def create_and_check_distilbert_for_multiple_choice(
self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
model = DistilBertForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous()
result = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
labels=choice_labels,
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def create_and_check_distilbert_for_multiple_choice(
self, config, input_ids, input_mask, sequence_labels,
token_labels, choice_labels):
config.num_choices = self.num_choices
model = DistilBertForMultipleChoice(config=config)
model.to(torch_device)
model.eval()
multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(
-1, self.num_choices, -1).contiguous()
multiple_choice_input_mask = input_mask.unsqueeze(1).expand(
-1, self.num_choices, -1).contiguous()
loss, logits = model(
multiple_choice_inputs_ids,
attention_mask=multiple_choice_input_mask,
labels=choice_labels,
)
result = {
"loss": loss,
"logits": logits,
}
self.parent.assertListEqual(list(result["logits"].size()),
[self.batch_size, self.num_choices])
self.check_loss_output(result)
def main():
# num_train_epochs = 8
# train_batch_size = 8
# max_seq_length = 512
# learning_rate = 1e-5
# warmup_proportion = 0.1
# gradient_accumulation_steps = 4
# data_dir = './Dataset/RACE/RACE/'
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--data_dir",
default=None,
type=str,
required=True,
help="The input data dir. Should contain the .csv files (or other data files) for the task.")
parser.add_argument("--bert_model", default=None, type=str, required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.")
parser.add_argument("--output_dir",
default=None,
type=str,
required=True,
help="The output directory where the model checkpoints will be written.")
parser.add_argument("--bert_type",
default=None,
type=int,
required=True,
help="0:bert, 1: distilbert")
## Other parameters
parser.add_argument("--max_seq_length",
default=128,
type=int,
help="The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
default=False,
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
default=False,
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument("--do_lower_case",
default=False,
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=8,
type=int,
help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--num_train_epochs",
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("--no_cuda",
default=False,
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument('--gradient_accumulation_steps',
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.")
args = parser.parse_args()
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: {}".format(
device, n_gpu, bool(args.local_rank != -1)))
args.train_batch_size = int(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_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True.")
if os.path.exists(args.output_dir) and os.listdir(args.output_dir):
if args.do_train:
raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
else:
os.makedirs(args.output_dir, exist_ok=True)
tokenizer = None
if args.bert_type == 0:
tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
elif args.bert_type == 1:
tokenizer = DistilBertTokenizerFast.from_pretrained(args.bert_model)
train_examples = None
num_train_steps = None
if args.do_train:
train_dir = os.path.join(args.data_dir, 'train')
train_examples = read_race_examples([train_dir + '/high', train_dir + '/middle'])
num_train_steps = int(
len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs)
# Prepare model
model = None
if args.bert_type == 0:
model = BertForMultipleChoice.from_pretrained(args.bert_model,
cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(
args.local_rank),
num_choices=4)
elif args.bert_type == 1:
model = DistilBertForMultipleChoice.from_pretrained(args.bert_model)
model.to(device)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
# Prepare optimizer
param_optimizer = list(model.named_parameters())
# hack to remove pooler, which is not used
# thus it produce None grad that break apex
param_optimizer = [n for n in param_optimizer if 'pooler' not in n[0]]
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}
]
t_total = num_train_steps
if args.local_rank != -1:
t_total = t_total // torch.distributed.get_world_size()
optimizer = None
if args.bert_type == 0:
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=t_total)
elif args.bert_type == 1:
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate)
global_step = 0
if args.do_train:
train_features = convert_examples_to_features(
train_examples, tokenizer, args.max_seq_length, True)
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_steps)
all_input_ids = torch.tensor(select_field(train_features, 'input_ids'), dtype=torch.long)
all_input_mask = torch.tensor(select_field(train_features, 'input_mask'), dtype=torch.long)
all_segment_ids = torch.tensor(select_field(train_features, 'segment_ids'), dtype=torch.long)
all_label = torch.tensor([f.label for f in train_features], dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label)
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)
model.train()
for ep in range(int(args.num_train_epochs)):
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
count = 0
logger.info("Trianing Epoch: {}/{}".format(ep + 1, int(args.num_train_epochs)))
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, label_ids = batch
if args.bert_type == 0:
loss = model(input_ids=input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask,
labels=label_ids)
elif args.bert_type == 1:
result = model(input_ids=input_ids,
# token_type_ids=segment_ids,
attention_mask=input_mask,
labels=label_ids)
loss = result['loss']
logits = result['logits']
#######
compare = np.array(label_ids.cpu()) == np.array(logits.argmax(axis=1).cpu())
count += np.sum(compare)
print("\nLabel: {}, Prediction: {}, Accuracy: {}"
.format(label_ids, logits.argmax(axis=1), count / (args.train_batch_size * (step + 1))))
#######
if n_gpu > 1:
loss = loss.mean()
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
loss.backward()
if (step + 1) % args.gradient_accumulation_steps == 0:
# modify learning rate with special warm up BERT uses
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.zero_grad()
global_step += 1
if global_step%100 == 0:
logger.info("Training loss: {}, global step: {}".format(tr_loss/nb_tr_steps, global_step))
## evaluate on dev set
if global_step % 1000 == 0:
dev_dir = os.path.join(args.data_dir, 'dev')
dev_set = [dev_dir+'/high', dev_dir+'/middle']
eval_examples = read_race_examples(dev_set)
eval_features = convert_examples_to_features(
eval_examples, tokenizer, args.max_seq_length, True)
logger.info("***** Running evaluation: Dev *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
all_input_ids = torch.tensor(select_field(eval_features, 'input_ids'), dtype=torch.long)
all_input_mask = torch.tensor(select_field(eval_features, 'input_mask'), dtype=torch.long)
all_segment_ids = torch.tensor(select_field(eval_features, 'segment_ids'), dtype=torch.long)
all_label = torch.tensor([f.label for f in eval_features], dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label)
# 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 step, batch in enumerate(eval_dataloader):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
with torch.no_grad():
if args.bert_type == 0:
tmp_eval_loss = model(input_ids, segment_ids, input_mask, label_ids)
logits = model(input_ids, segment_ids, input_mask)
elif args.bert_type == 1:
result = model(input_ids=input_ids,
attention_mask=input_mask,
labels=label_ids)
tmp_eval_loss = result['loss']
logits = result['logits']
logits = logits.argmax(axis=1)
logits = logits.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
result = {'dev_eval_loss': eval_loss,
'dev_eval_accuracy': eval_accuracy,
'global_step': global_step,
'loss': tr_loss/nb_tr_steps}
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "a+") as writer:
logger.info("***** Dev results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
# 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, "pytorch_model.bin")
torch.save(model_to_save.state_dict(), output_model_file)
model.load_state_dict(torch.load(os.path.join(args.output_dir, "pytorch_model.bin")))
if args.do_eval and args.local_rank == -1:
test_dir = os.path.join(args.data_dir, 'test')
test_high = [test_dir + '/high']
test_middle = [test_dir + '/middle']
## test high
eval_examples = read_race_examples(test_high)
eval_features = convert_examples_to_features(
eval_examples, tokenizer, args.max_seq_length, True)
logger.info("***** Running evaluation: test high *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
all_input_ids = torch.tensor(select_field(eval_features, 'input_ids'), dtype=torch.long)
all_input_mask = torch.tensor(select_field(eval_features, 'input_mask'), dtype=torch.long)
all_segment_ids = torch.tensor(select_field(eval_features, 'segment_ids'), dtype=torch.long)
all_label = torch.tensor([f.label for f in eval_features], dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label)
# 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()
high_eval_loss, high_eval_accuracy = 0, 0
high_nb_eval_steps, high_nb_eval_examples = 0, 0
for step, batch in enumerate(eval_dataloader):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
with torch.no_grad():
if args.bert_type == 0:
tmp_eval_loss = model(input_ids, segment_ids, input_mask, label_ids)
logits = model(input_ids, segment_ids, input_mask)
elif args.bert_type == 1:
result = model(input_ids=input_ids,
attention_mask=input_mask,
labels=label_ids)
tmp_eval_loss = result['loss']
logits = result['logits']
# logits = logits.argmax(axis=1)
logits = logits.detach().cpu().numpy()
label_ids = label_ids.to('cpu').numpy()
tmp_eval_accuracy = accuracy(logits, label_ids)
high_eval_loss += tmp_eval_loss.mean().item()
high_eval_accuracy += tmp_eval_accuracy
high_nb_eval_examples += input_ids.size(0)
high_nb_eval_steps += 1
eval_loss = high_eval_loss / high_nb_eval_steps
eval_accuracy = high_eval_accuracy / high_nb_eval_examples
result = {'high_eval_loss': eval_loss,
'high_eval_accuracy': eval_accuracy}
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "a+") as writer:
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
## test middle
eval_examples = read_race_examples(test_middle)
eval_features = convert_examples_to_features(
eval_examples, tokenizer, args.max_seq_length, True)
logger.info("***** Running evaluation: test middle *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
all_input_ids = torch.tensor(select_field(eval_features, 'input_ids'), dtype=torch.long)
all_input_mask = torch.tensor(select_field(eval_features, 'input_mask'), dtype=torch.long)
all_segment_ids = torch.tensor(select_field(eval_features, 'segment_ids'), dtype=torch.long)
all_label = torch.tensor([f.label for f in eval_features], dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label)
# 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()
middle_eval_loss, middle_eval_accuracy = 0, 0
middle_nb_eval_steps, middle_nb_eval_examples = 0, 0
for step, batch in enumerate(eval_dataloader):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
with torch.no_grad():
if args.bert_type == 0:
results = model(input_ids=input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask,
labels=label_ids)
elif args.bert_type == 1:
results = model(input_ids=input_ids,
attention_mask=input_mask,
labels=label_ids)
tmp_eval_loss = results['loss']
logits = results['logits']
logits = logits.detach().cpu().numpy()
label_ids = label_ids.to('cpu').numpy()
tmp_eval_accuracy = accuracy(logits, label_ids)
middle_eval_loss += tmp_eval_loss.mean().item()
middle_eval_accuracy += tmp_eval_accuracy
middle_nb_eval_examples += input_ids.size(0)
middle_nb_eval_steps += 1
eval_loss = middle_eval_loss / middle_nb_eval_steps
eval_accuracy = middle_eval_accuracy / middle_nb_eval_examples
result = {'middle_eval_loss': eval_loss,
'middle_eval_accuracy': eval_accuracy}
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "a+") as writer:
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
# all test
eval_loss = (middle_eval_loss + high_eval_loss) / (middle_nb_eval_steps + high_nb_eval_steps)
eval_accuracy = (middle_eval_accuracy + high_eval_accuracy) / (middle_nb_eval_examples + high_nb_eval_examples)
result = {'overall_eval_loss': eval_loss,
'overall_eval_accuracy': eval_accuracy}
with open(output_eval_file, "a+") as writer:
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
train_examples = read_mctest_examples([train_dir + '.tsv', train_dir + '.ans'])
num_train_steps = int(
len(train_examples) / train_batch_size / gradient_accumulation_steps * num_train_epochs)
tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')
train_features = convert_examples_to_features(
train_examples, tokenizer, max_seq_length, True)
train_data = MCTestDataset(train_features)
train_sampler = RandomSampler(train_data)
train_loader = DataLoader(train_data, sampler=train_sampler, batch_size=train_batch_size)
model = DistilBertForMultipleChoice.from_pretrained('distilbert-base-uncased')
model.to(device)
model = torch.nn.DataParallel(model)
optimizer = AdamW(model.parameters(), lr=learning_rate)
global_step = 0
count = 0
model.train()
for epoch in range(num_train_epochs):
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
print("Training Epoch: {}/{}".format(epoch + 1, int(num_train_epochs)))
for step, batch in enumerate(train_loader):
