def prepare_model_and_optimizer(self):
# Prepare model
self.config = BertConfig.from_json_file(self.args.config_file)
# Padding for divisibility by 8
if self.config.vocab_size % 8 != 0:
self.config.vocab_size += 8 - (self.config.vocab_size % 8)
self.model = BertForPreTraining(self.config)
self.another_model = BertForPreTraining(self.config)
self.model.to(self.device)
self.another_model.to(self.device)
param_optimizer = list(self.model.named_parameters())
no_decay = ['bias', 'gamma', 'beta', 'LayerNorm']
optimizer_grouped_parameters = []
names = []
for n, p in param_optimizer:
if not any(nd in n for nd in no_decay):
optimizer_grouped_parameters.append({
'params': [p],
'weight_decay': 0.01,
'name': n
})
names.append({'params': [n], 'weight_decay': 0.01})
if any(nd in n for nd in no_decay):
optimizer_grouped_parameters.append({
'params': [p],
'weight_decay': 0.00,
'name': n
})
names.append({'params': [n], 'weight_decay': 0.00})
if self.args.phase2:
max_steps = self.args.max_steps
tmp = max_steps * 10
r = self.args.phase1_end_step / tmp
lr = self.args.learning_rate * (1 - r)
else:
max_steps = int(self.args.max_steps / 9 * 10)
lr = self.args.learning_rate
if self.args.optimizer == "lamb":
self.optimizer = BertLAMB(optimizer_grouped_parameters,
lr=lr,
warmup=self.args.warmup_proportion
if not self.args.phase2 else -1,
t_total=max_steps)
elif self.args.optimizer == "adam":
self.optimizer = BertAdam(optimizer_grouped_parameters,
lr=lr,
warmup=self.args.warmup_proportion
if not self.args.phase2 else -1,
t_total=max_steps)
def test_adam(self):
w = torch.tensor([0.1, -0.2, -0.1], requires_grad=True)
target = torch.tensor([0.4, 0.2, -0.5])
criterion = torch.nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
optimizer = BertAdam(params=[w], lr=2e-1,
weight_decay=0.0,
max_grad_norm=-1)
for _ in range(100):
loss = criterion(w, target)
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist(), [0.4, 0.2, -0.5], tol=1e-2)
def set_optimizer(self, total_step, use_bert=True):
if use_bert:
self.optimizer = BertAdam(params=self.parameters(),
lr=self.config['lr'],
warmup=0.1,
t_total=total_step)
else:
self.optimizer = Adam(self.parameters(), lr=self.config['lr'])
def set_optimizer(self, total_step, use_bert=False):
if use_bert:
self.set_optimizer = BertAdam(params=self.parameters(),
lr=self.lr,
warmup=0.1,
t_total=total_step)
else:
self.set_optimizer = Adam(self.parameters(), lr=self.lr)
def tpu_training_loop(model, loader, device, context):
""" Called by torch_xla_py.data_parallel. This function is executed on each core of the TPU once per epoch"""
model.zero_grad()
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
param_optimizer = list(model.named_parameters())
optimizer_grouped_parameters = [{
'params': [p for n, p in param_optimizer if n not in no_decay],
'weight_decay_rate':
0.01
}, {
'params': [p for n, p in param_optimizer if n in no_decay],
'weight_decay_rate':
0.0
}]
optimizer = context.getattr_or(
'optimizer',
BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_steps))
tr_loss = None
pbar = None
if str(pbar_device) == str(device):
pbar = tqdm(total=int(pbar_steps),
desc=f"training",
dynamic_ncols=True)
tracker = tpu_xm.RateTracker()
model.train()
for step, batch in enumerate(loader):
input_ids, input_mask, segment_ids, label_ids, pos_ids = batch
loss, _ = model(input_ids,
segment_ids,
input_mask,
label_ids,
pos_ids=pos_ids)
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
loss.backward()
tracker.add(args.train_batch_size)
tr_loss = loss * args.gradient_accumulation_steps if step == 0 else tr_loss + loss * args.gradient_accumulation_steps
if pbar is not None:
pbar.update(1)
tpu_xm.optimizer_step(optimizer)
# optimizer.step()
optimizer.zero_grad()
return tr_loss.item() / step
def getOptimizer(args, model, train_steps):
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
p0 = [p for n, p in param_optimizer if any(nd in n for nd in no_decay)]
p1 = [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)]
optimizer_grouped_parameters = [{
'params': p1,
'weight_decay': 0.01
}, {
'params': p0,
'weight_decay': 0.0
}]
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=train_steps)
return optimizer
def prep_optimizer(args, model, num_train_optimization_steps):
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)
return optimizer
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 .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-large-cased, bert-base-multilingual-uncased, "
"bert-base-multilingual-cased, 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("--init_checkpoint",
default=None,
type=str,
required=True,
help="The checkpoint file from pretraining")
## 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",
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_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=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("--max_steps",
default=-1.0,
type=float,
help="Total number of training steps 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")
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: {}, 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):
print(
"WARNING: 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)
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 = read_swag_examples(os.path.join(
args.data_dir, 'train.csv'),
is_training=True)
num_train_optimization_steps = int(
len(train_examples) / args.train_batch_size /
args.gradient_accumulation_steps) * args.num_train_epochs
if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size(
)
# Prepare model
model = BertForMultipleChoice.from_pretrained(
args.bert_model,
cache_dir=os.path.join(PYTORCH_PRETRAINED_BERT_CACHE,
'distributed_{}'.format(args.local_rank)),
num_choices=4)
model.load_state_dict(torch.load(args.init_checkpoint, map_location='cpu'),
strict=False)
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)
# 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
}]
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
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_optimization_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 _ 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(train_dataloader, desc="Iteration")):
# Terminate early for benchmarking
if args.max_steps > 0 and global_step > args.max_steps:
break
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
loss = model(input_ids, segment_ids, input_mask, label_ids)
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args.fp16 and args.loss_scale != 1.0:
# rescale loss for fp16 training
# see https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html
loss = loss * args.loss_scale
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
if args.fp16:
optimizer.backward(loss)
else:
loss.backward()
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 = BertForMultipleChoice(config, num_choices=4)
# noinspection PyUnresolvedReferences
model.load_state_dict(torch.load(output_model_file))
else:
model = BertForMultipleChoice.from_pretrained(args.bert_model,
num_choices=4)
model.load_state_dict(torch.load(args.init_checkpoint,
map_location='cpu'),
strict=False)
model.to(device)
if args.do_eval and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
eval_examples = read_swag_examples(os.path.join(
args.data_dir, 'val.csv'),
is_training=True)
eval_features = convert_examples_to_features(eval_examples, tokenizer,
args.max_seq_length, True)
logger.info("***** Running evaluation *****")
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 input_ids, input_mask, segment_ids, label_ids in tqdm(
eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
tmp_eval_loss = model(input_ids, segment_ids, input_mask,
label_ids)
logits = model(input_ids, segment_ids, input_mask)
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
# noinspection PyUnboundLocalVariable
result = {
'eval_loss': eval_loss,
'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, "w") 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])))
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument(
"--bert_model",
default='pretrained/bert-base-uncased',
type=str,
required=False,
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(
"--output_dir",
default='tasks/QuestionAnswering/squad_output',
type=str,
required=False,
help=
"The output directory where the model checkpoints and predictions will be written."
