ColossalAI/applications/Chat/examples/inference.py

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import argparse
import torch
from coati.models.bloom import BLOOMActor
from coati.models.generation import generate
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from coati.models.gpt import GPTActor
from coati.models.opt import OPTActor
from transformers import AutoTokenizer
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from transformers.models.gpt2.tokenization_gpt2 import GPT2Tokenizer
def eval(args):
# configure model
if args.model == 'gpt2':
actor = GPTActor(pretrained=args.pretrain).to(torch.cuda.current_device())
elif args.model == 'bloom':
actor = BLOOMActor(pretrained=args.pretrain).to(torch.cuda.current_device())
elif args.model == 'opt':
actor = OPTActor(pretrained=args.pretrain).to(torch.cuda.current_device())
else:
raise ValueError(f'Unsupported model "{args.model}"')
state_dict = torch.load(args.model_path)
actor.load_state_dict(state_dict)
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# configure tokenizer
if args.model == 'gpt2':
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
tokenizer.pad_token = tokenizer.eos_token
elif args.model == 'bloom':
tokenizer = AutoTokenizer.from_pretrained('bigscience/bloom-560m')
tokenizer.pad_token = tokenizer.eos_token
elif args.model == 'opt':
tokenizer = AutoTokenizer.from_pretrained('facebook/opt-350m')
else:
raise ValueError(f'Unsupported model "{args.model}"')
actor.eval()
input = args.input
input_ids = tokenizer.encode(input, return_tensors='pt').to(torch.cuda.current_device())
outputs = generate(actor,
input_ids,
max_length=args.max_length,
do_sample=True,
top_k=50,
top_p=0.95,
num_return_sequences=1)
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output = tokenizer.batch_decode(outputs[0], skip_special_tokens=True)
print(output)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--model', default='gpt2', choices=['gpt2', 'bloom', 'opt'])
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# We suggest to use the pretrained model from HuggingFace, use pretrain to configure model
parser.add_argument('--pretrain', type=str, default=None)
parser.add_argument('--model_path', type=str, default=None)
parser.add_argument('--input', type=str, default='Question: How are you ? Answer:')
parser.add_argument('--max_length', type=int, default=100)
args = parser.parse_args()
eval(args)