v0.10.1rc1

2025-09-09 09:40:35 +08:00
parent d6f6ef41fe
commit 9149384e03
432 changed files with 84698 additions and 1 deletions
--- a/tests/e2e/multicard/test_offline_inference_distributed.py
+++ b/tests/e2e/multicard/test_offline_inference_distributed.py
@@ -0,0 +1,152 @@
+#
+# 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`.
+"""
+import os
+from unittest.mock import patch
+
+from modelscope import snapshot_download  # type: ignore
+from vllm import SamplingParams
+
+from tests.e2e.conftest import VllmRunner
+
+os.environ["PYTORCH_NPU_ALLOC_CONF"] = "max_split_size_mb:256"
+
+
+def test_models_distributed_QwQ():
+    example_prompts = [
+        "Hello, my name is",
+    ]
+    dtype = "half"
+    max_tokens = 5
+    with VllmRunner(
+            "Qwen/QwQ-32B",
+            dtype=dtype,
+            tensor_parallel_size=2,
+            distributed_executor_backend="mp",
+            enforce_eager=True,
+    ) as vllm_model:
+        vllm_model.generate_greedy(example_prompts, max_tokens)
+
+
+def test_models_distributed_DeepSeek_multistream_moe():
+    example_prompts = [
+        "Hello, my name is",
+    ]
+    dtype = "half"
+    max_tokens = 5
+    with VllmRunner(
+            "vllm-ascend/DeepSeek-V3-Pruning",
+            dtype=dtype,
+            tensor_parallel_size=2,
+            distributed_executor_backend="mp",
+            additional_config={
+                "torchair_graph_config": {
+                    "enabled": True,
+                    "enable_multistream_moe": True,
+                },
+                "ascend_scheduler_config": {
+                    "enabled": True,
+                },
+                "refresh": True,
+            },
+    ) as vllm_model:
+        vllm_model.generate_greedy(example_prompts, max_tokens)
+
+
+def test_models_distributed_Qwen3_W8A8():
+    example_prompts = [
+        "Hello, my name is",
+    ]
+    max_tokens = 5
+
+    with VllmRunner(
+            snapshot_download("vllm-ascend/Qwen3-8B-W8A8"),
+            max_model_len=8192,
+            dtype="auto",
+            tensor_parallel_size=2,
+            quantization="ascend",
+    ) as vllm_model:
+        vllm_model.generate_greedy(example_prompts, max_tokens)
+
+
+def test_models_distributed_Qwen3_W4A8DYNAMIC():
+    example_prompts = [
+        "Hello, my name is",
+    ]
+    max_tokens = 5
+
+    with VllmRunner(
+            snapshot_download("vllm-ascend/Qwen3-8B-W4A8"),
+            max_model_len=8192,
+            dtype="auto",
+            tensor_parallel_size=2,
+            quantization="ascend",
+    ) as vllm_model:
+        vllm_model.generate_greedy(example_prompts, max_tokens)
+
+
+@patch.dict(os.environ, {"VLLM_ASCEND_MLA_PA": "1"})
+def test_models_distributed_DeepSeek_W4A8DYNAMIC():
+    prompts = [
+        "Hello, my name is",
+    ]
+    max_tokens = 5
+    with VllmRunner(
+            snapshot_download("vllm-ascend/DeepSeek-V3-W4A8-Pruing"),
+            dtype="auto",
+            tensor_parallel_size=2,
+            quantization="ascend",
+            enforce_eager=True,
+            enable_expert_parallel=True,
+            additional_config={
+                "torchair_graph_config": {
+                    "enabled": False,
+                },
+                "ascend_scheduler_config": {
+                    "enabled": True,
+                }
+            },
+    ) as vllm_model:
+        vllm_model.generate_greedy(prompts, max_tokens)
+
+
+def test_sp_for_qwen3_moe() -> None:
+    example_prompts = [
+        "Hello, my name is",
+    ]
+    sampling_params = SamplingParams(max_tokens=5,
+                                     temperature=0.0,
+                                     top_k=50,
+                                     top_p=0.9)
+
+    with VllmRunner(snapshot_download("Qwen/Qwen3-30B-A3B"),
+                    dtype="auto",
+                    tensor_parallel_size=2,
+                    distributed_executor_backend="mp",
+                    compilation_config={
+                        "pass_config": {
+                            "enable_sequence_parallelism": True
+                        }
+                    },
+                    enable_expert_parallel=True,
+                    enforce_eager=True) as vllm_model:
+        vllm_model.generate(example_prompts, sampling_params)