)
## Other parameters
parser.add_argument(
"--train_file",
default='tasks/QuestionAnswering/squad_data/train-v1.1.json',
type=str,
help="SQuAD json for training. E.g., train-v1.1.json")
parser.add_argument(
"--predict_file",
default='tasks/QuestionAnswering/squad_data/dev-v1.1.json',
type=str,
help="SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json"
)
parser.add_argument("--vocab_size",
default=30522,
type=int,
help="The size of vocabulary.")
parser.add_argument(
"--max_seq_length",
default=384,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. Sequences "
"longer than this will be truncated, and sequences shorter than this will be padded."
)
parser.add_argument(
"--doc_stride",
default=128,
type=int,
help=
"When splitting up a long document into chunks, how much stride to take between chunks."
)
parser.add_argument(
"--max_query_length",
default=64,
type=int,
help=
"The maximum number of tokens for the question. Questions longer than this will "
"be truncated to this length.")
parser.add_argument(
"--do_train", default=1, type=int,
help="Whether to run training.") # , action='store_true'
parser.add_argument(
"--do_predict",
default=1,
type=int,
help="Whether to run eval on the dev set.") # , action='store_true'
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--predict_batch_size",
default=8,
type=int,
help="Total batch size for predictions.")
parser.add_argument("--learning_rate",
default=3e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--num_train_epochs",
default=2.0,
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(
"--n_best_size",
default=20,
type=int,
help=
"The total number of n-best predictions to generate in the nbest_predictions.json "
"output file.")
parser.add_argument(
"--max_answer_length",
default=30,
type=int,
help=
"The maximum length of an answer that can be generated. This is needed because the start "
"and end predictions are not conditioned on one another.")
parser.add_argument(
"--verbose_logging",
action='store_true',
help=
"If true, all of the warnings related to squad_data processing will be printed. "
"A number of warnings are expected for a normal SQuAD evaluation.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument('--seed',
type=int,
default=83,
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(
"--do_lower_case",
default=1,
type=int,
# action='store_true',
help=
"Whether to lower case the input text. True for uncased models, False for cased models."
)
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
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")
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)
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 os.path.exists(args.output_dir) and os.listdir(args.output_dir):
raise ValueError(
"Output directory () already exists and is not empty.")
os.makedirs(args.output_dir, exist_ok=True)
tokenizer = BertTokenizer.from_pretrained(args.bert_model)
train_data, dev_data = load_dataset(args)
# Prepare model
config = json.load(open(os.path.join(args.bert_model, BERT_CONFIG), "r"))
model = BertQA(args.vocab_size, **config)
model.load(os.path.join(args.bert_model, MODEL_NAME))
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.a_2', 'LayerNorm.b_2']
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 = args.num_train_steps
if args.local_rank != -1:
t_total = t_total // torch.distributed.get_world_size()
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=t_total)
global_step = 0
if args.do_train:
criterion = nn.CrossEntropyLoss()
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 _ in trange(int(args.num_train_epochs), desc="Epoch"):
for step, batch in enumerate(
tqdm(train_dataloader, desc="Iteration")):
batch = tuple(
t.to(device)
for t in batch) # multi-gpu does scattering it-self
input_ids, input_mask, segment_ids, start_positions, end_positions = batch
logits = model(x=input_ids,
segment_info=segment_ids,
mask=input_mask)
logits_start = logits['pred_start']
logtis_end = logits['pred_end']
ignored_index = logits_start.size(1)
start_positions.clamp_(0, ignored_index)
end_positions.clamp_(0, ignored_index)
loss = (criterion(logits_start, start_positions) +
criterion(logtis_end, end_positions)) / 2
if n_gpu > 1:
loss = loss.mean()
loss.backward()
if (step + 1) % 1 == 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
# 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)
# Load a trained model that you have fine-tuned
model.load(output_model_file)
model.to(device)
if args.do_predict and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
eval_examples = read_squad_examples(input_file=args.predict_file,
is_training=False)
eval_features = convert_examples_to_features(
examples=eval_examples,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length,
is_training=False)
# Run prediction for full squad_data
eval_sampler = SequentialSampler(dev_data)
eval_dataloader = DataLoader(dev_data,
sampler=eval_sampler,
batch_size=args.predict_batch_size)
model.eval()
all_results = []
logger.info("Start evaluating")
for input_ids, input_mask, segment_ids, example_indices in tqdm(
eval_dataloader, desc="Evaluating"):
if len(all_results) % 1000 == 0:
logger.info("Processing example: %d" % (len(all_results)))
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
with torch.no_grad():
logits = model(x=input_ids,
segment_info=segment_ids,
mask=input_mask)
batch_start_logits = logits['pred_start']
batch_end_logits = logits['pred_end']
for i, example_index in enumerate(example_indices):
start_logits = batch_start_logits[i].detach().cpu().tolist()
end_logits = batch_end_logits[i].detach().cpu().tolist()
eval_feature = eval_features[example_index.item()]
unique_id = int(eval_feature.unique_id)
all_results.append(
RawResult(unique_id=unique_id,
start_logits=start_logits,
end_logits=end_logits))
output_prediction_file = os.path.join(args.output_dir,
"predictions.json")
output_nbest_file = os.path.join(args.output_dir,
"nbest_predictions.json")
write_predictions(eval_examples, eval_features, all_results,
args.n_best_size, args.max_answer_length,
args.do_lower_case, output_prediction_file,
output_nbest_file, args.verbose_logging)
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument(
"--data_dir",
default="../bert_pytorch/tasks/MultipleChoice/swag_data/",
type=str,
required=False,
help=
"The input squad_data dir. Should contain the .csv files (or other squad_data files) for the task."
