def _get_scheduler(self, optimizer, scheduler: str, warmup_steps: int, t_total: int):
"""
Returns the correct learning rate scheduler
"""
scheduler = scheduler.lower()
if scheduler == 'constantlr':
return pytorch_transformers.ConstantLRSchedule(optimizer)
elif scheduler == 'warmupconstant':
return pytorch_transformers.WarmupConstantSchedule(optimizer, warmup_steps=warmup_steps)
elif scheduler == 'warmuplinear':
return pytorch_transformers.WarmupLinearSchedule(optimizer, warmup_steps=warmup_steps, t_total=t_total)
elif scheduler == 'warmupcosine':
return pytorch_transformers.WarmupCosineSchedule(optimizer, warmup_steps=warmup_steps, t_total=t_total)
elif scheduler == 'warmupcosinewithhardrestarts':
return pytorch_transformers.WarmupCosineWithHardRestartsSchedule(optimizer, warmup_steps=warmup_steps, t_total=t_total)
else:
raise ValueError("Unknown scheduler {}".format(scheduler))
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--device',
default='0,1,2,3',
type=str,
required=False,
help='设置使用哪些显卡')
parser.add_argument('--model_config',
default='config/model_config_small.json',
type=str,
required=False,
help='选择模型参数')
parser.add_argument('--tokenizer_path',
default='cache/vocab_small.txt',
type=str,
required=False,
help='选择词库')
parser.add_argument('--raw_data_path',
default='data/train.json',
type=str,
required=False,
help='原始训练语料')
parser.add_argument('--tokenized_data_path',
default='data/tokenized/',
type=str,
required=False,
help='tokenized语料存放位置')
parser.add_argument('--raw', action='store_true', help='是否先做tokenize')
parser.add_argument('--epochs',
default=5,
type=int,
required=False,
help='训练循环')
parser.add_argument('--batch_size',
default=8,
type=int,
required=False,
help='训练batch size')
parser.add_argument('--lr',
default=1.5e-4,
type=float,
required=False,
help='学习率')
parser.add_argument('--warmup_steps',
default=2000,
type=int,
required=False,
help='warm up步数')
parser.add_argument('--log_step',
default=1,
type=int,
required=False,
help='多少步汇报一次loss')
parser.add_argument('--stride',
default=768,
type=int,
required=False,
help='训练时取训练数据的窗口步长')
parser.add_argument('--gradient_accumulation',
default=1,
type=str,
required=False,
help='梯度积累')
parser.add_argument('--fp16', action='store_true', help='混合精度')
parser.add_argument('--fp16_opt_level',
default='O1',
type=str,
required=False)
parser.add_argument('--max_grad_norm',
default=1.0,
type=float,
required=False)
parser.add_argument('--num_pieces',
default=100,
type=int,
required=False,
help='将训练语料分成多少份')
parser.add_argument('--output_dir',
default='model/',
type=str,
required=False,
help='模型输出路径')
parser.add_argument('--pretrained_model',
default='',
type=str,
required=False,
help='模型训练起点路径')
parser.add_argument('--no_wordpiece',
action='store_true',
help='不做word piece切词')
args = parser.parse_args()
print('args:\n' + args.__repr__())
if args.no_wordpiece:
import tokenization_bert_without_wordpiece as tokenization_bert
else:
import tokenization_bert
os.environ["CUDA_VISIBLE_DEVICES"] = args.device # 此处设置程序使用哪些显卡
model_config = pytorch_transformers.modeling_gpt2.GPT2Config.from_json_file(
args.model_config)
print('config:\n' + model_config.to_json_string())
n_ctx = model_config.n_ctx
full_tokenizer = tokenization_bert.BertTokenizer(
vocab_file=args.tokenizer_path)
full_tokenizer.max_len = n_ctx
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print('using device:', device)
raw_data_path = args.raw_data_path
tokenized_data_path = args.tokenized_data_path
raw = args.raw # 选择是否从零开始构建数据集
epochs = args.epochs
batch_size = args.batch_size
lr = args.lr
warmup_steps = args.warmup_steps
log_step = args.log_step
stride = args.stride
gradient_accumulation = args.gradient_accumulation
fp16 = args.fp16 # 不支持半精度的显卡请勿打开
fp16_opt_level = args.fp16_opt_level
max_grad_norm = args.max_grad_norm
num_pieces = args.num_pieces
output_dir = args.output_dir
if raw:
print('building files')
build_files(raw_data_path=raw_data_path,
tokenized_data_path=tokenized_data_path,
full_tokenizer=full_tokenizer,
num_pieces=num_pieces)
print('files built')
if not args.pretrained_model:
model = pytorch_transformers.modeling_gpt2.GPT2LMHeadModel(
config=model_config)
else:
model = pytorch_transformers.modeling_gpt2.GPT2LMHeadModel.from_pretrained(
