def main():
args = _get_parser().parse_args()
args.device_ids = list(map(int, args.device_ids.split(',')))
set_seed(args)
sanity_checks(args)
init_gpu_params(args)
tokenizer = Tokenizer(
os.path.join(args.bert_model, "senti_vocab.txt"),
os.path.join(args.bert_model, "RoBERTa_Sentiment_kor"))
train_dataset = NSMCDataSet(data_split="train",
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
pad_to_max=args.pad_to_max)
train_sampler = RandomSampler(
train_dataset) if not args.multi_gpu else DistributedSampler(
train_dataset)
train_dataloader = DataLoader(train_dataset,
sampler=train_sampler,
batch_size=args.per_gpu_train_batch_size,
collate_fn=train_dataset.collate_fn)
model = RobertaForSequenceClassification(
classifier_dropout=args.classifier_dropout,
bert_model_dir=args.bert_model,
pre_trained_model=args.pretrained_bert_model)
no_decay = ["bias", "LayerNorm.weight"]
optimizer_grouped_parameters = [
{
"params": [
p for n, p in model.named_parameters()
if not any(nd in n for nd in no_decay)
],
"weight_decay":
args.weight_decay,
},
{
"params": [
p for n, p in model.named_parameters()
if any(nd in n for nd in no_decay)
],
"weight_decay":
0.0
},
]
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate)
t_total = len(train_dataloader
) // args.gradient_accumulation_steps * args.num_train_epochs
warmup_steps = math.ceil(t_total * args.warmup_proportion)
scheduler = get_linear_schedule_with_warmup(optimizer,
num_warmup_steps=warmup_steps,
num_training_steps=t_total)
model.zero_grad()
model.cuda()
if args.multi_gpu:
model = DistributedDataParallel(
model,
device_ids=[args.device_ids[args.local_rank]],
output_device=args.device_ids[args.local_rank])
if args.is_master:
logger.info(json.dumps(vars(args), indent=4))
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_dataset))
logger.info(" Num Epochs = %d", args.num_train_epochs)
logger.info(" Instantaneous batch size per GPU = %d",
args.per_gpu_train_batch_size)
logger.info(
" Total train batch size (w. parallel, distributed & accumulation) = %d",
args.per_gpu_train_batch_size * args.gradient_accumulation_steps *
(torch.distributed.get_world_size() if args.multi_gpu else 1),
)
logger.info(" Gradient Accumulation steps = %d",
args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", t_total)
global_steps = 0
for epoch in range(args.num_train_epochs):
if args.multi_gpu:
train_sampler.set_epoch(epoch)
loss_bce = nn.BCEWithLogitsLoss()
iter_loss = 0
model.train()
pbar = tqdm(train_dataloader, desc="Iter", disable=not args.is_master)
for step, batch in enumerate(pbar):
input_ids, attention_mask, labels = batch
inputs = {
"input_ids":
torch.tensor(input_ids, dtype=torch.long).cuda(),
"attention_mask":
torch.tensor(attention_mask, dtype=torch.long).cuda()
}
logits = model(**inputs)
labels = torch.tensor(labels, dtype=torch.float).cuda()
loss = loss_bce(input=logits.view(-1), target=labels.view(-1))
if args.gradient_accumulation_steps > 1:
loss /= args.gradient_accumulation_steps
loss.backward()
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.max_grad_norm > 0:
torch.nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
optimizer.step()
optimizer.zero_grad()
scheduler.step()
global_steps += 1
if global_steps % args.save_checkpoints_steps == 0 and args.is_master:
model_to_save = model.module if hasattr(
model, 'module') else model
save_path = os.path.join(args.save_checkpoints_dir,
f"step_{global_steps}.ckpt")
torch.save(model_to_save.state_dict(), save_path)
iter_loss += loss.item()
pbar.set_postfix({
"epoch":
epoch,
"global_steps":
global_steps,
"learning_rate":
f"{scheduler.get_last_lr()[0]:.10f}",
"avg_iter_loss":
f"{iter_loss / (step + 1) * args.gradient_accumulation_steps:.5f}",
"last_loss":
f"{loss.item() * args.gradient_accumulation_steps:.5f}"
})
pbar.close()
if args.is_master:
model_to_save = model.module if hasattr(model, 'module') else model
save_path = os.path.join(args.save_checkpoints_dir,
f"epoch_{epoch+1}.ckpt")
torch.save(model_to_save.state_dict(), save_path)
def main():
parser = argparse.ArgumentParser(description="Training")
parser.add_argument("--force",
action="store_true",
help="Overwrite dump_path if it already exists.")
