wehub-resource-sync
128 lines
4.8 KiB
Python
128 lines
4.8 KiB
Python
# Copyright (c) ModelScope Contributors. All rights reserved.
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import os
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from typing import List, Literal
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os.environ['CUDA_VISIBLE_DEVICES'] = '0'
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def infer_batch(engine: 'InferEngine', infer_requests: List['InferRequest']):
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request_config = RequestConfig(max_tokens=512, temperature=0)
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metric = InferStats()
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resp_list = engine.infer(infer_requests, request_config, metrics=[metric])
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query0 = infer_requests[0].messages[0]['content']
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print(f'query0: {query0}')
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print(f'response0: {resp_list[0].choices[0].message.content}')
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print(f'metric: {metric.compute()}')
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def infer_stream(engine: 'InferEngine', infer_request: 'InferRequest'):
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request_config = RequestConfig(max_tokens=512, temperature=0, stream=True)
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metric = InferStats()
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gen_list = engine.infer([infer_request], request_config, metrics=[metric])
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query = infer_request.messages[0]['content']
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print(f'query: {query}\nresponse: ', end='')
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for resp in gen_list[0]:
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if resp is None:
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continue
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print(resp.choices[0].delta.content, end='', flush=True)
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print()
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print(f'metric: {metric.compute()}')
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def get_message(mm_type: Literal['text', 'image', 'video', 'audio']):
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if mm_type == 'text':
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message = {'role': 'user', 'content': 'who are you?'}
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elif mm_type == 'image':
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message = {
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'role':
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'user',
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'content': [
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{
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'type': 'image',
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# url or local_path or PIL.Image or base64
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'image': 'http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/animal.png'
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},
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{
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'type': 'text',
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'text': 'How many sheep are there in the picture?'
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}
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]
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}
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elif mm_type == 'video':
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# # use base64
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# import base64
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# with open('baby.mp4', 'rb') as f:
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# vid_base64 = base64.b64encode(f.read()).decode('utf-8')
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# video = f'data:video/mp4;base64,{vid_base64}'
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# use url
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video = 'https://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/baby.mp4'
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message = {
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'role': 'user',
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'content': [{
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'type': 'video',
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'video': video
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}, {
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'type': 'text',
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'text': 'Describe this video.'
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}]
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}
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elif mm_type == 'audio':
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message = {
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'role':
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'user',
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'content': [{
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'type': 'audio',
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'audio': 'http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/weather.wav'
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}, {
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'type': 'text',
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'text': 'What does this audio say?'
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}]
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}
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return message
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def get_data(mm_type: Literal['text', 'image', 'video', 'audio']):
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data = {}
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if mm_type == 'text':
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messages = [{'role': 'user', 'content': 'who are you?'}]
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elif mm_type == 'image':
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# The number of <image> tags must be the same as len(images).
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messages = [{'role': 'user', 'content': '<image>How many sheep are there in the picture?'}]
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# Support URL/Path/base64/PIL.Image
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data['images'] = ['http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/animal.png']
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elif mm_type == 'video':
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messages = [{'role': 'user', 'content': '<video>Describe this video.'}]
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data['videos'] = ['https://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/baby.mp4']
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elif mm_type == 'audio':
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messages = [{'role': 'user', 'content': '<audio>What does this audio say?'}]
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data['audios'] = ['http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/weather.wav']
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data['messages'] = messages
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return data
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def run_client(host: str = '127.0.0.1', port: int = 8000):
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engine = InferClient(host=host, port=port)
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print(f'models: {engine.models}')
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# Here, `load_dataset` is used for convenience; `infer_batch` does not require creating a dataset.
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dataset = load_dataset(['AI-ModelScope/LaTeX_OCR:small#1000'], seed=42)[0]
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print(f'dataset: {dataset}')
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infer_requests = [InferRequest(**data) for data in dataset]
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infer_batch(engine, infer_requests)
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infer_stream(engine, InferRequest(messages=[get_message(mm_type='video')]))
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# This writing is equivalent to the above writing.
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infer_stream(engine, InferRequest(**get_data(mm_type='video')))
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if __name__ == '__main__':
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from swift import (DeployArguments, InferClient, InferEngine, InferRequest, InferStats, RequestConfig, load_dataset,
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run_deploy)
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# NOTE: In a real deployment scenario, please comment out the context of run_deploy.
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with run_deploy(
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DeployArguments(model='Qwen/Qwen2.5-VL-3B-Instruct', verbose=False, log_interval=-1,
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infer_backend='vllm')) as port:
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run_client(port=port)
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