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，设置为gradient accumulation的整数倍')
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=20, type=int, required=False, help='最短收录文章长度')
parser.add_argument('--n_ctx', default=50, type=int, required=False, help='训练样本长度')
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('--segment', action='store_true', help='中文以词为单位')
parser.add_argument('--bpe_token', action='store_true', help='subword')
parser.add_argument('--padding', action='store_true', help='padding')
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")
parser.add_argument('--max_steps_perEpoch_perPiece', default=1000000, type=int, required=False)
parser.add_argument('--steps_savemodel', default=10000, type=int, required=False, help='保存模型步数')
args = parser.parse_args()
print('args:\n' + args.__repr__())
if args.segment:
from tokenizations import tokenization_bert_word_level as tokenization_bert
else:
from tokenizations import tokenization_bert
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 = 999999
raw_data_path = args.raw_data_path
tokenized_data_path = args.tokenized_data_path
num_pieces = args.num_pieces
min_length = args.min_length
n_ctx = args.n_ctx
padding = args.padding
print('building files')
build_files(data_path=raw_data_path, tokenized_data_path=tokenized_data_path, num_pieces=num_pieces,
full_tokenizer=full_tokenizer, min_length=min_length,n_ctx=n_ctx, padding=padding)
print('files built')
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/config.json',
type=str,
required=False,
help='选择模型参数')
parser.add_argument('--tokenizer_path',
default='vocab/vocab.txt',
type=str,
required=False,
help='选择词库')
parser.add_argument('--raw_data_path',
default='data/train.txt',
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('--epochs',
default=1,
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，设置为gradient accumulation的整数倍')
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='model/',
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('--segment', action='store_true', help='中文以词为单位')
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__())
if args.segment:
from tokenizations import tokenization_bert_word_level as tokenization_bert
else:
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 = 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 = 999999
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
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
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)
assert log_step % gradient_accumulation == 0
if not os.path.exists(output_dir):
os.mkdir(output_dir)
if not args.pretrained_model:
model = transformers.modeling_gpt2.GPT2LMHeadModel(config=model_config)
else:
model = 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(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 = transformers.AdamW(model.parameters(),
lr=lr,
correct_bias=True)
scheduler = 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, device_ids=[int(i) for i in args.device.split(',')])
multi_gpu = True
print('starting training')
overall_step = 0
running_loss = 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(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
if start_point < len(tokens):
samples.append(tokens[len(tokens) - n_ctx:])
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_inputs = []
for ids in batch:
int_ids = [int(x) for x in ids]
batch_inputs.append(int_ids)
batch_inputs = torch.tensor(batch_inputs).long().to(device)
# forward pass
with torch.no_grad():
outputs = model.forward(input_ids=batch_inputs,
labels=batch_inputs)
loss, logits = outputs[:2]
loss.requires_grad_(True)
# 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 (overall_step + 1) % gradient_accumulation == 0:
running_loss += loss.item()
optimizer.step()
optimizer.zero_grad()
scheduler.step()
if (overall_step + 1) % log_step == 0:
tb_writer.add_scalar('loss',
loss.item() * gradient_accumulation,
overall_step)
print(
'now time: {}:{}. Step {} of piece {} of epoch {}, loss {}'
.format(
datetime.now().hour,
datetime.now().minute, step + 1, piece_num,
epoch + 1, running_loss * gradient_accumulation /
(log_step / gradient_accumulation)))
running_loss = 0
overall_step += 1
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))
