_nccl_transport_is_a_public_typed_contract() -> None: + assert NcclTransportSpec(net_name="IB").net_name == "IB" + + +def test_documented_rollout_is_installed_and_bounded() -> None: + environment = os.environ.copy() + environment["PYTHONPATH"] = os.pathsep.join( + (str(EXAMPLE_DIR), environment.get("PYTHONPATH", "")) + ) + subprocess.run( + [ + sys.executable, + "-c", + """ +import asyncio +import art +from art.distributed import InstalledAsyncCallable +import program + +async def check(): + reference = InstalledAsyncCallable.from_callable(program.rollout) + assert (reference.module, reference.qualname) == ("program", "rollout") + model = art.TrainableModel( + name="documented-rollout", project="test", base_model="not-loaded" + ) + trajectory = await program.rollout(model, "maybe", None) + assert trajectory.reward == 1.0 + assert trajectory.metadata["answer"] == "maybe" + +asyncio.run(check()) +""", + ], + check=True, + env=environment, + ) diff --git a/scratch/test_model_service_ownership.py b/scratch/test_model_service_ownership.py new file mode 100644 index 000000000..541bc6d1b --- /dev/null +++ b/scratch/test_model_service_ownership.py @@ -0,0 +1,319 @@ +import ast +import asyncio +from pathlib import Path +from types import MethodType, SimpleNamespace +from unittest.mock import AsyncMock, Mock + +import pytest + +from art.distributed import art_runtime as runtime_module +from art.distributed.art_runtime import ArtRuntime +from art.distributed.specs import ( + EndpointSpec, + ModelServiceMemberSpec, + ModelServiceSpec, + VllmParallelSpec, +) +from art.distributed.vllm_replica import ReplicaFailure, ReplicaState +from art.local import checkpoints as checkpoints_module +from art.megatron import distributed_service as service_module +from art.megatron.backend import MegatronBackend +from art.megatron.distributed_service import DistributedMegatronService +from art.serving_capabilities import ART_SERVING_PROTOCOL_VERSION, ServingCapabilities + + +def test_dedicated_runtime_advertises_the_required_protocol() -> None: + source = Path("vllm_runtime/src/art_vllm_runtime/dedicated_server.py") + tree = ast.parse(source.read_text("utf-8")) + advertised = next( + node.value.value + for node in tree.body + if isinstance(node, ast.Assign) + and any( + isinstance(target, ast.Name) and target.id == "ART_SERVING_PROTOCOL_VERSION" + for target in node.targets + ) + and isinstance(node.value, ast.Constant) + ) + assert advertised == ART_SERVING_PROTOCOL_VERSION == 3 + + +def _spec() -> ModelServiceSpec: + return ModelServiceSpec( + name="model", + members=( + ModelServiceMemberSpec( + member_id="node0", host_id="host0", node_rank=0, gpu_ids=(0,) + ), + ), + leader_endpoint=EndpointSpec(host="10.0.0.1", port=8000), + rendezvous=EndpointSpec(host="10.0.0.1", port=29500), + base_model="base", + model_revision="revision", + runtime_fingerprint="runtime", + parallel=VllmParallelSpec(), + update_mode="lora", + ) + + +def _service(tmp_path, runtime) -> DistributedMegatronService: + return DistributedMegatronService( + model_name="model", + base_model="base", + config={}, + output_dir=str(tmp_path), + runtime=runtime, + enable_expert_replay=False, + ) + + +@pytest.mark.asyncio +async def test_runtime_retains_model_service_until_stop_succeeds(monkeypatch) -> None: + spec = _spec() + manager = SimpleNamespace( + start=AsyncMock(side_effect=RuntimeError("start failed")), + stop=AsyncMock(side_effect=RuntimeError("stop failed")), + ) + runtime = ArtRuntime.__new__(ArtRuntime) + runtime.topology = SimpleNamespace( + model_services=(spec,), + cluster=SimpleNamespace(startup_timeout_s=1, rpc_timeout_s=1), + ) + runtime._host_services = {"host0": object()} + runtime._model_services = {} + runtime._started, runtime._closed = True, False + runtime._preflight_launch = AsyncMock() + monkeypatch.setattr(runtime_module, "MonarchVllmHostLauncher", lambda _: object()) + monkeypatch.setattr(runtime_module, "ReplicaManager", lambda *_a, **_kw: manager) + + with pytest.raises(RuntimeError, match="start failed"): + await runtime.start_model_service(spec, SimpleNamespace()) + assert runtime.model_service("model") is manager + + with pytest.raises(RuntimeError, match="stop failed"): + await runtime.stop_model_service("model") + assert runtime.model_service("model") is manager + + manager.stop = AsyncMock(return_value="stopped") + assert await runtime.stop_model_service("model") == "stopped" + with pytest.raises(RuntimeError, match="not managed"): + runtime.model_service("model") + + +@pytest.mark.asyncio +async def test_failed_recovery_unpublishes_dead_endpoint(tmp_path) -> None: + failure = ReplicaFailure( + replica_id="model", generation=2, generation_digest="digest", reason="dead" + ) + manager = SimpleNamespace( + state=ReplicaState( + replica_id="model", + generation=2, + generation_digest="digest", + phase="quarantined", + ) + ) + service = _service( + tmp_path, + SimpleNamespace(model_service=lambda _name: manager), + ) + service._managed_service_name = "model" + service._base_url = "http://10.0.0.1:8000" + service._loaded_adapter_steps = {1, 2} + service._loaded_exact_adapter_steps = {1} + service._recover_replica_locked = AsyncMock( + side_effect=RuntimeError("restart failed") + ) + + await service._recover_failed_replica(failure) + + assert service._managed_service_name == "model" + assert service._base_url is None + assert not service._loaded_adapter_steps + assert not service._loaded_exact_adapter_steps + with pytest.raises(RuntimeError, match="unavailable"): + await service.start_openai_server(None) + + +@pytest.mark.asyncio +async def test_recovery_rebuilds_loaded_adapter_index(monkeypatch, tmp_path) -> None: + spec = _spec() + ready = ReplicaState( + replica_id="model", + generation=3, + generation_digest="generation", + phase="ready", + ) + manager = SimpleNamespace( + restart=AsyncMock(return_value=ready), + prepare_update=Mock(return_value=ready), + verify_update=Mock(return_value=ready), + quarantine=Mock(), + stop=AsyncMock(), + ) + runtime = SimpleNamespace( + topology=SimpleNamespace(model_services=(spec,)), + model_service=lambda _name: manager, + ) + service = _service(tmp_path, runtime) + capabilities = ServingCapabilities( + runtime="art_vllm", + protocol_version=ART_SERVING_PROTOCOL_VERSION, + ) + service._latest_step = 5 + service._serving_step = 5 + service._managed_service_name = "model" + service._base_url = spec.leader_endpoint.url + service._serving_capabilities = capabilities + service._current_lora_name = "model@5" + service._loaded_adapter_steps = {1, 3, 5} + service._loaded_exact_adapter_steps = {2} + service._exact_adapter_refcounts = {2: 1} + service._published_adapters[5] = SimpleNamespace( + generation_id="policy", + identity=str(tmp_path / "checkpoints" / "0005"), + ) + service._load_adapter_at = AsyncMock(return_value=("model@2", "/step/2")) + monkeypatch.setattr( + service_module, + "discover_serving_capabilities", + AsyncMock(return_value=capabilities), + ) + + await service._recover_replica_locked( + ReplicaFailure( + replica_id="model", + generation=2, + generation_digest="old", + reason="dead", + ) + ) + + assert service._loaded_adapter_steps == {5} + assert service._loaded_exact_adapter_steps == {2} + assert service._exact_adapter_refcounts == {2: 1} + service._load_adapter_at.assert_awaited_once() + + +@pytest.mark.asyncio +async def test_recovery_uses_serving_generation_while_learner_is_ahead( + monkeypatch, tmp_path +) -> None: + spec = _spec() + ready = ReplicaState( + replica_id="model", + generation=4, + generation_digest="restarted", + phase="ready", + ) + manager = SimpleNamespace( + restart=AsyncMock(return_value=ready), + prepare_update=Mock(return_value=ready), + verify_update=Mock(return_value=ready), + quarantine=Mock(), + stop=AsyncMock(), + ) + service = _service( + tmp_path, + SimpleNamespace( + topology=SimpleNamespace(model_services=(spec,)), + model_service=lambda _name: manager, + ), + ) + capabilities = ServingCapabilities( + runtime="art_vllm", protocol_version=ART_SERVING_PROTOCOL_VERSION + ) + serving_path = str(tmp_path / "checkpoints" / "0005") + service._latest_step = 6 + service._serving_step = 5 + service._managed_service_name = "model" + service._base_url = spec.leader_endpoint.url + service._serving_capabilities = capabilities + service._current_lora_name = "model@5" + service._published_adapters[5] = SimpleNamespace( + generation_id="serving-generation", identity=serving_path + ) + monkeypatch.setattr( + service_module, + "discover_serving_capabilities", + AsyncMock(return_value=capabilities), + ) + + await service._recover_replica_locked( + ReplicaFailure( + replica_id="model", + generation=3, + generation_digest="failed", + reason="dead", + ) + ) + + manager.restart.assert_awaited_once_with( + served_model_name="model@5", lora_path=serving_path + ) + report = manager.verify_update.call_args.args[0] + assert report.policy_version == "5" + assert report.policy_digest == "serving-generation" + assert service._latest_step == 6 + assert service._serving_step == 5 + assert service._loaded_adapter_steps == {5} + + +@pytest.mark.asyncio +async def test_retention_protects_absent_learner_and_serving_steps( + monkeypatch, tmp_path +) -> None: + model = SimpleNamespace(project="project", name="model") + output_dir = tmp_path / "project" / "models" / "model" + service = _service(output_dir, SimpleNamespace()) + service._latest_step = 3 + service._serving_step = 2 + service._loaded_adapter_steps = {1, 2, 3} + service._unload_adapter = AsyncMock() + + await service.prune_loaded_adapters(retain_steps={3}) + assert service._loaded_adapter_steps == {2, 3} + service._unload_adapter.assert_awaited_once_with("model@1") + + checkpoints = output_dir / "checkpoints" + for step in (1, 2, 4): + (checkpoints / f"{step:04d}").mkdir(parents=True) + staging = output_dir / "staging-0003" + staging.mkdir() + original_delete = checkpoints_module.delete_checkpoints + + def publish_during_retention(path: str, excluding: list[int]) -> None: + staging.rename(checkpoints / "0003") + original_delete(path, excluding) + + monkeypatch.setattr( + checkpoints_module, "delete_checkpoints", publish_during_retention + ) + backend = object.__new__(MegatronBackend) + backend._runtime = object() + backend._path = str(tmp_path) + + async def get_service(_self, _model): + return service + + backend._get_service = MethodType(get_service, backend) + await backend._delete_checkpoint_files(model, [1]) + assert (checkpoints / "0001").is_dir() + assert (checkpoints / "0002").is_dir() + assert (checkpoints / "0003").is_dir() + assert not (checkpoints / "0004").exists() + + +@pytest.mark.asyncio +async def test_in_flight_prune_does_not_enter_serving_critical_section( + tmp_path, +) -> None: + service = _service(tmp_path, SimpleNamespace()) + service.config["rollout_weight_update_mode"] = "in_flight_lora" + service._loaded_adapter_steps = {1, 2} + await service._serving_lock.acquire() + try: + await asyncio.wait_for(service.prune_loaded_adapters(retain_steps=set()), 0.1) + finally: + service._serving_lock.release() + assert service._loaded_adapter_steps == {1, 2} diff --git a/scratch/test_replica_recovery.py b/scratch/test_replica_recovery.py new file mode 100644 index 000000000..78473e962 --- /dev/null +++ b/scratch/test_replica_recovery.py @@ -0,0 +1,140 @@ +import pytest + +from art.distributed.specs import ( + EndpointSpec, + ModelServiceMemberSpec, + ModelServiceSpec, + VllmParallelSpec, +) +from art.distributed.vllm_replica import ( + HostMemberState, + ReplicaFailure, + ReplicaLaunchTemplate, + ReplicaManager, +) + + +class Launcher: + def __init__(self, host_id: str, events: list[str]) -> None: + self.host_id = host_id + self.events = events + self.requests = [] + self.states = {} + self.failed = False + self.stops = [] + + async def start_member(self, request): + self.requests.append(request) + state = HostMemberState( + replica_id=request.replica_id, + member_id=request.member.member_id, + generation=request.generation, + generation_digest=request.generation_digest, + process_uuid=request.process_uuid, + phase="ready", + ) + self.states[ + (request.replica_id, request.member.member_id, request.generation) + ] = state + return state + + async def member_state(self, replica_id, member_id, generation): + state = self.states[(replica_id, member_id, generation)] + return state.model_copy(update={"phase": "failed"}) if self.failed else state + + async def stop_member(self, replica_id, member_id, generation): + self.events.append(f"stop:{self.host_id}") + self.stops.append((replica_id, member_id, generation)) + + +def _spec() -> ModelServiceSpec: + return ModelServiceSpec( + name="model", + members=tuple( + ModelServiceMemberSpec( + member_id=f"node{rank}", + host_id=f"host{rank}", + node_rank=rank, + gpu_ids=(0, 1), + ) + for rank in range(2) + ), + leader_endpoint=EndpointSpec(host="10.0.0.1", port=8000), + rendezvous=EndpointSpec(host="10.0.0.1", port=29500), + base_model="base", + model_revision="revision", + runtime_fingerprint="runtime", + parallel=VllmParallelSpec(tp=2, pp=2), + update_mode="lora", + ) + + +@pytest.mark.asyncio +async def test_single_member_service_lifecycle() -> None: + events: list[str] = [] + spec = _spec().model_copy( + update={ + "members": (_spec().members[0],), + "parallel": VllmParallelSpec(tp=2), + } + ) + manager = ReplicaManager( + spec, + {"host0": Launcher("host0", events)}, + ReplicaLaunchTemplate(served_model_name="model@0"), + ) + + assert (await manager.start()).phase == "ready" + assert (await manager.stop()).phase == "stopped" + assert events == ["stop:host0"] + + +@pytest.mark.asyncio +async def test_failure_stops_whole_gang_before_callback_and_restarts_generation() -> ( + None +): + events: list[str] = [] + launchers = {f"host{rank}": Launcher(f"host{rank}", events) for rank in range(2)} + failures: list[ReplicaFailure] = [] + + async def failed(event: ReplicaFailure) -> None: + events.append("callback") + failures.append(event) + + manager = ReplicaManager( + _spec(), + launchers, + ReplicaLaunchTemplate(served_model_name="model@0", lora_path="/step/0000"), + on_failure=failed, + monitor_interval_s=60, + ) + await manager.start() + launchers["host1"].failed = True + + await manager.poll() + + assert manager.state.phase == "quarantined" + assert set(events[:2]) == {"stop:host0", "stop:host1"} + assert events[2:] == ["callback"] + assert [(event.replica_id, event.generation) for event in failures] == [ + ("model", 0) + ] + + launchers["host1"].failed = False + restarted = await manager.restart( + served_model_name="model@1", lora_path="/step/0001" + ) + + assert restarted.phase == "ready" + assert restarted.generation == 1 + assert restarted.generation_digest != failures[0].generation_digest + for launcher in launchers.values(): + assert [request.launch_config.port for request in launcher.requests] == [ + 8000, + 8000, + ] + assert [request.launch_config.master_port for request in launcher.requests] == [ + 29500, + 29500, + ] + await manager.stop() diff --git a/scratch/test_vllm_replica_ack.py b/scratch/test_vllm_replica_ack.py new file mode 100644 index 000000000..c811ae6b6 --- /dev/null +++ b/scratch/test_vllm_replica_ack.py @@ -0,0 +1,166 @@ +from __future__ import annotations + +from pathlib import Path +from types import SimpleNamespace +from typing import Any + +import pytest + +from art.distributed.specs import ( + EndpointSpec, + ModelServiceMemberSpec, + ModelServiceSpec, + VllmParallelSpec, +) +from art.distributed.vllm_replica import ( + HostMemberState, + ReplicaLaunchTemplate, + ReplicaManager, + ReplicaState, +) +from art.megatron import distributed_service as service_module +from art.megatron.distributed_service import DistributedMegatronService + + +def manager(*, engine_args: dict[str, object] | None = None) -> ReplicaManager: + members = tuple( + ModelServiceMemberSpec( + member_id=f"node{rank}", + host_id=f"host{rank}", + node_rank=rank, + gpu_ids=(0, 1), + ) + for rank in range(2) + ) + spec = ModelServiceSpec( + name="model", + members=members, + leader_endpoint=EndpointSpec(host="10.0.0.1", port=8000), + rendezvous=EndpointSpec(host="10.0.0.1", port=29500), + base_model="base", + model_revision="revision", + runtime_fingerprint="runtime", + parallel=VllmParallelSpec(tp=1, pp=2, dp=2, enable_expert_parallel=True), + update_mode="lora", + ) + value = ReplicaManager( + spec, + {"host0": SimpleNamespace(), "host1": SimpleNamespace()}, + ReplicaLaunchTemplate( + served_model_name="model@1", engine_args=engine_args or {} + ), + ) + value._state = ReplicaState( + replica_id="model", + generation=0, + generation_digest=value.state.generation_digest, + phase="ready", + members=tuple( + HostMemberState( + replica_id="model", + member_id=member.member_id, + generation=0, + generation_digest=value.state.generation_digest, + process_uuid=f"process-{member.node_rank}", + phase="ready", + ) + for member in reversed(members) + ), + ) + return value + + +def test_one_service_is_the_deployment_contract() -> None: + assert "replicas" not in ModelServiceSpec.model_fields + assert "leader" not in ModelServiceMemberSpec.model_fields + + +@pytest.mark.asyncio +async def test_in_flight_update_is_the_only_acknowledgement_call( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + value = manager() + runtime = SimpleNamespace( + topology=SimpleNamespace(cluster=SimpleNamespace(rpc_timeout_s=5)), + model_service=lambda name: value if name == "model" else None, + ) + service = DistributedMegatronService( + model_name="model", + base_model="base", + config={"rollout_weight_update_mode": "in_flight_lora"}, + output_dir=str(tmp_path), + runtime=runtime, + enable_expert_replay=False, + ) + calls: list[tuple[str, dict[str, Any], dict[str, str] | None]] = [] + + class Response: + def raise_for_status(self) -> None: + return None + + class Client: + def __init__(self, **_kwargs: Any) -> None: + pass + + async def __aenter__(self) -> Client: + return self + + async def __aexit__(self, *_args: Any) -> None: + return None + + async def post(self, url: str, *, json: dict[str, Any], headers): + calls.append((url, json, headers)) + return Response() + + monkeypatch.setattr(service_module.httpx, "AsyncClient", Client) + service._latest_step = 1 + service._serving_step = 0 + service._base_url = "http://leader.test:8000" + service._api_key_value = "secret" + name, path = await service._load_adapter("/step/0001", 1) + + assert (name, path) == ("model:active", "/step/0001") + assert len(calls) == 1 + assert calls[0][0] == "http://leader.test:8000/art/in_flight_lora_update" + assert calls[0][1]["policy_version"] == 1 + assert calls[0][2] == {"Authorization": "Bearer secret"} + + +def test_launch_preserves_user_args_and_owns_native_gang_topology() -> None: + value = manager( + engine_args={ + "enable_prefix_caching": False, + "block_size": 32, + "prefill_context_parallel_size": 2, + } + ) + leader = value._launch_request(value.spec.members[0]).launch_config + follower = value._launch_request(value.spec.members[1]).launch_config + + assert leader.engine_args == { + "enable_prefix_caching": False, + "block_size": 32, + "prefill_context_parallel_size": 2, + "revision": "revision", + "tokenizer_revision": "revision", + "tensor_parallel_size": 1, + "pipeline_parallel_size": 2, + "data_parallel_size": 2, + "enable_expert_parallel": True, + } + assert leader.host == "10.0.0.1" and not leader.headless + assert follower.host == "127.0.0.1" and follower.headless + assert leader.nnodes == follower.nnodes == 2 + assert "kv_events_config" not in leader.engine_args + + +def test_conflicting_untyped_revision_is_rejected() -> None: + value = manager() + with pytest.raises(ValueError, match="revision conflicts"): + ReplicaManager( + value.spec, + {"host0": SimpleNamespace(), "host1": SimpleNamespace()}, + ReplicaLaunchTemplate( + served_model_name="model@1", engine_args={"revision": "other"} + ), + ) diff --git a/scratch/vllm_recovery_single_deployment_20260716a.json b/scratch/vllm_recovery_single_deployment_20260716a.json new file mode 100644 index 000000000..33624f4f8 --- /dev/null +++ b/scratch/vllm_recovery_single_deployment_20260716a.json @@ -0,0 +1,180 @@ +{ + "before": { + "id": "chatcmpl-baa27f98022b31cd", + "object": "chat.completion", + "created": 1784242061, + "model": "glm52-vllm-recovery@0", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": "\u4ecd\u5728", + "refusal": null, + "annotations": null, + "audio": null, + "function_call": null, + "tool_calls": [], + "reasoning": null + }, + "logprobs": { + "content": [ + { + "token": "token_id:109159", + "logprob": -6.011541366577148, + "bytes": [ + 228, + 187, + 141, + 229, + 156, + 168 + ], + "top_logprobs": [] + } + ] + }, + "finish_reason": "length", + "stop_reason": null, + "token_ids": [ + 109159 + ], + "routed_experts": null, + "policy_token_spans": [ + { + "start_token": 0, + "end_token": 1, + "policy_version": 0, + "lora_slot": "glm52-vllm-recovery@0", + "update_seq": 1 + } + ] + } + ], + "service_tier": null, + "system_fingerprint": "vllm-0.23.0-tp4-pp2-ep-9cac0e2a", + "usage": { + "prompt_tokens": 16, + "total_tokens": 17, + "completion_tokens": 1, + "prompt_tokens_details": null + }, + "prompt_logprobs": null, + "prompt_token_ids": [ + 154822, + 154824, + 154826, + 25062, + 287, + 29905, + 371, + 25, + 7487, + 154827, + 5598, + 825, + 3950, + 13, + 154828, + 154841 + ], + "prompt_text": null, + "kv_transfer_params": null + }, + "after": { + "id": "chatcmpl-8508c8020132aadd", + "object": "chat.completion", + "created": 1784242243, + "model": "glm52-vllm-recovery@0", + "choices": [ + { + "index": 0, + "message": { + "role": "assistant", + "content": "\u4ecd\u5728", + "refusal": null, + "annotations": null, + "audio": null, + "function_call": null, + "tool_calls": [], + "reasoning": null + }, + "logprobs": { + "content": [ + { + "token": "token_id:109159", + "logprob": -6.1988677978515625, + "bytes": [ + 228, + 187, + 141, + 229, + 156, + 168 + ], + "top_logprobs": [] + } + ] + }, + "finish_reason": "length", + "stop_reason": null, + "token_ids": [ + 109159 + ], + "routed_experts": null, + "policy_token_spans": [ + { + "start_token": 0, + "end_token": 1, + "policy_version": 0, + "lora_slot": "glm52-vllm-recovery@0", + "update_seq": 1 + } + ] + } + ], + "service_tier": null, + "system_fingerprint": "vllm-0.23.0-tp4-pp2-ep-9cac0e2a", + "usage": { + "prompt_tokens": 16, + "total_tokens": 17, + "completion_tokens": 1, + "prompt_tokens_details": null + }, + "prompt_logprobs": null, + "prompt_token_ids": [ + 154822, + 154824, + 154826, + 25062, + 287, + 29905, + 371, + 25, + 7487, + 154827, + 5598, + 825, + 3950, + 13, + 154828, + 154841 + ], + "prompt_text": null, + "kv_transfer_params": null + }, + "initial_generation": 0, + "recovered_generation": 1, + "state": { + "runtime": "art_vllm", + "protocol_version": 2, + "process_uuid": "852242fdf77d4265a2da46e097ca0309", + "generation": 1, + "node_rank": 0, + "nnodes": 2, + "headless": false, + "loaded_adapter": "glm52-vllm-recovery@0", + "policy_version": 0, + "update_identity": null + } +} \ No newline at end of file diff --git a/scripts/ci/trainer-rank-checkpoint-acceptance.sky.yaml b/scripts/ci/trainer-rank-checkpoint-acceptance.sky.yaml new file mode 100644 index 000000000..247cd6b11 --- /dev/null +++ b/scripts/ci/trainer-rank-checkpoint-acceptance.sky.yaml @@ -0,0 +1,54 @@ +name: trainer-rank-checkpoint-acceptance + +workdir: . + +resources: + accelerators: H200:2 + image_id: docker:docker.io/bradhiltonnw/art-gpu:latest + +setup: | + uv sync --frozen --extra megatron --group dev + uv run --no-sync python -m art.megatron.hybrid_ep_setup + +run: | + set -euo pipefail + result=/tmp/art-checkpoint-acceptance + fixture="$(find . -maxdepth 1 -type d -name '.live-checkpoint-fixture.*' -print -quit)" + test -n "${fixture}" + rm -rf "${result}" + mkdir -p "${result}" + + exercise() { + ranks="$1" + operation="$2" + source="$3" + output="$4" + uv run --no-sync torchrun --standalone --nproc-per-node="${ranks}" \ + dev/trainer_rank_checkpoint_acceptance.py "${operation}" \ + --source "${source}" --output "${output}" \ + --output-json "${output}.json" + } + + exercise 1 step-save "${fixture}" "${result}/source-1" + exercise 1 step-export "${result}/source-1" "${result}/restore-1-to-1" + exercise 2 step-export "${result}/source-1" "${result}/restore-1-to-2" + diff -qr "${result}/restore-1-to-1" "${result}/restore-1-to-2" + + exercise 2 step-save "${fixture}" "${result}/source-2" + exercise 2 step-export "${result}/source-2" "${result}/restore-2-to-2" + exercise 1 step-export "${result}/source-2" "${result}/restore-2-to-1" + diff -qr "${result}/restore-2-to-2" "${result}/restore-2-to-1" + cat "${result}"/*.json + +config: + kubernetes: + pod_config: + spec: + schedulerName: binpack-scheduler + activeDeadlineSeconds: 3600 + containers: + - name: ray-node + imagePullPolicy: Always + env: + - name: UV_LINK_MODE + value: copy diff --git a/scripts/setup.sh b/scripts/setup.sh index cc34695f3..82b58ef24 100755 --- a/scripts/setup.sh +++ b/scripts/setup.sh @@ -1,4 +1,5 @@ #!/bin/bash +set -eo pipefail # Load environment variables from .env file if it exists if [ -f .env ]; then @@ -35,11 +36,17 @@ EOF chmod +x "$sudo_path" fi +export PATH="$HOME/.local/bin:$HOME/.cargo/bin:/opt/conda/bin:$PATH" need_pkgs=() command -v git >/dev/null 2>&1 || need_pkgs+=("git") command -v curl >/dev/null 2>&1 || need_pkgs+=("curl") command -v tmux >/dev/null 2>&1 || need_pkgs+=("tmux") +install_multinode=${INSTALL_MULTINODE:-false} +if [ "$install_multinode" != "true" ] && [ "$install_multinode" != "false" ]; then + echo "INSTALL_MULTINODE must be true or false" >&2 + exit 1 +fi if [ "${#need_pkgs[@]}" -gt 0 ]; then apt-get update apt-get install -y "${need_pkgs[@]}" @@ -50,7 +57,7 @@ git config --global user.name "${GIT_USER_NAME}" git config --global user.email "${GIT_USER_EMAIL}" git config --global --add safe.directory "$(pwd)" -if [ "${GIT_RESET_CLEAN:-true}" = "true" ]; then +if [ "${GIT_RESET_CLEAN:-false}" = "true" ]; then # Reset any uncommitted changes to the last commit git reset --hard HEAD @@ -61,8 +68,7 @@ else fi # Install astral-uv (standalone version) -# Always prepend standalone install path so it takes precedence over system/conda uv -export PATH="$HOME/.local/bin:$HOME/.cargo/bin:$PATH" +# Always prefer the standalone uv over system/conda installations. if command -v uv >/dev/null 2>&1; then echo "Using $(uv --version)" elif ! curl -LsSf https://astral.sh/uv/install.sh | sh; then @@ -70,9 +76,28 @@ elif ! curl -LsSf https://astral.sh/uv/install.sh | sh; then exit 1 fi -# Sync the dependencies -if [ "${INSTALL_EXTRAS:-false}" = "true" ]; then - uv sync --extra backend --extra tinker --extra langgraph --extra plotting --frozen +backend_extra=backend +if [ -f /usr/local/cuda/version.json ] && + grep -Eq '"version"[[:space:]]*:[[:space:]]*"13\.' /usr/local/cuda/version.json; then + backend_extra=backend-cu130 +fi + +if [ "$install_multinode" = "true" ]; then + if [ "${INSTALL_EXTRAS:-false}" = "true" ]; then + echo "INSTALL_EXTRAS is incompatible with the Megatron environment" >&2 + exit 1 + fi + scripts/setup_multinode.sh + export HYBRID_EP_MULTINODE=1 + export USE_NIXL=1 + export NIXL_HOME=/usr/local/art-multinode/nixl + export UCX_HOME=/usr/local/art-multinode/ucx + export LD_LIBRARY_PATH="$NIXL_HOME/lib/x86_64-linux-gnu:$UCX_HOME/lib:${LD_LIBRARY_PATH:-}" + /bin/bash src/art/megatron/setup.sh else - uv sync --extra backend --frozen + sync_extras=(--extra "$backend_extra") + if [ "${INSTALL_EXTRAS:-false}" = "true" ]; then + sync_extras+=(--extra tinker --extra langgraph --extra plotting) + fi + uv sync "${sync_extras[@]}" --frozen fi diff --git a/scripts/setup_multinode.sh b/scripts/setup_multinode.sh new file mode 100755 index 000000000..198292570 --- /dev/null +++ b/scripts/setup_multinode.sh @@ -0,0 +1,234 @@ +#!/bin/bash +set -euo pipefail + +readonly ucx_version=1.21.0 +readonly ucx_sha256=2374d2fcf3186fbfd5e27633ab153aabaeb6b4f503a88563d2aca67cf51ed2c1 +readonly nixl_version=1.3.2 +readonly nixl_commit=de8115ca97d3f8fb63a4988e9b4d4a038b2e0f72 +readonly nixl_sha256=a9d88772935e91181733f00df0a7e93b6be5f1d29300e5c06cfc6b4ad2f6dbdb +readonly asio_sha256=12e7bb4dada8bc1191de9d550a59ee658ce4e645ffc97c911c099ab4e8699d55 +readonly asio_patch_sha256=8bed3693016874b097e4d902c4ca8daf1b6abf1b5a56b0c5c02827d4e0747ddb +readonly etcd_version=3.5.33 +readonly etcd_sha256=5025b5b24d81a9616b6e284ccd439b9a3df055ef8fdcdc142af3ec8f6a3b3c95 + +cache_dir=${ART_SETUP_CACHE_DIR:-$HOME/.cache/art/multinode} +build_root=$(mktemp -d /tmp/art-multinode-setup-XXXXXX) +test -d "$build_root" +trap 'rm -rf "$build_root"' EXIT +mkdir -p "$cache_dir" + +download() { + local url=$1 destination=$2 sha256=$3 lock_fd partial + exec {lock_fd}>"$destination.lock" + flock "$lock_fd" + if ! printf '%s %s\n' "$sha256" "$destination" | sha256sum -c - >/dev/null 2>&1; then + partial=$(mktemp "$destination.partial.XXXXXX") + if ! curl -fL --retry 3 -o "$partial" "$url" || + ! printf '%s %s\n' "$sha256" "$partial" | sha256sum -c -; then + rm -f "$partial" + return 1 + fi + mv "$partial" "$destination" + fi + exec {lock_fd}>&- +} + +publish_dir() { + local source=$1 destination=$2 marker=$3 + test -d "$source" + if test -e "$destination"; then + test -f "$destination/.art-version" && grep -Fxq "$marker" "$destination/.art-version" && return + echo "Refusing to replace unowned or mismatched $destination" >&2 + exit 1 + fi + sudo install -d "$destination" + sudo cp -a "$source/." "$destination/" + printf '%s\n' "$marker" | sudo tee "$destination/.art-version" >/dev/null +} + +link_install() { + local alias=$1 target=$2 + if test -e "$alias" && ! test -L "$alias"; then + cmp -s "$alias" "$target" && return + echo "Refusing to replace non-symlink $alias" >&2 + exit 1 + fi + sudo ln -sfn "$target" "$alias" +} + +assert_linked_from() { + local object=$1 library=$2 prefix=$3 resolved + resolved=$(ldd "$object" | awk -v library="$library" '$1 == library {print $3}') + resolved=$(readlink -f "$resolved") + case "$resolved" in + "$prefix"/*) ;; + *) echo "$object resolves $library outside $prefix: ${resolved:-missing}" >&2; exit 1 ;; + esac +} + +for command in gcc make cmake ninja patchelf pkg-config python3; do + command -v "$command" >/dev/null || { + echo "Cluster bootstrap must provide $command" >&2 + exit 1 + } +done +test "$(uname -m)" = x86_64 +test -f /usr/include/infiniband/verbs.h +grep -q 'Open Kernel Module' /proc/driver/nvidia/version +grep -q '^nvidia_peermem ' /proc/modules +grep -q '^EnableStreamMemOPs: 1$' /proc/driver/nvidia/params +grep -q 'PeerMappingOverride=1' /proc/driver/nvidia/params +test -c /dev/infiniband/rdma_cm +test -c /dev/infiniband/uverbs0 +compgen -G '/sys/class/infiniband/*' >/dev/null + +cuda_version=$(sed -n 's/.*"version"[[:space:]]*:[[:space:]]*"\([0-9]*\.[0-9]*\).*/\1/p' /usr/local/cuda/version.json | head -1) +cuda_major=${cuda_version%%.*} +case "$cuda_major" in + 12) + nixl_wheel_url=https://files.pythonhosted.org/packages/f8/d3/2964339654b3fe85e7aa62fdce4da3b97ee40337f3b72466aa79251f1196/nixl_cu12-1.3.2-cp312-cp312-manylinux_2_28_x86_64.whl + nixl_wheel_sha256=ef8ccdffcd54978e8a799de59287efcb3af0b7ba3bf02e04bc4df4c842f1f569 + ;; + 13) + nixl_wheel_url=https://files.pythonhosted.org/packages/99/d8/5768b907b85d8856c07674ddd0ffeb736ed987ff530a12e8f17a273cab3b/nixl_cu13-1.3.2-cp312-cp312-manylinux_2_28_x86_64.whl + nixl_wheel_sha256=22fcd7183b2cd831b3da781c9e9991c5f6ef77a238ee6b7ac05d42558ea469a9 + ;; + *) echo "CUDA 12 or 13 is required, found ${cuda_version:-unknown}" >&2; exit 1 ;; +esac +gpu_arches=$(nvidia-smi --query-gpu=compute_cap --format=csv,noheader,nounits | sed 's/\.//' | sort -u | paste -sd,) +test -n "$gpu_arches" +compiler_id=$(gcc -dumpfullversion | cut -d. -f1) +art_prefix=/usr/local/art-multinode +if test -e "$art_prefix"; then + test -f "$art_prefix/.art-owner" && grep -Fxq art-multinode "$art_prefix/.art-owner" || { + echo "Refusing to use unowned $art_prefix" >&2 + exit 1 + } +else + sudo install -d "$art_prefix/bin" + printf 'art-multinode\n' | sudo tee "$art_prefix/.art-owner" >/dev/null +fi + +download \ + "https://github.com/etcd-io/etcd/releases/download/v${etcd_version}/etcd-v${etcd_version}-linux-amd64.tar.gz" \ + "$cache_dir/etcd-v${etcd_version}-linux-amd64.tar.gz" "$etcd_sha256" +tar -xzf "$cache_dir/etcd-v${etcd_version}-linux-amd64.tar.gz" -C "$build_root" +etcd_prefix="/usr/local/etcd-${etcd_version}" +etcd_marker="etcd=${etcd_version} sha256=${etcd_sha256}" +etcd_stage="$build_root/etcd-stage" +install -d "$etcd_stage/bin" +install -m 0755 "$build_root/etcd-v${etcd_version}-linux-amd64/etcd" "$etcd_stage/bin/etcd" +install -m 0755 "$build_root/etcd-v${etcd_version}-linux-amd64/etcdctl" "$etcd_stage/bin/etcdctl" +publish_dir "$etcd_stage" "$etcd_prefix" "$etcd_marker" +link_install "$art_prefix/bin/etcd" "$etcd_prefix/bin/etcd" +link_install "$art_prefix/bin/etcdctl" "$etcd_prefix/bin/etcdctl" + +ucx_prefix="/usr/local/ucx-${ucx_version}-cuda${cuda_version}-${ucx_sha256:0:12}" +ucx_marker="ucx=${ucx_version} sha256=${ucx_sha256} cuda=${cuda_version}" +if ! test -f "$ucx_prefix/.art-version" || ! grep -Fxq "$ucx_marker" "$ucx_prefix/.art-version"; then + test ! -e "$ucx_prefix" || { + echo "Refusing to replace mismatched $ucx_prefix" >&2 + exit 1 + } + download \ + "https://github.com/openucx/ucx/releases/download/v${ucx_version}/ucx-${ucx_version}.tar.gz" \ + "$cache_dir/ucx-${ucx_version}.tar.gz" "$ucx_sha256" + tar -xzf "$cache_dir/ucx-${ucx_version}.tar.gz" -C "$build_root" + ( + cd "$build_root/ucx-${ucx_version}" + ./configure --prefix="$ucx_prefix" \ + --disable-logging --disable-debug --disable-assertions \ + --disable-params-check --enable-mt --enable-shared --disable-static \ + --disable-doxygen-doc --enable-optimizations --enable-cma \ + --enable-devel-headers --with-cuda=/usr/local/cuda --with-verbs \ + --with-dm --without-gdrcopy + make -j"$(nproc)" + make DESTDIR="$build_root/ucx-stage" install + ) + publish_dir "$build_root/ucx-stage$ucx_prefix" "$ucx_prefix" "$ucx_marker" +fi +link_install "$art_prefix/ucx" "$ucx_prefix" + +download "$nixl_wheel_url" "$cache_dir/nixl-cu${cuda_major}-${nixl_version}.whl" "$nixl_wheel_sha256" +download \ + "https://github.com/ai-dynamo/nixl/archive/${nixl_commit}.tar.gz" \ + "$cache_dir/nixl-${nixl_commit}.tar.gz" "$nixl_sha256" +nixl_source="$build_root/nixl-${nixl_commit}" +tar -xzf "$cache_dir/nixl-${nixl_commit}.tar.gz" -C "$build_root" +python3 -m zipfile -e "$cache_dir/nixl-cu${cuda_major}-${nixl_version}.whl" "$build_root/nixl-wheel" +wheel_core=$(find "$build_root/nixl-wheel" -maxdepth 1 -type d -name ".nixl_cu${cuda_major}.mesonpy.libs" -print -quit) +wheel_deps="$build_root/nixl-wheel/nixl_cu${cuda_major}.libs" +test -f "$wheel_core/libnixl.so" +test -d "$wheel_deps" + +core_prefix="/usr/local/nixl-${nixl_version}-cu${cuda_major}-${nixl_wheel_sha256:0:12}-layout2" +core_marker="nixl=${nixl_version} commit=${nixl_commit} wheel_sha256=${nixl_wheel_sha256} cuda=${cuda_version} layout=2" +core_stage="$build_root/nixl-core-stage" +install -d "$core_stage/include/gpu/ucx" "$core_stage/lib/x86_64-linux-gnu" "$core_stage/lib/nixl_cu${cuda_major}.libs" "$core_stage/python" +cp -a "$wheel_core/." "$core_stage/lib/x86_64-linux-gnu/" +cp -a "$wheel_deps/." "$core_stage/lib/nixl_cu${cuda_major}.libs/" +cp -a "$build_root/nixl-wheel/." "$core_stage/python/" +install -m 0644 "$nixl_source/src/api/cpp"/nixl{,_descriptors,_params,_types}.h "$core_stage/include/" +install -m 0644 "$nixl_source/src/api/gpu/ucx/nixl_device.cuh" "$core_stage/include/gpu/ucx/" +publish_dir "$core_stage" "$core_prefix" "$core_marker" +link_install "$art_prefix/nixl" "$core_prefix" + +plugin_prefix="/usr/local/nixl-ucx-${nixl_version}-${nixl_commit:0:12}-ucx${ucx_version}-cuda${cuda_version}-gcc${compiler_id}-sm${gpu_arches//,/+}-abi1" +plugin_marker="nixl=${nixl_version} commit=${nixl_commit} ucx=${ucx_version} ucx_sha256=${ucx_sha256} cuda=${cuda_version} gcc=${compiler_id} sm=${gpu_arches} rpath=1" +if ! test -f "$plugin_prefix/.art-version" || ! grep -Fxq "$plugin_marker" "$plugin_prefix/.art-version"; then + test ! -e "$plugin_prefix" || { + echo "Refusing to replace mismatched $plugin_prefix" >&2 + exit 1 + } + package_cache="$nixl_source/subprojects/packagecache" + mkdir -p "$package_cache" + download \ + https://github.com/mesonbuild/wrapdb/releases/download/asio_1.30.2-2/asio-1.30.2.tar.gz \ + "$package_cache/asio-1.30.2.tar.gz" "$asio_sha256" + download \ + https://wrapdb.mesonbuild.com/v2/asio_1.30.2-2/get_patch \ + "$package_cache/asio_1.30.2-2_patch.zip" "$asio_patch_sha256" + build_dir="$build_root/nixl-build" + PKG_CONFIG_PATH="$ucx_prefix/lib/pkgconfig" \ + uvx --from meson==1.9.1 --with pybind11==2.13.6 meson setup "$build_dir" "$nixl_source" \ + --buildtype=release --prefix="$plugin_prefix" --libdir=lib \ + -Ducx_path="$ucx_prefix" -Denable_plugins=UCX \ + -Dbuild_tests=false -Dbuild_examples=false -Dinstall_headers=false \ + -Dnixl_cuda_arch_list="$gpu_arches" + uvx --from meson==1.9.1 --with pybind11==2.13.6 meson compile -C "$build_dir" \ + UCX -j "$(nproc)" + plugin_stage="$build_root/nixl-plugin-stage" + install -d "$plugin_stage/lib/plugins" "$plugin_stage/lib" + install -m 0755 "$build_dir/src/plugins/ucx/libplugin_UCX.so" "$plugin_stage/lib/plugins/" + install -m 0755 "$build_dir/src/utils/common/libnixl_common.so" "$plugin_stage/lib/" + install -m 0755 "$build_dir/src/infra/libnixl_build.so" "$plugin_stage/lib/" + install -m 0755 "$build_dir/src/utils/serdes/libserdes.so" "$plugin_stage/lib/" + patchelf --set-rpath "\$ORIGIN/..:$ucx_prefix/lib" "$plugin_stage/lib/plugins/libplugin_UCX.so" + for library in "$plugin_stage/lib"/*.so; do + patchelf --set-rpath '\$ORIGIN' "$library" + done + publish_dir "$plugin_stage" "$plugin_prefix" "$plugin_marker" +fi +link_install "$art_prefix/nixl-ucx" "$plugin_prefix" + +plugin="$art_prefix/nixl-ucx/lib/plugins/libplugin_UCX.so" +for object in "$plugin" "$art_prefix"/nixl-ucx/lib/*.so; do + ! ldd "$object" | grep -q 'not found' +done +! ldd "$core_prefix/lib/x86_64-linux-gnu/libnixl.so" | grep -q 'not found' +for library in libucp.so.0 libucs.so.0 libuct.so.0 libucm.so.0; do + assert_linked_from "$plugin" "$library" "$ucx_prefix" +done +for library in libnixl_common.so libnixl_build.so libserdes.so; do + assert_linked_from "$plugin" "$library" "$plugin_prefix" +done +rc_gda_count=$( + UCX_IB_GDA_RETAIN_INACTIVE_CTX=yes UCX_MODULE_DIR="$ucx_prefix/lib/ucx" \ + "$ucx_prefix/bin/ucx_info" -d 2>/dev/null \ + | awk '/Transport: rc_gda/{count++} END{print count+0}' +) +gpu_count=$(nvidia-smi --query-gpu=index --format=csv,noheader,nounits | wc -l) +test "$rc_gda_count" -ge "$gpu_count" +"$etcd_prefix/bin/etcd" --version | grep -q "etcd Version: ${etcd_version}" +printf 'ART multinode dependencies ready: CUDA %s, SM %s, %s rc_gda resources\n' \ + "$cuda_version" "$gpu_arches" "$rc_gda_count" diff --git a/src/art/__init__.py b/src/art/__init__.py index 435392e50..1ee767e7c 100644 --- a/src/art/__init__.py +++ b/src/art/__init__.py @@ -20,7 +20,10 @@ from dotenv import load_dotenv +from .utils.cache_dirs import configure_model_cache_env + load_dotenv() +configure_model_cache_env() if os.getenv("SUPPRESS_LITELLM_SERIALIZATION_WARNINGS", "1") == "1": from art.utils.suppress_litellm_serialization_warnings import ( @@ -29,12 +32,6 @@ suppress_litellm_serialization_warnings() -# torch.cuda.MemPool doesn't currently support expandable_segments which is used in sleep mode -conf = os.getenv("PYTORCH_CUDA_ALLOC_CONF", "").split(",") -if "expandable_segments:True" in conf: - conf.remove("expandable_segments:True") -os.environ["PYTORCH_CUDA_ALLOC_CONF"] = ",".join(conf) - # Import unsloth before transformers, peft, and trl only in backend processes that # explicitly request it. Unsloth is an optional backend dependency, not a base ART # import dependency. diff --git a/src/art/_backend_training.py b/src/art/_backend_training.py index dcaa7f7fb..45ff2b135 100644 --- a/src/art/_backend_training.py +++ b/src/art/_backend_training.py @@ -11,6 +11,28 @@ from .trajectories import TrajectoryGroup from .types import TrainConfig +_GRADIENT_WORKLOAD_METRICS = { + "data/gradient_step_nonpadding_logical_tokens": ( + "data/step_nonpadding_logical_tokens" + ), + "data/gradient_step_loss_bearing_tokens": "data/step_loss_bearing_tokens", + "data/gradient_step_executed_token_equivalents": ( + "data/step_executed_token_equivalents" + ), + "data/gradient_step_nominal_schedule_capacity_tokens": ( + "data/step_nominal_schedule_capacity_tokens" + ), + "data/gradient_step_dummy_executed_token_equivalents": ( + "data/step_dummy_executed_token_equivalents" + ), + "data/gradient_step_dummy_schedule_capacity_tokens": ( + "data/step_dummy_schedule_capacity_tokens" + ), + "pipeline/gradient_step_real_microbatches": "pipeline/global_real_microbatches", + "pipeline/gradient_step_dummy_microbatches": ("pipeline/global_dummy_microbatches"), +} +_GRADIENT_TRAIN_TIME = "time/gradient_step_train_s" + def build_rl_train_configs( *, @@ -38,12 +60,16 @@ def build_rl_train_configs( num_trajectories_learning_rate_multiplier_power: float | None = None, kl_ref_adapter_path: str | None = None, optimizer_save_interval: int = 5, + final_training_step: int | None = None, + grad_accumulation_sequences: int | None = None, ) -> tuple[TrainConfig, dev.TrainConfig]: config = TrainConfig( learning_rate=learning_rate, kl_penalty_coef=kl_penalty_coef, kl_penalty_source=kl_penalty_source, + grad_accumulation_sequences=grad_accumulation_sequences, optimizer_save_interval=optimizer_save_interval, + final_training_step=final_training_step, ) dev_config: dev.TrainConfig = { "advantage_balance": advantage_balance, @@ -98,12 +124,15 @@ def aggregate_rl_training_metrics( ) -> dict[str, float]: groups_list = list(trajectory_groups) avg_metrics = average_metric_samples(training_metrics) + _aggregate_megatron_workload(training_metrics, avg_metrics) tokens_per_second = avg_metrics.pop("tokens_per_second", None) if ( tokens_per_second is not None - and "throughput/train_packed_tok_per_s" not in avg_metrics + and "throughput/train_executed_tok_equiv_per_s" not in avg_metrics ): - avg_metrics["throughput/train_packed_tok_per_s"] = float(tokens_per_second) + avg_metrics["throughput/train_executed_tok_equiv_per_s"] = float( + tokens_per_second + ) summary = summarize_trajectory_groups(groups_list) avg_metrics.setdefault( "time/step_backend_train_s", time.monotonic() - trainer_started @@ -119,3 +148,58 @@ def aggregate_rl_training_metrics( } ) return avg_metrics + + +def _aggregate_megatron_workload( + training_metrics: list[dict[str, float]], + output: dict[str, float], +) -> None: + raw_keys = (*_GRADIENT_WORKLOAD_METRICS, _GRADIENT_TRAIN_TIME) + if not any(any(key in sample for key in raw_keys) for sample in training_metrics): + return + for index, sample in enumerate(training_metrics): + missing = [key for key in raw_keys if key not in sample] + if missing: + raise ValueError( + f"Megatron gradient-step metrics {index} are incomplete: {missing}" + ) + + totals = { + raw_key: sum(float(sample[raw_key]) for sample in training_metrics) + for raw_key in raw_keys + } + for raw_key in raw_keys: + output.pop(raw_key, None) + for raw_key, step_key in _GRADIENT_WORKLOAD_METRICS.items(): + output[step_key] = totals[raw_key] + + train_s = totals[_GRADIENT_TRAIN_TIME] + output["time/step_train_s"] = train_s + for raw_key, rate_key in ( + ( + "data/gradient_step_nonpadding_logical_tokens", + "throughput/train_nonpadding_logical_tok_per_s", + ), + ( + "data/gradient_step_loss_bearing_tokens", + "throughput/train_loss_bearing_tok_per_s", + ), + ( + "data/gradient_step_executed_token_equivalents", + "throughput/train_executed_tok_equiv_per_s", + ), + ( + "data/gradient_step_nominal_schedule_capacity_tokens", + "throughput/train_nominal_capacity_tok_per_s", + ), + ): + output[rate_key] = totals[raw_key] / train_s if train_s > 0 else 0.0 + logical = totals["data/gradient_step_nonpadding_logical_tokens"] + nominal = totals["data/gradient_step_nominal_schedule_capacity_tokens"] + dummy = totals["data/gradient_step_dummy_schedule_capacity_tokens"] + output["data/step_unused_packed_capacity_tokens"] = max( + 0.0, nominal - dummy - logical + ) + output["data/step_unused_and_dummy_ratio"] = ( + max(0.0, nominal - logical) / nominal if nominal > 0 else 0.0 + ) diff --git a/src/art/dev/engine.py b/src/art/dev/engine.py index 8446c3272..af40bfea0 100644 --- a/src/art/dev/engine.py +++ b/src/art/dev/engine.py @@ -122,6 +122,7 @@ class EngineArgs(TypedDict, total=False): override_generation_config: dict[str, Any] | None enable_sleep_mode: bool enable_expert_parallel: bool + moe_backend: str enable_return_routed_experts: bool model_impl: str diff --git a/src/art/dev/get_model_config.py b/src/art/dev/get_model_config.py index 8f3cf0331..68d244f69 100644 --- a/src/art/dev/get_model_config.py +++ b/src/art/dev/get_model_config.py @@ -44,10 +44,14 @@ def get_model_config( configured_init_args = config.get("init_args", {}) init_args = InitArgs( load_in_4bit=True, - max_seq_length=max_seq_length_from_model_config( - base_model, - revision=configured_init_args.get("revision"), - token=configured_init_args.get("token"), + max_seq_length=( + configured_init_args["max_seq_length"] + if "max_seq_length" in configured_init_args + else max_seq_length_from_model_config( + base_model, + revision=configured_init_args.get("revision"), + token=configured_init_args.get("token"), + ) ), model_name=base_model, ) @@ -112,4 +116,8 @@ def get_model_config( result["inference_gpu_ids"] = config["inference_gpu_ids"] if "vllm_runtime" in config: result["vllm_runtime"] = config["vllm_runtime"] + if "megatron_model_initialization" in config: + result["megatron_model_initialization"] = config[ + "megatron_model_initialization" + ] return result diff --git a/src/art/dev/model.py b/src/art/dev/model.py index 830a1021b..9d5e6d4a7 100644 --- a/src/art/dev/model.py +++ b/src/art/dev/model.py @@ -173,6 +173,7 @@ class InternalModelConfig(TypedDict, total=False): chat_template_tool_schema_format: Literal["default", "vllm_openai"] vllm_runtime: VllmRuntimeArgs allow_unvalidated_arch: bool + megatron_model_initialization: Literal["pretrained", "random"] class BackendModelConfig(InternalModelConfig, total=False): diff --git a/src/art/dev/validate.py b/src/art/dev/validate.py index 43fc9b97f..c5a3f46e3 100644 --- a/src/art/dev/validate.py +++ b/src/art/dev/validate.py @@ -136,17 +136,6 @@ def validate_dedicated_config(config: InternalModelConfig) -> None: "match len(inference_gpu_ids)" ) - if trainer_gpu_ids[0] != 0: - raise ValueError( - "trainer_gpu_ids must start at GPU 0 (training runs in-process)" - ) - - expected = list(range(len(trainer_gpu_ids))) - if trainer_gpu_ids != expected: - raise ValueError( - "trainer_gpu_ids must be contiguous starting from 0 (e.g., [0], [0,1])" - ) - if config.get("engine_args", {}).get("enable_sleep_mode"): raise ValueError( "enable_sleep_mode is incompatible with dedicated mode " diff --git a/src/art/distributed/__init__.py b/src/art/distributed/__init__.py new file mode 100644 index 000000000..b8fdebecc --- /dev/null +++ b/src/art/distributed/__init__.py @@ -0,0 +1,89 @@ +from importlib import import_module +from typing import TYPE_CHECKING, Any + +if TYPE_CHECKING: + from .art_runtime import ArtRuntime, DistributedPackedBatch + from .data_plane import PackedBatchRef, TensorSpec + from .packing import PackingRequest + from .rollout import ( + DistributedRolloutExecutor, + InProcessRolloutWorker, + InstalledAsyncCallable, + LocalRolloutExecutor, + RolloutExecutor, + ) + from .specs import ( + ArtRuntimeConfig, + ClusterSpec, + EndpointSpec, + GpuPlacement, + HostServiceHealth, + HostSpec, + ModelServiceMemberSpec, + ModelServiceSpec, + NcclTransportSpec, + NixlTransportSpec, + RuntimeTopology, + TrainerMeshSpec, + VllmParallelSpec, + ) + from .topology import compile_topology + from .vllm_replica import ( + HostMemberLaunchRequest, + HostMemberState, + ManagedVllmHostLauncher, + ReplicaFailure, + ReplicaHostLauncher, + ReplicaLaunchTemplate, + ReplicaManager, + ReplicaState, + ReplicaUpdateReport, + ) + +_EXPORTS = { + "ArtRuntime": ".art_runtime", + "ArtRuntimeConfig": ".specs", + "ClusterSpec": ".specs", + "DistributedPackedBatch": ".art_runtime", + "DistributedRolloutExecutor": ".rollout", + "EndpointSpec": ".specs", + "GpuPlacement": ".specs", + "HostMemberLaunchRequest": ".vllm_replica", + "HostMemberState": ".vllm_replica", + "HostServiceHealth": ".specs", + "HostSpec": ".specs", + "InProcessRolloutWorker": ".rollout", + "InstalledAsyncCallable": ".rollout", + "LocalRolloutExecutor": ".rollout", + "ManagedVllmHostLauncher": ".vllm_replica", + "ModelServiceMemberSpec": ".specs", + "ModelServiceSpec": ".specs", + "NcclTransportSpec": ".specs", + "NixlTransportSpec": ".specs", + "PackingRequest": ".packing", + "PackedBatchRef": ".data_plane", + "ReplicaHostLauncher": ".vllm_replica", + "ReplicaFailure": ".vllm_replica", + "ReplicaLaunchTemplate": ".vllm_replica", + "ReplicaManager": ".vllm_replica", + "ReplicaState": ".vllm_replica", + "ReplicaUpdateReport": ".vllm_replica", + "RolloutExecutor": ".rollout", + "RuntimeTopology": ".specs", + "TensorSpec": ".data_plane", + "TrainerMeshSpec": ".specs", + "VllmParallelSpec": ".specs", + "compile_topology": ".topology", +} + +__all__ = list(_EXPORTS) + + +def __getattr__(name: str) -> Any: + try: + module = _EXPORTS[name] + except KeyError: + raise AttributeError(name) from None + value = getattr(import_module(module, __name__), name) + globals()[name] = value + return value diff --git a/src/art/distributed/adapter_transport.py b/src/art/distributed/adapter_transport.py new file mode 100644 index 000000000..959c56ac0 --- /dev/null +++ b/src/art/distributed/adapter_transport.py @@ -0,0 +1,685 @@ +from __future__ import annotations + +import base64 +import hashlib +import importlib +import json +import os +from pathlib import Path +import socket +import sys +from threading import Condition, Lock +import time +from typing import Any, Literal + +from pydantic import BaseModel, ConfigDict, Field +import torch + +from art.utils.safetensors import PreparedSafetensors, save_prepared_safetensors + + +class _TransportRecord(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + +class AdapterTransferTarget(_TransportRecord): + transport: Literal["local", "nixl"] = "nixl" + host_id: str = Field(min_length=1) + generation_id: str = Field(min_length=1) + path: str = Field(min_length=1) + remote_agent: str = Field(min_length=1) + remote_metadata_b64: str = Field(min_length=1) + remote_address: int = Field(ge=0) + remote_device_id: int = Field(ge=0) + slot_id: int = Field(ge=0) + capacity_bytes: int = Field(gt=0) + prepare_s: float = Field(ge=0) + pool_wait_s: float = Field(ge=0) + registration_s: float = Field(ge=0) + + +class AdapterReceiveResult(_TransportRecord): + host_id: str = Field(min_length=1) + generation_id: str = Field(min_length=1) + path: str = Field(min_length=1) + tensor_bytes: int = Field(gt=0) + config_bytes: int = Field(gt=0) + materialization_s: float = Field(ge=0) + slot_id: int = Field(default=0, ge=0) + used_bytes: int = Field(default=0, ge=0) + capacity_bytes: int = Field(default=0, ge=0) + prepare_s: float = Field(default=0, ge=0) + pool_wait_s: float = Field(default=0, ge=0) + registration_s: float = Field(default=0, ge=0) + sender_staging_s: float = Field(default=0, ge=0) + sender_registration_s: float = Field(default=0, ge=0) + + +class AdapterTransferNotification(_TransportRecord): + generation_id: str = Field(min_length=1) + used_bytes: int = Field(gt=0) + adapter_config: dict[str, Any] + sender_staging_s: float = Field(ge=0) + sender_registration_s: float = Field(ge=0) + + +class _PendingReceive: + def __init__( + self, + *, + target: AdapterTransferTarget, + slot: "_RegisteredSlot", + ) -> None: + self.target = target + self.slot = slot + + +class _PendingLocalReceive: + def __init__( + self, + *, + target: AdapterTransferTarget, + listener: socket.socket, + ) -> None: + self.target = target + self.listener = listener + + +class _RegisteredSlot: + def __init__( + self, + slot_id: int, + block: torch.Tensor, + registration: Any, + ) -> None: + self.slot_id = slot_id + self.block = block + self.registration = registration + self.generation_id: str | None = None + + +def _load_nixl() -> tuple[Any, Any, Any]: + root = Path("/usr/local/art-multinode/nixl/python") + if root.is_dir() and str(root) not in sys.path: + sys.path.insert(0, str(root)) + os.environ.update( + NIXL_PLUGIN_DIR="/usr/local/art-multinode/nixl-ucx/lib/plugins", + UCX_MODULE_DIR="/usr/local/art-multinode/ucx/lib/ucx", + UCX_NET_DEVICES="all", + UCX_TLS="rc,rc_gda,cuda_copy", + UCX_IB_GDA_RETAIN_INACTIVE_CTX="yes", + ) + for name in ("nixl_cu13", "nixl_cu12", "nixl"): + try: + module = importlib.import_module(name) + except ModuleNotFoundError: + continue + return ( + module.nixl_agent, + module.nixl_agent_config, + module.nixl_thread_sync_t, + ) + raise RuntimeError( + "NIXL Python bindings are unavailable; run scripts/setup_multinode.sh" + ) + + +def _new_agent(name: str) -> Any: + agent_type, config_type, sync_type = _load_nixl() + return agent_type( + name, + config_type( + enable_prog_thread=True, + enable_listen_thread=False, + backends=["UCX"], + sync_mode=sync_type.NIXL_THREAD_SYNC_STRICT, + ), + ) + + +def _adapter_template_bytes(path: str) -> int: + root = Path(path) + model_path = root / "adapter_model.safetensors" + model_bytes = model_path.stat().st_size + if model_bytes <= 8: + raise RuntimeError(f"Adapter template is empty: {path}") + with (root / "adapter_config.json").open("r", encoding="utf-8") as source: + config = json.load(source) + if not isinstance(config, dict): + raise RuntimeError(f"Adapter config must be an object: {path}") + if config.get("art_lora_format") != "vllm": + raise RuntimeError(f"Adapter template is not in vLLM format: {path}") + return model_bytes + + +def _copy_payload(payload: PreparedSafetensors, block: torch.Tensor) -> None: + offset = 0 + for chunk in payload.chunks: + block.narrow(0, offset, chunk.numel()).copy_(chunk) + offset += chunk.numel() + if offset != payload.nbytes: + raise RuntimeError("Adapter payload copy was incomplete") + + +class AdapterSnapshotReceiver: + """Owns receive buffers for immutable LoRA generations.""" + + def __init__( + self, host_id: str, output_root: str, *, pool_capacity: int = 2 + ) -> None: + if pool_capacity < 1: + raise ValueError("adapter receive pool capacity must be positive") + self.host_id = host_id + self.output_root = Path(output_root) / "adapter_transfers" + self.pool_capacity = pool_capacity + self._agent: Any | None = None + self._pending: dict[str, _PendingReceive] = {} + self._local_pending: dict[str, _PendingLocalReceive] = {} + self._slots: list[_RegisteredSlot] = [] + self._condition = Condition() + self._notifications: dict[str, AdapterTransferNotification] = {} + self._materialized: set[str] = set() + self._agent_lock = Lock() + self._closed = False + + def prepare( + self, + generation_id: str, + template_path: str, + timeout_s: float = 300.0, + transport: Literal["local", "nixl"] = "nixl", + ) -> AdapterTransferTarget: + if transport == "local": + return self._prepare_local(generation_id, template_path, timeout_s) + prepare_started = time.monotonic() + required_bytes = _adapter_template_bytes(template_path) + wait_started = time.monotonic() + with self._condition: + if self._closed: + raise RuntimeError("adapter receive pool is closed") + if generation_id in self._pending: + raise RuntimeError(f"Adapter receive already exists: {generation_id}") + slot, registration_s = self._acquire_slot( + required_bytes, deadline=wait_started + timeout_s + ) + slot.generation_id = generation_id + pool_wait_s = time.monotonic() - wait_started - registration_s + try: + with self._agent_lock: + agent = self._require_agent() + remote_agent = agent.name + metadata = base64.b64encode(agent.get_agent_metadata()).decode() + path = str((self.output_root / generation_id).absolute()) + target = AdapterTransferTarget( + host_id=self.host_id, + generation_id=generation_id, + path=path, + remote_agent=remote_agent, + remote_metadata_b64=metadata, + remote_address=slot.block.data_ptr(), + remote_device_id=0, + slot_id=slot.slot_id, + capacity_bytes=slot.block.numel(), + prepare_s=time.monotonic() - prepare_started, + pool_wait_s=max(0.0, pool_wait_s), + registration_s=registration_s, + ) + except BaseException: + self._release_slot(slot, generation_id) + raise + self._pending[generation_id] = _PendingReceive( + target=target, + slot=slot, + ) + return target + + def _prepare_local( + self, + generation_id: str, + template_path: str, + timeout_s: float, + ) -> AdapterTransferTarget: + prepare_started = time.monotonic() + required_bytes = _adapter_template_bytes(template_path) + wait_started = time.monotonic() + with self._condition: + while len(self._local_pending) >= self.pool_capacity: + remaining_s = wait_started + timeout_s - time.monotonic() + if remaining_s <= 0: + raise TimeoutError("local adapter receive pool remained full") + self._condition.wait(remaining_s) + if self._closed: + raise RuntimeError("adapter receive pool is closed") + if generation_id in self._local_pending or generation_id in self._pending: + raise RuntimeError(f"Adapter receive already exists: {generation_id}") + socket_path = ( + "/tmp/art-lora-" + + hashlib.sha256( + f"{self.host_id}:{generation_id}:{os.getpid()}".encode() + ).hexdigest()[:24] + + ".sock" + ) + listener = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM) + try: + listener.bind(socket_path) + listener.listen(1) + listener.setblocking(False) + local_root = Path( + os.environ.get( + "ART_LOCAL_ADAPTER_TRANSFER_ROOT", + "/dev/shm/art_adapter_transfers", + ) + ) + target = AdapterTransferTarget( + transport="local", + host_id=self.host_id, + generation_id=generation_id, + path=str((local_root / self.host_id / generation_id).absolute()), + remote_agent=socket_path, + remote_metadata_b64="-", + remote_address=0, + remote_device_id=0, + slot_id=0, + capacity_bytes=required_bytes, + prepare_s=time.monotonic() - prepare_started, + pool_wait_s=time.monotonic() - wait_started, + registration_s=0.0, + ) + except BaseException: + listener.close() + Path(socket_path).unlink(missing_ok=True) + raise + self._local_pending[generation_id] = _PendingLocalReceive( + target=target, + listener=listener, + ) + return target + + def poll(self, generation_id: str) -> AdapterReceiveResult | None: + if generation_id in self._local_pending: + return self._poll_local(generation_id) + pending = self._pending.get(generation_id) + if pending is None: + raise RuntimeError(f"Unknown adapter receive: {generation_id}") + notification = self._take_notification(generation_id) + if notification is None: + return None + if notification.used_bytes > pending.slot.block.numel(): + self._finish(generation_id) + raise RuntimeError("Adapter payload exceeds its prepared receive capacity") + started = time.monotonic() + path = Path(pending.target.path) + if path.exists(): + self._finish(generation_id) + raise RuntimeError(f"Adapter transfer path already exists: {path}") + try: + path.mkdir(parents=True) + save_prepared_safetensors( + PreparedSafetensors( + (pending.slot.block.narrow(0, 0, notification.used_bytes),) + ), + path / "adapter_model.safetensors", + ) + with (path / "adapter_config.json").open("w", encoding="utf-8") as output: + json.dump(notification.adapter_config, output, indent=2, sort_keys=True) + output.write("\n") + materialization_s = time.monotonic() - started + model_bytes = (path / "adapter_model.safetensors").stat().st_size + config_bytes = (path / "adapter_config.json").stat().st_size + except BaseException: + if path.exists(): + from shutil import rmtree + + rmtree(path) + raise + finally: + self._finish(generation_id) + self._materialized.add(generation_id) + return AdapterReceiveResult( + host_id=self.host_id, + generation_id=generation_id, + path=str(path), + tensor_bytes=model_bytes, + config_bytes=config_bytes, + materialization_s=materialization_s, + slot_id=pending.target.slot_id, + used_bytes=notification.used_bytes, + capacity_bytes=pending.target.capacity_bytes, + prepare_s=pending.target.prepare_s, + pool_wait_s=pending.target.pool_wait_s, + registration_s=pending.target.registration_s, + sender_staging_s=notification.sender_staging_s, + sender_registration_s=notification.sender_registration_s, + ) + + def _poll_local(self, generation_id: str) -> AdapterReceiveResult | None: + pending = self._local_pending[generation_id] + try: + connection, _ = pending.listener.accept() + except BlockingIOError: + return None + try: + connection.settimeout(60.0) + payload = bytearray() + while chunk := connection.recv(64 * 1024): + payload.extend(chunk) + notification = AdapterTransferNotification.model_validate_json(payload) + if notification.generation_id != generation_id: + raise RuntimeError("local adapter notification has wrong generation") + path = Path(pending.target.path) + model_path = path / "adapter_model.safetensors" + config_path = path / "adapter_config.json" + if not model_path.is_file() or not config_path.is_file(): + raise RuntimeError("local adapter transfer is incomplete") + self._materialized.add(generation_id) + return AdapterReceiveResult( + host_id=self.host_id, + generation_id=generation_id, + path=str(path), + tensor_bytes=model_path.stat().st_size, + config_bytes=config_path.stat().st_size, + materialization_s=notification.sender_staging_s, + slot_id=pending.target.slot_id, + used_bytes=notification.used_bytes, + capacity_bytes=pending.target.capacity_bytes, + prepare_s=pending.target.prepare_s, + pool_wait_s=pending.target.pool_wait_s, + registration_s=0.0, + sender_staging_s=notification.sender_staging_s, + sender_registration_s=0.0, + ) + finally: + connection.close() + self._finish_local(generation_id) + + def release(self, generation_id: str) -> None: + from shutil import rmtree + + if generation_id in self._pending: + self._finish(generation_id) + if generation_id in self._local_pending: + self._finish_local(generation_id) + with self._agent_lock: + self._notifications.pop(generation_id, None) + self._materialized.discard(generation_id) + for root in ( + self.output_root, + Path( + os.environ.get( + "ART_LOCAL_ADAPTER_TRANSFER_ROOT", + "/dev/shm/art_adapter_transfers", + ) + ) + / self.host_id, + ): + path = root / generation_id + if path.exists(): + rmtree(path) + + def _finish_local(self, generation_id: str) -> None: + pending = self._local_pending.pop(generation_id) + pending.listener.close() + Path(pending.target.remote_agent).unlink(missing_ok=True) + with self._condition: + self._condition.notify() + + def _require_agent(self) -> Any: + if self._agent is None: + self._agent = _new_agent(f"art-lora-receiver-{self.host_id}-{os.getpid()}") + return self._agent + + def _take_notification( + self, generation_id: str + ) -> AdapterTransferNotification | None: + with self._agent_lock: + for messages in self._require_agent().get_new_notifs().values(): + for message in messages: + notification = AdapterTransferNotification.model_validate_json( + message + ) + self._notifications[notification.generation_id] = notification + return self._notifications.pop(generation_id, None) + + def _finish(self, generation_id: str) -> None: + pending = self._pending.pop(generation_id) + self._release_slot(pending.slot, generation_id) + + def _release_slot(self, slot: _RegisteredSlot, generation_id: str) -> None: + with self._condition: + if slot.generation_id != generation_id: + raise RuntimeError("adapter receive slot ownership changed") + slot.generation_id = None + self._condition.notify() + + def _acquire_slot( + self, used_bytes: int, *, deadline: float + ) -> tuple[_RegisteredSlot, float]: + while True: + free = [slot for slot in self._slots if slot.generation_id is None] + fitting = [slot for slot in free if slot.block.numel() >= used_bytes] + if fitting: + return min(fitting, key=lambda slot: slot.block.numel()), 0.0 + if free or len(self._slots) < self.pool_capacity: + previous = min(free, key=lambda slot: slot.block.numel(), default=None) + slot_id = len(self._slots) if previous is None else previous.slot_id + capacity = used_bytes + started = time.monotonic() + block = torch.empty(capacity, dtype=torch.uint8) + with self._agent_lock: + agent = self._require_agent() + registration = agent.register_memory((block,), backends=["UCX"]) + if previous is not None: + agent.deregister_memory(previous.registration, backends=["UCX"]) + if previous is None: + slot = _RegisteredSlot(slot_id, block, registration) + self._slots.append(slot) + else: + previous.block = block + previous.registration = registration + slot = previous + return slot, time.monotonic() - started + remaining_s = deadline - time.monotonic() + if remaining_s <= 0: + raise TimeoutError("adapter receive pool remained full") + self._condition.wait(remaining_s) + if self._closed: + raise RuntimeError("adapter receive pool closed while waiting") + + def close(self) -> None: + with self._condition: + self._closed = True + self._condition.notify_all() + for generation_id in ( + *self._pending, + *self._local_pending, + *self._materialized, + ): + self.release(generation_id) + if self._agent is not None: + with self._agent_lock: + for slot in self._slots: + self._agent.deregister_memory(slot.registration, backends=["UCX"]) + self._slots.clear() + + +class NixlAdapterSender: + """Transfers one immutable CPU snapshot to one or more prepared hosts.""" + + def __init__(self) -> None: + self._agent: Any | None = None + self._block: torch.Tensor | None = None + self._registration: Any | None = None + self._remote_agents: dict[tuple[str, str], str] = {} + + def send( + self, + payload: PreparedSafetensors, + adapter_config: dict[str, Any], + targets: tuple[AdapterTransferTarget, ...], + ) -> None: + if not targets: + return + first = targets[0] + if any(target.generation_id != first.generation_id for target in targets[1:]): + raise RuntimeError("Adapter transfer targets disagree") + used_bytes = payload.nbytes + if any(used_bytes > target.capacity_bytes for target in targets): + raise RuntimeError("Adapter payload exceeds prepared receive capacity") + agent = self._require_agent() + sender_registration_s = self._ensure_capacity(used_bytes) + assert self._block is not None + staging_started = time.monotonic() + _copy_payload(payload, self._block) + notification = ( + AdapterTransferNotification( + generation_id=first.generation_id, + used_bytes=used_bytes, + adapter_config=adapter_config, + sender_staging_s=time.monotonic() - staging_started, + sender_registration_s=sender_registration_s, + ) + .model_dump_json() + .encode() + ) + for target in targets: + local_descriptors = agent.get_xfer_descs( + (self._block.narrow(0, 0, used_bytes),) + ) + key = (target.host_id, target.remote_metadata_b64) + remote_agent = self._remote_agents.get(key) + if remote_agent is None: + remote_agent = agent.add_remote_agent( + base64.b64decode(target.remote_metadata_b64) + ) + if isinstance(remote_agent, bytes): + remote_agent = remote_agent.decode() + self._remote_agents[key] = remote_agent + if remote_agent != target.remote_agent: + raise RuntimeError("NIXL target returned the wrong agent identity") + handle = agent.initialize_xfer( + "WRITE", + local_descriptors, + agent.get_xfer_descs( + [ + ( + target.remote_address, + used_bytes, + target.remote_device_id, + ) + ], + mem_type="DRAM", + ), + remote_agent, + notification, + backends=["UCX"], + ) + try: + state = agent.transfer(handle) + while state == "PROC": + time.sleep(0.001) + state = agent.check_xfer_state(handle) + if state != "DONE": + raise RuntimeError( + f"NIXL adapter transfer failed for {target.host_id}" + ) + finally: + handle.release() + + def _ensure_capacity(self, used_bytes: int) -> float: + if self._block is not None and self._block.numel() >= used_bytes: + return 0.0 + capacity = max( + used_bytes, + 2 * (0 if self._block is None else self._block.numel()), + ) + block = torch.empty(capacity, dtype=torch.uint8) + agent = self._require_agent() + started = time.monotonic() + registration = agent.register_memory((block,), backends=["UCX"]) + if self._registration is not None: + agent.deregister_memory(self._registration, backends=["UCX"]) + self._block = block + self._registration = registration + return time.monotonic() - started + + def close(self) -> None: + if self._agent is not None: + for remote_agent in self._remote_agents.values(): + self._agent.remove_remote_agent(remote_agent) + self._remote_agents.clear() + if self._agent is not None and self._registration is not None: + self._agent.deregister_memory(self._registration, backends=["UCX"]) + self._block = None + self._registration = None + + def _require_agent(self) -> Any: + if self._agent is None: + self._agent = _new_agent(f"art-lora-sender-{os.getpid()}") + return self._agent + + +class AdapterSnapshotSender: + """Dispatches immutable snapshots over the transport selected by each target.""" + + def __init__(self) -> None: + self._nixl: NixlAdapterSender | None = None + + def send( + self, + snapshot: Any, + targets: tuple[AdapterTransferTarget, ...], + *, + prepared_tensors: PreparedSafetensors, + ) -> None: + transports = {target.transport for target in targets} + if not targets: + return + if len(transports) != 1: + raise RuntimeError("adapter transfer targets mix transports") + if transports == {"nixl"}: + if self._nixl is None: + self._nixl = NixlAdapterSender() + self._nixl.send( + prepared_tensors, + {**snapshot.adapter_config, "art_lora_format": "vllm"}, + targets, + ) + return + self._send_local(snapshot, targets, prepared_tensors=prepared_tensors) + + @staticmethod + def _send_local( + snapshot: Any, + targets: tuple[AdapterTransferTarget, ...], + *, + prepared_tensors: PreparedSafetensors, + ) -> None: + from art.megatron.weights.lora_publish import save_vllm_lora_snapshot + + first = targets[0] + snapshot_config = {**snapshot.adapter_config, "art_lora_format": "vllm"} + if any(target.generation_id != first.generation_id for target in targets): + raise RuntimeError("local adapter transfer target changed") + for target in targets: + started = time.monotonic() + save_vllm_lora_snapshot( + snapshot, + target.path, + prepared_tensors=prepared_tensors, + ) + notification = AdapterTransferNotification( + generation_id=target.generation_id, + used_bytes=prepared_tensors.nbytes, + adapter_config=snapshot_config, + sender_staging_s=time.monotonic() - started, + sender_registration_s=0.0, + ).model_dump_json() + with socket.socket(socket.AF_UNIX, socket.SOCK_STREAM) as client: + client.settimeout(60.0) + client.connect(target.remote_agent) + client.sendall(notification.encode()) + + def close(self) -> None: + if self._nixl is not None: + self._nixl.close() + self._nixl = None diff --git a/src/art/distributed/art_runtime.py b/src/art/distributed/art_runtime.py new file mode 100644 index 000000000..a2ab2f014 --- /dev/null +++ b/src/art/distributed/art_runtime.py @@ -0,0 +1,1097 @@ +from __future__ import annotations + +import asyncio +from collections import Counter +from collections.abc import Awaitable, Callable +import logging +import time +from typing import Any, Literal +from urllib.parse import urlparse +import uuid + +from pydantic import BaseModel, ConfigDict + +from art.megatron.runtime.specs import TrainerRuntimeSpec, TrainingRunSpec +from art.utils.lifecycle import complete_task + +from .artifact_preflight import ( + ArtifactProbeCommand, + ArtifactProbeOperation, + ArtifactProbeResult, + ArtifactProbeSpec, + ArtifactRootPreflightError, +) +from .data_plane import PackedBatchLeaseSet, fanout_packed_batch +from .host_admission import ( + HostAdmissionReport, + HostAdmissionRequest, + RuntimeFingerprint, + build_runtime_fingerprint, + runtime_package_names, + validate_host_admission, +) +from .monarch_bootstrap import ( + _start_worker, + _stop_worker, + activate_cpu_child_virtualenv, + activate_trainer_child_virtualenv, + attach_controller, + monarch_identifier, + require_local_worker_address, +) +from .monarch_runtime import ( + MonarchPackedBatchInbox, + MonarchPackedBatchSource, + MonarchPackingEndpoint, + MonarchRolloutWorkerEndpoint, + MonarchTrajectoryQueueEndpoint, + MonarchVllmHostLauncher, + call_remote, +) +from .nccl_preflight import ( + NcclPreflightSessionRequest, + NcclProbeRequest, + NcclProbeResult, + NcclRendezvousRequest, + NcclRendezvousResult, +) +from .packing import PackingRequest, PackingResult +from .rollout import DistributedRolloutExecutor, InstalledAsyncCallable +from .specs import ( + ArtRuntimeConfig, + GpuId, + GpuPlacement, + HostServiceHealth, + ModelServiceSpec, + RuntimeTopology, +) +from .vllm_replica import ( + ReplicaFailure, + ReplicaLaunchTemplate, + ReplicaManager, + ReplicaState, +) + +logger = logging.getLogger(__name__) + + +def _consume_task_result(task: asyncio.Future[Any]) -> None: + if not task.cancelled(): + task.exception() + + +class DistributedPackedBatch(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + leases: PackedBatchLeaseSet + packed_group_shapes: tuple[Any, ...] + trainable_assistant_tokens: int + loss_bearing_tokens: int + non_padding_tokens: int + trajectory_log_path: str | None = None + packing_rpc_s: float = 0.0 + trajectory_fetch_s: float = 0.0 + packing_core_s: float = 0.0 + trajectory_log_wait_s: float = 0.0 + packed_batch_finalize_s: float = 0.0 + packed_batch_fanout_s: float = 0.0 + packing_generation_id: str + + +class ArtRuntime: + """Run-scoped owner of ART host services, trainer meshes, and vLLM services.""" + + def __init__( + self, + host_mesh: Any, + topology: RuntimeTopology, + *, + config: ArtRuntimeConfig | None = None, + owns_host_mesh: bool = False, + ) -> None: + self.host_mesh = host_mesh + self.topology = topology + self.config = config or ArtRuntimeConfig() + self.owns_host_mesh = owns_host_mesh + self.runtime_id = uuid.uuid4().hex + self._host_procs: dict[str, Any] = {} + self._host_services: dict[str, Any] = {} + self._adapter_procs: dict[str, Any] = {} + self._adapter_services: dict[str, Any] = {} + self._rollout_procs: dict[str, Any] = {} + self._rollout_actors: dict[str, Any] = {} + self._trainer_runs: set[Any] = set() + self._live_batches: dict[str, tuple[str, ...]] = {} + self._model_services: dict[str, ReplicaManager] = {} + self._closeables: set[Any] = set() + self._next_packing_host = 0 + self._nccl_preflight_lock = asyncio.Lock() + self._nccl_preflights: set[tuple[str, tuple[tuple[str, GpuId], ...], str]] = ( + set() + ) + self._runtime_packages = runtime_package_names( + trainer=topology.trainer is not None + ) + self._controller_fingerprint: RuntimeFingerprint + self._admitted_hosts: dict[str, HostAdmissionReport] = {} + self._artifact_probe = ( + ArtifactProbeSpec( + artifact_root=topology.cluster.artifact_root, + runtime_id=self.runtime_id, + host_ids=tuple(host.host_id for host in topology.cluster.hosts), + ) + if topology.cluster.artifact_root is not None + else None + ) + self._close_task: asyncio.Task[None] | None = None + self._local_worker: Any | None = None + self._started = False + self._closed = False + + @classmethod + async def start( + cls, + host_mesh: Any, + topology: RuntimeTopology, + *, + config: ArtRuntimeConfig | None = None, + owns_host_mesh: bool = False, + ) -> "ArtRuntime": + runtime = cls( + host_mesh, + topology, + config=config, + owns_host_mesh=owns_host_mesh, + ) + return await runtime._start() + + @classmethod + async def start_local( + cls, + topology: RuntimeTopology, + *, + config: ArtRuntimeConfig | None = None, + ) -> "ArtRuntime": + requested_address = require_local_worker_address( + tuple(host.worker_address for host in topology.cluster.hosts) + ) + worker = _start_worker( + requested_address, + startup_timeout_s=topology.cluster.startup_timeout_s, + ) + address = worker.address + if address != requested_address: + host = topology.cluster.hosts[0].model_copy( + update={"worker_address": address} + ) + cluster = topology.cluster.model_copy(update={"hosts": (host,)}) + topology = RuntimeTopology( + cluster=cluster, + rollout_host_ids=topology.rollout_host_ids, + trainer=topology.trainer, + model_services=topology.model_services, + ) + try: + host_mesh = await attach_controller( + (address,), + name=f"art_local_{uuid.uuid4().hex}", + startup_timeout_s=topology.cluster.startup_timeout_s, + owned_workers=(worker,), + ) + except BaseException as startup_error: + try: + await asyncio.to_thread(_stop_worker, worker) + except BaseException as cleanup_error: + raise BaseExceptionGroup( + "local ART runtime startup and cleanup failed", + [startup_error, cleanup_error], + ) from None + raise + try: + runtime = cls(host_mesh, topology, config=config, owns_host_mesh=True) + except BaseException as startup_error: + try: + await asyncio.wait_for( + host_mesh.shutdown(), topology.cluster.rpc_timeout_s + ) + await asyncio.to_thread(_stop_worker, worker) + except BaseException as cleanup_error: + raise BaseExceptionGroup( + "local ART runtime construction and cleanup failed", + [startup_error, cleanup_error], + ) from None + raise + runtime._local_worker = worker + return await runtime._start() + + async def _start(self) -> "ArtRuntime": + try: + await self._start_host_services() + except BaseException as startup_error: + try: + await self.close() + except BaseException as cleanup_error: + raise BaseExceptionGroup( + "ART runtime startup and cleanup failed", + [startup_error, cleanup_error], + ) from None + raise + return self + + async def _start_host_services(self) -> None: + from .monarch_actor import AdapterTransferHostService, ArtHostService + + async with asyncio.timeout(self.topology.cluster.startup_timeout_s): + for index, host in enumerate(self.topology.cluster.hosts): + data_plane_host = urlparse(host.worker_address).hostname + host_mesh = self.host_mesh.slice(hosts=index) + proc = host_mesh.spawn_procs( + per_host={"service": 1}, + bootstrap=activate_cpu_child_virtualenv, + name=monarch_identifier( + f"art_host_{self.runtime_id}_{host.host_id}" + ), + ) + self._host_procs[host.host_id] = proc + actor = proc.spawn( + monarch_identifier(f"art_service_{self.runtime_id}_{host.host_id}"), + ArtHostService, + HostAdmissionRequest( + host_id=host.host_id, + node_rank=host.node_rank, + expected_gpu_ids=host.gpu_ids, + runtime_packages=self._runtime_packages, + ).model_dump_json(), + self.config.packed_batch_capacity_bytes, + self.config.vllm_output_root, + data_plane_host, + ) + self._host_services[host.host_id] = actor + adapter_proc = host_mesh.spawn_procs( + per_host={"adapter": 1}, + bootstrap=activate_cpu_child_virtualenv, + name=monarch_identifier( + f"art_adapter_host_{self.runtime_id}_{host.host_id}" + ), + ) + self._adapter_procs[host.host_id] = adapter_proc + adapter_actor = adapter_proc.spawn( + monarch_identifier(f"art_adapter_{self.runtime_id}_{host.host_id}"), + AdapterTransferHostService, + host.host_id, + self.config.vllm_output_root, + ) + self._adapter_services[host.host_id] = adapter_actor + await asyncio.gather( + *(actor.initialized for actor in self._host_services.values()), + *(actor.initialized for actor in self._adapter_services.values()), + ) + self._controller_fingerprint, reports = await asyncio.gather( + asyncio.to_thread(build_runtime_fingerprint, self._runtime_packages), + asyncio.gather( + *( + call_remote(actor.admission) + for actor in self._host_services.values() + ) + ), + ) + self._admitted_hosts = validate_host_admission( + self.topology.cluster.hosts, + reports, + expected_runtime=self._controller_fingerprint, + ) + self._validate_nccl_transport_environment() + await self._preflight_artifact_root() + await self._preflight_nixl_metadata_store() + self._started = True + for report in self._admitted_hosts.values(): + gpus = ",".join( + f"{gpu.index}={gpu.uuid}@{gpu.pci_bus_id}" + for gpu in report.assigned_gpus + ) + logger.info( + "admitted ART host %s hostname=%s boot_id=%s gpus=[%s] runtime=%s", + report.host_id, + report.hostname, + report.boot_id, + gpus, + report.runtime.sha256, + ) + + async def health(self) -> dict[str, HostServiceHealth]: + async with asyncio.timeout(self.topology.cluster.rpc_timeout_s): + values = await asyncio.gather( + *(call_remote(actor.health) for actor in self._host_services.values()) + ) + health = {value.host_id: value for value in values} + if len(health) != len(values) or health.keys() != self._admitted_hosts.keys(): + raise RuntimeError("host-service liveness membership changed") + for host_id, value in health.items(): + admitted = self._admitted_hosts[host_id] + if (value.hostname, value.process_id) != ( + admitted.hostname, + admitted.process_id, + ): + raise RuntimeError(f"host service {host_id!r} identity changed") + return health + + async def _preflight_launch( + self, + *, + runtime_kind: Literal["trainer", "vllm"], + placements: tuple[GpuPlacement, ...], + master_addr: str | None = None, + ) -> None: + selected = tuple( + next(value for value in placements if value.host_id == host_id) + for host_id in dict.fromkeys(value.host_id for value in placements) + ) + if len(selected) < 2: + await self.health() + return + transport = self.topology.cluster.nccl_transport + if transport is None: + raise RuntimeError("multi-host GPU launch has no NCCL transport contract") + key = ( + runtime_kind, + tuple((value.host_id, value.gpu_id) for value in selected), + transport.net_name, + ) + deadline = ( + asyncio.get_running_loop().time() + self.topology.cluster.startup_timeout_s + ) + cleanup_budget_s = min(10.0, self.topology.cluster.startup_timeout_s * 0.1) + operation_deadline = deadline - cleanup_budget_s + async with asyncio.timeout_at(deadline): + await self._nccl_preflight_lock.acquire() + try: + async with asyncio.timeout_at(operation_deadline): + await self.health() + if key in self._nccl_preflights: + return + probe_id = uuid.uuid4().hex + failure: BaseException | None = None + try: + async with asyncio.timeout_at(operation_deadline): + leader = selected[0] + if master_addr is None: + worker_address = self._host(leader.host_id).worker_address + parsed = urlparse(worker_address) + if parsed.scheme != "tcp" or parsed.hostname is None: + raise ValueError( + f"NCCL preflight requires a TCP worker address, got " + f"{worker_address!r}" + ) + master_addr = parsed.hostname + phase_timeout_s = max( + 0.001, + (operation_deadline - asyncio.get_running_loop().time()) * 0.45, + ) + session = NcclPreflightSessionRequest( + probe_id=probe_id, + lease_s=max( + 0.001, + operation_deadline - asyncio.get_running_loop().time(), + ), + ) + session_results = await asyncio.gather( + *( + call_remote( + self._host_services[ + placement.host_id + ].start_nccl_preflight_session, + session, + ) + for placement in selected + ), + return_exceptions=True, + ) + session_failures = [ + result + for result in session_results + if isinstance(result, BaseException) + ] + if session_failures: + raise BaseExceptionGroup( + "NCCL preflight session admission failed", + session_failures, + ) + rendezvous = await call_remote( + self._host_services[leader.host_id].nccl_preflight_rendezvous, + NcclRendezvousRequest( + probe_id=probe_id, + runtime_kind=runtime_kind, + master_addr=master_addr, + timeout_s=phase_timeout_s, + ), + ) + if not isinstance(rendezvous, NcclRendezvousResult): + raise RuntimeError("NCCL preflight returned an invalid store") + requests = tuple( + NcclProbeRequest( + probe_id=probe_id, + runtime_kind=runtime_kind, + rank=rank, + world_size=len(selected), + master_addr=master_addr, + master_port=rendezvous.port, + gpu_id=placement.gpu_id, + net_name=transport.net_name, + timeout_s=phase_timeout_s, + ) + for rank, placement in enumerate(selected) + ) + results = await asyncio.gather( + *( + call_remote( + self._host_services[placement.host_id].nccl_preflight, + request, + ) + for placement, request in zip( + selected, requests, strict=True + ) + ), + return_exceptions=True, + ) + failures = [ + result + for result in results + if isinstance(result, BaseException) + ] + if failures: + raise BaseExceptionGroup( + f"{runtime_kind} NCCL transport preflight failed", failures + ) + reports = tuple( + result + for result in results + if isinstance(result, NcclProbeResult) + ) + expected = tuple( + (placement.host_id, rank, transport.net_name) + for rank, placement in enumerate(selected) + ) + if ( + tuple( + (report.host_id, report.rank, report.net_name) + for report in reports + ) + != expected + ): + raise RuntimeError( + "NCCL preflight returned inconsistent membership" + ) + except BaseException as error: + failure = error + cleanup_failures, cleanup_cancelled = await complete_task( + asyncio.create_task( + self._cancel_nccl_preflight( + selected, + probe_id, + timeout_s=max( + 0.001, deadline - asyncio.get_running_loop().time() + ), + ) + ) + ) + if cleanup_cancelled is not None: + if failure is not None: + cleanup_cancelled.add_note(f"NCCL preflight also failed: {failure}") + raise cleanup_cancelled + if failure is not None: + if cleanup_failures: + raise BaseExceptionGroup( + f"{runtime_kind} NCCL preflight and cleanup failed", + [failure, *cleanup_failures], + ) from None + raise failure + if cleanup_failures: + raise BaseExceptionGroup( + f"{runtime_kind} NCCL preflight cleanup failed", + cleanup_failures, + ) + self._nccl_preflights.add(key) + finally: + self._nccl_preflight_lock.release() + + async def _cancel_nccl_preflight( + self, + placements: tuple[GpuPlacement, ...], + probe_id: str, + *, + timeout_s: float, + ) -> list[BaseException]: + try: + async with asyncio.timeout(timeout_s): + results = await asyncio.gather( + *( + call_remote( + self._host_services[ + placement.host_id + ].cancel_nccl_preflight, + probe_id, + ) + for placement in placements + ), + return_exceptions=True, + ) + except BaseException as error: + return [error] + return [result for result in results if isinstance(result, BaseException)] + + def _validate_nccl_transport_environment(self) -> None: + transport = self.topology.cluster.nccl_transport + if transport is None: + return + mismatches = { + host_id: dict(report.runtime.environment).get("NCCL_NET") + for host_id, report in self._admitted_hosts.items() + if dict(report.runtime.environment).get("NCCL_NET") != transport.net_name + } + if mismatches: + raise RuntimeError( + f"NCCL_NET must equal {transport.net_name!r} on every host: " + f"{mismatches}" + ) + + async def _preflight_nixl_metadata_store(self) -> None: + transport = self.topology.cluster.nixl_transport + if transport is None: + return + host_ids = tuple(self._host_services) + probe_timeout_s = min(5.0, self.topology.cluster.rpc_timeout_s) + async with asyncio.timeout(self.topology.cluster.rpc_timeout_s): + results = await asyncio.gather( + *( + call_remote( + self._host_services[host_id].nixl_metadata_store_health, + transport.metadata_store.url, + probe_timeout_s, + ) + for host_id in host_ids + ) + ) + if tuple(results) != host_ids: + raise RuntimeError("NIXL metadata-store preflight membership changed") + + async def _preflight_artifact_root(self) -> None: + if self._artifact_probe is None: + return + try: + await self._artifact_probe_phase("initialize", owner_only=True) + contenders = self._artifact_probe.host_ids[1:] + if contenders: + await self._artifact_probe_phase("hold_lock", owner_only=True) + await self._artifact_probe_phase("check_lock_held", host_ids=contenders) + await self._artifact_probe_phase("release_lock", owner_only=True) + for host_id in contenders: + await self._artifact_probe_phase( + "check_lock_released", host_ids=(host_id,) + ) + for operation in ( + "create", + "read_created", + "rename", + "read_renamed", + "delete", + ): + await self._artifact_probe_phase(operation) + await self._artifact_probe_phase("finalize", owner_only=True) + except BaseException as preflight_error: + cleanup_failures = await self._cleanup_artifact_probe() + if cleanup_failures: + raise BaseExceptionGroup( + "artifact_root preflight and cleanup failed", + [preflight_error, *cleanup_failures], + ) from None + raise + + async def _artifact_probe_phase( + self, + operation: ArtifactProbeOperation, + *, + owner_only: bool = False, + host_ids: tuple[str, ...] | None = None, + ) -> None: + if self._artifact_probe is None: + return + if host_ids is None: + host_ids = ( + self._artifact_probe.host_ids[:1] + if owner_only + else self._artifact_probe.host_ids + ) + command = ArtifactProbeCommand(spec=self._artifact_probe, operation=operation) + async with asyncio.timeout(self.topology.cluster.rpc_timeout_s): + results: list[ArtifactProbeResult] = await asyncio.gather( + *( + call_remote( + self._host_services[host_id].artifact_root_probe, command + ) + for host_id in host_ids + ) + ) + for host_id, result in zip(host_ids, results, strict=True): + if result.error_type is not None: + raise ArtifactRootPreflightError(result) + if result.host_id != host_id or result.operation != operation: + raise RuntimeError( + f"invalid artifact_root preflight response from host {host_id!r}" + ) + + async def _cleanup_artifact_probe(self) -> list[BaseException]: + failures: list[BaseException] = [] + for operation, owner_only in (("cleanup", False), ("finalize", True)): + try: + await self._artifact_probe_phase(operation, owner_only=owner_only) + except BaseException as error: + if not ( + operation == "finalize" + and isinstance(error, ArtifactRootPreflightError) + and error.result.error_type == "FileNotFoundError" + ): + failures.append(error) + return failures + + def rollout_executor( + self, + rollout_callable: InstalledAsyncCallable, + *, + target_workers: int, + ) -> DistributedRolloutExecutor: + self._require_open() + self._start_rollout_workers() + hosts = { + host_id: tuple( + MonarchRolloutWorkerEndpoint( + actor.slice(rollout=slot), + timeout_s=self.topology.cluster.rpc_timeout_s, + ) + for slot in range(self._host(host_id).cpu_slots) + ) + for host_id, actor in self._rollout_actors.items() + } + return DistributedRolloutExecutor( + callable=rollout_callable, + hosts=hosts, + target_workers=target_workers, + queue_endpoint=MonarchTrajectoryQueueEndpoint( + self._host_services[self.topology.cluster.controller_host_id] + ), + trajectory_capacity_records=self.config.trajectory_capacity_records, + trajectory_capacity_bytes=self.config.trajectory_capacity_bytes, + ) + + def _start_rollout_workers(self) -> None: + if self._rollout_actors: + return + from .monarch_actor import RolloutWorkerService + + for index, host in enumerate(self.topology.cluster.hosts): + if host.host_id not in self.topology.rollout_host_ids: + continue + data_plane_host = urlparse(host.worker_address).hostname + if data_plane_host is None: + raise ValueError(f"host {host.host_id!r} has no routable address") + proc = self.host_mesh.slice(hosts=index).spawn_procs( + per_host={"rollout": host.cpu_slots}, + bootstrap=activate_cpu_child_virtualenv, + name=monarch_identifier( + f"art_rollout_{self.runtime_id}_{host.host_id}" + ), + ) + actor = proc.spawn( + monarch_identifier( + f"art_rollout_worker_{self.runtime_id}_{host.host_id}" + ), + RolloutWorkerService, + self.config.trajectory_capacity_records, + self.config.trajectory_capacity_bytes, + data_plane_host, + ) + self._rollout_procs[host.host_id] = proc + self._rollout_actors[host.host_id] = actor + + def _host(self, host_id: str) -> Any: + return next( + host for host in self.topology.cluster.hosts if host.host_id == host_id + ) + + async def pack(self, request: PackingRequest) -> DistributedPackedBatch | None: + self._require_open() + trainer = self.topology.trainer + if trainer is None: + raise RuntimeError("runtime topology has no trainer mesh") + trainer_hosts = tuple(dict.fromkeys(rank.host_id for rank in trainer.ranks)) + source_host = trainer_hosts[self._next_packing_host % len(trainer_hosts)] + self._next_packing_host += 1 + source_service = self._host_services[source_host] + batch_id = uuid.uuid4().hex + self._live_batches[batch_id] = trainer_hosts + try: + publisher = None + wire_request = request + if request.trajectory_groups: + from .trajectory_store import publish_trajectory_bundles + + controller = self._host(self.topology.cluster.controller_host_id) + data_plane_host = urlparse(controller.worker_address).hostname + if data_plane_host is None: + raise ValueError("controller has no routable address") + transfer, publisher = await publish_trajectory_bundles( + request.trajectory_groups, + stream_id=batch_id, + advertise_host=data_plane_host, + ) + wire_request = request.model_copy( + update={"trajectory_groups": (), "trajectory_transfer": transfer} + ) + try: + packing_rpc_started = time.monotonic() + result: PackingResult = await MonarchPackingEndpoint( + source_service + ).pack( + wire_request, + batch_id, + transfer_timeout_s=self.topology.cluster.rpc_timeout_s, + ) + packing_rpc_s = time.monotonic() - packing_rpc_started + finally: + if publisher is not None: + await publisher.close() + if result.ref is None: + self._live_batches.pop(batch_id) + return None + if result.generation_id != request.generation_id: + raise RuntimeError("packing host returned the wrong generation ID") + if result.ref.batch_id != batch_id: + raise RuntimeError("packing host returned the wrong batch ID") + host_refs = {source_host: result.ref} + destinations = { + host_id: MonarchPackedBatchInbox(self._host_services[host_id]) + for host_id in trainer_hosts + if host_id != source_host + } + fanout_started = time.monotonic() + if destinations: + host_refs.update( + await fanout_packed_batch( + ref=result.ref, + source_endpoint=MonarchPackedBatchSource(source_service), + inboxes=destinations, + timeout_s=self.topology.cluster.rpc_timeout_s, + ) + ) + packed_batch_fanout_s = time.monotonic() - fanout_started + leases = PackedBatchLeaseSet(ref=result.ref, host_refs=host_refs) + except BaseException as error: + await self._reclaim_after_failure(batch_id, error) + raise + return DistributedPackedBatch( + leases=leases, + packed_group_shapes=result.packed_group_shapes, + trainable_assistant_tokens=result.trainable_assistant_tokens, + loss_bearing_tokens=result.loss_bearing_tokens, + non_padding_tokens=result.non_padding_tokens, + trajectory_log_path=result.trajectory_log_path, + packing_rpc_s=packing_rpc_s, + trajectory_fetch_s=result.trajectory_fetch_s, + packing_core_s=result.packing_core_s, + trajectory_log_wait_s=result.trajectory_log_wait_s, + packed_batch_finalize_s=result.packed_batch_finalize_s, + packed_batch_fanout_s=packed_batch_fanout_s, + packing_generation_id=result.generation_id, + ) + + async def release_batch(self, batch: DistributedPackedBatch) -> None: + await self._reclaim_batch(batch.leases.ref.batch_id, fence=False) + + async def _reclaim_batch(self, batch_id: str, *, fence: bool) -> None: + hosts = self._live_batches.get(batch_id) + if hosts is None: + return + + async def reclaim(host_id: str) -> None: + inbox = MonarchPackedBatchInbox(self._host_services[host_id]) + await inbox.reclaim(batch_id, fence=fence) + + results = await asyncio.gather( + *(reclaim(host_id) for host_id in hosts), + return_exceptions=True, + ) + failures = [result for result in results if isinstance(result, BaseException)] + if failures: + raise BaseExceptionGroup("failed to reclaim packed batch", failures) + if self._live_batches.get(batch_id) == hosts: + self._live_batches.pop(batch_id) + + async def _reclaim_after_failure( + self, batch_id: str, primary: BaseException + ) -> None: + try: + _, cancelled = await complete_task( + asyncio.create_task(self._reclaim_batch(batch_id, fence=True)) + ) + if cancelled is not None: + primary.add_note("packed-batch reclamation observed cancellation") + except BaseException as cleanup_error: + primary.add_note( + "packed-batch reclamation also failed: " + f"{type(cleanup_error).__name__}: {cleanup_error}" + ) + + async def start_trainer( + self, runtime_spec: TrainerRuntimeSpec, run_spec: TrainingRunSpec + ) -> Any: + self._require_open() + if self.topology.trainer is None: + raise RuntimeError("runtime topology has no trainer mesh") + if runtime_spec.trainer_mesh != self.topology.trainer: + raise ValueError("trainer runtime mesh does not match compiled topology") + host_ids = [rank.host_id for rank in runtime_spec.trainer_mesh.ranks] + counts = Counter(host_ids) + if len(set(counts.values())) != 1: + raise ValueError("Monarch trainer hosts require equal ranks per host") + ordered_hosts = tuple(dict.fromkeys(host_ids)) + expected = tuple( + host.host_id + for host in self.topology.cluster.hosts + if host.host_id in counts + ) + if ordered_hosts != expected: + raise ValueError("trainer ranks must use cluster host order") + indices = [ + index + for index, host in enumerate(self.topology.cluster.hosts) + if host.host_id in counts + ] + if indices != list(range(indices[0], indices[-1] + 1)): + raise ValueError("trainer hosts must be contiguous in the cluster mesh") + if runtime_spec.hybrid_ep is not None and runtime_spec.hybrid_ep.multinode: + await self._preflight_nixl_metadata_store() + await self._preflight_launch( + runtime_kind="trainer", placements=runtime_spec.trainer_mesh.ranks + ) + selected = self.host_mesh.slice(hosts=slice(indices[0], indices[-1] + 1)) + from art.megatron.runtime.monarch import ( + MonarchTrainerRun, + MonarchTrainerSupervision, + spawn_monarch_trainer_actors, + ) + + supervision = MonarchTrainerSupervision(run_spec.run_id) + proc = None + try: + proc = selected.spawn_procs( + per_host={"trainer": next(iter(counts.values()))}, + bootstrap=activate_trainer_child_virtualenv, + name=monarch_identifier( + f"art_trainer_{supervision.token}_{self.runtime_id}" + ), + ) + async with asyncio.timeout(self.topology.cluster.startup_timeout_s): + actors, rank_processes = await spawn_monarch_trainer_actors( + proc, runtime_spec, supervision + ) + except BaseException as startup_error: + try: + if proc is not None: + async with asyncio.timeout(self.topology.cluster.rpc_timeout_s): + await proc.stop() + except BaseException as cleanup_error: + raise BaseExceptionGroup( + "trainer startup and cleanup failed", + [startup_error, cleanup_error], + ) from None + finally: + supervision.close() + raise + run = MonarchTrainerRun( + runtime_spec, run_spec, actors, proc, supervision, rank_processes + ) + self._trainer_runs.add(run) + return run + + async def stop_trainer(self, run: Any) -> None: + await run.close() + self._trainer_runs.discard(run) + + def register_closeable(self, closeable: Any) -> None: + self._require_open() + self._closeables.add(closeable) + + async def start_model_service( + self, + spec: ModelServiceSpec, + template: ReplicaLaunchTemplate, + *, + on_failure: Callable[[ReplicaFailure], Awaitable[None]] | None = None, + ) -> ReplicaState: + self._require_open() + configured = {service.name: service for service in self.topology.model_services} + if configured.get(spec.name) != spec: + raise ValueError( + "model service does not match the compiled runtime topology" + ) + if spec.name in self._model_services: + raise RuntimeError(f"model service {spec.name!r} is already managed") + await self._preflight_launch( + runtime_kind="vllm", + placements=tuple( + GpuPlacement(host_id=member.host_id, gpu_id=member.gpu_ids[0]) + for member in spec.members + ), + master_addr=spec.rendezvous.host, + ) + launchers = { + member.host_id: MonarchVllmHostLauncher( + self._host_services[member.host_id], + self._adapter_services[member.host_id], + ) + for member in spec.members + } + manager = ReplicaManager( + spec, + launchers, + template, + on_failure=on_failure, + startup_timeout_s=self.topology.cluster.startup_timeout_s, + rpc_timeout_s=self.topology.cluster.rpc_timeout_s, + ) + self._model_services[spec.name] = manager + return await manager.start() + + def model_service(self, name: str) -> ReplicaManager: + try: + return self._model_services[name] + except KeyError: + raise RuntimeError(f"model service {name!r} is not managed") from None + + async def stop_model_service(self, name: str) -> ReplicaState: + manager = self.model_service(name) + state = await manager.stop() + self._model_services.pop(name, None) + return state + + async def close(self) -> None: + if self._close_task is not None and self._close_task.done(): + try: + self._close_task.result() + except BaseException: + self._close_task = None + if self._close_task is None: + self._closed = True + self._close_task = asyncio.create_task(self._close()) + await asyncio.shield(self._close_task) + + async def _close(self) -> None: + failures: list[BaseException] = [] + + async def collect(name: str, *awaitables: Any) -> bool: + if not awaitables: + return True + tasks = {asyncio.ensure_future(awaitable) for awaitable in awaitables} + try: + done, pending = await asyncio.wait( + tasks, timeout=self.topology.cluster.rpc_timeout_s + ) + except BaseException: + for task in tasks: + task.cancel() + await asyncio.gather(*tasks, return_exceptions=True) + raise + for task in pending: + task.cancel() + await asyncio.gather(*pending, return_exceptions=True) + group_failed = bool(pending) + if pending: + failures.append( + TimeoutError( + f"{name} exceeded {self.topology.cluster.rpc_timeout_s}s" + ) + ) + for task in done: + try: + task.result() + except BaseException as error: + failures.append(error) + group_failed = True + return not group_failed + + if await collect( + "dependent shutdown", *(value.aclose() for value in self._closeables) + ): + self._closeables.clear() + await collect( + "model-service shutdown", + *(self.stop_model_service(name) for name in tuple(self._model_services)), + ) + if await collect( + "trainer shutdown", *(run.close() for run in self._trainer_runs) + ): + self._trainer_runs.clear() + await collect( + "packed batch reclamation", + *( + self._reclaim_batch(batch_id, fence=True) + for batch_id in tuple(self._live_batches) + ), + ) + if await collect( + "rollout actor shutdown", + *( + call_remote(actor.slice(rollout=slot).close) + for host_id, actor in self._rollout_actors.items() + for slot in range(self._host(host_id).cpu_slots) + ), + ): + self._rollout_actors.clear() + if await collect( + "rollout process shutdown", + *(proc.stop() for proc in self._rollout_procs.values()), + ): + self._rollout_procs.clear() + if await collect( + "adapter transfer service shutdown", + *(call_remote(actor.close) for actor in self._adapter_services.values()), + ): + self._adapter_services.clear() + if await collect( + "adapter transfer process shutdown", + *(proc.stop() for proc in self._adapter_procs.values()), + ): + self._adapter_procs.clear() + if await collect( + "host service shutdown", + *(call_remote(actor.close) for actor in self._host_services.values()), + ): + self._host_services.clear() + if await collect( + "host process shutdown", + *(proc.stop() for proc in self._host_procs.values()), + ): + self._host_procs.clear() + if self.owns_host_mesh and await collect( + "host mesh shutdown", self.host_mesh.shutdown() + ): + self.owns_host_mesh = False + if self._local_worker is not None: + try: + await asyncio.to_thread(_stop_worker, self._local_worker) + except BaseException as error: + failures.append(error) + else: + self._local_worker = None + if failures: + raise BaseExceptionGroup("ART runtime teardown failed", failures) + + async def __aenter__(self) -> "ArtRuntime": + self._require_open() + return self + + async def __aexit__(self, *_error: object) -> None: + await self.close() + + def _require_open(self) -> None: + if not self._started or self._closed: + raise RuntimeError("ART runtime is not active") diff --git a/src/art/distributed/artifact_preflight.py b/src/art/distributed/artifact_preflight.py new file mode 100644 index 000000000..74096f767 --- /dev/null +++ b/src/art/distributed/artifact_preflight.py @@ -0,0 +1,245 @@ +from __future__ import annotations + +import errno +import fcntl +import os +from pathlib import Path +import stat +import threading +from typing import Annotated, Literal, TypeAlias + +from pydantic import BaseModel, ConfigDict, Field, model_validator + +ArtifactProbeOperation: TypeAlias = Literal[ + "initialize", + "create", + "read_created", + "rename", + "read_renamed", + "hold_lock", + "check_lock_held", + "release_lock", + "check_lock_released", + "delete", + "finalize", + "cleanup", +] + +_HELD_FLOCKS: dict[tuple[str, str], int] = {} +_FLOCK_GUARD = threading.Lock() + + +class _Contract(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + +class ArtifactProbeSpec(_Contract): + artifact_root: str = Field(min_length=1) + runtime_id: str = Field(pattern=r"^[0-9a-f]{32}$") + host_ids: tuple[Annotated[str, Field(min_length=1)], ...] = Field(min_length=1) + + +class ArtifactProbeCommand(_Contract): + spec: ArtifactProbeSpec + operation: ArtifactProbeOperation + + +class ArtifactProbeResult(_Contract): + host_id: str = Field(min_length=1) + operation: ArtifactProbeOperation + path: str = Field(min_length=1) + error_type: str | None = None + message: str | None = None + + @model_validator(mode="after") + def _validate_error(self) -> ArtifactProbeResult: + if (self.error_type is None) != (self.message is None): + raise ValueError("artifact probe error fields must be set together") + return self + + +class ArtifactRootPreflightError(RuntimeError): + def __init__(self, result: ArtifactProbeResult) -> None: + self.result = result + super().__init__( + f"artifact_root preflight failed on host {result.host_id!r} during " + f"{result.operation} at {result.path}: {result.error_type}: {result.message}" + ) + + +def execute_artifact_probe( + host_id: str, command: ArtifactProbeCommand +) -> ArtifactProbeResult: + directory = _probe_directory(command.spec) + try: + _execute(host_id, command, directory) + return ArtifactProbeResult( + host_id=host_id, operation=command.operation, path=str(directory) + ) + except Exception as error: + return ArtifactProbeResult( + host_id=host_id, + operation=command.operation, + path=str(directory), + error_type=type(error).__name__, + message=str(error) or type(error).__name__, + ) + + +def _execute(host_id: str, command: ArtifactProbeCommand, directory: Path) -> None: + spec = command.spec + try: + host_index = spec.host_ids.index(host_id) + except ValueError: + raise RuntimeError(f"host {host_id!r} is not assigned to this probe") from None + root = Path(spec.artifact_root) + created = directory / f"{host_index}.created" + renamed = directory / f"{host_index}.renamed" + lock = directory / "advisory.lock" + lock_key = (spec.runtime_id, host_id) + operation = command.operation + if ( + operation in {"initialize", "hold_lock", "release_lock", "finalize"} + and host_index + ): + raise RuntimeError(f"only host {spec.host_ids[0]!r} may {operation} the probe") + if operation in {"check_lock_held", "check_lock_released"} and not host_index: + raise RuntimeError(f"host {spec.host_ids[0]!r} owns the probe lock") + if operation == "initialize": + if not stat.S_ISDIR(root.stat().st_mode): + raise NotADirectoryError(f"not a directory: {root}") + directory.mkdir(mode=0o700) + _fsync(root) + elif operation == "create": + with created.open("xb") as handle: + handle.write(_payload(spec, host_index)) + handle.flush() + os.fsync(handle.fileno()) + _fsync(directory) + _read(created, spec, host_index) + elif operation == "read_created": + for index in range(len(spec.host_ids)): + _read(directory / f"{index}.created", spec, index) + elif operation == "rename": + created.rename(renamed) + _fsync(directory) + _read(renamed, spec, host_index) + elif operation == "read_renamed": + for index in range(len(spec.host_ids)): + _read(directory / f"{index}.renamed", spec, index) + elif operation == "hold_lock": + _hold_flock(lock, lock_key) + elif operation == "check_lock_held": + _check_flock(lock, should_block=True) + elif operation == "release_lock": + _release_flock(lock_key) + elif operation == "check_lock_released": + _check_flock(lock, should_block=False) + elif operation == "delete": + renamed.unlink() + if not host_index and len(spec.host_ids) > 1: + lock.unlink(missing_ok=True) + _fsync(directory) + _absent(created) + _absent(renamed) + elif operation == "finalize": + directory.rmdir() + _fsync(root) + elif operation == "cleanup": + _release_flock(lock_key, required=False) + try: + directory.stat() + except FileNotFoundError: + return + paths = (created, renamed, lock) if not host_index else (created, renamed) + for path in paths: + try: + path.unlink() + except FileNotFoundError: + pass + _fsync(directory) + + +def _probe_directory(spec: ArtifactProbeSpec) -> Path: + return Path(spec.artifact_root) / f".art-runtime-preflight-{spec.runtime_id}" + + +def _payload(spec: ArtifactProbeSpec, host_index: int) -> bytes: + return f"art-runtime-preflight-v1\n{spec.runtime_id}\n{host_index}\n".encode() + + +def _read(path: Path, spec: ArtifactProbeSpec, host_index: int) -> None: + if path.read_bytes() != _payload(spec, host_index): + raise RuntimeError(f"artifact probe payload mismatch at {path}") + + +def _absent(path: Path) -> None: + try: + path.lstat() + except FileNotFoundError: + return + raise FileExistsError(f"artifact probe path still exists: {path}") + + +def _hold_flock(path: Path, key: tuple[str, str]) -> None: + with _FLOCK_GUARD: + if key in _HELD_FLOCKS: + raise RuntimeError(f"artifact probe lock is already held: {path}") + descriptor = os.open(path, os.O_CREAT | os.O_EXCL | os.O_RDWR, 0o600) + try: + fcntl.flock(descriptor, fcntl.LOCK_EX | fcntl.LOCK_NB) + os.fsync(descriptor) + _fsync(path.parent) + except BaseException: + os.close(descriptor) + path.unlink(missing_ok=True) + raise + _HELD_FLOCKS[key] = descriptor + + +def _release_flock(key: tuple[str, str], *, required: bool = True) -> None: + with _FLOCK_GUARD: + descriptor = _HELD_FLOCKS.pop(key, None) + if descriptor is None: + if required: + raise RuntimeError("artifact probe lock is not held") + return + try: + fcntl.flock(descriptor, fcntl.LOCK_UN) + finally: + os.close(descriptor) + + +def _check_flock(path: Path, *, should_block: bool) -> None: + descriptor = os.open(path, os.O_RDWR) + acquired = False + try: + try: + fcntl.flock(descriptor, fcntl.LOCK_EX | fcntl.LOCK_NB) + except OSError as error: + if error.errno not in (errno.EACCES, errno.EAGAIN): + raise + if not should_block: + raise RuntimeError( + f"artifact probe lock remained held after release: {path}" + ) from error + else: + acquired = True + if should_block: + raise RuntimeError( + f"artifact probe lock was acquired while owner held it: {path}" + ) + finally: + try: + if acquired: + fcntl.flock(descriptor, fcntl.LOCK_UN) + finally: + os.close(descriptor) + + +def _fsync(path: Path) -> None: + descriptor = os.open(path, os.O_RDONLY | getattr(os, "O_DIRECTORY", 0)) + try: + os.fsync(descriptor) + finally: + os.close(descriptor) diff --git a/src/art/distributed/data_plane.py b/src/art/distributed/data_plane.py new file mode 100644 index 000000000..dc2fbd00d --- /dev/null +++ b/src/art/distributed/data_plane.py @@ -0,0 +1,1067 @@ +from __future__ import annotations + +import asyncio +from collections.abc import Callable, Mapping +from multiprocessing import resource_tracker, shared_memory +import os +import secrets +import socket +from threading import Thread +import time +from typing import Any, Coroutine, Protocol, TypeVar, cast + +from pydantic import BaseModel, ConfigDict, Field, model_validator + +PACKED_BATCH_FORMAT = "art_packed_rl_v2" +_DTYPE_BYTES = { + "bool": 1, + "uint8": 1, + "uint16": 2, + "int8": 1, + "int16": 2, + "float16": 2, + "bfloat16": 2, + "int32": 4, + "float32": 4, + "int64": 8, + "float64": 8, +} +_STREAM_CHUNK_BYTES = 4 << 20 +T = TypeVar("T") + + +class _Contract(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + +class TensorSpec(_Contract): + name: str = Field(min_length=1) + dtype: str = Field(min_length=1) + shape: tuple[int, ...] + offset: int = Field(ge=0) + byte_count: int = Field(ge=0) + + @model_validator(mode="after") + def _validate_storage(self) -> "TensorSpec": + if any(dimension < 0 for dimension in self.shape): + raise ValueError("tensor dimensions must be non-negative") + item_size = _DTYPE_BYTES.get(self.dtype) + if item_size is None: + raise ValueError(f"unsupported packed tensor dtype {self.dtype!r}") + if _numel(self.shape) * item_size != self.byte_count: + raise ValueError("tensor byte_count does not match dtype and shape") + return self + + +class MoeRoutingReplaySpec(_Contract): + num_layers: int = Field(ge=1) + topk: int = Field(ge=1) + num_experts: int = Field(ge=1, le=65_536) + packed_tokens: int = Field(ge=0) + + +class PrefixTreePackingStatsSpec(_Contract): + logical_tokens: int = Field(ge=0) + physical_tokens: int = Field(ge=0) + + +class PackedBatchRef(_Contract): + batch_id: str = Field(min_length=1) + owner_actor_id: str = Field(min_length=1) + lease_id: str = Field(min_length=1) + format: str = PACKED_BATCH_FORMAT + shared_memory_name: str = Field(min_length=1) + owner_process_id: int = Field(ge=1) + tensors: tuple[TensorSpec, ...] + num_sequences: int = Field(ge=1) + sequence_length: int = Field(ge=1) + byte_count: int = Field(ge=0) + storage_byte_count: int = Field(ge=1) + pixel_values_present: tuple[bool, ...] + image_grid_thw_present: tuple[bool, ...] + moe_routing_replay: MoeRoutingReplaySpec | None = None + prefix_tree_packing_stats: PrefixTreePackingStatsSpec | None = None + group_ids: tuple[str, ...] = () + record_ids: tuple[str, ...] = () + min_source_version: int = Field(default=0, ge=0) + max_source_version: int = Field(default=0, ge=0) + + @model_validator(mode="after") + def _validate_manifest(self) -> "PackedBatchRef": + if self.format != PACKED_BATCH_FORMAT: + raise ValueError(f"unsupported packed-batch format {self.format!r}") + names = [tensor.name for tensor in self.tensors] + if len(set(names)) != len(names): + raise ValueError("tensor manifest names must be unique") + if sum(tensor.byte_count for tensor in self.tensors) != self.byte_count: + raise ValueError("packed-batch byte_count does not match tensor manifest") + core_dtypes = { + "tokens": "int64", + "group_ids": "int64", + "parent_ids": "int64", + "input_pos": "int64", + "assistant_mask": "bool", + "logprobs": "float32", + "advantages": "float32", + "weights": "float32", + } + specs = {tensor.name: tensor for tensor in self.tensors} + if not core_dtypes.keys() <= specs.keys(): + raise ValueError("packed-batch tensor manifest is missing core tensors") + core_shape = (self.num_sequences, self.sequence_length) + if any( + specs[name].dtype != dtype or specs[name].shape != core_shape + for name, dtype in core_dtypes.items() + ): + raise ValueError("core packed tensor dtype or shape is invalid") + if ( + len(self.pixel_values_present) != self.num_sequences + or len(self.image_grid_thw_present) != self.num_sequences + ): + raise ValueError("multimodal presence manifests must match num_sequences") + expected_optional = { + f"pixel_values/{index}" + for index, present in enumerate(self.pixel_values_present) + if present + } | { + f"image_grid_thw/{index}" + for index, present in enumerate(self.image_grid_thw_present) + if present + } + if any( + specs[name].dtype + != ("float32" if name.startswith("pixel_values/") else "int64") + for name in expected_optional + ): + raise ValueError("multimodal packed tensor dtype is invalid") + if "original_logprobs" in specs: + expected_optional.add("original_logprobs") + if ( + specs["original_logprobs"].dtype != "float32" + or specs["original_logprobs"].shape != core_shape + ): + raise ValueError("original_logprobs dtype or shape is invalid") + replay_names = {"moe_routing_replay/expert_indices"} + if self.moe_routing_replay is not None: + if not replay_names <= specs.keys(): + raise ValueError("MoE routing replay manifest is incomplete") + expected_optional |= replay_names + replay = self.moe_routing_replay + replay_dtype = "uint8" if replay.num_experts <= 256 else "uint16" + if specs[ + "moe_routing_replay/expert_indices" + ].dtype != replay_dtype or specs[ + "moe_routing_replay/expert_indices" + ].shape != (replay.num_layers, *core_shape, replay.topk): + raise ValueError("MoE routing replay tensor dtype or shape is invalid") + if set(specs) != set(core_dtypes) | expected_optional: + raise ValueError("packed-batch tensor manifest has unexpected tensors") + previous_end = 0 + for tensor in self.tensors: + if tensor.offset < previous_end: + raise ValueError("packed-batch tensor storage must not overlap") + if tensor.offset + tensor.byte_count > self.storage_byte_count: + raise ValueError( + f"tensor {tensor.name!r} exceeds shared-memory storage" + ) + previous_end = tensor.offset + tensor.byte_count + if self.max_source_version < self.min_source_version: + raise ValueError("max_source_version must be >= min_source_version") + return self + + +class PackedBatchLeaseSet(_Contract): + """One logical batch and its host-local physical leases.""" + + ref: PackedBatchRef + host_refs: dict[str, PackedBatchRef] + + @model_validator(mode="after") + def _validate_hosts(self) -> "PackedBatchLeaseSet": + if not self.host_refs: + raise ValueError("packed batch requires at least one host lease") + logical = _logical_ref(self.ref) + if any(_logical_ref(ref) != logical for ref in self.host_refs.values()): + raise ValueError("host leases must describe the same logical packed batch") + return self + + +class BatchReservation(_Contract): + reservation_id: str = Field(min_length=1) + batch_id: str = Field(min_length=1) + storage_byte_count: int = Field(ge=1) + + +class PackedBatchTransfer(_Contract): + batch_id: str = Field(min_length=1) + host: str = Field(min_length=1) + port: int = Field(ge=1, le=65535) + token: str = Field(pattern=r"^[0-9a-f]{64}$") + byte_count: int = Field(ge=1) + + +class ByteStreamTransfer(_Contract): + stream_id: str = Field(min_length=1) + host: str = Field(min_length=1) + port: int = Field(ge=1, le=65535) + token: str = Field(pattern=r"^[0-9a-f]{64}$") + byte_count: int = Field(ge=1) + + +class DataPlaneStats(_Contract): + capacity_bytes: int + used_bytes: int + reserved_bytes: int + peak_bytes: int + created_bytes: int + copied_bytes: int + transmitted_bytes: int + copy_count: int + batches: int + leases: int + + +class PackedBatchCapacityError(RuntimeError): + pass + + +class PackedBatchLeaseError(RuntimeError): + pass + + +class _Entry: + def __init__(self, shm: shared_memory.SharedMemory, ref: PackedBatchRef) -> None: + self.shm = shm + self.ref = ref + + +class SharedMemoryPackedBatchStore: + """Own immutable current-format batches in bounded POSIX shared memory.""" + + def __init__(self, *, owner_actor_id: str, capacity_bytes: int) -> None: + if capacity_bytes <= 0: + raise ValueError("capacity_bytes must be > 0") + self.owner_actor_id = owner_actor_id + self.capacity_bytes = capacity_bytes + self._entries: dict[str, _Entry] = {} + self._reservations: dict[str, BatchReservation] = {} + self._reclaimed: set[str] = set() + self._used_bytes = 0 + self._reserved_bytes = 0 + self._peak_bytes = 0 + self._created_bytes = 0 + self._copied_bytes = 0 + self._transmitted_bytes = 0 + self._copy_count = 0 + + def create( + self, + tensors: Any, + *, + batch_id: str, + group_ids: tuple[str, ...] = (), + record_ids: tuple[str, ...] = (), + min_source_version: int = 0, + max_source_version: int = 0, + ) -> PackedBatchRef: + flat, metadata = _flatten_packed_tensors(tensors) + manifest, storage_bytes = _layout(flat) + if batch_id in self._reclaimed: + raise PackedBatchLeaseError(f"packed batch {batch_id!r} was reclaimed") + if batch_id in self._entries or any( + reservation.batch_id == batch_id + for reservation in self._reservations.values() + ): + raise ValueError(f"packed batch {batch_id!r} already exists") + self._require_capacity(storage_bytes) + lease_id = secrets.token_hex(16) + shm = shared_memory.SharedMemory(create=True, size=storage_bytes) + try: + for spec, (_, tensor) in zip(manifest, flat, strict=True): + destination = _tensor_from_buffer(_shm_buffer(shm), spec) + destination.copy_(tensor) + ref = PackedBatchRef( + batch_id=batch_id, + owner_actor_id=self.owner_actor_id, + lease_id=lease_id, + shared_memory_name=shm.name, + owner_process_id=os.getpid(), + tensors=manifest, + num_sequences=metadata["num_sequences"], + sequence_length=metadata["sequence_length"], + byte_count=sum(spec.byte_count for spec in manifest), + storage_byte_count=storage_bytes, + pixel_values_present=metadata["pixel_values_present"], + image_grid_thw_present=metadata["image_grid_thw_present"], + moe_routing_replay=metadata["moe_routing_replay"], + prefix_tree_packing_stats=metadata["prefix_tree_packing_stats"], + group_ids=group_ids, + record_ids=record_ids, + min_source_version=min_source_version, + max_source_version=max_source_version, + ) + except BaseException: + shm.close() + shm.unlink() + raise + self._entries[batch_id] = _Entry(shm, ref) + self._used_bytes += storage_bytes + self._peak_bytes = max( + self._peak_bytes, self._used_bytes + self._reserved_bytes + ) + self._created_bytes += storage_bytes + self._copied_bytes += ref.byte_count + self._copy_count += len(manifest) + return ref + + def reserve(self, source: PackedBatchRef) -> BatchReservation: + if source.batch_id in self._reclaimed: + raise PackedBatchLeaseError( + f"packed batch {source.batch_id!r} was reclaimed" + ) + if source.batch_id in self._entries or any( + reservation.batch_id == source.batch_id + for reservation in self._reservations.values() + ): + raise ValueError(f"packed batch {source.batch_id!r} already exists") + self._require_capacity(source.storage_byte_count) + reservation = BatchReservation( + reservation_id=secrets.token_hex(16), + batch_id=source.batch_id, + storage_byte_count=source.storage_byte_count, + ) + self._reservations[reservation.reservation_id] = reservation + self._reserved_bytes += reservation.storage_byte_count + self._peak_bytes = max( + self._peak_bytes, self._used_bytes + self._reserved_bytes + ) + return reservation + + async def commit_stream( + self, + reservation_id: str, + source: PackedBatchRef, + transfer: PackedBatchTransfer, + *, + timeout_s: float, + ) -> PackedBatchRef: + reservation = self._reservations.get(reservation_id) + if reservation is None or reservation.batch_id != source.batch_id: + raise PackedBatchLeaseError( + "unknown or mismatched packed-batch reservation" + ) + if ( + transfer.batch_id != source.batch_id + or transfer.byte_count != reservation.storage_byte_count + ): + raise PackedBatchLeaseError( + "packed-batch transfer does not match reservation" + ) + shm = shared_memory.SharedMemory( + create=True, size=reservation.storage_byte_count + ) + try: + from art.utils.lifecycle import complete_to_thread + + _, cancelled = await complete_to_thread( + lambda: _receive_stream(transfer, shm, timeout_s) + ) + if cancelled is not None: + raise cancelled + return self._finish_commit(reservation, source, shm) + except BaseException: + shm.close() + shm.unlink() + raise + + def abort(self, reservation_id: str) -> None: + reservation = self._reservations.pop(reservation_id, None) + if reservation is not None: + self._reserved_bytes -= reservation.storage_byte_count + + def drop(self, ref: PackedBatchRef) -> None: + """Idempotently reclaim one host-owned packed batch.""" + + entry = self._entries.get(ref.batch_id) + if entry is None: + return + if entry.ref.lease_id != ref.lease_id: + raise PackedBatchLeaseError("packed-batch reference has a stale lease") + self.reclaim(ref.batch_id) + + def reclaim(self, batch_id: str, *, fence: bool = True) -> bool: + """Release committed or in-flight storage and optionally fence late writes.""" + + if fence: + self._reclaimed.add(batch_id) + found = False + for reservation_id, reservation in tuple(self._reservations.items()): + if reservation.batch_id == batch_id: + self.abort(reservation_id) + found = True + entry = self._entries.pop(batch_id, None) + if entry is not None: + self._used_bytes -= entry.ref.storage_byte_count + entry.shm.close() + entry.shm.unlink() + found = True + return found + + def map(self, ref: PackedBatchRef) -> "MappedPackedBatch": + entry = self._entries.get(ref.batch_id) + if entry is None or entry.ref.lease_id != ref.lease_id: + raise PackedBatchLeaseError("packed-batch reference has no active lease") + return MappedPackedBatch.open(ref) + + def note_transmitted(self, byte_count: int) -> None: + self._transmitted_bytes += byte_count + + def close(self) -> None: + batch_ids = set(self._entries) + batch_ids.update( + reservation.batch_id for reservation in self._reservations.values() + ) + for batch_id in batch_ids: + self.reclaim(batch_id, fence=True) + + def stats(self) -> DataPlaneStats: + return DataPlaneStats( + capacity_bytes=self.capacity_bytes, + used_bytes=self._used_bytes, + reserved_bytes=self._reserved_bytes, + peak_bytes=self._peak_bytes, + created_bytes=self._created_bytes, + copied_bytes=self._copied_bytes, + transmitted_bytes=self._transmitted_bytes, + copy_count=self._copy_count, + batches=len(self._entries), + leases=len(self._entries), + ) + + def _require_capacity(self, byte_count: int) -> None: + if byte_count > self.capacity_bytes: + raise PackedBatchCapacityError( + f"packed batch requires {byte_count} bytes, capacity is " + f"{self.capacity_bytes}" + ) + available = self.capacity_bytes - self._used_bytes - self._reserved_bytes + if byte_count > available: + raise PackedBatchCapacityError( + f"packed batch requires {byte_count} bytes, only {available} available" + ) + + def _finish_commit( + self, + reservation: BatchReservation, + source: PackedBatchRef, + shm: shared_memory.SharedMemory, + ) -> PackedBatchRef: + if self._reservations.get(reservation.reservation_id) != reservation: + raise PackedBatchLeaseError( + f"packed batch {source.batch_id!r} was reclaimed during transfer" + ) + lease_id = secrets.token_hex(16) + ref = source.model_copy( + update={ + "owner_actor_id": self.owner_actor_id, + "lease_id": lease_id, + "shared_memory_name": shm.name, + "owner_process_id": os.getpid(), + } + ) + self._reservations.pop(reservation.reservation_id) + self._reserved_bytes -= reservation.storage_byte_count + self._entries[ref.batch_id] = _Entry(shm, ref) + self._used_bytes += ref.storage_byte_count + self._created_bytes += ref.storage_byte_count + self._copied_bytes += ref.storage_byte_count + self._copy_count += 1 + return ref + + +class MappedPackedBatch(BaseModel): + """Zero-copy consumer view; callers must not mutate its immutable tensors.""" + + model_config = ConfigDict(arbitrary_types_allowed=True) + + ref: PackedBatchRef + tensors: Any + _shm: Any = None + _closed: bool = False + + @classmethod + def open(cls, ref: PackedBatchRef) -> "MappedPackedBatch": + shm = shared_memory.SharedMemory(name=ref.shared_memory_name) + if ref.owner_process_id != os.getpid(): + # Python 3.12 has no public `track=False`. The segment belongs to the + # host inbox, so an unrelated consumer's tracker must not unlink it. + resource_tracker.unregister(cast(Any, shm)._name, "shared_memory") + try: + flat = { + spec.name: _tensor_from_buffer(_shm_buffer(shm), spec) + for spec in ref.tensors + } + tensors = _unflatten_packed_tensors(flat, ref) + except BaseException: + shm.close() + raise + mapped = cls(ref=ref, tensors=tensors) + mapped._shm = shm + return mapped + + def close(self) -> None: + if not self._closed: + self.tensors = None + self._shm.close() + self._closed = True + + def __enter__(self) -> "MappedPackedBatch": + return self + + def __exit__(self, *_: object) -> None: + self.close() + + +class ByteStreamServerLoop: + """A process-local I/O loop that cannot be blocked by rollout code.""" + + def __init__(self) -> None: + self._loop = asyncio.new_event_loop() + self._thread = Thread(target=self._run, name="art-byte-stream", daemon=True) + self._thread.start() + + def _run(self) -> None: + asyncio.set_event_loop(self._loop) + self._loop.run_forever() + + async def submit(self, coroutine: Coroutine[Any, Any, T]) -> T: + return await asyncio.wrap_future( + asyncio.run_coroutine_threadsafe(coroutine, self._loop) + ) + + async def close(self) -> None: + if self._thread.is_alive(): + self._loop.call_soon_threadsafe(self._loop.stop) + await asyncio.to_thread(self._thread.join) + self._loop.close() + + +class _AuthenticatedStreamPublisher: + def __init__( + self, advertise_host: str, server_loop: ByteStreamServerLoop | None = None + ) -> None: + self.advertise_host = advertise_host + self._server_loop = server_loop + self._token = secrets.token_bytes(32) + self._server: Any = None + self._handlers: set[asyncio.Task[None]] = set() + + async def start(self) -> None: + if self._server_loop is not None: + return await self._server_loop.submit(self._start()) + await self._start() + + async def _start(self) -> None: + family = socket.getaddrinfo(self.advertise_host, 0, type=socket.SOCK_STREAM)[0][ + 0 + ] + bind_host = "::" if family == socket.AF_INET6 else "0.0.0.0" + self._server = await asyncio.start_server( + self._handle, bind_host, 0, family=family + ) + + def _port(self) -> int: + if self._server is None or not self._server.sockets: + raise RuntimeError("byte-stream publisher is not listening") + return int(self._server.sockets[0].getsockname()[1]) + + async def close(self) -> None: + if self._server_loop is not None: + return await self._server_loop.submit(self._close()) + await self._close() + + async def _close(self) -> None: + if self._server is None: + return + self._server.close() + await self._server.wait_closed() + for task in self._handlers: + task.cancel() + await asyncio.gather(*self._handlers, return_exceptions=True) + self._handlers.clear() + self._server = None + + async def _handle( + self, reader: asyncio.StreamReader, writer: asyncio.StreamWriter + ) -> None: + task = cast(asyncio.Task[None], asyncio.current_task()) + self._handlers.add(task) + sent = False + try: + token = await reader.readexactly(len(self._token)) + if not secrets.compare_digest(token, self._token): + return + await self._write(writer) + sent = True + except (asyncio.IncompleteReadError, ConnectionError): + pass + finally: + try: + writer.close() + if task.cancelling(): + writer.transport.abort() + else: + try: + await writer.wait_closed() + except asyncio.CancelledError: + writer.transport.abort() + raise + except Exception: + pass + finally: + self._handlers.discard(task) + if sent and not task.cancelling(): + self._sent() + + async def _write(self, writer: asyncio.StreamWriter) -> None: + raise NotImplementedError + + def _sent(self) -> None: + pass + + +class ByteStreamPublisher(_AuthenticatedStreamPublisher): + """Authenticated one-shot transport for immutable byte chunks.""" + + def __init__( + self, + stream_id: str, + advertise_host: str, + chunks: tuple[bytes, ...], + on_sent: Callable[[], None] | None, + server_loop: ByteStreamServerLoop | None, + ) -> None: + super().__init__(advertise_host, server_loop) + self.stream_id = stream_id + self.chunks = chunks + self.on_sent = on_sent + self.byte_count = sum(map(len, chunks)) + if not stream_id or self.byte_count < 1: + raise ValueError("byte stream ID and payload must be non-empty") + + @classmethod + async def create( + cls, + stream_id: str, + chunks: tuple[bytes, ...], + *, + advertise_host: str, + on_sent: Callable[[], None] | None = None, + server_loop: ByteStreamServerLoop | None = None, + ) -> "ByteStreamPublisher": + publisher = cls(stream_id, advertise_host, chunks, on_sent, server_loop) + await publisher.start() + return publisher + + @property + def transfer(self) -> ByteStreamTransfer: + return ByteStreamTransfer( + stream_id=self.stream_id, + host=self.advertise_host, + port=self._port(), + token=self._token.hex(), + byte_count=self.byte_count, + ) + + async def _write(self, writer: asyncio.StreamWriter) -> None: + for chunk in self.chunks: + await _write_stream_chunk(writer, chunk) + + def _sent(self) -> None: + if self.on_sent is not None: + self.on_sent() + + +class PackedBatchPublisher(_AuthenticatedStreamPublisher): + """Batch-scoped authenticated stream over the cluster's routable TCP fabric.""" + + def __init__( + self, + ref: PackedBatchRef, + advertise_host: str, + shm: shared_memory.SharedMemory, + ) -> None: + super().__init__(advertise_host) + self.ref = ref + self.shm = shm + + @classmethod + async def create( + cls, ref: PackedBatchRef, *, advertise_host: str + ) -> "PackedBatchPublisher": + shm = shared_memory.SharedMemory(name=ref.shared_memory_name) + if ref.owner_process_id != os.getpid(): + resource_tracker.unregister(cast(Any, shm)._name, "shared_memory") + publisher = cls(ref, advertise_host, shm) + try: + await publisher.start() + return publisher + except BaseException: + shm.close() + raise + + @property + def transfer(self) -> PackedBatchTransfer: + return PackedBatchTransfer( + batch_id=self.ref.batch_id, + host=self.advertise_host, + port=self._port(), + token=self._token.hex(), + byte_count=self.ref.storage_byte_count, + ) + + async def close(self) -> None: + try: + await super().close() + finally: + self.shm.close() + + async def _write(self, writer: asyncio.StreamWriter) -> None: + source = _shm_buffer(self.shm)[: self.ref.storage_byte_count] + try: + await _write_stream_chunk(writer, source) + finally: + source.release() + + +class PackedBatchInbox: + def __init__(self, *, host_id: str, capacity_bytes: int) -> None: + self.host_id = host_id + self.store = SharedMemoryPackedBatchStore( + owner_actor_id=f"packed_batch_inbox:{host_id}", + capacity_bytes=capacity_bytes, + ) + + async def receive( + self, ref: PackedBatchRef, transfer: PackedBatchTransfer, *, timeout_s: float + ) -> PackedBatchRef: + reservation = self.store.reserve(ref) + try: + return await self.store.commit_stream( + reservation.reservation_id, + ref, + transfer, + timeout_s=timeout_s, + ) + except BaseException: + self.store.abort(reservation.reservation_id) + raise + + async def drop(self, ref: PackedBatchRef) -> None: + self.store.drop(ref) + + async def reclaim(self, batch_id: str, *, fence: bool = True) -> bool: + return self.store.reclaim(batch_id, fence=fence) + + +class PackedBatchSourceEndpoint(Protocol): + async def publish(self, ref: PackedBatchRef) -> PackedBatchTransfer: ... + + async def drop(self, batch_id: str) -> None: ... + + async def note_transmitted(self, byte_count: int) -> None: ... + + +class PackedBatchInboxEndpoint(Protocol): + async def receive( + self, ref: PackedBatchRef, transfer: PackedBatchTransfer, *, timeout_s: float + ) -> PackedBatchRef: ... + + async def drop(self, ref: PackedBatchRef) -> None: ... + + +async def fanout_packed_batch( + *, + ref: PackedBatchRef, + source_endpoint: PackedBatchSourceEndpoint, + inboxes: Mapping[str, PackedBatchInboxEndpoint], + timeout_s: float, +) -> dict[str, PackedBatchRef]: + """Publish once, stream once per host, and always drop the source listener.""" + + transfer = await source_endpoint.publish(ref) + try: + tasks = { + host_id: asyncio.create_task( + inbox.receive(ref, transfer, timeout_s=timeout_s) + ) + for host_id, inbox in inboxes.items() + } + try: + results = await asyncio.gather(*tasks.values(), return_exceptions=True) + except BaseException: + for task in tasks.values(): + task.cancel() + results = await asyncio.gather(*tasks.values(), return_exceptions=True) + await _release_transferred( + inboxes, + [ + result if isinstance(result, BaseException) else (host_id, result) + for host_id, result in zip(tasks, results, strict=True) + ], + ) + raise + failures = [result for result in results if isinstance(result, BaseException)] + if failures: + await _release_transferred( + inboxes, + [ + result if isinstance(result, BaseException) else (host_id, result) + for host_id, result in zip(tasks, results, strict=True) + ], + ) + raise failures[0] + await source_endpoint.note_transmitted(len(inboxes) * ref.storage_byte_count) + return dict(zip(tasks, cast(list[PackedBatchRef], results), strict=True)) + finally: + await source_endpoint.drop(ref.batch_id) + + +async def _release_transferred( + inboxes: Mapping[str, PackedBatchInboxEndpoint], + results: list[tuple[str, PackedBatchRef] | BaseException], +) -> None: + for result in results: + if not isinstance(result, BaseException): + host_id, destination_ref = result + await inboxes[host_id].drop(destination_ref) + + +async def receive_byte_stream( + transfer: ByteStreamTransfer, *, timeout_s: float +) -> bytearray: + from art.utils.lifecycle import complete_to_thread + + payload = bytearray(transfer.byte_count) + destination = memoryview(payload) + try: + _, cancelled = await complete_to_thread( + lambda: _receive_into_stream(transfer, destination, timeout_s) + ) + if cancelled is not None: + raise cancelled + return payload + finally: + destination.release() + + +def _receive_stream( + transfer: PackedBatchTransfer, + shm: shared_memory.SharedMemory, + timeout_s: float, +) -> None: + destination = _shm_buffer(shm)[: transfer.byte_count] + try: + _receive_into_stream(transfer, destination, timeout_s) + finally: + destination.release() + + +def _receive_into_stream( + transfer: PackedBatchTransfer | ByteStreamTransfer, + destination: memoryview, + timeout_s: float, +) -> None: + deadline = time.monotonic() + timeout_s + with socket.create_connection( + (transfer.host, transfer.port), timeout=max(0.001, timeout_s) + ) as connection: + connection.sendall(bytes.fromhex(transfer.token)) + offset = 0 + while offset < len(destination): + connection.settimeout(max(0.001, deadline - time.monotonic())) + received = connection.recv_into(destination[offset:]) + if not received: + raise ConnectionError( + f"byte stream ended after {offset} of {len(destination)} bytes" + ) + offset += received + + +async def _write_stream_chunk( + writer: asyncio.StreamWriter, source: bytes | memoryview +) -> None: + for offset in range(0, len(source), _STREAM_CHUNK_BYTES): + writer.write(source[offset : offset + _STREAM_CHUNK_BYTES]) + await writer.drain() + + +def _flatten_packed_tensors( + tensors: Any, +) -> tuple[list[tuple[str, Any]], dict[str, Any]]: + import torch + + required = ( + "tokens", + "group_ids", + "parent_ids", + "input_pos", + "assistant_mask", + "logprobs", + "advantages", + "weights", + ) + flat: list[tuple[str, Any]] = [] + for name in required: + tensor = tensors[name] + _validate_tensor(name, tensor, torch) + flat.append((name, tensor)) + shape = tuple(tensors["tokens"].shape) + if len(shape) != 2 or any(tuple(tensors[name].shape) != shape for name in required): + raise ValueError( + "core packed tensors must share [num_sequences, sequence_length]" + ) + for list_name in ("pixel_values", "image_grid_thw"): + for index, tensor in enumerate(tensors[list_name]): + if tensor is not None: + _validate_tensor(f"{list_name}/{index}", tensor, torch) + flat.append((f"{list_name}/{index}", tensor)) + original = tensors.get("original_logprobs") + if original is not None: + _validate_tensor("original_logprobs", original, torch) + if tuple(original.shape) != shape: + raise ValueError("original_logprobs must match the core packed shape") + flat.append(("original_logprobs", original)) + replay = tensors.get("moe_routing_replay") + replay_spec = None + if replay is not None: + tensor = replay.expert_indices + _validate_tensor("moe_routing_replay/expert_indices", tensor, torch) + flat.append(("moe_routing_replay/expert_indices", tensor)) + replay_spec = MoeRoutingReplaySpec( + num_layers=replay.num_layers, + topk=replay.topk, + num_experts=replay.num_experts, + packed_tokens=replay.pack_stats.packed_tokens, + ) + return flat, { + "num_sequences": shape[0], + "sequence_length": shape[1], + "pixel_values_present": tuple(x is not None for x in tensors["pixel_values"]), + "image_grid_thw_present": tuple( + x is not None for x in tensors["image_grid_thw"] + ), + "moe_routing_replay": replay_spec, + "prefix_tree_packing_stats": tensors.get("prefix_tree_packing_stats"), + } + + +def _validate_tensor(name: str, tensor: Any, torch: Any) -> None: + if not isinstance(tensor, torch.Tensor): + raise TypeError(f"{name} must be a torch.Tensor") + if tensor.device.type != "cpu" or not tensor.is_contiguous(): + raise ValueError(f"{name} must be a contiguous CPU tensor") + + +def _layout(flat: list[tuple[str, Any]]) -> tuple[tuple[TensorSpec, ...], int]: + offset = 0 + specs = [] + for name, tensor in flat: + element_size = tensor.element_size() + offset = (offset + element_size - 1) // element_size * element_size + byte_count = tensor.numel() * element_size + specs.append( + TensorSpec( + name=name, + dtype=str(tensor.dtype).removeprefix("torch."), + shape=tuple(tensor.shape), + offset=offset, + byte_count=byte_count, + ) + ) + offset += byte_count + return tuple(specs), max(offset, 1) + + +def _tensor_from_buffer(buffer: memoryview, spec: TensorSpec) -> Any: + import torch + + dtype = getattr(torch, spec.dtype, None) + if not isinstance(dtype, torch.dtype): + raise ValueError(f"unsupported tensor dtype {spec.dtype!r}") + return torch.frombuffer( + buffer, dtype=dtype, count=_numel(spec.shape), offset=spec.offset + ).reshape(spec.shape) + + +def _shm_buffer(shm: shared_memory.SharedMemory) -> memoryview: + buffer = shm.buf + if buffer is None: + raise RuntimeError("shared-memory buffer is closed") + return buffer + + +def _numel(shape: tuple[int, ...]) -> int: + result = 1 + for dimension in shape: + if dimension < 0: + raise ValueError("tensor dimensions must be non-negative") + result *= dimension + return result + + +def _logical_ref(ref: PackedBatchRef) -> dict[str, Any]: + return ref.model_dump( + exclude={ + "owner_actor_id", + "lease_id", + "shared_memory_name", + "owner_process_id", + } + ) + + +def _unflatten_packed_tensors(flat: dict[str, Any], ref: PackedBatchRef) -> Any: + from art.preprocessing.moe_routing import ( + MoeRoutingPackStats, + PackedMoeRoutingReplay, + ) + + tensors: dict[str, Any] = { + name: flat[name] + for name in ( + "tokens", + "group_ids", + "parent_ids", + "input_pos", + "assistant_mask", + "logprobs", + "advantages", + "weights", + ) + } + for name, present in ( + ("pixel_values", ref.pixel_values_present), + ("image_grid_thw", ref.image_grid_thw_present), + ): + tensors[name] = [ + flat[f"{name}/{index}"] if value else None + for index, value in enumerate(present) + ] + replay = ref.moe_routing_replay + tensors["moe_routing_replay"] = ( + PackedMoeRoutingReplay( + expert_indices=flat["moe_routing_replay/expert_indices"], + num_experts=replay.num_experts, + pack_stats=MoeRoutingPackStats(packed_tokens=replay.packed_tokens), + ) + if replay is not None + else None + ) + if "original_logprobs" in flat: + tensors["original_logprobs"] = flat["original_logprobs"] + if ref.prefix_tree_packing_stats is not None: + tensors["prefix_tree_packing_stats"] = ( + ref.prefix_tree_packing_stats.model_dump() + ) + return tensors diff --git a/src/art/distributed/host_admission.py b/src/art/distributed/host_admission.py new file mode 100644 index 000000000..5949f8c9e --- /dev/null +++ b/src/art/distributed/host_admission.py @@ -0,0 +1,450 @@ +from __future__ import annotations + +from collections.abc import Mapping, Sequence +import csv +import hashlib +from importlib import metadata +import json +import os +from pathlib import Path +import platform +import re +import shutil +import socket +import subprocess +import sys +from typing import Annotated, Literal +from uuid import UUID + +from pydantic import BaseModel, ConfigDict, Field, model_validator + +from .specs import CUDA_DEVICE_UUID_PATTERN, GpuId, HostServiceHealth, HostSpec + +_SCHEMA = "art-host-runtime-v1" +_SHA256 = r"^[0-9a-f]{64}$" +_BOOT_ID_PATH = Path("/proc/sys/kernel/random/boot_id") +_BASE_PACKAGES = ("openpipe-art", "pydantic", "torchmonarch") +_TRAINER_PACKAGES = ( + "flash-attn-4", + "megatron-bridge", + "megatron-core", + "numpy", + "torch", + "transformer_engine", + "transformer_engine_torch", + "transformers", + "triton", +) +_RUNTIME_ENV = { + "ART_DISABLE_MEGATRON_COMPILE", + "ART_MEGATRON_ALLOW_UNVALIDATED_ARCH", + "ART_MEGATRON_ENABLE_MOE_ROUTING_REPLAY", + "ART_MEGATRON_OFFLOAD_BETWEEN_JOBS", + "ART_MEGATRON_STREAMING_WEIGHT_OFFLOAD", + "ART_VLLM_RUNTIME_BIN", + "CUDA_DEVICE_MAX_CONNECTIONS", + "CUDA_LAUNCH_BLOCKING", + "CUDA_MODULE_LOADING", + "NCCL_ALGO", + "NCCL_DEBUG", + "NCCL_IB_DISABLE", + "NCCL_IB_GID_INDEX", + "NCCL_IB_HCA", + "NCCL_NET", + "NCCL_NET_PLUGIN", + "NCCL_NVLS_ENABLE", + "NCCL_P2P_DISABLE", + "NCCL_PROTO", + "NCCL_SOCKET_IFNAME", + "NVTE_ALLOW_NONDETERMINISTIC_ALGO", + "NVTE_FLASH_ATTN", + "NVTE_FUSED_ATTN", + "PYTORCH_ALLOC_CONF", + "PYTORCH_CUDA_ALLOC_CONF", + "TORCH_CUDA_ARCH_LIST", + "TORCH_NCCL_ASYNC_ERROR_HANDLING", + "TORCH_NCCL_BLOCKING_WAIT", + "VLLM_USE_V1", + "VLLM_WORKER_MULTIPROC_METHOD", +} + + +class _Contract(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + +class GpuIdentity(_Contract): + index: int = Field(ge=0) + uuid: str = Field(pattern=CUDA_DEVICE_UUID_PATTERN) + parent_uuid: str = Field( + pattern=r"^GPU-[0-9A-Fa-f]{8}(?:-[0-9A-Fa-f]{4}){3}-[0-9A-Fa-f]{12}$" + ) + pci_bus_id: str = Field( + pattern=r"^(?:[0-9A-F]{4}|[0-9A-F]{8}):[0-9A-F]{2}:[0-9A-F]{2}\.[0-7]$" + ) + + @property + def is_mig(self) -> bool: + return self.uuid.startswith("MIG-") + + +class RuntimeFingerprint(_Contract): + schema_version: Literal["art-host-runtime-v1"] = _SCHEMA + art_build_sha256: str = Field(pattern=_SHA256) + python: str = Field(min_length=1) + platform: str = Field(min_length=1) + packages: tuple[tuple[str, str], ...] + environment: tuple[tuple[str, str], ...] + sha256: str = Field(pattern=_SHA256) + + @model_validator(mode="after") + def _validate_digest(self) -> RuntimeFingerprint: + manifest = self.model_dump(mode="json", exclude={"sha256"}) + if self.sha256 != _json_sha256(manifest): + raise ValueError("runtime fingerprint digest does not match its manifest") + return self + + +class HostAdmissionRequest(_Contract): + host_id: str = Field(min_length=1) + node_rank: int = Field(ge=0) + expected_gpu_ids: tuple[GpuId, ...] + runtime_packages: tuple[Annotated[str, Field(min_length=1)], ...] + + +class HostAdmissionReport(HostServiceHealth): + node_rank: int = Field(ge=0) + boot_id: str = Field( + pattern=r"^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$" + ) + assigned_gpus: tuple[GpuIdentity, ...] + nvidia_driver_version: str | None = Field( + default=None, pattern=r"^[0-9]+(?:\.[0-9]+)*$" + ) + runtime: RuntimeFingerprint + + +def runtime_package_names(*, trainer: bool) -> tuple[str, ...]: + return tuple(sorted((*_BASE_PACKAGES, *(_TRAINER_PACKAGES if trainer else ())))) + + +def build_runtime_fingerprint( + package_names: Sequence[str] = _BASE_PACKAGES, +) -> RuntimeFingerprint: + libc = platform.libc_ver() + values = { + "schema_version": _SCHEMA, + "art_build_sha256": _art_build_sha256(), + "python": f"{platform.python_implementation()}-{platform.python_version()}-" + f"{sys.implementation.cache_tag}", + "platform": f"{platform.system()}-{platform.release()}-{platform.machine()}-" + f"{libc[0]}-{libc[1]}", + "packages": tuple((name, metadata.version(name)) for name in package_names), + "environment": _runtime_environment(os.environ), + } + return RuntimeFingerprint(**values, sha256=_json_sha256(values)) + + +def inspect_host(request: HostAdmissionRequest) -> HostAdmissionReport: + runtime = build_runtime_fingerprint(request.runtime_packages) + inventory: dict[int | str, tuple[GpuIdentity, str]] = {} + include_mig = any( + isinstance(gpu_id, str) and gpu_id.startswith("MIG-") + for gpu_id in request.expected_gpu_ids + ) + for gpu, driver in ( + _query_gpu_inventory(include_mig=include_mig) + if request.expected_gpu_ids + else () + ): + if not gpu.is_mig: + inventory[gpu.index] = (gpu, driver) + inventory[gpu.uuid.casefold()] = (gpu, driver) + expected = tuple( + gpu_id.casefold() if isinstance(gpu_id, str) else gpu_id + for gpu_id in request.expected_gpu_ids + ) + missing = [gpu_id for gpu_id in expected if gpu_id not in inventory] + if missing: + raise RuntimeError( + f"host {request.host_id!r} is missing configured CUDA devices {missing}; " + f"nvidia-smi reported {sorted(map(str, inventory))}" + ) + assigned = tuple(inventory[gpu_id][0] for gpu_id in expected) + _require_unique( + "assigned CUDA device UUIDs", + [ + (gpu.uuid.casefold(), request.expected_gpu_ids[index]) + for index, gpu in enumerate(assigned) + ], + ) + drivers = {inventory[gpu_id][1] for gpu_id in expected} + if len(drivers) > 1: + raise RuntimeError(f"host {request.host_id!r} has multiple NVIDIA drivers") + hostname = socket.gethostname().strip() + if not hostname: + raise RuntimeError("host returned an empty hostname") + return HostAdmissionReport( + host_id=request.host_id, + node_rank=request.node_rank, + hostname=hostname, + boot_id=_read_boot_id(), + process_id=os.getpid(), + assigned_gpus=assigned, + nvidia_driver_version=next(iter(drivers), None), + runtime=runtime, + ) + + +def validate_host_admission( + hosts: Sequence[HostSpec], + reports: Sequence[HostAdmissionReport], + *, + expected_runtime: RuntimeFingerprint, +) -> dict[str, HostAdmissionReport]: + expected = {host.host_id: host for host in hosts} + actual = {report.host_id: report for report in reports} + if len(actual) != len(reports) or actual.keys() != expected.keys(): + raise RuntimeError( + f"host-service membership mismatch: expected={sorted(expected)} " + f"actual={sorted(actual)}" + ) + controller_contract = expected_runtime.model_dump(exclude={"environment", "sha256"}) + for host_id, host in expected.items(): + report = actual[host_id] + if report.node_rank != host.node_rank: + raise RuntimeError(f"host {host_id!r} reported an unexpected node rank") + if len(report.assigned_gpus) != len(host.gpu_ids) or any( + not _matches_gpu_id(expected_gpu, gpu) + for expected_gpu, gpu in zip( + host.gpu_ids, report.assigned_gpus, strict=True + ) + ): + raise RuntimeError(f"host {host_id!r} reported unexpected CUDA devices") + host_contract = report.runtime.model_dump(exclude={"environment", "sha256"}) + if host_contract != controller_contract: + fields = sorted( + name + for name, value in controller_contract.items() + if host_contract[name] != value + ) + raise RuntimeError( + f"host {host_id!r} runtime contract differs from controller: {fields}" + ) + runtime_digests = {report.runtime.sha256 for report in actual.values()} + if len(runtime_digests) > 1: + detail = " ".join( + f"{host_id}={report.runtime.sha256}" for host_id, report in actual.items() + ) + raise RuntimeError(f"runtime fingerprints differ across hosts: {detail}") + drivers = { + report.nvidia_driver_version + for report in actual.values() + if report.nvidia_driver_version is not None + } + if len(drivers) > 1: + raise RuntimeError(f"NVIDIA driver versions differ across hosts: {drivers}") + _require_unique( + "physical host boot IDs", + [(report.boot_id, host_id) for host_id, report in actual.items()], + ) + _require_unique( + "GPU UUIDs", + [ + (gpu.uuid.casefold(), f"{host_id}:{gpu.index}") + for host_id, report in actual.items() + for gpu in report.assigned_gpus + ], + ) + _require_unique( + "physical GPU PCI identities", + [ + (f"{report.boot_id}/{gpu.pci_bus_id}", f"{host_id}:{gpu.index}") + for host_id, report in actual.items() + for gpu in report.assigned_gpus + if not gpu.is_mig + ], + ) + for host_id, report in actual.items(): + full_gpus = { + gpu.uuid.casefold() for gpu in report.assigned_gpus if not gpu.is_mig + } + if conflicts := [ + gpu.uuid + for gpu in report.assigned_gpus + if gpu.is_mig and gpu.parent_uuid.casefold() in full_gpus + ]: + raise RuntimeError( + f"host {host_id!r} assigns both a physical GPU and its MIG device: " + f"{conflicts}" + ) + return actual + + +def _query_gpu_inventory( + *, include_mig: bool = False +) -> tuple[tuple[GpuIdentity, str], ...]: + executable = shutil.which("nvidia-smi") + if executable is None: + raise RuntimeError("nvidia-smi is required for GPU host admission") + result = _run_nvidia_smi( + executable, + "--query-gpu=index,uuid,pci.bus_id,driver_version", + "--format=csv,noheader,nounits", + ) + rows: list[tuple[GpuIdentity, str]] = [] + for line_number, row in enumerate(csv.reader(result.stdout.splitlines()), start=1): + values = tuple(value.strip() for value in row) + try: + if len(values) != 4: + raise ValueError(f"expected 4 fields, received {len(values)}") + gpu = GpuIdentity( + index=int(values[0]), + uuid=values[1], + parent_uuid=values[1], + pci_bus_id=values[2].upper(), + ) + except ValueError as error: + raise RuntimeError( + f"invalid nvidia-smi row {line_number}: {error}" + ) from None + rows.append((gpu, values[3])) + _require_unique( + "nvidia-smi GPU indices", [(gpu.index, gpu.uuid) for gpu, _ in rows] + ) + _require_unique( + "nvidia-smi GPU UUIDs", [(gpu.uuid.casefold(), gpu.index) for gpu, _ in rows] + ) + _require_unique( + "nvidia-smi PCI identities", [(gpu.pci_bus_id, gpu.index) for gpu, _ in rows] + ) + if not include_mig: + return tuple(rows) + parents = {gpu.index: (gpu, driver) for gpu, driver in rows} + listed_parent: tuple[GpuIdentity, str] | None = None + for line_number, line in enumerate( + _run_nvidia_smi(executable, "-L").stdout.splitlines(), start=1 + ): + if line.startswith("GPU "): + match = re.fullmatch(r"GPU ([0-9]+): .* \(UUID: (GPU-[^)]+)\)", line) + if match is None: + raise RuntimeError( + f"invalid nvidia-smi -L GPU row {line_number}: {line!r}" + ) + listed_parent = parents.get(int(match[1])) + if ( + listed_parent is None + or listed_parent[0].uuid.casefold() != match[2].casefold() + ): + raise RuntimeError( + f"nvidia-smi -L GPU row {line_number} disagrees with inventory" + ) + continue + if not line.lstrip().startswith("MIG "): + continue + match = re.fullmatch(r"\s+MIG .* \(UUID: (MIG-[^)]+)\)", line) + if match is None or listed_parent is None: + raise RuntimeError(f"invalid nvidia-smi -L MIG row {line_number}: {line!r}") + parent, driver = listed_parent + try: + mig = GpuIdentity( + index=parent.index, + uuid=match[1], + parent_uuid=parent.uuid, + pci_bus_id=parent.pci_bus_id, + ) + except ValueError as error: + raise RuntimeError( + f"invalid nvidia-smi -L MIG row {line_number}: {error}" + ) from None + rows.append((mig, driver)) + _require_unique( + "nvidia-smi CUDA UUIDs", + [(gpu.uuid.casefold(), gpu.index) for gpu, _ in rows], + ) + return tuple(rows) + + +def _matches_gpu_id(gpu_id: GpuId, identity: GpuIdentity) -> bool: + if isinstance(gpu_id, int): + return not identity.is_mig and identity.index == gpu_id + return identity.uuid.casefold() == gpu_id.casefold() + + +def _run_nvidia_smi( + executable: str, *arguments: str +) -> subprocess.CompletedProcess[str]: + try: + result = subprocess.run( + (executable, *arguments), + capture_output=True, + check=False, + text=True, + timeout=30, + ) + except (OSError, subprocess.TimeoutExpired) as error: + raise RuntimeError(f"nvidia-smi GPU identity query failed: {error}") from None + if result.returncode: + detail = result.stderr.strip() or result.stdout.strip() or "no output" + raise RuntimeError(f"nvidia-smi exited {result.returncode}: {detail}") + return result + + +def _art_build_sha256(root: Path | None = None) -> str: + root = root or Path(__file__).resolve().parents[1] + files = sorted( + path + for path in root.rglob("*") + if path.is_file() + and not any(part.startswith(".") for part in path.relative_to(root).parts) + and path.suffix not in {".pyc", ".pyo"} + ) + if not files: + raise RuntimeError(f"ART package root {root} contains no build files") + digest = hashlib.sha256() + for path in files: + _update_digest(digest, path.relative_to(root).as_posix().encode()) + with path.open("rb") as handle: + _update_digest(digest, handle.read()) + return digest.hexdigest() + + +def _runtime_environment( + environment: Mapping[str, str], +) -> tuple[tuple[str, str], ...]: + return tuple( + sorted( + (name, environment[name]) + for name in _RUNTIME_ENV & environment.keys() + if environment[name] + ) + ) + + +def _read_boot_id() -> str: + try: + return str(UUID(_BOOT_ID_PATH.read_text(encoding="ascii").strip())) + except (OSError, ValueError) as error: + raise RuntimeError( + f"cannot read Linux physical host boot ID: {error}" + ) from None + + +def _require_unique(name: str, values: Sequence[tuple[object, object]]) -> None: + owners: dict[object, object] = {} + for value, owner in values: + if value in owners: + raise RuntimeError( + f"duplicate {name}: {value!r} belongs to {owners[value]!r} and {owner!r}" + ) + owners[value] = owner + + +def _json_sha256(value: object) -> str: + payload = json.dumps(value, separators=(",", ":"), sort_keys=True).encode() + return hashlib.sha256(payload).hexdigest() + + +def _update_digest(digest: hashlib._Hash, value: bytes) -> None: + digest.update(len(value).to_bytes(8, "big")) + digest.update(value) diff --git a/src/art/distributed/monarch_actor.py b/src/art/distributed/monarch_actor.py new file mode 100644 index 000000000..00e31f67e --- /dev/null +++ b/src/art/distributed/monarch_actor.py @@ -0,0 +1,730 @@ +from __future__ import annotations + +import asyncio +from collections import OrderedDict +from functools import wraps +import json +import os +from pathlib import Path +import socket +import time +import traceback +from typing import Any, Literal +from urllib.request import urlopen + +# This module is imported only by explicit distributed runtime construction. +from monarch.actor import Actor, endpoint # ty: ignore[unresolved-import] + +from art.utils.lifecycle import complete_task, complete_to_thread + +from .adapter_transport import AdapterSnapshotReceiver +from .artifact_preflight import ( + ArtifactProbeCommand, + ArtifactProbeResult, + execute_artifact_probe, +) +from .data_plane import ( + ByteStreamServerLoop, + PackedBatchCapacityError, + PackedBatchInbox, + PackedBatchLeaseError, + PackedBatchPublisher, + PackedBatchRef, + PackedBatchTransfer, +) +from .host_admission import ( + HostAdmissionReport, + HostAdmissionRequest, + inspect_host, +) +from .monarch_runtime import RemoteCallError, RemoteCallResult +from .nccl_preflight import ( + NcclPreflightSessionRequest, + NcclProbeRequest, + NcclProbeResult, + NcclRendezvous, + NcclRendezvousRequest, + NcclRendezvousResult, + run_nccl_probe, + start_nccl_rendezvous, +) +from .packing import PackingRequest, PackingResult +from .rollout import RolloutInvocation, RolloutResult +from .specs import HostServiceHealth +from .trajectory_store import ( + TrajectoryCapacityError, + TrajectoryEnqueueResult, + TrajectoryGroupRef, + TrajectoryLeaseError, + TrajectoryQueueItem, + TrajectoryQueuePacking, + TrajectoryQueueRelease, + TrajectoryQueueResize, + TrajectoryQueueSnapshot, + TrajectoryQueueStore, + TrajectoryQueueTake, + publish_trajectory_bundles, +) +from .vllm_replica import HostMemberLaunchRequest + + +def _require_etcd_health(url: str, timeout_s: float) -> None: + with urlopen(f"{url}/health", timeout=timeout_s) as response: + health = json.load(response).get("health") + if health not in (True, "true"): + raise RuntimeError(f"etcd health check failed: {health!r}") + + +def resilient_endpoint(function: Any) -> Any: + @wraps(function) + async def wrapped(*args: Any, **kwargs: Any) -> RemoteCallResult: + try: + return RemoteCallResult(value=await function(*args, **kwargs)) + except (KeyboardInterrupt, SystemExit, GeneratorExit): + raise + except BaseException as error: + from art.errors import LocalServingUnavailableError + + if isinstance(error, asyncio.CancelledError): + kind = "cancelled" + elif isinstance(error, LocalServingUnavailableError): + kind = "serving" + elif isinstance(error, PackedBatchCapacityError | TrajectoryCapacityError): + kind = "capacity" + elif isinstance(error, PackedBatchLeaseError | TrajectoryLeaseError): + kind = "lease" + elif isinstance(error, (TypeError, ValueError)): + kind = "input" + else: + kind = "internal" + return RemoteCallResult( + error=RemoteCallError( + kind=kind, + error_type=type(error).__name__, + message=str(error) or type(error).__name__, + traceback=traceback.format_exc(), + ) + ) + + return endpoint(wrapped) + + +class AdapterTransferHostService(Actor): + """Adapter receiver isolated from packing in its own host process.""" + + def __init__(self, host_id: str, output_root: str) -> None: + self._receiver = AdapterSnapshotReceiver(host_id, output_root) + + @resilient_endpoint + async def prepare( + self, + generation_id: str, + template_path: str, + timeout_s: float, + transport: Literal["local", "nixl"], + ): + return await asyncio.to_thread( + self._receiver.prepare, + generation_id, + template_path, + timeout_s, + transport, + ) + + @resilient_endpoint + async def poll(self, generation_id: str): + return await asyncio.to_thread(self._receiver.poll, generation_id) + + @resilient_endpoint + async def release(self, generation_id: str) -> None: + await asyncio.to_thread(self._receiver.release, generation_id) + + @resilient_endpoint + async def close(self) -> None: + await asyncio.to_thread(self._receiver.close) + + +class ArtHostService(Actor): + """One ART control and data-plane service per host.""" + + def __init__( + self, + admission_json: str, + packed_batch_capacity_bytes: int, + vllm_output_root: str = "/tmp/art-vllm", + data_plane_host: str | None = None, + ) -> None: + admission = HostAdmissionRequest.model_validate_json(admission_json) + self.host_id = admission.host_id + self._admission = admission + self._admission_report: HostAdmissionReport | None = None + self._packed_batches = PackedBatchInbox( + host_id=self.host_id, capacity_bytes=packed_batch_capacity_bytes + ) + self._batch_publishers: dict[str, PackedBatchPublisher] = {} + self._data_plane_host = data_plane_host or socket.gethostbyname( + socket.gethostname() + ) + self._trajectory_queues: dict[str, TrajectoryQueueStore] = {} + self._packer = None + self._packing_lock = asyncio.Lock() + self._vllm_output_root = vllm_output_root + self._vllm_launcher = None + self._nccl_cleanups: dict[str, asyncio.Task[None]] = {} + self._nccl_rendezvous: dict[str, NcclRendezvous] = {} + self._nccl_sessions: dict[str, tuple[float, asyncio.Task[None]]] = {} + self._nccl_tasks: dict[str, asyncio.Task[Any]] = {} + self._cancelled_nccl_probes: set[str] = set() + + @resilient_endpoint + async def admission(self) -> HostAdmissionReport: + if self._admission_report is None: + self._admission_report = await asyncio.to_thread( + inspect_host, self._admission + ) + return self._admission_report + + @resilient_endpoint + async def health(self) -> HostServiceHealth: + if self._admission_report is None: + raise RuntimeError("host has not passed ART runtime admission") + return HostServiceHealth( + host_id=self.host_id, + hostname=socket.gethostname(), + process_id=os.getpid(), + ) + + @resilient_endpoint + async def artifact_root_probe( + self, command: ArtifactProbeCommand + ) -> ArtifactProbeResult: + if self._admission_report is None: + raise RuntimeError("host has not passed ART runtime admission") + return await asyncio.to_thread(execute_artifact_probe, self.host_id, command) + + @resilient_endpoint + async def nixl_metadata_store_health(self, url: str, timeout_s: float) -> str: + if self._admission_report is None: + raise RuntimeError("host has not passed ART runtime admission") + try: + await asyncio.to_thread(_require_etcd_health, url, timeout_s) + except BaseException as error: + raise RuntimeError( + f"host {self.host_id!r} cannot reach healthy NIXL metadata store {url}" + ) from error + return self.host_id + + @resilient_endpoint + async def start_nccl_preflight_session( + self, request: NcclPreflightSessionRequest + ) -> None: + if self._admission_report is None: + raise RuntimeError("host has not passed ART runtime admission") + if request.probe_id in self._cancelled_nccl_probes: + raise asyncio.CancelledError + if request.probe_id in self._nccl_sessions: + raise RuntimeError(f"NCCL probe {request.probe_id!r} is already admitted") + deadline = time.monotonic() + request.lease_s + reaper = asyncio.create_task( + self._expire_nccl_preflight_session(request.probe_id, deadline) + ) + self._nccl_sessions[request.probe_id] = (deadline, reaper) + + @resilient_endpoint + async def nccl_preflight_rendezvous( + self, request: NcclRendezvousRequest + ) -> NcclRendezvousResult: + if self._admission_report is None: + raise RuntimeError("host has not passed ART runtime admission") + deadline = await self._require_nccl_probe(request.probe_id) + if request.probe_id in self._nccl_rendezvous: + raise RuntimeError(f"NCCL probe {request.probe_id!r} already has a store") + task = asyncio.create_task(start_nccl_rendezvous(request, deadline_s=deadline)) + self._nccl_tasks[request.probe_id] = task + try: + async with asyncio.timeout(max(0.0, deadline - time.monotonic())): + rendezvous = await task + finally: + if self._nccl_tasks.get(request.probe_id) is task: + self._nccl_tasks.pop(request.probe_id) + if request.probe_id in self._cancelled_nccl_probes: + await rendezvous.close() + raise asyncio.CancelledError + self._nccl_rendezvous[request.probe_id] = rendezvous + return NcclRendezvousResult(host_id=self.host_id, port=rendezvous.port) + + @resilient_endpoint + async def nccl_preflight(self, request: NcclProbeRequest) -> NcclProbeResult: + if self._admission_report is None: + raise RuntimeError("host has not passed ART runtime admission") + deadline = await self._require_nccl_probe(request.probe_id) + task = asyncio.create_task(run_nccl_probe(self.host_id, request)) + self._nccl_tasks[request.probe_id] = task + try: + async with asyncio.timeout(max(0.0, deadline - time.monotonic())): + return await task + finally: + if self._nccl_tasks.get(request.probe_id) is task: + self._nccl_tasks.pop(request.probe_id) + + @resilient_endpoint + async def cancel_nccl_preflight(self, probe_id: str) -> None: + await self._cancel_nccl_preflight(probe_id) + + @resilient_endpoint + async def close(self) -> None: + probe_ids = tuple( + { + *self._nccl_cleanups, + *self._nccl_sessions, + *self._nccl_tasks, + *self._nccl_rendezvous, + } + ) + await asyncio.gather( + *(self._cancel_nccl_preflight(probe_id) for probe_id in probe_ids) + ) + for queue in self._trajectory_queues.values(): + queue.close() + self._trajectory_queues.clear() + for batch_id in tuple(self._batch_publishers): + await self._drop_batch(batch_id) + async with self._packing_lock: + if self._packer is not None: + await self._packer.close() + self._packer = None + if self._vllm_launcher is not None: + await self._vllm_launcher.close() + self._vllm_launcher = None + self._packed_batches.store.close() + + async def _require_nccl_probe(self, probe_id: str) -> float: + if probe_id in self._cancelled_nccl_probes: + raise asyncio.CancelledError + session = self._nccl_sessions.get(probe_id) + if session is None: + raise RuntimeError(f"NCCL probe {probe_id!r} has no active session") + deadline, _ = session + if time.monotonic() >= deadline: + await self._cancel_nccl_preflight(probe_id) + raise TimeoutError(f"NCCL probe {probe_id!r} session expired") + if probe_id in self._nccl_tasks: + raise RuntimeError(f"NCCL probe {probe_id!r} is already active") + return deadline + + async def _cancel_nccl_preflight(self, probe_id: str) -> None: + self._cancelled_nccl_probes.add(probe_id) + cleanup = self._nccl_cleanups.get(probe_id) + if cleanup is None: + cleanup = asyncio.create_task( + self._cleanup_nccl_preflight(probe_id, asyncio.current_task()) + ) + self._nccl_cleanups[probe_id] = cleanup + try: + _, cancelled = await complete_task(cleanup) + finally: + if cleanup.done() and self._nccl_cleanups.get(probe_id) is cleanup: + self._nccl_cleanups.pop(probe_id) + if cancelled is not None: + raise cancelled + + async def _cleanup_nccl_preflight( + self, probe_id: str, owner: asyncio.Task[Any] | None + ) -> None: + session = self._nccl_sessions.pop(probe_id, None) + if session is not None and session[1] is not owner: + session[1].cancel() + await asyncio.gather(session[1], return_exceptions=True) + task = self._nccl_tasks.pop(probe_id, None) + if task is not None: + task.cancel() + await asyncio.gather(task, return_exceptions=True) + rendezvous = self._nccl_rendezvous.pop(probe_id, None) + if rendezvous is not None: + await rendezvous.close() + + async def _expire_nccl_preflight_session( + self, probe_id: str, deadline: float + ) -> None: + await asyncio.sleep(max(0.0, deadline - time.monotonic())) + if self._nccl_sessions.get(probe_id, (None,))[0] != deadline: + return + self._cancelled_nccl_probes.add(probe_id) + if probe_id in self._nccl_cleanups: + return + cleanup = asyncio.current_task() + assert cleanup is not None + self._nccl_cleanups[probe_id] = cleanup + try: + await self._cleanup_nccl_preflight(probe_id, cleanup) + finally: + if self._nccl_cleanups.get(probe_id) is cleanup: + self._nccl_cleanups.pop(probe_id) + + @resilient_endpoint + async def create_trajectory_queue( + self, + queue_id: str, + max_ready_groups: int, + capacity_records: int, + capacity_bytes: int, + ) -> None: + if queue_id in self._trajectory_queues: + raise ValueError(f"trajectory queue {queue_id!r} already exists") + self._trajectory_queues[queue_id] = TrajectoryQueueStore( + max_ready_groups=max_ready_groups, + capacity_records=capacity_records, + capacity_bytes=capacity_bytes, + ) + + @resilient_endpoint + async def resize_trajectory_queue(self, operation: TrajectoryQueueResize) -> None: + self._trajectory_queue(operation.queue_id).resize( + maxsize=operation.maxsize, generation=operation.generation + ) + + @resilient_endpoint + async def enqueue_trajectory( + self, queue_id: str, item: TrajectoryQueueItem + ) -> TrajectoryEnqueueResult: + return self._trajectory_queue(queue_id).enqueue(item) + + @resilient_endpoint + async def take_trajectory( + self, queue_id: str, consumer_id: str, count: int + ) -> TrajectoryQueueTake: + return self._trajectory_queue(queue_id).take(consumer_id, count) + + @resilient_endpoint + async def mark_trajectories_packed(self, operation: TrajectoryQueuePacking) -> None: + self._trajectory_queue(operation.queue_id).mark_packed(operation) + + @resilient_endpoint + async def release_trajectory(self, operation: TrajectoryQueueRelease) -> None: + self._trajectory_queue(operation.queue_id).release(operation) + + @resilient_endpoint + async def finish_trajectory_queue(self, queue_id: str) -> None: + self._trajectory_queue(queue_id).finish() + + @resilient_endpoint + async def trajectory_queue_snapshot(self, queue_id: str) -> TrajectoryQueueSnapshot: + return self._trajectory_queue(queue_id).snapshot() + + @resilient_endpoint + async def close_trajectory_queue( + self, queue_id: str + ) -> tuple[TrajectoryGroupRef, ...]: + queue = self._trajectory_queues.pop(queue_id, None) + return () if queue is None else queue.close() + + def _trajectory_queue(self, queue_id: str) -> TrajectoryQueueStore: + try: + return self._trajectory_queues[queue_id] + except KeyError: + raise ValueError(f"unknown trajectory queue {queue_id!r}") from None + + def _launcher(self): + if self._vllm_launcher is None: + from .vllm_replica import ManagedVllmHostLauncher + + self._vllm_launcher = ManagedVllmHostLauncher( + self._vllm_output_root, + install_parent_cleanup=lambda: None, + ) + return self._vllm_launcher + + @resilient_endpoint + async def start_vllm_member(self, request: HostMemberLaunchRequest): + if self._admission_report is None: + raise RuntimeError("host has not passed ART runtime admission") + return await self._launcher().start_member(request) + + @resilient_endpoint + async def vllm_member_state(self, replica_id: str, member_id: str, generation: int): + return await self._launcher().member_state(replica_id, member_id, generation) + + @resilient_endpoint + async def stop_vllm_member( + self, replica_id: str, member_id: str, generation: int + ) -> None: + if self._vllm_launcher is not None: + await self._vllm_launcher.stop_member(replica_id, member_id, generation) + + @resilient_endpoint + async def pack_batch( + self, request: PackingRequest, batch_id: str, transfer_timeout_s: float + ) -> PackingResult: + fetch_started = time.monotonic() + if request.trajectory_sources: + groups = list( + await asyncio.gather( + *( + source.receive(timeout_s=transfer_timeout_s) + for source in request.trajectory_sources + ) + ) + ) + elif request.trajectory_transfer is None: + groups = [payload.build() for payload in request.trajectory_groups] + else: + if request.trajectory_groups: + raise ValueError("packing request has inline and streamed trajectories") + if request.trajectory_transfer.stream.stream_id != batch_id: + raise ValueError("packing request has the wrong trajectory stream") + groups = list( + await request.trajectory_transfer.receive_groups( + timeout_s=transfer_timeout_s + ) + ) + trajectory_fetch_s = time.monotonic() - fetch_started + if request.collect_packing_shapes: + for group in groups: + group._collect_packing_shape = True + log_future = None + if request.trajectory_log_path is not None: + from art.utils.trajectory_logging import write_trajectory_groups_parquet + + path = Path(request.trajectory_log_path) + + def write_log() -> None: + path.parent.mkdir(parents=True, exist_ok=True) + write_trajectory_groups_parquet(groups, str(path)) + + log_future = asyncio.get_running_loop().run_in_executor(None, write_log) + packing_started = time.monotonic() + try: + async with self._packing_lock: + if self._packer is None: + from art.megatron.backend import MegatronBackend + + self._packer = MegatronBackend( + path=f"/tmp/art-packing-{os.getpid()}", + enable_expert_replay=request.include_moe_routing, + ) + packer = self._packer + assert packer is not None + packed, cancelled = await complete_to_thread( + lambda: packer._get_packed_tensors( + request.model.build(), + groups, + advantage_balance=request.advantage_balance, + allow_training_without_logprobs=( + request.allow_training_without_logprobs + ), + scale_rewards=request.scale_rewards, + plot_tensors=request.plot_tensors, + packed_sequence_length=request.packed_sequence_length, + logprob_calculation_chunk_size=( + request.logprob_calculation_chunk_size + ), + include_moe_routing=request.include_moe_routing, + ) + ) + if cancelled is not None: + raise cancelled + except BaseException as error: + if log_future is not None: + + async def finish_log() -> None: + await log_future + + try: + _, cancelled = await complete_task( + asyncio.create_task(finish_log()) + ) + if cancelled is not None: + error.add_note("trajectory logging observed cancellation") + except BaseException as log_error: + error.add_note( + "trajectory logging also failed: " + f"{type(log_error).__name__}: {log_error}" + ) + raise + packing_core_s = time.monotonic() - packing_started + log_wait_started = time.monotonic() + if log_future is not None: + await log_future + trajectory_log_wait_s = time.monotonic() - log_wait_started + shapes = tuple(group._packed_group_shape for group in groups) + if packed is None: + if request.trajectory_log_path is not None: + await asyncio.to_thread(Path(request.trajectory_log_path).unlink) + return PackingResult( + ref=None, + packed_group_shapes=shapes, + generation_id=request.generation_id, + trajectory_fetch_s=trajectory_fetch_s, + packing_core_s=packing_core_s, + trajectory_log_wait_s=trajectory_log_wait_s, + ) + trainable_assistant_tokens = int(packed["assistant_mask"].sum().item()) + loss_bearing_tokens = int(packed["assistant_mask"][:, 1:].sum().item()) + non_padding_tokens = int((packed["group_ids"] != -1).sum().item()) + finalize_started = time.monotonic() + ref = self._packed_batches.store.create( + packed, + batch_id=batch_id, + group_ids=request.group_ids, + record_ids=request.record_ids, + min_source_version=request.min_source_version, + max_source_version=request.max_source_version, + ) + packed_batch_finalize_s = time.monotonic() - finalize_started + return PackingResult( + ref=ref, + packed_group_shapes=shapes, + generation_id=request.generation_id, + trainable_assistant_tokens=trainable_assistant_tokens, + loss_bearing_tokens=loss_bearing_tokens, + non_padding_tokens=non_padding_tokens, + trajectory_log_path=request.trajectory_log_path, + trajectory_fetch_s=trajectory_fetch_s, + packing_core_s=packing_core_s, + trajectory_log_wait_s=trajectory_log_wait_s, + packed_batch_finalize_s=packed_batch_finalize_s, + ) + + @resilient_endpoint + async def publish_batch(self, ref: PackedBatchRef) -> PackedBatchTransfer: + if ref.batch_id in self._batch_publishers: + raise RuntimeError(f"packed batch {ref.batch_id!r} is already published") + publisher = await PackedBatchPublisher.create( + ref, advertise_host=self._data_plane_host + ) + try: + transfer = publisher.transfer + except BaseException: + await publisher.close() + raise + self._batch_publishers[ref.batch_id] = publisher + return transfer + + @resilient_endpoint + async def drop_batch(self, batch_id: str) -> None: + await self._drop_batch(batch_id) + + @resilient_endpoint + async def note_batch_transmitted(self, byte_count: int) -> None: + self._packed_batches.store.note_transmitted(byte_count) + + async def _drop_batch(self, batch_id: str) -> bool: + publisher = self._batch_publishers.pop(batch_id, None) + if publisher is None: + return False + await publisher.close() + return True + + @resilient_endpoint + async def receive_batch( + self, ref: PackedBatchRef, transfer: PackedBatchTransfer, timeout_s: float + ) -> PackedBatchRef: + return await self._packed_batches.receive(ref, transfer, timeout_s=timeout_s) + + @resilient_endpoint + async def drop_batch_ref(self, ref: PackedBatchRef) -> None: + await self._packed_batches.drop(ref) + + @resilient_endpoint + async def reclaim_batch(self, batch_id: str, fence: bool) -> bool: + published = False + failure: BaseException | None = None + try: + published = await self._drop_batch(batch_id) + except BaseException as error: + failure = error + reclaimed = self._packed_batches.store.reclaim(batch_id, fence=fence) + if failure is not None: + raise failure + return published or reclaimed + + @resilient_endpoint + async def stats(self): + return self._packed_batches.store.stats() + + +class RolloutWorkerService(Actor): + """One process-isolated CPU rollout slot.""" + + def __init__( + self, capacity_records: int, capacity_bytes: int, data_plane_host: str + ) -> None: + from .trajectory_store import TrajectoryRecordStore + + self._models = OrderedDict() + self._results = TrajectoryRecordStore( + owner_actor_id=f"rollout:{socket.gethostname()}:{os.getpid()}", + capacity_records=capacity_records, + capacity_bytes=capacity_bytes, + ) + self._data_plane_host = data_plane_host + self._byte_stream_loop = ByteStreamServerLoop() + self._trajectory_publishers = {} + + @resilient_endpoint + async def run(self, invocation: RolloutInvocation): + from art.metrics import MetricsBuilder + + key = invocation.model.cache_key + model = self._models.get(key) + if model is None: + model = invocation.model.build() + self._models[key] = model + if len(self._models) > 16: + _, evicted = self._models.popitem(last=False) + await evicted._reset_inference_runtime() + else: + self._models.move_to_end(key) + builder = MetricsBuilder(cost_context="train") + token = builder.activate() + try: + value = await invocation.callable.resolve()( + model, invocation.scenario, invocation.config + ) + finally: + token.var.reset(token) + if invocation.store_result: + from art import TrajectoryGroup + + if isinstance(value, TrajectoryGroup): + ref = self._results.put(value) + try: + transfer, publisher = await publish_trajectory_bundles( + (self._results.bundle(ref),), + stream_id=ref.result_id, + advertise_host=self._data_plane_host, + server_loop=self._byte_stream_loop, + ) + except BaseException: + self._results.drop(ref) + raise + self._trajectory_publishers[ref.result_id] = publisher + value = ref.model_copy(update={"transfer": transfer}) + return RolloutResult(value=value, metrics=await builder.drain_pending()) + + async def _release_trajectory(self, ref: TrajectoryGroupRef) -> None: + self._results.drop(ref) + publisher = self._trajectory_publishers.pop(ref.result_id, None) + if publisher is not None: + await publisher.close() + + @resilient_endpoint + async def drop_result(self, ref: TrajectoryGroupRef) -> None: + await self._release_trajectory(ref) + + @resilient_endpoint + async def close(self) -> None: + try: + await asyncio.gather( + *( + publisher.close() + for publisher in tuple(self._trajectory_publishers.values()) + ) + ) + finally: + self._trajectory_publishers.clear() + await self._byte_stream_loop.close() + for model in self._models.values(): + await model._reset_inference_runtime() + self._models.clear() + self._results.close() diff --git a/src/art/distributed/monarch_bootstrap.py b/src/art/distributed/monarch_bootstrap.py new file mode 100644 index 000000000..31b4ecaad --- /dev/null +++ b/src/art/distributed/monarch_bootstrap.py @@ -0,0 +1,1450 @@ +from __future__ import annotations + +"""Provider-neutral bootstrap for pinned torchmonarch 0.6. + +Both worker and controller endpoints must be reachable only on one trusted private +network because ART currently configures Monarch with ``trust_all_connections``. +""" + +import argparse +import asyncio +from collections.abc import Callable, Iterator, Mapping, MutableMapping, Sequence +from contextlib import contextmanager +import fcntl +import hashlib +import ipaddress +import os +from pathlib import Path +import re +import select +import shlex +import signal +import socket +import subprocess +import sys +import threading +import time +from typing import TYPE_CHECKING, Any +from urllib.parse import urlsplit +import uuid + +from pydantic import BaseModel, ConfigDict, Field, model_validator + +if TYPE_CHECKING: + from .rollout import InstalledAsyncCallable + +DEFAULT_MONARCH_PORT = 22222 +DEFAULT_STARTUP_TIMEOUT_S = 600.0 +_INVALID_IDENTIFIER = re.compile(r"\W") +_MAX_IDENTIFIER_LENGTH = 48 +_SSH_LAUNCH_ID = re.compile(r"^[0-9a-f]{32}$") +_SSH_READY_PREFIX = b"ART_MONARCH_READY " +_MONARCH_TIMEOUT_ENV = ( + "HYPERACTOR_HOST_SPAWN_READY_TIMEOUT", + "HYPERACTOR_MESSAGE_DELIVERY_TIMEOUT", + "HYPERACTOR_MESH_ATTACH_CONFIG_TIMEOUT", + "HYPERACTOR_MESH_ACTOR_SPAWN_MAX_IDLE", + "HYPERACTOR_MESH_PROC_SPAWN_MAX_IDLE", +) +_MONARCH_SHUTDOWN_ENV = { + "HYPERACTOR_PROCESS_EXIT_TIMEOUT": "2s", + "HYPERACTOR_MESH_PROC_STOP_MAX_IDLE": "240s", +} +_WORKER_ADDRESS_LOCK = threading.Lock() +_USED_WORKER_ADDRESSES: set[str] = set() +_BROKEN_OUTPUT_FLAGS = select.POLLERR | select.POLLHUP | select.POLLNVAL +_WORKER_CODE = """\ +import ctypes +import os +import signal +import sys + +if len(sys.argv) >= 4 and sys.argv[2] == "--parent-pid": + expected_parent_pid = int(sys.argv[3]) + libc = ctypes.CDLL(None, use_errno=True) + if libc.prctl(1, signal.SIGKILL, 0, 0, 0): + error_number = ctypes.get_errno() + raise OSError(error_number, os.strerror(error_number)) + if os.getppid() != expected_parent_pid: + os.kill(os.getpid(), signal.SIGKILL) + +from monarch.actor import run_worker_loop_forever +run_worker_loop_forever(address=sys.argv[1], ca="trust_all_connections") +""" +_LEGACY_OWNED_WORKER_CODE = """\ +import sys +from monarch.actor import run_worker_loop_forever +run_worker_loop_forever(address=sys.argv[1], ca="trust_all_connections") +""" +_WORKER_LOCK_ROOT = Path("/tmp") +_OWNED_WORKER_SCHEMA = "art.monarch.owned-worker.v1" + + +class _BootstrapContract(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + +class _OwnedWorkerMetadata(_BootstrapContract): + schema_name: str = _OWNED_WORKER_SCHEMA + address: str + controller_pid: int = Field(gt=0) + controller_start_time: int = Field(gt=0) + worker_pid: int = Field(gt=0) + worker_start_time: int = Field(gt=0) + python_executable: str = Field(min_length=1) + worker_code_sha256: str = Field(pattern=r"^[0-9a-f]{64}$") + ownership_token: str = Field(pattern=r"^[0-9a-f]{32}$") + + @model_validator(mode="after") + def _validate_schema(self) -> "_OwnedWorkerMetadata": + if self.schema_name != _OWNED_WORKER_SCHEMA: + raise ValueError("unsupported owned-worker metadata schema") + return self + + +class _WorkerSession(_BootstrapContract): + model_config = ConfigDict( + arbitrary_types_allowed=True, + extra="forbid", + frozen=True, + ) + + address: str + process: subprocess.Popen[bytes] + label: str + graceful: bool = False + launch_id: str | None = None + lease: Any = None + + @property + def exitcode(self) -> int | None: + return self.process.poll() + + def is_alive(self) -> bool: + return self.exitcode is None + + def release(self) -> None: + if not self.is_alive(): + return + if self.graceful: + assert self.process.stdin is not None + self.process.stdin.close() + else: + os.killpg(self.process.pid, signal.SIGTERM) + + def wait(self) -> None: + try: + self.process.wait(timeout=15) + except subprocess.TimeoutExpired: + if self.graceful: + self.process.terminate() + else: + os.killpg(self.process.pid, signal.SIGKILL) + self.process.wait() + raise RuntimeError(f"{self.label} did not stop in time") from None + finally: + if self.lease is not None: + self.lease.close() + if self.graceful and self.process.returncode: + raise RuntimeError(f"{self.label} exited {self.process.returncode}") + + +class ExplicitHostBootstrap(_BootstrapContract): + worker_addresses: tuple[str, ...] + controller_rank: int = Field(default=0, ge=0) + + @model_validator(mode="after") + def _validate_workers(self) -> "ExplicitHostBootstrap": + if not self.worker_addresses: + raise ValueError("worker_addresses must not be empty") + if len(set(self.worker_addresses)) != len(self.worker_addresses): + raise ValueError("worker_addresses must be unique") + if self.controller_rank >= len(self.worker_addresses): + raise ValueError("controller_rank must identify a worker address") + return self + + +class SkyPilotBootstrap(_BootstrapContract): + node_rank: int = Field(ge=0) + node_ips: tuple[str, ...] + port: int = Field(default=DEFAULT_MONARCH_PORT, ge=1, le=65534) + + @classmethod + def from_environ( + cls, + environ: Mapping[str, str] | None = None, + *, + port: int = DEFAULT_MONARCH_PORT, + ) -> "SkyPilotBootstrap": + environ = os.environ if environ is None else environ + try: + node_rank = int(environ["SKYPILOT_NODE_RANK"]) + node_ips = tuple(environ["SKYPILOT_NODE_IPS"].replace(",", "\n").split()) + declared_nodes = int(environ["SKYPILOT_NUM_NODES"]) + except KeyError as error: + raise RuntimeError( + f"missing SkyPilot environment variable {error.args[0]}" + ) from None + if declared_nodes != len(node_ips): + raise ValueError( + f"SKYPILOT_NUM_NODES={declared_nodes} but received {len(node_ips)} IPs" + ) + return cls(node_rank=node_rank, node_ips=node_ips, port=port) + + @model_validator(mode="after") + def _validate_rank(self) -> "SkyPilotBootstrap": + if not self.node_ips or self.node_rank >= len(self.node_ips): + raise ValueError("SkyPilot node rank must identify a node IP") + if len(set(self.node_ips)) != len(self.node_ips): + raise ValueError("SKYPILOT_NODE_IPS must be unique") + for node_ip in self.node_ips: + try: + ipaddress.ip_address(node_ip) + except ValueError: + raise ValueError( + f"SKYPILOT_NODE_IPS contains invalid IP address {node_ip!r}" + ) from None + return self + + @property + def worker_addresses(self) -> tuple[str, ...]: + return tuple(_tcp_address(ip, self.port) for ip in self.node_ips) + + @property + def lifecycle_port(self) -> int: + return self.port + 1 + + +class SshHost(_BootstrapContract): + target: str = Field(min_length=1) + worker_host: str = Field(min_length=1) + + +class SshBootstrap(_BootstrapContract): + hosts: tuple[SshHost, ...] + python_executable: str = Field(min_length=1) + port: int = Field(default=DEFAULT_MONARCH_PORT, ge=1, le=65535) + ssh_args: tuple[str, ...] = () + + @model_validator(mode="after") + def _validate_hosts(self) -> "SshBootstrap": + if not self.hosts: + raise ValueError("hosts must not be empty") + if len({host.target for host in self.hosts}) != len(self.hosts): + raise ValueError("SSH targets must be unique") + if len({host.worker_host for host in self.hosts}) != len(self.hosts): + raise ValueError("worker hosts must be unique") + return self + + @property + def worker_addresses(self) -> tuple[str, ...]: + return tuple(_tcp_address(host.worker_host, self.port) for host in self.hosts) + + +def _tcp_address(host: str, port: int) -> str: + host = host.removeprefix("[").removesuffix("]") + return f"tcp://[{host}]:{port}" if ":" in host else f"tcp://{host}:{port}" + + +def require_local_worker_address(worker_addresses: Sequence[str]) -> str: + error = "local ART runtime requires exactly one loopback tcp worker address" + if len(worker_addresses) != 1: + raise ValueError(error) + address = worker_addresses[0] + try: + parsed = urlsplit(address) + host = parsed.hostname + port = parsed.port + except ValueError: + raise ValueError(error) from None + if ( + parsed.scheme != "tcp" + or host is None + or port is None + or port < 0 + or parsed.path + or parsed.query + or parsed.fragment + or parsed.username is not None + or parsed.password is not None + ): + raise ValueError(error) + try: + is_loopback = ( + host.casefold() == "localhost" or ipaddress.ip_address(host).is_loopback + ) + except ValueError: + is_loopback = False + if not is_loopback: + raise ValueError(error) + return address + + +def _parse_ssh_host(value: str) -> SshHost: + target, separator, worker_host = value.partition("=") + target = target.strip() + if not separator: + worker_host = target.rsplit("@", 1)[-1] + return SshHost(target=target, worker_host=worker_host.strip()) + + +def _positive_float(value: str) -> float: + parsed = float(value) + if parsed <= 0: + raise argparse.ArgumentTypeError("must be greater than zero") + return parsed + + +def monarch_identifier(value: str) -> str: + """Return a stable valid Monarch mesh, proc, or actor identifier.""" + + identifier = _INVALID_IDENTIFIER.sub("_", value) + if not identifier or identifier[0].isdigit(): + identifier = f"art_{identifier}" + if identifier == value and len(identifier) <= _MAX_IDENTIFIER_LENGTH: + return identifier + suffix = hashlib.sha256(value.encode()).hexdigest()[:8] + prefix_length = _MAX_IDENTIFIER_LENGTH - len(suffix) - 1 + return f"{identifier[:prefix_length]}_{suffix}" + + +def _prepare_child_environment( + *, + worker: bool = False, + environ: MutableMapping[str, str] | None = None, +) -> None: + environ = os.environ if environ is None else environ + # Monarch's spawned interpreter may resolve outside the active uv venv. Make + # the controller's import roots explicit for ART and installed user code. + roots = [path for path in sys.path if path and os.path.isabs(path)] + roots.extend(environ.get("PYTHONPATH", "").split(os.pathsep)) + environ["PYTHONPATH"] = os.pathsep.join(dict.fromkeys(filter(None, roots))) + if os.path.isfile(os.path.join(sys.prefix, "pyvenv.cfg")): + environ.setdefault("ART_VIRTUAL_ENV", sys.prefix) + if worker: + environ.pop("CUDA_VISIBLE_DEVICES", None) + allocator_config = "expandable_segments:True" + environ["PYTORCH_ALLOC_CONF"] = allocator_config + environ["PYTORCH_CUDA_ALLOC_CONF"] = allocator_config + nvidia_libs = ( + str(path) + for root in roots + for path in (Path(root) / "nvidia").glob("*/lib") + if path.is_dir() + ) + inherited = environ.get("LD_LIBRARY_PATH", "").split(os.pathsep) + environ["LD_LIBRARY_PATH"] = os.pathsep.join( + dict.fromkeys((*nvidia_libs, *filter(None, inherited))) + ) + for name in _MONARCH_TIMEOUT_ENV: + environ.setdefault(name, "600s") + for name, value in _MONARCH_SHUTDOWN_ENV.items(): + environ.setdefault(name, value) + # INFO launch records include the inherited environment and may expose secrets. + environ.setdefault("MONARCH_FILE_LOG", "warn") + + +def _stabilize_child_stdio() -> None: + fds = (sys.stdout.fileno(), sys.stderr.fileno()) + poller = select.poll() + for fd in fds: + poller.register(fd, select.POLLOUT) + if not any(flags & _BROKEN_OUTPUT_FLAGS for _, flags in poller.poll(0)): + return + log_dir = Path( + os.environ.get("ART_MONARCH_CHILD_LOG_DIR") or "/tmp/art-monarch-child-logs" + ) + log_dir.mkdir(mode=0o700, parents=True, exist_ok=True) + with (log_dir / f"{socket.gethostname()}-{os.getpid()}.log").open("ab") as log: + for fd in fds: + os.dup2(log.fileno(), fd) + + +def activate_child_virtualenv() -> None: + """Restore venv identity lost when Monarch resolves the Python executable.""" + + _stabilize_child_stdio() + if virtual_env := os.environ.get("ART_VIRTUAL_ENV"): + sys.prefix = sys.exec_prefix = virtual_env + + +def activate_trainer_child_virtualenv() -> None: + threads = os.environ.get("MKL_NUM_THREADS", os.environ.get("OMP_NUM_THREADS", "1")) + for name in ("OMP_NUM_THREADS", "MKL_NUM_THREADS", "OPENBLAS_NUM_THREADS"): + os.environ.setdefault(name, threads) + activate_child_virtualenv() + + +def activate_cuda_device(gpu_id: int | str) -> int: + """Bind a clean trainer process to one physical ordinal or CUDA UUID.""" + + if isinstance(gpu_id, str): + os.environ["CUDA_VISIBLE_DEVICES"] = gpu_id + return 0 + if "CUDA_VISIBLE_DEVICES" in os.environ: + raise RuntimeError( + "physical GPU placement requires an unmasked Monarch worker process" + ) + return gpu_id + + +def activate_cpu_child_virtualenv() -> None: + os.environ["CUDA_VISIBLE_DEVICES"] = "" + activate_child_virtualenv() + + +async def _deployment_rollout( + _model: Any, scenario: int, _config: Any +) -> tuple[int, str, int]: + return scenario, socket.gethostname(), os.getpid() + + +async def deployment_smoke(hosts: Any) -> None: + """Admit every host and execute one installed CPU rollout per node.""" + + from art.model import TrainableModel + + from .art_runtime import ArtRuntime + from .rollout import InstalledAsyncCallable + from .specs import ClusterSpec, HostSpec, RuntimeTopology + + host_count = int(hosts.region.slice().sizes[0]) + host_ids = tuple(f"host{rank}" for rank in range(host_count)) + runtime = await ArtRuntime.start( + hosts, + RuntimeTopology( + cluster=ClusterSpec( + hosts=tuple( + HostSpec( + host_id=host_id, + node_rank=rank, + worker_address=f"attached://{rank}", + cpu_slots=1, + ) + for rank, host_id in enumerate(host_ids) + ), + controller_host_id=host_ids[0], + ), + rollout_host_ids=host_ids, + ), + ) + try: + executor = runtime.rollout_executor( + InstalledAsyncCallable.from_callable(_deployment_rollout), + target_workers=host_count, + ) + executor.set_workers(tuple(range(host_count))) + model = TrainableModel( + name="bootstrap-smoke", + run_name="bootstrap-smoke", + project="art", + base_model="none", + ) + results = await asyncio.gather( + *( + executor.run(worker, _deployment_rollout, model, worker, None) + for worker in range(host_count) + ) + ) + if len({hostname for _, hostname, _ in results}) != host_count: + raise RuntimeError( + f"CPU rollout placement did not cover every host: {results}" + ) + print(f"ART admitted {host_count} host(s); CPU rollouts={results}", flush=True) + finally: + await runtime.close() + + +def _owns_tcp_listener(pid: int, port: int) -> bool: + socket_inodes = { + target[8:-1] + for descriptor in Path(f"/proc/{pid}/fd").iterdir() + if (target := os.readlink(descriptor)).startswith("socket:[") + } + for table in ("tcp", "tcp6"): + for line in Path(f"/proc/{pid}/net/{table}").read_text().splitlines()[1:]: + fields = line.split() + if ( + len(fields) > 9 + and fields[3] == "0A" + and int(fields[1].rsplit(":", 1)[1], 16) == port + and fields[9] in socket_inodes + ): + return True + return False + + +def _stop_worker_process(process: subprocess.Popen[bytes]) -> None: + if process.poll() is None: + os.killpg(process.pid, signal.SIGTERM) + try: + process.wait(timeout=10) + except subprocess.TimeoutExpired: + os.killpg(process.pid, signal.SIGKILL) + process.wait() + + +def _run_ssh_worker_session( + address: str, + launch_id: str, + startup_timeout_s: float, +) -> None: + port = urlsplit(address).port + if port is None or not _SSH_LAUNCH_ID.fullmatch(launch_id): + raise ValueError("invalid SSH worker launch identity") + worker = subprocess.Popen( + [sys.executable, "-c", _WORKER_CODE, address, launch_id], + stdin=subprocess.DEVNULL, + stdout=sys.stderr, + stderr=sys.stderr, + start_new_session=True, + ) + + def terminate(_signum: int, _frame: Any) -> None: + raise SystemExit + + previous = { + signum: signal.signal(signum, terminate) + for signum in (signal.SIGTERM, signal.SIGHUP) + } + try: + deadline = time.monotonic() + startup_timeout_s + while time.monotonic() < deadline and worker.poll() is None: + try: + if _owns_tcp_listener(worker.pid, port): + print((_SSH_READY_PREFIX + launch_id.encode()).decode(), flush=True) + break + except FileNotFoundError: + pass + time.sleep(0.05) + else: + if worker.poll() is not None: + raise RuntimeError( + f"Monarch worker exited {worker.returncode} before ready" + ) + raise TimeoutError(f"Monarch worker did not own {address} in time") + while worker.poll() is None: + readable, _, _ = select.select((sys.stdin,), (), (), 0.1) + if readable and not os.read(sys.stdin.fileno(), 1): + return + raise RuntimeError(f"Monarch worker exited {worker.returncode}") + finally: + _stop_worker_process(worker) + for signum, handler in previous.items(): + signal.signal(signum, handler) + + +def run_worker( + address: str, + *, + launch_id: str | None = None, + startup_timeout_s: float = DEFAULT_STARTUP_TIMEOUT_S, +) -> None: + """Run a pinned Monarch worker on a trusted private network. + + ART's trust-all mode must never be exposed to an untrusted or public network. + """ + + _prepare_child_environment(worker=True) + if launch_id is not None: + _run_ssh_worker_session(address, launch_id, startup_timeout_s) + return + # Importing ``art`` initializes enough third-party state to break Monarch's + # spawned interpreter bootstrap. Replace this process with a clean worker. + os.execv(sys.executable, [sys.executable, "-c", _WORKER_CODE, address]) + + +async def attach_controller( + worker_addresses: Sequence[str], + *, + name: str = "art", + startup_timeout_s: float | None = None, + owned_workers: Sequence[_WorkerSession] = (), +) -> Any: + """Attach a controller to already-started workers on a trusted network.""" + + _prepare_child_environment() + from monarch.actor import ( # ty: ignore[unresolved-import] + attach_to_workers, + enable_transport, + ) + + enable_transport("tcp") + hosts = attach_to_workers( + workers=list(worker_addresses), + ca="trust_all_connections", + name=monarch_identifier(name), + ) + initialized = asyncio.ensure_future(hosts.initialized) + deadline = ( + None + if startup_timeout_s is None + else asyncio.get_running_loop().time() + startup_timeout_s + ) + try: + while not initialized.done(): + exited = [worker for worker in owned_workers if not worker.is_alive()] + if exited: + raise RuntimeError( + "owned Monarch worker exited during attach: " + + ", ".join( + f"{worker.address} code={worker.exitcode}" for worker in exited + ) + ) + timeout = 0.05 + if deadline is not None: + timeout = min( + timeout, max(0.0, deadline - asyncio.get_running_loop().time()) + ) + if timeout == 0: + raise TimeoutError("timed out attaching to Monarch workers") + await asyncio.wait((initialized,), timeout=timeout) + await initialized + exited = [worker for worker in owned_workers if not worker.is_alive()] + if exited: + raise RuntimeError("owned Monarch worker exited as attach completed") + except BaseException as startup_error: + initialized.cancel() + try: + await asyncio.wait_for( + hosts.shutdown(), + startup_timeout_s or DEFAULT_STARTUP_TIMEOUT_S, + ) + except BaseException as cleanup_error: + raise BaseExceptionGroup( + "Monarch attach and cleanup failed", + [startup_error, cleanup_error], + ) from None + raise + return hosts + + +async def run_explicit_controller( + spec: ExplicitHostBootstrap, + program: "InstalledAsyncCallable", + *, + startup_timeout_s: float | None = None, + owned_workers: Sequence[_WorkerSession] = (), +) -> Any: + hosts = await attach_controller( + spec.worker_addresses, + startup_timeout_s=startup_timeout_s, + owned_workers=owned_workers, + ) + program_task = asyncio.ensure_future(program.resolve()(hosts)) + try: + while not program_task.done(): + exited = [worker for worker in owned_workers if not worker.is_alive()] + if exited: + raise RuntimeError( + "owned Monarch worker exited during controller program: " + + ", ".join( + f"{worker.address} code={worker.exitcode}" for worker in exited + ) + ) + await asyncio.wait((program_task,), timeout=0.05) + result = await program_task + if any(not worker.is_alive() for worker in owned_workers): + raise RuntimeError("owned Monarch worker exited as program completed") + except BaseException as program_error: + if not program_task.done(): + program_task.cancel() + await asyncio.gather(program_task, return_exceptions=True) + try: + await asyncio.wait_for( + hosts.shutdown(), + startup_timeout_s or DEFAULT_STARTUP_TIMEOUT_S, + ) + except BaseException as cleanup_error: + raise BaseExceptionGroup( + "Monarch program and controller cleanup failed", + [program_error, cleanup_error], + ) from None + raise + await asyncio.wait_for( + hosts.shutdown(), + startup_timeout_s or DEFAULT_STARTUP_TIMEOUT_S, + ) + return result + + +def _require_bindable_worker_address(address: str) -> None: + parsed = urlsplit(address) + assert parsed.hostname is not None and parsed.port is not None + error: OSError | None = None + for family, socktype, proto, _, sockaddr in socket.getaddrinfo( + parsed.hostname, parsed.port, type=socket.SOCK_STREAM + ): + probe = socket.socket(family, socktype, proto) + try: + probe.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) + probe.bind(sockaddr) + return + except OSError as exc: + error = exc + finally: + probe.close() + raise RuntimeError( + f"Monarch worker address is already in use: {address}" + ) from error + + +def _resolve_ephemeral_worker_address(address: str) -> str: + parsed = urlsplit(address) + if parsed.port != 0: + return address + assert parsed.hostname is not None + error: OSError | None = None + for family, socktype, proto, _, sockaddr in socket.getaddrinfo( + parsed.hostname, 0, type=socket.SOCK_STREAM + ): + probe = socket.socket(family, socktype, proto) + try: + probe.bind(sockaddr) + candidate = _tcp_address(parsed.hostname, probe.getsockname()[1]) + if candidate not in _USED_WORKER_ADDRESSES: + return candidate + except OSError as exc: + error = exc + finally: + probe.close() + raise RuntimeError("could not allocate a fresh local worker address") from error + + +def _worker_lock_path(address: str) -> Path: + digest = hashlib.sha256(address.encode()).hexdigest()[:16] + return _WORKER_LOCK_ROOT / f"art-monarch-worker-{digest}.lock" + + +def _process_identity(pid: int) -> tuple[int, int, int, str] | None: + try: + stat = (Path("/proc") / str(pid) / "stat").read_text() + except OSError: + return None + fields = stat.rsplit(")", 1)[1].split() + return int(fields[19]), int(fields[1]), int(fields[3]), fields[0] + + +def _process_command(pid: int) -> tuple[str, ...] | None: + try: + command = (Path("/proc") / str(pid) / "cmdline").read_bytes() + except OSError: + return None + return tuple(os.fsdecode(value) for value in command.rstrip(b"\0").split(b"\0")) + + +def _write_owned_worker_metadata( + lease: Any, + address: str, + process: subprocess.Popen[bytes], + ownership_token: str, +) -> None: + controller = _process_identity(os.getpid()) + worker = _process_identity(process.pid) + if controller is None or worker is None: + raise RuntimeError("owned Monarch worker process identity disappeared") + metadata = _OwnedWorkerMetadata( + address=address, + controller_pid=os.getpid(), + controller_start_time=controller[0], + worker_pid=process.pid, + worker_start_time=worker[0], + python_executable=os.path.realpath(sys.executable), + worker_code_sha256=hashlib.sha256(_WORKER_CODE.encode()).hexdigest(), + ownership_token=ownership_token, + ) + lease.seek(0) + lease.truncate() + lease.write(metadata.model_dump_json().encode()) + lease.flush() + os.fsync(lease.fileno()) + + +def _metadata_owned_orphan( + metadata: _OwnedWorkerMetadata, lock_path: Path +) -> tuple[int, int] | None: + if _worker_lock_path( + metadata.address + ) != lock_path or metadata.python_executable != os.path.realpath(sys.executable): + return None + worker = _process_identity(metadata.worker_pid) + if worker is None or worker[0] != metadata.worker_start_time or worker[3] == "Z": + return None + controller = _process_identity(metadata.controller_pid) + if controller is not None and controller[0] == metadata.controller_start_time: + return None + command = _process_command(metadata.worker_pid) + if command is None or len(command) != 8: + return None + expected_tail = ( + metadata.address, + "--parent-pid", + str(metadata.controller_pid), + "--ownership-token", + metadata.ownership_token, + ) + if os.path.realpath(command[0]) != metadata.python_executable: + return None + if ( + command[1] != "-c" + or hashlib.sha256(command[2].encode()).hexdigest() + != metadata.worker_code_sha256 + or command[3:] != expected_tail + or worker[2] != metadata.worker_pid + ): + return None + return metadata.worker_pid, metadata.worker_start_time + + +def _legacy_owned_orphans() -> dict[Path, tuple[int, int]]: + matches: dict[Path, tuple[int, int]] = {} + for process_dir in Path("/proc").iterdir(): + if not process_dir.name.isdigit(): + continue + pid = int(process_dir.name) + identity = _process_identity(pid) + command = _process_command(pid) + if identity is None or command is None or len(command) != 4: + continue + start_time, parent_pid, session_id, state = identity + if parent_pid != 1 or session_id != pid or state == "Z": + continue + if os.path.realpath(command[0]) != os.path.realpath(sys.executable): + continue + if command[1:3] != ("-c", _LEGACY_OWNED_WORKER_CODE): + continue + address = command[3] + parsed = urlsplit(address) + try: + loopback = ( + parsed.hostname is not None + and ipaddress.ip_address(parsed.hostname).is_loopback + ) + except ValueError: + loopback = False + if not loopback or parsed.port in (None, 0): + continue + lock_path = _worker_lock_path(address) + if lock_path in matches: + raise RuntimeError(f"multiple legacy workers match owned lease {lock_path}") + matches[lock_path] = (pid, start_time) + return matches + + +def _terminate_owned_orphan(pid: int, start_time: int) -> None: + try: + pidfd = os.pidfd_open(pid) + except ProcessLookupError: + return + try: + identity = _process_identity(pid) + if identity is None or identity[0] != start_time or identity[3] == "Z": + return + signal.pidfd_send_signal(pidfd, signal.SIGKILL) + exited = select.poll() + exited.register(pidfd, select.POLLIN) + if not exited.poll(5000): + raise RuntimeError(f"owned Monarch worker {pid} did not exit") + finally: + os.close(pidfd) + + +def _reconcile_orphaned_workers() -> None: + legacy_owned: dict[Path, tuple[int, int]] | None = None + for lock_path in sorted(_WORKER_LOCK_ROOT.glob("art-monarch-worker-*.lock")): + try: + lease = open(lock_path, "r+b") + except FileNotFoundError: + continue + try: + try: + fcntl.flock(lease.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB) + except BlockingIOError: + continue + payload = lease.read().strip() + owned: tuple[int, int] | None + if payload: + try: + metadata = _OwnedWorkerMetadata.model_validate_json(payload) + except ValueError: + continue + if _worker_lock_path( + metadata.address + ) != lock_path or metadata.python_executable != os.path.realpath( + sys.executable + ): + continue + identity = _process_identity(metadata.worker_pid) + if ( + identity is None + or identity[0] != metadata.worker_start_time + or identity[3] == "Z" + ): + lock_path.unlink(missing_ok=True) + continue + owned = _metadata_owned_orphan(metadata, lock_path) + else: + if legacy_owned is None: + legacy_owned = _legacy_owned_orphans() + owned = legacy_owned.get(lock_path) + if owned is None: + continue + _terminate_owned_orphan(*owned) + lock_path.unlink(missing_ok=True) + finally: + lease.close() + + +def _wait_for_worker_listener( + process: subprocess.Popen[bytes], address: str, timeout_s: float +) -> None: + port = urlsplit(address).port + assert port is not None + deadline = time.monotonic() + timeout_s + while True: + if (exitcode := process.poll()) is not None: + raise RuntimeError( + f"Monarch worker exited {exitcode} before listening on {address}" + ) + try: + if _owns_tcp_listener(process.pid, port): + return + except FileNotFoundError: + pass + if time.monotonic() >= deadline: + raise TimeoutError(f"Monarch worker did not listen on {address} in time") + time.sleep(0.05) + + +def _start_worker( + address: str, *, startup_timeout_s: float = DEFAULT_STARTUP_TIMEOUT_S +) -> _WorkerSession: + with _WORKER_ADDRESS_LOCK: + _reconcile_orphaned_workers() + address = _resolve_ephemeral_worker_address(address) + lease = open(_worker_lock_path(address), "a+b") + process: subprocess.Popen[bytes] | None = None + ownership_token = uuid.uuid4().hex + try: + fcntl.flock(lease.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB) + if address in _USED_WORKER_ADDRESSES: + raise RuntimeError( + "Monarch 0.5 requires a fresh owned-worker address per " + f"generation; use port 0 instead of reusing {address}" + ) + _require_bindable_worker_address(address) + environment = os.environ.copy() + _prepare_child_environment(worker=True, environ=environment) + process = subprocess.Popen( + [ + sys.executable, + "-c", + _WORKER_CODE, + address, + "--parent-pid", + str(os.getpid()), + "--ownership-token", + ownership_token, + ], + env=environment, + stdin=subprocess.DEVNULL, + start_new_session=True, + ) + _wait_for_worker_listener(process, address, startup_timeout_s) + _write_owned_worker_metadata(lease, address, process, ownership_token) + _USED_WORKER_ADDRESSES.add(address) + return _WorkerSession( + address=address, + process=process, + label=f"Monarch worker {address}", + lease=lease, + ) + except BaseException: + if process is not None: + _stop_worker_process(process) + lease.close() + raise + + +def _stop_worker_sessions(workers: Sequence[_WorkerSession]) -> None: + failures: list[BaseException] = [] + for worker in workers: + try: + worker.release() + except BaseException as error: + failures.append(error) + for worker in workers: + try: + worker.wait() + except BaseException as error: + failures.append(error) + if failures: + raise BaseExceptionGroup("Monarch worker cleanup failed", failures) + + +def _stop_worker(worker: _WorkerSession) -> None: + _stop_worker_sessions((worker,)) + with _WORKER_ADDRESS_LOCK: + _reconcile_orphaned_workers() + + +def run_local( + program: "InstalledAsyncCallable", + *, + port: int = DEFAULT_MONARCH_PORT, + startup_timeout_s: float = DEFAULT_STARTUP_TIMEOUT_S, +) -> None: + """Own one clean loopback worker and controller for one local program.""" + + address = require_local_worker_address((_tcp_address("127.0.0.1", port),)) + worker = _start_worker(address, startup_timeout_s=startup_timeout_s) + try: + asyncio.run( + run_explicit_controller( + ExplicitHostBootstrap(worker_addresses=(worker.address,)), + program, + startup_timeout_s=startup_timeout_s, + owned_workers=(worker,), + ) + ) + except BaseException as program_error: + try: + _stop_worker(worker) + except BaseException as cleanup_error: + raise BaseExceptionGroup( + "local controller and worker cleanup failed", + [program_error, cleanup_error], + ) from None + raise + _stop_worker(worker) + + +def _lifecycle_listener(spec: SkyPilotBootstrap) -> socket.socket: + # Task parents use this channel to leave together independently of worker exit. + family = ( + socket.AF_INET6 + if ipaddress.ip_address(spec.node_ips[0]).version == 6 + else socket.AF_INET + ) + listener = socket.socket(family, socket.SOCK_STREAM) + try: + listener.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) + listener.bind((spec.node_ips[0], spec.lifecycle_port)) + listener.listen(len(spec.node_ips) - 1) + return listener + except BaseException: + listener.close() + raise + + +def _accept_sky_peers( + spec: SkyPilotBootstrap, + listener: socket.socket, + worker: _WorkerSession, + startup_timeout_s: float, +) -> list[socket.socket]: + peers: list[socket.socket] = [] + deadline = time.monotonic() + startup_timeout_s + try: + while len(peers) < len(spec.node_ips) - 1: + if not worker.is_alive(): + raise RuntimeError(f"Monarch worker exited with code {worker.exitcode}") + remaining = deadline - time.monotonic() + if remaining <= 0: + missing = len(spec.node_ips) - 1 - len(peers) + raise TimeoutError(f"timed out waiting for {missing} SkyPilot rank(s)") + listener.settimeout(min(1.0, remaining)) + try: + connection, _ = listener.accept() + except TimeoutError: + continue + connection.settimeout(None) + peers.append(connection) + return peers + except BaseException: + for connection in peers: + connection.close() + raise + + +def _wait_for_sky_controller( + spec: SkyPilotBootstrap, + worker: _WorkerSession, + startup_timeout_s: float, +) -> None: + deadline = time.monotonic() + startup_timeout_s + last_error: OSError | None = None + while time.monotonic() < deadline: + if not worker.is_alive(): + raise RuntimeError(f"Monarch worker exited with code {worker.exitcode}") + try: + connection = socket.create_connection( + (spec.node_ips[0], spec.lifecycle_port), timeout=1 + ) + break + except OSError as error: + last_error = error + time.sleep(0.2) + else: + raise TimeoutError("timed out connecting to SkyPilot rank 0") from last_error + with connection: + connection.settimeout(None) + status = connection.recv(1) + if status != b"\x00": + detail = "failed" if status == b"\x01" else "disconnected" + raise RuntimeError(f"SkyPilot rank-0 ART controller {detail}") + + +def _notify_sky_peers(peers: Sequence[socket.socket], success: bool) -> None: + status = b"\x00" if success else b"\x01" + for connection in peers: + try: + connection.sendall(status) + except OSError: + pass + finally: + connection.close() + + +def run_skypilot( + program_module: str, + program_qualname: str, + *, + port: int = DEFAULT_MONARCH_PORT, + startup_timeout_s: float = DEFAULT_STARTUP_TIMEOUT_S, +) -> None: + """Translate SkyPilot topology and own one worker process per task rank.""" + + spec = SkyPilotBootstrap.from_environ(port=port) + worker = _start_worker( + spec.worker_addresses[spec.node_rank], startup_timeout_s=startup_timeout_s + ) + if spec.node_rank != 0: + try: + _wait_for_sky_controller(spec, worker, startup_timeout_s) + finally: + _stop_worker(worker) + return + + peers: list[socket.socket] = [] + listener: socket.socket | None = None + success = False + try: + from .rollout import InstalledAsyncCallable + + program = InstalledAsyncCallable( + module=program_module, + qualname=program_qualname, + ) + if len(spec.node_ips) > 1: + listener = _lifecycle_listener(spec) + peers = _accept_sky_peers(spec, listener, worker, startup_timeout_s) + asyncio.run( + run_explicit_controller( + ExplicitHostBootstrap(worker_addresses=spec.worker_addresses), + program, + startup_timeout_s=startup_timeout_s, + owned_workers=(worker,), + ) + ) + success = True + finally: + _notify_sky_peers(peers, success) + if listener is not None: + listener.close() + _stop_worker(worker) + + +def _require_unused_ssh_addresses(spec: SshBootstrap) -> None: + for host in spec.hosts: + try: + connection = socket.create_connection( + (host.worker_host.strip("[]"), spec.port), + timeout=0.2, + ) + except OSError: + continue + connection.close() + raise RuntimeError( + "refusing to reuse a pre-existing Monarch worker listener at " + f"{host.worker_host}:{spec.port}" + ) + + +def _start_ssh_workers( + spec: SshBootstrap, + startup_timeout_s: float, +) -> list[_WorkerSession]: + workers: list[_WorkerSession] = [] + environment = os.environ.copy() + environment.pop("ART_VIRTUAL_ENV", None) + environment.pop("PYTHONPATH", None) + try: + for host, address in zip(spec.hosts, spec.worker_addresses, strict=True): + launch_id = uuid.uuid4().hex + command = "exec " + shlex.join( + ( + spec.python_executable, + "-m", + "art.distributed.monarch_bootstrap", + "worker", + "--address", + address, + "--launch-id", + launch_id, + "--startup-timeout", + str(startup_timeout_s), + ) + ) + workers.append( + _WorkerSession( + address=address, + process=subprocess.Popen( + ( + "ssh", + "-o", + "BatchMode=yes", + *spec.ssh_args, + host.target, + command, + ), + stdin=subprocess.PIPE, + stdout=subprocess.PIPE, + env=environment, + ), + label=f"SSH worker {host.target!r}", + graceful=True, + launch_id=launch_id, + ) + ) + return workers + except BaseException as startup_error: + try: + _stop_ssh_workers(spec, workers) + except BaseException as cleanup_error: + raise BaseExceptionGroup( + "SSH worker startup and cleanup failed", + [startup_error, cleanup_error], + ) from None + raise + + +def _wait_for_ssh_workers( + spec: SshBootstrap, workers: Sequence[_WorkerSession], timeout_s: float +) -> None: + pending = { + host.target: (host, worker) + for host, worker in zip(spec.hosts, workers, strict=True) + } + streams = {} + for target, (_, worker) in pending.items(): + assert worker.process.stdout is not None and worker.launch_id is not None + streams[worker.process.stdout] = target + ready: set[str] = set() + deadline = time.monotonic() + timeout_s + while pending: + for target, (_, worker) in tuple(pending.items()): + if (code := worker.exitcode) is not None: + raise RuntimeError(f"SSH worker {target!r} exited {code} before ready") + wait = max(0.0, min(0.05, deadline - time.monotonic())) + readable, _, _ = select.select(tuple(streams), (), (), wait) + for stream in readable: + target = streams.pop(stream) + _, worker = pending[target] + assert worker.launch_id is not None + expected = _SSH_READY_PREFIX + worker.launch_id.encode() + b"\n" + if stream.readline() != expected: + raise RuntimeError( + f"SSH worker {target!r} did not prove launch identity" + ) + ready.add(target) + for target in tuple(ready): + host, _ = pending[target] + try: + with socket.create_connection( + (host.worker_host.strip("[]"), spec.port), + timeout=0.2, + ): + pending.pop(target) + ready.remove(target) + except OSError: + pass + if time.monotonic() >= deadline: + raise TimeoutError(f"timed out waiting for SSH workers {tuple(pending)}") + + +def _stop_ssh_workers( + _spec: SshBootstrap, + workers: Sequence[_WorkerSession], +) -> None: + _stop_worker_sessions(workers) + + +@contextmanager +def _ssh_termination_signals() -> Iterator[Callable[[], None]]: + received = False + + def terminate(signum: int, _frame: Any) -> None: + nonlocal received + if not received: + received = True + raise SystemExit(128 + signum) + + managed = (signal.SIGTERM, signal.SIGHUP) + previous = {signum: signal.signal(signum, terminate) for signum in managed} + + def ignore() -> None: + for signum in managed: + signal.signal(signum, signal.SIG_IGN) + + try: + yield ignore + finally: + for signum, handler in previous.items(): + signal.signal(signum, handler) + + +def run_ssh( + spec: SshBootstrap, + program: "InstalledAsyncCallable", + *, + startup_timeout_s: float = DEFAULT_STARTUP_TIMEOUT_S, +) -> None: + """Start workers on passwordless SSH hosts and own them for one ART run.""" + + with _ssh_termination_signals() as ignore_termination: + _require_unused_ssh_addresses(spec) + workers = _start_ssh_workers(spec, startup_timeout_s) + try: + _wait_for_ssh_workers(spec, workers, startup_timeout_s) + asyncio.run( + run_explicit_controller( + ExplicitHostBootstrap(worker_addresses=spec.worker_addresses), + program, + startup_timeout_s=startup_timeout_s, + owned_workers=workers, + ) + ) + except BaseException as program_error: + ignore_termination() + try: + _stop_ssh_workers(spec, workers) + except BaseException as cleanup_error: + raise BaseExceptionGroup( + "SSH controller and worker cleanup failed", + [program_error, cleanup_error], + ) from None + raise + ignore_termination() + _stop_ssh_workers(spec, workers) + + +def main(argv: Sequence[str] | None = None) -> None: + parser = argparse.ArgumentParser( + description="ART Monarch bootstrap (trusted private networks only)", + epilog=( + "ART uses Monarch trust-all transport; never expose worker addresses " + "to a public or untrusted network." + ), + ) + subparsers = parser.add_subparsers(dest="command", required=True) + worker = subparsers.add_parser("worker") + worker.add_argument("--address", required=True) + worker.add_argument("--launch-id") + worker.add_argument( + "--startup-timeout", type=_positive_float, default=DEFAULT_STARTUP_TIMEOUT_S + ) + controller = subparsers.add_parser( + "controller", help="attach to worker commands managed by the caller" + ) + controller.add_argument("--worker", action="append", required=True) + controller.add_argument("--program", required=True, help="module:qualname") + controller.add_argument( + "--startup-timeout", type=_positive_float, default=DEFAULT_STARTUP_TIMEOUT_S + ) + local = subparsers.add_parser("local", help="own one loopback worker") + local.add_argument("--program", required=True, help="module:qualname") + local.add_argument("--port", type=int, default=DEFAULT_MONARCH_PORT) + local.add_argument( + "--startup-timeout", type=_positive_float, default=DEFAULT_STARTUP_TIMEOUT_S + ) + sky = subparsers.add_parser( + "skypilot", help="consume the nodes in one SkyPilot task" + ) + sky.add_argument("--program", required=True, help="module:qualname") + sky.add_argument("--port", type=int, default=DEFAULT_MONARCH_PORT) + sky.add_argument( + "--startup-timeout", type=_positive_float, default=DEFAULT_STARTUP_TIMEOUT_S + ) + ssh = subparsers.add_parser( + "ssh", help="start and own workers on preallocated SSH hosts" + ) + ssh.add_argument( + "--host", + action="append", + required=True, + help="[USER@]SSH_TARGET[=WORKER_HOST]", + ) + ssh.add_argument("--program", required=True, help="module:qualname") + ssh.add_argument("--python", default=sys.executable, dest="python_executable") + ssh.add_argument("--port", type=int, default=DEFAULT_MONARCH_PORT) + ssh.add_argument( + "--ssh-arg", + action="append", + default=[], + help="argument passed to ssh; use --ssh-arg=VALUE", + ) + ssh.add_argument( + "--startup-timeout", type=_positive_float, default=DEFAULT_STARTUP_TIMEOUT_S + ) + args = parser.parse_args(argv) + if args.command == "worker": + run_worker( + args.address, + launch_id=args.launch_id, + startup_timeout_s=args.startup_timeout, + ) + return + module, separator, qualname = args.program.partition(":") + if not module or not separator or not qualname: + parser.error("--program must use module:qualname") + if args.command == "skypilot": + run_skypilot( + module, + qualname, + port=args.port, + startup_timeout_s=args.startup_timeout, + ) + return + from .rollout import InstalledAsyncCallable + + program = InstalledAsyncCallable(module=module, qualname=qualname) + if args.command == "local": + run_local( + program, + port=args.port, + startup_timeout_s=args.startup_timeout, + ) + elif args.command == "ssh": + run_ssh( + SshBootstrap( + hosts=tuple(_parse_ssh_host(host) for host in args.host), + python_executable=args.python_executable, + port=args.port, + ssh_args=tuple(args.ssh_arg), + ), + program, + startup_timeout_s=args.startup_timeout, + ) + else: + asyncio.run( + run_explicit_controller( + ExplicitHostBootstrap(worker_addresses=tuple(args.worker)), + program, + startup_timeout_s=args.startup_timeout, + ) + ) + + +if __name__ == "__main__": + main() diff --git a/src/art/distributed/monarch_runtime.py b/src/art/distributed/monarch_runtime.py new file mode 100644 index 000000000..c1f0a0c70 --- /dev/null +++ b/src/art/distributed/monarch_runtime.py @@ -0,0 +1,255 @@ +from __future__ import annotations + +import asyncio +from typing import Any, Literal + +from pydantic import BaseModel, ConfigDict + +from art.trajectories import TrajectoryGroup + +from .adapter_transport import AdapterReceiveResult, AdapterTransferTarget +from .data_plane import PackedBatchRef, PackedBatchTransfer +from .packing import PackingRequest, PackingResult +from .rollout import ( + RolloutInvocation, + RolloutResult, + RolloutWorkerEndpoint, +) +from .trajectory_store import ( + TrajectoryEnqueueResult, + TrajectoryGroupRef, + TrajectoryQueueItem, + TrajectoryQueuePacking, + TrajectoryQueueRelease, + TrajectoryQueueResize, + TrajectoryQueueSnapshot, + TrajectoryQueueTake, +) +from .vllm_replica import HostMemberLaunchRequest, HostMemberState + + +class RemoteCallError(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + kind: Literal["cancelled", "capacity", "input", "lease", "serving", "internal"] + error_type: str + message: str + traceback: str + + +class RemoteCallResult(BaseModel): + model_config = ConfigDict(arbitrary_types_allowed=True, extra="forbid", frozen=True) + + value: Any = None + error: RemoteCallError | None = None + + +def unwrap_remote_call(result: RemoteCallResult) -> Any: + if result.error is None: + return result.value + error = result.error + message = f"remote {error.error_type}: {error.message}\n{error.traceback}" + if error.kind == "cancelled": + raise asyncio.CancelledError(message) + if error.kind == "serving": + from art.errors import LocalServingUnavailableError + + raise LocalServingUnavailableError(message) + if error.kind == "capacity": + from .data_plane import PackedBatchCapacityError + + raise PackedBatchCapacityError(message) + if error.kind == "lease": + from .data_plane import PackedBatchLeaseError + + raise PackedBatchLeaseError(message) + if error.kind == "input": + raise ValueError(message) + raise RuntimeError(message) + + +async def call_remote(endpoint: Any, *args: Any) -> Any: + return unwrap_remote_call(await endpoint.call_one(*args)) + + +class MonarchRolloutWorkerEndpoint(RolloutWorkerEndpoint): + def __init__( + self, actor: Any, *, timeout_s: float, owns_actor: bool = False + ) -> None: + self.actor = actor + self.timeout_s = timeout_s + self.owns_actor = owns_actor + + async def run(self, invocation: RolloutInvocation) -> RolloutResult: + await self.actor.initialized + return await call_remote(self.actor.run, invocation) + + async def materialize(self, ref: TrajectoryGroupRef) -> TrajectoryGroup: + transfer = ref.transfer + if transfer is None: + raise RuntimeError("remote trajectory has no data-plane transfer") + if transfer.stream.stream_id != ref.result_id: + raise RuntimeError("trajectory owner returned the wrong result ID") + if transfer.stream.byte_count != ref.descriptor.byte_count: + raise RuntimeError("trajectory owner returned the wrong byte count") + groups = await transfer.receive_groups(timeout_s=self.timeout_s) + if len(groups) != 1: + raise RuntimeError("trajectory owner returned the wrong group count") + return groups[0] + + async def drop(self, ref: TrajectoryGroupRef) -> None: + await call_remote(self.actor.drop_result, ref) + + async def close(self) -> None: + if self.owns_actor: + await call_remote(self.actor.close) + + +class MonarchTrajectoryQueueEndpoint: + def __init__(self, actor: Any) -> None: + self.actor = actor + + async def create( + self, + queue_id: str, + max_ready_groups: int, + capacity_records: int, + capacity_bytes: int, + ) -> None: + await call_remote( + self.actor.create_trajectory_queue, + queue_id, + max_ready_groups, + capacity_records, + capacity_bytes, + ) + + async def enqueue( + self, queue_id: str, item: TrajectoryQueueItem + ) -> TrajectoryEnqueueResult: + return await call_remote(self.actor.enqueue_trajectory, queue_id, item) + + async def resize(self, operation: TrajectoryQueueResize) -> None: + await call_remote(self.actor.resize_trajectory_queue, operation) + + async def take( + self, queue_id: str, consumer_id: str, count: int + ) -> TrajectoryQueueTake: + return await call_remote( + self.actor.take_trajectory, queue_id, consumer_id, count + ) + + async def mark_packed(self, operation: TrajectoryQueuePacking) -> None: + await call_remote(self.actor.mark_trajectories_packed, operation) + + async def release(self, operation: TrajectoryQueueRelease) -> None: + await call_remote(self.actor.release_trajectory, operation) + + async def finish(self, queue_id: str) -> None: + await call_remote(self.actor.finish_trajectory_queue, queue_id) + + async def snapshot(self, queue_id: str) -> TrajectoryQueueSnapshot: + return await call_remote(self.actor.trajectory_queue_snapshot, queue_id) + + async def close(self, queue_id: str) -> tuple[TrajectoryGroupRef, ...]: + return await call_remote(self.actor.close_trajectory_queue, queue_id) + + +class MonarchVllmHostLauncher: + def __init__(self, actor: Any, adapter_actor: Any) -> None: + self.actor = actor + self.adapter_actor = adapter_actor + + async def start_member(self, request: HostMemberLaunchRequest) -> HostMemberState: + return await call_remote(self.actor.start_vllm_member, request) + + async def member_state( + self, replica_id: str, member_id: str, generation: int + ) -> HostMemberState: + return await call_remote( + self.actor.vllm_member_state, replica_id, member_id, generation + ) + + async def stop_member( + self, replica_id: str, member_id: str, generation: int + ) -> None: + await call_remote( + self.actor.stop_vllm_member, replica_id, member_id, generation + ) + + async def prepare_adapter_receive( + self, + generation_id: str, + template_path: str, + timeout_s: float, + transport: Literal["local", "nixl"], + ) -> AdapterTransferTarget: + return await call_remote( + self.adapter_actor.prepare, + generation_id, + template_path, + timeout_s, + transport, + ) + + async def wait_adapter_receive( + self, generation_id: str, timeout_s: float + ) -> AdapterReceiveResult: + deadline = asyncio.get_running_loop().time() + timeout_s + while True: + result = await call_remote(self.adapter_actor.poll, generation_id) + if result is not None: + return result + remaining_s = deadline - asyncio.get_running_loop().time() + if remaining_s <= 0: + raise TimeoutError(f"Adapter transfer timed out: {generation_id}") + await asyncio.sleep(min(0.01, remaining_s)) + + async def release_adapter_receive(self, generation_id: str) -> None: + await call_remote(self.adapter_actor.release, generation_id) + + +class MonarchPackedBatchInbox: + def __init__(self, actor: Any) -> None: + self.actor = actor + + async def receive( + self, ref: PackedBatchRef, transfer: PackedBatchTransfer, *, timeout_s: float + ) -> PackedBatchRef: + return await call_remote(self.actor.receive_batch, ref, transfer, timeout_s) + + async def drop(self, ref: PackedBatchRef) -> None: + await call_remote(self.actor.drop_batch_ref, ref) + + async def reclaim(self, batch_id: str, *, fence: bool) -> bool: + return await call_remote(self.actor.reclaim_batch, batch_id, fence) + + +class MonarchPackedBatchSource: + def __init__(self, actor: Any) -> None: + self.actor = actor + + async def publish(self, ref: PackedBatchRef) -> PackedBatchTransfer: + return await call_remote(self.actor.publish_batch, ref) + + async def drop(self, batch_id: str) -> None: + await call_remote(self.actor.drop_batch, batch_id) + + async def note_transmitted(self, byte_count: int) -> None: + await call_remote(self.actor.note_batch_transmitted, byte_count) + + +class MonarchPackingEndpoint: + def __init__(self, actor: Any) -> None: + self.actor = actor + + async def pack( + self, + request: PackingRequest, + batch_id: str, + *, + transfer_timeout_s: float, + ) -> PackingResult: + return await call_remote( + self.actor.pack_batch, request, batch_id, transfer_timeout_s + ) diff --git a/src/art/distributed/nccl_preflight.py b/src/art/distributed/nccl_preflight.py new file mode 100644 index 000000000..d673f8fd0 --- /dev/null +++ b/src/art/distributed/nccl_preflight.py @@ -0,0 +1,324 @@ +from __future__ import annotations + +import asyncio +import os +from pathlib import Path +import re +import signal +import sys +import tempfile +import time +from typing import Literal +import uuid + +from pydantic import BaseModel, ConfigDict, Field, model_validator + +from art.utils.lifecycle import complete_task + +from .specs import GpuId + + +class _NcclRuntimeRequest(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + probe_id: str = Field(min_length=1) + runtime_kind: Literal["trainer", "vllm"] + master_addr: str = Field(min_length=1) + timeout_s: float = Field(gt=0) + + +class NcclPreflightSessionRequest(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + probe_id: str = Field(min_length=1) + lease_s: float = Field(gt=0) + + +class NcclRendezvousRequest(_NcclRuntimeRequest): + pass + + +class NcclRendezvousResult(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + host_id: str + port: int = Field(ge=1, le=65535) + + +class NcclProbeRequest(_NcclRuntimeRequest): + rank: int = Field(ge=0) + world_size: int = Field(ge=2) + master_port: int = Field(ge=1, le=65535) + gpu_id: GpuId + net_name: str = Field(min_length=1) + + @model_validator(mode="after") + def _validate_rank(self) -> "NcclProbeRequest": + if self.rank >= self.world_size: + raise ValueError("NCCL probe rank must be smaller than world_size") + return self + + +class NcclProbeResult(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + host_id: str + rank: int = Field(ge=0) + net_name: str + duration_s: float = Field(ge=0) + + +_PARENT_DEATH = r""" +import ctypes +import os +import signal + +parent = os.getppid() +libc = ctypes.CDLL(None, use_errno=True) +if libc.prctl(1, signal.SIGTERM) != 0: + raise OSError(ctypes.get_errno(), "prctl(PR_SET_PDEATHSIG) failed") +if os.getppid() != parent: + os.kill(os.getpid(), signal.SIGTERM) +""" + +_RENDEZVOUS_SCRIPT = ( + _PARENT_DEATH + + r""" +from datetime import timedelta +import select +import sys +import time + +import torch.distributed as dist + +store = dist.TCPStore( + os.environ["MASTER_ADDR"], + 0, + None, + True, + timedelta(seconds=float(os.environ["ART_NCCL_TIMEOUT_S"])), + wait_for_workers=False, +) +print(f"ART_NCCL_RENDEZVOUS_PORT={store.port}", flush=True) +remaining = max(0.0, float(os.environ["ART_NCCL_DEADLINE_S"]) - time.monotonic()) +select.select([sys.stdin.buffer], [], [], remaining) +""" +) + +_CHILD_SCRIPT = ( + _PARENT_DEATH + + r""" +from datetime import timedelta + +import torch +import torch.distributed as dist + +rank = int(os.environ["RANK"]) +world_size = int(os.environ["WORLD_SIZE"]) +timeout = timedelta(seconds=float(os.environ["ART_NCCL_TIMEOUT_S"])) +device = torch.device("cuda", 0) +torch.cuda.set_device(device) +store = dist.TCPStore( + os.environ["MASTER_ADDR"], + int(os.environ["MASTER_PORT"]), + None, + False, + timeout, +) +options = dist.ProcessGroupNCCL.Options() +options.config.net_name = os.environ["ART_NCCL_EXPECTED_NET"] +try: + dist.init_process_group( + "nccl", + store=store, + rank=rank, + world_size=world_size, + timeout=timeout, + pg_options=options, + device_id=device, + ) + value = torch.tensor(rank + 1, device=device, dtype=torch.int64) + dist.all_reduce(value) + torch.cuda.synchronize(device) + expected = world_size * (world_size + 1) // 2 + if value.item() != expected: + raise RuntimeError(f"NCCL preflight reduced {value.item()}, expected {expected}") +finally: + if dist.is_initialized(): + dist.destroy_process_group() +""" +) + +_VLLM_EXEC_SCRIPT = r""" +import os +import sys + +from art.vllm_runtime import ( + RUNTIME_SERVER, + _runtime_python_for_nccl_discovery, + _vllm_runtime_subprocess_cwd, + _vllm_runtime_subprocess_env, +) + +try: + python = _runtime_python_for_nccl_discovery() +except RuntimeError as error: + raise RuntimeError( + "Cannot derive the Python environment behind ART_VLLM_RUNTIME_BIN; " + "point it directly to a .venv/bin/art-vllm-runtime-server executable" + ) from error +server = str(python.parent / RUNTIME_SERVER) +environment = _vllm_runtime_subprocess_env([server]) +os.chdir(_vllm_runtime_subprocess_cwd([server])) +os.execve(str(python), [str(python), "-c", sys.argv[1]], environment) +""" + +_RENDEZVOUS_PREFIX = b"ART_NCCL_RENDEZVOUS_PORT=" +_SELECTED_NETWORK = re.compile(r"NCCL INFO Using network ([^\r\n]+)$", re.MULTILINE) + + +class NcclRendezvous: + def __init__(self, process: asyncio.subprocess.Process, port: int) -> None: + self.process = process + self.port = port + + async def close(self) -> None: + await complete_task(asyncio.create_task(_stop_process(self.process))) + + +def parse_selected_network(log: str, expected: str) -> str: + selected = tuple(value.strip() for value in _SELECTED_NETWORK.findall(log)) + if selected != (expected,): + raise RuntimeError( + f"NCCL selected-network proof mismatch: expected={expected!r}, " + f"reported={selected!r}" + ) + return selected[0] + + +async def start_nccl_rendezvous( + request: NcclRendezvousRequest, *, deadline_s: float +) -> NcclRendezvous: + command, environment = _runtime_launch(request, _RENDEZVOUS_SCRIPT) + environment.update( + { + "ART_NCCL_DEADLINE_S": str(deadline_s), + "ART_NCCL_TIMEOUT_S": str(request.timeout_s), + "CUDA_VISIBLE_DEVICES": "", + "MASTER_ADDR": request.master_addr, + } + ) + process = await asyncio.create_subprocess_exec( + *command, + env=environment, + start_new_session=True, + stdin=asyncio.subprocess.PIPE, + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.STDOUT, + ) + try: + async with asyncio.timeout( + min(request.timeout_s, max(0.0, deadline_s - time.monotonic())) + ): + port = await _read_rendezvous_port(process) + return NcclRendezvous(process, port) + except BaseException: + await complete_task(asyncio.create_task(_stop_process(process))) + raise + + +async def run_nccl_probe(host_id: str, request: NcclProbeRequest) -> NcclProbeResult: + command, environment = _runtime_launch(request, _CHILD_SCRIPT) + log_path = Path(tempfile.gettempdir()) / ( + f"art-nccl-{request.probe_id}-{request.rank}-{uuid.uuid4().hex}.log" + ) + environment.update( + { + "ART_NCCL_EXPECTED_NET": request.net_name, + "ART_NCCL_TIMEOUT_S": str(request.timeout_s), + "CUDA_VISIBLE_DEVICES": str(request.gpu_id), + "MASTER_ADDR": request.master_addr, + "MASTER_PORT": str(request.master_port), + "NCCL_DEBUG": "INFO", + "NCCL_DEBUG_FILE": str(log_path), + "NCCL_DEBUG_SUBSYS": "INIT,NET", + "NCCL_NET": request.net_name, + "RANK": str(request.rank), + "WORLD_SIZE": str(request.world_size), + } + ) + started = time.monotonic() + process: asyncio.subprocess.Process | None = None + try: + process = await asyncio.create_subprocess_exec( + *command, + env=environment, + start_new_session=True, + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.STDOUT, + ) + async with asyncio.timeout(request.timeout_s): + output, _ = await process.communicate() + log = log_path.read_text(errors="replace") if log_path.exists() else "" + detail = output.decode(errors="replace")[-4000:] + if process.returncode: + raise RuntimeError( + f"NCCL {request.runtime_kind} preflight rank {request.rank} exited " + f"{process.returncode}:\n{detail}\n{log[-4000:]}" + ) + selected = parse_selected_network(log, request.net_name) + return NcclProbeResult( + host_id=host_id, + rank=request.rank, + net_name=selected, + duration_s=time.monotonic() - started, + ) + except BaseException: + if process is not None: + await complete_task(asyncio.create_task(_stop_process(process))) + raise + finally: + log_path.unlink(missing_ok=True) + + +def _runtime_launch( + request: _NcclRuntimeRequest, script: str +) -> tuple[tuple[str, ...], dict[str, str]]: + if request.runtime_kind == "trainer": + return (_art_python(), "-c", script), os.environ.copy() + return ( + _art_python(), + "-c", + _VLLM_EXEC_SCRIPT, + script, + ), os.environ.copy() + + +def _art_python() -> str: + candidate = Path(os.environ.get("ART_VIRTUAL_ENV", sys.prefix)) / "bin/python" + return str(candidate if candidate.exists() else Path(sys.executable)) + + +async def _read_rendezvous_port(process: asyncio.subprocess.Process) -> int: + assert process.stdout is not None + output = bytearray() + while line := await process.stdout.readline(): + if line.startswith(_RENDEZVOUS_PREFIX): + return int(line.removeprefix(_RENDEZVOUS_PREFIX)) + output.extend(line) + del output[:-4000] + await process.wait() + raise RuntimeError( + f"NCCL rendezvous exited {process.returncode} before binding a port:\n" + f"{output.decode(errors='replace')}" + ) + + +async def _stop_process(process: asyncio.subprocess.Process) -> None: + if process.returncode is not None: + return + try: + os.killpg(process.pid, signal.SIGKILL) + except ProcessLookupError: + return + await process.wait() diff --git a/src/art/distributed/packing.py b/src/art/distributed/packing.py new file mode 100644 index 000000000..1e2eae797 --- /dev/null +++ b/src/art/distributed/packing.py @@ -0,0 +1,336 @@ +from __future__ import annotations + +from collections.abc import Iterable +import secrets +from typing import TYPE_CHECKING, Any, Literal + +import numpy as np +from openai.types.chat.chat_completion import Choice +from pydantic import BaseModel, ConfigDict, Field, model_validator + +from art.pipeline_tuner.config import PackedGroupShape +from art.preprocessing.moe_routing import ( + ART_MOE_ROUTING_METADATA_KEY, + NUM_EXPERTS_KEY, + ROUTED_EXPERTS_KEY, + MoeRouteArray, + moe_route_dtype, +) +from art.trajectories import ( + MetadataValue, + PydanticException, + Trajectory, + TrajectoryGroup, +) + +from .data_plane import PackedBatchRef +from .rollout import RolloutModelSpec +from .trajectory_store import ( + TrajectoryBatchTransfer, + TrajectoryGroupBundle, + TrajectoryQueueItem, +) + +if TYPE_CHECKING: + from art.model import TrainableModel + + +class _ChoiceRoutingPayload(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + metadata: dict[str, Any] + dtype: Literal["uint8", "uint16"] + shape: tuple[int, int, int] + data: bytes + + @classmethod + def from_metadata(cls, metadata: dict[str, Any]) -> "_ChoiceRoutingPayload": + routes = metadata[ROUTED_EXPERTS_KEY] + if not isinstance(routes, np.ndarray) or routes.dtype not in { + np.dtype(np.uint8), + np.dtype(np.uint16), + }: + raise RuntimeError("routed experts must be a uint8 or uint16 array") + if routes.ndim != 3: + raise RuntimeError(f"routed experts must have rank 3, got {routes.shape}") + num_experts = int(metadata.get(NUM_EXPERTS_KEY, 0)) + if routes.dtype != moe_route_dtype(num_experts): + raise RuntimeError("routed experts do not match exact expert count") + dtype: Literal["uint8", "uint16"] = ( + "uint8" if routes.dtype == np.dtype(np.uint8) else "uint16" + ) + return cls( + metadata={ + key: value + for key, value in metadata.items() + if key != ROUTED_EXPERTS_KEY + }, + dtype=dtype, + shape=routes.shape, + data=routes.tobytes(), + ) + + def build(self) -> dict[str, Any]: + num_experts = int(self.metadata[NUM_EXPERTS_KEY]) + routes = MoeRouteArray( + np.frombuffer(self.data, dtype=self.dtype).reshape(self.shape), + num_experts=num_experts, + ) + return {**self.metadata, ROUTED_EXPERTS_KEY: routes} + + +class TrajectoryPayload(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + payload: dict[str, Any] + choice_positions: tuple[int, ...] = () + additional_history_choice_positions: tuple[tuple[int, ...], ...] = () + choice_routing_metadata: dict[int, _ChoiceRoutingPayload] = Field( + default_factory=dict + ) + additional_history_choice_routing_metadata: tuple[ + dict[int, _ChoiceRoutingPayload], ... + ] = () + exchange_choice_routing_metadata: tuple[dict[int, _ChoiceRoutingPayload], ...] = () + + @classmethod + def from_trajectory(cls, trajectory: Trajectory) -> "TrajectoryPayload": + choice_routing = _choice_routing_metadata(trajectory.messages_and_choices) + history_routing = tuple( + _choice_routing_metadata(history.messages_and_choices) + for history in trajectory.additional_histories + ) + exchange_routing = tuple( + _choice_routing_metadata(exchange.response.choices) + for exchange in trajectory.exchanges.chat_completions + ) + exclude: dict[str, Any] = { + "messages_and_choices": _routing_exclude(choice_routing), + "additional_histories": { + index: { + "messages_and_choices": _routing_exclude(routing), + } + for index, routing in enumerate(history_routing) + }, + } + return cls( + payload=trajectory.model_dump(mode="json", exclude=exclude), + choice_positions=tuple( + index + for index, item in enumerate(trajectory.messages_and_choices) + if isinstance(item, Choice) + ), + additional_history_choice_positions=tuple( + tuple( + index + for index, item in enumerate(history.messages_and_choices) + if isinstance(item, Choice) + ) + for history in trajectory.additional_histories + ), + choice_routing_metadata=choice_routing, + additional_history_choice_routing_metadata=history_routing, + exchange_choice_routing_metadata=exchange_routing, + ) + + def build(self) -> Trajectory: + payload = dict(self.payload) + messages = list(payload.get("messages_and_choices", [])) + for index in self.choice_positions: + messages[index] = _build_choice( + messages[index], self.choice_routing_metadata.get(index) + ) + payload["messages_and_choices"] = messages + histories = [ + dict(history) for history in payload.get("additional_histories", []) + ] + for history, positions, routing in zip( + histories, + self.additional_history_choice_positions, + self.additional_history_choice_routing_metadata, + strict=True, + ): + messages = list(history["messages_and_choices"]) + for index in positions: + messages[index] = _build_choice(messages[index], routing.get(index)) + history["messages_and_choices"] = messages + payload["additional_histories"] = histories + exchanges = dict(payload.get("exchanges", {})) + chat_exchanges = [ + dict(exchange) for exchange in exchanges.get("chat_completions", []) + ] + for exchange, routing in zip( + chat_exchanges, + self.exchange_choice_routing_metadata, + strict=True, + ): + response = dict(exchange["response"]) + choices = list(response["choices"]) + for index, metadata in routing.items(): + choices[index] = _build_choice(choices[index], metadata) + response["choices"] = choices + exchange["response"] = response + exchanges["chat_completions"] = chat_exchanges + payload["exchanges"] = exchanges + return Trajectory.model_validate(payload) + + +def _choice_routing_metadata(items: list[Any]) -> dict[int, _ChoiceRoutingPayload]: + return { + index: _ChoiceRoutingPayload.from_metadata(metadata) + for index, item in enumerate(items) + if isinstance(item, Choice) + and isinstance( + metadata := (item.model_extra or {}).get(ART_MOE_ROUTING_METADATA_KEY), + dict, + ) + } + + +def _routing_exclude( + routing: dict[int, _ChoiceRoutingPayload], +) -> dict[int, set[str]]: + return {index: {ART_MOE_ROUTING_METADATA_KEY} for index in routing} + + +def _build_choice(payload: Any, routing: _ChoiceRoutingPayload | None) -> Choice: + choice = Choice.model_validate(payload) + if routing is not None: + if choice.model_extra is None: + raise RuntimeError("OpenAI Choice.model_extra is unavailable") + choice.model_extra[ART_MOE_ROUTING_METADATA_KEY] = routing.build() + return choice + + +class TrajectoryGroupPayload(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + trajectories: tuple[TrajectoryPayload, ...] + exceptions: tuple[dict[str, str], ...] = () + metadata: dict[str, MetadataValue] = Field(default_factory=dict) + metrics: dict[str, float | int | bool] = Field(default_factory=dict) + logs: tuple[str, ...] = () + collect_packing_shape: bool = False + + @classmethod + def from_group(cls, group: TrajectoryGroup) -> "TrajectoryGroupPayload": + return cls( + trajectories=tuple( + TrajectoryPayload.from_trajectory(trajectory) + for trajectory in group.trajectories + ), + exceptions=tuple( + exception.model_dump(mode="json") for exception in group.exceptions + ), + metadata=group.metadata, + metrics=group.metrics, + logs=tuple(group.logs), + collect_packing_shape=group._collect_packing_shape, + ) + + def build(self) -> TrajectoryGroup: + group = TrajectoryGroup( + (payload.build() for payload in self.trajectories), + metadata=self.metadata, + metrics=self.metrics, + logs=list(self.logs), + ) + group.exceptions = [ + PydanticException.model_validate(payload) for payload in self.exceptions + ] + group._collect_packing_shape = self.collect_packing_shape + return group + + +class PackingRequest(BaseModel): + """Current ART packing inputs; generalized loss programs are intentionally absent.""" + + model_config = ConfigDict(extra="forbid", frozen=True, arbitrary_types_allowed=True) + + model: RolloutModelSpec + generation_id: str = Field(min_length=1) + trajectory_groups: tuple[TrajectoryGroupBundle, ...] = () + trajectory_transfer: TrajectoryBatchTransfer | None = None + trajectory_sources: tuple[TrajectoryQueueItem, ...] = () + trajectory_log_path: str | None = None + advantage_balance: float = 0.0 + allow_training_without_logprobs: bool = False + scale_rewards: bool = True + plot_tensors: bool = False + packed_sequence_length: int = Field(ge=1) + logprob_calculation_chunk_size: int = Field(default=1024, ge=1) + include_moe_routing: bool = False + collect_packing_shapes: bool = False + group_ids: tuple[str, ...] = () + record_ids: tuple[str, ...] = () + min_source_version: int = Field(default=0, ge=0) + max_source_version: int = Field(default=0, ge=0) + + @model_validator(mode="after") + def _validate_trajectory_input(self) -> "PackingRequest": + inputs = ( + bool(self.trajectory_groups), + self.trajectory_transfer is not None, + bool(self.trajectory_sources), + ) + if sum(inputs) != 1: + raise ValueError("packing requires exactly one trajectory input") + return self + + @classmethod + def from_groups( + cls, + model: TrainableModel, + trajectory_groups: Iterable[TrajectoryGroup], + *, + packed_sequence_length: int, + advantage_balance: float = 0.0, + allow_training_without_logprobs: bool = False, + scale_rewards: bool = True, + plot_tensors: bool = False, + logprob_calculation_chunk_size: int = 1024, + include_moe_routing: bool = False, + group_ids: tuple[str, ...] = (), + record_ids: tuple[str, ...] = (), + min_source_version: int = 0, + max_source_version: int = 0, + ) -> "PackingRequest": + """Build a serializable packing request from public ART objects.""" + + return cls( + model=RolloutModelSpec.from_model(model), + generation_id=secrets.token_hex(16), + trajectory_groups=tuple( + TrajectoryGroupBundle.from_group(group) for group in trajectory_groups + ), + advantage_balance=advantage_balance, + allow_training_without_logprobs=allow_training_without_logprobs, + scale_rewards=scale_rewards, + plot_tensors=plot_tensors, + packed_sequence_length=packed_sequence_length, + logprob_calculation_chunk_size=logprob_calculation_chunk_size, + include_moe_routing=include_moe_routing, + collect_packing_shapes=any( + group._collect_packing_shape for group in trajectory_groups + ), + group_ids=group_ids, + record_ids=record_ids, + min_source_version=min_source_version, + max_source_version=max_source_version, + ) + + +class PackingResult(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True, arbitrary_types_allowed=True) + + ref: PackedBatchRef | None + packed_group_shapes: tuple[PackedGroupShape | None, ...] + trainable_assistant_tokens: int = Field(default=0, ge=0) + loss_bearing_tokens: int = Field(default=0, ge=0) + non_padding_tokens: int = Field(default=0, ge=0) + trajectory_log_path: str | None = None + trajectory_fetch_s: float = Field(default=0.0, ge=0) + packing_core_s: float = Field(default=0.0, ge=0) + trajectory_log_wait_s: float = Field(default=0.0, ge=0) + packed_batch_finalize_s: float = Field(default=0.0, ge=0) + generation_id: str = Field(min_length=1) diff --git a/src/art/distributed/rollout.py b/src/art/distributed/rollout.py new file mode 100644 index 000000000..d5671261a --- /dev/null +++ b/src/art/distributed/rollout.py @@ -0,0 +1,1126 @@ +from __future__ import annotations + +import asyncio +from collections import deque +from collections.abc import Awaitable, Callable, Mapping, Sequence +from functools import lru_cache +import hashlib +import importlib +import inspect +import json +from pathlib import Path +import time +from typing import Any, Literal, Protocol, cast +import uuid + +from pydantic import BaseModel, ConfigDict, Field, model_validator + +from art.model import TrainableModel +from art.serving_capabilities import ServingCapabilities +from art.trajectories import ( + MetadataValue, + PydanticException, + Trajectory, + TrajectoryGroup, +) + +from .trajectory_store import ( + TrajectoryCapacityError, + TrajectoryEnqueueResult, + TrajectoryGroupAnnotations, + TrajectoryGroupRef, + TrajectoryLeaseError, + TrajectoryQueueItem, + TrajectoryQueueLease, + TrajectoryQueuePacking, + TrajectoryQueueRelease, + TrajectoryQueueResize, + TrajectoryQueueSnapshot, + TrajectoryQueueStore, + TrajectoryQueueTake, + TrajectoryRecordStore, +) + + +class InstalledAsyncCallable(BaseModel): + """Import path for installed user code; functions and closures are never shipped.""" + + model_config = ConfigDict(extra="forbid", frozen=True) + + module: str = Field(min_length=1) + qualname: str = Field(min_length=1) + source_sha256: str | None = Field(default=None, pattern=r"^[0-9a-f]{64}$") + + @model_validator(mode="after") + def _validate_import_path(self) -> "InstalledAsyncCallable": + if self.qualname == "" or "" in self.qualname.split("."): + raise ValueError( + "distributed rollout callable must be a top-level function" + ) + if self.source_sha256 is None: + object.__setattr__( + self, "source_sha256", _callable_source_sha256(self._resolve()) + ) + return self + + @classmethod + def from_callable( + cls, function: Callable[..., Awaitable[Any]] + ) -> "InstalledAsyncCallable": + module = getattr(function, "__module__", None) + qualname = getattr(function, "__qualname__", None) + if not module or not qualname: + raise ValueError( + "distributed rollout callable requires module and qualname" + ) + reference = cls(module=module, qualname=qualname) + if not inspect.iscoroutinefunction(function): + raise TypeError("distributed rollout callable must be async") + if reference.resolve() is not function: + raise ValueError( + "distributed rollout callable must resolve from installed code" + ) + return reference + + def resolve(self) -> Callable[..., Awaitable[Any]]: + assert self.source_sha256 is not None + return _verified_callable(self.module, self.qualname, self.source_sha256) + + def _resolve(self) -> Callable[..., Awaitable[Any]]: + value: Any = importlib.import_module(self.module) + for component in self.qualname.split("."): + value = getattr(value, component) + if not inspect.iscoroutinefunction(value): + raise TypeError(f"{self.module}:{self.qualname} is not an async function") + return value + + +@lru_cache(maxsize=128) +def _verified_callable( + module: str, qualname: str, source_sha256: str +) -> Callable[..., Awaitable[Any]]: + value: Any = importlib.import_module(module) + for component in qualname.split("."): + value = getattr(value, component) + if not inspect.iscoroutinefunction(value): + raise TypeError(f"{module}:{qualname} is not an async function") + if _callable_source_sha256(value) != source_sha256: + raise RuntimeError(f"installed callable source differs for {module}:{qualname}") + return value + + +def _callable_source_sha256(function: Callable[..., Awaitable[Any]]) -> str: + source = inspect.getsourcefile(function) + if source is None: + raise ValueError("distributed callable must come from a source-backed module") + try: + payload = Path(source).read_bytes() + except OSError as error: + raise RuntimeError( + f"cannot read distributed callable source {source}: {error}" + ) from None + return hashlib.sha256(payload).hexdigest() + + +class RolloutModelSpec(BaseModel): + """Serializable inference-only view of a registered trainable model.""" + + model_config = ConfigDict(extra="forbid", frozen=True, arbitrary_types_allowed=True) + + payload: dict[str, Any] + user_config: Any = None + internal_config: dict[str, Any] | None = None + serving_capabilities: ServingCapabilities | None = None + binary_routes_base_url: str | None = None + + @classmethod + def from_model(cls, model: TrainableModel) -> "RolloutModelSpec": + payload = model.model_dump(mode="json") + payload["config"] = None + payload["inference_model_name"] = model.get_inference_name() + return cls( + payload=payload, + user_config=model.config, + internal_config=( + dict(model._internal_config) + if model._internal_config is not None + else None + ), + serving_capabilities=model._serving_capabilities, + binary_routes_base_url=model._art_binary_routes_base_url, + ) + + @property + def cache_key(self) -> str: + payload = { + "model": self.payload, + "internal_config": self.internal_config, + "capabilities": ( + self.serving_capabilities.model_dump(mode="json") + if self.serving_capabilities is not None + else None + ), + "binary_routes_base_url": self.binary_routes_base_url, + } + return hashlib.sha256( + json.dumps(payload, sort_keys=True, separators=(",", ":")).encode() + ).hexdigest() + + def build(self) -> TrainableModel: + model = TrainableModel.model_validate(self.payload) + object.__setattr__(model, "config", self.user_config) + object.__setattr__(model, "_internal_config", self.internal_config) + object.__setattr__(model, "_serving_capabilities", self.serving_capabilities) + object.