96fa7ff63b
### What this PR does / why we need it? 1. Fix rank set in DP scenario. The new poc version of torch-npu support setting `ASCEND_RT_VISIBLE_DEVICES` dynamically, thus we could use the rank set in `DPEngineCoreProc` directly instead of calculating local rank across dp by hand in the patched `_init_data_parallel` Closes: https://github.com/vllm-project/vllm-ascend/issues/1170 2. Bump torch-npu version to 2.5.1.post1.dev20250528 Closes: https://github.com/vllm-project/vllm-ascend/pull/1242 Closes: https://github.com/vllm-project/vllm-ascend/issues/1232 ### How was this patch tested? CI passed with new added test. --------- Signed-off-by: MengqingCao <cmq0113@163.com> Signed-off-by: Icey <1790571317@qq.com> Co-authored-by: Icey <1790571317@qq.com>
67 lines
2.1 KiB
Python
67 lines
2.1 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Copyright 2023 The vLLM team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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Compare the outputs of vLLM with and without aclgraph.
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Run `pytest tests/multicard/test_data_parallel.py`.
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"""
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import os
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import pytest
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from tests.conftest import VllmRunner
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from tests.model_utils import check_outputs_equal
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MODELS = ["Qwen/Qwen2.5-0.5B-Instruct"]
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@pytest.mark.skipif(os.getenv("VLLM_USE_V1") == "0",
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reason="Data parallel only support on v1")
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("max_tokens", [32])
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def test_data_parallel_correctness(
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model: str,
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max_tokens: int,
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) -> None:
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example_prompts = [
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"Hello, my name is", "The president of the United States is",
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"The capital of France is", "The future of AI is"
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]
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with VllmRunner(model_name=model,
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max_model_len=1024,
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max_num_seqs=16,
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data_parallel_size=2,
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distributed_executor_backend="mp") as vllm_model:
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vllm_dp_outputs = vllm_model.generate_greedy(example_prompts,
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max_tokens)
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with VllmRunner(
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model_name=model,
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max_model_len=1024,
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max_num_seqs=16,
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) as vllm_model:
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vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
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check_outputs_equal(
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outputs_0_lst=vllm_outputs,
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outputs_1_lst=vllm_dp_outputs,
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name_0="vllm_outputs",
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name_1="vllm_dp_outputs",
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)
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