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(
"--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，设置为gradient accumulation的整数倍",
)
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("--segment", action="store_true", help="中文以词为单位")
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__())
if args.segment:
from tokenizations import tokenization_bert_word_level as tokenization_bert
else:
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 = 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 = 999999
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
min_length = args.min_length
output_dir = args.output_dir
tb_writer = SummaryWriter(log_dir=args.writer_dir)
assert log_step % gradient_accumulation == 0
if not os.path.exists(output_dir):
os.mkdir(output_dir)
if raw:
print("building files")
build_files(
data_path=raw_data_path,
tokenized_data_path=tokenized_data_path,
num_pieces=num_pieces,
full_tokenizer=full_tokenizer,
min_length=min_length,
)
print("files built")
if not args.pretrained_model:
model = transformers.modeling_gpt2.GPT2LMHeadModel(config=model_config)
else:
model = 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(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 = transformers.AdamW(model.parameters(), lr=lr, correct_bias=True)
scheduler = 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, device_ids=[int(i) for i in args.device.split(",")])
multi_gpu = True
print("starting training")
overall_step = 0
running_loss = 0
saving_time = datetime.now()
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(
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
if start_point < len(tokens):
samples.append(tokens[len(tokens) - n_ctx :])
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_inputs = []
for ids in batch:
int_ids = [int(x) for x in ids]
batch_inputs.append(int_ids)
batch_inputs = torch.tensor(batch_inputs).long().to(device)
# forward pass
outputs = model.forward(input_ids=batch_inputs, labels=batch_inputs)
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 (overall_step + 1) % gradient_accumulation == 0:
running_loss += loss.item()
optimizer.step()
optimizer.zero_grad()
scheduler.step()
if (overall_step + 1) % log_step == 0:
tb_writer.add_scalar(
"loss", loss.item() * gradient_accumulation, overall_step
)
print(
"now time: {}:{}. Step {} of piece {} of epoch {}, loss {}".format(
datetime.now().hour,
datetime.now().minute,
step + 1,
piece_num,
epoch + 1,
running_loss
* gradient_accumulation
/ (log_step / gradient_accumulation),
)
)
running_loss = 0
delta_time = datetime.now() - saving_time
if delta_time.seconds > 1800:
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)
)
saving_time = datetime.now()
overall_step += 1
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='設定要使用的顯卡，以逗號區隔')
parser.add_argument('--model_config',
type=str,
required=False,
help='模型參數設定檔的路徑')
parser.add_argument('--tokenizer_path',
type=str,
required=True,
help='選擇字典檔的路徑')
parser.add_argument('--raw_data_path',
type=str,
required=True,
help='訓練用語料庫的路徑')
parser.add_argument('--tokenized_data_path',
default='data/tokenized/',
type=str,
required=False,
help='語料庫 Tokenized 後的存放路徑')
parser.add_argument('--raw',
action='store_true',
help='是否已做過 Tokenization')
parser.add_argument('--epochs',
default=5,
type=int,
required=False,
help='設定 Epochs')
parser.add_argument('--batch_size',
default=8,
type=int,
required=False,
help='設定 Batch Size')
parser.add_argument('--lr',
default=3e-5,
type=float,
required=False,
help='設定 Learning Rate')
parser.add_argument('--warmup_steps',
default=0.1,
type=float,
required=False,
help='設定 Warmup Steps 的比例')
parser.add_argument('--log_step',
default=1,
type=int,
required=False,
help='Loss 紀錄的間隔，必須是 Gradient Accumulation 的整數倍')
parser.add_argument('--stride',
default=768,
type=int,
required=False,
help='設定訓練語料庫的窗口大小')
parser.add_argument('--gradient_accumulation',
default=1,
type=int,
required=False,
help='梯度累積')
parser.add_argument('--fp16', action='store_true', help='是否使用半精度浮點數')
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='將訓練語料庫分成多少份')
parser.add_argument('--min_length',
default=1,
type=int,
required=False,
help='文章最短長度，若文章長度不足將被捨棄')
