wehub-resource-sync
77 lines
2.5 KiB
Python
77 lines
2.5 KiB
Python
import os
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import torch
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from typing import Literal
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os.environ['CUDA_VISIBLE_DEVICES'] = '0'
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os.environ['ASCEND_RT_VISIBLE_DEVICES'] = '0'
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def _prepare(infer_backend: Literal['vllm', 'transformers', 'lmdeploy']):
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from swift.infer_engine import InferRequest
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if infer_backend == 'lmdeploy':
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from swift.infer_engine import LmdeployEngine
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engine = LmdeployEngine('Qwen/Qwen-VL-Chat', torch_dtype=torch.float32)
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elif infer_backend == 'transformers':
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from swift.infer_engine import TransformersEngine
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engine = TransformersEngine('Qwen/Qwen2-VL-7B-Instruct')
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elif infer_backend == 'vllm':
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from swift.infer_engine import VllmEngine
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engine = VllmEngine('Qwen/Qwen2-VL-7B-Instruct')
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infer_requests = [
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InferRequest([{
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'role': 'user',
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'content': '晚上睡不着觉怎么办'
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}]),
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InferRequest([{
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'role':
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'user',
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'content': [{
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'type': 'image_url',
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'image_url': 'http://modelscope-open.oss-cn-hangzhou.aliyuncs.com/images/cat.png'
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}]
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}])
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]
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return engine, infer_requests
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def test_infer(engine, infer_requests):
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from swift.infer_engine import RequestConfig
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from swift.metrics import InferStats
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request_config = RequestConfig(temperature=0)
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infer_stats = InferStats()
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response_list = engine.infer(infer_requests, request_config=request_config, metrics=[infer_stats])
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for response in response_list[:2]:
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print(response.choices[0].message.content)
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print(infer_stats.compute())
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def test_stream(engine, infer_requests):
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from swift.infer_engine import RequestConfig
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from swift.metrics import InferStats
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infer_stats = InferStats()
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request_config = RequestConfig(temperature=0, stream=True, logprobs=True)
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gen_list = engine.infer(infer_requests, request_config=request_config, metrics=[infer_stats])
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for response in gen_list[0]:
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if response is None:
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continue
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print(response.choices[0].delta.content, end='', flush=True)
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print()
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print(infer_stats.compute())
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gen_list = engine.infer(infer_requests, request_config=request_config, use_tqdm=True, metrics=[infer_stats])
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for response in gen_list[0]:
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pass
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print(infer_stats.compute())
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if __name__ == '__main__':
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engine, infer_requests = _prepare(infer_backend='transformers')
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test_infer(engine, infer_requests)
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test_stream(engine, infer_requests)
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