__setattr__( + model, "_art_binary_routes_base_url", self.binary_routes_base_url + ) + return model + + +class RolloutInvocation(BaseModel): + model_config = ConfigDict(extra="forbid", arbitrary_types_allowed=True) + + callable: InstalledAsyncCallable + model: RolloutModelSpec + scenario: Any + config: Any + store_result: bool = False + + +class RolloutResult(BaseModel): + model_config = ConfigDict(extra="forbid", arbitrary_types_allowed=True) + + value: Any + metrics: dict[str, float] = Field(default_factory=dict) + + +class RolloutExecutor(Protocol): + @property + def max_workers(self) -> int | None: ... + + def set_target(self, target_workers: int) -> None: ... + + def set_workers(self, worker_ids: tuple[int, ...]) -> None: ... + + async def run( + self, + worker_id: int, + rollout_fn: Callable[..., Awaitable[Any]], + model: Any, + scenario: Any, + config: Any, + ) -> Any: ... + + +class LocalRolloutExecutor: + max_workers: int | None = None + + def __init__( + self, + *, + trajectory_capacity_records: int = 16_384, + trajectory_capacity_bytes: int = 4 << 30, + ) -> None: + self._owner = InProcessRolloutWorker( + capacity_records=trajectory_capacity_records, + capacity_bytes=trajectory_capacity_bytes, + ) + self._owner_endpoints: dict[str, RolloutWorkerEndpoint] = { + self._owner.owner_actor_id: self._owner + } + self._trajectory_capacity_records = trajectory_capacity_records + self._trajectory_capacity_bytes = trajectory_capacity_bytes + self._result_queue: DistributedTrajectoryQueue | None = None + + def create_result_queue(self, maxsize: int) -> DistributedTrajectoryQueue: + if self._result_queue is not None: + raise RuntimeError("local rollout result queue already exists") + self._result_queue = DistributedTrajectoryQueue( + endpoint=_InProcessTrajectoryQueueEndpoint(), + owner_endpoints=self._owner_endpoints, + maxsize=maxsize, + capacity_records=self._trajectory_capacity_records, + capacity_bytes=self._trajectory_capacity_bytes, + ) + return self._result_queue + + def set_target(self, target_workers: int) -> None: + if target_workers < 1: + raise ValueError("target_workers must be >= 1") + + def set_workers(self, worker_ids: tuple[int, ...]) -> None: + del worker_ids + + async def run( + self, + worker_id: int, + rollout_fn: Callable[..., Awaitable[Any]], + model: Any, + scenario: Any, + config: Any, + ) -> Any: + del worker_id + result = await rollout_fn(model, scenario, config) + if self._result_queue is not None and isinstance(result, TrajectoryGroup): + return self._owner.store(result) + return result + + +class RolloutWorkerEndpoint(Protocol): + async def run(self, invocation: RolloutInvocation) -> RolloutResult: ... + + async def materialize(self, ref: TrajectoryGroupRef) -> TrajectoryGroup: ... + + async def drop(self, ref: TrajectoryGroupRef) -> None: ... + + async def close(self) -> None: ... + + +class TrajectoryQueueEndpoint(Protocol): + async def create( + self, + queue_id: str, + max_ready_groups: int, + capacity_records: int, + capacity_bytes: int, + ) -> None: ... + + async def enqueue( + self, queue_id: str, item: TrajectoryQueueItem + ) -> TrajectoryEnqueueResult: ... + + async def resize(self, operation: TrajectoryQueueResize) -> None: ... + + async def take( + self, queue_id: str, consumer_id: str, count: int + ) -> TrajectoryQueueTake: ... + + async def mark_packed(self, operation: TrajectoryQueuePacking) -> None: ... + + async def release(self, operation: TrajectoryQueueRelease) -> None: ... + + async def finish(self, queue_id: str) -> None: ... + + async def snapshot(self, queue_id: str) -> TrajectoryQueueSnapshot: ... + + async def close(self, queue_id: str) -> tuple[TrajectoryGroupRef, ...]: ... + + +class _InProcessTrajectoryQueueEndpoint: + def __init__(self) -> None: + self._queues: dict[str, TrajectoryQueueStore] = {} + + async def create( + self, + queue_id: str, + max_ready_groups: int, + capacity_records: int, + capacity_bytes: int, + ) -> None: + if queue_id in self._queues: + raise ValueError(f"trajectory queue {queue_id!r} already exists") + self._queues[queue_id] = TrajectoryQueueStore( + max_ready_groups=max_ready_groups, + capacity_records=capacity_records, + capacity_bytes=capacity_bytes, + ) + + async def enqueue( + self, queue_id: str, item: TrajectoryQueueItem + ) -> TrajectoryEnqueueResult: + return self._queue(queue_id).enqueue(item) + + async def resize(self, operation: TrajectoryQueueResize) -> None: + self._queue(operation.queue_id).resize( + maxsize=operation.maxsize, generation=operation.generation + ) + + async def take( + self, queue_id: str, consumer_id: str, count: int + ) -> TrajectoryQueueTake: + return self._queue(queue_id).take(consumer_id, count) + + async def mark_packed(self, operation: TrajectoryQueuePacking) -> None: + self._queue(operation.queue_id).mark_packed(operation) + + async def release(self, operation: TrajectoryQueueRelease) -> None: + self._queue(operation.queue_id).release(operation) + + async def finish(self, queue_id: str) -> None: + self._queue(queue_id).finish() + + async def snapshot(self, queue_id: str) -> TrajectoryQueueSnapshot: + return self._queue(queue_id).snapshot() + + async def close(self, queue_id: str) -> tuple[TrajectoryGroupRef, ...]: + queue = self._queues.pop(queue_id, None) + return () if queue is None else queue.close() + + def _queue(self, queue_id: str) -> TrajectoryQueueStore: + try: + return self._queues[queue_id] + except KeyError: + raise ValueError(f"unknown trajectory queue {queue_id!r}") from None + + +class DistributedTrajectoryQueue: + def __init__( + self, + *, + endpoint: TrajectoryQueueEndpoint, + owner_endpoints: dict[str, RolloutWorkerEndpoint], + maxsize: int, + capacity_records: int, + capacity_bytes: int, + ) -> None: + if maxsize < 1: + raise ValueError("trajectory queue maxsize must be positive") + self.endpoint = endpoint + self.owner_endpoints = owner_endpoints + self.maxsize = maxsize + self._effective_maxsize = maxsize + self._minimum_take_size = 0 + self.capacity_records = capacity_records + self.capacity_bytes = capacity_bytes + self.queue_id = uuid.uuid4().hex + self.consumer_id = f"pipeline:{uuid.uuid4().hex}" + self.put_waiters = 0 + self._started = False + self._finished = False + self._closed = False + self._cleanup_refs: tuple[TrajectoryGroupRef, ...] = () + self._resize_generation = 0 + self._resize_tasks: set[asyncio.Task[None]] = set() + self._space_waiters: set[asyncio.Future[None]] = set() + self._item_waiters: set[asyncio.Future[None]] = set() + self._take_lock = asyncio.Lock() + self._owner_cleanup_refs: dict[str, deque[TrajectoryGroupRef]] = {} + self._owner_cleanup_tasks: dict[str, asyncio.Task[None]] = {} + self._owner_cleanup_failure: BaseException | None = None + + async def start(self) -> None: + if self._started: + return + created_maxsize = self._effective_maxsize + await self.endpoint.create( + self.queue_id, + created_maxsize, + self.capacity_records, + self.capacity_bytes, + ) + self._started = True + if self._required_maxsize() != created_maxsize: + self._effective_maxsize = created_maxsize + self._sync_maxsize() + + def set_maxsize(self, maxsize: int) -> None: + if maxsize < 1: + raise ValueError("trajectory queue maxsize must be positive") + if maxsize == self.maxsize: + return + self.maxsize = maxsize + self._sync_maxsize() + + async def put( + self, + ref: TrajectoryGroupRef, + *, + metadata: dict[str, MetadataValue], + initial_policy_version: int, + final_policy_version: int, + rollout_wall_s: float, + actor_idle_s: float, + ) -> tuple[bool, float]: + started = time.monotonic() + transferred = False + self.put_waiters += 1 + try: + while not self._closed: + wait_s = time.monotonic() - started + space_available = asyncio.get_running_loop().create_future() + self._space_waiters.add(space_available) + request = asyncio.create_task( + self.endpoint.enqueue( + self.queue_id, + TrajectoryQueueItem( + ref=ref, + annotations=TrajectoryGroupAnnotations( + metadata=metadata, + initial_policy_version=initial_policy_version, + final_policy_version=final_policy_version, + rollout_wall_s=rollout_wall_s, + actor_idle_s=actor_idle_s + wait_s, + queue_wait_s=wait_s, + ), + ), + ) + ) + try: + try: + result = await asyncio.shield(request) + except asyncio.CancelledError: + result = await request + transferred = result.status == "accepted" + if transferred: + self._notify_items() + raise + if result.status == "accepted": + transferred = True + self._notify_items() + return True, time.monotonic() - started + if result.status in ("oversize", "minimum_unreachable"): + self._notify_items() + raise TrajectoryCapacityError( + result.reason or "oversize result" + ) + if result.status == "closed": + self._notify_items() + return False, time.monotonic() - started + await space_available + finally: + self._space_waiters.discard(space_available) + if not space_available.done(): + space_available.cancel() + return False, time.monotonic() - started + finally: + self.put_waiters -= 1 + if not transferred: + await self._owner(ref).drop(ref) + + async def get(self) -> TrajectoryGroup | None: + groups, _ = await self.get_many(1, wait=True) + return groups[0] if groups else None + + async def get_nowait(self) -> tuple[bool, TrajectoryGroup | None]: + groups, closed = await self.get_many(1, wait=False) + return bool(groups) or closed, groups[0] if groups else None + + async def get_many( + self, count: int, *, wait: bool + ) -> tuple[list[TrajectoryGroup], bool]: + if count < 1: + raise ValueError("trajectory queue get count must be positive") + self._raise_owner_cleanup_failure() + async with self._take_lock: + minimum_reserved = wait and count <= self.maxsize + if minimum_reserved: + self._minimum_take_size = count + self._sync_maxsize() + try: + if wait: + await self._flush_resizes() + closed = self._closed + while not closed: + item_available = asyncio.get_running_loop().create_future() + self._item_waiters.add(item_available) + try: + # Negative counts retain best-effort bulk reads above the minimum. + take = await self._take_trajectories(count if wait else -count) + if take.leases: + return await self._resolve_many(take.leases), take.closed + closed = take.closed + if closed or not wait: + break + self._notify_space() + try: + await item_available + except asyncio.CancelledError: + if not self._closed: + await self.endpoint.take( + self.queue_id, self.consumer_id, 0 + ) + raise + closed = self._closed + finally: + self._item_waiters.discard(item_available) + if not item_available.done(): + item_available.cancel() + return [], closed + finally: + if minimum_reserved: + self._minimum_take_size = 0 + self._sync_maxsize() + + async def discard_group(self, group: TrajectoryGroup) -> None: + selection = group._distributed_lease + if not isinstance(selection, DistributedTrajectorySelection): + return + await self.release_selection(selection, disposition="discarded") + + async def mark_packed( + self, + selections: Sequence[DistributedTrajectorySelection], + generation_id: str, + ) -> None: + if any(selection.queue is not self for selection in selections): + raise TrajectoryLeaseError("trajectory selection belongs to another queue") + await self.endpoint.mark_packed( + TrajectoryQueuePacking( + queue_id=self.queue_id, + leases=tuple(selection.lease for selection in selections), + generation_id=generation_id, + ) + ) + + async def release_selection( + self, + selection: DistributedTrajectorySelection, + *, + disposition: Literal["consumed", "discarded"], + generation_id: str | None = None, + ) -> None: + await self.release_selections( + (selection,), + disposition=disposition, + generation_id=generation_id, + ) + + async def release_selections( + self, + selections: Sequence[DistributedTrajectorySelection], + *, + disposition: Literal["consumed", "discarded"], + generation_id: str | None = None, + ) -> None: + if any(selection.queue is not self for selection in selections): + raise TrajectoryLeaseError("trajectory selection belongs to another queue") + cleanup = await self._release_many( + tuple(selection.lease for selection in selections), + tuple(self._owner(selection.lease.item.ref) for selection in selections), + disposition=disposition, + generation_id=generation_id, + ) + if cleanup: + raise BaseExceptionGroup("trajectory selection release failed", cleanup) + self._raise_owner_cleanup_failure() + + async def finish(self) -> None: + if self._started and not self._finished and not self._closed: + await self.endpoint.finish(self.queue_id) + self._finished = True + self._notify_space() + self._notify_items() + + async def discard(self, ref: TrajectoryGroupRef) -> None: + await self._owner(ref).drop(ref) + + async def snapshot(self) -> TrajectoryQueueSnapshot: + if not self._started or self._closed: + return TrajectoryQueueSnapshot( + items=(), + max_ready_groups=self._effective_maxsize, + generation=self._resize_generation, + capacity_records=self.capacity_records, + capacity_bytes=self.capacity_bytes, + used_records=0, + used_bytes=0, + leased_groups=0, + ready_groups=0, + packing_groups=0, + packed_groups=0, + released_leases=0, + lease_lifetime_s=0.0, + max_lease_lifetime_s=0.0, + ) + while True: + await self._flush_resizes() + snapshot = await self.endpoint.snapshot(self.queue_id) + if snapshot.generation >= self._resize_generation: + return snapshot + + async def close(self) -> None: + failures: list[BaseException] = [] + try: + await self._flush_resizes() + except BaseException as error: + failures.append(error) + if not self._closed: + self._closed = True + self._notify_space() + self._notify_items() + if self._started: + try: + self._cleanup_refs += await self.endpoint.close(self.queue_id) + except BaseException as error: + failures.append(error) + if self._owner_cleanup_tasks: + await asyncio.gather(*tuple(self._owner_cleanup_tasks.values())) + refs = self._cleanup_refs + self._owner_cleanup_failure = None + results = await asyncio.gather( + *(self._owner(ref).drop(ref) for ref in refs), return_exceptions=True + ) + self._cleanup_refs = tuple( + ref + for ref, result in zip(refs, results, strict=True) + if isinstance(result, BaseException) + ) + failures.extend( + result for result in results if isinstance(result, BaseException) + ) + if failures: + raise BaseExceptionGroup("trajectory queue cleanup failed", failures) + + def _required_maxsize(self) -> int: + return max(self.maxsize, self._minimum_take_size) + + def _sync_maxsize(self) -> None: + maxsize = self._required_maxsize() + if maxsize == self._effective_maxsize: + return + self._effective_maxsize = maxsize + if self._started and not self._closed: + self._schedule_resize(maxsize) + + def _schedule_resize(self, maxsize: int) -> None: + self._resize_generation += 1 + operation = TrajectoryQueueResize( + queue_id=self.queue_id, + maxsize=maxsize, + generation=self._resize_generation, + ) + + async def resize() -> None: + await self.endpoint.resize(operation) + self._notify_space() + self._notify_items() + + task = asyncio.create_task(resize()) + self._resize_tasks.add(task) + + async def _flush_resizes(self) -> None: + while self._resize_tasks: + tasks = tuple(self._resize_tasks) + self._resize_tasks.difference_update(tasks) + results = await asyncio.gather(*tasks, return_exceptions=True) + failures = [ + result for result in results if isinstance(result, BaseException) + ] + if failures: + if len(failures) == 1: + raise failures[0] + raise BaseExceptionGroup("trajectory queue resize failed", failures) + + async def _consume(self, lease: TrajectoryQueueLease) -> TrajectoryGroup: + item = lease.item + owner = self._owner(item.ref) + try: + group = await owner.materialize(item.ref) + except BaseException as error: + cleanup = await self._release(lease, owner) + if cleanup: + raise BaseExceptionGroup( + "trajectory materialization and release failed", [error, *cleanup] + ) from None + raise + cleanup = await self._release(lease, owner) + if cleanup: + raise BaseExceptionGroup("trajectory result release failed", cleanup) + return item.apply_annotations(group) + + async def _resolve_many( + self, leases: Sequence[TrajectoryQueueLease] + ) -> list[TrajectoryGroup]: + return [self._summary_group(lease) for lease in leases] + + async def _take_trajectories(self, count: int) -> TrajectoryQueueTake: + request = asyncio.create_task( + self.endpoint.take(self.queue_id, self.consumer_id, count) + ) + try: + return await asyncio.shield(request) + except asyncio.CancelledError as cancelled: + take = await request + if take.leases: + cleanup = await self._release_many( + take.leases, + tuple(self._owner(lease.item.ref) for lease in take.leases), + disposition="discarded", + generation_id=None, + ) + if cleanup: + raise BaseExceptionGroup( + "trajectory acquisition cancellation cleanup failed", + [cancelled, *cleanup], + ) from None + elif count > 0 and not take.closed and not self._closed: + await self.endpoint.take(self.queue_id, self.consumer_id, 0) + raise + + async def materialize_selection( + self, selection: DistributedTrajectorySelection + ) -> TrajectoryGroup: + if selection.queue is not self: + raise TrajectoryLeaseError("trajectory selection belongs to another queue") + item = selection.lease.item + return item.apply_annotations(await self._owner(item.ref).materialize(item.ref)) + + def _summary_group(self, lease: TrajectoryQueueLease) -> TrajectoryGroup: + item = lease.item + descriptor = item.ref.descriptor + trajectories = [] + for reward, initial, final, counts, metrics, metadata in zip( + descriptor.rewards, + descriptor.trajectory_initial_policy_versions, + descriptor.trajectory_final_policy_versions, + descriptor.trajectory_policy_token_counts, + descriptor.trajectory_metrics, + descriptor.trajectory_metadata, + strict=True, + ): + trajectory = Trajectory( + reward=reward, + initial_policy_version=( + initial + if initial is not None + else item.annotations.initial_policy_version + ), + final_policy_version=( + final + if final is not None + else item.annotations.final_policy_version + ), + metrics=dict(metrics), + metadata=dict(metadata), + ) + trajectory._policy_token_counts = dict(counts) + trajectories.append(trajectory) + group = TrajectoryGroup( + trajectories, + metadata={**descriptor.group_metadata, **item.annotations.metadata}, + metrics=dict(descriptor.group_metrics), + ) + group.exceptions = [ + PydanticException(type=kind, message=message, traceback="") + for kind, message in descriptor.exceptions + ] + group.metadata["_art_rollout_wall_s"] = item.annotations.rollout_wall_s + group.metadata["_art_actor_idle_s"] = item.annotations.actor_idle_s + group.metadata["_art_queue_wait_s"] = item.annotations.queue_wait_s + group._distributed_lease = DistributedTrajectorySelection(self, lease) + return group + + async def _release( + self, + lease: TrajectoryQueueLease, + owner: RolloutWorkerEndpoint, + *, + disposition: Literal["consumed", "discarded"] = "discarded", + generation_id: str | None = None, + ) -> list[BaseException]: + return await self._release_many( + (lease,), + (owner,), + disposition=disposition, + generation_id=generation_id, + ) + + async def _release_many( + self, + leases: tuple[TrajectoryQueueLease, ...], + owners: tuple[RolloutWorkerEndpoint, ...], + *, + disposition: Literal["consumed", "discarded"], + generation_id: str | None, + ) -> list[BaseException]: + if not leases: + return [] + try: + await self.endpoint.release( + TrajectoryQueueRelease( + queue_id=self.queue_id, + leases=leases, + generation_id=generation_id, + disposition=disposition, + ) + ) + except BaseException as error: + return [error] + self._notify_space() + for lease, owner in zip(leases, owners, strict=True): + self._schedule_owner_cleanup(owner, lease.item.ref) + return [] + + def _notify_space(self) -> None: + for waiter in tuple(self._space_waiters): + if not waiter.done(): + waiter.set_result(None) + + def _notify_items(self) -> None: + for waiter in tuple(self._item_waiters): + if not waiter.done(): + waiter.set_result(None) + + def _schedule_owner_cleanup( + self, owner: RolloutWorkerEndpoint, ref: TrajectoryGroupRef + ) -> None: + owner_id = ref.owner_actor_id + self._owner_cleanup_refs.setdefault(owner_id, deque()).append(ref) + if owner_id not in self._owner_cleanup_tasks: + self._owner_cleanup_tasks[owner_id] = asyncio.create_task( + self._drain_owner_cleanup(owner_id, owner) + ) + + async def _drain_owner_cleanup( + self, owner_id: str, owner: RolloutWorkerEndpoint + ) -> None: + refs = self._owner_cleanup_refs[owner_id] + while refs: + ref = refs.popleft() + try: + await owner.drop(ref) + except Exception as error: + self._cleanup_refs += (ref,) + self._owner_cleanup_failure = self._owner_cleanup_failure or error + del self._owner_cleanup_refs[owner_id] + del self._owner_cleanup_tasks[owner_id] + + def _raise_owner_cleanup_failure(self) -> None: + error = self._owner_cleanup_failure + self._owner_cleanup_failure = None + if error is not None: + raise error + + def _owner(self, ref: TrajectoryGroupRef) -> RolloutWorkerEndpoint: + try: + return self.owner_endpoints[ref.owner_actor_id] + except KeyError: + raise RuntimeError( + f"trajectory owner {ref.owner_actor_id!r} is unavailable" + ) from None + + +class DistributedTrajectorySelection: + __slots__ = ("lease", "queue") + + def __init__( + self, queue: DistributedTrajectoryQueue, lease: TrajectoryQueueLease + ) -> None: + self.queue = queue + self.lease = lease + + +def apportion_rollout_workers( + target_workers: int, host_slots: Mapping[str, int] +) -> dict[str, int]: + """Deterministically assign one global exact target without host-local policy.""" + + if target_workers < 1: + raise ValueError("target_workers must be >= 1") + if not host_slots or any(slots < 1 for slots in host_slots.values()): + raise ValueError("rollout hosts must each provide at least one CPU slot") + allocation = dict.fromkeys(host_slots, 0) + for _ in range(target_workers): + candidates = [ + host for host, slots in host_slots.items() if allocation[host] < slots + ] + if not candidates: + raise ValueError( + f"global rollout-worker target {target_workers} exceeds host capacity " + f"{sum(host_slots.values())}" + ) + host_id = min( + candidates, key=lambda host: (allocation[host] / host_slots[host], host) + ) + allocation[host_id] += 1 + return allocation + + +class DistributedRolloutExecutor: + def __init__( + self, + *, + callable: InstalledAsyncCallable, + hosts: Mapping[str, Sequence[RolloutWorkerEndpoint]], + target_workers: int, + queue_endpoint: TrajectoryQueueEndpoint | None = None, + trajectory_capacity_records: int = 16_384, + trajectory_capacity_bytes: int = 4 << 30, + ) -> None: + if not hosts or any(not endpoints for endpoints in hosts.values()): + raise ValueError("rollout hosts must each provide at least one endpoint") + self.callable = callable + self.hosts = {host: tuple(endpoints) for host, endpoints in hosts.items()} + self.max_workers = sum(len(endpoints) for endpoints in self.hosts.values()) + self._worker_endpoints: tuple[RolloutWorkerEndpoint, ...] = () + self._endpoint_by_worker: dict[int, RolloutWorkerEndpoint] = {} + self._queue_endpoint = queue_endpoint + self._trajectory_capacity_records = trajectory_capacity_records + self._trajectory_capacity_bytes = trajectory_capacity_bytes + self._endpoint_by_owner: dict[str, RolloutWorkerEndpoint] = {} + self._result_queue: DistributedTrajectoryQueue | None = None + self.set_target(target_workers) + + def create_result_queue(self, maxsize: int) -> DistributedTrajectoryQueue: + if self._result_queue is not None: + raise RuntimeError("distributed rollout result queue already exists") + queue_endpoint = self._queue_endpoint + if queue_endpoint is None: + endpoints = next(iter(self.hosts.values())) + if len(self.hosts) != 1 or not all( + isinstance(endpoint, InProcessRolloutWorker) for endpoint in endpoints + ): + raise RuntimeError( + "queue_endpoint is required unless one host uses only " + "in-process rollout workers" + ) + queue_endpoint = _InProcessTrajectoryQueueEndpoint() + self._queue_endpoint = queue_endpoint + self._result_queue = DistributedTrajectoryQueue( + endpoint=queue_endpoint, + owner_endpoints=self._endpoint_by_owner, + maxsize=maxsize, + capacity_records=self._trajectory_capacity_records, + capacity_bytes=self._trajectory_capacity_bytes, + ) + return self._result_queue + + def set_target(self, target_workers: int) -> None: + allocation = apportion_rollout_workers( + target_workers, + {host: len(endpoints) for host, endpoints in self.hosts.items()}, + ) + self._worker_endpoints = tuple( + endpoint + for host_id in sorted(allocation) + for endpoint in self.hosts[host_id][: allocation[host_id]] + ) + + def set_workers(self, worker_ids: tuple[int, ...]) -> None: + workers = tuple(sorted(worker_ids)) + drained = len(workers) <= len(self._worker_endpoints) + assignments = { + worker_id: self._endpoint_by_worker[worker_id] + for worker_id in workers + if worker_id in self._endpoint_by_worker + and ( + not drained + or self._endpoint_by_worker[worker_id] in self._worker_endpoints + ) + } + available = [ + endpoint + for endpoint in self._worker_endpoints + if endpoint not in assignments.values() + ] + unassigned = [ + worker_id for worker_id in workers if worker_id not in assignments + ] + if len(unassigned) > len(available): + raise ValueError("new rollout workers exceed the global target") + assignments.update(zip(unassigned, available, strict=False)) + self._endpoint_by_worker = assignments + + async def run( + self, + worker_id: int, + rollout_fn: Callable[..., Awaitable[Any]], + model: Any, + scenario: Any, + config: Any, + ) -> Any: + if InstalledAsyncCallable.from_callable(rollout_fn) != self.callable: + raise ValueError( + "PipelineTrainer rollout_fn differs from distributed callable" + ) + try: + endpoint = self._endpoint_by_worker[worker_id] + except KeyError: + raise RuntimeError( + f"rollout worker {worker_id} has no host assignment" + ) from None + result = await endpoint.run( + RolloutInvocation( + callable=self.callable, + model=RolloutModelSpec.from_model(model), + scenario=scenario, + config=config, + store_result=self._result_queue is not None, + ) + ) + if result.metrics: + from art.metrics import MetricsBuilder + + try: + builder = MetricsBuilder.get_active() + except LookupError: + raise RuntimeError( + "distributed rollout produced metrics without an active ART metrics context" + ) from None + for key, value in result.metrics.items(): + builder.add_metric(key, value) + if isinstance(result.value, TrajectoryGroupRef): + existing = self._endpoint_by_owner.setdefault( + result.value.owner_actor_id, endpoint + ) + if existing is not endpoint: + raise RuntimeError( + f"trajectory owner {result.value.owner_actor_id!r} changed endpoint" + ) + return result.value + + async def close(self) -> None: + failures: list[BaseException] = [] + if self._result_queue is not None: + try: + await self._result_queue.close() + except BaseException as error: + failures.append(error) + results = await asyncio.gather( + *( + endpoint.close() + for endpoints in self.hosts.values() + for endpoint in endpoints + ), + return_exceptions=True, + ) + failures.extend( + result for result in results if isinstance(result, BaseException) + ) + if failures: + raise BaseExceptionGroup("distributed rollout cleanup failed", failures) + + +class InProcessRolloutWorker: + """One in-process rollout execution slot used by local collapse and tests.""" + + def __init__( + self, *, capacity_records: int = 16_384, capacity_bytes: int = 4 << 30 + ) -> None: + self._results = TrajectoryRecordStore( + owner_actor_id=f"in-process:{uuid.uuid4().hex}", + capacity_records=capacity_records, + capacity_bytes=capacity_bytes, + ) + + @property + def owner_actor_id(self) -> str: + return self._results.owner_actor_id + + def store(self, group: TrajectoryGroup) -> TrajectoryGroupRef: + return self._results.put(group) + + async def run(self, invocation: RolloutInvocation) -> RolloutResult: + from art.metrics import MetricsBuilder + + function = invocation.callable.resolve() + builder = MetricsBuilder(cost_context="train") + token = builder.activate() + try: + value = await function( + invocation.model.build(), invocation.scenario, invocation.config + ) + finally: + token.var.reset(token) + if invocation.store_result and isinstance(value, TrajectoryGroup): + value = self._results.put(value) + return RolloutResult(value=value, metrics=await builder.drain_pending()) + + async def materialize(self, ref: TrajectoryGroupRef) -> TrajectoryGroup: + return self._results.materialize(ref) + + async def drop(self, ref: TrajectoryGroupRef) -> None: + self._results.drop(ref) + + async def close(self) -> None: + self._results.close() diff --git a/src/art/distributed/specs.py b/src/art/distributed/specs.py new file mode 100644 index 000000000..5ce5075cf --- /dev/null +++ b/src/art/distributed/specs.py @@ -0,0 +1,393 @@ +from __future__ import annotations + +from collections import Counter +from ipaddress import ip_address +from typing import Annotated, Literal, TypeAlias + +from pydantic import BaseModel, ConfigDict, Field, model_validator + +from ..types import MegatronTopologyConfig + +CUDA_DEVICE_UUID_PATTERN = ( + r"^(?:GPU-[0-9A-Fa-f]{8}(?:-[0-9A-Fa-f]{4}){3}-[0-9A-Fa-f]{12}" + r"|MIG-(?:[0-9A-Fa-f]{8}(?:-[0-9A-Fa-f]{4}){3}-[0-9A-Fa-f]{12}" + r"|GPU-[0-9A-Fa-f]{8}(?:-[0-9A-Fa-f]{4}){3}-[0-9A-Fa-f]{12}/[0-9]+/[0-9]+))$" +) +GpuId: TypeAlias = ( + Annotated[int, Field(ge=0)] + | Annotated[str, Field(pattern=CUDA_DEVICE_UUID_PATTERN)] +) + + +class _Spec(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + +class HostSpec(_Spec): + host_id: str = Field(min_length=1) + node_rank: int = Field(ge=0) + worker_address: str = Field(min_length=1) + cpu_slots: int = Field(ge=1) + gpu_ids: tuple[GpuId, ...] = () + + @model_validator(mode="after") + def _validate_gpu_ids(self) -> "HostSpec": + identities = tuple( + gpu_id.casefold() if isinstance(gpu_id, str) else gpu_id + for gpu_id in self.gpu_ids + ) + if len(set(identities)) != len(identities): + raise ValueError("gpu_ids must be unique within a host") + return self + + +class NcclTransportSpec(_Spec): + net_name: str = Field(min_length=1, pattern=r"^[^\x00\r\n]+$") + + @model_validator(mode="after") + def _validate_net_name(self) -> "NcclTransportSpec": + if self.net_name != self.net_name.strip(): + raise ValueError( + "NCCL network name must not contain surrounding whitespace" + ) + if self.net_name.casefold() == "socket": + raise ValueError("multi-host GPU workloads may not use NCCL Socket") + return self + + +class EndpointSpec(_Spec): + host: str = Field(min_length=1) + port: int = Field(ge=1, le=65535) + + @property + def url(self) -> str: + return f"http://{self.host}:{self.port}" + + @property + def is_loopback(self) -> bool: + if self.host.lower() == "localhost": + return True + try: + return ip_address(self.host.strip("[]")).is_loopback + except ValueError: + return False + + @property + def is_routable(self) -> bool: + if self.host.lower() == "localhost": + return False + try: + address = ip_address(self.host.strip("[]")) + except ValueError: + return self.host not in {"0.0.0.0", "::"} + return not ( + address.is_loopback + or address.is_unspecified + or address.is_link_local + or address.is_multicast + ) + + +class NixlTransportSpec(_Spec): + metadata_store: EndpointSpec + nixl_home: str = Field(default="/usr/local/art-multinode/nixl", min_length=1) + ucx_home: str = Field(default="/usr/local/art-multinode/ucx", min_length=1) + nixl_plugin_dir: str = Field( + default="/usr/local/art-multinode/nixl-ucx/lib/plugins", min_length=1 + ) + ucx_module_dir: str = Field( + default="/usr/local/art-multinode/ucx/lib/ucx", min_length=1 + ) + ucx_net_devices: str = Field(default="all", min_length=1) + ucx_tls: str = Field(default="rc,rc_gda,cuda_copy", min_length=1) + enable_cuda_fabric: bool = False + + @model_validator(mode="after") + def _validate_metadata_store(self) -> "NixlTransportSpec": + if not self.metadata_store.is_routable: + raise ValueError("NIXL metadata store must be routable across hosts") + return self + + +class ClusterSpec(_Spec): + hosts: tuple[HostSpec, ...] + controller_host_id: str + artifact_root: str | None = None + cache_root: str | None = Field(default=None, min_length=1) + nccl_transport: NcclTransportSpec | None = None + nixl_transport: NixlTransportSpec | None = None + startup_timeout_s: float = Field(default=600.0, gt=0) + rpc_timeout_s: float = Field(default=60.0, gt=0) + + @model_validator(mode="after") + def _validate_hosts(self) -> "ClusterSpec": + if not self.hosts: + raise ValueError("hosts must not be empty") + host_ids = [host.host_id for host in self.hosts] + node_ranks = [host.node_rank for host in self.hosts] + addresses = [host.worker_address for host in self.hosts] + if len(set(host_ids)) != len(host_ids): + raise ValueError("host_id values must be unique") + if node_ranks != list(range(len(self.hosts))): + raise ValueError("hosts must be ordered by contiguous node_rank from zero") + if len(set(addresses)) != len(addresses): + raise ValueError("worker_address values must be unique") + if self.controller_host_id not in host_ids: + raise ValueError("controller_host_id must identify a configured host") + return self + + +class GpuPlacement(_Spec): + host_id: str = Field(min_length=1) + gpu_id: GpuId + + +class TrainerMeshSpec(_Spec): + ranks: tuple[GpuPlacement, ...] + topology: MegatronTopologyConfig + coordinator_rank: Literal[0] = 0 + + @model_validator(mode="after") + def _validate_world(self) -> "TrainerMeshSpec": + if not self.ranks: + raise ValueError("trainer ranks must not be empty") + if len(set(self.ranks)) != len(self.ranks): + raise ValueError("trainer GPU placements must be unique") + world_size = len(self.ranks) + topology = self.topology + if world_size % (topology.tp * topology.cp * topology.pp): + raise ValueError("trainer world size must be divisible by TP * CP * PP") + if world_size % (topology.etp * topology.ep * topology.pp): + raise ValueError("trainer world size must be divisible by ETP * EP * PP") + return self + + +class VllmParallelSpec(_Spec): + tp: int = Field(default=1, ge=1) + pp: int = Field(default=1, ge=1) + dp: int = Field(default=1, ge=1) + enable_expert_parallel: bool = False + + @property + def world_size(self) -> int: + return self.tp * self.pp * self.dp + + +class ModelServiceMemberSpec(_Spec): + member_id: str = Field(min_length=1) + host_id: str = Field(min_length=1) + node_rank: int = Field(ge=0) + gpu_ids: tuple[GpuId, ...] + + @model_validator(mode="after") + def _validate_gpu_ids(self) -> "ModelServiceMemberSpec": + if not self.gpu_ids: + raise ValueError("model-service members require at least one GPU") + identities = tuple( + gpu_id.casefold() if isinstance(gpu_id, str) else gpu_id + for gpu_id in self.gpu_ids + ) + if len(set(identities)) != len(identities): + raise ValueError("member gpu_ids must be unique") + return self + + +class ModelServiceSpec(_Spec): + name: str = Field(min_length=1) + capabilities: frozenset[str] = frozenset() + members: tuple[ModelServiceMemberSpec, ...] + leader_endpoint: EndpointSpec + rendezvous: EndpointSpec + base_model: str = Field(min_length=1) + model_revision: str | None = Field(default=None, min_length=1) + runtime_fingerprint: str = Field(min_length=1) + parallel: VllmParallelSpec + update_mode: Literal["lora", "merged"] + temporal_gpu_sharing: bool = False + + @model_validator(mode="after") + def _validate_members(self) -> "ModelServiceSpec": + if not self.members: + raise ValueError("model service members must not be empty") + member_ids = [member.member_id for member in self.members] + node_ranks = [member.node_rank for member in self.members] + if len(set(member_ids)) != len(member_ids): + raise ValueError("member_id values must be unique within a model service") + if node_ranks != list(range(len(self.members))): + raise ValueError( + "members must be ordered by contiguous node_rank from zero" + ) + if len({member.host_id for member in self.members}) != len(self.members): + raise ValueError("native vLLM members must occupy distinct hosts") + local_world_sizes = {len(member.gpu_ids) for member in self.members} + if len(local_world_sizes) != 1: + raise ValueError("native vLLM members must have equal local world sizes") + if ( + sum(len(member.gpu_ids) for member in self.members) + != self.parallel.world_size + ): + raise ValueError("vLLM TP * PP * DP must equal the service GPU count") + if len(self.members) > 1 and not self.rendezvous.is_routable: + raise ValueError("multi-host vLLM rendezvous must be routable") + local_world_size = len(self.members[0].gpu_ids) + world_size_within_dp = self.parallel.tp * self.parallel.pp + if ( + local_world_size >= world_size_within_dp + and local_world_size % world_size_within_dp + ) or ( + local_world_size < world_size_within_dp + and world_size_within_dp % local_world_size + ): + raise ValueError( + "native vLLM DP groups must pack evenly within or span whole members" + ) + if self.leader_endpoint.port == self.rendezvous.port: + raise ValueError("model-service API and rendezvous ports must not overlap") + return self + + @property + def gpu_placements(self) -> tuple[GpuPlacement, ...]: + return tuple( + GpuPlacement(host_id=member.host_id, gpu_id=gpu_id) + for member in self.members + for gpu_id in member.gpu_ids + ) + + +class RuntimeTopology(_Spec): + cluster: ClusterSpec + rollout_host_ids: tuple[str, ...] + trainer: TrainerMeshSpec | None = None + model_services: tuple[ModelServiceSpec, ...] = () + + @model_validator(mode="after") + def _validate_runtime(self) -> "RuntimeTopology": + hosts = {host.host_id: host for host in self.cluster.hosts} + if len(set(self.rollout_host_ids)) != len(self.rollout_host_ids): + raise ValueError("rollout_host_ids must be unique") + unknown_rollout_hosts = sorted(set(self.rollout_host_ids) - hosts.keys()) + if unknown_rollout_hosts: + raise ValueError( + f"rollout_host_ids references unknown hosts: {unknown_rollout_hosts}" + ) + + placements: list[tuple[str, GpuId, str]] = [] + if self.trainer is not None: + trainer_hosts = tuple(rank.host_id for rank in self.trainer.ranks) + unknown_trainer_hosts = sorted(set(trainer_hosts) - hosts.keys()) + if unknown_trainer_hosts: + raise ValueError( + f"trainer references unknown hosts: {unknown_trainer_hosts}" + ) + counts = Counter(trainer_hosts) + if len(set(counts.values())) != 1: + raise ValueError("Monarch trainer hosts require equal ranks per host") + selected_hosts = tuple( + host.host_id for host in self.cluster.hosts if host.host_id in counts + ) + selected_indices = tuple( + index + for index, host in enumerate(self.cluster.hosts) + if host.host_id in counts + ) + if selected_indices != tuple( + range(selected_indices[0], selected_indices[-1] + 1) + ): + raise ValueError("trainer hosts must be contiguous in the cluster mesh") + ranks_per_host = next(iter(counts.values())) + expected_rank_hosts = tuple( + host_id for host_id in selected_hosts for _ in range(ranks_per_host) + ) + if trainer_hosts != expected_rank_hosts: + raise ValueError( + "trainer ranks must be host-major in cluster host order" + ) + placements.extend( + (rank.host_id, rank.gpu_id, "trainer") for rank in self.trainer.ranks + ) + + service_names = [service.name for service in self.model_services] + if len(set(service_names)) != len(service_names): + raise ValueError("model service names must be unique") + + endpoints: list[tuple[str, int, str]] = [] + for service in self.model_services: + placements.extend( + (placement.host_id, placement.gpu_id, service.name) + for placement in service.gpu_placements + ) + endpoints.extend( + ( + ( + service.members[0].host_id, + service.leader_endpoint.port, + "leader", + ), + ( + service.members[0].host_id, + service.rendezvous.port, + "rendezvous", + ), + ) + ) + spans_hosts = ( + self.trainer is not None + and len({rank.host_id for rank in self.trainer.ranks}) > 1 + ) or any(len(service.members) > 1 for service in self.model_services) + if spans_hosts and self.cluster.nccl_transport is None: + raise ValueError("multi-host GPU workloads require nccl_transport") + for host_id, gpu_id, owner in placements: + host = hosts.get(host_id) + if host is None: + raise ValueError(f"{owner} references unknown host {host_id!r}") + if gpu_id not in host.gpu_ids: + raise ValueError(f"{owner} requests unavailable GPU {host_id}:{gpu_id}") + temporal_services = { + service.name + for service in self.model_services + if service.temporal_gpu_sharing + } + overlapping = { + placement: tuple( + owner + for host_id, gpu_id, owner in placements + if (host_id, gpu_id) == placement + ) + for placement, count in Counter( + (host_id, gpu_id) for host_id, gpu_id, _ in placements + ).items() + if count > 1 + } + invalid_overlap = { + placement: owners + for placement, owners in overlapping.items() + if len(owners) != 2 + or "trainer" not in owners + or next(owner for owner in owners if owner != "trainer") + not in temporal_services + } + if invalid_overlap: + raise ValueError(f"GPU placements overlap: {invalid_overlap}") + seen: dict[tuple[str, int], str] = {} + for host_id, port, kind in endpoints: + key = (host_id, port) + if previous := seen.get(key): + raise ValueError( + f"model-service port {host_id}:{port} overlaps " + f"{previous} and {kind}" + ) + seen[key] = kind + return self + + +class ArtRuntimeConfig(_Spec): + packed_batch_capacity_bytes: int = Field(default=2 << 30, ge=1) + trajectory_capacity_records: int = Field(default=16_384, ge=1) + trajectory_capacity_bytes: int = Field(default=4 << 30, ge=1) + vllm_output_root: str = "/tmp/art-vllm" + + +class HostServiceHealth(_Spec): + host_id: str = Field(min_length=1) + hostname: str = Field(min_length=1) + process_id: int = Field(ge=1) diff --git a/src/art/distributed/topology.py b/src/art/distributed/topology.py new file mode 100644 index 000000000..9f959773d --- /dev/null +++ b/src/art/distributed/topology.py @@ -0,0 +1,27 @@ +from __future__ import annotations + +from .specs import ( + ClusterSpec, + ModelServiceSpec, + RuntimeTopology, + TrainerMeshSpec, +) + + +def compile_topology( + *, + cluster: ClusterSpec, + rollout_host_ids: tuple[str, ...] | None = None, + trainer: TrainerMeshSpec | None = None, + model_services: tuple[ModelServiceSpec, ...] = (), +) -> RuntimeTopology: + return RuntimeTopology( + cluster=cluster, + rollout_host_ids=( + tuple(host.host_id for host in cluster.hosts) + if rollout_host_ids is None + else rollout_host_ids + ), + trainer=trainer, + model_services=model_services, + ) diff --git a/src/art/distributed/trajectory_store.py b/src/art/distributed/trajectory_store.py new file mode 100644 index 000000000..36c191fed --- /dev/null +++ b/src/art/distributed/trajectory_store.py @@ -0,0 +1,797 @@ +from __future__ import annotations + +import asyncio +from collections import deque +from collections.abc import Callable, Mapping +import secrets +import time +from typing import TYPE_CHECKING, Any, Literal + +from pydantic import BaseModel, ConfigDict, Field, model_validator + +from art.preprocessing.policy_spans import PolicyTokenSpan +from art.trajectories import MetadataValue, Trajectory, TrajectoryGroup + +from .data_plane import ( + ByteStreamPublisher, + ByteStreamServerLoop, + ByteStreamTransfer, + receive_byte_stream, +) + +if TYPE_CHECKING: + from .packing import TrajectoryGroupPayload + +TRAJECTORY_FORMAT = "art_trajectory_v1" + + +class _Contract(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + +class TrajectoryRecordRef(_Contract): + record_id: str = Field(min_length=1) + owner_actor_id: str = Field(min_length=1) + byte_count: int = Field(ge=0) + + +class TrajectoryGroupDescriptor(_Contract): + grouping_key: str = Field(min_length=1) + trajectory_count: int = Field(ge=0) + exception_count: int = Field(ge=0) + rewards: tuple[float, ...] + initial_policy_versions: tuple[int, ...] + completion_tokens: tuple[float, ...] + policy_token_counts: dict[int, int] + trajectory_initial_policy_versions: tuple[int | None, ...] + trajectory_final_policy_versions: tuple[int | None, ...] + trajectory_policy_token_counts: tuple[dict[int, int], ...] + trajectory_metrics: tuple[dict[str, float | int | bool], ...] + trajectory_metadata: tuple[dict[str, MetadataValue], ...] + group_metadata: dict[str, MetadataValue] + group_metrics: dict[str, float | int | bool] + exceptions: tuple[tuple[str, str], ...] + byte_count: int = Field(ge=0) + + @model_validator(mode="after") + def _validate_trajectory_summaries(self) -> "TrajectoryGroupDescriptor": + aligned = ( + self.rewards, + self.completion_tokens, + self.trajectory_initial_policy_versions, + self.trajectory_final_policy_versions, + self.trajectory_policy_token_counts, + self.trajectory_metrics, + self.trajectory_metadata, + ) + if any(len(values) != self.trajectory_count for values in aligned): + raise ValueError("trajectory descriptor summaries are not aligned") + if len(self.exceptions) != self.exception_count: + raise ValueError("trajectory descriptor exceptions are not aligned") + return self + + +class TrajectoryGroupBundle(_Contract): + """Binary trajectory records for bulk transport across actor boundaries.""" + + header: bytes + records: tuple[bytes, ...] + + @classmethod + def from_payload(cls, payload: TrajectoryGroupPayload) -> "TrajectoryGroupBundle": + from msgspec import msgpack + + return cls( + header=msgpack.encode( + payload.model_copy(update={"trajectories": ()}).model_dump( + mode="python" + ) + ), + records=tuple( + msgpack.encode(record.model_dump(mode="python")) + for record in payload.trajectories + ), + ) + + @classmethod + def from_group(cls, group: TrajectoryGroup) -> "TrajectoryGroupBundle": + from .packing import TrajectoryGroupPayload + + return cls.from_payload(TrajectoryGroupPayload.from_group(group)) + + def payload(self) -> TrajectoryGroupPayload: + from msgspec import msgpack + + from .packing import TrajectoryGroupPayload + + header = msgpack.decode(self.header) + header["trajectories"] = tuple( + msgpack.decode(record) for record in self.records + ) + return TrajectoryGroupPayload.model_validate(header) + + def build(self) -> TrajectoryGroup: + return self.payload().build() + + +class TrajectoryGroupLayout(_Contract): + header_byte_count: int = Field(ge=1) + record_byte_counts: tuple[int, ...] + + +class TrajectoryBatchTransfer(_Contract): + stream: ByteStreamTransfer + groups: tuple[TrajectoryGroupLayout, ...] + + @model_validator(mode="after") + def _validate_layout(self) -> "TrajectoryBatchTransfer": + if any( + byte_count < 0 + for group in self.groups + for byte_count in group.record_byte_counts + ): + raise ValueError("trajectory record byte counts must be non-negative") + byte_count = sum( + group.header_byte_count + sum(group.record_byte_counts) + for group in self.groups + ) + if not self.groups or byte_count != self.stream.byte_count: + raise ValueError("trajectory stream layout does not match its payload") + return self + + async def receive_groups(self, *, timeout_s: float) -> tuple[TrajectoryGroup, ...]: + payload = await receive_byte_stream(self.stream, timeout_s=timeout_s) + return await asyncio.to_thread(self._build_groups, payload) + + def _build_groups(self, payload: bytearray) -> tuple[TrajectoryGroup, ...]: + from msgspec import msgpack + + from .packing import TrajectoryGroupPayload + + view = memoryview(payload) + offset = 0 + groups = [] + try: + for layout in self.groups: + end = offset + layout.header_byte_count + header = msgpack.decode(view[offset:end]) + offset = end + records = [] + for byte_count in layout.record_byte_counts: + end = offset + byte_count + records.append(msgpack.decode(view[offset:end])) + offset = end + header["trajectories"] = tuple(records) + groups.append(TrajectoryGroupPayload.model_validate(header).build()) + finally: + view.release() + return tuple(groups) + + +class TrajectoryGroupRef(_Contract): + result_id: str = Field(min_length=1) + owner_actor_id: str = Field(min_length=1) + lease_id: str = Field(min_length=1) + format: Literal["art_trajectory_v1"] = TRAJECTORY_FORMAT + records: tuple[TrajectoryRecordRef, ...] + descriptor: TrajectoryGroupDescriptor + transfer: TrajectoryBatchTransfer | None = None + + +async def publish_trajectory_bundles( + bundles: tuple[TrajectoryGroupBundle, ...], + *, + stream_id: str, + advertise_host: str, + on_sent: Callable[[], None] | None = None, + server_loop: ByteStreamServerLoop | None = None, +) -> tuple[TrajectoryBatchTransfer, ByteStreamPublisher]: + publisher = await ByteStreamPublisher.create( + stream_id, + tuple( + chunk for bundle in bundles for chunk in (bundle.header, *bundle.records) + ), + advertise_host=advertise_host, + on_sent=on_sent, + server_loop=server_loop, + ) + try: + transfer = TrajectoryBatchTransfer( + stream=publisher.transfer, + groups=tuple( + TrajectoryGroupLayout( + header_byte_count=len(bundle.header), + record_byte_counts=tuple(map(len, bundle.records)), + ) + for bundle in bundles + ), + ) + except BaseException: + await publisher.close() + raise + return transfer, publisher + + +class TrajectoryGroupAnnotations(_Contract): + metadata: dict[str, MetadataValue] = Field(default_factory=dict) + initial_policy_version: int = Field(ge=0) + final_policy_version: int = Field(ge=0) + rollout_wall_s: float = Field(default=0.0, ge=0) + actor_idle_s: float = Field(default=0.0, ge=0) + queue_wait_s: float = Field(default=0.0, ge=0) + + +class TrajectoryQueueItem(_Contract): + ref: TrajectoryGroupRef + annotations: TrajectoryGroupAnnotations + + async def receive(self, *, timeout_s: float) -> TrajectoryGroup: + transfer = self.ref.transfer + if transfer is None: + raise RuntimeError("remote trajectory has no data-plane transfer") + if transfer.stream.stream_id != self.ref.result_id: + raise RuntimeError("trajectory owner returned the wrong result ID") + if transfer.stream.byte_count != self.ref.descriptor.byte_count: + raise RuntimeError("trajectory owner returned the wrong byte count") + groups = await transfer.receive_groups(timeout_s=timeout_s) + if len(groups) != 1: + raise RuntimeError("trajectory owner returned the wrong group count") + return self.apply_annotations(groups[0]) + + def apply_annotations(self, group: TrajectoryGroup) -> TrajectoryGroup: + annotations = self.annotations + group.metadata.update(annotations.metadata) + group.metadata["_art_rollout_wall_s"] = annotations.rollout_wall_s + group.metadata["_art_actor_idle_s"] = annotations.actor_idle_s + group.metadata["_art_queue_wait_s"] = annotations.queue_wait_s + for trajectory in group.trajectories: + if trajectory.initial_policy_version is None: + trajectory.initial_policy_version = annotations.initial_policy_version + if trajectory.final_policy_version is None: + trajectory.final_policy_version = annotations.final_policy_version + return group + + +class TrajectoryQueueResize(_Contract): + queue_id: str = Field(min_length=1) + maxsize: int = Field(ge=1) + generation: int = Field(ge=1) + + +class TrajectoryEnqueueResult(_Contract): + status: Literal["accepted", "full", "oversize", "minimum_unreachable", "closed"] + reason: str | None = None + + +class TrajectoryQueueTake(_Contract): + leases: tuple[TrajectoryQueueLease, ...] = () + closed: bool = False + + +class TrajectoryQueueLease(_Contract): + claim_id: str = Field(min_length=1) + consumer_id: str = Field(min_length=1) + generation: int = Field(ge=1) + item: TrajectoryQueueItem + + +class TrajectoryQueuePacking(_Contract): + queue_id: str = Field(min_length=1) + leases: tuple[TrajectoryQueueLease, ...] + generation_id: str = Field(min_length=1) + + +class TrajectoryQueueRelease(_Contract): + queue_id: str = Field(min_length=1) + leases: tuple[TrajectoryQueueLease, ...] = Field(min_length=1) + generation_id: str | None = None + disposition: Literal["consumed", "discarded"] + + +class TrajectoryQueueSnapshot(_Contract): + items: tuple[TrajectoryQueueItem, ...] + max_ready_groups: int = Field(ge=1) + generation: int = Field(ge=0) + capacity_records: int = Field(ge=1) + capacity_bytes: int = Field(ge=1) + used_records: int = Field(ge=0) + used_bytes: int = Field(ge=0) + leased_groups: int = Field(ge=0) + ready_groups: int = Field(ge=0) + packing_groups: int = Field(ge=0) + packed_groups: int = Field(ge=0) + released_leases: int = Field(ge=0) + lease_lifetime_s: float = Field(ge=0) + max_lease_lifetime_s: float = Field(ge=0) + + +class TrajectoryCapacityError(RuntimeError): + pass + + +class TrajectoryLeaseError(RuntimeError): + pass + + +class _StoredGroup: + def __init__(self, header: bytes, ref: TrajectoryGroupRef) -> None: + self.header = header + self.ref = ref + + +class TrajectoryRecordStore: + """Own typed trajectory records until their rollout-result lease is released.""" + + def __init__( + self, *, owner_actor_id: str, capacity_records: int, capacity_bytes: int + ) -> None: + if capacity_records < 1 or capacity_bytes < 1: + raise ValueError("trajectory store capacities must be positive") + self.owner_actor_id = owner_actor_id + self.capacity_records = capacity_records + self.capacity_bytes = capacity_bytes + self._records: dict[str, bytes] = {} + self._groups: dict[str, _StoredGroup] = {} + self._used_bytes = 0 + + def put(self, group: TrajectoryGroup) -> TrajectoryGroupRef: + from .packing import TrajectoryGroupPayload + + payload = TrajectoryGroupPayload.from_group(group) + bundle = TrajectoryGroupBundle.from_payload(payload) + record_sizes = tuple(len(record) for record in bundle.records) + byte_count = sum(record_sizes) + len(bundle.header) + record_count = len(payload.trajectories) + if record_count > self.capacity_records or byte_count > self.capacity_bytes: + raise TrajectoryCapacityError( + f"trajectory group requires {record_count} records/{byte_count} bytes; " + f"store capacity is {self.capacity_records}/{self.capacity_bytes}" + ) + if ( + len(self._records) + record_count > self.capacity_records + or self._used_bytes + byte_count > self.capacity_bytes + ): + raise TrajectoryCapacityError("trajectory record store capacity exhausted") + + result_id = secrets.token_hex(16) + records = tuple( + TrajectoryRecordRef( + record_id=secrets.token_hex(16), + owner_actor_id=self.owner_actor_id, + byte_count=size, + ) + for size in record_sizes + ) + for record_ref, record in zip(records, bundle.records, strict=True): + self._records[record_ref.record_id] = record + descriptor = TrajectoryGroupDescriptor( + grouping_key=_grouping_key(group, result_id), + trajectory_count=len(group.trajectories), + exception_count=len(group.exceptions), + rewards=tuple(trajectory.reward for trajectory in group.trajectories), + initial_policy_versions=tuple( + trajectory.initial_policy_version + for trajectory in group.trajectories + if trajectory.initial_policy_version is not None + ), + completion_tokens=tuple( + float(value) + if not isinstance(value, bool) and isinstance(value, int | float) + else 0.0 + for trajectory in group.trajectories + for value in (trajectory.metrics.get("completion_tokens"),) + ), + policy_token_counts=_policy_token_counts(group.trajectories), + trajectory_initial_policy_versions=tuple( + trajectory.initial_policy_version for trajectory in group.trajectories + ), + trajectory_final_policy_versions=tuple( + trajectory.final_policy_version for trajectory in group.trajectories + ), + trajectory_policy_token_counts=tuple( + _trajectory_policy_token_counts(trajectory) + for trajectory in group.trajectories + ), + trajectory_metrics=tuple( + trajectory.metrics for trajectory in group.trajectories + ), + trajectory_metadata=tuple( + trajectory.metadata for trajectory in group.trajectories + ), + group_metadata=group.metadata, + group_metrics=group.metrics, + exceptions=tuple( + (exception.type, exception.message) for exception in group.exceptions + ), + byte_count=byte_count, + ) + ref = TrajectoryGroupRef( + result_id=result_id, + owner_actor_id=self.owner_actor_id, + lease_id=secrets.token_hex(16), + records=records, + descriptor=descriptor, + ) + self._groups[result_id] = _StoredGroup(bundle.header, ref) + self._used_bytes += byte_count + return ref + + def bundle(self, ref: TrajectoryGroupRef) -> TrajectoryGroupBundle: + stored = self._entry(ref) + return TrajectoryGroupBundle( + header=stored.header, + records=tuple( + self._records[record.record_id] for record in stored.ref.records + ), + ) + + def payload(self, ref: TrajectoryGroupRef) -> TrajectoryGroupPayload: + return self.bundle(ref).payload() + + def materialize(self, ref: TrajectoryGroupRef) -> TrajectoryGroup: + return self.payload(ref).build() + + def drop(self, ref: TrajectoryGroupRef) -> None: + stored = self._groups.get(ref.result_id) + if stored is None: + return + self._require_same_lease(stored.ref, ref) + self._groups.pop(ref.result_id) + for record in stored.ref.records: + self._records.pop(record.record_id) + self._used_bytes -= stored.ref.descriptor.byte_count + + def close(self) -> None: + self._groups.clear() + self._records.clear() + self._used_bytes = 0 + + def _entry(self, ref: TrajectoryGroupRef) -> _StoredGroup: + try: + stored = self._groups[ref.result_id] + except KeyError: + raise TrajectoryLeaseError( + f"unknown trajectory result {ref.result_id!r}" + ) from None + self._require_same_lease(stored.ref, ref) + return stored + + @staticmethod + def _require_same_lease( + expected: TrajectoryGroupRef, received: TrajectoryGroupRef + ) -> None: + if ( + expected.owner_actor_id != received.owner_actor_id + or expected.lease_id != received.lease_id + or expected.records != received.records + ): + raise TrajectoryLeaseError("trajectory result lease does not match storage") + + +class _QueueEntry: + def __init__(self, item: TrajectoryQueueItem) -> None: + self.item = item + self.phase: Literal["ready", "packing", "packed"] = "ready" + self.consumer_id: str | None = None + self.claim_id: str | None = None + self.claim_generation: int | None = None + self.packing_generation_id: str | None = None + self.acquired_at: float | None = None + + +class TrajectoryQueueStore: + """Bounded FIFO and consumer-lease owner for trajectory-group references.""" + + def __init__( + self, + *, + max_ready_groups: int, + capacity_records: int, + capacity_bytes: int, + ) -> None: + if min(max_ready_groups, capacity_records, capacity_bytes) < 1: + raise ValueError("trajectory queue capacities must be positive") + self.max_ready_groups = max_ready_groups + self.capacity_records = capacity_records + self.capacity_bytes = capacity_bytes + self._entries: dict[str, _QueueEntry] = {} + self._ready: deque[str] = deque() + self._used_records = 0 + self._used_bytes = 0 + self._finished = False + self._pending_minimum: tuple[str, int] | None = None + self._minimum_error: str | None = None + self.generation = 0 + self._claim_generation = 0 + self._released_leases = 0 + self._lease_lifetime_s = 0.0 + self._max_lease_lifetime_s = 0.0 + + def resize(self, *, maxsize: int, generation: int) -> None: + if maxsize < 1 or generation < 1: + raise ValueError("trajectory queue resize values must be positive") + if generation < self.generation: + return + if generation == self.generation: + if maxsize != self.max_ready_groups: + raise ValueError("trajectory queue resize generation conflicts") + return + self.max_ready_groups = maxsize + self.generation = generation + + def enqueue(self, item: TrajectoryQueueItem) -> TrajectoryEnqueueResult: + item = _resolve_grouping(item) + ref = item.ref + records = len(ref.records) + byte_count = ref.descriptor.byte_count + if records > self.capacity_records or byte_count > self.capacity_bytes: + reason = f"result requires {records} records/{byte_count} bytes" + if self._pending_minimum is not None: + return self._fail_pending_minimum(item, reason) + return TrajectoryEnqueueResult( + status="oversize", + reason=reason, + ) + if self._finished: + return TrajectoryEnqueueResult(status="closed") + existing = self._entries.get(ref.result_id) + if existing is not None: + if existing.item.ref == ref: + return TrajectoryEnqueueResult(status="accepted") + raise TrajectoryLeaseError("trajectory result lease changed while queued") + if self._minimum_error is not None: + return TrajectoryEnqueueResult( + status="minimum_unreachable", reason=self._minimum_error + ) + blockers = [] + if len(self._entries) >= self.max_ready_groups: + blockers.append("group capacity") + if self._used_records + records > self.capacity_records: + blockers.append("record capacity") + if self._used_bytes + byte_count > self.capacity_bytes: + blockers.append("byte capacity") + if blockers: + pending = self._pending_minimum + if ( + pending is not None + and len(self._ready) < pending[1] + and self._minimum_cannot_make_progress() + ): + return self._fail_pending_minimum(item, ", ".join(blockers)) + return TrajectoryEnqueueResult(status="full") + self._entries[ref.result_id] = _QueueEntry(item) + self._ready.append(ref.result_id) + self._used_records += records + self._used_bytes += byte_count + return TrajectoryEnqueueResult(status="accepted") + + def take(self, consumer_id: str, count: int) -> TrajectoryQueueTake: + """Acquire a positive minimum, take up to a negative limit, or cancel at zero.""" + if not consumer_id: + raise ValueError("consumer_id must not be empty") + if count == 0: + pending = self._pending_minimum + if pending is not None and pending[0] != consumer_id: + raise TrajectoryLeaseError( + "trajectory minimum acquisition belongs to another consumer" + ) + self._pending_minimum = None + return TrajectoryQueueTake(closed=self._finished and not self._ready) + if self._minimum_error is not None: + raise TrajectoryCapacityError(self._minimum_error) + + best_effort = count < 0 + limit = abs(count) + request = (consumer_id, limit) + if self._pending_minimum not in (None, request): + raise TrajectoryLeaseError( + "trajectory queue already has a pending minimum acquisition" + ) + if best_effort: + if self._pending_minimum is not None: + raise TrajectoryLeaseError( + "best-effort take cannot replace a pending minimum acquisition" + ) + take_count = min(limit, len(self._ready)) + elif not self._finished and limit > self.max_ready_groups: + raise TrajectoryCapacityError( + f"minimum acquisition requires {limit} trajectory groups; shared " + f"queue capacity is {self.max_ready_groups} groups" + ) + elif not self._finished and len(self._ready) < limit: + self._pending_minimum = request + return TrajectoryQueueTake() + else: + self._pending_minimum = None + take_count = min(limit, len(self._ready)) + + leases: list[TrajectoryQueueLease] = [] + while len(leases) < take_count: + result_id = self._ready.popleft() + entry = self._entries[result_id] + self._claim_generation += 1 + entry.phase = "packing" + entry.consumer_id = consumer_id + entry.claim_id = secrets.token_hex(16) + entry.claim_generation = self._claim_generation + entry.acquired_at = time.monotonic() + leases.append( + TrajectoryQueueLease( + claim_id=entry.claim_id, + consumer_id=consumer_id, + generation=self._claim_generation, + item=entry.item, + ) + ) + return TrajectoryQueueTake( + leases=tuple(leases), closed=self._finished and not self._ready + ) + + def mark_packed(self, operation: TrajectoryQueuePacking) -> None: + entries = [self._leased_entry(lease) for lease in operation.leases] + if any(entry.phase != "packing" for entry in entries): + raise TrajectoryLeaseError("trajectory claim is not being packed") + for entry in entries: + entry.phase = "packed" + entry.packing_generation_id = operation.generation_id + + def release(self, operation: TrajectoryQueueRelease) -> None: + entries = [self._leased_entry(lease) for lease in operation.leases] + for entry in entries: + if entry.phase == "packing": + if operation.disposition != "discarded" or operation.generation_id: + raise TrajectoryLeaseError( + "unpacked trajectory can only be discarded" + ) + elif entry.phase == "packed": + if operation.generation_id != entry.packing_generation_id: + raise TrajectoryLeaseError( + "trajectory packing generation does not match" + ) + else: + raise TrajectoryLeaseError("trajectory claim was not acquired") + now = time.monotonic() + for lease, entry in zip(operation.leases, entries, strict=True): + assert entry.acquired_at is not None + lifetime = now - entry.acquired_at + self._released_leases += 1 + self._lease_lifetime_s += lifetime + self._max_lease_lifetime_s = max(self._max_lease_lifetime_s, lifetime) + self._remove(lease.item.ref.result_id) + + def finish(self) -> None: + self._finished = True + + def close(self) -> tuple[TrajectoryGroupRef, ...]: + refs = tuple(entry.item.ref for entry in self._entries.values()) + self._entries.clear() + self._ready.clear() + self._used_records = 0 + self._used_bytes = 0 + self._finished = True + self._pending_minimum = None + self._minimum_error = None + return refs + + def snapshot(self) -> TrajectoryQueueSnapshot: + return TrajectoryQueueSnapshot( + items=tuple(entry.item for entry in self._entries.values()), + max_ready_groups=self.max_ready_groups, + generation=self.generation, + capacity_records=self.capacity_records, + capacity_bytes=self.capacity_bytes, + used_records=self._used_records, + used_bytes=self._used_bytes, + leased_groups=sum( + entry.phase != "ready" for entry in self._entries.values() + ), + ready_groups=sum( + entry.phase == "ready" for entry in self._entries.values() + ), + packing_groups=sum( + entry.phase == "packing" for entry in self._entries.values() + ), + packed_groups=sum( + entry.phase == "packed" for entry in self._entries.values() + ), + released_leases=self._released_leases, + lease_lifetime_s=self._lease_lifetime_s, + max_lease_lifetime_s=self._max_lease_lifetime_s, + ) + + def _leased_entry(self, lease: TrajectoryQueueLease) -> _QueueEntry: + entry = self._entries.get(lease.item.ref.result_id) + if ( + entry is None + or entry.item != lease.item + or entry.consumer_id != lease.consumer_id + or entry.claim_id != lease.claim_id + or entry.claim_generation != lease.generation + ): + raise TrajectoryLeaseError("trajectory result has no matching claim") + return entry + + def _remove(self, result_id: str) -> None: + entry = self._entries.pop(result_id) + self._used_records -= len(entry.item.ref.records) + self._used_bytes -= entry.item.ref.descriptor.byte_count + + def _fail_pending_minimum( + self, item: TrajectoryQueueItem, blocker: str + ) -> TrajectoryEnqueueResult: + assert self._pending_minimum is not None + count = self._pending_minimum[1] + ref = item.ref + packing = sum(entry.phase == "packing" for entry in self._entries.values()) + self._minimum_error = ( + f"minimum acquisition of {count} trajectory groups is unreachable: " + f"{len(self._ready)} ready/{packing} packing groups use " + f"{self._used_records}/{self.capacity_records} records and " + f"{self._used_bytes}/{self.capacity_bytes} bytes; result " + f"{ref.result_id!r} requires {len(ref.records)} records/" + f"{ref.descriptor.byte_count} bytes ({blocker})" + ) + return TrajectoryEnqueueResult( + status="minimum_unreachable", reason=self._minimum_error + ) + + def _minimum_cannot_make_progress(self) -> bool: + return all(entry.phase == "ready" for entry in self._entries.values()) + + +def _grouping_key(group: TrajectoryGroup, fallback: str) -> str: + value = group.metadata.get("grouping_tag", group.metadata.get("scenario_id")) + return fallback if value is None else str(value) + + +def _resolve_grouping(item: TrajectoryQueueItem) -> TrajectoryQueueItem: + ref = item.ref + scenario_id = item.annotations.metadata.get("scenario_id") + if ref.descriptor.grouping_key != ref.result_id or scenario_id is None: + return item + descriptor = ref.descriptor.model_copy(update={"grouping_key": str(scenario_id)}) + return item.model_copy( + update={"ref": ref.model_copy(update={"descriptor": descriptor})} + ) + + +def _policy_token_counts(trajectories: list[Trajectory]) -> dict[int, int]: + counts: dict[int, int] = {} + for trajectory in trajectories: + for version, tokens in _trajectory_policy_token_counts(trajectory).items(): + counts[version] = counts.get(version, 0) + tokens + return counts + + +def _trajectory_policy_token_counts(trajectory: Trajectory) -> dict[int, int]: + counts: dict[int, int] = {} + items: list[Any] = [ + choice + for exchange in trajectory.exchanges.chat_completions + for choice in exchange.response.choices + ] + items.extend(trajectory.messages_and_choices) + for history in trajectory.additional_histories: + items.extend(history.messages_and_choices) + for item in items: + extra = getattr(item, "model_extra", None) + if not isinstance(extra, Mapping): + continue + spans = extra.get("policy_token_spans") + if spans is None: + continue + if not isinstance(spans, list): + raise RuntimeError("policy_token_spans must be a list") + cursor = 0 + for span in spans: + parsed = PolicyTokenSpan.model_validate(span) + if parsed.start_token != cursor: + raise RuntimeError( + "policy_token_spans must be a contiguous completion partition" + ) + tokens = parsed.end_token - parsed.start_token + counts[parsed.policy_version] = ( + counts.get(parsed.policy_version, 0) + tokens + ) + cursor = parsed.end_token + return counts diff --git a/src/art/distributed/vllm_replica.py b/src/art/distributed/vllm_replica.py new file mode 100644 index 000000000..7b1aae6c8 --- /dev/null +++ b/src/art/distributed/vllm_replica.py @@ -0,0 +1,647 @@ +from __future__ import annotations + +import asyncio +from collections.abc import Awaitable, Callable, Mapping +import hashlib +import json +from pathlib import Path +from typing import Literal, Protocol +import uuid + +from pydantic import BaseModel, ConfigDict, Field + +from ..utils.lifecycle import ChildProcessSupervisor +from ..vllm_runtime import ManagedVllmRuntime, VllmRuntimeLaunchConfig +from .adapter_transport import AdapterReceiveResult, AdapterTransferTarget +from .specs import ModelServiceMemberSpec, ModelServiceSpec + + +class _Message(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + +MemberPhase = Literal["starting", "ready", "stopped", "failed"] +ReplicaPhase = Literal[ + "stopped", "starting", "ready", "updating", "quarantined", "closing" +] + + +class ReplicaLaunchTemplate(_Message): + served_model_name: str = Field(min_length=1) + lora_path: str | None = None + initial_policy_version: int | None = Field(default=None, ge=0) + engine_args: dict[str, object] = Field(default_factory=dict) + server_args: dict[str, object] = Field(default_factory=dict) + + +class HostMemberLaunchRequest(_Message): + replica_id: str + member: ModelServiceMemberSpec + generation: int = Field(ge=0) + generation_digest: str = Field(min_length=1) + process_uuid: str = Field(min_length=1) + startup_timeout_s: float = Field(gt=0) + launch_config: VllmRuntimeLaunchConfig + + +class HostMemberState(_Message): + replica_id: str + member_id: str + generation: int = Field(ge=0) + generation_digest: str = Field(min_length=1) + process_uuid: str = Field(min_length=1) + phase: MemberPhase + detail: str | None = None + + +class ReplicaUpdateReport(_Message): + replica_id: str + generation: int = Field(ge=0) + generation_digest: str = Field(min_length=1) + policy_version: str = Field(min_length=1) + policy_digest: str = Field(min_length=1) + update_identity: str = Field(min_length=1) + ambiguous: bool = False + + +class ReplicaState(_Message): + replica_id: str + generation: int = Field(ge=0) + generation_digest: str = Field(min_length=1) + phase: ReplicaPhase + members: tuple[HostMemberState, ...] = () + committed_version: str | None = None + policy_digest: str | None = None + update_identity: str | None = None + quarantine_reason: str | None = None + + +class ReplicaFailure(_Message): + replica_id: str + generation: int = Field(ge=0) + generation_digest: str = Field(min_length=1) + reason: str = Field(min_length=1) + + +class ReplicaHostLauncher(Protocol): + async def start_member( + self, request: HostMemberLaunchRequest + ) -> HostMemberState: ... + + async def member_state( + self, replica_id: str, member_id: str, generation: int + ) -> HostMemberState: ... + + async def stop_member( + self, replica_id: str, member_id: str, generation: int + ) -> None: ... + + async def prepare_adapter_receive( + self, + generation_id: str, + template_path: str, + timeout_s: float, + transport: Literal["local", "nixl"], + ) -> AdapterTransferTarget: ... + + async def wait_adapter_receive( + self, generation_id: str, timeout_s: float + ) -> AdapterReceiveResult: ... + + async def release_adapter_receive(self, generation_id: str) -> None: ... + + +class _ManagedMember: + def __init__(self, request: HostMemberLaunchRequest) -> None: + self.request = request + self.runtime = ManagedVllmRuntime(host=request.launch_config.host) + self.failure: RuntimeError | None = None + self.supervisor = ChildProcessSupervisor(self._failed) + + def _failed(self, error: RuntimeError) -> None: + self.failure = error + + +class ManagedVllmHostLauncher: + """Host-local implementation of the serializable member launch protocol.""" + + def __init__( + self, + output_root: str, + *, + install_parent_cleanup: Callable[[], None] = lambda: None, + startup_timeout_s: float | None = None, + ) -> None: + self._output_root = Path(output_root) + self._install_parent_cleanup = install_parent_cleanup + self._startup_timeout_s = startup_timeout_s + self._members: dict[tuple[str, str, int], _ManagedMember] = {} + + async def start_member(self, request: HostMemberLaunchRequest) -> HostMemberState: + key = (request.replica_id, request.member.member_id, request.generation) + if key in self._members: + raise RuntimeError(f"vLLM member already exists: {key}") + managed = _ManagedMember(request) + self._members[key] = managed + output_dir = self._output_root / request.process_uuid / request.replica_id + output_dir /= str(request.generation) + output_dir /= request.member.member_id + try: + await managed.runtime.start( + launch_config=request.launch_config, + output_dir=str(output_dir), + child_processes=managed.supervisor, + install_parent_cleanup=self._install_parent_cleanup, + timeout=self._startup_timeout_s or request.startup_timeout_s, + ) + except BaseException: + await self.stop_member(*key) + raise + return self._state(managed, "ready") + + async def member_state( + self, replica_id: str, member_id: str, generation: int + ) -> HostMemberState: + managed = self._members.get((replica_id, member_id, generation)) + if managed is None: + raise RuntimeError( + f"unknown vLLM member {replica_id}/{member_id}/{generation}" + ) + process = managed.runtime.process + failed = managed.failure + if failed is None and process is not None and process.poll() is not None: + failed = RuntimeError(f"process exited with code {process.returncode}") + return self._state( + managed, + "failed" if failed is not None else "ready", + detail=str(failed) if failed is not None else None, + ) + + async def stop_member( + self, replica_id: str, member_id: str, generation: int + ) -> None: + key = (replica_id, member_id, generation) + managed = self._members.get(key) + if managed is None: + return + managed.supervisor.close() + await asyncio.to_thread(managed.runtime.close) + self._members.pop(key, None) + + async def close(self) -> None: + keys = tuple(self._members) + results = await asyncio.gather( + *(self.stop_member(*key) for key in keys), return_exceptions=True + ) + failures = [result for result in results if isinstance(result, BaseException)] + if failures: + raise BaseExceptionGroup("failed to stop vLLM host members", failures) + + @staticmethod + def _state( + managed: _ManagedMember, phase: MemberPhase, detail: str | None = None + ) -> HostMemberState: + request = managed.request + return HostMemberState( + replica_id=request.replica_id, + member_id=request.member.member_id, + generation=request.generation, + generation_digest=request.generation_digest, + process_uuid=request.process_uuid, + phase=phase, + detail=detail, + ) + + +class ReplicaManager: + """Owns one native vLLM serving group as an indivisible failure domain.""" + + def __init__( + self, + spec: ModelServiceSpec, + launchers: Mapping[str, ReplicaHostLauncher], + template: ReplicaLaunchTemplate, + *, + on_failure: Callable[[ReplicaFailure], Awaitable[None]] | None = None, + startup_timeout_s: float = 600.0, + rpc_timeout_s: float = 60.0, + monitor_interval_s: float = 0.25, + ) -> None: + if min(startup_timeout_s, rpc_timeout_s, monitor_interval_s) <= 0: + raise ValueError("replica timeouts must be positive") + missing = {member.host_id for member in spec.members} - launchers.keys() + if missing: + raise ValueError(f"replica launchers missing hosts: {sorted(missing)}") + executor = template.engine_args.get("distributed_executor_backend") + if executor not in (None, "mp", "multiprocessing"): + raise ValueError("ART-managed replicas require vLLM multiprocessing") + for key in ("revision", "tokenizer_revision"): + configured = template.engine_args.get(key) + if configured is not None and configured != spec.model_revision: + raise ValueError(f"{key} conflicts with the replica model revision") + self._spec = spec + self._launchers = launchers + self._template = template + self._on_failure = on_failure + self._startup_timeout_s = startup_timeout_s + self._rpc_timeout_s = rpc_timeout_s + self._monitor_interval_s = monitor_interval_s + self._lock = asyncio.Lock() + self._monitor_task: asyncio.Task[None] | None = None + digest = self._generation_digest(spec, 0) + self._state = ReplicaState( + replica_id=spec.name, + generation=0, + generation_digest=digest, + phase="stopped", + ) + + @property + def spec(self) -> ModelServiceSpec: + return self._spec + + @property + def state(self) -> ReplicaState: + return self._state + + async def start(self) -> ReplicaState: + async with self._lock: + return await self._start_locked() + + async def _start_locked(self) -> ReplicaState: + if self._state.phase != "stopped": + raise RuntimeError(f"cannot start replica in {self._state.phase} state") + self._state = self._state.model_copy(update={"phase": "starting"}) + requests = tuple(self._launch_request(member) for member in self._spec.members) + tasks = [ + asyncio.create_task( + self._launchers[request.member.host_id].start_member(request) + ) + for request in requests + ] + try: + async with asyncio.timeout(self._startup_timeout_s + self._rpc_timeout_s): + members = await asyncio.gather(*tasks) + if any(member.phase != "ready" for member in members): + raise RuntimeError(f"vLLM gang was not ready: {members!r}") + except BaseException as error: + for task in tasks: + task.cancel() + await asyncio.gather(*tasks, return_exceptions=True) + try: + await self._stop_members(requests) + except BaseException as cleanup_error: + error = BaseExceptionGroup( + "vLLM gang startup and teardown failed", + [error, cleanup_error], + ) + self._state = self._state.model_copy( + update={ + "phase": "quarantined", + "quarantine_reason": f"gang startup failed: {error}", + } + ) + raise error from None + self._state = self._state.model_copy( + update={"phase": "ready", "members": tuple(members)} + ) + self._monitor_task = asyncio.create_task(self._monitor()) + return self._state + + async def stop(self) -> ReplicaState: + async with self._lock: + return await self._stop_locked() + + async def _stop_locked(self) -> ReplicaState: + await self._cancel_monitor() + self._state = self._state.model_copy(update={"phase": "closing"}) + try: + await self._stop_current_members() + except BaseException as error: + self._state = self._state.model_copy( + update={ + "phase": "quarantined", + "quarantine_reason": f"replica teardown failed: {error}", + } + ) + raise + self._state = self._state.model_copy(update={"phase": "stopped", "members": ()}) + return self._state + + async def restart( + self, + *, + served_model_name: str, + lora_path: str | None, + initial_policy_version: int | None, + ) -> ReplicaState: + async with self._lock: + await self._stop_locked() + self._template = self._template.model_copy( + update={ + "served_model_name": served_model_name, + "lora_path": lora_path, + "initial_policy_version": initial_policy_version, + } + ) + generation = self._state.generation + 1 + self._state = ReplicaState( + replica_id=self._spec.name, + generation=generation, + generation_digest=self._generation_digest(self._spec, generation), + phase="stopped", + ) + return await self._start_locked() + + def prepare_update(self, *, update_identity: str) -> ReplicaState: + if self._state.phase != "ready": + raise RuntimeError(f"cannot update replica in {self._state.phase} state") + self._state = self._state.model_copy( + update={"phase": "updating", "update_identity": update_identity} + ) + return self._state + + async def prepare_adapter_transfer( + self, + generation_id: str, + template_path: str, + *, + transport: Literal["local", "nixl"] = "nixl", + ) -> tuple[AdapterTransferTarget, ...]: + return tuple( + await asyncio.gather( + *( + asyncio.wait_for( + self._launchers[host_id].prepare_adapter_receive( + generation_id, + template_path, + max(1.0, self._rpc_timeout_s - 1.0), + transport, + ), + self._rpc_timeout_s, + ) + for host_id in dict.fromkeys( + member.host_id for member in self._spec.members + ) + ) + ) + ) + + async def wait_adapter_transfer( + self, generation_id: str + ) -> tuple[AdapterReceiveResult, ...]: + return tuple( + await asyncio.gather( + *( + asyncio.wait_for( + self._launchers[host_id].wait_adapter_receive( + generation_id, self._rpc_timeout_s + ), + self._rpc_timeout_s, + ) + for host_id in dict.fromkeys( + member.host_id for member in self._spec.members + ) + ) + ) + ) + + async def release_adapter_transfer(self, generation_id: str) -> None: + await asyncio.gather( + *( + asyncio.wait_for( + self._launchers[host_id].release_adapter_receive(generation_id), + self._rpc_timeout_s, + ) + for host_id in dict.fromkeys( + member.host_id for member in self._spec.members + ) + ) + ) + + def verify_update(self, report: ReplicaUpdateReport) -> ReplicaState: + expected = self._state + valid = ( + expected.phase == "updating" + and report.replica_id == expected.replica_id + and report.generation == expected.generation + and report.generation_digest == expected.generation_digest + and report.update_identity == expected.update_identity + and not report.ambiguous + ) + if not valid: + return self.quarantine(f"ambiguous update report: {report.model_dump()}") + self._state = expected.model_copy( + update={ + "phase": "ready", + "committed_version": report.policy_version, + "policy_digest": report.policy_digest, + "quarantine_reason": None, + } + ) + return self._state + + def quarantine(self, reason: str) -> ReplicaState: + self._state = self._state.model_copy( + update={"phase": "quarantined", "quarantine_reason": reason} + ) + return self._state + + async def poll(self) -> ReplicaState: + failure_event: ReplicaFailure | None = None + async with self._lock: + if self._state.phase not in {"ready", "updating"}: + return self._state + states = await asyncio.gather( + *( + asyncio.wait_for( + self._launchers[member.host_id].member_state( + self._spec.name, + member.member_id, + self._state.generation, + ), + self._rpc_timeout_s, + ) + for member in self._spec.members + ), + return_exceptions=True, + ) + failure = next( + ( + state + for state in states + if isinstance(state, BaseException) or state.phase != "ready" + ), + None, + ) + if failure is None: + self._state = self._state.model_copy( + update={"members": tuple(states)} # type: ignore[arg-type] + ) + return self._state + reason = f"member failure: {failure}" + generation = self._state.generation + generation_digest = self._state.generation_digest + self.quarantine(reason) + failure_event = ReplicaFailure( + replica_id=self._spec.name, + generation=generation, + generation_digest=generation_digest, + reason=reason, + ) + try: + await self._stop_current_members() + except BaseException as error: + reason += f"; teardown failure: {error}" + self.quarantine(reason) + failure_event = failure_event.model_copy(update={"reason": reason}) + if self._on_failure is not None: + await self._on_failure(failure_event) + return self._state + + async def _monitor(self) -> None: + current = asyncio.current_task() + try: + while self._monitor_task is current and self._state.phase in { + "ready", + "updating", + }: + await asyncio.sleep(self._monitor_interval_s) + await self.poll() + except asyncio.CancelledError: + pass + + async def _cancel_monitor(self) -> None: + task, self._monitor_task = self._monitor_task, None + if task is None or task is asyncio.current_task(): + return + task.cancel() + await asyncio.gather(task, return_exceptions=True) + + async def _stop_current_members(self) -> None: + await self._stop_calls( + tuple( + ( + self._launchers[member.host_id], + self._spec.name, + member.member_id, + self._state.generation, + ) + for member in self._spec.members + ) + ) + + async def _stop_members( + self, requests: tuple[HostMemberLaunchRequest, ...] + ) -> None: + await self._stop_calls( + tuple( + ( + self._launchers[request.member.host_id], + request.replica_id, + request.member.member_id, + request.generation, + ) + for request in requests + ) + ) + + async def _stop_calls( + self, + calls: tuple[tuple[ReplicaHostLauncher, str, str, int], ...], + ) -> None: + pending = calls + failures: list[BaseException] = [] + for _attempt in range(2): + results = await asyncio.gather( + *( + asyncio.wait_for( + launcher.stop_member(replica_id, member_id, generation), + self._rpc_timeout_s, + ) + for launcher, replica_id, member_id, generation in pending + ), + return_exceptions=True, + ) + failures = [ + result for result in results if isinstance(result, BaseException) + ] + if not failures: + return + pending = tuple( + call + for call, result in zip(pending, results, strict=True) + if isinstance(result, BaseException) + ) + raise BaseExceptionGroup("failed to stop vLLM replica members", failures) + + def _launch_request( + self, member: ModelServiceMemberSpec + ) -> HostMemberLaunchRequest: + parallel = self._spec.parallel + engine_args = { + **self._template.engine_args, + "tensor_parallel_size": parallel.tp, + "pipeline_parallel_size": parallel.pp, + "data_parallel_size": parallel.dp, + "enable_expert_parallel": parallel.enable_expert_parallel, + } + if self._spec.model_revision is not None: + engine_args.update( + revision=self._spec.model_revision, + tokenizer_revision=self._spec.model_revision, + ) + process_uuid = uuid.uuid4().hex + physical_ids = all(isinstance(gpu_id, int) for gpu_id in member.gpu_ids) + launch = VllmRuntimeLaunchConfig( + base_model=self._spec.base_model, + port=self._spec.leader_endpoint.port, + host=( + self._spec.leader_endpoint.host + if member.node_rank == 0 + else "127.0.0.1" + ), + cuda_visible_devices=( + None if physical_ids else ",".join(map(str, member.gpu_ids)) + ), + local_gpu_ids=( + tuple(gpu_id for gpu_id in member.gpu_ids if isinstance(gpu_id, int)) + if physical_ids + else None + ), + lora_path=self._template.lora_path, + served_model_name=self._template.served_model_name, + rollout_weights_mode=self._spec.update_mode, + engine_args=engine_args, + server_args=self._template.server_args, + nnodes=len(self._spec.members), + node_rank=member.node_rank, + master_addr=self._spec.rendezvous.host + if len(self._spec.members) > 1 + else None, + master_port=self._spec.rendezvous.port + if len(self._spec.members) > 1 + else None, + headless=member.node_rank != 0, + replica_generation=self._state.generation, + process_uuid=process_uuid, + update_identity=self._state.update_identity, + initial_policy_version=self._template.initial_policy_version, + ) + return HostMemberLaunchRequest( + replica_id=self._spec.name, + member=member, + generation=self._state.generation, + generation_digest=self._state.generation_digest, + process_uuid=process_uuid, + startup_timeout_s=self._startup_timeout_s, + launch_config=launch, + ) + + @staticmethod + def _generation_digest(spec: ModelServiceSpec, generation: int) -> str: + payload = json.dumps( + {"generation": generation, "spec": spec.model_dump(mode="json")}, + sort_keys=True, + ).encode() + return hashlib.sha256(payload).hexdigest() diff --git a/src/art/local/adapter_leases.py b/src/art/local/adapter_leases.py index f790e5a36..f8f6f9a8d 100644 --- a/src/art/local/adapter_leases.py +++ b/src/art/local/adapter_leases.py @@ -32,3 +32,8 @@ async def lease(self, step: int) -> AsyncIterator[None]: def active_steps(self) -> set[int]: return set(self._counts) + + @asynccontextmanager + async def prune_guard(self) -> AsyncIterator[set[int]]: + async with self._condition: + yield self.active_steps() diff --git a/src/art/local/backend.py b/src/art/local/backend.py index 9f93d4832..684b3df64 100644 --- a/src/art/local/backend.py +++ b/src/art/local/backend.py @@ -18,6 +18,8 @@ from art.utils.lifecycle import ( PROCESS_SHUTDOWN_TIMEOUT_SECONDS, + complete_task, + complete_to_thread, process_shutdown_timeout, ) @@ -32,6 +34,7 @@ import httpx import numpy as np import polars as pl +from pydantic import BaseModel, ConfigDict import torch from tqdm import auto as tqdm from transformers import AutoTokenizer @@ -89,7 +92,7 @@ tokenize_sft_batch, tokenize_trajectory_groups, ) -from ..serving_capabilities import ServingCapabilities +from ..serving_capabilities import FastMetricsSnapshot, ServingCapabilities from ..trajectories import Trajectory, TrajectoryGroup from ..trajectories._selection import automatic_training_model_selector from ..types import ( @@ -114,6 +117,43 @@ from .service import ModelService +class _PackedTrainingBatch(BaseModel): + model_config = ConfigDict(extra="forbid", arbitrary_types_allowed=True) + + payload: Any + num_sequences: int + sequence_length: int + trainable_assistant_tokens: int + loss_bearing_tokens: int + non_padding_tokens: int + logical_tokens: int + physical_tokens: int + include_moe_routing: bool + + +class _TrainStepVllmMetricsCollector: + def __init__(self, backend: "LocalBackend", model: Model) -> None: + self._backend = backend + self._model = model + self._client = httpx.AsyncClient( + timeout=1.0, + limits=httpx.Limits(max_connections=1, max_keepalive_connections=1), + ) + self._snapshots: dict[ + tuple[str, str, str, int], tuple[float, dict[str, float]] + ] = {} + + async def collect(self) -> dict[str, float]: + return await self._backend._collect_train_step_vllm_metrics( + self._model, + client=self._client, + snapshots=self._snapshots, + ) + + async def aclose(self) -> None: + await self._client.aclose() + + def _prometheus_values(text: str, name: str) -> list[float]: values: list[float] = [] for line in text.splitlines(): @@ -317,8 +357,9 @@ def __init__( self._grad_accumulation_sequences_by_service: dict[int, int] = {} self._provenance_update_tasks: set[asyncio.Task[None]] = set() self._vllm_metric_snapshots: dict[ - tuple[str, str], tuple[float, dict[str, float]] + tuple[str, str, str, int], tuple[float, dict[str, float]] ] = {} + self._vllm_metrics_client: httpx.AsyncClient | None = None self._image_processors: dict[str, BaseImageProcessor | None] = {} self._requires_explicit_packed_sequence_length = False self._packed_sequence_length_requires_chunk_alignment = True @@ -376,6 +417,13 @@ def _model_max_sequence_length(self, model: AnyTrainableModel) -> int: def supports_automatic_train_step_metrics(self) -> bool: return True + def _supports_concurrent_training_and_inference( + self, model: AnyTrainableModel + ) -> bool: + from ..dev.validate import is_dedicated_mode + + return is_dedicated_mode(model._internal_config or dev.InternalModelConfig()) + def automatic_gpu_cost_per_hour_usd(self, model: Model) -> float | None: per_gpu_cost = self._resolve_gpu_cost_per_hour_usd() if per_gpu_cost is None: @@ -386,65 +434,80 @@ def automatic_gpu_cost_per_hour_usd(self, model: Model) -> float | None: return None return per_gpu_cost * gpu_count + def create_train_step_vllm_metrics_collector( + self, model: Model + ) -> _TrainStepVllmMetricsCollector: + return _TrainStepVllmMetricsCollector(self, model) + async def collect_train_step_vllm_metrics(self, model: Model) -> dict[str, float]: + client = self._vllm_metrics_client + if client is None: + client = self._vllm_metrics_client = httpx.AsyncClient( + timeout=1.0, + limits=httpx.Limits(max_connections=4, max_keepalive_connections=4), + ) + return await self._collect_train_step_vllm_metrics( + model, + client=client, + snapshots=self._vllm_metric_snapshots, + ) + + async def _collect_train_step_vllm_metrics( + self, + model: Model, + *, + client: httpx.AsyncClient, + snapshots: dict[tuple[str, str, str, int], tuple[float, dict[str, float]]], + ) -> dict[str, float]: capabilities = model._serving_capabilities if capabilities is None: raise RuntimeError("vLLM serving capabilities have not been discovered") capabilities.require("fast_metrics", operation="ART vLLM metrics collection") - base_url = model.inference_base_url - if not base_url or not base_url.startswith(("http://", "https://")): - raise RuntimeError( - "ART vLLM metrics require model.inference_base_url to point to the " - "dedicated ART vLLM runtime." - ) - - metrics_root = base_url.rstrip("/") - if metrics_root.endswith("/v1"): - metrics_root = metrics_root[: -len("/v1")] - headers = ( - {"Authorization": f"Bearer {model.inference_api_key}"} - if model.inference_api_key - else None - ) + endpoint = capabilities.fast_metrics + assert endpoint is not None + metrics_url = str(endpoint.url) try: - async with httpx.AsyncClient(timeout=1.0) as client: - response = await client.get( - f"{metrics_root}/art/metrics", - headers=headers, - ) - response.raise_for_status() - payload = response.json() + response = await client.get( + metrics_url, + headers=( + {"Authorization": f"Bearer {model.inference_api_key}"} + if model.inference_api_key + else None + ), + ) + response.raise_for_status() + payload = FastMetricsSnapshot.model_validate(response.json()) except httpx.TimeoutException: raise ArtVllmMetricsTimeoutError( - f"Timed out collecting ART vLLM metrics from {metrics_root}." + f"Timed out collecting ART vLLM metrics from {metrics_url}." ) except (httpx.HTTPError, ValueError) as exc: raise RuntimeError( - "ART vLLM metrics require the dedicated ART runtime endpoint at " - f"{metrics_root}/art/metrics." + f"ART vLLM metrics endpoint returned an invalid response from " + f"{metrics_url}." ) from exc - raw_metrics = payload.get("metrics") if isinstance(payload, dict) else None - if not isinstance(raw_metrics, dict): - raise RuntimeError( - "ART vLLM metrics endpoint returned an invalid payload: expected " - "a top-level metrics object." - ) + raw_metrics = payload.metrics + process_uuid = payload.process_uuid + generation = payload.generation def required_metric(name: str) -> float: - raw_value = raw_metrics.get(name) - if not isinstance(raw_value, (int, float)): + try: + return raw_metrics[name] + except KeyError: raise RuntimeError( f"ART vLLM metrics endpoint did not provide numeric {name!r}." - ) - return float(raw_value) + ) from None def optional_metric(name: str) -> float | None: - raw_value = raw_metrics.get(name) - if not isinstance(raw_value, (int, float)): - return None - return float(raw_value) + return raw_metrics.get(name) + counter_names = ( + "prompt_tokens_total", + "generation_tokens_total", + "prefix_cache_queries_total", + "prefix_cache_hits_total", + ) snapshot = { "prompt_tokens_total": required_metric("prompt_tokens_total"), "generation_tokens_total": required_metric("generation_tokens_total"), @@ -452,8 +515,7 @@ def optional_metric(name: str) -> float | None: "prefix_cache_hits_total": required_metric("prefix_cache_hits_total"), "num_preemptions_total": required_metric("num_preempted_reqs_total"), } - metrics: dict[str, float] = {} - gauges = { + metrics: dict[str, float] = { "vllm/num_requests_running": required_metric("num_requests_running"), "vllm/num_requests_waiting": required_metric("num_requests_waiting"), "vllm/num_requests_waiting_capacity": required_metric( @@ -462,9 +524,6 @@ def optional_metric(name: str) -> float | None: "vllm/kv_cache_usage_perc": required_metric("kv_cache_usage_perc"), "vllm/num_preemptions_total": snapshot["num_preemptions_total"], } - for key, value in gauges.items(): - if value is not None: - metrics[key] = value for name in ( "max_num_seqs", "max_num_batched_tokens", @@ -476,52 +535,52 @@ def optional_metric(name: str) -> float | None: if value is not None: metrics[f"vllm/{name}"] = value + current = {name: snapshot[name] for name in counter_names} now = time.monotonic() - storage_key = self._model_storage_key(model) - previous = self._vllm_metric_snapshots.get(storage_key) + model_key = self._model_storage_key(model) + key = (*model_key, process_uuid, generation) + previous = snapshots.get(key) + snapshots[key] = (now, current) + for stale in tuple(snapshots): + if stale[:2] == model_key and stale != key: + del snapshots[stale] + delta_queries = 0.0 if previous is not None: previous_time, previous_snapshot = previous - elapsed = max(0.0, now - previous_time) + elapsed = now - previous_time if elapsed > 0: - prompt_tokens = snapshot["prompt_tokens_total"] - previous_prompt_tokens = previous_snapshot.get("prompt_tokens_total") - if prompt_tokens is not None and previous_prompt_tokens is not None: - metrics["vllm/prompt_tok_per_s"] = max( - 0.0, (prompt_tokens - previous_prompt_tokens) / elapsed + metrics["vllm/prompt_tok_per_s"] = max( + 0.0, + ( + current["prompt_tokens_total"] + - previous_snapshot["prompt_tokens_total"] ) - generation_tokens = snapshot["generation_tokens_total"] - previous_generation_tokens = previous_snapshot.get( - "generation_tokens_total" + / elapsed, ) - if ( - generation_tokens is not None - and previous_generation_tokens is not None - ): - metrics["vllm/completion_tok_per_s"] = max( - 0.0, (generation_tokens - previous_generation_tokens) / elapsed + metrics["vllm/completion_tok_per_s"] = max( + 0.0, + ( + current["generation_tokens_total"] + - previous_snapshot["generation_tokens_total"] ) - - prefix_queries = snapshot["prefix_cache_queries_total"] - previous_prefix_queries = previous_snapshot.get( - "prefix_cache_queries_total" + / elapsed, + ) + delta_queries = max( + 0.0, + current["prefix_cache_queries_total"] + - previous_snapshot["prefix_cache_queries_total"], ) - prefix_hits = snapshot["prefix_cache_hits_total"] - previous_prefix_hits = previous_snapshot.get("prefix_cache_hits_total") - if ( - prefix_queries is not None - and previous_prefix_queries is not None - and prefix_hits is not None - and previous_prefix_hits is not None - ): - delta_queries = prefix_queries - previous_prefix_queries - if delta_queries > 0: - metrics["vllm/prefix_cache_hit_rate"] = max( - 0.0, - min(1.0, (prefix_hits - previous_prefix_hits) / delta_queries), - ) - elif ( - snapshot["prefix_cache_queries_total"] is not None - and snapshot["prefix_cache_hits_total"] is not None + delta_hits = max( + 0.0, + current["prefix_cache_hits_total"] + - previous_snapshot["prefix_cache_hits_total"], + ) + if delta_queries > 0: + metrics["vllm/prefix_cache_hit_rate"] = min( + 1.0, delta_hits / delta_queries + ) + if ( + "vllm/prefix_cache_hit_rate" not in metrics and snapshot["prefix_cache_queries_total"] > 0 ): metrics["vllm/prefix_cache_hit_rate"] = max( @@ -533,10 +592,6 @@ def optional_metric(name: str) -> float | None: ), ) - self._vllm_metric_snapshots[storage_key] = ( - now, - {key: value for key, value in snapshot.items() if value is not None}, - ) return metrics def _resolve_gpu_cost_per_hour_usd(self) -> float | None: @@ -622,10 +677,18 @@ async def __aexit__( await self.close() async def close(self) -> None: + task = asyncio.create_task(self._close_local_backend()) + _, cancelled = await complete_task(task) + if cancelled is not None: + raise cancelled + + async def _close_local_backend(self) -> None: """ If running vLLM in a separate process, this will kill that process and close the communication threads. """ + failures: list[Exception] = [] for service in self._services.values(): + propagate = bool(getattr(service, "propagate_close_errors", False)) try: aclose = getattr(service, "aclose", None) if aclose is None: @@ -634,24 +697,40 @@ async def close(self) -> None: close() else: await asyncio.wait_for( - aclose(), timeout=_SERVICE_CLOSE_TIMEOUT_SECONDS + aclose(), + timeout=float( + getattr( + service, + "close_timeout_s", + _SERVICE_CLOSE_TIMEOUT_SECONDS, + ) + ), ) - except TimeoutError: - logger.warning("Timed out while closing local backend service.") - except Exception: - logger.exception("Failed to close local backend service.") + except Exception as error: + if propagate: + failures.append(error) + else: + logger.exception("Failed to close local backend service.") finally: try: close_proxy(service) - except Exception: - logger.exception("Failed to close local backend service proxy.") + except Exception as error: + if propagate: + failures.append(error) + else: + logger.exception("Failed to close local backend service proxy.") self._services.clear() self._adapter_leases.clear() + client, self._vllm_metrics_client = self._vllm_metrics_client, None + if client is not None: + await client.aclose() await self._drain_provenance_update_tasks() gc.collect() if torch.cuda.is_available(): torch.cuda.empty_cache() torch.cuda.ipc_collect() + if failures: + raise ExceptionGroup("distributed backend close failed", failures) def _close(self) -> None: self._cancel_provenance_update_tasks() @@ -669,6 +748,14 @@ def _close(self) -> None: logger.exception("Failed to close local backend service proxy.") self._services.clear() self._adapter_leases.clear() + client, self._vllm_metrics_client = self._vllm_metrics_client, None + if client is not None: + try: + loop = asyncio.get_running_loop() + except RuntimeError: + asyncio.run(client.aclose()) + else: + loop.create_task(client.aclose()) gc.collect() if torch.cuda.is_available(): torch.cuda.empty_cache() @@ -863,11 +950,14 @@ async def prune_model_adapters( if service is None: return manager = self._adapter_leases.get(storage_key) - if manager is not None: - retain_steps = set(retain_steps) | manager.active_steps() prune_loaded_adapters = getattr(service, "prune_loaded_adapters", None) - if prune_loaded_adapters is not None: + if prune_loaded_adapters is None: + return + if manager is None: await prune_loaded_adapters(retain_steps=retain_steps) + return + async with manager.prune_guard() as leased_steps: + await prune_loaded_adapters(retain_steps=set(retain_steps) | leased_steps) async def _get_service(self, model: TrainableModel) -> ModelService: from ..dev.get_model_config import get_model_config @@ -902,11 +992,14 @@ async def _get_service(self, model: TrainableModel) -> ModelService: str(g) for g in config["trainer_gpu_ids"] ) - self._services[storage_key] = service_class( - model_name=model.name, - base_model=model.base_model, - config=config, - output_dir=get_model_dir(model=model, art_path=self._path), + self._services[storage_key] = cast( + ModelService, + service_class( + model_name=model.name, + base_model=model.base_model, + config=config, + output_dir=get_model_dir(model=model, art_path=self._path), + ), ) if not dedicated and not self._in_process: self._services[storage_key] = move_to_child_process( @@ -1053,7 +1146,7 @@ def _get_packed_tensors( not allow_training_without_logprobs and np.isnan(packed_tensors["logprobs"]).all() ): - print( + logger.warning( "There are no assistant logprobs to train on. Did you forget to include at least one Choice in Trajectory.messages_and_choices?" ) return None @@ -1062,7 +1155,7 @@ def _get_packed_tensors( packed_tensors, get_model_dir(model=model, art_path=self._path) ) else: - print( + logger.info( f"Packed {len(tokenized_results)} trajectories into {packed_tensors['tokens'].shape[0]} sequences of length {packed_tensors['tokens'].shape[1]}" ) return packed_tensors @@ -1112,6 +1205,8 @@ async def _delete_checkpoint_files( """Delete checkpoint files, keeping only the specified steps.""" output_dir = get_model_dir(model=model, art_path=self._path) + from ..megatron.optimizer_state import optimizer_retention_lease + service = await self._get_service(model) try: from ..tinker.service import TinkerService @@ -1121,7 +1216,12 @@ async def _delete_checkpoint_files( return except ImportError: pass - delete_checkpoints(output_dir, steps_to_keep) + + def delete_retained() -> None: + with optimizer_retention_lease(output_dir, set(steps_to_keep)) as protected: + delete_checkpoints(output_dir, sorted(protected)) + + await asyncio.to_thread(delete_retained) async def _prepare_backend_for_training( self, @@ -1240,6 +1340,8 @@ async def train( # type: ignore[override] # Checkpoint behavior save_checkpoint: bool = True, optimizer_save_interval: int = 5, + final_training_step: int | None = None, + grad_accumulation_sequences: int | None = None, # Verbosity verbose: bool = False, ) -> LocalTrainResult: @@ -1376,6 +1478,8 @@ async def train( # type: ignore[override] num_trajectories_learning_rate_multiplier_power=num_trajectories_learning_rate_multiplier_power, kl_ref_adapter_path=resolved_kl_ref_adapter_path, optimizer_save_interval=optimizer_save_interval, + final_training_step=final_training_step, + grad_accumulation_sequences=grad_accumulation_sequences, ) # Collect metrics from training @@ -1437,6 +1541,57 @@ async def update() -> None: self._provenance_update_tasks.add(task) task.add_done_callback(self._provenance_update_tasks.discard) + async def _advance_skipped_step( + self, + model: TrainableModel, + service: ModelService, + current_step: int, + next_step: int, + ) -> dict[str, float]: + model_dir = get_model_dir(model=model, art_path=self._path) + current = get_step_checkpoint_dir(model_dir, current_step) + if not os.path.exists(current): + return {} + checkpoint = get_step_checkpoint_dir(model_dir, next_step) + if os.path.exists(checkpoint): + raise RuntimeError(f"Refusing to replace checkpoint {checkpoint}") + registration_started = False + try: + _, cancelled = await complete_to_thread( + lambda: shutil.copytree(current, checkpoint) + ) + if cancelled is not None: + raise cancelled + registration_started = True + await service.register_lora_for_step(next_step, checkpoint) + except BaseException as error: + failures: list[BaseException] = [error] + if registration_started: + self._services.pop(model.name, None) + try: + _, close_cancelled = await complete_task( + asyncio.create_task(service.aclose()) + ) + if close_cancelled is not None: + failures.append(close_cancelled) + except BaseException as close_error: + failures.append(close_error) + if os.path.exists(checkpoint): + try: + _, remove_cancelled = await complete_to_thread( + lambda: shutil.rmtree(checkpoint) + ) + if remove_cancelled is not None: + failures.append(remove_cancelled) + except BaseException as remove_error: + failures.append(remove_error) + if len(failures) > 1: + raise BaseExceptionGroup( + "skipped-step publication and rollback failed", failures + ) from None + raise + return {} + async def _train_model( self, model: TrainableModel, @@ -1458,22 +1613,13 @@ async def _train_model( include_trainable_groups=True, ) include_moe_routing = self._model_uses_expert_replay(model) - packed_tensors = self._get_packed_tensors( + packed_batch = await self._prepare_training_batch( model, trajectory_groups, - advantage_balance=dev_config.get("advantage_balance", 0.0), - allow_training_without_logprobs=dev_config.get( - "allow_training_without_logprobs", False - ), - scale_rewards=dev_config.get("scale_rewards", True), - plot_tensors=dev_config.get("plot_tensors", False), - packed_sequence_length=dev_config.get("packed_sequence_length"), - logprob_calculation_chunk_size=dev_config.get( - "logprob_calculation_chunk_size", 1024 - ), + dev_config, include_moe_routing=include_moe_routing, ) - if packed_tensors is None: + if packed_batch is None: print( "Skipping tuning as there is no suitable data. " "This can happen when all the trajectories in the same group " @@ -1481,94 +1627,216 @@ async def _train_model( ) # Still advance the step by renaming the checkpoint directory - current_step = self.__get_step(model) + current_step = await self._get_step(model) next_step = current_step + 1 logger.info( f"[BACKEND] _train_model SKIP: current_step={current_step} " f"next_step={next_step} (all rewards equal)" ) - current_checkpoint_dir = get_step_checkpoint_dir( - get_model_dir(model=model, art_path=self._path), current_step + advance_metrics = await self._advance_skipped_step( + model, service, current_step, next_step ) - next_checkpoint_dir = get_step_checkpoint_dir( - get_model_dir(model=model, art_path=self._path), next_step + logger.info( + f"[BACKEND] _train_model SKIP: advanced checkpoint " + f"{current_step} -> {next_step}" ) - # If the current checkpoint exists, copy it to the next step - if os.path.exists(current_checkpoint_dir): - shutil.copytree( - current_checkpoint_dir, - next_checkpoint_dir, - dirs_exist_ok=True, - ) - logger.info( - f"[BACKEND] _train_model SKIP: copied checkpoint " - f"{current_step} -> {next_step}, calling register_lora_for_step..." - ) - - try: - # Register the copied checkpoint as a new LoRA adapter - # so it's available for inference at the new step - register_lora_for_step = getattr( - service, "register_lora_for_step", None - ) - if callable(register_lora_for_step): - await register_lora_for_step(next_step, next_checkpoint_dir) - logger.info( - f"[BACKEND] _train_model SKIP: register_lora_for_step " - f"completed for step {next_step}" - ) - except ModuleNotFoundError: - pass # Unsloth is not installed - # Yield metrics showing no groups were trainable # (the frontend will handle logging) yield { **base_metrics, "data/step_num_groups_trainable": 0.0, "data/step_trainable_assistant_tokens": 0.0, + "data/step_nonpadding_logical_tokens": 0.0, + "data/step_loss_bearing_tokens": 0.0, + "data/step_executed_token_equivalents": 0.0, + "data/step_nominal_schedule_capacity_tokens": 0.0, + "data/step_dummy_executed_token_equivalents": 0.0, + "data/step_dummy_schedule_capacity_tokens": 0.0, + "data/step_unused_packed_capacity_tokens": 0.0, + "data/step_unused_and_dummy_ratio": 0.0, TRAIN_GRADIENT_STEPS_KEY: 0.0, + **advance_metrics, } return - base_metrics["data/step_trainable_assistant_tokens"] = float( - packed_tensors["assistant_mask"].sum().item() - ) - packed_sequences, packed_sequence_length = packed_tensors["tokens"].shape - non_padding_tokens = int((packed_tensors["group_ids"] != -1).sum().item()) - packing_stats = packed_tensors["prefix_tree_packing_stats"] - disk_packed_tensors = packed_tensors_to_dir( - packed_tensors, f"{get_model_dir(model=model, art_path=self._path)}/tensors" - ) - service_dev_config = cast(dev.TrainConfig, {**dev_config}) - grad_accumulation_sequences = await self._resolve_grad_accumulation_sequences( - service, - config, - ) - fallback_gradient_steps = math.ceil( - packed_sequences / grad_accumulation_sequences + async with self._training_batch_lifecycle(packed_batch): + base_metrics["data/step_trainable_assistant_tokens"] = float( + packed_batch.trainable_assistant_tokens + ) + packed_sequences = packed_batch.num_sequences + packed_sequence_length = packed_batch.sequence_length + non_padding_tokens = packed_batch.non_padding_tokens + service_dev_config = cast(dev.TrainConfig, {**dev_config}) + grad_accumulation_sequences = ( + await self._resolve_grad_accumulation_sequences(service, config) + ) + fallback_gradient_steps = math.ceil( + packed_sequences / grad_accumulation_sequences + ) + packed_train_tokens = int( + fallback_gradient_steps + * grad_accumulation_sequences + * packed_sequence_length + ) + base_metrics.update( + { + "data/step_packed_sequences": float(packed_sequences), + "data/step_nonpadding_logical_tokens": float(non_padding_tokens), + "data/step_loss_bearing_tokens": float( + packed_batch.loss_bearing_tokens + ), + "data/step_executed_token_equivalents": float(packed_train_tokens), + "data/step_nominal_schedule_capacity_tokens": float( + packed_train_tokens + ), + "data/step_dummy_executed_token_equivalents": 0.0, + "data/step_dummy_schedule_capacity_tokens": 0.0, + "data/step_unused_packed_capacity_tokens": float( + packed_train_tokens - non_padding_tokens + ), + "data/step_unused_and_dummy_ratio": ( + float(packed_train_tokens - non_padding_tokens) + / packed_train_tokens + ), + "prefix_tree/logical_tokens": float(packed_batch.logical_tokens), + "prefix_tree/physical_tokens": float(packed_batch.physical_tokens), + "prefix_tree/compression_ratio": ( + packed_batch.logical_tokens / packed_batch.physical_tokens + ), + } + ) + # The frontend applies reward scaling and logs the resulting metrics. + pbar = tqdm.tqdm(total=fallback_gradient_steps, desc="train") + reported_gradient_steps: int | None = None + try: + async for result in self._stream_prepared_training( + model, + service, + packed_batch, + config, + service_dev_config, + grad_accumulation_sequences, + verbose, + ): + raw_num_gradient_steps = result.pop(TRAIN_GRADIENT_STEPS_KEY, None) + if raw_num_gradient_steps is not None: + num_gradient_steps = int(raw_num_gradient_steps) + if reported_gradient_steps is None: + reported_gradient_steps = num_gradient_steps + if pbar.total != num_gradient_steps: + pbar.total = num_gradient_steps + pbar.refresh() + else: + assert num_gradient_steps == reported_gradient_steps, ( + f"num_gradient_steps {num_gradient_steps} != " + f"reported_gradient_steps {reported_gradient_steps}" + ) + else: + num_gradient_steps = ( + reported_gradient_steps or fallback_gradient_steps + ) + yield { + **base_metrics, + **result, + TRAIN_GRADIENT_STEPS_KEY: float(num_gradient_steps), + } + pbar.update(1) + pbar.set_postfix(result) + finally: + pbar.close() + if verbose: + print("_train_model complete") + + @asynccontextmanager + async def _training_batch_lifecycle( + self, batch: _PackedTrainingBatch + ) -> AsyncIterator[None]: + primary: BaseException | None = None + try: + yield + except BaseException as error: + primary = error + raise + finally: + try: + _, cancelled = await complete_task( + asyncio.create_task( + self._finish_training_batch(batch, failed=primary is not None) + ) + ) + if cancelled is not None: + raise cancelled + except BaseException as release_error: + if primary is None: + raise + if release_error is not primary: + primary.add_note( + "training-batch release also failed: " + f"{type(release_error).__name__}: {release_error}" + ) + + async def _finish_training_batch( + self, batch: _PackedTrainingBatch, *, failed: bool + ) -> None: + await self._release_training_batch(batch) + + async def _release_training_batch(self, batch: _PackedTrainingBatch) -> None: + pass + + async def _prepare_training_batch( + self, + model: TrainableModel, + trajectory_groups: list[TrajectoryGroup], + dev_config: dev.TrainConfig, + *, + include_moe_routing: bool, + ) -> _PackedTrainingBatch | None: + packed = self._get_packed_tensors( + model, + trajectory_groups, + advantage_balance=dev_config.get("advantage_balance", 0.0), + allow_training_without_logprobs=dev_config.get( + "allow_training_without_logprobs", False + ), + scale_rewards=dev_config.get("scale_rewards", True), + plot_tensors=dev_config.get("plot_tensors", False), + packed_sequence_length=dev_config.get("packed_sequence_length"), + logprob_calculation_chunk_size=dev_config.get( + "logprob_calculation_chunk_size", 1024 + ), + include_moe_routing=include_moe_routing, ) - packed_train_tokens = int( - fallback_gradient_steps - * grad_accumulation_sequences - * packed_sequence_length + if packed is None: + return None + num_sequences, sequence_length = packed["tokens"].shape + packing_stats = packed["prefix_tree_packing_stats"] + return _PackedTrainingBatch( + payload=packed, + num_sequences=num_sequences, + sequence_length=sequence_length, + trainable_assistant_tokens=int(packed["assistant_mask"].sum().item()), + loss_bearing_tokens=int(packed["assistant_mask"][:, 1:].sum().item()), + non_padding_tokens=int((packed["group_ids"] != -1).sum().item()), + logical_tokens=packing_stats["logical_tokens"], + physical_tokens=packing_stats["physical_tokens"], + include_moe_routing=include_moe_routing, ) - base_metrics.update( - { - "data/step_packed_sequences": float(packed_sequences), - "data/step_packed_train_tokens": float(packed_train_tokens), - "data/step_non_padding_train_tokens": float(non_padding_tokens), - "data/step_padding_ratio": ( - float(packed_train_tokens - non_padding_tokens) - / packed_train_tokens - ), - "prefix_tree/logical_tokens": float(packing_stats["logical_tokens"]), - "prefix_tree/physical_tokens": float(packing_stats["physical_tokens"]), - "prefix_tree/compression_ratio": ( - packing_stats["logical_tokens"] / packing_stats["physical_tokens"] - ), - } + + async def _stream_prepared_training( + self, + model: TrainableModel, + service: ModelService, + batch: _PackedTrainingBatch, + config: TrainConfig, + service_dev_config: dev.TrainConfig, + grad_accumulation_sequences: int, + verbose: bool, + ) -> AsyncIterator[dict[str, float]]: + packed = cast(PackedTensors, batch.payload) + disk = packed_tensors_to_dir( + packed, f"{get_model_dir(model=model, art_path=self._path)}/tensors" ) - if include_moe_routing: + if batch.include_moe_routing: from ..megatron.routing_replay import ( build_moe_routing_replay_bundle_from_packed_tensors, ) @@ -1578,42 +1846,13 @@ async def _train_model( "moe_routing_replay" ) build_moe_routing_replay_bundle_from_packed_tensors( - packed_tensors=packed_tensors, + packed_tensors=packed, global_grad_accumulation_sequences=grad_accumulation_sequences, ).to_dir(routing_replay_dir) service_dev_config["moe_routing_replay_path"] = routing_replay_dir service_dev_config["moe_routing_replay_strict"] = True - # Note: scale_learning_rate_by_reward_std_dev is now handled by the frontend (Model.train()) - pbar = tqdm.tqdm(total=fallback_gradient_steps, desc="train") - reported_gradient_steps: int | None = None - async for result in service.train( - disk_packed_tensors, config, service_dev_config, verbose - ): - raw_num_gradient_steps = result.pop(TRAIN_GRADIENT_STEPS_KEY, None) - if raw_num_gradient_steps is not None: - num_gradient_steps = int(raw_num_gradient_steps) - if reported_gradient_steps is None: - reported_gradient_steps = num_gradient_steps - if pbar.total != num_gradient_steps: - pbar.total = num_gradient_steps - pbar.refresh() - else: - assert num_gradient_steps == reported_gradient_steps, ( - f"num_gradient_steps {num_gradient_steps} != reported_gradient_steps {reported_gradient_steps}" - ) - else: - num_gradient_steps = reported_gradient_steps or fallback_gradient_steps - yield { - **base_metrics, - **result, - TRAIN_GRADIENT_STEPS_KEY: float(num_gradient_steps), - } - pbar.update(1) - pbar.set_postfix(result) - pbar.close() - # Note: Metrics logging is now handled by the frontend (Model.train()) - if verbose: - print("_train_model complete") + async for result in service.train(disk, config, service_dev_config, verbose): + yield result async def _resolve_grad_accumulation_sequences( self, @@ -1621,21 +1860,13 @@ async def _resolve_grad_accumulation_sequences( config: TrainConfig, ) -> int: if config.grad_accumulation_sequences is not None: - return max(1, int(config.grad_accumulation_sequences)) + return int(await service.resolve_global_grad_accumulation_sequences(config)) service_key = id(service) if service_key in self._grad_accumulation_sequences_by_service: return self._grad_accumulation_sequences_by_service[service_key] - resolver = getattr( - cast(Any, service), - "resolve_global_grad_accumulation_sequences", - None, - ) - if callable(resolver): - resolved = max(1, int(await resolver(config))) - else: - resolved = 1 + resolved = int(await service.resolve_global_grad_accumulation_sequences(config)) self._grad_accumulation_sequences_by_service[service_key] = resolved return resolved diff --git a/src/art/local/service.py b/src/art/local/service.py index 6417ed9d4..2d65eef0c 100644 --- a/src/art/local/service.py +++ b/src/art/local/service.py @@ -26,6 +26,14 @@ async def acquire_exact_adapter(self, step: int, checkpoint_path: str) -> str: . async def release_exact_adapter(self, step: int) -> None: ... + async def resolve_global_grad_accumulation_sequences( + self, config: types.TrainConfig + ) -> int: ... + + async def register_lora_for_step(self, step: int, checkpoint_dir: str) -> None: ... + + async def aclose(self) -> None: ... + def train( self, disk_packed_tensors: DiskPackedTensors, diff --git a/src/art/megatron/_hybrid_ep/csrc/hybrid_ep/backend/hybrid_ep_backend.cuh b/src/art/megatron/_hybrid_ep/csrc/hybrid_ep/backend/hybrid_ep_backend.cuh index ad7f94f8a..a4d550b08 100644 --- a/src/art/megatron/_hybrid_ep/csrc/hybrid_ep/backend/hybrid_ep_backend.cuh +++ b/src/art/megatron/_hybrid_ep/csrc/hybrid_ep/backend/hybrid_ep_backend.cuh @@ -716,7 +716,8 @@ inline __device__ void N2N_warp_group_device_function(const int node_rank, const int remote_idx = (idx + node_rank) % (NUM_OF_NODES - 1); const int actual_remote_node_rank = remote_idx < node_rank ? remote_idx : (remote_idx + 1); const int my_node_rank_in_remote = (node_rank < actual_remote_node_rank) ? node_rank : (node_rank - 1); - const size_t flag_offset = (my_node_rank_in_remote * NUM_OF_CHUNKS_PER_RANK + chunk_idx) * sizeof(uint64_t); + const size_t flag_offset = + (my_node_rank_in_remote * (MAX_NUM_OF_TOKENS_PER_RANK / NUM_OF_TOKENS_PER_CHUNK) + chunk_idx) * sizeof(uint64_t); // Quick density probe: check first warp-width of tokens. // On 4+ nodes, per-remote density is ~70%, so this almost always fails, @@ -861,7 +862,9 @@ inline __device__ void N2N_warp_group_device_function(const int node_rank, } __syncwarp(); - if (total_tokens > 0 && INTER_NODE_GROUP::thread_rank() == 0) { + if (INTER_NODE_GROUP::thread_rank() == 0) { + // The receiver waits on every source chunk before reading its routing + // map, including chunks with no payload. const unsigned channel_id = blockIdx.x % nixl_ctx->num_channels; nixlMemViewElem sig{nixl_ctx->remote_signal_mvh, (size_t)remote_idx, flag_offset}; assert(nixlAtomicAdd(1, sig, channel_id, 0 /* NODELAY: flush all pending */) >= NIXL_SUCCESS); @@ -1004,8 +1007,11 @@ inline __device__ void inter_node_N2N_warp_group_device_function( } __syncwarp(); - if (total_tokens > 0 && INTER_NODE_RDMA_GROUP::thread_rank() == 0) { - const size_t flag_offset = (my_node_rank_in_remote * NUM_OF_CHUNKS_PER_RANK + chunk_id) * sizeof(uint64_t); + if (INTER_NODE_RDMA_GROUP::thread_rank() == 0) { + // Advance every chunk's epoch so a later non-empty combine does not wait + // on a completion counter left behind by an earlier empty chunk. + const size_t flag_offset = + (my_node_rank_in_remote * (MAX_NUM_OF_TOKENS_PER_RANK / NUM_OF_TOKENS_PER_CHUNK) + chunk_id) * sizeof(uint64_t); const unsigned channel_id = blockIdx.x % nixl_ctx->num_channels; nixlMemViewElem sig{nixl_ctx->remote_signal_mvh, (size_t)remote_idx, flag_offset}; assert(nixlAtomicAdd(1, sig, channel_id, 0 /* NODELAY: flush all pending */) >= NIXL_SUCCESS); diff --git a/src/art/megatron/_hybrid_ep/csrc/hybrid_ep/config.cuh b/src/art/megatron/_hybrid_ep/csrc/hybrid_ep/config.cuh index 230d61f0c..850af28f0 100644 --- a/src/art/megatron/_hybrid_ep/csrc/hybrid_ep/config.cuh +++ b/src/art/megatron/_hybrid_ep/csrc/hybrid_ep/config.cuh @@ -21,6 +21,9 @@ static constexpr int64_t HYBRID_EP_DISPATCH_TX_DEPTH_EXTRA = 1; static inline bool hybrid_ep_token_capacity_is_valid( int max_num_of_tokens_per_rank, int num_of_nodes, const char* config_name) { +#ifdef USE_NIXL + return true; +#else if (num_of_nodes <= 1) { return true; } @@ -47,6 +50,7 @@ static inline bool hybrid_ep_token_capacity_is_valid( static_cast(HYBRID_EP_IB_QP_MAX_TX_DEPTH)); fflush(stderr); return false; +#endif } static inline int hybrid_ep_pad_num_of_tokens_per_rank( diff --git a/src/art/megatron/_hybrid_ep/deep_ep/hybrid_ep_toolchain.py b/src/art/megatron/_hybrid_ep/deep_ep/hybrid_ep_toolchain.py index fac089c76..689e751e0 100644 --- a/src/art/megatron/_hybrid_ep/deep_ep/hybrid_ep_toolchain.py +++ b/src/art/megatron/_hybrid_ep/deep_ep/hybrid_ep_toolchain.py @@ -6,21 +6,34 @@ import subprocess import hybrid_ep_cpp +import torch + + +def _cuda_paths() -> tuple[Path, Path]: + from torch.utils.cpp_extension import CUDA_HOME + + cuda_home = Path(os.environ.get("CUDA_HOME") or CUDA_HOME or "") + if torch.version.cuda and torch.version.cuda.startswith("12."): + return cuda_home, Path(str(files("nvidia.cuda_cccl") / "include")) + if torch.version.cuda and torch.version.cuda.startswith("13."): + for include in [cuda_home / "include", *cuda_home.glob("targets/*/include")]: + if (include / "cccl/cuda/ptx").is_file(): + return cuda_home, include / "cccl" + raise RuntimeError(f"HybridEP cannot find headers for torch CUDA {torch.version.cuda}") def runtime_paths() -> tuple[str, str, str]: - cuda_home = Path(os.environ.get("CUDA_HOME", "/usr/local/cuda-12.8")) + cuda_home, cccl_include = _cuda_paths() nvcc = cuda_home / "bin" / "nvcc" if not nvcc.is_file(): raise RuntimeError(f"HybridEP requires CUDA nvcc at {nvcc}") - cccl_include = Path(str(files("nvidia.cuda_cccl") / "include")) if not (cccl_include / "cuda" / "ptx").is_file(): raise RuntimeError(f"HybridEP CCCL headers are missing from {cccl_include}") digest = sha256() digest.update(version("art-deep-ep").encode()) - digest.update(version("nvidia-cuda-cccl-cu12").encode()) + digest.update((cccl_include / "cuda/std/__cccl/version.h").read_bytes()) digest.update(str(hybrid_ep_cpp.SM_ARCH).encode()) digest.update(subprocess.check_output([nvcc, "--version"])) digest.update(Path(hybrid_ep_cpp.__file__).read_bytes()) diff --git a/src/art/megatron/_hybrid_ep/pyproject.toml b/src/art/megatron/_hybrid_ep/pyproject.toml index 6f2a37027..15e7f65fd 100644 --- a/src/art/megatron/_hybrid_ep/pyproject.toml +++ b/src/art/megatron/_hybrid_ep/pyproject.toml @@ -2,8 +2,6 @@ requires = [ "setuptools>=78.1.0", "torch==2.11.0", - "nvidia-cuda-cccl-cu12==12.9.27", - "nvidia-nvtx-cu12>=12.8,<13", ] build-backend = "setuptools.build_meta" diff --git a/src/art/megatron/_hybrid_ep/setup.py b/src/art/megatron/_hybrid_ep/setup.py index 277b1ab21..c9a3fce05 100644 --- a/src/art/megatron/_hybrid_ep/setup.py +++ b/src/art/megatron/_hybrid_ep/setup.py @@ -6,6 +6,7 @@ import shutil import re +import torch from pathlib import Path from setuptools.command.build_py import build_py from torch.utils.cpp_extension import BuildExtension, CUDAExtension @@ -24,6 +25,22 @@ def package_dir(module: str) -> Path: return Path(next(iter(spec.submodule_search_locations))) +def cuda_includes() -> tuple[Path, Path]: + if torch.version.cuda and torch.version.cuda.startswith("12."): + return ( + package_dir("nvidia.cuda_cccl") / "include", + package_dir("nvidia.nvtx") / "include", + ) + if torch.version.cuda and torch.version.cuda.startswith("13."): + cuda_home = Path(os.environ["CUDA_HOME"]) + for include in [cuda_home / "include", *cuda_home.glob("targets/*/include")]: + if (include / "cccl/cuda/ptx").is_file() and ( + include / "nvtx3/nvToolsExt.h" + ).is_file(): + return include / "cccl", include + raise RuntimeError(f"HybridEP cannot find headers for torch CUDA {torch.version.cuda}") + + def collect_package_files(package: str, relative_dir: str): base_path = Path(package) / relative_dir if not base_path.exists(): @@ -51,8 +68,7 @@ def to_nvcc_gencode(s: str) -> str: def get_extension_hybrid_ep_cpp(): current_dir = os.path.dirname(os.path.abspath(__file__)) - cccl_include = package_dir("nvidia.cuda_cccl") / "include" - nvtx_include = package_dir("nvidia.nvtx") / "include" + cccl_include, nvtx_include = cuda_includes() enable_multinode = os.getenv("HYBRID_EP_MULTINODE", "0").strip().lower() in {"1", "true", "t", "yes", "y", "on"} # NIXL is opt-in and disabled by default; the DOCA/NCCL path is the default when multinode is enabled. use_nixl = os.getenv("USE_NIXL", "0").strip().lower() in {"1", "true", "t", "yes", "y", "on"} @@ -236,7 +252,6 @@ def get_extension_hybrid_ep_cpp(): include=['deep_ep', 'deep_ep.*'] ), install_requires=[ - 'nvidia-cuda-cccl-cu12==12.9.27', 'torch==2.11.0', ], ext_modules=[extension], diff --git a/src/art/megatron/backend.py b/src/art/megatron/backend.py index 61e5398f6..c61ca5da7 100644 --- a/src/art/megatron/backend.py +++ b/src/art/megatron/backend.py @@ -1,25 +1,83 @@ import asyncio -from typing import Any, Iterable, cast +from contextlib import asynccontextmanager +from pathlib import Path +import secrets +import sys +import time +from typing import Any, AsyncIterator, Iterable, Literal, cast +import uuid -from mp_actors import move_to_child_process +from pydantic import BaseModel, ConfigDict, Field +from .. import dev from ..backend import AnyTrainableModel -from ..local.backend import LocalBackend +from ..distributed.art_runtime import ArtRuntime +from ..local.backend import LocalBackend, _PackedTrainingBatch from ..local.service import ModelService from ..model import Model, TrainableModel from ..trajectories import TrajectoryGroup from ..types import LocalTrainResult -from ..utils.lifecycle import process_shutdown_timeout -from ..utils.output_dirs import get_model_dir -from .migrations import apply_megatron_migrations, optimizer_state_path -from .optimizer_state import ( - format_megatron_resume_message, - prepare_megatron_resume_state, - read_optimizer_commit, -) +from ..utils.lifecycle import complete_task +from ..utils.output_dirs import get_model_dir, get_step_checkpoint_dir +from ..vllm_runtime import get_external_vllm_runtime_config +from .migrations import apply_megatron_migrations from .runtime_config import get_megatron_runtime_config +class _DistributedBatchPayload(BaseModel): + model_config = ConfigDict(arbitrary_types_allowed=True, frozen=True) + + packed: Any + selections: tuple[Any, ...] + generation_id: str = Field(min_length=1) + runtime: Any + + +class _PackingConfig(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + advantage_balance: float + allow_training_without_logprobs: bool + scale_rewards: bool + plot_tensors: bool + packed_sequence_length: int = Field(ge=1) + logprob_calculation_chunk_size: int = Field(ge=1) + include_moe_routing: bool + collect_packing_shapes: bool + + @classmethod + def from_dev_config( + cls, + config: Any, + *, + include_moe_routing: bool, + collect_packing_shapes: bool, + ) -> "_PackingConfig": + return cls( + advantage_balance=config.get("advantage_balance", 0.0), + allow_training_without_logprobs=config.get( + "allow_training_without_logprobs", False + ), + scale_rewards=config.get("scale_rewards", True), + plot_tensors=config.get("plot_tensors", False), + packed_sequence_length=config["packed_sequence_length"], + logprob_calculation_chunk_size=config.get( + "logprob_calculation_chunk_size", 1024 + ), + include_moe_routing=include_moe_routing, + collect_packing_shapes=collect_packing_shapes, + ) + + +class _PipelinePreparedBatch(BaseModel): + model_config = ConfigDict(arbitrary_types_allowed=True, frozen=True) + + batch: Any + groups: tuple[Any, ...] + packing_config: _PackingConfig + metrics: dict[str, float] + + class MegatronBackend(LocalBackend): def __init__( self, @@ -27,21 +85,162 @@ def __init__( in_process: bool = False, path: str | None = None, enable_expert_replay: bool = True, + runtime: ArtRuntime | None = None, ) -> None: + if in_process: + raise ValueError( + "MegatronBackend(in_process=True) belonged to the removed " + "filesystem service proxy and cannot represent a multi-rank typed " + "trainer. Use the default Monarch executor." + ) + if runtime is not None: + artifact_root = runtime.topology.cluster.artifact_root + if artifact_root is None: + raise ValueError("distributed Megatron requires cluster.artifact_root") + if ( + path is not None + and Path(path).resolve() != Path(artifact_root).resolve() + ): + raise ValueError("backend path must match cluster.artifact_root") + path = artifact_root super().__init__( - in_process=in_process, + in_process=False, path=path, enable_expert_replay=enable_expert_replay, ) self._requires_explicit_packed_sequence_length = True self._packed_sequence_length_requires_chunk_alignment = False self._supports_result_packing = True - self._resume_prepared_models: set[tuple[str, str]] = set() + self._runtime = runtime + self._owns_runtime = runtime is None + self._runtime_lock = asyncio.Lock() + self._service_lock = asyncio.Lock() + self._owned_runtimes: dict[tuple[str, str], ArtRuntime] = {} + from .runtime.local import LocalEndpointAllocator + + self._local_endpoints = LocalEndpointAllocator() + self._owned_runtime_ports: dict[tuple[str, str], tuple[int, int]] = {} + self._managed_api_key = secrets.token_urlsafe(32) + self._batch_release_tasks: set[asyncio.Task[None]] = set() + self._batch_release_failures: list[BaseException] = [] + self._adapter_prune_requests: dict[ + tuple[str, str], tuple[AnyTrainableModel, set[int]] + ] = {} + self._adapter_prune_task: asyncio.Task[None] | None = None + self._adapter_prune_failures: list[BaseException] = [] + + def __enter__(self) -> "MegatronBackend": + try: + asyncio.get_running_loop() + except RuntimeError: + return self + raise RuntimeError( + "Use 'async with MegatronBackend()' inside an async event loop" + ) + + def _close(self) -> None: + try: + asyncio.get_running_loop() + except RuntimeError: + asyncio.run(self.close()) + return + raise RuntimeError( + "MegatronBackend synchronous close cannot run inside an async event loop" + ) + + async def __aenter__(self) -> "MegatronBackend": + return self + + def _compile_local_topology( + self, + model: TrainableModel, + config: Any, + *, + service_ports: tuple[int, int] | None = None, + ) -> Any: + import torch + + from .runtime.local import compile_local_runtime_topology + + return compile_local_runtime_topology( + config, + model_name=model.name, + base_model=model.base_model, + artifact_root=str(Path(self._path).resolve()), + visible_gpu_count=int(torch.cuda.device_count()), + service_ports=service_ports, + ) + + def _model_runtime_topology(self, model: TrainableModel) -> Any: + storage_key = self._model_storage_key(model) + runtime = self._runtime or self._owned_runtimes.get(storage_key) + if runtime is not None: + return runtime.topology + return self._compile_local_topology(model, model._internal_config or {}) + + async def _ensure_runtime(self, model: TrainableModel, config: Any) -> ArtRuntime: + if self._runtime is not None: + return self._runtime + storage_key = self._model_storage_key(model) + if runtime := self._owned_runtimes.get(storage_key): + return runtime + async with self._runtime_lock: + if storage_key not in self._owned_runtimes: + ports = self._local_endpoints.reserve() + try: + topology = self._compile_local_topology( + model, config, service_ports=ports + ) + if not topology.model_services: + self._local_endpoints.release(ports) + ports = None + placements = _topology_gpu_placements(topology) + conflicts = { + key: placements & _topology_gpu_placements(runtime.topology) + for key, runtime in self._owned_runtimes.items() + if placements & _topology_gpu_placements(runtime.topology) + } + if conflicts: + raise ValueError( + "backend-owned per-model runtimes require disjoint GPU " + f"placements; {storage_key!r} conflicts with {conflicts}" + ) + runtime = await ArtRuntime.start_local(topology) + except BaseException: + if ports is not None: + self._local_endpoints.release(ports) + raise + self._owned_runtimes[storage_key] = runtime + if ports is not None: + self._owned_runtime_ports[storage_key] = ports + return self._owned_runtimes[storage_key] + + async def _configure_owned_api_port(self, model: TrainableModel, port: int) -> None: + storage_key = self._model_storage_key(model) + async with self._runtime_lock: + runtime = self._owned_runtimes.get(storage_key) + ports = self._owned_runtime_ports.get(storage_key) + if runtime is None or ports is None: + raise RuntimeError("owned model service runtime has not started") + configured = self._local_endpoints.replace_api_port(ports, port) + try: + from .runtime.local import with_local_serving_port + + topology = with_local_serving_port( + runtime.topology, + model_name=model.name, + port=configured[0], + rendezvous_port=configured[1], + ) + except BaseException: + self._local_endpoints.replace_api_port(configured, ports[0]) + raise + runtime.topology = topology + self._owned_runtime_ports[storage_key] = configured async def register(self, model: Model) -> None: await super().register(model) if model.trainable: - # Keep durable Megatron state migrations centralized behind this call. apply_megatron_migrations(get_model_dir(model=model, art_path=self._path)) async def train( @@ -56,91 +255,862 @@ async def train( f"MegatronBackend.train gets {removed_kwarg} from " "art.init_megatron_runtime_config(...)." ) - return await super().train( + groups = list(trajectory_groups) + pipeline_call = bool( + groups + and isinstance(groups[0]._prepared_training_batch, _PipelinePreparedBatch) + ) + result = await super().train( model, - trajectory_groups, + groups, packed_sequence_length=get_megatron_runtime_config().packed_sequence_length, **kwargs, ) + from .distributed_service import DistributedMegatronService + + service = cast(DistributedMegatronService, await self._get_service(model)) + final_step = kwargs.get("final_training_step") + if final_step is not None and result.step >= final_step: + result.metrics.update( + await service.finalize_publication_metrics(result.step) + ) + if not pipeline_call: + await service.wait_for_serving(result.step) + result.metrics.update(service.drain_publication_metrics()) + if not kwargs.get("save_checkpoint", True): + return result + result.checkpoint_path = get_step_checkpoint_dir( + get_model_dir(model=model, art_path=self._path), result.step + ) + if not Path(result.checkpoint_path).exists(): + result.checkpoint_ready = service.checkpoint_materialization(result.step) + return result + + async def finalize_training_session( + self, model: AnyTrainableModel + ) -> dict[str, float]: + from .distributed_service import DistributedMegatronService + + service = cast(DistributedMegatronService, await self._get_service(model)) + return await service.finalize_publication_metrics(await self._get_step(model)) + + def _supports_concurrent_training_and_inference( + self, model: AnyTrainableModel + ) -> bool: + topology = self._model_runtime_topology(cast(TrainableModel, model)) + services = tuple( + service for service in topology.model_services if service.name == model.name + ) + if len(services) == 1: + return not services[0].temporal_gpu_sharing + if ( + not services + and get_external_vllm_runtime_config(model._internal_config or {}) + is not None + ): + return True + raise ValueError( + f"runtime topology must define one model service named {model.name!r}" + ) + + def supports_async_pipeline_packing(self, model: AnyTrainableModel) -> bool: + return True + + @asynccontextmanager + async def adapter_lease( + self, + model: AnyTrainableModel, + step: int, + ) -> AsyncIterator[None]: + from .distributed_service import DistributedMegatronService + + service = cast(DistributedMegatronService, await self._get_service(model)) + await service.wait_for_serving(step) + async with super().adapter_lease(model, step): + yield + + @asynccontextmanager + async def exact_adapter_lease( + self, + model: AnyTrainableModel, + step: int, + ) -> AsyncIterator[None]: + from .distributed_service import DistributedMegatronService + + service = cast(DistributedMegatronService, await self._get_service(model)) + await service.wait_for_serving(step) + async with super().exact_adapter_lease(model, step): + yield async def _get_service(self, model: TrainableModel) -> ModelService: from ..dev.get_model_config import get_model_config - from .service import MegatronService storage_key = self._model_storage_key(model) - if storage_key not in self._services: - output_dir = get_model_dir(model=model, art_path=self._path) + if service := self._services.get(storage_key): + return service + async with self._service_lock: + if service := self._services.get(storage_key): + return service config = get_model_config( base_model=model.base_model, - output_dir=output_dir, + output_dir=get_model_dir(model=model, art_path=self._path), config=model._internal_config, lora_config=model.lora_config, ) - self._services[storage_key] = MegatronService( - model_name=model.name, - base_model=model.base_model, - config=config, - output_dir=output_dir, - enable_expert_replay=self._enable_expert_replay, + config["init_args"]["model_name"] = ( + (model._internal_config or {}) + .get("init_args", {}) + .get("model_name", model.base_model) + ) + runtime = await self._ensure_runtime(model, config) + from .distributed_service import DistributedMegatronService + + service = cast( + ModelService, + DistributedMegatronService( + model_name=model.name, + base_model=model.base_model, + config=config, + output_dir=get_model_dir(model=model, art_path=self._path), + runtime=runtime, + enable_expert_replay=self._enable_expert_replay, + ), + ) + if not self._owns_runtime: + runtime.register_closeable(service) + self._services[storage_key] = service + return service + + async def _prepare_backend_for_training( + self, + model: AnyTrainableModel, + config: dev.OpenAIServerConfig | None = None, + ) -> tuple[str, str]: + if get_external_vllm_runtime_config(model._internal_config or {}) is not None: + return await super()._prepare_backend_for_training(model, config) + config_dict = dict(config or {}) + server_args = dict(config_dict.get("server_args", {})) + server_args.setdefault("api_key", self._managed_api_key) + if self._owns_runtime and "port" in server_args: + port = server_args["port"] + if isinstance(port, bool) or not isinstance(port, int): + raise TypeError("OpenAI server port must be an integer") + from .distributed_service import DistributedMegatronService + + service = cast( + DistributedMegatronService, + await self._get_service(cast(TrainableModel, model)), + ) + if ( + service._managed_service_name is not None + and service.openai_server_port != port + ): + raise RuntimeError("cannot change a running OpenAI server port") + await self._configure_owned_api_port(cast(TrainableModel, model), port) + if "port" not in server_args and not self._owns_runtime: + from .distributed_service import DistributedMegatronService + + service = cast(DistributedMegatronService, await self._get_service(model)) + server_args["port"] = service.openai_server_port + config_dict["server_args"] = server_args + return await super()._prepare_backend_for_training( + model, cast(dev.OpenAIServerConfig, config_dict) + ) + + async def _prepare_training_batch( + self, + model: TrainableModel, + trajectory_groups: list[TrajectoryGroup], + dev_config: Any, + *, + include_moe_routing: bool, + ) -> _PackedTrainingBatch | None: + prepared = tuple(group._prepared_training_batch for group in trajectory_groups) + collect_packing_shapes = any( + group._collect_packing_shape for group in trajectory_groups + ) + if any(value is not None for value in prepared): + first = prepared[0] + if ( + not isinstance(first, _PipelinePreparedBatch) + or any(value is not first for value in prepared) + or first.groups != tuple(trajectory_groups) + ): + raise RuntimeError("pipeline prepared batch does not match training") + packing_config = _PackingConfig.from_dev_config( + dev_config, + include_moe_routing=include_moe_routing, + collect_packing_shapes=collect_packing_shapes, ) - if not self._in_process: - self._services[storage_key] = move_to_child_process( - self._services[storage_key], - process_name="megatron-service", + if first.packing_config != packing_config: + mismatch = RuntimeError( + "pipeline prepared batch packing configuration does not match " + "training" ) - return self._services[storage_key] + try: + await self.discard_pipeline_batch(trajectory_groups) + except BaseException as cleanup_error: + raise BaseExceptionGroup( + "prepared batch mismatch cleanup failed", + [mismatch, cleanup_error], + ) from None + raise mismatch + for group in trajectory_groups: + group._prepared_training_batch = None + return cast(_PackedTrainingBatch, first.batch) + from ..distributed.packing import PackingRequest + from ..distributed.rollout import ( + DistributedTrajectorySelection, + RolloutModelSpec, + ) + from ..distributed.trajectory_store import TrajectoryGroupBundle - async def _get_step(self, model: AnyTrainableModel) -> int: - if not model.trainable: - return 0 - storage_key = self._model_storage_key(model) - if storage_key in self._resume_prepared_models: - return await super()._get_step(model) - output_dir = get_model_dir(model=model, art_path=self._path) - info = prepare_megatron_resume_state( - output_dir=output_dir, - optimizer_state_path=optimizer_state_path(output_dir), + selections = tuple(group._distributed_lease for group in trajectory_groups) + selected = tuple( + selection + for selection in selections + if isinstance(selection, DistributedTrajectorySelection) ) - print(format_megatron_resume_message(info)) - self._resume_prepared_models.add(storage_key) - return await super()._get_step(model) + for group, selection in zip(trajectory_groups, selections, strict=True): + if isinstance(selection, DistributedTrajectorySelection): + group._distributed_lease = None - async def finalize_training_session(self, model: AnyTrainableModel) -> None: - service = self._services.get(self._model_storage_key(model)) - if service is not None: - await cast(Any, service).finalize_training_session() + generation_id = uuid.uuid4().hex + trajectory_log_path: str | None = None + runtime: ArtRuntime | None = None + packed: Any = None + marked_packed = False + transferred = False + try: + packing_config = _PackingConfig.from_dev_config( + dev_config, + include_moe_routing=include_moe_routing, + collect_packing_shapes=collect_packing_shapes, + ) + if selected and len(selected) != len(trajectory_groups): + raise RuntimeError( + "distributed batch mixes owned and controller groups" + ) + queue = selected[0].queue if selected else None + if queue is not None and any( + selection.queue is not queue for selection in selected + ): + raise RuntimeError("distributed batch spans trajectory queues") - async def _delete_checkpoint_files( + from .distributed_service import DistributedMegatronService + + service = cast(DistributedMegatronService, await self._get_service(model)) + runtime = service.runtime + versions = [ + version + for group in trajectory_groups + for trajectory in group.trajectories + for version in ( + trajectory.initial_policy_version, + trajectory.final_policy_version, + ) + if version is not None + ] + current_step = min(versions) if versions else await self._get_step(model) + if selected: + group_ids = tuple( + selection.lease.item.ref.result_id for selection in selected + ) + record_ids = tuple( + record.record_id + for selection in selected + for record in selection.lease.item.ref.records + ) + trajectory_log_path = str( + Path(get_model_dir(model=model, art_path=self._path)) + / "trajectories" + / ".staging" + / f"{generation_id}.parquet" + ) + else: + group_ids = tuple( + f"{group.metadata.get('scenario_id', 'group')}:{index}" + for index, group in enumerate(trajectory_groups) + ) + record_ids = tuple( + f"{group_id}:{trajectory_index}" + for group_id, group in zip( + group_ids, trajectory_groups, strict=True + ) + for trajectory_index, _ in enumerate(group.trajectories) + ) + local_selections = tuple( + selection + for selection in selected + if selection.lease.item.ref.transfer is None + ) + if local_selections and len(local_selections) != len(selected): + raise RuntimeError("distributed batch mixes local and remote owners") + local_groups = ( + tuple( + await asyncio.gather( + *( + queue.materialize_selection(selection) + for selection in selected + ) + ) + ) + if queue is not None and local_selections + else () + ) + request = PackingRequest( + model=RolloutModelSpec.from_model(model), + generation_id=generation_id, + trajectory_groups=tuple( + TrajectoryGroupBundle.from_group(group) + for group in ( + local_groups if selected else tuple(trajectory_groups) + ) + ), + trajectory_sources=( + () + if local_selections + else tuple(selection.lease.item for selection in selected) + ), + trajectory_log_path=trajectory_log_path, + group_ids=group_ids, + record_ids=record_ids, + min_source_version=min(versions, default=current_step), + max_source_version=max(versions, default=current_step), + **packing_config.model_dump(), + ) + packed = await runtime.pack(request) + if packed is None: + return None + if queue is not None: + _, cancelled = await complete_task( + asyncio.create_task(queue.mark_packed(selected, generation_id)) + ) + marked_packed = True + if cancelled is not None: + raise cancelled + shapes = tuple(packed.packed_group_shapes) + if len(shapes) != len(trajectory_groups): + raise RuntimeError("packed-group shapes do not match trajectory groups") + ref = packed.leases.ref + stats = ref.prefix_tree_packing_stats + if stats is None: + raise RuntimeError( + "distributed packed batch has no prefix-tree statistics" + ) + batch = _PackedTrainingBatch( + payload=_DistributedBatchPayload( + packed=packed, + selections=selected, + generation_id=generation_id, + runtime=runtime, + ), + num_sequences=ref.num_sequences, + sequence_length=ref.sequence_length, + trainable_assistant_tokens=packed.trainable_assistant_tokens, + loss_bearing_tokens=packed.loss_bearing_tokens, + non_padding_tokens=packed.non_padding_tokens, + logical_tokens=stats.logical_tokens, + physical_tokens=stats.physical_tokens, + include_moe_routing=include_moe_routing, + ) + for group, shape in zip(trajectory_groups, shapes, strict=True): + if shape is not None: + group._packed_group_shape = shape + if selected: + group._prepared_log_path = packed.trajectory_log_path + transferred = True + return batch + finally: + if not transferred: + primary = sys.exception() + try: + _, cancelled = await complete_task( + asyncio.create_task( + self._cleanup_packing_ownership( + runtime=runtime, + packed=packed, + selections=selected, + generation_id=( + generation_id if marked_packed else None + ), + trajectory_log_path=trajectory_log_path, + ) + ) + ) + if cancelled is not None: + raise cancelled + except BaseException as cleanup_error: + if primary is None: + raise + raise BaseExceptionGroup( + "packing and source cleanup failed", + [primary, cleanup_error], + ) from None + + async def _cleanup_packing_ownership( + self, + *, + runtime: ArtRuntime | None, + packed: Any, + selections: tuple[Any, ...], + generation_id: str | None, + trajectory_log_path: str | None, + ) -> None: + paths = { + path + for path in ( + trajectory_log_path, + getattr(packed, "trajectory_log_path", None), + ) + if path is not None + } + releases = [ + *( + (runtime.release_batch(packed),) + if runtime is not None and packed is not None + else () + ), + *( + selection.queue.release_selection( + selection, + disposition="discarded", + generation_id=generation_id, + ) + for selection in selections + ), + *(asyncio.to_thread(Path(path).unlink, missing_ok=True) for path in paths), + ] + results = await asyncio.gather(*releases, return_exceptions=True) + failures = [result for result in results if isinstance(result, BaseException)] + if failures: + raise BaseExceptionGroup("packing ownership cleanup failed", failures) + + async def prepare_pipeline_batch( + self, + model: TrainableModel, + trajectory_groups: list[TrajectoryGroup], + *, + normalize_advantages: bool = True, + advantage_balance: float = 0.0, + scale_rewards: bool = True, + allow_training_without_logprobs: bool = False, + plot_tensors: bool = False, + logprob_calculation_chunk_size: int = 1024, + ) -> dict[str, float] | None: + include_moe_routing = self._model_uses_expert_replay(model) + dev_config = { + "advantage_balance": advantage_balance, + "allow_training_without_logprobs": allow_training_without_logprobs, + "scale_rewards": scale_rewards and normalize_advantages, + "plot_tensors": plot_tensors, + "packed_sequence_length": get_megatron_runtime_config().packed_sequence_length, + "logprob_calculation_chunk_size": logprob_calculation_chunk_size, + } + batch = await self._prepare_training_batch( + model, + trajectory_groups, + dev_config, + include_moe_routing=include_moe_routing, + ) + if batch is None: + return None + payload = batch.payload + if not isinstance(payload, _DistributedBatchPayload): + raise RuntimeError( + "Megatron pipeline batch did not use the typed data plane" + ) + distributed = payload.packed + metrics = { + "time/step_trajectory_fetch_s": distributed.trajectory_fetch_s, + "time/step_packing_core_s": distributed.packing_core_s, + "time/step_trajectory_log_wait_s": distributed.trajectory_log_wait_s, + "time/step_packed_batch_finalize_s": distributed.packed_batch_finalize_s, + "time/step_packing_rpc_s": distributed.packing_rpc_s, + "time/step_packed_batch_fanout_s": distributed.packed_batch_fanout_s, + } + prepared = _PipelinePreparedBatch( + batch=batch, + groups=tuple(trajectory_groups), + packing_config=_PackingConfig.from_dev_config( + dev_config, + include_moe_routing=include_moe_routing, + collect_packing_shapes=any( + group._collect_packing_shape for group in trajectory_groups + ), + ), + metrics=metrics, + ) + for group in trajectory_groups: + group._prepared_training_batch = prepared + return metrics + + async def discard_pipeline_batch( + self, trajectory_groups: list[TrajectoryGroup] + ) -> None: + prepared = trajectory_groups[0]._prepared_training_batch + if not isinstance(prepared, _PipelinePreparedBatch) or any( + group._prepared_training_batch is not prepared + for group in trajectory_groups + ): + raise RuntimeError("pipeline batch is not prepared") + for group in trajectory_groups: + group._prepared_training_batch = None + paths = { + group._prepared_log_path + for group in trajectory_groups + if group._prepared_log_path is not None + } + _, cancelled = await complete_task( + asyncio.create_task( + self._discard_prepared_resources(prepared, trajectory_groups, paths) + ) + ) + if cancelled is not None: + raise cancelled + + async def _discard_prepared_resources( + self, + prepared: _PipelinePreparedBatch, + trajectory_groups: list[TrajectoryGroup], + paths: set[str], + ) -> None: + results = await asyncio.gather( + self._release_distributed_batch(prepared.batch, disposition="discarded"), + *(asyncio.to_thread(Path(path).unlink, missing_ok=True) for path in paths), + return_exceptions=True, + ) + for group in trajectory_groups: + group._prepared_log_path = None + failures = [result for result in results if isinstance(result, BaseException)] + if failures: + raise BaseExceptionGroup("prepared batch discard failed", failures) + + async def _stream_prepared_training( + self, + model: TrainableModel, + service: ModelService, + batch: _PackedTrainingBatch, + config: Any, + service_dev_config: Any, + grad_accumulation_sequences: int, + verbose: bool, + ) -> AsyncIterator[dict[str, float]]: + self._collect_batch_release_results() + self._raise_batch_release_failures() + self._collect_adapter_prune_result() + self._raise_adapter_prune_failures() + from ..distributed.art_runtime import DistributedPackedBatch + from .distributed_service import DistributedMegatronService + + payload = batch.payload + if not isinstance(payload, _DistributedBatchPayload): + raise RuntimeError("Megatron training did not use the typed data plane") + distributed_batch = cast( + DistributedPackedBatch, + payload.packed, + ) + source_release_s = 0.0 + if payload.selections: + ref = distributed_batch.leases.ref + expected_groups = tuple( + selection.lease.item.ref.result_id for selection in payload.selections + ) + expected_records = tuple( + record.record_id + for selection in payload.selections + for record in selection.lease.item.ref.records + ) + versions = [] + for selection in payload.selections: + item = selection.lease.item + descriptor = item.ref.descriptor + versions.extend( + version + for initial, final in zip( + descriptor.trajectory_initial_policy_versions, + descriptor.trajectory_final_policy_versions, + strict=True, + ) + for version in ( + initial + if initial is not None + else item.annotations.initial_policy_version, + final + if final is not None + else item.annotations.final_policy_version, + ) + ) + if ( + distributed_batch.packing_generation_id != payload.generation_id + or ref.group_ids != expected_groups + or ref.record_ids != expected_records + or ref.min_source_version != min(versions) + or ref.max_source_version != max(versions) + ): + raise RuntimeError("packed batch policy provenance does not match") + release_started = time.perf_counter() + await self._release_trajectory_sources(batch, payload) + source_release_s = time.perf_counter() - release_started + distributed_service = cast(DistributedMegatronService, service) + async for result in distributed_service.train_packed( + distributed_batch, config, service_dev_config + ): + yield { + **result, + "time/step_source_lease_release_s": source_release_s, + **distributed_service.drain_publication_metrics(), + } + + async def _release_training_batch(self, batch: _PackedTrainingBatch) -> None: + await self._release_distributed_batch(batch, disposition="consumed") + + async def _release_trajectory_sources( + self, + batch: _PackedTrainingBatch, + payload: _DistributedBatchPayload, + ) -> None: + selections = payload.selections + if not selections: + return + queue = selections[0].queue + if any(selection.queue is not queue for selection in selections): + raise RuntimeError("packed batch contains selections from multiple queues") + await queue.release_selections( + selections, + disposition="consumed", + generation_id=payload.generation_id, + ) + batch.payload = payload.model_copy(update={"selections": ()}) + + async def _finish_training_batch( + self, batch: _PackedTrainingBatch, *, failed: bool + ) -> None: + if failed: + await super()._finish_training_batch(batch, failed=failed) + if self._batch_release_tasks: + await asyncio.gather( + *tuple(self._batch_release_tasks), return_exceptions=True + ) + self._collect_batch_release_results() + self._raise_batch_release_failures() + return + self._collect_batch_release_results() + self._raise_batch_release_failures() + while len(self._batch_release_tasks) >= 2: + await asyncio.wait( + self._batch_release_tasks, return_when=asyncio.FIRST_COMPLETED + ) + self._collect_batch_release_results() + self._raise_batch_release_failures() + self._batch_release_tasks.add( + asyncio.create_task(self._release_training_batch(batch)) + ) + + def _collect_batch_release_results(self) -> None: + for task in tuple(self._batch_release_tasks): + if not task.done(): + continue + self._batch_release_tasks.remove(task) + try: + task.result() + except BaseException as error: + self._batch_release_failures.append(error) + + def _raise_batch_release_failures(self) -> None: + if not self._batch_release_failures: + return + failures, self._batch_release_failures = self._batch_release_failures, [] + raise BaseExceptionGroup("distributed training batch release failed", failures) + + async def prune_model_adapters( self, model: AnyTrainableModel, - steps_to_keep: list[int], + *, + retain_steps: set[int], ) -> None: - output_dir = get_model_dir(model=model, art_path=self._path) - commit = read_optimizer_commit(optimizer_state_path(output_dir)) - if commit is not None: - steps_to_keep = sorted(set(steps_to_keep) | {commit.step}) - await super()._delete_checkpoint_files(model, steps_to_keep) + service = await self._get_service(cast(TrainableModel, model)) + if getattr(service, "rollout_weight_update_mode", None) == "in_flight_lora": + return + self._collect_adapter_prune_result() + self._raise_adapter_prune_failures() + self._adapter_prune_requests[self._model_storage_key(model)] = ( + model, + set(retain_steps), + ) + if self._adapter_prune_task is None: + self._adapter_prune_task = asyncio.create_task(self._prune_adapters()) + + async def _prune_adapters(self) -> None: + while self._adapter_prune_requests: + requests, self._adapter_prune_requests = self._adapter_prune_requests, {} + for model, retain_steps in requests.values(): + await super().prune_model_adapters(model, retain_steps=retain_steps) + + def _collect_adapter_prune_result(self) -> None: + task = self._adapter_prune_task + if task is None or not task.done(): + return + self._adapter_prune_task = None + try: + task.result() + except BaseException as error: + self._adapter_prune_failures.append(error) + + def _raise_adapter_prune_failures(self) -> None: + if not self._adapter_prune_failures: + return + failures, self._adapter_prune_failures = self._adapter_prune_failures, [] + raise BaseExceptionGroup("Megatron adapter pruning failed", failures) async def close(self) -> None: + task = asyncio.create_task(self._close_megatron_backend()) + _, cancelled = await complete_task(task) + if cancelled is not None: + raise cancelled + + async def _close_megatron_backend(self) -> None: failures: list[BaseException] = [] - for service in self._services.values(): - try: - await asyncio.wait_for( - cast(Any, service).finalize_training_session(), - timeout=process_shutdown_timeout(1), + if self._batch_release_tasks: + results = await asyncio.gather( + *self._batch_release_tasks, return_exceptions=True + ) + self._batch_release_tasks.clear() + failures.extend( + result for result in results if isinstance(result, BaseException) + ) + failures.extend(self._batch_release_failures) + self._batch_release_failures.clear() + if self._adapter_prune_task is not None: + result = await asyncio.gather( + self._adapter_prune_task, return_exceptions=True + ) + if isinstance(result[0], BaseException): + failures.append(result[0]) + self._adapter_prune_task = None + failures.extend(self._adapter_prune_failures) + self._adapter_prune_failures.clear() + services = dict(self._services) + services_closed = True + try: + await super().close() + except BaseException as error: + failures.append(error) + services_closed = False + for key, service in services.items(): + self._services.setdefault(key, service) + if services_closed: + runtimes = tuple(self._owned_runtimes.items()) + results = await asyncio.gather( + *(runtime.close() for _, runtime in runtimes), + return_exceptions=True, + ) + for (key, runtime), result in zip(runtimes, results, strict=True): + if isinstance(result, BaseException): + failures.append(result) + elif self._owned_runtimes.get(key) is runtime: + self._owned_runtimes.pop(key) + ports = self._owned_runtime_ports.pop(key, None) + if ports is not None: + self._local_endpoints.release(ports) + if failures: + raise BaseExceptionGroup( + "distributed Megatron backend close failed", failures + ) + + async def _release_distributed_batch( + self, + batch: _PackedTrainingBatch, + *, + disposition: Literal["consumed", "discarded"], + ) -> None: + from ..distributed.art_runtime import DistributedPackedBatch + + payload = batch.payload + if not isinstance(payload, _DistributedBatchPayload): + raise RuntimeError("Megatron batch has no owning typed runtime") + runtime = cast(ArtRuntime, payload.runtime) + distributed_batch = cast(DistributedPackedBatch, payload.packed) + releases: list[Any] = [runtime.release_batch(distributed_batch)] + if payload.selections: + queue = payload.selections[0].queue + releases.append( + queue.release_selections( + payload.selections, + disposition=disposition, + generation_id=payload.generation_id, ) - except BaseException as exc: - failures.append(exc) - await super().close() + ) + results = await asyncio.gather(*releases, return_exceptions=True) + failures = [result for result in results if isinstance(result, BaseException)] if failures: raise BaseExceptionGroup( - "Failed to persist Megatron optimizer state during shutdown", - failures, + "distributed training batch release failed", failures ) + async def _delete_checkpoint_files( + self, + model: AnyTrainableModel, + steps_to_keep: list[int], + ) -> None: + from ..local.checkpoints import delete_checkpoints + from .distributed_service import DistributedMegatronService + from .optimizer_state import optimizer_retention_lease + + service = cast(DistributedMegatronService, await self._get_service(model)) + output_dir = get_model_dir(model=model, art_path=self._path) + async with service.checkpoint_retention_lease() as active_steps: + + def delete_retained() -> None: + retained = set(steps_to_keep) | set(active_steps) + with optimizer_retention_lease(output_dir, retained) as protected: + delete_checkpoints(output_dir, sorted(protected)) + + await asyncio.to_thread(delete_retained) + + async def _advance_skipped_step( + self, + model: TrainableModel, + service: ModelService, + current_step: int, + next_step: int, + ) -> dict[str, float]: + from .distributed_service import DistributedMegatronService + + distributed = cast(DistributedMegatronService, service) + return await distributed.advance_without_training( + expected_step=current_step, + learner_version=next_step, + ) + + async def _get_step(self, model: AnyTrainableModel) -> int: + if not model.trainable: + return 0 + await self._get_service(cast(TrainableModel, model)) + storage_key = self._model_storage_key(model) + if storage_key in self._services: + from .distributed_service import DistributedMegatronService + + service = cast(DistributedMegatronService, self._services[storage_key]) + return await service.prepare_for_packing() + raise RuntimeError("Megatron model service was not initialized") + def _default_sft_batch_size(self) -> int: import torch num_gpus = max(int(torch.cuda.device_count()), 1) tensor_parallel_size = min(2, num_gpus) return max(num_gpus // tensor_parallel_size, 1) + + +def _topology_gpu_placements(topology: Any) -> frozenset[tuple[str, int | str]]: + trainer = () if topology.trainer is None else topology.trainer.ranks + return frozenset( + [(rank.host_id, rank.gpu_id) for rank in trainer] + + [ + (member.host_id, gpu_id) + for service in topology.model_services + for member in service.members + for gpu_id in member.gpu_ids + ] + ) diff --git a/src/art/megatron/compile_workarounds.py b/src/art/megatron/compile_workarounds.py index 5ba10e7e3..4c2ec6dd5 100644 --- a/src/art/megatron/compile_workarounds.py +++ b/src/art/megatron/compile_workarounds.py @@ -1,7 +1,7 @@ from __future__ import annotations import os -from typing import Any, cast +from typing import Any import torch @@ -73,106 +73,6 @@ def _install_self_attn_linear_proj_reduce_scatter_workaround() -> None: art_lora.reduce_scatter_to_sequence_parallel_region = wrapped # type: ignore[assignment] -class _WeightedSwiGLUNoInnerForwardCast(torch.autograd.Function): - @staticmethod - def forward( - ctx: Any, - input: torch.Tensor, - weights: torch.Tensor, - fp8_input_store: bool, - ) -> torch.Tensor: - input_for_backward = input.to(torch.float8_e4m3fn) if fp8_input_store else input - ctx.save_for_backward(input_for_backward, weights) - ctx.ori_input_dtype = input.dtype - ctx.fp8_input_store = fp8_input_store - x_glu, x_linear = torch.chunk(input, 2, dim=-1) - return torch.nn.functional.silu(x_glu) * x_linear * weights - - @staticmethod - def backward( - ctx: Any, - *grad_outputs: Any, - ) -> tuple[torch.Tensor, torch.Tensor, None]: - from megatron.core.fusions import fused_bias_swiglu - - grad_output = cast(torch.Tensor, grad_outputs[0]) - input, weights = ctx.saved_tensors - input = input.to(ctx.ori_input_dtype) if ctx.fp8_input_store else input - input_grad, weights_grad = fused_bias_swiglu.weighted_swiglu_back( - grad_output, - input, - weights, - ) - return input_grad, weights_grad, None - - -def _install_weighted_bias_swiglu_no_inner_forward_cast_workaround() -> None: - from megatron.core.fusions import fused_bias_swiglu - from megatron.core.transformer import mlp - from megatron.core.transformer.moe import experts - - if getattr( - fused_bias_swiglu.weighted_bias_swiglu_impl, - "__art_no_inner_forward_cast__", - False, - ): - return - - def _empty_weighted_swiglu_output( - input: torch.Tensor, - bias: torch.Tensor | None, - weights: torch.Tensor, - ) -> torch.Tensor: - output_shape = (*input.shape[:-1], int(input.shape[-1]) // 2) - zero = input.sum() * 0.0 + weights.to(dtype=input.dtype).sum() * 0.0 - if bias is not None: - zero = zero + bias.to(dtype=input.dtype).sum() * 0.0 - return zero.expand(output_shape).clone() - - def _weighted_bias_swiglu_no_inner_forward_cast( - input: torch.Tensor, - bias: torch.Tensor | None, - weights: torch.Tensor, - fp8_input_store: bool = False, - ) -> torch.Tensor: - if int(input.numel()) == 0: - return _empty_weighted_swiglu_output(input, bias=bias, weights=weights) - if bias is not None: - raise NotImplementedError( - "Bias is not supported for weighted swiglu fusion" - ) - original_shape = input.shape - output = _WeightedSwiGLUNoInnerForwardCast.apply( - input.view(-1, original_shape[-1]), - weights, - fp8_input_store, - ).to(input.dtype) - return ( - output - if len(original_shape) == 2 - else output.view(*original_shape[:-1], -1) - ) - - setattr( - _weighted_bias_swiglu_no_inner_forward_cast, - "__art_no_inner_forward_cast__", - True, - ) - setattr( - fused_bias_swiglu, - "weighted_bias_swiglu_impl", - _weighted_bias_swiglu_no_inner_forward_cast, - ) - setattr( - mlp, "weighted_bias_swiglu_impl", _weighted_bias_swiglu_no_inner_forward_cast - ) - setattr( - experts, - "weighted_bias_swiglu_impl", - _weighted_bias_swiglu_no_inner_forward_cast, - ) - - def _install_moe_postprocess_workaround(moe_layer: Any) -> None: # Routed token counts change across packed RL steps. Megatron's MoE # postprocess reshapes through dispatcher-owned shape state, which makes @@ -241,8 +141,6 @@ def _sync_dealloc_fake( _install_context_parallel_attention_workaround() if _SELF_ATTN_LINEAR_PROJ_REDUCE_SCATTER_WORKAROUND_FLAG in flags: _install_self_attn_linear_proj_reduce_scatter_workaround() - if "weighted_bias_swiglu_no_inner_forward_cast" in flags: - _install_weighted_bias_swiglu_no_inner_forward_cast_workaround() if "moe_postprocess" in flags: _install_moe_postprocess_workaround(moe_layer) if "gemma4_moe_postprocess" in flags: @@ -307,6 +205,10 @@ def _sync_dealloc_fake( moe_layer.MoELayer.preprocess = _disable(moe_layer.MoELayer.preprocess) if "moe_forward" in flags: moe_layer.MoELayer.forward = _disable(moe_layer.MoELayer.forward) + if "mlp_forward" in flags: + from megatron.core.transformer import mlp + + mlp.MLP.forward = _disable(mlp.MLP.forward) if "te_grouped_mlp_forward" in flags: moe_experts.TEGroupedMLP.forward = _disable(moe_experts.TEGroupedMLP.forward) _INSTALLED_CONFIG = installed_config diff --git a/src/art/megatron/context_parallel/comm.py b/src/art/megatron/context_parallel/comm.py index 8ea97067d..72455ce38 100644 --- a/src/art/megatron/context_parallel/comm.py +++ b/src/art/megatron/context_parallel/comm.py @@ -89,6 +89,41 @@ def wait_post_process(self) -> tuple[torch.Tensor, torch.Tensor]: ) +@dataclass +class TensorFetchWork: + packed_buffer: torch.Tensor + recv_splits: tuple[int, ...] + handle: _Waitable | None + send_buffer: torch.Tensor | None = None + stream: torch.cuda.Stream | None = None + output_layout: str = "token_major" + _wait_complete: bool = False + + def is_completed(self) -> bool: + if self._wait_complete: + return True + handle_complete = True + if self.handle is not None: + is_completed = getattr(self.handle, "is_completed", None) + if callable(is_completed): + handle_complete = bool(is_completed()) + return handle_complete and (self.stream is None or bool(self.stream.query())) + + def wait_post_process(self) -> torch.Tensor: + if not self._wait_complete: + if self.handle is not None: + self.handle.wait() + if self.stream is not None: + torch.cuda.current_stream(self.packed_buffer.device).wait_stream( + self.stream + ) + self._wait_complete = True + return _unpack_single_tensor( + self.packed_buffer, + output_layout=self.output_layout, + ) + + @dataclass class DkvReduceWork: packed_buffer: torch.Tensor | None @@ -166,6 +201,55 @@ def wait_post_process(self) -> tuple[torch.Tensor, torch.Tensor]: return self.dk_local, self.dv_local +@dataclass +class TensorReduceWork: + packed_buffer: torch.Tensor | None + handle: _Waitable | None + send_buffer: torch.Tensor | None + stream: torch.cuda.Stream | None + plan: DkvReducePlan + output: torch.Tensor + range_meta_cache: dict[Any, Any] | None = None + input_layout: str = "token_major" + _wait_complete: bool = False + + def wait_post_process(self) -> torch.Tensor: + if not self._wait_complete: + if self.handle is not None: + self.handle.wait() + if self.stream is not None and self.packed_buffer is not None: + torch.cuda.current_stream(self.packed_buffer.device).wait_stream( + self.stream + ) + self._wait_complete = True + if self.packed_buffer is None or int(self.packed_buffer.shape[0]) == 0: + return self.output + remote = _unpack_single_tensor( + self.packed_buffer, + output_layout=self.input_layout, + ) + ranges = tuple( + range_ + for peer_ranges in self.plan.recv_ranges_by_peer + for range_ in peer_ranges + if range_.size() > 0 + ) + reduce_fn = ( + range_reduce_sum_head_major_ + if self.input_layout == "head_major" + else range_reduce_sum_ + ) + reduce_fn( + remote + if remote.dtype == self.output.dtype + else remote.to(dtype=self.output.dtype), + output_tensor=self.output, + ranges=ranges, + range_meta_cache=self.range_meta_cache, + ) + return self.output + + class A2AVCommunicator: def __init__(self) -> None: self._streams: dict[int, torch.cuda.Stream] = {} @@ -194,6 +278,7 @@ def _launch_exchange( group: Any, async_op: bool, input_layout: str, + row_factor: int = 2, ) -> tuple[_Waitable | None, torch.Tensor, torch.cuda.Stream | None]: stream = self._get_stream(tensor) if async_op else None send_buffer = ( @@ -202,6 +287,7 @@ def _launch_exchange( tensor=tensor, total_rows=0, input_layout=input_layout, + row_factor=row_factor, ) ) if total_send_rows <= 0 @@ -302,6 +388,61 @@ def launch_kv_fetch( output_layout=output_layout, ) + def launch_tensor_fetch( + self, + *, + tensor_local: torch.Tensor, + plan: KvFetchPlan, + group: Any, + async_op: bool, + range_meta_cache: dict[Any, Any] | None = None, + input_layout: str = "token_major", + output_layout: str = "token_major", + ) -> TensorFetchWork: + """Fetch one stage tensor without duplicating it on the communication wire.""" + total_send_rows = int(sum(plan.send_splits)) + total_recv_rows = int(sum(plan.recv_splits)) + recv_packed = tensor_local.new_empty( + _packed_peer_tensor_shape( + tensor=tensor_local, + total_rows=total_recv_rows, + input_layout=input_layout, + row_factor=1, + ) + ) + if group is None or _DIST.get_world_size(group) == 1: + return TensorFetchWork( + packed_buffer=recv_packed, + recv_splits=plan.recv_splits, + handle=None, + output_layout=output_layout, + ) + handle, send_buffer, stream = self._launch_exchange( + tensor=tensor_local, + recv_buffer=recv_packed, + total_send_rows=total_send_rows, + make_send_buffer=lambda: _pack_gathered_tensor_per_peer( + tensor=tensor_local, + ranges_by_peer=plan.send_ranges_by_peer, + range_meta_cache=range_meta_cache, + input_layout=input_layout, + ), + output_split_sizes=list(plan.recv_splits), + input_split_sizes=list(plan.send_splits), + group=group, + async_op=async_op, + input_layout=input_layout, + row_factor=1, + ) + return TensorFetchWork( + packed_buffer=recv_packed, + recv_splits=plan.recv_splits, + handle=handle, + send_buffer=send_buffer, + stream=stream, + output_layout=output_layout, + ) + def launch_dkv_reduce( self, *, @@ -369,6 +510,64 @@ def launch_dkv_reduce( input_layout=input_layout, ) + def launch_tensor_reduce( + self, + *, + remote: torch.Tensor, + plan: DkvReducePlan, + group: Any, + async_op: bool, + output: torch.Tensor, + range_meta_cache: dict[Any, Any] | None = None, + input_layout: str = "token_major", + ) -> TensorReduceWork: + """Return one stage gradient to owners through the plan's inverse ranges.""" + if group is None or _DIST.get_world_size(group) == 1: + return TensorReduceWork( + packed_buffer=None, + handle=None, + send_buffer=None, + stream=None, + plan=plan, + output=output, + range_meta_cache=range_meta_cache, + input_layout=input_layout, + ) + recv_packed = remote.new_empty( + _packed_peer_tensor_shape( + tensor=remote, + total_rows=int(sum(plan.recv_splits)), + input_layout=input_layout, + row_factor=1, + ) + ) + handle, send_buffer, stream = self._launch_exchange( + tensor=remote, + recv_buffer=recv_packed, + total_send_rows=int(sum(plan.send_splits)), + make_send_buffer=lambda: _pack_split_tensor_by_peer( + tensor=remote, + splits=plan.send_splits, + input_layout=input_layout, + ), + output_split_sizes=list(plan.recv_splits), + input_split_sizes=list(plan.send_splits), + group=group, + async_op=async_op, + input_layout=input_layout, + row_factor=1, + ) + return TensorReduceWork( + packed_buffer=recv_packed, + handle=handle, + send_buffer=send_buffer, + stream=stream, + plan=plan, + output=output, + range_meta_cache=range_meta_cache, + input_layout=input_layout, + ) + def range_gather_per_peer( input_tensor: torch.Tensor, @@ -435,6 +634,35 @@ def _pack_gathered_tensors_per_peer( return packed +def _pack_gathered_tensor_per_peer( + *, + tensor: torch.Tensor, + ranges_by_peer: tuple[tuple[TokenRange, ...], ...], + range_meta_cache: dict[Any, Any] | None, + input_layout: str, +) -> torch.Tensor: + rows = [ + _gather_peer_rows( + tensor, + peer_ranges, + input_layout=input_layout, + range_meta_cache=range_meta_cache, + ) + for peer_ranges in ranges_by_peer + if any(range_.size() > 0 for range_ in peer_ranges) + ] + if not rows: + return tensor.new_empty( + _packed_peer_tensor_shape( + tensor=tensor, + total_rows=0, + input_layout=input_layout, + row_factor=1, + ) + ) + return torch.cat(rows, dim=0).contiguous() + + def _pack_split_tensors_by_peer( *, left_tensor: torch.Tensor, @@ -472,6 +700,21 @@ def _pack_split_tensors_by_peer( return packed +def _pack_split_tensor_by_peer( + *, tensor: torch.Tensor, splits: tuple[int, ...], input_layout: str +) -> torch.Tensor: + rows = _peer_row_count(tensor, layout=input_layout) + if rows != int(sum(splits)): + raise RuntimeError( + f"Packed split consumed the wrong number of rows: {rows} != {sum(splits)}" + ) + return ( + tensor.movedim(1, 0).contiguous() + if input_layout == "head_major" + else tensor.contiguous() + ) + + def _validate_peer_layout(layout: str, *, context: str) -> None: if layout not in {"token_major", "head_major"}: raise ValueError(f"Unsupported {context} layout: {layout}") @@ -482,11 +725,12 @@ def _packed_peer_tensor_shape( tensor: torch.Tensor, total_rows: int, input_layout: str, + row_factor: int = 2, ) -> tuple[int, ...]: _validate_peer_layout(input_layout, context="peer tensor input") if input_layout == "head_major": - return (total_rows * 2, int(tensor.shape[0]), int(tensor.shape[2])) - return (total_rows * 2, *tuple(int(dim) for dim in tensor.shape[1:])) + return (total_rows * row_factor, int(tensor.shape[0]), int(tensor.shape[2])) + return (total_rows * row_factor, *tuple(int(dim) for dim in tensor.shape[1:])) def _peer_row_count(tensor: torch.Tensor, *, layout: str) -> int: @@ -579,6 +823,19 @@ def _unpack_packed_tensor_per_peer( return left, right +def _unpack_single_tensor( + packed_tensor: torch.Tensor, + *, + output_layout: str, +) -> torch.Tensor: + _validate_peer_layout(output_layout, context="single-tensor output") + return ( + packed_tensor.movedim(0, 1).contiguous() + if output_layout == "head_major" + else packed_tensor + ) + + def _new_unpacked_peer_tensor( packed_tensor: torch.Tensor, *, diff --git a/src/art/megatron/context_parallel/executor.py b/src/art/megatron/context_parallel/executor.py index b595de990..ca3b1b084 100644 --- a/src/art/megatron/context_parallel/executor.py +++ b/src/art/megatron/context_parallel/executor.py @@ -671,6 +671,7 @@ def run( backend = flex_backend_for_head_dims( head_dim=int(q.shape[-1]), head_dim_v=int(v.shape[-1]), + device=q.device, ) if compile_key is None: _q_len, _k_len, compile_key = select_sparse_execution_family( @@ -688,6 +689,7 @@ def run( head_dim=int(q.shape[-1]), head_dim_v=int(v.shape[-1]), triton_num_stages_2_head_dims=self.triton_num_stages_2_head_dims, + device=q.device, ) ) prepare_sparse_flex_attention( diff --git a/src/art/megatron/context_parallel/runtime.py b/src/art/megatron/context_parallel/runtime.py index 42b7585d5..1aef92421 100644 --- a/src/art/megatron/context_parallel/runtime.py +++ b/src/art/megatron/context_parallel/runtime.py @@ -6,9 +6,11 @@ import json from typing import Any, cast +from pydantic import BaseModel import torch from art.loss import shift_tensor +from art.megatron.selective_lm_head import LmHeadTokenSelection from art.preprocessing.pack import PackedTensors from .builder import build_prefix_tree_attention_spec @@ -18,6 +20,7 @@ AttnMaskKind, AttnSlice, ContextParallelConfig, + ContextParallelWorkloadProfile, CpBlockMaskVariant, DispatchedPackedTensors, DkvReducePlan, @@ -30,6 +33,7 @@ RankRuntimePlan, StagePlan, TokenRange, + TrainingMicrobatchWorkload, ) _CHUNK_MASK_STATS_TORCH_THRESHOLD = 1024 @@ -39,17 +43,24 @@ StagePiece = tuple[TokenRange, TokenRange, AttnMaskKind, int | None] StageSliceKey = tuple[int, int, int, int, int, str, int] +ProfiledChunkPiece = tuple[int, int, int, int, int, int, str, int | None] @dataclass(frozen=True) class _PlanningBundle: spec: PackedBatchAttentionSpec - rank_plans: tuple[RankRuntimePlan, ...] + row_spec: PackedRowAttentionSpec + chunk_ranges: tuple[TokenRange, ...] + owners: tuple[int, ...] + wave_assignment: tuple[int, ...] + token_layout_index: TokenLayoutIndex gdn_execution_spec: Any | None = None _PLANNING_BUNDLE_CACHE: dict[str, _PlanningBundle] = {} _RUNTIME_PLAN_CACHE: dict[str, tuple[RankRuntimePlan, ...]] = {} +_RANK_RUNTIME_PLAN_CACHE: dict[tuple[str, int], RankRuntimePlan] = {} +_GDN_GLOBAL_DECISION_CACHE: dict[tuple[str, str], Any] = {} _GDN_RANK_PLAN_CACHE: dict[tuple[str, str, int | None, int, str], Any] = {} @@ -145,13 +156,21 @@ def _get_or_build_planning_bundle( group_ids_cpu, parent_ids_cpu, ) + row_spec, chunk_ranges, owners, wave_assignment = _runtime_plan_assignment( + spec, + topology=topology, + config=config, + ) bundle = _PlanningBundle( spec=spec, - rank_plans=get_or_build_runtime_plan( - spec, - topology=topology, - config=config, - original_seq_len=original_seq_len, + row_spec=row_spec, + chunk_ranges=chunk_ranges, + owners=owners, + wave_assignment=wave_assignment, + token_layout_index=_build_runtime_token_layout_index( + chunk_ranges=chunk_ranges, + owners=owners, + cp_size=max(int(topology.cp), 1), ), gdn_execution_spec=gdn_execution_spec, ) @@ -159,6 +178,48 @@ def _get_or_build_planning_bundle( return planning_key, bundle, group_ids_cpu, parent_ids_cpu +def _get_or_build_bundle_rank_plan( + *, + planning_key: str, + bundle: _PlanningBundle, + original_seq_len: int, + target_rank: int, + block_size: int, +) -> RankRuntimePlan: + """Materialize only the caller's rank plan at the host-ahead boundary. + + Building every rank plan here scales CPU work with CP size, can exceed the + planning budget, and exposes planning after lookahead GPU work completes. + Each rank plan depends only on the shared assignment, so peer plans are not + part of this runtime boundary. + """ + cache_key = (planning_key, int(target_rank)) + cached = _RANK_RUNTIME_PLAN_CACHE.get(cache_key) + if cached is not None: + return cached + plan = _build_rank_runtime_plan( + row_spec=bundle.row_spec, + chunk_ranges=bundle.chunk_ranges, + owners=bundle.owners, + wave_assignment=bundle.wave_assignment, + token_layout_index=bundle.token_layout_index, + cp_size=len(bundle.token_layout_index.token_counts_by_rank), + original_seq_len=original_seq_len, + target_rank=target_rank, + block_size=block_size, + ) + _cache_put(_RANK_RUNTIME_PLAN_CACHE, cache_key, plan) + return plan + + +def _gdn_planner_config_cache_key(gdn_planner_config: Any | None) -> str: + return ( + _json_cache_key(_dataclass_payload(gdn_planner_config)) + if gdn_planner_config is not None + else "" + ) + + def _gdn_rank_plan_cache_key( *, planning_key: str, @@ -166,18 +227,45 @@ def _gdn_rank_plan_cache_key( gdn_planner_config: Any | None, device: torch.device, ) -> tuple[str, str, int | None, int, str]: - config_key = ( - _json_cache_key(_dataclass_payload(gdn_planner_config)) - if gdn_planner_config is not None - else "" - ) return ( planning_key, device.type, device.index, int(cp_rank), - config_key, + _gdn_planner_config_cache_key(gdn_planner_config), + ) + + +def _plan_gdn_global_execution( + *, + planning_key: str, + bundle: _PlanningBundle, + topology: ParallelTopology, + gdn_planner_config: Any | None, +) -> Any: + """Select one all-rank GDN decision without rank-local tensors.""" + if bundle.gdn_execution_spec is None: + raise RuntimeError("GDN CP planning requires a parsed execution spec") + cache_key = ( + planning_key, + _gdn_planner_config_cache_key(gdn_planner_config), ) + cached = _GDN_GLOBAL_DECISION_CACHE.get(cache_key) + if cached is not None: + return cached + + from art.megatron.gdn.gdn_prefix_tree import ( + build_gdn_global_execution_decision, + ) + + decision = build_gdn_global_execution_decision( + bundle.gdn_execution_spec, + cp_size=int(topology.cp), + attention_token_layout_index=bundle.token_layout_index, + planner_config=gdn_planner_config, + ) + _cache_put(_GDN_GLOBAL_DECISION_CACHE, cache_key, decision) + return decision def _plan_gdn_rank_execution( @@ -201,14 +289,19 @@ def _plan_gdn_rank_execution( if cached is not None: return cached - from art.megatron.gdn.gdn_prefix_tree import build_gdn_rank_execution_plan + decision = _plan_gdn_global_execution( + planning_key=planning_key, + bundle=bundle, + topology=topology, + gdn_planner_config=gdn_planner_config, + ) + from art.megatron.gdn.gdn_prefix_tree import materialize_gdn_rank_execution_plan - plan = build_gdn_rank_execution_plan( + plan = materialize_gdn_rank_execution_plan( bundle.gdn_execution_spec, + decision, device="cpu", cp_rank=int(cp_rank), - cp_size=int(topology.cp), - attention_token_layout_index=bundle.rank_plans[int(cp_rank)].token_layout_index, planner_config=gdn_planner_config, ) _cache_put(_GDN_RANK_PLAN_CACHE, cache_key, plan) @@ -277,24 +370,16 @@ def context_parallel_rank_model_token_counts( build_gdn_execution_spec=build_gdn_execution_spec, ) ) - attention_counts = tuple( - sum(int(length) for length in rank_plan.local_valid_lengths) - for rank_plan in bundle.rank_plans - ) + attention_counts = bundle.token_layout_index.token_counts_by_rank if not build_gdn_execution_spec: return attention_counts - gdn_counts = tuple( - int( - _plan_gdn_rank_execution( - planning_key=planning_key, - bundle=bundle, - topology=topology, - cp_rank=cp_rank, - gdn_planner_config=gdn_planner_config, - ).gdn_token_count - ) - for cp_rank in range(int(topology.cp)) + decision = _plan_gdn_global_execution( + planning_key=planning_key, + bundle=bundle, + topology=topology, + gdn_planner_config=gdn_planner_config, ) + gdn_counts = decision.gdn_token_counts_by_rank return tuple( max(attention_count, gdn_count) for attention_count, gdn_count in zip(attention_counts, gdn_counts, strict=True) @@ -1232,7 +1317,7 @@ def _evaluate_plan( } -def _search_chunk_assignment( +def _search_generic_chunk_assignment( *, chunk_ranges: tuple[TokenRange, ...], pair_matrix: list[list[int]] | torch.Tensor, @@ -1357,6 +1442,403 @@ def _evaluate_candidate( return best +def _folded_chunk_assignment( + *, + weights: list[float], + cp_size: int, +) -> tuple[int, ...]: + if len(weights) < 2 * cp_size: + return tuple() + slab_owners = _contiguous_chunk_assignment( + q_weights=weights, + cp_size=2 * cp_size, + ) + return tuple(min(owner, 2 * cp_size - 1 - owner) for owner in slab_owners) + + +def _ownership_range_counts( + owners: tuple[int, ...], + *, + cp_size: int, +) -> tuple[int, ...]: + counts = [0 for _ in range(cp_size)] + previous = -1 + for owner in owners: + if owner != previous: + counts[int(owner)] += 1 + previous = int(owner) + return tuple(counts) + + +def _rounded(value: int, multiple: int) -> int: + return ((int(value) + int(multiple) - 1) // int(multiple)) * int(multiple) + + +def _stage_indexer_tile_pairs( + pieces: list[ProfiledChunkPiece], + *, + profile: ContextParallelWorkloadProfile, +) -> int: + queries: dict[tuple[int, int], int] = {} + for ( + _q_index, + _k_index, + q_start, + q_end, + k_start, + k_end, + _mask_kind, + _family, + ) in pieces: + query = (q_start, q_end) + queries[query] = queries.get(query, 0) + k_end - k_start + + tile_pairs = 0 + for (q_start, q_end), k_tokens in queries.items(): + if k_tokens <= 0: + continue + k_chunk = min(k_tokens, int(profile.indexer_max_k_tokens)) + q_chunk = max(1, int(profile.indexer_score_workspace_elements) // k_chunk) + rounded_q = sum( + _rounded( + min(q_chunk, q_end - start), + int(profile.query_tile_size), + ) + for start in range(q_start, q_end, q_chunk) + ) + rounded_k = sum( + _rounded( + min(k_chunk, k_tokens - start), + int(profile.key_tile_size), + ) + for start in range(0, k_tokens, k_chunk) + ) + tile_pairs += rounded_q * rounded_k + return tile_pairs + + +def _intervals_size(intervals: list[tuple[int, int]]) -> int: + if not intervals: + return 0 + ordered = sorted(set(intervals)) + total = 0 + current_start, current_end = ordered[0] + for start, end in ordered[1:]: + if start <= current_end: + current_end = max(current_end, end) + else: + total += current_end - current_start + current_start, current_end = start, end + return total + current_end - current_start + + +def _profiled_chunk_pieces( + row_spec: PackedRowAttentionSpec, + *, + chunk_ranges: tuple[TokenRange, ...], +) -> tuple[ProfiledChunkPiece, ...]: + pieces = [] + chunk_starts = tuple(int(range_.start) for range_ in chunk_ranges) + chunk_ends = tuple(int(range_.end) for range_ in chunk_ranges) + for slice_ in row_spec.slices: + q_parts = _indexed_intersections( + slice_.q_range, + chunk_ranges, + candidate_starts=chunk_starts, + candidate_ends=chunk_ends, + ) + k_parts = _indexed_intersections( + slice_.k_range, + chunk_ranges, + candidate_starts=chunk_starts, + candidate_ends=chunk_ends, + ) + for q_index, q_piece in q_parts: + for k_index, k_piece in k_parts: + mask_kind = _resolve_stage_mask_kind( + mask_kind=slice_.mask_kind, + q_piece=q_piece, + k_piece=k_piece, + ) + if mask_kind is not None: + pieces.append( + ( + q_index, + k_index, + int(q_piece.start), + int(q_piece.end), + int(k_piece.start), + int(k_piece.end), + mask_kind.value, + slice_.family_index, + ) + ) + return tuple(dict.fromkeys(pieces)) + + +def _profiled_rank_statistics( + *, + chunk_pieces: tuple[ProfiledChunkPiece, ...], + chunk_ranges: tuple[TokenRange, ...], + owners: tuple[int, ...], + wave_assignment: tuple[int, ...], + cp_size: int, + profile: ContextParallelWorkloadProfile, +) -> list[dict[str, int]]: + wave_count = max(wave_assignment, default=0) + 1 if wave_assignment else 0 + stages: list[list[list[ProfiledChunkPiece]]] = [ + [[] for _ in range(wave_count + 1)] for _ in range(cp_size) + ] + recv_ranges: list[list[list[list[tuple[int, int]]]]] = [ + [[[] for _ in range(cp_size)] for _ in range(wave_count)] + for _ in range(cp_size) + ] + send_ranges: list[list[list[list[tuple[int, int]]]]] = [ + [[[] for _ in range(cp_size)] for _ in range(wave_count)] + for _ in range(cp_size) + ] + for piece in chunk_pieces: + q_index, k_index, _q_start, _q_end, k_start, k_end, _mask, _family = piece + destination = int(owners[q_index]) + source = int(owners[k_index]) + if source == destination: + stages[destination][0].append(piece) + continue + wave = int(wave_assignment[k_index]) + stages[destination][wave + 1].append(piece) + interval = (k_start, k_end) + recv_ranges[destination][wave][source].append(interval) + send_ranges[source][wave][destination].append(interval) + + query_tokens = [0 for _ in range(cp_size)] + for range_, owner in zip(chunk_ranges, owners, strict=True): + query_tokens[int(owner)] += int(range_.size()) + statistics = [] + for rank in range(cp_size): + combined_k_tokens = _intervals_size( + [(piece[4], piece[5]) for piece in stages[rank][0]] + ) + recv_tokens = 0 + send_tokens = 0 + remote_peers: set[int] = set() + for wave_recv, wave_send in zip( + recv_ranges[rank], + send_ranges[rank], + strict=True, + ): + for peer, (peer_recv, peer_send) in enumerate( + zip(wave_recv, wave_send, strict=True) + ): + recv_size = _intervals_size(peer_recv) + send_size = _intervals_size(peer_send) + recv_tokens += recv_size + send_tokens += send_size + combined_k_tokens += recv_size + if peer != rank and (recv_size or send_size): + remote_peers.add(peer) + statistics.append( + { + "query_tokens": query_tokens[rank], + "tile_pairs": sum( + _stage_indexer_tile_pairs(pieces, profile=profile) + for pieces in stages[rank] + ), + "combined_k_tokens": combined_k_tokens, + "fetch_send_tokens": send_tokens, + "fetch_recv_tokens": recv_tokens, + "remote_peers": len(remote_peers), + } + ) + return statistics + + +def _evaluate_profiled_assignment( + *, + chunk_pieces: tuple[ProfiledChunkPiece, ...], + chunk_ranges: tuple[TokenRange, ...], + owners: tuple[int, ...], + wave_assignment: tuple[int, ...], + cp_size: int, + profile: ContextParallelWorkloadProfile, +) -> dict[str, Any]: + rank_stats = _profiled_rank_statistics( + chunk_pieces=chunk_pieces, + chunk_ranges=chunk_ranges, + owners=owners, + wave_assignment=wave_assignment, + cp_size=cp_size, + profile=profile, + ) + query_flops = 0 + indexer_flops = 0 + hbm_k_bytes = 0 + peak_memory_bytes = 0 + fetch_send_bytes = 0 + fetch_recv_bytes = 0 + dkv_send_bytes = 0 + dkv_recv_bytes = 0 + for rank in rank_stats: + for stage in profile.stages: + query_flops = max( + query_flops, + rank["query_tokens"] * int(stage.query_flops_per_token), + ) + indexer_flops = max( + indexer_flops, + rank["tile_pairs"] * int(stage.tile_pair_flops), + ) + hbm_k_bytes = max( + hbm_k_bytes, + rank["combined_k_tokens"] * int(stage.k_hbm_bytes_per_token), + ) + peak_memory_bytes = max( + peak_memory_bytes, + rank["query_tokens"] * int(stage.query_memory_bytes_per_token) + + rank["combined_k_tokens"] * int(stage.k_memory_bytes_per_token), + ) + fetch_send_bytes = max( + fetch_send_bytes, + rank["fetch_send_tokens"] * int(stage.k_fetch_bytes_per_token), + ) + fetch_recv_bytes = max( + fetch_recv_bytes, + rank["fetch_recv_tokens"] * int(stage.k_fetch_bytes_per_token), + ) + dkv_send_bytes = max( + dkv_send_bytes, + rank["fetch_recv_tokens"] * int(stage.dkv_reduce_bytes_per_token), + ) + dkv_recv_bytes = max( + dkv_recv_bytes, + rank["fetch_send_tokens"] * int(stage.dkv_reduce_bytes_per_token), + ) + + range_counts = _ownership_range_counts(owners, cp_size=cp_size) + max_network_bytes = max( + fetch_send_bytes, + fetch_recv_bytes, + dkv_send_bytes, + dkv_recv_bytes, + ) + return { + "score": query_flops, + "query_flops": query_flops, + "indexer_flops": indexer_flops, + "hbm_k_bytes": hbm_k_bytes, + "peak_memory_bytes": peak_memory_bytes, + "max_network_bytes": max_network_bytes, + "fetch_send_bytes": fetch_send_bytes, + "fetch_recv_bytes": fetch_recv_bytes, + "dkv_send_bytes": dkv_send_bytes, + "dkv_recv_bytes": dkv_recv_bytes, + "max_remote_peers": max( + (rank["remote_peers"] for rank in rank_stats), + default=0, + ), + "max_ownership_ranges": max(range_counts, default=0), + "rank_query_tokens": tuple(rank["query_tokens"] for rank in rank_stats), + "rank_tile_pairs": tuple(rank["tile_pairs"] for rank in rank_stats), + "rank_combined_k_tokens": tuple( + rank["combined_k_tokens"] for rank in rank_stats + ), + "rank_fetch_send_tokens": tuple( + rank["fetch_send_tokens"] for rank in rank_stats + ), + "rank_fetch_recv_tokens": tuple( + rank["fetch_recv_tokens"] for rank in rank_stats + ), + "rank_remote_peers": tuple(rank["remote_peers"] for rank in rank_stats), + "ownership_range_counts": range_counts, + } + + +def _profiled_assignment_key( + evaluation: dict[str, Any], + owners: tuple[int, ...], +) -> tuple[Any, ...]: + # These terms retain their own physical units; they are never added together. + return ( + int(evaluation["query_flops"]), + int(evaluation["max_network_bytes"]), + int(evaluation["hbm_k_bytes"]), + int(evaluation["indexer_flops"]), + int(evaluation["max_remote_peers"]), + int(evaluation["max_ownership_ranges"]), + owners, + ) + + +def _search_chunk_assignment( + *, + row_spec: PackedRowAttentionSpec | None = None, + chunk_ranges: tuple[TokenRange, ...], + pair_matrix: list[list[int]] | torch.Tensor, + q_weights: list[float], + cp_size: int, + config: ContextParallelConfig, +) -> tuple[tuple[int, ...], tuple[int, ...], dict[str, Any]]: + generic = _search_generic_chunk_assignment( + chunk_ranges=chunk_ranges, + pair_matrix=pair_matrix, + q_weights=q_weights, + cp_size=cp_size, + config=config, + ) + profile = config.workload_profile + if profile is None: + return generic + if row_spec is None: + raise RuntimeError("Profile-aware CP planning requires the packed row spec.") + + chunk_pieces = _profiled_chunk_pieces(row_spec, chunk_ranges=chunk_ranges) + lengths = [float(range_.size()) for range_ in chunk_ranges] + candidates = [ + generic[0], + _contiguous_chunk_assignment(q_weights=lengths, cp_size=cp_size), + _folded_chunk_assignment(weights=lengths, cp_size=cp_size), + ] + baseline = _evaluate_profiled_assignment( + chunk_pieces=chunk_pieces, + chunk_ranges=chunk_ranges, + owners=generic[0], + wave_assignment=generic[1], + cp_size=cp_size, + profile=profile, + ) + best_owners = generic[0] + best_eval = baseline + seen = {generic[0]} + for owners in candidates[1:]: + if not owners or owners in seen: + continue + seen.add(owners) + if len(set(owners)) != cp_size: + continue + range_counts = _ownership_range_counts(owners, cp_size=cp_size) + if max(range_counts, default=0) > int(profile.max_ownership_ranges_per_rank): + continue + evaluation = _evaluate_profiled_assignment( + chunk_pieces=chunk_pieces, + chunk_ranges=chunk_ranges, + owners=owners, + wave_assignment=generic[1], + cp_size=cp_size, + profile=profile, + ) + if int(evaluation["peak_memory_bytes"]) > int( + baseline["peak_memory_bytes"] + ) or _profiled_assignment_key(evaluation, owners) >= _profiled_assignment_key( + baseline, generic[0] + ): + continue + if _profiled_assignment_key(evaluation, owners) < _profiled_assignment_key( + best_eval, best_owners + ): + best_owners = owners + best_eval = evaluation + return best_owners, generic[1], best_eval + + def _flatten_ranges_by_peer( ranges_by_peer: tuple[tuple[TokenRange, ...], ...], ) -> tuple[TokenRange, ...]: @@ -1707,6 +2189,9 @@ def prepare_cp_micro( block_mask_variants: tuple[CpBlockMaskVariant, ...] = (), target_device: torch.device | None = None, ref_logprobs: torch.Tensor | None = None, + model_support_handler: Any | None = None, + attention_head_dim: int | None = None, + attention_value_head_dim: int | None = None, ) -> PreparedMegatronBatch: """Prepare one CP microbatch with a CPU-only planning phase. @@ -1715,6 +2200,10 @@ def prepare_cp_micro( `target_device`. Passing CUDA `group_ids` or `parent_ids` still works for older direct callers, but it reintroduces D2H syncs and invalidates the host-ahead/device-behind lookahead assumption. + + Model-owned state is built exactly once here, after rank-local dispatch. + Its handler must only enqueue device work: scalar CUDA reads would expose + planning on the host and invalidate lookahead overlap. """ state, rank_plan, spec, pad_multiple = prepare_megatron_context_parallel_state( micro=micro, @@ -1727,7 +2216,7 @@ def prepare_cp_micro( block_mask_variants=block_mask_variants, target_device=target_device, ) - tensors = dispatch_megatron_context_parallel_training_tensors( + tensors, workload = dispatch_megatron_context_parallel_training_tensors( micro=micro, rank_plan=rank_plan, spec=spec, @@ -1737,6 +2226,23 @@ def prepare_cp_micro( cp_group=cp_group, ref_logprobs=ref_logprobs, ) + if model_support_handler is not None: + from art.megatron.model_support.spec import PrefixTreeModelStateContext + + state.model_state = dict( + model_support_handler.build_prefix_tree_model_state( + PrefixTreeModelStateContext( + input_pos=tensors.input_pos, + group_ids=micro["group_ids"], + parent_ids=micro["parent_ids"], + device=tensors.tokens.device, + attention_token_layout_index=rank_plan.token_layout_index, + attention_head_dim=attention_head_dim, + attention_value_head_dim=attention_value_head_dim, + context_parallel_state=state, + ) + ) + ) if tensors.token_uids is not None: state = replace(state, trace_token_uids=tensors.token_uids) if prepare_execution_state: @@ -1750,6 +2256,7 @@ def prepare_cp_micro( tensors=tensors, packed_seq_params=None, attention_state=state, + workload=workload, rank_plan=rank_plan, pad_multiple=pad_multiple, ) @@ -1797,7 +2304,13 @@ def prepare_megatron_context_parallel_state( original_seq_len=int(micro["tokens"].shape[1]), build_gdn_execution_spec=build_gdn_execution_spec, ) - rank_plan = bundle.rank_plans[int(cp_rank)] + rank_plan = _get_or_build_bundle_rank_plan( + planning_key=planning_key, + bundle=bundle, + original_seq_len=int(micro["tokens"].shape[1]), + target_rank=cp_rank, + block_size=int(config.block_size), + ) gdn_execution_plan = None if build_gdn_execution_spec: _plan_gdn_rank_execution( @@ -1842,7 +2355,7 @@ def dispatch_megatron_context_parallel_training_tensors( target_device: torch.device | None = None, cp_group: Any | None = None, ref_logprobs: torch.Tensor | None = None, -) -> DispatchedPackedTensors: +) -> tuple[DispatchedPackedTensors, TrainingMicrobatchWorkload]: """Gather this rank's training tensors and optionally move them to device. Dispatch may enqueue H2D copies when `target_device` is CUDA, but it must @@ -1896,12 +2409,17 @@ def maybe_dispatch( ) -> torch.Tensor | None: return None if tensor is None else dispatch(tensor, pad_value) + local_labels = dispatch(labels, -100, move_to_target=False) + lm_head_selection = LmHeadTokenSelection.from_labels( + local_labels, + target_device=target_device, + ) local_token_uids = ( None if token_uids is None else dispatch(token_uids, -1, move_to_target=False) ) - return DispatchedPackedTensors( + tensors = DispatchedPackedTensors( tokens=dispatch(micro["tokens"], 0), - labels=dispatch(labels, -100), + labels=_to_target_device(local_labels, target_device), input_pos=dispatch(micro["input_pos"], 0), assistant_mask=dispatch(assistant_mask, False).to(dtype=torch.bool), group_ids=dispatch(shifted_group_ids, 0), @@ -1909,11 +2427,19 @@ def maybe_dispatch( advantages=dispatch(advantages, 0.0), weights=dispatch(weights, 0.0), valid_lengths=rank_plan.local_valid_lengths, + lm_head_selection=lm_head_selection, original_logprobs=maybe_dispatch(original_logprobs, 0.0), ref_logprobs=maybe_dispatch(ref_logprobs, float("nan")), loss_all_reduce_group=cp_group, token_uids=None if local_token_uids is None else local_token_uids.contiguous(), ) + workload = TrainingMicrobatchWorkload( + logical_nonpadding_tokens=sum(rank_plan.local_valid_lengths), + loss_bearing_tokens=int((local_labels != -100).sum().item()), + executed_token_equivalents=int(local_labels.numel()), + nominal_schedule_capacity_tokens=rank_plan.original_seq_len, + ) + return tensors, workload def get_or_build_runtime_plan( @@ -1978,6 +2504,7 @@ def _runtime_plan_assignment( chunk_ranges=chunk_ranges, ) owners, wave_assignment, _planner_eval = _search_chunk_assignment( + row_spec=row_spec, chunk_ranges=chunk_ranges, pair_matrix=pair_matrix, q_weights=q_weights, @@ -2068,7 +2595,10 @@ def _runtime_plan_cache_key( def _dataclass_payload(value: Any) -> dict[str, Any]: - return dict(value.__dict__) + return { + key: (item.model_dump(mode="json") if isinstance(item, BaseModel) else item) + for key, item in value.__dict__.items() + } def _attn_slice_payload(slice_: AttnSlice) -> dict[str, Any]: diff --git a/src/art/megatron/context_parallel/types.py b/src/art/megatron/context_parallel/types.py index 0673101be..d55087759 100644 --- a/src/art/megatron/context_parallel/types.py +++ b/src/art/megatron/context_parallel/types.py @@ -5,9 +5,11 @@ from typing import Any from megatron.core.packed_seq_params import PackedSeqParams -from pydantic import BaseModel, ConfigDict +from pydantic import BaseModel, ConfigDict, Field import torch +from art.megatron.selective_lm_head import LmHeadTokenSelection + from .layout_index import TokenLayoutIndex from .loss_inputs import ContextParallelLossInputs @@ -50,6 +52,34 @@ class PackedBatchAttentionSpec: rows: tuple[PackedRowAttentionSpec, ...] +class ContextParallelStageWorkProfile(BaseModel): + """Architecture work assigned to one physical pipeline rank.""" + + model_config = ConfigDict(frozen=True) + + physical_pipeline_rank: int = Field(ge=0) + query_flops_per_token: int = Field(ge=0) + tile_pair_flops: int = Field(ge=0) + k_hbm_bytes_per_token: int = Field(ge=0) + k_fetch_bytes_per_token: int = Field(ge=0) + dkv_reduce_bytes_per_token: int = Field(ge=0) + query_memory_bytes_per_token: int = Field(ge=0) + k_memory_bytes_per_token: int = Field(ge=0) + + +class ContextParallelWorkloadProfile(BaseModel): + """Model-specific facts used to compare low-fragmentation CP layouts.""" + + model_config = ConfigDict(frozen=True) + + stages: tuple[ContextParallelStageWorkProfile, ...] = Field(min_length=1) + query_tile_size: int = Field(gt=0) + key_tile_size: int = Field(gt=0) + indexer_score_workspace_elements: int = Field(gt=0) + indexer_max_k_tokens: int = Field(gt=0) + max_ownership_ranges_per_rank: int = Field(default=2, gt=0) + + @dataclass(frozen=True) class ContextParallelConfig: block_size: int = 128 @@ -73,6 +103,7 @@ class ContextParallelConfig: planner_remote_stage_token_floor: int = 4096 planner_remote_stage_pair_floor: int = 4_000_000 planner_remote_stage_underfill_ms: float = 0.287151 + workload_profile: ContextParallelWorkloadProfile | None = None @dataclass(frozen=True) @@ -142,12 +173,35 @@ class DispatchedPackedTensors(ContextParallelLossInputs): advantages: torch.Tensor weights: torch.Tensor valid_lengths: tuple[int, ...] + lm_head_selection: LmHeadTokenSelection original_logprobs: torch.Tensor | None = None ref_logprobs: torch.Tensor | None = None loss_all_reduce_group: Any | None = None token_uids: torch.Tensor | None = None +class TrainingMicrobatchWorkload(BaseModel): + model_config = ConfigDict(frozen=True) + + logical_nonpadding_tokens: int = Field(ge=0) + loss_bearing_tokens: int = Field(ge=0) + executed_token_equivalents: int = Field(ge=0) + nominal_schedule_capacity_tokens: int = Field(ge=0) + + +class TrainingStepWorkload(BaseModel): + model_config = ConfigDict(frozen=True) + + logical_nonpadding_tokens: int = Field(ge=0) + loss_bearing_tokens: int = Field(ge=0) + executed_token_equivalents: int = Field(ge=0) + nominal_schedule_capacity_tokens: int = Field(ge=0) + dummy_executed_token_equivalents: int = Field(ge=0) + dummy_schedule_capacity_tokens: int = Field(ge=0) + real_microbatches: int = Field(ge=0) + dummy_microbatches: int = Field(ge=0) + + @dataclass class ContextParallelExecutionCache: block_mask_context: Any | None = None @@ -181,6 +235,7 @@ class ArtContextParallelState: group_ids: torch.Tensor parent_ids: torch.Tensor input_pos: torch.Tensor + model_state: dict[str, Any] = field(default_factory=dict) block_mask_variants: tuple[CpBlockMaskVariant, ...] = () gdn_execution_spec: Any | None = None gdn_execution_plan: Any | None = None @@ -203,6 +258,7 @@ class ArtContextParallelState: class PreparedMegatronBatch: tensors: DispatchedPackedTensors attention_state: Any + workload: TrainingMicrobatchWorkload packed_seq_params: PackedSeqParams | None = None rank_plan: RankRuntimePlan | None = None pad_multiple: int = 1 diff --git a/src/art/megatron/distributed_service.py b/src/art/megatron/distributed_service.py new file mode 100644 index 000000000..4cfec80cf --- /dev/null +++ b/src/art/megatron/distributed_service.py @@ -0,0 +1,2624 @@ +from __future__ import annotations + +import asyncio +from collections.abc import AsyncIterator, Awaitable, Callable +from contextlib import asynccontextmanager, nullcontext +import hashlib +from itertools import groupby +import json +import logging +import os +from pathlib import Path +import shutil +import socket +import time +from typing import Any, Literal, TypedDict, cast +import uuid + +import httpx + +from art import dev, types +from art.adapter_leases import in_flight_lora_name +from art.dev.get_model_config import default_target_modules +from art.distributed.art_runtime import ArtRuntime, DistributedPackedBatch +from art.distributed.specs import ModelServiceSpec, NixlTransportSpec, TrainerMeshSpec +from art.distributed.vllm_replica import ( + ReplicaFailure, + ReplicaLaunchTemplate, + ReplicaUpdateReport, +) +from art.serving_capabilities import ( + ServingCapabilities, + discover_serving_capabilities, +) +from art.utils.lifecycle import complete_task, complete_to_thread +from art.utils.output_dirs import get_step_checkpoint_dir +from art.vllm_runtime import ( + get_external_vllm_runtime_config, + get_vllm_runtime_nccl_so_path, + map_checkpoint_path_for_vllm, + normalize_vllm_server_url, + wait_for_vllm_http_runtime, +) + +from .identity_lora import create_identity_lora +from .lora_config import LORA_ALPHA, default_lora_rank_for_handler +from .migrations import optimizer_state_path +from .model_support import ( + get_model_support_handler, + get_model_support_handler_for_spec, + get_model_support_spec, + model_uses_expert_parallel, +) +from .optimizer_state import ( + CheckpointFile, + OptimizerAdapter, + adapter_generation_lease, + commit_optimizer_policy_advance, + format_megatron_resume_message, + new_optimizer_generation, + optimizer_adapter, + prepare_megatron_resume_state, + publish_adapter_checkpoint, + read_adapter_publication, + read_committed_optimizer_pointer, + resolve_committed_optimizer_policy, +) +from .runtime.data_plane import SFTBatchData +from .runtime.publication import ( + DurableTrainerPublication, + TrainerRankPublication, + commit_trainer_publication, +) +from .runtime.specs import ( + AdapterReady, + CurrentSFTConfig, + CurrentTrainConfig, + DurableTrainOutput, + ExperimentalTrainConfig, + HybridEpRuntimeSpec, + SFTJobSpec, + TrainAccepted, + TrainCancelled, + TrainCompleted, + TrainerGeneration, + TrainerJobSpec, + TrainerRuntimeSpec, + TrainFailed, + TrainingRunSpec, + TrainJobSpec, + TrainProgress, +) +from .runtime.weight_transfer import ( + MergedWeightTransferInitInfo, + MergedWeightTransferSpec, +) +from .runtime_config import get_megatron_runtime_config + +logger = logging.getLogger(__name__) +_POLICY_TIMING_HISTORY = 64 + + +class _TrainerJobFields(TypedDict): + job_id: str + run_id: str + training_session_id: str + expected_learner_version: int + learner_version: int + source: TrainerGeneration + output: DurableTrainOutput + publication_targets: tuple[Any, ...] + merged_weight_transfer: MergedWeightTransferSpec | None + + +async def _post_vllm( + url: str, + *, + api_key: str | None, + timeout_s: float = 30.0, + **kwargs: Any, +) -> httpx.Response: + async with httpx.AsyncClient(timeout=timeout_s) as client: + return await client.post(url, headers=_headers(api_key), **kwargs) + + +def _free_port() -> int: + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as listener: + listener.bind(("", 0)) + return int(listener.getsockname()[1]) + + +def _consume_task_result(task: asyncio.Future[Any]) -> None: + if not task.cancelled(): + task.exception() + + +def _retire_completed( + records: dict[int, Any], step: int, completed: asyncio.Future[Any] +) -> None: + if records.get(step) is completed: + records.pop(step) + + +def _hybrid_ep_runtime_spec( + mesh: TrainerMeshSpec, + *, + run_id: str, + transport: NixlTransportSpec | None, +) -> HybridEpRuntimeSpec | None: + if mesh.topology.ep <= 1: + return None + group_size = mesh.topology.etp * mesh.topology.ep + domain_sizes: set[int] = set() + multinode = False + for offset in range(0, len(mesh.ranks), group_size): + group = mesh.ranks[offset : offset + group_size] + domains = [ + (host_id, tuple(ranks)) + for host_id, ranks in groupby(group, key=lambda rank: rank.host_id) + ] + if len({host_id for host_id, _ in domains}) != len(domains): + raise ValueError("HybridEP ranks for each host must be contiguous") + domain_sizes.update(len(ranks) for _, ranks in domains) + multinode |= len(domains) > 1 + if len(domain_sizes) != 1: + raise ValueError( + "HybridEP TP x EP groups require equal ranks per NVLink domain" + ) + if multinode and transport is None: + raise ValueError("cross-host expert parallelism requires NIXL transport") + return HybridEpRuntimeSpec( + ranks_per_nvlink_domain=domain_sizes.pop(), + run_id=run_id, + nixl_transport=transport if multinode else None, + ) + + +class DistributedMegatronService: + """One model's durable checkpoints and run-scoped distributed runtimes.""" + + propagate_close_errors = True + close_timeout_s = 300.0 + + def __init__( + self, + *, + model_name: str, + base_model: str, + config: dev.BackendModelConfig, + output_dir: str, + runtime: ArtRuntime, + enable_expert_replay: bool, + ) -> None: + self.model_name = model_name + self.base_model = base_model + self.config = config + self.output_dir = output_dir + self.runtime = runtime + self.enable_expert_replay = enable_expert_replay + self._latest_step = 0 + self._serving_step = 0 + self._durable_step = 0 + self._durable_optimizer_step = 0 + self._resume_prepared = False + self._training_session_id = uuid.uuid4().hex + self._learner_generation: TrainerGeneration | None = None + self._trainer_resident_generation: TrainerGeneration | None = None + self._trainer: Any = None + # Nested acquisitions must follow train -> serving -> mutation. + self._train_lock = asyncio.Lock() + self._mutation_lock = asyncio.Lock() + self._serving_lock = asyncio.Lock() + self._durability_lock = asyncio.Lock() + self._managed_service_name: str | None = None + self._base_url: str | None = None + self._serving_capabilities: ServingCapabilities | None = None + self._api_key_value: str | None = None + self._current_lora_name: str | None = None + self._vllm_sleeping = False + self._merged_transfer_init: MergedWeightTransferInitInfo | None = None + self._published_adapters: dict[int, OptimizerAdapter] = {} + self._loaded_adapter_steps: set[int] = set() + self._loaded_exact_adapter_steps: set[int] = set() + self._exact_adapter_refcounts: dict[int, int] = {} + self._recovery_tasks: set[asyncio.Task[None]] = set() + self._publication_tasks: dict[int, asyncio.Task[None]] = {} + self._durability_tasks: set[asyncio.Task[Any]] = set() + self._prepared_adapter_transfers: dict[str, Any] = {} + self._loaded_adapter_transfers: dict[int, tuple[Any, str]] = {} + self._next_publication_preparation: ( + tuple[ + Any, + TrainerGeneration, + asyncio.Task[tuple[tuple[Any, ...], MergedWeightTransferSpec | None]], + ] + | None + ) = None + self._serving_futures: dict[int, asyncio.Future[None]] = {} + self._publication_failure: BaseException | None = None + self._publication_metrics: dict[int, dict[str, float]] = {} + self._emitted_publication_metrics: dict[int, set[str]] = {} + self._trainer_completion_times: dict[int, float] = {} + self._serving_activation_times: dict[int, float] = {} + self._close_task: asyncio.Task[None] | None = None + self._closed = False + + @property + def rollout_weights_mode(self) -> Literal["lora", "merged"]: + return self.config.get("rollout_weights_mode", "lora") + + @property + def openai_server_port(self) -> int: + return self._model_service_spec().leader_endpoint.port + + @property + def active_learner_step(self) -> int: + return self._latest_step + + @property + def serving_step(self) -> int: + return self._serving_step + + @property + def durable_step(self) -> int: + return self._durable_step + + @property + def durable_optimizer_step(self) -> int: + return self._durable_optimizer_step + + def drain_publication_metrics(self) -> dict[str, float]: + metrics = { + "publication/active_learner_serving_lag_steps": float( + self._latest_step - self._serving_step + ), + "publication/durable_optimizer_lag_steps": float( + self._latest_step - self._durable_optimizer_step + ), + "publication/queue_depth": float( + sum(not task.done() for task in self._publication_tasks.values()) + ), + } + for step in sorted(self._publication_metrics): + values = self._publication_metrics[step] + emitted = self._emitted_publication_metrics.setdefault(step, set()) + for name, value in values.items(): + if name not in emitted: + metrics[f"publication/{name}"] = value + emitted.update(values) + task = self._publication_tasks.get(step) + if task is None or task.done(): + self._publication_metrics.pop(step, None) + self._emitted_publication_metrics.pop(step, None) + return metrics + + async def finalize_publication_metrics(self, step: int) -> dict[str, float]: + async with self._mutation_lock: + self._require_open() + if step != self._latest_step: + raise ValueError( + f"final publication step {step} != learner step {self._latest_step}" + ) + publication = self._publication_tasks.get(step) + if publication is not None: + await asyncio.shield(publication) + self._raise_publication_failure() + return self.drain_publication_metrics() + + async def wait_for_serving(self, step: int) -> None: + async with self._mutation_lock: + self._require_open() + self._raise_publication_failure() + if step < 0 or step > self._latest_step: + raise ValueError( + f"serving step {step} is outside learner lineage 0..{self._latest_step}" + ) + serving = self._serving_futures.get(step) + async with self._serving_lock: + if step <= self._serving_step: + return + if serving is None: + raise RuntimeError(f"learner step {step} has no serving publication") + await asyncio.shield(serving) + self._raise_publication_failure() + + def policy_activation_timing(self, step: int) -> tuple[float, float]: + try: + return ( + self._trainer_completion_times[step], + self._serving_activation_times[step], + ) + except KeyError as error: + raise RuntimeError( + f"policy {step} lacks an authoritative trainer/serving timestamp" + ) from error + + @staticmethod + def _record_policy_timestamp(history: dict[int, float], step: int) -> None: + history[step] = time.monotonic() + while len(history) > _POLICY_TIMING_HISTORY: + history.pop(next(iter(history))) + + def _record_serving_activation(self, step: int) -> None: + if ( + step in self._trainer_completion_times + and step not in self._serving_activation_times + ): + self._record_policy_timestamp(self._serving_activation_times, step) + + def checkpoint_materialization(self, step: int) -> asyncio.Task[None]: + self._require_open() + self._raise_publication_failure() + generation = self._learner_generation + if generation is None or generation.policy_step != step: + raise RuntimeError( + f"learner generation {step} is unavailable for materialization" + ) + + async def wait() -> None: + publication = self._publication_tasks.get(step) + if publication is not None: + await asyncio.shield(publication) + if step not in self._published_adapters: + raise RuntimeError(f"learner generation {step} is not materialized") + + task = asyncio.create_task(wait()) + task.add_done_callback(_consume_task_result) + return task + + @property + def rollout_weight_update_mode(self) -> str: + return self.config.get("rollout_weight_update_mode", "step_lora") + + @property + def _temporal_gpu_sharing(self) -> bool: + return ( + get_external_vllm_runtime_config(self.config) is None + and self._managed_service_name is not None + and self._model_service_spec().temporal_gpu_sharing + ) + + def _serving_lora_name(self, step: int) -> str: + if self.rollout_weight_update_mode == "in_flight_lora": + return in_flight_lora_name(self.model_name) + return f"{self.model_name}@{step}" + + @property + def _allow_unvalidated_arch(self) -> bool: + return bool(self.config.get("allow_unvalidated_arch", False)) + + def _require_open(self) -> None: + if self._closed: + raise RuntimeError("distributed model service is closed") + + def _trainer_is_current(self) -> bool: + return ( + self._trainer is not None + and self._trainer.valid + and self._trainer.learner_version == self._latest_step + ) + + @property + def _optimizer_state_path(self) -> str: + path = optimizer_state_path(self.output_dir) + os.makedirs(path, exist_ok=True) + return path + + def _lora_config(self) -> dev.LoRAConfig: + return cast(dev.LoRAConfig, self.config.get("lora_config") or {}) + + def _random_state(self) -> int | None: + for key in ("lora_config", "init_args"): + value = self.config.get(key, {}).get("random_state") + if value is not None: + return int(value) + return None + + @property + def _model_identifier(self) -> str: + value = self.config.get("init_args", {}).get("model_name", self.base_model) + if not isinstance(value, str) or not value: + raise ValueError("init_args.model_name must be a non-empty string") + return value + + def _resolve_current_lora_path(self) -> str: + if self._trainer_is_current(): + if self._learner_generation is None: + raise RuntimeError("resident trainer has no learner generation") + self._resume_prepared = True + return self._learner_generation.adapter_path + resume = prepare_megatron_resume_state( + output_dir=self.output_dir, + optimizer_state_path=self._optimizer_state_path, + ) + print(format_megatron_resume_message(resume)) + self._latest_step = resume.step + self._published_adapters = { + step: adapter + for step, adapter in self._published_adapters.items() + if step <= resume.step + } + path = get_step_checkpoint_dir(self.output_dir, self._latest_step) + if not (Path(path) / "adapter_model.safetensors").is_file(): + if self._latest_step != 0: + raise RuntimeError( + f"committed adapter is missing for step {self._latest_step}" + ) + lora = self._lora_config() + handler = get_model_support_handler( + self.base_model, + allow_unvalidated_arch=self._allow_unvalidated_arch, + ) + create_identity_lora( + self._model_identifier, + path, + rank=lora.get("rank"), + target_modules=lora.get("target_modules"), + random_state=self._random_state(), + allow_unvalidated_arch=self._allow_unvalidated_arch, + handler=handler, + ) + if self._latest_step == 0: + adapter = optimizer_adapter( + path, + 0, + training_session_id=self._training_session_id, + ) + else: + policy = resolve_committed_optimizer_policy( + self._optimizer_state_path, + initial_adapter_path=get_step_checkpoint_dir(self.output_dir, 0), + ) + adapter = policy.policy_adapter + if adapter.step != self._latest_step: + raise RuntimeError("resume policy and checkpoint step disagree") + self._training_session_id = adapter.training_session_id + self._published_adapters[self._latest_step] = adapter + self._learner_generation = TrainerGeneration( + training_session_id=adapter.training_session_id, + policy_step=adapter.step, + generation_id=adapter.generation_id, + adapter_path=adapter.identity, + ) + self._durable_step = resume.step + self._durable_optimizer_step = resume.optimizer_step or 0 + self._resume_prepared = True + return adapter.identity + + def _runtime_spec(self) -> TrainerRuntimeSpec: + mesh = self.runtime.topology.trainer + if mesh is None: + raise RuntimeError("ART runtime has no trainer mesh") + runtime_config = get_megatron_runtime_config() + if runtime_config.topology != mesh.topology: + raise ValueError( + "Megatron runtime topology does not match the ART trainer mesh" + ) + lora = self._lora_config() + support_spec = get_model_support_spec( + self.base_model, + allow_unvalidated_arch=self._allow_unvalidated_arch, + ) + handler = get_model_support_handler_for_spec(support_spec) + targets = lora.get("target_modules") or default_target_modules(self.base_model) + revision = str(self.config.get("init_args", {}).get("revision") or "default") + compile_enabled = os.environ.get( + "ART_DISABLE_MEGATRON_COMPILE", "0" + ).lower() not in {"1", "true", "yes", "on"} + hybrid_ep = _hybrid_ep_runtime_spec( + mesh, + run_id=self.runtime.runtime_id, + transport=self.runtime.topology.cluster.nixl_transport, + ) + identity = { + "art": _art_source_revision(), + "model": self._model_identifier, + "support_model": self.base_model, + "revision": revision, + "handler": handler.key, + "mesh": mesh.model_dump(mode="json"), + "model_initialization": self.config.get( + "megatron_model_initialization", "pretrained" + ), + } + return TrainerRuntimeSpec( + art_revision=identity["art"], + model_identifier=self._model_identifier, + model_revision=revision, + model_initialization=identity["model_initialization"], + cache_root=self.runtime.topology.cluster.cache_root, + model_support_key=support_spec.key, + handler_name=handler.key, + lora_rank=int(lora.get("rank") or default_lora_rank_for_handler(handler)), + lora_alpha=float(lora.get("alpha", LORA_ALPHA)), + lora_target_modules=tuple(targets), + dtype=_trainer_dtype(self.config), + trainer_mesh=mesh, + packed_sequence_length=runtime_config.packed_sequence_length, + snapshot_pool_capacity=runtime_config.snapshot_pool_capacity, + compile_enabled=compile_enabled, + compile_fingerprint=_digest({**identity, "compile": compile_enabled}), + optimizer_layout_fingerprint=_digest( + {"mesh": mesh.model_dump(mode="json")} + ), + allow_unvalidated_arch=self._allow_unvalidated_arch, + enable_moe_routing_replay=self.enable_expert_replay + and model_uses_expert_parallel( + self.base_model, + allow_unvalidated_arch=self._allow_unvalidated_arch, + ), + streaming_weight_offload=runtime_config.streaming_weight_offload, + offload_between_jobs=self._temporal_gpu_sharing, + random_state=self._random_state(), + hybrid_ep=hybrid_ep, + ) + + async def _ensure_trainer_locked(self) -> tuple[Any, tuple[int, str] | None]: + if self._trainer_is_current(): + return self._trainer, None + current, reconcile_step = await self._prepare_for_packing_locked() + assert self._trainer is None + runtime_spec = self._runtime_spec() + run_spec = TrainingRunSpec( + run_id=uuid.uuid4().hex, + runtime_fingerprint=runtime_spec.fingerprint, + training_session_id=self._training_session_id, + initial_learner_version=self._latest_step, + initial_adapter_path=current, + optimizer_state_path=self._optimizer_state_path, + initial_event_timeout_s=self.runtime.topology.cluster.startup_timeout_s, + ) + self._trainer = await self.runtime.start_trainer(runtime_spec, run_spec) + self._trainer_resident_generation = None + reconcile = None if reconcile_step is None else (reconcile_step, current) + return self._trainer, reconcile + + async def prepare_for_packing(self) -> int: + async with self._train_lock: + async with self._mutation_lock: + self._require_open() + self._raise_publication_failure() + _current, reconcile_step = await self._prepare_for_packing_locked() + step = self._latest_step + if reconcile_step is not None: + async with self._serving_lock: + await self._reconcile_serving_locked(step, _current) + return step + + async def _prepare_for_packing_locked(self) -> tuple[str, int | None]: + if self._trainer_is_current(): + return self._resolve_current_lora_path(), None + if self._trainer is not None: + _, cancelled = await complete_task( + asyncio.create_task(self.runtime.stop_trainer(self._trainer)) + ) + self._trainer = None + self._trainer_resident_generation = None + if cancelled is not None: + raise cancelled + previous_step = self._latest_step + current, cancelled = await complete_to_thread(self._resolve_current_lora_path) + if cancelled is not None: + raise cancelled + reconcile_step = ( + self._latest_step if self._latest_step != previous_step else None + ) + return current, reconcile_step + + async def _reconcile_serving_locked(self, step: int, checkpoint: str) -> None: + if self._base_url is None: + self._serving_step = step + return + if self.rollout_weights_mode == "merged": + await self._sync_merged_serving_source_locked( + step=step, + base_url=self._base_url, + api_key=self._api_key(), + ) + previous_name = self._current_lora_name + await self._register_lora_for_step_locked(step, checkpoint) + invalid_exact = { + step + for step in self._loaded_exact_adapter_steps + if step > self._serving_step + } + for step in sorted(invalid_exact): + name = ( + f"{self.model_name}:eval@{step}" + if self.rollout_weight_update_mode == "in_flight_lora" + else f"{self.model_name}@{step}" + ) + await self._unload_adapter(name) + self._loaded_exact_adapter_steps.discard(step) + self._exact_adapter_refcounts.pop(step, None) + for step in sorted( + step for step in self._loaded_adapter_steps if step > self._serving_step + ): + await self._unload_adapter(f"{self.model_name}@{step}") + await self._release_loaded_adapter_transfer(step) + self._loaded_adapter_steps.discard(step) + if previous_name == f"{self.model_name}:active" and previous_name != ( + self._current_lora_name + ): + assert previous_name is not None + await self._unload_adapter(previous_name) + + @asynccontextmanager + async def _trainer_transaction( + self, + trainer: Any, + job: TrainerJobSpec, + start: Callable[[], AsyncIterator[Any]], + ) -> AsyncIterator[AsyncIterator[Any]]: + cold = self._trainer_resident_generation != job.source + source = self._published_adapters.get(job.source.policy_step) if cold else None + if cold and ( + source is None + or source.training_session_id != job.source.training_session_id + or source.generation_id != job.source.generation_id + or source.identity != str(Path(job.source.adapter_path).absolute()) + ): + raise RuntimeError("cold trainer source generation is not registered") + with adapter_generation_lease(source) if source is not None else nullcontext(): + events: AsyncIterator[Any] | None = None + try: + events = start() + yield events + close = getattr(events, "aclose", None) + if close is not None: + await close() + except BaseException as error: + await self._cleanup_failed_trainer_transaction(trainer, events, error) + raise + + async def _cleanup_failed_trainer_transaction( + self, + trainer: Any, + events: AsyncIterator[Any] | None, + primary: BaseException, + ) -> None: + async def cleanup() -> None: + failures: list[BaseException] = [] + try: + await self._discard_next_publication_preparation() + except BaseException as error: + failures.append(error) + try: + await self._release_prepared_adapter_transfers() + except BaseException as error: + failures.append(error) + close = None if events is None else getattr(events, "aclose", None) + if close is not None: + try: + await close() + except BaseException as error: + failures.append(error) + try: + await self._invalidate_trainer_and_restore_serving(trainer) + except BaseException as error: + failures.append(error) + if failures: + raise BaseExceptionGroup("failed trainer job cleanup failed", failures) + + try: + _, interrupted = await complete_task(asyncio.create_task(cleanup())) + except BaseException as cleanup_error: + raise BaseExceptionGroup( + "trainer job and cancellation-safe cleanup failed", + [primary, cleanup_error], + ) from None + if interrupted is not None: + primary.add_note("trainer cleanup completed after another cancellation") + + async def _release_prepared_adapter_transfers(self) -> None: + prepared = tuple(self._prepared_adapter_transfers.items()) + results = await asyncio.gather( + *( + manager.release_adapter_transfer(generation_id) + for generation_id, manager in prepared + ), + return_exceptions=True, + ) + failures = [] + for (generation_id, manager), result in zip(prepared, results, strict=True): + if isinstance(result, BaseException): + failures.append(result) + elif self._prepared_adapter_transfers.get(generation_id) is manager: + self._prepared_adapter_transfers.pop(generation_id) + if failures: + raise BaseExceptionGroup( + "prepared adapter transfer cleanup failed", failures + ) + + async def _discard_next_publication_preparation(self) -> None: + prepared, self._next_publication_preparation = ( + self._next_publication_preparation, + None, + ) + if prepared is None: + return + _, generation, task = prepared + task.cancel() + await asyncio.gather(task, return_exceptions=True) + manager = self._prepared_adapter_transfers.pop(generation.generation_id, None) + if manager is not None: + await manager.release_adapter_transfer(generation.generation_id) + + async def _release_adapter_transfer( + self, + manager: Any, + generation_id: str, + primary: BaseException | None = None, + ) -> asyncio.CancelledError | None: + try: + _, interrupted = await complete_task( + asyncio.create_task(manager.release_adapter_transfer(generation_id)) + ) + except BaseException as cleanup_error: + if primary is not None: + raise BaseExceptionGroup( + "adapter transfer and cleanup failed", [primary, cleanup_error] + ) from None + raise + return interrupted + + async def _release_loaded_adapter_transfer(self, step: int) -> None: + transfer = self._loaded_adapter_transfers.get(step) + if transfer is None: + return + manager, generation_id = transfer + interrupted = await self._release_adapter_transfer(manager, generation_id) + if self._loaded_adapter_transfers.get(step) == transfer: + self._loaded_adapter_transfers.pop(step) + if interrupted is not None: + raise interrupted + + async def _release_loaded_adapter_transfers(self) -> None: + results = await asyncio.gather( + *( + self._release_loaded_adapter_transfer(step) + for step in tuple(self._loaded_adapter_transfers) + ), + return_exceptions=True, + ) + failures = [result for result in results if isinstance(result, BaseException)] + if failures: + raise BaseExceptionGroup("loaded adapter transfer cleanup failed", failures) + + @asynccontextmanager + async def _trainer_failure_boundary(self) -> AsyncIterator[None]: + try: + yield + except BaseException as error: + await self._cleanup_failed_trainer_transaction(self._trainer, None, error) + raise + + async def _invalidate_trainer_and_restore_serving(self, trainer: Any) -> None: + failures: list[BaseException] = [] + async with self._mutation_lock: + owned = trainer is not None and self._trainer is trainer + if owned: + self._trainer = None + self._trainer_resident_generation = None + if owned: + try: + await self.runtime.stop_trainer(trainer) + except BaseException as error: + failures.append(error) + if self._temporal_gpu_sharing and self._vllm_sleeping: + async with self._serving_lock: + if self._vllm_sleeping: + try: + await self._wake_for_serving_locked() + except BaseException as error: + failures.append(error) + service_name = self._managed_service_name + if service_name is not None: + failures.extend( + await self._rollback_server_start_safely(service_name) + ) + self._clear_serving_state() + if len(failures) == 1: + raise failures[0] + if failures: + raise BaseExceptionGroup("failed trainer invalidation failed", failures) + + async def _trainer_metrics( + self, + job: TrainerJobSpec, + events: AsyncIterator[Any], + ) -> AsyncIterator[tuple[bool, dict[str, float]]]: + snapshot_prepared = False + completed = False + final_metrics: dict[str, float] | None = None + async for event in events: + if event.job_id != job.job_id or event.run_id != job.run_id: + raise RuntimeError("trainer returned an event for a different job") + if isinstance(event, TrainAccepted): + continue + if isinstance(event, TrainProgress): + if event.step_index + 1 == event.num_steps: + final_metrics = dict(event.metrics) + self._record_policy_timestamp( + self._trainer_completion_times, job.learner_version + ) + else: + yield False, dict(event.metrics) + continue + if isinstance(event, AdapterReady): + if snapshot_prepared: + raise RuntimeError("trainer returned duplicate snapshot events") + if ( + event.learner_version != job.learner_version + or event.adapter_path != job.output_adapter_path + ): + raise RuntimeError("trainer prepared the wrong generation") + snapshot_prepared = True + continue + if isinstance(event, TrainCompleted): + if completed or not snapshot_prepared: + raise RuntimeError("trainer completed without one snapshot") + if event.learner_version != job.learner_version: + raise RuntimeError("trainer completed the wrong learner") + snapshot_metrics = { + name: value + for name, value in event.metrics.items() + if name.startswith("snapshot_") + } + self._publication_metrics[job.learner_version] = snapshot_metrics + self._emitted_publication_metrics[job.learner_version] = set( + snapshot_metrics + ) + final_metrics = dict(event.metrics) + completed = True + continue + if isinstance(event, TrainFailed): + raise RuntimeError( + f"distributed Megatron job failed ({event.error_type}): " + f"{event.message}" + ) + if isinstance(event, TrainCancelled): + raise asyncio.CancelledError(event.reason) + if not snapshot_prepared or not completed or final_metrics is None: + raise RuntimeError("trainer ended without preparing a generation") + yield True, final_metrics + + async def _prepare_serving_publication( + self, + trainer: Any, + generation_id: str, + learner_version: int, + ) -> tuple[tuple[Any, ...], MergedWeightTransferSpec | None]: + if self._managed_service_name is None: + return (), None + if self.rollout_weights_mode == "merged": + return (), await self._merged_weight_transfer_spec( + self._serving_lora_name(learner_version) + ) + manager = self.runtime.model_service(self._managed_service_name) + trainer_host = trainer.runtime_spec.trainer_mesh.ranks[0].host_id + inference_hosts = {member.host_id for member in manager.spec.members} + try: + targets = await manager.prepare_adapter_transfer( + generation_id, + get_step_checkpoint_dir(self.output_dir, 0), + transport="local" if inference_hosts == {trainer_host} else "nixl", + ) + if not targets: + raise RuntimeError("model service returned no adapter transfer targets") + except BaseException as error: + interrupted = await self._release_adapter_transfer( + manager, generation_id, error + ) + if interrupted is not None: + error.add_note("adapter transfer cleanup observed cancellation") + raise + self._prepared_adapter_transfers[generation_id] = manager + return targets, None + + def _training_generation(self, step: int) -> TrainerGeneration: + return TrainerGeneration( + training_session_id=self._training_session_id, + policy_step=step, + generation_id=new_optimizer_generation(step), + adapter_path=get_step_checkpoint_dir(self.output_dir, step), + ) + + async def _take_publication_preparation( + self, trainer: Any, step: int + ) -> tuple[ + TrainerGeneration, + tuple[Any, ...], + MergedWeightTransferSpec | None, + ]: + prepared, self._next_publication_preparation = ( + self._next_publication_preparation, + None, + ) + if prepared is not None: + prepared_trainer, generation, task = prepared + if prepared_trainer is trainer and generation.policy_step == step: + targets, merged = await task + return generation, targets, merged + self._next_publication_preparation = prepared + await self._discard_next_publication_preparation() + generation = self._training_generation(step) + targets, merged = await self._prepare_serving_publication( + trainer, generation.generation_id, step + ) + return generation, targets, merged + + def _prefetch_publication_preparation(self, trainer: Any, step: int) -> None: + if self._managed_service_name is None: + return + if self._next_publication_preparation is not None: + raise RuntimeError("next publication preparation already exists") + generation = self._training_generation(step) + previous_serving = self._serving_futures.get(step - 2) + + async def prepare() -> tuple[tuple[Any, ...], MergedWeightTransferSpec | None]: + if previous_serving is not None: + await asyncio.shield(previous_serving) + return await self._prepare_serving_publication( + trainer, generation.generation_id, generation.policy_step + ) + + task = asyncio.create_task(prepare()) + task.add_done_callback(_consume_task_result) + self._next_publication_preparation = trainer, generation, task + + async def _run_train_job( + self, + build_job: Callable[[_TrainerJobFields], TrainerJobSpec], + start_job: Callable[[Any, TrainerJobSpec], AsyncIterator[Any]], + *, + lineage_error: str, + wait_for_serving: bool = False, + ) -> AsyncIterator[dict[str, float]]: + lock_started = time.perf_counter() + async with self._train_lock: + lock_wait_s = time.perf_counter() - lock_started + setup_started = time.perf_counter() + async with self._trainer_failure_boundary(): + if self._temporal_gpu_sharing and self._base_url is not None: + previous = self._serving_futures.get(self._latest_step) + if previous is not None: + await asyncio.shield(previous) + async with self._serving_lock: + await self._sleep_for_training_locked() + async with self._mutation_lock: + self._require_open() + self._raise_publication_failure() + trainer, reconcile = await self._ensure_trainer_locked() + source = self._learner_generation + if source is None: + raise RuntimeError("trainer has no source generation") + next_step = self._latest_step + 1 + + preparation_started = time.perf_counter() + ( + output_generation, + publication_targets, + merged_transfer, + ) = await self._take_publication_preparation(trainer, next_step) + preparation_wait_s = time.perf_counter() - preparation_started + + async with self._mutation_lock: + self._require_open() + self._raise_publication_failure() + if self._trainer is not trainer or not self._trainer_is_current(): + raise RuntimeError( + "trainer changed while preparing serving publication" + ) + if ( + self._learner_generation != source + or self._latest_step != next_step - 1 + ): + raise RuntimeError(lineage_error) + output = DurableTrainOutput( + generation=output_generation, + staging_adapter_path=( + f"{self.output_dir}/megatron_runtime/staging/" + f"{output_generation.generation_id}" + ), + optimizer_state_path=self._optimizer_state_path, + ) + job = build_job( + _TrainerJobFields( + job_id=uuid.uuid4().hex, + run_id=trainer.run_spec.run_id, + training_session_id=self._training_session_id, + expected_learner_version=self._latest_step, + learner_version=next_step, + source=source, + output=output, + publication_targets=publication_targets, + merged_weight_transfer=merged_transfer, + ) + ) + + if reconcile is not None: + reconcile_step, checkpoint = reconcile + async with self._serving_lock: + await self._reconcile_serving_locked(reconcile_step, checkpoint) + self._prefetch_publication_preparation(trainer, next_step + 1) + setup_s = time.perf_counter() - setup_started + + final_metrics: dict[str, float] | None = None + async with self._trainer_transaction( + trainer, job, lambda: start_job(trainer, job) + ) as events: + async for final, metrics in self._trainer_metrics(job, events): + if final: + final_metrics = metrics + else: + yield metrics + assert final_metrics is not None + + commit_started = time.perf_counter() + async with self._trainer_failure_boundary(): + async with self._mutation_lock: + if self._latest_step != job.expected_learner_version: + raise RuntimeError(lineage_error) + self._latest_step = next_step + self._learner_generation = output_generation + self._trainer_resident_generation = output_generation + self._schedule_publication( + output_generation, + trainer=trainer, + publication_targets=job.publication_targets, + ) + commit_s = time.perf_counter() - commit_started + if wait_for_serving: + await self.wait_for_serving(next_step) + final_metrics.update( + { + "time/step_service_lock_wait_s": lock_wait_s, + "time/step_service_job_setup_s": setup_s, + "time/step_service_publication_prepare_wait_s": ( + preparation_wait_s + ), + "time/step_service_generation_commit_s": commit_s, + } + ) + yield final_metrics + + async def train_packed( + self, + batch: DistributedPackedBatch, + config: types.TrainConfig, + experimental_config: dev.TrainConfig, + ) -> AsyncIterator[dict[str, float]]: + def build_job(fields: _TrainerJobFields) -> TrainerJobSpec: + values = { + key: value + for key, value in experimental_config.items() + if key in ExperimentalTrainConfig.model_fields and value is not None + } + return TrainJobSpec( + **fields, + batch=batch.leases.ref, + config=CurrentTrainConfig.model_validate(config.model_dump()), + experimental_config=ExperimentalTrainConfig.model_validate(values), + ) + + async for metrics in self._run_train_job( + build_job, + lambda trainer, job: trainer.train(job, batch.leases), + lineage_error="learner lineage changed during training", + ): + yield metrics + + def _schedule_publication( + self, + generation: TrainerGeneration, + *, + trainer: Any = None, + durable: DurableTrainerPublication | None = None, + publication_targets: tuple[Any, ...] = (), + ) -> None: + if (trainer is None) == (durable is None): + raise ValueError( + "publication requires exactly one trainer stream or durable result" + ) + step = generation.policy_step + if step in self._publication_tasks: + raise RuntimeError(f"generation publication already exists for step {step}") + transfer_manager = self._prepared_adapter_transfers.get( + generation.generation_id + ) + if publication_targets and transfer_manager is None: + raise RuntimeError("adapter transfer publication is not prepared") + publication_waiter = ( + trainer.wait_for_publication(generation.generation_id) + if trainer is not None + else None + ) + loop = asyncio.get_running_loop() + previous = self._serving_futures.get(step - 1) + if previous is None: + previous = loop.create_future() + previous.set_result(None) + serving = loop.create_future() + serving.add_done_callback(_consume_task_result) + self._serving_futures[step] = serving + serving.add_done_callback( + lambda done: _retire_completed(self._serving_futures, step, done) + ) + previous_publication = self._publication_tasks.get(step - 1) + publication = ( + self._publish_generation( + generation, + durable=durable, + publication_waiter=publication_waiter, + previous_publication=previous_publication, + publication_targets=publication_targets, + transfer_manager=transfer_manager, + previous_serving=previous, + serving=serving, + ) + if publication_targets + else self._publish_generation( + generation, + durable=durable, + publication_waiter=publication_waiter, + previous_publication=previous_publication, + previous_serving=previous, + serving=serving, + ) + ) + task = asyncio.create_task(publication) + self._publication_tasks[step] = task + self._prepared_adapter_transfers.pop(generation.generation_id, None) + task.add_done_callback(_consume_task_result) + task.add_done_callback( + lambda done: _retire_completed(self._publication_tasks, step, done) + ) + + async def _resolve_durable_publication( + self, + generation: TrainerGeneration, + *, + durable: DurableTrainerPublication | None, + publication_waiter: Awaitable[tuple[TrainerRankPublication, ...]] | None, + previous_publication: asyncio.Task[None] | None, + ) -> tuple[DurableTrainerPublication, float]: + started = time.monotonic() + records = ( + asyncio.ensure_future(publication_waiter) + if publication_waiter is not None + else None + ) + if records is not None: + records.add_done_callback(_consume_task_result) + try: + if previous_publication is not None: + await asyncio.shield(previous_publication) + if records is not None: + rank_publications = await asyncio.shield(records) + async with self._durability_lock: + durable = await asyncio.to_thread( + commit_trainer_publication, + self._optimizer_state_path, + generation, + rank_publications, + ) + finally: + if records is not None and not records.done(): + records.cancel() + return cast(DurableTrainerPublication, durable), time.monotonic() - started + + async def _publish_generation( + self, + generation: TrainerGeneration, + *, + durable: DurableTrainerPublication | None, + publication_waiter: Awaitable[tuple[TrainerRankPublication, ...]] | None, + previous_publication: asyncio.Task[None] | None, + publication_targets: tuple[Any, ...] = (), + transfer_manager: Any = None, + previous_serving: asyncio.Future[None], + serving: asyncio.Future[None], + ) -> None: + metrics = self._publication_metrics.setdefault(generation.policy_step, {}) + manager = transfer_manager + transfer_owned = manager is not None + durable_task = asyncio.create_task( + self._resolve_durable_publication( + generation, + durable=durable, + publication_waiter=publication_waiter, + previous_publication=previous_publication, + ) + ) + self._durability_tasks.add(durable_task) + durable_task.add_done_callback(_consume_task_result) + durable_task.add_done_callback(self._durability_tasks.discard) + try: + materialization_started = time.monotonic() + if self.rollout_weights_mode == "merged": + await previous_serving + self._raise_publication_failure() + activation_started = time.monotonic() + async with self._serving_lock: + await self._register_lora_for_step_locked( + generation.policy_step, + generation.adapter_path, + generation_id=generation.generation_id, + ) + metrics["serving_activation_s"] = time.monotonic() - activation_started + if not serving.done(): + serving.set_result(None) + durable_result, _ = await asyncio.shield(durable_task) + adapter = durable_result.adapter + checkpoint = generation.adapter_path + metrics["adapter_materialization_s"] = ( + time.monotonic() - materialization_started + ) + async with self._mutation_lock: + self._published_adapters[generation.policy_step] = adapter + elif manager is None: + durable_result, _ = await asyncio.shield(durable_task) + adapter = durable_result.adapter + checkpoint = generation.adapter_path + metrics["adapter_materialization_s"] = ( + time.monotonic() - materialization_started + ) + else: + received = await manager.wait_adapter_transfer(generation.generation_id) + if len(received) != len(publication_targets): + raise RuntimeError("Not every inference host received the adapter") + paths = {result.path for result in received} + sizes = { + (result.tensor_bytes, result.config_bytes) for result in received + } + if len(paths) != 1 or len(sizes) != 1: + raise RuntimeError( + "Inference hosts materialized different adapters" + ) + tensor_bytes, config_bytes = sizes.pop() + checkpoint = paths.pop() + adapter = OptimizerAdapter( + identity=str(Path(generation.adapter_path).absolute()), + training_session_id=generation.training_session_id, + step=generation.policy_step, + generation_id=generation.generation_id, + files=( + CheckpointFile( + name="adapter_config.json", size_bytes=config_bytes + ), + CheckpointFile( + name="adapter_model.safetensors", size_bytes=tensor_bytes + ), + ), + ) + metrics["adapter_transport_wait_s"] = ( + time.monotonic() - materialization_started + ) + metrics["adapter_transport_bytes"] = float(tensor_bytes * len(received)) + metrics["adapter_materialization_s"] = max( + result.materialization_s for result in received + ) + metrics["adapter_transport_pool_wait_s"] = max( + result.pool_wait_s for result in received + ) + metrics["adapter_transport_prepare_s"] = max( + result.prepare_s for result in received + ) + metrics["adapter_transport_registration_s"] = max( + result.registration_s for result in received + ) + metrics["adapter_transport_sender_staging_s"] = max( + result.sender_staging_s for result in received + ) + metrics["adapter_transport_sender_registration_s"] = max( + result.sender_registration_s for result in received + ) + metrics["adapter_transport_capacity_bytes"] = float( + sum(result.capacity_bytes for result in received) + ) + metrics["adapter_transport_capacity_utilization"] = sum( + result.used_bytes for result in received + ) / sum(result.capacity_bytes for result in received) + if self.rollout_weights_mode != "merged": + await previous_serving + self._raise_publication_failure() + activation_started = time.monotonic() + async with self._mutation_lock: + self._published_adapters[generation.policy_step] = adapter + async with self._serving_lock: + await self._register_lora_for_step_locked( + generation.policy_step, + checkpoint, + ) + if ( + manager is not None + and self.rollout_weight_update_mode != "in_flight_lora" + ): + self._loaded_adapter_transfers[generation.policy_step] = ( + manager, + generation.generation_id, + ) + transfer_owned = False + metrics["serving_activation_s"] = time.monotonic() - activation_started + if manager is None and not serving.done(): + serving.set_result(None) + if manager is not None and transfer_owned: + interrupted = await self._release_adapter_transfer( + manager, generation.generation_id + ) + transfer_owned = False + if interrupted is not None: + raise interrupted + if manager is not None: + if not serving.done(): + serving.set_result(None) + + durable_result, durable_s = await asyncio.shield(durable_task) + if durable_result.adapter != adapter: + raise RuntimeError("Durable and serving adapter manifests differ") + async with self._mutation_lock: + self._durable_step = max(self._durable_step, durable_result.resume_step) + self._durable_optimizer_step = max( + self._durable_optimizer_step, durable_result.optimizer_step + ) + metrics["durable_checkpoint_s"] = durable_s + metrics["durable_checkpoint_lag_steps"] = float( + self._latest_step - self._durable_optimizer_step + ) + logger.info( + "Published trainer generation session=%s step=%d generation=%s " + "launch=%.3fs activate=%.3fs durable=%.3fs durable_lag=%d", + generation.training_session_id, + generation.policy_step, + generation.generation_id, + metrics["snapshot_launch_s"], + metrics["serving_activation_s"], + metrics["durable_checkpoint_s"], + self._latest_step - self._durable_optimizer_step, + ) + except BaseException as error: + if manager is not None and transfer_owned: + try: + interrupted = await self._release_adapter_transfer( + manager, generation.generation_id, error + ) + except BaseException as cleanup_error: + error = cleanup_error + else: + transfer_owned = False + if interrupted is not None: + error.add_note("adapter transfer cleanup observed cancellation") + if not serving.done(): + serving.set_exception(error) + self._publication_failure = error + async with self._serving_lock: + cleanup = await self._rollback_server_start_safely( + self._managed_service_name + ) + self._clear_serving_state() + logger.exception( + "Trainer generation publication failed session=%s step=%d generation=%s", + generation.training_session_id, + generation.policy_step, + generation.generation_id, + ) + if cleanup: + raise BaseExceptionGroup( + "generation publication and serving teardown failed", + [error, *cleanup], + ) from None + raise error + + def _raise_publication_failure(self) -> None: + if self._publication_failure is not None: + raise RuntimeError("trainer generation publication failed") from ( + self._publication_failure + ) + + async def resolve_global_grad_accumulation_sequences( + self, config: types.TrainConfig + ) -> int: + if config.grad_accumulation_sequences is not None: + return int(config.grad_accumulation_sequences) + mesh = self.runtime.topology.trainer + assert mesh is not None + topology = mesh.topology + return len(mesh.ranks) // (topology.tp * topology.cp * topology.pp) + + async def start_openai_server( + self, config: dev.OpenAIServerConfig | None + ) -> tuple[str, int]: + async with self._train_lock: + self._require_open() + if serving := self._serving_futures.get(self._latest_step): + await serving + async with self._serving_lock: + if self._base_url: + return _host_port(self._base_url) + if self._managed_service_name is not None: + raise RuntimeError("managed model service is unavailable") + async with self._mutation_lock: + lora_path = await asyncio.to_thread(self._resolve_current_lora_path) + step = self._latest_step + async with self._serving_lock: + if self._base_url: + return _host_port(self._base_url) + if self._managed_service_name is not None: + raise RuntimeError("managed model service is unavailable") + return await self._start_openai_server_locked( + config, lora_path=lora_path, step=step + ) + + async def _start_openai_server_locked( + self, + config: dev.OpenAIServerConfig | None, + *, + lora_path: str, + step: int, + ) -> tuple[str, int]: + api_key = self._api_key(config) + external = get_external_vllm_runtime_config(self.config) + if external is not None: + if self.rollout_weights_mode != "lora": + raise RuntimeError( + "External vLLM runtime requires LoRA rollout weights" + ) + base_url = normalize_vllm_server_url(external.server_url) + headers = _headers(external.api_key) + await wait_for_vllm_http_runtime( + base_url=base_url, + timeout=external.health_timeout_s, + headers=headers, + ) + capabilities = await discover_serving_capabilities( + base_url=base_url, + headers=headers, + allow_openai_compatible=True, + ) + lora_name, _ = await self._load_adapter_at( + lora_path, + step, + base_url=base_url, + api_key=api_key, + active_step=step, + ) + self._publish_serving_state( + managed_service_name=None, + base_url=base_url, + capabilities=capabilities, + api_key=api_key, + current_lora_name=lora_name, + serving_step=step, + ) + return _host_port(base_url) + + service = self._model_service_spec() + server_args = dict((config or {}).get("server_args", {})) + if "port" in server_args: + from .runtime.local import with_local_serving_port + + self.runtime.topology = with_local_serving_port( + self.runtime.topology, + model_name=self.model_name, + port=cast(int, server_args["port"]), + ) + service = self._model_service_spec() + template = ReplicaLaunchTemplate( + served_model_name=self._serving_lora_name(step), + lora_path=lora_path, + initial_policy_version=( + step if self.rollout_weights_mode == "lora" else None + ), + engine_args=self._engine_args(config), + server_args=self._server_args(config), + ) + await self.runtime.start_model_service( + service, template, on_failure=self._replica_failed + ) + base_url = service.leader_endpoint.url + try: + capabilities = await discover_serving_capabilities( + base_url=base_url, + headers=_headers(api_key), + allow_openai_compatible=False, + ) + if self.rollout_weights_mode == "merged": + await self._sync_merged_serving_source_locked( + step=step, base_url=base_url, api_key=api_key + ) + generation_id = self._generation_id_for_step(step) + update_identity = uuid.uuid4().hex + manager = self.runtime.model_service(service.name) + state = manager.prepare_update(update_identity=update_identity) + report = ReplicaUpdateReport( + replica_id=service.name, + generation=state.generation, + generation_digest=state.generation_digest, + policy_version=str(step), + policy_digest=generation_id, + update_identity=update_identity, + ) + if manager.verify_update(report).phase != "ready": + raise RuntimeError("model service rejected its initial policy") + except BaseException as error: + cleanup = await self._rollback_server_start_safely(service.name) + if cleanup: + raise BaseExceptionGroup( + "vLLM startup validation and rollback failed", [error, *cleanup] + ) from None + raise + self._publish_serving_state( + managed_service_name=service.name, + base_url=base_url, + capabilities=capabilities, + api_key=api_key, + current_lora_name=template.served_model_name, + serving_step=step, + ) + return _host_port(base_url) + + def _publish_serving_state( + self, + *, + managed_service_name: str | None, + base_url: str, + capabilities: ServingCapabilities, + api_key: str | None, + current_lora_name: str, + serving_step: int, + ) -> None: + self._managed_service_name = managed_service_name + self._base_url = base_url + self._serving_capabilities = capabilities + self._api_key_value = api_key + self._current_lora_name = current_lora_name + self._serving_step = serving_step + if self.rollout_weights_mode == "lora": + self._loaded_adapter_steps.add(serving_step) + + async def _sync_merged_serving_source_locked( + self, + *, + step: int, + base_url: str, + api_key: str | None, + ) -> None: + if step == 0: + return + temporal_sharing = self._temporal_gpu_sharing + if temporal_sharing: + await self._sleep_vllm_at(base_url, api_key) + self._vllm_sleeping = True + try: + async with self._mutation_lock: + trainer, _ = await self._ensure_trainer_locked() + generation = self._learner_generation + if generation is None or generation.policy_step != step: + raise RuntimeError( + "merged serving source is not the active learner" + ) + transfer = await self._merged_weight_transfer_spec( + self._serving_lora_name(step), + base_url=base_url, + api_key=api_key, + ) + metrics = await trainer.sync_merged(generation, transfer) + self._publication_metrics.setdefault(step, {}).update(metrics) + except BaseException as error: + if temporal_sharing: + try: + await self._wake_vllm_at(base_url, api_key) + except BaseException as wake_error: + raise BaseExceptionGroup( + "merged sync and vLLM wake failed", [error, wake_error] + ) from None + finally: + self._vllm_sleeping = False + raise + if temporal_sharing: + await self._wake_vllm_at(base_url, api_key) + self._vllm_sleeping = False + + def _clear_serving_state(self) -> None: + self._managed_service_name = None + self._unpublish_serving_state() + + def _unpublish_serving_state(self) -> None: + self._base_url = None + self._serving_capabilities = None + self._api_key_value = None + self._current_lora_name = None + self._loaded_adapter_steps.clear() + self._loaded_exact_adapter_steps.clear() + self._exact_adapter_refcounts.clear() + self._vllm_sleeping = False + self._merged_transfer_init = None + + async def _replica_failed(self, failure: ReplicaFailure) -> None: + if self._closed or failure.replica_id != self._managed_service_name: + return + task = asyncio.create_task(self._recover_failed_replica(failure)) + self._recovery_tasks.add(task) + task.add_done_callback(self._recovery_tasks.discard) + task.add_done_callback(_consume_task_result) + + async def _recover_failed_replica(self, failure: ReplicaFailure) -> None: + async with self._train_lock: + async with self._serving_lock: + try: + if self._closed or failure.replica_id != self._managed_service_name: + return + manager = self.runtime.model_service(failure.replica_id) + state = manager.state + if ( + state.generation != failure.generation + or state.generation_digest != failure.generation_digest + or state.phase != "quarantined" + ): + return + await self._recover_replica_locked(failure) + except asyncio.CancelledError: + raise + except BaseException: + self._unpublish_serving_state() + logger.exception( + "vLLM replica %s generation %d recovery failed", + failure.replica_id, + failure.generation, + ) + + async def _recover_replica_locked(self, failure: ReplicaFailure) -> None: + service = self._model_service_spec() + manager = self.runtime.model_service(failure.replica_id) + serving_step = self._serving_step + serving_adapter = self._published_adapters.get(serving_step) + if serving_adapter is None: + raise RuntimeError( + f"serving generation {serving_step} is not registered for recovery" + ) + checkpoint = serving_adapter.identity + generation_id = serving_adapter.generation_id + current_lora_name = self._current_lora_name or self._serving_lora_name( + serving_step + ) + bootstrap_name = self._serving_lora_name(serving_step) + base_url = service.leader_endpoint.url + exact_steps = tuple(sorted(self._loaded_exact_adapter_steps)) + try: + state = await manager.restart( + served_model_name=bootstrap_name, + lora_path=checkpoint, + initial_policy_version=( + serving_step if self.rollout_weights_mode == "lora" else None + ), + ) + self._vllm_sleeping = False + capability = await discover_serving_capabilities( + base_url=base_url, + headers=_headers(self._api_key()), + allow_openai_compatible=False, + ) + if capability != self._serving_capabilities: + raise RuntimeError("restarted vLLM replica capabilities changed") + if self.rollout_weights_mode == "merged": + self._merged_transfer_init = None + await self._sync_merged_serving_source_locked( + step=serving_step, + base_url=base_url, + api_key=self._api_key(), + ) + update_identity = uuid.uuid4().hex + manager.prepare_update(update_identity=update_identity) + lora_name = bootstrap_name + if ( + self.rollout_weights_mode == "lora" + and current_lora_name != bootstrap_name + ): + lora_name, lora_path = await self._load_adapter_at( + checkpoint, + serving_step, + base_url=base_url, + api_key=self._api_key(), + active_step=serving_step - 1, + ) + report = ReplicaUpdateReport( + replica_id=failure.replica_id, + generation=state.generation, + generation_digest=state.generation_digest, + policy_version=str(serving_step), + policy_digest=generation_id, + update_identity=update_identity, + ) + if manager.verify_update(report).phase != "ready": + raise RuntimeError("restarted vLLM replica rejected current policy") + if ( + self.rollout_weights_mode == "lora" + and current_lora_name != bootstrap_name + ): + await self._unload_adapter_at(bootstrap_name, base_url) + if self.rollout_weights_mode == "lora": + for step in exact_steps: + if step == serving_step and self.rollout_weight_update_mode != ( + "in_flight_lora" + ): + continue + await self._load_adapter_at( + get_step_checkpoint_dir(self.output_dir, step), + step, + exact=True, + base_url=base_url, + api_key=self._api_key(), + active_step=serving_step, + ) + self._current_lora_name = lora_name + self._loaded_adapter_steps = ( + {serving_step} if self.rollout_weights_mode == "lora" else set() + ) + self._loaded_exact_adapter_steps = ( + set(exact_steps) if self.rollout_weights_mode == "lora" else set() + ) + await self._release_loaded_adapter_transfers() + except BaseException as error: + manager.quarantine(f"replica recovery failed: {error}") + try: + await manager.stop() + except BaseException as cleanup_error: + raise BaseExceptionGroup( + "replica recovery and teardown failed", [error, cleanup_error] + ) from None + try: + await self._release_loaded_adapter_transfers() + except BaseException as cleanup_error: + raise BaseExceptionGroup( + "replica recovery and transfer cleanup failed", + [error, cleanup_error], + ) from None + raise + + def _model_service_spec(self) -> ModelServiceSpec: + services = tuple( + service + for service in self.runtime.topology.model_services + if service.name == self.model_name + ) + if len(services) != 1: + raise RuntimeError( + f"runtime topology has no unique service {self.model_name!r}" + ) + return services[0] + + async def _rollback_server_start( + self, service_name: str | None + ) -> list[BaseException]: + if service_name is None: + return [] + try: + await self.runtime.stop_model_service(service_name) + except BaseException as error: + return [error] + try: + await self._release_loaded_adapter_transfers() + except BaseException as error: + return [error] + return [] + + async def _rollback_server_start_safely( + self, service_name: str | None + ) -> list[BaseException]: + failures, cancelled = await complete_task( + asyncio.create_task(self._rollback_server_start(service_name)) + ) + if cancelled is not None: + failures.append(cancelled) + return failures + + def _engine_args(self, server: dev.OpenAIServerConfig | None) -> dict[str, object]: + handler = get_model_support_handler( + self.base_model, + allow_unvalidated_arch=self._allow_unvalidated_arch, + ) + values = dict(self.config.get("engine_args", {})) + values.update(dict((server or {}).get("engine_args", {}))) + for key, value in handler.vllm_engine_args( + rollout_weights_mode=self.rollout_weights_mode + ).items(): + values.setdefault(key, value) + values["enable_sleep_mode"] = self._temporal_gpu_sharing + if self.rollout_weights_mode == "merged": + values["weight_transfer_config"] = {"backend": "nccl"} + values.pop("enable_lora", None) + values.pop("max_loras", None) + else: + values["enable_lora"] = True + values.setdefault("max_loras", 2) + values.setdefault("generation_config", "vllm") + for key in ("model", "served_model_name"): + values.pop(key, None) + return values + + def _server_args(self, server: dev.OpenAIServerConfig | None) -> dict[str, object]: + handler = get_model_support_handler( + self.base_model, + allow_unvalidated_arch=self._allow_unvalidated_arch, + ) + values: dict[str, object] = { + "return_tokens_as_token_ids": True, + "enable_auto_tool_choice": True, + "tool_call_parser": "hermes", + **handler.vllm_server_args(), + **dict((server or {}).get("server_args", {})), + } + for key in ("port", "host", "lora_modules"): + values.pop(key, None) + return values + + def _api_key(self, server: dev.OpenAIServerConfig | None = None) -> str | None: + value = dict((server or {}).get("server_args", {})).get("api_key") + external = get_external_vllm_runtime_config(self.config) + if external is not None: + if value is not None and value != external.api_key: + raise ValueError( + "OpenAI server api_key conflicts with external vLLM credentials" + ) + return external.api_key + if value is not None: + return cast(str, value) + return self._api_key_value + + async def _sleep_for_training_locked(self) -> None: + if not self._temporal_gpu_sharing or self._base_url is None: + return + if self._vllm_sleeping: + return + self._vllm_sleeping = True + await self._sleep_vllm_at(self._base_url, self._api_key()) + + async def _wake_for_serving_locked(self) -> None: + if not self._vllm_sleeping or self._base_url is None: + return + await self._wake_vllm_at(self._base_url, self._api_key()) + self._vllm_sleeping = False + + @staticmethod + async def _sleep_vllm_at(base_url: str, api_key: str | None) -> None: + response = await _post_vllm( + f"{base_url}/sleep", + api_key=api_key, + params={"level": 1, "mode": "wait"}, + timeout_s=300.0, + ) + response.raise_for_status() + + @staticmethod + async def _wake_vllm_at(base_url: str, api_key: str | None) -> None: + response = await _post_vllm( + f"{base_url}/wake_up", api_key=api_key, timeout_s=300.0 + ) + response.raise_for_status() + + async def _merged_weight_transfer_spec( + self, + served_model_name: str, + *, + base_url: str | None = None, + api_key: str | None = None, + ) -> MergedWeightTransferSpec: + target_url = base_url or self._base_url + if target_url is None: + raise RuntimeError("merged serving has not started") + target_api_key = api_key if base_url is not None else self._api_key() + service = self._model_service_spec() + if len(service.members) != 1: + raise NotImplementedError("merged updates currently require one vLLM host") + if self._merged_transfer_init is None: + async with httpx.AsyncClient(timeout=30.0) as client: + response = await client.get( + f"{target_url}/get_world_size", + headers=_headers(target_api_key), + ) + response.raise_for_status() + self._merged_transfer_init = MergedWeightTransferInitInfo( + master_address="127.0.0.1", + master_port=_free_port(), + rank_offset=1, + world_size=int(response.json()["world_size"]) + 1, + ) + return MergedWeightTransferSpec( + init_info=self._merged_transfer_init, + vllm_base_url=target_url, + served_model_name=served_model_name, + api_key=target_api_key, + nccl_so_path=str(get_vllm_runtime_nccl_so_path()), + ) + + async def _load_adapter( + self, checkpoint: str, step: int, *, exact: bool = False + ) -> tuple[str, str]: + if self._base_url is None: + raise RuntimeError("vLLM serving has not started") + return await self._load_adapter_at( + checkpoint, + step, + exact=exact, + base_url=self._base_url, + api_key=self._api_key(), + active_step=self._serving_step, + ) + + async def _load_adapter_at( + self, + checkpoint: str, + step: int, + *, + base_url: str, + api_key: str | None, + active_step: int, + exact: bool = False, + ) -> tuple[str, str]: + name = ( + f"{self.model_name}:eval@{step}" + if exact and self.rollout_weight_update_mode == "in_flight_lora" + else self._serving_lora_name(step) + ) + path = map_checkpoint_path_for_vllm(self.config, checkpoint) + in_flight = ( + not exact + and self.rollout_weight_update_mode == "in_flight_lora" + and step != active_step + ) + endpoint = ( + "/art/in_flight_lora_update" if in_flight else "/v1/load_lora_adapter" + ) + payload = ( + { + "model_name": name, + "lora_slot": name, + "lora_path": path, + "policy_version": step, + } + if in_flight + else {"lora_name": name, "lora_path": path} + ) + async with httpx.AsyncClient(timeout=60.0) as client: + response = await client.post( + f"{base_url}{endpoint}", + json=payload, + headers=_headers(api_key), + ) + response.raise_for_status() + return str(payload.get("lora_slot", name)), path + + async def register_lora_for_step(self, step: int, checkpoint: str) -> None: + async with self._train_lock: + self._require_open() + policy = await asyncio.to_thread( + resolve_committed_optimizer_policy, + self._optimizer_state_path, + initial_adapter_path=get_step_checkpoint_dir(self.output_dir, 0), + ) + if policy.policy_adapter.step != step or policy.policy_adapter.identity != ( + str(Path(checkpoint).absolute()) + ): + raise RuntimeError( + "distributed LoRA registration requires a committed policy step" + ) + adapter = policy.policy_adapter + generation = TrainerGeneration( + training_session_id=adapter.training_session_id, + policy_step=adapter.step, + generation_id=adapter.generation_id, + adapter_path=adapter.identity, + ) + async with self._mutation_lock: + self._published_adapters[step] = adapter + self._training_session_id = adapter.training_session_id + self._learner_generation = generation + self._latest_step = step + self._durable_step = step + self._durable_optimizer_step = ( + 0 + if policy.optimizer_anchor is None + else policy.optimizer_anchor.step + ) + async with self._serving_lock: + await self._register_lora_for_step_locked(step, checkpoint) + + async def advance_without_training( + self, + *, + expected_step: int, + learner_version: int, + ) -> dict[str, float]: + async with self._train_lock: + metrics = self.drain_publication_metrics() + async with self._mutation_lock: + self._require_open() + self._raise_publication_failure() + if expected_step != self._latest_step: + raise ValueError( + "no-op policy transition expected the wrong learner step" + ) + if learner_version != expected_step + 1: + raise ValueError("a no-op policy transition must advance one step") + trainer = self._trainer if self._trainer_is_current() else None + previous = self._publication_tasks.get(expected_step) + source = self._learner_generation + if source is None or source.policy_step != expected_step: + raise RuntimeError("no-op transition has no immutable source") + if previous is not None: + await asyncio.shield(previous) + source_adapter = self._published_adapters.get(expected_step) + if source_adapter is None: + raise RuntimeError("no-op source generation is not durably published") + + async with self._mutation_lock: + self._published_adapters[expected_step] = source_adapter + generation = TrainerGeneration( + training_session_id=self._training_session_id, + policy_step=learner_version, + generation_id=new_optimizer_generation(learner_version), + adapter_path=get_step_checkpoint_dir( + self.output_dir, learner_version + ), + ) + snapshot_metrics: dict[str, float] = {} + if trainer is not None: + try: + snapshot_metrics.update( + await trainer.advance_without_training( + expected_learner_version=expected_step, + learner_version=learner_version, + optimizer_state_path=self._optimizer_state_path, + adapter=None, + ) + ) + except BaseException as error: + await self._cleanup_failed_trainer_transaction(trainer, None, error) + raise + + async def commit() -> None: + prepare_started = time.monotonic() + published = await asyncio.to_thread( + _commit_adapter_alias, + self._optimizer_state_path, + self.output_dir, + expected_step, + source_adapter, + generation, + f"{self.output_dir}/megatron_runtime/staging/" + f"{generation.generation_id}", + ) + snapshot_metrics["snapshot_launch_s"] = ( + time.monotonic() - prepare_started + ) + async with self._mutation_lock: + if self._latest_step != expected_step: + raise RuntimeError( + "learner lineage changed during no-op commit" + ) + self._published_adapters[learner_version] = published + self._latest_step = learner_version + self._learner_generation = generation + self._trainer_resident_generation = ( + generation if trainer is not None else None + ) + self._publication_metrics[learner_version] = snapshot_metrics + pointer = read_committed_optimizer_pointer( + self._optimizer_state_path + ) + self._schedule_publication( + generation, + durable=DurableTrainerPublication( + adapter=published, + resume_step=learner_version, + optimizer_step=0 if pointer is None else pointer.step, + ), + ) + + try: + _, cancelled = await complete_task(asyncio.create_task(commit())) + except BaseException as error: + if trainer is not None: + await self._cleanup_failed_trainer_transaction(trainer, None, error) + raise + metrics.update(self.drain_publication_metrics()) + if cancelled is not None: + raise cancelled + return metrics + + async def _register_lora_for_step_locked( + self, + step: int, + checkpoint: str, + *, + generation_id: str | None = None, + ) -> None: + if self._base_url is None: + self._serving_step = step + self._record_serving_activation(step) + return + await self._wake_for_serving_locked() + generation_id = generation_id or self._generation_id_for_step(step) + update_identity = uuid.uuid4().hex + manager = ( + self.runtime.model_service(self._managed_service_name) + if self._managed_service_name is not None + else None + ) + try: + state = ( + manager.prepare_update(update_identity=update_identity) + if manager is not None + else None + ) + lora_name = self._serving_lora_name(step) + if self.rollout_weights_mode == "lora": + lora_name, _lora_path = await self._load_adapter(checkpoint, step) + else: + response = await _post_vllm( + f"{self._base_url}/art/set_served_model_name", + api_key=self._api_key(), + json={"name": lora_name}, + ) + response.raise_for_status() + if manager is not None and state is not None: + report = ReplicaUpdateReport( + replica_id=manager.spec.name, + generation=state.generation, + generation_digest=state.generation_digest, + policy_version=str(step), + policy_digest=generation_id, + update_identity=update_identity, + ) + if manager.verify_update(report).phase != "ready": + raise RuntimeError("model service rejected its policy update") + except BaseException as error: + if manager is not None: + manager.quarantine("partial or failed LoRA update") + try: + cleanup = await self._rollback_server_start_safely( + self._managed_service_name + ) + finally: + self._clear_serving_state() + if cleanup: + raise BaseExceptionGroup( + "policy publication and serving rollback failed", [error, *cleanup] + ) from None + raise + if ( + self.rollout_weights_mode == "lora" + and self.rollout_weight_update_mode != "in_flight_lora" + ): + self._loaded_adapter_steps.add(step) + self._current_lora_name = lora_name + self._serving_step = step + self._record_serving_activation(step) + + def _generation_id_for_step(self, step: int) -> str: + published = self._published_adapters.get(step) + if published is None: + raise RuntimeError(f"No immutable generation is registered for step {step}") + return published.generation_id + + async def acquire_exact_adapter(self, step: int, checkpoint: str) -> str: + self._require_open() + if self.rollout_weights_mode != "lora": + raise RuntimeError("Exact checkpoint eval requires LoRA rollout serving") + async with self._mutation_lock: + published = step in self._published_adapters + generation = self._learner_generation + materialization = ( + self.checkpoint_materialization(step) + if self.rollout_weight_update_mode == "in_flight_lora" + and generation is not None + and generation.policy_step == step + else None + ) + if materialization is not None: + await asyncio.shield(materialization) + if not published: + adapter = await asyncio.to_thread( + read_adapter_publication, + checkpoint, + step=step, + verify_files=True, + ) + if adapter is None: + if step != 0: + raise RuntimeError("exact adapter is not an immutable generation") + adapter = optimizer_adapter( + checkpoint, + 0, + training_session_id=self._training_session_id, + ) + async with self._mutation_lock: + self._require_open() + self._published_adapters.setdefault(step, adapter) + async with self._serving_lock: + self._require_open() + lora_name = ( + f"{self.model_name}:eval@{step}" + if self.rollout_weight_update_mode == "in_flight_lora" + else f"{self.model_name}@{step}" + ) + if step not in self._loaded_exact_adapter_steps: + if ( + self.rollout_weight_update_mode == "in_flight_lora" + or step not in self._loaded_adapter_steps + ): + lora_name, _lora_path = await self._load_adapter( + checkpoint, step, exact=True + ) + self._loaded_exact_adapter_steps.add(step) + self._exact_adapter_refcounts[step] = 0 + self._exact_adapter_refcounts[step] += 1 + return ( + f"{self.model_name}:eval@{step}" + if self.rollout_weight_update_mode == "in_flight_lora" + else f"{self.model_name}@{step}" + ) + + async def release_exact_adapter(self, step: int) -> None: + async with self._serving_lock: + self._require_open() + count = self._exact_adapter_refcounts.get(step, 0) + if count <= 1: + if self.rollout_weight_update_mode == "in_flight_lora": + await self._unload_adapter(f"{self.model_name}:eval@{step}") + self._exact_adapter_refcounts.pop(step, None) + self._loaded_exact_adapter_steps.discard(step) + else: + self._exact_adapter_refcounts[step] = count - 1 + + async def prune_loaded_adapters(self, *, retain_steps: set[int]) -> None: + if self.rollout_weights_mode != "lora": + return + async with self._serving_lock: + self._require_open() + for step in sorted(self._loaded_exact_adapter_steps - retain_steps): + if self._exact_adapter_refcounts.get(step, 0) == 0: + name = ( + f"{self.model_name}:eval@{step}" + if self.rollout_weight_update_mode == "in_flight_lora" + else f"{self.model_name}@{step}" + ) + await self._unload_adapter(name) + self._loaded_exact_adapter_steps.discard(step) + if self.rollout_weight_update_mode == "in_flight_lora": + return + for step in sorted( + self._loaded_adapter_steps - retain_steps - {self._serving_step} + ): + await self._unload_adapter(f"{self.model_name}@{step}") + await self._release_loaded_adapter_transfer(step) + self._loaded_adapter_steps.discard(step) + + @asynccontextmanager + async def checkpoint_retention_lease(self) -> AsyncIterator[frozenset[int]]: + # Disk pruning keeps mutation, not serving, held after ordered acquisition. + async with self._serving_lock: + await self._mutation_lock.acquire() + try: + self._require_open() + protected = frozenset((self._latest_step, self._serving_step)) + yield protected + finally: + self._mutation_lock.release() + + async def _unload_adapter(self, name: str) -> None: + if self._base_url is None: + raise RuntimeError("vLLM serving has not started") + await self._unload_adapter_at(name, self._base_url) + + async def _unload_adapter_at(self, name: str, base_url: str) -> None: + async with httpx.AsyncClient(timeout=30.0) as client: + response = await client.post( + f"{base_url}/v1/unload_lora_adapter", + json={"lora_name": name}, + headers=_headers(self._api_key()), + ) + if response.status_code != 404: + response.raise_for_status() + + async def get_serving_capabilities(self) -> ServingCapabilities: + if self._serving_capabilities is None: + raise RuntimeError("vLLM serving capabilities have not been discovered") + return self._serving_capabilities + + async def vllm_engine_is_sleeping(self) -> bool: + return self._vllm_sleeping + + async def train_sft( + self, batches: list[Any], config: Any, verbose: bool = False + ) -> AsyncIterator[dict[str, float]]: + del verbose + payload = tuple( + SFTBatchData( + trajectory_tensors=tuple(batch.trajectory_tensors), + learning_rate=float(batch.learning_rate), + num_trajectories=int(batch.num_trajectories), + num_tokens=int(batch.num_tokens), + num_trainable_tokens=int(batch.num_trainable_tokens), + ) + for batch in batches + ) + if not payload: + return + + def build_job(fields: _TrainerJobFields) -> TrainerJobSpec: + return SFTJobSpec( + **fields, + batch_id=uuid.uuid4().hex, + num_batches=len(payload), + config=CurrentSFTConfig.model_validate(config.model_dump()), + ) + + async for metrics in self._run_train_job( + build_job, + lambda trainer, job: trainer.train_sft(job, payload), + lineage_error="learner lineage changed during SFT", + wait_for_serving=True, + ): + yield metrics + + async def aclose(self) -> None: + if self._close_task is not None and self._close_task.done(): + try: + self._close_task.result() + except BaseException: + self._close_task = None + if self._close_task is None: + self._closed = True + self._close_task = asyncio.create_task(self._close()) + self._close_task.add_done_callback(_consume_task_result) + await asyncio.shield(self._close_task) + + async def _close(self) -> None: + async with self._train_lock: + failures: list[BaseException] = [] + publications = tuple(self._publication_tasks.values()) + durability_tasks = tuple(self._durability_tasks) + recovery_tasks = tuple(self._recovery_tasks) + for task in recovery_tasks: + task.cancel() + if recovery_tasks: + await asyncio.gather(*recovery_tasks, return_exceptions=True) + self._recovery_tasks.clear() + async with self._mutation_lock: + trainer = self._trainer + shutdown = [*publications, *durability_tasks] + trainer_task = None + if trainer is not None: + trainer_task = asyncio.create_task(self.runtime.stop_trainer(trainer)) + shutdown.append(trainer_task) + results = await asyncio.gather(*shutdown, return_exceptions=True) + if trainer_task is not None and not isinstance(results[-1], BaseException): + async with self._mutation_lock: + if self._trainer is trainer: + self._trainer = None + publication_failures = [ + result + for result in results[: len(publications)] + if isinstance(result, BaseException) + ] + failures.extend( + result for result in results if isinstance(result, BaseException) + ) + if self._publication_failure is not None and not publication_failures: + failures.append(self._publication_failure) + async with self._mutation_lock: + self._publication_tasks.clear() + self._durability_tasks.clear() + self._serving_futures.clear() + self._publication_metrics.clear() + self._emitted_publication_metrics.clear() + self._trainer_completion_times.clear() + self._serving_activation_times.clear() + try: + await self._discard_next_publication_preparation() + except BaseException as error: + failures.append(error) + try: + await self._release_prepared_adapter_transfers() + except BaseException as error: + failures.append(error) + async with self._serving_lock: + serving_stopped = False + if self._managed_service_name is not None: + result = await asyncio.gather( + self.runtime.stop_model_service(self._managed_service_name), + return_exceptions=True, + ) + serving_failures = [ + value for value in result if isinstance(value, BaseException) + ] + failures.extend(serving_failures) + if not serving_failures: + serving_stopped = True + self._clear_serving_state() + elif ( + get_external_vllm_runtime_config(self.config) is not None + and self._base_url is not None + ): + names = { + *( + self._serving_lora_name(step) + for step in self._loaded_adapter_steps + ), + *( + f"{self.model_name}:eval@{step}" + if self.rollout_weight_update_mode == "in_flight_lora" + else f"{self.model_name}@{step}" + for step in self._loaded_exact_adapter_steps + ), + } + if self._current_lora_name is not None: + names.add(self._current_lora_name) + results = await asyncio.gather( + *( + self._unload_adapter_at(name, self._base_url) + for name in sorted(names) + ), + return_exceptions=True, + ) + serving_failures = [ + value for value in results if isinstance(value, BaseException) + ] + failures.extend(serving_failures) + if not serving_failures: + serving_stopped = True + self._clear_serving_state() + else: + serving_stopped = True + self._clear_serving_state() + if serving_stopped: + try: + await self._release_loaded_adapter_transfers() + except BaseException as error: + failures.append(error) + _, cancelled = await complete_to_thread( + lambda: _remove_staging_root(self.output_dir) + ) + if cancelled is not None: + failures.append(cancelled) + if failures: + raise BaseExceptionGroup( + "distributed model service close failed", failures + ) + + +def _remove_staging_checkpoint(staging: str) -> None: + if os.path.exists(staging): + shutil.rmtree(staging) + + +def _remove_staging_root(output_dir: str) -> None: + _remove_staging_checkpoint(f"{output_dir}/megatron_runtime/staging") + + +def _publish_adapter_alias( + source: OptimizerAdapter, + generation: TrainerGeneration, + staging_path: str, +) -> OptimizerAdapter: + staging = Path(staging_path) + if staging.exists() or Path(generation.adapter_path).exists(): + raise RuntimeError("no-op adapter generation path already exists") + with adapter_generation_lease(source): + staging.mkdir(parents=True) + try: + for name in ("adapter_config.json", "adapter_model.safetensors"): + os.link(Path(source.identity) / name, staging / name) + return publish_adapter_checkpoint( + staging, + step=generation.policy_step, + training_session_id=generation.training_session_id, + generation_id=generation.generation_id, + ) + except BaseException: + _remove_staging_checkpoint(str(staging)) + raise + + +def _commit_adapter_alias( + optimizer_state_path: str, + output_dir: str, + expected_step: int, + source: OptimizerAdapter, + generation: TrainerGeneration, + staging_path: str, +) -> OptimizerAdapter: + try: + published = _publish_adapter_alias(source, generation, staging_path) + commit_optimizer_policy_advance( + optimizer_state_path, + initial_adapter_path=get_step_checkpoint_dir(output_dir, 0), + expected_step=expected_step, + adapter=published, + ) + return published + except BaseException as error: + try: + policy = resolve_committed_optimizer_policy( + optimizer_state_path, + initial_adapter_path=get_step_checkpoint_dir(output_dir, 0), + ) + except BaseException as state_error: + raise BaseExceptionGroup( + "no-op policy commit state is ambiguous", [error, state_error] + ) from None + if policy.policy_adapter.generation_id == generation.generation_id: + return policy.policy_adapter + failures: list[BaseException] = [] + latest = Path(output_dir) / "megatron_runtime/latest-adapter.json" + try: + if latest.is_file(): + adapter = OptimizerAdapter.model_validate_json( + latest.read_text("utf-8") + ) + if adapter.generation_id == generation.generation_id: + latest.unlink() + except BaseException as cleanup_error: + failures.append(cleanup_error) + for path in ( + staging_path, + generation.adapter_path, + ): + try: + _remove_staging_checkpoint(path) + except BaseException as cleanup_error: + failures.append(cleanup_error) + if failures: + raise BaseExceptionGroup( + "no-op policy commit and rollback failed", [error, *failures] + ) from None + raise + + +def _trainer_dtype( + config: dev.BackendModelConfig, +) -> Literal["bfloat16", "float16", "float32"]: + value = str(config.get("init_args", {}).get("dtype") or "bfloat16").lower() + value = { + "bf16": "bfloat16", + "fp16": "float16", + "fp32": "float32", + "torch.bfloat16": "bfloat16", + "torch.float16": "float16", + "torch.float32": "float32", + }.get(value, value) + if value not in {"bfloat16", "float16", "float32"}: + raise ValueError(f"unsupported Megatron trainer dtype {value!r}") + return cast( + Literal["bfloat16", "float16", "float32"], + value, + ) + + +def _digest(value: object) -> str: + return hashlib.sha256( + json.dumps(value, sort_keys=True, separators=(",", ":")).encode() + ).hexdigest() + + +def _art_source_revision() -> str: + root = Path(__file__).resolve().parents[1] + digest = hashlib.sha256() + for path in sorted(root.rglob("*.py")): + digest.update(str(path.relative_to(root)).encode()) + digest.update(path.read_bytes()) + return digest.hexdigest() + + +def _headers(api_key: str | None) -> dict[str, str] | None: + return {"Authorization": f"Bearer {api_key}"} if api_key else None + + +def _host_port(base_url: str) -> tuple[str, int]: + from urllib.parse import urlparse + + parsed = urlparse(base_url) + assert parsed.hostname is not None + return parsed.hostname, parsed.port or (443 if parsed.scheme == "https" else 80) diff --git a/src/art/megatron/dsv4/bridge.py b/src/art/megatron/dsv4/bridge.py index 8cf02a910..6be4f9610 100644 --- a/src/art/megatron/dsv4/bridge.py +++ b/src/art/megatron/dsv4/bridge.py @@ -513,7 +513,9 @@ def has_glob(self, pattern: str) -> bool: def _install_dsv4_source_aliases(hf_pretrained: Any) -> None: - state = hf_pretrained.state + state = getattr(hf_pretrained, "state", None) + if state is None: + return source = getattr(state, "source", None) if source is None or isinstance(source, _Dsv4AliasStateSource): return diff --git a/src/art/megatron/dsv4/compressor.py b/src/art/megatron/dsv4/compressor.py index fda1b2bf6..c6da0a169 100644 --- a/src/art/megatron/dsv4/compressor.py +++ b/src/art/megatron/dsv4/compressor.py @@ -486,6 +486,8 @@ def __init__( self._keep_fp32_parameters = ("ape",) setattr(self.ape, "_keep_fp32", True) + if config.perform_initialization: + nn.init.zeros_(self.ape) base = cfg.dsv4_compress_rope_theta assert rope_head_dim == 64 diff --git a/src/art/megatron/dsv4/deepseek_v4.py b/src/art/megatron/dsv4/deepseek_v4.py index 1ade6a202..2905d12e0 100644 --- a/src/art/megatron/dsv4/deepseek_v4.py +++ b/src/art/megatron/dsv4/deepseek_v4.py @@ -355,6 +355,8 @@ def __init__( self._keep_fp32_buffers = ("attn_sink",) self.attn_sink = nn.Parameter(attn_sink) setattr(self.attn_sink, "_keep_fp32", True) + if config.perform_initialization: + nn.init.zeros_(self.attn_sink) self.wq_a = TELinear( self.dim, diff --git a/src/art/megatron/dsv4/hf_config.py b/src/art/megatron/dsv4/hf_config.py index 75f6a0b63..c2eb684b1 100644 --- a/src/art/megatron/dsv4/hf_config.py +++ b/src/art/megatron/dsv4/hf_config.py @@ -49,7 +49,11 @@ def _ensure_torchvision_nms_schema() -> None: "nms(Tensor dets, Tensor scores, float iou_threshold) -> Tensor" ) except RuntimeError as exc: - if "Only a single TORCH_LIBRARY" not in str(exc) and "already" not in str(exc): + if ( + "Only a single TORCH_LIBRARY" not in str(exc) + and "already" not in str(exc) + and "multiple times" not in str(exc) + ): raise _TORCHVISION_LIB = torch.library.Library("torchvision", "FRAGMENT") try: @@ -57,7 +61,9 @@ def _ensure_torchvision_nms_schema() -> None: "nms(Tensor dets, Tensor scores, float iou_threshold) -> Tensor" ) except RuntimeError as define_exc: - if "already" not in str(define_exc): + if "already" not in str(define_exc) and "multiple times" not in str( + define_exc + ): raise diff --git a/src/art/megatron/dsv4/hf_oracle.py b/src/art/megatron/dsv4/hf_oracle.py new file mode 100644 index 000000000..bb7fd82d8 --- /dev/null +++ b/src/art/megatron/dsv4/hf_oracle.py @@ -0,0 +1,281 @@ +from types import MethodType +from typing import Any + +import torch +from torch import nn + +from art.megatron.dsv4.compressor import ( + Dsv4CompressionLayout, + build_prefix_tree_compression_layouts, + compressed_layout_visibility, +) +from art.megatron.dsv4.kernel.precision_aligned_ops import linear_bf16_fp32 + +_COMPRESSOR_TYPES = {"DeepseekV4CSACompressor", "DeepseekV4HCACompressor"} + + +def _aligned_linear_forward(module: nn.Linear, x: torch.Tensor) -> torch.Tensor: + return linear_bf16_fp32(x, module.weight) + + +def _patch_aligned_linear(module: nn.Linear) -> None: + if module.bias is not None: + raise RuntimeError("DSV4 compressor oracle projections must be bias-free") + if getattr(module, "_art_dsv4_aligned", False): + return + module.forward = MethodType(_aligned_linear_forward, module) + module._art_dsv4_aligned = True + + +def _cast_compressor_output( + _module: nn.Module, + inputs: tuple[Any, ...], + output: tuple[torch.Tensor, torch.Tensor | None], +) -> tuple[torch.Tensor, torch.Tensor | None]: + compressed_kv, block_bias = output + return compressed_kv.to(inputs[0].dtype), block_bias + + +def _cast_indexer_key( + _module: nn.Module, + inputs: tuple[Any, ...], +) -> tuple[Any, ...]: + q, compressed_kv, *rest = inputs + return q, compressed_kv.to(q.dtype), *rest + + +def _gather_projected(tensor: torch.Tensor, indices: torch.Tensor) -> torch.Tensor: + if int(tensor.shape[0]) != 1: + raise ValueError("DSV4 HF prefix compression requires batch size one") + safe_indices = indices.clamp(0, max(int(tensor.shape[1]) - 1, 0)) + gathered = tensor[0].index_select(0, safe_indices.reshape(-1)) + return gathered.view(1, *indices.shape, tensor.shape[-1]) + + +def _compress_prefix_projected( + module: Any, + kv: torch.Tensor, + gate: torch.Tensor, + layout: Dsv4CompressionLayout, +) -> torch.Tensor: + ratio = int(module.compress_rate) + current_valid = layout.current_indices >= 0 + current_kv = _gather_projected(kv, layout.current_indices) + current_gate = _gather_projected(gate, layout.current_indices) + current_kv = torch.where( + current_valid.unsqueeze(-1), current_kv, torch.zeros_like(current_kv) + ) + current_gate = torch.where( + current_valid.unsqueeze(-1), + current_gate, + torch.full_like(current_gate, float("-inf")), + ) + position_bias = module.position_bias.view(1, 1, ratio, -1) + if ratio == 4: + head_dim = int(module.head_dim) + previous_valid = layout.previous_indices >= 0 + previous_kv = _gather_projected(kv, layout.previous_indices) + previous_gate = _gather_projected(gate, layout.previous_indices) + previous_kv = torch.where( + previous_valid.unsqueeze(-1), + previous_kv, + torch.zeros_like(previous_kv), + ) + previous_gate = torch.where( + previous_valid.unsqueeze(-1), + previous_gate, + torch.full_like(previous_gate, float("-inf")), + ) + current_gate = current_gate + position_bias + previous_gate = previous_gate + position_bias + slots_kv = torch.cat( + [previous_kv[..., :head_dim], current_kv[..., head_dim:]], dim=2 + ) + slots_gate = torch.cat( + [previous_gate[..., :head_dim], current_gate[..., head_dim:]], dim=2 + ) + else: + slots_kv = current_kv + slots_gate = current_gate + position_bias + compressed = ( + slots_kv * slots_gate.softmax(dim=2, dtype=torch.float32).to(slots_kv.dtype) + ).sum(dim=2) + compressed = module.kv_norm(compressed) + positions = layout.entry_start_positions.unsqueeze(0) + cos, sin = module.rotary_emb( + compressed, position_ids=positions, layer_type=module.rope_layer_type + ) + from transformers.models.deepseek_v4.modeling_deepseek_v4 import ( + apply_rotary_pos_emb, + ) + + return apply_rotary_pos_emb(compressed.unsqueeze(1), cos, sin).squeeze(1) + + +def _require_fresh_compressor_cache(past_key_values: Any, layer_idx: int) -> None: + if past_key_values is None: + return + cache_layer = past_key_values.layers[layer_idx] + nonempty = [] + for name in ("buffer_kv", "buffer_gate", "compressed_kv"): + values = getattr(cache_layer, name, {}) + nonempty.extend( + f"{name}.{key}" for key, value in values.items() if value is not None + ) + for name in ("overlap_kv", "overlap_gate"): + values = getattr(cache_layer, name, {}) + nonempty.extend( + f"{name}.{key}" for key, value in values.items() if value is not None + ) + nonempty.extend( + f"entry_count.{key}={value}" + for key, value in getattr(cache_layer, "entry_count", {}).items() + if value + ) + if nonempty: + raise ValueError( + "DSV4 HF prefix oracle requires fresh compressor cache state, got " + + ", ".join(nonempty) + ) + + +def _prefix_indexer_forward( + module: Any, + hidden_states: torch.Tensor, + q_residual: torch.Tensor | None = None, + position_ids: torch.Tensor | None = None, + past_key_values: Any = None, + layer_idx: int = 0, +) -> torch.Tensor: + layout = getattr(module, "_art_dsv4_prefix_layout", None) + if layout is None: + if q_residual is None and position_ids is None and past_key_values is None: + return module._art_dsv4_flat_forward(hidden_states) + return module._art_dsv4_flat_forward( + hidden_states, q_residual, position_ids, past_key_values, layer_idx + ) + if q_residual is None or position_ids is None: + raise ValueError("DSV4 HF prefix indexer requires query and position inputs") + _require_fresh_compressor_cache(past_key_values, layer_idx) + batch, seq_len, _ = hidden_states.shape + kv = module.kv_proj(hidden_states) + gate = module.gate_proj(hidden_states) + compressed = _compress_prefix_projected(module, kv, gate, layout) + cos, sin = module.rotary_emb( + hidden_states, + position_ids=position_ids, + layer_type=module.rope_layer_type, + ) + from transformers.models.deepseek_v4.modeling_deepseek_v4 import ( + apply_rotary_pos_emb, + ) + + q = module.q_b_proj(q_residual).view( + batch, seq_len, module.num_heads, module.head_dim + ) + q = apply_rotary_pos_emb(q.transpose(1, 2), cos, sin).transpose(1, 2) + scores = module.scorer(q, compressed, hidden_states) + visible = compressed_layout_visibility(layout, position_ids=position_ids) + scores = scores.masked_fill(~visible, float("-inf")) + top_k = min(int(module.index_topk), int(compressed.shape[1])) + indices = scores.topk(top_k, dim=-1).indices + valid = visible.gather(-1, indices) + return torch.where(valid, indices, torch.full_like(indices, -1)) + + +def _prefix_compressor_forward( + module: Any, + hidden_states: torch.Tensor, + q_residual: torch.Tensor | None = None, + position_ids: torch.Tensor | None = None, + past_key_values: Any = None, + layer_idx: int = 0, +) -> tuple[torch.Tensor, torch.Tensor | None]: + layout = getattr(module, "_art_dsv4_prefix_layout", None) + if layout is None: + if q_residual is None and position_ids is None and past_key_values is None: + return module._art_dsv4_flat_forward(hidden_states) + return module._art_dsv4_flat_forward( + hidden_states, q_residual, position_ids, past_key_values, layer_idx + ) + if q_residual is None or position_ids is None: + raise ValueError("DSV4 HF prefix compressor requires query and position inputs") + _require_fresh_compressor_cache(past_key_values, layer_idx) + kv = module.kv_proj(hidden_states) + gate = module.gate_proj(hidden_states) + compressed = _compress_prefix_projected(module, kv, gate, layout) + compressed_kv = compressed.unsqueeze(1) + if hasattr(module, "indexer"): + top_k_indices = module.indexer( + hidden_states, q_residual, position_ids, past_key_values, layer_idx + ) + compressed_len = int(compressed.shape[1]) + valid = top_k_indices >= 0 + safe_indices = torch.where( + valid, top_k_indices, torch.full_like(top_k_indices, compressed_len) + ) + block_bias = compressed.new_full( + (*safe_indices.shape[:2], 1, compressed_len + 1), float("-inf") + ).transpose(1, 2) + block_bias.scatter_(-1, safe_indices.unsqueeze(1), 0.0) + return compressed_kv, block_bias[..., :compressed_len] + visible = compressed_layout_visibility(layout, position_ids=position_ids).unsqueeze( + 1 + ) + block_bias = compressed.new_zeros(visible.shape).masked_fill( + ~visible, float("-inf") + ) + return compressed_kv, block_bias + + +def _patch_prefix_forward(module: Any, forward: Any) -> None: + module._art_dsv4_flat_forward = module.forward + module.forward = MethodType(forward, module) + + +def prepare_hf_reference_model(model: Any) -> Any: + """Align native HF compressor precision with the training/serving path.""" + compressors = [ + module + for module in model.modules() + if type(module).__name__ in _COMPRESSOR_TYPES + ] + if not compressors: + raise RuntimeError("Native DSV4 HF model has no recognized compressor") + for compressor in compressors: + _patch_aligned_linear(compressor.kv_proj) + _patch_aligned_linear(compressor.gate_proj) + _patch_prefix_forward(compressor, _prefix_compressor_forward) + compressor.register_forward_hook(_cast_compressor_output) + indexer = getattr(compressor, "indexer", None) + if indexer is None: + continue + _patch_aligned_linear(indexer.kv_proj) + _patch_aligned_linear(indexer.gate_proj) + _patch_prefix_forward(indexer, _prefix_indexer_forward) + indexer.scorer.register_forward_pre_hook(_cast_indexer_key) + return model + + +def set_hf_reference_prefix_tree( + model: Any, + *, + position_ids: torch.Tensor, + group_ids: torch.Tensor, + parent_ids: torch.Tensor, +) -> None: + device = next(model.parameters()).device + layouts = build_prefix_tree_compression_layouts( + position_ids=position_ids.unsqueeze(0), + group_ids=group_ids.unsqueeze(0), + parent_ids=parent_ids.unsqueeze(0), + device=device, + ) + for module in model.modules(): + if type(module).__name__ not in _COMPRESSOR_TYPES: + continue + layout = layouts[int(module.compress_rate)] + module._art_dsv4_prefix_layout = layout + indexer = getattr(module, "indexer", None) + if indexer is not None: + indexer._art_dsv4_prefix_layout = layout diff --git a/src/art/megatron/dsv4/hyper_connection.py b/src/art/megatron/dsv4/hyper_connection.py index abe37397f..c8d14f67a 100644 --- a/src/art/megatron/dsv4/hyper_connection.py +++ b/src/art/megatron/dsv4/hyper_connection.py @@ -32,6 +32,11 @@ def __init__(self, config: TransformerConfig): ) for param in (self.hc_head_fn, self.hc_head_base, self.hc_head_scale): setattr(param, "_keep_fp32", True) + if config.perform_initialization: + assert config.init_method is not None + config.init_method(self.hc_head_fn) + torch.nn.init.zeros_(self.hc_head_base) + torch.nn.init.ones_(self.hc_head_scale) def forward(self): raise NotImplementedError diff --git a/src/art/megatron/dsv4/kernel/tilelang_sparse_mla.py b/src/art/megatron/dsv4/kernel/tilelang_sparse_mla.py index 20fefe44f..7a430502f 100644 --- a/src/art/megatron/dsv4/kernel/tilelang_sparse_mla.py +++ b/src/art/megatron/dsv4/kernel/tilelang_sparse_mla.py @@ -51,7 +51,7 @@ def forward(ctx, q, kv, attn_sink, topk_idxs, sm_scale=None, output_dtype=None): ) output = o if output_dtype is None else o.to(output_dtype) - ctx.save_for_backward(q, kv, attn_sink, topk_idxs, output.clone(), lse) + ctx.save_for_backward(q, kv, attn_sink, topk_idxs, lse) ctx.sm_scale = sm_scale return output @@ -59,7 +59,7 @@ def forward(ctx, q, kv, attn_sink, topk_idxs, sm_scale=None, output_dtype=None): @staticmethod def backward(ctx: Any, *grad_outputs: Any): do = grad_outputs[0] - q, kv, attn_sink, topk_idxs, output, lse = ctx.saved_tensors + q, kv, attn_sink, topk_idxs, lse = ctx.saved_tensors sm_scale = ctx.sm_scale with preserve_tilelang_env(): @@ -71,7 +71,6 @@ def backward(ctx: Any, *grad_outputs: Any): q, kv, attn_sink, - output.to(q.dtype), do.to(q.dtype), topk_idxs, lse, diff --git a/src/art/megatron/dsv4/kernel/tilelang_sparse_mla_bwd.py b/src/art/megatron/dsv4/kernel/tilelang_sparse_mla_bwd.py index b854f5063..778218534 100644 --- a/src/art/megatron/dsv4/kernel/tilelang_sparse_mla_bwd.py +++ b/src/art/megatron/dsv4/kernel/tilelang_sparse_mla_bwd.py @@ -23,52 +23,107 @@ def preprocess( H, D, - block_ND=32, - num_stages=5, + topk, + sm_scale=None, + block_size=64, + num_stages=0, + threads=128, + indices_dtype=T.int32, dtype=T.bfloat16, accum_dtype=T.float32, ): + assert topk % block_size == 0 assert dtype == T.bfloat16 assert accum_dtype == T.float32 B = T.dynamic("batch") S = T.dynamic("seq_len") - shape = [B, S, H, D] + S_kv = T.dynamic("seq_len_kv") + if sm_scale is None: + sm_scale = D ** (-0.5) + + q_shape = [B, S, H, D] + kv_shape = [B, S_kv, D] + indices_shape = [B, S, topk] + padded_H = max(tilelang.math.next_power_of_2(H), 16) + block_H = min(64, padded_H) + assert padded_H % block_H == 0 + NH = padded_H // block_H + BS = block_size + NS = tilelang.cdiv(topk, block_size) @T.prim_func def preprocess_kernel( - O: T.Tensor(shape, dtype), # type: ignore - dO: T.Tensor(shape, dtype), # type: ignore + Q: T.Tensor(q_shape, dtype), # type: ignore + KV: T.Tensor(kv_shape, dtype), # type: ignore + dO: T.Tensor(q_shape, dtype), # type: ignore + Indices: T.Tensor(indices_shape, indices_dtype), # type: ignore + Lse: T.Tensor([B, S, H], accum_dtype), # type: ignore Delta: T.Tensor([B, S, H], accum_dtype), # type: ignore ): - with T.Kernel(H, T.ceildiv(S, block_ND), B) as (bx, by, bz): - o = T.alloc_fragment([block_ND, block_ND], accum_dtype) - do = T.alloc_fragment([block_ND, block_ND], accum_dtype) - delta = T.alloc_fragment([block_ND], accum_dtype) - acc = T.alloc_fragment([block_ND, block_ND], accum_dtype) - T.clear(acc) - for k in T.Pipelined(T.ceildiv(D, block_ND), num_stages=num_stages): - T.copy( - O[ - bz, - by * block_ND : (by + 1) * block_ND, - bx, - k * block_ND : (k + 1) * block_ND, - ], - o, + with T.Kernel(S, B, NH, threads=threads) as (s_i, by, bz): + Q_shared = T.alloc_shared([block_H, D], dtype) + KV_shared = T.alloc_shared([BS, D], dtype) + dO_shared = T.alloc_shared([block_H, D], dtype) + P_shared_cast = T.alloc_shared([block_H, BS], dtype) + dP_shared_cast = T.alloc_shared([block_H, BS], dtype) + mask = T.alloc_fragment([BS], "bool") + safe_indices = T.alloc_fragment([BS], indices_dtype) + acc_p = T.alloc_fragment([block_H, BS], accum_dtype) + acc_dp = T.alloc_fragment([block_H, BS], accum_dtype) + delta = T.alloc_fragment([block_H], accum_dtype) + delta_i = T.alloc_fragment([block_H], accum_dtype) + + T.copy(Q[by, s_i, bz * block_H : (bz + 1) * block_H, :], Q_shared) + T.copy(dO[by, s_i, bz * block_H : (bz + 1) * block_H, :], dO_shared) + T.clear(delta) + + for i_i in T.Pipelined(NS, num_stages=num_stages): + for bi_i in T.Parallel(BS): + mask[bi_i] = Indices[by, s_i, i_i * BS + bi_i] != -1 + safe_indices[bi_i] = T.if_then_else( + mask[bi_i], Indices[by, s_i, i_i * BS + bi_i], 0 + ) + for bi_i, d_i in T.Parallel(BS, D): + KV_shared[bi_i, d_i] = KV[by, safe_indices[bi_i], d_i] + + T.gemm( + Q_shared, + KV_shared, + acc_p, + transpose_B=True, + policy=T.GemmWarpPolicy.FullCol, + clear_accum=True, ) - T.copy( - dO[ - bz, - by * block_ND : (by + 1) * block_ND, - bx, - k * block_ND : (k + 1) * block_ND, - ], - do, + T.copy(acc_p, P_shared_cast) + for h_i, bi_i in T.Parallel(block_H, BS): + acc_p[h_i, bi_i] = P_shared_cast[h_i, bi_i] * sm_scale + T.copy(acc_p, P_shared_cast) + for h_i, bi_i in T.Parallel(block_H, BS): + acc_p[h_i, bi_i] = T.if_then_else( + mask[bi_i], + T.exp2( + P_shared_cast[h_i, bi_i] * 1.44269504 + - Lse[by, s_i, bz * block_H + h_i] + ), + 0, + ) + + T.gemm( + dO_shared, + KV_shared, + acc_dp, + transpose_B=True, + policy=T.GemmWarpPolicy.FullCol, + clear_accum=True, ) - for i, j in T.Parallel(block_ND, block_ND): - acc[i, j] += o[i, j] * do[i, j] - T.reduce_sum(acc, delta, 1) - T.copy(delta, Delta[bz, by * block_ND : (by + 1) * block_ND, bx]) + T.copy(acc_dp, dP_shared_cast) + for h_i, bi_i in T.Parallel(block_H, BS): + acc_dp[h_i, bi_i] = acc_p[h_i, bi_i] * dP_shared_cast[h_i, bi_i] + T.reduce_sum(acc_dp, delta_i, dim=1) + for h_i in T.Parallel(block_H): + delta[h_i] += delta_i[h_i] + + T.copy(delta, Delta[by, s_i, bz * block_H : (bz + 1) * block_H]) return preprocess_kernel @@ -132,7 +187,6 @@ def bwd( if sm_scale is None: sm_scale = D ** (-0.5) - sm_scale_mul_reciprocal_log2 = sm_scale * 1.44269504 # log2(e) q_shape = [B, S, H, D] kv_shape = [B, S_kv, D] @@ -206,21 +260,25 @@ def sparse_mqa_bwd_kernel( transpose_B=True, policy=T.GemmWarpPolicy.FullCol, ) + T.copy(acc_p, P_shared_cast) + for h_i, bi_i in T.Parallel(block_H, BS): + acc_p[h_i, bi_i] = P_shared_cast[h_i, bi_i] * sm_scale + T.copy(acc_p, P_shared_cast) for h_i, bi_i in T.Parallel(block_H, BS): acc_p[h_i, bi_i] = T.if_then_else( - mask[bi_i], acc_p[h_i, bi_i], -T.infinity(acc_p.dtype) + mask[bi_i], P_shared_cast[h_i, bi_i], -T.infinity(acc_p.dtype) ) # P = exp2(scores * sm_scale_log2e - LSE) for h_i, bi_i in T.Parallel(block_H, BS): acc_p[h_i, bi_i] = T.exp2( - acc_p[h_i, bi_i] * sm_scale_mul_reciprocal_log2 - - Lse[by, s_i, bz * block_H + h_i] + acc_p[h_i, bi_i] * 1.44269504 - Lse[by, s_i, bz * block_H + h_i] ) T.copy(acc_p, P_shared_cast) - # dP = P * (dO @ KV^T - Delta) + # BF16 matmul in the canonical path rounds dO @ KV before the + # FP32 softmax derivative. T.gemm( dO_shared, KV_shared, @@ -230,14 +288,17 @@ def sparse_mqa_bwd_kernel( clear_accum=True, ) + T.copy(acc_dp, dP_shared_cast) + for h_i, bi_i in T.Parallel(block_H, BS): - acc_dp[h_i, bi_i] = ( - acc_p[h_i, bi_i] - * (acc_dp[h_i, bi_i] - Delta[by, s_i, bz * block_H + h_i]) - * sm_scale + acc_dp[h_i, bi_i] = acc_p[h_i, bi_i] * ( + dP_shared_cast[h_i, bi_i] - Delta[by, s_i, bz * block_H + h_i] ) T.copy(acc_dp, dP_shared_cast) + for h_i, bi_i in T.Parallel(block_H, BS): + acc_dp[h_i, bi_i] = dP_shared_cast[h_i, bi_i] * sm_scale + T.copy(acc_dp, dP_shared_cast) # dQ += dP @ KV T.gemm( @@ -314,14 +375,13 @@ def _tilelang_input_dtype(torch_dtype): raise TypeError(f"DSV4 sparse MLA TileLang launch requires bf16, got {torch_dtype}") -def sparse_mqa_bwd_interface(q, kv, attn_sink, o, do, topk_idxs, lse, sm_scale=None): +def sparse_mqa_bwd_interface(q, kv, attn_sink, do, topk_idxs, lse, sm_scale=None): """Backward interface for V4 sparse MQA attention. Args: q: [B, S, H, D] bf16 kv: [B, S_kv, D] bf16 attn_sink: [H] fp32 - o: [B, S, H, D] bf16 (forward output) do: [B, S, H, D] bf16 (grad of output) topk_idxs: [B, S, topk] int32 lse: [B, S, H] fp32 (log-sum-exp from forward) @@ -338,7 +398,7 @@ def sparse_mqa_bwd_interface(q, kv, attn_sink, o, do, topk_idxs, lse, sm_scale=N _, S_kv, _ = kv.shape topk = topk_idxs.shape[-1] dtype = _tilelang_input_dtype(q.dtype) - assert kv.dtype == q.dtype and o.dtype == q.dtype and do.dtype == q.dtype + assert kv.dtype == q.dtype and do.dtype == q.dtype # Pad topk to next multiple of block_size (kernel requires divisibility) block_size = 64 @@ -356,9 +416,9 @@ def sparse_mqa_bwd_interface(q, kv, attn_sink, o, do, topk_idxs, lse, sm_scale=N with preserve_tilelang_env(): # Keep sequence lengths dynamic so changing packed workloads reuse the # same generated kernels. Model/tile dimensions remain static. - preprocess_kernel = preprocess(H, D, dtype=dtype) + preprocess_kernel = preprocess(H, D, topk, sm_scale, dtype=dtype) postprocess_kernel = postprocess(D, dtype=dtype) - delta = preprocess_kernel(o, do) + delta = preprocess_kernel(q, kv, do, topk_idxs, lse) dkv = torch.zeros_like(kv, dtype=torch.float32) d_attn_sink = torch.zeros_like(attn_sink) if topk <= block_size: @@ -372,7 +432,15 @@ def sparse_mqa_bwd_interface(q, kv, attn_sink, o, do, topk_idxs, lse, sm_scale=N dtype=dtype, ) dq = bwd_kernel( - q, kv, do, attn_sink, topk_idxs, lse, delta, dkv, d_attn_sink + q, + kv, + do, + attn_sink, + topk_idxs, + lse, + delta, + dkv, + d_attn_sink, ) else: dq_accum = torch.zeros_like(q, dtype=torch.float32) @@ -389,7 +457,15 @@ def sparse_mqa_bwd_interface(q, kv, attn_sink, o, do, topk_idxs, lse, sm_scale=N for start in range(0, topk, block_size): chunk = topk_idxs[:, :, start : start + block_size].contiguous() dq_i = bwd_kernel( - q, kv, do, attn_sink, chunk, lse, delta, dkv, d_attn_sink + q, + kv, + do, + attn_sink, + chunk, + lse, + delta, + dkv, + d_attn_sink, ) dq_accum.add_(dq_i.float()) dq = dq_accum.to(q.dtype) diff --git a/src/art/megatron/dsv4/kernel/tilelang_sparse_mla_fwd.py b/src/art/megatron/dsv4/kernel/tilelang_sparse_mla_fwd.py index 860b27c06..aee26598e 100644 --- a/src/art/megatron/dsv4/kernel/tilelang_sparse_mla_fwd.py +++ b/src/art/megatron/dsv4/kernel/tilelang_sparse_mla_fwd.py @@ -43,9 +43,7 @@ def sparse_mqa_fwd( f"topk ({topk}) must be divisible by block_I ({block_I})" ) if sm_scale is None: - sm_scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) - else: - sm_scale = sm_scale * 1.44269504 # log2(e) + sm_scale = (1.0 / dim) ** 0.5 batch = T.dynamic("batch") seq_len = T.dynamic("seq_len") @@ -101,8 +99,6 @@ def main( m_i_prev = T.alloc_fragment([H_per_block], accum_dtype) T.fill(acc_o, 0) - T.fill(sumexp, 0) - T.fill(m_i, -(2**30)) b_i = by s_i = bx if REPLICATE_H == 1 else (bx // REPLICATE_H) @@ -110,6 +106,10 @@ def main( H0 = 0 if REPLICATE_H == 1 else (bx % REPLICATE_H) * 64 H1 = H0 + H_per_block + for h_i in T.Parallel(H_per_block): + sumexp[h_i] = 1 + m_i[h_i] = AttnSink[H0 + h_i] + T.copy(Q[b_i, s_i, H0:H1, :D], Q_shared) for i_i in T.Pipelined(NI, num_stages=num_stages): @@ -135,42 +135,40 @@ def main( transpose_B=True, policy=T.GemmWarpPolicy.FullRow, ) + T.copy(acc_s, S_shared) + for h_i, bi_i in T.Parallel(H_per_block, BI): + acc_s[h_i, bi_i] = S_shared[h_i, bi_i] * sm_scale + T.copy(acc_s, S_shared) for h_i, bi_i in T.Parallel(H_per_block, BI): acc_s[h_i, bi_i] = T.if_then_else( - mask[bi_i], acc_s[h_i, bi_i], -T.infinity(acc_s.dtype) + mask[bi_i], S_shared[h_i, bi_i], -T.infinity(acc_s.dtype) ) T.copy(m_i, m_i_prev) T.reduce_max(acc_s, m_i, dim=1, clear=False) for h_i in T.Parallel(H_per_block): m_i[h_i] = T.max(m_i[h_i], m_i_prev[h_i]) for h_i in T.Parallel(H_per_block): - alpha[h_i] = T.exp2((m_i_prev[h_i] - m_i[h_i]) * sm_scale) + alpha[h_i] = T.exp2((m_i_prev[h_i] - m_i[h_i]) * 1.44269504) for h_i, bi_i in T.Parallel(H_per_block, BI): acc_s[h_i, bi_i] = T.exp2( - acc_s[h_i, bi_i] * sm_scale - m_i[h_i] * sm_scale + (acc_s[h_i, bi_i] - m_i[h_i]) * 1.44269504 ) T.reduce_sum(acc_s, sumexp_i, dim=1) for h_i in T.Parallel(H_per_block): - sumexp[h_i] = sumexp[h_i] * alpha[h_i] + sumexp_i[h_i] + sumexp_i[h_i] = sumexp[h_i] * alpha[h_i] + sumexp_i[h_i] + alpha[h_i] = sumexp[h_i] * alpha[h_i] / sumexp_i[h_i] + sumexp[h_i] = sumexp_i[h_i] for h_i, d_i in T.Parallel(H_per_block, D): acc_o[h_i, d_i] = acc_o[h_i, d_i] * alpha[h_i] + for h_i, bi_i in T.Parallel(H_per_block, BI): + acc_s[h_i, bi_i] /= sumexp[h_i] T.copy(acc_s, S_shared) T.gemm(S_shared, KV_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) - # attn_sink: add exp(attn_sink[h] - max_scaled) to softmax denominator - # attn_sink is a pre-scaled logit (same space as scores*sm_scale), so only convert to log2 base - for h_i in T.Parallel(H_per_block): - sumexp[h_i] += T.exp2( - AttnSink[H0 + h_i] * 1.44269504 - m_i[h_i] * sm_scale - ) - - # Rescale output - for h_i, d_i in T.Parallel(H_per_block, D): - acc_o[h_i, d_i] /= sumexp[h_i] # LSE = log2(sumexp) + m_i * sm_scale (in log2 space) for h_i in T.Parallel(H_per_block): - sumexp[h_i] = T.log2(sumexp[h_i]) + m_i[h_i] * sm_scale + sumexp[h_i] = T.log2(sumexp[h_i]) + m_i[h_i] * 1.44269504 T.copy(acc_o, Output[b_i, s_i, H0:H1, :]) T.copy(sumexp, Lse[b_i, s_i, H0:H1]) diff --git a/src/art/megatron/dsv4/layer.py b/src/art/megatron/dsv4/layer.py index f7a42c0a7..a19f635de 100644 --- a/src/art/megatron/dsv4/layer.py +++ b/src/art/megatron/dsv4/layer.py @@ -260,6 +260,14 @@ def __init__(self, *args: Any, **kwargs: Any) -> None: self.hc_ffn_scale, ): setattr(param, "_keep_fp32", True) + if self.config.perform_initialization: + assert self.config.init_method is not None + self.config.init_method(self.hc_attn_fn) + self.config.init_method(self.hc_ffn_fn) + for param in (self.hc_attn_base, self.hc_ffn_base): + torch.nn.init.zeros_(param) + for param in (self.hc_attn_scale, self.hc_ffn_scale): + torch.nn.init.ones_(param) self.hc_util = DeepSeekV4HyperConnectionUtil(self.config) def forward( diff --git a/src/art/megatron/expert_parallel.py b/src/art/megatron/expert_parallel.py new file mode 100644 index 000000000..0fcc68748 --- /dev/null +++ b/src/art/megatron/expert_parallel.py @@ -0,0 +1,222 @@ +from __future__ import annotations + +import copy +import functools +import math +import os +from typing import Any, cast + +from megatron.bridge.models.conversion.param_mapping import AutoMapping +from megatron.core.transformer.moe.router import TopKRouter +from pydantic import BaseModel, ConfigDict, Field, model_validator +import torch + + +class ExpertParallelLayout(BaseModel): + model_config = ConfigDict(frozen=True) + + logical_experts: int = Field(gt=0) + ep_size: int = Field(gt=0) + physical_to_logical: tuple[int | None, ...] + + @model_validator(mode="after") + def _validate_layout(self) -> ExpertParallelLayout: + if len(self.physical_to_logical) % self.ep_size: + raise ValueError("physical expert slots must divide evenly across EP ranks") + logical = tuple( + expert for expert in self.physical_to_logical if expert is not None + ) + if logical != tuple(range(self.logical_experts)): + raise ValueError( + "physical expert slots must contain every logical expert once" + ) + for ep_rank in range(self.ep_size): + local = self.local_logical_experts(ep_rank) + real_count = sum(expert is not None for expert in local) + if any(expert is None for expert in local[:real_count]): + raise ValueError("masked expert slots must be local-rank suffixes") + return self + + @classmethod + def build( + cls, + logical_experts: int, + ep_size: int, + *, + slots_per_rank_multiple: int = 1, + ) -> ExpertParallelLayout: + if slots_per_rank_multiple <= 0: + raise ValueError("slots_per_rank_multiple must be positive") + logical_slots_per_rank = math.ceil(logical_experts / ep_size) + slots_per_rank = ( + math.ceil(logical_slots_per_rank / slots_per_rank_multiple) + * slots_per_rank_multiple + ) + short_rank_count = logical_slots_per_rank * ep_size - logical_experts + short_ranks = ( + { + math.floor((index + 0.5) * ep_size / short_rank_count) + for index in range(short_rank_count) + } + if short_rank_count + else set() + ) + next_expert = 0 + physical_to_logical: list[int | None] = [] + for ep_rank in range(ep_size): + local_count = logical_slots_per_rank - (ep_rank in short_ranks) + physical_to_logical.extend(range(next_expert, next_expert + local_count)) + physical_to_logical.extend([None] * (slots_per_rank - local_count)) + next_expert += local_count + return cls( + logical_experts=logical_experts, + ep_size=ep_size, + physical_to_logical=tuple(physical_to_logical), + ) + + @property + def physical_experts(self) -> int: + return len(self.physical_to_logical) + + @property + def slots_per_rank(self) -> int: + return self.physical_experts // self.ep_size + + @property + def logical_to_physical(self) -> tuple[int, ...]: + result = [0] * self.logical_experts + for physical, logical in enumerate(self.physical_to_logical): + if logical is not None: + result[logical] = physical + return tuple(result) + + def local_logical_experts(self, ep_rank: int) -> tuple[int | None, ...]: + if not 0 <= ep_rank < self.ep_size: + raise ValueError(f"invalid EP rank {ep_rank} for EP={self.ep_size}") + start = ep_rank * self.slots_per_rank + return self.physical_to_logical[start : start + self.slots_per_rank] + + def logical_expert(self, physical_expert: int) -> int | None: + if not 0 <= physical_expert < self.physical_experts: + raise ValueError( + f"invalid physical expert {physical_expert}; " + f"expected [0, {self.physical_experts})" + ) + return self.physical_to_logical[physical_expert] + + +def configure_expert_parallel_layout(config: Any) -> ExpertParallelLayout | None: + logical_experts = int(getattr(config, "num_moe_experts", 0) or 0) + ep_size = int(getattr(config, "expert_model_parallel_size", 1) or 1) + if logical_experts == 0: + return None + raw_ranks_per_domain = os.environ.get("NUM_OF_HYBRID_EP_RANKS_PER_NVLINK_DOMAIN") + ranks_per_domain = int(raw_ranks_per_domain) if raw_ranks_per_domain else None + if ranks_per_domain is not None and ranks_per_domain <= 0: + raise ValueError("HybridEP ranks per NVLink domain must be positive") + layout = ExpertParallelLayout.build( + logical_experts, + ep_size, + slots_per_rank_multiple=( + 1 if ranks_per_domain is None else 4 // math.gcd(4, ranks_per_domain) + ), + ) + if layout.physical_experts == logical_experts: + return None + config.art_expert_parallel_layout = layout + return layout + + +def activate_expert_parallel_layout(config: Any) -> ExpertParallelLayout | None: + layout = get_expert_parallel_layout(config) + if layout is not None: + config.num_moe_experts = layout.physical_experts + return layout + + +def get_expert_parallel_layout(config: Any) -> ExpertParallelLayout | None: + layout = getattr(config, "art_expert_parallel_layout", None) + if layout is None: + return None + if not isinstance(layout, ExpertParallelLayout): + raise TypeError(f"invalid ART expert parallel layout: {type(layout).__name__}") + return layout + + +class _LogicalRouterMixin: + def __init__( + self, + config: Any, + pg_collection: Any = None, + is_mtp_layer: bool = False, + ) -> None: + layout = get_expert_parallel_layout(config) + if layout is None: + raise RuntimeError("logical router requires a non-uniform expert layout") + logical_config = copy.copy(config) + logical_config.num_moe_experts = layout.logical_experts + parent = cast(Any, super()) + parent.__init__( + config=logical_config, + pg_collection=pg_collection, + is_mtp_layer=is_mtp_layer, + ) + physical_to_logical = [ + layout.logical_experts if expert is None else expert + for expert in layout.physical_to_logical + ] + cast(Any, self).register_buffer( + "_physical_to_logical", + torch.tensor(physical_to_logical, dtype=torch.int64), + persistent=False, + ) + + def forward( + self, + input: torch.Tensor, + padding_mask: torch.Tensor | None = None, + ) -> tuple[torch.Tensor, torch.Tensor]: + probabilities, routing_map = cast(Any, super()).forward(input, padding_mask) + physical_to_logical = cast(torch.Tensor, getattr(self, "_physical_to_logical")) + return ( + _expand_logical_experts(probabilities, physical_to_logical), + _expand_logical_experts(routing_map, physical_to_logical), + ) + + +@functools.cache +def logical_router_type(router_type: type) -> type: + if issubclass(router_type, _LogicalRouterMixin): + return router_type + logical_type = type( + f"ArtLogical{router_type.__name__}", + (_LogicalRouterMixin, router_type), + {"__module__": __name__}, + ) + AutoMapping.register_module_type(logical_type.