)
parser.add_argument(
"--bert_model",
default='converted/base-uncased',
type=str,
required=False,
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(
"--output_dir",
default='tasks/MultipleChoice/swag_output/',
type=str,
required=False,
help="The output directory where the model checkpoints will be written."
)
## Other parameters
parser.add_argument(
"--max_seq_length",
default=80,
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("--vocab_size",
default=30522,
type=int,
help="The size of vocabulary.")
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_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
default=4,
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",
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=4,
help=
"Number of updates steps to accumulate before performing a backward/update pass."
)
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")
args = parser.parse_args()
###### config setting ######
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')
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 = 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)
###### fastNLP.DataSet loading ######
train_data, dev_data = load_dataset(args)
###### model initializing ######
config = json.load(open(os.path.join(args.bert_model, BERT_CONFIG), "r"))
model = BertMC(args.vocab_size, num_choices=4, **config)
model.load(os.path.join(args.bert_model, MODEL_NAME))
model.to(device)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
###### ptimizer initializing ######
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.a_2', 'LayerNorm.b_2']
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 = args.num_train_steps
if args.local_rank != -1:
t_total = t_total // torch.distributed.get_world_size()
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=t_total)
global_step = 0
if args.do_train:
criterion = nn.CrossEntropyLoss()
train_dataloader = DataLoader(train_data,
sampler=RandomSampler(train_data),
batch_size=args.train_batch_size)
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(train_dataloader, desc="Iteration")):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
logits = model(x=input_ids,
segment_info=segment_ids,
mask=input_mask)['pred']
loss = criterion(logits, label_ids)
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
# 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")
if args.do_train:
torch.save(model_to_save.state_dict(), output_model_file)
# Load a trained model that you have fine-tuned
model.load(output_model_file)
model.to(device)
if args.do_eval and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
eval_dataloader = DataLoader(dev_data,
sampler=SequentialSampler(dev_data),
batch_size=args.eval_batch_size)
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
for input_ids, input_mask, segment_ids, label_ids in eval_dataloader:
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
# TODO
logits = model(x=input_ids,
segment_info=segment_ids,
mask=input_mask)['pred']
tmp_eval_loss = criterion(logits, label_ids)
if n_gpu > 1:
tmp_eval_loss = tmp_eval_loss.mean()
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
loss = tr_loss / nb_tr_steps if args.do_train else None
result = {
'eval_loss': eval_loss,
'eval_accuracy': eval_accuracy,
'global_step': global_step,
'loss': loss
}
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "w") 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])))
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--train_file",
default=None,
type=str,
required=True,
help="The input train corpus.")
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."
)
## Other parameters
parser.add_argument(
"--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
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",
action='store_true',
help="Whether to run training.")
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=3e-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(
"--on_memory",
action='store_true',
help="Whether to load train samples into memory or use disk")
parser.add_argument(
"--do_lower_case",
action='store_true',
help=
"Whether to lower case the input text. True for uncased models, False for cased models."
)
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 accumualte 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")
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: {}, 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):
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)
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:
print("Loading Train Dataset", args.train_file)
train_dataset = BERTDataset(args.train_file,
tokenizer,
seq_len=args.max_seq_length,
corpus_lines=None,
on_memory=args.on_memory)
num_train_optimization_steps = int(
len(train_dataset) / args.train_batch_size /
args.gradient_accumulation_steps) * args.num_train_epochs
if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size(
)
# Prepare model
model = BertForPreTraining.from_pretrained(args.bert_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)
# 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
if args.do_train:
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_dataset))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps)
if args.local_rank == -1:
train_sampler = RandomSampler(train_dataset)
else:
#TODO: check if this works with current data generator from disk that relies on next(file)
# (it doesn't return item back by index)
train_sampler = DistributedSampler(train_dataset)
train_dataloader = DataLoader(train_dataset,
sampler=train_sampler,
batch_size=args.train_batch_size)
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(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)
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
# Save a trained model
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, "pytorch_model.bin")
if args.do_train:
torch.save(model_to_save.state_dict(), output_model_file)
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--data_dir",
default='./data',
type=str,
help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
parser.add_argument("--bert_model", default='bert-base-uncased', type=str,
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("--pre_training_path", default='./pre_training', type=str,
help="model pre training")
parser.add_argument("--save_path", default='./output', type=str,
help="model save path")
parser.add_argument("--ngpu", default=1, type=int,
help="use gpu number")
parser.add_argument("--load_model", default=False, action='store_true',
help="model load")
parser.add_argument("--save_model", default=False, action='store_true',
help="model save ")
parser.add_argument("--load_path", default='./output', type=str,
help="model save path")
parser.add_argument("--is_test", default='./output', type=str,
help="model save path")
parser.add_argument("--task_name",
default='cloth',
type=str,
help="The name of the task to train.")
parser.add_argument("--output_dir",
default='EXP/',
type=str,
required=True,
help="The output directory where the model checkpoints will be written.")