parser.add_argument(
"--dump_path",
type=str,
required=True,
help="The output directory (log, checkpoints, parameters, etc.)")
parser.add_argument(
"--data_file",
type=str,
required=True,
help=
"The binarized file (tokenized + tokens_to_ids) and grouped by sequence.",
)
parser.add_argument(
"--student_type",
type=str,
choices=["distilbert", "roberta", "gpt2"],
required=True,
help="The student type (DistilBERT, RoBERTa).",
)
parser.add_argument("--student_config",
type=str,
required=True,
help="Path to the student configuration.")
parser.add_argument("--student_pretrained_weights",
default=None,
type=str,
help="Load student initialization checkpoint.")
parser.add_argument("--teacher_type",
choices=["bert", "roberta", "gpt2"],
required=True,
help="Teacher type (BERT, RoBERTa).")
parser.add_argument("--teacher_name",
type=str,
required=True,
help="The teacher model.")
parser.add_argument("--temperature",
default=2.0,
type=float,
help="Temperature for the softmax temperature.")
parser.add_argument(
"--alpha_ce",
default=0.5,
type=float,
help="Linear weight for the distillation loss. Must be >=0.")
parser.add_argument(
"--alpha_mlm",
default=0.0,
type=float,
help=
"Linear weight for the MLM loss. Must be >=0. Should be used in coonjunction with `mlm` flag.",
)
parser.add_argument("--alpha_clm",
default=0.5,
type=float,
help="Linear weight for the CLM loss. Must be >=0.")
parser.add_argument("--alpha_mse",
default=0.0,
type=float,
help="Linear weight of the MSE loss. Must be >=0.")
parser.add_argument(
"--alpha_cos",
default=0.0,
type=float,
help="Linear weight of the cosine embedding loss. Must be >=0.")
parser.add_argument(
"--mlm",
action="store_true",
help=
"The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.")
parser.add_argument(
"--mlm_mask_prop",
default=0.15,
type=float,
help="Proportion of tokens for which we need to make a prediction.",
)
parser.add_argument("--word_mask",
default=0.8,
type=float,
help="Proportion of tokens to mask out.")
parser.add_argument("--word_keep",
default=0.1,
type=float,
help="Proportion of tokens to keep.")
parser.add_argument("--word_rand",
default=0.1,
type=float,
help="Proportion of tokens to randomly replace.")
parser.add_argument(
"--mlm_smoothing",
default=0.7,
type=float,
help=
"Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).",
)
parser.add_argument("--token_counts",
type=str,
help="The token counts in the data_file for MLM.")
parser.add_argument(
"--restrict_ce_to_mask",
action="store_true",
help=
"If true, compute the distilation loss only the [MLM] prediction distribution.",
)
parser.add_argument(
"--freeze_pos_embs",
action="store_true",
help=
"Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only.",
)
parser.add_argument(
"--freeze_token_type_embds",
action="store_true",
help=
"Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only.",
)
parser.add_argument("--n_epoch",
type=int,
default=3,
help="Number of pass on the whole dataset.")
parser.add_argument("--batch_size",
type=int,
default=5,
help="Batch size (for each process).")
parser.add_argument(
"--group_by_size",
action="store_false",
help=
"If true, group sequences that have similar length into the same batch. Default is true.",
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=50,
help="Gradient accumulation for larger training batches.",
)
parser.add_argument("--warmup_prop",
default=0.05,
type=float,
help="Linear warmup proportion.")
parser.add_argument("--weight_decay",
default=0.0,
type=float,
help="Weight deay if we apply some.")
parser.add_argument("--learning_rate",
default=5e-4,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--adam_epsilon",
default=1e-6,
type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm",
default=5.0,
type=float,
help="Max gradient norm.")
parser.add_argument("--initializer_range",
default=0.02,
type=float,
help="Random initialization range.")
parser.add_argument(
"--fp16",
action="store_true",
help=
"Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
)
parser.add_argument(
"--fp16_opt_level",
type=str,
default="O1",
help=
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html",
)
parser.add_argument("--n_gpu",
type=int,
default=1,
help="Number of GPUs in the node.")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="Distributed training - Local rank")
parser.add_argument("--seed", type=int, default=56, help="Random seed")
parser.add_argument("--log_interval",
type=int,
default=500,
help="Tensorboard logging interval.")
