def __init__(self, model, optimizer, processor, args):
self.args = args
self.model = model
self.optimizer = optimizer
self.processor = processor
self.train_examples = self.processor.get_train_examples(args.data_dir)
self.tokenizer = BertTokenizer.from_pretrained(
args.model, is_lowercase=args.is_lowercase)
timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
self.snapshot_path = os.path.join(self.args.save_path,
self.processor.NAME,
'%s.pt' % timestamp)
self.num_train_optimization_steps = int(
len(self.train_examples) / args.batch_size /
args.gradient_accumulation_steps) * args.epochs
if args.local_rank != -1:
self.num_train_optimization_steps = args.num_train_optimization_steps // torch.distributed.get_world_size(
)
self.log_header = 'Epoch Iteration Progress Dev/Acc. Dev/Pr. Dev/Re. Dev/F1 Dev/Loss'
self.log_template = ' '.join(
'{:>5.0f},{:>9.0f},{:>6.0f}/{:<5.0f} {:>6.4f},{:>8.4f},{:8.4f},{:8.4f},{:10.4f}'
.split(','))
self.iterations, self.nb_tr_steps, self.tr_loss = 0, 0, 0
self.best_dev_f1, self.unimproved_iters = 0, 0
self.early_stop = False
def __init__(self, model, processor, args, split='dev'):
self.args = args
self.model = model
self.processor = processor
self.tokenizer = BertTokenizer.from_pretrained(
args.model, is_lowercase=args.is_lowercase)
if split == 'test':
self.eval_examples = self.processor.get_test_examples(
args.data_dir)
else:
self.eval_examples = self.processor.get_dev_examples(args.data_dir)
def main():
args = run_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port),
redirect_output=True)
ptvsd.wait_for_attach()
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
logger.info(
"device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".
format(device, n_gpu, bool(args.local_rank != -1), args.fp16))
if args.gradient_accumulation_steps < 1:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
.format(args.gradient_accumulation_steps))
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if not args.do_train and not args.do_infer:
raise ValueError(
"At least one of `do_train` or `do_infer` must be True.")
if os.path.exists(args.output_dir) and os.listdir(
args.output_dir) and args.do_train:
raise ValueError(
"Output directory ({}) already exists and is not empty.".format(
args.output_dir))
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
processor = ColaProcessor()
label_list = processor.get_labels()
num_labels = len(label_list)
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=args.do_lower_case)
train_examples = None
num_train_optimization_steps = None
if args.do_train:
train_examples = processor.get_train_examples(args.data_dir)
num_train_optimization_steps = int(
len(train_examples) / args.train_batch_size /
args.gradient_accumulation_steps) * args.num_train_epochs
if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size(
)
# Prepare model
if args.upper_model == "Linear":
model = BertForSequenceClassification.from_pretrained(
args.bert_model, num_labels=num_labels)
elif args.upper_model == "CNN":
model = BertCnn.from_pretrained(args.bert_model,
num_labels=num_labels,
seq_len=args.max_seq_length)
else:
model = BertForSequenceClassification.from_pretrained(
args.bert_model, num_labels=num_labels)
if args.fp16:
model.half()
model.to(device)
if args.local_rank != -1:
try:
from apex.parallel import DistributedDataParallel as DDP
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
)
model = DDP(model)
elif n_gpu > 1:
# model = torch.nn.DataParallel(model)
model = DataParallelModel(model)
# Prepare optimizer
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
}]
if args.fp16:
try:
from apex.optimizers import FP16_Optimizer
from apex.optimizers import FusedAdam
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
)
optimizer = FusedAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
bias_correction=False,
max_grad_norm=1.0)
if args.loss_scale == 0:
optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
else:
optimizer = FP16_Optimizer(optimizer,
static_loss_scale=args.loss_scale)
else:
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
global_step = 0
nb_tr_steps = 0
tr_loss = 0
if args.do_train:
train_features = convert_examples_to_features(train_examples,
label_list,
args.max_seq_length,
tokenizer)
eval_examples = processor.get_dev_examples(args.data_dir)
eval_features = convert_examples_to_features(eval_examples, label_list,
args.max_seq_length,
tokenizer)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps)
all_input_ids = torch.tensor([f.input_ids for f in train_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in train_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in train_features],
dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in train_features],
dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
if args.local_rank == -1:
train_sampler = RandomSampler(train_data)
else:
train_sampler = DistributedSampler(train_data)
train_dataloader = DataLoader(train_data,
sampler=train_sampler,
batch_size=args.train_batch_size)
for _ in trange(int(args.num_train_epochs), desc="Epoch"):
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(
