afdbf77483
### What this PR does / why we need it? - Add a new runner to the continuous integration system and keep the original CI runner until the new runner runs stably - Add distributed test cases ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? CI passed --------- Signed-off-by: wangli <wangli858794774@gmail.com>
81 lines
2.8 KiB
Python
81 lines
2.8 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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# Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
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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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"""Compare the short outputs of HF and vLLM when using greedy sampling.
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Run `pytest tests/test_offline_inference.py`.
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"""
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import os
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import pytest
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import vllm # noqa: F401
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from conftest import VllmRunner
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import vllm_ascend # noqa: F401
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MODELS = [
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"Qwen/Qwen2.5-0.5B-Instruct",
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]
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os.environ["PYTORCH_NPU_ALLOC_CONF"] = "max_split_size_mb:256"
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@pytest.mark.parametrize("model", MODELS)
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@pytest.mark.parametrize("dtype", ["half", "float16"])
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@pytest.mark.parametrize("max_tokens", [5])
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def test_models(model: str, dtype: str, max_tokens: int) -> None:
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# 5042 tokens for gemma2
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# gemma2 has alternating sliding window size of 4096
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# we need a prompt with more than 4096 tokens to test the sliding window
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prompt = "The following numbers of the sequence " + ", ".join(
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str(i) for i in range(1024)) + " are:"
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example_prompts = [prompt]
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with VllmRunner(model,
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max_model_len=8192,
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dtype=dtype,
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enforce_eager=False,
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gpu_memory_utilization=0.7) as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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@pytest.mark.multinpu
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@pytest.mark.parametrize("model, distributed_executor_backend", [
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("Qwen/QwQ-32B", "mp"),
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])
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def test_models_distributed(vllm_runner, model: str,
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distributed_executor_backend: str) -> None:
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example_prompts = [
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"vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.",
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"Briefly describe the major milestones in the development of artificial intelligence from 1950 to 2020.",
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"Compare and contrast artificial intelligence with human intelligence in terms of processing information.",
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]
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dtype = "half"
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max_tokens = 5
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with vllm_runner(
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model,
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dtype=dtype,
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tensor_parallel_size=4,
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distributed_executor_backend=distributed_executor_backend,
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) as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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if __name__ == "__main__":
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import pytest
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pytest.main([__file__])
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