add qwen3-moe optimization

tests/e2e/multicard/test_qwen3_moe.py

@@ -0,0 +1,72 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2023 The vLLM team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# This file is a part of the vllm-ascend project.
+#
+"""
+Compare the outputs of vLLM with and without aclgraph.
+
+Run `pytest tests/multicard/test_data_parallel.py`.
+"""
+
+import os
+import subprocess
+import sys
+from unittest.mock import patch
+
+import pytest
+
+MODELS = ["vllm-ascend/Qwen3-30B-A3B-Puring"]
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("max_tokens", [32])
+@patch.dict(os.environ, {"ASCEND_RT_VISIBLE_DEVICES": "0,1,2,3"})
+def test_qwen3_moe_inference(model, max_tokens):
+    script = "examples/offline_data_parallel.py"
+
+    env = os.environ.copy()
+
+    cmd = [
+        sys.executable,
+        script,
+        "--model",
+        model,
+        "--dp-size",
+        "2",
+        "--tp-size",
+        "2",
+        "--node-size",
+        "1",
+        "--node-rank",
+        "0",
+        "--trust-remote-code",
+        "--enforce-eager",
+    ]
+
+    print(f"Running subprocess: {' '.join(cmd)}")
+    proc = subprocess.run(cmd,
+                          env=env,
+                          stdout=subprocess.PIPE,
+                          stderr=subprocess.STDOUT,
+                          timeout=600)
+    output = proc.stdout.decode()
+
+    print(output)
+
+    assert "DP rank 0 needs to process" in output
+    assert "DP rank 1 needs to process" in output
+    assert "Generated text:" in output
+    assert proc.returncode == 0

vllm_ascend/models/qwen3_moe.py

@@ -16,8 +16,24 @@
 # Adapted from vllm/model_executor/models/qwen3_moe.py
 # This file is a part of the vllm-ascend project.
 
+from typing import Optional
+
+import torch
+import vllm
+from torch import nn
+from transformers import PretrainedConfig
+from vllm.attention import AttentionMetadata
+from vllm.distributed import get_tensor_model_parallel_world_size, get_tp_group
+from vllm.distributed.parallel_state import get_dp_group
+from vllm.forward_context import get_forward_context
+from vllm.model_executor.layers.linear import ReplicatedLinear
+from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.models.qwen3_moe import Qwen3MoeForCausalLM
 
+from vllm_ascend.ascend_config import get_ascend_config
+from vllm_ascend.distributed.parallel_state import get_ep_group
+from vllm_ascend.ops.fused_moe import AscendFusedMoE
+
 
 class CustomQwen3MoeForCausalLM(Qwen3MoeForCausalLM):
     packed_modules_mapping = {
@@ -33,3 +49,86 @@ class CustomQwen3MoeForCausalLM(Qwen3MoeForCausalLM):
         "experts":
         ["experts.0.gate_proj", "experts.0.up_proj", "experts.0.down_proj"],
     }
+
+
+class AscendQwen3MoeSparseMoeBlock(nn.Module):
+    top_k: int
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+        self.tp_size = get_tensor_model_parallel_world_size()
+        if self.tp_size > config.num_experts:
+            raise ValueError(
+                f"Tensor parallel size {self.tp_size} is greater than "
+                f"the number of experts {config.num_experts}.")
+
+        ascend_config = get_ascend_config()
+        self.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled
+        self.enable_multistream_moe = \
+            ascend_config.torchair_graph_config.enable_multistream_moe
+
+        self.gate = ReplicatedLinear(config.hidden_size,
+                                     config.num_experts,
+                                     bias=False,
+                                     quant_config=None,
+                                     prefix=f"{prefix}.gate")
+
+        self.experts = AscendFusedMoE(
+            num_experts=config.num_experts,
+            top_k=config.num_experts_per_tok,
+            hidden_size=config.hidden_size,
+            intermediate_size=config.moe_intermediate_size,
+            reduce_results=False,
+            renormalize=config.norm_topk_prob,
+            quant_config=quant_config,
+            prefix=f"{prefix}.experts")
+
+        self.top_k = config.num_experts_per_tok
+
+        self.dp_size = get_dp_group().world_size
+
+        self.tp_group = get_tp_group().device_group
+        self.tp_rank = get_tp_group().rank_in_group
+        self.ep_group = get_ep_group()
+
+        self.params_dtype = torch.get_default_dtype()
+
+    def forward(
+            self,
+            hidden_states: torch.Tensor,
+            attn_metadata: Optional[AttentionMetadata] = None) -> torch.Tensor:
+        if attn_metadata is None:
+            attn_metadata = get_forward_context().attn_metadata
+        # when profile runs, force experts to load balanced tokens
+        # to avoid high memory consumption on a single rank.
+        # TODO: need a better flag to indicate whether in profile run or not.
+        if attn_metadata is None:
+            # for profile run
+            is_prefill = True
+            enable_force_load_balance = True
+        else:
+            # is_prefill = attn_metadata.num_prefills > 0
+            enable_force_load_balance = False
+            if hasattr(attn_metadata, 'with_prefill_across_dp'):
+                is_prefill = attn_metadata.with_prefill_across_dp
+
+        # router_logits: (num_tokens, n_experts)
+        router_logits, _ = self.gate(hidden_states)
+
+        hidden_states = self.experts(
+            hidden_states=hidden_states,
+            router_logits=router_logits,
+            is_prefill=is_prefill,
+            top_k=self.top_k,
+            enable_force_load_balance=enable_force_load_balance,
+            shared_experts=None)
+
+        return hidden_states
+
+
+vllm.model_executor.models.qwen3_moe.Qwen3MoeSparseMoeBlock = AscendQwen3MoeSparseMoeBlock

vllm_ascend/ops/fused_moe.py

@@ -118,9 +118,13 @@
     top_k: int,
     expert_map: torch.Tensor = None,
     moe_all_to_all_group_name: Optional[str] = None,
-    shared_experts: Optional[Any] = None
+    shared_experts: Optional[Any] = None,
+    global_batch_size: int = 256,
 ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
-    global_bs = 0
+
+    ep_group = get_ep_group().device_group
+    all_to_all_group_size = torch.distributed.get_world_size(ep_group)
+    global_bs = global_batch_size * all_to_all_group_size
     moe_expert_num = len(expert_map)
     kwargs_mc2 = {
         "x": hidden_states,
@@ -132,11 +136,8 @@
     }
 
     rank = torch.distributed.get_rank()
-
     quant_mode = 0
-    ep_group = get_ep_group().device_group
     local_rank = torch.distributed.get_rank(group=ep_group)
-    all_to_all_group_size = torch.distributed.get_world_size(ep_group)
 
     tp_size = get_etp_group().world_size
     tp_rank = rank % tp_size
@@ -204,7 +205,7 @@
         "expert_shard_type": 0,
         "shared_expert_rank_num": 0,
         "moe_expert_num": moe_expert_num,
-        "global_bs": 0,
+        "global_bs": global_bs,
     }
     tp_recv_counts = output[5]
     stage3_kwargs = {
@@ -1037,7 +1038,8 @@
                 top_k=top_k,
                 expert_map=expert_map,
                 moe_all_to_all_group_name=self.moe_all_to_all_group_name,
-                shared_experts=shared_experts)
+                shared_experts=shared_experts,
+                global_batch_size=self.global_batch_size)
         elif fused_moe_state == FusedMoEState.AllGather:
             return fused_experts(hidden_states=x,
                                  w1=layer.w13_weight,