parser.add_argument("--checkpoint_interval",
type=int,
default=4000,
help="Checkpoint interval.")
args = parser.parse_args()
sanity_checks(args)
# ARGS #
init_gpu_params(args)
set_seed(args)
if args.is_master:
if os.path.exists(args.dump_path):
if not args.force:
raise ValueError(
f"Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite it"
"Use `--force` if you want to overwrite it")
else:
shutil.rmtree(args.dump_path)
if not os.path.exists(args.dump_path):
os.makedirs(args.dump_path)
logger.info(
f"Experiment will be dumped and logged in {args.dump_path}")
# SAVE PARAMS #
logger.info(f"Param: {args}")
with open(os.path.join(args.dump_path, "parameters.json"), "w") as f:
json.dump(vars(args), f, indent=4)
git_log(args.dump_path)
student_config_class, student_model_class, _ = MODEL_CLASSES[
args.student_type]
teacher_config_class, teacher_model_class, teacher_tokenizer_class = MODEL_CLASSES[
args.teacher_type]
# TOKENIZER #
tokenizer = teacher_tokenizer_class.from_pretrained(args.teacher_name)
special_tok_ids = {}
for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
idx = tokenizer.all_special_tokens.index(tok_symbol)
special_tok_ids[tok_name] = tokenizer.all_special_ids[idx]
logger.info(f"Special tokens {special_tok_ids}")
args.special_tok_ids = special_tok_ids
args.max_model_input_size = tokenizer.max_model_input_sizes[
args.teacher_name]
# DATA LOADER #
logger.info(f"Loading data from {args.data_file}")
with open(args.data_file, "rb") as fp:
data = pickle.load(fp)
if args.mlm:
logger.info(
f"Loading token counts from {args.token_counts} (already pre-computed)"
)
with open(args.token_counts, "rb") as fp:
counts = pickle.load(fp)
token_probs = np.maximum(counts, 1)**-args.mlm_smoothing
for idx in special_tok_ids.values():
token_probs[idx] = 0.0 # do not predict special tokens
token_probs = torch.from_numpy(token_probs)
else:
token_probs = None
train_lm_seq_dataset = LmSeqsDataset(params=args, data=data)
logger.info(f"Data loader created.")
# STUDENT #
logger.info(f"Loading student config from {args.student_config}")
stu_architecture_config = student_config_class.from_pretrained(
args.student_config)
stu_architecture_config.output_hidden_states = True
if args.student_pretrained_weights is not None:
logger.info(
f"Loading pretrained weights from {args.student_pretrained_weights}"
)
student = student_model_class.from_pretrained(
args.student_pretrained_weights, config=stu_architecture_config)
else:
student = student_model_class(stu_architecture_config)
if args.n_gpu > 0:
student.to(f"cuda:{args.local_rank}")
logger.info(f"Student loaded.")
# TEACHER #
teacher = teacher_model_class.from_pretrained(args.teacher_name,
output_hidden_states=True)
if args.n_gpu > 0:
teacher.to(f"cuda:{args.local_rank}")
logger.info(f"Teacher loaded from {args.teacher_name}.")
# FREEZING #
if args.freeze_pos_embs:
freeze_pos_embeddings(student, args)
if args.freeze_token_type_embds:
freeze_token_type_embeddings(student, args)
# SANITY CHECKS #
assert student.config.vocab_size == teacher.config.vocab_size
assert student.config.hidden_size == teacher.config.hidden_size
assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
if args.mlm:
assert token_probs.size(0) == stu_architecture_config.vocab_size
# DISTILLER #
torch.cuda.empty_cache()
distiller = Distiller(params=args,
dataset=train_lm_seq_dataset,
token_probs=token_probs,
student=student,
teacher=teacher)
distiller.train()
logger.info("Let's go get some drinks.")
def main():
parser = argparse.ArgumentParser(description="Training")
parser.add_argument(
"--dump_path",
type=str,
required=True,
help="The output directory (log, checkpoints, parameters, etc.)")
parser.add_argument(
"--data_file",
type=str,
required=True,
help=
"The binarized file (tokenized + tokens_to_ids) and grouped by sequence."