args.pretrained_model)
model.train()
model.to(device)
multi_gpu = False
full_len = 0
print('calculating total steps')
for i in tqdm(range(num_pieces)):
with open(tokenized_data_path + 'tokenized_train_{}.txt'.format(i),
'r') as f:
full_len += len([int(item) for item in f.read().strip().split()])
total_steps = int(full_len / stride * epochs / batch_size /
gradient_accumulation)
print('total steps = {}'.format(total_steps))
optimizer = pytorch_transformers.AdamW(model.parameters(),
lr=lr,
correct_bias=True)
scheduler = pytorch_transformers.WarmupLinearSchedule(
optimizer, warmup_steps=warmup_steps, t_total=total_steps)
if fp16:
try:
from apex import amp
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
model, optimizer = amp.initialize(model,
optimizer,
opt_level=fp16_opt_level)
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
model = DataParallel(model)
multi_gpu = True
print('starting training')
for epoch in range(epochs):
print('epoch {}'.format(epoch + 1))
now = datetime.now()
print('time: {}'.format(now))
x = np.linspace(0, num_pieces - 1, num_pieces, dtype=np.int32)
random.shuffle(x)
piece_num = 0
for i in x:
running_loss = 0
with open(tokenized_data_path + 'tokenized_train_{}.txt'.format(i),
'r') as f:
line = f.read().strip()
tokens = line.split()
tokens = [int(token) for token in tokens]
start_point = 0
samples = []
while start_point < len(tokens) - n_ctx:
samples.append(tokens[start_point:start_point + n_ctx])
start_point += stride
random.shuffle(samples)
for step in range(len(samples) // batch_size):
# prepare data
batch = samples[step * batch_size:(step + 1) * batch_size]
batch_labels = []
batch_inputs = []
for ids in batch:
int_ids_for_labels = [int(x) for x in ids]
int_ids_for_inputs = [int(x) for x in ids]
batch_labels.append(int_ids_for_labels)
batch_inputs.append(int_ids_for_inputs)
batch_labels = torch.tensor(batch_labels).long().to(device)
batch_inputs = torch.tensor(batch_inputs).long().to(device)
# forward pass
outputs = model.forward(input_ids=batch_inputs,
labels=batch_labels)
loss, logits = outputs[:2]
# get loss
if multi_gpu:
loss = loss.mean()
if gradient_accumulation > 1:
loss = loss / gradient_accumulation
# loss backward
if fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
torch.nn.utils.clip_grad_norm_(
amp.master_params(optimizer), max_grad_norm)
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(),
max_grad_norm)
# optimizer step
if (step + 1) % gradient_accumulation == 0:
running_loss += loss.item()
scheduler.step()
optimizer.step()
optimizer.zero_grad()
if (step + 1) % log_step == 0:
print(
'now time: {}:{}. Step {} of piece {} of epoch {}, loss {}'
.format(
datetime.now().hour,
datetime.now().minute,
(step + 1) // gradient_accumulation, piece_num,
epoch + 1,
running_loss * gradient_accumulation / log_step))
running_loss = 0
piece_num += 1
print('saving model for epoch {}'.format(epoch + 1))
if not os.path.exists(output_dir + 'model_epoch{}'.format(epoch + 1)):
os.mkdir(output_dir + 'model_epoch{}'.format(epoch + 1))
model_to_save = model.module if hasattr(model, 'module') else model
model_to_save.save_pretrained(output_dir +
'model_epoch{}'.format(epoch + 1))
# torch.save(scheduler.state_dict(), output_dir + 'model_epoch{}/scheduler.pt'.format(epoch + 1))
# torch.save(optimizer.state_dict(), output_dir + 'model_epoch{}/optimizer.pt'.format(epoch + 1))
print('epoch {} finished'.format(epoch + 1))
then = datetime.now()
print('time: {}'.format(then))
print('time for one epoch: {}'.format(then - now))
print('training finished')
if not os.path.exists(output_dir + 'final_model'):
os.mkdir(output_dir + 'final_model')
model_to_save = model.module if hasattr(model, 'module') else model
model_to_save.save_pretrained(output_dir + 'final_model')
def main():
if raw:
print('building files')
build_files(data_path=raw_data_path)
print('files built')
model = pytorch_transformers.modeling_gpt2.GPT2LMHeadModel(
config=model_config)
model.to(device)
num_parameters = 0
parameters = model.parameters()
for parameter in parameters:
num_parameters += parameter.numel()
print('number of parameters: {}'.format(num_parameters))
multi_gpu = False
full_len = 0
print('calculating total steps')
for i in tqdm(range(num_pieces)):
with open(tokenized_data_path + 'tokenized_train_{}.txt'.format(i),
'r') as f:
full_len += len([int(item) for item in f.read().strip().split()])
total_steps = int(full_len / stride * epochs / batch_size /
gradient_accumulation)
print('total steps = {}'.format(total_steps))
optimizer = pytorch_transformers.AdamW(model.parameters(),
lr=lr,
correct_bias=True)
scheduler = pytorch_transformers.WarmupLinearSchedule(
optimizer, warmup_steps=warmup_steps, t_total=total_steps)
if fp16:
try:
from apex import amp
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
model, optimizer = amp.initialize(model,
optimizer,
opt_level=fp16_opt_level)
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
model = DataParallel(model)
multi_gpu = True
print('starting training')
for epoch in range(epochs):
print('epoch {}'.format(epoch + 1))
now = datetime.now()
print('time: {}'.format(now))
x = np.linspace(0, num_pieces - 1, num_pieces, dtype=np.int32)
random.shuffle(x)
piece_num = 0
for i in x:
running_loss = 0
with open(tokenized_data_path + 'tokenized_train_{}.txt'.format(i),
'r') as f:
line = f.read().strip()
tokens = line.split()
tokens = [int(token) for token in tokens]
start_point = 0
samples = []
while start_point < len(tokens) - n_ctx:
samples.append(tokens[start_point:start_point + n_ctx])
start_point += stride
random.shuffle(samples)
for step in range(len(samples) // batch_size):
# prepare data
batch = samples[step * batch_size:(step + 1) * batch_size]
batch_labels = []
batch_inputs = []
for ids in batch:
int_ids_for_labels = [int(x) for x in ids]
int_ids_for_inputs = [int(x) for x in ids]
batch_labels.append(int_ids_for_labels)
batch_inputs.append(int_ids_for_inputs)
batch_labels = torch.tensor(batch_labels).long().to(device)
batch_inputs = torch.tensor(batch_inputs).long().to(device)
# forward pass
outputs = model.forward(input_ids=batch_inputs,
labels=batch_labels)
loss, logits = outputs[:2]
# get loss
if multi_gpu:
loss = loss.mean()
if gradient_accumulation > 1:
loss = loss / gradient_accumulation
# loss backward
if fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
torch.nn.utils.clip_grad_norm_(
amp.master_params(optimizer), max_grad_norm)
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(),
max_grad_norm)
# optimizer step
if (step + 1) % gradient_accumulation == 0:
running_loss += loss.item()
scheduler.step()
optimizer.step()
optimizer.zero_grad()
if (step + 1) % log_step == 0:
print('step {} of piece {} of epoch {}, loss {}'.format(
(step + 1) // gradient_accumulation, piece_num,
epoch + 1,
running_loss * gradient_accumulation / log_step))
running_loss = 0
piece_num += 1
print('saving model for epoch {}'.format(epoch + 1))
if not os.path.exists(output_dir + 'model_epoch{}'.format(epoch + 1)):
os.mkdir(output_dir + 'model_epoch{}'.format(epoch + 1))
model_to_save = model.module if hasattr(model, 'module') else model
model_to_save.save_pretrained(output_dir +
'model_epoch{}'.format(epoch + 1))
# torch.save(scheduler.state_dict(), output_dir + 'model_epoch{}/scheduler.pt'.format(epoch + 1))
# torch.save(optimizer.state_dict(), output_dir + 'model_epoch{}/optimizer.pt'.format(epoch + 1))
print('epoch {} finished'.format(epoch + 1))
then = datetime.now()
print('time: {}'.format(then))
print('time for one epoch: {}'.format(then - now))
print('training finished')
if not os.path.exists(output_dir + 'final_model'):
os.mkdir(output_dir + 'final_model')
model_to_save = model.module if hasattr(model, 'module') else model
model_to_save.save_pretrained(output_dir + 'final_model')
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--device',
default='0,1,2,3',
type=str,
required=False,
help='设置使用哪些显卡')
parser.add_argument('--model_config',
default='config/model_config_small.json',
type=str,
required=False,
help='选择模型参数')
parser.add_argument('--tokenizer_path',
default='cache/vocab_small.txt',
type=str,
required=False,
help='选择词库')
parser.add_argument('--tokenized_data_path',
default='data/tokenized/',
type=str,
required=False,
help='tokenized语料存放位置')
parser.add_argument('--epochs',
default=5,
type=int,
required=False,
help='训练循环')
parser.add_argument('--batch_size',
default=8,
type=int,
required=False,
help='训练batch size')
parser.add_argument('--lr',
default=1.5e-4,
type=float,
required=False,
help='学习率')
parser.add_argument('--warmup_steps',
default=2000,
type=int,
required=False,
help='warm up步数')
parser.add_argument('--log_step',
default=1,
type=int,
required=False,
help='多少步汇报一次loss')
parser.add_argument('--save_per_step',
default=10000,
type=int,
required=False,
help='多少步保存一次模型')
parser.add_argument('--stride',
default=768,
type=int,
required=False,
help='训练时取训练数据的窗口步长')
parser.add_argument('--gradient_accumulation',
default=1,
type=int,
required=False,
help='梯度积累')
parser.add_argument('--fp16', action='store_true', help='混合精度')
parser.add_argument('--fp16_opt_level',
default='O1',
type=str,
required=False)
parser.add_argument('--max_grad_norm',
default=1.0,
type=float,
required=False)
parser.add_argument('--num_pieces',
default=100,
type=int,
required=False,
help='将训练语料分成多少份')
parser.add_argument('--min_length',
default=128,
type=int,
required=False,
help='最短收录文章长度')
parser.add_argument('--output_dir',
default='model/',
type=str,
required=False,
help='模型输出路径')
parser.add_argument('--pretrained_model',
default='',
type=str,
required=False,
help='模型训练起点路径')
parser.add_argument('--writer_dir',
default='tensorboard_summary/',
type=str,
required=False,
help='Tensorboard路径')
parser.add_argument('--bpe_token', action='store_true', help='subword')
parser.add_argument('--encoder_json',
default="tokenizations/encoder.json",
type=str,
help="encoder.json")
parser.add_argument('--vocab_bpe',
default="tokenizations/vocab.bpe",
type=str,
help="vocab.bpe")
args = parser.parse_args()
print('args:\n' + args.__repr__())
from tokenizations import tokenization_bert
os.environ["CUDA_VISIBLE_DEVICES"] = args.device # 此处设置程序使用哪些显卡
model_config = pytorch_transformers.modeling_gpt2.GPT2Config.from_json_file(
args.model_config)
print('config:\n' + model_config.to_json_string())
n_ctx = model_config.n_ctx
if args.bpe_token:
full_tokenizer = get_encoder(args.encoder_json, args.vocab_bpe)
else:
full_tokenizer = tokenization_bert.BertTokenizer(
vocab_file=args.tokenizer_path)
full_tokenizer.max_len = n_ctx
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print('using device:', device)
tokenized_data_path = args.tokenized_data_path
epochs = args.epochs
batch_size = args.batch_size
lr = args.lr
warmup_steps = args.warmup_steps
log_step = args.log_step
stride = args.stride
gradient_accumulation = args.gradient_accumulation
fp16 = args.fp16 # 不支持半精度的显卡请勿打开
fp16_opt_level = args.fp16_opt_level
max_grad_norm = args.max_grad_norm
num_pieces = args.num_pieces
min_length = args.min_length
output_dir = args.output_dir
# tb_writer = SummaryWriter(log_dir=args.writer_dir)
if not os.path.exists(output_dir):
os.mkdir(output_dir)
if not args.pretrained_model:
model = pytorch_transformers.modeling_gpt2.GPT2LMHeadModel(
config=model_config)
else:
model = pytorch_transformers.modeling_gpt2.GPT2LMHeadModel.from_pretrained(
args.pretrained_model)
model.train()
model.to(device)
num_parameters = 0
parameters = model.parameters()
for parameter in parameters:
num_parameters += parameter.numel()
print('number of parameters: {}'.format(num_parameters))
multi_gpu = False
full_len = 0
print('calculating total steps')
for i in tqdm(range(num_pieces)):
with open(
os.path.join(tokenized_data_path,
'tokenized_train_{}.txt'.format(i)), 'r') as f:
full_len += len([int(item) for item in f.read().strip().split()])
total_steps = int(full_len / stride * epochs / batch_size /
gradient_accumulation)
print('total steps = {}'.format(total_steps))
optimizer = pytorch_transformers.AdamW(model.parameters(),
lr=lr,
correct_bias=True)
scheduler = pytorch_transformers.WarmupLinearSchedule(
optimizer, warmup_steps=warmup_steps, t_total=total_steps)
if fp16:
try:
from apex import amp
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
model, optimizer = amp.initialize(model,
optimizer,
opt_level=fp16_opt_level)
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
model = DataParallel(model)
multi_gpu = True
print('starting training')
overall_step = 0
running_loss = 0
running_step = 0
for epoch in range(epochs):
print('epoch {}'.format(epoch + 1))
now = datetime.now()
print('time: {}'.format(now))
x = np.linspace(0, num_pieces - 1, num_pieces, dtype=np.int32)
