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>
81 lines
3.1 KiB
Python
81 lines
3.1 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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# This file is a part of the vllm-ascend project.
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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/multicard/test_torchair_graph_mode.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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os.environ["PYTORCH_NPU_ALLOC_CONF"] = "max_split_size_mb:256"
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@pytest.mark.skipif(os.getenv("VLLM_USE_V1") == "0",
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reason="torchair graph is not supported on v0")
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def test_e2e_deepseekv3_with_torchair(monkeypatch: pytest.MonkeyPatch):
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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_WORKER_MULTIPROC_METHOD", "spawn")
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example_prompts = [
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"Hello, my name is",
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"The president of the United States is",
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"The capital of France is",
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"The future of AI is",
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]
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dtype = "half"
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max_tokens = 5
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# torchair is only work without chunked-prefill now
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with VllmRunner(
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"vllm-ascend/DeepSeek-V3-Pruning",
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dtype=dtype,
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tensor_parallel_size=4,
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distributed_executor_backend="mp",
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additional_config={
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"torchair_graph_config": {
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"enabled": True,
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},
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"ascend_scheduler_config": {
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"enabled": True,
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},
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"refresh": True,
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},
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enforce_eager=False,
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) as vllm_model:
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# use greedy sampler to make sure the generated results are fix
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vllm_output = vllm_model.generate_greedy(example_prompts,
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max_tokens)
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# NOTE: vllm-ascend/DeepSeek-V3-Pruning is a random weight of
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# DeepSeek-V3 with 2 hidden layers, thus the golden results seems
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# inaccurate. This will only change if accuracy improves with the
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# official weights of DeepSeek-V3.
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golden_results = [
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'Hello, my name is feasibility伸 spazio debtor添',
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'The president of the United States is begg"""\n杭州风和 bestimm',
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'The capital of France is frequentlyশามalinkAllowed',
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'The future of AI is deleting俯احت怎么样了حراف',
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]
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assert len(golden_results) == len(vllm_output)
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for i in range(len(vllm_output)):
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assert golden_results[i] == vllm_output[i][1]
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print(f"Generated text: {vllm_output[i][1]!r}")
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