[BugFix] Fix the bug that qwen3 moe doesn't work with aclgraph (#2183)

What's the PR does:
1. Move AscendSparseMoeBlock to qwen3 model, since it's only used by
qwen3 model.
2. Disable AscendSparseMoeBlock if aclgraph is enabled,
AscendSparseMoeBlock doesn't work with aclgraph currently.

- vLLM version: v0.10.0
- vLLM main:
cdfd6871a5

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>

tests/e2e/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.e2e.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=2,
+            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=2,
+            enable_expert_parallel=True,
+            distributed_executor_backend="mp",
+    ) as vllm_model:
+        vllm_model.generate_greedy(example_prompts, max_tokens)