## Other parameters
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("--train_batch_size",
default=4,
type=int,
help="Total batch size for training.")
parser.add_argument("--cache_size",
default=256,
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("--num_log_steps",
default=10,
type=int,
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.")
parser.add_argument('--optimize_on_cpu',
default=False,
action='store_true',
help="Whether to perform optimization and keep the optimizer averages on CPU")
parser.add_argument('--fp16',
default=False,
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument('--loss_scale',
type=float, default=128,
help='Loss scaling, positive power of 2 values can improve fp16 convergence.')
args = parser.parse_args()
if not args.do_train and not args.do_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True.")
suffix = time.strftime('%Y%m%d-%H%M%S')
args.output_dir = os.path.join(args.output_dir, suffix)
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))
os.makedirs(args.output_dir, exist_ok=True)
bert_list = []
model_list = []
for m in args.bert_model.split('+'):
bert_list .append(m)
model_list.append(chose_model_model(m, args))
logging = get_logger(os.path.join(args.output_dir, 'log.txt'))
data_file = []
for m in bert_list:
data_file.append({'train': 'train', 'valid': 'dev', 'test': 'test'})
for key in data_file[-1].keys():
data_file[-1][key] = data_file[-1][key] + '-' + m + '.pt'
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:
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')
if args.fp16:
logging("16-bits training currently not supported in distributed training")
args.fp16 = False # (see https://github.com/pytorch/pytorch/pull/13496)
logging("device {} n_gpu {} distributed training {}".format(device, n_gpu, bool(args.local_rank != -1)))
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 = 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)
task_name = args.task_name.lower()
num_train_steps = []
train_data = []
if args.do_train:
for id, m in enumerate(bert_list):
train_data.append(data_utils.Loader(args.data_dir, data_file[id]['train'], args.cache_size, args.train_batch_size,
device))
num_train_steps.append(int(
train_data[-1].data_num / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs))
# Prepare model
# model = RobertaForCloze.from_pretrained("roberta-base",
# cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(args.local_rank),
# #proxies={ "socks":"127.0.0.1:1080",}
#
# tokenizer = chose_model_token(args.bert_model,args)
# tokenizer = chose_model_token(args.bert_model,args)
# model.resize_token_embeddings(len(tokenizer))
# model = torch.load()
if args.fp16:
for id, model in enumerate(model_list):
model_list[id].half()
for id, model in enumerate(model_list):
model_list[id].to(device)
if args.local_rank != -1:
for id, model in enumerate(model_list):
model_list[id] = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank],
output_device=args.local_rank)
elif n_gpu > 1:
for id, model in enumerate(model_list):
model_list[id] = torch.nn.DataParallel(model)
# Prepare optimizer
param_optimizer = []
if args.fp16:
for model in model_list:
param_optimizer.append((n, param.clone().detach().to('cpu').float().requires_grad_()) \
for n, param in model.named_parameters())
elif args.optimize_on_cpu:
for model in model_list:
param_optimizer.append((n, param.clone().detach().to('cpu').requires_grad_()) \
for n, param in model.named_parameters())
else:
for model in model_list:
param_optimizer.append(list(model.named_parameters()))
no_decay = ['bias', 'gamma', 'beta']
optimizer_grouped_parameters = []
for p_o in param_optimizer:
optimizer_grouped_parameters.append([
{'params': [p for n, p in p_o if not any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.01},
{'params': [p for n, p in p_o if any(nd in n for nd in no_decay)], 'weight_decay_rate': 0.0}
])
global_step = 0
if args.load_model:
if args.ngpu > 1:
for id, m in enumerate(args.bert_list):
print(' model is loading...... PATH:' + m + '/' + m + '_' + str(
args.ngpu) + '.bin')
model_list[id] = torch.load(args.load_path + '/' + m + '_' + str(args.ngpu) + '.bin')
else:
for id, m in enumerate(args.bert_list):
print(' model is loading...... PATH:' + args.load_path + '/' + m + '.bin')
model_list[id] = torch.load(args.load_path + '/' + m + '.bin')
if args.do_train:
# import time
for id, model in enumerate(model_list):
start = time.time()
logging("***** Running training *****")
logging(" Batch size = {}".format(args.train_batch_size))
logging(" Num steps = {}".format(num_train_steps[id]))
model.train()
loss_history = []
acc_history = []
t_total = num_train_steps[id]
if args.local_rank != -1:
t_total = t_total // torch.distributed.get_world_size()
optimizer = (BertAdam(optimizer_grouped_parameters[id],
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=t_total))
for _ in range(int(args.num_train_epochs)):
tr_loss = 0
tr_acc = 0
nb_tr_examples, nb_tr_steps = 0, 0
for inp, tgt in train_data[id].data_iter():
loss, acc = model(inp, tgt)
# print(loss)
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
acc = acc.sum()
if args.fp16 and args.loss_scale != 1.0:
# rescale loss for fp16 training
# see https://docs.nvidia.com/deeplearning/sdk/mixed-precision-training/index.html
loss = loss * args.loss_scale
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
loss.backward()
# print(loss.shape)
tr_loss += loss.item()
tr_acc += acc.item()
# print(tr_acc)
nb_tr_examples += inp[-1].sum()
nb_tr_steps += 1
if (nb_tr_steps + 1) % args.gradient_accumulation_steps == 0:
if args.fp16 or args.optimize_on_cpu:
if args.fp16 and args.loss_scale != 1.0:
# scale down gradients for fp16 training
for param in model.parameters():
if param.grad is not None:
param.grad.data = param.grad.data / args.loss_scale
is_nan = set_optimizer_params_grad(param_optimizer, model.named_parameters(), test_nan=True)
if is_nan:
logging("FP16 TRAINING: Nan in gradients, reducing loss scaling")
args.loss_scale = args.loss_scale / 2
model.zero_grad()
continue
optimizer.step()
copy_optimizer_params_to_model(model.named_parameters(), param_optimizer)
else:
optimizer.step()
model.zero_grad()
global_step += 1
if (global_step % args.num_log_steps == 0):
