def main():
parser = ArgumentParser()
parser.add_argument("--model-config",
type=str,
default="openai-gpt",
help="Path, url or short name of the model")
parser.add_argument("--device",
type=str,
default="cuda" if torch.cuda.is_available() else "cpu",
help="Device (cuda or cpu)")
parser.add_argument("--outlens", type=int, default=30)
parser.add_argument("--beam", type=int, default=1)
parser.add_argument("--checkpoints", type=str)
parser.add_argument("--data", type=str, default="file")
args = parser.parse_args()
args.load_model = True
model = BertModel(None, args)
state_dict = convert_model(torch.load(args.checkpoints)['sd'])
model.load_state_dict(state_dict)
model.to(args.device)
tokenizer = BertWordPieceTokenizer("bert-base-chinese",
cache_dir="temp_cache_dir")
generate(model,
tokenizer,
args.device,
args.data,
sample=True,
top_k=5,
beam_size=6,
outlens=30)
def model_init(app):
ArgsSet = type('ArgsSet',(object,),{})
client = ArgsSet()
parser = ArgumentParser()
parser.add_argument("--model-config", type=str, default="openai-gpt",
help="Path, url or short name of the model")
parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available()
else "cpu", help="Device (cuda or cpu)")
parser.add_argument("--outlens", type=int, default=30)
parser.add_argument("--beam", type=int, default=1)
parser.add_argument("--gpt-checkpoints", type=str)
parser.add_argument("--port", type=int, default=8866)
args = parser.parse_args()
args.load_model = True
args.fp32_embedding = False
args.fp32_layernorm = False
args.fp32_tokentypes = False
args.layernorm_epsilon = 1e-12
gpt = BertModel(None, args)
state_dict = convert_model(torch.load(args.gpt_checkpoints)['sd'])
gpt.load_state_dict(state_dict)
gpt.to(args.device)
gpt.eval()
tokenizer = BertWordPieceTokenizer("bert-base-chinese", cache_dir="temp_cache_dir")
print(" Load model from {}".format(args.gpt_checkpoints))
client.tokenizer = tokenizer
client.gpt =gpt
client.gpt_beam = SequenceGenerator(gpt, tokenizer, beam_size=args.beam, max_lens=args.outlens)
client.device = args.device
client.port = args.port
client.generator = sample_sequence
return client
old_vocab = train_dataset.vocab
vocab = torchtext.legacy.vocab.Vocab(
counter=old_vocab.freqs, specials=['<unk>', '<pad>', '<MASK>'])
with open(args.save_vocab, 'wb') as f:
torch.save(vocab, f)
pad_id = vocab.stoi['<pad>']
sep_id = vocab.stoi['<sep>']
cls_id = vocab.stoi['<cls>']
train_dataset, dev_dataset = SQuAD1(vocab=vocab)
train_dataset = process_raw_data(train_dataset)
dev_dataset = process_raw_data(dev_dataset)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
embed_layer = BertEmbedding(len(vocab), args.emsize)
pretrained_bert = BertModel(len(vocab), args.emsize, args.nhead, args.nhid,
args.nlayers, embed_layer, args.dropout)
pretrained_bert.load_state_dict(torch.load(args.bert_model))
model = QuestionAnswerTask(pretrained_bert).to(device)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.1)
best_f1 = None
train_loss_log, val_loss_log = [], []
for epoch in range(1, args.epochs + 1):
epoch_start_time = time.time()
train()
val_loss, val_exact, val_f1 = evaluate(dev_dataset, vocab)
val_loss_log.append(val_loss)
print('-' * 89)
print('| end of epoch {:3d} | time: {:5.2f}s | valid loss {:5.2f} | '
'exact {:8.3f}% | '
def main(train_file,
dev_file,
target_dir,
epochs=10,
batch_size=32,
lr=2e-05,
patience=3,
max_grad_norm=10.0,
checkpoint=None):
bert_tokenizer = BertTokenizer.from_pretrained('bert-base-chinese',
do_lower_case=True)
device = torch.device("cuda")
print(20 * "=", " Preparing for training ", 20 * "=")
# 保存模型的路径
if not os.path.exists(target_dir):
os.makedirs(target_dir)
# -------------------- Data loading ------------------- #
print("\t* Loading training data...")
train_data = DataPrecessForSentence(bert_tokenizer, train_file)
train_loader = DataLoader(train_data, shuffle=True, batch_size=batch_size)
print("\t* Loading validation data...")
dev_data = DataPrecessForSentence(bert_tokenizer, dev_file)
dev_loader = DataLoader(dev_data, shuffle=True, batch_size=batch_size)
# -------------------- Model definition ------------------- #
print("\t* Building model...")
model = BertModel().to(device)
# -------------------- Preparation for training ------------------- #
# 待优化的参数
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
optimizer = AdamW(optimizer_grouped_parameters, lr=lr)
#optimizer = torch.optim.Adam(optimizer_grouped_parameters, lr=lr)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
mode="max",
factor=0.85,
patience=0)
best_score = 0.0
start_epoch = 1
# Data for loss curves plot
epochs_count = []
train_losses = []
valid_losses = []
# Continuing training from a checkpoint if one was given as argument
if checkpoint:
checkpoint = torch.load(checkpoint)
start_epoch = checkpoint["epoch"] + 1
best_score = checkpoint["best_score"]
print("\t* Training will continue on existing model from epoch {}...".
format(start_epoch))
model.load_state_dict(checkpoint["model"])
optimizer.load_state_dict(checkpoint["optimizer"])
epochs_count = checkpoint["epochs_count"]
train_losses = checkpoint["train_losses"]
valid_losses = checkpoint["valid_losses"]
# Compute loss and accuracy before starting (or resuming) training.
_, valid_loss, valid_accuracy, auc = validate(model, dev_loader)
print(
"\t* Validation loss before training: {:.4f}, accuracy: {:.4f}%, auc: {:.4f}"
.format(valid_loss, (valid_accuracy * 100), auc))
# -------------------- Training epochs ------------------- #
print("\n", 20 * "=", "Training Bert model on device: {}".format(device),
20 * "=")
patience_counter = 0
for epoch in range(start_epoch, epochs + 1):
epochs_count.append(epoch)
print("* Training epoch {}:".format(epoch))
epoch_time, epoch_loss, epoch_accuracy = train(model, train_loader,
optimizer, epoch,
max_grad_norm)
train_losses.append(epoch_loss)
print("-> Training time: {:.4f}s, loss = {:.4f}, accuracy: {:.4f}%".
format(epoch_time, epoch_loss, (epoch_accuracy * 100)))
print("* Validation for epoch {}:".format(epoch))
epoch_time, epoch_loss, epoch_accuracy, epoch_auc = validate(
model, dev_loader)
valid_losses.append(epoch_loss)
print(
"-> Valid. time: {:.4f}s, loss: {:.4f}, accuracy: {:.4f}%, auc: {:.4f}\n"
.format(epoch_time, epoch_loss, (epoch_accuracy * 100), epoch_auc))
# Update the optimizer's learning rate with the scheduler.
scheduler.step(epoch_accuracy)
# Early stopping on validation accuracy.
if epoch_accuracy < best_score:
patience_counter += 1
else:
best_score = epoch_accuracy
patience_counter = 0
torch.save(
{
"epoch": epoch,
"model": model.state_dict(),
"best_score": best_score,
"epochs_count": epochs_count,
"train_losses": train_losses,
"valid_losses": valid_losses
}, os.path.join(target_dir, "best.pth.tar"))
if patience_counter >= patience:
print("-> Early stopping: patience limit reached, stopping...")
break
# We use only ~1% data to fine-tune the model.
train, dev = SQuAD1()
raw_train = list(train)[:1024]
raw_dev = list(dev)[:128]
convert_to_arrow(raw_train, "train_arrow")
convert_to_arrow(raw_dev, "dev_arrow")
base_url = 'https://pytorch.s3.amazonaws.com/models/text/torchtext_bert_example/'
vocab_path = download_from_url(base_url + 'bert_vocab.txt')
data_module = QuestionAnswerDataModule(train_arrow_path='train_arrow',
dev_arrow_path='dev_arrow',
vocab_filepath=vocab_path,
batch_size=BATCH_SIZE)
# Load pretrained model and generate task
# default parameters from the pretrained model
vocab_size, emsize, nhead, nhid, nlayers, dropout = 99230, 768, 12, 3072, 12, 0.2
pretrained_bert = BertModel(vocab_size, emsize, nhead, nhid, nlayers,
dropout)
pretrained_model_path = download_from_url(base_url + 'ns_bert.pt')
pretrained_bert.load_state_dict(
torch.load(pretrained_model_path, map_location='cpu'))
qa_model = QuestionAnswerModel(pretrained_bert)
task = QuestionAnswerTask(qa_model, LR)
trainer = Trainer(gpus=0,
max_epochs=EPOCH,
progress_bar_refresh_rate=40,
fast_dev_run=True)
trainer.fit(task, data_module)
def run_main(args, rank=None):
# Set the random seed manually for reproducibility.
torch.manual_seed(args.seed)
if args.parallel == 'DDP':
n = torch.cuda.device_count() // args.world_size
device = list(range(rank * n, (rank + 1) * n))
else:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
vocab = torch.load(args.save_vocab)
cls_id = vocab.stoi['<cls>']
pad_id = vocab.stoi['<pad>']
sep_id = vocab.stoi['<sep>']
if args.dataset == 'WikiText103':
from torchtext.experimental.datasets import WikiText103
train_dataset, valid_dataset, test_dataset = WikiText103(vocab=vocab)
elif args.dataset == 'BookCorpus':
from data import BookCorpus
train_dataset, valid_dataset, test_dataset = BookCorpus(vocab, min_sentence_len=60)
if rank is not None:
chunk_len = len(train_dataset.data) // args.world_size
train_dataset.data = train_dataset.data[(rank * chunk_len):((rank + 1) * chunk_len)]
if args.checkpoint != 'None':
model = torch.load(args.checkpoint)
else:
pretrained_bert = BertModel(len(vocab), args.emsize, args.nhead, args.nhid, args.nlayers, args.dropout)
pretrained_bert.load_state_dict(torch.load(args.bert_model))
model = NextSentenceTask(pretrained_bert)
if args.parallel == 'DDP':
model = model.to(device[0])
model = DDP(model, device_ids=device)
else:
model = model.to(device)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.1)
best_val_loss = None
train_loss_log, val_loss_log = [], []
for epoch in range(1, args.epochs + 1):
epoch_start_time = time.time()
train(process_raw_data(train_dataset, args), model, train_loss_log, device, optimizer,
criterion, epoch, scheduler, cls_id, sep_id, pad_id, args, rank)
val_loss = evaluate(process_raw_data(valid_dataset, args), model, device, criterion,
cls_id, sep_id, pad_id, args)
val_loss_log.append(val_loss)
if (rank is None) or (rank == 0):
print('-' * 89)
print('| end of epoch {:3d} | time: {:5.2f}s '
'| valid loss {:8.5f} | '.format(epoch,
(time.time() - epoch_start_time),
val_loss))
print('-' * 89)
if not best_val_loss or val_loss < best_val_loss:
if rank is None:
with open(args.save, 'wb') as f:
torch.save(model, f)
elif rank == 0:
with open(args.save, 'wb') as f:
torch.save(model.state_dict(), f)
best_val_loss = val_loss
else:
scheduler.step()
if args.parallel == 'DDP':
rank0_devices = [x - rank * len(device) for x in device]
device_pairs = zip(rank0_devices, device)
map_location = {'cuda:%d' % x: 'cuda:%d' % y for x, y in device_pairs}
model.load_state_dict(torch.load(args.save, map_location=map_location))
test_loss = evaluate(process_raw_data(test_dataset, args), model, device, criterion,
cls_id, sep_id, pad_id, args)
if rank == 0:
wrap_up(train_loss_log, val_loss_log, test_loss, args, model.module, 'ns_loss.txt', 'ns_model.pt')
else:
with open(args.save, 'rb') as f:
model = torch.load(f)
test_loss = evaluate(process_raw_data(test_dataset, args), model, device, criterion,
cls_id, sep_id, pad_id, args)
wrap_up(train_loss_log, val_loss_log, test_loss, args, model, 'ns_loss.txt', 'ns_model.pt')
def model_init(app):
ArgsSet = type('ArgsSet', (object, ), {})
client = ArgsSet()
parser = ArgumentParser()
parser.add_argument("--model-config",
type=str,
default="openai-gpt",
help="Path, url or short name of the model")
parser.add_argument("--device",
type=str,
default="cuda" if torch.cuda.is_available() else "cpu",
help="Device (cuda or cpu)")
parser.add_argument("--outlens", type=int, default=30)
parser.add_argument("--beam", type=int, default=1)
parser.add_argument("--fuse-checkpoints", type=str)
parser.add_argument("--gpt-checkpoints", type=str)
parser.add_argument("--qa-style-checkpoints", type=str)
parser.add_argument("--multi-task", type=str)
parser.add_argument("--split-sentence-with-task-embedding-checkpoints",
type=str)
parser.add_argument("--special-cls-checkpoints", type=str)
parser.add_argument("--port", type=int, default=8866)
args = parser.parse_args()
args.load_model = True
args.fp32_embedding = False
args.fp32_layernorm = False
args.fp32_tokentypes = False
args.layernorm_epsilon = 1e-12
fuse_model = BertModel(None, args)
state_dict = convert_model(torch.load(args.fuse_checkpoints)['sd'])
fuse_model.load_state_dict(state_dict)
fuse_model.to(args.device)
fuse_model.eval()
print("| Load model from {}".format(args.fuse_checkpoints))
gpt = BertModel(None, args)
state_dict = convert_model(torch.load(args.gpt_checkpoints)['sd'])
gpt.load_state_dict(state_dict)
gpt.to(args.device)
gpt.eval()
tokenizer = BertWordPieceTokenizer("bert-base-chinese",
cache_dir="temp_cache_dir")
print(" Load model from {}".format(args.gpt_checkpoints))
# Load bert checkpoints
args.load_model = False
args.fp32_embedding = False
args.fp32_layernorm = False
args.fp32_tokentypes = False
args.layernorm_epsilon = 1e-12
bert = BertModel(None, args)
bert.to(args.device)
bert.eval()
client.tokenizer = tokenizer
client.fuse_model = fuse_model
client.fuse_beam = SequenceGenerator(fuse_model,
tokenizer,
beam_size=args.beam,
max_lens=args.outlens)
client.gpt = gpt
client.gpt_beam = SequenceGenerator(gpt,
tokenizer,
beam_size=args.beam,
max_lens=args.outlens)
client.bert = bert
client.device = args.device
client.port = args.port
client.generator = sample_sequence
# multi task model
multi_task = BertModel(None, args)
state_dict = convert_model(torch.load(args.multi_task)['sd'])
print("| Load model from {}".format(args.multi_task))
multi_task.load_state_dict(state_dict)
multi_task.to(args.device)
multi_task.eval()
client.multi_task_model = multi_task
client.multi_task_beam = SequenceGenerator(multi_task,
tokenizer,
beam_size=args.beam,
max_lens=args.outlens)
# qa style model
qa_style = BertModel(None, args)
state_dict = convert_model(torch.load(args.qa_style_checkpoints)['sd'])
qa_style.load_state_dict(state_dict)
qa_style.to(args.device)
qa_style.eval()
print(" Load model from {}".format(args.qa_style_checkpoints))
client.qa_task_model = qa_style
# special cls tokens
special_cls_model = BertModel(None, args)
special_cls_model.eval()
state_dict = convert_model(torch.load(args.special_cls_checkpoints)['sd'])
special_cls_model.load_state_dict(state_dict)
special_cls_model.to(args.device)
special_cls_model.eval()
print(" Load model from {}".format(args.special_cls_checkpoints))
client.special_cls_model = special_cls_model
client.special_beam = SequenceGenerator(special_cls_model,
tokenizer,
beam_size=args.beam,
max_lens=args.outlens)
# split sentence model with task embedding
split_sentence_model = BertModel(None, args)
split_sentence_model.eval()
state_dict = convert_model(
torch.load(args.split_sentence_with_task_embedding_checkpoints)['sd'])
split_sentence_model.load_state_dict(state_dict)
split_sentence_model.to(args.device)
split_sentence_model.eval()
print(" Load model from {}".format(
args.split_sentence_with_task_embedding_checkpoints))
client.split_sentence_model = split_sentence_model
client.split_sentence_beam = SequenceGenerator(split_sentence_model,
tokenizer,
beam_size=args.beam,
max_lens=args.outlens)
return client
import re
import torch
import sentencepiece as spm
from model import BertModel
sp = spm.SentencePieceProcessor()
sp.load('resource/sentencepiece.unigram.35000.model')
vocab_size = sp.get_piece_size()
n_embedding = 512
n_layer = 8
model = BertModel(vocab_size, n_embedding, n_layer)
model.eval()
model.load_state_dict(torch.load('resource/model.{}.{}.th'.format(n_embedding, n_layer),
map_location='cpu'))
# you should enable cuda if it is available
# model.cuda()
# if you are using a GPU that has tensor cores (nvidia volta, Turing architecture), you can enable half precision
# inference and training, we recommend to use the nvidia official apex to make everything as clean as possible from
# apex import amp [model] = amp.initialize([model], opt_level="O2")
device = model.embedding.weight.data.device
def clean_text(txt):
txt = txt.lower()
txt = re.sub('\s*', '', txt)
return txt
