fef18b60bc
Refactor E2E CI to make it clear and faster
1. remove some uesless e2e test
2. remove some uesless function
3. Make sure all test runs with VLLMRunner to avoid oom error
4. Make sure all ops test end with torch.empty_cache to avoid oom error
5. run the test one by one to avoid resource limit error
- vLLM version: v0.10.1.1
- vLLM main:
a344a5aa0a
Signed-off-by: wangxiyuan <wangxiyuan1007@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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from vllm import SamplingParams
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from vllm.assets.audio import AudioAsset
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from vllm.assets.image import ImageAsset
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from tests.e2e.conftest import VllmRunner
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os.environ["PYTORCH_NPU_ALLOC_CONF"] = "max_split_size_mb:256"
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@pytest.mark.skip(reason="fix me")
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def test_multimodal_vl(prompt_template):
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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 VllmRunner("Qwen/Qwen2.5-VL-3B-Instruct",
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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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},
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enforce_eager=True) 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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def test_multimodal_audio():
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audio_prompt = "".join([
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f"Audio {idx+1}: <|audio_bos|><|AUDIO|><|audio_eos|>\n"
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for idx in range(2)
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])
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question = "What sport and what nursery rhyme are referenced?"
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prompt = ("<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n"
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"<|im_start|>user\n"
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f"{audio_prompt}{question}<|im_end|>\n"
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"<|im_start|>assistant\n")
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mm_data = {
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"audio": [
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asset.audio_and_sample_rate for asset in
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[AudioAsset("mary_had_lamb"),
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AudioAsset("winning_call")]
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]
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}
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inputs = {"prompt": prompt, "multi_modal_data": mm_data}
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sampling_params = SamplingParams(temperature=0.2,
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max_tokens=10,
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stop_token_ids=None)
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with VllmRunner("Qwen/Qwen2-Audio-7B-Instruct",
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max_model_len=4096,
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max_num_seqs=5,
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dtype="bfloat16",
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limit_mm_per_prompt={"audio": 2},
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gpu_memory_utilization=0.9) as runner:
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runner.generate(inputs, sampling_params=sampling_params)
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