parser.add_argument('--output_dir', type=str, required=True, help='模型輸出路徑')
parser.add_argument('--pretrained_model',
default='',
type=str,
required=False,
help='模型起始路徑')
parser.add_argument('--writer_dir',
default='tensorboard_summary/',
type=str,
required=False,
help='Tensorboard 輸出路徑')
parser.add_argument('--segment', action='store_true', help='是否以詞為單位')
parser.add_argument('--bpe_token',
action='store_true',
help='使用 Byte Pair Encoding')
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')
parser.add_argument('--timezone',
default=8,
type=int,
help='手動指定時區，預設為 GMT+8')
parser.add_argument('--epoch_save',
default=1,
type=int,
help='每隔幾個 Epoch 就存一次權重')
args = parser.parse_args()
print(f'Arguments: {args.__repr__()}')
if args.segment:
from tokenizations import tokenization_bert_word_level as tokenization_bert
else:
from tokenizations import tokenization_bert
# 設定要使用的顯卡
os.environ['CUDA_VISIBLE_DEVICES'] = args.device
model_config = transformers.GPT2Config.from_json_file(args.model_config)
print(f'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,
do_lower_case=False,
do_basic_tokenize=False)
full_tokenizer.max_len = 999999
device = 'cuda' if torch.cuda.is_available() else 'cpu'
print(f'Using Device: {device.upper()}')
raw_data_path = args.raw_data_path
tokenized_data_path = args.tokenized_data_path
raw = args.raw
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
# 不支援半精度浮點數的顯卡不要使用
fp16 = args.fp16
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
tz = args.timezone
strlen = lambda n: len(str(n))
get_time = lambda: datetime.utcnow() + timedelta(hours=tz)
tb_writer = SummaryWriter(log_dir=args.writer_dir)
assert log_step % gradient_accumulation == 0
os.makedirs(output_dir, exist_ok=True)
if raw:
print('Building from Raw Data')
build_files(data_path=raw_data_path,
tokenized_data_path=tokenized_data_path,
num_pieces=num_pieces,
tokenizer=full_tokenizer,
min_length=min_length)
if not args.pretrained_model:
model = transformers.GPT2LMHeadModel(config=model_config)
else:
model = transformers.GPT2LMHeadModel.from_pretrained(
args.pretrained_model)
if torch.cuda.device_count() == 2:
device_map = {
0: [0, 1, 2, 3, 4],
1: [5, 6, 7, 8, 9, 10, 11],
}
model.parallelize(device_map)
# model.parallelize()
print('Model Parallelism!')
model.train()
if torch.cuda.device_count() < 2:
model.to(device)
num_parameters = 0
parameters = model.parameters()
for parameter in parameters:
num_parameters += parameter.numel()
print(f'Number of Parameters: {num_parameters}')
multi_gpu = False
full_len = 0
print('Calculating Total Steps')
for i in tqdm(range(num_pieces)):
_fpath = os.path.join(tokenized_data_path, f'tokenized_train_{i}.txt')
with open(_fpath, '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)
warmup_steps = int(total_steps * warmup_steps)
print(f'Total Steps: {total_steps}')
optimizer = transformers.AdamW(model.parameters(),
lr=lr,
correct_bias=True)
scheduler = transformers.get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=warmup_steps,
num_training_steps=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(f'Using {torch.cuda.device_count()} GPUs')
# model = DataParallel(
# model, device_ids=[int(i) for i in args.device.split(',')])
# model.to(f'cuda:{model.device_ids[0]}')
# multi_gpu = True
with TimeCost('Training'):
print('Training Begin')
overall_step = 0
running_loss = 0
for epoch in range(epochs):
now = get_time()
print(f'Epoch {epoch + 1} - Time: {now}')
x = np.linspace(0, num_pieces - 1, num_pieces, dtype=np.int32)
random.shuffle(x)
piece_num = 0
for i in x:
_fpath = os.path.join(tokenized_data_path,
f'tokenized_train_{i}.txt')
with open(_fpath, '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
if start_point < len(tokens):
idx = len(tokens) - n_ctx
samples.append(tokens[idx:])
print(f'Tokenize {i} Sample Size: {len(samples)}')
random.shuffle(samples)
# 捨棄最後一個不足一個完整 Batch 的 Step
_steps = len(samples) // batch_size
# 若 Samples 數量小於 Batch Size 會發生完全沒有 Steps 可以訓練的問題
# 不要把 num_pieces 設定的太大，也可以解決這個問題
_steps = 1 if _steps <= 0 else _steps
for step in range(_steps):
# prepare data
batch = samples[step * batch_size:(step + 1) * batch_size]
batch_inputs = []
for ids in batch:
int_ids = [int(x) for x in ids]
batch_inputs.append(int_ids)
