[Feat] Support MLP_TP feature, exclude MOE layer

tests/ut/distributed/test_parallel_state.py

@@ -41,6 +41,7 @@ def test_init_ascend_model_parallel(mock_distributed, parallel_config):
     mock_ascend_config.finegrained_tp_config.lmhead_tensor_parallel_size = 2
     mock_ascend_config.finegrained_tp_config.oproj_tensor_parallel_size = 2
     mock_ascend_config.finegrained_tp_config.embedding_tensor_parallel_size = 2
+    mock_ascend_config.finegrained_tp_config.mlp_tensor_parallel_size = 2
     mock_ascend_config.flashcomm2_oproj_tensor_parallel_size = 2
     mock_ascend_config.pd_tp_ratio = 2
     mock_ascend_config.num_head_replica = 0

vllm_ascend/distributed/parallel_state.py

@@ -96,25 +96,11 @@ def init_ascend_model_parallel(parallel_config: ParallelConfig, ):
                                      backend,
                                      group_name="mc2")
 
-    # Initialize specialized tensor parallel (TP) process groups for fine-grained model parallelism
-    # on Ascend hardware. This enables independent TP configurations for three critical components:
-
-    # 1. ** LM Head **:
-    # The final linear layer that maps hidden states to vocabulary logits.
-    # Controlled by `lmhead_tensor_parallel_size`.
-
-    # 2. ** o_proj **:
-    # The output projection in attention blocks (e.g., in Multi-Head Attention).
-    # Controlled by `oproj_tensor_parallel_size`.
-
-    # 3. ** Embedding **:
-    # The token embedding table at the input and/or output of the model.
-    # Controlled by `embedding_tensor_parallel_size`.
-
-    # 4. ** MLP **:
-    # The feed-forward network layers within transformer blocks.
-    # Controlled by `mlp_tensor_parallel_size`.
-
+    # Initialize fine-grained TP process groups on Ascend for four components:
+    # 1. LM Head: output logits projection (`lmhead_tensor_parallel_size`)
+    # 2. O Proj: attention output projection (`oproj_tensor_parallel_size`)
+    # 3. Embedding: The token embedding table at the input of the model (`embedding_tensor_parallel_size`)
+    # 4. MLP: feed-forward network in transformer blocks (`mlp_tensor_parallel_size`)
     _group_cache = {}
 
     def _create_or_get_group(group_size: int,
@@ -149,9 +135,9 @@ def _create_or_get_group(group_size: int,
     embedding_tp_size = get_ascend_config(
     ).finegrained_tp_config.embedding_tensor_parallel_size
     mlp_tp_size = get_ascend_config(
-    ).finegrained_tp_config.embedding_tensor_parallel_size
+    ).finegrained_tp_config.mlp_tensor_parallel_size
 
-    global _OTP, _LMTP, _EMBED_TP
+    global _OTP, _LMTP, _EMBED_TP, _MLP_TP
 
     if otp_size > 0:
         _OTP = _create_or_get_group(otp_size, "otp")

vllm_ascend/ops/linear_op.py

@@ -36,6 +36,8 @@
 Row parallel op follows a similar approach - inherit from RowColumnParallelOp and register the new class in get_row_parallel_op.
 """
 
+import re
+from functools import lru_cache
 from typing import Optional, Union
 
 import torch
@@ -605,7 +607,8 @@ def update_attrs(self):
 def _get_column_parallel_op(
         prefix, layer
 ) -> Optional[Union[MLPColumnParallelOp, SequenceColumnParallelOp]]:
-    if mlp_tp_enable() and "gate_up_proj" in prefix:
+    if "gate_up_proj" in prefix and mlp_tp_enable(
+    ) and not is_moe_layer(prefix):
         return MLPColumnParallelOp(layer)
     if enable_sp():
         if "shared_expert" in prefix:
@@ -629,7 +632,7 @@ def _get_row_parallel_op(
 ) -> Optional[Union[MLPRowParallelOp, OProjRowParallelOp,
                     Flashcomm2OProjRowParallelOp, MatmulAllreduceRowParallelOp,
                     SequenceRowParallelOp]]:
-    if "down_proj" in prefix and mlp_tp_enable():
+    if "down_proj" in prefix and mlp_tp_enable() and not is_moe_layer(prefix):
         return MLPRowParallelOp(layer)
     if "o_proj" in prefix and oproj_tp_enable():
         return OProjRowParallelOp(layer)
@@ -681,3 +684,27 @@ def get_replicated_op(disable_tp, prefix,
         return None
 
     return CustomReplicatedOp(layer)
+
+
+def is_moe_layer(prefix: str) -> bool:
+
+    @lru_cache(maxsize=1)
+    def get_moe_params():
+        from vllm.config import get_current_vllm_config
+        vllm_config = get_current_vllm_config()
+        config = vllm_config.model_config.hf_config
+        n_routed_experts = getattr(config, 'n_routed_experts', 0)
+        first_k_dense_replace = getattr(config, 'first_k_dense_replace',
+                                        float('inf'))
+        moe_layer_freq = getattr(config, 'moe_layer_freq', 1)
+        return n_routed_experts, first_k_dense_replace, moe_layer_freq
+
+    match = re.search(r'layers\.(\d+)\.', prefix)
+    if match is None:
+        return False
+    layer_idx = int(match.group(1))
+
+    n_routed_experts, first_k_dense_replace, moe_layer_freq = get_moe_params()
+
+    return (n_routed_experts is not None and layer_idx >= first_k_dense_replace
+            and layer_idx % moe_layer_freq == 0)