d6be63e11d
### What this PR does / why we need it? Add Qwen3-0.6B-Base for integration test Signed-off-by: wangli <wangli858794774@gmail.com>
90 lines
3.2 KiB
Python
90 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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# 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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import pytest
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import vllm # noqa: F401
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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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"vllm-ascend/Qwen2.5-0.5B-Instruct-w8a8",
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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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os.environ["VLLM_USE_MODELSCOPE"] = "True"
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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.parametrize("model", MULTIMODALITY_MODELS)
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@pytest.mark.skipif(os.getenv("VLLM_USE_V1") == "1",
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reason="qwen2.5_vl is not supported on v1")
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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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if __name__ == "__main__":
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import pytest
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pytest.main([__file__])
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