a25209252f
This PR add 310 e2e test back to ensure the related PR will be tested on
310.
1. for light e2e, we'll run 310p test if the changed files are located
in `vllm_ascend/_310p`
2. for full e2e, we'll always run 310p test
3. for main2main test, we'll stop run 310p test
- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef
Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
76 lines
2.6 KiB
Python
76 lines
2.6 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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import pytest
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from vllm.assets.image import ImageAsset
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from tests.e2e.conftest import VllmRunner
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@pytest.mark.parametrize("dtype", ["float16"])
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@pytest.mark.parametrize("max_tokens", [5])
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def test_llm_models(dtype: str, max_tokens: int) -> None:
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example_prompts = [
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"Hello, my name is",
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"The future of AI is",
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]
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with VllmRunner("Qwen/Qwen3-0.6B",
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tensor_parallel_size=1,
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dtype=dtype,
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max_model_len=2048,
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enforce_eager=True) as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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def test_multimodal_vl():
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image = ImageAsset("cherry_blossom").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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placeholder = "<|image_pad|>"
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prompts = [
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("<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
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f"<|im_start|>user\n<|vision_start|>{placeholder}<|vision_end|>"
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f"{q}<|im_end|>\n<|im_start|>assistant\n") for q in img_questions
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]
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with VllmRunner("Qwen/Qwen2.5-VL-3B-Instruct",
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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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},
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max_model_len=8192,
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enforce_eager=True,
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limit_mm_per_prompt={"image": 1}) as vllm_model:
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outputs = vllm_model.generate_greedy(
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prompts=prompts,
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images=images,
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max_tokens=64,
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)
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assert len(outputs) == len(prompts)
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for _, output_str in outputs:
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assert output_str, "Generated output should not be empty."
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