logging('step: {} | train loss: {} | train acc {}'.format(
global_step, tr_loss / nb_tr_examples, tr_acc / nb_tr_examples))
loss_history.append([global_step, tr_loss])
acc_history.append([global_step, tr_acc])
tr_loss = 0
tr_acc = 0
nb_tr_examples = 0
save_history_path = "./Cord_Pic"
end = time.time()
print(end - start)
loss_history = np.array(loss_history)
acc_history = np.array(acc_history)
np.save(save_history_path + '/' + bert_list[id] + '.loss_history.npy', loss_history) # 保存为.npy格式
np.save(save_history_path + '/' + bert_list[id] + '.acc_history.npy', acc_history) # 保存为.npy格式
# 读取
# a = np.load('a.npy')
# a = a.tolist()
if args.save_model:
if args.ngpu > 1:
print(' model is saving...... PATH:' + args.load_path + '/' + bert_list[id] + '_' + str(
args.ngpu) + '.bin')
torch.save(model, args.load_path + '/' + bert_list[id] + '_' + str(args.ngpu) + '.bin')
else:
print(' model is saving...... PATH:' + args.load_path + '/' + bert_list[id] + '.bin')
torch.save(model, args.load_path + '/' + bert_list[id] + '.bin')
if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
logging("***** Running evaluation *****")
logging(" Batch size = {}".format(args.eval_batch_size))
valid_data = []
for id, m in enumerate(bert_list):
valid_data.append(data_utils.Loader(args.data_dir, data_file[id]['valid'], args.cache_size, args.eval_batch_size, device))
# Run prediction for full data
for id, model in enumerate(model_list):
out = []
for inp, tgt in valid_data[id].data_iter(shuffle=False):
with torch.no_grad():
one_out = model(inp, tgt)
out.append(one_out)
output = torch.tensor([valid_data[id].data_num, 4])
for batch in range(int(valid_data[id].data_num / args.eval_batch_size)):
output[batch * args.eval_batch_size : (batch + 1) * args.eval_batch_size] = out[batch]
torch.save(output,bert_list[id] + '_out.pt')
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."
)
parser.add_argument("--word_embedding_file",
default='emb/crawl-300d-2M.vec',
type=str,
help="The input directory of word embeddings.")
parser.add_argument("--index_path",
default='emb/p_index.bin',
type=str,
help="The input directory of word embedding index.")
parser.add_argument("--word_embedding_info",
default='emb/vocab_info.txt',
type=str,
help="The input directory of word embedding info.")
parser.add_argument("--data_file",
default='',
type=str,
help="The input directory of input data file.")
## 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=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("--max_ngram_length",
default=16,
type=int,
help="The maximum total ngram sequence")
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_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("--embedding_size",
default=300,
type=int,
help="Total batch size for embeddings.")
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",
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(
'--num_eval_epochs',
type=int,
default=0,
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(
'--single',
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.")
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()
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 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]()
num_labels = num_labels_task[task_name]
label_list = processor.get_labels()
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=args.do_lower_case)
logger.info("loading embeddings ... ")
if args.do_train:
emb_dict, emb_vec, vocab_list, emb_vocab_size = load_vectors(
args.word_embedding_file)
write_vocab_info(args.word_embedding_info, emb_vocab_size, vocab_list)
if args.do_eval:
emb_vocab_size, vocab_list = load_vocab_info(args.word_embedding_info)
#emb_dict, emb_vec, vocab_list, emb_vocab_size = load_vectors(args.word_embedding_file)
#write_vocab_info(args.word_embedding_info, emb_vocab_size, vocab_list)
logger.info("loading p index ...")
if not os.path.exists(args.index_path):
p = load_embeddings_and_save_index(range(emb_vocab_size), emb_vec,
args.index_path)
else:
p = load_embedding_index(args.index_path,
emb_vocab_size,
num_dim=args.embedding_size)
train_examples = None
num_train_optimization_steps = None
w2i, i2w, vocab_size = {}, {}, 1
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
if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size(
)
train_features, w2i, i2w, vocab_size = convert_examples_to_features_gnrt_train(\
train_examples, label_list, args.max_seq_length, args.max_ngram_length, tokenizer, emb_dict)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples))
logger.info(" Num token vocab = %d", vocab_size)
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps)
all_ngram_ids = torch.tensor([f.ngram_ids for f in train_features],
dtype=torch.long)
all_ngram_labels = torch.tensor(
[f.ngram_labels for f in train_features], dtype=torch.long)
all_ngram_masks = torch.tensor([f.ngram_masks for f in train_features],
dtype=torch.long)
all_ngram_embeddings = torch.tensor(
[f.ngram_embeddings for f in train_features], dtype=torch.float)
# Prepare model
cache_dir = args.cache_dir if args.cache_dir else os.path.join(
PYTORCH_PRETRAINED_BERT_CACHE, 'distributed_{}'.format(
args.local_rank))
model = BertForNgramClassification.from_pretrained(
args.bert_model,
cache_dir=cache_dir,
num_labels=num_labels,
embedding_size=args.embedding_size,
max_seq_length=args.max_seq_length,
max_ngram_length=args.max_ngram_length)
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)
# 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
}]
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:
train_data = TensorDataset(all_ngram_ids, all_ngram_labels,
all_ngram_masks, all_ngram_embeddings)
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 ind in trange(int(args.num_train_epochs), desc="Epoch"):
tr_loss = 0
nb_tr_steps = 0
for step, batch in enumerate(
tqdm(train_dataloader, desc="Iteration")):
batch = tuple(t.to(device) for t in batch)
ngram_ids, ngram_labels, ngram_masks, ngram_embeddings = batch
loss = model(ngram_ids, ngram_masks, ngram_embeddings)
if n_gpu > 1:
loss = loss.mean()
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
loss.backward()
tr_loss += loss.item()
nb_tr_steps += 1
if (step + 1) % args.gradient_accumulation_steps == 0:
optimizer.step()