tqdm(train_dataloader, desc="Iteration")):
model.train()
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
predictions = model(input_ids, segment_ids, input_mask,
label_ids)
for i in range(len(predictions)):
predictions[i] = predictions[i].view(-1, num_labels)
loss_fct = CrossEntropyLoss()
loss_fct_parallel = DataParallelCriterion(loss_fct)
loss = loss_fct_parallel(predictions, label_ids.view(-1))
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
optimizer.backward(loss)
else:
loss.backward()
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
# modify learning rate with special warm up BERT uses
# if args.fp16 is False, BertAdam is used that handles this automatically
lr_this_step = args.learning_rate * warmup_linear(
global_step / num_train_optimization_steps,
args.warmup_proportion)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step()
optimizer.zero_grad()
global_step += 1
# do eval
if global_step % args.eval_freq == 0 and global_step > 0:
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
all_input_ids = torch.tensor(
[f.input_ids for f in eval_features], dtype=torch.long)
all_input_mask = torch.tensor(
[f.input_mask for f in eval_features],
dtype=torch.long)
all_segment_ids = torch.tensor(
[f.segment_ids for f in eval_features],
dtype=torch.long)
all_label_ids = torch.tensor(
[f.label_id for f in eval_features], dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(
eval_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
for input_ids, input_mask, segment_ids, label_ids in tqdm(
eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
eval_preds = model(input_ids, segment_ids,
input_mask, label_ids)
# 计算loss
for i in range(len(eval_preds)):
eval_preds[i] = eval_preds[i].view(-1, num_labels)
loss = loss_fct_parallel(eval_preds,
label_ids.view(-1))
if n_gpu > 1:
loss = loss.mean(
) # mean() to average on multi-gpu.
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
tmp_eval_loss = loss
eval_preds = torch.cat(
eval_preds) # shape: [batch_size, num_labels]
logits = eval_preds.detach().cpu().numpy()
label_ids = label_ids.to('cpu').numpy()
tmp_eval_accuracy = accuracy(logits, label_ids)
eval_loss += tmp_eval_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_examples
loss = tr_loss / nb_tr_steps if args.do_train else None
result = {
'eval_loss': eval_loss,
'eval_accuracy': eval_accuracy,
'global_step': global_step,
'loss': loss
}
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
if args.do_train:
# Save a trained model and the associated configuration
model_to_save = model.module if hasattr(
model, 'module') else model # Only save the model it-self
output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
torch.save(model_to_save.state_dict(), output_model_file)
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
with open(output_config_file, 'w') as f:
f.write(model_to_save.config.to_json_string())
if args.do_infer:
infer_examples = processor.get_infer_examples(args.data_dir)
infer_features = convert_examples_to_features(infer_examples,
label_list,
args.max_seq_length,
tokenizer)
logger.info("***** Running Inference *****")
logger.info(" Num examples = %d", len(infer_examples))
logger.info(" Batch size = %d", args.infer_batch_size)
all_input_ids = torch.tensor([f.input_ids for f in infer_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in infer_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in infer_features],
dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in infer_features],
dtype=torch.long)
infer_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
# Run prediction for full data
infer_sampler = SequentialSampler(infer_data)
infer_dataloader = DataLoader(infer_data,
sampler=infer_sampler,
batch_size=args.infer_batch_size)
model.eval()
for input_ids, input_mask, segment_ids, label_ids in tqdm(
infer_dataloader, desc="Inference"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
infer_preds = model(input_ids, segment_ids, input_mask,
label_ids)
for i in range(len(infer_preds)):
infer_preds[i] = infer_preds[i].view(-1, num_labels)
infer_preds = torch.cat(
infer_preds) # shape: [batch_size, num_labels]
logits = infer_preds.detach().cpu().numpy()
outputs = np.argmax(logits, axis=1)
print(outputs)
logger.info("***** Infer finished *****")
args.batch_size = args.batch_size // args.gradient_accumulation_steps
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if os.path.exists(args.output_dir) and os.listdir(args.output_dir):
raise ValueError(
"Output directory ({}) already exists and is not empty.".format(
args.output_dir))
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
tokenizer = BertTokenizer.from_pretrained(args.model,
is_lowercase=args.do_lower_case)
print("Loading Train Dataset", args.data_path)
train_dataset = BERTDataset(args.data_path,
tokenizer,
seq_len=args.max_seq_length,
on_memory=args.on_memory)
num_train_optimization_steps = int(
len(train_dataset) / args.batch_size /
args.gradient_accumulation_steps) * args.epochs
# Prepare model
model = BertForPreTraining.from_pretrained(args.model,
savedir=args.output_dir,
dropout=args.dropout)
if args.fp16: