aeb5aa8b88
### What this PR does / why we need it?
Add `__main__` guard to all offline examples.
- vLLM version: v0.9.2
- vLLM main:
76b494444f
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Signed-off-by: shen-shanshan <467638484@qq.com>
90 lines
3.0 KiB
Python
90 lines
3.0 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-project/vllm/examples/offline_inference/audio_language.py
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#
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"""
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This example shows how to use vLLM for running offline inference
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with the correct prompt format on audio language models.
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For most models, the prompt format should follow corresponding examples
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on HuggingFace model repository.
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"""
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import os
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from vllm import LLM, SamplingParams
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from vllm.assets.audio import AudioAsset
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os.environ["VLLM_USE_MODELSCOPE"] = "True"
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os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
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audio_assets = [AudioAsset("mary_had_lamb"), AudioAsset("winning_call")]
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question_per_audio_count = {
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1: "What is recited in the audio?",
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2: "What sport and what nursery rhyme are referenced?"
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}
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def prepare_inputs(audio_count: int):
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audio_in_prompt = "".join([
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f"Audio {idx+1}: <|audio_bos|><|AUDIO|><|audio_eos|>\n"
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for idx in range(audio_count)
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])
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question = question_per_audio_count[audio_count]
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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_in_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 audio_assets[:audio_count]]
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}
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# Merge text prompt and audio data into inputs
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inputs = {"prompt": prompt, "multi_modal_data": mm_data}
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return inputs
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def main(audio_count: int):
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# NOTE: The default `max_num_seqs` and `max_model_len` may result in OOM on
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# lower-end GPUs.
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# Unless specified, these settings have been tested to work on a single L4.
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# `limit_mm_per_prompt`: the max num items for each modality per prompt.
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llm = LLM(model="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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limit_mm_per_prompt={"audio": audio_count},
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enforce_eager=True)
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inputs = prepare_inputs(audio_count)
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sampling_params = SamplingParams(temperature=0.2,
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max_tokens=64,
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stop_token_ids=None)
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outputs = llm.generate(inputs, sampling_params=sampling_params)
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for o in outputs:
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generated_text = o.outputs[0].text
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print(generated_text)
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if __name__ == "__main__":
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audio_count = 2
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main(audio_count)
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