[Bugfix] Fix the bug that qwen3 moe doesn't work with aclgraph

tests/multicard/test_qwen3_moe.py

@@ -0,0 +1,55 @@
+#
+# 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.
+# Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
+#
+"""Compare the short outputs of HF and vLLM when using greedy sampling.
+
+Run `pytest tests/test_offline_inference.py`.
+"""
+
+from tests.conftest import VllmRunner
+
+
+def test_models_distributed_Qwen3_MOE_TP2():
+    example_prompts = [
+        "Hello, my name is",
+    ]
+    dtype = "half"
+    max_tokens = 5
+    with VllmRunner(
+            "Qwen/Qwen3-30B-A3B",
+            dtype=dtype,
+            tensor_parallel_size=4,
+            distributed_executor_backend="mp",
+    ) as vllm_model:
+        vllm_model.generate_greedy(example_prompts, max_tokens)
+
+
+def test_models_distributed_Qwen3_MOE_TP2_WITH_EP():
+    example_prompts = [
+        "Hello, my name is",
+    ]
+    dtype = "half"
+    max_tokens = 5
+    with VllmRunner(
+            "Qwen/Qwen3-30B-A3B",
+            dtype=dtype,
+            tensor_parallel_size=4,
+            enable_expert_parallel=True,
+            distributed_executor_backend="mp",
+    ) as vllm_model:
+        vllm_model.generate_greedy(example_prompts, max_tokens)

vllm_ascend/models/qwen3_moe.py

@@ -22,7 +22,7 @@
 from torch import nn
 from transformers import PretrainedConfig
 from vllm.compilation.decorators import support_torch_compile
-from vllm.config import CacheConfig, VllmConfig
+from vllm.config import CacheConfig, CompilationLevel, VllmConfig
 from vllm.distributed import get_pp_group
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.logits_processor import LogitsProcessor
@@ -32,7 +32,8 @@
 from vllm.model_executor.models.interfaces import SupportsPP
 from vllm.model_executor.models.qwen3_moe import (Qwen3MoeAttention,
                                                   Qwen3MoeForCausalLM,
-                                                  Qwen3MoeMLP, Qwen3MoeModel)
+                                                  Qwen3MoeMLP, Qwen3MoeModel,
+                                                  Qwen3MoeSparseMoeBlock)
 from vllm.model_executor.models.utils import (
     extract_layer_index, make_empty_intermediate_tensors_factory, make_layers,
     maybe_prefix)
@@ -78,12 +79,21 @@ def __init__(
         layer_idx = extract_layer_index(prefix)
         mlp_only_layers = ([] if not hasattr(config, "mlp_only_layers") else
                            config.mlp_only_layers)
+        use_aclgraph = (vllm_config is not None
+                        and vllm_config.compilation_config.level
+                        == CompilationLevel.PIECEWISE
+                        and not vllm_config.model_config.enforce_eager)
         if (layer_idx not in mlp_only_layers) and (
                 config.num_experts > 0 and
             (layer_idx + 1) % config.decoder_sparse_step == 0):
-            self.mlp = AscendSparseMoeBlock(config=config,
-                                            quant_config=quant_config,
-                                            prefix=f"{prefix}.mlp")
+            if not use_aclgraph:
+                self.mlp = AscendSparseMoeBlock(config=config,
+                                                quant_config=quant_config,
+                                                prefix=f"{prefix}.mlp")
+            else:
+                self.mlp = Qwen3MoeSparseMoeBlock(config=config,
+                                                  quant_config=quant_config,
+                                                  prefix=f"{prefix}.mlp")
         else:
             self.mlp = Qwen3MoeMLP(hidden_size=config.hidden_size,
                                    intermediate_size=config.intermediate_size,