def virtual_adversarial_loss():
"""Computes virtual adversarial loss.
Uses lm_inputs and constructs the language model graph if it hasn't yet
been constructed.
Also ensures that the LM input states are saved for LSTM state-saving
BPTT.
Returns:
loss: float scalar.
"""
if self.lm_inputs is None:
self.language_model_graph(compute_loss=False)
def logits_from_embedding(embedded, return_next_state=False):
_, next_state, logits, _ = self.cl_loss_from_embedding(
embedded, inputs=self.lm_inputs, return_intermediates=True)
if return_next_state:
return next_state, logits
else:
return logits
next_state, lm_cl_logits = logits_from_embedding(
self.tensors['lm_embedded'], return_next_state=True)
va_loss = adv_lib.virtual_adversarial_loss(
lm_cl_logits, self.tensors['lm_embedded'], self.lm_inputs,
logits_from_embedding)
with tf.control_dependencies(
[self.lm_inputs.save_state(next_state)]):
va_loss = tf.identity(va_loss)
return va_loss
def virtual_adversarial_loss():
"""Computes virtual adversarial loss.
Uses lm_inputs and constructs the language model graph if it hasn't yet
been constructed.
Also ensures that the LM input states are saved for LSTM state-saving
BPTT.
Returns:
loss: float scalar.
"""
if self.lm_inputs is None:
self.language_model_graph(compute_loss=False)
def logits_from_embedding(embedded, return_next_state=False):
_, next_state, logits, _ = self.cl_loss_from_embedding(
embedded, inputs=self.lm_inputs, return_intermediates=True)
if return_next_state:
return next_state, logits
else:
return logits
next_state, lm_cl_logits = logits_from_embedding(
self.tensors['lm_embedded'], return_next_state=True)
va_loss = adv_lib.virtual_adversarial_loss(
lm_cl_logits, self.tensors['lm_embedded'], self.lm_inputs,
logits_from_embedding)
with tf.control_dependencies([self.lm_inputs.save_state(next_state)]):
va_loss = tf.identity(va_loss)
return va_loss
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument(
"--data_dir",
default=None,
type=str,
required=True,
help=
"The input data dir. Should contain the .tsv 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-large-cased, bert-base-multilingual-uncased, "
"bert-base-multilingual-cased, bert-base-chinese.")
parser.add_argument("--task_name",
default=None,
type=str,
required=True,
help="The name of the task to train.")
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help=
"The output directory where the model predictions and checkpoints will be written."
)
## Other parameters
parser.add_argument(
"--cache_dir",
default="",
type=str,
help=
"Where do you want to store the pre-trained models downloaded from s3")
parser.add_argument(
"--max_seq_length",
default=32,
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("--tag_space",
default=128,
type=int,
help="dimension of linear transformation.")
parser.add_argument("--rnn_hidden_size",
default=None,
type=int,
help="dimension of document level rnn layer.")
parser.add_argument(
"--dropout",
default=0.1,
type=float,
help="dropout for outputs other than the original bert model")
parser.add_argument("--use_crf",
action='store_true',
help="Whether to use crf layer.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument(
"--do_resume",
action='store_true',
help="Whether to run eval on the resumed pretrained model.")
parser.add_argument(
"--do_lower_case",
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=16,
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",
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."
)
parser.add_argument(
'--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument(
'--loss_scale',
type=float,
default=0,
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('--server_ip',
type=str,
default='',
help="Can be used for distant debugging.")
parser.add_argument('--server_port',
type=str,
default='',
help="Can be used for distant debugging.")
parser.add_argument('--adv_reg_coeff',
default='0.0',
type=float,
help='Regularization coefficient of adversarial loss')
parser.add_argument(
'--va_reg_coeff',
default='0.0',
type=float,
help='Regularization coefficient of virtual adversarial loss')
parser.add_argument('--adv_perturb_norm_length',
default='8.0',
type=float,
help='Norm length of adversarial perturbation to be')
parser.add_argument(
'--va_perturb_norm_length',
default='4.0',
type=float,
help='Norm length of virtual adversarial perturbation to be')
args = parser.parse_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()
processors = {"pico": PICOProcessor, "nicta": NICTAProcessor}
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_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) 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)
task_name = args.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
label_list = processor.get_labels()
label_map = {label: i for i, label in enumerate(label_list)}
num_labels = len(label_map)
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
num_train_optimization_steps_epoch = int(
len(train_examples) / args.train_batch_size /
args.gradient_accumulation_steps)
if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size(
)
# Prepare model
cache_dir = args.cache_dir if args.cache_dir else os.path.join(
str(PYTORCH_PRETRAINED_BERT_CACHE), 'distributed_{}'.format(
args.local_rank))
model = BertForSequentialClassification.from_pretrained(
args.bert_model,
cache_dir=cache_dir,
num_labels=num_labels,
tag_space=args.tag_space,
use_crf=args.use_crf,
rnn_hidden_size=args.rnn_hidden_size,
dropout=args.dropout)
# print(count_parameters(model))
# 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)
if args.do_train:
# 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:
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)
model.train()
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(minibatches(train_examples,
label_map,
tokenizer,
args.train_batch_size,
args.max_seq_length,
shuffle=True),
desc="Iteration",
total=num_train_optimization_steps_epoch)):
input_ids = torch.tensor(batch.input_ids,
dtype=torch.long).to(device)
segment_ids = torch.tensor(batch.segment_ids,
dtype=torch.long).to(device)
input_mask = torch.tensor(batch.input_mask,
dtype=torch.long).to(device)
label_ids = torch.tensor(batch.label_ids,
dtype=torch.long).to(device)
document_mask = torch.tensor(batch.document_mask,
dtype=torch.float).to(device)
loss, logits, embeddings = model(input_ids, segment_ids,
input_mask, document_mask,
label_ids)
if args.adv_reg_coeff:
adv_loss = adversarial_loss(
embeddings, segment_ids, input_mask, document_mask,
label_ids, loss, model,
args.adv_perturb_norm_length)[0]
loss += args.adv_reg_coeff * adv_loss
if args.va_reg_coeff:
va_loss = virtual_adversarial_loss(
logits, embeddings, segment_ids, input_mask,
document_mask, num_labels, model,
args.va_perturb_norm_length)
loss += args.va_reg_coeff * va_loss
# 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
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())
# Load a trained model and config that you have fine-tuned
config = BertConfig(output_config_file)
model = BertForSequentialClassification(
config,
num_labels=num_labels,
tag_space=args.tag_space,
use_crf=args.use_crf,
rnn_hidden_size=args.rnn_hidden_size,
dropout=args.dropout)
model.load_state_dict(torch.load(output_model_file))
elif args.do_resume:
output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
# Load a trained model and config that you have fine-tuned
config = BertConfig(output_config_file)
model = BertForSequentialClassification(
config,
num_labels=num_labels,
tag_space=args.tag_space,
use_crf=args.use_crf,
rnn_hidden_size=args.rnn_hidden_size,
dropout=args.dropout)
model.load_state_dict(torch.load(output_model_file))
else:
model = BertForSequentialClassification.from_pretrained(
args.bert_model,
num_labels=num_labels,
tag_space=args.tag_space,
use_crf=args.use_crf,
rnn_hidden_size=args.rnn_hidden_size,
dropout=args.dropout)
model.to(device)
if args.do_eval and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
eval_examples = processor.get_test_examples(
args.data_dir) ## eval on dev/test sets
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
model.eval()
preds_all = []
labels_all = []
for step, batch in enumerate(
tqdm(minibatches(eval_examples,
label_map,
tokenizer,
args.eval_batch_size,
args.max_seq_length,
shuffle=False),
desc="Evaluating")):
input_ids = torch.tensor(batch.input_ids,
dtype=torch.long).to(device)
segment_ids = torch.tensor(batch.segment_ids,
dtype=torch.long).to(device)
input_mask = torch.tensor(batch.input_mask,
dtype=torch.long).to(device)
document_mask = torch.tensor(batch.document_mask,
dtype=torch.float).to(device)
with torch.no_grad():
preds, _, _ = model(input_ids, segment_ids, input_mask,
document_mask)
preds = preds.cpu().tolist()
document_lens = np.sum(batch.document_mask, axis=1)
for pred, label, document_len in zip(preds, batch.label_ids,
document_lens):
preds_all += pred[:document_len]
labels_all += label[:document_len]
eval_acc, eval_prec, eval_recall, eval_f1 = accuracy(
preds_all, labels_all)
print(confusion_matrix(labels_all, preds_all))
eval_sents = [
sent for example in eval_examples for sent in example.document
]
with open(os.path.join(args.output_dir, 'eval_text'), 'w') as ofile:
for sent, pred, label in zip(eval_sents, preds_all, labels_all):
ofile.write('{}\t{}\t{}\n'.format(label_list[label],
label_list[pred], sent))
loss = tr_loss / nb_tr_steps if args.do_train else None
result = {'global_step': global_step, 'loss': loss}
for tag in processor.get_labels():
result.update({
tag: {
"precision": eval_prec[label_map[tag]],
"recall": eval_recall[label_map[tag]],
"f1": eval_f1[label_map[tag]]
}
})
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
fold_num = args.output_dir.split('/')[-1]
params_log = ', '.join(['{}: {}'.format(attr, getattr(args, attr)) for attr in dir(args) \
if not callable(getattr(args, attr)) and not attr.startswith("__")])
with open(output_eval_file, "a") as writer:
logger.info("***** Eval results for Fold {}*****".format(fold_num))
writer.write(
"\n***** Eval results for Fold {}*****\n".format(fold_num))
writer.write(params_log + '\n')
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))