# # 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. # """ Compare the outputs of vLLM with and without aclgraph. Run `pytest tests/multicard/test_data_parallel.py`. """ import os import pytest from tests.conftest import VllmRunner from tests.model_utils import check_outputs_equal MODELS = ["Qwen/Qwen2.5-0.5B-Instruct"] @pytest.mark.skipif(os.getenv("VLLM_USE_V1") == "0", reason="Data parallel only support on v1") @pytest.mark.parametrize("model", MODELS) @pytest.mark.parametrize("max_tokens", [32]) def test_data_parallel_correctness( model: str, max_tokens: int, ) -> None: example_prompts = [ "Hello, my name is", "The president of the United States is", "The capital of France is", "The future of AI is" ] with VllmRunner(model_name=model, max_model_len=1024, max_num_seqs=16, data_parallel_size=2, distributed_executor_backend="mp") as vllm_model: vllm_dp_outputs = vllm_model.generate_greedy(example_prompts, max_tokens) with VllmRunner( model_name=model, max_model_len=1024, max_num_seqs=16, ) as vllm_model: vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens) check_outputs_equal( outputs_0_lst=vllm_outputs, outputs_1_lst=vllm_dp_outputs, name_0="vllm_outputs", name_1="vllm_dp_outputs", )