0644113c35
This PR comments out newly added vlm e2e test of ascend scheduler scenario because I found that when running in multi-batch this will stuck. Need to add this back after dealing with this issue. Signed-off-by: whx-sjtu <2952154980@qq.com>
125 lines
4.8 KiB
Python
125 lines
4.8 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 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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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=False) as vllm_model:
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outputs = vllm_model.generate_greedy(prompts=prompts,
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images=images,
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max_tokens=64)
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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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@pytest.mark.skip(reason="This e2e test will stuck in multi-batch scenario. "
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"Add this back after fixing the issue.")
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def test_multimodal_ascend_scheduler(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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additional_config={
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'ascend_scheduler_config': {
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'enabled': True,
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},
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},
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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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outputs = vllm_model.generate_greedy(prompts=prompts,
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images=images,
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max_tokens=64)
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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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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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outputs = runner.generate(inputs, sampling_params=sampling_params)
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assert outputs is not None, "Generated outputs should not be None."
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assert len(outputs) > 0, "Generated outputs should not be empty."
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