optimizer.zero_grad()
global_step += 1
loss = tr_loss / nb_tr_steps if args.do_train else None
result = {
'loss': loss,
}
output_eval_file = os.path.join(args.output_dir,
"train_results.txt")
with open(output_eval_file, "a") as writer:
#logger.info("***** Training results *****")
writer.write("epoch" + str(ind) + '\n')
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
writer.write('\n')
model_to_save = model.module if hasattr(model, 'module') else model
output_model_file = os.path.join(args.output_dir,
"epoch" + str(ind) + 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
if args.do_eval and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
eval_examples = processor.get_gnrt_dev_examples(args.data_file)
eval_features, w2i, i2w, vocab_size = convert_examples_to_features_gnrt_eval(
eval_examples, label_list, args.max_seq_length,
args.max_ngram_length, tokenizer, w2i, i2w, vocab_size)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Num token vocab = %d", vocab_size)
logger.info(" Batch size = %d", args.eval_batch_size)
all_token_ids = torch.tensor([f.token_ids for f in eval_features],
dtype=torch.long)
# all_flaw_labels: indexes of wrong words predicted by disc
all_flaw_labels = torch.tensor([f.flaw_labels for f in eval_features],
dtype=torch.long)
all_ngram_ids = torch.tensor([f.ngram_ids for f in eval_features],
dtype=torch.long)
all_ngram_mask = torch.tensor([f.ngram_mask for f in eval_features],
dtype=torch.long)
all_ngram_labels = torch.tensor(
[f.ngram_labels for f in eval_features], dtype=torch.long)
all_label_id = torch.tensor([f.label_id for f in eval_features],
dtype=torch.long)
eval_data = TensorDataset(all_token_ids, all_ngram_ids, all_ngram_mask,
all_ngram_labels, all_label_id,
all_flaw_labels)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
if args.single:
eval_range = trange(int(args.num_eval_epochs),
int(args.num_eval_epochs + 1),
desc="Epoch")
else:
eval_range = trange(int(args.num_eval_epochs), desc="Epoch")
for epoch in eval_range:
output_file = os.path.join(
args.data_dir, "epoch" + str(epoch) + "gnrt_outputs.tsv")
with open(output_file, "w") as csv_file:
writer = csv.writer(csv_file, delimiter='\t')
writer.writerow(["sentence", "label"])
output_model_file = os.path.join(
args.output_dir, "epoch" + str(epoch) + WEIGHTS_NAME)
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
config = BertConfig(output_config_file)
model = BertForNgramClassification(
config,
num_labels=num_labels,
embedding_size=args.embedding_size,
max_seq_length=args.max_seq_length,
max_ngram_length=args.max_ngram_length)
model.load_state_dict(torch.load(output_model_file))
model.to(device)
model.eval()
for token_ids, ngram_ids, ngram_mask, ngram_labels, label_id, flaw_labels in tqdm(
eval_dataloader, desc="Evaluating"):
ngram_ids = ngram_ids.to(device)
ngram_mask = ngram_mask.to(device)
with torch.no_grad():
logits = model(ngram_ids, ngram_mask)
logits = logits.detach().cpu().numpy()
flaw_labels = flaw_labels.to('cpu').numpy()
label_id = label_id.to('cpu').numpy()
token_ids = token_ids.to('cpu').numpy()
masks = ngram_mask.to('cpu').numpy()
with open(output_file, "a") as csv_file:
for i in range(len(label_id)):
correct_tokens = look_up_words(logits[i], masks[i],
vocab_list, p)
token_new = replace_token(token_ids[i], flaw_labels[i],
correct_tokens, i2w)
token_new = ' '.join(token_new)
label = str(label_id[i])
writer = csv.writer(csv_file, delimiter='\t')
writer.writerow([token_new, label])
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("--src_file", default=None, type=str,
help="The input data file name.")
parser.add_argument("--tgt_file", default=None, type=str,
help="The output data file name.")
parser.add_argument("--dev_src_file", default=None, type=str,
help="The input data file name.")
parser.add_argument("--dev_tgt_file", default=None, type=str,
help="The output data file name.")
parser.add_argument("--ks_src_file", default=None, type=str,
help="The input data file name.")
parser.add_argument("--ks_tgt_file", default=None, type=str,
help="The output data file name.")
parser.add_argument("--ks_dev_src_file", default=None, type=str,
help="The input data file name.")
parser.add_argument("--ks_dev_tgt_file", default=None, type=str,
help="The output data file name.")
parser.add_argument("--predict_input_file", default=None, type=str,
help="predict_input_file")
parser.add_argument("--predict_output_file", default=None, type=str,
help="predict_output_file")
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("--config_path", default=None, type=str,
help="Bert config file path.")
parser.add_argument("--output_dir",
default=None,
type=str,
required=True,
help="The output directory where the model predictions and checkpoints will be written.")
parser.add_argument("--log_dir",
default='',
type=str,
required=True,
help="The output directory where the log will be written.")
parser.add_argument("--model_recover_path",
default=None,
type=str,
required=True,
help="The file of fine-tuned pretraining model.")
parser.add_argument("--optim_recover_path",
default=None,
type=str,
help="The file of pretraining optimizer.")
parser.add_argument("--predict_bleu",
default=0.5,
type=float,
help="The Predicted Bleu for KS Predict ")
parser.add_argument("--train_vae",
action='store_true',
help="Whether to train vae.")
# 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",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_predict",
action='store_true',
help="Whether to run ks predict.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval on the dev set.")
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=64,
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("--label_smoothing", default=0, type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--weight_decay",
default=0.01,
type=float,
help="The weight decay rate for Adam.")
parser.add_argument("--finetune_decay",
action='store_true',
help="Weight decay to the original weights.")
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("--hidden_dropout_prob", default=0.1, type=float,
help="Dropout rate for hidden states.")
parser.add_argument("--attention_probs_dropout_prob", default=0.1, type=float,
help="Dropout rate for attention probabilities.")