)
parser.add_argument("--token_counts",
type=str,
required=True,
help="The token counts in the data_file for MLM.")
parser.add_argument("--force",
action='store_true',
help="Overwrite dump_path if it already exists.")
parser.add_argument("--vocab_size",
default=30522,
type=int,
help="The vocabulary size.")
parser.add_argument(
"--max_position_embeddings",
default=512,
type=int,
help="Maximum sequence length we can model (including [CLS] and [SEP])."
)
parser.add_argument(
"--sinusoidal_pos_embds",
action='store_false',
help=
"If true, the position embeddings are simply fixed with sinusoidal embeddings."
)
parser.add_argument("--n_layers",
default=6,
type=int,
help="Number of Transformer blocks.")
parser.add_argument("--n_heads",
default=12,
type=int,
help="Number of heads in the self-attention module.")
parser.add_argument(
"--dim",
default=768,
type=int,
help="Dimension through the network. Must be divisible by n_heads")
parser.add_argument("--hidden_dim",
default=3072,
type=int,
help="Intermediate dimension in the FFN.")
parser.add_argument("--dropout", default=0.1, type=float, help="Dropout.")
parser.add_argument("--attention_dropout",
default=0.1,
type=float,
help="Dropout in self-attention.")
parser.add_argument("--activation",
default='gelu',
type=str,
help="Activation to use in self-attention")
parser.add_argument(
"--tie_weights_",
action='store_false',
help=
"If true, we tie the embeddings matrix with the projection over the vocabulary matrix. Default is true."
)
parser.add_argument("--from_pretrained_weights",
default=None,
type=str,
help="Load student initialization checkpoint.")
parser.add_argument(
"--from_pretrained_config",
default=None,
type=str,
help="Load student initialization architecture config.")
parser.add_argument("--teacher_type",
default="bert",
choices=["bert", "roberta"],
help="Teacher type (BERT, RoBERTa).")
parser.add_argument("--teacher_name",
default="bert-base-uncased",
type=str,
help="The teacher model.")
parser.add_argument("--temperature",
default=2.,
type=float,
help="Temperature for the softmax temperature.")
parser.add_argument(
"--alpha_ce",
default=0.5,
type=float,
help="Linear weight for the distillation loss. Must be >=0.")
parser.add_argument("--alpha_mlm",
default=0.5,
type=float,
help="Linear weight for the MLM loss. Must be >=0.")
parser.add_argument("--alpha_mse",
default=0.0,
type=float,
help="Linear weight of the MSE loss. Must be >=0.")
parser.add_argument(
"--alpha_cos",
default=0.0,
type=float,
help="Linear weight of the cosine embedding loss. Must be >=0.")
parser.add_argument(
"--mlm_mask_prop",
default=0.15,
type=float,
help="Proportion of tokens for which we need to make a prediction.")
parser.add_argument("--word_mask",
default=0.8,
type=float,
help="Proportion of tokens to mask out.")
parser.add_argument("--word_keep",
default=0.1,
type=float,
help="Proportion of tokens to keep.")
parser.add_argument("--word_rand",
default=0.1,
type=float,
help="Proportion of tokens to randomly replace.")
parser.add_argument(
"--mlm_smoothing",
default=0.7,
type=float,
help=
"Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)."
)
parser.add_argument(
"--restrict_ce_to_mask",
action='store_true',
help=
"If true, compute the distilation loss only the [MLM] prediction distribution."
)
parser.add_argument("--n_epoch",
type=int,
default=3,
help="Number of pass on the whole dataset.")
parser.add_argument("--batch_size",
type=int,
default=5,
help="Batch size (for each process).")
parser.add_argument(
"--tokens_per_batch",
type=int,
default=-1,
help=
"If specified, modify the batches so that they have approximately this number of tokens."
)
parser.add_argument(
"--shuffle",
action='store_false',
help="If true, shuffle the sequence order. Default is true.")
parser.add_argument(
"--group_by_size",
action='store_false',
help=
"If true, group sequences that have similar length into the same batch. Default is true."
