Support quantization for tokamax gmm v2

docs/guides/optimization/benchmark_and_performance.md

@@ -191,7 +191,7 @@ Different quantization recipes are available, including` "int8", "fp8", "fp8_ful
 
 For v6e and earlier generation TPUs, use the "int8" recipe. For v7x and later generation TPUs, use "fp8_full". GPUs should use “fp8_gpu” for NVIDIA and "nanoo_fp8" for AMD.
 
-See [](quantization-doc).
+See [Quantization guide](../../reference/core_concepts/quantization.md).
 
 ### Choose sharding strategy
 

docs/reference/core_concepts/moe_configuration.md

@@ -83,6 +83,8 @@ Dropping:
 
 `use_tokamax_gmm`: If enabled, use Tokamax library's Ragged Dot for matmul. Recommended for dropless configurations.
 
+`use_gmm_v2`: If enabled, use the Tokamax GMM v2 kernel for grouped matrix multiplication. Requires `use_tokamax_gmm` to be True.
+
 `megablox`: If enabled, use Megablox for sparse matrix operations. Effective only when `use_tokamax_gmm` is False.
 
 `capacity_factor`: A scalar multiplier for expert capacity. Effective only when `sparse_matmul` is False.

docs/reference/models/supported_models_and_architectures.md

@@ -12,7 +12,7 @@ MaxText is an open-source, high-performance LLM framework written in Python/JAX.
 
 - **Supported Precisions**: FP32, BF16, INT8, and FP8.
 - **Ahead-of-Time Compilation (AOT)**: For faster model development/prototyping and earlier OOM detection.
-- **Quantization**: Via **Qwix** (recommended) and AQT (deprecated). See the [Quantization guide](quantization-doc).
+- **Quantization**: Via **Qwix** (recommended) and AQT (deprecated). See the [Quantization guide](../core_concepts/quantization.md).
 - **Diagnostics**: Simple logging via `max_logging`, profiling in **XProf**, and visualization in **TensorBoard**.
 - **Multi-Token Prediction (MTP)**: Enables token efficient training with multi-token prediction.
 - **Elastic Training**: Fault-tolerant and dynamic scale-up/scale-down on Cloud TPUs with Pathways.

src/maxtext/common/checkpointing.py

@@ -752,6 +752,9 @@ def _should_save_checkpoint_at_step(checkpoint_manager, step, config, force):
   """Returns whether MaxText should build and dispatch checkpoint args."""
   if force:
     return True
+  if step == 0 and not config.save_checkpoint_on_start:
+    # if step = 0, `step % config.checkpoint_period == 0` is always true, force skip
+    return False
   if config.enable_continuous_checkpointing:
     base_checkpoint_due = bool(checkpoint_manager.should_save(step))
   else:

src/maxtext/configs/base.yml

@@ -51,6 +51,7 @@ load_full_state_path: ""
 # async_checkpointing is true, else a synchronous one is used. If you have
 # problems with the checkpointer we recommend trying the synchronous one.
 enable_checkpointing: true
+save_checkpoint_on_start: true
 save_checkpoint_on_completion: true
 async_checkpointing: true
 checkpoint_period: 10_000
@@ -141,7 +142,7 @@ kv_quant_dtype: "int8"
 checkpoint_is_quantized: false # set to true if reading from a saved aqt quantized checkpoint
 # saves params quantized on fly at following path
 save_quantized_params_path: ""
-#used to configure the mode in which model is called
+# used to configure the mode in which model is called
 # when left as is, corresponds to training
 # accepted values are "inference"
 model_call_mode: ""
@@ -267,6 +268,11 @@ wo_tile_drhs_mlp_dim: 1024
 
 merge_gating_gmm: false
 
+# Use tokamax library for gmm kernel implementation
+use_tokamax_gmm: false
+# Whether to use Tokamax GMM v2 for MoE kernel. Requires use_tokamax_gmm=true.
+use_gmm_v2: false
+
 norm_topk_prob: false # boolean to enable the top-k probability normalization. qwen3-specific normalization of router weights.
 