_device = 'cuda:0' if torch.cuda.device_count(
) > 1 else device
batch_inputs = torch.tensor(batch_inputs).long().to(
_device)
# forward pass
outputs = model.forward(input_ids=batch_inputs,
labels=batch_inputs)
loss, _ = 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 (overall_step + 1) % gradient_accumulation == 0:
running_loss += loss.item()
optimizer.step()
optimizer.zero_grad()
scheduler.step()
if (overall_step + 1) % log_step == 0:
tb_writer.add_scalar(
'loss',
loss.item() * gradient_accumulation, overall_step)
ts = datetime.utcnow() + timedelta(hours=8)
ts = ts.strftime('%H:%M:%S')
display_loss = running_loss * gradient_accumulation
display_loss /= log_step / gradient_accumulation
print(
f'Time {ts} - '
f'Epoch {epoch + 1:{strlen(epochs)}d}/{epochs} - '
f'Step {step + 1:{strlen(_steps)}d}/{_steps} - '
f'Piece {piece_num + 1:{strlen(num_pieces)}d}/{num_pieces} - '
f'Loss {display_loss:.4f}')
running_loss = 0
overall_step += 1
piece_num += 1
if (epoch + 1) % args.epoch_save == 0:
print(f'Saving Model of Epoch {epoch + 1}')
model_output_dir = os.path.join(output_dir,
f'model_epoch{epoch + 1}')
os.makedirs(model_output_dir, exist_ok=True)
model_to_save = model.module if hasattr(model,
'module') else model
model_to_save.save_pretrained(model_output_dir)
then = get_time()
print(f'Epoch {epoch + 1} Finished - Time: {then}')
delta = (then - now).total_seconds()
mm, ss = delta // 60, delta % 60
hh, mm = mm // 60, mm % 60
print(
f'Time Cost of the Epoch {epoch + 1} - {hh:.0f}:{mm:.0f}:{ss:.2f}'
)
print('Training Done')
model_output_dir = os.path.join(output_dir, 'final_model')
os.makedirs(model_output_dir, exist_ok=True)
model_to_save = model.module if hasattr(model, 'module') else model
model_to_save.save_pretrained(model_output_dir)
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=64, 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，设置为gradient accumulation的整数倍')
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('--max_length', default=256, 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('--segment', action='store_true', help='中文以词为单位')
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")
parser.add_argument('--max_steps_perEpoch_perPiece', default=1000000, type=int, required=False)
parser.add_argument('--steps_savemodel', default=10000, type=int, required=False, help='保存模型步数')
parser.add_argument('--padding', action='store_true', help='输入是否定长')
args = parser.parse_args()
print('args:\n' + args.__repr__())
if args.segment:
from tokenizations import tokenization_bert_word_level as tokenization_bert
else:
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 = 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 = 999999
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
min_length = args.min_length
output_dir = args.output_dir
padding = args.padding
max_length = args.max_length
#tb_writer = SummaryWriter(log_dir=args.writer_dir)
assert log_step % gradient_accumulation == 0
if not os.path.exists(output_dir):
os.mkdir(output_dir)
if not args.pretrained_model:
model = transformers.modeling_gpt2.GPT2LMHeadModel(config=model_config)
else:
model = 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
optimizer = transformers.AdamW(model.parameters(), lr=lr, correct_bias=True)
#scheduler = 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, device_ids=[int(i) for i in args.device.split(',')])
multi_gpu = True
print('starting training')
step_loss = 0
running_loss = 10
loss_ = 10
iter = iterData(args.tokenized_data_path, rate=1.0, batch_size=batch_size, epochs=epochs)
step = 0
epoch0 = -1
while True:
data = next(iter)
if data=='__STOP__':
break
epoch, epochs, idx_file, nb_files, batch_inputs = data
random.shuffle(batch_inputs)
batch_inputs = torch.tensor(batch_inputs).long().to(device)
# forward pass
outputs = model.forward(input_ids=batch_inputs, labels=batch_inputs)
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()
optimizer.step()
optimizer.zero_grad()
step_loss += 1
#scheduler.step()
if (step + 1) % log_step == 0:
loss_ = running_loss * gradient_accumulation / (log_step / gradient_accumulation)
print('now time: {}:{}. step: {}, progress-innerEpoch: {}/{}, progress-outerEpoch: {}/{}, loss {}'.format(
datetime.now().hour,
datetime.now().minute,
