Add CI integration for Spark-X2.5 models

.ci/scripts/test_torchao_huggingface_checkpoints.sh

@@ -11,7 +11,7 @@ MODEL_NAME=""
 # Parse args
 if [[ $# -lt 1 ]]; then
   echo "Usage: $0 <model_name> [--test_with_runner]"
-  echo "Supported model_name values: qwen3_4b, phi_4_mini, lfm2_5_1_2b"
+  echo "Supported model_name values: qwen3_4b, phi_4_mini, lfm2_5_1_2b, spark_x2_5_1_7b, spark_x2_5_4b"
   exit 1
 fi
 
@@ -28,7 +28,7 @@ while [[ $# -gt 0 ]]; do
       ;;
     -h|--help)
       echo "Usage: $0 <model_name> [--test_with_runner] [--use_torchao_kernels]"
-      echo "  model_name: qwen3_4b | phi_4_mini | lfm2_5_1_2b"
+      echo "  model_name: qwen3_4b | phi_4_mini | lfm2_5_1_2b | spark_x2_5_1_7b | spark_x2_5_4b"
       echo "  --test_with_runner: build ET + run llama_main to sanity-check the export"
       echo "  --use_torchao_kernels: use torchao kernels for linear and tied embedding"
       exit 0
@@ -125,9 +125,61 @@ case "$MODEL_NAME" in
       ${BACKEND_ARGS}
     ;;
 
+  spark_x2_5_1_7b)
+    echo "Running Spark-X2.5-1.7B export..."
+    HF_MODEL_DIR="${RUNNER_TEMP:-/tmp}/hf_cache/spark_x2_5_1_7b"
+    mkdir -p "${HF_MODEL_DIR}"
+    hf download --local-dir "${HF_MODEL_DIR}" XHToken/Spark-X2.5-1.7B
+    EXPECTED_MODEL_SIZE_UPPER_BOUND=$((2 * 1024 * 1024 * 1024)) # 2GB
+    $PYTHON_EXECUTABLE -m executorch.examples.models.spark_x2_5.convert_weights \
+      $HF_MODEL_DIR \
+      pytorch_model_converted.bin
+
+    $PYTHON_EXECUTABLE -m executorch.examples.models.llama.export_llama \
+      --model "spark_x2_5_1_7b" \
+      --checkpoint pytorch_model_converted.bin \
+      --params examples/models/spark_x2_5/config/spark_x2_5_1_7b_config.json \
+      --output_name $MODEL_OUT \
+      -kv \
+      --use_sdpa_with_kv_cache \
+      --max_context_length 1024 \
+      --max_seq_length 1024 \
+      --metadata '{"get_bos_id":0, "get_eos_ids":[1]}' \
+      --verbose \
+      --dtype fp32 \
+      -qmode 8da4w \
+      ${BACKEND_ARGS}
+    ;;
+
+  spark_x2_5_4b)
+    echo "Running Spark-X2.5-4B export..."
+    HF_MODEL_DIR="${RUNNER_TEMP:-/tmp}/hf_cache/spark_x2_5_4b"
+    mkdir -p "${HF_MODEL_DIR}"
+    hf download --local-dir "${HF_MODEL_DIR}" XHToken/Spark-X2.5-4B
+    EXPECTED_MODEL_SIZE_UPPER_BOUND=$((5 * 1024 * 1024 * 1024)) # 5GB
+    $PYTHON_EXECUTABLE -m executorch.examples.models.spark_x2_5.convert_weights \
+      $HF_MODEL_DIR \
+      pytorch_model_converted.bin
+
+    $PYTHON_EXECUTABLE -m executorch.examples.models.llama.export_llama \
+      --model "spark_x2_5_4b" \
+      --checkpoint pytorch_model_converted.bin \
+      --params examples/models/spark_x2_5/config/spark_x2_5_4b_config.json \
+      --output_name $MODEL_OUT \
+      -kv \
+      --use_sdpa_with_kv_cache \
+      --max_context_length 1024 \
+      --max_seq_length 1024 \
+      --metadata '{"get_bos_id":0, "get_eos_ids":[1]}' \
+      --verbose \
+      --dtype fp32 \
+      -qmode 8da4w \
+      ${BACKEND_ARGS}
+    ;;
+
   *)
     echo "Error: unsupported model_name '$MODEL_NAME'"
-    echo "Supported values: qwen3_4b, phi_4_mini, lfm2_5_1_2b"
+    echo "Supported values: qwen3_4b, phi_4_mini, lfm2_5_1_2b, spark_x2_5_1_7b, spark_x2_5_4b"
     exit 1
     ;;
 esac

