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>
115 lines
3.7 KiB
Python
115 lines
3.7 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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# Adapted from vllm/tests/basic_correctness/test_basic_correctness.py
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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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from unittest.mock import patch
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from modelscope import snapshot_download # type: ignore
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from vllm import SamplingParams
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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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def test_models_distributed_QwQ():
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example_prompts = [
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"Hello, my name is",
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]
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dtype = "half"
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max_tokens = 5
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with VllmRunner(
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"Qwen/QwQ-32B",
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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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) as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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def test_models_distributed_DeepSeek():
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example_prompts = [
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"Hello, my name is",
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]
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dtype = "half"
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max_tokens = 5
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with VllmRunner(
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"deepseek-ai/DeepSeek-V2-Lite",
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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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) as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_TOPK_OPTIMIZE": "1"})
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def test_models_distributed_topk() -> 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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sampling_params = SamplingParams(max_tokens=5,
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temperature=0.0,
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top_k=50,
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top_p=0.9)
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with VllmRunner(
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"deepseek-ai/DeepSeek-V2-Lite",
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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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) as vllm_model:
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vllm_model.generate(example_prompts, sampling_params)
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@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_DBO": "1"})
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def test_models_distributed_DeepSeek_dbo():
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example_prompts = ["The president of the United States is"] * 41
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dtype = "half"
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sampling_params = SamplingParams(max_tokens=100, temperature=0.0)
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with VllmRunner(
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"deepseek-ai/DeepSeek-V2-Lite",
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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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) as vllm_model:
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vllm_model.generate(example_prompts, sampling_params)
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def test_models_distributed_DeepSeek_W8A8():
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example_prompts = [
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"Hello, my name is",
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]
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max_tokens = 5
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with VllmRunner(
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snapshot_download("vllm-ascend/DeepSeek-V2-Lite-W8A8"),
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max_model_len=8192,
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enforce_eager=True,
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dtype="auto",
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tensor_parallel_size=4,
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quantization="ascend",
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) as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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