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>
130 lines
4.7 KiB
Python
130 lines
4.7 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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import pytest
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import vllm # noqa: F401
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from modelscope import snapshot_download # type: ignore[import-untyped]
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from vllm import SamplingParams
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from vllm.assets.image import ImageAsset
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import vllm_ascend # noqa: F401
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from tests.conftest import VllmRunner
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MODELS = [
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"Qwen/Qwen2.5-0.5B-Instruct",
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"Qwen/Qwen3-0.6B-Base",
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]
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MULTIMODALITY_MODELS = ["Qwen/Qwen2.5-VL-3B-Instruct"]
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QUANTIZATION_MODELS = [
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"vllm-ascend/Qwen2.5-0.5B-Instruct-W8A8",
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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=True,
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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.parametrize("model", QUANTIZATION_MODELS)
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@pytest.mark.parametrize("max_tokens", [5])
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def test_quantization_models(model: str, max_tokens: int) -> None:
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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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# NOTE: Using quantized model repo id from modelscope encounters an issue,
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# this pr (https://github.com/vllm-project/vllm/pull/19212) fix the issue,
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# after it is being merged, there's no need to download model explicitly.
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model_path = snapshot_download(model)
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with VllmRunner(model_path,
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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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gpu_memory_utilization=0.7,
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quantization="ascend") as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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@pytest.mark.parametrize("model", MULTIMODALITY_MODELS)
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def test_multimodal(model, prompt_template, vllm_runner):
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image = ImageAsset("cherry_blossom") \
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.pil_image.convert("RGB")
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img_questions = [
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"What is the content of this image?",
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"Describe the content of this image in detail.",
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"What's in the image?",
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"Where is this image taken?",
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]
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images = [image] * len(img_questions)
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prompts = prompt_template(img_questions)
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with vllm_runner(model,
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max_model_len=4096,
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mm_processor_kwargs={
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"min_pixels": 28 * 28,
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"max_pixels": 1280 * 28 * 28,
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"fps": 1,
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}) as vllm_model:
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vllm_model.generate_greedy(prompts=prompts,
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images=images,
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max_tokens=64)
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@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_TOPK_OPTIMIZE": "1"})
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def test_models_topk() -> None:
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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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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("Qwen/Qwen2.5-0.5B-Instruct",
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max_model_len=8192,
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dtype="float16",
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enforce_eager=True,
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gpu_memory_utilization=0.7) as vllm_model:
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vllm_model.generate(example_prompts, sampling_params)
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