random.shuffle(x)
piece_num = 0
for i in x:
with open(
os.path.join(tokenized_data_path,
'tokenized_train_{}.txt'.format(i)),
'r') as f:
line = f.read().strip()
tokens = line.split()
tokens = [int(token) for token in tokens]
start_point = 0
samples = []
while start_point < len(tokens) - n_ctx:
samples.append(tokens[start_point:start_point + n_ctx])
start_point += stride
random.shuffle(samples)
for step in range(len(samples) // batch_size): # drop last
# prepare data
batch = samples[step * batch_size:(step + 1) * batch_size]
batch_labels = []
batch_inputs = []
for ids in batch:
int_ids_for_labels = [int(x) for x in ids]
int_ids_for_inputs = [int(x) for x in ids]
batch_labels.append(int_ids_for_labels)
batch_inputs.append(int_ids_for_inputs)
batch_labels = torch.tensor(batch_labels).long().to(device)
batch_inputs = torch.tensor(batch_inputs).long().to(device)
# forward pass
outputs = model.forward(input_ids=batch_inputs,
labels=batch_labels)
loss, logits = outputs[:2]
# get loss
if multi_gpu:
loss = loss.mean()
if gradient_accumulation > 1:
loss = loss / gradient_accumulation
# loss backward
if fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
torch.nn.utils.clip_grad_norm_(
amp.master_params(optimizer), max_grad_norm)
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(),
max_grad_norm)
# optimizer step
running_loss += loss.item()
running_step += 1
mean_loss = running_loss * gradient_accumulation / running_step
if (step + 1) % gradient_accumulation == 0:
optimizer.step()
optimizer.zero_grad()
scheduler.step()
overall_step += 1
# how many steps to print loss log
if overall_step % log_step == 0:
now_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
print(
'now time: {}: Step {} of piece {} of epoch {}. Global Step: {}, Mean Loss: {}'
.format(now_time,
(step + 1) // gradient_accumulation,
piece_num, epoch + 1, overall_step,
mean_loss))
# how many steps to save a checkpoint
if overall_step % args.save_per_step == 0:
if not os.path.exists(
os.path.join(
output_dir, "model_step_%d" %
(overall_step + 1))):
os.mkdir(
os.path.join(
output_dir,
"model_step_%d" % (overall_step + 1)))
model_to_save = model.module if hasattr(
model, 'module') else model
model_to_save.save_pretrained(
os.path.join(output_dir,
"model_step_%d" % (overall_step + 1)))
piece_num += 1
# save model per epoch
print('saving model for epoch {}'.format(epoch + 1))
if not os.path.exists(
os.path.join(output_dir, 'model_epoch{}'.format(epoch + 1))):
os.mkdir(
os.path.join(output_dir, 'model_epoch{}'.format(epoch + 1)))
model_to_save = model.module if hasattr(model, 'module') else model
model_to_save.save_pretrained(
os.path.join(output_dir, 'model_epoch{}'.format(epoch + 1)))
print('epoch {} finished'.format(epoch + 1))
then = datetime.now()
print('time: {}'.format(then))
print('time for one epoch: {}'.format(then - now))
# save the final model
print('training finished')
if not os.path.exists(os.path.join(output_dir, 'final_model')):
os.mkdir(os.path.join(output_dir, 'final_model'))
model_to_save = model.module if hasattr(model, 'module') else model
model_to_save.save_pretrained(os.path.join(output_dir, 'final_model'))
def train(self):
if not self.pretrained_model:
model = GPT2KWModel(config=self.model_config)
else:
model = GPT2KWModel.from_pretrained(self.pretrained_model)
model.train()
model.to(self.device)
# 计算模型参数量
num_parameters = 0
parameters = model.parameters()
for parameter in parameters:
num_parameters += parameter.numel()
self.print_and_log('模型参数量: {}'.format(num_parameters))
self.print_and_log("开始加载训练集")
train_loader = self.create_dataloader()
self.print_and_log("训练集加载完毕")
epoch_steps = int(train_loader.sampler.num_samples / self.batch_size / self.accumulation_steps)
total_steps = epoch_steps * self.epochs
self.print_and_log('epoch 步数 = {}'.format(epoch_steps))
self.print_and_log('总步数 = {}'.format(total_steps))
optimizer = pytorch_transformers.AdamW(model.parameters(), lr=self.lr, correct_bias=True)
scheduler = pytorch_transformers.WarmupLinearSchedule(optimizer, warmup_steps=self.warmup_steps, t_total=total_steps)
if self.fp16:
try:
from apex import amp
except ImportError:
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use fp16 training.")