 # when moe weight matrices are sharded on both fsdp and fsdp-transpose axes, use two separate all-gather calls
@@ -1268,11 +1274,6 @@ use_qk_norm_in_gdn: true
 # The ratio of dimension to apply ROPE on
 partial_rotary_factor: 1.0
 
-# Use tokamax library for gmm kernel implementation
-use_tokamax_gmm: false
-# Whether to use GMM v2 for MoE.
-# Requires use_tokamax_gmm: true. Currently incompatible with quantization.
-use_gmm_v2: false
 use_tokamax_splash: false
 # Setting this flag will use a non-pallas implementation.
 use_jax_splash: false

src/maxtext/configs/pyconfig.py

@@ -38,7 +38,6 @@
 from maxtext.utils import max_logging
 
 logger = logging.getLogger(__name__)
-logger.setLevel(os.environ.get("LOGLEVEL", "INFO"))
 
 _BASE_CONFIG_ATTR = "base_config"
 _MAX_PREFIX = "M_"

src/maxtext/configs/types.py

@@ -358,6 +358,7 @@ class Checkpointing(BaseModel):
   source_checkpoint_layout: Literal["orbax", "safetensors", "safetensors_dynamic"] = Field(
       "orbax", description="The layout of the source checkpoint to load."
   )
+  save_checkpoint_on_start: bool = Field(True, description="If True, saves an initial checkpoint upon training start.")
   save_checkpoint_on_completion: bool = Field(
       True, description="If True, saves a final checkpoint upon training completion."
   )
@@ -450,7 +451,7 @@ class Quantization(BaseModel):
   use_qwix_quantization: bool = Field(False, description="Whether to use qwix for quantization.")
   use_manual_quantization: bool = Field(
       False,
-      description="Whether to use manual quantization for batch split. Only used if use_batch_split_schedule is True.",
+      description="Whether to use manual quantization for batch split. Only used if `use_batch_split_schedule=True`.",
   )
   weight_quantization_calibration_method: str = Field(
       "absmax",
@@ -626,20 +627,6 @@ class Attention(BaseModel):
   use_post_attn_norm: bool = Field(False, description="Apply LayerNorm after the attention block.")
   use_post_ffw_norm: bool = Field(False, description="Apply LayerNorm after the feed-forward block.")
   use_ragged_attention: bool = Field(False, description="Whether to use ragged attention kernels.")
-  use_tokamax_gmm: bool = Field(
-      False,
-      description="Whether to use the Tokamax library for GMM kernel implementation.",
-  )
-  use_gmm_v2: bool = Field(
-      False,
-      description=(
-          "Whether to use GMM v2 (with bf16 activations and weights) for MoE."
-          " Requires use_tokamax_gmm: true. Currently incompatible with quantization."
-      ),
-  )
-  num_moe_emb_chunks: int = Field(
-      0, description="Number of chunks for overlapping token all-gather and GMM computation along embedding dimension."
-  )
   ragged_block_size: int = Field(256, description="Block size for ragged attention.")
   enable_padding_causal_mask: bool = Field(True, description="Temporary flag for Transformer Engine padding.")
   use_tokamax_splash: bool = Field(False, description="Whether to use tokamax splash attention.")
@@ -998,6 +985,21 @@ class MoEKernels(BaseModel):
 
   merge_gating_gmm: bool = Field(False, description="whether to merge the two gating gmm kernels into one.")
 
+  num_moe_emb_chunks: int = Field(
+      0, description="Number of chunks for overlapping token all-gather and GMM computation along embedding dimension."
+  )
+
+  # tokamax gmm
+  use_tokamax_gmm: bool = Field(
+      False,
+      description="Whether to use the Tokamax library for GMM kernel implementation.",
+  )
+
+  use_gmm_v2: bool = Field(
+      False,
+      description="Whether to use Tokamax GMM v2 for MoE kernel.",
+  )
+
 
 class DeepSeekMoE(BaseModel):
   """Configuration specific to DeepSeek-style MoE layers."""
@@ -3772,10 +3774,15 @@ def calculate_global_batch_sizes(per_device_batch_size, expansion_factor, num_de
     if self.share_kv_projections and self.attention_type == "mla":
       raise ValueError("`share_kv_projections` is not compatible with `attention_type='mla'`.")
 
-    if self.use_gmm_v2 and (self.quantization or self.use_qwix_quantization):
-      raise ValueError("Quantization with GMM v2 is not supported yet.")
-    if self.use_gmm_v2 and not self.use_tokamax_gmm:
-      raise ValueError("GMM v2 requires `use_tokamax_gmm=true`.")
+    if self.use_manual_quantization and not self.use_batch_split_schedule:
+      raise ValueError("manual quantization is only used when `use_batch_split_schedule=True`.")
+
+    # Validation for GMM v2
+    if self.use_gmm_v2:
+      if not self.use_tokamax_gmm:
+        raise ValueError("GMM v2 requires `use_tokamax_gmm=True`.")
+      if self.use_batch_split_schedule:
+        raise ValueError("GMM v2 is not supported with a batch split schedule.")
 
     for val in self.compress_ratios:
       if val != 0 and val < 4: