SUMIN/HuggingFace_transformer
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fix learning rate/fp16 and warmup problem for all examples

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Matej Svejda
2019-01-27 14:07:24 +01:00
parent 01ff4f82ba
commit 9c6a48c8c3
5 changed files with 39 additions and 46 deletions
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8
examples/run_classifier.py
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@@ -33,7 +33,7 @@ from torch.utils.data.distributed import DistributedSampler
from pytorch_pretrained_bert.tokenization import BertTokenizer from pytorch_pretrained_bert.tokenization import BertTokenizer
from pytorch_pretrained_bert.modeling import BertForSequenceClassification from pytorch_pretrained_bert.modeling import BertForSequenceClassification
from pytorch_pretrained_bert.optimization import BertAdam from pytorch_pretrained_bert.optimization import BertAdam, warmup_linear
from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
@@ -536,6 +536,12 @@ def main():
nb_tr_examples += input_ids.size(0) nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1 nb_tr_steps += 1
if (step + 1) % args.gradient_accumulation_steps == 0: 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/t_total, args.warmup_proportion)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step() optimizer.step()
optimizer.zero_grad() optimizer.zero_grad()
global_step += 1 global_step += 1

20
examples/run_lm_finetuning.py
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@@ -31,7 +31,7 @@ from torch.utils.data.distributed import DistributedSampler
from pytorch_pretrained_bert.tokenization import BertTokenizer from pytorch_pretrained_bert.tokenization import BertTokenizer
from pytorch_pretrained_bert.modeling import BertForPreTraining from pytorch_pretrained_bert.modeling import BertForPreTraining
from pytorch_pretrained_bert.optimization import BertAdam from pytorch_pretrained_bert.optimization import BertAdam, warmup_linear
from torch.utils.data import Dataset from torch.utils.data import Dataset
import random import random
@@ -42,12 +42,6 @@ logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
def warmup_linear(x, warmup=0.002):
if x < warmup:
return x/warmup
return 1.0 - x
class BERTDataset(Dataset): class BERTDataset(Dataset):
def __init__(self, corpus_path, tokenizer, seq_len, encoding="utf-8", corpus_lines=None, on_memory=True): def __init__(self, corpus_path, tokenizer, seq_len, encoding="utf-8", corpus_lines=None, on_memory=True):
self.vocab = tokenizer.vocab self.vocab = tokenizer.vocab
@@ -527,7 +521,7 @@ def main():
train_dataset = BERTDataset(args.train_file, tokenizer, seq_len=args.max_seq_length, train_dataset = BERTDataset(args.train_file, tokenizer, seq_len=args.max_seq_length,
corpus_lines=None, on_memory=args.on_memory) corpus_lines=None, on_memory=args.on_memory)
num_train_steps = int( num_train_steps = int(
len(train_dataset) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs) len(train_dataset) / args.train_batch_size * args.num_train_epochs)
# Prepare model # Prepare model
model = BertForPreTraining.from_pretrained(args.bert_model) model = BertForPreTraining.from_pretrained(args.bert_model)
@@ -607,10 +601,12 @@ def main():
nb_tr_examples += input_ids.size(0) nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1 nb_tr_steps += 1
if (step + 1) % args.gradient_accumulation_steps == 0: if (step + 1) % args.gradient_accumulation_steps == 0:
# modify learning rate with special warm up BERT uses if args.fp16:
lr_this_step = args.learning_rate * warmup_linear(global_step/num_train_steps, args.warmup_proportion) # modify learning rate with special warm up BERT uses
for param_group in optimizer.param_groups: # if args.fp16 is False, BertAdam is used that handles this automatically
param_group['lr'] = lr_this_step lr_this_step = args.learning_rate * warmup_linear(global_step/num_train_steps, args.warmup_proportion)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step() optimizer.step()
optimizer.zero_grad() optimizer.zero_grad()
global_step += 1 global_step += 1

19
examples/run_squad.py
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@@ -36,7 +36,7 @@ from torch.utils.data.distributed import DistributedSampler
from pytorch_pretrained_bert.tokenization import whitespace_tokenize, BasicTokenizer, BertTokenizer from pytorch_pretrained_bert.tokenization import whitespace_tokenize, BasicTokenizer, BertTokenizer
from pytorch_pretrained_bert.modeling import BertForQuestionAnswering from pytorch_pretrained_bert.modeling import BertForQuestionAnswering
from pytorch_pretrained_bert.optimization import BertAdam from pytorch_pretrained_bert.optimization import BertAdam, warmup_linear
from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
@@ -670,11 +670,6 @@ def _compute_softmax(scores):
probs.append(score / total_sum) probs.append(score / total_sum)
return probs return probs
def warmup_linear(x, warmup=0.002):
if x < warmup:
return x/warmup
return 1.0 - x
def main(): def main():
parser = argparse.ArgumentParser() parser = argparse.ArgumentParser()
@@ -794,7 +789,7 @@ def main():
train_examples = read_squad_examples( train_examples = read_squad_examples(
input_file=args.train_file, is_training=True) input_file=args.train_file, is_training=True)
num_train_steps = int( num_train_steps = int(
len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs) len(train_examples) / args.train_batch_size * args.num_train_epochs)
# Prepare model # Prepare model
model = BertForQuestionAnswering.from_pretrained(args.bert_model, model = BertForQuestionAnswering.from_pretrained(args.bert_model,
@@ -905,10 +900,12 @@ def main():
else: else:
loss.backward() loss.backward()
if (step + 1) % args.gradient_accumulation_steps == 0: if (step + 1) % args.gradient_accumulation_steps == 0:
# modify learning rate with special warm up BERT uses if args.fp16:
lr_this_step = args.learning_rate * warmup_linear(global_step/t_total, args.warmup_proportion) # modify learning rate with special warm up BERT uses
for param_group in optimizer.param_groups: # if args.fp16 is False, BertAdam is used that handles this automatically
param_group['lr'] = lr_this_step lr_this_step = args.learning_rate * warmup_linear(global_step/t_total, args.warmup_proportion)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step() optimizer.step()
optimizer.zero_grad() optimizer.zero_grad()
global_step += 1 global_step += 1

19
examples/run_squad2.py
View File

@@ -36,7 +36,7 @@ from torch.utils.data.distributed import DistributedSampler
from pytorch_pretrained_bert.tokenization import whitespace_tokenize, BasicTokenizer, BertTokenizer from pytorch_pretrained_bert.tokenization import whitespace_tokenize, BasicTokenizer, BertTokenizer
from pytorch_pretrained_bert.modeling import BertForQuestionAnswering from pytorch_pretrained_bert.modeling import BertForQuestionAnswering
from pytorch_pretrained_bert.optimization import BertAdam from pytorch_pretrained_bert.optimization import BertAdam, warmup_linear
from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
@@ -759,11 +759,6 @@ def _compute_softmax(scores):
probs.append(score / total_sum) probs.append(score / total_sum)
return probs return probs
def warmup_linear(x, warmup=0.002):
if x < warmup:
return x/warmup
return 1.0 - x
def main(): def main():
parser = argparse.ArgumentParser() parser = argparse.ArgumentParser()
@@ -887,7 +882,7 @@ def main():
train_examples = read_squad_examples( train_examples = read_squad_examples(
input_file=args.train_file, is_training=True) input_file=args.train_file, is_training=True)
num_train_steps = int( num_train_steps = int(
len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs) len(train_examples) / args.train_batch_size * args.num_train_epochs)
# Prepare model # Prepare model
model = BertForQuestionAnswering.from_pretrained(args.bert_model, model = BertForQuestionAnswering.from_pretrained(args.bert_model,
@@ -999,10 +994,12 @@ def main():
else: else:
loss.backward() loss.backward()
if (step + 1) % args.gradient_accumulation_steps == 0: if (step + 1) % args.gradient_accumulation_steps == 0:
# modify learning rate with special warm up BERT uses if args.fp16:
lr_this_step = args.learning_rate * warmup_linear(global_step/t_total, args.warmup_proportion) # modify learning rate with special warm up BERT uses
for param_group in optimizer.param_groups: # if args.fp16 is False, BertAdam is used that handles this automatically
param_group['lr'] = lr_this_step lr_this_step = args.learning_rate * warmup_linear(global_step/t_total, args.warmup_proportion)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step() optimizer.step()
optimizer.zero_grad() optimizer.zero_grad()
global_step += 1 global_step += 1

19
examples/run_swag.py
View File

@@ -29,7 +29,7 @@ from torch.utils.data.distributed import DistributedSampler
from pytorch_pretrained_bert.tokenization import BertTokenizer from pytorch_pretrained_bert.tokenization import BertTokenizer
from pytorch_pretrained_bert.modeling import BertForMultipleChoice from pytorch_pretrained_bert.modeling import BertForMultipleChoice
from pytorch_pretrained_bert.optimization import BertAdam from pytorch_pretrained_bert.optimization import BertAdam, warmup_linear
from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s', logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
@@ -233,11 +233,6 @@ def select_field(features, field):
for feature in features for feature in features
] ]
def warmup_linear(x, warmup=0.002):
if x < warmup:
return x/warmup
return 1.0 - x
def main(): def main():
parser = argparse.ArgumentParser() parser = argparse.ArgumentParser()
@@ -358,7 +353,7 @@ def main():
if args.do_train: if args.do_train:
train_examples = read_swag_examples(os.path.join(args.data_dir, 'train.csv'), is_training = True) train_examples = read_swag_examples(os.path.join(args.data_dir, 'train.csv'), is_training = True)
num_train_steps = int( num_train_steps = int(
len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs) len(train_examples) / args.train_batch_size * args.num_train_epochs)
# Prepare model # Prepare model
model = BertForMultipleChoice.from_pretrained(args.bert_model, model = BertForMultipleChoice.from_pretrained(args.bert_model,
@@ -457,10 +452,12 @@ def main():
else: else:
loss.backward() loss.backward()
if (step + 1) % args.gradient_accumulation_steps == 0: if (step + 1) % args.gradient_accumulation_steps == 0:
# modify learning rate with special warm up BERT uses if args.fp16:
lr_this_step = args.learning_rate * warmup_linear(global_step/t_total, args.warmup_proportion) # modify learning rate with special warm up BERT uses
for param_group in optimizer.param_groups: # if args.fp16 is False, BertAdam is used that handles this automatically
param_group['lr'] = lr_this_step lr_this_step = args.learning_rate * warmup_linear(global_step/t_total, args.warmup_proportion)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step() optimizer.step()
optimizer.zero_grad() optimizer.zero_grad()
global_step += 1 global_step += 1