model, optimizer = amp.initialize(model, optimizer, opt_level=self.fp16_opt_level)
if torch.cuda.device_count() > 1:
model = DataParallel(model)
multi_gpu = True
else:
multi_gpu = False
overall_step = 0
for epoch in range(self.epochs):
self.print_and_log('epoch {}'.format(epoch + 1))
now = datetime.now()
self.print_and_log('time: {}'.format(now))
optimizer.zero_grad()
running_loss = 0
for i, batch_data in enumerate(train_loader):
if torch.cuda.is_available():
keyword_ids = batch_data[0].to(self.device, non_blocking=True)
passage_ids = batch_data[1].to(self.device, non_blocking=True)
label_ids = passage_ids.clone().to(self.device, non_blocking=True)
else:
keyword_ids = batch_data[0]
passage_ids = batch_data[1]
label_ids = passage_ids.clone()
outputs = model(input_ids=passage_ids, keyword_ids=keyword_ids, labels=label_ids)
loss, logits = outputs[:2]
if multi_gpu:
loss = loss.mean()
if self.gradient_accumulation > 1:
loss = loss / self.gradient_accumulation
# loss backward
if self.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), self.max_grad_norm)
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), self.max_grad_norm)
if (i + 1) % self.gradient_accumulation == 0:
running_loss += loss.item()
scheduler.step()
optimizer.step()
optimizer.zero_grad()
overall_step += 1
#if (overall_step + 1) % self.log_step == 0:
# self.tb_writer.add_scalar('loss', loss.item(), overall_step)
if (overall_step + 1) % self.log_step == 0 and running_loss != 0:
self.print_and_log('now time: {}:{}. Step {} of epoch {}, loss {}'.format(
datetime.now().hour,
datetime.now().minute,
overall_step + 1,
epoch + 1,
running_loss * self.gradient_accumulation / self.log_step))
running_loss = 0
if not os.path.exists(self.output_dir + 'model_epoch{}'.format(epoch + 1)):
os.makedirs(self.output_dir + 'model_epoch{}'.format(epoch + 1))
model_to_save = model.module if hasattr(model, 'module') else model
model_to_save.save_pretrained(self.output_dir + 'model_epoch{}'.format(epoch + 1))
# torch.save(scheduler.state_dict(), output_dir + 'model_epoch{}/scheduler.pt'.format(epoch + 1))
# torch.save(optimizer.state_dict(), output_dir + 'model_epoch{}/optimizer.pt'.format(epoch + 1))
then = datetime.now()
self.print_and_log('time: {}'.format(then))
self.print_and_log('time for one epoch: {}'.format(then - now))
self.print_and_log('training finished')
self.f_log.close()
if not os.path.exists(self.output_dir + 'final_model'):
os.makedirs(self.output_dir + 'final_model')
model_to_save = model.module if hasattr(model, 'module') else model
model_to_save.save_pretrained(self.output_dir + 'final_model')
def train(self):
if not self.pretrained_model:
model = pytorch_transformers.modeling_gpt2.GPT2LMHeadModel(
config=self.model_config)
else:
model = pytorch_transformers.modeling_gpt2.GPT2LMHeadModel.from_pretrained(
self.pretrained_model)
model.train()
model.to(self.device)
# 计算模型参数量
num_parameters = 0
parameters = model.parameters()
for parameter in parameters:
num_parameters += parameter.numel()
self.print_and_log('模型参数量 = {}'.format(num_parameters))
if self.do_tokenize:
self.print_and_log("开始加载训练集")
self.tokenize_and_save()
self.print_and_log("训练集加载完毕")
full_len = 0
for i in range(self.split_num):
with open(
self.tokenized_data_path +
'tokenized_train_{}.txt'.format(i), 'r') as f:
full_len += len(
[int(item) for item in f.read().strip().split()])
sample_num = int(full_len / self.stride)
epoch_steps = int(full_len / self.stride / self.batch_size /
self.gradient_accumulation)
total_steps = int(full_len / self.stride * self.epochs /
self.batch_size / self.gradient_accumulation)
self.print_and_log('样本数 = {}'.format(sample_num))
self.print_and_log('epoch 步数 = {}'.format(epoch_steps))
self.print_and_log('总步数 = {}'.format(total_steps))
optimizer = pytorch_transformers.AdamW(model.parameters(),
lr=self.lr,
correct_bias=True)
scheduler = pytorch_transformers.WarmupLinearSchedule(
optimizer, warmup_steps=self.warmup_steps, t_total=total_steps)
if self.fp16:
try:
from apex import amp
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
model, optimizer = amp.initialize(model,
optimizer,
opt_level=self.fp16_opt_level)
if torch.cuda.device_count() > 1:
model = DataParallel(model)
multi_gpu = True
else:
multi_gpu = False
overall_step = 0
running_loss = 0
for epoch in range(self.epochs):
self.print_and_log('epoch {}'.format(epoch + 1))
now = datetime.now()
self.print_and_log('time: {}'.format(now))
optimizer.zero_grad()
split_indices = np.linspace(0,
self.split_num - 1,
self.split_num,
dtype=np.int32)
random.shuffle(split_indices)
for split_index in split_indices:
with open(
self.tokenized_data_path +
'tokenized_train_{}.txt'.format(split_index),
'r') as f:
line = f.read().strip()
all_ids = line.split()
all_ids = [int(x) for x in all_ids]
start_point = 0
samples = []
while start_point < len(all_ids) - self.n_ctx:
samples.append(all_ids[start_point:start_point +
self.n_ctx])
start_point += self.stride
random.shuffle(samples)
for i in range(len(samples) // self.batch_size): # drop last
batch = samples[i * self.batch_size:(i + 1) *
self.batch_size]
batch_labels = torch.tensor(batch, dtype=torch.long).to(
self.device)
batch_inputs = torch.tensor(batch, dtype=torch.long).to(
self.device)
outputs = model.forward(input_ids=batch_inputs,
labels=batch_labels)
loss, logits = outputs[:2]
if multi_gpu:
loss = loss.mean()
if self.gradient_accumulation > 1:
loss = loss / self.gradient_accumulation
# loss backward
if self.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
torch.nn.utils.clip_grad_norm_(
amp.master_params(optimizer),
self.max_grad_norm)
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(),
self.max_grad_norm)
if (i + 1) % self.gradient_accumulation == 0:
running_loss += loss.item()
scheduler.step()
optimizer.step()
optimizer.zero_grad()
overall_step += 1
if (overall_step +
1) % self.log_step == 0 and running_loss != 0:
self.print_and_log(
'now time: {}:{}. Step {} of epoch {}, loss {}'.