step+1,
idx_file+1,
nb_files,
epoch + 1,
epochs,
loss_))
running_loss = 0
if step%args.steps_savemodel==0:
print('saving model for epoch {}'.format(epoch + 1))
output_dir_ = output_dir + 'model_epoch{}_step{}_loss-{}'.format(epoch + 1, step,'%0.2f'%loss_)
if not os.path.exists(output_dir_):
os.mkdir(output_dir_)
model_to_save = model.module if hasattr(model, 'module') else model
model_to_save.save_pretrained(output_dir_)
step += 1
if epoch!=epoch0:
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))
epoch0 = epoch
print('epoch {} finished'.format(epoch + 1))
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')
print('training finished')
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--device',
default='0',
type=str,
required=False,
help='设置使用哪些显卡')
parser.add_argument('--model_config',
default='gpt2/config.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=150,
type=int,
required=False,
help='训练循环')
parser.add_argument('--batch_size',
default=1,
type=int,
required=False,
help='训练batch size')
parser.add_argument('--lr',
default=1e-4,
type=float,
required=False,
help='学习率')
parser.add_argument('--warmup_steps',
default=100,
type=int,
required=False,
help='warm up步数')
# parser.add_argument('--log_step', default=2, type=int, required=False, help='多少步汇报一次loss，设置为gradient accumulation的整数倍')
parser.add_argument('--stride',
default=384,
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('--output_dir',
default='model_classfier/',
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('--segment', action='store_true', help='中文以词为单位')
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__())
if args.segment:
from tokenizations import tokenization_bert_word_level as tokenization_bert
else:
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 = transformers.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 = 999999
device = 'cuda:0' 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
# min_length = args.min_length
output_dir = args.output_dir
# tb_writer = SummaryWriter(log_dir=args.writer_dir)
# assert log_step % gradient_accumulation == 0
if not os.path.exists(output_dir):
os.mkdir(output_dir)
if raw:
print('building files')
resources, resources_id, input_question_list, max_aq_len = build_files(
data_path=raw_data_path, full_tokenizer=full_tokenizer)
print('files built')
input_ids = [] * len(resources_id)
# labels = []
for i in range(len(resources_id)):
inputsss, _ = sliding_window(max_len=512,
resources=resources_id[i],
stride=512 - 128)
input_ids.append(inputsss)
# labels = labels + [choices['label']] * len(inputsss)
print('sliding built')
if True: # shuffle
index = [i for i in range(len(input_ids))]
random.shuffle(index)
new_input_ids = [input_ids[i] for i in index]
new_input_question_list = [input_question_list[i] for i in index]
input_ids = new_input_ids
val_rate = 0.1
split = int((1 - val_rate) * len(input_ids))
val_input_ids = input_ids[split:]
val_input_question_list = input_question_list[split:]
input_ids = input_ids[:split]
input_question_list = input_question_list[:split]
# train_dataset = my_dataset(x=input_ids, y=labels, token_type_ids=token_type_ids)
# train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=1)
# if not args.pretrained_model:
# model = transformers.models.gpt2.GPT2LMHeadModel(config=model_config)
# else:
# model = transformers.models.gpt2.GPT2LMHeadModel.from_pretrained(args.pretrained_model)
model = modelMy(args, device)
model.to(device)
# old_parameter = model.f**k.weight.clone()
# num_parameters = 0
# parameters = model.parameters()
# for parameter in parameters:
# num_parameters += parameter.numel()
# print('number of parameters: {}'.format(num_parameters))
# param_optimizer = [p for n, p in model.named_parameters() if p.requires_grad]
multi_gpu = False
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()])
optimizer = transformers.optimization.AdamW(model.parameters(),
lr=lr,
weight_decay=0.01,
correct_bias=True)
scheduler = transformers.optimization.get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=1500,
num_training_steps=args.epochs * len(input_ids))
# scheduler = transformers.optimization.get_linear_schedule_with_warmup(optimizer, num_warmup_steps=10, num_training_steps = args.)
# from pytorch_pretrained_bert.optimization import BertAdam
# optimizer = BertAdam(model.parameters(),
# lr=0.1,
# warmup=0.1,
# t_total=100)
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
model = DataParallel(
model, device_ids=[int(i) for i in args.device.split(',')])
multi_gpu = True
print('starting training')
overall_step = 0
running_loss = 0
best_loss = 9999999
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)
acc_s = 0
piece_num = 0
model.train()
for step in range(len(input_ids)): # paper by paper
# print ("step:{}".format(step))
# if (overall_step + 2) % gradient_accumulation == 0:
# break
batch_inputs = input_ids[step]
batch_inputs = torch.tensor(batch_inputs).long().to(
device).unsqueeze(0)
batch_questions = [
z['Question_token'] for z in input_question_list[step][:]
]
batch_questions = torch.tensor(batch_questions).long().to(
device).unsqueeze(0)
batch_choices = [
z['Choices_token'] for z in input_question_list[step][:]
]
batch_choices = torch.tensor(batch_choices).long().to(
device).unsqueeze(0)
batch_labels = [z['Goal'] for z in input_question_list[step][:]]
batch_labels = torch.tensor(batch_labels).long().to(
device).unsqueeze(0)
# forward pass
outputs = model.forward(inputs=batch_inputs,
questions=batch_questions,
choices=batch_choices,
labels=batch_labels)
loss, pred, acc = outputs
acc_s += (acc.cpu())
running_loss += loss.item()
# get loss
if multi_gpu:
loss = loss.mean()
if gradient_accumulation > 1:
loss = loss / gradient_accumulation
# loss backward
loss.backward()
if (overall_step + 1) % gradient_accumulation == 0:
optimizer.step()
scheduler.step()
overall_step += 1
# print("backwards")
# if (overall_step + 1) % 1 == 0:
overall_step += 1
piece_num += 1
running_loss = running_loss / len(input_ids)
print('now time: {}:{}. epoch {}, loss {}, acc {:.6f}'.format(
datetime.now().hour,
datetime.now().minute, epoch + 1, running_loss * 1000,
acc_s / len(input_ids)
# acc_s / 16
# acc_s / 800
))
#---------------------------------
running_loss = running_loss * gradient_accumulation / len(resources)
if running_loss < best_loss:
best_loss = running_loss
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 + 'loss.best', optimizer,
epoch)
running_loss = 0
model.eval()
val_accs = 0
for stepp in range(len(val_input_ids)):
batch_inputs = val_input_ids[stepp]
batch_inputs = torch.tensor(batch_inputs).long().to(
device).unsqueeze(0)
batch_questions = [
z['Question_token'] for z in val_input_question_list[stepp][:]
]
batch_questions = torch.tensor(batch_questions).long().to(
device).unsqueeze(0)
batch_choices = [
z['Choices_token'] for z in val_input_question_list[stepp][:]
]
batch_choices = torch.tensor(batch_choices).long().to(
device).unsqueeze(0)
batch_labels = [
z['Goal'] for z in val_input_question_list[stepp][:]
]
batch_labels = torch.tensor(batch_labels).long().to(
device).unsqueeze(0)
# forward pass
outputs = model.forward(inputs=batch_inputs,
questions=batch_questions,
choices=batch_choices,
labels=batch_labels,
training=False)
loss, pred, acc = outputs
val_accs += (acc)
print('validation acc {}'.format(val_accs / (len(val_input_ids))))
# print('validation acc {}'.format(val_accs))
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.best', optimizer, epoch)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--device', default='0', type=str, required=False, help='设置使用哪些显卡')
parser.add_argument('--model_config', default='gpt2/config.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=100, type=int, required=False, help='训练循环')
parser.add_argument('--batch_size', default=1, 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=10000, type=int, required=False, help='warm up步数')
