69b817ed65
This PR added the unit test framework to enable ut for vLLM Ascend. Unit test runs on CPU machines. It'll be ran once lint check is passed the same as e2e test. For unit test, this PR created a new folder called `ut` under `tests` module. All the test file in `ut` should keep the same with the code in `vllm-ascend`. The file name should be start with `test_` prefix. For example, in this PR. the `test_ascend_config.py` is added for `ascend_config.py` test. A new fille `worker/test_worker_v1.py` is also added as the placeholder. This file should be the unit test for `vllm-ascend/worker/worker_v1.py`. Additional, a new `fake_weight` folder is added, it contains the config.json from `facebook/opt-125m`, so that the test will not always visit huggingface. TODO: We should add all the unit test file one by one in the future. Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
96 lines
3.2 KiB
Python
96 lines
3.2 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/compile/test_aclgraph.py`.
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"""
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import os
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import pytest
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import torch
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from vllm import LLM, SamplingParams
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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="aclgraph 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_models(
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model: str,
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max_tokens: int,
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monkeypatch: pytest.MonkeyPatch,
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) -> None:
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with monkeypatch.context() as m:
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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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# aclgraph only support on v1
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m.setenv("VLLM_USE_V1", "1")
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sampling_params = SamplingParams(max_tokens=max_tokens,
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temperature=0.0)
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# TODO: change to use vllmrunner when the registry of custom op is solved
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# while running pytest
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vllm_model = LLM(model)
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vllm_aclgraph_outputs = vllm_model.generate(prompts, sampling_params)
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del vllm_model
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torch.npu.empty_cache()
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vllm_model = LLM(model, enforce_eager=True)
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vllm_eager_outputs = vllm_model.generate(prompts, sampling_params)
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del vllm_model
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torch.npu.empty_cache()
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vllm_aclgraph_outputs_list = []
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for output in vllm_aclgraph_outputs:
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vllm_aclgraph_outputs_list.append(
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(output.outputs[0].index, output.outputs[0].text))
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vllm_eager_outputs_list = []
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for output in vllm_eager_outputs:
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vllm_eager_outputs_list.append(
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(output.outputs[0].index, output.outputs[0].text))
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check_outputs_equal(
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outputs_0_lst=vllm_eager_outputs_list,
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outputs_1_lst=vllm_aclgraph_outputs_list,
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name_0="vllm_eager_outputs",
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name_1="vllm_aclgraph_outputs",
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)
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@pytest.mark.skipif(os.getenv("VLLM_USE_V1") == "0",
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reason="aclgraph only support on v1")
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def test_deepseek_raises_error(monkeypatch: pytest.MonkeyPatch) -> None:
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with monkeypatch.context() as m:
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m.setenv("VLLM_USE_MODELSCOPE", "True")
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m.setenv("VLLM_USE_V1", "1")
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with pytest.raises(NotImplementedError) as excinfo:
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VllmRunner("deepseek-ai/DeepSeek-V2-Lite-Chat",
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max_model_len=1024,
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enforce_eager=False)
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assert "ACL Graph does not support deepseek" in str(excinfo.value)
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