format(
datetime.now().hour,
datetime.now().minute, overall_step + 1,
epoch + 1, running_loss *
self.gradient_accumulation / self.log_step))
running_loss = 0
if not os.path.exists(self.output_dir +
'model_epoch{}'.format(epoch + 1)):
os.makedirs(self.output_dir +
'model_epoch{}'.format(epoch + 1))
gpt2_model = model.transformer
model_to_save = gpt2_model.module if hasattr(
gpt2_model, 'module') else gpt2_model
model_to_save.save_pretrained(self.output_dir +
'model_epoch{}'.format(epoch + 1))
# torch.save(scheduler.state_dict(), output_dir + 'model_epoch{}/scheduler.pt'.format(epoch + 1))
# torch.save(optimizer.state_dict(), output_dir + 'model_epoch{}/optimizer.pt'.format(epoch + 1))
then = datetime.now()
self.print_and_log('time: {}'.format(then))
self.print_and_log('time for one epoch: {}'.format(then - now))
self.print_and_log('training finished')
self.f_log.close()
if not os.path.exists(self.output_dir + 'final_model'):
os.makedirs(self.output_dir + 'final_model')
gpt2_model = model.transformer
model_to_save = gpt2_model.module if hasattr(gpt2_model,
'module') else gpt2_model
model_to_save.save_pretrained(self.output_dir + 'final_model')
def main():
if raw:
build_files(data_path=RAW_DATA_PATH)
exit(1)
model = pytorch_transformers.modeling_gpt2.GPT2LMHeadModel(
config=model_config)
MULTI_GPU = False
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
model = DataParallel(model)
MULTI_GPU = True
model.to(device)
total_tokens = 0
for i in tqdm(range(num_pieces)):
with open(tokenized_data_path + 'tokenized_train_{}.txt'.format(i),
'r') as f:
total_tokens += len(f.read().split())
num_chunks = total_tokens // stride
total_steps = int(num_chunks * EPOCHS / BATCH_SIZE)
print('total steps = {}'.format(total_steps))
optimizer = pytorch_transformers.AdamW(model.parameters(),
lr=LR,
correct_bias=True)
scheduler = pytorch_transformers.WarmupLinearSchedule(
optimizer, warmup_steps=WARMUP_STEPS, t_total=total_steps)
if fp16:
try:
from apex import amp
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
model, optimizer = amp.initialize(model,
optimizer,
opt_level=fp16_opt_level)
print('starting training')
for epoch in range(EPOCHS):
print('epoch {}'.format(epoch))
now = datetime.now()
print('time: {}'.format(now))
x = np.linspace(0, num_pieces - 1, num_pieces, dtype=np.int32)
random.shuffle(x)
piece_num = 0
for i in x:
with open(tokenized_data_path + 'tokenized_train_{}.txt'.format(i),
'r') as f:
running_loss = 0
line = f.read()
tokens = line.split()
tokens = [int(token) for token in tokens]
start_point = 0
chunks = []
while start_point < len(tokens) - n_ctx:
chunks.append(tokens[start_point:start_point + n_ctx])
start_point += stride
random.shuffle(chunks)
for step in range(len(chunks) // BATCH_SIZE):
batch = chunks[step * BATCH_SIZE:(step + 1) * BATCH_SIZE]
batch_labels = []
batch_inputs = []
for ids in batch:
int_ids_for_labels = [int(x) for x in ids]
int_ids_for_inputs = [int(x) for x in ids]
batch_labels.append(int_ids_for_labels)
batch_inputs.append(int_ids_for_inputs)
batch_labels = torch.tensor(batch_labels).long().to(device)
batch_inputs = torch.tensor(batch_inputs).long().to(device)
optimizer.zero_grad()
outputs = model.forward(input_ids=batch_inputs,
labels=batch_labels)
loss, logits = outputs[:2]
if MULTI_GPU:
loss = loss.mean()
if fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
torch.nn.utils.clip_grad_norm_(
amp.master_params(optimizer), max_grad_norm)
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(),
max_grad_norm)
running_loss += loss.item()
scheduler.step()
optimizer.step()
if (step + 1) % LOG_STEP == 0:
print(
'step {} of piece {} of epoch {}, loss {}'.format(
step + 1, piece_num, epoch + 1,
running_loss / LOG_STEP))
running_loss = 0
piece_num += 1
print('saving model for epoch {}'.format(epoch))
if not os.path.exists('./model/model_epoch{}'.format(epoch + 1)):
os.mkdir('./model/model_epoch{}'.format(epoch + 1))
model.save_pretrained('./model/model_epoch{}'.format(epoch + 1))
torch.save(scheduler.state_dict(),
'./model/model_epoch{}/scheduler.pt'.format(epoch + 1))
torch.save(optimizer.state_dict(),
'./model/model_epoch{}/optimizer.pt'.format(epoch + 1))
print('epoch {} finished'.format(epoch + 1))
then = datetime.now()
print('time: {}'.format(then))
print('time for one epoch: {}'.format(then - now))
print('training finished')
if not os.path.exists('./model/final_model'):
os.mkdir('./model/final_model')
model.save_pretrained('./model/final_model')
torch.save(scheduler.state_dict(), './model/final_model/scheduler.pt')
torch.save(optimizer.state_dict(), './model/final_model/optimizer.pt')