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jupyter_main.py
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import torch
from torch.utils.data import DataLoader # 데이터로더
from gluonnlp.data import SentencepieceTokenizer
from kogpt2.utils import get_tokenizer
from kogpt2.utils import download, tokenizer
from kogpt2.model.torch_gpt2 import GPT2Config, GPT2LMHeadModel
from kogpt2.data import Read_Dataset
import gluonnlp
from kogpt2.model.sample import sample_sequence
from tqdm import tqdm
import subprocess
from tensorboardX import SummaryWriter
import re
def auto_enter(text):
text = (text.replace(" ", "\n"))
text = text.split("\n")
text = [t.lstrip() for t in text if t != '']
return "\n\n".join(text)
def get_gpu_memory_map():
"""Get the current gpu usage.
Returns
-------
usage: dict
Keys are device ids as integers.
Values are memory usage as integers in MB.
"""
result = subprocess.check_output(
[
'nvidia-smi', '--query-gpu=memory.used',
'--format=csv,nounits,noheader'
], encoding='utf-8')
# Convert lines into a dictionary
gpu_memory = [int(x) for x in result.strip().split('\n')]
gpu_memory_map = dict(zip(range(len(gpu_memory)), gpu_memory))
return gpu_memory_map
def main(epoch = 200, save_path = './checkpoint/', load_path = './checkpoint/KoGPT2_checkpoint_long.tar',
data_file_path = 'dataset/lyrics_dataset.txt', batch_size = 8, summary_url = 'runs/', new = 0, text_size = 100):
ctx = 'cuda'
cachedir = '~/kogpt2/'
summary = SummaryWriter(summary_url)
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
}
# download model
model_info = pytorch_kogpt2
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)
# KoGPT-2 언어 모델 학습을 위한 GPT2LMHeadModel 선언
kogpt2model = GPT2LMHeadModel(config=GPT2Config.from_dict(kogpt2_config))
# model_path 로부터 다운로드 받은 내용을 load_state_dict 으로 업로드
kogpt2model.load_state_dict(torch.load(model_path))
device = torch.device(ctx)
kogpt2model.to(device)
count = 0
# 불러오기 부분
try:
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()
except:
print("count 0 : ", load_path)
else:
print("count check : ",re.findall("\d+", load_path))
count = max([int(i) for i in (re.findall("\d+", load_path))])
if new:
count = 0
# 추가로 학습하기 위해 .train() 사용
kogpt2model.train()
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>')
tok_path = get_tokenizer()
model, vocab = kogpt2model, vocab_b_obj
sentencepieceTokenizer = SentencepieceTokenizer(tok_path)
dataset = Read_Dataset(data_file_path, vocab, sentencepieceTokenizer)
data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True, pin_memory=True)
learning_rate = 3e-5
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
## train
# vocab.token_to_idx["\n"] = vocab.token_to_idx["<unused0>"]
# del vocab.token_to_idx["<unused0>"]
# vocab.token_to_idx["<|endoftext|>"] = vocab.token_to_idx["<unused1>"]
# del vocab.token_to_idx["<unused1>"]
model = model.to(ctx)
tok = SentencepieceTokenizer(tok_path)
print('KoGPT-2 Transfer Learning Start')
avg_loss = (0.0, 0.0)
for epoch in range(epoch):
for data in data_loader:
optimizer.zero_grad()
data = torch.stack(data) # list of Tensor로 구성되어 있기 때문에 list를 stack을 통해 변환해준다.
data = data.transpose(1,0)
data = data.to(ctx)
model = model.to(ctx)
outputs = model(data, labels=data)
loss, logits = outputs[:2]
loss = loss.to(ctx)
loss.backward()
avg_loss = (avg_loss[0] * 0.99 + loss, avg_loss[1] * 0.99 + 1.0)
optimizer.step()
if count % 10 == 0:
print('epoch no.{0} train no.{1} loss = {2:.5f} avg_loss = {3:.5f}' . format(epoch, count, loss, avg_loss[0] / avg_loss[1]))
summary.add_scalar('loss/avg_loss', avg_loss[0] / avg_loss[1], count)
summary.add_scalar('loss/loss', loss, count)
# print("save")
# torch.save({
# 'epoch': epoch,
# 'train_no': count,
# 'model_state_dict': model.state_dict(),
# 'optimizer_state_dict': optimizer.state_dict(),
# 'loss': loss
# }, save_path + 'KoGPT2_checkpoint_' + str(count) + '.tar')
#generator 진행
if (count > 0 and count % 1000 == 0) or (len(data) < batch_size):
sent = sample_sequence(model.to("cpu"), tok, vocab, sent="사랑", text_size=text_size, temperature=0.7, top_p=0.8, top_k=40)
sent = sent.replace("<unused0>", "\n") # 비효율적이지만 엔터를 위해서 등장
sent = auto_enter(sent)
print(sent)
summary.add_text('Text', sent, count)
del sent
pass
#########################################
if (count > 0 and count % 18500 == 0):
# 모델 저장
try:
torch.save({
'epoch': epoch,
'train_no': count,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'loss': loss
}, save_path + 'KoGPT2_checkpoint_' + str(count) + '.tar')
except:
pass
count += 1