# parser.add_argument('--log_step', default=2, type=int, required=False, help='多少步汇报一次loss，设置为gradient accumulation的整数倍')
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=1, type=int, required=False, help='最短收录文章长度')
parser.add_argument('--output_dir', default='model_classfier/', 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('--segment', action='store_true', help='中文以词为单位')
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__())
if args.segment:
from tokenizations import tokenization_bert_word_level as tokenization_bert
else:
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 = transformers.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 = 999999
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
print('using device:', device)
raw_data_path = args.raw_data_path
log_step = args.log_step
gradient_accumulation = args.gradient_accumulation
output_dir = args.output_dir
assert log_step % gradient_accumulation == 0
if not os.path.exists(output_dir):
os.mkdir(output_dir)
if True:
print('building files')
resources,resources_id,input_question_list, max_aq_len = build_files(data_path=raw_data_path, full_tokenizer=full_tokenizer)
print('files built')
input_ids = [] * len(resources_id)
# labels = []
for i in range(len(resources_id)):
inputsss,_ = sliding_window( max_len = 512, resources = resources_id[i], stride=384)
input_ids.append(inputsss)
# labels = labels + [choices['label']] * len(inputsss)
print('sliding built')
val_input_ids = input_ids
val_input_question_list = input_question_list
# if False: # shuffle
# index = [i for i in range(len(token_type_ids))]
# random.shuffle(index)
# new_input_ids = [input_ids[i] for i in index]
# new_labels = [labels[i] for i in index]
# new_token_type_ids = [token_type_ids[i] for i in index]
# input_ids = new_input_ids
# labels = new_labels
# token_type_ids = new_token_type_ids
# train_dataset = my_dataset(x=input_ids, y=labels, token_type_ids=token_type_ids)
# train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=1)
# if not args.pretrained_model:
# model = transformers.models.gpt2.GPT2LMHeadModel(config=model_config)
# else:
# model = transformers.models.gpt2.GPT2LMHeadModel.from_pretrained(args.pretrained_model)
model = modelMy(args,device)
model.load_pretrained(output_dir + 'loss.best')
model.to(device)
num_parameters = 0
parameters = model.parameters()
for parameter in parameters:
num_parameters += parameter.numel()
print('number of parameters: {}'.format(num_parameters))
print('calculating total steps')
model.eval()
pred_list = []
for stepp in range(len(val_input_ids)):
batch_inputs = val_input_ids[stepp]
batch_inputs = torch.tensor(batch_inputs).long().to(device).unsqueeze(0)
batch_questions = [z['Question_token'] for z in val_input_question_list[stepp][:]]
batch_questions = torch.tensor(batch_questions).long().to(device).unsqueeze(0)
batch_choices = [z['Choices_token'] for z in val_input_question_list[stepp][:]]
batch_choices = torch.tensor(batch_choices).long().to(device).unsqueeze(0)
# forward pass
outputs = model.forward(inputs=batch_inputs, questions=batch_questions, choices=batch_choices,
labels=None,training=False)
pred = outputs
pred_list = pred_list + pred.squeeze(0).tolist()
f = open('data/answer.data', 'wb')
# 将文件中的变量加载到当前工作区
pickle.dump(pred_list, f)
f.close()
import csv
headers = ['id', 'label']
rows = []
n_id = 101
for choice in pred_list:
if choice == 0:
charr = 'A'
if choice == 1:
charr = 'B'
if choice == 2:
charr = 'C'
if choice == 3:
charr = 'D'
rows.append([n_id, charr])
with open('submit.csv', 'w')as f:
f_csv = csv.writer(f)
f_csv.writerow(headers)
f_csv.writerows(rows)
