138 lines
4.7 KiB
Python
138 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/e2e/pull_request/one_card/test_vlm.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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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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WHISPER_MODELS = [
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"openai-mirror/whisper-large-v3-turbo",
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]
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@patch.dict(os.environ, {"VLLM_WORKER_MULTIPROC_METHOD": "spawn"})
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def test_multimodal_vl(vl_config):
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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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prompts = vl_config["prompt_fn"](img_questions)
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with VllmRunner(
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vl_config["model"],
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mm_processor_kwargs=vl_config["mm_processor_kwargs"],
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max_model_len=8192,
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cudagraph_capture_sizes=[1, 2, 4, 8],
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limit_mm_per_prompt={"image": 1},
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) 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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@patch.dict(os.environ, {"VLLM_WORKER_MULTIPROC_METHOD": "spawn"})
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def test_multimodal_vl_language_model_only():
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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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max_tokens = 5
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with VllmRunner(
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"Qwen/Qwen3-VL-8B-Instruct",
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max_model_len=4096,
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cudagraph_capture_sizes=[1, 2, 4, 8],
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gpu_memory_utilization=0.90,
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language_model_only=True,
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) as vllm_model:
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vllm_model.generate_greedy(example_prompts, max_tokens)
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@patch.dict(os.environ, {"VLLM_WORKER_MULTIPROC_METHOD": "spawn"})
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def test_multimodal_audio():
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audio_prompt = "".join([f"Audio {idx + 1}: <|audio_bos|><|AUDIO|><|audio_eos|>\n" for idx in range(2)])
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question = "What sport and what nursery rhyme are referenced?"
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prompt = (
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"<|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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)
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mm_data = {
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"audio": [asset.audio_and_sample_rate for asset in [AudioAsset("mary_had_lamb"), AudioAsset("winning_call")]]
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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, max_tokens=10, stop_token_ids=None)
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with VllmRunner(
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"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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cudagraph_capture_sizes=[1, 2, 4, 8],
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gpu_memory_utilization=0.9,
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) 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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@pytest.mark.parametrize("model", WHISPER_MODELS)
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@patch.dict(os.environ, {"VLLM_WORKER_MULTIPROC_METHOD": "spawn"})
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def test_whisper(model) -> None:
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prompts = ["<|startoftranscript|><|en|><|transcribe|><|notimestamps|>"]
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audios = [AudioAsset("mary_had_lamb").audio_and_sample_rate]
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sampling_params = SamplingParams(temperature=0.2, max_tokens=10, stop_token_ids=None)
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with VllmRunner(
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model, max_model_len=448, max_num_seqs=5, dtype="bfloat16", block_size=128, gpu_memory_utilization=0.9
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) as runner:
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outputs = runner.generate(prompts=prompts, audios=audios, 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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