.github/workflows/trunk.yml

@@ -576,7 +576,7 @@ jobs:
       contents: read
     strategy:
       matrix:
-        model: [qwen3_4b, phi_4_mini, lfm2_5_1_2b]
+        model: [qwen3_4b, phi_4_mini, lfm2_5_1_2b, spark_x2_5_1_7b, spark_x2_5_4b]
         runner: [mt-l-x86iavx512-8-64]
         docker-image: [executorch-ubuntu-22.04-clang12]
         backend: [xnnpack]
@@ -593,6 +593,14 @@ jobs:
             runner: mt-l-arm64g4-16-62
             docker-image: executorch-ubuntu-22.04-gcc11-aarch64
             backend: torchao
+          - model: spark_x2_5_1_7b
+            runner: mt-l-arm64g4-16-62
+            docker-image: executorch-ubuntu-22.04-gcc11-aarch64
+            backend: torchao
+          - model: spark_x2_5_4b
+            runner: mt-l-arm64g4-16-62
+            docker-image: executorch-ubuntu-22.04-gcc11-aarch64
+            backend: torchao
       fail-fast: false
     with:
       runner: ${{ matrix.runner }}

examples/models/spark_x2_5/convert_weights.py

@@ -4,7 +4,6 @@
 from typing import Dict
 
 import torch
-
 from executorch.examples.models.checkpoint import get_mapped_key
 from safetensors.torch import load_file
 
@@ -92,12 +91,37 @@ def load_checkpoint(input_dir: str) -> Dict[str, torch.Tensor]:
     raise FileNotFoundError(f"Could not find safetensors checkpoint in {input_dir}")
 
 
+def sanitize_tokenizer(input_dir: str) -> None:
+    """Make tokenizer.json loadable by the C++ HF tokenizer.
+
+    Spark-X2.5 ships a no-op normalizer ({"type": "Sequence", "normalizers":
+    []}) that the C++ tokenizer rejects; a null normalizer is equivalent and
+    accepted.
+    """
+    path = os.path.join(input_dir, "tokenizer.json")
+    if not os.path.exists(path):
+        return
+    with open(path) as f:
+        tokenizer = json.load(f)
+    normalizer = tokenizer.get("normalizer")
+    if (
+        isinstance(normalizer, dict)
+        and normalizer.get("type") == "Sequence"
+        and not normalizer.get("normalizers")
+    ):
+        tokenizer["normalizer"] = None
+        with open(path, "w") as f:
+            json.dump(tokenizer, f)
+        print("Replaced no-op normalizer in tokenizer.json with null")
+
+
 def convert_weights(input_dir: str, output_file: str) -> None:
     """Convert Spark-X2.5 HF weights to Meta format.
 
     Reads model hyperparameters from config.json in input_dir so the function
     signature stays compatible with download_and_convert_hf_checkpoint.
     """
+    sanitize_tokenizer(input_dir)
     config_path = os.path.join(input_dir, "config.json")
     if os.path.exists(config_path):
         with open(config_path) as f:

examples/models/spark_x2_5/test_spark_x2_5.py

@@ -115,6 +115,37 @@ def test_spark_x2_5_architecture_configs_match_expected_shapes() -> None:
         )
 
 
+def test_sanitize_tokenizer_replaces_noop_normalizer(tmp_path) -> None:
+    from executorch.examples.models.spark_x2_5.convert_weights import sanitize_tokenizer
+
+    tokenizer = {
+        "normalizer": {"type": "Sequence", "normalizers": []},
+        "model": {"type": "BPE"},
+    }
+    tokenizer_path = tmp_path / "tokenizer.json"
+    tokenizer_path.write_text(json.dumps(tokenizer))
+
+    sanitize_tokenizer(str(tmp_path))
+
+    sanitized = json.loads(tokenizer_path.read_text())
+    assert sanitized["normalizer"] is None
+    assert sanitized["model"] == {"type": "BPE"}
+
+
+def test_sanitize_tokenizer_keeps_meaningful_normalizer(tmp_path) -> None:
+    from executorch.examples.models.spark_x2_5.convert_weights import sanitize_tokenizer
+
+    normalizer = {"type": "Sequence", "normalizers": [{"type": "NFC"}]}
+    tokenizer = {"normalizer": normalizer, "model": {"type": "BPE"}}
+    tokenizer_path = tmp_path / "tokenizer.json"
+    tokenizer_path.write_text(json.dumps(tokenizer))
+
+    sanitize_tokenizer(str(tmp_path))
+
+    sanitized = json.loads(tokenizer_path.read_text())
+    assert sanitized["normalizer"] == normalizer
+
+
 def test_spark_x2_5_xnnpack_q8da4w_config_enables_xnnpack_backend() -> None:
     cfg = OmegaConf.load(CONFIG_DIR / "spark_x2_5_xnnpack_q8da4w.yaml")
     assert cfg.base.metadata == '{"get_bos_id": 0, "get_eos_ids":[1]}'