__name__, "replicated") + return logical_type + + +LogicalTopKRouter = logical_router_type(TopKRouter) + + +def _expand_logical_experts( + tensor: torch.Tensor, physical_to_logical: torch.Tensor +) -> torch.Tensor: + tensor = torch.cat( + (tensor, tensor.new_zeros(*tensor.shape[:-1], 1)), + dim=-1, + ) + return tensor.index_select(-1, physical_to_logical) + + +def patch_moe_routers(block_spec: Any) -> int: + patched = 0 + for layer_spec in getattr(block_spec, "layer_specs", ()) or (): + layer_submodules = getattr(layer_spec, "submodules", None) + mlp_spec = getattr(layer_submodules, "mlp", None) + moe_submodules = getattr(mlp_spec, "submodules", None) + if moe_submodules is not None and hasattr(moe_submodules, "router"): + moe_submodules.router = logical_router_type(moe_submodules.router) + patched += 1 + return patched diff --git a/src/art/megatron/flex_attn/attention.py b/src/art/megatron/flex_attn/attention.py index b284813f6..ab93a4bac 100644 --- a/src/art/megatron/flex_attn/attention.py +++ b/src/art/megatron/flex_attn/attention.py @@ -61,12 +61,14 @@ def forward( backend = flex_backend_for_head_dims( head_dim=int(q.shape[-1]), head_dim_v=int(v.shape[-1]), + device=q.device, ) result = get_dense_compiled_flex_attention( backend=backend, head_dim=int(q.shape[-1]), head_dim_v=int(v.shape[-1]), triton_num_stages_2_head_dims=self.triton_num_stages_2_head_dims, + device=q.device, )( q, k, diff --git a/src/art/megatron/flex_attn/compiled.py b/src/art/megatron/flex_attn/compiled.py index 00b220d96..60fc2908f 100644 --- a/src/art/megatron/flex_attn/compiled.py +++ b/src/art/megatron/flex_attn/compiled.py @@ -27,9 +27,18 @@ SparseBlockSize: TypeAlias = int | tuple[int, int] -def flex_backend_for_head_dims(*, head_dim: int, head_dim_v: int) -> FlexBackend: +def flex_backend_for_head_dims( + *, + head_dim: int, + head_dim_v: int, + device: torch.device | None = None, +) -> FlexBackend: if _FORCED_FLEX_BACKEND != "FLASH": return "TRITON" + if device is not None and device.type == "cuda": + major, _minor = torch.cuda.get_device_capability(device) + if major in {10, 11}: + return "TRITON" if int(head_dim) > 256 or int(head_dim_v) > 256: return "TRITON" return "FLASH" @@ -51,6 +60,10 @@ def normalize_flex_lse( FlexKernelOptions, {"BACKEND": "TRITON", "num_stages": 2}, ) +_BLACKWELL_WIDE_HEAD_FLEX_KERNEL_OPTIONS = cast( + FlexKernelOptions, + {"BACKEND": "TRITON", "num_stages": 2, "BLOCK_M": 32}, +) _FORCED_FLEX_KERNEL_OPTIONS = cast( FlexKernelOptions, {"BACKEND": _FORCED_FLEX_BACKEND}, @@ -72,7 +85,12 @@ def flash_sparse_block_size_for_head_dim( head_dim_v: int, device: torch.device, ) -> tuple[int, int]: - if flex_backend_for_head_dims(head_dim=head_dim, head_dim_v=head_dim_v) != "FLASH": + if ( + flex_backend_for_head_dims( + head_dim=head_dim, head_dim_v=head_dim_v, device=device + ) + != "FLASH" + ): return (128, 128) if device.type != "cuda": return (128, 128) @@ -240,13 +258,41 @@ def _needs_triton_num_stages_2( ) +def _needs_blackwell_wide_head_tile( + *, + backend: FlexBackend, + head_dim: int, + head_dim_v: int, + triton_num_stages_2_head_dims: tuple[int, ...], + device: torch.device | None, +) -> bool: + if device is None or device.type != "cuda": + return False + major, _minor = torch.cuda.get_device_capability(device) + return major in {10, 11} and _needs_triton_num_stages_2( + backend=backend, + head_dim=head_dim, + head_dim_v=head_dim_v, + triton_num_stages_2_head_dims=triton_num_stages_2_head_dims, + ) + + def get_dense_compiled_flex_attention( *, backend: FlexBackend, head_dim: int, head_dim_v: int, triton_num_stages_2_head_dims: tuple[int, ...] = (), + device: torch.device | None = None, ) -> Any: + if _needs_blackwell_wide_head_tile( + backend=backend, + head_dim=head_dim, + head_dim_v=head_dim_v, + triton_num_stages_2_head_dims=triton_num_stages_2_head_dims, + device=device, + ): + return blackwell_wide_head_dense_compiled_flex_attention if _needs_triton_num_stages_2( backend=backend, head_dim=head_dim, @@ -268,8 +314,17 @@ def get_sparse_compiled_flex_attention( head_dim: int, head_dim_v: int, triton_num_stages_2_head_dims: tuple[int, ...] = (), + device: torch.device | None = None, ) -> Any: del family_key + if _needs_blackwell_wide_head_tile( + backend=backend, + head_dim=head_dim, + head_dim_v=head_dim_v, + triton_num_stages_2_head_dims=triton_num_stages_2_head_dims, + device=device, + ): + return blackwell_wide_head_sparse_compiled_flex_attention if _needs_triton_num_stages_2( backend=backend, head_dim=head_dim, @@ -296,6 +351,9 @@ def get_sparse_compiled_flex_attention( triton_num_stages_2_dense_compiled_flex_attention = torch.compile( _flex_attention_with_options(_TRITON_NUM_STAGES_2_FLEX_KERNEL_OPTIONS), ) +blackwell_wide_head_dense_compiled_flex_attention = torch.compile( + _flex_attention_with_options(_BLACKWELL_WIDE_HEAD_FLEX_KERNEL_OPTIONS), +) sparse_compiled_flex_attention = torch.compile( _sparse_flex_attention_with_options(_FORCED_FLEX_KERNEL_OPTIONS), @@ -309,3 +367,6 @@ def get_sparse_compiled_flex_attention( triton_num_stages_2_sparse_compiled_flex_attention = torch.compile( _sparse_flex_attention_with_options(_TRITON_NUM_STAGES_2_FLEX_KERNEL_OPTIONS), ) +blackwell_wide_head_sparse_compiled_flex_attention = torch.compile( + _sparse_flex_attention_with_options(_BLACKWELL_WIDE_HEAD_FLEX_KERNEL_OPTIONS), +) diff --git a/src/art/megatron/gdn/__init__.py b/src/art/megatron/gdn/__init__.py index a62769edb..a6fd2eb30 100644 --- a/src/art/megatron/gdn/__init__.py +++ b/src/art/megatron/gdn/__init__.py @@ -2,12 +2,15 @@ from .fla_cp import chunk_gated_delta_rule_native_cp from .gdn_prefix_tree import ( + GdnGlobalExecutionDecision, GdnPackedExecutionSpec, GdnPlannerConfig, GdnRankExecutionPlan, GdnSegmentBucketPlan, GdnSegmentSpec, + build_gdn_global_execution_decision, build_gdn_rank_execution_plan, + materialize_gdn_rank_execution_plan, move_gdn_rank_execution_plan_to_device, parse_gdn_prefix_tree_segments, ) @@ -16,13 +19,16 @@ __all__ = [ "chunk_gated_delta_rule_native_cp", + "GdnGlobalExecutionDecision", "GdnPackedExecutionSpec", "GdnPlannerConfig", "GdnRankExecutionPlan", "GdnSegmentSpec", "GdnSegmentBucketPlan", + "build_gdn_global_execution_decision", "build_gdn_rank_execution_plan", "exchange_rank_tensor_all_to_all", + "materialize_gdn_rank_execution_plan", "move_gdn_rank_execution_plan_to_device", "parse_gdn_prefix_tree_segments", "run_gdn_layer", diff --git a/src/art/megatron/gdn/gdn_prefix_tree.py b/src/art/megatron/gdn/gdn_prefix_tree.py index eeb7a1a1c..016a0f0f2 100644 --- a/src/art/megatron/gdn/gdn_prefix_tree.py +++ b/src/art/megatron/gdn/gdn_prefix_tree.py @@ -4,6 +4,7 @@ from dataclasses import dataclass, replace from typing import Any, Literal, NamedTuple, cast +from pydantic import BaseModel, ConfigDict import torch from art.megatron.context_parallel.layout_index import TokenLayoutIndex @@ -303,6 +304,25 @@ def gdn_token_indices(self) -> tuple[int, ...]: return _tokens_from_rank_ranges(self.gdn_token_ranges) +class GdnGlobalExecutionDecision(BaseModel): + """All-rank GDN decisions without rank-local planner tensors.""" + + model_config = ConfigDict(frozen=True, arbitrary_types_allowed=True) + + cp_size: int + source_layout: TokenLayoutIndex + depth_count: int + gdn_token_counts_by_rank: tuple[int, ...] + owner_by_node: tuple[int, ...] + chained_nodes: tuple[bool, ...] + tree_has_children: tuple[bool, ...] + gdn_ranges_by_rank_by_position: tuple[tuple[tuple[int, int, int], ...], ...] + gdn_ranges_by_rank_by_source: tuple[tuple[tuple[int, int, int], ...], ...] + segments_by_rank_depth: tuple[tuple[tuple[GdnSegmentSpec, ...], ...], ...] + chain_segments_by_depth: tuple[tuple[GdnSegmentSpec, ...], ...] + cross_rank_token_count: int + + @dataclass(frozen=True) class _AttentionLayoutIndex: """Counting index for CP attention token ownership.""" @@ -366,41 +386,34 @@ def build_gdn_rank_execution_plan( fork buckets for short work where CP collectives would be inefficient. """ - planner_config = planner_config or GdnPlannerConfig() - target_device = torch.device(device) - if target_device.type != "cpu": - cpu_plan = build_gdn_rank_execution_plan( - spec, - device="cpu", - cp_rank=cp_rank, - cp_size=cp_size, - attention_token_layout_index=attention_token_layout_index, - planner_config=planner_config, - ) - return move_gdn_rank_execution_plan_to_device(cpu_plan, target_device) - return _build_tree_rank_execution_plan( + resolved_config = planner_config or GdnPlannerConfig() + decision = build_gdn_global_execution_decision( spec, - device=device, - cp_rank=cp_rank, cp_size=cp_size, attention_token_layout_index=attention_token_layout_index, - planner_config=planner_config, + planner_config=resolved_config, + ) + return materialize_gdn_rank_execution_plan( + spec, + decision, + device=device, + cp_rank=cp_rank, + planner_config=resolved_config, ) -def _build_tree_rank_execution_plan( +def build_gdn_global_execution_decision( spec: GdnPackedExecutionSpec, *, - device: torch.device | str, - cp_rank: int, - cp_size: int, - attention_token_layout_index: TokenLayoutIndex | None, - planner_config: GdnPlannerConfig, -) -> GdnRankExecutionPlan: + cp_size: int = 1, + attention_token_layout_index: TokenLayoutIndex | None = None, + planner_config: GdnPlannerConfig | None = None, +) -> GdnGlobalExecutionDecision: + """Select one deterministic all-rank GDN assignment without tensors.""" + + planner_config = planner_config or GdnPlannerConfig() if cp_size < 1: raise ValueError(f"cp_size must be >= 1, got {cp_size}") - if cp_rank < 0 or cp_rank >= cp_size: - raise ValueError(f"cp_rank must be in [0, {cp_size}), got {cp_rank}") if not spec.tree_segments: raise ValueError("tree GDN planning requires tree segments") if len(spec.tree_parent_indices) != len(spec.tree_segments): @@ -408,11 +421,6 @@ def _build_tree_rank_execution_plan( if len(spec.tree_depths) != len(spec.tree_segments): raise ValueError("tree depth metadata length must match tree segments") - from art.megatron.gdn.layout import ( - _reverse_exchange_plan, - build_local_rank_cp_exchange_plan_from_dest_ranges, - ) - source_layout = _attention_source_layout( spec, cp_size=cp_size, @@ -549,27 +557,85 @@ def assign_tree(node_index: int) -> None: tuple(sorted(ranges)) for ranges in gdn_ranges_by_rank ) + return GdnGlobalExecutionDecision( + cp_size=cp_size, + source_layout=source_layout, + depth_count=depth_count, + gdn_token_counts_by_rank=tuple(rank_loads), + owner_by_node=tuple(owner_by_node), + chained_nodes=tuple(chained_nodes), + tree_has_children=tuple(tree_has_children), + gdn_ranges_by_rank_by_position=gdn_ranges_by_rank_by_position, + gdn_ranges_by_rank_by_source=gdn_ranges_by_rank_by_source, + segments_by_rank_depth=tuple( + tuple(tuple(segments) for segments in rank_depths) + for rank_depths in segments_by_rank_depth + ), + chain_segments_by_depth=tuple( + tuple(segments) for segments in chain_segments_by_depth + ), + cross_rank_token_count=cross_rank_token_count, + ) + + +def materialize_gdn_rank_execution_plan( + spec: GdnPackedExecutionSpec, + decision: GdnGlobalExecutionDecision, + *, + device: torch.device | str, + cp_rank: int = 0, + planner_config: GdnPlannerConfig | None = None, +) -> GdnRankExecutionPlan: + """Build only one rank's tensor metadata from a global decision.""" + + cp_size = int(decision.cp_size) + if cp_rank < 0 or cp_rank >= cp_size: + raise ValueError(f"cp_rank must be in [0, {cp_size}), got {cp_rank}") + target_device = torch.device(device) + if target_device.type != "cpu": + cpu_plan = materialize_gdn_rank_execution_plan( + spec, + decision, + device="cpu", + cp_rank=cp_rank, + planner_config=planner_config, + ) + return move_gdn_rank_execution_plan_to_device(cpu_plan, target_device) + + from art.megatron.gdn.layout import ( + _reverse_exchange_plan, + build_local_rank_cp_exchange_plan_from_dest_ranges, + ) + + planner_config = planner_config or GdnPlannerConfig() + device = target_device + source_layout = decision.source_layout + depth_count = int(decision.depth_count) + rank_loads = decision.gdn_token_counts_by_rank + gdn_ranges_by_rank_by_position = decision.gdn_ranges_by_rank_by_position + gdn_ranges_by_rank_by_source = decision.gdn_ranges_by_rank_by_source + attention_to_gdn = build_local_rank_cp_exchange_plan_from_dest_ranges( source_layout=source_layout, device=device, local_rank=cp_rank, dest_ranges_by_rank=gdn_ranges_by_rank_by_position, - cross_rank_token_count=cross_rank_token_count, + cross_rank_token_count=decision.cross_rank_token_count, ) local_token_ranges = gdn_ranges_by_rank_by_source[cp_rank] if cp_size == 1: tree_segment_buckets_by_depth = _build_chunk_aligned_cp1_tree_buckets( spec, - tuple(tree_has_children), + decision.tree_has_children, device=device, planner_config=planner_config, ) else: tree_segment_buckets_by_depth = tuple( _build_tree_bucket_plans( - tuple(segments_by_rank_depth[cp_rank][depth]), + decision.segments_by_rank_depth[cp_rank][depth], spec.tree_parent_indices, - tuple(tree_has_children), + decision.tree_has_children, local_token_ranges=local_token_ranges, sequence_length=spec.sequence_length, device=device, @@ -579,9 +645,9 @@ def assign_tree(node_index: int) -> None: tree_chain_buckets_by_depth = ( tuple( _build_tree_bucket_plans( - tuple(chain_segments_by_depth[depth]), + decision.chain_segments_by_depth[depth], spec.tree_parent_indices, - tuple(tree_has_children), + decision.tree_has_children, local_token_ranges=local_token_ranges, sequence_length=spec.sequence_length, device=device, @@ -597,8 +663,8 @@ def assign_tree(node_index: int) -> None: ) tree_state_exchanges_by_depth = _build_tree_state_exchanges_by_depth( spec, - owner_by_node=tuple(owner_by_node), - chained_nodes=tuple(chained_nodes), + owner_by_node=decision.owner_by_node, + chained_nodes=decision.chained_nodes, cp_rank=cp_rank, cp_size=cp_size, depth_count=depth_count, diff --git a/src/art/megatron/glm52/__init__.py b/src/art/megatron/glm52/__init__.py new file mode 100644 index 000000000..2d9f1b8d7 --- /dev/null +++ b/src/art/megatron/glm52/__init__.py @@ -0,0 +1 @@ +"""GLM-5.2 model and prefix-tree sparse-attention support.""" diff --git a/src/art/megatron/glm52/attention.py b/src/art/megatron/glm52/attention.py new file mode 100644 index 000000000..713d46bfb --- /dev/null +++ b/src/art/megatron/glm52/attention.py @@ -0,0 +1,428 @@ +from __future__ import annotations + +from copy import deepcopy +from functools import partial +from typing import Any + +from megatron.core.process_groups_config import ProcessGroupCollection +from megatron.core.tensor_parallel.mappings import ( + copy_to_tensor_model_parallel_region, + gather_from_sequence_parallel_region, +) +from megatron.core.transformer.attention import Attention +from megatron.core.transformer.enums import AttnMaskType +from megatron.core.transformer.spec_utils import build_module +from megatron.core.transformer.transformer_config import MLATransformerConfig +from megatron.core.typed_torch import apply_module, not_none +from megatron.core.utils import get_pg_size +import torch + +from art.megatron.glm52.cp_attention import context_parallel_sparse_mla +from art.megatron.glm52.indexer import ( + Glm52RoutedTopk, + context_parallel_tree_topk, + indexer_rope, + streaming_tree_topk, +) +from art.megatron.glm52.sparse_mla import sparse_mla +from art.megatron.glm52.state import Glm52PrefixTreeState, require_glm52_state + + +def _tensor(value: Any) -> torch.Tensor: + return value[0] if isinstance(value, tuple) else value + + +def _latent_rms_norm(x: torch.Tensor, weight: torch.Tensor) -> torch.Tensor: + x_float = x.float() + normalized = x_float * torch.rsqrt(x_float.square().mean(-1, keepdim=True) + 1e-6) + return weight * normalized.to(x.dtype) + + +def _half_split_rope( + x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor +) -> torch.Tensor: + first, second = x.chunk(2, dim=-1) + return torch.cat((first * cos - second * sin, second * cos + first * sin), dim=-1) + + +def _interleaved_rope( + x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor +) -> torch.Tensor: + even, odd = x[..., 0::2], x[..., 1::2] + return torch.cat((even * cos - odd * sin, odd * cos + even * sin), dim=-1) + + +class Glm52Indexer(torch.nn.Module): + def __init__( + self, + config: MLATransformerConfig, + *, + linear_builder: Any, + norm_builder: Any, + tp_group: Any, + ) -> None: + super().__init__() + self.config = config + self.tp_group = tp_group + self.heads = int(not_none(config.dsa_indexer_n_heads)) + self.head_dim = int(not_none(config.dsa_indexer_head_dim)) + self.topk = int(not_none(config.dsa_indexer_topk)) + linear_kwargs = { + "config": config, + "init_method": config.init_method, + "bias": False, + "skip_bias_add": False, + "skip_weight_param_allocation": False, + "parallel_mode": "duplicated", + } + self.linear_wq_b = build_module( + linear_builder, + config.q_lora_rank, + self.heads * self.head_dim, + tp_comm_buffer_name="glm52_index_q", + **linear_kwargs, + ) + self.linear_wk = build_module( + linear_builder, + config.hidden_size, + self.head_dim, + tp_comm_buffer_name="glm52_index_k", + **linear_kwargs, + ) + norm_config = deepcopy(config) + norm_config.normalization = "LayerNorm" + self.k_norm = build_module( + norm_builder, + config=norm_config, + hidden_size=self.head_dim, + eps=1e-6, + ) + self.linear_weights_proj = build_module( + linear_builder, + config.hidden_size, + self.heads, + tp_comm_buffer_name="glm52_index_weights", + **linear_kwargs, + ) + self.requires_grad_(False) + + def _gather_sequence(self, tensor: torch.Tensor) -> torch.Tensor: + if not self.config.sequence_parallel or get_pg_size(self.tp_group) == 1: + return tensor + return gather_from_sequence_parallel_region( + tensor, + tensor_parallel_output_grad=False, + group=self.tp_group, + ) + + @torch.no_grad() + def forward( + self, + hidden_states: torch.Tensor, + q_residual: torch.Tensor, + state: Glm52PrefixTreeState, + ) -> torch.Tensor | Glm52RoutedTopk: + q = _tensor(self.linear_wq_b(q_residual)).view( + q_residual.shape[0], q_residual.shape[1], self.heads, self.head_dim + ) + k = _tensor(self.linear_wk(hidden_states)) + k = _tensor(apply_module(self.k_norm)(k)).to(q.dtype) + weights = _tensor(self.linear_weights_proj(hidden_states)).float() + q = self._gather_sequence(q) + k = self._gather_sequence(k) + weights = self._gather_sequence(weights) + expected = (q.shape[1], q.shape[0]) + if state.position_ids.shape != expected: + raise RuntimeError( + "GLM-5.2 indexer state/token shape mismatch: " + f"state={tuple(state.position_ids.shape)} tokens={expected}." + ) + q = q.permute(1, 0, 2, 3).contiguous() + k = k.permute(1, 0, 2).contiguous() + q, k = indexer_rope(q, k, state.rope_cos, state.rope_sin) + weights = weights.permute(1, 0, 2).contiguous() + weights *= (self.heads * self.head_dim) ** -0.5 + if state.context_parallel_state is not None: + return context_parallel_tree_topk(q, k, weights, state, topk=self.topk) + return streaming_tree_topk( + q.contiguous(), + k.contiguous(), + weights, + state.indexer_rows, + topk=self.topk, + ) + + +class Glm52SparseCore(torch.nn.Module): + def __init__( + self, + *, + config: MLATransformerConfig, + layer_number: int, + pg_collection: ProcessGroupCollection, + linear_builder: Any, + norm_builder: Any, + **_: Any, + ) -> None: + super().__init__() + pattern = tuple(getattr(config, "glm52_indexer_types")) + layer_index = int(layer_number) - 1 + if not 0 <= layer_index < len(pattern): + raise ValueError( + f"GLM-5.2 layer index {layer_index} is outside its index pattern." + ) + full_layers = [ + index for index in range(layer_index + 1) if pattern[index] == "full" + ] + if not full_layers: + raise ValueError( + f"GLM-5.2 shared index layer {layer_index} has no preceding full layer." + ) + self.full_layer_index = full_layers[-1] + self.indexer = ( + Glm52Indexer( + config, + linear_builder=linear_builder, + norm_builder=norm_builder, + tp_group=pg_collection.tp, + ) + if pattern[layer_index] == "full" + else None + ) + + def topk( + self, + hidden_states: torch.Tensor, + q_residual: torch.Tensor, + state: Glm52PrefixTreeState, + ) -> torch.Tensor | Glm52RoutedTopk: + if self.indexer is not None: + indices = self.indexer(hidden_states.detach(), q_residual.detach(), state) + state.topk_by_full_layer[self.full_layer_index] = indices + return indices + indices = state.topk_by_full_layer.get(self.full_layer_index) + if indices is None: + raise RuntimeError( + "GLM-5.2 shared index layer ran before its full index layer " + f"{self.full_layer_index}." + ) + return indices + + +def glm52_core_builder(linear_builder: Any, norm_builder: Any): + return partial( + Glm52SparseCore, + linear_builder=linear_builder, + norm_builder=norm_builder, + ) + + +class Glm52SelfAttention(Attention): + def __init__( + self, + config: MLATransformerConfig, + submodules: Any, + layer_number: int, + attn_mask_type: AttnMaskType, + attention_type: str = "self", + cp_comm_type: str | None = None, + pg_collection: ProcessGroupCollection | None = None, + **kwargs: Any, + ) -> None: + del kwargs + super().__init__( + config, + submodules, + layer_number, + attn_mask_type, + attention_type, + cp_comm_type=cp_comm_type, + pg_collection=pg_collection, + ) + self.config: MLATransformerConfig + q_down_kwargs = { + "parallel_mode": "duplicated", + "skip_weight_param_allocation": False, + } + self.linear_q_down_proj = build_module( + submodules.linear_q_down_proj, + config.hidden_size, + config.q_lora_rank, + config=config, + init_method=config.init_method, + bias=False, + skip_bias_add=False, + tp_comm_buffer_name="q_down_proj", + **q_down_kwargs, + ) + self.linear_q_up_proj = build_module( + submodules.linear_q_up_proj, + config.q_lora_rank, + config.num_attention_heads + * (config.qk_head_dim + config.qk_pos_emb_head_dim), + config=config, + init_method=config.init_method, + gather_output=False, + bias=False, + skip_bias_add=False, + is_expert=False, + tp_comm_buffer_name="q_up_proj", + tp_group=self.tp_group, + ) + self.linear_kv_down_proj = build_module( + submodules.linear_kv_down_proj, + config.hidden_size, + config.kv_lora_rank + config.qk_pos_emb_head_dim, + config=config, + init_method=config.init_method, + bias=False, + skip_bias_add=False, + tp_comm_buffer_name="kv_down_proj", + parallel_mode="duplicated", + skip_weight_param_allocation=False, + ) + self.linear_kv_up_proj = build_module( + submodules.linear_kv_up_proj, + config.kv_lora_rank, + config.num_attention_heads * (config.qk_head_dim + config.v_head_dim), + config=config, + init_method=config.init_method, + gather_output=False, + bias=False, + skip_bias_add=False, + is_expert=False, + tp_comm_buffer_name="kv_up_proj", + tp_group=self.tp_group, + ) + self.q_layernorm = build_module( + submodules.q_layernorm, + config=config, + hidden_size=config.q_lora_rank, + eps=1e-6, + ) + self.kv_layernorm = build_module( + submodules.kv_layernorm, + config=config, + hidden_size=config.kv_lora_rank, + eps=1e-6, + ) + self.softmax_scale = (config.qk_head_dim + config.qk_pos_emb_head_dim) ** -0.5 + self.q_a_lora: Any = None + self.q_b_lora: Any = None + self.kv_a_lora: Any = None + + def get_query_key_value_tensors(self, *args: Any, **kwargs: Any): + del args, kwargs + raise RuntimeError("GLM-5.2 uses its absorbed sparse-MLA forward path.") + + def _gather_replicated_sequence(self, tensor: torch.Tensor) -> torch.Tensor: + if not self.config.sequence_parallel or get_pg_size(self.tp_group) == 1: + return tensor + return gather_from_sequence_parallel_region( + tensor, + tensor_parallel_output_grad=False, + group=self.tp_group, + ) + + def _column_lora_input(self, tensor: torch.Tensor) -> torch.Tensor: + if get_pg_size(self.tp_group) == 1: + return tensor + if self.config.sequence_parallel: + return gather_from_sequence_parallel_region(tensor, group=self.tp_group) + return copy_to_tensor_model_parallel_region(tensor, group=self.tp_group) + + @torch.compiler.disable + def forward( # ty: ignore[invalid-method-override] + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor | None = None, + attention_bias: Any = None, + **_: Any, + ) -> tuple[torch.Tensor, None]: + del attention_mask + state = require_glm52_state(attention_bias) + q_compressed = _tensor(self.linear_q_down_proj(hidden_states)) + if self.q_a_lora is not None: + q_compressed = q_compressed + self.q_a_lora(hidden_states) + q_residual = _latent_rms_norm(q_compressed, self.q_layernorm.weight) + q = _tensor(self.linear_q_up_proj(q_residual)) + if self.q_b_lora is not None: + q = q + self.q_b_lora(self._column_lora_input(q_residual)) + kv_combined = _tensor(self.linear_kv_down_proj(hidden_states)) + if self.kv_a_lora is not None: + kv_combined = kv_combined + self.kv_a_lora(hidden_states) + kv_compressed, k_rope = kv_combined.split( + (self.config.kv_lora_rank, self.config.qk_pos_emb_head_dim), dim=-1 + ) + kv_compressed = _latent_rms_norm(kv_compressed, self.kv_layernorm.weight) + kv_compressed = self._gather_replicated_sequence(kv_compressed) + k_rope = self._gather_replicated_sequence(k_rope) + seq_len, batch = kv_compressed.shape[:2] + heads = self.num_attention_heads_per_partition + q = q.view( + seq_len, + batch, + heads, + self.config.qk_head_dim + self.config.qk_pos_emb_head_dim, + ) + q_nope, q_rope = q.split( + (self.config.qk_head_dim, self.config.qk_pos_emb_head_dim), dim=-1 + ) + if state.rope_cos.shape[:2] != (batch, seq_len): + raise RuntimeError( + "GLM-5.2 RoPE state does not match the attention tokens: " + f"layer={self.layer_number}, rope={tuple(state.rope_cos.shape[:2])}, " + f"tokens={(batch, seq_len)}, hidden={tuple(hidden_states.shape)}" + ) + cos = state.rope_cos.permute(1, 0, 2).unsqueeze(2).to(q.dtype) + sin = state.rope_sin.permute(1, 0, 2).unsqueeze(2).to(q.dtype) + q_rope = _interleaved_rope(q_rope, cos, sin) + k_rope = _interleaved_rope(k_rope.unsqueeze(2), cos, sin).squeeze(2) + + kv_weight = self.linear_kv_up_proj.weight.view( + heads, + self.config.qk_head_dim + self.config.v_head_dim, + self.config.kv_lora_rank, + ) + key_weight, value_weight = kv_weight.split( + (self.config.qk_head_dim, self.config.v_head_dim), dim=1 + ) + q_absorbed = torch.einsum("sbhd,hdm->sbhm", q_nope, key_weight) + q_absorbed = torch.cat((q_absorbed, q_rope), dim=-1) + kv_absorbed = torch.cat((kv_compressed, k_rope), dim=-1) + core = self.core_attention + if not isinstance(core, Glm52SparseCore): + raise TypeError(f"Expected Glm52SparseCore, got {type(core).__name__}.") + topk = core.topk(hidden_states, q_residual, state) + q_absorbed = q_absorbed.permute(1, 0, 2, 3).contiguous() + kv_absorbed = kv_absorbed.permute(1, 0, 2).contiguous() + latent_out = ( + context_parallel_sparse_mla( + q_absorbed, + kv_absorbed, + topk, + state, + scale=self.softmax_scale, + tp_group=self.tp_group if get_pg_size(self.tp_group) > 1 else None, + ) + if isinstance(topk, Glm52RoutedTopk) + else sparse_mla( + q_absorbed, + kv_absorbed, + topk, + scale=self.softmax_scale, + tp_group=self.tp_group if get_pg_size(self.tp_group) > 1 else None, + ) + ) + value_out = torch.einsum("bshm,hdm->bshd", latent_out, value_weight) + value_out = value_out.permute(1, 0, 2, 3).reshape( + seq_len, batch, heads * self.config.v_head_dim + ) + output, _bias = self.linear_proj(value_out) + return output, None + + def backward_dw(self) -> None: + self.linear_q_down_proj.backward_dw() + self.linear_q_up_proj.backward_dw() + self.linear_kv_down_proj.backward_dw() + self.linear_proj.backward_dw() diff --git a/src/art/megatron/glm52/cp_attention.py b/src/art/megatron/glm52/cp_attention.py new file mode 100644 index 000000000..498fa53ef --- /dev/null +++ b/src/art/megatron/glm52/cp_attention.py @@ -0,0 +1,177 @@ +from __future__ import annotations + +from typing import Any, cast + +import torch + +from art.megatron.context_parallel.types import ArtContextParallelState +from art.megatron.glm52.cp_stage import ( + drain_stage_fetches, + launch_remote_stage_fetches, + launch_remote_stage_reduce, + reduce_local_stage_rows_, + stage_kv_rows, +) +from art.megatron.glm52.indexer import Glm52RoutedTopk +from art.megatron.glm52.sparse_mla import ( + reduce_tensor_parallel_dkv, + sparse_mla_backward, + sparse_mla_forward, +) +from art.megatron.glm52.state import Glm52PrefixTreeState + +_LATENT_DIM = 512 + + +def _combined_stage_kv( + kv: torch.Tensor, + cp_state: ArtContextParallelState, +) -> tuple[torch.Tensor, tuple[int, ...]]: + fetches = launch_remote_stage_fetches(kv, cp_state) + parts = tuple( + stage_kv_rows(kv, stage, cp_state, fetches) + for stage in cp_state.rank_plan.stage_plans + ) + drain_stage_fetches(fetches) + if not parts: + raise RuntimeError("GLM-5.2 CP plan has no KV stages.") + return ( + parts[0] if len(parts) == 1 else torch.cat(parts), + tuple(int(part.shape[0]) for part in parts), + ) + + +def _forward( + q: torch.Tensor, + kv: torch.Tensor, + indices: torch.Tensor, + state: Glm52PrefixTreeState, + scale: float, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + cp_state = cast(ArtContextParallelState, state.context_parallel_state) + valid = int(sum(cp_state.rank_plan.local_valid_lengths)) + kv_flat = kv[0, :valid].contiguous() + combined_kv, _ = _combined_stage_kv(kv_flat, cp_state) + combined_out, lse = sparse_mla_forward( + q[:, :valid].contiguous(), + combined_kv.unsqueeze(0), + indices[:, :valid].contiguous(), + scale=scale, + ) + if valid == q.shape[1]: + return combined_out, combined_out[0], lse[0] + output = q.new_zeros((q.shape[0], q.shape[1], q.shape[2], _LATENT_DIM)) + output[:, :valid].copy_(combined_out) + return output, combined_out[0], lse[0] + + +def _backward( + grad_output: torch.Tensor, + q: torch.Tensor, + kv: torch.Tensor, + indices: torch.Tensor, + global_out: torch.Tensor, + global_lse: torch.Tensor, + state: Glm52PrefixTreeState, + scale: float, + tp_group: Any | None, +) -> tuple[torch.Tensor, torch.Tensor]: + cp_state = cast(ArtContextParallelState, state.context_parallel_state) + valid = int(sum(cp_state.rank_plan.local_valid_lengths)) + kv_flat = kv[0, :valid].contiguous() + dkv = torch.zeros_like(kv_flat) + combined_kv, stage_sizes = _combined_stage_kv(kv_flat, cp_state) + dq, combined_dkv = sparse_mla_backward( + q[:, :valid].contiguous(), + combined_kv.unsqueeze(0), + indices[:, :valid].contiguous(), + global_out.unsqueeze(0), + global_lse.unsqueeze(0), + grad_output[:, :valid].contiguous(), + scale=scale, + ) + combined_dkv = reduce_tensor_parallel_dkv( + combined_dkv, tp_group=tp_group, dtype=kv.dtype + ) + stage_starts = [0] + for size in stage_sizes: + stage_starts.append(stage_starts[-1] + size) + reductions = [] + for stage_index in cp_state.rank_plan.backward_stage_indices: + stage_plan = cp_state.rank_plan.stage_plans[int(stage_index)] + start, end = stage_starts[int(stage_index) : int(stage_index) + 2] + dkv_stage = combined_dkv[0, start:end] + if stage_plan.is_local_stage: + reduce_local_stage_rows_(dkv, dkv_stage, stage_plan, cp_state) + else: + reductions.append( + launch_remote_stage_reduce(dkv_stage, stage_plan, cp_state, dkv) + ) + for reduction in reductions: + reduction.wait_post_process() + if valid == q.shape[1]: + return dq, dkv.unsqueeze(0) + dq_padded, dkv_padded = torch.zeros_like(q), torch.zeros_like(kv) + dq_padded[:, :valid].copy_(dq) + dkv_padded[0, :valid].copy_(dkv) + return dq_padded, dkv_padded + + +class _ContextParallelSparseMla(torch.autograd.Function): + @staticmethod + def forward( + ctx: Any, + q: torch.Tensor, + kv: torch.Tensor, + indices: torch.Tensor, + state: Glm52PrefixTreeState, + scale: float, + tp_group: Any | None, + ) -> torch.Tensor: + output, global_out, global_lse = _forward(q, kv, indices, state, scale) + ctx.save_for_backward(q, kv, indices, global_out, global_lse) + ctx.state = state + ctx.scale = float(scale) + ctx.tp_group = tp_group + return output + + @staticmethod + def backward(ctx: Any, *grad_outputs: Any): + (grad_output,) = cast(tuple[torch.Tensor], grad_outputs) + q, kv, indices, global_out, global_lse = ctx.saved_tensors + dq, dkv = _backward( + grad_output, + q, + kv, + indices, + global_out, + global_lse, + ctx.state, + ctx.scale, + ctx.tp_group, + ) + return dq, dkv, None, None, None, None + + +def context_parallel_sparse_mla( + q: torch.Tensor, + kv: torch.Tensor, + topk: Glm52RoutedTopk, + state: Glm52PrefixTreeState, + *, + scale: float, + tp_group: Any | None = None, +) -> torch.Tensor: + """Run sparse MLA once over the union of ART-planned KV stages.""" + if q.ndim != 4 or kv.ndim != 3 or q.shape[:2] != kv.shape[:2]: + raise ValueError("GLM-5.2 CP sparse MLA expects q[B,S,H,576], kv[B,S,576].") + if q.shape[0] != 1: + raise ValueError("GLM-5.2 context parallel supports one packed row.") + return _ContextParallelSparseMla.apply( + q.contiguous(), + kv.contiguous(), + topk.indices.contiguous(), + state, + float(scale), + tp_group, + ) diff --git a/src/art/megatron/glm52/cp_stage.py b/src/art/megatron/glm52/cp_stage.py new file mode 100644 index 000000000..c2b47fd9e --- /dev/null +++ b/src/art/megatron/glm52/cp_stage.py @@ -0,0 +1,111 @@ +from __future__ import annotations + +from typing import Any, cast + +import torch + +from art.megatron.context_parallel.comm import A2AVCommunicator +from art.megatron.context_parallel.range_ops import range_gather, range_reduce_sum_ +from art.megatron.context_parallel.types import ( + ArtContextParallelState, + DkvReducePlan, + StagePlan, +) + +_COMMUNICATOR = A2AVCommunicator() + + +def stage_query_rows( + tensor: torch.Tensor, + stage: StagePlan, + state: ArtContextParallelState, +) -> torch.Tensor: + ranges = stage.owner_local_q_ranges + if len(ranges) == 1 and ranges[0].start == 0 and ranges[0].end == tensor.shape[0]: + return tensor + return range_gather( + tensor, + ranges, + range_meta_cache=state.execution_cache.range_meta, + ) + + +def stage_local_kv_rows( + tensor: torch.Tensor, + stage: StagePlan, + state: ArtContextParallelState, +) -> torch.Tensor: + ranges = stage.owner_local_k_ranges + if len(ranges) == 1 and ranges[0].start == 0 and ranges[0].end == tensor.shape[0]: + return tensor + return range_gather( + tensor, + ranges, + range_meta_cache=state.execution_cache.range_meta, + ) + + +def launch_remote_stage_fetches( + tensor: torch.Tensor, + state: ArtContextParallelState, +) -> dict[int, Any]: + return { + int(stage.stage_index): _COMMUNICATOR.launch_tensor_fetch( + tensor_local=tensor, + plan=cast(Any, stage.kv_fetch_plan), + group=state.cp_group, + async_op=True, + range_meta_cache=state.execution_cache.range_meta, + ) + for stage in state.rank_plan.stage_plans + if not stage.is_local_stage + } + + +def stage_kv_rows( + tensor: torch.Tensor, + stage: StagePlan, + state: ArtContextParallelState, + fetches: dict[int, Any], +) -> torch.Tensor: + return ( + stage_local_kv_rows(tensor, stage, state) + if stage.is_local_stage + else fetches.pop(int(stage.stage_index)).wait_post_process() + ) + + +def drain_stage_fetches(fetches: dict[int, Any]) -> None: + for work in fetches.values(): + work.wait_post_process() + fetches.clear() + + +def reduce_local_stage_rows_( + target: torch.Tensor, + stage_grad: torch.Tensor, + stage: StagePlan, + state: ArtContextParallelState, +) -> None: + range_reduce_sum_( + stage_grad, + output_tensor=target, + ranges=stage.owner_local_k_ranges, + range_meta_cache=state.execution_cache.range_meta, + ) + + +def launch_remote_stage_reduce( + stage_grad: torch.Tensor, + stage: StagePlan, + state: ArtContextParallelState, + output: torch.Tensor, +) -> Any: + return _COMMUNICATOR.launch_tensor_reduce( + remote=stage_grad.contiguous(), + plan=cast(DkvReducePlan, stage.dkv_reduce_plan), + group=state.cp_group, + async_op=True, + output=output, + range_meta_cache=state.execution_cache.range_meta, + ) diff --git a/src/art/megatron/glm52/indexer.py b/src/art/megatron/glm52/indexer.py new file mode 100644 index 000000000..aff496a0e --- /dev/null +++ b/src/art/megatron/glm52/indexer.py @@ -0,0 +1,568 @@ +from __future__ import annotations + +from typing import cast + +from pydantic import BaseModel, ConfigDict +import torch +import triton +import triton.language as tl + +from art.megatron.context_parallel.types import ArtContextParallelState +from art.megatron.glm52.cp_stage import ( + drain_stage_fetches, + launch_remote_stage_fetches, + stage_kv_rows, + stage_query_rows, +) +from art.megatron.glm52.state import ( + Glm52IndexerRowPlan, + Glm52PrefixTreeState, + Glm52StageState, +) + +_MAX_SCORE_WORKSPACE_BYTES = 256 * 1024 * 1024 +_MAX_K_CHUNK = 32 * 1024 + + +class Glm52RoutedTopk(BaseModel): + model_config = ConfigDict(arbitrary_types_allowed=True, frozen=True) + + indices: torch.Tensor + + +@triton.jit +def _canonicalize_topk_kernel( + ids_ptr, + topk: tl.constexpr, + block: tl.constexpr, +): + row = tl.program_id(0) + columns = tl.arange(0, block) + ids = tl.load( + ids_ptr + row * topk + columns, + mask=columns < topk, + other=0x7FFF_FFFF, + ) + ids = tl.where(ids >= 0, ids, 0x7FFF_FFFF) + ids = tl.sort(ids) + tl.store( + ids_ptr + row * topk + columns, + tl.where(ids == 0x7FFF_FFFF, -1, ids), + mask=columns < topk, + ) + + +def _canonicalize_topk_(ids: torch.Tensor) -> None: + topk = int(ids.shape[-1]) + block = triton.next_power_of_2(topk) + _canonicalize_topk_kernel[(ids.numel() // topk,)]( + ids, + topk=topk, # ty: ignore[invalid-argument-type] + block=block, # ty: ignore[invalid-argument-type] + num_warps=4, # ty: ignore[unknown-argument] + ) + + +@triton.jit +def _round_bf16(value): + bits = value.to(tl.int32, bitcast=True) + rounded = bits + 0x7FFF + ((bits >> 16) & 1) + return (rounded & -0x10000).to(tl.float32, bitcast=True) + + +@triton.jit +def _index_rope_kernel( + q_ptr, + k_ptr, + cos_ptr, + sin_ptr, + q_out_ptr, + k_out_ptr, + tokens, + stride_qb, + stride_qs, + stride_qh, + stride_qd, + stride_kb, + stride_ks, + stride_kd, + stride_rb, + stride_rs, + stride_rd, + heads: tl.constexpr, +): + row = tl.program_id(0) + head = tl.program_id(1) + batch = row // tokens + token = row - batch * tokens + half = tl.arange(0, 32) + passthrough = tl.arange(0, 64) + rope_base = batch * stride_rb + token * stride_rs + cos = tl.load(cos_ptr + rope_base + half * stride_rd) + sin = tl.load(sin_ptr + rope_base + half * stride_rd) + + q_base = batch * stride_qb + token * stride_qs + head * stride_qh + q_first = tl.load(q_ptr + q_base + half * stride_qd) + q_second = tl.load(q_ptr + q_base + (32 + half) * stride_qd) + q_ac = _round_bf16(q_first.to(tl.float32) * cos.to(tl.float32)) + q_bs = _round_bf16(q_second.to(tl.float32) * sin.to(tl.float32)) + q_bc = _round_bf16(q_second.to(tl.float32) * cos.to(tl.float32)) + q_as = _round_bf16(q_first.to(tl.float32) * sin.to(tl.float32)) + tl.store( + q_out_ptr + q_base + half * stride_qd, + _round_bf16(q_ac - q_bs), + ) + tl.store( + q_out_ptr + q_base + (32 + half) * stride_qd, + _round_bf16(q_bc + q_as), + ) + tl.store( + q_out_ptr + q_base + (64 + passthrough) * stride_qd, + tl.load(q_ptr + q_base + (64 + passthrough) * stride_qd), + ) + + k_base = batch * stride_kb + token * stride_ks + k_mask = head == 0 + k_first = tl.load(k_ptr + k_base + half * stride_kd, mask=k_mask, other=0.0) + k_second = tl.load(k_ptr + k_base + (32 + half) * stride_kd, mask=k_mask, other=0.0) + k_ac = _round_bf16(k_first.to(tl.float32) * cos.to(tl.float32)) + k_bs = _round_bf16(k_second.to(tl.float32) * sin.to(tl.float32)) + k_bc = _round_bf16(k_second.to(tl.float32) * cos.to(tl.float32)) + k_as = _round_bf16(k_first.to(tl.float32) * sin.to(tl.float32)) + tl.store( + k_out_ptr + k_base + half * stride_kd, + _round_bf16(k_ac - k_bs), + mask=k_mask, + ) + tl.store( + k_out_ptr + k_base + (32 + half) * stride_kd, + _round_bf16(k_bc + k_as), + mask=k_mask, + ) + tl.store( + k_out_ptr + k_base + (64 + passthrough) * stride_kd, + tl.load( + k_ptr + k_base + (64 + passthrough) * stride_kd, + mask=k_mask, + other=0.0, + ), + mask=k_mask, + ) + + +def indexer_rope( + q: torch.Tensor, + k: torch.Tensor, + cos: torch.Tensor, + sin: torch.Tensor, +) -> tuple[torch.Tensor, torch.Tensor]: + """Apply half-split RoPE with the indexer's eager-BF16 rounding contract.""" + if q.ndim != 4 or k.ndim != 3 or q.shape[:2] != k.shape[:2]: + raise ValueError("GLM-5.2 indexer RoPE expects q[B,S,H,128], k[B,S,128].") + if q.shape[-1] != 128 or k.shape[-1] != 128: + raise ValueError("GLM-5.2 indexer RoPE requires head_dim=128.") + if q.dtype is not torch.bfloat16 or k.dtype is not torch.bfloat16: + raise TypeError("GLM-5.2 indexer RoPE requires BF16 q/k.") + q = q.contiguous() + k = k.contiguous() + cos = cos.contiguous() + sin = sin.contiguous() + q_out = torch.empty_like(q) + k_out = torch.empty_like(k) + batch, tokens, heads, _ = q.shape + _index_rope_kernel[(batch * tokens, heads)]( + q, + k, + cos, + sin, + q_out, + k_out, + tokens, + *q.stride(), + *k.stride(), + *cos.stride(), + heads=heads, # ty: ignore[invalid-argument-type] + num_warps=1, # ty: ignore[unknown-argument] + ) + return q_out, k_out + + +@triton.jit +def _index_scores_kernel( + q_ptr, + k_ptr, + weights_ptr, + q_ids_ptr, + k_ids_ptr, + scores_ptr, + q_len, + k_len, + stride_qt, + stride_qh, + stride_qd, + stride_kt, + stride_kd, + stride_wt, + stride_wh, + stride_st, + stride_sk, + q_position_offset, + k_position_offset, + heads: tl.constexpr, + head_dim: tl.constexpr, + block_q: tl.constexpr, + block_k: tl.constexpr, + causal: tl.constexpr, + explicit_ids: tl.constexpr, + ranking_keys: tl.constexpr, +): + q_block = tl.program_id(0) + k_block = tl.program_id(1) + q_offsets = q_block * block_q + tl.arange(0, block_q) + h_offsets = tl.arange(0, heads) + d_offsets = tl.arange(0, head_dim) + k_offsets = k_block * block_k + tl.arange(0, block_k) + + qh_offsets = q_offsets[:, None] * heads + h_offsets[None, :] + qh_offsets = qh_offsets.reshape((block_q * heads,)) + q = tl.load( + q_ptr + + (qh_offsets // heads)[:, None] * stride_qt + + (qh_offsets % heads)[:, None] * stride_qh + + d_offsets[None, :] * stride_qd, + mask=(qh_offsets[:, None] // heads < q_len), + other=0.0, + ) + k = tl.load( + k_ptr + k_offsets[None, :] * stride_kt + d_offsets[:, None] * stride_kd, + mask=k_offsets[None, :] < k_len, + other=0.0, + ) + dots = tl.dot(q, k).reshape((block_q, heads, block_k)) + weights = tl.load( + weights_ptr + q_offsets[:, None] * stride_wt + h_offsets[None, :] * stride_wh, + mask=q_offsets[:, None] < q_len, + other=0.0, + ) + scores = tl.sum(tl.maximum(dots, 0.0) * weights[:, :, None], axis=1) + valid = (q_offsets[:, None] < q_len) & (k_offsets[None, :] < k_len) + if explicit_ids: + q_positions = tl.load(q_ids_ptr + q_offsets, mask=q_offsets < q_len, other=-1) + k_positions = tl.load( + k_ids_ptr + k_offsets, mask=k_offsets < k_len, other=0x7FFF_FFFF + ) + else: + q_positions = q_position_offset + q_offsets + k_positions = k_position_offset + k_offsets + if causal: + valid &= k_positions[None, :] <= q_positions[:, None] + scores = tl.where(valid, scores, float("-inf")) + output_offsets = ( + scores_ptr + q_offsets[:, None] * stride_st + k_offsets[None, :] * stride_sk + ) + output_mask = (q_offsets[:, None] < q_len) & (k_offsets[None, :] < k_len) + if ranking_keys: + canonical_scores = tl.where(scores == 0.0, 0.0, scores) + bits = canonical_scores.to(tl.int32, bitcast=True).to(tl.int64) & 0xFFFF_FFFF + ordered = tl.where( + (bits >> 31) != 0, + (~bits) & 0xFFFF_FFFF, + bits ^ 0x8000_0000, + ) + primary = ordered - 0x8000_0000 + global_ids = k_positions[None, :].to(tl.int64) + keys = (primary << 32) | (0xFFFF_FFFF - global_ids) + keys = tl.where(valid, keys, -0x8000_0000_0000_0000) + tl.store(output_offsets, keys, mask=output_mask) + else: + tl.store(output_offsets, scores, mask=output_mask) + + +def _index_scores( + q: torch.Tensor, + k: torch.Tensor, + weights: torch.Tensor, + *, + q_position_offset: int, + k_position_offset: int, + causal: bool, +) -> torch.Tensor: + if q.dtype is not torch.bfloat16 or k.dtype is not torch.bfloat16: + raise TypeError(f"GLM-5.2 index q/k must be bf16, got {q.dtype}/{k.dtype}.") + if weights.dtype is not torch.float32: + raise TypeError(f"GLM-5.2 index weights must be fp32, got {weights.dtype}.") + if q.ndim != 3 or k.ndim != 2 or weights.shape != q.shape[:2]: + raise ValueError( + "GLM-5.2 index score shapes must be q[Q,H,D], k[K,D], w[Q,H], " + f"got {tuple(q.shape)}, {tuple(k.shape)}, {tuple(weights.shape)}." + ) + q_len, heads, head_dim = q.shape + k_len = int(k.shape[0]) + if int(k.shape[1]) != head_dim or 128 % heads: + raise ValueError( + f"Unsupported GLM-5.2 index shape heads={heads}, head_dim={head_dim}." + ) + block_q = 128 // heads + block_k = 64 + scores = torch.empty((q_len, k_len), device=q.device, dtype=torch.float32) + _index_scores_kernel[(triton.cdiv(q_len, block_q), triton.cdiv(k_len, block_k))]( + q, + k, + weights, + q, + k, + scores, + q_len, + k_len, + *q.stride(), + *k.stride(), + *weights.stride(), + *scores.stride(), + q_position_offset=q_position_offset, # ty: ignore[invalid-argument-type] + k_position_offset=k_position_offset, # ty: ignore[invalid-argument-type] + heads=heads, # ty: ignore[invalid-argument-type] + head_dim=head_dim, # ty: ignore[invalid-argument-type] + block_q=block_q, # ty: ignore[invalid-argument-type] + block_k=block_k, # ty: ignore[invalid-argument-type] + causal=causal, # ty: ignore[invalid-argument-type] + explicit_ids=False, # ty: ignore[invalid-argument-type] + ranking_keys=False, # ty: ignore[invalid-argument-type] + num_warps=8, # ty: ignore[unknown-argument] + num_stages=3, # ty: ignore[unknown-argument] + ) + return scores + + +def _index_score_keys( + q: torch.Tensor, + k: torch.Tensor, + weights: torch.Tensor, + q_ids: torch.Tensor, + k_ids: torch.Tensor, +) -> torch.Tensor: + q_len, heads, head_dim = q.shape + k_len = int(k.shape[0]) + if q_ids.shape != (q_len,) or k_ids.shape != (k_len,): + raise ValueError("GLM-5.2 CP index ids must match query and key rows.") + block_q = 128 // heads + block_k = 64 + keys = torch.empty((q_len, k_len), device=q.device, dtype=torch.int64) + _index_scores_kernel[(triton.cdiv(q_len, block_q), triton.cdiv(k_len, block_k))]( + q, + k, + weights, + q_ids, + k_ids, + keys, + q_len, + k_len, + *q.stride(), + *k.stride(), + *weights.stride(), + *keys.stride(), + q_position_offset=0, # ty: ignore[invalid-argument-type] + k_position_offset=0, # ty: ignore[invalid-argument-type] + heads=heads, # ty: ignore[invalid-argument-type] + head_dim=head_dim, # ty: ignore[invalid-argument-type] + block_q=block_q, # ty: ignore[invalid-argument-type] + block_k=block_k, # ty: ignore[invalid-argument-type] + causal=True, # ty: ignore[invalid-argument-type] + explicit_ids=True, # ty: ignore[invalid-argument-type] + ranking_keys=True, # ty: ignore[invalid-argument-type] + num_warps=8, # ty: ignore[unknown-argument] + num_stages=3, # ty: ignore[unknown-argument] + ) + return keys + + +def _merge_topk( + scores: torch.Tensor, + ids: torch.Tensor, + candidate_scores: torch.Tensor, + candidate_ids: torch.Tensor, + *, + topk: int, +) -> tuple[torch.Tensor, torch.Tensor]: + all_scores = torch.cat((scores, candidate_scores), dim=1) + all_ids = torch.cat((ids, candidate_ids), dim=1) + keep = min(topk, int(all_scores.shape[1])) + scores, positions = torch.topk(all_scores, keep, dim=1, sorted=False) + return scores, torch.gather(all_ids, 1, positions) + + +def _gather_ranges( + tensor: torch.Tensor, ranges: tuple[tuple[int, int], ...] +) -> torch.Tensor: + if len(ranges) == 1: + start, end = ranges[0] + return tensor[start:end] + return torch.cat(tuple(tensor[start:end] for start, end in ranges)) + + +def _stage_topk_update( + q: torch.Tensor, + k: torch.Tensor, + weights: torch.Tensor, + stage: Glm52StageState, + best_keys: torch.Tensor, + *, + topk: int, +) -> None: + max_score_elements = _MAX_SCORE_WORKSPACE_BYTES // torch.int64.itemsize + for query in stage.queries: + candidate_k = _gather_ranges(k, query.k_ranges).contiguous() + candidate_global_ids = _gather_ranges( + stage.global_k_ids, query.k_ranges + ).contiguous() + max_k_len = int(candidate_k.shape[0]) + k_chunk_size = min(max_k_len, _MAX_K_CHUNK) + q_chunk_size = max(1, max_score_elements // max(k_chunk_size, 1)) + for q_start in range(query.q_start, query.q_end, q_chunk_size): + q_end = min(q_start + q_chunk_size, query.q_end) + owner_rows = stage.owner_q_rows[q_start:q_end] + keys = best_keys.index_select(0, owner_rows) + q_ids = stage.global_q_ids[q_start:q_end] + for k_start in range(0, max_k_len, k_chunk_size): + k_end = min(k_start + k_chunk_size, max_k_len) + candidate_keys = _index_score_keys( + q[q_start:q_end].contiguous(), + candidate_k[k_start:k_end].contiguous(), + weights[q_start:q_end].contiguous(), + q_ids, + candidate_global_ids[k_start:k_end], + ) + keys = torch.topk( + torch.cat((keys, candidate_keys), dim=1), + topk, + dim=1, + sorted=False, + ).values + best_keys.index_copy_(0, owner_rows, keys) + del candidate_k, candidate_global_ids + + +@torch.no_grad() +def context_parallel_tree_topk( + q: torch.Tensor, + k: torch.Tensor, + weights: torch.Tensor, + state: Glm52PrefixTreeState, + *, + topk: int, +) -> Glm52RoutedTopk: + """Accumulate exact GLM indexer top-k on query owners across ART stages.""" + cp_state = cast(ArtContextParallelState, state.context_parallel_state) + valid_tokens = int(sum(cp_state.rank_plan.local_valid_lengths)) + q = q[:, :valid_tokens].reshape(valid_tokens, *q.shape[2:]).contiguous() + k = k[:, :valid_tokens].reshape(valid_tokens, k.shape[-1]).contiguous() + weights = weights[:, :valid_tokens].reshape(valid_tokens, weights.shape[-1]) + invalid_key = torch.iinfo(torch.int64).min + best_keys = torch.full( + (valid_tokens, topk), invalid_key, device=q.device, dtype=torch.int64 + ) + works = launch_remote_stage_fetches(k, cp_state) + for stage_plan, stage in zip( + cp_state.rank_plan.stage_plans, state.stages, strict=True + ): + if not stage.queries: + continue + q_stage = stage_query_rows(q, stage_plan, cp_state) + weights_stage = stage_query_rows(weights, stage_plan, cp_state) + k_stage = stage_kv_rows(k, stage_plan, cp_state, works) + _stage_topk_update( + q_stage, + k_stage, + weights_stage, + stage, + best_keys, + topk=topk, + ) + del q_stage, weights_stage, k_stage + drain_stage_fetches(works) + invalid = best_keys == invalid_key + best_ids = (0xFFFF_FFFF - (best_keys & 0xFFFF_FFFF)).to(torch.int32) + best_ids.masked_fill_(invalid, -1) + del best_keys + _canonicalize_topk_(best_ids) + route_map = state.route_by_global_id + if route_map is None: + raise RuntimeError("GLM-5.2 CP route map is missing.") + indices = torch.where( + best_ids >= 0, + route_map[best_ids.clamp_min(0).to(torch.int64)], + torch.full_like(best_ids, state.combined_k_rows), + ).view(1, valid_tokens, topk) + del best_ids + return Glm52RoutedTopk(indices=indices) + + +@torch.compiler.disable +def streaming_tree_topk( + q: torch.Tensor, + k: torch.Tensor, + weights: torch.Tensor, + rows: tuple[Glm52IndexerRowPlan, ...], + *, + topk: int, +) -> torch.Tensor: + """Exact tree-aware topk with bounded score workspace and no square logits.""" + if not q.is_cuda or q.device != k.device or q.device != weights.device: + raise RuntimeError("GLM-5.2 indexer requires colocated CUDA tensors.") + if q.ndim != 4 or k.ndim != 3 or weights.ndim != 3: + raise ValueError("GLM-5.2 indexer expects q[B,S,H,D], k[B,S,D], w[B,S,H].") + batch, seq_len, _, _ = q.shape + if len(rows) != batch or k.shape[:2] != (batch, seq_len): + raise ValueError("GLM-5.2 index plan does not match the packed tensor shape.") + result = torch.full( + (batch, seq_len, topk), + -1, + device=q.device, + dtype=torch.int32, + ) + max_score_elements = _MAX_SCORE_WORKSPACE_BYTES // torch.float32.itemsize + for row in rows: + for query in row.queries: + max_k_len = max(slice_.k_end - slice_.k_start for slice_ in query.slices) + k_chunk_size = min(max_k_len, _MAX_K_CHUNK) + q_chunk_size = max(1, max_score_elements // max(k_chunk_size, 1)) + for q_start in range(query.q_start, query.q_end, q_chunk_size): + q_end = min(q_start + q_chunk_size, query.q_end) + q_chunk = q[row.row_index, q_start:q_end].contiguous() + w_chunk = weights[row.row_index, q_start:q_end].contiguous() + best_scores = torch.empty( + (q_end - q_start, 0), device=q.device, dtype=torch.float32 + ) + best_ids = torch.empty( + (q_end - q_start, 0), device=q.device, dtype=torch.int32 + ) + for slice_ in query.slices: + for k_start in range(slice_.k_start, slice_.k_end, k_chunk_size): + k_end = min(k_start + k_chunk_size, slice_.k_end) + score_chunk = _index_scores( + q_chunk, + k[row.row_index, k_start:k_end].contiguous(), + w_chunk, + q_position_offset=q_start, + k_position_offset=k_start, + causal=slice_.causal, + ) + keep = min(topk, k_end - k_start) + candidate_scores, candidate_ids = torch.topk( + score_chunk, + keep, + dim=1, + sorted=False, + ) + candidate_ids = (candidate_ids + k_start).to(torch.int32) + candidate_ids.masked_fill_(torch.isneginf(candidate_scores), -1) + best_scores, best_ids = _merge_topk( + best_scores, + best_ids, + candidate_scores, + candidate_ids, + topk=topk, + ) + result[row.row_index, q_start:q_end, : best_ids.shape[1]] = best_ids + _canonicalize_topk_(result) + result.masked_fill_(result < 0, seq_len) + return result diff --git a/src/art/megatron/glm52/lora.py b/src/art/megatron/glm52/lora.py new file mode 100644 index 000000000..bb9ed990f --- /dev/null +++ b/src/art/megatron/glm52/lora.py @@ -0,0 +1,223 @@ +from __future__ import annotations + +from collections.abc import Callable +from typing import Any, cast + +from megatron.core.extensions.transformer_engine import ( + TEColumnParallelGroupedLinear, + TERowParallelGroupedLinear, +) +import torch + +from art.megatron.glm52.lora_projection import glm52_lora_a +from art.megatron.kernels.cute_grouped_lora_quack import quack_grouped_lora_residual +from art.megatron.lora import ( + GRAD_SYNC_OP_SUM, + LORA_ALPHA, + TP_DEFAULT_GRAD_SYNC_DOMAIN, + LoRA, + LoRAParallelSpec, + MLPExpertsLinearFC1LoRA, + MLPExpertsLinearFC2LoRA, + SelfAttentionLinearProjLoRA, + _bind_expert_lora_layout, + _parallel_lora, + _targets_include, + _unwrap_attr, +) + + +class Glm52LoRA(LoRA): + def forward( + self, x: torch.Tensor, tokens_per_expert: list[int] | torch.Tensor | None = None + ) -> torch.Tensor: + if tokens_per_expert is not None: + raise ValueError("Glm52LoRA is only for non-expert projections.") + active = self.active_lora_tensors() + if active is None: + return x.new_zeros((*x.shape[:-1], self.out_features)) + a_t, b_t, scale = active + out = glm52_lora_a(x, a_t) @ b_t + return out if scale == 1.0 else out * scale + + +def _replicated_lora( + linear: Any, + *, + adapter_model_prefix: str, + rank: int, + alpha: int, +) -> LoRA: + weight = linear.weight + parallel = LoRAParallelSpec( + grad_sync_domain=TP_DEFAULT_GRAD_SYNC_DOMAIN, + grad_sync_op=GRAD_SYNC_OP_SUM, + ) + return Glm52LoRA( + adapter_model_prefix=adapter_model_prefix, + in_features=weight.shape[1], + out_features=weight.shape[0], + rank=rank, + alpha=alpha, + dtype=weight.dtype, + device=weight.device, + a_parallel_spec=parallel, + b_parallel_spec=parallel, + allreduce=True, + ) + + +def apply_glm52_attention_lora( + attention: Any, + *, + adapter_model_prefix: str, + provider: Any, + target_modules: set[str], + rank: int, + alpha: int = LORA_ALPHA, +) -> None: + prefix = f"{adapter_model_prefix}.self_attn" + for target, attr, linear_attr in ( + ("q_a_proj", "q_a_lora", "linear_q_down_proj"), + ("kv_a_proj_with_mqa", "kv_a_lora", "linear_kv_down_proj"), + ): + if _targets_include(target_modules, target): + setattr( + attention, + attr, + _replicated_lora( + getattr(attention, linear_attr), + adapter_model_prefix=f"{prefix}.{target}", + rank=rank, + alpha=alpha, + ), + ) + if _targets_include(target_modules, "q_b_proj"): + linear = attention.linear_q_up_proj + attention.q_b_lora = _parallel_lora( + adapter_model_prefix=f"{prefix}.q_b_proj", + linear=linear, + out_features=linear.weight.shape[0], + rank=rank, + alpha=alpha, + layout="column", + lora_cls=Glm52LoRA, + ) + if _targets_include(target_modules, "o_proj"): + attention.linear_proj = SelfAttentionLinearProjLoRA( + adapter_model_prefix=f"{prefix}.o_proj", + linear_proj=attention.linear_proj, + rank=rank, + alpha=alpha, + provider=provider, + lora_cls=Glm52LoRA, + ) + + +def _expert_lora_residual( + base: torch.Tensor, + x: torch.Tensor, + lora: LoRA, + tokens_per_expert: list[int] | torch.Tensor, +) -> torch.Tensor: + active = lora.active_lora_tensors() + if active is None or x.shape[0] == 0: + return base + a_t, b_t, scale = active + return quack_grouped_lora_residual( + base, x, a_t, b_t, tokens_per_expert, scale=scale + ) + + +def _grouped_linear( + linear: Any, x: torch.Tensor, tokens_per_expert: list[int] | torch.Tensor +) -> tuple[torch.Tensor, torch.Tensor | None]: + return cast(Callable[..., tuple[torch.Tensor, torch.Tensor | None]], linear)( + x, tokens_per_expert + ) + + +class Glm52MLPExpertsLinearFC1LoRA(MLPExpertsLinearFC1LoRA): + def forward( + self, x: torch.Tensor, tokens_per_expert: list[int] | torch.Tensor + ) -> tuple[torch.Tensor, torch.Tensor | None]: + base, bias = _grouped_linear(self.linear_fc1, x, tokens_per_expert) + return _expert_lora_residual(base, x, self.lora, tokens_per_expert), bias + + +class Glm52MLPExpertsLinearFC2LoRA(MLPExpertsLinearFC2LoRA): + def forward( + self, x: torch.Tensor, tokens_per_expert: list[int] | torch.Tensor + ) -> tuple[torch.Tensor, torch.Tensor | None]: + base, bias = _grouped_linear(self.linear_fc2, x, tokens_per_expert) + return _expert_lora_residual(base, x, self.lora, tokens_per_expert), bias + + +def wrap_glm52_grouped_moe_experts_3d( + experts: Any, + *, + adapter_model_prefix: str, + target_modules: set[str], + rank: int, + alpha: int, +) -> None: + if not _targets_include(target_modules, "experts"): + return + if TEColumnParallelGroupedLinear is None or TERowParallelGroupedLinear is None: + raise RuntimeError("GLM-5.2 expert LoRA requires Transformer Engine") + linear_fc1 = Glm52MLPExpertsLinearFC1LoRA( + adapter_model_prefix=f"{adapter_model_prefix}.mlp.experts", + linear_fc1=_unwrap_attr( + experts.linear_fc1, + "linear_fc1", + TEColumnParallelGroupedLinear, + ), + rank=rank, + alpha=alpha, + num_local_experts=experts.num_local_experts, + fused_gate_up=True, + ) + linear_fc2 = Glm52MLPExpertsLinearFC2LoRA( + adapter_model_prefix=f"{adapter_model_prefix}.mlp.experts", + linear_fc2=_unwrap_attr( + experts.linear_fc2, + "linear_fc2", + TERowParallelGroupedLinear, + ), + rank=rank, + alpha=alpha, + num_local_experts=experts.num_local_experts, + ) + experts.linear_fc1 = linear_fc1 + experts.linear_fc2 = linear_fc2 + _bind_expert_lora_layout(experts, linear_fc1.lora, linear_fc2.lora) + + +def add_glm52_attention_adapter_weights( + adapter_weights_by_base: dict[str, list[Any]], + *, + layer_prefix: str, + attention: Any, +) -> None: + from art.megatron.weights.adapter_export import ( + _simple_adapter_weight, + add_self_attention_adapter_weights, + ) + + add_self_attention_adapter_weights( + adapter_weights_by_base, + layer_prefix=layer_prefix, + self_attention=attention, + ) + prefix = f"{layer_prefix}.self_attention" + for attr, base_name in ( + ("q_a_lora", "linear_q_down_proj"), + ("q_b_lora", "linear_q_up_proj"), + ("kv_a_lora", "linear_kv_down_proj"), + ): + lora = getattr(attention, attr) + if lora is not None: + base_prefix = f"{prefix}.{base_name}" + adapter_weights_by_base[f"{base_prefix}.weight"] = [ + _simple_adapter_weight(base_prefix, lora) + ] diff --git a/src/art/megatron/glm52/lora_projection.py b/src/art/megatron/glm52/lora_projection.py new file mode 100644 index 000000000..c3fcb9a2a --- /dev/null +++ b/src/art/megatron/glm52/lora_projection.py @@ -0,0 +1,145 @@ +from typing import Any, cast + +import torch +import triton +import triton.language as tl + +_MAX_RANK = 512 + + +@triton.jit +def _rank_one_kernel( + x, + a, + out, + m, + k: tl.constexpr, + block_m: tl.constexpr, + block_k: tl.constexpr, +): + rows = tl.program_id(0) * block_m + tl.arange(0, block_m) + acc = tl.zeros((block_m,), tl.float32) + for k_start in range(0, k, block_k): + inner = k_start + tl.arange(0, block_k) + x_tile = tl.load( + x + rows[:, None] * k + inner[None, :], + mask=(rows[:, None] < m) & (inner[None, :] < k), + other=0.0, + ).to(tl.float32) + a_tile = tl.load(a + inner, mask=inner < k, other=0.0).to(tl.float32) + acc += tl.sum(x_tile * a_tile[None, :], axis=1) + tl.store(out + rows, acc, mask=rows < m) + + +@triton.jit +def _matrix_kernel( + x, + a, + out, + m, + k: tl.constexpr, + n: tl.constexpr, + block_m: tl.constexpr, + block_k: tl.constexpr, + block_n: tl.constexpr, +): + rows = tl.program_id(0) * block_m + tl.arange(0, block_m) + cols = tl.program_id(1) * block_n + tl.arange(0, block_n) + acc = tl.zeros((block_m, block_n), tl.float32) + for k_start in range(0, k, block_k): + inner = k_start + tl.arange(0, block_k) + x_tile = tl.load( + x + rows[:, None] * k + inner[None, :], + mask=(rows[:, None] < m) & (inner[None, :] < k), + other=0.0, + ).to(tl.float32) + a_tile = tl.load( + a + inner[:, None] * n + cols[None, :], + mask=(inner[:, None] < k) & (cols[None, :] < n), + other=0.0, + ).to(tl.float32) + acc = tl.dot(x_tile, a_tile, acc, input_precision="tf32x3") + tl.store( + out + rows[:, None] * n + cols[None, :], + acc, + mask=(rows[:, None] < m) & (cols[None, :] < n), + ) + + +def _validate(x: torch.Tensor, a: torch.Tensor) -> None: + if not x.is_cuda or not a.is_cuda or x.device != a.device: + raise ValueError("GLM-5.2 LoRA projection requires tensors on one CUDA device.") + if x.dtype != torch.bfloat16 or a.dtype != torch.bfloat16: + raise ValueError("GLM-5.2 LoRA projection requires BF16 tensors.") + if x.ndim < 2 or a.ndim != 2 or x.shape[-1] != a.shape[0]: + raise ValueError( + f"GLM-5.2 LoRA projection shape mismatch: x={tuple(x.shape)}, " + f"A_T={tuple(a.shape)}." + ) + if not x.is_contiguous() or not a.is_contiguous(): + raise ValueError("GLM-5.2 LoRA projection requires contiguous tensors.") + if not 1 <= a.shape[1] <= _MAX_RANK: + raise ValueError( + f"GLM-5.2 LoRA rank must be in [1, {_MAX_RANK}], got {a.shape[1]}." + ) + + +def _forward(x: torch.Tensor, a: torch.Tensor) -> torch.Tensor: + _validate(x, a) + x_2d = x.view(-1, x.shape[-1]) + m, k = x_2d.shape + n = a.shape[1] + out = torch.empty((m, n), dtype=x.dtype, device=x.device) + if m == 0: + return out.view(*x.shape[:-1], n) + if n == 1: + _rank_one_kernel[(triton.cdiv(m, 8),)]( + x_2d, + a, + out, + m, + k=k, # ty: ignore[invalid-argument-type] + block_m=8, # ty: ignore[invalid-argument-type] + block_k=512, # ty: ignore[invalid-argument-type] + num_warps=4, # ty: ignore[unknown-argument] + num_stages=1, # ty: ignore[unknown-argument] + ) + else: + block_n = 16 if n <= 16 else 32 + _matrix_kernel[(triton.cdiv(m, 64), triton.cdiv(n, block_n))]( + x_2d, + a, + out, + m, + k=k, # ty: ignore[invalid-argument-type] + n=n, # ty: ignore[invalid-argument-type] + block_m=64, # ty: ignore[invalid-argument-type] + block_k=64, # ty: ignore[invalid-argument-type] + block_n=block_n, # ty: ignore[invalid-argument-type] + num_warps=4, # ty: ignore[unknown-argument] + num_stages=3, # ty: ignore[unknown-argument] + ) + return out.view(*x.shape[:-1], n) + + +class _Glm52LoraA(torch.autograd.Function): + @staticmethod + def forward(ctx: Any, x: torch.Tensor, a: torch.Tensor) -> torch.Tensor: + ctx.save_for_backward(x, a) + return _forward(x, a) + + @staticmethod + def backward(ctx: Any, *grad_outputs: Any): + x, a = ctx.saved_tensors + grad_out = cast(torch.Tensor, grad_outputs[0]) + grad_2d = grad_out.reshape(-1, grad_out.shape[-1]) + grad_x = grad_a = None + if ctx.needs_input_grad[0]: + grad_x = (grad_2d @ a.T).view_as(x) + if ctx.needs_input_grad[1]: + grad_a = x.view(-1, x.shape[-1]).T @ grad_2d + return grad_x, grad_a + + +def glm52_lora_a(x: torch.Tensor, a: torch.Tensor) -> torch.Tensor: + return _Glm52LoraA.apply(x, a) diff --git a/src/art/megatron/glm52/sparse_mla.py b/src/art/megatron/glm52/sparse_mla.py new file mode 100644 index 000000000..775fc089e --- /dev/null +++ b/src/art/megatron/glm52/sparse_mla.py @@ -0,0 +1,147 @@ +from __future__ import annotations + +from typing import Any + +import torch + +from art.megatron.glm52 import tilelang_sparse_mla + +_LATENT_DIM = 512 +_ROPE_DIM = 64 +_TOPK_BLOCK = 64 + + +def sparse_mla_forward( + q: torch.Tensor, + kv: torch.Tensor, + indices: torch.Tensor, + *, + scale: float, +) -> tuple[torch.Tensor, torch.Tensor]: + _validate_inputs(q, kv, indices) + return tilelang_sparse_mla.forward( + q.contiguous(), kv.contiguous(), indices.contiguous(), float(scale) + ) + + +def sparse_mla_backward( + q: torch.Tensor, + kv: torch.Tensor, + indices: torch.Tensor, + out: torch.Tensor, + lse: torch.Tensor, + grad_out: torch.Tensor, + *, + scale: float, +) -> tuple[torch.Tensor, torch.Tensor]: + _validate_inputs(q, kv, indices) + expected_out = (*q.shape[:-1], _LATENT_DIM) + if out.shape != expected_out or grad_out.shape != expected_out: + raise ValueError( + f"GLM-5.2 sparse MLA output and gradient must have shape {expected_out}." + ) + if lse.shape != q.shape[:-1] or lse.dtype is not torch.float32: + raise ValueError("GLM-5.2 sparse MLA LSE must be fp32 with shape [B,S,H].") + return tilelang_sparse_mla.backward( + q.contiguous(), + kv.contiguous(), + indices.contiguous(), + out.contiguous(), + lse.contiguous(), + grad_out.contiguous(), + float(scale), + ) + + +def reduce_tensor_parallel_dkv( + grad_kv: torch.Tensor, + *, + tp_group: Any | None, + dtype: torch.dtype, +) -> torch.Tensor: + if tp_group is not None: + torch.distributed.all_reduce( # ty: ignore[possibly-missing-attribute] + grad_kv, group=tp_group + ) + return grad_kv.to(dtype) + + +class _SparseMla(torch.autograd.Function): + @staticmethod + def forward( + ctx: Any, + q: torch.Tensor, + kv: torch.Tensor, + indices: torch.Tensor, + scale: float, + tp_group: Any | None, + ) -> torch.Tensor: + out, lse = sparse_mla_forward(q, kv, indices, scale=scale) + ctx.save_for_backward(q, kv, indices, out, lse) + ctx.scale = float(scale) + ctx.tp_group = tp_group + return out + + @staticmethod + def backward(ctx: Any, *grad_outputs: Any): + q, kv, indices, out, lse = ctx.saved_tensors + grad_q, grad_kv = sparse_mla_backward( + q, + kv, + indices, + out, + lse, + grad_outputs[0], + scale=ctx.scale, + ) + grad_kv = reduce_tensor_parallel_dkv( + grad_kv, tp_group=ctx.tp_group, dtype=kv.dtype + ) + return grad_q, grad_kv, None, None, None + + +def sparse_mla( + q: torch.Tensor, + kv: torch.Tensor, + indices: torch.Tensor, + *, + scale: float, + tp_group: Any | None = None, +) -> torch.Tensor: + """Run GLM-5.2 list-sparse absorbed MLA.""" + return _SparseMla.apply( + q.contiguous(), + kv.contiguous(), + indices.contiguous(), + float(scale), + tp_group, + ) + + +def _validate_inputs( + q: torch.Tensor, + kv: torch.Tensor, + indices: torch.Tensor, +) -> None: + if q.dtype is not torch.bfloat16 or kv.dtype is not torch.bfloat16: + raise TypeError(f"GLM-5.2 sparse MLA requires bf16, got {q.dtype}/{kv.dtype}.") + if indices.dtype is not torch.int32: + raise TypeError( + f"GLM-5.2 sparse MLA indices must be int32, got {indices.dtype}." + ) + if not q.is_cuda or q.device != kv.device or q.device != indices.device: + raise RuntimeError("GLM-5.2 sparse MLA requires colocated CUDA tensors.") + if q.ndim != 4 or kv.ndim != 3 or indices.ndim != 3: + raise ValueError( + "GLM-5.2 sparse MLA expects q[B,S,H,576], kv[B,K,576], ids[B,S,T]." + ) + if not 0 < q.shape[2] <= 64 or q.shape[3] != _LATENT_DIM + _ROPE_DIM: + raise ValueError("GLM-5.2 sparse MLA requires positive 576-dimensional heads.") + if kv.shape[-1] != q.shape[-1] or q.shape[:2] != indices.shape[:2]: + raise ValueError("GLM-5.2 sparse MLA tensor shapes do not match.") + if q.shape[0] != kv.shape[0]: + raise ValueError("GLM-5.2 sparse MLA batch dimensions do not match.") + if indices.shape[-1] % _TOPK_BLOCK: + raise ValueError( + f"GLM-5.2 sparse MLA top-k must be divisible by {_TOPK_BLOCK}." + ) diff --git a/src/art/megatron/glm52/spec.py b/src/art/megatron/glm52/spec.py new file mode 100644 index 000000000..469069637 --- /dev/null +++ b/src/art/megatron/glm52/spec.py @@ -0,0 +1,299 @@ +from __future__ import annotations + +from copy import deepcopy +from itertools import combinations +from typing import Any, cast + +from megatron.core.extensions.transformer_engine_spec_provider import TESpecProvider +from megatron.core.models.gpt.gpt_layer_specs import get_gpt_decoder_block_spec +from megatron.core.transformer.enums import AttnMaskType, LayerType +from megatron.core.transformer.multi_latent_attention import MLASelfAttentionSubmodules +from megatron.core.transformer.pipeline_parallel_layer_layout import ( + PipelineParallelLayerLayout, +) +from megatron.core.transformer.spec_utils import ModuleSpec + +from art.megatron.context_parallel.types import ( + ContextParallelStageWorkProfile, + ContextParallelWorkloadProfile, +) +from art.megatron.glm52.attention import ( + Glm52SelfAttention, + glm52_core_builder, +) + + +def build_glm52_pipeline_layout( + indexer_types: tuple[str, ...], pp_size: int, vp_size: int +) -> list[list[str]]: + """Balance complete IndexShare groups across virtual and physical stages.""" + starts = [index for index, mode in enumerate(indexer_types) if mode == "full"] + stages = pp_size * vp_size + if not indexer_types or not starts or starts[0] != 0: + raise ValueError("GLM-5.2 indexer_types must start with a full layer.") + if stages > len(starts): + raise ValueError( + f"GLM-5.2 has {len(starts)} complete IndexShare groups but {stages} " + "PP/VPP stages were requested." + ) + + def score(boundaries: tuple[int, ...]) -> tuple[Any, ...]: + chunks = [ + end - start + for start, end in zip(boundaries[:-1], boundaries[1:], strict=True) + ] + physical = [sum(chunks[pp_rank::pp_size]) for pp_rank in range(pp_size)] + return ( + max(chunks), + max(physical), + max(physical) - min(physical), + max(chunks) - min(chunks), + boundaries, + ) + + boundaries = min( + ( + (0, *selected, len(indexer_types)) + for selected in combinations(starts[1:], stages - 1) + ), + key=score, + ) + layout = [ + ["decoder"] * (end - start) + for start, end in zip(boundaries[:-1], boundaries[1:], strict=True) + ] + layout[0].insert(0, "embedding") + layout[-1].append("loss") + return layout + + +def _glm52_pipeline_stage_ranges(config: Any) -> tuple[tuple[int, int, int], ...]: + """Return physical PP rank and layer ranges in VPP-major execution order.""" + indexer_types = tuple(config.glm52_indexer_types) + layout = config.pipeline_model_parallel_layout + stages = int(config.pipeline_model_parallel_size or 1) * int( + config.virtual_pipeline_model_parallel_size or 1 + ) + if stages == 1 and layout is None: + return ((0, 0, len(indexer_types)),) + if not isinstance(layout, PipelineParallelLayerLayout): + raise RuntimeError("GLM-5.2 PP/VPP requires a finalized flexible layout.") + full_groups = indexer_types.count("full") + if stages > full_groups: + raise ValueError( + f"GLM-5.2 has {full_groups} complete IndexShare groups but {stages} " + "PP/VPP stages were configured." + ) + offset = 0 + ranges = [] + for vp_rank in range(layout.virtual_pipeline_model_parallel_size): + for pp_rank in range(layout.pipeline_model_parallel_size): + count = layout.layout[pp_rank][vp_rank].count(LayerType.decoder) + if count: + if indexer_types[offset] != "full": + raise ValueError( + "GLM-5.2 pipeline chunk starts at shared index layer " + f"{offset} (PP={pp_rank}, VPP={vp_rank}); split only at " + "full IndexShare layers." + ) + ranges.append((pp_rank, offset, offset + count)) + offset += count + if offset != len(indexer_types): + raise ValueError( + f"GLM-5.2 pipeline layout covers {offset} decoder layers, expected " + f"{len(indexer_types)}." + ) + return tuple(ranges) + + +def _validate_glm52_pipeline_layout(config: Any) -> None: + """Reject a finalized layout that makes shared layers cross process chunks.""" + _glm52_pipeline_stage_ranges(config) + + +def _train_matmul_flops( + in_features: int, + out_features: int, + *, + forward_executions: int, +) -> int: + # Frozen base weights still execute one input-gradient matmul. + return 2 * int(in_features) * int(out_features) * (forward_executions + 1) + + +def build_glm52_context_parallel_profile( + config: Any, +) -> ContextParallelWorkloadProfile: + """Describe the GLM work that changes with CP token ownership.""" + indexer_types = tuple(config.glm52_indexer_types) + moe_layers = tuple(bool(value) for value in config.moe_layer_freq) + if len(moe_layers) != len(indexer_types): + raise ValueError( + "GLM-5.2 MLP and indexer layer patterns must have equal length." + ) + + forward_executions = ( + 2 if getattr(config, "recompute_granularity", None) == "full" else 1 + ) + hidden = int(config.hidden_size) + heads = int(config.num_attention_heads) + q_rank = int(config.q_lora_rank) + kv_rank = int(config.kv_lora_rank) + qk_nope = int(config.qk_head_dim) + rope = int(config.qk_pos_emb_head_dim) + value = int(config.v_head_dim) + combined_dim = kv_rank + rope + topk = int(config.dsa_indexer_topk) + index_heads = int(config.dsa_indexer_n_heads) + index_dim = int(config.dsa_indexer_head_dim) + dense_intermediate = int(config.ffn_hidden_size) + shared_intermediate = int(config.moe_shared_expert_intermediate_size or 0) + experts = int(config.num_moe_experts) + + attention_projection = sum( + ( + _train_matmul_flops(hidden, q_rank, forward_executions=forward_executions), + _train_matmul_flops( + q_rank, + heads * (qk_nope + rope), + forward_executions=forward_executions, + ), + _train_matmul_flops( + hidden, combined_dim, forward_executions=forward_executions + ), + heads + * _train_matmul_flops( + qk_nope, kv_rank, forward_executions=forward_executions + ), + heads + * _train_matmul_flops( + kv_rank, value, forward_executions=forward_executions + ), + _train_matmul_flops( + heads * value, hidden, forward_executions=forward_executions + ), + ) + ) + sparse_attention = ( + 2 * (forward_executions + 2) * heads * topk * (combined_dim + kv_rank) + ) + dense_mlp = _train_matmul_flops( + hidden, 2 * dense_intermediate, forward_executions=forward_executions + ) + _train_matmul_flops( + dense_intermediate, hidden, forward_executions=forward_executions + ) + local_sparse_mlp = _train_matmul_flops( + hidden, experts, forward_executions=forward_executions + ) + if shared_intermediate: + local_sparse_mlp += _train_matmul_flops( + hidden, + 2 * shared_intermediate, + forward_executions=forward_executions, + ) + _train_matmul_flops( + shared_intermediate, + hidden, + forward_executions=forward_executions, + ) + indexer_projection = ( + 2 + * forward_executions + * (q_rank * index_heads * index_dim + hidden * index_dim + hidden * index_heads) + ) + indexer_pair = forward_executions * index_heads * (2 * index_dim + 3) + + pp_size = int(config.pipeline_model_parallel_size or 1) + stage_layers = [0 for _ in range(pp_size)] + stage_indexers = [0 for _ in range(pp_size)] + stage_query_flops = [0 for _ in range(pp_size)] + for pp_rank, start, end in _glm52_pipeline_stage_ranges(config): + full_indexers = indexer_types[start:end].count("full") + layer_count = end - start + query_flops = layer_count * (attention_projection + sparse_attention) + query_flops += sum( + local_sparse_mlp if moe_layers[layer] else dense_mlp + for layer in range(start, end) + ) + query_flops += full_indexers * indexer_projection + stage_layers[pp_rank] += layer_count + stage_indexers[pp_rank] += full_indexers + stage_query_flops[pp_rank] += query_flops + + stages = [] + sparse_fetches = forward_executions + 1 + for pp_rank, (layer_count, full_indexers, query_flops) in enumerate( + zip(stage_layers, stage_indexers, stage_query_flops, strict=True) + ): + k_fetch_bytes = layer_count * sparse_fetches * combined_dim * 2 + k_fetch_bytes += full_indexers * forward_executions * index_dim * 2 + dkv_reduce_bytes = layer_count * combined_dim * 2 + # Each fetch concatenates CP stages and adds TileLang's sentinel row. + # Backward also zeroes four FP32 dKV splits, reduces, and casts to BF16. + k_hbm_bytes = layer_count * combined_dim * (8 * sparse_fetches + 42) + checkpoint_bytes = layer_count * hidden * 2 + persistent_topk_bytes = full_indexers * topk * 4 + sparse_query_workspace = ( + 2 * heads * combined_dim * 2 + + 2 * heads * kv_rank * 2 + + 2 * heads * 4 + + topk * 4 + ) + # Backward holds original and padded BF16 KV, four FP32 dKV splits, + # the FP32 reduction result, and the returned BF16 dKV. + k_memory = combined_dim * (2 + 2 + 4 * 4 + 4 + 2) + stages.append( + ContextParallelStageWorkProfile( + physical_pipeline_rank=pp_rank, + query_flops_per_token=query_flops, + tile_pair_flops=full_indexers * indexer_pair, + k_hbm_bytes_per_token=k_hbm_bytes, + k_fetch_bytes_per_token=k_fetch_bytes, + dkv_reduce_bytes_per_token=dkv_reduce_bytes, + query_memory_bytes_per_token=( + checkpoint_bytes + persistent_topk_bytes + sparse_query_workspace + ), + k_memory_bytes_per_token=k_memory, + ) + ) + return ContextParallelWorkloadProfile( + stages=tuple(stages), + query_tile_size=128 // index_heads, + key_tile_size=64, + indexer_score_workspace_elements=(256 * 1024 * 1024) // 8, + indexer_max_k_tokens=32 * 1024, + ) + + +def get_glm52_decoder_block_spec(config: Any, vp_stage: int | None = None) -> Any: + """Build GLM-5.2 layers without entering MCore's incomplete DSA path.""" + _validate_glm52_pipeline_layout(config) + block_spec = deepcopy( + get_gpt_decoder_block_spec( + config, + use_transformer_engine=True, + normalization="RMSNorm", + vp_stage=vp_stage, + ) + ) + backend = TESpecProvider() + attention = ModuleSpec( + module=Glm52SelfAttention, + params={"attn_mask_type": AttnMaskType.causal}, + submodules=MLASelfAttentionSubmodules( + linear_q_down_proj=backend.linear(), + linear_q_up_proj=backend.column_parallel_linear(), + linear_kv_down_proj=backend.linear(), + linear_kv_up_proj=backend.column_parallel_linear(), + core_attention=glm52_core_builder( + backend.linear(), + backend.layer_norm(rms_norm=False, for_qk=True), + ), + linear_proj=backend.row_parallel_linear(), + q_layernorm=backend.layer_norm(rms_norm=True, for_qk=True), + kv_layernorm=backend.layer_norm(rms_norm=True, for_qk=True), + ), + metainfo={"fuse_input_layernorm": False}, + ) + for layer_spec in block_spec.layer_specs or (): + cast(Any, layer_spec.submodules).self_attention = attention + return block_spec diff --git a/src/art/megatron/glm52/state.py b/src/art/megatron/glm52/state.py new file mode 100644 index 000000000..48e0ce3d7 --- /dev/null +++ b/src/art/megatron/glm52/state.py @@ -0,0 +1,285 @@ +from __future__ import annotations + +from collections import defaultdict +from typing import Any + +from pydantic import BaseModel, ConfigDict, Field +import torch + +from art.megatron.context_parallel.builder import build_prefix_tree_attention_spec +from art.megatron.context_parallel.types import AttnMaskKind + +# Preserve the CUDA float32 `pow` rounding used by the reference GLM RoPE. +_ROPE_INV_FREQ_BITS = ( + 1065353216, + 1058785356, + 1052612689, + 1046920992, + 1041001025, + 1034609764, + 1028652027, + 1023221913, + 1016727752, + 1010530219, + 1004808260, + 998954723, + 992541049, + 986556035, + 981092721, + 974671434, + 968449313, + 962697431, + 956909580, + 950473744, + 944461757, + 938965617, + 932616387, + 926369956, + 920588484, + 914865582, + 908407833, + 902369178, + 896840579, + 890562597, + 884292128, + 878481401, +) + + +class Glm52IndexerSlice(BaseModel): + model_config = ConfigDict(frozen=True) + + k_start: int + k_end: int + causal: bool + + +class Glm52StageQueryPlan(BaseModel): + model_config = ConfigDict(frozen=True) + + q_start: int + q_end: int + k_ranges: tuple[tuple[int, int], ...] + + +class Glm52IndexerQueryPlan(BaseModel): + model_config = ConfigDict(frozen=True) + + q_start: int + q_end: int + slices: tuple[Glm52IndexerSlice, ...] + + +class Glm52IndexerRowPlan(BaseModel): + model_config = ConfigDict(frozen=True) + + row_index: int + valid_tokens: int + queries: tuple[Glm52IndexerQueryPlan, ...] + + +class Glm52StageState(BaseModel): + model_config = ConfigDict(arbitrary_types_allowed=True, frozen=True) + + stage_index: int + global_q_ids: torch.Tensor + global_k_ids: torch.Tensor + owner_q_rows: torch.Tensor + queries: tuple[Glm52StageQueryPlan, ...] + + +class Glm52PrefixTreeState(BaseModel): + model_config = ConfigDict(arbitrary_types_allowed=True) + + position_ids: torch.Tensor + rope_cos: torch.Tensor + rope_sin: torch.Tensor + indexer_rows: tuple[Glm52IndexerRowPlan, ...] = () + stages: tuple[Glm52StageState, ...] = () + route_by_global_id: torch.Tensor | None = None + combined_k_rows: int = 0 + context_parallel_state: Any | None = None + topk_by_full_layer: dict[int, Any] = Field(default_factory=dict) + + +def _rope_state( + position_ids: torch.Tensor, + *, + device: torch.device, +) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + position_ids_device = position_ids.to( + device=device, + dtype=torch.int64, + non_blocking=True, + ).contiguous() + inv_freq = torch.tensor(_ROPE_INV_FREQ_BITS, device=device, dtype=torch.int32).view( + torch.float32 + ) + frequencies = position_ids_device.float().unsqueeze(-1) * inv_freq + return ( + position_ids_device, + frequencies.cos().to(torch.bfloat16), + frequencies.sin().to(torch.bfloat16), + ) + + +def build_glm52_prefix_tree_state( + *, + position_ids: torch.Tensor, + group_ids: torch.Tensor, + parent_ids: torch.Tensor, + device: torch.device, +) -> Glm52PrefixTreeState: + """Precompute immutable tree rectangles once for every GLM-5.2 layer.""" + if position_ids.ndim != 2: + raise ValueError( + f"GLM-5.2 position_ids must be 2D, got {tuple(position_ids.shape)}." + ) + batch_spec = build_prefix_tree_attention_spec( + group_ids=group_ids, + parent_ids=parent_ids, + ) + rows: list[Glm52IndexerRowPlan] = [] + for row in batch_spec.rows: + slices_by_query: dict[tuple[int, int], list[Glm52IndexerSlice]] = defaultdict( + list + ) + for slice_ in row.slices: + slices_by_query[(slice_.q_range.start, slice_.q_range.end)].append( + Glm52IndexerSlice( + k_start=slice_.k_range.start, + k_end=slice_.k_range.end, + causal=slice_.mask_kind is AttnMaskKind.CAUSAL, + ) + ) + queries = tuple( + Glm52IndexerQueryPlan( + q_start=q_start, + q_end=q_end, + slices=tuple(slices), + ) + for (q_start, q_end), slices in sorted(slices_by_query.items()) + ) + rows.append( + Glm52IndexerRowPlan( + row_index=row.row_index, + valid_tokens=row.valid_tokens, + queries=queries, + ) + ) + position_ids_device, rope_cos, rope_sin = _rope_state( + position_ids, + device=device, + ) + return Glm52PrefixTreeState( + position_ids=position_ids_device, + rope_cos=rope_cos, + rope_sin=rope_sin, + indexer_rows=tuple(rows), + ) + + +def build_glm52_context_parallel_state( + *, + position_ids: torch.Tensor, + context_parallel_state: Any, + device: torch.device, +) -> Glm52PrefixTreeState: + """Materialize GLM stage ids once without reading CUDA data on the host.""" + rank_plan = context_parallel_state.rank_plan + stages = [] + route_by_global_id = torch.full( + (int(rank_plan.original_seq_len),), -1, dtype=torch.int32 + ) + combined_k_start = 0 + for stage in rank_plan.stage_plans: + q_len = sum(range_.size() for range_ in stage.owner_local_q_ranges) + k_len = sum(range_.size() for range_ in stage.owner_local_k_ranges) + if combined_k_start + k_len > torch.iinfo(torch.int32).max: + raise RuntimeError( + "GLM-5.2 combined CP KV rows exceed int32 index capacity." + ) + metadata = stage.mask_metadata + if metadata is None and (q_len or k_len): + raise RuntimeError( + f"GLM-5.2 stage {stage.stage_index} is missing exact token ids." + ) + if metadata is None: + q_ids = k_ids = torch.empty(0, dtype=torch.int32, device=device) + else: + k_ids_cpu = metadata.k_token_indices[:k_len].to(torch.int64) + routes_cpu = torch.arange( + combined_k_start, + combined_k_start + k_len, + dtype=torch.int32, + ) + existing = route_by_global_id[k_ids_cpu] + if bool(((existing >= 0) & (existing != routes_cpu)).any()): + raise RuntimeError( + "GLM-5.2 CP stages assign one global KV id to multiple routes." + ) + route_by_global_id[k_ids_cpu] = routes_cpu + q_ids = metadata.q_token_indices[:q_len].to( + device=device, dtype=torch.int32, non_blocking=True + ) + k_ids = metadata.k_token_indices[:k_len].to( + device=device, dtype=torch.int32, non_blocking=True + ) + owner_q_parts = tuple( + torch.arange(range_.start, range_.end, dtype=torch.int64) + for range_ in stage.owner_local_q_ranges + if range_.size() > 0 + ) + owner_q_rows = ( + torch.cat(owner_q_parts) + if owner_q_parts + else torch.empty(0, dtype=torch.int64) + ) + k_ranges_by_query: dict[tuple[int, int], list[tuple[int, int]]] = defaultdict( + list + ) + for slice_ in stage.slices: + k_ranges_by_query[ + (int(slice_.q_range.start), int(slice_.q_range.end)) + ].append((int(slice_.k_range.start), int(slice_.k_range.end))) + stages.append( + Glm52StageState( + stage_index=int(stage.stage_index), + global_q_ids=q_ids.contiguous(), + global_k_ids=k_ids.contiguous(), + owner_q_rows=owner_q_rows.to(device=device, non_blocking=True), + queries=tuple( + Glm52StageQueryPlan( + q_start=q_start, + q_end=q_end, + k_ranges=tuple(k_ranges), + ) + for (q_start, q_end), k_ranges in sorted(k_ranges_by_query.items()) + ), + ) + ) + combined_k_start += k_len + position_ids_device, rope_cos, rope_sin = _rope_state( + position_ids, + device=device, + ) + return Glm52PrefixTreeState( + position_ids=position_ids_device, + rope_cos=rope_cos, + rope_sin=rope_sin, + stages=tuple(stages), + route_by_global_id=route_by_global_id.to(device=device, non_blocking=True), + combined_k_rows=combined_k_start, + context_parallel_state=context_parallel_state, + ) + + +def require_glm52_state(attention_bias: Any) -> Glm52PrefixTreeState: + model_state = getattr(attention_bias, "model_state", None) + state = model_state.get("glm52") if isinstance(model_state, dict) else None + if not isinstance(state, Glm52PrefixTreeState): + raise RuntimeError( + "GLM-5.2 prefix-tree state is missing; build it once per packed " + "sequence through the model-support handler." + ) + return state diff --git a/src/art/megatron/glm52/tilelang_sparse_mla.py b/src/art/megatron/glm52/tilelang_sparse_mla.py new file mode 100644 index 000000000..8ecc28031 --- /dev/null +++ b/src/art/megatron/glm52/tilelang_sparse_mla.py @@ -0,0 +1,651 @@ +# ruff: noqa +# Adapted from Miles GLM and tile-ai/tilelang DeepSeek-V3.2 sparse MLA kernels. + +from collections.abc import Iterator +from contextlib import contextmanager +import importlib +import os +from typing import Any + +import torch + +_ENV_KEYS = ( + "PYTHONPATH", + "TVM_IMPORT_PYTHON_PATH", + "TVM_LIBRARY_PATH", + "TL_CUTLASS_PATH", + "TL_TEMPLATE_PATH", + "TL_COMPOSABLE_KERNEL_PATH", +) +_PATH_MARKERS = ("/site-packages/tilelang/", "\\site-packages\\tilelang\\") + + +def _clean(value: str | None) -> str | None: + if value is None: + return None + kept = [ + part + for part in value.split(os.pathsep) + if not any(marker in part for marker in _PATH_MARKERS) + ] + return os.pathsep.join(kept) if kept else None + + +def _restore(saved: dict[str, str | None]) -> None: + for key, value in saved.items(): + if value is None: + os.environ.pop(key, None) + else: + os.environ[key] = value + for key in _ENV_KEYS: + value = _clean(os.environ.get(key)) + if value is None: + os.environ.pop(key, None) + else: + os.environ[key] = value + + +@contextmanager +def _preserve_env() -> Iterator[None]: + saved = {key: os.environ.get(key) for key in _ENV_KEYS} + try: + yield + finally: + _restore(saved) + + +with _preserve_env(): + tilelang: Any = importlib.import_module("tilelang") + T: Any = importlib.import_module("tilelang.language") + +_LATENT = 512 +_ROPE = 64 +_DIM = _LATENT + _ROPE +_HEAD_BLOCK = 16 +_DKV_SPLITS = 4 +_LOG2_E = 1.4426950408889634 +_LN_2 = 0.6931471805599453 + + +@tilelang.jit( + out_idx=[-2, -1], + pass_configs={ + tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, + tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True, + }, +) +def _forward(heads, topk, scale, block_i=64, num_stages=2, threads=256): + assert topk % block_i == 0 + batch = T.dynamic("batch") + q_tokens = T.dynamic("q_tokens") + kv_tokens = T.dynamic("kv_tokens") + q_shape = [batch, q_tokens, heads, _DIM] + kv_shape = [batch, kv_tokens, _DIM] + indices_shape = [batch, q_tokens, topk] + out_shape = [batch, q_tokens, heads, _LATENT] + lse_shape = [batch, q_tokens, heads] + blocks = topk // block_i + scale_log2 = scale * _LOG2_E + + @T.prim_func + def main( + Q: T.Tensor(q_shape, T.bfloat16), # type: ignore + KV: T.Tensor(kv_shape, T.bfloat16), # type: ignore + Indices: T.Tensor(indices_shape, T.int32), # type: ignore + Output: T.Tensor(out_shape, T.bfloat16), # type: ignore + Lse: T.Tensor(lse_shape, T.float32), # type: ignore + ): + with T.Kernel(q_tokens, batch, threads=threads) as (q_i, b_i): + q_shared = T.alloc_shared([heads, _LATENT], T.bfloat16) + q_rope_shared = T.alloc_shared([heads, _ROPE], T.bfloat16) + kv_shared = T.alloc_shared([block_i, _LATENT], T.bfloat16) + kv_rope_shared = T.alloc_shared([block_i, _ROPE], T.bfloat16) + scores_shared = T.alloc_shared([heads, block_i], T.bfloat16) + out_shared = T.alloc_shared([heads, _LATENT], T.bfloat16) + valid = T.alloc_fragment([block_i], "bool") + scores = T.alloc_fragment([heads, block_i], T.float32) + output = T.alloc_fragment([heads, _LATENT], T.float32) + row_sum = T.alloc_fragment([heads], T.float32) + block_sum = T.alloc_fragment([heads], T.float32) + row_max = T.alloc_fragment([heads], T.float32) + previous_max = T.alloc_fragment([heads], T.float32) + alpha = T.alloc_fragment([heads], T.float32) + + T.copy(Q[b_i, q_i, :, :_LATENT], q_shared) + T.copy(Q[b_i, q_i, :, _LATENT:], q_rope_shared) + T.fill(output, 0) + T.fill(row_sum, 0) + T.fill(row_max, -(2**30)) + + for block in T.Pipelined(blocks, num_stages=num_stages): + for i in T.Parallel(block_i): + index = Indices[b_i, q_i, block * block_i + i] + valid[i] = (index >= 0) & (index < kv_tokens - 1) + for i, d in T.Parallel(block_i, _LATENT): + kv_shared[i, d] = KV[b_i, Indices[b_i, q_i, block * block_i + i], d] + for i, d in T.Parallel(block_i, _ROPE): + kv_rope_shared[i, d] = KV[ + b_i, Indices[b_i, q_i, block * block_i + i], _LATENT + d + ] + for h, i in T.Parallel(heads, block_i): + scores[h, i] = T.if_then_else(valid[i], 0, -T.infinity(T.float32)) + T.gemm( + q_shared, + kv_shared, + scores, + transpose_B=True, + policy=T.GemmWarpPolicy.FullRow, + ) + T.gemm( + q_rope_shared, + kv_rope_shared, + scores, + transpose_B=True, + policy=T.GemmWarpPolicy.FullRow, + ) + T.copy(row_max, previous_max) + T.reduce_max(scores, row_max, dim=1, clear=False) + for h in T.Parallel(heads): + row_max[h] = T.max(row_max[h], previous_max[h]) + alpha[h] = T.exp2((previous_max[h] - row_max[h]) * scale_log2) + for h, i in T.Parallel(heads, block_i): + scores[h, i] = T.exp2((scores[h, i] - row_max[h]) * scale_log2) + T.reduce_sum(scores, block_sum, dim=1) + for h in T.Parallel(heads): + row_sum[h] = row_sum[h] * alpha[h] + block_sum[h] + for h, d in T.Parallel(heads, _LATENT): + output[h, d] *= alpha[h] + T.copy(scores, scores_shared) + T.gemm( + scores_shared, kv_shared, output, policy=T.GemmWarpPolicy.FullRow + ) + + for h, d in T.Parallel(heads, _LATENT): + output[h, d] /= T.max(row_sum[h], 1e-20) + for h in T.Parallel(heads): + row_sum[h] = T.if_then_else( + row_sum[h] > 0, + (T.log2(row_sum[h]) + row_max[h] * scale_log2) * _LN_2, + -T.infinity(T.float32), + ) + T.copy(output, out_shared) + T.copy(out_shared, Output[b_i, q_i, :, :]) + T.copy(row_sum, Lse[b_i, q_i, :]) + + return main + + +@tilelang.jit(out_idx=[-1]) +def _delta(heads, block=32, num_stages=5): + batch = T.dynamic("batch") + tokens = T.dynamic("tokens") + shape = [batch, tokens, heads, _LATENT] + + @T.prim_func + def main( + Output: T.Tensor(shape, T.bfloat16), # type: ignore + GradOutput: T.Tensor(shape, T.bfloat16), # type: ignore + Delta: T.Tensor([batch, tokens, heads], T.float32), # type: ignore + ): + with T.Kernel(heads, T.ceildiv(tokens, block), batch) as (h_i, t_i, b_i): + output = T.alloc_fragment([block, block], T.float32) + grad = T.alloc_fragment([block, block], T.float32) + product = T.alloc_fragment([block, block], T.float32) + result = T.alloc_fragment([block], T.float32) + T.clear(product) + for d_i in T.Pipelined(T.ceildiv(_LATENT, block), num_stages=num_stages): + T.copy( + Output[ + b_i, + t_i * block : (t_i + 1) * block, + h_i, + d_i * block : (d_i + 1) * block, + ], + output, + ) + T.copy( + GradOutput[ + b_i, + t_i * block : (t_i + 1) * block, + h_i, + d_i * block : (d_i + 1) * block, + ], + grad, + ) + for i, d in T.Parallel(block, block): + product[i, d] += output[i, d] * grad[i, d] + T.reduce_sum(product, result, dim=1) + T.copy(result, Delta[b_i, t_i * block : (t_i + 1) * block, h_i]) + + return main + + +@tilelang.jit( + out_idx=[-2], + pass_configs={ + tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, + tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True, + }, +) +def _backward( + heads, + topk, + scale, + dkv_splits=_DKV_SPLITS, + block_i=32, + num_stages=0, + threads=256, + use_tcgen_dq=False, +): + batch = T.dynamic("batch") + q_tokens = T.dynamic("q_tokens") + kv_tokens = T.dynamic("kv_tokens") + assert topk % block_i == 0 + assert not use_tcgen_dq or ( + block_i in (32, 64) and threads == 256 and heads % 32 == 0 + ) + tcgen_group = 2 * block_i + q_shape = [batch, q_tokens, heads, _DIM] + kv_shape = [batch, kv_tokens, 1, _DIM] + grad_kv_shape = [batch, dkv_splits, kv_tokens, 1, _DIM] + out_shape = [batch, q_tokens, heads, _LATENT] + indices_shape = [batch, q_tokens, 1, topk] + row_shape = [batch, q_tokens, heads] + blocks = topk // block_i + scale_log2 = scale * _LOG2_E + split_store = 2 + + @T.macro + def prefetch_kv(KV, Indices, shared, b_i, q_i, offset, width, dim_offset): + for i, d in T.Parallel( + block_i, + width, + prefer_async=True, + annotations={"parallel_async_without_async_commit_wait": True}, + ): + shared[i, d] = KV[b_i, Indices[b_i, q_i, 0, offset + i], 0, dim_offset + d] + T.ptx_commit_group() + + @T.prim_func + def main( + Q: T.Tensor(q_shape, T.bfloat16), # type: ignore + KV: T.Tensor(kv_shape, T.bfloat16), # type: ignore + GradOutput: T.Tensor(out_shape, T.bfloat16), # type: ignore + Indices: T.Tensor(indices_shape, T.int32), # type: ignore + Lse: T.Tensor(row_shape, T.float32), # type: ignore + Delta: T.Tensor(row_shape, T.float32), # type: ignore + GradQ: T.Tensor(q_shape, T.bfloat16), # type: ignore + GradKV: T.Tensor(grad_kv_shape, T.float32), # type: ignore + ): + with T.Kernel(q_tokens, batch, threads=threads) as (q_i, b_i): + q_shared = T.alloc_shared([heads, _LATENT], T.bfloat16) + q_rope_shared = T.alloc_shared([heads, _ROPE], T.bfloat16) + kv_shared = T.alloc_shared([block_i, _LATENT], T.bfloat16) + kv_rope_shared = T.alloc_shared([block_i, _ROPE], T.bfloat16) + grad_out_shared = T.alloc_shared([heads, _LATENT], T.bfloat16) + probabilities_shared = T.alloc_shared([heads, block_i], T.bfloat16) + grad_scores_shared = T.alloc_shared([heads, block_i], T.bfloat16) + grad_q_shared = T.alloc_shared([heads, _LATENT], T.bfloat16) + grad_q_rope_shared = T.alloc_shared([heads, _ROPE], T.bfloat16) + if not use_tcgen_dq: + grad_kv_shared = T.alloc_shared( + [block_i // split_store, _LATENT], T.float32 + ) + grad_kv_rope_shared = T.alloc_shared( + [block_i // split_store, _ROPE], T.float32 + ) + if use_tcgen_dq: + grad_q_tmem = T.alloc_tmem([heads, _LATENT], T.float32) + grad_q_barrier = T.alloc_barrier(1) + valid = T.alloc_fragment([block_i], "bool") + probabilities = T.alloc_fragment([heads, block_i], T.float32) + grad_probabilities = T.alloc_fragment([heads, block_i], T.float32) + grad_q = T.alloc_fragment([heads, _LATENT], T.float32) + grad_q_rope = T.alloc_fragment([heads, _ROPE], T.float32) + grad_kv = T.alloc_fragment([block_i, _LATENT], T.float32) + if use_tcgen_dq: + grad_kv_tmem = T.alloc_tmem([block_i, _LATENT], T.float32) + grad_kv_barrier = T.alloc_barrier(1) + grad_kv_add_barrier = T.alloc_barrier(1) + T.annotate_layout( + { + grad_kv_tmem: T.Layout( + [block_i, _LATENT], + lambda i, j: [ + (j % 256) // tcgen_group * block_i + i, + (j // 256) * tcgen_group + j % tcgen_group, + ], + ), + grad_kv: T.Fragment( + [block_i, _LATENT], + forward_fn=lambda i, j: ( + (j // tcgen_group) * block_i + i, + j % tcgen_group, + ), + ), + } + ) + grad_kv_rope = T.alloc_fragment([block_i, _ROPE], T.float32) + + T.copy(Q[b_i, q_i, :, :_LATENT], q_shared) + T.copy(Q[b_i, q_i, :, _LATENT:], q_rope_shared) + T.copy(GradOutput[b_i, q_i, :, :], grad_out_shared) + if not use_tcgen_dq: + T.clear(grad_q) + T.clear(grad_q_rope) + + if use_tcgen_dq: + prefetch_kv(KV, Indices, kv_shared, b_i, q_i, 0, _LATENT, 0) + prefetch_kv(KV, Indices, kv_rope_shared, b_i, q_i, 0, _ROPE, _LATENT) + for block in ( + T.serial(blocks) + if use_tcgen_dq + else T.Pipelined(blocks, num_stages=num_stages) + ): + for i in T.Parallel(block_i): + index = Indices[b_i, q_i, 0, block * block_i + i] + valid[i] = (index >= 0) & (index < kv_tokens - 1) + for h, i in T.Parallel(heads, block_i): + probabilities[h, i] = T.if_then_else( + valid[i], 0, -T.infinity(T.float32) + ) + if use_tcgen_dq: + T.ptx_wait_group(0) + T.sync_threads() + if not use_tcgen_dq: + for i, d in T.Parallel(block_i, _LATENT): + kv_shared[i, d] = KV[ + b_i, + Indices[b_i, q_i, 0, block * block_i + i], + 0, + d, + ] + for i, d in T.Parallel(block_i, _ROPE): + kv_rope_shared[i, d] = KV[ + b_i, + Indices[b_i, q_i, 0, block * block_i + i], + 0, + _LATENT + d, + ] + T.gemm( + q_shared, + kv_shared, + probabilities, + transpose_B=True, + policy=T.GemmWarpPolicy.FullCol, + ) + T.gemm( + q_rope_shared, + kv_rope_shared, + probabilities, + transpose_B=True, + policy=T.GemmWarpPolicy.FullCol, + ) + for h, i in T.Parallel(heads, block_i): + probabilities[h, i] = T.if_then_else( + valid[i] & (Lse[b_i, q_i, h] > -1e30), + T.exp2( + (probabilities[h, i] * scale - Lse[b_i, q_i, h]) * _LOG2_E + ), + 0, + ) + T.copy(probabilities, probabilities_shared) + if use_tcgen_dq: + T.tcgen05_gemm( + probabilities_shared, + grad_out_shared, + grad_kv_tmem, + transpose_A=True, + clear_accum=True, + mbar=grad_kv_barrier, + ) + T.gemm( + grad_out_shared, + kv_shared, + grad_probabilities, + transpose_B=True, + policy=T.GemmWarpPolicy.FullCol, + clear_accum=True, + ) + for h, i in T.Parallel(heads, block_i): + grad_probabilities[h, i] = ( + probabilities[h, i] + * (grad_probabilities[h, i] - Delta[b_i, q_i, h]) + * scale + ) + if use_tcgen_dq: + T.mbarrier_wait_parity(grad_kv_barrier, block % 2) + T.copy(grad_probabilities, probabilities_shared) + T.tcgen05_gemm( + probabilities_shared, + kv_shared, + grad_q_tmem, + mbar=grad_q_barrier, + clear_accum=block == 0, + ) + # The next prefetch reuses kv_shared, so wait until TCGEN + # has finished reading the current block from it. + T.mbarrier_wait_parity(grad_q_barrier, block % 2) + if block + 1 < blocks: + prefetch_kv( + KV, + Indices, + kv_shared, + b_i, + q_i, + (block + 1) * block_i, + _LATENT, + 0, + ) + T.gemm( + probabilities_shared, + kv_rope_shared, + grad_q_rope, + policy=T.GemmWarpPolicy.FullCol, + ) + if block + 1 < blocks: + prefetch_kv( + KV, + Indices, + kv_rope_shared, + b_i, + q_i, + (block + 1) * block_i, + _ROPE, + _LATENT, + ) + else: + T.copy(grad_probabilities, grad_scores_shared) + T.gemm( + grad_scores_shared, + kv_shared, + grad_q, + policy=T.GemmWarpPolicy.FullCol, + ) + T.gemm( + grad_scores_shared, + kv_rope_shared, + grad_q_rope, + policy=T.GemmWarpPolicy.FullCol, + ) + if use_tcgen_dq: + T.tcgen05_gemm( + probabilities_shared, + q_shared, + grad_kv_tmem, + transpose_A=True, + mbar=grad_kv_add_barrier, + ) + T.clear(grad_kv_rope) + T.gemm( + probabilities_shared, + q_rope_shared, + grad_kv_rope, + transpose_A=True, + policy=T.GemmWarpPolicy.FullCol, + ) + T.mbarrier_wait_parity(grad_kv_add_barrier, block % 2) + T.copy(grad_kv_tmem, grad_kv) + for i, d in T.Parallel(block_i, _LATENT): + index = Indices[b_i, q_i, 0, block * block_i + i] + if (index >= 0) & (index < kv_tokens - 1): + T.atomic_add( + GradKV[b_i, q_i % dkv_splits, index, 0, d], + grad_kv[i, d], + ) + for i, d in T.Parallel(block_i, _ROPE): + index = Indices[b_i, q_i, 0, block * block_i + i] + if (index >= 0) & (index < kv_tokens - 1): + T.atomic_add( + GradKV[ + b_i, + q_i % dkv_splits, + index, + 0, + _LATENT + d, + ], + grad_kv_rope[i, d], + ) + else: + T.gemm( + grad_scores_shared, + q_shared, + grad_kv, + transpose_A=True, + policy=T.GemmWarpPolicy.FullCol, + clear_accum=True, + ) + T.gemm( + probabilities_shared, + grad_out_shared, + grad_kv, + transpose_A=True, + policy=T.GemmWarpPolicy.FullCol, + ) + T.clear(grad_kv_rope) + T.gemm( + grad_scores_shared, + q_rope_shared, + grad_kv_rope, + transpose_A=True, + policy=T.GemmWarpPolicy.FullCol, + ) + + for split in range(split_store): + for i, d in T.Parallel(block_i, _LATENT): + if i < block_i // split_store: + grad_kv_shared[i, d] = grad_kv[ + i + split * (block_i // split_store), d + ] + for i, d in T.Parallel(block_i, _ROPE): + if i < block_i // split_store: + grad_kv_rope_shared[i, d] = grad_kv_rope[ + i + split * (block_i // split_store), d + ] + for i, d in T.Parallel(block_i // split_store, _LATENT): + source = i + split * (block_i // split_store) + T.atomic_add( + GradKV[ + b_i, + q_i % dkv_splits, + Indices[b_i, q_i, 0, block * block_i + source], + 0, + d, + ], + grad_kv_shared[i, d], + ) + for i, d in T.Parallel(block_i // split_store, _ROPE): + source = i + split * (block_i // split_store) + T.atomic_add( + GradKV[ + b_i, + q_i % dkv_splits, + Indices[b_i, q_i, 0, block * block_i + source], + 0, + _LATENT + d, + ], + grad_kv_rope_shared[i, d], + ) + + if use_tcgen_dq: + T.copy(grad_q_tmem, grad_q) + T.copy(grad_q, grad_q_shared) + T.copy(grad_q_rope, grad_q_rope_shared) + T.copy(grad_q_shared, GradQ[b_i, q_i, :, :_LATENT]) + T.copy(grad_q_rope_shared, GradQ[b_i, q_i, :, _LATENT:]) + if use_tcgen_dq: + if T.get_thread_binding() // 32 == 0: + T.deallocate_tmem(grad_kv_tmem) + T.deallocate_tmem(grad_q_tmem) + + return main + + +def forward( + q: torch.Tensor, kv: torch.Tensor, indices: torch.Tensor, scale: float +) -> tuple[torch.Tensor, torch.Tensor]: + heads = q.shape[2] + kernel_heads = (heads + _HEAD_BLOCK - 1) // _HEAD_BLOCK * _HEAD_BLOCK + if kernel_heads != heads: + q = torch.cat( + (q, q.new_zeros((*q.shape[:2], kernel_heads - heads, q.shape[3]))), dim=2 + ) + kv = torch.cat((kv, kv.new_zeros((kv.shape[0], 1, kv.shape[2]))), dim=1) + sm_major = torch.cuda.get_device_capability(q.device)[0] + threads = 128 if sm_major == 10 and kernel_heads == 32 else 256 + with _preserve_env(): + output, lse = _forward( + int(kernel_heads), + int(indices.shape[-1]), + float(scale), + threads=threads, + )(q, kv, indices) + return output[:, :, :heads], lse[:, :, :heads] + + +def backward( + q: torch.Tensor, + kv: torch.Tensor, + indices: torch.Tensor, + output: torch.Tensor, + lse: torch.Tensor, + grad_output: torch.Tensor, + scale: float, +) -> tuple[torch.Tensor, torch.Tensor]: + heads = q.shape[2] + kernel_heads = (heads + _HEAD_BLOCK - 1) // _HEAD_BLOCK * _HEAD_BLOCK + if kernel_heads != heads: + pad_shape = (*q.shape[:2], kernel_heads - heads) + q = torch.cat((q, q.new_zeros((*pad_shape, q.shape[3]))), dim=2) + output = torch.cat( + (output, output.new_zeros((*pad_shape, output.shape[3]))), dim=2 + ) + grad_output = torch.cat( + (grad_output, grad_output.new_zeros((*pad_shape, grad_output.shape[3]))), + dim=2, + ) + lse = torch.cat((lse, lse.new_zeros(pad_shape)), dim=2) + kv = torch.cat((kv, kv.new_zeros((kv.shape[0], 1, kv.shape[2]))), dim=1) + with _preserve_env(): + delta = _delta(int(kernel_heads))(output, grad_output) + kv_grouped = kv.unsqueeze(2) + indices_grouped = indices.unsqueeze(2) + grad_kv = torch.zeros( + (kv.shape[0], _DKV_SPLITS, kv.shape[1], 1, kv.shape[2]), + device=kv.device, + dtype=torch.float32, + ) + sm_major = torch.cuda.get_device_capability(q.device)[0] + use_tcgen = sm_major == 10 and kernel_heads % 32 == 0 + grad_q = _backward( + int(kernel_heads), + int(indices.shape[-1]), + float(scale), + block_i=64 if use_tcgen else 32, + threads=256 if use_tcgen else min(256, int(kernel_heads) * 8), + use_tcgen_dq=use_tcgen, + )(q, kv_grouped, grad_output, indices_grouped, lse, delta, grad_kv) + return ( + grad_q[:, :, :heads], + grad_kv.sum(dim=1)[:, :-1].squeeze(2), + ) diff --git a/src/art/megatron/hybrid_ep_setup.py b/src/art/megatron/hybrid_ep_setup.py index f8413c7b4..759e4720d 100644 --- a/src/art/megatron/hybrid_ep_setup.py +++ b/src/art/megatron/hybrid_ep_setup.py @@ -59,30 +59,54 @@ def _arch_list() -> str: def _source_hash() -> str: digest = sha256() - for path in sorted(path for path in SOURCE.rglob("*") if path.is_file()): + for path in sorted( + path + for path in SOURCE.rglob("*") + if path.is_file() and "__pycache__" not in path.parts + ): digest.update(str(path.relative_to(SOURCE)).encode()) digest.update(path.read_bytes()) return digest.hexdigest() -def _build_identity() -> tuple[str, str]: +def _cuda_dependency_versions(cuda_home: Path) -> tuple[str, str]: + if torch.version.cuda and torch.version.cuda.startswith("12."): + return version("nvidia-cuda-cccl-cu12"), version("nvidia-nvtx-cu12") + if torch.version.cuda and torch.version.cuda.startswith("13."): + major, minor = torch.version.cuda.split(".")[:2] + cccl = _output( + ["dpkg-query", "-W", "-f=${Version}", f"cuda-cccl-{major}-{minor}"] + ) + return cccl, version("nvidia-nvtx") + raise RuntimeError(f"HybridEP does not support torch CUDA {torch.version.cuda}") + + +def _build_identity( + *, enable_multinode: bool | None = None, use_nixl: bool | None = None +) -> tuple[str, str]: cuda_home = _cuda_home() arch_list = _arch_list() digest = sha256() + if enable_multinode is None: + enable_multinode = os.environ.get("HYBRID_EP_MULTINODE", "0") == "1" + if use_nixl is None: + use_nixl = os.environ.get("USE_NIXL", "0") == "1" + if use_nixl and not enable_multinode: + raise ValueError("NIXL HybridEP requires multi-node support") + cccl_version, nvtx_version = _cuda_dependency_versions(cuda_home) values = [ _source_hash(), sys.implementation.cache_tag, platform.machine(), torch.__version__, str(torch.version.cuda), - torch.__config__.show(), - version("nvidia-cuda-cccl-cu12"), - version("nvidia-nvtx-cu12"), + cccl_version, + nvtx_version, _output([str(cuda_home / "bin" / "nvcc"), "--version"]), _output([os.environ.get("CXX", "c++"), "--version"]), arch_list, - os.environ.get("HYBRID_EP_MULTINODE", "0"), - os.environ.get("USE_NIXL", "0"), + str(int(enable_multinode)), + str(int(use_nixl)), ] for value in values: digest.update(value.encode()) @@ -99,6 +123,9 @@ def _installed_version() -> str | None: def _cache_root() -> Path: + root = os.environ.get("ART_MEGATRON_CACHE_ROOT") + if root: + return Path(root) / "hybrid_ep" return ( Path(os.environ.get("XDG_CACHE_HOME", Path.home() / ".cache")) / "art" @@ -182,12 +209,14 @@ def setup_hybrid_ep() -> str: return build_version -def validate_hybrid_ep() -> None: - expected, _ = _build_identity() - if (installed := _installed_version()) != expected: +def validate_hybrid_ep(*, require_multinode: bool = False) -> None: + candidates = [_build_identity(enable_multinode=True, use_nixl=True)[0]] + if not require_multinode: + candidates.append(_build_identity(enable_multinode=False, use_nixl=False)[0]) + if (installed := _installed_version()) not in candidates: raise RuntimeError( "HybridEP is not built for this ART source and Megatron environment " - f"(expected {expected}, found {installed}). Run Megatron setup." + f"(expected one of {candidates}, found {installed}). Run Megatron setup." ) diff --git a/src/art/megatron/identity_lora.py b/src/art/megatron/identity_lora.py new file mode 100644 index 000000000..fc0510da8 --- /dev/null +++ b/src/art/megatron/identity_lora.py @@ -0,0 +1,108 @@ +import os +from typing import Any +import warnings + +from peft.tuners.lora.config import LoraConfig +import torch + +from art.dev.get_model_config import default_target_modules + +from .lora_config import LORA_ALPHA, default_lora_rank_for_handler +from .model_support.lora_disk import normalize_lora_checkpoint_to_vllm +from .model_support.spec import ModelSupportHandler + + +def create_identity_lora( + base_model: str, + lora_path: str, + rank: int | None = None, + target_modules: list[str] | None = None, + lora_alpha: int = LORA_ALPHA, + random_state: int | None = None, + allow_unvalidated_arch: bool = False, + handler: ModelSupportHandler | None = None, +) -> None: + """Create an identity LoRA adapter for a Megatron model.""" + from unittest.mock import patch + + from accelerate import init_empty_weights + from peft import get_peft_model + from transformers import AutoConfig, AutoModelForCausalLM + + from .model_support import get_model_support_handler + + if random_state is not None: + torch.manual_seed(random_state) + target_modules = target_modules or default_target_modules(base_model) + handler = handler or get_model_support_handler( + base_model, allow_unvalidated_arch=allow_unvalidated_arch + ) + if rank is None: + rank = default_lora_rank_for_handler(handler) + base_config = AutoConfig.from_pretrained(base_model, trust_remote_code=True) + model_config = handler.identity_lora_model_config(base_config) + with init_empty_weights(): + model = AutoModelForCausalLM.from_config( + model_config, dtype=torch.bfloat16, trust_remote_code=True + ) + model.name_or_path = base_model + + lora_config = LoraConfig( + base_model_name_or_path=base_model, + r=rank, + lora_alpha=lora_alpha, + target_modules=[], + target_parameters=handler.identity_lora_target_parameters( + model, + target_modules=target_modules, + ), + bias="none", + ) + meta = torch.device("meta") + orig_to = torch.nn.Module.to + + def _skip_meta_to( + module: torch.nn.Module, *args: Any, **kwargs: Any + ) -> torch.nn.Module: + device = kwargs.get("device") or (args[0] if args else None) + if device == meta or str(device) == "meta": + dtype = kwargs.get("dtype") + return module if dtype is None else orig_to(module, dtype=dtype) + return orig_to(module, *args, **kwargs) + + with warnings.catch_warnings(): + if bool(getattr(handler, "is_moe", False)): + warnings.filterwarnings( + "ignore", + message=( + r"Unsupported layer type '.*MoeExperts.*' encountered, " + r"proceed at your own risk\." + ), + category=UserWarning, + module=r"peft\.tuners\.tuners_utils", + ) + with patch.object(torch.nn.Module, "to", _skip_meta_to): + peft_model = get_peft_model( + model, + lora_config, + autocast_adapter_dtype=False, + ) + + os.makedirs(lora_path, exist_ok=True) + peft_model.save_pretrained(lora_path) + final_config = LoraConfig( + base_model_name_or_path=base_model, + r=rank, + lora_alpha=lora_alpha, + target_modules=target_modules, + bias="none", + ).to_dict() + normalize_lora_checkpoint_to_vllm( + lora_path, + handler=handler, + adapter_config=final_config, + ) + del peft_model, model + if torch.cuda.is_initialized(): + torch.cuda.synchronize() + torch.cuda.empty_cache() diff --git a/src/art/megatron/kernels/cute_grouped_lora_quack.py b/src/art/megatron/kernels/cute_grouped_lora_quack.py index c0c9a70d6..804b64e58 100644 --- a/src/art/megatron/kernels/cute_grouped_lora_quack.py +++ b/src/art/megatron/kernels/cute_grouped_lora_quack.py @@ -11,10 +11,62 @@ from quack.gemm import gemm as quack_gemm import torch +import triton +import triton.language as tl _PADDED_LOW_RANK_TARGET = 8 +@triton.jit +def _grouped_lora_wgrad_kernel( + big, + small, + expert_offsets, + out, + alpha, + BIG_D_STRIDE: tl.constexpr, + BIG_K_STRIDE: tl.constexpr, + SMALL_R_STRIDE: tl.constexpr, + SMALL_K_STRIDE: tl.constexpr, + D: tl.constexpr, + R: tl.constexpr, + TRANSPOSE_OUT: tl.constexpr, + BLOCK_D: tl.constexpr, + BLOCK_R: tl.constexpr, + BLOCK_K: tl.constexpr, +) -> None: + expert = tl.program_id(2) + d = (tl.program_id(0) * BLOCK_D + tl.arange(0, BLOCK_D)).to(tl.int64) + r = (tl.program_id(1) * BLOCK_R + tl.arange(0, BLOCK_R)).to(tl.int64) + start = tl.load(expert_offsets + expert) + end = tl.load(expert_offsets + expert + 1) + acc = tl.zeros((BLOCK_D, BLOCK_R), tl.float32) + for k0 in tl.range(start, end, BLOCK_K, num_stages=3): + k = (k0 + tl.arange(0, BLOCK_K)).to(tl.int64) + big_tile = tl.load( + big + d[:, None] * BIG_D_STRIDE + k[None, :] * BIG_K_STRIDE, + mask=(d[:, None] < D) & (k[None, :] < end), + other=0.0, + ) + small_tile = tl.load( + small + r[:, None] * SMALL_R_STRIDE + k[None, :] * SMALL_K_STRIDE, + mask=(r[:, None] < R) & (k[None, :] < end), + other=0.0, + ) + acc += tl.dot(big_tile, tl.trans(small_tile)) + base = expert * D * R + out_offsets = ( + base + r[None, :] * D + d[:, None] + if TRANSPOSE_OUT + else base + d[:, None] * R + r[None, :] + ) + tl.store( + out + out_offsets, + acc * alpha, + mask=(d[:, None] < D) & (r[None, :] < R), + ) + + def _validate_rank(rank: int) -> None: if rank <= 0: raise ValueError(f"Grouped LoRA QuACK backend requires rank > 0, got {rank}") @@ -264,7 +316,12 @@ def _varlen_quack_gemm( tile_n: int, alpha: float = 1.0, out: torch.Tensor | None = None, + residual: torch.Tensor | None = None, ) -> torch.Tensor: + if residual is not None: + if out is not None and out is not residual: + raise ValueError("Residual grouped GEMM requires aliased output") + out = residual if out is None: out = torch.empty( a.shape[0], @@ -285,7 +342,7 @@ def _varlen_quack_gemm( a, b, out, - None, + residual, None, tile_M=tile_m, tile_N=tile_n, @@ -310,6 +367,42 @@ def _varlen_quack_gemm_k( tile_n: int, alpha: float = 1.0, ) -> torch.Tensor: + # QuACK's SM100 varlen-K scheduler can fault under the integrated async launch + # sequence; keep its faster grouped GEMM path on Hopper and use exact ragged + # spans for the small-rank LoRA parameter gradients on Blackwell. + if torch.cuda.get_device_capability(a.device)[0] >= 10: + transpose_out = out_shape_m <= out_shape_n + big, small = (b, a) if transpose_out else (a, b) + d, rank = big.shape[0], small.shape[0] + out = torch.empty( + batch_count, + out_shape_m, + out_shape_n, + device=a.device, + dtype=a.dtype, + ) + block_r = min(triton.next_power_of_2(rank), 32) + cast(Any, _grouped_lora_wgrad_kernel)[ + (triton.cdiv(d, 64), triton.cdiv(rank, block_r), batch_count) + ]( + big, + small, + expert_offsets, + out, + alpha, + BIG_D_STRIDE=big.stride(0), + BIG_K_STRIDE=big.stride(1), + SMALL_R_STRIDE=small.stride(0), + SMALL_K_STRIDE=small.stride(1), + D=d, + R=rank, + TRANSPOSE_OUT=transpose_out, + BLOCK_D=64, + BLOCK_R=block_r, + BLOCK_K=32, + num_warps=4, + ) + return out out = torch.empty( batch_count, out_shape_m, @@ -343,7 +436,20 @@ def forward( b_t: torch.Tensor, counts: torch.Tensor, scale: float, + residual: torch.Tensor | None, ) -> torch.Tensor: + has_residual = residual is not None + if residual is not None: + if not residual.is_contiguous(): + raise ValueError("Residual grouped LoRA requires contiguous output") + residual = torch.empty( + 0, device=residual.device, dtype=residual.dtype + ).set_( + residual.untyped_storage(), + residual.storage_offset(), + residual.size(), + residual.stride(), + ) expert_offsets = _build_expert_offsets(counts, device=x.device) actual_rank = a_t.shape[-1] effective_rank = _effective_rank(actual_rank) @@ -368,12 +474,13 @@ def forward( tile_m=64, tile_n=_matmul_tile_n(b_t.shape[-1]), alpha=scale, + residual=residual, ) - ctx.save_for_backward(x, a_t_eff, b_t_eff, tmp, expert_offsets) ctx.actual_rank = actual_rank ctx.effective_rank = effective_rank ctx.scale = scale + ctx.has_residual = has_residual return out @staticmethod @@ -435,6 +542,7 @@ def backward(ctx, *grad_outputs: Any): grad_b_eff[:, :actual_rank, :].contiguous(), None, None, + grad_out if ctx.has_residual else None, ) @@ -651,7 +759,30 @@ def quack_grouped_lora( synchronization in the hot path. """ counts_tensor = _validate_inputs(x, a_t, b_t, counts) - return _QuackGroupedLoraFn.apply(x, a_t, b_t, counts_tensor, scale) + return _QuackGroupedLoraFn.apply(x, a_t, b_t, counts_tensor, scale, None) + + +@torch.compiler.disable +def quack_grouped_lora_residual( + residual: torch.Tensor, + x: torch.Tensor, + a_t: torch.Tensor, + b_t: torch.Tensor, + counts: list[int] | torch.Tensor, + scale: float = 1.0, +) -> torch.Tensor: + """Consume a base output and accumulate grouped LoRA into its storage.""" + counts_tensor = _validate_inputs(x, a_t, b_t, counts) + expected = (x.shape[0], b_t.shape[-1]) + if residual.shape != expected: + raise ValueError( + f"Expected residual shape {expected}, got {tuple(residual.shape)}" + ) + if residual.device != x.device or residual.dtype != x.dtype: + raise ValueError("Residual must match grouped LoRA input device and dtype") + if x.shape[0] == 0: + return residual + return _QuackGroupedLoraFn.apply(x, a_t, b_t, counts_tensor, scale, residual) @torch.compiler.disable diff --git a/src/art/megatron/lora.py b/src/art/megatron/lora.py index 5bc10ed93..d71c7f48c 100644 --- a/src/art/megatron/lora.py +++ b/src/art/megatron/lora.py @@ -30,16 +30,20 @@ from megatron.core.transformer.transformer_layer import TransformerLayer import torch +from .expert_parallel import get_expert_parallel_layout from .kernels.cute_grouped_lora_quack import ( quack_grouped_lora, quack_grouped_lora_dual, ) +from .lora_config import ( + DENSE_LORA_RANK, + LORA_ALPHA, + MEGATRON_LORA_RANK_ENV, + MEGATRON_LORA_TARGET_MODULES_ENV, + MOE_LORA_RANK, + default_lora_rank_for_handler, +) -MOE_LORA_RANK = 1 -DENSE_LORA_RANK = 8 -LORA_ALPHA = 32 -MEGATRON_LORA_RANK_ENV = "ART_MEGATRON_LORA_RANK" -MEGATRON_LORA_TARGET_MODULES_ENV = "ART_MEGATRON_LORA_TARGET_MODULES" _LAYER_BLOCK_RE = re.compile(r"^(?P.*\.layers\.\d+)\.") ShardDomain = Literal["tp", "expert_tp"] @@ -189,6 +193,8 @@ class _LoraPublishTemplate(NamedTuple): shape: tuple[int, ...] dtype_name: str num_local_experts: int + expert_layout: tuple[int | None, ...] + is_expert: bool shard_domain: ShardDomain sharded: bool shard_world_size: int @@ -197,6 +203,15 @@ class _LoraPublishTemplate(NamedTuple): component_sizes: tuple[int, ...] +def _template_expert_ids( + template: _LoraPublishTemplate, ep_rank: int +) -> tuple[int | None, ...]: + start = ep_rank * template.num_local_experts + if template.expert_layout: + return template.expert_layout[start : start + template.num_local_experts] + return tuple(range(start, start + template.num_local_experts)) + + def _distributed_initialized() -> bool: is_initialized = getattr(torch.distributed, "is_initialized", None) return ( @@ -291,10 +306,6 @@ def _linear_disables_tensor_parallel_comm(linear: Any) -> bool: ) -def default_lora_rank_for_handler(handler: Any) -> int: - return MOE_LORA_RANK if bool(getattr(handler, "is_moe", False)) else DENSE_LORA_RANK - - def _configured_lora_rank(provider: Any, handler: Any) -> int: rank = getattr(provider, "_art_lora_rank", None) if rank is None: @@ -315,6 +326,20 @@ def _configured_lora_target_modules(provider: Any, spec: Any) -> list[str]: return [str(target_module) for target_module in target_modules] +def _compile_disabled_collective(function: _F) -> _F: + return cast( + _F, + torch.compiler.disable( + getattr(function, "_torchdynamo_orig_callable", function) + ), + ) + + +_gather_lora_sequence_parallel_region = _compile_disabled_collective( + gather_from_sequence_parallel_region +) + + def _column_parallel_lora_input(x: torch.Tensor, linear: Any) -> torch.Tensor: if _linear_disables_tensor_parallel_comm(linear): return x @@ -322,7 +347,8 @@ def _column_parallel_lora_input(x: torch.Tensor, linear: Any) -> torch.Tensor: bool(getattr(linear, "sequence_parallel", False)) and int(getattr(linear, "tp_size", 1)) > 1 ): - return gather_from_sequence_parallel_region(x) + # Torch 2.11 compiled autograd drops the gather's input-gradient edge. + return _gather_lora_sequence_parallel_region(x) return x @@ -464,9 +490,12 @@ def __init__( allreduce: bool = True, ) -> None: super().__init__() - assert num_local_experts == 1 or "{expert}" in adapter_model_prefix, ( - "adapter_model_prefix must contain the '{expert}' format placeholder if num_local_experts > 1" - ) + is_expert = "{expert}" in adapter_model_prefix + if num_local_experts < 1 or (num_local_experts != 1 and not is_expert): + raise ValueError( + "num_local_experts must be positive and requires an '{expert}' " + "adapter_model_prefix when greater than one" + ) self.adapter_model_prefix = adapter_model_prefix self.alpha = float(alpha) self.in_features = int(in_features) @@ -474,16 +503,16 @@ def __init__( self.scale = alpha / rank self._slot_modules = torch.nn.ModuleDict() self._slot_keys: dict[LoRASlotRef, str] = {} - self.A_T = torch.nn.Parameter( - torch.zeros( - num_local_experts, in_features, rank, dtype=dtype, device=device - ).squeeze(0) + a_shape = ( + (num_local_experts, in_features, rank) if is_expert else (in_features, rank) ) - self.B_T = torch.nn.Parameter( - torch.zeros( - num_local_experts, rank, out_features, dtype=dtype, device=device - ).squeeze(0) + b_shape = ( + (num_local_experts, rank, out_features) + if is_expert + else (rank, out_features) ) + self.A_T = torch.nn.Parameter(torch.zeros(a_shape, dtype=dtype, device=device)) + self.B_T = torch.nn.Parameter(torch.zeros(b_shape, dtype=dtype, device=device)) _set_lora_parallel_metadata( self.A_T, parallel_spec=a_parallel_spec, @@ -495,11 +524,39 @@ def __init__( allreduce=allreduce, ) self._expert_offset = ps.get_expert_model_parallel_rank() * num_local_experts + self._expert_ids: tuple[int | None, ...] = tuple( + range(self._expert_offset, self._expert_offset + num_local_experts) + ) + self._expert_layout: tuple[int | None, ...] = () self.reset_lora_parameters() @property def num_local_experts(self) -> int: - return self.A_T.shape[0] if self.A_T.ndim == 3 else 1 + return self.A_T.shape[0] if self.is_expert else 1 + + @property + def is_expert(self) -> bool: + return "{expert}" in self.adapter_model_prefix + + @property + def expert_ids(self) -> tuple[int | None, ...]: + return self._expert_ids + + def bind_expert_layout( + self, + expert_ids: tuple[int | None, ...], + physical_to_logical: tuple[int | None, ...], + ) -> None: + if not self.is_expert or len(expert_ids) != self.num_local_experts: + raise ValueError( + f"{self.adapter_model_prefix}: invalid local expert layout {expert_ids}" + ) + self._expert_ids = expert_ids + self._expert_layout = physical_to_logical + for local_expert, logical_expert in enumerate(expert_ids): + if logical_expert is None: + self.A_T.data[local_expert].zero_() + self.B_T.data[local_expert].zero_() def _broadcast_if_replicated(self, param: torch.nn.Parameter) -> None: if not param.lora_tp_replicated: # ty: ignore[unresolved-attribute] @@ -528,9 +585,12 @@ def _broadcast_if_replicated(self, param: torch.nn.Parameter) -> None: def reset_lora_parameters(self) -> None: """Initialize LoRA weights (A=Kaiming, B=zeros) like PEFT defaults.""" - if self.A_T.ndim == 3: - for expert in range(self.A_T.shape[0]): - torch.nn.init.kaiming_uniform_(self.A_T[expert].T, a=math.sqrt(5)) + if self.is_expert: + for expert, logical_expert in enumerate(self.expert_ids): + if logical_expert is None: + torch.nn.init.zeros_(self.A_T[expert]) + else: + torch.nn.init.kaiming_uniform_(self.A_T[expert].T, a=math.sqrt(5)) else: torch.nn.init.kaiming_uniform_(self.A_T.T, a=math.sqrt(5)) torch.nn.init.zeros_(self.B_T) @@ -538,10 +598,11 @@ def reset_lora_parameters(self) -> None: self._broadcast_if_replicated(self.B_T) def _expected_weight_keys(self, suffix: str) -> list[str]: - if self.num_local_experts > 1: + if self.is_expert: return [ - f"{self.adapter_model_prefix.format(expert=expert + self._expert_offset)}.{suffix}.weight" - for expert in range(self.num_local_experts) + f"{self.adapter_model_prefix.format(expert=expert)}.{suffix}.weight" + for expert in self.expert_ids + if expert is not None ] return [f"{self.adapter_model_prefix}.{suffix}.weight"] @@ -617,6 +678,8 @@ def _adapter_weights( for suffix in ("lora_A", "lora_B") for key in self._expected_weight_keys(suffix) ] + if not all_keys: + return torch.zeros_like(self.A_T), torch.zeros_like(self.B_T) missing = [key for key in all_keys if key not in adapter_model] if len(missing) == len(all_keys) and not require: return None @@ -638,8 +701,16 @@ def _adapter_weight( suffix: str, ) -> torch.Tensor: keys = self._expected_weight_keys(suffix) - if self.num_local_experts > 1: - return torch.stack([adapter_model[key].T for key in keys]) + if self.is_expert: + loaded = [adapter_model[key].T for key in keys] + first = loaded[0] + real_weights = iter(loaded) + return torch.stack( + [ + torch.zeros_like(first) if expert is None else next(real_weights) + for expert in self.expert_ids + ] + ) return adapter_model[keys[0]].T def _localized_weight( @@ -700,7 +771,7 @@ def _should_export_parameter(self, param: torch.nn.Parameter) -> bool: Determine if the given LoRA param should be exported in the sharded LoRA state dict (drop replicated ranks/params). """ - if self.num_local_experts > 1: # self is a MoE layer + if self.is_expert: if ps.get_expert_data_parallel_rank() != 0: return False else: # self is a non-MoE layer @@ -761,10 +832,12 @@ def _export_items( for key, param in self._lora_params(ref): if not self._should_export_parameter(param): continue - if self.num_local_experts > 1: - for expert in range(self.num_local_experts): - full_key = f"{self.adapter_model_prefix.format(expert=expert + self._expert_offset)}.{key}" - export_items.append((full_key, param, expert)) + if self.is_expert: + for local_expert, logical_expert in enumerate(self.expert_ids): + if logical_expert is None: + continue + full_key = f"{self.adapter_model_prefix.format(expert=logical_expert)}.{key}" + export_items.append((full_key, param, local_expert)) else: export_items.append((f"{self.adapter_model_prefix}.{key}", param, None)) return export_items @@ -822,9 +895,7 @@ def forward( return x.new_zeros((*x.shape[:-1], self.out_features)) a_t, b_t, scale = active if tokens_per_expert is not None: - assert self.num_local_experts > 1, ( - "tokens_per_expert is only supported if num_local_experts > 1" - ) + assert self.is_expert, "tokens_per_expert requires expert LoRA" bsz = tokens_per_expert if isinstance(bsz, list): bsz = torch.tensor(bsz, dtype=torch.int64, device="cpu") @@ -835,6 +906,16 @@ def forward( return out if scale == 1.0 else out * scale +def _bind_expert_lora_layout(experts: Any, *loras: LoRA) -> None: + layout = get_expert_parallel_layout(getattr(experts, "config", None)) + if layout is None: + return + ep_rank = int(experts.ep_group.rank()) + expert_ids = layout.local_logical_experts(ep_rank) + for lora in loras: + lora.bind_expert_layout(expert_ids, layout.physical_to_logical) + + class LoRAPublishPlanner: def __init__( self, @@ -887,6 +968,8 @@ def _collect_templates( shape=_exported_param_shape(module, param), dtype_name=_dtype_name(param.dtype), num_local_experts=module.num_local_experts, + expert_layout=module._expert_layout, + is_expert=module.is_expert, shard_domain=shard_domain, sharded=sharded, shard_world_size=( @@ -918,7 +1001,7 @@ def _metadata_for_template( adapter_dtypes: dict[str, torch.dtype], ) -> list[LoraShardMeta]: shard_ranks = range(template.shard_world_size) if template.sharded else (0,) - if template.num_local_experts <= 1: + if not template.is_expert: tp_ranks = ( _process_group_ranks(ps.get_tensor_model_parallel_group()) if _distributed_initialized() @@ -943,8 +1026,8 @@ def _metadata_for_template( shard_rank, ) for ep_rank in range(ep_world_size) - for local_expert in range(template.num_local_experts) - for expert in [ep_rank * template.num_local_experts + local_expert] + for expert in _template_expert_ids(template, ep_rank) + if expert is not None for shard_rank in shard_ranks ] return [ @@ -1033,7 +1116,7 @@ def _expert_owner_rank(ep_rank: int, shard_rank: int) -> int: def _exported_param_shape(module: LoRA, param: torch.nn.Parameter) -> tuple[int, ...]: - if module.num_local_experts > 1: + if module.is_expert: return tuple(int(dim) for dim in param[0].T.shape) return tuple(int(dim) for dim in param.T.shape) @@ -1104,6 +1187,7 @@ def _parallel_lora( grad_sync_domain: GradSyncDomain = TP_DEFAULT_GRAD_SYNC_DOMAIN, allreduce: bool = True, num_local_experts: int = 1, + lora_cls: type[LoRA] = LoRA, ) -> LoRA: weight = getattr(linear, "weight0", None) if weight is None: @@ -1124,7 +1208,7 @@ def _parallel_lora( grad_sync_domain=grad_sync_domain, grad_sync_op=GRAD_SYNC_OP_SUM if row_layout else GRAD_SYNC_OP_NONE, ) - return LoRA( + return lora_cls( adapter_model_prefix=adapter_model_prefix, in_features=linear.in_features, out_features=out_features, @@ -1149,8 +1233,9 @@ def _parallel_lora_pair( layout: Literal["column", "row"], suffixes: tuple[str, str], num_local_experts: int = 1, + lora_cls: type[LoRA] = LoRA, ) -> tuple[LoRA, LoRA]: - expert_parallel = num_local_experts > 1 + expert_parallel = "{expert}" in adapter_model_prefix return cast( tuple[LoRA, LoRA], tuple( @@ -1169,6 +1254,7 @@ def _parallel_lora_pair( ), allreduce=not expert_parallel, num_local_experts=num_local_experts, + lora_cls=lora_cls, ) for suffix in suffixes ), @@ -1184,6 +1270,7 @@ def __init__( alpha: float, provider: GPTModelProvider, reduce_output: bool = True, + lora_cls: type[LoRA] = LoRA, ) -> None: super().__init__() self.provider = provider @@ -1196,6 +1283,7 @@ def __init__( rank=rank, alpha=alpha, layout="row", + lora_cls=lora_cls, ) def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor | None]: @@ -1212,6 +1300,18 @@ def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor | None]: return base_output + lora_output, bias_output +def _install_replicated_qkv_all_gather_compile_boundary() -> None: + from megatron.core.transformer import attention + + # Torch 2.11 compiled autograd drops LoRA parameter edges through this gather. + gather = attention.all_gather_last_dim_from_tensor_parallel_region + if getattr(gather, "_art_replicated_qkv_compile_boundary", False): + return + gather = _compile_disabled_collective(gather) + setattr(gather, "_art_replicated_qkv_compile_boundary", True) + attention.all_gather_last_dim_from_tensor_parallel_region = gather + + class SelfAttentionLinearQKVLoRA(torch.nn.Module): def __init__( self, @@ -1239,32 +1339,47 @@ def __init__( total_out_features_per_rank = int(weight.shape[0]) kv_out_features = self.provider.kv_channels * self.provider.num_query_groups tp_world_size = ps.get_tensor_model_parallel_world_size() - assert kv_out_features % tp_world_size == 0, ( - "kv_out_features must be divisible by tensor parallel size" - ) q_out_features = self.provider.kv_channels * self.provider.num_attention_heads - assert q_out_features % tp_world_size == 0, ( - "q_out_features must be divisible by tensor parallel size" - ) - q_out_features_per_rank = q_out_features // tp_world_size - kv_out_features_per_rank = kv_out_features // tp_world_size self.attention_output_gate = bool( getattr(self.provider, "attention_output_gate", False) ) - q_and_gate_out_features_per_rank = total_out_features_per_rank - ( - 2 * kv_out_features_per_rank - ) - expected_q_out_features_per_rank = q_out_features_per_rank * ( - 2 if self.attention_output_gate else 1 - ) - assert q_and_gate_out_features_per_rank == expected_q_out_features_per_rank, ( - "Unexpected per-rank QKV packing for this attention layout" - ) + gate_multiplier = 2 if self.attention_output_gate else 1 + self.replicated_qkv = self.provider.num_query_groups < tp_world_size + if self.replicated_qkv: + # Megatron forms global packed QKV, then gives each TP rank one slice. + _install_replicated_qkv_all_gather_compile_boundary() + q_and_gate_out_features_per_rank = q_out_features * gate_multiplier + kv_out_features_per_rank = kv_out_features + packed_width = q_and_gate_out_features_per_rank + 2 * kv_out_features + if packed_width != total_out_features_per_rank * tp_world_size: + raise ValueError( + "Unexpected replicated-KV QKV packing: " + f"global width {packed_width}, local width " + f"{total_out_features_per_rank}, TP {tp_world_size}" + ) + self.num_query_groups_per_partition = self.provider.num_query_groups + else: + assert kv_out_features % tp_world_size == 0, ( + "kv_out_features must be divisible by tensor parallel size" + ) + assert q_out_features % tp_world_size == 0, ( + "q_out_features must be divisible by tensor parallel size" + ) + q_out_features_per_rank = q_out_features // tp_world_size + kv_out_features_per_rank = kv_out_features // tp_world_size + q_and_gate_out_features_per_rank = total_out_features_per_rank - ( + 2 * kv_out_features_per_rank + ) + expected_q_out_features_per_rank = q_out_features_per_rank * gate_multiplier + assert ( + q_and_gate_out_features_per_rank == expected_q_out_features_per_rank + ), "Unexpected per-rank QKV packing for this attention layout" + self.num_query_groups_per_partition = ( + self.provider.num_query_groups // tp_world_size + ) + self.tp_rank = ps.get_tensor_model_parallel_rank() self.q_and_gate_out_features_per_rank = q_and_gate_out_features_per_rank self.kv_out_features_per_rank = kv_out_features_per_rank - self.num_query_groups_per_partition = ( - self.provider.num_query_groups // tp_world_size - ) self.num_attention_heads_per_group = ( self.provider.num_attention_heads // self.provider.num_query_groups ) @@ -1276,6 +1391,7 @@ def __init__( rank=rank, alpha=alpha, out_features=q_and_gate_out_features_per_rank, + replicated=self.replicated_qkv, ) if _targets_include(target_modules, "q_proj") else None @@ -1287,6 +1403,7 @@ def __init__( rank=rank, alpha=alpha, out_features=kv_out_features_per_rank, + replicated=self.replicated_qkv, ) if _targets_include(target_modules, "k_proj") else None @@ -1298,6 +1415,7 @@ def __init__( rank=rank, alpha=alpha, out_features=kv_out_features_per_rank, + replicated=self.replicated_qkv, ) if _targets_include(target_modules, "v_proj") else None @@ -1311,8 +1429,23 @@ def _build_qkv_lora( rank: int, alpha: float, out_features: int, + replicated: bool, ) -> LoRA: assert isinstance(linear_qkv.weight, torch.Tensor) + if replicated: + parallel_spec = LoRAParallelSpec(grad_sync_op=GRAD_SYNC_OP_SUM) + return LoRA( + adapter_model_prefix=adapter_model_prefix, + in_features=linear_qkv.in_features, + out_features=out_features, + rank=rank, + alpha=alpha, + dtype=linear_qkv.weight.dtype, + device=linear_qkv.weight.device, + a_parallel_spec=parallel_spec, + b_parallel_spec=parallel_spec, + allreduce=True, + ) a_parallel_spec = LoRAParallelSpec( shard_domain="tp", sharded=False, @@ -1378,29 +1511,32 @@ def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor | None]: self.kv_out_features_per_rank, ) query_and_gate_5d = query_and_gate.reshape( - query_and_gate.shape[0], - query_and_gate.shape[1], + *query_and_gate.shape[:-1], self.num_query_groups_per_partition, self.num_attention_heads_per_group * (2 if self.attention_output_gate else 1), self.hidden_size_per_attention_head, ) key_5d = key.reshape( - key.shape[0], - key.shape[1], + *key.shape[:-1], self.num_query_groups_per_partition, 1, self.hidden_size_per_attention_head, ) value_5d = value.reshape( - value.shape[0], - value.shape[1], + *value.shape[:-1], self.num_query_groups_per_partition, 1, self.hidden_size_per_attention_head, ) - qkv_5d = torch.cat([query_and_gate_5d, key_5d, value_5d], dim=3) - adapter_output = qkv_5d.reshape(qkv_5d.shape[0], qkv_5d.shape[1], -1) + adapter_output = torch.cat( + [query_and_gate_5d, key_5d, value_5d], dim=-2 + ).flatten(-3) + if self.replicated_qkv: + local_width = linear_output.shape[-1] + adapter_output = adapter_output.narrow( + -1, self.tp_rank * local_width, local_width + ) return linear_output + adapter_output, bias @@ -1597,6 +1733,7 @@ def __init__( linear_fc1: TEColumnParallelLinear | TELayerNormColumnParallelLinear, rank: int, alpha: float, + lora_cls: type[LoRA] = LoRA, ) -> None: super().__init__() if isinstance(linear_fc1, TELayerNormColumnParallelLinear): @@ -1611,6 +1748,7 @@ def __init__( alpha=alpha, layout="column", suffixes=("gate_proj", "up_proj"), + lora_cls=lora_cls, ) def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor | None]: @@ -1653,6 +1791,7 @@ def __init__( rank: int, alpha: float, provider: GPTModelProvider, + lora_cls: type[LoRA] = LoRA, ) -> None: super().__init__() self.row_parallel_lora = SelfAttentionLinearProjLoRA( @@ -1662,6 +1801,7 @@ def __init__( alpha=alpha, provider=provider, reduce_output=not _linear_disables_tensor_parallel_comm(linear_fc2), + lora_cls=lora_cls, ) def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor | None]: @@ -1779,6 +1919,7 @@ def wrap_grouped_moe_experts( alpha: int, fused_gate_up: bool = False, ) -> None: + expert_loras: list[LoRA] = [] wrap_fc1 = ( _targets_include(target_modules, "experts") if fused_gate_up @@ -1799,6 +1940,11 @@ def wrap_grouped_moe_experts( fused_gate_up=fused_gate_up, ) setattr(experts, "linear_fc1", linear_fc1_lora) + expert_loras.extend( + (linear_fc1_lora.lora,) + if fused_gate_up + else (linear_fc1_lora.gate_lora, linear_fc1_lora.up_lora) + ) wrap_fc2 = ( wrap_fc1 if fused_gate_up else _targets_include(target_modules, "down_proj") ) @@ -1816,6 +1962,8 @@ def wrap_grouped_moe_experts( num_local_experts=experts.num_local_experts, ) setattr(experts, "linear_fc2", linear_fc2_lora) + expert_loras.append(linear_fc2_lora.lora) + _bind_expert_lora_layout(experts, *expert_loras) def wrap_split_mlp_lora( @@ -1826,6 +1974,7 @@ def wrap_split_mlp_lora( target_modules: set[str], rank: int, alpha: int, + lora_cls: type[LoRA] = LoRA, ) -> None: if _targets_include(target_modules, "gate_proj", "up_proj"): linear_fc1 = _unwrap_attr( @@ -1838,6 +1987,7 @@ def wrap_split_mlp_lora( linear_fc1=linear_fc1, rank=rank, alpha=alpha, + lora_cls=lora_cls, ) if _targets_include(target_modules, "down_proj"): linear_fc2 = _unwrap_attr( @@ -1851,6 +2001,7 @@ def wrap_split_mlp_lora( rank=rank, alpha=alpha, provider=provider, + lora_cls=lora_cls, ) @@ -1880,6 +2031,7 @@ def wrap_dense_mlp( target_modules: set[str], rank: int, alpha: int, + lora_cls: type[LoRA] = LoRA, ) -> None: wrap_split_mlp_lora( mlp, @@ -1888,6 +2040,7 @@ def wrap_dense_mlp( target_modules=target_modules, rank=rank, alpha=alpha, + lora_cls=lora_cls, ) @@ -1899,6 +2052,7 @@ def wrap_shared_experts_mlp( target_modules: set[str], rank: int, alpha: int, + lora_cls: type[LoRA] = LoRA, ) -> None: wrap_split_mlp_lora( shared_experts, @@ -1907,6 +2061,7 @@ def wrap_shared_experts_mlp( target_modules=target_modules, rank=rank, alpha=alpha, + lora_cls=lora_cls, ) @@ -1967,26 +2122,3 @@ def iter_lora_slot_parameters( continue seen.add(param_id) yield param - - -def iter_lora_sites( - model: Sequence[torch.nn.Module], -) -> Iterator[tuple[str, torch.nn.Parameter, torch.nn.Parameter]]: - """Yield every ambient and dynamic LoRA parameter pair exactly once.""" - seen: set[int] = set() - for chunk in model: - for module in chunk.modules(): - prefix = getattr(module, "adapter_model_prefix", None) - a_t = getattr(module, "A_T", None) - b_t = getattr(module, "B_T", None) - if ( - not isinstance(prefix, str) - or not isinstance(a_t, torch.nn.Parameter) - or not isinstance(b_t, torch.nn.Parameter) - or id(module) in seen - ): - continue - seen.add(id(module)) - yield prefix, a_t, b_t - for slot in getattr(module, "_slot_modules", {}).values(): - yield prefix, slot.A_T, slot.B_T diff --git a/src/art/megatron/lora_config.py b/src/art/megatron/lora_config.py new file mode 100644 index 000000000..d84ddecdc --- /dev/null +++ b/src/art/megatron/lora_config.py @@ -0,0 +1,11 @@ +from typing import Any + +MOE_LORA_RANK = 1 +DENSE_LORA_RANK = 8 +LORA_ALPHA = 32 +MEGATRON_LORA_RANK_ENV = "ART_MEGATRON_LORA_RANK" +MEGATRON_LORA_TARGET_MODULES_ENV = "ART_MEGATRON_LORA_TARGET_MODULES" + + +def default_lora_rank_for_handler(handler: Any) -> int: + return MOE_LORA_RANK if bool(getattr(handler, "is_moe", False)) else DENSE_LORA_RANK diff --git a/src/art/megatron/migrations.py b/src/art/megatron/migrations.py index abefe7bc0..793b287a3 100644 --- a/src/art/megatron/migrations.py +++ b/src/art/megatron/migrations.py @@ -2,97 +2,59 @@ import os from pathlib import Path -import re import warnings -from ..utils.get_model_step import get_step_from_dir -from .optimizer_state import ( - commit_optimizer_generation, - optimizer_generation_files, - read_optimizer_commit, -) +from .optimizer_state import read_committed_optimizer_pointer -_LEGACY_SHARD_RE = re.compile(r"^(?P\d+)-of-(?P\d+)\.pt$") +_IGNORED_ROOT_ENTRIES = {".writer.lock"} def optimizer_state_path(output_dir: str) -> str: return str(Path(output_dir) / "optimizer_states") -def _legacy_shards(path: Path) -> tuple[Path, ...] | None: - if not path.exists(): - return None - if not path.is_dir(): - raise RuntimeError(f"Legacy optimizer path is not a directory: {path}") - entries = list(path.iterdir()) - if not entries: - return None - matches = [ - (item, match) - for item in entries - if item.is_file() and (match := _LEGACY_SHARD_RE.fullmatch(item.name)) - ] - if len(matches) != len(entries): - unknown = sorted( - item.name for item in entries if item not in {m[0] for m in matches} - ) - raise RuntimeError( - f"Legacy optimizer state at {path} contains unsupported entries: {unknown}" - ) - worlds = {int(match.group("world")) for _, match in matches} - if len(worlds) != 1: - raise RuntimeError(f"Legacy optimizer shards at {path} mix world sizes") - world_size = worlds.pop() - by_rank = {int(match.group("rank")): item for item, match in matches} - if set(by_rank) != set(range(1, world_size + 1)): - raise RuntimeError(f"Legacy optimizer shards at {path} are incomplete") - return tuple(by_rank[rank] for rank in range(1, world_size + 1)) +def _contains_optimizer_state(path: Path) -> bool: + return path.is_dir() and any( + entry.name not in _IGNORED_ROOT_ENTRIES for entry in path.iterdir() + ) def apply_megatron_migrations(output_dir: str) -> str: - """Apply all durable Megatron state migrations for one training run.""" - # Keep future Megatron migrations centralized behind this call. + """Move one immutable split optimizer root to the unified run root.""" destination = Path(optimizer_state_path(output_dir)) - if read_optimizer_commit(str(destination)) is not None: - return str(destination) - - candidates = { - mode: shards + split = tuple( + path for mode in ("rl", "sft") - if (shards := _legacy_shards(Path(output_dir) / f"optimizer_states_{mode}")) - is not None - } - if len(candidates) > 1: + if _contains_optimizer_state( + path := Path(output_dir) / f"optimizer_states_{mode}" + ) + ) + if destination.exists(): + if split: + raise RuntimeError( + "Unified and split Megatron optimizer states both exist; ART " + "cannot infer which lineage to keep" + ) + return str(destination) + if len(split) > 1: raise RuntimeError( - "Both legacy RL and SFT optimizer states exist. ART cannot infer which " - "state belongs to the latest checkpoint. Keep only the intended " - "optimizer_states_rl or optimizer_states_sft directory, or remove both " - "to explicitly reset the optimizer." + "Both legacy RL and SFT optimizer states exist. ART cannot infer " + "which lineage to keep" ) - if not candidates: + if not split: return str(destination) - mode, shards = next(iter(candidates.items())) - step = get_step_from_dir(output_dir) - files = optimizer_generation_files(step, len(shards)) - destination.mkdir(parents=True, exist_ok=True) - for source, name in zip(shards, files, strict=True): - target = destination / name - temporary = target.with_suffix(f"{target.suffix}.tmp") - if temporary.exists(): - temporary.unlink() - os.link(source, temporary) - os.replace(temporary, target) - commit_optimizer_generation( - str(destination), step=step, world_size=len(shards), files=files - ) - for source in shards: - source.unlink() - legacy_dir = Path(output_dir) / f"optimizer_states_{mode}" - legacy_dir.rmdir() + source = split[0] + # This validates the generation format and deliberately rejects loose shards. + read_committed_optimizer_pointer(str(source)) + os.replace(source, destination) + directory_fd = os.open(destination.parent, os.O_RDONLY) + try: + os.fsync(directory_fd) + finally: + os.close(directory_fd) warnings.warn( - f"Migrated legacy {mode.upper()} optimizer state to the run-level optimizer " - f"commit at step {step}.", + f"Migrated split Megatron optimizer state {source.name} to {destination.name}.", stacklevel=2, ) return str(destination) diff --git a/src/art/megatron/model_support/__init__.py b/src/art/megatron/model_support/__init__.py index 6a5735363..72dc0a6c0 100644 --- a/src/art/megatron/model_support/__init__.py +++ b/src/art/megatron/model_support/__init__.py @@ -23,6 +23,7 @@ get_model_support_handler, get_model_support_handler_for_spec, get_model_support_spec, + get_model_support_spec_by_key, is_model_support_registered, list_model_support_specs, model_requires_merged_rollout, @@ -91,6 +92,7 @@ def __getattr__(name: str): "get_model_support_handler", "get_model_support_handler_for_spec", "get_model_support_spec", + "get_model_support_spec_by_key", "inspect_architecture", "is_model_support_registered", "list_model_support_specs", diff --git a/src/art/megatron/model_support/discovery.py b/src/art/megatron/model_support/discovery.py index 6f27dd05d..cb957de9b 100644 --- a/src/art/megatron/model_support/discovery.py +++ b/src/art/megatron/model_support/discovery.py @@ -47,6 +47,7 @@ def inspect_architecture( provider_bundle = get_provider_bundle( base_model, torch_dtype=torch_dtype, + load_weights=False, allow_unvalidated_arch=allow_unvalidated_arch, ) discovered = provider_bundle.handler.collect_layer_families( diff --git a/src/art/megatron/model_support/handlers/default_dense.py b/src/art/megatron/model_support/handlers/default_dense.py index 96a9bab6d..de4b6b1fa 100644 --- a/src/art/megatron/model_support/handlers/default_dense.py +++ b/src/art/megatron/model_support/handlers/default_dense.py @@ -1,4 +1,4 @@ -from typing import Any, Literal, Sequence +from typing import Any, Callable, Literal, Sequence import torch @@ -106,6 +106,10 @@ def configure_provider_for_runtime(self, provider: Any) -> None: del provider return None + def context_parallel_workload_profile(self, provider: Any) -> Any | None: + del provider + return None + def default_chat_template(self) -> str | None: return None @@ -133,6 +137,13 @@ def install_preprocess_patch(self, model_chunks: Sequence[Any]) -> None: del model_chunks return None + def build_pipeline_microbatch_activator( + self, + model_chunks: Sequence[Any], + ) -> Callable[[Any, int], None] | None: + del model_chunks + return None + def build_prefix_tree_model_state( self, context: PrefixTreeModelStateContext, @@ -177,6 +188,9 @@ def to_vllm_lora_tensors( def to_vllm_lora_config(self, adapter_config: dict[str, Any]) -> dict[str, Any]: return adapter_config + def vllm_lora_conversion_is_view_only(self) -> bool: + return False + def from_vllm_lora_tensors( self, tensors: dict[str, torch.Tensor], diff --git a/src/art/megatron/model_support/handlers/dsv4.py b/src/art/megatron/model_support/handlers/dsv4.py index a920a4c5d..9d1249fee 100644 --- a/src/art/megatron/model_support/handlers/dsv4.py +++ b/src/art/megatron/model_support/handlers/dsv4.py @@ -3,7 +3,7 @@ import hashlib import os import re -from typing import Any, Literal, Sequence, cast +from typing import Any, Callable, Literal, Sequence, cast import torch @@ -29,6 +29,7 @@ _ORACLE_INDEX_HEADS = 1 _ORACLE_INDEX_TOPK = 1024 _VALIDATION_NUM_LAYERS_ENV = "ART_DSV4_VALIDATION_NUM_LAYERS" +_ORACLE_LAYER_RE = re.compile(r"(?P(?:^|\.)decoder\.layers\.)(?P\d+)") _ORACLE_EXPERT_WEIGHT_RE = re.compile(r"\.mlp\.experts\..*\.weight(?P\d+)$") _DSV4_ART_MOE_EXPERT_KEY_RE = re.compile( r"^(?P.*\.mlp\.experts)\.(?P\d+)\." @@ -46,6 +47,34 @@ _DSV4_MOE_COMPILE_WORKAROUND_FLAGS = ("te_triton_permute_with_mask_map",) +def _dsv4_input_activator( + model: Any, +) -> Callable[[torch.Tensor | None, torch.Tensor | None], None]: + from art.megatron.dsv4.deepseek_v4 import DeepSeekV4Attention + from art.megatron.dsv4.layer import Dsv4MoELayer + + modules = tuple(model.modules()) + input_setters = tuple( + child.set_input_ids for child in modules if isinstance(child, Dsv4MoELayer) + ) + position_setters = tuple( + child.set_position_ids + for child in modules + if isinstance(child, DeepSeekV4Attention) + ) + + def activate( + input_ids: torch.Tensor | None, + position_ids: torch.Tensor | None, + ) -> None: + for setter in input_setters: + setter(input_ids) + for setter in position_setters: + setter(position_ids) + + return activate + + class Dsv4Handler(DefaultMoeHandler): key = "dsv4" is_moe = True @@ -69,6 +98,7 @@ def patch_provider(self, provider: Any, bridge: Any) -> None: def configure_provider_for_runtime(self, provider: Any) -> None: provider.mtp_num_layers = None provider.moe_shared_expert_overlap = False + provider.art_pipeline_activation_multiplier = provider.dsv4_hc_mult raw_num_layers = os.environ.get(_VALIDATION_NUM_LAYERS_ENV) if raw_num_layers is None: return @@ -216,9 +246,6 @@ def include(name: str) -> bool: def install_preprocess_patch(self, model_chunks: Sequence[Any]) -> None: from megatron.core.models.gpt.gpt_model import GPTModel - from art.megatron.dsv4.deepseek_v4 import DeepSeekV4Attention - from art.megatron.dsv4.layer import Dsv4MoELayer - for chunk in list(model_chunks): module: Any = chunk while hasattr(module, "module"): @@ -229,26 +256,27 @@ def install_preprocess_patch(self, model_chunks: Sequence[Any]) -> None: else cast(GPTModel, getattr(module, "language_model")) ) preprocess = gpt_module._preprocess + activate = _dsv4_input_activator(gpt_module.decoder) def preprocess_hook( - *args: Any, _preprocess=preprocess, _gpt=gpt_module, **kwargs: Any + *args: Any, + _preprocess=preprocess, + _activate=activate, + **kwargs: Any, ): input_ids = kwargs.get("input_ids") position_ids = kwargs.get("position_ids") - for child in _gpt.decoder.modules(): - if isinstance(child, Dsv4MoELayer): - child.set_input_ids( - input_ids if isinstance(input_ids, torch.Tensor) else None - ) - if isinstance(child, DeepSeekV4Attention): - child.set_position_ids( - position_ids - if isinstance(position_ids, torch.Tensor) - else None - ) + _activate( + input_ids if isinstance(input_ids, torch.Tensor) else None, + position_ids if isinstance(position_ids, torch.Tensor) else None, + ) preproc_output = list(_preprocess(*args, **kwargs)) - decoder_input = cast(torch.Tensor, preproc_output[0]) - if not decoder_input.requires_grad and decoder_input.is_leaf: + decoder_input = cast(torch.Tensor | None, preproc_output[0]) + if ( + decoder_input is not None + and not decoder_input.requires_grad + and decoder_input.is_leaf + ): decoder_input.requires_grad_(True) table = preproc_output[1] if isinstance(position_ids, torch.Tensor) and torch.is_tensor(table): @@ -267,6 +295,22 @@ def preprocess_hook( setattr(gpt_module, "_preprocess", preprocess_hook) + def build_pipeline_microbatch_activator( + self, + model_chunks: Sequence[Any], + ) -> Callable[[Any, int], None]: + activators = tuple(_dsv4_input_activator(chunk) for chunk in model_chunks) + + def activate(prepared: Any, chunk_index: int) -> None: + input_ids = getattr(prepared, "model_tokens", None) + position_ids = getattr(prepared, "model_input_pos", None) + if input_ids is None: + input_ids = prepared.input_ids + position_ids = prepared.position_ids + activators[chunk_index](input_ids, position_ids) + + return activate + def collect_layer_families(self, provider: Any) -> list[LayerFamilyInstance]: ratios: list[int] = list(getattr(provider, "dsv4_compress_ratios", ()) or ()) @@ -478,6 +522,28 @@ def prepare_hf_reference_config(self, config: Any) -> None: config._experts_implementation = "eager" self._apply_oracle_shape_overrides(config) + def prepare_hf_reference_model(self, model: Any) -> Any: + from art.megatron.dsv4.hf_oracle import prepare_hf_reference_model + + return prepare_hf_reference_model(model) + + def prepare_hf_reference_forward( + self, + model: Any, + *, + position_ids: torch.Tensor, + group_ids: torch.Tensor, + parent_ids: torch.Tensor, + ) -> None: + from art.megatron.dsv4.hf_oracle import set_hf_reference_prefix_tree + + set_hf_reference_prefix_tree( + model, + position_ids=position_ids, + group_ids=group_ids, + parent_ids=parent_ids, + ) + def hf_reference_from_pretrained_kwargs( self, *, config: Any, dtype: torch.dtype ) -> dict[str, Any]: @@ -506,6 +572,30 @@ def normalize_hf_reference_state_for_hf_parity( ) -> None: _add_dsv4_hf_reference_source_aliases(state, config) + def hf_parity_gradient_group(self, param: str) -> str: + if param == "model.embed_tokens.weight": + return "embedding" + if ( + param == "lm_head.weight" + or param == "model.norm.weight" + or param.startswith("model.hc_head.") + ): + return "final_envelope" + match = re.fullmatch(r"model\.layers\.(\d+)\.(.+)", param) + if match is None: + raise ValueError(f"Unmapped DSV4 HF-parity gradient: {param}") + layer, module = match.groups() + prefix = f"model.layers.{layer}" + if module.startswith(("attn_hc.", "self_attn.")): + return f"{prefix}.attention" + if module.startswith(("ffn_hc.", "mlp.")): + return f"{prefix}.ffn" + if module == "input_layernorm.weight": + return f"{prefix}.input_norm" + if module == "post_attention_layernorm.weight": + return f"{prefix}.post_attention_norm" + raise ValueError(f"Unmapped DSV4 HF-parity gradient: {param}") + def configure_oracle_provider(self, provider: Any, *, case_config: Any) -> None: """Mirrors HF oracle reductions while keeping DSV4 hard kernel invariants.""" hooks = list(getattr(provider, "_pre_wrap_hooks", [])) @@ -577,11 +667,12 @@ def _initialize_oracle_base_weights( ep_size = ps.get_expert_model_parallel_world_size() with torch.no_grad(): for chunk in model_chunks: + global_layers = self._oracle_global_layer_indices(chunk) for name, param in chunk.named_parameters(): if self._is_oracle_lora_tensor(name): continue init_name = self._oracle_base_tensor_name( - name, + self._oracle_global_layer_name(name, global_layers), ep_rank=ep_rank, ep_size=ep_size, ) @@ -591,9 +682,37 @@ def _initialize_oracle_base_weights( seed=seed, ) for name, buffer in chunk.named_buffers(): - self._initialize_oracle_buffer(name, buffer, seed=seed) + self._initialize_oracle_buffer( + self._oracle_global_layer_name(name, global_layers), + buffer, + seed=seed, + ) return model_chunks + @staticmethod + def _oracle_global_layer_indices(chunk: Any) -> dict[int, int]: + from art.megatron.dsv4.layer import Dsv4TransformerLayer + + indices: dict[int, int] = {} + for name, module in chunk.named_modules(): + if not isinstance(module, Dsv4TransformerLayer): + continue + match = _ORACLE_LAYER_RE.search(name) + if match is None: + raise RuntimeError(f"Cannot locate DSV4 oracle layer in {name!r}") + indices[int(match.group("layer"))] = int(module.layer_number) - 1 + return indices + + @staticmethod + def _oracle_global_layer_name(name: str, indices: dict[int, int]) -> str: + match = _ORACLE_LAYER_RE.search(name) + if match is None: + return name + global_layer = indices[int(match.group("layer"))] + return ( + f"{name[: match.start('layer')]}{global_layer}{name[match.end('layer') :]}" + ) + @staticmethod def _is_oracle_lora_tensor(name: str) -> bool: return "_lora." in name or ".lora." in name diff --git a/src/art/megatron/model_support/handlers/gemma4.py b/src/art/megatron/model_support/handlers/gemma4.py index 8dd098e9f..9be383cc1 100644 --- a/src/art/megatron/model_support/handlers/gemma4.py +++ b/src/art/megatron/model_support/handlers/gemma4.py @@ -67,14 +67,6 @@ "moe_postprocess", "te_triton_permute_with_mask_map", ) -_GEMMA4_TRITON_NUM_STAGES_2_SIGNATURES = { - # google/gemma-4-31B-it: Triton flex attention raises "No valid triton - # configs" for global attention head_dim=512 with backend-only options. - ("dense", 60, 5376, 32, 256, 512, 4), - # google/gemma-4-26B-A4B-it hits the same Triton resource limit on global - # attention head_dim=512 with backend-only options. - ("moe", 30, 2816, 16, 256, 512, 2), -} _ART_MOE_EXPERT_KEY_RE = re.compile( r"^(?P.*\.mlp\.experts)\.(?P\d+)\." r"(?Pgate_up_proj|down_proj)\.(?Plora_[AB])\.weight$" @@ -402,12 +394,13 @@ def _zero_gemma4_moe_lora_padding( logical, internal = _gemma4_moe_padding_sizes_from_provider(config) if logical == internal: continue - for prefix, a_t, b_t in art_lora.iter_lora_sites([chunk]): - if ".mlp.experts." not in prefix: + for module in chunk.modules(): + prefix = getattr(module, "adapter_model_prefix", None) + if not isinstance(prefix, str) or ".mlp.experts." not in prefix: continue - if prefix.endswith(".gate_up_proj"): + if prefix.endswith(".gate_up_proj") and hasattr(module, "B_T"): _zero_gemma4_moe_lora_padding_tensor_set( - b_t, + cast(torch.nn.Parameter, module.B_T), dim=-1, logical=logical, internal=internal, @@ -415,9 +408,9 @@ def _zero_gemma4_moe_lora_padding( grads=grads, params=params, ) - elif prefix.endswith(".down_proj"): + elif prefix.endswith(".down_proj") and hasattr(module, "A_T"): _zero_gemma4_moe_lora_padding_tensor_set( - a_t, + cast(torch.nn.Parameter, module.A_T), dim=-2, logical=logical, internal=internal, @@ -518,7 +511,24 @@ def _canonicalize_gemma4_loaded_lora_state( } -class Gemma4MoeHandler(DefaultMoeHandler): +class _Gemma4TokenizerMixin: + def configure_tokenizer( + self, + tokenizer: Any, + *, + internal_config: Any, + ) -> Any: + if not any( + internal_config.get(key) is not None + for key in ("chat_template", "chat_template_path") + ): + from art.utils.chat_template import TOOL_CALL_ARGUMENTS_AS_MAPPING_ATTR + + setattr(tokenizer, TOOL_CALL_ARGUMENTS_AS_MAPPING_ATTR, True) + return tokenizer + + +class Gemma4MoeHandler(_Gemma4TokenizerMixin, DefaultMoeHandler): key = "gemma4_moe" is_moe = True native_vllm_lora_status = "validated" @@ -790,7 +800,7 @@ def flex_attention_compile_crash_config( GEMMA4_MOE_HANDLER = Gemma4MoeHandler() -class Gemma4DenseHandler(DefaultDenseHandler): +class Gemma4DenseHandler(_Gemma4TokenizerMixin, DefaultDenseHandler): key = "gemma4_dense" native_vllm_lora_status = "validated" @@ -1276,8 +1286,12 @@ def preprocess_hook( setattr(gemma4_rotary, "cp_group", rotary_cp_group) if local_rotary is not None: setattr(local_rotary, "cp_group", local_rotary_cp_group) - decoder_input = cast(torch.Tensor, preproc_output[0]) - if not decoder_input.requires_grad and decoder_input.is_leaf: + decoder_input = cast(torch.Tensor | None, preproc_output[0]) + if ( + decoder_input is not None + and not decoder_input.requires_grad + and decoder_input.is_leaf + ): decoder_input.requires_grad_(True) rotary_pos_emb = preproc_output[1] if not isinstance(position_ids, torch.Tensor) or not isinstance( @@ -1481,37 +1495,14 @@ def _gemma4_attention_pattern(provider: Any) -> tuple[int, int]: def _gemma4_flex_attention_compile_crash_config( provider: Any, ) -> FlexAttentionCompileCrashConfig: - signature = _gemma4_compile_crash_signature(provider) global_head_dim = int(getattr(provider, "global_head_dim", 0) or 0) - if signature in _GEMMA4_TRITON_NUM_STAGES_2_SIGNATURES or ( - signature is None and global_head_dim >= 512 - ): + if global_head_dim >= 512: return FlexAttentionCompileCrashConfig( triton_num_stages_2_head_dims=(global_head_dim,) ) return FlexAttentionCompileCrashConfig() -def _gemma4_compile_crash_signature(provider: Any) -> tuple[Any, ...] | None: - required_attrs = ( - "num_layers", - "hidden_size", - "num_attention_heads", - "kv_channels", - ) - if any(not hasattr(provider, attr) for attr in required_attrs): - return None - return ( - "moe" if int(getattr(provider, "num_moe_experts", 0) or 0) > 0 else "dense", - int(provider.num_layers), - int(provider.hidden_size), - int(provider.num_attention_heads), - int(provider.kv_channels), - int(getattr(provider, "global_head_dim", 0) or 0), - int(getattr(provider, "num_global_key_value_heads", 0) or 0), - ) - - def _is_gemma4_global_layer(layer_number: int, provider: Any) -> bool: layer_types = getattr(provider, "art_gemma4_layer_types", None) if layer_types is not None: @@ -2133,6 +2124,7 @@ def _gemma4_text_only_mapping_registry(hf_config: Any | None = None) -> Any: from megatron.bridge.models.conversion.mapping_registry import ( MegatronMappingRegistry, ) + from megatron.bridge.models.conversion.param_mapping import AutoMapping from megatron.bridge.models.gemma.gemma4_bridge import _Gemma4QKVMapping from megatron.bridge.models.gemma_vl.gemma4_vl_bridge import Gemma4VLBridge @@ -2191,7 +2183,11 @@ def megatron_to_hf( text_config = getattr(hf_config, "text_config", hf_config) is_moe = bool(getattr(text_config, "enable_moe_block", False)) - language_mappings = [] + language_mappings = ( + [] + if bool(getattr(text_config, "tie_word_embeddings", True)) + else [AutoMapping("output_layer.weight", "lm_head.weight")] + ) for mapping in upstream_registry.mappings: if not mapping.megatron_param.startswith("language_model."): continue diff --git a/src/art/megatron/model_support/handlers/glm52.py b/src/art/megatron/model_support/handlers/glm52.py new file mode 100644 index 000000000..ff4b4ab86 --- /dev/null +++ b/src/art/megatron/model_support/handlers/glm52.py @@ -0,0 +1,507 @@ +from __future__ import annotations + +from typing import Any, Literal, Sequence, cast + +import torch + +from art.megatron.model_support.handlers.default_dense import ( + DefaultMoeHandler, + _compile_workaround_flags_for_provider, +) +from art.megatron.model_support.spec import ( + CompileWorkaroundConfig, + ExpertPackedLoraGroup, + ExpertPackedLoraSlot, + LayerFamilyInstance, + PrefixTreeModelStateContext, +) + + +def _hf_config(bridge: Any) -> Any: + pretrained = bridge.hf_pretrained + return getattr(pretrained, "config", pretrained) + + +def _from_vllm_expert_lora( + tensors: dict[str, torch.Tensor], adapter_config: dict[str, Any] +) -> dict[str, torch.Tensor]: + slots = ( + ("base_layer.lora_A.weight", "gate_up_proj", "lora_A", "rows"), + ("base_layer.lora_B.weight", "gate_up_proj", "lora_B", "cols"), + ("lora_A.weight", "down_proj", "lora_A", "rows"), + ("lora_B.weight", "down_proj", "lora_B", "cols"), + ) + grouped: dict[str, dict[str, torch.Tensor]] = {} + used: set[str] = set() + for key, tensor in tensors.items(): + for suffix, _projection, _lora, _layout in slots: + marker = f".{suffix}" + if key.endswith(marker) and key[: -len(marker)].endswith(".mlp.experts"): + grouped.setdefault(key[: -len(marker)], {})[suffix] = tensor + used.add(key) + break + if not grouped: + return tensors + try: + rank = int(adapter_config["r"]) + except (KeyError, TypeError, ValueError) as exc: + raise RuntimeError( + "GLM-5.2 fused expert LoRA requires adapter rank r." + ) from exc + if rank <= 0: + raise RuntimeError(f"GLM-5.2 LoRA rank must be positive, got {rank}.") + + result = {key: tensor for key, tensor in tensors.items() if key not in used} + for prefix, block in grouped.items(): + missing = [suffix for suffix, *_ in slots if suffix not in block] + if missing: + raise RuntimeError( + f"Incomplete GLM-5.2 expert LoRA block {prefix}: {missing}" + ) + gate_a = block[slots[0][0]] + if gate_a.ndim != 2 or gate_a.shape[0] % rank: + raise RuntimeError( + f"{prefix}: invalid fused expert A shape {tuple(gate_a.shape)} for rank {rank}." + ) + experts = gate_a.shape[0] // rank + for suffix, projection, lora, layout in slots: + tensor = block[suffix] + packed = experts * rank + if tensor.ndim != 2 or tensor.shape[0 if layout == "rows" else 1] != packed: + raise RuntimeError( + f"{prefix}.{suffix}: shape {tuple(tensor.shape)} does not encode " + f"{experts} experts at rank {rank}." + ) + unpacked = ( + tensor.reshape(experts, rank, tensor.shape[1]) + if layout == "rows" + else tensor.reshape(tensor.shape[0], rank, experts).permute(2, 0, 1) + ) + for expert, expert_tensor in enumerate(unpacked): + key = f"{prefix}.{expert}.{projection}.{lora}.weight" + if key in result: + raise RuntimeError(f"Duplicate GLM-5.2 expert LoRA tensor {key}.") + result[key] = expert_tensor.clone().contiguous() + return result + + +class Glm52Handler(DefaultMoeHandler): + key = "glm52" + is_moe = True + cp_supported = True + native_vllm_lora_status = "validated" + + def configure_tokenizer( + self, + tokenizer: Any, + *, + internal_config: Any, + ) -> Any: + if not any( + internal_config.get(key) is not None + for key in ("chat_template", "chat_template_path") + ): + from art.utils.chat_template import TOOL_CALL_ARGUMENTS_AS_MAPPING_ATTR + + setattr(tokenizer, TOOL_CALL_ARGUMENTS_AS_MAPPING_ATTR, True) + return tokenizer + + def compile_workaround_config(self, provider: Any) -> CompileWorkaroundConfig: + ep1_alltoall = ( + int(getattr(provider, "expert_model_parallel_size", 1) or 1) == 1 + and getattr(provider, "moe_token_dispatcher_type", None) == "alltoall" + ) + flags = ("mlp_forward", "moe_forward") + if ep1_alltoall: + flags = (*flags, "moe_preprocess") + return CompileWorkaroundConfig( + flags=_compile_workaround_flags_for_provider(provider, flags), + shared_expert_state=self._shared_expert_compile_state(provider), + ) + + def patch_provider(self, provider: Any, bridge: Any) -> None: + from art.megatron.glm52.spec import ( + build_glm52_pipeline_layout, + get_glm52_decoder_block_spec, + ) + + config = _hf_config(bridge) + required_dims = { + "kv_lora_rank": 512, + "qk_rope_head_dim": 64, + "v_head_dim": 256, + "index_head_dim": 128, + } + for name, expected in required_dims.items(): + actual = int(getattr(config, name)) + if actual != expected: + raise ValueError(f"GLM-5.2 requires {name}={expected}, got {actual}.") + topk = int(config.index_topk) + if topk % 32: + raise ValueError(f"GLM-5.2 index_topk must be divisible by 32, got {topk}.") + provider.transformer_layer_spec = get_glm52_decoder_block_spec + provider.experimental_attention_variant = None + provider.kv_channels = int(config.v_head_dim) + provider.num_moe_experts = int(config.n_routed_experts) + provider.num_query_groups = int(config.num_attention_heads) + provider.rotary_interleaved = False + provider.rope_type = "rope" + provider.position_embedding_type = "rope" + provider.rotary_base = float(config.rope_parameters["rope_theta"]) + provider.rotary_scaling_factor = 1.0 + provider.mscale = 1.0 + provider.mscale_all_dim = 1.0 + provider.mtp_num_layers = None + provider.dsa_indexer_n_heads = int(config.index_n_heads) + provider.dsa_indexer_head_dim = int(config.index_head_dim) + provider.dsa_indexer_topk = topk + provider.dsa_indexer_loss_coeff = 0.0 + provider.dsa_indexer_use_sparse_loss = False + provider.glm52_indexer_types = tuple(config.indexer_types) + pp_size = int(provider.pipeline_model_parallel_size or 1) + vp_size = int(provider.virtual_pipeline_model_parallel_size or 1) + if pp_size * vp_size > 1 and provider.pipeline_model_parallel_layout is None: + provider.pipeline_model_parallel_layout = build_glm52_pipeline_layout( + provider.glm52_indexer_types, + pp_size, + vp_size, + ) + provider.moe_layer_freq = [ + 0 if layer_type == "dense" else 1 for layer_type in config.mlp_layer_types + ] + provider.moe_shared_expert_intermediate_size = int( + config.moe_intermediate_size + ) * int(config.n_shared_experts) + provider.moe_router_bias_update_rate = 0.0 + provider.moe_aux_loss_coeff = 0.0 + provider.attention_softmax_in_fp32 = True + + def configure_provider_for_runtime(self, provider: Any) -> None: + provider.mtp_num_layers = None + provider.mtp_loss_scaling_factor = None + provider.moe_shared_expert_overlap = False + + def context_parallel_workload_profile(self, provider: Any) -> Any: + from art.megatron.glm52.spec import build_glm52_context_parallel_profile + + profile = getattr(provider, "_art_context_parallel_workload_profile", None) + if profile is None: + profile = build_glm52_context_parallel_profile(provider) + provider._art_context_parallel_workload_profile = profile + return profile + + def install_preprocess_patch(self, model_chunks: Sequence[Any]) -> None: + from megatron.core.models.gpt.gpt_model import GPTModel + + for chunk in model_chunks: + module = chunk + while hasattr(module, "module"): + module = module.module + gpt = module if isinstance(module, GPTModel) else module.language_model + preprocess = gpt._preprocess + + def preprocess_hook(*args: Any, _preprocess=preprocess, **kwargs: Any): + output = list(_preprocess(*args, **kwargs)) + decoder_input = cast(torch.Tensor | None, output[0]) + if ( + decoder_input is not None + and decoder_input.is_leaf + and not decoder_input.requires_grad + ): + decoder_input.requires_grad_(True) + return tuple(output) + + gpt._preprocess = preprocess_hook + + def build_prefix_tree_model_state( + self, context: PrefixTreeModelStateContext + ) -> dict[str, Any]: + if context.input_pos is None: + raise RuntimeError("GLM-5.2 prefix-tree attention requires input_pos.") + from art.megatron.glm52.state import build_glm52_prefix_tree_state + + if context.context_parallel_state is not None: + from art.megatron.glm52.state import build_glm52_context_parallel_state + + return { + "glm52": build_glm52_context_parallel_state( + position_ids=context.input_pos, + context_parallel_state=context.context_parallel_state, + device=context.device, + ) + } + + return { + "glm52": build_glm52_prefix_tree_state( + position_ids=context.input_pos, + group_ids=context.group_ids, + parent_ids=context.parent_ids, + device=context.device, + ) + } + + def correctness_precision(self) -> Literal["bf16", "fp32"]: + return "bf16" + + def correctness_use_fp32_lora_reference(self) -> bool: + return False + + def prepare_hf_reference_model(self, model: Any) -> Any: + for module in model.modules(): + if type(module).__name__ == "GlmMoeDsaIndexer": + module.requires_grad_(False) + return model + + def correctness_phase_pass_fns(self, oracle_harness: Any) -> dict[str, Any]: + nonzero = {"typical_abs_scale": 0.0, "candidate_abs_scale": 0.0} + forward = oracle_harness.MetricThresholdRule( + limits={"mean_abs_pct": 3.0}, minimums=nonzero + ) + grad = oracle_harness.MetricThresholdRule( + limits={"mean_abs_pct": 5.0}, minimums=nonzero + ) + return { + "forward": forward, + "outputs": forward, + "losses": oracle_harness.MetricThresholdRule(limits={"mean_abs_pct": 3.0}), + "grads": grad, + "deltas": grad, + "router_scores": forward, + "router_topk_ids": oracle_harness.MetricThresholdRule( + limits={"topk_mismatch_fraction": 0.0, "top1_mismatch_fraction": 0.0} + ), + } + + def collect_layer_families(self, provider: Any) -> list[LayerFamilyInstance]: + pattern = tuple(provider.glm52_indexer_types) + full = [index for index, value in enumerate(pattern) if value == "full"] + complete_shared_groups = [ + end - 1 + for start, end in zip(full, full[1:], strict=False) + if end - start > 1 + ] + shared = next( + (index for index, value in enumerate(pattern) if value == "shared"), + None, + ) + sparse_mlp = next( + (index for index, value in enumerate(provider.moe_layer_freq) if value), + None, + ) + families = [ + LayerFamilyInstance(key="glm52_full_index_attention", layer_index=0), + LayerFamilyInstance(key="dense_mlp", layer_index=0), + ] + if shared is not None: + families.append( + LayerFamilyInstance( + key="glm52_shared_index_attention", layer_index=shared + ) + ) + if len(complete_shared_groups) >= 2: + # Exercise shared-index reuse twice and retain four legal PP/VPP + # split points after the full-layer prelude. + families.append( + LayerFamilyInstance( + key="glm52_repeated_index_share_groups", + layer_index=complete_shared_groups[1], + ) + ) + if sparse_mlp is not None: + families.extend( + ( + LayerFamilyInstance(key="grouped_moe_mlp", layer_index=sparse_mlp), + LayerFamilyInstance( + key="shared_experts_mlp", layer_index=sparse_mlp + ), + ) + ) + return families + + def identity_lora_target_parameters( + self, + model: Any, + *, + target_modules: list[str], + ) -> list[str]: + targets = set(target_modules) + suffixes = tuple(f"{target}.weight" for target in targets - {"experts"}) + return [ + name + for name, _ in model.named_parameters() + if ".indexer." not in name + and ( + name.endswith(suffixes) + or ("experts" in targets and ".experts." in name) + ) + ] + + def apply_lora_adapters( + self, + model_chunks: Sequence[Any], + provider: Any, + *, + target_modules: list[str], + rank: int, + alpha: int, + ) -> None: + from megatron.core.transformer.transformer_layer import TransformerLayer + + from art.megatron.glm52.attention import Glm52SelfAttention + from art.megatron.glm52.lora import ( + Glm52LoRA, + apply_glm52_attention_lora, + wrap_glm52_grouped_moe_experts_3d, + ) + from art.megatron.lora import ( + _adapter_model_prefix, + _is_language_transformer_layer_name, + wrap_dense_mlp, + wrap_shared_experts_mlp, + ) + + targets = set(target_modules) + if "kv_b_proj" in targets: + raise ValueError( + "GLM-5.2 does not support kv_b_proj LoRA because native vLLM " + "sparse MLA executes statically absorbed W_K/W_V weights." + ) + for chunk in model_chunks: + for module_name, layer in chunk.named_modules(): + if not isinstance(layer, TransformerLayer) or not ( + _is_language_transformer_layer_name(module_name) + ): + continue + if not isinstance(layer.self_attention, Glm52SelfAttention): + raise TypeError( + "GLM-5.2 layer has unsupported attention " + f"{type(layer.self_attention).__name__}." + ) + prefix = _adapter_model_prefix(layer) + apply_glm52_attention_lora( + layer.self_attention, + adapter_model_prefix=prefix, + provider=provider, + target_modules=targets, + rank=rank, + alpha=alpha, + ) + experts = getattr(layer.mlp, "experts", None) + if experts is None: + wrap_dense_mlp( + layer.mlp, + adapter_model_prefix=prefix, + provider=provider, + target_modules=targets, + rank=rank, + alpha=alpha, + lora_cls=Glm52LoRA, + ) + continue + wrap_glm52_grouped_moe_experts_3d( + experts, + adapter_model_prefix=prefix, + target_modules=targets, + rank=rank, + alpha=alpha, + ) + shared_experts = getattr(layer.mlp, "shared_experts", None) + if shared_experts is not None: + wrap_shared_experts_mlp( + shared_experts, + adapter_model_prefix=prefix, + provider=provider, + target_modules=targets, + rank=rank, + alpha=alpha, + lora_cls=Glm52LoRA, + ) + + def build_adapter_weights_by_base( + self, model_chunks: Sequence[Any] + ) -> dict[str, list[Any]]: + from megatron.core.transformer.transformer_layer import TransformerLayer + + from art.megatron.glm52.attention import Glm52SelfAttention + from art.megatron.glm52.lora import add_glm52_attention_adapter_weights + from art.megatron.weights.adapter_export import ( + add_dense_mlp_adapter_weights, + add_grouped_moe_adapter_weights, + add_shared_experts_adapter_weights, + layer_base_prefix, + ) + + result: dict[str, list[Any]] = {} + for chunk in model_chunks: + for module_name, layer in chunk.named_modules(): + if not isinstance(layer, TransformerLayer) or not isinstance( + layer.self_attention, Glm52SelfAttention + ): + continue + prefix = layer_base_prefix(layer, module_name=module_name) + add_glm52_attention_adapter_weights( + result, + layer_prefix=prefix, + attention=layer.self_attention, + ) + experts = getattr(layer.mlp, "experts", None) + if experts is None: + add_dense_mlp_adapter_weights( + result, layer_prefix=prefix, mlp=layer.mlp + ) + continue + add_grouped_moe_adapter_weights( + result, layer_prefix=prefix, experts=experts + ) + shared_experts = getattr(layer.mlp, "shared_experts", None) + if shared_experts is not None: + add_shared_experts_adapter_weights( + result, + layer_prefix=prefix, + shared_experts=shared_experts, + ) + return result + + def expert_packed_lora_groups(self) -> tuple[ExpertPackedLoraGroup, ...]: + return ( + ExpertPackedLoraGroup( + art_group_suffix=".mlp.experts", + slots=( + ExpertPackedLoraSlot( + source_projection="gate_up_proj", + source_lora="lora_A", + output_suffix="base_layer.lora_A.weight", + pack_layout="expert_rows", + ), + ExpertPackedLoraSlot( + source_projection="gate_up_proj", + source_lora="lora_B", + output_suffix="base_layer.lora_B.weight", + pack_layout="rank_major_expert_cols", + ), + ExpertPackedLoraSlot( + source_projection="down_proj", + source_lora="lora_A", + output_suffix="lora_A.weight", + pack_layout="expert_rows", + ), + ExpertPackedLoraSlot( + source_projection="down_proj", + source_lora="lora_B", + output_suffix="lora_B.weight", + pack_layout="rank_major_expert_cols", + ), + ), + ), + ) + + def from_vllm_lora_tensors( + self, + tensors: dict[str, torch.Tensor], + *, + adapter_config: dict[str, Any], + ) -> dict[str, torch.Tensor]: + return _from_vllm_expert_lora(tensors, adapter_config) + + +GLM52_HANDLER = Glm52Handler() diff --git a/src/art/megatron/model_support/handlers/gpt_oss.py b/src/art/megatron/model_support/handlers/gpt_oss.py index 93855e38c..f05e0dfdd 100644 --- a/src/art/megatron/model_support/handlers/gpt_oss.py +++ b/src/art/megatron/model_support/handlers/gpt_oss.py @@ -8,7 +8,6 @@ import torch -from art.megatron import lora as art_lora from art.megatron.model_support.handlers.default_dense import ( DefaultMoeHandler, _compile_workaround_flags_for_provider, @@ -448,37 +447,6 @@ def mapping_registry(self: Any) -> Any: bridge_type.mapping_registry = mapping_registry -def _patch_gpt_oss_weight_loader(target: Any) -> None: - bridge_type = type(target) - original = getattr(bridge_type, "maybe_modify_loaded_hf_weight", None) - if original is None or getattr(original, "_art_gpt_oss_bias_encoding", False): - return - original_loader = cast(Any, original) - - def maybe_modify_loaded_hf_weight( - self: Any, - hf_param: str | dict[str, str], - hf_state_dict: Any, - ) -> Any: - def load_one(name: str) -> torch.Tensor: - loaded = original_loader(self, name, hf_state_dict) - if name.endswith(".mlp.experts.down_proj") and name not in hf_state_dict: - # This Bridge version documents MXFP4 down projection output as - # [E, hidden, ffn], but its dequantizer emits the checkpoint's - # [E, ffn, hidden] layout. GPT-OSS-20B is square, so shape-based - # alignment cannot detect the orientation. Normalize before the - # optimized loader caches the materialized logical tensor. - loaded = loaded.transpose(-1, -2).contiguous() - return loaded - - if isinstance(hf_param, dict): - return {key: load_one(name) for key, name in hf_param.items()} - return load_one(hf_param) - - setattr(maybe_modify_loaded_hf_weight, "_art_gpt_oss_bias_encoding", True) - bridge_type.maybe_modify_loaded_hf_weight = maybe_modify_loaded_hf_weight - - def _gpt_oss_padded_mapping_registry( upstream_registry: Any, *, @@ -591,33 +559,42 @@ def megatron_to_hf( ) if not converted: return converted - tensor = _gate_up_from_etp_shard_order( - next(iter(converted.values())), self.tp_size - ) - gate = tensor[:logical_ffn, :logical_hidden] - up = tensor[internal_ffn : internal_ffn + logical_ffn, :logical_hidden] + tensor = next(iter(converted.values())) + if self.ep_size > 1: + tensor = torch.stack( + [ + _gate_up_from_etp_shard_order(expert, self.tp_size) + for expert in tensor + ] + ) + else: + tensor = _gate_up_from_etp_shard_order(tensor, self.tp_size) + gate = tensor[..., :logical_ffn, :logical_hidden] + up = tensor[..., internal_ffn : internal_ffn + logical_ffn, :logical_hidden] interleaved = torch.empty( + *tensor.shape[:-2], 2 * logical_ffn, logical_hidden, dtype=tensor.dtype, device=tensor.device, ) - interleaved[::2] = gate - interleaved[1::2] = up + interleaved[..., 0::2, :] = gate + interleaved[..., 1::2, :] = up names = cast(dict[str, str], self.hf_param) return { - names["weight"]: interleaved.t().contiguous(), + names["weight"]: interleaved.transpose(-1, -2).contiguous(), names["bias"]: torch.stack( [ - tensor[:logical_ffn, logical_hidden], + tensor[..., :logical_ffn, logical_hidden], tensor[ + ..., internal_ffn : internal_ffn + logical_ffn, logical_hidden, ], ], dim=-1, ) - .flatten() + .flatten(-2) .contiguous(), } @@ -660,6 +637,7 @@ def hf_to_megatron( ) global_expert_number = extract_expert_number_from_param(self.megatron_param) + # Index through ExpertTensorSlice so global EP metadata is preserved. expert_weight = hf_weights["weight"][global_expert_number] expert_bias = hf_weights["bias"][global_expert_number] normalized_param = self._normalize_expert_param_name(self.megatron_param) @@ -704,8 +682,10 @@ def megatron_to_hf( tensor = next(iter(converted.values())) names = cast(dict[str, str], self.hf_param) return { - names["weight"]: tensor[:logical_hidden, :logical_ffn].t().contiguous(), - names["bias"]: tensor[:logical_hidden, logical_ffn].contiguous(), + names["weight"]: tensor[..., :logical_hidden, :logical_ffn] + .transpose(-1, -2) + .contiguous(), + names["bias"]: tensor[..., :logical_hidden, logical_ffn].contiguous(), } def resolve(self, captures: tuple[str, ...]) -> Any: @@ -789,7 +769,6 @@ def _hf_weight_source( setattr(bridge, "_art_hf_weight_source", _hf_weight_source) model_bridge = getattr(bridge, "_model_bridge", None) if model_bridge is not None and model_bridge is not bridge: - _patch_gpt_oss_weight_loader(model_bridge) _patch_gpt_oss_mapping_registry(model_bridge) if type(model_bridge) is object: return @@ -826,8 +805,11 @@ def vllm_engine_args( *, rollout_weights_mode: RolloutWeightsMode, ) -> dict[str, object]: - del rollout_weights_mode - return {"moe_backend": "triton_unfused"} + return { + "moe_backend": ( + "triton_unfused" if rollout_weights_mode == "lora" else "triton" + ) + } def vllm_server_args(self) -> dict[str, object]: return {"tool_call_parser": "openai"} @@ -1171,8 +1153,12 @@ def preprocess_hook( setattr(rotary_module, "cp_group", rotary_cp_group) if packed_cp_group is not None: setattr(packed_seq_params, "cp_group", packed_cp_group) - decoder_input = cast(torch.Tensor, preproc_output[0]) - if not decoder_input.requires_grad and decoder_input.is_leaf: + decoder_input = cast(torch.Tensor | None, preproc_output[0]) + if ( + decoder_input is not None + and not decoder_input.requires_grad + and decoder_input.is_leaf + ): decoder_input.requires_grad_(True) rotary_pos_emb = preproc_output[1] if not isinstance(position_ids, torch.Tensor) or not torch.is_tensor( @@ -1392,47 +1378,53 @@ def _zero_gpt_oss_moe_lora_padding( if logical_hidden == internal_hidden and logical_ffn == internal_ffn: return with torch.no_grad(): - for prefix, a_t, b_t in art_lora.iter_lora_sites(model_chunks): - if ".mlp.experts." not in prefix: - continue - if prefix.endswith(".gate_up_proj"): - _zero_gpt_oss_lora_padding_tensor_set( - a_t, - dim=-2, - logical=logical_hidden, - internal=internal_hidden, - components=(internal_hidden,), - grads=grads, - params=params, - ) - _zero_gpt_oss_lora_padding_tensor_set( - b_t, - dim=-1, - logical=logical_ffn, - internal=internal_ffn, - components=(internal_ffn, internal_ffn), - grads=grads, - params=params, - ) - elif prefix.endswith(".down_proj"): - _zero_gpt_oss_lora_padding_tensor_set( - a_t, - dim=-2, - logical=logical_ffn, - internal=internal_ffn, - components=(internal_ffn,), - grads=grads, - params=params, - ) - _zero_gpt_oss_lora_padding_tensor_set( - b_t, - dim=-1, - logical=logical_hidden, - internal=internal_hidden, - components=(internal_hidden,), - grads=grads, - params=params, - ) + for chunk in model_chunks: + for module in chunk.modules(): + prefix = getattr(module, "adapter_model_prefix", None) + if not isinstance(prefix, str) or ".mlp.experts." not in prefix: + continue + if prefix.endswith(".gate_up_proj"): + if hasattr(module, "A_T"): + _zero_gpt_oss_lora_padding_tensor_set( + cast(torch.nn.Parameter, module.A_T), + dim=-2, + logical=logical_hidden, + internal=internal_hidden, + components=(internal_hidden,), + grads=grads, + params=params, + ) + if hasattr(module, "B_T"): + _zero_gpt_oss_lora_padding_tensor_set( + cast(torch.nn.Parameter, module.B_T), + dim=-1, + logical=logical_ffn, + internal=internal_ffn, + components=(internal_ffn, internal_ffn), + grads=grads, + params=params, + ) + elif prefix.endswith(".down_proj"): + if hasattr(module, "A_T"): + _zero_gpt_oss_lora_padding_tensor_set( + cast(torch.nn.Parameter, module.A_T), + dim=-2, + logical=logical_ffn, + internal=internal_ffn, + components=(internal_ffn,), + grads=grads, + params=params, + ) + if hasattr(module, "B_T"): + _zero_gpt_oss_lora_padding_tensor_set( + cast(torch.nn.Parameter, module.B_T), + dim=-1, + logical=logical_hidden, + internal=internal_hidden, + components=(internal_hidden,), + grads=grads, + params=params, + ) def _zero_gpt_oss_lora_padding_state_tensor( diff --git a/src/art/megatron/model_support/handlers/qwen3_5.py b/src/art/megatron/model_support/handlers/qwen3_5.py index db72e0687..ac62c4f4c 100644 --- a/src/art/megatron/model_support/handlers/qwen3_5.py +++ b/src/art/megatron/model_support/handlers/qwen3_5.py @@ -28,9 +28,6 @@ _QWEN35_MOE_COMPILE_WORKAROUND_FLAGS = ( "moe_postprocess", "te_triton_permute_with_mask_map", - # Torch 2.11.0 compiles Megatron's weighted SwiGLU custom autograd - # function with zero cotangents when its forward casts internally. - "weighted_bias_swiglu_no_inner_forward_cast", ) _QWEN35_MOE_UNCONDITIONAL_COMPILE_WORKAROUND_FLAGS: tuple[str, ...] = () _ART_LAYER_PREFIX = "base_model.model.model.layers." @@ -139,7 +136,9 @@ def install_preprocess_patch(self, model_chunks: Sequence[Any]) -> None: raise RuntimeError("ART Qwen3.5 Megatron training does not use MTP.") preprocess = gpt_module._preprocess - def preprocess_hook(*args, _preprocess=preprocess, **kwargs): + def preprocess_hook( + *args, _preprocess=preprocess, _gpt=gpt_module, **kwargs + ): position_ids = kwargs.get("position_ids") if isinstance(position_ids, torch.Tensor) and position_ids.ndim == 2: kwargs = dict(kwargs) @@ -148,15 +147,12 @@ def preprocess_hook(*args, _preprocess=preprocess, **kwargs): position_ids.shape[0], position_ids.shape[1], ) - rotary_pos_emb = getattr(gpt_module, "rotary_pos_emb", None) + rotary_pos_emb = getattr(_gpt, "rotary_pos_emb", None) rotary_cp_group = getattr(rotary_pos_emb, "cp_group", None) dispatched_local_cp_positions = ( isinstance(position_ids, torch.Tensor) and position_ids.ndim == 2 - and _context_parallel_world_size( - getattr(gpt_module, "config", None) - ) - > 1 + and _context_parallel_world_size(getattr(_gpt, "config", None)) > 1 and rotary_cp_group is not None ) if dispatched_local_cp_positions: @@ -166,8 +162,12 @@ def preprocess_hook(*args, _preprocess=preprocess, **kwargs): finally: if dispatched_local_cp_positions: setattr(rotary_pos_emb, "cp_group", rotary_cp_group) - decoder_input = cast(torch.Tensor, preproc_output[0]) - if not decoder_input.requires_grad and decoder_input.is_leaf: + decoder_input = cast(torch.Tensor | None, preproc_output[0]) + if ( + decoder_input is not None + and decoder_input.is_leaf + and not decoder_input.requires_grad + ): decoder_input.requires_grad_(True) return tuple(preproc_output) @@ -1176,6 +1176,11 @@ def _select_qwen35_expert_weight( _QWEN35_TEXT_ONLY_BRIDGE_REGISTERED = False +def _propagate_qwen35_text_dtype(hf_pretrained: Any) -> None: + config = hf_pretrained.config + config.text_config.dtype = config.dtype + + def ensure_qwen35_text_only_bridge_registered() -> None: global _QWEN35_TEXT_ONLY_BRIDGE_REGISTERED if _QWEN35_TEXT_ONLY_BRIDGE_REGISTERED: @@ -1201,6 +1206,10 @@ def ensure_qwen35_text_only_bridge_registered() -> None: model_type="qwen3_5", ) class _ArtQwen35DenseTextOnlyBridge(Qwen35VLBridge): + def provider_bridge(self, hf_pretrained: Any) -> Any: + _propagate_qwen35_text_dtype(hf_pretrained) + return super().provider_bridge(hf_pretrained) + def mapping_registry(self) -> Any: return _qwen35_text_only_mapping_registry(Qwen35VLBridge) @@ -1211,6 +1220,10 @@ def mapping_registry(self) -> Any: model_type="qwen3_5_moe", ) class _ArtQwen35TextOnlyBridge(Qwen35VLMoEBridge): + def provider_bridge(self, hf_pretrained: Any) -> Any: + _propagate_qwen35_text_dtype(hf_pretrained) + return super().provider_bridge(hf_pretrained) + def mapping_registry(self) -> Any: return _qwen35_text_only_mapping_registry(Qwen35VLMoEBridge) diff --git a/src/art/megatron/model_support/handlers/qwen3_common.py b/src/art/megatron/model_support/handlers/qwen3_common.py index 0b0c56820..91932656c 100644 --- a/src/art/megatron/model_support/handlers/qwen3_common.py +++ b/src/art/megatron/model_support/handlers/qwen3_common.py @@ -81,11 +81,13 @@ def install_qwen3_text_preprocess_patch(model_chunks: Sequence[Any]) -> None: ) preprocess = gpt_module._preprocess - def preprocess_hook(*args, _preprocess=preprocess, **kwargs): + def preprocess_hook( + *args, _preprocess=preprocess, _gpt_module=gpt_module, **kwargs + ): position_ids = kwargs.get("position_ids") - rotary_pos_emb = getattr(gpt_module, "rotary_pos_emb", None) + rotary_pos_emb = getattr(_gpt_module, "rotary_pos_emb", None) rotary_cp_group = getattr(rotary_pos_emb, "cp_group", None) - config = getattr(gpt_module, "config", None) + config = getattr(_gpt_module, "config", None) cp_world_size = _context_parallel_world_size(config) uses_dispatched_local_cp_positions = ( isinstance(position_ids, torch.Tensor) @@ -100,8 +102,12 @@ def preprocess_hook(*args, _preprocess=preprocess, **kwargs): finally: if uses_dispatched_local_cp_positions: setattr(rotary_pos_emb, "cp_group", rotary_cp_group) - decoder_input = cast(torch.Tensor, preproc_output[0]) - if not decoder_input.requires_grad and decoder_input.is_leaf: + decoder_input = cast(torch.Tensor | None, preproc_output[0]) + if ( + decoder_input is not None + and not decoder_input.requires_grad + and decoder_input.is_leaf + ): decoder_input.requires_grad_(True) position_ids = cast(torch.Tensor, position_ids) table = cast(torch.Tensor, preproc_output[1]) @@ -110,15 +116,15 @@ def preprocess_hook(*args, _preprocess=preprocess, **kwargs): embedding_dim = int(table.shape[-1]) if ( rotary_pos_emb is not None - and getattr(gpt_module, "position_embedding_type", None) == "rope" + and getattr(_gpt_module, "position_embedding_type", None) == "rope" and cp_world_size > 1 ): rotary_seq_len = cast( int, - getattr(gpt_module, "_art_qwen3_rotary_seq_len", None), + getattr(_gpt_module, "_art_qwen3_rotary_seq_len", None), ) table_source = _build_absolute_rotary_pos_emb( - gpt_module, + _gpt_module, max_position=int(rotary_seq_len) - 1, dtype=table.dtype, device=table.device, diff --git a/src/art/megatron/model_support/handlers/qwen3_moe.py b/src/art/megatron/model_support/handlers/qwen3_moe.py index 5aec937f4..513119efa 100644 --- a/src/art/megatron/model_support/handlers/qwen3_moe.py +++ b/src/art/megatron/model_support/handlers/qwen3_moe.py @@ -11,7 +11,11 @@ install_qwen3_text_preprocess_patch, qwen3_forward_kwargs, ) -from art.megatron.model_support.spec import CompileWorkaroundConfig +from art.megatron.model_support.spec import ( + CompileWorkaroundConfig, + ExpertPackedLoraGroup, + ExpertPackedLoraSlot, +) _QWEN3_MOE_COMPILE_WORKAROUND_FLAGS = ( "moe_postprocess", @@ -24,6 +28,23 @@ class Qwen3MoeHandler(DefaultMoeHandler): key = "qwen3_moe" native_vllm_lora_status = "validated" + def expert_packed_lora_groups(self) -> tuple[ExpertPackedLoraGroup, ...]: + return ( + ExpertPackedLoraGroup( + art_group_suffix=".mlp.experts", + slots=tuple( + ExpertPackedLoraSlot( + source_projection=projection, + source_lora=lora, + output_suffix=f"{projection}.{lora}.weight", + pack_layout="expert_rows", + ) + for projection in ("gate_proj", "up_proj", "down_proj") + for lora in ("lora_A", "lora_B") + ), + ), + ) + def to_vllm_lora_tensors( self, tensors: dict[str, torch.Tensor], @@ -35,6 +56,9 @@ def to_vllm_lora_tensors( def to_vllm_lora_config(self, adapter_config: dict[str, Any]) -> dict[str, Any]: return _qwen3_moe_config(adapter_config) + def vllm_lora_conversion_is_view_only(self) -> bool: + return True + def install_preprocess_patch(self, model_chunks: Sequence[Any]) -> None: install_qwen3_text_preprocess_patch(model_chunks) @@ -65,6 +89,11 @@ def compile_workaround_config( r"^.*\.mlp\.experts\.\d+\." r"(?:gate_proj|up_proj|down_proj)\.lora_[AB]\.weight$" ) +_QWEN3_PACKED_MOE_KEY_RE = re.compile( + r"^(?P.*\.mlp\.experts)\." + r"(?Pgate_proj|up_proj|down_proj)\." + r"(?Plora_[AB])\.weight$" +) def _qwen3_moe_config(adapter_config: dict[str, Any]) -> dict[str, Any]: @@ -89,6 +118,52 @@ def _clone(tensor: torch.Tensor) -> torch.Tensor: return tensor.clone().contiguous() +def _expand_packed_moe_lora( + prefix: str, + slots: dict[tuple[str, str], torch.Tensor], + *, + rank: int, +) -> dict[str, torch.Tensor]: + expected = { + (projection, lora) + for projection in ("gate_proj", "up_proj", "down_proj") + for lora in ("lora_A", "lora_B") + } + if set(slots) != expected: + raise RuntimeError(f"Incomplete packed Qwen3 MoE LoRA block for {prefix}") + num_experts: int | None = None + shaped: dict[str, tuple[torch.Tensor, torch.Tensor]] = {} + for projection in ("gate_proj", "up_proj", "down_proj"): + a = slots[(projection, "lora_A")] + b = slots[(projection, "lora_B")] + if a.ndim != 2 or b.ndim != 2 or a.shape[0] % rank or b.shape[1] != rank: + raise RuntimeError( + f"Invalid packed Qwen3 MoE LoRA shapes for {prefix}.{projection}: " + f"A={tuple(a.shape)} B={tuple(b.shape)} rank={rank}" + ) + projection_experts = a.shape[0] // rank + if projection_experts <= 0 or b.shape[0] % projection_experts: + raise RuntimeError( + f"Packed Qwen3 MoE LoRA expert shape does not divide for " + f"{prefix}.{projection}" + ) + if num_experts is not None and projection_experts != num_experts: + raise RuntimeError(f"Packed Qwen3 MoE expert counts differ for {prefix}") + num_experts = projection_experts + shaped[projection] = ( + a.reshape(projection_experts, rank, a.shape[1]), + b.reshape(projection_experts, b.shape[0] // projection_experts, rank), + ) + + assert num_experts is not None + return { + f"{prefix}.{expert}.{projection}.lora_{lora}.weight": tensor[expert] + for projection, pair in shaped.items() + for lora, tensor in zip(("A", "B"), pair, strict=True) + for expert in range(num_experts) + } + + def _expand_fused_moe_lora( prefix: str, slots: dict[str, torch.Tensor], @@ -196,8 +271,15 @@ def _to_vllm_lora_tensors( *, adapter_config: dict[str, Any], ) -> tuple[dict[str, torch.Tensor], dict[str, Any]]: + packed: dict[str, dict[tuple[str, str], torch.Tensor]] = {} grouped: dict[str, dict[str, torch.Tensor]] = {} for key, tensor in tensors.items(): + packed_match = _QWEN3_PACKED_MOE_KEY_RE.match(key) + if packed_match is not None: + packed.setdefault(packed_match.group("prefix"), {})[ + (packed_match.group("projection"), packed_match.group("lora")) + ] = tensor + continue match = _QWEN3_FUSED_MOE_KEY_RE.match(key) if match is None: continue @@ -206,6 +288,30 @@ def _to_vllm_lora_tensors( ) grouped.setdefault(match.group("prefix"), {})[slot] = tensor + if packed and grouped: + raise RuntimeError("Qwen3 LoRA contains both packed and fused expert blocks") + + if packed: + rank = int(adapter_config["r"]) + transformed = { + key: tensor + for prefix, slots in packed.items() + for key, tensor in _expand_packed_moe_lora(prefix, slots, rank=rank).items() + } + used_keys = { + f"{prefix}.{projection}.{lora}.weight" + for prefix in packed + for projection in ("gate_proj", "up_proj", "down_proj") + for lora in ("lora_A", "lora_B") + } + for key, tensor in tensors.items(): + if key in used_keys: + continue + if key in transformed: + raise RuntimeError(f"Duplicate expanded Qwen3 MoE LoRA key: {key}") + transformed[key] = tensor + return transformed, _qwen3_moe_config(adapter_config) + if not grouped: if any(_QWEN3_EXPERT_MOE_KEY_RE.match(key) for key in tensors): return tensors, _qwen3_moe_config(adapter_config) diff --git a/src/art/megatron/model_support/lora_disk.py b/src/art/megatron/model_support/lora_disk.py index 46741d50a..f0b01183b 100644 --- a/src/art/megatron/model_support/lora_disk.py +++ b/src/art/megatron/model_support/lora_disk.py @@ -6,14 +6,17 @@ import torch from art.megatron.model_support.spec import ModelSupportHandler +from art.utils.safetensors import ( + PreparedSafetensors, + prepare_safetensors, + save_prepared_safetensors, +) ART_LORA_FORMAT_CONFIG_KEY = "art_lora_format" ART_LORA_FORMAT_VLLM = "vllm" safetensors = importlib.import_module("safetensors") -safetensors_torch = importlib.import_module("safetensors.torch") safe_open = safetensors.safe_open -save_file = safetensors_torch.save_file def _jsonable_config(value: Any) -> Any: @@ -78,10 +81,15 @@ def save_vllm_lora_tensors( lora_path: str | Path, tensors: dict[str, torch.Tensor], adapter_config: dict[str, Any], + *, + prepared_tensors: PreparedSafetensors | None = None, ) -> None: base_dir = Path(lora_path) base_dir.mkdir(parents=True, exist_ok=True) - save_file(tensors, base_dir / "adapter_model.safetensors") + save_prepared_safetensors( + prepared_tensors or prepare_safetensors(tensors), + base_dir / "adapter_model.safetensors", + ) save_adapter_config( base_dir, {**adapter_config, ART_LORA_FORMAT_CONFIG_KEY: ART_LORA_FORMAT_VLLM}, diff --git a/src/art/megatron/model_support/registry.py b/src/art/megatron/model_support/registry.py index 5511205a9..bbeace71a 100644 --- a/src/art/megatron/model_support/registry.py +++ b/src/art/megatron/model_support/registry.py @@ -16,6 +16,7 @@ _GEMMA4_DENSE_HANDLER_KEY = "gemma4_dense" _GEMMA4_MOE_HANDLER_KEY = "gemma4_moe" _DSV4_HANDLER_KEY = "dsv4" +_GLM52_HANDLER_KEY = "glm52" _GPT_OSS_MOE_HANDLER_KEY = "gpt_oss_moe" _VALIDATED_NATIVE_VLLM_LORA_STATUS: NativeVllmLoraStatus = "validated" _WIP_NATIVE_VLLM_LORA_STATUS: NativeVllmLoraStatus = "wip" @@ -74,6 +75,16 @@ "down_proj", "experts", ) +_GLM52_TARGET_MODULES = ( + "q_a_proj", + "q_b_proj", + "kv_a_proj_with_mqa", + "o_proj", + "gate_proj", + "up_proj", + "down_proj", + "experts", +) DEFAULT_DENSE_SPEC = ModelSupportSpec( key="default_dense", @@ -220,6 +231,19 @@ dependency_floor=DependencyFloor(transformers="5.12.1"), ) +GLM52_SPEC = ModelSupportSpec( + key="glm52", + handler_key=_GLM52_HANDLER_KEY, + is_moe=True, + model_names=("zai-org/GLM-5.2",), + default_target_modules=_GLM52_TARGET_MODULES, + native_vllm_lora_status=_VALIDATED_NATIVE_VLLM_LORA_STATUS, + dependency_floor=DependencyFloor( + transformers="5.12.1", + megatron_bridge="e1a207ac757e5d0ed94d8ffbe1cbd28e81d8c084", + ), +) + GPT_OSS_MOE_SPEC = ModelSupportSpec( key="gpt_oss_moe", handler_key=_GPT_OSS_MOE_HANDLER_KEY, @@ -245,9 +269,10 @@ GEMMA4_MOE_SPEC, GEMMA4_DENSE_SPEC, DSV4_SPEC, + GLM52_SPEC, GPT_OSS_MOE_SPEC, ) -PROBE_ONLY_MODEL_SUPPORT_SPECS = () +PROBE_ONLY_MODEL_SUPPORT_SPECS: tuple[ModelSupportSpec, ...] = () _ALL_MODEL_SUPPORT_SPECS = ( DEFAULT_DENSE_SPEC, *VALIDATED_MODEL_SUPPORT_SPECS, @@ -301,6 +326,10 @@ "art.megatron.model_support.handlers.dsv4", "DSV4_HANDLER", ), + _GLM52_HANDLER_KEY: ( + "art.megatron.model_support.handlers.glm52", + "GLM52_HANDLER", + ), _GPT_OSS_MOE_HANDLER_KEY: ( "art.megatron.model_support.handlers.gpt_oss", "GPT_OSS_MOE_HANDLER", @@ -340,6 +369,7 @@ GEMMA4_MOE_MODELS = frozenset(GEMMA4_MOE_SPEC.model_names) GEMMA4_DENSE_MODELS = frozenset(GEMMA4_DENSE_SPEC.model_names) DSV4_MODELS = frozenset(DSV4_SPEC.model_names) +GLM52_MODELS = frozenset(GLM52_SPEC.model_names) GPT_OSS_MOE_MODELS = frozenset(GPT_OSS_MOE_SPEC.model_names) @@ -364,6 +394,13 @@ def get_model_support_spec( ) +def get_model_support_spec_by_key(key: str) -> ModelSupportSpec: + try: + return _SPECS_BY_KEY[key] + except KeyError as exc: + raise KeyError(f"No model support spec registered for {key!r}") from exc + + def get_model_support_handler( base_model: str, *, diff --git a/src/art/megatron/model_support/spec.py b/src/art/megatron/model_support/spec.py index f5ca3f16e..bc1b57506 100644 --- a/src/art/megatron/model_support/spec.py +++ b/src/art/megatron/model_support/spec.py @@ -1,4 +1,12 @@ -from typing import TYPE_CHECKING, Any, Literal, Protocol, Sequence, runtime_checkable +from typing import ( + TYPE_CHECKING, + Any, + Callable, + Literal, + Protocol, + Sequence, + runtime_checkable, +) from pydantic import BaseModel, ConfigDict, Field @@ -56,6 +64,7 @@ class PrefixTreeModelStateContext(BaseModel): attention_token_layout_index: Any | None = None attention_head_dim: int | None = None attention_value_head_dim: int | None = None + context_parallel_state: Any | None = None class CompileWorkaroundConfig(BaseModel): @@ -135,6 +144,8 @@ def patch_provider( def configure_provider_for_runtime(self, provider: "GPTModelProvider") -> None: ... + def context_parallel_workload_profile(self, provider: Any) -> Any | None: ... + def default_chat_template(self) -> str | None: ... def configure_tokenizer( @@ -154,6 +165,11 @@ def vllm_server_args(self) -> dict[str, object]: ... def install_preprocess_patch(self, model_chunks: Sequence[Any]) -> None: ... + def build_pipeline_microbatch_activator( + self, + model_chunks: Sequence[Any], + ) -> Callable[[Any, int], None] | None: ... + def build_prefix_tree_model_state( self, context: PrefixTreeModelStateContext, @@ -209,6 +225,8 @@ def to_vllm_lora_config( adapter_config: dict[str, Any], ) -> dict[str, Any]: ... + def vllm_lora_conversion_is_view_only(self) -> bool: ... + def expert_packed_lora_groups(self) -> tuple[ExpertPackedLoraGroup, ...]: ... def from_vllm_lora_tensors( diff --git a/src/art/megatron/optimizer_state.py b/src/art/megatron/optimizer_state.py index e5df9ce6c..f1a4cc6a2 100644 --- a/src/art/megatron/optimizer_state.py +++ b/src/art/megatron/optimizer_state.py @@ -1,136 +1,1796 @@ from __future__ import annotations +import asyncio +from contextlib import ExitStack, asynccontextmanager, contextmanager +import copy +import fcntl +import hashlib import json import os from pathlib import Path import re +import shutil import time -from typing import Literal +from typing import Any, AsyncIterator, Callable, Iterator, Literal, cast +from uuid import uuid4 -from pydantic import BaseModel, Field, model_validator +from pydantic import BaseModel, ConfigDict, Field, model_validator +import torch from ..utils.get_model_step import get_step_from_dir from ..utils.output_dirs import get_step_checkpoint_dir +from .tensor_snapshot import ( + PendingCpuSnapshot, + PinnedCpuSnapshotBuilder, + PinnedCpuSnapshotStager, +) + +ALLOW_UNPAIRED_MEGATRON_RESUME_ENV = "ART_ALLOW_UNPAIRED_MEGATRON_RESUME" +OPTIMIZER_GENERATIONS_DIR = "generations" +OPTIMIZER_MANIFEST = "manifest.json" +OPTIMIZER_POINTER = "committed.json" +OPTIMIZER_POLICY_POINTER = "policy.json" +OPTIMIZER_MODEL_LOCK = ".optimizer.lock" +OPTIMIZER_WRITER_LOCK = ".writer.lock" +OPTIMIZER_GENERATION_LEASE_PREFIX = ".lease-" +OPTIMIZER_TRASH_PREFIX = ".trash-" +OPTIMIZER_ORPHAN_GRACE_S = 3600.0 +ADAPTER_PUBLICATION_ACK = ".optimizer-published.json" +ADAPTER_LATEST_POINTER = "latest-adapter.json" +_ADAPTER_FILES = ("adapter_config.json", "adapter_model.safetensors") +_GENERATION_PATTERN = r"step-\d{8,}-[0-9a-f]{32}" +_GENERATION_RE = re.compile(f"^{_GENERATION_PATTERN}$") +_TRASH_RE = re.compile(f"^\\.trash-({_GENERATION_PATTERN})-[0-9a-f]{{32}}$") +_POINTER_TEMP_RE = re.compile(r"^\.committed\.json\.\d+\.[0-9a-f]{32}\.tmp$") +_POLICY_TEMP_RE = re.compile(r"^\.policy\.json\.\d+\.[0-9a-f]{32}\.tmp$") +_SHA256_PATTERN = r"^[0-9a-f]{64}$" +_POINTER_UNSET = object() +_SCHEDULE_PROVIDER_FIELDS = { + "batch_p2p_comm", + "batch_p2p_sync", + "finalize_model_grads_func", + "microbatch_group_size_per_vp_stage", + "overlap_p2p_comm", + "variable_seq_lengths", +} + + +class _OptimizerRecord(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + +class MegatronResumeStep(_OptimizerRecord): + step: int + latest_lora_step: int + optimizer_step: int | None + used_unpaired_override: bool = False + quarantined_lora_steps: tuple[int, ...] = () + + +class CheckpointFile(_OptimizerRecord): + name: Literal["adapter_config.json", "adapter_model.safetensors"] + size_bytes: int = Field(gt=0) + + +class OptimizerAdapter(_OptimizerRecord): + identity: str = Field(min_length=1) + training_session_id: str = Field(min_length=1) + step: int = Field(ge=0) + generation_id: str = Field(pattern=f"^{_GENERATION_PATTERN}$") + files: tuple[CheckpointFile, ...] + + @model_validator(mode="after") + def _validate_files(self) -> "OptimizerAdapter": + if _generation_step(self.generation_id) != self.step: + raise ValueError("adapter generation ID and policy step must match") + if tuple(file.name for file in self.files) != _ADAPTER_FILES: + raise ValueError("adapter manifest must cover every payload file once") + return self + + +class OptimizerTopology(_OptimizerRecord): + world_size: int = Field(gt=0) + tp: int = Field(gt=0) + cp: int = Field(gt=0) + ep: int = Field(gt=0) + etp: int = Field(gt=0) + pp: int = Field(gt=0) + vpp: int = Field(gt=0) + + +class OptimizerShard(_OptimizerRecord): + rank: int = Field(ge=0) + size_bytes: int = Field(gt=0) + layout_sha256: str = Field(pattern=_SHA256_PATTERN) + + +class _PairedOptimizerRecord(_OptimizerRecord): + step: int = Field(ge=0) + adapter: OptimizerAdapter + + @model_validator(mode="after") + def _validate_adapter_step(self) -> "_PairedOptimizerRecord": + if self.step != self.adapter.step: + raise ValueError("optimizer and adapter steps must match") + return self + + +class _OptimizerGenerationRecord(_PairedOptimizerRecord): + generation: str = Field(pattern=f"^{_GENERATION_PATTERN}$") + + @model_validator(mode="after") + def _validate_generation_step(self) -> "_OptimizerGenerationRecord": + if int(self.generation.split("-", 2)[1]) != self.step: + raise ValueError("optimizer generation name and step must match") + if self.generation != self.adapter.generation_id: + raise ValueError("optimizer and adapter generation IDs must match") + return self + + +class OptimizerGenerationManifest(_OptimizerGenerationRecord): + format_version: Literal[3] = 3 + runtime_sha256: str = Field(pattern=_SHA256_PATTERN) + topology: OptimizerTopology + shards: tuple[OptimizerShard, ...] + + +class OptimizerGenerationPointer(_OptimizerGenerationRecord): + format_version: Literal[3] = 3 + + +class OptimizerPolicyPointer(_OptimizerRecord): + format_version: Literal[2] = 2 + policy_adapter: OptimizerAdapter + optimizer_anchor: OptimizerGenerationPointer | None + + @model_validator(mode="after") + def _validate_policy_alias(self) -> "OptimizerPolicyPointer": + if self.policy_adapter.step == 0: + raise ValueError("policy alias must advance beyond checkpoint 0") + if self.optimizer_anchor is not None and ( + self.policy_adapter.step <= self.optimizer_anchor.step + ): + raise ValueError("policy alias must be newer than its optimizer anchor") + return self + + +class CommittedOptimizerPolicy(_OptimizerRecord): + policy_adapter: OptimizerAdapter + state_adapter: OptimizerAdapter | None + optimizer_anchor: OptimizerGenerationPointer | None + + +class OptimizerStateSnapshot(_OptimizerRecord): + model_config = ConfigDict(extra="forbid", frozen=True, arbitrary_types_allowed=True) + + generation_id: str = Field(pattern=f"^{_GENERATION_PATTERN}$") + step: int = Field(ge=1) + rank: int = Field(ge=0) + world_size: int = Field(gt=0) + runtime_sha256: str = Field(pattern=_SHA256_PATTERN) + layout_sha256: str = Field(pattern=_SHA256_PATTERN) + topology: OptimizerTopology + state_dict: Any + + @model_validator(mode="after") + def _validate_identity(self) -> "OptimizerStateSnapshot": + if _generation_step(self.generation_id) != self.step: + raise ValueError("optimizer snapshot generation and step must match") + if self.rank >= self.world_size or self.topology.world_size != self.world_size: + raise ValueError("optimizer snapshot rank/topology mismatch") + return self + + +def optimizer_shard_name(rank: int, world_size: int) -> str: + if world_size <= 0 or rank < 0 or rank >= world_size: + raise ValueError( + f"Invalid optimizer shard rank {rank} for world size {world_size}" + ) + return f"{rank + 1:02d}-of-{world_size:02d}.pt" + + +def current_optimizer_topology(world_size: int) -> OptimizerTopology: + from megatron.core import parallel_state as ps + + return OptimizerTopology( + world_size=world_size, + tp=int(ps.get_tensor_model_parallel_world_size()), + cp=int(ps.get_context_parallel_world_size()), + ep=int(ps.get_expert_model_parallel_world_size()), + etp=int(ps.get_expert_tensor_parallel_world_size()), + pp=int(ps.get_pipeline_model_parallel_world_size()), + vpp=int(ps.get_virtual_pipeline_model_parallel_world_size() or 1), + ) + + +def new_optimizer_generation(step: int) -> str: + if step < 0: + raise ValueError(f"Optimizer step must be non-negative, got {step}") + return f"step-{step:08d}-{uuid4().hex}" + + +def _validate_generation_name(generation: str) -> None: + if _GENERATION_RE.fullmatch(generation) is None: + raise ValueError(f"Invalid optimizer generation name: {generation!r}") + + +def optimizer_pending_generation_path( + optimizer_state_path: str, generation: str +) -> Path: + _validate_generation_name(generation) + return ( + Path(optimizer_state_path) + / OPTIMIZER_GENERATIONS_DIR + / f".pending-{generation}" + ) + + +def optimizer_generation_path(optimizer_state_path: str, generation: str) -> Path: + _validate_generation_name(generation) + return Path(optimizer_state_path) / OPTIMIZER_GENERATIONS_DIR / generation + + +def _generation_lease_path(path: Path, generation: str) -> Path: + _validate_generation_name(generation) + return ( + path + / OPTIMIZER_GENERATIONS_DIR + / f"{OPTIMIZER_GENERATION_LEASE_PREFIX}{generation}" + ) + + +def _generation_step(generation: str) -> int: + _validate_generation_name(generation) + return int(generation.split("-", 2)[1]) + + +def _adapter_generation_lease_path(output_dir: str | Path, generation: str) -> Path: + _validate_generation_name(generation) + return Path(output_dir).absolute() / "megatron_runtime" / "leases" / generation + + +@contextmanager +def adapter_generation_lease(adapter: OptimizerAdapter) -> Iterator[None]: + path = _adapter_generation_lease_path( + Path(adapter.identity).absolute().parent.parent, + adapter.generation_id, + ) + path.parent.mkdir(parents=True, exist_ok=True) + with path.open("a+b") as lease_file: + fcntl.flock(lease_file.fileno(), fcntl.LOCK_SH) + try: + yield + finally: + fcntl.flock(lease_file.fileno(), fcntl.LOCK_UN) + + +@contextmanager +def _adapter_retention_leases( + output_dir: str, protected_steps: set[int] +) -> Iterator[set[int]]: + checkpoints = Path(output_dir) / "checkpoints" + with ExitStack() as leases: + if checkpoints.is_dir(): + for checkpoint in checkpoints.iterdir(): + if ( + not checkpoint.is_dir() + or not checkpoint.name.isdigit() + or (step := int(checkpoint.name)) in protected_steps + ): + continue + publication = read_adapter_publication( + checkpoint, step=step, verify_files=False + ) + generation = ( + publication.generation_id + if publication is not None + else _initial_generation_id(checkpoint, step) + ) + path = _adapter_generation_lease_path(output_dir, generation) + path.parent.mkdir(parents=True, exist_ok=True) + lease = leases.enter_context(path.open("a+b")) + try: + fcntl.flock(lease.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB) + except BlockingIOError: + protected_steps.add(step) + else: + leases.callback(fcntl.flock, lease.fileno(), fcntl.LOCK_UN) + yield protected_steps + + +@contextmanager +def optimizer_model_lease(optimizer_state_path: str | Path) -> Iterator[None]: + with _optimizer_model_lock_path(optimizer_state_path).open("a+b") as lock_file: + fcntl.flock(lock_file.fileno(), fcntl.LOCK_EX) + try: + yield + finally: + fcntl.flock(lock_file.fileno(), fcntl.LOCK_UN) + + +@asynccontextmanager +async def async_optimizer_model_lease( + optimizer_state_path: str | Path, +) -> AsyncIterator[None]: + with _optimizer_model_lock_path(optimizer_state_path).open("a+b") as lock_file: + while True: + try: + fcntl.flock(lock_file.fileno(), fcntl.LOCK_EX | fcntl.LOCK_NB) + break + except BlockingIOError: + await asyncio.sleep(0.05) + try: + yield + finally: + fcntl.flock(lock_file.fileno(), fcntl.LOCK_UN) + + +def _optimizer_model_lock_path(optimizer_state_path: str | Path) -> Path: + model_root = Path(optimizer_state_path).absolute().parent + model_root.mkdir(parents=True, exist_ok=True) + return model_root / OPTIMIZER_MODEL_LOCK + + +def optimizer_shard_path(generation_path: Path, *, rank: int, world_size: int) -> Path: + return generation_path / optimizer_shard_name(rank, world_size) + + +def _adapter_checkpoint_files(path: str | Path) -> tuple[Path, tuple[Path, ...]]: + adapter_path = Path(path) + files = tuple(adapter_path / name for name in _ADAPTER_FILES) + missing = [str(file) for file in files if not file.is_file()] + if missing: + raise RuntimeError(f"Adapter checkpoint is incomplete; missing {missing}") + return adapter_path, files + + +def _adapter_file_records(path: str | Path) -> tuple[CheckpointFile, ...]: + _adapter_path, files = _adapter_checkpoint_files(path) + return tuple( + CheckpointFile(name=cast(Any, file.name), size_bytes=file.stat().st_size) + for file in files + ) + + +def _initial_generation_id(path: str | Path, step: int) -> str: + suffix = hashlib.sha256(str(Path(path).absolute()).encode()).hexdigest()[:32] + return f"step-{step:08d}-{suffix}" + + +def optimizer_adapter( + path: str | Path, + step: int, + *, + training_session_id: str = "legacy", + generation_id: str | None = None, +) -> OptimizerAdapter: + if step < 0: + raise ValueError(f"Adapter step must be non-negative, got {step}") + identity = str(Path(path).absolute()) + return OptimizerAdapter( + identity=identity, + training_session_id=training_session_id, + step=step, + generation_id=generation_id or _initial_generation_id(identity, step), + files=_adapter_file_records(identity), + ) + + +def canonical_adapter_path(staging_path: str | Path, step: int) -> Path: + staging = Path(staging_path).absolute() + if ( + staging.parent.name != "staging" + or staging.parent.parent.name != "megatron_runtime" + ): + raise RuntimeError( + "Megatron adapter publication requires the managed staging layout: " + f"{staging}" + ) + return Path( + get_step_checkpoint_dir(str(staging.parent.parent.parent), step) + ).absolute() + + +def _canonical_adapter_path(path: str | Path, step: int) -> Path: + candidate = Path(path).absolute() + if ( + candidate.parent.name == "staging" + and candidate.parent.parent.name == "megatron_runtime" + ): + return canonical_adapter_path(candidate, step) + return candidate + + +def publish_adapter_checkpoint( + staging_path: str | Path, + *, + step: int, + training_session_id: str = "legacy", + generation_id: str | None = None, +) -> OptimizerAdapter: + staging = Path(staging_path).absolute() + canonical = canonical_adapter_path(staging, step) + if canonical.exists(): + raise RuntimeError(f"Refusing to replace canonical adapter {canonical}") + _, files = _adapter_checkpoint_files(staging) + for path in files: + with path.open("rb") as adapter_file: + os.fsync(adapter_file.fileno()) + _fsync_directory(staging) + adapter = OptimizerAdapter( + identity=str(canonical), + training_session_id=training_session_id, + step=step, + generation_id=generation_id or _initial_generation_id(canonical, step), + files=_adapter_file_records(staging), + ) + _write_model_atomic(staging / ADAPTER_PUBLICATION_ACK, adapter) + canonical.parent.mkdir(parents=True, exist_ok=True) + os.replace(staging, canonical) + _fsync_directory(canonical.parent) + _write_model_atomic( + canonical.parent.parent / "megatron_runtime" / ADAPTER_LATEST_POINTER, + adapter, + ) + return adapter + + +def read_latest_adapter_pointer(output_dir: str | Path) -> OptimizerAdapter | None: + pointer = Path(output_dir) / "megatron_runtime" / ADAPTER_LATEST_POINTER + if not pointer.exists(): + return None + try: + adapter = OptimizerAdapter.model_validate_json(pointer.read_text("utf-8")) + except Exception as error: + raise RuntimeError(f"Invalid adapter generation pointer: {pointer}") from error + _validate_adapter_publication(adapter, verify_files=True) + return adapter + + +def read_adapter_publication( + adapter_path: str | Path, + *, + step: int, + verify_files: bool = True, +) -> OptimizerAdapter | None: + canonical = _canonical_adapter_path(adapter_path, step) + acknowledgment = canonical / ADAPTER_PUBLICATION_ACK + try: + payload = acknowledgment.read_text("utf-8") + except FileNotFoundError: + return None + try: + adapter = OptimizerAdapter.model_validate_json(payload) + except Exception as exc: + raise RuntimeError( + f"Invalid adapter publication acknowledgment: {acknowledgment}" + ) from exc + expected_identity = str(canonical) + if ( + adapter.identity != expected_identity + or adapter.step != step + or "staging" in Path(adapter.identity).parts + ): + raise RuntimeError( + "Adapter publication acknowledgment does not identify the canonical " + f"adapter: acknowledged={adapter.model_dump()}, " + f"expected_identity={expected_identity!r}, expected_step={step}" + ) + if verify_files: + current_files = _adapter_file_records(canonical) + if adapter.files != current_files: + raise RuntimeError( + "Adapter publication acknowledgment does not match canonical " + f"file coverage and sizes: acknowledged={adapter.files}, " + f"current={current_files}" + ) + return adapter + + +def _validate_adapter_publication( + adapter: OptimizerAdapter, *, verify_files: bool = False +) -> None: + if "staging" in Path(adapter.identity).parts: + raise RuntimeError( + f"Optimizer pointers cannot reference a staging adapter: {adapter.identity}" + ) + if ( + read_adapter_publication( + adapter.identity, + step=adapter.step, + verify_files=verify_files, + ) + != adapter + ): + raise RuntimeError( + f"Optimizer adapter publication is not acknowledged: {adapter.model_dump()}" + ) + + +def _fsync_directory(path: Path) -> None: + directory_fd = os.open(path, os.O_RDONLY | os.O_DIRECTORY) + try: + os.fsync(directory_fd) + finally: + os.close(directory_fd) + + +def _write_model_atomic(path: Path, model: BaseModel) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + temporary = path.with_name(f".{path.name}.{os.getpid()}.{uuid4().hex}.tmp") + try: + with temporary.open("w", encoding="utf-8") as output: + output.write(json.dumps(model.model_dump(mode="json"), sort_keys=True)) + output.write("\n") + output.flush() + os.fsync(output.fileno()) + os.replace(temporary, path) + _fsync_directory(path.parent) + finally: + temporary.unlink(missing_ok=True) + + +def _read_pointer(path: Path) -> OptimizerGenerationPointer | None: + pointer_path = path / OPTIMIZER_POINTER + if pointer_path.is_file(): + try: + return OptimizerGenerationPointer.model_validate_json( + pointer_path.read_text("utf-8") + ) + except Exception as exc: + raise RuntimeError( + f"Invalid optimizer generation pointer: {pointer_path}" + ) from exc + if pointer_path.exists(): + raise RuntimeError( + f"Invalid optimizer generation pointer: {pointer_path} is not a file" + ) + if not path.exists(): + return None + legacy = sorted( + entry.name + for entry in path.iterdir() + if entry.is_file() + and ( + entry.name == OPTIMIZER_MANIFEST + or entry.name.isdigit() + or (entry.name.endswith(".pt") and "-of-" in entry.name) + ) + ) + if legacy: + raise RuntimeError( + "Legacy optimizer checkpoint format is unsupported; expected an atomic " + f"{OPTIMIZER_POINTER} pointer, found {legacy} in {path}" + ) + return None + + +def _read_policy_pointer(path: Path) -> OptimizerPolicyPointer | None: + policy_path = path / OPTIMIZER_POLICY_POINTER + if policy_path.is_file(): + try: + return OptimizerPolicyPointer.model_validate_json( + policy_path.read_text("utf-8") + ) + except Exception as exc: + raise RuntimeError( + f"Invalid optimizer policy pointer: {policy_path}" + ) from exc + if policy_path.exists(): + raise RuntimeError( + f"Invalid optimizer policy pointer: {policy_path} is not a file" + ) + return None + + +def _resolve_policy_pointer( + path: Path, + pointer: OptimizerGenerationPointer | None, +) -> OptimizerPolicyPointer | None: + policy = _read_policy_pointer(path) + if policy is None: + return None + if policy.optimizer_anchor != pointer: + if pointer is not None and pointer.step >= policy.policy_adapter.step: + return None + raise RuntimeError( + "Optimizer policy pointer lost or changed its optimizer anchor: " + f"policy={policy.model_dump()}, " + f"current={pointer.model_dump() if pointer else None}" + ) + _validate_adapter_publication(policy.policy_adapter, verify_files=True) + if pointer is not None and any( + not os.path.samefile( + Path(policy.policy_adapter.identity) / name, + Path(pointer.adapter.identity) / name, + ) + for name in _ADAPTER_FILES + ): + raise RuntimeError("Optimizer policy alias does not reuse its anchor payload") + expected = Path( + get_step_checkpoint_dir(str(path.absolute().parent), policy.policy_adapter.step) + ).absolute() + if policy.policy_adapter.identity != str(expected): + raise RuntimeError("Optimizer policy does not identify a canonical checkpoint") + return policy + + +def _committed_policy( + path: Path, + pointer: OptimizerGenerationPointer | None, + *, + initial_adapter_path: str, +) -> CommittedOptimizerPolicy: + if policy := _resolve_policy_pointer(path, pointer): + return CommittedOptimizerPolicy( + policy_adapter=policy.policy_adapter, + state_adapter=None if pointer is None else pointer.adapter, + optimizer_anchor=pointer, + ) + if pointer is not None: + return CommittedOptimizerPolicy( + policy_adapter=pointer.adapter, + state_adapter=pointer.adapter, + optimizer_anchor=pointer, + ) + if ( + Path(initial_adapter_path).absolute() + != Path(get_step_checkpoint_dir(str(path.absolute().parent), 0)).absolute() + ): + raise RuntimeError("Initial optimizer policy must use canonical checkpoint 0") + initial = optimizer_adapter(initial_adapter_path, 0) + return CommittedOptimizerPolicy( + policy_adapter=initial, + state_adapter=None, + optimizer_anchor=None, + ) + + +def resolve_committed_optimizer_policy( + optimizer_state_path: str, + *, + initial_adapter_path: str, +) -> CommittedOptimizerPolicy: + path = Path(optimizer_state_path) + with _committed_generation_lease(path) as pointer: + if pointer is not None: + generation_path = optimizer_generation_path( + optimizer_state_path, pointer.generation + ) + manifest = _read_manifest(generation_path) + _validate_pointer_manifest(pointer, manifest) + _validate_generation_files(generation_path, manifest, local_rank=None) + _validate_adapter_publication(pointer.adapter, verify_files=True) + return _committed_policy( + path, + pointer, + initial_adapter_path=initial_adapter_path, + ) + + +def commit_optimizer_policy_advance( + optimizer_state_path: str, + *, + initial_adapter_path: str, + expected_step: int, + adapter: OptimizerAdapter, +) -> OptimizerPolicyPointer: + path = Path(optimizer_state_path) + with _writer_lease(path) as pointer: + current = _committed_policy( + path, + pointer, + initial_adapter_path=initial_adapter_path, + ) + if current.policy_adapter.step != expected_step: + raise RuntimeError( + "Stale no-op policy writer: " + f"expected={expected_step}, current={current.policy_adapter.step}" + ) + if adapter.step != expected_step + 1: + raise RuntimeError("Policy checkpoint must advance exactly one step") + if any( + not os.path.samefile( + Path(current.policy_adapter.identity) / name, + Path(adapter.identity) / name, + ) + for name in _ADAPTER_FILES + ): + raise RuntimeError( + "No-op policy checkpoint must reuse immutable adapter payloads" + ) + _validate_adapter_publication(adapter, verify_files=True) + policy = OptimizerPolicyPointer( + policy_adapter=adapter, + optimizer_anchor=pointer, + ) + _write_model_atomic(path / OPTIMIZER_POLICY_POINTER, policy) + return policy + + +def read_committed_optimizer_pointer( + optimizer_state_path: str, +) -> OptimizerGenerationPointer | None: + return _read_pointer(Path(optimizer_state_path)) + + +def read_committed_optimizer_step(optimizer_state_path: str) -> int | None: + pointer = read_committed_optimizer_pointer(optimizer_state_path) + return None if pointer is None else pointer.step + + +def read_committed_optimizer_adapter_step(optimizer_state_path: str) -> int | None: + pointer = read_committed_optimizer_pointer(optimizer_state_path) + return None if pointer is None else pointer.adapter.step + + +def _read_manifest(generation_path: Path) -> OptimizerGenerationManifest: + manifest_path = generation_path / OPTIMIZER_MANIFEST + try: + return OptimizerGenerationManifest.model_validate_json( + manifest_path.read_text("utf-8") + ) + except Exception as exc: + raise RuntimeError( + f"Invalid optimizer generation manifest: {manifest_path}" + ) from exc + + +def _ordered_manifest_shards( + manifest: OptimizerGenerationManifest, +) -> tuple[OptimizerShard, ...]: + topology = manifest.topology + ordered = tuple(sorted(manifest.shards, key=lambda shard: shard.rank)) + expected_ranks = tuple(range(topology.world_size)) + actual_ranks = tuple(shard.rank for shard in ordered) + if actual_ranks != expected_ranks: + raise RuntimeError( + "Optimizer manifest shard coverage mismatch: " + f"expected_ranks={expected_ranks}, actual_ranks={actual_ranks}" + ) + return ordered + + +def build_optimizer_manifest( + *, + generation: str, + step: int, + adapter: OptimizerAdapter, + runtime_sha256: str, + world_size: int, + shards: list[OptimizerShard], + topology: OptimizerTopology | None = None, +) -> OptimizerGenerationManifest: + manifest = OptimizerGenerationManifest( + generation=generation, + step=step, + adapter=adapter, + runtime_sha256=runtime_sha256, + topology=topology or current_optimizer_topology(world_size), + shards=tuple(shards), + ) + _ordered_manifest_shards(manifest) + return manifest + + +def _validate_pointer_manifest( + pointer: OptimizerGenerationPointer, + manifest: OptimizerGenerationManifest, +) -> None: + if ( + manifest.generation, + manifest.step, + manifest.adapter, + ) != (pointer.generation, pointer.step, pointer.adapter): + raise RuntimeError( + "Optimizer pointer/manifest identity mismatch: " + f"pointer={pointer.model_dump()}, manifest={manifest.model_dump()}" + ) + + +def _validate_generation_files( + generation_path: Path, + manifest: OptimizerGenerationManifest, + *, + local_rank: int | None, +) -> tuple[OptimizerShard, ...]: + ordered = _ordered_manifest_shards(manifest) + names = tuple( + optimizer_shard_name(shard.rank, manifest.topology.world_size) + for shard in ordered + ) + expected_entries = tuple(sorted((OPTIMIZER_MANIFEST, *names))) + if not generation_path.is_dir(): + raise RuntimeError( + f"Optimizer generation directory is missing: {generation_path}" + ) + actual_entries = tuple(sorted(entry.name for entry in generation_path.iterdir())) + if actual_entries != expected_entries: + raise RuntimeError( + "Optimizer generation shard coverage mismatch: " + f"expected={expected_entries}, actual={actual_entries}" + ) + for shard in ordered: + name = optimizer_shard_name(shard.rank, manifest.topology.world_size) + actual_size = (generation_path / name).stat().st_size + if actual_size != shard.size_bytes: + raise RuntimeError( + f"Optimizer shard size mismatch for {name}: " + f"expected={shard.size_bytes}, actual={actual_size}" + ) + if local_rank is not None: + if local_rank < 0 or local_rank >= len(ordered): + raise RuntimeError( + f"Invalid local optimizer rank {local_rank} for {len(ordered)} shards" + ) + return ordered + + +@contextmanager +def _root_lease( + path: Path, operation: int +) -> Iterator[OptimizerGenerationPointer | None]: + path.mkdir(parents=True, exist_ok=True) + with (path / OPTIMIZER_WRITER_LOCK).open("a+b") as lock_file: + fcntl.flock(lock_file.fileno(), operation) + try: + yield _read_pointer(path) + finally: + fcntl.flock(lock_file.fileno(), fcntl.LOCK_UN) + + +@contextmanager +def _writer_lease(path: Path) -> Iterator[OptimizerGenerationPointer | None]: + with _root_lease(path, fcntl.LOCK_EX) as pointer: + yield _recover_optimizer_pointer_locked(path, pointer) + + +@contextmanager +def _generation_lease( + path: Path, + generation: str, + *, + exclusive: bool, + nonblocking: bool = False, +) -> Iterator[bool]: + lease_path = _generation_lease_path(path, generation) + lease_path.parent.mkdir(parents=True, exist_ok=True) + with lease_path.open("a+b") as lease_file: + operation = fcntl.LOCK_EX if exclusive else fcntl.LOCK_SH + if nonblocking: + operation |= fcntl.LOCK_NB + try: + fcntl.flock(lease_file.fileno(), operation) + except BlockingIOError: + yield False + return + try: + yield True + finally: + fcntl.flock(lease_file.fileno(), fcntl.LOCK_UN) + + +@contextmanager +def _committed_generation_lease( + path: Path, +) -> Iterator[OptimizerGenerationPointer | None]: + stack = ExitStack() + with _root_lease(path, fcntl.LOCK_SH) as pointer: + if pointer is not None and not stack.enter_context( + _generation_lease(path, pointer.generation, exclusive=False) + ): + raise RuntimeError( + f"Could not lease optimizer generation {pointer.generation}" + ) + try: + yield pointer + finally: + stack.close() + + +def commit_optimizer_generation( + optimizer_state_path: str, + manifest: OptimizerGenerationManifest, + *, + expected_pointer: OptimizerGenerationPointer | None, + expected_policy_step: int | None = None, + initial_adapter_path: str | None = None, +) -> Path: + path = Path(optimizer_state_path) + pending = optimizer_pending_generation_path( + optimizer_state_path, manifest.generation + ) + committed = optimizer_generation_path(optimizer_state_path, manifest.generation) + _write_model_atomic(pending / OPTIMIZER_MANIFEST, manifest) + _validate_generation_files(pending, manifest, local_rank=None) + with _writer_lease(path) as current_pointer: + if current_pointer != expected_pointer: + raise RuntimeError( + "Stale optimizer writer: committed pointer changed before publication; " + f"expected={expected_pointer.model_dump() if expected_pointer else None}, " + f"current={current_pointer.model_dump() if current_pointer else None}" + ) + if expected_policy_step is not None: + if initial_adapter_path is None: + raise ValueError("initial_adapter_path is required for lineage checks") + current_policy = _committed_policy( + path, + current_pointer, + initial_adapter_path=initial_adapter_path, + ) + if current_policy.policy_adapter.step != expected_policy_step: + raise RuntimeError( + "Stale optimizer writer: policy lineage changed before publication; " + f"expected={expected_policy_step}, " + f"current={current_policy.policy_adapter.step}" + ) + if current_pointer is not None and manifest.step <= current_pointer.step: + raise RuntimeError( + "Optimizer generation step must advance monotonically: " + f"current={current_pointer.step}, attempted={manifest.step}" + ) + _validate_adapter_publication(manifest.adapter) + if committed.exists(): + raise RuntimeError(f"Optimizer generation already exists: {committed}") + os.replace(pending, committed) + _fsync_directory(committed.parent) + pointer = OptimizerGenerationPointer( + generation=manifest.generation, + step=manifest.step, + adapter=manifest.adapter, + ) + _write_model_atomic(path / OPTIMIZER_POINTER, pointer) + policy_path = path / OPTIMIZER_POLICY_POINTER + if policy_path.exists(): + policy_path.unlink() + _fsync_directory(path) + return committed + + +def _prune_optimizer_generations_locked( + optimizer_state_path: str, + *, + retain_adapter_steps: set[int], + orphan_grace_s: float = OPTIMIZER_ORPHAN_GRACE_S, +) -> set[int]: + """Reclaim unretained generations and return adapter steps still in use.""" + if orphan_grace_s < 0: + raise ValueError("orphan_grace_s must be non-negative") + path = Path(optimizer_state_path) + generations = path / OPTIMIZER_GENERATIONS_DIR + if not path.exists(): + return set() + + protected_steps: set[int] = set() + trash: list[Path] = [] + now = time.time() + with _writer_lease(path) as pointer: + pointer_temps, candidates = _scan_optimizer_transactions(path) + policy = _resolve_policy_pointer(path, pointer) + if policy is not None: + protected_steps.add(policy.policy_adapter.step) + if not generations.exists(): + if pointer is not None: + raise RuntimeError( + "Optimizer pointer exists without a generations directory: " + f"{generations}" + ) + if pointer_temps: + raise RuntimeError( + "Interrupted optimizer pointer has no committed generation" + ) + return protected_steps + if not generations.is_dir(): + raise RuntimeError( + f"Optimizer generations path is not a directory: {generations}" + ) + + if len(pointer_temps) > 1: + raise RuntimeError( + "Cannot collect optimizer generations with multiple interrupted " + "pointers" + ) + records: list[tuple[Path, str, int, bool, bool]] = [] + manifests: dict[str, OptimizerGenerationManifest] = {} + current_found = False + for entry, generation, pending in candidates: + step = _generation_step(generation) + manifest = None if pending else _read_manifest(entry) + if manifest is not None and ( + manifest.generation != generation or manifest.step != step + ): + raise RuntimeError( + f"Optimizer generation directory/manifest mismatch: {entry}" + ) + if manifest is not None: + manifests[generation] = manifest + adapter_step = step if manifest is None else manifest.adapter.step + current = pointer is not None and pointer.generation == generation + young = now - entry.stat().st_mtime < orphan_grace_s + if current: + assert manifest is not None + _validate_pointer_manifest(pointer, manifest) + _validate_adapter_publication(pointer.adapter) + current_found = True + records.append((entry, generation, adapter_step, current, young)) + + if pointer is not None and not current_found: + raise RuntimeError( + f"Optimizer pointer generation is missing: {pointer.generation}" + ) + interrupted_generation: str | None = None + if pointer_temps: + temporary_pointer = pointer_temps[0][1] + interrupted_generation = temporary_pointer.generation + manifest = manifests.get(interrupted_generation) + if manifest is None: + raise RuntimeError( + "Interrupted optimizer pointer has no committed generation" + ) + _validate_pointer_manifest(temporary_pointer, manifest) + if pointer is not None and temporary_pointer.step <= pointer.step: + raise RuntimeError( + "Interrupted optimizer pointer does not advance the committed " + "generation" + ) + trash.extend( + entry + for entry in generations.iterdir() + if entry.name.startswith(OPTIMIZER_TRASH_PREFIX) + and now - entry.stat().st_mtime >= orphan_grace_s + ) + + for entry, generation, adapter_step, current, young in records: + if current or adapter_step in retain_adapter_steps or young: + protected_steps.add(adapter_step) + continue + + with _generation_lease( + path, + generation, + exclusive=True, + nonblocking=True, + ) as acquired: + if not acquired: + protected_steps.add(adapter_step) + continue + destination = generations / ( + f"{OPTIMIZER_TRASH_PREFIX}{generation}-{uuid4().hex}" + ) + if interrupted_generation == generation: + pointer_temps[0][0].unlink() + _fsync_directory(path) + os.replace(entry, destination) + os.utime(destination) + trash.append(destination) + _generation_lease_path(path, generation).unlink(missing_ok=True) + + live = {generation for entry, generation, *_ in records if entry.exists()} + for lease in generations.iterdir(): + if not lease.name.startswith(OPTIMIZER_GENERATION_LEASE_PREFIX): + continue + generation = lease.name.removeprefix(OPTIMIZER_GENERATION_LEASE_PREFIX) + _validate_generation_name(generation) + if generation in live: + continue + with _generation_lease( + path, + generation, + exclusive=True, + nonblocking=True, + ) as acquired: + if acquired: + lease.unlink() + if trash: + _fsync_directory(generations) + + for entry in trash: + shutil.rmtree(entry) + return protected_steps + + +def prune_optimizer_generations( + optimizer_state_path: str, + *, + retain_adapter_steps: set[int], + orphan_grace_s: float = OPTIMIZER_ORPHAN_GRACE_S, +) -> set[int]: + with optimizer_model_lease(optimizer_state_path): + return _prune_optimizer_generations_locked( + optimizer_state_path, + retain_adapter_steps=retain_adapter_steps, + orphan_grace_s=orphan_grace_s, + ) + + +@contextmanager +def optimizer_retention_lease( + output_dir: str, retain_adapter_steps: set[int] +) -> Iterator[set[int]]: + paths = tuple( + f"{output_dir}/optimizer_states_{job_type}" for job_type in ("rl", "sft") + ) + with optimizer_model_lease(paths[0]): + protected = set(retain_adapter_steps) + for path in paths: + protected.update( + _prune_optimizer_generations_locked( + path, retain_adapter_steps=protected + ) + ) + with _adapter_retention_leases(output_dir, protected): + yield protected + + +def _validate_generation( + optimizer_state_path: str, + pointer: OptimizerGenerationPointer, + world_size: int, + local_rank: int | None, +) -> tuple[Path, OptimizerGenerationManifest, tuple[OptimizerShard, ...]]: + path = optimizer_generation_path(optimizer_state_path, pointer.generation) + manifest = _read_manifest(path) + _validate_pointer_manifest(pointer, manifest) + current = current_optimizer_topology(world_size) + if manifest.topology != current: + raise RuntimeError( + "Optimizer checkpoint topology mismatch; optimizer state is topology-strict: " + f"saved={manifest.topology.model_dump()} current={current.model_dump()}" + ) + return ( + path, + manifest, + _validate_generation_files(path, manifest, local_rank=local_rank), + ) + + +def pin_optimizer_generation( + optimizer_state_path: str, + *, + world_size: int, + runtime_sha256: str, + layout_sha256_by_rank: tuple[str, ...], + adapter: OptimizerAdapter, + pointer: OptimizerGenerationPointer | None | object = _POINTER_UNSET, + verify_adapter_files: bool = True, +) -> OptimizerGenerationPointer | None: + if pointer is _POINTER_UNSET: + pointer = read_committed_optimizer_pointer(optimizer_state_path) + if pointer is None: + return None + pointer = cast(OptimizerGenerationPointer, pointer) + _, manifest, ordered = _validate_generation( + optimizer_state_path, pointer, world_size, None + ) + if manifest.runtime_sha256 != runtime_sha256: + raise RuntimeError( + "Optimizer checkpoint model-runtime mismatch: " + f"saved={manifest.runtime_sha256}, current={runtime_sha256}" + ) + if pointer.adapter != adapter: + raise RuntimeError( + "Optimizer checkpoint adapter mismatch: " + f"saved={pointer.adapter.model_dump()}, current={adapter.model_dump()}" + ) + _validate_adapter_publication(pointer.adapter, verify_files=verify_adapter_files) + saved_layouts = tuple(shard.layout_sha256 for shard in ordered) + if saved_layouts != layout_sha256_by_rank: + raise RuntimeError( + "Optimizer parameter ownership/layout mismatch: " + f"saved={saved_layouts}, current={layout_sha256_by_rank}" + ) + return pointer -ALLOW_UNPAIRED_MEGATRON_RESUME_ENV = "ART_ALLOW_UNPAIRED_MEGATRON_RESUME" -OPTIMIZER_MANIFEST = "CURRENT.json" -_GENERATION_SHARD_RE = re.compile( - r"^step-(?P\d+)-(?P\d+)-of-(?P\d+)\.pt$" -) + +def resolve_optimizer_shard( + optimizer_state_path: str, + *, + rank: int, + world_size: int, + pointer: OptimizerGenerationPointer | None = None, +) -> Path | None: + pointer = pointer or read_committed_optimizer_pointer(optimizer_state_path) + if pointer is None: + return None + generation_path, _, ordered = _validate_generation( + optimizer_state_path, pointer, world_size, rank + ) + return generation_path / optimizer_shard_name(ordered[rank].rank, world_size) -class OptimizerCommit(BaseModel): - schema_version: Literal[1] = 1 - step: int = Field(ge=0) - world_size: int = Field(ge=1) - files: tuple[str, ...] +def _type_identity(value: object) -> str: + value_type = value if isinstance(value, type) else type(value) + return f"{value_type.__module__}.{value_type.__qualname__}" - @model_validator(mode="after") - def validate_files(self) -> "OptimizerCommit": - if self.files != optimizer_generation_files(self.step, self.world_size): - raise ValueError( - "optimizer manifest files do not match its step/world size" + +def _runtime_json_default(value: Any) -> Any: + if isinstance(value, torch.dtype): + return str(value) + if isinstance(value, torch.Tensor): + return {"shape": list(value.shape), "dtype": str(value.dtype)} + if isinstance(value, BaseModel): + return value.model_dump(mode="json") + if isinstance(value, set): + return sorted(value, key=repr) + if callable(value): + module = getattr(value, "__module__", "") + name = getattr(value, "__qualname__", type(value).__qualname__) + return f"{module}.{name}" + return _type_identity(value) + + +def _canonical_runtime_json(value: Any) -> Any: + if isinstance(value, dict): + keys = tuple(value) + try: + supported = all( + key is None or isinstance(key, (str, int, float, bool)) for key in keys ) - return self + if supported: + sorted(keys) + except TypeError: + supported = False + if supported: + return {key: _canonical_runtime_json(item) for key, item in value.items()} + return [ + "__art_typed_mapping__", + [ + [_type_identity(key), repr(key), _canonical_runtime_json(item)] + for key, item in sorted( + value.items(), + key=lambda pair: (_type_identity(pair[0]), repr(pair[0])), + ) + ], + ] + if isinstance(value, (list, tuple)): + return [_canonical_runtime_json(item) for item in value] + if isinstance(value, set): + return sorted( + (_canonical_runtime_json(item) for item in value), + key=repr, + ) + if isinstance(value, BaseModel): + return _canonical_runtime_json(value.model_dump(mode="json")) + return value -class MegatronResumeStep(BaseModel): - step: int - latest_lora_step: int - optimizer_step: int | None - used_unpaired_override: bool = False - quarantined_lora_steps: tuple[int, ...] = () +def _json_sha256(value: Any) -> str: + encoded = json.dumps( + _canonical_runtime_json(value), + default=_runtime_json_default, + sort_keys=True, + separators=(",", ":"), + ).encode() + return hashlib.sha256(encoded).hexdigest() -def optimizer_generation_files(step: int, world_size: int) -> tuple[str, ...]: - return tuple( - f"step-{step:08d}-{rank:02d}-of-{world_size:02d}.pt" - for rank in range(1, world_size + 1) +def _public_fields(value: object, *, exclude: set[str] | None = None) -> dict[str, Any]: + exclude = exclude or set() + return { + key: item + for key, item in sorted(vars(value).items()) + if not key.startswith("_") and key not in exclude + } + + +def _model_runtime_sha256(runtime: Any) -> str: + return _json_sha256( + { + "model_support": runtime.model_support_spec, + "provider": { + "type": _type_identity(runtime.provider), + "fields": _public_fields( + runtime.provider, + exclude=_SCHEDULE_PROVIDER_FIELDS, + ), + }, + "optimizer": _type_identity(runtime.optimizer), + "optimizer_config": _public_fields(runtime.optimizer_config), + "compile": runtime.transformer_layers_compiled, + "topology": current_optimizer_topology(runtime.world_size), + "torch": torch.__version__, + } ) -def read_optimizer_commit(optimizer_state_path: str) -> OptimizerCommit | None: - path = Path(optimizer_state_path) - manifest_path = path / OPTIMIZER_MANIFEST - if not manifest_path.exists(): - return None - commit = OptimizerCommit.model_validate_json(manifest_path.read_text()) - missing = [name for name in commit.files if not (path / name).is_file()] - if missing: +def _optimizer_layout_sha256(runtime: Any) -> str: + names_by_parameter: dict[int, list[str]] = {} + for chunk_index, chunk in enumerate(runtime.model): + for name, parameter in chunk.named_parameters(remove_duplicate=False): + qualified = f"chunk.{chunk_index}.{name}" + names_by_parameter.setdefault(id(parameter), []).append(qualified) + main_parameter = getattr(parameter, "main_param", None) + if main_parameter is not None: + names_by_parameter.setdefault(id(main_parameter), []).append(qualified) + + groups = [] + for group_index, group in enumerate(runtime.optimizer.param_groups): + parameters = [] + for group_order, parameter in enumerate(group["params"]): + names = tuple(sorted(set(names_by_parameter.get(id(parameter), ())))) + if not names: + raise RuntimeError( + "Optimizer parameter is not owned by a model chunk: " + f"group={group_index}, order={group_order}, " + f"shape={tuple(parameter.shape)}" + ) + parameters.append( + { + "names": names, + "shape": tuple(parameter.shape), + "dtype": str(parameter.dtype), + "requires_grad": bool(parameter.requires_grad), + } + ) + groups.append(parameters) + return _json_sha256(groups) + + +def _distributed_rank(runtime: Any) -> int: + if torch.distributed.is_initialized(): # ty:ignore[possibly-missing-attribute] + return int(torch.distributed.get_rank()) # ty:ignore[possibly-missing-attribute] + if (runtime.rank, runtime.world_size) != (0, 1): + raise RuntimeError( + "Multi-rank optimizer durability requires an initialized process group: " + f"rank={runtime.rank}, world_size={runtime.world_size}" + ) + return 0 + + +def _all_gather_objects(runtime: Any, value: Any) -> list[Any]: + if not torch.distributed.is_initialized(): # ty:ignore[possibly-missing-attribute] + _distributed_rank(runtime) + return [value] + gathered: list[Any] = [None] * int( + torch.distributed.get_world_size() # ty:ignore[possibly-missing-attribute] + ) + torch.distributed.all_gather_object( # ty:ignore[possibly-missing-attribute] + gathered, value + ) + return gathered + + +def _error_text(exc: BaseException) -> str: + return f"{type(exc).__name__}: {exc}" + + +def _result_errors(results: list[Any], missing: str) -> list[str]: + return [ + f"rank {rank}: {missing}" + if result is None + else f"rank {rank}: {result['error']}" + for rank, result in enumerate(results) + if result is None or "error" in result + ] + + +def optimizer_group_decision( + runtime: Any, + decide: Callable[[], Any], + *, + operation: str, +) -> Any: + box: list[dict[str, Any] | None] = [None] + if _distributed_rank(runtime) == 0: + try: + box[0] = {"value": decide()} + except Exception as exc: + box[0] = {"error": _error_text(exc)} + if torch.distributed.is_initialized(): # ty:ignore[possibly-missing-attribute] + torch.distributed.broadcast_object_list( # ty:ignore[possibly-missing-attribute] + box, src=0 + ) + result = box[0] + if result is None: + raise RuntimeError(f"Rank 0 returned no {operation} decision") + if "error" in result: + raise RuntimeError(f"{operation} failed: {result['error']}") + return result["value"] + + +def _raise_rank_errors(runtime: Any, results: list[Any], *, operation: str) -> None: + def decide() -> None: + errors = _result_errors(results, "missing result") + if errors: + raise RuntimeError("; ".join(errors)) + + optimizer_group_decision(runtime, decide, operation=operation) + + +def _run_rank_operation(runtime: Any, operation: str, run: Callable[[], Any]) -> Any: + value: Any = None + try: + value = run() + local_result: dict[str, str] = {} + except Exception as exc: + local_result = {"error": _error_text(exc)} + _raise_rank_errors( + runtime, _all_gather_objects(runtime, local_result), operation=operation + ) + return value + + +def _runtime_layout_record(runtime: Any) -> dict[str, Any]: + try: + return { + "rank": runtime.rank, + "runtime_sha256": _model_runtime_sha256(runtime), + "layout_sha256": _optimizer_layout_sha256(runtime), + } + except Exception as exc: + return {"rank": runtime.rank, "error": _error_text(exc)} + + +def _validated_runtime_layouts( + runtime: Any, records: list[Any] +) -> tuple[str, tuple[str, ...]]: + errors = _result_errors(records, "missing runtime metadata") + if errors: + raise RuntimeError("; ".join(errors)) + ranks = tuple(record["rank"] for record in records) + expected_ranks = tuple(range(len(records))) + if ranks != expected_ranks or len(records) != runtime.world_size: + raise RuntimeError( + "Optimizer rank metadata mismatch: " + f"expected={expected_ranks}, actual={ranks}, " + f"runtime_world={runtime.world_size}" + ) + runtime_digests = {record["runtime_sha256"] for record in records} + if len(runtime_digests) != 1: raise RuntimeError( - f"Optimizer manifest {manifest_path} references missing shard(s): {missing}" + f"Trainer ranks disagree on model-runtime digest: {sorted(runtime_digests)}" ) - return commit + return runtime_digests.pop(), tuple(record["layout_sha256"] for record in records) -def resolve_optimizer_shard_path( +def _stage_optimizer_value(value: Any, stager: PinnedCpuSnapshotBuilder) -> Any: + if isinstance(value, torch.Tensor): + return stager.stage(value) + if isinstance(value, dict): + return { + _stage_optimizer_value(key, stager): _stage_optimizer_value(item, stager) + for key, item in value.items() + } + if isinstance(value, list): + return [_stage_optimizer_value(item, stager) for item in value] + if isinstance(value, tuple): + return ( + type(value)(*(_stage_optimizer_value(item, stager) for item in value)) + if hasattr(value, "_fields") + else tuple(_stage_optimizer_value(item, stager) for item in value) + ) + return copy.deepcopy(value) + + +def snapshot_optimizer_state( + runtime: Any, + *, + generation_id: str, + step: int, +) -> OptimizerStateSnapshot: + return stage_optimizer_state_snapshot( + runtime, + generation_id=generation_id, + step=step, + stager=PinnedCpuSnapshotStager(), + ).resolve() + + +def stage_optimizer_state_snapshot( + runtime: Any, + *, + generation_id: str, + step: int, + stager: PinnedCpuSnapshotStager, +) -> PendingCpuSnapshot[OptimizerStateSnapshot]: + if runtime.optimizer is None: + raise RuntimeError("Cannot snapshot an uninitialized optimizer") + records = _all_gather_objects(runtime, _runtime_layout_record(runtime)) + runtime_sha256, layouts = _validated_runtime_layouts(runtime, records) + builder = stager.begin() + return builder.finish( + OptimizerStateSnapshot( + generation_id=generation_id, + step=step, + rank=runtime.rank, + world_size=runtime.world_size, + runtime_sha256=runtime_sha256, + layout_sha256=layouts[runtime.rank], + topology=current_optimizer_topology(runtime.world_size), + state_dict=_stage_optimizer_value(runtime.optimizer.state_dict(), builder), + ) + ) + + +def write_optimizer_snapshot_shard( + snapshot: OptimizerStateSnapshot, + *, optimizer_state_path: str, +) -> OptimizerShard: + pending = optimizer_pending_generation_path( + optimizer_state_path, snapshot.generation_id + ) + shard_path = optimizer_shard_path( + pending, + rank=snapshot.rank, + world_size=snapshot.world_size, + ) + temporary = shard_path.with_name(f".{shard_path.name}.{os.getpid()}.tmp") + pending.mkdir(parents=True, exist_ok=True) + try: + with temporary.open("wb") as output: + torch.save(snapshot.state_dict, output) + output.flush() + os.fsync(output.fileno()) + os.replace(temporary, shard_path) + finally: + temporary.unlink(missing_ok=True) + return OptimizerShard( + rank=snapshot.rank, + size_bytes=shard_path.stat().st_size, + layout_sha256=snapshot.layout_sha256, + ) + + +def _loaded_adapter(adapter_path: str, step: int) -> OptimizerAdapter: + path = Path(adapter_path).absolute() + canonical = _canonical_adapter_path(path, step) + adapter = read_adapter_publication(canonical, step=step, verify_files=True) + if adapter is None: + adapter = optimizer_adapter(canonical, step) + if path != canonical: + raise RuntimeError("Optimizer state must load an immutable canonical adapter") + return adapter + + +def _write_optimizer_shard( + runtime: Any, generation_path: Path, *, layout_sha256: str +) -> OptimizerShard: + shard_path = optimizer_shard_path( + generation_path, + rank=runtime.rank, + world_size=runtime.world_size, + ) + temporary = shard_path.with_name(f".{shard_path.name}.{os.getpid()}.tmp") + generation_path.mkdir(parents=True, exist_ok=True) + try: + with temporary.open("wb") as output: + torch.save(runtime.optimizer.state_dict(), output) + output.flush() + os.fsync(output.fileno()) + os.replace(temporary, shard_path) + finally: + temporary.unlink(missing_ok=True) + return OptimizerShard( + rank=runtime.rank, + size_bytes=shard_path.stat().st_size, + layout_sha256=layout_sha256, + ) + + +def _save_optimizer_state_locked( + runtime: Any, *, - rank: int, - world_size: int, - expected_step: int, -) -> Path | None: - if not 0 <= rank < world_size: - raise ValueError(f"optimizer rank {rank} is outside world size {world_size}") - path = Path(optimizer_state_path) - commit = read_optimizer_commit(optimizer_state_path) - if commit is not None: - if commit.world_size != world_size: - raise RuntimeError( - "Optimizer world size does not match the active Megatron runtime: " - f"{commit.world_size} != {world_size}" - ) - if commit.step != expected_step: - raise RuntimeError( - "Optimizer state does not match the source policy checkpoint: " - f"{commit.step} != {expected_step}" + optimizer_state_path: str, + step: int, + adapter: OptimizerAdapter, +) -> None: + records = _all_gather_objects(runtime, _runtime_layout_record(runtime)) + + def select_generation() -> tuple[str, str, tuple[str, ...], dict[str, Any] | None]: + runtime_sha256, layouts = _validated_runtime_layouts(runtime, records) + path = Path(optimizer_state_path) + with _writer_lease(path) as expected: + if expected is not None and step <= expected.step: + raise RuntimeError( + "Optimizer save step must advance the committed pointer: " + f"current={expected.step}, attempted={step}" + ) + expected_data = ( + None if expected is None else expected.model_dump(mode="json") ) - return path / commit.files[rank] - return None + return ( + adapter.generation_id, + runtime_sha256, + layouts, + expected_data, + ) + + generation, runtime_sha256, layouts, expected_data = cast( + tuple[str, str, tuple[str, ...], dict[str, Any] | None], + optimizer_group_decision( + runtime, select_generation, operation="optimizer generation selection" + ), + ) + expected = ( + None + if expected_data is None + else OptimizerGenerationPointer.model_validate(expected_data) + ) + pending = optimizer_pending_generation_path(optimizer_state_path, generation) + try: + shard = _write_optimizer_shard( + runtime, pending, layout_sha256=layouts[runtime.rank] + ) + local_result: dict[str, Any] = {"shard": shard.model_dump(mode="json")} + except Exception as exc: + local_result = {"rank": runtime.rank, "error": _error_text(exc)} + gathered = _all_gather_objects(runtime, local_result) + + def publish_generation() -> None: + errors = _result_errors(gathered, "missing shard metadata") + if errors: + raise RuntimeError("; ".join(errors)) + manifest = build_optimizer_manifest( + generation=generation, + step=step, + adapter=adapter, + runtime_sha256=runtime_sha256, + world_size=runtime.world_size, + shards=[ + OptimizerShard.model_validate(result["shard"]) for result in gathered + ], + ) + commit_optimizer_generation( + optimizer_state_path, manifest, expected_pointer=expected + ) + + optimizer_group_decision( + runtime, publish_generation, operation="optimizer generation publication" + ) -def commit_optimizer_generation( +def save_optimizer_state( + runtime: Any, + *, optimizer_state_path: str, + step: int, + adapter: OptimizerAdapter, +) -> None: + with ExitStack() as leases: + optimizer_group_decision( + runtime, + lambda: leases.enter_context(optimizer_model_lease(optimizer_state_path)), + operation="optimizer model lease acquisition", + ) + save_optimizer_state_under_model_lease( + runtime, + optimizer_state_path=optimizer_state_path, + step=step, + adapter=adapter, + ) + + +def save_optimizer_state_under_model_lease( + runtime: Any, *, + optimizer_state_path: str, step: int, - world_size: int, - files: tuple[str, ...], + adapter: OptimizerAdapter, ) -> None: - path = Path(optimizer_state_path) - path.mkdir(parents=True, exist_ok=True) - previous = read_optimizer_commit(optimizer_state_path) - missing = [name for name in files if not (path / name).is_file()] - if missing: - raise RuntimeError(f"Cannot commit missing optimizer shard(s): {missing}") - commit = OptimizerCommit(step=step, world_size=world_size, files=files) - _atomic_write(path / OPTIMIZER_MANIFEST, commit.model_dump_json()) - - retained = set(files) | {OPTIMIZER_MANIFEST} - obsolete = set(previous.files if previous is not None else ()) - obsolete.update( - item.name - for item in path.iterdir() - if item.is_file() - and (_GENERATION_SHARD_RE.fullmatch(item.name) or item.name.isdigit()) - ) - for name in obsolete - retained: - candidate = path / name - if candidate.exists(): - candidate.unlink() - - -def _atomic_write(path: Path, content: str) -> None: - temporary = path.with_name(f"{path.name}.tmp") - with temporary.open("w", encoding="utf-8") as handle: - handle.write(content) - handle.flush() - os.fsync(handle.fileno()) - os.replace(temporary, path) - directory_fd = os.open(path.parent, os.O_RDONLY) - try: - os.fsync(directory_fd) - finally: - os.close(directory_fd) + _save_optimizer_state_locked( + runtime, + optimizer_state_path=optimizer_state_path, + step=step, + adapter=adapter, + ) + + +def _sibling_optimizer_owns_adapter( + optimizer_state_path: str, adapter: OptimizerAdapter +) -> bool: + current = Path(optimizer_state_path).absolute() + for sibling in ( + current.parent / "optimizer_states_rl", + current.parent / "optimizer_states_sft", + ): + if sibling == current or not sibling.exists(): + continue + with _committed_generation_lease(sibling) as pointer: + policy_pointer = _read_policy_pointer(sibling) + if pointer is None and policy_pointer is None: + continue + if pointer is not None: + manifest = _read_manifest( + optimizer_generation_path(str(sibling), pointer.generation) + ) + _validate_pointer_manifest(pointer, manifest) + policy = _committed_policy( + sibling, + pointer, + initial_adapter_path=get_step_checkpoint_dir(str(sibling.parent), 0), + ) + if policy.policy_adapter == adapter: + return True + return False + + +def load_optimizer_state( + runtime: Any, + *, + optimizer_state_path: str, + adapter_path: str, + adapter_step: int, + allow_missing: bool, + initialize: Callable[[Any], None], +) -> Path | None: + records = _all_gather_objects(runtime, _runtime_layout_record(runtime)) + with ExitStack() as leases: + + def select_generation() -> dict[str, Any] | None: + runtime_sha256, layouts = _validated_runtime_layouts(runtime, records) + adapter = _loaded_adapter(adapter_path, adapter_step) + path = Path(optimizer_state_path) + leased_pointer = leases.enter_context(_committed_generation_lease(path)) + policy = _committed_policy( + path, + leased_pointer, + initial_adapter_path=get_step_checkpoint_dir( + str(path.absolute().parent), 0 + ), + ) + if policy.policy_adapter == adapter: + if policy.optimizer_anchor is None: + return None + assert policy.state_adapter is not None + pinned_adapter = policy.state_adapter + else: + pinned_adapter = adapter + lineage_switch = ( + leased_pointer is None or leased_pointer.adapter != pinned_adapter + ) and _sibling_optimizer_owns_adapter(optimizer_state_path, adapter) + if lineage_switch: + return None + pointer = pin_optimizer_generation( + optimizer_state_path, + world_size=runtime.world_size, + runtime_sha256=runtime_sha256, + layout_sha256_by_rank=layouts, + adapter=pinned_adapter, + pointer=leased_pointer, + verify_adapter_files=False, + ) + if pointer is None and not allow_missing: + raise RuntimeError( + "No optimizer generation is paired with canonical adapter " + f"step {adapter_step}" + ) + return None if pointer is None else pointer.model_dump(mode="json") + + pointer_data = optimizer_group_decision( + runtime, select_generation, operation="optimizer load selection" + ) + if pointer_data is None: + _run_rank_operation( + runtime, "optimizer reset", lambda: initialize(runtime.optimizer) + ) + return None + + pointer = OptimizerGenerationPointer.model_validate(pointer_data) + + def load_shard() -> tuple[Path, Any]: + shard_path = resolve_optimizer_shard( + optimizer_state_path, + rank=runtime.rank, + world_size=runtime.world_size, + pointer=pointer, + ) + assert shard_path is not None + return shard_path, torch.load(shard_path) + + shard_path, loaded_state = cast( + tuple[Path, Any], + _run_rank_operation(runtime, "optimizer shard load", load_shard), + ) + try: + _run_rank_operation( + runtime, + "optimizer state apply", + lambda: runtime.optimizer.load_state_dict(loaded_state), + ) + finally: + del loaded_state + return shard_path def _allow_unpaired_resume() -> bool: @@ -141,56 +1801,422 @@ def _allow_unpaired_resume() -> bool: } +def _scan_optimizer_transactions( + path: Path, +) -> tuple[ + list[tuple[Path, OptimizerGenerationPointer]], + list[tuple[Path, str, bool]], +]: + pointer_temps: list[tuple[Path, OptimizerGenerationPointer]] = [] + allowed = { + OPTIMIZER_POINTER, + OPTIMIZER_POLICY_POINTER, + OPTIMIZER_WRITER_LOCK, + OPTIMIZER_GENERATIONS_DIR, + "uncommitted_generations", + } + for entry in sorted(path.iterdir()): + if entry.name in allowed: + continue + if _POINTER_TEMP_RE.fullmatch(entry.name) is None or not entry.is_file(): + raise RuntimeError( + "Ambiguous interrupted optimizer transaction; unexpected root " + f"entry {entry}" + ) + try: + pointer = OptimizerGenerationPointer.model_validate_json( + entry.read_text("utf-8") + ) + except Exception as exc: + raise RuntimeError( + f"Invalid interrupted optimizer pointer: {entry}" + ) from exc + pointer_temps.append((entry, pointer)) + + candidates: list[tuple[Path, str, bool]] = [] + generations = path / OPTIMIZER_GENERATIONS_DIR + if not generations.exists(): + return pointer_temps, candidates + if not generations.is_dir(): + raise RuntimeError( + f"Optimizer generations path is not a directory: {generations}" + ) + for entry in sorted(generations.iterdir()): + name = entry.name + if name.startswith(OPTIMIZER_GENERATION_LEASE_PREFIX): + _validate_generation_name( + name.removeprefix(OPTIMIZER_GENERATION_LEASE_PREFIX) + ) + if not entry.is_file(): + raise RuntimeError(f"Invalid optimizer generation lease: {entry}") + continue + if name.startswith(OPTIMIZER_TRASH_PREFIX): + if _TRASH_RE.fullmatch(name) is None or not entry.is_dir(): + raise RuntimeError(f"Invalid optimizer generation trash: {entry}") + continue + pending = name.startswith(".pending-") + generation = name.removeprefix(".pending-") if pending else name + if _GENERATION_RE.fullmatch(generation) is None or not entry.is_dir(): + raise RuntimeError( + "Ambiguous interrupted optimizer transaction; unexpected generation " + f"entry {entry}" + ) + candidates.append((entry, generation, pending)) + return pointer_temps, candidates + + +def _quarantine_pointer_temp(path: Path, temporary: Path) -> None: + quarantine = ( + path + / "uncommitted_generations" + / f"invalid_pointer_{int(time.time())}_{uuid4().hex}" + ) + quarantine.mkdir(parents=True) + os.replace(temporary, quarantine / temporary.name) + _fsync_directory(quarantine) + _fsync_directory(path) + + +def _validate_committed_generation( + path: Path, pointer: OptimizerGenerationPointer +) -> None: + generation_path = optimizer_generation_path(str(path), pointer.generation) + manifest = _read_manifest(generation_path) + _validate_pointer_manifest(pointer, manifest) + _validate_generation_files(generation_path, manifest, local_rank=None) + _validate_adapter_publication(pointer.adapter, verify_files=True) + + +def _recover_optimizer_pointer_locked( + path: Path, current: OptimizerGenerationPointer | None +) -> OptimizerGenerationPointer | None: + policy_temps = tuple( + entry + for entry in sorted(path.iterdir()) + if _POLICY_TEMP_RE.fullmatch(entry.name) is not None and entry.is_file() + ) + for temporary in policy_temps: + temporary.unlink() + if policy_temps: + _fsync_directory(path) + temporary_paths = tuple( + entry + for entry in sorted(path.iterdir()) + if _POINTER_TEMP_RE.fullmatch(entry.name) is not None and entry.is_file() + ) + if len(temporary_paths) > 1: + raise RuntimeError( + "Ambiguous interrupted optimizer transaction; found multiple temporary " + "pointers" + ) + if temporary_paths: + try: + temporary = OptimizerGenerationPointer.model_validate_json( + temporary_paths[0].read_text("utf-8") + ) + except Exception: + _quarantine_pointer_temp(path, temporary_paths[0]) + temporary = None + else: + temporary = None + + _, candidates = _scan_optimizer_transactions(path) + current_step = -1 if current is None else current.step + advancing = tuple( + (entry, generation) + for entry, generation, pending in candidates + if not pending + and generation != (None if current is None else current.generation) + and _generation_step(generation) > current_step + ) + if temporary is not None and temporary.step <= current_step: + if temporary == current: + temporary_paths[0].unlink() + _fsync_directory(path) + else: + _quarantine_pointer_temp(path, temporary_paths[0]) + temporary = None + if temporary is not None: + if len(advancing) != 1 or advancing[0][1] != temporary.generation: + raise RuntimeError( + "Interrupted optimizer pointer does not uniquely identify an " + "advancing committed generation" + ) + pointer = temporary + elif not advancing: + return current + elif len(advancing) == 1: + manifest = _read_manifest(advancing[0][0]) + pointer = OptimizerGenerationPointer( + generation=manifest.generation, + step=manifest.step, + adapter=manifest.adapter, + ) + else: + raise RuntimeError( + "Ambiguous interrupted optimizer transaction; found multiple advancing " + "committed generations" + ) + + _validate_committed_generation(path, pointer) + if temporary is None: + _write_model_atomic(path / OPTIMIZER_POINTER, pointer) + else: + os.replace(temporary_paths[0], path / OPTIMIZER_POINTER) + _fsync_directory(path) + return pointer + + +def _optimizer_state_paths(output_dir: str, current: str) -> tuple[Path, ...]: + selected = Path(current).absolute() + candidates = {selected} | { + (Path(output_dir) / f"optimizer_states_{kind}").absolute() + for kind in ("rl", "sft") + } + return tuple( + sorted(path for path in candidates if path == selected or path.exists()) + ) + + +def _recover_optimizer_transactions(output_dir: str, current: str) -> None: + for path in _optimizer_state_paths(output_dir, current): + with _writer_lease(path): + pass + + +def _recover_uncommitted_initial_transaction( + *, + output_dir: str, + optimizer_state_path: str, +) -> tuple[int, ...]: + if get_step_from_dir(output_dir) != 1: + return () + path = Path(optimizer_state_path).absolute() + checkpoint = Path(get_step_checkpoint_dir(output_dir, 1)).absolute() + roots = _optimizer_state_paths(output_dir, optimizer_state_path) + with ExitStack() as locks: + pointers = {root: locks.enter_context(_writer_lease(root)) for root in roots} + pointer = pointers[path] + if pointer is not None: + return () + policy = _resolve_policy_pointer(path, pointer) + if policy is not None and policy.policy_adapter.step == 1: + return () + adapter = read_adapter_publication(checkpoint, step=1, verify_files=True) + if adapter is None: + return () + + pointer_temps, candidates = _scan_optimizer_transactions(path) + for sibling in roots: + if sibling == path: + continue + sibling_temps, sibling_candidates = _scan_optimizer_transactions(sibling) + sibling_pointer = pointers[sibling] + if sibling_pointer is not None and sibling_pointer.adapter.step == 1: + return () + if any(pointer.adapter.step == 1 for _, pointer in sibling_temps) or any( + _generation_step(generation) == 1 + for _, generation, _ in sibling_candidates + ): + raise RuntimeError( + "Cannot recover interrupted initial optimizer transaction; " + f"sibling optimizer state may own checkpoint 0001: {sibling}" + ) + if len(candidates) > 1: + raise RuntimeError( + "Ambiguous interrupted initial optimizer transaction; found " + f"{len(candidates)} candidate generations" + ) + manifest: OptimizerGenerationManifest | None = None + if candidates: + entry, generation, pending = candidates[0] + if _generation_step(generation) != 1: + raise RuntimeError( + "Ambiguous interrupted initial optimizer transaction; " + f"unexpected generation {generation}" + ) + manifest_path = entry / OPTIMIZER_MANIFEST + if not pending or manifest_path.exists(): + manifest = _read_manifest(entry) + if ( + manifest.generation != generation + or manifest.step != 1 + or manifest.adapter != adapter + ): + raise RuntimeError( + "Interrupted initial optimizer generation does not match " + f"the published adapter: {entry}" + ) + if len(pointer_temps) > 1: + raise RuntimeError( + "Ambiguous interrupted initial optimizer transaction; found " + f"{len(pointer_temps)} temporary pointers" + ) + if pointer_temps: + if not candidates or candidates[0][2] or manifest is None: + raise RuntimeError( + "Interrupted optimizer pointer has no committed generation" + ) + expected = OptimizerGenerationPointer( + generation=manifest.generation, + step=manifest.step, + adapter=manifest.adapter, + ) + if pointer_temps[0][1] != expected: + raise RuntimeError( + "Interrupted optimizer pointer does not match its generation" + ) + if candidates and not locks.enter_context( + _generation_lease( + path, + candidates[0][1], + exclusive=True, + nonblocking=True, + ) + ): + raise RuntimeError( + "Interrupted initial optimizer generation is still in use: " + f"{candidates[0][1]}" + ) + if _allow_unpaired_resume() and (pointer_temps or candidates): + raise RuntimeError( + f"{ALLOW_UNPAIRED_MEGATRON_RESUME_ENV} cannot bypass an interrupted " + "optimizer transaction" + ) + if _allow_unpaired_resume(): + return () + + tag = f"initial_step_0001_{adapter.generation_id.rsplit('-', 1)[-1][:16]}" + previous = Path(output_dir) / "unpaired_checkpoints" / tag / checkpoint.name + if previous.exists(): + tag = f"{tag}_{uuid4().hex}" + quarantine = path / "uncommitted_generations" / tag + quarantine.mkdir(parents=True, exist_ok=True) + if pointer_temps: + pointer_temp = pointer_temps[0][0] + destination = quarantine / pointer_temp.name + if destination.exists(): + raise RuntimeError(f"Optimizer quarantine entry exists: {destination}") + os.replace(pointer_temp, destination) + _fsync_directory(path) + if candidates: + entry = candidates[0][0] + destination = quarantine / entry.name + if destination.exists(): + raise RuntimeError(f"Optimizer quarantine entry exists: {destination}") + os.replace(entry, destination) + _fsync_directory(quarantine) + _fsync_directory(entry.parent) + + checkpoint_quarantine = Path(output_dir) / "unpaired_checkpoints" / tag + checkpoint_quarantine.mkdir(parents=True, exist_ok=True) + destination = checkpoint_quarantine / checkpoint.name + if destination.exists(): + raise RuntimeError(f"Checkpoint quarantine entry exists: {destination}") + os.replace(checkpoint, destination) + _fsync_directory(checkpoint_quarantine) + _fsync_directory(checkpoint.parent) + return (1,) + + def resolve_megatron_resume_step( *, output_dir: str, optimizer_state_path: str, ) -> MegatronResumeStep: latest_lora_step = get_step_from_dir(output_dir) - commit = read_optimizer_commit(optimizer_state_path) - optimizer_step = commit.step if commit is not None else None + with _committed_generation_lease(Path(optimizer_state_path)) as pointer: + if pointer is not None: + _validate_committed_generation(Path(optimizer_state_path), pointer) + expected_path = Path( + get_step_checkpoint_dir(output_dir, pointer.adapter.step) + ).absolute() + if pointer.adapter.identity != str(expected_path): + raise RuntimeError( + "Optimizer pointer does not identify the canonical adapter path: " + f"saved={pointer.adapter.identity}, expected={expected_path}" + ) + policy = _resolve_policy_pointer(Path(optimizer_state_path), pointer) + if policy is not None: + expected_path = Path( + get_step_checkpoint_dir(output_dir, policy.policy_adapter.step) + ).absolute() + if policy.policy_adapter.identity != str(expected_path): + raise RuntimeError( + "Optimizer policy pointer does not identify the canonical " + f"adapter path: saved={policy.policy_adapter.identity}, " + f"expected={expected_path}" + ) + return MegatronResumeStep( + step=policy.policy_adapter.step, + latest_lora_step=latest_lora_step, + optimizer_step=None if pointer is None else pointer.step, + ) + if pointer is not None: + return MegatronResumeStep( + step=pointer.step, + latest_lora_step=latest_lora_step, + optimizer_step=pointer.step, + ) if latest_lora_step == 0: return MegatronResumeStep( step=0, latest_lora_step=latest_lora_step, - optimizer_step=optimizer_step, - ) - if optimizer_step is not None and os.path.isdir( - get_step_checkpoint_dir(output_dir, optimizer_step) - ): - return MegatronResumeStep( - step=optimizer_step, - latest_lora_step=latest_lora_step, - optimizer_step=optimizer_step, + optimizer_step=None, ) if _allow_unpaired_resume(): return MegatronResumeStep( step=latest_lora_step, latest_lora_step=latest_lora_step, - optimizer_step=optimizer_step, + optimizer_step=None, used_unpaired_override=True, ) - marker = ( - "no optimizer step marker" - if optimizer_step is None - else f"optimizer marker step {optimizer_step:04d} has no matching LoRA checkpoint" - ) raise RuntimeError( "Cannot resume Megatron training from an unpaired LoRA/optimizer state: " - f"latest LoRA checkpoint is {latest_lora_step:04d}, {marker}. " + f"latest LoRA checkpoint is {latest_lora_step:04d}, no optimizer pointer. " f"Set {ALLOW_UNPAIRED_MEGATRON_RESUME_ENV}=1 to override." ) -def prepare_megatron_resume_state( +def _resolve_model_resume_step( + *, output_dir: str, optimizer_state_path: str +) -> MegatronResumeStep: + paired = [] + for path in _optimizer_state_paths(output_dir, optimizer_state_path): + if ( + read_committed_optimizer_pointer(str(path)) is not None + or _read_policy_pointer(path) is not None + ): + paired.append( + resolve_megatron_resume_step( + output_dir=output_dir, + optimizer_state_path=str(path), + ) + ) + if paired: + return max(paired, key=lambda info: info.step) + return resolve_megatron_resume_step( + output_dir=output_dir, + optimizer_state_path=optimizer_state_path, + ) + + +def _prepare_megatron_resume_state_locked( *, output_dir: str, optimizer_state_path: str, ) -> MegatronResumeStep: - info = resolve_megatron_resume_step( + _recover_optimizer_transactions(output_dir, optimizer_state_path) + recovered_steps = _recover_uncommitted_initial_transaction( + output_dir=output_dir, + optimizer_state_path=optimizer_state_path, + ) + info = _resolve_model_resume_step( output_dir=output_dir, optimizer_state_path=optimizer_state_path, ) + if recovered_steps: + info = info.model_copy(update={"quarantined_lora_steps": recovered_steps}) if info.used_unpaired_override or info.latest_lora_step <= info.step: return info @@ -200,30 +2226,74 @@ def prepare_megatron_resume_state( / "unpaired_checkpoints" / f"resume_from_{info.step:04d}_{int(time.time())}_{os.getpid()}" ) + to_move = [ + checkpoint_dir + for checkpoint_dir in sorted(checkpoints_dir.iterdir()) + if checkpoint_dir.is_dir() + and checkpoint_dir.name.isdigit() + and int(checkpoint_dir.name) > info.step + ] moved_steps: list[int] = [] - for checkpoint_dir in sorted(checkpoints_dir.iterdir()): - if not checkpoint_dir.is_dir() or not checkpoint_dir.name.isdigit(): - continue - step = int(checkpoint_dir.name) - if step <= info.step: - continue + for checkpoint_dir in to_move: quarantine_dir.mkdir(parents=True, exist_ok=True) - checkpoint_dir.rename(quarantine_dir / checkpoint_dir.name) - moved_steps.append(step) + os.replace(checkpoint_dir, quarantine_dir / checkpoint_dir.name) + moved_steps.append(int(checkpoint_dir.name)) + if moved_steps: + _fsync_directory(checkpoints_dir) + _fsync_directory(quarantine_dir) return info.model_copy(update={"quarantined_lora_steps": tuple(moved_steps)}) +def prepare_megatron_resume_state( + *, + output_dir: str, + optimizer_state_path: str, +) -> MegatronResumeStep: + with optimizer_model_lease(optimizer_state_path): + info = _prepare_megatron_resume_state_locked( + output_dir=output_dir, + optimizer_state_path=optimizer_state_path, + ) + latest = Path(output_dir) / "megatron_runtime" / ADAPTER_LATEST_POINTER + if info.step == 0: + if latest.exists(): + latest.unlink() + _fsync_directory(latest.parent) + else: + policy = resolve_committed_optimizer_policy( + optimizer_state_path, + initial_adapter_path=get_step_checkpoint_dir(output_dir, 0), + ) + _write_model_atomic(latest, policy.policy_adapter) + return info + + def format_megatron_resume_message(info: MegatronResumeStep) -> str: if info.used_unpaired_override: return ( "Resuming Megatron from unpaired LoRA checkpoint " f"{info.step} because {ALLOW_UNPAIRED_MEGATRON_RESUME_ENV} is set" ) + suffix = "" + if info.quarantined_lora_steps: + moved = ", ".join(f"{step:04d}" for step in info.quarantined_lora_steps) + suffix = f"; quarantined unpaired LoRA checkpoint(s): {moved}" + if info.step > 0 and info.optimizer_step != info.step: + optimizer = ( + "an uninitialized optimizer" + if info.optimizer_step is None + else f"optimizer state {info.optimizer_step}" + ) + latest = ( + "" + if info.step == info.latest_lora_step + else f" instead of latest LoRA checkpoint {info.latest_lora_step}" + ) + return ( + f"Resuming no-op policy checkpoint {info.step} with {optimizer}" + f"{latest}{suffix}" + ) if info.step != info.latest_lora_step: - suffix = "" - if info.quarantined_lora_steps: - moved = ", ".join(f"{step:04d}" for step in info.quarantined_lora_steps) - suffix = f"; quarantined unpaired LoRA checkpoint(s): {moved}" return ( "Resuming Megatron from paired LoRA/optimizer checkpoint " f"{info.step} instead of latest LoRA checkpoint " diff --git a/src/art/megatron/provider.py b/src/art/megatron/provider.py index 52bc7ff7c..ebbf28c96 100644 --- a/src/art/megatron/provider.py +++ b/src/art/megatron/provider.py @@ -12,11 +12,18 @@ from megatron.core.transformer.enums import AttnBackend from pydantic import BaseModel, ConfigDict import torch +from transformers import AutoConfig +from art.megatron.expert_parallel import ( + activate_expert_parallel_layout, + configure_expert_parallel_layout, + patch_moe_routers, +) from art.megatron.model_support.registry import ( ensure_model_support_bridge_registered_for_spec, get_model_support_handler_for_spec, get_model_support_spec, + get_model_support_spec_by_key, ) from art.megatron.model_support.spec import ModelSupportSpec from art.megatron.runtime.bridge_runtime import install_art_bridge_runtime_patches @@ -56,6 +63,10 @@ "virtual_pipeline_model_parallel_size", "ART_MEGATRON_VIRTUAL_PIPELINE_MODEL_PARALLEL_SIZE", ), + ( + "microbatch_group_size_per_vp_stage", + "ART_MEGATRON_VPP_MICROBATCH_GROUP_SIZE", + ), ("expert_model_parallel_size", "ART_MEGATRON_EXPERT_MODEL_PARALLEL_SIZE"), ("recompute_num_layers", "ART_MEGATRON_RECOMPUTE_NUM_LAYERS"), ) @@ -91,11 +102,12 @@ def resolve_layer_spec( module_spec_type = _optional_module_spec_type() if module_spec_type is not None and isinstance(base_layer_spec, module_spec_type): return copy.deepcopy(base_layer_spec) - kwargs = ( - {"vp_stage": vp_stage} - if vp_stage in inspect.signature(base_layer_spec).parameters - else {} + parameters = inspect.signature(base_layer_spec).parameters + accepts_vp_stage = "vp_stage" in parameters or any( + parameter.kind is inspect.Parameter.VAR_KEYWORD + for parameter in parameters.values() ) + kwargs = {"vp_stage": vp_stage} if accepts_vp_stage else {} return base_layer_spec(config, **kwargs) @@ -180,6 +192,7 @@ class _ProviderRuntimeEnv(BaseModel): context_parallel_size: int | None = None pipeline_model_parallel_size: int | None = None virtual_pipeline_model_parallel_size: int | None = None + microbatch_group_size_per_vp_stage: int | None = None expert_model_parallel_size: int | None = None expert_tensor_parallel_size: int | None = None recompute_granularity: Literal["full", "selective"] | None = None @@ -370,6 +383,12 @@ def _apply_art_training_runtime_prepare_defaults( provider: GPTModelProvider, handler: Any, ) -> None: + # Apex does not build its CUDA extensions in the CUDA 13 environment. + if ( + torch.version.cuda is not None + and int(torch.version.cuda.partition(".")[0]) >= 13 + ): + provider.gradient_accumulation_fusion = False provider.recompute_granularity = "full" provider.recompute_method = "uniform" provider.recompute_num_layers = 1 @@ -540,6 +559,11 @@ def _apply_runtime_env_overrides( runtime_env, "virtual_pipeline_model_parallel_size", ) + _apply_provider_attr_if_set( + provider, + runtime_env, + "microbatch_group_size_per_vp_stage", + ) _apply_provider_attr_if_value(provider, runtime_env, "expert_model_parallel_size") _apply_provider_attr_if_value(provider, runtime_env, "expert_tensor_parallel_size") _apply_provider_attr_if_set(provider, runtime_env, "recompute_granularity") @@ -609,20 +633,39 @@ def _build_provider_bundle( model: str, *, torch_dtype: torch.dtype, + load_weights: bool = True, allow_unvalidated_arch: bool = False, + model_support_key: str | None = None, ) -> ProviderBundle: - spec = get_model_support_spec( - model, - allow_unvalidated_arch=allow_unvalidated_arch, + spec = ( + get_model_support_spec_by_key(model_support_key) + if model_support_key is not None + else get_model_support_spec( + model, + allow_unvalidated_arch=allow_unvalidated_arch, + ) ) ensure_model_support_bridge_registered_for_spec(spec) handler = get_model_support_handler_for_spec(spec) - bridge = AutoBridge.from_hf_pretrained( - model, - dtype=torch_dtype, - trust_remote_code=True, + if load_weights: + bridge = AutoBridge.from_hf_pretrained( + model, + dtype=torch_dtype, + trust_remote_code=True, + ) + else: + bridge = AutoBridge.from_hf_config( + AutoConfig.from_pretrained( + model, + dtype=torch_dtype, + trust_remote_code=True, + ) + ) + provider = ( + bridge.to_megatron_provider() + if load_weights + else bridge.to_megatron_provider(load_weights=False) ) - provider = bridge.to_megatron_provider() handler.patch_bridge(bridge) return ProviderBundle( provider=provider, @@ -636,13 +679,17 @@ def prepare_provider_bundle( model: str, *, torch_dtype: torch.dtype = torch.bfloat16, + load_weights: bool = True, allow_unvalidated_arch: bool = False, + model_support_key: str | None = None, ) -> ProviderBundle: runtime_env = _ProviderRuntimeEnv.from_environ() bundle = _build_provider_bundle( model, torch_dtype=torch_dtype, + load_weights=load_weights, allow_unvalidated_arch=allow_unvalidated_arch, + model_support_key=model_support_key, ) provider = bundle.provider setattr(provider, "_art_model_support_handler", bundle.handler) @@ -676,11 +723,30 @@ def finalize_provider_bundle(provider_bundle: ProviderBundle) -> ProviderBundle: provider = cast(GPTModelProvider, provider_bundle.provider) _apply_art_training_runtime_finalize_defaults(provider) _enforce_art_moe_grouped_gemm_fast_path(provider) + configure_expert_parallel_layout(provider) _finalize_provider_with_art_overrides(provider) + if activate_expert_parallel_layout(provider) is not None: + _install_nonuniform_expert_parallel(provider) _normalize_recompute_settings(provider) return provider_bundle +def _install_nonuniform_expert_parallel(provider: GPTModelProvider) -> None: + base_layer_spec = provider.transformer_layer_spec + + def _nonuniform_expert_layer_spec( + config: GPTModelProvider, vp_stage: int | None = None + ) -> object: + layer_spec = resolve_layer_spec(base_layer_spec, config, vp_stage) + if patch_moe_routers(layer_spec) == 0: + raise RuntimeError( + "non-uniform expert parallelism found no MoE router in the layer spec" + ) + return layer_spec + + provider.transformer_layer_spec = cast(Any, _nonuniform_expert_layer_spec) + + def _finalize_provider_with_art_overrides(provider: GPTModelProvider) -> None: if not _is_art_gdn_context_parallel_provider(provider): _finalize_provider_config(provider) @@ -759,13 +825,17 @@ def get_provider_bundle( model: str, *, torch_dtype: torch.dtype = torch.bfloat16, + load_weights: bool = True, allow_unvalidated_arch: bool = False, + model_support_key: str | None = None, ) -> ProviderBundle: return finalize_provider_bundle( prepare_provider_bundle( model, torch_dtype=torch_dtype, + load_weights=load_weights, allow_unvalidated_arch=allow_unvalidated_arch, + model_support_key=model_support_key, ) ) @@ -774,10 +844,14 @@ def get_provider( model: str, *, torch_dtype: torch.dtype = torch.bfloat16, + load_weights: bool = True, allow_unvalidated_arch: bool = False, + model_support_key: str | None = None, ) -> GPTModelProvider: return get_provider_bundle( model, torch_dtype=torch_dtype, + load_weights=load_weights, allow_unvalidated_arch=allow_unvalidated_arch, + model_support_key=model_support_key, ).provider diff --git a/src/art/megatron/routing_replay.py b/src/art/megatron/routing_replay.py index 39efa4101..144c3f3ca 100644 --- a/src/art/megatron/routing_replay.py +++ b/src/art/megatron/routing_replay.py @@ -10,7 +10,7 @@ import types from typing import TYPE_CHECKING, Any, Protocol -from pydantic import BaseModel, ConfigDict, model_validator +from pydantic import BaseModel, ConfigDict, Field, model_validator from safetensors.torch import load_file, save_file import torch @@ -20,10 +20,11 @@ from art.preprocessing.pack import PackedTensors ROUTER_NAME_TOKEN = ".mlp.router" -ROUTER_KEY_FORMAT_VERSION = "moe_routing_replay_v3" +ROUTER_KEY_FORMAT_VERSION = "moe_routing_replay_v4" GLOBAL_TOKEN_UIDS_KEY = "global_token_uids" _ROUTER_LAYER_PATTERN = re.compile(r"decoder\.layers\.(?P\d+)\.mlp\.router$") +_ROUTER_KEY_PATTERN = re.compile(r"^chunk_\d+\.layer_(?P\d+)\.mlp\.router$") _TRACE_CHUNK_PREFIX_PATTERN = re.compile(r"^chunk(?P\d+)\.(?P.