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change loss and optimizer to new API

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This commit is contained in:
yzy5630 2019-07-17 21:22:34 +08:00
parent 123da5a2fa
commit d6522e2873
2 changed files with 24 additions and 42 deletions
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29
examples/lm_finetuning/finetune_on_pregenerated.py
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@ -155,11 +155,10 @@ def main():
help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n")
parser.add_argument("--warmup_proportion",
default=0.1,
type=float,
help="Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--warmup_steps",
default=0,
type=int,
help="Linear warmup over warmup_steps.")
parser.add_argument("--learning_rate",
default=3e-5,
type=float,
@ -270,13 +269,9 @@ def main():
optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
else:
optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale)
warmup_linear = WarmupLinearSchedule(warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
else:
optimizer = AdamW(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
scheduler = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=num_train_optimization_steps)
global_step = 0
logging.info("***** Running training *****")
@ -298,7 +293,8 @@ def main():
for step, batch in enumerate(train_dataloader):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, lm_label_ids, is_next = batch
loss = model(input_ids, segment_ids, input_mask, lm_label_ids, is_next)
outputs = model(input_ids, segment_ids, input_mask, lm_label_ids, is_next)
loss = outputs[0]
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args.gradient_accumulation_steps > 1:
@ -314,18 +310,13 @@ def main():
mean_loss = tr_loss * args.gradient_accumulation_steps / nb_tr_steps
pbar.set_postfix_str(f"Loss: {mean_loss:.5f}")
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.get_lr(global_step, args.warmup_proportion)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
scheduler.step() # Update learning rate schedule
optimizer.step()
optimizer.zero_grad()
global_step += 1
# Save a trained model
if torch.distributed.get_rank() == 0:
if n_gpu > 1 and torch.distributed.get_rank() == 0 or n_gpu <=1 :
logging.info("** ** * Saving fine-tuned model ** ** * ")
model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self

37
examples/lm_finetuning/simple_lm_finetuning.py
View File

@ -438,11 +438,10 @@ def main():
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument("--warmup_proportion",
default=0.1,
type=float,
help="Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--warmup_steps",
default=0,
type=int,
help="Linear warmup over warmup_steps.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
@ -504,7 +503,7 @@ def main():
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) and torch.distributed.get_rank() == 0:
if not os.path.exists(args.output_dir) and ( n_gpu > 1 and torch.distributed.get_rank() == 0 or n_gpu <=1 ):
os.makedirs(args.output_dir)
tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
@ -558,14 +557,10 @@ def main():
optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
else:
optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale)
warmup_linear = WarmupLinearSchedule(warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
else:
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)
scheduler = WarmupLinearSchedule(optimizer, warmup_steps=args.warmup_steps, t_total=num_train_optimization_steps)
global_step = 0
if args.do_train:
@ -589,7 +584,8 @@ def main():
for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, lm_label_ids, is_next = batch
loss = model(input_ids, segment_ids, input_mask, lm_label_ids, is_next)
outputs = model(input_ids, segment_ids, input_mask, lm_label_ids, is_next)
loss = outputs[0]
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args.gradient_accumulation_steps > 1:
@ -602,22 +598,17 @@ def main():
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.get_lr(global_step, args.warmup_proportion)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
scheduler.step() # Update learning rate schedule
optimizer.step()
optimizer.zero_grad()
global_step += 1
# Save a trained model
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)
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
if args.do_train and torch.distributed.get_rank() == 0:
if args.do_train and ( n_gpu > 1 and torch.distributed.get_rank() == 0 or n_gpu <=1):
logger.info("** ** * Saving fine - tuned model ** ** * ")
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)
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
torch.save(model_to_save.state_dict(), output_model_file)
model_to_save.config.to_json_file(output_config_file)
tokenizer.save_vocabulary(args.output_dir)