File size: 2,161 Bytes
96fe658 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 |
# Copyright (c) Alibaba, Inc. and its affiliates.
# demo_seq_cls: https://github.com/modelscope/ms-swift/blob/main/examples/train/seq_cls/qwen2_vl/infer.py
import os
from typing import List
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
def infer_batch(engine: 'InferEngine', infer_requests: List['InferRequest']):
resp_list = engine.infer(infer_requests)
query0 = infer_requests[0].messages[0]['content']
query1 = infer_requests[1].messages[0]['content']
print(f'query0: {query0}')
print(f'response0: {resp_list[0].choices[0].message.content}')
print(f'query1: {query1}')
print(f'response1: {resp_list[1].choices[0].message.content}')
if __name__ == '__main__':
# This is an example of BERT with LoRA.
from swift.llm import InferEngine, InferRequest, PtEngine, load_dataset, safe_snapshot_download, BaseArguments
from swift.tuners import Swift
adapter_path = safe_snapshot_download('swift/test_bert')
args = BaseArguments.from_pretrained(adapter_path)
args.max_length = 512
args.truncation_strategy = 'right'
# method1
model, processor = args.get_model_processor()
model = Swift.from_pretrained(model, adapter_path)
template = args.get_template(processor)
engine = PtEngine.from_model_template(model, template, max_batch_size=64)
# method2
# engine = PtEngine(args.model, adapters=[adapter_path], max_batch_size=64,
# task_type=args.task_type, num_labels=args.num_labels)
# template = args.get_template(engine.processor)
# engine.default_template = template
# Here, `load_dataset` is used for convenience; `infer_batch` does not require creating a dataset.
dataset = load_dataset(['DAMO_NLP/jd:cls#1000'], seed=42)[0]
print(f'dataset: {dataset}')
infer_requests = [InferRequest(messages=data['messages']) for data in dataset]
infer_batch(engine, infer_requests)
infer_batch(engine, [
InferRequest(messages=[{
'role': 'user',
'content': '今天天气真好呀'
}]),
InferRequest(messages=[{
'role': 'user',
'content': '真倒霉'
}])
])
|