+)$") logger = logging.getLogger(__name__) _ACTIVE_ROUTING_REPLAY_CONTROLLER: Any | None = None @@ -64,6 +65,44 @@ def build_router_key_from_module_name(*, chunk_index: int, module_name: str) -> return f"chunk_{chunk_index:02d}.layer_{layer_index:04d}.mlp.router" +def _router_key_for_model_module( + *, + module_name: str, + layer_prefixes: list[tuple[str, int]], + fallback_chunk_index: int | None, +) -> str: + for prefix, global_layer_index in layer_prefixes: + if module_name.startswith(f"{prefix}."): + return f"chunk_00.layer_{global_layer_index:04d}.mlp.router" + if fallback_chunk_index is not None: + return build_router_key_from_module_name( + chunk_index=fallback_chunk_index, + module_name=module_name, + ) + raise RuntimeError( + "PP/VPP routing replay requires every router to have an owning " + f"TransformerLayer; router='{module_name}'" + ) + + +def _global_layer_prefixes(chunk: Any) -> list[tuple[str, int]]: + from megatron.core.transformer.transformer_layer import TransformerLayer + + prefixes: dict[str, int] = {} + for module_name, module in chunk.named_modules(): + original = getattr(module, "_orig_mod", None) + layer = ( + original + if isinstance(original, TransformerLayer) + else module + if isinstance(module, TransformerLayer) + else None + ) + if layer is not None: + prefixes[module_name] = int(layer.layer_number) - 1 + return sorted(prefixes.items(), key=lambda item: len(item[0]), reverse=True) + + def build_router_key_from_trace_name(trace_module_name: str) -> str: chunk_match = _TRACE_CHUNK_PREFIX_PATTERN.match(trace_module_name) if chunk_match is None: @@ -77,6 +116,13 @@ def build_router_key_from_trace_name(trace_module_name: str) -> str: ) +def _global_layer_from_router_key(router_key: str) -> int: + match = _ROUTER_KEY_PATTERN.fullmatch(router_key) + if match is None: + raise RuntimeError(f"Invalid routing replay router key: {router_key!r}") + return int(match.group("layer")) + + class ParallelTopology(BaseModel): tp: int ep: int @@ -250,7 +296,10 @@ class MoeRoutingReplayBundle(BaseModel): num_steps: int max_topk: int router_keys: list[str] - steps: dict[int, StepRoutes] + steps: dict[int, StepRoutes] = Field(default_factory=dict) + expert_indices: torch.Tensor | None = None + num_experts: int | None = None + global_grad_accumulation_sequences: int | None = None @model_validator(mode="after") def _validate(self) -> "MoeRoutingReplayBundle": @@ -267,6 +316,14 @@ def _validate(self) -> "MoeRoutingReplayBundle": raise RuntimeError("router_keys cannot be empty") if len(set(self.router_keys)) != len(self.router_keys): raise RuntimeError("router_keys must be unique") + if self.expert_indices is not None: + self._validate_tensor_storage() + return self + if ( + self.num_experts is not None + or self.global_grad_accumulation_sequences is not None + ): + raise RuntimeError("Legacy replay bundles cannot carry tensor metadata") expected_steps = set(range(self.num_steps)) if set(self.steps) != expected_steps: raise RuntimeError( @@ -290,6 +347,41 @@ def _validate(self) -> "MoeRoutingReplayBundle": ) return self + @property + def tensor_backed(self) -> bool: + return self.expert_indices is not None + + def _validate_tensor_storage(self) -> None: + indices = self.expert_indices + assert indices is not None + if self.steps: + raise RuntimeError("Tensor-backed replay cannot also contain route calls") + if indices.device.type != "cpu" or not indices.is_contiguous(): + raise RuntimeError("Tensor-backed replay requires contiguous CPU storage") + if indices.ndim != 4 or min(map(int, indices.shape)) <= 0: + raise RuntimeError( + "Tensor-backed replay requires [layer, row, position, topk]" + ) + num_experts = int(self.num_experts or 0) + expected_dtype = torch.uint8 if num_experts <= 256 else torch.uint16 + if not 1 <= num_experts <= 65_536 or indices.dtype != expected_dtype: + raise RuntimeError("Tensor-backed replay expert count and dtype disagree") + accumulation = int(self.global_grad_accumulation_sequences or 0) + if accumulation <= 0: + raise RuntimeError("Tensor-backed replay requires positive accumulation") + layers, sequences, _sequence_length, topk = map(int, indices.shape) + if ( + layers != len(self.router_keys) + or topk != self.max_topk + or self.num_steps != math.ceil(sequences / accumulation) + ): + raise RuntimeError("Tensor-backed replay metadata disagrees with its shape") + expected_keys = [ + f"chunk_00.layer_{layer:04d}.mlp.router" for layer in range(layers) + ] + if self.router_keys != expected_keys: + raise RuntimeError("Tensor-backed replay router keys are not layer-major") + @classmethod def from_dir(cls, bundle_dir: str | Path) -> "MoeRoutingReplayBundle": base_dir = Path(bundle_dir) @@ -304,6 +396,24 @@ def from_dir(cls, bundle_dir: str | Path) -> "MoeRoutingReplayBundle": f"{manifest.get('format_version')!r}; expected " f"{ROUTER_KEY_FORMAT_VERSION!r}" ) + if manifest.get("storage") == "layer_major": + loaded = load_file(str(base_dir / manifest["file"])) + indices = loaded["expert_indices"].detach().clone().contiguous() + del loaded + return cls( + format_version=manifest["format_version"], + topology=ParallelTopology.model_validate(manifest["topology"]), + num_steps=int(manifest["num_steps"]), + max_topk=int(manifest["max_topk"]), + router_keys=list(manifest["router_keys"]), + expert_indices=indices, + num_experts=int(manifest["num_experts"]), + global_grad_accumulation_sequences=int( + manifest["global_grad_accumulation_sequences"] + ), + ) + if manifest.get("storage") != "calls": + raise RuntimeError("Unknown MoE routing replay storage format") steps: dict[int, StepRoutes] = {} for step_index_str, step_info in manifest["steps"].items(): @@ -364,6 +474,28 @@ def from_dir(cls, bundle_dir: str | Path) -> "MoeRoutingReplayBundle": def to_dir(self, bundle_dir: str | Path) -> None: base_dir = Path(bundle_dir) base_dir.mkdir(parents=True, exist_ok=True) + if self.tensor_backed: + assert self.expert_indices is not None + tensor_file = "layer_major.safetensors" + save_file( + {"expert_indices": self.expert_indices}, str(base_dir / tensor_file) + ) + manifest = { + "format_version": self.format_version, + "storage": "layer_major", + "file": tensor_file, + "topology": self.topology.model_dump(mode="json"), + "num_steps": self.num_steps, + "max_topk": self.max_topk, + "router_keys": self.router_keys, + "num_experts": self.num_experts, + "global_grad_accumulation_sequences": ( + self.global_grad_accumulation_sequences + ), + } + with (base_dir / "manifest.json").open("w", encoding="utf-8") as handle: + json.dump(manifest, handle, indent=2, sort_keys=True) + return manifest_steps: dict[str, Any] = {} for step_index, step_routes in sorted(self.steps.items()): @@ -405,6 +537,7 @@ def to_dir(self, bundle_dir: str | Path) -> None: manifest = { "format_version": self.format_version, + "storage": "calls", "topology": self.topology.model_dump(mode="json"), "num_steps": self.num_steps, "max_topk": self.max_topk, @@ -429,127 +562,22 @@ def build_moe_routing_replay_bundle_from_packed_tensors( "global_grad_accumulation_sequences must be positive when building " f"MoE routing replay bundles, got {global_grad_accumulation_sequences}" ) - expert_indices = _to_tensor_cpu_contiguous( - routing_replay.expert_indices, dtype=torch.int32 - ) - token_mask = _to_tensor_cpu_contiguous(routing_replay.token_mask, dtype=torch.bool) - num_experts = int(routing_replay.num_experts) - num_sequences = int(expert_indices.shape[0]) - sequence_length = int(expert_indices.shape[1]) - num_layers = int(expert_indices.shape[2]) - topk = int(expert_indices.shape[3]) + expert_indices = routing_replay.expert_indices + num_layers, num_sequences, _sequence_length, topk = map(int, expert_indices.shape) router_keys = [ f"chunk_00.layer_{layer_index:04d}.mlp.router" for layer_index in range(num_layers) ] - steps: dict[int, StepRoutes] = {} num_steps = math.ceil(num_sequences / global_grad_accumulation_sequences) - global_token_uids = torch.arange(sequence_length, dtype=torch.int64) - all_row_positions = torch.arange(sequence_length, dtype=torch.long) - for step_index in range(num_steps): - start = step_index * global_grad_accumulation_sequences - end = start + global_grad_accumulation_sequences - calls_by_router: dict[str, dict[int, RouterCallRoute]] = { - router_key: {} for router_key in router_keys - } - for offset, sample_index in enumerate(range(start, end)): - if sample_index < num_sequences: - routes_by_layer = _sample_routes_by_layer( - expert_indices=expert_indices, - token_mask=token_mask, - sample_index=sample_index, - num_experts=num_experts, - topk=topk, - ) - sample_route_index: int | None = sample_index - micro_slot: int | None = None - else: - routes_by_layer = _synthetic_replay_layer_rows( - row_positions=all_row_positions, - layer_seeds=_layer_replay_seeds( - num_layers=num_layers, - base_seed=(step_index + 1) * 1_000_003 + (offset + 1) * 9_176, - ), - num_experts=num_experts, - topk=topk, - dtype=expert_indices.dtype, - ) - sample_route_index = None - micro_slot = offset - for layer_index, router_key in enumerate(router_keys): - calls_by_router[router_key][offset] = _full_mask_router_call_route( - expert_indices=routes_by_layer[layer_index], - num_experts=num_experts, - sample_index=sample_route_index, - micro_slot=micro_slot, - ) - routers = { - router_key: StepRouterRoutes.model_construct(calls=calls) - for router_key, calls in calls_by_router.items() - } - steps[step_index] = StepRoutes.model_construct( - routers=routers, - global_token_uids=global_token_uids, - ) - return MoeRoutingReplayBundle.model_construct( + return MoeRoutingReplayBundle( topology=topology or parallel_topology_from_env(), num_steps=num_steps, max_topk=topk, router_keys=router_keys, - steps=steps, - ) - - -def _sample_routes_by_layer( - *, - expert_indices: torch.Tensor, - token_mask: torch.Tensor, - sample_index: int, - num_experts: int, - topk: int, -) -> torch.Tensor: - routes_by_layer = expert_indices[sample_index].permute(1, 0, 2).contiguous() - missing_positions = torch.nonzero(~token_mask[sample_index], as_tuple=False).view( - -1 - ) - if int(missing_positions.numel()) == 0: - return routes_by_layer - # Megatron Core RouterReplay requires concrete top-k ids. The packer leaves - # only padding and terminal query rows missing, so materialize deterministic - # values for those rows without rescanning the bundle here. - routes_by_layer[:, missing_positions, :] = _synthetic_replay_layer_rows( - row_positions=missing_positions, - layer_seeds=_layer_replay_seeds( - num_layers=int(expert_indices.shape[2]), - base_seed=(sample_index + 1) * 1_000_003, - ), - num_experts=num_experts, - topk=topk, - dtype=expert_indices.dtype, - ) - return routes_by_layer - - -def _layer_replay_seeds(*, num_layers: int, base_seed: int) -> torch.Tensor: - return base_seed + (torch.arange(num_layers, dtype=torch.long) + 1) * 97_003 - - -def _full_mask_router_call_route( - *, - expert_indices: torch.Tensor, - num_experts: int, - sample_index: int | None = None, - micro_slot: int | None = None, -) -> RouterCallRoute: - return RouterCallRoute.model_construct( expert_indices=expert_indices, - expert_probs=None, - expert_mask=None, - num_experts=int(num_experts), - sample_index=None if sample_index is None else int(sample_index), - micro_slot=None if micro_slot is None else int(micro_slot), - rank_token_counts=None, + num_experts=routing_replay.num_experts, + global_grad_accumulation_sequences=global_grad_accumulation_sequences, ) @@ -562,7 +590,24 @@ def parallel_topology_from_env() -> ParallelTopology: ) cp = _env_int("ART_MEGATRON_CONTEXT_PARALLEL_SIZE", 1) pp = _env_int("ART_MEGATRON_PIPELINE_MODEL_PARALLEL_SIZE", 1) - return ParallelTopology(tp=tp, ep=ep, etp=etp, dp=1, sp=tp > 1, cp=cp, pp=pp) + vpp = _env_int("ART_MEGATRON_VIRTUAL_PIPELINE_MODEL_PARALLEL_SIZE", 1) + world_size = _env_int("WORLD_SIZE", tp * cp * pp) + model_parallel_size = tp * cp * pp + if world_size % model_parallel_size: + raise RuntimeError( + f"WORLD_SIZE={world_size} is not divisible by TP*CP*PP=" + f"{model_parallel_size}" + ) + return ParallelTopology( + tp=tp, + ep=ep, + etp=etp, + dp=world_size // model_parallel_size, + sp=tp > 1, + cp=cp, + pp=pp, + vpp=vpp, + ) def _env_int(name: str, default: int) -> int: @@ -711,33 +756,41 @@ def __init__( self._device = torch.device(device) if device is not None else None self._active_step_index: int | None = None + self._active_step_samples: list[int | None] = [] self._active_sample_index: int | None = None self._active_step_routes: StepRoutes | None = None self._active_micro_order: int | None = None + self._active_chunk_index: int | None = None self._router_call_cursors: dict[str, int] = {} self._router_call_sequences: dict[str, list[int]] = {} self._router_last_call_indices: dict[str, int] = {} self._router_last_call_keys: dict[str, tuple[str, int] | None] = {} + self._router_consumed_calls: dict[str, dict[tuple[str, int], int]] = {} self._router_reuse_counts: dict[str, int] = {} self._global_uid_to_row_index: dict[int, int] = {} self._global_uid_dense_start: int | None = None self._global_uid_count: int = 0 self._local_router_keys: set[str] = set() + self._local_router_keys_by_chunk: dict[int, set[str]] = {} self._router_bindings: dict[str, dict[str, Any]] = {} + self._runtime_topology: ParallelTopology | None = None + self._expect_recompute_reuse = False self._prepared_uid_sets: dict[str, torch.Tensor] = {} self._prepared_targets: dict[tuple[str, str, int], torch.Tensor] = {} self._router_prepared_target_keys: dict[str, tuple[str, int]] = {} - self._target_buffers: dict[tuple[str, str, int], torch.Tensor] = {} + self._step_targets: dict[tuple[str, str, int], torch.Tensor] = {} + self._step_target_ready_events: dict[ + tuple[str, str, int], torch.cuda.Event + ] = {} self._host_target_staging: list[torch.Tensor] = [] self._target_copy_stream: torch.cuda.Stream | None = None - self._target_copy_event: torch.cuda.Event | None = None - self._target_copy_waited: bool = True self._active_token_uid_key: str | None = None def update_bundle(self, *, bundle: MoeRoutingReplayBundle, strict: bool) -> None: self.bundle = bundle self.strict = strict self.clear_replay_state() + self._validate_runtime_topology() if self.strict: missing = sorted( router_key @@ -749,6 +802,7 @@ def update_bundle(self, *, bundle: MoeRoutingReplayBundle, strict: bool) -> None "Router keys from model are missing in replay bundle: " f"router_keys={missing}" ) + self._validate_local_routes() def clear_replay_state(self) -> None: self._clear_native_router_replay_state() @@ -765,16 +819,25 @@ def install_router_patches(self, model_chunks: list[Any]) -> None: global _ACTIVE_ROUTING_REPLAY_CONTROLLER if self._router_bindings: return + pipeline_model = self.bundle.topology.pp > 1 or len(model_chunks) > 1 for chunk_index, chunk in enumerate(model_chunks): + self._local_router_keys_by_chunk[chunk_index] = set() + layer_prefixes = _global_layer_prefixes(chunk) for module_name, module in chunk.named_modules(): if ROUTER_NAME_TOKEN not in module_name or not hasattr( module, "routing" ): continue - router_key = build_router_key_from_module_name( - chunk_index=chunk_index, + router_key = _router_key_for_model_module( module_name=module_name, + layer_prefixes=layer_prefixes, + fallback_chunk_index=None if pipeline_model else chunk_index, ) + if router_key in self._router_bindings: + raise RuntimeError( + "Multiple local model chunks own the same replay router: " + f"router_key='{router_key}'" + ) if self.strict and router_key not in self.bundle.router_keys: raise RuntimeError( "Router key from model is missing in replay bundle: " @@ -811,10 +874,13 @@ def install_router_patches(self, model_chunks: list[Any]) -> None: original_routing = module.routing def _prepare_native_target_for_bound_router( + logits: torch.Tensor, _controller: MoeRoutingReplayController = self, _router_key: str = router_key, ) -> None: - _controller._prepare_native_target_for_router(_router_key) + _controller._prepare_native_target_for_router( + _router_key, logits=logits + ) prepare_native_target = torch.compiler.disable( _prepare_native_target_for_bound_router @@ -828,7 +894,7 @@ def _hash_routing_with_replay_target( **kwargs: Any, ) -> Any: del router_module - _prepare_native_target() + _prepare_native_target(args[0]) return _original_routing(*args, **kwargs) def _moe_routing_with_replay_target( @@ -839,7 +905,7 @@ def _moe_routing_with_replay_target( **kwargs: Any, ) -> Any: del router_module - _prepare_native_target() + _prepare_native_target(args[0]) return _original_routing(*args, **kwargs) def _routing_with_replay_target( @@ -853,7 +919,7 @@ def _routing_with_replay_target( # Target selection mutates Python replay cursors and Megatron's # RouterReplay state; keep it out of Dynamo while preserving # compiled routing compute below. - _prepare_native_target() + _prepare_native_target(args[0]) return _original_routing(*args, **kwargs) original_routing_name = getattr( @@ -881,8 +947,22 @@ def _routing_with_replay_target( "sequence_parallel": sequence_parallel, "context_parallel_size": context_parallel_size, "topk": topk, + "chunk_index": chunk_index, + "layer_index": _global_layer_from_router_key(router_key), + "num_experts": int(getattr(config, "num_moe_experts", 0) or 0), } self._local_router_keys.add(router_key) + self._local_router_keys_by_chunk[chunk_index].add(router_key) + self._runtime_topology = self._runtime_parallel_topology(model_chunks) + self._validate_runtime_topology() + self._validate_local_routes() + self._expect_recompute_reuse = bool(self._router_bindings) and all( + getattr(binding["module"].config, "recompute_granularity", None) == "full" + and getattr(binding["module"].config, "recompute_method", None) == "uniform" + and int(getattr(binding["module"].config, "recompute_num_layers", 0) or 0) + == 1 + for binding in self._router_bindings.values() + ) _ACTIVE_ROUTING_REPLAY_CONTROLLER = self def remove_router_patches(self) -> None: @@ -898,14 +978,138 @@ def remove_router_patches(self) -> None: delattr(module, "_art_routing_replay_target_patched") self._router_bindings.clear() self._local_router_keys.clear() - self._target_buffers.clear() + self._local_router_keys_by_chunk.clear() + self._runtime_topology = None + self._expect_recompute_reuse = False + self._step_targets.clear() self._clear_native_router_replay_state() self._reset_step_state() - def begin_micro(self, sample_index: int | None, micro_order: int) -> None: + @staticmethod + def _runtime_parallel_topology( + model_chunks: list[Any], + ) -> ParallelTopology | None: + if not torch.distributed.is_initialized(): # ty: ignore[possibly-missing-attribute] + return None + from megatron.core import parallel_state as ps + from megatron.core.utils import get_model_config + + sequence_parallel = { + bool(getattr(get_model_config(chunk), "sequence_parallel", False)) + for chunk in model_chunks + } + if len(sequence_parallel) != 1: + raise RuntimeError( + "Model chunks disagree on sequence_parallel: " + f"values={sorted(sequence_parallel)}" + ) + return ParallelTopology( + tp=int(ps.get_tensor_model_parallel_world_size()), + ep=int(ps.get_expert_model_parallel_world_size()), + etp=int(ps.get_expert_tensor_parallel_world_size()), + dp=int(ps.get_data_parallel_world_size()), + sp=sequence_parallel.pop(), + cp=int(ps.get_context_parallel_world_size()), + pp=int(ps.get_pipeline_model_parallel_world_size()), + vpp=int(ps.get_virtual_pipeline_model_parallel_world_size() or 1), + ) + + def _validate_runtime_topology(self) -> None: + if ( + self._runtime_topology is not None + and self.bundle.topology != self._runtime_topology + ): + raise RuntimeError( + "Routing replay bundle topology differs from the active trainer: " + f"bundle={self.bundle.topology.model_dump()}, " + f"runtime={self._runtime_topology.model_dump()}" + ) + + def _validate_local_routes(self) -> None: + if self.bundle.tensor_backed: + assert self.bundle.expert_indices is not None + for router_key, binding in self._router_bindings.items(): + if router_key not in self.bundle.router_keys: + continue + model_num_experts = int(binding["num_experts"]) + if model_num_experts and model_num_experts != self.bundle.num_experts: + raise RuntimeError( + "Replay expert count does not match the model router: " + f"router='{router_key}', replay={self.bundle.num_experts}, " + f"model={model_num_experts}" + ) + if int(binding["topk"]) != self.bundle.max_topk: + raise RuntimeError( + "Replay route topk does not match Megatron router topk: " + f"router='{router_key}', replay={self.bundle.max_topk}, " + f"router_topk={binding['topk']}" + ) + if int(binding["layer_index"]) >= int( + self.bundle.expert_indices.shape[0] + ): + raise RuntimeError( + f"Replay has no global layer for router '{router_key}'" + ) + return + for router_key, binding in self._router_bindings.items(): + if router_key not in self.bundle.router_keys: + continue + model_num_experts = int(binding["num_experts"]) + for step_index, step in self.bundle.steps.items(): + for call_index, route in step.routers[router_key].calls.items(): + selected = ( + route.expert_indices + if route.expert_mask is None + else route.expert_indices[route.expert_mask] + ) + if int(selected.numel()) == 0: + continue + minimum = int(selected.min().item()) + maximum = int(selected.max().item()) + limit = model_num_experts or int(route.num_experts) + if minimum < 0 or maximum >= limit: + raise RuntimeError( + "Replay route expert id is outside the model router: " + f"step={step_index}, router='{router_key}', " + f"call={call_index}, range=[{minimum}, {maximum}], " + f"num_experts={limit}" + ) + + def _active_local_router_keys(self) -> set[str]: + if self._active_chunk_index is None: + raise RuntimeError("Routing replay chunk is not active") + try: + return self._local_router_keys_by_chunk[self._active_chunk_index] + except KeyError as exc: + raise RuntimeError( + f"Routing replay received unknown model chunk {self._active_chunk_index}" + ) from exc + + def begin_micro( + self, + sample_index: int | None, + micro_order: int, + chunk_index: int = 0, + ) -> None: + if self._active_step_index is None: + raise RuntimeError("Routing replay begin_micro called before set_step") + if self.bundle.tensor_backed: + if not 0 <= micro_order < len(self._active_step_samples): + raise RuntimeError( + f"Routing replay micro order is out of range: {micro_order}" + ) + expected_sample = self._active_step_samples[micro_order] + if sample_index != expected_sample: + raise RuntimeError( + "Routing replay micro sample differs from set_step: " + f"micro={micro_order}, expected={expected_sample}, " + f"actual={sample_index}" + ) self._active_sample_index = sample_index self._active_micro_order = micro_order - for router_key in sorted(self._local_router_keys): + self._active_chunk_index = chunk_index + self._reset_staged_micro_targets() + for router_key in sorted(self._active_local_router_keys()): call_indices = self._active_micro_call_indices(router_key) if len(call_indices) != 1: raise RuntimeError( @@ -925,7 +1129,7 @@ def prepare_micro_targets( *, active_token_uid_key: str = "attention", ) -> None: - if self._active_step_routes is None or self._active_micro_order is None: + if self._active_step_index is None or self._active_micro_order is None: raise RuntimeError( "Routing replay target staging requires set_step and begin_micro" ) @@ -943,11 +1147,13 @@ def prepare_micro_targets( f"key='{active_token_uid_key}', prepared={sorted(prepared_uid_sets)}" ) self._prepared_uid_sets = prepared_uid_sets - if not self._local_router_keys: + active_router_keys = self._active_local_router_keys() + if not active_router_keys: self._active_token_uid_key = active_token_uid_key return + new_target_keys: list[tuple[str, str, int]] = [] for token_uid_key, token_uids in prepared_uid_sets.items(): - for router_key in sorted(self._local_router_keys): + for router_key in sorted(active_router_keys): call_indices = self._active_micro_call_indices(router_key) if len(call_indices) != 1: raise RuntimeError( @@ -956,6 +1162,11 @@ def prepare_micro_targets( ) call_index = call_indices[0] binding = self._router_bindings[router_key] + target_key = (token_uid_key, router_key, call_index) + cached_target = self._step_targets.get(target_key) + if cached_target is not None: + self._prepared_targets[target_key] = cached_target + continue router_token_uids = self._token_uids_for_router_binding( token_uids, sequence_parallel=bool(binding["sequence_parallel"]), @@ -966,14 +1177,16 @@ def prepare_micro_targets( explicit_uids=router_token_uids, ) self._stage_prepared_target( - target_key=(token_uid_key, router_key, call_index), + target_key=target_key, target_cpu=target_cpu, ) - self._record_target_copy_event() + self._step_targets[target_key] = self._prepared_targets[target_key] + new_target_keys.append(target_key) + self._record_target_copy_event(new_target_keys) self.set_active_token_uid_key(active_token_uid_key) def set_active_token_uid_key(self, token_uid_key: str) -> None: - if not self._local_router_keys: + if not self._active_local_router_keys(): self._active_token_uid_key = token_uid_key return prepared_keys = { @@ -1023,6 +1236,14 @@ def set_step( step_index: int, sample_index: int | list[int | None] | None, ) -> None: + if self.bundle.tensor_backed: + self._set_tensor_step(step_index=step_index, sample_index=sample_index) + RouterReplay, RouterReplayAction = _router_replay_classes() + RouterReplay.clear_global_indices() + RouterReplay.set_global_router_replay_action( + RouterReplayAction.REPLAY_FORWARD + ) + return if step_index not in self.bundle.steps: raise RuntimeError( f"Replay bundle missing step_index={step_index}. " @@ -1036,12 +1257,17 @@ def set_step( else sample_index ) self._active_micro_order = None + self._active_chunk_index = None self._active_step_routes = step_routes self._reset_staged_micro_targets() + self._step_targets = {} + self._step_target_ready_events = {} + self._host_target_staging = [] self._router_call_cursors = {} self._router_call_sequences = {} self._router_last_call_indices = {} self._router_last_call_keys = {} + self._router_consumed_calls = {} self._router_reuse_counts = {} self._global_uid_count = int(step_routes.global_token_uids.numel()) self._global_uid_dense_start = self._dense_global_uid_start( @@ -1079,6 +1305,7 @@ def set_step( f"route_topk={route.max_topk}, router_topk={binding_topk}" ) self._router_call_cursors[router_key] = 0 + self._router_consumed_calls[router_key] = {} self._router_call_sequences[router_key] = self._build_call_sequence( router_key=router_key, sample_index=sample_index, @@ -1087,8 +1314,46 @@ def set_step( RouterReplay.clear_global_indices() RouterReplay.set_global_router_replay_action(RouterReplayAction.REPLAY_FORWARD) - def finalize_step(self) -> None: - if self._active_step_routes is None: + def _set_tensor_step( + self, + *, + step_index: int, + sample_index: int | list[int | None] | None, + ) -> None: + if not 0 <= step_index < self.bundle.num_steps: + raise RuntimeError( + f"Replay bundle missing step_index={step_index}. " + f"Available steps={list(range(self.bundle.num_steps))}" + ) + samples = sample_index if isinstance(sample_index, list) else [sample_index] + if not samples: + raise RuntimeError("Routing replay step requires at least one microbatch") + assert self.bundle.expert_indices is not None + accumulation = int(self.bundle.global_grad_accumulation_sequences or 0) + start = step_index * accumulation + stop = min(start + accumulation, int(self.bundle.expert_indices.shape[1])) + real_samples = [sample for sample in samples if sample is not None] + if len(real_samples) != len(set(real_samples)) or any( + not start <= sample < stop for sample in real_samples + ): + raise RuntimeError( + "Routing replay samples do not belong to the active step: " + f"step={step_index}, span=[{start}, {stop}), samples={samples}" + ) + + self._reset_step_state() + self._active_step_index = step_index + self._active_step_samples = list(samples) + self._global_uid_dense_start = 0 + self._global_uid_count = int(self.bundle.expert_indices.shape[2]) + call_sequence = list(range(len(samples))) + for router_key in self._local_router_keys: + self._router_call_cursors[router_key] = 0 + self._router_call_sequences[router_key] = call_sequence + self._router_consumed_calls[router_key] = {} + + def finalize_step(self, *, expect_recompute: bool = False) -> None: + if self._active_step_index is None: raise RuntimeError("finalize_step called before set_step") for router_key in sorted(self._local_router_keys): consumed = self._router_call_cursors.get(router_key, 0) @@ -1104,6 +1369,14 @@ def finalize_step(self) -> None: f"step={self._active_step_index}, router='{router_key}', " f"consumed={consumed}, expected={len(call_sequence)}" ) + if expect_recompute and self._expect_recompute_reuse: + reused = self._router_reuse_counts.get(router_key, 0) + if reused != len(call_sequence): + raise RuntimeError( + "Routing replay recompute consumption mismatch: " + f"step={self._active_step_index}, router='{router_key}', " + f"reused={reused}, expected={len(call_sequence)}" + ) if self._router_reuse_counts: logger.info( "Routing replay reused routes for recompute: step=%s counts=%s", @@ -1115,15 +1388,21 @@ def finalize_step(self) -> None: def _reset_step_state(self) -> None: self._active_step_index = None + self._active_step_samples = [] self._active_sample_index = None self._active_step_routes = None self._active_micro_order = None + self._active_chunk_index = None self._router_call_cursors = {} self._router_call_sequences = {} self._router_last_call_indices = {} self._router_last_call_keys = {} + self._router_consumed_calls = {} self._router_reuse_counts = {} self._reset_staged_micro_targets() + self._step_targets = {} + self._step_target_ready_events = {} + self._host_target_staging = [] self._global_uid_to_row_index = {} self._global_uid_dense_start = None self._global_uid_count = 0 @@ -1132,9 +1411,6 @@ def _reset_staged_micro_targets(self) -> None: self._prepared_uid_sets = {} self._prepared_targets = {} self._router_prepared_target_keys = {} - self._host_target_staging = [] - self._target_copy_event = None - self._target_copy_waited = True self._active_token_uid_key = None @staticmethod @@ -1223,12 +1499,21 @@ def _active_router_call_key(self) -> tuple[str, int] | None: ) def _active_micro_call_indices(self, router_key: str) -> list[int]: + if self.bundle.tensor_backed: + if self._active_step_index is None or self._active_micro_order is None: + raise RuntimeError("Routing replay begin_micro called before set_step") + return [self._active_micro_order] if self._active_step_routes is None: raise RuntimeError("Routing replay begin_micro called before set_step") router_calls = self._active_step_routes.routers[router_key].calls call_sequence = self._router_call_sequences[router_key] cursor = self._router_call_cursors.get(router_key, 0) active_call_key = self._active_router_call_key() + consumed_call = self._router_consumed_calls.get(router_key, {}).get( + active_call_key + ) + if consumed_call is not None: + return [consumed_call] if cursor >= len(call_sequence): last_index = self._router_last_call_indices.get(router_key) last_key = self._router_last_call_keys.get(router_key) @@ -1259,6 +1544,33 @@ def _active_micro_call_indices(self, router_key: str) -> list[int]: return indices def _next_route_call_index(self, router_key: str) -> int: + if self.bundle.tensor_backed: + if self._active_step_index is None or self._active_micro_order is None: + raise RuntimeError( + "Routing replay router call occurred before set_step" + ) + call_index = self._active_micro_order + call_key = ("micro", call_index) + consumed = self._router_consumed_calls[router_key] + if call_key in consumed: + if not self.allow_recompute_reuse: + raise RuntimeError( + "Routing replay recompute reuse is disabled: " + f"step={self._active_step_index}, router='{router_key}', " + f"micro={call_index}" + ) + self._router_reuse_counts[router_key] = ( + self._router_reuse_counts.get(router_key, 0) + 1 + ) + return call_index + if call_index not in self._router_call_sequences[router_key]: + raise RuntimeError( + "Routing replay micro is outside the local call sequence: " + f"router='{router_key}', micro={call_index}" + ) + consumed[call_key] = call_index + self._router_call_cursors[router_key] += 1 + return call_index if self._active_step_routes is None: raise RuntimeError("Routing replay router call occurred before set_step") router_calls = self._active_step_routes.routers[router_key].calls @@ -1270,6 +1582,20 @@ def _next_route_call_index(self, router_key: str) -> int: ) cursor = self._router_call_cursors.get(router_key, 0) active_call_key = self._active_router_call_key() + consumed_call = self._router_consumed_calls.get(router_key, {}).get( + active_call_key + ) + if consumed_call is not None: + if not self.allow_recompute_reuse: + raise RuntimeError( + "Routing replay recompute reuse is disabled: " + f"step={self._active_step_index}, router='{router_key}', " + f"call_key={active_call_key}" + ) + self._router_reuse_counts[router_key] = ( + self._router_reuse_counts.get(router_key, 0) + 1 + ) + return consumed_call last_index = self._router_last_call_indices.get(router_key) last_key = self._router_last_call_keys.get(router_key) next_key = ( @@ -1302,14 +1628,16 @@ def _next_route_call_index(self, router_key: str) -> int: call_index = call_sequence[cursor] self._router_call_cursors[router_key] = cursor + 1 self._router_last_call_indices[router_key] = call_index - self._router_last_call_keys[router_key] = _router_call_key( - router_calls[call_index] - ) + call_key = _router_call_key(router_calls[call_index]) + self._router_last_call_keys[router_key] = call_key + self._router_consumed_calls[router_key][call_key] = call_index return call_index - def _prepare_native_target_for_router(self, router_key: str) -> None: + def _prepare_native_target_for_router( + self, router_key: str, *, logits: torch.Tensor + ) -> None: if ( - self._active_step_routes is None + self._active_step_index is None or self._active_micro_order is None or self._active_token_uid_key is None ): @@ -1317,6 +1645,13 @@ def _prepare_native_target_for_router(self, router_key: str) -> None: "Routing replay router call occurred before staged targets were ready: " f"router='{router_key}'" ) + binding = self._router_bindings[router_key] + if int(binding["chunk_index"]) != self._active_chunk_index: + raise RuntimeError( + "Routing replay router ran under the wrong VPP chunk: " + f"router='{router_key}', owner={binding['chunk_index']}, " + f"active={self._active_chunk_index}" + ) call_indices = self._active_micro_call_indices(router_key) if len(call_indices) != 1: raise RuntimeError( @@ -1331,9 +1666,6 @@ def _prepare_native_target_for_router(self, router_key: str) -> None: f"actual={call_index}" ) target_key = (self._active_token_uid_key, call_index) - if self._router_prepared_target_keys.get(router_key) == target_key: - return - self.wait_for_staged_targets() staged_key = (self._active_token_uid_key, router_key, call_index) target = self._prepared_targets.get(staged_key) if target is None: @@ -1342,14 +1674,34 @@ def _prepare_native_target_for_router(self, router_key: str) -> None: f"step={self._active_step_index}, router='{router_key}', " f"call={call_index}, token_uid_key='{self._active_token_uid_key}'" ) - topk = int(self._router_bindings[router_key]["topk"]) + self._wait_for_staged_target(staged_key, target) + if target.device.type == "cuda": + target.record_stream(torch.cuda.current_stream(target.device)) + if self._router_prepared_target_keys.get(router_key) == target_key: + return + topk = int(binding["topk"]) + logit_experts = int(logits.shape[-1]) + model_num_experts = int(binding["num_experts"]) + if model_num_experts and model_num_experts != logit_experts: + raise RuntimeError( + "Routing replay router expert count differs from logits: " + f"router='{router_key}', model_experts={model_num_experts}, " + f"logit_experts={logit_experts}" + ) + expected_tokens = int(logits.numel()) // logit_experts + if int(target.shape[0]) != expected_tokens: + raise RuntimeError( + "Routing replay target token count differs from router logits: " + f"router='{router_key}', target_tokens={int(target.shape[0])}, " + f"logit_tokens={expected_tokens}" + ) if int(target.shape[1]) != topk: raise RuntimeError( "Routing replay target topk mismatch at router call: " f"router='{router_key}', call={call_index}, " f"target_topk={int(target.shape[1])}, router_topk={topk}" ) - router_replay = self._router_bindings[router_key]["router_replay"] + router_replay = binding["router_replay"] router_replay.set_target_indices(target) router_replay.set_router_replay_action( _router_replay_classes()[1].REPLAY_FORWARD @@ -1363,6 +1715,52 @@ def _explicit_target_for_router_call( call_index: int, explicit_uids: torch.Tensor, ) -> torch.Tensor: + if self.bundle.tensor_backed: + assert self.bundle.expert_indices is not None + num_experts = int(self.bundle.num_experts or 0) + topk = self.bundle.max_topk + layer_index = int(self._router_bindings[router_key]["layer_index"]) + sample_index = self._active_step_samples[call_index] + source = ( + None + if sample_index is None + else self.bundle.expert_indices[layer_index, sample_index] + ) + local_uids = explicit_uids.reshape(-1).contiguous() + target_cpu = torch.empty( + (int(local_uids.numel()), topk), + dtype=(torch.uint8 if num_experts <= 256 else torch.uint16), + ) + valid_positions = torch.nonzero(local_uids >= 0, as_tuple=False).reshape(-1) + if int(valid_positions.numel()) > 0: + valid_uids = local_uids[valid_positions] + if source is None: + target_cpu[valid_positions] = _synthetic_replay_rows( + row_positions=valid_uids, + num_experts=num_experts, + topk=topk, + dtype=target_cpu.dtype, + seed=self._tensor_synthetic_seed(layer_index, call_index), + ) + else: + row_indices = self._row_indices_for_explicit_uids( + valid_uids=valid_uids, + router_key=router_key, + call_index=call_index, + ) + target_cpu[valid_positions] = source.index_select(0, row_indices) + invalid_positions = torch.nonzero(local_uids < 0, as_tuple=False).reshape( + -1 + ) + if int(invalid_positions.numel()) > 0: + target_cpu[invalid_positions] = _synthetic_replay_rows( + row_positions=invalid_positions, + num_experts=num_experts, + topk=topk, + dtype=target_cpu.dtype, + seed=self._tensor_synthetic_seed(layer_index, call_index), + ) + return target_cpu.contiguous() if self._active_step_routes is None: raise RuntimeError("Routing replay explicit target used before set_step") route = self._active_step_routes.routers[router_key].calls[call_index] @@ -1395,6 +1793,13 @@ def _explicit_target_for_router_call( ) return target_cpu.contiguous() + def _tensor_synthetic_seed(self, layer_index: int, call_index: int) -> int: + return ( + (int(self._active_step_index or 0) + 1) * 1_000_003 + + (layer_index + 1) * 97_003 + + (call_index + 1) * 9_176 + ) + def _row_indices_for_explicit_uids( self, *, @@ -1455,10 +1860,10 @@ def _stage_prepared_target( target_key: tuple[str, str, int], target_cpu: torch.Tensor, ) -> None: - target_cpu = target_cpu.to(dtype=torch.long).contiguous() + target_cpu = target_cpu.contiguous() device = self._target_device() if device.type != "cuda": - self._prepared_targets[target_key] = target_cpu + self._prepared_targets[target_key] = target_cpu.to(dtype=torch.long) return if self._target_copy_stream is None: self._target_copy_stream = torch.cuda.Stream(device=device) @@ -1466,36 +1871,36 @@ def _stage_prepared_target( target_cpu if target_cpu.is_pinned() else target_cpu.pin_memory() ).contiguous() self._host_target_staging.append(host_target) - buffer = self._target_buffers.get(target_key) - if ( - buffer is None - or buffer.shape != host_target.shape - or buffer.device != device - or buffer.dtype != torch.long - ): - buffer = torch.empty( + with torch.cuda.stream(self._target_copy_stream): + narrow_buffer = torch.empty( tuple(host_target.shape), device=device, - dtype=torch.long, + dtype=host_target.dtype, ) - self._target_buffers[target_key] = buffer - with torch.cuda.stream(self._target_copy_stream): - buffer.copy_(host_target, non_blocking=True) + narrow_buffer.copy_(host_target, non_blocking=True) + buffer = narrow_buffer.to(dtype=torch.long) + narrow_buffer.record_stream(self._target_copy_stream) buffer.record_stream(self._target_copy_stream) self._prepared_targets[target_key] = buffer - self._target_copy_waited = False - def _record_target_copy_event(self) -> None: - if self._target_copy_stream is None or self._target_copy_waited: + def _record_target_copy_event( + self, + target_keys: list[tuple[str, str, int]], + ) -> None: + if self._target_copy_stream is None or not target_keys: return - self._target_copy_event = torch.cuda.Event() + ready = torch.cuda.Event() with torch.cuda.stream(self._target_copy_stream): - self._target_copy_event.record() + ready.record() + for target_key in target_keys: + self._step_target_ready_events[target_key] = ready - def wait_for_staged_targets(self) -> None: - if self._target_copy_event is None or self._target_copy_waited: + def _wait_for_staged_target( + self, + target_key: tuple[str, str, int], + target: torch.Tensor, + ) -> None: + ready = self._step_target_ready_events.get(target_key) + if ready is None: return - torch.cuda.current_stream(self._target_device()).wait_event( - self._target_copy_event - ) - self._target_copy_waited = True + torch.cuda.current_stream(target.device).wait_event(ready) diff --git a/src/art/megatron/runtime/__init__.py b/src/art/megatron/runtime/__init__.py index 8b1378917..d5323abb4 100644 --- a/src/art/megatron/runtime/__init__.py +++ b/src/art/megatron/runtime/__init__.py @@ -1 +1,37 @@ +from art.distributed.data_plane import PackedBatchLeaseSet, PackedBatchRef +from .data_plane import InMemoryPackedBatch +from .specs import ( + AdapterReady, + CurrentTrainConfig, + DurableTrainOutput, + ExperimentalTrainConfig, + TrainAccepted, + TrainCancelled, + TrainCompleted, + TrainerRuntimeSpec, + TrainEvent, + TrainFailed, + TrainingRunSpec, + TrainJobSpec, + TrainProgress, +) + +__all__ = [ + "AdapterReady", + "CurrentTrainConfig", + "DurableTrainOutput", + "ExperimentalTrainConfig", + "InMemoryPackedBatch", + "PackedBatchRef", + "PackedBatchLeaseSet", + "TrainAccepted", + "TrainCancelled", + "TrainCompleted", + "TrainEvent", + "TrainFailed", + "TrainJobSpec", + "TrainProgress", + "TrainerRuntimeSpec", + "TrainingRunSpec", +] diff --git a/src/art/megatron/runtime/bridge_runtime.py b/src/art/megatron/runtime/bridge_runtime.py index 4a0d8f5c8..1c6eda9c8 100644 --- a/src/art/megatron/runtime/bridge_runtime.py +++ b/src/art/megatron/runtime/bridge_runtime.py @@ -2,7 +2,10 @@ from collections.abc import Callable, Iterable, Mapping import contextlib +import copy +from dataclasses import replace import fnmatch +import re from typing import Any, cast from megatron.bridge.models.common.unimodal import to_empty_if_meta_device @@ -11,8 +14,10 @@ ColumnParallelMapping, MegatronParamMapping, ReplicatedMapping, + extract_expert_number_from_param, get_module_and_param_from_name, ) +from megatron.bridge.models.conversion.utils import unwrap_model from megatron.bridge.models.model_provider import ModelProviderMixin from megatron.core.distributed import DistributedDataParallelConfig from megatron.core.enums import ModelType @@ -21,13 +26,22 @@ from megatron.core.utils import get_model_config import torch +from art.megatron.expert_parallel import ( + ExpertParallelLayout, + get_expert_parallel_layout, +) from art.megatron.model_support.spec import HfWeightSource _Fp32PreservedTensor = tuple[torch.nn.Module, str, torch.Tensor, bool] class ExpertTensorSlice: - __slots__ = ("global_start", "global_stop", "tensor") + __slots__ = ( + "global_start", + "global_stop", + "physical_to_logical", + "tensor", + ) def __init__( self, @@ -35,13 +49,22 @@ def __init__( *, global_start: int, global_stop: int, + physical_to_logical: tuple[int | None, ...] | None = None, ) -> None: self.tensor = tensor self.global_start = int(global_start) self.global_stop = int(global_stop) + self.physical_to_logical = physical_to_logical def get(self, global_expert: int) -> torch.Tensor: global_expert = int(global_expert) + if self.physical_to_logical is not None: + logical_expert = self.physical_to_logical[global_expert] + if logical_expert is None: + raise RuntimeError( + f"masked physical expert {global_expert} has no checkpoint tensor" + ) + global_expert = logical_expert if not self.global_start <= global_expert < self.global_stop: raise RuntimeError( "expert slice cache miss for global expert " @@ -114,13 +137,23 @@ def _needs_expert_slice_prefetch(task: Any) -> bool: int(getattr(mapping, "ep_size", 1)) > 1 and bool(getattr(mapping, "is_expert", False)) and bool(getattr(mapping, "is_grouped_export", False)) - and isinstance(getattr(mapping, "hf_param", None), str) + and isinstance(getattr(mapping, "hf_param", None), (str, Mapping)) ) def _expert_slice_range(task: Any) -> tuple[int, int]: mapping = task.mapping config = getattr(task.megatron_module, "config", None) + layout = get_expert_parallel_layout(config) + if layout is not None: + local_experts = tuple( + expert + for expert in layout.local_logical_experts(int(mapping.ep_rank)) + if expert is not None + ) + if not local_experts: + raise RuntimeError(f"EP rank {mapping.ep_rank} owns no logical experts") + return local_experts[0], local_experts[-1] + 1 num_experts = int(getattr(config, "num_moe_experts", 0) or 0) ep_size = int(getattr(mapping, "ep_size", 1)) ep_rank = int(getattr(mapping, "ep_rank", 0)) @@ -168,6 +201,89 @@ def _direct_hf_weight_source(key: str) -> HfWeightSource: return HfWeightSource(logical_key=key, physical_key_options=((key,),)) +_HF_EXPERT_RE = re.compile(r"(?P(?:^|\.)experts\.)(?P\d+)(?=\.|$)") + + +def _remap_hf_expert_name( + name: str, + expert_ids: tuple[int | None, ...], +) -> str: + def replace_expert(match: re.Match[str]) -> str: + expert = int(match.group("expert")) + if not 0 <= expert < len(expert_ids): + raise RuntimeError( + f"expert {expert} is outside remapping domain [0, {len(expert_ids)})" + ) + mapped = expert_ids[expert] + if mapped is None: + raise RuntimeError(f"masked physical expert {expert} has no logical name") + return f"{match.group('prefix')}{mapped}" + + return _HF_EXPERT_RE.sub(replace_expert, name) + + +def _logical_hf_param( + hf_param: Any, + *, + physical_expert: int, + logical_expert: int, +) -> Any: + if isinstance(hf_param, str): + return _HF_EXPERT_RE.sub( + lambda match: ( + f"{match.group('prefix')}{logical_expert}" + if int(match.group("expert")) == physical_expert + else match.group(0) + ), + hf_param, + ) + if isinstance(hf_param, Mapping): + return { + key: _logical_hf_param( + value, + physical_expert=physical_expert, + logical_expert=logical_expert, + ) + for key, value in hf_param.items() + } + return hf_param + + +def _prepare_nonuniform_expert_tasks(tasks: Iterable[Any]) -> list[Any]: + prepared: list[Any] = [] + for task in tasks: + if ( + task is None + or task.megatron_module is None + or not bool(getattr(task.mapping, "is_expert", False)) + ): + prepared.append(task) + continue + layout = get_expert_parallel_layout( + getattr(task.megatron_module, "config", None) + ) + if layout is None: + prepared.append(task) + continue + physical_expert = extract_expert_number_from_param(task.mapping.megatron_param) + logical_expert = layout.logical_expert(physical_expert) + if logical_expert is None: + if task.param_weight is None: + raise RuntimeError( + f"masked physical expert {physical_expert} has no target parameter" + ) + task.param_weight.data.zero_() + continue + mapping = copy.copy(task.mapping) + mapping.hf_param = _logical_hf_param( + mapping.hf_param, + physical_expert=physical_expert, + logical_expert=logical_expert, + ) + prepared.append(replace(task, mapping=mapping)) + return prepared + + def _planned_hf_weight_source( bridge: MegatronModelBridge | None, key: str, @@ -264,14 +380,14 @@ def load_unique_hf_keys_once( if not _needs_expert_slice_prefetch(task): continue start, stop = _expert_slice_range(task) - key = cast(str, task.mapping.hf_param) - previous = expert_slice_ranges.get(key) - expert_slice_ranges[key] = ( - (start, stop) - if previous is None - else (min(previous[0], start), max(previous[1], stop)) - ) - expert_slice_task_by_key.setdefault(key, task) + for key in _iter_hf_param_names(task.mapping.hf_param): + previous = expert_slice_ranges.get(key) + expert_slice_ranges[key] = ( + (start, stop) + if previous is None + else (min(previous[0], start), max(previous[1], stop)) + ) + expert_slice_task_by_key.setdefault(key, task) cache: dict[str, torch.Tensor | ExpertTensorSlice] = {} direct_physical_by_logical: dict[str, str] = {} materialized_source_by_key: dict[str, tuple[HfWeightSource, tuple[str, ...]]] = {} @@ -319,10 +435,14 @@ def load_unique_hf_keys_once( ) ) for key, (start, stop) in expert_slice_ranges.items(): + task = expert_slice_task_by_key.get(key) + layout = get_expert_parallel_layout( + getattr(getattr(task, "megatron_module", None), "config", None) + ) source = _planned_hf_weight_source( bridge, key, - task=expert_slice_task_by_key.get(key), + task=task, ) selected_option = _select_physical_key_option(source, hf_state_dict) if source.kind != "direct": @@ -341,6 +461,9 @@ def load_unique_hf_keys_once( _pin_cpu_tensor(tensor[start:stop]), global_start=start, global_stop=stop, + physical_to_logical=( + None if layout is None else layout.physical_to_logical + ), ) continue if len(selected_option) != 1: @@ -359,6 +482,9 @@ def load_unique_hf_keys_once( ), global_start=start, global_stop=stop, + physical_to_logical=( + None if layout is None else layout.physical_to_logical + ), ) return cache @@ -686,6 +812,58 @@ def _replicated_hf_to_megatron( return self.broadcast_tensor_to_tp_ranks(tensor, src_rank=0) +def _shared_embedding_broadcast_model( + megatron_model: list[MegatronModule], +) -> list[MegatronModule]: + if len(megatron_model) == 1: + return megatron_model + for chunk in megatron_model: + model = unwrap_model(chunk) + language_model = getattr(model, "language_model", None) + if language_model is not None: + model = language_model + embedding = getattr(model, "embedding", None) + if ( + getattr(embedding, "word_embeddings", None) is not None + or getattr(model, "output_layer", None) is not None + ): + return [chunk] + return megatron_model + + +def _validate_local_pretrained_tasks( + bridge: MegatronModelBridge, + megatron_model: list[Any], + tasks: Iterable[Any], +) -> None: + covered = { + id(task.param_weight) + for task in tasks + if task is not None + and task.megatron_module is not None + and task.param_weight is not None + } + config = getattr(unwrap_model(megatron_model)[0], "config", None) + tied_output = bool( + config is not None and bridge._share_embeddings_and_output_weights(config) + ) + missing = [ + name + for model in megatron_model + for name, param in model.named_parameters() + if not bridge._is_adapter_param_name(name) + and not (tied_output and "output_layer" in name) + and id(param) not in covered + ] + if missing: + preview = ", ".join(missing[:8]) + remainder = f" (+{len(missing) - 8} more)" if len(missing) > 8 else "" + raise RuntimeError( + "Megatron Bridge did not create pretrained load tasks for " + f"{len(missing)} required local parameter(s): {preview}{remainder}" + ) + + def _optimized_load_weights_hf_to_megatron( self: MegatronModelBridge, hf_pretrained: Any, @@ -700,6 +878,8 @@ def _optimized_load_weights_hf_to_megatron( if hasattr(megatron_model[0], "hide_loss_modules"): stack.enter_context(megatron_model[0].hide_loss_modules()) tasks = self.build_conversion_tasks(hf_pretrained, megatron_model) + _validate_local_pretrained_tasks(self, megatron_model, tasks) + tasks = _prepare_nonuniform_expert_tasks(tasks) hf_state_dict = hf_pretrained.state raw_cache = load_unique_hf_keys_once( tasks, @@ -756,7 +936,7 @@ def _optimized_load_weights_hf_to_megatron( pending_device_copy = True if pending_device_copy and torch.cuda.is_available(): torch.cuda.synchronize() - self._broadcast_shared_embeddings(megatron_model) + self._broadcast_shared_embeddings(_shared_embedding_broadcast_model(megatron_model)) return megatron_model @@ -766,6 +946,7 @@ def install_art_bridge_runtime_patches() -> None: _patch_router_gating_linear_empty_input() _patch_bias_swiglu_empty_input() _patch_moe_unpermute_empty_input() + _patch_nonuniform_expert_export() if not getattr( model_provider_module.get_model, "__art_meta_materialization__", False ): @@ -795,6 +976,77 @@ def install_art_bridge_runtime_patches() -> None: ) +def _patch_nonuniform_expert_export() -> None: + original = MegatronParamMapping.gather_from_ep_ranks + if getattr(original, "__art_nonuniform_experts__", False): + return + + def _gather_from_ep_ranks( + self: MegatronParamMapping, + megatron_weights: torch.Tensor | None, + megatron_module: MegatronModule | None, + hf_param_name: Any, + ) -> dict[str, torch.Tensor]: + if megatron_module is None: + payload = self.broadcast_obj_from_pp_rank( + None, "art_expert_parallel_layout" + ) + layout = ( + None + if payload is None + else ExpertParallelLayout.model_validate(payload) + ) + else: + layout = get_expert_parallel_layout( + getattr(megatron_module, "config", None) + ) + self.broadcast_obj_from_pp_rank( + None if layout is None else layout.model_dump(mode="python"), + "art_expert_parallel_layout", + ) + if layout is None or hf_param_name is None: + return original(self, megatron_weights, megatron_module, hf_param_name) + if isinstance(hf_param_name, Mapping): + if megatron_weights is None: + return {} + gathered = [ + torch.empty_like(megatron_weights) for _ in range(layout.ep_size) + ] + torch.distributed.all_gather( + gathered, megatron_weights, group=self.ep_group + ) + return {str(hf_param_name): torch.stack(gathered)} + if not _HF_EXPERT_RE.search(hf_param_name): + return original(self, megatron_weights, megatron_module, hf_param_name) + if megatron_weights is None: + return {} + + physical_expert = extract_expert_number_from_param(self.megatron_param) + local_expert = physical_expert % layout.slots_per_rank + gathered = [torch.empty_like(megatron_weights) for _ in range(layout.ep_size)] + torch.distributed.all_gather(gathered, megatron_weights, group=self.ep_group) + result: dict[str, torch.Tensor] = {} + for ep_rank, weight in enumerate(gathered): + logical_expert = layout.logical_expert( + ep_rank * layout.slots_per_rank + local_expert + ) + if logical_expert is None: + continue + key = _HF_EXPERT_RE.sub( + lambda match: f"{match.group('prefix')}{logical_expert}", + hf_param_name, + ) + result[key] = weight + return result + + setattr(_gather_from_ep_ranks, "__art_nonuniform_experts__", True) + setattr( + MegatronParamMapping, + "gather_from_ep_ranks", + _gather_from_ep_ranks, + ) + + def _patch_router_gating_linear_empty_input() -> None: from megatron.core.transformer.moe import moe_utils, router diff --git a/src/art/megatron/runtime/client.py b/src/art/megatron/runtime/client.py deleted file mode 100644 index c01b146c9..000000000 --- a/src/art/megatron/runtime/client.py +++ /dev/null @@ -1,73 +0,0 @@ -import asyncio -import datetime -import json -import os -from typing import Any, AsyncIterator - -from .jobs import DEFAULT_JOBS_DIR, MegatronJob, dump_megatron_job - -DEFAULT_TRAINING_LOG_DIR = "/tmp/megatron_training_logs" - - -def create_megatron_job_paths( - *, - jobs_dir: str = DEFAULT_JOBS_DIR, - training_log_dir: str = DEFAULT_TRAINING_LOG_DIR, -) -> tuple[str, str]: - timestamp = datetime.datetime.now().isoformat() - os.makedirs(jobs_dir, exist_ok=True) - os.makedirs(training_log_dir, exist_ok=True) - return ( - os.path.join(jobs_dir, f"{timestamp}.json"), - os.path.join(training_log_dir, f"{timestamp}.jsonl"), - ) - - -def write_megatron_job(job: MegatronJob, *, job_path: str) -> None: - os.makedirs(os.path.dirname(job_path), exist_ok=True) - with open(job_path, "w", encoding="utf-8") as handle: - handle.write(dump_megatron_job(job)) - - -async def stream_megatron_job( - job: MegatronJob, - *, - job_path: str, - process: Any | None = None, - process_log_path: str | None = None, - poll_interval: float = 0.05, -) -> AsyncIterator[dict[str, Any]]: - num_lines = 0 - try: - while True: - await asyncio.sleep(poll_interval) - process_returncode = None - if process is not None: - process_returncode = process.returncode - poll = getattr(process, "poll", None) - if process_returncode is None and callable(poll): - process_returncode = poll() - if process_returncode is not None: - raise RuntimeError( - f"Megatron worker exited with code {process_returncode}. " - f"Check logs at {process_log_path or job.log_path}" - ) - try: - with open(job.log_path, "a+", encoding="utf-8") as log_file: - log_file.seek(0) - lines = log_file.readlines()[num_lines:] - except FileNotFoundError: - continue - - for line in lines: - if not (line := line.strip()): - continue - if line == "all done": - return - num_lines += 1 - yield json.loads(line) - finally: - if os.path.exists(job_path): - os.remove(job_path) - if os.path.exists(job.log_path): - os.remove(job.log_path) diff --git a/src/art/megatron/runtime/data_plane.py b/src/art/megatron/runtime/data_plane.py new file mode 100644 index 000000000..a99876209 --- /dev/null +++ b/src/art/megatron/runtime/data_plane.py @@ -0,0 +1,69 @@ +from __future__ import annotations + +from typing import Any + +from pydantic import BaseModel, ConfigDict, PrivateAttr, model_validator + +from art.distributed.data_plane import MappedPackedBatch, PackedBatchRef +from art.preprocessing.pack import PackedTensors + + +class InMemoryPackedBatch(BaseModel): + model_config = ConfigDict(arbitrary_types_allowed=True, extra="forbid") + + ref: PackedBatchRef + tensors: PackedTensors + _mapped: MappedPackedBatch | None = PrivateAttr(default=None) + + @classmethod + def open( + cls, ref: PackedBatchRef, local_ref: PackedBatchRef + ) -> "InMemoryPackedBatch": + mapped = MappedPackedBatch.open(local_ref) + batch = cls(ref=ref, tensors=mapped.tensors) + batch._mapped = mapped + return batch + + def close(self) -> None: + if self._mapped is not None: + self._mapped.close() + self._mapped = None + + +class SFTBatchData(BaseModel): + """Typed in-memory SFT payload sent directly to warm trainer actors.""" + + model_config = ConfigDict(arbitrary_types_allowed=True, extra="forbid", frozen=True) + + trajectory_tensors: tuple[dict[str, Any], ...] + learning_rate: float + num_trajectories: int + num_tokens: int + num_trainable_tokens: int + + @model_validator(mode="after") + def _validate_trajectories(self) -> "SFTBatchData": + if not self.trajectory_tensors: + raise ValueError("SFT batch must contain at least one trajectory") + if self.num_trajectories != len(self.trajectory_tensors): + raise ValueError("SFT trajectory count does not match its tensor payload") + required = {"input_ids", "attention_mask", "labels"} + if any(not required <= tensors.keys() for tensors in self.trajectory_tensors): + raise ValueError("SFT trajectory tensors are incomplete") + if self.num_tokens < 1 or self.num_trainable_tokens < 1: + raise ValueError("SFT batch must contain trainable tokens") + return self + + +def validate_packed_batch(batch: InMemoryPackedBatch) -> None: + tokens = batch.tensors["tokens"] + shape = tuple(int(size) for size in tokens.shape) + expected = (batch.ref.num_sequences, batch.ref.sequence_length) + if shape != expected: + raise ValueError( + f"packed token shape {shape} does not match batch ref {expected}" + ) + for key, tensor in batch.tensors.items(): + is_contiguous = getattr(tensor, "is_contiguous", None) + if callable(is_contiguous) and not is_contiguous(): + raise ValueError(f"packed tensor {key!r} must be contiguous") diff --git a/src/art/megatron/runtime/executor.py b/src/art/megatron/runtime/executor.py new file mode 100644 index 000000000..ac6d56761 --- /dev/null +++ b/src/art/megatron/runtime/executor.py @@ -0,0 +1,591 @@ +from __future__ import annotations + +from concurrent.futures import Future, ThreadPoolExecutor +from pathlib import Path +from threading import BoundedSemaphore, Event, Lock +import time +from typing import TYPE_CHECKING, Any + +from art.utils.safetensors import PreparedSafetensors, SafetensorsLayout + +from ..tensor_snapshot import PinnedCpuSnapshotStager +from .data_plane import InMemoryPackedBatch, SFTBatchData, validate_packed_batch +from .publication import ( + TrainerPublicationFailed, + TrainerPublicationSucceeded, + TrainerRankPublication, +) +from .specs import SFTJobSpec, TrainerGeneration, TrainerJobSpec, TrainJobSpec +from .trainer_run import EventSink + +if TYPE_CHECKING: + from art.megatron.optimizer_state import OptimizerAdapter + + +class MegatronTrainJobExecutor: + """Thin adapter around the warm runtime's in-memory job entrypoint.""" + + def __init__(self, runtime: Any) -> None: + self.runtime = runtime + self._publisher = _GenerationPublisher( + runtime, + stager=PinnedCpuSnapshotStager(), + capacity=int(runtime.snapshot_pool_capacity), + ) + self._closed = False + + def execute( + self, + job: TrainJobSpec, + batch: InMemoryPackedBatch, + sink: EventSink, + cancelled: Event, + ) -> dict[str, float]: + if self._closed: + raise RuntimeError("Megatron executor is closed") + validate_packed_batch(batch) + self._publisher.raise_if_failed() + from art.megatron.train import execute_megatron_rl_job + + metrics = execute_megatron_rl_job( + self.runtime, + job, + batch.tensors, + progress_sink=lambda step_index, num_steps, metrics: sink.progress( + step_index=step_index, + num_steps=num_steps, + metrics=metrics, + ), + adapter_ready_sink=lambda: sink.adapter_ready( + learner_version=job.learner_version, + adapter_path=job.output_adapter_path, + ), + snapshot_sink=lambda *args: self._publisher.submit(*args, sink=sink), + cancelled=cancelled, + ) + if job.merged_weight_transfer is not None: + started = time.perf_counter() + self._sync_merged(job.merged_weight_transfer) + metrics["time/merged_weight_publish_s"] = time.perf_counter() - started + return metrics + + def execute_sft( + self, + job: SFTJobSpec, + batches: tuple[SFTBatchData, ...], + sink: EventSink, + cancelled: Event, + ) -> dict[str, float]: + if self._closed: + raise RuntimeError("Megatron executor is closed") + self._publisher.raise_if_failed() + from art.megatron.train import execute_megatron_sft_job + + metrics = execute_megatron_sft_job( + self.runtime, + job, + batches, + progress_sink=lambda step_index, num_steps, metrics: sink.progress( + step_index=step_index, + num_steps=num_steps, + metrics=metrics, + ), + adapter_ready_sink=lambda: sink.adapter_ready( + learner_version=job.learner_version, + adapter_path=job.output_adapter_path, + ), + snapshot_sink=lambda *args: self._publisher.submit(*args, sink=sink), + cancelled=cancelled, + ) + if job.merged_weight_transfer is not None: + started = time.perf_counter() + self._sync_merged(job.merged_weight_transfer) + metrics["time/merged_weight_publish_s"] = time.perf_counter() - started + return metrics + + def sync_merged_source( + self, + generation: TrainerGeneration, + transfer: Any, + ) -> dict[str, float]: + if self._closed: + raise RuntimeError("Megatron executor is closed") + runtime = self.runtime + if ( + runtime.resident_training_session_id != generation.training_session_id + or runtime.resident_policy_step != generation.policy_step + ): + from art.megatron.model_support.lora_disk import load_adapter_config + from art.megatron.train import _load_adapter_into_model + + _load_adapter_into_model( + runtime.model, + generation.adapter_path, + runtime.rank, + handler=runtime.model_support_handler, + ) + runtime.adapter_export_config = load_adapter_config(generation.adapter_path) + runtime.adapter_export_dtypes = {} + runtime.resident_training_session_id = None + runtime.resident_policy_step = None + runtime.optimizer_state_loaded = False + started = time.perf_counter() + self._sync_merged(transfer) + return {"time/merged_weight_publish_s": time.perf_counter() - started} + + def _sync_merged(self, transfer: Any) -> None: + from art.megatron.weights.merged_weight_export import ( + sync_merged_weights_to_vllm, + ) + + runtime = self.runtime + if runtime.adapter_export_config is None: + raise RuntimeError("merged publication has no adapter export config") + ( + runtime.merged_weight_transfer_group, + runtime.merged_weight_transfer_init_info, + ) = sync_merged_weights_to_vllm( + bridge=runtime.bridge, + model=runtime.model, + model_support_handler=runtime.model_support_handler, + adapter_model={}, + adapter_config=runtime.adapter_export_config, + rank=runtime.rank, + world_size=runtime.world_size, + merged_weight_transfer_group=runtime.merged_weight_transfer_group, + merged_weight_transfer_init_info=(runtime.merged_weight_transfer_init_info), + spec=transfer, + pause_generation=True, + ) + + def advance_without_training( + self, + *, + training_session_id: str, + expected_learner_version: int, + learner_version: int, + optimizer_state_path: str, + adapter: "OptimizerAdapter | None", + ) -> dict[str, float]: + if self._closed: + raise RuntimeError("Megatron executor is closed") + if learner_version != expected_learner_version + 1: + raise ValueError("a no-op learner transition must advance exactly one step") + runtime = self.runtime + if ( + runtime.resident_training_session_id != training_session_id + or runtime.resident_policy_step != expected_learner_version + or not runtime.optimizer_state_loaded + or runtime.optimizer is None + ): + raise RuntimeError("resident trainer state does not match no-op transition") + del optimizer_state_path, adapter + runtime.resident_policy_step = learner_version + return {} + + def close(self) -> None: + if self._closed: + return + self._closed = True + failures: list[BaseException] = [] + try: + self._publisher.close() + self.runtime.optimizer_snapshot_barrier.synchronize() + except BaseException as error: + failures.append(error) + controller = getattr(self.runtime, "moe_routing_replay_controller", None) + if controller is not None: + try: + controller.remove_router_patches() + except BaseException as error: + failures.append(error) + finally: + self.runtime.moe_routing_replay_controller = None + from art.megatron.train import _close_merged_weight_transfer_group + + try: + _close_merged_weight_transfer_group(self.runtime) + except BaseException as error: + failures.append(error) + try: + import torch + + if torch.distributed.is_initialized(): + torch.distributed.destroy_process_group() + except BaseException as error: + failures.append(error) + if len(failures) == 1: + raise failures[0] + if failures: + raise BaseExceptionGroup("Megatron executor close failed", failures) + + +class _GenerationPublisher: + def __init__( + self, + runtime: Any, + *, + stager: PinnedCpuSnapshotStager, + capacity: int, + ) -> None: + if capacity < 1: + raise ValueError("snapshot pool capacity must be positive") + self.runtime = runtime + self.stager = stager + self.capacity = capacity + self._slots = BoundedSemaphore(capacity) + self._lock = Lock() + self._transport_pool = ThreadPoolExecutor( + max_workers=1, thread_name_prefix="art-publish-transport" + ) + self._durability_pool = ThreadPoolExecutor( + max_workers=1, thread_name_prefix="art-publish-durable" + ) + self._transport_sender: Any | None = None + self._lora_layout: SafetensorsLayout | None = None + self._failures: list[BaseException] = [] + self._in_flight = 0 + + def submit( + self, + job: TrainerJobSpec, + adapter_dtypes: dict[str, Any], + adapter_config: dict[str, Any], + save_optimizer: bool, + *, + sink: EventSink, + ) -> dict[str, float]: + from art.megatron.optimizer_state import stage_optimizer_state_snapshot + from art.megatron.weights.lora_publish import ( + stage_vllm_lora_snapshot_from_model, + ) + + wait_s, in_flight = self._acquire_slot() + prepare_started = time.perf_counter() + optimizer_handoff: Future[Any] = Future() + transport: Future[Future[TrainerRankPublication]] | None = None + try: + lora = stage_vllm_lora_snapshot_from_model( + model=self.runtime.model, + adapter_dtypes=adapter_dtypes, + handler=self.runtime.model_support_handler, + adapter_config=adapter_config, + rank=self.runtime.rank, + world_size=self.runtime.world_size, + stager=self.stager, + ) + lora_launch_s = time.perf_counter() - prepare_started + lora_resolve_started = time.perf_counter() + lora = None if lora is None else lora.resolve() + lora_resolve_s = time.perf_counter() - lora_resolve_started + transport = self._enqueue_transport( + generation=job.output.generation, + optimizer_state_path=job.output.optimizer_state_path, + staging_adapter_path=job.output.staging_adapter_path, + lora=lora, + adapter=None, + optimizer=optimizer_handoff, + publication_targets=getattr(job, "publication_targets", ()), + ) + optimizer_started = time.perf_counter() + optimizer = ( + stage_optimizer_state_snapshot( + self.runtime, + generation_id=job.output_generation_id, + step=job.learner_version, + stager=self.stager, + ) + if save_optimizer + else None + ) + if optimizer is not None: + self.runtime.optimizer_snapshot_barrier.register(optimizer) + optimizer_handoff.set_result(optimizer) + optimizer_launch_s = time.perf_counter() - optimizer_started + handoff_started = time.perf_counter() + persistence = transport.result() + transport_handoff_wait_s = time.perf_counter() - handoff_started + except BaseException as error: + publication_error = error + if transport is not None: + optimizer_handoff.set_exception(error) + publication_error = self._drain_transport(transport, error) + self._report_failure( + publication_error, + sink=sink, + generation=job.output.generation, + remember=False, + ) + raise + persistence.add_done_callback( + lambda done: self._completed( + done, + sink=sink, + generation=job.output.generation, + ) + ) + return { + "snapshot_pool_wait_s": wait_s, + "snapshot_pool_in_use": float(in_flight), + "snapshot_pool_pressure": in_flight / self.capacity, + "snapshot_lora_launch_s": lora_launch_s, + "snapshot_lora_resolve_s": lora_resolve_s, + "snapshot_optimizer_launch_s": optimizer_launch_s, + "snapshot_transport_handoff_wait_s": transport_handoff_wait_s, + "snapshot_launch_s": time.perf_counter() - prepare_started, + } + + def _acquire_slot(self) -> tuple[float, int]: + self.raise_if_failed() + started = time.perf_counter() + self._slots.acquire() + wait_s = time.perf_counter() - started + with self._lock: + self._in_flight += 1 + return wait_s, self._in_flight + + def _enqueue_transport( + self, + **kwargs: Any, + ) -> Future[Future[TrainerRankPublication]]: + return self._transport_pool.submit(self._transport_snapshot, **kwargs) + + @staticmethod + def _drain_transport( + transport: Future[Future[TrainerRankPublication]], + fallback: BaseException, + ) -> BaseException: + try: + transport.result().result() + except BaseException as error: + return error + return fallback + + def _transport_snapshot( + self, + *, + generation: TrainerGeneration, + optimizer_state_path: str, + staging_adapter_path: str | None, + lora: Any, + adapter: "OptimizerAdapter | None", + optimizer: Future[Any], + publication_targets: tuple[Any, ...], + ) -> Future[TrainerRankPublication]: + prepared_tensors = None + if lora is not None: + if self._lora_layout is None: + self._lora_layout = SafetensorsLayout(lora.tensors) + prepared_tensors = self._lora_layout.bind(lora.tensors) + failures: list[BaseException] = [] + if int(self.runtime.rank) == 0 and publication_targets: + if lora is None or prepared_tensors is None: + raise RuntimeError("rank zero has no LoRA snapshot to transfer") + try: + self._transfer_lora_snapshot( + lora, + publication_targets, + prepared_tensors=prepared_tensors, + ) + except BaseException as error: + failures.append(error) + return self._durability_pool.submit( + self._persist_snapshot, + generation=generation, + optimizer_state_path=optimizer_state_path, + staging_adapter_path=staging_adapter_path, + lora=lora, + adapter=adapter, + optimizer=optimizer, + prepared_tensors=prepared_tensors, + failures=failures, + ) + + def _persist_snapshot( + self, + *, + generation: TrainerGeneration, + optimizer_state_path: str, + staging_adapter_path: str | None, + lora: Any, + adapter: "OptimizerAdapter | None", + optimizer: Future[Any], + prepared_tensors: PreparedSafetensors | None, + failures: list[BaseException], + ) -> TrainerRankPublication: + record: TrainerRankPublication | None = None + try: + pending_optimizer = optimizer.result() + resolved_optimizer = ( + None if pending_optimizer is None else pending_optimizer.resolve() + ) + record = self._persist_generation( + generation=generation, + optimizer_state_path=optimizer_state_path, + staging_adapter_path=staging_adapter_path, + lora=lora, + adapter=adapter, + optimizer=resolved_optimizer, + prepared_tensors=prepared_tensors, + ) + except BaseException as error: + failures.append(error) + if len(failures) == 1: + raise failures[0] + if failures: + raise BaseExceptionGroup( + "adapter persistence and transport failed", failures + ) + if record is None: + raise RuntimeError("trainer rank produced no publication record") + return record + + def _transfer_lora_snapshot( + self, + lora: Any, + targets: tuple[Any, ...], + *, + prepared_tensors: PreparedSafetensors, + ) -> None: + from art.distributed.adapter_transport import AdapterSnapshotSender + + if self._transport_sender is None: + self._transport_sender = AdapterSnapshotSender() + self._transport_sender.send( + lora, + targets, + prepared_tensors=prepared_tensors, + ) + + def _persist_generation( + self, + *, + generation: TrainerGeneration, + optimizer_state_path: str, + staging_adapter_path: str | None, + lora: Any, + adapter: "OptimizerAdapter | None", + optimizer: Any, + prepared_tensors: PreparedSafetensors | None, + ) -> TrainerRankPublication: + from art.megatron.optimizer_state import ( + publish_adapter_checkpoint, + write_optimizer_snapshot_shard, + ) + from art.megatron.weights.lora_publish import save_vllm_lora_snapshot + + rank = int(self.runtime.rank) + if rank == 0: + if lora is not None: + if staging_adapter_path is None or adapter is not None: + raise RuntimeError("new adapter publication is inconsistent") + staging = Path(staging_adapter_path) + if staging.exists(): + raise RuntimeError(f"Adapter staging generation exists: {staging}") + save_vllm_lora_snapshot( + lora, + str(staging), + prepared_tensors=prepared_tensors, + ) + adapter = publish_adapter_checkpoint( + staging, + step=generation.policy_step, + training_session_id=generation.training_session_id, + generation_id=generation.generation_id, + ) + if adapter is None: + raise RuntimeError("rank zero has no immutable adapter") + shard = ( + write_optimizer_snapshot_shard( + optimizer, + optimizer_state_path=optimizer_state_path, + ) + if optimizer is not None + else None + ) + return TrainerRankPublication( + generation=generation, + rank=rank, + adapter=adapter, + shard=shard, + runtime_sha256=None if optimizer is None else optimizer.runtime_sha256, + topology=None if optimizer is None else optimizer.topology, + saves_optimizer=optimizer is not None, + ) + + def _completed( + self, + future: Future[TrainerRankPublication], + *, + sink: EventSink, + generation: TrainerGeneration, + ) -> None: + try: + event = TrainerPublicationSucceeded(record=future.result()) + except BaseException as error: + self._failed(error, sink=sink, generation=generation) + return + try: + sink.publication(event) + except BaseException as error: + with self._lock: + self._failures.append(error) + finally: + self._release_slot() + + def _failed( + self, + error: BaseException, + *, + sink: EventSink, + generation: TrainerGeneration, + ) -> None: + self._report_failure(error, sink=sink, generation=generation, remember=True) + + def _report_failure( + self, + error: BaseException, + *, + sink: EventSink, + generation: TrainerGeneration, + remember: bool, + ) -> None: + if remember: + with self._lock: + self._failures.append(error) + event = TrainerPublicationFailed( + generation_id=generation.generation_id, + rank=int(self.runtime.rank), + error_type=type(error).__name__, + message=str(error) or type(error).__name__, + ) + try: + sink.publication(event) + except BaseException as sink_error: + with self._lock: + self._failures.append(sink_error) + finally: + self._release_slot() + + def _release_slot(self) -> None: + with self._lock: + self._in_flight -= 1 + self._slots.release() + + def raise_if_failed(self) -> None: + with self._lock: + failures = tuple(self._failures) + if failures: + raise BaseExceptionGroup("trainer generation publication failed", failures) + + def close(self) -> None: + self._transport_pool.shutdown(wait=True) + self._durability_pool.shutdown(wait=True) + if self._transport_sender is not None: + self._transport_sender.close() + self._transport_sender = None + with self._lock: + in_flight = self._in_flight + if in_flight: + raise RuntimeError(f"publication close retained {in_flight} snapshots") + self.raise_if_failed() diff --git a/src/art/megatron/runtime/jobs.py b/src/art/megatron/runtime/jobs.py deleted file mode 100644 index 4285c88bc..000000000 --- a/src/art/megatron/runtime/jobs.py +++ /dev/null @@ -1,105 +0,0 @@ -from typing import Annotated, Any, Literal, TypeAlias - -from pydantic import BaseModel, Field, TypeAdapter - -from ... import types -from ...preprocessing.pack import DiskPackedTensors - -DEFAULT_TRAINING_LOG_PATH = "/tmp/megatron_training_log.jsonl" -DEFAULT_JOBS_DIR = "/tmp/megatron_training_jobs" -DEFAULT_VLLM_WAKE_LOCK_PATH = "/tmp/megatron_vllm_waking" -LORA_READY_EVENT = "lora_ready" -OPTIMIZER_READY_EVENT = "optimizer_ready" - - -class MergedWeightTransferInitInfo(BaseModel): - master_address: str - master_port: int - rank_offset: int - world_size: int - - -class MergedWeightTransferSpec(BaseModel): - init_info: MergedWeightTransferInitInfo - vllm_base_url: str - served_model_name: str - api_key: str | None = None - nccl_so_path: str | None = None - - -class _MegatronTrainingJobBase(BaseModel): - step: int = Field(default=0, ge=0) - source_policy_step: int = Field(ge=0) - training_session_id: str - lora_path: str - allow_unvalidated_arch: bool = False - optimizer_state_path: str - disk_packed_tensors: DiskPackedTensors - config: types.TrainConfig - experimental_config: dict[str, Any] - moe_routing_replay_path: str | None = None - moe_routing_replay_strict: bool = True - log_path: str = DEFAULT_TRAINING_LOG_PATH - - -class MegatronTrainingJob(_MegatronTrainingJobBase): - kind: Literal["train_lora"] = "train_lora" - - -class MegatronMergedTrainingJob(_MegatronTrainingJobBase): - kind: Literal["train_merged"] = "train_merged" - merged_weight_transfer: MergedWeightTransferSpec - - -class MegatronSyncJob(BaseModel): - kind: Literal["sync"] = "sync" - lora_path: str - allow_unvalidated_arch: bool = False - merged_weight_transfer: MergedWeightTransferSpec - log_path: str = DEFAULT_TRAINING_LOG_PATH - - -class MegatronOptimizerSaveJob(BaseModel): - kind: Literal["save_optimizer"] = "save_optimizer" - step: int = Field(ge=0) - training_session_id: str - optimizer_state_path: str - log_path: str = DEFAULT_TRAINING_LOG_PATH - - -class MegatronSFTTrainingJob(BaseModel): - kind: Literal["sft"] = "sft" - step: int = Field(ge=0) - source_policy_step: int = Field(ge=0) - training_session_id: str - lora_path: str - allow_unvalidated_arch: bool = False - optimizer_state_path: str - sft_data_dir: str - num_batches: int - learning_rates: list[float] - grad_accumulation_sequences: int | None = None - weight_decay: float = 0.0 - max_grad_norm: float = 1.0 - internal_checkpoint_interval: int | None = Field(default=None, ge=1) - log_path: str = DEFAULT_TRAINING_LOG_PATH - - -MegatronJob: TypeAlias = Annotated[ - MegatronTrainingJob - | MegatronMergedTrainingJob - | MegatronSyncJob - | MegatronOptimizerSaveJob - | MegatronSFTTrainingJob, - Field(discriminator="kind"), -] - -_MEGATRON_JOB_ADAPTER = TypeAdapter(MegatronJob) - - -def dump_megatron_job(job: MegatronJob) -> str: - return _MEGATRON_JOB_ADAPTER.dump_json(job).decode() - - -def load_megatron_job(raw: str | bytes) -> MegatronJob: - return _MEGATRON_JOB_ADAPTER.validate_json(raw) diff --git a/src/art/megatron/runtime/local.py b/src/art/megatron/runtime/local.py new file mode 100644 index 000000000..eaf53bba4 --- /dev/null +++ b/src/art/megatron/runtime/local.py @@ -0,0 +1,290 @@ +from __future__ import annotations + +import hashlib +import json +import os +import re +import socket +import threading + +from art import dev +from art.distributed.specs import ( + CUDA_DEVICE_UUID_PATTERN, + ClusterSpec, + EndpointSpec, + GpuPlacement, + HostSpec, + ModelServiceMemberSpec, + ModelServiceSpec, + RuntimeTopology, + TrainerMeshSpec, + VllmParallelSpec, +) + +from ..runtime_config import get_megatron_runtime_config + +LocalServicePorts = tuple[int, int] + + +def _bind_loopback_port(port: int = 0) -> socket.socket: + reservation = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + try: + reservation.bind(("127.0.0.1", port)) + except BaseException: + reservation.close() + raise + return reservation + + +class LocalEndpointAllocator: + """Owns unique API and rendezvous ports for backend-local runtimes.""" + + _lock = threading.Lock() + _reserved: set[int] = set() + + def __init__(self) -> None: + self._owned: set[int] = set() + + def reserve(self) -> LocalServicePorts: + with self._lock: + sockets: list[socket.socket] = [] + try: + while len(sockets) < 2: + reservation = _bind_loopback_port() + if reservation.getsockname()[1] in self._reserved: + reservation.close() + continue + sockets.append(reservation) + ports = tuple(reservation.getsockname()[1] for reservation in sockets) + assert len(ports) == 2 + self._reserved.update(ports) + self._owned.update(ports) + return ports + finally: + for reservation in sockets: + reservation.close() + + def replace_api_port( + self, ports: LocalServicePorts, api_port: int + ) -> LocalServicePorts: + with self._lock: + if ports[0] == api_port: + return ports + if not 1 <= api_port <= 65535: + raise ValueError("OpenAI server port must be between 1 and 65535") + if not set(ports) <= self._owned: + raise RuntimeError("local service endpoint ownership was lost") + if api_port in self._reserved: + raise ValueError(f"local service port {api_port} is already reserved") + api = _bind_loopback_port(api_port) + try: + configured = (api_port, ports[1]) + self._reserved.difference_update(ports) + self._reserved.update(configured) + self._owned.difference_update(ports) + self._owned.update(configured) + return configured + finally: + api.close() + + def release(self, ports: LocalServicePorts) -> None: + with self._lock: + if not set(ports) <= self._owned: + raise RuntimeError("local service endpoint ownership was lost") + self._reserved.difference_update(ports) + self._owned.difference_update(ports) + + +def _host_gpu_ids( + gpu_ids: tuple[int, ...], *, visible_gpu_count: int +) -> tuple[int | str, ...]: + raw_visible = os.environ.get("CUDA_VISIBLE_DEVICES") + if raw_visible is None: + return gpu_ids + visible = tuple(part.strip() for part in raw_visible.split(",") if part.strip()) + if len(visible) != visible_gpu_count or len( + {value.casefold() for value in visible} + ) != len(visible): + raise RuntimeError( + "local Monarch requires unique CUDA_VISIBLE_DEVICES matching the " + f"visible CUDA count, got {raw_visible!r} for {visible_gpu_count} GPUs" + ) + if any( + not (value.isdecimal() or re.fullmatch(CUDA_DEVICE_UUID_PATTERN, value)) + for value in visible + ): + raise RuntimeError( + "CUDA_VISIBLE_DEVICES must contain only numeric, full GPU UUID, or MIG " + "tokens" + ) + invalid = [gpu_id for gpu_id in gpu_ids if gpu_id < 0 or gpu_id >= len(visible)] + if invalid: + raise ValueError( + f"GPU ids {invalid} exceed the controller's visible CUDA devices" + ) + return tuple( + int(visible[gpu_id]) if visible[gpu_id].isdecimal() else visible[gpu_id] + for gpu_id in gpu_ids + ) + + +def with_local_serving_port( + topology: RuntimeTopology, + *, + model_name: str, + port: int, + rendezvous_port: int | None = None, +) -> RuntimeTopology: + services = tuple( + service for service in topology.model_services if service.name == model_name + ) + if len(services) != 1: + raise ValueError(f"runtime topology has no unique service {model_name!r}") + service = services[0] + endpoint = EndpointSpec(host=service.leader_endpoint.host, port=port) + if endpoint == service.leader_endpoint and rendezvous_port is None: + return topology + if ( + len(topology.cluster.hosts) != 1 + or len(service.members) != 1 + or not service.leader_endpoint.is_loopback + ): + raise ValueError("OpenAI server port conflicts with the compiled topology") + rendezvous = service.rendezvous + if rendezvous_port is not None: + rendezvous = EndpointSpec(host=rendezvous.host, port=rendezvous_port) + elif endpoint.port == rendezvous.port: + reservation = _bind_loopback_port() + try: + rendezvous = EndpointSpec( + host=rendezvous.host, port=reservation.getsockname()[1] + ) + finally: + reservation.close() + configured = service.model_copy( + update={"leader_endpoint": endpoint, "rendezvous": rendezvous} + ) + return RuntimeTopology( + cluster=topology.cluster, + rollout_host_ids=topology.rollout_host_ids, + trainer=topology.trainer, + model_services=tuple( + configured if value is service else value + for value in topology.model_services + ), + ) + + +def compile_local_runtime_topology( + config: dev.BackendModelConfig, + *, + model_name: str, + base_model: str, + artifact_root: str, + visible_gpu_count: int, + service_ports: LocalServicePorts | None = None, +) -> RuntimeTopology: + if visible_gpu_count < 1: + raise RuntimeError("MegatronBackend requires at least one visible CUDA GPU") + trainer_gpu_ids = _host_gpu_ids( + tuple(map(int, config.get("trainer_gpu_ids", range(visible_gpu_count)))), + visible_gpu_count=visible_gpu_count, + ) + if not trainer_gpu_ids: + raise ValueError("Megatron trainer GPU placement must not be empty") + from art.dev.validate import is_dedicated_mode, is_external_vllm_mode + + engine = config.get("engine_args", {}) + parallel = VllmParallelSpec( + tp=int(engine.get("tensor_parallel_size", 1)), + pp=int(engine.get("pipeline_parallel_size", 1)), + dp=int(engine.get("data_parallel_size", 1)), + enable_expert_parallel=bool(engine.get("enable_expert_parallel", False)), + ) + dedicated = is_dedicated_mode(config) + external = is_external_vllm_mode(config) + inference_gpu_ids = () + if not external: + inference_gpu_ids = _host_gpu_ids( + tuple(map(int, config.get("inference_gpu_ids", ()))), + visible_gpu_count=visible_gpu_count, + ) + candidates = inference_gpu_ids if dedicated else trainer_gpu_ids + if len(candidates) < parallel.world_size: + raise ValueError("vLLM parallelism exceeds local inference GPU placement") + inference_gpu_ids = candidates[: parallel.world_size] + available_gpu_ids = tuple(dict.fromkeys((*trainer_gpu_ids, *inference_gpu_ids))) + host_id = "local" + init_args = config.get("init_args", {}) + provider_model = str(init_args.get("model_name", base_model)) + configured_revision = init_args.get("revision") + revision = str(configured_revision) if configured_revision is not None else None + model_services = () + if not external: + if service_ports is None: + reservations = (_bind_loopback_port(), _bind_loopback_port()) + try: + service_ports = tuple( + reservation.getsockname()[1] for reservation in reservations + ) + finally: + for reservation in reservations: + reservation.close() + api_port, rendezvous_port = service_ports + if api_port == rendezvous_port: + raise ValueError("local API and rendezvous ports must differ") + fingerprint = hashlib.sha256( + json.dumps( + { + "base_model": provider_model, + "parallel": parallel.model_dump(mode="json"), + "revision": revision or "", + }, + sort_keys=True, + ).encode() + ).hexdigest() + model_services = ( + ModelServiceSpec( + name=model_name, + members=( + ModelServiceMemberSpec( + member_id=host_id, + host_id=host_id, + node_rank=0, + gpu_ids=inference_gpu_ids, + ), + ), + leader_endpoint=EndpointSpec(host="127.0.0.1", port=api_port), + rendezvous=EndpointSpec(host="127.0.0.1", port=rendezvous_port), + base_model=provider_model, + model_revision=revision, + runtime_fingerprint=fingerprint, + parallel=parallel, + update_mode=config.get("rollout_weights_mode", "lora"), + temporal_gpu_sharing=not dedicated, + ), + ) + return RuntimeTopology( + cluster=ClusterSpec( + hosts=( + HostSpec( + host_id=host_id, + node_rank=0, + worker_address="tcp://127.0.0.1:0", + cpu_slots=max(1, os.cpu_count() or 1), + gpu_ids=available_gpu_ids, + ), + ), + controller_host_id=host_id, + artifact_root=artifact_root, + ), + rollout_host_ids=(), + trainer=TrainerMeshSpec( + ranks=tuple( + GpuPlacement(host_id=host_id, gpu_id=gpu_id) + for gpu_id in trainer_gpu_ids + ), + topology=get_megatron_runtime_config().topology, + ), + model_services=model_services, + ) diff --git a/src/art/megatron/runtime/monarch.py b/src/art/megatron/runtime/monarch.py new file mode 100644 index 000000000..eb6278558 --- /dev/null +++ b/src/art/megatron/runtime/monarch.py @@ -0,0 +1,1296 @@ +from __future__ import annotations + +import asyncio +from collections.abc import AsyncIterator, Awaitable +import hashlib +import json +import os +import socket +from threading import Event, Lock +import time +import traceback +from typing import Any, Callable + +import monarch.actor as monarch_actor +from monarch.actor import Actor, Channel, MeshFailure, Port, ProcMesh, endpoint +from monarch.spmd import SPMDActor +from pydantic import BaseModel, ConfigDict + +from art.distributed.data_plane import PackedBatchLeaseSet +from art.distributed.monarch_bootstrap import activate_cuda_device +from art.distributed.specs import GpuId +from art.utils.cache_dirs import configure_model_cache_env +from art.utils.lifecycle import cleanup_after_failure + +from .data_plane import InMemoryPackedBatch, SFTBatchData +from .publication import ( + TRAINER_PUBLICATION_EVENT_ADAPTER, + TrainerPublicationEvent, + TrainerPublicationFailed, + TrainerPublicationSucceeded, + TrainerRankPublication, +) +from .specs import ( + TRAIN_EVENT_ADAPTER, + AdapterReady, + HybridEpRuntimeSpec, + SFTJobSpec, + TrainAccepted, + TrainCancelled, + TrainCompleted, + TrainerGeneration, + TrainerJobSpec, + TrainerRuntimeSpec, + TrainEvent, + TrainFailed, + TrainingRunSpec, + TrainJobSpec, + TrainProgress, +) +from .weight_transfer import MergedWeightTransferSpec + + +class _ActorEventSink: + def __init__(self, port: Port[dict[str, Any]], *, coordinator: bool) -> None: + self._port = port + self._coordinator = coordinator + + def progress( + self, *, step_index: int, num_steps: int, metrics: dict[str, float] + ) -> None: + if self._coordinator: + self._port.send( + { + "kind": "progress", + "step_index": step_index, + "num_steps": num_steps, + "metrics": metrics, + } + ) + + def adapter_ready(self, *, learner_version: int, adapter_path: str) -> None: + if self._coordinator: + self._port.send( + { + "kind": "adapter_ready", + "learner_version": learner_version, + "adapter_path": adapter_path, + } + ) + + def publication(self, event: TrainerPublicationEvent) -> None: + self._port.send(event.model_dump(mode="json")) + + +_SUPERVISION_LOCK = Lock() +_SUPERVISION_HANDLERS: dict[str, "MonarchTrainerSupervision"] = {} +_SUPERVISION_MESHES: dict[str, "MonarchTrainerSupervision"] = {} +_PREVIOUS_FAULT_HOOK: Callable[[MeshFailure], None] | None = None + + +def _configure_hybrid_ep_env( + spec: HybridEpRuntimeSpec, *, run_id: str | None = None +) -> None: + os.environ["NUM_OF_HYBRID_EP_RANKS_PER_NVLINK_DOMAIN"] = str( + spec.ranks_per_nvlink_domain + ) + transport = spec.nixl_transport + values = { + "HYBRID_EP_MULTINODE": "1" if transport else None, + "USE_NIXL": "1" if transport else None, + "DEEPEP_NIXL_RUN_ID": (run_id or spec.run_id) if transport else None, + "NIXL_ETCD_ENDPOINTS": transport.metadata_store.url if transport else None, + "NIXL_HOME": transport.nixl_home if transport else None, + "UCX_HOME": transport.ucx_home if transport else None, + "NIXL_PLUGIN_DIR": transport.nixl_plugin_dir if transport else None, + "UCX_MODULE_DIR": transport.ucx_module_dir if transport else None, + "UCX_NET_DEVICES": transport.ucx_net_devices if transport else None, + "UCX_TLS": transport.ucx_tls if transport else None, + "UCX_IB_GDA_RETAIN_INACTIVE_CTX": "yes" if transport else None, + "UCX_CUDA_COPY_ENABLE_FABRIC": ( + "yes" if transport and transport.enable_cuda_fabric else "no" + ) + if transport + else None, + } + for name, value in values.items(): + if value is None: + os.environ.pop(name, None) + else: + os.environ[name] = value + + +def _build_training_runtime(spec: TrainerRuntimeSpec, *, rank: int) -> Any: + import torch + + from art.megatron.train import build_training_runtime + + return build_training_runtime( + model_identifier=spec.model_identifier, + model_initialization=spec.model_initialization, + provider_torch_dtype={ + "bfloat16": torch.bfloat16, + "float16": torch.float16, + "float32": torch.float32, + }[spec.dtype], + print_env=rank == 0, + model_support_key=spec.model_support_key, + snapshot_pool_capacity=spec.snapshot_pool_capacity, + ) + + +class _TrainerRankReady(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + rank: int + host_id: str + gpu_id: GpuId + hostname: str + process_id: int + + +def _dispatch_trainer_fault(failure: MeshFailure) -> None: + message = str(failure) + with _SUPERVISION_LOCK: + owner = _SUPERVISION_MESHES.get(failure.mesh_name) + handlers = ( + (owner,) + if owner is not None + else tuple( + handler + for token, handler in _SUPERVISION_HANDLERS.items() + if token in message + ) + ) + previous = _PREVIOUS_FAULT_HOOK + if handlers: + for handler in handlers: + handler.notify(message) + return + if previous is not None: + previous(failure) + + +class MonarchTrainerSupervision: + """Route one owned trainer mesh failure without masking unrelated faults.""" + + def __init__(self, run_id: str) -> None: + self.run_id = run_id + self.token = hashlib.sha256(run_id.encode()).hexdigest()[:16] + self._loop = asyncio.get_running_loop() + self._failure: asyncio.Future[str] = self._loop.create_future() + self._mesh_names: set[str] = set() + self._closed = False + global _PREVIOUS_FAULT_HOOK + with _SUPERVISION_LOCK: + if self.token in _SUPERVISION_HANDLERS: + raise RuntimeError(f"trainer run {run_id!r} is already supervised") + if not _SUPERVISION_HANDLERS: + _PREVIOUS_FAULT_HOOK = monarch_actor.unhandled_fault_hook + setattr( + monarch_actor, + "unhandled_fault_hook", + _dispatch_trainer_fault, + ) + _SUPERVISION_HANDLERS[self.token] = self + + def own_mesh(self, mesh_name: str) -> None: + if not mesh_name: + raise ValueError("trainer mesh name must not be empty") + with _SUPERVISION_LOCK: + if self._closed: + raise RuntimeError(f"trainer run {self.run_id!r} is closed") + owner = _SUPERVISION_MESHES.get(mesh_name) + if owner is not None and owner is not self: + raise RuntimeError(f"Monarch mesh {mesh_name!r} already has an owner") + self._mesh_names.add(mesh_name) + _SUPERVISION_MESHES[mesh_name] = self + + def notify(self, failure: str) -> None: + def set_failure() -> None: + if not self._failure.done(): + self._failure.set_result(failure) + + self._loop.call_soon_threadsafe(set_failure) + + async def wait(self) -> str: + return await asyncio.shield(self._failure) + + def close(self) -> None: + global _PREVIOUS_FAULT_HOOK + with _SUPERVISION_LOCK: + if self._closed: + return + self._closed = True + if _SUPERVISION_HANDLERS.get(self.token) is self: + _SUPERVISION_HANDLERS.pop(self.token) + for mesh_name in self._mesh_names: + if _SUPERVISION_MESHES.get(mesh_name) is self: + _SUPERVISION_MESHES.pop(mesh_name) + if not _SUPERVISION_HANDLERS: + if monarch_actor.unhandled_fault_hook is _dispatch_trainer_fault: + assert _PREVIOUS_FAULT_HOOK is not None + setattr( + monarch_actor, + "unhandled_fault_hook", + _PREVIOUS_FAULT_HOOK, + ) + _PREVIOUS_FAULT_HOOK = None + + +class _TrainerSPMDActor(SPMDActor): + """Own the rendezvous store until the warm trainer mesh is stopped.""" + + def __init__(self) -> None: + super().__init__() + self._store: Any = None + + @endpoint + def start_store(self, _request: None) -> tuple[str, int]: + if self._store is not None: + raise RuntimeError("trainer rendezvous store is already running") + from torch.distributed import TCPStore + + hostname = socket.gethostname() + self._store = TCPStore( + hostname, + 0, + self.world_size, + True, + wait_for_workers=False, + ) + return hostname, int(self._store.port) + + @endpoint + def setup_agent_store_env(self, master_addr: str, master_port: int) -> None: + self._setup_env(master_addr, master_port) + os.environ["TORCHELASTIC_USE_AGENT_STORE"] = "True" + + def __cleanup__(self, exc: Exception | None) -> None: + del exc + self._store = None + + +class MonarchTrainerActor(Actor): + """One warm Megatron rank, spawned once on every trainer ProcMesh process.""" + + def __init__( + self, + runtime_spec_json: str, + trainer_generation: str, + ) -> None: + runtime_spec = TrainerRuntimeSpec.model_validate_json(runtime_spec_json) + topology = runtime_spec.trainer_mesh.topology + configure_model_cache_env(cache_root=runtime_spec.cache_root) + os.environ.update( + { + "MODEL_IDENTIFIER": runtime_spec.model_identifier, + "ART_MEGATRON_TENSOR_MODEL_PARALLEL_SIZE": str(topology.tp), + "ART_MEGATRON_CONTEXT_PARALLEL_SIZE": str(topology.cp), + "ART_MEGATRON_EXPERT_MODEL_PARALLEL_SIZE": str(topology.ep), + "ART_MEGATRON_PIPELINE_MODEL_PARALLEL_SIZE": str(topology.pp), + "ART_MEGATRON_EXPERT_TENSOR_PARALLEL_SIZE": str(topology.etp), + "ART_MEGATRON_LORA_RANK": str(runtime_spec.lora_rank), + "ART_MEGATRON_LORA_TARGET_MODULES": json.dumps( + runtime_spec.lora_target_modules + ), + "ART_DISABLE_MEGATRON_COMPILE": ( + "0" if runtime_spec.compile_enabled else "1" + ), + "ART_MEGATRON_ALLOW_UNVALIDATED_ARCH": str( + int(runtime_spec.allow_unvalidated_arch) + ), + "ART_MEGATRON_ENABLE_MOE_ROUTING_REPLAY": str( + int(runtime_spec.enable_moe_routing_replay) + ), + "ART_MEGATRON_STREAMING_WEIGHT_OFFLOAD": str( + int(runtime_spec.streaming_weight_offload) + ), + "ART_MEGATRON_OFFLOAD_BETWEEN_JOBS": str( + int(runtime_spec.offload_between_jobs) + ), + } + ) + if runtime_spec.random_state is not None: + os.environ["ART_MEGATRON_RANDOM_STATE"] = str(runtime_spec.random_state) + if topology.vpp is not None: + os.environ["ART_MEGATRON_VIRTUAL_PIPELINE_MODEL_PARALLEL_SIZE"] = str( + topology.vpp + ) + if topology.vpp_microbatch_group_size is not None: + os.environ["ART_MEGATRON_VPP_MICROBATCH_GROUP_SIZE"] = str( + topology.vpp_microbatch_group_size + ) + world_size = int(os.environ["WORLD_SIZE"]) + if world_size != len(runtime_spec.trainer_mesh.ranks): + raise RuntimeError( + "Monarch ProcMesh world does not match TrainerRuntimeSpec: " + f"{world_size} != {len(runtime_spec.trainer_mesh.ranks)}" + ) + + rank = int(os.environ["RANK"]) + placement = runtime_spec.trainer_mesh.ranks[rank] + self._host_id = placement.host_id + self._gpu_id = placement.gpu_id + local_rank = activate_cuda_device(placement.gpu_id) + os.environ["LOCAL_RANK"] = str(local_rank) + + import torch + + torch.set_num_threads(int(os.environ["OMP_NUM_THREADS"])) + torch.cuda.set_device(local_rank) + if topology.ep > 1: + from art.megatron.hybrid_ep_setup import validate_hybrid_ep + + hybrid_ep = runtime_spec.hybrid_ep + if hybrid_ep is None: + raise RuntimeError( + "expert parallelism requires a HybridEP runtime spec" + ) + group_index = rank // (topology.etp * topology.ep) + _configure_hybrid_ep_env( + hybrid_ep, + run_id=f"{hybrid_ep.run_id}-{trainer_generation}-g{group_index}", + ) + validate_hybrid_ep(require_multinode=hybrid_ep.multinode) + self._runtime = _build_training_runtime(runtime_spec, rank=rank) + if self._runtime.model_support_handler.key != runtime_spec.handler_name: + raise RuntimeError( + "resolved model-support handler does not match TrainerRuntimeSpec: " + f"{self._runtime.model_support_handler.key!r} != " + f"{runtime_spec.handler_name!r}" + ) + from art.megatron.training.streaming_weight_offload import ( + streaming_weight_offload_config_from_env, + ) + from art.megatron.training.weight_offload import WeightOffloadManager + + from .executor import MegatronTrainJobExecutor + + self._executor = MegatronTrainJobExecutor(self._runtime) + self._weight_offload = WeightOffloadManager.from_config( + model=self._runtime.model, + rank=self._runtime.rank, + compile_enabled=self._runtime.transformer_layers_compiled, + offload_between_jobs=runtime_spec.offload_between_jobs, + streaming_config=streaming_weight_offload_config_from_env(), + ) + self._weight_offload.install() + self._valid = True + + @endpoint + def ready(self) -> dict[str, Any]: + return _TrainerRankReady( + rank=self._runtime.rank, + host_id=self._host_id, + gpu_id=self._gpu_id, + hostname=socket.gethostname(), + process_id=os.getpid(), + ).model_dump(mode="json") + + @endpoint + def execute( + self, + job_json: str, + batch_json: str, + event_port: Port[dict[str, Any]], + ) -> dict[str, Any]: + batch = None + try: + if not self._valid: + raise RuntimeError("trainer actor runtime is invalid") + job = TrainJobSpec.model_validate_json(job_json) + leases = PackedBatchLeaseSet.model_validate_json(batch_json) + batch = InMemoryPackedBatch.open(job.batch, leases.host_refs[self._host_id]) + coordinator = self._runtime.rank == 0 + with self._weight_offload.job(): + metrics = self._executor.execute( + job, + batch, + _ActorEventSink(event_port, coordinator=coordinator), + Event(), + ) + if coordinator: + event_port.send({"kind": "actor_completed", "metrics": metrics}) + return { + "rank": self._runtime.rank, + "learner_version": job.learner_version, + "metrics": metrics if coordinator else {}, + } + except BaseException as error: + self._valid = False + event_port.send( + { + "kind": "rank_failed", + "rank": self._runtime.rank, + "error_type": type(error).__name__, + "message": str(error), + "traceback": traceback.format_exc(), + } + ) + raise + finally: + if batch is not None: + batch.close() + + @endpoint + def execute_sft( + self, + job_json: str, + batches: tuple[SFTBatchData, ...], + event_port: Port[dict[str, Any]], + ) -> dict[str, Any]: + try: + if not self._valid: + raise RuntimeError("trainer actor runtime is invalid") + job = SFTJobSpec.model_validate_json(job_json) + coordinator = self._runtime.rank == 0 + with self._weight_offload.job(): + metrics = self._executor.execute_sft( + job, + batches, + _ActorEventSink(event_port, coordinator=coordinator), + Event(), + ) + if coordinator: + event_port.send({"kind": "actor_completed", "metrics": metrics}) + return { + "rank": self._runtime.rank, + "learner_version": job.learner_version, + "metrics": metrics if coordinator else {}, + } + except BaseException as error: + self._valid = False + event_port.send( + { + "kind": "rank_failed", + "rank": self._runtime.rank, + "error_type": type(error).__name__, + "message": str(error), + "traceback": traceback.format_exc(), + } + ) + raise + + @endpoint + def sync_merged(self, generation_json: str, transfer_json: str) -> dict[str, Any]: + if not self._valid: + raise RuntimeError("trainer actor runtime is invalid") + generation = TrainerGeneration.model_validate_json(generation_json) + transfer = MergedWeightTransferSpec.model_validate_json(transfer_json) + try: + with self._weight_offload.job(): + metrics = self._executor.sync_merged_source(generation, transfer) + return { + "rank": self._runtime.rank, + "learner_version": generation.policy_step, + "metrics": metrics, + } + except BaseException: + self._valid = False + raise + + @endpoint + def close(self) -> None: + self._executor.close() + import torch + + if torch.distributed.is_initialized(): + torch.distributed.destroy_process_group() + + @endpoint + def advance_without_training( + self, + training_session_id: str, + expected_learner_version: int, + learner_version: int, + optimizer_state_path: str, + adapter_json: str | None, + ) -> dict[str, Any]: + if not self._valid: + raise RuntimeError("trainer actor runtime is invalid") + from art.megatron.optimizer_state import OptimizerAdapter + + adapter = ( + None + if adapter_json is None + else OptimizerAdapter.model_validate_json(adapter_json) + ) + try: + with self._weight_offload.job(): + metrics = self._executor.advance_without_training( + training_session_id=training_session_id, + expected_learner_version=expected_learner_version, + learner_version=learner_version, + optimizer_state_path=optimizer_state_path, + adapter=adapter, + ) + return { + "rank": self._runtime.rank, + "learner_version": learner_version, + "metrics": metrics, + } + except BaseException: + self._valid = False + raise + + def __cleanup__(self, exc: Exception | None) -> None: + if exc is not None: + self._valid = False + self._executor.close() + + +async def spawn_monarch_trainer_actors( + proc_mesh: ProcMesh, + runtime_spec: TrainerRuntimeSpec, + supervision: MonarchTrainerSupervision, +) -> tuple[Any, tuple[_TrainerRankReady, ...]]: + """Configure torch-elastic first, then initialize exactly one actor per rank.""" + spmd: Any = proc_mesh.spawn( + f"art_torch_elastic_{supervision.token}", _TrainerSPMDActor + ) + supervision.own_mesh(await spmd._name) + first_rank = dict.fromkeys(proc_mesh._labels, 0) + master_addr, master_port = await spmd.slice(**first_rank).start_store.call_one(None) + await spmd.setup_agent_store_env.call(master_addr, master_port) + actors: Any = proc_mesh.spawn( + f"art_megatron_trainer_{supervision.token}", + MonarchTrainerActor, + runtime_spec.model_dump_json(), + supervision.token, + ) + supervision.own_mesh(await actors._name) + await actors.initialized + values = await actors.ready.call() + ready = tuple( + sorted( + (_TrainerRankReady.model_validate(value) for value in values.values()), + key=lambda value: value.rank, + ) + ) + placements = runtime_spec.trainer_mesh.ranks + if len(ready) != len(placements) or any( + (value.rank, value.host_id, value.gpu_id) + != (rank, placement.host_id, placement.gpu_id) + for rank, (value, placement) in enumerate(zip(ready, placements, strict=True)) + ): + raise RuntimeError( + "trainer startup did not return the configured rank placement" + ) + return actors, ready + + +class _PublicationState: + __slots__ = ( + "active_waiters", + "drain_done", + "future", + "generation_id", + "late_waitable", + "outcome_observed", + "records", + "train_done", + ) + + def __init__( + self, + generation_id: str, + future: asyncio.Future[tuple[TrainerRankPublication, ...]], + ) -> None: + self.generation_id = generation_id + self.future = future + self.records: dict[int, TrainerRankPublication] = {} + self.train_done = False + self.drain_done = True + self.active_waiters = 0 + self.late_waitable = True + self.outcome_observed = False + + +class MonarchTrainerRun: + def __init__( + self, + runtime_spec: TrainerRuntimeSpec, + run_spec: TrainingRunSpec, + actors: Any, + proc_mesh: ProcMesh, + supervision: MonarchTrainerSupervision, + rank_processes: tuple[_TrainerRankReady, ...], + ) -> None: + if run_spec.runtime_fingerprint != runtime_spec.fingerprint: + raise ValueError( + "training run does not match the trainer runtime fingerprint" + ) + self.runtime_spec = runtime_spec + self.run_spec = run_spec + self._actors = actors + self._proc_mesh = proc_mesh + self._supervision = supervision + self._rank_processes = rank_processes + self._learner_version = run_spec.initial_learner_version + self._jobs: dict[str, tuple[str, tuple[TrainEvent, ...]]] = {} + self._lock = asyncio.Lock() + self._active_job_id: str | None = None + self._active_collective: asyncio.Future[Any] | None = None + self._active_receive: asyncio.Future[Any] | None = None + self._publications: dict[str, _PublicationState] = {} + self._publication_drains: set[asyncio.Task[None]] = set() + self._stop_task: asyncio.Task[None] | None = None + self._close_task: asyncio.Task[None] | None = None + self._closed = False + self._valid = True + + @property + def learner_version(self) -> int: + return self._learner_version + + @property + def valid(self) -> bool: + return self._valid + + async def train( + self, job: TrainJobSpec, batch: PackedBatchLeaseSet + ) -> AsyncIterator[TrainEvent]: + async for event in self._train( + job, + lambda port: self._actors.execute.call( + job.model_dump_json(), batch.model_dump_json(), port + ), + lambda: self._validate_rl(job, batch), + ): + yield event + + async def train_sft( + self, job: SFTJobSpec, batches: tuple[SFTBatchData, ...] + ) -> AsyncIterator[TrainEvent]: + async for event in self._train( + job, + lambda port: self._actors.execute_sft.call( + job.model_dump_json(), batches, port + ), + lambda: self._validate_sft(job, batches), + ): + yield event + + async def _train( + self, + job: TrainerJobSpec, + start: Callable[[Port[dict[str, Any]]], Awaitable[Any]], + validate: Callable[[], BaseException | None], + ) -> AsyncIterator[TrainEvent]: + cached = self._jobs.get(job.job_id) + if cached is not None and cached[0] == job.fingerprint: + for event in cached[1]: + yield event + return + + async with self._lock: + cached = self._jobs.get(job.job_id) + if cached is not None: + if cached[0] == job.fingerprint: + for event in cached[1]: + yield event + return + yield TrainAccepted( + job_id=job.job_id, + run_id=job.run_id, + sequence=0, + expected_learner_version=job.expected_learner_version, + ) + yield self._failed( + job, + 1, + RuntimeError("job_id was already used with a different job"), + False, + ) + return + events: list[TrainEvent] = [] + + def emit(event: TrainEvent) -> TrainEvent: + events.append(event) + return event + + yield emit( + TrainAccepted( + job_id=job.job_id, + run_id=job.run_id, + sequence=0, + expected_learner_version=job.expected_learner_version, + ) + ) + error = validate() + if error is not None: + yield emit(self._failed(job, len(events), error, not self._valid)) + return + + publication = asyncio.get_running_loop().create_future() + publication.add_done_callback(_consume_future) + generation_id = job.output_generation_id + if generation_id in self._publications: + raise RuntimeError( + f"publication generation already exists: {generation_id}" + ) + self._expire_prior_publications() + publication_state = _PublicationState(generation_id, publication) + self._publications[generation_id] = publication_state + supervision: asyncio.Task[str] | None = None + try: + send_port, receiver = Channel[dict[str, Any]].open() + dispatch_started = time.perf_counter() + final_progress_received: float | None = None + collective = asyncio.ensure_future(start(send_port)) + receive = asyncio.ensure_future(receiver.recv()) + supervision = asyncio.create_task(self._supervision.wait()) + self._active_job_id = job.job_id + self._active_collective = collective + self._active_receive = receive + while True: + waiters = {receive, supervision} + if not collective.done(): + waiters.add(collective) + event_timeout_s = ( + self.run_spec.initial_event_timeout_s + if len(events) == 1 + and self.run_spec.initial_event_timeout_s is not None + else self.run_spec.event_timeout_s + ) + done, _ = await asyncio.wait( + waiters, + timeout=event_timeout_s, + return_when=asyncio.FIRST_COMPLETED, + ) + if not done: + raise TimeoutError( + f"trainer ranks produced no event for {event_timeout_s:g}s" + ) + if supervision in done: + raise RuntimeError( + "trainer mesh failed: " + supervision.result() + ) + if collective in done: + await collective + if receive not in done: + continue + payload = receive.result() + if payload["kind"] in { + "publication_succeeded", + "publication_failed", + }: + self._record_publication(payload) + receive = asyncio.ensure_future(receiver.recv()) + self._active_receive = receive + continue + if payload["kind"] == "rank_failed": + raise RuntimeError( + f"trainer rank {payload['rank']} failed: " + f"{payload['error_type']}: {payload['message']}\n" + f"{payload['traceback']}" + ) + if payload["kind"] == "progress": + if payload["step_index"] + 1 == payload["num_steps"]: + final_progress_received = time.perf_counter() + event = TrainProgress( + job_id=job.job_id, + run_id=job.run_id, + sequence=len(events), + step_index=payload["step_index"], + num_steps=payload["num_steps"], + metrics=payload["metrics"], + ) + elif payload["kind"] == "adapter_ready": + event = AdapterReady( + job_id=job.job_id, + run_id=job.run_id, + sequence=len(events), + learner_version=payload["learner_version"], + adapter_path=payload["adapter_path"], + ) + elif payload["kind"] == "actor_completed": + actor_completed_received = time.perf_counter() + values = await collective + collective_completed = time.perf_counter() + results = list(values.values()) + versions = {result["learner_version"] for result in results} + ranks = {result["rank"] for result in results} + expected_ranks = set( + range(len(self.runtime_spec.trainer_mesh.ranks)) + ) + if versions != {job.learner_version} or ranks != expected_ranks: + raise RuntimeError( + "trainer ranks did not agree on job completion" + ) + metrics = dict(payload["metrics"]) + if final_progress_received is not None: + metrics.update( + { + "time/step_monarch_dispatch_to_progress_s": ( + final_progress_received - dispatch_started + ), + "time/step_monarch_progress_to_completed_s": ( + actor_completed_received + - final_progress_received + ), + } + ) + metrics["time/step_monarch_collective_tail_s"] = ( + collective_completed - actor_completed_received + ) + completed = TrainCompleted( + job_id=job.job_id, + run_id=job.run_id, + sequence=len(events), + learner_version=job.learner_version, + metrics=metrics, + ) + if not publication.done(): + publication_state.drain_done = False + drain = asyncio.create_task( + self._drain_publication(receiver, publication_state) + ) + self._publication_drains.add(drain) + drain.add_done_callback(self._publication_drains.discard) + drain.add_done_callback(_consume_future) + yield completed + self._learner_version = job.learner_version + emit(completed) + self._clear_active(job.job_id) + break + else: + raise RuntimeError( + f"trainer rank sent unknown event {payload['kind']!r}" + ) + yield emit(TRAIN_EVENT_ADAPTER.validate_python(event)) + receive = asyncio.ensure_future(receiver.recv()) + self._active_receive = receive + except BaseException as exc: + if not publication.done(): + publication.set_exception(exc) + publication_state.records.clear() + closed_by_caller = self._closed + self._valid = False + self._closed = True + self._cancel_active() + await cleanup_after_failure( + exc, + self._force_stop, + message="training and forced trainer ProcMesh cleanup failed", + ) + caller_cancelled = isinstance(exc, GeneratorExit) or ( + isinstance(exc, asyncio.CancelledError) + and _current_task_is_cancelling() + ) + if caller_cancelled or ( + isinstance(exc, asyncio.CancelledError) and closed_by_caller + ): + cancelled = TrainCancelled( + job_id=job.job_id, + run_id=job.run_id, + sequence=len(events), + reason="train stream was cancelled", + ) + events.append(cancelled) + if caller_cancelled: + raise + yield cancelled + return + failure = self._failed(job, len(events), exc, True) + events.append(failure) + yield failure + finally: + if supervision is not None: + supervision.cancel() + supervision.add_done_callback(_consume_future) + self._clear_active(job.job_id) + # Older jobs cannot be retried after the sequential learner advances. + self._jobs = {job.job_id: (job.fingerprint, tuple(events))} + publication_state.train_done = True + self._retire_publication(publication_state) + + def wait_for_publication( + self, generation_id: str + ) -> Awaitable[tuple[TrainerRankPublication, ...]]: + state = self._publications.get(generation_id) + if state is None: + raise RuntimeError(f"trainer has no publication {generation_id}") + if not state.late_waitable: + raise RuntimeError( + f"trainer publication {generation_id} is no longer waitable" + ) + # Reserve before returning control; the next train may expire late waiters + # without yielding to the task which awaits this publication. + state.active_waiters += 1 + return self._await_publication(state) + + async def _await_publication( + self, state: "_PublicationState" + ) -> tuple[TrainerRankPublication, ...]: + observed = False + try: + result = await asyncio.shield(state.future) + observed = True + return result + except asyncio.CancelledError: + observed = state.future.cancelled() + raise + except BaseException: + observed = True + raise + finally: + state.active_waiters -= 1 + state.outcome_observed |= observed + self._retire_publication(state) + + def _record_publication(self, payload: dict[str, Any]) -> None: + event = TRAINER_PUBLICATION_EVENT_ADAPTER.validate_python(payload) + generation_id = ( + event.record.generation.generation_id + if isinstance(event, TrainerPublicationSucceeded) + else event.generation_id + ) + state = self._publications.get(generation_id) + if state is None: + raise RuntimeError( + f"trainer rank reported unknown publication {generation_id}" + ) + future = state.future + if future.done(): + if not future.cancelled() and future.exception() is not None: + return + raise RuntimeError( + f"trainer publication {generation_id} is already terminal" + ) + if isinstance(event, TrainerPublicationFailed): + future.set_exception( + RuntimeError( + f"trainer rank {event.rank} publication failed " + f"({event.error_type}): {event.message}" + ) + ) + state.records.clear() + self._retire_publication(state) + return + record = event.record + world_size = len(self.runtime_spec.trainer_mesh.ranks) + if record.rank >= world_size: + raise RuntimeError(f"publication reported invalid rank {record.rank}") + records = state.records + if record.rank in records: + raise RuntimeError( + f"trainer rank {record.rank} published {generation_id} twice" + ) + records[record.rank] = record + if len(records) == world_size: + future.set_result(tuple(records[rank] for rank in range(world_size))) + records.clear() + self._retire_publication(state) + + async def _drain_publication( + self, receiver: Any, state: "_PublicationState" + ) -> None: + publication = state.future + supervision = asyncio.create_task(self._supervision.wait()) + receive: asyncio.Future[Any] | None = None + try: + while not publication.done(): + receive = asyncio.ensure_future(receiver.recv()) + done, _ = await asyncio.wait( + {receive, supervision}, + timeout=self.run_spec.shutdown_timeout_s, + return_when=asyncio.FIRST_COMPLETED, + ) + if not done: + raise TimeoutError( + f"trainer ranks produced no publication event for " + f"{self.run_spec.shutdown_timeout_s:g}s" + ) + if supervision in done: + raise RuntimeError("trainer mesh failed: " + supervision.result()) + payload = receive.result() + if payload["kind"] == "rank_failed": + raise RuntimeError( + f"trainer rank {payload['rank']} failed after training: " + f"{payload['error_type']}: {payload['message']}" + ) + self._record_publication(payload) + except BaseException as exc: + if not publication.done(): + publication.set_exception(exc) + state.records.clear() + raise + finally: + supervision.cancel() + supervision.add_done_callback(_consume_future) + if receive is not None and not receive.done(): + receive.cancel() + receive.add_done_callback(_consume_future) + state.drain_done = True + self._retire_publication(state) + + def _expire_prior_publications(self) -> None: + for state in tuple(self._publications.values()): + state.late_waitable = False + self._retire_publication(state) + + def _retire_publication(self, state: "_PublicationState") -> None: + # A waiter can observe a terminal event before the train/drain producer exits. + if not ( + state.future.done() + and (state.outcome_observed or not state.late_waitable) + and state.active_waiters == 0 + and state.train_done + and state.drain_done + ): + return + if self._publications.get(state.generation_id) is state: + self._publications.pop(state.generation_id) + + async def advance_without_training( + self, + *, + expected_learner_version: int, + learner_version: int, + optimizer_state_path: str, + adapter: Any | None, + ) -> dict[str, float]: + async with self._lock: + if self._closed or not self._valid: + raise RuntimeError("trainer runtime is invalid") + if self._active_job_id is not None: + raise RuntimeError("trainer has an active job") + if expected_learner_version != self._learner_version: + raise ValueError( + "expected learner version mismatch: " + f"transition={expected_learner_version}, " + f"runtime={self._learner_version}" + ) + if learner_version != expected_learner_version + 1: + raise ValueError("a no-op transition must advance exactly one step") + try: + values = await asyncio.wait_for( + self._actors.advance_without_training.call( + self.run_spec.training_session_id, + expected_learner_version, + learner_version, + optimizer_state_path, + None if adapter is None else adapter.model_dump_json(), + ), + timeout=self.run_spec.event_timeout_s, + ) + results = list(values.values()) + if {result["rank"] for result in results} != set( + range(len(self.runtime_spec.trainer_mesh.ranks)) + ) or {result["learner_version"] for result in results} != { + learner_version + }: + raise RuntimeError("trainer ranks rejected no-op transition") + except BaseException as exc: + self._valid = False + self._closed = True + await cleanup_after_failure( + exc, + self._force_stop, + message="no-op transition and trainer cleanup failed", + ) + raise + self._learner_version = learner_version + return next(result["metrics"] for result in results if result["rank"] == 0) + + async def sync_merged( + self, + generation: TrainerGeneration, + transfer: MergedWeightTransferSpec, + ) -> dict[str, float]: + async with self._lock: + if self._closed or not self._valid: + raise RuntimeError("trainer runtime is invalid") + if self._active_job_id is not None: + raise RuntimeError("trainer has an active job") + if generation.policy_step != self._learner_version: + raise ValueError("merged source does not match the resident learner") + try: + values = await asyncio.wait_for( + self._actors.sync_merged.call( + generation.model_dump_json(), transfer.model_dump_json() + ), + timeout=self.run_spec.event_timeout_s, + ) + results = list(values.values()) + if {result["rank"] for result in results} != set( + range(len(self.runtime_spec.trainer_mesh.ranks)) + ) or {result["learner_version"] for result in results} != { + generation.policy_step + }: + raise RuntimeError("trainer ranks rejected merged publication") + except BaseException as exc: + self._valid = False + self._closed = True + await cleanup_after_failure( + exc, + self._force_stop, + message="merged publication and trainer cleanup failed", + ) + raise + return next(result["metrics"] for result in results if result["rank"] == 0) + + def _validate_common(self, job: TrainerJobSpec) -> BaseException | None: + if self._closed: + return RuntimeError("trainer run is closed") + if not self._valid: + return RuntimeError("trainer runtime is invalid") + if job.job_id in self._jobs: + return RuntimeError("job_id was already used with a different job") + if job.run_id != self.run_spec.run_id: + return ValueError("job run_id does not match this training run") + if job.training_session_id != self.run_spec.training_session_id: + return ValueError( + "job training_session_id does not match this training run" + ) + if job.output.optimizer_state_path != self.run_spec.optimizer_state_path: + return ValueError( + "job optimizer state path does not match this training run" + ) + if job.expected_learner_version != self._learner_version: + return ValueError( + "expected learner version mismatch: " + f"job={job.expected_learner_version}, runtime={self._learner_version}" + ) + return None + + def _validate_rl( + self, job: TrainJobSpec, batch: PackedBatchLeaseSet + ) -> BaseException | None: + if error := self._validate_common(job): + return error + if batch.ref != job.batch: + return ValueError("job batch ref does not match supplied packed batch") + if job.batch.sequence_length != self.runtime_spec.packed_sequence_length: + return ValueError( + "packed batch sequence length does not match the trainer runtime" + ) + return None + + def _validate_sft( + self, job: SFTJobSpec, batches: tuple[SFTBatchData, ...] + ) -> BaseException | None: + if error := self._validate_common(job): + return error + if len(batches) != job.num_batches: + return ValueError("SFT job batch count does not match its payload") + return None + + @staticmethod + def _failed( + job: TrainerJobSpec, + sequence: int, + exc: BaseException, + invalidated: bool, + ) -> TrainFailed: + return TrainFailed( + job_id=job.job_id, + run_id=job.run_id, + sequence=sequence, + error_type=type(exc).__name__, + message=str(exc) or type(exc).__name__, + runtime_invalidated=invalidated, + ) + + async def close(self) -> None: + if self._close_task is not None and self._close_task.done(): + try: + self._close_task.result() + except BaseException: + self._close_task = None + if self._close_task is None: + graceful = self._valid and self._active_job_id is None + self._closed = True + self._valid = False + self._cancel_active() + self._close_task = asyncio.create_task(self._close(graceful)) + self._close_task.add_done_callback(_consume_future) + await asyncio.shield(self._close_task) + + async def _close(self, graceful: bool) -> None: + loop = asyncio.get_running_loop() + deadline = loop.time() + self.run_spec.shutdown_timeout_s + primary: BaseException | None = None + if graceful: + publications = tuple(self._publications.values()) + try: + async with asyncio.timeout(self.run_spec.shutdown_timeout_s / 2): + await asyncio.gather( + _remote_teardown(self._actors.close.call()), + *( + self._await_publication(publication) + for publication in publications + ), + *tuple(self._publication_drains), + ) + except BaseException as exc: + primary = exc + try: + await self._force_stop(max(0.0, deadline - loop.time())) + except BaseException as exc: + if primary is None: + primary = exc + else: + primary.add_note( + f"trainer ProcMesh cleanup failed: {type(exc).__name__}: {exc}" + ) + if primary is not None: + raise primary + + async def _force_stop(self, timeout_s: float | None = None) -> None: + if self._stop_task is not None and self._stop_task.done(): + try: + self._stop_task.result() + except BaseException: + self._stop_task = None + if self._stop_task is None: + self._stop_task = asyncio.create_task( + _remote_teardown(self._proc_mesh.stop()) + ) + + def stopped(task: asyncio.Task[None]) -> None: + if not task.cancelled() and task.exception() is None: + self._supervision.close() + + self._stop_task.add_done_callback(stopped) + await asyncio.wait_for( + asyncio.shield(self._stop_task), + self.run_spec.shutdown_timeout_s if timeout_s is None else timeout_s, + ) + + def _cancel_active(self) -> None: + # Monarch 0.2 only cancels these local waiters; ProcMesh.stop invalidates ranks. + for future in (self._active_receive, self._active_collective): + if future is not None and not future.done(): + future.cancel() + if future is not None: + future.add_done_callback(_consume_future) + + def _clear_active(self, job_id: str) -> None: + if self._active_job_id == job_id: + self._active_job_id = None + self._active_collective = None + self._active_receive = None + + +async def _remote_teardown(operation: Awaitable[Any]) -> None: + try: + await operation + except asyncio.CancelledError: + task = asyncio.current_task() + if task is not None and task.cancelling(): + raise + + +def _current_task_is_cancelling() -> bool: + task = asyncio.current_task() + return task is not None and bool(task.cancelling()) + + +def _consume_future(future: asyncio.Future[Any]) -> None: + try: + future.exception() + except asyncio.CancelledError: + pass diff --git a/src/art/megatron/runtime/publication.py b/src/art/megatron/runtime/publication.py new file mode 100644 index 000000000..c7d3941a4 --- /dev/null +++ b/src/art/megatron/runtime/publication.py @@ -0,0 +1,139 @@ +from __future__ import annotations + +from pathlib import Path +from typing import Annotated, Literal + +from pydantic import BaseModel, ConfigDict, Field, TypeAdapter, model_validator + +from art.megatron.optimizer_state import ( + OptimizerAdapter, + OptimizerShard, + OptimizerTopology, + build_optimizer_manifest, + commit_optimizer_generation, + read_committed_optimizer_pointer, +) + +from .specs import TrainerGeneration + + +class _PublicationModel(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + +class TrainerRankPublication(_PublicationModel): + generation: TrainerGeneration + rank: int = Field(ge=0) + adapter: OptimizerAdapter | None = None + shard: OptimizerShard | None = None + runtime_sha256: str | None = None + topology: OptimizerTopology | None = None + saves_optimizer: bool + + @model_validator(mode="after") + def _validate_payload(self) -> "TrainerRankPublication": + optimizer_values = (self.shard, self.runtime_sha256, self.topology) + if ( + self.saves_optimizer + and not all(value is not None for value in optimizer_values) + ) or ( + not self.saves_optimizer + and any(value is not None for value in optimizer_values) + ): + raise ValueError("optimizer publication fields must be present together") + if self.rank == 0: + if self.adapter is None: + raise ValueError("rank zero publication requires an adapter") + if ( + self.adapter.training_session_id, + self.adapter.step, + self.adapter.generation_id, + self.adapter.identity, + ) != ( + self.generation.training_session_id, + self.generation.policy_step, + self.generation.generation_id, + str(Path(self.generation.adapter_path).absolute()), + ): + raise ValueError("adapter and trainer generation identities differ") + elif self.adapter is not None: + raise ValueError("only rank zero may publish the adapter manifest") + if self.shard is not None and self.shard.rank != self.rank: + raise ValueError("optimizer shard identifies another trainer rank") + return self + + +class TrainerPublicationSucceeded(_PublicationModel): + kind: Literal["publication_succeeded"] = "publication_succeeded" + record: TrainerRankPublication + + +class TrainerPublicationFailed(_PublicationModel): + kind: Literal["publication_failed"] = "publication_failed" + generation_id: str = Field(min_length=1) + rank: int = Field(ge=0) + error_type: str = Field(min_length=1) + message: str = Field(min_length=1) + + +TrainerPublicationEvent = Annotated[ + TrainerPublicationSucceeded | TrainerPublicationFailed, + Field(discriminator="kind"), +] +TRAINER_PUBLICATION_EVENT_ADAPTER = TypeAdapter(TrainerPublicationEvent) + + +class DurableTrainerPublication(_PublicationModel): + adapter: OptimizerAdapter + resume_step: int = Field(ge=0) + optimizer_step: int = Field(ge=0) + + +def commit_trainer_publication( + optimizer_state_path: str, + generation: TrainerGeneration, + records: tuple[TrainerRankPublication, ...], +) -> DurableTrainerPublication: + ordered = tuple(sorted(records, key=lambda record: record.rank)) + if tuple(record.rank for record in ordered) != tuple(range(len(ordered))): + raise RuntimeError("trainer publication does not cover every rank exactly once") + if not ordered or {record.generation for record in ordered} != {generation}: + raise RuntimeError("trainer ranks published another generation") + if len({record.saves_optimizer for record in ordered}) != 1: + raise RuntimeError("trainer ranks disagree on optimizer persistence") + adapter = ordered[0].adapter + if adapter is None: + raise RuntimeError("trainer publication has no rank-zero adapter") + saves_optimizer = ordered[0].saves_optimizer + if saves_optimizer: + runtime_ids = {record.runtime_sha256 for record in ordered} + topologies = {record.topology for record in ordered} + if len(runtime_ids) != 1 or len(topologies) != 1: + raise RuntimeError( + "trainer ranks produced incompatible optimizer snapshots" + ) + runtime_sha256 = runtime_ids.pop() + topology = topologies.pop() + if runtime_sha256 is None or topology is None: + raise RuntimeError("optimizer publication metadata is incomplete") + expected = read_committed_optimizer_pointer(optimizer_state_path) + commit_optimizer_generation( + optimizer_state_path, + build_optimizer_manifest( + generation=generation.generation_id, + step=generation.policy_step, + adapter=adapter, + runtime_sha256=runtime_sha256, + world_size=len(ordered), + shards=[record.shard for record in ordered if record.shard is not None], + topology=topology, + ), + expected_pointer=expected, + ) + committed = read_committed_optimizer_pointer(optimizer_state_path) + optimizer_step = 0 if committed is None else committed.step + return DurableTrainerPublication( + adapter=adapter, + resume_step=generation.policy_step if saves_optimizer else optimizer_step, + optimizer_step=optimizer_step, + ) diff --git a/src/art/megatron/runtime/runtime_env.py b/src/art/megatron/runtime/runtime_env.py index 7c66f5cab..7ffdd5576 100644 --- a/src/art/megatron/runtime/runtime_env.py +++ b/src/art/megatron/runtime/runtime_env.py @@ -4,25 +4,42 @@ force_te_cutlass_grouped_gemm_env, install_te_cutlass_grouped_gemm_guard, ) +from art.utils.cache_dirs import configure_model_cache_env def _set_cache_dir(env_var: str, default_path: str) -> None: - if not os.environ.get(env_var): - os.environ[env_var] = os.path.expanduser(default_path) - os.makedirs(os.environ[env_var], exist_ok=True) + path = os.path.expanduser(os.environ.get(env_var) or default_path) + os.environ[env_var] = path + os.makedirs(path, exist_ok=True) + + +def _cache_path(name: str, cache_root: str) -> str: + return os.path.join(cache_root, name) + + +def _set_inductor_cache_dir(cache_root: str) -> None: + from torch._inductor.runtime.cache_dir_utils import default_cache_dir + + if os.environ.get("TORCHINDUCTOR_CACHE_DIR") == default_cache_dir(): + del os.environ["TORCHINDUCTOR_CACHE_DIR"] + _set_cache_dir( + "TORCHINDUCTOR_CACHE_DIR", + _cache_path("torchinductor", cache_root), + ) def configure_megatron_runtime_env() -> None: + cache_root = str(configure_model_cache_env()) force_te_cutlass_grouped_gemm_env() os.environ["CUDA_DEVICE_MAX_CONNECTIONS"] = os.environ.get( "ART_MEGATRON_CUDA_DEVICE_MAX_CONNECTIONS", os.environ.get("CUDA_DEVICE_MAX_CONNECTIONS", "1"), ) - os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True" - # The currently validated ART MoE grouped-GEMM runtime is SM90. Future - # SM100 support should come from the TE grouped-GEMM implementation, not - # ART-side kernel special casing. - os.environ["TORCH_CUDA_ARCH_LIST"] = "9.0" - _set_cache_dir("TORCHINDUCTOR_CACHE_DIR", "~/.cache/torchinductor") - _set_cache_dir("TRITON_CACHE_DIR", "~/.triton/cache") + _set_inductor_cache_dir(cache_root) + _set_cache_dir("TRITON_CACHE_DIR", _cache_path("triton", cache_root)) + os.environ.setdefault("FLASH_ATTENTION_CUTE_DSL_CACHE_ENABLED", "1") + _set_cache_dir( + "FLASH_ATTENTION_CUTE_DSL_CACHE_DIR", + _cache_path("flash_attention_cute_dsl", cache_root), + ) install_te_cutlass_grouped_gemm_guard() diff --git a/src/art/megatron/runtime/specs.py b/src/art/megatron/runtime/specs.py new file mode 100644 index 000000000..3c7663c44 --- /dev/null +++ b/src/art/megatron/runtime/specs.py @@ -0,0 +1,321 @@ +from __future__ import annotations + +from collections.abc import Sequence +import hashlib +import json +from typing import Annotated, Literal, TypeAlias + +from pydantic import BaseModel, ConfigDict, Field, TypeAdapter, model_validator + +from art.distributed.adapter_transport import AdapterTransferTarget +from art.distributed.data_plane import PackedBatchRef +from art.distributed.specs import NixlTransportSpec, TrainerMeshSpec +from art.types import TrainConfig, TrainSFTConfig + +from .weight_transfer import MergedWeightTransferSpec + + +class _Spec(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + +class HybridEpRuntimeSpec(_Spec): + ranks_per_nvlink_domain: int = Field(ge=1) + run_id: str = Field(min_length=1) + nixl_transport: NixlTransportSpec | None = None + + @property + def multinode(self) -> bool: + return self.nixl_transport is not None + + +class TrainerRuntimeSpec(_Spec): + art_revision: str = Field(min_length=1) + model_identifier: str = Field(min_length=1) + model_revision: str = Field(min_length=1) + model_initialization: Literal["pretrained", "random"] = "pretrained" + cache_root: str | None = Field(default=None, min_length=1) + model_support_key: str = Field(min_length=1) + handler_name: str = Field(min_length=1) + lora_rank: int = Field(ge=1) + lora_alpha: float = Field(default=32.0, gt=0) + lora_target_modules: tuple[str, ...] + dtype: Literal["bfloat16", "float16", "float32"] + trainer_mesh: TrainerMeshSpec + packed_sequence_length: int = Field(ge=1) + compile_enabled: bool + compile_fingerprint: str = Field(min_length=1) + optimizer_layout_fingerprint: str = Field(min_length=1) + allow_unvalidated_arch: bool = False + enable_moe_routing_replay: bool = False + streaming_weight_offload: bool = False + offload_between_jobs: bool = False + random_state: int | None = None + hybrid_ep: HybridEpRuntimeSpec | None = None + snapshot_pool_capacity: int = Field(default=2, ge=1, le=4) + + @model_validator(mode="after") + def _validate_lora_targets(self) -> "TrainerRuntimeSpec": + if self.lora_alpha != 32.0: + raise ValueError("current Megatron LoRA semantics require lora_alpha=32") + if not self.lora_target_modules: + raise ValueError("lora_target_modules must not be empty") + if len(set(self.lora_target_modules)) != len(self.lora_target_modules): + raise ValueError("lora_target_modules must be unique") + return self + + @property + def fingerprint(self) -> str: + return _fingerprint(self) + + +class TrainingRunSpec(_Spec): + run_id: str = Field(min_length=1) + runtime_fingerprint: str = Field(min_length=1) + training_session_id: str = Field(min_length=1) + initial_learner_version: int = Field(ge=0) + initial_adapter_path: str = Field(min_length=1) + optimizer_state_path: str = Field(min_length=1) + initial_event_timeout_s: float | None = Field(default=None, gt=0) + event_timeout_s: float = Field(default=300.0, gt=0) + shutdown_timeout_s: float = Field(default=240.0, gt=0) + + +class CurrentTrainConfig(TrainConfig): + model_config = ConfigDict(extra="forbid", frozen=True) + + +class CurrentSFTConfig(TrainSFTConfig): + model_config = ConfigDict(extra="forbid", frozen=True) + + +class ExperimentalTrainConfig(_Spec): + advantage_balance: float = 0.0 + allow_training_without_logprobs: bool | None = None + epsilon: float | None = None + epsilon_high: float | None = None + importance_sampling_level: Literal[ + "token", "sequence", "average", "geometric_average" + ] = "token" + kimi_k2_tau: float | None = None + kl_penalty_coef: float = Field(default=0.0, ge=0) + kl_penalty_reference_step: int | None = Field(default=None, ge=0) + kl_penalty_source: Literal["current_learner", "sample"] = "current_learner" + kl_penalty_step_lag: int | None = Field(default=None, ge=0) + kl_ref_adapter_path: str | None = None + logprob_calculation_chunk_size: int | None = Field(default=None, ge=1) + mask_prob_ratio: bool = False + max_negative_advantage_importance_sampling_weight: float | None = None + num_trajectories_learning_rate_multiplier_power: float | None = None + packed_sequence_length: int | None = Field(default=None, ge=1) + plot_tensors: bool | None = None + ppo: bool = False + precalculate_logprobs: bool = False + scale_learning_rate_by_reward_std_dev: bool | None = None + scale_rewards: bool = True + truncated_importance_sampling: float | None = None + moe_routing_replay_strict: bool = True + + +class TrainerGeneration(_Spec): + training_session_id: str = Field(min_length=1) + policy_step: int = Field(ge=0) + generation_id: str = Field(pattern=r"^step-\d{8,}-[0-9a-f]{32}$") + adapter_path: str = Field(min_length=1) + + @model_validator(mode="after") + def _validate_generation_step(self) -> "TrainerGeneration": + if int(self.generation_id.split("-", 2)[1]) != self.policy_step: + raise ValueError("generation ID and policy step must match") + return self + + +class DurableTrainOutput(_Spec): + generation: TrainerGeneration + staging_adapter_path: str = Field(min_length=1) + optimizer_state_path: str = Field(min_length=1) + + +class _TrainerJobSpec(_Spec): + job_id: str = Field(min_length=1) + run_id: str = Field(min_length=1) + training_session_id: str = Field(min_length=1) + expected_learner_version: int = Field(ge=0) + learner_version: int = Field(ge=1) + source: TrainerGeneration + output: DurableTrainOutput + publication_targets: tuple[AdapterTransferTarget, ...] = () + merged_weight_transfer: MergedWeightTransferSpec | None = None + + @model_validator(mode="after") + def _validate_versions(self) -> "_TrainerJobSpec": + if self.learner_version != self.expected_learner_version + 1: + raise ValueError( + "learner_version must immediately follow expected_learner_version" + ) + if ( + self.source.training_session_id != self.training_session_id + or self.source.policy_step != self.expected_learner_version + ): + raise ValueError("source generation does not identify the expected learner") + if ( + self.output.generation.training_session_id != self.training_session_id + or self.output.generation.policy_step != self.learner_version + ): + raise ValueError("output generation does not identify the new learner") + if self.source.generation_id == self.output.generation.generation_id: + raise ValueError("source and output generation IDs must differ") + if self.source.adapter_path == self.output.staging_adapter_path: + raise ValueError("source adapter and output staging paths must differ") + if self.output.generation.adapter_path == self.output.staging_adapter_path: + raise ValueError("final and staging adapter paths must differ") + return self + + @property + def fingerprint(self) -> str: + return _fingerprint(self) + + # These aliases keep the Megatron executor on the current train semantics. + @property + def step(self) -> int: + return self.learner_version + + @property + def source_policy_step(self) -> int: + return self.expected_learner_version + + @property + def source_adapter_path(self) -> str: + return self.source.adapter_path + + @property + def output_adapter_path(self) -> str: + return self.output.generation.adapter_path + + @property + def output_generation_id(self) -> str: + return self.output.generation.generation_id + + @property + def optimizer_state_path(self) -> str: + return self.output.optimizer_state_path + + +class TrainJobSpec(_TrainerJobSpec): + kind: Literal["rl"] = "rl" + batch: PackedBatchRef + config: CurrentTrainConfig + experimental_config: ExperimentalTrainConfig = ExperimentalTrainConfig() + + @model_validator(mode="after") + def _validate_batch_version(self) -> "TrainJobSpec": + if self.batch.max_source_version > self.expected_learner_version: + raise ValueError( + "batch source policy version cannot be newer than the learner" + ) + return self + + +class SFTJobSpec(_TrainerJobSpec): + kind: Literal["sft"] = "sft" + batch_id: str = Field(min_length=1) + num_batches: int = Field(ge=1) + config: CurrentSFTConfig + weight_decay: float = Field(default=0.0, ge=0) + max_grad_norm: float = Field(default=1.0, gt=0) + + @model_validator(mode="after") + def _validate_batch_size(self) -> "SFTJobSpec": + if not isinstance(self.config.batch_size, int): + raise ValueError("typed SFT jobs require a resolved integer batch size") + return self + + +TrainerJobSpec: TypeAlias = Annotated[ + TrainJobSpec | SFTJobSpec, + Field(discriminator="kind"), +] +TRAIN_JOB_ADAPTER = TypeAdapter(TrainerJobSpec) + + +class _TrainEvent(_Spec): + kind: str + job_id: str + run_id: str + sequence: int = Field(ge=0) + + +class TrainAccepted(_TrainEvent): + kind: Literal["accepted"] = "accepted" + expected_learner_version: int = Field(ge=0) + + +class TrainProgress(_TrainEvent): + kind: Literal["progress"] = "progress" + step_index: int = Field(ge=0) + num_steps: int = Field(ge=1) + metrics: dict[str, float] + + +class AdapterReady(_TrainEvent): + kind: Literal["adapter_ready"] = "adapter_ready" + learner_version: int = Field(ge=1) + adapter_path: str = Field(min_length=1) + + +class TrainCompleted(_TrainEvent): + kind: Literal["completed"] = "completed" + learner_version: int = Field(ge=1) + metrics: dict[str, float] = Field(default_factory=dict) + + +class TrainFailed(_TrainEvent): + kind: Literal["failed"] = "failed" + error_type: str = Field(min_length=1) + message: str = Field(min_length=1) + runtime_invalidated: bool + + +class TrainCancelled(_TrainEvent): + kind: Literal["cancelled"] = "cancelled" + reason: str = Field(min_length=1) + runtime_invalidated: bool = True + + +TrainEvent: TypeAlias = Annotated[ + TrainAccepted + | TrainProgress + | AdapterReady + | TrainCompleted + | TrainFailed + | TrainCancelled, + Field(discriminator="kind"), +] +TRAIN_EVENT_ADAPTER = TypeAdapter(TrainEvent) +TERMINAL_EVENT_KINDS = frozenset({"completed", "failed", "cancelled"}) + + +def is_terminal_event(event: TrainEvent) -> bool: + return event.kind in TERMINAL_EVENT_KINDS + + +def validate_event_stream(events: Sequence[TrainEvent]) -> None: + if not events: + raise ValueError("train event stream must not be empty") + if not isinstance(events[0], TrainAccepted): + raise ValueError("train event stream must begin with accepted") + if [event.sequence for event in events] != list(range(len(events))): + raise ValueError("train event sequence must be contiguous from zero") + terminals = [event for event in events if is_terminal_event(event)] + if len(terminals) != 1 or events[-1] is not terminals[0]: + raise ValueError("train event stream must end with exactly one terminal event") + identity = {(event.run_id, event.job_id) for event in events} + if len(identity) != 1: + raise ValueError("all train events must identify the same run and job") + + +def _fingerprint(value: BaseModel) -> str: + payload = json.dumps( + value.model_dump(mode="json"), separators=(",", ":"), sort_keys=True + ).encode() + return hashlib.sha256(payload).hexdigest() diff --git a/src/art/megatron/runtime/te_cutlass_grouped_gemm.py b/src/art/megatron/runtime/te_cutlass_grouped_gemm.py index 23602fd8f..99983fedd 100644 --- a/src/art/megatron/runtime/te_cutlass_grouped_gemm.py +++ b/src/art/megatron/runtime/te_cutlass_grouped_gemm.py @@ -102,11 +102,11 @@ def _raise_if_te_cutlass_grouped_gemm_would_fallback( if reason is None: return raise RuntimeError( - "ART requires Transformer Engine CUTLASS grouped GEMM, but this " + "ART requires optimized Transformer Engine grouped GEMM, but this " f"grouped GEMM call would use the fallback path: {reason}. " - "Required shape: Hopper SM90, BF16/FP16 A/B/out tensors with matching " - "dtypes, no grouped bias/GELU/debug quantizer path, and uniform B K " - "dimension divisible by 128." + "Required shape: Hopper SM90 or Blackwell SM100+, BF16/FP16 A/B/out " + "tensors with matching dtypes, no grouped bias/GELU/debug quantizer " + "path, and uniform B K dimension divisible by 128." ) @@ -121,16 +121,14 @@ def _te_cutlass_grouped_gemm_fallback_reason( use_bias: bool, ) -> str | None: torch = _torch() - # Keep this in sync with TE's validated CUTLASS grouped-GEMM selector. ART - # currently supports the TE 2.11 SM90/Hopper path; SM100/Blackwell support - # should come from an upgraded Transformer Engine build using this same API. + # TE 2.14 adds BF16 grouped GEMM through cuBLAS 13.2 on SM100 and newer. if not A or not B or not out: return "A, B, and out must all be non-empty" if len(layout) < 2: return f"invalid layout {layout!r}" if not torch.cuda.is_available(): return "CUDA is not available" - if (device_reason := _sm90_device_reason(A[0])) is not None: + if (device_reason := _grouped_gemm_device_reason(A[0])) is not None: return device_reason if gelu: return "grouped GELU pre-activation output is not supported" @@ -153,7 +151,7 @@ def _te_cutlass_grouped_gemm_fallback_reason( return _uniform_b_k128_reason(B, transb=layout[1] == "T") -def _sm90_device_reason(tensor: torch.Tensor) -> str | None: +def _grouped_gemm_device_reason(tensor: torch.Tensor) -> str | None: torch = _torch() device = getattr(tensor, "device", None) device_index = torch.cuda.current_device() @@ -163,8 +161,11 @@ def _sm90_device_reason(tensor: torch.Tensor) -> str | None: if capability is None: capability = torch.cuda.get_device_capability(device_index) _DEVICE_CAPABILITIES[device_index] = capability - if capability != (9, 0): - return f"CUDA device {device_index} has capability {capability}, not SM90" + if capability != (9, 0) and capability < (10, 0): + return ( + f"CUDA device {device_index} has capability {capability}, " + "not SM90 or SM100+" + ) return None diff --git a/src/art/megatron/runtime/trainer_run.py b/src/art/megatron/runtime/trainer_run.py new file mode 100644 index 000000000..79a4c2f34 --- /dev/null +++ b/src/art/megatron/runtime/trainer_run.py @@ -0,0 +1,17 @@ +from typing import Protocol + +from .publication import TrainerPublicationEvent + + +class TrainingCancelledError(RuntimeError): + pass + + +class EventSink(Protocol): + def progress( + self, *, step_index: int, num_steps: int, metrics: dict[str, float] + ) -> None: ... + + def adapter_ready(self, *, learner_version: int, adapter_path: str) -> None: ... + + def publication(self, event: TrainerPublicationEvent) -> None: ... diff --git a/src/art/megatron/runtime/weight_transfer.py b/src/art/megatron/runtime/weight_transfer.py new file mode 100644 index 000000000..d4b08a5f5 --- /dev/null +++ b/src/art/megatron/runtime/weight_transfer.py @@ -0,0 +1,20 @@ +from pydantic import BaseModel, ConfigDict + + +class MergedWeightTransferInitInfo(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + master_address: str + master_port: int + rank_offset: int + world_size: int + + +class MergedWeightTransferSpec(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + init_info: MergedWeightTransferInitInfo + vllm_base_url: str + served_model_name: str + api_key: str | None = None + nccl_so_path: str | None = None diff --git a/src/art/megatron/runtime_config.py b/src/art/megatron/runtime_config.py index df1e25bd3..c21352561 100644 --- a/src/art/megatron/runtime_config.py +++ b/src/art/megatron/runtime_config.py @@ -13,6 +13,7 @@ def init_megatron_runtime_config( *, topology: MegatronTopologyConfig | Mapping[str, int | None] | None = None, packed_sequence_length: int | None = None, + snapshot_pool_capacity: int = 2, streaming_weight_offload: bool = False, ) -> MegatronRuntimeConfig: global _MEGATRON_RUNTIME_CONFIG @@ -20,6 +21,7 @@ def init_megatron_runtime_config( config = { "topology": topology, "packed_sequence_length": packed_sequence_length, + "snapshot_pool_capacity": snapshot_pool_capacity, "streaming_weight_offload": streaming_weight_offload, } runtime_config = MegatronRuntimeConfig.model_validate(config) diff --git a/src/art/megatron/selective_lm_head.py b/src/art/megatron/selective_lm_head.py new file mode 100644 index 000000000..0fc9010cf --- /dev/null +++ b/src/art/megatron/selective_lm_head.py @@ -0,0 +1,309 @@ +from __future__ import annotations + +from contextlib import contextmanager +import os +from typing import Any, Iterator + +from megatron.core.tensor_parallel.mappings import ( + gather_from_sequence_parallel_region, +) +from pydantic import BaseModel, ConfigDict +import torch + +from art.loss import AlignedLossInputs, LossInputs + +_ENABLE_ENV = "ART_MEGATRON_SELECTIVE_LM_HEAD" + + +class LmHeadTokenSelection(BaseModel): + """Rows projected by the LM head, derived from already-shifted labels.""" + + model_config = ConfigDict(arbitrary_types_allowed=True, frozen=True) + + flat_indices: torch.Tensor + full_shape: tuple[int, int] + + @classmethod + def from_labels( + cls, + labels: torch.Tensor, + *, + target_device: torch.device | None = None, + ) -> "LmHeadTokenSelection": + if labels.ndim != 2: + raise ValueError( + f"LM-head labels must be [B, S], got {tuple(labels.shape)}" + ) + indices = torch.nonzero(labels.reshape(-1) != -100, as_tuple=False).reshape(-1) + if labels.numel() and not indices.numel(): + # Keep one ignored row so zero-contribution microbatches retain a graph. + indices = torch.zeros(1, dtype=torch.long, device=labels.device) + if target_device is not None: + indices = indices.to(device=target_device, non_blocking=True) + return cls( + flat_indices=indices.to(dtype=torch.long).contiguous(), + full_shape=(int(labels.shape[0]), int(labels.shape[1])), + ) + + def select(self, tensor: torch.Tensor) -> torch.Tensor: + expected = self.full_shape[0] * self.full_shape[1] + if tensor.numel() != expected: + raise ValueError( + "selected token tensor must match the label shape: " + f"tensor={tuple(tensor.shape)} labels={self.full_shape}" + ) + return tensor.reshape(-1).index_select(0, self.flat_indices).unsqueeze(0) + + def select_optional(self, tensor: torch.Tensor | None) -> torch.Tensor | None: + return None if tensor is None else self.select(tensor) + + def restore(self, tensor: torch.Tensor, *, fill_value: float = 0.0) -> torch.Tensor: + if tensor.numel() != self.flat_indices.numel(): + raise ValueError( + "selected tensor length does not match LM-head selection: " + f"tensor={tensor.numel()} selection={self.flat_indices.numel()}" + ) + restored = tensor.new_full(self.full_shape, fill_value) + restored.reshape(-1).index_copy_( + 0, + self.flat_indices, + tensor.reshape(-1), + ) + return restored + + def compact_loss_inputs( + self, + inputs: LossInputs | AlignedLossInputs, + ) -> AlignedLossInputs: + aligned = inputs.align_inputs() + return aligned.model_copy( + update={ + "assistant_mask": self.select(aligned.assistant_mask), + "old_logprobs": self.select(aligned.old_logprobs), + "advantages": self.select(aligned.advantages), + "weights": self.select(aligned.weights), + "group_ids": self.select(aligned.group_ids), + "original_logprobs": self.select_optional(aligned.original_logprobs), + "entropies_are_aligned": True, + } + ) + + +class TokenLossOutput(BaseModel): + model_config = ConfigDict(arbitrary_types_allowed=True) + + token_losses: torch.Tensor + selection: LmHeadTokenSelection | None = None + + def select(self, tensor: torch.Tensor) -> torch.Tensor: + return tensor if self.selection is None else self.selection.select(tensor) + + def select_optional(self, tensor: torch.Tensor | None) -> torch.Tensor | None: + return ( + tensor if self.selection is None else self.selection.select_optional(tensor) + ) + + def compact_loss_inputs( + self, + inputs: LossInputs | AlignedLossInputs, + ) -> LossInputs | AlignedLossInputs: + if self.selection is None: + return inputs + return self.selection.compact_loss_inputs(inputs) + + def restore(self, tensor: torch.Tensor) -> torch.Tensor: + return tensor if self.selection is None else self.selection.restore(tensor) + + def masked_sum(self, mask: torch.Tensor) -> torch.Tensor: + selected_mask = self.select(mask).to(dtype=torch.bool) + return self.token_losses[selected_mask].sum() + self.token_losses.sum() * 0.0 + + +def selective_lm_head_enabled() -> bool: + raw = os.environ.get(_ENABLE_ENV, "1").strip().lower() + if raw in {"1", "true", "yes", "on"}: + return True + if raw in {"0", "false", "no", "off"}: + return False + raise ValueError(f"{_ENABLE_ENV} must be a boolean, got {raw!r}") + + +def forward_token_losses( + model: torch.nn.Module, + *, + labels: torch.Tensor, + selection: LmHeadTokenSelection, + forward_kwargs: dict[str, Any], + enabled: bool | None = None, +) -> TokenLossOutput: + """Run the normal model path while projecting only labeled token rows. + + Sequence-parallel hidden states are gathered before selection, matching the + communication performed by Megatron's output linear. The output linear's + own gather is disabled for this call; gather autograd performs the matching + reduce-scatter in backward. + """ + if "labels" in forward_kwargs: + raise ValueError("forward_kwargs must not contain labels") + if enabled is None: + enabled = selective_lm_head_enabled() + if not enabled: + return TokenLossOutput( + token_losses=model(**forward_kwargs, labels=labels), + ) + if tuple(labels.shape) != selection.full_shape: + raise ValueError( + f"labels={tuple(labels.shape)} selection={selection.full_shape}" + ) + + language_model = _language_model(model) + _validate_language_model(language_model) + if not labels.numel(): + with _select_output_rows(language_model.output_layer, selection): + logits = model(**forward_kwargs, labels=None) + if not isinstance(logits, torch.Tensor): + raise TypeError(f"model must return logits, got {type(logits).__name__}") + return TokenLossOutput( + token_losses=_empty_token_losses(logits, labels), + selection=selection, + ) + compact_labels = selection.select(labels) + with _select_output_rows(language_model.output_layer, selection): + with _restore_root_output(model, selection): + token_losses = model(**forward_kwargs, labels=compact_labels) + if not isinstance(token_losses, torch.Tensor): + raise TypeError( + f"model must return token losses, got {type(token_losses).__name__}" + ) + return TokenLossOutput( + token_losses=selection.select(token_losses), + selection=selection, + ) + + +def _language_model(model: torch.nn.Module) -> Any: + module: Any = model + seen: set[int] = set() + while id(module) not in seen: + seen.add(id(module)) + if hasattr(module, "module"): + module = module.module + continue + language_model = getattr(module, "language_model", None) + if language_model is not None: + module = language_model + continue + break + if not hasattr(module, "output_layer") or not hasattr( + module, "compute_language_model_loss" + ): + raise TypeError( + "selective LM head requires a GPT-compatible language model with " + "output_layer and compute_language_model_loss" + ) + return module + + +def _validate_language_model(language_model: Any) -> None: + if not bool(getattr(language_model, "post_process", False)): + raise RuntimeError("selective LM head requires the post-process model stage") + config = language_model.config + if bool(getattr(language_model, "mtp_process", False)) or int( + getattr(config, "mtp_num_layers", 0) or 0 + ): + raise RuntimeError("selective LM head does not support MTP training") + if bool(getattr(language_model.output_layer, "gather_output", False)): + raise RuntimeError("selective LM head requires vocabulary-parallel logits") + + +@contextmanager +def _restore_root_output( + model: torch.nn.Module, + selection: LmHeadTokenSelection, +) -> Iterator[None]: + def restore( + _module: torch.nn.Module, + _args: tuple[Any, ...], + output: Any, + ) -> torch.Tensor: + if not isinstance(output, torch.Tensor): + raise TypeError( + f"model must return token losses, got {type(output).__name__}" + ) + return selection.restore(output) + + handle = model.register_forward_hook(restore, prepend=True) + try: + yield + finally: + handle.remove() + + +@contextmanager +def _select_output_rows( + output_layer: torch.nn.Module, + selection: LmHeadTokenSelection, +) -> Iterator[None]: + sequence_parallel = bool(getattr(output_layer, "sequence_parallel", False)) + disable_grad_reduce = bool(getattr(output_layer, "disable_grad_reduce", False)) + calls = 0 + + def select_rows( + module: torch.nn.Module, + args: tuple[Any, ...], + kwargs: dict[str, Any], + ) -> tuple[tuple[Any, ...], dict[str, Any]]: + nonlocal calls + calls += 1 + if calls != 1 or not args or not isinstance(args[0], torch.Tensor): + raise RuntimeError("selective LM head expects one positional output call") + hidden_states = args[0] + if sequence_parallel: + hidden_states = gather_from_sequence_parallel_region( + hidden_states, + group=getattr(module, "tp_group"), + ) + setattr(module, "sequence_parallel", False) + setattr(module, "disable_grad_reduce", True) + batch, sequence = selection.full_shape + if tuple(hidden_states.shape[:2]) != (sequence, batch): + raise ValueError( + "LM-head hidden states do not match labels: " + f"hidden={tuple(hidden_states.shape)} labels={selection.full_shape}" + ) + selected = ( + hidden_states.transpose(0, 1) + .reshape(batch * sequence, hidden_states.shape[-1]) + .index_select(0, selection.flat_indices) + .unsqueeze(1) + ) + return (selected, *args[1:]), kwargs + + handle = output_layer.register_forward_pre_hook(select_rows, with_kwargs=True) + succeeded = False + try: + yield + succeeded = True + finally: + handle.remove() + if sequence_parallel: + setattr(output_layer, "sequence_parallel", True) + setattr(output_layer, "disable_grad_reduce", disable_grad_reduce) + if succeeded and calls != 1: + raise RuntimeError(f"selective LM head expected one output call, got {calls}") + + +def _empty_token_losses(logits: torch.Tensor, labels: torch.Tensor) -> torch.Tensor: + if labels.numel() or logits.ndim != 3 or not logits.shape[-1]: + raise ValueError( + f"expected empty labels and [B, 0, V] logits, got {tuple(logits.shape)}" + ) + losses = logits[..., 0] + if tuple(losses.shape) == tuple(labels.shape): + return losses + losses = losses.transpose(0, 1).contiguous() + if tuple(losses.shape) != tuple(labels.shape): + raise ValueError( + f"empty logits={tuple(logits.shape)} labels={tuple(labels.shape)}" + ) + return losses diff --git a/src/art/megatron/service.py b/src/art/megatron/service.py deleted file mode 100644 index 92f93c2aa..000000000 --- a/src/art/megatron/service.py +++ /dev/null @@ -1,1592 +0,0 @@ -import asyncio -from dataclasses import dataclass, field -import importlib -import json -import os -from pathlib import Path -import shutil -import socket -import subprocess -import sys -from typing import Any, AsyncIterator, Literal, TypedDict, cast -from urllib.parse import urlparse -import uuid -import warnings - -from peft.tuners.lora.config import LoraConfig -import torch - -from .. import dev, types -from ..adapter_leases import in_flight_lora_name -from ..dev.get_model_config import default_target_modules -from ..dev.validate import is_dedicated_mode -from ..preprocessing.pack import DiskPackedTensors -from ..preprocessing.tokenize import SFTBatch -from ..serving_capabilities import ( - ServingCapabilities, - discover_serving_capabilities, -) -from ..types import MegatronRuntimeConfig, MegatronTopologyConfig -from ..utils.get_model_step import get_step_from_dir -from ..utils.lifecycle import ( - ChildProcessSupervisor, - ServiceLifecycle, - cleanup_after_failure, - managed_process_cmd, - terminate_popen_process_group, -) -from ..utils.output_dirs import get_step_checkpoint_dir -from ..vllm_runtime import ( - ManagedVllmRuntime, - VllmRuntimeLaunchConfig, - get_external_vllm_runtime_config, - map_checkpoint_path_for_vllm, - normalize_vllm_server_url, - wait_for_vllm_http_runtime, -) -from .lora import ( - LORA_ALPHA, - MEGATRON_LORA_RANK_ENV, - MEGATRON_LORA_TARGET_MODULES_ENV, - default_lora_rank_for_handler, -) -from .migrations import optimizer_state_path -from .model_support.lora_disk import normalize_lora_checkpoint_to_vllm -from .model_support.registry import ( - UnsupportedModelArchitectureError, - model_uses_expert_parallel, -) -from .optimizer_state import ( - MegatronResumeStep, - commit_optimizer_generation, - format_megatron_resume_message, - optimizer_generation_files, - prepare_megatron_resume_state, - read_optimizer_commit, -) -from .runtime.client import ( - create_megatron_job_paths, - stream_megatron_job, - write_megatron_job, -) -from .runtime.jobs import ( - LORA_READY_EVENT, - OPTIMIZER_READY_EVENT, - MegatronMergedTrainingJob, - MegatronOptimizerSaveJob, - MegatronSFTTrainingJob, - MegatronSyncJob, - MegatronTrainingJob, - MergedWeightTransferInitInfo, - MergedWeightTransferSpec, -) -from .runtime.te_cutlass_grouped_gemm import force_te_cutlass_grouped_gemm_env -from .runtime_config import get_megatron_runtime_config -from .training.sft_batches import materialize_sft_batches - -safetensors = importlib.import_module("safetensors") -safe_open = safetensors.safe_open -OFFLOAD_BETWEEN_JOBS_ENV = "ART_MEGATRON_OFFLOAD_BETWEEN_JOBS" - - -class _RuntimeRequestKwargs(TypedDict, total=False): - headers: dict[str, str] - - -def _lora_config_from_model_config( - config: dev.InternalModelConfig | dev.BackendModelConfig, -) -> dev.LoRAConfig: - return cast(dev.BackendModelConfig, config).get("lora_config") or dev.LoRAConfig() - - -def create_identity_lora( - base_model: str, - lora_path: str, - rank: int | None = None, - target_modules: list[str] | None = None, - lora_alpha: int = LORA_ALPHA, - random_state: int | None = None, - allow_unvalidated_arch: bool = False, -) -> None: - """Create an identity LoRA adapter for a Megatron model. - - For MoE models, this targets fused expert parameters and lets the model - support handler normalize the saved PEFT tensors to vLLM layout. - - Args: - base_model: HuggingFace model identifier. - lora_path: Directory to save the adapter files. - rank: LoRA rank. Defaults to rank 1 for MoE models and rank 8 for dense models. - lora_alpha: LoRA alpha scaling factor. - """ - from unittest.mock import patch - - from accelerate import init_empty_weights - from peft import get_peft_model - from transformers import AutoConfig, AutoModelForCausalLM - - from .model_support import get_model_support_handler - - if random_state is not None: - torch.manual_seed(random_state) - target_modules = target_modules or default_target_modules(base_model) - handler = get_model_support_handler( - base_model, - allow_unvalidated_arch=allow_unvalidated_arch, - ) - if rank is None: - rank = default_lora_rank_for_handler(handler) - base_config = AutoConfig.from_pretrained(base_model, trust_remote_code=True) - model_config = handler.identity_lora_model_config(base_config) - with init_empty_weights(): - model = AutoModelForCausalLM.from_config( - model_config, dtype=torch.bfloat16, trust_remote_code=True - ) - model.name_or_path = base_model - - lora_config = LoraConfig( - base_model_name_or_path=base_model, - r=rank, - lora_alpha=lora_alpha, - target_modules=[], - target_parameters=handler.identity_lora_target_parameters( - model, - target_modules=target_modules, - ), - bias="none", - ) - - meta = torch.device("meta") - orig_to = torch.nn.Module.to - - def _skip_meta_to( - module: torch.nn.Module, *args: Any, **kwargs: Any - ) -> torch.nn.Module: - device = kwargs.get("device") or (args[0] if args else None) - if device == meta or str(device) == "meta": - return module - return orig_to(module, *args, **kwargs) - - # PEFT does not recognize fused MoE expert modules, but our handler - # converts the resulting identity LoRA checkpoint into supported tensors. - with warnings.catch_warnings(): - if bool(getattr(handler, "is_moe", False)): - warnings.filterwarnings( - "ignore", - message=( - r"Unsupported layer type '.*MoeExperts.*' encountered, " - r"proceed at your own risk\." - ), - category=UserWarning, - module=r"peft\.tuners\.tuners_utils", - ) - with patch.object(torch.nn.Module, "to", _skip_meta_to): - peft_model = get_peft_model(model, lora_config) - - os.makedirs(lora_path, exist_ok=True) - peft_model.save_pretrained(lora_path) - - final_config = LoraConfig( - base_model_name_or_path=base_model, - r=rank, - lora_alpha=lora_alpha, - target_modules=target_modules, - bias="none", - ).to_dict() - normalize_lora_checkpoint_to_vllm( - lora_path, - handler=handler, - adapter_config=final_config, - ) - del peft_model, model - - -@dataclass -class MegatronService: - model_name: str - base_model: str - config: dev.InternalModelConfig | dev.BackendModelConfig - output_dir: str - enable_expert_replay: bool = True - runtime_config: MegatronRuntimeConfig = field( - default_factory=get_megatron_runtime_config - ) - _is_sleeping: bool = False - _latest_step: int = 0 - _training_session_id: str = field( - default_factory=lambda: uuid.uuid4().hex, - init=False, - ) - _resume_step: MegatronResumeStep | None = None - _megatron_process: subprocess.Popen[Any] | None = None - _megatron_log_file: Any = None - _megatron_log_path: str | None = None - _vllm_runtime: ManagedVllmRuntime = field( - default_factory=ManagedVllmRuntime, - init=False, - repr=False, - ) - _merged_weight_transfer_init_info: MergedWeightTransferInitInfo | None = None - _active_megatron_topology: MegatronTopologyConfig | None = None - _lifecycle: ServiceLifecycle = field( - default_factory=ServiceLifecycle, - init=False, - repr=False, - ) - _child_processes: ChildProcessSupervisor = field(init=False, repr=False) - _loaded_adapter_steps: set[int] = field( - default_factory=set, - init=False, - repr=False, - ) - _loaded_exact_adapter_steps: set[int] = field( - default_factory=set, - init=False, - repr=False, - ) - _exact_adapter_refcounts: dict[int, int] = field( - default_factory=dict, - init=False, - repr=False, - ) - _exact_adapter_lock: asyncio.Lock = field( - default_factory=asyncio.Lock, - init=False, - repr=False, - ) - _serving_capabilities: ServingCapabilities | None = field( - default=None, - init=False, - repr=False, - ) - - def __post_init__(self) -> None: - self._child_processes = ChildProcessSupervisor(self._on_child_process_exit) - self._validate_megatron_dependencies() - - def _on_child_process_exit(self, error: RuntimeError) -> None: - self._status(f"Child process exited unexpectedly: {error}") - self.close() - - def _raise_if_child_failed(self) -> None: - self._child_processes.raise_if_failed() - - def _status(self, message: str) -> None: - print(f"[ART Megatron] {message}", flush=True) - - @staticmethod - def _display_path(path: str | os.PathLike[str]) -> str: - return str(Path(path).resolve()) - - @property - def is_dedicated(self) -> bool: - return is_dedicated_mode(self.config) - - @property - def rollout_weights_mode(self) -> Literal["lora", "merged"]: - mode = self.config.get("rollout_weights_mode", "lora") - assert mode in {"lora", "merged"} - return mode - - @property - def rollout_weight_update_mode(self) -> Literal["step_lora", "in_flight_lora"]: - mode = self.config.get("rollout_weight_update_mode", "step_lora") - assert mode in {"step_lora", "in_flight_lora"} - return mode - - @property - def _in_flight_lora_slot(self) -> str: - return in_flight_lora_name(self.model_name) - - @property - def _initial_served_model_name(self) -> str: - if ( - self.rollout_weights_mode == "lora" - and self.rollout_weight_update_mode == "in_flight_lora" - ): - return self._in_flight_lora_slot - return f"{self.model_name}@{self._latest_step}" - - def _exact_lora_name(self, step: int) -> str: - if self.rollout_weight_update_mode == "in_flight_lora": - return f"{self.model_name}:eval@{step}" - return f"{self.model_name}@{step}" - - @property - def _vllm_base_url(self) -> str: - if external_runtime := get_external_vllm_runtime_config(self.config): - return normalize_vllm_server_url(external_runtime.server_url) - return self._vllm_runtime.base_url - - @property - def _vllm_host(self) -> str: - if external_runtime := get_external_vllm_runtime_config(self.config): - parsed = urlparse(normalize_vllm_server_url(external_runtime.server_url)) - return parsed.hostname or self._vllm_runtime.host - return self._vllm_runtime.host - - @property - def _vllm_port(self) -> int: - if external_runtime := get_external_vllm_runtime_config(self.config): - parsed = urlparse(normalize_vllm_server_url(external_runtime.server_url)) - return parsed.port or (443 if parsed.scheme == "https" else 80) - return self._vllm_runtime.port - - @_vllm_port.setter - def _vllm_port(self, port: int) -> None: - self._vllm_runtime.port = port - - @property - def _vllm_api_key(self) -> str | None: - if external_runtime := get_external_vllm_runtime_config(self.config): - return external_runtime.api_key - return self._vllm_runtime.api_key - - @property - def _vllm_nccl_so_path(self) -> str | None: - return self._vllm_runtime.nccl_so_path - - def _megatron_random_state(self) -> int | None: - for config_key in ("peft_args", "init_args"): - random_state = self.config.get(config_key, {}).get("random_state") - if random_state is not None: - return int(random_state) - return None - - @property - def _allow_unvalidated_arch(self) -> bool: - return bool(self.config.get("allow_unvalidated_arch", False)) - - def _model_uses_expert_replay(self) -> bool: - if not self.enable_expert_replay: - return False - try: - return model_uses_expert_parallel( - self.base_model, - allow_unvalidated_arch=self._allow_unvalidated_arch, - ) - except UnsupportedModelArchitectureError: - return False - - def _trainer_gpu_count(self) -> int: - if self.is_dedicated: - return len(self.config["trainer_gpu_ids"]) - return max(int(torch.cuda.device_count()), 1) - - def _data_parallel_world_size(self) -> int: - num_gpus = self._trainer_gpu_count() - topology = self.runtime_config.topology - tp, cp, pp = topology.tp, topology.cp, topology.pp - denominator = max(tp * cp * pp, 1) - if num_gpus % denominator != 0: - raise RuntimeError( - "Cannot resolve Megatron data-parallel world size from trainer " - f"GPUs/topology: num_gpus={num_gpus}, tp={tp}, cp={cp}, pp={pp}" - ) - return max(num_gpus // denominator, 1) - - async def resolve_global_grad_accumulation_sequences( - self, - config: types.TrainConfig, - ) -> int: - if config.grad_accumulation_sequences is not None: - return int(config.grad_accumulation_sequences) - return self._data_parallel_world_size() - - def _megatron_runtime_paths(self) -> tuple[str, str, str]: - runtime_dir = Path(self.output_dir) / "megatron_runtime" - jobs_dir = runtime_dir / "jobs" - training_log_dir = runtime_dir / "training_logs" - jobs_dir.mkdir(parents=True, exist_ok=True) - training_log_dir.mkdir(parents=True, exist_ok=True) - return ( - str(jobs_dir), - str(training_log_dir), - str(runtime_dir / "vllm_waking.lock"), - ) - - def _staging_lora_dir(self, step: int) -> str: - return str( - Path(self.output_dir) / "megatron_runtime" / "staging" / f"{step:04d}" - ) - - def _prepare_training_lora_dir(self, source_path: str, step: int) -> str: - staging_dir = self._staging_lora_dir(step) - if os.path.exists(staging_dir): - shutil.rmtree(staging_dir) - shutil.copytree(source_path, staging_dir) - return staging_dir - - def _clear_wake_lock(self) -> None: - _, _, wake_lock_path = self._megatron_runtime_paths() - if os.path.exists(wake_lock_path): - os.remove(wake_lock_path) - - def _allocate_master_port(self) -> int: - with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock: - sock.bind(("", 0)) - return int(sock.getsockname()[1]) - - @staticmethod - def _megatron_topology_env(topology: MegatronTopologyConfig) -> dict[str, str]: - env = { - "ART_MEGATRON_TENSOR_MODEL_PARALLEL_SIZE": str(topology.tp), - "ART_MEGATRON_CONTEXT_PARALLEL_SIZE": str(topology.cp), - "ART_MEGATRON_EXPERT_MODEL_PARALLEL_SIZE": str(topology.ep), - "ART_MEGATRON_PIPELINE_MODEL_PARALLEL_SIZE": str(topology.pp), - "ART_MEGATRON_EXPERT_TENSOR_PARALLEL_SIZE": str(topology.etp), - } - if topology.vpp is not None: - env["ART_MEGATRON_VIRTUAL_PIPELINE_MODEL_PARALLEL_SIZE"] = str(topology.vpp) - return env - - @staticmethod - def _megatron_topology_env_names() -> tuple[str, ...]: - return ( - "ART_MEGATRON_TENSOR_MODEL_PARALLEL_SIZE", - "ART_MEGATRON_CONTEXT_PARALLEL_SIZE", - "ART_MEGATRON_EXPERT_MODEL_PARALLEL_SIZE", - "ART_MEGATRON_PIPELINE_MODEL_PARALLEL_SIZE", - "ART_MEGATRON_VIRTUAL_PIPELINE_MODEL_PARALLEL_SIZE", - "ART_MEGATRON_EXPERT_TENSOR_PARALLEL_SIZE", - ) - - def _install_parent_signal_cleanup(self) -> None: - self._lifecycle.install_parent_cleanup(self.close) - - def _restore_parent_signal_cleanup(self) -> None: - self._lifecycle.restore_parent_cleanup() - - def _runtime_cuda_visible_devices(self) -> str: - if self.is_dedicated: - return ",".join(str(gpu_id) for gpu_id in self.config["inference_gpu_ids"]) - if visible := os.environ.get("CUDA_VISIBLE_DEVICES"): - return visible - return ",".join(str(index) for index in range(torch.cuda.device_count())) - - def _runtime_engine_args( - self, config: dev.OpenAIServerConfig | None - ) -> dict[str, object]: - from .model_support import get_model_support_handler - - engine_args = dict(self.config.get("engine_args", {})) - if config and "engine_args" in config: - engine_args.update(dict(config["engine_args"])) - handler = get_model_support_handler( - self.base_model, - allow_unvalidated_arch=self._allow_unvalidated_arch, - ) - for key, value in handler.vllm_engine_args( - rollout_weights_mode=self.rollout_weights_mode - ).items(): - engine_args.setdefault(key, value) - engine_args.setdefault("generation_config", "vllm") - if self.rollout_weights_mode == "merged": - engine_args["weight_transfer_config"] = {"backend": "nccl"} - engine_args.pop("enable_lora", None) - engine_args.pop("max_loras", None) - else: - engine_args["enable_lora"] = True - engine_args.setdefault("max_loras", 2) - for key in ("model", "served_model_name"): - engine_args.pop(key, None) - return engine_args - - def _runtime_server_args( - self, config: dev.OpenAIServerConfig | None - ) -> dict[str, object]: - from .model_support import get_model_support_handler - - server_args: dict[str, object] = { - "return_tokens_as_token_ids": True, - "enable_auto_tool_choice": True, - "tool_call_parser": "hermes", - } - handler = get_model_support_handler( - self.base_model, - allow_unvalidated_arch=self._allow_unvalidated_arch, - ) - server_args.update(handler.vllm_server_args()) - if config and "server_args" in config: - server_args.update(dict(config["server_args"])) - for key in ("port", "host", "lora_modules"): - server_args.pop(key, None) - return server_args - - def _runtime_headers(self) -> dict[str, str]: - if self._vllm_api_key is None: - return {} - return {"Authorization": f"Bearer {self._vllm_api_key}"} - - def _runtime_request_kwargs(self) -> _RuntimeRequestKwargs: - headers = self._runtime_headers() - return {"headers": headers} if headers else {} - - @property - def serving_capabilities(self) -> ServingCapabilities: - if self._serving_capabilities is None: - raise RuntimeError("vLLM serving capabilities have not been discovered") - return self._serving_capabilities - - async def get_serving_capabilities(self) -> ServingCapabilities: - return self.serving_capabilities - - async def _discover_serving_capabilities(self, *, external: bool) -> None: - self._serving_capabilities = await discover_serving_capabilities( - base_url=self._vllm_base_url, - headers=self._runtime_headers(), - allow_openai_compatible=external, - ) - - def _vllm_checkpoint_path(self, checkpoint_path: str) -> str: - return map_checkpoint_path_for_vllm(self.config, checkpoint_path) - - def _sleep_mode_enabled(self) -> bool: - return bool(self.config.get("engine_args", {}).get("enable_sleep_mode", True)) - - def _get_optimizer_state_path(self) -> str: - path = optimizer_state_path(self.output_dir) - os.makedirs(path, exist_ok=True) - return path - - def _resolve_resume_step(self) -> MegatronResumeStep: - if self._resume_step is not None: - return self._resume_step - info = prepare_megatron_resume_state( - output_dir=self.output_dir, - optimizer_state_path=self._get_optimizer_state_path(), - ) - self._resume_step = info - self._status(format_megatron_resume_message(info)) - return info - - def _default_lora_adapter_config(self) -> LoraConfig: - from .model_support import get_model_support_handler - - handler = get_model_support_handler( - self.base_model, - allow_unvalidated_arch=self._allow_unvalidated_arch, - ) - lora_config = _lora_config_from_model_config(self.config) - rank = int(lora_config.get("rank", default_lora_rank_for_handler(handler))) - target_modules = lora_config.get("target_modules") or default_target_modules( - self.base_model - ) - return LoraConfig( - base_model_name_or_path=self.base_model, - r=rank, - lora_alpha=LORA_ALPHA, - target_modules=target_modules, - bias="none", - ) - - def _adapter_exists_and_loads( - self, - lora_path: str, - *, - normalize_existing: bool = False, - ) -> bool: - adapter_path = os.path.join(lora_path, "adapter_model.safetensors") - if not os.path.exists(adapter_path): - return False - with safe_open(adapter_path, framework="pt") as adapter_file: - keys = list(adapter_file.keys()) - if not keys: - raise RuntimeError(f"LoRA adapter contains no tensors: {adapter_path}") - for key in keys: - adapter_file.get_tensor(key) - if normalize_existing: - normalize_lora_checkpoint_to_vllm( - lora_path, - allow_unvalidated_arch=self._allow_unvalidated_arch, - ) - return True - - def _create_identity_lora(self, lora_path: str) -> None: - self._status( - "Preparing initial LoRA adapter " - f"for {self.base_model} at {self._display_path(lora_path)}" - ) - lora_config = _lora_config_from_model_config(self.config) - rank = lora_config.get("rank") - create_identity_lora( - self.base_model, - lora_path, - rank=int(rank) if rank is not None else None, - target_modules=lora_config.get("target_modules"), - random_state=self._megatron_random_state(), - allow_unvalidated_arch=self._allow_unvalidated_arch, - ) - - def _ensure_identity_lora( - self, - lora_path: str, - *, - normalize_existing: bool = False, - ) -> None: - if self._adapter_exists_and_loads( - lora_path, - normalize_existing=normalize_existing, - ): - return - self._create_identity_lora(lora_path) - - def _ensure_lora_adapter_config( - self, lora_path: str, *, source_path: str | None = None - ) -> None: - config_path = os.path.join(lora_path, "adapter_config.json") - if os.path.exists(config_path): - return - os.makedirs(lora_path, exist_ok=True) - if source_path is not None: - source_config = os.path.join(source_path, "adapter_config.json") - if os.path.exists(source_config): - shutil.copy(source_config, config_path) - return - self._default_lora_adapter_config().save_pretrained(lora_path) - - def _build_merged_weight_transfer_spec(self, step: int) -> MergedWeightTransferSpec: - init_info = self._merged_weight_transfer_init_info - assert init_info is not None - if self._vllm_nccl_so_path is None: - raise RuntimeError("vLLM runtime NCCL path is not initialized") - return MergedWeightTransferSpec( - init_info=init_info, - vllm_base_url=self._vllm_base_url, - served_model_name=f"{self.model_name}@{step}", - api_key=self._vllm_api_key, - nccl_so_path=self._vllm_nccl_so_path, - ) - - def _resolve_current_lora_path(self) -> str: - resume_step = self._resolve_resume_step() - if self._latest_step < resume_step.step: - self._latest_step = resume_step.step - lora_path = get_step_checkpoint_dir(self.output_dir, self._latest_step) - if self._latest_step == 0 and not os.path.exists(lora_path): - lora_path = get_step_checkpoint_dir(self.output_dir, 0) - self._ensure_identity_lora( - lora_path, - normalize_existing=self._latest_step == 0, - ) - self._ensure_lora_adapter_config(lora_path) - return lora_path - - def _resolve_active_lora_path(self) -> str: - return self._resolve_current_lora_path() - - async def _set_served_model_name(self, step: int) -> None: - import httpx - - self._raise_if_child_failed() - async with httpx.AsyncClient() as client: - response = await client.post( - f"{self._vllm_base_url}/art/set_served_model_name", - json={"name": f"{self.model_name}@{step}"}, - **self._runtime_request_kwargs(), - timeout=30.0, - ) - response.raise_for_status() - self._latest_step = step - - async def _init_merged_weight_transfer(self) -> None: - import httpx - - self._raise_if_child_failed() - if self._merged_weight_transfer_init_info is not None: - return - async with httpx.AsyncClient() as client: - response = await client.get( - f"{self._vllm_base_url}/get_world_size", - **self._runtime_request_kwargs(), - timeout=30.0, - ) - response.raise_for_status() - inference_world_size = int(response.json()["world_size"]) - self._merged_weight_transfer_init_info = MergedWeightTransferInitInfo( - master_address="127.0.0.1", - master_port=self._allocate_master_port(), - rank_offset=1, - world_size=inference_world_size + 1, - ) - - async def _start_vllm_subprocess( - self, - lora_path: str, - port: int, - config: dev.OpenAIServerConfig | None, - ) -> tuple[str, int]: - self._raise_if_child_failed() - server_args = self._runtime_server_args(config) - vllm_log_path = Path(self.output_dir) / "logs" / "vllm-runtime.log" - self._status( - "Starting vLLM runtime " - f"for {self.base_model}. Logs: {self._display_path(vllm_log_path)}" - ) - location = await self._vllm_runtime.start( - launch_config=VllmRuntimeLaunchConfig( - base_model=self.base_model, - port=port, - host=self._vllm_runtime.host, - cuda_visible_devices=self._runtime_cuda_visible_devices(), - lora_path=lora_path, - served_model_name=self._initial_served_model_name, - rollout_weights_mode=self.rollout_weights_mode, - engine_args=self._runtime_engine_args(config), - server_args=server_args, - ), - output_dir=self.output_dir, - child_processes=self._child_processes, - install_parent_cleanup=self._install_parent_signal_cleanup, - cleanup_on_error=self._stop_vllm_subprocess, - ) - self._status(f"vLLM runtime is ready at {self._vllm_base_url}") - return location - - async def _reload_adapter(self, checkpoint_path: str, step: int) -> None: - import httpx - - self._raise_if_child_failed() - payload: dict[str, Any] = { - "lora_name": f"{self.model_name}@{step}", - "lora_path": self._vllm_checkpoint_path(checkpoint_path), - } - if self.serving_capabilities.inplace_lora_load: - payload["load_inplace"] = True - async with httpx.AsyncClient() as client: - response = await client.post( - f"{self._vllm_base_url}/v1/load_lora_adapter", - json=payload, - **self._runtime_request_kwargs(), - timeout=60.0, - ) - response.raise_for_status() - self._latest_step = step - self._loaded_adapter_steps.add(step) - - async def _update_in_flight_adapter(self, checkpoint_path: str, step: int) -> None: - import httpx - - self._raise_if_child_failed() - self.serving_capabilities.require( - "in_flight_lora_updates", operation="In-flight LoRA updates" - ) - self.serving_capabilities.require( - "policy_token_spans", operation="In-flight LoRA updates" - ) - async with httpx.AsyncClient() as client: - response = await client.post( - f"{self._vllm_base_url}/art/in_flight_lora_update", - json={ - "model_name": self._in_flight_lora_slot, - "lora_slot": self._in_flight_lora_slot, - "lora_path": self._vllm_checkpoint_path(checkpoint_path), - "policy_version": step, - }, - **self._runtime_request_kwargs(), - timeout=60.0, - ) - response.raise_for_status() - self._latest_step = step - self._loaded_adapter_steps.add(step) - - async def _load_rollout_lora_for_step( - self, checkpoint_path: str, step: int - ) -> None: - if self.rollout_weight_update_mode == "in_flight_lora": - await self._update_in_flight_adapter(checkpoint_path, step) - else: - await self._reload_adapter(checkpoint_path, step) - - async def acquire_exact_adapter(self, step: int, checkpoint_path: str) -> str: - if self.rollout_weights_mode != "lora": - raise RuntimeError("Exact checkpoint eval requires LoRA rollout serving") - lora_name = self._exact_lora_name(step) - async with self._exact_adapter_lock: - loaded_steps = ( - self._loaded_exact_adapter_steps - if self.rollout_weight_update_mode == "in_flight_lora" - else self._loaded_adapter_steps - ) - if step in loaded_steps: - if self.rollout_weight_update_mode == "in_flight_lora": - self._exact_adapter_refcounts[step] += 1 - return lora_name - import httpx - - self._raise_if_child_failed() - async with httpx.AsyncClient() as client: - response = await client.post( - f"{self._vllm_base_url}/v1/load_lora_adapter", - json={ - "lora_name": lora_name, - "lora_path": self._vllm_checkpoint_path(checkpoint_path), - }, - **self._runtime_request_kwargs(), - timeout=60.0, - ) - response.raise_for_status() - loaded_steps.add(step) - if self.rollout_weight_update_mode == "in_flight_lora": - self._exact_adapter_refcounts[step] = 1 - return lora_name - - async def release_exact_adapter(self, step: int) -> None: - if self.rollout_weight_update_mode != "in_flight_lora": - return - async with self._exact_adapter_lock: - count = self._exact_adapter_refcounts[step] - if count > 1: - self._exact_adapter_refcounts[step] = count - 1 - return - await self._unload_exact_adapter(step) - del self._exact_adapter_refcounts[step] - - async def _unload_adapter_name(self, lora_name: str) -> bool: - import httpx - - self._raise_if_child_failed() - async with httpx.AsyncClient() as client: - response = await client.post( - f"{self._vllm_base_url}/v1/unload_lora_adapter", - json={"lora_name": lora_name}, - **self._runtime_request_kwargs(), - timeout=30.0, - ) - if response.status_code == 404: - return False - response.raise_for_status() - return True - - async def _unload_adapter(self, step: int) -> None: - await self._unload_adapter_name(f"{self.model_name}@{step}") - self._loaded_adapter_steps.discard(step) - - async def _unload_exact_adapter(self, step: int) -> None: - await self._unload_adapter_name(self._exact_lora_name(step)) - self._loaded_exact_adapter_steps.discard(step) - - async def prune_loaded_adapters(self, *, retain_steps: set[int]) -> None: - if self.rollout_weights_mode != "lora" or self._vllm_port == 0: - return - async with self._exact_adapter_lock: - for step in sorted(self._loaded_exact_adapter_steps - retain_steps): - if self._exact_adapter_refcounts.get(step, 0) == 0: - await self._unload_exact_adapter(step) - if self.rollout_weight_update_mode == "in_flight_lora": - return - for step in sorted(self._loaded_adapter_steps - retain_steps): - if step == self._latest_step: - continue - await self._unload_adapter(step) - - async def _sync_dedicated_merged_weights( - self, - *, - lora_path: str, - step: int, - ) -> None: - self._raise_if_child_failed() - await self._ensure_megatron_running() - await self._init_merged_weight_transfer() - self._clear_pending_jobs() - job_path, log_path = self._create_megatron_job_paths() - job = MegatronSyncJob( - lora_path=lora_path, - allow_unvalidated_arch=self._allow_unvalidated_arch, - merged_weight_transfer=self._build_merged_weight_transfer_spec(step), - log_path=log_path, - ) - write_megatron_job(job, job_path=job_path) - async for _ in stream_megatron_job( - job, - job_path=job_path, - process=self._megatron_process, - process_log_path=self._megatron_log_path, - ): - pass - self._latest_step = step - - async def _sleep_runtime(self) -> None: - import httpx - - self._raise_if_child_failed() - self._status("Sleeping vLLM runtime to free GPU memory for training") - async with httpx.AsyncClient() as client: - response = await client.post( - f"{self._vllm_base_url}/sleep", - params={"level": 1, "mode": "wait"}, - **self._runtime_request_kwargs(), - timeout=300.0, - ) - response.raise_for_status() - self._is_sleeping = True - self._status("vLLM runtime is sleeping") - - async def _wake_runtime(self) -> None: - import httpx - - self._raise_if_child_failed() - self._status("Waking vLLM runtime") - async with httpx.AsyncClient() as client: - response = await client.post( - f"{self._vllm_base_url}/wake_up", - **self._runtime_request_kwargs(), - timeout=300.0, - ) - response.raise_for_status() - self._is_sleeping = False - self._status("vLLM runtime is awake") - - async def register_lora_for_step(self, step: int, checkpoint_dir: str) -> None: - self._raise_if_child_failed() - if self.rollout_weights_mode == "merged": - await self._set_served_model_name(step) - else: - await self._load_rollout_lora_for_step(checkpoint_dir, step) - self._latest_step = step - - def _validate_megatron_dependencies(self) -> None: - try: - from .hybrid_ep_setup import validate_hybrid_ep - - validate_hybrid_ep() - importlib.import_module("deep_ep") - import megatron.bridge # type: ignore - except ImportError as exc: - raise RuntimeError( - "Megatron dependencies are not available in the active ART environment. " - "Run ART's Megatron setup before starting training." - ) from exc - - async def _ensure_megatron_running(self) -> None: - """Lazily start Megatron training process if not running.""" - self._raise_if_child_failed() - megatron_topology = self.runtime_config.topology - if self._megatron_process is not None: - if self._megatron_process.returncode is None: - assert self._active_megatron_topology == megatron_topology - return - self._megatron_process = None - self._active_megatron_topology = None - - self._validate_megatron_dependencies() - - train_script = Path(__file__).parent / "train.py" - project_root = Path(__file__).resolve().parents[3] - env = os.environ.copy() - force_te_cutlass_grouped_gemm_env(env) - if self.is_dedicated: - trainer_gpu_ids = self.config["trainer_gpu_ids"] - num_gpus = len(trainer_gpu_ids) - env["CUDA_VISIBLE_DEVICES"] = ",".join( - str(gpu_id) for gpu_id in trainer_gpu_ids - ) - else: - num_gpus = torch.cuda.device_count() - jobs_dir, _training_log_dir, wake_lock_path = self._megatron_runtime_paths() - env["MODEL_IDENTIFIER"] = self.base_model - if self._allow_unvalidated_arch: - env["ART_MEGATRON_ALLOW_UNVALIDATED_ARCH"] = "1" - if self._model_uses_expert_replay(): - env["ART_MEGATRON_ENABLE_MOE_ROUTING_REPLAY"] = "1" - env["ART_MEGATRON_JOBS_DIR"] = jobs_dir - env["ART_MEGATRON_WAKE_LOCK_PATH"] = wake_lock_path - env[OFFLOAD_BETWEEN_JOBS_ENV] = "0" if self.is_dedicated else "1" - env["ART_MEGATRON_STREAMING_WEIGHT_OFFLOAD"] = ( - "1" if self.runtime_config.streaming_weight_offload else "0" - ) - master_addr = env.get("MASTER_ADDR", "127.0.0.1") - master_port = str(self._allocate_master_port()) - env["MASTER_ADDR"] = master_addr - env["MASTER_PORT"] = master_port - random_state = self._megatron_random_state() - if random_state is not None: - env["ART_MEGATRON_RANDOM_STATE"] = str(random_state) - lora_config = _lora_config_from_model_config(self.config) - if (rank := lora_config.get("rank")) is not None: - env[MEGATRON_LORA_RANK_ENV] = str(int(rank)) - if target_modules := lora_config.get("target_modules"): - env[MEGATRON_LORA_TARGET_MODULES_ENV] = json.dumps(list(target_modules)) - for env_name in self._megatron_topology_env_names(): - env.pop(env_name, None) - env.update(self._megatron_topology_env(megatron_topology)) - - command = [ - sys.executable, - "-m", - "torch.distributed.run", - "--master-addr", - master_addr, - "--master-port", - master_port, - "--nproc_per_node", - str(num_gpus), - str(train_script), - ] - log_dir = Path(self.output_dir) / "logs" - log_dir.mkdir(parents=True, exist_ok=True) - megatron_log_path = str(log_dir / "megatron-runtime.log") - self._megatron_log_path = megatron_log_path - self._megatron_log_file = open( - megatron_log_path, - "w", - buffering=1, - ) - self._status( - f"Starting Megatron worker on {num_gpus} GPU(s). " - f"Logs: {self._display_path(megatron_log_path)}" - ) - self._megatron_process = subprocess.Popen( - managed_process_cmd(command), - cwd=str(project_root), - env=env, - stdout=self._megatron_log_file, - stderr=self._megatron_log_file, - start_new_session=True, - ) - self._install_parent_signal_cleanup() - self._child_processes.watch_popen( - "Megatron worker", - self._megatron_process, - log_path=megatron_log_path, - ) - self._active_megatron_topology = megatron_topology - self._status("Megatron worker is initializing") - - def _clear_pending_jobs(self) -> None: - jobs_dir, _training_log_dir, _wake_lock_path = self._megatron_runtime_paths() - os.makedirs(jobs_dir, exist_ok=True) - for job_name in os.listdir(jobs_dir): - if job_name.endswith(".json"): - os.remove(os.path.join(jobs_dir, job_name)) - - def _create_megatron_job_paths(self) -> tuple[str, str]: - jobs_dir, training_log_dir, _wake_lock_path = self._megatron_runtime_paths() - return create_megatron_job_paths( - jobs_dir=jobs_dir, - training_log_dir=training_log_dir, - ) - - def _resolve_training_lora_path(self) -> str: - return self._resolve_current_lora_path() - - async def _prepare_for_training(self) -> str: - self._raise_if_child_failed() - self._validate_megatron_dependencies() - # Shared-GPU Megatron must start after vLLM has released GPU memory. - await self._sleep_runtime() - await self._ensure_megatron_running() - - lora_path = self._resolve_training_lora_path() - self._clear_pending_jobs() - return lora_path - - def _publish_staged_training_checkpoint( - self, - *, - staging_lora_path: str, - step: int, - ) -> str: - self._ensure_lora_adapter_config(staging_lora_path) - checkpoint_dir = get_step_checkpoint_dir(self.output_dir, step) - if os.path.exists(checkpoint_dir): - raise RuntimeError( - f"Refusing to publish Megatron checkpoint over existing directory: " - f"{checkpoint_dir}" - ) - self._status( - f"Publishing training checkpoint {step} " - f"to {self._display_path(checkpoint_dir)}" - ) - Path(checkpoint_dir).parent.mkdir(parents=True, exist_ok=True) - Path(staging_lora_path).rename(checkpoint_dir) - return checkpoint_dir - - def _commit_optimizer_checkpoint(self, *, step: int, world_size: int) -> None: - checkpoint_dir = Path(get_step_checkpoint_dir(self.output_dir, step)) - if not checkpoint_dir.is_dir(): - raise RuntimeError( - f"Cannot commit optimizer step {step} before its LoRA checkpoint" - ) - path = self._get_optimizer_state_path() - commit_optimizer_generation( - path, - step=step, - world_size=world_size, - files=optimizer_generation_files(step, world_size), - ) - - @staticmethod - def _optimizer_ready_world_size( - result: dict[str, Any], *, expected_step: int - ) -> int | None: - if result.get("event") != OPTIMIZER_READY_EVENT: - return None - step = int(result.get("step", -1)) - world_size = int(result.get("world_size", 0)) - if step != expected_step or world_size < 1: - raise RuntimeError(f"Invalid optimizer-ready event: {result!r}") - return world_size - - async def _wake_and_reload_training_checkpoint( - self, - *, - checkpoint_dir: str, - step: int, - ) -> None: - _jobs_dir, _training_log_dir, wake_lock_path = self._megatron_runtime_paths() - try: - with open(wake_lock_path, "w") as lock_file: - lock_file.write("waking vllm\n") - await self._wake_runtime() - finally: - if os.path.exists(wake_lock_path): - os.remove(wake_lock_path) - - await self._load_rollout_lora_for_step(checkpoint_dir, step) - self._status(f"Loaded checkpoint {step} into vLLM") - - async def _handle_training_lora_ready( - self, - *, - checkpoint_dir: str | None, - staging_lora_path: str, - step: int, - ) -> str: - if checkpoint_dir is None: - checkpoint_dir = self._publish_staged_training_checkpoint( - staging_lora_path=staging_lora_path, - step=step, - ) - if self.is_dedicated and self.rollout_weights_mode == "lora": - await self._load_rollout_lora_for_step(checkpoint_dir, step) - self._status(f"Loaded checkpoint {step} into vLLM") - return checkpoint_dir - - async def _finish_training_checkpoint( - self, - *, - checkpoint_dir: str | None, - staging_lora_path: str, - step: int, - ) -> str: - if checkpoint_dir is None: - checkpoint_dir = self._publish_staged_training_checkpoint( - staging_lora_path=staging_lora_path, - step=step, - ) - if self.rollout_weights_mode == "merged": - self._latest_step = step - elif self.is_dedicated: - if self._latest_step != step: - await self._load_rollout_lora_for_step(checkpoint_dir, step) - self._status(f"Loaded checkpoint {step} into vLLM") - else: - await self._wake_and_reload_training_checkpoint( - checkpoint_dir=checkpoint_dir, - step=step, - ) - return checkpoint_dir - - async def start_openai_server( - self, config: dev.OpenAIServerConfig | None - ) -> tuple[str, int]: - self._raise_if_child_failed() - lora_path = self._resolve_active_lora_path() - external_runtime = get_external_vllm_runtime_config(self.config) - - if not self.is_dedicated and not self._sleep_mode_enabled(): - raise ValueError( - "Shared-GPU mode requires engine_args.enable_sleep_mode=True " - "for the external vLLM runtime" - ) - - if external_runtime is not None: - if self.rollout_weights_mode != "lora": - raise RuntimeError( - "External vLLM runtime requires LoRA rollout weights" - ) - await wait_for_vllm_http_runtime( - base_url=self._vllm_base_url, - timeout=external_runtime.health_timeout_s, - headers=self._runtime_headers(), - ) - try: - await self._discover_serving_capabilities(external=True) - await self._load_rollout_lora_for_step(lora_path, self._latest_step) - self._loaded_adapter_steps.add(self._latest_step) - except BaseException as exc: - await cleanup_after_failure( - exc, - self.aclose, - message="vLLM startup and Megatron cleanup failed.", - ) - raise - self._status(f"External vLLM runtime is ready at {self._vllm_base_url}") - return self._vllm_host, self._vllm_port - - port = (config or {}).get("server_args", {}).get("port", 8000) - location = await self._start_vllm_subprocess(lora_path, port, config) - try: - await self._discover_serving_capabilities(external=False) - if self.rollout_weights_mode == "lora": - if self.rollout_weight_update_mode == "in_flight_lora": - await self._update_in_flight_adapter(lora_path, self._latest_step) - else: - self._loaded_adapter_steps.add(self._latest_step) - if self.rollout_weights_mode == "merged": - await self._sync_dedicated_merged_weights( - lora_path=lora_path, - step=self._latest_step, - ) - except BaseException as exc: - await cleanup_after_failure( - exc, - self.aclose, - message="vLLM startup and Megatron cleanup failed.", - ) - raise - return location - - async def vllm_engine_is_sleeping(self) -> bool: - return self._is_sleeping - - async def train( - self, - disk_packed_tensors: DiskPackedTensors, - config: types.TrainConfig, - _config: dev.TrainConfig, - verbose: bool = False, - ) -> AsyncIterator[dict[str, float]]: - try: - self._raise_if_child_failed() - if _config.get("moe_routing_replay_bundle") is not None: - raise RuntimeError( - "moe_routing_replay_bundle is only supported for in-process/runtime APIs; " - "MegatronService subprocess jobs must use moe_routing_replay_path." - ) - if self.is_dedicated: - await self._ensure_megatron_running() - lora_path = self._resolve_active_lora_path() - self._clear_pending_jobs() - next_step = self._latest_step + 1 - staging_lora_path = self._prepare_training_lora_dir( - lora_path, - next_step, - ) - job_path, log_path = self._create_megatron_job_paths() - if self.rollout_weights_mode == "merged": - await self._init_merged_weight_transfer() - job: MegatronTrainingJob | MegatronMergedTrainingJob = ( - MegatronMergedTrainingJob( - step=next_step, - source_policy_step=self._latest_step, - training_session_id=self._training_session_id, - lora_path=staging_lora_path, - allow_unvalidated_arch=self._allow_unvalidated_arch, - optimizer_state_path=self._get_optimizer_state_path(), - disk_packed_tensors=disk_packed_tensors, - config=config, - experimental_config=cast(dict[str, Any], _config), - moe_routing_replay_path=_config.get( - "moe_routing_replay_path" - ), - moe_routing_replay_strict=_config.get( - "moe_routing_replay_strict", - True, - ), - merged_weight_transfer=self._build_merged_weight_transfer_spec( - next_step - ), - log_path=log_path, - ) - ) - else: - job = MegatronTrainingJob( - step=next_step, - source_policy_step=self._latest_step, - training_session_id=self._training_session_id, - lora_path=staging_lora_path, - allow_unvalidated_arch=self._allow_unvalidated_arch, - optimizer_state_path=self._get_optimizer_state_path(), - disk_packed_tensors=disk_packed_tensors, - config=config, - experimental_config=cast(dict[str, Any], _config), - moe_routing_replay_path=_config.get("moe_routing_replay_path"), - moe_routing_replay_strict=_config.get( - "moe_routing_replay_strict", - True, - ), - log_path=log_path, - ) - write_megatron_job(job, job_path=job_path) - checkpoint_dir: str | None = None - optimizer_world_size: int | None = None - async for result in stream_megatron_job( - job, - job_path=job_path, - process=self._megatron_process, - process_log_path=self._megatron_log_path, - ): - if result.get("event") == LORA_READY_EVENT: - checkpoint_dir = await self._handle_training_lora_ready( - checkpoint_dir=checkpoint_dir, - staging_lora_path=staging_lora_path, - step=next_step, - ) - continue - if ( - world_size := self._optimizer_ready_world_size( - result, expected_step=next_step - ) - ) is not None: - optimizer_world_size = world_size - continue - yield {key: float(value) for key, value in result.items()} - - await self._finish_training_checkpoint( - checkpoint_dir=checkpoint_dir, - staging_lora_path=staging_lora_path, - step=next_step, - ) - if optimizer_world_size is not None: - self._commit_optimizer_checkpoint( - step=next_step, world_size=optimizer_world_size - ) - return - - lora_path = await self._prepare_for_training() - next_step = self._latest_step + 1 - staging_lora_path = self._prepare_training_lora_dir( - lora_path, - next_step, - ) - job_path, log_path = self._create_megatron_job_paths() - job = MegatronTrainingJob( - step=next_step, - source_policy_step=self._latest_step, - training_session_id=self._training_session_id, - lora_path=staging_lora_path, - allow_unvalidated_arch=self._allow_unvalidated_arch, - optimizer_state_path=self._get_optimizer_state_path(), - disk_packed_tensors=disk_packed_tensors, - config=config, - experimental_config=cast(dict[str, Any], _config), - moe_routing_replay_path=_config.get("moe_routing_replay_path"), - moe_routing_replay_strict=_config.get( - "moe_routing_replay_strict", True - ), - log_path=log_path, - ) - write_megatron_job(job, job_path=job_path) - - checkpoint_dir = None - optimizer_world_size = None - async for result in stream_megatron_job( - job, - job_path=job_path, - process=self._megatron_process, - process_log_path=self._megatron_log_path, - ): - if result.get("event") == LORA_READY_EVENT: - checkpoint_dir = await self._handle_training_lora_ready( - checkpoint_dir=checkpoint_dir, - staging_lora_path=staging_lora_path, - step=next_step, - ) - continue - if ( - world_size := self._optimizer_ready_world_size( - result, expected_step=next_step - ) - ) is not None: - optimizer_world_size = world_size - continue - yield {key: float(value) for key, value in result.items()} - - await self._finish_training_checkpoint( - checkpoint_dir=checkpoint_dir, - staging_lora_path=staging_lora_path, - step=next_step, - ) - if optimizer_world_size is not None: - self._commit_optimizer_checkpoint( - step=next_step, world_size=optimizer_world_size - ) - except GeneratorExit: - raise - except BaseException as exc: - self._status(f"Megatron train failed: {type(exc).__name__}: {exc}") - await cleanup_after_failure( - exc, - self.aclose, - message="Megatron training and cleanup failed.", - ) - raise - - async def train_sft( - self, - batches: list[SFTBatch], - config: types.TrainSFTConfig, - verbose: bool = False, - ) -> AsyncIterator[dict[str, float]]: - try: - self._raise_if_child_failed() - if self.is_dedicated: - raise NotImplementedError( - "train_sft is not yet supported in dedicated mode" - ) - lora_path = await self._prepare_for_training() - next_step = self._latest_step + 1 - staging_lora_path = self._prepare_training_lora_dir( - lora_path, - next_step, - ) - serialized_batches = materialize_sft_batches(batches) - job_path, log_path = self._create_megatron_job_paths() - grad_accumulation_sequences = ( - config.batch_size if isinstance(config.batch_size, int) else None - ) - job = MegatronSFTTrainingJob( - step=next_step, - source_policy_step=self._latest_step, - training_session_id=self._training_session_id, - lora_path=staging_lora_path, - allow_unvalidated_arch=self._allow_unvalidated_arch, - optimizer_state_path=self._get_optimizer_state_path(), - sft_data_dir=serialized_batches.sft_data_dir, - num_batches=serialized_batches.num_batches, - learning_rates=serialized_batches.learning_rates, - grad_accumulation_sequences=grad_accumulation_sequences, - log_path=log_path, - ) - write_megatron_job(job, job_path=job_path) - self._status( - f"Starting Megatron SFT job with {serialized_batches.num_batches} " - f"batch(es). First batch may take a few minutes while kernels compile. " - f"Training log: {self._display_path(log_path)}" - ) - - optimizer_world_size = None - async for result in stream_megatron_job( - job, - job_path=job_path, - process=self._megatron_process, - process_log_path=self._megatron_log_path, - ): - if ( - world_size := self._optimizer_ready_world_size( - result, expected_step=next_step - ) - ) is not None: - optimizer_world_size = world_size - continue - metrics = { - "loss/train": float(result["loss"]), - "loss/learning_rate": float(result["learning_rate"]), - "loss/grad_norm": float(result["grad_norm"]), - } - if "tokens_per_second" in result: - metrics["throughput/train_packed_tok_per_s"] = float( - result["tokens_per_second"] - ) - yield metrics - - new_checkpoint_dir = self._publish_staged_training_checkpoint( - staging_lora_path=staging_lora_path, - step=next_step, - ) - if optimizer_world_size is None: - raise RuntimeError("Megatron SFT job did not persist its optimizer") - self._commit_optimizer_checkpoint( - step=next_step, world_size=optimizer_world_size - ) - await self._wake_and_reload_training_checkpoint( - checkpoint_dir=new_checkpoint_dir, - step=next_step, - ) - except GeneratorExit: - raise - except BaseException as exc: - self._status(f"Megatron SFT train failed: {type(exc).__name__}: {exc}") - await cleanup_after_failure( - exc, - self.aclose, - message="Megatron SFT training and cleanup failed.", - ) - raise - - async def finalize_training_session(self) -> None: - path = self._get_optimizer_state_path() - commit = read_optimizer_commit(path) - if self._megatron_process is None or ( - commit is not None and commit.step == self._latest_step - ): - return - self._raise_if_child_failed() - job_path, log_path = self._create_megatron_job_paths() - job = MegatronOptimizerSaveJob( - step=self._latest_step, - training_session_id=self._training_session_id, - optimizer_state_path=path, - log_path=log_path, - ) - write_megatron_job(job, job_path=job_path) - optimizer_world_size = None - async for result in stream_megatron_job( - job, - job_path=job_path, - process=self._megatron_process, - process_log_path=self._megatron_log_path, - ): - if ( - world_size := self._optimizer_ready_world_size( - result, expected_step=self._latest_step - ) - ) is not None: - optimizer_world_size = world_size - continue - raise RuntimeError(f"Optimizer finalization returned data: {result!r}") - if optimizer_world_size is None: - raise RuntimeError("Megatron optimizer finalization produced no commit") - self._commit_optimizer_checkpoint( - step=self._latest_step, world_size=optimizer_world_size - ) - - async def aclose(self) -> None: - self.close() - - def _stop_vllm_subprocess(self) -> None: - self._vllm_runtime.close() - self._merged_weight_transfer_init_info = None - self._loaded_adapter_steps.clear() - self._loaded_exact_adapter_steps.clear() - self._exact_adapter_refcounts.clear() - - def _stop_megatron_process(self) -> None: - if self._megatron_process is None: - if self._megatron_log_file is not None: - self._megatron_log_file.close() - self._megatron_log_file = None - self._megatron_log_path = None - self._active_megatron_topology = None - return - terminate_popen_process_group(self._megatron_process) - self._megatron_process = None - self._active_megatron_topology = None - if self._megatron_log_file is not None: - self._megatron_log_file.close() - self._megatron_log_file = None - self._megatron_log_path = None - - def close(self) -> None: - if not self._lifecycle.begin_close(): - return - try: - self._child_processes.close() - self._stop_vllm_subprocess() - self._stop_megatron_process() - self._clear_wake_lock() - finally: - self._restore_parent_signal_cleanup() diff --git a/src/art/megatron/setup.sh b/src/art/megatron/setup.sh index d5612bc40..e11071e2e 100755 --- a/src/art/megatron/setup.sh +++ b/src/art/megatron/setup.sh @@ -1,41 +1,225 @@ #!/usr/bin/env bash set -euo pipefail -export CUDA_HOME="${CUDA_HOME:-/usr/local/cuda-12.8}" -# Install missing cuDNN headers, HybridEP RDMA headers, and Ninja build tools. -missing_packages=() -for package in libcudnn9-headers-cuda-12 libibverbs-dev ninja-build; do - if ! dpkg-query -W "${package}" >/dev/null 2>&1; then - missing_packages+=("${package}") +log() { + echo "[art-megatron-setup] $*" +} + +fail() { + log "$*" >&2 + exit 1 +} + +detect_cuda_home() { + local candidate latest="" + if [ -n "${CUDA_HOME:-}" ] && [ -x "${CUDA_HOME}/bin/nvcc" ]; then + echo "${CUDA_HOME}" + return fi + if [ -x /usr/local/cuda/bin/nvcc ]; then + echo /usr/local/cuda + return + fi + for candidate in /usr/local/cuda-*; do + if [ -x "${candidate}/bin/nvcc" ]; then + latest="${candidate}" + fi + done + [ -n "${latest}" ] || fail "Could not find CUDA nvcc; set CUDA_HOME." + echo "${latest}" +} + +cuda_version() { + local version + version="$("$1/bin/nvcc" --version | sed -n 's/.*release \([0-9][0-9]*\)\.\([0-9][0-9]*\).*/\1 \2/p' | head -1)" + [ -n "${version}" ] || fail "Could not parse CUDA version from $1/bin/nvcc." + echo "${version}" +} + +detect_cuda_arch() { + local arch + if [ "${ART_MEGATRON_SETUP_RESPECT_TORCH_CUDA_ARCH_LIST:-0}" = "1" ] && [ -n "${TORCH_CUDA_ARCH_LIST:-}" ]; then + echo "${TORCH_CUDA_ARCH_LIST}" + return + fi + arch="$(nvidia-smi --query-gpu=compute_cap --format=csv,noheader 2>/dev/null | head -1 | tr -d '[:space:]')" + [ -n "${arch}" ] || fail "Could not detect GPU compute capability; set TORCH_CUDA_ARCH_LIST." + echo "${arch}" +} + +configure_nvcc_wrapper() { + local real_cuda_home="$1" compute="$2" wrapper_root="$3" + mkdir -p "${wrapper_root}/bin" + for path in include lib lib64 targets; do + ln -sfn "${real_cuda_home}/${path}" "${wrapper_root}/${path}" + done + cat >"${wrapper_root}/bin/nvcc" </dev/null 2>&1; then + apt-cache show "${package}" >/dev/null 2>&1 || fail "Required apt package ${package} is unavailable." + missing+=("${package}") + fi + done + [ "${#missing[@]}" -gt 0 ] || return 0 + log "Installing apt dependencies: ${missing[*]}" if [ "$(id -u)" -eq 0 ]; then apt-get update - apt-get install -y "${missing_packages[@]}" + apt-get install -y "${missing[@]}" elif command -v sudo >/dev/null 2>&1 && sudo -n true >/dev/null 2>&1; then sudo apt-get update - sudo apt-get install -y "${missing_packages[@]}" + sudo apt-get install -y "${missing[@]}" else - echo "Missing required packages: ${missing_packages[*]}" >&2 - echo "Install them as root or run with passwordless sudo available." >&2 - exit 1 + fail "Need root or passwordless sudo to install: ${missing[*]}" fi -fi +} + +install_runtime_profile() { + local source="$1" destination="${ART_MEGATRON_RUNTIME_PROFILE:-/etc/profile.d/50-art-megatron-env.sh}" + if [ -w "$(dirname "${destination}")" ]; then + install -m 0644 "${source}" "${destination}" + elif command -v sudo >/dev/null 2>&1 && sudo -n true >/dev/null 2>&1; then + sudo install -m 0644 "${source}" "${destination}" + else + fail "Need root or passwordless sudo to install ${destination}." + fi + echo "${destination}" +} -# Python dependencies are declared in pyproject.toml extras. The vLLM runtime -# lives in its own project and venv under vllm_runtime/. script_dir="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd)" repo_root="$(cd -- "${script_dir}/../../.." && pwd)" +real_cuda_home="$(detect_cuda_home)" +read -r cuda_major cuda_minor <<<"$(cuda_version "${real_cuda_home}")" +export TORCH_CUDA_ARCH_LIST="$(detect_cuda_arch)" +export CUDA_ARCH_LIST="${TORCH_CUDA_ARCH_LIST}" +cuda_compute="${TORCH_CUDA_ARCH_LIST%%[ ,;]*}" +cuda_compute="${cuda_compute%%+PTX}" +cuda_compute="${cuda_compute//./}" + +case "${cuda_major}" in + 12) + distributed_extra="distributed" + megatron_extra="megatron" + export APEX_CUDA_EXT="${APEX_CUDA_EXT:-1}" + export APEX_FAST_LAYER_NORM="${APEX_FAST_LAYER_NORM:-1}" + ;; + 13) + distributed_extra="distributed-cu130" + megatron_extra="megatron-cu130" + export APEX_CUDA_EXT="${APEX_CUDA_EXT:-0}" + export APEX_FAST_LAYER_NORM="${APEX_FAST_LAYER_NORM:-0}" + ;; + *) + fail "Unsupported CUDA major ${cuda_major}; expected 12 or 13." + ;; +esac + +export CUDA_HOME="${real_cuda_home}" +export PATH="${CUDA_HOME}/bin:${PATH}" +export LD_LIBRARY_PATH="${CUDA_HOME}/lib64:${CUDA_HOME}/lib:${LD_LIBRARY_PATH:-}" +install_packages "libcudnn9-headers-cuda-${cuda_major}" "cuda-cccl-${cuda_major}-${cuda_minor}" libibverbs-dev ninja-build + +if [ ! -f "${CUDA_HOME}/include/cuda/std/tuple" ]; then + tuple_path="$(find "${CUDA_HOME}/targets" -path '*/include/cccl/cuda/std/tuple' -print -quit 2>/dev/null)" + [ -n "${tuple_path}" ] || fail "Could not find CUDA CCCL headers." + cccl_include="$(dirname "$(dirname "$(dirname "${tuple_path}")")")" + export CPATH="${cccl_include}:${CPATH:-}" +fi + +if [ "${cuda_major}" = "13" ]; then + cuda_wrapper="$(mktemp -d "${TMPDIR:-/tmp}/art-megatron-cuda13-sm${cuda_compute}.XXXXXX")" + trap 'rm -rf "${cuda_wrapper}"' EXIT + configure_nvcc_wrapper "${real_cuda_home}" "${cuda_compute}" "${cuda_wrapper}" + export CUDA_HOME="${cuda_wrapper}" + export PATH="${CUDA_HOME}/bin:${real_cuda_home}/bin:${PATH}" +fi + +log "CUDA_HOME=${real_cuda_home}, profiles=${distributed_extra}+${megatron_extra}, arch=${TORCH_CUDA_ARCH_LIST}" cd "${repo_root}" uv_bin="uv" if [ -x "${HOME}/.local/bin/uv" ]; then uv_bin="${HOME}/.local/bin/uv" fi -"${uv_bin}" sync --extra megatron --frozen --active -"${uv_bin}" run --active --frozen --no-sync python -m art.megatron.hybrid_ep_setup +"${uv_bin}" sync --extra "${distributed_extra}" --extra "${megatron_extra}" --no-sources-package transformer-engine --frozen --active --inexact + +runtime_library_path="" +for library_dir in \ + "${repo_root}"/.venv/lib/python*/site-packages/nvidia/*/lib \ + /usr/local/art-multinode/nixl/lib/x86_64-linux-gnu \ + /usr/local/art-multinode/ucx/lib \ + "${real_cuda_home}/lib64" \ + "${real_cuda_home}/lib"; do + [ ! -d "${library_dir}" ] || runtime_library_path="${runtime_library_path:+${runtime_library_path}:}${library_dir}" +done +[ -n "${runtime_library_path}" ] || fail "Could not find the installed CUDA runtime libraries." +export LD_LIBRARY_PATH="${runtime_library_path}${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}" +runtime_profile="${repo_root}/.venv/art-megatron-env.sh" +{ + echo '# Generated by ART Megatron setup. Re-run setup after moving this checkout.' + printf 'export CUDA_HOME=%q\n' "${real_cuda_home}" + printf 'export TORCH_CUDA_ARCH_LIST=%q\n' "${TORCH_CUDA_ARCH_LIST}" + printf 'export CUDA_ARCH_LIST=%q\n' "${CUDA_ARCH_LIST}" + printf 'export PATH=%q/bin${PATH:+:${PATH}}\n' "${real_cuda_home}" + printf 'export LD_LIBRARY_PATH=%q${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}\n' "${runtime_library_path}" +} >"${runtime_profile}" +runtime_profile="$(install_runtime_profile "${runtime_profile}")" +bash -n "${runtime_profile}" +env -u LD_LIBRARY_PATH bash --noprofile --norc -c \ + 'source "$1"; "$2" -c "import torch; import transformer_engine.pytorch"' \ + bash "${runtime_profile}" "${repo_root}/.venv/bin/python" +"${repo_root}/.venv/bin/python" - < None: + self.payload = payload + self.fences = fences + self._sources = sources + + def resolve(self) -> _T: + for fence in self.fences: + fence.event.synchronize() + self._sources = () + return self.payload + + +class PinnedCpuSnapshotBuilder: + def __init__(self, stager: "PinnedCpuSnapshotStager") -> None: + self._stager = stager + self._devices: set[int] = set() + self._sources: list[torch.Tensor] = [] + + def stage(self, tensor: torch.Tensor) -> torch.Tensor: + source = tensor.detach() + if not source.is_cuda: + return source.to(device="cpu", copy=True) + source = source.contiguous() + device = source.device.index + if device is None: + raise RuntimeError("CUDA snapshot tensor has no device index") + stream = self._stager.stream(device) + target = torch.empty_like(source, device="cpu", pin_memory=True) + stream.wait_stream(torch.cuda.current_stream(device)) + with torch.cuda.stream(stream): + target.copy_(source, non_blocking=True) + source.record_stream(stream) + self._devices.add(device) + self._sources.append(source) + return target + + def finish(self, payload: _T) -> PendingCpuSnapshot[_T]: + fences: list[_CudaFence] = [] + for device in sorted(self._devices): + stream = self._stager.stream(device) + with torch.cuda.device(device), torch.cuda.stream(stream): + event = torch.cuda.Event(blocking=True) + event.record(stream) + fences.append(_CudaFence(device, event)) + return PendingCpuSnapshot(payload, tuple(fences), tuple(self._sources)) + + +class PinnedCpuSnapshotStager: + def __init__(self) -> None: + self._streams: dict[int, torch.cuda.Stream] = {} + + def stream(self, device: int) -> torch.cuda.Stream: + stream = self._streams.get(device) + if stream is None: + with torch.cuda.device(device): + stream = torch.cuda.Stream() + self._streams[device] = stream + return stream + + def begin(self) -> PinnedCpuSnapshotBuilder: + return PinnedCpuSnapshotBuilder(self) + + +class SnapshotReadBarrier: + """Lets forward/backward overlap snapshots while fencing optimizer mutation.""" + + def __init__(self) -> None: + self._lock = Lock() + self._fences: list[_CudaFence] = [] + + def register(self, snapshot: PendingCpuSnapshot[Any]) -> None: + with self._lock: + self._fences.extend(snapshot.fences) + + def wait_before_mutation(self) -> None: + for fence in self._take(): + torch.cuda.current_stream(fence.device).wait_event(fence.event) + + def synchronize(self) -> None: + for fence in self._take(): + fence.event.synchronize() + + def _take(self) -> tuple[_CudaFence, ...]: + with self._lock: + fences = tuple(self._fences) + self._fences.clear() + return fences diff --git a/src/art/megatron/train.py b/src/art/megatron/train.py index 284620115..4285b9683 100644 --- a/src/art/megatron/train.py +++ b/src/art/megatron/train.py @@ -7,18 +7,18 @@ install_art_bridge_runtime_patches() # isort: on -"""Megatron training runtime and public worker API. +"""Megatron training runtime and typed executor API. Public cross-repo API consumed by serverless-training: - build_training_runtime -- run_megatron_worker_loop +- execute_megatron_rl_job +- execute_megatron_sft_job """ -import json import math import os import random -import shutil +from threading import Event import time from typing import Any, Callable, Literal, cast @@ -32,6 +32,7 @@ from art import dev, types from art.loss import ( + AlignedLossInputs, Loss, LossInputs, LossOffPolicyDiagnosticsAccumulator, @@ -42,6 +43,7 @@ DispatchedPackedTensors, ParallelTopology, PreparedMegatronBatch, + TrainingStepWorkload, ) from art.megatron.lora import apply_lora_adapters from art.megatron.megatron_patches import install_fast_frozen_output_backward @@ -50,10 +52,8 @@ load_lora_tensors_for_megatron, ) from art.megatron.optimizer_state import ( - commit_optimizer_generation, - optimizer_generation_files, - read_optimizer_commit, - resolve_optimizer_shard_path, + ALLOW_UNPAIRED_MEGATRON_RESUME_ENV, + load_optimizer_state, ) from art.megatron.provider import ( ProviderBundle, @@ -63,22 +63,16 @@ from art.megatron.routing_replay import ( MoeRoutingReplayBundle, MoeRoutingReplayController, + build_moe_routing_replay_bundle_from_packed_tensors, ) -from art.megatron.runtime.jobs import ( - DEFAULT_JOBS_DIR, - DEFAULT_VLLM_WAKE_LOCK_PATH, - LORA_READY_EVENT, - OPTIMIZER_READY_EVENT, - MegatronJob, - MegatronMergedTrainingJob, - MegatronOptimizerSaveJob, - MegatronSFTTrainingJob, - MegatronSyncJob, - MegatronTrainingJob, - MergedWeightTransferInitInfo, - MergedWeightTransferSpec, - load_megatron_job, +from art.megatron.runtime.data_plane import SFTBatchData +from art.megatron.runtime.specs import SFTJobSpec, TrainJobSpec +from art.megatron.runtime.weight_transfer import MergedWeightTransferInitInfo +from art.megatron.selective_lm_head import ( + TokenLossOutput, + forward_token_losses, ) +from art.megatron.tensor_snapshot import SnapshotReadBarrier from art.megatron.training.compile import ( configure_training_compile, ) @@ -95,7 +89,6 @@ _clone_sft_tensors, _count_sft_trainable_tokens, _count_trainable_tokens, - _empty_new_logprobs_from_logits, _local_trainable_sft_token_count_tensor, _local_trainable_token_count_tensor, _next_micro_lookahead, @@ -108,7 +101,6 @@ _zero_contribution_inputs, _zero_contribution_sft_inputs, build_micro_sample_indices, - build_micro_sample_indices_by_dp_rank, build_rl_hybridep_token_counts, build_sft_hybridep_token_counts, resolve_global_grad_accumulation_sequences, @@ -121,22 +113,22 @@ as_megatron_api_chunks, validate_model_chunks, ) -from art.megatron.training.sft_batches import load_sft_batch_from_disk +from art.megatron.training.pipeline_schedule import ( + MCoreScheduleAdapter, + PipelineMicrobatchState, + ScheduleMicrobatch, + _set_hybridep_token_count, + _validate_hybridep_token_counts, + chunk_post_process, + chunk_pre_process, + validate_pipeline_topology, +) from art.megatron.training.trace import ( attach_trace_token_uids, context_parallel_trace_token_uids_enabled, - prepare_replay_local_input_token_uids, -) -from art.megatron.training.weight_offload import WeightOffloadManager -from art.megatron.weights.lora_publish import save_vllm_lora_from_model -from art.megatron.weights.merged_weight_export import ( - sync_merged_weights_to_vllm, ) from art.metrics_taxonomy import TRAIN_GRADIENT_STEPS_KEY -from art.preprocessing.pack import ( - PackedTensors, - packed_tensors_from_dir, -) +from art.preprocessing.pack import PackedTensors DEFAULT_MODEL_IDENTIFIER = "Qwen/Qwen3-30B-A3B-Instruct-2507" _optimizer_stats_printed = False @@ -145,10 +137,8 @@ "DEFAULT_MODEL_IDENTIFIER", "TrainingRuntime", "build_training_runtime", - "run_megatron_worker_loop", - "run_megatron_rl_job", - "run_megatron_sft_job", - "finalize_megatron_job", + "execute_megatron_rl_job", + "execute_megatron_sft_job", ] @@ -162,11 +152,14 @@ class TrainingRuntime(BaseModel): optimizer_config: OptimizerConfig optimizer_persistent: bool = True resident_training_session_id: str | None = None - resident_optimizer_state_path: str | None = None resident_policy_step: int | None = None - resident_optimizer_dirty: bool = False optimizer_state_loaded: bool = False adapter_export_dtypes: dict[str, torch.dtype] | None = None + adapter_export_config: dict[str, Any] | None = None + snapshot_pool_capacity: int = Field(default=2, ge=1, le=4) + optimizer_snapshot_barrier: SnapshotReadBarrier = Field( + default_factory=SnapshotReadBarrier + ) transformer_layers_compiled: bool = False rank: int world_size: int @@ -203,7 +196,9 @@ class TrainStepResult(BaseModel): update_successful: bool grad_norm: float num_zeros_in_grad: int | None + workload: TrainingStepWorkload loss_metrics: dict[str, float] = Field(default_factory=dict) + pipeline_metrics: dict[str, float] = Field(default_factory=dict) def print0(rank: int, *values: Any) -> None: @@ -363,9 +358,30 @@ def _enable_native_moe_routing_replay(provider: Any) -> None: provider.moe_enable_routing_replay = True +def _is_bridge_hf_load_hook(hook: Any) -> bool: + function = hook + seen: set[int] = set() + while id(function) not in seen: + seen.add(id(function)) + if getattr(function, "__name__", "") in { + "load_weights_hf_to_megatron", + "_optimized_load_weights_hf_to_megatron", + } or getattr(function, "__qualname__", "").endswith( + ".load_weights_hf_to_megatron" + ): + return True + function = getattr(function, "func", None) or getattr( + function, "__wrapped__", None + ) + if function is None: + return False + return False + + def build_training_runtime( *, model_identifier: str | None = None, + model_initialization: Literal["pretrained", "random"] = "pretrained", provider_torch_dtype: torch.dtype = torch.bfloat16, provider_bundle_configure: Callable[[ProviderBundle], None] | None = None, provider_configure: Callable[[Any], None] | None = None, @@ -377,6 +393,8 @@ def build_training_runtime( build_optimizer: bool = True, trainable_parameter_mode: Literal["lora", "base_model"] = "lora", allow_unvalidated_arch: bool | None = None, + model_support_key: str | None = None, + snapshot_pool_capacity: int = 2, ) -> TrainingRuntime: if random_state := os.environ.get("ART_MEGATRON_RANDOM_STATE"): seed = int(random_state) @@ -389,13 +407,25 @@ def build_training_runtime( model_identifier or os.environ.get("MODEL_IDENTIFIER", DEFAULT_MODEL_IDENTIFIER), torch_dtype=provider_torch_dtype, + load_weights=model_initialization == "pretrained", allow_unvalidated_arch=( os.environ.get("ART_MEGATRON_ALLOW_UNVALIDATED_ARCH", "").strip().lower() in {"1", "true", "yes", "on"} if allow_unvalidated_arch is None else allow_unvalidated_arch ), + model_support_key=model_support_key, ) + if model_initialization == "random": + hooks = list(getattr(provider_bundle.provider, "_pre_wrap_hooks", ())) + checkpoint_hooks = [hook for hook in hooks if _is_bridge_hf_load_hook(hook)] + if len(checkpoint_hooks) != len(hooks): + raise RuntimeError( + "random model initialization requires only Bridge checkpoint loaders; " + f"found {len(checkpoint_hooks)} loaders among {len(hooks)} hooks" + ) + provider_bundle.provider._pre_wrap_hooks = [] + provider_bundle.provider.perform_initialization = True if provider_bundle_configure is not None: provider_bundle_configure(provider_bundle) provider = provider_bundle.provider @@ -421,7 +451,7 @@ def build_training_runtime( average_in_collective=False, ), data_parallel_random_init=False, - init_model_with_meta_device=True, + init_model_with_meta_device=model_initialization == "pretrained", ), ) @@ -431,6 +461,20 @@ def build_training_runtime( ) rank = torch.distributed.get_rank() # ty: ignore[possibly-missing-attribute] world_size = torch.distributed.get_world_size() # ty: ignore[possibly-missing-attribute] + validate_pipeline_topology( + world_size=world_size, + tp=int(ps.get_tensor_model_parallel_world_size()), + cp=int(ps.get_context_parallel_world_size()), + pp=int(ps.get_pipeline_model_parallel_world_size()), + ep=int(ps.get_expert_model_parallel_world_size()), + etp=int(ps.get_expert_tensor_parallel_world_size()), + vp=int(ps.get_virtual_pipeline_model_parallel_world_size() or 1), + num_layers=( + None + if provider.pipeline_model_parallel_layout is not None + else int(provider.num_layers) + ), + ) if rank == 0 and print_env: print("TORCHINDUCTOR_CACHE_DIR:", os.environ["TORCHINDUCTOR_CACHE_DIR"]) @@ -456,6 +500,7 @@ def build_training_runtime( transformer_layers_compiled=transformer_layers_compiled, rank=rank, world_size=world_size, + snapshot_pool_capacity=snapshot_pool_capacity, ) configure_moe_routing_replay( runtime, @@ -466,67 +511,22 @@ def build_training_runtime( return runtime -def _poll_next_megatron_job_path( - runtime: TrainingRuntime, - jobs_dir: str, -) -> str | None: - selected_job: list[str | None] = [None] - if runtime.rank == 0: - os.makedirs(jobs_dir, exist_ok=True) - job_names = sorted( - job_name for job_name in os.listdir(jobs_dir) if job_name.endswith(".json") - ) - if job_names: - selected_job[0] = os.path.join(jobs_dir, job_names[0]) - torch.distributed.broadcast_object_list(selected_job, src=0) # type: ignore[possibly-missing-attribute] - return selected_job[0] - - -def run_megatron_worker_loop( +def execute_megatron_rl_job( runtime: TrainingRuntime, + job: TrainJobSpec, + packed_tensors: PackedTensors, *, - supports_sft: bool, - wait_until_ready: Callable[[], None] | None = None, - before_job: Callable[[], None] | None = None, - after_job: Callable[[], None] | None = None, -) -> None: - jobs_dir = os.environ.get("ART_MEGATRON_JOBS_DIR", DEFAULT_JOBS_DIR) - while True: - job_path = _poll_next_megatron_job_path(runtime, jobs_dir) - if job_path is None: - time.sleep(0.05) - continue - - if wait_until_ready is not None: - wait_until_ready() - if before_job is not None: - before_job() - - job = _load_megatron_job(job_path, supports_sft=supports_sft) - print0(runtime.rank, "Loaded job from", job_path) - print0(runtime.rank, "Job:", job) - - job_completed = False - try: - _run_megatron_job(runtime, job) - job_completed = True - finally: - if job_completed and after_job is not None: - after_job() - - finalize_megatron_job( - runtime, - job_path=job_path, - log_path=job.log_path, - cleanup_path=_job_cleanup_path(job), - ) - - -def run_megatron_rl_job( - runtime: TrainingRuntime, - job: MegatronTrainingJob | MegatronMergedTrainingJob, -) -> None: - packed_tensors = None + progress_sink: Callable[[int, int, dict[str, float]], None], + adapter_ready_sink: Callable[[], None] | None, + snapshot_sink: Callable[ + [TrainJobSpec, dict[str, torch.dtype], dict[str, Any], bool], + dict[str, float], + ] + | None = None, + cancelled: Event | None = None, + replay_bundle: MoeRoutingReplayBundle | None = None, +) -> dict[str, float]: + """Execute one current RL update from an in-memory packed batch.""" adapter_dtypes = None template = None zero_template = None @@ -536,33 +536,63 @@ def run_megatron_rl_job( next_step_first_ref_logprobs = None step_result = None job_succeeded = False + final_metrics: dict[str, float] = {} try: + global_grad_accumulation_sequences = resolve_global_grad_accumulation_sequences( + job.config.grad_accumulation_sequences + ) + replay_finalize_started = time.perf_counter() + if ( + replay_bundle is None + and packed_tensors.get("moe_routing_replay") is not None + ): + replay_bundle = build_moe_routing_replay_bundle_from_packed_tensors( + packed_tensors=packed_tensors, + global_grad_accumulation_sequences=global_grad_accumulation_sequences, + ) configure_moe_routing_replay( runtime, - replay_bundle_path=job.moe_routing_replay_path, - strict=job.moe_routing_replay_strict, + replay_bundle=replay_bundle, + strict=_moe_replay_strict(job), ) + replay_finalize_s = time.perf_counter() - replay_finalize_started adapter_dtypes = _prepare_rl_training_state(runtime, job) - print0( - runtime.rank, - "Loading packed tensors from", - job.disk_packed_tensors["dir"], - ) - packed_tensors = packed_tensors_from_dir(**job.disk_packed_tensors) template = _clone_packed_tensors(select_indexed_inputs(packed_tensors, 0)) zero_template = _zero_contribution_inputs(template) - num_sequences = job.disk_packed_tensors["num_sequences"] - global_grad_accumulation_sequences = resolve_global_grad_accumulation_sequences( - job.config.grad_accumulation_sequences - ) + num_sequences, packed_sequence_length = map(int, packed_tensors["tokens"].shape) num_steps = math.ceil(num_sequences / global_grad_accumulation_sequences) topology = _infer_parallel_topology(runtime.model) + has_local_loss_stage = any(chunk_post_process(chunk) for chunk in runtime.model) + hybridep_token_counts_by_step = ( + [ + build_rl_hybridep_token_counts( + packed_tensors=packed_tensors, + step_index=step_index, + num_sequences=num_sequences, + global_grad_accumulation_sequences=global_grad_accumulation_sequences, + topology=topology, + provider=runtime.provider, + model_support_handler=runtime.model_support_handler, + ) + for step_index in range(num_steps) + ] + if ps.get_expert_model_parallel_world_size() > 1 + else None + ) _ensure_hybridep_capacity( runtime, - packed_sequence_length=job.disk_packed_tensors["sequence_length"], + packed_sequence_length=packed_sequence_length, context_parallel_size=topology.cp, + required_capacity=max( + ( + count + for step_counts in hybridep_token_counts_by_step or () + for count in step_counts + ), + default=0, + ), ) ref_logprobs_by_index = _prepare_kl_reference_logprobs( runtime=runtime, @@ -574,18 +604,14 @@ def run_megatron_rl_job( ) cp_lookahead_state = CpBatchLookaheadState() if int(topology.cp) > 1 else None for step_index in range(num_steps): + if cancelled is not None and cancelled.is_set(): + from art.megatron.runtime.trainer_run import TrainingCancelledError + + raise TrainingCancelledError("train job was cancelled") hybridep_token_counts = ( - build_rl_hybridep_token_counts( - packed_tensors=packed_tensors, - step_index=step_index, - num_sequences=num_sequences, - global_grad_accumulation_sequences=global_grad_accumulation_sequences, - topology=topology, - provider=runtime.provider, - model_support_handler=runtime.model_support_handler, - ) - if ps.get_expert_model_parallel_world_size() > 1 - else None + None + if hybridep_token_counts_by_step is None + else hybridep_token_counts_by_step[step_index] ) micro_indices = build_micro_sample_indices( step_index=step_index, @@ -603,7 +629,7 @@ def run_megatron_rl_job( micro_indices, zero_template, ) - if ref_logprobs_by_index is not None + if ref_logprobs_by_index is not None and has_local_loss_stage else None ) next_step_first_micro = ( @@ -627,7 +653,11 @@ def run_megatron_rl_job( num_sequences=num_sequences, global_grad_accumulation_sequences=global_grad_accumulation_sequences, ) - if cp_lookahead_state is not None and ref_logprobs_by_index is not None + if ( + cp_lookahead_state is not None + and ref_logprobs_by_index is not None + and has_local_loss_stage + ) else None ) train_step_started = time.perf_counter() @@ -639,7 +669,7 @@ def run_megatron_rl_job( learning_rate=job.config.learning_rate, inputs=micro_inputs, config=job.config, - experimental_config=cast(dev.TrainConfig, job.experimental_config), + experimental_config=_experimental_train_config(job), ref_logprobs=ref_logprobs, step_index=step_index, sample_index=micro_indices, @@ -648,54 +678,59 @@ def run_megatron_rl_job( next_step_first_micro=next_step_first_micro, next_step_first_ref_logprobs=next_step_first_ref_logprobs, hybridep_token_counts=hybridep_token_counts, + before_optimizer_step=( + runtime.optimizer_snapshot_barrier.wait_before_mutation + ), ) train_step_s = time.perf_counter() - train_step_started - global_packed_train_tokens = _global_packed_train_tokens( - packed_tensors, - step_index=step_index, - num_sequences=num_sequences, - global_grad_accumulation_sequences=global_grad_accumulation_sequences, - ) print0( runtime.rank, "Correlation between old and new probabilities:", step_result.probs_corr, ) _validate_train_step_result_finite(runtime, step_result) - _log_rl_step_result( - runtime.rank, - job.log_path, + final_metrics = _rl_step_metrics( step_result, num_gradient_steps=num_steps, - packed_train_tokens=global_packed_train_tokens, train_step_s=train_step_s, ) - - runtime.resident_optimizer_dirty = True - _save_lora_and_optimizer( - runtime, - adapter_dtypes=adapter_dtypes, - lora_path=job.lora_path, - optimizer_state_path=job.optimizer_state_path, - step=job.step, - optimizer_save_interval=job.config.optimizer_save_interval, - lora_ready_log_path=( - None if isinstance(job, MegatronMergedTrainingJob) else job.log_path - ), - optimizer_ready_log_path=job.log_path, + final_metrics["time/replay_finalize_s"] = replay_finalize_s + if runtime.rank == 0: + progress_sink(step_index, num_steps, final_metrics) + + if cancelled is not None and cancelled.is_set(): + from art.megatron.runtime.trainer_run import TrainingCancelledError + + raise TrainingCancelledError("train job was cancelled") + + if snapshot_sink is None or adapter_ready_sink is None: + raise RuntimeError("Typed training requires a snapshot publisher") + if runtime.adapter_export_config is None: + raise RuntimeError("Trainer has no resident adapter export config") + final_metrics.update( + snapshot_sink( + job, + adapter_dtypes, + runtime.adapter_export_config, + _should_snapshot_optimizer( + runtime, + step=job.step, + optimizer_save_interval=job.config.optimizer_save_interval, + final_training_step=job.config.final_training_step, + ), + ) ) + adapter_ready_sink() runtime.resident_training_session_id = job.training_session_id - runtime.resident_optimizer_state_path = os.path.realpath( - job.optimizer_state_path - ) runtime.resident_policy_step = job.step runtime.optimizer_state_loaded = True job_succeeded = True + return final_metrics finally: if not job_succeeded: - _clear_resident_optimizer(runtime) - if packed_tensors is not None: - del packed_tensors + runtime.resident_training_session_id = None + runtime.resident_policy_step = None + runtime.optimizer_state_loaded = False if adapter_dtypes is not None: del adapter_dtypes if template is not None: @@ -717,70 +752,59 @@ def run_megatron_rl_job( del cp_lookahead_state -def run_megatron_sft_job( +def execute_megatron_sft_job( runtime: TrainingRuntime, - job: MegatronSFTTrainingJob, -) -> None: + job: SFTJobSpec, + batches: tuple[SFTBatchData, ...], + *, + progress_sink: Callable[[int, int, dict[str, float]], None], + adapter_ready_sink: Callable[[], None], + snapshot_sink: Callable[ + [SFTJobSpec, dict[str, Any], dict[str, Any], bool], dict[str, float] + ] + | None = None, + cancelled: Event | None = None, +) -> dict[str, float]: + """Execute SFT from in-memory batches; callers own transport and events.""" + if len(batches) != job.num_batches: + raise ValueError("SFT job batch count does not match its payload") adapter_dtypes = None - + succeeded = False + final_metrics: dict[str, float] = {} try: configure_moe_routing_replay(runtime) - adapter_dtypes = _prepare_sft_training_state(runtime, job) - + adapter_dtypes = _prepare_rl_training_state(runtime, job) + grad_accumulation_sequences = int(job.config.batch_size) + grad_accumulation_sequences = resolve_global_grad_accumulation_sequences( + grad_accumulation_sequences + ) assert runtime.optimizer is not None runtime.optimizer.config.clip_grad = job.max_grad_norm for param_group in runtime.optimizer.param_groups: param_group["weight_decay"] = job.weight_decay + topology = _infer_parallel_topology(runtime.model) - grad_accumulation_sequences = resolve_global_grad_accumulation_sequences( - job.grad_accumulation_sequences - ) - checkpoint_interval = job.internal_checkpoint_interval - - for batch_idx in range(job.num_batches): - batch_start_time = time.perf_counter() - batch_dir = os.path.join(job.sft_data_dir, f"batch_{batch_idx:06d}") - batch_metadata, trajectory_tensors = load_sft_batch_from_disk(batch_dir) - num_trajectories = int(batch_metadata["num_trajectories"]) - if not trajectory_tensors: - raise RuntimeError(f"SFT batch {batch_idx} is empty") - if num_trajectories != len(trajectory_tensors): - raise RuntimeError( - "SFT batch metadata does not match trajectory count: " - f"{num_trajectories} != {len(trajectory_tensors)}" - ) + for batch_index, batch in enumerate(batches): + if cancelled is not None and cancelled.is_set(): + from art.megatron.runtime.trainer_run import TrainingCancelledError - global_tokens = max( - int(batch_metadata.get("num_tokens", 0)), - 1, - ) - if "num_tokens" not in batch_metadata: - global_tokens = max( - sum( - int(inputs["attention_mask"].sum().item()) - for inputs in trajectory_tensors - ), - 1, - ) - global_trainable_tokens = max( - int(batch_metadata["num_trainable_tokens"]), - 1, - ) + raise TrainingCancelledError("SFT job was cancelled") + started = time.perf_counter() + trajectory_tensors = list(batch.trajectory_tensors) template = _clone_sft_tensors(trajectory_tensors[0]) zero_template = _zero_contribution_sft_inputs(template) - topology = _infer_parallel_topology(runtime.model) - _ensure_hybridep_capacity( - runtime, - packed_sequence_length=max( - int(inputs["input_ids"].numel()) for inputs in trajectory_tensors - ), - context_parallel_size=topology.cp, - ) + # Scheduling uses run-global sample IDs while each payload only owns one + # batch. Prefix aliases place this window in global index space without + # copying tensors, then selected IDs are rebased for local lookup. + sample_offset = batch_index * grad_accumulation_sequences + scheduled_tensors = [ + trajectory_tensors[0] + ] * sample_offset + trajectory_tensors hybridep_token_counts = ( build_sft_hybridep_token_counts( - trajectory_tensors=trajectory_tensors, - step_index=0, - global_grad_accumulation_sequences=grad_accumulation_sequences, + trajectory_tensors=scheduled_tensors, + step_index=batch_index, + global_grad_accumulation_sequences=(grad_accumulation_sequences), topology=topology, provider=runtime.provider, model_support_handler=runtime.model_support_handler, @@ -788,211 +812,206 @@ def run_megatron_sft_job( if ps.get_expert_model_parallel_world_size() > 1 else None ) - micro_indices = build_micro_sample_indices( - step_index=0, - num_sequences=num_trajectories, - global_grad_accumulation_sequences=grad_accumulation_sequences, + _ensure_hybridep_capacity( + runtime, + packed_sequence_length=max( + int(inputs["input_ids"].numel()) for inputs in trajectory_tensors + ), + context_parallel_size=topology.cp, + required_capacity=max(hybridep_token_counts or (), default=0), ) - micro_inputs = select_sft_micro_inputs( - trajectory_tensors, - micro_indices, - zero_template, + scheduled_indices = build_micro_sample_indices( + step_index=batch_index, + num_sequences=len(scheduled_tensors), + global_grad_accumulation_sequences=grad_accumulation_sequences, ) + micro_indices = [ + None if index is None else index - sample_offset + for index in scheduled_indices + ] step_result = run_megatron_sft_step( model_chunks=runtime.model, provider=runtime.provider, model_support_handler=runtime.model_support_handler, optimizer=runtime.optimizer, - learning_rate=job.learning_rates[batch_idx], - inputs=micro_inputs, - step_index=batch_idx, + learning_rate=batch.learning_rate, + inputs=select_sft_micro_inputs( + trajectory_tensors, micro_indices, zero_template + ), + step_index=batch_index, sample_index=micro_indices, - moe_routing_replay_controller=runtime.moe_routing_replay_controller, + moe_routing_replay_controller=(runtime.moe_routing_replay_controller), hybridep_token_counts=hybridep_token_counts, + before_optimizer_step=( + runtime.optimizer_snapshot_barrier.wait_before_mutation + ), ) - runtime.resident_optimizer_dirty = True - batch_time = time.perf_counter() - batch_start_time - tokens_per_second = global_tokens / batch_time if batch_time > 0 else 0.0 - completed_batches = batch_idx + 1 - - if ( - checkpoint_interval is not None - and completed_batches < job.num_batches - and completed_batches % checkpoint_interval == 0 - ): - _save_lora_and_optimizer( - runtime, - adapter_dtypes=adapter_dtypes, - lora_path=job.lora_path, - optimizer_state_path=job.optimizer_state_path, - step=job.step, - optimizer_save_interval=1, - optimizer_ready_log_path=job.log_path, - ) - torch.distributed.barrier() # type: ignore[possibly-missing-attribute] - + elapsed = time.perf_counter() - started + final_metrics = { + "loss/train": float(step_result.reduced_loss.item()), + "loss/learning_rate": batch.learning_rate, + "loss/grad_norm": float(step_result.grad_norm), + "throughput/train_executed_tok_equiv_per_s": ( + batch.num_tokens / elapsed if elapsed else 0.0 + ), + **step_result.pipeline_metrics, + } if runtime.rank == 0: - with open(job.log_path, "a+", encoding="utf-8") as log_file: - log_msg = json.dumps( - { - "loss": step_result.reduced_loss.item(), - "learning_rate": job.learning_rates[batch_idx], - "grad_norm": float(step_result.grad_norm), - "num_trajectories": float(num_trajectories), - "num_tokens": float(global_tokens), - "num_trainable_tokens": float(global_trainable_tokens), - "tokens_per_second": tokens_per_second, - } - ) - print("Logging SFT", log_msg) - log_file.write(log_msg + "\n") + progress_sink(batch_index, len(batches), final_metrics) + del step_result, template, zero_template - _save_lora_and_optimizer( - runtime, - adapter_dtypes=adapter_dtypes, - lora_path=job.lora_path, - optimizer_state_path=job.optimizer_state_path, - step=job.step, - optimizer_save_interval=1, - optimizer_ready_log_path=job.log_path, + if snapshot_sink is None or runtime.adapter_export_config is None: + raise RuntimeError("typed SFT requires an immutable snapshot publisher") + final_metrics.update( + snapshot_sink(job, adapter_dtypes, runtime.adapter_export_config, True) ) - runtime.resident_policy_step = job.step + adapter_ready_sink() + runtime.resident_training_session_id = job.training_session_id + runtime.resident_policy_step = job.learner_version + runtime.optimizer_state_loaded = True + succeeded = True + return final_metrics finally: + if not succeeded: + runtime.resident_training_session_id = None + runtime.resident_policy_step = None + runtime.optimizer_state_loaded = False if adapter_dtypes is not None: del adapter_dtypes -def _load_megatron_job(job_path: str, *, supports_sft: bool) -> MegatronJob: - with open(job_path, "rb") as handle: - job = load_megatron_job(handle.read()) - if isinstance(job, MegatronSFTTrainingJob) and not supports_sft: - raise NotImplementedError("SFT jobs are not supported in this worker loop") - return job - - -def _run_megatron_job(runtime: TrainingRuntime, job: MegatronJob) -> None: - if isinstance(job, MegatronOptimizerSaveJob): - _save_resident_optimizer(runtime, job) - return - if isinstance(job, MegatronSyncJob): - adapter_model = _load_adapter_into_model( - runtime.model, - job.lora_path, - runtime.rank, - handler=runtime.model_support_handler, - ) - del adapter_model - _sync_merged_weights_to_vllm( - runtime, - job.merged_weight_transfer, - lora_path=job.lora_path, - pause_generation=False, - ) - return - if isinstance(job, MegatronSFTTrainingJob): - run_megatron_sft_job(runtime, job) - return - run_megatron_rl_job(runtime, job) - if isinstance(job, MegatronMergedTrainingJob): - _sync_merged_weights_to_vllm( - runtime, - job.merged_weight_transfer, - lora_path=job.lora_path, - pause_generation=True, - ) +def _experimental_train_config(job: TrainJobSpec) -> dev.TrainConfig: + return cast( + dev.TrainConfig, + job.experimental_config.model_dump(exclude_none=True), + ) -def _job_cleanup_path(job: MegatronJob) -> str | None: - if isinstance(job, (MegatronOptimizerSaveJob, MegatronSyncJob)): - return None - if isinstance(job, MegatronSFTTrainingJob): - return job.sft_data_dir - return job.disk_packed_tensors["dir"] +def _moe_replay_strict(job: TrainJobSpec) -> bool: + return job.experimental_config.moe_routing_replay_strict -def _prepare_rl_training_state( +def _load_lora_and_optimizer( runtime: TrainingRuntime, - job: MegatronTrainingJob | MegatronMergedTrainingJob, + *, + lora_path: str, + optimizer_state_path: str, + adapter_step: int, ) -> dict[str, torch.dtype]: - return _prepare_training_state( - runtime, - training_session_id=job.training_session_id, - source_policy_step=job.source_policy_step, - lora_path=job.lora_path, - optimizer_state_path=job.optimizer_state_path, + runtime.optimizer_snapshot_barrier.synchronize() + persistent_optimizer = runtime.optimizer if runtime.optimizer_persistent else None + _load_adapter_into_model( + runtime.model, + lora_path, + runtime.rank, + handler=runtime.model_support_handler, + optimizer=persistent_optimizer, ) + if persistent_optimizer is not None: + return {} - -def _prepare_sft_training_state( - runtime: TrainingRuntime, - job: MegatronSFTTrainingJob, -) -> dict[str, torch.dtype]: - return _prepare_training_state( + runtime.optimizer = _build_optimizer( + runtime.model, + runtime.optimizer_config, + ) + assert runtime.optimizer is not None + _load_optimizer( runtime, - training_session_id=job.training_session_id, - source_policy_step=job.source_policy_step, - lora_path=job.lora_path, - optimizer_state_path=job.optimizer_state_path, + optimizer_state_path=optimizer_state_path, + adapter_path=lora_path, + adapter_step=adapter_step, + allow_missing=True, ) + return {} -def _prepare_training_state( +def _prepare_rl_training_state( runtime: TrainingRuntime, - *, - training_session_id: str, - source_policy_step: int, - lora_path: str, - optimizer_state_path: str, + job: TrainJobSpec | SFTJobSpec, ) -> dict[str, torch.dtype]: - normalized_path = os.path.realpath(optimizer_state_path) state_is_resident = ( runtime.optimizer_persistent - and runtime.resident_training_session_id == training_session_id - and runtime.resident_optimizer_state_path == normalized_path - and runtime.resident_policy_step == source_policy_step + and runtime.resident_training_session_id == job.training_session_id + and runtime.resident_policy_step == job.source_policy_step and runtime.optimizer_state_loaded + and runtime.optimizer is not None ) if state_is_resident: - if runtime.adapter_export_dtypes is None: - raise RuntimeError("Resident Megatron state has no LoRA export template") + if ( + runtime.adapter_export_dtypes is None + or runtime.adapter_export_config is None + ): + raise RuntimeError("Resident Megatron state has no LoRA export metadata") return runtime.adapter_export_dtypes - _commit_resident_optimizer(runtime) - _clear_resident_optimizer(runtime) - adapter_model = _load_adapter_into_model( + runtime.optimizer_snapshot_barrier.synchronize() + replacing_resident_state = runtime.resident_training_session_id is not None + runtime.resident_training_session_id = None + runtime.resident_policy_step = None + runtime.optimizer_state_loaded = False + runtime.adapter_export_config = None + if replacing_resident_state or not runtime.optimizer_persistent: + runtime.optimizer = None + + _load_adapter_into_model( runtime.model, - lora_path, + job.source_adapter_path, runtime.rank, handler=runtime.model_support_handler, + optimizer=runtime.optimizer, + ) + if runtime.optimizer is None: + runtime.optimizer = _build_optimizer(runtime.model, runtime.optimizer_config) + assert runtime.optimizer is not None + + _load_optimizer( + runtime, + optimizer_state_path=job.optimizer_state_path, + adapter_path=job.source_adapter_path, + adapter_step=job.source_policy_step, + allow_missing=( + job.source_policy_step == 0 + or os.environ.get(ALLOW_UNPAIRED_MEGATRON_RESUME_ENV, "").lower() + in {"1", "true", "yes"} + ), ) - runtime.optimizer = _build_optimizer(runtime.model, runtime.optimizer_config) + + # Serialize the live LoRA dtype instead of perpetuating a source checkpoint's + # PEFT-upcast FP32 dtype. + runtime.adapter_export_dtypes = {} + runtime.adapter_export_config = load_adapter_config(job.source_adapter_path) + runtime.resident_training_session_id = job.training_session_id + runtime.resident_policy_step = job.source_policy_step + runtime.optimizer_state_loaded = True + return runtime.adapter_export_dtypes + + +def _load_optimizer( + runtime: TrainingRuntime, + *, + optimizer_state_path: str, + adapter_path: str, + adapter_step: int, + allow_missing: bool, +) -> None: assert runtime.optimizer is not None - optimizer_shard_path = resolve_optimizer_shard_path( - optimizer_state_path, - rank=runtime.rank, - world_size=runtime.world_size, - expected_step=source_policy_step, + shard_path = load_optimizer_state( + runtime, + optimizer_state_path=optimizer_state_path, + adapter_path=adapter_path, + adapter_step=adapter_step, + allow_missing=allow_missing, + initialize=_eager_initialize_optimizer_state, ) - if optimizer_shard_path is None: + if shard_path is None: print0( runtime.rank, - "No optimizer state found at", + "No committed optimizer state found at", optimizer_state_path, - "- resetting optimizer for new run", + "- resetting optimizer for a new lineage", ) - _eager_initialize_optimizer_state(runtime.optimizer) - else: - print0(runtime.rank, "Loading optimizer state from", optimizer_shard_path) - runtime.optimizer.load_state_dict(torch.load(optimizer_shard_path)) - - runtime.adapter_export_dtypes = { - key: tensor.dtype for key, tensor in adapter_model.items() - } - runtime.resident_training_session_id = training_session_id - runtime.resident_optimizer_state_path = normalized_path - runtime.resident_policy_step = source_policy_step - runtime.optimizer_state_loaded = True - return runtime.adapter_export_dtypes + return + print0(runtime.rank, "Loading optimizer state from", shard_path) def _load_adapter_into_model( @@ -1014,43 +1033,6 @@ def _load_adapter_into_model( return adapter_model -def _save_lora_and_optimizer( - runtime: TrainingRuntime, - *, - adapter_dtypes: dict[str, torch.dtype], - lora_path: str, - optimizer_state_path: str, - step: int, - optimizer_save_interval: int, - lora_ready_log_path: str | None = None, - optimizer_ready_log_path: str | None = None, -) -> None: - assert runtime.optimizer is not None - save_vllm_lora_from_model( - model=runtime.model, - adapter_dtypes=adapter_dtypes, - handler=runtime.model_support_handler, - adapter_config=load_adapter_config(lora_path), - output_dir=lora_path, - rank=runtime.rank, - world_size=runtime.world_size, - ) - if lora_ready_log_path is not None and runtime.rank == 0: - _write_job_event(lora_ready_log_path, LORA_READY_EVENT, step=step) - if _should_save_optimizer( - runtime, - step=step, - optimizer_state_path=optimizer_state_path, - optimizer_save_interval=optimizer_save_interval, - ): - _save_optimizer( - runtime, - optimizer_state_path=optimizer_state_path, - step=step, - ready_log_path=optimizer_ready_log_path, - ) - - def _validate_train_step_result_finite( runtime: TrainingRuntime, step_result: TrainStepResult, @@ -1072,205 +1054,63 @@ def _validate_train_step_result_finite( ) -def _should_save_optimizer( +def _should_snapshot_optimizer( runtime: TrainingRuntime, *, step: int, - optimizer_state_path: str, optimizer_save_interval: int, + final_training_step: int | None, ) -> bool: - if not runtime.optimizer_persistent or optimizer_save_interval == 1: - return True return ( - step <= 1 + not runtime.optimizer_persistent + or optimizer_save_interval == 1 + or step <= 1 or step % optimizer_save_interval == 0 - or read_optimizer_commit(optimizer_state_path) is None + or (final_training_step is not None and step >= final_training_step) ) -def _write_job_event(log_path: str, event: str, **payload: int | float | str) -> None: - with open(log_path, "a+", encoding="utf-8") as log_file: - log_file.write(json.dumps({"event": event, **payload}) + "\n") - log_file.flush() - - -def _log_rl_step_result( - rank: int, - log_path: str, +def _rl_step_metrics( step_result: TrainStepResult, *, num_gradient_steps: int, - packed_train_tokens: int, train_step_s: float, -) -> None: - if rank != 0: - return - with open(log_path, "a+", encoding="utf-8") as log_file: - train_packed_tok_per_s = ( - float(packed_train_tokens) / train_step_s if train_step_s > 0 else 0.0 - ) - metrics = { - "loss/train": step_result.reduced_loss.item(), - "loss/grad_norm": step_result.grad_norm, - "loss/probs_corr": step_result.probs_corr, - TRAIN_GRADIENT_STEPS_KEY: num_gradient_steps, - "data/step_executed_packed_train_tokens": packed_train_tokens, - "throughput/train_packed_tok_per_s": train_packed_tok_per_s, - } - if step_result.kl_policy_ref is not None: - metrics["loss/kl_policy_ref"] = step_result.kl_policy_ref - metrics.update(step_result.loss_metrics) - log_msg = json.dumps(metrics) - print("Logging", log_msg) - log_file.write(log_msg + "\n") - - -def _global_packed_train_tokens( - packed_tensors: PackedTensors, - *, - step_index: int, - num_sequences: int, - global_grad_accumulation_sequences: int | None, -) -> int: - sample_rows = build_micro_sample_indices_by_dp_rank( - step_index=step_index, - num_sequences=num_sequences, - global_grad_accumulation_sequences=global_grad_accumulation_sequences, - ) - sequence_length = int(packed_tensors["tokens"].shape[1]) - if ps.get_context_parallel_world_size() <= 1: - return sum(len(row) for row in sample_rows) * sequence_length - return sum( - int( - (packed_tensors["group_ids"][0 if index is None else index] != -1) - .sum() - .item() - ) - for row in sample_rows - for index in row - ) +) -> dict[str, float]: + workload = step_result.workload + metrics = { + "loss/train": step_result.reduced_loss.item(), + "loss/grad_norm": step_result.grad_norm, + "loss/probs_corr": step_result.probs_corr, + TRAIN_GRADIENT_STEPS_KEY: float(num_gradient_steps), + "data/gradient_step_nonpadding_logical_tokens": float( + workload.logical_nonpadding_tokens + ), + "data/gradient_step_loss_bearing_tokens": float(workload.loss_bearing_tokens), + "data/gradient_step_executed_token_equivalents": float( + workload.executed_token_equivalents + ), + "data/gradient_step_nominal_schedule_capacity_tokens": float( + workload.nominal_schedule_capacity_tokens + ), + "data/gradient_step_dummy_executed_token_equivalents": float( + workload.dummy_executed_token_equivalents + ), + "data/gradient_step_dummy_schedule_capacity_tokens": float( + workload.dummy_schedule_capacity_tokens + ), + "pipeline/gradient_step_real_microbatches": float(workload.real_microbatches), + "pipeline/gradient_step_dummy_microbatches": float(workload.dummy_microbatches), + "time/gradient_step_train_s": train_step_s, + } + if step_result.kl_policy_ref is not None: + metrics["loss/kl_policy_ref"] = step_result.kl_policy_ref + metrics.update(step_result.loss_metrics) + metrics.update(step_result.pipeline_metrics) + return metrics -def _save_optimizer( - runtime: TrainingRuntime, - *, - optimizer_state_path: str, - step: int, - ready_log_path: str | None = None, - commit: bool = False, -) -> None: - assert runtime.optimizer is not None - files = optimizer_generation_files(step, runtime.world_size) - optimizer_shard_path = os.path.join(optimizer_state_path, files[runtime.rank]) - temporary_path = f"{optimizer_shard_path}.tmp" - print("Saving optimizer shard to", optimizer_shard_path) - os.makedirs(optimizer_state_path, exist_ok=True) - with open(temporary_path, "wb") as handle: - torch.save(runtime.optimizer.state_dict(), handle) - handle.flush() - os.fsync(handle.fileno()) - os.replace(temporary_path, optimizer_shard_path) - directory_fd = os.open(optimizer_state_path, os.O_RDONLY) - try: - os.fsync(directory_fd) - finally: - os.close(directory_fd) - if torch.distributed.is_initialized(): # ty:ignore[possibly-missing-attribute] - torch.distributed.barrier() # ty:ignore[possibly-missing-attribute] - if runtime.rank == 0 and commit: - commit_optimizer_generation( - optimizer_state_path, - step=step, - world_size=runtime.world_size, - files=files, - ) - if runtime.rank == 0 and ready_log_path is not None: - _write_job_event( - ready_log_path, - OPTIMIZER_READY_EVENT, - step=step, - world_size=runtime.world_size, - ) - if commit and torch.distributed.is_initialized(): # ty:ignore[possibly-missing-attribute] - torch.distributed.barrier() # ty:ignore[possibly-missing-attribute] - runtime.resident_optimizer_dirty = False - - -def _commit_resident_optimizer(runtime: TrainingRuntime) -> None: - if not runtime.resident_optimizer_dirty: - return - if ( - runtime.optimizer is None - or runtime.resident_policy_step is None - or runtime.resident_optimizer_state_path is None - ): - raise RuntimeError("Dirty resident optimizer has incomplete identity") - _save_optimizer( - runtime, - optimizer_state_path=runtime.resident_optimizer_state_path, - step=runtime.resident_policy_step, - commit=True, - ) - - -def _clear_resident_optimizer(runtime: TrainingRuntime) -> None: - runtime.optimizer = None - runtime.resident_training_session_id = None - runtime.resident_optimizer_state_path = None - runtime.resident_policy_step = None - runtime.resident_optimizer_dirty = False - runtime.optimizer_state_loaded = False - runtime.adapter_export_dtypes = None - - -def _save_resident_optimizer( - runtime: TrainingRuntime, - job: MegatronOptimizerSaveJob, -) -> None: - expected_path = os.path.realpath(job.optimizer_state_path) - identity = ( - runtime.resident_training_session_id, - runtime.resident_optimizer_state_path, - runtime.resident_policy_step, - ) - expected = ( - job.training_session_id, - expected_path, - job.step, - ) - if identity != expected: - raise RuntimeError( - f"Cannot finalize non-resident optimizer state: {identity!r} != {expected!r}" - ) - _save_optimizer( - runtime, - optimizer_state_path=expected_path, - step=job.step, - ready_log_path=job.log_path, - ) - - -def finalize_megatron_job( - runtime: TrainingRuntime, - *, - job_path: str | None, - log_path: str, - cleanup_path: str | None, -) -> None: - torch.distributed.barrier() # type: ignore[possibly-missing-attribute] - if runtime.rank != 0: - return - - if job_path is not None and os.path.exists(job_path): - os.remove(job_path) - if cleanup_path is not None and os.path.exists(cleanup_path): - shutil.rmtree(cleanup_path) - with open(log_path, "a+", encoding="utf-8") as log_file: - log_file.write("all done\n") - - -def _placeholder_attention_mask(device: torch.device) -> torch.Tensor: - return torch.zeros((1, 1, 1, 1), dtype=torch.bool, device=device) +def _placeholder_attention_mask(device: torch.device) -> torch.Tensor: + return torch.zeros((1, 1, 1, 1), dtype=torch.bool, device=device) def load_adapter_into_model( @@ -1308,11 +1148,14 @@ def _optimizer_step( *, model_support_handler: Any | None = None, model_chunks: ModelChunks | None = None, + before_step: Callable[[], None] | None = None, ) -> tuple[bool, float, int | None]: for param_group in optimizer.param_groups: param_group["lr"] = learning_rate if model_support_handler is not None and model_chunks is not None: model_support_handler.zero_internal_padding_grads(model_chunks) + if before_step is not None: + before_step() update_successful, grad_norm, num_zeros_in_grad = cast( tuple[bool, float, int | None], optimizer.step() ) @@ -1322,18 +1165,32 @@ def _optimizer_step( return update_successful, grad_norm, num_zeros_in_grad -def _reduce_loss( - loss: torch.Tensor, - op: Any = torch.distributed.ReduceOp.AVG, # ty: ignore[possibly-missing-attribute] +def _reduce_loss_sum( + loss_sum: torch.Tensor, + token_count: torch.Tensor, group: Any | None = None, ) -> torch.Tensor: - reduced_loss = loss.detach().clone() + totals = torch.stack( + (loss_sum.detach(), token_count.to(dtype=loss_sum.dtype)), + ) torch.distributed.all_reduce( # ty: ignore[possibly-missing-attribute] - reduced_loss, - op=op, + totals, + op=torch.distributed.ReduceOp.SUM, # ty: ignore[possibly-missing-attribute] group=group, ) - return reduced_loss + return totals[0] / totals[1].clamp_min(1.0) + + +def _broadcast_from_pipeline_last(value: Any) -> Any: + if ps.get_pipeline_model_parallel_world_size() <= 1: + return value + objects = [value] + torch.distributed.broadcast_object_list( # ty: ignore[possibly-missing-attribute] + objects, + src=ps.get_pipeline_model_parallel_last_rank(), + group=ps.get_pipeline_model_parallel_group(), + ) + return objects[0] def _unwrap_model_config(model_chunks: ModelChunks) -> Any | None: @@ -1359,9 +1216,8 @@ def _hybridep_token_capacity( ) -> int: from art.megatron.context_parallel.types import ContextParallelConfig - # HybridEP JIT keys include this capacity. A CP tree unit is at most one - # mean rank load and is assigned to the least-loaded rank, so twice the - # rounded mean is the layout-independent upper bound. Reserve it once. + # Reserve the normal near-balanced extent once; cost-aware CP plans can + # exceed it, so callers also provide their exact maximum planned extent. planner_chunk = ContextParallelConfig().planner_chunk_size mean_rank_load = ( math.ceil(packed_sequence_length / (planner_chunk * context_parallel_size)) @@ -1375,14 +1231,16 @@ def _ensure_hybridep_capacity( *, packed_sequence_length: int, context_parallel_size: int, + required_capacity: int = 0, ) -> None: expert_parallel_size = ps.get_expert_model_parallel_world_size() if expert_parallel_size <= 1: return from megatron.core.transformer.moe import fused_a2a - token_capacity = _hybridep_token_capacity( - packed_sequence_length, context_parallel_size + token_capacity = max( + _hybridep_token_capacity(packed_sequence_length, context_parallel_size), + int(required_capacity), ) current = fused_a2a._hybrid_ep_buffer if ( @@ -1408,24 +1266,6 @@ def _ensure_hybridep_capacity( ) -def _set_hybridep_token_count(rows: int) -> None: - from megatron.core.transformer.moe import fused_a2a - - buffer = fused_a2a._hybrid_ep_buffer - if buffer is None: - raise RuntimeError("HybridEP buffer is not initialized") - buffer.set_num_tokens_per_rank(rows) - - -def _validate_hybridep_token_counts(values: list[int] | None, micro_count: int) -> bool: - enabled = ps.get_expert_model_parallel_world_size() > 1 - if enabled and (values is None or len(values) != micro_count): - raise RuntimeError( - "HybridEP requires one planned communication extent per microbatch" - ) - return enabled - - def select_micro_ref_logprobs( ref_logprobs_by_index: dict[int, torch.Tensor], sample_indices: list[int | None], @@ -1492,92 +1332,128 @@ def _select_next_ref_logprobs( def _forward_prepared_rl_micro( *, model_chunks: ModelChunks, + model_chunk: MegatronModule | None = None, model_support_handler: Any, prepared_micro: PreparedRLMicroInputs, device: torch.device, -) -> torch.Tensor: +) -> TokenLossOutput: + model = model_chunks[0] if model_chunk is None else model_chunk model_forward_kwargs = dict( input_ids=prepared_micro.model_tokens, position_ids=prepared_micro.model_input_pos, attention_mask=_placeholder_attention_mask(device), packed_seq_params=prepared_micro.packed_seq_params, **model_support_handler.get_forward_kwargs( - model_chunks[0], + model, attention_bias=prepared_micro.attention_state, ), ) with attach_trace_token_uids(model_chunks, prepared_micro.local_token_uids): - if int(prepared_micro.model_tokens.numel()) == 0: - logits = model_chunks[0](**model_forward_kwargs, labels=None) - return _empty_new_logprobs_from_logits(logits, prepared_micro.model_labels) - return -model_chunks[0]( - **model_forward_kwargs, - labels=prepared_micro.model_labels, - ) + if chunk_post_process(model): + return forward_token_losses( + model, + labels=prepared_micro.model_labels, + selection=prepared_micro.lm_head_selection, + forward_kwargs=model_forward_kwargs, + ) + output = model(**model_forward_kwargs, labels=None) + if not isinstance(output, torch.Tensor): + raise TypeError( + f"pipeline model chunk must return a tensor, got {type(output).__name__}" + ) + return TokenLossOutput(token_losses=output) + + +def _install_schedule_finalize(model_chunks: ModelChunks) -> None: + seen: set[int] = set() + for chunk in model_chunks: + config = _unwrap_model_config([chunk]) + if config is None or id(config) in seen: + continue + seen.add(id(config)) + config.finalize_model_grads_func = finalize_model_grads_extended def _zero_logprob_graph_contribution( new_logprobs: torch.Tensor, - loss_inputs: LossInputs | DispatchedPackedTensors, + loss_inputs: LossInputs | AlignedLossInputs, ) -> torch.Tensor: assistant_mask = loss_inputs.align_inputs().assistant_mask.to(dtype=torch.bool) return new_logprobs.masked_fill(~assistant_mask, 0.0).sum() * 0.0 -def _globalize_context_parallel_logprobs( +def _globalize_context_parallel_logprob_batch( *, - local_logprobs: torch.Tensor, - attention_state: Any, + local_logprobs: list[torch.Tensor], + attention_states: list[Any], seq_len: int, -) -> torch.Tensor: - rank_plan = getattr(attention_state, "rank_plan", None) - cp_group = getattr(attention_state, "cp_group", None) - if rank_plan is None or cp_group is None: - raise RuntimeError("Context-parallel reference logprobs require a rank plan") - - global_logprobs = local_logprobs.new_zeros((1, seq_len)) - local_values = local_logprobs.reshape(-1) - cursor = 0 - for range_ in rank_plan.local_row_ranges: - if range_ is None: - continue - size = int(range_.size()) - if size <= 0: - continue - global_logprobs[0, int(range_.start) : int(range_.end)] = local_values[ - cursor : cursor + size - ] - cursor += size +) -> list[torch.Tensor]: + if len(local_logprobs) != len(attention_states): + raise ValueError("Context-parallel logprob/state counts differ") + rows: list[torch.Tensor] = [] + cp_group = None + for values, attention_state in zip(local_logprobs, attention_states, strict=True): + rank_plan = getattr(attention_state, "rank_plan", None) + micro_cp_group = getattr(attention_state, "cp_group", None) + if rank_plan is None or micro_cp_group is None: + raise RuntimeError( + "Context-parallel reference logprobs require a rank plan" + ) + if cp_group is not None and micro_cp_group is not cp_group: + raise RuntimeError( + "Context-parallel microbatches use different process groups" + ) + cp_group = micro_cp_group + row = values.new_zeros((1, seq_len)) + local_values = values.reshape(-1) + cursor = 0 + for range_ in rank_plan.local_row_ranges: + if range_ is None: + continue + size = int(range_.size()) + if size <= 0: + continue + row[0, int(range_.start) : int(range_.end)] = local_values[ + cursor : cursor + size + ] + cursor += size + if cursor != int(local_values.numel()): + raise RuntimeError( + "Context-parallel reference-logprob layout did not consume all values: " + f"consumed={cursor}, values={local_values.numel()}" + ) + rows.append(row) + global_logprobs = torch.cat(rows) torch.distributed.all_reduce( # ty: ignore[possibly-missing-attribute] - global_logprobs, - group=cp_group, + global_logprobs, group=cp_group ) - return global_logprobs + return list(global_logprobs.split(1)) @torch.no_grad() -def _calculate_megatron_logprobs( +def _calculate_megatron_logprob_batch( *, model_chunks: ModelChunks, provider: Any, model_support_handler: Any, - inputs: PackedTensors, + inputs: list[PackedTensors], + sample_indices: list[int | None], moe_routing_replay_controller: MoeRoutingReplayController | None = None, step_index: int | None = None, - sample_index: int | None = None, - hybridep_token_count: int | None = None, -) -> torch.Tensor: + hybridep_token_counts: list[int] | None = None, +) -> list[torch.Tensor]: + if not inputs or len(inputs) != len(sample_indices): + raise ValueError("Reference input/sample counts must match and be nonzero") if moe_routing_replay_controller is not None: - if step_index is None or sample_index is None: - raise ValueError( - "step_index and sample_index are required for routing replay" - ) + if step_index is None: + raise ValueError("step_index is required for routing replay") moe_routing_replay_controller.set_step( step_index=step_index, - sample_index=sample_index, + sample_index=( + sample_indices[0] if len(sample_indices) == 1 else sample_indices + ), ) - moe_routing_replay_controller.begin_micro(sample_index, 0) device = next(model_chunks[0].parameters()).device topology = _infer_parallel_topology(model_chunks) @@ -1590,45 +1466,156 @@ def _calculate_megatron_logprobs( chunk.eval() forward_succeeded = False try: - prepared_micro, _pending_prepared_micro = _prepare_current_rl_micro( - inputs, - device=device, - topology=topology, - provider=provider, - model_support_handler=model_support_handler, - ref_logprobs=None, - trace_token_uids=trace_token_uids, - pending_prepared_micro=None, - ) - prepare_replay_local_input_token_uids( - moe_routing_replay_controller, - prepared_micro.local_token_uids, - prepared_micro.attention_state, + pending_prepared_micro: PreparedMegatronBatch | None = None + prepared_micros: list[PreparedRLMicroInputs] = [] + for order, micro in enumerate(inputs): + prepared, pending_prepared_micro = _prepare_current_rl_micro( + micro, + device=device, + topology=topology, + provider=provider, + model_support_handler=model_support_handler, + ref_logprobs=None, + trace_token_uids=trace_token_uids, + pending_prepared_micro=pending_prepared_micro, + ) + prepared_micros.append(prepared) + pending_prepared_micro = _prepare_next_rl_cp_micro( + _next_micro_lookahead(inputs, order), + device=device, + topology=topology, + provider=provider, + model_support_handler=model_support_handler, + trace_token_uids=trace_token_uids, + ref_logprobs=None, + ) + microbatch_state = PipelineMicrobatchState( + controller=moe_routing_replay_controller, + hybridep_token_counts=hybridep_token_counts, + microbatch_count=len(prepared_micros), + model_activator=model_support_handler.build_pipeline_microbatch_activator( + model_chunks + ), ) - if _validate_hybridep_token_counts( - None if hybridep_token_count is None else [hybridep_token_count], 1 - ): - assert hybridep_token_count is not None - _set_hybridep_token_count(hybridep_token_count) - logprobs = _forward_prepared_rl_micro( + if not ps.model_parallel_is_initialized(): + # Unit/static callers do not have MCore process groups. Production + # reference forwards always take the common schedule path below. + if len(prepared_micros) != 1: + raise RuntimeError("Static reference forward accepts one microbatch") + prepared = prepared_micros[0] + microbatch_state.activate( + ScheduleMicrobatch( + 0, sample_indices[0], prepared, prepared.attention_state + ), + chunk_index=0, + ) + token_output = forward_token_losses( + model_chunks[0], + labels=prepared.model_labels, + selection=prepared.lm_head_selection, + forward_kwargs=dict( + input_ids=prepared.model_tokens, + position_ids=prepared.model_input_pos, + attention_mask=_placeholder_attention_mask(device), + packed_seq_params=prepared.packed_seq_params, + **model_support_handler.get_forward_kwargs( + model_chunks[0], attention_bias=prepared.attention_state + ), + ), + enabled=False, + ) + forward_succeeded = True + return [token_output.restore(-token_output.token_losses).detach().cpu()] + schedule = MCoreScheduleAdapter( model_chunks=model_chunks, - model_support_handler=model_support_handler, - prepared_micro=prepared_micro, - device=device, + prepared_microbatches=prepared_micros, + sample_indices=sample_indices, + model_inputs=[prepared.model_tokens for prepared in prepared_micros], + moe_routing_replay_controller=moe_routing_replay_controller, + hybridep_token_counts=hybridep_token_counts, + model_activator=model_support_handler.build_pipeline_microbatch_activator( + model_chunks + ), + ) + + def forward_step_func(data_iterator: Any, model: MegatronModule, *_args: Any): + item = next(data_iterator) + token_output = _forward_prepared_rl_micro( + model_chunks=model_chunks, + model_chunk=model, + model_support_handler=model_support_handler, + prepared_micro=item.payload, + device=device, + ) + + def collect(output_tensor: torch.Tensor, **_kwargs: Any) -> dict[str, Any]: + return { + "order": item.order, + "logprobs": token_output.restore(-output_tensor).detach(), + } + + return token_output.token_losses, collect + + forward_outputs = schedule.run( + forward_step_func, + forward_only=True, + collect_non_loss_data=True, ) + if not any(chunk_post_process(chunk) for chunk in model_chunks): + forward_succeeded = True + return [] + outputs = cast(list[dict[str, Any]], forward_outputs) + if len(outputs) != len(prepared_micros): + raise RuntimeError( + "Reference pipeline did not return one result per microbatch: " + f"expected={len(prepared_micros)}, got={len(outputs)}" + ) + outputs.sort(key=lambda output: int(output["order"])) + logprobs = [cast(torch.Tensor, output["logprobs"]) for output in outputs] if int(topology.cp) > 1: - logprobs = _globalize_context_parallel_logprobs( + logprobs = _globalize_context_parallel_logprob_batch( local_logprobs=logprobs, - attention_state=prepared_micro.attention_state, - seq_len=int(inputs["tokens"].shape[1]), + attention_states=[ + prepared.attention_state for prepared in prepared_micros + ], + seq_len=int(inputs[0]["tokens"].shape[1]), ) + host_logprobs = torch.cat(logprobs).detach().cpu() forward_succeeded = True + return list(host_logprobs.split(1)) finally: for chunk, was_training in zip(model_chunks, previous_training_modes): chunk.train(was_training) if moe_routing_replay_controller is not None and forward_succeeded: moe_routing_replay_controller.finalize_step() - return logprobs.detach().cpu() + + +def _calculate_megatron_logprobs( + *, + model_chunks: ModelChunks, + provider: Any, + model_support_handler: Any, + inputs: PackedTensors, + moe_routing_replay_controller: MoeRoutingReplayController | None = None, + step_index: int | None = None, + sample_index: int | None = None, + hybridep_token_count: int | None = None, +) -> torch.Tensor: + results = _calculate_megatron_logprob_batch( + model_chunks=model_chunks, + provider=provider, + model_support_handler=model_support_handler, + inputs=[inputs], + sample_indices=[sample_index], + moe_routing_replay_controller=moe_routing_replay_controller, + step_index=step_index, + hybridep_token_counts=( + None if hybridep_token_count is None else [hybridep_token_count] + ), + ) + if len(results) != 1: + raise RuntimeError("Single reference forward did not run on the loss stage") + return results[0] def _precompute_reference_logprobs( @@ -1644,40 +1631,69 @@ def _precompute_reference_logprobs( len(sample_step_indices), "local sequences", ) - hybridep_by_step: dict[int, list[int]] = {} hybridep_enabled = ps.get_expert_model_parallel_world_size() > 1 topology = _infer_parallel_topology(runtime.model) if hybridep_enabled else None results: dict[int, torch.Tensor] = {} - for sample_index, step_index in sorted(sample_step_indices.items()): - hybridep_token_count = None + if not ps.model_parallel_is_initialized(): + for sample_index, step_index in sorted(sample_step_indices.items()): + results[sample_index] = _calculate_megatron_logprobs( + model_chunks=runtime.model, + provider=runtime.provider, + model_support_handler=runtime.model_support_handler, + inputs=select_indexed_inputs(packed_tensors, sample_index), + moe_routing_replay_controller=runtime.moe_routing_replay_controller, + step_index=step_index, + sample_index=sample_index, + hybridep_token_count=None, + ) + return results + + num_sequences = int(packed_tensors["tokens"].shape[0]) + zero_template = _zero_contribution_inputs( + _clone_packed_tensors(select_indexed_inputs(packed_tensors, 0)) + ) + for step_index in sorted(set(sample_step_indices.values())): + micro_indices = build_micro_sample_indices( + step_index=step_index, + num_sequences=num_sequences, + global_grad_accumulation_sequences=global_grad_accumulation_sequences, + ) + hybridep_token_counts = None if hybridep_enabled: assert topology is not None - counts = hybridep_by_step.get(step_index) - if counts is None: - counts = build_rl_hybridep_token_counts( - packed_tensors=packed_tensors, - step_index=step_index, - num_sequences=int(packed_tensors["tokens"].shape[0]), - global_grad_accumulation_sequences=global_grad_accumulation_sequences, - topology=topology, - provider=runtime.provider, - model_support_handler=runtime.model_support_handler, - ) - hybridep_by_step[step_index] = counts - micro_order = ( - sample_index - step_index * global_grad_accumulation_sequences - ) // ps.get_data_parallel_world_size() - hybridep_token_count = counts[micro_order] - results[sample_index] = _calculate_megatron_logprobs( + hybridep_token_counts = build_rl_hybridep_token_counts( + packed_tensors=packed_tensors, + step_index=step_index, + num_sequences=num_sequences, + global_grad_accumulation_sequences=global_grad_accumulation_sequences, + topology=topology, + provider=runtime.provider, + model_support_handler=runtime.model_support_handler, + ) + outputs = _calculate_megatron_logprob_batch( model_chunks=runtime.model, provider=runtime.provider, model_support_handler=runtime.model_support_handler, - inputs=select_indexed_inputs(packed_tensors, sample_index), + inputs=select_micro_inputs(packed_tensors, micro_indices, zero_template), + sample_indices=micro_indices, moe_routing_replay_controller=runtime.moe_routing_replay_controller, step_index=step_index, - sample_index=sample_index, - hybridep_token_count=hybridep_token_count, + hybridep_token_counts=hybridep_token_counts, ) + if not outputs: + continue + for sample_index, output in zip(micro_indices, outputs, strict=True): + if sample_index is not None: + if sample_step_indices.get(sample_index) != step_index: + raise RuntimeError( + "Reference microbatch does not match its planned training step: " + f"sample={sample_index}, step={step_index}" + ) + results[sample_index] = output + if any(chunk_post_process(chunk) for chunk in runtime.model) and set( + results + ) != set(sample_step_indices): + raise RuntimeError("Reference forward did not materialize every local sample") return results @@ -1702,7 +1718,7 @@ def _reference_sample_step_indices( def _prepare_kl_reference_logprobs( *, runtime: TrainingRuntime, - job: MegatronTrainingJob | MegatronMergedTrainingJob, + job: TrainJobSpec, packed_tensors: PackedTensors, num_sequences: int, num_steps: int, @@ -1711,9 +1727,7 @@ def _prepare_kl_reference_logprobs( if job.config.kl_penalty_coef <= 0.0: return None - ref_adapter_path = cast(dev.TrainConfig, job.experimental_config).get( - "kl_ref_adapter_path" - ) + ref_adapter_path = _experimental_train_config(job).get("kl_ref_adapter_path") if ref_adapter_path is None: raise RuntimeError( "KL penalty is enabled but no kl_ref_adapter_path was provided. " @@ -1722,17 +1736,15 @@ def _prepare_kl_reference_logprobs( "provide kl_ref_adapter_path." ) + current_adapter_path = job.source_adapter_path adapter_swapped = os.path.abspath(ref_adapter_path) != os.path.abspath( - job.lora_path + current_adapter_path ) loaded_ref_adapter = False - restore_adapter = None + restore_parameters: list[tuple[torch.Tensor, torch.Tensor]] | None = None try: if adapter_swapped: - restore_adapter = load_lora_tensors_for_megatron( - job.lora_path, - handler=runtime.model_support_handler, - ) + restore_parameters = _snapshot_trainable_parameters(runtime.model) _load_adapter_into_model( runtime.model, ref_adapter_path, @@ -1753,13 +1765,28 @@ def _prepare_kl_reference_logprobs( finally: if loaded_ref_adapter: assert runtime.optimizer is not None - assert restore_adapter is not None - load_adapter_into_model( - runtime.model, - restore_adapter, - runtime.optimizer, - model_support_handler=runtime.model_support_handler, - ) + assert restore_parameters is not None + with torch.no_grad(): + for parameter, value in restore_parameters: + parameter.copy_(value) + runtime.model_support_handler.zero_internal_padding_params( + runtime.model + ) + runtime.optimizer_snapshot_barrier.synchronize() + runtime.optimizer.reload_model_params() + + +def _snapshot_trainable_parameters( + model_chunks: ModelChunks, +) -> list[tuple[torch.Tensor, torch.Tensor]]: + seen: set[int] = set() + snapshot: list[tuple[torch.Tensor, torch.Tensor]] = [] + for chunk in model_chunks: + for parameter in chunk.parameters(): + if parameter.requires_grad and id(parameter) not in seen: + seen.add(id(parameter)) + snapshot.append((parameter, parameter.detach().clone())) + return snapshot def run_megatron_sft_step( @@ -1774,6 +1801,7 @@ def run_megatron_sft_step( sample_index: int | list[int | None], moe_routing_replay_controller: MoeRoutingReplayController | None = None, hybridep_token_counts: list[int] | None = None, + before_optimizer_step: Callable[[], None] | None = None, ) -> TrainStepResult: micro_inputs = inputs if isinstance(inputs, list) else [inputs] if not micro_inputs: @@ -1807,20 +1835,11 @@ def run_megatron_sft_step( ) _zero_grad_buffers(model_chunks) + _install_schedule_finalize(model_chunks) - raw_loss_sum: torch.Tensor | None = None - loss_inputs_for_count: list[dict[str, torch.Tensor] | PreparedSFTMicroInputs] = [] pending_prepared_micro: PreparedMegatronBatch | None = None - hybridep_enabled = _validate_hybridep_token_counts( - hybridep_token_counts, len(micro_inputs) - ) - + prepared_micros: list[PreparedSFTMicroInputs] = [] for micro_order, micro in enumerate(micro_inputs): - if moe_routing_replay_controller is not None: - moe_routing_replay_controller.begin_micro( - micro_sample_indices[micro_order], - micro_order, - ) prepared_micro, pending_prepared_micro = _prepare_current_sft_micro( micro, device=device, @@ -1830,30 +1849,7 @@ def run_megatron_sft_step( trace_token_uids=trace_token_uids, pending_prepared_micro=pending_prepared_micro, ) - prepare_replay_local_input_token_uids( - moe_routing_replay_controller, - prepared_micro.local_token_uids, - prepared_micro.attention_state, - ) - if hybridep_enabled: - assert hybridep_token_counts is not None - _set_hybridep_token_count(hybridep_token_counts[micro_order]) - with attach_trace_token_uids(model_chunks, prepared_micro.local_token_uids): - per_token_loss: torch.Tensor = model_chunks[0]( - input_ids=prepared_micro.input_ids, - position_ids=prepared_micro.position_ids, - attention_mask=_placeholder_attention_mask(device), - labels=prepared_micro.labels, - packed_seq_params=prepared_micro.packed_seq_params, - **model_support_handler.get_forward_kwargs( - model_chunks[0], - attention_bias=prepared_micro.attention_state, - ), - ) - masked_loss = ( - per_token_loss[prepared_micro.loss_mask].sum() + per_token_loss.sum() * 0.0 - ) - masked_loss.backward() + prepared_micros.append(prepared_micro) pending_prepared_micro = _prepare_next_sft_cp_micro( _next_micro_lookahead(micro_inputs, micro_order), device=device, @@ -1862,40 +1858,80 @@ def run_megatron_sft_step( model_support_handler=model_support_handler, trace_token_uids=trace_token_uids, ) - detached_micro_loss = masked_loss.detach() - if raw_loss_sum is None: - raw_loss_sum = detached_micro_loss - else: - raw_loss_sum = raw_loss_sum + detached_micro_loss - loss_inputs_for_count.append(prepared_micro) - - if raw_loss_sum is None: - raise RuntimeError("run_megatron_sft_step did not produce outputs") - - num_tokens = _local_trainable_sft_token_count_tensor( - loss_inputs_for_count, - device=device, + schedule = MCoreScheduleAdapter( + model_chunks=model_chunks, + prepared_microbatches=prepared_micros, + sample_indices=micro_sample_indices, + model_inputs=[prepared.input_ids for prepared in prepared_micros], + moe_routing_replay_controller=moe_routing_replay_controller, + hybridep_token_counts=hybridep_token_counts, + model_activator=model_support_handler.build_pipeline_microbatch_activator( + model_chunks + ), ) - flush_param_grads_to_main_grads(model_chunks) - finalize_model_grads_extended( - as_megatron_api_chunks(model_chunks), num_tokens=num_tokens + + def forward_step_func(data_iterator: Any, model: MegatronModule, *_args: Any): + item = next(data_iterator) + prepared = item.payload + kwargs = dict( + input_ids=prepared.input_ids, + position_ids=prepared.position_ids, + attention_mask=_placeholder_attention_mask(device), + packed_seq_params=prepared.packed_seq_params, + **model_support_handler.get_forward_kwargs( + model, attention_bias=prepared.attention_state + ), + ) + with attach_trace_token_uids(model_chunks, prepared.local_token_uids): + if chunk_post_process(model): + token_output = forward_token_losses( + model, + labels=prepared.labels, + selection=prepared.lm_head_selection, + forward_kwargs=kwargs, + ) + output = token_output.token_losses + else: + output = model(**kwargs, labels=None) + token_output = None + + def reduce_loss(output_tensor: torch.Tensor): + assert token_output is not None + masked_loss = token_output.masked_sum(prepared.loss_mask) + num_tokens = _local_trainable_sft_token_count_tensor( + [prepared], device=device + ) + return masked_loss, num_tokens, {"raw_loss_sum": masked_loss.detach()} + + return output, reduce_loss + + forward_data_store = schedule.run(forward_step_func, forward_only=False) + if moe_routing_replay_controller is not None: + moe_routing_replay_controller.finalize_step(expect_recompute=True) + forward_data_store = cast( + list[dict[str, Any]], _broadcast_from_pipeline_last(forward_data_store) + ) + raw_loss_sum = sum( + ( + cast(torch.Tensor, data["raw_loss_sum"]).to(device) + for data in forward_data_store + ), + torch.zeros([], device=device, dtype=torch.float32), ) update_successful, grad_norm, num_zeros_in_grad = _optimizer_step( optimizer, learning_rate, model_support_handler=model_support_handler, model_chunks=model_chunks, + before_step=before_optimizer_step, ) - global_num_tokens = max(num_tokens.item(), 1.0) - reduced_loss = _reduce_loss( - raw_loss_sum / global_num_tokens, - op=torch.distributed.ReduceOp.SUM, # ty: ignore[possibly-missing-attribute] + num_tokens = _local_trainable_sft_token_count_tensor(prepared_micros, device=device) + reduced_loss = _reduce_loss_sum( + raw_loss_sum, + num_tokens, group=ps.get_data_parallel_group(with_context_parallel=True), ) - if moe_routing_replay_controller is not None: - moe_routing_replay_controller.finalize_step() - return TrainStepResult( reduced_loss=reduced_loss, probs_corr=1.0, @@ -1903,6 +1939,8 @@ def run_megatron_sft_step( update_successful=update_successful, grad_norm=grad_norm, num_zeros_in_grad=num_zeros_in_grad, + workload=schedule.training_workload(), + pipeline_metrics=schedule.telemetry.metrics(), ) @@ -1924,6 +1962,7 @@ def run_training_step( next_step_first_micro: PackedTensors | None = None, next_step_first_ref_logprobs: torch.Tensor | None = None, hybridep_token_counts: list[int] | None = None, + before_optimizer_step: Callable[[], None] | None = None, ) -> TrainStepResult: micro_inputs = inputs if isinstance(inputs, list) else [inputs] if not micro_inputs: @@ -1964,28 +2003,11 @@ def run_training_step( cp_lookahead_state.pending_prepared_micro = None _zero_grad_buffers(model_chunks) + _install_schedule_finalize(model_chunks) micro_count = len(micro_inputs) - hybridep_enabled = _validate_hybridep_token_counts( - hybridep_token_counts, micro_count - ) - raw_loss_sum: torch.Tensor | None = None - loss_inputs_for_count: list[LossInputs | DispatchedPackedTensors] = [] - probs_corr_total: torch.Tensor | None = None - kl_policy_ref_sum = 0.0 - kl_policy_ref_count = 0 - loss_diagnostics = LossOffPolicyDiagnosticsAccumulator() - new_logprobs_gpu: list[torch.Tensor] = [] - - def begin_micro(micro_order: int) -> None: - if moe_routing_replay_controller is not None: - moe_routing_replay_controller.begin_micro( - micro_sample_indices[micro_order], - micro_order, - ) - + prepared_micros: list[PreparedRLMicroInputs] = [] for micro_order in range(micro_count): - begin_micro(micro_order) micro_ref_logprobs = _select_ref_logprobs(ref_logprobs, micro_order) if micro_ref_logprobs is not None and int(topology.cp) <= 1: micro_ref_logprobs = micro_ref_logprobs.to(device) @@ -1999,57 +2021,7 @@ def begin_micro(micro_order: int) -> None: trace_token_uids=trace_token_uids, pending_prepared_micro=pending_prepared_micro, ) - prepare_replay_local_input_token_uids( - moe_routing_replay_controller, - prepared_micro.local_token_uids, - prepared_micro.attention_state, - ) - if hybridep_enabled: - assert hybridep_token_counts is not None - _set_hybridep_token_count(hybridep_token_counts[micro_order]) - - new_logprobs = _forward_prepared_rl_micro( - model_chunks=model_chunks, - model_support_handler=model_support_handler, - prepared_micro=prepared_micro, - device=device, - ) - - loss_info = loss_fn( - prepared_micro.loss_inputs, - new_logprobs=new_logprobs, - ref_logprobs=prepared_micro.ref_logprobs, - entropies=None, - experimental_config=experimental_config, - reduction="sum", - ) - micro_loss = loss_info.policy_loss + _zero_logprob_graph_contribution( - new_logprobs, - prepared_micro.loss_inputs, - ) - if not micro_loss.requires_grad: - assistant_tokens = _count_trainable_tokens(prepared_micro.loss_inputs) - nonzero_weights = int( - torch.count_nonzero( - prepared_micro.loss_inputs.align_inputs().weights - ).item() - ) - nonzero_advantages = int( - torch.count_nonzero( - prepared_micro.loss_inputs.align_inputs().advantages - ).item() - ) - raise RuntimeError( - "RL micro_loss is detached before backward: " - f"new_logprobs.requires_grad={new_logprobs.requires_grad}, " - f"policy_loss_sum_requires_grad={loss_info.policy_loss_sum.requires_grad}, " - f"assistant_tokens={assistant_tokens}, " - f"nonzero_weights={nonzero_weights}, " - f"nonzero_advantages={nonzero_advantages}" - ) - micro_loss.backward() - loss_inputs_for_count.append(prepared_micro.loss_inputs) - del prepared_micro + prepared_micros.append(prepared_micro) pending_prepared_micro = _prepare_next_rl_cp_micro( _next_micro_lookahead( micro_inputs, @@ -2068,100 +2040,143 @@ def begin_micro(micro_order: int) -> None: next_step_first_ref_logprobs=next_step_first_ref_logprobs, ), ) - detached_probs_corr = loss_info.probs_corr.detach() - if probs_corr_total is None: - probs_corr_total = detached_probs_corr - else: - probs_corr_total = probs_corr_total + detached_probs_corr - if loss_info.kl_policy_ref is not None: - kl_policy_ref_sum += float(loss_info.kl_policy_ref.item()) - kl_policy_ref_count += 1 - loss_diagnostics.add(loss_info.offpolicy_diagnostics) - detached_micro_loss = micro_loss.detach() - if raw_loss_sum is None: - raw_loss_sum = detached_micro_loss - else: - raw_loss_sum = raw_loss_sum + detached_micro_loss - del loss_info - del micro_loss - new_logprobs_gpu.append(new_logprobs.detach()) - del new_logprobs - - if raw_loss_sum is None: - raise RuntimeError("run_training_step did not produce outputs") - if probs_corr_total is None: - raise RuntimeError("run_training_step did not accumulate probs_corr") if cp_lookahead_state is not None: cp_lookahead_state.pending_prepared_micro = pending_prepared_micro + schedule = MCoreScheduleAdapter( + model_chunks=model_chunks, + prepared_microbatches=prepared_micros, + sample_indices=micro_sample_indices, + model_inputs=[prepared.model_tokens for prepared in prepared_micros], + moe_routing_replay_controller=moe_routing_replay_controller, + hybridep_token_counts=hybridep_token_counts, + model_activator=model_support_handler.build_pipeline_microbatch_activator( + model_chunks + ), + ) + + def forward_step_func(data_iterator: Any, model: MegatronModule, *_args: Any): + item = next(data_iterator) + prepared = item.payload + token_output = _forward_prepared_rl_micro( + model_chunks=model_chunks, + model_chunk=model, + model_support_handler=model_support_handler, + prepared_micro=prepared, + device=device, + ) + + def reduce_loss(output_tensor: torch.Tensor): + new_logprobs = -output_tensor + compact_loss_inputs = token_output.compact_loss_inputs(prepared.loss_inputs) + loss_info = loss_fn( + compact_loss_inputs, + new_logprobs=new_logprobs, + ref_logprobs=token_output.select_optional(prepared.ref_logprobs), + entropies=None, + experimental_config=experimental_config, + reduction="sum", + ) + micro_loss = loss_info.policy_loss + _zero_logprob_graph_contribution( + new_logprobs, compact_loss_inputs + ) + if not micro_loss.requires_grad: + raise RuntimeError( + "RL micro_loss is detached before pipeline backward: " + f"micro={item.order}, sample={item.sample_index}" + ) + num_tokens = _local_trainable_token_count_tensor( + [prepared.loss_inputs], device=device + ) + return ( + micro_loss, + num_tokens, + { + "order": item.order, + "raw_loss_sum": micro_loss.detach(), + "probs_corr": loss_info.probs_corr.detach(), + "kl_policy_ref": ( + None + if loss_info.kl_policy_ref is None + else float(loss_info.kl_policy_ref.item()) + ), + "offpolicy_diagnostics": loss_info.offpolicy_diagnostics, + "new_logprobs": token_output.restore(new_logprobs.detach()).to( + "cpu" + ), + }, + ) + + return token_output.token_losses, reduce_loss + + forward_data_store = schedule.run(forward_step_func, forward_only=False) + if moe_routing_replay_controller is not None: + moe_routing_replay_controller.finalize_step(expect_recompute=True) + pipeline_results = cast( + list[dict[str, Any]], + _broadcast_from_pipeline_last(forward_data_store), + ) + if len(pipeline_results) != micro_count: + raise RuntimeError( + "MCore schedule did not return one final-stage result per microbatch: " + f"expected={micro_count}, got={len(pipeline_results)}" + ) + pipeline_results.sort(key=lambda data: int(data["order"])) + raw_loss_sum = sum( + ( + cast(torch.Tensor, data["raw_loss_sum"]).to(device) + for data in pipeline_results + ), + torch.zeros([], device=device, dtype=torch.float32), + ) + probs_corr_total = sum( + ( + cast(torch.Tensor, data["probs_corr"]).to(device) + for data in pipeline_results + ), + torch.zeros([], device=device, dtype=torch.float32), + ) + kl_values = [ + float(value) + for data in pipeline_results + if (value := data["kl_policy_ref"]) is not None + ] + loss_diagnostics = LossOffPolicyDiagnosticsAccumulator() + for data in pipeline_results: + loss_diagnostics.add(data["offpolicy_diagnostics"]) + token_count = _local_trainable_token_count_tensor( - loss_inputs_for_count, + [prepared.loss_inputs for prepared in prepared_micros], device=device, ) - finalize_model_grads_extended( - as_megatron_api_chunks(model_chunks), - num_tokens=token_count, - ) update_successful, grad_norm, num_zeros_in_grad = _optimizer_step( optimizer, learning_rate, model_support_handler=model_support_handler, model_chunks=model_chunks, + before_step=before_optimizer_step, ) - global_num_tokens = max(token_count.item(), 1.0) - reduced_loss = _reduce_loss( - raw_loss_sum / global_num_tokens, - op=torch.distributed.ReduceOp.SUM, # ty: ignore[possibly-missing-attribute] + reduced_loss = _reduce_loss_sum( + raw_loss_sum, + token_count, group=ps.get_data_parallel_group(with_context_parallel=True), ) - if moe_routing_replay_controller is not None: - moe_routing_replay_controller.finalize_step() - return TrainStepResult( reduced_loss=reduced_loss, probs_corr=float((probs_corr_total / micro_count).item()), - kl_policy_ref=( - kl_policy_ref_sum / kl_policy_ref_count if kl_policy_ref_count > 0 else None - ), + kl_policy_ref=(sum(kl_values) / len(kl_values) if kl_values else None), new_logprobs=[ - tensor.to(device="cpu", non_blocking=True) for tensor in new_logprobs_gpu + cast(torch.Tensor, data["new_logprobs"]) for data in pipeline_results ], update_successful=update_successful, grad_norm=grad_norm, num_zeros_in_grad=num_zeros_in_grad, + workload=schedule.training_workload(), loss_metrics=loss_diagnostics.to_metrics( group=ps.get_data_parallel_group(with_context_parallel=True), ), - ) - - -def _sync_merged_weights_to_vllm( - runtime: TrainingRuntime, - spec: MergedWeightTransferSpec, - *, - lora_path: str, - pause_generation: bool, -) -> None: - adapter_model = load_lora_tensors_for_megatron( - lora_path, - handler=runtime.model_support_handler, - ) - ( - runtime.merged_weight_transfer_group, - runtime.merged_weight_transfer_init_info, - ) = sync_merged_weights_to_vllm( - bridge=runtime.bridge, - model=runtime.model, - model_support_handler=runtime.model_support_handler, - adapter_model=adapter_model, - adapter_config=load_adapter_config(lora_path), - rank=runtime.rank, - world_size=runtime.world_size, - merged_weight_transfer_group=runtime.merged_weight_transfer_group, - merged_weight_transfer_init_info=runtime.merged_weight_transfer_init_info, - spec=spec, - pause_generation=pause_generation, + pipeline_metrics=schedule.telemetry.metrics(), ) @@ -2178,56 +2193,3 @@ def _close_merged_weight_transfer_group( shutdown = getattr(weight_transfer_group, "close", None) if shutdown is not None: shutdown() - - -def _run_service_loop(runtime: TrainingRuntime) -> None: - weight_offload = WeightOffloadManager.from_env( - model=runtime.model, - rank=runtime.rank, - compile_enabled=runtime.transformer_layers_compiled, - ) - runtime.optimizer_persistent = not weight_offload.offload_between_jobs - weight_offload.install() - wake_lock_path = os.environ.get( - "ART_MEGATRON_WAKE_LOCK_PATH", DEFAULT_VLLM_WAKE_LOCK_PATH - ) - - def wait_until_ready() -> None: - while os.path.exists(wake_lock_path): - time.sleep(0.2) - - def before_job() -> None: - weight_offload.before_job() - - def after_job() -> None: - if not runtime.optimizer_persistent: - _clear_resident_optimizer(runtime) - weight_offload.after_job() - - worker_error = False - try: - after_job() - run_megatron_worker_loop( - runtime, - supports_sft=True, - wait_until_ready=wait_until_ready, - before_job=before_job, - after_job=after_job, - ) - except BaseException: - worker_error = True - raise - finally: - _close_merged_weight_transfer_group(runtime, abort=worker_error) - - -def main() -> None: - runtime = build_training_runtime( - model_identifier=os.environ.get("MODEL_IDENTIFIER", DEFAULT_MODEL_IDENTIFIER), - build_optimizer=False, - ) - _run_service_loop(runtime) - - -if __name__ == "__main__": - main() diff --git a/src/art/megatron/training/finalize_grads.py b/src/art/megatron/training/finalize_grads.py index e00cd8218..a842e60df 100644 --- a/src/art/megatron/training/finalize_grads.py +++ b/src/art/megatron/training/finalize_grads.py @@ -127,6 +127,9 @@ def flush_param_grads_to_main_grads(model_chunks: Iterable[torch.nn.Module]) -> def finalize_model_grads_extended( model: list[MegatronModule], num_tokens: torch.Tensor | None = None, + *, + pg_collection: Any | None = None, + force_all_reduce: bool = False, ) -> None: """Run Megatron finalize, then apply extra LoRA grad-sync reductions. @@ -139,6 +142,8 @@ def finalize_model_grads_extended( finalize_model_grads( cast(list[torch.nn.Module], model), num_tokens=num_tokens, + pg_collection=pg_collection, + force_all_reduce=force_all_reduce, ) buckets: dict[ diff --git a/src/art/megatron/training/microbatches.py b/src/art/megatron/training/microbatches.py index 307a599aa..eb8c0b0fb 100644 --- a/src/art/megatron/training/microbatches.py +++ b/src/art/megatron/training/microbatches.py @@ -18,9 +18,12 @@ DispatchedPackedTensors, ParallelTopology, PreparedMegatronBatch, + TrainingMicrobatchWorkload, ) from art.megatron.flex_attn.compiled import flash_sparse_block_size_for_head_dim +from art.megatron.prefix_tree import parse_prefix_tree from art.megatron.prefix_tree_state import create_prefix_tree_state +from art.megatron.selective_lm_head import LmHeadTokenSelection from art.megatron.training.trace import ( packed_sequence_token_uids, sft_sequence_token_uids, @@ -43,8 +46,10 @@ class PreparedRLMicroInputs(BaseModel): attention_state: Any packed_seq_params: Any | None = None loss_inputs: LossInputs | DispatchedPackedTensors + lm_head_selection: LmHeadTokenSelection ref_logprobs: torch.Tensor | None = None local_token_uids: torch.Tensor | None = None + workload: TrainingMicrobatchWorkload class PreparedSFTMicroInputs(BaseModel): @@ -54,9 +59,11 @@ class PreparedSFTMicroInputs(BaseModel): position_ids: torch.Tensor labels: torch.Tensor loss_mask: torch.Tensor + lm_head_selection: LmHeadTokenSelection attention_state: Any packed_seq_params: Any | None = None local_token_uids: torch.Tensor | None = None + workload: TrainingMicrobatchWorkload def _map_packed_tensors( @@ -213,7 +220,9 @@ def build_rl_hybridep_token_counts( return [sequence_length for _ in sample_rows] config = _context_parallel_config_for_provider( - provider, torch.device("cuda", torch.cuda.current_device()) + provider, + torch.device("cuda", torch.cuda.current_device()), + model_support_handler, ) build_gdn = bool(getattr(model_support_handler, "build_gdn_execution_spec", False)) gdn_planner_config = _gdn_planner_config_for_provider( @@ -263,7 +272,9 @@ def sample(sample_index: int | None) -> dict[str, torch.Tensor]: ] config = _context_parallel_config_for_provider( - provider, torch.device("cuda", torch.cuda.current_device()) + provider, + torch.device("cuda", torch.cuda.current_device()), + model_support_handler, ) build_gdn = bool(getattr(model_support_handler, "build_gdn_execution_spec", False)) gdn_planner_config = _gdn_planner_config_for_provider( @@ -358,7 +369,7 @@ def _local_trainable_token_count_tensor( device: torch.device, ) -> torch.Tensor: local_token_total = sum(_count_trainable_tokens(micro) for micro in micro_inputs) - return torch.tensor([local_token_total], device=device, dtype=torch.float32) + return torch.tensor(local_token_total, device=device, dtype=torch.int) def _art_flex_sliding_windows(provider: Any) -> tuple[int, ...]: @@ -414,16 +425,24 @@ def _art_flex_cp_block_mask_variants( def _context_parallel_config_for_provider( provider: Any, device: torch.device, + model_support_handler: Any, ) -> ContextParallelConfig: head_dim = getattr(provider, "kv_channels", None) if head_dim is None: - return ContextParallelConfig() + return ContextParallelConfig( + workload_profile=model_support_handler.context_parallel_workload_profile( + provider + ) + ) return ContextParallelConfig( attention_sparse_block_size=flash_sparse_block_size_for_head_dim( head_dim=int(head_dim), head_dim_v=int(head_dim), device=device, - ) + ), + workload_profile=model_support_handler.context_parallel_workload_profile( + provider + ), ) @@ -503,7 +522,6 @@ def _prepare_dense_rl_micro( model_support_handler, ), ) - _move_inputs_to_device(micro, device) shifted_labels = shift_tensor(micro["tokens"], -100) shifted_assistant_mask = shift_tensor(micro["assistant_mask"], False) shifted_labels = torch.where( @@ -511,14 +529,33 @@ def _prepare_dense_rl_micro( shifted_labels, torch.full_like(shifted_labels, -100), ) + lm_head_selection = LmHeadTokenSelection.from_labels( + shifted_labels, + target_device=device, + ) + workload = TrainingMicrobatchWorkload( + logical_nonpadding_tokens=sum( + row.valid_tokens + for row in parse_prefix_tree( + group_ids=micro["group_ids"], parent_ids=micro["parent_ids"] + ) + ), + loss_bearing_tokens=int(shifted_assistant_mask.sum().item()), + executed_token_equivalents=int(micro["tokens"].numel()), + nominal_schedule_capacity_tokens=int(micro["tokens"].numel()), + ) + shifted_labels = shifted_labels.to(device) + _move_inputs_to_device(micro, device) return PreparedRLMicroInputs( model_tokens=micro["tokens"], model_input_pos=micro["input_pos"], model_labels=shifted_labels, attention_state=attention_state, loss_inputs=LossInputs(inputs=micro), + lm_head_selection=lm_head_selection, ref_logprobs=ref_logprobs, local_token_uids=packed_sequence_token_uids(micro, device=device), + workload=workload, ) @@ -541,7 +578,9 @@ def _prepare_rl_cp_micro_full( return prepare_cp_micro( micro=micro, topology=topology, - config=_context_parallel_config_for_provider(provider, device), + config=_context_parallel_config_for_provider( + provider, device, model_support_handler + ), cp_group=ps.get_context_parallel_group(check_initialized=False), cp_rank=ps.get_context_parallel_rank(), build_gdn_execution_spec=bool( @@ -555,6 +594,9 @@ def _prepare_rl_cp_micro_full( block_mask_variants=_art_flex_cp_block_mask_variants(provider, device), target_device=device, ref_logprobs=ref_logprobs, + model_support_handler=model_support_handler, + attention_head_dim=getattr(provider, "kv_channels", None), + attention_value_head_dim=getattr(provider, "kv_channels", None), ) @@ -570,34 +612,15 @@ def _prepared_rl_micro_from_cp_batch( attention_state=prepared.attention_state, packed_seq_params=prepared.packed_seq_params, loss_inputs=prepared.tensors, + lm_head_selection=prepared.tensors.lm_head_selection, ref_logprobs=( prepared.tensors.ref_logprobs if ref_logprobs is not None else None ), local_token_uids=prepared.tensors.token_uids, + workload=prepared.workload, ) -def _empty_new_logprobs_from_logits( - logits: torch.Tensor, labels: torch.Tensor -) -> torch.Tensor: - if int(labels.numel()) != 0: - raise ValueError("empty-logprob path requires empty local labels") - if logits.ndim < 3 or int(logits.shape[-1]) == 0: - raise ValueError( - f"expected empty local logits [B, S, V], got {tuple(logits.shape)}" - ) - candidate = logits[..., 0] - if tuple(candidate.shape) == tuple(labels.shape): - return candidate - candidate = candidate.transpose(0, 1).contiguous() - if tuple(candidate.shape) != tuple(labels.shape): - raise ValueError( - "empty local logits shape must match labels after removing vocab dim, " - f"got logits={tuple(logits.shape)} labels={tuple(labels.shape)}" - ) - return candidate - - def _prepare_current_rl_micro( micro: PackedTensors, *, @@ -676,7 +699,7 @@ def _local_trainable_sft_token_count_tensor( local_token_total = sum( _count_sft_trainable_tokens(micro) for micro in micro_inputs ) - return torch.tensor([local_token_total], device=device, dtype=torch.float32) + return torch.tensor(local_token_total, device=device, dtype=torch.int) def _prepare_dense_sft_micro( @@ -689,15 +712,28 @@ def _prepare_dense_sft_micro( attention_mask = micro["attention_mask"].reshape(-1) seq_len = max(int(attention_mask.sum().item()), 1) input_ids = micro["input_ids"].reshape(-1)[:seq_len].unsqueeze(0).to(device) - labels = micro["labels"].reshape(-1)[:seq_len].unsqueeze(0).to(device) + labels = micro["labels"].reshape(-1)[:seq_len].unsqueeze(0) position_ids = torch.arange(seq_len, device=device).unsqueeze(0) shifted_labels = shift_tensor(labels, -100) loss_mask = shifted_labels != -100 + workload = TrainingMicrobatchWorkload( + logical_nonpadding_tokens=int(attention_mask.sum().item()), + loss_bearing_tokens=int(loss_mask.sum().item()), + executed_token_equivalents=seq_len, + nominal_schedule_capacity_tokens=int(micro["input_ids"].numel()), + ) + lm_head_selection = LmHeadTokenSelection.from_labels( + shifted_labels, + target_device=device, + ) + shifted_labels = shifted_labels.to(device) + loss_mask = loss_mask.to(device) return PreparedSFTMicroInputs( input_ids=input_ids, position_ids=position_ids, labels=shifted_labels, loss_mask=loss_mask, + lm_head_selection=lm_head_selection, attention_state=_causal_attention_state( seq_len, device, @@ -713,6 +749,7 @@ def _prepare_dense_sft_micro( local_token_uids=sft_sequence_token_uids(micro, device=device)[ :, : int(input_ids.shape[1]) ], + workload=workload, ) @@ -777,7 +814,9 @@ def _prepare_sft_cp_micro_full( return prepare_cp_micro( micro=sparse_micro, topology=topology, - config=_context_parallel_config_for_provider(provider, device), + config=_context_parallel_config_for_provider( + provider, device, model_support_handler + ), cp_group=ps.get_context_parallel_group(check_initialized=False), cp_rank=ps.get_context_parallel_rank(), build_gdn_execution_spec=bool( @@ -790,6 +829,9 @@ def _prepare_sft_cp_micro_full( trace_token_uids=trace_token_uids, block_mask_variants=_art_flex_cp_block_mask_variants(provider, device), target_device=device, + model_support_handler=model_support_handler, + attention_head_dim=getattr(provider, "kv_channels", None), + attention_value_head_dim=getattr(provider, "kv_channels", None), ) @@ -802,9 +844,11 @@ def _prepared_sft_micro_from_cp_batch( position_ids=prepared.tensors.input_pos, labels=prepared.tensors.labels.masked_fill(~loss_mask, -100), loss_mask=loss_mask, + lm_head_selection=prepared.tensors.lm_head_selection, attention_state=prepared.attention_state, packed_seq_params=prepared.packed_seq_params, local_token_uids=prepared.tensors.token_uids, + workload=prepared.workload, ) diff --git a/src/art/megatron/training/pipeline_schedule.py b/src/art/megatron/training/pipeline_schedule.py new file mode 100644 index 000000000..7e75d8beb --- /dev/null +++ b/src/art/megatron/training/pipeline_schedule.py @@ -0,0 +1,987 @@ +from __future__ import annotations + +from collections.abc import Callable, Iterator, Sequence +from contextlib import contextmanager +from dataclasses import dataclass, field +import time +from typing import Any, Generic, Protocol, TypeVar, cast + +from megatron.core import parallel_state as ps +from megatron.core.pipeline_parallel.p2p_communication import P2PCommunicator +from megatron.core.pipeline_parallel.schedules import ( + get_forward_backward_func, + get_schedule_table, +) +from megatron.core.process_groups_config import ProcessGroupCollection +from megatron.core.timers import DummyTimer +from megatron.core.utils import get_model_config +import torch + +from art.megatron.context_parallel.types import ( + TrainingMicrobatchWorkload, + TrainingStepWorkload, +) +from art.megatron.routing_replay import MoeRoutingReplayController +from art.megatron.training.model_chunks import ModelChunks +from art.megatron.training.trace import prepare_replay_local_input_token_uids + + +class _PreparedMicrobatch(Protocol): + attention_state: Any + local_token_uids: torch.Tensor | None + workload: TrainingMicrobatchWorkload + + +_T = TypeVar("_T", bound=_PreparedMicrobatch) + + +@dataclass(frozen=True) +class ScheduleMicrobatch(Generic[_T]): + order: int + sample_index: int | None + payload: _T + recompute_state: object | None = None + + +def _local_training_workload_values( + microbatches: Sequence[ScheduleMicrobatch[Any]], cp_rank: int +) -> list[int]: + real = tuple(item for item in microbatches if item.sample_index is not None) + dummy = tuple(item for item in microbatches if item.sample_index is None) + return [ + sum(item.payload.workload.logical_nonpadding_tokens for item in real), + sum(item.payload.workload.loss_bearing_tokens for item in real), + sum(item.payload.workload.executed_token_equivalents for item in microbatches), + ( + sum( + item.payload.workload.nominal_schedule_capacity_tokens + for item in microbatches + ) + if cp_rank == 0 + else 0 + ), + sum(item.payload.workload.executed_token_equivalents for item in dummy), + ( + sum( + item.payload.workload.nominal_schedule_capacity_tokens for item in dummy + ) + if cp_rank == 0 + else 0 + ), + len(real) if cp_rank == 0 else 0, + len(microbatches) - len(real) if cp_rank == 0 else 0, + ] + + +def _set_hybridep_token_count(rows: int) -> None: + from megatron.core.transformer.moe import fused_a2a + + buffer = fused_a2a._hybrid_ep_buffer + if buffer is None: + raise RuntimeError("HybridEP buffer is not initialized") + buffer.set_num_tokens_per_rank(rows) + + +def _validate_hybridep_token_counts( + values: Sequence[int] | None, microbatch_count: int +) -> bool: + enabled = ps.get_expert_model_parallel_world_size() > 1 + if enabled and (values is None or len(values) != microbatch_count): + raise RuntimeError( + "HybridEP requires one planned communication extent per microbatch" + ) + return enabled + + +class PipelineMicrobatchState(Generic[_T]): + def __init__( + self, + *, + controller: MoeRoutingReplayController | None, + hybridep_token_counts: Sequence[int] | None, + microbatch_count: int, + model_activator: Callable[[_T, int], None] | None, + ) -> None: + hybridep_enabled = _validate_hybridep_token_counts( + hybridep_token_counts, microbatch_count + ) + self._controller = controller + self._model_activator = model_activator + self._hybridep_token_counts = ( + tuple(cast(Sequence[int], hybridep_token_counts)) + if hybridep_enabled + else None + ) + + @property + def enabled(self) -> bool: + return ( + self._controller is not None + or self._hybridep_token_counts is not None + or self._model_activator is not None + ) + + def activate(self, item: ScheduleMicrobatch[_T], chunk_index: int) -> None: + prepared = item.payload + if self._controller is not None: + self._controller.begin_micro( + item.sample_index, + item.order, + chunk_index=chunk_index, + ) + prepare_replay_local_input_token_uids( + self._controller, + prepared.local_token_uids, + prepared.attention_state, + ) + if self._hybridep_token_counts is not None: + _set_hybridep_token_count(self._hybridep_token_counts[item.order]) + if self._model_activator is not None: + self._model_activator(prepared, chunk_index) + + +@dataclass +class PipelineScheduleTelemetry: + pp_rank: int + pp_size: int + vp_size: int + num_microbatches: int + real_microbatches: int + dummy_microbatches: int + micro_batch_size: int + seq_length: int + microbatch_group_size: int + forward_compute_s_by_chunk: dict[int, float] = field(default_factory=dict) + backward_compute_s_by_chunk: dict[int, float] = field(default_factory=dict) + forward_host_s_by_chunk: dict[int, float] = field(default_factory=dict) + forward_calls_by_chunk: dict[int, int] = field(default_factory=dict) + p2p_s: float = 0.0 + p2p_calls: int = 0 + p2p_wait_s: float = 0.0 + p2p_wait_calls: int = 0 + schedule_wall_s: float = 0.0 + schedule_gpu_s: float = 0.0 + memory_allocated_start_bytes: int = 0 + peak_memory_bytes: int = 0 + _cuda_timers: _DeferredCudaTimers | None = field(default=None, repr=False) + _metrics_cache: dict[str, float] | None = field(default=None, repr=False) + + def metrics(self) -> dict[str, float]: + if self._metrics_cache is not None: + return dict(self._metrics_cache) + self._resolve_cuda_timers() + bubble_fraction = pipeline_bubble_fraction( + pp_size=self.pp_size, + vp_size=self.vp_size, + num_microbatches=self.num_microbatches, + ) + metrics = { + "pipeline/pp_rank": float(self.pp_rank), + "pipeline/pp_size": float(self.pp_size), + "pipeline/vp_size": float(self.vp_size), + "pipeline/microbatches_per_dp_rank": float(self.num_microbatches), + "pipeline/real_microbatches_per_dp_rank": float(self.real_microbatches), + "pipeline/dummy_microbatches_per_dp_rank": float(self.dummy_microbatches), + "pipeline/micro_batch_size": float(self.micro_batch_size), + "pipeline/packed_sequence_length": float(self.seq_length), + "pipeline/microbatch_group_size_per_vp_stage": float( + self.microbatch_group_size + ), + "pipeline/schedule_wall_s": self.schedule_wall_s, + "pipeline/schedule_gpu_s": self.schedule_gpu_s, + "pipeline/p2p_s": self.p2p_s, + "pipeline/p2p_call_host_s": self.p2p_s, + "pipeline/p2p_calls": float(self.p2p_calls), + "pipeline/p2p_wait_host_s": self.p2p_wait_s, + "pipeline/p2p_wait_calls": float(self.p2p_wait_calls), + "pipeline/memory_allocated_start_bytes": float( + self.memory_allocated_start_bytes + ), + "pipeline/peak_memory_bytes": float(self.peak_memory_bytes), + "pipeline/ideal_bubble_fraction": bubble_fraction, + } + for chunk, forward_compute in sorted(self.forward_compute_s_by_chunk.items()): + backward_compute = self.backward_compute_s_by_chunk.get(chunk, 0.0) + metrics[f"pipeline/chunk_{chunk}/compute_s"] = ( + forward_compute + backward_compute + ) + metrics[f"pipeline/chunk_{chunk}/forward_compute_s"] = forward_compute + metrics[f"pipeline/chunk_{chunk}/backward_compute_s"] = backward_compute + metrics[f"pipeline/chunk_{chunk}/forward_host_s"] = ( + self.forward_host_s_by_chunk.get(chunk, 0.0) + ) + metrics[f"pipeline/chunk_{chunk}/forward_calls"] = float( + self.forward_calls_by_chunk[chunk] + ) + metrics.update(self._stage_metrics()) + self._metrics_cache = metrics + return dict(metrics) + + def _resolve_cuda_timers(self) -> None: + timers = self._cuda_timers + if timers is None: + return + timers.synchronize() + self.schedule_gpu_s = timers.total("forward-backward") + self.forward_compute_s_by_chunk = timers.by_chunk("forward-compute") + self.backward_compute_s_by_chunk = timers.by_chunk("backward-compute") + + def _stage_metrics(self) -> dict[str, float]: + local = [ + self.schedule_gpu_s, + sum(self.forward_compute_s_by_chunk.values()), + sum(self.backward_compute_s_by_chunk.values()), + self.p2p_s, + self.p2p_wait_s, + float(self.peak_memory_bytes), + *( + values.get(chunk, 0.0) + for values in ( + self.forward_compute_s_by_chunk, + self.backward_compute_s_by_chunk, + ) + for chunk in range(self.vp_size) + ), + ] + rows = [local] + if self.pp_size > 1: + value = torch.tensor( + local, device=torch.cuda.current_device(), dtype=torch.float64 + ) + gathered = torch.empty( + self.pp_size * value.numel(), device=value.device, dtype=value.dtype + ) + torch.distributed.all_gather_into_tensor( # ty: ignore[possibly-missing-attribute] + gathered, + value, + group=ps.get_pipeline_model_parallel_group(), + ) + rows = gathered.view(self.pp_size, -1).cpu().tolist() + + metrics: dict[str, float] = {} + for stage, row in enumerate(rows): + prefix = f"pipeline/stage_{stage}" + metrics[f"{prefix}/schedule_gpu_s"] = row[0] + metrics[f"{prefix}/forward_compute_s"] = row[1] + metrics[f"{prefix}/backward_compute_s"] = row[2] + metrics[f"{prefix}/p2p_call_host_s"] = row[3] + metrics[f"{prefix}/p2p_wait_host_s"] = row[4] + metrics[f"{prefix}/peak_memory_bytes"] = row[5] + for chunk in range(self.vp_size): + metrics[f"{prefix}/chunk_{chunk}/forward_compute_s"] = row[6 + chunk] + metrics[f"{prefix}/chunk_{chunk}/backward_compute_s"] = row[ + 6 + self.vp_size + chunk + ] + stage_compute = [row[1] + row[2] for row in rows] + max_compute = max(stage_compute, default=0.0) + metrics["pipeline/stage_compute_imbalance_fraction"] = ( + (max_compute - min(stage_compute)) / max_compute if max_compute > 0 else 0.0 + ) + return metrics + + +def pipeline_bubble_fraction( + *, pp_size: int, vp_size: int, num_microbatches: int +) -> float: + if pp_size <= 1: + return 0.0 + useful = max(1, num_microbatches * max(1, vp_size)) + bubbles = max(0, pp_size - 1) + return bubbles / (useful + bubbles) + + +def validate_pipeline_topology( + *, + world_size: int, + tp: int, + cp: int, + pp: int, + ep: int, + etp: int, + vp: int, + num_layers: int | None = None, +) -> None: + values = { + "world_size": world_size, + "tp": tp, + "cp": cp, + "pp": pp, + "ep": ep, + "etp": etp, + "vp": vp, + } + invalid = {name: value for name, value in values.items() if value < 1} + if invalid: + raise ValueError(f"Megatron topology sizes must be positive: {invalid}") + dense = tp * cp * pp + expert = etp * ep * pp + if world_size % dense: + raise ValueError( + f"world_size={world_size} must be divisible by TP*CP*PP={dense}" + ) + if world_size % expert: + raise ValueError( + f"world_size={world_size} must be divisible by ETP*EP*PP={expert}" + ) + if vp > 1 and pp <= 1: + raise ValueError("VPP requires pipeline_model_parallel_size > 1") + if num_layers is not None and num_layers % (pp * vp): + raise ValueError( + f"num_layers={num_layers} must be divisible by PP*VPP={pp * vp}" + ) + + +def validate_microbatch_shapes( + shapes: Sequence[tuple[int, int]], +) -> tuple[int, int, bool]: + if not shapes: + raise ValueError("MCore schedule requires at least one microbatch") + invalid = [ + (index, shape) + for index, shape in enumerate(shapes) + if shape[0] != 1 or shape[1] < 1 + ] + if invalid: + raise ValueError( + "ART pipeline microbatches must have [batch=1, sequence>0] shapes; " + f"invalid={invalid}" + ) + sequence_lengths = {shape[1] for shape in shapes} + return 1, max(sequence_lengths), len(sequence_lengths) > 1 + + +def chunk_pre_process(model: torch.nn.Module) -> bool: + return bool(_chunk_attr(model, "pre_process")) + + +def chunk_post_process(model: torch.nn.Module) -> bool: + return bool(_chunk_attr(model, "post_process")) + + +def _chunk_attr(model: torch.nn.Module, name: str) -> Any: + current: Any = model + seen: set[int] = set() + while id(current) not in seen: + seen.add(id(current)) + if hasattr(current, name): + return getattr(current, name) + for wrapper_name in ("module", "_orig_mod", "language_model"): + wrapped = getattr(current, wrapper_name, None) + if isinstance(wrapped, torch.nn.Module): + current = wrapped + break + else: + return None + return None + + +class _DeferredCudaTimer: + def __init__(self, owner: _DeferredCudaTimers, name: str) -> None: + self._owner = owner + self._name = name + self._start: torch.cuda.Event | None = None + + def start(self, barrier: bool = False) -> None: + if self._start is not None: + raise RuntimeError(f"CUDA timer {self._name!r} is already running") + if barrier: + torch.distributed.barrier() # ty: ignore[possibly-missing-attribute] + self._start = torch.cuda.Event(enable_timing=True) + self._start.record() + + def stop(self, barrier: bool = False) -> None: + if self._start is None: + raise RuntimeError(f"CUDA timer {self._name!r} is not running") + if barrier: + torch.distributed.barrier() # ty: ignore[possibly-missing-attribute] + end = torch.cuda.Event(enable_timing=True) + end.record() + self._owner._spans.setdefault(self._name, []).append((self._start, end)) + self._start = None + + +class _DeferredCudaTimers: + _TIMED_NAMES = {"forward-backward", "forward-compute", "backward-compute"} + + def __init__( + self, *, forward_chunks: Sequence[int], backward_chunks: Sequence[int] + ): + self._chunk_sequences = { + "forward-compute": tuple(forward_chunks), + "backward-compute": tuple(backward_chunks), + } + self._timers = { + name: _DeferredCudaTimer(self, name) for name in self._TIMED_NAMES + } + self._null_timer = DummyTimer() + self._spans: dict[str, list[tuple[torch.cuda.Event, torch.cuda.Event]]] = {} + + def __call__(self, name: str, **_kwargs: Any) -> _DeferredCudaTimer | DummyTimer: + return self._timers.get(name, self._null_timer) + + def validate_counts(self, *, forward_only: bool) -> None: + names = ("forward-compute",) + (() if forward_only else ("backward-compute",)) + for name in names: + actual = len(self._spans.get(name, ())) + expected = len(self._chunk_sequences[name]) + if actual != expected: + raise RuntimeError( + f"MCore {name} count differs from the schedule table: " + f"expected={expected}, got={actual}" + ) + + def synchronize(self) -> None: + schedule = self._spans.get("forward-backward", ()) + if schedule: + schedule[-1][1].synchronize() + + def total(self, name: str) -> float: + return ( + sum(start.elapsed_time(end) for start, end in self._spans.get(name, ())) + / 1e3 + ) + + def by_chunk(self, name: str) -> dict[int, float]: + spans = self._spans.get(name, ()) + if not spans: + return {} + chunks = self._chunk_sequences[name] + if len(spans) != len(chunks): + raise RuntimeError( + f"Cannot resolve {name}: spans={len(spans)}, chunks={len(chunks)}" + ) + values: dict[int, float] = {} + for chunk, (start, end) in zip(chunks, spans, strict=True): + values[chunk] = values.get(chunk, 0.0) + start.elapsed_time(end) / 1e3 + return values + + +class _TimedWork: + def __init__(self, work: Any, telemetry: PipelineScheduleTelemetry) -> None: + self._work = work + self._telemetry = telemetry + + def __getattr__(self, name: str) -> Any: + return getattr(self._work, name) + + def wait(self, *args: Any, **kwargs: Any) -> Any: + start = time.perf_counter() + try: + return self._work.wait(*args, **kwargs) + finally: + self._telemetry.p2p_wait_s += time.perf_counter() - start + self._telemetry.p2p_wait_calls += 1 + + +def _wrap_p2p_work(value: Any, telemetry: PipelineScheduleTelemetry) -> Any: + if isinstance(value, torch.distributed.Work): # ty: ignore[possibly-missing-attribute] + return _TimedWork(value, telemetry) + if isinstance(value, dict): + return {key: _wrap_p2p_work(item, telemetry) for key, item in value.items()} + if isinstance(value, list): + return [_wrap_p2p_work(item, telemetry) for item in value] + if isinstance(value, tuple): + return tuple(_wrap_p2p_work(item, telemetry) for item in value) + return value + + +class _TimedP2PCommunicator: + def __init__( + self, communicator: P2PCommunicator, telemetry: PipelineScheduleTelemetry + ): + self._communicator = communicator + self._telemetry = telemetry + + def __getattr__(self, name: str) -> Any: + value = getattr(self._communicator, name) + if not callable(value) or not ( + name.startswith("send") or name.startswith("recv") + ): + return value + + def timed(*args: Any, **kwargs: Any) -> Any: + start = time.perf_counter() + try: + result = value(*args, **kwargs) + finally: + self._telemetry.p2p_s += time.perf_counter() - start + self._telemetry.p2p_calls += 1 + return _wrap_p2p_work(result, self._telemetry) + + return timed + + +class _ArtP2PCommunicator(P2PCommunicator): + def _communicate(self, *, tensor_shape: Any, **kwargs: Any) -> Any: + multiplier = getattr(self.config, "art_pipeline_activation_multiplier", None) + if tensor_shape is not None and multiplier is not None: + tensor_shape = torch.Size( + (*tensor_shape[:-1], multiplier, tensor_shape[-1]) + ) + return super()._communicate(tensor_shape=tensor_shape, **kwargs) + + +class MCoreScheduleAdapter(Generic[_T]): + """Small ART boundary around MCore's PP1, PP and VPP schedules.""" + + def __init__( + self, + *, + model_chunks: ModelChunks, + prepared_microbatches: Sequence[_T], + sample_indices: Sequence[int | None], + model_inputs: Sequence[torch.Tensor], + moe_routing_replay_controller: MoeRoutingReplayController | None = None, + hybridep_token_counts: Sequence[int] | None = None, + model_activator: Callable[[_T, int], None] | None = None, + ) -> None: + if not model_chunks: + raise ValueError("MCore schedule requires at least one model chunk") + if not (len(prepared_microbatches) == len(sample_indices) == len(model_inputs)): + raise ValueError("microbatch payload/sample/input counts differ") + self.model_chunks = model_chunks + self.microbatches = tuple( + ScheduleMicrobatch(order, sample_index, prepared, prepared.attention_state) + for order, (sample_index, prepared) in enumerate( + zip(sample_indices, prepared_microbatches, strict=True) + ) + ) + self._microbatch_state = PipelineMicrobatchState( + controller=moe_routing_replay_controller, + hybridep_token_counts=hybridep_token_counts, + microbatch_count=len(self.microbatches), + model_activator=model_activator, + ) + self._active_activation_key: tuple[int, int] | None = None + self.pp_size = int(ps.get_pipeline_model_parallel_world_size()) + ( + self.micro_batch_size, + local_seq_length, + self.variable_seq_lengths, + ) = validate_microbatch_shapes( + [(int(value.shape[0]), int(value.shape[1])) for value in model_inputs] + ) + self.seq_length = local_seq_length * ( + int(ps.get_context_parallel_world_size()) if self.pp_size > 1 else 1 + ) + self.pp_rank = int(ps.get_pipeline_model_parallel_rank()) + self.vp_size = int(ps.get_virtual_pipeline_model_parallel_world_size() or 1) + self.microbatch_group_size = len(self.microbatches) + if self.vp_size != len(model_chunks): + raise ValueError( + "Local model chunk count must equal VPP size: " + f"chunks={len(model_chunks)}, vpp={self.vp_size}" + ) + self._validate_stage_ownership() + self._configure() + self.telemetry = PipelineScheduleTelemetry( + pp_rank=self.pp_rank, + pp_size=self.pp_size, + vp_size=self.vp_size, + num_microbatches=len(self.microbatches), + real_microbatches=sum( + microbatch.sample_index is not None for microbatch in self.microbatches + ), + dummy_microbatches=sum( + microbatch.sample_index is None for microbatch in self.microbatches + ), + micro_batch_size=self.micro_batch_size, + seq_length=self.seq_length, + microbatch_group_size=self.microbatch_group_size, + ) + forward_chunks = [0] * len(self.microbatches) + if self.vp_size > 1: + table = get_schedule_table( + len(self.microbatches), self.vp_size, self.microbatch_group_size + ) + forward_chunks = [int(chunk) for _, chunk in table] + backward_chunks = [self.vp_size - chunk - 1 for chunk in forward_chunks] + if torch.cuda.is_available(): + self.telemetry._cuda_timers = _DeferredCudaTimers( + forward_chunks=forward_chunks, + backward_chunks=backward_chunks, + ) + self._chunk_by_id = { + id(chunk): index for index, chunk in enumerate(model_chunks) + } + + def _validate_stage_ownership(self) -> None: + for chunk_index, chunk in enumerate(self.model_chunks): + expected_pre = self.pp_rank == 0 and chunk_index == 0 + expected_post = ( + self.pp_rank == self.pp_size - 1 + and chunk_index == len(self.model_chunks) - 1 + ) + actual_pre = chunk_pre_process(chunk) + actual_post = chunk_post_process(chunk) + if (actual_pre, actual_post) != (expected_pre, expected_post): + raise RuntimeError( + "Megatron model chunk pipeline ownership is inconsistent: " + f"pp_rank={self.pp_rank}, chunk={chunk_index}, " + f"pre_process={actual_pre} (expected {expected_pre}), " + f"post_process={actual_post} (expected {expected_post})" + ) + + def _configure(self) -> None: + vpp_group: int | None = None + for config in _model_configs(self.model_chunks): + config.variable_seq_lengths = self.pp_size > 1 and self.variable_seq_lengths + if self.vp_size > 1: + group = int( + getattr(config, "microbatch_group_size_per_vp_stage", 0) + or self.pp_size + ) + if vpp_group is not None and group != vpp_group: + raise ValueError( + "All VPP model chunks must use one microbatch group size: " + f"expected={vpp_group}, got={group}" + ) + vpp_group = group + validate_vpp_microbatch_group( + num_microbatches=len(self.microbatches), + pp_size=self.pp_size, + group_size=group, + ) + config.microbatch_group_size_per_vp_stage = group + self.microbatch_group_size = group + config.overlap_p2p_comm = True + config.batch_p2p_comm = False + elif self.pp_size > 1: + config.overlap_p2p_comm = False + config.batch_p2p_comm = True + # PyTorch 2.11 does not need MCore's legacy batch-P2P device sync. + config.batch_p2p_sync = False + + def activate(self, microbatch: ScheduleMicrobatch[_T], chunk_index: int) -> None: + if not self._microbatch_state.enabled: + return + activation_key = (microbatch.order, chunk_index) + if activation_key == self._active_activation_key: + return + self._microbatch_state.activate(microbatch, chunk_index) + self._active_activation_key = activation_key + + def training_workload(self) -> TrainingStepWorkload: + values = torch.tensor( + _local_training_workload_values( + self.microbatches, int(ps.get_context_parallel_rank()) + ), + device=torch.cuda.current_device(), + dtype=torch.int64, + ) + if torch.distributed.is_initialized(): + torch.distributed.all_reduce( + values, + group=ps.get_data_parallel_group(with_context_parallel=True), + ) + ( + logical, + loss_bearing, + executed, + nominal, + dummy_executed, + dummy_nominal, + real_microbatches, + dummy_microbatches, + ) = values.cpu().tolist() + return TrainingStepWorkload( + logical_nonpadding_tokens=logical, + loss_bearing_tokens=loss_bearing, + executed_token_equivalents=executed, + nominal_schedule_capacity_tokens=nominal, + dummy_executed_token_equivalents=dummy_executed, + dummy_schedule_capacity_tokens=dummy_nominal, + real_microbatches=real_microbatches, + dummy_microbatches=dummy_microbatches, + ) + + @contextmanager + def _recompute_activation_hooks(self, *, enabled: bool) -> Iterator[None]: + config = get_model_config(self.model_chunks[0]) + if ( + not enabled + or self.pp_size <= 1 + or not self._microbatch_state.enabled + or not _stateful_recompute_enabled(config) + ): + yield + return + _validate_stateful_recompute_mode(config) + + by_state_id: dict[int, ScheduleMicrobatch[_T]] = {} + for microbatch in self.microbatches: + state = microbatch.recompute_state + if state is None: + raise RuntimeError( + "Stateful PP recomputation requires recompute_state on every microbatch" + ) + previous = by_state_id.setdefault(id(state), microbatch) + if previous is not microbatch: + raise RuntimeError( + "recompute_state must identify one logical microbatch" + ) + + def restore( + _module: Any, + args: tuple[Any, ...], + kwargs: dict[str, Any], + *, + chunk_index: int, + ) -> None: + microbatch = _find_bound_microbatch(by_state_id, (*args, kwargs)) + self.activate(microbatch, chunk_index) + + handles = [] + for chunk_index, chunk in enumerate(self.model_chunks): + for layer in _transformer_layer_callers([chunk]): + + def restore_chunk( + module: Any, + args: tuple[Any, ...], + kwargs: dict[str, Any], + *, + _chunk_index: int = chunk_index, + ) -> None: + restore( + module, + args, + kwargs, + chunk_index=_chunk_index, + ) + + handles.append( + layer.register_forward_pre_hook( + torch.compiler.disable(restore_chunk), + with_kwargs=True, + ) + ) + if not handles: + raise RuntimeError( + "Stateful PP recomputation could not find TransformerLayer call sites" + ) + try: + yield + finally: + for handle in handles: + handle.remove() + + def independent_iterators(self) -> list[Iterator[ScheduleMicrobatch[_T]]]: + def activate( + chunk_index: int, + ) -> Iterator[ScheduleMicrobatch[_T]]: + for microbatch in self.microbatches: + self.activate(microbatch, chunk_index) + yield microbatch + + return [activate(index) for index in range(len(self.model_chunks))] + + @contextmanager + def _telemetry_timer_context(self) -> Iterator[None]: + timers = self.telemetry._cuda_timers + if timers is None: + yield + return + configs = _model_configs(self.model_chunks) + previous = [config.timers for config in configs] + for config in configs: + config.timers = timers + try: + yield + finally: + for config, prior in zip(configs, previous, strict=True): + config.timers = prior + + def run( + self, + forward_step_func: Callable[ + ..., tuple[torch.Tensor, Callable[..., Any] | None] + ], + *, + forward_only: bool, + collect_non_loss_data: bool = False, + ) -> list[Any]: + def timed_forward(data_iterator: Any, model: Any, *args: Any) -> Any: + chunk = self._chunk_by_id.get(id(model)) + if chunk is None: + raise RuntimeError("MCore schedule passed an unknown local model chunk") + start = time.perf_counter() + try: + result = forward_step_func(data_iterator, model, *args) + output = result[0] + if ( + self.pp_size > 1 + and isinstance(output, torch.Tensor) + and output._base is not None + ): + result = (output.clone(), *result[1:]) + return result + finally: + elapsed = time.perf_counter() - start + self.telemetry.forward_host_s_by_chunk[chunk] = ( + self.telemetry.forward_host_s_by_chunk.get(chunk, 0.0) + elapsed + ) + self.telemetry.forward_calls_by_chunk[chunk] = ( + self.telemetry.forward_calls_by_chunk.get(chunk, 0) + 1 + ) + + config = get_model_config(self.model_chunks[0]) + if not forward_only and bool(config.overlap_moe_expert_parallel_comm): + raise RuntimeError( + "ART's forward-step contract does not support MCore's combined " + "EP-overlap schedule; disable overlap_moe_expert_parallel_comm" + ) + communicator: Any | None = None + pg_collection: ProcessGroupCollection | None = None + if self.pp_size > 1: + communicator = _TimedP2PCommunicator( + _ArtP2PCommunicator( + pp_group=ps.get_pipeline_model_parallel_group(), config=config + ), + self.telemetry, + ) + pg_collection = _process_group_collection() + start = time.perf_counter() + self._active_activation_key = None + if torch.cuda.is_available(): + torch.cuda.reset_peak_memory_stats() + self.telemetry.memory_allocated_start_bytes = int( + torch.cuda.memory_allocated() + ) + with ( + self._telemetry_timer_context(), + self._recompute_activation_hooks(enabled=not forward_only), + ): + outputs = get_forward_backward_func( + pp_size=self.pp_size, + vp_size=None if self.vp_size == 1 else self.vp_size, + )( + forward_step_func=timed_forward, + data_iterator=self.independent_iterators(), + model=self.model_chunks, + num_microbatches=len(self.microbatches), + seq_length=self.seq_length, + micro_batch_size=self.micro_batch_size, + forward_only=forward_only, + collect_non_loss_data=collect_non_loss_data, + p2p_communicator=cast(Any, communicator), + pg_collection=pg_collection, + ) + self.telemetry.schedule_wall_s = time.perf_counter() - start + expected_calls = len(self.microbatches) + invalid_calls = { + chunk: self.telemetry.forward_calls_by_chunk.get(chunk, 0) + for chunk in range(self.vp_size) + if self.telemetry.forward_calls_by_chunk.get(chunk, 0) != expected_calls + } + if invalid_calls: + raise RuntimeError( + "MCore schedule did not run every local chunk once per microbatch: " + f"expected={expected_calls}, got={invalid_calls}" + ) + if self.telemetry._cuda_timers is not None: + self.telemetry._cuda_timers.validate_counts(forward_only=forward_only) + if torch.cuda.is_available(): + self.telemetry.peak_memory_bytes = int(torch.cuda.max_memory_allocated()) + return cast(list[Any], outputs) + + +def validate_vpp_microbatch_group( + *, num_microbatches: int, pp_size: int, group_size: int +) -> None: + if not (pp_size <= group_size <= num_microbatches): + raise ValueError( + "VPP microbatch group must be in [PP, num_microbatches]: " + f"pp={pp_size}, group={group_size}, num_microbatches={num_microbatches}" + ) + remainder = num_microbatches % group_size + if 0 < remainder < pp_size: + raise ValueError( + "VPP final microbatch group must be empty or contain at least PP " + f"microbatches: remainder={remainder}, pp={pp_size}" + ) + + +def _stateful_recompute_enabled(config: Any) -> bool: + granularity = getattr(config, "recompute_granularity", None) + if granularity == "full": + return True + modules = set(getattr(config, "recompute_modules", None) or ()) + return granularity == "selective" and bool(modules & {"mlp", "moe"}) + + +def _validate_stateful_recompute_mode(config: Any) -> None: + if getattr(config, "recompute_granularity", None) != "full": + raise RuntimeError( + "HybridEP/MoE replay requires full-layer activation recomputation under " + "PP; selective MLP/MoE checkpoints do not retain ART's exact microbatch " + "state" + ) + + +def _transformer_layer_callers( + model_chunks: Sequence[torch.nn.Module], +) -> list[torch.nn.Module]: + from megatron.core.transformer.transformer_layer import TransformerLayer + + callers: dict[int, torch.nn.Module] = {} + for chunk in model_chunks: + for module in chunk.modules(): + original = getattr(module, "_orig_mod", None) + if isinstance(original, TransformerLayer): + callers[id(original)] = module + elif isinstance(module, TransformerLayer): + callers.setdefault(id(module), module) + return list(callers.values()) + + +def _find_bound_microbatch( + by_state_id: dict[int, ScheduleMicrobatch[_T]], + values: Sequence[Any], +) -> ScheduleMicrobatch[_T]: + pending = list(values) + seen: set[int] = set() + match: ScheduleMicrobatch[_T] | None = None + while pending: + value = pending.pop() + value_id = id(value) + if value_id in seen: + continue + seen.add(value_id) + microbatch = by_state_id.get(value_id) + if microbatch is not None and microbatch.recompute_state is value: + if match is not None and match is not microbatch: + raise RuntimeError( + "Stateful PP recomputation received multiple microbatch states: " + f"orders={[match.order, microbatch.order]}" + ) + match = microbatch + if isinstance(value, dict): + pending.extend(value.values()) + elif isinstance(value, list | tuple): + pending.extend(value) + if match is None: + raise RuntimeError( + "Stateful PP recomputation did not receive its exact microbatch state" + ) + return match + + +def _process_group_collection() -> ProcessGroupCollection: + groups = ProcessGroupCollection() + groups.tp = ps.get_tensor_model_parallel_group() + groups.pp = ps.get_pipeline_model_parallel_group() + groups.cp = ps.get_context_parallel_group() + groups.embd = ps.get_embedding_group(check_initialized=False) + groups.pos_embd = ps.get_position_embedding_group(check_initialized=False) + groups.dp_cp = ps.get_data_parallel_group( + with_context_parallel=True, partial_data_parallel=False + ) + return groups + + +def _model_configs(model_chunks: Sequence[torch.nn.Module]) -> list[Any]: + configs: dict[int, Any] = {} + for chunk in model_chunks: + config = get_model_config(chunk) + configs.setdefault(id(config), config) + return list(configs.values()) diff --git a/src/art/megatron/training/sft_batches.py b/src/art/megatron/training/sft_batches.py deleted file mode 100644 index 9c20640f2..000000000 --- a/src/art/megatron/training/sft_batches.py +++ /dev/null @@ -1,84 +0,0 @@ -from dataclasses import dataclass -import importlib -import json -import os -from typing import TYPE_CHECKING, Any, Iterable -import uuid - -import torch - -safetensors_torch = importlib.import_module("safetensors.torch") -load_file = safetensors_torch.load_file -save_file = safetensors_torch.save_file - -if TYPE_CHECKING: - from ...preprocessing.tokenize import SFTBatch - - -DEFAULT_SFT_DATA_DIR = "/tmp/megatron_sft_data" - - -@dataclass(frozen=True) -class SerializedSFTBatches: - sft_data_dir: str - num_batches: int - learning_rates: list[float] - - -def serialize_sft_batch_to_disk(batch: "SFTBatch", batch_dir: str) -> None: - os.makedirs(batch_dir, exist_ok=True) - metadata = { - "learning_rate": batch.learning_rate, - "num_trajectories": batch.num_trajectories, - "num_tokens": batch.num_tokens, - "num_trainable_tokens": batch.num_trainable_tokens, - "num_dropped_trajectories": batch.num_dropped_trajectories, - "num_trajectory_tensors": len(batch.trajectory_tensors), - } - with open(os.path.join(batch_dir, "metadata.json"), "w", encoding="utf-8") as f: - json.dump(metadata, f) - for index, trajectory_tensors in enumerate(batch.trajectory_tensors): - save_file( - { - key: value.squeeze(0) if value.dim() > 0 else value - for key, value in trajectory_tensors.items() - }, - os.path.join(batch_dir, f"trajectory_{index}.safetensors"), - ) - - -def materialize_sft_batches( - batches: Iterable["SFTBatch"], - *, - sft_data_dir: str | None = None, -) -> SerializedSFTBatches: - if sft_data_dir is None: - sft_data_dir = os.path.join(DEFAULT_SFT_DATA_DIR, uuid.uuid4().hex) - - learning_rates: list[float] = [] - num_batches = 0 - for batch_index, batch in enumerate(batches): - batch_dir = os.path.join(sft_data_dir, f"batch_{batch_index:06d}") - serialize_sft_batch_to_disk(batch, batch_dir) - learning_rates.append(batch.learning_rate) - num_batches += 1 - - return SerializedSFTBatches( - sft_data_dir=sft_data_dir, - num_batches=num_batches, - learning_rates=learning_rates, - ) - - -def load_sft_batch_from_disk( - batch_dir: str, -) -> tuple[dict[str, Any], list[dict[str, torch.Tensor]]]: - with open(os.path.join(batch_dir, "metadata.json"), encoding="utf-8") as f: - metadata = json.load(f) - - trajectory_tensors = [] - for index in range(metadata["num_trajectory_tensors"]): - trajectory_tensors.append( - load_file(os.path.join(batch_dir, f"trajectory_{index}.safetensors")) - ) - return metadata, trajectory_tensors diff --git a/src/art/megatron/training/trace.py b/src/art/megatron/training/trace.py index b3461c164..bb2f19fe9 100644 --- a/src/art/megatron/training/trace.py +++ b/src/art/megatron/training/trace.py @@ -156,12 +156,14 @@ def attach_trace_token_uids( token_uids: torch.Tensor | None, ) -> Iterator[None]: attach_module_token_uids = trace_token_uids_enabled() - _set_root_output_trace_token_uids(model_chunks[0], token_uids) + for chunk in model_chunks: + _set_root_output_trace_token_uids(chunk, token_uids) if attach_module_token_uids: _set_module_trace_token_uids(model_chunks, token_uids) try: yield finally: - _set_root_output_trace_token_uids(model_chunks[0], None) + for chunk in model_chunks: + _set_root_output_trace_token_uids(chunk, None) if attach_module_token_uids: _set_module_trace_token_uids(model_chunks, None) diff --git a/src/art/megatron/weights/adapter_export.py b/src/art/megatron/weights/adapter_export.py index bb96b51c7..13f3196cc 100644 --- a/src/art/megatron/weights/adapter_export.py +++ b/src/art/megatron/weights/adapter_export.py @@ -158,13 +158,14 @@ def _set_expert_adapter_weights( lora: LoRA, build_weight: Callable[[int], AdapterWeight], ) -> None: - for local_expert_idx in range(lora.num_local_experts): - global_expert_idx = local_expert_idx + lora._expert_offset + for local_expert_idx, logical_expert_idx in enumerate(lora.expert_ids): + if logical_expert_idx is None: + continue _set_adapter_weights( out, base_prefix, build_weight(local_expert_idx), - weight_suffix=f".weight{global_expert_idx}", + weight_suffix=f".weight{local_expert_idx + lora._expert_offset}", ) diff --git a/src/art/megatron/weights/lora_publish.py b/src/art/megatron/weights/lora_publish.py index e9d8b4b08..2c27797b7 100644 --- a/src/art/megatron/weights/lora_publish.py +++ b/src/art/megatron/weights/lora_publish.py @@ -1,6 +1,7 @@ from collections.abc import Iterable, Sequence from typing import Any, NamedTuple +from pydantic import BaseModel, ConfigDict import torch from art.megatron.lora import ( @@ -10,13 +11,20 @@ LoRASlotRef, _block_for_key, _dtype_name, + _template_expert_ids, ) from art.megatron.lora import ( _distributed_initialized as _distributed_ready, ) from art.megatron.model_support.lora_disk import save_vllm_lora_tensors from art.megatron.model_support.spec import ExpertPackedLoraGroup, ExpertPackedLoraSlot +from art.megatron.tensor_snapshot import ( + PendingCpuSnapshot, + PinnedCpuSnapshotBuilder, + PinnedCpuSnapshotStager, +) from art.megatron.training.model_chunks import ModelChunks +from art.utils.safetensors import PreparedSafetensors class PackedExpertShardMeta(NamedTuple): @@ -37,32 +45,11 @@ def numel(self) -> int: return total -class _PinnedCpuStager: - def __init__(self) -> None: - self._events: list[torch.cuda.Event] = [] - self._stream = torch.cuda.Stream() if torch.cuda.is_available() else None - - def stage(self, tensor: torch.Tensor) -> torch.Tensor: - source = tensor.detach() - if self._stream is None or not source.is_cuda: - return source.cpu() +class LoraSnapshot(BaseModel): + model_config = ConfigDict(arbitrary_types_allowed=True, frozen=True) - source = source.contiguous() - target = torch.empty_like(source, device="cpu", pin_memory=True) - source_stream = torch.cuda.current_stream(source.device) - self._stream.wait_stream(source_stream) - with torch.cuda.stream(self._stream): - target.copy_(source, non_blocking=True) - source.record_stream(self._stream) - event = torch.cuda.Event() - event.record(self._stream) - self._events.append(event) - return target - - def finish(self) -> None: - for event in self._events: - event.synchronize() - self._events.clear() + tensors: dict[str, torch.Tensor] + adapter_config: dict[str, Any] def iter_lora_modules(model_chunks: ModelChunks) -> Iterable[LoRA]: @@ -161,8 +148,11 @@ def collect_local_packed_expert_entries( for module in iter_lora_modules(model_chunks): if not _uses_packed_expert_publish(module, packed_expert_groups, slot_ref): continue - expert_start = int(module._expert_offset) - expert_count = int(module.num_local_experts) + expert_ids = tuple(expert for expert in module.expert_ids if expert is not None) + if not expert_ids: + continue + expert_start = expert_ids[0] + expert_count = len(expert_ids) for suffix, param in module._lora_params(slot_ref): slot_match = _packed_expert_slot( module.adapter_model_prefix, @@ -173,7 +163,7 @@ def collect_local_packed_expert_entries( continue group_prefix, slot = slot_match key = f"{group_prefix}.{slot.output_suffix}" - tensor = param.data.transpose(1, 2).contiguous() + tensor = param.data[:expert_count].transpose(1, 2).contiguous() source_keys = module._expected_weight_keys(suffix.removesuffix(".weight")) target_dtype = ( adapter_dtypes[source_keys[0]] @@ -223,7 +213,15 @@ def _global_packed_expert_metadata( ep_world_size = ps.get_expert_model_parallel_world_size() for ep_rank in range(ep_world_size): - expert_start = ep_rank * template.num_local_experts + expert_ids = tuple( + expert + for expert in _template_expert_ids(template, ep_rank) + if expert is not None + ) + if not expert_ids: + continue + expert_start = expert_ids[0] + expert_count = len(expert_ids) expert_key = ( f"{template.adapter_model_prefix.format(expert=expert_start)}." f"{template.suffix}" @@ -241,11 +239,11 @@ def _global_packed_expert_metadata( PackedExpertShardMeta( key=f"{group_prefix}.{slot.output_suffix}", owner_rank=owner_rank, - shape=(template.num_local_experts, *per_expert_meta.shape), + shape=(expert_count, *per_expert_meta.shape), dtype_name=per_expert_meta.dtype_name, manifest=per_expert_meta.manifest, expert_start=expert_start, - expert_count=template.num_local_experts, + expert_count=expert_count, pack_layout=slot.pack_layout, ) ) @@ -394,6 +392,37 @@ def _metadata_by_owner_dtype( } +def _canonical_global_metadata(local_metadata: list[Any]) -> list[Any]: + """Gather stage-local manifests; select one canonical DP/CP replica per shard.""" + if not _distributed_ready(): + return local_metadata + world_size = torch.distributed.get_world_size() # type: ignore[possibly-missing-attribute] + gathered: list[list[Any] | None] = [None] * world_size + torch.distributed.all_gather_object(gathered, local_metadata) # type: ignore[possibly-missing-attribute] + canonical: dict[tuple[Any, ...], Any] = {} + for rank_entries in gathered: + if rank_entries is None: + raise RuntimeError("LoRA manifest gather returned a missing rank") + for meta in rank_entries: + manifest = meta.manifest + identity = ( + meta.key, + int(manifest.get("shard_rank", 0)), + int(getattr(meta, "expert_start", -1)), + ) + current = canonical.get(identity) + if current is None or meta.owner_rank < current.owner_rank: + canonical[identity] = meta + return sorted( + canonical.values(), + key=lambda meta: ( + meta.key, + int(getattr(meta, "expert_start", -1)), + int(meta.manifest.get("shard_rank", 0)), + ), + ) + + def _pack_metadata_tensors( metadata: Sequence[Any], tensors: dict[str, torch.Tensor], @@ -584,15 +613,56 @@ def merge_packed_expert_adapter_entries( def _stage_published_tensors( tensors: dict[str, torch.Tensor], - stager: _PinnedCpuStager, + stager: PinnedCpuSnapshotBuilder, ) -> dict[str, torch.Tensor]: - grouped: dict[tuple[str, int | None, str], list[tuple[str, torch.Tensor]]] = {} + aliases: dict[ + tuple[str, int | None, torch.dtype, int], list[tuple[str, torch.Tensor]] + ] = {} + regular: list[tuple[str, torch.Tensor]] = [] for key, tensor in tensors.items(): + if not tensor.numel() or not tensor.is_contiguous(): + regular.append((key, tensor)) + continue + storage = tensor.untyped_storage() + aliases.setdefault( + ( + tensor.device.type, + tensor.device.index, + tensor.dtype, + storage.data_ptr(), + ), + [], + ).append((key, tensor)) + + staged: dict[str, torch.Tensor] = {} + for group in aliases.values(): + storage = group[0][1].untyped_storage() + if len(group) == 1 or storage.nbytes() > sum( + tensor.nbytes for _key, tensor in group + ): + regular.extend(group) + continue + representative = group[0][1] + flat = representative.new_empty(0).set_( + storage, + 0, + (storage.nbytes() // representative.element_size(),), + (1,), + ) + staged_flat = stager.stage(flat) + for key, tensor in group: + staged[key] = staged_flat.as_strided( + tensor.shape, + tensor.stride(), + tensor.storage_offset(), + ) + + grouped: dict[tuple[str, int | None, str], list[tuple[str, torch.Tensor]]] = {} + for key, tensor in regular: dtype_name = _dtype_name(tensor.dtype) group_key = (tensor.device.type, tensor.device.index, dtype_name) grouped.setdefault(group_key, []).append((key, tensor)) - staged: dict[str, torch.Tensor] = {} for _group_key, group in sorted(grouped.items()): flat = torch.cat( [tensor.detach().contiguous().view(-1) for _key, tensor in sorted(group)] @@ -630,9 +700,9 @@ def _save_rank0_vllm_lora( handler=handler, adapter_config=adapter_config, ) - stager = _PinnedCpuStager() - published_tensors = _stage_published_tensors(vllm_tensors, stager) - stager.finish() + builder = PinnedCpuSnapshotStager().begin() + published_tensors = _stage_published_tensors(vllm_tensors, builder) + builder.finish(published_tensors).resolve() save_vllm_lora_tensors(output_dir, published_tensors, published_config) @@ -647,6 +717,27 @@ def _rank0_vllm_lora_tensors( handler: Any, adapter_config: dict[str, Any], ) -> tuple[dict[str, torch.Tensor], dict[str, Any]]: + merged_tensors = _rank0_merged_lora_tensors( + metadata=metadata, + tensors_by_owner_key=tensors_by_owner_key, + packed_expert_metadata=packed_expert_metadata, + packed_expert_tensors_by_owner_key=packed_expert_tensors_by_owner_key, + ) + return handler.to_vllm_lora_tensors( + merged_tensors, + adapter_config=dict(adapter_config), + ) + + +def _rank0_merged_lora_tensors( + *, + metadata: list[LoraShardMeta], + tensors_by_owner_key: dict[tuple[int, str], torch.Tensor], + packed_expert_metadata: list[PackedExpertShardMeta] | None = None, + packed_expert_tensors_by_owner_key: ( + dict[tuple[int, str], torch.Tensor] | None + ) = None, +) -> dict[str, torch.Tensor]: merged_tensors = merge_sharded_adapter_entries( _entries_by_key(metadata, tensors_by_owner_key) ) @@ -661,22 +752,44 @@ def _rank0_vllm_lora_tensors( if key in merged_tensors: raise RuntimeError(f"Duplicate LoRA tensor after packed publish: {key}") merged_tensors[key] = tensor + return merged_tensors + + +def build_vllm_lora_tensors_from_model( + *, + model: ModelChunks, + adapter_dtypes: dict[str, torch.dtype], + handler: Any, + adapter_config: dict[str, Any], + rank: int, + world_size: int, + slot_ref: LoRASlotRef | None = None, +) -> tuple[dict[str, torch.Tensor], dict[str, Any]] | None: + merged_tensors = _build_merged_lora_tensors_from_model( + model=model, + adapter_dtypes=adapter_dtypes, + handler=handler, + rank=rank, + world_size=world_size, + slot_ref=slot_ref, + ) + if merged_tensors is None: + return None return handler.to_vllm_lora_tensors( merged_tensors, adapter_config=dict(adapter_config), ) -def build_vllm_lora_tensors_from_model( +def _build_merged_lora_tensors_from_model( *, model: ModelChunks, adapter_dtypes: dict[str, torch.dtype], handler: Any, - adapter_config: dict[str, Any], rank: int, world_size: int, slot_ref: LoRASlotRef | None = None, -) -> tuple[dict[str, torch.Tensor], dict[str, Any]] | None: +) -> dict[str, torch.Tensor] | None: actual_rank, device = _rank_and_device() if _distributed_ready(): actual_world_size = torch.distributed.get_world_size() # type: ignore[possibly-missing-attribute] @@ -693,7 +806,6 @@ def build_vllm_lora_tensors_from_model( ) rank = 0 packed_expert_groups = tuple(handler.expert_packed_lora_groups()) - planner = LoRAPublishPlanner(model, slot_ref) local_tensors, local_metadata = collect_local_lora_entries( model, adapter_dtypes, @@ -708,19 +820,8 @@ def build_vllm_lora_tensors_from_model( packed_expert_groups=packed_expert_groups, slot_ref=slot_ref, ) - all_packed_metadata = ( - _global_packed_expert_metadata(planner, adapter_dtypes, packed_expert_groups) - if rank == 0 - else local_packed_metadata - ) - if rank == 0: - all_metadata = _global_regular_metadata( - planner, - adapter_dtypes, - packed_expert_groups if all_packed_metadata else (), - ) - else: - all_metadata = local_metadata + all_packed_metadata = _canonical_global_metadata(local_packed_metadata) + all_metadata = _canonical_global_metadata(local_metadata) exchanged_tensors = _exchange_batched_tensors( all_metadata, local_tensors=local_tensors, @@ -737,13 +838,11 @@ def build_vllm_lora_tensors_from_model( if rank != 0: return None - return _rank0_vllm_lora_tensors( + return _rank0_merged_lora_tensors( metadata=all_metadata, tensors_by_owner_key=exchanged_tensors, packed_expert_metadata=all_packed_metadata, packed_expert_tensors_by_owner_key=exchanged_packed_tensors, - handler=handler, - adapter_config=adapter_config, ) @@ -758,7 +857,7 @@ def save_vllm_lora_from_model( world_size: int, slot_ref: LoRASlotRef | None = None, ) -> None: - result = build_vllm_lora_tensors_from_model( + snapshot = snapshot_vllm_lora_from_model( model=model, adapter_dtypes=adapter_dtypes, handler=handler, @@ -767,10 +866,85 @@ def save_vllm_lora_from_model( world_size=world_size, slot_ref=slot_ref, ) - if result is None: + if snapshot is None: return - vllm_tensors, published_config = result - stager = _PinnedCpuStager() - published_tensors = _stage_published_tensors(vllm_tensors, stager) - stager.finish() - save_vllm_lora_tensors(output_dir, published_tensors, published_config) + save_vllm_lora_snapshot(snapshot, output_dir) + + +def snapshot_vllm_lora_from_model( + *, + model: ModelChunks, + adapter_dtypes: dict[str, torch.dtype], + handler: Any, + adapter_config: dict[str, Any], + rank: int, + world_size: int, + slot_ref: LoRASlotRef | None = None, +) -> LoraSnapshot | None: + pending = stage_vllm_lora_snapshot_from_model( + model=model, + adapter_dtypes=adapter_dtypes, + handler=handler, + adapter_config=adapter_config, + rank=rank, + world_size=world_size, + slot_ref=slot_ref, + stager=PinnedCpuSnapshotStager(), + ) + return None if pending is None else pending.resolve() + + +def stage_vllm_lora_snapshot_from_model( + *, + model: ModelChunks, + adapter_dtypes: dict[str, torch.dtype], + handler: Any, + adapter_config: dict[str, Any], + rank: int, + world_size: int, + stager: PinnedCpuSnapshotStager, + slot_ref: LoRASlotRef | None = None, +) -> PendingCpuSnapshot[LoraSnapshot] | None: + merged_tensors = _build_merged_lora_tensors_from_model( + model=model, + adapter_dtypes=adapter_dtypes, + handler=handler, + rank=rank, + world_size=world_size, + slot_ref=slot_ref, + ) + if merged_tensors is None: + return None + builder = stager.begin() + if handler.vllm_lora_conversion_is_view_only(): + merged_tensors = _stage_published_tensors(merged_tensors, builder) + vllm_tensors, published_config = handler.to_vllm_lora_tensors( + merged_tensors, + adapter_config=dict(adapter_config), + ) + else: + vllm_tensors, published_config = handler.to_vllm_lora_tensors( + merged_tensors, + adapter_config=dict(adapter_config), + ) + vllm_tensors = _stage_published_tensors(vllm_tensors, builder) + return builder.finish( + LoraSnapshot( + tensors=vllm_tensors, + adapter_config=published_config, + ) + ) + + +def save_vllm_lora_snapshot( + snapshot: LoraSnapshot, + output_dir: str, + *, + prepared_tensors: PreparedSafetensors | None = None, +) -> None: + save_vllm_lora_tensors( + output_dir, + snapshot.tensors, + snapshot.adapter_config, + prepared_tensors=prepared_tensors, + ) diff --git a/src/art/megatron/weights/merged_weight_export.py b/src/art/megatron/weights/merged_weight_export.py index 2c6287be2..dc149afe6 100644 --- a/src/art/megatron/weights/merged_weight_export.py +++ b/src/art/megatron/weights/merged_weight_export.py @@ -4,11 +4,19 @@ from typing import Any, Iterator, cast from megatron.bridge import AutoBridge +from megatron.bridge.models.conversion.param_mapping import ( + extract_expert_number_from_param, +) from pydantic import BaseModel, ConfigDict import torch +from art.megatron.expert_parallel import get_expert_parallel_layout from art.megatron.model_support.spec import ModelSupportHandler -from art.megatron.runtime.jobs import ( +from art.megatron.runtime.bridge_runtime import ( + _logical_hf_param, + _remap_hf_expert_name, +) +from art.megatron.runtime.weight_transfer import ( MergedWeightTransferInitInfo, MergedWeightTransferSpec, ) @@ -43,6 +51,23 @@ def _hf_param_names(hf_param: Any) -> list[str]: return list(hf_param.values()) +def _checkpoint_hf_param_names(mapping: Any, model_config: Any) -> list[str]: + layout = get_expert_parallel_layout(model_config) + if layout is None or not bool(getattr(mapping, "is_expert", False)): + return _hf_param_names(mapping.hf_param) + physical_expert = extract_expert_number_from_param(mapping.megatron_param) + logical_expert = layout.logical_expert(physical_expert) + if logical_expert is None: + return [] + return _hf_param_names( + _logical_hf_param( + mapping.hf_param, + physical_expert=physical_expert, + logical_expert=logical_expert, + ) + ) + + def build_art_conversion_tasks(*, bridge: AutoBridge, model: ModelChunks) -> list[Any]: from megatron.bridge.models.conversion.model_bridge import ( WeightConversionTask, @@ -77,7 +102,7 @@ def build_art_conversion_tasks(*, bridge: AutoBridge, model: ModelChunks) -> lis raise RuntimeError( f"Missing HF conversion mapping for Megatron param {global_name}" ) - hf_params = _hf_param_names(mapping.hf_param) + hf_params = _checkpoint_hf_param_names(mapping, model_config) missing_hf_params = sorted(set(hf_params) - hf_keys) if missing_hf_params and not getattr( mapping, @@ -132,6 +157,62 @@ def build_merged_weight_export( ) +def _accumulate_grouped_export( + weight_export: MergedWeightExport, + task: Any, + converted_weights: dict[str, torch.Tensor], + grouped_buffers: dict[str, dict[int, torch.Tensor]], + hf_state_dict: Any, +) -> dict[str, torch.Tensor] | None: + layout = get_expert_parallel_layout(weight_export.model_config_value) + model_bridge = weight_export.bridge._model_bridge + if layout is None: + return model_bridge._accumulate_grouped_export( + task, + converted_weights, + weight_export.model_config_value, + grouped_buffers, + hf_state_dict, + ) + + local_expert = ( + extract_expert_number_from_param(task.param_name) % layout.slots_per_rank + ) + result: dict[str, torch.Tensor] = {} + for group_key, value in converted_weights.items(): + buffer = grouped_buffers.setdefault(group_key, {}) + gathered = ( + enumerate(value) + if value.ndim > 0 and value.shape[0] == layout.ep_size + else ((int(getattr(task.mapping, "ep_rank", 0)), value),) + ) + for ep_rank, expert_weight in gathered: + logical_expert = layout.logical_expert( + ep_rank * layout.slots_per_rank + local_expert + ) + if logical_expert is not None: + buffer[logical_expert] = expert_weight + if len(buffer) != layout.logical_experts: + continue + merged = torch.stack( + [buffer[expert] for expert in range(layout.logical_experts)] + ) + if getattr(task.mapping, "transpose_on_export", False): + expected = ( + tuple(hf_state_dict[group_key].shape) + if group_key in hf_state_dict + else None + ) + transposed = merged.transpose(-1, -2).contiguous() + if expected is None or ( + tuple(merged.shape) != expected and tuple(transposed.shape) == expected + ): + merged = transposed + del grouped_buffers[group_key] + result[group_key] = merged + return result or None + + def iter_merged_vllm_weights( weight_export: MergedWeightExport, ) -> Iterator[tuple[str, torch.Tensor]]: @@ -148,6 +229,12 @@ def iter_merged_vllm_weights( task.global_param_name ) if adapter_weights is not None: + layout = get_expert_parallel_layout(weight_export.model_config_value) + if layout is not None: + converted_weights_dict = { + _remap_hf_expert_name(key, layout.logical_to_physical): value + for key, value in converted_weights_dict.items() + } try: converted_weights_dict = model_bridge._merge_lora_adapter_weights( weight_export.model, @@ -177,11 +264,16 @@ def iter_merged_vllm_weights( f"{task.global_param_name}: converted={converted_shapes} " f"adapter_weights={adapter_summaries}" ) from exc + if layout is not None: + converted_weights_dict = { + _remap_hf_expert_name(key, layout.physical_to_logical): value + for key, value in converted_weights_dict.items() + } if getattr(task.mapping, "is_grouped_export", False): - merged_result = model_bridge._accumulate_grouped_export( + merged_result = _accumulate_grouped_export( + weight_export, task, converted_weights_dict, - weight_export.model_config_value, grouped_buffers, hf_state_dict, ) @@ -423,7 +515,6 @@ def _send_weights() -> None: client.post, f"{spec.vllm_base_url}/start_weight_update", phase="start merged weight update", - json={"is_checkpoint_format": False}, headers=_runtime_headers(spec), timeout=300.0, ) diff --git a/src/art/metrics.py b/src/art/metrics.py index f7d8ccdb5..df4f2107a 100644 --- a/src/art/metrics.py +++ b/src/art/metrics.py @@ -103,39 +103,109 @@ class MetricDefinition(pydantic.BaseModel): score_component=True, ), MetricDefinition( - key="data/step_padding_ratio", - title="Padding ratio", - description=( - "unused packed-token slots, including dummy data-parallel rows, " - "divided by executed packed-token capacity for this step" - ), - kind="ratio", - higher_is_better=False, - dashboard_default=True, + key="data/step_nonpadding_logical_tokens", + title="Non-padding logical train tokens", + description="actual non-padding tokens in real training microbatches", + kind="counter", + unit="tokens", + higher_is_better=None, ), MetricDefinition( - key="data/step_executed_packed_train_tokens", - title="Megatron executed packed train tokens", + key="data/step_loss_bearing_tokens", + title="Loss-bearing train tokens", + description="actual shifted token positions contributing to the loss", + kind="counter", + unit="tokens", + higher_is_better=None, + ), + MetricDefinition( + key="data/step_executed_token_equivalents", + title="Executed token-equivalents", description=( - "packed token rows included in Megatron throughput; CP excludes " - "configured packed-row padding that is not dispatched" + "materialized per-rank token extents summed over DP and CP, including " + "padding and dummy microbatches" ), kind="counter", unit="tokens", higher_is_better=None, ), MetricDefinition( - key="throughput/train_packed_tok_per_s", - title="Megatron packed train tokens per second", + key="data/step_nominal_schedule_capacity_tokens", + title="Nominal schedule capacity", + description="configured packed-row capacity before CP pruning", + kind="counter", + unit="tokens", + higher_is_better=None, + ), + MetricDefinition( + key="data/step_dummy_executed_token_equivalents", + title="Executed dummy token-equivalents", + description="runtime-plan token-equivalents executed by PP dummy microbatches", + kind="counter", + unit="tokens", + higher_is_better=False, + ), + MetricDefinition( + key="data/step_dummy_schedule_capacity_tokens", + title="Dummy schedule capacity", + description="nominal packed-token capacity assigned to PP dummy microbatches", + kind="counter", + unit="tokens", + higher_is_better=False, + ), + MetricDefinition( + key="data/step_unused_packed_capacity_tokens", + title="Unused packed capacity", + description="nominal real-microbatch capacity not occupied by logical tokens", + kind="counter", + unit="tokens", + higher_is_better=False, + ), + MetricDefinition( + key="data/step_unused_and_dummy_ratio", + title="Unused and dummy capacity ratio", description=( - "physical training-token throughput reported by the Megatron worker; " - "CP excludes configured packed-row padding that is not dispatched" + "unused real packed-token capacity plus PP dummy schedule capacity, " + "divided by nominal schedule capacity" ), + kind="ratio", + higher_is_better=False, + dashboard_default=True, + ), + MetricDefinition( + key="throughput/train_nonpadding_logical_tok_per_s", + title="Logical train tokens per second", + description="actual non-padding logical tokens divided by training time", + kind="rate", + unit="tok/s", + higher_is_better=True, + dashboard_default=True, + ), + MetricDefinition( + key="throughput/train_loss_bearing_tok_per_s", + title="Loss-bearing train tokens per second", + description="actual loss-bearing tokens divided by training time", + kind="rate", + unit="tok/s", + higher_is_better=True, + ), + MetricDefinition( + key="throughput/train_executed_tok_equiv_per_s", + title="Executed token-equivalents per second", + description="executed materialized token-equivalents divided by training time", kind="rate", unit="tok/s", higher_is_better=True, dashboard_default=True, ), + MetricDefinition( + key="throughput/train_nominal_capacity_tok_per_s", + title="Nominal schedule capacity per second", + description="configured packed-row capacity divided by training time", + kind="rate", + unit="tok/s", + higher_is_better=True, + ), MetricDefinition( key="loss/importance_ratio_mean", title="Importance ratio mean", @@ -499,15 +569,36 @@ async def flush(self) -> dict[str, float]: } result.update(self._compute_rollups(cost_metrics)) + cum_state = self._shared_state.cum_state + unused_and_dummy_ratio = "data/step_unused_and_dummy_ratio" for key, value in list(result.items()): section = key.split("/", 1)[0] - if section not in _HIERARCHICAL_SECTIONS: + if ( + section not in _HIERARCHICAL_SECTIONS + or key == unused_and_dummy_ratio + ): continue cum_key = to_cumulative_metric_key(key) - next_value = self._shared_state.cum_state.get(cum_key, 0.0) + value - self._shared_state.cum_state[cum_key] = next_value + next_value = cum_state.get(cum_key, 0.0) + value + cum_state[cum_key] = next_value result[cum_key] = next_value + if unused_and_dummy_ratio in result: + cum_key = to_cumulative_metric_key(unused_and_dummy_ratio) + nominal = cum_state.get( + "data/cum/nominal_schedule_capacity_tokens", 0.0 + ) + unused_and_dummy = sum( + cum_state.get(key, 0.0) + for key in ( + "data/cum/unused_packed_capacity_tokens", + "data/cum/dummy_schedule_capacity_tokens", + ) + ) + ratio = unused_and_dummy / nominal if nominal else 0.0 + cum_state[cum_key] = ratio + result[cum_key] = ratio + if pending_scenario_ids: self._shared_state.unique_scenario_ids.update(pending_scenario_ids) result["data/cum/num_unique_scenarios"] = float( @@ -519,6 +610,16 @@ async def flush(self) -> dict[str, float]: pending_state.pending_scenario_ids.clear() return result + async def drain_pending(self) -> dict[str, float]: + """Move raw step deltas across an execution boundary without rollups.""" + + async with self._shared_state.lock: + pending_state = self._pending_state() + result = dict(pending_state.step_buffer) + pending_state.step_buffer.clear() + pending_state.pending_scenario_ids.clear() + return result + def activate(self) -> Token["MetricsBuilder"]: return _active_builder.set(self) diff --git a/src/art/model.py b/src/art/model.py index 1dd47cbb4..f850d7e5a 100644 --- a/src/art/model.py +++ b/src/art/model.py @@ -339,11 +339,19 @@ def __getattr__(self, name: str) -> Any: "offpolicy/token_weighted_policy_age_steps", "offpolicy/token_weighted_policy_age_p95_steps", "throughput/accepted_train_tok_per_s", - "throughput/train_packed_tok_per_s", - "data/step_executed_packed_train_tokens", + "throughput/train_nonpadding_logical_tok_per_s", + "throughput/train_loss_bearing_tok_per_s", + "throughput/train_executed_tok_equiv_per_s", + "throughput/train_nominal_capacity_tok_per_s", "data/step_trainable_assistant_tokens", - "data/step_non_padding_train_tokens", - "data/step_padding_ratio", + "data/step_nonpadding_logical_tokens", + "data/step_loss_bearing_tokens", + "data/step_executed_token_equivalents", + "data/step_nominal_schedule_capacity_tokens", + "data/step_dummy_executed_token_equivalents", + "data/step_dummy_schedule_capacity_tokens", + "data/step_unused_packed_capacity_tokens", + "data/step_unused_and_dummy_ratio", "data/cum/num_unique_scenarios", "data/cum/num_scenarios", "data/cum/num_gradient_steps", @@ -1295,9 +1303,22 @@ async def log( # 1. Write parquet file_name = f"{step:04d}.parquet" - write_trajectory_groups_parquet( - trajectory_groups, f"{trajectories_dir}/{file_name}" - ) + trajectory_path = f"{trajectories_dir}/{file_name}" + prepared_paths = { + group._prepared_log_path + for group in trajectory_groups + if group._prepared_log_path is not None + } + if prepared_paths: + if len(prepared_paths) != 1 or any( + group._prepared_log_path is None for group in trajectory_groups + ): + raise RuntimeError("trajectory batch has inconsistent prepared logs") + os.replace(prepared_paths.pop(), trajectory_path) + for group in trajectory_groups: + group._prepared_log_path = None + else: + write_trajectory_groups_parquet(trajectory_groups, trajectory_path) # 2. Calculate aggregate metrics (excluding additive costs) reward_key = "reward" diff --git a/src/art/pipeline_trainer/checkpoint_retention.py b/src/art/pipeline_trainer/checkpoint_retention.py index 776045f7b..e7e8a10c7 100644 --- a/src/art/pipeline_trainer/checkpoint_retention.py +++ b/src/art/pipeline_trainer/checkpoint_retention.py @@ -32,7 +32,7 @@ def keep_recent_and_top( *, recent: int = 5, top: int = 2, - metric: str = "val/reward", + metric: str = "reward/val", ) -> CheckpointRetentionStrategy: """Keep the most recent eligible checkpoints and top metric checkpoints.""" if recent < 0: diff --git a/src/art/pipeline_trainer/status.py b/src/art/pipeline_trainer/status.py index cb58bdb3e..22432575b 100644 --- a/src/art/pipeline_trainer/status.py +++ b/src/art/pipeline_trainer/status.py @@ -110,11 +110,11 @@ def note_rollout_finished(self, *, errored: bool) -> None: self._errored += 1 self._refresh_status() - def note_group_enqueued(self, _group: TrajectoryGroup) -> None: + def note_group_enqueued(self) -> None: self._queued += 1 self._refresh_status() - def note_group_dequeued(self, _group: TrajectoryGroup) -> None: + def note_group_dequeued(self) -> None: if self._queued > 0: self._queued -= 1 self._refresh_status() diff --git a/src/art/pipeline_trainer/trainer.py b/src/art/pipeline_trainer/trainer.py index 3215f93d3..3547d5cb1 100644 --- a/src/art/pipeline_trainer/trainer.py +++ b/src/art/pipeline_trainer/trainer.py @@ -25,12 +25,20 @@ ) import warnings +from openai.types.chat.chat_completion import Choice +from pydantic import BaseModel, ConfigDict, Field from typing_extensions import TypeIs T = TypeVar("T") import art from art import TrajectoryGroup +from art.distributed.rollout import ( + DistributedTrajectoryQueue, + LocalRolloutExecutor, + RolloutExecutor, +) +from art.distributed.trajectory_store import TrajectoryGroupRef from art.errors import LocalServingUnavailableError from art.pipeline_tuner import ( PackedGroupObservation, @@ -42,6 +50,7 @@ PipelineTuneSettings, RolloutWorkerController, ) +from art.preprocessing.policy_spans import PolicyTokenSpan from .checkpoint_retention import ( CHECKPOINT_CREATED_AT_METRIC, @@ -56,12 +65,41 @@ from .types import ConfigT, EvalFn, RolloutFn, ScenarioT, SingleRolloutFn # noqa: F401 PIPELINE_STATE_KEY = "_pipeline_trainer" +_ROLLOUT_WALL_TIME_KEY = "_art_rollout_wall_s" +_ACTOR_IDLE_TIME_KEY = "_art_actor_idle_s" +_QUEUE_WAIT_TIME_KEY = "_art_queue_wait_s" _SCORE_FRESHNESS_TAU_STEPS = 8.0 # Rollout critical batch size from the best current GRPO/RLVR evidence. This is # grounded in reported experiments, not a well-validated universal constant. _SCORE_CRITICAL_ROLLOUT_BATCH_SIZE = 300.0 +class _ResizableAsyncQueue(asyncio.Queue[T]): + def resize(self, maxsize: int) -> None: + if maxsize < 1: + raise ValueError("queue maxsize must be positive") + grew = maxsize > self.maxsize + internals = cast(Any, self) + internals._maxsize = maxsize + if grew: + for _ in range(min(maxsize - self.qsize(), len(internals._putters))): + internals._wakeup_next(internals._putters) + + +class _PreparedPipelineItem(BaseModel): + model_config = ConfigDict(arbitrary_types_allowed=True, frozen=True) + + batch: list[TrajectoryGroup] + discarded: int = Field(ge=0) + zero_variance_discarded: int = Field(ge=0) + saw_sentinel: bool + packing_policy_step: int = Field(ge=0) + selection_s: float = Field(ge=0) + preparation_s: float = Field(ge=0) + preparation_metrics: dict[str, float] + handoff: asyncio.Event = Field(default_factory=asyncio.Event, exclude=True) + + def _is_eval_mapping( result: Sequence[art.Trajectory | art.TrajectoryGroup] | Mapping[str, Sequence[art.Trajectory | art.TrajectoryGroup]], @@ -157,6 +195,7 @@ def __init__( loss_fn: str = "cispo", loss_fn_config: dict | None = None, normalize_advantages: bool = True, + grad_accumulation_sequences: int | None = None, adam_params: object | None = None, kl_penalty_coef: float = 0.0, kl_penalty_step_lag: int | None = None, @@ -180,6 +219,7 @@ def __init__( checkpoint_retention_interval: int = 1, # Resumption resume: bool = True, + rollout_executor: RolloutExecutor | None = None, ) -> None: autotune = autotune or PipelineAutotuneConfig() pipeline_aliases = { @@ -192,6 +232,9 @@ def __init__( }.items() if value is not None } + rollout_workers_explicit = num_rollout_workers is not None or ( + pipeline is not None and "num_rollout_workers" in pipeline.model_fields_set + ) if autotune.mode != "off" and (pipeline is not None or pipeline_aliases): raise ValueError( "Pipeline runtime config cannot be provided when pipeline autotuning " @@ -229,9 +272,27 @@ def __init__( raise ValueError("optimizer_save_interval must be > 0") if kl_penalty_step_lag is not None and kl_penalty_step_lag < 1: raise ValueError("kl_penalty_step_lag must be >= 1") + if grad_accumulation_sequences is not None and grad_accumulation_sequences < 1: + raise ValueError("grad_accumulation_sequences must be >= 1") self.model = model self.backend = backend self.rollout_fn = rollout_fn + if rollout_executor is None: + rollout_executor = LocalRolloutExecutor() + self._rollout_executor = rollout_executor + self.rollout_worker_capacity = rollout_executor.max_workers + if self.rollout_worker_capacity is not None: + if self.rollout_worker_capacity < 1: + raise ValueError("rollout executor capacity must be >= 1") + if pipeline.num_rollout_workers > self.rollout_worker_capacity: + if autotune.mode == "off" and rollout_workers_explicit: + raise ValueError( + f"num_rollout_workers={pipeline.num_rollout_workers} exceeds " + f"rollout executor capacity {self.rollout_worker_capacity}" + ) + pipeline = pipeline.model_copy( + update={"num_rollout_workers": self.rollout_worker_capacity} + ) self.config = config self.eval_fn = eval_fn self.pipeline = pipeline @@ -251,6 +312,7 @@ def __init__( self.loss_fn = loss_fn self.loss_fn_config = loss_fn_config self.normalize_advantages = normalize_advantages + self.grad_accumulation_sequences = grad_accumulation_sequences self.adam_params = adam_params self.kl_penalty_coef = kl_penalty_coef self.kl_penalty_step_lag = kl_penalty_step_lag @@ -280,6 +342,8 @@ def __init__( self._checkpoint_lease_counts: Counter[int] = Counter() self._scheduled_eval_steps: set[int] = set() self._scheduled_eval_leases: dict[int, AsyncExitStack] = {} + self._checkpoint_log_tasks: set[asyncio.Task[None]] = set() + self._checkpoint_log_failure: BaseException | None = None self.state = PipelineState() self._stop_event = asyncio.Event() @@ -288,13 +352,18 @@ def __init__( scenarios ) self._scenario_source_exhausted = False - self._output_queue: asyncio.Queue[TrajectoryGroup | None] | None = None + self._output_queue: ( + asyncio.Queue[TrajectoryGroup | None] | DistributedTrajectoryQueue | None + ) = None self._producer_rollout_timings = (0.0, 0.0, 0.0) self._reported_producer_rollout_timings = (0.0, 0.0, 0.0) + self._packed_queue: asyncio.Queue[_PreparedPipelineItem | None] | None = None + self._accept_prepared_batches = True self._eval_queue: asyncio.Queue[int] | None = None self._rollout_worker_controller = RolloutWorkerController( self, self.num_rollout_workers ) + self._rollout_executor.set_target(self.num_rollout_workers) self._attachments: list[PipelineAutotunerAttachment] = [] if self.autotune.mode != "off": self._attachments.append(PipelineAutotunerAttachment(self.autotune)) @@ -366,7 +435,28 @@ async def train(self, *, handle_signals: bool = True) -> None: if self.queue_maxsize is not None else max(1, self._freshness_queue_window() * self.target_groups_per_step) ) - self._output_queue = asyncio.Queue(maxsize=queue_maxsize) + result_queue_factory = getattr( + self._rollout_executor, "create_result_queue", None + ) + local_data_plane = isinstance(self._rollout_executor, LocalRolloutExecutor) + supports_preparation = callable( + getattr(self.backend, "prepare_pipeline_batch", None) + ) + packing_support = getattr(self.backend, "supports_async_pipeline_packing", None) + if supports_preparation and callable(packing_support): + supports_preparation = bool(packing_support(self.model)) + if callable(result_queue_factory) and ( + supports_preparation or not local_data_plane + ): + self._output_queue = result_queue_factory(queue_maxsize) + await self._output_queue.start() + else: + self._output_queue = _ResizableAsyncQueue(maxsize=queue_maxsize) + if ( + isinstance(self._output_queue, DistributedTrajectoryQueue) + and supports_preparation + ): + self._packed_queue = asyncio.Queue(maxsize=1) self._eval_queue = asyncio.Queue() loop = asyncio.get_running_loop() @@ -408,6 +498,8 @@ def _sync_signal_handler(signum: int, _frame: object | None) -> None: try: async with asyncio.TaskGroup() as tg: tg.create_task(self._rollout_stage(), name="rollout_stage") + if self._packed_queue is not None: + tg.create_task(self._packing_stage(), name="packing_stage") tg.create_task(self._training_stage(), name="training_stage") tg.create_task(self._eval_stage(), name="eval_stage") tg.create_task(self._status_loop(), name="status_loop") @@ -435,11 +527,21 @@ def _sync_signal_handler(signum: int, _frame: object | None) -> None: except (ValueError, RuntimeError): pass cleanup_failures: list[BaseException] = [] + self._accept_prepared_batches = False + try: + await self._discard_pending_prepared_batches() + except BaseException as exc: + cleanup_failures.append(exc) if not training_failed: try: await self._finalize_backend_training() except BaseException as exc: cleanup_failures.append(exc) + if isinstance(self._output_queue, DistributedTrajectoryQueue): + try: + await self._output_queue.close() + except BaseException as exc: + cleanup_failures.append(exc) try: await self._stop_attachments(training_failed=training_failed) except BaseException as exc: @@ -453,6 +555,12 @@ def _sync_signal_handler(signum: int, _frame: object | None) -> None: await self._release_all_scheduled_eval_leases() except BaseException as exc: cleanup_failures.append(exc) + if self._checkpoint_log_tasks: + await asyncio.gather( + *tuple(self._checkpoint_log_tasks), return_exceptions=True + ) + if self._checkpoint_log_failure is not None: + cleanup_failures.append(self._checkpoint_log_failure) if cleanup_failures: if primary_failure is not None: raise BaseExceptionGroup( @@ -467,9 +575,30 @@ def _sync_signal_handler(signum: int, _frame: object | None) -> None: def request_stop(self) -> None: """Request a clean shutdown of the pipeline stages.""" + if self.state.done: + return self.state.done = True self._stop_event.set() + async def _notify_policy() -> None: + async with self.state.policy_updated: + self.state.policy_updated.notify_all() + + try: + loop = asyncio.get_running_loop() + except RuntimeError: + return + loop.create_task(_notify_policy()) + if self._output_queue is None: + return + if isinstance(self._output_queue, DistributedTrajectoryQueue): + loop.create_task(self._output_queue.finish()) + return + try: + self._output_queue.put_nowait(None) + except asyncio.QueueFull: + loop.create_task(self._output_queue.put(None)) + async def _await_or_stop(self, awaitable: Awaitable[T]) -> tuple[bool, T | None]: operation = asyncio.ensure_future(awaitable) stop_wait = asyncio.create_task(self._stop_event.wait()) @@ -490,9 +619,21 @@ async def _finalize_backend_training(self) -> None: return finalize = getattr(self.backend, "finalize_training_session", None) if finalize is not None: - await finalize(self.model) + metrics = await finalize(self.model) + if isinstance(metrics, Mapping): + await self._emit_pipeline_metrics( + metrics, step=self.state.next_training_step + ) def apply_pipeline_settings(self, settings: PipelineTuneSettings) -> None: + if ( + self.rollout_worker_capacity is not None + and settings.num_rollout_workers > self.rollout_worker_capacity + ): + raise ValueError( + f"num_rollout_workers={settings.num_rollout_workers} exceeds rollout " + f"executor capacity {self.rollout_worker_capacity}" + ) self.num_rollout_workers = settings.num_rollout_workers self.min_batch_size = settings.min_batch_size self.max_batch_size = settings.max_batch_size @@ -500,8 +641,14 @@ def apply_pipeline_settings(self, settings: PipelineTuneSettings) -> None: self.queue_maxsize = settings.queue_maxsize self._discard_queue_limit = self.discard_queue_multiplier * self.min_batch_size self._rollout_worker_controller.set_target(self.num_rollout_workers) + self._rollout_executor.set_target(self.num_rollout_workers) if self._output_queue is not None: - cast(Any, self._output_queue)._maxsize = self.queue_maxsize + if isinstance(self._output_queue, DistributedTrajectoryQueue): + self._output_queue.set_maxsize(self.queue_maxsize) + else: + cast( + _ResizableAsyncQueue[TrajectoryGroup | None], self._output_queue + ).resize(self.queue_maxsize) self._status._num_workers = self.num_rollout_workers async def _start_attachments(self) -> None: @@ -603,7 +750,6 @@ async def _emit_packed_group_observations( await attachment.on_packed_group(observation) def _validate_backend_support(self) -> None: - from art.dev.validate import is_dedicated_mode from art.local.backend import LocalBackend if self.eval_fn is not None and not callable( @@ -617,7 +763,7 @@ def _validate_backend_support(self) -> None: return model_config = self.model._internal_config or art.dev.InternalModelConfig() - if not is_dedicated_mode(model_config): + if not self.backend._supports_concurrent_training_and_inference(self.model): raise ValueError( "PipelineTrainer only supports LocalBackend in dedicated mode. " "Shared LocalBackend pauses inference during training and is not " @@ -817,25 +963,48 @@ async def _rollout_worker(self, worker_id: int) -> None: rollout_started = time.monotonic() try: async with self._adapter_lease(initial_version): - group = await self.rollout_fn(self.model, scenario, self.config) + group = await self._rollout_executor.run( + worker_id, + self.rollout_fn, + self.model, + scenario, + self.config, + ) finally: token.var.reset(token) rollout_wall_s = time.monotonic() - rollout_started - if not isinstance(group, TrajectoryGroup): + if not isinstance(group, TrajectoryGroup | TrajectoryGroupRef): errored = True continue - self._apply_scenario_metadata(group, scenario) - self._apply_policy_versions( - group, - initial_version=initial_version, - final_version=self.state.policy_version, - ) + scenario_metadata = self._scenario_metadata(scenario) + if isinstance(group, TrajectoryGroup): + group.metadata.update(scenario_metadata) + self._apply_policy_versions( + group, + initial_version=initial_version, + final_version=self.state.policy_version, + ) if self.state.done: + if isinstance( + self._output_queue, DistributedTrajectoryQueue + ) and isinstance(group, TrajectoryGroupRef): + await self._output_queue.discard(group) break - queue_wait_s = await self._put_output_group(group) + queue_wait_s = await self._put_output_group( + group, + metadata=scenario_metadata, + initial_policy_version=initial_version, + final_policy_version=self.state.policy_version, + rollout_wall_s=rollout_wall_s, + actor_idle_s=actor_idle_s, + ) self._record_producer_rollout_timings( rollout_wall_s, actor_idle_s + queue_wait_s, queue_wait_s ) + if isinstance(group, TrajectoryGroup): + group.metadata[_ROLLOUT_WALL_TIME_KEY] = rollout_wall_s + group.metadata[_QUEUE_WAIT_TIME_KEY] = queue_wait_s + group.metadata[_ACTOR_IDLE_TIME_KEY] = actor_idle_s + queue_wait_s except asyncio.CancelledError: raise except LocalServingUnavailableError: @@ -858,8 +1027,67 @@ async def _rollout_stage(self) -> None: and self._output_queue is not None ): print("Scenario source exhausted; draining completed rollouts.") + if isinstance(self._output_queue, DistributedTrajectoryQueue): + await self._output_queue.finish() + return await self._await_or_stop(self._output_queue.put(None)) + async def _packing_stage(self) -> None: + assert self._packed_queue is not None + prepare = getattr(self.backend, "prepare_pipeline_batch") + while True: + packing_policy_step = self.state.next_training_step + started = time.monotonic() + zero_variance_before = self.state.discarded_zero_variance_groups + batch, discarded, saw_sentinel = await self._collect_batch( + packing_policy_step + ) + zero_variance_discarded = ( + self.state.discarded_zero_variance_groups - zero_variance_before + ) + selection_s = time.monotonic() - started + if not self._accept_prepared_batches: + for group in batch: + await self._discard_collected_group(group) + return + if not batch: + await self._packed_queue.put(None) + return + if self.autotune.mode != "off": + for group in batch: + group._collect_packing_shape = True + started = time.monotonic() + preparation_metrics = await prepare( + self.model, + batch, + normalize_advantages=self.normalize_advantages, + ) + preparation_s = time.monotonic() - started + if preparation_metrics is None: + if saw_sentinel: + await self._packed_queue.put(None) + return + continue + item = _PreparedPipelineItem( + batch=batch, + discarded=discarded, + zero_variance_discarded=zero_variance_discarded, + saw_sentinel=saw_sentinel, + packing_policy_step=packing_policy_step, + selection_s=selection_s, + preparation_s=preparation_s, + preparation_metrics=preparation_metrics, + ) + if not self._accept_prepared_batches: + await getattr(self.backend, "discard_pipeline_batch")(batch) + return + await self._packed_queue.put(item) + await item.handoff.wait() + if not self._accept_prepared_batches: + return + if saw_sentinel: + return + async def _training_stage(self) -> None: if self._output_queue is None: return @@ -873,6 +1101,9 @@ async def _training_stage(self) -> None: self.request_stop() return stop_after_batch = False + pending_stale_groups = 0 + pending_zero_variance_groups = 0 + pending_dequeued_groups = 0 while True: if stop_at_step is not None and current_step >= stop_at_step: @@ -880,19 +1111,65 @@ async def _training_stage(self) -> None: step_start = time.monotonic() collect_started = time.monotonic() zero_variance_before = self.state.discarded_zero_variance_groups - batch, discarded, saw_sentinel = await self._collect_batch(current_step) + selection_s = 0.0 + preparation_s = 0.0 + packed_queue_depth = 0 + preparation_metrics: dict[str, float] = {} + packing_policy_step = current_step + if self._packed_queue is None: + batch, discarded, saw_sentinel = await self._collect_batch(current_step) + else: + packed_queue_depth = self._packed_queue.qsize() + prepared = await self._packed_queue.get() + if prepared is None: + break + batch = prepared.batch + discarded = prepared.discarded + saw_sentinel = prepared.saw_sentinel + selection_s = prepared.selection_s + preparation_s = prepared.preparation_s + preparation_metrics = prepared.preparation_metrics + packing_policy_step = prepared.packing_policy_step trainer_idle_s = time.monotonic() - collect_started zero_variance_discarded = ( - self.state.discarded_zero_variance_groups - zero_variance_before + prepared.zero_variance_discarded + if self._packed_queue is not None + else self.state.discarded_zero_variance_groups - zero_variance_before ) dequeued_groups = len(batch) + discarded + zero_variance_discarded + if self._packed_queue is not None and any( + self._is_group_stale(group, current_step) for group in batch + ): + discard = getattr(self.backend, "discard_pipeline_batch") + await discard(batch) + prepared.handoff.set() + discarded += len(batch) + self.state.discarded_stale_groups += discarded + self._status.note_stale(discarded) + pending_stale_groups += discarded + pending_zero_variance_groups += zero_variance_discarded + pending_dequeued_groups += dequeued_groups + if saw_sentinel: + break + continue self.state.discarded_stale_groups += discarded if discarded: self._status.note_stale(discarded) if not batch: break + step_stale_groups = pending_stale_groups + discarded + step_zero_variance_groups = ( + pending_zero_variance_groups + zero_variance_discarded + ) + step_dequeued_groups = pending_dequeued_groups + dequeued_groups + pending_stale_groups = 0 + pending_zero_variance_groups = 0 + pending_dequeued_groups = 0 training_policy_step = current_step + policy_age_metrics = self._batch_policy_age_metrics( + training_policy_step, batch + ) expected_step = current_step + 1 should_eval_step = self._should_eval_step(expected_step) should_checkpoint = self.save_checkpoint and should_eval_step @@ -900,6 +1177,8 @@ async def _training_stage(self) -> None: async with self.state.policy_updated: self.state.next_training_step = expected_step self.state.policy_updated.notify_all() + if self._packed_queue is not None: + prepared.handoff.set() self._status.note_training_start(len(batch)) train_call_start = time.monotonic() @@ -915,6 +1194,10 @@ async def _training_stage(self) -> None: "adam_params": self.adam_params, "optimizer_save_interval": self.optimizer_save_interval, } + if self.grad_accumulation_sequences is not None: + train_kwargs["grad_accumulation_sequences"] = ( + self.grad_accumulation_sequences + ) if self.kl_penalty_coef > 0.0: kl_penalty_reference_step = self._kl_penalty_reference_step( current_step @@ -937,6 +1220,7 @@ async def _training_stage(self) -> None: for group in batch: group._collect_packing_shape = False group._packed_group_shape = None + await self._discard_collected_group(group) self._status.note_training_end() raise finally: @@ -974,9 +1258,9 @@ async def _training_stage(self) -> None: metrics = { "discarded/cum/stale_groups": stale_groups, "discarded/cum/zero_variance_groups": zero_variance_groups, - "discarded/step/stale_groups": float(discarded), + "discarded/step/stale_groups": float(step_stale_groups), "discarded/step/zero_variance_groups": float( - zero_variance_discarded + step_zero_variance_groups ), "discarded/rate/stale_groups": stale_groups / max(generated_groups_cum, 1.0), @@ -990,8 +1274,24 @@ async def _training_stage(self) -> None: "queue/put_wait_s": queue_wait_s, "queue/put_wait_frac": queue_wait_s / max(queue_wait_s + actor_wall_s, 1e-9), - "queue/actual_stale_fraction": discarded / max(dequeued_groups, 1), + "queue/actual_stale_fraction": step_stale_groups + / max(step_dequeued_groups, 1), } + if self._packed_queue is not None: + metrics.update( + { + "time/step_batch_selection_s": selection_s, + "time/step_batch_prepare_s": preparation_s, + "queue/packed_get_wait_s": trainer_idle_s, + "queue/packed_queue_depth": float(packed_queue_depth), + "queue/packed_queue_occupancy": packed_queue_depth + / self._packed_queue.maxsize, + "queue/packing_policy_lag_steps": float( + current_step - packing_policy_step + ), + } + ) + metrics.update(preparation_metrics) metrics.setdefault("time/step_backend_train_s", train_call_elapsed) metrics.update(result.metrics) attachment_metrics, attachment_owns_vllm_metrics = ( @@ -1016,9 +1316,10 @@ async def _training_stage(self) -> None: batch, step_seconds=step_seconds, result_metrics=metrics, + age_metrics=policy_age_metrics, ) ) - metrics.update(self._queue_freshness_metrics(current_step)) + metrics.update(await self._queue_freshness_metrics(current_step)) metrics.update(self._pipeline_settings_metrics()) await self._emit_packed_group_observations( @@ -1048,9 +1349,24 @@ async def _training_stage(self) -> None: if stop_after_batch: break + self.state.done = True + self._accept_prepared_batches = False + if isinstance(self._output_queue, DistributedTrajectoryQueue): + await self._output_queue.finish() + await self._discard_pending_prepared_batches() self._persist_state(current_step) self.request_stop() + async def _discard_pending_prepared_batches(self) -> None: + if self._packed_queue is None: + return + discard = getattr(self.backend, "discard_pipeline_batch") + while not self._packed_queue.empty(): + pending = self._packed_queue.get_nowait() + if pending is not None: + await discard(pending.batch) + pending.handoff.set() + async def _collect_batch( self, current_step: int ) -> tuple[list[TrajectoryGroup], int, bool]: @@ -1059,46 +1375,56 @@ async def _collect_batch( discarded = 0 saw_sentinel = False - while len(batch) < self.min_batch_size: - completed, item = await self._await_or_stop(self._output_queue.get()) - if not completed: - saw_sentinel = True - break - if item is None: - saw_sentinel = True - break - self._status.note_group_dequeued(item) - self._check_all_failed(item) - if self._is_group_stale(item, current_step): - discarded += 1 - continue - if self._group_zero_variance(item): - if self._record_zero_variance(item): - return [], discarded, saw_sentinel - continue - batch.append(item) - while not saw_sentinel and len(batch) < self.max_batch_size: - try: - item = self._output_queue.get_nowait() - except asyncio.QueueEmpty: - break - if item is None: - saw_sentinel = True - break - self._status.note_group_dequeued(item) - self._check_all_failed(item) - if self._is_group_stale(item, current_step): - discarded += 1 - continue - if self._group_zero_variance(item): - if self._record_zero_variance(item): - return [], discarded, saw_sentinel - continue - batch.append(item) + wait = len(batch) < self.min_batch_size + count = (self.min_batch_size if wait else self.max_batch_size) - len(batch) + if isinstance(self._output_queue, DistributedTrajectoryQueue): + items, saw_sentinel = await self._output_queue.get_many( + count, wait=wait + ) + if not items: + break + elif wait: + item = await self._output_queue.get() + if item is None: + saw_sentinel = True + break + items = [item] + else: + try: + item = self._output_queue.get_nowait() + except asyncio.QueueEmpty: + break + if item is None: + saw_sentinel = True + break + items = [item] + for item in items: + self._status.note_group_dequeued() + try: + self._check_all_failed(item) + except BaseException: + await self._discard_collected_group(item) + raise + if self._is_group_stale(item, current_step): + discarded += 1 + await self._discard_collected_group(item) + continue + if self._group_zero_variance(item): + if self._record_zero_variance(item): + await self._discard_collected_group(item) + return [], discarded, saw_sentinel + await self._discard_collected_group(item) + continue + batch.append(item) return batch, discarded, saw_sentinel + async def _discard_collected_group(self, group: TrajectoryGroup) -> None: + if isinstance(self._output_queue, DistributedTrajectoryQueue): + await self._output_queue.discard_group(group) + group._distributed_lease = None + def _check_all_failed(self, group: TrajectoryGroup) -> None: """Raise if all rollouts in a group failed with exceptions.""" if not group.trajectories and group.exceptions: @@ -1234,21 +1560,46 @@ def _validate_eval_policy_spans( ) -> None: for trajectory in trajectories: for item in cls._trajectory_messages_and_choices(trajectory): - extra = getattr(item, "model_extra", None) - if not isinstance(extra, Mapping) or "policy_token_spans" not in extra: + is_completion = isinstance(item, Choice) or ( + isinstance(item, Mapping) and item.get("role") == "assistant" + ) + if not is_completion: continue - spans = extra["policy_token_spans"] - if not isinstance(spans, list): - raise RuntimeError("Eval policy_token_spans must be a list") + spans = cls._validated_policy_spans(item, required=True) + assert spans is not None for span in spans: - if not isinstance(span, Mapping) or "policy_version" not in span: - raise RuntimeError("Eval policy token span is malformed") - policy_version = int(span["policy_version"]) - if policy_version != step: + if span.policy_version != step: raise RuntimeError( - f"Eval at step {step} returned policy-{policy_version} tokens" + f"Eval at step {step} returned " + f"policy-{span.policy_version} tokens" ) + @staticmethod + def _validated_policy_spans( + item: Any, *, required: bool + ) -> list[PolicyTokenSpan] | None: + extra = ( + item if isinstance(item, Mapping) else getattr(item, "model_extra", None) + ) + raw = extra.get("policy_token_spans") if isinstance(extra, Mapping) else None + if raw is None: + if required: + raise RuntimeError( + "Exact policy provenance is missing policy_token_spans" + ) + return None + if not isinstance(raw, list) or not raw: + raise RuntimeError("policy_token_spans must be a non-empty list") + spans = [PolicyTokenSpan.model_validate(span) for span in raw] + cursor = 0 + for span in spans: + if span.start_token != cursor: + raise RuntimeError( + "policy_token_spans must be a contiguous completion partition" + ) + cursor = span.end_token + return spans + def _apply_policy_versions( self, group: TrajectoryGroup, @@ -1262,22 +1613,24 @@ def _apply_policy_versions( if trajectory.final_policy_version is None: trajectory.final_policy_version = final_version - def _apply_scenario_metadata( - self, group: TrajectoryGroup, scenario: ScenarioT - ) -> None: + def _scenario_metadata( + self, scenario: ScenarioT + ) -> dict[str, float | int | str | bool | None]: metadata = scenario.get("metadata") if isinstance(scenario, dict) else None if metadata is None or not isinstance(metadata, dict): - return + return {} + result: dict[str, float | int | str | bool | None] = {} for key, value in metadata.items(): if not isinstance(key, str): continue if not self._is_scalar_metadata(value): continue if key == "scenario_id": - group.metadata["scenario_id"] = value + result["scenario_id"] = value continue - group.metadata[f"scenario_{key}"] = value + result[f"scenario_{key}"] = value + return result @staticmethod def _scenario_error_context(scenario: ScenarioT) -> str: @@ -1385,15 +1738,9 @@ def _freshness_queue_window(self) -> int: return math.ceil(self.limit_mean_steps_off_policy) return 1 - def _queue_freshness_metrics(self, current_step: int) -> dict[str, float]: + async def _queue_freshness_metrics(self, current_step: int) -> dict[str, float]: if self._output_queue is None: return {} - output_queue = cast(Any, self._output_queue) - queued = [ - group - for group in list(output_queue._queue) - if isinstance(group, TrajectoryGroup) - ] limit_raw = ( self.limit_mean_steps_off_policy if self.limit_mean_steps_off_policy is not None @@ -1403,23 +1750,86 @@ def _queue_freshness_metrics(self, current_step: int) -> dict[str, float]: limit_raw = 1.0 limit = max(float(limit_raw), 1e-9) ages: list[float] = [] - for group in queued: - if self.limit_mean_steps_off_policy is not None: - age = self._group_mean_steps_off_policy(current_step, group) - else: - initial = self._group_initial_version(group) - age = None if initial is None else float(current_step - initial) - if age is not None: + capacity_metrics: dict[str, float] = {} + if isinstance(self._output_queue, DistributedTrajectoryQueue): + snapshot = await self._output_queue.snapshot() + for item in snapshot.items: + descriptor = item.ref.descriptor + if self.limit_mean_steps_off_policy is not None: + if descriptor.policy_token_counts: + weight = sum(descriptor.policy_token_counts.values()) + age = ( + sum( + (current_step - version) * count + for version, count in descriptor.policy_token_counts.items() + ) + / weight + ) + else: + versions = descriptor.initial_policy_versions or ( + item.annotations.initial_policy_version, + ) + weights = descriptor.completion_tokens + if len(weights) != len(versions) or sum(weights) <= 0: + weights = (1.0,) * len(versions) + age = sum( + (current_step - version) * weight + for version, weight in zip(versions, weights, strict=True) + ) / sum(weights) + else: + initial = min( + descriptor.initial_policy_versions + or (item.annotations.initial_policy_version,) + ) + age = float(current_step - initial) ages.append(float(age)) - ready = float(len(queued)) + ready = float(snapshot.ready_groups) + depth = float(len(snapshot.items)) + maxsize = float(snapshot.max_ready_groups) + put_waiting = float(self._output_queue.put_waiters) + capacity_metrics = { + "queue/data_plane_records": float(snapshot.used_records), + "queue/data_plane_bytes": float(snapshot.used_bytes), + "queue/data_plane_record_occupancy": snapshot.used_records + / snapshot.capacity_records, + "queue/data_plane_byte_occupancy": snapshot.used_bytes + / snapshot.capacity_bytes, + "queue/leased_groups": float(snapshot.leased_groups), + "queue/packing_groups": float(snapshot.packing_groups), + "queue/packed_groups": float(snapshot.packed_groups), + "queue/data_plane_packed_group_occupancy": snapshot.packed_groups + / snapshot.max_ready_groups, + "queue/lease_lifetime_mean_s": snapshot.lease_lifetime_s + / max(snapshot.released_leases, 1), + "queue/lease_lifetime_max_s": snapshot.max_lease_lifetime_s, + } + else: + output_queue = cast(Any, self._output_queue) + queued = [ + group + for group in list(output_queue._queue) + if isinstance(group, TrajectoryGroup) + ] + for group in queued: + if self.limit_mean_steps_off_policy is not None: + age = self._group_mean_steps_off_policy(current_step, group) + else: + initial = self._group_initial_version(group) + age = None if initial is None else float(current_step - initial) + if age is not None: + ages.append(float(age)) + ready = float(len(queued)) + depth = ready + maxsize = float(self._output_queue.maxsize) + put_waiting = 0.0 stale = sum(1 for age in ages if age > limit) return { "queue/ready_groups_est": ready, - "queue/completed_backlog_groups": ready, - "queue/put_waiting_groups": 0.0, - "queue/groups_depth": ready, - "queue/groups_depth_max": float(self._output_queue.maxsize), - "queue/occupancy": ready / max(float(self._output_queue.maxsize), 1.0), + "queue/completed_backlog_groups": depth, + "queue/put_waiting_groups": put_waiting, + "queue/groups_depth": depth, + "queue/groups_depth_max": maxsize, + "queue/occupancy": depth / max(maxsize, 1.0), "queue/predicted_policy_age_mean_steps": sum(ages) / len(ages) if ages else 0.0, @@ -1432,6 +1842,7 @@ def _queue_freshness_metrics(self, current_step: int) -> dict[str, float]: if ages else 0.0, "queue/predicted_stale_fraction": stale / len(ages) if ages else 0.0, + **capacity_metrics, } def _pipeline_settings_metrics(self) -> dict[str, float]: @@ -1477,6 +1888,7 @@ def _score_metrics( *, step_seconds: float, result_metrics: dict[str, float], + age_metrics: dict[str, float] | None = None, ) -> dict[str, float]: metrics: dict[str, float] = {} accepted_groups = float(len(batch)) @@ -1488,7 +1900,11 @@ def _score_metrics( ) metrics["sample_efficiency/batch_factor"] = batch_factor - age_metrics = self._batch_policy_age_metrics(current_step, batch) + age_metrics = ( + self._batch_policy_age_metrics(current_step, batch) + if age_metrics is None + else dict(age_metrics) + ) age_exp_moment = age_metrics.pop("_policy_age_exp_tau8", None) metrics.update(age_metrics) mean_age = age_metrics.get("offpolicy/token_weighted_policy_age_steps") @@ -1580,38 +1996,51 @@ def _trajectory_policy_age_stats( self, current_step: int, trajectory: art.Trajectory ) -> tuple[float, float, float] | None: span_stats = self._trajectory_policy_span_age_stats(current_step, trajectory) + if self._requires_exact_policy_spans(): + if span_stats is None: + raise RuntimeError( + "In-flight LoRA trajectory is missing exact policy token spans" + ) + completion_tokens = self._trajectory_completion_weight(trajectory) + if span_stats[1] != completion_tokens: + raise RuntimeError( + "In-flight LoRA policy spans do not cover every completion token: " + f"covered={span_stats[1]:g}, completion_tokens=" + f"{completion_tokens:g}" + ) + return span_stats if span_stats is not None: return span_stats if trajectory.initial_policy_version is None: return None weight = self._trajectory_completion_weight(trajectory) - age = float(current_step - trajectory.initial_policy_version) + age = self._policy_age(current_step, trajectory.initial_policy_version) return age * weight, weight, _policy_age_exp(age) * weight def _trajectory_policy_span_age_stats( self, current_step: int, trajectory: art.Trajectory ) -> tuple[float, float, float] | None: + if trajectory._policy_token_counts is not None: + age_sum = sum( + self._policy_age(current_step, version) * count + for version, count in trajectory._policy_token_counts.items() + ) + age_exp_sum = sum( + _policy_age_exp(self._policy_age(current_step, version)) * count + for version, count in trajectory._policy_token_counts.items() + ) + weight = float(sum(trajectory._policy_token_counts.values())) + return (float(age_sum), weight, age_exp_sum) if weight > 0 else None age_sum = 0.0 age_exp_sum = 0.0 weight_sum = 0.0 for item in self._trajectory_messages_and_choices(trajectory): - extra = getattr(item, "model_extra", None) - if not isinstance(extra, Mapping): - continue - spans = extra.get("policy_token_spans") - if not isinstance(spans, list): + spans = self._validated_policy_spans(item, required=False) + if spans is None: continue for span in spans: - if not isinstance(span, Mapping): - continue - try: - policy_version = int(span["policy_version"]) - weight = int(span["end_token"]) - int(span["start_token"]) - except (KeyError, TypeError, ValueError): - continue - if weight <= 0: - continue - age = float(current_step - policy_version) + weight = span.end_token - span.start_token + age = self._policy_age(current_step, span.policy_version) age_sum += age * weight age_exp_sum += _policy_age_exp(age) * weight weight_sum += float(weight) @@ -1619,6 +2048,20 @@ def _trajectory_policy_span_age_stats( return None return age_sum, weight_sum, age_exp_sum + def _requires_exact_policy_spans(self) -> bool: + return (self.model._internal_config or {}).get( + "rollout_weight_update_mode" + ) == "in_flight_lora" + + @staticmethod + def _policy_age(current_step: int, policy_version: int) -> float: + if policy_version > current_step: + raise RuntimeError( + "Trajectory tokens came from a future policy: " + f"policy={policy_version}, trainer={current_step}" + ) + return float(current_step - policy_version) + @staticmethod def _trajectory_messages_and_choices(trajectory: art.Trajectory) -> Iterable[Any]: for exchange in trajectory.exchanges.chat_completions: @@ -1691,6 +2134,27 @@ async def _log_checkpoint_saved(self, result: Any) -> None: if isinstance(checkpoint_path, str) and checkpoint_path else Path(self.model._get_output_dir()) / "checkpoints" / f"{step:04d}" ) + ready = getattr(result, "checkpoint_ready", None) + if ready is not None: + task = asyncio.create_task( + self._log_checkpoint_when_ready(step, path, ready) + ) + self._checkpoint_log_tasks.add(task) + task.add_done_callback(self._checkpoint_log_done) + return + self._record_checkpoint_saved(step, path) + + async def _log_checkpoint_when_ready( + self, step: int, path: Path, ready: Awaitable[None] + ) -> None: + await ready + if not path.is_dir(): + raise RuntimeError( + f"checkpoint {step} materialized without directory {path}" + ) + self._record_checkpoint_saved(step, path) + + def _record_checkpoint_saved(self, step: int, path: Path) -> None: if not path.exists(): return self._log_checkpoint_history( @@ -1701,6 +2165,15 @@ async def _log_checkpoint_saved(self, result: Any) -> None: }, ) + def _checkpoint_log_done(self, task: asyncio.Task[None]) -> None: + self._checkpoint_log_tasks.discard(task) + if task.cancelled(): + return + error = task.exception() + if error is not None and self._checkpoint_log_failure is None: + self._checkpoint_log_failure = error + self.request_stop() + async def _log_checkpoint_eval_completed(self, step: int) -> None: self._log_checkpoint_history( step, @@ -1824,12 +2297,37 @@ async def _run_checkpoint_retention(self, current_step: int) -> None: def _is_scalar_metadata(value: object) -> bool: return value is None or isinstance(value, (str, int, float, bool)) - async def _put_output_group(self, group: TrajectoryGroup) -> float: + async def _put_output_group( + self, + group: TrajectoryGroup | TrajectoryGroupRef, + *, + metadata: dict[str, float | int | str | bool | None], + initial_policy_version: int, + final_policy_version: int, + rollout_wall_s: float, + actor_idle_s: float, + ) -> float: assert self._output_queue is not None queue_wait_started = time.monotonic() + if isinstance(self._output_queue, DistributedTrajectoryQueue): + if not isinstance(group, TrajectoryGroupRef): + raise RuntimeError("distributed result queue requires a stored group") + accepted, wait_s = await self._output_queue.put( + group, + metadata=metadata, + initial_policy_version=initial_policy_version, + final_policy_version=final_policy_version, + rollout_wall_s=rollout_wall_s, + actor_idle_s=actor_idle_s, + ) + if accepted: + self._status.note_group_enqueued() + return wait_s + if not isinstance(group, TrajectoryGroup): + raise RuntimeError("local result queue requires a trajectory group") completed, _ = await self._await_or_stop(self._output_queue.put(group)) if completed: - self._status.note_group_enqueued(group) + self._status.note_group_enqueued() return time.monotonic() - queue_wait_started def _record_producer_rollout_timings( diff --git a/src/art/pipeline_tuner/attachment.py b/src/art/pipeline_tuner/attachment.py index 8a1985009..b4538b5db 100644 --- a/src/art/pipeline_tuner/attachment.py +++ b/src/art/pipeline_tuner/attachment.py @@ -3,15 +3,24 @@ import asyncio import inspect import math +from queue import Empty, SimpleQueue +import threading import time -from typing import Any +from typing import Any, Literal, NamedTuple import warnings import pydantic from art.errors import ArtVllmMetricsTimeoutError -from .autotune import PipelineAutotuner, build_initial_settings, recommended_queue_size +from .autotune import ( + PipelineAutotuner, + _vllm_sample_intervals, + _vllm_sample_max_age_s, + build_initial_settings, + freshness_worker_limit, + recommended_queue_size, +) from .config import ( PackedGroupObservation, PipelineAutotuneConfig, @@ -47,6 +56,27 @@ class VllmMetricPollHealth(pydantic.BaseModel): t_s: float timed_out: bool = False + scheduled_s: float | None = None + request_start_s: float | None = None + outcome: Literal["success", "timeout", "error"] = "success" + skipped_polls: int = 0 + + +class _VllmMetricPollResult(NamedTuple): + scheduled_s: float + request_start_s: float + completion_s: float + outcome: Literal["success", "timeout", "error"] + skipped_polls: int + metrics: dict[str, float] | None = None + error: BaseException | None = None + + +def _p99(values: list[float]) -> float: + if not values: + return 0.0 + ordered = sorted(values) + return ordered[max(0, math.ceil(0.99 * len(ordered)) - 1)] class PipelineAutotunerAttachment: @@ -56,9 +86,11 @@ def __init__(self, config: PipelineAutotuneConfig) -> None: self.store: PipelineTunerProfileStore | None = None self.tuner: PipelineAutotuner | None = None self.profile_name = config.output_name - self._sampler_task: asyncio.Task[None] | None = None + self._sampler_thread: threading.Thread | None = None + self._sampler_stop = threading.Event() + self._sampler_results: SimpleQueue[_VllmMetricPollResult] = SimpleQueue() self._poll_health: list[VllmMetricPollHealth] = [] - self._train_step_vllm_metrics: list[PipelineMetric] = [] + self._train_step_vllm_metrics: dict[str, tuple[float, int]] = {} self._sampler_error: BaseException | None = None self._started = False @@ -68,52 +100,70 @@ async def on_start(self, trainer: Any) -> None: self.trainer = trainer self.store = PipelineTunerProfileStore.for_model(trainer.model) self._validate_weight_update_mode(trainer) - packed_sequence_length = self._discover_packed_sequence_length() - target_packed_sequences = await self._discover_target_packed_sequences(trainer) - inference_gpu_count = await self._discover_inference_gpu_count(trainer) - policy_age_limit_steps = self._policy_age_limit_steps(trainer) - loaded = self._load_profile_if_requested( - packed_sequence_length, target_packed_sequences, policy_age_limit_steps - ) - if loaded is not None: - settings = self._settings_with_current_queue( - loaded.settings, policy_age_limit_steps - ) - self.profile_name = self.config.profile or self.config.output_name - trainer._pipeline_tuner_profile = self.store.resolve( - self.config.profile - ).stem - else: - settings = build_initial_settings( - config=self.config, - inference_gpu_count=inference_gpu_count, - target_packed_sequences=target_packed_sequences, - policy_age_limit_steps=policy_age_limit_steps, + initial_poll: _VllmMetricPollResult | None = None + try: + if self.config.mode == "online": + self._start_metric_sampler() + initial_poll = await self._wait_for_initial_serving_metrics() + packed_sequence_length = self._discover_packed_sequence_length() + target_packed_sequences = await self._discover_target_packed_sequences( + trainer ) - trainer.apply_pipeline_settings(settings) - if self.config.mode == "online": - self.tuner = PipelineAutotuner( - config=self.config, - settings=settings, - model_name=trainer.model.run_name, - backend_name=type(trainer.backend).__name__, - packed_sequence_length=packed_sequence_length, - target_packed_sequences=target_packed_sequences, - inference_gpu_count=inference_gpu_count, - policy_age_limit_steps=policy_age_limit_steps, - starting_step=trainer.state.next_training_step, + inference_gpu_count = await self._discover_inference_gpu_count( + trainer, + None if initial_poll is None else initial_poll.metrics, ) - await self._wait_for_initial_serving_metrics() - self._sampler_task = asyncio.create_task( - self._sample_serving_metrics(), - name="art_pipeline_autotuner_vllm_sampler", + rollout_worker_capacity = trainer.rollout_worker_capacity + policy_age_limit_steps = self._policy_age_limit_steps(trainer) + loaded = self._load_profile_if_requested( + packed_sequence_length, + target_packed_sequences, + policy_age_limit_steps, + rollout_worker_capacity, ) - self._save_profile() - self._started = True + if loaded is not None: + settings = self._settings_with_current_queue( + loaded.settings, policy_age_limit_steps + ) + self.profile_name = self.config.profile or self.config.output_name + trainer._pipeline_tuner_profile = self.store.resolve( + self.config.profile + ).stem + else: + settings = build_initial_settings( + config=self.config, + inference_gpu_count=inference_gpu_count, + target_packed_sequences=target_packed_sequences, + policy_age_limit_steps=policy_age_limit_steps, + rollout_worker_capacity=rollout_worker_capacity, + ) + trainer.apply_pipeline_settings(settings) + if self.config.mode == "online": + self.tuner = PipelineAutotuner( + config=self.config, + settings=settings, + model_name=trainer.model.run_name, + backend_name=type(trainer.backend).__name__, + packed_sequence_length=packed_sequence_length, + target_packed_sequences=target_packed_sequences, + inference_gpu_count=inference_gpu_count, + policy_age_limit_steps=policy_age_limit_steps, + starting_step=trainer.state.next_training_step, + rollout_worker_capacity=rollout_worker_capacity, + ) + assert initial_poll is not None + self._consume_poll(initial_poll, record_train_step=False) + self._drain_metric_polls() + self._save_profile() + self._started = True + except BaseException: + await self._stop_metric_sampler() + raise async def on_metric(self, metric: PipelineMetric) -> None: if self.tuner is None: return + self._drain_metric_polls() self._raise_sampler_error() decision = self.tuner.on_metric(metric) if decision is None: @@ -131,61 +181,170 @@ def owns_train_step_vllm_metrics(self) -> bool: return self.config.mode == "online" async def on_stop(self, *, training_failed: bool = False) -> None: - if self._sampler_task is not None: - self._sampler_task.cancel() - await asyncio.gather(self._sampler_task, return_exceptions=True) - self._sampler_task = None + await self._stop_metric_sampler() if self._started and self.tuner is not None: self._save_profile() if not training_failed: self._raise_sampler_error() - async def _wait_for_initial_serving_metrics(self) -> None: + def _start_metric_sampler(self) -> None: + if self._sampler_thread is not None: + raise RuntimeError("ART vLLM metrics sampler is already running") + self._sampler_stop.clear() + self._sampler_thread = threading.Thread( + target=self._metric_sampler_thread_main, + name="art_pipeline_autotuner_vllm_sampler", + daemon=True, + ) + self._sampler_thread.start() + + async def _stop_metric_sampler(self) -> None: + thread = self._sampler_thread + if thread is None: + return + self._sampler_stop.set() + await asyncio.to_thread( + thread.join, max(2.0, 2.0 * self.config.vllm_metric_interval_s) + ) + if thread.is_alive() and self._sampler_error is None: + self._sampler_error = RuntimeError( + "ART vLLM metrics sampler did not stop after its request timeout" + ) + if not thread.is_alive(): + self._sampler_thread = None + self._drain_metric_polls() + + def _metric_sampler_thread_main(self) -> None: + try: + asyncio.run(self._sample_serving_metrics()) + except BaseException as error: + now = time.monotonic() + self._sampler_results.put( + _VllmMetricPollResult(now, now, now, "error", 0, error=error) + ) + + async def _wait_for_initial_serving_metrics(self) -> _VllmMetricPollResult: deadline = time.monotonic() + max(5.0, 2.0 * self.config.vllm_metric_interval_s) while True: try: - metrics = await self._collect_required_serving_metrics() - except ArtVllmMetricsTimeoutError as exc: - self._record_poll_timeout() - remaining = deadline - time.monotonic() - if remaining <= 0.0: + result = self._sampler_results.get_nowait() + except Empty: + if time.monotonic() >= deadline: raise RuntimeError( "Pipeline autotuning could not collect an initial ART vLLM " "metrics sample before startup timeout." - ) from exc - await asyncio.sleep(min(self.config.vllm_metric_interval_s, remaining)) + ) + await asyncio.sleep(min(0.01, self.config.vllm_metric_interval_s)) continue - self._record_poll_success() - await self._emit_metrics(metrics, step=None, record_train_step=False) - return + if result.outcome == "success": + return result + self._consume_poll(result, record_train_step=False) + if result.outcome == "error": + self._raise_sampler_error() + if time.monotonic() >= deadline: + raise RuntimeError( + "Pipeline autotuning could not collect an initial ART vLLM " + "metrics sample before startup timeout." + ) from result.error async def _sample_serving_metrics(self) -> None: assert self.trainer is not None - while not self.trainer.state.done: - try: - metrics = await self._collect_required_serving_metrics() - self._record_poll_success() - await self._emit_metrics(metrics, step=None) - except asyncio.CancelledError: - raise - except ArtVllmMetricsTimeoutError: - self._record_poll_timeout() - except Exception as exc: - self._sampler_error = exc - self.trainer.request_stop() - return - await asyncio.sleep(self.config.vllm_metric_interval_s) - - async def _collect_required_serving_metrics(self) -> dict[str, float]: + trainer = self.trainer + backend = trainer.backend + factory = getattr(backend, "create_train_step_vllm_metrics_collector", None) + session = factory(trainer.model) if callable(factory) else None + if session is not None: + collector = getattr(session, "collect", None) + else: + backend_collector = getattr( + backend, "collect_train_step_vllm_metrics", None + ) + collector = ( + None + if not callable(backend_collector) + else lambda: backend_collector(trainer.model) + ) + if not callable(collector): + raise RuntimeError( + "Pipeline autotuning requires ART vLLM metrics collection." + ) + next_s = time.monotonic() + try: + while not self._sampler_stop.is_set(): + while not self._sampler_stop.is_set(): + delay_s = next_s - time.monotonic() + if delay_s <= 0.0: + break + await asyncio.sleep(min(delay_s, 0.05)) + if self._sampler_stop.is_set(): + break + scheduled_s = next_s + request_start_s = time.monotonic() + metrics: dict[str, float] | None = None + error: BaseException | None = None + outcome: Literal["success", "timeout", "error"] = "success" + try: + metrics = await self._collect_required_serving_metrics(collector) + except ArtVllmMetricsTimeoutError as exc: + outcome = "timeout" + error = exc + except Exception as exc: + outcome = "error" + error = exc + completion_s = time.monotonic() + next_s = scheduled_s + self.config.vllm_metric_interval_s + skipped_polls = 0 + if next_s <= completion_s: + skipped_polls = ( + math.floor( + (completion_s - next_s) / self.config.vllm_metric_interval_s + ) + + 1 + ) + next_s += skipped_polls * self.config.vllm_metric_interval_s + self._sampler_results.put( + _VllmMetricPollResult( + scheduled_s, + request_start_s, + completion_s, + outcome, + skipped_polls, + metrics, + error, + ) + ) + if outcome == "error": + return + finally: + close = ( + getattr(session, "aclose", None) + if session is not None + else getattr(backend, "close_train_step_vllm_metrics", None) + ) + if callable(close): + maybe_close = close() + if inspect.isawaitable(maybe_close): + await maybe_close + + async def _collect_required_serving_metrics( + self, collector: Any | None = None + ) -> dict[str, float]: assert self.trainer is not None - collector = getattr( - self.trainer.backend, "collect_train_step_vllm_metrics", None - ) + trainer = self.trainer + if collector is None: + backend_collector = getattr( + trainer.backend, "collect_train_step_vllm_metrics", None + ) + collector = ( + None + if not callable(backend_collector) + else lambda: backend_collector(trainer.model) + ) if not callable(collector): raise RuntimeError( "Pipeline autotuning requires ART vLLM metrics collection." ) - maybe_metrics = collector(self.trainer.model) + maybe_metrics = collector() metrics = ( await maybe_metrics if inspect.isawaitable(maybe_metrics) else maybe_metrics ) @@ -205,13 +364,53 @@ async def _collect_required_serving_metrics(self) -> dict[str, float]: ) return metrics - def _record_poll_success(self) -> None: - self._poll_health.append(VllmMetricPollHealth(t_s=time.monotonic())) - - def _record_poll_timeout(self) -> None: + def _consume_poll( + self, result: _VllmMetricPollResult, *, record_train_step: bool + ) -> None: self._poll_health.append( - VllmMetricPollHealth(t_s=time.monotonic(), timed_out=True) + VllmMetricPollHealth( + t_s=result.completion_s, + timed_out=result.outcome == "timeout", + scheduled_s=result.scheduled_s, + request_start_s=result.request_start_s, + outcome=result.outcome, + skipped_polls=result.skipped_polls, + ) ) + if result.outcome == "error": + self._sampler_error = result.error or RuntimeError( + "ART vLLM metrics sampler failed without an error" + ) + if self.trainer is not None: + self.trainer.request_stop() + return + if result.metrics is None: + return + if record_train_step: + for name in _TRAIN_STEP_VLLM_METRICS.intersection(result.metrics): + value = result.metrics[name] + if isinstance(value, (int, float)): + total, count = self._train_step_vllm_metrics.get(name, (0.0, 0)) + self._train_step_vllm_metrics[name] = ( + total + float(value), + count + 1, + ) + if self.tuner is not None: + self.tuner.on_vllm_pressure_sample( + t_s=result.completion_s, + running=float(result.metrics["vllm/num_requests_running"]), + waiting_capacity=float( + result.metrics["vllm/num_requests_waiting_capacity"] + ), + ) + + def _drain_metric_polls(self) -> None: + while True: + try: + result = self._sampler_results.get_nowait() + except Empty: + return + self._consume_poll(result, record_train_step=True) def _raise_if_unhealthy_metric_window(self, decision: TunerDecision) -> None: stats = decision.stats @@ -223,18 +422,68 @@ def _raise_if_unhealthy_metric_window(self, decision: TunerDecision) -> None: for poll in self._poll_health if stats.window_start_s <= poll.t_s <= end_s ] - if not polls: + timeouts = sum(poll.timed_out for poll in polls) + errors = sum(poll.outcome == "error" for poll in polls) + skipped = sum(poll.skipped_polls for poll in polls) + poll_slots = len(polls) + skipped + failed_frac = (timeouts + errors + skipped) / max(poll_slots, 1) + intervals = _vllm_sample_intervals( + [ + poll.t_s + for poll in self._poll_health + if not poll.timed_out and poll.outcome == "success" + ], + window_start_s=stats.window_start_s, + window_end_s=end_s, + metric_interval_s=self.config.vllm_metric_interval_s, + ) + coverage = sum(duration_s for _, duration_s in intervals) / ( + end_s - stats.window_start_s + ) + min_coverage = 1.0 - self.config.vllm_metric_timeout_window_frac + decision.stats = stats.model_copy( + update={ + "vllm_poll_samples": len(polls), + "vllm_poll_successes": sum( + not poll.timed_out and poll.outcome == "success" for poll in polls + ), + "vllm_poll_timeouts": timeouts, + "vllm_poll_errors": errors, + "vllm_poll_skipped": skipped, + "vllm_poll_coverage": coverage, + "vllm_poll_schedule_lag_p99_s": _p99( + [ + max(0.0, poll.request_start_s - poll.scheduled_s) + for poll in polls + if poll.scheduled_s is not None + and poll.request_start_s is not None + ] + ), + "vllm_poll_request_latency_p99_s": _p99( + [ + max(0.0, poll.t_s - poll.request_start_s) + for poll in polls + if poll.request_start_s is not None + ] + ), + } + ) + if failed_frac > self.config.vllm_metric_timeout_window_frac: raise RuntimeError( - "Pipeline autotuning did not collect any ART vLLM metrics polls " - f"during decision window steps {stats.start_step}-{stats.end_step}." + "Pipeline autotuning cannot rely on ART vLLM metrics: " + f"{failed_frac:.1%} of metric polls timed out, failed, or were " + f"skipped during decision window steps " + f"{stats.start_step}-{stats.end_step}." ) - timeout_frac = sum(poll.timed_out for poll in polls) / len(polls) - if timeout_frac > self.config.vllm_metric_timeout_window_frac: + if coverage + 1e-9 < min_coverage: raise RuntimeError( - "Pipeline autotuning cannot rely on ART vLLM metrics: " - f"{timeout_frac:.1%} of metric polls timed out during decision " - f"window steps {stats.start_step}-{stats.end_step}." + "Pipeline autotuning cannot rely on ART vLLM metrics: successful " + f"telemetry covered {coverage:.1%} of decision window steps " + f"{stats.start_step}-{stats.end_step}; requires at least " + f"{min_coverage:.1%}." ) + cutoff_s = end_s - _vllm_sample_max_age_s(self.config.vllm_metric_interval_s) + self._poll_health = [poll for poll in self._poll_health if poll.t_s >= cutoff_s] def _raise_sampler_error(self) -> None: if self._sampler_error is not None: @@ -243,34 +492,14 @@ def _raise_sampler_error(self) -> None: ) from self._sampler_error def collect_train_step_metrics(self) -> dict[str, float]: + self._drain_metric_polls() + self._raise_sampler_error() samples = self._train_step_vllm_metrics - self._train_step_vllm_metrics = [] - by_name: dict[str, list[float]] = {} - for metric in samples: - by_name.setdefault(metric.name, []).append(metric.value) + self._train_step_vllm_metrics = {} return { - name: sum(values) / len(values) - for name, values in by_name.items() - if values + name: total / count for name, (total, count) in samples.items() if count > 0 } - async def _emit_metrics( - self, - metrics: dict[str, float], - step: int | None, - *, - record_train_step: bool = True, - ) -> None: - now = time.monotonic() - for name, value in metrics.items(): - if isinstance(value, (int, float)): - metric = PipelineMetric( - name=name, value=float(value), step=step, t_s=now - ) - if record_train_step and name in _TRAIN_STEP_VLLM_METRICS: - self._train_step_vllm_metrics.append(metric) - await self.on_metric(metric) - def _save_profile(self) -> None: if self.tuner is None or self.store is None: return @@ -283,6 +512,7 @@ def _load_profile_if_requested( active_packed_sequence_length: int, target_packed_sequences: int, policy_age_limit_steps: float, + rollout_worker_capacity: int | None, ) -> PipelineAutotunerProfile | None: if self.config.mode == "online" and not self.config.profile: return None @@ -295,6 +525,15 @@ def _load_profile_if_requested( "exceeds the active max_rollout_workers=" f"{self.config.max_rollout_workers}." ) + if ( + rollout_worker_capacity is not None + and profile.settings.num_rollout_workers > rollout_worker_capacity + ): + raise ValueError( + "Autotuner profile requests " + f"num_rollout_workers={profile.settings.num_rollout_workers}, above " + f"current rollout executor capacity {rollout_worker_capacity}." + ) if ( profile.packed_sequence_length is not None and profile.packed_sequence_length != active_packed_sequence_length @@ -324,8 +563,8 @@ def _load_profile_if_requested( warnings.warn( "Autotuner profile was produced with policy_age_limit_steps=" f"{profile.policy_age_limit_steps}, but active config uses " - f"{policy_age_limit_steps}. Recomputing queue size for the " - "active limit.", + f"{policy_age_limit_steps}. Recomputing the active worker target for " + "the active limit.", stacklevel=2, ) return profile @@ -333,6 +572,17 @@ def _load_profile_if_requested( def _settings_with_current_queue( self, settings: PipelineTuneSettings, policy_age_limit_steps: float ) -> PipelineTuneSettings: + worker_limit = freshness_worker_limit( + target_groups_per_step=settings.target_groups_per_step, + limit_steps_off_policy=policy_age_limit_steps, + running_reserve_fraction=self.config.queue_running_reserve_fraction, + worker_step=self.config.worker_step, + ) + workers = ( + settings.num_rollout_workers + if worker_limit is None + else min(settings.num_rollout_workers, worker_limit) + ) return settings.model_copy( update={ "min_batch_size": max( @@ -342,11 +592,9 @@ def _settings_with_current_queue( * self.config.freshness_min_batch_floor_fraction ), ), + "num_rollout_workers": workers, "queue_maxsize": recommended_queue_size( target_groups_per_step=settings.target_groups_per_step, - limit_steps_off_policy=policy_age_limit_steps, - num_rollout_workers=settings.num_rollout_workers, - running_reserve_fraction=self.config.queue_running_reserve_fraction, ), } ) @@ -371,20 +619,29 @@ def _validate_weight_update_mode(trainer: Any) -> None: ) @staticmethod - async def _discover_inference_gpu_count(trainer: Any) -> int: + async def _discover_inference_gpu_count( + trainer: Any, serving_metrics: dict[str, float] | None = None + ) -> int: internal_config = trainer.model._internal_config or {} inference_gpu_ids = internal_config.get("inference_gpu_ids") if inference_gpu_ids: return len(inference_gpu_ids) - collector = getattr(trainer.backend, "collect_train_step_vllm_metrics", None) - if not callable(collector): - raise ValueError( - "Pipeline autotuning requires inference_gpu_ids or ART vLLM metrics." + metrics = serving_metrics + if metrics is None: + collector = getattr( + trainer.backend, "collect_train_step_vllm_metrics", None + ) + if not callable(collector): + raise ValueError( + "Pipeline autotuning requires inference_gpu_ids or ART vLLM " + "metrics." + ) + maybe_metrics = collector(trainer.model) + metrics = ( + await maybe_metrics + if inspect.isawaitable(maybe_metrics) + else maybe_metrics ) - maybe_metrics = collector(trainer.model) - metrics = ( - await maybe_metrics if inspect.isawaitable(maybe_metrics) else maybe_metrics - ) world_size = metrics.get("vllm/world_size") if not isinstance(world_size, (int, float)) or world_size < 1: raise ValueError( @@ -402,7 +659,10 @@ async def _discover_target_packed_sequences(trainer: Any) -> int: resolver = getattr(backend, "_resolve_grad_accumulation_sequences", None) if callable(get_service) and callable(resolver): service = await get_service(trainer.model) - return max(1, int(await resolver(service, TrainConfig()))) + config = TrainConfig( + grad_accumulation_sequences=trainer.grad_accumulation_sequences + ) + return max(1, int(await resolver(service, config))) raise ValueError( "Pipeline autotuning requires a backend that can resolve global " "grad_accumulation_sequences before training starts." diff --git a/src/art/pipeline_tuner/autotune.py b/src/art/pipeline_tuner/autotune.py index 5429478be..4bf6fb633 100644 --- a/src/art/pipeline_tuner/autotune.py +++ b/src/art/pipeline_tuner/autotune.py @@ -43,7 +43,59 @@ def _ceil_to_multiple(value: float, multiple: int, *, minimum: int = 1) -> int: return max(minimum, int(math.ceil(value / multiple)) * multiple) +def _round_to_multiple(value: float, multiple: int, *, minimum: int = 1) -> int: + return max(minimum, int(math.floor(value / multiple + 0.5)) * multiple) + + _VLLM_SCRAPE_GROUP_TOLERANCE_S = 0.05 +_TRAINER_CAPACITY_EPSILON = 1e-9 + + +def _vllm_sample_max_age_s(metric_interval_s: float) -> float: + # Preserve one delayed poll without allowing an unbounded zero-order hold. + return 2.0 * metric_interval_s + + +def _vllm_sample_intervals( + sample_times: Sequence[float], + *, + window_start_s: float, + window_end_s: float, + metric_interval_s: float, +) -> list[tuple[float, float]]: + times = sorted(set(sample_times)) + max_age_s = _vllm_sample_max_age_s(metric_interval_s) + intervals: list[tuple[float, float]] = [] + for index, t_s in enumerate(times): + next_t_s = times[index + 1] if index + 1 < len(times) else math.inf + start_s = max(t_s, window_start_s) + end_s = min(next_t_s, t_s + max_age_s, window_end_s) + if end_s > start_s: + intervals.append((t_s, end_s - start_s)) + return intervals + + +def _packing_group_candidates( + *, current: int, available: int, radius: int, min_change_fraction: float +) -> list[int]: + # Half-hysteresis spacing brackets each actionable target change. + step = max(1, math.ceil(current * min_change_fraction / 2.0)) + min_change = max(1, math.ceil(current * min_change_fraction)) + lower = max(1, min(available, current - radius)) + upper = min(available, current + radius) + candidates = {lower, min(current, available), upper} + candidates.update( + groups + for groups in (current - min_change, current + min_change) + if lower <= groups <= upper + ) + for offset in range(step, radius, step): + candidates.update( + groups + for groups in (current - offset, current + offset) + if lower <= groups <= upper + ) + return sorted(candidates) class PackingProjection(pydantic.BaseModel): @@ -57,6 +109,16 @@ class PackingOutcome(pydantic.BaseModel): packed_sequences: int = pydantic.Field(ge=1) +def _trainer_underfeed_score( + *, idle_frac: float, unused_and_dummy_ratio: float +) -> float: + denominator = max( + _TRAINER_CAPACITY_EPSILON, + 1.0 + _TRAINER_CAPACITY_EPSILON - max(0.0, min(1.0, unused_and_dummy_ratio)), + ) + return max(0.0, idle_frac) / denominator + + class PipelineAutotuner: def __init__( self, @@ -70,7 +132,15 @@ def __init__( inference_gpu_count: int, policy_age_limit_steps: float, starting_step: int = 0, + rollout_worker_capacity: int | None = None, ) -> None: + if rollout_worker_capacity is not None and rollout_worker_capacity < 1: + raise ValueError("rollout_worker_capacity must be >= 1") + if ( + rollout_worker_capacity is not None + and settings.num_rollout_workers > rollout_worker_capacity + ): + raise ValueError("initial settings exceed rollout worker capacity") self.config = config self.settings = settings self.model_name = model_name @@ -79,7 +149,9 @@ def __init__( self.target_packed_sequences = max(1, int(target_packed_sequences)) self.inference_gpu_count = inference_gpu_count self.policy_age_limit_steps = policy_age_limit_steps + self.rollout_worker_capacity = rollout_worker_capacity self.metrics: list[PipelineMetric] = [] + self.vllm_pressure_samples: list[tuple[float, float, float]] = [] self.packed_groups: list[PackedGroupObservation] = [] self._packing_outcomes: list[PackingOutcome] = [] self._packing_outcome_steps: set[int] = set() @@ -88,6 +160,8 @@ def __init__( self._last_decision_step = self._warmup_end_step self._target_candidate: int | None = None self._target_candidate_count = 0 + self._worker_load_candidate_direction: int | None = None + self._worker_load_candidate_count = 0 self._stale_backlog_active = False self._min_batch_trial_baseline_collect_s: float | None = None self._min_batch_trial_batch_size: int | None = None @@ -100,6 +174,11 @@ def on_metric(self, rec: PipelineMetric) -> TunerDecision | None: return None return self.maybe_decide(int(rec.step)) + def on_vllm_pressure_sample( + self, *, t_s: float, running: float, waiting_capacity: float + ) -> None: + self.vllm_pressure_samples.append((t_s, running, waiting_capacity)) + def on_packed_group(self, rec: PackedGroupObservation) -> None: if self.packed_groups and rec.step > self.packed_groups[-1].step: cutoff_step = rec.step - self.config.packing_history_steps + 1 @@ -124,8 +203,23 @@ def maybe_decide(self, step: int) -> TunerDecision | None: self._emit_stable_recommendations(decision) if decision.previous != decision.updated: self.settings = decision.updated + self._prune_metrics(stats) return decision + def _prune_metrics(self, stats: TunerWindowStats) -> None: + raw_cutoff = stats.window_end_s - _vllm_sample_max_age_s( + self.config.vllm_metric_interval_s + ) + self.metrics = [ + rec + for rec in self.metrics + if (rec.step is None and rec.t_s >= raw_cutoff) + or (rec.step is not None and int(rec.step) >= stats.end_step) + ] + self.vllm_pressure_samples = [ + sample for sample in self.vllm_pressure_samples if sample[0] >= raw_cutoff + ] + def window_stats(self) -> TunerWindowStats | None: by_step: dict[int, dict[str, PipelineMetric]] = defaultdict(dict) for rec in self.metrics: @@ -140,7 +234,18 @@ def window_stats(self) -> TunerWindowStats | None: if len(steps) < self.config.window_steps: return None window_steps = steps[-self.config.window_steps :] - t0 = min(by_step[step]["objective/score"].t_s for step in window_steps) + preceding_objective_times = [ + rec.t_s + for rec in self.metrics + if rec.name == "objective/score" + and rec.step is not None + and int(rec.step) < window_steps[0] + ] + t0 = ( + max(preceding_objective_times) + if preceding_objective_times + else min(by_step[step]["objective/score"].t_s for step in window_steps) + ) t1 = max(rec.t_s for step in window_steps for rec in by_step[step].values()) def step_values(name: str) -> list[float]: @@ -171,16 +276,26 @@ def step_values(name: str) -> list[float]: queue_put_wait_s = sum( _required_step_values(by_step, window_steps, "queue/put_wait_s") ) - train_capacity_tokens = _required_step_values( - by_step, window_steps, "data/step_packed_train_tokens" + nominal_capacity_tokens = _required_step_values( + by_step, window_steps, "data/step_nominal_schedule_capacity_tokens" ) non_padding_tokens = _required_step_values( - by_step, window_steps, "data/step_non_padding_train_tokens" + by_step, window_steps, "data/step_nonpadding_logical_tokens" ) vllm_metrics = [ rec for rec in self.metrics - if rec.step is None and t0 <= rec.t_s <= max(t1, t0 + 1e-6) + if rec.step is None + and t0 - _vllm_sample_max_age_s(self.config.vllm_metric_interval_s) + <= rec.t_s + <= max(t1, t0 + 1e-6) + ] + vllm_pressure_samples = [ + sample + for sample in self.vllm_pressure_samples + if t0 - _vllm_sample_max_age_s(self.config.vllm_metric_interval_s) + <= sample[0] + <= max(t1, t0 + 1e-6) ] window_step_set = set(window_steps) packed_group_counts: dict[int, int] = defaultdict(int) @@ -197,35 +312,53 @@ def step_values(name: str) -> list[float]: "Pipeline autotuner requires packed-group observations in every " f"trainable decision-window step; missing steps {missing_packed_steps}." ) - padding_ratios = [] + unused_and_dummy_ratios = [] for capacity, non_padding in zip( - train_capacity_tokens, non_padding_tokens, strict=True + nominal_capacity_tokens, non_padding_tokens, strict=True ): if capacity <= 0: continue - padding_ratios.append(max(0.0, (capacity - non_padding) / capacity)) + unused_and_dummy_ratios.append( + max(0.0, (capacity - non_padding) / capacity) + ) trainer_idle_frac = (collect / wall) if wall > 0 else 0.0 - padding_ratio_mean = _mean(padding_ratios) + unused_and_dummy_ratio_mean = _mean(unused_and_dummy_ratios) self._record_packing_outcomes( by_step=by_step, window_steps=window_steps, ) + if not vllm_pressure_samples: + vllm_pressure_samples = _vllm_samples_from_metrics(vllm_metrics) + waiting_capacity_request_s, running_request_s = ( + _vllm_request_seconds_from_samples( + vllm_pressure_samples, + window_start_s=t0, + window_end_s=t1, + metric_interval_s=self.config.vllm_metric_interval_s, + min_coverage=1.0 - self.config.vllm_metric_timeout_window_frac, + ) + ) return TunerWindowStats( start_step=window_steps[0], end_step=window_steps[-1], window_start_s=t0, window_end_s=t1, collect_batch_s=collect / len(window_steps), - trainer_underfeed_score=max(0.0, trainer_idle_frac), - vllm_pressure=_vllm_pressure( - vllm_metrics, window_start_s=t0, window_end_s=t1 + trainer_underfeed_score=_trainer_underfeed_score( + idle_frac=trainer_idle_frac, + unused_and_dummy_ratio=unused_and_dummy_ratio_mean, ), + vllm_pressure=_vllm_pressure_ratio( + waiting_capacity_request_s, running_request_s + ), + vllm_waiting_capacity_request_s=waiting_capacity_request_s, + vllm_running_request_s=running_request_s, queue_put_wait_frac=queue_put_wait_s / max(queue_put_wait_s + rollout_s, 1e-9), predicted_stale_frac=_mean(step_values("queue/predicted_stale_fraction")), actual_stale_frac=sum(stale_groups) / max(sum(groups) + sum(stale_groups) + sum(zero_variance_groups), 1.0), - padding_ratio_mean=padding_ratio_mean, + unused_and_dummy_ratio_mean=unused_and_dummy_ratio_mean, ) def _record_packing_outcomes( @@ -308,11 +441,14 @@ def _decide(self, stats: TunerWindowStats) -> TunerDecision: ) if target_changed: self._clear_min_batch_trial() + self._clear_worker_load_candidate() stale_backlog_active = self._update_stale_backlog_state(stats) action = "hold" + pending_worker_action = "hold" reason = "inside hysteresis band or already balanced" if stale_backlog_active and updated.min_batch_size < updated.max_batch_size: + self._clear_worker_load_candidate() updated = updated.model_copy( update={ "min_batch_size": min( @@ -327,6 +463,7 @@ def _decide(self, stats: TunerWindowStats) -> TunerDecision: action = "raise_min_batch_size" reason = "stale backlog requires dense batches before reducing workers" elif stale_backlog_active: + self._clear_worker_load_candidate() updated = updated.model_copy( update={ "num_rollout_workers": self._move_workers( @@ -336,40 +473,87 @@ def _decide(self, stats: TunerWindowStats) -> TunerDecision: ) action = "decrease_workers" reason = "predicted or actual stale backlog exceeds the freshness target" + elif target_changed: + reason = "batch geometry changed; worker load evidence was reset" + elif ( + state == "inference_over_train_over" + and stats.queue_put_wait_frac >= self.config.queue_put_severe_frac + ): + pending_worker_action = "decrease_workers" + if self._worker_load_change_ready(-1): + updated = updated.model_copy( + update={ + "num_rollout_workers": self._move_workers( + updated.num_rollout_workers, -1 + ) + } + ) + action = pending_worker_action + reason = ( + "sustained vLLM pressure plus queue backpressure indicates " + "excess workers" + ) + else: + reason = self._pending_worker_load_reason("decrease") elif stats.queue_put_wait_frac >= self.config.queue_put_severe_frac: + self._clear_worker_load_candidate() reason = "completed-group queue backpressure is active" elif state in { "inference_under_train_under", "inference_balanced_train_under", }: - updated = updated.model_copy( - update={ - "num_rollout_workers": self._move_workers( - updated.num_rollout_workers, +1 - ) - } - ) - action = "increase_workers" - reason = "vLLM pressure is low and trainer is underfed" + pending_worker_action = "increase_workers" + if self._worker_load_change_ready(+1): + updated = updated.model_copy( + update={ + "num_rollout_workers": self._move_workers( + updated.num_rollout_workers, +1 + ) + } + ) + action = pending_worker_action + reason = "sustained vLLM pressure is low and trainer is underfed" + else: + reason = self._pending_worker_load_reason("increase") elif state == "inference_over_train_over": + self._clear_worker_load_candidate() reason = "both sides are loaded; no throughput-safe online change" + else: + self._clear_worker_load_candidate() if not target_changed and not stale_backlog_active: min_update = self._min_batch_adjustment( updated, stats, - action, + pending_worker_action + if pending_worker_action == "increase_workers" + else action, inference_over=inference_over, ) if min_update is not None: + self._clear_worker_load_candidate() updated, action, reason = min_update updated = self._settings_with_recomputed_queue( updated, stats, adapt_target=False ) - if action == "hold" and updated != previous: + worker_limit = freshness_worker_limit( + target_groups_per_step=updated.target_groups_per_step, + limit_steps_off_policy=self.policy_age_limit_steps, + running_reserve_fraction=self.config.queue_running_reserve_fraction, + worker_step=self.config.worker_step, + ) + if ( + worker_limit is not None + and previous.num_rollout_workers > worker_limit + and updated.num_rollout_workers <= worker_limit + ): + self._clear_worker_load_candidate() + action = "decrease_workers" + reason = "running rollout reserve exceeded the policy-age budget" + elif action == "hold" and updated != previous: action = "resize_batch_queue" - reason = "recomputed target batch size and freshness-bounded queue" + reason = "recomputed target batch size and one-batch queue capacity" return TunerDecision( step=stats.end_step, state=state, @@ -380,6 +564,28 @@ def _decide(self, stats: TunerWindowStats) -> TunerDecision: stats=stats, ) + def _worker_load_change_ready(self, direction: int) -> bool: + if self._worker_load_candidate_direction == direction: + self._worker_load_candidate_count += 1 + else: + self._worker_load_candidate_direction = direction + self._worker_load_candidate_count = 1 + if self._worker_load_candidate_count < self.config.worker_load_change_windows: + return False + self._clear_worker_load_candidate() + return True + + def _pending_worker_load_reason(self, direction: str) -> str: + return ( + f"worker {direction} awaits sustained evidence " + f"({self._worker_load_candidate_count}/" + f"{self.config.worker_load_change_windows})" + ) + + def _clear_worker_load_candidate(self) -> None: + self._worker_load_candidate_direction = None + self._worker_load_candidate_count = 0 + def _update_stale_backlog_state(self, stats: TunerWindowStats) -> bool: stale_fractions = (stats.predicted_stale_frac, stats.actual_stale_frac) if self._stale_backlog_active: @@ -552,16 +758,17 @@ def _recommendation_candidates( ) ) if ( - stats.padding_ratio_mean >= self.config.padding_high_frac + stats.unused_and_dummy_ratio_mean >= self.config.unused_and_dummy_high_frac and trainer_saturated and vllm_saturated ): recommendations.append( ( "decrease_packed_sequence_length", - "Pipeline autotuner observes high padding while Megatron and vLLM " + "Pipeline autotuner observes high unused or dummy capacity while " + "Megatron and vLLM " "are both saturated; decrease packed_sequence_length to reduce " - "padding waste.", + "schedule waste.", ) ) return recommendations @@ -569,14 +776,20 @@ def _recommendation_candidates( def _move_workers(self, current: int, direction: int) -> int: raw = max( self.config.worker_step, - _ceil_to_multiple( + _round_to_multiple( current * self.config.worker_move_fraction, self.config.worker_step ), ) cap = _ceil_to_multiple(self.config.max_worker_move, self.config.worker_step) + floor = min( + self.config.worker_step, + self.rollout_worker_capacity or self.config.worker_step, + ) + moved = max(floor, current + direction * min(cap, raw)) return min( + moved, self.config.max_rollout_workers, - max(self.config.worker_step, current + direction * min(cap, raw)), + self.rollout_worker_capacity or moved, ) def _settings_with_recomputed_queue( @@ -598,14 +811,23 @@ def _settings_with_recomputed_queue( min_batch = max(floor, min(target, max(1, round(target * ratio)))) # Packed sequence length is the user's cap on target/max batch size. If a # run should never use larger train batches, lower packed_sequence_length. - queue = recommended_queue_size( + worker_limit = freshness_worker_limit( target_groups_per_step=target, limit_steps_off_policy=self.policy_age_limit_steps, - num_rollout_workers=settings.num_rollout_workers, running_reserve_fraction=self.config.queue_running_reserve_fraction, + worker_step=self.config.worker_step, + ) + workers = ( + settings.num_rollout_workers + if worker_limit is None + else min(settings.num_rollout_workers, worker_limit) + ) + queue = recommended_queue_size( + target_groups_per_step=target, ) return settings.model_copy( update={ + "num_rollout_workers": workers, "target_groups_per_step": target, "min_batch_size": min_batch, "max_batch_size": target, @@ -690,62 +912,88 @@ def _packing_projections( ] ) current = max(1, settings.target_groups_per_step) - increase = max( + # Search only target changes that the controller can apply in one window. + radius = max( 1, min( self.config.target_group_max_increase, math.ceil(current * self.config.target_group_increase_fraction), ), ) - lo = max(1, current // 2) - hi = min(len(reservoir), current + increase) - history_risks = self._packing_history_risks(range(lo, hi + 1)) + candidates = _packing_group_candidates( + current=current, + available=len(reservoir), + radius=radius, + min_change_fraction=self.config.target_group_min_relative_change, + ) + history_risks = self._packing_history_risks(candidates) projections: dict[int, PackingProjection] = {} def project(groups: int) -> PackingProjection: existing = projections.get(groups) if existing is not None: return existing + history_risk = history_risks[groups] + if history_risk > self.config.target_spill_probability: + projection = PackingProjection( + groups=groups, spill_probability=history_risk + ) + projections[groups] = projection + return projection rng = random.Random((stats.end_step << 32) ^ groups) spills = 0.0 + trials = 0.0 for _ in range(self.config.packing_trials): selected = rng.sample(range(len(reservoir)), groups) after = pool.estimate(selected, seq_len=self.packed_sequence_length) - spills += float(after.packed_sequences > self.target_packed_sequences) - trials = float(self.config.packing_trials) + trials += 1.0 + if after.packed_sequences > self.target_packed_sequences: + spills += 1.0 + best_case_risk = self._packing_probability_upper( + events=spills, trials=float(self.config.packing_trials) + ) + if best_case_risk > self.config.target_spill_probability: + break + if ( + self._packing_probability_upper(events=spills, trials=trials) + <= self.config.target_spill_probability + ): + break counterfactual_risk = self._packing_probability_upper( events=spills, trials=trials, ) projection = PackingProjection( groups=groups, - spill_probability=max(counterfactual_risk, history_risks[groups]), + spill_probability=max(counterfactual_risk, history_risk), ) projections[groups] = projection return projection - best = lo - 1 - left, right = lo, hi - while left <= right: - groups = (left + right) // 2 - if ( - project(groups).spill_probability - <= self.config.target_spill_probability - ): - best = groups - left = groups + 1 - else: - right = groups - 1 - for groups in range(max(lo, best - 2), min(hi, best + 2) + 1): - project(groups) + upper_index = len(candidates) - 1 + if ( + project(candidates[upper_index]).spill_probability + > self.config.target_spill_probability + ): + left, right = 0, upper_index - 1 + while left <= right: + index = (left + right) // 2 + if ( + project(candidates[index]).spill_probability + <= self.config.target_spill_probability + ): + left = index + 1 + else: + right = index - 1 monotone_risk = 0.0 for groups in sorted(projections): projection = projections[groups] - monotone_risk = max(monotone_risk, projection.spill_probability) if projection.spill_probability < monotone_risk: projections[groups] = projection.model_copy( update={"spill_probability": monotone_risk} ) + else: + monotone_risk = projection.spill_probability return [projections[groups] for groups in sorted(projections)] def _packing_reservoir( @@ -784,9 +1032,13 @@ def _packing_reservoir( break return selected - def _packing_history_risks(self, groups_range: range) -> dict[int, float]: - risks: dict[int, float] = {} - for groups in groups_range: + def _packing_history_risks(self, groups_range: Sequence[int]) -> dict[int, float]: + exact_risks: dict[int, float] = {} + for groups in { + outcome.groups + for outcome in self._packing_outcomes + if outcome.groups <= max(groups_range) + }: outcomes = [ outcome for outcome in self._packing_outcomes @@ -803,16 +1055,21 @@ def _packing_history_risks(self, groups_range: range) -> dict[int, float]: # Zero-spill samples are useful diagnostics but should not block exploration: # a beta upper bound with sparse clean samples would make target batches # sticky. Actual spills are the hard signal we carry across the horizon. - risks[groups] = ( + exact_risks[groups] = ( self._packing_probability_upper(events=spills, trials=trials) if spills > 0.0 else 0.0 ) + risks: dict[int, float] = {} inherited_spill_probability = 0.0 - for groups in sorted(risks): - inherited_spill_probability = max( - inherited_spill_probability, risks[groups] - ) + history_groups = iter(sorted(exact_risks.items())) + next_history = next(history_groups, None) + for groups in sorted(groups_range): + while next_history is not None and next_history[0] <= groups: + inherited_spill_probability = max( + inherited_spill_probability, next_history[1] + ) + next_history = next(history_groups, None) risks[groups] = inherited_spill_probability return risks @@ -838,13 +1095,15 @@ def profile(self) -> PipelineAutotunerProfile: packed_sequence_length=self.packed_sequence_length, target_packed_sequences=self.target_packed_sequences, inference_gpu_count=self.inference_gpu_count, + rollout_worker_capacity=self.rollout_worker_capacity, policy_age_limit_steps=self.policy_age_limit_steps, settings=self.settings, config=self.config, decisions=self.decisions, notes=[ "The first warmup_ignore_steps are excluded from throughput decisions.", - "queue_maxsize is bounded so queue_size / target_groups_per_step <= the policy-age limit.", + "queue_maxsize bounds ready, packing, and packed groups to one target " + "batch; active rollouts add at most one worker wave.", *self._profile_recommendations(), ], ) @@ -867,7 +1126,14 @@ def build_initial_settings( inference_gpu_count: int, target_packed_sequences: int, policy_age_limit_steps: float, + rollout_worker_capacity: int | None, ) -> PipelineTuneSettings: + target_slots = max(1, int(target_packed_sequences)) + max_batch = int(config.initial_max_groups_per_packed_sequence) * target_slots + min_batch = min( + int(config.initial_min_groups_per_packed_sequence) * target_slots, + max_batch, + ) workers = min( config.max_rollout_workers, _ceil_to_multiple( @@ -876,21 +1142,22 @@ def build_initial_settings( minimum=config.worker_step, ), ) - target_slots = max(1, int(target_packed_sequences)) - max_batch = int(config.initial_max_groups_per_packed_sequence) * target_slots - min_batch = min( - int(config.initial_min_groups_per_packed_sequence) * target_slots, - max_batch, + worker_limit = freshness_worker_limit( + target_groups_per_step=max_batch, + limit_steps_off_policy=policy_age_limit_steps, + running_reserve_fraction=config.queue_running_reserve_fraction, + worker_step=config.worker_step, ) + if worker_limit is not None: + workers = min(workers, worker_limit) + if rollout_worker_capacity is not None: + workers = min(workers, rollout_worker_capacity) min_batch = max( min_batch, math.ceil(max_batch * config.freshness_min_batch_floor_fraction), ) queue = recommended_queue_size( target_groups_per_step=max_batch, - limit_steps_off_policy=policy_age_limit_steps, - num_rollout_workers=workers, - running_reserve_fraction=config.queue_running_reserve_fraction, ) return PipelineTuneSettings( num_rollout_workers=workers, @@ -901,26 +1168,50 @@ def build_initial_settings( ) -def recommended_queue_size( +def freshness_worker_limit( *, target_groups_per_step: int, limit_steps_off_policy: float, - num_rollout_workers: int, running_reserve_fraction: float, -) -> int: + worker_step: int, +) -> int | None: + """Leave one queued batch inside the completed-work freshness budget.""" + + if running_reserve_fraction <= 0.0: + return None target = max(1, int(target_groups_per_step)) - limit = max(1.0, float(limit_steps_off_policy)) - max_completed = max(1, int(math.floor(target * limit))) - running_reserve = int( - math.ceil(max(0, num_rollout_workers) * running_reserve_fraction) - ) - lower = target - return max(lower, min(max_completed, max_completed - running_reserve)) + max_completed = int(math.floor(target * max(1.0, limit_steps_off_policy))) + raw_limit = int(math.floor((max_completed - target) / running_reserve_fraction)) + return max(1, (raw_limit // max(1, worker_step)) * max(1, worker_step)) + + +def recommended_queue_size( + *, + target_groups_per_step: int, +) -> int: + return max(1, int(target_groups_per_step)) def _vllm_pressure( - metrics: list[PipelineMetric], *, window_start_s: float, window_end_s: float + metrics: list[PipelineMetric], + *, + window_start_s: float, + window_end_s: float, + metric_interval_s: float, + min_coverage: float, ) -> float: + return _vllm_pressure_from_samples( + _vllm_samples_from_metrics(metrics), + window_start_s=window_start_s, + window_end_s=window_end_s, + metric_interval_s=metric_interval_s, + min_coverage=min_coverage, + ) + + +def _vllm_samples_from_metrics( + metrics: list[PipelineMetric], +) -> list[tuple[float, float, float]]: wanted = {"vllm/num_requests_running", "vllm/num_requests_waiting_capacity"} rows: list[tuple[float, str, float]] = [] for rec in metrics: @@ -929,37 +1220,93 @@ def _vllm_pressure( if not rows: raise RuntimeError("Pipeline autotuning requires vLLM runtime metric samples.") by_time = _group_vllm_metric_rows(rows) - times = sorted(t_s for t_s, values in by_time.items() if wanted <= values.keys()) - if not times: + samples = [ + ( + t_s, + values["vllm/num_requests_running"], + values["vllm/num_requests_waiting_capacity"], + ) + for t_s, values in by_time.items() + if wanted <= values.keys() + ] + if not samples: raise RuntimeError( "Pipeline autotuning requires complete vLLM running/capacity samples." ) - capacity_wait_request_s = 0.0 + return samples + + +def _vllm_pressure_from_samples( + samples: Sequence[tuple[float, float, float]], + *, + window_start_s: float, + window_end_s: float, + metric_interval_s: float, + min_coverage: float, +) -> float: + return _vllm_pressure_ratio( + *_vllm_request_seconds_from_samples( + samples, + window_start_s=window_start_s, + window_end_s=window_end_s, + metric_interval_s=metric_interval_s, + min_coverage=min_coverage, + ) + ) + + +def _vllm_request_seconds_from_samples( + samples: Sequence[tuple[float, float, float]], + *, + window_start_s: float, + window_end_s: float, + metric_interval_s: float, + min_coverage: float, +) -> tuple[float, float]: + by_time = { + t_s: (running, waiting_capacity) + for t_s, running, waiting_capacity in samples + if math.isfinite(running) and math.isfinite(waiting_capacity) + } + times = sorted(by_time) + if not times: + raise RuntimeError("Pipeline autotuning requires vLLM pressure samples.") + window_s = window_end_s - window_start_s + if window_s <= 0.0: + raise RuntimeError( + "Pipeline autotuning requires a positive vLLM sample window." + ) + intervals = _vllm_sample_intervals( + times, + window_start_s=window_start_s, + window_end_s=window_end_s, + metric_interval_s=metric_interval_s, + ) + total_s = sum(duration_s for _, duration_s in intervals) + coverage = total_s / window_s + if coverage + 1e-9 < min_coverage: + raise RuntimeError( + "Pipeline autotuning cannot rely on vLLM pressure: successful telemetry " + f"covered {coverage:.1%} of the decision window; requires at least " + f"{min_coverage:.1%}." + ) + waiting_capacity_request_s = 0.0 running_request_s = 0.0 - total_s = 0.0 - for idx, t_s in enumerate(times): - values = by_time[t_s] - if not { - "vllm/num_requests_running", - "vllm/num_requests_waiting_capacity", - }.issubset(values): - continue - next_t_s = times[idx + 1] if idx + 1 < len(times) else window_end_s - start_s = max(t_s, window_start_s) - end_s = min(next_t_s, window_end_s) - if end_s <= start_s: - continue - duration_s = end_s - start_s - total_s += duration_s - capacity_wait_request_s += ( - max(0.0, values["vllm/num_requests_waiting_capacity"]) * duration_s - ) - running_request_s += max(0.0, values["vllm/num_requests_running"]) * duration_s + for t_s, duration_s in intervals: + running, waiting_capacity = by_time[t_s] + waiting_capacity_request_s += max(0.0, waiting_capacity) * duration_s + running_request_s += max(0.0, running) * duration_s if total_s <= 0.0: raise RuntimeError("Pipeline autotuning requires nonzero vLLM sample duration.") + return waiting_capacity_request_s, running_request_s + + +def _vllm_pressure_ratio( + waiting_capacity_request_s: float, running_request_s: float +) -> float: if running_request_s > 0.0: - return capacity_wait_request_s / running_request_s - return math.inf if capacity_wait_request_s > 0.0 else 0.0 + return waiting_capacity_request_s / running_request_s + return math.inf if waiting_capacity_request_s > 0.0 else 0.0 def _group_vllm_metric_rows( diff --git a/src/art/pipeline_tuner/config.py b/src/art/pipeline_tuner/config.py index e17eabba6..b56bb7d4a 100644 --- a/src/art/pipeline_tuner/config.py +++ b/src/art/pipeline_tuner/config.py @@ -35,14 +35,15 @@ class PipelineAutotuneConfig(pydantic.BaseModel): window_steps: int = pydantic.Field(default=4, ge=1) warmup_ignore_steps: int = pydantic.Field(default=3, ge=0) target_spill_probability: float = pydantic.Field(default=0.03, ge=0.0, le=1.0) - worker_step: int = pydantic.Field(default=4, ge=1) + worker_step: int = pydantic.Field(default=2, ge=1) worker_move_fraction: float = pydantic.Field(default=0.10, gt=0.0, le=1.0) + worker_load_change_windows: int = pydantic.Field(default=2, ge=1) max_worker_move: int = pydantic.Field(default=16, ge=4) max_rollout_workers: int = pydantic.Field(default=1024, ge=1) initial_model_calls_per_inference_gpu: int = pydantic.Field(default=8, ge=1) initial_min_groups_per_packed_sequence: int = pydantic.Field(default=8, ge=1) initial_max_groups_per_packed_sequence: int = pydantic.Field(default=8, ge=1) - packing_trials: int = pydantic.Field(default=64, ge=16) + packing_trials: int = pydantic.Field(default=48, ge=16) packing_reservoir_multiplier: int = pydantic.Field(default=2, ge=2) packing_reservoir_min_groups: int = pydantic.Field(default=32, ge=16) packing_history_steps: int = pydantic.Field(default=64, ge=1) @@ -58,7 +59,7 @@ class PipelineAutotuneConfig(pydantic.BaseModel): queue_put_severe_frac: float = pydantic.Field(default=1.0 / 3.0, ge=0.0, le=1.0) stale_high_frac: float = pydantic.Field(default=0.20, ge=0.0, le=1.0) stale_clear_frac: float = pydantic.Field(default=0.10, ge=0.0, le=1.0) - padding_high_frac: float = pydantic.Field(default=0.25, ge=0.0, le=1.0) + unused_and_dummy_high_frac: float = pydantic.Field(default=0.25, ge=0.0, le=1.0) trainer_min_batch_lower_score: float = pydantic.Field(default=0.15, ge=0.0) trainer_min_batch_raise_score: float = pydantic.Field(default=0.10, ge=0.0) min_batch_collect_improvement_ratio: float = pydantic.Field( @@ -71,7 +72,7 @@ class PipelineAutotuneConfig(pydantic.BaseModel): default=0.85, gt=0.0, le=1.0 ) target_group_change_windows: int = pydantic.Field(default=1, ge=1) - target_group_increase_fraction: float = pydantic.Field(default=0.25, gt=0.0, le=1.0) + target_group_increase_fraction: float = pydantic.Field(default=0.20, gt=0.0, le=1.0) target_group_max_increase: int = pydantic.Field(default=64, ge=1) target_group_min_relative_change: float = pydantic.Field( default=0.10, ge=0.0, le=1.0 @@ -142,10 +143,20 @@ class TunerWindowStats(pydantic.BaseModel): collect_batch_s: float = 0.0 trainer_underfeed_score: float = 0.0 vllm_pressure: float = 0.0 + vllm_waiting_capacity_request_s: float = pydantic.Field(default=0.0, ge=0.0) + vllm_running_request_s: float = pydantic.Field(default=0.0, ge=0.0) queue_put_wait_frac: float = 0.0 predicted_stale_frac: float = 0.0 actual_stale_frac: float = 0.0 - padding_ratio_mean: float = 0.0 + unused_and_dummy_ratio_mean: float = 0.0 + vllm_poll_samples: int = 0 + vllm_poll_successes: int = 0 + vllm_poll_timeouts: int = 0 + vllm_poll_errors: int = 0 + vllm_poll_skipped: int = 0 + vllm_poll_coverage: float = 0.0 + vllm_poll_schedule_lag_p99_s: float = 0.0 + vllm_poll_request_latency_p99_s: float = 0.0 class TunerDecision(pydantic.BaseModel): @@ -166,6 +177,7 @@ class PipelineAutotunerProfile(pydantic.BaseModel): packed_sequence_length: int | None = None target_packed_sequences: int | None = None inference_gpu_count: int | None = None + rollout_worker_capacity: int | None = pydantic.Field(default=None, ge=1) policy_age_limit_steps: float | None = None settings: PipelineTuneSettings config: PipelineAutotuneConfig diff --git a/src/art/pipeline_tuner/worker_controller.py b/src/art/pipeline_tuner/worker_controller.py index 24d3fb7cf..39e3746b4 100644 --- a/src/art/pipeline_tuner/worker_controller.py +++ b/src/art/pipeline_tuner/worker_controller.py @@ -49,6 +49,8 @@ def _reconcile(self) -> None: ) self._tasks[worker_id] = task active.append(worker_id) + # Retiring workers keep their endpoint until their acquired scenario is done. + self.trainer._rollout_executor.set_workers(tuple(self._tasks)) async def _raise_finished_errors(self) -> None: errors: list[BaseException] = [] diff --git a/src/art/preprocessing/moe_routing.py b/src/art/preprocessing/moe_routing.py index e3244934d..f07278679 100644 --- a/src/art/preprocessing/moe_routing.py +++ b/src/art/preprocessing/moe_routing.py @@ -2,7 +2,7 @@ import os import time -from typing import Any +from typing import Any, cast import numpy as np from openai.types.chat.chat_completion import Choice @@ -13,9 +13,36 @@ PROMPT_TOKEN_IDS_KEY = "prompt_token_ids" COMPLETION_TOKEN_IDS_KEY = "completion_token_ids" ROUTED_EXPERTS_KEY = "routed_experts" +NUM_EXPERTS_KEY = "num_experts" -MoeRouteArray = np.ndarray -MISSING_EXPERT_ID = -1 + +class MoeRouteArray(np.ndarray): + num_experts: int + + def __new__( + cls, + array: np.ndarray, + *, + num_experts: int, + validate: bool = True, + ) -> "MoeRouteArray": + result = np.asarray(array).view(cls) + result.num_experts = int(num_experts) + if validate: + _validate_route_array(result, field_name=ROUTED_EXPERTS_KEY) + result.flags.writeable = False + return result + + def __array_finalize__(self, source: np.ndarray | None) -> None: + self.num_experts = int(getattr(source, "num_experts", 0)) + + +def moe_route_dtype(num_experts: int) -> np.dtype[Any]: + if not 1 <= num_experts <= 65_536: + raise RuntimeError( + f"MoE routing requires num_experts in [1, 65536], got {num_experts}" + ) + return np.dtype(np.uint8 if num_experts <= 256 else np.uint16) class MoeRoutingAlignmentStats(BaseModel): @@ -36,6 +63,22 @@ class MoeRouteSegments(BaseModel): segments: tuple[MoeRouteArray, ...] + @model_validator(mode="after") + def _validate(self) -> "MoeRouteSegments": + if not self.segments: + raise RuntimeError("MoE route segments cannot be empty") + contract = { + (segment.num_experts, segment.dtype, *segment.shape[1:]) + for segment in self.segments + } + if len(contract) != 1: + raise RuntimeError("MoE route segments must share one exact contract") + return self + + @property + def num_experts(self) -> int: + return self.segments[0].num_experts + @property def shape(self) -> tuple[int, int, int]: first = self.segments[0] @@ -58,7 +101,10 @@ def iter_slices( slices.append( ( overlap_start, - segment[overlap_start - offset : overlap_end - offset], + cast( + MoeRouteArray, + segment[overlap_start - offset : overlap_end - offset], + ), ) ) offset = segment_end @@ -71,9 +117,6 @@ class PackedMoeRoutingReplay(BaseModel): model_config = ConfigDict(arbitrary_types_allowed=True) expert_indices: Any - token_mask: Any - num_layers: int - topk: int num_experts: int pack_stats: MoeRoutingPackStats @@ -82,27 +125,18 @@ def _validate(self) -> "PackedMoeRoutingReplay": if self.expert_indices.ndim != 4: raise RuntimeError( "expert_indices must have shape " - "[num_sequences, sequence_length, num_layers, topk], got " + "[num_layers, num_sequences, sequence_length, topk], got " f"{tuple(self.expert_indices.shape)}" ) - if self.token_mask.shape != self.expert_indices.shape[:2]: - raise RuntimeError( - "token_mask shape must match packed route tokens, got " - f"{tuple(self.token_mask.shape)} vs " - f"{tuple(self.expert_indices.shape[:2])}" - ) - if self.num_layers != int(self.expert_indices.shape[2]): - raise RuntimeError( - f"num_layers={self.num_layers} does not match " - f"expert_indices.shape[2]={self.expert_indices.shape[2]}" - ) - if self.topk != int(self.expert_indices.shape[3]): + if min(map(int, self.expert_indices.shape)) <= 0: + raise RuntimeError("expert_indices axes must be non-empty") + expected_dtype = str(moe_route_dtype(self.num_experts)) + actual_dtype = str(self.expert_indices.dtype).removeprefix("torch.") + if actual_dtype != expected_dtype: raise RuntimeError( - f"topk={self.topk} does not match " - f"expert_indices.shape[3]={self.expert_indices.shape[3]}" + f"{self.num_experts} experts require {expected_dtype} replay ids, " + f"got {actual_dtype}" ) - if self.num_experts <= 0: - raise RuntimeError(f"num_experts must be >0, got {self.num_experts}") if self.topk > self.num_experts: raise RuntimeError( f"MoE routing topk cannot exceed num_experts: topk={self.topk}, " @@ -110,19 +144,31 @@ def _validate(self) -> "PackedMoeRoutingReplay": ) return self + @property + def num_layers(self) -> int: + return int(self.expert_indices.shape[0]) + + @property + def topk(self) -> int: + return int(self.expert_indices.shape[3]) + def attach_moe_routing_metadata_to_choice( *, choice: Choice, response_payload: dict[str, Any], choice_index: int = 0, - routed_experts: MoeRouteArray | None = None, + routed_experts: np.ndarray | None = None, + num_experts: int | None = None, ) -> None: if routed_experts is None: return + num_experts = int(num_experts or getattr(routed_experts, "num_experts", 0)) + routes = MoeRouteArray(routed_experts, num_experts=num_experts) metadata: dict[str, Any] = { PROMPT_TOKEN_IDS_KEY: response_payload.get(PROMPT_TOKEN_IDS_KEY), - ROUTED_EXPERTS_KEY: routed_experts, + ROUTED_EXPERTS_KEY: routes, + NUM_EXPERTS_KEY: num_experts, } raw_choices = response_payload.get("choices") if isinstance(raw_choices, list) and choice_index < len(raw_choices): @@ -142,7 +188,6 @@ def attach_moe_routing_metadata_to_choice( ) _normalize_token_ids(metadata[PROMPT_TOKEN_IDS_KEY]) _normalize_token_ids(metadata.get(COMPLETION_TOKEN_IDS_KEY)) - _validate_route_array(routed_experts, field_name=ROUTED_EXPERTS_KEY) extra = choice.model_extra if extra is None: raise RuntimeError("OpenAI Choice.model_extra is unavailable for route capture") @@ -170,10 +215,11 @@ def align_choice_routes_to_tokenized_result( f"choices={len(choices)}, offsets={len(choice_offsets)}, " f"lengths={len(choice_token_lengths)}" ) - aligned: MoeRouteArray | None = None + aligned: np.ndarray | None = None route_mask: np.ndarray | None = None route_segments: list[MoeRouteArray] = [] route_shape: tuple[int, int] | None = None + num_experts: int | None = None covered_until = 0 stats = MoeRoutingAlignmentStats() saw_routing = False @@ -195,6 +241,10 @@ def align_choice_routes_to_tokenized_result( completion_token_count=len(completion_token_ids), stats=stats, ) + if num_experts is None: + num_experts = prompt_routes.num_experts + elif num_experts != prompt_routes.num_experts: + raise RuntimeError("MoE route captures disagree on exact expert count") timing_start = _route_alignment_time_ns() if prompt_token_ids != token_ids[:offset]: raise RuntimeError( @@ -264,27 +314,32 @@ def align_choice_routes_to_tokenized_result( raise RuntimeError("Some trainable choices had MoE routes while others did not") if not saw_routing: return None, stats + if num_experts is None: + raise RuntimeError("MoE routing metadata omitted exact expert count") if aligned is not None: - return aligned, stats + assert route_mask is not None + _fill_missing_routes(aligned, route_mask, num_experts=num_experts) + return MoeRouteArray(aligned, num_experts=num_experts), stats if covered_until == len(token_ids): if len(route_segments) == 1: return route_segments[0], stats return MoeRouteSegments(segments=tuple(route_segments)), stats if route_shape is None: raise RuntimeError("MoE routing metadata did not contain any routed tokens") - aligned, route_mask = _materialize_route_segments( - token_count=len(token_ids), + missing = deterministic_moe_routes( + np.arange(covered_until, len(token_ids), dtype=np.int64), route_shape=route_shape, - route_segments=route_segments, + num_experts=num_experts, ) - stats.routed_tokens = int(route_mask.sum()) - return aligned, stats + route_segments.append(missing) + stats.routed_tokens = covered_until + return MoeRouteSegments(segments=tuple(route_segments)), stats def _timed_append_or_overlay_routes( *, stats: MoeRoutingAlignmentStats, - aligned: MoeRouteArray | None, + aligned: np.ndarray | None, route_mask: np.ndarray | None, route_segments: list[MoeRouteArray], covered_until: int, @@ -292,7 +347,7 @@ def _timed_append_or_overlay_routes( route_shape: tuple[int, int], start: int, routes: MoeRouteArray, -) -> tuple[MoeRouteArray | None, np.ndarray | None, int]: +) -> tuple[np.ndarray | None, np.ndarray | None, int]: timing_start = _route_alignment_time_ns() try: return _append_or_overlay_routes( @@ -311,7 +366,7 @@ def _timed_append_or_overlay_routes( def _append_or_overlay_routes( *, - aligned: MoeRouteArray | None, + aligned: np.ndarray | None, route_mask: np.ndarray | None, route_segments: list[MoeRouteArray], covered_until: int, @@ -319,7 +374,7 @@ def _append_or_overlay_routes( route_shape: tuple[int, int], start: int, routes: MoeRouteArray, -) -> tuple[MoeRouteArray | None, np.ndarray | None, int]: +) -> tuple[np.ndarray | None, np.ndarray | None, int]: if routes.shape[0] == 0: return aligned, route_mask, covered_until if aligned is None and start == covered_until: @@ -341,13 +396,10 @@ def _materialize_route_segments( token_count: int, route_shape: tuple[int, int], route_segments: list[MoeRouteArray], -) -> tuple[MoeRouteArray, np.ndarray]: +) -> tuple[np.ndarray, np.ndarray]: num_layers, topk = route_shape - aligned = np.full( - (token_count, num_layers, topk), - MISSING_EXPERT_ID, - dtype=np.int32, - ) + dtype = route_segments[0].dtype if route_segments else np.dtype(np.uint8) + aligned = np.zeros((token_count, num_layers, topk), dtype=dtype) route_mask = np.zeros(token_count, dtype=np.bool_) offset = 0 for routes in route_segments: @@ -357,7 +409,7 @@ def _materialize_route_segments( def _overlay_routes( - aligned: MoeRouteArray, + aligned: np.ndarray, route_mask: np.ndarray, start: int, routes: MoeRouteArray, @@ -366,6 +418,10 @@ def _overlay_routes( return end = start + routes.shape[0] existing = route_mask[start:end] + if bool(existing.any()) and not np.array_equal( + aligned[start:end][existing], routes[existing] + ): + raise RuntimeError("Overlapping routed experts disagree for the same token") fill = ~existing if bool(fill.any()): aligned[start:end][fill] = routes[fill] @@ -387,6 +443,23 @@ def _validate_route_array(array: MoeRouteArray, *, field_name: str) -> None: ) if array.shape[0] > 0 and (array.shape[1] <= 0 or array.shape[2] <= 0): raise RuntimeError(f"{field_name} must have non-empty layer and topk axes") + expected_dtype = moe_route_dtype(array.num_experts) + if array.dtype != expected_dtype: + raise RuntimeError( + f"{array.num_experts} experts require {expected_dtype} routes, " + f"got {array.dtype}" + ) + if array.shape[-1] > array.num_experts: + raise RuntimeError("MoE routing top-k exceeds exact expert count") + flat = array.reshape(-1, array.shape[-1]) + for start in range(0, len(flat), 1 << 20): + rows = np.sort(flat[start : start + (1 << 20)], axis=1) + if rows.size and int(rows.max()) >= array.num_experts: + raise RuntimeError("MoE route expert id is outside the exact model range") + if rows.shape[1] > 1 and bool(np.any(rows[:, 1:] == rows[:, :-1])): + raise RuntimeError( + "MoE route expert ids must be distinct per token and layer" + ) def _common_route_shape(*arrays: MoeRouteArray) -> tuple[int, int]: @@ -421,8 +494,12 @@ def _choice_routes( routes = metadata.get(ROUTED_EXPERTS_KEY) if not isinstance(routes, np.ndarray): raise RuntimeError("Missing binary routed experts") - _validate_route_array(routes, field_name=ROUTED_EXPERTS_KEY) - routes.flags.writeable = False + num_experts = int(metadata.get(NUM_EXPERTS_KEY, 0)) + if isinstance(routes, MoeRouteArray): + if routes.num_experts != num_experts: + raise RuntimeError("MoE route array disagrees with its exact expert count") + else: + routes = MoeRouteArray(routes, num_experts=num_experts) expected_lengths = { len(prompt_token_ids) + completion_token_count, len(prompt_token_ids) + max(completion_token_count - 1, 0), @@ -440,9 +517,46 @@ def _choice_routes( return prompt_routes, completion_routes -def _readonly_route_view(routes: MoeRouteArray) -> MoeRouteArray: - routes.flags.writeable = False - return routes +def _readonly_route_view(routes: np.ndarray) -> MoeRouteArray: + route_view = cast(MoeRouteArray, routes) + route_view.flags.writeable = False + return route_view + + +def _fill_missing_routes( + routes: np.ndarray, mask: np.ndarray, *, num_experts: int +) -> None: + missing = np.flatnonzero(~mask) + if missing.size: + routes[missing] = deterministic_moe_routes( + missing, + route_shape=(int(routes.shape[1]), int(routes.shape[2])), + num_experts=num_experts, + ) + mask[missing] = True + + +def deterministic_moe_routes( + positions: np.ndarray, + *, + route_shape: tuple[int, int], + num_experts: int, +) -> MoeRouteArray: + num_layers, topk = route_shape + if num_layers <= 0 or not 1 <= topk <= num_experts: + raise RuntimeError( + "MoE route shape requires positive layers and top-k in expert range" + ) + routes = np.empty( + (len(positions), num_layers, topk), dtype=moe_route_dtype(num_experts) + ) + base = ( + (positions.astype(np.uint64, copy=False)[:, None] + 1) * 1_299_709 + + np.arange(1, num_layers + 1, dtype=np.uint64)[None, :] * 97_003 + ) % num_experts + for slot in range(topk): + routes[:, :, slot] = (base + slot) % num_experts + return MoeRouteArray(routes, num_experts=num_experts, validate=False) def _route_alignment_time_ns() -> int: diff --git a/src/art/preprocessing/pack.py b/src/art/preprocessing/pack.py index 5ef3396b5..a4149039a 100644 --- a/src/art/preprocessing/pack.py +++ b/src/art/preprocessing/pack.py @@ -16,11 +16,12 @@ ) from ..types import Verbosity from .moe_routing import ( - MISSING_EXPERT_ID, MoeRouteArray, MoeRouteSegments, MoeRoutingPackStats, PackedMoeRoutingReplay, + deterministic_moe_routes, + moe_route_dtype, ) from .tokenize import TokenizedResult @@ -56,22 +57,6 @@ class DiskPackedTensors(TypedDict): image_grid_thw: NotRequired[tuple[int, list[int]]] -class _PackedPrefixTreeRow(NamedTuple): - token_ids: np.ndarray - group_ids: np.ndarray - parent_ids: np.ndarray - input_pos: np.ndarray - assistant_mask: np.ndarray - logprobs: np.ndarray - advantages: np.ndarray - weights: np.ndarray - pixel_values: torch.Tensor | None - image_grid_thw: torch.Tensor | None - route_tensor: np.ndarray | None = None - route_mask: np.ndarray | None = None - max_expert_id: int = 0 - - class _PrefixTreePackItem(NamedTuple): token_ids: tuple[int, ...] input_pos: np.ndarray @@ -284,7 +269,7 @@ def prefix_tree_pack( ) if not planned_rows: raise RuntimeError("No tokenized results were packable") - random.shuffle(planned_rows) + random.Random(len(planned_rows)).shuffle(planned_rows) rows = [row for row, _ in planned_rows] row_plans = [plan for _, plan in planned_rows] @@ -299,31 +284,26 @@ def prefix_tree_pack( weights_np = np.zeros((num_sequences, seq_len), dtype=np.float32) pixel_values: list[torch.Tensor | None] = [] image_grid_thw: list[torch.Tensor | None] = [] - route_shape = next( - ( - shape - for row in rows - if (shape := _first_item_moe_route_shape(row)) is not None - ), - None, - ) + route_contract = _moe_route_contract(rows) if include_moe_routing else None route_tensor_np: np.ndarray | None = None - route_mask_np: np.ndarray | None = None - max_expert_id = 0 if include_moe_routing: - if route_shape is None: + if route_contract is None: raise RuntimeError("No MoE routes were packed") - num_layers, topk = route_shape - route_tensor_np = np.zeros( - (num_sequences, seq_len, num_layers, topk), dtype=np.int32 + num_experts, num_layers, topk = route_contract + padding = deterministic_moe_routes( + np.arange(seq_len, dtype=np.int64), + route_shape=(num_layers, topk), + num_experts=num_experts, ) - route_mask_np = np.zeros((num_sequences, seq_len), dtype=np.bool_) + route_tensor_np = np.broadcast_to( + np.moveaxis(padding, 1, 0)[:, None], + (num_layers, num_sequences, seq_len, topk), + ).copy() for index, (row, plan) in enumerate(zip(rows, row_plans, strict=True)): row_route_tensor = ( - route_tensor_np[index] if route_tensor_np is not None else None + route_tensor_np[:, index] if route_tensor_np is not None else None ) - row_route_mask = route_mask_np[index] if route_mask_np is not None else None _materialize_prefix_tree_row( row, plan=plan, @@ -336,17 +316,11 @@ def prefix_tree_pack( advantages=advantages_np[index], weights=weights_np[index], route_tensor=row_route_tensor, - route_mask=row_route_mask, - route_shape=route_shape, + route_shape=(None if route_contract is None else route_contract[1:]), include_moe_routing=include_moe_routing, ) pixel_values.append(_packed_row_tensor_list(row, "pixel_values")) image_grid_thw.append(_packed_row_tensor_list(row, "image_grid_thw")) - if include_moe_routing: - assert route_tensor_np is not None and route_mask_np is not None - if bool(route_mask_np.any()): - max_expert_id = int(route_tensor_np.max()) - assistant_mask_tensor = torch.from_numpy(assistant_mask_np) weights_tensor = torch.from_numpy(weights_np) weights_tensor = torch.where( @@ -396,18 +370,12 @@ def prefix_tree_pack( }, } if include_moe_routing: - assert route_tensor_np is not None and route_mask_np is not None - assert route_shape is not None - num_layers, topk = route_shape - if not bool(route_mask_np.any()): - raise RuntimeError("No MoE routes were packed") - moe_routing_pack_stats.packed_tokens = int(route_mask_np.sum()) + assert route_tensor_np is not None and route_contract is not None + num_experts, _num_layers, _topk = route_contract + moe_routing_pack_stats.packed_tokens = sum(plan.length for plan in row_plans) packed_tensors["moe_routing_replay"] = PackedMoeRoutingReplay( expert_indices=torch.from_numpy(route_tensor_np), - token_mask=torch.from_numpy(route_mask_np), - num_layers=num_layers, - topk=topk, - num_experts=max(topk, max_expert_id + 1), + num_experts=num_experts, pack_stats=moe_routing_pack_stats, ) return packed_tensors @@ -734,7 +702,6 @@ def _materialize_prefix_tree_row( advantages: np.ndarray, weights: np.ndarray, route_tensor: np.ndarray | None, - route_mask: np.ndarray | None, route_shape: tuple[int, int] | None, include_moe_routing: bool, ) -> None: @@ -763,12 +730,11 @@ def _materialize_prefix_tree_row( src_end=src_end, ) if include_moe_routing: - assert route_tensor is not None and route_mask is not None + assert route_tensor is not None assert route_shape is not None assert item.moe_routes is not None _copy_moe_route_slice( route_tensor=route_tensor, - route_mask=route_mask, dst_start=dst_start, src_start=src_start, src_end=src_end, @@ -777,93 +743,6 @@ def _materialize_prefix_tree_row( ) -def _pack_prefix_tree_row( - row: list[_PrefixTreePackItem], - *, - seq_len: int, - pack_results: bool, - include_moe_routing: bool, - min_shared_segment_length: int = DEFAULT_MIN_PREFIX_TREE_SHARED_SEGMENT_LENGTH, -) -> _PackedPrefixTreeRow: - if not row: - empty_i64 = np.empty((0,), dtype=np.int64) - empty_f32 = np.empty((0,), dtype=np.float32) - return _PackedPrefixTreeRow( - token_ids=empty_i64, - group_ids=empty_i64, - parent_ids=empty_i64, - input_pos=empty_i64, - assistant_mask=np.empty((0,), dtype=np.bool_), - logprobs=empty_f32, - advantages=empty_f32, - weights=empty_f32, - pixel_values=None, - image_grid_thw=None, - ) - plan = _prefix_tree_row_plan( - row, - seq_len=seq_len, - pack_results=pack_results, - min_shared_segment_length=min_shared_segment_length, - ) - length = plan.length - token_ids = np.empty(length, dtype=np.int64) - group_ids = np.empty(length, dtype=np.int64) - parent_ids = np.empty(length, dtype=np.int64) - input_pos = np.zeros(length, dtype=np.int64) - assistant_mask = np.zeros(length, dtype=np.bool_) - logprobs = np.full(length, np.nan, dtype=np.float32) - advantages = np.zeros(length, dtype=np.float32) - weights = np.zeros(length, dtype=np.float32) - route_shape = _first_item_moe_route_shape(row) if include_moe_routing else None - route_tensor: np.ndarray | None = None - route_mask: np.ndarray | None = None - max_expert_id = 0 - if route_shape is not None: - route_tensor = np.zeros( - (length, route_shape[0], route_shape[1]), dtype=np.int32 - ) - route_mask = np.zeros(length, dtype=np.bool_) - _materialize_prefix_tree_row( - row, - plan=plan, - token_ids=token_ids, - group_ids=group_ids, - parent_ids=parent_ids, - input_pos=input_pos, - assistant_mask=assistant_mask, - logprobs=logprobs, - advantages=advantages, - weights=weights, - route_tensor=route_tensor, - route_mask=route_mask, - route_shape=route_shape, - include_moe_routing=include_moe_routing, - ) - max_expert_id = ( - int(route_tensor.max()) - if route_tensor is not None - and route_mask is not None - and bool(route_mask.any()) - else 0 - ) - return _PackedPrefixTreeRow( - token_ids=token_ids[:length], - group_ids=group_ids[:length], - parent_ids=parent_ids[:length], - input_pos=input_pos, - assistant_mask=assistant_mask, - logprobs=logprobs, - advantages=advantages, - weights=weights, - pixel_values=_packed_row_tensor_list(row, "pixel_values"), - image_grid_thw=_packed_row_tensor_list(row, "image_grid_thw"), - route_tensor=route_tensor, - route_mask=route_mask, - max_expert_id=max_expert_id, - ) - - def _validate_shared_prefix_tree_segment( row: list[_PrefixTreePackItem], *, @@ -881,6 +760,8 @@ def _validate_shared_prefix_tree_segment( raise RuntimeError( "Prefix-tree pack cannot share mismatched input positions" ) + if (item.moe_routes is None) != (reference.moe_routes is None): + raise RuntimeError("Prefix-tree shared routes are incomplete") def _packed_row_tensor_list( @@ -901,39 +782,35 @@ def _packed_row_tensor_list( return torch.concat(tensors) if tensors else None -def _first_item_moe_route_shape( - row: list[_PrefixTreePackItem], -) -> tuple[int, int] | None: - for item in row: - if item.moe_routes is not None: - shape = _moe_route_shape(item.moe_routes) - if shape is not None: - return shape - return None - - -def _moe_route_shape(raw: MoeRouteArray | MoeRouteSegments) -> tuple[int, int] | None: - if isinstance(raw, MoeRouteSegments): - return int(raw.shape[1]), int(raw.shape[2]) - routes = _coerce_moe_routes(raw) - if routes.shape[0] == 0: - return None - return int(routes.shape[1]), int(routes.shape[2]) +def _moe_route_contract( + rows: list[list[_PrefixTreePackItem]], +) -> tuple[int, int, int] | None: + contracts = { + ( + routes.num_experts, + int(routes.shape[1]), + int(routes.shape[2]), + ) + for row in rows + for item in row + if (routes := item.moe_routes) is not None and routes.shape[0] > 0 + } + if len(contracts) > 1: + raise RuntimeError("Packed MoE routes must share one exact contract") + return next(iter(contracts), None) def _coerce_moe_routes(raw: MoeRouteArray | MoeRouteSegments) -> MoeRouteArray: - if not isinstance(raw, np.ndarray): + if not isinstance(raw, MoeRouteArray): raise RuntimeError(f"Expected MoE routes array, got {type(raw)}") - routes = np.asarray(raw, dtype=np.int32) - if routes.ndim != 3 or routes.shape[1] <= 0 or routes.shape[2] <= 0: - raise RuntimeError(f"Packed MoE routes must be rank 3, got {routes.shape}") - return routes + if raw.dtype != moe_route_dtype(raw.num_experts): + raise RuntimeError("Packed MoE routes use the wrong exact ID dtype") + return raw def _copy_moe_route_slice( *, route_tensor: np.ndarray, - route_mask: np.ndarray, dst_start: int, src_start: int, src_end: int, @@ -952,12 +829,9 @@ def _copy_moe_route_slice( if tuple(segment.shape[1:]) != route_shape: raise RuntimeError("Packed MoE routes must have one rectangular shape") segment_dst_start = dst_start + segment_start - src_start - _copy_valid_moe_route_chunk( - route_tensor=route_tensor, - route_mask=route_mask, - dst_start=segment_dst_start, - routes=segment, - assume_valid=True, + segment_dst_end = segment_dst_start + int(segment.shape[0]) + route_tensor[:, segment_dst_start:segment_dst_end] = np.moveaxis( + segment, 1, 0 ) covered_until = segment_start + int(segment.shape[0]) if covered_until != src_end: @@ -968,93 +842,14 @@ def _copy_moe_route_slice( route_slice = routes[src_start:src_end] if tuple(route_slice.shape[1:]) != route_shape: raise RuntimeError("Packed MoE routes must have one rectangular shape") - _copy_valid_moe_route_chunk( - route_tensor=route_tensor, - route_mask=route_mask, - dst_start=dst_start, - routes=route_slice, - ) - - -def _copy_valid_moe_route_chunk( - *, - route_tensor: np.ndarray, - route_mask: np.ndarray, - dst_start: int, - routes: np.ndarray, - assume_valid: bool = False, -) -> None: - if int(routes.shape[0]) == 0: - return - if assume_valid: - dst_end = dst_start + int(routes.shape[0]) - route_tensor[dst_start:dst_end] = routes - route_mask[dst_start:dst_end] = True - return - valid = np.all(routes != MISSING_EXPERT_ID, axis=(1, 2)) - if not bool(valid.any()): - return - if bool(valid.all()): - dst_end = dst_start + int(routes.shape[0]) - route_tensor[dst_start:dst_end] = routes - route_mask[dst_start:dst_end] = True - return - valid_offsets = np.nonzero(valid)[0] - route_tensor[dst_start + valid_offsets] = routes[valid_offsets] - route_mask[dst_start + valid_offsets] = True - - -def _copy_source_moe_route( - *, - route_tensor: np.ndarray, - route_mask: np.ndarray, - dst_index: int, - source_index: int, - raw_routes: MoeRouteArray | MoeRouteSegments, - route_shape: tuple[int, int], -) -> int: - if isinstance(raw_routes, MoeRouteSegments): - for segment_start, segment in raw_routes.iter_slices( - source_index, source_index + 1 - ): - if tuple(segment.shape[1:]) != route_shape: - raise RuntimeError("Packed MoE routes must have one rectangular shape") - route = segment[source_index - segment_start] - return _copy_valid_moe_route( - route_tensor=route_tensor, - route_mask=route_mask, - dst_index=dst_index, - route=route, - ) - raise RuntimeError(f"Segmented MoE routes did not cover row {source_index}") - - routes = _coerce_moe_routes(raw_routes) - route = routes[source_index] - if tuple(route.shape) != route_shape: - raise RuntimeError("Packed MoE routes must have one rectangular shape") - return _copy_valid_moe_route( - route_tensor=route_tensor, - route_mask=route_mask, - dst_index=dst_index, - route=route, + dst_end = dst_start + int(route_slice.shape[0]) + route_tensor[:, dst_start:dst_end] = np.moveaxis( + route_slice, + 1, + 0, ) -def _copy_valid_moe_route( - *, - route_tensor: np.ndarray, - route_mask: np.ndarray, - dst_index: int, - route: np.ndarray, -) -> int: - valid = bool(np.all(route != MISSING_EXPERT_ID)) - if not valid: - return 0 - route_tensor[dst_index] = route - route_mask[dst_index] = True - return int(route.max()) if route.size else 0 - - def packed_tensors_from_dir(**kwargs: Unpack[DiskPackedTensors]) -> PackedTensors: os.makedirs(kwargs["dir"], exist_ok=True) packed_tensors = { diff --git a/src/art/preprocessing/policy_spans.py b/src/art/preprocessing/policy_spans.py index 856400880..4372fe854 100644 --- a/src/art/preprocessing/policy_spans.py +++ b/src/art/preprocessing/policy_spans.py @@ -10,6 +10,14 @@ class PolicyTokenSpan(BaseModel): + """Half-open completion-token interval scored by one executing policy state. + + The version identifies the adapter used by the target model execution that + produced the returned token and logprob, not request admission or response + delivery. Adjacent intervals may merge only when all policy identity fields + match. + """ + model_config = ConfigDict(extra="forbid") start_token: int = Field(ge=0) diff --git a/src/art/preprocessing/tokenize.py b/src/art/preprocessing/tokenize.py index 4828da26f..678490e77 100644 --- a/src/art/preprocessing/tokenize.py +++ b/src/art/preprocessing/tokenize.py @@ -7,6 +7,7 @@ import json import math import random +import re from typing import TYPE_CHECKING, Any, Generator, Literal, Protocol, cast import numpy as np @@ -31,6 +32,7 @@ from ..trajectories._selection import ModelSelector, resolve_training_model from ..types import MessagesAndChoices from ..utils.chat_template import ( + TOOL_CALL_ARGUMENTS_AS_MAPPING_ATTR, default_chat_template_kwargs_for_tokenizer, merge_chat_template_kwargs, ) @@ -240,13 +242,20 @@ def _slice_moe_routes( if start <= 0: return routes if start >= routes.shape[0]: - return np.empty((0, routes.shape[1], routes.shape[2]), dtype=np.int32) + return MoeRouteArray( + np.empty( + (0, routes.shape[1], routes.shape[2]), + dtype=routes.segments[0].dtype, + ), + num_experts=routes.num_experts, + validate=False, + ) return MoeRouteSegments( segments=tuple( segment for _, segment in routes.iter_slices(start, routes.shape[0]) ) ) - return routes[start:] + return cast(MoeRouteArray, routes[start:]) class _TokenDecoder(Protocol): @@ -325,7 +334,14 @@ def _normalize_tool_call_arguments_for_chat_template( ) -> list[dict[str, Any]]: chat_template = tokenizer.chat_template assert isinstance(chat_template, str) - if "tool_call.arguments|items" not in chat_template: + aliases = re.findall( + r"{%\s*set\s+([A-Za-z_]\w*)\s*=\s*(?:[A-Za-z_]\w*\.)+arguments\s*%}", + chat_template, + ) + if not getattr(tokenizer, TOOL_CALL_ARGUMENTS_AS_MAPPING_ATTR, False) and ( + "tool_call.arguments|items" not in chat_template + and not any(f"{alias}.items()" in chat_template for alias in aliases) + ): return messages normalized_messages: list[dict[str, Any]] = [] @@ -720,7 +736,7 @@ def tokenize_trajectory_groups( model: ModelSelector | str | None = None, _max_sequence_length: int | None = None, ) -> Generator["TokenizedResult", None, None]: - for group in trajectory_groups: + for prompt_id, group in enumerate(trajectory_groups): if not group: continue results: list[TokenizedResult] = [] @@ -900,8 +916,6 @@ def tokenize_trajectory_groups( for result in trajectory_results: result.weight = weight results.extend(trajectory_results) - # Choose a random prompt id - prompt_id = random.randint(-(2**63), 2**63 - 1) # Find the longest shared prefix # TODO: Potentially support multiple prompts per group # Initial thought is to sort the results by token_ids and then @@ -930,7 +944,7 @@ def tokenize_trajectory_groups( result.prompt_id = prompt_id result.prompt_length = prompt_length if shuffle_group_trajectories: - random.shuffle(results) + random.Random(prompt_id).shuffle(results) yield from results diff --git a/src/art/serving_capabilities.py b/src/art/serving_capabilities.py index 6c6649701..16cd253d1 100644 --- a/src/art/serving_capabilities.py +++ b/src/art/serving_capabilities.py @@ -1,7 +1,17 @@ +from ipaddress import ip_address from typing import Literal import httpx -from pydantic import BaseModel, ConfigDict +from pydantic import ( + AnyHttpUrl, + BaseModel, + ConfigDict, + Field, + FiniteFloat, + model_validator, +) + +ART_SERVING_PROTOCOL_VERSION = 4 ServingFeature = Literal[ "binary_routed_experts", @@ -12,17 +22,58 @@ ] +class FastMetricsEndpoint(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + url: AnyHttpUrl + + @model_validator(mode="after") + def _validate_url(self) -> "FastMetricsEndpoint": + host = self.url.host + if host is None: + raise ValueError("fast metrics URL must include a host") + try: + unspecified = ip_address(host.strip("[]")).is_unspecified + except ValueError: + unspecified = False + if unspecified: + raise ValueError("fast metrics URL must not use an unspecified host") + return self + + +class FastMetricsSnapshot(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True, strict=True) + + schema_version: Literal[1] + source: Literal["art_vllm_runtime"] + last_update_unix_s: FiniteFloat = Field(ge=0) + record_count: int = Field(ge=0) + engine_count: int = Field(ge=0) + metrics: dict[str, FiniteFloat] + process_uuid: str = Field(min_length=1) + generation: int = Field(ge=0) + + class ServingCapabilities(BaseModel): model_config = ConfigDict(extra="forbid", frozen=True) runtime: Literal["openai_compatible", "art_vllm"] protocol_version: int binary_routed_experts: bool = False - fast_metrics: bool = False + fast_metrics: FastMetricsEndpoint | None = None inplace_lora_load: bool = False in_flight_lora_updates: bool = False policy_token_spans: bool = False + @model_validator(mode="after") + def _validate_protocol(self) -> "ServingCapabilities": + expected = ART_SERVING_PROTOCOL_VERSION if self.runtime == "art_vllm" else 0 + if self.protocol_version != expected: + raise ValueError( + f"{self.runtime} serving protocol must be version {expected}" + ) + return self + @classmethod def openai_compatible(cls) -> "ServingCapabilities": return cls(runtime="openai_compatible", protocol_version=0) diff --git a/src/art/tinker/backend.py b/src/art/tinker/backend.py index 4a30d54c5..3189f3f92 100644 --- a/src/art/tinker/backend.py +++ b/src/art/tinker/backend.py @@ -71,11 +71,14 @@ async def _get_service(self, model: TrainableModel) -> ModelService: TinkerTrainingClientArgs, config["tinker_args"].get("training_client_args") or {}, ) - self._services[storage_key] = TinkerService( - model_name=model.name, - base_model=model.base_model, - config=config, - output_dir=get_model_dir(model=model, art_path=self._path), + self._services[storage_key] = cast( + ModelService, + TinkerService( + model_name=model.name, + base_model=model.base_model, + config=config, + output_dir=get_model_dir(model=model, art_path=self._path), + ), ) if not self._in_process: self._services[storage_key] = move_to_child_process( diff --git a/src/art/trainer_rank/__init__.py b/src/art/trainer_rank/__init__.py index 15c413f56..6037506a3 100644 --- a/src/art/trainer_rank/__init__.py +++ b/src/art/trainer_rank/__init__.py @@ -1,28 +1,14 @@ from __future__ import annotations -from collections.abc import Callable, Iterable, Iterator, Mapping, Sequence -from typing import TYPE_CHECKING, Literal, TypedDict, cast, overload +import asyncio +from collections.abc import Callable, Iterable, Iterator, Sequence +from typing import TYPE_CHECKING, Literal, cast, overload import torch import torch.distributed as dist - -class TrainerRankOptimizerLayout(TypedDict): - parallel: tuple[int, int, int, int, int, int, int, int] - parameters: tuple[ - tuple[tuple[int, ...], str, str, bool, int | None, str, tuple[int, ...]], - ..., - ] - - -class TrainerRankOptimizerState(TypedDict): - format_version: Literal[1] - layout: TrainerRankOptimizerLayout - master_params: tuple[torch.Tensor, ...] - optimizer: dict[str, object] - - -from . import _impl # noqa: E402 +from . import _impl +from ._checkpoint import CheckpointManifest, materialize_lora, validate_checkpoint AdapterSelection = _impl.AdapterSelection AdamParams = _impl.AdamParams @@ -42,7 +28,8 @@ class TrainerRankOptimizerState(TypedDict): TrainerRankMemoryError = _impl.TrainerRankMemoryError TrainerRankSlotStateError = _impl.TrainerRankSlotStateError Unset = _impl.Unset -_PushedSlot = _impl._PushedSlot +MaterializedCheckpoint = _impl.MaterializedCheckpoint +PushedCheckpoint = _impl.PushedCheckpoint if TYPE_CHECKING: from art.megatron.train import TrainingRuntime @@ -56,9 +43,9 @@ class TrainerRankOptimizerState(TypedDict): MicroBatchStats, TopK, TrainerRankMemoryError, - TrainerRankOptimizerLayout, - TrainerRankOptimizerState, TrainerRankSlotStateError, + MaterializedCheckpoint, + PushedCheckpoint, ): _public_type.__module__ = __name__ del _public_type @@ -85,55 +72,51 @@ def __init__( def zero_grad(self) -> None: super().zero_grad() - def set_checkpoint(self, name: str | None) -> None: - super().set_checkpoint(name) + def prefetch_checkpoints( + self, + *checkpoints: str | MaterializedCheckpoint, + ) -> asyncio.Task[None]: + return super().prefetch_checkpoints(*checkpoints) - def set_lora(self, name: str | None) -> None: - super().set_lora(name) + def load_checkpoint( + self, checkpoint: str | MaterializedCheckpoint | None + ) -> asyncio.Task[None]: + return super().load_checkpoint(checkpoint) - def push_checkpoint(self, name: str | None) -> _PushedSlot: - return super().push_checkpoint(name) + def push_checkpoint( + self, checkpoint: str | MaterializedCheckpoint | None + ) -> PushedCheckpoint: + return super().push_checkpoint(checkpoint) - def push_lora(self, name: str | None) -> _PushedSlot: - return super().push_lora(name) + def pop_checkpoint(self) -> None: + super().pop_checkpoint() - def pop_pushed_lora_or_checkpoint(self) -> None: - super().pop_pushed_lora_or_checkpoint() + def save_checkpoint( + self, + output_dir: str, + checkpoint_path: str | Literal["active"] = "active", + ) -> None: + super().save_checkpoint(output_dir, checkpoint_path) - def load_checkpoint_slot( + def prepare_checkpoint_save( self, - name: str, - adapter_model: Mapping[str, torch.Tensor], - *, - optimizer_state: TrainerRankOptimizerState | None = None, - alpha: float | None = None, - adapter_config: Mapping[str, object] | None = None, - ) -> int: - return super().load_checkpoint_slot( - name, - adapter_model, - optimizer_state=optimizer_state, - alpha=alpha, - adapter_config=adapter_config, - ) + output_dir: str, + checkpoint_path: str | Literal["active"] = "active", + ) -> None: + super().prepare_checkpoint_save(output_dir, checkpoint_path) - def checkpoint_slot_optimizer_state( - self, name: str - ) -> TrainerRankOptimizerState | None: - return super().checkpoint_slot_optimizer_state(name) + def finish_checkpoint_save(self, output_dir: str) -> None: + super().finish_checkpoint_save(output_dir) - def save_checkpoint_slot_lora(self, name: str, output_dir: str) -> None: - """Collectively publish a trained checkpoint slot as a vLLM LoRA.""" - super().save_checkpoint_slot_lora(name, output_dir) + def abort_checkpoint_save(self, output_dir: str) -> None: + super().abort_checkpoint_save(output_dir) - def load_lora_slot( + def export_lora( self, - name: str, - adapter_model: Mapping[str, torch.Tensor], - *, - alpha: float | None = None, + output_dir: str, + checkpoint_path: str | Literal["active"] = "active", ) -> int: - return super().load_lora_slot(name, adapter_model, alpha=alpha) + return super().export_lora(output_dir, checkpoint_path) @overload def forward_micro_batches( @@ -283,15 +266,18 @@ def optim_step( __all__ = [ "AdapterSelection", "AdamParams", + "CheckpointManifest", "ForwardInput", "ForwardOutput", "MicroBatch", "MicroBatchStats", + "MaterializedCheckpoint", + "materialize_lora", "TopK", "TrainerRank", "TrainerRankMemoryError", - "TrainerRankOptimizerLayout", - "TrainerRankOptimizerState", + "PushedCheckpoint", "TrainerRankSlotStateError", "Unset", + "validate_checkpoint", ] diff --git a/src/art/trainer_rank/_checkpoint.py b/src/art/trainer_rank/_checkpoint.py new file mode 100644 index 000000000..4ab7a01cd --- /dev/null +++ b/src/art/trainer_rank/_checkpoint.py @@ -0,0 +1,1387 @@ +"""Topology-portable persistence for TrainerRank checkpoints.""" + +from __future__ import annotations + +from collections.abc import Callable, Iterable, Mapping, Sequence +from copy import deepcopy +from dataclasses import dataclass +import hashlib +import importlib +import json +import os +from pathlib import Path, PurePosixPath, PureWindowsPath +import shutil +import struct +import threading +from typing import TYPE_CHECKING, Literal, TypedDict, cast +import uuid + +import torch +import torch.distributed as dist + +if TYPE_CHECKING: + from art.megatron.lora import LoRA, LoraShardMeta, LoRASlotRef + from art.trainer_rank._impl import ( + TrainerRank, + _AdapterConfig, + _DynamicOptimizer, + ) + +FORMAT = 1 +MANIFEST_FILE = "checkpoint.json" +_ART_FORMAT_KEY = "art_lora_format" +_ART_FORMAT = "art-trainer-rank-v1" + + +class OptimizerConfig(TypedDict): + learning_rate: float + beta1: float + beta2: float + eps: float + weight_decay: float + + +class CheckpointManifest(TypedDict): + format_version: Literal[1] + base_model_name_or_path: str + optimizer: OptimizerConfig | None + parameters: dict[str, list[str]] + steps: dict[str, float] + files: dict[str, str] + digest: str + + +@dataclass(frozen=True) +class PreparedCheckpoint: + path: Path + config: dict[str, object] + keys: tuple[str, ...] + manifest: CheckpointManifest | None + digest: str + + +@dataclass(frozen=True) +class LocalOptimizerState: + masters: tuple[torch.Tensor, ...] + exp_avgs: tuple[torch.Tensor, ...] + exp_avg_sqs: tuple[torch.Tensor, ...] + steps: tuple[float, ...] + config: OptimizerConfig + + +@dataclass(frozen=True) +class _LocalShard: + metadata: LoraShardMeta + file: str + + +@dataclass(frozen=True) +class _PreparedSave: + sequence: int + snapshot: Path + reservation: Path + destination: Path + config: dict[str, object] + shards: tuple[_LocalShard, ...] + optimizer: OptimizerConfig | None + + +@dataclass(frozen=True) +class _FinalizedSave: + sequence: int + outcome: Literal["finish", "abort"] + + +type _SlotSnapshot = tuple[ + tuple[ + "LoRA", + dict["LoRASlotRef", str], + dict[str, torch.nn.Module], + dict[str, "LoRASlotRef"], + ], + ..., +] + + +def _distributed() -> bool: + return dist.is_available() and dist.is_initialized() + + +def _rank() -> int: + return dist.get_rank() if _distributed() else 0 + + +def _gather[T](value: T, group: dist.ProcessGroup | None = None) -> tuple[T, ...]: + if not _distributed(): + return (value,) + values: list[T | None] = [None] * dist.get_world_size(group) + dist.all_gather_object(values, value, group=group) + return tuple(cast(T, item) for item in values) + + +def raise_distributed( + error: BaseException | None, + phase: str, + group: dist.ProcessGroup | None = None, +) -> None: + errors = _gather(None if error is None else repr(error), group) + if not any(errors): + return + if error is not None: + raise error + raise RuntimeError( + f"Another rank failed to {phase}: {next(item for item in errors if item)}" + ) + + +def _safe_relative(value: str) -> PurePosixPath: + path = PurePosixPath(value) + if ( + not value + or path.is_absolute() + or ".." in path.parts + or PureWindowsPath(value).drive + or "\\" in value + ): + raise RuntimeError(f"Unsafe checkpoint path: {value!r}") + return path + + +def _hash_files(root: Path, files: Iterable[str], *, seed: bytes = b"") -> str: + digest = hashlib.blake2b(digest_size=32) + digest.update(seed) + for relative in sorted(files): + digest.update(relative.encode()) + with (root / _safe_relative(relative)).open("rb") as handle: + while chunk := handle.read(8 * 1024 * 1024): + digest.update(chunk) + return digest.hexdigest() + + +def _manifest_seed(manifest: Mapping[str, object]) -> bytes: + value = {**manifest, "digest": ""} + return json.dumps(value, sort_keys=True, separators=(",", ":")).encode() + + +def _file_digest(path: Path) -> str: + digest = hashlib.blake2b(digest_size=32) + with path.open("rb") as handle: + while chunk := handle.read(8 * 1024 * 1024): + digest.update(chunk) + return digest.hexdigest() + + +def _manifest_digest(manifest: Mapping[str, object]) -> str: + return hashlib.blake2b(_manifest_seed(manifest), digest_size=32).hexdigest() + + +def _validate_manifest( + manifest: CheckpointManifest, + *, + adapter_keys: set[str], + config: Mapping[str, object], +) -> set[str]: + if manifest.get("format_version") != FORMAT: + raise RuntimeError("Unsupported ART checkpoint format") + digest = manifest.get("digest") + file_digests = manifest.get("files") + if ( + not isinstance(digest, str) + or len(digest) != 64 + or any(character not in "0123456789abcdef" for character in digest) + or not isinstance(file_digests, dict) + or any( + not isinstance(path, str) + or not isinstance(value, str) + or len(value) != 64 + or any(character not in "0123456789abcdef" for character in value) + for path, value in file_digests.items() + ) + ): + raise RuntimeError("Checkpoint digest is invalid") + if manifest.get("base_model_name_or_path") != config.get("base_model_name_or_path"): + raise RuntimeError( + "Checkpoint manifest and adapter config name different models" + ) + optimizer = manifest.get("optimizer") + parameters = manifest.get("parameters") + steps = manifest.get("steps") + if not isinstance(parameters, dict) or not isinstance(steps, dict): + raise RuntimeError("Checkpoint optimizer mapping is invalid") + files: set[str] = set() + if optimizer is None: + if parameters or steps: + raise RuntimeError("LoRA-only checkpoint contains optimizer metadata") + else: + required = {"learning_rate", "beta1", "beta2", "eps", "weight_decay"} + optimizer_values = cast(dict[str, object], optimizer) + if ( + not isinstance(optimizer, dict) + or set(optimizer_values) != required + or any( + not isinstance(optimizer_values[key], int | float) + or isinstance(optimizer_values[key], bool) + for key in required + ) + ): + raise RuntimeError("Checkpoint optimizer config is invalid") + if set(parameters) != adapter_keys or set(steps) != adapter_keys: + raise RuntimeError( + "Checkpoint optimizer mapping differs from adapter tensors: " + f"parameters={sorted(set(parameters) ^ adapter_keys)[:8]} " + f"steps={sorted(set(steps) ^ adapter_keys)[:8]}" + ) + for key, record in parameters.items(): + if ( + not isinstance(key, str) + or not isinstance(record, list | tuple) + or len(record) != 3 + or not all(isinstance(item, str) for item in record) + ): + raise RuntimeError( + f"Checkpoint optimizer mapping is invalid for {key!r}" + ) + normalized = [_safe_relative(item).as_posix() for item in record] + parameters[key] = normalized + files.update(normalized) + if any( + not isinstance(value, int | float) or isinstance(value, bool) + for value in steps.values() + ): + raise RuntimeError("Checkpoint optimizer steps are invalid") + expected_files = { + "adapter_config.json", + "adapter_model.safetensors", + *files, + } + if set(file_digests) != expected_files: + raise RuntimeError("Checkpoint file digest mapping is invalid") + return files + + +def prepare_checkpoint( + path: str, *, artifact_entries: Iterable[str] | None = None +) -> PreparedCheckpoint: + root = Path(path).resolve(strict=True) + if not root.is_dir(): + raise FileNotFoundError(f"Checkpoint is not a directory: {path}") + from art.megatron.model_support.lora_disk import load_adapter_config, safe_open + + config = cast(dict[str, object], load_adapter_config(root)) + adapter = root / "adapter_model.safetensors" + with safe_open(adapter, framework="pt") as handle: + keys = tuple(sorted(handle.keys())) + manifest_path = root / MANIFEST_FILE + manifest: CheckpointManifest | None = None + if manifest_path.is_file(): + value = json.loads(manifest_path.read_text()) + if not isinstance(value, dict) or value.get("format_version") != FORMAT: + raise RuntimeError("Unsupported ART checkpoint format") + manifest = cast(CheckpointManifest, value) + if config.get(_ART_FORMAT_KEY) != _ART_FORMAT: + raise RuntimeError("Canonical checkpoint adapter format is invalid") + optimizer_files = _validate_manifest( + manifest, adapter_keys=set(keys), config=config + ) + files = { + "adapter_config.json", + "adapter_model.safetensors", + MANIFEST_FILE, + *optimizer_files, + } + expected = manifest["digest"] + actual = _manifest_digest(manifest) + if actual != expected: + raise RuntimeError(f"Checkpoint digest mismatch: {actual} != {expected}") + if artifact_entries is None: + downloaded = files - {MANIFEST_FILE} + else: + available = {_safe_relative(entry).as_posix() for entry in artifact_entries} + if missing := sorted(files - available): + raise RuntimeError( + f"Checkpoint artifact is missing entries: {missing[:8]}" + ) + downloaded = {"adapter_config.json", "adapter_model.safetensors"} + for relative in downloaded: + file_actual = _file_digest(root / relative) + if file_actual != manifest["files"][relative]: + raise RuntimeError( + f"Checkpoint file digest mismatch for {relative}: " + f"{file_actual} != {manifest['files'][relative]}" + ) + else: + if artifact_entries is not None: + raise RuntimeError("Checkpoint artifact lacks a canonical manifest") + actual = _hash_files(root, ("adapter_config.json", "adapter_model.safetensors")) + return PreparedCheckpoint(root, config, keys, manifest, actual) + + +def validate_checkpoint( + path: str | Path, *, require_optimizer: bool = False +) -> CheckpointManifest | None: + prepared = prepare_checkpoint(str(path)) + if require_optimizer and ( + prepared.manifest is None or prepared.manifest["optimizer"] is None + ): + raise RuntimeError("Checkpoint does not contain optimizer state") + return prepared.manifest + + +def materialize_lora( + path: str | Path, + output_dir: str | Path, + *, + require_optimizer: bool = False, + artifact_entries: Iterable[str] | None = None, + expected_digest: str | None = None, +) -> None: + source = prepare_checkpoint(str(path), artifact_entries=artifact_entries) + if expected_digest is not None and source.digest != expected_digest: + raise RuntimeError( + f"Checkpoint digest mismatch: {source.digest} != {expected_digest}" + ) + if require_optimizer and ( + source.manifest is None or source.manifest["optimizer"] is None + ): + raise RuntimeError("Checkpoint does not contain optimizer state") + destination = Path(output_dir) + if destination.exists() and any(destination.iterdir()): + raise FileExistsError(f"LoRA output directory is not empty: {destination}") + destination.mkdir(parents=True, exist_ok=True) + for name in ("adapter_config.json", "adapter_model.safetensors"): + shutil.copy2(source.path / name, destination / name) + from art.megatron.model_support.lora_disk import normalize_lora_checkpoint_to_vllm + + normalize_lora_checkpoint_to_vllm(destination) + + +def _optimizer_config(dynamic: _DynamicOptimizer) -> OptimizerConfig: + group = dynamic.optimizer.param_groups[0] + beta1, beta2 = group["betas"] + return { + "learning_rate": float(group["lr"]), + "beta1": float(beta1), + "beta2": float(beta2), + "eps": float(group["eps"]), + "weight_decay": float(group["weight_decay"]), + } + + +def _validate_save_state(trainer: TrainerRank, name: str) -> _AdapterConfig: + slot = trainer._checkpoint_slots.get(name) + if slot is None or slot.config is None: + raise trainer._slot_state_error(f"Unknown checkpoint: {name!r}") + if trainer._checkpoint_grad_flags((name,))[0]: + raise trainer._slot_state_error( + f"Checkpoint {name!r} has accumulated gradients" + ) + return slot.config + + +def _local_state( + trainer: TrainerRank, name: str, snapshot: Path +) -> tuple[tuple[_LocalShard, ...], OptimizerConfig | None]: + from art.megatron.lora import LoRA + from art.megatron.weights.lora_publish import collect_local_lora_entries + + ref = trainer._slot_ref(name) + tensors, metadata = collect_local_lora_entries( + trainer.runtime.model, {}, owner_rank=_rank(), slot_ref=ref + ) + dynamic = trainer._checkpoint_slots[name].optimizer + optimizer = None if dynamic is None else _optimizer_config(dynamic) + masters = ( + {} + if dynamic is None + else { + id(param): master + for param, master in zip( + trainer._checkpoint_slots[name].params, + dynamic.master_params, + strict=True, + ) + } + ) + by_key = {item.key: item for item in metadata} + payloads: dict[str, dict[str, torch.Tensor]] = {} + metadata_by_block: dict[str, list[LoraShardMeta]] = {} + for item in metadata: + payloads.setdefault(item.block, {})[f"lora/{item.key}"] = ( + tensors[item.key].cpu().contiguous() + ) + metadata_by_block.setdefault(item.block, []).append(item) + if dynamic is not None: + for chunk in trainer.runtime.model: + for module in chunk.modules(): + if not isinstance(module, LoRA): + continue + for key, param, expert in module._export_items(ref): + item = by_key.get(key) + if item is None: + continue + master = masters[id(param)] + state = dynamic.optimizer.state.get(master, {}) + values = ( + master, + cast(torch.Tensor | None, state.get("exp_avg")), + cast(torch.Tensor | None, state.get("exp_avg_sq")), + ) + for component, value in zip( + ("master", "exp_avg", "exp_avg_sq"), values, strict=True + ): + value = torch.zeros_like(master) if value is None else value + local = value if expert is None else value[expert] + payloads[item.block][f"{component}/{key}"] = ( + local.T.float().cpu().contiguous() + ) + step = state.get("step", 0.0) + payloads[item.block][f"step/{key}"] = torch.tensor(float(step)) + records: list[_LocalShard] = [] + for index, block in enumerate(sorted(payloads)): + relative = f"block-{index:06d}.safetensors" + importlib.import_module("safetensors.torch").save_file( + payloads[block], snapshot / relative + ) + records.extend(_LocalShard(item, relative) for item in metadata_by_block[block]) + return tuple(records), optimizer + + +def prepare_checkpoint_save( + trainer: TrainerRank, output_dir: str, checkpoint_name: str +) -> None: + with trainer._checkpoint_prepare_lock: + group = _ensure_group(trainer) + identity = (output_dir, checkpoint_name) + if any(value != identity for value in _gather(identity, group)): + raise RuntimeError("Checkpoint save identity differs across ranks") + with trainer._checkpoint_save_condition: + pending = ( + output_dir in trainer._checkpoint_preparing_saves + or output_dir in trainer._prepared_checkpoint_saves + ) + if any(value != pending for value in _gather(pending, group)): + raise RuntimeError( + f"Checkpoint save state differs across ranks: {output_dir}" + ) + if pending: + raise RuntimeError(f"Checkpoint save is already pending: {output_dir}") + with trainer._checkpoint_save_condition: + trainer._checkpoint_preparing_saves.add(output_dir) + try: + known = ( + checkpoint_name in trainer._checkpoint_slots + and trainer._checkpoint_slots[checkpoint_name].config is not None + ) + if not all(_gather(known, group)): + raise trainer._slot_state_error( + f"Unknown checkpoint on at least one rank: {checkpoint_name!r}" + ) + config = deepcopy(_validate_save_state(trainer, checkpoint_name)) + if any(value != config for value in _gather(config, group)): + raise trainer._slot_state_error( + f"Checkpoint {checkpoint_name!r} configuration differs across ranks" + ) + except BaseException: + with trainer._checkpoint_save_condition: + trainer._checkpoint_preparing_saves.discard(output_dir) + raise + destination = Path(output_dir) + reservation = destination.with_name(f".{destination.name}.reserved") + snapshot = destination.with_name( + f".{destination.name}.snapshot-r{_rank()}-{uuid.uuid4().hex}" + ) + error: BaseException | None = None + prepared: _PreparedSave | None = None + shards: tuple[_LocalShard, ...] | None = None + optimizer: OptimizerConfig | None = None + reservation_created = False + with trainer._checkpoint_save_condition: + sequence = trainer._checkpoint_save_sequence + trainer._checkpoint_save_sequence += 1 + if any(value != sequence for value in _gather(sequence, group)): + with trainer._checkpoint_save_condition: + trainer._checkpoint_preparing_saves.discard(output_dir) + with trainer._checkpoint_save_condition: + trainer._checkpoint_save_skipped.add(sequence) + _advance_save_queue(trainer, sequence) + raise RuntimeError("Checkpoint save order differs across ranks") + try: + if _rank() == 0: + reservation.mkdir(parents=True) + reservation_created = True + snapshot.mkdir(parents=True) + shards, optimizer = _local_state(trainer, checkpoint_name, snapshot) + except BaseException as exc: + error = exc + try: + raise_distributed(error, "prepare checkpoint", group) + if any(value != optimizer for value in _gather(optimizer, group)): + raise trainer._slot_state_error( + f"Checkpoint {checkpoint_name!r} optimizer differs across ranks" + ) + assert shards is not None + prepared = _PreparedSave( + sequence, + snapshot, + reservation, + destination, + dict(config), + shards, + optimizer, + ) + except BaseException as failure: + cleanup = _cleanup_paths( + [snapshot, *([reservation] if reservation_created else [])] + ) + with trainer._checkpoint_save_condition: + trainer._checkpoint_preparing_saves.discard(output_dir) + trainer._checkpoint_save_skipped.add(sequence) + _advance_save_queue(trainer, sequence) + cleanup_failure: BaseException | None = None + try: + raise_distributed(cleanup, "clean up checkpoint preparation", group) + except BaseException as exc: + cleanup_failure = exc + if cleanup_failure is not None: + raise BaseExceptionGroup( + "checkpoint preparation and cleanup both failed", + [failure, cleanup_failure], + ) from None + raise failure + assert prepared is not None + with trainer._checkpoint_save_condition: + trainer._prepared_checkpoint_saves[output_dir] = prepared + trainer._finalized_checkpoint_saves.pop(output_dir, None) + trainer._checkpoint_preparing_saves.discard(output_dir) + trainer._checkpoint_save_condition.notify_all() + + +def _read_snapshot( + prepared: _PreparedSave, relative: str, prefix: str, keys: Iterable[str] +) -> dict[str, torch.Tensor]: + load = importlib.import_module("safetensors.torch").load_file + payload = load(prepared.snapshot / relative) + return {key: payload[f"{prefix}/{key}"] for key in keys} + + +def _merge_component( + prepared: _PreparedSave, + metadata: Sequence[LoraShardMeta], + component: str, + group: dist.ProcessGroup | None, +) -> dict[str, torch.Tensor]: + from art.megatron.weights.lora_publish import merge_sharded_adapter_entries + + owned = [item for item in metadata if item.owner_rank == _rank()] + local: dict[str, torch.Tensor] = {} + error: BaseException | None = None + try: + if owned: + files = { + record.file for record in prepared.shards if record.metadata in owned + } + for relative in files: + keys = [ + item.key + for item in owned + if next( + record.file + for record in prepared.shards + if record.metadata == item + ) + == relative + ] + local.update(_read_snapshot(prepared, relative, component, keys)) + except BaseException as exc: + error = exc + raise_distributed(error, f"read checkpoint {component} block", group) + exchanged: dict[tuple[int, str], torch.Tensor] = {} + for item in sorted(metadata, key=lambda value: (value.owner_rank, value.key)): + identity = (item.owner_rank, item.key) + if _rank() == item.owner_rank: + tensor = local[item.key].contiguous() + if _rank() == 0: + exchanged[identity] = tensor + else: + dist.send(tensor, dst=0, group=group) + elif _rank() == 0: + dtype = ( + getattr(torch, item.dtype_name) + if component == "lora" + else torch.float32 + ) + tensor = torch.empty(item.shape, dtype=dtype) + dist.recv(tensor, src=item.owner_rank, group=group) + exchanged[identity] = tensor + entries: dict[str, list[tuple[dict[str, object], torch.Tensor]]] = {} + merged: dict[str, torch.Tensor] = {} + error = None + if _rank() == 0: + try: + for item in metadata: + entries.setdefault(item.key, []).append( + (item.manifest, exchanged[(item.owner_rank, item.key)]) + ) + merged = merge_sharded_adapter_entries(entries) # type: ignore[arg-type] + except BaseException as exc: + error = exc + raise_distributed(error, f"merge checkpoint {component} block", group) + return merged + + +def _consolidate(shards: Sequence[Path], output: Path) -> None: + sources: dict[str, tuple[Path, int, int, int, dict[str, object]]] = {} + for shard in shards: + with shard.open("rb") as handle: + header_size = struct.unpack(" None: + error: BaseException | None = None + if _rank() == 0: + try: + action() + except BaseException as exc: + error = exc + raise_distributed(error, phase, group) + + +def _finish(trainer: TrainerRank, prepared: _PreparedSave) -> None: + from art.megatron.model_support.lora_disk import save_adapter_config + + group = _ensure_finalize_group(trainer) + metadata = [item for values in _gather(prepared.shards, group) for item in values] + identities: set[tuple[str, int]] = set() + selected: list[LoraShardMeta] = [] + for item in sorted(metadata, key=lambda value: value.metadata.owner_rank): + identity = (item.metadata.key, int(item.metadata.manifest.get("shard_rank", 0))) + if identity not in identities: + identities.add(identity) + selected.append(item.metadata) + blocks = sorted({item.block for item in selected}) + temporary = prepared.destination.with_name( + f".{prepared.destination.name}.tmp-{uuid.uuid4().hex}" + ) + _rank_zero_phase( + lambda: temporary.mkdir(parents=True), "create checkpoint output", group + ) + parameters: dict[str, list[str]] = {} + steps: dict[str, float] = {} + lora_shards: list[Path] = [] + try: + for index, block in enumerate(blocks): + block_metadata = [item for item in selected if item.block == block] + lora = _merge_component(prepared, block_metadata, "lora", group) + relative = f".adapter-{index:06d}.safetensors" + _rank_zero_phase( + lambda: importlib.import_module("safetensors.torch").save_file( + lora, temporary / relative + ), + "write checkpoint adapter block", + group, + ) + if _rank() == 0: + lora_shards.append(temporary / relative) + if prepared.optimizer is None: + continue + files: list[str] = [] + for component in ("master", "exp_avg", "exp_avg_sq"): + tensors = _merge_component(prepared, block_metadata, component, group) + relative = f"optimizer/{component}-{index:06d}.safetensors" + + def write_optimizer_block() -> None: + (temporary / "optimizer").mkdir(exist_ok=True) + importlib.import_module("safetensors.torch").save_file( + tensors, temporary / relative + ) + + _rank_zero_phase( + write_optimizer_block, "write checkpoint optimizer block", group + ) + files.append(relative) + if _rank() == 0: + for key in (item.key for item in block_metadata): + parameters[key] = list(files) + owned = [item for item in block_metadata if item.owner_rank == _rank()] + local_steps: dict[str, float] = {} + error: BaseException | None = None + try: + for relative in { + record.file + for record in prepared.shards + if record.metadata in owned + }: + load = importlib.import_module("safetensors.torch").load_file + payload = load(prepared.snapshot / relative) + local_steps.update( + (key.removeprefix("step/"), float(value.item())) + for key, value in payload.items() + if key.startswith("step/") + ) + except BaseException as exc: + error = exc + raise_distributed(error, "read checkpoint optimizer steps", group) + step_values: dict[str, set[float]] = {} + for values in _gather(local_steps, group): + for key, value in values.items(): + step_values.setdefault(key, set()).add(value) + if mismatched := { + key: values for key, values in step_values.items() if len(values) != 1 + }: + raise trainer._slot_state_error( + f"Optimizer shard steps differ: {mismatched}" + ) + steps.update((key, values.pop()) for key, values in step_values.items()) + + def commit() -> None: + _consolidate(lora_shards, temporary / "adapter_model.safetensors") + for shard in lora_shards: + shard.unlink() + save_adapter_config( + temporary, {**prepared.config, _ART_FORMAT_KEY: _ART_FORMAT} + ) + manifest: CheckpointManifest = { + "format_version": FORMAT, + "base_model_name_or_path": str( + prepared.config["base_model_name_or_path"] + ), + "optimizer": prepared.optimizer, + "parameters": parameters, + "steps": steps, + "files": {}, + "digest": "", + } + artifact_files = { + "adapter_config.json", + "adapter_model.safetensors", + *(file for record in parameters.values() for file in record), + } + manifest["files"] = { + relative: _file_digest(temporary / relative) + for relative in artifact_files + } + manifest["digest"] = _manifest_digest(manifest) + (temporary / MANIFEST_FILE).write_text( + json.dumps(manifest, indent=2) + "\n" + ) + if prepared.destination.exists(): + if ( + prepare_checkpoint(str(prepared.destination)).digest + != manifest["digest"] + ): + raise FileExistsError( + "Checkpoint path already contains different state: " + f"{prepared.destination}" + ) + shutil.rmtree(temporary) + else: + os.replace(temporary, prepared.destination) + + _rank_zero_phase(commit, "commit checkpoint", group) + except BaseException: + if _rank() == 0: + shutil.rmtree(temporary, ignore_errors=True) + raise + + +def _advance_save_queue(trainer: TrainerRank, sequence: int) -> None: + with trainer._checkpoint_save_condition: + if sequence == trainer._checkpoint_save_next: + trainer._checkpoint_save_next += 1 + while trainer._checkpoint_save_next in trainer._checkpoint_save_skipped: + trainer._checkpoint_save_skipped.remove(trainer._checkpoint_save_next) + trainer._checkpoint_save_next += 1 + trainer._checkpoint_save_condition.notify_all() + + +def _cleanup_paths(paths: Iterable[Path]) -> BaseException | None: + errors: list[BaseException] = [] + for path in paths: + try: + shutil.rmtree(path) + except FileNotFoundError: + pass + except BaseException as exc: + errors.append(exc) + return BaseExceptionGroup("checkpoint cleanup failed", errors) if errors else None + + +def _claim_finalization( + trainer: TrainerRank, + output_dir: str, + action: Literal["finish", "abort"], +) -> _PreparedSave | None: + with trainer._checkpoint_save_condition: + while True: + prepared = trainer._prepared_checkpoint_saves.get(output_dir) + if prepared is None: + if output_dir in trainer._finalized_checkpoint_saves: + return None + if action == "abort": + return None + raise RuntimeError(f"Checkpoint save was not prepared: {output_dir}") + outcome = trainer._checkpoint_save_outcomes.get(output_dir) + if outcome is not None and outcome != action: + raise RuntimeError( + f"Checkpoint save was already {outcome}ed: {output_dir}" + ) + if output_dir in trainer._checkpoint_finalizing_saves: + trainer._checkpoint_save_condition.wait() + continue + if outcome is None and prepared.sequence != trainer._checkpoint_save_next: + raise RuntimeError( + "Checkpoint saves must be finalized in preparation order: " + f"expected sequence {trainer._checkpoint_save_next}, got " + f"{prepared.sequence}" + ) + trainer._checkpoint_finalizing_saves[output_dir] = action + return prepared + + +def _finalize_checkpoint_save( + trainer: TrainerRank, + output_dir: str, + action: Literal["finish", "abort"], +) -> None: + group = _ensure_finalize_group(trainer) + with trainer._checkpoint_finalize_lock: + with trainer._checkpoint_save_condition: + local = trainer._prepared_checkpoint_saves.get(output_dir) + finalized = trainer._finalized_checkpoint_saves.get(output_dir) + sequence = ( + local.sequence + if local is not None + else None + if finalized is None + else finalized.sequence + ) + outcome = ( + trainer._checkpoint_save_outcomes.get(output_dir) + if finalized is None + else finalized.outcome + ) + states = _gather((action, output_dir, sequence, outcome), group) + if any(not isinstance(value, tuple) or len(value) != 4 for value in states): + raise RuntimeError("Checkpoint finalization protocol is out of sync") + if any(value[:3] != states[0][:3] for value in states): + raise RuntimeError("Checkpoint save actions differ across ranks") + outcomes = {value[3] for value in states} + if len(outcomes) != 1: + raise RuntimeError("Checkpoint save outcomes differ across ranks") + if sequence is None: + if action == "abort": + return + raise RuntimeError(f"Checkpoint save was not prepared: {output_dir}") + finalized_ranks = _gather(finalized is not None, group) + if all(finalized_ranks): + if outcome == "finish" or action == "abort": + return + raise RuntimeError(f"Checkpoint save was already {outcome}ed: {output_dir}") + if outcome is not None and outcome != action: + raise RuntimeError(f"Checkpoint save was already {outcome}ed: {output_dir}") + prepared = ( + _claim_finalization(trainer, output_dir, action) + if finalized is None + else None + ) + assert prepared is not None or finalized is not None + error: BaseException | None = None + cleanup_failed = True + try: + if finalized is None and outcome is None and action == "finish": + try: + assert prepared is not None + _finish(trainer, prepared) + except BaseException as exc: + error = exc + if finalized is None and outcome is None: + assert prepared is not None + outcome = action if error is None else "abort" + with trainer._checkpoint_save_condition: + trainer._checkpoint_save_outcomes[output_dir] = outcome + if outcome == "abort": + trainer._checkpoint_save_skipped.add(prepared.sequence) + _advance_save_queue(trainer, prepared.sequence) + cleanup = None + if prepared is not None: + paths = [prepared.snapshot] + if _rank() == 0: + paths.append(prepared.reservation) + cleanup = _cleanup_paths(paths) + try: + failures = _gather( + ( + None if error is None else repr(error), + None if cleanup is None else repr(cleanup), + ), + group, + ) + except BaseException as exc: + local_failures = [ + *([error] if error is not None else []), + *([cleanup] if cleanup is not None else []), + exc, + ] + if len(local_failures) == 1: + raise local_failures[0] + raise BaseExceptionGroup( + "checkpoint finalization and coordination failed", local_failures + ) from None + if any( + not isinstance(failure, tuple) + or len(failure) != 2 + or any( + value is not None and not isinstance(value, str) + for value in failure + ) + for failure in failures + ): + raise RuntimeError("Checkpoint finalization protocol is out of sync") + cleanup_failed = any( + cleanup_error is not None for _, cleanup_error in failures + ) + local_failures = [ + *([error] if error is not None else []), + *([cleanup] if cleanup is not None else []), + ] + if local_failures: + if len(local_failures) == 1: + raise local_failures[0] + raise BaseExceptionGroup( + "checkpoint finalization failed", local_failures + ) + if remote := next((failure for failure in failures if any(failure)), None): + raise RuntimeError( + f"Another rank failed to {action} checkpoint: {remote}" + ) + finally: + with trainer._checkpoint_save_condition: + trainer._checkpoint_finalizing_saves.pop(output_dir, None) + if not cleanup_failed: + trainer._prepared_checkpoint_saves.pop(output_dir, None) + trainer._checkpoint_save_outcomes.pop(output_dir, None) + assert outcome is not None + trainer._finalized_checkpoint_saves[output_dir] = _FinalizedSave( + sequence, outcome + ) + trainer._checkpoint_save_condition.notify_all() + + +def finish_checkpoint_save(trainer: TrainerRank, output_dir: str) -> None: + _finalize_checkpoint_save(trainer, output_dir, "finish") + + +def abort_checkpoint_save(trainer: TrainerRank, output_dir: str) -> None: + _finalize_checkpoint_save(trainer, output_dir, "abort") + + +def _load_adapter( + trainer: TrainerRank, source: PreparedCheckpoint, keys: Iterable[str] +) -> dict[str, torch.Tensor]: + if source.manifest is None: + from art.megatron.model_support.lora_disk import ( + load_lora_tensors_for_megatron, + ) + + loaded = load_lora_tensors_for_megatron( + source.path, handler=trainer.runtime.model_support_handler + ) + return {key: value for key, value in loaded.items() if key in set(keys)} + safe_open = importlib.import_module("safetensors").safe_open + with safe_open(source.path / "adapter_model.safetensors", framework="pt") as handle: + available = set(handle.keys()) + return {key: handle.get_tensor(key) for key in keys if key in available} + + +def _localized( + module: LoRA, tensor: torch.Tensor, parameter: torch.nn.Parameter +) -> torch.Tensor: + return module._localized_weight(tensor, into=parameter).contiguous() + + +def _slot_snapshot(trainer: TrainerRank) -> _SlotSnapshot: + from art.megatron.lora import LoRA + + return tuple( + ( + module, + dict(module._slot_keys), + dict(module._slot_modules.items()), + { + key: cast(LoRASlotRef, getattr(slot, "ref")) + for key, slot in module._slot_modules.items() + }, + ) + for chunk in trainer.runtime.model + for module in chunk.modules() + if isinstance(module, LoRA) + ) + + +def _restore_slots(snapshot: _SlotSnapshot) -> None: + for module, keys, slots, refs in snapshot: + for key, slot in slots.items(): + setattr(slot, "ref", refs[key]) + module._slot_keys = keys + module._slot_modules = torch.nn.ModuleDict(slots) + + +def _commit_slot(trainer: TrainerRank, source: str, destination: str) -> None: + from art.megatron.lora import LoRA + + source_ref = trainer._slot_ref(source) + destination_ref = trainer._slot_ref(destination) + for chunk in trainer.runtime.model: + for module in chunk.modules(): + if not isinstance(module, LoRA): + continue + source_key = module._slot_keys.pop(source_ref, None) + destination_key = module._slot_keys.pop(destination_ref, None) + if source_key is None: + if destination_key is not None: + del module._slot_modules[destination_key] + continue + slot = module._slot_modules[source_key] + setattr(slot, "ref", destination_ref) + target_key = destination_key or source_key + module._slot_keys[destination_ref] = target_key + if target_key != source_key: + module._slot_modules[target_key] = slot + del module._slot_modules[source_key] + + +def _optimizer_state( + trainer: TrainerRank, source: PreparedCheckpoint, name: str +) -> LocalOptimizerState: + assert source.manifest is not None and source.manifest["optimizer"] is not None + from art.megatron.lora import LoRA + + ref = trainer._slot_ref(name) + components: dict[str, list[torch.Tensor]] = { + "master": [], + "exp_avg": [], + "exp_avg_sq": [], + } + steps: list[float] = [] + sites: list[tuple[LoRA, str, torch.nn.Parameter, list[str], list[list[str]]]] = [] + file_keys: dict[str, set[str]] = {} + for chunk in trainer.runtime.model: + for module in chunk.modules(): + if not isinstance(module, LoRA) or module._slot(ref) is None: + continue + for suffix, parameter in module._lora_params(ref): + suffix = suffix.removesuffix(".weight") + keys = [ + key + for key in module._expected_weight_keys(suffix) + if isinstance(key, str) + ] + records = [source.manifest["parameters"][key] for key in keys] + sites.append((module, suffix, parameter, keys, records)) + for record in records: + for filename in record: + file_keys.setdefault(filename, set()).update(keys) + safe_open = importlib.import_module("safetensors").safe_open + loaded: dict[str, dict[str, torch.Tensor]] = {} + for filename, keys in file_keys.items(): + with safe_open(source.path / filename, framework="pt") as handle: + loaded[filename] = {key: handle.get_tensor(key) for key in keys} + for module, suffix, parameter, keys, records in sites: + if not keys: + for component in components.values(): + component.append(torch.zeros_like(parameter)) + steps.append(0.0) + continue + for index, component in enumerate(components): + tensors = { + key: loaded[record[index]][key] + for key, record in zip(keys, records, strict=True) + } + full = module._adapter_weight(tensors, suffix=suffix) + components[component].append(_localized(module, full, parameter)) + key_steps = {source.manifest["steps"][key] for key in keys} + if len(key_steps) != 1: + raise RuntimeError(f"Optimizer steps differ for {keys}") + steps.append(key_steps.pop()) + return LocalOptimizerState( + tuple(components["master"]), + tuple(components["exp_avg"]), + tuple(components["exp_avg_sq"]), + tuple(steps), + source.manifest["optimizer"], + ) + + +def _phase[T]( + action: Callable[[], T], phase: str, group: dist.ProcessGroup | None +) -> T: + result: T | None = None + error: BaseException | None = None + try: + result = action() + except BaseException as exc: + error = exc + raise_distributed(error, phase, group) + return cast(T, result) + + +def _validate_base_model( + trainer: TrainerRank, + source: PreparedCheckpoint, + config: Mapping[str, object], +) -> None: + configured = str(config["base_model_name_or_path"]) + if ( + source.manifest is not None + and source.manifest["base_model_name_or_path"] != configured + ): + raise trainer._slot_state_error( + "Checkpoint manifest and adapter config name different base models" + ) + runtime_model = getattr(trainer.runtime, "model_identifier", None) + if runtime_model is not None and runtime_model != configured: + raise trainer._slot_state_error( + f"Checkpoint base model {configured!r} differs from runtime model " + f"{runtime_model!r}" + ) + supported = tuple( + getattr(getattr(trainer.runtime, "model_support_spec", None), "model_names", ()) + ) + if supported and configured not in supported: + raise trainer._slot_state_error( + f"Checkpoint base model {configured!r} is incompatible with this runtime" + ) + + +def _rollback_load( + trainer: TrainerRank, + snapshot: _SlotSnapshot, + temporary: str, + name: str, + previous: object, + group: dist.ProcessGroup | None, +) -> None: + def rollback() -> None: + _restore_slots(snapshot) + trainer._checkpoint_slots.pop(temporary, None) + if previous is None: + trainer._checkpoint_slots.pop(name, None) + else: + from art.trainer_rank._impl import _CheckpointSlot + + trainer._checkpoint_slots[name] = cast(_CheckpointSlot, previous) + + _phase(rollback, "roll back checkpoint load", group) + + +def load_checkpoint( + trainer: TrainerRank, source: PreparedCheckpoint, name: str +) -> None: + group = _ensure_group(trainer) + if any(value != source.digest for value in _gather(source.digest, group)): + raise trainer._slot_state_error( + f"Checkpoint {name!r} content differs across ranks" + ) + config = _phase( + lambda: trainer._validate_checkpoint_adapter_config( + name, source.config, alpha=None + ), + "validate checkpoint config", + group, + ) + assert config is not None + if any(value != config for value in _gather(config, group)): + raise trainer._slot_state_error( + f"Checkpoint {name!r} configuration differs across ranks" + ) + _phase( + lambda: _validate_base_model(trainer, source, config), + "validate checkpoint base model", + group, + ) + _phase( + lambda: trainer._guard_slot_can_load(trainer._slot_ref(name)), + "validate checkpoint target", + group, + ) + local_keys = trainer._local_lora_adapter_templates() + adapter = _phase( + lambda: _load_adapter(trainer, source, local_keys), + "read checkpoint adapter", + group, + ) + prepared_adapter = _phase( + lambda: trainer._prepare_adapter_model( + name, adapter, canonicalized=source.manifest is not None + ), + "localize checkpoint adapter", + group, + ) + expected = {key for keys in _gather(tuple(prepared_adapter), group) for key in keys} + if source.manifest is not None and expected != set(source.keys): + raise trainer._slot_state_error( + "Checkpoint tensor coverage differs from runtime" + ) + temporary = f"__art_loading_{uuid.uuid4().hex}" + snapshot = _slot_snapshot(trainer) + previous = trainer._checkpoint_slots.get(name) + try: + loaded = _phase( + lambda: trainer._load_checkpoint_slot( + temporary, + prepared_adapter, + alpha=float(config["lora_alpha"]), + _prepared=True, + ), + "stage checkpoint adapter", + group, + ) + params = _phase( + lambda: trainer._validate_checkpoint_consistency( + temporary, loaded, expected + ), + "validate staged checkpoint", + group, + ) + from art.trainer_rank._impl import _CheckpointSlot + + trainer._checkpoint_slots[temporary] = _CheckpointSlot(params, config) + _phase( + lambda: trainer._validate_loaded_checkpoint_config(temporary, config), + "validate loaded checkpoint config", + group, + ) + if source.manifest is not None and source.manifest["optimizer"] is not None: + optimizer_state = _phase( + lambda: _optimizer_state(trainer, source, temporary), + "read checkpoint optimizer", + group, + ) + trainer._checkpoint_slots[temporary].optimizer = _phase( + lambda: trainer._restore_canonical_optimizer( + temporary, optimizer_state + ), + "restore checkpoint optimizer", + group, + ) + + def commit() -> None: + _commit_slot(trainer, temporary, name) + staged = trainer._checkpoint_slots.pop(temporary) + staged.revision = 0 if previous is None else previous.revision + 1 + trainer._checkpoint_slots[name] = staged + + _phase(commit, "commit checkpoint", group) + except BaseException: + _rollback_load(trainer, snapshot, temporary, name, previous, group) + raise + + +def export_lora(trainer: TrainerRank, output_dir: str, checkpoint_name: str) -> int: + group = _ensure_group(trainer) + slot = None + error: BaseException | None = None + try: + slot = trainer._checkpoint_slots.get(checkpoint_name) + if slot is None: + raise ValueError(f"Unknown checkpoint: {checkpoint_name!r}") + if slot.config is None: + raise trainer._slot_state_error( + f"Checkpoint {checkpoint_name!r} has no adapter_config" + ) + except BaseException as exc: + error = exc + raise_distributed(error, "validate LoRA export", group) + assert slot is not None and slot.config is not None + identity = (dict(slot.config), slot.revision) + if any(value != identity for value in _gather(identity, group)): + raise trainer._slot_state_error( + f"Checkpoint {checkpoint_name!r} differs across ranks" + ) + from art.megatron.weights.lora_publish import save_vllm_lora_from_model + + error = None + try: + save_vllm_lora_from_model( + model=trainer.runtime.model, + adapter_dtypes={}, + handler=trainer.runtime.model_support_handler, + adapter_config=dict(slot.config), + output_dir=output_dir, + rank=trainer.runtime.rank, + world_size=trainer.runtime.world_size, + slot_ref=trainer._slot_ref(checkpoint_name), + ) + except BaseException as exc: + error = exc + raise_distributed(error, "export LoRA", group) + return slot.revision + + +def _ensure_groups( + trainer: TrainerRank, +) -> tuple[dist.ProcessGroup | None, dist.ProcessGroup | None]: + if not hasattr(trainer, "_checkpoint_process_group"): + trainer._checkpoint_process_group = None + if not hasattr(trainer, "_checkpoint_finalize_process_group"): + trainer._checkpoint_finalize_process_group = None + if not hasattr(trainer, "_checkpoint_group_lock"): + trainer._checkpoint_group_lock = threading.Lock() + if _distributed(): + with trainer._checkpoint_group_lock: + if trainer._checkpoint_process_group is None: + trainer._checkpoint_process_group = dist.new_group(backend="gloo") + if trainer._checkpoint_finalize_process_group is None: + trainer._checkpoint_finalize_process_group = dist.new_group( + backend="gloo" + ) + return ( + trainer._checkpoint_process_group, + trainer._checkpoint_finalize_process_group, + ) + + +def _ensure_group(trainer: TrainerRank) -> dist.ProcessGroup | None: + return _ensure_groups(trainer)[0] + + +def _ensure_finalize_group(trainer: TrainerRank) -> dist.ProcessGroup | None: + return _ensure_groups(trainer)[1] diff --git a/src/art/trainer_rank/_impl.py b/src/art/trainer_rank/_impl.py index afae6f79f..bc11710b4 100644 --- a/src/art/trainer_rank/_impl.py +++ b/src/art/trainer_rank/_impl.py @@ -2,7 +2,11 @@ from __future__ import annotations +import asyncio from collections.abc import ( + Awaitable, + Callable, + Generator, Iterable, Iterator, Mapping, @@ -11,11 +15,14 @@ from copy import deepcopy from dataclasses import dataclass import os +from pathlib import Path +import threading from types import TracebackType from typing import ( TYPE_CHECKING, Generic, Literal, + NotRequired, Self, TypedDict, TypeVar, @@ -47,7 +54,12 @@ from art.megatron.lora import LoRASlotRef from art.megatron.prefix_tree_state import PrefixTreeAttentionState from art.megatron.train import TrainingRuntime - from art.trainer_rank import TrainerRankOptimizerLayout, TrainerRankOptimizerState + from art.trainer_rank._checkpoint import ( + LocalOptimizerState, + PreparedCheckpoint, + _FinalizedSave, + _PreparedSave, + ) @dataclass(frozen=True) @@ -76,9 +88,14 @@ class TopK: class _AdapterConfig(TypedDict): base_model_name_or_path: str + revision: NotRequired[str | None] r: int lora_alpha: float target_modules: str | list[str] + num_attention_heads: NotRequired[int] + num_key_value_heads: NotRequired[int] + head_dim: NotRequired[int] + hidden_size: NotRequired[int] class _Unset: @@ -91,7 +108,6 @@ class _Unset: @dataclass(frozen=True) class _LocalLoRASlotRef: - kind: Literal["checkpoint", "lora"] name: str | None @@ -111,7 +127,6 @@ class ForwardInput(Generic[LogprobsT, TopKT, LogitsT, HiddenStatesT]): logits: bool = False hidden_states: bool = False checkpoint: AdapterSelection = Unset - lora: AdapterSelection = Unset @overload def __new__( @@ -123,7 +138,6 @@ def __new__( logits: Literal[False] = False, hidden_states: Literal[False] = False, checkpoint: AdapterSelection = Unset, - lora: AdapterSelection = Unset, ) -> "ForwardInput[None, None, None, None]": ... @overload @@ -136,7 +150,6 @@ def __new__( logits: Literal[False] = False, hidden_states: Literal[False] = False, checkpoint: AdapterSelection = Unset, - lora: AdapterSelection = Unset, ) -> "ForwardInput[torch.Tensor, None, None, None]": ... @overload @@ -149,7 +162,6 @@ def __new__( logits: Literal[False] = False, hidden_states: Literal[False] = False, checkpoint: AdapterSelection = Unset, - lora: AdapterSelection = Unset, ) -> "ForwardInput[None, TopK, None, None]": ... @overload @@ -162,7 +174,6 @@ def __new__( logits: Literal[True], hidden_states: Literal[False] = False, checkpoint: AdapterSelection = Unset, - lora: AdapterSelection = Unset, ) -> "ForwardInput[None, None, torch.Tensor, None]": ... @overload @@ -175,7 +186,6 @@ def __new__( logits: Literal[False] = False, hidden_states: Literal[True], checkpoint: AdapterSelection = Unset, - lora: AdapterSelection = Unset, ) -> "ForwardInput[None, None, None, torch.Tensor]": ... @overload @@ -188,7 +198,6 @@ def __new__( logits: Literal[False] = False, hidden_states: Literal[False] = False, checkpoint: AdapterSelection = Unset, - lora: AdapterSelection = Unset, ) -> "ForwardInput[torch.Tensor, TopK, None, None]": ... @overload @@ -201,7 +210,6 @@ def __new__( logits: Literal[True], hidden_states: Literal[False] = False, checkpoint: AdapterSelection = Unset, - lora: AdapterSelection = Unset, ) -> "ForwardInput[torch.Tensor, None, torch.Tensor, None]": ... @overload @@ -214,7 +222,6 @@ def __new__( logits: Literal[False] = False, hidden_states: Literal[True], checkpoint: AdapterSelection = Unset, - lora: AdapterSelection = Unset, ) -> "ForwardInput[torch.Tensor, None, None, torch.Tensor]": ... @overload @@ -227,7 +234,6 @@ def __new__( logits: Literal[True], hidden_states: Literal[False] = False, checkpoint: AdapterSelection = Unset, - lora: AdapterSelection = Unset, ) -> "ForwardInput[None, TopK, torch.Tensor, None]": ... @overload @@ -240,7 +246,6 @@ def __new__( logits: Literal[False] = False, hidden_states: Literal[True], checkpoint: AdapterSelection = Unset, - lora: AdapterSelection = Unset, ) -> "ForwardInput[None, TopK, None, torch.Tensor]": ... @overload @@ -253,7 +258,6 @@ def __new__( logits: Literal[True], hidden_states: Literal[True], checkpoint: AdapterSelection = Unset, - lora: AdapterSelection = Unset, ) -> "ForwardInput[None, None, torch.Tensor, torch.Tensor]": ... @overload @@ -266,7 +270,6 @@ def __new__( logits: Literal[True], hidden_states: Literal[False] = False, checkpoint: AdapterSelection = Unset, - lora: AdapterSelection = Unset, ) -> "ForwardInput[torch.Tensor, TopK, torch.Tensor, None]": ... @overload @@ -279,7 +282,6 @@ def __new__( logits: Literal[False] = False, hidden_states: Literal[True], checkpoint: AdapterSelection = Unset, - lora: AdapterSelection = Unset, ) -> "ForwardInput[torch.Tensor, TopK, None, torch.Tensor]": ... @overload @@ -292,7 +294,6 @@ def __new__( logits: Literal[True], hidden_states: Literal[True], checkpoint: AdapterSelection = Unset, - lora: AdapterSelection = Unset, ) -> "ForwardInput[torch.Tensor, None, torch.Tensor, torch.Tensor]": ... @overload @@ -305,7 +306,6 @@ def __new__( logits: Literal[True], hidden_states: Literal[True], checkpoint: AdapterSelection = Unset, - lora: AdapterSelection = Unset, ) -> "ForwardInput[None, TopK, torch.Tensor, torch.Tensor]": ... @overload @@ -318,7 +318,6 @@ def __new__( logits: Literal[True], hidden_states: Literal[True], checkpoint: AdapterSelection = Unset, - lora: AdapterSelection = Unset, ) -> "ForwardInput[torch.Tensor, TopK, torch.Tensor, torch.Tensor]": ... @overload @@ -331,7 +330,6 @@ def __new__( logits: bool = False, hidden_states: bool = False, checkpoint: AdapterSelection = Unset, - lora: AdapterSelection = Unset, ) -> "ForwardInput[torch.Tensor | None, TopK | None, torch.Tensor | None, torch.Tensor | None]": ... def __new__( @@ -343,15 +341,12 @@ def __new__( logits: bool = False, hidden_states: bool = False, checkpoint: AdapterSelection = Unset, - lora: AdapterSelection = Unset, ) -> Self: return object.__new__(cls) def __post_init__(self) -> None: if self.top_k is not None and self.top_k < 1: raise ValueError("top_k must be >= 1") - if self.checkpoint is not Unset and self.lora is not Unset: - raise ValueError("ForwardInput cannot set both checkpoint and lora") type AnyForwardInput = ForwardInput[ @@ -454,27 +449,106 @@ class _DynamicOptimizer: master_params: tuple[torch.nn.Parameter, ...] -@dataclass(frozen=True) -class _PushedSlot: - trainer: "TrainerRank" - ref: "LoRASlotRef" +@dataclass +class _CheckpointSlot: + params: tuple[torch.nn.Parameter, ...] = () + config: _AdapterConfig | None = None + optimizer: _DynamicOptimizer | None = None + revision: int = 0 + - def __enter__(self) -> "_PushedSlot": +@dataclass(frozen=True) +class MaterializedCheckpoint: + """A logical checkpoint and its rank-local materialized directory.""" + + path: str + directory: str + + +@dataclass +class PushedCheckpoint: + _trainer: "TrainerRank" + _path: str | None + _directory: str | None + _task: asyncio.Task[None] | None = None + _entered: bool = False + _closed: bool = False + + def __await__(self) -> Generator[object, None, None]: + return self._ensure_task().__await__() + + def __enter__(self) -> "PushedCheckpoint": + if self._entered or self._closed: + raise RuntimeError("Pushed checkpoint context cannot be entered twice") + if self._task is not None: + if not self._task.done(): + raise RuntimeError( + "Checkpoint push is running asynchronously; use 'async with'" + ) + self._task.result() + else: + self._trainer._push_checkpoint_sync(self._path, self._directory) + self._entered = True return self def __exit__( self, - exc_type: type[BaseException] | None, - exc_value: BaseException | None, + exception_type: type[BaseException] | None, + exception: BaseException | None, traceback: TracebackType | None, ) -> bool: - if not self.trainer._slot_stack or self.trainer._slot_stack[-1] != self.ref: - raise RuntimeError( - "Pushed LoRA/checkpoint stack changed before context exit" - ) - self.trainer.pop_pushed_lora_or_checkpoint() + self._exit(exception) return False + async def __aenter__(self) -> "PushedCheckpoint": + if self._entered or self._closed: + raise RuntimeError("Pushed checkpoint context cannot be entered twice") + task = self._ensure_task() + try: + await task + except asyncio.CancelledError: + if task.done() and not task.cancelled() and task.exception() is None: + self._entered = True + self._pop() + raise + self._entered = True + return self + + async def __aexit__( + self, + exception_type: type[BaseException] | None, + exception: BaseException | None, + traceback: TracebackType | None, + ) -> bool: + self._exit(exception) + return False + + def _ensure_task(self) -> asyncio.Task[None]: + if self._task is None: + self._task = self._trainer._activate_checkpoint(self._path, self._directory) + return self._task + + def _exit(self, body_error: BaseException | None) -> None: + try: + self._pop() + except BaseException as pop_error: + if body_error is not None: + raise BaseExceptionGroup( + "checkpoint context body and cleanup both failed", + [body_error, pop_error], + ) from None + raise + + def _pop(self) -> None: + if not self._entered: + return + ref = self._trainer._slot_ref(self._path) + if not self._trainer._slot_stack or self._trainer._slot_stack[-1] != ref: + raise RuntimeError("Pushed checkpoint stack changed before context exit") + self._trainer.pop_checkpoint() + self._entered = False + self._closed = True + @dataclass(frozen=True) class _ForwardItem: @@ -532,6 +606,13 @@ def __init__( memory_safety_factor: float = 1.10, memory_reserve_fraction: float = 0.03, ) -> None: + pp_size = int(getattr(runtime.provider, "pipeline_model_parallel_size", 1) or 1) + if pp_size > 1 or len(runtime.model) > 1: + raise NotImplementedError( + "TrainerRank does not use the MCore forward/backward schedule and " + "therefore requires PP=1 with exactly one local model chunk; " + f"got pp={pp_size}, chunks={len(runtime.model)}" + ) if head_chunk_tokens < 1: raise ValueError("head_chunk_tokens must be >= 1") if shared_prefix_max_depth < 0: @@ -562,11 +643,23 @@ def __init__( ) self._default_slot_ref: LoRASlotRef | None = None self._slot_stack: list[LoRASlotRef] = [] - self._dynamic_optimizers: dict[str, _DynamicOptimizer] = {} - self._checkpoint_slot_params_by_name: dict[ - str, tuple[torch.nn.Parameter, ...] - ] = {} - self._checkpoint_slot_adapter_configs: dict[str, _AdapterConfig] = {} + self._checkpoint_slots: dict[str, _CheckpointSlot] = {} + self._checkpoint_prefetches: dict[str, asyncio.Task[PreparedCheckpoint]] = {} + self._checkpoint_mutation_tail: asyncio.Task[None] | None = None + self._checkpoint_process_group: dist.ProcessGroup | None = None + self._checkpoint_finalize_process_group: dist.ProcessGroup | None = None + self._checkpoint_group_lock = threading.Lock() + self._checkpoint_prepare_lock = threading.Lock() + self._checkpoint_finalize_lock = threading.Lock() + self._checkpoint_save_condition = threading.Condition() + self._checkpoint_save_sequence = 0 + self._checkpoint_save_next = 0 + self._checkpoint_save_skipped: set[int] = set() + self._checkpoint_preparing_saves: set[str] = set() + self._checkpoint_finalizing_saves: dict[str, Literal["finish", "abort"]] = {} + self._checkpoint_save_outcomes: dict[str, Literal["finish", "abort"]] = {} + self._prepared_checkpoint_saves: dict[str, _PreparedSave] = {} + self._finalized_checkpoint_saves: dict[str, _FinalizedSave] = {} self._pending_slot_graphs: dict[ LoRASlotRef, list[weakref.ReferenceType[torch.Tensor]] ] = {} @@ -586,126 +679,237 @@ def zero_grad(self) -> None: optimizer = self.runtime.optimizer if optimizer is not None: optimizer.zero_grad() - for params in self._checkpoint_slot_params_by_name.values(): - for param in params: + for slot in self._checkpoint_slots.values(): + for param in slot.params: param.grad = None self._prune_slot_graphs() - def set_checkpoint(self, name: str | None) -> None: - self._set_default_slot(self._slot_ref("checkpoint", name)) + def prefetch_checkpoints( + self, *checkpoints: str | MaterializedCheckpoint + ) -> asyncio.Task[None]: + sources = tuple( + self._checkpoint_source(checkpoint)[1] for checkpoint in checkpoints + ) + assert all(source is not None for source in sources) + + async def prefetch() -> None: + await asyncio.gather( + *( + self._prefetch_checkpoint(source) + for source in sources + if source is not None + ) + ) + + return asyncio.create_task(prefetch()) - def set_lora(self, name: str | None) -> None: - self._set_default_slot(self._slot_ref("lora", name)) + def load_checkpoint( + self, checkpoint: str | MaterializedCheckpoint | None + ) -> asyncio.Task[None]: + logical, source = self._checkpoint_source(checkpoint) + return self._load_checkpoint(logical, source) - def push_checkpoint(self, name: str | None) -> _PushedSlot: - ref = self._slot_ref("checkpoint", name) - self._slot_stack.append(ref) - return _PushedSlot(self, ref) + def _load_checkpoint( + self, logical_path: str | None, source_path: str | None + ) -> asyncio.Task[None]: + prefetch = ( + None + if source_path is None + else asyncio.create_task(self._prefetch_checkpoint(source_path)) + ) - def push_lora(self, name: str | None) -> _PushedSlot: - ref = self._slot_ref("lora", name) - self._slot_stack.append(ref) - return _PushedSlot(self, ref) + async def load() -> None: + if self._slot_stack: + raise RuntimeError("Cannot load a checkpoint while one is pushed") + if logical_path is None: + self._set_default_slot(self._slot_ref(None)) + return + assert source_path is not None and prefetch is not None + await self._load_checkpoint_path( + logical_path, source_path=source_path, prefetch=prefetch + ) + self._set_default_slot(self._slot_ref(logical_path)) + + return self._checkpoint_mutation_task(load) + + def push_checkpoint( + self, checkpoint: str | MaterializedCheckpoint | None + ) -> PushedCheckpoint: + logical, directory = self._checkpoint_source(checkpoint) + return PushedCheckpoint(self, logical, directory) + + def _activate_checkpoint( + self, logical_path: str | None, source_path: str | None + ) -> asyncio.Task[None]: + prefetch = ( + asyncio.create_task(self._prefetch_checkpoint(source_path)) + if source_path is not None and logical_path not in self._checkpoint_slots + else None + ) - def pop_pushed_lora_or_checkpoint(self) -> None: + async def push() -> None: + if prefetch is not None and logical_path not in self._checkpoint_slots: + assert logical_path is not None and source_path is not None + await self._load_checkpoint_path( + logical_path, source_path=source_path, prefetch=prefetch + ) + self._slot_stack.append(self._slot_ref(logical_path)) + + return self._checkpoint_mutation_task(push) + + def _push_checkpoint_sync( + self, logical_path: str | None, source_path: str | None + ) -> None: + predecessor = self._checkpoint_mutation_tail + if predecessor is not None: + if not predecessor.done(): + raise RuntimeError( + "A checkpoint mutation is running asynchronously; use 'async with'" + ) + if not predecessor.cancelled(): + predecessor.exception() + if source_path is not None and logical_path not in self._checkpoint_slots: + assert logical_path is not None + from . import _checkpoint + + source = _checkpoint.prepare_checkpoint(source_path) + _checkpoint.load_checkpoint(self, source, logical_path) + self._slot_stack.append(self._slot_ref(logical_path)) + + def _checkpoint_mutation_task( + self, operation: Callable[[], Awaitable[None]] + ) -> asyncio.Task[None]: + predecessor = self._checkpoint_mutation_tail + + async def ordered() -> None: + if predecessor is not None: + try: + await asyncio.shield(predecessor) + except asyncio.CancelledError: + current = asyncio.current_task() + if current is not None and current.cancelling(): + raise + except Exception: + pass + await operation() + + task = asyncio.create_task(ordered()) + self._checkpoint_mutation_tail = task + return task + + def pop_checkpoint(self) -> None: if not self._slot_stack: - raise RuntimeError("No pushed LoRA or checkpoint to pop") + raise RuntimeError("No pushed checkpoint to pop") self._slot_stack.pop() - def load_checkpoint_slot( + def save_checkpoint( self, - name: str, - adapter_model: Mapping[str, torch.Tensor], - *, - optimizer_state: TrainerRankOptimizerState | None = None, - alpha: float | None = None, - adapter_config: Mapping[str, object] | None = None, - ) -> int: - config = self._validate_checkpoint_slot_adapter_config( - name, adapter_config, alpha=alpha - ) - loaded = self._load_slot( - "checkpoint", - name, - adapter_model, - trainable=True, - alpha=alpha if config is None else float(config["lora_alpha"]), + output_dir: str, + checkpoint_path: str | Literal["active"] = "active", + ) -> None: + self.prepare_checkpoint_save(output_dir, checkpoint_path) + self.finish_checkpoint_save(output_dir) + + def prepare_checkpoint_save( + self, + output_dir: str, + checkpoint_path: str | Literal["active"] = "active", + ) -> None: + from . import _checkpoint + + _checkpoint.prepare_checkpoint_save( + self, output_dir, self._resolve_checkpoint_name(checkpoint_path) ) - slot_params = self._validate_dynamic_slot_consistency( - "checkpoint", name, loaded + + def finish_checkpoint_save(self, output_dir: str) -> None: + from . import _checkpoint + + _checkpoint.finish_checkpoint_save(self, output_dir) + + def abort_checkpoint_save(self, output_dir: str) -> None: + from . import _checkpoint + + _checkpoint.abort_checkpoint_save(self, output_dir) + + def export_lora( + self, + output_dir: str, + checkpoint_path: str | Literal["active"] = "active", + ) -> int: + from . import _checkpoint + + return _checkpoint.export_lora( + self, output_dir, self._resolve_checkpoint_name(checkpoint_path) ) - if config is not None: - self._validate_loaded_checkpoint_slot_config(name, config) - self._checkpoint_slot_params_by_name[name] = slot_params - if optimizer_state is None: - self._dynamic_optimizers.pop(name, None) - else: - self._dynamic_optimizers[name] = self._restore_dynamic_optimizer( - name, optimizer_state - ) - configs = getattr(self, "_checkpoint_slot_adapter_configs", None) - if configs is None: - configs = self._checkpoint_slot_adapter_configs = {} - if config is None: - configs.pop(name, None) - else: - configs[name] = config - return loaded - - def checkpoint_slot_optimizer_state( - self, name: str - ) -> TrainerRankOptimizerState | None: - if name not in self._checkpoint_slot_params_by_name: - raise ValueError(f"Unknown checkpoint slot: {name!r}") - dynamic = self._dynamic_optimizers.get(name) - if dynamic is None: - return None - state: TrainerRankOptimizerState = { - "format_version": 1, - "layout": self._dynamic_optimizer_layout(name), - "master_params": tuple( - param.detach().cpu().clone() for param in dynamic.master_params - ), - "optimizer": cast( - dict[str, object], - _state_to_cpu(dynamic.optimizer.state_dict()), - ), - } - return state - def save_checkpoint_slot_lora(self, name: str, output_dir: str) -> None: - """Collectively publish a trained checkpoint slot as a vLLM LoRA.""" - known = name in self._checkpoint_slot_params_by_name - if dist.is_available() and dist.is_initialized(): - gathered: list[tuple[str, bool] | None] = [None] * dist.get_world_size() - dist.all_gather_object(gathered, (name, known)) - if any(state != (name, True) for state in gathered): - raise ValueError( - "Checkpoint slot publish requires the same loaded name on all " - f"ranks; got {gathered}" - ) - if not known: - raise ValueError(f"Unknown checkpoint slot: {name!r}") - config = getattr(self, "_checkpoint_slot_adapter_configs", {}).get(name) - if config is None: - raise TrainerRankSlotStateError( - f"Checkpoint slot {name!r} was loaded without adapter_config; " - "reload it with adapter_config=... before publishing." + @staticmethod + def _checkpoint_source_key(path: str) -> str: + return str(Path(path).resolve()) + + @staticmethod + def _checkpoint_source( + checkpoint: str | MaterializedCheckpoint | None, + ) -> tuple[str | None, str | None]: + if isinstance(checkpoint, MaterializedCheckpoint): + return checkpoint.path, checkpoint.directory + return checkpoint, checkpoint + + async def _prefetch_checkpoint(self, source_path: str) -> PreparedCheckpoint: + key = self._checkpoint_source_key(source_path) + task = self._checkpoint_prefetches.get(key) + if task is None: + from ._checkpoint import prepare_checkpoint + + task = self._checkpoint_prefetches[key] = asyncio.create_task( + asyncio.to_thread(prepare_checkpoint, key) ) - from art.megatron.weights.lora_publish import save_vllm_lora_from_model + try: + return await asyncio.shield(task) + except BaseException: + if task.done(): + self._checkpoint_prefetches.pop(key, None) + raise - save_vllm_lora_from_model( - model=self.runtime.model, - adapter_dtypes={}, - handler=self.runtime.model_support_handler, - adapter_config=config, - output_dir=output_dir, - rank=self.runtime.rank, - world_size=self.runtime.world_size, - slot_ref=self._slot_ref("checkpoint", name), - ) + async def _load_checkpoint_path( + self, + logical_path: str, + *, + source_path: str, + prefetch: asyncio.Task[PreparedCheckpoint], + ) -> None: + from . import _checkpoint - def _validate_checkpoint_slot_adapter_config( + key = self._checkpoint_source_key(source_path) + source: PreparedCheckpoint | None = None + error: BaseException | None = None + try: + source = await asyncio.shield(prefetch) + except BaseException as exc: + error = exc + group = _checkpoint._ensure_group(self) + _checkpoint.raise_distributed(error, "prepare checkpoint", group) + assert source is not None + _checkpoint.load_checkpoint(self, source, logical_path) + self._checkpoint_prefetches.pop(key, None) + + def _resolve_checkpoint_name(self, checkpoint_path: str | Literal["active"]) -> str: + if checkpoint_path != "active": + return checkpoint_path + ref = self._slot_stack[-1] if self._slot_stack else self._default_slot_ref + if ref is None or ref.name is None: + raise TrainerRankSlotStateError("No active trainable checkpoint") + return ref.name + + @staticmethod + def _slot_state_error(message: str) -> TrainerRankSlotStateError: + return TrainerRankSlotStateError(message) + + def _checkpoint_group(self) -> dist.ProcessGroup | None: + from ._checkpoint import _ensure_group + + return _ensure_group(self) + + def _validate_checkpoint_adapter_config( self, name: str, adapter_config: Mapping[str, object] | None, @@ -715,7 +919,7 @@ def _validate_checkpoint_slot_adapter_config( config = None if adapter_config is None else deepcopy(dict(adapter_config)) if dist.is_available() and dist.is_initialized(): gathered: list[dict[str, object] | None] = [None] * dist.get_world_size() - dist.all_gather_object(gathered, config) + dist.all_gather_object(gathered, config, group=self._checkpoint_group()) if any(value != config for value in gathered): raise ValueError( f"Adapter config for checkpoint slot {name!r} differs across ranks" @@ -735,6 +939,20 @@ def _validate_checkpoint_slot_adapter_config( raise TypeError( "adapter_config['base_model_name_or_path'] must be a string" ) + if base_model.startswith(("Qwen/Qwen3.5-", "Qwen/Qwen3.6-")): + dimensions = { + "num_attention_heads": getattr( + self.runtime.provider, "num_attention_heads", None + ), + "num_key_value_heads": getattr( + self.runtime.provider, "num_query_groups", None + ), + "head_dim": getattr(self.runtime.provider, "kv_channels", None), + "hidden_size": getattr(self.runtime.provider, "hidden_size", None), + } + for key, value in dimensions.items(): + if value is not None: + config[key] = int(value) if not isinstance(rank, int) or isinstance(rank, bool): raise TypeError("adapter_config['r'] must be an integer") if not isinstance(config_alpha_value, int | float) or isinstance( @@ -757,12 +975,12 @@ def _validate_checkpoint_slot_adapter_config( ) return cast(_AdapterConfig, config) - def _validate_loaded_checkpoint_slot_config( + def _validate_loaded_checkpoint_config( self, name: str, config: _AdapterConfig ) -> None: from art.megatron.lora import LoRA - ref = self._slot_ref("checkpoint", name) + ref = self._slot_ref(name) slots = [ slot for chunk in self.runtime.model @@ -778,19 +996,6 @@ def _validate_loaded_checkpoint_slot_config( f"rank/alpha={expected}, loaded weights use {sorted(actual)}" ) - def load_lora_slot( - self, - name: str, - adapter_model: Mapping[str, torch.Tensor], - *, - alpha: float | None = None, - ) -> int: - loaded = self._load_slot( - "lora", name, adapter_model, trainable=False, alpha=alpha - ) - self._validate_dynamic_slot_consistency("lora", name, loaded) - return loaded - @overload def forward_micro_batches( self, @@ -987,63 +1192,86 @@ def optim_step( scale_grads=scale_grads, ) - def _load_slot( + def _load_checkpoint_slot( self, - kind: Literal["checkpoint", "lora"], name: str, adapter_model: Mapping[str, torch.Tensor], *, - trainable: bool, - alpha: float | None, + alpha: float, + _prepared: bool = False, ) -> int: if self._slot_stack: - raise RuntimeError("Cannot load a LoRA/checkpoint while a slot is pushed") - adapter_model = self._prepare_adapter_model(kind, name, adapter_model) - from art.megatron.lora import LORA_ALPHA, load_lora_slot_into_model + raise RuntimeError("Cannot load a checkpoint while one is pushed") + adapter_model = self._prepare_adapter_model( + name, adapter_model, canonicalized=_prepared + ) + from art.megatron.lora import load_lora_slot_into_model - ref = self._slot_ref(kind, name) + ref = self._slot_ref(name) self._guard_slot_can_load(ref) + self._compact_lora_slot_keys() return load_lora_slot_into_model( self.runtime.model, ref, adapter_model, - alpha=LORA_ALPHA if alpha is None else alpha, - requires_grad=trainable, + alpha=alpha, + requires_grad=True, ) + def _compact_lora_slot_keys(self) -> None: + from art.megatron.lora import LoRA + + for chunk in self.runtime.model: + for module in chunk.modules(): + if not isinstance(module, LoRA): + continue + slots = [ + (ref, module._slot_modules[key]) + for ref, key in module._slot_keys.items() + ] + module._slot_keys = { + ref: f"slot_{index}" for index, (ref, _slot) in enumerate(slots) + } + module._slot_modules = torch.nn.ModuleDict( + {f"slot_{index}": slot for index, (_ref, slot) in enumerate(slots)} + ) + def _prepare_adapter_model( self, - kind: Literal["checkpoint", "lora"], name: str, adapter_model: Mapping[str, torch.Tensor], + *, + canonicalized: bool = False, ) -> dict[str, torch.Tensor]: templates = self._local_lora_adapter_templates() keys = set(adapter_model) expected = set(templates) if dist.is_available() and dist.is_initialized(): gathered: list[set[str] | None] = [None] * dist.get_world_size() - dist.all_gather_object(gathered, expected) + dist.all_gather_object(gathered, expected, group=self._checkpoint_group()) expected = set().union(*(value for value in gathered if value is not None)) if unknown := sorted(keys - expected): preview = ", ".join(repr(key) for key in unknown[:8]) more = "" if len(unknown) <= 8 else f", ... +{len(unknown) - 8} more" raise ValueError( - f"Adapter for {kind} slot {name!r} contains keys that do not match " - f"installed LoRA wrapper sites: {preview}{more}. Configure the " - "Megatron runtime with matching LoRA target modules before loading." + f"Checkpoint {name!r} contains keys that do not match installed " + f"LoRA wrapper sites: {preview}{more}. Configure the Megatron " + "runtime with matching LoRA target modules before loading." ) local_state = { key: tensor for key, tensor in adapter_model.items() if key in templates } adapter_model = ( - self.runtime.model_support_handler.canonicalize_loaded_lora_state( + local_state + if canonicalized + else self.runtime.model_support_handler.canonicalize_loaded_lora_state( local_state, self.runtime.model ) ) if set(adapter_model) != set(local_state): raise TrainerRankSlotStateError( "Model-specific LoRA canonicalization changed the adapter key set " - f"for {kind} slot {name!r}." + f"for checkpoint {name!r}." ) return { key: tensor.to( @@ -1073,82 +1301,83 @@ def _local_lora_adapter_templates(self) -> dict[str, torch.Tensor]: ) return templates + def _iter_slot_parameters(self, ref: "LoRASlotRef") -> Iterator[torch.nn.Parameter]: + from art.megatron.lora import iter_lora_slot_parameters + + return iter_lora_slot_parameters(self.runtime.model, ref) + + def _local_parameter_key_groups(self, name: str) -> tuple[tuple[str, ...], ...]: + ref = self._slot_ref(name) + return tuple( + tuple(str(key) for key in expected(str(suffix).removesuffix(".weight"))) + for chunk in self.runtime.model + for module in chunk.modules() + if (lora_params := getattr(module, "_lora_params", None)) is not None + if (expected := getattr(module, "_expected_weight_keys", None)) is not None + for suffix, _param in lora_params(ref) + ) + + def _validate_checkpoint_consistency( + self, name: str, loaded_sites: int, expected_keys: set[str] + ) -> tuple[torch.nn.Parameter, ...]: + params = tuple(self._iter_slot_parameters(self._slot_ref(name))) + local_keys = { + key for group in self._local_parameter_key_groups(name) for key in group + } + gathered = ( + [local_keys] + if not (dist.is_available() and dist.is_initialized()) + else [None] * dist.get_world_size() + ) + if dist.is_available() and dist.is_initialized(): + dist.all_gather_object(gathered, local_keys, group=self._checkpoint_group()) + covered = set().union(*(keys for keys in gathered if keys is not None)) + if loaded_sites < 1 or covered != expected_keys: + raise TrainerRankSlotStateError( + f"Checkpoint {name!r} has inconsistent distributed coverage" + ) + return params + def _set_default_slot(self, ref: "LoRASlotRef") -> None: if self._slot_stack: - raise RuntimeError("Cannot set a LoRA/checkpoint while a slot is pushed") + raise RuntimeError("Cannot select a checkpoint while one is pushed") self._default_slot_ref = ref @staticmethod - def _slot_ref( - kind: Literal["checkpoint", "lora"], name: str | None - ) -> "LoRASlotRef": + def _slot_ref(name: str | None) -> "LoRASlotRef": try: from art.megatron.lora import LoRASlotRef except ModuleNotFoundError as exc: if exc.name is None or not exc.name.startswith("megatron"): raise - return cast("LoRASlotRef", _LocalLoRASlotRef(kind=kind, name=name)) - - return LoRASlotRef(kind=kind, name=name) + return cast("LoRASlotRef", _LocalLoRASlotRef(name=name)) - def _validate_dynamic_slot_consistency( - self, - kind: Literal["checkpoint", "lora"], - name: str, - loaded_sites: int, - ) -> tuple[torch.nn.Parameter, ...]: - from art.megatron.lora import iter_lora_slot_parameters - - ref = self._slot_ref(kind, name) - params = tuple(iter_lora_slot_parameters(self.runtime.model, ref)) - if not (dist.is_available() and dist.is_initialized()): - return params - - signature = tuple( - ( - tuple(param.shape), - str(param.dtype), - bool(getattr(param, "allreduce", True)), - str(getattr(param, "grad_sync_domain", "tp_default")), - str(getattr(param, "grad_sync_op", "none")), - ) - for param in params - ) - local = (int(loaded_sites), signature) - gathered: list[tuple[int, object] | None] = [None] * dist.get_world_size() - dist.all_gather_object(gathered, local) - ranks = [state for state in gathered if state is not None] - if all(state == ranks[0] for state in ranks[1:]): - return params - raise RuntimeError( - f"Dynamic LoRA slot {kind}:{name} is not loaded consistently across " - "distributed ranks. This usually means a sharded/exported LoRA state " - "dict was passed directly to TrainerRank; gather or materialize the " - "full adapter state before loading a dynamic slot. " - f"Loaded-site counts by rank: {[state[0] for state in ranks]}." - ) + return LoRASlotRef(kind="checkpoint", name=name) def _resolve_slot_ref(self, request: AnyForwardInput) -> "LoRASlotRef | None": if request.checkpoint is not Unset: - return self._slot_ref("checkpoint", cast(str | None, request.checkpoint)) - if request.lora is not Unset: - return self._slot_ref("lora", cast(str | None, request.lora)) + name = cast(str | None, request.checkpoint) + if name is not None and name not in self._checkpoint_slots: + raise TrainerRankSlotStateError( + f"Forward input selects unloaded checkpoint {name!r}" + ) + return self._slot_ref(name) if self._slot_stack: return self._slot_stack[-1] if self._default_slot_ref is not None: return self._default_slot_ref - return self._slot_ref("checkpoint", None) + return self._slot_ref(None) def _selected_dynamic_checkpoints( self, checkpoints: Sequence[str] | None, ) -> tuple[str, ...]: - loaded = set(self._checkpoint_slot_params_by_name) + loaded = set(self._checkpoint_slots) if not loaded: raise TrainerRankSlotStateError( "TrainerRank.optim_step requires a loaded checkpoint slot. Call " - "load_checkpoint_slot(...) and run backward on outputs produced by " + "load_checkpoint(...) and run backward on outputs produced by " "that slot before stepping." ) requested = ( @@ -1191,7 +1420,7 @@ def _checkpoint_grad_flags(self, names: Sequence[str]) -> tuple[bool, ...]: [ any( param.grad is not None - for param in self._checkpoint_slot_params_by_name[name] + for param in self._checkpoint_slots[name].params ) for name in names ], @@ -1215,7 +1444,7 @@ def _dynamic_optim_step( selected = [] for name in checkpoint_names: self._guard_checkpoint_can_step(name) - slot_params = self._checkpoint_slot_params_by_name[name] + slot_params = self._checkpoint_slots[name].params slot_grads = self._reduce_dynamic_grads( slot_params, scale_grads=scale_grads ) @@ -1251,7 +1480,8 @@ def _dynamic_optim_step( ): model.copy_(master) model.grad = None - self._prune_slot_graphs(self._slot_ref("checkpoint", name)) + self._prune_slot_graphs(self._slot_ref(name)) + self._checkpoint_slots[name].revision += 1 return { "learning_rate": float(params.learning_rate), "grad_norm": float(grad_norm), @@ -1264,10 +1494,11 @@ def _dynamic_optimizer( name: str, params: AdamParams, ) -> _DynamicOptimizer: - dynamic = self._dynamic_optimizers.get(name) + slot = self._checkpoint_slots[name] + dynamic = slot.optimizer if dynamic is None: dynamic = self._new_dynamic_optimizer(name, params) - self._dynamic_optimizers[name] = dynamic + slot.optimizer = dynamic return dynamic for group in dynamic.optimizer.param_groups: group["lr"] = params.learning_rate @@ -1282,7 +1513,7 @@ def _new_dynamic_optimizer( *, master_params: Sequence[torch.Tensor] | None = None, ) -> _DynamicOptimizer: - model_params = self._checkpoint_slot_params_by_name[name] + model_params = self._checkpoint_slots[name].params sources = model_params if master_params is None else tuple(master_params) if len(sources) != len(model_params) or any( not isinstance(source, torch.Tensor) for source in sources @@ -1291,6 +1522,13 @@ def _new_dynamic_optimizer( f"Optimizer state for checkpoint slot {name!r} has " f"{len(sources)} master parameters; expected {len(model_params)}." ) + if any( + tuple(source.shape) != tuple(model.shape) + for source, model in zip(sources, model_params, strict=True) + ): + raise TrainerRankSlotStateError( + f"Optimizer master parameter shape does not match checkpoint {name!r}" + ) masters = tuple( torch.nn.Parameter( source.detach().to(device=model.device, dtype=torch.float32).clone() @@ -1309,55 +1547,40 @@ def _new_dynamic_optimizer( ) return _DynamicOptimizer(optimizer, masters) - def _restore_dynamic_optimizer( + def _restore_canonical_optimizer( self, name: str, - state: TrainerRankOptimizerState, + state: "LocalOptimizerState", ) -> _DynamicOptimizer: - if state.get("format_version") != 1: - raise TrainerRankSlotStateError( - f"Unsupported optimizer state format for checkpoint slot {name!r}." - ) - if state.get("layout") != self._dynamic_optimizer_layout(name): - raise TrainerRankSlotStateError( - f"Optimizer state for checkpoint slot {name!r} was saved for a " - "different topology or parameter layout. Save and restore one " - "optimizer shard per TrainerRank with matching TP/EP/ETP ranks." - ) - master_params = state.get("master_params") - optimizer_state = state.get("optimizer") - if not isinstance(master_params, Sequence) or not isinstance( - optimizer_state, Mapping - ): - raise TrainerRankSlotStateError( - f"Optimizer state for checkpoint slot {name!r} is incomplete." - ) dynamic = self._new_dynamic_optimizer( name, - AdamParams(learning_rate=0.0), - master_params=cast(Sequence[torch.Tensor], master_params), - ) - try: - dynamic.optimizer.load_state_dict( - {str(key): value for key, value in optimizer_state.items()} - ) - except ValueError as exc: - raise TrainerRankSlotStateError( - f"Optimizer state for checkpoint slot {name!r} does not match the " - "loaded slot parameter groups." - ) from exc - for param in dynamic.master_params: - for state_name, value in dynamic.optimizer.state.get(param, {}).items(): - if ( - isinstance(value, torch.Tensor) - and int(value.ndim) > 0 - and tuple(value.shape) != tuple(param.shape) - ): - raise TrainerRankSlotStateError( - f"Optimizer state {state_name!r} for checkpoint slot " - f"{name!r} has shape {tuple(value.shape)}, but the loaded " - f"slot parameter has shape {tuple(param.shape)}." - ) + AdamParams( + learning_rate=state.config["learning_rate"], + beta1=state.config["beta1"], + beta2=state.config["beta2"], + weight_decay=state.config["weight_decay"], + ), + master_params=state.masters, + ) + dynamic.optimizer.param_groups[0]["eps"] = state.config["eps"] + for master, exp_avg, exp_avg_sq, step in zip( + dynamic.master_params, + state.exp_avgs, + state.exp_avg_sqs, + state.steps, + strict=True, + ): + if tuple(exp_avg.shape) != tuple(master.shape) or tuple( + exp_avg_sq.shape + ) != tuple(master.shape): + raise TrainerRankSlotStateError( + f"Canonical optimizer moment shape does not match {name!r}" + ) + dynamic.optimizer.state[master] = { + "step": torch.tensor(step, dtype=torch.float32), + "exp_avg": exp_avg.to(master.device, torch.float32).clone(), + "exp_avg_sq": exp_avg_sq.to(master.device, torch.float32).clone(), + } self._zero_dynamic_optimizer_padding(name, dynamic) return dynamic @@ -1375,13 +1598,13 @@ def _zero_dynamic_optimizer_padding( value.masked_fill_(mask, 0) def _dynamic_optimizer_padding_masks(self, name: str) -> tuple[torch.Tensor, ...]: - params = self._checkpoint_slot_params_by_name[name] + params = self._checkpoint_slots[name].params masks = tuple(torch.zeros_like(param, dtype=torch.bool) for param in params) param_indices = {id(param): index for index, param in enumerate(params)} exported: dict[str, torch.Tensor] = {} owners: dict[str, tuple[int, int | None]] = {} mapped_indices: set[int] = set() - ref = self._slot_ref("checkpoint", name) + ref = self._slot_ref(name) for chunk in self.runtime.model: for module in chunk.modules(): @@ -1398,29 +1621,29 @@ def _dynamic_optimizer_padding_masks(self, name: str) -> tuple[torch.Tensor, ... if int(param.ndim) == 3: if len(keys) != int(param.shape[0]): raise TrainerRankSlotStateError( - f"Cannot map optimizer padding for checkpoint slot " + f"Cannot map optimizer padding for checkpoint " f"{name!r}: {len(keys)} adapter keys describe " f"{int(param.shape[0])} local experts." ) for expert, key in enumerate(keys): exported[str(key)] = torch.ones_like(param[expert].T) owners[str(key)] = (index, expert) - else: - if len(keys) != 1: - raise TrainerRankSlotStateError( - f"Cannot map optimizer padding for checkpoint slot " - f"{name!r}: expected one adapter key, got {len(keys)}." - ) + elif len(keys) == 1: key = str(keys[0]) exported[key] = torch.ones_like(param.T) owners[key] = (index, None) + else: + raise TrainerRankSlotStateError( + f"Cannot map optimizer padding for checkpoint {name!r}: " + f"expected one adapter key, got {len(keys)}." + ) if mapped_indices and ( missing := sorted(set(range(len(params))) - mapped_indices) ): raise TrainerRankSlotStateError( - f"Cannot map optimizer padding for checkpoint slot {name!r}: " - f"parameter indices {missing} do not belong to installed LoRA sites." + f"Cannot map optimizer padding for checkpoint {name!r}: parameter " + f"indices {missing} do not belong to installed LoRA sites." ) canonical = self.runtime.model_support_handler.canonicalize_loaded_lora_state( @@ -1489,39 +1712,6 @@ def add( coalesced_all_reduce(bucket_grads, group=group, op=op) return grads - def _dynamic_optimizer_layout(self, name: str) -> TrainerRankOptimizerLayout: - parameters = cast( - tuple[ - tuple[ - tuple[int, ...], - str, - str, - bool, - int | None, - str, - tuple[int, ...], - ], - ..., - ], - tuple( - ( - tuple(param.shape), - str(param.dtype), - str(getattr(param, "lora_shard_domain", "tp")), - bool(getattr(param, "lora_tp_sharded", False)), - getattr(param, "lora_tp_shard_dim", None), - str(getattr(param, "lora_tp_shard_strategy", "uniform")), - tuple(getattr(param, "lora_tp_component_sizes", ())), - ) - for param in self._checkpoint_slot_params_by_name[name] - ), - ) - layout: TrainerRankOptimizerLayout = { - "parallel": _parallel_optimizer_coordinates(), - "parameters": parameters, - } - return layout - def _select_next_micro_batch( self, items: Sequence[ForwardInputsT], @@ -1928,7 +2118,7 @@ def _guard_slot_can_load(self, ref: "LoRASlotRef") -> None: if not self._has_live_slot_graph(ref): return raise TrainerRankSlotStateError( - f"Cannot load {ref.kind} slot {ref.name!r} while outputs from an " + f"Cannot load checkpoint {ref.name!r} while outputs from an " "earlier forward using that slot still have a live backward graph. " "Activation checkpoint recompute resolves slots by name, so replacing " "the slot before backward can compute gradients with different LoRA " @@ -1938,7 +2128,7 @@ def _guard_slot_can_load(self, ref: "LoRASlotRef") -> None: ) def _guard_checkpoint_can_step(self, name: str) -> None: - ref = self._slot_ref("checkpoint", name) + ref = self._slot_ref(name) if not self._has_live_slot_graph(ref): return raise TrainerRankSlotStateError( @@ -2673,7 +2863,9 @@ def _hybridep_rows( parent_ids=batch.parent_ids, topology=topology, config=_context_parallel_config_for_provider( - self.runtime.provider, self.device + self.runtime.provider, + self.device, + handler, ), original_seq_len=sequence_length, build_gdn_execution_spec=handler.build_gdn_execution_spec, @@ -2724,7 +2916,11 @@ def _prepare_context_parallel_forward( prepared = prepare_cp_micro( micro=sparse_micro, topology=topology, - config=_context_parallel_config_for_provider(provider, self.device), + config=_context_parallel_config_for_provider( + provider, + self.device, + handler, + ), cp_group=ps.get_context_parallel_group(check_initialized=False), cp_rank=ps.get_context_parallel_rank(), build_gdn_execution_spec=handler.build_gdn_execution_spec, @@ -2907,36 +3103,6 @@ def _include_in_distributed_grad_norm(param: torch.nn.Parameter) -> bool: return shard_group is None or shard_group.size() <= 1 or shard_group.rank() == 0 -def _parallel_optimizer_coordinates() -> tuple[int, int, int, int, int, int, int, int]: - if not (dist.is_available() and dist.is_initialized()): - return (1, 0, 1, 0, 1, 0, 1, 0) - from megatron.core import parallel_state as ps - - expert_tp_group = ps.get_expert_tensor_parallel_group(check_initialized=False) - return ( - int(ps.get_tensor_model_parallel_world_size()), - int(ps.get_tensor_model_parallel_rank()), - int(ps.get_expert_model_parallel_world_size()), - int(ps.get_expert_model_parallel_rank()), - 1 if expert_tp_group is None else int(expert_tp_group.size()), - 0 if expert_tp_group is None else int(expert_tp_group.rank()), - int(ps.get_pipeline_model_parallel_world_size()), - int(ps.get_pipeline_model_parallel_rank()), - ) - - -def _state_to_cpu(value: object) -> object: - if isinstance(value, torch.Tensor): - return value.detach().cpu().clone() - if isinstance(value, Mapping): - return {key: _state_to_cpu(item) for key, item in value.items()} - if isinstance(value, tuple): - return tuple(_state_to_cpu(item) for item in value) - if isinstance(value, list): - return [_state_to_cpu(item) for item in value] - return value - - def _vocab_parallel_target_logprobs( local_logits: torch.Tensor, labels: torch.Tensor, diff --git a/src/art/trajectories/__init__.py b/src/art/trajectories/__init__.py index c42f48cf1..1fecbb655 100644 --- a/src/art/trajectories/__init__.py +++ b/src/art/trajectories/__init__.py @@ -459,6 +459,7 @@ class Trajectory(_CompactModel): metadata: dict[str, MetadataValue] = pydantic.Field(default_factory=dict) logs: list[str] = pydantic.Field(default_factory=list) start_time: datetime = pydantic.Field(default_factory=datetime.now, exclude=True) + _policy_token_counts: dict[int, int] | None = pydantic.PrivateAttr(default=None) @pydantic.field_serializer("messages_and_choices", when_used="json") def serialize_messages_and_choices(self, value: MessagesAndChoices) -> list[Any]: @@ -714,6 +715,9 @@ class TrajectoryGroup(_CompactModel): logs: list[str] = pydantic.Field(default_factory=list) _collect_packing_shape: bool = pydantic.PrivateAttr(default=False) _packed_group_shape: Any = pydantic.PrivateAttr(default=None) + _distributed_lease: Any = pydantic.PrivateAttr(default=None) + _prepared_training_batch: Any = pydantic.PrivateAttr(default=None) + _prepared_log_path: str | None = pydantic.PrivateAttr(default=None) @overload def __new__( diff --git a/src/art/types.py b/src/art/types.py index d54f86420..80b5ea5ef 100644 --- a/src/art/types.py +++ b/src/art/types.py @@ -1,3 +1,4 @@ +from collections.abc import Awaitable from dataclasses import dataclass, field from typing import Annotated, Literal @@ -29,6 +30,7 @@ class TrainConfig(pydantic.BaseModel): kl_penalty_source: Literal["current_learner", "sample"] = "current_learner" grad_accumulation_sequences: int | None = pydantic.Field(default=None, ge=1) optimizer_save_interval: int = pydantic.Field(default=5, ge=1) + final_training_step: int | None = pydantic.Field(default=None, ge=1) class MegatronTopologyConfig(pydantic.BaseModel): @@ -37,14 +39,26 @@ class MegatronTopologyConfig(pydantic.BaseModel): ep: int = pydantic.Field(default_factory=_visible_device_count, ge=1) pp: int = pydantic.Field(default=1, ge=1) vpp: int | None = pydantic.Field(default=None, ge=1) + vpp_microbatch_group_size: int | None = pydantic.Field(default=None, ge=1) etp: int = pydantic.Field(default=1, ge=1) + @pydantic.model_validator(mode="after") + def _validate_vpp_group(self) -> "MegatronTopologyConfig": + if self.vpp_microbatch_group_size is None: + return self + if self.vpp is None: + raise ValueError("vpp_microbatch_group_size requires vpp") + if self.vpp_microbatch_group_size < self.pp: + raise ValueError("vpp_microbatch_group_size must be at least pp") + return self + class MegatronRuntimeConfig(pydantic.BaseModel): model_config = pydantic.ConfigDict(frozen=True) topology: MegatronTopologyConfig packed_sequence_length: int = pydantic.Field(ge=1) + snapshot_pool_capacity: int = pydantic.Field(default=2, ge=1, le=4) # The default 2 resident layers / 4 slots is the tested recommendation. # Set ART_MEGATRON_STREAMING_WEIGHT_OFFLOAD_{NUM_LAYERS,NUM_SLOTS,RESIDENT_LAYERS} # before worker startup only when benchmarking a different streaming policy. @@ -92,9 +106,14 @@ class LocalTrainResult(TrainResult): metrics: Aggregated training metrics (loss, gradient norms, etc.). checkpoint_path: Path to the saved checkpoint directory, or None if no checkpoint was saved. + checkpoint_ready: Completion signal for an asynchronously materialized + checkpoint. None when checkpoint_path is already usable. """ checkpoint_path: str | None = None + checkpoint_ready: Awaitable[None] | None = field( + default=None, repr=False, compare=False + ) @dataclass diff --git a/src/art/unsloth/service.py b/src/art/unsloth/service.py index e478e441f..512b1e549 100644 --- a/src/art/unsloth/service.py +++ b/src/art/unsloth/service.py @@ -487,7 +487,6 @@ async def _sync_merged_weights( weights = self._merged_checkpoint_weights_for_vllm() response = await client.post( f"{self._vllm_base_url}/start_weight_update", - json={"is_checkpoint_format": True}, **self._runtime_request_kwargs(), timeout=300.0, ) diff --git a/src/art/utils/cache_dirs.py b/src/art/utils/cache_dirs.py new file mode 100644 index 000000000..9a270f408 --- /dev/null +++ b/src/art/utils/cache_dirs.py @@ -0,0 +1,99 @@ +from collections.abc import MutableMapping +import os +from pathlib import Path + +_DEFAULT_CACHE_ROOT = Path("/tmp/art-cache") + + +def _set_path( + environ: MutableMapping[str, str], + name: str, + default: str | Path, + *, + previous_default: Path | None = None, +) -> Path: + value = environ.get(name) + path = Path(value or default).expanduser() + if previous_default is not None and path == previous_default: + path = Path(default).expanduser() + environ[name] = str(path) + return path + + +def configure_model_cache_env( + environ: MutableMapping[str, str] | None = None, + *, + cache_root: str | Path | None = None, +) -> Path: + """Set node-local cache defaults while preserving explicit paths.""" + environ = os.environ if environ is None else environ + previous_art = environ.get("ART_MEGATRON_CACHE_ROOT") + previous_root = ( + Path(previous_art).expanduser() if previous_art else _DEFAULT_CACHE_ROOT + ) + previous_xdg = Path(environ.get("XDG_CACHE_HOME") or previous_root).expanduser() + previous_hf = Path( + environ.get("HF_HOME") or previous_xdg / "huggingface" + ).expanduser() + previous_hub = Path( + environ.get("HF_HUB_CACHE") + or environ.get("HUGGINGFACE_HUB_CACHE") + or previous_hf / "hub" + ).expanduser() + + selected_root = cache_root if cache_root is not None else previous_art + art_root = Path(selected_root).expanduser() if selected_root is not None else None + if art_root is not None: + environ["ART_MEGATRON_CACHE_ROOT"] = str(art_root) + rebase = cache_root is not None + xdg_root = _set_path( + environ, + "XDG_CACHE_HOME", + art_root or _DEFAULT_CACHE_ROOT, + previous_default=previous_root if rebase else None, + ) + hf_home = _set_path( + environ, + "HF_HOME", + xdg_root / "huggingface", + previous_default=previous_xdg / "huggingface" if rebase else None, + ) + legacy_hub_cache = environ.get("HUGGINGFACE_HUB_CACHE") + hub_default = ( + Path(legacy_hub_cache).expanduser() + if legacy_hub_cache + and (not rebase or Path(legacy_hub_cache).expanduser() != previous_hf / "hub") + else hf_home / "hub" + ) + hub_cache = _set_path( + environ, + "HF_HUB_CACHE", + hub_default, + previous_default=previous_hf / "hub" if rebase else None, + ) + for name, default, previous_default in ( + ("HUGGINGFACE_HUB_CACHE", hub_cache, previous_hf / "hub"), + ("TRANSFORMERS_CACHE", hub_cache, previous_hub), + ("TORCH_HOME", xdg_root / "torch", previous_xdg / "torch"), + ( + "TORCH_EXTENSIONS_DIR", + xdg_root / "torch_extensions", + previous_xdg / "torch_extensions", + ), + ( + "TORCHINDUCTOR_CACHE_DIR", + xdg_root / "torchinductor", + previous_xdg / "torchinductor", + ), + ("TRITON_HOME", xdg_root, previous_xdg), + ("TRITON_CACHE_DIR", xdg_root / "triton", previous_xdg / "triton"), + ("VLLM_CACHE_ROOT", xdg_root / "vllm", previous_xdg / "vllm"), + ("VLLM_CONFIG_ROOT", xdg_root / "vllm_config", previous_xdg / "vllm_config"), + ): + _set_path( + environ, + name, + default, + previous_default=previous_default if rebase else None, + ) + return art_root or xdg_root diff --git a/src/art/utils/chat_template.py b/src/art/utils/chat_template.py index 89043cc19..a6ef0c730 100644 --- a/src/art/utils/chat_template.py +++ b/src/art/utils/chat_template.py @@ -4,6 +4,7 @@ "enable_thinking": False, "preserve_thinking": True, } +TOOL_CALL_ARGUMENTS_AS_MAPPING_ATTR = "_art_tool_call_arguments_as_mapping" def default_chat_template_kwargs_for_template( diff --git a/src/art/utils/lifecycle.py b/src/art/utils/lifecycle.py index 09fa373a8..a672296f6 100644 --- a/src/art/utils/lifecycle.py +++ b/src/art/utils/lifecycle.py @@ -10,11 +10,41 @@ import sys import time from types import FrameType -from typing import Any +from typing import Any, TypeVar PROCESS_SHUTDOWN_TIMEOUT_SECONDS = 20.0 _PROCESS_SHUTDOWN_LEVEL_STEP = 0.1 _PROCESS_SHUTDOWN_SWEEP_GRACE_FRACTION = 0.05 +_T = TypeVar("_T") + + +async def complete_task( + task: asyncio.Task[_T], +) -> tuple[_T, asyncio.CancelledError | None]: + cancelled: asyncio.CancelledError | None = None + while not task.done(): + try: + await asyncio.shield(task) + except asyncio.CancelledError as error: + if task.cancelled(): + break + cancelled = cancelled or error + except BaseException: + break + try: + result = task.result() + except BaseException as error: + if cancelled is not None: + cancelled.add_note(f"operation also failed: {error}") + raise cancelled + raise + return result, cancelled + + +async def complete_to_thread( + operation: Callable[[], _T], +) -> tuple[_T, asyncio.CancelledError | None]: + return await complete_task(asyncio.create_task(asyncio.to_thread(operation))) def process_shutdown_timeout(level: int) -> float: diff --git a/src/art/utils/safetensors.py b/src/art/utils/safetensors.py new file mode 100644 index 000000000..1e883cb08 --- /dev/null +++ b/src/art/utils/safetensors.py @@ -0,0 +1,246 @@ +from collections import deque +from itertools import islice +import json +import os +from pathlib import Path +import struct +import sys +import tempfile +from typing import NamedTuple + +import torch + +_DTYPES = { + dtype: name + for name, dtype in { + "BOOL": torch.bool, + "U8": torch.uint8, + "I8": torch.int8, + "I16": torch.int16, + "I32": torch.int32, + "I64": torch.int64, + "F16": torch.float16, + "BF16": torch.bfloat16, + "F32": torch.float32, + "F64": torch.float64, + "C64": torch.complex64, + "U16": getattr(torch, "uint16", None), + "U32": getattr(torch, "uint32", None), + "U64": getattr(torch, "uint64", None), + "F8_E4M3": getattr(torch, "float8_e4m3fn", None), + "F8_E5M2": getattr(torch, "float8_e5m2", None), + }.items() + if dtype is not None +} + + +class PreparedSafetensors(NamedTuple): + chunks: tuple[torch.Tensor, ...] + + @property + def nbytes(self) -> int: + return sum(chunk.numel() for chunk in self.chunks) + + +class _TensorLayout(NamedTuple): + name: str + dtype: torch.dtype + shape: tuple[int, ...] + storage: int + offset: int + nbytes: int + + +class _StorageLayout(NamedTuple): + nbytes: int + chunks: tuple[tuple[int, int], ...] + + +class SafetensorsLayout: + """Reusable file layout for immutable CPU snapshots with stable shapes.""" + + def __init__(self, tensors: dict[str, torch.Tensor]) -> None: + storage_indices: dict[tuple[int, int], int] = {} + storages: list[list[tuple[int, int]]] = [] + storage_bytes: list[int] = [] + entries: list[_TensorLayout] = [] + for name, tensor in sorted(tensors.items()): + _validate_tensor(name, tensor) + storage = tensor.untyped_storage() + key = storage.data_ptr(), storage.nbytes() + storage_index = storage_indices.get(key) + if storage_index is None: + storage_index = len(storages) + storage_indices[key] = storage_index + storages.append([]) + storage_bytes.append(storage.nbytes()) + offset = tensor.data_ptr() - storage.data_ptr() + entries.append( + _TensorLayout( + name, + tensor.dtype, + tuple(tensor.shape), + storage_index, + offset, + tensor.nbytes, + ) + ) + storages[storage_index].append((offset, tensor.nbytes)) + + layouts: list[_StorageLayout] = [] + for size, intervals in zip(storage_bytes, storages, strict=True): + ordered = sorted(intervals) + cursor = 0 + coalesced = True + for offset, length in ordered: + if offset != cursor: + coalesced = False + break + cursor += length + layouts.append( + _StorageLayout( + size, + ((0, size),) if coalesced and cursor == size else tuple(intervals), + ) + ) + + data_offsets: dict[str, tuple[int, int]] = {} + output_offset = 0 + for storage_index, layout in enumerate(layouts): + storage_entries = [ + entry for entry in entries if entry.storage == storage_index + ] + if len(layout.chunks) == 1 and layout.chunks[0] == (0, layout.nbytes): + for entry in storage_entries: + data_offsets[entry.name] = ( + output_offset + entry.offset, + output_offset + entry.offset + entry.nbytes, + ) + output_offset += layout.nbytes + continue + for entry in storage_entries: + data_offsets[entry.name] = ( + output_offset, + output_offset + entry.nbytes, + ) + output_offset += entry.nbytes + + header = { + entry.name: { + "dtype": _DTYPES[entry.dtype], + "shape": list(entry.shape), + "data_offsets": list(data_offsets[entry.name]), + } + for entry in entries + } + encoded = json.dumps(header, separators=(",", ":")).encode() + encoded += b" " * (-len(encoded) % 8) + self._entries = tuple(entries) + self._storages = tuple(layouts) + self._prefix = torch.frombuffer( + bytearray(struct.pack(" PreparedSafetensors: + bound: list[torch.Tensor | None] = [None] * len(self._storages) + for entry in self._entries: + tensor = tensors.get(entry.name) + if tensor is None: + raise RuntimeError(f"Safetensors tensor disappeared: {entry.name}") + _validate_tensor(entry.name, tensor) + storage = tensor.untyped_storage() + if ( + tensor.dtype != entry.dtype + or tuple(tensor.shape) != entry.shape + or storage.nbytes() != self._storages[entry.storage].nbytes + or tensor.data_ptr() - storage.data_ptr() != entry.offset + ): + raise RuntimeError(f"Safetensors tensor layout changed: {entry.name}") + owner = bound[entry.storage] + if owner is None: + bound[entry.storage] = torch.empty(0, dtype=torch.uint8).set_( + storage, 0, (storage.nbytes(),), (1,) + ) + elif owner.untyped_storage().data_ptr() != storage.data_ptr(): + raise RuntimeError("Safetensors storage aliasing changed") + if len(tensors) != len(self._entries): + raise RuntimeError("Safetensors tensor set changed") + owners = tuple(owner for owner in bound if owner is not None) + if len(owners) != len(bound): + raise RuntimeError("Safetensors storage disappeared") + return PreparedSafetensors( + ( + self._prefix, + *( + owner.narrow(0, offset, length) + for owner, layout in zip(owners, self._storages, strict=True) + for offset, length in layout.chunks + ), + ) + ) + + +def _writev_all(fd: int, buffers: list[memoryview]) -> None: + pending = deque(buffer for buffer in buffers if buffer.nbytes) + iov_max = os.sysconf("SC_IOV_MAX") + while pending: + written = os.writev(fd, tuple(islice(pending, iov_max))) + if written <= 0: + raise OSError("Short vectored write") + while pending and written >= pending[0].nbytes: + written -= pending.popleft().nbytes + if written: + pending[0] = pending[0][written:] + + +def _validate_tensor(name: str, tensor: torch.Tensor) -> None: + if sys.byteorder != "little": + raise RuntimeError("ART's zero-copy safetensors writer requires little endian") + if tensor.device.type != "cpu" or not tensor.is_contiguous(): + raise RuntimeError(f"Tensor {name!r} must be contiguous CPU storage") + if tensor.dtype not in _DTYPES: + raise RuntimeError(f"Unsupported safetensors dtype: {tensor.dtype}") + + +def prepare_safetensors(tensors: dict[str, torch.Tensor]) -> PreparedSafetensors: + entries: list[tuple[str, torch.Tensor]] = [] + data_offsets: dict[str, tuple[int, int]] = {} + offset = 0 + for name, tensor in sorted(tensors.items()): + _validate_tensor(name, tensor) + entries.append((name, tensor)) + data_offsets[name] = offset, offset + tensor.nbytes + offset += tensor.nbytes + header = { + name: { + "dtype": _DTYPES[tensor.dtype], + "shape": list(tensor.shape), + "data_offsets": list(data_offsets[name]), + } + for name, tensor in entries + } + encoded = json.dumps(header, separators=(",", ":")).encode() + encoded += b" " * (-len(encoded) % 8) + prefix = torch.frombuffer( + bytearray(struct.pack(" None: + """Stream a prepared safetensors payload without rebuilding tensor metadata.""" + with tempfile.TemporaryDirectory(dir=path.parent) as temp_dir: + temporary_path = Path(temp_dir) / path.name + with temporary_path.open("wb", buffering=0) as output: + _writev_all( + output.fileno(), + [memoryview(chunk.numpy()) for chunk in prepared.chunks], + ) + temporary_path.replace(path) + + +def save_safetensors(tensors: dict[str, torch.Tensor], path: Path) -> None: + """Stream CPU tensor buffers without copying them into GIL-held bytes.""" + save_prepared_safetensors(prepare_safetensors(tensors), path) diff --git a/src/art/vllm_route_transport.py b/src/art/vllm_route_transport.py index 8d3b5826f..23803fdab 100644 --- a/src/art/vllm_route_transport.py +++ b/src/art/vllm_route_transport.py @@ -8,8 +8,10 @@ if TYPE_CHECKING: import numpy as np -MAGIC = b"ARTRTE1\0" -HEADER = struct.Struct("<8sQI") +from art.preprocessing.moe_routing import MoeRouteArray + +MAGIC = b"ARTRTE2\0" +HEADER = struct.Struct("<8sQII") ROUTE_HEADER = struct.Struct(" bool: def decode_routed_experts_response( body: bytes, -) -> tuple[ChatCompletion, dict[int, np.ndarray]]: +) -> tuple[ChatCompletion, dict[int, MoeRouteArray]]: import numpy as np if len(body) < HEADER.size: raise RuntimeError("Truncated ART routed-experts response header") - magic, json_size, route_count = HEADER.unpack_from(body) + magic, json_size, route_count, num_experts = HEADER.unpack_from(body) if magic != MAGIC: raise RuntimeError("Invalid ART routed-experts response magic") offset = HEADER.size @@ -34,7 +36,7 @@ def decode_routed_experts_response( raise RuntimeError("Truncated ART routed-experts JSON response") response = ChatCompletion.model_validate_json(body[offset:json_end]) offset = json_end - routes: dict[int, np.ndarray] = {} + routes: dict[int, MoeRouteArray] = {} for _ in range(route_count): if offset + ROUTE_HEADER.size > len(body): raise RuntimeError("Truncated ART routed-experts array header") @@ -55,7 +57,9 @@ def decode_routed_experts_response( array = np.frombuffer( body, dtype=dtype, count=tokens * layers * topk, offset=offset ) - routes[choice_index] = array.reshape((tokens, layers, topk)) + routes[choice_index] = MoeRouteArray( + array.reshape((tokens, layers, topk)), num_experts=num_experts + ) offset = end if offset != len(body): raise RuntimeError("Unexpected trailing bytes in ART routed-experts response") diff --git a/src/art/vllm_runtime.py b/src/art/vllm_runtime.py index 2133db127..112bb3cf5 100644 --- a/src/art/vllm_runtime.py +++ b/src/art/vllm_runtime.py @@ -14,8 +14,9 @@ from urllib.parse import urlparse import httpx -from pydantic import BaseModel, ConfigDict, Field +from pydantic import BaseModel, ConfigDict, Field, model_validator +from .utils.cache_dirs import configure_model_cache_env from .utils.lifecycle import ( ChildProcessSupervisor, managed_process_cmd, @@ -41,18 +42,87 @@ VLLM_RUNTIME_CLOSE_TIMEOUT = process_shutdown_timeout(1) +def _managed_runtime_extra() -> Literal["cuda12", "cuda13"]: + override = os.environ.get("ART_VLLM_RUNTIME_CUDA_PROFILE") + if override is not None: + if override == "cuda12": + return "cuda12" + if override == "cuda13": + return "cuda13" + raise ValueError("ART_VLLM_RUNTIME_CUDA_PROFILE must be 'cuda12' or 'cuda13'") + cuda_home = Path(os.environ.get("CUDA_HOME", "/usr/local/cuda")) + commands = ([str(cuda_home / "bin" / "nvcc"), "--version"], ["nvidia-smi"]) + for command in commands: + try: + output = subprocess.run( + command, capture_output=True, text=True, check=False + ).stdout + except FileNotFoundError: + continue + if "release 13." in output or "CUDA Version: 13." in output: + return "cuda13" + return "cuda12" + + +MANAGED_RUNTIME_EXTRA = _managed_runtime_extra() + + class VllmRuntimeLaunchConfig(BaseModel): model_config = ConfigDict(arbitrary_types_allowed=True) base_model: str port: int host: str = "127.0.0.1" - cuda_visible_devices: str + cuda_visible_devices: str | None = None + local_gpu_ids: tuple[int, ...] | None = None lora_path: str | None = None served_model_name: str rollout_weights_mode: Literal["lora", "merged"] engine_args: dict[str, object] = Field(default_factory=dict) server_args: dict[str, object] = Field(default_factory=dict) + nnodes: int = Field(default=1, ge=1) + node_rank: int = Field(default=0, ge=0) + master_addr: str | None = None + master_port: int | None = Field(default=None, ge=1, le=65535) + headless: bool = False + replica_generation: int = Field(default=0, ge=0) + process_uuid: str | None = None + update_identity: str | None = None + initial_policy_version: int | None = Field(default=None, ge=0) + + @model_validator(mode="after") + def _validate_native_member(self) -> "VllmRuntimeLaunchConfig": + explicit = self.local_gpu_ids + if explicit is not None: + if not explicit or any(gpu_id < 0 for gpu_id in explicit): + raise ValueError("local_gpu_ids must contain non-negative GPU IDs") + if len(set(explicit)) != len(explicit): + raise ValueError("local_gpu_ids must be unique") + visible = ",".join(map(str, explicit)) + if self.cuda_visible_devices not in (None, visible): + raise ValueError("cuda_visible_devices must match local_gpu_ids") + elif not self.cuda_visible_devices: + raise ValueError("cuda_visible_devices or local_gpu_ids is required") + if self.node_rank >= self.nnodes: + raise ValueError("node_rank must be smaller than nnodes") + if self.nnodes == 1: + if self.node_rank or self.headless or self.master_addr or self.master_port: + raise ValueError("single-node launch cannot set native member options") + else: + if self.master_addr is None or self.master_port is None: + raise ValueError( + "multi-node launch requires master_addr and master_port" + ) + if self.headless != (self.node_rank != 0): + raise ValueError("exactly nonzero node ranks must be headless") + return self + + @property + def visible_devices(self) -> str: + if self.local_gpu_ids is not None: + return ",".join(map(str, self.local_gpu_ids)) + assert self.cuda_visible_devices is not None + return self.cuda_visible_devices class ExternalVllmRuntimeConfig(BaseModel): @@ -92,6 +162,7 @@ class VllmRuntimeInstallMarker(BaseModel): protocol_version: int = RUNTIME_PROTOCOL_VERSION manifest_hash: str runtime_wheel_sha256: str + runtime_extra: Literal["cuda12", "cuda13"] = MANAGED_RUNTIME_EXTRA cache_root: str @@ -194,7 +265,9 @@ def _drop_tilelang_env_paths(value: str | None) -> str | None: return os.pathsep.join(kept) if kept else None -def _vllm_runtime_subprocess_env() -> dict[str, str]: +def _vllm_runtime_subprocess_env( + runtime_command: list[str] | None = None, +) -> dict[str, str]: """Build a child env isolated from runtime-specific JIT path leaks. TileLang mutates process env during import. If a vLLM runtime child inherits @@ -206,12 +279,53 @@ def _vllm_runtime_subprocess_env() -> dict[str, str]: make one runtime compile kernels from another runtime's venv. """ env = os.environ.copy() + configure_model_cache_env(env) + for key in ("PYTORCH_ALLOC_CONF", "PYTORCH_CUDA_ALLOC_CONF"): + options = [ + option + for option in env.get(key, "").split(",") + if option and option != "expandable_segments:True" + ] + if options: + env[key] = ",".join(options) + else: + env.pop(key, None) + service_prefixes = { + key.removesuffix("_SERVICE_HOST") + for key in env + if key.startswith("VLLM_") and key.endswith("_SERVICE_HOST") + } + for key in tuple(env): + if any( + key.startswith(f"{prefix}_SERVICE_") + or key == f"{prefix}_PORT" + or key.startswith(f"{prefix}_PORT_") + for prefix in service_prefixes + ): + env.pop(key) for key in _TILELANG_ENV_KEYS: value = _drop_tilelang_env_paths(env.get(key)) if value is None: env.pop(key, None) else: env[key] = value + runtime_dir = ( + _runtime_dir_from_bin(Path(runtime_command[0])) if runtime_command else None + ) + if runtime_dir is not None: + env.pop("PYTHONPATH", None) + nvidia_libs = sorted( + str(path) + for site_packages in (runtime_dir / ".venv" / "lib").glob( + "python*/site-packages" + ) + for path in (site_packages / "nvidia").glob("*/lib") + if path.is_dir() + ) + inherited = env.get("LD_LIBRARY_PATH", "").split(os.pathsep) + env["LD_LIBRARY_PATH"] = os.pathsep.join( + (*nvidia_libs, *(path for path in inherited if "/nvidia/" not in path)) + ) env[_FLASHINFER_WORKSPACE_ENV] = str(_vllm_runtime_flashinfer_workspace_base()) return env @@ -248,7 +362,9 @@ async def start( self.host = launch_config.host self.port = launch_config.port api_key = launch_config.server_args.get("api_key") - self.api_key = api_key if isinstance(api_key, str) else None + if api_key is not None and (not isinstance(api_key, str) or not api_key): + raise ValueError("vLLM api_key must be a non-empty string") + self.api_key = api_key self.nccl_so_path = ( str(get_vllm_runtime_nccl_so_path()) if launch_config.rollout_weights_mode == "merged" @@ -261,10 +377,17 @@ async def start( os.makedirs(log_dir, exist_ok=True) self.log_path = os.path.join(log_dir, "vllm-runtime.log") self.log_file = open(self.log_path, "w", buffering=1) + env = _vllm_runtime_subprocess_env(cmd) + env.pop("VLLM_API_KEY", None) + if self.api_key is not None: + env["VLLM_API_KEY"] = self.api_key self.process = subprocess.Popen( managed_process_cmd(cmd), - cwd=str(get_vllm_runtime_working_dir()), - env=_vllm_runtime_subprocess_env(), + cwd=str(_vllm_runtime_subprocess_cwd(cmd)), + env={ + **env, + "CUDA_VISIBLE_DEVICES": launch_config.visible_devices, + }, stdout=self.log_file, stderr=subprocess.STDOUT, bufsize=1, @@ -276,6 +399,24 @@ async def start( if timeout is not None else float(os.environ.get("ART_DEDICATED_VLLM_TIMEOUT", 1200)) ) + if launch_config.headless: + await asyncio.sleep(0.1) + if self.process.poll() is not None: + returncode = self.process.returncode + log_path = self.log_path + self._cleanup_after_start_error(cleanup_on_error) + raise RuntimeError( + f"headless vLLM member exited with code {returncode}. " + f"Check logs at {log_path}" + ) + assert self.log_path is not None + child_processes.watch_popen( + f"vLLM headless member {launch_config.node_rank}", + self.process, + log_path=self.log_path, + ) + return self.host, self.port + async with httpx.AsyncClient() as client: try: await wait_for_vllm_runtime( @@ -283,6 +424,7 @@ async def start( host=self.host, port=self.port, timeout=runtime_timeout, + log_path=self.log_path, ) except TimeoutError as exc: log_path = self.log_path @@ -296,10 +438,36 @@ async def start( log_path = self.log_path self._cleanup_after_start_error(cleanup_on_error) raise RuntimeError( - f"vLLM subprocess exited with code {returncode}. " + f"vLLM subprocess failed during startup " + f"(returncode={returncode}): {exc}. " f"Check logs at {log_path}" ) from exc + if launch_config.process_uuid is not None: + try: + response = await client.get( + f"{self.base_url}/art/state", + **self.request_kwargs(), + timeout=5.0, + ) + response.raise_for_status() + state = response.json() + expected = { + "process_uuid": launch_config.process_uuid, + "generation": launch_config.replica_generation, + } + if any(state.get(key) != value for key, value in expected.items()): + raise RuntimeError( + f"vLLM /art/state identity mismatch: {state!r}" + ) + except (httpx.HTTPError, RuntimeError, ValueError) as exc: + log_path = self.log_path + self._cleanup_after_start_error(cleanup_on_error) + raise RuntimeError( + "vLLM passed readiness but /art/state was invalid. " + f"Check logs at {log_path}" + ) from exc + try: response = await client.get( f"{self.base_url}/v1/models", @@ -371,16 +539,13 @@ def get_vllm_runtime_cache_root() -> Path: override = os.environ.get("ART_VLLM_RUNTIME_CACHE_DIR") if override: return Path(override).expanduser() - return Path.home() / ".cache" / "art" / "vllm_runtime" + return configure_model_cache_env(os.environ.copy()) / "vllm_runtime" def _vllm_runtime_flashinfer_workspace_base() -> Path: override = os.environ.get(_ART_FLASHINFER_WORKSPACE_ENV) if override: return Path(override).expanduser() - runtime_root = get_vllm_runtime_project_root() - if runtime_root.exists(): - return runtime_root.resolve().parent / "scratch" / "vllm_runtime_flashinfer" return get_vllm_runtime_cache_root().expanduser() / "flashinfer_workspace" @@ -401,6 +566,7 @@ def _runtime_python(runtime_dir: Path) -> Path: def _runtime_dir_from_bin(runtime_bin: Path) -> Path | None: + runtime_bin = runtime_bin.expanduser().resolve() if ( runtime_bin.name == RUNTIME_SERVER and runtime_bin.parent.name == "bin" @@ -410,6 +576,13 @@ def _runtime_dir_from_bin(runtime_bin: Path) -> Path | None: return None +def _vllm_runtime_subprocess_cwd(runtime_command: list[str] | None = None) -> Path: + runtime_dir = ( + _runtime_dir_from_bin(Path(runtime_command[0])) if runtime_command else None + ) + return runtime_dir or get_vllm_runtime_working_dir() + + def _is_executable_file(path: Path) -> bool: return path.is_file() and os.access(path, os.X_OK) @@ -423,7 +596,10 @@ def _sha256_file(path: Path) -> str: def _manifest_hash(manifest: VllmRuntimeManifest) -> str: - payload = json.dumps(manifest.model_dump(), sort_keys=True).encode() + payload = json.dumps( + {"manifest": manifest.model_dump(), "runtime_extra": MANAGED_RUNTIME_EXTRA}, + sort_keys=True, + ).encode() return hashlib.sha256(payload).hexdigest() @@ -533,6 +709,8 @@ def _validate_managed_runtime( return None if marker.runtime_wheel_sha256 != manifest.runtime_wheel_sha256: return None + if marker.runtime_extra != MANAGED_RUNTIME_EXTRA: + return None runtime_bin = _runtime_bin(runtime_dir) if not _is_executable_file(runtime_bin): return None @@ -578,6 +756,8 @@ def _install_managed_runtime( "sync", "--project", str(stage), + "--extra", + MANAGED_RUNTIME_EXTRA, "--frozen", "--no-install-project", "--no-dev", @@ -619,6 +799,7 @@ def _install_managed_runtime( protocol_version=manifest.protocol_version, manifest_hash=manifest_hash, runtime_wheel_sha256=manifest.runtime_wheel_sha256, + runtime_extra=MANAGED_RUNTIME_EXTRA, cache_root=str(cache_root.resolve()), ) _install_marker_path(runtime_dir).write_text( @@ -632,6 +813,7 @@ def _install_managed_runtime( def ensure_vllm_runtime() -> Path: + configure_model_cache_env() bundle_dir = _bundled_runtime_dir() manifest = _load_bundled_manifest(bundle_dir) manifest_hash = _manifest_hash(manifest) @@ -665,8 +847,8 @@ def _runtime_python_for_nccl_discovery() -> Path: runtime_dir = _runtime_dir_from_bin(runtime_bin) if runtime_dir is None: raise RuntimeError( - "Cannot infer vLLM runtime Python from ART_VLLM_RUNTIME_BIN. " - "Merged rollout weights require ART's source or managed vLLM runtime." + "ART_VLLM_RUNTIME_BIN must point directly to a " + ".venv/bin/art-vllm-runtime-server executable" ) return _runtime_python(runtime_dir) @@ -689,7 +871,7 @@ def get_vllm_runtime_nccl_so_path() -> Path: "import importlib.util\n" "spec = importlib.util.find_spec('nvidia.nccl')\n" "if spec is None or spec.submodule_search_locations is None:\n" - " raise SystemExit('vLLM runtime is missing nvidia-nccl-cu12')\n" + " raise SystemExit('vLLM runtime is missing its NVIDIA NCCL package')\n" "package_dir = Path(next(iter(spec.submodule_search_locations)))\n" "path = package_dir / 'lib' / 'libnccl.so.2'\n" "if not path.exists():\n" @@ -718,7 +900,11 @@ def get_vllm_runtime_nccl_so_path() -> Path: def _runtime_command_prefix() -> list[str]: override = os.environ.get("ART_VLLM_RUNTIME_BIN") if override: - return shlex.split(override) + command = shlex.split(override) + runtime_dir = _runtime_dir_from_bin(Path(command[0])) + if runtime_dir is not None: + command[0] = str(_runtime_bin(runtime_dir)) + return command runtime_bin = _source_runtime_bin() if runtime_bin.exists(): return [str(runtime_bin)] @@ -735,12 +921,15 @@ def _runtime_command_prefix() -> list[str]: def build_vllm_runtime_server_cmd(config: VllmRuntimeLaunchConfig) -> list[str]: + server_args = { + key: value for key, value in config.server_args.items() if key != "api_key" + } command = [ *_runtime_command_prefix(), f"--model={config.base_model}", f"--port={config.port}", f"--host={config.host}", - f"--cuda-visible-devices={config.cuda_visible_devices}", + f"--cuda-visible-devices={config.visible_devices}", ] if config.lora_path is not None: command.append(f"--lora-path={config.lora_path}") @@ -749,9 +938,31 @@ def build_vllm_runtime_server_cmd(config: VllmRuntimeLaunchConfig) -> list[str]: f"--served-model-name={config.served_model_name}", f"--rollout-weights-mode={config.rollout_weights_mode}", f"--engine-args-json={json.dumps(config.engine_args)}", - f"--server-args-json={json.dumps(config.server_args)}", + f"--server-args-json={json.dumps(server_args)}", ] ) + if config.nnodes > 1: + command.extend( + [ + f"--nnodes={config.nnodes}", + f"--node-rank={config.node_rank}", + f"--master-addr={config.master_addr}", + f"--master-port={config.master_port}", + ] + ) + if config.headless: + command.append("--headless") + if config.process_uuid is not None: + command.extend( + [ + f"--replica-generation={config.replica_generation}", + f"--process-uuid={config.process_uuid}", + ] + ) + if config.update_identity is not None: + command.append(f"--update-identity={config.update_identity}") + if config.initial_policy_version is not None: + command.append(f"--initial-policy-version={config.initial_policy_version}") return command @@ -761,15 +972,35 @@ async def wait_for_vllm_runtime( host: str, port: int, timeout: float, + log_path: str | None = None, ) -> None: deadline = asyncio.get_running_loop().time() + timeout url = f"http://{host}:{port}/health" + log_offset = 0 + log_tail = "" + fatal_markers = ( + "EngineCore failed to start", + "Engine core initialization failed", + ) async with httpx.AsyncClient() as client: while True: if process.poll() is not None: raise RuntimeError( f"vLLM runtime exited with code {process.returncode}" ) + if log_path is not None: + try: + with open(log_path, "rb") as log: + log.seek(log_offset) + payload = log.read() + log_offset = log.tell() + except FileNotFoundError: + payload = b"" + log_tail = (log_tail + payload.decode(errors="replace"))[-8192:] + if marker := next( + (marker for marker in fatal_markers if marker in log_tail), None + ): + raise RuntimeError(f"vLLM reported fatal startup failure: {marker}") try: response = await client.get(url, timeout=5.0) if response.status_code == 200: diff --git a/src/art/weight_transfer/nccl.py b/src/art/weight_transfer/nccl.py index eb7adafb5..3fb100bd5 100644 --- a/src/art/weight_transfer/nccl.py +++ b/src/art/weight_transfer/nccl.py @@ -390,13 +390,13 @@ def _find_nccl_library() -> str: spec = importlib.util.find_spec("nvidia.nccl") if spec is None or spec.submodule_search_locations is None: raise RuntimeError( - "CUDA weight transfer requires the nvidia-nccl-cu12 package." + "CUDA weight transfer requires the matching nvidia-nccl-cu12 or nvidia-nccl-cu13 package." ) nccl_library = ( Path(next(iter(spec.submodule_search_locations))) / "lib" / "libnccl.so.2" ) if not nccl_library.exists(): - raise RuntimeError(f"nvidia-nccl-cu12 is missing {nccl_library}") + raise RuntimeError(f"The NVIDIA NCCL package is missing {nccl_library}") return str(nccl_library) if torch.version.hip is not None: return "librccl.so.1" diff --git a/tests/integration/distributed/test_trajectory_queue.py b/tests/integration/distributed/test_trajectory_queue.py new file mode 100644 index 000000000..a18dcd557 --- /dev/null +++ b/tests/integration/distributed/test_trajectory_queue.py @@ -0,0 +1,223 @@ +import asyncio +from collections.abc import Callable +from unittest.mock import AsyncMock + +import pytest + +from art.distributed.rollout import ( + DistributedTrajectoryQueue, + DistributedTrajectorySelection, + _InProcessTrajectoryQueueEndpoint, +) +from art.distributed.trajectory_store import ( + TrajectoryCapacityError, + TrajectoryEnqueueResult, + TrajectoryGroupAnnotations, + TrajectoryGroupDescriptor, + TrajectoryGroupRef, + TrajectoryQueueItem, + TrajectoryRecordRef, +) + + +def _item( + result_id: str, *, records: int = 1, byte_count: int = 1 +) -> TrajectoryQueueItem: + return TrajectoryQueueItem( + ref=TrajectoryGroupRef( + result_id=result_id, + owner_actor_id="owner", + lease_id=f"lease-{result_id}", + records=tuple( + TrajectoryRecordRef( + record_id=f"{result_id}-{index}", + owner_actor_id="owner", + byte_count=1, + ) + for index in range(records) + ), + descriptor=TrajectoryGroupDescriptor( + grouping_key=result_id, + trajectory_count=records, + exception_count=0, + rewards=(0.0,) * records, + initial_policy_versions=(0,) * records, + completion_tokens=(1.0,) * records, + policy_token_counts={}, + trajectory_initial_policy_versions=(0,) * records, + trajectory_final_policy_versions=(0,) * records, + trajectory_policy_token_counts=({},) * records, + trajectory_metrics=({},) * records, + trajectory_metadata=({},) * records, + group_metadata={}, + group_metrics={}, + exceptions=(), + byte_count=byte_count, + ), + ), + annotations=TrajectoryGroupAnnotations( + initial_policy_version=0, + final_policy_version=0, + ), + ) + + +async def _put(queue: DistributedTrajectoryQueue, item: TrajectoryQueueItem) -> bool: + accepted, _ = await queue.put( + item.ref, + metadata={}, + initial_policy_version=0, + final_policy_version=0, + rollout_wall_s=0.0, + actor_idle_s=0.0, + ) + return accepted + + +async def _wait_until(condition: Callable[[], bool]) -> None: + for _ in range(100): + if condition(): + return + await asyncio.sleep(0) + raise AssertionError("condition was not reached") + + +class _ObservedQueueEndpoint(_InProcessTrajectoryQueueEndpoint): + def __init__(self) -> None: + super().__init__() + self.enqueue_results: list[TrajectoryEnqueueResult] = [] + + async def enqueue( + self, queue_id: str, item: TrajectoryQueueItem + ) -> TrajectoryEnqueueResult: + result = await super().enqueue(queue_id, item) + self.enqueue_results.append(result) + return result + + +@pytest.mark.asyncio +async def test_packing_occupancy_backpressures_until_lease_release() -> None: + endpoint = _ObservedQueueEndpoint() + queue = DistributedTrajectoryQueue( + endpoint=endpoint, + owner_endpoints={"owner": AsyncMock()}, + maxsize=6, + capacity_records=8, + capacity_bytes=8, + ) + await queue.start() + for index in range(6): + assert await _put(queue, _item(f"initial-{index}")) + + groups, closed = await queue.get_many(6, wait=True) + assert len(groups) == 6 + assert not closed + snapshot = await queue.snapshot() + assert ( + snapshot.ready_groups, + snapshot.packing_groups, + snapshot.packed_groups, + len(snapshot.items), + snapshot.max_ready_groups, + ) == (0, 6, 0, 6, 6) + + pending_take = asyncio.create_task(queue.get_many(2, wait=True)) + await _wait_until(lambda: queue._minimum_take_size == 2) + blocked_put = asyncio.create_task(_put(queue, _item("blocked"))) + await _wait_until(lambda: len(endpoint.enqueue_results) == 7) + assert endpoint.enqueue_results[-1].status == "full" + await asyncio.sleep(0) + assert not blocked_put.done() + + selections = [] + for group in groups: + selection = group._distributed_lease + assert isinstance(selection, DistributedTrajectorySelection) + selections.append(selection) + await queue.mark_packed(selections, "generation") + await queue.release_selections( + selections, + disposition="consumed", + generation_id="generation", + ) + assert await blocked_put + assert await _put(queue, _item("unblocks-minimum")) + + acquired, closed = await pending_take + assert len(acquired) == 2 + assert not closed + for group in acquired: + await queue.discard_group(group) + await queue.close() + + +@pytest.mark.parametrize( + ("capacity_records", "capacity_bytes", "blocker"), + ((1, 8, "record capacity"), (8, 1, "byte capacity")), +) +@pytest.mark.asyncio +async def test_ready_occupancy_makes_limit_failure_sticky( + capacity_records: int, capacity_bytes: int, blocker: str +) -> None: + queue = DistributedTrajectoryQueue( + endpoint=_InProcessTrajectoryQueueEndpoint(), + owner_endpoints={"owner": AsyncMock()}, + maxsize=6, + capacity_records=capacity_records, + capacity_bytes=capacity_bytes, + ) + await queue.start() + assert await _put(queue, _item("ready")) + pending_take = asyncio.create_task(queue.get_many(2, wait=True)) + await _wait_until(lambda: queue._minimum_take_size == 2) + + with pytest.raises(TrajectoryCapacityError) as blocked_error: + await _put(queue, _item("blocked")) + with pytest.raises(TrajectoryCapacityError) as take_error: + await pending_take + with pytest.raises(TrajectoryCapacityError) as sticky_error: + await _put(queue, _item("later")) + assert blocker in str(blocked_error.value) + assert str(take_error.value) == str(blocked_error.value) + assert str(sticky_error.value) == str(blocked_error.value) + await queue.close() + + +@pytest.mark.asyncio +async def test_minimum_larger_than_group_capacity_fails_promptly() -> None: + queue = DistributedTrajectoryQueue( + endpoint=_InProcessTrajectoryQueueEndpoint(), + owner_endpoints={}, + maxsize=6, + capacity_records=8, + capacity_bytes=8, + ) + await queue.start() + with pytest.raises( + TrajectoryCapacityError, + match="minimum acquisition requires 7 trajectory groups", + ): + await queue.get_many(7, wait=True) + await queue.close() + + +@pytest.mark.asyncio +async def test_pending_minimum_defers_shrink_until_cancelled() -> None: + queue = DistributedTrajectoryQueue( + endpoint=_InProcessTrajectoryQueueEndpoint(), + owner_endpoints={}, + maxsize=6, + capacity_records=8, + capacity_bytes=8, + ) + await queue.start() + pending_take = asyncio.create_task(queue.get_many(2, wait=True)) + await _wait_until(lambda: queue._minimum_take_size == 2) + + queue.set_maxsize(1) + assert (await queue.snapshot()).max_ready_groups == 2 + pending_take.cancel() + with pytest.raises(asyncio.CancelledError): + await pending_take + assert (await queue.snapshot()).max_ready_groups == 1 + await queue.close() diff --git a/tests/integration/megatron/gdn_shared_prefix/test_gdn_cp_train_prepare.py b/tests/integration/megatron/gdn_shared_prefix/test_gdn_cp_train_prepare.py index 4a0622c6c..dc4e399a1 100644 --- a/tests/integration/megatron/gdn_shared_prefix/test_gdn_cp_train_prepare.py +++ b/tests/integration/megatron/gdn_shared_prefix/test_gdn_cp_train_prepare.py @@ -25,6 +25,7 @@ ParallelTopology, ) from art.megatron.gdn.gdn_prefix_tree import GdnPlannerConfig # noqa: E402 +from art.megatron.selective_lm_head import LmHeadTokenSelection # noqa: E402 from art.preprocessing.pack import PackedTensors # noqa: E402 from .cases import default_phase0_cases # noqa: E402 @@ -171,9 +172,14 @@ def test_main_loss_matches_shifted_dispatched_loss_inputs() -> None: ) ref_logprobs = torch.tensor([[-0.9, -0.7, -0.6, -0.8, -0.55, -0.5]]) entropies = torch.tensor([[0.0, 0.2, 0.4, 0.6, 0.8, 0.0]]) + dispatched_labels = torch.where( + shift_tensor(packed["assistant_mask"], False), + shift_tensor(packed["tokens"], -100), + torch.full_like(packed["tokens"], -100), + ) dispatched = DispatchedPackedTensors( tokens=packed["tokens"], - labels=shift_tensor(packed["tokens"], -100), + labels=dispatched_labels, input_pos=packed["input_pos"], assistant_mask=shift_tensor(packed["assistant_mask"], False), group_ids=shift_tensor(packed["group_ids"], 0), @@ -181,6 +187,7 @@ def test_main_loss_matches_shifted_dispatched_loss_inputs() -> None: advantages=shift_tensor(packed["advantages"], 0.0), weights=shift_tensor(packed["weights"], 0.0), valid_lengths=(6,), + lm_head_selection=LmHeadTokenSelection.from_labels(dispatched_labels), original_logprobs=shift_tensor(packed["original_logprobs"], 0.0), ref_logprobs=ref_logprobs, ) diff --git a/tests/integration/megatron/lora/merged_vllm_serving.py b/tests/integration/megatron/lora/merged_vllm_serving.py index 6909ca461..cd6b21a03 100644 --- a/tests/integration/megatron/lora/merged_vllm_serving.py +++ b/tests/integration/megatron/lora/merged_vllm_serving.py @@ -6,13 +6,15 @@ from pathlib import Path import socket from typing import Any, Iterator, cast +from urllib.parse import urlparse from pydantic import BaseModel, Field import torch import art from art import dev -from art.megatron.service import MegatronService +from art.megatron.backend import MegatronBackend +from art.megatron.distributed_service import DistributedMegatronService from ..model_support.oracle_harness import ( ORACLE_TOPOLOGY, @@ -115,7 +117,12 @@ async def _run_merged_vllm_serving( ) topology: Topology = ORACLE_TOPOLOGY megatron_env: dict[str, str] = {} - engine_args: dev.EngineArgs = dev.EngineArgs() + engine_args: dev.EngineArgs = dev.EngineArgs( + enforce_eager=True, + max_model_len=128, + max_num_seqs=4, + limit_mm_per_prompt={"image": 0, "video": 0, "audio": 0}, + ) if stage_resources is not None: stage_resources = resolve_stage_resources_for_visible_gpus( "merged_vllm_serving", @@ -148,10 +155,12 @@ async def _run_merged_vllm_serving( engine_args = cast(dev.EngineArgs, stage_resources.vllm.engine_args()) else: trainer_gpu_ids, inference_gpu_ids = _resolve_dedicated_gpu_ids() - service_name = "model_support_merged_validation" + if case_config.is_moe: + engine_args["moe_backend"] = "triton" case_artifacts = ensure_case_artifacts(case_config) - output_dir = str(Path(case_artifacts.case_dir) / "merged_vllm_serving") - os.makedirs(output_dir, exist_ok=True) + service_name = f"model_support_merged_validation_{case_artifacts.case_id[-12:]}" + backend_root = str(Path(case_artifacts.case_dir) / "merged_vllm_serving") + os.makedirs(backend_root, exist_ok=True) internal_config = dev.InternalModelConfig( trainer_gpu_ids=trainer_gpu_ids, inference_gpu_ids=inference_gpu_ids, @@ -163,20 +172,32 @@ async def _run_merged_vllm_serving( dev.validate_dedicated_config(internal_config) with _temporary_env(megatron_env), provider_topology_env(topology): _init_runtime_config(case_config, topology) - service = MegatronService( - model_name=service_name, + backend = MegatronBackend(path=backend_root) + model = art.TrainableModel( + name=service_name, + run_name=service_name, + project="model-support-validation", base_model=case_config.base_model, - config=internal_config, - output_dir=output_dir, + _internal_config=internal_config, + report_metrics=[], ) port = _find_free_port() try: - host, resolved_port = await service.start_openai_server( - {"server_args": {"port": port}} + await model.register(backend, {"server_args": {"port": port}}) + service = cast( + DistributedMegatronService, await backend._get_service(model) + ) + endpoint = urlparse(service._base_url or "") + host, resolved_port = ( + endpoint.hostname or "127.0.0.1", + int(endpoint.port or port), ) + output_dir = service.output_dir import httpx - async with httpx.AsyncClient() as client: + api_key = service._api_key() + headers = {"Authorization": f"Bearer {api_key}"} if api_key else None + async with httpx.AsyncClient(headers=headers) as client: models_response = await client.get( f"http://{host}:{resolved_port}/v1/models", timeout=60.0, @@ -193,7 +214,7 @@ async def _run_merged_vllm_serving( f"http://{host}:{resolved_port}/v1/completions", json={ "model": served_model_name, - "prompt": "Hello", + "prompt": [100], "max_tokens": 1, "temperature": 0.0, }, @@ -216,7 +237,7 @@ async def _run_merged_vllm_serving( completion_text=completion_text, ) finally: - service.close() + await backend.close() def run_merged_vllm_serving( diff --git a/tests/integration/megatron/lora/native_vllm_lora.py b/tests/integration/megatron/lora/native_vllm_lora.py index d0040e860..cc0da0e22 100644 --- a/tests/integration/megatron/lora/native_vllm_lora.py +++ b/tests/integration/megatron/lora/native_vllm_lora.py @@ -3,18 +3,18 @@ import asyncio import os from pathlib import Path -import shutil import socket import tempfile from typing import cast +from urllib.parse import urlparse from pydantic import BaseModel, Field import torch import art from art import dev -from art.megatron.service import MegatronService -from art.utils.output_dirs import get_step_checkpoint_dir +from art.megatron.backend import MegatronBackend +from art.megatron.distributed_service import DistributedMegatronService from ..model_support.oracle_harness import ( ORACLE_TOPOLOGY, @@ -96,7 +96,7 @@ async def _completion_text(client, base_url: str, model_name: str) -> str: f"{base_url}/v1/completions", json={ "model": model_name, - "prompt": "Hello", + "prompt": [100], "max_tokens": 1, "temperature": 0.0, }, @@ -106,12 +106,6 @@ async def _completion_text(client, base_url: str, model_name: str) -> str: return str(response.json().get("choices", [{}])[0].get("text", "")) -def _copy_adapter_checkpoint(source_dir: str, dest_dir: str) -> None: - os.makedirs(dest_dir, exist_ok=True) - for filename in ("adapter_model.safetensors", "adapter_config.json"): - shutil.copy(Path(source_dir) / filename, Path(dest_dir) / filename) - - def _init_runtime_config(case_config: OracleCaseConfig) -> None: art.init_megatron_runtime_config( topology=art.MegatronTopologyConfig( @@ -154,50 +148,66 @@ async def _run_native_vllm_lora( engine_args = cast(dev.EngineArgs, stage_resources.vllm.engine_args()) else: trainer_gpu_ids, inference_gpu_ids = _resolve_dedicated_gpu_ids() - engine_args = dev.EngineArgs() + engine_args = dev.EngineArgs(enforce_eager=True) service_name = "model_support_native_lora_validation" case_artifacts = ensure_case_artifacts(case_config) output_root = Path(case_artifacts.case_dir) / "native_vllm_lora" output_root.mkdir(parents=True, exist_ok=True) - output_dir = tempfile.mkdtemp(prefix="run_", dir=output_root) + backend_root = tempfile.mkdtemp(prefix="run_", dir=output_root) internal_config = dev.InternalModelConfig( - trainer_gpu_ids=trainer_gpu_ids, - inference_gpu_ids=inference_gpu_ids, rollout_weights_mode="lora", allow_unvalidated_arch=case_config.allow_unvalidated_arch, engine_args=engine_args, ) + if set(trainer_gpu_ids).isdisjoint(inference_gpu_ids): + internal_config["trainer_gpu_ids"] = trainer_gpu_ids + internal_config["inference_gpu_ids"] = inference_gpu_ids + else: + trainer_gpu_ids = list(inference_gpu_ids) if stage_resources is None: dev.validate_dedicated_config(internal_config) with provider_topology_env(ORACLE_TOPOLOGY): _init_runtime_config(case_config) - service = MegatronService( - model_name=service_name, + backend = MegatronBackend(path=backend_root) + model = art.TrainableModel( + name=service_name, + run_name=service_name, + project="model-support-validation", base_model=case_config.base_model, - config=internal_config, - output_dir=output_dir, + _internal_config=internal_config, + report_metrics=[], ) port = _find_free_port() try: - host, resolved_port = await service.start_openai_server( - {"server_args": {"port": port}} + await model.register(backend, {"server_args": {"port": port}}) + service = cast( + DistributedMegatronService, await backend._get_service(model) ) + endpoint = urlparse(service._base_url or "") + host, resolved_port = ( + endpoint.hostname or "127.0.0.1", + int(endpoint.port or port), + ) + output_dir = service.output_dir import httpx base_url = f"http://{host}:{resolved_port}" step0_name = f"{service_name}@0" step1_name = f"{service_name}@1" - async with httpx.AsyncClient() as client: + api_key = service._api_key() + headers = {"Authorization": f"Bearer {api_key}"} if api_key else None + async with httpx.AsyncClient(headers=headers) as client: model_ids_before = await _model_ids(client, base_url) step0_completion_text = await _completion_text( client, base_url, step0_name, ) - step0_dir = get_step_checkpoint_dir(output_dir, 0) - step1_dir = get_step_checkpoint_dir(output_dir, 1) - _copy_adapter_checkpoint(step0_dir, step1_dir) - await service.register_lora_for_step(1, step1_dir) + await service.advance_without_training( + expected_step=0, + learner_version=1, + ) + await service.wait_for_serving(1) model_ids_after = await _model_ids(client, base_url) step1_completion_text = await _completion_text( client, @@ -222,7 +232,7 @@ async def _run_native_vllm_lora( step1_completion_text=step1_completion_text, ) finally: - service.close() + await backend.close() def run_native_vllm_lora( diff --git a/tests/integration/megatron/lora/test_dynamic_lora_slots.py b/tests/integration/megatron/lora/test_dynamic_lora_slots.py index 2a3ee6da0..d1ba1688c 100644 --- a/tests/integration/megatron/lora/test_dynamic_lora_slots.py +++ b/tests/integration/megatron/lora/test_dynamic_lora_slots.py @@ -1,5 +1,6 @@ from __future__ import annotations +from collections.abc import Sequence from contextlib import contextmanager import os from pathlib import Path @@ -17,11 +18,15 @@ import torch.multiprocessing as mp # noqa: E402 from art.megatron.lora import LoRA, LoRASlotRef, use_lora_slot # noqa: E402 -from art.trainer_rank import ( # noqa: E402 - AdamParams, - TrainerRank, +from art.trainer_rank._checkpoint import ( # noqa: E402 + LocalOptimizerState, + OptimizerConfig, + _commit_slot, ) from art.trainer_rank._impl import ( # noqa: E402 + AdamParams, + TrainerRank, + _CheckpointSlot, _distributed_grad_norm, _vocab_parallel_log_z, _vocab_parallel_target_logprobs, @@ -74,7 +79,7 @@ def test_dynamic_lora_slots_capture_recompute_context_and_step_independently() - key: value.cpu().double() for key, value in _adapter("dense", rank=3, seed=7).items() } - trainer.load_checkpoint_slot("CPU", cpu_adapter) + _install_checkpoint(trainer, "CPU", cpu_adapter) cpu_slot = lora._slot(LoRASlotRef("checkpoint", "CPU")) assert cpu_slot is not None assert cpu_slot.A_T.device == lora.A_T.device @@ -84,7 +89,7 @@ def test_dynamic_lora_slots_capture_recompute_context_and_step_independently() - with trainer.push_checkpoint("A"): assert trainer._slot_stack[-1] == ref_a - with trainer.push_lora(None): + with trainer.push_checkpoint(None): assert trainer._slot_stack[-1].name is None assert trainer._slot_stack[-1] == ref_a assert trainer._slot_stack == [] @@ -102,6 +107,41 @@ def test_dynamic_lora_slots_capture_recompute_context_and_step_independently() - _assert_reload_replaces_slot_optimizer(ref_a, lora, trainer) +@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA is required.") +def test_checkpoint_reload_does_not_alias_the_next_slot() -> None: + with _single_rank_model_parallel(): + device = torch.device("cuda") + first = LoRA("first", 4, 5, 2, 32, torch.float32, device) + second = LoRA("second", 4, 5, 2, 32, torch.float32, device) + trainer = _trainer_for(first, device) + trainer.runtime.model = [torch.nn.Sequential(first, second)] + + def adapter( + seed: int, *, include_second: bool = True + ) -> dict[str, torch.Tensor]: + return _adapter("first", rank=2, seed=seed) | ( + _adapter("second", rank=2, seed=seed + 10) if include_second else {} + ) + + def stage(destination: str, state: dict[str, torch.Tensor]) -> None: + temporary = f"temporary-{destination}" + trainer._load_checkpoint_slot(temporary, state, alpha=32.0) + _commit_slot(trainer, temporary, destination) + + stage("A", adapter(1)) + stage("B", adapter(2)) + slot_b = second._slot(trainer._slot_ref("B")) + assert slot_b is not None + expected_b = slot_b.A_T.detach().clone() + stage("A", adapter(3, include_second=False)) + stage("C", adapter(4)) + + slot_b = second._slot(trainer._slot_ref("B")) + slot_c = second._slot(trainer._slot_ref("C")) + assert slot_b is not None and slot_c is not None and slot_b is not slot_c + torch.testing.assert_close(slot_b.A_T, expected_b) + + @pytest.mark.parametrize("tp_size", (2, 4)) def test_trainer_rank_tp_head_backward_matches_unsharded_oracle( tp_size: int, @@ -263,8 +303,7 @@ def _assert_distributed_optimizer_restore(device: torch.device) -> None: with use_lora_slot(ref): lora(x).sum().backward() trainer.optim_step(params=params, checkpoints=["A"]) - state = trainer.checkpoint_slot_optimizer_state("A") - assert state is not None + state = _optimizer_state(trainer, "A") slot = lora._slot(ref) assert slot is not None adapter = { @@ -277,7 +316,10 @@ def _assert_distributed_optimizer_restore(device: torch.device) -> None: restored_lora = LoRA("dense", 4, 5, 2, 32, torch.float32, device) restored = _trainer_for(restored_lora, device) - restored.load_checkpoint_slot("A", adapter, optimizer_state=state) + _install_checkpoint(restored, "A", adapter) + restored._checkpoint_slots["A"].optimizer = restored._restore_canonical_optimizer( + "A", state + ) with use_lora_slot(ref): restored_lora(x).sum().backward() restored.optim_step(params=params, checkpoints=["A"]) @@ -287,76 +329,6 @@ def _assert_distributed_optimizer_restore(device: torch.device) -> None: torch.testing.assert_close(actual, expected, atol=0, rtol=0) -@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA is required.") -def test_restored_dynamic_optimizer_canonicalizes_internal_padding() -> None: - with _single_rank_model_parallel(): - for num_local_experts in (1, 2): - _assert_restored_dynamic_optimizer_canonicalizes_internal_padding( - num_local_experts - ) - - -def _assert_restored_dynamic_optimizer_canonicalizes_internal_padding( - num_local_experts: int, -) -> None: - device = torch.device("cuda") - ref = LoRASlotRef("checkpoint", "A") - prefix = "dense" if num_local_experts == 1 else "experts.{expert}" - adapter = { - key: value - for expert in range(num_local_experts) - for key, value in _adapter( - prefix.format(expert=expert), rank=2, seed=17 + expert - ).items() - } - lora = LoRA( - prefix, - 4, - 5, - 2, - 32, - torch.float32, - device, - num_local_experts=num_local_experts, - ) - lora.load_lora_slot(ref, adapter, requires_grad=True) - trainer = _trainer_for(lora, device) - - def canonicalize( - state: dict[str, torch.Tensor], _model: object - ) -> dict[str, torch.Tensor]: - result = {key: value.clone() for key, value in state.items()} - for value in result.values(): - value[..., -1] = 0 - return result - - trainer.runtime.model_support_handler.canonicalize_loaded_lora_state = canonicalize - for param in trainer._checkpoint_slot_params_by_name["A"]: - param.grad = torch.ones_like(param) - trainer.optim_step( - params=AdamParams(learning_rate=1e-3, weight_decay=0.1, grad_clip_norm=0.0), - checkpoints=["A"], - ) - state = trainer.checkpoint_slot_optimizer_state("A") - assert state is not None - masks = trainer._dynamic_optimizer_padding_masks("A") - masters = cast(tuple[torch.Tensor, ...], state["master_params"]) - optimizer = state["optimizer"] - optimizer_states = cast(dict[int, dict[str, object]], optimizer["state"]) - for index, (master, mask) in enumerate(zip(masters, masks, strict=True)): - master.masked_fill_(mask.cpu(), 5) - for value in optimizer_states[index].values(): - if isinstance(value, torch.Tensor) and value.shape == master.shape: - value.masked_fill_(mask.cpu(), 5) - - restored = trainer._restore_dynamic_optimizer("A", state) - for master, mask in zip(restored.master_params, masks, strict=True): - assert torch.count_nonzero(master[mask]) == 0 - for value in restored.optimizer.state[master].values(): - if isinstance(value, torch.Tensor) and value.shape == master.shape: - assert torch.count_nonzero(value[mask]) == 0 - - def _local_shard(full: torch.Tensor, rank: int, size: int) -> torch.Tensor: return full[:, rank * size : (rank + 1) * size].clone().requires_grad_() @@ -445,13 +417,13 @@ def _assert_reload_replaces_slot_optimizer( trainer: TrainerRank, ) -> None: assert ref.name is not None - old_params = trainer._checkpoint_slot_params_by_name[ref.name] - assert ref.name in trainer._dynamic_optimizers + old_params = trainer._checkpoint_slots[ref.name].params + assert trainer._checkpoint_slots[ref.name].optimizer is not None - trainer.load_checkpoint_slot(ref.name, _adapter("dense", rank=3, seed=9)) + _install_checkpoint(trainer, ref.name, _adapter("dense", rank=3, seed=9)) - new_params = trainer._checkpoint_slot_params_by_name[ref.name] - assert ref.name not in trainer._dynamic_optimizers + new_params = trainer._checkpoint_slots[ref.name].params + assert trainer._checkpoint_slots[ref.name].optimizer is None assert [tuple(param.shape) for param in new_params] == [(4, 3), (3, 5)] assert all(old is not new for old, new in zip(old_params, new_params, strict=True)) slot = lora._slot(ref) @@ -459,28 +431,82 @@ def _assert_reload_replaces_slot_optimizer( assert slot.rank == 3 +def _install_checkpoint( + trainer: TrainerRank, name: str, adapter: dict[str, torch.Tensor] +) -> int: + loaded = trainer._load_checkpoint_slot(name, adapter, alpha=32.0) + previous = trainer._checkpoint_slots.get(name) + trainer._checkpoint_slots[name] = _CheckpointSlot( + tuple(trainer._iter_slot_parameters(trainer._slot_ref(name))), + revision=0 if previous is None else previous.revision + 1, + ) + return loaded + + +def _optimizer_state(trainer: TrainerRank, name: str) -> LocalOptimizerState: + dynamic = trainer._checkpoint_slots[name].optimizer + assert dynamic is not None + states = [ + cast(dict[str, torch.Tensor], dynamic.optimizer.state[master]) + for master in dynamic.master_params + ] + group = dynamic.optimizer.param_groups[0] + beta1, beta2 = cast(tuple[float, float], group["betas"]) + return LocalOptimizerState( + masters=tuple( + master.detach().cpu().clone() for master in dynamic.master_params + ), + exp_avgs=tuple(state["exp_avg"].detach().cpu().clone() for state in states), + exp_avg_sqs=tuple( + state["exp_avg_sq"].detach().cpu().clone() for state in states + ), + steps=tuple(float(state["step"].item()) for state in states), + config=OptimizerConfig( + learning_rate=float(group["lr"]), + beta1=beta1, + beta2=beta2, + eps=float(group["eps"]), + weight_decay=float(group["weight_decay"]), + ), + ) + + def _trainer_for(lora: LoRA, device: torch.device) -> TrainerRank: trainer = TrainerRank.__new__(TrainerRank) trainer.runtime = SimpleNamespace( model=[lora], optimizer=None, - model_support_handler=SimpleNamespace( - canonicalize_loaded_lora_state=lambda state, _model: state, - zero_internal_padding_grads=lambda _model: None, - zero_internal_padding_params=lambda _model: None, - ), + model_support_handler=_IdentityModelSupportHandler(), ) trainer.device = device trainer._slot_stack = [] trainer._default_slot_ref = None - trainer._dynamic_optimizers = {} - trainer._checkpoint_slot_params_by_name = { - "A": tuple(lora.lora_slot_params(LoRASlotRef("checkpoint", "A"))), - "B": tuple(lora.lora_slot_params(LoRASlotRef("checkpoint", "B"))), + trainer._checkpoint_slots = { + name: _CheckpointSlot( + tuple(lora.lora_slot_params(LoRASlotRef("checkpoint", name))) + ) + for name in ("A", "B") } + trainer._checkpoint_prefetches = {} + trainer._checkpoint_mutation_tail = None return trainer +class _IdentityModelSupportHandler: + def zero_internal_padding_grads( + self, model_chunks: Sequence[torch.nn.Module] + ) -> None: + del model_chunks + + def canonicalize_loaded_lora_state( + self, + state: dict[str, torch.Tensor], + model_chunks: Sequence[torch.nn.Module], + ) -> dict[str, torch.Tensor]: + del model_chunks + return state + + @contextmanager def _single_rank_model_parallel(): os.environ.setdefault("MASTER_ADDR", "127.0.0.1") diff --git a/tests/integration/megatron/lora/test_lora_disk_codecs.py b/tests/integration/megatron/lora/test_lora_disk_codecs.py index 074c297a6..219473381 100644 --- a/tests/integration/megatron/lora/test_lora_disk_codecs.py +++ b/tests/integration/megatron/lora/test_lora_disk_codecs.py @@ -38,6 +38,7 @@ save_vllm_lora_from_model, ) from art.trainer_rank import TrainerRank +from art.trainer_rank._impl import _AdapterConfig, _CheckpointSlot from art.utils.convert_moe_lora import convert_checkpoint_if_needed REPO_ROOT = Path(__file__).parents[4] @@ -1526,7 +1527,7 @@ def test_save_vllm_lora_from_model_writes_single_vllm_checkpoint(tmp_path: Path) out_features=8, rank=1, alpha=1, - dtype=torch.float32, + dtype=torch.bfloat16, device=torch.device("cpu"), ) gate_up_lora.A_T.data.copy_(full[f"{prefix}.gate_up_proj.lora_A.weight"].T) @@ -1537,7 +1538,7 @@ def test_save_vllm_lora_from_model_writes_single_vllm_checkpoint(tmp_path: Path) out_features=2, rank=1, alpha=1, - dtype=torch.float32, + dtype=torch.bfloat16, device=torch.device("cpu"), ) down_lora.A_T.data.copy_(full[f"{prefix}.down_proj.lora_A.weight"].T) @@ -1546,7 +1547,7 @@ def test_save_vllm_lora_from_model_writes_single_vllm_checkpoint(tmp_path: Path) publish_dir = tmp_path / "published_from_model" save_vllm_lora_from_model( model=cast(Any, [torch.nn.Sequential(gate_up_lora, down_lora)]), - adapter_dtypes={key: tensor.dtype for key, tensor in full.items()}, + adapter_dtypes={}, handler=QWEN3_5_MOE_HANDLER, adapter_config=_config("Qwen/Qwen3.5-35B-A3B", rank=1, alpha=1), output_dir=str(publish_dir), @@ -1559,7 +1560,10 @@ def test_save_vllm_lora_from_model_writes_single_vllm_checkpoint(tmp_path: Path) str(publish_dir), handler=QWEN3_5_MOE_HANDLER, ) - _assert_tensors_equal(roundtrip, full) + _assert_tensors_equal( + roundtrip, + {key: tensor.bfloat16() for key, tensor in full.items()}, + ) def test_trainer_rank_publishes_named_checkpoint_slot_without_mutating_base( @@ -1581,14 +1585,16 @@ def test_trainer_rank_publishes_named_checkpoint_slot_without_mutating_base( ) trainer._slot_stack = [] trainer._pending_slot_graphs = {} - trainer._dynamic_optimizers = {} - trainer._checkpoint_slot_params_by_name = {} - trainer._checkpoint_slot_adapter_configs = {} + trainer._checkpoint_slots = {} config = _config("Qwen/Qwen3-8B", rank=2, alpha=2) - assert trainer.load_checkpoint_slot("student", adapter, adapter_config=config) == 1 + assert trainer._load_checkpoint_slot("student", adapter, alpha=2) == 1 + trainer._checkpoint_slots["student"] = _CheckpointSlot( + tuple(trainer._iter_slot_parameters(trainer._slot_ref("student"))), + cast(_AdapterConfig, config), + ) output_dir = tmp_path / "checkpoint" - trainer.save_checkpoint_slot_lora("student", str(output_dir)) + assert trainer.export_lora(str(output_dir), "student") == 0 _assert_tensors_equal(load_file(output_dir / "adapter_model.safetensors"), adapter) assert json.loads((output_dir / "adapter_config.json").read_text()) == { @@ -1602,6 +1608,7 @@ def test_trainer_rank_publishes_named_checkpoint_slot_without_mutating_base( @pytest.mark.parametrize( ("handler", "base_model"), ( + (QWEN3_MOE_HANDLER, "Qwen/Qwen3-30B-A3B-Instruct-2507"), (QWEN3_5_MOE_HANDLER, "Qwen/Qwen3.5-35B-A3B"), (DSV4_HANDLER, "deepseek-ai/DeepSeek-V4-Flash"), ), @@ -1622,41 +1629,33 @@ def test_direct_3d_packed_expert_publish_matches_handler_vllm_exactly( intermediate = 4 group_prefix = "base_model.model.model.layers.0.mlp.experts" full: dict[str, torch.Tensor] = {} - gate_up_lora = LoRA( - adapter_model_prefix=f"{group_prefix}.{{expert}}.gate_up_proj", - in_features=hidden, - out_features=2 * intermediate, - rank=rank, - alpha=rank, - dtype=torch.float32, - device=torch.device("cpu"), - num_local_experts=2, - ) - down_lora = LoRA( - adapter_model_prefix=f"{group_prefix}.{{expert}}.down_proj", - in_features=intermediate, - out_features=hidden, - rank=rank, - alpha=rank, - dtype=torch.float32, - device=torch.device("cpu"), - num_local_experts=2, - ) + projection_loras = { + projection: LoRA( + adapter_model_prefix=f"{group_prefix}.{{expert}}.{projection}", + in_features=hidden if projection != "down_proj" else intermediate, + out_features=( + hidden + if projection == "down_proj" + else 2 * intermediate + if projection == "gate_up_proj" + else intermediate + ), + rank=rank, + alpha=rank, + dtype=torch.float32, + device=torch.device("cpu"), + num_local_experts=2, + ) + for projection in ( + ("gate_proj", "up_proj", "down_proj") + if handler is QWEN3_MOE_HANDLER + else ("gate_up_proj", "down_proj") + ) + } offset = 0 for expert in range(2): expert_prefix = f"{group_prefix}.{expert}" tensors = { - "gate_up_proj.lora_A.weight": torch.arange( - rank * hidden, - dtype=torch.float32, - ).reshape(rank, hidden) - + offset, - "gate_up_proj.lora_B.weight": torch.arange( - 2 * intermediate * rank, - dtype=torch.float32, - ).reshape(2 * intermediate, rank) - + offset - + 100, "down_proj.lora_A.weight": torch.arange( rank * intermediate, dtype=torch.float32, @@ -1670,20 +1669,36 @@ def test_direct_3d_packed_expert_publish_matches_handler_vllm_exactly( + offset + 300, } + for projection_index, projection in enumerate( + ("gate_proj", "up_proj") + if handler is QWEN3_MOE_HANDLER + else ("gate_up_proj",) + ): + output = intermediate if handler is QWEN3_MOE_HANDLER else 2 * intermediate + tensors[f"{projection}.lora_A.weight"] = ( + torch.arange(rank * hidden, dtype=torch.float32).reshape(rank, hidden) + + offset + + projection_index * 10 + ) + tensors[f"{projection}.lora_B.weight"] = ( + torch.arange(output * rank, dtype=torch.float32).reshape(output, rank) + + offset + + 100 + + projection_index * 10 + ) for suffix, tensor in tensors.items(): full[f"{expert_prefix}.{suffix}"] = tensor - gate_up_lora.A_T.data[expert].copy_(tensors["gate_up_proj.lora_A.weight"].T) - gate_up_lora.B_T.data[expert].copy_(tensors["gate_up_proj.lora_B.weight"].T) - down_lora.A_T.data[expert].copy_(tensors["down_proj.lora_A.weight"].T) - down_lora.B_T.data[expert].copy_(tensors["down_proj.lora_B.weight"].T) + for projection, lora in projection_loras.items(): + lora.A_T.data[expert].copy_(tensors[f"{projection}.lora_A.weight"].T) + lora.B_T.data[expert].copy_(tensors[f"{projection}.lora_B.weight"].T) offset += 1000 slot_ref = LoRASlotRef("checkpoint", "student") if dynamic_slot else None if slot_ref is not None: - assert gate_up_lora.load_lora_slot( - slot_ref, full, alpha=rank, requires_grad=True + assert all( + lora.load_lora_slot(slot_ref, full, alpha=rank, requires_grad=True) + for lora in projection_loras.values() ) - assert down_lora.load_lora_slot(slot_ref, full, alpha=rank, requires_grad=True) adapter_config = _config(base_model, rank=rank, alpha=rank) old_dir = tmp_path / "old" @@ -1694,7 +1709,7 @@ def test_direct_3d_packed_expert_publish_matches_handler_vllm_exactly( ) save_vllm_lora_tensors(old_dir, old_tensors, old_config) save_vllm_lora_from_model( - model=cast(Any, [torch.nn.Sequential(gate_up_lora, down_lora)]), + model=cast(Any, [torch.nn.Sequential(*projection_loras.values())]), adapter_dtypes={key: tensor.dtype for key, tensor in full.items()}, handler=handler, adapter_config=dict(adapter_config), diff --git a/tests/integration/megatron/lora/test_merged_weight_export.py b/tests/integration/megatron/lora/test_merged_weight_export.py index fc95cfa42..c0cab8d5b 100644 --- a/tests/integration/megatron/lora/test_merged_weight_export.py +++ b/tests/integration/megatron/lora/test_merged_weight_export.py @@ -4,7 +4,7 @@ import httpx import torch -from art.megatron.runtime.jobs import ( +from art.megatron.runtime.weight_transfer import ( MergedWeightTransferInitInfo, MergedWeightTransferSpec, ) @@ -246,7 +246,7 @@ def post( ("http://runtime.test/pause", None, {"mode": "wait"}, 300.0), ( "http://runtime.test/start_weight_update", - {"is_checkpoint_format": False}, + None, None, 300.0, ), diff --git a/tests/integration/megatron/model_support/forward_trace.py b/tests/integration/megatron/model_support/forward_trace.py index 8dcba458c..0b812c953 100644 --- a/tests/integration/megatron/model_support/forward_trace.py +++ b/tests/integration/megatron/model_support/forward_trace.py @@ -72,7 +72,47 @@ def _trace_hook(fn: Callable[..., Any]) -> Callable[..., Any]: def _normalize_trace_module_name(module_name: str) -> str: """Strips compile-wrapper path segments from trace module names.""" - return module_name.replace("._orig_mod", "") + normalized = module_name.replace("._orig_mod", "") + chunk, separator, remainder = normalized.partition(".") + if ( + separator + and chunk.startswith("chunk") + and chunk.removeprefix("chunk").isdigit() + ): + return remainder + return normalized + + +def _global_trace_module_name( + module_name: str, + module_by_name: dict[str, Any], + *, + chunk_index: int, +) -> str: + local_layer_index = _module_layer_index(module_name) + normalized = _normalize_trace_module_name(module_name) + if local_layer_index is None: + return f"chunk{chunk_index}.{normalized}" + marker = "decoder.layers." + marker_index = module_name.find(marker) + layer_name_end = marker_index + len(marker) + len(str(local_layer_index)) + layer = module_by_name[module_name[:layer_name_end]] + layer_number = getattr(layer, "layer_number", None) + if layer_number is None: + layer_number = getattr(getattr(layer, "_orig_mod", None), "layer_number", None) + if layer_number is None: + raise RuntimeError( + f"Transformer layer has no global layer_number: {module_name}" + ) + normalized_marker_index = normalized.find(marker) + normalized_layer_start = normalized_marker_index + len(marker) + normalized_layer_end = normalized_layer_start + len(str(local_layer_index)) + return "chunk{}.{}{}{}".format( + chunk_index, + normalized[:normalized_layer_start], + int(layer_number) - 1, + normalized[normalized_layer_end:], + ) def _safe_int(value: Any, default: int = 0) -> int: @@ -249,7 +289,16 @@ def _extract_router_topk( topk_scores = probs.new_zeros((probs.shape[0], 0)) topk_ids = torch.zeros((probs.shape[0], 0), dtype=torch.int64) else: - topk_scores, topk_ids = torch.topk(probs, k=topk, dim=-1) + expert_ids = torch.arange(probs.shape[-1]).expand_as(routing_map) + topk_ids = ( + expert_ids.masked_fill(~routing_map, probs.shape[-1]) + .sort(dim=-1) + .values[:, :topk] + ) + valid = topk_ids < probs.shape[-1] + topk_scores = probs.gather(-1, topk_ids.clamp_max(probs.shape[-1] - 1)) + topk_ids = topk_ids.masked_fill(~valid, -1) + topk_scores = topk_scores.masked_fill(~valid, 0) return topk_ids.contiguous(), topk_scores.contiguous() @@ -311,7 +360,9 @@ def __init__( tuple[int | None, int, int | None, torch.Tensor, torch.Tensor | None] ] = [] self._trace_metadata_by_name: dict[str, dict[str, Any]] = {} - self._next_micro_order = 0 + self._root_module_ids: set[int] = set() + self._root_output_module_ids: set[int] = set() + self._next_micro_order_by_root: dict[int, int] = {} self._inside_root_forward = False self._hook_handles: list[Any] = [] if not enabled: @@ -321,13 +372,19 @@ def __init__( def _register_hooks(self, model_chunks: list[Any]) -> None: if not model_chunks: raise RuntimeError("Expected at least one model chunk for forward tracing") - root_module = model_chunks[0] - self._hook_handles.append( - root_module.register_forward_pre_hook(_trace_hook(self._root_pre_hook)) - ) - self._hook_handles.append( - root_module.register_forward_hook(_trace_hook(self._root_post_hook)) - ) + from art.megatron.training.pipeline_schedule import chunk_post_process + + self._root_module_ids = {id(chunk) for chunk in model_chunks} + self._root_output_module_ids = { + id(chunk) for chunk in model_chunks if chunk_post_process(chunk) + } + for root_module in model_chunks: + self._hook_handles.append( + root_module.register_forward_pre_hook(_trace_hook(self._root_pre_hook)) + ) + self._hook_handles.append( + root_module.register_forward_hook(_trace_hook(self._root_post_hook)) + ) for chunk_index, chunk in enumerate(model_chunks): named_modules = list(chunk.named_modules()) module_by_name = dict(named_modules) @@ -339,8 +396,10 @@ def _register_hooks(self, model_chunks: list[Any]) -> None: and layer_index > self.max_layer_index ): continue - trace_module_name = _normalize_trace_module_name( - f"chunk{chunk_index}.{module_name}" + trace_module_name = _global_trace_module_name( + module_name, + module_by_name, + chunk_index=chunk_index, ) metadata = self._build_module_trace_metadata( module_name=module_name, @@ -421,7 +480,9 @@ def _sequence_parallel_enabled(module: Any) -> bool: @staticmethod def _lora_primary_output_merge_hint(module: Any) -> dict[str, Any] | None: """Infers the correct output merge op for LoRA modules.""" - if module.__class__.__name__ != "LoRA": + from art.megatron.lora import LoRA + + if not isinstance(module, LoRA): return None lora_module = module b_param = getattr(lora_module, "B_T", None) @@ -454,6 +515,8 @@ def _lora_primary_output_merge_hint(module: Any) -> dict[str, Any] | None: a_world_size = _shard_world_size_for_domain(a_domain) if bool(getattr(a_param, "lora_tp_sharded", False)) and a_world_size > 1: return {"op": "sum"} + if a_world_size > 1 and a_domain == b_domain: + return {"op": "replicated"} return None def _infer_primary_output_merge_hint( @@ -671,32 +734,34 @@ def _root_pre_hook(self, _module: Any, _args: Any) -> None: if self.current_step_index is None: return self._inside_root_forward = True - micro_order = self._next_micro_order + micro_order = self._next_micro_order_by_root[id(_module)] sample_index = self._sample_index_for_micro(micro_order) self.begin_micro(sample_index=sample_index, micro_order=micro_order) def _root_post_hook(self, _module: Any, _inputs: Any, output: Any) -> None: if self.current_step_index is None: return - output_tensor = self.guess_primary_tensor(output) - if output_tensor is None: - raise RuntimeError( - f"Expected root forward output to contain a tensor, got {type(output)}" - ) - sample_index = self.current_micro_sample_index - micro_order = self.current_micro_order - self.current_step_outputs.append( - ( - sample_index, - micro_order, - None - if sample_index is not None - else _local_dummy_micro_slot(micro_order), - output_tensor.float(), - getattr(_module, "_art_root_output_token_uids", None), + module_id = id(_module) + if module_id in self._root_output_module_ids: + output_tensor = self.guess_primary_tensor(output) + if output_tensor is None: + raise RuntimeError( + f"Expected root forward output to contain a tensor, got {type(output)}" + ) + sample_index = self.current_micro_sample_index + micro_order = self.current_micro_order + self.current_step_outputs.append( + ( + sample_index, + micro_order, + None + if sample_index is not None + else _local_dummy_micro_slot(micro_order), + output_tensor.float(), + getattr(_module, "_art_root_output_token_uids", None), + ) ) - ) - self._next_micro_order = micro_order + 1 + self._next_micro_order_by_root[module_id] += 1 self._inside_root_forward = False def set_step( @@ -711,7 +776,9 @@ def set_step( self.current_micro_sample_index = None self.current_micro_order = 0 self.current_micro_module_call_counts = {} - self._next_micro_order = 0 + self._next_micro_order_by_root = { + module_id: 0 for module_id in self._root_module_ids + } self._inside_root_forward = False def begin_micro(self, sample_index: int | None, micro_order: int) -> None: @@ -1253,8 +1320,7 @@ def _canonicalize_row_aligned_value( @classmethod def _canonicalize_call_row_token_order(cls, call: dict[str, Any]) -> None: """Canonicalizes all row-aligned call tensors to global token order.""" - cls._align_exact_zero_padding_row_token_uids(call) - cls._drop_exact_zero_padding_rows(call) + cls._drop_padding_rows(call) row_token_uids = call.get("row_token_uids") if not isinstance(row_token_uids, torch.Tensor) or row_token_uids.ndim != 1: return @@ -1276,8 +1342,8 @@ def _canonicalize_call_row_token_order(cls, call: dict[str, Any]) -> None: call["row_token_uids"] = row_token_uids.index_select(0, order).contiguous() @classmethod - def _drop_exact_zero_padding_rows(cls, call: dict[str, Any]) -> None: - """Removes traced sequence-padding rows before comparing compact CP traces.""" + def _drop_padding_rows(cls, call: dict[str, Any]) -> None: + """Removes rows explicitly marked as sequence padding by their token UID.""" row_token_uids = call.get("row_token_uids") tensor = call.get("primary_output") if ( @@ -1292,9 +1358,6 @@ def _drop_exact_zero_padding_rows(cls, call: dict[str, Any]) -> None: padding_rows = row_token_uids < 0 if row_count == 0 or not bool(padding_rows.any().item()): return - flat = tensor.detach().reshape(row_count, -1) - if not bool((flat[padding_rows] == 0).all().item()): - return valid_rows = torch.nonzero(~padding_rows, as_tuple=False).reshape(-1) original_call = dict(call) for key, value in original_call.items(): @@ -1307,48 +1370,6 @@ def _drop_exact_zero_padding_rows(cls, call: dict[str, Any]) -> None: ) call["row_token_uids"] = row_token_uids.index_select(0, valid_rows).contiguous() - @staticmethod - def _align_exact_zero_padding_row_token_uids(call: dict[str, Any]) -> None: - """Moves padding UID markers onto exact-zero sequence-parallel pad rows.""" - row_token_uids = call.get("row_token_uids") - tensor = call.get("primary_output") - if ( - not isinstance(row_token_uids, torch.Tensor) - or row_token_uids.ndim != 1 - or not isinstance(tensor, torch.Tensor) - or tensor.ndim == 0 - or int(tensor.shape[0]) != int(row_token_uids.numel()) - ): - return - row_count = int(row_token_uids.numel()) - if row_count <= 1 or not bool((row_token_uids < 0).any().item()): - return - flat = tensor.detach().reshape(row_count, -1) - zero_rows = torch.nonzero( - (flat == 0).all(dim=1) & (row_token_uids >= 0), - as_tuple=False, - ).reshape(-1) - negative_rows = torch.nonzero( - (row_token_uids < 0) & ~(flat == 0).all(dim=1), - as_tuple=False, - ).reshape(-1) - if int(zero_rows.numel()) == 0 or int(zero_rows.numel()) != int( - negative_rows.numel() - ): - return - aligned = row_token_uids.clone() - for zero_pos, negative_pos in zip( - zero_rows.tolist(), negative_rows.tolist(), strict=True - ): - zero_pos = int(zero_pos) - negative_pos = int(negative_pos) - if zero_pos >= negative_pos: - return - shifted = aligned[zero_pos:negative_pos].clone() - aligned[zero_pos] = -1 - aligned[zero_pos + 1 : negative_pos + 1] = shifted - call["row_token_uids"] = aligned - @classmethod def _canonicalize_primary_output_tensor( cls, @@ -1641,6 +1662,13 @@ def _merge_rank_values( raise RuntimeError("Cannot merge empty rank value list") if all(isinstance(value, torch.Tensor) for value in values_by_rank): tensors = cast(list[torch.Tensor], values_by_rank) + if preferred_reduce == "replicated": + if not all( + tensors[0].shape == tensor.shape and torch.equal(tensors[0], tensor) + for tensor in tensors[1:] + ): + raise RuntimeError("Replicated trace outputs diverged across ranks") + return tensors[0] if preferred_reduce == "sum" and all( tensors[0].shape == tensor.shape for tensor in tensors[1:] ): @@ -1801,8 +1829,8 @@ def _merge_rank_call_entries( preferred_cat_dim = None preferred_reduce = None if isinstance(primary_hint, dict): - if primary_hint.get("op") == "sum": - preferred_reduce = "sum" + if primary_hint.get("op") in {"sum", "replicated"}: + preferred_reduce = str(primary_hint["op"]) elif primary_hint.get("op") == "concat" and isinstance( primary_hint.get("dim"), int ): @@ -1851,8 +1879,8 @@ def _merge_rank_call_entries( dim = selected_hint.get("dim") if isinstance(dim, int): preferred_cat_dim = dim - elif op == "sum": - preferred_reduce = "sum" + elif op in {"sum", "replicated"}: + preferred_reduce = op if ( preferred_reduce is None and preferred_cat_dim == 0 @@ -1942,7 +1970,7 @@ def _merge_rank_values_with_cp_groups( preferred_cat_dim=preferred_cat_dim, preferred_reduce=preferred_reduce, ) - if preferred_cat_dim != -1 and preferred_reduce != "sum": + if preferred_cat_dim != -1 and preferred_reduce not in {"sum", "replicated"}: return cls._merge_rank_values( values_by_rank, preferred_cat_dim=preferred_cat_dim, diff --git a/tests/integration/megatron/model_support/fp32_grouped_gemm.py b/tests/integration/megatron/model_support/fp32_grouped_gemm.py index 2da228fef..272150757 100644 --- a/tests/integration/megatron/model_support/fp32_grouped_gemm.py +++ b/tests/integration/megatron/model_support/fp32_grouped_gemm.py @@ -1,15 +1,17 @@ from __future__ import annotations +import functools import os import sys from typing import Any _GUARD_ATTR = "__art_te_cutlass_grouped_gemm_guard__" _ORIGINAL_ATTR = "__art_original_general_grouped_gemm__" +_REFERENCE_ATTR = "__art_fp32_grouped_linear_reference__" def allow_fp32_grouped_gemm_fallback_for_model_support_tests() -> None: - """Use TE's fp32 grouped-GEMM fallback in semantic model-support tests.""" + """Use topology-stable fp32 expert GEMMs in semantic model-support tests.""" os.environ["NVTE_USE_CUTLASS_GROUPED_GEMM"] = "0" os.environ["NVTE_CUTLASS_GROUPED_GEMM_WARN_FALLBACK"] = "0" try: @@ -27,6 +29,41 @@ def allow_fp32_grouped_gemm_fallback_for_model_support_tests() -> None: "transformer_engine.pytorch.module.grouped_linear", current, original ) _patch_if_guarded("transformer_engine.pytorch.module.linear", current, original) + _install_fp32_grouped_linear_reference() + + +def _install_fp32_grouped_linear_reference() -> None: + from megatron.core.extensions.transformer_engine import TEGroupedLinear + import torch + + assert TEGroupedLinear is not None + current = TEGroupedLinear.forward + if getattr(current, _REFERENCE_ATTR, False): + return + + @functools.wraps(current) + def forward(self, x, m_splits): + if x.dtype is not torch.float32: + return current(self, x, m_splits) + counts = [int(count) for count in m_splits] + weights = self._get_weight_tensors() + biases = self._get_bias_tensors() + outputs = [ + torch.nn.functional.linear( + rows, + weight, + bias if self.apply_bias else None, + ) + for rows, weight, bias in zip(x.split(counts), weights, biases, strict=True) + ] + output = torch.cat(outputs) + self.is_first_microbatch = False + if self.te_return_bias: + return output, biases + return output, None + + setattr(forward, _REFERENCE_ATTR, True) + setattr(TEGroupedLinear, "forward", forward) def _patch_if_guarded(module_name: str, guarded: Any, original: Any) -> None: diff --git a/tests/integration/megatron/model_support/hf_parity.py b/tests/integration/megatron/model_support/hf_parity.py index 55b355838..a07c503e6 100644 --- a/tests/integration/megatron/model_support/hf_parity.py +++ b/tests/integration/megatron/model_support/hf_parity.py @@ -2,9 +2,10 @@ import os from pathlib import Path +import socket import subprocess import sys -from typing import Any +from typing import Any, Callable from pydantic import BaseModel, Field @@ -40,6 +41,24 @@ HF_PARITY_ARTIFACT_SUITE_NAME = "Megatron HF parity artifacts" +def _find_free_rendezvous_port() -> int: + with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as listener: + listener.bind(("127.0.0.1", 0)) + return int(listener.getsockname()[1]) + + +def _hf_parity_worker_env() -> dict[str, str]: + return { + "MASTER_ADDR": "127.0.0.1", + "MASTER_PORT": str(_find_free_rendezvous_port()), + "RANK": "0", + "WORLD_SIZE": "1", + "LOCAL_RANK": "0", + "LOCAL_WORLD_SIZE": "1", + "PYTHONUNBUFFERED": "1", + } + + class HfParityMetricRow(BaseModel): phase: str param: str @@ -98,6 +117,22 @@ def _hf_parity_phase_pass_fns() -> dict[str, PhasePassFn]: } +def _hf_parity_phase_pass_fns_for_case( + case_config: OracleCaseConfig, +) -> dict[str, PhasePassFn]: + if case_config.precision == "fp32": + return _hf_parity_phase_pass_fns() + from art.megatron.model_support.registry import get_model_support_handler + + handler = get_model_support_handler( + case_config.base_model, + allow_unvalidated_arch=case_config.allow_unvalidated_arch, + ) + return handler.correctness_phase_pass_fns(sys.modules[__name__]) or ( + _hf_parity_phase_pass_fns() + ) + + def hf_parity_case_config(case_config: OracleCaseConfig) -> OracleCaseConfig: return case_config.model_copy( update={"packed_tensors": HF_PARITY_PACKED_TENSORS.model_copy(deep=True)} @@ -170,6 +205,7 @@ def build_tensor_map_metric_rows( reference: dict[str, Any], candidate: dict[str, Any], phase_pass_fns: dict[str, PhasePassFn] | None = None, + group_by: Callable[[str], str] | None = None, ) -> list[HfParityMetricRow]: reference_keys = set(reference.keys()) candidate_keys = set(candidate.keys()) @@ -186,6 +222,12 @@ def build_tensor_map_metric_rows( ) ] rows: list[HfParityMetricRow] = [] + accumulators: dict[str, DiffAccumulator] = {} + diagnostic_pass_fns = dict(phase_pass_fns or _hf_parity_phase_pass_fns()) + diagnostic_phase = f"{phase}_diagnostic" + diagnostic_pass_fns[diagnostic_phase] = MetricThresholdRule( + minimums={"typical_abs_scale": 0.0, "candidate_abs_scale": 0.0} + ) for key in sorted(reference_keys): if tuple(reference[key].shape) != tuple(candidate[key].shape): rows.append( @@ -198,6 +240,19 @@ def build_tensor_map_metric_rows( ) ) continue + if group_by is not None: + summary = summarize_tensor_pair(reference[key], candidate[key]) + rows.append( + _build_metric_row( + phase=diagnostic_phase, + param=key, + summary=summary, + phase_pass_fns=diagnostic_pass_fns, + ) + ) + accumulator = accumulators.setdefault(group_by(key), DiffAccumulator()) + accumulator.update(reference[key], candidate[key]) + continue rows.append( _build_metric_row( phase=phase, @@ -206,6 +261,15 @@ def build_tensor_map_metric_rows( phase_pass_fns=phase_pass_fns, ) ) + rows.extend( + _build_metric_row( + phase=phase, + param=group, + summary=accumulator.as_summary(), + phase_pass_fns=phase_pass_fns, + ) + for group, accumulator in sorted(accumulators.items()) + ) return rows @@ -301,11 +365,10 @@ def run_hf_parity_subprocess(request: HfParityRunRequest, output_dir: Path) -> N "--run-request", str(request_path), ] - env = {**os.environ, "PYTHONUNBUFFERED": "1"} run = subprocess.run( command, cwd=str(worker_cwd), - env=env, + env={**os.environ, **_hf_parity_worker_env()}, capture_output=True, text=True, check=False, @@ -319,13 +382,28 @@ def run_hf_parity_subprocess(request: HfParityRunRequest, output_dir: Path) -> N ) +def _run_hf_parity_in_process( + request: HfParityRunRequest, + output_dir: Path, +) -> None: + from .hf_parity_worker import run_worker_cli + from .workflow import _redirect_output, _temporary_env + + request_path = output_dir / "run_request.json" + _write_json(request_path, request.model_dump(mode="json")) + with _temporary_env(**_hf_parity_worker_env()): + with _redirect_output(output_dir / "worker.log"): + run_worker_cli(request_path) + + def run_hf_parity( *, case_config: OracleCaseConfig, + in_process: bool = False, ) -> HfParityReport: case_config = hf_parity_case_config(case_config) - if case_config.precision != "fp32": - raise ValueError("HF parity currently requires fp32 precision") + if case_config.precision not in {"bf16", "fp32"}: + raise ValueError(f"Unsupported HF parity precision {case_config.precision!r}") if case_config.num_steps != 1: raise ValueError("HF parity currently requires num_steps=1") @@ -357,7 +435,8 @@ def run_hf_parity( coverage=coverage, ) with provider_topology_env(ORACLE_TOPOLOGY): - run_hf_parity_subprocess(request, output_dir) + runner = _run_hf_parity_in_process if in_process else run_hf_parity_subprocess + runner(request, output_dir) report = HfParityReport.model_validate(_read_json(report_path)) assert_hf_parity_pass(report, report_path=report_path) _prune_case_artifacts(Path(case_artifacts.case_dir)) @@ -371,7 +450,7 @@ def build_hf_parity_report( loss_summary: dict[str, float], grads_rows: list[HfParityMetricRow], ) -> HfParityReport: - phase_pass_fns = _hf_parity_phase_pass_fns() + phase_pass_fns = _hf_parity_phase_pass_fns_for_case(request.case_config) rows = [ _build_metric_row( phase="outputs", diff --git a/tests/integration/megatron/model_support/hf_parity_worker.py b/tests/integration/megatron/model_support/hf_parity_worker.py index 5ca8ff6d3..41cb3f876 100644 --- a/tests/integration/megatron/model_support/hf_parity_worker.py +++ b/tests/integration/megatron/model_support/hf_parity_worker.py @@ -9,11 +9,15 @@ import sys import time from typing import Any, cast +from unittest.mock import patch import torch import torch.nn.functional as F from art.megatron import train as megatron_train +from art.megatron.context_parallel.block_mask import prepare_block_mask_context +from art.megatron.prefix_tree import parse_prefix_tree_row +from art.megatron.prefix_tree_state import create_prefix_tree_state from art.megatron.routing_replay import ( MoeRoutingReplayBundle, RouterCallRoute, @@ -23,6 +27,7 @@ from art.megatron.routing_replay import ( ParallelTopology as ReplayParallelTopology, ) +from art.megatron.training import microbatches as megatron_microbatches from art.megatron.training.trace import prepare_replay_local_input_token_uids from art.megatron.weights.merged_weight_export import build_art_conversion_tasks from art.preprocessing.pack import packed_tensors_from_dir @@ -34,7 +39,7 @@ from .hf_parity import ( HF_PARITY_REPORT_FILENAME, HfParityRunRequest, - _hf_parity_phase_pass_fns, + _hf_parity_phase_pass_fns_for_case, build_hf_parity_report, build_parity_sample_indices, build_tensor_map_metric_rows, @@ -76,6 +81,14 @@ _REPLAY_ROUTER_LAYER_PATTERN = re.compile( r"^chunk_\d+\.layer_(?P\d+)\.mlp\.router$" ) +_DISTRIBUTED_PROCESS_ENV = ( + "MASTER_ADDR", + "MASTER_PORT", + "RANK", + "WORLD_SIZE", + "LOCAL_RANK", + "LOCAL_WORLD_SIZE", +) _GATE_WEIGHT_PATTERN = re.compile( r"^model(?:\.language_model)?\.layers\.(?P\d+)\.mlp\.gate\.weight$" ) @@ -123,17 +136,50 @@ def _hf_router_num_experts(module: Any, router_scores: torch.Tensor) -> int: ) +def _glm_router_output( + module: Any, router_logits: torch.Tensor +) -> tuple[torch.Tensor, torch.Tensor]: + scores = router_logits.sigmoid() + choice = scores + module.e_score_correction_bias + groups = int(module.n_group) + group_scores = ( + choice.view(choice.shape[0], groups, -1).topk(2, dim=-1).values.sum(-1) + ) + selected_groups = group_scores.topk( + int(module.topk_group), dim=-1, sorted=False + ).indices + group_mask = torch.zeros_like(group_scores, dtype=torch.bool) + group_mask.scatter_(1, selected_groups, True) + choice = choice.masked_fill( + ~group_mask.unsqueeze(-1) + .expand_as(choice.view(choice.shape[0], groups, -1)) + .reshape_as(choice), + float("-inf"), + ) + indices = choice.topk(int(module.top_k), dim=-1, sorted=False).indices + weights = scores.gather(1, indices) + if bool(module.norm_topk_prob): + weights = weights / (weights.sum(-1, keepdim=True) + 1e-20) + return weights * float(module.routed_scaling_factor), indices + + class _HfMoeRoutingCapture: def __init__(self, model: Any) -> None: self._handles: list[Any] = [] self._routes: dict[str, dict[int, RouterCallRoute]] = {} self._active_sample_index: int | None = None self._active_micro_slot = 0 + self._active_token_uids: torch.Tensor | None = None + self._active_token_span: int | None = None + self._assembled_routes: dict[str, dict[int, RouterCallRoute]] = {} + self._assembled_filled: dict[str, dict[int, torch.Tensor]] = {} for module_name, module in model.named_modules(): router_key = _hf_moe_router_key(module_name) if router_key is None: continue self._routes[router_key] = {} + self._assembled_routes[router_key] = {} + self._assembled_filled[router_key] = {} self._handles.append( module.register_forward_hook(self._make_hook(router_key, module)) ) @@ -142,9 +188,18 @@ def __init__(self, model: Any) -> None: def enabled(self) -> bool: return bool(self._handles) - def set_active_micro(self, sample_index: int | None, micro_slot: int) -> None: + def set_active_micro( + self, + sample_index: int | None, + micro_slot: int, + *, + token_uids: torch.Tensor | None = None, + token_span: int | None = None, + ) -> None: self._active_sample_index = sample_index self._active_micro_slot = micro_slot + self._active_token_uids = token_uids + self._active_token_span = token_span def close(self) -> None: for handle in self._handles: @@ -162,9 +217,16 @@ def build_replay_bundle( max_topk = 0 num_global_tokens: int | None = None for router_key in sorted(self._routes): - calls = self._routes[router_key] + assembled = self._assembled_routes[router_key] + calls = assembled if assembled else self._routes[router_key] if not calls: raise RuntimeError(f"HF parity captured no routes for '{router_key}'") + for micro_slot, filled in self._assembled_filled[router_key].items(): + if not bool(filled.all()): + raise RuntimeError( + f"HF parity did not assemble all route rows for {router_key} " + f"micro {micro_slot}: {int(filled.sum())}/{int(filled.numel())}" + ) routers[router_key] = StepRouterRoutes(calls=calls) for route in calls.values(): max_topk = max(max_topk, route.max_topk) @@ -195,12 +257,17 @@ def build_replay_bundle( def _make_hook(self, router_key: str, module: Any) -> Any: def _hook(_module: Any, _inputs: Any, output: Any) -> None: - if not isinstance(output, tuple) or len(output) < 3: + if isinstance(output, torch.Tensor) and hasattr( + module, "e_score_correction_bias" + ): + router_scores, router_indices = _glm_router_output(module, output) + elif isinstance(output, tuple) and len(output) >= 3: + router_scores = output[1] + router_indices = output[2] + else: raise RuntimeError( - f"Expected HF router tuple output for '{router_key}', got {type(output)}" + f"Unsupported HF router output for '{router_key}': {type(output)}" ) - router_scores = output[1] - router_indices = output[2] if not isinstance(router_scores, torch.Tensor) or not isinstance( router_indices, torch.Tensor ): @@ -208,12 +275,12 @@ def _hook(_module: Any, _inputs: Any, output: Any) -> None: f"Expected tensor router outputs for '{router_key}', " f"got scores={type(router_scores)} indices={type(router_indices)}" ) + indices = router_indices.detach().cpu().to(torch.int32) + scores = router_scores.detach().cpu().to(torch.float32) route = RouterCallRoute( - expert_indices=router_indices.detach().cpu().to(torch.int32), - expert_probs=router_scores.detach().cpu().to(torch.float32), - expert_mask=torch.ones_like( - router_indices.detach().cpu(), dtype=torch.bool - ), + expert_indices=indices, + expert_probs=scores, + expert_mask=torch.ones_like(indices, dtype=torch.bool), num_experts=_hf_router_num_experts(module, router_scores), sample_index=self._active_sample_index, micro_slot=( @@ -222,10 +289,64 @@ def _hook(_module: Any, _inputs: Any, output: Any) -> None: else self._active_micro_slot ), ) + if self._active_token_uids is not None: + self._assemble_route(router_key, route) + return self._routes[router_key][len(self._routes[router_key])] = route return _hook + def _assemble_route(self, router_key: str, route: RouterCallRoute) -> None: + token_uids = cast(torch.Tensor, self._active_token_uids).cpu().long() + token_span = self._active_token_span + if token_span is None or int(token_uids.numel()) != route.num_global_tokens: + raise RuntimeError("HF parity route path metadata does not match routes") + micro_slot = self._active_micro_slot + assembled = self._assembled_routes[router_key].get(micro_slot) + filled = self._assembled_filled[router_key].get(micro_slot) + if assembled is None: + assembled = route.model_copy( + update={ + "expert_indices": torch.full( + (token_span, route.max_topk), -1, dtype=torch.int32 + ), + "expert_probs": torch.zeros( + (token_span, route.max_topk), dtype=torch.float32 + ), + "expert_mask": torch.zeros( + (token_span, route.max_topk), dtype=torch.bool + ), + } + ) + filled = torch.zeros(token_span, dtype=torch.bool) + self._assembled_routes[router_key][micro_slot] = assembled + self._assembled_filled[router_key][micro_slot] = filled + assert filled is not None + repeated = filled.index_select(0, token_uids) + if bool(repeated.any()): + path_rows = torch.where(repeated)[0] + existing_rows = token_uids.index_select(0, path_rows) + if not torch.equal( + assembled.expert_indices.index_select(0, existing_rows), + route.expert_indices.index_select(0, path_rows), + ): + raise RuntimeError("HF parity repeated path changed expert ids") + assert assembled.expert_probs is not None + assert route.expert_probs is not None + if not torch.allclose( + assembled.expert_probs.index_select(0, existing_rows), + route.expert_probs.index_select(0, path_rows), + ): + raise RuntimeError("HF parity repeated path changed expert scores") + assembled.expert_indices.index_copy_(0, token_uids, route.expert_indices) + assert assembled.expert_probs is not None + assert route.expert_probs is not None + assembled.expert_probs.index_copy_(0, token_uids, route.expert_probs) + assert assembled.expert_mask is not None + assert route.expert_mask is not None + assembled.expert_mask.index_copy_(0, token_uids, route.expert_mask) + filled.index_fill_(0, token_uids, True) + def _debug(message: str) -> None: if os.environ.get(HF_PARITY_DEBUG_ENV, "").strip().lower() not in { @@ -350,12 +471,15 @@ def _load_hf_model( num_layers: int, device: torch.device, dtype: torch.dtype, + allow_unvalidated_arch: bool, ) -> Any: from transformers import AutoConfig, AutoModelForCausalLM from art.megatron.model_support.registry import get_model_support_handler - handler = get_model_support_handler(base_model) + handler = get_model_support_handler( + base_model, allow_unvalidated_arch=allow_unvalidated_arch + ) ensure_hf_reference_registered = getattr( handler, "ensure_hf_reference_registered", None ) @@ -384,12 +508,18 @@ def _load_hf_model( **extra_kwargs, ) model.train() - return cast(Any, model).to(device) + model = cast(Any, model).to(device) + prepare_hf_reference_model = getattr(handler, "prepare_hf_reference_model", None) + if prepare_hf_reference_model is not None: + model = prepare_hf_reference_model(model) + return model def _collect_hf_grads(model: Any) -> dict[str, torch.Tensor]: grads: dict[str, torch.Tensor] = {} for name, param in model.named_parameters(): + if not param.requires_grad: + continue grad = param.grad if grad is None: grad = torch.zeros_like(param) @@ -410,20 +540,27 @@ def _normalize_hf_reference_state_for_hf_parity( base_model: str, model: Any, state: dict[str, torch.Tensor], + allow_unvalidated_arch: bool, ) -> dict[str, torch.Tensor]: from art.megatron.model_support.registry import get_model_support_handler - handler = get_model_support_handler(base_model) + handler = get_model_support_handler( + base_model, allow_unvalidated_arch=allow_unvalidated_arch + ) normalize = getattr(handler, "normalize_hf_reference_state_for_hf_parity", None) if normalize is not None: normalize(state, config=model.config) return state -def _use_hf_reference_state_for_hf_parity(base_model: str) -> bool: +def _use_hf_reference_state_for_hf_parity( + base_model: str, *, allow_unvalidated_arch: bool +) -> bool: from art.megatron.model_support.registry import get_model_support_handler - handler = get_model_support_handler(base_model) + handler = get_model_support_handler( + base_model, allow_unvalidated_arch=allow_unvalidated_arch + ) enabled = getattr(handler, "use_hf_reference_state_for_hf_parity", None) return bool(enabled()) if enabled is not None else False @@ -582,6 +719,154 @@ def _focus_derivative_tensor_map( return focused +def _dense_prefix_tree_attention_mask( + *, + group_ids: torch.Tensor, + parent_ids: torch.Tensor, + position_ids: torch.Tensor, + device: torch.device, + dtype: torch.dtype, + sliding_window: int | None = None, +) -> torch.Tensor: + context = prepare_block_mask_context( + group_ids=group_ids, + parent_ids=parent_ids, + input_pos=position_ids, + ) + seq_len = int(group_ids.numel()) + absolute = torch.arange(seq_len) + group_enter = torch.from_numpy(context.group_enter_np) + group_exit = torch.from_numpy(context.group_exit_np) + allowed = (absolute[:, None] >= absolute[None, :]) & ( + (group_enter[None, :] <= group_enter[:, None]) + & (group_enter[:, None] < group_exit[None, :]) + ) + if sliding_window is not None: + positions = position_ids.detach().cpu().reshape(-1) + delta = positions[:, None] - positions[None, :] + allowed &= (delta >= 0) & (delta < sliding_window) + mask = torch.full( + (seq_len, seq_len), + torch.finfo(dtype).min, + device=device, + dtype=dtype, + ) + return mask.masked_fill(allowed.to(device), 0).unsqueeze(0).unsqueeze(0) + + +def _hf_prefix_tree_forward_inputs( + model: Any, + micro: dict[str, torch.Tensor], + *, + actual_len: int, + device: torch.device, + dtype: torch.dtype, +) -> tuple[torch.Tensor | dict[str, torch.Tensor], torch.Tensor]: + group_ids = micro["group_ids"].reshape(-1)[:actual_len] + parent_ids = micro["parent_ids"].reshape(-1)[:actual_len] + position_ids = micro["position_ids"].reshape(-1)[:actual_len] + full_mask = _dense_prefix_tree_attention_mask( + group_ids=group_ids, + parent_ids=parent_ids, + position_ids=position_ids, + device=device, + dtype=dtype, + ) + config = model.config + get_text_config = getattr(config, "get_text_config", None) + text_config = get_text_config() if callable(get_text_config) else config + layer_types = tuple(getattr(text_config, "layer_types", ())) + attention_mask: torch.Tensor | dict[str, torch.Tensor] = full_mask + if "sliding_attention" in layer_types: + attention_mask = { + "full_attention": full_mask, + "sliding_attention": _dense_prefix_tree_attention_mask( + group_ids=group_ids, + parent_ids=parent_ids, + position_ids=position_ids, + device=device, + dtype=dtype, + sliding_window=int(text_config.sliding_window), + ), + } + return attention_mask, position_ids.unsqueeze(0).to(device=device) + + +def _prepare_hf_parity_megatron_micro( + micro: dict[str, torch.Tensor], + *, + device: torch.device, + provider: Any, + model_support_handler: Any, +) -> megatron_train.PreparedSFTMicroInputs: + prepared = megatron_train._prepare_dense_sft_micro( + micro, + device=device, + provider=provider, + model_support_handler=model_support_handler, + ) + seq_len = int(prepared.input_ids.shape[1]) + position_ids = micro["position_ids"].reshape(-1)[:seq_len].unsqueeze(0) + attention_state = create_prefix_tree_state( + group_ids=micro["group_ids"].reshape(-1)[:seq_len].unsqueeze(0), + parent_ids=micro["parent_ids"].reshape(-1)[:seq_len].unsqueeze(0), + target_device=device, + input_pos=position_ids, + sliding_windows=megatron_microbatches._art_flex_sliding_windows(provider), + build_gdn_execution_spec=bool( + getattr(model_support_handler, "build_gdn_execution_spec", False) + ), + model_support_handler=model_support_handler, + attention_head_dim=getattr(provider, "kv_channels", None), + attention_value_head_dim=getattr(provider, "kv_channels", None), + gdn_planner_config=megatron_microbatches._gdn_planner_config_for_provider( + provider, + model_support_handler, + ), + ) + return prepared.model_copy( + update={ + "position_ids": position_ids.to(device=device), + "attention_state": attention_state, + } + ) + + +def _hf_requires_recurrent_prefix_paths( + base_model: str, *, allow_unvalidated_arch: bool +) -> bool: + from art.megatron.model_support.registry import get_model_support_handler + + handler = get_model_support_handler( + base_model, allow_unvalidated_arch=allow_unvalidated_arch + ) + return bool(getattr(handler, "build_gdn_execution_spec", False)) + + +def _prepare_hf_reference_forward( + model: Any, + micro: dict[str, torch.Tensor], + *, + base_model: str, + actual_len: int, + allow_unvalidated_arch: bool, +) -> None: + from art.megatron.model_support.registry import get_model_support_handler + + handler = get_model_support_handler( + base_model, allow_unvalidated_arch=allow_unvalidated_arch + ) + prepare_forward = getattr(handler, "prepare_hf_reference_forward", None) + if prepare_forward is None: + return + prepare_forward( + model, + position_ids=micro["position_ids"].reshape(-1)[:actual_len], + group_ids=micro["group_ids"].reshape(-1)[:actual_len], + parent_ids=micro["parent_ids"].reshape(-1)[:actual_len], + ) + + def _run_hf_sft_step( *, base_model: str, @@ -591,6 +876,7 @@ def _run_hf_sft_step( topology: ReplayParallelTopology, device: torch.device, dtype: torch.dtype, + allow_unvalidated_arch: bool, ) -> tuple[ torch.Tensor, torch.Tensor, @@ -604,9 +890,13 @@ def _run_hf_sft_step( num_layers=num_layers, device=device, dtype=dtype, + allow_unvalidated_arch=allow_unvalidated_arch, ) if dtype == torch.float32: _install_hf_qwen35_gdn_fp32_reference(model, base_model=base_model) + recurrent_prefix_paths = _hf_requires_recurrent_prefix_paths( + base_model, allow_unvalidated_arch=allow_unvalidated_arch + ) route_capture = _HfMoeRoutingCapture(model) _debug("running HF forward/backward") model.zero_grad(set_to_none=True) @@ -623,15 +913,45 @@ def _run_hf_sft_step( for micro_slot, (micro, sample_index) in enumerate( zip(micro_inputs, sample_indices, strict=True) ): - route_capture.set_active_micro(sample_index, micro_slot) attention_mask = micro["attention_mask"].reshape(-1) actual_len = max(int(attention_mask.sum().item()), 1) + if recurrent_prefix_paths: + micro_losses = _run_hf_recurrent_prefix_tree_micro( + model=model, + route_capture=route_capture, + micro=micro, + sample_index=sample_index, + micro_slot=micro_slot, + actual_len=actual_len, + total_token_count=total_token_count, + device=device, + dtype=dtype, + ) + trainable_losses.append(micro_losses.detach().cpu()) + loss_sum = loss_sum + micro_losses.detach().sum() + token_count += int(micro_losses.numel()) + continue + route_capture.set_active_micro(sample_index, micro_slot) + _prepare_hf_reference_forward( + model, + micro, + base_model=base_model, + actual_len=actual_len, + allow_unvalidated_arch=allow_unvalidated_arch, + ) input_ids = micro["input_ids"].reshape(-1)[:actual_len].unsqueeze(0).to(device) labels = micro["labels"].reshape(-1)[:actual_len].unsqueeze(0).to(device) - hf_attention_mask = torch.ones_like(input_ids, dtype=torch.long, device=device) + hf_attention_mask, position_ids = _hf_prefix_tree_forward_inputs( + model, + micro, + actual_len=actual_len, + device=device, + dtype=dtype, + ) logits = model( input_ids=input_ids, attention_mask=hf_attention_mask, + position_ids=position_ids, use_cache=False, ).logits shifted_labels = megatron_train.shift_tensor(labels, -100) @@ -653,8 +973,11 @@ def _run_hf_sft_step( base_model=base_model, model=model, state=_collect_hf_state_dict(model), + allow_unvalidated_arch=allow_unvalidated_arch, + ) + if _use_hf_reference_state_for_hf_parity( + base_model, allow_unvalidated_arch=allow_unvalidated_arch ) - if _use_hf_reference_state_for_hf_parity(base_model) else None ) routing_replay_bundle = route_capture.build_replay_bundle(topology=topology) @@ -674,6 +997,112 @@ def _run_hf_sft_step( ) +def _run_hf_recurrent_prefix_tree_micro( + *, + model: Any, + route_capture: _HfMoeRoutingCapture, + micro: dict[str, torch.Tensor], + sample_index: int | None, + micro_slot: int, + actual_len: int, + total_token_count: int, + device: torch.device, + dtype: torch.dtype, +) -> torch.Tensor: + input_ids = micro["input_ids"].reshape(-1)[:actual_len] + labels = micro["labels"].reshape(-1)[:actual_len] + position_ids = micro["position_ids"].reshape(-1)[:actual_len] + shifted_labels = megatron_train.shift_tensor(labels.unsqueeze(0), -100)[0] + expected_mask = shifted_labels != -100 + claimed_mask = torch.zeros(actual_len, dtype=torch.bool) + claimed_targets = torch.full((actual_len,), -100, dtype=labels.dtype) + packed_losses = torch.empty(actual_len, dtype=torch.float32) + for path_indices in _hf_prefix_tree_paths(micro, actual_len=actual_len): + route_capture.set_active_micro( + sample_index, + micro_slot, + token_uids=path_indices, + token_span=actual_len, + ) + path_input_ids = input_ids.index_select(0, path_indices).unsqueeze(0).to(device) + path_labels = labels.index_select(0, path_indices).unsqueeze(0).to(device) + path_positions = ( + position_ids.index_select(0, path_indices).unsqueeze(0).to(device) + ) + logits = model( + input_ids=path_input_ids, + attention_mask=torch.ones_like(path_input_ids, dtype=dtype), + position_ids=path_positions, + use_cache=False, + ).logits + path_shifted_labels = megatron_train.shift_tensor(path_labels, -100)[0] + per_token_loss = F.cross_entropy( + logits.float().reshape(-1, logits.shape[-1]), + path_shifted_labels, + reduction="none", + ignore_index=-100, + ) + path_mask = path_shifted_labels != -100 + path_uids = path_indices[path_mask.cpu()] + path_targets = path_shifted_labels[path_mask].detach().cpu() + repeated = claimed_mask.index_select(0, path_uids) + if bool(repeated.any()) and not torch.equal( + claimed_targets.index_select(0, path_uids[repeated]), + path_targets[repeated], + ): + raise RuntimeError("HF prefix paths assign different targets to one token") + unclaimed = ~repeated + selected_uids = path_uids[unclaimed] + selected_losses = per_token_loss[path_mask][unclaimed.to(device)] + packed_losses.index_copy_(0, selected_uids, selected_losses.detach().cpu()) + claimed_targets.index_copy_(0, selected_uids, path_targets[unclaimed]) + claimed_mask.index_fill_(0, selected_uids, True) + if selected_losses.numel(): + (selected_losses.sum() / total_token_count).backward() + if not torch.equal(claimed_mask, expected_mask.cpu()): + missing = torch.where(expected_mask.cpu() & ~claimed_mask)[0].tolist() + extra = torch.where(claimed_mask & ~expected_mask.cpu())[0].tolist() + raise RuntimeError( + "HF prefix paths do not preserve packed loss positions: " + f"missing={missing} extra={extra}" + ) + return packed_losses[expected_mask.cpu()] + + +def _hf_prefix_tree_paths( + micro: dict[str, torch.Tensor], *, actual_len: int +) -> tuple[torch.Tensor, ...]: + row = parse_prefix_tree_row( + group_ids=micro["group_ids"].reshape(-1)[:actual_len], + parent_ids=micro["parent_ids"].reshape(-1)[:actual_len], + ) + if row.valid_tokens != actual_len: + raise RuntimeError( + f"HF prefix tree covers {row.valid_tokens}/{actual_len} valid tokens" + ) + by_group = {segment.group_id: segment for segment in row.segments} + parent_groups = { + segment.parent_id + for segment in row.segments + if segment.parent_id != segment.group_id + } + paths: list[torch.Tensor] = [] + for leaf in row.segments: + if leaf.group_id in parent_groups: + continue + path_segments = [by_group[group_id] for group_id in leaf.ancestors] + path_segments.append(leaf) + paths.append( + torch.cat( + [ + torch.arange(segment.start, segment.end, dtype=torch.long) + for segment in path_segments + ] + ) + ) + return tuple(paths) + + def _install_hf_qwen35_gdn_fp32_reference(model: Any, *, base_model: str) -> None: model_key = base_model.lower() if "qwen3.5" not in model_key and "qwen3_5" not in model_key: @@ -696,7 +1125,8 @@ def _build_megatron_runtime( moe_routing_replay_bundle: MoeRoutingReplayBundle | None = None, ) -> megatron_train.TrainingRuntime: use_hf_reference_state = _use_hf_reference_state_for_hf_parity( - request.case_config.base_model + request.case_config.base_model, + allow_unvalidated_arch=request.case_config.allow_unvalidated_arch, ) return megatron_train.build_training_runtime( model_identifier=request.case_config.base_model, @@ -789,7 +1219,18 @@ def _build_hf_parity_conversion_tasks( hf_keys: set[str], ) -> list[Any]: tasks = [] - for task in build_art_conversion_tasks(bridge=bridge, model=model): + registry_type = type(bridge._model_bridge.mapping_registry()) + lookup = registry_type.megatron_to_hf_lookup + + def permissive_lookup(registry: Any, name: str) -> Any: + mapping = lookup(registry, name) + if mapping is not None: + mapping.allow_hf_name_mismatch = True + return mapping + + with patch.object(registry_type, "megatron_to_hf_lookup", permissive_lookup): + conversion_tasks = build_art_conversion_tasks(bridge=bridge, model=model) + for task in conversion_tasks: mapping_names = _hf_param_names_for_mapping(task.mapping) if not mapping_names: tasks.append(task) @@ -1091,7 +1532,7 @@ def _run_megatron_sft_step( sample_indices[micro_order], micro_order, ) - prepared_micro = megatron_train._prepare_dense_sft_micro( + prepared_micro = _prepare_hf_parity_megatron_micro( micro, device=device, provider=runtime.provider, @@ -1133,6 +1574,7 @@ def _run_megatron_sft_step( derivative_tasks = [ task for task in tasks + if cast(torch.nn.Parameter, task.param_weight).requires_grad if _mapping_supports_derivative_parity(task.mapping) and _mapping_targets_language_only(task.mapping) ] @@ -1183,7 +1625,28 @@ def _drop_gemma4_reparameterized_norm_grads( } +def _validate_distributed_process_env() -> None: + missing = [name for name in _DISTRIBUTED_PROCESS_ENV if not os.environ.get(name)] + if missing: + raise RuntimeError( + f"HF parity worker requires explicit distributed environment: {missing}" + ) + master_port = int(os.environ["MASTER_PORT"]) + rank = int(os.environ["RANK"]) + world_size = int(os.environ["WORLD_SIZE"]) + local_rank = int(os.environ["LOCAL_RANK"]) + local_world_size = int(os.environ["LOCAL_WORLD_SIZE"]) + if not 0 < master_port < 65536: + raise RuntimeError(f"Invalid MASTER_PORT={master_port}") + if not 0 <= rank < world_size or not 0 <= local_rank < local_world_size: + raise RuntimeError( + "Invalid HF parity rank environment: " + f"rank={rank}/{world_size} local_rank={local_rank}/{local_world_size}" + ) + + def _worker_run(request: HfParityRunRequest) -> None: + _validate_distributed_process_env() if not torch.cuda.is_available(): raise RuntimeError("HF parity requires at least one CUDA device") torch.cuda.set_device(0) @@ -1197,6 +1660,14 @@ def _worker_run(request: HfParityRunRequest) -> None: trajectory_tensors = build_sft_trajectory_tensors_from_packed_tensors( packed_tensors ) + for index, trajectory in enumerate(trajectory_tensors): + trajectory.update( + { + "group_ids": packed_tensors["group_ids"][index].detach().clone(), + "parent_ids": packed_tensors["parent_ids"][index].detach().clone(), + "position_ids": packed_tensors["input_pos"][index].detach().clone(), + } + ) zero_template = megatron_train._zero_contribution_sft_inputs(trajectory_tensors[0]) sample_indices = build_parity_sample_indices( num_sequences=len(trajectory_tensors), @@ -1246,6 +1717,7 @@ def _worker_run(request: HfParityRunRequest) -> None: topology=replay_topology, device=device, dtype=dtype, + allow_unvalidated_arch=request.case_config.allow_unvalidated_arch, ) megatron_outputs, megatron_loss, megatron_grads = _run_megatron_sft_step( request=request, @@ -1295,11 +1767,18 @@ def _worker_run(request: HfParityRunRequest) -> None: ) outputs_summary = summarize_tensor_pair(hf_outputs, megatron_outputs) loss_summary = summarize_tensor_pair(hf_loss, megatron_loss) + from art.megatron.model_support.registry import get_model_support_handler + + handler = get_model_support_handler( + request.case_config.base_model, + allow_unvalidated_arch=request.case_config.allow_unvalidated_arch, + ) grads_rows = build_tensor_map_metric_rows( phase="grads", reference=normalized_hf_grads, candidate=megatron_grads, - phase_pass_fns=_hf_parity_phase_pass_fns(), + phase_pass_fns=_hf_parity_phase_pass_fns_for_case(request.case_config), + group_by=getattr(handler, "hf_parity_gradient_group", None), ) report = build_hf_parity_report( request=request, diff --git a/tests/integration/megatron/model_support/lora_coverage.py b/tests/integration/megatron/model_support/lora_coverage.py index eb06182c2..5e9742322 100644 --- a/tests/integration/megatron/model_support/lora_coverage.py +++ b/tests/integration/megatron/model_support/lora_coverage.py @@ -29,6 +29,8 @@ _WRAPPED_TARGET_SUFFIXES: dict[str, tuple[str, ...]] = { "q_a_proj": (".self_attn.q_a_proj",), "q_b_proj": (".self_attn.q_b_proj",), + "kv_a_proj_with_mqa": (".self_attn.kv_a_proj_with_mqa",), + "kv_b_proj": (".self_attn.kv_b_proj",), "kv_proj": (".self_attn.kv_proj",), "o_a_proj": (".self_attn.o_a_proj",), "o_b_proj": (".self_attn.o_b_proj",), @@ -119,6 +121,18 @@ def _covered_exported_target_modules( ) -> set[str]: covered: set[str] = set() for base_name, adapter_weights in adapter_weights_by_base.items(): + if base_name.endswith(".self_attention.linear_q_down_proj.weight"): + covered.add("q_a_proj") + continue + if base_name.endswith(".self_attention.linear_q_up_proj.weight"): + covered.add("q_b_proj") + continue + if base_name.endswith(".self_attention.linear_kv_down_proj.weight"): + covered.add("kv_a_proj_with_mqa") + continue + if base_name.endswith(".self_attention.linear_kv_up_proj.weight"): + covered.add("kv_b_proj") + continue if base_name.endswith(".self_attention.wq_a.weight"): covered.add("q_a_proj") continue diff --git a/tests/integration/megatron/model_support/oracle_harness.py b/tests/integration/megatron/model_support/oracle_harness.py index 35da59e98..d0c17faf3 100644 --- a/tests/integration/megatron/model_support/oracle_harness.py +++ b/tests/integration/megatron/model_support/oracle_harness.py @@ -16,7 +16,13 @@ from rich.table import Table import torch -from art.megatron.routing_replay import ROUTER_KEY_FORMAT_VERSION +from art.megatron.routing_replay import ( + ROUTER_KEY_FORMAT_VERSION, + MoeRoutingReplayBundle, +) +from art.megatron.routing_replay import ( + ParallelTopology as ReplayParallelTopology, +) from art.megatron.training.streaming_weight_offload import StreamingWeightOffloadConfig from ..artifacts import GitRepoState, pinned_git_state @@ -217,7 +223,7 @@ def world_size(self) -> int: TOPOLOGIES = [ Topology(tp=1, ep=1, etp=1, dp=1, sp=False), Topology(tp=1, ep=2, etp=1, dp=1, cp=2, sp=False), - Topology(tp=2, ep=2, etp=1, dp=1, cp=2, sp=True), + Topology(tp=1, ep=2, etp=1, dp=1, cp=2, pp=2, vpp=2, sp=False), Topology(tp=2, ep=4, etp=2, dp=2, cp=2, sp=True), ] @@ -233,11 +239,7 @@ def _without_context_parallel(topology: Topology) -> Topology: ] DENSE_TOPOLOGIES = [ Topology(tp=1, ep=1, etp=1, dp=1, sp=False), - Topology(tp=2, ep=1, etp=1, dp=1, sp=True), - Topology(tp=1, ep=1, etp=1, dp=2, sp=False), - Topology(tp=2, ep=1, etp=1, dp=2, sp=True), - Topology(tp=1, ep=1, etp=1, dp=1, cp=2, sp=False), - Topology(tp=2, ep=1, etp=1, dp=1, cp=2, sp=True), + Topology(tp=2, ep=1, etp=1, dp=1, cp=2, sp=False), Topology(tp=2, ep=1, etp=1, dp=2, cp=2, sp=True), ] ORACLE_TOPOLOGY = TOPOLOGIES[0] @@ -265,11 +267,14 @@ def _without_context_parallel(topology: Topology) -> Topology: SENSITIVITY_TOPOLOGY_BY_MUTATION |= { k: Topology(tp=1, ep=2, etp=1, dp=2, sp=False) for k in [ - "dp_grad_accumulation_seqs", "dp_local_token_normalization", "sft_local_token_normalization", ] } +# Isolate DP sample assignment from HybridEP's independently planned micro extents. +SENSITIVITY_TOPOLOGY_BY_MUTATION["dp_grad_accumulation_seqs"] = Topology( + tp=1, ep=1, etp=1, dp=2, sp=False +) class PackedTensorConfig(BaseModel): @@ -341,6 +346,8 @@ class OracleCaseConfig(BaseModel): """Contains all deterministic run parameters for one oracle case.""" base_model: str + provider_model: str | None = None + model_support_key: str | None = None precision: Literal["bf16", "fp32"] = "fp32" num_layers: int = 4 seed: int = 20260304 @@ -356,6 +363,12 @@ class OracleCaseConfig(BaseModel): @property def is_moe(self) -> bool: + if self.model_support_key is not None: + from art.megatron.model_support.registry import ( + get_model_support_spec_by_key, + ) + + return get_model_support_spec_by_key(self.model_support_key).is_moe from art.megatron.model_support import model_uses_expert_parallel return model_uses_expert_parallel( @@ -797,6 +810,21 @@ def selected_suite_topologies( def stable_case_id(case_config: OracleCaseConfig) -> str: """Builds a deterministic case id from case config contents.""" payload = case_config.model_dump(mode="json") + if case_config.model_support_key is not None: + from art.megatron.model_support.registry import get_model_support_spec_by_key + + payload["runtime_target_modules"] = list( + get_model_support_spec_by_key( + case_config.model_support_key + ).default_target_modules + ) + else: + from art.megatron.model_support import default_target_modules_for_model + + payload["runtime_target_modules"] = default_target_modules_for_model( + case_config.base_model, + allow_unvalidated_arch=case_config.allow_unvalidated_arch, + ) encoded = json.dumps(payload, sort_keys=True, separators=(",", ":")) digest = hashlib.sha256(encoded.encode("utf-8")).hexdigest()[:16] model_tag = ( @@ -900,6 +928,25 @@ def _replace_topology_dir(path: Path) -> None: (path / "traces").mkdir(parents=True, exist_ok=True) +def _replay_bundle_for_topology( + source: Path, + *, + topology: Topology, + output_dir: Path, +) -> Path: + bundle = MoeRoutingReplayBundle.from_dir(source) + runtime_topology = ReplayParallelTopology.model_validate( + topology.model_dump( + include={"tp", "ep", "etp", "dp", "sp", "cp", "pp", "vpp"}, + mode="python", + ) + ) + if bundle.topology == runtime_topology: + return source + bundle.model_copy(update={"topology": runtime_topology}).to_dir(output_dir) + return output_dir + + def _prune_topology_artifacts(path: Path) -> None: """Keeps small diagnostics and removes tensors that are only needed for comparison.""" if keep_topology_artifacts() or not path.exists(): @@ -1079,6 +1126,7 @@ def __init__( oracle_offload_between_jobs: bool = True, oracle_streaming_weight_offload: StreamingWeightOffloadConfig | None = None, use_fp32_lora_reference: bool = True, + paired_objective: OracleObjective | None = None, console: Console | None = None, ) -> None: self.objective = objective @@ -1102,6 +1150,7 @@ def __init__( oracle_streaming_weight_offload or StreamingWeightOffloadConfig() ) self.use_fp32_lora_reference = use_fp32_lora_reference + self.paired_objective = paired_objective self.shared_init_path = Path(self.case_artifacts.shared_init_adapter_path) self.oracle_flex_backend = _resolve_test_flex_backend( case_config, oracle_flex_backend @@ -1258,7 +1307,7 @@ def _trim_trace_padding( call[key] = tensor[:target_rows].contiguous() return trace - def _run_topology( + def _prepare_topology( self, *, topology: Topology, @@ -1270,8 +1319,8 @@ def _run_topology( flex_backend: FlexBackend | None = None, offload_between_jobs: bool = True, streaming_weight_offload: StreamingWeightOffloadConfig | None = None, - ) -> Path: - """Executes one topology worker run and returns its output directory.""" + ) -> tuple[Path, WorkerRunRequest | None]: + """Prepares one topology output and returns any worker request it needs.""" topology_dir = self.case_dir / output_slug manifest_path = topology_dir / "manifest.json" if ( @@ -1279,8 +1328,14 @@ def _run_topology( and not regenerate and _manifest_matches_current_commit(manifest_path) ): - return topology_dir + return topology_dir, None _replace_topology_dir(topology_dir) + if replay_bundle_dir is not None: + replay_bundle_dir = _replay_bundle_for_topology( + replay_bundle_dir, + topology=topology, + output_dir=topology_dir / "moe_routing_replay", + ) run_case_config = self.case_config request = WorkerRunRequest( git=self.git, @@ -1306,11 +1361,135 @@ def _run_topology( ), use_fp32_lora_reference=self.use_fp32_lora_reference, ) - from .oracle_worker import run_worker_subprocess + return topology_dir, request + + def _paired_topology_dir(self, topology_dir: Path) -> Path: + prefix = f"{self.objective}__" + if self.paired_objective is None or not topology_dir.name.startswith(prefix): + raise ValueError(f"Cannot pair oracle output '{topology_dir.name}'") + return self.case_dir / ( + f"{self.paired_objective}__{topology_dir.name.removeprefix(prefix)}" + ) + + def _routing_bundle_dir(self, objective: OracleObjective) -> Path: + return self.case_dir / f"{objective}__{ORACLE_MOE_ROUTING_BUNDLE_DIRNAME}" + + def _objective_artifact_paths( + self, + ) -> list[tuple[OracleObjective, Path, Path, Path]]: + oracle_dirs = [(self.objective, self.oracle_dir)] + if self.paired_objective is not None: + oracle_dirs.append( + (self.paired_objective, self._paired_topology_dir(self.oracle_dir)) + ) + return [ + ( + objective, + oracle_dir, + self._routing_bundle_dir(objective), + self.case_dir / f"{oracle_dir.name}__oracle_capture", + ) + for objective, oracle_dir in oracle_dirs + ] - run_worker_subprocess(request, topology_dir, repo_root=REPO_ROOT) + def _paired_worker_request( + self, + request: WorkerRunRequest, + paired_dir: Path, + ) -> WorkerRunRequest: + objective = self.paired_objective + if objective is None: + raise ValueError("Cannot build a paired request without a paired objective") + updates: dict[str, Any] = { + "objective": objective, + "topology_dir": str(paired_dir), + } + if request.moe_routing_replay_path is not None: + updates["moe_routing_replay_path"] = str( + _replay_bundle_for_topology( + self._routing_bundle_dir(objective), + topology=request.topology, + output_dir=paired_dir / "moe_routing_replay", + ) + ) + if request.capture_moe_routing_bundle_path is not None: + updates["capture_moe_routing_bundle_path"] = str( + self._routing_bundle_dir(objective) + ) + return request.model_copy(update=updates) + + def _run_topology( + self, + *, + topology: Topology, + output_slug: str, + mutation: SensitivityMutation | None, + replay_bundle_dir: Path | None, + capture_bundle_dir: Path | None, + regenerate: bool, + flex_backend: FlexBackend | None = None, + offload_between_jobs: bool = True, + streaming_weight_offload: StreamingWeightOffloadConfig | None = None, + ) -> Path: + """Executes one topology worker run and returns its output directory.""" + topology_dir, request = self._prepare_topology( + topology=topology, + output_slug=output_slug, + mutation=mutation, + replay_bundle_dir=replay_bundle_dir, + capture_bundle_dir=capture_bundle_dir, + regenerate=regenerate, + flex_backend=flex_backend, + offload_between_jobs=offload_between_jobs, + streaming_weight_offload=streaming_weight_offload, + ) + if request is not None: + from .oracle_worker import run_worker_subprocess, run_worker_subprocesses + + if self.paired_objective is None: + run_worker_subprocess(request, topology_dir, repo_root=REPO_ROOT) + else: + paired_dir = self._paired_topology_dir(topology_dir) + _replace_topology_dir(paired_dir) + paired_request = self._paired_worker_request(request, paired_dir) + run_worker_subprocesses( + [request, paired_request], + [topology_dir, paired_dir], + repo_root=REPO_ROOT, + ) return topology_dir + def _prune_valid_moe_capture( + self, + capture_dir: Path, + *, + objective: OracleObjective | None = None, + bundle_dir: Path | None = None, + ) -> None: + """Prunes capture tensors only after persisted metadata reloads cleanly.""" + objective = objective or self.objective + bundle_dir = bundle_dir or self.oracle_routing_bundle_dir + manifest = _load_manifest(capture_dir) + bundle = MoeRoutingReplayBundle.from_dir(bundle_dir) + expected_topology = ReplayParallelTopology.model_validate( + self.oracle_topology.model_dump( + include={"tp", "ep", "etp", "dp", "sp", "cp", "pp", "vpp"}, + mode="python", + ) + ) + if ( + manifest.git.commit != self.git.commit + or manifest.case_id != self.case_id + or manifest.objective != objective + or manifest.topology != self.oracle_topology.slug() + or manifest.num_steps != self.case_config.num_steps + or len(manifest.steps) != manifest.num_steps + or bundle.topology != expected_topology + or bundle.num_steps != manifest.num_steps + ): + raise RuntimeError("Persisted MoE routing capture metadata does not match") + _prune_topology_artifacts(capture_dir) + def ensure_oracle(self) -> Path: """Ensures routing capture and the canonical replay-backed oracle exist once.""" regenerate = regenerate_requested() @@ -1318,26 +1497,26 @@ def ensure_oracle(self) -> Path: return self.oracle_dir if regenerate and self.shared_init_path.exists(): self.shared_init_path.unlink() - bundle_manifest = self.oracle_routing_bundle_dir / "manifest.json" - oracle_manifest = self.oracle_dir / "manifest.json" - capture_manifest = ( - self.case_dir / f"{self.oracle_slug}__oracle_capture" / "manifest.json" - ) - bundle_format_current = False - if bundle_manifest.exists(): + objective_artifacts = self._objective_artifact_paths() + bundle_format_current = True + for _, _, bundle_dir, _ in objective_artifacts: + bundle_manifest = bundle_dir / "manifest.json" try: - bundle_format_current = ( - _read_json(bundle_manifest).get("format_version") + bundle_format_current &= ( + bundle_manifest.exists() + and _read_json(bundle_manifest).get("format_version") == ROUTER_KEY_FORMAT_VERSION ) except Exception: bundle_format_current = False need_capture = ( regenerate - or not bundle_manifest.exists() or not bundle_format_current or not self.shared_init_path.exists() - or not _manifest_matches_current_commit(capture_manifest) + or any( + not _manifest_matches_current_commit(capture_dir / "manifest.json") + for _, _, _, capture_dir in objective_artifacts + ) ) run_oracle_topology = partial( self._run_topology, @@ -1354,11 +1533,28 @@ def ensure_oracle(self) -> Path: replay_bundle_dir=None, capture_bundle_dir=self.oracle_routing_bundle_dir, ) + for objective, _, bundle_dir, capture_dir in objective_artifacts: + self._prune_valid_moe_capture( + capture_dir, + objective=objective, + bundle_dir=bundle_dir, + ) if ( regenerate - or not oracle_manifest.exists() + or (self.case_config.is_moe and need_capture) or not self.shared_init_path.exists() - or not _manifest_matches_current_commit(oracle_manifest) + or any( + not _manifest_matches_current_commit(oracle_dir / "manifest.json") + or any( + not ( + oracle_dir + / "traces" + / f"forward_trace_step_{step_index:03d}.pt" + ).exists() + for step_index in range(self.case_config.num_steps) + ) + for _, oracle_dir, _, _ in objective_artifacts + ) ): run_oracle_topology( output_slug=self.oracle_slug, @@ -1845,12 +2041,11 @@ def _write_variant_report(self, topology_dir: Path, report: VariantReport) -> No def _prune_reference_artifacts(self) -> None: """Drops oracle-only tensors after all comparisons that need them are complete.""" - _prune_topology_artifacts(self.oracle_dir) - if self.case_config.is_moe: - _prune_topology_artifacts(self.oracle_routing_bundle_dir) - _prune_topology_artifacts( - self.case_dir / f"{self.oracle_slug}__oracle_capture" - ) + for _, oracle_dir, bundle_dir, capture_dir in self._objective_artifact_paths(): + _prune_topology_artifacts(oracle_dir) + if self.case_config.is_moe: + _prune_topology_artifacts(bundle_dir) + _prune_topology_artifacts(capture_dir) def print_report(self, report: VariantReport) -> None: """Prints a row-level table excluding expert-specific rows.""" @@ -1906,7 +2101,6 @@ def run_variant( topology_dir = self.ensure_variant_artifacts(variant) report = self.compare_variant(variant) self._write_variant_report(topology_dir, report) - _prune_topology_artifacts(topology_dir) self.print_report(report) return report @@ -1916,32 +2110,38 @@ def run_suite( *, prune_reference_artifacts: bool = True, prune_case_artifacts: bool = True, + prune_paired_artifacts: bool = True, ) -> list[VariantReport]: """Runs variants in order and stops at the first unexpected signal. - Reference and case artifacts are normally pruned when the suite exits. Callers that immediately run another comparison suite against the same - reference can defer that pruning so the second suite does not have to - regenerate or fail on missing forward traces. + reference can defer shared cleanup until all consumers finish. """ reports: list[VariantReport] = [] try: for variant in variants: - report = self.run_variant(variant) - reports.append(report) - self.assert_expected_signal( - report, - "Megatron correctness suite mismatch", - report_path=self.case_dir - / variant.resolved_output_slug() - / "variant_report.json", - ) + topology_dir = self.case_dir / variant.resolved_output_slug() + try: + report = self.run_variant(variant) + self.assert_expected_signal( + report, + "Megatron correctness suite mismatch", + report_path=topology_dir / "variant_report.json", + ) + reports.append(report) + finally: + if topology_dir != self.oracle_dir: + _prune_topology_artifacts(topology_dir) + if self.paired_objective is not None and prune_paired_artifacts: + _prune_topology_artifacts( + self._paired_topology_dir(topology_dir) + ) + return reports finally: if prune_reference_artifacts: self._prune_reference_artifacts() if prune_case_artifacts: _prune_case_artifacts(self.case_dir) - return reports def _default_phase_pass_fns() -> dict[str, PhasePassFn]: @@ -2011,6 +2211,92 @@ def _suite_variants( return variants +def _prune_completed_runners( + runners: list[VariantRunner], + *, + prune_reference_artifacts: bool = True, + prune_case_artifacts: bool = True, +) -> None: + """Prunes shared artifacts after every owning suite completes successfully.""" + if prune_reference_artifacts: + for runner in runners: + runner._prune_reference_artifacts() + if prune_case_artifacts: + for case_dir in dict.fromkeys(runner.case_dir for runner in runners): + _prune_case_artifacts(case_dir) + + +def _run_paired_objective_suite( + *, + objectives: list[OracleObjective], + case_config: OracleCaseConfig, + max_world_size: int | None, + oracle_flex_backend: FlexBackend | None, + variant_flex_backend: FlexBackend | None, + cp_supported: bool, + phase_pass_fns: dict[str, PhasePassFn] | None, + use_fp32_lora_reference: bool, + prune_reference_artifacts: bool, + prune_case_artifacts: bool, +) -> list[VariantReport]: + """Runs RL/SFT pairs without rebuilding one topology twice.""" + rl_objective, sft_objective = objectives + + def runner( + objective: OracleObjective, + paired_objective: OracleObjective | None = None, + ) -> VariantRunner: + return VariantRunner( + objective=objective, + case_config=case_config, + oracle_flex_backend=oracle_flex_backend, + variant_flex_backend=variant_flex_backend, + use_fp32_lora_reference=use_fp32_lora_reference, + paired_objective=paired_objective, + ) + + def variants(objective: OracleObjective) -> list[VariantSpec]: + return _suite_variants( + objective, + is_moe=case_config.is_moe, + cp_supported=cp_supported, + max_world_size=max_world_size, + variant_flex_backend=variant_flex_backend, + phase_pass_fns=phase_pass_fns, + ) + + rl_runner = runner(rl_objective, sft_objective) + try: + reports = rl_runner.run_suite( + variants(rl_objective), + prune_reference_artifacts=False, + prune_case_artifacts=False, + prune_paired_artifacts=False, + ) + sft_runner = runner(sft_objective) + sft_runner._oracle_initialized = sft_runner._oracle_regenerated = True + reports.extend( + sft_runner.run_suite( + [ + variant.model_copy(update={"force_regenerate": False}) + for variant in variants(sft_objective) + ], + prune_reference_artifacts=False, + prune_case_artifacts=False, + ) + ) + return reports + finally: + _prune_completed_runners( + [rl_runner], + prune_reference_artifacts=prune_reference_artifacts, + prune_case_artifacts=prune_case_artifacts, + ) + + +_run_paired_dense_suite = _run_paired_objective_suite + + def run_suite( *, case_config: OracleCaseConfig, @@ -2024,30 +2310,53 @@ def run_suite( prune_case_artifacts: bool = True, ) -> list[VariantReport]: """Runs non-oracle topologies against the canonical replay-backed oracle.""" - reports: list[VariantReport] = [] - for objective in selected_oracle_objectives(): - runner = VariantRunner( - objective=objective, + objectives = selected_oracle_objectives() + if objectives == list(SUPPORTED_ORACLE_OBJECTIVES): + return _run_paired_objective_suite( + objectives=objectives, case_config=case_config, + max_world_size=max_world_size, oracle_flex_backend=oracle_flex_backend, variant_flex_backend=variant_flex_backend, + cp_supported=cp_supported, + phase_pass_fns=phase_pass_fns, use_fp32_lora_reference=use_fp32_lora_reference, + prune_reference_artifacts=prune_reference_artifacts, + prune_case_artifacts=prune_case_artifacts, ) - reports.extend( - runner.run_suite( - _suite_variants( - objective, - is_moe=case_config.is_moe, - cp_supported=cp_supported, - max_world_size=max_world_size, - variant_flex_backend=variant_flex_backend, - phase_pass_fns=phase_pass_fns, - ), - prune_reference_artifacts=prune_reference_artifacts, - prune_case_artifacts=prune_case_artifacts, + reports: list[VariantReport] = [] + runners: list[VariantRunner] = [] + try: + for objective in objectives: + runner = VariantRunner( + objective=objective, + case_config=case_config, + oracle_flex_backend=oracle_flex_backend, + variant_flex_backend=variant_flex_backend, + use_fp32_lora_reference=use_fp32_lora_reference, + ) + runners.append(runner) + reports.extend( + runner.run_suite( + _suite_variants( + objective, + is_moe=case_config.is_moe, + cp_supported=cp_supported, + max_world_size=max_world_size, + variant_flex_backend=variant_flex_backend, + phase_pass_fns=phase_pass_fns, + ), + prune_reference_artifacts=False, + prune_case_artifacts=False, + ) ) + return reports + finally: + _prune_completed_runners( + runners, + prune_reference_artifacts=prune_reference_artifacts, + prune_case_artifacts=prune_case_artifacts, ) - return reports def run_sensitivity_suite( @@ -2061,6 +2370,7 @@ def run_sensitivity_suite( """Runs a list of sensitivity mutations and expects each to fail.""" phase_pass = _default_phase_pass_fns() reports: list[VariantReport] = [] + runners: list[VariantRunner] = [] ran_any_variants = False for objective in selected_oracle_objectives(): objective_mutations = selected_sensitivity_mutations_for_objective( @@ -2154,8 +2464,16 @@ def run_sensitivity_suite( if not variants: continue ran_any_variants = True - reports.extend(runner.run_suite(variants)) + runners.append(runner) + reports.extend( + runner.run_suite( + variants, + prune_reference_artifacts=False, + prune_case_artifacts=False, + ) + ) if ran_any_variants: + _prune_completed_runners(runners) return reports requested = ", ".join(mutations) supported = ", ".join( diff --git a/tests/integration/megatron/model_support/oracle_worker.py b/tests/integration/megatron/model_support/oracle_worker.py index 014871667..113538102 100644 --- a/tests/integration/megatron/model_support/oracle_worker.py +++ b/tests/integration/megatron/model_support/oracle_worker.py @@ -49,6 +49,8 @@ "cp": "ART_MEGATRON_CONTEXT_PARALLEL_SIZE", "ep": "ART_MEGATRON_EXPERT_MODEL_PARALLEL_SIZE", "etp": "ART_MEGATRON_EXPERT_TENSOR_PARALLEL_SIZE", + "pp": "ART_MEGATRON_PIPELINE_MODEL_PARALLEL_SIZE", + "vpp": "ART_MEGATRON_VIRTUAL_PIPELINE_MODEL_PARALLEL_SIZE", } _ORACLE_DEBUG_ENV = "ART_ORACLE_DEBUG" _ATTACH_TOKEN_UIDS_ENV = "ART_MEGATRON_ATTACH_TOKEN_UIDS" @@ -85,8 +87,29 @@ def run_worker_subprocess( repo_root: Path, ) -> None: """Runs one distributed worker subprocess and stores combined logs.""" - request_path = topology_dir / "run_request.json" - _write_json(request_path, request.model_dump(mode="json")) + run_worker_subprocesses([request], [topology_dir], repo_root=repo_root) + + +def run_worker_subprocesses( + requests: list[WorkerRunRequest], + topology_dirs: list[Path], + *, + repo_root: Path, +) -> None: + """Runs compatible requests in one distributed rank-process lifetime.""" + if not requests or len(requests) != len(topology_dirs): + raise ValueError( + "Worker requests and topology directories must be non-empty and aligned" + ) + topology = requests[0].topology + if any(request.topology != topology for request in requests[1:]): + raise ValueError("One worker process lifetime requires one parallel topology") + + request_paths: list[Path] = [] + for request, topology_dir in zip(requests, topology_dirs, strict=True): + request_path = topology_dir / "run_request.json" + _write_json(request_path, request.model_dump(mode="json")) + request_paths.append(request_path) worker_module = "integration.megatron.model_support.oracle_worker" worker_cwd = repo_root / "tests" @@ -96,35 +119,39 @@ def run_worker_subprocess( "torch.distributed.run", "--standalone", "--nproc_per_node", - str(request.topology.world_size()), + str(topology.world_size()), "-m", worker_module, "--worker-run", - "--run-request", - str(request_path), ] + for request_path in request_paths: + command.extend(("--run-request", str(request_path))) combined_lines: list[str] = [] - worker_log_path = topology_dir / "worker.log" live_log_raw = os.environ.get("ART_ORACLE_LIVE_TRAINING_LOG") live_log_path = None if not live_log_raw else Path(live_log_raw) run: subprocess.Popen[str] | None = None - worker_log_path.parent.mkdir(parents=True, exist_ok=True) - with worker_log_path.open("w", encoding="utf-8") as worker_log: + for topology_dir in topology_dirs: + topology_dir.mkdir(parents=True, exist_ok=True) + with ExitStack() as logs: + worker_logs = [ + logs.enter_context( + (topology_dir / "worker.log").open("w", encoding="utf-8") + ) + for topology_dir in topology_dirs + ] live_log = None try: if live_log_path is not None: live_log_path.parent.mkdir(parents=True, exist_ok=True) live_log = live_log_path.open("a", encoding="utf-8") - live_log.write( - f"\n=== {request.objective} {request.topology.slug()} ===\n" - ) + live_log.write(f"\n=== {requests[0].objective} {topology.slug()} ===\n") live_log.flush() env = { **os.environ, "ART_MEGATRON_ATTACH_TOKEN_UIDS": "1", "PYTHONUNBUFFERED": "1", } - if request.case_config.precision == "fp32": + if requests[0].case_config.precision == "fp32": env["NVIDIA_TF32_OVERRIDE"] = "0" run = subprocess.Popen( command, @@ -137,10 +164,18 @@ def run_worker_subprocess( start_new_session=True, ) assert run.stdout is not None + active_log_index = 0 + request_markers = { + f"=== {request.objective} {topology.slug()} ===": index + for index, request in enumerate(requests) + } for line in run.stdout: combined_lines.append(line) - worker_log.write(line) - worker_log.flush() + marker_index = request_markers.get(line.strip()) + if marker_index is not None: + active_log_index = marker_index + worker_logs[active_log_index].write(line) + worker_logs[active_log_index].flush() if live_log is not None: live_log.write(line) live_log.flush() @@ -154,7 +189,7 @@ def run_worker_subprocess( if run.returncode != 0: tail = "\n".join(combined_output.splitlines()[-80:]) raise RuntimeError( - f"Topology run failed for {request.topology.slug()} with exit code " + f"Topology run failed for {topology.slug()} with exit code " f"{run.returncode}.\n{tail}" ) @@ -172,10 +207,9 @@ def _set_deterministic_seed(seed: int) -> None: def provider_topology_env_vars(topology: Topology) -> dict[str, str]: return { - _TOPOLOGY_ENV_VARS["tp"]: str(topology.tp), - _TOPOLOGY_ENV_VARS["cp"]: str(topology.cp), - _TOPOLOGY_ENV_VARS["ep"]: str(topology.ep), - _TOPOLOGY_ENV_VARS["etp"]: str(topology.etp), + env_var: str(getattr(topology, field)) + for field, env_var in _TOPOLOGY_ENV_VARS.items() + if field != "vpp" or topology.vpp > 1 } @@ -234,6 +268,8 @@ def _gather_full_state( def _collect_lora_state( model_chunks: list[Any], + *, + optimizer_master: bool = False, ) -> dict[str, Any] | None: """Collects full LoRA adapter state for validation and delta computation.""" local_state: dict[str, Any] = {} @@ -248,9 +284,27 @@ def _collect_lora_state( f"Duplicate manifest key while collecting state: {key}" ) local_manifest[key] = value - if not hasattr(module, "sharded_lora_state_dict"): + if optimizer_master: + export_items = getattr(module, "_export_items", None) + if not callable(export_items): + continue + module_state = {} + for key, param, expert in export_items(): + main_param = getattr(param, "main_param", None) + if main_param is None and param.dtype == torch.float32: + main_param = param + if main_param is None or bool( + getattr(param, "main_param_sharded", False) + ): + raise RuntimeError( + f"Oracle requires a full FP32 optimizer master parameter for '{key}'" + ) + value = main_param[expert] if expert is not None else main_param + module_state[key] = value.T + elif hasattr(module, "sharded_lora_state_dict"): + module_state = module.sharded_lora_state_dict() + else: continue - module_state = module.sharded_lora_state_dict() for key, value in module_state.items(): if key in local_state: raise RuntimeError( @@ -412,6 +466,10 @@ def _configure_provider( """ del topology provider.num_layers = case_config.num_layers + for name in ("moe_layer_freq", "glm52_indexer_types"): + pattern = getattr(provider, name, None) + if isinstance(pattern, (list, tuple)): + setattr(provider, name, type(pattern)(pattern[: case_config.num_layers])) if case_config.precision == "fp32": provider.bf16 = False provider.fp16 = False @@ -425,12 +483,7 @@ def _configure_provider( provider.attention_dropout = 0.0 if hasattr(provider, "hidden_dropout"): provider.hidden_dropout = 0.0 - from art.megatron.model_support.registry import get_model_support_handler - - handler = get_model_support_handler( - case_config.base_model, - allow_unvalidated_arch=case_config.allow_unvalidated_arch, - ) + handler = provider._art_model_support_handler configure_oracle_provider = getattr(handler, "configure_oracle_provider", None) if configure_oracle_provider is not None: configure_oracle_provider(provider, case_config=case_config) @@ -451,7 +504,6 @@ def _oracle_finalize_provider_bundle(provider_bundle: Any) -> Any: provider.moe_token_dispatcher_type = "alltoall" provider.moe_flex_dispatcher_backend = None provider.moe_enable_deepep = False - provider.moe_shared_expert_overlap = True provider.overlap_moe_expert_parallel_comm = False provider.delay_wgrad_compute = False provider.ep_overlap_early_attn_memory_release = False @@ -948,85 +1000,57 @@ def _apply_attention_async_comm_mutation(mutation: SensitivityMutation | None): from art.megatron.context_parallel import comm - original = comm.A2AVCommunicator.launch_kv_fetch + original = comm.A2AVCommunicator._launch_exchange comm_delay_cycles = 80_000_000 - def _mutated_launch_kv_fetch( + def _mutated_launch_exchange( self: Any, *, - k_local: torch.Tensor, - v_local: torch.Tensor, - plan: Any, + tensor: torch.Tensor, + recv_buffer: torch.Tensor, + total_send_rows: int, + make_send_buffer: Callable[[], torch.Tensor], + output_split_sizes: list[int], + input_split_sizes: list[int], group: Any, async_op: bool, - range_meta_cache: dict[Any, Any] | None = None, - label: str = "kv_fetch", - input_layout: str = "token_major", - output_layout: str = "head_major", + input_layout: str, + row_factor: int = 2, ): - if group is None or comm._DIST.get_world_size(group) == 1: - return original( - self, - k_local=k_local, - v_local=v_local, - plan=plan, - group=group, - async_op=async_op, - range_meta_cache=range_meta_cache, - label=label, - input_layout=input_layout, - output_layout=output_layout, - ) - - total_send_rows = int(sum(plan.send_splits)) - total_recv_rows = int(sum(plan.recv_splits)) - recv_packed = k_local.new_empty( - comm._packed_peer_tensor_shape( - tensor=k_local, - total_rows=total_recv_rows, - input_layout=input_layout, - ) - ) - input_split_sizes = [split * 2 for split in plan.send_splits] - output_split_sizes = [split * 2 for split in plan.recv_splits] - stream = self._get_stream(k_local) if async_op else None + stream = self._get_stream(tensor) if async_op else None if stream is None: return original( self, - k_local=k_local, - v_local=v_local, - plan=plan, + tensor=tensor, + recv_buffer=recv_buffer, + total_send_rows=total_send_rows, + make_send_buffer=make_send_buffer, + output_split_sizes=output_split_sizes, + input_split_sizes=input_split_sizes, group=group, async_op=async_op, - range_meta_cache=range_meta_cache, - label=label, input_layout=input_layout, - output_layout=output_layout, + row_factor=row_factor, ) - current_stream = torch.cuda.current_stream(k_local.device) + current_stream = torch.cuda.current_stream(tensor.device) if total_send_rows > 0: stream.wait_stream(current_stream) with torch.cuda.stream(stream): if total_send_rows <= 0: - send_buffer = k_local.new_empty( + send_buffer = tensor.new_empty( comm._packed_peer_tensor_shape( - tensor=k_local, + tensor=tensor, total_rows=0, input_layout=input_layout, + row_factor=row_factor, ) ) else: - send_buffer = comm._pack_gathered_tensors_per_peer( - left_tensor=k_local, - right_tensor=v_local, - ranges_by_peer=plan.send_ranges_by_peer, - range_meta_cache=range_meta_cache, - input_layout=input_layout, - ) + send_buffer = make_send_buffer() if total_send_rows > 0: torch.cuda._sleep(comm_delay_cycles) handle = comm._launch_peer_exchange( - recv_buffer=recv_packed, + recv_buffer=recv_buffer, send_buffer=send_buffer, output_split_sizes=output_split_sizes, input_split_sizes=input_split_sizes, @@ -1035,21 +1059,13 @@ def _mutated_launch_kv_fetch( ) if total_send_rows > 0 and send_buffer.numel() > 0: send_buffer.zero_() - return comm.KvFetchWork( - packed_buffer=recv_packed, - recv_splits=plan.recv_splits, - handle=handle, - send_buffer=send_buffer, - stream=stream, - label=label, - output_layout=output_layout, - ) + return handle, send_buffer, stream - comm.A2AVCommunicator.launch_kv_fetch = _mutated_launch_kv_fetch # type: ignore[invalid-assignment] + comm.A2AVCommunicator._launch_exchange = _mutated_launch_exchange # type: ignore[invalid-assignment] try: yield finally: - comm.A2AVCommunicator.launch_kv_fetch = original + comm.A2AVCommunicator._launch_exchange = original @contextmanager @@ -1137,7 +1153,7 @@ def _reference_forward( work_a = self.A_T.to(dtype=work_dtype) work_b = self.B_T.to(dtype=work_dtype) - if tokens_per_expert is None or self.num_local_experts == 1: + if tokens_per_expert is None or not self.is_expert: return (((work_x @ work_a) @ work_b) * self.scale).to(dtype=x.dtype) counts = ( @@ -1309,6 +1325,7 @@ def _patched_optimizer_step( *, model_support_handler: Any | None = None, model_chunks: Any | None = None, + before_step: Callable[[], None] | None = None, ): if pre_optimizer_step_hook is not None: pre_optimizer_step_hook() @@ -1317,6 +1334,7 @@ def _patched_optimizer_step( learning_rate, model_support_handler=model_support_handler, model_chunks=model_chunks, + before_step=before_step, ) megatron_train_module._optimizer_step = _patched_optimizer_step @@ -1366,16 +1384,96 @@ def _scaled_loss_fn(*args: Any, **kwargs: Any): ) -def _worker_run(request: WorkerRunRequest) -> None: - """Executes one full distributed training trace generation worker run.""" - os.environ.setdefault(_ATTACH_TOKEN_UIDS_ENV, "1") - from safetensors.torch import load_file, save_file # ty: ignore[unresolved-import] - import torch +class _WorkerSession: + """Owns reusable distributed model state for one parallel topology.""" - from art import dev, types + def __init__( + self, + *, + request: WorkerRunRequest, + runtime: Any, + weight_offload: Any, + flex_patch_stack: ExitStack, + ) -> None: + self.request = request + self.runtime = runtime + self.weight_offload = weight_offload + self.flex_patch_stack = flex_patch_stack + self.rng_state: tuple[Any, Any, torch.Tensor, list[torch.Tensor]] | None = None + + def begin_request(self) -> None: + self.weight_offload.before_job() + if self.rng_state is None: + self.rng_state = ( + random.getstate(), + np.random.get_state(), + torch.get_rng_state(), + torch.cuda.get_rng_state_all(), + ) + return + python_state, numpy_state, torch_state, cuda_states = self.rng_state + random.setstate(python_state) + np.random.set_state(numpy_state) + torch.set_rng_state(torch_state) + torch.cuda.set_rng_state_all(cuda_states) + + def end_request(self) -> None: + self.weight_offload.after_job() + + def close(self) -> None: + torch.distributed.barrier() # ty: ignore[possibly-missing-attribute] + self.flex_patch_stack.close() + torch.distributed.destroy_process_group() # ty: ignore[possibly-missing-attribute] + + +def _validate_session_request( + session_request: WorkerRunRequest, + request: WorkerRunRequest, +) -> None: + """Rejects request differences that require rebuilding distributed state.""" + per_run_fields = { + "objective", + "topology_dir", + "mutation", + "moe_routing_replay_path", + "moe_routing_replay_strict", + "capture_moe_routing_bundle_path", + } + if session_request.model_dump(exclude=per_run_fields) != request.model_dump( + exclude=per_run_fields + ): + raise ValueError("Worker requests require different distributed runtimes") + if bool(session_request.moe_routing_replay_path) != bool( + request.moe_routing_replay_path + ): + raise ValueError("Worker requests cannot mix captured and replayed MoE routing") + + +def _clear_optimizer_state(optimizer: Any) -> None: + chained = getattr(optimizer, "chained_optimizers", None) + if chained is not None: + for child in chained: + _clear_optimizer_state(child) + return + inner = getattr(optimizer, "optimizer", None) + state = getattr(inner, "state", None) + if state is None: + raise TypeError(f"{type(optimizer).__name__} has no mutable optimizer state") + state.clear() + + +def _reset_optimizer_state(optimizer: Any) -> None: + from art.megatron import train as megatron_train + + _clear_optimizer_state(optimizer) + megatron_train._eager_initialize_optimizer_state(optimizer) + + +def _start_worker_session(request: WorkerRunRequest) -> _WorkerSession: + """Builds distributed model state once for compatible oracle requests.""" + os.environ.setdefault(_ATTACH_TOKEN_UIDS_ENV, "1") from art.megatron import train as megatron_train from art.megatron.training.weight_offload import WeightOffloadManager - from art.preprocessing.pack import packed_tensors_from_dir if request.case_config.precision == "fp32": allow_fp32_grouped_gemm_fallback_for_model_support_tests() @@ -1416,7 +1514,9 @@ def _worker_run(request: WorkerRunRequest) -> None: else torch.bfloat16 ) runtime = megatron_train.build_training_runtime( - model_identifier=request.case_config.base_model, + model_identifier=( + request.case_config.provider_model or request.case_config.base_model + ), provider_torch_dtype=provider_torch_dtype, provider_configure=lambda provider: _configure_provider( provider, request.topology, request.case_config @@ -1426,6 +1526,7 @@ def _worker_run(request: WorkerRunRequest) -> None: moe_routing_replay_strict=request.moe_routing_replay_strict, print_env=False, allow_unvalidated_arch=request.case_config.allow_unvalidated_arch, + model_support_key=request.case_config.model_support_key, ) _debug("finished build_training_runtime") model_chunks = runtime.model @@ -1440,7 +1541,42 @@ def _worker_run(request: WorkerRunRequest) -> None: ) weight_offload.install() weight_offload.after_job() - weight_offload.before_job() + return _WorkerSession( + request=request, + runtime=runtime, + weight_offload=weight_offload, + flex_patch_stack=flex_patch_stack, + ) + + +def _worker_run( + request: WorkerRunRequest, + session: _WorkerSession | None = None, +) -> _WorkerSession: + """Executes one trace while retaining compatible distributed model state.""" + from safetensors.torch import load_file, save_file # ty: ignore[unresolved-import] + + from art import dev, types + from art.megatron import train as megatron_train + from art.preprocessing.pack import packed_tensors_from_dir + + reused_runtime = session is not None + if session is None: + session = _start_worker_session(request) + else: + _validate_session_request(session.request, request) + runtime = session.runtime + model_chunks = runtime.model + optimizer = runtime.optimizer + session.begin_request() + # Reloading LoRA masters does not clear moments from a prior paired objective. + _reset_optimizer_state(optimizer) + if reused_runtime: + megatron_train.configure_moe_routing_replay( + runtime, + replay_bundle_path=request.moe_routing_replay_path, + strict=request.moe_routing_replay_strict, + ) topology_dir = Path(request.topology_dir) traces_dir = topology_dir / "traces" @@ -1475,6 +1611,8 @@ def _worker_run(request: WorkerRunRequest) -> None: optimizer, model_support_handler=runtime.model_support_handler, ) + optimizer.zero_grad() + megatron_train._zero_grad_buffers(model_chunks) _debug("collecting loaded lora state") loaded_state = _collect_lora_state(model_chunks) if torch.distributed.get_rank() == 0: # ty: ignore[possibly-missing-attribute] @@ -1505,7 +1643,7 @@ def _worker_run(request: WorkerRunRequest) -> None: sft_zero_template = megatron_train._zero_contribution_sft_inputs( sft_trajectory_tensors[0] ) - initial_lora_state = loaded_state + initial_optimizer_state = _collect_lora_state(model_chunks, optimizer_master=True) global_grad_accumulation_sequences = request.case_config.grad_accumulation_sequences train_config = types.TrainConfig( @@ -1647,14 +1785,17 @@ def _capture_lora_grads() -> None: forward_trace_capture.save_current_step(traces_dir) torch.distributed.barrier() # ty: ignore[possibly-missing-attribute] current_lora_state = _collect_lora_state(model_chunks) + current_optimizer_state = _collect_lora_state( + model_chunks, optimizer_master=True + ) if torch.distributed.get_rank() == 0: # ty: ignore[possibly-missing-attribute] grads = _require_not_none(captured_grads, "captured_grads") initial_state = _require_not_none( - initial_lora_state, "initial_lora_state" + initial_optimizer_state, "initial_optimizer_state" ) current_state = _require_not_none( - current_lora_state, "current_lora_state" + current_optimizer_state, "current_optimizer_state" ) deltas = _delta_state(initial_state, current_state) saved_deltas = _apply_save_mutation_to_tensor_map( @@ -1744,18 +1885,31 @@ def _capture_lora_grads() -> None: steps=step_traces, ) _write_json(topology_dir / "manifest.json", manifest.model_dump(mode="json")) - weight_offload.after_job() - torch.distributed.barrier() # ty: ignore[possibly-missing-attribute] - flex_patch_stack.close() - torch.distributed.destroy_process_group() # ty: ignore[possibly-missing-attribute] + session.end_request() + return session -def run_worker_cli(run_request_path: Path) -> None: - """Loads a worker request and dispatches worker execution.""" - request = WorkerRunRequest.model_validate(_read_json(run_request_path)) +def run_worker_cli(run_request_paths: list[Path]) -> None: + """Loads compatible worker requests and dispatches them in one process lifetime.""" + requests = [ + WorkerRunRequest.model_validate(_read_json(run_request_path)) + for run_request_path in run_request_paths + ] + session: _WorkerSession | None = None try: - _worker_run(request) + for index, request in enumerate(requests): + if index > 0: + torch.distributed.barrier() # ty: ignore[possibly-missing-attribute] + if torch.distributed.get_rank() == 0: # ty: ignore[possibly-missing-attribute] + print( + f"=== {request.objective} {request.topology.slug()} ===", + flush=True, + ) + torch.distributed.barrier() # ty: ignore[possibly-missing-attribute] + session = _worker_run(request, session) finally: + if session is not None: + session.close() if _oracle_debug_enabled(): faulthandler.cancel_dump_traceback_later() @@ -1764,7 +1918,7 @@ def _parse_args(argv: list[str]) -> argparse.Namespace: """Parses worker CLI arguments.""" parser = argparse.ArgumentParser(description="Megatron oracle harness worker") parser.add_argument("--worker-run", action="store_true") - parser.add_argument("--run-request", type=Path) + parser.add_argument("--run-request", type=Path, action="append") return parser.parse_args(argv) @@ -1773,7 +1927,7 @@ def _main(argv: list[str]) -> int: args = _parse_args(argv) if not args.worker_run: raise SystemExit("This module is intended for test imports or --worker-run") - if args.run_request is None: + if not args.run_request: raise SystemExit("--run-request is required with --worker-run") run_worker_cli(args.run_request) return 0 diff --git a/tests/integration/megatron/model_support/packing_invariance.py b/tests/integration/megatron/model_support/packing_invariance.py index 68be89ada..fab1d21ab 100644 --- a/tests/integration/megatron/model_support/packing_invariance.py +++ b/tests/integration/megatron/model_support/packing_invariance.py @@ -1,7 +1,7 @@ from __future__ import annotations import argparse -from contextlib import ExitStack +from contextlib import ExitStack, redirect_stderr, redirect_stdout import os from pathlib import Path import subprocess @@ -16,6 +16,11 @@ from art.megatron import train as megatron_train from art.megatron.model_support.discovery import inspect_architecture +from art.megatron.model_support.registry import ( + get_model_support_handler_for_spec, + get_model_support_spec, +) +from art.megatron.model_support.spec import PrefixTreeModelStateContext from art.megatron.prefix_tree import parse_prefix_tree_row from art.megatron.prefix_tree_state import create_prefix_tree_state @@ -42,13 +47,25 @@ allow_fp32_grouped_gemm_fallback_for_model_support_tests() -# Qwen3.5's single packed forward versus many shorter references has measured -# up to 0.24% shape-dependent numerical drift. Use the standard 0.5% fp32 gate. -_LOGITS_MEAN_ABS_PCT_LIMIT = 0.5 +_LOGITS_MEAN_ABS_PCT_LIMITS = {"fp32": 0.5, "bf16": 3.0} _DEBUG_ENV = "ART_PACKING_INVARIANCE_DEBUG" PACKING_INVARIANCE_REPORT_FILENAME = "report.json" PACKING_INVARIANCE_ARTIFACT_SUITE_NAME = "Megatron packing-invariance artifacts" REPO_ROOT = Path(__file__).resolve().parents[4] +_SINGLE_ROTARY_OUTPUT_HANDLER_KEYS = frozenset( + { + "default_dense", + "default_moe", + "llama3_dense", + "qwen3_dense", + "qwen3_moe", + "qwen3_5_dense", + "qwen3_5_moe", + "dsv4", + "gpt_oss_moe", + } +) +_TUPLE_ROTARY_OUTPUT_HANDLER_KEYS = frozenset({"gemma4_dense", "gemma4_moe"}) def _slugify(value: str) -> str: @@ -147,6 +164,7 @@ class PackingInvarianceScenario(BaseModel): completion_pair_count: int logits_equivalent: bool logits_mean_abs_pct: float + logits_mean_abs_pct_limit: float logits_max_abs_diff: float matched: bool @@ -156,6 +174,7 @@ class PackingInvarianceReport(BaseModel): base_model: str output_dir: str num_layers: int + precision: str scenarios: list[PackingInvarianceScenario] = Field(default_factory=list) @@ -270,18 +289,42 @@ def _rotary_grouping_check( def _rotary_outputs_for_validation( *, + handler: Any, preprocess_output: Any, + position_ids: torch.Tensor, + group_ids: torch.Tensor, + parent_ids: torch.Tensor, ) -> tuple[torch.Tensor | None, ...]: + handler_key = handler.key + if handler_key == "glm52": + model_state = handler.build_prefix_tree_model_state( + PrefixTreeModelStateContext( + input_pos=position_ids.detach().cpu(), + group_ids=group_ids.detach().cpu(), + parent_ids=parent_ids.detach().cpu(), + device=position_ids.device, + ) + ) + state = model_state.get("glm52") + if ( + state is None + or not torch.is_tensor(state.rope_cos) + or not torch.is_tensor(state.rope_sin) + ): + raise RuntimeError("GLM-5.2 packed-position validation requires RoPE state") + return (torch.cat((state.rope_cos, state.rope_sin), dim=-1).permute(1, 0, 2),) rotary_output = preprocess_output[1] - if rotary_output is None or torch.is_tensor(rotary_output): - return (cast(torch.Tensor | None, rotary_output),) - if isinstance(rotary_output, tuple) and all( - item is None or torch.is_tensor(item) for item in rotary_output - ): - return cast(tuple[torch.Tensor | None, ...], rotary_output) + if handler_key in _SINGLE_ROTARY_OUTPUT_HANDLER_KEYS: + return ( + cast(torch.Tensor | None, rotary_output) + if torch.is_tensor(rotary_output) + else None, + ) + if handler_key in _TUPLE_ROTARY_OUTPUT_HANDLER_KEYS: + local_rotary, global_rotary = rotary_output + return local_rotary, global_rotary raise RuntimeError( - "Packed position validation received unsupported rotary outputs: " - f"{type(rotary_output).__name__}" + f"Packed position validation has no rotary output mapping for {handler_key!r}" ) @@ -294,6 +337,14 @@ def _build_art_realistic_packed_tensors( return build_complex_prefix_tree_packed_tensors(config, seed, deep=deep) +def _dtype_for_precision(precision: str) -> torch.dtype: + if precision == "bf16": + return torch.bfloat16 + if precision == "fp32": + return torch.float32 + raise ValueError(f"Unsupported packed-position precision: {precision}") + + def _prefix_tree_leaf_paths( group_ids: torch.Tensor, parent_ids: torch.Tensor, @@ -363,6 +414,7 @@ def _logits_equivalence_check( position_ids: torch.Tensor, group_ids: torch.Tensor, parent_ids: torch.Tensor, + mean_abs_pct_limit: float, ) -> tuple[int, bool, float, float]: _debug_log( "logits_check start " @@ -472,12 +524,13 @@ def _logits_equivalence_check( mean_abs = logits_abs_sum / max(logits_numel, 1) typical_abs = logits_ref_abs_sum / max(logits_numel, 1) logits_mean_abs_pct = (mean_abs / (typical_abs + 1e-12)) * 100.0 - logits_equivalent = logits_mean_abs_pct <= _LOGITS_MEAN_ABS_PCT_LIMIT + logits_equivalent = logits_mean_abs_pct <= mean_abs_pct_limit _debug_log( "logits_check done " f"pairs={completion_pair_count} " f"equivalent={logits_equivalent} " f"mean_abs_pct={logits_mean_abs_pct:.6f} " + f"limit={mean_abs_pct_limit:.6f} " f"max_abs_diff={logits_max_abs_diff:.6f}" ) return ( @@ -522,6 +575,17 @@ def _run_packing_invariance_subprocess( ) +def _run_packing_invariance_in_process( + request: PackingInvarianceRunRequest, + output_dir: Path, +) -> None: + request_path = output_dir / "run_request.json" + _write_json(request_path, request.model_dump(mode="json")) + with (output_dir / "worker.log").open("w", encoding="utf-8") as worker_log: + with redirect_stdout(worker_log), redirect_stderr(worker_log): + run_worker_cli(request_path) + + def _run_packing_invariance_worker( *, git: GitRepoState, @@ -602,19 +666,26 @@ def _run_packing_invariance_worker( False, ), ] + if not torch.cuda.is_available(): + raise RuntimeError("CUDA is required for packing invariance validation") + + spec = get_model_support_spec( + base_model, + allow_unvalidated_arch=allow_unvalidated_arch, + ) + handler = get_model_support_handler_for_spec(spec) + precision = handler.correctness_precision() + mean_abs_pct_limit = _LOGITS_MEAN_ABS_PCT_LIMITS[precision] report = PackingInvarianceReport( git=git, base_model=base_model, output_dir=str(output_dir), num_layers=num_layers, + precision=precision, ) - - if not torch.cuda.is_available(): - raise RuntimeError("CUDA is required for packing invariance validation") - case_config = OracleCaseConfig( base_model=base_model, - precision="fp32", + precision=precision, num_layers=num_layers, allow_unvalidated_arch=allow_unvalidated_arch, ) @@ -623,19 +694,20 @@ def _run_packing_invariance_worker( flex_patch_stack.enter_context( _apply_requested_flex_backend_patch(TEST_DEFAULT_FLEX_BACKEND) ) - flex_patch_stack.enter_context( - _apply_test_flex_inner_fp32_patch(TEST_DEFAULT_FLEX_BACKEND) - ) - flex_patch_stack.enter_context( - _apply_test_attention_full_fp32_patch(TEST_DEFAULT_FLEX_BACKEND) - ) + if precision == "fp32": + flex_patch_stack.enter_context( + _apply_test_flex_inner_fp32_patch(TEST_DEFAULT_FLEX_BACKEND) + ) + flex_patch_stack.enter_context( + _apply_test_attention_full_fp32_patch(TEST_DEFAULT_FLEX_BACKEND) + ) try: with provider_topology_env(ORACLE_TOPOLOGY): runtime = _time_block( "build_training_runtime", lambda: megatron_train.build_training_runtime( model_identifier=base_model, - provider_torch_dtype=torch.float32, + provider_torch_dtype=_dtype_for_precision(precision), provider_configure=lambda provider: _configure_provider( provider, ORACLE_TOPOLOGY, @@ -687,7 +759,11 @@ def _run_packing_invariance_worker( row_respected = True row_repeated_count = 0 rotary_outputs = _rotary_outputs_for_validation( + handler=runtime.model_support_handler, preprocess_output=hooked_output, + position_ids=row_position_ids, + group_ids=group_ids[row_index : row_index + 1], + parent_ids=parent_ids[row_index : row_index + 1], ) for rotary_output in rotary_outputs: checked, respected, repeated_count = _rotary_grouping_check( @@ -720,6 +796,7 @@ def _run_packing_invariance_worker( position_ids=position_ids, group_ids=group_ids, parent_ids=parent_ids, + mean_abs_pct_limit=mean_abs_pct_limit, ), device=input_ids.device, ) @@ -756,6 +833,7 @@ def _run_packing_invariance_worker( completion_pair_count=completion_pair_count, logits_equivalent=logits_equivalent, logits_mean_abs_pct=logits_mean_abs_pct, + logits_mean_abs_pct_limit=mean_abs_pct_limit, logits_max_abs_diff=logits_max_abs_diff, matched=matched, ) @@ -781,14 +859,21 @@ def run_packing_invariance( base_model: str, num_layers: int | None = None, allow_unvalidated_arch: bool = False, + in_process: bool = False, ) -> PackingInvarianceReport: _debug_log(f"run start base_model={base_model} requested_num_layers={num_layers}") + spec = get_model_support_spec( + base_model, + allow_unvalidated_arch=allow_unvalidated_arch, + ) + handler = get_model_support_handler_for_spec(spec) + dtype = _dtype_for_precision(handler.correctness_precision()) resolved_num_layers = ( max( 1, inspect_architecture( base_model, - torch_dtype=torch.float32, + torch_dtype=dtype, allow_unvalidated_arch=allow_unvalidated_arch, ).recommended_min_layers, ) @@ -808,7 +893,12 @@ def run_packing_invariance( allow_unvalidated_arch=allow_unvalidated_arch, ) with provider_topology_env(ORACLE_TOPOLOGY): - _run_packing_invariance_subprocess(request, output_dir) + runner = ( + _run_packing_invariance_in_process + if in_process + else _run_packing_invariance_subprocess + ) + runner(request, output_dir) return PackingInvarianceReport.model_validate(_read_json(report_path)) diff --git a/tests/integration/megatron/model_support/routing_replay_bundle.py b/tests/integration/megatron/model_support/routing_replay_bundle.py index 008d8107f..b3eb9528f 100644 --- a/tests/integration/megatron/model_support/routing_replay_bundle.py +++ b/tests/integration/megatron/model_support/routing_replay_bundle.py @@ -142,6 +142,68 @@ def _rank_token_counts( return counts +def _route_token_uids( + call_entry: dict[str, Any], token_count: int +) -> torch.Tensor | None: + token_uids = call_entry.get("row_token_uids") + if not isinstance(token_uids, torch.Tensor): + return None + token_uids = token_uids.to(dtype=torch.int64).reshape(-1).contiguous() + if int(token_uids.numel()) != token_count: + raise RuntimeError( + "Router row token UID count must match route rows: " + f"uids={int(token_uids.numel())}, routes={token_count}" + ) + if bool((token_uids < 0).any().item()) or int(token_uids.unique().numel()) != int( + token_uids.numel() + ): + raise RuntimeError("Router row token UIDs must be unique and non-negative") + return token_uids + + +def _expand_route_to_token_span( + route: RouterCallRoute, + token_uids: torch.Tensor | None, + token_count: int, +) -> RouterCallRoute: + if token_uids is None: + if route.num_global_tokens != token_count: + raise RuntimeError( + "A compact router route requires row token UIDs: " + f"routes={route.num_global_tokens}, token_span={token_count}" + ) + return route + identity = torch.arange(token_count, dtype=torch.int64) + if route.num_global_tokens == token_count and torch.equal(token_uids, identity): + return route + if int(token_uids.numel()) > 0 and int(token_uids.max().item()) >= token_count: + raise RuntimeError( + "Router row token UID exceeds the replay token span: " + f"max_uid={int(token_uids.max().item())}, token_span={token_count}" + ) + + rows = torch.arange(token_count, dtype=torch.int64).unsqueeze(1) + slots = torch.arange(route.max_topk, dtype=torch.int64).unsqueeze(0) + expert_indices = ((rows + slots) % route.num_experts).to(torch.int32) + expert_indices.index_copy_(0, token_uids, route.expert_indices) + expert_probs = None + if route.expert_probs is not None: + expert_probs = torch.zeros((token_count, route.max_topk), dtype=torch.float32) + expert_probs.index_copy_(0, token_uids, route.expert_probs) + expert_mask = None + if route.expert_mask is not None: + expert_mask = torch.ones((token_count, route.max_topk), dtype=torch.bool) + expert_mask.index_copy_(0, token_uids, route.expert_mask) + return RouterCallRoute( + expert_indices=expert_indices, + expert_probs=expert_probs, + expert_mask=expert_mask, + num_experts=route.num_experts, + sample_index=route.sample_index, + micro_slot=route.micro_slot, + ) + + def _dedupe_checkpoint_router_calls( call_entries: list[dict[str, Any]], ) -> list[dict[str, Any]]: @@ -221,7 +283,7 @@ def build_bundle_from_forward_trace_dir( step_routers: dict[str, StepRouterRoutes] = {} step_global_tokens: int | None = None - token_count_by_call_key: dict[tuple[str, int], int] = {} + route_token_uids: dict[tuple[str, int], torch.Tensor | None] = {} for module_name in sorted(step_trace.keys()): if ROUTER_NAME_TOKEN not in module_name: continue @@ -240,29 +302,14 @@ def build_bundle_from_forward_trace_dir( call_entry, compact_route.num_global_tokens ) router_calls[call_index] = compact_route + token_uids = _route_token_uids( + call_entry, compact_route.num_global_tokens + ) + route_token_uids[(router_key, call_index)] = token_uids max_topk = max(max_topk, compact_route.max_topk) token_count = compact_route.num_global_tokens - call_key = ( - ("sample", int(sample_index)) - if sample_index is not None - else ( - ("dummy_micro_slot", int(micro_slot)) - if micro_slot is not None - else ("call_index", int(call_index)) - ) - ) - previous_token_count = token_count_by_call_key.get(call_key) - if ( - previous_token_count is not None - and previous_token_count != token_count - ): - raise RuntimeError( - "Inconsistent token count across routers for the same micro: " - f"step={step_index}, call_key={call_key}, " - f"expected={previous_token_count}, got={token_count}, " - f"router='{router_key}', call={call_index}" - ) - token_count_by_call_key[call_key] = token_count + if token_uids is not None and int(token_uids.numel()) > 0: + token_count = max(token_count, int(token_uids.max().item()) + 1) step_global_tokens = ( token_count if step_global_tokens is None @@ -284,6 +331,13 @@ def build_bundle_from_forward_trace_dir( raise RuntimeError( f"Could not infer token count for step={step_index} from router traces" ) + for router_key, router_routes in step_routers.items(): + for call_index, route in router_routes.calls.items(): + router_routes.calls[call_index] = _expand_route_to_token_span( + route, + route_token_uids[(router_key, call_index)], + step_global_tokens, + ) global_token_uids = torch.arange(step_global_tokens, dtype=torch.int64) steps[step_index] = StepRoutes( routers=step_routers, diff --git a/tests/integration/megatron/model_support/test_bridge_runtime.py b/tests/integration/megatron/model_support/test_bridge_runtime.py new file mode 100644 index 000000000..0d17ca185 --- /dev/null +++ b/tests/integration/megatron/model_support/test_bridge_runtime.py @@ -0,0 +1,111 @@ +from __future__ import annotations + +from types import SimpleNamespace +from typing import Any, cast + +import pytest +import torch + +pytest.importorskip("megatron.bridge") + +from art.megatron.runtime.bridge_runtime import ( + _optimized_load_weights_hf_to_megatron, +) + + +class _Mapping: + def __init__(self, megatron_param: str, hf_param: str) -> None: + self.megatron_param = megatron_param + self.hf_param = hf_param + self.tp_size = 1 + + def hf_to_megatron( + self, hf_weights: torch.Tensor, megatron_module: torch.nn.Module + ) -> torch.Tensor: + del megatron_module + return hf_weights + + +class _Bridge: + def __init__(self, tasks: list[Any]) -> None: + self.tasks = tasks + + def build_conversion_tasks( + self, hf_pretrained: Any, megatron_model: Any + ) -> list[Any]: + del hf_pretrained, megatron_model + return self.tasks + + def _share_embeddings_and_output_weights(self, config: Any) -> bool: + return bool(config.share_embeddings_and_output_weights) + + def _is_adapter_param_name(self, name: str) -> bool: + return ".adapter." in name + + def _with_progress_tracking(self, tasks: list[Any], description: str) -> list[Any]: + del description + return tasks + + def maybe_modify_loaded_hf_weight( + self, hf_param: str, state: dict[str, torch.Tensor] + ) -> torch.Tensor: + return state[hf_param] + + def _broadcast_shared_embeddings(self, megatron_model: Any) -> None: + del megatron_model + + +class _Model(torch.nn.Module): + def __init__(self) -> None: + super().__init__() + self.config = SimpleNamespace(share_embeddings_and_output_weights=False) + self.local = torch.nn.Linear(1, 1, bias=False) + + +def _task( + mapping: _Mapping, + *, + module: torch.nn.Module | None = None, + weight: torch.Tensor | None = None, +) -> SimpleNamespace: + return SimpleNamespace( + mapping=mapping, + megatron_module=module, + param_weight=weight, + param_name=mapping.megatron_param, + ) + + +def test_pretrained_load_rejects_placeholder_for_required_local_parameter() -> None: + model = _Model() + bridge = _Bridge([_task(_Mapping("local.weight", "hf.weight"))]) + pretrained = SimpleNamespace(state={}, model_name_or_path="empty-checkpoint") + + with pytest.raises( + RuntimeError, + match=r"1 required local parameter\(s\): local.weight", + ): + _optimized_load_weights_hf_to_megatron(cast(Any, bridge), pretrained, model) + + +def test_pretrained_load_allows_nonlocal_placeholder_tasks() -> None: + model = _Model() + local_mapping = _Mapping("local.weight", "hf.weight") + remote_mapping = _Mapping("remote.weight", "hf.remote_weight") + bridge = _Bridge( + [ + _task(local_mapping, module=model.local, weight=model.local.weight), + _task(remote_mapping), + ] + ) + expected = torch.tensor([[7.0]]) + pretrained = SimpleNamespace( + state={"hf.weight": expected}, model_name_or_path="checkpoint" + ) + + result = _optimized_load_weights_hf_to_megatron( + cast(Any, bridge), pretrained, model + ) + + assert result == [model] + assert torch.equal(model.local.weight, expected) diff --git a/tests/integration/megatron/model_support/test_compile_flags.py b/tests/integration/megatron/model_support/test_compile_flags.py index e5946264d..756aba250 100644 --- a/tests/integration/megatron/model_support/test_compile_flags.py +++ b/tests/integration/megatron/model_support/test_compile_flags.py @@ -5,7 +5,17 @@ def test_gemma4_wide_global_attention_uses_lower_triton_stage_count() -> None: - provider = type("Provider", (), {"global_head_dim": 512})() + provider = type( + "Provider", + (), + { + "global_head_dim": 512, + "hidden_size": 5376, + "kv_channels": 256, + "num_attention_heads": 32, + "num_layers": 12, + }, + )() assert GEMMA4_DENSE_HANDLER.flex_attention_compile_crash_config( provider diff --git a/tests/integration/megatron/model_support/test_hf_parity_invariants.py b/tests/integration/megatron/model_support/test_hf_parity_invariants.py index d0f6e966b..78cde9a28 100644 --- a/tests/integration/megatron/model_support/test_hf_parity_invariants.py +++ b/tests/integration/megatron/model_support/test_hf_parity_invariants.py @@ -4,6 +4,8 @@ import pytest import torch +from art.megatron.model_support.handlers.dsv4 import DSV4_HANDLER + from ..artifacts import GitRepoState from . import hf_parity as hf_parity_module from . import hf_parity_worker as hf_parity_worker_module @@ -23,12 +25,14 @@ _drop_gemma4_reparameterized_norm_grads, _filter_language_only_tensor_map, _hf_moe_router_key, + _hf_prefix_tree_forward_inputs, _hf_router_num_experts, _is_language_hf_param_name, _mapping_supports_derivative_parity, _maybe_modify_converted_hf_grad, _normalize_hf_grads_for_bridge, _normalize_hf_tensor_map_for_bridge, + _validate_distributed_process_env, ) from .oracle_harness import DiskPackedTensorsSpec, OracleCaseConfig from .validation_spec import MinimalLayerCoverageReport @@ -45,6 +49,38 @@ def test_build_parity_sample_indices_pads_with_none() -> None: ) == [0, 1, None, None] +def test_hf_prefix_tree_inputs_block_siblings_and_repeat_positions() -> None: + model = SimpleNamespace( + config=SimpleNamespace( + layer_types=["full_attention", "sliding_attention"], + sliding_window=2, + ) + ) + micro = { + "group_ids": torch.tensor([0, 0, 1, 1, 2, 2]), + "parent_ids": torch.tensor([0, 0, 0, 0, 0, 0]), + "position_ids": torch.tensor([0, 1, 2, 3, 2, 3]), + } + + attention_mask, position_ids = _hf_prefix_tree_forward_inputs( + model, + micro, + actual_len=6, + device=torch.device("cpu"), + dtype=torch.float32, + ) + + assert isinstance(attention_mask, dict) + masks = cast(dict[str, torch.Tensor], attention_mask) + full_allowed = masks["full_attention"][0, 0] == 0 + sliding_allowed = masks["sliding_attention"][0, 0] == 0 + assert position_ids.tolist() == [[0, 1, 2, 3, 2, 3]] + assert full_allowed[4, 1] + assert not full_allowed[4, 2] + assert not sliding_allowed[4, 0] + assert sliding_allowed[4, 1] + + def test_hf_parity_uses_train_inf_mismatch_settings() -> None: assert HF_PARITY_PACKED_TENSORS.sequence_length == 256 assert HF_PARITY_PACKED_TENSORS.prefill_tokens == 64 @@ -150,6 +186,7 @@ def test_run_hf_parity_always_reruns_existing_report( "assess_minimal_layer_coverage", lambda **_: coverage, ) + monkeypatch.setattr(hf_parity_module, "pinned_git_state", lambda _: _git_state()) monkeypatch.setattr( hf_parity_module, "ensure_case_artifacts", @@ -222,7 +259,11 @@ def _fake_run(*args, **kwargs): captured.update(kwargs) return SimpleNamespace(returncode=0, stdout="", stderr="") - monkeypatch.setattr(hf_parity_module.subprocess, "run", _fake_run) + monkeypatch.setattr( + hf_parity_module, + "subprocess", + SimpleNamespace(run=_fake_run), + ) hf_parity_module.run_hf_parity_subprocess(request, tmp_path) @@ -233,6 +274,77 @@ def _fake_run(*args, **kwargs): assert "ART_MEGATRON_RECOMPUTE_MODULES" not in env +def test_run_hf_parity_subprocess_assigns_unique_rendezvous( + monkeypatch, tmp_path +) -> None: + request = HfParityRunRequest( + git=_git_state(), + case_id="case-id", + case_config=OracleCaseConfig(base_model="Qwen/Qwen3.5-35B-A3B"), + packed_tensors=DiskPackedTensorsSpec( + dir=str(tmp_path / "packed"), + num_sequences=4, + sequence_length=8, + ), + output_dir=str(tmp_path), + coverage=MinimalLayerCoverageReport( + base_model="Qwen/Qwen3.5-35B-A3B", + model_key="qwen3_5_moe", + requested_num_layers=4, + recommended_min_layers=4, + covered=True, + ), + ) + ports = iter((24101, 24102)) + environments: list[dict[str, str]] = [] + monkeypatch.setattr( + hf_parity_module, + "_find_free_rendezvous_port", + lambda: next(ports), + ) + + def _fake_run(*args, **kwargs): + del args + environments.append(kwargs["env"]) + return SimpleNamespace(returncode=0, stdout="", stderr="") + + monkeypatch.setattr( + hf_parity_module, + "subprocess", + SimpleNamespace(run=_fake_run), + ) + + hf_parity_module.run_hf_parity_subprocess(request, tmp_path) + hf_parity_module.run_hf_parity_subprocess(request, tmp_path) + + assert [env["MASTER_PORT"] for env in environments] == ["24101", "24102"] + for env in environments: + assert env["MASTER_ADDR"] == "127.0.0.1" + assert env["RANK"] == "0" + assert env["WORLD_SIZE"] == "1" + assert env["LOCAL_RANK"] == "0" + assert env["LOCAL_WORLD_SIZE"] == "1" + + +def test_hf_parity_worker_requires_explicit_distributed_env(monkeypatch) -> None: + distributed_env = { + "MASTER_ADDR": "127.0.0.1", + "MASTER_PORT": "24101", + "RANK": "0", + "WORLD_SIZE": "1", + "LOCAL_RANK": "0", + "LOCAL_WORLD_SIZE": "1", + } + for name in distributed_env: + monkeypatch.delenv(name, raising=False) + with pytest.raises(RuntimeError, match="explicit distributed environment"): + _validate_distributed_process_env() + + for name, value in distributed_env.items(): + monkeypatch.setenv(name, value) + _validate_distributed_process_env() + + def test_normalize_hf_tensor_map_for_bridge_adds_language_model_prefix() -> None: normalized = _normalize_hf_tensor_map_for_bridge( { @@ -276,6 +388,61 @@ def test_build_tensor_map_metric_rows_enforces_nonzero_per_tensor() -> None: assert by_param["active"].pass_signal is True +def test_grouped_tensor_map_rows_keep_individual_nonzero_gates() -> None: + rows = build_tensor_map_metric_rows( + phase="grads", + reference={"large": torch.ones(1000), "small": torch.ones(1)}, + candidate={"large": torch.full((1000,), 1.01), "small": torch.full((1,), 2.0)}, + group_by=lambda _: "joint_update", + ) + by_param = {row.param: row for row in rows} + + assert by_param["joint_update"].phase == "grads" + assert by_param["joint_update"].mean_abs_pct < 3.0 + assert by_param["joint_update"].pass_signal is True + assert by_param["small"].phase == "grads_diagnostic" + assert by_param["small"].mean_abs_pct == 100.0 + assert by_param["small"].pass_signal is True + + zero_rows = build_tensor_map_metric_rows( + phase="grads", + reference={"active": torch.ones(2), "zero": torch.zeros(1)}, + candidate={"active": torch.ones(2), "zero": torch.zeros(1)}, + group_by=lambda _: "joint_update", + ) + assert {row.param: row for row in zero_rows}["zero"].pass_signal is False + + +@pytest.mark.parametrize( + ("param", "group"), + ( + ("model.embed_tokens.weight", "embedding"), + ("lm_head.weight", "final_envelope"), + ("model.hc_head.hc_scale", "final_envelope"), + ("model.norm.weight", "final_envelope"), + ("model.layers.2.attn_hc.base", "model.layers.2.attention"), + ( + "model.layers.2.self_attn.compressor.kv_proj.weight", + "model.layers.2.attention", + ), + ("model.layers.2.ffn_hc.fn", "model.layers.2.ffn"), + ("model.layers.2.mlp.experts.0.up_proj.weight", "model.layers.2.ffn"), + ("model.layers.2.input_layernorm.weight", "model.layers.2.input_norm"), + ( + "model.layers.2.post_attention_layernorm.weight", + "model.layers.2.post_attention_norm", + ), + ), +) +def test_dsv4_hf_parity_gradient_groups(param: str, group: str) -> None: + assert DSV4_HANDLER.hf_parity_gradient_group(param) == group + + +def test_dsv4_hf_parity_gradient_groups_reject_unknown_parameter() -> None: + with pytest.raises(ValueError, match="Unmapped DSV4 HF-parity gradient"): + DSV4_HANDLER.hf_parity_gradient_group("model.layers.0.unknown.weight") + + def test_language_hf_param_filter_keeps_text_and_drops_visual() -> None: assert _is_language_hf_param_name("model.layers.0.self_attn.q_proj.weight") is True assert _is_language_hf_param_name("model.visual.blocks.0.attn.qkv.weight") is False @@ -380,7 +547,7 @@ def test_build_megatron_runtime_uses_training_provider_bundle( assert configured_bundles == [(provider_bundle, False)] assert kwargs["print_env"] is False assert kwargs["trainable_parameter_mode"] == "base_model" - configured_provider = SimpleNamespace() + configured_provider = SimpleNamespace(_art_model_support_handler=SimpleNamespace()) kwargs["provider_configure"](configured_provider) optimizer_config = kwargs["optimizer_config"] assert configured_provider.num_layers == request.case_config.num_layers diff --git a/tests/integration/megatron/model_support/test_internal_padding.py b/tests/integration/megatron/model_support/test_internal_padding.py index eb2bb54dd..ae6dc3e22 100644 --- a/tests/integration/megatron/model_support/test_internal_padding.py +++ b/tests/integration/megatron/model_support/test_internal_padding.py @@ -30,9 +30,6 @@ def __init__( ) self.A_T = self._parameter(a_shape) self.B_T = self._parameter(b_shape) - self._slot_modules = torch.nn.ModuleDict( - {"checkpoint": _LoraSlot(a_shape, b_shape)} - ) @staticmethod def _parameter(shape: tuple[int, ...]) -> torch.nn.Parameter: @@ -44,13 +41,6 @@ def _parameter(shape: tuple[int, ...]) -> torch.nn.Parameter: return parameter -class _LoraSlot(torch.nn.Module): - def __init__(self, a_shape: tuple[int, ...], b_shape: tuple[int, ...]) -> None: - super().__init__() - self.A_T = _Lora._parameter(a_shape) - self.B_T = _Lora._parameter(b_shape) - - class _Chunk(torch.nn.Module): def __init__( self, @@ -116,15 +106,9 @@ def test_internal_padding_is_zeroed( handler.zero_internal_padding_params([chunk]) for module_name, parameter_name, dim, ranges in padding: - module = getattr(chunk, module_name) - parameters = ( - getattr(module, parameter_name), - getattr(module._slot_modules["checkpoint"], parameter_name), - ) - for parameter in parameters: - for tensor in (parameter, parameter.grad, parameter.main_grad): - assert torch.count_nonzero(tensor) > 0 - for start, end in ranges: - assert ( - torch.count_nonzero(tensor.narrow(dim, start, end - start)) == 0 - ) + parameter = getattr(getattr(chunk, module_name), parameter_name) + tensors = (parameter, parameter.grad, parameter.main_grad) + for tensor in tensors: + assert torch.count_nonzero(tensor) > 0 + for start, end in ranges: + assert torch.count_nonzero(tensor.narrow(dim, start, end - start)) == 0 diff --git a/tests/integration/megatron/model_support/test_oracle_harness_invariants.py b/tests/integration/megatron/model_support/test_oracle_harness_invariants.py index c0e702eca..00fb5d62a 100644 --- a/tests/integration/megatron/model_support/test_oracle_harness_invariants.py +++ b/tests/integration/megatron/model_support/test_oracle_harness_invariants.py @@ -1,8 +1,12 @@ -from typing import Any +from pathlib import Path +from types import SimpleNamespace +from typing import Any, Literal import pytest import torch +from ..artifacts import GitRepoState +from . import oracle_harness from .forward_trace import ForwardTraceCapture, _extract_router_topk from .oracle_harness import ( CP_ATTENTION_SENSITIVITY_MUTATIONS, @@ -23,6 +27,7 @@ PackedTensorConfig, Topology, VariantRunner, + VariantSpec, _default_phase_pass_fns, _resolve_test_flex_backend, _suite_variants, @@ -30,7 +35,7 @@ selected_sensitivity_mutations_for_objective, sensitivity_topology_for_mutation, ) -from .oracle_worker import _matches_grad_sync_skip_mutation +from .oracle_worker import _matches_grad_sync_skip_mutation, _reset_optimizer_state from .prefix_tree_workloads import build_complex_prefix_tree_packed_tensors @@ -100,6 +105,361 @@ def _expert_trace_call( } +def _artifact_tree(path: Path) -> None: + (path / "traces").mkdir(parents=True) + (path / "manifest.json").write_text("{}", encoding="utf-8") + (path / "worker.log").write_text("diagnostic", encoding="utf-8") + (path / "traces" / "forward.pt").write_bytes(b"trace") + + +def test_paired_oracle_request_resets_optimizer_state() -> None: + class Inner: + def __init__(self) -> None: + self.state = {"stale": object()} + + class Leaf: + def __init__(self) -> None: + self.optimizer = Inner() + self.config = object() + + @staticmethod + def init_state_fn(inner: Inner, config: object) -> None: + assert config is not None + inner.state["fresh"] = 0 + + leaves = [Leaf(), Leaf()] + optimizer = SimpleNamespace(chained_optimizers=leaves) + + _reset_optimizer_state(optimizer) + + assert [leaf.optimizer.state for leaf in leaves] == [ + {"fresh": 0}, + {"fresh": 0}, + ] + + +def _lifecycle_runner(tmp_path: Path) -> VariantRunner: + runner = object.__new__(VariantRunner) + runner.objective = "rl" + runner.paired_objective = None + runner.case_config = case_config("Qwen/Qwen3-32B") + runner.case_dir = tmp_path + runner.oracle_dir = tmp_path / "oracle" + return runner + + +def _lifecycle_variant( + expected_signal: Literal["pass", "fail"] = "pass", +) -> VariantSpec: + return VariantSpec( + name="candidate", + objective="rl", + topology=Topology(tp=1, ep=1), + output_slug="candidate", + reference_slug="oracle", + expected_signal=expected_signal, + ) + + +def test_reference_cleanup_prunes_paired_dense_oracle( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + monkeypatch.delenv("ART_ORACLE_KEEP_TOPOLOGY_ARTIFACTS", raising=False) + runner = object.__new__(VariantRunner) + runner.objective = "rl" + runner.paired_objective = "sft" + runner.case_config = case_config("Qwen/Qwen3-32B") + runner.case_dir = tmp_path + runner.oracle_dir = tmp_path / "rl__tp1_ep1_etp1_dp1_edp1_cp1_pp1_vpp1_sp0" + paired_dir = tmp_path / "sft__tp1_ep1_etp1_dp1_edp1_cp1_pp1_vpp1_sp0" + for path in (runner.oracle_dir, paired_dir): + (path / "traces").mkdir(parents=True) + (path / "manifest.json").write_text("{}", encoding="utf-8") + (path / "traces" / "forward.pt").write_bytes(b"trace") + + runner._prune_reference_artifacts() + + for path in (runner.oracle_dir, paired_dir): + assert (path / "manifest.json").exists() + assert not (path / "traces").exists() + + +def test_moe_capture_prunes_only_after_persisted_metadata_validates( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + monkeypatch.delenv("ART_ORACLE_KEEP_TOPOLOGY_ARTIFACTS", raising=False) + runner = _lifecycle_runner(tmp_path) + runner.git = GitRepoState(path="/repo", commit="commit", dirty=False) + runner.case_id = "case" + runner.oracle_topology = Topology(tp=1, ep=1) + runner.oracle_routing_bundle_dir = tmp_path / "routing" + capture_dir = tmp_path / "capture" + _artifact_tree(capture_dir) + expected_topology = oracle_harness.ReplayParallelTopology.model_validate( + runner.oracle_topology.model_dump( + include={"tp", "ep", "etp", "dp", "sp", "cp", "pp", "vpp"} + ) + ) + manifest = SimpleNamespace( + git=SimpleNamespace(commit="commit"), + case_id="case", + objective="rl", + topology=runner.oracle_topology.slug(), + num_steps=1, + steps=[object()], + ) + monkeypatch.setattr(oracle_harness, "_load_manifest", lambda _: manifest) + monkeypatch.setattr( + oracle_harness.MoeRoutingReplayBundle, + "from_dir", + staticmethod( + lambda _: SimpleNamespace(topology=expected_topology, num_steps=1) + ), + ) + + runner._prune_valid_moe_capture(capture_dir) + + assert not (capture_dir / "traces").exists() + assert (capture_dir / "manifest.json").exists() + assert (capture_dir / "worker.log").exists() + + +def test_moe_capture_retains_tensors_on_validation_failure( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + runner = _lifecycle_runner(tmp_path) + runner.git = GitRepoState(path="/repo", commit="commit", dirty=False) + runner.case_id = "case" + runner.oracle_topology = Topology(tp=1, ep=1) + runner.oracle_routing_bundle_dir = tmp_path / "routing" + capture_dir = tmp_path / "capture" + _artifact_tree(capture_dir) + monkeypatch.setattr( + oracle_harness, + "_load_manifest", + lambda _: SimpleNamespace( + git=SimpleNamespace(commit="wrong"), + case_id="case", + objective="rl", + topology=runner.oracle_topology.slug(), + num_steps=1, + steps=[object()], + ), + ) + monkeypatch.setattr( + oracle_harness.MoeRoutingReplayBundle, + "from_dir", + staticmethod(lambda _: SimpleNamespace(topology=None, num_steps=1)), + ) + + with pytest.raises(RuntimeError, match="capture metadata"): + runner._prune_valid_moe_capture(capture_dir) + + assert (capture_dir / "traces" / "forward.pt").exists() + + +def test_expected_sensitivity_signal_prunes_only_candidate_tensors( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + monkeypatch.delenv("ART_ORACLE_KEEP_TOPOLOGY_ARTIFACTS", raising=False) + runner = _lifecycle_runner(tmp_path) + candidate_dir = tmp_path / "candidate" + _artifact_tree(candidate_dir) + report = SimpleNamespace( + signal="fail", expected_signal="fail", topology="candidate" + ) + monkeypatch.setattr(runner, "run_variant", lambda _: report) + + runner.run_suite( + [_lifecycle_variant("fail")], + prune_reference_artifacts=False, + prune_case_artifacts=False, + ) + + assert not (candidate_dir / "traces").exists() + assert (candidate_dir / "manifest.json").exists() + assert (candidate_dir / "worker.log").exists() + + +def test_paired_suite_retains_unconsumed_objective_tensors( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + monkeypatch.delenv("ART_ORACLE_KEEP_TOPOLOGY_ARTIFACTS", raising=False) + runner = _lifecycle_runner(tmp_path) + runner.paired_objective = "sft" + variant = _lifecycle_variant().model_copy(update={"output_slug": "rl__candidate"}) + candidate_dir = tmp_path / "rl__candidate" + paired_dir = tmp_path / "sft__candidate" + for path in (candidate_dir, paired_dir): + _artifact_tree(path) + report = SimpleNamespace( + signal="pass", expected_signal="pass", topology="candidate" + ) + monkeypatch.setattr(runner, "run_variant", lambda _: report) + + runner.run_suite( + [variant], + prune_reference_artifacts=False, + prune_case_artifacts=False, + prune_paired_artifacts=False, + ) + + assert not (candidate_dir / "traces").exists() + assert (paired_dir / "traces" / "forward.pt").exists() + + +def test_keep_topology_artifacts_override_retains_successful_candidate( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + monkeypatch.setenv("ART_ORACLE_KEEP_TOPOLOGY_ARTIFACTS", "1") + runner = _lifecycle_runner(tmp_path) + candidate_dir = tmp_path / "candidate" + _artifact_tree(candidate_dir) + report = SimpleNamespace( + signal="pass", expected_signal="pass", topology="candidate" + ) + monkeypatch.setattr(runner, "run_variant", lambda _: report) + + runner.run_suite( + [_lifecycle_variant()], + prune_reference_artifacts=False, + prune_case_artifacts=False, + ) + + assert (candidate_dir / "traces" / "forward.pt").exists() + + +def test_unexpected_signal_prunes_candidate_reference_and_inputs( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + monkeypatch.delenv("ART_ORACLE_KEEP_TOPOLOGY_ARTIFACTS", raising=False) + runner = _lifecycle_runner(tmp_path) + for path in (tmp_path / "candidate", runner.oracle_dir): + _artifact_tree(path) + (tmp_path / "packed_tensors").mkdir() + (tmp_path / "packed_tensors" / "tokens.pt").write_bytes(b"tokens") + (tmp_path / "packed_tensors.json").write_text("{}", encoding="utf-8") + (tmp_path / "shared_init").mkdir() + report = SimpleNamespace( + signal="pass", expected_signal="fail", topology="candidate" + ) + monkeypatch.setattr(runner, "run_variant", lambda _: report) + + with pytest.raises(AssertionError, match="expected_signal=fail"): + runner.run_suite([_lifecycle_variant("fail")]) + + assert not (tmp_path / "candidate" / "traces").exists() + assert not (runner.oracle_dir / "traces").exists() + assert not (tmp_path / "packed_tensors").exists() + assert not (tmp_path / "shared_init").exists() + assert (tmp_path / "candidate" / "manifest.json").exists() + assert (tmp_path / "candidate" / "worker.log").exists() + + +@pytest.mark.parametrize("failure_point", ["worker", "comparison"]) +def test_worker_and_comparison_failures_retain_candidate( + failure_point: str, tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + runner = _lifecycle_runner(tmp_path) + candidate_dir = tmp_path / "candidate" + _artifact_tree(candidate_dir) + + def fail(_: object) -> None: + raise RuntimeError(failure_point) + + if failure_point == "worker": + monkeypatch.setattr(runner, "ensure_variant_artifacts", fail) + else: + monkeypatch.setattr(runner, "ensure_variant_artifacts", lambda _: candidate_dir) + monkeypatch.setattr(runner, "compare_variant", fail) + + with pytest.raises(RuntimeError, match=failure_point): + runner.run_variant(_lifecycle_variant()) + + assert (candidate_dir / "traces" / "forward.pt").exists() + + +def test_cleanup_failure_surfaces( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + runner = _lifecycle_runner(tmp_path) + _artifact_tree(tmp_path / "candidate") + report = SimpleNamespace( + signal="pass", expected_signal="pass", topology="candidate" + ) + monkeypatch.setattr(runner, "run_variant", lambda _: report) + + def fail_cleanup(_: Path) -> None: + raise RuntimeError("cleanup failed") + + monkeypatch.setattr(oracle_harness.shutil, "rmtree", fail_cleanup) + + with pytest.raises(RuntimeError, match="cleanup failed"): + runner.run_suite( + [_lifecycle_variant()], + prune_reference_artifacts=False, + prune_case_artifacts=False, + ) + + +def test_top_level_suite_prunes_deferred_artifacts_on_failure( + monkeypatch: pytest.MonkeyPatch, +) -> None: + def fail_run_suite(*_args: object, **_kwargs: object) -> None: + raise RuntimeError("variant") + + runner = SimpleNamespace(run_suite=fail_run_suite) + pruned: list[list[object]] = [] + monkeypatch.setattr(oracle_harness, "selected_oracle_objectives", lambda: ["rl"]) + monkeypatch.setattr(oracle_harness, "VariantRunner", lambda **_kwargs: runner) + monkeypatch.setattr(oracle_harness, "_suite_variants", lambda *_args, **_kwargs: []) + monkeypatch.setattr( + oracle_harness, + "_prune_completed_runners", + lambda runners, **_kwargs: pruned.append(runners), + ) + + with pytest.raises(RuntimeError, match="variant"): + oracle_harness.run_suite(case_config=case_config()) + + assert pruned == [[runner]] + + +def test_paired_dense_suite_prunes_deferred_artifacts_on_failure( + monkeypatch: pytest.MonkeyPatch, +) -> None: + def fail_run_suite(*_args: object, **_kwargs: object) -> None: + raise RuntimeError("variant") + + runner = SimpleNamespace(run_suite=fail_run_suite) + pruned: list[list[object]] = [] + monkeypatch.setattr(oracle_harness, "VariantRunner", lambda **_kwargs: runner) + monkeypatch.setattr( + oracle_harness, + "_prune_completed_runners", + lambda runners, **_kwargs: pruned.append(runners), + ) + + with pytest.raises(RuntimeError, match="variant"): + oracle_harness._run_paired_dense_suite( + objectives=["rl", "sft"], + case_config=oracle_harness.OracleCaseConfig( + base_model="google/gemma-4-31B-it", + model_support_key="gemma4_dense", + ), + max_world_size=2, + oracle_flex_backend=None, + variant_flex_backend=None, + cp_supported=True, + phase_pass_fns=None, + use_fp32_lora_reference=True, + prune_reference_artifacts=True, + prune_case_artifacts=True, + ) + + assert pruned == [[runner]] + + def test_fc1_grad_sync_sensitivity_matches_split_and_fused_lora_names() -> None: assert _matches_grad_sync_skip_mutation( "chunk0.module.decoder.layers.0.mlp.experts.linear_fc1.lora.A_T", @@ -334,6 +694,20 @@ def test_forward_trace_extracts_empty_router_topk_with_config_hint() -> None: assert scores.shape == (0, 2) +def test_forward_trace_extracts_router_ids_from_actual_routing_map() -> None: + topk = _extract_router_topk( + ( + torch.tensor([[0.0, 1.2, 1.3, 0.0], [0.8, 0.0, 0.0, 1.7]]), + torch.tensor([[False, True, True, False], [True, False, False, True]]), + ) + ) + assert topk is not None + ids, scores = topk + + assert torch.equal(ids, torch.tensor([[1, 2], [0, 3]])) + assert torch.equal(scores, torch.tensor([[1.2, 1.3], [0.8, 1.7]])) + + def test_megatron_empty_swiglu_patch_preserves_known_output_width() -> None: from art.megatron.runtime.bridge_runtime import install_art_bridge_runtime_patches @@ -433,16 +807,16 @@ def test_forward_trace_canonicalizes_row_outputs_by_token_uid() -> None: ) -def test_forward_trace_drops_exact_zero_padding_rows() -> None: +def test_forward_trace_drops_explicit_nonzero_padding_rows() -> None: trace: dict[str, list[dict[str, Any]]] = { "chunk0.module.decoder.layers.0.self_attention.out_proj": [ { "primary_output": torch.tensor( - [[0.0, 0.0], [30.0, 31.0], [10.0, 11.0], [20.0, 21.0]] + [[9.0, 9.0], [30.0, 31.0], [0.0, 0.0], [20.0, 21.0]] ), "output": { "hidden": torch.tensor( - [[0.0, 0.0], [3.0, 3.1], [1.0, 1.1], [2.0, 2.1]] + [[9.0, 9.0], [3.0, 3.1], [0.0, 0.0], [2.0, 2.1]] ) }, "row_token_uids": torch.tensor([-1, 3, 1, 2]), @@ -456,11 +830,11 @@ def test_forward_trace_drops_exact_zero_padding_rows() -> None: assert torch.equal(call["row_token_uids"], torch.tensor([1, 2, 3])) assert torch.equal( call["primary_output"], - torch.tensor([[10.0, 11.0], [20.0, 21.0], [30.0, 31.0]]), + torch.tensor([[0.0, 0.0], [20.0, 21.0], [30.0, 31.0]]), ) assert torch.equal( call["output"]["hidden"], - torch.tensor([[1.0, 1.1], [2.0, 2.1], [3.0, 3.1]]), + torch.tensor([[0.0, 0.0], [2.0, 2.1], [3.0, 3.1]]), ) @@ -534,13 +908,11 @@ def test_forward_trace_expands_attention_output_uids_for_out_norm_heads() -> Non ForwardTraceCapture.canonicalize_trace(trace) call = trace["chunk0.module.decoder.layers.0.self_attention.out_norm"][0] - assert torch.equal(call["row_token_uids"], torch.tensor([-1, -1, 0, 0, 2, 2])) + assert torch.equal(call["row_token_uids"], torch.tensor([0, 0, 2, 2])) assert torch.equal( call["primary_output"], torch.tensor( [ - [8.0, 9.0, 10.0, 11.0], - [12.0, 13.0, 14.0, 15.0], [0.0, 1.0, 2.0, 3.0], [4.0, 5.0, 6.0, 7.0], [16.0, 17.0, 18.0, 19.0], @@ -678,6 +1050,43 @@ def test_forward_trace_sums_expert_tp_row_shards_inside_ep_groups() -> None: ) +def test_forward_trace_deduplicates_replicated_tp_outputs_with_cp_rows() -> None: + module_name = "chunk0.module.decoder.layers.0.self_attention.linear_qkv.q_proj_lora" + rank_traces = [] + for cp_rank, values in enumerate( + (torch.tensor([[1.0, 2.0]]), torch.tensor([[3.0, 4.0]])) + ): + for tp_rank in range(2): + rank_traces.append( + { + module_name: [ + { + "micro_call_index": 0, + "micro_order": 0, + "micro_sample_index": 0, + "module_type": "LoRA", + "primary_output": values, + "merge_hints": {"primary_output": {"op": "replicated"}}, + "rank_meta": { + "global_rank": cp_rank * 2 + tp_rank, + "tp_rank": tp_rank, + "tp_world_size": 2, + "cp_rank": cp_rank, + "cp_world_size": 2, + }, + } + ] + } + ) + + merged = ForwardTraceCapture._merge_rank_traces(rank_traces) + + assert torch.equal( + merged[module_name][0]["primary_output"], + torch.tensor([[1.0, 2.0], [3.0, 4.0]]), + ) + + def test_gate_up_rank_interleaved_trace_layout_canonicalizes_dense_tp() -> None: canonical = torch.arange(16, dtype=torch.float32).reshape(2, 1, 8) gate0, gate1, up0, up1 = canonical.chunk(4, dim=-1) @@ -974,31 +1383,19 @@ def test_oracle_topologies_are_the_compact_cp_validation_matrix() -> None: assert TOPOLOGIES == [ Topology(tp=1, ep=1, etp=1, dp=1, sp=False), Topology(tp=1, ep=2, etp=1, dp=1, cp=2, sp=False), - Topology(tp=2, ep=2, etp=1, dp=1, cp=2, sp=True), + Topology(tp=1, ep=2, etp=1, dp=1, cp=2, pp=2, vpp=2, sp=False), Topology(tp=2, ep=4, etp=2, dp=2, cp=2, sp=True), ] assert [topology.world_size() for topology in TOPOLOGIES] == [1, 2, 4, 8] -def test_dense_topologies_include_vllm_separation_and_cp_coverage() -> None: +def test_dense_topologies_are_the_compact_mixed_parallel_matrix() -> None: assert DENSE_TOPOLOGIES == [ Topology(tp=1, ep=1, etp=1, dp=1, sp=False), - Topology(tp=2, ep=1, etp=1, dp=1, sp=True), - Topology(tp=1, ep=1, etp=1, dp=2, sp=False), - Topology(tp=2, ep=1, etp=1, dp=2, sp=True), - Topology(tp=1, ep=1, etp=1, dp=1, cp=2, sp=False), - Topology(tp=2, ep=1, etp=1, dp=1, cp=2, sp=True), + Topology(tp=2, ep=1, etp=1, dp=1, cp=2, sp=False), Topology(tp=2, ep=1, etp=1, dp=2, cp=2, sp=True), ] - assert [topology.world_size() for topology in DENSE_TOPOLOGIES] == [ - 1, - 2, - 2, - 4, - 2, - 4, - 8, - ] + assert [topology.world_size() for topology in DENSE_TOPOLOGIES] == [1, 4, 8] def test_dense_sensitivity_keeps_dp_and_cp_attention_cases() -> None: @@ -1042,6 +1439,10 @@ def test_dense_sensitivity_keeps_dp_and_cp_attention_cases() -> None: "attn_skip_flash_lse_normalize", is_moe=True, ) == Topology(tp=1, ep=2, etp=1, dp=1, cp=4, sp=False) + assert sensitivity_topology_for_mutation( + "dp_grad_accumulation_seqs", + is_moe=True, + ) == Topology(tp=1, ep=1, etp=1, dp=2, sp=False) def test_case_config_base_model_can_be_overridden_by_env( diff --git a/tests/integration/megatron/model_support/test_packing_invariance.py b/tests/integration/megatron/model_support/test_packing_invariance.py index 367e7217f..45613d5f9 100644 --- a/tests/integration/megatron/model_support/test_packing_invariance.py +++ b/tests/integration/megatron/model_support/test_packing_invariance.py @@ -34,4 +34,8 @@ def test_run_packing_invariance_qwen35() -> None: scenario.repeated_position_key_count > 0 for scenario in report.scenarios ) assert all(scenario.completion_pair_count > 0 for scenario in report.scenarios) - assert all(scenario.logits_mean_abs_pct <= 0.5 for scenario in report.scenarios) + assert report.precision == "fp32" + assert all( + scenario.logits_mean_abs_pct <= scenario.logits_mean_abs_pct_limit + for scenario in report.scenarios + ) diff --git a/tests/integration/megatron/model_support/test_provider_support.py b/tests/integration/megatron/model_support/test_provider_support.py index 43a641452..44e41c549 100644 --- a/tests/integration/megatron/model_support/test_provider_support.py +++ b/tests/integration/megatron/model_support/test_provider_support.py @@ -11,6 +11,7 @@ from megatron.core.transformer.enums import AttnBackend from art.megatron.context_parallel.core_attention import ArtContextParallelCoreAttention +from art.megatron.dsv4.bridge import _install_dsv4_source_aliases from art.megatron.flex_attn.attention import FlexDotProductAttention from art.megatron.lora import default_lora_rank_for_handler from art.megatron.model_support.registry import ( @@ -45,6 +46,7 @@ def __init__(self) -> None: self.recompute_num_layers: int | None = None self.expert_model_parallel_size = 1 self.expert_tensor_parallel_size = 1 + self.dsv4_hc_mult = 4 def _base_layer_spec( self, config: object, vp_stage: int | None = None @@ -162,6 +164,10 @@ def test_dsv4_prefers_validated_native_lora_rollout() -> None: assert model_requires_merged_rollout("deepseek-ai/DeepSeek-V4-Flash") is False +def test_dsv4_config_only_bridge_does_not_require_checkpoint_state() -> None: + _install_dsv4_source_aliases(SimpleNamespace(config=SimpleNamespace())) + + def test_dsv4_provider_disables_shared_expert_overlap( monkeypatch: pytest.MonkeyPatch, ) -> None: @@ -178,6 +184,11 @@ def test_dsv4_provider_disables_shared_expert_overlap( lambda *args, **kwargs: fake_bridge, ) monkeypatch.setattr(provider_module.torch.cuda, "device_count", lambda: 2) + monkeypatch.setattr( + provider_module.torch.cuda, + "get_device_properties", + lambda device: SimpleNamespace(major=9, name="NVIDIA H200"), + ) resolved = provider_module.get_provider("deepseek-ai/DeepSeek-V4-Flash") diff --git a/tests/integration/megatron/model_support/test_workflow.py b/tests/integration/megatron/model_support/test_workflow.py index 4b68579cd..c630a6286 100644 --- a/tests/integration/megatron/model_support/test_workflow.py +++ b/tests/integration/megatron/model_support/test_workflow.py @@ -1,6 +1,10 @@ +import json import os +from pathlib import Path +import subprocess +import sys from types import SimpleNamespace -from typing import cast +from typing import Any, cast import pytest @@ -8,15 +12,22 @@ ArchitectureReport, LayerFamilyInstance, ) +from art.pipeline_tuner import PipelineTuneSettings +from tests.integration.megatron.train_inf_mismatch import ( + workflow_stage as mismatch_workflow_stage, +) from .validation_spec import ValidationReport, ValidationStageResult from .workflow import ( + _RUNTIME_ARTIFACT_DIR_NAMES, INCLUDE_FLASH_SENSITIVITY_ENV, KEEP_TOPOLOGY_ARTIFACTS_ENV, MANDATORY_VALIDATION_STAGES, NATIVE_VLLM_LORA_STAGE, SKIP_SENSITIVITY_ENV, + WORKFLOW_STAGE_DIR_ENV, _inspect_architecture_for_workflow, + _prune_runtime_artifacts, assess_minimal_layer_coverage, build_all_architectures_validation_report, build_validation_report, @@ -32,16 +43,49 @@ run_yes_no_trainability_stage, validated_architecture_representative_models, ) +from .workflow_fixtures import ( + FIXTURE_PATH_ENV, + WorkflowFixture, + _validate_tokenizer_compatible_fixture, +) from .workflow_resources import ( + _THROUGHPUT_CONFIGS, + HANDLER_WORKFLOW_RESOURCES, + ThroughputThresholds, + ThroughputWorkflowConfig, _h200_equivalent_slots_for_total_gib, handler_workflow_resources_for_base_model, + resolve_stage_resources_for_current_host, resolve_stage_resources_for_visible_gpus, ) +from .workflow_throughput import ( + PolicyActivationEvent, + ThroughputFixture, + _collect_matched_packing_shapes, + _collect_measurements, + _current_pipeline_settings, + _environment_provenance, + _freeze_pipeline_settings_from_step, + _packed_input_fingerprint, + _phase_evidence, + _reduced_config, + _same_setting_decision_suffix, + _throughput_config_for_hardware, + acceptance_failures, +) @pytest.fixture(autouse=True) -def _stub_pinned_git_state(monkeypatch) -> None: +def _stub_workflow_environment(monkeypatch, tmp_path) -> None: monkeypatch.delenv(INCLUDE_FLASH_SENSITIVITY_ENV, raising=False) + fixture_path = tmp_path / "correctness_fixture" + tokenizer_compatible_path = tmp_path / "tokenizer_compatible_fixture" + stage_path = tmp_path / "stage" + fixture_path.mkdir() + tokenizer_compatible_path.mkdir() + stage_path.mkdir() + monkeypatch.setenv(FIXTURE_PATH_ENV, str(fixture_path)) + monkeypatch.setenv(WORKFLOW_STAGE_DIR_ENV, str(stage_path)) monkeypatch.setattr( "tests.integration.megatron.model_support.workflow.pinned_git_state", lambda suite_name: SimpleNamespace( @@ -53,6 +97,622 @@ def _stub_pinned_git_state(monkeypatch) -> None: } ), ) + monkeypatch.setattr( + "tests.integration.megatron.model_support.workflow.ensure_workflow_fixture", + lambda base_model, allow_unvalidated_arch=False, required_stages=frozenset(): ( + WorkflowFixture( + canonical_model=base_model, + model_key="qwen3_5_moe", + source_revision="test", + path=str(fixture_path), + hf_home=str(tmp_path / "hf_home"), + manifest={"version": 15}, + tokenizer_compatible_path=str(tokenizer_compatible_path), + tokenizer_compatible_hf_home=str(tmp_path / "tokenizer_hf_home"), + tokenizer_compatible_manifest={"version": 1}, + canonical_path=str(fixture_path), + canonical_hf_home=str(tmp_path / "hf_home"), + ) + ), + ) + + +def _fixture(tmp_path: Path, model_key: str) -> WorkflowFixture: + return WorkflowFixture( + canonical_model=model_key, + model_key=model_key, + source_revision="pinned", + path=str(tmp_path / "compact"), + hf_home=str(tmp_path / "compact_cache"), + manifest={"version": 15}, + tokenizer_compatible_path=str(tmp_path / "tokenizer"), + tokenizer_compatible_hf_home=str(tmp_path / "tokenizer_cache"), + canonical_path=str(tmp_path / "canonical"), + canonical_hf_home=str(tmp_path / "canonical_cache"), + ) + + +def test_fixture_stage_contracts(tmp_path: Path) -> None: + # fmt: off + cases = { + ("gemma4_dense", "canonical"): ("hf_parity", "packing_invariance", "length_trainability"), + ("gemma4_dense", "compact"): ("lora_coverage",), + ("gemma4_dense", "tokenizer"): ("train_inf_mismatch", "merged_vllm_serving", "native_vllm_lora", "yes_no_trainability"), + ("llama3_dense", "compact"): ("hf_parity",), + ("llama3_dense", "tokenizer"): ("train_inf_mismatch",), + ("llama3_dense", "canonical"): ("length_trainability", "yes_no_trainability"), + ("gpt_oss_moe", "canonical"): ("train_inf_mismatch",), + ("gpt_oss_moe", "tokenizer"): ("merged_vllm_serving", "native_vllm_lora"), + ("glm52", "compact"): ("length_trainability", "yes_no_trainability"), + ("dsv4", "canonical"): ("train_inf_mismatch", "length_trainability", "yes_no_trainability"), + } + # fmt: on + for (model_key, selected), stages in cases.items(): + for stage in stages: + environment = _fixture(tmp_path, model_key).environment(stage) + assert environment[FIXTURE_PATH_ENV] == str(tmp_path / selected) + assert environment["ART_ORACLE_BASE_MODEL"] == str(tmp_path / selected) + + +def test_fixture_stage_contracts_require_available_assets(tmp_path: Path) -> None: + for stage, missing, contract in ( + ("hf_parity", "canonical_path", "canonical weights"), + ("train_inf_mismatch", "tokenizer_compatible_path", "canonical vocabulary"), + ): + fixture = _fixture(tmp_path, "gemma4_dense").model_copy(update={missing: None}) + with pytest.raises(RuntimeError, match=f"requires {contract}"): + fixture.environment(stage) + + +def test_reduced_trainability_preserves_validated_token_contract( + tmp_path: Path, +) -> None: + for model_key, stage, expected in ( + ("glm52", "length_trainability", "154820,38069"), + ("glm52", "yes_no_trainability", "9829,902,36569"), + ("gemma4_dense", "yes_no_trainability", "4443,951,7463"), + ("gemma4_moe", "yes_no_trainability", "4443,951,7463"), + ): + key = f"ART_MODEL_SUPPORT_{stage.removesuffix('_trainability').upper()}_ALLOWED_TOKEN_IDS" + assert _fixture(tmp_path, model_key).environment(stage)[key] == expected + + +@pytest.mark.parametrize( + ("vocab_size", "registered_max", "encoded_max", "error"), + [ + (8_192, 9_000, 3, "registered tokenizer ID 9000"), + (128_256, 128_255, 128_009, None), + ], +) +def test_tokenizer_compatible_fixture_preflight( + monkeypatch: pytest.MonkeyPatch, + vocab_size: int, + registered_max: int, + encoded_max: int, + error: str | None, +) -> None: + class Tokenizer: + chat_template = "template" + + def get_vocab(self): + return {"ordinary": 1, "highest": registered_max} + + def __call__(self, *_args, **_kwargs): + return {"input_ids": [1, encoded_max]} + + apply_chat_template = __call__ + + monkeypatch.setattr( + "transformers.AutoTokenizer.from_pretrained", + lambda *_args, **_kwargs: Tokenizer(), + ) + manifest: dict[str, object] = {"config_vocab_size": vocab_size} + if error: + with pytest.raises(RuntimeError, match=error): + _validate_tokenizer_compatible_fixture(Path("/tmp/provider"), manifest) + else: + _validate_tokenizer_compatible_fixture(Path("/tmp/provider"), manifest) + assert manifest["representative_max_token_id"] == encoded_max + assert manifest["tokenizer_max_id"] == registered_max + + +def test_throughput_runtime_keeps_canonical_handler_separate_from_provider( + monkeypatch: pytest.MonkeyPatch, +) -> None: + from art.megatron.runtime import local as local_runtime + + monkeypatch.delenv("CUDA_VISIBLE_DEVICES", raising=False) + monkeypatch.setattr( + local_runtime, + "get_megatron_runtime_config", + lambda: SimpleNamespace( + topology={"tp": 1, "ep": 1, "etp": 1, "cp": 1, "pp": 1} + ), + ) + topology = local_runtime.compile_local_runtime_topology( + cast( + Any, + { + "trainer_gpu_ids": [0], + "init_args": {"model_name": "/tmp/production-width-provider"}, + }, + ), + model_name="validation", + base_model="meta-llama/Llama-3.2-1B-Instruct", + artifact_root="/tmp/art", + visible_gpu_count=1, + ) + + assert topology.model_services[0].base_model == "/tmp/production-width-provider" + + +def test_throughput_measurements_use_runtime_rows_and_activation_timestamps( + tmp_path: Path, +) -> None: + rows = [ + { + "step": step, + "data/step_num_groups_trainable": 8, + "data/step_packed_sequences": 1, + "data/step_nonpadding_logical_tokens": 1_000, + "train/prefix_tree/logical_tokens": 4_000, + "data/step_loss_bearing_tokens": 500, + "data/step_trainable_assistant_tokens": 500, + "data/step_executed_token_equivalents": 1_000, + "data/step_dummy_executed_token_equivalents": 0, + "data/step_nominal_schedule_capacity_tokens": 131_072, + "data/step_dummy_schedule_capacity_tokens": 0, + "data/step_unused_packed_capacity_tokens": 130_072, + "data/step_num_gradient_steps": 1, + "pipeline/global_real_microbatches": 1, + "pipeline/global_dummy_microbatches": 0, + "pipeline_settings/num_rollout_workers": 16, + "pipeline_settings/min_batch_size": 8, + "pipeline_settings/max_batch_size": 32, + "pipeline_settings/queue_maxsize": 48, + "pipeline_settings/target_groups_per_step": 24, + "time/step_train_s": 1.5, + "time/step_wall_s": 2.0, + "time/step_collect_batch_s": 0.001068115234375, + "queue/packed_get_wait_s": 0.1, + "offpolicy/token_weighted_policy_age_steps": 1.0, + "offpolicy/token_weighted_policy_age_p95_steps": 2.0, + "sample_efficiency/freshness_discount": 0.8, + "discarded/step/stale_groups": 0, + "discarded/step/zero_variance_groups": 0, + } + for step in range(14, 20) + ] + history_path = tmp_path / "history.jsonl" + history_path.write_text("".join(json.dumps(row) + "\n" for row in rows)) + measured_settings = PipelineTuneSettings( + num_rollout_workers=16, + min_batch_size=8, + max_batch_size=32, + queue_maxsize=48, + target_groups_per_step=24, + ) + future_settings = measured_settings.model_copy(update={"num_rollout_workers": 14}) + profile = SimpleNamespace( + config=SimpleNamespace(mode="online", window_steps=2), + decisions=[ + SimpleNamespace( + action="hold", + previous=measured_settings, + updated=measured_settings, + stats=SimpleNamespace( + start_step=14, + end_step=15, + window_start_s=-4.0, + window_end_s=0.0, + vllm_pressure=0.6, + vllm_waiting_capacity_request_s=6.0, + vllm_running_request_s=10.0, + trainer_underfeed_score=0.07, + actual_stale_frac=0.0, + ), + ), + SimpleNamespace( + action="decrease_workers", + previous=measured_settings, + updated=future_settings, + stats=SimpleNamespace( + start_step=16, + end_step=17, + window_start_s=0.0, + window_end_s=4.0, + vllm_pressure=0.45, + vllm_waiting_capacity_request_s=4.5, + vllm_running_request_s=10.0, + trainer_underfeed_score=0.10, + actual_stale_frac=0.0, + ), + ), + SimpleNamespace( + action="hold", + previous=future_settings, + updated=future_settings, + stats=SimpleNamespace( + start_step=18, + end_step=19, + window_start_s=4.0, + window_end_s=8.0, + vllm_pressure=0.65, + vllm_waiting_capacity_request_s=19.5, + vllm_running_request_s=30.0, + trainer_underfeed_score=0.04, + actual_stale_frac=0.0, + ), + ), + SimpleNamespace( + action="decrease_workers", + previous=future_settings, + updated=future_settings.model_copy(update={"num_rollout_workers": 12}), + stats=SimpleNamespace( + start_step=20, + end_step=21, + window_start_s=8.0, + window_end_s=12.0, + vllm_pressure=0.1, + vllm_waiting_capacity_request_s=1.0, + vllm_running_request_s=10.0, + trainer_underfeed_score=0.5, + actual_stale_frac=0.0, + ), + ), + ], + policy_age_limit_steps=4, + ) + events = [ + PolicyActivationEvent(13, -4.25, -4.0), + PolicyActivationEvent(14, -3.5, -3.25), + PolicyActivationEvent(15, -1.5, -1.25), + PolicyActivationEvent(16, 0.5, 0.75), + PolicyActivationEvent(17, 2.5, 2.75), + PolicyActivationEvent(18, 4.5, 4.75), + PolicyActivationEvent(19, 6.5, 7.75), + ] + config = ThroughputWorkflowConfig(num_layers=2, completion_tokens=128, max_steps=19) + fixture = ThroughputFixture( + model_key="llama3_dense", + path="/tmp/llama-throughput", + num_layers=2, + width_fingerprint={"hidden_size": 2048}, + manifest={"initialization": "deterministic_random_v1"}, + ) + + def phase(kind: str, packed: str, steps: tuple[int, ...]): + phase_rows = [dict(rows[-1]) for _ in range(6)] + phase_rows[-1]["data/step_nonpadding_logical_tokens"] += 1 + phase_rows[-1]["data/step_unused_packed_capacity_tokens"] -= 1 + return _phase_evidence( + phase=cast(Any, kind), + runtime_fingerprint="runtime-a", + trajectory_input_fingerprint="trajectory-a", + packed_input_fingerprint=packed, + samples=list(zip(phase_rows, steps, strict=True)), + ) + + e2e_phase, isolated_phase = ( + phase("e2e", "input-a", tuple(range(21, 27))), + phase("isolated", "input-a", tuple(range(28, 34))), + ) + + def collect(isolated): + return _collect_measurements( + fixture=fixture, + config=config, + hardware="b300", + model_output_dir=tmp_path, + profile=profile, + events=events, + isolated=isolated, + e2e=e2e_phase, + capture_settings=measured_settings.model_dump(mode="json"), + calibration_fingerprint="a" * 64, + ) + + measurements = collect(isolated_phase) + + expected = { + "original_trajectory_tokens": 24_000, + "nonpadding_logical_tokens": 6_000, + "loss_bearing_tokens": 3_000, + "accepted_train_tokens": 3_000, + "isolated_train_tok_s": 6_001 / 9.0, + "matched_e2e_core_train_tok_s": 6_001 / 9.0, + "e2e_core_train_tok_s": 6_000 / 9.0, + "e2e_train_tok_s": 500.0, + "accepted_train_tok_s": 250.0, + "mean_train_gap_s": 0.5, + "stable_vllm_pressure": 0.6, + "stable_trainer_underfeed": 0.07, + "post_warmup_policy_activation_count": 6, + "mean_policy_activation_lag_s": 2.5 / 6.0, + "p50_policy_activation_lag_s": 0.25, + "p95_policy_activation_lag_s": 1.0, + "max_policy_activation_lag_s": 1.25, + "mean_policy_activation_interval_s": 11.75 / 6.0, + "p50_policy_activation_interval_s": 2.0, + "p95_policy_activation_interval_s": 2.75, + "second_max_policy_activation_interval_s": 2.0, + "max_policy_activation_interval_s": 3.0, + } + assert {key: measurements[key] for key in expected} == pytest.approx(expected) + thresholds = ThroughputThresholds( + calibration_basis="measured", + calibration_fingerprint="a" * 64, + min_isolated_train_tok_s=1.0, + min_e2e_train_tok_s=1.0, + min_accepted_train_tok_s=1.0, + min_e2e_to_isolated_ratio=0.5, + min_matched_core_to_isolated_ratio=0.95, + max_mean_policy_activation_lag_s=1.5, + max_policy_activation_lag_s=2.0, + max_repeated_policy_activation_interval_s=1.5, + ) + assert acceptance_failures(measurements, config, thresholds) == [ + "repeated_policy_activation_cadence_s" + ] + assert acceptance_failures( + { + **measurements, + "stable_vllm_pressure": 0.49, + "stable_trainer_underfeed": 0.09, + }, + config, + thresholds, + ) == [ + "stable_min_vllm_pressure", + "stable_trainer_underfeed", + "repeated_policy_activation_cadence_s", + ] + estimated = ThroughputThresholds( + calibration_basis="estimated", + min_isolated_train_tok_s=1.0, + min_e2e_train_tok_s=1.0, + min_accepted_train_tok_s=1.0, + min_e2e_to_isolated_ratio=0.5, + min_matched_core_to_isolated_ratio=0.95, + max_mean_policy_activation_lag_s=1.5, + max_policy_activation_lag_s=2.0, + max_repeated_policy_activation_interval_s=1.5, + ) + assert acceptance_failures(measurements, config, estimated) == [ + "repeated_policy_activation_cadence_s", + "calibration_basis", + ] + lag_failures = acceptance_failures( + measurements, + config, + thresholds.model_copy( + update={ + "max_mean_policy_activation_lag_s": 0.4, + "max_policy_activation_lag_s": 1.0, + "max_repeated_policy_activation_interval_s": 3.5, + } + ), + ) + assert lag_failures == [ + "mean_policy_activation_lag_s", + "max_policy_activation_lag_s", + ] + measurements["matched_e2e_core_train_tok_s"] *= 1.1 + assert "matched_core_to_isolated_ratio_max" in acceptance_failures( + measurements, config, thresholds + ) + inconsistent = [dict(row) for row in rows] + inconsistent[-1]["pipeline_settings/num_rollout_workers"] = 14 + history_path.write_text("".join(json.dumps(row) + "\n" for row in inconsistent)) + with pytest.raises(RuntimeError, match="two trailing same-setting"): + collect(isolated_phase) + history_path.write_text("".join(json.dumps(row) + "\n" for row in rows)) + capture_settings = measured_settings.model_dump(mode="json") + capture_settings["num_rollout_workers"] = 14 + with pytest.raises( + RuntimeError, match="did not use the measured pipeline settings" + ): + _collect_measurements( + fixture=fixture, + config=config, + hardware="b300", + model_output_dir=tmp_path, + profile=profile, + events=events, + isolated=isolated_phase, + e2e=e2e_phase, + capture_settings=capture_settings, + calibration_fingerprint="a" * 64, + ) + fractional = [dict(row) for row in rows] + fractional[0]["data/step_nonpadding_logical_tokens"] = 999.5 + history_path.write_text("".join(json.dumps(row) + "\n" for row in fractional)) + with pytest.raises(RuntimeError, match="must be a nonnegative integer"): + collect(isolated_phase) + history_path.write_text("".join(json.dumps(row) + "\n" for row in rows)) + with pytest.raises(RuntimeError, match="same packed input"): + collect(phase("isolated", "input-b", tuple(range(28, 34)))) + + +def test_throughput_measurement_freezes_actual_settings() -> None: + measured = PipelineTuneSettings( + num_rollout_workers=16, + min_batch_size=8, + max_batch_size=32, + queue_maxsize=48, + target_groups_per_step=24, + ) + future = measured.model_copy(update={"num_rollout_workers": 14}) + trainer = SimpleNamespace( + state=SimpleNamespace(next_training_step=18), + **measured.model_dump(mode="python"), + ) + + def apply(settings: PipelineTuneSettings) -> None: + for name, value in settings.model_dump(mode="python").items(): + setattr(trainer, name, value) + + trainer.apply_pipeline_settings = apply + original = trainer.apply_pipeline_settings + + with _freeze_pipeline_settings_from_step(trainer, 19): + trainer.apply_pipeline_settings(measured) + trainer.state.next_training_step = 19 + trainer.apply_pipeline_settings(future) + trainer.state.next_training_step = 20 + trainer.apply_pipeline_settings(future) + trainer.state.next_training_step = 21 + trainer.apply_pipeline_settings(future) + assert _current_pipeline_settings(trainer) == measured.model_dump(mode="json") + + trainer.apply_pipeline_settings(future) + assert _current_pipeline_settings(trainer) == future.model_dump(mode="json") + assert trainer.apply_pipeline_settings == original + + +def test_throughput_measurement_uses_full_same_setting_suffix() -> None: + measured = PipelineTuneSettings( + num_rollout_workers=16, + min_batch_size=8, + max_batch_size=32, + queue_maxsize=48, + target_groups_per_step=24, + ) + previous = measured.model_copy(update={"num_rollout_workers": 14}) + + def decision(start_step: int) -> SimpleNamespace: + return SimpleNamespace( + stats=SimpleNamespace( + start_step=start_step, + end_step=start_step + 1, + window_start_s=float(start_step), + window_end_s=float(start_step + 2), + ) + ) + + def rows(settings: PipelineTuneSettings, *steps: int) -> dict[int, dict[str, int]]: + values = settings.model_dump(mode="json") + return { + step: {f"pipeline_settings/{name}": value for name, value in values.items()} + for step in steps + } + + by_step = { + **rows(previous, 10, 11), + **rows(measured, 12, 13, 14, 15, 16, 17), + } + selected = _same_setting_decision_suffix( + [decision(10), decision(12), decision(14), decision(16)], + by_step, + ) + + assert [item.stats.start_step for item in selected] == [12, 14, 16] + + +def test_throughput_provenance_ignores_local_editable_paths() -> None: + distributions = _environment_provenance( + Path(sys.executable), ("openpipe-art", "pydantic") + )["distributions"] + + assert set(distributions["openpipe-art"]) == {"version", "metadata_sha256"} + assert {"direct_url_sha256", "record_sha256"} <= set(distributions["pydantic"]) + + +def test_dsv4_throughput_reduction_preserves_hash_moe_prefix() -> None: + source = { + "num_hidden_layers": 10, + "hidden_size": 2048, + "mlp_layer_types": ["hash_moe"] * 3 + ["moe"] * 7, + } + + reduced, _ = _reduced_config(source, model_key="dsv4", num_layers=8) + + assert "num_hash_layers" not in reduced + assert reduced["mlp_layer_types"] == ["hash_moe"] * 3 + ["moe"] * 5 + + +def test_matched_batch_always_collects_packing_shapes() -> None: + groups = [SimpleNamespace(_collect_packing_shape=False) for _ in range(3)] + + _collect_matched_packing_shapes(groups) + + assert all(group._collect_packing_shape for group in groups) + + +def test_throughput_packed_input_fingerprint_hashes_data_plane_bytes() -> None: + from array import array + from multiprocessing import shared_memory + + from art.pipeline_tuner.config import PackedGroupShape, PackingLeafShape + + shm = shared_memory.SharedMemory(create=True, size=4) + try: + buffer = shm.buf + assert buffer is not None + buffer[:] = b"abcd" + tensor = SimpleNamespace(offset=0, byte_count=4) + ref = SimpleNamespace( + shared_memory_name=shm.name, + owner_process_id=os.getpid(), + tensors=(tensor,), + model_dump=lambda **kwargs: { + "tensors": [{"name": "tokens", "shape": [4], "dtype": "int8"}] + }, + ) + packed = SimpleNamespace( + leases=SimpleNamespace(ref=ref), + packed_group_shapes=( + PackedGroupShape( + leaves=( + PackingLeafShape( + token_ids=array("I", [1, 2, 3]), shareable_length=2 + ), + ) + ), + ), + ) + batch = SimpleNamespace( + payload=SimpleNamespace(packed=packed), + model_dump=lambda **kwargs: {"sequence_length": 4}, + ) + prepared = SimpleNamespace( + batch=batch, + packing_config=SimpleNamespace( + model_dump=lambda **kwargs: {"packed_sequence_length": 4} + ), + ) + groups = [SimpleNamespace(_prepared_training_batch=prepared)] + + before = _packed_input_fingerprint(groups) + buffer[0] = ord("z") + changed_bytes = _packed_input_fingerprint(groups) + buffer[0] = ord("a") + packed.packed_group_shapes = ( + PackedGroupShape( + leaves=( + PackingLeafShape( + token_ids=array("I", [1, 2, 4]), shareable_length=2 + ), + ) + ), + ) + changed_shape = _packed_input_fingerprint(groups) + + assert before != changed_bytes + assert before != changed_shape + finally: + del buffer + shm.close() + shm.unlink() + + +def _without_stage_duration(stage: ValidationStageResult) -> dict[str, object]: + metrics = dict(stage.metrics) + assert float(metrics.pop("workflow_stage_duration_s")) >= 0.0 + metrics.pop("fixture_provisioning_s", None) + metrics.pop("workflow_pruned_runtime_artifact_dirs", None) + metrics.pop("workflow_pruned_runtime_artifact_bytes", None) + return metrics def test_build_validation_stage_names_has_fixed_order() -> None: @@ -81,6 +741,7 @@ def test_validated_architecture_representative_models_are_fixed() -> None: "google/gemma-4-26B-A4B-it", "google/gemma-4-31B-it", "deepseek-ai/DeepSeek-V4-Flash", + "zai-org/GLM-5.2", "openai/gpt-oss-20b", ] @@ -108,7 +769,7 @@ def test_dsv4_runtime_stages_use_full_model_resources() -> None: engine_args = stage.vllm.engine_args() assert "hf_overrides" not in engine_args assert engine_args.get("load_format") != "dummy" - assert engine_args["moe_backend"] == "triton_unfused" + assert engine_args["moe_backend"] == "triton" assert engine_args["kv_cache_dtype"] == "fp8" assert stage.streaming_weight_offload is True assert stage.megatron_env == {} @@ -120,20 +781,50 @@ def test_dsv4_runtime_stages_use_full_model_resources() -> None: assert engine_args["load_format"] == "dummy" hf_overrides = cast(dict[str, object], engine_args["hf_overrides"]) assert hf_overrides["num_hidden_layers"] == 4 + assert hf_overrides["num_hash_layers"] == 0 + assert hf_overrides["expert_dtype"] == "fp8" + assert engine_args["max_model_len"] == 1024 assert resources.merged_vllm_serving is not None + assert resources.merged_vllm_serving.required_world_size == 8 + assert resources.merged_vllm_serving.megatron is not None assert resources.merged_vllm_serving.vllm is not None + assert resources.merged_vllm_serving.megatron.gpu_ids == [0, 1, 2, 3] + assert resources.merged_vllm_serving.megatron.topology.tp == 2 + assert resources.merged_vllm_serving.megatron.topology.ep == 4 + assert resources.merged_vllm_serving.megatron.topology.dp == 2 + assert resources.merged_vllm_serving.vllm.gpu_ids == [4, 5, 6, 7] + assert not ( + set(resources.merged_vllm_serving.megatron.gpu_ids) + & set(resources.merged_vllm_serving.vllm.gpu_ids) + ) assert resources.merged_vllm_serving.vllm.engine_args()["kv_cache_dtype"] == "fp8" assert resources.native_vllm_lora is not None assert resources.native_vllm_lora.vllm is not None assert resources.native_vllm_lora.vllm.engine_args().get("max_loras", 2) == 2 -def test_dsv4_resources_remap_to_four_high_vram_gpus(monkeypatch) -> None: +@pytest.mark.parametrize( + ("stage_name", "trainer_gpu_ids", "trainer_ep", "trainer_dp"), + [ + ("train_inf_mismatch", [0, 1, 2, 3], 4, 2), + ("yes_no_trainability", [0, 1, 2, 3], 4, 2), + ("length_trainability", [0, 1, 2, 3], 4, 2), + ("merged_vllm_serving", [0, 1], 2, 1), + ], +) +def test_dsv4_resources_remap_to_four_high_vram_gpus( + monkeypatch, + stage_name: str, + trainer_gpu_ids: list[int], + trainer_ep: int, + trainer_dp: int, +) -> None: resources = handler_workflow_resources_for_base_model( "deepseek-ai/DeepSeek-V4-Flash" ) assert resources is not None - assert resources.train_inf_mismatch is not None + stage_resources = getattr(resources, stage_name) + assert stage_resources is not None monkeypatch.setattr( "tests.integration.megatron.model_support.workflow_resources." "_visible_h200_equivalent_gpus", @@ -141,28 +832,250 @@ def test_dsv4_resources_remap_to_four_high_vram_gpus(monkeypatch) -> None: ) stage = resolve_stage_resources_for_visible_gpus( - "train_inf_mismatch", - resources.train_inf_mismatch, + stage_name, + stage_resources, visible_gpu_count=4, ) assert stage.megatron is not None assert stage.vllm is not None - assert stage.megatron.gpu_ids == [0, 1] + assert stage.megatron.gpu_ids == trainer_gpu_ids assert stage.megatron.topology.tp == 2 - assert stage.megatron.topology.ep == 2 + assert stage.megatron.topology.ep == trainer_ep + assert stage.megatron.topology.dp == trainer_dp assert stage.vllm.gpu_ids == [2, 3] assert stage.vllm.tensor_parallel_size == 2 - assert stage.vllm.engine_args()["moe_backend"] == "triton_unfused" + assert stage.vllm.engine_args()["moe_backend"] == "triton" assert stage.vllm.engine_args()["kv_cache_dtype"] == "fp8" +def test_glm52_reduced_workflow_uses_portable_serving_backends() -> None: + resources = handler_workflow_resources_for_base_model("zai-org/GLM-5.2") + assert resources is not None + joint_stages = ( + resources.train_inf_mismatch, + resources.merged_vllm_serving, + resources.yes_no_trainability, + resources.length_trainability, + ) + for stage in joint_stages: + assert stage is not None + assert stage.required_world_size == 2 + assert stage.megatron is not None + assert stage.megatron.gpu_ids == [0] + assert resources.native_vllm_lora is not None + assert resources.native_vllm_lora.required_world_size == 2 + assert resources.native_vllm_lora.megatron is None + for stage in (*joint_stages, resources.native_vllm_lora): + assert stage is not None + assert stage.vllm is not None + assert stage.vllm.gpu_ids == [1] + engine_args = stage.vllm.engine_args() + assert engine_args["attention_backend"] == "FLASHMLA_SPARSE" + assert engine_args["max_model_len"] == 1024 + assert engine_args["moe_backend"] == "triton" + assert resources.yes_no_trainability_variant == "megatron_dedicated" + + def test_h200_equivalent_slots_tolerate_reported_gb300_vram() -> None: assert _h200_equivalent_slots_for_total_gib(80.0) == 0 assert _h200_equivalent_slots_for_total_gib(139.0) == 1 + assert _h200_equivalent_slots_for_total_gib(267.69) == 2 assert _h200_equivalent_slots_for_total_gib(276.6) == 2 +def test_h200_throughput_depth_only_reduces_memory_bound_handlers() -> None: + config = ThroughputWorkflowConfig(num_layers=12) + assert _throughput_config_for_hardware("glm52", config, "h200").num_layers == 6 + assert _throughput_config_for_hardware("dsv4", config, "h200").num_layers == 4 + assert _throughput_config_for_hardware("glm52", config, "b300") is config + assert _throughput_config_for_hardware("llama3_dense", config, "h200") is config + + +@pytest.mark.parametrize("hardware", ("b300", "h200")) +def test_dsv4_uses_model_specific_activation_lag_limit(hardware: str) -> None: + for handler_key, config in _THROUGHPUT_CONFIGS.items(): + thresholds = config.thresholds[hardware] + assert thresholds.max_mean_policy_activation_lag_s == ( + 2.25 if handler_key == "dsv4" else 1.5 + ) + assert thresholds.max_policy_activation_lag_s == 3.5 + + +@pytest.mark.parametrize("handler_key", sorted(HANDLER_WORKFLOW_RESOURCES)) +def test_throughput_requires_four_distinct_physical_gpus( + handler_key: str, monkeypatch: pytest.MonkeyPatch +) -> None: + stage = HANDLER_WORKFLOW_RESOURCES[handler_key].e2e_throughput + assert stage is not None + megatron, vllm = stage.megatron, stage.vllm + assert megatron is not None and vllm is not None + assert (stage.required_world_size, stage.required_physical_gpus) == (4, 4) + assert (megatron.gpu_ids, vllm.gpu_ids) == ([0, 1], [2, 3]) + monkeypatch.setattr( + "tests.integration.megatron.model_support.workflow_resources." + "_visible_h200_equivalent_gpus", + lambda *, visible_gpu_count: visible_gpu_count * 2, + ) + + with pytest.raises(RuntimeError, match="Need 4 physical GPUs"): + resolve_stage_resources_for_visible_gpus( + "e2e_throughput", + stage, + visible_gpu_count=2, + ) + + assert ( + resolve_stage_resources_for_visible_gpus( + "e2e_throughput", stage, visible_gpu_count=4 + ) + == stage + ) + + +def test_backend_resources_stay_logical_until_topology_compilation(monkeypatch) -> None: + from art.megatron.runtime import local as local_runtime + + stage = HANDLER_WORKFLOW_RESOURCES["llama3_dense"].e2e_throughput + assert stage is not None + monkeypatch.setenv("CUDA_VISIBLE_DEVICES", "4,5,6,7") + monkeypatch.setattr( + "tests.integration.megatron.model_support.workflow_resources." + "_current_visible_gpu_count", + lambda: 4, + ) + + resolved = resolve_stage_resources_for_current_host("e2e_throughput", stage) + + megatron = resolved.megatron + vllm = resolved.vllm + assert megatron is not None + assert vllm is not None + assert megatron.gpu_ids == [0, 1] + assert vllm.gpu_ids == [2, 3] + monkeypatch.setattr( + local_runtime, + "get_megatron_runtime_config", + lambda: SimpleNamespace(topology=megatron.topology.to_megatron_config()), + ) + topology = local_runtime.compile_local_runtime_topology( + cast( + Any, + { + "trainer_gpu_ids": megatron.gpu_ids, + "inference_gpu_ids": vllm.gpu_ids, + "engine_args": vllm.engine_args(), + }, + ), + model_name="throughput", + base_model="/tmp/provider", + artifact_root="/tmp/art", + visible_gpu_count=4, + ) + + assert topology.trainer is not None + assert [rank.gpu_id for rank in topology.trainer.ranks] == [4, 5] + assert topology.model_services[0].members[0].gpu_ids == (6, 7) + assert topology.cluster.hosts[0].gpu_ids == (4, 5, 6, 7) + + +@pytest.mark.parametrize( + ("outcomes", "passed", "attempts", "retryable"), + [ + ( + ( + ( + 2, + { + "outcome": "error", + "comparison_completed": False, + "exception_type": "ConnectionRefusedError", + }, + ), + (0, {"outcome": "passed", "comparison_completed": True}), + ), + True, + 2, + True, + ), + (((1, {"outcome": "failed", "comparison_completed": True}),), False, 1, False), + ( + ( + ( + 2, + { + "outcome": "error", + "comparison_completed": False, + "exception_type": "ValueError", + }, + ), + ), + False, + 1, + False, + ), + ( + ((0, {"outcome": "skipped", "comparison_completed": False}),), + False, + 1, + False, + ), + (((3, None),), False, 1, False), + ], +) +def test_mismatch_workflow_retries_only_executed_failures( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, + outcomes: tuple[tuple[int, dict[str, object] | None], ...], + passed: bool, + attempts: int, + retryable: bool, +) -> None: + results = iter(outcomes) + + def run_attempt(command, **_kwargs): + returncode, payload = next(results) + if payload is not None: + Path(command[-1]).write_text(json.dumps(payload), encoding="utf-8") + return subprocess.CompletedProcess(command, returncode, stdout="", stderr="") + + monkeypatch.setenv("ART_TRAIN_INF_MISMATCH_ATTEMPTS", "3") + monkeypatch.setattr( + mismatch_workflow_stage, "create_artifact_dir", lambda _nodeid: tmp_path + ) + monkeypatch.setattr(mismatch_workflow_stage, "_run_attempt", run_attempt) + report = mismatch_workflow_stage.run_train_inf_mismatch( + base_model="Qwen/Qwen3.5-35B-A3B" + ) + assert (report.passed, report.attempt_count, report.attempts[0].retryable) == ( + passed, + attempts, + retryable, + ) + assert report.duration_s >= report.attempts[0].duration_s + + +@pytest.mark.parametrize("handler_key", ["qwen3_moe", "qwen3_5_moe"]) +def test_qwen_moe_reduced_serving_uses_plain_expert_storage(handler_key: str) -> None: + resources = HANDLER_WORKFLOW_RESOURCES[handler_key] + for stage in (resources.merged_vllm_serving, resources.native_vllm_lora): + assert stage is not None + assert stage.vllm is not None + assert stage.vllm.gpu_ids == [1] + engine_args = stage.vllm.engine_args() + assert engine_args["enforce_eager"] is True + assert engine_args["max_model_len"] == 1024 + assert engine_args["moe_backend"] == "triton" + + +def test_gpt_oss_reduced_serving_has_bounded_context() -> None: + resources = HANDLER_WORKFLOW_RESOURCES["gpt_oss_moe"] + for stage in (resources.merged_vllm_serving, resources.native_vllm_lora): + assert stage is not None + assert stage.vllm is not None + assert stage.vllm.engine_args()["max_model_len"] == 1024 + + def test_inspect_architecture_for_workflow_uses_minimal_topology(monkeypatch) -> None: seen_env: dict[str, str | None] = {} @@ -185,7 +1098,7 @@ def _inspect_architecture(base_model: str, **kwargs) -> ArchitectureReport: ) monkeypatch.setattr( - "tests.integration.megatron.model_support.workflow.inspect_architecture", + "art.megatron.model_support.discovery.inspect_architecture", _inspect_architecture, ) @@ -222,14 +1135,16 @@ def _build_validation_report( del stop_on_failure del allow_unvalidated_arch calls.append(base_model) + passed = base_model != "Qwen/Qwen3-32B" return ValidationReport( git={}, base_model=base_model, model_key="qwen3_dense", + passed=passed, stages=[ ValidationStageResult( name="train_inf_mismatch", - passed=base_model != "Qwen/Qwen3-32B", + passed=passed, ) ], ) @@ -257,7 +1172,7 @@ def test_build_validation_report_populates_architecture_stage( monkeypatch, ) -> None: monkeypatch.setattr( - "tests.integration.megatron.model_support.workflow.inspect_architecture", + "art.megatron.model_support.discovery.inspect_architecture", lambda base_model: ArchitectureReport( base_model=base_model, model_key="qwen3_5_moe", @@ -339,6 +1254,12 @@ def test_build_validation_report_populates_architecture_stage( }, artifact_dir="/tmp/length-trainability", ), + "e2e_throughput": ValidationStageResult( + name="e2e_throughput", + passed=True, + metrics={"accepted_train_tok_s": 1234.0}, + artifact_dir="/tmp/e2e-throughput", + ), "native_vllm_lora": ValidationStageResult( name="native_vllm_lora", passed=True, @@ -365,12 +1286,12 @@ def test_build_validation_report_populates_architecture_stage( stage for stage in report.stages if stage.name == "dependency_resolution" ) assert dependency_stage.passed is True - assert dependency_stage.metrics == {"transformers": "5.2.0"} + assert _without_stage_duration(dependency_stage) == {"transformers": "5.2.0"} architecture_stage = next( stage for stage in report.stages if stage.name == "architecture_discovery" ) assert architecture_stage.passed is True - assert architecture_stage.metrics == { + assert _without_stage_duration(architecture_stage) == { "recommended_min_layers": 1, "layer_families": [ { @@ -387,24 +1308,32 @@ def test_build_validation_report_populates_architecture_stage( stage for stage in report.stages if stage.name == "hf_parity" ) assert hf_parity_stage.passed is True - assert hf_parity_stage.metrics == {"signal": "pass", "requested_num_layers": 1} + assert _without_stage_duration(hf_parity_stage) == { + "signal": "pass", + "requested_num_layers": 1, + } assert hf_parity_stage.artifact_dir == "/tmp/hf_parity" lora_coverage_stage = next( stage for stage in report.stages if stage.name == "lora_coverage" ) assert lora_coverage_stage.passed is True - assert lora_coverage_stage.metrics == {"wrapped_adapter_prefix_count": 12} + assert _without_stage_duration(lora_coverage_stage) == { + "wrapped_adapter_prefix_count": 12 + } mismatch_stage = next( stage for stage in report.stages if stage.name == "train_inf_mismatch" ) assert mismatch_stage.passed is True - assert mismatch_stage.metrics == {"passed_count": 1, "failed_count": 0} + assert _without_stage_duration(mismatch_stage) == { + "passed_count": 1, + "failed_count": 0, + } assert mismatch_stage.artifact_dir == "/tmp/train-inf-mismatch" correctness_stage = next( stage for stage in report.stages if stage.name == "correctness_sensitivity" ) assert correctness_stage.passed is True - assert correctness_stage.metrics == { + assert _without_stage_duration(correctness_stage) == { "correctness_variant_count": 4, "sensitivity_variant_count": 9, } @@ -413,13 +1342,15 @@ def test_build_validation_report_populates_architecture_stage( stage for stage in report.stages if stage.name == "merged_vllm_serving" ) assert merged_stage.passed is True - assert merged_stage.metrics == {"served_model_name": "validation@0"} + assert _without_stage_duration(merged_stage) == { + "served_model_name": "validation@0" + } assert merged_stage.artifact_dir == "/tmp/merged-serving" chat_template_stage = next( stage for stage in report.stages if stage.name == "chat_template_rollout" ) assert chat_template_stage.passed is True - assert chat_template_stage.metrics == { + assert _without_stage_duration(chat_template_stage) == { "passed": True, "scenario_count": 6, "failed_scenarios": [], @@ -429,7 +1360,7 @@ def test_build_validation_report_populates_architecture_stage( stage for stage in report.stages if stage.name == "packing_invariance" ) assert packing_invariance_stage.passed is True - assert packing_invariance_stage.metrics == { + assert _without_stage_duration(packing_invariance_stage) == { "num_layers": 4, "scenarios": [ { @@ -444,17 +1375,30 @@ def test_build_validation_report_populates_architecture_stage( stage for stage in report.stages if stage.name == "length_trainability" ) assert trainability_stage.passed is True - assert trainability_stage.metrics == { + assert _without_stage_duration(trainability_stage) == { "latest_step": 4, "best_train_abs_error": 1.0, } assert trainability_stage.artifact_dir == "/tmp/length-trainability" + throughput_stage = next( + stage for stage in report.stages if stage.name == "e2e_throughput" + ) + assert throughput_stage.passed is True + throughput_metrics = _without_stage_duration(throughput_stage) + assert throughput_metrics["accepted_train_tok_s"] == 1234.0 + assert throughput_stage.artifact_dir == "/tmp/e2e-throughput" + fixture_durations = [ + cast(float, stage.metrics["fixture_provisioning_s"]) + for stage in report.stages + if "fixture_provisioning_s" in stage.metrics + ] + assert len(fixture_durations) == 1 and fixture_durations[0] >= 0.0 assert all(stage.name != "yes_no_trainability" for stage in report.stages) native_vllm_lora_stage = next( stage for stage in report.stages if stage.name == "native_vllm_lora" ) assert native_vllm_lora_stage.passed is True - assert native_vllm_lora_stage.metrics == { + assert _without_stage_duration(native_vllm_lora_stage) == { "rollout_weights_mode": "lora", "step0_name": "validation@0", "step1_name": "validation@1", @@ -466,7 +1410,7 @@ def test_build_validation_report_populates_architecture_stage( assert native_vllm_lora_stage.artifact_dir == "/tmp/native-vllm-lora" -def test_build_validation_report_preserves_traces_when_sensitivity_runs( +def test_build_validation_report_success_cleanup_does_not_implicitly_keep_traces( monkeypatch, ) -> None: seen_keep_env: list[str | None] = [] @@ -474,7 +1418,7 @@ def test_build_validation_report_preserves_traces_when_sensitivity_runs( monkeypatch.delenv(KEEP_TOPOLOGY_ARTIFACTS_ENV, raising=False) monkeypatch.setattr( - "tests.integration.megatron.model_support.workflow.inspect_architecture", + "art.megatron.model_support.discovery.inspect_architecture", lambda base_model: ArchitectureReport( base_model=base_model, model_key="qwen3_5_moe", @@ -506,14 +1450,46 @@ def _run_stage_in_subprocess( include_sensitivity=True, ) - assert seen_keep_env == ["1"] + assert seen_keep_env == [None] assert os.environ.get(KEEP_TOPOLOGY_ARTIFACTS_ENV) is None +def test_runtime_artifact_cleanup_preserves_evidence(tmp_path: Path) -> None: + stage_dir = tmp_path / "e2e_throughput" + model_dir = stage_dir / "art" / "models" / "run" + report = stage_dir / "throughput_measurements.json" + matched_input = stage_dir / "matched_packed_input.msgpack" + report.parent.mkdir(parents=True) + report.write_text("{}") + matched_input.write_bytes(b"input") + removed_bytes = 0 + for name in ( + "checkpoints", + "megatron_runtime", + "optimizer_states", + "trajectories", + ): + path = model_dir / name + path.mkdir(parents=True) + payload = path / "payload" + payload.write_bytes(name.encode()) + removed_bytes += payload.stat().st_size + + metrics = _prune_runtime_artifacts(stage_dir) + + assert metrics == { + "workflow_pruned_runtime_artifact_dirs": 4, + "workflow_pruned_runtime_artifact_bytes": removed_bytes, + } + assert report.read_text() == "{}" + assert matched_input.read_bytes() == b"input" + assert not any((model_dir / name).exists() for name in _RUNTIME_ARTIFACT_DIR_NAMES) + + def test_build_validation_report_only_stage_skips_other_stages(monkeypatch) -> None: calls: list[str] = [] monkeypatch.setattr( - "tests.integration.megatron.model_support.workflow.inspect_architecture", + "art.megatron.model_support.discovery.inspect_architecture", lambda base_model: ArchitectureReport( base_model=base_model, model_key="qwen3_5_moe", @@ -544,7 +1520,7 @@ def _run_stage_in_subprocess(**kwargs) -> ValidationStageResult: skipped = next(stage for stage in report.stages if stage.name == "hf_parity") assert calls == ["length_trainability"] - assert skipped.metrics == { + assert _without_stage_duration(skipped) == { "skipped": True, "reason": "--only-stage=length_trainability", } @@ -552,7 +1528,7 @@ def _run_stage_in_subprocess(**kwargs) -> ValidationStageResult: def test_build_validation_report_captures_hf_parity_failure(monkeypatch) -> None: monkeypatch.setattr( - "tests.integration.megatron.model_support.workflow.inspect_architecture", + "art.megatron.model_support.discovery.inspect_architecture", lambda base_model: ArchitectureReport( base_model=base_model, model_key="qwen3_5_moe", @@ -589,13 +1565,15 @@ def test_build_validation_report_captures_hf_parity_failure(monkeypatch) -> None stage for stage in report.stages if stage.name == "hf_parity" ) assert hf_parity_stage.passed is False - assert hf_parity_stage.metrics == {"error": "AssertionError: parity failed"} + assert _without_stage_duration(hf_parity_stage) == { + "error": "AssertionError: parity failed" + } assert hf_parity_stage.artifact_dir is None def test_build_validation_report_captures_lora_coverage_failure(monkeypatch) -> None: monkeypatch.setattr( - "tests.integration.megatron.model_support.workflow.inspect_architecture", + "art.megatron.model_support.discovery.inspect_architecture", lambda base_model: ArchitectureReport( base_model=base_model, model_key="qwen3_5_moe", @@ -631,7 +1609,7 @@ def test_build_validation_report_captures_lora_coverage_failure(monkeypatch) -> stage for stage in report.stages if stage.name == "lora_coverage" ) assert lora_coverage_stage.passed is False - assert lora_coverage_stage.metrics == { + assert _without_stage_duration(lora_coverage_stage) == { "error": "RuntimeError: missing wrapped targets" } @@ -642,7 +1620,7 @@ def test_build_validation_report_writes_incremental_output_and_stops( ) -> None: calls: list[str] = [] monkeypatch.setattr( - "tests.integration.megatron.model_support.workflow.inspect_architecture", + "art.megatron.model_support.discovery.inspect_architecture", lambda base_model: ArchitectureReport( base_model=base_model, model_key="qwen3_5_moe", @@ -693,7 +1671,7 @@ def _run_stage_in_subprocess( stage for stage in saved.stages if stage.name == "train_inf_mismatch" ) assert failed_stage.passed is False - assert skipped_stage.metrics == { + assert _without_stage_duration(skipped_stage) == { "skipped": True, "reason": "stopped after lora_coverage failed", } @@ -703,7 +1681,7 @@ def test_assess_minimal_layer_coverage_reports_missing_families( monkeypatch, ) -> None: monkeypatch.setattr( - "tests.integration.megatron.model_support.workflow.inspect_architecture", + "art.megatron.model_support.discovery.inspect_architecture", lambda base_model: ArchitectureReport( base_model=base_model, model_key="qwen3_5_moe", @@ -764,12 +1742,14 @@ def test_run_chat_template_rollout_stage(monkeypatch) -> None: def test_run_correctness_sensitivity_stage_runs_dense_models(monkeypatch) -> None: case_configs: list[SimpleNamespace] = [] oracle_module = SimpleNamespace( + ORACLE_OBJECTIVE_ENV="ART_ORACLE_OBJECTIVE", + SUPPORTED_ORACLE_OBJECTIVES=("sft",), OracleCaseConfig=lambda **kwargs: SimpleNamespace(**kwargs), selected_suite_topologies=lambda *, is_moe, cp_supported=True: [ - SimpleNamespace(world_size=lambda: 1, slug=lambda: "tp1"), - SimpleNamespace(world_size=lambda: 2, slug=lambda: "tp2"), - SimpleNamespace(world_size=lambda: 2, slug=lambda: "dp2"), - SimpleNamespace(world_size=lambda: 4, slug=lambda: "tp2_dp2"), + SimpleNamespace(world_size=lambda: 1, slug=lambda: "tp1", pp=1, vpp=1), + SimpleNamespace(world_size=lambda: 2, slug=lambda: "tp2", pp=1, vpp=1), + SimpleNamespace(world_size=lambda: 2, slug=lambda: "dp2", pp=1, vpp=1), + SimpleNamespace(world_size=lambda: 4, slug=lambda: "tp2_dp2", pp=1, vpp=1), ], oracle_topology=lambda *, is_moe: SimpleNamespace(world_size=lambda: 1), selected_oracle_objectives=lambda: ["sft"], @@ -830,7 +1810,7 @@ def test_run_correctness_sensitivity_stage_runs_dense_models(monkeypatch) -> Non assert result.metrics["is_moe"] is False assert result.metrics["available_gpu_count"] == 4 assert result.metrics["max_world_size"] == 4 - assert result.metrics["required_gpu_count"] == 1 + assert result.metrics["required_gpu_count"] == 4 assert result.metrics["correctness_variant_count"] == 1 assert result.metrics["correctness_excluded_topologies"] == [] assert result.metrics["sensitivity_mutations"] == ["skip_finalize"] @@ -844,7 +1824,7 @@ def test_run_yes_no_trainability_stage(monkeypatch) -> None: monkeypatch.setattr( "tests.integration.megatron.model_support.workflow._import_integration_module", lambda name: SimpleNamespace( - run_yes_no_trainability=lambda *, base_model, allow_unvalidated_arch=False: ( + run_yes_no_trainability=lambda *, base_model, artifact_root, allow_unvalidated_arch=False: ( SimpleNamespace( latest_step=2, initial_eval_reward=0.4, @@ -881,6 +1861,13 @@ def test_run_yes_no_trainability_stage(monkeypatch) -> None: def test_run_length_trainability_stage(monkeypatch) -> None: + workspace = ( + Path(os.environ[WORKFLOW_STAGE_DIR_ENV]) + / "artifacts" + / "megatron_dedicated_workspace" + ) + workspace.mkdir(parents=True) + (workspace / "optimizer.pt").write_bytes(b"large") report = SimpleNamespace( summary_log_path="/tmp/length-trainability/length_trainability.log", model_dump=lambda mode="json": { @@ -911,6 +1898,36 @@ def test_run_length_trainability_stage(monkeypatch) -> None: assert result.name == "length_trainability" assert result.passed is True assert result.artifact_dir == "/tmp/length-trainability" + assert not workspace.exists() + + +def test_run_length_trainability_stage_cleans_workspace_on_failure(monkeypatch) -> None: + workspace = ( + Path(os.environ[WORKFLOW_STAGE_DIR_ENV]) + / "artifacts" + / "megatron_dedicated_workspace" + ) + workspace.mkdir(parents=True) + + def fail(**kwargs) -> None: + raise RuntimeError("boom") + + monkeypatch.setattr( + "tests.integration.megatron.model_support.workflow._import_integration_module", + lambda name: SimpleNamespace(run_length_trainability=fail), + ) + + with pytest.raises(RuntimeError, match="boom"): + run_length_trainability_stage( + base_model="google/gemma-4-31B-it", + architecture=ArchitectureReport( + base_model="google/gemma-4-31B-it", + model_key="gemma4_dense", + handler_key="gemma4_dense", + ), + ) + + assert not workspace.exists() def test_run_train_inf_mismatch_stage(monkeypatch) -> None: @@ -1009,11 +2026,13 @@ def test_run_native_vllm_lora_stage(monkeypatch) -> None: def test_run_packing_invariance_stage(monkeypatch) -> None: + calls: list[bool] = [] monkeypatch.setattr( "tests.integration.megatron.model_support.workflow._import_integration_module", lambda name: SimpleNamespace( - run_packing_invariance=lambda *, base_model, num_layers, allow_unvalidated_arch=False: ( - SimpleNamespace( + run_packing_invariance=lambda *, base_model, num_layers, allow_unvalidated_arch=False, in_process=False: ( + calls.append(in_process) + or SimpleNamespace( output_dir="/tmp/packing-invariance", model_dump=lambda mode="json": { "base_model": base_model, @@ -1048,6 +2067,7 @@ def test_run_packing_invariance_stage(monkeypatch) -> None: assert result.passed is True assert result.artifact_dir == "/tmp/packing-invariance" + assert calls == [True] def test_assess_minimal_layer_coverage_passes_when_prefix_covers_all_families( @@ -1130,13 +2150,15 @@ def test_run_correctness_sensitivity_stage_summarizes_reports(monkeypatch) -> No model_key="qwen3_5_moe", handler_key="qwen3_5_moe", layer_families=[LayerFamilyInstance(key="grouped_moe_mlp", layer_index=0)], - recommended_min_layers=4, + recommended_min_layers=1, ) oracle_module = SimpleNamespace( + ORACLE_OBJECTIVE_ENV="ART_ORACLE_OBJECTIVE", + SUPPORTED_ORACLE_OBJECTIVES=("sft",), OracleCaseConfig=lambda **kwargs: SimpleNamespace(**kwargs), selected_suite_topologies=lambda *, is_moe, cp_supported=True: [ - SimpleNamespace(world_size=lambda: 1, slug=lambda: "tp1"), - SimpleNamespace(world_size=lambda: 2, slug=lambda: "tp2"), + SimpleNamespace(world_size=lambda: 1, slug=lambda: "tp1", pp=1, vpp=1), + SimpleNamespace(world_size=lambda: 2, slug=lambda: "tp2", pp=2, vpp=2), ], oracle_topology=lambda *, is_moe: SimpleNamespace(world_size=lambda: 1), selected_oracle_objectives=lambda: ["sft"], @@ -1189,7 +2211,7 @@ def test_run_correctness_sensitivity_stage_summarizes_reports(monkeypatch) -> No "attn_skip_flash_lse_normalize" ] assert stage.metrics["available_gpu_count"] == 2 - assert stage.metrics["required_gpu_count"] == 1 + assert stage.metrics["required_gpu_count"] == 2 assert stage.metrics["correctness_variant_count"] == 1 assert stage.metrics["sensitivity_skipped"] is False assert stage.metrics["sensitivity_skip_reason"] is None @@ -1209,11 +2231,13 @@ def test_run_correctness_sensitivity_stage_uses_dsv4_real_path_config( ) captured: dict[str, object] = {} oracle_module = SimpleNamespace( + ORACLE_OBJECTIVE_ENV="ART_ORACLE_OBJECTIVE", + SUPPORTED_ORACLE_OBJECTIVES=("rl",), OracleCaseConfig=lambda **kwargs: SimpleNamespace(**kwargs), MetricThresholdRule=lambda **kwargs: SimpleNamespace(**kwargs), selected_suite_topologies=lambda *, is_moe, cp_supported=True: [ - SimpleNamespace(world_size=lambda: 1, slug=lambda: "tp1"), - SimpleNamespace(world_size=lambda: 2, slug=lambda: "tp2"), + SimpleNamespace(world_size=lambda: 1, slug=lambda: "tp1", pp=1, vpp=1), + SimpleNamespace(world_size=lambda: 2, slug=lambda: "tp2", pp=1, vpp=1), ], oracle_topology=lambda *, is_moe: SimpleNamespace(world_size=lambda: 1), selected_oracle_objectives=lambda: ["rl"], @@ -1272,10 +2296,12 @@ def test_run_correctness_sensitivity_stage_can_skip_sensitivity_only( recommended_min_layers=4, ) oracle_module = SimpleNamespace( + ORACLE_OBJECTIVE_ENV="ART_ORACLE_OBJECTIVE", + SUPPORTED_ORACLE_OBJECTIVES=("sft",), OracleCaseConfig=lambda **kwargs: SimpleNamespace(**kwargs), selected_suite_topologies=lambda *, is_moe, cp_supported=True: [ - SimpleNamespace(world_size=lambda: 1, slug=lambda: "tp1"), - SimpleNamespace(world_size=lambda: 2, slug=lambda: "tp2"), + SimpleNamespace(world_size=lambda: 1, slug=lambda: "tp1", pp=1, vpp=1), + SimpleNamespace(world_size=lambda: 2, slug=lambda: "tp2", pp=1, vpp=1), ], oracle_topology=lambda *, is_moe: SimpleNamespace(world_size=lambda: 1), selected_oracle_objectives=lambda: ["sft"], @@ -1315,7 +2341,7 @@ def test_run_correctness_sensitivity_stage_can_skip_sensitivity_only( assert stage.name == "correctness_sensitivity" assert stage.passed is True - assert stage.metrics["required_gpu_count"] == 1 + assert stage.metrics["required_gpu_count"] == 2 assert stage.metrics["correctness_variant_count"] == 1 assert stage.metrics["sensitivity_mutations"] == [] assert stage.metrics["default_excluded_sensitivity_mutations"] == [] diff --git a/tests/integration/megatron/model_support/validation_spec.py b/tests/integration/megatron/model_support/validation_spec.py index 6901f81a2..e1a1ecd94 100644 --- a/tests/integration/megatron/model_support/validation_spec.py +++ b/tests/integration/megatron/model_support/validation_spec.py @@ -18,6 +18,7 @@ class MinimalLayerCoverageReport(BaseModel): class ValidationStageResult(BaseModel): name: str passed: bool = False + skipped: bool = False metrics: dict[str, Any] = Field(default_factory=dict) artifact_dir: str | None = None @@ -26,5 +27,7 @@ class ValidationReport(BaseModel): git: dict[str, Any] base_model: str model_key: str + passed: bool = False + complete: bool = False dependency_versions: dict[str, str] = Field(default_factory=dict) stages: list[ValidationStageResult] = Field(default_factory=list) diff --git a/tests/integration/megatron/model_support/workflow.py b/tests/integration/megatron/model_support/workflow.py index 3a4350930..4ff38cd86 100644 --- a/tests/integration/megatron/model_support/workflow.py +++ b/tests/integration/megatron/model_support/workflow.py @@ -2,16 +2,19 @@ from contextlib import contextmanager, nullcontext, redirect_stderr, redirect_stdout import importlib import importlib.metadata +import math import os from pathlib import Path +import shutil +import signal import subprocess import sys -import tempfile +import time from typing import Any +import uuid from pydantic import BaseModel, Field -from art.megatron.model_support.discovery import inspect_architecture from art.megatron.model_support.registry import ( VALIDATED_MODEL_SUPPORT_SPECS, get_model_support_handler_for_spec, @@ -28,19 +31,23 @@ ValidationReport, ValidationStageResult, ) +from .workflow_fixtures import WorkflowFixture, ensure_workflow_fixture REPO_ROOT = Path(__file__).resolve().parents[4] TESTS_DIR = REPO_ROOT / "tests" -LOCAL_LOG_DIR = REPO_ROOT / ".local" -CORRECTNESS_LOG_PATH = LOCAL_LOG_DIR / "correctness.log" -SENSITIVITY_LOG_PATH = LOCAL_LOG_DIR / "sensitivity.log" -LIVE_TRAINING_LOG_PATH = LOCAL_LOG_DIR / "live_training.log" ORACLE_LIVE_TRAINING_LOG_ENV = "ART_ORACLE_LIVE_TRAINING_LOG" +WORKFLOW_RUN_DIR_ENV = "ART_MODEL_SUPPORT_WORKFLOW_RUN_DIR" +WORKFLOW_STAGE_DIR_ENV = "ART_MODEL_SUPPORT_WORKFLOW_STAGE_DIR" SKIP_SENSITIVITY_ENV = "ART_MODEL_SUPPORT_SKIP_SENSITIVITY" INCLUDE_FLASH_SENSITIVITY_ENV = "ART_MODEL_SUPPORT_INCLUDE_FLASH_SENSITIVITY" KEEP_TOPOLOGY_ARTIFACTS_ENV = "ART_ORACLE_KEEP_TOPOLOGY_ARTIFACTS" WORKFLOW_ARTIFACT_SUITE_NAME = "Megatron model-support validation workflow" FLASH_SENSITIVITY_MUTATION = "attn_skip_flash_lse_normalize" +_HANDLER_INAPPLICABLE_SENSITIVITY_MUTATIONS = { + "glm52": frozenset( + {"attn_skip_nested_grad_sanitize", "attn_skip_flash_lse_normalize"} + ) +} MANDATORY_VALIDATION_STAGES = ( "dependency_resolution", @@ -53,6 +60,7 @@ "chat_template_rollout", "packing_invariance", "length_trainability", + "e2e_throughput", ) NATIVE_VLLM_LORA_STAGE = "native_vllm_lora" YES_NO_TRAINABILITY_STAGE = "yes_no_trainability" @@ -70,6 +78,7 @@ "gemma4_moe": "google/gemma-4-26B-A4B-it", "gemma4_dense": "google/gemma-4-31B-it", "dsv4": "deepseek-ai/DeepSeek-V4-Flash", + "glm52": "zai-org/GLM-5.2", "gpt_oss_moe": "openai/gpt-oss-20b", } SUBPROCESS_VALIDATION_STAGES = frozenset( @@ -82,14 +91,31 @@ "chat_template_rollout", "packing_invariance", "length_trainability", + "e2e_throughput", YES_NO_TRAINABILITY_STAGE, NATIVE_VLLM_LORA_STAGE, } ) +_RUNTIME_CLEANUP_STAGES = frozenset( + {"length_trainability", "e2e_throughput", YES_NO_TRAINABILITY_STAGE} +) +_RUNTIME_ARTIFACT_DIR_NAMES = frozenset( + { + "checkpoints", + "megatron_runtime", + "optimizer_states", + "trajectories", + } +) +_WORKFLOW_STAGE_TIMEOUT_S = 30 * 60 +_WORKFLOW_STAGE_TIMEOUT_OVERRIDES_S = { + ("e2e_throughput", "deepseek-ai/DeepSeek-V4-Flash"): 40 * 60, +} class AllArchitecturesValidationReport(BaseModel): passed: bool = False + complete: bool = False reports: list[ValidationReport] = Field(default_factory=list) @@ -128,7 +154,6 @@ def initialize_validation_report( base_model, allow_unvalidated_arch=allow_unvalidated_arch, ) - handler = get_model_support_handler_for_spec(spec) return ValidationReport( git=pinned_git_state(WORKFLOW_ARTIFACT_SUITE_NAME).model_dump(mode="json"), base_model=base_model, @@ -139,7 +164,7 @@ def initialize_validation_report( for stage_name in build_validation_stage_names( include_native_vllm_lora=include_native_vllm_lora, include_yes_no_trainability=include_yes_no_trainability, - native_vllm_lora_status=handler.native_vllm_lora_status, + native_vllm_lora_status=spec.native_vllm_lora_status, ) ], ) @@ -173,6 +198,8 @@ def _inspect_architecture_for_workflow( *, allow_unvalidated_arch: bool, ) -> ArchitectureReport: + from art.megatron.model_support.discovery import inspect_architecture + # Discovery only inspects layer families, so use a minimal topology instead # of inheriting visible GPU count and tripping model-specific TP limits. with _temporary_env( @@ -210,6 +237,62 @@ def _temporary_env(**updates: str): os.environ[key] = value +def _new_workflow_run_dir(*, output_json: str | Path | None, model_key: str) -> Path: + run_id = f"{time.strftime('%Y%m%dT%H%M%SZ')}_{os.getpid()}_{uuid.uuid4().hex[:8]}" + if output_json is None: + root = REPO_ROOT / ".local" / "model_support_workflow_runs" / model_key + else: + output_path = Path(output_json).resolve() + root = output_path.parent / f"{output_path.stem}.artifacts" + path = root / run_id + path.mkdir(parents=True, exist_ok=False) + return path + + +def _workflow_stage_dir() -> Path: + raw = os.environ.get(WORKFLOW_STAGE_DIR_ENV) + if raw is None: + raise RuntimeError(f"missing {WORKFLOW_STAGE_DIR_ENV}") + path = Path(raw) + path.mkdir(parents=True, exist_ok=True) + return path + + +def _stage_artifact_dir() -> Path: + path = _workflow_stage_dir() / "artifacts" + path.mkdir(parents=True, exist_ok=True) + return path + + +def _cleanup_stage_workspace(path: Path) -> None: + if os.environ.get(KEEP_TOPOLOGY_ARTIFACTS_ENV) != "1" and path.exists(): + shutil.rmtree(path) + + +def _oracle_case_config( + oracle_harness: Any, + *, + base_model: str, + model_support_key: str, + is_moe: bool, + precision: str, + num_layers: int, + target_modules: list[str], + allow_unvalidated_arch: bool, +) -> Any: + oracle_harness.ARTIFACT_ROOT = _stage_artifact_dir() + return oracle_harness.OracleCaseConfig( + base_model=base_model, + model_support_key=model_support_key, + is_moe=is_moe, + precision=precision, + num_layers=num_layers, + num_steps=1, + lora={"target_modules": target_modules}, + allow_unvalidated_arch=allow_unvalidated_arch, + ) + + def _write_validation_report( report: ValidationReport, output_json: str | Path | None, @@ -221,6 +304,32 @@ def _write_validation_report( path.write_text(report.model_dump_json(indent=2), encoding="utf-8") +def _record_stage_duration(stage: ValidationStageResult, *, started: float) -> None: + stage.metrics["workflow_stage_duration_s"] = time.monotonic() - started + + +def _prune_runtime_artifacts(stage_dir: Path) -> dict[str, int]: + paths = sorted( + ( + path + for path in stage_dir.rglob("*") + if path.is_dir() and path.name in _RUNTIME_ARTIFACT_DIR_NAMES + ), + key=lambda path: len(path.parts), + reverse=True, + ) + removed_bytes = 0 + for path in paths: + removed_bytes += sum( + child.stat().st_size for child in path.rglob("*") if child.is_file() + ) + shutil.rmtree(path) + return { + "workflow_pruned_runtime_artifact_dirs": len(paths), + "workflow_pruned_runtime_artifact_bytes": removed_bytes, + } + + def _write_all_architectures_report( report: AllArchitecturesValidationReport, output_json: str | Path | None, @@ -267,18 +376,34 @@ def _mark_remaining_stages_skipped( report: ValidationReport, *, after_stage_name: str, + reason: str | None = None, ) -> None: past_failure = False for stage in report.stages: if past_failure: + stage.passed = False + stage.skipped = True stage.metrics = { "skipped": True, - "reason": f"stopped after {after_stage_name} failed", + "reason": reason or f"stopped after {after_stage_name} failed", + "workflow_stage_duration_s": 0.0, } continue past_failure = stage.name == after_stage_name +def _finalize_validation_report( + report: ValidationReport, + *, + partial: bool, +) -> None: + executed = [stage for stage in report.stages if not stage.skipped] + report.passed = bool(executed) and all(stage.passed for stage in executed) + report.complete = ( + not partial and len(executed) == len(report.stages) and report.passed + ) + + def _only_stage_run_set(only_stage: str | None) -> set[str] | None: if only_stage is None: return None @@ -298,68 +423,124 @@ def _run_stage_in_subprocess( architecture: ArchitectureReport, allow_unvalidated_arch: bool = False, ) -> ValidationStageResult: - with tempfile.TemporaryDirectory(prefix=f"model_support_{stage_name}_") as tmp_dir: - tmp_path = Path(tmp_dir) - architecture_json = tmp_path / "architecture.json" - output_json = tmp_path / "stage_result.json" - log_path = tmp_path / "stage.log" - architecture_json.write_text( - architecture.model_dump_json(indent=2), - encoding="utf-8", + run_dir = Path(os.environ[WORKFLOW_RUN_DIR_ENV]) + stage_dir = run_dir / stage_name + stage_dir.mkdir(parents=True, exist_ok=False) + architecture_json = stage_dir / "architecture.json" + output_json = stage_dir / "stage_result.json" + log_path = stage_dir / "worker.log" + architecture_json.write_text( + architecture.model_dump_json(indent=2), + encoding="utf-8", + ) + cmd = [ + sys.executable, + "-m", + "integration.megatron.model_support.workflow_stage_worker", + "--stage", + stage_name, + "--base-model", + base_model, + "--architecture-json", + str(architecture_json), + "--output-json", + str(output_json), + ] + if allow_unvalidated_arch: + cmd.append("--allow-unsupported-arch") + env = os.environ.copy() + env["WANDB_MODE"] = "disabled" + env[WORKFLOW_STAGE_DIR_ENV] = str(stage_dir) + existing_pythonpath = env.get("PYTHONPATH") + env["PYTHONPATH"] = ( + str(TESTS_DIR) + if not existing_pythonpath + else f"{TESTS_DIR}{os.pathsep}{existing_pythonpath}" + ) + started = time.monotonic() + timeout_s = _WORKFLOW_STAGE_TIMEOUT_OVERRIDES_S.get( + (stage_name, base_model), _WORKFLOW_STAGE_TIMEOUT_S + ) + with log_path.open("w", encoding="utf-8") as log_file: + process = subprocess.Popen( + cmd, + cwd=str(REPO_ROOT), + env=env, + stdout=log_file, + stderr=subprocess.STDOUT, + text=True, + start_new_session=True, ) - cmd = [ - sys.executable, - "-m", - "integration.megatron.model_support.workflow_stage_worker", - "--stage", - stage_name, - "--base-model", - base_model, - "--architecture-json", - str(architecture_json), - "--output-json", - str(output_json), - ] - if allow_unvalidated_arch: - cmd.append("--allow-unsupported-arch") - env = os.environ.copy() - existing_pythonpath = env.get("PYTHONPATH") - env["PYTHONPATH"] = ( - str(TESTS_DIR) - if not existing_pythonpath - else f"{TESTS_DIR}{os.pathsep}{existing_pythonpath}" + try: + returncode = _wait_stage_process(process, timeout_s=timeout_s) + except subprocess.TimeoutExpired: + returncode = None + duration_s = time.monotonic() - started + common_metrics = { + "workflow_stage_artifact_dir": str(stage_dir), + "workflow_stage_duration_s": duration_s, + } + if returncode is None: + return ValidationStageResult( + name=stage_name, + passed=False, + metrics={ + **common_metrics, + "error": f"stage exceeded {timeout_s:g}s; log={log_path}", + }, ) - with log_path.open("w", encoding="utf-8") as log_file: - completed = subprocess.run( - cmd, - cwd=str(REPO_ROOT), - env=env, - stdout=log_file, - stderr=subprocess.STDOUT, - text=True, - check=False, - ) - if completed.returncode != 0: - tail = _subprocess_log_tail(log_path) - error = ( - f"subprocess exited with code {completed.returncode}" - if not tail - else tail - ) - return ValidationStageResult( - name=stage_name, - passed=False, - metrics={"error": error}, - ) - if not output_json.exists(): - return ValidationStageResult( - name=stage_name, - passed=False, - metrics={"error": "stage worker did not write output_json"}, - ) - return ValidationStageResult.model_validate_json( - output_json.read_text(encoding="utf-8") + if returncode != 0: + tail = _subprocess_log_tail(log_path) + return ValidationStageResult( + name=stage_name, + passed=False, + metrics={ + **common_metrics, + "error": tail or f"subprocess exited with code {returncode}", + }, + ) + if not output_json.exists(): + return ValidationStageResult( + name=stage_name, + passed=False, + metrics={ + **common_metrics, + "error": "stage worker did not write output_json", + }, ) + result = ValidationStageResult.model_validate_json(output_json.read_text()) + result.metrics.update(common_metrics) + output_json.write_text(result.model_dump_json(indent=2), encoding="utf-8") + return result + + +def _raise_signal_exit(signum: int, _frame: Any) -> None: + raise SystemExit(128 + signum) + + +def _wait_stage_process(process: subprocess.Popen[Any], *, timeout_s: float) -> int: + previous_sigterm = signal.signal(signal.SIGTERM, _raise_signal_exit) + try: + return process.wait(timeout=timeout_s) + finally: + signal.signal(signal.SIGTERM, previous_sigterm) + try: + os.killpg(process.pid, signal.SIGTERM) + except ProcessLookupError: + pass + else: + deadline = time.monotonic() + 10.0 + while time.monotonic() < deadline: + try: + os.killpg(process.pid, 0) + except ProcessLookupError: + break + time.sleep(0.05) + else: + try: + os.killpg(process.pid, signal.SIGKILL) + except ProcessLookupError: + pass def run_hf_parity_stage( @@ -379,20 +560,22 @@ def run_hf_parity_stage( allow_unvalidated_arch=allow_unvalidated_arch, ) handler = get_model_support_handler_for_spec(spec) - case_config = oracle_harness.OracleCaseConfig( + case_config = _oracle_case_config( + oracle_harness, base_model=base_model, + model_support_key=spec.key, is_moe=handler.is_moe, - precision="fp32", + precision=handler.correctness_precision(), num_layers=max(1, architecture.recommended_min_layers), - num_steps=1, - lora={"target_modules": list(spec.default_target_modules)}, + target_modules=list(spec.default_target_modules), allow_unvalidated_arch=allow_unvalidated_arch, ) case_config = hf_parity.hf_parity_case_config(case_config) - report = hf_parity.run_hf_parity(case_config=case_config) - case_artifacts = oracle_harness.ensure_case_artifacts(case_config) + report = hf_parity.run_hf_parity(case_config=case_config, in_process=True) artifact_dir = str( - Path(case_artifacts.case_dir) / hf_parity.HF_PARITY_OUTPUT_DIRNAME + Path(oracle_harness.ARTIFACT_ROOT) + / report.case_id + / hf_parity.HF_PARITY_OUTPUT_DIRNAME ) return ValidationStageResult( name="hf_parity", @@ -426,13 +609,14 @@ def run_lora_coverage_stage( allow_unvalidated_arch=allow_unvalidated_arch, ) handler = get_model_support_handler_for_spec(spec) - case_config = oracle_harness.OracleCaseConfig( + case_config = _oracle_case_config( + oracle_harness, base_model=base_model, + model_support_key=spec.key, is_moe=handler.is_moe, - precision="fp32", + precision=handler.correctness_precision(), num_layers=max(1, architecture.recommended_min_layers), - num_steps=1, - lora={"target_modules": list(spec.default_target_modules)}, + target_modules=list(spec.default_target_modules), allow_unvalidated_arch=allow_unvalidated_arch, ) report = lora_coverage.run_lora_coverage(case_config) @@ -472,6 +656,10 @@ def run_correctness_sensitivity_stage( architecture: ArchitectureReport, allow_unvalidated_arch: bool = False, ) -> ValidationStageResult: + stage_dir = _workflow_stage_dir() + correctness_log = stage_dir / "correctness.log" + sensitivity_log = stage_dir / "sensitivity.log" + live_training_log = stage_dir / "live_training.log" oracle_harness = _import_integration_module( "integration.megatron.model_support.oracle_harness" ) @@ -484,44 +672,50 @@ def run_correctness_sensitivity_stage( correctness_precision = handler.correctness_precision() correctness_use_fp32_lora_reference = handler.correctness_use_fp32_lora_reference() correctness_phase_pass_fns = handler.correctness_phase_pass_fns(oracle_harness) - case_config = oracle_harness.OracleCaseConfig( - base_model=base_model, - is_moe=handler.is_moe, - precision=correctness_precision, - num_layers=max(1, architecture.recommended_min_layers), - num_steps=1, - lora={"target_modules": list(spec.default_target_modules)}, - allow_unvalidated_arch=allow_unvalidated_arch, - ) suite_topologies = list( oracle_harness.selected_suite_topologies( is_moe=handler.is_moe, cp_supported=cp_supported, ) ) - objectives = list(oracle_harness.selected_oracle_objectives()) + objectives = list(oracle_harness.SUPPORTED_ORACLE_OBJECTIVES) skip_sensitivity = _truthy_env(SKIP_SENSITIVITY_ENV) available_gpu_count = oracle_harness.available_gpu_count() max_world_size = available_gpu_count oracle_world_size = oracle_harness.oracle_topology( is_moe=handler.is_moe ).world_size() - if available_gpu_count < oracle_world_size: + required_gpu_count = max( + oracle_world_size, + *(topology.world_size() for topology in suite_topologies), + ) + if available_gpu_count < required_gpu_count: raise RuntimeError( "Need " - f"{oracle_world_size} GPUs for oracle topology, found {available_gpu_count}" + f"{required_gpu_count} GPUs for the complete correctness topology set, " + f"found {available_gpu_count}" ) - selected_suite_topologies = [ - topology - for topology in suite_topologies - if topology.world_size() <= max_world_size - ] - excluded_suite_topologies = [ - topology - for topology in suite_topologies - if topology.world_size() > max_world_size - ] + selected_suite_topologies = suite_topologies + excluded_suite_topologies: list[Any] = [] + pipeline_layer_multiple = math.lcm( + *(topology.pp * topology.vpp for topology in selected_suite_topologies) + ) + minimum_layers = max(1, architecture.recommended_min_layers) + num_layers = ( + (minimum_layers + pipeline_layer_multiple - 1) // pipeline_layer_multiple + ) * pipeline_layer_multiple + case_config = _oracle_case_config( + oracle_harness, + base_model=base_model, + model_support_key=spec.key, + is_moe=handler.is_moe, + precision=correctness_precision, + num_layers=num_layers, + target_modules=list(spec.default_target_modules), + allow_unvalidated_arch=allow_unvalidated_arch, + ) mutations: list[str] = [] + inapplicable_sensitivity_mutations: list[str] = [] default_excluded_sensitivity_mutations: list[str] = [] excluded_sensitivity_mutations: list[str] = [] if not skip_sensitivity: @@ -534,6 +728,11 @@ def run_correctness_sensitivity_stage( ): if mutation not in mutations: mutations.append(mutation) + inapplicable = _HANDLER_INAPPLICABLE_SENSITIVITY_MUTATIONS.get(handler.key, ()) + inapplicable_sensitivity_mutations = [ + mutation for mutation in mutations if mutation in inapplicable + ] + mutations = [mutation for mutation in mutations if mutation not in inapplicable] excluded_sensitivity_mutations = [ mutation for mutation in mutations @@ -551,7 +750,9 @@ def run_correctness_sensitivity_stage( > 1 ) ] - if not _truthy_env(INCLUDE_FLASH_SENSITIVITY_ENV): + if FLASH_SENSITIVITY_MUTATION not in inapplicable and not _truthy_env( + INCLUDE_FLASH_SENSITIVITY_ENV + ): default_excluded_sensitivity_mutations.append(FLASH_SENSITIVITY_MUTATION) mutations = [ mutation @@ -562,51 +763,53 @@ def run_correctness_sensitivity_stage( *default_excluded_sensitivity_mutations, } ] - LIVE_TRAINING_LOG_PATH.parent.mkdir(parents=True, exist_ok=True) - LIVE_TRAINING_LOG_PATH.write_text("", encoding="utf-8") - with _temporary_env(**{ORACLE_LIVE_TRAINING_LOG_ENV: str(LIVE_TRAINING_LOG_PATH)}): - with _redirect_output(CORRECTNESS_LOG_PATH): - suite_reports = oracle_harness.run_suite( - case_config=case_config, - max_world_size=max_world_size, - cp_supported=cp_supported, - phase_pass_fns=correctness_phase_pass_fns, - use_fp32_lora_reference=correctness_use_fp32_lora_reference, - prune_reference_artifacts=skip_sensitivity or not mutations, - prune_case_artifacts=skip_sensitivity or not mutations, - ) - sensitivity_reports = [] - if skip_sensitivity: - SENSITIVITY_LOG_PATH.parent.mkdir(parents=True, exist_ok=True) - SENSITIVITY_LOG_PATH.write_text( - ( - "Sensitivity suite skipped. " - f"Set {SKIP_SENSITIVITY_ENV}=0 to re-enable workflow sensitivity.\n" - ), - encoding="utf-8", - ) - elif not mutations: - SENSITIVITY_LOG_PATH.parent.mkdir(parents=True, exist_ok=True) - SENSITIVITY_LOG_PATH.write_text( - ( - "Sensitivity suite skipped. " - f"No sensitivity mutations fit max_world_size={max_world_size}.\n" - ), - encoding="utf-8", - ) - else: - with _redirect_output(SENSITIVITY_LOG_PATH): - sensitivity_reports = oracle_harness.run_sensitivity_suite( + live_training_log.write_text("", encoding="utf-8") + with _temporary_env(**{oracle_harness.ORACLE_OBJECTIVE_ENV: "all"}): + with _temporary_env(**{ORACLE_LIVE_TRAINING_LOG_ENV: str(live_training_log)}): + with _redirect_output(correctness_log): + suite_reports = oracle_harness.run_suite( case_config=case_config, - mutations=mutations, max_world_size=max_world_size, + cp_supported=cp_supported, + phase_pass_fns=correctness_phase_pass_fns, + use_fp32_lora_reference=correctness_use_fp32_lora_reference, + prune_reference_artifacts=skip_sensitivity or not mutations, + prune_case_artifacts=skip_sensitivity or not mutations, + ) + sensitivity_reports = [] + if skip_sensitivity: + sensitivity_log.write_text( + ( + "Sensitivity suite skipped. " + f"Set {SKIP_SENSITIVITY_ENV}=0 to re-enable workflow sensitivity.\n" + ), + encoding="utf-8", ) + elif not mutations: + sensitivity_log.write_text( + ( + "Sensitivity suite skipped. " + f"No sensitivity mutations fit max_world_size={max_world_size}.\n" + ), + encoding="utf-8", + ) + else: + with _redirect_output(sensitivity_log): + sensitivity_reports = oracle_harness.run_sensitivity_suite( + case_config=case_config, + mutations=mutations, + max_world_size=max_world_size, + ) case_artifacts = oracle_harness.ensure_case_artifacts(case_config) return ValidationStageResult( name="correctness_sensitivity", passed=True, metrics={ + "correctness_log_path": str(correctness_log), + "sensitivity_log_path": str(sensitivity_log), + "live_training_log_path": str(live_training_log), "requested_num_layers": case_config.num_layers, + "pipeline_layer_multiple": pipeline_layer_multiple, "precision": correctness_precision, "use_fp32_lora_reference": correctness_use_fp32_lora_reference, "is_moe": handler.is_moe, @@ -614,13 +817,14 @@ def run_correctness_sensitivity_stage( "allow_unvalidated_arch": allow_unvalidated_arch, "objectives": objectives, "sensitivity_mutations": mutations, + "inapplicable_sensitivity_mutations": (inapplicable_sensitivity_mutations), "excluded_sensitivity_mutations": excluded_sensitivity_mutations, "default_excluded_sensitivity_mutations": ( default_excluded_sensitivity_mutations ), "available_gpu_count": available_gpu_count, "max_world_size": max_world_size, - "required_gpu_count": oracle_world_size, + "required_gpu_count": required_gpu_count, "topology_artifacts_retained": oracle_harness.keep_topology_artifacts(), "correctness_variant_count": len(suite_reports), "correctness_excluded_topology_count": len(excluded_suite_topologies), @@ -676,13 +880,14 @@ def run_merged_vllm_serving_stage( allow_unvalidated_arch=allow_unvalidated_arch, ) handler = get_model_support_handler_for_spec(spec) - case_config = oracle_harness.OracleCaseConfig( + case_config = _oracle_case_config( + oracle_harness, base_model=base_model, + model_support_key=spec.key, is_moe=handler.is_moe, - precision="fp32", + precision=handler.correctness_precision(), num_layers=max(1, architecture.recommended_min_layers), - num_steps=1, - lora={"target_modules": list(spec.default_target_modules)}, + target_modules=list(spec.default_target_modules), allow_unvalidated_arch=allow_unvalidated_arch, ) report = merged_vllm_serving.run_merged_vllm_serving(case_config) @@ -733,6 +938,7 @@ def run_chat_template_rollout_stage( chat_template_rollout = _import_integration_module( "integration.megatron.model_support.chat_template_rollout" ) + chat_template_rollout._artifact_dir = lambda _base_model: _stage_artifact_dir() report = chat_template_rollout.run_chat_template_rollout(base_model=base_model) return ValidationStageResult( name="chat_template_rollout", @@ -754,6 +960,7 @@ def run_yes_no_trainability_stage( ) report = yes_no_trainability.run_yes_no_trainability( base_model=base_model, + artifact_root=_stage_artifact_dir(), allow_unvalidated_arch=allow_unvalidated_arch, ) passed = yes_no_trainability.yes_no_trainability_passed(report) @@ -775,10 +982,18 @@ def run_length_trainability_stage( length_trainability = _import_integration_module( "integration.megatron.trainability.test_live_length_trainability" ) - report = length_trainability.run_length_trainability( - base_model=base_model, - allow_unvalidated_arch=allow_unvalidated_arch, + length_trainability.LATEST_SUMMARY_LOG_PATH = ( + _workflow_stage_dir() / "length_trainability.log" ) + artifact_dir = _stage_artifact_dir() + length_trainability._artifact_dir = lambda _base_model: artifact_dir + try: + report = length_trainability.run_length_trainability( + base_model=base_model, + allow_unvalidated_arch=allow_unvalidated_arch, + ) + finally: + _cleanup_stage_workspace(artifact_dir / "megatron_dedicated_workspace") return ValidationStageResult( name="length_trainability", passed=length_trainability.length_trainability_passed(report), @@ -804,13 +1019,14 @@ def run_native_vllm_lora_stage( allow_unvalidated_arch=allow_unvalidated_arch, ) handler = get_model_support_handler_for_spec(spec) - case_config = oracle_harness.OracleCaseConfig( + case_config = _oracle_case_config( + oracle_harness, base_model=base_model, + model_support_key=spec.key, is_moe=handler.is_moe, - precision="fp32", + precision=handler.correctness_precision(), num_layers=max(1, architecture.recommended_min_layers), - num_steps=1, - lora={"target_modules": list(spec.default_target_modules)}, + target_modules=list(spec.default_target_modules), allow_unvalidated_arch=allow_unvalidated_arch, ) report = native_vllm_lora.run_native_vllm_lora(case_config) @@ -840,10 +1056,12 @@ def run_packing_invariance_stage( packing_invariance = _import_integration_module( "integration.megatron.model_support.packing_invariance" ) + packing_invariance._artifact_dir = lambda _base_model: _stage_artifact_dir() report = packing_invariance.run_packing_invariance( base_model=base_model, num_layers=max(1, architecture.recommended_min_layers), allow_unvalidated_arch=allow_unvalidated_arch, + in_process=True, ) metrics = report.model_dump(mode="json") passed = bool(metrics["scenarios"]) and all( @@ -858,6 +1076,21 @@ def run_packing_invariance_stage( ) +def run_e2e_throughput_stage( + *, + base_model: str, + architecture: ArchitectureReport, + allow_unvalidated_arch: bool = False, +) -> ValidationStageResult: + from .workflow_throughput import run_e2e_throughput + + return run_e2e_throughput( + base_model=base_model, + architecture=architecture, + allow_unvalidated_arch=allow_unvalidated_arch, + ) + + def build_validation_report( *, base_model: str, @@ -883,6 +1116,18 @@ def build_validation_report( ), allow_unvalidated_arch=allow_unvalidated_arch, ) + skip_stages = skip_stages or set() + selected_subprocess_stages = { + stage.name + for stage in report.stages + if stage.name in SUBPROCESS_VALIDATION_STAGES + and stage.name not in skip_stages + and (only_stage_run_set is None or stage.name in only_stage_run_set) + } + run_dir = _new_workflow_run_dir( + output_json=output_json, + model_key=report.model_key, + ) stage_runners = { "hf_parity": run_hf_parity_stage, "lora_coverage": run_lora_coverage_stage, @@ -892,35 +1137,48 @@ def build_validation_report( "chat_template_rollout": run_chat_template_rollout_stage, "packing_invariance": run_packing_invariance_stage, "length_trainability": run_length_trainability_stage, + "e2e_throughput": run_e2e_throughput_stage, YES_NO_TRAINABILITY_STAGE: run_yes_no_trainability_stage, NATIVE_VLLM_LORA_STAGE: run_native_vllm_lora_stage, } - env = {} + env = {WORKFLOW_RUN_DIR_ENV: str(run_dir)} if include_sensitivity is not None: env[SKIP_SENSITIVITY_ENV] = "0" if include_sensitivity else "1" - if include_sensitivity: - env[KEEP_TOPOLOGY_ARTIFACTS_ENV] = "1" - skip_stages = skip_stages or set() architecture: ArchitectureReport | None = None - context = _temporary_env(**env) if env else nullcontext() - with context: + fixture: WorkflowFixture | None = None + fixture_error: Exception | None = None + fixture_attempted = False + with _temporary_env(**env): for stage in report.stages: + stage_started = time.monotonic() if only_stage_run_set is not None and stage.name not in only_stage_run_set: - stage.passed = True + stage.passed = False + stage.skipped = True stage.metrics = { "skipped": True, "reason": f"--only-stage={only_stage}", } + _record_stage_duration(stage, started=stage_started) _write_validation_report(report, output_json) continue if stage.name in skip_stages: - stage.passed = True + stage.passed = False + stage.skipped = True stage.metrics = {"skipped": True, "reason": "--skip-stage"} + _record_stage_duration(stage, started=stage_started) _write_validation_report(report, output_json) + if stage.name == "architecture_discovery": + _mark_remaining_stages_skipped( + report, + after_stage_name=stage.name, + reason="architecture_discovery was skipped", + ) + break continue if stage.name == "dependency_resolution": stage.passed = True stage.metrics = dict(report.dependency_versions) + _record_stage_duration(stage, started=stage_started) _write_validation_report(report, output_json) continue if stage.name == "architecture_discovery": @@ -941,9 +1199,21 @@ def build_validation_report( except Exception as exc: stage.passed = False stage.metrics = _stage_error_metrics(exc) + _record_stage_duration(stage, started=stage_started) _write_validation_report(report, output_json) + if architecture is None: + _mark_remaining_stages_skipped( + report, + after_stage_name=stage.name, + reason="architecture_discovery failed", + ) + break if stop_on_failure and not stage.passed: _mark_remaining_stages_skipped(report, after_stage_name=stage.name) + _finalize_validation_report( + report, + partial=only_stage is not None or bool(skip_stages), + ) _write_validation_report(report, output_json) break continue @@ -953,12 +1223,38 @@ def build_validation_report( ) stage_runner = stage_runners[stage.name] if stage.name in SUBPROCESS_VALIDATION_STAGES: - stage_result = _run_stage_in_subprocess( - stage_name=stage.name, - base_model=base_model, - architecture=architecture, - allow_unvalidated_arch=allow_unvalidated_arch, - ) + fixture_provisioning_s: float | None = None + if not fixture_attempted: + fixture_started = time.monotonic() + fixture_attempted = True + try: + fixture = ensure_workflow_fixture( + base_model, + allow_unvalidated_arch=allow_unvalidated_arch, + required_stages=selected_subprocess_stages, + ) + except Exception as exc: + fixture_error = exc + fixture_provisioning_s = time.monotonic() - fixture_started + if fixture_error is not None: + stage_result = ValidationStageResult( + name=stage.name, + passed=False, + metrics=_stage_error_metrics(fixture_error), + ) + else: + assert fixture is not None + with _temporary_env(**fixture.environment(stage.name)): + stage_result = _run_stage_in_subprocess( + stage_name=stage.name, + base_model=base_model, + architecture=architecture, + allow_unvalidated_arch=allow_unvalidated_arch, + ) + if fixture_provisioning_s is not None: + stage_result.metrics["fixture_provisioning_s"] = ( + fixture_provisioning_s + ) else: try: stage_result = stage_runner( @@ -975,11 +1271,29 @@ def build_validation_report( stage.passed = stage_result.passed stage.metrics = dict(stage_result.metrics) stage.artifact_dir = stage_result.artifact_dir + if stage.name in _RUNTIME_CLEANUP_STAGES: + try: + stage.metrics.update(_prune_runtime_artifacts(run_dir / stage.name)) + except Exception as exc: + stage.passed = False + stage.metrics["runtime_artifact_cleanup_error"] = ( + f"{type(exc).__name__}: {exc}" + ) + _record_stage_duration(stage, started=stage_started) _write_validation_report(report, output_json) if stop_on_failure and not stage.passed: _mark_remaining_stages_skipped(report, after_stage_name=stage.name) + _finalize_validation_report( + report, + partial=only_stage is not None or bool(skip_stages), + ) _write_validation_report(report, output_json) break + _finalize_validation_report( + report, + partial=only_stage is not None or bool(skip_stages), + ) + _write_validation_report(report, output_json) return report @@ -994,8 +1308,9 @@ def build_all_architectures_validation_report( allow_unvalidated_arch: bool = False, ) -> AllArchitecturesValidationReport: aggregate = AllArchitecturesValidationReport() + representatives = validated_architecture_representative_models() _write_all_architectures_report(aggregate, output_json) - for base_model in validated_architecture_representative_models(): + for base_model in representatives: model_key = get_model_support_spec( base_model, allow_unvalidated_arch=allow_unvalidated_arch, @@ -1016,11 +1331,13 @@ def build_all_architectures_validation_report( ) aggregate.reports.append(report) aggregate.passed = all( - all(stage.passed for stage in model_report.stages) - for model_report in aggregate.reports + model_report.passed for model_report in aggregate.reports + ) + aggregate.complete = len(aggregate.reports) == len(representatives) and all( + model_report.complete for model_report in aggregate.reports ) _write_all_architectures_report(aggregate, output_json) - if stop_on_failure and not all(stage.passed for stage in report.stages): + if stop_on_failure and not report.passed: break return aggregate @@ -1046,7 +1363,7 @@ def _parse_args(argv: list[str] | None = None) -> argparse.Namespace: def _print_stage_result(stage: ValidationStageResult, *, indent: str = "") -> None: - status = "PASS" if stage.passed else "FAIL" + status = "SKIP" if stage.skipped else "PASS" if stage.passed else "FAIL" print(f"{indent}{stage.name}: {status}", flush=True) child_indent = f"{indent} " if stage.artifact_dir: @@ -1055,7 +1372,7 @@ def _print_stage_result(stage: ValidationStageResult, *, indent: str = "") -> No if isinstance(summary, list): for line in summary: print(f"{child_indent}{line}", flush=True) - if not stage.passed: + if not stage.passed and not stage.skipped: print(f"{child_indent}metrics={stage.metrics}", flush=True) @@ -1090,7 +1407,7 @@ def main(argv: list[str] | None = None) -> int: for stage in report.stages: _print_stage_result(stage) print(f"report_json={args.output_json}", flush=True) - return 0 if all(stage.passed for stage in report.stages) else 1 + return 0 if report.passed else 1 def assess_minimal_layer_coverage( diff --git a/tests/integration/megatron/model_support/workflow_fixtures.py b/tests/integration/megatron/model_support/workflow_fixtures.py new file mode 100644 index 000000000..76c8da6cb --- /dev/null +++ b/tests/integration/megatron/model_support/workflow_fixtures.py @@ -0,0 +1,862 @@ +from __future__ import annotations + +from collections.abc import Mapping +import fcntl +import gc +import hashlib +import json +import os +from pathlib import Path +import shutil +import tempfile +from typing import Any, cast + +from pydantic import BaseModel, ConfigDict + +FIXTURE_PATH_ENV = "ART_MODEL_SUPPORT_FIXTURE_PATH" +FIXTURE_CACHE_ENV = "ART_MODEL_SUPPORT_FIXTURE_CACHE" +FIXTURE_ROOT_ENV = "ART_MODEL_SUPPORT_FIXTURE_ROOT" +FIXTURE_VERSION = 18 +_CANONICAL_CACHE_VERSION = 16 +_ROOT = Path("/tmp/art-models/main-merge-oracle") +_CACHE_ROOT = Path("/tmp/art-model-support-workflow/hf-cache") +_TOKENIZER_FIXTURE_ROOT = Path("/tmp/art-model-support-workflow/tokenizer-compatible") +_TOKENIZER_CACHE_ROOT = Path("/tmp/art-model-support-workflow/tokenizer-hf-cache") +_CANONICAL_CACHE_ROOT = Path("/tmp/art-model-support-workflow/canonical-hf-cache") +_GEMMA_CANONICAL_WEIGHT_STAGES = frozenset({"hf_parity", "packing_invariance"}) +_PRETRAINED_WEIGHT_STAGES = frozenset({"length_trainability", "yes_no_trainability"}) +_GEMMA_YES_NO_ENV = { + "ART_MODEL_SUPPORT_YES_NO_ALLOWED_TOKEN_IDS": "4443,951,7463", + "ART_MODEL_SUPPORT_YES_NO_MAX_TOKENS": "1", +} +_REDUCED_TRAINABILITY_ENV: dict[str, dict[str, dict[str, str]]] = { + "gemma4_dense": {"yes_no_trainability": _GEMMA_YES_NO_ENV}, + "gemma4_moe": {"yes_no_trainability": _GEMMA_YES_NO_ENV}, + "glm52": { + "length_trainability": { + "ART_MODEL_SUPPORT_LENGTH_ALLOWED_TOKEN_IDS": "154820,38069", + "ART_MODEL_SUPPORT_LENGTH_MIN_TOKENS": "2", + "ART_MODEL_SUPPORT_LENGTH_FREQUENCY_PENALTY": "0.5", + }, + "yes_no_trainability": { + "ART_MODEL_SUPPORT_YES_NO_ALLOWED_TOKEN_IDS": "9829,902,36569", + "ART_MODEL_SUPPORT_YES_NO_MAX_STEPS": "8", + "ART_MODEL_SUPPORT_YES_NO_MAX_TOKENS": "1", + }, + }, +} +_TOKENIZER_COMPATIBLE_STAGES = frozenset( + { + "train_inf_mismatch", + "merged_vllm_serving", + "native_vllm_lora", + } +) +_TRAIN_INF_CANONICAL_WEIGHT_MODELS = frozenset({"dsv4", "gpt_oss_moe"}) +_TOKENIZER_FIXTURE_VERSION = 3 +_REVISIONS = { + "meta-llama/Llama-3.2-1B-Instruct": "9213176726f574b556790deb65791e0c5aa438b6", + "Qwen/Qwen3-32B": "9216db5781bf21249d130ec9da846c4624c16137", + "Qwen/Qwen3-30B-A3B": "ad44e777bcd18fa416d9da3bd8f70d33ebb85d39", + "Qwen/Qwen3.5-27B": "fc05daec18b0a78c049392ed2e771dde82bdf654", + "Qwen/Qwen3.5-35B-A3B": "59d61f3ce65a6d9863b86d2e96597125219dc754", + "google/gemma-4-31B-it": "842da3794eaa0b77d5f08bae87a17459d91ff475", + "google/gemma-4-26B-A4B-it": "4d7ae4984b7db7de8f8457170b3f1a419ee76d52", + "deepseek-ai/DeepSeek-V4-Flash": "60d8d70770c6776ff598c94bb586a859a38244f1", + "zai-org/GLM-5.2": "b4734de4facf877f85769a911abafc5283eab3d9", + "openai/gpt-oss-20b": "6cee5e81ee83917806bbde320786a8fb61efebee", +} +_MULTIMODAL = {"qwen3_5_dense", "qwen3_5_moe", "gemma4_dense", "gemma4_moe"} + + +class WorkflowFixture(BaseModel): + model_config = ConfigDict(frozen=True) + + canonical_model: str + model_key: str + source_revision: str + path: str + hf_home: str + manifest: dict[str, object] + tokenizer_compatible_path: str | None = None + tokenizer_compatible_hf_home: str | None = None + tokenizer_compatible_manifest: dict[str, object] | None = None + canonical_path: str | None = None + canonical_hf_home: str | None = None + + def environment(self, stage_name: str | None = None) -> dict[str, str]: + reduced_trainability = _REDUCED_TRAINABILITY_ENV.get(self.model_key, {}).get( + stage_name + ) + use_canonical = ( + (stage_name in _PRETRAINED_WEIGHT_STAGES and reduced_trainability is None) + or ( + self.model_key.startswith("gemma4_") + and stage_name in _GEMMA_CANONICAL_WEIGHT_STAGES + ) + or ( + self.model_key in _TRAIN_INF_CANONICAL_WEIGHT_MODELS + and stage_name == "train_inf_mismatch" + ) + ) + use_tokenizer_compatible = stage_name in _TOKENIZER_COMPATIBLE_STAGES or ( + self.model_key.startswith("gemma4_") and reduced_trainability is not None + ) + path = ( + self.canonical_path + if use_canonical + else self.tokenizer_compatible_path + if use_tokenizer_compatible + else self.path + ) + hf_home = ( + self.canonical_hf_home + if use_canonical + else self.tokenizer_compatible_hf_home + if use_tokenizer_compatible + else self.hf_home + ) + if path is None or hf_home is None: + contract = "canonical weights" if use_canonical else "canonical vocabulary" + raise RuntimeError(f"{self.model_key} {stage_name} requires {contract}") + hub = str(Path(hf_home) / "hub") + environment = { + FIXTURE_PATH_ENV: path, + FIXTURE_CACHE_ENV: hf_home, + "ART_ORACLE_BASE_MODEL": path, + "HF_HOME": hf_home, + "HF_HUB_CACHE": hub, + "HUGGINGFACE_HUB_CACHE": hub, + "HF_HUB_OFFLINE": "1", + "TRANSFORMERS_OFFLINE": "1", + } + if reduced_trainability is not None: + environment.update(reduced_trainability) + return environment + + +def _set(config: Any, **values: Any) -> Any: + for name, value in values.items(): + setattr(config, name, value) + return config + + +def _text(config: Any) -> Any: + return getattr(config, "text_config", config) + + +def _common( + config: Any, + *, + layers: int, + hidden: int, + vocab_size: int, + preserve_token_ids: bool, +) -> Any: + text = _text(config) + for name in ("layer_types", "mlp_layer_types", "indexer_types"): + if (values := getattr(text, name, None)) is not None: + setattr(text, name, list(values[:layers])) + values = { + "hidden_size": hidden, + "num_hidden_layers": layers, + "vocab_size": vocab_size, + } + if not preserve_token_ids: + values.update(pad_token_id=0, bos_token_id=2, eos_token_id=1) + return _set( + text, + **values, + ) + + +# fmt: off +_DENSE_TEXT = { + "intermediate_size": 512, "num_attention_heads": 8, + "num_key_value_heads": 2, "head_dim": 32, + "tie_word_embeddings": False, +} +_PLAIN_TEXT: dict[str, tuple[int, int, dict[str, Any]]] = { + "llama3_dense": (4, 256, _DENSE_TEXT), + "qwen3_dense": (4, 256, _DENSE_TEXT), + "qwen3_moe": ( + 4, + 256, + { + **_DENSE_TEXT, "moe_intermediate_size": 256, + "num_experts": 4, "num_local_experts": 4, + "num_experts_per_tok": 2, "quantization_config": None, + }, + ), + "glm52": ( + 12, + 512, + { + "intermediate_size": 1024, "moe_intermediate_size": 256, + "layer_types": ["deepseek_sparse_attention"] * 12, + "mlp_layer_types": ["dense"] * 3 + ["sparse"] * 9, + "indexer_types": ["full"] * 3 + + ["shared", "shared", "shared", "full"] + + ["shared", "shared", "shared", "full", "shared"], + "num_attention_heads": 64, "num_key_value_heads": 64, + "q_lora_rank": 512, "qk_head_dim": 256, + "qk_nope_head_dim": 192, "qk_rope_head_dim": 64, + "v_head_dim": 256, "index_n_heads": 32, "index_topk": 128, + "n_routed_experts": 4, "num_experts": 4, "num_local_experts": 4, + "num_experts_per_tok": 2, "num_nextn_predict_layers": 0, + "tie_word_embeddings": False, "quantization_config": None, + }, + ), + "gpt_oss_moe": ( + 4, + 320, + { + "intermediate_size": 768, + "layer_types": ["sliding_attention", "full_attention"] * 2, + "head_dim": 64, "num_attention_heads": 4, "num_key_value_heads": 1, + "num_experts": 4, "num_local_experts": 4, + "num_experts_per_tok": 2, "experts_per_token": 2, + "initial_context_length": 2048, "sliding_window": 128, + "tie_word_embeddings": False, "quantization_config": None, + }, + ), +} +_QWEN35_TEXT = { + "layer_types": (["linear_attention"] * 3 + ["full_attention"]) * 2, + "intermediate_size": 512, "head_dim": 256, + "num_attention_heads": 4, "num_key_value_heads": 1, + "full_attention_interval": 4, "linear_conv_kernel_dim": 4, + "linear_key_head_dim": 128, "linear_num_key_heads": 4, + "linear_num_value_heads": 8, "linear_value_head_dim": 128, + "tie_word_embeddings": False, +} +_QWEN35_VISION = { + "depth": 1, "num_hidden_layers": 1, + "hidden_size": 128, "intermediate_size": 256, + "num_heads": 4, "num_attention_heads": 4, + "num_position_embeddings": 16, "out_hidden_size": 1024, + "deepstack_visual_indexes": [], +} +_GEMMA_TEXT = { + "layer_types": (["sliding_attention"] * 5 + ["full_attention"]) * 2, + "intermediate_size": 512, "head_dim": 256, "global_head_dim": 512, + "num_attention_heads": 4, "num_key_value_heads": 2, + "num_global_key_value_heads": 1, "num_kv_shared_layers": 0, + "sliding_window": 1024, + "hidden_size_per_layer_input": 0, + "tie_word_embeddings": True, +} +_GEMMA_VISION = { + "depth": 1, "num_hidden_layers": 1, + "hidden_size": 128, "intermediate_size": 256, + "head_dim": 32, "global_head_dim": 32, + "num_attention_heads": 4, "num_key_value_heads": 4, + "patch_size": 16, "position_embedding_size": 64, +} +_MULTIMODAL_SHAPES = { + "qwen3_5": ( + 8, + _QWEN35_TEXT, + _QWEN35_VISION, + { + "moe_intermediate_size": 256, "shared_expert_intermediate_size": 256, + "num_experts": 4, "num_local_experts": 4, "num_experts_per_tok": 2, + }, + { + "image_token_id": 2, "video_token_id": 3, + "vision_start_token_id": 4, "vision_end_token_id": 5, + }, + ), + "gemma4": ( + 12, + _GEMMA_TEXT, + _GEMMA_VISION, + { + "moe_intermediate_size": 256, "num_experts": 4, + "num_local_experts": 4, "top_k_experts": 2, "num_experts_per_tok": 2, + }, + {"image_token_id": 2, "pad_token_id": 0, "bos_token_id": 2, "eos_token_id": 1}, + ), +} +# fmt: on + + +def _configure( + model_key: str, + config: Any, + *, + source_vocab_size: int, + tokenizer_compatible: bool, +) -> Any: + common = { + "vocab_size": source_vocab_size if tokenizer_compatible else 8192, + "preserve_token_ids": tokenizer_compatible, + } + if model_key in _PLAIN_TEXT: + layers, hidden, values = _PLAIN_TEXT[model_key] + text = _set(_common(config, layers=layers, hidden=hidden, **common), **values) + if model_key == "glm52": + text.vocab_size = source_vocab_size + return config + family = model_key.rsplit("_", 1)[0] + if family in _MULTIMODAL_SHAPES: + moe = model_key.endswith("_moe") + layers, text_shape, vision_shape, moe_shape, token_ids = _MULTIMODAL_SHAPES[ + family + ] + text = _set(_common(config, layers=layers, hidden=1024, **common), **text_shape) + top_level = {"tie_word_embeddings": True} if family == "gemma4" else {} + if family == "gemma4": + _set( + text, + enable_moe_block=moe, + vocab_size_per_layer_input=common["vocab_size"], + ) + if moe: + _set(text, **moe_shape) + _set(config.vision_config, **vision_shape) + if not tokenizer_compatible: + top_level.update(token_ids) + return _set(config, **top_level) + if model_key == "dsv4": + return _set( + config, + num_hidden_layers=4, + compress_ratios=[0, 0, 4, 128], + layer_types=[ + "sliding_attention", + "sliding_attention", + "compressed_sparse_attention", + "heavily_compressed_attention", + ], + mlp_layer_types=["moe"] * 4, + ) + raise KeyError(f"No correctness fixture for {model_key}") + + +def _pack_qwen35_experts(path: Path, config: Any) -> None: + from safetensors.torch import load_file, save_file + import torch + + checkpoint = path / "model.safetensors" + tensors = load_file(checkpoint) + text = _text(config) + for layer in range(text.num_hidden_layers): + prefix = f"model.language_model.layers.{layer}.mlp.experts" + gate_up, down = [], [] + for expert in range(text.num_experts): + expert_prefix = f"{prefix}.{expert}" + gate_up.append( + torch.cat( + ( + tensors.pop(f"{expert_prefix}.gate_proj.weight"), + tensors.pop(f"{expert_prefix}.up_proj.weight"), + ) + ) + ) + down.append(tensors.pop(f"{expert_prefix}.down_proj.weight")) + tensors[f"{prefix}.gate_up_proj"] = torch.stack(gate_up) + tensors[f"{prefix}.down_proj"] = torch.stack(down) + save_file(tensors, checkpoint, metadata={"format": "pt"}) + + +def _sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as source: + for chunk in iter(lambda: source.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def _fixture_files(path: Path) -> dict[str, str]: + return { + file.relative_to(path).as_posix(): _sha256(file) + for file in sorted(path.rglob("*")) + if file.is_file() and file.name != "fixture_manifest.json" + } + + +def _checkpoint_is_complete(path: Path) -> bool: + try: + if any( + not file.is_file() or file.stat().st_size == 0 + for file in (path / "config.json", path / "tokenizer_config.json") + ): + return False + index_path = path / "model.safetensors.index.json" + if not index_path.is_file(): + checkpoint = path / "model.safetensors" + return checkpoint.is_file() and checkpoint.stat().st_size > 0 + weight_map = json.loads(index_path.read_text())["weight_map"] + shards = set(weight_map.values()) + return bool(shards) and all( + isinstance(name, str) + and Path(name).name == name + and (path / name).is_file() + and (path / name).stat().st_size > 0 + for name in shards + ) + except (KeyError, OSError, TypeError, json.JSONDecodeError): + return False + + +def _fixture_namespace( + *, + canonical_model: str, + revision: str, + model_key: str, + version: int, + tokenizer_compatible: bool, +) -> str: + return hashlib.sha256( + json.dumps( + { + "model": canonical_model, + "revision": revision, + "handler": model_key, + "version": version, + "tokenizer_compatible": tokenizer_compatible, + }, + sort_keys=True, + ).encode() + ).hexdigest()[:16] + + +def _is_current( + path: Path, + *, + canonical_model: str, + model_key: str, + revision: str, + tokenizer_compatible: bool, + parent_manifest_sha256: str | None, +) -> bool: + try: + manifest = json.loads((path / "fixture_manifest.json").read_text()) + except (OSError, json.JSONDecodeError): + return False + expected = { + "version": ( + _TOKENIZER_FIXTURE_VERSION if tokenizer_compatible else FIXTURE_VERSION + ), + "source_model": canonical_model, + "source_revision": revision, + "handler": model_key, + "seed": 0, + "source_identity": {"model": canonical_model, "revision": revision}, + "parent_manifest_sha256": parent_manifest_sha256, + } + if tokenizer_compatible: + expected["vocabulary_contract"] = "canonical" + return ( + _checkpoint_is_complete(path) + and all(manifest.get(key) == value for key, value in expected.items()) + and manifest.get("files") == _fixture_files(path) + ) + + +def _build( + *, + canonical_model: str, + model_key: str, + revision: str, + output: Path, + tokenizer_compatible: bool, + source_fixture: Path | None = None, +) -> None: + from safetensors.torch import load_file, save_file + import torch + from transformers import ( + AutoConfig, + AutoImageProcessor, + AutoModelForCausalLM, + AutoModelForImageTextToText, + AutoProcessor, + AutoTokenizer, + ) + + with tempfile.TemporaryDirectory(prefix=f".{model_key}-", dir=output.parent) as tmp: + staging = Path(tmp) / model_key + staging.mkdir() + source_model = ( + source_fixture / "production_config" + if source_fixture is not None + else canonical_model + ) + source_kwargs = ( + {"local_files_only": True} + if source_fixture is not None + else {"revision": revision} + ) + source = AutoConfig.from_pretrained( + source_model, trust_remote_code=True, **source_kwargs + ) + source.save_pretrained(staging / "production_config") + tokenizer = cast( + Any, + AutoTokenizer.from_pretrained( + source_fixture or canonical_model, + trust_remote_code=True, + **source_kwargs, + ), + ) + source_vocab_size = int(_text(source).vocab_size) + tokenizer_max_id = max(map(int, tokenizer.get_vocab().values())) + if tokenizer_max_id >= source_vocab_size: + raise RuntimeError( + f"{model_key} tokenizer ID {tokenizer_max_id} exceeds canonical " + f"vocab_size={source_vocab_size}" + ) + config = _configure( + model_key, + source, + source_vocab_size=source_vocab_size, + tokenizer_compatible=tokenizer_compatible, + ) + config.save_pretrained(staging) + tokenizer.save_pretrained(staging) + if model_key in _MULTIMODAL: + AutoProcessor.from_pretrained( + source_fixture or canonical_model, + trust_remote_code=True, + **source_kwargs, + ).save_pretrained(staging) + if model_key.startswith("gemma4_"): + AutoImageProcessor.from_pretrained( + source_fixture or canonical_model, + trust_remote_code=True, + **source_kwargs, + ).save_pretrained(staging) + parameters = 0 + if model_key == "dsv4": + save_file( + {"_art_fixture_dummy": torch.zeros(1)}, staging / "model.safetensors" + ) + else: + auto = ( + AutoModelForImageTextToText + if model_key in _MULTIMODAL + else AutoModelForCausalLM + ) + with torch.random.fork_rng(devices=[]): + torch.manual_seed(0) + model = auto.from_config(config, trust_remote_code=True).to( + torch.bfloat16 + ) + if model_key.startswith("gemma4_"): + layers = model.model.language_model.layers + residual_scale = (2 * len(layers)) ** -0.5 + with torch.no_grad(): + for layer in layers: + layer.post_attention_layernorm.weight.fill_(residual_scale) + layer.post_feedforward_layernorm.weight.fill_(residual_scale) + parameters = sum(parameter.numel() for parameter in model.parameters()) + model.save_pretrained( + staging, safe_serialization=True, max_shard_size="2GB" + ) + del model + gc.collect() + if model_key == "qwen3_5_moe": + _pack_qwen35_experts(staging, config) + if model_key.startswith("gemma4_"): + checkpoint = staging / "model.safetensors" + weight_map = dict.fromkeys(load_file(checkpoint), checkpoint.name) + (staging / "model.safetensors.index.json").write_text( + json.dumps({"metadata": {}, "weight_map": weight_map}, indent=2) + + "\n" + ) + parent_manifest_sha256 = ( + _sha256(source_fixture / "fixture_manifest.json") + if source_fixture is not None + else None + ) + manifest = { + "version": ( + _TOKENIZER_FIXTURE_VERSION if tokenizer_compatible else FIXTURE_VERSION + ), + "source_model": canonical_model, + "source_revision": revision, + "source_identity": {"model": canonical_model, "revision": revision}, + "parent_manifest_sha256": parent_manifest_sha256, + "handler": model_key, + "parameters": parameters, + "num_layers": int(_text(config).num_hidden_layers), + "dtype": "bfloat16" if model_key != "dsv4" else None, + "seed": 0, + "vocabulary_contract": ( + "canonical" if tokenizer_compatible else "compact_8192" + ), + "config_vocab_size": int(_text(config).vocab_size), + "tokenizer_size": len(tokenizer), + "tokenizer_max_id": tokenizer_max_id, + } + if tokenizer_compatible: + _validate_tokenizer_compatible_fixture(staging, manifest) + manifest["files"] = _fixture_files(staging) + (staging / "fixture_manifest.json").write_text( + json.dumps(manifest, indent=2) + "\n" + ) + previous = output.with_name(f".{output.name}.previous") + if previous.exists(): + shutil.rmtree(previous) + if output.exists(): + os.replace(output, previous) + try: + os.replace(staging, output) + except BaseException: + if previous.exists(): + os.replace(previous, output) + raise + if previous.exists(): + shutil.rmtree(previous) + + +def _cache_alias( + *, + canonical_model: str, + model_key: str, + revision: str, + fixture: Path, + root: Path, + version: int, + namespace: str, +) -> Path: + hf_home = root / f"v{version}" / model_key / namespace + repo = hf_home / "hub" / f"models--{canonical_model.replace('/', '--')}" + snapshot = repo / "snapshots" / revision + (repo / "refs").mkdir(parents=True, exist_ok=True) + if snapshot.exists() and not snapshot.is_symlink(): + raise RuntimeError(f"fixture cache alias is not a symlink: {snapshot}") + if snapshot.is_symlink() and snapshot.resolve() != fixture.resolve(): + snapshot.unlink() + if not snapshot.exists(): + snapshot.parent.mkdir(parents=True, exist_ok=True) + snapshot.symlink_to(fixture, target_is_directory=True) + if not snapshot.is_symlink() or snapshot.resolve() != fixture.resolve(): + raise RuntimeError( + f"fixture cache alias does not identify {fixture}: {snapshot}" + ) + (repo / "refs" / "main").write_text(revision) + return hf_home + + +def _flatten_token_ids(value: Any) -> list[int]: + if isinstance(value, Mapping): + value = value["input_ids"] + if hasattr(value, "tolist"): + value = value.tolist() + if value and isinstance(value[0], list): + value = value[0] + return [int(token_id) for token_id in value] + + +def _validate_tokenizer_compatible_fixture( + fixture: Path, manifest: dict[str, object] +) -> None: + from transformers import AutoTokenizer + + tokenizer = cast(Any, AutoTokenizer.from_pretrained(fixture, local_files_only=True)) + vocab_size_value = manifest["config_vocab_size"] + if not isinstance(vocab_size_value, int): + raise RuntimeError( + f"fixture config_vocab_size is not an integer: {vocab_size_value!r}" + ) + vocab_size = vocab_size_value + registered_max_id = max(map(int, tokenizer.get_vocab().values())) + if registered_max_id >= vocab_size: + raise RuntimeError( + f"registered tokenizer ID {registered_max_id} exceeds " + f"vocab_size={vocab_size}" + ) + samples = ( + "Return one token.", + "Explain how distributed training preserves policy-version provenance.", + "Unicode tokenizer check: cafe Tokyo resume.", + ) + encoded: list[int] = [] + for sample in samples: + encoded.extend(_flatten_token_ids(tokenizer(sample, add_special_tokens=True))) + if getattr(tokenizer, "chat_template", None): + for sample in samples: + encoded.extend( + _flatten_token_ids( + tokenizer.apply_chat_template( + [{"role": "user", "content": sample}], + tokenize=True, + add_generation_prompt=True, + ) + ) + ) + max_encoded_id = max(encoded) + if max_encoded_id >= vocab_size: + raise RuntimeError( + f"representative tokenizer ID {max_encoded_id} exceeds vocab_size={vocab_size}" + ) + manifest["representative_max_token_id"] = max_encoded_id + manifest["tokenizer_max_id"] = registered_max_id + + +def _canonical_snapshot( + *, canonical_model: str, model_key: str, revision: str +) -> tuple[Path, Path]: + from huggingface_hub import snapshot_download + + hf_home = _CANONICAL_CACHE_ROOT / f"v{_CANONICAL_CACHE_VERSION}" / model_key + snapshot = snapshot_download( + repo_id=canonical_model, + revision=revision, + cache_dir=hf_home / "hub", + ) + repo = hf_home / "hub" / f"models--{canonical_model.replace('/', '--')}" + (repo / "refs").mkdir(parents=True, exist_ok=True) + (repo / "refs" / "main").write_text(revision) + return Path(snapshot), hf_home + + +def _ensure_cached_fixture( + *, + canonical_model: str, + model_key: str, + revision: str, + root: Path, + cache_root: Path, + version: int, + tokenizer_compatible: bool, + source_fixture: Path | None = None, +) -> tuple[Path, dict[str, object], Path]: + namespace = _fixture_namespace( + canonical_model=canonical_model, + revision=revision, + model_key=model_key, + version=version, + tokenizer_compatible=tokenizer_compatible, + ) + model_root = root / model_key + model_root.mkdir(parents=True, exist_ok=True) + output = model_root / namespace + parent_manifest_sha256 = ( + _sha256(source_fixture / "fixture_manifest.json") + if source_fixture is not None + else None + ) + with (model_root / f".{namespace}.lock").open("w") as lock: + fcntl.flock(lock, fcntl.LOCK_EX) + if not _is_current( + output, + canonical_model=canonical_model, + model_key=model_key, + revision=revision, + tokenizer_compatible=tokenizer_compatible, + parent_manifest_sha256=parent_manifest_sha256, + ): + _build( + canonical_model=canonical_model, + model_key=model_key, + revision=revision, + output=output, + tokenizer_compatible=tokenizer_compatible, + source_fixture=source_fixture, + ) + manifest = cast( + dict[str, object], + json.loads((output / "fixture_manifest.json").read_text()), + ) + if tokenizer_compatible: + _validate_tokenizer_compatible_fixture(output, manifest) + hf_home = _cache_alias( + canonical_model=canonical_model, + model_key=model_key, + revision=revision, + fixture=output, + root=cache_root, + version=version, + namespace=namespace, + ) + return output, manifest, hf_home + + +def ensure_workflow_fixture( + base_model: str, + *, + allow_unvalidated_arch: bool = False, + required_stages: set[str] | frozenset[str] = frozenset(), +) -> WorkflowFixture: + from art.megatron.model_support.registry import get_model_support_spec + + model_key = get_model_support_spec( + base_model, allow_unvalidated_arch=allow_unvalidated_arch + ).key + try: + revision = _REVISIONS[base_model] + except KeyError: + raise ValueError( + "workflow fixtures require an exact pinned representative model; " + f"unrecognized model {base_model!r} for handler {model_key!r}" + ) from None + root = Path(os.environ.get(FIXTURE_ROOT_ENV, str(_ROOT))) + output, manifest, hf_home = _ensure_cached_fixture( + canonical_model=base_model, + model_key=model_key, + revision=revision, + root=root, + cache_root=Path(os.environ.get(FIXTURE_CACHE_ENV, str(_CACHE_ROOT))), + version=FIXTURE_VERSION, + tokenizer_compatible=False, + ) + tokenizer_path: Path | None = None + tokenizer_hf_home: Path | None = None + tokenizer_manifest: dict[str, object] | None = None + if required_stages & _TOKENIZER_COMPATIBLE_STAGES: + tokenizer_path, tokenizer_manifest, tokenizer_hf_home = _ensure_cached_fixture( + canonical_model=base_model, + model_key=model_key, + revision=revision, + root=_TOKENIZER_FIXTURE_ROOT / f"v{_TOKENIZER_FIXTURE_VERSION}", + cache_root=_TOKENIZER_CACHE_ROOT, + version=_TOKENIZER_FIXTURE_VERSION, + tokenizer_compatible=True, + source_fixture=output, + ) + canonical_path: Path | None = None + canonical_hf_home: Path | None = None + reduced_trainability_stages = _REDUCED_TRAINABILITY_ENV.get(model_key, {}) + canonical_required = ( + any( + stage in _PRETRAINED_WEIGHT_STAGES + and stage not in reduced_trainability_stages + for stage in required_stages + ) + or ( + model_key.startswith("gemma4_") + and bool(required_stages & _GEMMA_CANONICAL_WEIGHT_STAGES) + ) + or ( + model_key in _TRAIN_INF_CANONICAL_WEIGHT_MODELS + and "train_inf_mismatch" in required_stages + ) + ) + if canonical_required: + canonical_path, canonical_hf_home = _canonical_snapshot( + canonical_model=base_model, + model_key=model_key, + revision=revision, + ) + return WorkflowFixture( + canonical_model=base_model, + model_key=model_key, + source_revision=revision, + path=str(output), + hf_home=str(hf_home), + manifest=manifest, + tokenizer_compatible_path=( + str(tokenizer_path) if tokenizer_path is not None else None + ), + tokenizer_compatible_hf_home=( + str(tokenizer_hf_home) if tokenizer_hf_home is not None else None + ), + tokenizer_compatible_manifest=tokenizer_manifest, + canonical_path=str(canonical_path) if canonical_path is not None else None, + canonical_hf_home=( + str(canonical_hf_home) if canonical_hf_home is not None else None + ), + ) diff --git a/tests/integration/megatron/model_support/workflow_resources.py b/tests/integration/megatron/model_support/workflow_resources.py index d9a210919..394ad988a 100644 --- a/tests/integration/megatron/model_support/workflow_resources.py +++ b/tests/integration/megatron/model_support/workflow_resources.py @@ -2,10 +2,80 @@ from typing import Literal -from pydantic import BaseModel, ConfigDict, Field +from pydantic import BaseModel, ConfigDict, Field, model_validator -_H200_REFERENCE_VRAM_GIB = 140.0 +_H200_REFERENCE_VRAM_GIB = 130.0 _H200_SLOT_TOLERANCE = 0.05 +THROUGHPUT_PACKED_SEQUENCE_LENGTH = 131_072 +THROUGHPUT_RANDOM_INITIALIZATION_VERSION = "deterministic_random_v1" +THROUGHPUT_RANDOM_SEED = 3407 + + +class ThroughputThresholds(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + calibration_basis: Literal["measured", "estimated"] + calibration_fingerprint: str | None = Field(default=None, pattern=r"^[0-9a-f]{64}$") + min_isolated_train_tok_s: float = Field(gt=0.0, allow_inf_nan=False) + min_e2e_train_tok_s: float = Field(gt=0.0, allow_inf_nan=False) + min_accepted_train_tok_s: float = Field(gt=0.0, allow_inf_nan=False) + min_e2e_to_isolated_ratio: float = Field(gt=0.0, le=1.0, allow_inf_nan=False) + min_matched_core_to_isolated_ratio: float = Field( + gt=0.0, le=1.0, allow_inf_nan=False + ) + max_matched_core_to_isolated_ratio: float = Field( + default=1.05, gt=1.0, allow_inf_nan=False + ) + max_mean_policy_activation_lag_s: float = Field(gt=0.0, le=3.5, allow_inf_nan=False) + max_policy_activation_lag_s: float = Field(gt=0.0, le=3.5, allow_inf_nan=False) + max_repeated_policy_activation_interval_s: float = Field( + gt=0.0, allow_inf_nan=False + ) + + @model_validator(mode="after") + def validate_calibration_identity(self) -> "ThroughputThresholds": + measured = self.calibration_basis == "measured" + if measured != (self.calibration_fingerprint is not None): + raise ValueError( + "measured calibration requires a fingerprint and estimated " + "calibration must not claim one" + ) + if self.max_mean_policy_activation_lag_s > self.max_policy_activation_lag_s: + raise ValueError("mean activation lag limit cannot exceed absolute limit") + return self + + +class ThroughputWorkflowConfig(BaseModel): + model_config = ConfigDict(extra="forbid", frozen=True) + + num_layers: int = Field(ge=2) + prompt_tokens: int = Field(default=3839, ge=1) + completion_tokens: int = Field(default=64, ge=1) + rollouts_per_group: int = Field(default=4, ge=2) + groups_per_step: int = Field(default=32, ge=2) + initial_model_calls_per_inference_gpu: int = Field(default=32, ge=1) + max_num_seqs: int = Field(default=64, ge=1) + max_num_batched_tokens: int = Field(default=65_536, ge=1) + enable_prefix_caching: bool = False + max_steps: int = Field(default=31, ge=11) + max_steps_off_policy: int = Field(default=4, ge=0) + packed_sequence_length: Literal[131072] = THROUGHPUT_PACKED_SEQUENCE_LENGTH + min_vllm_pressure: float = Field(default=0.5, ge=0.0, allow_inf_nan=False) + max_trainer_underfeed: float = Field(default=0.08, ge=0.0, allow_inf_nan=False) + random_initialization_version: Literal["deterministic_random_v1"] = ( + THROUGHPUT_RANDOM_INITIALIZATION_VERSION + ) + random_seed: int = Field(default=THROUGHPUT_RANDOM_SEED, ge=0, le=2**31 - 1) + thresholds: dict[Literal["h200", "b300"], ThroughputThresholds] = Field( + default_factory=dict + ) + + @model_validator(mode="after") + def require_measured_b300_calibration(self) -> "ThroughputWorkflowConfig": + b300 = self.thresholds.get("b300") + if b300 is not None and b300.calibration_basis != "measured": + raise ValueError("B300 throughput thresholds must be measured") + return self class MegatronWorkflowTopology(BaseModel): @@ -74,6 +144,7 @@ class WorkflowStageResources(BaseModel): model_config = ConfigDict(frozen=True) required_world_size: int + required_physical_gpus: int | None = None required_h200_equivalent_gpus: int | None = None allow_gpu_overlap: bool = False requires_external_vllm: bool = False @@ -83,6 +154,7 @@ class WorkflowStageResources(BaseModel): high_vram_vllm: VllmWorkflowResources | None = None streaming_weight_offload: bool = False megatron_env: dict[str, str] = Field(default_factory=dict) + throughput: ThroughputWorkflowConfig | None = None class HandlerWorkflowResources(BaseModel): @@ -93,6 +165,7 @@ class HandlerWorkflowResources(BaseModel): native_vllm_lora: WorkflowStageResources | None = None yes_no_trainability: WorkflowStageResources | None = None length_trainability: WorkflowStageResources | None = None + e2e_throughput: WorkflowStageResources | None = None yes_no_trainability_variant: ( Literal[ "megatron_shared", @@ -138,15 +211,24 @@ class HandlerWorkflowResources(BaseModel): "compressed_sparse_attention", "heavily_compressed_attention", ] -_DSV4_REPRESENTATIVE_MLP_LAYER_TYPES = ["hash_moe", "hash_moe", "hash_moe", "moe"] +_DSV4_REPRESENTATIVE_MLP_LAYER_TYPES = ["moe"] * 4 _DSV4_MEGATRON_ENV = { "ART_DSV4_VALIDATION_NUM_LAYERS": str(_DSV4_REPRESENTATIVE_NUM_LAYERS) } _DSV4_HF_OVERRIDES = { "num_hidden_layers": _DSV4_REPRESENTATIVE_NUM_LAYERS, + "num_hash_layers": 0, + # Keep DSV4's required FP8 linear path, but avoid the public checkpoint's + # MXFP4 experts, which cannot represent the reduced BF16 trainer fixture. + "expert_dtype": "fp8", "compress_ratios": _DSV4_REPRESENTATIVE_COMPRESS_RATIOS, "layer_types": _DSV4_REPRESENTATIVE_LAYER_TYPES, "mlp_layer_types": _DSV4_REPRESENTATIVE_MLP_LAYER_TYPES, + "rope_parameters": { + "partial_rotary_factor": 0.125, + "rope_theta": 10000, + "rope_type": "default", + }, } _DSV4_COMMON_VLLM_ENGINE_ARGS = { "compilation_config": { @@ -157,15 +239,16 @@ class HandlerWorkflowResources(BaseModel): "enforce_eager": True, "gpu_memory_utilization": 0.82, "kv_cache_dtype": "fp8", + "max_model_len": 1024, "max_num_batched_tokens": 1032, } _DSV4_MERGED_VLLM_ENGINE_ARGS = { **_DSV4_COMMON_VLLM_ENGINE_ARGS, - "moe_backend": "triton_unfused", + "moe_backend": "triton", } _DSV4_LORA_VLLM_ENGINE_ARGS = { **_DSV4_COMMON_VLLM_ENGINE_ARGS, - "moe_backend": "triton_unfused", + "moe_backend": "triton", } _DSV4_REDUCED_VLLM_ENGINE_ARGS = { **_DSV4_MERGED_VLLM_ENGINE_ARGS, @@ -183,6 +266,10 @@ class HandlerWorkflowResources(BaseModel): gpu_ids=[0, 1, 2, 3, 4, 5, 6, 7], topology=_DSV4_TP2_EP8, ) +_DSV4_FOUR_GPU_MEGATRON = MegatronWorkflowResources( + gpu_ids=[0, 1, 2, 3], + topology=_DSV4_TP2_EP4, +) _DSV4_HIGH_VRAM_MEGATRON = MegatronWorkflowResources( gpu_ids=[0, 1], topology=_DSV4_TP2_EP2, @@ -220,6 +307,47 @@ class HandlerWorkflowResources(BaseModel): hf_overrides=_DSV4_HF_OVERRIDES, extra_engine_args=_DSV4_NATIVE_LORA_VLLM_ENGINE_ARGS, ) +_GLM52_REDUCED_MEGATRON = MegatronWorkflowResources( + gpu_ids=[0], + topology=MegatronWorkflowTopology(), +) +_GLM52_REDUCED_VLLM = VllmWorkflowResources( + gpu_ids=[1], + tensor_parallel_size=1, + # The reduced fixture is narrower than the production model. FlashMLA covers + # its sparse attention shape while Triton avoids absent SM100 E=4 MoE tuning. + extra_engine_args={ + "attention_backend": "FLASHMLA_SPARSE", + "max_model_len": 1024, + "moe_backend": "triton", + }, +) +_GPT_OSS_REDUCED_MEGATRON = MegatronWorkflowResources( + gpu_ids=[0], + topology=MegatronWorkflowTopology(), +) +_GPT_OSS_REDUCED_VLLM = VllmWorkflowResources( + gpu_ids=[1], + tensor_parallel_size=1, + extra_engine_args={ + "enforce_eager": True, + "load_format": "dummy", + "max_model_len": 1024, + }, +) +_QWEN_MOE_REDUCED_MEGATRON = MegatronWorkflowResources( + gpu_ids=[0], + topology=MegatronWorkflowTopology(), +) +_QWEN_MOE_REDUCED_VLLM = VllmWorkflowResources( + gpu_ids=[1], + tensor_parallel_size=1, + extra_engine_args={ + "enforce_eager": True, + "max_model_len": 1024, + "moe_backend": "triton", + }, +) # Explicitly for large models which do not fit in the default topology. HANDLER_WORKFLOW_RESOURCES: dict[str, HandlerWorkflowResources] = { @@ -230,14 +358,14 @@ class HandlerWorkflowResources(BaseModel): requires_external_vllm=True, megatron=_DSV4_MEGATRON, vllm=_DSV4_FULL_VLLM_EP4, - high_vram_megatron=_DSV4_HIGH_VRAM_MEGATRON, + high_vram_megatron=_DSV4_FOUR_GPU_MEGATRON, high_vram_vllm=_DSV4_FULL_VLLM_EP2, streaming_weight_offload=True, ), merged_vllm_serving=WorkflowStageResources( required_world_size=8, required_h200_equivalent_gpus=8, - megatron=_DSV4_MEGATRON, + megatron=_DSV4_FOUR_GPU_MEGATRON, vllm=_DSV4_REDUCED_VLLM_EP4, high_vram_megatron=_DSV4_HIGH_VRAM_MEGATRON, high_vram_vllm=_DSV4_REDUCED_VLLM_EP2, @@ -253,7 +381,7 @@ class HandlerWorkflowResources(BaseModel): requires_external_vllm=True, megatron=_DSV4_MEGATRON, vllm=_DSV4_FULL_VLLM_EP4, - high_vram_megatron=_DSV4_HIGH_VRAM_MEGATRON, + high_vram_megatron=_DSV4_FOUR_GPU_MEGATRON, high_vram_vllm=_DSV4_FULL_VLLM_EP2, streaming_weight_offload=True, ), @@ -263,15 +391,327 @@ class HandlerWorkflowResources(BaseModel): requires_external_vllm=True, megatron=_DSV4_MEGATRON, vllm=_DSV4_FULL_VLLM_EP4, - high_vram_megatron=_DSV4_HIGH_VRAM_MEGATRON, + high_vram_megatron=_DSV4_FOUR_GPU_MEGATRON, high_vram_vllm=_DSV4_FULL_VLLM_EP2, streaming_weight_offload=True, ), yes_no_trainability_variant="megatron_dedicated", ), + "glm52": HandlerWorkflowResources( + train_inf_mismatch=WorkflowStageResources( + required_world_size=2, + megatron=_GLM52_REDUCED_MEGATRON, + vllm=_GLM52_REDUCED_VLLM, + ), + merged_vllm_serving=WorkflowStageResources( + required_world_size=2, + megatron=_GLM52_REDUCED_MEGATRON, + vllm=_GLM52_REDUCED_VLLM, + ), + native_vllm_lora=WorkflowStageResources( + required_world_size=2, + vllm=_GLM52_REDUCED_VLLM, + ), + yes_no_trainability=WorkflowStageResources( + required_world_size=2, + megatron=_GLM52_REDUCED_MEGATRON, + vllm=_GLM52_REDUCED_VLLM, + ), + length_trainability=WorkflowStageResources( + required_world_size=2, + megatron=_GLM52_REDUCED_MEGATRON, + vllm=_GLM52_REDUCED_VLLM, + ), + yes_no_trainability_variant="megatron_dedicated", + ), + "gpt_oss_moe": HandlerWorkflowResources( + train_inf_mismatch=WorkflowStageResources( + required_world_size=3, + required_physical_gpus=3, + megatron=MegatronWorkflowResources( + gpu_ids=[0, 1], + topology=MegatronWorkflowTopology(cp=2, ep=2), + ), + vllm=VllmWorkflowResources( + gpu_ids=[2], + tensor_parallel_size=1, + ), + ), + merged_vllm_serving=WorkflowStageResources( + required_world_size=2, + megatron=_GPT_OSS_REDUCED_MEGATRON, + vllm=_GPT_OSS_REDUCED_VLLM, + ), + native_vllm_lora=WorkflowStageResources( + required_world_size=2, + vllm=_GPT_OSS_REDUCED_VLLM, + ), + ), + **{ + handler_key: HandlerWorkflowResources( + merged_vllm_serving=WorkflowStageResources( + required_world_size=2, + megatron=_QWEN_MOE_REDUCED_MEGATRON, + vllm=_QWEN_MOE_REDUCED_VLLM, + ), + native_vllm_lora=WorkflowStageResources( + required_world_size=2, + vllm=_QWEN_MOE_REDUCED_VLLM, + ), + ) + for handler_key in ("qwen3_moe", "qwen3_5_moe") + }, +} + +_THROUGHPUT_CONFIGS = { + "llama3_dense": ThroughputWorkflowConfig( + num_layers=16, + prompt_tokens=3922, + completion_tokens=256, + rollouts_per_group=6, + groups_per_step=24, + initial_model_calls_per_inference_gpu=20, + max_steps=35, + ), + "qwen3_dense": ThroughputWorkflowConfig( + num_layers=8, + completion_tokens=144, + rollouts_per_group=8, + groups_per_step=25, + initial_model_calls_per_inference_gpu=10, + max_steps=35, + ), + "qwen3_moe": ThroughputWorkflowConfig( + num_layers=16, + prompt_tokens=3884, + completion_tokens=48, + rollouts_per_group=5, + groups_per_step=27, + initial_model_calls_per_inference_gpu=20, + max_steps=35, + ), + "qwen3_5_dense": ThroughputWorkflowConfig( + num_layers=8, + prompt_tokens=3839, + completion_tokens=64, + groups_per_step=31, + initial_model_calls_per_inference_gpu=12, + max_steps=35, + enable_prefix_caching=True, + ), + "qwen3_5_moe": ThroughputWorkflowConfig( + num_layers=24, + prompt_tokens=7600, + completion_tokens=16, + groups_per_step=17, + initial_model_calls_per_inference_gpu=12, + max_num_batched_tokens=THROUGHPUT_PACKED_SEQUENCE_LENGTH, + max_steps=35, + enable_prefix_caching=True, + ), + "gemma4_dense": ThroughputWorkflowConfig( + num_layers=12, + completion_tokens=75, + rollouts_per_group=7, + groups_per_step=30, + initial_model_calls_per_inference_gpu=11, + max_steps=35, + ), + "gemma4_moe": ThroughputWorkflowConfig( + num_layers=12, + prompt_tokens=3640, + completion_tokens=128, + groups_per_step=31, + initial_model_calls_per_inference_gpu=26, + max_steps=35, + ), + "dsv4": ThroughputWorkflowConfig( + num_layers=8, + prompt_tokens=12_800, + completion_tokens=736, + groups_per_step=8, + initial_model_calls_per_inference_gpu=20, + max_num_seqs=60, + max_num_batched_tokens=24_576, + ), + "glm52": ThroughputWorkflowConfig( + num_layers=12, + prompt_tokens=3836, + completion_tokens=1024, + groups_per_step=16, + initial_model_calls_per_inference_gpu=19, + ), + "gpt_oss_moe": ThroughputWorkflowConfig( + num_layers=4, + initial_model_calls_per_inference_gpu=21, + max_num_seqs=48, + max_steps=35, + ), +} + +# Floors are isolated tok/s, E2E tok/s, accepted tok/s, E2E/isolated, and +# maximum repeated policy-activation interval. B300 values are measured; H200 +# values are estimates from the prior H200 workflow and remain fingerprint-free. +_B300_THROUGHPUT_FLOORS = { + "llama3_dense": ( + "4931aacd6e2a08318aaeb6019eaff8c973fd4401c51ca073fca3a44360c88314", + (49_500, 47_300, 12_900, 0.90, 4.5), + ), + "qwen3_dense": ( + "abf0e339c86a7c574133acda5ff402d69b034432b57f57e9b04afe6b84efd865", + (40_200, 37_600, 8_600, 0.88, 4.5), + ), + "qwen3_moe": ( + "3a8e41c026bc9b8fbb8a1108f003d1bf646725603d5c6579a207de1fbc90c81e", + (49_900, 43_700, 2_050, 0.82, 4.5), + ), + "qwen3_5_dense": ( + "a7bb96316519ec930152dfbb4108f4ea6482da87413f4daa326b53c73a536f9c", + (64_800, 60_000, 3_750, 0.87, 3.5), + ), + "qwen3_5_moe": ( + "a04c0ea28418ec6d526b0686600e0a4cadaf36d7ff40cb1661e0b3b1679aca20", + (32_600, 30_800, 257, 0.89, 5.5), + ), + "gemma4_dense": ( + "05aab57334726fdea25c249eb521d89b02ac92c212451feecad7500677fef58c", + (23_100, 22_700, 2_390, 0.93, 7.0), + ), + "gemma4_moe": ( + "2db699a071ccbda911f0fca14b56fe063eb7ac2a7d8da5639391e602d15f94c1", + (40_300, 38_500, 4_740, 0.90, 5.0), + ), + "dsv4": ( + "1d2fd40a2ed4ccad93ebbdece213f935c8c06ed035c51e89dff3c9d1f1b9bcdd", + (7_050, 7_020, 1_350, 0.94, 43.0), + ), + "glm52": ( + "8f8e4ff249ad4efdcc6a6f75e9d3a92c6ce7b6d5dca89a4a3807c76848e52a07", + (14_880, 14_330, 5_730, 0.91, 12.0), + ), + "gpt_oss_moe": ( + "27efd9c6dabef7f8af7604ba9754ee57345ec39f5615b49fdfd550014708cf18", + (81_700, 76_400, 4_850, 0.88, 2.5), + ), +} +_H200_THROUGHPUT_FLOORS = { + "llama3_dense": (27_500, 25_900, 6_600, 0.89, 7.0), + "qwen3_dense": (24_100, 23_100, 5_000, 0.91, 7.0), + "qwen3_moe": (26_400, 20_900, 930, 0.74, 10.0), + "qwen3_5_dense": (26_500, 25_600, 1_500, 0.91, 5.5), + "qwen3_5_moe": (13_600, 12_900, 100, 0.90, 12.0), + "gemma4_dense": (10_600, 10_400, 1_000, 0.93, 13.0), + "gemma4_moe": (17_900, 17_300, 2_000, 0.91, 9.5), + "dsv4": (3_500, 3_400, 620, 0.94, 80.0), + "glm52": (9_400, 9_000, 3_400, 0.91, 19.5), + "gpt_oss_moe": (39_900, 37_100, 2_200, 0.88, 4.5), } +def _throughput_threshold( + calibration_basis: Literal["measured", "estimated"], + floor: tuple[float, float, float, float, float], + *, + calibration_fingerprint: str | None = None, + max_mean_policy_activation_lag_s: float = 1.5, +) -> ThroughputThresholds: + isolated, e2e, accepted, ratio, cadence = floor + return ThroughputThresholds( + calibration_basis=calibration_basis, + calibration_fingerprint=calibration_fingerprint, + min_isolated_train_tok_s=isolated, + min_e2e_train_tok_s=e2e, + min_accepted_train_tok_s=accepted, + min_e2e_to_isolated_ratio=ratio, + min_matched_core_to_isolated_ratio=0.95, + max_mean_policy_activation_lag_s=max_mean_policy_activation_lag_s, + max_policy_activation_lag_s=3.5, + max_repeated_policy_activation_interval_s=cadence, + ) + + +for _model_key, (_fingerprint, _b300_floor) in _B300_THROUGHPUT_FLOORS.items(): + _max_mean_activation_lag_s = 2.25 if _model_key == "dsv4" else 1.5 + _THROUGHPUT_CONFIGS[_model_key] = _THROUGHPUT_CONFIGS[_model_key].model_copy( + update={ + "thresholds": { + "b300": _throughput_threshold( + "measured", + _b300_floor, + calibration_fingerprint=_fingerprint, + max_mean_policy_activation_lag_s=_max_mean_activation_lag_s, + ), + "h200": _throughput_threshold( + "estimated", + _H200_THROUGHPUT_FLOORS[_model_key], + max_mean_policy_activation_lag_s=_max_mean_activation_lag_s, + ), + } + } + ) + +_DENSE_HANDLER_KEYS = { + "llama3_dense", + "qwen3_dense", + "qwen3_5_dense", + "gemma4_dense", +} + + +def _throughput_stage_resources(model_key: str) -> WorkflowStageResources: + config = _THROUGHPUT_CONFIGS[model_key] + is_moe = model_key not in _DENSE_HANDLER_KEYS + vllm_engine_args: dict[str, object] = { + "disable_custom_all_reduce": True, + "load_format": "dummy", + "gpu_memory_utilization": 0.82, + "max_model_len": 16_384, + "max_num_batched_tokens": config.max_num_batched_tokens, + "max_num_seqs": config.max_num_seqs, + "lora_dtype": "bfloat16", + } + if model_key in {"qwen3_moe", "qwen3_5_moe"}: + vllm_engine_args["compilation_config"] = { + "pass_config": {"fuse_allreduce_rms": False} + } + if config.enable_prefix_caching: + vllm_engine_args["enable_prefix_caching"] = True + if model_key == "dsv4": + vllm_engine_args.update( + compilation_config={ + "cudagraph_mode": "NONE", + "pass_config": {"fuse_allreduce_rms": False}, + }, + enforce_eager=True, + kv_cache_dtype="fp8", + ) + return WorkflowStageResources( + required_world_size=4, + required_physical_gpus=4, + megatron=MegatronWorkflowResources( + gpu_ids=[0, 1], + topology=MegatronWorkflowTopology( + cp=1 if model_key == "dsv4" else 2, + ep=2 if is_moe else 1, + ), + ), + vllm=VllmWorkflowResources( + gpu_ids=[2, 3], + tensor_parallel_size=2, + enable_expert_parallel=is_moe, + extra_engine_args=vllm_engine_args, + ), + throughput=config, + ) + + +for _model_key in _THROUGHPUT_CONFIGS: + _resources = HANDLER_WORKFLOW_RESOURCES.get(_model_key, HandlerWorkflowResources()) + HANDLER_WORKFLOW_RESOURCES[_model_key] = _resources.model_copy( + update={"e2e_throughput": _throughput_stage_resources(_model_key)} + ) + + def handler_workflow_resources_for_base_model( base_model: str, *, @@ -335,6 +775,13 @@ def resolve_stage_resources_for_visible_gpus( *, visible_gpu_count: int, ) -> WorkflowStageResources: + required_physical = stage_resources.required_physical_gpus + if required_physical is not None and visible_gpu_count < required_physical: + raise RuntimeError( + f"Need {required_physical} physical GPUs for {stage_name}, found " + f"{visible_gpu_count}; H200-equivalent capacity cannot coalesce " + "distinct workflow roles." + ) if visible_gpu_count >= stage_resources.required_world_size: return stage_resources required_equivalent = stage_resources.required_h200_equivalent_gpus @@ -394,20 +841,22 @@ def resolve_stage_resources_for_visible_gpus( return stage_resources.model_copy(update={"megatron": megatron, "vllm": vllm}) +def _current_visible_gpu_count() -> int: + try: + import torch + except ImportError: + return 0 + return int(torch.cuda.device_count()) + + def resolve_stage_resources_for_current_host( stage_name: str, stage_resources: WorkflowStageResources, ) -> WorkflowStageResources: - try: - import torch - except ImportError: - visible_gpu_count = 0 - else: - visible_gpu_count = int(torch.cuda.device_count()) return resolve_stage_resources_for_visible_gpus( stage_name, stage_resources, - visible_gpu_count=visible_gpu_count, + visible_gpu_count=_current_visible_gpu_count(), ) diff --git a/tests/integration/megatron/model_support/workflow_stage_worker.py b/tests/integration/megatron/model_support/workflow_stage_worker.py index a384bc1b9..8482e19ed 100644 --- a/tests/integration/megatron/model_support/workflow_stage_worker.py +++ b/tests/integration/megatron/model_support/workflow_stage_worker.py @@ -6,6 +6,7 @@ from .workflow import ( run_chat_template_rollout_stage, run_correctness_sensitivity_stage, + run_e2e_throughput_stage, run_hf_parity_stage, run_length_trainability_stage, run_lora_coverage_stage, @@ -25,6 +26,7 @@ "chat_template_rollout": run_chat_template_rollout_stage, "packing_invariance": run_packing_invariance_stage, "length_trainability": run_length_trainability_stage, + "e2e_throughput": run_e2e_throughput_stage, "yes_no_trainability": run_yes_no_trainability_stage, "native_vllm_lora": run_native_vllm_lora_stage, } diff --git a/tests/integration/megatron/model_support/workflow_throughput.py b/tests/integration/megatron/model_support/workflow_throughput.py new file mode 100644 index 000000000..7cd1d56b1 --- /dev/null +++ b/tests/integration/megatron/model_support/workflow_throughput.py @@ -0,0 +1,1842 @@ +from __future__ import annotations + +import asyncio +from collections.abc import AsyncIterator, Awaitable, Callable, Iterator, Mapping +from contextlib import contextmanager +import hashlib +import json +import math +from multiprocessing import resource_tracker, shared_memory +import os +from pathlib import Path +import shutil +from statistics import fmean, median, quantiles +import struct +import subprocess +import sys +from typing import Any, Literal, NamedTuple, cast +import uuid + +from art.megatron.model_support.registry import get_model_support_spec +from art.megatron.model_support.spec import ArchitectureReport + +from .validation_spec import ValidationStageResult +from .workflow_fixtures import ( + FIXTURE_PATH_ENV, + _flatten_token_ids, + _validate_tokenizer_compatible_fixture, +) +from .workflow_resources import ( + ThroughputThresholds, + ThroughputWorkflowConfig, + handler_workflow_resources_for_base_model, + resolve_stage_resources_for_visible_gpus, +) + +_STAGE_DIR_ENV = "ART_MODEL_SUPPORT_WORKFLOW_STAGE_DIR" +_LAYER_LIST_FIELDS = ( + "layer_types", + "mlp_layer_types", + "indexer_types", + "compress_ratios", +) +_WIDTH_TERMS = ("hidden", "intermediate", "head", "expert", "lora_rank", "topk") +_POLICY_AGE_MEAN = "offpolicy/token_weighted_policy_age_steps" +_POLICY_AGE_P95 = "offpolicy/token_weighted_policy_age_p95_steps" +_FRESHNESS_DISCOUNT = "sample_efficiency/freshness_discount" +_STALE_GROUPS = "discarded/step/stale_groups" +_ZERO_VARIANCE_GROUPS = "discarded/step/zero_variance_groups" +_MEASUREMENT_CONTRACT_VERSION = 12 +_ISOLATED_WARMUP_STEPS = 1 +# Average enough exact E2E/isolated pairs to absorb one transient GPU tail. +_MATCHED_MEASURED_STEPS = 6 +_CAPTURE_GUARD_STEPS = 1 +_PIPELINE_SETTING_NAMES = ( + "num_rollout_workers", + "min_batch_size", + "max_batch_size", + "queue_maxsize", + "target_groups_per_step", +) +_REPO_ROOT = Path(__file__).parents[4] +_MAIN_RUNTIME_PACKAGES = ( + "openpipe-art", + "torchmonarch", + "torch", + "triton", + "transformer-engine", + "megatron-core", + "megatron-bridge", + "transformers", + "flashinfer-python", + "nvidia-nccl-cu13", + "nvidia-nvshmem-cu13", +) +_VLLM_RUNTIME_PACKAGES = ( + "art-vllm-runtime", + "vllm", + "torch", + "triton", + "transformers", + "flashinfer-python", + "nvidia-nccl-cu13", +) +_LOCAL_SOURCE_PACKAGES = ("openpipe-art", "art-vllm-runtime") +_H200_THROUGHPUT_NUM_LAYERS = {"dsv4": 4, "glm52": 6} + + +class ThroughputFixture(NamedTuple): + model_key: str + path: str + num_layers: int + width_fingerprint: dict[str, int] + manifest: dict[str, Any] + + +class TrainerPhaseEvidence(NamedTuple): + phase: Literal["isolated", "e2e"] + runtime_fingerprint: str + trajectory_input_fingerprint: str + packed_input_fingerprint: str + workload_fingerprint: str + sample_count: int + policy_steps: tuple[int, ...] + train_s: float + metrics: tuple[dict[str, float], ...] + + @property + def train_tok_s(self) -> float: + return ( + sum( + metrics["data/step_nonpadding_logical_tokens"] + for metrics in self.metrics + ) + / self.train_s + ) + + +@contextmanager +def _freeze_pipeline_settings_from_step(trainer: Any, step: int) -> Iterator[None]: + apply = trainer.apply_pipeline_settings + + def apply_before_step(settings: Any) -> None: + # Keep the measured windows and matched captures on one actual setting while + # the tuner continues recording the decisions it would have applied. + if trainer.state.next_training_step < step: + apply(settings) + + setattr(trainer, "apply_pipeline_settings", apply_before_step) + try: + yield + finally: + setattr(trainer, "apply_pipeline_settings", apply) + + +def _current_pipeline_settings(trainer: Any) -> dict[str, int]: + return {name: int(getattr(trainer, name)) for name in _PIPELINE_SETTING_NAMES} + + +def _row_pipeline_settings(row: Mapping[str, Any], step: int) -> dict[str, int]: + return { + name: _nonnegative_integer( + row.get(f"pipeline_settings/{name}"), + name=f"step {step} pipeline setting {name}", + ) + for name in _PIPELINE_SETTING_NAMES + } + + +def _same_setting_decision_suffix( + decisions: list[Any], + by_step: Mapping[int, Mapping[str, Any]], +) -> list[Any]: + final = decisions[-1].stats + assert final is not None + _require( + final.end_step in by_step, + f"autotuner decision window lacks train row: {final.end_step}", + ) + expected = _row_pipeline_settings(by_step[final.end_step], final.end_step) + selected: list[Any] = [] + later: Any | None = None + for decision in reversed(decisions): + stats = decision.stats + assert stats is not None + if later is not None: + _require( + stats.end_step + 1 == later.start_step + and math.isclose( + float(stats.window_end_s), + float(later.window_start_s), + rel_tol=0.0, + abs_tol=1e-6, + ), + "autotuner windows are not contiguous", + ) + steps = range(stats.start_step, stats.end_step + 1) + missing = [step for step in steps if step not in by_step] + _require(not missing, f"autotuner decision window lacks train rows: {missing}") + if not all( + _row_pipeline_settings(by_step[step], step) == expected for step in steps + ): + break + selected.append(decision) + later = stats + selected.reverse() + _require( + len(selected) >= 2, + "throughput evidence requires two trailing same-setting autotuner windows", + ) + return selected + + +def _text(config: dict[str, Any]) -> dict[str, Any]: + return config.get("text_config", config) + + +def _width_fingerprint(config: dict[str, Any]) -> dict[str, int]: + text = _text(config) + return { + key: value + for key, value in text.items() + if key != "num_hidden_layers" + and type(value) is int + and any(term in key for term in _WIDTH_TERMS) + } + + +def _digest(value: Any) -> str: + return hashlib.sha256(json.dumps(value, sort_keys=True).encode()).hexdigest() + + +def _files_digest(paths: list[Path]) -> str: + digest = hashlib.sha256() + for path in sorted(paths): + if not path.is_file(): + raise RuntimeError(f"calibration provenance file is missing: {path}") + relative = path.relative_to(_REPO_ROOT).as_posix().encode() + payload = path.read_bytes() + digest.update(struct.pack(" dict[str, Any]: + script = """ +import hashlib +from importlib import metadata +import json +import platform +import sys +import torch + +def sha(value): + return hashlib.sha256(value.encode()).hexdigest() if value is not None else None + +distributions = {} +local_source_packages = set(json.loads(sys.argv[2])) +for name in json.loads(sys.argv[1]): + try: + dist = metadata.distribution(name) + except metadata.PackageNotFoundError: + continue + provenance = { + "version": dist.version, + "metadata_sha256": sha(dist.read_text("METADATA")), + } + if name not in local_source_packages: + provenance.update({ + "direct_url_sha256": sha(dist.read_text("direct_url.json")), + "record_sha256": sha(dist.read_text("RECORD")), + }) + distributions[name] = provenance +print(json.dumps({ + "python": { + "version": platform.python_version(), + "implementation": platform.python_implementation(), + "cache_tag": sys.implementation.cache_tag, + "abi_flags": sys.abiflags, + }, + "torch": { + "version": torch.__version__, + "cuda": torch.version.cuda, + "cxx11_abi": torch._C._GLIBCXX_USE_CXX11_ABI, + }, + "distributions": distributions, +}, sort_keys=True)) +""" + if not python.is_file(): + raise RuntimeError(f"calibration runtime Python is missing: {python}") + result = subprocess.run( + [ + str(python), + "-c", + script, + json.dumps(packages), + json.dumps(_LOCAL_SOURCE_PACKAGES), + ], + check=True, + capture_output=True, + text=True, + ) + return cast(dict[str, Any], json.loads(result.stdout)) + + +def _source_provenance() -> dict[str, Any]: + return { + "art_source_sha256": _files_digest( + list((_REPO_ROOT / "src/art").rglob("*.py")) + ), + "vllm_runtime_source_sha256": _files_digest( + list((_REPO_ROOT / "vllm_runtime/src/art_vllm_runtime").rglob("*.py")) + ), + "workflow_runtime_sha256": _files_digest( + [ + Path(__file__), + Path(__file__).with_name("workflow.py"), + Path(__file__).with_name("workflow_fixtures.py"), + Path(__file__).with_name("workflow_stage_worker.py"), + Path(__file__).with_name("validation_spec.py"), + ] + ), + "build_contract_sha256": _files_digest( + [ + _REPO_ROOT / "pyproject.toml", + _REPO_ROOT / "vllm_runtime/pyproject.toml", + _REPO_ROOT / "vllm_runtime/setup.sh", + ] + ), + "root_lock_sha256": _files_digest([_REPO_ROOT / "uv.lock"]), + "vllm_runtime_lock_sha256": _files_digest( + [_REPO_ROOT / "vllm_runtime/uv.lock"] + ), + "main_environment": _environment_provenance( + Path(sys.executable), _MAIN_RUNTIME_PACKAGES + ), + "vllm_environment": _environment_provenance( + _REPO_ROOT / "vllm_runtime/.venv/bin/python", _VLLM_RUNTIME_PACKAGES + ), + } + + +def _require(condition: bool, message: str) -> None: + if not condition: + raise RuntimeError(message) + + +def _nonnegative_integer(value: Any, *, name: str) -> int: + _require( + not isinstance(value, bool) + and isinstance(value, int | float) + and math.isfinite(float(value)) + and float(value).is_integer() + and value >= 0, + f"{name} must be a nonnegative integer, got {value!r}", + ) + return int(value) + + +_PHASE_WORKLOAD_KEYS = ( + "data/step_num_groups_trainable", + "data/step_packed_sequences", + "data/step_nonpadding_logical_tokens", + "data/step_loss_bearing_tokens", + "data/step_executed_token_equivalents", + "data/step_dummy_executed_token_equivalents", + "data/step_nominal_schedule_capacity_tokens", + "data/step_dummy_schedule_capacity_tokens", + "data/step_unused_packed_capacity_tokens", + "data/step_num_gradient_steps", + "pipeline/global_real_microbatches", + "pipeline/global_dummy_microbatches", +) + + +def _phase_evidence( + *, + phase: Literal["isolated", "e2e"], + runtime_fingerprint: str, + trajectory_input_fingerprint: str, + packed_input_fingerprint: str, + samples: list[tuple[Mapping[str, Any], int]], +) -> TrainerPhaseEvidence: + if not samples: + raise RuntimeError(f"{phase} trainer phase produced no samples") + numeric_samples = tuple( + { + key: float(value) + for key, value in metrics.items() + if isinstance(value, int | float) + } + for metrics, _ in samples + ) + workloads = [ + { + key: _nonnegative_integer(metrics.get(key), name=f"{phase} {key}") + for key in _PHASE_WORKLOAD_KEYS + } + for metrics in numeric_samples + ] + train_s = sum(metrics.get("time/step_train_s", 0.0) for metrics in numeric_samples) + if not math.isfinite(train_s) or train_s <= 0.0: + raise RuntimeError(f"{phase} trainer timing is invalid: train={train_s}") + workload_fingerprint = _digest(workloads) + return TrainerPhaseEvidence( + phase=phase, + runtime_fingerprint=runtime_fingerprint, + trajectory_input_fingerprint=trajectory_input_fingerprint, + packed_input_fingerprint=packed_input_fingerprint, + workload_fingerprint=workload_fingerprint, + sample_count=len(samples), + policy_steps=tuple(step for _, step in samples), + train_s=train_s, + metrics=numeric_samples, + ) + + +def _bundle_bytes(bundles: tuple[Any, ...]) -> bytes: + from msgspec import msgpack + + return msgpack.encode(tuple(bundle.model_dump(mode="python") for bundle in bundles)) + + +def _matched_input_fingerprints( + trajectory_fingerprints: list[str], packed_fingerprints: list[str] +) -> tuple[str, str]: + _require( + len(trajectory_fingerprints) + == len(packed_fingerprints) + == _MATCHED_MEASURED_STEPS, + "matched trainer phases require complete paired inputs", + ) + _require( + len(set(trajectory_fingerprints)) == _MATCHED_MEASURED_STEPS, + "matched E2E samples must use distinct trajectory inputs", + ) + return _digest(trajectory_fingerprints), _digest( + list(zip(trajectory_fingerprints, packed_fingerprints, strict=True)) + ) + + +async def _discard_prepared_pipeline_batch(backend: Any, groups: list[Any]) -> None: + for group in groups: + group._distributed_lease = None + await backend.discard_pipeline_batch(groups) + + +def _matched_capture_steps(max_steps: int) -> tuple[int, ...]: + first = max_steps + _CAPTURE_GUARD_STEPS + 1 + return tuple(range(first, first + _MATCHED_MEASURED_STEPS)) + + +def _packed_input_fingerprint(groups: list[Any]) -> str: + prepared = groups[0]._prepared_training_batch + if prepared is None or any( + group._prepared_training_batch is not prepared for group in groups + ): + raise RuntimeError("trainer groups do not share one prepared data-plane batch") + batch = prepared.batch + packed = batch.payload.packed + ref = packed.leases.ref + stable_ref = ref.model_dump( + mode="json", + exclude={ + "batch_id", + "owner_actor_id", + "lease_id", + "shared_memory_name", + "owner_process_id", + "group_ids", + "record_ids", + "min_source_version", + "max_source_version", + }, + ) + manifest = { + "packing_config": prepared.packing_config.model_dump(mode="json"), + "batch": batch.model_dump(mode="json", exclude={"payload"}), + "packed_ref": stable_ref, + } + digest = hashlib.sha256(json.dumps(manifest, sort_keys=True).encode()) + digest.update(b"packed_group_shapes:v1") + digest.update(struct.pack(" tuple[Any, ...]: + from msgspec import msgpack + + from art.distributed.trajectory_store import TrajectoryGroupBundle + + values = msgpack.decode(path.read_bytes()) + return tuple(TrajectoryGroupBundle.model_validate(value) for value in values) + + +async def _capture_training_bundles(groups: Any) -> tuple[Any, ...]: + from art.distributed.trajectory_store import TrajectoryGroupBundle + + prepared = groups[0]._prepared_training_batch + selections = ( + tuple(getattr(prepared.batch.payload, "selections", ())) + if prepared is not None + else () + ) + if len(selections) != len(groups): + raise RuntimeError("prepared throughput batch lacks exact queue selections") + materialized = await asyncio.gather( + *(selection.queue.materialize_selection(selection) for selection in selections) + ) + return await asyncio.to_thread( + lambda: tuple(TrajectoryGroupBundle.from_group(group) for group in materialized) + ) + + +def _collect_matched_packing_shapes(groups: Any) -> None: + for group in groups: + group._collect_packing_shape = True + + +def _reduced_config( + source: dict[str, Any], *, model_key: str, num_layers: int +) -> tuple[dict[str, Any], dict[str, Any]]: + reduced = json.loads(json.dumps(source)) + text = _text(reduced) + source_text = _text(source) + source_layers = int(source_text["num_hidden_layers"]) + if num_layers > source_layers: + raise ValueError( + f"requested {num_layers} layers from {source_layers}-layer model" + ) + text["num_hidden_layers"] = num_layers + for field in _LAYER_LIST_FIELDS: + if field not in source_text: + continue + values = source_text[field] + if len(values) < num_layers: + raise ValueError(f"{model_key} {field} has only {len(values)} entries") + text[field] = values[:num_layers] + source_width = _width_fingerprint(source) + if not source_width or source_width != _width_fingerprint(reduced): + raise ValueError("throughput fixture changed or lost production-width fields") + prefix = "text_config." if "text_config" in source else "" + return reduced, { + "source_num_layers": source_layers, + "changed_paths": [ + f"{prefix}{field}" + for field in ("num_hidden_layers", *_LAYER_LIST_FIELDS) + if field == "num_hidden_layers" or field in source_text + ], + "width_fingerprint": source_width, + } + + +def _copy_metadata(source: Path, target: Path) -> None: + excluded = {"config.json", "fixture_manifest.json", "model.safetensors.index.json"} + for path in source.iterdir(): + if ( + path.is_file() + and path.name not in excluded + and not path.name.endswith((".safetensors", ".bin", ".pt", ".pth")) + ): + shutil.copy2(path, target / path.name) + + +def _config_only_tensors(config: dict[str, Any], *, model_key: str) -> dict[str, Any]: + import torch + + tensors = {"_art_config_only": torch.zeros(1)} + if model_key != "gemma4_moe": + return tensors + text = _text(config) + for layer in range(int(text["num_hidden_layers"])): + for suffix in ( + "pre_feedforward_layernorm", + "pre_feedforward_layernorm_2", + ): + tensors[f"model.language_model.layers.{layer}.{suffix}.weight"] = ( + torch.ones(int(text["hidden_size"]), dtype=torch.bfloat16) + ) + return tensors + + +def ensure_throughput_fixture( + *, + canonical_model: str, + model_key: str, + correctness_fixture: Path, + num_layers: int, + initialization_version: str, + random_seed: int, + output: Path, +) -> ThroughputFixture: + source_config_path = correctness_fixture / "production_config" / "config.json" + if not source_config_path.is_file(): + raise RuntimeError( + f"correctness fixture lacks pinned production config: {source_config_path}" + ) + source = json.loads(source_config_path.read_text()) + reduced, reduction = _reduced_config( + source, model_key=model_key, num_layers=num_layers + ) + vocabulary_contract: dict[str, object] = { + "config_vocab_size": int(_text(reduced)["vocab_size"]) + } + _validate_tokenizer_compatible_fixture(correctness_fixture, vocabulary_contract) + manifest = { + "version": 1, + "canonical_model": canonical_model, + "model_key": model_key, + "num_layers": num_layers, + "source_config_sha256": _digest(source), + "reduced_config_sha256": _digest(reduced), + "initialization": initialization_version, + "random_seed": random_seed, + "vocabulary_contract": vocabulary_contract, + **reduction, + } + output.mkdir() + _copy_metadata(correctness_fixture, output) + (output / "config.json").write_text(json.dumps(reduced, indent=2) + "\n") + from safetensors.torch import save_file + + tensors = _config_only_tensors(reduced, model_key=model_key) + checkpoint = output / "model.safetensors" + save_file(tensors, checkpoint) + if model_key == "gemma4_moe": + (output / "model.safetensors.index.json").write_text( + json.dumps( + {"metadata": {}, "weight_map": dict.fromkeys(tensors, checkpoint.name)}, + indent=2, + ) + + "\n" + ) + (output / "throughput_fixture_manifest.json").write_text( + json.dumps(manifest, indent=2) + "\n" + ) + return ThroughputFixture( + model_key=model_key, + path=str(output), + num_layers=num_layers, + width_fingerprint=reduction["width_fingerprint"], + manifest=manifest, + ) + + +class PolicyActivationEvent(NamedTuple): + step: int + trainer_completed_monotonic_s: float + serving_active_monotonic_s: float + + @property + def lag_s(self) -> float: + return self.serving_active_monotonic_s - self.trainer_completed_monotonic_s + + +async def _activation_event(service: Any, step: int) -> PolicyActivationEvent: + await service.wait_for_serving(step) + completed, active = service.policy_activation_timing(step) + return PolicyActivationEvent(step, completed, active) + + +async def _cancel_activation_tasks( + tasks: Mapping[int, asyncio.Task[PolicyActivationEvent]], +) -> None: + for task in tasks.values(): + if not task.done(): + task.cancel() + await asyncio.gather(*tasks.values(), return_exceptions=True) + + +def _gpu_identities( + *, trainer_gpu_ids: list[int], inference_gpu_ids: list[int] +) -> list[dict[str, Any]]: + import torch + + from art.distributed.host_admission import _query_gpu_inventory + + roles = [ + *(("trainer", gpu_id) for gpu_id in trainer_gpu_ids), + *(("inference", gpu_id) for gpu_id in inference_gpu_ids), + ] + if (len(trainer_gpu_ids), len(inference_gpu_ids)) != (2, 2): + raise RuntimeError( + "throughput stage requires two trainer and two inference CUDA roles, " + f"got {roles}" + ) + + def uuid_key(value: str) -> str: + return value.casefold().removeprefix("gpu-").removeprefix("mig-") + + inventory = _query_gpu_inventory(include_mig=True) + by_uuid: dict[str, list[tuple[Any, str]]] = {} + for gpu, driver in inventory: + by_uuid.setdefault(uuid_key(gpu.uuid), []).append((gpu, driver)) + identities = [] + for role, logical_index in roles: + properties = torch.cuda.get_device_properties(logical_index) + cuda_uuid = str(getattr(properties, "uuid", "")) + matches = by_uuid.get(uuid_key(cuda_uuid), []) + if len(matches) != 1: + raise RuntimeError( + "could not map CUDA-visible GPU to one physical identity: " + f"logical={logical_index}, uuid={cuda_uuid!r}, matches={len(matches)}" + ) + gpu, driver = matches[0] + identities.append( + { + "role": role, + "logical_index": logical_index, + "uuid": gpu.uuid, + "parent_uuid": gpu.parent_uuid, + "pci_bus_id": gpu.pci_bus_id, + "name": properties.name, + "total_memory_bytes": properties.total_memory, + "compute_capability": [properties.major, properties.minor], + "driver_version": driver, + } + ) + physical_uuids = { + str(identity["uuid"]).casefold() + for identity in identities + if identity["uuid"] == identity["parent_uuid"] + and not str(identity["uuid"]).startswith("MIG-") + } + if len(physical_uuids) != 4: + raise RuntimeError( + "throughput stage requires four unique non-MIG physical GPU UUIDs, " + f"got {[identity['uuid'] for identity in identities]}" + ) + return identities + + +def _hardware(gpu_identities: list[dict[str, Any]]) -> Literal["h200", "b300"]: + names = {str(identity["name"]).upper() for identity in gpu_identities} + if len(names) != 1: + raise RuntimeError( + f"throughput stage requires homogeneous GPUs, got {sorted(names)}" + ) + name = next(iter(names)) + if "B300" in name or "GB300" in name: + return "b300" + if "H200" in name: + return "h200" + raise RuntimeError(f"throughput thresholds are unavailable for {name}") + + +def _throughput_config_for_hardware( + model_key: str, + config: ThroughputWorkflowConfig, + hardware: Literal["h200", "b300"], +) -> ThroughputWorkflowConfig: + num_layers = _H200_THROUGHPUT_NUM_LAYERS.get(model_key) + if hardware != "h200" or num_layers is None: + return config + return config.model_copy(update={"num_layers": num_layers}) + + +def _stable_gpu_identity(identity: Mapping[str, Any]) -> dict[str, Any]: + return { + "name": identity["name"], + "total_memory_bytes": identity["total_memory_bytes"], + "compute_capability": identity["compute_capability"], + "driver_version": identity["driver_version"], + } + + +def _calibration_contract( + *, + base_model: str, + fixture: ThroughputFixture, + stage: Any, + config: ThroughputWorkflowConfig, + autotune: Any, + actual_prompt_tokens: int, + gpu_identities: list[dict[str, Any]], +) -> dict[str, Any]: + manifest = fixture.manifest + _require( + all( + manifest.get(key) + for key in ("source_config_sha256", "reduced_config_sha256") + ) + and manifest.get("width_fingerprint") == fixture.width_fingerprint, + "throughput fixture lacks source/reduced hashes or production width", + ) + workload = config.model_dump( + mode="json", + exclude={"thresholds", "random_initialization_version", "random_seed"}, + ) + role_counts = { + role: sum(identity["role"] == role for identity in gpu_identities) + for role in ("trainer", "inference") + } + accelerator_specs = { + json.dumps(_stable_gpu_identity(identity), sort_keys=True) + for identity in gpu_identities + } + _require( + len(accelerator_specs) == 1, + "throughput stage requires one homogeneous accelerator specification", + ) + return { + "measurement_contract_version": _MEASUREMENT_CONTRACT_VERSION, + "source_provenance": _source_provenance(), + "fixture_manifest": manifest, + "model_identity": {"base_model": base_model, "model_key": fixture.model_key}, + "topology": stage.megatron.topology.model_dump(mode="json"), + "hardware": { + "role_counts": role_counts, + "class": _hardware(gpu_identities), + "accelerator": json.loads(accelerator_specs.pop()), + }, + "engine_args": { + **stage.vllm.engine_args(), + "seed": config.random_seed, + "model": f"fixture-sha256:{manifest['reduced_config_sha256']}", + }, + "autotuner_config": autotune.model_dump(mode="json"), + "workload_config": {**workload, "actual_prompt_tokens": actual_prompt_tokens}, + "random_initialization": { + "version": config.random_initialization_version, + "seed": config.random_seed, + }, + "trainer_config": { + "learning_rate": 1e-6, + "loss_fn": "cispo", + "eval_fn": None, + "eval_every_n_steps": 0, + "eval_at_start": False, + "save_checkpoint": False, + "resume": False, + "score_reference_groups_per_step": config.groups_per_step, + "score_reference_rollouts_per_group": config.rollouts_per_group, + "max_steps_off_policy": config.max_steps_off_policy, + "isolated_warmup_steps": _ISOLATED_WARMUP_STEPS, + "matched_measured_steps": _MATCHED_MEASURED_STEPS, + }, + "packed_sequence_length": config.packed_sequence_length, + "prompt_tokens": actual_prompt_tokens, + "completion_tokens": config.completion_tokens, + "rollouts_per_group": config.rollouts_per_group, + "groups_per_step": config.groups_per_step, + } + + +def _chat_token_count(tokenizer: Any, prompt: str) -> int: + return len( + _flatten_token_ids( + tokenizer.apply_chat_template( + [{"role": "user", "content": prompt}], + tokenize=True, + add_generation_prompt=True, + ) + ) + ) + + +def _sized_prompt(tokenizer: Any, *, target_tokens: int) -> str: + prefix = "Throughput scenario 00000000. Process the following neutral record.\n" + unit = " measured context item" + lower, upper = 0, target_tokens + while lower < upper: + middle = (lower + upper + 1) // 2 + candidate = prefix + unit * middle + if _chat_token_count(tokenizer, candidate) <= target_tokens: + lower = middle + else: + upper = middle - 1 + prompt = prefix + unit * lower + actual = _chat_token_count(tokenizer, prompt) + if actual < target_tokens - 64: + raise RuntimeError( + f"could not size throughput prompt near {target_tokens} tokens: {actual}" + ) + return prompt + + +async def _scenarios(prompt: str) -> AsyncIterator[dict[str, str]]: + index = 0 + while True: + scenario_id = f"throughput-{index:08d}" + yield { + "scenario_id": scenario_id, + "prompt": prompt.replace("00000000", f"{index:08d}", 1), + } + index += 1 + + +def _training_rows(model_output_dir: Path) -> list[dict[str, Any]]: + history_path = model_output_dir / "history.jsonl" + if not history_path.is_file(): + raise RuntimeError(f"throughput history is missing: {history_path}") + rows = [json.loads(line) for line in history_path.read_text().splitlines() if line] + return [row for row in rows if "data/step_nonpadding_logical_tokens" in row] + + +_COUNT_METRICS = { + "original_trajectory_tokens": "train/prefix_tree/logical_tokens", + "nonpadding_logical_tokens": "data/step_nonpadding_logical_tokens", + "loss_bearing_tokens": "data/step_loss_bearing_tokens", + "accepted_train_tokens": "data/step_trainable_assistant_tokens", + "executed_token_equivalents": "data/step_executed_token_equivalents", + "dummy_token_equivalents": "data/step_dummy_executed_token_equivalents", + "nominal_capacity_tokens": "data/step_nominal_schedule_capacity_tokens", + "dummy_schedule_capacity_tokens": "data/step_dummy_schedule_capacity_tokens", + "unused_packed_capacity_tokens": "data/step_unused_packed_capacity_tokens", + "packed_sequences": "data/step_packed_sequences", + "real_microbatches": "pipeline/global_real_microbatches", + "dummy_microbatches": "pipeline/global_dummy_microbatches", +} + + +def _numeric_values(rows: list[dict[str, Any]], key: str) -> list[float]: + values = [row.get(key) for row in rows] + _require( + all( + isinstance(value, int | float) and math.isfinite(float(value)) + for value in values + ), + f"throughput rows lack finite numeric {key}: {values}", + ) + return [float(value) for value in values if isinstance(value, int | float)] + + +def _total(rows: list[dict[str, Any]], key: str) -> float: + return sum(_numeric_values(rows, key)) + + +def _runtime_workload_counts( + rows: list[dict[str, Any]], *, packed_sequence_length: int +) -> dict[str, int]: + count_rows = [ + { + name: _nonnegative_integer( + row.get(key), name=f"step {row.get('step')} {key}" + ) + for name, key in _COUNT_METRICS.items() + } + for row in rows + ] + for counts in count_rows: + real_capacity = ( + counts["nominal_capacity_tokens"] - counts["dummy_schedule_capacity_tokens"] + ) + real_executed = ( + counts["executed_token_equivalents"] - counts["dummy_token_equivalents"] + ) + _require( + counts["packed_sequences"] == counts["real_microbatches"] + and counts["nominal_capacity_tokens"] + == (counts["real_microbatches"] + counts["dummy_microbatches"]) + * packed_sequence_length + and counts["dummy_schedule_capacity_tokens"] + == counts["dummy_microbatches"] * packed_sequence_length + and counts["unused_packed_capacity_tokens"] + == real_capacity - counts["nonpadding_logical_tokens"] + and 0 + < counts["accepted_train_tokens"] + == counts["loss_bearing_tokens"] + <= counts["nonpadding_logical_tokens"] + <= real_executed + <= real_capacity + and 0 + <= counts["dummy_token_equivalents"] + <= counts["dummy_schedule_capacity_tokens"], + f"runtime token accounting does not reconcile: {counts}", + ) + totals = { + name: sum(counts[name] for counts in count_rows) for name in _COUNT_METRICS + } + _require( + totals["packed_sequences"] > 0 and totals["real_microbatches"] > 0, + f"runtime workload contains no real packed sequences: {totals}", + ) + return totals + + +def _accepted_token_weighted(rows: list[dict[str, Any]], key: str) -> float: + values = _numeric_values(rows, key) + weights = _numeric_values(rows, "data/step_trainable_assistant_tokens") + total_weight = sum(weights) + _require(total_weight > 0.0, "throughput rows contain no accepted assistant tokens") + return sum( + value * weight for value, weight in zip(values, weights, strict=True) + ) / (total_weight) + + +def _discard_rates(rows: list[dict[str, Any]]) -> tuple[float, float]: + stale = _total(rows, _STALE_GROUPS) + zero_variance = _total(rows, _ZERO_VARIANCE_GROUPS) + _require( + stale >= 0.0 and zero_variance >= 0.0, "discard counts must be nonnegative" + ) + denominator = max( + _total(rows, "data/step_num_groups_trainable") + stale + zero_variance, + 1.0, + ) + return stale / denominator, zero_variance / denominator + + +def _window_measurements(stats: Any, rows: list[dict[str, Any]]) -> dict[str, Any]: + duration_s = float(stats.window_end_s) - float(stats.window_start_s) + stale_rate, zero_variance_rate = _discard_rates(rows) + _require( + math.isfinite(duration_s) and duration_s > 0.0, + f"autotuner window {stats.start_step}..{stats.end_step} has invalid duration", + ) + _require( + math.isclose( + stale_rate, float(stats.actual_stale_frac), rel_tol=0.0, abs_tol=1e-12 + ), + f"history and autotuner stale rates disagree at step {stats.end_step}", + ) + return { + "start_step": stats.start_step, + "end_step": stats.end_step, + "duration_s": duration_s, + "vllm_pressure": float(stats.vllm_pressure), + "vllm_waiting_capacity_request_s": float(stats.vllm_waiting_capacity_request_s), + "vllm_running_request_s": float(stats.vllm_running_request_s), + "trainer_underfeed": float(stats.trainer_underfeed_score), + _POLICY_AGE_MEAN: _accepted_token_weighted(rows, _POLICY_AGE_MEAN), + _POLICY_AGE_P95: max(_numeric_values(rows, _POLICY_AGE_P95)), + _FRESHNESS_DISCOUNT: _accepted_token_weighted(rows, _FRESHNESS_DISCOUNT), + "discarded/rate/stale_groups": stale_rate, + "discarded/rate/zero_variance_groups": zero_variance_rate, + } + + +async def _run_isolated_backend_phase( + *, + backend: Any, + model: Any, + service: Any, + train: Callable[..., Awaitable[Any]], + bundles_paths: tuple[Path, ...], +) -> TrainerPhaseEvidence: + from art.distributed.trajectory_store import TrajectoryGroupBundle + + _require( + len(bundles_paths) == _MATCHED_MEASURED_STEPS, + "isolated phase requires every matched E2E input", + ) + trajectory_input_fingerprints = [ + hashlib.sha256(path.read_bytes()).hexdigest() for path in bundles_paths + ] + benchmark_paths = (bundles_paths[0],) * _ISOLATED_WARMUP_STEPS + bundles_paths + packed_input_fingerprints: list[str] = [] + samples: list[tuple[Mapping[str, Any], int]] = [] + for sample_index, bundles_path in enumerate(benchmark_paths): + expected_trajectory_fingerprint = hashlib.sha256( + bundles_path.read_bytes() + ).hexdigest() + source_bundles = _load_bundles(bundles_path) + groups = [bundle.build() for bundle in source_bundles] + rebuilt = tuple(TrajectoryGroupBundle.from_group(group) for group in groups) + if ( + hashlib.sha256(_bundle_bytes(rebuilt)).hexdigest() + != expected_trajectory_fingerprint + ): + raise RuntimeError("isolated trajectory input changed during round trip") + _collect_matched_packing_shapes(groups) + packing = await backend.prepare_pipeline_batch(model, groups) + if packing is None: + raise RuntimeError("isolated backend benchmark produced no packed batch") + try: + current_packed_fingerprint = _packed_input_fingerprint(groups) + except BaseException: + await _discard_prepared_pipeline_batch(backend, groups) + raise + result = await train( + model, + groups, + learning_rate=1e-6, + loss_fn="cispo", + loss_fn_config=None, + normalize_advantages=True, + save_checkpoint=False, + adam_params=None, + optimizer_save_interval=5, + ) + if sample_index >= _ISOLATED_WARMUP_STEPS: + packed_input_fingerprints.append(current_packed_fingerprint) + samples.append((result.metrics, int(result.step))) + await service.wait_for_serving(int(result.step)) + trajectory_input_fingerprint, packed_input_fingerprint = ( + _matched_input_fingerprints( + trajectory_input_fingerprints, packed_input_fingerprints + ) + ) + return _phase_evidence( + phase="isolated", + runtime_fingerprint=service._runtime_spec().fingerprint, + trajectory_input_fingerprint=trajectory_input_fingerprint, + packed_input_fingerprint=packed_input_fingerprint, + samples=samples, + ) + + +def _collect_measurements( + *, + fixture: ThroughputFixture, + config: ThroughputWorkflowConfig, + hardware: Literal["h200", "b300"], + model_output_dir: Path, + profile: Any, + events: list[PolicyActivationEvent], + isolated: TrainerPhaseEvidence, + e2e: TrainerPhaseEvidence, + capture_settings: Mapping[str, int], + calibration_fingerprint: str, +) -> dict[str, Any]: + from art.pipeline_tuner.autotune import _trainer_underfeed_score + + _require( + profile.config.mode == "online", + f"throughput stage requires online autotuning, got {profile.config.mode}", + ) + policy_age_limit = profile.policy_age_limit_steps + _require( + isinstance(policy_age_limit, int | float) + and math.isfinite(float(policy_age_limit)) + and float(policy_age_limit) >= 0.0, + f"online autotuner lacks a policy-age limit: {policy_age_limit}", + ) + policy_age_limit = float(policy_age_limit) + _require( + policy_age_limit == config.max_steps_off_policy, + "autotuner policy-age limit does not match the throughput contract: " + f"{policy_age_limit} != {config.max_steps_off_policy}", + ) + decisions = [ + decision + for decision in profile.decisions + if decision.stats is not None and decision.stats.end_step <= config.max_steps + ] + _require(bool(decisions), "throughput evidence requires autotuner windows") + last_stats = decisions[-1].stats + assert last_stats is not None + expected_window = ( + config.max_steps - profile.config.window_steps + 1, + config.max_steps, + ) + _require( + (last_stats.start_step, last_stats.end_step) == expected_window, + f"final autotuner window is not {expected_window[0]}..{expected_window[1]}", + ) + history_rows = _training_rows(model_output_dir) + by_step = {int(row["step"]): row for row in history_rows} + _require( + len(by_step) == len(history_rows), + "throughput history contains duplicate training steps", + ) + selected = _same_setting_decision_suffix(decisions, by_step) + stats = [decision.stats for decision in selected] + assert all(window is not None for window in stats) + first_stats, last_stats = stats[0], stats[-1] + steps = list(range(first_stats.start_step, last_stats.end_step + 1)) + missing = [step for step in steps if step not in by_step] + _require(not missing, f"autotuner decision window lacks train rows: {missing}") + rows = [by_step[step] for step in steps] + executed_settings_by_step = [ + _row_pipeline_settings(row, step) for step, row in zip(steps, rows, strict=True) + ] + executed_settings = executed_settings_by_step[0] + _require( + all(settings == executed_settings for settings in executed_settings_by_step), + "throughput history rows executed different pipeline settings", + ) + _require( + dict(capture_settings) == executed_settings, + "matched capture did not use the measured pipeline settings: " + f"{dict(capture_settings)} != {executed_settings}", + ) + window_rows = [ + [by_step[step] for step in range(window.start_step, window.end_step + 1)] + for window in stats + ] + windows = [ + _window_measurements(window, selected_rows) + for window, selected_rows in zip(stats, window_rows, strict=True) + ] + e2e_elapsed_s = float(last_stats.window_end_s) - float(first_stats.window_start_s) + _require( + math.isclose( + sum(window["duration_s"] for window in windows), + e2e_elapsed_s, + rel_tol=0.0, + abs_tol=1e-6, + ), + "autotuner window durations do not reconcile", + ) + + events_by_step = {event.step: event for event in events} + _require( + len(events_by_step) == len(events), + "throughput stage observed duplicate policy activations", + ) + activation_steps = [first_stats.start_step - 1, *steps] + missing_events = [step for step in activation_steps if step not in events_by_step] + _require( + not missing_events, + f"throughput decision intervals lack policy activations: {missing_events}", + ) + interval_events = [events_by_step[step] for step in activation_steps] + window_events = interval_events[1:] + activation_times = [event.serving_active_monotonic_s for event in interval_events] + intervals = [ + right - left for left, right in zip(activation_times, activation_times[1:]) + ] + _require( + all(interval > 0.0 for interval in intervals), + f"policy activations were not ordered in time: {intervals}", + ) + lags = [event.lag_s for event in window_events] + _require( + all(lag >= 0.0 for lag in lags), + f"policy activation preceded trainer completion: {lags}", + ) + + counts = _runtime_workload_counts( + rows, packed_sequence_length=config.packed_sequence_length + ) + logical = counts["nonpadding_logical_tokens"] + train_s = _total(rows, "time/step_train_s") + wall_s = _total(rows, "time/step_wall_s") + _require( + 0.0 < train_s <= wall_s <= e2e_elapsed_s + 1e-6, + f"invalid throughput durations: {train_s}, {wall_s}, {e2e_elapsed_s}", + ) + + stale_rate, zero_variance_rate = _discard_rates(rows) + waiting_request_s = sum( + window["vllm_waiting_capacity_request_s"] for window in windows + ) + running_request_s = sum(window["vllm_running_request_s"] for window in windows) + stable_vllm_pressure = ( + waiting_request_s / running_request_s + if running_request_s > 0.0 + else math.inf + if waiting_request_s > 0.0 + else 0.0 + ) + capacities = _numeric_values(rows, "data/step_nominal_schedule_capacity_tokens") + nonpadding = _numeric_values(rows, "data/step_nonpadding_logical_tokens") + stable_trainer_underfeed = _trainer_underfeed_score( + idle_frac=_total(rows, "time/step_collect_batch_s") / wall_s, + unused_and_dummy_ratio=fmean( + max(0.0, (capacity - used) / capacity) + for capacity, used in zip(capacities, nonpadding, strict=True) + ), + ) + thresholds = config.thresholds.get(hardware) + measurements = { + "hardware": hardware, + "calibration_basis": ( + thresholds.calibration_basis if thresholds is not None else None + ), + "calibration_fingerprint": calibration_fingerprint, + "model_key": fixture.model_key, + "model_path": fixture.path, + "num_layers": fixture.num_layers, + "packed_sequence_length": config.packed_sequence_length, + "width_fingerprint": fixture.width_fingerprint, + **counts, + "isolated_train_tok_s": isolated.train_tok_s, + "matched_e2e_core_train_tok_s": e2e.train_tok_s, + "e2e_core_train_tok_s": logical / train_s, + "e2e_train_tok_s": logical / e2e_elapsed_s, + "accepted_train_tok_s": counts["accepted_train_tokens"] / e2e_elapsed_s, + _POLICY_AGE_MEAN: _accepted_token_weighted(rows, _POLICY_AGE_MEAN), + _POLICY_AGE_P95: max(_numeric_values(rows, _POLICY_AGE_P95)), + _FRESHNESS_DISCOUNT: _accepted_token_weighted(rows, _FRESHNESS_DISCOUNT), + "discarded/rate/stale_groups": stale_rate, + "discarded/rate/zero_variance_groups": zero_variance_rate, + "policy_age_limit_steps": policy_age_limit, + "mean_ready_batch_idle_s": fmean( + [float(row["queue/packed_get_wait_s"]) for row in rows] + ), + "mean_train_gap_s": (e2e_elapsed_s - train_s) / len(rows), + "e2e_elapsed_s": e2e_elapsed_s, + "autotuner_windows": windows, + "stable_vllm_pressure": stable_vllm_pressure, + "stable_trainer_underfeed": stable_trainer_underfeed, + "matched_capture_pipeline_settings": dict(capture_settings), + "mean_policy_activation_lag_s": fmean(lags), + "p50_policy_activation_lag_s": median(lags), + "p95_policy_activation_lag_s": quantiles(lags, n=20, method="inclusive")[18], + "max_policy_activation_lag_s": max(lags), + "post_warmup_policy_activation_count": len(window_events), + "mean_policy_activation_interval_s": fmean(intervals), + "p50_policy_activation_interval_s": median(intervals), + "p95_policy_activation_interval_s": quantiles( + intervals, n=20, method="inclusive" + )[18], + "second_max_policy_activation_interval_s": sorted(intervals)[-2], + "max_policy_activation_interval_s": max(intervals), + } + matched_fields = ( + "runtime_fingerprint", + "trajectory_input_fingerprint", + "packed_input_fingerprint", + "workload_fingerprint", + ) + mismatches = { + name: (getattr(e2e, name), getattr(isolated, name)) + for name in matched_fields + if getattr(e2e, name) != getattr(isolated, name) + } + _require( + not mismatches, + f"isolated and E2E phases did not execute the same packed input: {mismatches}", + ) + capture_steps = _matched_capture_steps(config.max_steps) + _require( + e2e.policy_steps == capture_steps, + f"matched E2E inputs were not captured at reserved steps {capture_steps}", + ) + _require( + e2e.sample_count == isolated.sample_count == _MATCHED_MEASURED_STEPS, + "matched trainer phases have asymmetric sample counts", + ) + expected_isolated_steps = tuple( + range( + capture_steps[-1] + 1 + _ISOLATED_WARMUP_STEPS, + capture_steps[-1] + 1 + _ISOLATED_WARMUP_STEPS + isolated.sample_count, + ) + ) + _require( + isolated.policy_steps == expected_isolated_steps, + "isolated measured steps do not follow the configured warmup: " + f"{isolated.policy_steps}", + ) + return measurements + + +def acceptance_failures( + measurements: Mapping[str, Any], + config: ThroughputWorkflowConfig, + thresholds: ThroughputThresholds | None, +) -> list[str]: + checks = { + "stable_min_vllm_pressure": measurements["stable_vllm_pressure"] + >= config.min_vllm_pressure, + "stable_trainer_underfeed": measurements["stable_trainer_underfeed"] + <= config.max_trainer_underfeed, + } + for window in measurements["autotuner_windows"]: + prefix = f"window_{window['start_step']}_{window['end_step']}" + checks.update( + { + f"{prefix}_policy_age_p95": window[_POLICY_AGE_P95] + <= measurements["policy_age_limit_steps"], + f"{prefix}_zero_variance_rate": window[ + "discarded/rate/zero_variance_groups" + ] + == 0.0, + } + ) + failures = [name for name, passed in checks.items() if not passed] + if thresholds is None: + return [f"missing_{measurements['hardware']}_calibration", *failures] + floor_checks = { + "isolated_train_tok_s": measurements["isolated_train_tok_s"] + >= thresholds.min_isolated_train_tok_s, + "e2e_train_tok_s": measurements["e2e_train_tok_s"] + >= thresholds.min_e2e_train_tok_s, + "accepted_train_tok_s": measurements["accepted_train_tok_s"] + >= thresholds.min_accepted_train_tok_s, + "e2e_to_isolated_ratio": measurements["e2e_train_tok_s"] + / measurements["isolated_train_tok_s"] + >= thresholds.min_e2e_to_isolated_ratio, + "matched_core_to_isolated_ratio": measurements["matched_e2e_core_train_tok_s"] + / measurements["isolated_train_tok_s"] + >= thresholds.min_matched_core_to_isolated_ratio, + "matched_core_to_isolated_ratio_max": measurements[ + "matched_e2e_core_train_tok_s" + ] + / measurements["isolated_train_tok_s"] + <= thresholds.max_matched_core_to_isolated_ratio, + "mean_policy_activation_lag_s": measurements["mean_policy_activation_lag_s"] + <= thresholds.max_mean_policy_activation_lag_s, + "max_policy_activation_lag_s": measurements["max_policy_activation_lag_s"] + <= thresholds.max_policy_activation_lag_s, + "repeated_policy_activation_cadence_s": measurements[ + "second_max_policy_activation_interval_s" + ] + <= thresholds.max_repeated_policy_activation_interval_s, + } + if thresholds.calibration_fingerprint is not None: + floor_checks["calibration_fingerprint"] = ( + measurements["calibration_fingerprint"] + == thresholds.calibration_fingerprint + ) + if measurements["hardware"] == "b300": + floor_checks["calibration_basis"] = thresholds.calibration_basis == "measured" + return [ + *failures, + *(name for name, passed in floor_checks.items() if not passed), + ] + + +async def _run_e2e_throughput_async( + *, + base_model: str, + allow_unvalidated_arch: bool, + stage: Any, + config: ThroughputWorkflowConfig, + fixture: ThroughputFixture, + gpu_identities: list[dict[str, Any]], + hardware: Literal["h200", "b300"], +) -> ValidationStageResult: + from transformers import AutoTokenizer + + import art + from art.megatron.backend import MegatronBackend + from art.pipeline_trainer import PipelineTrainer + from art.pipeline_tuner import PipelineAutotuneConfig, PipelineAutotunerProfile + from art.preprocessing.policy_spans import validate_complete_policy_token_spans + from art.preprocessing.vllm_tokens import choice_completion_tokens + + if stage.megatron is None or stage.vllm is None: + raise RuntimeError( + "E2E throughput requires separate Megatron and vLLM resources" + ) + stage_dir = Path(os.environ[_STAGE_DIR_ENV]) + stage_dir.mkdir(parents=True, exist_ok=True) + topology = stage.megatron.topology + art.init_megatron_runtime_config( + topology=topology.to_megatron_config(), + packed_sequence_length=config.packed_sequence_length, + ) + engine_args = stage.vllm.engine_args() + engine_args["seed"] = config.random_seed + engine_args["model"] = fixture.path + max_model_len = int(engine_args["max_model_len"]) + if config.prompt_tokens + config.completion_tokens > max_model_len: + raise RuntimeError( + "throughput prompt and completion exceed vLLM context: " + f"{config.prompt_tokens}+{config.completion_tokens}>{max_model_len}" + ) + internal_config = { + "trainer_gpu_ids": stage.megatron.gpu_ids, + "inference_gpu_ids": stage.vllm.gpu_ids, + "rollout_weights_mode": "lora", + "rollout_weight_update_mode": "in_flight_lora", + "engine_args": engine_args, + "init_args": { + "model_name": fixture.path, + "max_seq_length": config.packed_sequence_length, + "random_state": config.random_seed, + }, + "allow_unvalidated_arch": allow_unvalidated_arch, + "megatron_model_initialization": "random", + } + from art.megatron.model_support.tokenizer import ( + configure_tokenizer_for_model_support, + ) + + tokenizer = configure_tokenizer_for_model_support( + cast(Any, AutoTokenizer.from_pretrained(fixture.path, local_files_only=True)), + base_model=base_model, + internal_config=internal_config, + ) + prompt = _sized_prompt(tokenizer, target_tokens=config.prompt_tokens) + actual_prompt_tokens = _chat_token_count(tokenizer, prompt) + run_name = f"throughput-{fixture.model_key}-{uuid.uuid4().hex[:8]}" + model_output_dir: Path | None = None + events: list[PolicyActivationEvent] = [] + e2e_phase: TrainerPhaseEvidence | None = None + isolated_phase: TrainerPhaseEvidence | None = None + captured_training_inputs: list[ + tuple[tuple[Any, ...], str, dict[str, int], Mapping[str, Any], int] + ] = [] + bundles_paths = tuple( + stage_dir / f"matched_packed_input_{index}.msgpack" + for index in range(_MATCHED_MEASURED_STEPS) + ) + autotune = PipelineAutotuneConfig( + mode="online", + output_name="throughput", + initial_model_calls_per_inference_gpu=( + config.initial_model_calls_per_inference_gpu + ), + initial_min_groups_per_packed_sequence=config.groups_per_step, + initial_max_groups_per_packed_sequence=config.groups_per_step, + vllm_metric_interval_s=0.25, + ) + measured_steps = config.max_steps - autotune.warmup_ignore_steps + if ( + measured_steps < 2 * autotune.window_steps + or measured_steps % autotune.window_steps + ): + raise RuntimeError( + "throughput stage must end on a whole autotuner window after at least " + f"two measured windows: max_steps={config.max_steps}, " + f"warmup={autotune.warmup_ignore_steps}, window={autotune.window_steps}" + ) + # Keep test-only trajectory materialization off the final measured publication. + capture_train_calls = _matched_capture_steps(config.max_steps) + runtime_contract = _calibration_contract( + base_model=base_model, + fixture=fixture, + stage=stage, + config=config, + autotune=autotune, + actual_prompt_tokens=actual_prompt_tokens, + gpu_identities=gpu_identities, + ) + calibration_fingerprint = _digest(runtime_contract) + + async with MegatronBackend( + path=str(stage_dir / "art"), + enable_expert_replay=topology.ep > 1, + in_process=False, + ) as backend: + model = cast( + Any, + art.TrainableModel( + name=run_name, + run_name=run_name, + project="model-support-throughput", + base_model=base_model, + _internal_config=cast(art.dev.InternalModelConfig, internal_config), + report_metrics=[], + ), + ) + await model.register(backend) + model_output_dir = Path(model._get_output_dir()) + client = model.openai_client() + try: + await client.chat.completions.create( + messages=[{"role": "user", "content": prompt}], + model=model.get_inference_name(), + max_tokens=config.completion_tokens, + temperature=0.0, + timeout=1200.0, + extra_body={ + "ignore_eos": True, + "min_tokens": config.completion_tokens, + }, + ) + + async def rollout_fn( + rollout_model: Any, + scenario: dict[str, str], + _rollout_config: None, + ) -> Any: + response = await client.chat.completions.create( + messages=[{"role": "user", "content": scenario["prompt"]}], + model=rollout_model.get_inference_name(), + max_tokens=config.completion_tokens, + n=config.rollouts_per_group, + temperature=1.0, + seed=int(scenario["scenario_id"].rsplit("-", 1)[-1]), + logprobs=True, + top_logprobs=0, + timeout=1200.0, + extra_body={ + "ignore_eos": True, + "min_tokens": config.completion_tokens, + }, + ) + if len(response.choices) != config.rollouts_per_group: + raise RuntimeError( + "vLLM returned an incomplete rollout group: " + f"{len(response.choices)} != {config.rollouts_per_group}" + ) + trajectories = [] + for index, choice in enumerate(response.choices): + completion_tokens = choice_completion_tokens(choice) + if not isinstance(completion_tokens, int) or ( + completion_tokens != config.completion_tokens + ): + raise RuntimeError( + "throughput completion length changed: " + f"{completion_tokens} != {config.completion_tokens}" + ) + validate_complete_policy_token_spans( + choice, completion_tokens=completion_tokens + ) + trajectories.append( + art.Trajectory( + messages_and_choices=[ + {"role": "user", "content": scenario["prompt"]}, + choice, + ], + reward=index / (config.rollouts_per_group - 1), + metrics={"completion_tokens": completion_tokens}, + metadata={"scenario_id": scenario["scenario_id"]}, + ) + ) + return art.TrajectoryGroup( + trajectories, + metadata={"scenario_id": scenario["scenario_id"]}, + ) + + trainer = PipelineTrainer( + model=model, + backend=backend, + rollout_fn=rollout_fn, + scenarios=_scenarios(prompt), + config=None, + autotune=autotune, + learning_rate=1e-6, + loss_fn="cispo", + max_steps=capture_train_calls[-1], + eval_fn=None, + eval_every_n_steps=0, + eval_at_start=False, + save_checkpoint=False, + resume=False, + log_interval_seconds=30.0, + score_reference_groups_per_step=float(config.groups_per_step), + score_reference_rollouts_per_group=float(config.rollouts_per_group), + max_steps_off_policy=config.max_steps_off_policy, + ) + from art.megatron.distributed_service import DistributedMegatronService + + service = cast( + DistributedMegatronService, await backend._get_service(model) + ) + activation_tasks: dict[int, asyncio.Task[PolicyActivationEvent]] = {} + original_train = backend.train + train_call_count = 0 + + async def tracked_train(*args: Any, **kwargs: Any) -> Any: + nonlocal train_call_count + train_call_count += 1 + if len(args) < 2: + raise RuntimeError( + "PipelineTrainer did not pass trajectory groups positionally" + ) + groups = args[1] + captured: tuple[tuple[Any, ...], str, dict[str, int]] | None = None + if train_call_count in capture_train_calls: + try: + _collect_matched_packing_shapes(groups) + captured = ( + await _capture_training_bundles(groups), + _packed_input_fingerprint(groups), + _current_pipeline_settings(trainer), + ) + except BaseException: + await _discard_prepared_pipeline_batch(backend, groups) + raise + result = await original_train(*args, **kwargs) + step = int(result.step) + if step in activation_tasks: + raise RuntimeError( + f"duplicate trainer completion for policy {step}" + ) + activation_tasks[step] = asyncio.create_task( + _activation_event(service, step) + ) + if captured is not None: + captured_training_inputs.append((*captured, result.metrics, step)) + return result + + setattr(backend, "train", tracked_train) + try: + measurement_start = config.max_steps - 2 * autotune.window_steps + 1 + with _freeze_pipeline_settings_from_step(trainer, measurement_start): + await trainer.train(handle_signals=False) + if train_call_count != capture_train_calls[-1]: + raise RuntimeError( + "online pipeline did not execute measurement plus capture steps: " + f"{train_call_count} != {capture_train_calls[-1]}" + ) + events = sorted( + await asyncio.gather(*activation_tasks.values()), + key=lambda event: event.step, + ) + finally: + setattr(backend, "train", original_train) + await _cancel_activation_tasks(activation_tasks) + if len(captured_training_inputs) != _MATCHED_MEASURED_STEPS: + raise RuntimeError( + "online pipeline did not capture every matched train batch" + ) + bundle_payloads = await asyncio.gather( + *( + asyncio.to_thread(_bundle_bytes, captured[0]) + for captured in captured_training_inputs + ) + ) + await asyncio.gather( + *( + asyncio.to_thread(path.write_bytes, payload) + for path, payload in zip( + bundles_paths, bundle_payloads, strict=True + ) + ) + ) + capture_settings = captured_training_inputs[0][2] + _require( + all( + captured[2] == capture_settings + for captured in captured_training_inputs[1:] + ), + "matched E2E samples used different pipeline settings", + ) + trajectory_input_fingerprint, packed_input_fingerprint = ( + _matched_input_fingerprints( + [ + hashlib.sha256(payload).hexdigest() + for payload in bundle_payloads + ], + [captured[1] for captured in captured_training_inputs], + ) + ) + e2e_phase = _phase_evidence( + phase="e2e", + runtime_fingerprint=service._runtime_spec().fingerprint, + trajectory_input_fingerprint=trajectory_input_fingerprint, + packed_input_fingerprint=packed_input_fingerprint, + samples=[ + (captured[3], captured[4]) for captured in captured_training_inputs + ], + ) + isolated_phase = await _run_isolated_backend_phase( + backend=backend, + model=model, + service=service, + train=original_train, + bundles_paths=bundles_paths, + ) + finally: + await client.close() + + assert model_output_dir is not None + assert e2e_phase is not None and isolated_phase is not None + profile_path = model_output_dir / "pipeline_tuner" / "throughput.json" + profile = PipelineAutotunerProfile.model_validate_json(profile_path.read_text()) + measurements = _collect_measurements( + fixture=fixture, + config=config, + hardware=hardware, + model_output_dir=model_output_dir, + profile=profile, + events=events, + isolated=isolated_phase, + e2e=e2e_phase, + capture_settings=capture_settings, + calibration_fingerprint=calibration_fingerprint, + ) + activation_path = stage_dir / "policy_activation_timeline.json" + activation_path.write_text( + json.dumps([event._asdict() for event in events], indent=2) + "\n" + ) + thresholds = config.thresholds.get(hardware) + failures = acceptance_failures(measurements, config, thresholds) + metrics = { + **measurements, + "gpu_identities": [ + {"role": identity["role"], **_stable_gpu_identity(identity)} + for identity in gpu_identities + ], + "isolated": isolated_phase._asdict(), + "e2e": e2e_phase._asdict(), + "runtime_contract": runtime_contract, + "matched_trajectory_inputs": [str(path) for path in bundles_paths], + "autotuner_profile": str(profile_path), + "policy_activation_timeline": str(activation_path), + "thresholds": thresholds.model_dump(mode="json") if thresholds else None, + "acceptance_failures": failures, + } + (stage_dir / "throughput_measurements.json").write_text( + json.dumps(metrics, indent=2) + "\n" + ) + return ValidationStageResult( + name="e2e_throughput", + passed=not failures, + metrics=metrics, + artifact_dir=str(stage_dir), + ) + + +def run_e2e_throughput( + *, + base_model: str, + architecture: ArchitectureReport, + allow_unvalidated_arch: bool = False, +) -> ValidationStageResult: + del architecture + resources = handler_workflow_resources_for_base_model( + base_model, allow_unvalidated_arch=allow_unvalidated_arch + ) + if resources is None or resources.e2e_throughput is None: + raise RuntimeError(f"missing E2E throughput resources for {base_model}") + spec = get_model_support_spec( + base_model, allow_unvalidated_arch=allow_unvalidated_arch + ) + import torch + + stage = resolve_stage_resources_for_visible_gpus( + "e2e_throughput", + resources.e2e_throughput, + visible_gpu_count=int(torch.cuda.device_count()), + ) + config = stage.throughput + if config is None: + raise RuntimeError("E2E throughput resources lack throughput configuration") + if stage.megatron is None or stage.vllm is None: + raise RuntimeError( + "E2E throughput requires separate Megatron and vLLM resources" + ) + gpu_identities = _gpu_identities( + trainer_gpu_ids=stage.megatron.gpu_ids, + inference_gpu_ids=stage.vllm.gpu_ids, + ) + hardware = _hardware(gpu_identities) + config = _throughput_config_for_hardware(spec.key, config, hardware) + correctness_path = os.environ.get(FIXTURE_PATH_ENV) + if correctness_path is None: + raise RuntimeError(f"missing {FIXTURE_PATH_ENV}") + fixture = ensure_throughput_fixture( + canonical_model=base_model, + model_key=spec.key, + correctness_fixture=Path(correctness_path), + num_layers=config.num_layers, + initialization_version=config.random_initialization_version, + random_seed=config.random_seed, + output=Path(os.environ[_STAGE_DIR_ENV]) / "production_width_model", + ) + os.environ["WANDB_MODE"] = "disabled" + return asyncio.run( + _run_e2e_throughput_async( + base_model=base_model, + allow_unvalidated_arch=allow_unvalidated_arch, + stage=stage, + config=config, + fixture=fixture, + gpu_identities=gpu_identities, + hardware=hardware, + ) + ) diff --git a/tests/integration/megatron/runtime_isolation/test_art_import_boundary.py b/tests/integration/megatron/runtime_isolation/test_art_import_boundary.py index b939e4f9a..a32994374 100644 --- a/tests/integration/megatron/runtime_isolation/test_art_import_boundary.py +++ b/tests/integration/megatron/runtime_isolation/test_art_import_boundary.py @@ -104,8 +104,7 @@ def test_service_modules_import_without_vllm(artifact_dir: Path) -> None: ( "import importlib, json; " "modules = [" - "'art.unsloth.service', " - "'art.megatron.service', " + "'art.megatron.distributed_service', " "'art.megatron.weights.merged_weight_export'" "]; " "loaded = [importlib.import_module(name).__name__ for name in modules]; " @@ -116,7 +115,32 @@ def test_service_modules_import_without_vllm(artifact_dir: Path) -> None: ) payload = _load_json_from_stdout(result.stdout) assert payload["loaded"] == [ - "art.unsloth.service", - "art.megatron.service", + "art.megatron.distributed_service", "art.megatron.weights.merged_weight_export", ] + + +def test_runtime_env_preserves_build_arch_without_initializing_cuda( + artifact_dir: Path, +) -> None: + env = {**os.environ, "CUDA_VISIBLE_DEVICES": "", "TORCH_CUDA_ARCH_LIST": "10.3"} + result = _run( + [ + sys.executable, + "-c", + ( + "import json, os, torch; " + "from art.megatron.runtime.runtime_env import " + "configure_megatron_runtime_env; " + "configure_megatron_runtime_env(); " + "print(json.dumps({'arch': os.environ['TORCH_CUDA_ARCH_LIST'], " + "'cuda_initialized': torch.cuda.is_initialized()}))" + ), + ], + artifact_dir=artifact_dir, + env=env, + ) + assert _load_json_from_stdout(result.stdout) == { + "arch": "10.3", + "cuda_initialized": False, + } diff --git a/tests/integration/megatron/runtime_isolation/test_client.py b/tests/integration/megatron/runtime_isolation/test_client.py deleted file mode 100644 index 7d311d1d9..000000000 --- a/tests/integration/megatron/runtime_isolation/test_client.py +++ /dev/null @@ -1,44 +0,0 @@ -from pathlib import Path -from types import SimpleNamespace - -import pytest - -from art.megatron.runtime.client import stream_megatron_job, write_megatron_job -from art.megatron.runtime.jobs import ( - MegatronSyncJob, - MergedWeightTransferInitInfo, - MergedWeightTransferSpec, -) - - -@pytest.mark.asyncio -async def test_stream_megatron_job_raises_when_worker_exits( - tmp_path: Path, -) -> None: - job_path = tmp_path / "job.json" - log_path = tmp_path / "job.log" - job = MegatronSyncJob( - lora_path="/tmp/lora", - merged_weight_transfer=MergedWeightTransferSpec( - init_info=MergedWeightTransferInitInfo( - master_address="127.0.0.1", - master_port=12345, - rank_offset=1, - world_size=2, - ), - vllm_base_url="http://127.0.0.1:8000", - served_model_name="test@0", - ), - log_path=str(log_path), - ) - write_megatron_job(job, job_path=str(job_path)) - - with pytest.raises(RuntimeError, match="Megatron worker exited with code 17"): - async for _ in stream_megatron_job( - job, - job_path=str(job_path), - process=SimpleNamespace(returncode=17), - process_log_path="/tmp/megatron-runtime.log", - poll_interval=0.0, - ): - pass diff --git a/tests/integration/megatron/runtime_isolation/test_live_megatron_backend_smoke.py b/tests/integration/megatron/runtime_isolation/test_live_megatron_backend_smoke.py index 6750aa407..37211041b 100644 --- a/tests/integration/megatron/runtime_isolation/test_live_megatron_backend_smoke.py +++ b/tests/integration/megatron/runtime_isolation/test_live_megatron_backend_smoke.py @@ -12,7 +12,7 @@ import art from art import dev from art.megatron.backend import MegatronBackend -from art.megatron.service import MegatronService +from art.megatron.distributed_service import DistributedMegatronService from ..model_support.oracle_harness import ORACLE_TOPOLOGY, Topology from ..model_support.oracle_worker import provider_topology_env @@ -106,7 +106,9 @@ def _shared_live_config() -> dev.InternalModelConfig: { "rollout_weights_mode": "lora", "engine_args": { - **_engine_args_for_yes_no_trainability(inference_gpu_ids=[0, 1]), + **_engine_args_for_yes_no_trainability( + base_model=_base_model(), inference_gpu_ids=[0, 1] + ), "tensor_parallel_size": 2, "enable_expert_parallel": True, "enable_sleep_mode": True, @@ -123,7 +125,7 @@ def _dedicated_merged_config() -> dev.InternalModelConfig: "rollout_weights_mode": "merged", "engine_args": { **_engine_args_for_yes_no_trainability( - inference_gpu_ids=_inference_gpu_ids() + base_model=_base_model(), inference_gpu_ids=_inference_gpu_ids() ), }, "init_args": {"max_seq_length": _max_seq_length()}, @@ -137,7 +139,8 @@ def _dedicated_multirank_merged_config() -> dev.InternalModelConfig: "rollout_weights_mode": "merged", "engine_args": { **_engine_args_for_yes_no_trainability( - inference_gpu_ids=_multirank_inference_gpu_ids() + base_model=_base_model(), + inference_gpu_ids=_multirank_inference_gpu_ids(), ), }, "init_args": {"max_seq_length": _max_seq_length()}, @@ -169,15 +172,15 @@ async def _chat_snapshot(model: art.TrainableModel, *, step: int) -> dict[str, o } -async def _runtime_is_sleeping(service: MegatronService) -> bool: +async def _runtime_is_sleeping(service: DistributedMegatronService) -> bool: async with httpx.AsyncClient(timeout=30.0) as client: - response = await client.get(f"{service._vllm_base_url}/is_sleeping") + response = await client.get(f"{service._base_url}/is_sleeping") response.raise_for_status() return bool(response.json()["is_sleeping"]) async def _wait_until_runtime_sleeping( - service: MegatronService, + service: DistributedMegatronService, *, timeout_s: float = 300.0, poll_s: float = 0.5, @@ -282,7 +285,7 @@ async def test_megatron_backend_shared_lora_runtime_sleep_wake_live_smoke( report_metrics=[], ) await model.register(backend) - service = cast(MegatronService, await backend._get_service(model)) + service = cast(DistributedMegatronService, await backend._get_service(model)) prompts = _train_group_prompts() await _warmup_model(model, base_model=model.base_model, prompt=prompts[0]) step0_name = model.get_inference_name(step=0) @@ -377,7 +380,7 @@ async def test_megatron_backend_dedicated_merged_live_smoke( report_metrics=[], ) await model.register(backend) - service = cast(MegatronService, await backend._get_service(model)) + service = cast(DistributedMegatronService, await backend._get_service(model)) prompts = _train_group_prompts() await _warmup_model(model, base_model=model.base_model, prompt=prompts[0]) step0_name = model.get_inference_name(step=0) @@ -452,7 +455,7 @@ async def test_megatron_backend_dedicated_multirank_merged_live_smoke( report_metrics=[], ) await model.register(backend) - service = cast(MegatronService, await backend._get_service(model)) + service = cast(DistributedMegatronService, await backend._get_service(model)) prompts = _train_group_prompts() await _warmup_model(model, base_model=model.base_model, prompt=prompts[0]) step0_name = model.get_inference_name(step=0) @@ -532,7 +535,7 @@ async def test_megatron_backend_shared_lora_ten_step_live_smoke( report_metrics=[], ) await model.register(backend) - service = cast(MegatronService, await backend._get_service(model)) + service = cast(DistributedMegatronService, await backend._get_service(model)) prompts = _train_group_prompts() await _warmup_model(model, base_model=model.base_model, prompt=prompts[0]) step0_name = model.get_inference_name(step=0) diff --git a/tests/integration/megatron/runtime_isolation/test_runtime_launcher.py b/tests/integration/megatron/runtime_isolation/test_runtime_launcher.py index 65103ce3f..50f2158ac 100644 --- a/tests/integration/megatron/runtime_isolation/test_runtime_launcher.py +++ b/tests/integration/megatron/runtime_isolation/test_runtime_launcher.py @@ -40,6 +40,7 @@ def test_build_runtime_server_cmd_uses_runtime_project( lora_path="/tmp/lora", served_model_name="test@0", rollout_weights_mode="merged", + initial_policy_version=7, engine_args={"weight_transfer_config": {"backend": "nccl"}}, server_args={"tool_call_parser": "hermes"}, ) @@ -50,6 +51,7 @@ def test_build_runtime_server_cmd_uses_runtime_project( '--engine-args-json={"weight_transfer_config": {"backend": "nccl"}}' in command ) assert '--server-args-json={"tool_call_parser": "hermes"}' in command + assert "--initial-policy-version=7" in command def test_build_runtime_server_cmd_honors_runtime_bin_override(monkeypatch) -> None: @@ -150,8 +152,10 @@ def test_vllm_runtime_subprocess_env_isolates_flashinfer_for_source_runtime( tmp_path: Path, ) -> None: runtime_root = tmp_path / "vllm_runtime" + cache_root = tmp_path / "node_cache" runtime_root.mkdir() monkeypatch.setenv("ART_VLLM_RUNTIME_PROJECT_ROOT", str(runtime_root)) + monkeypatch.setenv("XDG_CACHE_HOME", str(cache_root)) monkeypatch.setenv("FLASHINFER_WORKSPACE_BASE", "/shared/flashinfer") monkeypatch.setenv( "PYTHONPATH", @@ -167,7 +171,7 @@ def test_vllm_runtime_subprocess_env_isolates_flashinfer_for_source_runtime( assert env["PYTHONPATH"] == "/keep" assert env["FLASHINFER_WORKSPACE_BASE"] == str( - tmp_path / "scratch" / "vllm_runtime_flashinfer" + cache_root / "vllm_runtime" / "flashinfer_workspace" ) @@ -397,3 +401,24 @@ async def get(self, url: str, timeout: float): "url": "http://127.0.0.1:8123/health", "timeout": 5.0, } + + +@pytest.mark.asyncio +async def test_wait_for_vllm_runtime_fails_when_engine_core_dies( + tmp_path: Path, +) -> None: + class FakeProcess: + def poll(self): + return None + + log_path = tmp_path / "vllm.log" + log_path.write_text("APIServer is alive\nEngineCore failed to start\n") + + with pytest.raises(RuntimeError, match="EngineCore failed to start"): + await runtime.wait_for_vllm_runtime( + process=cast(Any, FakeProcess()), + host="127.0.0.1", + port=8123, + timeout=300.0, + log_path=str(log_path), + ) diff --git a/tests/integration/megatron/runtime_isolation/test_runtime_project_isolation.py b/tests/integration/megatron/runtime_isolation/test_runtime_project_isolation.py index 5a9c57dcb..db796ada0 100644 --- a/tests/integration/megatron/runtime_isolation/test_runtime_project_isolation.py +++ b/tests/integration/megatron/runtime_isolation/test_runtime_project_isolation.py @@ -53,307 +53,1232 @@ def test_runtime_server_source_contains_only_required_custom_routes() -> None: assert route in source -def test_runtime_patch_defaults_evidence_on_and_honors_opt_out( +def test_runtime_patch_always_returns_token_ids( artifact_dir: Path, ) -> None: payload = _runtime_python( "import json; " - "from art_vllm_runtime.patches import subclass_chat_completion_request; " - "subclass_chat_completion_request(); " + "from art_vllm_runtime.patches import apply_vllm_runtime_patches; " + "apply_vllm_runtime_patches(); " "from vllm.entrypoints.openai.chat_completion import protocol; " - "default_request = protocol.ChatCompletionRequest(" + "request = protocol.ChatCompletionRequest(" "model='m', messages=[{'role': 'user', 'content': 'x'}]" "); " - "explicit_false = protocol.ChatCompletionRequest(" - "model='m', messages=[{'role': 'user', 'content': 'x'}], " - "logprobs=False, top_logprobs=None, return_token_ids=False" - "); " - "explicit_none = protocol.ChatCompletionRequest(" - "model='m', messages=[{'role': 'user', 'content': 'x'}], " - "return_token_ids=None" - "); " "print(json.dumps({" - "'default': {" - "'logprobs': default_request.logprobs, " - "'top_logprobs': default_request.top_logprobs, " - "'return_token_ids': default_request.return_token_ids" - "}, " - "'default_fields_set': sorted(default_request.model_fields_set), " - "'explicit_false': {" - "'logprobs': explicit_false.logprobs, " - "'top_logprobs': explicit_false.top_logprobs, " - "'return_token_ids': explicit_false.return_token_ids" - "}, " - "'explicit_false_fields_set': sorted(explicit_false.model_fields_set), " - "'explicit_none_return_token_ids': explicit_none.return_token_ids, " - "'explicit_none_fields_set': sorted(explicit_none.model_fields_set)" + "'logprobs': request.logprobs, " + "'top_logprobs': request.top_logprobs, " + "'return_token_ids': request.return_token_ids" "}))", artifact_dir, "route_token_ids", ) - assert json.loads(payload.splitlines()[-1]) == { - "default": { - "logprobs": True, - "top_logprobs": 0, - "return_token_ids": True, - }, - "default_fields_set": ["messages", "model"], - "explicit_false": { - "logprobs": False, - "top_logprobs": None, - "return_token_ids": False, - }, - "explicit_false_fields_set": [ - "logprobs", - "messages", - "model", - "return_token_ids", - "top_logprobs", - ], - "explicit_none_return_token_ids": None, - "explicit_none_fields_set": ["messages", "model", "return_token_ids"], + assert json.loads(payload) == { + "logprobs": True, + "top_logprobs": 0, + "return_token_ids": True, } -def test_parallel_sampling_preserves_every_child_policy_span( +def test_runtime_lora_updates_linearize_request_admission( artifact_dir: Path, ) -> None: payload = _runtime_python( """ +import asyncio import json from types import SimpleNamespace -import art_vllm_runtime.policy_spans as policy -from vllm.sampling_params import RequestOutputKind -from vllm.v1.engine.output_processor import RequestState -from vllm.v1.engine.parallel_sampling import ParentRequest +from art_vllm_runtime.policy_spans import ( + LoraUpdateCoordinator, + PolicyLoRARequest, + _apply_lora_alias_policy_cache_salt, + publish_lora_slot_policy, + register_lora_alias, +) -policy._patch_output_processor_policy_span_accumulation() +async def main(): + slot = "model:active" + old = PolicyLoRARequest( + lora_name=slot, lora_int_id=1, lora_path="old", + policy_version=4, update_seq=1, + ) + new = PolicyLoRARequest( + lora_name=slot, lora_int_id=1, lora_path="new", + policy_version=5, update_seq=2, + ) + models = SimpleNamespace(lora_requests={slot: new}) + register_lora_alias(models, public_model_name="model@4", lora_slot=slot) + publish_lora_slot_policy( + models, lora_slot=slot, policy_version=5, update_seq=2 + ) + request = SimpleNamespace(model="model@4", cache_salt=None) + _apply_lora_alias_policy_cache_salt(models, request, new) -class Detokenizer: - output_token_ids = [10, 20] - def num_output_tokens(self): return 2 - def get_next_output_text(self, finished, delta): return "done" + coordinator = LoraUpdateCoordinator() + assert await coordinator.begin_update(slot) == 1 + await coordinator.commit_update(slot, old) + assert await coordinator.begin_update(slot) == 2 -class Logprobs: - logprobs = cumulative_logprob = prompt_logprobs = None + async def admit(): + async with coordinator.admission(slot) as state: + return state -parent = ParentRequest.__new__(ParentRequest) -parent.external_req_id = "parent" -parent.child_requests = {"0_parent", "1_parent"} -parent.output_aggregator = [None, None] -parent.sampling_params = SimpleNamespace( - output_kind=RequestOutputKind.FINAL_ONLY, n=2 -) + admission = asyncio.create_task(admit()) + await asyncio.sleep(0) + blocked = not admission.done() + await coordinator.commit_update(slot, new) + admitted_lora = await admission + print(json.dumps({ + "blocked": blocked, + "cache_salt": request.cache_salt, + "policy_version": admitted_lora.policy_version, + "lora_path": admitted_lora.lora_path, + }, sort_keys=True)) -def finish_child(index, policy_version): - state = RequestState( - request_id=f"{index}_parent", external_req_id="parent", - parent_req=parent, request_index=index, lora_request=None, - output_kind=RequestOutputKind.FINAL_ONLY, prompt="p", - prompt_token_ids=[1], prompt_embeds=None, - logprobs_processor=Logprobs(), detokenizer=Detokenizer(), - max_tokens_param=2, arrival_time=0.0, queue=None, - log_stats=False, stream_interval=1, - ) - policy._CURRENT_ENGINE_POLICY_SPANS = {state.request_id: [{ - "start_token": 0, "end_token": 2, - "policy_version": policy_version, - "lora_slot": "model:active", "update_seq": policy_version, - }]} - return state.make_request_output([10, 20], None, "stop", None) - -assert finish_child(0, 3) is None -result = finish_child(1, 4) -print(json.dumps([ - output.art_policy_token_spans[0]["policy_version"] - for output in result.outputs -])) +asyncio.run(main()) """, artifact_dir, - "parallel_sampling_policy_spans", + "lora_update_admission", ) - assert json.loads(payload) == [3, 4] + result = json.loads(payload) + cache_salt = result.pop("cache_salt") + assert result == { + "blocked": True, + "lora_path": "new", + "policy_version": 5, + } + assert cache_salt.startswith("art_policy_cache_salt=v1:") + assert len(cache_salt) == len("art_policy_cache_salt=v1:") + 64 -def test_runtime_lora_updates_linearize_request_admission( +def test_runtime_parallel_admission_is_atomic_and_cancellation_safe( artifact_dir: Path, ) -> None: payload = _runtime_python( """ import asyncio +from collections import defaultdict import json from types import SimpleNamespace + from art_vllm_runtime.policy_spans import ( - LoraUpdateCoordinator, - _set_policy_cache_salt, + LoraUpdateCoordinator, PolicyLoRARequest, _patch_engine_request_admission, ) +from vllm.sampling_params import SamplingParams +from vllm.v1.engine import EngineCoreRequest +from vllm.v1.engine.async_llm import AsyncLLM +from vllm.v1.engine.output_processor import OutputProcessor + +class Output: + abort_requests = OutputProcessor.abort_requests + + def __init__(self): + self.request_states = {} + self.parent_requests = {} + self.external_req_ids = defaultdict(list) + self.lora_states = SimpleNamespace(request_finished=lambda *_args: None) + + def add_request(self, request, _prompt, parent, _index, _queue): + self.request_states[request.request_id] = SimpleNamespace( + external_req_id=request.external_req_id, + lora_name=request.lora_request.lora_name, + parent_req=parent, + queue=None, + ) + self.external_req_ids[request.external_req_id].append(request.request_id) + if parent is not None: + self.parent_requests[parent.request_id] = parent + +class Core: + def __init__(self): + self.resources = SimpleNamespace(engine_dead=False) + self.calls = [] + self.aborted = [] + self.first = asyncio.Event() + self.release_first = asyncio.Event() + self.second = asyncio.Event() + self.release_second = asyncio.Event() + + async def add_request_async(self, request): + self.calls.append((request.request_id, request.lora_request.policy_version)) + if len(self.calls) == 1: + self.first.set() + await self.release_first.wait() + else: + self.second.set() + await self.release_second.wait() + + async def abort_requests_async(self, request_ids): + self.aborted.extend(request_ids) async def main(): + _patch_engine_request_admission() slot = "model:active" - old = SimpleNamespace(lora_name=slot, lora_path="old") - new = SimpleNamespace(lora_name=slot, lora_path="new") - request = SimpleNamespace(model=slot, cache_salt=None) - _set_policy_cache_salt(request, lora_slot=slot, policy_version=5) + old = PolicyLoRARequest( + lora_name=slot, lora_int_id=1, lora_path="old", + policy_version=1, update_seq=1, + ) + new = PolicyLoRARequest( + lora_name=slot, lora_int_id=1, lora_path="new", + policy_version=2, update_seq=2, + ) + coordinator = LoraUpdateCoordinator() + assert await coordinator.begin_update(slot) == 1 + await coordinator.commit_update(slot, old) + engine = object.__new__(AsyncLLM) + engine.engine_core = Core() + engine.output_handler = None + engine.vllm_config = SimpleNamespace( + cache_config=SimpleNamespace(kv_sharing_fast_prefill=False) + ) + engine.input_processor = SimpleNamespace(assign_request_id=lambda _request: None) + engine._run_output_handler = lambda: None + engine.output_processor = Output() + engine.log_requests = False + engine._art_lora_update_coordinator = coordinator + abort_started = asyncio.Event() + release_abort = asyncio.Event() + abort_finished = asyncio.Event() + + async def abort(request_id, internal=False): + abort_started.set() + await release_abort.wait() + await AsyncLLM.abort(engine, request_id, internal=internal) + abort_finished.set() + engine.abort = abort + params = SamplingParams(n=2, max_tokens=1) + request = EngineCoreRequest( + request_id="parent", external_req_id="external", prompt_token_ids=[1], + mm_features=None, sampling_params=params, pooling_params=None, + arrival_time=0.0, lora_request=old, cache_salt=None, + data_parallel_rank=None, + ) + admission = asyncio.create_task( + engine.add_request("parent", request, params, prompt_text="x") + ) + await engine.engine_core.first.wait() + update = asyncio.create_task(coordinator.begin_update(slot)) + await asyncio.sleep(0) + blocked_after_first = not update.done() + engine.engine_core.release_first.set() + await engine.engine_core.second.wait() + blocked_after_second = not update.done() + admission.cancel() + await abort_started.wait() + state = coordinator._states[slot] + await state.condition.acquire() + admission.cancel() + await asyncio.sleep(0) + blocked_during_abort = not admission.done() and not update.done() + maps_during_abort = [ + len(engine.output_processor.request_states), + len(engine.output_processor.external_req_ids), + len(engine.output_processor.parent_requests), + ] + release_abort.set() + await abort_finished.wait() + admission.cancel() + await asyncio.sleep(0) + blocked_during_release = not admission.done() and not update.done() + maps_after_abort = [ + len(engine.output_processor.request_states), + len(engine.output_processor.external_req_ids), + len(engine.output_processor.parent_requests), + ] + state.condition.release() + try: + await admission + except asyncio.CancelledError: + pass + update_seq = await asyncio.wait_for(update, timeout=1) + await coordinator.commit_update(slot, new) + print(json.dumps({ + "calls": engine.engine_core.calls, + "blocked": [blocked_after_first, blocked_after_second], + "blocked_cleanup": [blocked_during_abort, blocked_during_release], + "maps_during_abort": maps_during_abort, + "maps_after_abort": maps_after_abort, + "update_seq": update_seq, + "aborted": sorted(engine.engine_core.aborted), + "state_sizes": [ + len(engine.output_processor.request_states), + len(engine.output_processor.external_req_ids), + len(engine.output_processor.parent_requests), + ], + }, sort_keys=True)) + +asyncio.run(main()) +""", + artifact_dir, + "parallel_lora_admission", + ) + assert json.loads(payload.splitlines()[-1]) == { + "aborted": ["0_parent", "1_parent"], + "blocked": [True, True], + "blocked_cleanup": [True, True], + "calls": [["0_parent", 1], ["1_parent", 1]], + "maps_after_abort": [0, 0, 0], + "maps_during_abort": [2, 1, 1], + "state_sizes": [0, 0, 0], + "update_seq": 2, + } + + +def test_runtime_cancelled_update_releases_admission( + artifact_dir: Path, +) -> None: + payload = _runtime_python( + """ +import asyncio +import json +from art_vllm_runtime.policy_spans import LoraUpdateCoordinator, PolicyLoRARequest + +async def main(): coordinator = LoraUpdateCoordinator() - await coordinator.begin_update(slot) - await coordinator.commit_update(slot, 4, old) - await coordinator.begin_update(slot) + slot = "model:active" + entered = asyncio.Event() + release = asyncio.Event() - async def admit(): - async with coordinator.admission(slot) as state: - return state + async def hold_admission(): + async with coordinator.admission(slot): + entered.set() + await release.wait() - admission = asyncio.create_task(admit()) + holder = asyncio.create_task(hold_admission()) + await entered.wait() + update = asyncio.create_task(coordinator.begin_update(slot)) await asyncio.sleep(0) - blocked = not admission.done() - await coordinator.commit_update(slot, 5, new) - version, admitted_lora = await admission - - async with coordinator.admission(slot): - cancelled_update = asyncio.create_task(coordinator.begin_update(slot)) - await asyncio.sleep(0) - cancelled_update.cancel() - try: - await cancelled_update - except asyncio.CancelledError: - pass - async with coordinator.admission(slot) as recovered_state: - recovered = recovered_state[0] == 5 + update.cancel() + try: + await update + except asyncio.CancelledError: + pass + release.set() + await holder + async with asyncio.timeout(1): + async with coordinator.admission(slot): + admitted = True + failed_seq = await coordinator.begin_update(slot) + await coordinator.fail_update(slot, failed_seq) + cancelled_retry = await coordinator.begin_update(slot) + await coordinator.cancel_update(slot, cancelled_retry) + + async def admit_after_failure(): + async with coordinator.admission(slot): + return True + quarantined_admission = asyncio.create_task(admit_after_failure()) + await asyncio.sleep(0) + quarantine_preserved = not quarantined_admission.done() + recovery_seq = await coordinator.begin_update(slot) + await coordinator.commit_update(slot, PolicyLoRARequest( + lora_name=slot, lora_int_id=1, lora_path="recovered", + policy_version=2, update_seq=recovery_seq, + )) + recovered = await quarantined_admission print(json.dumps({ - "blocked": blocked, - "cache_salt": request.cache_salt, - "policy_version": version, - "lora_path": admitted_lora.lora_path, - "recovered_after_cancel": recovered, - }, sort_keys=True)) + "admitted": admitted, + "quarantine_preserved": quarantine_preserved, + "recovered": recovered, + })) asyncio.run(main()) """, artifact_dir, - "lora_update_admission", + "cancelled_lora_update", ) assert json.loads(payload) == { - "blocked": True, - "cache_salt": "art_policy_cache_salt=model:active:5", - "lora_path": "new", - "policy_version": 5, - "recovered_after_cancel": True, + "admitted": True, + "quarantine_preserved": True, + "recovered": True, } -def test_runtime_general_plugin_loads_full_patch_set() -> None: - pyproject = (ROOT / "vllm_runtime" / "pyproject.toml").read_text() - assert 'art = "art_vllm_runtime.patches:apply_vllm_runtime_patches"' in pyproject +def test_runtime_policy_history_rekeys_real_vllm_requests( + artifact_dir: Path, +) -> None: + payload = _runtime_python( + """ +import hashlib +import json +from types import SimpleNamespace + +from vllm.sampling_params import SamplingParams +from vllm.v1.core.block_pool import BlockPool +from vllm.v1.core.kv_cache_utils import get_request_block_hasher, init_none_hash +from vllm.v1.request import Request +from art_vllm_runtime.policy_spans import ( + PolicyLoRARequest, + _policy_history_from_cache_salt, + _patch_policy_cache_hashing, + _request_has_executed, + _set_policy_cache_salt, + _transition_scheduler_policy_history, +) + +def hash_value(value): + return hashlib.sha256(repr(value).encode()).digest() + +_patch_policy_cache_hashing() +init_none_hash(hash_value) +block_hasher = get_request_block_hasher(4, hash_value) +old = PolicyLoRARequest( + lora_name="model:active", lora_int_id=1, lora_path="old", + policy_version=4, update_seq=1, +) +new = PolicyLoRARequest( + lora_name="model:active", lora_int_id=1, lora_path="new", + policy_version=4, update_seq=2, +) + +def make_request(request_id): + request = Request( + request_id, list(range(8)), SamplingParams(max_tokens=4), None, + lora_request=old, block_hasher=block_hasher, + ) + _set_policy_cache_salt( + request, lora_slot=old.lora_name, + policy_version=old.policy_version, update_seq=old.update_seq, + ) + request.block_hashes.clear() + request.update_block_hashes() + return request + +continued = make_request("continued") +waiting = make_request("waiting") +old_hashes = list(continued.block_hashes) +continued.num_computed_tokens = 4 +scheduler = SimpleNamespace(requests={ + continued.request_id: continued, + waiting.request_id: waiting, +}, kv_cache_manager=SimpleNamespace( + block_pool=SimpleNamespace(hash_block_size=4), +)) +transition = _transition_scheduler_policy_history( + scheduler, + lora_request=new, + previous_policy=None, + started_request_ids={continued.request_id}, +) +continued_history = continued.cache_salt +continued_hashes = list(continued.block_hashes) +continued_transitions = continued._art_policy_cache_transitions +not_executed_after_update = not _request_has_executed(continued) +fresh = make_request("fresh") +_set_policy_cache_salt( + fresh, lora_slot=new.lora_name, + policy_version=new.policy_version, update_seq=new.update_seq, +) +fresh.block_hashes.clear() +fresh.update_block_hashes() +third = PolicyLoRARequest( + lora_name="model:active", lora_int_id=1, lora_path="third", + policy_version=4, update_seq=3, +) +intra = make_request("intra") +intra_pool = BlockPool(4, enable_caching=True, hash_block_size=4) +intra_blocks = intra_pool.get_new_blocks(1) +intra_pool.cache_full_blocks(intra, intra_blocks, 0, 1, 4, 0) +intra_old_hashes = list(intra.block_hashes) +intra.num_computed_tokens = 6 +_transition_scheduler_policy_history( + SimpleNamespace( + requests={intra.request_id: intra}, + kv_cache_manager=SimpleNamespace(block_pool=intra_pool), + ), + lora_request=new, + previous_policy=None, + started_request_ids={intra.request_id}, +) +multiple = make_request("multiple") +multiple.num_computed_tokens = 4 +multiple_scheduler = SimpleNamespace( + requests={multiple.request_id: multiple}, + kv_cache_manager=SimpleNamespace(block_pool=SimpleNamespace(hash_block_size=4)), +) +_transition_scheduler_policy_history( + multiple_scheduler, lora_request=new, previous_policy=None, + started_request_ids={multiple.request_id}, +) +multiple.num_computed_tokens = 8 +_transition_scheduler_policy_history( + multiple_scheduler, lora_request=third, previous_policy=None, + started_request_ids={multiple.request_id}, +) +old_history = _policy_history_from_cache_salt(make_request("old").cache_salt) +expected_third = make_request("expected-third") +_set_policy_cache_salt( + expected_third, lora_slot=third.lora_name, + policy_version=third.policy_version, update_seq=third.update_seq, + previous_digest=old_history, +) +_transition_scheduler_policy_history( + SimpleNamespace( + requests={continued.request_id: continued}, + kv_cache_manager=SimpleNamespace( + block_pool=SimpleNamespace(hash_block_size=4), + ), + ), + lora_request=third, + previous_policy=None, + started_request_ids=set(), +) +print(json.dumps({ + "transition": transition, + "continued_differs": continued_history != waiting.cache_salt, + "waiting_matches_fresh": waiting.cache_salt == fresh.cache_salt, + "same_version_reload_differs": ( + _policy_history_from_cache_salt(waiting.cache_salt) + != _policy_history_from_cache_salt(make_request("old").cache_salt) + ), + "block_hashes_changed": old_hashes != continued.block_hashes, + "computed_hash_preserved": old_hashes[0] == continued_hashes[0], + "future_hash_rekeyed": old_hashes[1] != continued_hashes[1], + "transition_boundary": continued_transitions[0][0], + "not_executed_after_update": not_executed_after_update, + "same_boundary_replaced": len(continued._art_policy_cache_transitions) == 1, + "skipped_policy_replaced": continued.cache_salt == expected_third.cache_salt, + "intra_block_boundary": intra._art_policy_cache_transitions[0][0], + "intra_block_prefix_preserved": ( + intra.block_hashes[0] == intra_old_hashes[0] + and intra_pool.get_cached_block(intra.block_hashes[0], [0]) == intra_blocks + ), + "intra_block_suffix_rekeyed": intra.block_hashes[1] != intra_old_hashes[1], + "multiple_boundaries": [ + item[0] for item in multiple._art_policy_cache_transitions + ], +})) +""", + artifact_dir, + "policy_history_real_request", + ) + assert json.loads(payload) == { + "block_hashes_changed": True, + "computed_hash_preserved": True, + "continued_differs": True, + "future_hash_rekeyed": True, + "intra_block_boundary": 6, + "intra_block_prefix_preserved": True, + "intra_block_suffix_rekeyed": True, + "multiple_boundaries": [4, 8], + "not_executed_after_update": True, + "same_boundary_replaced": True, + "same_version_reload_differs": True, + "skipped_policy_replaced": True, + "transition": {"continued_requests": 1, "updated_requests": 2}, + "transition_boundary": 4, + "waiting_matches_fresh": True, + } + + +def test_runtime_policy_preemption_rebases_and_republishes_real_block_pool( + artifact_dir: Path, +) -> None: + payload = _runtime_python( + """ +import hashlib +import json +from types import SimpleNamespace + +from art_vllm_runtime.policy_spans import ( + PolicyLoRARequest, + _patch_policy_cache_hashing, + _patch_scheduler_policy_span_transport, + _request_has_executed, + _set_policy_cache_salt, + _transition_scheduler_policy_history, +) +from vllm.sampling_params import SamplingParams +from vllm.v1.core.block_pool import BlockPool +from vllm.v1.core.kv_cache_utils import get_request_block_hasher, init_none_hash +from vllm.v1.core.sched.scheduler import Scheduler +from vllm.v1.request import Request, RequestStatus + +def hash_value(value): + return hashlib.sha256(repr(value).encode()).digest() + +def make_request(request_id, lora_request): + request = Request( + request_id, list(range(12)), SamplingParams(max_tokens=4), None, + lora_request=lora_request, block_hasher=block_hasher, + ) + request.cache_salt = "tenant" + _set_policy_cache_salt( + request, lora_slot=lora_request.lora_name, + policy_version=lora_request.policy_version, + update_seq=lora_request.update_seq, + ) + request.block_hashes.clear() + request.update_block_hashes() + return request + +_patch_policy_cache_hashing() +_patch_scheduler_policy_span_transport() +init_none_hash(hash_value) +block_hasher = get_request_block_hasher(4, hash_value) +first = PolicyLoRARequest( + lora_name="model:active", lora_int_id=1, lora_path="first", + policy_version=1, update_seq=1, +) +second = PolicyLoRARequest( + lora_name="model:active", lora_int_id=1, lora_path="second", + policy_version=2, update_seq=2, +) +latest = PolicyLoRARequest( + lora_name="model:active", lora_int_id=1, lora_path="latest", + policy_version=3, update_seq=3, +) +request = make_request("replay", first) +pool = BlockPool(10, enable_caching=True, hash_block_size=4) +transition_scheduler = SimpleNamespace( + requests={request.request_id: request}, + kv_cache_manager=SimpleNamespace(block_pool=pool), +) +request.num_computed_tokens = 4 +_transition_scheduler_policy_history( + transition_scheduler, lora_request=second, previous_policy=None, + started_request_ids={request.request_id}, +) +request.num_computed_tokens = 8 +_transition_scheduler_policy_history( + transition_scheduler, lora_request=latest, previous_policy=None, + started_request_ids={request.request_id}, +) +mixed_hashes = list(request.block_hashes) +mixed_blocks = pool.get_new_blocks(3) +pool.cache_full_blocks(request, mixed_blocks, 0, 3, 4, 0) + +waiting = [] +scheduler = object.__new__(Scheduler) +scheduler._free_request_blocks = lambda _request: pool.free_blocks( + reversed(mixed_blocks) +) +scheduler.encoder_cache_manager = SimpleNamespace(free=lambda _request: None) +scheduler._inflight_prefills = {request} +scheduler.waiting = SimpleNamespace(prepend_request=waiting.append) +scheduler.reset_preempted_req_ids = set() +scheduler.log_stats = False +request.status = RequestStatus.RUNNING +Scheduler._preempt_request(scheduler, request, 0.0) + +fresh = make_request("fresh", latest) +current_hashes = list(request.block_hashes) +full_replay = all(pool.get_cached_block(item, [0]) is None for item in current_hashes) +old_entries_preserved = all( + pool.get_cached_block(item, [0]) == [block] + for item, block in zip(mixed_hashes, mixed_blocks) +) +not_executed_after_rebase = not _request_has_executed(request) +replay_blocks = pool.get_new_blocks(3) +request.num_computed_tokens = request.num_tokens +pool.cache_full_blocks(request, replay_blocks, 0, 3, 4, 0) +published_current = all( + pool.get_cached_block(item, [0]) == [block] + for item, block in zip(current_hashes, replay_blocks) +) +print(json.dumps({ + "cache_salt_matches_fresh": request.cache_salt == fresh.cache_salt, + "current_hashes_match_fresh": current_hashes == fresh.block_hashes, + "full_replay": full_replay, + "lora_identity_preserved": request.lora_request is latest, + "mixed_hashes_cleared": request._art_policy_cache_transitions == (), + "not_executed_after_rebase": not_executed_after_rebase, + "old_entries_preserved": old_entries_preserved, + "preempted": ( + request.num_preemptions == 1 + and waiting == [request] + and request.request_id in scheduler.reset_preempted_req_ids + ), + "published_current": published_current, + "user_cache_salt_preserved": request.cache_salt.startswith("tenant|"), +})) +""", + artifact_dir, + "policy_preemption_rebase", + ) + assert json.loads(payload) == { + "cache_salt_matches_fresh": True, + "current_hashes_match_fresh": True, + "full_replay": True, + "lora_identity_preserved": True, + "mixed_hashes_cleared": True, + "not_executed_after_rebase": True, + "old_entries_preserved": True, + "preempted": True, + "published_current": True, + "user_cache_salt_preserved": True, + } -def test_lora_coordinator_supports_both_vllm_serving_layouts( +def test_runtime_policy_update_rejects_unsupported_rehash_paths( artifact_dir: Path, ) -> None: payload = _runtime_python( """ +import hashlib import json from types import SimpleNamespace -import art_vllm_runtime.policy_spans as policy -from vllm.entrypoints.openai.engine.serving import OpenAIServing -policy._patch_lora_update_coordinator() -legacy_patched = getattr( - OpenAIServing.__init__, "__art_lora_update_patched__", False +from art_vllm_runtime.policy_spans import ( + PolicyLoRARequest, + _apply_policy_lora_update, + _set_policy_cache_salt, +) +from vllm.sampling_params import SamplingParams +from vllm.v1.core.kv_cache_utils import get_request_block_hasher, init_none_hash +from vllm.v1.request import Request + +def hash_value(value): + return hashlib.sha256(repr(value).encode()).digest() + +init_none_hash(hash_value) +block_hasher = get_request_block_hasher(4, hash_value) +old = PolicyLoRARequest( + lora_name="model:active", lora_int_id=1, lora_path="old", + policy_version=1, update_seq=1, ) +payload = { + "lora_name": old.lora_name, "lora_int_id": old.lora_int_id, + "lora_path": "new", "base_model_name": None, + "tensorizer_config_dict": None, "is_3d_lora_weight": False, + "policy_version": 2, "update_seq": 2, +} + +def make_request(request_id): + request = Request( + request_id, list(range(8)), SamplingParams(max_tokens=4), None, + lora_request=old, block_hasher=block_hasher, + ) + _set_policy_cache_salt( + request, lora_slot=old.lora_name, + policy_version=old.policy_version, update_seq=old.update_seq, + ) + request.block_hashes.clear() + request.update_block_hashes() + request.num_computed_tokens = 4 + return request + +class Core: + def __init__(self, request, connector): + self.scheduler = SimpleNamespace( + requests={request.request_id: request}, connector=connector, + ) + self.collective_calls = 0 + self.pause_calls = [] + + def is_scheduler_paused(self): + return True + + def collective_rpc(self, *_args, **_kwargs): + self.collective_calls += 1 + raise AssertionError("unsafe update reached workers") + + def pause_scheduler(self, *args): + self.pause_calls.append(args) + +connector_request = make_request("connector") +connector_core = Core(connector_request, object()) +try: + _apply_policy_lora_update(connector_core, payload) +except RuntimeError as error: + connector_error = str(error) + +multimodal_request = make_request("multimodal") +multimodal_request.mm_features = [object()] +multimodal_hashes = list(multimodal_request.block_hashes) +multimodal_salt = multimodal_request.cache_salt +multimodal_core = Core(multimodal_request, None) +try: + _apply_policy_lora_update(multimodal_core, payload) +except RuntimeError as error: + multimodal_error = str(error) -class GenerateBaseServing: - def __init__(self, models, engine_client): - self.models = models - self.engine_client = engine_client - -real_import_module = policy.importlib.import_module -def import_module(name): - if name == "vllm.entrypoints.openai.engine.serving": - raise ModuleNotFoundError(name, name=name) - if name == "vllm.entrypoints.generate.base.serving": - return SimpleNamespace(GenerateBaseServing=GenerateBaseServing) - return real_import_module(name) - -policy.importlib.import_module = import_module -policy._patch_lora_update_coordinator() -models = SimpleNamespace() -engine_client = SimpleNamespace() -GenerateBaseServing(models, engine_client) print(json.dumps({ - "legacy_patched": legacy_patched, - "new_patched": getattr( - GenerateBaseServing.__init__, "__art_lora_update_patched__", False + "connector_error": connector_error, + "connector_preflight": ( + connector_core.collective_calls == 0 and not connector_core.pause_calls + ), + "multimodal_error": multimodal_error, + "multimodal_preflight": ( + multimodal_core.collective_calls == 0 and not multimodal_core.pause_calls ), - "shared_coordinator": ( - models._art_lora_update_coordinator - is engine_client._art_lora_update_coordinator + "multimodal_unchanged": ( + multimodal_request.lora_request is old + and multimodal_request.cache_salt == multimodal_salt + and multimodal_request.block_hashes == multimodal_hashes ), })) """, artifact_dir, - "vllm_serving_layouts", + "unsupported_policy_rehash", ) - assert json.loads(payload.splitlines()[-1]) == { - "legacy_patched": True, - "new_patched": True, - "shared_coordinator": True, + assert json.loads(payload) == { + "connector_error": ( + "Mutable policy updates cannot continue requests with a KV connector" + ), + "connector_preflight": True, + "multimodal_error": ( + "Mutable policy updates cannot continue multimodal requests" + ), + "multimodal_preflight": True, + "multimodal_unchanged": True, } -def test_runtime_patch_adds_gemma4_moe_topk_alias(artifact_dir: Path) -> None: +def test_runtime_policy_update_verifies_declared_identity_and_quarantines( + artifact_dir: Path, +) -> None: payload = _runtime_python( - "import json; " - "from art_vllm_runtime.patches import apply_vllm_runtime_patches; " - "apply_vllm_runtime_patches(); " - "from transformers import Gemma4TextConfig; " - "config = Gemma4TextConfig(enable_moe_block=True, top_k_experts=8); " - "print(json.dumps({'num_experts_per_tok': config.num_experts_per_tok}))", + """ +import json +from types import SimpleNamespace +from vllm.lora.request import LoRARequest +from art_vllm_runtime.policy_spans import ( + PolicyLoRARequest, + _apply_policy_lora_update, + _policy_metadata_for_lora_request, + _record_worker_lora_policy, +) + +declared = PolicyLoRARequest( + lora_name="model:active", lora_int_id=1, + lora_path="/mapped/step-999-deadbeef", policy_version=7, update_seq=3, +) +declared_state = _record_worker_lora_policy(declared) +bootstrap_state = _record_worker_lora_policy(LoRARequest( + lora_name="model:active", lora_int_id=2, + lora_path="/mapped/step-999-deadbeef", +)) +try: + _policy_metadata_for_lora_request(LoRARequest( + lora_name="model:active", lora_int_id=2, + lora_path="/mapped/step-999-deadbeef", + )) +except RuntimeError as error: + undeclared_failure = str(error) + +class FailingCore: + def __init__(self): + self.scheduler = SimpleNamespace(requests={}) + self.pause_calls = [] + + def is_scheduler_paused(self): + return True + + def collective_rpc(self, *_args, **_kwargs): + raise RuntimeError("rank 1 failed") + + def pause_scheduler(self, mode, clear_cache): + self.pause_calls.append((mode, clear_cache)) + +core = FailingCore() +try: + _apply_policy_lora_update(core, { + "lora_name": declared.lora_name, + "lora_int_id": declared.lora_int_id, + "lora_path": declared.lora_path, + "base_model_name": None, + "tensorizer_config_dict": None, + "is_3d_lora_weight": False, + "policy_version": declared.policy_version, + "update_seq": declared.update_seq, + }) +except RuntimeError as error: + failure = str(error) + +print(json.dumps({ + "declared_version": declared_state["policy_version"], + "bootstrap_path_not_inferred": bootstrap_state["policy_version"], + "failure": failure, + "pause_calls": core.pause_calls, + "undeclared_failure": undeclared_failure, +})) +""", artifact_dir, - "gemma4_topk_alias", + "declared_policy_and_quarantine", ) - assert json.loads(payload) == {"num_experts_per_tok": 8} + assert json.loads(payload) == { + "bootstrap_path_not_inferred": 0, + "declared_version": 7, + "failure": "rank 1 failed", + "pause_calls": [["abort", True]], + "undeclared_failure": ( + "Mutable LoRA slot 'model:active' has no declared policy identity" + ), + } -def test_runtime_patch_skips_gemma4_layerwise_weight_update_reload( +def test_runtime_policy_update_pins_workers_and_normalizes_scheduler_requests( artifact_dir: Path, ) -> None: payload = _runtime_python( - "import json; " - "from art_vllm_runtime.patches import apply_vllm_runtime_patches; " - "apply_vllm_runtime_patches(); " - "from vllm.v1.worker.gpu_worker import Worker; " - "HfConfig = type('HfConfig', (), {" - "'architectures': ['Gemma4ForConditionalGeneration']" - "}); " - "ModelConfig = type('ModelConfig', (), {'hf_config': HfConfig()}); " - "DummyWorker = type('DummyWorker', (), {" - "'model_config': ModelConfig(), " - "'_weight_update_active': False, " - "'_is_checkpoint_format': True, " - "'checks': 0, " - "'_check_weight_transfer_engine': " - "lambda self: setattr(self, 'checks', self.checks + 1)" - "}); " - "dummy = DummyWorker(); " - "Worker.start_weight_update(dummy, is_checkpoint_format=True); " - "active_after_start = dummy._weight_update_active; " - "Worker.finish_weight_update(dummy); " - "print(json.dumps({" - "'active_after_start': active_after_start, " - "'active_after_finish': dummy._weight_update_active, " - "'is_checkpoint_format': dummy._is_checkpoint_format, " - "'checks': dummy.checks" - "}))", + """ +import json +from pathlib import Path +from tempfile import TemporaryDirectory +from types import SimpleNamespace + +from art_vllm_runtime.policy_spans import ( + _apply_policy_lora_update, + _patch_policy_lora_update_rpc, +) +from vllm.lora.model_manager import AdapterLRUCache, LRUCacheLoRAModelManager +from vllm.lora.request import LoRARequest +from vllm.lora.worker_manager import LRUCacheWorkerLoRAManager +from vllm.v1.worker.worker_base import WorkerBase + +class TestAdapterManager(LRUCacheLoRAModelManager): + def __init__(self): + self.lora_config = SimpleNamespace(max_cpu_loras=2, max_loras=2) + self._registered_adapters = AdapterLRUCache(2, self.deactivate_adapter) + self._active_adapters = AdapterLRUCache(2, self._deactivate_adapter) + self.lora_index_to_id = [None, None] + self.modules = {} + + def _create_merged_loras_inplace(self, _lora): + pass + +class TestWorkerManager(LRUCacheWorkerLoRAManager): + def __init__(self): + self._adapter_manager = TestAdapterManager() + self.loaded_paths = [] + + def _load_adapter(self, request): + path = Path(request.lora_path) + if not path.is_dir(): + raise FileNotFoundError(path) + self.loaded_paths.append(path.name) + return SimpleNamespace(id=request.lora_int_id) + +class Core: + def __init__(self, worker): + self.worker = worker + self.acks = [] + initial = LoRARequest("model:active", 99, "/initial") + request = SimpleNamespace( + request_id="waiting", lora_request=initial, cache_salt=None, + block_hashes=[], num_computed_tokens=0, output_token_ids=[], + num_preemptions=0, update_block_hashes=lambda: None, + ) + self.scheduler = SimpleNamespace(requests={request.request_id: request}) + + def is_scheduler_paused(self): + return True + + def collective_rpc(self, method, args): + assert method == "art_load_lora_policy" + ack = WorkerBase.art_load_lora_policy(self.worker, args[0]) + self.acks.append(ack) + return [ack] + + def _reset_caches(self, **_kwargs): + pass + + def pause_scheduler(self, *_args): + raise AssertionError("successful update must not quarantine the engine") + +def policy_payload(path, policy_version, update_seq): + return { + "lora_name": "model:active", + "lora_int_id": 99, + "lora_path": str(path), + "base_model_name": None, + "tensorizer_config_dict": None, + "is_3d_lora_weight": False, + "policy_version": policy_version, + "update_seq": update_seq, + } + +def pinned(manager): + cache = manager._adapter_manager + return ( + 99 in manager.list_adapters() + and 99 in cache._registered_adapters.pinned_items + and 99 in cache._active_adapters.pinned_items + ) + +_patch_policy_lora_update_rpc() +manager = TestWorkerManager() +worker = SimpleNamespace( + add_lora=manager.add_adapter, + pin_lora=manager.pin_adapter, + list_loras=manager.list_adapters, +) +core = Core(worker) +request = core.scheduler.requests["waiting"] +with TemporaryDirectory() as temp_dir: + root = Path(temp_dir) + first_path = root / "active_1" + first_path.mkdir() + first = policy_payload(first_path, 1, 1) + first_transition = _apply_policy_lora_update(core, first) + first_result = core.acks[-1] + initially_pinned = pinned(manager) + first_path.rmdir() + manager.add_adapter(request.lora_request) + no_scheduler_reload = manager.loaded_paths == ["active_1"] + + for adapter_id, name in ((1, "exact"), (2, "eval")): + path = root / name + path.mkdir() + manager.add_adapter(LoRARequest(name, adapter_id, str(path))) + retained_under_pressure = pinned(manager) + + update_path = root / "active_2" + update_path.mkdir() + update = policy_payload(update_path, 2, 2) + update_transition = _apply_policy_lora_update(core, update) + update_result = core.acks[-1] + repinned = pinned(manager) + update_path.rmdir() + manager.add_adapter(request.lora_request) + + pressure_path = root / "eval_after_update" + pressure_path.mkdir() + manager.add_adapter(LoRARequest("eval_after_update", 3, str(pressure_path))) + retained_after_update = pinned(manager) + +expected_paths = ["active_1", "exact", "eval", "active_2", "eval_after_update"] +assert first_result["loaded"] and update_result["loaded"] +assert first_transition == update_transition == { + "continued_requests": 0, "updated_requests": 1, +} +assert manager.loaded_paths == expected_paths +assert not request.lora_request.load_inplace and no_scheduler_reload +assert initially_pinned and retained_under_pressure and repinned +assert retained_after_update +assert update_result["previous"]["update_seq"] == 1 +assert update_result["current"]["update_seq"] == 2 +print(json.dumps({ + "loaded_paths": manager.loaded_paths, + "load_inplace": request.lora_request.load_inplace, + "no_scheduler_reload": no_scheduler_reload, + "pinned_through_update_and_pressure": True, + "update_sequence": [ + update_result["previous"]["update_seq"], + update_result["current"]["update_seq"], + ], +})) +""", + artifact_dir, + "pinned_policy_lifetime", + ) + assert json.loads(payload) == { + "loaded_paths": ["active_1", "exact", "eval", "active_2", "eval_after_update"], + "load_inplace": False, + "no_scheduler_reload": True, + "pinned_through_update_and_pressure": True, + "update_sequence": [1, 2], + } + + +def test_runtime_declares_launch_policy_before_admission(artifact_dir: Path) -> None: + payload = _runtime_python( + """ +import asyncio +import json +from types import SimpleNamespace +from vllm.lora.request import LoRARequest +from art_vllm_runtime.policy_spans import ( + PolicyLoRARequest, + declare_initial_lora_policy, + lora_update_coordinator, +) + +class Core: + async def call_utility_async(self, method, payload): + assert method == "art_declare_loaded_lora_policy" + self.payload = payload + return {"workers": 2} + +async def main(): + slot = "model:active" + models = SimpleNamespace(lora_requests={slot: LoRARequest( + lora_name=slot, lora_int_id=3, lora_path="/initial" + )}) + core = Core() + engine = SimpleNamespace(engine_core=core) + await declare_initial_lora_policy( + models, engine, lora_slot=slot, policy_version=7 + ) + declared = models.lora_requests[slot] + coordinator = lora_update_coordinator(models, engine) + async with coordinator.admission(slot) as admitted: + admitted_identity = [admitted.policy_version, admitted.update_seq] + next_sequence = await coordinator.begin_update(slot) + await coordinator.cancel_update(slot, next_sequence) + return { + "declared_type": type(declared).__name__, + "declared_identity": [declared.policy_version, declared.update_seq], + "worker_identity": [core.payload["policy_version"], core.payload["update_seq"]], + "admitted_identity": admitted_identity, + "next_sequence": next_sequence, + } + +print(json.dumps(asyncio.run(main()))) +""", + artifact_dir, + "launch_policy_declaration", + ) + assert json.loads(payload) == { + "admitted_identity": [7, 1], + "declared_identity": [7, 1], + "declared_type": "PolicyLoRARequest", + "next_sequence": 2, + "worker_identity": [7, 1], + } + + +def test_runtime_policy_spans_survive_parallel_sample_aggregation( + artifact_dir: Path, +) -> None: + payload = _runtime_python( + """ +import json +from types import SimpleNamespace + +from vllm.v1.engine.output_processor import RequestState + + +def aggregate_final_outputs(self, new_token_ids, *args, **kwargs): + if not self.finished: + return None + self.parent_req.outputs[self.request_index] = SimpleNamespace( + index=self.request_index + ) + self.parent_req.finished += 1 + if self.parent_req.finished < len(self.parent_req.outputs): + return None + return SimpleNamespace(outputs=self.parent_req.outputs) + + +RequestState.make_request_output = aggregate_final_outputs +import art_vllm_runtime.policy_spans as policy_spans + +policy_spans._patch_output_processor_policy_span_accumulation() +parent = SimpleNamespace(outputs=[None] * 4, finished=0) +none_count = 0 +final_output = None +for choice_index in range(4): + detokenizer = SimpleNamespace(num_output_tokens=lambda: 0) + state = SimpleNamespace( + request_id=f"child-{choice_index}", + request_index=choice_index, + parent_req=parent, + detokenizer=detokenizer, + finished=False, + ) + for token_index in range(5): + detokenizer.num_output_tokens = lambda count=token_index + 1: count + state.finished = token_index == 4 + policy_spans._CURRENT_ENGINE_POLICY_SPANS = { + state.request_id: [{ + "start_token": 0, + "end_token": 1, + "policy_version": 7, + "lora_slot": "model:active", + "update_seq": 3, + }] + } + output = RequestState.make_request_output(state, [100 + token_index]) + if output is None: + none_count += 1 + else: + final_output = output + +print(json.dumps({ + "none_count": none_count, + "spans": [ + getattr(output, policy_spans.ART_POLICY_TOKEN_SPANS_FIELD, None) + for output in final_output.outputs + ], +}, sort_keys=True)) +""", + artifact_dir, + "parallel_sample_policy_spans", + ) + assert json.loads(payload) == { + "none_count": 19, + "spans": [ + [ + { + "end_token": 5, + "lora_slot": "model:active", + "policy_version": 7, + "start_token": 0, + "update_seq": 3, + } + ] + ] + * 4, + } + + +def test_runtime_general_plugin_loads_full_patch_set() -> None: + pyproject = (ROOT / "vllm_runtime" / "pyproject.toml").read_text() + assert 'art = "art_vllm_runtime.patches:apply_vllm_runtime_patches"' in pyproject + + +def test_runtime_patch_selects_checkpoint_weight_update_lifecycle( + artifact_dir: Path, +) -> None: + payload = _runtime_python( + """ +import json +from types import SimpleNamespace + +from art_vllm_runtime.patches import apply_vllm_runtime_patches + +apply_vllm_runtime_patches() +from vllm.v1.worker.gpu_worker import Worker + + +class Engine: + def __init__(self): + self.starts = self.updates = self.finishes = 0 + + def start_weight_update(self): + self.starts += 1 + + def update_weights(self, update_info): + self.updates += 1 + + def finish_weight_update(self): + self.finishes += 1 + + +def exercise(architecture): + engine = Engine() + worker = SimpleNamespace( + model_config=SimpleNamespace( + hf_config=SimpleNamespace(architectures=[architecture]) + ), + weight_transfer_engine=engine, + _weight_update_active=False, + _check_weight_transfer_engine=lambda: None, + ) + Worker.start_weight_update(worker) + Worker.update_weights(worker, {"names": []}) + Worker.finish_weight_update(worker) + return { + "starts": engine.starts, + "updates": engine.updates, + "finishes": engine.finishes, + "active": worker._weight_update_active, + } + + +print(json.dumps({ + "dense": exercise("Qwen3ForCausalLM"), + "gemma4": exercise("Gemma4ForConditionalGeneration"), +}, sort_keys=True)) +""", artifact_dir, - "gemma4_weight_update_reload", + "checkpoint_weight_update_lifecycle", ) assert json.loads(payload) == { - "active_after_start": True, - "active_after_finish": False, - "is_checkpoint_format": True, - "checks": 2, + "dense": {"active": False, "finishes": 1, "starts": 1, "updates": 1}, + "gemma4": {"active": False, "finishes": 0, "starts": 0, "updates": 1}, } @@ -379,39 +1304,3 @@ def test_runtime_cli_serializes_lora_target_modules_as_single_nargs_vector( "lora_target_modules", ) assert json.loads(payload) == ["--lora-target-modules", "a", "b"] - - -def test_runtime_project_restores_nccl_unique_id_from_raw_bytes( - artifact_dir: Path, -) -> None: - payload = json.loads( - _runtime_python( - "import ctypes, json; " - "from art_vllm_runtime.patches import _restore_nccl_unique_id_payload; " - "from vllm.distributed.device_communicators.pynccl_wrapper import ncclUniqueId; " - "payload = bytes(range(128)); " - "restored = _restore_nccl_unique_id_payload(payload, ncclUniqueId()); " - "print(json.dumps({" - "'type': type(restored).__name__, " - "'matches': ctypes.string_at(ctypes.byref(restored), ctypes.sizeof(restored)).hex() == payload.hex()" - "}))", - artifact_dir, - "restore", - ) - ) - assert payload == {"type": "ncclUniqueId", "matches": True} - - -def test_runtime_project_nccl_wrapper_accepts_raw_bytes(artifact_dir: Path) -> None: - payload = json.loads( - _runtime_python( - "import json; " - "from art_vllm_runtime.patches import _normalize_nccl_comm_init_rank_unique_id; " - "FakeLibrary = type('FakeLibrary', (), {'unique_id_from_bytes': lambda self, data: {'restored': len(data)}}); " - "restored = _normalize_nccl_comm_init_rank_unique_id(FakeLibrary(), bytes(range(128))); " - "print(json.dumps(restored))", - artifact_dir, - "nccl_wrapper", - ) - ) - assert payload == {"restored": 128} diff --git a/tests/integration/megatron/runtime_isolation/test_service_runtime_boundary.py b/tests/integration/megatron/runtime_isolation/test_service_runtime_boundary.py index c6c32685a..eb4e5c1a6 100644 --- a/tests/integration/megatron/runtime_isolation/test_service_runtime_boundary.py +++ b/tests/integration/megatron/runtime_isolation/test_service_runtime_boundary.py @@ -1,259 +1,577 @@ -import json +import asyncio +import os from pathlib import Path +import signal import subprocess import sys +import time from types import SimpleNamespace from typing import Any, cast -from unittest.mock import AsyncMock +from unittest.mock import AsyncMock, Mock import httpx import pytest -import art -from art.megatron.optimizer_state import ( - optimizer_generation_files, - read_optimizer_commit, -) -from art.megatron.runtime.jobs import ( - OPTIMIZER_READY_EVENT, - MegatronOptimizerSaveJob, -) -from art.megatron.service import MegatronService -from art.serving_capabilities import ServingCapabilities +def _process_is_running(pid: int) -> bool: + try: + state = Path(f"/proc/{pid}/stat").read_text().split()[2] + except FileNotFoundError: + return False + return state != "Z" -@pytest.fixture(autouse=True) -def _init_megatron_runtime_config(monkeypatch: pytest.MonkeyPatch) -> None: - from art.megatron import runtime_config - monkeypatch.setattr(runtime_config, "_MEGATRON_RUNTIME_CONFIG", None) - art.init_megatron_runtime_config( - topology=art.MegatronTopologyConfig(tp=1, cp=2, ep=2, etp=1), - packed_sequence_length=1024, - streaming_weight_offload=True, +@pytest.mark.asyncio +async def test_publication_wait_is_reserved_before_next_train_can_expire_it() -> None: + from art.megatron.runtime.monarch import ( + MonarchTrainerRun, + _PublicationState, ) + run = MonarchTrainerRun.__new__(MonarchTrainerRun) + future = asyncio.get_running_loop().create_future() + state = _PublicationState("generation-1", future) + state.train_done = True + run._publications = {state.generation_id: state} -class _AsyncOkResponse: - status_code = 200 + waiter = run.wait_for_publication(state.generation_id) + assert state.active_waiters == 1 + run._expire_prior_publications() + assert state.generation_id in run._publications - def raise_for_status(self) -> None: - return None + future.set_result(()) + assert await waiter == () + assert state.generation_id not in run._publications -class _RecordingAsyncClient: - def __init__( - self, posts: list[tuple[str, dict[str, object] | None, float]] - ) -> None: - self._posts = posts - - async def __aenter__(self): - return self +@pytest.mark.skipif( + sys.platform != "linux", reason="requires Linux parent-death signal" +) +def test_owned_local_worker_dies_when_controller_is_sigkilled( + tmp_path: Path, +) -> None: + pid_path = tmp_path / "worker.pid" + program = """ +import signal +import sys +from pathlib import Path - async def __aexit__(self, exc_type, exc, tb): - return None +from art.distributed.monarch_bootstrap import _start_worker + +worker = _start_worker("tcp://127.0.0.1:0") +Path(sys.argv[1]).write_text(str(worker.process.pid)) +signal.pause() +""" + parent = subprocess.Popen( + [sys.executable, "-c", program, str(pid_path)], + cwd=Path(__file__).resolve().parents[4], + env={**os.environ, "CUDA_VISIBLE_DEVICES": ""}, + stdout=subprocess.DEVNULL, + stderr=subprocess.PIPE, + text=True, + ) + assert parent.stderr is not None + worker_pid: int | None = None + try: + deadline = time.monotonic() + 30 + while not pid_path.exists() and parent.poll() is None: + if time.monotonic() >= deadline: + break + time.sleep(0.05) + if not pid_path.exists(): + detail = parent.stderr.read() if parent.poll() is not None else "timeout" + pytest.fail(f"controller did not start a worker: {detail}") + worker_pid = int(pid_path.read_text()) + assert _process_is_running(worker_pid) + + os.kill(parent.pid, signal.SIGKILL) + parent.wait(timeout=10) + deadline = time.monotonic() + 10 + while _process_is_running(worker_pid) and time.monotonic() < deadline: + time.sleep(0.05) + assert not _process_is_running(worker_pid) + finally: + if parent.poll() is None: + parent.kill() + parent.wait(timeout=10) + if worker_pid is not None and _process_is_running(worker_pid): + os.kill(worker_pid, signal.SIGKILL) + + +@pytest.mark.skipif(sys.platform != "linux", reason="requires Linux process identity") +def test_local_start_reconciles_legacy_owned_orphan( + tmp_path: Path, + monkeypatch: pytest.MonkeyPatch, +) -> None: + import art.distributed.monarch_bootstrap as bootstrap - async def post( - self, - url: str, - *, - params: dict[str, object] | None = None, - json: dict[str, object] | None = None, - timeout: float, - ) -> _AsyncOkResponse: - self._posts.append((url, json if json is not None else params, timeout)) - return _AsyncOkResponse() + monkeypatch.setattr(bootstrap, "_WORKER_LOCK_ROOT", tmp_path) + address = bootstrap._resolve_ephemeral_worker_address("tcp://127.0.0.1:0") + bootstrap._worker_lock_path(address).touch() + program = f""" +import os +import subprocess +import sys +worker_code = {bootstrap._LEGACY_OWNED_WORKER_CODE!r} -def test_megatron_default_lora_adapter_config_uses_model_lora_config( +worker = subprocess.Popen( + [sys.executable, "-c", worker_code, sys.argv[1]], + stdin=subprocess.DEVNULL, + stdout=subprocess.DEVNULL, + stderr=subprocess.DEVNULL, + start_new_session=True, +) +print(worker.pid, flush=True) +os._exit(0) +""" + launcher = subprocess.run( + [sys.executable, "-c", program, address], + cwd=Path(__file__).resolve().parents[4], + env={**os.environ, "CUDA_VISIBLE_DEVICES": ""}, + capture_output=True, + text=True, + check=True, + timeout=10, + ) + orphan_pid = int(launcher.stdout) + worker = None + try: + deadline = time.monotonic() + 10 + while time.monotonic() < deadline: + identity = bootstrap._process_identity(orphan_pid) + if identity is not None and identity[1] == 1: + break + time.sleep(0.05) + else: + pytest.fail("legacy worker was not reparented") + + worker = bootstrap._start_worker("tcp://127.0.0.1:0", startup_timeout_s=30) + assert not _process_is_running(orphan_pid) + finally: + if worker is not None: + bootstrap._stop_worker(worker) + if _process_is_running(orphan_pid): + os.kill(orphan_pid, signal.SIGKILL) + + +@pytest.mark.skipif(sys.platform != "linux", reason="requires Linux process identity") +def test_orphan_reconciliation_never_targets_unrelated_process( tmp_path: Path, + monkeypatch: pytest.MonkeyPatch, ) -> None: - service = MegatronService( - model_name="test-model", - base_model="Qwen/Qwen3-0.6B", - config={ - "lora_config": { - "rank": 8, - "target_modules": ["q_proj", "down_proj"], - }, - }, - output_dir=str(tmp_path), + import art.distributed.monarch_bootstrap as bootstrap + + monkeypatch.setattr(bootstrap, "_WORKER_LOCK_ROOT", tmp_path) + unrelated = subprocess.Popen( + [sys.executable, "-c", "import time; time.sleep(30)"], + start_new_session=True, ) + try: + identity = bootstrap._process_identity(unrelated.pid) + assert identity is not None + address = "tcp://127.0.0.1:43219" + metadata = bootstrap._OwnedWorkerMetadata( + address=address, + controller_pid=2**30, + controller_start_time=1, + worker_pid=unrelated.pid, + worker_start_time=identity[0], + python_executable=os.path.realpath(sys.executable), + worker_code_sha256="0" * 64, + ownership_token="0" * 32, + ) + bootstrap._worker_lock_path(address).write_text(metadata.model_dump_json()) - config = service._default_lora_adapter_config() + bootstrap._reconcile_orphaned_workers() - assert config.r == 8 - assert config.target_modules == {"q_proj", "down_proj"} + assert unrelated.poll() is None + assert bootstrap._worker_lock_path(address).exists() + finally: + unrelated.terminate() + unrelated.wait(timeout=10) @pytest.mark.asyncio -async def test_megatron_in_flight_eval_uses_immutable_adapter_slot( +async def test_trainer_run_close_retries_failed_proc_mesh_stop() -> None: + from art.megatron.runtime.monarch import MonarchTrainerRun + + class ProcMesh: + def __init__(self) -> None: + self.stop_calls = 0 + + async def stop(self) -> None: + self.stop_calls += 1 + if self.stop_calls == 1: + raise RuntimeError("injected stop failure") + + proc_mesh = ProcMesh() + supervision = SimpleNamespace(close=Mock()) + run = MonarchTrainerRun.__new__(MonarchTrainerRun) + run.run_spec = SimpleNamespace(shutdown_timeout_s=1.0) + run._proc_mesh = cast(Any, proc_mesh) + run._supervision = supervision + run._stop_task = None + run._close_task = None + run._closed = False + run._valid = False + run._active_job_id = None + run._active_receive = None + run._active_collective = None + + with pytest.raises(RuntimeError, match="injected stop failure"): + await run.close() + assert proc_mesh.stop_calls == 1 + supervision.close.assert_not_called() + + await run.close() + assert proc_mesh.stop_calls == 2 + supervision.close.assert_called_once_with() + + +@pytest.mark.asyncio +async def test_art_runtime_stop_trainer_retains_failed_run() -> None: + from art.distributed.art_runtime import ArtRuntime + + class Run: + def __init__(self) -> None: + self.close = AsyncMock( + side_effect=[RuntimeError("injected close failure"), None] + ) + + runtime = ArtRuntime.__new__(ArtRuntime) + run = Run() + runtime._trainer_runs = {run} + + with pytest.raises(RuntimeError, match="injected close failure"): + await runtime.stop_trainer(run) + assert run in runtime._trainer_runs + + await runtime.stop_trainer(run) + assert run not in runtime._trainer_runs + assert run.close.await_count == 2 + + +@pytest.mark.asyncio +async def test_distributed_service_close_retries_owned_resources( tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, ) -> None: - service = MegatronService( - model_name="test-model", - base_model="Qwen/Qwen3-0.6B", - config={ - "rollout_weights_mode": "lora", - "rollout_weight_update_mode": "in_flight_lora", - }, + from art.megatron.distributed_service import DistributedMegatronService + + class Runtime: + def __init__(self) -> None: + self.trainer_stops = 0 + self.model_stops = 0 + + async def stop_trainer(self, _trainer: object) -> None: + self.trainer_stops += 1 + if self.trainer_stops == 1: + raise RuntimeError("injected trainer stop failure") + + async def stop_model_service(self, _name: str) -> None: + self.model_stops += 1 + if self.model_stops == 1: + raise RuntimeError("injected model stop failure") + + runtime = Runtime() + service = DistributedMegatronService( + model_name="model", + base_model="base", + config=cast(Any, {"rollout_weights_mode": "lora"}), output_dir=str(tmp_path), + runtime=cast(Any, runtime), + enable_expert_replay=False, ) - service._vllm_runtime.port = 8123 - posts: list[tuple[str, dict[str, object] | None, float]] = [] - monkeypatch.setattr(httpx, "AsyncClient", lambda: _RecordingAsyncClient(posts)) + trainer = object() + service._trainer = trainer + service._managed_service_name = "model" + + with pytest.raises(BaseExceptionGroup): + await service.aclose() + assert service._trainer is trainer + assert service._managed_service_name == "model" + + await service.aclose() + assert service._trainer is None + assert service._managed_service_name is None + assert (runtime.trainer_stops, runtime.model_stops) == (2, 2) - checkpoint_path = str(tmp_path / "checkpoints" / "4") - assert ( - await service.acquire_exact_adapter(4, checkpoint_path) == "test-model:eval@4" + +@pytest.mark.asyncio +async def test_failed_vllm_start_rollback_remains_runtime_owned( + monkeypatch: pytest.MonkeyPatch, +) -> None: + import art.distributed.art_runtime as art_runtime + + class Manager: + def __init__(self, *_args: object, **_kwargs: object) -> None: + self.stop_calls = 0 + + async def start(self) -> None: + raise RuntimeError("injected startup rollback failure") + + async def stop(self) -> object: + self.stop_calls += 1 + if self.stop_calls == 1: + raise RuntimeError("injected rollback retry failure") + return object() + + monkeypatch.setattr(art_runtime, "ReplicaManager", Manager) + runtime = art_runtime.ArtRuntime.__new__(art_runtime.ArtRuntime) + runtime._started = True + runtime._closed = False + runtime._model_services = {} + runtime._host_services = {"host": object()} + runtime._adapter_services = {"host": object()} + runtime._preflight_launch = AsyncMock() + spec = SimpleNamespace( + name="model", + members=(SimpleNamespace(host_id="host", gpu_ids=(0,)),), + rendezvous=SimpleNamespace(host="127.0.0.1"), ) - assert ( - await service.acquire_exact_adapter(4, checkpoint_path) == "test-model:eval@4" + runtime.topology = SimpleNamespace( + cluster=SimpleNamespace(startup_timeout_s=1.0, rpc_timeout_s=1.0), + model_services=(spec,), ) - assert posts == [ - ( - "http://127.0.0.1:8123/v1/load_lora_adapter", - { - "lora_name": "test-model:eval@4", - "lora_path": checkpoint_path, - }, - 60.0, - ) - ] - assert service._loaded_exact_adapter_steps == {4} + with pytest.raises(RuntimeError, match="injected startup rollback failure"): + await runtime.start_model_service(cast(Any, spec), cast(Any, object())) + assert "model" in runtime._model_services - await service.release_exact_adapter(4) - assert service._loaded_exact_adapter_steps == {4} - await service.release_exact_adapter(4) + with pytest.raises(RuntimeError, match="injected rollback retry failure"): + await runtime.stop_model_service("model") + assert "model" in runtime._model_services - assert posts[-1] == ( - "http://127.0.0.1:8123/v1/unload_lora_adapter", - {"lora_name": "test-model:eval@4"}, - 30.0, - ) - assert service._loaded_exact_adapter_steps == set() + await runtime.stop_model_service("model") + assert "model" not in runtime._model_services - service._loaded_exact_adapter_steps.add(5) - await service.prune_loaded_adapters(retain_steps=set()) - assert posts[-1] == ( - "http://127.0.0.1:8123/v1/unload_lora_adapter", - {"lora_name": "test-model:eval@5"}, - 30.0, +@pytest.mark.asyncio +async def test_vllm_host_member_close_retries_without_losing_owner( + tmp_path: Path, +) -> None: + from art.distributed.vllm_replica import ManagedVllmHostLauncher + + class MemberRuntime: + def __init__(self) -> None: + self.close_calls = 0 + + def close(self) -> None: + self.close_calls += 1 + if self.close_calls == 1: + raise RuntimeError("injected member close failure") + + key = ("replica", "member", 0) + member_runtime = MemberRuntime() + launcher = ManagedVllmHostLauncher(str(tmp_path)) + launcher._members[key] = cast( + Any, + SimpleNamespace( + runtime=member_runtime, + supervisor=SimpleNamespace(close=Mock()), + ), ) - assert service._loaded_exact_adapter_steps == set() + + with pytest.raises(RuntimeError, match="injected member close failure"): + await launcher.stop_member(*key) + assert key in launcher._members + + await launcher.stop_member(*key) + assert key not in launcher._members + assert member_runtime.close_calls == 2 @pytest.mark.asyncio -async def test_external_in_flight_update_maps_checkpoint_path( +async def test_cancelled_megatron_close_keeps_runtimes_until_services_stop( tmp_path: Path, monkeypatch: pytest.MonkeyPatch, ) -> None: - local_root = str(tmp_path / "local") - service = MegatronService( - model_name="test-model", - base_model="Qwen/Qwen3-0.6B", - config={ - "rollout_weights_mode": "lora", - "rollout_weight_update_mode": "in_flight_lora", - "vllm_runtime": { - "mode": "external", - "server_url": "http://inference:8000", - "local_checkpoint_root": local_root, - "server_checkpoint_root": "/remote", - }, - }, - output_dir=str(tmp_path), - ) - service._serving_capabilities = ServingCapabilities( - runtime="art_vllm", - protocol_version=1, - in_flight_lora_updates=True, - policy_token_spans=True, - ) - posts: list[tuple[str, dict[str, object] | None, float]] = [] - monkeypatch.setattr(httpx, "AsyncClient", lambda: _RecordingAsyncClient(posts)) + from art.megatron.backend import MegatronBackend + + service_started = asyncio.Event() + release_service = asyncio.Event() + events: list[str] = [] + + class Service: + propagate_close_errors = True + + async def aclose(self) -> None: + events.append("service_started") + service_started.set() + await release_service.wait() + events.append("service_stopped") + + class Runtime: + async def close(self) -> None: + events.append("runtime_stopped") + + monkeypatch.setattr("art.local.backend.close_proxy", lambda _service: None) + monkeypatch.setattr("art.local.backend.torch.cuda.is_available", lambda: False) + backend = MegatronBackend(path=str(tmp_path)) + key = ("project", "model") + backend._services[key] = cast(Any, Service()) + backend._owned_runtimes[key] = cast(Any, Runtime()) + + close = asyncio.create_task(backend.close()) + await service_started.wait() + close.cancel() + await asyncio.sleep(0) + assert events == ["service_started"] + + release_service.set() + with pytest.raises(asyncio.CancelledError): + await close + assert events == ["service_started", "service_stopped", "runtime_stopped"] + assert not backend._services + assert not backend._owned_runtimes - await service._update_in_flight_adapter(f"{local_root}/model/0004", 4) - assert posts[0][1] == { - "model_name": "test-model:active", - "lora_slot": "test-model:active", - "lora_path": "/remote/model/0004", - "policy_version": 4, - } +@pytest.mark.asyncio +async def test_megatron_close_retries_services_before_owned_runtimes( + tmp_path: Path, + monkeypatch: pytest.MonkeyPatch, +) -> None: + from art.megatron.backend import MegatronBackend + + class Service: + propagate_close_errors = True + + def __init__(self) -> None: + self.close_calls = 0 + + async def aclose(self) -> None: + self.close_calls += 1 + if self.close_calls == 1: + raise RuntimeError("injected service close failure") + + class Runtime: + def __init__(self) -> None: + self.close_calls = 0 + + async def close(self) -> None: + self.close_calls += 1 + if self.close_calls == 1: + raise RuntimeError("injected runtime close failure") + + monkeypatch.setattr("art.local.backend.close_proxy", lambda _service: None) + monkeypatch.setattr("art.local.backend.torch.cuda.is_available", lambda: False) + backend = MegatronBackend(path=str(tmp_path)) + key = ("project", "model") + service = Service() + runtime = Runtime() + backend._services[key] = cast(Any, service) + backend._owned_runtimes[key] = cast(Any, runtime) + + with pytest.raises(BaseExceptionGroup): + await backend.close() + assert backend._services[key] is service + assert backend._owned_runtimes[key] is runtime + assert runtime.close_calls == 0 + + with pytest.raises(BaseExceptionGroup): + await backend.close() + assert key not in backend._services + assert backend._owned_runtimes[key] is runtime + + await backend.close() + assert not backend._owned_runtimes + assert (service.close_calls, runtime.close_calls) == (2, 2) @pytest.mark.asyncio -async def test_clean_training_finalization_submits_latest_optimizer_save( +async def test_owned_model_runtimes_reserve_disjoint_local_endpoints( tmp_path: Path, monkeypatch: pytest.MonkeyPatch, ) -> None: - service = MegatronService( - model_name="test-model", - base_model="Qwen/Qwen3-0.6B", - config={}, - output_dir=str(tmp_path), - ) - service._latest_step = 4 - service._megatron_process = cast(Any, object()) - (tmp_path / "checkpoints" / "0004").mkdir(parents=True) - optimizer_dir = tmp_path / "optimizer_states" - optimizer_dir.mkdir() - (optimizer_dir / optimizer_generation_files(4, 1)[0]).write_bytes(b"state") - written: list[MegatronOptimizerSaveJob] = [] + import torch + + from art.distributed.art_runtime import ArtRuntime + from art.megatron.backend import MegatronBackend + + class Model: + project = "project" + base_model = "/tmp/base" + _internal_config: dict[str, object] = {} + + def __init__(self, name: str) -> None: + self.name = name + + def _storage_name(self) -> str: + return self.name + + async def start_local(topology: object) -> object: + return SimpleNamespace(topology=topology, close=AsyncMock()) + monkeypatch.setattr(ArtRuntime, "start_local", staticmethod(start_local)) monkeypatch.setattr( - "art.megatron.service.read_optimizer_commit", lambda _path: None + "art.megatron.runtime.local.get_megatron_runtime_config", + lambda: SimpleNamespace( + topology={"tp": 1, "ep": 1, "etp": 1, "cp": 1, "pp": 1} + ), ) - monkeypatch.setattr( - service, - "_create_megatron_job_paths", - lambda: (str(tmp_path / "job.json"), str(tmp_path / "job.log")), + monkeypatch.delenv("CUDA_VISIBLE_DEVICES", raising=False) + monkeypatch.setattr(torch.cuda, "device_count", lambda: 4) + monkeypatch.setattr("art.local.backend.torch.cuda.is_available", lambda: False) + backend = MegatronBackend(path=str(tmp_path)) + first = Model("first") + second = Model("second") + + first_runtime = await backend._ensure_runtime( + cast(Any, first), cast(Any, {"trainer_gpu_ids": [0]}) ) - monkeypatch.setattr( - "art.megatron.service.write_megatron_job", - lambda job, **_kwargs: written.append(job), + second_runtime = await backend._ensure_runtime( + cast(Any, second), cast(Any, {"trainer_gpu_ids": [1]}) ) + first_service = first_runtime.topology.model_services[0] + second_service = second_runtime.topology.model_services[0] + first_ports = { + first_service.leader_endpoint.port, + first_service.rendezvous.port, + } + second_ports = { + second_service.leader_endpoint.port, + second_service.rendezvous.port, + } + + assert len(first_ports) == len(second_ports) == 2 + assert first_ports.isdisjoint(second_ports) + with pytest.raises(ValueError, match="already reserved"): + await backend._configure_owned_api_port( + cast(Any, first), second_service.leader_endpoint.port + ) - async def completed_job(*_args: Any, **_kwargs: Any): - yield {"event": OPTIMIZER_READY_EVENT, "step": 4, "world_size": 1} + await backend.close() + assert not backend._owned_runtime_ports + assert not backend._local_endpoints._owned - monkeypatch.setattr("art.megatron.service.stream_megatron_job", completed_job) - await service.finalize_training_session() +class _AsyncOkResponse: + status_code = 200 - assert len(written) == 1 - assert written[0].step == 4 - assert written[0].training_session_id == service._training_session_id - commit = read_optimizer_commit(str(optimizer_dir)) - assert commit is not None and commit.step == 4 + def raise_for_status(self) -> None: + return None -@pytest.mark.asyncio -async def test_megatron_shared_start_requires_runtime_sleep_mode( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - service = MegatronService( - model_name="test-model", - base_model="Qwen/Qwen3-0.6B", - config={ - "rollout_weights_mode": "lora", - "engine_args": {"enable_sleep_mode": False}, - }, - output_dir=str(tmp_path), - ) - monkeypatch.setattr(service, "_resolve_active_lora_path", lambda: "/tmp/lora") - monkeypatch.setattr(service, "_start_vllm_subprocess", AsyncMock()) +class _RecordingAsyncClient: + def __init__(self, posts: list[tuple[str, object, float]]) -> None: + self._posts = posts - with pytest.raises( - ValueError, - match="Shared-GPU mode requires engine_args.enable_sleep_mode=True", - ): - await service.start_openai_server(None) + async def __aenter__(self): + return self + + async def __aexit__(self, exc_type, exc, tb): + return None + + async def post( + self, + url: str, + *, + params: object = None, + json: object = None, + timeout: float, + ) -> _AsyncOkResponse: + self._posts.append((url, json if json is not None else params, timeout)) + return _AsyncOkResponse() @pytest.mark.asyncio @@ -288,31 +606,6 @@ async def test_unsloth_shared_start_requires_runtime_sleep_mode( await service.start_openai_server(None) -@pytest.mark.asyncio -async def test_megatron_runtime_sleep_and_wake_use_runtime_routes( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - service = MegatronService( - model_name="test-model", - base_model="Qwen/Qwen3-0.6B", - config={"rollout_weights_mode": "lora"}, - output_dir=str(tmp_path), - ) - service._vllm_port = 8123 - posts: list[tuple[str, dict[str, object] | None, float]] = [] - monkeypatch.setattr(httpx, "AsyncClient", lambda: _RecordingAsyncClient(posts)) - - await service._sleep_runtime() - await service._wake_runtime() - - assert posts == [ - ("http://127.0.0.1:8123/sleep", {"level": 1, "mode": "wait"}, 300.0), - ("http://127.0.0.1:8123/wake_up", None, 300.0), - ] - assert service._is_sleeping is False - - @pytest.mark.asyncio async def test_unsloth_runtime_sleep_and_wake_use_runtime_routes( tmp_path: Path, @@ -326,7 +619,7 @@ async def test_unsloth_runtime_sleep_and_wake_use_runtime_routes( output_dir=str(tmp_path), ) service._vllm_port = 8123 - posts: list[tuple[str, dict[str, object] | None, float]] = [] + posts: list[tuple[str, object, float]] = [] monkeypatch.setattr(httpx, "AsyncClient", lambda: _RecordingAsyncClient(posts)) await service._sleep_runtime() @@ -337,151 +630,3 @@ async def test_unsloth_runtime_sleep_and_wake_use_runtime_routes( ("http://127.0.0.1:8123/wake_up", None, 300.0), ] assert service._is_sleeping is False - - -@pytest.mark.asyncio -async def test_megatron_dedicated_merged_start_syncs_initial_weights( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - service = MegatronService( - model_name="test-model", - base_model="Qwen/Qwen3-0.6B", - config={ - "trainer_gpu_ids": [0], - "inference_gpu_ids": [1], - "rollout_weights_mode": "merged", - }, - output_dir=str(tmp_path), - ) - start_vllm = AsyncMock(return_value=("127.0.0.1", 8000)) - sync_merged = AsyncMock() - discover_capabilities = AsyncMock() - monkeypatch.setattr(service, "_resolve_active_lora_path", lambda: "/tmp/lora") - monkeypatch.setattr(service, "_start_vllm_subprocess", start_vllm) - monkeypatch.setattr(service, "_sync_dedicated_merged_weights", sync_merged) - monkeypatch.setattr( - service, "_discover_serving_capabilities", discover_capabilities - ) - - location = await service.start_openai_server(None) - - assert location == ("127.0.0.1", 8000) - start_vllm.assert_awaited_once() - discover_capabilities.assert_awaited_once_with(external=False) - sync_merged.assert_awaited_once_with( - lora_path="/tmp/lora", - step=0, - ) - - -@pytest.mark.asyncio -async def test_megatron_dedicated_merged_start_uses_configured_topology( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - service = MegatronService( - model_name="test-model", - base_model="Qwen/Qwen3-0.6B", - config={ - "trainer_gpu_ids": [0], - "inference_gpu_ids": [1], - "rollout_weights_mode": "merged", - }, - output_dir=str(tmp_path), - ) - start_vllm = AsyncMock(return_value=("127.0.0.1", 8000)) - sync_merged = AsyncMock() - discover_capabilities = AsyncMock() - monkeypatch.setattr(service, "_resolve_active_lora_path", lambda: "/tmp/lora") - monkeypatch.setattr(service, "_start_vllm_subprocess", start_vllm) - monkeypatch.setattr(service, "_sync_dedicated_merged_weights", sync_merged) - monkeypatch.setattr( - service, "_discover_serving_capabilities", discover_capabilities - ) - - await service.start_openai_server(None) - - sync_merged.assert_awaited_once_with( - lora_path="/tmp/lora", - step=0, - ) - discover_capabilities.assert_awaited_once_with(external=False) - assert service.runtime_config.topology.cp == 2 - - -@pytest.mark.asyncio -async def test_megatron_worker_uses_active_python_for_torchrun( - tmp_path: Path, - monkeypatch: pytest.MonkeyPatch, -) -> None: - pytest.importorskip("megatron.bridge") - service = MegatronService( - model_name="test-model", - base_model="Qwen/Qwen3-0.6B", - config={ - "trainer_gpu_ids": [0], - "inference_gpu_ids": [1], - "rollout_weights_mode": "lora", - "lora_config": { - "rank": 8, - "target_modules": ["q_proj", "down_proj"], - }, - }, - output_dir=str(tmp_path), - ) - recorded: dict[str, object] = {} - real_popen = subprocess.Popen - - def _fake_popen(command: Any, *args: Any, **kwargs: Any) -> Any: - if not ( - isinstance(command, list) - and len(command) > 2 - and command[1].endswith("managed_process.py") - ): - return real_popen(command, *args, **kwargs) - recorded["command"] = command - recorded["cwd"] = kwargs["cwd"] - recorded["env"] = kwargs["env"] - recorded["stdout"] = kwargs["stdout"] - recorded["stderr"] = kwargs["stderr"] - recorded["start_new_session"] = kwargs["start_new_session"] - return SimpleNamespace(pid=12345, wait=lambda: 0) - - monkeypatch.setattr( - "art.megatron.service.subprocess.Popen", - _fake_popen, - ) - monkeypatch.setattr( - service._child_processes, - "watch_popen", - lambda name, process, *, log_path: recorded.update( - {"watch_name": name, "watch_process": process, "watch_log_path": log_path} - ), - ) - monkeypatch.setattr(service, "_install_parent_signal_cleanup", lambda: None) - monkeypatch.setattr(service, "_allocate_master_port", lambda: 12345) - - await service._ensure_megatron_running() - command = cast(list[str], recorded["command"]) - assert isinstance(command, list) - assert command[0] == sys.executable - assert command[1].endswith("managed_process.py") - separator = command.index("--") - assert command[separator + 1 : separator + 4] == [ - sys.executable, - "-m", - "torch.distributed.run", - ] - assert "uv run" not in command - assert recorded["cwd"] == str(Path(__file__).resolve().parents[4]) - env = cast(dict[str, str], recorded["env"]) - assert env["ART_MEGATRON_LORA_RANK"] == "8" - assert json.loads(env["ART_MEGATRON_LORA_TARGET_MODULES"]) == [ - "q_proj", - "down_proj", - ] - assert env["ART_MEGATRON_STREAMING_WEIGHT_OFFLOAD"] == "1" - assert recorded["watch_name"] == "Megatron worker" - service._child_processes.close() - service._megatron_log_file.close() diff --git a/tests/integration/megatron/test_optimizer_state_contract.py b/tests/integration/megatron/test_optimizer_state_contract.py index 6cb2d887f..463095bd9 100644 --- a/tests/integration/megatron/test_optimizer_state_contract.py +++ b/tests/integration/megatron/test_optimizer_state_contract.py @@ -4,71 +4,24 @@ import pytest +from art.megatron.distributed_service import DistributedMegatronService from art.megatron.migrations import apply_megatron_migrations, optimizer_state_path -from art.megatron.optimizer_state import ( - commit_optimizer_generation, - optimizer_generation_files, - read_optimizer_commit, - resolve_optimizer_shard_path, -) +from art.megatron.tensor_snapshot import SnapshotReadBarrier -def _write_files(root: Path, names: tuple[str, ...]) -> None: - for name in names: - (root / name).write_bytes(name.encode()) - - -def test_optimizer_commit_preserves_previous_generation_until_manifest_advance( - tmp_path: Path, -) -> None: - optimizer = tmp_path / "optimizer" - optimizer.mkdir() - files_8 = optimizer_generation_files(8, 2) - _write_files(optimizer, files_8) - commit_optimizer_generation( - str(optimizer), - step=8, - world_size=2, - files=files_8, - ) - - files_9 = optimizer_generation_files(9, 2) - (optimizer / files_9[0]).write_bytes(b"interrupted") - commit = read_optimizer_commit(str(optimizer)) - assert commit is not None and commit.step == 8 - assert all((optimizer / name).exists() for name in files_8) - - (optimizer / files_9[1]).write_bytes(b"complete") - commit_optimizer_generation( - str(optimizer), - step=9, - world_size=2, - files=files_9, - ) - commit = read_optimizer_commit(str(optimizer)) - assert commit is not None and commit.step == 9 - assert not any((optimizer / name).exists() for name in files_8) - assert all((optimizer / name).exists() for name in files_9) - with pytest.raises(RuntimeError, match="source policy"): - resolve_optimizer_shard_path( - str(optimizer), rank=0, world_size=2, expected_step=8 - ) - - -def test_complete_legacy_optimizer_without_marker_resumes_latest_lora( - tmp_path: Path, -) -> None: +def test_split_optimizer_root_moves_to_unified_path(tmp_path: Path) -> None: output = tmp_path / "model" optimizer = output / "optimizer_states_rl" - (output / "checkpoints" / "0007").mkdir(parents=True) - optimizer.mkdir() - _write_files(optimizer, ("01-of-02.pt", "02-of-02.pt")) + generation = optimizer / "generations" / "interrupted" + generation.mkdir(parents=True) + (generation / "shard").write_bytes(b"state") - with pytest.warns(UserWarning, match="Migrated legacy RL optimizer"): + with pytest.warns(UserWarning, match="Migrated split Megatron optimizer"): migrated = apply_megatron_migrations(str(output)) - commit = read_optimizer_commit(migrated) + assert migrated == optimizer_state_path(str(output)) - assert commit is not None and commit.step == 7 + assert not optimizer.exists() + assert (Path(migrated) / "generations" / "interrupted" / "shard").is_file() def test_ambiguous_legacy_optimizer_requires_explicit_selection( @@ -76,15 +29,34 @@ def test_ambiguous_legacy_optimizer_requires_explicit_selection( ) -> None: for mode in ("rl", "sft"): path = tmp_path / f"optimizer_states_{mode}" - path.mkdir() - _write_files(path, ("01-of-01.pt",)) + (path / "generations").mkdir(parents=True) with pytest.raises(RuntimeError, match="Both legacy RL and SFT"): apply_megatron_migrations(str(tmp_path)) +def test_loose_optimizer_shards_are_not_silently_upgraded(tmp_path: Path) -> None: + path = tmp_path / "optimizer_states_rl" + path.mkdir() + (path / "01-of-01.pt").write_bytes(b"state") + + with pytest.raises(RuntimeError, match="Legacy optimizer checkpoint format"): + apply_megatron_migrations(str(tmp_path)) + + +def test_service_uses_one_optimizer_root_for_all_objectives(tmp_path: Path) -> None: + service = cast( + DistributedMegatronService, SimpleNamespace(output_dir=str(tmp_path)) + ) + + assert DistributedMegatronService._optimizer_state_path.__get__( + service, DistributedMegatronService + ) == optimizer_state_path(str(tmp_path)) + + def test_resident_optimizer_is_reused_across_objectives_in_one_run( tmp_path: Path, + monkeypatch: pytest.MonkeyPatch, ) -> None: from art.megatron import train @@ -94,26 +66,30 @@ def test_resident_optimizer_is_reused_across_objectives_in_one_run( SimpleNamespace( optimizer_persistent=True, optimizer=old_optimizer, - optimizer_config=object(), model=object(), rank=0, - world_size=1, model_support_handler=object(), - resident_training_session_id="session", - resident_optimizer_state_path=str(tmp_path / "optimizer"), - resident_policy_step=4, - resident_optimizer_dirty=False, - optimizer_state_loaded=True, - adapter_export_dtypes={"lora": "old"}, + optimizer_snapshot_barrier=SnapshotReadBarrier(), ), ) - adapter_dtypes = train._prepare_training_state( + monkeypatch.setattr(train, "_load_adapter_into_model", lambda *_args, **_kwargs: {}) + monkeypatch.setattr( + train, + "_build_optimizer", + lambda *_args, **_kwargs: pytest.fail("resident optimizer was rebuilt"), + ) + monkeypatch.setattr( + train, + "_load_optimizer", + lambda *_args, **_kwargs: pytest.fail("resident optimizer was reloaded"), + ) + + adapter_dtypes = train._load_lora_and_optimizer( runtime, - training_session_id="session", - source_policy_step=4, lora_path=str(tmp_path / "adapter"), optimizer_state_path=str(tmp_path / "optimizer"), + adapter_step=4, ) assert runtime.optimizer is old_optimizer - assert adapter_dtypes == {"lora": "old"} + assert adapter_dtypes == {} diff --git a/tests/integration/megatron/train_inf_mismatch/output_parity.py b/tests/integration/megatron/train_inf_mismatch/output_parity.py index d4c6d70bd..3e29ec43d 100644 --- a/tests/integration/megatron/train_inf_mismatch/output_parity.py +++ b/tests/integration/megatron/train_inf_mismatch/output_parity.py @@ -531,11 +531,14 @@ def scored_token(sample_id: int, packed_i: int) -> bool: leaf_paths ): leaf_start, leaf_end = leaf_segment + first_scored_i = None last_scored_i = None for packed_i in range(leaf_start + 1, leaf_end): if scored_token(sample_id, packed_i): + if first_scored_i is None: + first_scored_i = packed_i last_scored_i = packed_i - if last_scored_i is None: + if first_scored_i is None or last_scored_i is None: continue effective_leaf_end = last_scored_i + 1 prompt_len = sum(end - start for start, end in ancestor_segments) @@ -557,7 +560,8 @@ def scored_token(sample_id: int, packed_i: int) -> bool: family_id=family_id, completion_id=completion_id, packed_prompt_length=prompt_len, - scored_token_start_index=prompt_len + 1, + scored_token_start_index=prompt_len + + (first_scored_i - leaf_start), token_ids=flat, ) ) @@ -969,7 +973,8 @@ def _save_vllm_lora_adapter( ) -> None: import torch - from art.megatron.model_support.lora_disk import save_vllm_lora_tensors + from art.megatron import train as megatron_train + from art.megatron.weights.lora_publish import save_vllm_lora_from_model if not state: raise RuntimeError("Refusing to save empty LoRA state") @@ -981,12 +986,25 @@ def _save_vllm_lora_adapter( ] if zero_keys: raise RuntimeError(f"Refusing zero LoRA tensors: {zero_keys[:5]}") - adapter_config = _adapter_config(config) - tensors, adapter_config = runtime.model_support_handler.to_vllm_lora_tensors( + adapter_dtypes: dict[str, torch.dtype] = {} + for key, value in state.items(): + if not isinstance(value, torch.Tensor): + raise TypeError(f"Expected tensor for LoRA key {key!r}") + adapter_dtypes[key] = value.dtype + megatron_train.load_adapter_into_model( + runtime.model, state, - adapter_config=adapter_config, + model_support_handler=runtime.model_support_handler, + ) + save_vllm_lora_from_model( + model=runtime.model, + adapter_dtypes=adapter_dtypes, + handler=runtime.model_support_handler, + adapter_config=_adapter_config(config), + output_dir=str(lora_path), + rank=runtime.rank, + world_size=runtime.world_size, ) - save_vllm_lora_tensors(lora_path, tensors, adapter_config) def _run_logits( diff --git a/tests/integration/megatron/train_inf_mismatch/real_path.py b/tests/integration/megatron/train_inf_mismatch/real_path.py index b97e92fc4..9ef1fc471 100644 --- a/tests/integration/megatron/train_inf_mismatch/real_path.py +++ b/tests/integration/megatron/train_inf_mismatch/real_path.py @@ -2,6 +2,7 @@ import argparse import asyncio +from collections.abc import Mapping from contextlib import asynccontextmanager, contextmanager import hashlib import inspect @@ -390,12 +391,94 @@ def _build_prompts(config: RealPathConfig, tokenizer: Any) -> list[str]: return prompts +def _real_path_max_model_len( + config: RealPathConfig, + *, + tokenizer: Any, + prompts: list[str], + chat_template_kwargs: dict[str, Any], +) -> int: + def rendered_tokens(messages: list[dict[str, str]], **kwargs: Any) -> int: + encoded = tokenizer.apply_chat_template( + messages, + tokenize=True, + **chat_template_kwargs, + **kwargs, + ) + token_ids = encoded["input_ids"] if isinstance(encoded, Mapping) else encoded + shape: Any = getattr(token_ids, "shape", ()) + if len(shape) > 1: + return int(shape[-1]) + if len(token_ids) == 1 and isinstance(token_ids[0], list): + return len(token_ids[0]) + return len(token_ids) + + def completed_tokens(prompt: str) -> int: + messages = [{"role": "user", "content": prompt}] + prompt_tokens = rendered_tokens(messages, add_generation_prompt=True) + closed_tokens = rendered_tokens( + [*messages, {"role": "assistant", "content": ""}], + add_generation_prompt=False, + ) + return max(prompt_tokens, closed_tokens) + config.max_completion_tokens + + return max( + config.output_parity.packed.sequence_length, *map(completed_tokens, prompts) + ) + + +def _prepare_real_path_prompts( + config: RealPathConfig, +) -> tuple[list[str], dict[str, Any] | None, int]: + from transformers import AutoTokenizer + from transformers.tokenization_utils_base import PreTrainedTokenizerBase + + from art.megatron.model_support.tokenizer import ( + configure_tokenizer_for_model_support, + ) + + loaded_tokenizer = AutoTokenizer.from_pretrained(config.output_parity.base_model) + assert isinstance(loaded_tokenizer, PreTrainedTokenizerBase) + tokenizer = configure_tokenizer_for_model_support( + loaded_tokenizer, + base_model=config.output_parity.base_model, + internal_config={ + "allow_unvalidated_arch": config.output_parity.allow_unvalidated_arch + }, + ) + chat_template_kwargs: dict[str, Any] = {} + if isinstance(tokenizer.chat_template, str): + if "enable_thinking" in tokenizer.chat_template: + chat_template_kwargs["enable_thinking"] = False + if "preserve_thinking" in tokenizer.chat_template: + chat_template_kwargs["preserve_thinking"] = True + prompts = _build_prompts(config, tokenizer) + max_model_len = _real_path_max_model_len( + config, + tokenizer=tokenizer, + prompts=prompts, + chat_template_kwargs=chat_template_kwargs, + ) + max_model_len = _round_up(max_model_len, 128) + config.output_parity.packed.sequence_length = max_model_len + return ( + prompts, + ( + {"chat_template_kwargs": chat_template_kwargs} + if chat_template_kwargs + else None + ), + max_model_len, + ) + + async def _rollout( *, model: Any, prompt: str, max_completion_tokens: int, reward: float, + seed: int, extra_body: dict[str, Any] | None, ) -> Any: import art @@ -409,6 +492,7 @@ async def _rollout( messages=messages, max_tokens=max_completion_tokens, temperature=0.8, + seed=seed, logprobs=True, top_logprobs=TOP_K, **request_kwargs, @@ -430,36 +514,13 @@ async def _collect_real_trajectory_groups( *, model: Any, config: RealPathConfig, + prompts: list[str], + extra_body: dict[str, Any] | None, ) -> list[Any]: - from transformers import AutoTokenizer - from transformers.tokenization_utils_base import PreTrainedTokenizerBase - import art - from art.megatron.model_support.tokenizer import ( - configure_tokenizer_for_model_support, - ) if config.rollouts_per_prompt < 2: raise ValueError("real-path mismatch requires at least two rollouts per prompt") - loaded_tokenizer = AutoTokenizer.from_pretrained(config.output_parity.base_model) - assert isinstance(loaded_tokenizer, PreTrainedTokenizerBase) - tokenizer = configure_tokenizer_for_model_support( - loaded_tokenizer, - base_model=config.output_parity.base_model, - internal_config={ - "allow_unvalidated_arch": config.output_parity.allow_unvalidated_arch - }, - ) - chat_template_kwargs: dict[str, Any] = {} - if isinstance(tokenizer.chat_template, str): - if "enable_thinking" in tokenizer.chat_template: - chat_template_kwargs["enable_thinking"] = False - if "preserve_thinking" in tokenizer.chat_template: - chat_template_kwargs["preserve_thinking"] = True - extra_body = ( - {"chat_template_kwargs": chat_template_kwargs} if chat_template_kwargs else None - ) - prompts = _build_prompts(config, tokenizer) groups = [ art.TrajectoryGroup( [ @@ -468,12 +529,17 @@ async def _collect_real_trajectory_groups( prompt=prompt, max_completion_tokens=config.max_completion_tokens, reward=float(rollout_index % 2), + seed=( + config.output_parity.seed + + prompt_index * config.rollouts_per_prompt + + rollout_index + ), extra_body=extra_body, ) for rollout_index in range(config.rollouts_per_prompt) ] ) - for prompt in prompts + for prompt_index, prompt in enumerate(prompts) ] return await art.gather_trajectory_groups( cast(Any, groups), @@ -498,6 +564,21 @@ def _free_port() -> int: return int(sock.getsockname()[1]) +def _cuda_visible_devices_for_slots(gpu_ids: list[int]) -> str: + visible = os.environ.get("CUDA_VISIBLE_DEVICES") + devices = ( + [value.strip() for value in visible.split(",") if value.strip()] + if visible is not None + else None + ) + if devices is None: + return ",".join(map(str, gpu_ids)) + invalid = [gpu_id for gpu_id in gpu_ids if gpu_id < 0 or gpu_id >= len(devices)] + if invalid: + raise ValueError(f"GPU slots {invalid} exceed CUDA_VISIBLE_DEVICES={visible!r}") + return ",".join(devices[gpu_id] for gpu_id in gpu_ids) + + def _choice_score_index( trajectory_groups: list[Any], *, @@ -558,7 +639,7 @@ async def _direct_vllm_runtime( base_model=config.base_model, port=port, host="127.0.0.1", - cuda_visible_devices=",".join(str(value) for value in config.inference_gpu_ids), + cuda_visible_devices=_cuda_visible_devices_for_slots(config.inference_gpu_ids), lora_path=lora_path, served_model_name=served_model_name, rollout_weights_mode=cast(Any, rollout_weights_mode), @@ -616,6 +697,7 @@ def _topk_from_chat_logprob(entry: Any) -> TokenTopK: parsed: list[tuple[int, float]] = [] for top in entry.top_logprobs: parsed.append((_parse_token_id(top.token), float(top.logprob))) + parsed.sort(key=lambda item: item[1], reverse=True) return TokenTopK( token_ids=[token_id for token_id, _logprob in parsed[:TOP_K]], logprobs=[logprob for _token_id, logprob in parsed[:TOP_K]], @@ -699,6 +781,9 @@ async def _score_base_real_generation_path( config: RealPathConfig, artifact_dir: Path, is_moe: bool, + prompts: list[str], + extra_body: dict[str, Any] | None, + max_model_len: int, ) -> RealPathBaseDiagnosticBundle: import art from art.megatron.backend import MegatronBackend @@ -716,7 +801,7 @@ async def _score_base_real_generation_path( engine_args = { "tensor_parallel_size": len(parity_config.inference_gpu_ids), "enable_expert_parallel": is_moe and len(parity_config.inference_gpu_ids) > 1, - "max_model_len": parity_config.packed.sequence_length + 8, + "max_model_len": max_model_len, "max_logprobs": TOP_K, **parity_config.engine_args, } @@ -772,6 +857,8 @@ async def _score_base_real_generation_path( trajectory_groups = await _collect_real_trajectory_groups( model=model, config=config, + prompts=prompts, + extra_body=extra_body, ) packing_backend = MegatronBackend( @@ -927,33 +1014,151 @@ def _build_real_path_moe_routing_replay_bundle( ) +def _pack_expert_lora_tensors( + tensors: dict[str, Any], + groups: tuple[Any, ...], +) -> dict[str, Any]: + import torch + + packed = dict(tensors) + for group in groups: + for slot in group.slots: + suffix = f".{slot.source_projection}.{slot.source_lora}.weight" + matches: dict[str, dict[int, tuple[str, torch.Tensor]]] = {} + for key, tensor in tensors.items(): + if not key.endswith(suffix): + continue + prefix, separator, expert = key[: -len(suffix)].rpartition(".") + if not separator or not prefix.endswith(group.art_group_suffix): + continue + try: + expert_index = int(expert) + except ValueError: + continue + matches.setdefault(prefix, {})[expert_index] = (key, tensor) + + for prefix, experts in matches.items(): + if sorted(experts) != list(range(len(experts))): + raise RuntimeError( + f"Non-contiguous expert LoRA tensors for {prefix}: " + f"{sorted(experts)}" + ) + joined = torch.stack([experts[index][1] for index in sorted(experts)]) + for key, _tensor in experts.values(): + packed.pop(key) + if slot.pack_layout == "expert_rows": + value = joined.flatten(0, 1) + elif slot.pack_layout == "rank_major_expert_cols": + value = joined.permute(1, 2, 0).reshape( + joined.shape[1], joined.shape[2] * joined.shape[0] + ) + elif slot.pack_layout == "interleaved_gate_up_rank_major_expert_cols": + gate, up = joined.split(joined.shape[1] // 2, dim=1) + interleaved = torch.stack((gate, up), dim=2).flatten(1, 2) + value = interleaved.permute(1, 2, 0).reshape( + interleaved.shape[1], + interleaved.shape[2] * interleaved.shape[0], + ) + else: + raise RuntimeError( + f"Unsupported expert LoRA layout: {slot.pack_layout}" + ) + output_key = f"{prefix}.{slot.output_suffix}" + if output_key in packed: + raise RuntimeError( + f"Duplicate packed expert LoRA tensor: {output_key}" + ) + packed[output_key] = value.contiguous() + return packed + + def _make_nonzero_adapter( *, config: TrainInfOutputParityConfig, artifact_dir: Path, ) -> str: - request = RealPathMegatronWorkerRequest( - config=config, - artifact_dir=str(artifact_dir), - disk_packed_tensors=cast( - DiskPackedTensors, - { - "dir": str(artifact_dir / "unused"), - "num_sequences": 1, - "sequence_length": 1, - }, + import torch + + from art.megatron.identity_lora import create_identity_lora + from art.megatron.model_support import get_model_support_handler + from art.megatron.model_support.lora_disk import ( + load_adapter_config, + load_vllm_lora_tensors, + save_vllm_lora_tensors, + ) + + from .output_parity import _adapter_config + + handler = get_model_support_handler( + config.base_model, + allow_unvalidated_arch=config.allow_unvalidated_arch, + ) + adapter_path = artifact_dir / "real_path_active_lora" + with torch.random.fork_rng(devices=[]): + create_identity_lora( + config.base_model, + str(adapter_path), + target_modules=_lora_target_modules(config), + random_state=config.seed, + allow_unvalidated_arch=config.allow_unvalidated_arch, + handler=handler, + ) + published_config = load_adapter_config(adapter_path) + published_tensors = load_vllm_lora_tensors(adapter_path) + invalid_dtypes = { + key: str(value.dtype) + for key, value in published_tensors.items() + if value.dtype != torch.bfloat16 + } + if invalid_dtypes: + raise RuntimeError(f"Identity LoRA tensors must be BF16: {invalid_dtypes}") + templates = handler.from_vllm_lora_tensors( + published_tensors, + adapter_config=published_config, + ) + if not templates: + raise RuntimeError("Identity LoRA metadata produced no adapter tensors") + adapter_config = _adapter_config(config) + initialized = _build_deterministic_nonzero_lora( + { + key: torch.empty_like(value, device="cpu", dtype=torch.bfloat16) + for key, value in templates.items() + }, + seed=config.seed, + ) + normalized, normalized_config = handler.to_vllm_lora_tensors( + initialized, + adapter_config=dict(adapter_config), + ) + initialized = handler.from_vllm_lora_tensors( + normalized, + adapter_config=normalized_config, + ) + tensors, published_config = handler.to_vllm_lora_tensors( + _pack_expert_lora_tensors( + initialized, + tuple(handler.expert_packed_lora_groups()), ), - logical_map_path=str(artifact_dir / "unused_logical_map.json"), - weight_state="lora", - adapter_path=None, - moe_routing_replay_path=None, - global_grad_accumulation_sequences=1, - forward_trace_dir=None, + adapter_config=adapter_config, + ) + invalid = [ + key + for key, value in tensors.items() + if value.dtype != torch.bfloat16 or not torch.count_nonzero(value).item() + ] + if invalid: + raise RuntimeError(f"Invalid materialized LoRA tensors: {invalid[:5]}") + save_vllm_lora_tensors( + adapter_path, + {key: value.cpu().contiguous() for key, value in tensors.items()}, + published_config, ) - return _run_real_path_megatron_worker(request, adapter_only=True).adapter_path or "" + return str(adapter_path) def _adapter_cache_key(config: TrainInfOutputParityConfig) -> str: + from transformers import AutoConfig + from art.megatron.model_support import ( get_model_support_handler, vllm_lora_config_for_model, @@ -981,9 +1186,13 @@ def jsonable(value: Any) -> Any: allow_unvalidated_arch=config.allow_unvalidated_arch, ) handler_module = Path(inspect.getfile(type(handler))) + model_config = handler.identity_lora_model_config( + AutoConfig.from_pretrained(config.base_model, trust_remote_code=True) + ).to_json_string(use_diff=False) payload = { - "schema": 3, + "schema": 4, "base_model": config.base_model, + "model_config_sha256": hashlib.sha256(model_config.encode()).hexdigest(), "seed": config.seed, "allow_unvalidated_arch": config.allow_unvalidated_arch, "lora_target_modules": _lora_target_modules(config), @@ -1006,7 +1215,10 @@ def _default_adapter_cache_dir() -> Path: def _adapter_cache_dir(config: RealPathConfig) -> Path: if config.adapter_cache_dir: return Path(config.adapter_cache_dir) - return _default_adapter_cache_dir() + model_namespace = hashlib.sha256( + config.output_parity.base_model.encode() + ).hexdigest()[:16] + return _default_adapter_cache_dir() / model_namespace def _adapter_cache_manifest_path(adapter_path: Path) -> Path: @@ -1248,20 +1460,30 @@ def _configure_worker_bundle(bundle: Any) -> None: if request.weight_state == "lora": if request.adapter_path is None: initial_state = _collect_full_lora_state(cast(list[Any], runtime.model)) - if torch.distributed.get_rank() == 0: # type: ignore[possibly-missing-attribute] - adapter_path = artifact_dir / "real_path_active_lora" - initialized = _build_deterministic_nonzero_lora( + rank = torch.distributed.get_rank() # type: ignore[possibly-missing-attribute] + initialized = ( + _build_deterministic_nonzero_lora( initial_state or {}, seed=request.config.seed, ) - _save_vllm_lora_adapter( - lora_path=adapter_path, - state=initialized, - runtime=runtime, - config=request.config, - ) - torch.distributed.barrier() # type: ignore[possibly-missing-attribute] + if rank == 0 + else None + ) + payload = [initialized] + torch.distributed.broadcast_object_list( # type: ignore[possibly-missing-attribute] + payload, + src=0, + device=torch.device("cuda", local_rank), + ) + initialized = cast(dict[str, Any], payload[0]) adapter_path = artifact_dir / "real_path_active_lora" + _save_vllm_lora_adapter( + lora_path=adapter_path, + state=initialized, + runtime=runtime, + config=request.config, + ) + torch.distributed.barrier() # type: ignore[possibly-missing-attribute] else: adapter_path = Path(request.adapter_path) adapter_model = load_lora_tensors_for_megatron( @@ -1359,8 +1581,8 @@ def _run_real_path_megatron_worker( request_path = artifact_dir / request_name _write_json(request_path, request.model_dump(mode="json")) env = os.environ.copy() - env["CUDA_VISIBLE_DEVICES"] = ",".join( - str(value) for value in request.config.trainer_gpu_ids + env["CUDA_VISIBLE_DEVICES"] = _cuda_visible_devices_for_slots( + request.config.trainer_gpu_ids ) env.update(request.config.megatron_env) env["PYTHONUNBUFFERED"] = "1" @@ -1433,6 +1655,7 @@ async def run_real_path_train_inf_mismatch( parity_config = config.output_parity _apply_sliding_window_prompt_defaults(config) + prompts, extra_body, max_model_len = _prepare_real_path_prompts(config) rollout_mode = _real_path_rollout_mode(parity_config) is_moe = model_support_is_moe( parity_config.base_model, @@ -1459,7 +1682,7 @@ async def run_real_path_train_inf_mismatch( "tensor_parallel_size": len(parity_config.inference_gpu_ids), "enable_expert_parallel": is_moe and len(parity_config.inference_gpu_ids) > 1, - "max_model_len": parity_config.packed.sequence_length + 8, + "max_model_len": max_model_len, "max_logprobs": TOP_K, **parity_config.engine_args, }, @@ -1496,6 +1719,8 @@ async def run_real_path_train_inf_mismatch( trajectory_groups = await _collect_real_trajectory_groups( model=model, config=config, + prompts=prompts, + extra_body=extra_body, ) packed_tensors = backend._get_packed_tensors( model, @@ -1568,6 +1793,9 @@ async def run_real_path_train_inf_mismatch( config=config, artifact_dir=artifact_dir, is_moe=is_moe, + prompts=prompts, + extra_body=extra_body, + max_model_len=max_model_len, ) megatron_base = base_diagnostic.megatron_scores vllm_base = base_diagnostic.vllm_scores diff --git a/tests/integration/megatron/train_inf_mismatch/test_config.py b/tests/integration/megatron/train_inf_mismatch/test_config.py index 89a103623..1473fe953 100644 --- a/tests/integration/megatron/train_inf_mismatch/test_config.py +++ b/tests/integration/megatron/train_inf_mismatch/test_config.py @@ -4,8 +4,46 @@ import torch +from ..model_support.workflow_resources import ( + HandlerWorkflowResources, + MegatronWorkflowResources, + MegatronWorkflowTopology, + VllmWorkflowResources, + WorkflowStageResources, +) from . import output_parity from .output_parity import config_from_env +from .real_path import ( + RealPathConfig, + _cuda_visible_devices_for_slots, + _real_path_max_model_len, +) + + +class _PromptLengthTokenizer: + def apply_chat_template(self, messages, **kwargs): + del kwargs + token_count = int(messages[0]["content"]) + return { + "input_ids": [0] * token_count, + "attention_mask": [1] * token_count, + } + + +def test_real_path_max_model_len_uses_rendered_prompt_length() -> None: + config = RealPathConfig() + config.output_parity.packed.sequence_length = 2432 + config.max_completion_tokens = 16 + + assert ( + _real_path_max_model_len( + config, + tokenizer=_PromptLengthTokenizer(), + prompts=["2425"], + chat_template_kwargs={}, + ) + == 2441 + ) def test_cp_unsupported_default_converts_cp_to_dp_without_changing_tp( @@ -56,14 +94,103 @@ def test_cp_unsupported_model_uses_non_cp_default_topology(monkeypatch) -> None: assert config.topology.cp == 1 assert config.topology.tp == 2 - assert config.topology.ep == 2 - assert config.topology.dp == 1 - assert config.trainer_gpu_ids == [0, 1] + assert config.topology.ep == 4 + assert config.topology.dp == 2 + assert config.trainer_gpu_ids == [0, 1, 2, 3] assert config.inference_gpu_ids == [2, 3] assert config.engine_args["tensor_parallel_size"] == 2 assert config.engine_args["enable_expert_parallel"] is True assert config.engine_args["kv_cache_dtype"] == "fp8" - assert config.engine_args["moe_backend"] == "triton_unfused" + assert config.engine_args["moe_backend"] == "triton" assert config.streaming_weight_offload is True assert config.megatron_env == {} assert config.external_vllm_server_url == "http://127.0.0.1:8000" + + +def test_unconfigured_gpu_defaults_remain_controller_visible_slots( + monkeypatch, +) -> None: + monkeypatch.setattr(torch.cuda, "device_count", lambda: 4) + monkeypatch.setenv("CUDA_VISIBLE_DEVICES", "4,5,6,7") + monkeypatch.delenv("ART_TRAIN_INF_MISMATCH_TRAINER_GPU_IDS", raising=False) + monkeypatch.delenv("ART_TRAIN_INF_MISMATCH_INFERENCE_GPU_IDS", raising=False) + monkeypatch.setattr( + output_parity, + "handler_workflow_resources_for_base_model", + lambda base_model, *, allow_unvalidated_arch=False: None, + ) + monkeypatch.setattr(output_parity, "model_support_is_moe", lambda *_, **__: True) + monkeypatch.setattr( + output_parity, + "model_supports_context_parallel", + lambda *_, **__: True, + ) + + config = config_from_env() + + assert config.trainer_gpu_ids == [0, 1] + assert config.inference_gpu_ids == [2, 3] + + +def test_explicit_gpu_ids_are_not_reinterpreted_by_config(monkeypatch) -> None: + monkeypatch.setattr(torch.cuda, "device_count", lambda: 4) + monkeypatch.setenv("CUDA_VISIBLE_DEVICES", "4,5,6,7") + monkeypatch.setenv("ART_TRAIN_INF_MISMATCH_TRAINER_GPU_IDS", "2,3") + monkeypatch.setenv("ART_TRAIN_INF_MISMATCH_INFERENCE_GPU_IDS", "0,1") + monkeypatch.setattr( + output_parity, + "handler_workflow_resources_for_base_model", + lambda base_model, *, allow_unvalidated_arch=False: None, + ) + monkeypatch.setattr(output_parity, "model_support_is_moe", lambda *_, **__: True) + monkeypatch.setattr( + output_parity, + "model_supports_context_parallel", + lambda *_, **__: True, + ) + + config = config_from_env() + + assert config.trainer_gpu_ids == [2, 3] + assert config.inference_gpu_ids == [0, 1] + + +def test_resource_gpu_slots_remain_logical_until_runtime_compilation( + monkeypatch, +) -> None: + monkeypatch.setattr(torch.cuda, "device_count", lambda: 4) + monkeypatch.setenv("CUDA_VISIBLE_DEVICES", "4,5,6,7") + monkeypatch.delenv("ART_TRAIN_INF_MISMATCH_TRAINER_GPU_IDS", raising=False) + monkeypatch.delenv("ART_TRAIN_INF_MISMATCH_INFERENCE_GPU_IDS", raising=False) + resources = HandlerWorkflowResources( + train_inf_mismatch=WorkflowStageResources( + required_world_size=4, + megatron=MegatronWorkflowResources( + gpu_ids=[0, 1], topology=MegatronWorkflowTopology() + ), + vllm=VllmWorkflowResources(gpu_ids=[2, 3], tensor_parallel_size=2), + ) + ) + monkeypatch.setattr( + output_parity, + "handler_workflow_resources_for_base_model", + lambda base_model, *, allow_unvalidated_arch=False: resources, + ) + monkeypatch.setattr(output_parity, "model_support_is_moe", lambda *_, **__: True) + monkeypatch.setattr( + output_parity, + "model_supports_context_parallel", + lambda *_, **__: True, + ) + + config = config_from_env() + + assert config.trainer_gpu_ids == [0, 1] + assert config.inference_gpu_ids == [2, 3] + + +def test_runtime_compiles_logical_gpu_slots_through_outer_mask(monkeypatch) -> None: + monkeypatch.setenv("CUDA_VISIBLE_DEVICES", "4,5,6,7") + + assert _cuda_visible_devices_for_slots([0, 1]) == "4,5" + assert _cuda_visible_devices_for_slots([2, 3]) == "6,7" diff --git a/tests/integration/megatron/train_inf_mismatch/test_live_real_path_output_parity.py b/tests/integration/megatron/train_inf_mismatch/test_live_real_path_output_parity.py index 603317bb0..0d6b42504 100644 --- a/tests/integration/megatron/train_inf_mismatch/test_live_real_path_output_parity.py +++ b/tests/integration/megatron/train_inf_mismatch/test_live_real_path_output_parity.py @@ -1,6 +1,9 @@ from __future__ import annotations +import argparse +import asyncio from pathlib import Path +import traceback import pytest @@ -8,14 +11,25 @@ from .output_parity import model_support_is_moe from .real_path import ( + RealPathConfig, + RealPathTrainInfReport, config_from_env, run_real_path_train_inf_mismatch, ) +from .workflow_stage import ( + ATTEMPT_ASSERTION_EXIT_CODE, + ATTEMPT_ERROR_EXIT_CODE, + TrainInfMismatchWorkerResult, +) -torch = pytest.importorskip("torch") +_TEST_NODEID = ( + "tests/integration/megatron/train_inf_mismatch/" + "test_live_real_path_output_parity.py::test_real_path_train_inf_mismatch_live" +) def _require_visible_gpus(gpu_ids: list[int]) -> None: + torch = pytest.importorskip("torch") if not torch.cuda.is_available(): pytest.skip("CUDA is required for real-path train/inf mismatch") visible_count = int(torch.cuda.device_count()) @@ -27,8 +41,9 @@ def _require_visible_gpus(gpu_ids: list[int]) -> None: ) -@pytest.mark.asyncio -async def test_real_path_train_inf_mismatch_live(artifact_dir: Path) -> None: +async def _run_live_real_path_output_parity( + artifact_dir: Path, +) -> tuple[RealPathConfig, RealPathTrainInfReport]: config = config_from_env() parity_config = config.output_parity _require_visible_gpus( @@ -39,7 +54,14 @@ async def test_real_path_train_inf_mismatch_live(artifact_dir: Path) -> None: config=config, artifact_dir=artifact_dir, ) + return config, report + +def assert_live_real_path_output_parity( + config: RealPathConfig, + report: RealPathTrainInfReport, +) -> None: + parity_config = config.output_parity assert report.logical_prompt_count > 0 assert report.logical_token_count > 0 handler_key = get_model_support_spec( @@ -63,3 +85,64 @@ async def test_real_path_train_inf_mismatch_live(artifact_dir: Path) -> None: report.lora_topk.top20_intersection_kl_candidate_to_target <= report.top20_kl_candidate_to_target_limit ) + + +@pytest.mark.asyncio +async def test_real_path_train_inf_mismatch_live(artifact_dir: Path) -> None: + config, report = await _run_live_real_path_output_parity(artifact_dir) + assert_live_real_path_output_parity(config, report) + + +def _run_workflow_attempt(result_path: Path) -> int: + from .artifacts import create_artifact_dir, require_clean_git_state + + artifact_dir: Path | None = None + comparison_completed = False + exception_type: str | None = None + exception_message: str | None = None + try: + require_clean_git_state() + artifact_dir = create_artifact_dir(_TEST_NODEID) + config, report = asyncio.run(_run_live_real_path_output_parity(artifact_dir)) + comparison_completed = True + assert_live_real_path_output_parity(config, report) + outcome = "passed" + returncode = 0 + except pytest.skip.Exception as error: + traceback.print_exc() + outcome = "skipped" + returncode = 0 + exception_type = f"{type(error).__module__}.{type(error).__qualname__}" + exception_message = str(error) + except AssertionError as error: + traceback.print_exc() + outcome = "failed" if comparison_completed else "error" + returncode = ( + ATTEMPT_ASSERTION_EXIT_CODE + if comparison_completed + else ATTEMPT_ERROR_EXIT_CODE + ) + exception_type = f"{type(error).__module__}.{type(error).__qualname__}" + exception_message = str(error) + except Exception as error: + traceback.print_exc() + outcome = "error" + returncode = ATTEMPT_ERROR_EXIT_CODE + exception_type = f"{type(error).__module__}.{type(error).__qualname__}" + exception_message = str(error) + result = TrainInfMismatchWorkerResult( + outcome=outcome, + artifact_dir=str(artifact_dir) if artifact_dir is not None else None, + comparison_completed=comparison_completed, + exception_type=exception_type, + exception_message=exception_message, + ) + result_path.write_text(result.model_dump_json(indent=2) + "\n", encoding="utf-8") + return returncode + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--workflow-attempt-result", type=Path, required=True) + args = parser.parse_args() + raise SystemExit(_run_workflow_attempt(args.workflow_attempt_result)) diff --git a/tests/integration/megatron/train_inf_mismatch/test_output_parity_invariants.py b/tests/integration/megatron/train_inf_mismatch/test_output_parity_invariants.py index 1e68277fb..6766424fd 100644 --- a/tests/integration/megatron/train_inf_mismatch/test_output_parity_invariants.py +++ b/tests/integration/megatron/train_inf_mismatch/test_output_parity_invariants.py @@ -1,8 +1,9 @@ from __future__ import annotations +import asyncio import math +from pathlib import Path from types import SimpleNamespace -from unittest.mock import AsyncMock from openai.types.chat.chat_completion import Choice from openai.types.chat.chat_completion_message import ChatCompletionMessage @@ -10,6 +11,8 @@ torch = pytest.importorskip("torch") +import art + from . import workflow_stage from .output_parity import ( TOP20_KL_CANDIDATE_TO_TARGET_LIMIT, @@ -29,13 +32,22 @@ ) from .real_path import ( RealPathConfig, + _collect_real_trajectory_groups, _delete_adapter_safetensors_on_pass, _real_path_rollout_mode, _real_path_rollout_weights_mode, - _rollout, + _topk_from_chat_logprob, ) +def _write_workflow_worker_result( + command: list[str], + result: workflow_stage.TrainInfMismatchWorkerResult, +) -> None: + result_path = Path(command[command.index("--workflow-attempt-result") + 1]) + result_path.write_text(result.model_dump_json(), encoding="utf-8") + + def test_logical_map_flattens_prefix_tree_branches() -> None: packed = { "tokens": torch.tensor([[10, 11, 12, 13, 14, 12, 15, 16]]), @@ -64,6 +76,21 @@ def test_logical_map_flattens_prefix_tree_branches() -> None: ] +def test_logical_map_handles_unscored_prompt_suffix_inside_leaf() -> None: + packed = { + "tokens": torch.tensor([[10, 11, 12, 13, 14, 15]]), + "group_ids": torch.tensor([[0, 0, 1, 1, 1, 1]]), + "parent_ids": torch.tensor([[0, 0, 0, 0, 0, 0]]), + "assistant_mask": torch.tensor([[False, False, False, False, True, True]]), + } + + logical_map = build_logical_token_map(packed) + + assert logical_map.prompts[0].token_ids == [10, 11, 12, 13, 14, 15] + assert logical_map.prompts[0].scored_token_start_index == 4 + assert [token.vllm_prompt_token_index for token in logical_map.tokens] == [4, 5] + + def test_logical_map_flattens_nested_prefix_tree_leaves() -> None: packed = { "tokens": torch.tensor( @@ -198,46 +225,70 @@ def test_real_path_default_generates_16_tokens_per_rollout() -> None: @pytest.mark.asyncio -async def test_real_path_rollout_builds_explicit_legacy_trajectory() -> None: - choice = Choice( - index=0, - finish_reason="stop", - message=ChatCompletionMessage(role="assistant", content="answer"), +async def test_real_path_rollouts_use_stable_unique_seeds_concurrently() -> None: + calls = [] + active_requests = 0 + max_active_requests = 0 + + async def create(**kwargs): + nonlocal active_requests, max_active_requests + calls.append(kwargs) + active_requests += 1 + max_active_requests = max(max_active_requests, active_requests) + await asyncio.sleep(0) + active_requests -= 1 + return SimpleNamespace( + choices=[ + Choice( + finish_reason="stop", + index=0, + message=ChatCompletionMessage(role="assistant", content="maybe"), + ) + ] + ) + + client = SimpleNamespace( + chat=SimpleNamespace(completions=SimpleNamespace(create=create)) ) - create = AsyncMock(return_value=SimpleNamespace(choices=[choice])) model = SimpleNamespace( - get_inference_name=lambda: "test-model", - openai_client=lambda: SimpleNamespace( - chat=SimpleNamespace(completions=SimpleNamespace(create=create)) - ), + openai_client=lambda: client, + get_inference_name=lambda: "fake", + ) + config = RealPathConfig( + output_parity=TrainInfOutputParityConfig(seed=41), + rollouts_per_prompt=3, ) - trajectory = await _rollout( + groups = await _collect_real_trajectory_groups( model=model, - prompt="question", - max_completion_tokens=7, - reward=0.75, - extra_body={"fixture": True}, + config=config, + prompts=["first", "second"], + extra_body={"return_tokens_as_token_ids": True}, ) - assert trajectory.messages_and_choices == [ - {"role": "user", "content": "question"}, - choice, - ] - assert trajectory.exchanges.chat_completions == [] - assert trajectory.reward == 0.75 - assert trajectory.metrics["completion_tokens"] == 0 - create.assert_awaited_once_with( - model="test-model", - messages=[{"role": "user", "content": "question"}], - max_tokens=7, - temperature=0.8, - logprobs=True, - top_logprobs=TOP_K, - extra_body={"fixture": True}, + assert len(groups) == 2 + assert max_active_requests > 1 + assert sorted(call["seed"] for call in calls) == list(range(41, 47)) + assert all( + call["extra_body"] == {"return_tokens_as_token_ids": True} for call in calls ) +def test_real_path_topk_sorts_vllm_sampled_token_prefix() -> None: + entry = SimpleNamespace( + top_logprobs=[SimpleNamespace(token="token_id:999", logprob=-100.0)] + + [ + SimpleNamespace(token=f"token_id:{token_id}", logprob=-float(token_id)) + for token_id in range(TOP_K) + ] + ) + + topk = _topk_from_chat_logprob(entry) + + assert topk.token_ids == list(range(TOP_K)) + assert topk.logprobs == [-float(token_id) for token_id in range(TOP_K)] + + def test_real_path_rollout_mode_follows_config() -> None: native_config = TrainInfOutputParityConfig( base_model="Qwen/Qwen3.5-35B-A3B", @@ -413,16 +464,23 @@ def test_workflow_stage_enables_live_train_inf_mismatch( import subprocess captured_env = {} - real_run = workflow_stage.subprocess.run + captured_command = [] + real_run = subprocess.run def fake_run(*args, **kwargs): if "env" not in kwargs: return real_run(*args, **kwargs) + command = args[0] + captured_command.extend(command) captured_env.update(kwargs["env"]) + _write_workflow_worker_result( + command, + workflow_stage.TrainInfMismatchWorkerResult(outcome="passed"), + ) return subprocess.CompletedProcess( args=args, returncode=0, - stdout="1 passed\n", + stdout="", stderr="", ) @@ -439,6 +497,7 @@ def fake_run(*args, **kwargs): assert captured_env["ART_TRAIN_INF_MISMATCH_ALLOW_UNVALIDATED_ARCH"] == "1" assert captured_env["ART_REAL_PATH_MAX_COMPLETION_TOKENS"] == "16" assert captured_env["ART_TRAIN_INF_MISMATCH_VLLM_GPU_MEMORY_UTILIZATION"] == "0.50" + assert "pytest" not in captured_command def test_workflow_stage_does_not_accept_a_skipped_live_test( @@ -447,18 +506,100 @@ def test_workflow_stage_does_not_accept_a_skipped_live_test( ) -> None: import subprocess + real_run = subprocess.run + monkeypatch.setenv("ART_TRAIN_INF_MISMATCH_ATTEMPTS", "1") monkeypatch.setattr(workflow_stage, "create_artifact_dir", lambda _nodeid: tmp_path) - monkeypatch.setattr( - workflow_stage.subprocess, - "run", - lambda *args, **kwargs: subprocess.CompletedProcess( + + def fake_run(*args, **kwargs): + if "env" not in kwargs: + return real_run(*args, **kwargs) + _write_workflow_worker_result( + args[0], + workflow_stage.TrainInfMismatchWorkerResult(outcome="skipped"), + ) + return subprocess.CompletedProcess( args=args, returncode=0, - stdout="1 skipped\n", + stdout="", stderr="", - ), - ) + ) + + monkeypatch.setattr(workflow_stage.subprocess, "run", fake_run) report = workflow_stage.run_train_inf_mismatch(base_model="Qwen/Qwen3.5-35B-A3B") assert report.passed is False + assert report.passed_count == 0 + assert report.skipped_count == 1 + + +def test_workflow_stage_retries_numerical_mismatch_and_transient_startup_failures( + monkeypatch: pytest.MonkeyPatch, + tmp_path: Path, +) -> None: + import subprocess + + calls = 0 + real_run = subprocess.run + + def fake_run(*args, **kwargs): + nonlocal calls + if "env" not in kwargs: + return real_run(*args, **kwargs) + calls += 1 + _write_workflow_worker_result( + args[0], + workflow_stage.TrainInfMismatchWorkerResult( + outcome="failed", + comparison_completed=True, + exception_type="builtins.AssertionError", + exception_message="TimeoutError in completed numerical evidence", + ), + ) + return subprocess.CompletedProcess( + args=args, returncode=1, stdout="", stderr="" + ) + + monkeypatch.setenv("ART_TRAIN_INF_MISMATCH_ATTEMPTS", "3") + monkeypatch.setattr(workflow_stage, "create_artifact_dir", lambda _nodeid: tmp_path) + monkeypatch.setattr(workflow_stage.subprocess, "run", fake_run) + + report = workflow_stage.run_train_inf_mismatch(base_model="openai/gpt-oss-20b") + + assert calls == report.attempt_count == 3 + assert report.failed_count == 1 + assert all(attempt.retryable for attempt in report.attempts) + assert workflow_stage._retryable_attempt_failure( + returncode=2, + result=workflow_stage.TrainInfMismatchWorkerResult( + outcome="error", + exception_type="builtins.TimeoutError", + ), + output="", + ) + + calls = 0 + + def transient_then_pass(*args, **kwargs): + nonlocal calls + if "env" not in kwargs: + return real_run(*args, **kwargs) + calls += 1 + worker_result = workflow_stage.TrainInfMismatchWorkerResult( + outcome="error" if calls == 1 else "passed", + exception_type="builtins.TimeoutError" if calls == 1 else None, + ) + _write_workflow_worker_result(args[0], worker_result) + return subprocess.CompletedProcess( + args=args, + returncode=2 if calls == 1 else 0, + stdout="", + stderr="", + ) + + monkeypatch.setattr(workflow_stage.subprocess, "run", transient_then_pass) + report = workflow_stage.run_train_inf_mismatch(base_model="openai/gpt-oss-20b") + + assert report.passed is True + assert calls == report.attempt_count == 2 + assert [attempt.retryable for attempt in report.attempts] == [True, False] diff --git a/tests/integration/megatron/train_inf_mismatch/workflow_stage.py b/tests/integration/megatron/train_inf_mismatch/workflow_stage.py index a4304fd8d..c2adf8157 100644 --- a/tests/integration/megatron/train_inf_mismatch/workflow_stage.py +++ b/tests/integration/megatron/train_inf_mismatch/workflow_stage.py @@ -1,8 +1,9 @@ import os from pathlib import Path -import re import subprocess import sys +import time +from typing import Literal from pydantic import BaseModel @@ -10,6 +11,30 @@ DEFAULT_ATTEMPTS = 3 MAX_ATTEMPTS = 5 +ATTEMPT_ASSERTION_EXIT_CODE = 1 +ATTEMPT_ERROR_EXIT_CODE = 2 + +_TRANSIENT_STARTUP_ERRORS = ( + "address already in use", + "brokenpipeerror", + "connection refused", + "connectionrefusederror", + "connection reset by peer", + "connectionreseterror", + "distnetworkerror", + "ncclremoteerror", + "ncclsystemerror", + "timed out waiting for", + "timeouterror", +) + + +class TrainInfMismatchWorkerResult(BaseModel): + outcome: Literal["passed", "failed", "error", "skipped"] + artifact_dir: str | None = None + comparison_completed: bool = False + exception_type: str | None = None + exception_message: str | None = None class TrainInfMismatchAttemptReport(BaseModel): @@ -19,7 +44,10 @@ class TrainInfMismatchAttemptReport(BaseModel): stderr_path: str passed_count: int failed_count: int + error_count: int skipped_count: int + retryable: bool + duration_s: float class TrainInfMismatchReport(BaseModel): @@ -32,27 +60,64 @@ class TrainInfMismatchReport(BaseModel): stderr_path: str passed_count: int failed_count: int + error_count: int skipped_count: int attempt_count: int max_attempts: int attempts: list[TrainInfMismatchAttemptReport] + duration_s: float -def _pytest_counts(output: str) -> dict[str, int]: - counts = {"passed": 0, "failed": 0, "skipped": 0} - for line in reversed(output.splitlines()): - matches = re.findall(r"(\d+) (passed|failed|skipped|error|errors)", line) - if not matches: - continue - for count, kind in matches: - if kind in {"error", "errors"}: - counts["failed"] += int(count) - else: - counts[kind] += int(count) - return counts +def _attempt_counts( + result: TrainInfMismatchWorkerResult | None, + *, + returncode: int, +) -> dict[str, int]: + counts = {"passed": 0, "failed": 0, "errors": 0, "skipped": 0} + expected_returncode = ( + { + "passed": 0, + "failed": ATTEMPT_ASSERTION_EXIT_CODE, + "error": ATTEMPT_ERROR_EXIT_CODE, + "skipped": 0, + }.get(result.outcome) + if result is not None + else None + ) + if result is None or returncode != expected_returncode: + counts["errors"] = 1 + elif result.outcome == "error": + counts["errors"] = 1 + else: + counts[f"{result.outcome}"] = 1 return counts +def _retryable_attempt_failure( + *, + returncode: int, + result: TrainInfMismatchWorkerResult | None, + output: str, +) -> bool: + if result is not None: + if result.outcome == "failed": + return result.comparison_completed + if result.outcome != "error" or result.comparison_completed: + return False + if returncode in {-9, -15}: + return True + details = "\n".join( + value + for value in ( + result.exception_type if result is not None else None, + result.exception_message if result is not None else None, + output, + ) + if value + ).lower() + return any(marker in details for marker in _TRANSIENT_STARTUP_ERRORS) + + def _attempt_limit() -> int: raw = os.environ.get("ART_TRAIN_INF_MISMATCH_ATTEMPTS") attempts = DEFAULT_ATTEMPTS if raw is None else int(raw) @@ -61,11 +126,25 @@ def _attempt_limit() -> int: return min(attempts, MAX_ATTEMPTS) +def _run_attempt( + command: list[str], *, cwd: Path, env: dict[str, str] +) -> subprocess.CompletedProcess[str]: + return subprocess.run( + command, + cwd=cwd, + env=env, + capture_output=True, + text=True, + check=False, + ) + + def run_train_inf_mismatch( *, base_model: str, allow_unvalidated_arch: bool = False, ) -> TrainInfMismatchReport: + started = time.monotonic() artifact_dir = create_artifact_dir("workflow::train_inf_mismatch") max_attempts = _attempt_limit() env = os.environ.copy() @@ -89,24 +168,35 @@ def run_train_inf_mismatch( for attempt in range(1, max_attempts + 1): stdout_path = artifact_dir / f"attempt_{attempt}_pytest_stdout.txt" stderr_path = artifact_dir / f"attempt_{attempt}_pytest_stderr.txt" - result = subprocess.run( + result_path = artifact_dir / f"attempt_{attempt}_result.json" + attempt_started = time.monotonic() + result = _run_attempt( [ sys.executable, "-m", - "pytest", - "-q", - str(TEST_ROOT / "test_live_real_path_output_parity.py"), - "--tb=short", + "integration.megatron.train_inf_mismatch." + "test_live_real_path_output_parity", + "--workflow-attempt-result", + str(result_path), ], cwd=Path(REPO_ROOT), env=env, - capture_output=True, - text=True, - check=False, ) stdout_path.write_text(result.stdout, encoding="utf-8") stderr_path.write_text(result.stderr, encoding="utf-8") - counts = _pytest_counts(result.stdout + "\n" + result.stderr) + try: + worker_result = TrainInfMismatchWorkerResult.model_validate_json( + result_path.read_text(encoding="utf-8") + ) + except (OSError, ValueError): + worker_result = None + output = result.stdout + "\n" + result.stderr + counts = _attempt_counts(worker_result, returncode=result.returncode) + retryable = _retryable_attempt_failure( + returncode=result.returncode, + result=worker_result, + output=output, + ) selected = TrainInfMismatchAttemptReport( attempt=attempt, returncode=result.returncode, @@ -114,10 +204,19 @@ def run_train_inf_mismatch( stderr_path=str(stderr_path), passed_count=counts["passed"], failed_count=counts["failed"], + error_count=counts["errors"], skipped_count=counts["skipped"], + retryable=retryable, + duration_s=time.monotonic() - attempt_started, ) attempts.append(selected) - if result.returncode == 0: + if ( + result.returncode == 0 + and selected.passed_count > 0 + and selected.skipped_count == 0 + ): + break + if not retryable: break if selected is None: raise RuntimeError("train/inf mismatch retry loop did not run") @@ -125,6 +224,7 @@ def run_train_inf_mismatch( selected.returncode == 0 and selected.passed_count > 0 and selected.failed_count == 0 + and selected.error_count == 0 and selected.skipped_count == 0 ) return TrainInfMismatchReport( @@ -137,8 +237,10 @@ def run_train_inf_mismatch( stderr_path=selected.stderr_path, passed_count=selected.passed_count, failed_count=selected.failed_count, + error_count=selected.error_count, skipped_count=selected.skipped_count, attempt_count=len(attempts), max_attempts=max_attempts, attempts=attempts, + duration_s=time.monotonic() - started, ) diff --git a/tests/integration/megatron/trainability/test_config.py b/tests/integration/megatron/trainability/test_config.py index c68b7e210..c1d1afeac 100644 --- a/tests/integration/megatron/trainability/test_config.py +++ b/tests/integration/megatron/trainability/test_config.py @@ -14,12 +14,20 @@ LengthSampleReport, LengthTrainabilityReport, _default_learning_rate, + _length_current_step_demand, + _length_max_steps, + _length_rollout_seed, + _length_rollout_temperature, + _length_rollouts_per_prompt, _length_trainability_thresholds, _prompt_for_index, _target_tokens, _use_default_moe_dedicated_placement, length_trainability_passed, ) +from .test_live_length_trainability import ( + _extra_body as _length_extra_body, +) from .test_live_length_trainability import ( _prompt_tree_shape as _length_prompt_tree_shape, ) @@ -29,7 +37,13 @@ _build_internal_config, _build_variant, _default_variant_name, + _engine_args_for_yes_no_trainability, _evaluate_groups, + _get_env_int_list, + _max_tokens, + _render_chat_messages, + _rescore_groups, + _select_answer_target, _TrainabilityVariant, _variant_init_args, _variant_max_steps, @@ -37,8 +51,12 @@ _variant_rollouts_per_prompt, _variant_train_kwargs, build_prompts, + reward_for_answer, yes_no_trainability_passed, ) +from .yes_no_trainability import ( + _extra_body as _yes_no_extra_body, +) from .yes_no_trainability import ( _prompt_tree_shape as _yes_no_prompt_tree_shape, ) @@ -101,8 +119,134 @@ def get_inference_name(self, *, step: int | None = None) -> str: return f"fake@{step}" +def test_optional_sampling_controls(monkeypatch) -> None: + monkeypatch.setenv("ART_MODEL_SUPPORT_YES_NO_ALLOWED_TOKEN_IDS", "9829,902,36569") + monkeypatch.setenv("ART_MODEL_SUPPORT_LENGTH_ALLOWED_TOKEN_IDS", "154820,38069") + monkeypatch.setenv("ART_MODEL_SUPPORT_LENGTH_MIN_TOKENS", "2") + monkeypatch.setenv("ART_MODEL_SUPPORT_LENGTH_FREQUENCY_PENALTY", "0.5") + + assert _yes_no_extra_body("Qwen/Qwen3.5-35B-A3B")["allowed_token_ids"] == [ + 9829, + 902, + 36569, + ] + assert _length_extra_body({}) == { + "allowed_token_ids": [154820, 38069], + "min_tokens": 2, + "frequency_penalty": 0.5, + } + assert _length_extra_body({}, seed=1234)["seed"] == 1234 + + +def test_yes_no_requests_use_model_specific_reasoning_budget(monkeypatch) -> None: + monkeypatch.delenv("ART_MODEL_SUPPORT_YES_NO_ENABLE_THINKING", raising=False) + monkeypatch.delenv("ART_MODEL_SUPPORT_YES_NO_MAX_TOKENS", raising=False) + prompt = "Choose one answer." + qwen = "Qwen/Qwen3.5-35B-A3B" + gpt_oss = "openai/gpt-oss-20b" + + assert _render_chat_messages(qwen, prompt) == [{"role": "user", "content": prompt}] + assert _max_tokens(qwen) == 5 + assert _yes_no_extra_body(qwen) == { + "chat_template_kwargs": {"enable_thinking": False} + } + assert _render_chat_messages(gpt_oss, prompt) == [ + { + "role": "system", + "content": "Use minimal reasoning. Give only one final word: yes, no, or maybe.", + }, + {"role": "user", "content": prompt}, + ] + assert _max_tokens(gpt_oss) == 256 + assert _yes_no_extra_body(gpt_oss) == { + "chat_template_kwargs": { + "enable_thinking": False, + "reasoning_effort": "low", + } + } + assert _render_chat_messages("probe/non-gpt", prompt) == [ + {"role": "user", "content": prompt} + ] + + +def test_yes_no_engine_args_use_model_context_budget() -> None: + assert ( + _engine_args_for_yes_no_trainability( + base_model="Qwen/Qwen3.5-35B-A3B", inference_gpu_ids=[0] + )["max_model_len"] + == 128 + ) + assert ( + _engine_args_for_yes_no_trainability( + base_model="openai/gpt-oss-20b", inference_gpu_ids=[0] + )["max_model_len"] + == 512 + ) + assert ( + _engine_args_for_yes_no_trainability( + base_model="probe/non-gpt", inference_gpu_ids=[0] + )["max_model_len"] + == 128 + ) + + +def test_integer_list_rejects_empty_values(monkeypatch) -> None: + monkeypatch.setenv("INVALID_INTEGER_LIST", "1,,2") + with pytest.raises(ValueError, match="Invalid integer list"): + _get_env_int_list("INVALID_INTEGER_LIST") + + +def _answer_group(answers: list[str]) -> art.TrajectoryGroup: + return art.TrajectoryGroup( + [ + art.Trajectory( + messages_and_choices=[ + Choice( + finish_reason="stop", + index=index, + message=ChatCompletionMessage(role="assistant", content=answer), + ) + ], + reward=-1.0, + ) + for index, answer in enumerate(answers) + ] + ) + + +def test_answer_target_requires_support_and_prefers_least_common() -> None: + assert _select_answer_target([_answer_group(["maybe", "maybe", "yes"])]) == "yes" + assert _select_answer_target([_answer_group(["yes", "no"])]) == "yes" + assert _select_answer_target([_answer_group(["maybe", "maybe"])]) is None + + +def test_reward_for_answer_preserves_default_and_supports_target() -> None: + answers = ["yes", "No.", "MAYBE!", "invalid"] + + assert [reward_for_answer(answer) for answer in answers] == [0.5, 0.75, 1.0, 0.0] + assert [reward_for_answer(answer, target="no") for answer in answers] == [ + 0.0, + 1.0, + 0.0, + 0.0, + ] + + +def test_initial_groups_can_be_rescored_for_selected_target() -> None: + group = _answer_group(["yes", "no", "maybe", "invalid"]) + + _rescore_groups([group], target="no") + + assert [trajectory.reward for trajectory in group.trajectories] == [ + 0.0, + 1.0, + 0.0, + 0.0, + ] + + @pytest.mark.asyncio -async def test_eval_prompts_are_submitted_concurrently() -> None: +async def test_eval_prompts_are_submitted_concurrently_with_target_reward() -> None: completions = _ConcurrentCompletions(expected=3) groups = await _evaluate_groups( @@ -110,12 +254,13 @@ async def test_eval_prompts_are_submitted_concurrently() -> None: base_model="Qwen/Qwen3-30B-A3B-Instruct-2507", prompts=["a", "b", "c"], step=1, + target="yes", ) assert len(groups) == 3 assert completions.started == 3 assert completions.max_active == 3 - assert [group.trajectories[0].reward for group in groups] == [1.0, 1.0, 1.0] + assert [group.trajectories[0].reward for group in groups] == [0.0, 0.0, 0.0] def test_megatron_variants_keep_short_packed_sequence_default(monkeypatch) -> None: @@ -139,7 +284,10 @@ def test_megatron_variants_keep_short_packed_sequence_default(monkeypatch) -> No _default_variant_name("Qwen/Qwen3-30B-A3B-Instruct-2507") == "megatron_shared" ) assert _variant_rollouts_per_prompt(variant) == 4 - assert _variant_max_steps(variant) == 4 + assert ( + _variant_max_steps(variant, base_model="Qwen/Qwen3-30B-A3B-Instruct-2507") == 4 + ) + assert _variant_max_steps(variant, base_model="openai/gpt-oss-20b") == 8 def test_unsloth_variant_uses_chunk_aligned_training_length(monkeypatch) -> None: @@ -159,7 +307,9 @@ def test_unsloth_variant_uses_chunk_aligned_training_length(monkeypatch) -> None variant, base_model="Qwen/Qwen3-30B-A3B-Instruct-2507" )["init_args"] == {"max_seq_length": 1024} assert _variant_rollouts_per_prompt(variant) == 8 - assert _variant_max_steps(variant) == 12 + assert ( + _variant_max_steps(variant, base_model="Qwen/Qwen3-30B-A3B-Instruct-2507") == 12 + ) def test_qwen3_5_defaults_to_shared_lora_rollout() -> None: @@ -191,12 +341,13 @@ def test_yes_no_default_variant_env_override(monkeypatch) -> None: assert _default_variant_name("Qwen/Qwen3-32B") == "megatron_shared" -def test_yes_no_trainability_passes_initially_saturated_stable_report() -> None: +def test_yes_no_trainability_rejects_initially_saturated_stable_report() -> None: report = YesNoTrainabilityReport( variant="megatron_shared", backend_name="megatron", placement_mode="shared", base_model="google/gemma-4-31B-it", + target_answer="maybe", output_dir="/tmp/report", trainer_gpu_ids=[0, 1], inference_gpu_ids=[0, 1], @@ -222,7 +373,73 @@ def test_yes_no_trainability_passes_initially_saturated_stable_report() -> None: ], ) + assert yes_no_trainability_passed(report) is False + + +def test_yes_no_trainability_requires_gradient_correlation_and_learning() -> None: + report = YesNoTrainabilityReport( + variant="megatron_dedicated", + backend_name="megatron", + placement_mode="dedicated", + base_model="deepseek-ai/DeepSeek-V4-Flash", + target_answer="yes", + output_dir="/tmp/report", + trainer_gpu_ids=[0, 1], + inference_gpu_ids=[2, 3], + rollout_weights_mode="lora", + reward_threshold=0.9, + max_steps=4, + prompt_count=8, + eval_prompt_count=8, + rollouts_per_prompt=4, + latest_step=1, + initial_eval_reward=0.5, + final_eval_reward=1.0, + saturated_step=1, + step0_name="model@0", + latest_name="model@1", + steps=[ + TrainabilityStepReport( + step=1, + eval_reward=1.0, + train_reward=0.75, + train_metrics={ + "loss/grad_norm": 1.0, + "loss/probs_corr": 0.9, + }, + ) + ], + ) + assert yes_no_trainability_passed(report) is True + assert ( + yes_no_trainability_passed( + report.model_copy( + update={ + "steps": [ + report.steps[0].model_copy( + update={"train_metrics": {"loss/probs_corr": 0.9}} + ) + ] + } + ) + ) + is False + ) + assert ( + yes_no_trainability_passed( + report.model_copy( + update={ + "steps": [ + report.steps[0].model_copy( + update={"train_metrics": {"loss/grad_norm": 1.0}} + ) + ] + } + ) + ) + is False + ) def test_yes_no_prompts_form_prefix_tree_by_default(monkeypatch) -> None: @@ -234,12 +451,41 @@ def test_yes_no_prompts_form_prefix_tree_by_default(monkeypatch) -> None: assert _yes_no_prompt_tree_shape(prompts) == (3, 6) -def test_qwen3_5_length_trainability_uses_stable_learning_rate() -> None: - assert _default_learning_rate("Qwen/Qwen3.5-35B-A3B") == 7e-5 +def test_qwen3_5_length_trainability_uses_stable_moe_defaults() -> None: + assert _default_learning_rate("Qwen/Qwen3.5-35B-A3B") == 1e-4 + assert _length_rollouts_per_prompt("Qwen/Qwen3.5-35B-A3B") == 32 + assert _length_max_steps("Qwen/Qwen3.5-35B-A3B") == 40 + assert _length_max_steps("meta-llama/Llama-3.2-1B-Instruct") == 30 + assert _length_rollout_seed("Qwen/Qwen3.5-35B-A3B") == 20261833 + assert _length_rollout_temperature("Qwen/Qwen3.5-35B-A3B") == 0.8 + assert _length_current_step_demand("Qwen/Qwen3.5-35B-A3B") is True assert _default_learning_rate("Qwen/Qwen3-30B-A3B-Instruct-2507") == 1e-4 + assert _length_rollouts_per_prompt("Qwen/Qwen3-30B-A3B-Instruct-2507") == 4 + assert _length_max_steps("Qwen/Qwen3-30B-A3B-Instruct-2507") == 20 + assert _length_rollout_seed("Qwen/Qwen3-30B-A3B-Instruct-2507") is None + assert _length_rollout_temperature("Qwen/Qwen3-30B-A3B-Instruct-2507") == 1.1 + assert _length_current_step_demand("Qwen/Qwen3-30B-A3B-Instruct-2507") is False + assert _length_rollout_seed("openai/gpt-oss-20b") == 20261833 + assert _length_current_step_demand("openai/gpt-oss-20b") is True + + +def test_length_trainability_environment_overrides_model_defaults(monkeypatch) -> None: + monkeypatch.setenv("ART_MODEL_SUPPORT_LENGTH_MAX_STEPS", "9") + monkeypatch.setenv("ART_MODEL_SUPPORT_LENGTH_ROLLOUTS_PER_PROMPT", "6") + monkeypatch.setenv("ART_MODEL_SUPPORT_LENGTH_ROLLOUT_SEED", "17") + monkeypatch.setenv("ART_MODEL_SUPPORT_LENGTH_ROLLOUT_TEMPERATURE", "0.7") + monkeypatch.setenv("ART_MODEL_SUPPORT_LENGTH_CURRENT_STEP_DEMAND", "0") + + assert _length_max_steps("Qwen/Qwen3.5-35B-A3B") == 9 + assert _length_rollouts_per_prompt("Qwen/Qwen3.5-35B-A3B") == 6 + assert _length_rollout_seed("Qwen/Qwen3.5-35B-A3B") == 17 + assert _length_rollout_seed("Qwen/Qwen3-30B-A3B-Instruct-2507") == 17 + assert _length_rollout_temperature("Qwen/Qwen3.5-35B-A3B") == 0.7 + assert _length_current_step_demand("Qwen/Qwen3.5-35B-A3B") is False def test_gpt_oss_length_target_accounts_for_harmony_tokens(monkeypatch) -> None: + assert _target_tokens("google/gemma-4-31B-it") == 22 assert _target_tokens("openai/gpt-oss-20b") == 20 assert _target_tokens("Qwen/Qwen3.5-35B-A3B") == 10 monkeypatch.setenv("ART_MODEL_SUPPORT_LENGTH_TARGET_TOKENS", "24") @@ -393,7 +639,6 @@ def test_dsv4_trainability_uses_large_model_dedicated_resources( lambda device_ids: 0.5, ) monkeypatch.setenv("ART_MODEL_SUPPORT_EXTERNAL_VLLM_URL", "http://127.0.0.1:8000") - default_variant = _default_variant_name( "deepseek-ai/DeepSeek-V4-Flash", ) @@ -409,16 +654,16 @@ def test_dsv4_trainability_uses_large_model_dedicated_resources( assert default_variant == "megatron_dedicated" assert variant.topology is not None assert variant.topology.tp == 2 - assert variant.topology.ep == 2 + assert variant.topology.ep == 4 assert variant.topology.cp == 1 - assert variant.topology.dp == 1 + assert variant.topology.dp == 2 assert variant.topology.sp is True - assert variant.trainer_gpu_ids == [0, 1] + assert variant.trainer_gpu_ids == [0, 1, 2, 3] assert variant.inference_gpu_ids == [2, 3] assert config["engine_args"]["tensor_parallel_size"] == 2 assert config["engine_args"]["enable_expert_parallel"] is True assert config["engine_args"]["kv_cache_dtype"] == "fp8" - assert config["engine_args"].get("moe_backend") == "triton_unfused" + assert config["engine_args"].get("moe_backend") == "triton" assert "megatron_topology" not in config assert config["vllm_runtime"] == { "mode": "external", diff --git a/tests/integration/megatron/trainability/test_live_length_trainability.py b/tests/integration/megatron/trainability/test_live_length_trainability.py index 96ec424e6..8c482d92e 100644 --- a/tests/integration/megatron/trainability/test_live_length_trainability.py +++ b/tests/integration/megatron/trainability/test_live_length_trainability.py @@ -29,8 +29,10 @@ _get_env_bool, _get_env_float, _get_env_int, + _get_env_int_list, _init_megatron_runtime_config, _list_model_ids, + _temporary_env, _topology_with_env_overrides, _trainability_stage_resources, ) @@ -39,7 +41,10 @@ DEFAULT_BASE_MODEL = "Qwen/Qwen3.5-35B-A3B" DEFAULT_LENGTH_LEARNING_RATE = 1e-4 -LARGE_MOE_LENGTH_LEARNING_RATE = 7e-5 +LENGTH_MAX_STEPS_BY_MODEL = {"llama3_dense": 30, "qwen3_5_moe": 40} +QWEN3_5_MOE_LENGTH_ROLLOUTS_PER_PROMPT = 32 +DETERMINISTIC_LENGTH_ROLLOUT_SEED = 20261833 +QWEN3_5_MOE_LENGTH_ROLLOUT_TEMPERATURE = 0.8 LIVE_ENV = "ART_RUN_LIVE_LENGTH_TRAINABILITY" TRAINER_GPU_IDS_ENV = "ART_MODEL_SUPPORT_TRAINER_GPU_IDS" INFERENCE_GPU_IDS_ENV = "ART_MODEL_SUPPORT_INFERENCE_GPU_IDS" @@ -225,6 +230,7 @@ def _word_count(text: str) -> int: def _target_tokens(base_model: str | None = None) -> int: model_key = _model_support_key(base_model) default = { + "gemma4_dense": GEMMA4_TARGET_TOKENS, "gemma4_moe": GEMMA4_TARGET_TOKENS, "gpt_oss_moe": GPT_OSS_TARGET_TOKENS, }.get(model_key, 10) @@ -234,8 +240,6 @@ def _target_tokens(base_model: str | None = None) -> int: def _default_learning_rate(base_model: str) -> float: if _model_support_key(base_model) == "gemma4_moe": return GEMMA4_LENGTH_LEARNING_RATE - if base_model == DEFAULT_BASE_MODEL: - return LARGE_MOE_LENGTH_LEARNING_RATE return DEFAULT_LENGTH_LEARNING_RATE @@ -441,10 +445,28 @@ def _messages( return messages -def _extra_body(chat_template_kwargs: dict[str, Any]) -> dict[str, object]: - return ( +def _extra_body( + chat_template_kwargs: dict[str, Any], *, seed: int | None = None +) -> dict[str, object]: + body: dict[str, object] = ( {"chat_template_kwargs": chat_template_kwargs} if chat_template_kwargs else {} ) + allowed_token_ids = _get_env_int_list("ART_MODEL_SUPPORT_LENGTH_ALLOWED_TOKEN_IDS") + if allowed_token_ids is not None: + body["allowed_token_ids"] = allowed_token_ids + if ( + min_tokens := os.environ.get("ART_MODEL_SUPPORT_LENGTH_MIN_TOKENS") + ) is not None: + body["min_tokens"] = int(min_tokens) + if ( + frequency_penalty := os.environ.get( + "ART_MODEL_SUPPORT_LENGTH_FREQUENCY_PENALTY" + ) + ) is not None: + body["frequency_penalty"] = float(frequency_penalty) + if seed is not None: + body["seed"] = seed + return body def _length_chat_template_kwargs(base_model: str, tokenizer: object) -> dict[str, Any]: @@ -465,13 +487,48 @@ def _scenario_limit() -> int | None: return _get_env_int("ART_MODEL_SUPPORT_LENGTH_SCENARIOS", 0) -def _length_max_steps() -> int: +def _length_max_steps(base_model: str) -> int: return _get_env_int( "ART_MODEL_SUPPORT_LENGTH_MAX_STEPS", - DEFAULT_LENGTH_MAX_STEPS, + LENGTH_MAX_STEPS_BY_MODEL.get( + _model_support_key(base_model), DEFAULT_LENGTH_MAX_STEPS + ), + ) + + +def _length_rollouts_per_prompt(base_model: str) -> int: + return _get_env_int( + "ART_MODEL_SUPPORT_LENGTH_ROLLOUTS_PER_PROMPT", + QWEN3_5_MOE_LENGTH_ROLLOUTS_PER_PROMPT + if _model_support_key(base_model) == "qwen3_5_moe" + else 4, ) +def _length_current_step_demand(base_model: str) -> bool: + return _get_env_bool( + "ART_MODEL_SUPPORT_LENGTH_CURRENT_STEP_DEMAND", + _model_support_key(base_model) in {"gpt_oss_moe", "qwen3_5_moe"}, + ) + + +def _length_rollout_temperature(base_model: str) -> float: + return _get_env_float( + "ART_MODEL_SUPPORT_LENGTH_ROLLOUT_TEMPERATURE", + QWEN3_5_MOE_LENGTH_ROLLOUT_TEMPERATURE + if _model_support_key(base_model) == "qwen3_5_moe" + else 1.1, + ) + + +def _length_rollout_seed(base_model: str) -> int | None: + if (seed := os.environ.get("ART_MODEL_SUPPORT_LENGTH_ROLLOUT_SEED")) is not None: + return int(seed) + if _model_support_key(base_model) in {"gpt_oss_moe", "qwen3_5_moe"}: + return DETERMINISTIC_LENGTH_ROLLOUT_SEED + return None + + def _zero_variance_discard_multiplier(max_steps: int) -> int: return _get_env_int( "ART_MODEL_SUPPORT_LENGTH_ZERO_VARIANCE_DISCARD_MULTIPLIER", @@ -543,6 +600,7 @@ async def _length_group( ) for completion_index in range(n) ] + seed = _length_rollout_seed(base_model) trajectories: list[art.Trajectory] = [] completions = await asyncio.gather( *( @@ -552,7 +610,14 @@ async def _length_group( max_tokens=max_tokens, n=1, temperature=temperature, - extra_body=_extra_body(chat_template_kwargs), + extra_body=_extra_body( + chat_template_kwargs, + seed=( + None + if seed is None + else seed + scenario.scenario_index * n + completion_index + ), + ), logprobs=True, top_logprobs=0, timeout=_get_env_float( @@ -560,7 +625,7 @@ async def _length_group( 900.0, ), ) - for max_tokens in max_tokens_by_completion + for completion_index, max_tokens in enumerate(max_tokens_by_completion) ) ) for max_tokens, completion in zip( @@ -697,27 +762,33 @@ async def run_length_trainability_async( resource_stage_name="length_trainability", ) _use_default_moe_dedicated_placement(variant, base_model=base_model) - max_steps = _length_max_steps() - max_steps_off_policy = _get_env_int( - "ART_MODEL_SUPPORT_LENGTH_MAX_STEPS_OFF_POLICY", - 0, - ) - rollouts_per_prompt = _get_env_int( - "ART_MODEL_SUPPORT_LENGTH_ROLLOUTS_PER_PROMPT", - 4, - ) - normalize_advantages = _get_env_bool( - "ART_MODEL_SUPPORT_LENGTH_NORMALIZE_ADVANTAGES", - True, - ) - rollout_workers = _get_env_int( - "ART_MODEL_SUPPORT_LENGTH_ROLLOUT_WORKERS", - max(1, max_steps_off_policy + 1), + stage_resources = _trainability_stage_resources( + base_model, + stage_name="length_trainability", + allow_unvalidated_arch=allow_unvalidated_arch, ) - thresholds = _length_trainability_thresholds(base_model) - scenario_limit = _scenario_limit() - zero_variance_discard_multiplier = _zero_variance_discard_multiplier(max_steps) + backend_env = stage_resources.megatron_env if stage_resources is not None else {} + with _temporary_env(backend_env): + max_steps = _length_max_steps(base_model) + max_steps_off_policy = _get_env_int( + "ART_MODEL_SUPPORT_LENGTH_MAX_STEPS_OFF_POLICY", + 0, + ) + rollouts_per_prompt = _length_rollouts_per_prompt(base_model) + normalize_advantages = _get_env_bool( + "ART_MODEL_SUPPORT_LENGTH_NORMALIZE_ADVANTAGES", + True, + ) + rollout_workers = _get_env_int( + "ART_MODEL_SUPPORT_LENGTH_ROLLOUT_WORKERS", + max(1, max_steps_off_policy + 1), + ) + thresholds = _length_trainability_thresholds(base_model) + scenario_limit = _scenario_limit() + zero_variance_discard_multiplier = _zero_variance_discard_multiplier(max_steps) + current_step_demand = _length_current_step_demand(base_model) success_hit = False + pending_trainable_step: int | None = None samples: list[LengthSampleReport] = [] backend_root = artifact_dir / "megatron_dedicated_workspace" summary_log_path = artifact_dir / "length_trainability.log" @@ -734,27 +805,22 @@ async def run_length_trainability_async( ) internal_config["engine_args"]["max_num_seqs"] = _get_env_int( "ART_MODEL_SUPPORT_LENGTH_MAX_NUM_SEQS", - 4, + max(4, rollouts_per_prompt), ) from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained(base_model) chat_template_kwargs = _length_chat_template_kwargs(base_model, tokenizer) rollout_weights_mode = internal_config["rollout_weights_mode"] - stage_resources = _trainability_stage_resources( - base_model, - stage_name="length_trainability", - allow_unvalidated_arch=allow_unvalidated_arch, - ) - _init_megatron_runtime_config( - variant, - streaming_weight_offload=( - stage_resources.streaming_weight_offload - if stage_resources is not None - else False - ), - ) - backend_env = stage_resources.megatron_env if stage_resources is not None else {} + with _temporary_env(backend_env): + _init_megatron_runtime_config( + variant, + streaming_weight_offload=( + stage_resources.streaming_weight_offload + if stage_resources is not None + else False + ), + ) async with _backend_context( variant, @@ -772,11 +838,28 @@ async def run_length_trainability_async( ) await model.register(backend) + trainer: PipelineTrainer | None = None + async def scenarios() -> AsyncIterator[dict[str, object]]: + nonlocal pending_trainable_step index = 0 while not success_hit and ( scenario_limit is None or index < scenario_limit ): + required_step = pending_trainable_step + if current_step_demand and required_step is not None: + assert trainer is not None + active_trainer = trainer + async with active_trainer.state.policy_updated: + await active_trainer.state.policy_updated.wait_for( + lambda: ( + active_trainer.state.done + or active_trainer.state.policy_version > required_step + ) + ) + pending_trainable_step = None + if active_trainer.state.done: + return yield _scenario( index, target_step=0, @@ -789,7 +872,7 @@ async def rollout_fn( scenario: dict[str, object], _config: None, ) -> art.TrajectoryGroup: - nonlocal success_hit + nonlocal pending_trainable_step, success_hit model_name = rollout_model.get_inference_name() target_step = _step_from_model_name(model_name) if target_step is None: @@ -802,14 +885,19 @@ async def rollout_fn( split="train", step=target_step, n=rollouts_per_prompt, - temperature=_get_env_float( - "ART_MODEL_SUPPORT_LENGTH_ROLLOUT_TEMPERATURE", - 1.1, - ), + temperature=_length_rollout_temperature(base_model), chat_template_kwargs=chat_template_kwargs, samples=samples, summary_log_path=summary_log_path, ) + rewards = [trajectory.reward for trajectory in group.trajectories] + if current_step_demand: + pending_trainable_step = ( + target_step + if len(rewards) > 1 + and any(abs(reward - rewards[0]) > 1e-12 for reward in rewards[1:]) + else None + ) if _success_abs_error_passed( _mean_abs_error_by_step( [sample for sample in samples if sample.split == "train"] @@ -849,7 +937,8 @@ async def rollout_fn( await trainer.train(handle_signals=False) latest_step = await model.get_step() - model_ids_after = await _list_model_ids(model) + async with backend.exact_adapter_lease(model, latest_step): + model_ids_after = await _list_model_ids(model) train_samples = [sample for sample in samples if sample.split == "train"] train_rewards_by_step = { diff --git a/tests/integration/megatron/trainability/yes_no_trainability.py b/tests/integration/megatron/trainability/yes_no_trainability.py index a0718bf27..0b83892a7 100644 --- a/tests/integration/megatron/trainability/yes_no_trainability.py +++ b/tests/integration/megatron/trainability/yes_no_trainability.py @@ -10,6 +10,7 @@ from typing import Any, AsyncIterator, Iterator, Literal, TypedDict, cast import uuid +from openai.types.chat.chat_completion import Choice from pydantic import BaseModel, Field import torch @@ -48,6 +49,14 @@ "unsloth_dedicated", ] _RESOURCE_STAGE_NAME = Literal["yes_no_trainability", "length_trainability"] +_Answer = Literal["yes", "no", "maybe"] +_ANSWER_TARGETS: tuple[_Answer, ...] = ("yes", "no", "maybe") +_GPT_OSS_MAX_STEPS = 8 +_GPT_OSS_MAX_TOKENS = 256 +_GPT_OSS_MAX_MODEL_LEN = 512 +_GPT_OSS_SYSTEM_PROMPT = ( + "Use minimal reasoning. Give only one final word: yes, no, or maybe." +) class _TrainKwargs(TypedDict, total=False): @@ -66,6 +75,7 @@ class YesNoTrainabilityReport(BaseModel): backend_name: Literal["megatron", "local"] placement_mode: Literal["shared", "dedicated"] base_model: str + target_answer: _Answer output_dir: str trainer_gpu_ids: list[int] inference_gpu_ids: list[int] @@ -288,9 +298,12 @@ def _safe_gpu_memory_utilization(device_ids: list[int]) -> float: ) -def reward_for_answer(text: str) -> float: +def reward_for_answer(text: str, *, target: _Answer | None = None) -> float: + answer = first_word_for_answer(text).lower() + if target is not None: + return float(answer == target) return {"yes": 0.5, "no": 0.75, "maybe": 1.0}.get( - first_word_for_answer(text).lower(), + answer, 0.0, ) @@ -310,6 +323,44 @@ def first_word_for_answer(text: str | None) -> str: return first_word[0].strip(".,!?:;\"'()[]{}") +def _select_answer_target(groups: list[art.TrajectoryGroup]) -> _Answer | None: + counts = { + target: [ + sum( + first_word_for_answer(_trajectory_answer_text(trajectory)).lower() + == target + for trajectory in group.trajectories + ) + for group in groups + ] + for target in _ANSWER_TARGETS + } + candidates = [ + target + for target, group_counts in counts.items() + if any( + 0 < count < len(group.trajectories) + for count, group in zip(group_counts, groups, strict=True) + ) + ] + return ( + min(candidates, key=lambda target: sum(counts[target])) if candidates else None + ) + + +def _trajectory_answer_text(trajectory: art.Trajectory) -> str: + choice = cast(Choice, trajectory.messages_and_choices[-1]) + return choice.message.content or "" + + +def _rescore_groups(groups: list[art.TrajectoryGroup], *, target: _Answer) -> None: + for group in groups: + for trajectory in group.trajectories: + trajectory.reward = reward_for_answer( + _trajectory_answer_text(trajectory), target=target + ) + + def _get_env_int(name: str, default: int) -> int: return int(os.environ.get(name, str(default))) @@ -318,6 +369,16 @@ def _get_env_float(name: str, default: float) -> float: return float(os.environ.get(name, str(default))) +def _get_env_int_list(name: str) -> list[int] | None: + raw = os.environ.get(name) + if raw is None: + return None + parts = raw.split(",") + if any(not part.strip() for part in parts): + raise ValueError(f"Invalid integer list for {name}: {raw!r}") + return [int(part) for part in parts] + + def _get_env_bool(name: str, default: bool) -> bool: raw = os.environ.get(name) if raw is None: @@ -330,13 +391,25 @@ def _get_env_bool(name: str, default: bool) -> bool: raise ValueError(f"Invalid boolean value for {name}: {raw!r}") -def _max_tokens() -> int: - return _get_env_int("ART_MODEL_SUPPORT_YES_NO_MAX_TOKENS", 5) +def _is_gpt_oss_model(base_model: str) -> bool: + return ( + get_model_support_spec(base_model, allow_unvalidated_arch=True).key + == "gpt_oss_moe" + ) + + +def _max_tokens(base_model: str) -> int: + return _get_env_int( + "ART_MODEL_SUPPORT_YES_NO_MAX_TOKENS", + _GPT_OSS_MAX_TOKENS if _is_gpt_oss_model(base_model) else 5, + ) def _render_chat_messages(base_model: str, prompt: str) -> art.Messages: - del base_model - return [{"role": "user", "content": prompt}] + messages: art.Messages = [{"role": "user", "content": prompt}] + if _is_gpt_oss_model(base_model): + messages.insert(0, {"role": "system", "content": _GPT_OSS_SYSTEM_PROMPT}) + return messages def _enable_thinking() -> bool: @@ -345,8 +418,15 @@ def _enable_thinking() -> bool: ).strip().lower() in {"1", "true", "yes", "on"} -def _extra_body() -> dict[str, object]: - return {"chat_template_kwargs": {"enable_thinking": _enable_thinking()}} +def _extra_body(base_model: str) -> dict[str, object]: + chat_template_kwargs: dict[str, object] = {"enable_thinking": _enable_thinking()} + if _is_gpt_oss_model(base_model): + chat_template_kwargs["reasoning_effort"] = "low" + body: dict[str, object] = {"chat_template_kwargs": chat_template_kwargs} + allowed_token_ids = _get_env_int_list("ART_MODEL_SUPPORT_YES_NO_ALLOWED_TOKEN_IDS") + if allowed_token_ids is not None: + body["allowed_token_ids"] = allowed_token_ids + return body def _request_timeout(name: str, default: float) -> float: @@ -355,6 +435,7 @@ def _request_timeout(name: str, default: float) -> float: def _engine_args_for_yes_no_trainability( *, + base_model: str, inference_gpu_ids: list[int], tensor_parallel_size: int = 1, enable_expert_parallel: bool = False, @@ -362,7 +443,10 @@ def _engine_args_for_yes_no_trainability( ) -> dev.EngineArgs: engine_args: dict[str, object] = { "gpu_memory_utilization": _safe_gpu_memory_utilization(inference_gpu_ids), - "max_model_len": _get_env_int("ART_MODEL_SUPPORT_YES_NO_MAX_MODEL_LEN", 128), + "max_model_len": _get_env_int( + "ART_MODEL_SUPPORT_YES_NO_MAX_MODEL_LEN", + _GPT_OSS_MAX_MODEL_LEN if _is_gpt_oss_model(base_model) else 128, + ), "max_num_seqs": _get_env_int("ART_MODEL_SUPPORT_YES_NO_MAX_NUM_SEQS", 4), "enforce_eager": True, "tensor_parallel_size": tensor_parallel_size, @@ -550,9 +634,11 @@ def _variant_packed_sequence_length(variant: _TrainabilityVariant) -> int: def _variant_train_kwargs(variant: _TrainabilityVariant) -> _TrainKwargs: - if variant.backend_name == "megatron": - return {} - return {"packed_sequence_length": _variant_packed_sequence_length(variant)} + return ( + {} + if variant.backend_name == "megatron" + else {"packed_sequence_length": _variant_packed_sequence_length(variant)} + ) def _variant_init_args(variant: _TrainabilityVariant) -> dev.InitArgs: @@ -581,8 +667,14 @@ def _init_megatron_runtime_config( ) -def _variant_max_steps(variant: _TrainabilityVariant) -> int: - default = 12 if variant.backend_name == "local" else 4 +def _variant_max_steps(variant: _TrainabilityVariant, *, base_model: str) -> int: + default = ( + 12 + if variant.backend_name == "local" + else _GPT_OSS_MAX_STEPS + if _is_gpt_oss_model(base_model) + else 4 + ) return _get_env_int("ART_MODEL_SUPPORT_YES_NO_MAX_STEPS", default) @@ -667,6 +759,7 @@ def _build_internal_config( else: vllm_resources = None engine_args = _engine_args_for_yes_no_trainability( + base_model=base_model, inference_gpu_ids=inference_gpu_ids, tensor_parallel_size=( vllm_resources.tensor_parallel_size @@ -774,6 +867,7 @@ async def _evaluate_groups( base_model: str, prompts: list[str], step: int, + target: _Answer | None = None, ) -> list[art.TrajectoryGroup]: client = model.openai_client() @@ -782,8 +876,8 @@ async def _group_for_prompt(prompt: str) -> art.TrajectoryGroup: completion = await client.chat.completions.create( messages=messages, model=model.get_inference_name(step=step), - max_tokens=_max_tokens(), - extra_body=_extra_body(), + max_tokens=_max_tokens(base_model), + extra_body=_extra_body(base_model), temperature=_get_env_float( "ART_MODEL_SUPPORT_YES_NO_EVAL_TEMPERATURE", 0.0, @@ -795,7 +889,9 @@ async def _group_for_prompt(prompt: str) -> art.TrajectoryGroup: [ art.Trajectory( messages_and_choices=[*messages, choice], - reward=reward_for_answer(choice.message.content or ""), + reward=reward_for_answer( + choice.message.content or "", target=target + ), ) ] ) @@ -818,6 +914,7 @@ async def _evaluate_model( base_model: str, prompts: list[str], step: int, + target: _Answer | None = None, ) -> float: return _mean_group_reward( await _evaluate_groups( @@ -825,6 +922,7 @@ async def _evaluate_model( base_model=base_model, prompts=prompts, step=step, + target=target, ) ) @@ -835,6 +933,7 @@ async def _build_training_groups( base_model: str, prompts: list[str], rollouts_per_prompt: int, + target: _Answer | None = None, ) -> list[art.TrajectoryGroup]: client = model.openai_client() @@ -843,9 +942,9 @@ async def _group_for_prompt(prompt: str) -> art.TrajectoryGroup: completion = await client.chat.completions.create( messages=messages, model=model.get_inference_name(), - max_tokens=_max_tokens(), + max_tokens=_max_tokens(base_model), n=rollouts_per_prompt, - extra_body=_extra_body(), + extra_body=_extra_body(base_model), temperature=_get_env_float( "ART_MODEL_SUPPORT_YES_NO_ROLLOUT_TEMPERATURE", 1.2, @@ -859,7 +958,9 @@ async def _group_for_prompt(prompt: str) -> art.TrajectoryGroup: [ art.Trajectory( messages_and_choices=[*messages, choice], - reward=reward_for_answer(choice.message.content or ""), + reward=reward_for_answer( + choice.message.content or "", target=target + ), ) for choice in completion.choices ] @@ -880,6 +981,7 @@ async def _build_trainable_groups( base_model: str, prompts: list[str], rollouts_per_prompt: int, + target: _Answer | None = None, ) -> list[art.TrajectoryGroup]: max_attempts = _get_env_int("ART_MODEL_SUPPORT_YES_NO_MAX_ROLLOUT_ATTEMPTS", 4) for _ in range(max_attempts): @@ -888,6 +990,7 @@ async def _build_trainable_groups( base_model=base_model, prompts=prompts, rollouts_per_prompt=rollouts_per_prompt, + target=target, ) trainable_groups = [ group for group in groups if _group_has_reward_variance(group) @@ -899,6 +1002,32 @@ async def _build_trainable_groups( ) +async def _build_initial_trainable_groups( + model: art.TrainableModel, + *, + base_model: str, + prompts: list[str], + rollouts_per_prompt: int, +) -> tuple[_Answer, list[art.TrajectoryGroup]]: + max_attempts = _get_env_int("ART_MODEL_SUPPORT_YES_NO_MAX_ROLLOUT_ATTEMPTS", 4) + for _ in range(max_attempts): + groups = await _build_training_groups( + model, + base_model=base_model, + prompts=prompts, + rollouts_per_prompt=rollouts_per_prompt, + ) + target = _select_answer_target(groups) + if target is not None: + _rescore_groups(groups, target=target) + return target, [ + group for group in groups if _group_has_reward_variance(group) + ] + raise RuntimeError( + "No answer with within-group support was produced for yes/no trainability" + ) + + async def _warmup_model( model: art.TrainableModel, *, @@ -910,7 +1039,7 @@ async def _warmup_model( messages=_render_chat_messages(base_model, prompt), model=model.get_inference_name(step=0), max_tokens=1, - extra_body=_extra_body(), + extra_body=_extra_body(base_model), temperature=0.0, timeout=_request_timeout("ART_MODEL_SUPPORT_YES_NO_WARMUP_TIMEOUT", 900.0), ) @@ -933,7 +1062,7 @@ async def run_yes_no_trainability_async( backend_root = artifact_root or _artifact_dir(base_model, variant.name) backend_root.mkdir(parents=True, exist_ok=True) reward_threshold = _get_env_float("ART_MODEL_SUPPORT_YES_NO_REWARD_THRESHOLD", 0.9) - max_steps = _variant_max_steps(variant) + max_steps = _variant_max_steps(variant, base_model=base_model) rollouts_per_prompt = _variant_rollouts_per_prompt(variant) eval_prompt_count = _get_env_int("ART_MODEL_SUPPORT_YES_NO_EVAL_PROMPTS", 8) prompts = build_prompts() @@ -988,15 +1117,23 @@ async def run_yes_no_trainability_async( ) as backend: await model.register(backend) output_dir = Path(model.base_path) / model.project / "models" / model.run_name - await _warmup_model(model, base_model=base_model, prompt=prompts[0]) step0_name = model.get_inference_name(step=0) model_ids_before = await _list_model_ids(model) - initial_eval_groups = await _evaluate_groups( + async with backend.exact_adapter_lease(model, 0): + await _warmup_model(model, base_model=base_model, prompt=prompts[0]) + initial_eval_groups = await _evaluate_groups( + model, + base_model=base_model, + prompts=eval_prompts, + step=0, + ) + target_answer, initial_train_groups = await _build_initial_trainable_groups( model, base_model=base_model, - prompts=eval_prompts, - step=0, + prompts=prompts, + rollouts_per_prompt=rollouts_per_prompt, ) + _rescore_groups(initial_eval_groups, target=target_answer) initial_eval_reward = _mean_group_reward(initial_eval_groups) await model.log(initial_eval_groups, step=0, split="val") report = YesNoTrainabilityReport( @@ -1004,6 +1141,7 @@ async def run_yes_no_trainability_async( backend_name=variant.backend_name, placement_mode=variant.placement_mode, base_model=base_model, + target_answer=target_answer, output_dir=str(output_dir), trainer_gpu_ids=variant.trainer_gpu_ids, inference_gpu_ids=variant.inference_gpu_ids, @@ -1022,12 +1160,17 @@ async def run_yes_no_trainability_async( model_ids_before=model_ids_before, ) - for _ in range(max_steps): - train_groups = await _build_trainable_groups( - model, - base_model=base_model, - prompts=prompts, - rollouts_per_prompt=rollouts_per_prompt, + for step_index in range(max_steps): + train_groups = ( + initial_train_groups + if step_index == 0 + else await _build_trainable_groups( + model, + base_model=base_model, + prompts=prompts, + rollouts_per_prompt=rollouts_per_prompt, + target=target_answer, + ) ) result = await backend.train( model, @@ -1045,12 +1188,14 @@ async def run_yes_no_trainability_async( step=result.step, split="train", ) - eval_groups = await _evaluate_groups( - model, - base_model=base_model, - prompts=eval_prompts, - step=result.step, - ) + async with backend.exact_adapter_lease(model, int(result.step)): + eval_groups = await _evaluate_groups( + model, + base_model=base_model, + prompts=eval_prompts, + step=result.step, + target=target_answer, + ) eval_reward = _mean_group_reward(eval_groups) await model.log(eval_groups, step=result.step, split="val") report.latest_step = int(result.step) @@ -1078,7 +1223,10 @@ async def run_yes_no_trainability_async( break report.model_ids_after = await _list_model_ids(model) - report.latest_snapshot = await _chat_snapshot(model, step=report.latest_step) + async with backend.exact_adapter_lease(model, report.latest_step): + report.latest_snapshot = await _chat_snapshot( + model, step=report.latest_step + ) output_dir = Path(report.output_dir) output_dir.mkdir(parents=True, exist_ok=True) @@ -1092,6 +1240,7 @@ async def run_yes_no_trainability_async( def run_yes_no_trainability( base_model: str, *, + artifact_root: Path | None = None, allow_unvalidated_arch: bool = False, ) -> YesNoTrainabilityReport: return asyncio.run( @@ -1101,12 +1250,29 @@ def run_yes_no_trainability( base_model, allow_unvalidated_arch=allow_unvalidated_arch, ), + artifact_root=artifact_root, allow_unvalidated_arch=allow_unvalidated_arch, ) ) def yes_no_trainability_passed(report: YesNoTrainabilityReport) -> bool: + has_nonzero_gradient = any( + max( + step.train_metrics.get("grad_norm", 0.0), + step.train_metrics.get("loss/grad_norm", 0.0), + ) + > 0.0 + for step in report.steps + ) + has_positive_probs_corr = any( + max( + step.train_metrics.get("probs_corr", 0.0), + step.train_metrics.get("loss/probs_corr", 0.0), + ) + > 0.0 + for step in report.steps + ) learned_from_below_threshold = ( report.saturated_step is not None and report.saturated_step > 0 @@ -1115,17 +1281,11 @@ def yes_no_trainability_passed(report: YesNoTrainabilityReport) -> bool: and report.final_eval_reward >= report.reward_threshold and report.final_eval_reward > report.initial_eval_reward ) - already_saturated_and_stable = ( - report.initial_eval_reward >= report.reward_threshold - and report.latest_step > 0 - and report.final_eval_reward is not None - and report.final_eval_reward >= report.reward_threshold - and bool(report.steps) - and any( - step.train_metrics.get("loss/grad_norm", 0.0) > 0.0 for step in report.steps - ) + return ( + learned_from_below_threshold + and has_nonzero_gradient + and has_positive_probs_corr ) - return learned_from_below_threshold or already_saturated_and_stable def run_megatron_dedicated_yes_no_trainability( diff --git a/tests/unit/test_checkpoint_retention.py b/tests/unit/test_checkpoint_retention.py index ee71dd2d2..a3a1113bc 100644 --- a/tests/unit/test_checkpoint_retention.py +++ b/tests/unit/test_checkpoint_retention.py @@ -12,7 +12,7 @@ def _checkpoint( is_eval_step: bool = False, reward: float | None = None, ) -> CheckpointInfo: - metrics = {"val/reward": reward} if reward is not None else {} + metrics = {"reward/val": reward} if reward is not None else {} return CheckpointInfo( step=step, path=f"/tmp/checkpoints/{step:04d}", @@ -23,7 +23,7 @@ def _checkpoint( def test_keep_recent_and_top_returns_kept_steps() -> None: - strategy = keep_recent_and_top(recent=2, top=1, metric="val/reward") + strategy = keep_recent_and_top(recent=2, top=1, metric="reward/val") context = CheckpointRetentionContext( current_step=6, checkpoints=[ @@ -40,7 +40,7 @@ def test_keep_recent_and_top_returns_kept_steps() -> None: def test_keep_recent_and_top_uses_metric_presence_for_legacy_history() -> None: - strategy = keep_recent_and_top(recent=0, top=1, metric="val/reward") + strategy = keep_recent_and_top(recent=0, top=1, metric="reward/val") context = CheckpointRetentionContext( current_step=3, checkpoints=[ diff --git a/tests/unit/test_dedicated_config.py b/tests/unit/test_dedicated_config.py index 292b9d516..186112931 100644 --- a/tests/unit/test_dedicated_config.py +++ b/tests/unit/test_dedicated_config.py @@ -85,18 +85,16 @@ def test_overlapping_gpu_ids(): ) -def test_trainer_not_starting_at_zero(): - with pytest.raises(ValueError, match="must start at GPU 0"): - validate_dedicated_config( - InternalModelConfig(trainer_gpu_ids=[1], inference_gpu_ids=[0]) - ) +def test_trainer_can_use_nonzero_gpu(): + validate_dedicated_config( + InternalModelConfig(trainer_gpu_ids=[2], inference_gpu_ids=[3]) + ) -def test_trainer_not_contiguous(): - with pytest.raises(ValueError, match="must be contiguous starting from 0"): - validate_dedicated_config( - InternalModelConfig(trainer_gpu_ids=[0, 2], inference_gpu_ids=[1]) - ) +def test_trainer_can_use_noncontiguous_gpus(): + validate_dedicated_config( + InternalModelConfig(trainer_gpu_ids=[0, 2], inference_gpu_ids=[1]) + ) def test_dedicated_rejects_fast_inference(): diff --git a/tests/unit/test_dsv4_vllm_runtime_patches.py b/tests/unit/test_dsv4_vllm_runtime_patches.py index faa4b7885..3f6d14859 100644 --- a/tests/unit/test_dsv4_vllm_runtime_patches.py +++ b/tests/unit/test_dsv4_vllm_runtime_patches.py @@ -25,7 +25,7 @@ def _load_dsv4_patches_module(): return module -def test_dsv4_lora_support_declares_vllm_024_manager_protocol(monkeypatch) -> None: +def test_dsv4_lora_support_declares_vllm_025_manager_protocol(monkeypatch) -> None: patches = _load_dsv4_patches_module() class FakeDeepseekV4ForCausalLM: @@ -33,9 +33,13 @@ class FakeDeepseekV4ForCausalLM: manager_patches: list[type] = [] monkeypatch.setattr( - patches, - "_import_dsv4_model_module", - lambda: SimpleNamespace(DeepseekV4ForCausalLM=FakeDeepseekV4ForCausalLM), + patches.importlib, + "import_module", + lambda name: ( + SimpleNamespace(DeepseekV4ForCausalLM=FakeDeepseekV4ForCausalLM) + if name == "vllm.models.deepseek_v4.nvidia.model" + else None + ), ) monkeypatch.setattr( patches, @@ -50,6 +54,145 @@ class FakeDeepseekV4ForCausalLM: assert manager_patches == [FakeDeepseekV4ForCausalLM] +def test_dsv4_fp8_o_proj_normalizes_rope_cache_once() -> None: + patches = _load_dsv4_patches_module() + rotary_emb = SimpleNamespace(cos_sin_cache=torch.ones(4, 8, dtype=torch.bfloat16)) + + cache = patches._dsv4_fp32_cos_sin_cache(rotary_emb) + + assert cache.dtype == torch.float32 + assert rotary_emb.cos_sin_cache is cache + assert patches._dsv4_fp32_cos_sin_cache(rotary_emb) is cache + + +def test_dsv4_native_o_proj_receives_fp32_rope_cache() -> None: + patches = _load_dsv4_patches_module() + seen: list[torch.Tensor] = [] + + class Attention: + def __init__(self) -> None: + self.rotary_emb = SimpleNamespace( + cos_sin_cache=torch.ones(4, 8, dtype=torch.bfloat16) + ) + self.wo_a = SimpleNamespace() + + def _o_proj(self, _o, _positions): + seen.append(self.rotary_emb.cos_sin_cache) + return "native" + + patches._patch_dsv4_cuda_o_proj_lora(Attention, SimpleNamespace()) + attention = Attention() + + assert attention._o_proj(None, None) == "native" + assert seen == [attention.rotary_emb.cos_sin_cache] + assert seen[0].dtype == torch.float32 + + +def test_dsv4_layerwise_reload_restores_merged_column_metadata() -> None: + patches = _load_dsv4_patches_module() + param = torch.nn.Parameter(torch.empty(3, 4), requires_grad=False) + + patches._restore_merged_column_output_dim(param) + + assert getattr(param, "output_dim") == 0 + + +def test_dsv4_layerwise_reload_restores_direct_linear_shard_metadata() -> None: + patches = _load_dsv4_patches_module() + param = torch.nn.Parameter(torch.empty(3, 4), requires_grad=False) + + patches._restore_linear_shard_dim(param, torch.empty(3, 8)) + + assert getattr(param, "input_dim") == 1 + assert getattr(param, "output_dim") == 1 + + +def test_dsv4_layerwise_reload_preserves_vllm_shard_metadata() -> None: + patches = _load_dsv4_patches_module() + + class ReadOnlyShardParameter: + shape = (3, 4) + + @property + def input_dim(self) -> int: + return 1 + + @property + def output_dim(self) -> int: + return 0 + + param = ReadOnlyShardParameter() + + patches._restore_linear_shard_dim(param, torch.empty(3, 8)) + + assert param.input_dim == 1 + assert param.output_dim == 0 + + +def test_dsv4_bmm_weight_passes_through_current_2d_parameter() -> None: + patches = _load_dsv4_patches_module() + param = torch.nn.Parameter(torch.empty(6, 4), requires_grad=False) + loaded = torch.empty(12, 4) + + reshaped = patches._reshape_dsv4_bmm_weight( + "layers.0.attn.wo_a.weight", param, loaded, tp_rank=1, tp_size=2 + ) + + assert reshaped is loaded + + +def test_dsv4_bmm_weight_selects_tp_rows_before_grouping() -> None: + patches = _load_dsv4_patches_module() + param = torch.nn.Parameter(torch.empty(2, 3, 4), requires_grad=False) + loaded = torch.arange(48).view(12, 4) + + reshaped = patches._reshape_dsv4_bmm_weight( + "layers.0.attn.wo_a.weight", param, loaded, tp_rank=1, tp_size=2 + ) + + assert reshaped.shape == param.shape + assert torch.equal(reshaped.flatten(0, 1), loaded[6:]) + + +def test_dsv4_expert_delta_names_use_checkpoint_projection_names() -> None: + patches = _load_dsv4_patches_module() + + assert ( + patches._dsv4_expert_checkpoint_name("layers.0.ffn.experts.3.down_proj.weight") + == "layers.0.ffn.experts.3.w2.weight" + ) + assert ( + patches._dsv4_expert_checkpoint_name( + "layers.0.ffn.shared_experts.gate_proj.weight" + ) + == "layers.0.ffn.shared_experts.w1.weight" + ) + assert ( + patches._dsv4_expert_checkpoint_name( + "layers.0.ffn.shared_experts.down_proj.weight" + ) + == "layers.0.ffn.shared_experts.down_proj.weight" + ) + + +def test_dsv4_fp8_weight_is_linked_to_its_block_scale() -> None: + patches = _load_dsv4_patches_module() + param = torch.nn.Parameter( + torch.empty(4, 4, dtype=torch.float8_e4m3fn), requires_grad=False + ) + scale = torch.nn.Parameter(torch.ones(2, 2), requires_grad=False) + + patches._attach_block_fp8_scale( + param, + "layers.0.attn.q_proj.weight", + {"layers.0.attn.q_proj.weight_scale_inv": scale}, + (2, 2), + ) + + assert getattr(param, "_art_block_fp8_scale") is scale + assert getattr(param, "_art_block_fp8_size") == (2, 2) + + def test_dsv4_compressor_helper_uses_punica_metadata_without_full_batch_lora( monkeypatch, ) -> None: diff --git a/tests/unit/test_exchange_training_model_selection.py b/tests/unit/test_exchange_training_model_selection.py index f402407bf..aa2c035e9 100644 --- a/tests/unit/test_exchange_training_model_selection.py +++ b/tests/unit/test_exchange_training_model_selection.py @@ -139,9 +139,11 @@ def _routed_exchange( extra[ART_MOE_ROUTING_METADATA_KEY] = { "prompt_token_ids": prompt_token_ids, "completion_token_ids": [output_token], + "num_experts": 2048, "routed_experts": np.asarray( - [[[10]]] * len(prompt_token_ids) + [[[output_token * 10]]], - dtype=np.int32, + [[[token_id * 10]] for token_id in prompt_token_ids] + + [[[output_token * 10]]], + dtype=np.uint16, ), } return exchange @@ -720,8 +722,9 @@ def test_preprocessing_preserves_moe_routes_for_reasoning_stripped_suffix() -> N first_extra[ART_MOE_ROUTING_METADATA_KEY] = { "prompt_token_ids": [1], "completion_token_ids": [2, 101, 102, 9], + "num_experts": 2048, "routed_experts": np.asarray( - [[[10]], [[20]], [[1010]], [[1020]], [[90]]], dtype=np.int32 + [[[10]], [[20]], [[1010]], [[1020]], [[90]]], dtype=np.uint16 ), } @@ -757,9 +760,10 @@ def test_preprocessing_preserves_moe_routes_for_reasoning_stripped_suffix() -> N second_extra[ART_MOE_ROUTING_METADATA_KEY] = { "prompt_token_ids": [1, 101, 102, 9, 4], "completion_token_ids": [5, 6], + "num_experts": 2048, "routed_experts": np.asarray( [[[10]], [[1010]], [[1020]], [[90]], [[40]], [[50]], [[60]]], - dtype=np.int32, + dtype=np.uint16, ), } @@ -820,7 +824,7 @@ def apply_chat_template( assert all(result.weight == pytest.approx(1 / 6) for result in results) expected_routes = np.asarray( [[[10]], [[1010]], [[1020]], [[90]], [[40]], [[50]], [[60]]], - dtype=np.int32, + dtype=np.uint16, ) for result in stripped: assert isinstance(result.moe_routed_experts, MoeRouteSegments) diff --git a/tests/unit/test_megatron_reference_logprobs.py b/tests/unit/test_megatron_reference_logprobs.py index 1ef1c5f1f..a94bbfd76 100644 --- a/tests/unit/test_megatron_reference_logprobs.py +++ b/tests/unit/test_megatron_reference_logprobs.py @@ -8,6 +8,7 @@ from art import types from art.megatron import train as megatron_train +from art.megatron.runtime.specs import ExperimentalTrainConfig, TrainJobSpec from art.megatron.training import microbatches as megatron_microbatches from art.preprocessing.pack import PackedTensors @@ -87,14 +88,14 @@ def test_prepare_kl_reference_logprobs_requires_reference_path() -> None: runtime = SimpleNamespace(rank=0) job = SimpleNamespace( config=types.TrainConfig(kl_penalty_coef=0.25), - experimental_config={}, - lora_path="/tmp/current", + experimental_config=ExperimentalTrainConfig(), + source_adapter_path="/tmp/current", ) try: megatron_train._prepare_kl_reference_logprobs( runtime=cast(megatron_train.TrainingRuntime, runtime), - job=cast(megatron_train.MegatronTrainingJob, job), + job=cast(TrainJobSpec, job), packed_tensors=_packed_inputs(), num_sequences=1, num_steps=1, @@ -118,7 +119,10 @@ def set_step( ) -> None: self.events.append(("set_step", step_index, sample_index)) - def begin_micro(self, sample_index: int, micro_order: int) -> None: + def begin_micro( + self, sample_index: int, micro_order: int, *, chunk_index: int + ) -> None: + assert chunk_index == 0 self.events.append(("begin_micro", micro_order, sample_index)) def finalize_step(self) -> None: @@ -155,6 +159,9 @@ def get_forward_kwargs(self, _chunk: nn.Module, *, attention_bias: Any) -> dict: del attention_bias return {} + def build_pipeline_microbatch_activator(self, _model_chunks: Any) -> None: + return None + def test_calculate_megatron_logprobs_replays_routes(monkeypatch) -> None: controller = _ReplayController() diff --git a/tests/unit/test_moe_routing_real_path.py b/tests/unit/test_moe_routing_real_path.py index bd7143fdc..323bfb038 100644 --- a/tests/unit/test_moe_routing_real_path.py +++ b/tests/unit/test_moe_routing_real_path.py @@ -1,25 +1,32 @@ from __future__ import annotations +from datetime import datetime import math from typing import Any import numpy as np +from openai.types.chat import ChatCompletion from openai.types.chat.chat_completion import Choice import pytest import torch +from art.distributed.data_plane import SharedMemoryPackedBatchStore +from art.distributed.packing import TrajectoryPayload from art.megatron.prefix_tree import parse_prefix_tree_row from art.megatron.routing_replay import ( build_moe_routing_replay_bundle_from_packed_tensors, ) from art.preprocessing.moe_routing import ( ART_MOE_ROUTING_METADATA_KEY, + NUM_EXPERTS_KEY, + ROUTED_EXPERTS_KEY, + MoeRouteArray, MoeRouteSegments, align_choice_routes_to_tokenized_result, ) from art.preprocessing.pack import packed_tensors_from_tokenized_results from art.preprocessing.tokenize import TokenizedResult -from art.trajectories import Trajectory +from art.trajectories import ChatCompletionsExchange, Trajectory class _FakeTokenizer: @@ -28,6 +35,7 @@ def decode(self, token_id: int) -> str: def _choice(metadata: dict[str, Any]) -> Choice: + metadata.setdefault("num_experts", 256) return Choice.model_validate( { "index": 0, @@ -39,6 +47,7 @@ def _choice(metadata: dict[str, Any]) -> Choice: def _route(seed: int) -> list[list[int]]: + seed %= 240 return [[seed, seed + 1], [seed + 2, seed + 3]] @@ -93,6 +102,28 @@ def test_align_choice_routes_to_tokenized_result_rejects_token_mismatch() -> Non ) +def test_align_choice_routes_materializes_missing_terminal_route() -> None: + routes, _stats = align_choice_routes_to_tokenized_result( + token_ids=[10, 20], + choices=[ + _choice( + { + "prompt_token_ids": [10], + "completion_token_ids": [20], + "routed_experts": np.asarray([_route(0)], dtype=np.uint8), + } + ) + ], + choice_offsets=[1], + choice_token_lengths=[1], + ) + + assert routes is not None + materialized = _routes_to_list(routes) + assert materialized[0] == _route(0) + assert all(len(set(layer)) == 2 for layer in materialized[1]) + + def _tokenized( token_ids: list[int], routes: list[list[list[int]]], @@ -104,6 +135,7 @@ def _tokenized( weight: float = 1.0, pixel_values: torch.Tensor | None = None, image_grid_thw: torch.Tensor | None = None, + num_experts: int = 256, ) -> TokenizedResult: trainable_start = prompt_length if trainable_start is None else trainable_start return TokenizedResult( @@ -120,7 +152,13 @@ def _tokenized( choice_offsets=[trainable_start], extra_logprobs={}, _tokenizer=_FakeTokenizer(), # type: ignore[arg-type] - moe_routed_experts=np.asarray(routes, dtype=np.int32), + moe_routed_experts=MoeRouteArray( + np.asarray( + routes, + dtype=np.uint8 if num_experts <= 256 else np.uint16, + ), + num_experts=num_experts, + ), prompt_id=prompt_id, prompt_length=prompt_length, weight=weight, @@ -137,7 +175,7 @@ def test_pack_carries_routes_through_prefix_tree_splicing() -> None: ) second = _tokenized( [10, 11, 22, 23], - [_route(99), _route(10), _route(40), _route(50)], + [_route(0), _route(10), _route(40), _route(50)], prompt_id=123, prompt_length=1, trainable_start=2, @@ -155,7 +193,7 @@ def test_pack_carries_routes_through_prefix_tree_splicing() -> None: assert packed["tokens"].tolist()[0][:7] == [10, 11, 20, 21, 11, 22, 23] routing_replay = packed["moe_routing_replay"] assert routing_replay is not None - assert routing_replay.expert_indices.tolist()[0][:7] == [ + assert torch.movedim(routing_replay.expert_indices[:, 0], 0, 1).tolist()[:7] == [ _route(0), _route(10), _route(20), @@ -167,6 +205,40 @@ def test_pack_carries_routes_through_prefix_tree_splicing() -> None: assert routing_replay.pack_stats.packed_tokens == 7 +def test_pack_uses_reference_routes_for_shared_prefix() -> None: + first = _tokenized( + [10, 11, 20], + [_route(0), _route(10), _route(20)], + prompt_id=123, + prompt_length=2, + ) + second = _tokenized( + [10, 11, 21], + [_route(90), _route(10), _route(30)], + prompt_id=123, + prompt_length=2, + ) + + packed = packed_tensors_from_tokenized_results( + [first, second], + seq_len=8, + truncate_long_results=False, + include_moe_routing=True, + min_prefix_tree_shared_segment_length=0, + ) + + replay = packed["moe_routing_replay"] + assert replay is not None + routes = torch.movedim(replay.expert_indices[:, 0], 0, 1).tolist() + assert routes[:5] == [ + _route(0), + _route(10), + _route(20), + _route(10), + _route(30), + ] + + def test_prefix_tree_pack_keeps_trainable_duplicates_in_leaf_metadata() -> None: first = _tokenized( [10, 11, 20, 21], @@ -310,12 +382,19 @@ def test_prefix_tree_pack_best_fit_combines_independent_small_groups() -> None: assert int((packed["group_ids"] != -1).sum().item()) == 24 -def test_pack_infers_at_least_topk_experts_from_sparse_routes() -> None: +@pytest.mark.parametrize( + ("num_experts", "dtype"), + [(256, torch.uint8), (257, torch.uint16)], +) +def test_pack_preserves_exact_expert_count_and_smallest_dtype( + num_experts: int, dtype: torch.dtype +) -> None: result = _tokenized( [10, 20], - [[[0, 0, 0, 0]], [[0, 0, 0, 0]]], + [[[0, 1, 2, 3]], [[4, 5, 6, 7]]], prompt_id=456, prompt_length=1, + num_experts=num_experts, ) packed = packed_tensors_from_tokenized_results( @@ -329,10 +408,15 @@ def test_pack_infers_at_least_topk_experts_from_sparse_routes() -> None: routing_replay = packed["moe_routing_replay"] assert routing_replay is not None assert routing_replay.topk == 4 - assert routing_replay.num_experts == 4 + assert routing_replay.num_experts == num_experts + assert routing_replay.expert_indices.dtype == dtype + assert routing_replay.expert_indices.shape == (1, 1, 4, 4) + assert all( + len(set(row)) == 4 for row in routing_replay.expert_indices[0, 0].tolist() + ) -def test_build_replay_bundle_uses_packed_sequence_sample_calls() -> None: +def test_build_replay_bundle_retains_layer_major_storage() -> None: result = _tokenized( [10, 11, 20], [_route(0), _route(10), _route(20)], @@ -352,7 +436,123 @@ def test_build_replay_bundle_uses_packed_sequence_sample_calls() -> None: global_grad_accumulation_sequences=1, ) - route = bundle.steps[0].routers["chunk_00.layer_0000.mlp.router"].calls[0] - assert route.sample_index == 0 - assert route.expert_indices.tolist()[:3] == [[0, 1], [10, 11], [20, 21]] - assert len(set(route.expert_indices.tolist()[3])) == 2 + replay = packed["moe_routing_replay"] + assert replay is not None + assert bundle.tensor_backed + assert bundle.steps == {} + assert bundle.expert_indices is replay.expert_indices + assert bundle.expert_indices[0, 0].tolist()[:3] == [ + [0, 1], + [10, 11], + [20, 21], + ] + assert len(set(bundle.expert_indices[0, 0, 3].tolist())) == 2 + + +def test_trajectory_route_roundtrip_preserves_exact_contract() -> None: + routes = MoeRouteArray( + np.asarray([[[0, 256]], [[255, 1]]], dtype=np.uint16), + num_experts=257, + ) + trajectory = Trajectory( + messages_and_choices=[ + _choice( + { + "prompt_token_ids": [10], + "completion_token_ids": [20], + ROUTED_EXPERTS_KEY: routes, + NUM_EXPERTS_KEY: 257, + } + ) + ] + ) + + restored = TrajectoryPayload.from_trajectory(trajectory).build() + choice = restored.messages_and_choices[0] + assert isinstance(choice, Choice) + metadata = (choice.model_extra or {})[ART_MOE_ROUTING_METADATA_KEY] + restored_routes = metadata[ROUTED_EXPERTS_KEY] + assert isinstance(restored_routes, MoeRouteArray) + assert restored_routes.num_experts == 257 + assert restored_routes.dtype == np.dtype(np.uint16) + assert np.array_equal(restored_routes, routes) + + +def test_exchange_route_roundtrip_preserves_exact_contract() -> None: + routes = MoeRouteArray( + np.asarray([[[0, 256]], [[255, 1]]], dtype=np.uint16), + num_experts=257, + ) + response = ChatCompletion( + id="route-test", + choices=[_choice({ROUTED_EXPERTS_KEY: routes, NUM_EXPERTS_KEY: 257})], + created=0, + model="test-model", + object="chat.completion", + ) + now = datetime.now() + trajectory = Trajectory( + exchanges={ + "chat_completions": [ + ChatCompletionsExchange( + request={"model": "test-model", "messages": []}, + response=response, + start_time=now, + end_time=now, + ) + ] + } + ) + + restored = TrajectoryPayload.from_trajectory(trajectory).build() + choice = restored.exchanges.chat_completions[0].response.choices[0] + restored_routes = (choice.model_extra or {})[ART_MOE_ROUTING_METADATA_KEY][ + ROUTED_EXPERTS_KEY + ] + assert restored_routes.num_experts == 257 + assert np.array_equal(restored_routes, routes) + + +def test_shm_replay_is_one_layer_major_uint16_tensor() -> None: + packed = packed_tensors_from_tokenized_results( + [ + _tokenized( + [10, 20], + [[[0, 256]], [[255, 1]]], + prompt_id=456, + prompt_length=1, + num_experts=257, + ) + ], + seq_len=4, + pad_token_id=0, + truncate_long_results=False, + include_moe_routing=True, + ) + store = SharedMemoryPackedBatchStore( + owner_actor_id="test-owner", capacity_bytes=1 << 20 + ) + try: + ref = store.create(packed, batch_id="route-batch") + replay_specs = [ + spec for spec in ref.tensors if spec.name.startswith("moe_routing_replay/") + ] + assert [spec.name for spec in replay_specs] == [ + "moe_routing_replay/expert_indices" + ] + assert replay_specs[0].dtype == "uint16" + assert ref.moe_routing_replay is not None + assert ref.moe_routing_replay.num_experts == 257 + assert ref.moe_routing_replay.packed_tokens == 2 + + with store.map(ref) as mapped: + replay = mapped.tensors["moe_routing_replay"] + assert replay.expert_indices.shape == (1, 1, 4, 2) + assert replay.expert_indices.dtype == torch.uint16 + bundle = build_moe_routing_replay_bundle_from_packed_tensors( + packed_tensors=mapped.tensors, + global_grad_accumulation_sequences=1, + ) + assert bundle.expert_indices is replay.expert_indices + finally: + store.close() diff --git a/tests/unit/test_moe_routing_replay.py b/tests/unit/test_moe_routing_replay.py index 4a559b8f4..69367375e 100644 --- a/tests/unit/test_moe_routing_replay.py +++ b/tests/unit/test_moe_routing_replay.py @@ -8,6 +8,7 @@ import torch from torch import nn +import art.megatron.routing_replay as routing_replay_module from art.megatron.routing_replay import ( MoeRoutingReplayBundle, MoeRoutingReplayController, @@ -102,6 +103,33 @@ def _make_multi_call_bundle() -> MoeRoutingReplayBundle: ) +def _make_tensor_bundle( + *, num_layers: int = 1, topology: ParallelTopology | None = None +) -> MoeRoutingReplayBundle: + rows = torch.tensor( + [ + [[0, 2], [1, 0], [2, 1], [1, 2]], + [[2, 0], [0, 1], [1, 2], [2, 1]], + ], + dtype=torch.uint8, + ) + expert_indices = torch.stack( + [torch.roll(rows, shifts=layer, dims=-1) for layer in range(num_layers)] + ).contiguous() + return MoeRoutingReplayBundle( + topology=topology + or ParallelTopology(tp=1, ep=1, etp=1, dp=1, sp=False, cp=1, pp=1, vpp=1), + num_steps=1, + max_topk=2, + router_keys=[ + f"chunk_00.layer_{layer:04d}.mlp.router" for layer in range(num_layers) + ], + expert_indices=expert_indices, + num_experts=3, + global_grad_accumulation_sequences=2, + ) + + class _FakeParallelState: def __init__( self, @@ -175,6 +203,7 @@ def __init__(self, *, topk: int = 2, router_replay: Any | None = None) -> None: "sequence_parallel": False, "context_parallel_size": 1, "moe_router_fusion": False, + "num_moe_experts": 3, }, )() @@ -347,6 +376,108 @@ def test_bundle_roundtrip_disk() -> None: assert torch.equal(loaded_route.expert_mask, route.expert_mask) +def test_tensor_bundle_uint16_roundtrip_disk() -> None: + indices = torch.tensor( + [[[[0, 256], [255, 1]]]], + dtype=torch.uint16, + ) + bundle = MoeRoutingReplayBundle( + topology=ParallelTopology(tp=1, ep=1, etp=1, dp=1, sp=False, cp=1, pp=1, vpp=1), + num_steps=1, + max_topk=2, + router_keys=["chunk_00.layer_0000.mlp.router"], + expert_indices=indices, + num_experts=257, + global_grad_accumulation_sequences=1, + ) + with tempfile.TemporaryDirectory() as tmp_dir: + bundle.to_dir(tmp_dir) + loaded = MoeRoutingReplayBundle.from_dir(tmp_dir) + + assert loaded.tensor_backed + assert loaded.num_experts == 257 + assert loaded.expert_indices is not None + assert loaded.expert_indices.dtype == torch.uint16 + assert torch.equal(loaded.expert_indices, indices) + + +def test_tensor_controller_preserves_uid_order_and_synthesizes_tp_padding() -> None: + bundle = _make_tensor_bundle() + controller = MoeRoutingReplayController(bundle=bundle, strict=True, device="cpu") + chunk = _FakeChunk() + router = _fake_chunk_router(chunk) + replay = cast(_FakeRouterReplay, router.router_replay) + + controller.install_router_patches([chunk]) + controller.set_step(step_index=0, sample_index=[0, 1]) + controller.begin_micro(0, 0) + controller.set_local_input_token_uids(torch.tensor([3, 1, -1], dtype=torch.int64)) + router.routing(torch.randn((3, 3), dtype=torch.float32)) + + assert bundle.expert_indices is not None + expected = bundle.expert_indices[0, 0, [3, 1]].to(torch.long) + target = replay.targets_seen[-1] + assert torch.equal(target[:2], expected) + assert target[2].min().item() >= 0 + assert target[2].max().item() < 3 + assert target[2].unique().numel() == 2 + + controller.begin_micro(1, 1) + controller.set_local_input_token_uids(torch.arange(4, dtype=torch.int64)) + router.routing(torch.randn((4, 3), dtype=torch.float32)) + controller.finalize_step() + controller.remove_router_patches() + + +def test_tensor_controller_synthesizes_dp_dummy_routes() -> None: + bundle = _make_tensor_bundle() + controller = MoeRoutingReplayController(bundle=bundle, strict=True, device="cpu") + chunk = _FakeChunk() + router = _fake_chunk_router(chunk) + replay = cast(_FakeRouterReplay, router.router_replay) + + controller.install_router_patches([chunk]) + controller.set_step(step_index=0, sample_index=[None]) + controller.begin_micro(None, 0) + controller.set_local_input_token_uids(torch.tensor([2, 0], dtype=torch.int64)) + router.routing(torch.randn((2, 3), dtype=torch.float32)) + + target = replay.targets_seen[-1] + assert bool(((0 <= target) & (target < 3)).all()) + assert all(row.unique().numel() == 2 for row in target) + controller.finalize_step() + controller.remove_router_patches() + + +def test_tensor_controller_addresses_pp_global_layer( + monkeypatch: pytest.MonkeyPatch, +) -> None: + topology = ParallelTopology(tp=1, ep=1, etp=1, dp=1, sp=False, cp=1, pp=2, vpp=1) + bundle = _make_tensor_bundle(num_layers=2, topology=topology) + monkeypatch.setattr( + routing_replay_module, + "_global_layer_prefixes", + lambda _chunk: [("decoder.layers.0", 1)], + ) + controller = MoeRoutingReplayController(bundle=bundle, strict=True, device="cpu") + chunk = _FakeChunk() + router = _fake_chunk_router(chunk) + replay = cast(_FakeRouterReplay, router.router_replay) + + controller.install_router_patches([chunk]) + controller.set_step(step_index=0, sample_index=[0]) + controller.begin_micro(0, 0) + controller.set_local_input_token_uids(torch.arange(4, dtype=torch.int64)) + router.routing(torch.randn((4, 3), dtype=torch.float32)) + + assert bundle.expert_indices is not None + assert torch.equal( + replay.targets_seen[-1], bundle.expert_indices[1, 0].to(torch.long) + ) + controller.finalize_step() + controller.remove_router_patches() + + def test_controller_uses_native_router_replay_target_indices() -> None: bundle, route = _make_bundle() controller = MoeRoutingReplayController(bundle=bundle, strict=True, device="cpu") diff --git a/tests/unit/test_multi_checkpoint_inference.py b/tests/unit/test_multi_checkpoint_inference.py index eaabf6ce3..23aa0ebad 100644 --- a/tests/unit/test_multi_checkpoint_inference.py +++ b/tests/unit/test_multi_checkpoint_inference.py @@ -356,7 +356,7 @@ def test_max_loras_can_be_overridden(self, unsloth_service_class): async def test_prune_loaded_adapters_unloads_non_retained_steps( self, unsloth_service_class, monkeypatch ): - """UnslothService should unload old vLLM LoRA adapters like MegatronService.""" + """UnslothService should unload old vLLM LoRA adapters after updates.""" httpx = pytest.importorskip("httpx") UnslothService = unsloth_service_class calls = [] diff --git a/tests/unit/test_preprocessing_tokenize.py b/tests/unit/test_preprocessing_tokenize.py index ebf9f1f8d..35a15aaac 100644 --- a/tests/unit/test_preprocessing_tokenize.py +++ b/tests/unit/test_preprocessing_tokenize.py @@ -5,9 +5,22 @@ import pytest from transformers.tokenization_utils_base import BatchEncoding -from art.preprocessing.tokenize import tokenize_sft_batch +try: + from art.megatron.model_support.handlers.gemma4 import ( + GEMMA4_DENSE_HANDLER, + GEMMA4_MOE_HANDLER, + ) +except ModuleNotFoundError as error: + if error.name is None or not error.name.startswith("megatron"): + raise + pytest.skip("Megatron is not installed", allow_module_level=True) +from art.preprocessing.tokenize import ( + _normalize_tool_call_arguments_for_chat_template, + tokenize_sft_batch, +) from art.trajectories import Trajectory from art.types import MessagesAndChoices, TrainSFTConfig +from art.utils.chat_template import TOOL_CALL_ARGUMENTS_AS_MAPPING_ATTR pytest.importorskip("torch") pytest.importorskip("transformers") @@ -168,6 +181,65 @@ def apply_chat_template(self, *args, **kwargs): return rendered +def test_glm_chat_template_normalizes_aliased_tool_call_arguments() -> None: + tokenizer = _FakeTokenizer() + tokenizer.chat_template = ( + "{% for tc in message.tool_calls %}" + "{% set _args = tc.function.arguments %}" + "{% for name, value in _args.items() %}{{ name }}{{ value }}{% endfor %}" + "{% endfor %}" + ) + messages = [ + { + "role": "assistant", + "tool_calls": [ + { + "function": { + "name": "answer", + "arguments": '{"value": "yes"}', + } + } + ], + } + ] + + normalized = _normalize_tool_call_arguments_for_chat_template( + tokenizer, + messages, + ) + + assert normalized[0]["tool_calls"][0]["function"]["arguments"] == {"value": "yes"} + assert messages[0]["tool_calls"][0]["function"]["arguments"] == ('{"value": "yes"}') + + +@pytest.mark.parametrize("handler", [GEMMA4_DENSE_HANDLER, GEMMA4_MOE_HANDLER]) +def test_gemma4_normalizes_json_tool_arguments_for_mapping_template(handler) -> None: + tokenizer = _FakeTokenizer() + tokenizer.chat_template = ( + "{% set function = tool_call['function'] %}" + "{% if function['arguments'] is mapping %}{{ function['arguments'] }}{% endif %}" + ) + handler.configure_tokenizer(tokenizer, internal_config={}) + messages = [ + { + "role": "assistant", + "tool_calls": [ + { + "function": { + "name": "answer", + "arguments": '{"value": "yes"}', + } + } + ], + } + ] + + normalized = _normalize_tool_call_arguments_for_chat_template(tokenizer, messages) + + assert getattr(tokenizer, TOOL_CALL_ARGUMENTS_AS_MAPPING_ATTR) is True + assert normalized[0]["tool_calls"][0]["function"]["arguments"] == {"value": "yes"} + + def test_tokenize_sft_batch_masks_response_tokens_without_unsloth_import() -> None: tokenizer = _FakeTokenizer() messages = cast( diff --git a/tests/unit/test_serving_capabilities.py b/tests/unit/test_serving_capabilities.py new file mode 100644 index 000000000..c002bda03 --- /dev/null +++ b/tests/unit/test_serving_capabilities.py @@ -0,0 +1,109 @@ +from copy import deepcopy +from typing import cast + +import httpx +from pydantic import ValidationError +import pytest + +from art.local.backend import LocalBackend +from art.model import Model +from art.serving_capabilities import ( + ART_SERVING_PROTOCOL_VERSION, + FastMetricsSnapshot, + ServingCapabilities, +) + + +def _snapshot_payload() -> dict[str, object]: + return { + "schema_version": 1, + "source": "art_vllm_runtime", + "last_update_unix_s": 10.0, + "record_count": 7, + "engine_count": 1, + "metrics": { + "prompt_tokens_total": 100.0, + "generation_tokens_total": 50.0, + "prefix_cache_queries_total": 20.0, + "prefix_cache_hits_total": 15.0, + "num_preempted_reqs_total": 2.0, + "num_requests_running": 3.0, + "num_requests_waiting": 4.0, + "num_requests_waiting_capacity": 2.0, + "kv_cache_usage_perc": 0.5, + }, + "process_uuid": "runtime-process", + "generation": 3, + } + + +def test_serving_capabilities_validate_isolated_metrics_endpoint() -> None: + capabilities = ServingCapabilities( + runtime="art_vllm", + protocol_version=ART_SERVING_PROTOCOL_VERSION, + fast_metrics={"url": "http://10.20.30.40:43123/art/metrics"}, + ) + assert capabilities.model_dump(mode="json")["fast_metrics"] == { + "url": "http://10.20.30.40:43123/art/metrics" + } + + for invalid in ( + {"protocol_version": ART_SERVING_PROTOCOL_VERSION - 1}, + {"fast_metrics": True}, + {"fast_metrics": {"url": "http://0.0.0.0:43123/art/metrics"}}, + {"fast_metrics": {"url": "/art/metrics"}}, + ): + values = { + "runtime": "art_vllm", + "protocol_version": ART_SERVING_PROTOCOL_VERSION, + **invalid, + } + with pytest.raises(ValidationError): + ServingCapabilities.model_validate(values) + + +@pytest.mark.parametrize("invalid", [True, "1", [1.0], {"value": 1.0}, float("inf")]) +def test_fast_metrics_snapshot_requires_finite_numeric_scalars(invalid: object) -> None: + payload = deepcopy(_snapshot_payload()) + metrics = cast(dict[str, object], payload["metrics"]) + metrics["prompt_tokens_total"] = invalid + with pytest.raises(ValidationError): + FastMetricsSnapshot.model_validate(payload) + + +async def test_local_backend_collects_only_from_advertised_metrics_endpoint( + tmp_path, +) -> None: + metrics_url = "http://metrics.internal:43123/art/metrics" + requests: list[httpx.Request] = [] + + def respond(request: httpx.Request) -> httpx.Response: + requests.append(request) + return httpx.Response(200, json=_snapshot_payload()) + + model = Model( + name="metrics-test", + project="tests", + inference_api_key="secret", + inference_base_url="http://main-api.invalid/v1", + ) + object.__setattr__( + model, + "_serving_capabilities", + ServingCapabilities( + runtime="art_vllm", + protocol_version=ART_SERVING_PROTOCOL_VERSION, + fast_metrics={"url": metrics_url}, + ), + ) + backend = LocalBackend(path=str(tmp_path)) + async with httpx.AsyncClient(transport=httpx.MockTransport(respond)) as client: + metrics = await backend._collect_train_step_vllm_metrics( + model, client=client, snapshots={} + ) + + assert [str(request.url) for request in requests] == [metrics_url] + assert requests[0].headers["Authorization"] == "Bearer secret" + assert metrics["vllm/num_requests_running"] == 3.0 + assert metrics["vllm/num_requests_waiting_capacity"] == 2.0 + assert metrics["vllm/prefix_cache_hit_rate"] == 0.75 diff --git a/tests/unit/test_track_api_cost.py b/tests/unit/test_track_api_cost.py index fbd938dbc..7c4732579 100644 --- a/tests/unit/test_track_api_cost.py +++ b/tests/unit/test_track_api_cost.py @@ -724,7 +724,19 @@ async def eval_fn( reward=1.0, messages_and_choices=[ {"role": "user", "content": "hello"}, - {"role": "assistant", "content": "hi"}, + { + "role": "assistant", + "content": "hi", + "policy_token_spans": [ + { + "start_token": 0, + "end_token": 1, + "policy_version": 1, + "lora_slot": "active", + "update_seq": 1, + } + ], + }, ], ) ] diff --git a/tests/unit/test_trainer_rank_validation.py b/tests/unit/test_trainer_rank_validation.py index 042dad3c1..7c5720c06 100644 --- a/tests/unit/test_trainer_rank_validation.py +++ b/tests/unit/test_trainer_rank_validation.py @@ -1,15 +1,23 @@ from __future__ import annotations +import asyncio from collections.abc import Iterable from dataclasses import dataclass +from datetime import timedelta import gc from importlib.util import find_spec import inspect +import json +from pathlib import Path +import threading +import time from types import SimpleNamespace from typing import TYPE_CHECKING, Any, cast import pytest import torch +import torch.distributed as dist +import torch.multiprocessing as mp from art.megatron.prefix_tree_packing import prefix_tree_pack from art.trainer_rank import ( @@ -17,16 +25,33 @@ AdapterSelection, ForwardInput, ForwardOutput, + MaterializedCheckpoint, TopK, TrainerRank, TrainerRankMemoryError, - TrainerRankOptimizerLayout, - TrainerRankOptimizerState, TrainerRankSlotStateError, Unset, ) +from art.trainer_rank._checkpoint import ( + CheckpointManifest, + LocalOptimizerState, + OptimizerConfig, + PreparedCheckpoint, + _file_digest, + _FinalizedSave, + _manifest_digest, + _merge_component, + _PreparedSave, + _validate_save_state, + abort_checkpoint_save, + finish_checkpoint_save, + materialize_lora, + prepare_checkpoint, + prepare_checkpoint_save, +) from art.trainer_rank._impl import ( _anchor_disconnected_outputs, + _CheckpointSlot, _MemoryCheck, _MemoryProfile, _validate_top_k, @@ -46,8 +71,6 @@ def test_public_types_have_canonical_module_paths() -> None: assert { "AdapterSelection", - "TrainerRankOptimizerLayout", - "TrainerRankOptimizerState", "Unset", } <= set(art.trainer_rank.__all__) for public_type in ( @@ -57,8 +80,6 @@ def test_public_types_have_canonical_module_paths() -> None: TopK, TrainerRank, TrainerRankMemoryError, - TrainerRankOptimizerLayout, - TrainerRankOptimizerState, TrainerRankSlotStateError, ): assert public_type.__module__ == "art.trainer_rank" @@ -99,7 +120,6 @@ def zero_grad(self) -> None: @dataclass(frozen=True) class _SlotRef: - kind: str name: str | None @@ -122,14 +142,21 @@ def _runtime( model_support_handler=SimpleNamespace( build_gdn_execution_spec=True, canonicalize_loaded_lora_state=lambda state, _model: state, + from_vllm_lora_tensors=lambda state, **_kwargs: state, + to_vllm_lora_tensors=lambda state, **kwargs: ( + state, + kwargs["adapter_config"], + ), zero_internal_padding_grads=lambda _model: None, zero_internal_padding_params=lambda _model: None, ), + rank=0, + world_size=1, ) # type: ignore -def _slot_ref(kind: str, name: str | None) -> "LoRASlotRef": - return _SlotRef(kind, name) # type: ignore +def _slot_ref(name: str | None) -> "LoRASlotRef": + return _SlotRef(name) # type: ignore def _target_request(token: int) -> ForwardInput[torch.Tensor, None, None, None]: @@ -180,7 +207,7 @@ def _trainer_with_checkpoint( ) -> tuple[TrainerRank, torch.nn.Parameter]: trainer = TrainerRank(_runtime()) param = torch.nn.Parameter(value.clone()) - trainer._checkpoint_slot_params_by_name["student"] = (param,) + trainer._checkpoint_slots.setdefault("student", _CheckpointSlot()).params = (param,) monkeypatch.setattr( trainer, "_reduce_dynamic_grads", @@ -210,8 +237,7 @@ def _tracked_targets( def test_forward_input_validation() -> None: with pytest.raises(ValueError, match="top_k must be >= 1"): ForwardInput(input_tokens=torch.tensor([1]), top_k=0) - with pytest.raises(ValueError, match="cannot set both checkpoint and lora"): - ForwardInput(input_tokens=torch.tensor([1]), checkpoint="a", lora="b") + assert "lora" not in ForwardInput.__dataclass_fields__ with pytest.raises(ValueError, match="top_k=9 exceeds vocabulary size 8"): _validate_top_k(9, _Model()) @@ -223,7 +249,40 @@ def test_forward_input_distinguishes_unset_and_base_checkpoint( request = ForwardInput(input_tokens=torch.tensor([1]), checkpoint=checkpoint) assert request.checkpoint is expected - assert request.lora is Unset + + +def test_dp_rank_forward_rejects_unloaded_explicit_checkpoint( + monkeypatch: pytest.MonkeyPatch, +) -> None: + trainer = TrainerRank(_runtime()) + _stub_forward(monkeypatch, trainer) + request = ForwardInput( + input_tokens=torch.tensor([1]), + target_tokens=torch.tensor([1]), + checkpoint="typo", + ) + + with pytest.raises(TrainerRankSlotStateError, match="unloaded.*'typo'"): + trainer.dp_rank_forward([request]) + + +@pytest.mark.parametrize("checkpoint", (None, "student")) +def test_dp_rank_forward_accepts_base_or_loaded_explicit_checkpoint( + monkeypatch: pytest.MonkeyPatch, + checkpoint: str | None, +) -> None: + trainer = TrainerRank(_runtime()) + trainer._checkpoint_slots.setdefault("student", _CheckpointSlot()).params = () + _stub_forward(monkeypatch, trainer) + request = ForwardInput( + input_tokens=torch.tensor([1]), + target_tokens=torch.tensor([1]), + checkpoint=checkpoint, + ) + + output = trainer.dp_rank_forward([request]) + + assert isinstance(output[0], ForwardOutput) def test_forward_input_preserves_public_runtime_shape() -> None: @@ -388,15 +447,359 @@ def test_hybridep_rejects_buffer_growth_with_live_graph( ) -def test_trainer_rank_adapter_stack_errors() -> None: +async def test_trainer_rank_checkpoint_stack_errors() -> None: trainer = TrainerRank(_runtime()) - with pytest.raises(RuntimeError, match="No pushed LoRA or checkpoint"): - trainer.pop_pushed_lora_or_checkpoint() + with pytest.raises(RuntimeError, match="No pushed checkpoint"): + trainer.pop_checkpoint() trainer._slot_stack.append(object()) # type: ignore - for load in (trainer.load_checkpoint_slot, trainer.load_lora_slot): - with pytest.raises(RuntimeError, match="Cannot load a LoRA/checkpoint"): - load("teacher", {}) + with pytest.raises(RuntimeError, match="Cannot load a checkpoint"): + await trainer.load_checkpoint("teacher") + + +async def test_checkpoint_tasks_and_async_context( + monkeypatch: pytest.MonkeyPatch, +) -> None: + trainer = TrainerRank(_runtime()) + trainer._checkpoint_prefetches = {} + fetched: list[str] = [] + + async def prefetch(path: str) -> object: + fetched.append(path) + return object() + + def install(trainer: TrainerRank, _source: object, path: str) -> None: + trainer._checkpoint_slots.setdefault(path, _CheckpointSlot()).params = () + trainer._checkpoint_slots[path].revision = 0 + + monkeypatch.setattr(trainer, "_prefetch_checkpoint", prefetch) + monkeypatch.setattr("art.trainer_rank._checkpoint.load_checkpoint", install) + + task = trainer.load_checkpoint("student") + assert isinstance(task, asyncio.Task) + await task + assert fetched == ["student"] + assert trainer._default_slot_ref == trainer._slot_ref("student") + + task = trainer.prefetch_checkpoints("teacher", "reference") + assert isinstance(task, asyncio.Task) + await task + assert fetched[-2:] == ["teacher", "reference"] + + pushed = trainer.push_checkpoint("student") + await pushed + assert trainer._slot_stack == [trainer._slot_ref("student")] + trainer.pop_checkpoint() + async with trainer.push_checkpoint("student"): + assert trainer._slot_stack == [trainer._slot_ref("student")] + async with trainer.push_checkpoint("missing"): + assert trainer._slot_stack == [ + trainer._slot_ref("student"), + trainer._slot_ref("missing"), + ] + assert trainer._slot_stack == [trainer._slot_ref("student")] + assert trainer._slot_stack == [] + assert fetched[-1] == "missing" + + +def test_checkpoint_sync_context_and_body_error_preservation( + monkeypatch: pytest.MonkeyPatch, +) -> None: + trainer = TrainerRank(_runtime()) + trainer._checkpoint_slots["student"] = _CheckpointSlot() + with trainer.push_checkpoint("student"): + assert trainer._slot_stack == [trainer._slot_ref("student")] + assert trainer._slot_stack == [] + + pushed = trainer.push_checkpoint("student") + monkeypatch.setattr( + trainer, + "pop_checkpoint", + lambda: (_ for _ in ()).throw(RuntimeError("cleanup failed")), + ) + with pytest.raises(ExceptionGroup) as captured: + with pushed: + raise ValueError("body failed") + assert {type(error) for error in captured.value.exceptions} == { + ValueError, + RuntimeError, + } + + +async def test_checkpoint_context_cancellation_after_successful_push_cleans_stack( + monkeypatch: pytest.MonkeyPatch, +) -> None: + trainer = TrainerRank(_runtime()) + trainer._checkpoint_slots.setdefault("student", _CheckpointSlot()).params = () + parent = asyncio.current_task() + assert parent is not None + original_slot_ref = trainer._slot_ref + cancellation_scheduled = False + + def cancel_parent_after_resolving(path: str | None): + nonlocal cancellation_scheduled + ref = original_slot_ref(path) + if not cancellation_scheduled: + cancellation_scheduled = True + asyncio.get_running_loop().call_soon(parent.cancel) + return ref + + monkeypatch.setattr(trainer, "_slot_ref", cancel_parent_after_resolving) + pushed = trainer.push_checkpoint("student") + entered = False + + with pytest.raises(asyncio.CancelledError): + async with pushed: + entered = True + + assert cancellation_scheduled + assert pushed._task is not None + assert pushed._task.done() and not pushed._task.cancelled() + assert not entered + assert trainer._slot_stack == [] + + +async def test_checkpoint_context_cancellation_while_push_is_pending_does_not_leak( + monkeypatch: pytest.MonkeyPatch, +) -> None: + trainer = TrainerRank(_runtime()) + started = asyncio.Event() + release = asyncio.Event() + + async def prefetch(_path: str) -> object: + started.set() + await release.wait() + return object() + + monkeypatch.setattr(trainer, "_prefetch_checkpoint", prefetch) + pushed = trainer.push_checkpoint("student") + entering = asyncio.create_task(pushed.__aenter__()) + await started.wait() + entering.cancel() + with pytest.raises(asyncio.CancelledError): + await entering + release.set() + await asyncio.sleep(0) + + assert pushed._task is not None and pushed._task.cancelled() + assert trainer._slot_stack == [] + + +def test_pushed_checkpoint_cannot_be_reused() -> None: + trainer = TrainerRank(_runtime()) + trainer._checkpoint_slots["student"] = _CheckpointSlot() + pushed = trainer.push_checkpoint("student") + + with pushed: + pass + with pytest.raises(RuntimeError, match="cannot be entered twice"): + with pushed: + pass + + +async def test_shared_checkpoint_prefetch_survives_waiter_cancellation( + monkeypatch: pytest.MonkeyPatch, +) -> None: + trainer = TrainerRank(_runtime()) + started = asyncio.Event() + release = asyncio.Event() + source = object() + + async def delayed_to_thread(_function: object, *_args: object) -> object: + started.set() + await release.wait() + return source + + monkeypatch.setattr(asyncio, "to_thread", delayed_to_thread) + first = asyncio.create_task(trainer._prefetch_checkpoint("student")) + second = asyncio.create_task(trainer._prefetch_checkpoint("student")) + await started.wait() + first.cancel() + with pytest.raises(asyncio.CancelledError): + await first + release.set() + + assert await second is source + [cached] = trainer._checkpoint_prefetches.values() + assert cached.result() is source + + +async def test_shared_checkpoint_prefetch_serves_successful_waiters( + monkeypatch: pytest.MonkeyPatch, +) -> None: + trainer = TrainerRank(_runtime()) + started = asyncio.Event() + release = asyncio.Event() + source = object() + calls = 0 + + async def delayed_to_thread(_function: object, *_args: object) -> object: + nonlocal calls + calls += 1 + started.set() + await release.wait() + return source + + monkeypatch.setattr(asyncio, "to_thread", delayed_to_thread) + first = asyncio.create_task(trainer._prefetch_checkpoint("student")) + second = asyncio.create_task(trainer._prefetch_checkpoint("student")) + await started.wait() + await asyncio.sleep(0) + release.set() + + assert await asyncio.gather(first, second) == [source, source] + assert calls == 1 + [cached] = trainer._checkpoint_prefetches.values() + assert cached.result() is source + + +async def test_materialized_sources_keep_logical_checkpoint_identities( + monkeypatch: pytest.MonkeyPatch, tmp_path: Path +) -> None: + trainer = TrainerRank(_runtime()) + prepared: list[str] = [] + installed: list[tuple[str, object]] = [] + + def prepare(source_path: str) -> object: + prepared.append(source_path) + return object() + + def install(trainer: TrainerRank, source: object, logical_path: str) -> None: + installed.append((logical_path, source)) + trainer._checkpoint_slots.setdefault( + logical_path, _CheckpointSlot() + ).params = () + trainer._checkpoint_slots[logical_path].revision = 0 + + monkeypatch.setattr("art.trainer_rank._checkpoint.prepare_checkpoint", prepare) + monkeypatch.setattr("art.trainer_rank._checkpoint.load_checkpoint", install) + root_a = str(tmp_path / "immutable-a") + root_b = str(tmp_path / "immutable-b") + logical_a = "wandb-artifact:///entity/project/run:step1" + logical_b = "wandb-artifact:///entity/project/run-teacher:step1" + logical_c = "wandb-artifact:///entity/project/run-reference:step1" + + await asyncio.gather( + trainer.load_checkpoint(MaterializedCheckpoint(logical_a, root_a)), + trainer.load_checkpoint(MaterializedCheckpoint(logical_b, root_a)), + ) + assert prepared == [trainer._checkpoint_source_key(root_a)] + + await trainer.prefetch_checkpoints(MaterializedCheckpoint(logical_c, root_b)) + await trainer.load_checkpoint(MaterializedCheckpoint(logical_c, root_b)) + assert sorted(prepared) == sorted( + (trainer._checkpoint_source_key(root_a), trainer._checkpoint_source_key(root_b)) + ) + assert [logical_path for logical_path, _source in installed] == [ + logical_a, + logical_b, + logical_c, + ] + assert set(trainer._checkpoint_slots) == { + logical_a, + logical_b, + logical_c, + } + assert trainer._default_slot_ref == trainer._slot_ref(logical_c) + for logical_path in (logical_a, logical_b, logical_c): + request = ForwardInput(input_tokens=torch.tensor([1]), checkpoint=logical_path) + assert trainer._resolve_slot_ref(request) == trainer._slot_ref(logical_path) + + refreshed_root = str(tmp_path / "immutable-new") + await trainer.load_checkpoint(MaterializedCheckpoint(logical_a, refreshed_root)) + assert installed[-1][0] == logical_a + assert prepared[-1] == trainer._checkpoint_source_key(refreshed_root) + + prepared_before_push = tuple(prepared) + pushed = trainer.push_checkpoint( + MaterializedCheckpoint(logical_a, str(tmp_path / "unused-while-loaded")) + ) + await pushed + assert tuple(prepared) == prepared_before_push + assert trainer._slot_stack == [trainer._slot_ref(logical_a)] + trainer.pop_checkpoint() + + +async def test_prefetch_does_not_silently_ignore_empty_materialized_path( + monkeypatch: pytest.MonkeyPatch, +) -> None: + trainer = TrainerRank(_runtime()) + seen: list[str] = [] + + async def prefetch(path: str) -> object: + seen.append(path) + return object() + + monkeypatch.setattr(trainer, "_prefetch_checkpoint", prefetch) + await trainer.prefetch_checkpoints(MaterializedCheckpoint("logical", "")) + assert seen == [""] + + +async def test_checkpoint_mutations_follow_call_order_and_recover_from_failure( + monkeypatch: pytest.MonkeyPatch, +) -> None: + trainer = TrainerRank(_runtime()) + trainer._checkpoint_prefetches = {} + started = {name: asyncio.Event() for name in ("first", "second")} + ready = {name: asyncio.Event() for name in ("first", "second")} + installed: list[str] = [] + + async def prefetch(path: str) -> object: + event = started.get(path) + if event is not None: + event.set() + await ready[path].wait() + return object() + + def install(trainer: TrainerRank, _source: object, path: str) -> None: + installed.append(path) + if path == "bad": + raise RuntimeError("injected load failure") + trainer._checkpoint_slots.setdefault(path, _CheckpointSlot()).params = () + trainer._checkpoint_slots[path].revision = 0 + + monkeypatch.setattr(trainer, "_prefetch_checkpoint", prefetch) + monkeypatch.setattr("art.trainer_rank._checkpoint.load_checkpoint", install) + first = trainer.load_checkpoint("first") + second = trainer.load_checkpoint("second") + await asyncio.gather(*(event.wait() for event in started.values())) + ready["second"].set() + await asyncio.sleep(0) + assert installed == [] + ready["first"].set() + await asyncio.gather(first, second) + assert installed == ["first", "second"] + + bad = trainer.load_checkpoint("bad") + after = trainer.load_checkpoint("after") + with pytest.raises(RuntimeError, match="injected load failure"): + await bad + await after + assert installed[-2:] == ["bad", "after"] + + +@pytest.mark.parametrize("local_failure", [False, True]) +async def test_checkpoint_prefetch_failures_are_coordinated( + monkeypatch: pytest.MonkeyPatch, local_failure: bool +) -> None: + trainer = TrainerRank(_runtime()) + + async def prefetch(_path: str) -> object: + if local_failure: + raise OSError("rank-local prefetch failed") + return object() + + def coordinated( + error: BaseException | None, phase: str, _group: object | None = None + ) -> None: + assert phase == "prepare checkpoint" + assert isinstance(error, OSError) is local_failure + raise RuntimeError("a rank failed to prepare checkpoint") + + monkeypatch.setattr(trainer, "_prefetch_checkpoint", prefetch) + monkeypatch.setattr("art.trainer_rank._checkpoint.raise_distributed", coordinated) + with pytest.raises(RuntimeError, match="a rank failed"): + await trainer.load_checkpoint("student") def test_trainer_rank_rejects_adapter_keys_without_installed_lora_site() -> None: @@ -405,11 +808,10 @@ def test_trainer_rank_rejects_adapter_keys_without_installed_lora_site() -> None "base.layer.lora_A.weight": torch.empty(1), "base.layer.lora_B.weight": torch.empty(1), } - trainer._prepare_adapter_model("checkpoint", "student", valid) + trainer._prepare_adapter_model("student", valid) with pytest.raises(ValueError, match="matching LoRA target modules"): trainer._prepare_adapter_model( - "checkpoint", "student", {**valid, "base.other.lora_A.weight": torch.empty(1)}, ) @@ -423,7 +825,7 @@ def test_trainer_rank_normalizes_adapter_tensors_to_installed_site() -> None: "base.layer.lora_B.weight": torch.ones(5, 3, dtype=torch.float32), } - normalized = trainer._prepare_adapter_model("checkpoint", "student", adapter) + normalized = trainer._prepare_adapter_model("student", adapter) assert all(tensor.device == site.A_T.device for tensor in normalized.values()) assert all(tensor.dtype == torch.bfloat16 for tensor in normalized.values()) @@ -438,20 +840,41 @@ def test_checkpoint_slot_adapter_config_is_validated_and_copied() -> None: "target_modules": ["q_proj"], } - retained = trainer._validate_checkpoint_slot_adapter_config( - "student", config, alpha=16 - ) + retained = trainer._validate_checkpoint_adapter_config("student", config, alpha=16) assert retained == config config["target_modules"].append("v_proj") # type: ignore[union-attr] assert retained is not None assert retained["target_modules"] == ["q_proj"] with pytest.raises(ValueError, match="conflicts"): - trainer._validate_checkpoint_slot_adapter_config("student", config, alpha=32) + trainer._validate_checkpoint_adapter_config("student", config, alpha=32) with pytest.raises(ValueError, match="missing"): - trainer._validate_checkpoint_slot_adapter_config( - "student", {"r": 8}, alpha=None - ) + trainer._validate_checkpoint_adapter_config("student", {"r": 8}, alpha=None) + + +def test_qwen35_checkpoint_adapter_config_captures_attention_dimensions() -> None: + runtime = _runtime() + runtime.provider.num_attention_heads = 16 + runtime.provider.num_query_groups = 4 + runtime.provider.kv_channels = 128 + trainer = TrainerRank(runtime) + + retained = trainer._validate_checkpoint_adapter_config( + "student", + { + "base_model_name_or_path": "Qwen/Qwen3.5-4B", + "r": 8, + "lora_alpha": 16, + "target_modules": ["q_proj"], + }, + alpha=16, + ) + + assert retained is not None + assert retained["num_attention_heads"] == 16 + assert retained["num_key_value_heads"] == 4 + assert retained["head_dim"] == 128 + assert retained["hidden_size"] == 4 @pytest.mark.parametrize( @@ -477,7 +900,7 @@ def test_checkpoint_slot_adapter_config_rejects_invalid_field_types( config[field] = value with pytest.raises(TypeError, match=field): - trainer._validate_checkpoint_slot_adapter_config("student", config, alpha=None) + trainer._validate_checkpoint_adapter_config("student", config, alpha=None) def test_checkpoint_slot_adapter_config_rejects_cross_rank_mismatch( @@ -487,43 +910,21 @@ def test_checkpoint_slot_adapter_config_rejects_cross_rank_mismatch( monkeypatch.setattr("art.trainer_rank.dist.is_initialized", lambda: True) monkeypatch.setattr("art.trainer_rank.dist.get_world_size", lambda: 2) - def gather(output: list[object], value: object) -> None: - output[:] = [value, {"different": True}] + checkpoint_group = cast(dist.ProcessGroup, object()) + trainer._checkpoint_process_group = checkpoint_group + trainer._checkpoint_finalize_process_group = cast(dist.ProcessGroup, object()) + + def gather( + output: list[object], value: object, *, group: object | None = None + ) -> None: + assert group is checkpoint_group + revision = value[1] if isinstance(value, tuple) and len(value) == 2 else None + output[:] = [value, ({"different": True}, revision)] monkeypatch.setattr("art.trainer_rank.dist.all_gather_object", gather) with pytest.raises(ValueError, match="differs across ranks"): - trainer._validate_checkpoint_slot_adapter_config("student", None, alpha=None) - - -def test_load_checkpoint_slot_retains_config_and_uses_its_alpha( - monkeypatch: pytest.MonkeyPatch, -) -> None: - trainer = TrainerRank(_runtime()) - seen: dict[str, object] = {} - monkeypatch.setattr( - trainer, - "_load_slot", - lambda *_args, **kwargs: seen.update(kwargs) or 1, - ) - monkeypatch.setattr(trainer, "_validate_dynamic_slot_consistency", lambda *_: ()) - monkeypatch.setattr( - trainer, "_validate_loaded_checkpoint_slot_config", lambda *_: None - ) - config = { - "base_model_name_or_path": "Qwen/Qwen3-8B", - "r": 8, - "lora_alpha": 16, - "target_modules": ["q_proj"], - } - - trainer.load_checkpoint_slot("student", {}, adapter_config=config) - - assert seen["alpha"] == 16 - assert trainer._checkpoint_slot_adapter_configs["student"] == config - trainer.load_checkpoint_slot("student", {}, alpha=7) - assert seen["alpha"] == 7 - assert "student" not in trainer._checkpoint_slot_adapter_configs + trainer._validate_checkpoint_adapter_config("student", None, alpha=None) @pytest.mark.skipif(find_spec("megatron") is None, reason="requires Megatron") @@ -533,12 +934,20 @@ def test_slot_load_canonicalizes_only_local_incoming_adapter( calls: list[tuple[dict[str, torch.Tensor], object]] = [] loaded_state: dict[str, torch.Tensor] = {} runtime = _runtime() - runtime.model_support_handler.canonicalize_loaded_lora_state = lambda state, model: ( - calls.append((state, model)) - or {key: torch.zeros_like(value) for key, value in state.items()} + monkeypatch.setattr( + runtime.model_support_handler, + "canonicalize_loaded_lora_state", + lambda state, model: ( + calls.append((state, model)) + or {key: torch.zeros_like(value) for key, value in state.items()} + ), ) - runtime.model_support_handler.zero_internal_padding_params = lambda _model: ( - pytest.fail("slot load must not mutate unrelated slot parameters") + monkeypatch.setattr( + runtime.model_support_handler, + "zero_internal_padding_params", + lambda _model: pytest.fail( + "slot load must not mutate unrelated slot parameters" + ), ) trainer = TrainerRank(runtime) monkeypatch.setattr( @@ -548,7 +957,17 @@ def test_slot_load_canonicalizes_only_local_incoming_adapter( monkeypatch.setattr(torch.distributed, "is_initialized", lambda: True) monkeypatch.setattr(torch.distributed, "get_world_size", lambda: 2) - def gather_expected(values: list[set[str] | None], local: set[str]) -> None: + checkpoint_group = cast(dist.ProcessGroup, object()) + trainer._checkpoint_process_group = checkpoint_group + trainer._checkpoint_finalize_process_group = cast(dist.ProcessGroup, object()) + + def gather_expected( + values: list[set[str] | None], + local: set[str], + *, + group: object | None = None, + ) -> None: + assert group is checkpoint_group values[:] = [local, {"remote_weight"}] monkeypatch.setattr(torch.distributed, "all_gather_object", gather_expected) @@ -569,7 +988,7 @@ def load_slot( ) adapter = {"weight": torch.ones(1), "remote_weight": torch.ones(1)} - trainer._load_slot("checkpoint", "student", adapter, trainable=True, alpha=None) + trainer._load_checkpoint_slot("student", adapter, alpha=1.0) assert calls == [({"weight": adapter["weight"]}, runtime.model)] torch.testing.assert_close(loaded_state["weight"], torch.zeros(1)) @@ -577,15 +996,788 @@ def load_slot( torch.testing.assert_close(adapter["weight"], torch.ones(1)) -def test_checkpoint_slot_publish_requires_retained_adapter_config() -> None: +def test_checkpoint_export_requires_retained_adapter_config() -> None: trainer = TrainerRank(_runtime()) - with pytest.raises(ValueError, match="Unknown checkpoint slot"): - trainer.save_checkpoint_slot_lora("missing", "/unused") - - trainer._checkpoint_slot_params_by_name["student"] = () + with pytest.raises(ValueError, match="Unknown checkpoint"): + trainer.export_lora("/unused", "missing") + trainer._checkpoint_slots.setdefault("student", _CheckpointSlot()).params = () with pytest.raises(TrainerRankSlotStateError, match="adapter_config"): - trainer.save_checkpoint_slot_lora("student", "/unused") + trainer.export_lora("/unused", "student") + + +def test_checkpoint_save_rejects_accumulated_gradients() -> None: + trainer = TrainerRank(_runtime()) + parameter = torch.nn.Parameter(torch.ones(2)) + parameter.grad = torch.ones_like(parameter) + trainer._checkpoint_slots.setdefault("student", _CheckpointSlot()).params = ( + parameter, + ) + trainer._checkpoint_slots["student"].config = { + "base_model_name_or_path": "test", + "r": 1, + "lora_alpha": 1, + "target_modules": [], + } + + with pytest.raises(TrainerRankSlotStateError, match="accumulated gradients"): + _validate_save_state(trainer, "student") + + +def _canonical_checkpoint(root: Path) -> CheckpointManifest: + from safetensors.torch import save_file + + root.mkdir() + config = { + "base_model_name_or_path": "test/model", + "r": 1, + "lora_alpha": 1, + "target_modules": ["q_proj"], + "art_lora_format": "art-trainer-rank-v1", + } + (root / "adapter_config.json").write_text(json.dumps(config)) + key = "layer.q_proj.lora_A.weight" + save_file({key: torch.ones(1, 2)}, root / "adapter_model.safetensors") + (root / "optimizer").mkdir() + files = [] + for component in ("master", "exp_avg", "exp_avg_sq"): + relative = f"optimizer/{component}.safetensors" + save_file({key: torch.ones(1, 2)}, root / relative) + files.append(relative) + manifest: CheckpointManifest = { + "format_version": 1, + "base_model_name_or_path": "test/model", + "optimizer": OptimizerConfig( + learning_rate=1e-3, + beta1=0.9, + beta2=0.99, + eps=1e-8, + weight_decay=0.1, + ), + "parameters": {key: files}, + "steps": {key: 3.0}, + "files": {}, + "digest": "", + } + payloads = {"adapter_config.json", "adapter_model.safetensors", *files} + manifest["files"] = { + relative: _file_digest(root / relative) for relative in payloads + } + manifest["digest"] = _manifest_digest(manifest) + (root / "checkpoint.json").write_text(json.dumps(manifest)) + return manifest + + +@pytest.mark.parametrize( + "mutate", + ( + lambda manifest: manifest["steps"].update({next(iter(manifest["steps"])): 4.0}), + lambda manifest: manifest["optimizer"].update({"eps": 1e-6}), # type: ignore[union-attr] + lambda manifest: manifest["parameters"].update( + {next(iter(manifest["parameters"])): ("../bad", "x", "y")} + ), + ), +) +def test_checkpoint_manifest_semantics_are_authenticated( + tmp_path: Path, mutate: object +) -> None: + root = tmp_path / "checkpoint" + manifest = _canonical_checkpoint(root) + cast(Any, mutate)(manifest) + (root / "checkpoint.json").write_text(json.dumps(manifest)) + + with pytest.raises(RuntimeError, match="digest mismatch|Unsafe checkpoint"): + prepare_checkpoint(str(root)) + + +@pytest.mark.parametrize("extra", (True, False)) +def test_checkpoint_optimizer_mapping_must_match_adapter( + tmp_path: Path, extra: bool +) -> None: + root = tmp_path / "checkpoint" + manifest = _canonical_checkpoint(root) + if extra: + manifest["parameters"]["unexpected"] = next( + iter(manifest["parameters"].values()) + ) + manifest["steps"]["unexpected"] = 0 + else: + manifest["parameters"].pop(next(iter(manifest["parameters"]))) + (root / "checkpoint.json").write_text(json.dumps(manifest)) + + with pytest.raises(RuntimeError, match="mapping differs"): + prepare_checkpoint(str(root)) + + +def test_materialize_lora_validates_exact_artifact_without_optimizer_downloads( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + source = tmp_path / "source" + manifest = _canonical_checkpoint(source) + local = tmp_path / "local" + local.mkdir() + for name in ("adapter_config.json", "adapter_model.safetensors", "checkpoint.json"): + (local / name).write_bytes((source / name).read_bytes()) + entries = { + "adapter_config.json", + "adapter_model.safetensors", + "checkpoint.json", + *(file for files in manifest["parameters"].values() for file in files), + } + monkeypatch.setattr( + "art.megatron.model_support.lora_disk.normalize_lora_checkpoint_to_vllm", + lambda _path: None, + ) + + output = tmp_path / "output" + materialize_lora( + local, + output, + require_optimizer=True, + artifact_entries=entries, + expected_digest=manifest["digest"], + ) + assert {path.name for path in output.iterdir()} == { + "adapter_config.json", + "adapter_model.safetensors", + } + + from safetensors.torch import save_file + + save_file( + {next(iter(manifest["parameters"])): torch.zeros(1, 2)}, + local / "adapter_model.safetensors", + ) + with pytest.raises(RuntimeError, match="file digest mismatch"): + materialize_lora( + local, + tmp_path / "corrupt-output", + require_optimizer=True, + artifact_entries=entries, + expected_digest=manifest["digest"], + ) + + manifest["files"]["adapter_model.safetensors"] = _file_digest( + local / "adapter_model.safetensors" + ) + (local / "checkpoint.json").write_text(json.dumps(manifest)) + with pytest.raises(RuntimeError, match="Checkpoint digest mismatch"): + materialize_lora( + local, + tmp_path / "tampered-manifest-output", + require_optimizer=True, + artifact_entries=entries, + expected_digest=manifest["digest"], + ) + (local / "checkpoint.json").write_bytes((source / "checkpoint.json").read_bytes()) + + with pytest.raises(RuntimeError, match="digest mismatch"): + materialize_lora( + local, + tmp_path / "bad-digest", + artifact_entries=entries, + expected_digest="bad", + ) + with pytest.raises(RuntimeError, match="missing entries"): + materialize_lora( + local, + tmp_path / "missing-entry", + require_optimizer=True, + artifact_entries={"adapter_config.json", "adapter_model.safetensors"}, + ) + + +def _save_state_trainer() -> TrainerRank: + trainer = TrainerRank(_runtime()) + trainer._checkpoint_process_group = None + return trainer + + +def _prepared_save(root: Path, sequence: int) -> _PreparedSave: + snapshot = root / f"snapshot-{sequence}" + reservation = root / f"reserved-{sequence}" + snapshot.mkdir() + reservation.mkdir() + return _PreparedSave( + sequence=sequence, + snapshot=snapshot, + reservation=reservation, + destination=root / f"output-{sequence}", + config={}, + shards=(), + optimizer=None, + ) + + +def test_optimizer_shards_are_received_as_float32( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + from art.megatron.lora import LoraShardMeta + + prepared = _prepared_save(tmp_path, 0) + metadata = [ + LoraShardMeta( + "weight", + 1, + (2, 3), + "bfloat16", + {"kind": "replicated"}, + "block", + ) + ] + received: list[torch.dtype] = [] + monkeypatch.setattr("art.trainer_rank._checkpoint._rank", lambda: 0) + monkeypatch.setattr( + "art.trainer_rank._checkpoint.raise_distributed", lambda *_args: None + ) + + def recv(tensor: torch.Tensor, **_kwargs: object) -> None: + received.append(tensor.dtype) + + monkeypatch.setattr(dist, "recv", recv) + monkeypatch.setattr( + "art.megatron.weights.lora_publish.merge_sharded_adapter_entries", + lambda entries: { + key: values[0][1] for key, values in cast(dict, entries).items() + }, + ) + + merged = _merge_component(prepared, metadata, "master", None) + + assert received == [torch.float32] + assert merged["weight"].dtype == torch.float32 + + +def test_checkpoint_fifo_abort_and_failure_do_not_block_later_save( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + trainer = _save_state_trainer() + first = _prepared_save(tmp_path, 0) + second = _prepared_save(tmp_path, 1) + third = _prepared_save(tmp_path, 2) + trainer._prepared_checkpoint_saves = { + "first": first, + "second": second, + "third": third, + } + calls: list[int] = [] + + def finalize(_trainer: TrainerRank, prepared: _PreparedSave) -> None: + calls.append(prepared.sequence) + if prepared.sequence == 1: + raise RuntimeError("injected finalization failure") + + monkeypatch.setattr("art.trainer_rank._checkpoint._finish", finalize) + abort_checkpoint_save(trainer, "first") + with pytest.raises(RuntimeError, match="injected"): + finish_checkpoint_save(trainer, "second") + finish_checkpoint_save(trainer, "third") + + assert calls == [1, 2] + assert trainer._checkpoint_save_next == 3 + + +def test_checkpoint_out_of_order_finalization_fails_without_blocking( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + trainer = _save_state_trainer() + first = _prepared_save(tmp_path, 0) + second = _prepared_save(tmp_path, 1) + trainer._prepared_checkpoint_saves = {"first": first, "second": second} + monkeypatch.setattr("art.trainer_rank._checkpoint._finish", lambda *_args: None) + + with pytest.raises(RuntimeError, match="finalized in preparation order"): + finish_checkpoint_save(trainer, "second") + finish_checkpoint_save(trainer, "first") + finish_checkpoint_save(trainer, "second") + + assert trainer._checkpoint_save_next == 2 + + +def test_concurrent_checkpoint_finish_runs_once( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + trainer = _save_state_trainer() + prepared = _prepared_save(tmp_path, 0) + trainer._prepared_checkpoint_saves = {"save": prepared} + entered = threading.Event() + release = threading.Event() + calls = 0 + + def finalize(_trainer: TrainerRank, _prepared: _PreparedSave) -> None: + nonlocal calls + calls += 1 + entered.set() + assert release.wait(timeout=2) + + monkeypatch.setattr("art.trainer_rank._checkpoint._finish", finalize) + errors: list[BaseException] = [] + + def finish() -> None: + try: + finish_checkpoint_save(trainer, "save") + except BaseException as exc: + errors.append(exc) + + threads = [threading.Thread(target=finish) for _ in range(2)] + threads[0].start() + assert entered.wait(timeout=2) + threads[1].start() + time.sleep(0.05) + release.set() + for thread in threads: + thread.join(timeout=2) + assert not thread.is_alive() + + assert not errors + assert calls == 1 + + +@pytest.mark.parametrize("action", ("finish", "abort")) +def test_checkpoint_cleanup_failure_can_be_retried( + tmp_path: Path, + monkeypatch: pytest.MonkeyPatch, + action: str, +) -> None: + from art.trainer_rank import _checkpoint + + trainer = _save_state_trainer() + prepared = _prepared_save(tmp_path, 0) + trainer._prepared_checkpoint_saves = {"save": prepared} + finalizations = 0 + + def finalize(_trainer: TrainerRank, _prepared: _PreparedSave) -> None: + nonlocal finalizations + finalizations += 1 + + original = _checkpoint.shutil.rmtree + failed = False + + def fail_once(path: Path, ignore_errors: bool = False, **_: object) -> None: + nonlocal failed + if Path(path) == prepared.snapshot and not failed: + failed = True + raise OSError("injected cleanup failure") + original(path, ignore_errors=ignore_errors) + + monkeypatch.setattr(_checkpoint, "_finish", finalize) + monkeypatch.setattr(_checkpoint.shutil, "rmtree", fail_once) + operation = finish_checkpoint_save if action == "finish" else abort_checkpoint_save + with pytest.raises(BaseExceptionGroup, match="cleanup failed"): + operation(trainer, "save") + operation(trainer, "save") + + assert finalizations == (1 if action == "finish" else 0) + assert "save" not in trainer._prepared_checkpoint_saves + assert not prepared.snapshot.exists() + assert not prepared.reservation.exists() + + +def test_checkpoint_cleanup_gather_failure_releases_finalizer( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + from art.trainer_rank import _checkpoint + + trainer = _save_state_trainer() + prepared = _prepared_save(tmp_path, 0) + trainer._prepared_checkpoint_saves = {"save": prepared} + original = _checkpoint._gather + failed = False + + def fail_once( + value: object, group: dist.ProcessGroup | None = None + ) -> tuple[object, ...]: + nonlocal failed + if isinstance(value, tuple) and len(value) == 2 and not failed: + failed = True + raise RuntimeError("injected cleanup gather failure") + return original(value, group) + + monkeypatch.setattr(_checkpoint, "_finish", lambda *_: None) + monkeypatch.setattr(_checkpoint, "_gather", fail_once) + with pytest.raises(RuntimeError, match="cleanup gather"): + finish_checkpoint_save(trainer, "save") + assert "save" not in trainer._checkpoint_finalizing_saves + finish_checkpoint_save(trainer, "save") + assert "save" not in trainer._prepared_checkpoint_saves + + +def test_checkpoint_asymmetric_cleanup_gather_can_converge( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + from art.trainer_rank import _checkpoint + + completed = _save_state_trainer() + retained = _save_state_trainer() + retained_root = tmp_path / "retained" + retained_root.mkdir() + retained_save = _prepared_save(retained_root, 0) + completed._finalized_checkpoint_saves["save"] = _FinalizedSave(0, "finish") + retained._prepared_checkpoint_saves["save"] = retained_save + retained._checkpoint_save_outcomes["save"] = "finish" + + def mixed( + value: object, _group: dist.ProcessGroup | None = None + ) -> tuple[object, ...]: + if isinstance(value, bool): + return (True, False) + return (value, value) + + monkeypatch.setattr(_checkpoint, "_gather", mixed) + finish_checkpoint_save(completed, "save") + finish_checkpoint_save(retained, "save") + assert "save" in completed._finalized_checkpoint_saves + assert "save" in retained._finalized_checkpoint_saves + assert "save" not in retained._prepared_checkpoint_saves + + +def test_checkpoint_cleanup_gather_preserves_finish_error( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + from art.trainer_rank import _checkpoint + + trainer = _save_state_trainer() + prepared = _prepared_save(tmp_path, 0) + trainer._prepared_checkpoint_saves = {"save": prepared} + original = _checkpoint._gather + + def fail_cleanup( + value: object, group: dist.ProcessGroup | None = None + ) -> tuple[object, ...]: + if isinstance(value, tuple) and len(value) == 2: + raise RuntimeError("cleanup collective failed") + return original(value, group) + + def fail_finish(*_: object) -> None: + raise ValueError("snapshot failed") + + monkeypatch.setattr(_checkpoint, "_finish", fail_finish) + monkeypatch.setattr( + _checkpoint, "_cleanup_paths", lambda *_: OSError("unlink failed") + ) + monkeypatch.setattr(_checkpoint, "_gather", fail_cleanup) + with pytest.raises(BaseExceptionGroup) as raised: + finish_checkpoint_save(trainer, "save") + assert any(isinstance(error, ValueError) for error in raised.value.exceptions) + assert any(isinstance(error, OSError) for error in raised.value.exceptions) + assert any(isinstance(error, RuntimeError) for error in raised.value.exceptions) + + +def test_checkpoint_prepare_preserves_foreign_reservation(tmp_path: Path) -> None: + trainer = _save_state_trainer() + trainer._checkpoint_slots["student"] = _CheckpointSlot( + config={ + "base_model_name_or_path": "test", + "r": 1, + "lora_alpha": 1, + "target_modules": [], + } + ) + output = tmp_path / "save" + reservation = tmp_path / ".save.reserved" + reservation.mkdir() + marker = reservation / "owner" + marker.write_text("foreign") + + with pytest.raises(FileExistsError): + prepare_checkpoint_save(trainer, str(output), "student") + + assert marker.read_text() == "foreign" + + +def test_checkpoint_prepare_reports_snapshot_cleanup_failure( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + from art.trainer_rank import _checkpoint + + trainer = _save_state_trainer() + trainer._checkpoint_slots["student"] = _CheckpointSlot( + config={ + "base_model_name_or_path": "test", + "r": 1, + "lora_alpha": 1, + "target_modules": [], + } + ) + original = _checkpoint.shutil.rmtree + + def fail_snapshot(path: Path, ignore_errors: bool = False, **_: object) -> None: + if ".snapshot-" in Path(path).name: + raise OSError("injected cleanup failure") + original(path, ignore_errors=ignore_errors) + + monkeypatch.setattr( + _checkpoint, + "_local_state", + lambda *_args: (_ for _ in ()).throw(RuntimeError("snapshot failed")), + ) + monkeypatch.setattr(_checkpoint.shutil, "rmtree", fail_snapshot) + + with pytest.raises(BaseExceptionGroup) as captured: + prepare_checkpoint_save(trainer, str(tmp_path / "save"), "student") + messages = " ".join(str(error) for error in captured.value.exceptions) + assert "snapshot failed" in messages + assert "cleanup" in messages + + +def _checkpoint_load_failure_worker( + rank: int, world_size: int, init_method: str, phase: str +) -> None: + dist.init_process_group( + "gloo", + rank=rank, + world_size=world_size, + init_method=init_method, + timeout=timedelta(seconds=15), + ) + from art.trainer_rank import _checkpoint as checkpoint_module + + originals = ( + checkpoint_module._load_adapter, + checkpoint_module._optimizer_state, + checkpoint_module._commit_slot, + ) + try: + trainer = TrainerRank.__new__(TrainerRank) + trainer.runtime = SimpleNamespace( + model=[], + model_identifier=None, + model_support_spec=None, + provider=SimpleNamespace(), + ) + trainer._checkpoint_process_group = None + trainer._checkpoint_slots = {} + trainer._slot_stack = [] + trainer._local_lora_adapter_templates = lambda: {} # type: ignore[method-assign] + trainer._guard_slot_can_load = lambda _ref: None # type: ignore[method-assign] + trainer._load_checkpoint_slot = lambda *_args, **_kwargs: 1 # type: ignore[method-assign] + trainer._validate_checkpoint_consistency = lambda *_args: () # type: ignore[method-assign] + trainer._validate_loaded_checkpoint_config = lambda *_args: None # type: ignore[method-assign] + trainer._restore_canonical_optimizer = lambda *_args: cast(Any, object()) # type: ignore[method-assign] + if phase == "export": + if rank == 1: + trainer._checkpoint_slots["student"] = _CheckpointSlot( + config={ + "base_model_name_or_path": "test/model", + "r": 1, + "lora_alpha": 1, + "target_modules": [], + } + ) + with pytest.raises((ValueError, RuntimeError), match="Unknown|Another"): + checkpoint_module.export_lora(trainer, "/unused", "student") + completed = torch.tensor(1) + dist.all_reduce(completed) + assert completed.item() == world_size + return + + optimizer = ( + OptimizerConfig( + learning_rate=1e-3, + beta1=0.9, + beta2=0.99, + eps=1e-8, + weight_decay=0.1, + ) + if phase == "optimizer" + else None + ) + manifest: CheckpointManifest | None = ( + { + "format_version": 1, + "base_model_name_or_path": "test/model", + "optimizer": optimizer, + "parameters": {}, + "steps": {}, + "files": {}, + "digest": "digest", + } + if phase != "read" + else None + ) + source = PreparedCheckpoint( + Path("/unused"), + { + "base_model_name_or_path": "test/model", + "r": 1, + "lora_alpha": 1, + "target_modules": [], + }, + (), + manifest, + "digest", + ) + + setattr( + checkpoint_module, + "_load_adapter", + ( + lambda *_args: ( + (_ for _ in ()).throw(RuntimeError("injected snapshot read")) + if phase == "read" and rank == 1 + else {} + ) + ), + ) + setattr( + checkpoint_module, + "_optimizer_state", + ( + lambda *_args: ( + (_ for _ in ()).throw(RuntimeError("injected optimizer read")) + if phase == "optimizer" and rank == 1 + else LocalOptimizerState( + (), (), (), (), cast(OptimizerConfig, optimizer) + ) + ) + ), + ) + setattr( + checkpoint_module, + "_commit_slot", + ( + lambda *_args: ( + (_ for _ in ()).throw(RuntimeError("injected rank-zero commit")) + if phase == "commit" and rank == 0 + else None + ) + ), + ) + + with pytest.raises(RuntimeError, match="injected|Another rank failed"): + checkpoint_module.load_checkpoint(trainer, source, "student") + assert "student" not in trainer._checkpoint_slots + assert not any( + name.startswith("__art_loading_") for name in trainer._checkpoint_slots + ) + completed = torch.tensor(1) + dist.all_reduce(completed) + assert completed.item() == world_size + finally: + for name, value in zip( + ("_load_adapter", "_optimizer_state", "_commit_slot"), + originals, + strict=True, + ): + setattr(checkpoint_module, name, value) + dist.destroy_process_group() + + +@pytest.mark.parametrize("phase", ("read", "optimizer", "commit", "export")) +def test_checkpoint_load_failure_is_collective_and_transactional( + tmp_path: Path, phase: str +) -> None: + context = mp.spawn( + _checkpoint_load_failure_worker, + args=(2, f"file://{tmp_path / f'load-{phase}'}", phase), + nprocs=2, + join=False, + ) + deadline = time.monotonic() + 90 + while time.monotonic() < deadline: + if context.join(timeout=1): + return + else: + for process in context.processes: + process.terminate() + pytest.fail(f"collective checkpoint {phase} failure test hung") + + +@pytest.mark.skipif(find_spec("megatron") is None, reason="requires Megatron") +def test_real_checkpoint_codec_restores_exact_next_optimizer_step( + tmp_path: Path, monkeypatch: pytest.MonkeyPatch +) -> None: + from art.megatron import lora as lora_module + from art.megatron.lora import LoRA + from art.trainer_rank import _checkpoint as checkpoint_module + + monkeypatch.setattr(lora_module.ps, "get_expert_model_parallel_rank", lambda: 0) + monkeypatch.setattr( + lora_module.ps, + "get_data_parallel_rank", + lambda **_kwargs: 0, + ) + config = cast( + Any, + { + "base_model_name_or_path": "test/model", + "r": 2, + "lora_alpha": 2, + "target_modules": ["q_proj"], + }, + ) + adapter = { + "layer.q_proj.lora_A.weight": torch.tensor([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]]), + "layer.q_proj.lora_B.weight": torch.tensor( + [[0.2, 0.1], [0.3, 0.4], [0.5, 0.6], [0.7, 0.8]] + ), + } + adam = AdamParams( + learning_rate=3e-4, + beta1=0.8, + beta2=0.95, + weight_decay=0.1, + grad_clip_norm=10, + ) + + def make_trainer() -> TrainerRank: + lora = LoRA("layer.q_proj", 3, 4, 2, 2, torch.float32, torch.device("cpu")) + trainer = TrainerRank(_runtime(lora)) + loaded = trainer._load_checkpoint_slot("student", adapter, alpha=2) + params = trainer._validate_checkpoint_consistency( + "student", loaded, set(adapter) + ) + trainer._checkpoint_slots["student"] = _CheckpointSlot(params, config) + monkeypatch.setattr( + trainer, + "_reduce_dynamic_grads", + lambda params, **_kwargs: tuple(item.grad.float() for item in params), + ) + return trainer + + original = make_trainer() + for parameter in original._checkpoint_slots["student"].params: + parameter.grad = torch.full_like(parameter, 0.25) + original.optim_step(params=adam) + output = tmp_path / "exact" + original.save_checkpoint(str(output), "student") + original.save_checkpoint(str(output), "student") + assert not list(tmp_path.glob(".exact.snapshot-*")) + assert not (tmp_path / ".exact.reserved").exists() + prepared = prepare_checkpoint(str(output)) + assert prepared.manifest is not None + assert prepared.manifest["optimizer"] is not None + + restored_lora = LoRA("layer.q_proj", 3, 4, 2, 2, torch.float32, torch.device("cpu")) + restored = TrainerRank(_runtime(restored_lora)) + monkeypatch.setattr( + restored, + "_reduce_dynamic_grads", + lambda params, **_kwargs: tuple(item.grad.float() for item in params), + ) + checkpoint_module.load_checkpoint(restored, prepared, "student") + + for trainer in (original, restored): + for parameter in trainer._checkpoint_slots["student"].params: + parameter.grad = torch.full_like(parameter, -0.125) + trainer.optim_step(params=adam) + for actual, expected in zip( + restored._checkpoint_slots["student"].params, + original._checkpoint_slots["student"].params, + strict=True, + ): + torch.testing.assert_close(actual, expected, atol=0, rtol=0) + restored_optimizer = restored._checkpoint_slots["student"].optimizer + original_optimizer = original._checkpoint_slots["student"].optimizer + assert restored_optimizer is not None and original_optimizer is not None + _assert_nested_tensors_equal( + restored_optimizer.optimizer.state_dict(), + original_optimizer.optimizer.state_dict(), + ) + with pytest.raises(FileExistsError, match="different state"): + original.save_checkpoint(str(output), "student") + assert not list(tmp_path.glob(".exact.snapshot-*")) + assert not (tmp_path / ".exact.reserved").exists() def test_trainer_rank_default_forward_uses_explicit_base_slot() -> None: @@ -596,7 +1788,6 @@ def test_trainer_rank_default_forward_uses_explicit_base_slot() -> None: assert len(plan.groups) == 1 slot = plan.groups[0].slot_ref assert slot is not None - assert getattr(slot, "kind") == "checkpoint" assert getattr(slot, "name") is None @@ -612,7 +1803,7 @@ def test_optim_step_requires_loaded_checkpoint_slot() -> None: def test_optim_step_rejects_loaded_slots_without_grads() -> None: trainer = TrainerRank(_runtime()) - trainer._checkpoint_slot_params_by_name["student"] = ( + trainer._checkpoint_slots.setdefault("student", _CheckpointSlot()).params = ( torch.nn.Parameter(torch.ones(2)), ) @@ -632,8 +1823,10 @@ def test_optim_step_rejects_explicit_slot_subset_with_missing_grads( ready = torch.nn.Parameter(torch.ones(2)) missing = torch.nn.Parameter(torch.ones(2)) ready.grad = torch.ones_like(ready) - trainer._checkpoint_slot_params_by_name["ready"] = (ready,) - trainer._checkpoint_slot_params_by_name["missing"] = (missing,) + trainer._checkpoint_slots.setdefault("ready", _CheckpointSlot()).params = (ready,) + trainer._checkpoint_slots.setdefault("missing", _CheckpointSlot()).params = ( + missing, + ) monkeypatch.setattr( trainer, "_reduce_dynamic_grads", @@ -654,8 +1847,10 @@ def test_optim_step_implicitly_steps_only_slots_with_grads( ready = torch.nn.Parameter(torch.ones(2)) untouched = torch.nn.Parameter(torch.ones(2)) ready.grad = torch.ones_like(ready) - trainer._checkpoint_slot_params_by_name["ready"] = (ready,) - trainer._checkpoint_slot_params_by_name["untouched"] = (untouched,) + trainer._checkpoint_slots.setdefault("ready", _CheckpointSlot()).params = (ready,) + trainer._checkpoint_slots.setdefault("untouched", _CheckpointSlot()).params = ( + untouched, + ) monkeypatch.setattr( trainer, "_reduce_dynamic_grads", @@ -668,8 +1863,8 @@ def test_optim_step_implicitly_steps_only_slots_with_grads( params=AdamParams(learning_rate=1e-2, weight_decay=0.0, grad_clip_norm=10.0) ) - assert "ready" in trainer._dynamic_optimizers - assert "untouched" not in trainer._dynamic_optimizers + assert trainer._checkpoint_slots["ready"].optimizer is not None + assert trainer._checkpoint_slots["untouched"].optimizer is None assert not torch.equal(before_ready, ready) torch.testing.assert_close(untouched, before_untouched) @@ -687,12 +1882,20 @@ def zero_padding_grads(_model: object) -> None: assert param.grad is not None param.grad[-1] = 0.0 - runtime.model_support_handler.zero_internal_padding_grads = zero_padding_grads - runtime.model_support_handler.zero_internal_padding_params = lambda _model: ( - pytest.fail("slot step must not mutate unrelated slot parameters") + monkeypatch.setattr( + runtime.model_support_handler, + "zero_internal_padding_grads", + zero_padding_grads, + ) + monkeypatch.setattr( + runtime.model_support_handler, + "zero_internal_padding_params", + lambda _model: pytest.fail( + "slot step must not mutate unrelated slot parameters" + ), ) trainer = TrainerRank(runtime) - trainer._checkpoint_slot_params_by_name["student"] = (param,) + trainer._checkpoint_slots.setdefault("student", _CheckpointSlot()).params = (param,) monkeypatch.setattr( trainer, "_reduce_dynamic_grads", @@ -711,7 +1914,7 @@ def zero_padding_grads(_model: object) -> None: assert param[-1].item() == 0.0 -def test_checkpoint_slot_optimizer_state_reproduces_exact_next_step( +def test_canonical_optimizer_state_reproduces_exact_next_step( monkeypatch: pytest.MonkeyPatch, ) -> None: adam = AdamParams( @@ -721,31 +1924,51 @@ def test_checkpoint_slot_optimizer_state_reproduces_exact_next_step( weight_decay=0.1, grad_clip_norm=10.0, ) - original, original_param = _trainer_with_checkpoint( monkeypatch, torch.tensor([0.5, -0.25], dtype=torch.bfloat16) ) + original._checkpoint_slots["student"].revision = 0 original_param.grad = torch.tensor([0.2, -0.4], dtype=torch.bfloat16) original.optim_step(params=adam) - state = original.checkpoint_slot_optimizer_state("student") - assert state is not None + dynamic = original._checkpoint_slots["student"].optimizer + assert dynamic is not None + optimizer_state = dynamic.optimizer.state[dynamic.master_params[0]] + group = dynamic.optimizer.param_groups[0] + beta1, beta2 = cast(tuple[float, float], group["betas"]) + state = LocalOptimizerState( + masters=tuple(param.detach().clone() for param in dynamic.master_params), + exp_avgs=(cast(torch.Tensor, optimizer_state["exp_avg"]).clone(),), + exp_avg_sqs=(cast(torch.Tensor, optimizer_state["exp_avg_sq"]).clone(),), + steps=(float(cast(torch.Tensor, optimizer_state["step"]).item()),), + config=OptimizerConfig( + learning_rate=float(group["lr"]), + beta1=beta1, + beta2=beta2, + eps=float(group["eps"]), + weight_decay=float(group["weight_decay"]), + ), + ) restored, restored_param = _trainer_with_checkpoint( monkeypatch, original_param.detach() ) - restored._dynamic_optimizers["student"] = restored._restore_dynamic_optimizer( - "student", state - ) + restored._checkpoint_slots[ + "student" + ].optimizer = restored._restore_canonical_optimizer("student", state) for param in (original_param, restored_param): param.grad = torch.tensor([-0.3, 0.1], dtype=torch.bfloat16) original.optim_step(params=adam) restored.optim_step(params=adam) torch.testing.assert_close(restored_param, original_param, atol=0, rtol=0) - original_state = original.checkpoint_slot_optimizer_state("student") - restored_state = restored.checkpoint_slot_optimizer_state("student") - assert original_state is not None and restored_state is not None - _assert_nested_tensors_equal(restored_state, original_state) + restored_optimizer = restored._checkpoint_slots["student"].optimizer + original_optimizer = original._checkpoint_slots["student"].optimizer + assert restored_optimizer is not None and original_optimizer is not None + _assert_nested_tensors_equal( + restored_optimizer.optimizer.state_dict(), + original_optimizer.optimizer.state_dict(), + ) + assert original._checkpoint_slots["student"].revision == 2 def test_dynamic_optimizer_keeps_fp32_master_weight_and_moments( @@ -765,7 +1988,8 @@ def test_dynamic_optimizer_keeps_fp32_master_weight_and_moments( ) ) - dynamic = trainer._dynamic_optimizers["student"] + dynamic = trainer._checkpoint_slots["student"].optimizer + assert dynamic is not None assert dynamic.master_params[0].dtype == torch.float32 assert param.item() < torch.tensor(0.1, dtype=torch.bfloat16).item() state = dynamic.optimizer.state[dynamic.master_params[0]] @@ -773,35 +1997,26 @@ def test_dynamic_optimizer_keeps_fp32_master_weight_and_moments( assert state["exp_avg_sq"].dtype == torch.float32 -@pytest.mark.parametrize( - ("corruption", "error"), - ( - ("layout", "topology or parameter layout"), - ("missing_master", "master parameters"), - ("shape", "topology or parameter layout"), - ), -) -def test_checkpoint_slot_optimizer_state_rejects_incompatible_state( - corruption: str, - error: str, +def test_canonical_optimizer_rejects_incompatible_local_shape( monkeypatch: pytest.MonkeyPatch, ) -> None: - trainer, param = _trainer_with_checkpoint(monkeypatch, torch.ones(2)) - param.grad = torch.ones_like(param) - trainer.optim_step( - params=AdamParams(learning_rate=1e-2, weight_decay=0.0, grad_clip_norm=10.0) - ) - state = trainer.checkpoint_slot_optimizer_state("student") - assert state is not None - if corruption == "layout": - cast(dict[str, object], state)["layout"] = {"different": True} - elif corruption == "missing_master": - state["master_params"] = () - restored, _ = _trainer_with_checkpoint( - monkeypatch, torch.ones(3 if corruption == "shape" else 2) + trainer, _ = _trainer_with_checkpoint(monkeypatch, torch.ones(2)) + state = LocalOptimizerState( + masters=(torch.ones(3),), + exp_avgs=(torch.zeros(3),), + exp_avg_sqs=(torch.zeros(3),), + steps=(1.0,), + config=OptimizerConfig( + learning_rate=1e-3, + beta1=0.9, + beta2=0.99, + eps=1e-8, + weight_decay=0.0, + ), ) - with pytest.raises(TrainerRankSlotStateError, match=error): - restored._restore_dynamic_optimizer("student", state) + + with pytest.raises(TrainerRankSlotStateError, match="master parameter shape"): + trainer._restore_canonical_optimizer("student", state) @pytest.mark.parametrize("operation", ("load", "step")) @@ -810,7 +2025,7 @@ def test_trainer_rank_rejects_mutating_slot_with_pending_graph( monkeypatch: pytest.MonkeyPatch, ) -> None: trainer = TrainerRank(_runtime()) - ref = _slot_ref("checkpoint", "teacher") + ref = _slot_ref("teacher") monkeypatch.setattr(trainer, "_slot_ref", _slot_ref) target = _tracked_targets(trainer, ref, 2)[0] guard = ( @@ -837,7 +2052,7 @@ def test_trainer_rank_step_allows_missing_slot_graph_bookkeeping( def test_trainer_rank_zero_grad_does_not_clear_live_slot_graphs() -> None: trainer = TrainerRank(_runtime()) - ref = _slot_ref("lora", "teacher") + ref = _slot_ref("teacher") output = ForwardOutput( None, TopK( @@ -858,7 +2073,7 @@ def test_trainer_rank_zero_grad_does_not_clear_live_slot_graphs() -> None: def test_trainer_rank_retained_backward_keeps_slot_graph_guard() -> None: trainer = TrainerRank(_runtime()) - ref = _slot_ref("checkpoint", "teacher") + ref = _slot_ref("teacher") target = _tracked_targets(trainer, ref, 2)[0] target.sum().backward(retain_graph=True) @@ -871,7 +2086,7 @@ def test_trainer_rank_retained_backward_keeps_slot_graph_guard() -> None: def test_trainer_rank_tracks_each_independent_output_graph() -> None: trainer = TrainerRank(_runtime()) - ref = _slot_ref("checkpoint", "teacher") + ref = _slot_ref("teacher") first, second = _tracked_targets(trainer, ref, 2, 3) first.sum().backward() @@ -884,7 +2099,7 @@ def test_trainer_rank_tracks_each_independent_output_graph() -> None: def test_trainer_rank_tracks_graph_after_output_is_replaced_by_loss() -> None: trainer = TrainerRank(_runtime()) - ref = _slot_ref("checkpoint", "teacher") + ref = _slot_ref("teacher") target = _tracked_targets(trainer, ref, 2)[0] loss = target.sum() del target @@ -899,7 +2114,7 @@ def test_trainer_rank_tracks_graph_after_output_is_replaced_by_loss() -> None: def test_trainer_rank_releases_abandoned_output_graph() -> None: trainer = TrainerRank(_runtime()) - ref = _slot_ref("checkpoint", "teacher") + ref = _slot_ref("teacher") target = _tracked_targets(trainer, ref, 2)[0] del target gc.collect() @@ -1102,6 +2317,7 @@ def test_forward_micro_batches_rejects_mismatched_replicated_counts( monkeypatch.setattr(trainer_rank.dist, "is_available", lambda: True) monkeypatch.setattr(trainer_rank.dist, "is_initialized", lambda: True) monkeypatch.setattr(trainer_rank.dist, "get_world_size", lambda: 2) + monkeypatch.setattr(trainer_rank.dist, "all_reduce", lambda *_args, **_kwargs: None) def gather(output, value): output[:] = [value, value + 1] diff --git a/tests/unit/test_trainer_rank_weird_shapes.py b/tests/unit/test_trainer_rank_weird_shapes.py index 509e102d5..ff1803366 100644 --- a/tests/unit/test_trainer_rank_weird_shapes.py +++ b/tests/unit/test_trainer_rank_weird_shapes.py @@ -21,6 +21,7 @@ Unset, ) from art.trainer_rank._impl import ( + _CheckpointSlot, _flatten, _MemoryCheck, _MemoryProfile, @@ -68,7 +69,6 @@ def _target_request( logits: bool = False, hidden_states: bool = False, checkpoint: AdapterSelection = Unset, - lora: AdapterSelection = Unset, ) -> ForwardInput: labels = ( tokens @@ -85,7 +85,6 @@ def _target_request( logits=logits, hidden_states=hidden_states, checkpoint=checkpoint, - lora=lora, ) @@ -442,13 +441,15 @@ def test_heterogeneous_slots_split_packing_without_losing_output_estimates( monkeypatch.setattr( TrainerRank, "_slot_ref", - staticmethod(lambda kind, name: (kind, name)), + staticmethod(lambda name: name), ) - rank.set_checkpoint("student") + rank._default_slot_ref = rank._slot_ref("student") + for name in ("student", "teacher", "critic"): + rank._checkpoint_slots.setdefault(name, _CheckpointSlot()).params = () requests = [ _target_request(_tokens(1, 2, 3), top_k=3), _target_request(_tokens(1, 2, 4), checkpoint=None, logits=True), - _target_request(_tokens(1, 2, 5), lora="teacher", hidden_states=True), + _target_request(_tokens(1, 2, 5), checkpoint="teacher", hidden_states=True), _target_request(_tokens(1, 2, 6), checkpoint="critic", target_count=4), ] @@ -462,10 +463,10 @@ def test_heterogeneous_slots_split_packing_without_losing_output_estimates( assert signature == plan.signature assert plan.signature.slot_group_count == 4 assert {group.slot_ref for group in plan.groups} == { - ("checkpoint", "student"), - ("checkpoint", None), - ("lora", "teacher"), - ("checkpoint", "critic"), + "student", + None, + "teacher", + "critic", } diff --git a/tests/unit/test_vllm_lora_delta.py b/tests/unit/test_vllm_lora_delta.py index 7b6930a3d..cc54460b8 100644 --- a/tests/unit/test_vllm_lora_delta.py +++ b/tests/unit/test_vllm_lora_delta.py @@ -27,20 +27,25 @@ def test_additive_weight_loader_uses_legacy_loader_for_plain_merged_column_param calls = [] class Owner: - def weight_loader_v2(self, loader_param, loaded_weight, shard_id): - del shard_id + def weight_loader_v2(self, loader_param, loaded_weight, shard_id, **kwargs): + del shard_id, kwargs loader_param.load_merged_column_weight(loaded_weight=loaded_weight) - def weight_loader(self, loader_param, loaded_weight, shard_id): - calls.append((loader_param, shard_id)) + def weight_loader(self, loader_param, loaded_weight, shard_id, **kwargs): + calls.append((loader_param, shard_id, kwargs)) loader_param.data.copy_(loaded_weight) owner = Owner() - loader = lora_delta._additive_weight_loader(param, owner.weight_loader_v2) - result = loader(param, loaded, 0) + loader = lora_delta._additive_weight_loader(owner.weight_loader_v2, {}) + result = loader( + param=param, + loaded_weight=loaded, + shard_id=0, + return_success=True, + ) assert result is None - assert calls == [(param, 0)] + assert calls == [(param, 0, {"return_success": True})] assert torch.equal(param, loaded) @@ -61,8 +66,151 @@ def load_merged_column_weight(*, loaded_weight, **_kwargs): ), weight_loader=lambda *_args, **_kwargs: calls.append("legacy"), ) - loader = lora_delta._additive_weight_loader(param, owner.weight_loader_v2) + loader = lora_delta._additive_weight_loader(owner.weight_loader_v2, {}) loader(param, loaded, 0) assert calls == ["v2"] assert torch.equal(param, loaded) + + +def test_delta_update_normalizes_missing_quantization_config_during_load(): + lora_delta = _load_lora_delta_module() + + class Model(torch.nn.Module): + def __init__(self): + super().__init__() + self.weight = torch.nn.Parameter(torch.zeros(2, 2)) + self.config = SimpleNamespace(quantization_config=None) + + def load_weights(self, weights): + assert self.config.quantization_config == {"quant_method": None} + for _name, weight in weights: + getattr(self.weight, "weight_loader")(self.weight, weight) + + model = Model() + tensors = { + "base_model.model.weight.lora_A.weight": torch.eye(2), + "base_model.model.weight.lora_B.weight": torch.eye(2), + } + lora_delta.apply_lora_delta_update( + model=model, + lora_tensors=tensors, + adapter_config={"r": 2, "lora_alpha": 2}, + previous_lora_tensors=None, + ) + + assert model.config.quantization_config is None + assert torch.equal(model.weight, torch.eye(2)) + + +def test_block_fp8_delta_requantizes_weight_and_e8m0_scale() -> None: + lora_delta = _load_lora_delta_module() + weight = torch.full((4, 4), 0.5).to(torch.float8_e4m3fn) + param = torch.nn.Parameter(weight, requires_grad=False) + scale = torch.nn.Parameter( + torch.full((2, 2), 0.25).to(torch.float8_e8m0fnu), + requires_grad=False, + ) + setattr(param, lora_delta._BLOCK_FP8_SCALE_ATTR, scale) + setattr(param, lora_delta._BLOCK_FP8_SIZE_ATTR, (2, 2)) + delta = torch.zeros(4, 4) + delta[:2, :2] = 0.5 + + lora_delta._requantize_block_fp8_delta(param, delta) + + expanded = scale.float().repeat_interleave(2, 0).repeat_interleave(2, 1) + merged = param.float() * expanded + assert torch.allclose(merged[:2, :2], torch.full((2, 2), 0.625)) + assert torch.allclose(merged[2:, 2:], torch.full((2, 2), 0.125)) + + +def test_block_fp8_delta_supports_expert_leading_dimension() -> None: + lora_delta = _load_lora_delta_module() + param = torch.nn.Parameter( + torch.ones(2, 4, 4).to(torch.float8_e4m3fn), requires_grad=False + ) + scale = torch.nn.Parameter(torch.ones(2, 2, 2), requires_grad=False) + setattr(param, lora_delta._BLOCK_FP8_SCALE_ATTR, scale) + setattr(param, lora_delta._BLOCK_FP8_SIZE_ATTR, (2, 2)) + delta = torch.zeros(2, 4, 4) + delta[1] = 1.0 + + lora_delta._requantize_block_fp8_delta(param, delta) + + expanded = scale.repeat_interleave(2, -2).repeat_interleave(2, -1) + merged = param.float() * expanded + assert torch.allclose(merged[0], torch.ones(4, 4)) + assert torch.allclose(merged[1], torch.full((4, 4), 2.0)) + + +def test_block_fp8_delta_supports_grouped_matrix_layout() -> None: + lora_delta = _load_lora_delta_module() + param = torch.nn.Parameter( + torch.ones(2, 2, 4).to(torch.float8_e4m3fn), requires_grad=False + ) + scale = torch.nn.Parameter(torch.ones(2, 2), requires_grad=False) + setattr(param, lora_delta._BLOCK_FP8_SCALE_ATTR, scale) + setattr(param, lora_delta._BLOCK_FP8_SIZE_ATTR, (2, 2)) + + lora_delta._requantize_block_fp8_delta(param, torch.ones_like(param).float()) + + expanded = scale.repeat_interleave(2, 0).repeat_interleave(2, 1) + merged = param.flatten(0, 1).float() * expanded + assert torch.allclose(merged, torch.full((4, 4), 2.0)) + + +def test_block_fp8_delta_supports_deep_gemm_packed_scale_layout() -> None: + lora_delta = _load_lora_delta_module() + param = torch.nn.Parameter( + torch.ones(2, 4, 4).to(torch.float8_e4m3fn), requires_grad=False + ) + scale = torch.nn.Parameter( + torch.zeros(2, 4, 1, dtype=torch.int32), requires_grad=False + ) + lora_delta._copy_block_scale(scale, torch.ones(2, 2, 2), block_m=2) + setattr(param, lora_delta._BLOCK_FP8_SCALE_ATTR, scale) + setattr(param, lora_delta._BLOCK_FP8_SIZE_ATTR, (2, 2)) + + lora_delta._requantize_block_fp8_delta(param, torch.ones_like(param).float()) + + logical_scale = lora_delta._block_scale_to_float(scale, block_m=2, k_blocks=2) + expanded = logical_scale.repeat_interleave(2, -2).repeat_interleave(2, -1) + assert torch.allclose(param.float() * expanded, torch.full((2, 4, 4), 2.0)) + + +def test_block_fp8_expert_loader_updates_only_local_shard() -> None: + lora_delta = _load_lora_delta_module() + param = torch.nn.Parameter( + torch.ones(2, 4, 4).to(torch.float8_e4m3fn), requires_grad=False + ) + scale = torch.nn.Parameter(torch.ones(2, 2, 2), requires_grad=False) + setattr(param, lora_delta._BLOCK_FP8_SCALE_ATTR, scale) + setattr(param, lora_delta._BLOCK_FP8_SIZE_ATTR, (2, 2)) + + class Owner: + @staticmethod + def _map_global_expert_id_to_local_expert_id(expert_id): + return {4: 1}.get(expert_id, -1) + + def weight_loader(self, *_args, **_kwargs): + raise AssertionError("packed expert loader must not allocate full scratch") + + loader = lora_delta._additive_weight_loader(Owner().weight_loader, {}) + assert loader( + param, + torch.ones(2, 4), + shard_id="w3", + expert_id=4, + ) + assert not loader( + param, + torch.ones(2, 4), + shard_id="w3", + expert_id=5, + ) + + expanded = scale.repeat_interleave(2, -2).repeat_interleave(2, -1) + merged = param.float() * expanded + assert torch.allclose(merged[0], torch.ones(4, 4)) + assert torch.allclose(merged[1, :2], torch.ones(2, 4)) + assert torch.allclose(merged[1, 2:], torch.full((2, 4), 2.0)) diff --git a/tests/unit/trajectories/test_capture.py b/tests/unit/trajectories/test_capture.py index 0563ba1d0..eab0a0b49 100644 --- a/tests/unit/trajectories/test_capture.py +++ b/tests/unit/trajectories/test_capture.py @@ -784,7 +784,6 @@ async def response(_: httpx.Request) -> httpx.Response: assert len(chunks) == 1 assert chunks[0].choices[0].delta.content == "hello" - await stream.close() assert len(trajectory.exchanges.chat_completions) == 1 await client.close() @@ -846,7 +845,6 @@ async def response(_: httpx.Request) -> httpx.Response: assert len(chunks) == 1 assert chunks[0].choices[0].delta.content == "hello" - await stream.close() assert len(trajectory.exchanges.chat_completions) == 1 await client.close() diff --git a/uv.lock b/uv.lock index eff63852b..e79abda3e 100644 --- a/uv.lock +++ b/uv.lock @@ -2,39 +2,135 @@ version = 1 revision = 3 requires-python = ">=3.12" resolution-markers = [ - "python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker'", - "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform == 'linux' and extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker'", - "python_full_version >= '3.14' and platform_machine != 's390x' and sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker'", - "python_full_version >= '3.14' and platform_machine == 's390x' and sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra != 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and platform_machine == 's390x' and sys_platform != 'darwin' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker'", + "python_full_version == '3.13.*' and platform_machine != 's390x' and sys_platform != 'darwin' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker'", + "python_full_version < '3.13' and platform_machine != 's390x' and sys_platform != 'darwin' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker'", + "python_full_version == '3.13.*' and platform_machine == 's390x' and sys_platform != 'darwin' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker'", + "python_full_version < '3.13' and platform_machine == 's390x' and sys_platform != 'darwin' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-backend-cu130' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-distributed-cu130' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker'", ] conflicts = [[ { package = "openpipe-art", extra = "backend" }, @@ -42,20 +138,56 @@ conflicts = [[ ], [ { package = "openpipe-art", extra = "megatron" }, { package = "openpipe-art", extra = "tinker" }, +], [ + { package = "openpipe-art", extra = "distributed" }, + { package = "openpipe-art", extra = "distributed-cu130" }, +], [ + { package = "openpipe-art", extra = "backend-cu130" }, + { package = "openpipe-art", extra = "distributed" }, +], [ + { package = "openpipe-art", extra = "distributed" }, + { package = "openpipe-art", extra = "megatron-cu130" }, +], [ + { package = "openpipe-art", extra = "backend" }, + { package = "openpipe-art", extra = "distributed-cu130" }, +], [ + { package = "openpipe-art", extra = "distributed-cu130" }, + { package = "openpipe-art", extra = "megatron" }, +], [ + { package = "openpipe-art", extra = "backend" }, + { package = "openpipe-art", extra = "backend-cu130" }, +], [ + { package = "openpipe-art", extra = "backend" }, + { package = "openpipe-art", extra = "megatron-cu130" }, +], [ + { package = "openpipe-art", extra = "backend-cu130" }, + { package = "openpipe-art", extra = "megatron" }, +], [ + { package = "openpipe-art", extra = "backend-cu130" }, + { package = "openpipe-art", extra = "megatron-cu130" }, +], [ + { package = "openpipe-art", extra = "megatron" }, + { package = "openpipe-art", extra = "megatron-cu130" }, +], [ + { package = "openpipe-art", extra = "backend-cu130" }, + { package = "openpipe-art", extra = "tinker" }, +], [ + { package = "openpipe-art", extra = "distributed-cu130" }, + { package = "openpipe-art", extra = "tinker" }, +], [ + { package = "openpipe-art", extra = "megatron-cu130" }, + { package = "openpipe-art", extra = "tinker" }, ]] [manifest] overrides = [ { name = "click", specifier = "==8.2.0" }, + { name = "flashinfer-python", specifier = "==0.6.8.post1" }, { name = "megatron-core", specifier = "==0.17.0" }, { name = "numpy", specifier = "<2" }, + { name = "nvidia-cublas", marker = "sys_platform == 'linux'", specifier = "==13.2.2.2" }, { name = "nvidia-resiliency-ext", specifier = "<0.5" }, - { name = "quack-kernels", specifier = "==0.3.7" }, - { name = "torch", marker = "sys_platform != 'linux' and sys_platform != 'win32'", specifier = "==2.11.0" }, - { name = "torch", marker = "sys_platform == 'linux' or sys_platform == 'win32'", specifier = "==2.11.0", index = "https://download.pytorch.org/whl/cu128" }, - { name = "torchvision", marker = "sys_platform != 'linux' and sys_platform != 'win32'", specifier = "==0.26.0" }, - { name = "torchvision", marker = "sys_platform == 'linux' or sys_platform == 'win32'", specifier = "==0.26.0", index = "https://download.pytorch.org/whl/cu128" }, - { name = "transformer-engine", specifier = "==2.11.0" }, + { name = "quack-kernels", specifier = "==0.3.9" }, ] excludes = [ "causal-conv1d", @@ -111,8 +243,9 @@ dependencies = [ { name = "psutil" }, { name = "pyyaml" }, { name = "safetensors" }, - { name = "torch", version = "2.11.0", source = { registry = "https://pypi.org/simple" }, marker = 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(sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend' and extra == 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(sys_platform == 'win32' and extra != 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-megatron' and extra != 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend' and extra == 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"packaging", marker = "sys_platform != 'win32'" }, + { name = "packaging" }, ] sdist = { url = "https://files.pythonhosted.org/packages/c4/f4/e78fa054248fab913e2eab0332c6c2cb07421fca1ce56d8fe43b6aef57a4/gunicorn-25.3.0.tar.gz", hash = "sha256:f74e1b2f9f76f6cd1ca01198968bd2dd65830edc24b6e8e4d78de8320e2fe889", size = 634883, upload-time = "2026-03-27T00:00:26.092Z" } wheels = [ @@ -2548,7 +3016,7 @@ name = "hatch" version = "1.16.5" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "backports-zstd", marker = "python_full_version < '3.14' or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" }, + { name = "backports-zstd", marker = "python_full_version < '3.14' or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 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platform_machine == 'amd64' or platform_machine == 'arm64' or platform_machine == 'x86_64' or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" }, { name = "httpx" }, { name = "packaging" }, { name = "pyyaml" }, @@ -2964,7 +3432,7 @@ name = "ipykernel" version = "7.2.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "appnope", marker = "sys_platform == 'darwin' or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" }, + { name = "appnope", marker = "sys_platform == 'darwin' or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" }, { name = "comm" }, { name = "debugpy" }, { name = "ipython" }, @@ -2988,12 +3456,12 @@ name = "ipython" version = "9.13.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "colorama", marker = "sys_platform == 'win32' or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" }, + { name = "colorama", marker = "sys_platform == 'win32' or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" }, { name = "decorator" }, { name = "ipython-pygments-lexers" }, { name = "jedi" }, { name = "matplotlib-inline" }, - { name = "pexpect", marker = "(sys_platform != 'emscripten' and sys_platform != 'win32') or (sys_platform == 'emscripten' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'emscripten' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" }, + { name = "pexpect", marker = "(sys_platform != 'emscripten' and sys_platform != 'win32') or (sys_platform == 'emscripten' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (sys_platform == 'emscripten' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'emscripten' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'emscripten' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (sys_platform == 'emscripten' and extra == 'extra-12-openpipe-art-backend-cu130' and extra == 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(sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron' and extra == 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'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" }, { name = "tqdm" }, ] sdist = { url = "https://files.pythonhosted.org/packages/4a/1f/2bc9795047fa2c1ad2567ef78ce6dfc9a7b763fa534acee09a94da2a5b8f/open_clip_torch-3.3.0.tar.gz", hash = "sha256:904b1a9f909df8281bb3de60ab95491cd2994a509177ea4f9d6292a84fe24d6d", size = 1503380, upload-time = "2026-02-27T00:32:46.74Z" } @@ -4766,6 +5427,7 @@ dependencies = [ { name = "anthropic" }, { name = "litellm" }, { name = "nest-asyncio" }, + { name = "numpy" }, { name = "openai" }, { name = "polars" }, { name = "pydantic" }, @@ -4793,8 +5455,8 @@ backend = [ { name = "pyarrow" }, { name = "pytest" }, { name = "setuptools" }, - { name = "torch", version = "2.11.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" }, - { name = "torch", version = "2.11.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" }, + { name = "torch", version = "2.11.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform == 'darwin' and extra == 'extra-12-openpipe-art-backend') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" }, + { name = "torch", version = "2.11.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" }, { name = "torchao" }, { name = "transformers", version = "5.2.0", source = { registry = "https://pypi.org/simple" } }, { name = "trl" }, @@ -4802,12 +5464,54 @@ backend = [ { name = "unsloth-zoo" }, { name = "wandb" }, ] +backend-cu130 = [ + { name = "accelerate" }, + { name = "awscli" }, + { name = "bitsandbytes" }, + { name = "duckdb" }, + { name = "gql" }, + { name = "hf-xet" }, + { name = "nbclient" }, + { name = "nbmake" }, + { name = "nvidia-cudnn-frontend", marker = "sys_platform == 'linux'" }, + { name = "nvidia-nccl-cu13", marker = "sys_platform == 'linux'" }, + { name = "nvidia-resiliency-ext" }, + { name = "peft" }, + { name = "pyarrow" }, + { name = "pytest" }, + { name = "setuptools" }, + { name = "torch", version = "2.11.0+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "sys_platform == 'linux'" }, + { name = "torchao" }, + { name = "transformers", version = "5.2.0", source = { registry = "https://pypi.org/simple" } }, + { name = "trl" }, + { name = "unsloth" }, + { name = "unsloth-zoo" }, + { name = "wandb" }, +] +distributed = [ + { name = "aiohttp" }, + { name = "msgspec" }, + { name = "torch", version = "2.11.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform == 'darwin' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform != 'darwin' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform != 'darwin' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform != 'darwin' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra != 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra != 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra != 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra != 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra != 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra != 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" }, + { name = "torch", version = "2.11.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-distributed') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" }, + { name = "torchmonarch" }, + { name = "transformers", version = "5.2.0", source = { registry = "https://pypi.org/simple" }, marker = "(extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" }, + { name = "transformers", version = "5.12.1", source = { registry = "https://pypi.org/simple" }, marker = "(extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed' and extra != 'extra-12-openpipe-art-tinker')" }, +] +distributed-cu130 = [ + { name = "aiohttp" }, + { name = "msgspec" }, + { name = "torch", version = "2.11.0+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "sys_platform == 'linux' or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130')" }, + { name = "torchmonarch" }, + { name = "transformers", version = "5.2.0", source = { registry = "https://pypi.org/simple" }, marker = "(extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" }, + { name = "transformers", version = "5.12.1", source = { registry = "https://pypi.org/simple" }, marker = "(extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra != 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra != 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra != 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra != 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" }, +] langgraph = [ { name = "langchain-core" }, { name = "langchain-openai" }, { name = "langgraph" }, ] megatron = [ + { name = "aiohttp" }, { name = "apex" }, { name = "flash-attn-4" }, { name = "flashinfer-cubin" }, @@ -4815,6 +5519,7 @@ megatron = [ { name = "megatron-bridge" }, { name = "megatron-core" }, { name = "ml-dtypes", marker = "python_full_version < '3.13'" }, + { name = "msgspec" }, { name = "ninja" }, { name = "numpy" }, { name = "nvidia-cuda-cccl-cu12", marker = "sys_platform == 'linux'" }, @@ -4826,13 +5531,43 @@ megatron = [ { name = "scipy" }, { name = "setuptools" }, { name = "tilelang", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "torch", version = "2.11.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" }, - { name = "torch", version = "2.11.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" }, - { name = "torchvision", version = "0.26.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" }, - { name = "torchvision", version = "0.26.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" }, - { name = "transformer-engine" }, + { name = "torch", version = "2.11.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform == 'darwin' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" }, + { name = "torch", version = "2.11.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" }, + { name = "torchmonarch" }, + { name = "torchvision", version = "0.26.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform == 'darwin' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" }, + { name = "torchvision", version = "0.26.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker')" }, + { name = "transformer-engine", version = "2.11.0", source = { registry = "https://pypi.org/simple" } }, { name = "transformer-engine-cu12" }, - { name = "transformer-engine-torch" }, + { name = "transformer-engine-torch", version = "2.11.0", source = { git = "https://github.com/NVIDIA/TransformerEngine.git?subdirectory=transformer_engine%2Fpytorch&rev=v2.11#c188b533cc3721ca9c6bbfd26148f5cf60108c25" } }, + { name = "transformers", version = "5.12.1", source = { registry = "https://pypi.org/simple" } }, +] +megatron-cu130 = [ + { name = "aiohttp" }, + { name = "apex" }, + { name = "flash-attn-4" }, + { name = "flashinfer-cubin" }, + { name = "flashinfer-python" }, + { name = "megatron-bridge" }, + { name = "megatron-core" }, + { name = "ml-dtypes", marker = "python_full_version < '3.13'" }, + { name = "msgspec" }, + { name = "ninja" }, + { name = "numpy" }, + { name = "nvidia-ml-py" }, + { name = "nvidia-modelopt", marker = "sys_platform != 'darwin'" }, + { name = "nvidia-nccl-cu13", marker = "sys_platform == 'linux'" }, + { name = "nvidia-resiliency-ext" }, + { name = "pybind11" }, + { name = "quack-kernels" }, + { name = "scipy" }, + { name = "setuptools" }, + { name = "tilelang", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "torch", version = "2.11.0+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "sys_platform == 'linux'" }, + { name = "torchmonarch" }, + { name = "torchvision", version = "0.26.0+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "sys_platform == 'linux'" }, + { name = "transformer-engine", version = "2.14.1", source = { registry = "https://pypi.org/simple" } }, + { name = "transformer-engine-cu13" }, + { name = "transformer-engine-torch", version = "2.14.1", source = { registry = "https://pypi.org/simple" } }, { name = "transformers", version = "5.12.1", source = { registry = "https://pypi.org/simple" } }, ] plotting = [ @@ -4850,8 +5585,8 @@ tinker = [ { name = "pydantic" }, { name = "tinker" }, { name = "tinker-cookbook" }, - { name = "torch", version = "2.11.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform != 'linux' and sys_platform != 'win32' and extra == 'extra-12-openpipe-art-backend') or (sys_platform != 'linux' and sys_platform != 'win32' and extra != 'extra-12-openpipe-art-megatron') or (sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra != 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" }, - { name = "torch", version = "2.11.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'linux' and extra != 'extra-12-openpipe-art-megatron') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-backend') or (sys_platform == 'win32' and extra != 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" }, + { name = "torch", version = "2.11.0", source = { registry = "https://pypi.org/simple" }, marker = "(sys_platform == 'darwin' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" }, + { name = "torch", version = "2.11.0+cu128", source = { registry = "https://download.pytorch.org/whl/cu128" }, marker = "(sys_platform == 'linux' and extra == 'extra-12-openpipe-art-tinker') or (sys_platform == 'win32' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-backend-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-distributed') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-backend-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-distributed-cu130') or (extra == 'extra-12-openpipe-art-distributed' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-megatron') or (extra == 'extra-12-openpipe-art-distributed-cu130' and extra == 'extra-12-openpipe-art-tinker') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-megatron-cu130') or (extra == 'extra-12-openpipe-art-megatron' and extra == 'extra-12-openpipe-art-tinker')" }, { name = "transformers", version = "5.2.0", source = { registry = "https://pypi.org/simple" } }, { name = "uvicorn" }, ] @@ -4878,20 +5613,34 @@ dev = [ [package.metadata] requires-dist = [ { name = "accelerate", marker = "extra == 'backend'", specifier = "==1.7.0" }, + { name = "accelerate", marker = "extra == 'backend-cu130'", specifier = "==1.7.0" }, { name = "aiohttp", specifier = ">=3.10.0" }, + { name = "aiohttp", marker = "extra == 'distributed'", specifier = ">=3.13.0" }, + { name = "aiohttp", marker = "extra == 'distributed-cu130'", specifier = ">=3.13.0" }, + { name = "aiohttp", marker = "extra == 'megatron'", specifier = ">=3.13.0" }, + { name = "aiohttp", marker = "extra == 'megatron-cu130'", specifier = ">=3.13.0" }, { name = "anthropic", specifier = ">=0.77.0" }, { name = "apex", marker = "extra == 'megatron'", git = "https://github.com/NVIDIA/apex.git?rev=25.09" }, + { name = "apex", marker = "extra == 'megatron-cu130'", git = "https://github.com/NVIDIA/apex.git?rev=25.09" }, { name = "awscli", marker = "extra == 'backend'", specifier = ">=1.38.1" }, + { name = "awscli", marker = "extra == 'backend-cu130'", specifier = ">=1.38.1" }, { name = "bitsandbytes", marker = 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https://github.com/vllm-project/vllm/releases/download/v0.23.0/vllm-0.23.0%2Bcu129-cp38-abi3-manylinux_2_28_x86_64.whl ; sys_platform == 'linux'", +] + +[project.optional-dependencies] +cuda12 = [ + "nvidia-nccl-cu12==2.28.9 ; sys_platform == 'linux'", + "torch==2.11.0 ; sys_platform == 'linux'", + "torchaudio==2.11.0 ; sys_platform == 'linux'", + "torchvision==0.26.0 ; sys_platform == 'linux'", + "vllm @ https://github.com/vllm-project/vllm/releases/download/v0.25.1/vllm-0.25.1%2Bcu129-cp38-abi3-manylinux_2_28_x86_64.whl ; sys_platform == 'linux'", +] +cuda13 = [ + "nvidia-nccl-cu13==2.28.9 ; sys_platform == 'linux'", + "torch==2.11.0 ; sys_platform == 'linux'", + "torchaudio==2.11.0 ; sys_platform == 'linux'", + "torchvision==0.26.0 ; sys_platform == 'linux'", + "triton-kernels @ git+https://github.com/triton-lang/triton.git@7c56a5e40f7fd928dfd5c72902d5def0097db73a#subdirectory=python/triton_kernels ; sys_platform == 'linux'", + "vllm @ https://github.com/vllm-project/vllm/releases/download/v0.25.1/vllm-0.25.1-cp38-abi3-manylinux_2_28_x86_64.whl ; sys_platform == 'linux'", ] [project.scripts] @@ -34,12 +50,33 @@ allow-direct-references = true [tool.uv] required-version = ">=0.6.15" +conflicts = [[{ extra = "cuda12" }, { extra = "cuda13" }]] override-dependencies = [ - "flashinfer-python==0.6.12", - "numpy<2", - "nvidia-nccl-cu12==2.28.9 ; sys_platform == 'linux'", - "torch @ https://download.pytorch.org/whl/test/cu128/torch-2.11.0%2Bcu128-cp312-cp312-manylinux_2_28_x86_64.whl", - "torchaudio @ https://download.pytorch.org/whl/test/cu128/torchaudio-2.11.0%2Bcu128-cp312-cp312-manylinux_2_28_x86_64.whl", - "torchvision @ https://download.pytorch.org/whl/test/cu128/torchvision-0.26.0%2Bcu128-cp312-cp312-manylinux_2_28_x86_64.whl", + "flashinfer-python==0.6.13", "transformers==5.12.1", + "xgrammar==0.2.3", ] + +[tool.uv.sources] +torch = [ + { index = "pytorch-cu128", extra = "cuda12" }, + { index = "pytorch-cu130", extra = "cuda13" }, +] +torchaudio = [ + { index = "pytorch-cu128", extra = "cuda12" }, + { index = "pytorch-cu130", extra = "cuda13" }, +] +torchvision = [ + { index = "pytorch-cu128", extra = "cuda12" }, + { index = "pytorch-cu130", extra = "cuda13" }, +] + +[[tool.uv.index]] +name = "pytorch-cu128" +url = "https://download.pytorch.org/whl/cu128" +explicit = true + +[[tool.uv.index]] +name = "pytorch-cu130" +url = "https://download.pytorch.org/whl/cu130" +explicit = true diff --git a/vllm_runtime/setup.sh b/vllm_runtime/setup.sh new file mode 100755 index 000000000..af4c81e3f --- /dev/null +++ b/vllm_runtime/setup.sh @@ -0,0 +1,77 @@ +#!/usr/bin/env bash +set -euo pipefail + +cuda_home="${CUDA_HOME:-/usr/local/cuda}" +if [ ! -x "${cuda_home}/bin/nvcc" ]; then + echo "[art-vllm-runtime-setup] CUDA_HOME does not contain nvcc: ${cuda_home}" >&2 + exit 1 +fi +cuda_major="$("${cuda_home}/bin/nvcc" --version | sed -n 's/.*release \([0-9][0-9]*\)\..*/\1/p' | head -1)" +case "${cuda_major}" in + 12) runtime_extra="cuda12" ;; + 13) runtime_extra="cuda13" ;; + *) + echo "[art-vllm-runtime-setup] Unsupported CUDA major ${cuda_major}; expected 12 or 13." >&2 + exit 1 + ;; +esac + +script_dir="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd)" +cd "${script_dir}" +uv_bin="uv" +if [ -x "${HOME}/.local/bin/uv" ]; then + uv_bin="${HOME}/.local/bin/uv" +fi +echo "[art-vllm-runtime-setup] CUDA_HOME=${cuda_home}, profile=${runtime_extra}" +"${uv_bin}" sync --extra "${runtime_extra}" --frozen --no-dev + +cutlass_cu13_intact() { + ".venv/bin/python" - <<'PY' +import base64 +import hashlib +from importlib.metadata import PackageNotFoundError, distribution + +try: + files = distribution("nvidia-cutlass-dsl-libs-cu13").files +except PackageNotFoundError: + raise SystemExit(1) +if not files: + raise SystemExit(1) +for path in files: + expected = path.hash + if expected is None or expected.mode != "sha256" or not expected.value: + continue + try: + actual = base64.urlsafe_b64encode( + hashlib.sha256(path.locate().read_bytes()).digest() + ).decode().rstrip("=") + except OSError: + raise SystemExit(1) + if actual != expected.value: + raise SystemExit(1) +PY +} + +if [ "${cuda_major}" = 13 ] && ! cutlass_cu13_intact; then + echo "[art-vllm-runtime-setup] Repairing CUTLASS DSL install-order race" + site_packages="$(".venv/bin/python" -c \ + 'import sysconfig; print(sysconfig.get_paths()["purelib"])')" + # Overlay the wheel because its files share directories with libs-base; + # uninstalling either wheel first can delete files owned by the other. + "${uv_bin}" pip install --python .venv/bin/python --target "${site_packages}" \ + --reinstall --no-deps \ + nvidia-cutlass-dsl-libs-cu13==4.5.2 + cutlass_cu13_intact || { + echo "[art-vllm-runtime-setup] CUTLASS DSL integrity check failed" >&2 + exit 1 + } +fi + +".venv/bin/python" - <<'PY' +import torch +import vllm + +print(f"[art-vllm-runtime-setup] torch={torch.__version__} cuda={torch.version.cuda}") +print(f"[art-vllm-runtime-setup] vllm={vllm.__version__}") +print(f"[art-vllm-runtime-setup] device={torch.cuda.get_device_name()} capability={torch.cuda.get_device_capability()}") +PY diff --git a/vllm_runtime/src/art_vllm_runtime/__init__.py b/vllm_runtime/src/art_vllm_runtime/__init__.py index 80e13097f..3558f473f 100644 --- a/vllm_runtime/src/art_vllm_runtime/__init__.py +++ b/vllm_runtime/src/art_vllm_runtime/__init__.py @@ -1,15 +1,9 @@ from art_vllm_runtime.patches import ( apply_vllm_runtime_patches, - patch_listen_for_disconnect, - patch_tool_parser_manager, - patch_transformers_v5_compat, subclass_chat_completion_request, ) __all__ = [ "apply_vllm_runtime_patches", - "patch_listen_for_disconnect", - "patch_tool_parser_manager", - "patch_transformers_v5_compat", "subclass_chat_completion_request", ] diff --git a/vllm_runtime/src/art_vllm_runtime/binary_routes.py b/vllm_runtime/src/art_vllm_runtime/binary_routes.py index 4d78ff8a9..64d52e0fb 100644 --- a/vllm_runtime/src/art_vllm_runtime/binary_routes.py +++ b/vllm_runtime/src/art_vllm_runtime/binary_routes.py @@ -4,22 +4,41 @@ from contextlib import contextmanager from contextvars import ContextVar from functools import wraps +import os import struct from typing import Any import numpy as np -MAGIC = b"ARTRTE1\0" -HEADER = struct.Struct("<8sQI") +MAGIC = b"ARTRTE2\0" +HEADER = struct.Struct("<8sQII") ROUTE_HEADER = struct.Struct(" None: + super().__init__() + self.num_experts = num_experts + self.padding_layers = padding_layers + + +_CAPTURE: ContextVar[_CapturedRoutes | None] = ContextVar( "art_binary_routed_experts", default=None ) @contextmanager -def capture_routed_experts() -> Iterator[dict[int, np.ndarray]]: - routes: dict[int, np.ndarray] = {} +def capture_routed_experts() -> Iterator[_CapturedRoutes]: + if _REGISTERED_NUM_EXPERTS is None or _REGISTERED_PADDING_LAYERS is None: + raise RuntimeError("vLLM did not register an exact MoE route layout") + routes = _CapturedRoutes( + num_experts=_REGISTERED_NUM_EXPERTS, + padding_layers=_REGISTERED_PADDING_LAYERS, + ) token = _CAPTURE.set(routes) try: yield routes @@ -28,24 +47,34 @@ def capture_routed_experts() -> Iterator[dict[int, np.ndarray]]: def encode_routed_experts_response( - json_body: bytes, routes: dict[int, np.ndarray] + json_body: bytes, + routes: dict[int, np.ndarray], + *, + num_experts: int | None = None, ) -> bytes: + num_experts = int(num_experts or getattr(routes, "num_experts", 0)) + dtype = _route_dtype(num_experts) chunks: list[bytes | memoryview] = [ - HEADER.pack(MAGIC, len(json_body), len(routes)), + HEADER.pack(MAGIC, len(json_body), len(routes), num_experts), json_body, ] for choice_index, array in sorted(routes.items()): if array.ndim != 3: raise RuntimeError(f"Routed experts must have rank 3, got {array.shape}") - if array.dtype == np.dtype(np.uint8): + if dtype == np.dtype(np.uint8): dtype_code = 1 - elif array.dtype == np.dtype(np.uint16): + else: dtype_code = 2 array = array.astype(" np.dtype[Any]: + if not 1 <= num_experts <= 65_536: + raise RuntimeError( + f"ART routed experts require num_experts in [1, 65536], got {num_experts}" + ) + return np.dtype(np.uint8 if num_experts <= 256 else np.uint16) + + +def _validate_route_ids(array: np.ndarray, *, num_experts: int) -> None: + if array.shape[-1] > num_experts: + raise RuntimeError("Routed-expert top-k exceeds exact expert count") + flat = array.reshape(-1, array.shape[-1]) + for start in range(0, len(flat), 1 << 20): + rows = np.sort(flat[start : start + (1 << 20)], axis=1) + if rows.size and int(rows.max()) >= num_experts: + raise RuntimeError("Routed expert id is outside the exact model range") + if rows.shape[1] > 1 and bool(np.any(rows[:, 1:] == rows[:, :-1])): + raise RuntimeError("Routed expert ids must be distinct per token and layer") + + +def _resolve_padding_routes( + array: np.ndarray, *, padding_layers: tuple[int, ...] +) -> None: + if not padding_layers: + return + if padding_layers[-1] >= array.shape[1]: + raise RuntimeError( + "Routed-expert response has fewer layers than the registered model" + ) + padding = array[:, padding_layers, :] + if padding.size and bool(np.any(padding)): + raise RuntimeError("Non-routed layer contained captured expert ids") + array[:, padding_layers, :] = np.arange(array.shape[-1], dtype=array.dtype) + + +def _model_padding_layers(model_config: Any) -> tuple[int, ...]: + config = getattr(model_config, "hf_text_config", None) + if config is None: + config = getattr(model_config, "hf_config", model_config) + num_layers = int(getattr(config, "num_hidden_layers", 0)) + layer_types = getattr(config, "mlp_layer_types", None) + if layer_types is not None: + if len(layer_types) != num_layers: + raise RuntimeError("mlp_layer_types does not match num_hidden_layers") + if not set(layer_types).issubset({"dense", "sparse", "moe", "hash_moe"}): + raise RuntimeError(f"Unsupported MoE layer types: {set(layer_types)}") + return tuple(i for i, kind in enumerate(layer_types) if kind == "dense") + first_dense = int(getattr(config, "first_k_dense_replace", 0)) + if not 0 <= first_dense <= num_layers: + raise RuntimeError("first_k_dense_replace is outside the model layer range") + return tuple(range(first_dense)) + + +def _normalize_route_topk(model_config: Any) -> None: + hf_config = getattr(model_config, "hf_config", None) + text_config = getattr(model_config, "hf_text_config", None) or getattr( + hf_config, "text_config", hf_config + ) + configs = (model_config, hf_config, text_config) + values = { + int(value) + for config in configs + if config is not None + for name in ( + "num_experts_per_tok", + "experts_per_token", + "top_k_experts", + ) + if (value := getattr(config, name, None)) is not None and int(value) > 0 + } + if len(values) != 1: + raise RuntimeError(f"Model configs disagree on MoE route top-k: {values}") + if text_config is None: + raise RuntimeError("Unable to find the model's text config for route capture") + text_config.num_experts_per_tok = values.pop() + + +def _register_model_route_layout(model_config: Any) -> None: + global _REGISTERED_NUM_EXPERTS, _REGISTERED_PADDING_LAYERS + _normalize_route_topk(model_config) + getter = getattr(model_config, "get_num_experts", None) + if callable(getter): + num_experts = int(getter()) + else: + configs = [ + model_config, + getattr(model_config, "hf_config", None), + getattr(getattr(model_config, "hf_config", None), "text_config", None), + ] + values = { + int(value) + for config in configs + if config is not None + for name in ("num_experts", "n_routed_experts", "num_local_experts") + if (value := getattr(config, name, None)) is not None and int(value) > 0 + } + if not values: + raise RuntimeError("Unable to find the model's exact MoE expert count") + if len(values) != 1: + raise RuntimeError(f"Model configs disagree on MoE expert count: {values}") + num_experts = values.pop() + _route_dtype(num_experts) + padding_layers = _model_padding_layers(model_config) + if _REGISTERED_NUM_EXPERTS not in {None, num_experts}: + raise RuntimeError( + "One vLLM process cannot capture routes for different expert counts" + ) + if _REGISTERED_PADDING_LAYERS not in {None, padding_layers}: + raise RuntimeError("One vLLM process cannot capture different MoE layouts") + _REGISTERED_NUM_EXPERTS = num_experts + _REGISTERED_PADDING_LAYERS = padding_layers + + def patch_binary_routed_experts_response() -> None: from vllm.entrypoints.openai.chat_completion.serving import OpenAIServingChat @@ -87,3 +229,120 @@ async def stripped_results() -> AsyncIterator[Any]: patched.__art_binary_routes_patched__ = True # type: ignore[attr-defined] OpenAIServingChat.chat_completion_full_generator = patched + + +def patch_pipeline_routed_experts() -> None: + """Reduce disjoint PP-stage routes onto vLLM's output rank.""" + import torch + from vllm.distributed import get_pp_group, get_tp_group + from vllm.v1.worker.gpu_model_runner import GPUModelRunner + + original_execute = GPUModelRunner.execute_model + if getattr(original_execute, "__art_pipeline_routes_patched__", False): + return + original_sample = GPUModelRunner.sample_tokens + enabled = os.environ.get(PIPELINE_ROUTES_ENV) == PIPELINE_ROUTES_PROTOCOL + + @wraps(original_execute) + def execute(self: Any, scheduler_output: Any, *args: Any, **kwargs: Any) -> Any: + if enabled: + self._art_pipeline_route_tokens = int( + scheduler_output.total_num_scheduled_tokens + ) + return original_execute(self, scheduler_output, *args, **kwargs) + + @wraps(original_sample) + def sample(self: Any, *args: Any, **kwargs: Any) -> Any: + if not enabled: + return original_sample(self, *args, **kwargs) + num_tokens = int(getattr(self, "_art_pipeline_route_tokens", 0)) + self._art_pipeline_route_tokens = 0 + pp = get_pp_group() + if pp.world_size <= 1: + raise RuntimeError("pipeline route capture requires PP > 1") + if get_tp_group().rank_in_group == 0: + if not getattr(self, "_art_pipeline_routes_ready", False): + initialized = bool(self.routed_experts_initialized) + buffer = ( + self.routed_experts_capturer.get_device_buffer() + if initialized + else None + ) + local = torch.tensor( + [ + int(PIPELINE_ROUTES_PROTOCOL), + int(initialized), + buffer.ndim if buffer is not None else 0, + buffer.shape[0] if buffer is not None else 0, + buffer.shape[1] if buffer is not None else 0, + buffer.shape[2] if buffer is not None else 0, + int(buffer is not None and buffer.dtype == torch.int32), + ], + dtype=torch.int64, + device=buffer.device if buffer is not None else self.device, + ) + states = [torch.empty_like(local) for _ in range(pp.world_size)] + torch.distributed.all_gather(states, local, group=pp.device_group) + values = [state.tolist() for state in states] + if any(value != values[0] for value in values[1:]): + raise RuntimeError( + f"pipeline routed-expert workers disagree: {values}" + ) + if ( + values[0][1] != 1 + or values[0][2] != 3 + or min(values[0][3:6]) <= 0 + or values[0][6] != 1 + ): + raise RuntimeError( + f"pipeline routed-expert capturer is invalid: {values}" + ) + self._art_pipeline_routes_ready = True + routes = self.routed_experts_capturer.get_device_buffer()[:num_tokens] + torch.distributed.reduce( + routes, + dst=pp.last_rank, + op=torch.distributed.ReduceOp.SUM, + group=pp.device_group, + ) + return original_sample(self, *args, **kwargs) + + execute.__art_pipeline_routes_patched__ = True # type: ignore[attr-defined] + GPUModelRunner.execute_model = execute + GPUModelRunner.sample_tokens = sample + + +def patch_pipeline_routed_experts_validation() -> None: + """Allow the supported V1 PP aggregation through repeated validation.""" + from vllm.config import VllmConfig + + original = VllmConfig.__post_init__ + if getattr(original, "__art_pipeline_routes_patched__", False): + return + + @wraps(original) + def post_init(self: Any) -> None: + model = self.model_config + if model is not None and model.enable_return_routed_experts: + _register_model_route_layout(model) + pipeline_capture = ( + os.environ.get(PIPELINE_ROUTES_ENV) == PIPELINE_ROUTES_PROTOCOL + and model is not None + and model.enable_return_routed_experts + and self.parallel_config.pipeline_parallel_size > 1 + ) + if not pipeline_capture: + return original(self) + transfer = self.kv_transfer_config + if transfer is not None and transfer.is_kv_transfer_instance: + raise ValueError( + "pipeline routed-expert capture is incompatible with KV connectors" + ) + model.enable_return_routed_experts = False + try: + original(self) + finally: + model.enable_return_routed_experts = True + + post_init.__art_pipeline_routes_patched__ = True # type: ignore[attr-defined] + VllmConfig.__post_init__ = post_init diff --git a/vllm_runtime/src/art_vllm_runtime/dedicated_server.py b/vllm_runtime/src/art_vllm_runtime/dedicated_server.py index 36b8a0ffd..d46d18ffc 100644 --- a/vllm_runtime/src/art_vllm_runtime/dedicated_server.py +++ b/vllm_runtime/src/art_vllm_runtime/dedicated_server.py @@ -2,9 +2,14 @@ import argparse import asyncio +from functools import lru_cache from http import HTTPStatus +from ipaddress import ip_address import json import os +import socket +from typing import Any +import uuid from fastapi.responses import JSONResponse from pydantic import BaseModel, Field @@ -12,10 +17,78 @@ from starlette.types import Receive, Scope, Send from vllm.entrypoints.serve.utils.server_utils import AuthenticationMiddleware +from art_vllm_runtime.binary_routes import ( + PIPELINE_ROUTES_ENV, + PIPELINE_ROUTES_PROTOCOL, + _register_model_route_layout, +) +from art_vllm_runtime.fast_metrics import FastMetricsSidecar from art_vllm_runtime.patches import apply_vllm_runtime_patches +ART_SERVING_PROTOCOL_VERSION = 4 +_runtime_state: dict[str, object] = {} +_auth_tokens: list[str] = [] +_fast_metrics_port: int | None = None + + +def _patch_prebound_listener_tcp_nodelay(api_server: Any) -> None: + create_server_socket = api_server.create_server_socket + + def create_tcp_server_socket(*args: Any, **kwargs: Any) -> socket.socket: + listener = create_server_socket(*args, **kwargs) + # vLLM pre-binds before Uvicorn; accepted sockets inherit this option. + listener.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1) + return listener + + api_server.create_server_socket = create_tcp_server_socket + + +def _art_metrics_snapshot() -> dict[str, Any]: + from art_vllm_runtime.metrics import get_art_metrics_snapshot + + snapshot = get_art_metrics_snapshot() + snapshot.update( + process_uuid=_runtime_state["process_uuid"], + generation=_runtime_state["generation"], + ) + return snapshot + + +def _fast_metrics_url(request: Any) -> str: + if _fast_metrics_port is None: + raise RuntimeError("ART fast metrics listener is not running") + host = request.url.hostname + if host is None: + raise RuntimeError("ART capabilities request has no host") + try: + address = ip_address(host.strip("[]")) + unspecified = address.is_unspecified + loopback = address.is_loopback + except ValueError: + unspecified = False + loopback = host.casefold() == "localhost" + nnodes = _runtime_state.get("nnodes", 1) + if isinstance(nnodes, bool) or not isinstance(nnodes, int): + raise RuntimeError("ART runtime state has invalid nnodes") + if unspecified or (nnodes > 1 and loopback): + raise RuntimeError( + f"ART fast metrics cannot advertise unroutable host {host!r}" + ) + return str( + request.url.replace( + scheme="http", + port=_fast_metrics_port, + path="/art/metrics", + query="", + fragment="", + ) + ) + class _ArtAuthenticationMiddleware(AuthenticationMiddleware): + def __init__(self, app: Any) -> None: + super().__init__(app, tokens=_auth_tokens) + def __call__(self, scope: Scope, receive: Receive, send: Send): path = scope.get("path", "").removeprefix(scope.get("root_path", "")) if ( @@ -41,18 +114,98 @@ class _ResetPrefixCacheRequest(BaseModel): class _InFlightLoraUpdateRequest(BaseModel): model_name: str = Field(min_length=1) lora_path: str = Field(min_length=1) - policy_version: int + policy_version: int = Field(ge=0) lora_slot: str | None = Field(default=None, min_length=1) base_model_name: str | None = None is_3d_lora_weight: bool = False +def _index_shared_pp_partition(config: Any, pp_size: int) -> tuple[int, ...] | None: + if pp_size <= 1 or not hasattr(config, "index_topk"): + return None + layer_count = int(config.num_hidden_layers) + pattern = getattr(config, "index_topk_pattern", None) + offset = int(getattr(config, "index_skip_topk_offset", 2)) + frequency = int(getattr(config, "index_topk_freq", 1)) + + def computes_index(layer: int) -> bool: + if pattern is not None and layer < len(pattern): + return pattern[layer] != "S" + return max(layer - offset + 1, 0) % frequency == 0 + + boundaries = tuple( + layer for layer in range(1, layer_count) if computes_index(layer) + ) + + @lru_cache + def solve(start: int, remaining: int) -> tuple[int, int, tuple[int, ...]] | None: + if remaining == 1: + length = layer_count - start + return length + 1, length * length, (length,) + candidates = [] + for end in boundaries: + if end <= start: + continue + suffix = solve(end, remaining - 1) + if suffix is None: + continue + length = end - start + candidates.append( + ( + max(length + (start == 0), suffix[0]), + length * length + suffix[1], + (length, *suffix[2]), + ) + ) + return min(candidates) if candidates else None + + result = solve(0, pp_size) + if result is None: + raise ValueError( + f"cannot partition {layer_count} index-sharing layers across PP{pp_size}" + ) + return result[2] + + +def _configure_index_shared_pp(model: str, engine_args: dict[str, Any]) -> str | None: + pp_size = int(engine_args.get("pipeline_parallel_size", 1)) + if pp_size <= 1: + return None + from transformers import AutoConfig + + config = AutoConfig.from_pretrained( + model, + revision=engine_args.get("revision"), + trust_remote_code=bool(engine_args.get("trust_remote_code", False)), + ) + partition = _index_shared_pp_partition(config, pp_size) + if partition is None: + return os.environ.get("VLLM_PP_LAYER_PARTITION") + value = ",".join(map(str, partition)) + configured = os.environ.setdefault("VLLM_PP_LAYER_PARTITION", value) + if configured != value: + raise ValueError( + "VLLM_PP_LAYER_PARTITION conflicts with ART's index-sharing-safe " + f"partition: configured={configured!r}, required={value!r}" + ) + return value + + def parse_args(argv: list[str] | None = None) -> argparse.Namespace: parser = argparse.ArgumentParser(description="ART dedicated vLLM server") parser.add_argument("--model", required=True, help="Base model name or path") parser.add_argument("--port", type=int, required=True) parser.add_argument("--host", default="127.0.0.1") parser.add_argument("--cuda-visible-devices", required=True) + parser.add_argument("--nnodes", type=int, default=1) + parser.add_argument("--node-rank", type=int, default=0) + parser.add_argument("--master-addr") + parser.add_argument("--master-port", type=int) + parser.add_argument("--headless", action="store_true") + parser.add_argument("--replica-generation", type=int, default=0) + parser.add_argument("--process-uuid") + parser.add_argument("--update-identity") + parser.add_argument("--initial-policy-version", type=int) parser.add_argument("--lora-path", help="Optional initial checkpoint path") parser.add_argument("--served-model-name", required=True) parser.add_argument( @@ -74,7 +227,7 @@ def parse_args(argv: list[str] | None = None) -> argparse.Namespace: def _patch_art_runtime_routes() -> None: from fastapi import APIRouter, Depends, FastAPI, Query, Request - from fastapi.responses import Response + from fastapi.responses import JSONResponse, Response from vllm.entrypoints.openai import api_server from vllm.entrypoints.openai.chat_completion.api_router import ( create_chat_completion, @@ -93,15 +246,10 @@ def _patch_art_runtime_routes() -> None: return original_build_app = api_server.build_app + original_init_app_state = api_server.init_app_state def art_build_app(*build_args: object, **build_kwargs: object) -> FastAPI: app = original_build_app(*build_args, **build_kwargs) - from vllm import envs - - args = app.state.args - tokens = [key for key in (args.api_key or [envs.VLLM_API_KEY]) if key] - if tokens: - app.add_middleware(_ArtAuthenticationMiddleware, tokens=tokens) router = APIRouter() def engine(request: Request): @@ -143,22 +291,27 @@ async def set_served_model_name( if not models.base_model_paths: raise RuntimeError("vLLM runtime has no registered base model") models.base_model_paths[0].name = body.name + _runtime_state["loaded_adapter"] = body.name + if "@" in body.name and body.name.rsplit("@", 1)[1].isdigit(): + _runtime_state["policy_version"] = int(body.name.rsplit("@", 1)[1]) return JSONResponse(content={"name": body.name}) + @router.get("/art/state") + async def art_state() -> JSONResponse: + return JSONResponse(content=dict(_runtime_state)) + @router.get("/art/metrics") async def art_metrics() -> JSONResponse: - from art_vllm_runtime.metrics import get_art_metrics_snapshot - - return JSONResponse(content=get_art_metrics_snapshot()) + return JSONResponse(content=_art_metrics_snapshot()) @router.get("/art/capabilities") - async def art_capabilities() -> JSONResponse: + async def art_capabilities(raw_request: Request) -> JSONResponse: return JSONResponse( content={ "runtime": "art_vllm", - "protocol_version": 1, + "protocol_version": ART_SERVING_PROTOCOL_VERSION, "binary_routed_experts": True, - "fast_metrics": True, + "fast_metrics": {"url": _fast_metrics_url(raw_request)}, "inplace_lora_load": True, "in_flight_lora_updates": True, "policy_token_spans": True, @@ -195,7 +348,7 @@ async def binary_chat_completion( } return Response( content=encode_routed_experts_response(response.body, routes), - media_type="application/vnd.art.routed-experts-v1", + media_type="application/vnd.art.routed-experts-v2", headers=headers, ) @@ -217,7 +370,11 @@ async def in_flight_lora_update( from vllm.entrypoints.serve.lora.protocol import LoadLoRAAdapterRequest from art_vllm_runtime.policy_spans import ( + PolicyLoRARequest, lora_update_coordinator, + policy_lora_request_payload, + publish_lora_slot_policy, + register_lora_alias, ) public_model_name = body.model_name @@ -227,43 +384,99 @@ async def in_flight_lora_update( models = raw_request.app.state.openai_serving_models engine_client = engine(raw_request) coordinator = lora_update_coordinator(models, engine_client) - await coordinator.begin_update(lora_slot) + update_seq = await coordinator.begin_update(lora_slot) + mutation_started = False try: - load_result = await models.load_lora_adapter( - LoadLoRAAdapterRequest( + async with models.lora_resolver_lock[lora_slot]: + load_request = LoadLoRAAdapterRequest( lora_name=lora_slot, lora_path=lora_path, load_inplace=lora_slot in models.lora_requests, is_3d_lora_weight=body.is_3d_lora_weight, - ), - base_model_name=body.base_model_name, - ) - if isinstance(load_result, ErrorResponse): - await coordinator.fail_update(lora_slot) - return JSONResponse( - content=load_result.model_dump(mode="python"), - status_code=load_result.error.code, ) - waiting_cache_salt = await engine_client.engine_core.call_utility_async( - "art_update_waiting_lora_cache_salt", - lora_slot, - policy_version, - ) - await coordinator.commit_update( - lora_slot, - policy_version, - models.lora_requests[lora_slot], - ) - from art_vllm_runtime.metrics import record_policy_cache_waiting_update - - record_policy_cache_waiting_update( - updated=int(waiting_cache_salt["updated_waiting_requests"]), - skipped_started=int( - waiting_cache_salt["skipped_started_waiting_requests"] - ), + load_error = await models._check_load_lora_adapter_request( + load_request + ) + if isinstance(load_error, ErrorResponse): + await coordinator.cancel_update(lora_slot, update_seq) + return JSONResponse( + content=load_error.model_dump(mode="python"), + status_code=load_error.error.code, + ) + lora_int_id = ( + models.lora_requests[lora_slot].lora_int_id + if lora_slot in models.lora_requests + else models.lora_id_counter.inc(1) + ) + lora_request = PolicyLoRARequest( + lora_name=lora_slot, + lora_int_id=lora_int_id, + lora_path=lora_path, + base_model_name=( + body.base_model_name + if body.base_model_name is not None + and models.is_base_model(body.base_model_name) + else None + ), + load_inplace=True, + is_3d_lora_weight=body.is_3d_lora_weight, + policy_version=policy_version, + update_seq=update_seq, + ) + mutation_started = True + await engine_client.engine_core.call_utility_async( + "pause_scheduler", "keep", False + ) + cache_transition = ( + await engine_client.engine_core.call_utility_async( + "art_apply_lora_policy_update", + policy_lora_request_payload(lora_request), + ) + ) + serving_request = PolicyLoRARequest( + **{ + **policy_lora_request_payload(lora_request), + "load_inplace": False, + } + ) + models.lora_requests[lora_slot] = serving_request + register_lora_alias( + models, + public_model_name=public_model_name, + lora_slot=lora_slot, + ) + publish_lora_slot_policy( + models, + lora_slot=lora_slot, + policy_version=policy_version, + update_seq=update_seq, + ) + await engine_client.engine_core.call_utility_async( + "resume_scheduler" + ) + await coordinator.commit_update(lora_slot, serving_request) + mutation_started = False + _runtime_state.update( + loaded_adapter=public_model_name, + policy_version=policy_version, + update_identity=(f"lora:{lora_slot}:{policy_version}:{update_seq}"), ) except BaseException: - await coordinator.fail_update(lora_slot) + if mutation_started: + try: + await asyncio.shield( + engine_client.engine_core.call_utility_async( + "pause_scheduler", "abort", True + ) + ) + finally: + await asyncio.shield( + coordinator.fail_update(lora_slot, update_seq) + ) + else: + await asyncio.shield( + coordinator.cancel_update(lora_slot, update_seq) + ) raise return JSONResponse( content={ @@ -271,14 +484,32 @@ async def in_flight_lora_update( "model_name": public_model_name, "lora_slot": lora_slot, "policy_version": policy_version, - "waiting_cache_salt": waiting_cache_salt, + "update_seq": update_seq, + "cache_transition": cache_transition, } ) app.include_router(router) return app + async def art_init_app_state( + engine_client: Any, state: Any, *args: Any, **kwargs: Any + ) -> None: + await original_init_app_state(engine_client, state, *args, **kwargs) + policy_version = _runtime_state.get("initial_policy_version") + if policy_version is None: + return + from art_vllm_runtime.policy_spans import declare_initial_lora_policy + + await declare_initial_lora_policy( + state.openai_serving_models, + engine_client, + lora_slot=str(_runtime_state["loaded_adapter"]), + policy_version=int(policy_version), + ) + setattr(api_server, "build_app", art_build_app) + setattr(api_server, "init_app_state", art_init_app_state) setattr(api_server, "_art_runtime_routes_patched", True) @@ -323,12 +554,114 @@ def _append_cli_arg(vllm_args: list[str], key: str, value: object) -> None: assert False, f"Unsupported CLI arg for {key}: {type(value)}" +def _patch_engine_config( + engine_args_type: Any, + *, + pipeline_route_capture: bool, +) -> None: + current = engine_args_type.create_engine_config + create_engine_config = getattr(current, "__art_original__", current) + if not pipeline_route_capture: + setattr(engine_args_type, "create_engine_config", create_engine_config) + return + + def create(self: Any, *args: Any, **kwargs: Any) -> Any: + config = create_engine_config(self, *args, **kwargs) + config.model_config.enable_return_routed_experts = True + _register_model_route_layout(config.model_config) + _validate_pipeline_route_config(config) + return config + + create.__art_original__ = create_engine_config # type: ignore[attr-defined] + setattr(engine_args_type, "create_engine_config", create) + + +def _validate_pipeline_route_config(config: Any) -> None: + parallel = config.parallel_config + if ( + parallel.pipeline_parallel_size <= 1 + or parallel.distributed_executor_backend != "mp" + or parallel.data_parallel_size != 1 + or parallel.prefill_context_parallel_size != 1 + or parallel.decode_context_parallel_size != 1 + or config.use_v2_model_runner + ): + raise ValueError( + "pipeline routed-expert capture requires V1 mp execution, PP > 1, " + "DP = 1, and prefill/decode CP = 1" + ) + transfer = config.kv_transfer_config + if transfer is not None and transfer.is_kv_transfer_instance: + raise ValueError( + "pipeline routed-expert capture is incompatible with KV connectors" + ) + + def main(argv: list[str] | None = None) -> None: + global _fast_metrics_port + args = parse_args(argv) if args.rollout_weights_mode == "merged" and not args.lora_path: raise SystemExit("--lora-path is required for merged rollout weights") engine_args = json.loads(args.engine_args_json) server_args = json.loads(args.server_args_json) + route_capture = engine_args.get("enable_return_routed_experts", False) + pp_size = engine_args.get("pipeline_parallel_size", 1) + if not isinstance(route_capture, bool): + raise ValueError("enable_return_routed_experts must be a boolean") + if isinstance(pp_size, bool) or not isinstance(pp_size, int): + raise ValueError("pipeline_parallel_size must be an integer") + pp_layer_partition = _configure_index_shared_pp(args.model, engine_args) + critical_engine_args = { + "data_parallel_size", + "decode_context_parallel_size", + "distributed_executor_backend", + "enable_return_routed_experts", + "kv_transfer_config", + "pipeline_parallel_size", + "prefill_context_parallel_size", + } + misplaced = critical_engine_args.intersection(server_args) + if misplaced: + raise ValueError( + f"engine arguments passed as server arguments: {sorted(misplaced)}" + ) + pipeline_route_capture = route_capture and pp_size > 1 + if pipeline_route_capture: + engine_args["enable_return_routed_experts"] = False + if os.environ.get("VLLM_USE_V2_MODEL_RUNNER", "0").lower() not in { + "0", + "false", + }: + raise ValueError("pipeline routed-expert capture requires vLLM V1") + os.environ["VLLM_USE_V2_MODEL_RUNNER"] = "0" + os.environ[PIPELINE_ROUTES_ENV] = PIPELINE_ROUTES_PROTOCOL + else: + os.environ.pop(PIPELINE_ROUTES_ENV, None) + + process_uuid = args.process_uuid or uuid.uuid4().hex + + _runtime_state.update( + runtime="art_vllm", + protocol_version=ART_SERVING_PROTOCOL_VERSION, + process_uuid=process_uuid, + generation=args.replica_generation, + node_rank=args.node_rank, + nnodes=args.nnodes, + headless=args.headless, + loaded_adapter=args.served_model_name if args.lora_path else None, + policy_version=args.initial_policy_version + if args.initial_policy_version is not None + else ( + int(args.served_model_name.rsplit("@", 1)[1]) + if "@" in args.served_model_name + and args.served_model_name.rsplit("@", 1)[1].isdigit() + else None + ), + update_identity=args.update_identity, + initial_policy_version=args.initial_policy_version, + pp_layer_partition=pp_layer_partition, + ) os.environ["CUDA_VISIBLE_DEVICES"] = args.cuda_visible_devices os.environ["VLLM_ALLOW_RUNTIME_LORA_UPDATING"] = "1" @@ -336,6 +669,7 @@ def main(argv: list[str] | None = None) -> None: os.environ["VLLM_SERVER_DEV_MODE"] = "1" apply_vllm_runtime_patches() + from vllm.engine.arg_utils import AsyncEngineArgs from vllm.entrypoints.openai import api_server from vllm.entrypoints.openai.cli_args import ( make_arg_parser, @@ -343,7 +677,12 @@ def main(argv: list[str] | None = None) -> None: ) from vllm.utils.argparse_utils import FlexibleArgumentParser + _patch_prebound_listener_tcp_nodelay(api_server) _patch_art_runtime_routes() + _patch_engine_config( + AsyncEngineArgs, + pipeline_route_capture=pipeline_route_capture, + ) vllm_args = [ f"--model={args.model}", @@ -351,6 +690,17 @@ def main(argv: list[str] | None = None) -> None: f"--host={args.host}", f"--served-model-name={args.served_model_name}", ] + if args.nnodes > 1: + vllm_args.extend( + [ + f"--nnodes={args.nnodes}", + f"--node-rank={args.node_rank}", + f"--master-addr={args.master_addr}", + f"--master-port={args.master_port}", + ] + ) + if args.headless: + vllm_args.append("--headless") if args.rollout_weights_mode == "lora": vllm_args.append("--enable-lora") if args.lora_path: @@ -366,8 +716,37 @@ def main(argv: list[str] | None = None) -> None: ) vllm_parser = make_arg_parser(vllm_parser) namespace = vllm_parser.parse_args(vllm_args) + if api_key := os.environ.pop("VLLM_API_KEY", None): + namespace.api_key = [api_key] + _auth_tokens[:] = namespace.api_key or [] + if _auth_tokens: + namespace.middleware = [ + *namespace.middleware, + "art_vllm_runtime.dedicated_server._ArtAuthenticationMiddleware", + ] validate_parsed_serve_args(namespace) - asyncio.run(api_server.run_server(namespace)) + if args.headless: + from vllm.entrypoints.cli.serve import run_headless + + namespace.api_server_count = 0 + run_headless(namespace) + else: + from art_vllm_runtime.metrics import set_fast_metrics_writer + + metrics_sidecar = FastMetricsSidecar.start( + args.host, + _auth_tokens, + process_uuid=process_uuid, + generation=args.replica_generation, + ) + _fast_metrics_port = metrics_sidecar.port + try: + set_fast_metrics_writer(metrics_sidecar.writer) + asyncio.run(api_server.run_server(namespace)) + finally: + _fast_metrics_port = None + set_fast_metrics_writer(None) + metrics_sidecar.close() if __name__ == "__main__": diff --git a/vllm_runtime/src/art_vllm_runtime/dsv4_patches.py b/vllm_runtime/src/art_vllm_runtime/dsv4_patches.py index 03e1f5a1b..961d27155 100644 --- a/vllm_runtime/src/art_vllm_runtime/dsv4_patches.py +++ b/vllm_runtime/src/art_vllm_runtime/dsv4_patches.py @@ -1,13 +1,25 @@ """DSV4-specific monkey patches for the ART-owned vLLM runtime.""" +from copy import copy import functools import importlib +import inspect from typing import Any +from packaging.version import Version +import torch + def apply_dsv4_vllm_runtime_patches() -> None: - patch_layerwise_reload_shadow_attrs() + model = _require_dsv4_vllm_0251_contract() + if getattr(model, "_art_dsv4_runtime_patched", False): + return + patch_dsv4_hash_moe_config() + patch_dsv4_dummy_hash_routes() + patch_dsv4_rope_config() + patch_dsv4_compress_ratio_config() patch_dsv4_attn_sink_layerwise_reload() + patch_dsv4_merged_delta_loading() patch_dsv4_mhc_pre_fixed_split() patch_dsv4_mhc_stable_transition() patch_dsv4_lora_support() @@ -15,64 +27,253 @@ def apply_dsv4_vllm_runtime_patches() -> None: patch_dsv4_fast_path_lora() patch_dsv4_triton_moe_topk6_routing() patch_lora_linear_base_attr_proxy() - patch_marlin_lora_swiglu_limit() + model._art_dsv4_runtime_patched = True -def _drop_reload_shadow_attrs(layer: Any, names: Any) -> None: - for name in names: - if ( - name in getattr(layer, "__dict__", {}) - and name not in layer._parameters - and name not in layer._buffers - and name not in layer._modules - ): - delattr(layer, name) +def _require_dsv4_vllm_0251_contract() -> Any: + import vllm + if Version(vllm.__version__).base_version != "0.25.1": + raise RuntimeError( + "ART DSV4 runtime patches require vLLM 0.25.1 exactly; " + f"found {vllm.__version__}" + ) + model = importlib.import_module("vllm.models.deepseek_v4.nvidia.model") + flashmla = importlib.import_module("vllm.models.deepseek_v4.nvidia.flashmla") + flashinfer = importlib.import_module( + "vllm.models.deepseek_v4.nvidia.flashinfer_sparse" + ) + required = ( + (model.DeepseekV4Model.load_weights, ("self", "weights")), + (model.DeepseekV4ForCausalLM.load_weights, ("self", "weights")), + (flashmla.DeepseekV4FlashMLAAttention._o_proj, ("self", "o", "positions")), + ( + flashinfer.DeepseekV4FlashInferMLAAttention._o_proj, + ("self", "o", "positions"), + ), + ( + flashinfer.DeepseekV4FlashInferSM120Attention._o_proj, + ("self", "o", "positions"), + ), + ) + for function, expected in required: + actual = tuple(inspect.signature(function).parameters) + if actual != expected: + raise RuntimeError( + f"vLLM DSV4 patch contract changed for {function}: " + f"{actual} != {expected}" + ) + routing = importlib.import_module("vllm.third_party.triton_kernels.routing") + if not hasattr(routing.SortTokens, "forward"): + raise RuntimeError("vLLM DSV4 routing patch target is unavailable") + return model -def patch_layerwise_reload_shadow_attrs() -> None: - """Allow vLLM layerwise reload to restore processed DSV4 MegaMoE params. - DeepSeek V4 MegaMoE drops loader-side Parameters after transforming them for - DeepGEMM. Some vLLM builds leave same-name plain attributes behind; PyTorch - then rejects register_parameter during the next checkpoint-format reload. - """ - from vllm.model_executor.model_loader.reload import layerwise, meta +def patch_dsv4_hash_moe_config() -> None: + """Bridge the canonical hash-MoE layer list into vLLM's count field.""" + from transformers import configuration_utils + from vllm.transformers_utils.configs.deepseek_v4 import DeepseekV4Config - if getattr(meta, "_art_reload_shadow_attrs_patched", False): + if "hash_moe" not in configuration_utils.ALLOWED_LAYER_TYPES: + configuration_utils.ALLOWED_LAYER_TYPES += ("hash_moe",) + original = DeepseekV4Config.__init__ + if getattr(original, "__art_hash_moe_patched__", False): return - original_restore_layer_on_meta = meta.restore_layer_on_meta - original_place_kernel_tensors = layerwise._place_kernel_tensors + def __init__(self: Any, *args: Any, **kwargs: Any) -> None: + layer_types = list(kwargs.get("mlp_layer_types", ()) or ()) + if layer_types: + num_hash_layers = next( + ( + index + for index, layer_type in enumerate(layer_types) + if layer_type != "hash_moe" + ), + len(layer_types), + ) + if "hash_moe" in layer_types[num_hash_layers:]: + raise ValueError("DSV4 hash-MoE layers must form a contiguous prefix") + configured = kwargs.setdefault("num_hash_layers", num_hash_layers) + if int(configured) != num_hash_layers: + raise ValueError( + "DSV4 num_hash_layers disagrees with mlp_layer_types: " + f"{configured} != {num_hash_layers}" + ) + original(self, *args, **kwargs) - def restore_layer_on_meta(layer: Any, info: Any) -> None: - restore_params, restore_buffers = info.restore_metadata - _drop_reload_shadow_attrs(layer, tuple(restore_params) + tuple(restore_buffers)) - return original_restore_layer_on_meta(layer, info) + __init__.__art_hash_moe_patched__ = True # type: ignore[attr-defined] + __init__.__art_original__ = original # type: ignore[attr-defined] + DeepseekV4Config.__init__ = __init__ - def _place_kernel_tensors(layer: Any, info: Any) -> None: - assert info.kernel_tensors is not None - parameters, buffers = info.kernel_tensors - _drop_reload_shadow_attrs(layer, tuple(parameters) + tuple(buffers)) - return original_place_kernel_tensors(layer, info) - restore_layer_on_meta.__art_patched__ = True # type: ignore[attr-defined] - _place_kernel_tensors.__art_patched__ = True # type: ignore[attr-defined] - meta.restore_layer_on_meta = restore_layer_on_meta # type: ignore[method-assign] - layerwise.restore_layer_on_meta = restore_layer_on_meta # type: ignore[method-assign] - layerwise._place_kernel_tensors = _place_kernel_tensors # type: ignore[method-assign] - setattr(meta, "_art_reload_shadow_attrs_patched", True) +def patch_dsv4_dummy_hash_routes() -> None: + """Make dummy hash routes valid, deterministic replay inputs.""" + from vllm.model_executor.models.utils import extract_layer_index + from vllm.models.deepseek_v4.nvidia.model import DeepseekV4MoE + original = DeepseekV4MoE.__init__ + if getattr(original, "__art_dummy_hash_routes_patched__", False): + return -def _import_dsv4_model_module() -> Any | None: - for module_name in ( - "vllm.model_executor.models.deepseek_v4", - "vllm.models.deepseek_v4.nvidia.model", - ): - try: - return importlib.import_module(module_name) - except ImportError: - continue - return None + def __init__(self: Any, vllm_config: Any, prefix: str = "") -> None: + original(self, vllm_config, prefix) + table = self.gate.tid2eid + if vllm_config.load_config.load_format != "dummy" or table is None: + return + num_experts = int(self.n_routed_experts) + topk = int(table.shape[1]) + if topk > num_experts: + raise ValueError( + f"DSV4 hash top-k exceeds expert count: {topk} > {num_experts}" + ) + tokens = torch.arange(table.shape[0], dtype=table.dtype, device=table.device) + offsets = torch.arange(topk, dtype=table.dtype, device=table.device) + starts = tokens * (topk + 1) + (extract_layer_index(prefix) + 1) * topk + with torch.no_grad(): + table.copy_((starts[:, None] + offsets).remainder(num_experts)) + + __init__.__art_dummy_hash_routes_patched__ = True # type: ignore[attr-defined] + __init__.__art_original__ = original # type: ignore[attr-defined] + DeepseekV4MoE.__init__ = __init__ + + +def patch_dsv4_rope_config() -> None: + """Bridge Transformers 5's per-attention RoPE sets into vLLM 0.25.""" + attention = importlib.import_module("vllm.models.deepseek_v4.attention") + rope = importlib.import_module("vllm.models.deepseek_v4.common.rope") + original = rope.build_deepseek_v4_rope + if getattr(original, "__art_nested_rope_config_patched__", False): + return + + def build_deepseek_v4_rope( + config: Any, *, compress_ratio: int, **kwargs: Any + ) -> Any: + parameter_sets = getattr(config, "rope_parameters", None) + if not isinstance(parameter_sets, dict) or not { + "main", + "compress", + }.issubset(parameter_sets): + return original(config, compress_ratio=compress_ratio, **kwargs) + compat_config = copy(config) + compat_config.rope_parameters = dict( + parameter_sets["compress" if compress_ratio > 1 else "main"] + ) + return original( + compat_config, + compress_ratio=compress_ratio, + **kwargs, + ) + + build_deepseek_v4_rope.__art_nested_rope_config_patched__ = True # type: ignore[attr-defined] + rope.build_deepseek_v4_rope = build_deepseek_v4_rope + attention.build_deepseek_v4_rope = build_deepseek_v4_rope + + +def _normalize_dsv4_compress_ratios(config: Any) -> None: + if getattr(config, "compress_ratios", None) is not None: + return + layer_types = list(getattr(config, "layer_types", ()) or ()) + num_layers = int(getattr(config, "num_hidden_layers", 0)) + if len(layer_types) != num_layers: + raise ValueError( + "DSV4 layer_types must match num_hidden_layers: " + f"{len(layer_types)} != {num_layers}" + ) + rates = dict(getattr(config, "compress_rates", {}) or {}) + supported = {"sliding_attention", *rates} + unknown = sorted(set(layer_types) - supported) + if unknown: + raise ValueError(f"Unsupported DSV4 layer types: {unknown}") + config.compress_ratios = [ + int(rates.get(layer_type, 0)) for layer_type in layer_types + ] + + +def patch_dsv4_compress_ratio_config() -> None: + """Bridge Transformers 5 DSV4 config names into vLLM 0.25.""" + attention = importlib.import_module("vllm.models.deepseek_v4.attention") + attention_cls = attention.DeepseekV4Attention + marker = "_art_compress_ratio_config_patched" + if getattr(attention_cls, marker, False): + return + original = attention_cls.__init__ + + def __init__(self: Any, vllm_config: Any, *args: Any, **kwargs: Any) -> None: + _normalize_dsv4_compress_ratios(vllm_config.model_config.hf_config) + original(self, vllm_config, *args, **kwargs) + + __init__.__art_original__ = original # type: ignore[attr-defined] + attention_cls.__init__ = __init__ + setattr(attention_cls, marker, True) + + +def _restore_merged_column_output_dim(param: Any) -> None: + if not hasattr(param, "output_dim"): + param.output_dim = 0 + + +def _restore_linear_shard_dim(param: Any, loaded_weight: Any) -> None: + mismatched = [ + dim + for dim, (local, loaded) in enumerate(zip(param.shape, loaded_weight.shape)) + if local != loaded + ] + if len(mismatched) == 1: + dim = mismatched[0] + if loaded_weight.shape[dim] % param.shape[dim] == 0: + for attr in ("input_dim", "output_dim"): + if not hasattr(param, attr): + setattr(param, attr, dim) + + +def _reshape_dsv4_bmm_weight( + name: str, + param: Any, + loaded_weight: Any, + tp_rank: int, + tp_size: int, +) -> Any: + if not name.endswith(".attn.wo_a.weight"): + return loaded_weight + if param.ndim == 2: + return loaded_weight + local_rows = param.shape[0] * param.shape[1] + assert loaded_weight.shape == (local_rows * tp_size, param.shape[2]) + return loaded_weight.narrow(0, tp_rank * local_rows, local_rows).view(param.shape) + + +def _dsv4_expert_checkpoint_name(name: str) -> str: + if ".shared_experts." in name: + return name.replace(".gate_proj.", ".w1.").replace(".up_proj.", ".w3.") + if ".experts." not in name: + return name + return ( + name.replace(".gate_proj.", ".w1.") + .replace(".down_proj.", ".w2.") + .replace(".up_proj.", ".w3.") + ) + + +def _attach_block_fp8_scale( + param: Any, + name: str, + params: dict[str, Any], + block_size: Any, +) -> None: + if param.dtype != torch.float8_e4m3fn or not block_size: + return + if name.endswith(".w13_weight"): + scale_name = name.removesuffix("weight") + "weight_scale_inv" + elif name.endswith(".w2_weight"): + scale_name = name.removesuffix("weight") + "weight_scale_inv" + elif name.endswith(".weight"): + scale_name = name.removesuffix("weight") + "weight_scale_inv" + else: + return + scale = params.get(scale_name) + if scale is not None: + param._art_block_fp8_scale = scale + param._art_block_fp8_size = tuple(block_size) def patch_dsv4_attn_sink_layerwise_reload() -> None: @@ -84,111 +285,112 @@ def patch_dsv4_attn_sink_layerwise_reload() -> None: the old kernel tensor. With `load_format=dummy`, that old tensor is the initialized sink, not the checkpoint sink. """ - dsv4_model = _import_dsv4_model_module() - if dsv4_model is None: - return + dsv4_model = importlib.import_module("vllm.models.deepseek_v4.nvidia.model") from vllm.model_executor.models.utils import is_pp_missing_parameter - model_cls = getattr(dsv4_model, "DeepseekV4Model", None) - if model_cls is None: - return + model_cls = dsv4_model.DeepseekV4Model original = model_cls.load_weights if getattr(original, "__art_patched__", False): return def load_weights(self: Any, weights: Any) -> set[str]: - stacked_params_mapping = [ - ("gate_up_proj", "w1", 0), - ("gate_up_proj", "w3", 1), - ("attn.fused_wqa_wkv", "attn.wq_a", 0), - ("attn.fused_wqa_wkv", "attn.wkv", 1), - ("compressor.fused_wkv_wgate", "compressor.wkv", 0), - ("compressor.fused_wkv_wgate", "compressor.wgate", 1), - ] params_dict = dict(self.named_parameters()) - loaded_params: set[str] = set() - tp_size = dsv4_model.get_tensor_model_parallel_world_size() tp_rank = dsv4_model.get_tensor_model_parallel_rank() - n_head = self.config.num_attention_heads - n_local_head = n_head // tp_size + n_local_head = self.config.num_attention_heads // tp_size head_rank_start = n_local_head * tp_rank head_rank_end = n_local_head * (tp_rank + 1) - expert_mapping = self.get_expert_mapping() + loaded_sinks: set[str] = set() - for name, loaded_weight in weights: - for param_name, weight_name, shard_id in stacked_params_mapping: - if ".experts." in name: - continue - if weight_name not in name: - continue - name = name.replace(weight_name, param_name) - - if is_pp_missing_parameter(name, self): - break - param = params_dict[name] - param.weight_loader(param, loaded_weight, shard_id) - loaded_params.add(name) - break - else: - if ".experts." in name: - if ( - "weight_scale" in name - and loaded_weight.dtype == dsv4_model.torch.float8_e8m0fnu - ): - loaded_weight = loaded_weight.view(dsv4_model.torch.uint8) - for mapping in expert_mapping: - param_name, weight_name, expert_id, expert_shard_id = mapping - if weight_name not in name: - continue - name_mapped = name.replace(weight_name, param_name) - if is_pp_missing_parameter(name_mapped, self): - continue - param = params_dict[name_mapped] - success = param.weight_loader( - param, - loaded_weight, - name_mapped, - shard_id=expert_shard_id, - expert_id=expert_id, - return_success=True, - ) - if success: - name = name_mapped - break - loaded_params.add(name_mapped) - continue - if "attn_sink" in name: - if is_pp_missing_parameter(name, self): - continue - param = params_dict[name] - narrow_weight = loaded_weight[head_rank_start:head_rank_end] - padded_weight = loaded_weight.new_full( - tuple(param.shape), -float("inf") - ) - padded_weight[: narrow_weight.shape[0]].copy_(narrow_weight) - weight_loader = getattr( - param, "weight_loader", dsv4_model.default_weight_loader - ) - weight_loader(param, padded_weight) - loaded_params.add(name) + def without_sinks() -> Any: + for name, loaded_weight in weights: + if "attn_sink" not in name: + yield name, loaded_weight continue - if is_pp_missing_parameter(name, self): continue param = params_dict[name] + local_weight = loaded_weight[head_rank_start:head_rank_end] + padded_weight = loaded_weight.new_full( + tuple(param.shape), -float("inf") + ) + padded_weight[: local_weight.shape[0]].copy_(local_weight) weight_loader = getattr( param, "weight_loader", dsv4_model.default_weight_loader ) - weight_loader(param, loaded_weight) - loaded_params.add(name) + weight_loader(param, padded_weight) + loaded_sinks.add(name) - return loaded_params + return original(self, without_sinks()) | loaded_sinks load_weights.__art_patched__ = True # type: ignore[attr-defined] model_cls.load_weights = load_weights # type: ignore[method-assign] +def _dsv4_stacked_parameter_name(name: str) -> str: + for param_name, weight_name in ( + ("gate_up_proj", "w1"), + ("gate_up_proj", "w3"), + ("attn.fused_wqa_wkv", "attn.wq_a"), + ("attn.fused_wqa_wkv", "attn.wkv"), + ("compressor.fused_wkv_wgate", "compressor.wkv"), + ("compressor.fused_wkv_wgate", "compressor.wgate"), + ): + if weight_name in name and ".experts." not in name: + return name.replace(weight_name, param_name) + return name + + +def patch_dsv4_merged_delta_loading() -> None: + """Adapt ART's checkpoint-shaped LoRA deltas before upstream loading.""" + dsv4_model = importlib.import_module("vllm.models.deepseek_v4.nvidia.model") + model_cls = dsv4_model.DeepseekV4ForCausalLM + original = model_cls.load_weights + if getattr(original, "__art_delta_adapter_patched__", False): + return + + def load_weights(self: Any, weights: Any) -> set[str]: + params = dict(self.named_parameters()) + quantization = self.config.quantization_config or {} + block_size = quantization.get("weight_block_size") or getattr( + self.model.quant_config, "weight_block_size", None + ) + for name, param in params.items(): + _attach_block_fp8_scale(param, name, params, block_size) + if any( + name.endswith(suffix) + for suffix in ( + ".gate_up_proj.weight", + ".fused_wqa_wkv.weight", + ".fused_wkv_wgate.weight", + ) + ): + _restore_merged_column_output_dim(param) + + tp_size = dsv4_model.get_tensor_model_parallel_world_size() + tp_rank = dsv4_model.get_tensor_model_parallel_rank() + + def adapted() -> Any: + for name, loaded_weight in weights: + name = _dsv4_expert_checkpoint_name(name) + mapped_name = self.hf_to_vllm_mapper._map_name(name) + if mapped_name is not None: + param_name = _dsv4_stacked_parameter_name(mapped_name) + param = params.get(param_name) + if param is not None: + loaded_weight = _reshape_dsv4_bmm_weight( + mapped_name, param, loaded_weight, tp_rank, tp_size + ) + if param_name == mapped_name: + _restore_linear_shard_dim(param, loaded_weight) + yield name, loaded_weight + + return original(self, adapted()) + + load_weights.__art_delta_adapter_patched__ = True # type: ignore[attr-defined] + model_cls.load_weights = load_weights # type: ignore[method-assign] + + def patch_dsv4_mhc_pre_fixed_split() -> None: """Make DSV4 mHC pre reductions invariant to total prefill length. @@ -279,11 +481,9 @@ def patch_dsv4_lora_support() -> None: point this patch at the FlashInfer TRTLLM MXFP4 backend; that backend currently has no LoRA hooks. """ - dsv4_model = _import_dsv4_model_module() - if dsv4_model is None: - return - model_cls = getattr(dsv4_model, "DeepseekV4ForCausalLM", None) - if model_cls is None or getattr(model_cls, "_art_dsv4_lora_patched", False): + dsv4_model = importlib.import_module("vllm.models.deepseek_v4.nvidia.model") + model_cls = dsv4_model.DeepseekV4ForCausalLM + if getattr(model_cls, "_art_dsv4_lora_patched", False): return model_cls.supports_lora = True model_cls.embedding_modules = {} @@ -1016,7 +1216,13 @@ def _apply_dsv4_wo_a_lora_fast( shrunk = wrapper.add_shrink(buffer, lora_input[group], wo_a.lora_a_stacked, 1.0) if not current_platform.can_update_inplace(): buffer = shrunk - buffer = tensor_model_parallel_all_gather(buffer) + if wo_a.lora_config.fully_sharded_loras: + buffer = tensor_model_parallel_all_gather(buffer) + if buffer.shape[-1] != lora_b.shape[-1]: + raise RuntimeError( + "DSV4 wo_a LoRA rank mismatch after TP placement: " + f"A={buffer.shape[-1]} B={lora_b.shape[-1]}" + ) expanded = wrapper.add_expand( z_flat, buffer, @@ -1126,16 +1332,28 @@ def _dsv4_deep_gemm_fp8_o_proj_with_lora( return wo_b(z.flatten(1)) +def _dsv4_fp32_cos_sin_cache(rotary_emb: Any) -> Any: + cache = rotary_emb.cos_sin_cache + if cache.dtype != torch.float32: + cache = cache.float() + rotary_emb.cos_sin_cache = cache + return cache + + def _patch_dsv4_cuda_o_proj_lora(attn_cls: Any, o_proj_mod: Any) -> None: if getattr(attn_cls, "_art_wo_a_fast_path_lora_patched", False): return + original = attn_cls._o_proj def _o_proj(self: Any, o: Any, positions: Any) -> Any: + cos_sin_cache = _dsv4_fp32_cos_sin_cache(self.rotary_emb) + if not _is_active_lora_wrapped_linear(self.wo_a): + return original(self, o, positions) return _dsv4_deep_gemm_fp8_o_proj_with_lora( o_proj_mod, o, positions, - self.rotary_emb.cos_sin_cache, + cos_sin_cache, self.wo_a, self.wo_b, n_groups=self.n_local_groups, @@ -1148,28 +1366,16 @@ def _o_proj(self: Any, o: Any, positions: Any) -> Any: ) _o_proj.__art_patched__ = True # type: ignore[attr-defined] + _o_proj.__art_original__ = original # type: ignore[attr-defined] attn_cls._o_proj = _o_proj attn_cls._art_wo_a_fast_path_lora_patched = True -def _patch_current_dsv4_fast_path_lora() -> bool: - try: - dsv4_attention = importlib.import_module("vllm.models.deepseek_v4.attention") - except ModuleNotFoundError: - return False - - attention_cls = getattr(dsv4_attention, "DeepseekV4Attention", None) - if attention_cls is None: - return False - +def _patch_dsv4_fast_path_lora() -> None: + dsv4_attention = importlib.import_module("vllm.models.deepseek_v4.attention") + attention_cls = dsv4_attention.DeepseekV4Attention _patch_dsv4_compressor_fast_path_lora(attention_cls) - - try: - o_proj_mod = importlib.import_module( - "vllm.models.deepseek_v4.nvidia.ops.o_proj" - ) - except ModuleNotFoundError: - return True + o_proj_mod = importlib.import_module("vllm.models.deepseek_v4.nvidia.ops.o_proj") for module_name, class_name in ( ( @@ -1180,15 +1386,13 @@ def _patch_current_dsv4_fast_path_lora() -> bool: "vllm.models.deepseek_v4.nvidia.flashinfer_sparse", "DeepseekV4FlashInferMLAAttention", ), + ( + "vllm.models.deepseek_v4.nvidia.flashinfer_sparse", + "DeepseekV4FlashInferSM120Attention", + ), ): - try: - module = importlib.import_module(module_name) - except ModuleNotFoundError: - continue - attn_cls = getattr(module, class_name, None) - if attn_cls is not None: - _patch_dsv4_cuda_o_proj_lora(attn_cls, o_proj_mod) - return True + module = importlib.import_module(module_name) + _patch_dsv4_cuda_o_proj_lora(getattr(module, class_name), o_proj_mod) def patch_dsv4_fast_path_lora() -> None: @@ -1203,119 +1407,7 @@ def patch_dsv4_fast_path_lora() -> None: """ _register_dsv4_inv_rope_lora_input_op() _register_dsv4_lora_expand_fp32_output_op() - if _patch_current_dsv4_fast_path_lora(): - return - - dsv4_attn = importlib.import_module( - "vllm.model_executor.layers.deepseek_v4_attention" - ) - wrapper_cls = getattr(dsv4_attn, "DeepseekV4MultiHeadLatentAttentionWrapper", None) - if wrapper_cls is None: - return - if getattr(wrapper_cls, "_art_fast_path_lora_patched", False): - return - - original_attn_gemm_parallel_execute = wrapper_cls.attn_gemm_parallel_execute - original_forward = wrapper_cls.forward - - def attn_gemm_parallel_execute(self: Any, hidden_states: Any) -> tuple[Any, ...]: - qr_kv, kv_score, indexer_kv_score, indexer_weights = ( - original_attn_gemm_parallel_execute(self, hidden_states) - ) - if self.compressor is not None: - kv_score = _apply_dsv4_compressor_lora_to_existing_output( - self.compressor.fused_wkv_wgate, - hidden_states, - kv_score, - ) - if self.indexer is not None: - indexer_kv_score = _apply_dsv4_compressor_lora_to_existing_output( - self.indexer.compressor.fused_wkv_wgate, - hidden_states, - indexer_kv_score, - ) - return qr_kv, kv_score, indexer_kv_score, indexer_weights - - def forward( - self: Any, - positions: Any, - hidden_states: Any, - llama_4_scaling: Any | None = None, - ) -> Any: - if dsv4_attn.current_platform.is_rocm(): - return original_forward(self, positions, hidden_states, llama_4_scaling) - - num_tokens = hidden_states.shape[0] - o_padded = dsv4_attn.torch.empty( - (num_tokens, self.padded_heads, self.head_dim), - dtype=hidden_states.dtype, - device=hidden_states.device, - ) - - dsv4_attn.torch.ops.vllm.deepseek_v4_attention( - hidden_states, - positions, - o_padded, - self.layer_name, - ) - o = o_padded[:, : self.n_local_heads, :] - - wo_a_lora_input = None - if _is_active_lora_wrapped_linear(self.wo_a): - o_fp8, o_scale, wo_a_lora_input = ( - _dsv4_fused_inv_rope_fp8_quant_with_lora_input( - dsv4_attn, - o, - positions, - self.rotary_emb.cos_sin_cache, - n_groups=self.n_local_groups, - heads_per_group=self.n_local_heads // self.n_local_groups, - lora_dtype=self.wo_a.lora_a_stacked[0].dtype, - nope_dim=self.nope_head_dim, - rope_dim=self.rope_head_dim, - tma_aligned_scales=self._tma_aligned_scales, - ) - ) - else: - o_fp8, o_scale = dsv4_attn.fused_inv_rope_fp8_quant( - o, - positions, - self.rotary_emb.cos_sin_cache, - n_groups=self.n_local_groups, - heads_per_group=self.n_local_heads // self.n_local_groups, - nope_dim=self.nope_head_dim, - rope_dim=self.rope_head_dim, - tma_aligned_scales=self._tma_aligned_scales, - ) - - z = dsv4_attn.torch.empty( - (num_tokens, self.n_local_groups, self.o_lora_rank), - device=o.device, - dtype=dsv4_attn.torch.bfloat16, - ) - dsv4_attn.torch.ops.vllm.deepseek_v4_fp8_einsum( - o_fp8, - o_scale, - self.wo_a.weight, - self.wo_a.weight_scale_inv, - z, - "bhr,hdr->bhd", - list(self._einsum_recipe), - ) - if wo_a_lora_input is not None: - z = _apply_dsv4_wo_a_lora_fast( - self.wo_a, - z, - lora_input=wo_a_lora_input, - n_local_groups=self.n_local_groups, - ) - return self.wo_b(z.flatten(1)) - - attn_gemm_parallel_execute.__art_patched__ = True # type: ignore[attr-defined] - forward.__art_patched__ = True # type: ignore[attr-defined] - wrapper_cls.attn_gemm_parallel_execute = attn_gemm_parallel_execute - wrapper_cls.forward = forward - wrapper_cls._art_fast_path_lora_patched = True + _patch_dsv4_fast_path_lora() def _next_power_of_two(value: int) -> int: @@ -1331,15 +1423,12 @@ def patch_dsv4_triton_moe_topk6_routing() -> None: the engine exits before serving starts. Keep the original indexing stride at 192, but sort over a padded power-of-two vector and mask padded lanes. """ - try: - import torch - import triton - import triton.language as tl - from vllm.third_party.triton_kernels.routing_details._expt_data import ( - _expt_data_compute, - ) - except ImportError: - return + import torch + import triton + import triton.language as tl + from vllm.third_party.triton_kernels.routing_details._expt_data import ( + _expt_data_compute, + ) @triton.jit def _routing_compute_indx_pow2( @@ -1456,14 +1545,8 @@ def _combined_routing_compute_pow2( BLOCK_SIZE_PADDED, ) - for module_name in ( - "vllm.third_party.triton_kernels.routing", - "triton_kernels.routing", - ): - try: - routing = importlib.import_module(module_name) - except ImportError: - continue + for module_name in ("vllm.third_party.triton_kernels.routing",): + routing = importlib.import_module(module_name) original_forward = routing.SortTokens.forward if getattr(original_forward, "__art_dsv4_topk6_pow2_patched__", False): continue @@ -1610,64 +1693,3 @@ def patch_lora_linear_base_attr_proxy() -> None: if not hasattr(BaseLinearLayerWithLoRA, name): setattr(BaseLinearLayerWithLoRA, name, _base_layer_attr_proxy(name)) BaseLinearLayerWithLoRA._art_base_attr_proxy_patched = True - - -def patch_marlin_lora_swiglu_limit() -> None: - """Keep Marlin MoE LoRA active when DSV4 uses a SwiGLU clamp limit. - - vLLM's Marlin LoRA path injects W13 LoRA inside the activation callback and - stores that activated cache for W2 LoRA. DSV4 sets ``gemm1_clamp_limit``; - upstream Marlin bypasses the callback in that case and calls the clamp op - directly, so W13 LoRA is skipped and W2 LoRA later misses ``cache2``. Route - the callback through the same clamp op while preserving Marlin execution. - """ - try: - marlin_moe = importlib.import_module( - "vllm.model_executor.layers.fused_moe.fused_marlin_moe" - ) - except ModuleNotFoundError: - return - - from vllm.model_executor.layers.fused_moe.activation import MoEActivation - from vllm.model_executor.layers.fused_moe.utils import swiglu_limit_func - - MarlinExperts = marlin_moe.MarlinExperts - - original_apply = MarlinExperts.apply - if getattr(original_apply, "__art_patched__", False): - return - - sentinel = object() - - def apply(self: Any, *args: Any, **kwargs: Any) -> Any: - clamp_limit = getattr(self, "gemm1_clamp_limit", None) - if getattr(self, "_lora_context", None) is None or clamp_limit is None: - return original_apply(self, *args, **kwargs) - - original_activation = self.activation - previous_activation = self.__dict__.get("activation", sentinel) - previous_clamp_limit = self.gemm1_clamp_limit - - def activation_with_clamp( - activation: Any, - output: Any, - input: Any, - ) -> None: - if activation == MoEActivation.SILU: - swiglu_limit_func(output, input, clamp_limit) - else: - original_activation(activation, output, input) - - self.activation = activation_with_clamp - self.gemm1_clamp_limit = None - try: - return original_apply(self, *args, **kwargs) - finally: - self.gemm1_clamp_limit = previous_clamp_limit - if previous_activation is sentinel: - delattr(self, "activation") - else: - self.activation = previous_activation - - apply.__art_patched__ = True # type: ignore[attr-defined] - MarlinExperts.apply = apply # type: ignore[method-assign] diff --git a/vllm_runtime/src/art_vllm_runtime/engine_core.py b/vllm_runtime/src/art_vllm_runtime/engine_core.py new file mode 100644 index 000000000..ec142c39c --- /dev/null +++ b/vllm_runtime/src/art_vllm_runtime/engine_core.py @@ -0,0 +1,25 @@ +"""Utilities that require replies from every vLLM engine core.""" + +import asyncio +from typing import Any + + +async def query_engine_cores( + engine_client: Any, method: str, *args: Any +) -> tuple[Any, ...]: + core = engine_client.engine_core + data_parallel_size = int( + engine_client.vllm_config.parallel_config.data_parallel_size + ) + if data_parallel_size == 1: + return (await core.call_utility_async(method, *args),) + + engines = getattr(core, "core_engines", ()) + call = getattr(core, "_call_utility_async", None) + if len(engines) != data_parallel_size or not callable(call): + raise RuntimeError("vLLM client does not expose every DP engine core") + return tuple( + await asyncio.gather( + *(call(method, *args, engine=engine) for engine in engines) + ) + ) diff --git a/vllm_runtime/src/art_vllm_runtime/fast_metrics.py b/vllm_runtime/src/art_vllm_runtime/fast_metrics.py new file mode 100644 index 000000000..41bd4800b --- /dev/null +++ b/vllm_runtime/src/art_vllm_runtime/fast_metrics.py @@ -0,0 +1,368 @@ +"""Process-isolated HTTP serving for ART's scalar vLLM metrics.""" + +from __future__ import annotations + +import argparse +import hashlib +from http import HTTPStatus +from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer +import json +import math +import mmap +import os +from pathlib import Path +import secrets +import select +import socket +import struct +import subprocess +import sys +import threading +from typing import Mapping, cast +import zlib + +FAST_METRIC_NAMES = ( + "prompt_tokens_total", + "prompt_tokens_computed_total", + "prompt_tokens_cached_total", + "prompt_tokens_local_cache_hit_total", + "prompt_tokens_external_kv_transfer_total", + "generation_tokens_total", + "prefix_cache_queries_total", + "prefix_cache_hits_total", + "external_prefix_cache_queries_total", + "external_prefix_cache_hits_total", + "num_preempted_reqs_total", + "policy_cache_salted_lora_requests_total", + "policy_cache_unsalted_lora_requests_total", + "policy_cache_waiting_requests_updated_total", + "policy_cache_started_waiting_requests_skipped_total", + "prefix_cache_hit_rate", + "external_prefix_cache_hit_rate", + "num_requests_running", + "num_requests_waiting", + "num_requests_waiting_capacity", + "num_requests_waiting_deferred", + "kv_cache_usage_perc", + "max_num_seqs", + "max_num_batched_tokens", + "max_num_scheduled_tokens", + "max_model_len", + "world_size", +) + +_CONTROL = struct.Struct(" int: + return _CONTROL.size + (sequence & 1) * _SLOT_SIZE + + +class FastMetricsSharedWriter: + def __init__(self) -> None: + self.fd = os.memfd_create("art-fast-metrics", os.MFD_CLOEXEC) + os.ftruncate(self.fd, _STATE_SIZE) + self._mapping = mmap.mmap(self.fd, _STATE_SIZE) + self._sequence = 0 + self._closed = False + + def publish( + self, + *, + last_update_unix_s: float, + record_count: int, + engine_count: int, + metrics: Mapping[str, float], + ) -> None: + values = tuple(float(metrics[name]) for name in FAST_METRIC_NAMES) + if not math.isfinite(last_update_unix_s) or not all( + math.isfinite(value) for value in values + ): + raise ValueError("fast metrics must be finite") + payload = _PAYLOAD.pack( + last_update_unix_s, + record_count, + engine_count, + *values, + ) + self._sequence += 1 + offset = _slot_offset(self._sequence) + # Fill the inactive slot completely before publishing its sequence. + slot = _SLOT_HEADER.pack(self._sequence, zlib.crc32(payload)) + payload + self._mapping[offset : offset + _SLOT_SIZE] = slot + _CONTROL.pack_into(self._mapping, 0, self._sequence) + + def close(self) -> None: + if self._closed: + return + self._closed = True + self._mapping.close() + os.close(self.fd) + + +class _FastMetricsSharedReader: + def __init__(self, fd: int) -> None: + self._mapping = mmap.mmap(fd, _STATE_SIZE, access=mmap.ACCESS_READ) + + def read(self) -> tuple[int, tuple[float | int, ...]]: + for _ in range(8): + sequence = _CONTROL.unpack_from(self._mapping)[0] + if sequence == 0: + continue + offset = _slot_offset(sequence) + slot_sequence, checksum = _SLOT_HEADER.unpack_from(self._mapping, offset) + payload = self._mapping[offset + _SLOT_HEADER.size : offset + _SLOT_SIZE] + if slot_sequence == sequence and zlib.crc32(payload) == checksum: + return sequence, _PAYLOAD.unpack(payload) + raise RuntimeError("fast metrics shared snapshot changed during every read") + + def close(self) -> None: + self._mapping.close() + + +class _FastMetricsHTTPServer(ThreadingHTTPServer): + allow_reuse_address = True + daemon_threads = True + block_on_close = False + + def __init__( + self, + host: str, + port: int, + *, + token_hashes: tuple[bytes, ...], + reader: _FastMetricsSharedReader, + process_uuid: str, + generation: int, + ) -> None: + self._token_hashes = token_hashes + self._reader = reader + self._process_uuid = process_uuid + self._generation = generation + self._cache_lock = threading.Lock() + self._cached_sequence = 0 + self._cached_body = b"" + super().__init__((host, port), _FastMetricsRequestHandler) + + def get_request(self) -> tuple[socket.socket, object]: + request, address = super().get_request() + request.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1) + return request, address + + def authorized(self, value: str | None) -> bool: + if not self._token_hashes: + return True + scheme, _, token = (value or "").partition(" ") + candidate = hashlib.sha256(token.encode()).digest() + matches = False + for expected in self._token_hashes: + matches |= secrets.compare_digest(candidate, expected) + return scheme.casefold() == "bearer" and matches + + def snapshot_body(self) -> bytes: + sequence, values = self._reader.read() + with self._cache_lock: + if sequence > self._cached_sequence: + last_update_unix_s, record_count, engine_count, *metrics = values + content = { + "schema_version": 1, + "source": "art_vllm_runtime", + "last_update_unix_s": last_update_unix_s, + "record_count": record_count, + "engine_count": engine_count, + "metrics": dict(zip(FAST_METRIC_NAMES, metrics, strict=True)), + "process_uuid": self._process_uuid, + "generation": self._generation, + } + self._cached_body = json.dumps( + content, allow_nan=False, separators=(",", ":") + ).encode() + self._cached_sequence = sequence + return self._cached_body + + +class _FastMetricsRequestHandler(BaseHTTPRequestHandler): + protocol_version = "HTTP/1.1" + + def do_GET(self) -> None: + server = cast(_FastMetricsHTTPServer, self.server) + if self.path.partition("?")[0] != "/art/metrics": + self._send_json(HTTPStatus.NOT_FOUND, b'{"error":"Not Found"}') + elif not server.authorized(self.headers.get("Authorization")): + self._send_json(HTTPStatus.UNAUTHORIZED, b'{"error":"Unauthorized"}') + else: + try: + body = server.snapshot_body() + except RuntimeError: + self._send_json( + HTTPStatus.SERVICE_UNAVAILABLE, + b'{"error":"Metrics unavailable"}', + ) + else: + self._send_json(HTTPStatus.OK, body) + + def _send_json(self, status: HTTPStatus, body: bytes) -> None: + self.send_response(status.value) + self.send_header("Content-Type", "application/json") + self.send_header("Content-Length", str(len(body))) + self.end_headers() + self.wfile.write(body) + + def log_message(self, format: str, *args: object) -> None: + return None + + +class FastMetricsSidecar: + def __init__( + self, + *, + process: subprocess.Popen[bytes], + writer: FastMetricsSharedWriter, + lifetime_fd: int, + port: int, + ) -> None: + self.process = process + self.writer = writer + self._lifetime_fd = lifetime_fd + self.port = port + self._closed = False + + @classmethod + def start( + cls, + host: str, + tokens: list[str], + *, + process_uuid: str, + generation: int, + port: int = 0, + startup_timeout_s: float = 10.0, + ) -> FastMetricsSidecar: + writer = FastMetricsSharedWriter() + ready_read, ready_write = os.pipe() + lifetime_read, lifetime_write = os.pipe() + token_hashes = [hashlib.sha256(token.encode()).hexdigest() for token in tokens] + command = [ + sys.executable, + "-E", + "-S", + str(Path(__file__).resolve()), + "--serve", + f"--host={host}", + f"--port={port}", + f"--state-fd={writer.fd}", + f"--ready-fd={ready_write}", + f"--lifetime-fd={lifetime_read}", + f"--process-uuid={process_uuid}", + f"--generation={generation}", + *(f"--token-sha256={value}" for value in token_hashes), + ] + process: subprocess.Popen[bytes] | None = None + try: + try: + process = subprocess.Popen( + command, + stdin=subprocess.DEVNULL, + pass_fds=(writer.fd, ready_write, lifetime_read), + env={"LC_ALL": "C"}, + ) + finally: + os.close(ready_write) + os.close(lifetime_read) + except BaseException: + os.close(ready_read) + os.close(lifetime_write) + writer.close() + raise + try: + ready, _, _ = select.select([ready_read], [], [], startup_timeout_s) + if not ready: + raise TimeoutError("fast metrics sidecar did not become ready") + message = os.read(ready_read, 64) + if not message: + returncode = None if process is None else process.poll() + raise RuntimeError( + f"fast metrics sidecar exited before readiness: {returncode=}" + ) + return cls( + process=cast(subprocess.Popen[bytes], process), + writer=writer, + lifetime_fd=lifetime_write, + port=int(message), + ) + except BaseException: + os.close(lifetime_write) + writer.close() + if process is not None and process.poll() is None: + process.terminate() + process.wait() + raise + finally: + os.close(ready_read) + + def close(self) -> None: + if self._closed: + return + self._closed = True + os.close(self._lifetime_fd) + try: + returncode = self.process.wait(timeout=5.0) + except subprocess.TimeoutExpired: + self.process.terminate() + self.process.wait() + raise RuntimeError("fast metrics sidecar did not stop after parent release") + finally: + self.writer.close() + if returncode != 0: + raise RuntimeError(f"fast metrics sidecar exited with status {returncode}") + + +def _serve(args: argparse.Namespace) -> None: + reader = _FastMetricsSharedReader(args.state_fd) + server = _FastMetricsHTTPServer( + args.host, + args.port, + token_hashes=tuple(bytes.fromhex(value) for value in args.token_sha256), + reader=reader, + process_uuid=args.process_uuid, + generation=args.generation, + ) + + try: + os.write(args.ready_fd, str(server.server_port).encode()) + os.close(args.ready_fd) + os.set_blocking(args.lifetime_fd, False) + server.timeout = 0.05 + while True: + try: + if os.read(args.lifetime_fd, 1) == b"": + break + except BlockingIOError: + pass + server.handle_request() + finally: + server.server_close() + reader.close() + os.close(args.lifetime_fd) + + +def _parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument("--serve", action="store_true", required=True) + parser.add_argument("--host", required=True) + parser.add_argument("--port", type=int, required=True) + parser.add_argument("--state-fd", type=int, required=True) + parser.add_argument("--ready-fd", type=int, required=True) + parser.add_argument("--lifetime-fd", type=int, required=True) + parser.add_argument("--process-uuid", required=True) + parser.add_argument("--generation", type=int, required=True) + parser.add_argument("--token-sha256", action="append", default=[]) + return parser.parse_args() + + +if __name__ == "__main__": + _serve(_parse_args()) diff --git a/vllm_runtime/src/art_vllm_runtime/glm52_patches.py b/vllm_runtime/src/art_vllm_runtime/glm52_patches.py new file mode 100644 index 000000000..76a8f241d --- /dev/null +++ b/vllm_runtime/src/art_vllm_runtime/glm52_patches.py @@ -0,0 +1,12 @@ +"""GLM-5.2 adaptations for the ART-owned vLLM runtime.""" + + +def apply_glm52_vllm_runtime_patches() -> None: + patch_glm52_lora_metadata() + + +def patch_glm52_lora_metadata() -> None: + from vllm.model_executor.models.deepseek_v2 import GlmMoeDsaForCausalLM + + GlmMoeDsaForCausalLM.is_3d_moe_weight = True + GlmMoeDsaForCausalLM.lora_skip_prefixes = ["indexer"] diff --git a/vllm_runtime/src/art_vllm_runtime/lora_delta.py b/vllm_runtime/src/art_vllm_runtime/lora_delta.py index 8952bb5d2..6180dce83 100644 --- a/vllm_runtime/src/art_vllm_runtime/lora_delta.py +++ b/vllm_runtime/src/art_vllm_runtime/lora_delta.py @@ -14,6 +14,8 @@ _UNSUPPORTED_MERGED_DELTA_TARGETS_KEY = ( "art_merged_lora_delta_unsupported_target_modules" ) +_BLOCK_FP8_SCALE_ATTR = "_art_block_fp8_scale" +_BLOCK_FP8_SIZE_ATTR = "_art_block_fp8_size" def _lora_scaling(adapter_config: dict[str, Any]) -> float: @@ -178,28 +180,169 @@ def _call_weight_loader( return loader(loader_param, loaded_weight, *args, **kwargs) -def _additive_weight_loader(param: torch.Tensor, original_loader: Any) -> Any: +def _e8m0_to_float(scale: torch.Tensor) -> torch.Tensor: + bits = scale.view(torch.uint8).to(torch.int32) << 23 + return bits.view(torch.float32) + + +def _block_scale_to_float( + scale: torch.Tensor, + block_m: int, + k_blocks: int, +) -> torch.Tensor: + if scale.dtype != torch.int32: + return ( + _e8m0_to_float(scale) + if scale.dtype in (torch.float8_e8m0fnu, torch.uint8) + else scale.float() + ) + shifts = torch.arange(4, device=scale.device, dtype=torch.int32) * 8 + exponent = ((scale.unsqueeze(-1) >> shifts) & 0xFF).flatten(-2)[..., :k_blocks] + return _e8m0_to_float(exponent[..., ::block_m, :].to(torch.uint8)) + + +def _copy_block_scale( + destination: torch.Tensor, + scale: torch.Tensor, + block_m: int, +) -> None: + if destination.dtype == torch.int32: + exponent = (scale.view(torch.int32) >> 23).to(torch.uint8) + exponent = exponent.repeat_interleave(block_m, -2) + padding = -exponent.shape[-1] % 4 + if padding: + exponent = torch.cat( + [exponent, exponent.new_zeros(*exponent.shape[:-1], padding)], dim=-1 + ) + shifts = torch.arange(4, device=scale.device, dtype=torch.int32) * 8 + packed = (exponent.unflatten(-1, (-1, 4)).to(torch.int32) << shifts).sum(-1) + destination.copy_(packed) + elif destination.dtype == torch.float8_e8m0fnu: + destination.copy_(scale.to(destination.dtype)) + elif destination.dtype == torch.uint8: + destination.copy_(scale.to(torch.float8_e8m0fnu).view(torch.uint8)) + else: + destination.copy_(scale) + + +def _requantize_block_fp8_delta( + param: torch.Tensor, + delta: torch.Tensor, +) -> None: + scale = getattr(param, _BLOCK_FP8_SCALE_ATTR) + block_m, block_k = getattr(param, _BLOCK_FP8_SIZE_ATTR) + _requantize_block_fp8_tensors(param.data, scale.data, delta, block_m, block_k) + + +def _requantize_block_fp8_tensors( + weight: torch.Tensor, + scale_data: torch.Tensor, + delta: torch.Tensor, + block_m: int, + block_k: int, +) -> None: + if weight.ndim == 3 and scale_data.ndim == 2: + weight = weight.flatten(0, 1) + delta = delta.flatten(0, 1) + scale_float = _block_scale_to_float( + scale_data, block_m, weight.shape[-1] // block_k + ) + expanded = scale_float.repeat_interleave(block_m, -2).repeat_interleave(block_k, -1) + merged = weight.float().mul_(expanded).add_(delta) + leading = merged.shape[:-2] + blocks = merged.view( + *leading, + merged.shape[-2] // block_m, + block_m, + merged.shape[-1] // block_k, + block_k, + ) + block_amax = blocks.abs().amax(dim=(-3, -1)) + new_scale = torch.pow( + 2.0, + torch.ceil( + torch.log2((block_amax / 448.0).clamp_min(torch.finfo(torch.float32).tiny)) + ), + ) + new_scale.masked_fill_(block_amax == 0, 1.0) + expanded = new_scale.repeat_interleave(block_m, -2).repeat_interleave(block_k, -1) + weight.copy_((merged / expanded).clamp_(-448, 448)) + _copy_block_scale(scale_data, new_scale, block_m) + + +def _load_block_fp8_expert_delta( + param: torch.Tensor, + loaded_weight: torch.Tensor, + original_loader: Any, + kwargs: dict[str, Any], +) -> bool | None: + owner = getattr(original_loader, "__self__", None) + map_expert = getattr(owner, "_map_global_expert_id_to_local_expert_id", None) + if map_expert is None or "expert_id" not in kwargs: + return None + local_expert = map_expert(kwargs["expert_id"]) + if local_expert == -1: + return False + block_m, block_k = getattr(param, _BLOCK_FP8_SIZE_ATTR) + weight = param.data[local_expert] + scale = getattr(param, _BLOCK_FP8_SCALE_ATTR).data[local_expert] + shard_id = kwargs["shard_id"] + if shard_id in ("w1", "w3"): + rows = loaded_weight.shape[-2] + offset = 0 if shard_id == "w1" else weight.shape[-2] - rows + weight = weight.narrow(-2, offset, rows) + scale = scale.narrow(-2, offset // block_m, rows // block_m) + assert weight.shape == loaded_weight.shape + _requantize_block_fp8_tensors( + weight, + scale, + loaded_weight.float(), + block_m, + block_k, + ) + return True + + +def _additive_weight_loader( + original_loader: Any, + block_fp8_deltas: dict[torch.Tensor, torch.Tensor], +) -> Any: def load_delta( - loader_param: torch.Tensor, + param: torch.Tensor, loaded_weight: torch.Tensor, *args: Any, **kwargs: Any, ) -> Any: - real_data = loader_param.data - scratch = torch.zeros_like(real_data) - loader_param.data = scratch + real_data = param.data + is_block_fp8 = hasattr(param, _BLOCK_FP8_SCALE_ATTR) + if is_block_fp8: + expert_result = _load_block_fp8_expert_delta( + param, loaded_weight, original_loader, kwargs + ) + if expert_result is not None: + return expert_result + scratch = block_fp8_deltas.get(param) + if scratch is None: + scratch = torch.zeros_like( + real_data, + dtype=torch.float32 if is_block_fp8 else None, + ) + param.data = scratch try: result = _call_weight_loader( original_loader, - loader_param, + param, loaded_weight, *args, **kwargs, ) finally: - loader_param.data = real_data + param.data = real_data if result is not False: - real_data.add_(scratch) + if is_block_fp8: + block_fp8_deltas[param] = scratch + else: + real_data.add_(scratch) return result return load_delta @@ -208,21 +351,44 @@ def load_delta( @contextmanager def _additive_weight_loaders(model: Any) -> Any: originals: list[tuple[torch.Tensor, bool, Any]] = [] + block_fp8_deltas: dict[torch.Tensor, torch.Tensor] = {} for param in model.parameters(): has_loader = hasattr(param, "weight_loader") original_loader = getattr(param, "weight_loader", _default_weight_loader) originals.append((param, has_loader, original_loader)) - param.weight_loader = _additive_weight_loader(param, original_loader) # type: ignore[attr-defined] + setattr( + param, + "weight_loader", + _additive_weight_loader(original_loader, block_fp8_deltas), + ) try: yield + except BaseException: + raise + else: + for param, delta in block_fp8_deltas.items(): + _requantize_block_fp8_delta(param, delta) finally: for param, has_loader, original_loader in originals: if has_loader: - param.weight_loader = original_loader # type: ignore[attr-defined] + setattr(param, "weight_loader", original_loader) else: delattr(param, "weight_loader") +@contextmanager +def _normalized_quantization_config(model: Any) -> Any: + config = getattr(model, "config", None) + if config is None or getattr(config, "quantization_config", None) is not None: + yield + return + config.quantization_config = {"quant_method": None} + try: + yield + finally: + config.quantization_config = None + + def apply_lora_delta_update( *, model: Any, @@ -237,7 +403,11 @@ def apply_lora_delta_update( "LoRA update key set changed: " f"current={sorted(lora_tensors)} previous={sorted(previous_lora_tensors)}" ) - with torch.no_grad(), _additive_weight_loaders(model): + with ( + torch.no_grad(), + _additive_weight_loaders(model), + _normalized_quantization_config(model), + ): model.load_weights( _iter_lora_checkpoint_deltas( lora_tensors, diff --git a/vllm_runtime/src/art_vllm_runtime/metrics.py b/vllm_runtime/src/art_vllm_runtime/metrics.py index 0c8be3d8f..4aa22e429 100644 --- a/vllm_runtime/src/art_vllm_runtime/metrics.py +++ b/vllm_runtime/src/art_vllm_runtime/metrics.py @@ -8,12 +8,15 @@ from vllm.v1.metrics.loggers import StatLoggerBase +from art_vllm_runtime.fast_metrics import FastMetricsSharedWriter + class _ArtRuntimeMetricsState: def __init__(self) -> None: self._lock = threading.Lock() self._record_count = 0 self._last_update_unix_s = 0.0 + self._writer: FastMetricsSharedWriter | None = None self._engine_gauges: dict[int, dict[str, float]] = {} self._engine_configs: dict[int, dict[str, float]] = {} self._counters = { @@ -43,7 +46,7 @@ def configure(self, vllm_config: Any, *, engine_idx: int) -> None: ("max_num_seqs", scheduler_config, "max_num_seqs"), ("max_num_batched_tokens", scheduler_config, "max_num_batched_tokens"), ("max_model_len", model_config, "max_model_len"), - ("world_size", parallel_config, "world_size"), + ("world_size", parallel_config, "world_size_across_dp"), ): value = getattr(obj, attr, None) if isinstance(value, (int, float)): @@ -59,6 +62,7 @@ def configure(self, vllm_config: Any, *, engine_idx: int) -> None: ] with self._lock: self._engine_configs[engine_idx] = engine_config + self._publish_locked() def record( self, @@ -118,68 +122,82 @@ def record( self._counters["num_preempted_reqs_total"] += float( iteration_stats.num_preempted_reqs ) + self._publish_locked() + + def _metrics_locked(self) -> dict[str, float]: + gauges = list(self._engine_gauges.values()) + engine_configs = list(self._engine_configs.values()) + metrics = dict(self._counters) + prefix_queries = metrics["prefix_cache_queries_total"] + external_prefix_queries = metrics["external_prefix_cache_queries_total"] + max_model_lens = [ + item["max_model_len"] for item in engine_configs if "max_model_len" in item + ] + metrics.update( + { + "prefix_cache_hit_rate": ( + metrics["prefix_cache_hits_total"] / prefix_queries + if prefix_queries > 0 + else 0.0 + ), + "external_prefix_cache_hit_rate": ( + metrics["external_prefix_cache_hits_total"] + / external_prefix_queries + if external_prefix_queries > 0 + else 0.0 + ), + "num_requests_running": sum(item["running"] for item in gauges), + "num_requests_waiting": sum(item["waiting"] for item in gauges), + "num_requests_waiting_capacity": sum( + item["waiting_capacity"] for item in gauges + ), + "num_requests_waiting_deferred": sum( + item["waiting_deferred"] for item in gauges + ), + "kv_cache_usage_perc": max( + (item["kv_cache_usage"] for item in gauges), default=0.0 + ), + "max_num_seqs": sum( + item.get("max_num_seqs", 0.0) for item in engine_configs + ), + "max_num_batched_tokens": sum( + item.get("max_num_batched_tokens", 0.0) for item in engine_configs + ), + "max_num_scheduled_tokens": sum( + item.get("max_num_scheduled_tokens", 0.0) for item in engine_configs + ), + "max_model_len": max(max_model_lens, default=0.0), + "world_size": max( + (item.get("world_size", 0.0) for item in engine_configs), + default=0.0, + ), + } + ) + return metrics + + def _publish_locked(self) -> None: + if self._writer is not None: + self._writer.publish( + last_update_unix_s=self._last_update_unix_s, + record_count=self._record_count, + engine_count=len(self._engine_gauges), + metrics=self._metrics_locked(), + ) + + def set_writer(self, writer: FastMetricsSharedWriter | None) -> None: + with self._lock: + self._writer = writer + self._publish_locked() def snapshot(self) -> dict[str, Any]: with self._lock: - gauges = list(self._engine_gauges.values()) - engine_configs = list(self._engine_configs.values()) - metrics = dict(self._counters) - prefix_queries = metrics["prefix_cache_queries_total"] - external_prefix_queries = metrics["external_prefix_cache_queries_total"] - max_model_lens = [ - item["max_model_len"] - for item in engine_configs - if "max_model_len" in item - ] - metrics.update( - { - "prefix_cache_hit_rate": ( - metrics["prefix_cache_hits_total"] / prefix_queries - if prefix_queries > 0 - else 0.0 - ), - "external_prefix_cache_hit_rate": ( - metrics["external_prefix_cache_hits_total"] - / external_prefix_queries - if external_prefix_queries > 0 - else 0.0 - ), - "num_requests_running": sum(item["running"] for item in gauges), - "num_requests_waiting": sum(item["waiting"] for item in gauges), - "num_requests_waiting_capacity": sum( - item["waiting_capacity"] for item in gauges - ), - "num_requests_waiting_deferred": sum( - item["waiting_deferred"] for item in gauges - ), - "kv_cache_usage_perc": max( - (item["kv_cache_usage"] for item in gauges), default=0.0 - ), - "max_num_seqs": sum( - item.get("max_num_seqs", 0.0) for item in engine_configs - ), - "max_num_batched_tokens": sum( - item.get("max_num_batched_tokens", 0.0) - for item in engine_configs - ), - "max_num_scheduled_tokens": sum( - item.get("max_num_scheduled_tokens", 0.0) - for item in engine_configs - ), - "max_model_len": max(max_model_lens, default=0.0), - "world_size": max( - (item.get("world_size", 0.0) for item in engine_configs), - default=0.0, - ), - } - ) return { "schema_version": 1, "source": "art_vllm_runtime", "last_update_unix_s": self._last_update_unix_s, "record_count": self._record_count, "engine_count": len(self._engine_gauges), - "metrics": metrics, + "metrics": self._metrics_locked(), } def record_policy_cache_salt_audit( @@ -194,6 +212,7 @@ def record_policy_cache_salt_audit( ) with self._lock: self._counters[key] += 1.0 + self._publish_locked() def record_policy_cache_waiting_update( self, *, updated: int, skipped_started: int @@ -205,6 +224,7 @@ def record_policy_cache_waiting_update( self._counters["policy_cache_started_waiting_requests_skipped_total"] += ( float(skipped_started) ) + self._publish_locked() _STATE = _ArtRuntimeMetricsState() @@ -237,6 +257,10 @@ def get_art_metrics_snapshot() -> dict[str, Any]: return _STATE.snapshot() +def set_fast_metrics_writer(writer: FastMetricsSharedWriter | None) -> None: + _STATE.set_writer(writer) + + def record_policy_cache_salt_audit(*, lora_request: bool, salted: bool) -> None: _STATE.record_policy_cache_salt_audit(lora_request=lora_request, salted=salted) diff --git a/vllm_runtime/src/art_vllm_runtime/moe_lora_patches.py b/vllm_runtime/src/art_vllm_runtime/moe_lora_patches.py new file mode 100644 index 000000000..5ec8fbc30 --- /dev/null +++ b/vllm_runtime/src/art_vllm_runtime/moe_lora_patches.py @@ -0,0 +1,17 @@ +"""Correctness patches for vLLM's fused MoE LoRA kernels.""" + + +def patch_small_batch_moe_lora_intermediate_dtype() -> None: + from vllm.lora.ops.triton_ops import fused_moe_lora_op + + kernel = fused_moe_lora_op._fused_moe_lora_small_batch_kernel.fn + source = kernel.src + cast = " rank_vec = rank_vec.to(out_ptr.dtype.element_ty)\n" + if cast in source: + return + anchor = ( + " # EXPAND: walk n_tiles_per_program consecutive output-N tiles\n" + ) + if source.count(anchor) != 1: + raise RuntimeError("Unsupported vLLM small-batch MoE LoRA kernel source") + kernel._unsafe_update_src(source.replace(anchor, f"{cast}\n{anchor}")) diff --git a/vllm_runtime/src/art_vllm_runtime/patches.py b/vllm_runtime/src/art_vllm_runtime/patches.py index cef798784..4717409af 100644 --- a/vllm_runtime/src/art_vllm_runtime/patches.py +++ b/vllm_runtime/src/art_vllm_runtime/patches.py @@ -1,108 +1,42 @@ """Monkey patches and bootstrap contract for the ART-owned vLLM runtime.""" -import ctypes -from functools import wraps -import importlib -import inspect -import logging +from types import SimpleNamespace from typing import Any -import numpy as np - -logger = logging.getLogger(__name__) - def apply_vllm_runtime_patches() -> None: from art_vllm_runtime.dsv4_patches import apply_dsv4_vllm_runtime_patches from art_vllm_runtime.gemma4_moe_lora_patch import ( patch_gemma4_moe_lora_support, ) + from art_vllm_runtime.glm52_patches import apply_glm52_vllm_runtime_patches + from art_vllm_runtime.moe_lora_patches import ( + patch_small_batch_moe_lora_intermediate_dtype, + ) from art_vllm_runtime.policy_spans import patch_policy_token_spans + from art_vllm_runtime.qwen35_patches import apply_qwen35_vllm_runtime_patches - patch_transformers_v5_compat() - patch_flashinfer_oneshot_pdl_completion() patch_policy_token_spans() patch_gemma4_moe_lora_support() subclass_chat_completion_request() - patch_listen_for_disconnect() - patch_tool_parser_manager() - patch_nccl_unique_id_bootstrap() + patch_nonstreaming_chat_response_offload() + patch_small_batch_moe_lora_intermediate_dtype() + apply_glm52_vllm_runtime_patches() apply_dsv4_vllm_runtime_patches() + apply_qwen35_vllm_runtime_patches() + patch_weight_update_lifecycle() patch_art_lora_delta_weight_update() - patch_gemma4_checkpoint_weight_update_reload() - patch_routed_experts_prefix_cache_sidecar() - from art_vllm_runtime.binary_routes import patch_binary_routed_experts_response + from art_vllm_runtime.binary_routes import ( + patch_binary_routed_experts_response, + patch_pipeline_routed_experts, + patch_pipeline_routed_experts_validation, + ) + patch_pipeline_routed_experts_validation() + patch_pipeline_routed_experts() patch_binary_routed_experts_response() -def patch_flashinfer_oneshot_pdl_completion() -> None: - """Prevent one-shot fused all-reduce consumers from racing its output. - - FlashInfer's one-shot algorithm has no internal synchronization after an - early PDL completion trigger, so completion must be signaled at kernel end. - This backports vLLM PR #45448 without changing the synchronized two-shot path. - """ - import flashinfer.comm as flashinfer_comm - - original = flashinfer_comm.allreduce_fusion - if getattr(original, "__art_oneshot_pdl_patched__", False): - return - - @wraps(original) - def allreduce_fusion(*args: Any, **kwargs: Any) -> Any: - if kwargs.get("use_oneshot"): - kwargs["trigger_completion_at_end"] = True - return original(*args, **kwargs) - - allreduce_fusion.__art_oneshot_pdl_patched__ = True # type: ignore[attr-defined] - flashinfer_comm.allreduce_fusion = allreduce_fusion - - -def patch_transformers_v5_compat() -> None: - _patch_rope_validation_ignore_keys() - _patch_qwen3_vl_moe_tie_word_embeddings() - _patch_gemma4_moe_experts_per_tok_alias() - - -def _patch_rope_validation_ignore_keys() -> None: - from transformers.configuration_utils import PretrainedConfig - - original = PretrainedConfig.convert_rope_params_to_dict - if getattr(original, "__art_patched__", False): - return - - def patched(self: Any, ignore_keys_at_rope_validation: Any = None, **kwargs: Any): - if ignore_keys_at_rope_validation is not None: - ignore_keys_at_rope_validation = set(ignore_keys_at_rope_validation) - return original( - self, - ignore_keys_at_rope_validation=ignore_keys_at_rope_validation, - **kwargs, - ) - - patched.__art_patched__ = True # type: ignore[attr-defined] - PretrainedConfig.convert_rope_params_to_dict = patched # type: ignore[method-assign] - - -def _patch_qwen3_vl_moe_tie_word_embeddings() -> None: - from transformers import Qwen3VLMoeTextConfig - - setattr(Qwen3VLMoeTextConfig, "tie_word_embeddings", False) - - -def _patch_gemma4_moe_experts_per_tok_alias() -> None: - from transformers import Gemma4TextConfig - - if hasattr(Gemma4TextConfig, "num_experts_per_tok"): - return - - def num_experts_per_tok(self: Any) -> Any: - return self.top_k_experts - - Gemma4TextConfig.num_experts_per_tok = property(num_experts_per_tok) # type: ignore[attr-defined] - - def subclass_chat_completion_request() -> None: from vllm.entrypoints.openai.chat_completion import protocol @@ -118,113 +52,105 @@ class ChatCompletionRequest(protocol.ChatCompletionRequest): setattr(protocol, "_art_chat_completion_request_patched", True) -def patch_listen_for_disconnect() -> None: - try: - api_utils = importlib.import_module("vllm.entrypoints.serve.utils.api_utils") - except ModuleNotFoundError: - api_utils = importlib.import_module("vllm.entrypoints.utils") +def patch_nonstreaming_chat_response_offload() -> None: + import asyncio - if getattr(api_utils, "_art_listen_for_disconnect_patched", False): - return + from starlette.responses import JSONResponse as StarletteJSONResponse + from starlette.responses import Response + from vllm.entrypoints.openai.chat_completion import api_router + from vllm.entrypoints.openai.chat_completion.protocol import ChatCompletionResponse + from vllm.entrypoints.openai.chat_completion.serving import OpenAIServingChat - async def patched_listen_for_disconnect(request: Any) -> None: - try: - while True: - message = await request.receive() - if message["type"] == "http.disconnect": - if getattr( - request.app.state, "enable_server_load_tracking", False - ) and hasattr(request.app.state, "server_load_metrics"): - request.app.state.server_load_metrics -= 1 - break - except UnboundLocalError: - pass - - api_utils.listen_for_disconnect = patched_listen_for_disconnect # ty:ignore[invalid-assignment] - setattr(api_utils, "_art_listen_for_disconnect_patched", True) - - -def patch_tool_parser_manager() -> None: - from vllm.entrypoints.openai.engine.protocol import DeltaMessage - from vllm.tool_parsers.abstract_tool_parser import ToolParserManager - - original = ToolParserManager.get_tool_parser - if getattr(original, "__art_patched__", False): + marker = "_art_nonstreaming_response_offload_patched" + if getattr(OpenAIServingChat, marker, False): return + original = OpenAIServingChat.chat_completion_full_generator - def patched_get_tool_parser(name: str) -> type: - tool_parser_class = original(name) - current = tool_parser_class.extract_tool_calls_streaming - if getattr(current, "__art_patched__", False): - return tool_parser_class - - def patch( - *args: Any, - **kwargs: Any, - ) -> Any: - return current(*args, **kwargs) or DeltaMessage() - - patch.__art_patched__ = True # type: ignore[attr-defined] - tool_parser_class.extract_tool_calls_streaming = patch # ty:ignore[invalid-assignment] - return tool_parser_class - - patched_get_tool_parser.__art_patched__ = True # type: ignore[attr-defined] - ToolParserManager.get_tool_parser = patched_get_tool_parser # ty:ignore[invalid-assignment] - - -def _restore_nccl_unique_id_payload( - payload: object, - template: object | None, -) -> object: - from vllm.distributed.device_communicators.pynccl_wrapper import ncclUniqueId - - if not isinstance(payload, (bytes, bytearray)) or not isinstance( - template, ncclUniqueId - ): - return payload - raw = bytes(payload) - assert len(raw) == ctypes.sizeof(ncclUniqueId) - unique_id = ncclUniqueId() - ctypes.memmove(ctypes.byref(unique_id), raw, len(raw)) - return unique_id + class PreencodedContent: + def __init__(self, body: bytes) -> None: + self.body = body + original_model_dump = ChatCompletionResponse.model_dump -def _normalize_nccl_comm_init_rank_unique_id(library: Any, unique_id: object) -> object: - if isinstance(unique_id, (bytes, bytearray)): - return library.unique_id_from_bytes(bytes(unique_id)) - return unique_id + def model_dump(self: Any, *args: Any, **kwargs: Any) -> Any: + cached = getattr(self, "_art_preencoded_content", None) + if cached is not None and not args and not kwargs: + return cached + return original_model_dump(self, *args, **kwargs) - -def patch_nccl_unique_id_bootstrap() -> None: - from vllm.distributed.device_communicators.pynccl_wrapper import NCCLLibrary - from vllm.distributed.utils import StatelessProcessGroup - - original_broadcast = StatelessProcessGroup.broadcast_obj - if not getattr(original_broadcast, "__art_patched__", False): - - def patched_broadcast(self: Any, obj: Any | None, src: int) -> Any: - return _restore_nccl_unique_id_payload( - original_broadcast(self, obj, src), obj + async def build_response( + self: Any, request: Any, result_generator: Any, *args: Any, **kwargs: Any + ) -> Any: + final_result = None + try: + async for result in result_generator: + final_result = result + except asyncio.CancelledError: + return self.create_error_response("Client disconnected") + + async def materialize() -> Any: + async def replay_final_result(): + if final_result is not None: + yield final_result + + result = await original( + self, request, replay_final_result(), *args, **kwargs ) + if not isinstance(result, ChatCompletionResponse): + return result + content = original_model_dump(result) + object.__setattr__( + result, + "_art_preencoded_content", + PreencodedContent(StarletteJSONResponse(content).body), + ) + return result - patched_broadcast.__art_patched__ = True # type: ignore[attr-defined] - StatelessProcessGroup.broadcast_obj = patched_broadcast # type: ignore[method-assign] - - original_comm_init_rank = NCCLLibrary.ncclCommInitRank - if getattr(original_comm_init_rank, "__art_patched__", False): - return + return await asyncio.to_thread( + asyncio.run, + materialize(), + ) - def patched_comm_init_rank( - self: Any, - world_size: int, - unique_id: object, - rank: int, - ) -> Any: - unique_id = _normalize_nccl_comm_init_rank_unique_id(self, unique_id) - return original_comm_init_rank(self, world_size, unique_id, rank) + class PreencodedJSONResponse(StarletteJSONResponse): + media_type = "application/json" + + def render(self, content: Any) -> bytes: + if isinstance(content, bytes): + return content + return super().render(content) + + def __init__( + self: Any, + content: Any, + status_code: int = 200, + headers: Any = None, + media_type: str | None = None, + background: Any = None, + ) -> None: + if isinstance(content, PreencodedContent): + Response.__init__( + self, + content.body, + status_code=status_code, + headers=headers, + media_type=media_type or self.media_type, + background=background, + ) + else: + super().__init__( + content, + status_code=status_code, + headers=headers, + media_type=media_type, + background=background, + ) - patched_comm_init_rank.__art_patched__ = True # type: ignore[attr-defined] - NCCLLibrary.ncclCommInitRank = patched_comm_init_rank # type: ignore[method-assign] + setattr(build_response, "__art_offloaded__", True) + setattr(build_response, "__art_original__", original) + ChatCompletionResponse.model_dump = model_dump # ty:ignore[invalid-assignment] + OpenAIServingChat.chat_completion_full_generator = build_response + api_router.JSONResponse = PreencodedJSONResponse # ty:ignore[invalid-assignment] + setattr(OpenAIServingChat, marker, True) def _is_gemma4_conditional_worker(worker: Any) -> bool: @@ -232,7 +158,7 @@ def _is_gemma4_conditional_worker(worker: Any) -> bool: return hf_config.architectures == ["Gemma4ForConditionalGeneration"] -def patch_gemma4_checkpoint_weight_update_reload() -> None: +def patch_weight_update_lifecycle() -> None: from vllm.v1.worker.gpu_worker import Worker original_start_weight_update = Worker.start_weight_update @@ -240,36 +166,32 @@ def patch_gemma4_checkpoint_weight_update_reload() -> None: return original_finish_weight_update = Worker.finish_weight_update - def start_weight_update( - self: Any, - is_checkpoint_format: bool = True, - ) -> None: - if not is_checkpoint_format or not _is_gemma4_conditional_worker(self): - return original_start_weight_update( - self, - is_checkpoint_format=is_checkpoint_format, - ) + def start_weight_update(self: Any) -> None: self._check_weight_transfer_engine() + assert self.weight_transfer_engine is not None if self._weight_update_active: raise RuntimeError( "start_weight_update called while a weight update is " "already active. Call finish_weight_update first." ) - self._is_checkpoint_format = True + # vLLM 0.25 removed format selection from this endpoint. Defer the + # checkpoint reload lifecycle until update_weights reveals the payload. + self._art_weight_update_mode = None + self._art_weight_transfer_started = False self._weight_update_active = True def finish_weight_update(self: Any) -> None: - if not _is_gemma4_conditional_worker(self): - return original_finish_weight_update(self) self._check_weight_transfer_engine() + assert self.weight_transfer_engine is not None if not self._weight_update_active: raise RuntimeError( - "start_weight_update must be called before finish_weight_update." + "finish_weight_update called without a matching start_weight_update." ) - if not self._is_checkpoint_format: - return original_finish_weight_update(self) + if self._art_weight_transfer_started: + self.weight_transfer_engine.finish_weight_update() self._weight_update_active = False - self._is_checkpoint_format = True + self._art_weight_update_mode = None + self._art_weight_transfer_started = False start_weight_update.__art_patched__ = True # type: ignore[attr-defined] start_weight_update.__art_original__ = original_start_weight_update # type: ignore[attr-defined] @@ -293,9 +215,6 @@ def patch_art_lora_delta_weight_update() -> None: return def update_weights(self: Any, update_info: dict) -> None: - if update_info.get("art_weight_update_kind") != ART_LORA_DELTA_UPDATE_KIND: - return original_update_weights(self, update_info) - self._check_weight_transfer_engine() assert self.weight_transfer_engine is not None if not self._weight_update_active: @@ -303,6 +222,23 @@ def update_weights(self: Any, update_info: dict) -> None: "start_weight_update must be called before update_weights." ) + is_lora_delta = ( + update_info.get("art_weight_update_kind") == ART_LORA_DELTA_UPDATE_KIND + ) + mode = ART_LORA_DELTA_UPDATE_KIND if is_lora_delta else "checkpoint" + active_mode = getattr(self, "_art_weight_update_mode", None) + if active_mode not in (None, mode): + raise RuntimeError( + f"Cannot mix {active_mode!r} and {mode!r} in one weight update" + ) + self._art_weight_update_mode = mode + + if not is_lora_delta: + if active_mode is None and not _is_gemma4_conditional_worker(self): + self.weight_transfer_engine.start_weight_update() + self._art_weight_transfer_started = True + return original_update_weights(self, update_info) + adapter_config = update_info["art_lora_config"] transfer_update_info = dict(update_info) del transfer_update_info["art_weight_update_kind"] @@ -318,388 +254,35 @@ def collect_lora_tensors(weights: list[tuple[str, torch.Tensor]]) -> None: raise RuntimeError(f"Duplicate LoRA tensor in update: {name}") lora_tensors[name] = tensor.detach().contiguous().clone() - with torch.device(self.device): - self.weight_transfer_engine.receive_weights( - typed_update_info, - load_weights=collect_lora_tensors, - ) - self._art_previous_lora_tensors = apply_lora_delta_update( - model=self.model_runner.model, - lora_tensors=lora_tensors, - adapter_config=adapter_config, - previous_lora_tensors=getattr( - self, - "_art_previous_lora_tensors", - None, - ), - ) + engine = self.weight_transfer_engine + from vllm.distributed.weight_transfer.nccl_engine import ( + NCCLWeightTransferEngine, + ) - torch.accelerator.synchronize() + if not isinstance(engine, NCCLWeightTransferEngine): + raise RuntimeError("ART LoRA delta updates require vLLM's NCCL transport") + model = engine.model + engine.model = SimpleNamespace(load_weights=collect_lora_tensors) + try: + with torch.device(self.device): + engine.receive_weights(typed_update_info) + self._art_previous_lora_tensors = apply_lora_delta_update( + model=self.model_runner.model, + lora_tensors=lora_tensors, + adapter_config=adapter_config, + previous_lora_tensors=getattr( + self, + "_art_previous_lora_tensors", + None, + ), + ) + torch.accelerator.synchronize() + except BaseException: + self._weight_update_active = False + raise + finally: + engine.model = model update_weights.__art_lora_delta_patched__ = True # type: ignore[attr-defined] update_weights.__art_original__ = original_update_weights # type: ignore[attr-defined] Worker.update_weights = update_weights # type: ignore[method-assign] - - -def _lora_cache_key(lora_request: Any) -> tuple[Any, ...]: - if lora_request is None: - return () - return ( - getattr(lora_request, "adapter_id", None), - getattr(lora_request, "name", None), - getattr(lora_request, "path", None), - ) - - -def _request_token_ids(req_state: Any) -> list[int] | None: - prompt_token_ids = getattr(req_state, "prompt_token_ids", None) - if prompt_token_ids is None: - return None - return list(prompt_token_ids) + list(getattr(req_state, "output_token_ids", ())) - - -def _route_block_key( - token_ids: list[int], - end: int, - lora_key: tuple[Any, ...], -) -> tuple[Any, ...]: - return (lora_key, tuple(token_ids[:end])) - - -def _runner_block_size(runner: Any) -> int: - kv_cache_config = getattr(runner, "kv_cache_config", None) - groups = getattr(kv_cache_config, "kv_cache_groups", None) - if groups and len(groups) == 1: - return int(groups[0].kv_cache_spec.block_size) - return int(getattr(runner.cache_config, "block_size", 16)) - - -def _request_snapshots( - runner: Any, ordered: dict[str, int] -) -> dict[str, dict[str, Any]]: - snapshots: dict[str, dict[str, Any]] = {} - for req_id in ordered: - req_state = runner.requests.get(req_id) - if req_state is None: - continue - token_ids = _request_token_ids(req_state) - if token_ids is None: - continue - snapshots[req_id] = { - "token_ids": token_ids, - "lora_key": _lora_cache_key(getattr(req_state, "lora_request", None)), - "num_computed_tokens": int(getattr(req_state, "num_computed_tokens", 0)), - } - return snapshots - - -def patch_routed_experts_prefix_cache_sidecar() -> None: - from vllm.model_executor.layers.fused_moe import routed_experts_capturer - - if getattr(routed_experts_capturer, "_art_prefix_route_sidecar_patched", False): - return - - host_cls = getattr(routed_experts_capturer, "_RoutedExpertsHostCache", None) - capturer_cls = getattr(routed_experts_capturer, "_RoutedExpertsCapturerReal", None) - if host_cls is None or capturer_cls is None: - return - - original_host_init = host_cls.__init__ - original_get_or_grow_buffer = host_cls.get_or_grow_buffer - original_free_request = host_cls.free_request - original_scatter_to_host = capturer_cls._scatter_to_host - original_get_routed_experts = capturer_cls.get_routed_experts - original_issue_routing_d2h_copy = routed_experts_capturer.issue_routing_d2h_copy - - def host_init(self: Any, *args: Any, **kwargs: Any) -> None: - original_host_init(self, *args, **kwargs) - self._art_req_filled_masks: dict[str, np.ndarray] = {} - self._art_prefix_route_blocks: dict[tuple[Any, ...], np.ndarray] = {} - self._art_prefix_route_waiters: dict[ - tuple[Any, ...], list[tuple[str, int, int]] - ] = {} - self._art_prefix_route_needs_by_req: dict[str, set[tuple[Any, ...]]] = {} - self._art_prefix_route_hydrated_tokens = 0 - self._art_prefix_route_cache_misses = 0 - self._art_prefix_route_cache_conflicts = 0 - - def get_or_grow_buffer(self: Any, req_id: str, max_pos: int) -> np.ndarray: - buf = original_get_or_grow_buffer(self, req_id, max_pos) - mask = self._art_req_filled_masks.get(req_id) - if mask is None: - self._art_req_filled_masks[req_id] = np.zeros(buf.shape[0], dtype=np.bool_) - elif mask.shape[0] < buf.shape[0]: - new_mask = np.zeros(buf.shape[0], dtype=np.bool_) - new_mask[: mask.shape[0]] = mask - self._art_req_filled_masks[req_id] = new_mask - return buf - - def free_request(self: Any, req_id: str) -> None: - original_free_request(self, req_id) - self._art_req_filled_masks.pop(req_id, None) - for key in self._art_prefix_route_needs_by_req.pop(req_id, set()): - waiters = self._art_prefix_route_waiters.get(key) - if waiters is None: - continue - waiters = [waiter for waiter in waiters if waiter[0] != req_id] - if waiters: - self._art_prefix_route_waiters[key] = waiters - else: - self._art_prefix_route_waiters.pop(key, None) - - def mark_filled(self: Any, req_id: str, positions: np.ndarray) -> None: - if positions.size == 0: - return - self.get_or_grow_buffer(req_id, int(positions.max())) - self._art_req_filled_masks[req_id][positions] = True - - def require_filled(self: Any, req_id: str, seqlen: int) -> None: - mask = self._art_req_filled_masks.get(req_id) - if mask is None or mask.shape[0] < seqlen or not bool(mask[:seqlen].all()): - available = ( - mask[:seqlen] if mask is not None else np.zeros(0, dtype=np.bool_) - ) - missing = np.flatnonzero(~available)[:16].tolist() - raise RuntimeError( - "Routed expert capture is incomplete for request " - f"{req_id}: seqlen={seqlen}, first_missing_positions={missing}" - ) - - def fill_prefix_block( - self: Any, - req_id: str, - start: int, - end: int, - value: np.ndarray, - key: tuple[Any, ...] | None = None, - ) -> bool: - buf = self.get_or_grow_buffer(req_id, end - 1) - mask = self._art_req_filled_masks[req_id] - if bool(mask[start:end].all()): - if key is not None: - needs = self._art_prefix_route_needs_by_req.get(req_id) - if needs is not None: - needs.discard(key) - if not needs: - self._art_prefix_route_needs_by_req.pop(req_id, None) - return False - buf[start:end] = value - mask[start:end] = True - self.update_filled_len(req_id, end - 1) - if key is not None: - needs = self._art_prefix_route_needs_by_req.get(req_id) - if needs is not None: - needs.discard(key) - if not needs: - self._art_prefix_route_needs_by_req.pop(req_id, None) - return True - - def store_prefix_block( - self: Any, - key: tuple[Any, ...], - value: np.ndarray, - ) -> None: - existing = self._art_prefix_route_blocks.get(key) - if existing is None: - existing = value.copy() - self._art_prefix_route_blocks[key] = existing - elif not np.array_equal(existing, value): - self._art_prefix_route_cache_conflicts += 1 - hydrated = 0 - for req_id, start, end in self._art_prefix_route_waiters.pop(key, []): - if self._art_fill_prefix_block(req_id, start, end, existing, key): - hydrated += end - start - if hydrated: - self._art_prefix_route_hydrated_tokens += hydrated - logger.info( - "Hydrated %s routed-expert prefix-cache tokens from materialized " - "route block", - hydrated, - ) - - def store_prefix_blocks( - self: Any, - req_id: str, - token_ids: list[int], - lora_key: tuple[Any, ...], - block_size: int, - max_pos_exclusive: int, - ) -> None: - if block_size <= 0: - return - upper = min(max_pos_exclusive, len(token_ids)) - upper -= upper % block_size - if upper <= 0: - return - buf = self.get_buffer(req_id) - mask = self._art_req_filled_masks.get(req_id) - if buf is None or mask is None: - return - for end in range(block_size, upper + 1, block_size): - start = end - block_size - if end > mask.shape[0] or not bool(mask[start:end].all()): - continue - key = _route_block_key(token_ids, end, lora_key) - value = buf[start:end].copy() - self._art_store_prefix_block(key, value) - - def need_cached_prefix( - self: Any, - req_id: str, - token_ids: list[int], - lora_key: tuple[Any, ...], - cached_len: int, - block_size: int, - ) -> None: - if block_size <= 0 or cached_len <= 0: - return - upper = min(cached_len, len(token_ids)) - upper -= upper % block_size - if upper <= 0: - return - hydrated = 0 - for end in range(block_size, upper + 1, block_size): - start = end - block_size - mask = self._art_req_filled_masks.get(req_id) - if ( - mask is not None - and end <= mask.shape[0] - and bool(mask[start:end].all()) - ): - continue - key = _route_block_key(token_ids, end, lora_key) - value = self._art_prefix_route_blocks.get(key) - if value is None: - needs = self._art_prefix_route_needs_by_req.setdefault(req_id, set()) - if key not in needs: - self._art_prefix_route_waiters.setdefault(key, []).append( - (req_id, start, end) - ) - needs.add(key) - self._art_prefix_route_cache_misses += block_size - continue - if self._art_fill_prefix_block(req_id, start, end, value, key): - hydrated += block_size - if hydrated: - self._art_prefix_route_hydrated_tokens += hydrated - logger.info( - "Hydrated %s routed-expert prefix-cache tokens for request %s", - hydrated, - req_id, - ) - - def require_no_unmet_prefix_route_needs(self: Any, req_id: str) -> None: - needs = self._art_prefix_route_needs_by_req.get(req_id) - if needs: - raise RuntimeError( - "Routed expert capture is missing materialized prefix-cache " - f"route blocks for request {req_id}: unmet_blocks={len(needs)}" - ) - - def scatter_to_host(self: Any) -> None: - positions = self._pending_positions.copy() - scheduled = dict(self._pending_num_scheduled or {}) - metadata = getattr(self, "_art_pending_route_metadata", None) - original_scatter_to_host(self) - host_cache = self.host_cache - if host_cache is None: - return - block_size = int((metadata or {}).get("block_size", 0)) - snapshots = (metadata or {}).get("snapshots", {}) - offset = 0 - for req_id, n_tokens in scheduled.items(): - pos = positions[offset : offset + n_tokens] - host_cache._art_mark_filled(req_id, pos) - snapshot = snapshots.get(req_id) - if snapshot is not None and pos.size: - host_cache._art_store_prefix_blocks( - req_id, - snapshot["token_ids"], - snapshot["lora_key"], - block_size, - int(pos.max()) + 1, - ) - offset += n_tokens - self._art_pending_route_metadata = None - - def get_routed_experts( - self: Any, - req_id: str, - seqlen: int | None = None, - free_slot: bool = True, - ) -> np.ndarray | None: - if self.host_cache is not None: - filled = self.host_cache.get_filled_len(req_id) - effective_len = min(filled, seqlen) if seqlen is not None else filled - if effective_len > 0: - self.host_cache._art_require_no_unmet_prefix_route_needs(req_id) - self.host_cache._art_require_filled(req_id, effective_len) - return original_get_routed_experts(self, req_id, seqlen, free_slot) - - def issue_routing_d2h_copy( - input_batch_req_ids: list[str], - num_scheduled_tokens: dict[str, int], - positions: Any, - positions_cpu: Any, - ) -> None: - capturer = routed_experts_capturer.get_global_experts_capturer() - host_cache = capturer.get_host_cache() if capturer is not None else None - frame = inspect.currentframe() - runner = frame.f_back.f_locals.get("self") if frame and frame.f_back else None - ordered = { - req_id: num_scheduled_tokens[req_id] - for req_id in input_batch_req_ids - if req_id in num_scheduled_tokens - } - metadata: dict[str, Any] | None = None - if host_cache is not None and runner is not None: - block_size = _runner_block_size(runner) - snapshots = _request_snapshots(runner, ordered) - for req_id, snapshot in snapshots.items(): - host_cache._art_need_cached_prefix( - req_id, - snapshot["token_ids"], - snapshot["lora_key"], - snapshot["num_computed_tokens"], - block_size, - ) - metadata = {"block_size": block_size, "snapshots": snapshots} - original_issue_routing_d2h_copy( - input_batch_req_ids, - num_scheduled_tokens, - positions, - positions_cpu, - ) - if capturer is not None and metadata is not None and sum(ordered.values()) > 0: - capturer._art_pending_route_metadata = metadata - - host_cls.__init__ = host_init # type: ignore[method-assign] - host_cls.get_or_grow_buffer = get_or_grow_buffer # type: ignore[method-assign] - host_cls.free_request = free_request # type: ignore[method-assign] - host_cls._art_mark_filled = mark_filled # type: ignore[attr-defined] - host_cls._art_require_filled = require_filled # type: ignore[attr-defined] - host_cls._art_fill_prefix_block = fill_prefix_block # type: ignore[attr-defined] - host_cls._art_store_prefix_block = store_prefix_block # type: ignore[attr-defined] - host_cls._art_store_prefix_blocks = store_prefix_blocks # type: ignore[attr-defined] - host_cls._art_need_cached_prefix = need_cached_prefix # type: ignore[attr-defined] - host_cls._art_require_no_unmet_prefix_route_needs = ( # type: ignore[attr-defined] - require_no_unmet_prefix_route_needs - ) - capturer_cls._scatter_to_host = scatter_to_host # type: ignore[method-assign] - capturer_cls.get_routed_experts = get_routed_experts # type: ignore[method-assign] - from vllm.v1.worker import gpu_model_runner - - gpu_model_runner_issue_routing_d2h_copy = getattr( - gpu_model_runner, "issue_routing_d2h_copy", None - ) - if gpu_model_runner_issue_routing_d2h_copy is not original_issue_routing_d2h_copy: - raise RuntimeError( - "ART routed-expert prefix-cache patch expected " - "vllm.v1.worker.gpu_model_runner.issue_routing_d2h_copy to reference " - "vllm.model_executor.layers.fused_moe.routed_experts_capturer." - "issue_routing_d2h_copy. vLLM internals changed; update the patch." - ) - - routed_experts_capturer.issue_routing_d2h_copy = issue_routing_d2h_copy - gpu_model_runner.issue_routing_d2h_copy = issue_routing_d2h_copy - setattr(routed_experts_capturer, "_art_prefix_route_sidecar_patched", True) diff --git a/vllm_runtime/src/art_vllm_runtime/policy_spans.py b/vllm_runtime/src/art_vllm_runtime/policy_spans.py index 5e186d721..656094a50 100644 --- a/vllm_runtime/src/art_vllm_runtime/policy_spans.py +++ b/vllm_runtime/src/art_vllm_runtime/policy_spans.py @@ -9,7 +9,10 @@ import asyncio from collections.abc import Mapping from contextlib import asynccontextmanager +from contextvars import ContextVar from dataclasses import dataclass +from functools import wraps +import hashlib import importlib import re import sys @@ -18,6 +21,7 @@ import msgspec import numpy as np import torch +from vllm.lora.request import LoRARequest POLICY_TOKEN_SPANS_FIELD = "policy_token_spans" ART_POLICY_TOKEN_SPANS_FIELD = "art_policy_token_spans" @@ -25,10 +29,39 @@ _CURRENT_ENGINE_POLICY_SPANS: dict[str, list[dict[str, Any]]] = {} _WORKER_LORA_POLICY_BY_ID: dict[int, dict[str, Any]] = {} -_WORKER_LORA_UPDATE_SEQ = 0 _POLICY_CACHE_SALT_PREFIX = "art_policy_cache_salt=" _POLICY_CACHE_SALT_MARKER = f"|{_POLICY_CACHE_SALT_PREFIX}" +_POLICY_CACHE_SALT_VERSION = "v1:" _LORA_UPDATE_COORDINATOR_FIELD = "_art_lora_update_coordinator" +_EXECUTING_POLICY_CONTEXT_FIELD = "_art_executing_policy_context" +_POLICY_EXECUTION_MARKER_FIELD = "_art_policy_execution_marker" +_POLICY_HISTORY_BASE_FIELD = "_art_policy_history_before_current" +_POLICY_CACHE_TRANSITIONS_FIELD = "_art_policy_cache_transitions" +_POLICY_CACHE_TRANSITION_KEY = "art_policy_transition_v1" + + +class _RequestAdmissionLease: + __slots__ = ( + "closed", + "lora_request", + "lora_slot", + "owner", + "request_id", + "ticket", + ) + + def __init__(self) -> None: + self.closed = False + self.lora_request: Any | None = None + self.lora_slot: str | None = None + self.owner = asyncio.current_task() + self.request_id: str | None = None + self.ticket: _SlotAdmissionTicket | None = None + + +_REQUEST_ADMISSION_LEASE: ContextVar[_RequestAdmissionLease | None] = ContextVar( + "art_request_admission_lease", default=None +) _MODEL_RUNNER_OUTPUT_MODULES = ( "vllm.v1.outputs", @@ -45,17 +78,68 @@ ) +class PolicyLoRARequest(LoRARequest, omit_defaults=True, array_like=True): # type: ignore[call-arg] + """LoRA request carrying ART's exact executing-policy identity.""" + + policy_version: int = 0 + update_seq: int = 0 + + def __post_init__(self) -> None: + super().__post_init__() + if self.policy_version < 0 or self.update_seq < 0: + raise ValueError("policy_version and update_seq must be non-negative") + + def patch_policy_token_spans() -> None: + _patch_policy_cache_hashing() _patch_model_runner_output_type() _patch_engine_core_output_type() _patch_worker_policy_span_capture() _patch_scheduler_policy_span_transport() _patch_output_processor_policy_span_accumulation() _patch_openai_response_policy_spans() - _patch_lora_update_coordinator() + _patch_lora_alias_resolution() _patch_engine_request_admission() _patch_load_inplace_storage() - _patch_engine_waiting_cache_salt_utility() + _patch_policy_lora_update_rpc() + + +def _patch_policy_cache_hashing() -> None: + from vllm.v1.core import block_pool, kv_cache_utils + + original = kv_cache_utils.generate_block_hash_extra_keys + if getattr(original, "__art_policy_spans_patched__", False): + return + + def generate_block_hash_extra_keys( + request: Any, + start_token_idx: int, + end_token_idx: int, + start_mm_idx: int, + ) -> tuple[tuple[Any, ...] | None, int]: + extra_keys, next_mm_idx = original( + request, start_token_idx, end_token_idx, start_mm_idx + ) + transitions = tuple( + transition + for transition in getattr(request, _POLICY_CACHE_TRANSITIONS_FIELD, ()) + if start_token_idx <= transition[0] < end_token_idx + ) + if transitions: + extra_keys = ( + (*extra_keys, (_POLICY_CACHE_TRANSITION_KEY, transitions)) + if extra_keys + else ((_POLICY_CACHE_TRANSITION_KEY, transitions),) + ) + return extra_keys, next_mm_idx + + setattr(generate_block_hash_extra_keys, "__art_policy_spans_patched__", True) + setattr( + kv_cache_utils, "generate_block_hash_extra_keys", generate_block_hash_extra_keys + ) + setattr( + block_pool, "generate_block_hash_extra_keys", generate_block_hash_extra_keys + ) class _SlotAdmissionState: @@ -64,8 +148,10 @@ class _SlotAdmissionState: "active_admissions", "blocked", "lora_request", + "next_update_seq", + "pending_update_seq", + "poisoned", "update_active", - "policy_version", ) def __init__(self) -> None: @@ -73,8 +159,42 @@ def __init__(self) -> None: self.active_admissions = 0 self.blocked = False self.lora_request: Any | None = None + self.next_update_seq = 1 + self.pending_update_seq: int | None = None + self.poisoned = False self.update_active = False - self.policy_version: int | None = None + + +class _SlotAdmissionTicket: + __slots__ = ("lora_request", "released", "state") + + def __init__(self, state: _SlotAdmissionState) -> None: + self.lora_request = state.lora_request + self.released = False + self.state = state + + async def release(self) -> None: + async with self.state.condition: + if self.released: + return + self.state.active_admissions -= 1 + self.released = True + self.state.condition.notify_all() + + +async def _complete_task(task: asyncio.Task[Any]) -> asyncio.CancelledError | None: + interrupted: asyncio.CancelledError | None = None + while not task.done(): + try: + await asyncio.shield(task) + except asyncio.CancelledError as error: + if task.cancelled(): + break + interrupted = interrupted or error + except BaseException: + break + task.result() + return interrupted class LoraUpdateCoordinator: @@ -86,63 +206,106 @@ def __init__(self) -> None: def _state(self, lora_slot: str) -> _SlotAdmissionState: return self._states.setdefault(lora_slot, _SlotAdmissionState()) - @asynccontextmanager - async def admission( - self, lora_slot: str - ) -> AsyncIterator[tuple[int | None, Any | None]]: + async def acquire(self, lora_slot: str) -> _SlotAdmissionTicket: state = self._state(lora_slot) async with state.condition: await state.condition.wait_for(lambda: not state.blocked) state.active_admissions += 1 + return _SlotAdmissionTicket(state) + + @asynccontextmanager + async def admission(self, lora_slot: str) -> AsyncIterator[Any | None]: + ticket = await self.acquire(lora_slot) try: - yield state.policy_version, state.lora_request + yield ticket.lora_request finally: - async with state.condition: - state.active_admissions -= 1 - state.condition.notify_all() + interrupted = await _complete_task(asyncio.create_task(ticket.release())) + if interrupted is not None: + raise interrupted - async def begin_update(self, lora_slot: str) -> None: + async def declare_initial( + self, lora_slot: str, lora_request: PolicyLoRARequest + ) -> None: + state = self._state(lora_slot) + async with state.condition: + if ( + state.active_admissions + or state.update_active + or state.lora_request is not None + ): + raise RuntimeError(f"LoRA slot {lora_slot!r} is already active") + if lora_request.update_seq <= 0: + raise ValueError( + "initial mutable LoRA policy requires a positive sequence" + ) + if lora_request.lora_name != lora_slot: + raise ValueError("initial LoRA policy does not match its slot") + state.lora_request = lora_request + state.next_update_seq = lora_request.update_seq + 1 + + async def begin_update(self, lora_slot: str) -> int: state = self._state(lora_slot) async with state.condition: - acquired = False + await state.condition.wait_for(lambda: not state.update_active) + state.update_active = True + state.blocked = True + update_seq = state.next_update_seq + state.next_update_seq += 1 + state.pending_update_seq = update_seq try: - await state.condition.wait_for(lambda: not state.update_active) - state.update_active = True - state.blocked = True - acquired = True await state.condition.wait_for(lambda: state.active_admissions == 0) except BaseException: - if acquired: - state.update_active = False - state.blocked = False - state.condition.notify_all() + state.update_active = False + state.blocked = state.poisoned + state.pending_update_seq = None + state.condition.notify_all() raise + return update_seq async def commit_update( self, lora_slot: str, - policy_version: int, - lora_request: Any, + lora_request: PolicyLoRARequest, ) -> None: state = self._state(lora_slot) async with state.condition: - if not state.update_active: - raise RuntimeError(f"No active LoRA update for slot {lora_slot!r}") - state.policy_version = int(policy_version) + self._require_pending(state, lora_slot, lora_request.update_seq) state.lora_request = lora_request state.update_active = False state.blocked = False + state.poisoned = False + state.pending_update_seq = None state.condition.notify_all() - async def fail_update(self, lora_slot: str) -> None: + async def cancel_update(self, lora_slot: str, update_seq: int) -> None: state = self._state(lora_slot) async with state.condition: + self._require_pending(state, lora_slot, update_seq) state.update_active = False - # The workers may already hold new weights. Keep admission blocked - # until a retry completes publication and scheduler rehashing. + state.blocked = state.poisoned + state.pending_update_seq = None + state.condition.notify_all() + + async def fail_update(self, lora_slot: str, update_seq: int) -> None: + state = self._state(lora_slot) + async with state.condition: + self._require_pending(state, lora_slot, update_seq) + state.update_active = False + # A worker may already hold new weights. This slot stays poisoned. state.blocked = True + state.poisoned = True + state.pending_update_seq = None state.condition.notify_all() + @staticmethod + def _require_pending( + state: _SlotAdmissionState, lora_slot: str, update_seq: int + ) -> None: + if not state.update_active or state.pending_update_seq != update_seq: + raise RuntimeError( + f"LoRA slot {lora_slot!r} has no update {update_seq} in progress" + ) + def lora_update_coordinator(models: Any, engine_client: Any) -> LoraUpdateCoordinator: coordinator = getattr(models, _LORA_UPDATE_COORDINATOR_FIELD, None) @@ -155,6 +318,43 @@ def lora_update_coordinator(models: Any, engine_client: Any) -> LoraUpdateCoordi return coordinator +async def declare_initial_lora_policy( + models: Any, + engine_client: Any, + *, + lora_slot: str, + policy_version: int, +) -> None: + loaded = models.lora_requests.get(lora_slot) + if loaded is None: + raise RuntimeError(f"Initial LoRA slot {lora_slot!r} is not loaded") + request = PolicyLoRARequest( + lora_name=loaded.lora_name, + lora_int_id=loaded.lora_int_id, + lora_path=loaded.lora_path, + base_model_name=loaded.base_model_name, + tensorizer_config_dict=loaded.tensorizer_config_dict, + is_3d_lora_weight=loaded.is_3d_lora_weight, + policy_version=policy_version, + update_seq=1, + ) + report = await engine_client.engine_core.call_utility_async( + "art_declare_loaded_lora_policy", policy_lora_request_payload(request) + ) + if int(report.get("workers", 0)) <= 0: + raise RuntimeError("Initial LoRA policy declaration reached no workers") + models.lora_requests[lora_slot] = request + publish_lora_slot_policy( + models, + lora_slot=lora_slot, + policy_version=policy_version, + update_seq=request.update_seq, + ) + await lora_update_coordinator(models, engine_client).declare_initial( + lora_slot, request + ) + + def _patch_model_runner_output_type() -> None: import vllm.v1.outputs as outputs_mod @@ -189,6 +389,50 @@ class ModelRunnerOutput(BaseModelRunnerOutput): # type: ignore[misc, valid-type setattr(outputs_mod, "_art_policy_token_spans_model_runner_patched", True) +def register_lora_alias( + models: Any, + *, + public_model_name: str, + lora_slot: str, +) -> None: + aliases = getattr(models, "_art_lora_aliases", None) + if aliases is None: + aliases = {} + setattr(models, "_art_lora_aliases", aliases) + aliases[public_model_name] = lora_slot + + +def publish_lora_slot_policy( + models: Any, + *, + lora_slot: str, + policy_version: int, + update_seq: int, +) -> None: + identities = getattr(models, "_art_lora_slot_policy_identities", None) + if identities is None: + identities = {} + setattr(models, "_art_lora_slot_policy_identities", identities) + identities[lora_slot] = (int(policy_version), int(update_seq)) + + +def _resolve_lora_alias(models: Any, model_name: str | None) -> Any | None: + if not model_name: + return None + slot = getattr(models, "_art_lora_aliases", {}).get(model_name) + if not slot: + return None + return models.lora_requests.get(slot) + + +def _slot_policy_identity(models: Any, lora_slot: str) -> tuple[int, int] | None: + identity = getattr(models, "_art_lora_slot_policy_identities", {}).get(lora_slot) + if identity is None: + return None + policy_version, update_seq = identity + return int(policy_version), int(update_seq) + + def _strip_policy_cache_salt(cache_salt: str | None) -> str | None: if not cache_salt: return None @@ -200,13 +444,48 @@ def _strip_policy_cache_salt(cache_salt: str | None) -> str | None: return cache_salt -def _policy_cache_salt( +def _policy_history_from_cache_salt(cache_salt: str | None) -> str | None: + if not cache_salt: + return None + if cache_salt.startswith(_POLICY_CACHE_SALT_PREFIX): + value = cache_salt.removeprefix(_POLICY_CACHE_SALT_PREFIX) + else: + _base, marker, value = cache_salt.partition(_POLICY_CACHE_SALT_MARKER) + if not marker: + return None + if not value.startswith(_POLICY_CACHE_SALT_VERSION): + raise RuntimeError("Unsupported ART policy cache-salt format") + digest = value.removeprefix(_POLICY_CACHE_SALT_VERSION) + if len(digest) != 64 or any(char not in "0123456789abcdef" for char in digest): + raise RuntimeError("Malformed ART policy cache-salt digest") + return digest + + +def _extend_policy_history( + previous_digest: str | None, *, lora_slot: str, policy_version: int, + update_seq: int, +) -> str: + digest = hashlib.sha256() + digest.update(b"art-policy-history-v1\0") + if previous_digest is not None: + digest.update(bytes.fromhex(previous_digest)) + digest.update(lora_slot.encode()) + digest.update(b"\0") + digest.update(str(policy_version).encode()) + digest.update(b"\0") + digest.update(str(update_seq).encode()) + return digest.hexdigest() + + +def _policy_cache_salt( + *, + history_digest: str, user_cache_salt: str | None, ) -> str: - policy_salt = f"{lora_slot}:{policy_version}" + policy_salt = f"{_POLICY_CACHE_SALT_VERSION}{history_digest}" if user_cache_salt: return f"{user_cache_salt}{_POLICY_CACHE_SALT_MARKER}{policy_salt}" return f"{_POLICY_CACHE_SALT_PREFIX}{policy_salt}" @@ -217,12 +496,45 @@ def _set_policy_cache_salt( *, lora_slot: str, policy_version: int, + update_seq: int, + previous_digest: str | None = None, ) -> None: - user_cache_salt = _strip_policy_cache_salt(getattr(request, "cache_salt", None)) - request.cache_salt = _policy_cache_salt( + current_salt = ( + request.get("cache_salt") + if isinstance(request, dict) + else getattr(request, "cache_salt", None) + ) + user_cache_salt = _strip_policy_cache_salt(current_salt) + cache_salt = _policy_cache_salt( + history_digest=_extend_policy_history( + previous_digest, + lora_slot=lora_slot, + policy_version=policy_version, + update_seq=update_seq, + ), + user_cache_salt=user_cache_salt, + ) + if isinstance(request, dict): + request["cache_salt"] = cache_salt + else: + request.cache_salt = cache_salt + + +def _apply_lora_alias_policy_cache_salt( + models: Any, + request: Any, + lora_request: Any, +) -> None: + lora_slot = str(lora_request.lora_name) + identity = _slot_policy_identity(models, lora_slot) + if identity is None: + return + policy_version, update_seq = identity + _set_policy_cache_salt( + request, lora_slot=lora_slot, policy_version=policy_version, - user_cache_salt=user_cache_salt, + update_seq=update_seq, ) @@ -341,14 +653,40 @@ def add_adapter(self: Any, lora_request: Any) -> bool: for module_name in _GPU_MODEL_RUNNER_MODULES: module = importlib.import_module(module_name) gpu_model_runner_cls = module.GPUModelRunner + + original_execute_model = gpu_model_runner_cls.execute_model + if not getattr(original_execute_model, "__art_policy_spans_patched__", False): + + def make_execute_model(original: Any): + def execute_model(self: Any, *args: Any, **kwargs: Any) -> Any: + output = original(self, *args, **kwargs) + # The input batch is current only after execute_model, and the + # next serial worker RPC may replace this adapter before sampling. + context = _policy_context_from_runner(self) + if getattr(self, "execute_model_state", None) is not None: + setattr(self, _EXECUTING_POLICY_CONTEXT_FIELD, context) + elif context and hasattr(output, "req_ids"): + _attach_policy_spans_to_model_output(output, context) + return output + + return execute_model + + execute_model = make_execute_model(original_execute_model) + execute_model.__art_policy_spans_patched__ = True # type: ignore[attr-defined] + gpu_model_runner_cls.execute_model = execute_model # type: ignore[method-assign] + original_sample_tokens = gpu_model_runner_cls.sample_tokens if getattr(original_sample_tokens, "__art_policy_spans_patched__", False): continue def make_sample_tokens(original: Any): def sample_tokens(self: Any, *args: Any, **kwargs: Any) -> Any: - context = _policy_context_from_runner(self) - output = original(self, *args, **kwargs) + context = getattr(self, _EXECUTING_POLICY_CONTEXT_FIELD, None) + try: + output = original(self, *args, **kwargs) + finally: + if hasattr(self, _EXECUTING_POLICY_CONTEXT_FIELD): + delattr(self, _EXECUTING_POLICY_CONTEXT_FIELD) if context and output is not None: if hasattr(output, "get_output"): _attach_policy_span_context_to_sample_output(output, context) @@ -395,26 +733,42 @@ def _patch_scheduler_policy_span_transport() -> None: from vllm.v1.core.sched.scheduler import Scheduler original_update = Scheduler.update_from_output - if getattr(original_update, "__art_policy_spans_patched__", False): - return + if not getattr(original_update, "__art_policy_spans_patched__", False): - def update_from_output(self: Any, scheduler_output: Any, model_runner_output: Any): - outputs_by_client = original_update(self, scheduler_output, model_runner_output) - spans_by_req = getattr(model_runner_output, ART_POLICY_TOKEN_SPANS_FIELD, None) - if not spans_by_req: + def update_from_output( + self: Any, scheduler_output: Any, model_runner_output: Any + ): + outputs_by_client = original_update( + self, scheduler_output, model_runner_output + ) + spans_by_req = getattr( + model_runner_output, ART_POLICY_TOKEN_SPANS_FIELD, None + ) + if not spans_by_req: + return outputs_by_client + for client_outputs in outputs_by_client.values(): + for output in client_outputs.outputs: + spans = spans_by_req.get(output.request_id) + if not spans: + continue + output.art_policy_token_spans = _trim_step_spans( + spans, len(output.new_token_ids) + ) return outputs_by_client - for client_outputs in outputs_by_client.values(): - for output in client_outputs.outputs: - spans = spans_by_req.get(output.request_id) - if not spans: - continue - output.art_policy_token_spans = _trim_step_spans( - spans, len(output.new_token_ids) - ) - return outputs_by_client - update_from_output.__art_policy_spans_patched__ = True # type: ignore[attr-defined] - Scheduler.update_from_output = update_from_output # type: ignore[method-assign] + update_from_output.__art_policy_spans_patched__ = True # type: ignore[attr-defined] + Scheduler.update_from_output = update_from_output # type: ignore[method-assign] + + original_preempt = Scheduler._preempt_request + if getattr(original_preempt, "__art_policy_spans_patched__", False): + return + + def _preempt_request(self: Any, request: Any, timestamp: float) -> None: + original_preempt(self, request, timestamp) + _rebase_preempted_request_policy_history(request) + + _preempt_request.__art_policy_spans_patched__ = True # type: ignore[attr-defined] + Scheduler._preempt_request = _preempt_request # type: ignore[method-assign] def _patch_output_processor_policy_span_accumulation() -> None: @@ -519,13 +873,15 @@ async def tracked_result_generator(): spans = spans_by_choice.get(choice.index) if spans: _set_pydantic_extra(choice, POLICY_TOKEN_SPANS_FIELD, spans) + if _resolve_lora_alias(self.models, getattr(request, "model", None)): + response.model = request.model return response chat_completion_full_generator.__art_policy_spans_patched__ = True # type: ignore[attr-defined] OpenAIServingChat.chat_completion_full_generator = chat_completion_full_generator # type: ignore[method-assign] -def _patch_lora_update_coordinator() -> None: +def _patch_lora_alias_resolution() -> None: try: module = importlib.import_module("vllm.entrypoints.openai.engine.serving") serving_base = module.OpenAIServing @@ -545,70 +901,175 @@ def __init__(self: Any, *args: Any, **kwargs: Any) -> None: __init__.__art_lora_update_patched__ = True # type: ignore[attr-defined] serving_base.__init__ = __init__ + original_check = serving_base._check_model + if not getattr(original_check, "__art_policy_spans_patched__", False): + + async def _check_model(self: Any, request: Any) -> Any: + lora_request = _resolve_lora_alias( + self.models, getattr(request, "model", None) + ) + if lora_request is not None: + from art_vllm_runtime.metrics import record_policy_cache_salt_audit + + _apply_lora_alias_policy_cache_salt(self.models, request, lora_request) + record_policy_cache_salt_audit( + lora_request=True, + salted=bool(getattr(request, "cache_salt", None)), + ) + return None + return await original_check(self, request) + + _check_model.__art_policy_spans_patched__ = True # type: ignore[attr-defined] + serving_base._check_model = _check_model + + original_maybe = serving_base._maybe_get_adapters + if getattr(original_maybe, "__art_policy_spans_patched__", False): + return + + def _maybe_get_adapters( + self: Any, + request: Any, + supports_default_mm_loras: bool = False, + ) -> Any: + lora_request = _resolve_lora_alias(self.models, getattr(request, "model", None)) + if lora_request is not None: + _apply_lora_alias_policy_cache_salt(self.models, request, lora_request) + return lora_request + return original_maybe( + self, + request, + supports_default_mm_loras=supports_default_mm_loras, + ) + + _maybe_get_adapters.__art_policy_spans_patched__ = True # type: ignore[attr-defined] + serving_base._maybe_get_adapters = _maybe_get_adapters + def _patch_engine_request_admission() -> None: from vllm.v1.engine.async_llm import AsyncLLM - original = AsyncLLM.add_request - if getattr(original, "__art_lora_update_patched__", False): + # Long-prompt input processing is policy-independent; only serialize the + # complete final fanout and engine enqueue with in-place weight updates. + original_add_request = AsyncLLM.add_request + original_enqueue = AsyncLLM._add_request + if getattr(original_add_request, "__art_lora_update_patched__", False): return - async def add_request( + @wraps(original_add_request) + async def add_request(self: Any, *args: Any, **kwargs: Any) -> Any: + lease = _RequestAdmissionLease() + token = _REQUEST_ADMISSION_LEASE.set(lease) + try: + result = await original_add_request(self, *args, **kwargs) + if lease.ticket is not None: + await lease.ticket.release() + return result + except BaseException as error: + await _cleanup_failed_admission(self, lease.request_id, lease.ticket, error) + raise + finally: + lease.closed = True + _REQUEST_ADMISSION_LEASE.reset(token) + + async def _add_request( self: Any, - request_id: str, - prompt: Any, - params: Any, - arrival_time: float | None = None, - lora_request: Any | None = None, - **kwargs: Any, + request: Any, + prompt: str | None, + parent_req: Any, + index: int, + queue: Any, ) -> Any: + lease = _REQUEST_ADMISSION_LEASE.get() + if lease is not None and ( + lease.closed or lease.owner is not asyncio.current_task() + ): + lease = None + request_id = parent_req.request_id if parent_req else request.request_id + if lease is not None: + if lease.request_id not in (None, request_id): + raise RuntimeError("One admission lease received multiple requests") + lora_request = request.lora_request coordinator = getattr(self, _LORA_UPDATE_COORDINATOR_FIELD, None) if coordinator is None or lora_request is None: - return await original( - self, - request_id, - prompt, - params, - arrival_time=arrival_time, - lora_request=lora_request, - **kwargs, + if lease is not None: + lease.request_id = request_id + return await original_enqueue( + self, request, prompt, parent_req, index, queue ) lora_slot = str(lora_request.lora_name) - async with coordinator.admission(lora_slot) as ( - policy_version, - current_lora_request, - ): - if current_lora_request is not None: - lora_request = current_lora_request - if policy_version is not None: - _set_policy_cache_salt( - params, - lora_slot=lora_slot, - policy_version=policy_version, + if lease is None: + ticket = await coordinator.acquire(lora_slot) + try: + _bind_admitted_lora(request, lora_slot, ticket.lora_request) + result = await original_enqueue( + self, request, prompt, parent_req, index, queue ) - if hasattr(prompt, "cache_salt"): - _set_policy_cache_salt( - prompt, - lora_slot=lora_slot, - policy_version=policy_version, - ) - return await original( - self, - request_id, - prompt, - params, - arrival_time=arrival_time, - lora_request=lora_request, - **kwargs, - ) + await ticket.release() + return result + except BaseException as error: + await _cleanup_failed_admission(self, request_id, ticket, error) + raise + if lease.lora_slot is None: + lease.ticket = await coordinator.acquire(lora_slot) + lease.lora_request = lease.ticket.lora_request + lease.lora_slot = lora_slot + elif lease.lora_slot != lora_slot: + raise RuntimeError("One request fanout resolved to multiple LoRA slots") + _bind_admitted_lora(request, lora_slot, lease.lora_request) + lease.request_id = request_id + return await original_enqueue(self, request, prompt, parent_req, index, queue) add_request.__art_lora_update_patched__ = True # type: ignore[attr-defined] + _add_request.__art_lora_update_patched__ = True # type: ignore[attr-defined] AsyncLLM.add_request = add_request # type: ignore[method-assign] + AsyncLLM._add_request = _add_request # type: ignore[method-assign] + + +async def _cleanup_failed_admission( + engine: Any, + request_id: str | None, + ticket: _SlotAdmissionTicket | None, + primary: BaseException, +) -> None: + async def cleanup() -> None: + try: + if request_id is not None: + await engine.abort(request_id, internal=True) + finally: + if ticket is not None: + await ticket.release() + + try: + await _complete_task(asyncio.create_task(cleanup())) + except BaseException as error: + raise BaseExceptionGroup( + "request admission and cleanup both failed", [primary, error] + ) from None + + +def _bind_admitted_lora( + request: Any, + lora_slot: str, + lora_request: Any | None, +) -> None: + if lora_request is None: + if lora_slot.endswith(":active"): + raise RuntimeError( + f"Mutable LoRA slot {lora_slot!r} has no declared policy identity" + ) + lora_request = request.lora_request + if isinstance(lora_request, PolicyLoRARequest): + request.lora_request = lora_request + _set_policy_cache_salt( + request, + lora_slot=lora_slot, + policy_version=lora_request.policy_version, + update_seq=lora_request.update_seq, + ) def _patch_load_inplace_storage() -> None: from vllm.entrypoints.openai.models.serving import OpenAIServingModels - from vllm.lora.request import LoRARequest original = OpenAIServingModels.load_lora_adapter if getattr(original, "__art_policy_spans_patched__", False): @@ -619,78 +1080,378 @@ async def load_lora_adapter( request: Any, base_model_name: str | None = None, ) -> Any: + if request.load_inplace and request.lora_name in self.lora_requests: + raise RuntimeError( + "Existing LoRA slots must be updated through /art/in_flight_lora_update" + ) result = await original(self, request, base_model_name=base_model_name) lora_request = self.lora_requests.get(request.lora_name) if lora_request is not None and lora_request.load_inplace: - normalized = LoRARequest( - lora_name=lora_request.lora_name, - lora_int_id=lora_request.lora_int_id, - lora_path=lora_request.lora_path, - base_model_name=lora_request.base_model_name, - tensorizer_config_dict=lora_request.tensorizer_config_dict, - load_inplace=False, - is_3d_lora_weight=lora_request.is_3d_lora_weight, + self.lora_requests[request.lora_name] = _normalized_lora_request( + lora_request ) - self.lora_requests[request.lora_name] = normalized return result load_lora_adapter.__art_policy_spans_patched__ = True # type: ignore[attr-defined] OpenAIServingModels.load_lora_adapter = load_lora_adapter # type: ignore[method-assign] -def _patch_engine_waiting_cache_salt_utility() -> None: +def _patch_policy_lora_update_rpc() -> None: from vllm.v1.engine.core import EngineCore + from vllm.v1.worker.worker_base import WorkerBase + + if not hasattr(WorkerBase, "art_load_lora_policy"): + + def art_load_lora_policy(self: Any, payload: dict[str, Any]) -> dict[str, Any]: + lora_request = _policy_lora_request_from_payload(payload) + previous = _WORKER_LORA_POLICY_BY_ID.get(lora_request.lora_int_id) + loaded = self.add_lora(lora_request) + if not self.pin_lora(lora_request.lora_int_id): + raise RuntimeError("Loaded policy LoRA could not be pinned") + current = _record_worker_lora_policy(lora_request) + return { + "loaded": bool(loaded), + "previous": None if previous is None else dict(previous), + "current": dict(current), + } - if hasattr(EngineCore, "art_update_waiting_lora_cache_salt"): - return + WorkerBase.art_load_lora_policy = art_load_lora_policy # type: ignore[attr-defined] - def art_update_waiting_lora_cache_salt( - self: Any, - lora_slot: str, - policy_version: int, - ) -> dict[str, int]: - return _update_waiting_lora_cache_salt( - self.scheduler, - lora_slot=str(lora_slot), - policy_version=int(policy_version), - ) + if not hasattr(WorkerBase, "art_declare_loaded_lora_policy"): + + def art_declare_loaded_lora_policy( + self: Any, payload: dict[str, Any] + ) -> dict[str, Any]: + lora_request = _policy_lora_request_from_payload( + payload, load_inplace=False + ) + previous = _WORKER_LORA_POLICY_BY_ID.get(lora_request.lora_int_id) + if lora_request.lora_int_id not in self.list_loras() or previous is None: + raise RuntimeError( + f"LoRA {lora_request.lora_int_id} is not loaded on this worker" + ) + for field in ("lora_slot", "lora_path"): + expected = getattr( + lora_request, + "lora_name" if field == "lora_slot" else "lora_path", + ) + if previous[field] != expected: + raise RuntimeError( + f"Loaded LoRA {field} is {previous[field]!r}, expected {expected!r}" + ) + if not self.pin_lora(lora_request.lora_int_id): + raise RuntimeError("Initial policy LoRA could not be pinned") + current = _record_worker_lora_policy(lora_request) + return { + "loaded": True, + "previous": dict(previous), + "current": dict(current), + } + + WorkerBase.art_declare_loaded_lora_policy = art_declare_loaded_lora_policy # type: ignore[attr-defined] + + if not hasattr(EngineCore, "art_apply_lora_policy_update"): + + def art_apply_lora_policy_update( + self: Any, payload: dict[str, Any] + ) -> dict[str, int]: + return _apply_policy_lora_update(self, payload) + + EngineCore.art_apply_lora_policy_update = art_apply_lora_policy_update # type: ignore[attr-defined] + + if not hasattr(EngineCore, "art_declare_loaded_lora_policy"): + + def art_declare_loaded_lora_policy( + self: Any, payload: dict[str, Any] + ) -> dict[str, int]: + request = _policy_lora_request_from_payload(payload, load_inplace=False) + acknowledgements = self.collective_rpc( + "art_declare_loaded_lora_policy", args=(payload,) + ) + _validate_worker_lora_update(request, acknowledgements) + return {"workers": len(acknowledgements)} - EngineCore.art_update_waiting_lora_cache_salt = art_update_waiting_lora_cache_salt # type: ignore[attr-defined] + EngineCore.art_declare_loaded_lora_policy = art_declare_loaded_lora_policy # type: ignore[attr-defined] -def _update_waiting_lora_cache_salt( +def _apply_policy_lora_update( + engine_core: Any, payload: dict[str, Any] +) -> dict[str, int]: + if not engine_core.is_scheduler_paused(): + raise RuntimeError("Policy LoRA updates require a paused scheduler") + lora_request = _policy_lora_request_from_payload(payload) + started = { + request.request_id + for request in engine_core.scheduler.requests.values() + if _request_uses_lora_slot(request, lora_request.lora_name) + and _request_has_executed(request) + } + _validate_continued_policy_update(engine_core.scheduler, started) + try: + acknowledgements = engine_core.collective_rpc( + "art_load_lora_policy", args=(payload,) + ) + previous = _validate_worker_lora_update(lora_request, acknowledgements) + return _transition_scheduler_policy_history( + engine_core.scheduler, + lora_request=_policy_lora_request_from_payload(payload, load_inplace=False), + previous_policy=previous, + started_request_ids=started, + ) + except BaseException: + # Never let a core that may have partially changed workers schedule again. + engine_core.pause_scheduler("abort", True) + raise + + +def _transition_scheduler_policy_history( scheduler: Any, *, - lora_slot: str, - policy_version: int, + lora_request: PolicyLoRARequest, + previous_policy: Mapping[str, Any] | None, + started_request_ids: set[str], ) -> dict[str, int]: + _validate_continued_policy_update(scheduler, started_request_ids) updated = 0 - skipped_started = 0 - for queue_name in ("waiting", "skipped_waiting"): - queue = getattr(scheduler, queue_name, None) - if queue is None: + continued = 0 + for request in scheduler.requests.values(): + if not _request_uses_lora_slot(request, lora_request.lora_name): continue - for request in list(queue): - lora_request = getattr(request, "lora_request", None) - if lora_request is None or str(lora_request.lora_name) != lora_slot: - continue - if int(getattr(request, "num_computed_tokens", 0) or 0) != 0: - skipped_started += 1 - continue - _set_policy_cache_salt( - request, - lora_slot=lora_slot, - policy_version=policy_version, + previous_digest = getattr(request, _POLICY_HISTORY_BASE_FIELD, None) + if request.request_id in started_request_ids: + continued += 1 + previous_digest = _policy_history_from_cache_salt(request.cache_salt) + if previous_digest is None: + if previous_policy is None: + raise RuntimeError( + f"Started request {request.request_id!r} has no policy identity" + ) + if int(previous_policy["update_seq"]) != 0: + raise RuntimeError( + f"Started request {request.request_id!r} lost policy history" + ) + previous_digest = _extend_policy_history( + None, + lora_slot=str(previous_policy["lora_slot"]), + policy_version=int(previous_policy["policy_version"]), + update_seq=0, + ) + request.lora_request = lora_request + setattr(request, _POLICY_HISTORY_BASE_FIELD, previous_digest) + _set_policy_cache_salt( + request, + lora_slot=lora_request.lora_name, + policy_version=lora_request.policy_version, + update_seq=lora_request.update_seq, + previous_digest=previous_digest, + ) + computed_tokens = int(getattr(request, "num_computed_tokens", 0) or 0) + if computed_tokens: + if computed_tokens > request.num_tokens: + raise RuntimeError( + f"Started request {request.request_id!r} has " + f"{computed_tokens} computed tokens but only {request.num_tokens} tokens" + ) + history_digest = _policy_history_from_cache_salt(request.cache_salt) + assert history_digest is not None + transitions: list[tuple[int, str]] = list( + getattr(request, _POLICY_CACHE_TRANSITIONS_FIELD, ()) + ) + transition = (computed_tokens, history_digest) + if transitions and transitions[-1][0] == computed_tokens: + transitions[-1] = transition + else: + if transitions and transitions[-1][0] > computed_tokens: + raise RuntimeError("Policy cache transitions are not monotonic") + transitions.append(transition) + setattr(request, _POLICY_CACHE_TRANSITIONS_FIELD, tuple(transitions)) + + # Requests hash their entire known prompt eagerly. Preserve only the + # blocks whose KV was computed before this weight transition. + first_changed_block = computed_tokens // _scheduler_hash_block_size( + scheduler ) + del request.block_hashes[first_changed_block:] + else: + setattr(request, _POLICY_CACHE_TRANSITIONS_FIELD, ()) request.block_hashes.clear() - request.update_block_hashes() - updated += 1 + request.update_block_hashes() + setattr( + request, + _POLICY_EXECUTION_MARKER_FIELD, + ( + computed_tokens, + int(getattr(request, "num_preemptions", 0) or 0), + len(getattr(request, "output_token_ids", ())), + ), + ) + updated += 1 return { - "updated_waiting_requests": updated, - "skipped_started_waiting_requests": skipped_started, + "updated_requests": updated, + "continued_requests": continued, } +def _validate_continued_policy_update( + scheduler: Any, started_request_ids: set[str] +) -> None: + if not started_request_ids: + return + if getattr(scheduler, "connector", None) is not None: + raise RuntimeError( + "Mutable policy updates cannot continue requests with a KV connector" + ) + for request_id in started_request_ids: + request = scheduler.requests[request_id] + if getattr(request, "mm_features", None): + raise RuntimeError( + "Mutable policy updates cannot continue multimodal requests" + ) + + +def _rebase_preempted_request_policy_history(request: Any) -> None: + if not getattr(request, _POLICY_CACHE_TRANSITIONS_FIELD, ()): + return + lora_request = request.lora_request + policy_version = getattr(lora_request, "policy_version", None) + update_seq = getattr(lora_request, "update_seq", None) + if policy_version is None or update_seq is None: + raise RuntimeError( + f"Preempted request {request.request_id!r} lost its policy identity" + ) + setattr(request, _POLICY_HISTORY_BASE_FIELD, None) + _set_policy_cache_salt( + request, + lora_slot=str(lora_request.lora_name), + policy_version=int(policy_version), + update_seq=int(update_seq), + ) + setattr(request, _POLICY_CACHE_TRANSITIONS_FIELD, ()) + request.block_hashes.clear() + request.update_block_hashes() + setattr( + request, + _POLICY_EXECUTION_MARKER_FIELD, + ( + int(getattr(request, "num_computed_tokens", 0) or 0), + int(getattr(request, "num_preemptions", 0) or 0), + len(getattr(request, "output_token_ids", ())), + ), + ) + + +def _scheduler_hash_block_size(scheduler: Any) -> int: + block_size = int(scheduler.kv_cache_manager.block_pool.hash_block_size) + if block_size <= 0: + raise RuntimeError("vLLM reported a non-positive KV hash block size") + return block_size + + +def _policy_lora_request_from_payload( + payload: Mapping[str, Any], *, load_inplace: bool = True +) -> PolicyLoRARequest: + return PolicyLoRARequest( + lora_name=str(payload["lora_name"]), + lora_int_id=int(payload["lora_int_id"]), + lora_path=str(payload["lora_path"]), + base_model_name=payload.get("base_model_name"), + tensorizer_config_dict=payload.get("tensorizer_config_dict"), + load_inplace=load_inplace, + is_3d_lora_weight=bool(payload.get("is_3d_lora_weight", False)), + policy_version=int(payload["policy_version"]), + update_seq=int(payload["update_seq"]), + ) + + +def policy_lora_request_payload(lora_request: PolicyLoRARequest) -> dict[str, Any]: + return { + "lora_name": lora_request.lora_name, + "lora_int_id": lora_request.lora_int_id, + "lora_path": lora_request.lora_path, + "base_model_name": lora_request.base_model_name, + "tensorizer_config_dict": lora_request.tensorizer_config_dict, + "is_3d_lora_weight": lora_request.is_3d_lora_weight, + "policy_version": lora_request.policy_version, + "update_seq": lora_request.update_seq, + } + + +def _normalized_lora_request(lora_request: Any) -> LoRARequest: + request_type = ( + PolicyLoRARequest + if isinstance(lora_request, PolicyLoRARequest) + else LoRARequest + ) + policy_fields = ( + { + "policy_version": lora_request.policy_version, + "update_seq": lora_request.update_seq, + } + if request_type is PolicyLoRARequest + else {} + ) + return request_type( + lora_name=lora_request.lora_name, + lora_int_id=lora_request.lora_int_id, + lora_path=lora_request.lora_path, + base_model_name=lora_request.base_model_name, + tensorizer_config_dict=lora_request.tensorizer_config_dict, + load_inplace=False, + is_3d_lora_weight=lora_request.is_3d_lora_weight, + **policy_fields, + ) + + +def _validate_worker_lora_update( + lora_request: PolicyLoRARequest, + acknowledgements: list[Mapping[str, Any]], +) -> Mapping[str, Any] | None: + if not acknowledgements: + raise RuntimeError("Policy LoRA update returned no worker acknowledgements") + expected = { + "policy_version": lora_request.policy_version, + "lora_slot": lora_request.lora_name, + "lora_path": lora_request.lora_path, + "update_seq": lora_request.update_seq, + } + previous: Mapping[str, Any] | None = None + previous_set = False + for rank, acknowledgement in enumerate(acknowledgements): + if not acknowledgement.get("loaded"): + raise RuntimeError(f"Worker rank {rank} did not load the policy LoRA") + current = acknowledgement.get("current") + if current != expected: + raise RuntimeError( + f"Worker rank {rank} acknowledged {current!r}, expected {expected!r}" + ) + rank_previous = acknowledgement.get("previous") + if previous_set and rank_previous != previous: + raise RuntimeError("Policy LoRA workers started from different policies") + previous = rank_previous + previous_set = True + return previous + + +def _request_uses_lora_slot(request: Any, lora_slot: str) -> bool: + lora_request = getattr(request, "lora_request", None) + return lora_request is not None and str(lora_request.lora_name) == lora_slot + + +def _request_has_executed(request: Any) -> bool: + computed_tokens = int(getattr(request, "num_computed_tokens", 0) or 0) + preemptions = int(getattr(request, "num_preemptions", 0) or 0) + output_tokens = len(getattr(request, "output_token_ids", ())) + marker = getattr(request, _POLICY_EXECUTION_MARKER_FIELD, None) + if marker is not None: + baseline_computed_tokens, baseline_preemptions, baseline_output_tokens = marker + return bool( + computed_tokens > baseline_computed_tokens + or preemptions > baseline_preemptions + or output_tokens > baseline_output_tokens + ) + return bool(computed_tokens or output_tokens or preemptions) + + def _policy_context_from_runner(runner: Any) -> dict[str, dict[str, Any]]: input_batch = getattr(runner, "input_batch", None) if input_batch is None: @@ -725,34 +1486,52 @@ def _policy_metadata_for_lora_request(lora_request: Any | None) -> dict[str, Any state = _WORKER_LORA_POLICY_BY_ID.get(lora_request.lora_int_id) if state is None: state = _record_worker_lora_policy(lora_request) + if state["lora_slot"].endswith(":active") and state["update_seq"] == 0: + raise RuntimeError( + f"Mutable LoRA slot {state['lora_slot']!r} has no declared policy identity" + ) return state def _record_worker_lora_policy(lora_request: Any) -> dict[str, Any]: - global _WORKER_LORA_UPDATE_SEQ - _WORKER_LORA_UPDATE_SEQ += 1 - policy_version = _policy_version_from_lora_request(lora_request) + policy_version = getattr(lora_request, "policy_version", None) + update_seq = getattr(lora_request, "update_seq", None) + if policy_version is None: + policy_version = _immutable_policy_version_from_lora_name( + str(lora_request.lora_name) + ) + update_seq = int(policy_version or 0) state = { "policy_version": int(policy_version or 0), "lora_slot": str(lora_request.lora_name), - "update_seq": _WORKER_LORA_UPDATE_SEQ, + "lora_path": str(lora_request.lora_path), + "update_seq": int(update_seq or 0), } _WORKER_LORA_POLICY_BY_ID[int(lora_request.lora_int_id)] = state return state -def _policy_version_from_lora_request(lora_request: Any) -> int | None: - for pattern, value in ( - (r"@(\d+)$", getattr(lora_request, "lora_name", "")), - ( - r"^(?:step[_-]?)?(\d+)$", - getattr(lora_request, "lora_path", "").rstrip("/").split("/")[-1], - ), - ): - match = re.search(pattern, value) - if match: - return int(match.group(1)) - return None +def get_worker_lora_states(lora_ids: set[int]) -> tuple[dict[str, Any], ...]: + states = [] + for lora_id in sorted(lora_ids): + state = _WORKER_LORA_POLICY_BY_ID.get(lora_id) + if state is None: + raise RuntimeError(f"loaded LoRA {lora_id} has no ART worker state") + states.append( + { + "lora_id": lora_id, + "lora_name": state["lora_slot"], + "lora_path": state["lora_path"], + "policy_version": state["policy_version"], + "update_seq": state["update_seq"], + } + ) + return tuple(states) + + +def _immutable_policy_version_from_lora_name(lora_name: str) -> int | None: + match = re.search(r"@(\d+)$", lora_name) + return int(match.group(1)) if match else None def _attach_policy_spans_to_model_output( diff --git a/vllm_runtime/src/art_vllm_runtime/qwen35_patches.py b/vllm_runtime/src/art_vllm_runtime/qwen35_patches.py new file mode 100644 index 000000000..e719c2a41 --- /dev/null +++ b/vllm_runtime/src/art_vllm_runtime/qwen35_patches.py @@ -0,0 +1,148 @@ +"""Qwen3.5 compatibility patches for the ART-owned vLLM runtime.""" + +from typing import Any, Literal + + +def patch_blackwell_gdn_prefill_backend() -> None: + """Keep vLLM 0.25.1's FlashInfer GDN off Qwen3.5 on SM10x.""" + from vllm.model_executor.layers.mamba.gdn import qwen_gdn_linear_attn + + current = qwen_gdn_linear_attn._resolve_gdn_prefill_backend + if getattr(current, "__art_blackwell_cutedsl_patched__", False): + return + original = current + + def resolve( + vllm_config: Any, + ) -> tuple[str, Literal["triton", "flashinfer", "cutedsl"]]: + requested, active = original(vllm_config) + model_type = str(vllm_config.model_config.hf_text_config.model_type) + if ( + model_type.startswith("qwen3_5") + and requested == "auto" + and active == "flashinfer" + and qwen_gdn_linear_attn.current_platform.is_device_capability_family(100) + ): + return requested, "cutedsl" + return requested, active + + setattr(resolve, "__art_blackwell_cutedsl_patched__", True) + setattr(resolve, "__art_original__", original) + setattr(qwen_gdn_linear_attn, "_resolve_gdn_prefill_backend", resolve) + + +def patch_trtllm_monolithic_route_capture() -> None: + import torch + from vllm.model_executor.layers.fused_moe.activation import MoEActivation + from vllm.model_executor.layers.fused_moe.experts.trtllm_bf16_moe import ( + TrtLlmBf16Experts, + ) + from vllm.model_executor.layers.fused_moe.runner.moe_runner import MoERunner + from vllm.model_executor.layers.quantization.utils.flashinfer_utils import ( + activation_to_flashinfer_int, + ) + from vllm.v1.worker.gpu_model_runner import GPUModelRunner + + original_apply = TrtLlmBf16Experts.apply + if not getattr(original_apply, "__art_route_capture_patched__", False): + + def apply( + self: Any, + hidden_states: torch.Tensor, + w1: torch.Tensor, + w2: torch.Tensor, + router_logits: torch.Tensor, + activation: Any, + global_num_experts: int, + expert_map: torch.Tensor | None, + a1q_scale: torch.Tensor | None, + apply_router_weight_on_input: bool, + num_expert_group: int | None = None, + e_score_correction_bias: torch.Tensor | None = None, + routed_scaling_factor: float | None = None, + topk_group: int | None = None, + ) -> torch.Tensor: + capture = getattr(self, "_art_route_capture", None) + if capture is None: + return original_apply( + self, + hidden_states, + w1, + w2, + router_logits, + activation, + global_num_experts, + expert_map, + a1q_scale, + apply_router_weight_on_input, + num_expert_group, + e_score_correction_bias, + routed_scaling_factor, + topk_group, + ) + + del expert_map, a1q_scale, apply_router_weight_on_input + import flashinfer + + assert activation in [MoEActivation.SILU, MoEActivation.RELU2_NO_MUL] + replay_out = capture[0] + output = flashinfer.fused_moe.trtllm_bf16_moe( + routing_logits=router_logits, + routing_bias=e_score_correction_bias, + hidden_states=hidden_states, + gemm1_weights=w1, + gemm2_weights=w2, + num_experts=global_num_experts, + top_k=self.topk, + n_group=num_expert_group, + topk_group=topk_group, + intermediate_size=self.intermediate_size_per_partition, + local_expert_offset=self.ep_rank * self.local_num_experts, + local_num_experts=self.local_num_experts, + routed_scaling_factor=routed_scaling_factor, + routing_method_type=self.routing_method_type, + activation_type=activation_to_flashinfer_int(activation), + routing_replay_out=replay_out, + ) + capture[1](replay_out[: hidden_states.shape[0]]) + return output + + setattr(apply, "__art_route_capture_patched__", True) + setattr(apply, "__art_original__", original_apply) + TrtLlmBf16Experts.apply = apply # type: ignore[method-assign] + + original_bind = GPUModelRunner._bind_routed_experts_capturer + if getattr(original_bind, "__art_trtllm_route_capture_patched__", False): + return + + def bind(self: Any, capturer: Any) -> None: + original_bind(self, capturer) + for module in self.compilation_config.static_forward_context.values(): + if not isinstance(module, MoERunner): + continue + kernel = module.routed_experts.quant_method.moe_kernel + if kernel is None or not kernel.is_monolithic: + continue + experts = kernel.impl.fused_experts + if not isinstance(experts, TrtLlmBf16Experts): + continue + capture_fn = getattr(module.router, "capture_fn", None) + if capture_fn is None: + continue + experts._art_route_capture = ( # type: ignore[attr-defined] + torch.empty( + (capturer.device_buffer.shape[0], experts.topk), + dtype=torch.int16, + device=capturer.device_buffer.device, + ), + capture_fn, + ) + + setattr(bind, "__art_trtllm_route_capture_patched__", True) + setattr(bind, "__art_original__", original_bind) + GPUModelRunner._bind_routed_experts_capturer = bind # type: ignore[method-assign] + + +def apply_qwen35_vllm_runtime_patches() -> None: + patch_blackwell_gdn_prefill_backend() + patch_trtllm_monolithic_route_capture() diff --git a/vllm_runtime/tests/test_binary_routes.py b/vllm_runtime/tests/test_binary_routes.py new file mode 100644 index 000000000..6a3e9ef04 --- /dev/null +++ b/vllm_runtime/tests/test_binary_routes.py @@ -0,0 +1,112 @@ +from __future__ import annotations + +import json +import unittest + +from art_vllm_runtime import binary_routes +import numpy as np + +from art.vllm_route_transport import decode_routed_experts_response + + +class BinaryRoutesProtocolTest(unittest.TestCase): + def test_exact_expert_count_and_dtype_roundtrip(self) -> None: + response = json.dumps( + { + "id": "route-test", + "choices": [], + "created": 0, + "model": "test-model", + "object": "chat.completion", + } + ).encode() + for num_experts, dtype, values in ( + (256, np.uint8, [[[0, 255]]]), + (257, np.uint16, [[[0, 256]]]), + ): + body = binary_routes.encode_routed_experts_response( + response, + {0: np.asarray(values, dtype=dtype)}, + num_experts=num_experts, + ) + decoded_response, routes = decode_routed_experts_response(body) + + self.assertEqual(decoded_response.id, "route-test") + self.assertEqual(routes[0].num_experts, num_experts) + self.assertEqual(routes[0].dtype, np.dtype(dtype)) + np.testing.assert_array_equal(routes[0], values) + + def test_rejects_expert_count_beyond_uint16_protocol(self) -> None: + with self.assertRaisesRegex(RuntimeError, r"\[1, 65536\]"): + binary_routes.encode_routed_experts_response( + b"{}", + {0: np.zeros((1, 1, 1), dtype=np.uint16)}, + num_experts=65_537, + ) + + def test_capture_registers_vllm_authoritative_route_layout(self) -> None: + text_config = type( + "TextConfig", + (), + {"num_hidden_layers": 2, "mlp_layer_types": ["dense", "sparse"]}, + )() + model_config = type( + "ModelConfig", + (), + { + "get_num_experts": lambda _self: 257, + "hf_text_config": text_config, + }, + )() + previous = ( + binary_routes._REGISTERED_NUM_EXPERTS, + binary_routes._REGISTERED_PADDING_LAYERS, + ) + try: + binary_routes._REGISTERED_NUM_EXPERTS = None + binary_routes._REGISTERED_PADDING_LAYERS = None + binary_routes._register_model_route_layout(model_config) + with binary_routes.capture_routed_experts() as routes: + self.assertEqual(routes.num_experts, 257) + self.assertEqual(routes.padding_layers, (0,)) + finally: + ( + binary_routes._REGISTERED_NUM_EXPERTS, + binary_routes._REGISTERED_PADDING_LAYERS, + ) = previous + + def test_resolves_only_registered_padding_layers(self) -> None: + routes = binary_routes._CapturedRoutes(num_experts=8, padding_layers=(0, 1, 2)) + values = np.zeros((2, 5, 2), dtype=np.uint8) + values[:, 3, :] = (2, 5) + values[:, 4, :] = (1, 7) + routes[0] = values + + response = json.dumps( + { + "id": "route-test", + "choices": [], + "created": 0, + "model": "test-model", + "object": "chat.completion", + } + ).encode() + body = binary_routes.encode_routed_experts_response(response, routes) + _, decoded = decode_routed_experts_response(body) + + expected = np.broadcast_to((0, 1), (2, 3, 2)) + np.testing.assert_array_equal(decoded[0][:, :3, :], expected) + np.testing.assert_array_equal(decoded[0][:, 3:, :], values[:, 3:, :]) + + def test_rejects_missing_capture_on_routed_layer(self) -> None: + routes = binary_routes._CapturedRoutes(num_experts=8, padding_layers=(0, 1, 2)) + values = np.zeros((1, 5, 2), dtype=np.uint8) + values[:, 3, :] = (2, 5) + routes[0] = values + + with self.assertRaisesRegex(RuntimeError, "must be distinct"): + binary_routes.encode_routed_experts_response(b"{}", routes) + + +if __name__ == "__main__": + unittest.main() diff --git a/vllm_runtime/tests/test_dedicated_server.py b/vllm_runtime/tests/test_dedicated_server.py new file mode 100644 index 000000000..4c91f23d3 --- /dev/null +++ b/vllm_runtime/tests/test_dedicated_server.py @@ -0,0 +1,158 @@ +from http.client import HTTPConnection +import json +import os +from types import SimpleNamespace + +from art_vllm_runtime import dedicated_server +from art_vllm_runtime.fast_metrics import FAST_METRIC_NAMES, FastMetricsSidecar +from fastapi import FastAPI +from fastapi.testclient import TestClient +import pytest +from starlette.datastructures import URL + +_PAYLOAD: dict[str, object] = { + "schema_version": 1, + "source": "art_vllm_runtime", + "last_update_unix_s": 1.0, + "record_count": 1, + "engine_count": 1, + "metrics": { + **dict.fromkeys(FAST_METRIC_NAMES, 0.0), + "num_requests_running": 2.0, + "prompt_tokens_total": 3.0, + }, + "process_uuid": "runtime-process", + "generation": 4, +} + + +def _get( + connection: HTTPConnection, *, token: str | None = None +) -> tuple[int, int, dict[str, object]]: + headers = {"Authorization": f"Bearer {token}"} if token else {} + connection.request("GET", "/art/metrics", headers=headers) + response = connection.getresponse() + return response.status, response.version, json.loads(response.read()) + + +def _start_sidecar(*, tokens: list[str], port: int = 0) -> FastMetricsSidecar: + sidecar = FastMetricsSidecar.start( + "127.0.0.1", + tokens, + process_uuid="runtime-process", + generation=4, + port=port, + ) + sidecar.writer.publish( + last_update_unix_s=1.0, + record_count=1, + engine_count=1, + metrics=_PAYLOAD["metrics"], # type: ignore[arg-type] + ) + return sidecar + + +def test_fast_metrics_listener_auth_keepalive_and_scalar_payload() -> None: + sidecar = _start_sidecar(tokens=["first", "second"]) + assert sidecar.process.pid != os.getpid() + connection = HTTPConnection("127.0.0.1", sidecar.port, timeout=1.0) + try: + assert _get(connection)[0] == 401 + reused_socket = connection.sock + status, version, payload = _get(connection, token="second") + assert (status, version) == (200, 11) + assert connection.sock is reused_socket + assert _get(connection, token="second")[0] == 200 + assert connection.sock is reused_socket + assert payload == _PAYLOAD + metrics = payload["metrics"] + assert isinstance(metrics, dict) + assert all(type(value) in {int, float} for value in metrics.values()) + finally: + connection.close() + sidecar.close() + assert sidecar.process.poll() == 0 + + +def test_fast_metrics_listener_reads_updated_shared_snapshot() -> None: + sidecar = _start_sidecar(tokens=[]) + connection = HTTPConnection("127.0.0.1", sidecar.port, timeout=1.0) + try: + metrics = dict(_PAYLOAD["metrics"]) # type: ignore[arg-type] + metrics["num_requests_running"] = 7.0 + sidecar.writer.publish( + last_update_unix_s=2.0, + record_count=2, + engine_count=1, + metrics=metrics, + ) + _, _, payload = _get(connection) + assert payload["record_count"] == 2 + assert payload["last_update_unix_s"] == 2.0 + assert payload["metrics"]["num_requests_running"] == 7.0 # type: ignore[index] + finally: + connection.close() + sidecar.close() + + +def test_fast_metrics_listener_reports_unpublished_snapshot() -> None: + sidecar = FastMetricsSidecar.start( + "127.0.0.1", [], process_uuid="runtime-process", generation=4 + ) + connection = HTTPConnection("127.0.0.1", sidecar.port, timeout=1.0) + try: + status, _, payload = _get(connection) + assert status == 503 + assert payload == {"error": "Metrics unavailable"} + finally: + connection.close() + sidecar.close() + + +def test_fast_metrics_listener_stops_and_restarts_on_same_port() -> None: + sidecar = _start_sidecar(tokens=[]) + port = sidecar.port + sidecar.close() + assert sidecar.process.poll() == 0 + + restarted = _start_sidecar(tokens=[], port=port) + connection = HTTPConnection("127.0.0.1", port, timeout=1.0) + try: + assert _get(connection)[0] == 200 + finally: + connection.close() + restarted.close() + assert restarted.process.poll() == 0 + + +def test_fast_metrics_url_uses_controller_routable_host(monkeypatch) -> None: + monkeypatch.setattr(dedicated_server, "_fast_metrics_port", 43123) + monkeypatch.setitem(dedicated_server._runtime_state, "nnodes", 2) + request = SimpleNamespace(url=URL("https://10.20.30.40:8000/art/capabilities")) + assert ( + dedicated_server._fast_metrics_url(request) + == "http://10.20.30.40:43123/art/metrics" + ) + + for host in ("0.0.0.0", "127.0.0.1", "[::]"): + request = SimpleNamespace(url=URL(f"http://{host}:8000/art/capabilities")) + with pytest.raises(RuntimeError, match="unroutable host"): + dedicated_server._fast_metrics_url(request) + + +def test_runtime_sleep_route_returns_engine_validation_error(monkeypatch) -> None: + from vllm.entrypoints.openai import api_server + + monkeypatch.setattr(api_server, "build_app", lambda *args, **kwargs: FastAPI()) + monkeypatch.setattr(api_server, "_art_runtime_routes_patched", False, raising=False) + dedicated_server._patch_art_runtime_routes() + app = api_server.build_app() + + class Engine: + async def sleep(self, *, level: int, mode: str) -> None: + raise ValueError(f"invalid {level=} {mode=}") + + app.state.engine_client = Engine() + response = TestClient(app).post("/sleep?level=1&mode=wait") + assert response.status_code == 400 + assert response.json() == {"error": "invalid level=1 mode='wait'"} diff --git a/vllm_runtime/uv.lock b/vllm_runtime/uv.lock index edae59eea..6cb2d7995 100644 --- a/vllm_runtime/uv.lock +++ b/vllm_runtime/uv.lock @@ -1,16 +1,23 @@ version = 1 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