def get_kogpt2_model(model_file, vocab_file, ctx="cpu"):
kogpt2model = GPT2LMHeadModel(config=GPT2Config.from_dict(kogpt2_config))
kogpt2model.load_state_dict(torch.load(model_file))
device = torch.device(ctx)
kogpt2model.to(device)
kogpt2model.eval()
vocab_b_obj = nlp.vocab.BERTVocab.from_sentencepiece(vocab_file,
mask_token=None,
sep_token=None,
cls_token=None,
unknown_token='<unk>',
padding_token='<pad>',
bos_token='<s>',
eos_token='</s>')
return kogpt2model, vocab_b_obj
def get_kogpt2_model(model_name_or_path, ctx="cuda", cachedir='~/kogpt2/'):
if model_name_or_path == "kogpt2":
# download model
model_info = pytorch_kogpt2
model_path = download(model_info['url'],
model_info['fname'],
model_info['chksum'],
cachedir=cachedir)
config = GPT2Config.from_dict(kogpt2_config)
model = GPT2LMHeadModel(config=config)
model.load_state_dict(torch.load(model_path), strict=False)
else:
config = GPT2Config.from_pretrained(args.model_name_or_path)
model = GPT2LMHeadModel.from_pretrained(
args.model_name_or_path,
from_tf=bool(".ckpt" in args.model_name_or_path),
config=config)
device = torch.device(ctx)
model.to(device)
model.train()
return model, config
def get_model_result(tmp_sent):
### 1. koGPT2 Config
ctx= 'cpu'#'cuda' #'cpu' #학습 Device CPU or GPU. colab의 경우 GPU 사용
cachedir='~/nlp/' # KoGPT-2 모델 다운로드 경로
epoch =200 # 학습 epoch
save_path = './checkpoint'
load_path = './checkpoint/narrativeKoGPT2_checkpoint.tar'
#use_cuda = True # Colab내 GPU 사용을 위한 값
pytorch_kogpt2 = {
'url':
'https://kobert.blob.core.windows.net/models/kogpt2/pytorch/pytorch_kogpt2_676e9bcfa7.params',
'fname': 'pytorch_kogpt2_676e9bcfa7.params',
'chksum': '676e9bcfa7'
}
kogpt2_config = {
"initializer_range": 0.02,
"layer_norm_epsilon": 1e-05,
"n_ctx": 1024,
"n_embd": 768,
"n_head": 12,
"n_layer": 12,
"n_positions": 1024,
"vocab_size": 50000
}
### 2. Vocab 불러오기
# download vocab
vocab_info = tokenizer
vocab_path = download(vocab_info['url'],
vocab_info['fname'],
vocab_info['chksum'],
cachedir=cachedir)
### 3. 체크포인트 및 디바이스 설정
# Device 설정
device = torch.device(ctx)
# 저장한 Checkpoint 불러오기
checkpoint = torch.load(load_path, map_location=device)
# KoGPT-2 언어 모델 학습을 위한 GPT2LMHeadModel 선언
kogpt2model = GPT2LMHeadModel(config=GPT2Config.from_dict(kogpt2_config))
kogpt2model.load_state_dict(checkpoint['model_state_dict'])
kogpt2model.eval()
vocab_b_obj = gluonnlp.vocab.BERTVocab.from_sentencepiece(vocab_path,
mask_token=None,
sep_token=None,
cls_token=None,
unknown_token='<unk>',
padding_token='<pad>',
bos_token='<s>',
eos_token='</s>')
### 4. Tokenizer
tok_path = get_tokenizer()
model, vocab = kogpt2model, vocab_b_obj
tok = SentencepieceTokenizer(tok_path)
### 5. Text Generation
result = []
usr_sent = tmp_sent
sent = ''
for j in range(10):
if sent == '':
sent = sent + usr_sent
else:
sent = generated_text
# print(sent) ## print result
result.append(sent)
toked = tok(sent)
count = 0
generated_text = ''
input_size = 50
if len(toked) > 1022:
break
while(1):
input_ids = torch.tensor([vocab[vocab.bos_token], ] + vocab[toked]).unsqueeze(0)
predicts = model(input_ids)
pred = predicts[0]
# print('predicts:', torch.argmax(pred, axis=-1).squeeze())
# gen = vocab.to_tokens(torch.argmax(pred, axis=-1).squeeze().tolist())[-1]
gen = topkSampling(pred, 10, vocab)
if '</s>' in gen:
gen = gen.replace('</s>', '')
# if gen == '</s>':
# print('to_tokens:',vocab.to_tokens(torch.argmax(pred, axis=-1).squeeze().tolist()))
if '.' in gen or count > input_size:
sent += gen.replace('▁', ' ').replace('</', '')
generated_text += gen.replace('▁', ' ').replace('</', '')
# sent += '\n'
# generated_text += '\n'
toked = tok(sent)
count = 0
break
# print('to_tokens:',vocab.to_tokens(torch.argmax(pred, axis=-1).squeeze().tolist()))
# if count >= input_size:
# break
sent += gen.replace('▁', ' ').replace('<', '')
generated_text += gen.replace('▁', ' ').replace('<', '')
toked = tok(sent)
count += 1
# print('result:')
# print(sent)
split = sent.split('\n')
# print(split)
if len(split) > 1:
# print(split[1])
if sent == split[1]:
break
result = ''.join(result)
return result
model_path = _download(model_info['url'],
model_info['fname'],
model_info['chksum'],
cachedir=cachedir)
# download vocab
vocab_info = tokenizer
vocab_path = _download(vocab_info['url'],
vocab_info['fname'],
vocab_info['chksum'],
cachedir=cachedir)
####################################################################################
model_file = model_path # 다운로드된 모델 파일 경로
vocab_file = vocab_path # 다운로드된 사전 파일 경로
ctx = "cpu" # cpu, gpu 여부
kogpt2model = GPT2LMHeadModel(config=GPT2Config.from_dict(kogpt2_config))
kogpt2model.load_state_dict(torch.load(model_file))
device = torch.device(ctx)
kogpt2model.to(device)
kogpt2model.eval()
vocab_b_obj = nlp.vocab.BERTVocab.from_sentencepiece(vocab_file,
mask_token=None,
sep_token=None,
cls_token=None,
unknown_token='<unk>',
padding_token='<pad>',
bos_token='<s>',
eos_token='</s>')
####################################################################################
model, vocab = kogpt2model, vocab_b_obj
tok = SentencepieceTokenizer(tok_path)