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