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# Copyright (c) Alibaba, Inc. and its affiliates.
import asyncio
import os
from typing import List
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()}')
# metric.reset() # reuse
def infer_async_batch(engine: 'InferEngine', infer_requests: List['InferRequest']):
# The asynchronous interface below is equivalent to the synchronous interface above.
request_config = RequestConfig(max_tokens=512, temperature=0)
async def _run():
tasks = [engine.infer_async(infer_request, request_config) for infer_request in infer_requests]
return await asyncio.gather(*tasks)
resp_list = asyncio.run(_run())
query0 = infer_requests[0].messages[0]['content']
print(f'query0: {query0}')
print(f'response0: {resp_list[0].choices[0].message.content}')
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()}')
if __name__ == '__main__':
from swift.llm import InferEngine, InferRequest, PtEngine, RequestConfig, load_dataset
from swift.plugin import InferStats
model = 'Qwen/Qwen2.5-1.5B-Instruct'
infer_backend = 'pt'
if infer_backend == 'pt':
engine = PtEngine(model, max_batch_size=64)
elif infer_backend == 'vllm':
from swift.llm import VllmEngine
engine = VllmEngine(model, max_model_len=8192)
elif infer_backend == 'lmdeploy':
from swift.llm import LmdeployEngine
engine = LmdeployEngine(model)
# Here, `load_dataset` is used for convenience; `infer_batch` does not require creating a dataset.
dataset = load_dataset(['AI-ModelScope/alpaca-gpt4-data-zh#1000'], seed=42)[0]
print(f'dataset: {dataset}')
infer_requests = [InferRequest(**data) for data in dataset]
# if infer_backend in {'vllm', 'lmdeploy'}:
# infer_async_batch(engine, infer_requests)
infer_batch(engine, infer_requests)
messages = [{'role': 'user', 'content': 'who are you?'}]
infer_stream(engine, InferRequest(messages=messages))
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