mirror of https://github.com/hpcaitech/ColossalAI
53 lines
2.0 KiB
Python
53 lines
2.0 KiB
Python
import argparse
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import torch
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from chatgpt.nn import BLOOMActor, GPTActor, OPTActor
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from transformers import AutoTokenizer
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from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer
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def eval(args):
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# configure model
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if args.model == 'gpt2':
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model = GPTActor(pretrained=args.pretrain).to(torch.cuda.current_device())
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elif args.model == 'bloom':
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model = BLOOMActor(pretrained=args.pretrain).to(torch.cuda.current_device())
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elif args.model == 'opt':
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model = OPTActor(pretrained=args.pretrain).to(torch.cuda.current_device())
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else:
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raise ValueError(f'Unsupported model "{args.model}"')
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# configure tokenizer
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if args.model == 'gpt2':
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tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
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tokenizer.pad_token = tokenizer.eos_token
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elif args.model == 'bloom':
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tokenizer = AutoTokenizer.from_pretrained(args.pretrain)
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tokenizer.pad_token = tokenizer.eos_token
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elif args.model == 'opt':
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tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m")
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else:
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raise ValueError(f'Unsupported model "{args.model}"')
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model.eval()
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input = args.input
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input_ids = tokenizer.encode(input, return_tensors='pt').to(torch.cuda.current_device())
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outputs = model.generate(input_ids,
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max_length=args.max_length,
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do_sample=True,
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top_k=50,
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top_p=0.95,
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num_return_sequences=1)
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output = tokenizer.batch_decode(outputs[0], skip_special_tokens=True)
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print(output)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt'])
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parser.add_argument('--pretrain', type=str, default=None)
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parser.add_argument('--input', type=str, default='Q: How are you ? A:')
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parser.add_argument('--max_length', type=int, default=100)
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args = parser.parse_args()
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eval(args)
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