)
parser.add_argument(
"--gradient_accumulation_steps",
type=int,
default=50,
help="Gradient accumulation for larger training batches.")
parser.add_argument("--warmup_prop",
default=0.05,
type=float,
help="Linear warmup proportion.")
parser.add_argument("--weight_decay",
default=0.0,
type=float,
help="Weight deay if we apply some.")
parser.add_argument("--learning_rate",
default=5e-4,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--adam_epsilon",
default=1e-6,
type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm",
default=5.0,
type=float,
help="Max gradient norm.")
parser.add_argument("--initializer_range",
default=0.02,
type=float,
help="Random initialization range.")
parser.add_argument(
'--fp16',
action='store_true',
help=
"Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"
)
parser.add_argument(
'--fp16_opt_level',
type=str,
default='O1',
help=
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html")
parser.add_argument("--n_gpu",
type=int,
default=1,
help="Number of GPUs in the node.")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="Distributed training - Local rank")
parser.add_argument("--seed", type=int, default=56, help="Random seed")
parser.add_argument("--log_interval",
type=int,
default=500,
help="Tensorboard logging interval.")
parser.add_argument("--checkpoint_interval",
type=int,
default=4000,
help="Checkpoint interval.")
args = parser.parse_args()
## ARGS ##
init_gpu_params(args)
set_seed(args)
if args.is_master:
if os.path.exists(args.dump_path):
if not args.force:
raise ValueError(
f'Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite it'
'Use `--force` if you want to overwrite it')
else:
shutil.rmtree(args.dump_path)
if not os.path.exists(args.dump_path):
os.makedirs(args.dump_path)
logger.info(
f'Experiment will be dumped and logged in {args.dump_path}')
### SAVE PARAMS ###
logger.info(f'Param: {args}')
with open(os.path.join(args.dump_path, 'parameters.json'), 'w') as f:
json.dump(vars(args), f, indent=4)
git_log(args.dump_path)
assert (args.from_pretrained_weights is None and args.from_pretrained_config is None) or \
(args.from_pretrained_weights is not None and args.from_pretrained_config is not None)
### TOKENIZER ###
if args.teacher_type == 'bert':
tokenizer = BertTokenizer.from_pretrained(args.teacher_name)
elif args.teacher_type == 'roberta':
tokenizer = RobertaTokenizer.from_pretrained(args.teacher_name)
special_tok_ids = {}
for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
idx = tokenizer.all_special_tokens.index(tok_symbol)
special_tok_ids[tok_name] = tokenizer.all_special_ids[idx]
logger.info(f'Special tokens {special_tok_ids}')
args.special_tok_ids = special_tok_ids
## DATA LOADER ##
logger.info(f'Loading data from {args.data_file}')
with open(args.data_file, 'rb') as fp:
data = pickle.load(fp)
assert os.path.isfile(args.token_counts)
logger.info(
f'Loading token counts from {args.token_counts} (already pre-computed)'
)
with open(args.token_counts, 'rb') as fp:
counts = pickle.load(fp)
assert len(counts) == args.vocab_size
token_probs = np.maximum(counts, 1)**-args.mlm_smoothing
for idx in special_tok_ids.values():
token_probs[idx] = 0. # do not predict special tokens
token_probs = torch.from_numpy(token_probs)
train_dataloader = Dataset(params=args, data=data)
logger.info(f'Data loader created.')
## STUDENT ##
if args.from_pretrained_weights is not None:
assert os.path.isfile(args.from_pretrained_weights)
assert os.path.isfile(args.from_pretrained_config)
logger.info(
f'Loading pretrained weights from {args.from_pretrained_weights}')
logger.info(
f'Loading pretrained config from {args.from_pretrained_config}')
stu_architecture_config = DistilBertConfig.from_json_file(
args.from_pretrained_config)
stu_architecture_config.output_hidden_states = True
student = DistilBertForMaskedLM.from_pretrained(
args.from_pretrained_weights, config=stu_architecture_config)
else:
args.vocab_size_or_config_json_file = args.vocab_size
stu_architecture_config = DistilBertConfig(**vars(args),
output_hidden_states=True)
student = DistilBertForMaskedLM(stu_architecture_config)
if args.n_gpu > 0:
student.to(f'cuda:{args.local_rank}')
logger.info(f'Student loaded.')
## TEACHER ##
if args.teacher_type == 'bert':
teacher = BertForMaskedLM.from_pretrained(args.teacher_name,
output_hidden_states=True)
elif args.teacher_type == 'roberta':
teacher = RobertaForMaskedLM.from_pretrained(args.teacher_name,
output_hidden_states=True)
if args.n_gpu > 0:
teacher.to(f'cuda:{args.local_rank}')
logger.info(f'Teacher loaded from {args.teacher_name}.')
## DISTILLER ##
torch.cuda.empty_cache()
distiller = Distiller(params=args,
dataloader=train_dataloader,
token_probs=token_probs,
student=student,
teacher=teacher)
distiller.train()
logger.info("Let's go get some drinks.")