[Bugfix] Fix deepseek V0 issue and add acc ci for it

Signed-off-by: MengqingCao <cmq0113@163.com>

Author: MengqingCao

.github/workflows/vllm_ascend_test.yaml

@@ -112,6 +112,7 @@ jobs:
         run: |
           if [[ "${{ matrix.os }}" == "linux-arm64-npu-1" ]]; then
             pytest -sv tests/singlecard/test_offline_inference.py
+            pytest -sv tests/singlecard --ignore=tests/singlecard/test_offline_inference.py
             pytest -sv tests/ops
             pytest -sv tests/compile
           else
@@ -126,6 +127,7 @@ jobs:
         run: |
           if [[ "${{ matrix.os }}" == "linux-arm64-npu-1" ]]; then
             pytest -sv tests/singlecard/test_offline_inference.py
+            pytest -sv tests/singlecard --ignore=tests/singlecard/test_offline_inference.py
             pytest -sv tests/ops
           else
             pytest -sv -k "QwQ" tests/multicard/test_offline_inference_distributed.py

tests/multicard/test_accuracy.py

@@ -0,0 +1,71 @@
+#
+# 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-project/blob/main/tests/entrypoints/llm/test_accuracy.py
+#
+
+import gc
+import multiprocessing
+from multiprocessing import Queue
+
+import lm_eval
+import pytest
+import torch
+
+# pre-trained model path on Hugging Face.
+MODELS = ["deepseek-ai/DeepSeek-V2-Lite"]
+# Math reasoning benchmark (Grade School Math 8K).
+TASK = "gsm8k"
+# Answer validation requiring format consistency.
+FILTER = "exact_match,strict-match"
+# 3% relative tolerance for numerical accuracy.
+RTOL = 0.03
+# Baseline accuracy after VLLM optimization.
+EXPECTED_VALUE = 0.316
+
+
+def run_test(model_name, queue, more_args=None):
+    model_args = f"pretrained={model_name},max_model_len=4096,trust_remote_code=True,tensor_parallel_size=4"
+    if more_args is not None:
+        model_args = f"{model_args},{more_args}"
+    results = lm_eval.simple_evaluate(
+        model="vllm",
+        model_args=model_args,
+        tasks=TASK,
+        batch_size="auto",
+    )
+    result = results["results"][TASK][FILTER]
+    print("result:", result)
+    queue.put(result)
+    del results
+    torch.npu.empty_cache()
+    gc.collect()
+
+
+@pytest.mark.parametrize("model", MODELS)
+def test_lm_eval_accuracy(model, monkeypatch: pytest.MonkeyPatch):
+    with monkeypatch.context():
+        result_queue: Queue[float] = multiprocessing.Queue()
+        p = multiprocessing.Process(target=run_test,
+                                    args=(
+                                        model,
+                                        result_queue,
+                                    ))
+        p.start()
+        p.join()
+        result = result_queue.get()
+        assert (EXPECTED_VALUE - RTOL < result < EXPECTED_VALUE + RTOL), \
+            f"Expected: {EXPECTED_VALUE}±{RTOL} | Measured: {result}"

tests/singlecard/test_accuracy.py

@@ -63,4 +63,4 @@ def test_lm_eval_accuracy(monkeypatch: pytest.MonkeyPatch):
         p.join()
         result = result_queue.get()
         assert (EXPECTED_VALUE - RTOL < result < EXPECTED_VALUE + RTOL), \
-            f"Expected: {EXPECTED_VALUE}±{RTOL} | Measured: {result}"
+            f"Expected: {EXPECTED_VALUE}±{RTOL} | Measured: {result}"

vllm_ascend/ops/fused_moe.py

@@ -198,6 +198,7 @@ def fused_experts(
     num_experts = w1.shape[0]
     dtype = hidden_states.dtype
     device = hidden_states.device
+    topk_weights = topk_weights.to(dtype)
     # assert dtype in [torch.float32, torch.float16, torch.bfloat16
     #                  ], "Only float32, float16, and bfloat16 are supported"