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('--fp32_embedding', action='store_true',
help="Whether to use 32-bit float precision instead of 16-bit for embeddings")
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('--amp', action='store_true',
help="Whether to use amp for fp16")
parser.add_argument('--from_scratch', action='store_true',
help="Initialize parameters with random values (i.e., training from scratch).")
parser.add_argument('--new_segment_ids', action='store_true',
help="Use new segment ids for bi-uni-directional LM.")
parser.add_argument('--new_pos_ids', action='store_true',
help="Use new position ids for LMs.")
parser.add_argument('--tokenized_input', action='store_true',
help="Whether the input is tokenized.")
parser.add_argument('--max_len_a', type=int, default=0,
help="Truncate_config: maximum length of segment A.")
parser.add_argument('--max_len_b', type=int, default=0,
help="Truncate_config: maximum length of segment B.")
parser.add_argument('--trunc_seg', default='',
help="Truncate_config: first truncate segment A/B (option: a, b).")
parser.add_argument('--always_truncate_tail', action='store_true',
help="Truncate_config: Whether we should always truncate tail.")
parser.add_argument("--mask_prob", default=0.15, type=float,
help="Number of prediction is sometimes less than max_pred when sequence is short.")
parser.add_argument("--mask_prob_eos", default=0, type=float,
help="Number of prediction is sometimes less than max_pred when sequence is short.")
parser.add_argument('--max_pred', type=int, default=20,
help="Max tokens of prediction.")
parser.add_argument("--num_workers", default=0, type=int,
help="Number of workers for the data loader.")
parser.add_argument('--mask_source_words', action='store_true',
help="Whether to mask source words for training")
parser.add_argument('--skipgram_prb', type=float, default=0.0,
help='prob of ngram mask')
parser.add_argument('--skipgram_size', type=int, default=1,
help='the max size of ngram mask')
parser.add_argument('--mask_whole_word', action='store_true',
help="Whether masking a whole word.")
parser.add_argument('--do_l2r_training', action='store_true',
help="Whether to do left to right training")
parser.add_argument('--has_sentence_oracle', action='store_true',
help="Whether to have sentence level oracle for training. "
"Only useful for summary generation")
parser.add_argument('--max_position_embeddings', type=int, default=None,
help="max position embeddings")
parser.add_argument('--relax_projection', action='store_true',
help="Use different projection layers for tasks.")
parser.add_argument('--ffn_type', default=0, type=int,
help="0: default mlp; 1: W((Wx+b) elem_prod x);")
parser.add_argument('--num_qkv', default=0, type=int,
help="Number of different <Q,K,V>.")
parser.add_argument('--seg_emb', action='store_true',
help="Using segment embedding for self-attention.")
parser.add_argument('--s2s_special_token', action='store_true',
help="New special tokens ([S2S_SEP]/[S2S_CLS]) of S2S.")
parser.add_argument('--s2s_add_segment', action='store_true',
help="Additional segmental for the encoder of S2S.")
parser.add_argument('--s2s_share_segment', action='store_true',
help="Sharing segment embeddings for the encoder of S2S (used with --s2s_add_segment).")
parser.add_argument('--pos_shift', action='store_true',
help="Using position shift for fine-tuning.")
args = parser.parse_args()
assert Path(args.model_recover_path).exists(
), "--model_recover_path doesn't exist"
args.output_dir = args.output_dir.replace(
'[PT_OUTPUT_DIR]', os.getenv('PT_OUTPUT_DIR', ''))
args.log_dir = args.log_dir.replace(
'[PT_OUTPUT_DIR]', os.getenv('PT_OUTPUT_DIR', ''))
os.makedirs(args.output_dir, exist_ok=True)
os.makedirs(args.log_dir, exist_ok=True)
handler = logging.FileHandler(os.path.join(args.log_dir, "train.log"), encoding='UTF-8')
handler.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
handler.setFormatter(formatter)
console = logging.StreamHandler()
console.setLevel(logging.DEBUG)
logger.addHandler(handler)
logger.addHandler(console)
json.dump(args.__dict__, open(os.path.join(
args.output_dir, 'opt.json'), 'w'), sort_keys=True, indent=2)
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
dist.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 = 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 args.local_rank not in (-1, 0):
# Make sure only the first process in distributed training will download model & vocab
dist.barrier()
tokenizer = BertTokenizer.from_pretrained(
args.bert_model, do_lower_case=args.do_lower_case)
if args.max_position_embeddings:
tokenizer.max_len = args.max_position_embeddings
data_tokenizer = WhitespaceTokenizer() if args.tokenized_input else tokenizer
if args.local_rank == 0:
dist.barrier()
print("Loading QKR Train Dataset", args.data_dir)
bi_uni_pipeline = [seq2seq_loader.Preprocess4Seq2seq(args.max_pred, args.mask_prob, list(tokenizer.vocab.keys(
)), tokenizer.convert_tokens_to_ids, args.max_seq_length, new_segment_ids=args.new_segment_ids, truncate_config={'max_len_a': args.max_len_a, 'max_len_b': args.max_len_b, 'trunc_seg': args.trunc_seg, 'always_truncate_tail': args.always_truncate_tail}, mask_source_words=args.mask_source_words, skipgram_prb=args.skipgram_prb, skipgram_size=args.skipgram_size, mask_whole_word=args.mask_whole_word, mode="s2s", has_oracle=args.has_sentence_oracle, num_qkv=args.num_qkv, s2s_special_token=args.s2s_special_token, s2s_add_segment=args.s2s_add_segment, s2s_share_segment=args.s2s_share_segment, pos_shift=args.pos_shift)]
file_oracle = None
if args.has_sentence_oracle:
file_oracle = os.path.join(args.data_dir, 'train.oracle')
fn_src = os.path.join(
args.data_dir, args.src_file if args.src_file else 'train.src')
fn_tgt = os.path.join(
args.data_dir, args.tgt_file if args.tgt_file else 'train.tgt')
train_dataset = seq2seq_loader.Seq2SeqDataset(
fn_src, fn_tgt, args.train_batch_size, data_tokenizer, args.max_seq_length, file_oracle=file_oracle, bi_uni_pipeline=bi_uni_pipeline)
if args.local_rank == -1:
train_sampler = RandomSampler(train_dataset, replacement=False)
_batch_size = args.train_batch_size
else:
train_sampler = DistributedSampler(train_dataset)
_batch_size = args.train_batch_size // dist.get_world_size()
train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=_batch_size, sampler=train_sampler,
num_workers=args.num_workers, collate_fn=seq2seq_loader.batch_list_to_batch_tensors, pin_memory=False)
print("Loading KS Train Dataset", args.data_dir)
ks_fn_src = os.path.join(
args.data_dir, args.ks_src_file)
ks_fn_tgt = os.path.join(
args.data_dir, args.ks_tgt_file)
ks_train_dataset = seq2seq_loader.Seq2SeqDataset(
ks_fn_src, ks_fn_tgt, args.train_batch_size, data_tokenizer, args.max_seq_length, file_oracle=file_oracle,
bi_uni_pipeline=bi_uni_pipeline)
if args.local_rank == -1:
ks_train_sampler = RandomSampler(ks_train_dataset, replacement=False)
_batch_size = args.train_batch_size
else:
ks_train_sampler = DistributedSampler(ks_train_dataset)
_batch_size = args.train_batch_size // dist.get_world_size()
ks_train_dataloader = torch.utils.data.DataLoader(ks_train_dataset, batch_size=_batch_size, sampler=ks_train_sampler,
num_workers=args.num_workers,
collate_fn=seq2seq_loader.batch_list_to_batch_tensors,
pin_memory=False)
logger.info("Loading QKR Eval Dataset from {}".format(args.data_dir))
fn_src = os.path.join(
args.data_dir, args.dev_src_file)
fn_tgt = os.path.join(
args.data_dir, args.dev_tgt_file)
dev_reddit_dataset = seq2seq_loader.Seq2SeqDataset(
fn_src, fn_tgt, args.eval_batch_size, data_tokenizer, args.max_seq_length, file_oracle=file_oracle,
bi_uni_pipeline=bi_uni_pipeline)
if args.local_rank == -1:
dev_reddit_sampler = RandomSampler(dev_reddit_dataset, replacement=False)
_batch_size = args.eval_batch_size
else:
dev_reddit_sampler = DistributedSampler(dev_reddit_dataset)
_batch_size = args.eval_batch_size // dist.get_world_size()
dev_reddit_dataloader = torch.utils.data.DataLoader(dev_reddit_dataset, batch_size=_batch_size,
sampler=dev_reddit_sampler,
num_workers=args.num_workers,
collate_fn=seq2seq_loader.batch_list_to_batch_tensors,
pin_memory=False)
logger.info("Loading KS Eval Dataset from {}".format(args.data_dir))
ks_dev_fn_src = os.path.join(
args.data_dir, args.ks_dev_src_file)
ks_dev_fn_tgt = os.path.join(
args.data_dir, args.ks_dev_tgt_file)
ks_dev_reddit_dataset = seq2seq_loader.Seq2SeqDataset(
ks_dev_fn_src, ks_dev_fn_tgt, args.eval_batch_size, data_tokenizer, args.max_seq_length, file_oracle=file_oracle,
bi_uni_pipeline=bi_uni_pipeline)
if args.local_rank == -1:
ks_dev_reddit_sampler = RandomSampler(ks_dev_reddit_dataset, replacement=False)
_batch_size = args.eval_batch_size
else:
ks_dev_reddit_sampler = DistributedSampler(ks_dev_reddit_dataset)
_batch_size = args.eval_batch_size // dist.get_world_size()
ks_dev_reddit_dataloader = torch.utils.data.DataLoader(ks_dev_reddit_dataset, batch_size=_batch_size,
sampler=ks_dev_reddit_sampler,
num_workers=args.num_workers,
collate_fn=seq2seq_loader.batch_list_to_batch_tensors,
pin_memory=False)
# note: args.train_batch_size has been changed to (/= args.gradient_accumulation_steps)
# t_total = int(math.ceil(len(train_dataset.ex_list) / args.train_batch_size)
t_total = int(len(train_dataloader) * args.num_train_epochs /
args.gradient_accumulation_steps)
amp_handle = None
if args.fp16 and args.amp:
from apex import amp
amp_handle = amp.init(enable_caching=True)
logger.info("enable fp16 with amp")
# Prepare model
recover_step = _get_max_epoch_model(args.output_dir)
cls_num_labels = 2
type_vocab_size = 6 + \
(1 if args.s2s_add_segment else 0) if args.new_segment_ids else 2
num_sentlvl_labels = 2 if args.has_sentence_oracle else 0
relax_projection = 4 if args.relax_projection else 0
if args.local_rank not in (-1, 0):
# Make sure only the first process in distributed training will download model & vocab
dist.barrier()
if (recover_step is None) and (args.model_recover_path is None):
# if _state_dict == {}, the parameters are randomly initialized
# if _state_dict == None, the parameters are initialized with bert-init
_state_dict = {} if args.from_scratch else None
model = BertForPreTrainingLossMask.from_pretrained(
args.bert_model, state_dict=_state_dict, num_labels=cls_num_labels, num_rel=0, type_vocab_size=type_vocab_size, config_path=args.config_path, task_idx=3, num_sentlvl_labels=num_sentlvl_labels, max_position_embeddings=args.max_position_embeddings, label_smoothing=args.label_smoothing, fp32_embedding=args.fp32_embedding, relax_projection=relax_projection, new_pos_ids=args.new_pos_ids, ffn_type=args.ffn_type, hidden_dropout_prob=args.hidden_dropout_prob, attention_probs_dropout_prob=args.attention_probs_dropout_prob, num_qkv=args.num_qkv, seg_emb=args.seg_emb)
global_step = 0
else:
if recover_step:
logger.info("***** Recover model: %d *****", recover_step)
model_recover = torch.load(os.path.join(
args.output_dir, "model.{0}.bin".format(recover_step)), map_location='cpu')
# recover_step == number of epochs
global_step = math.floor(
recover_step * t_total / args.num_train_epochs)
elif args.model_recover_path:
logger.info("***** Recover model: %s *****",
args.model_recover_path)
model_recover = torch.load(
args.model_recover_path, map_location='cpu')
global_step = 0
model = BertForPreTrainingLossMask.from_pretrained(
args.bert_model, state_dict=model_recover, num_labels=cls_num_labels, num_rel=0, type_vocab_size=type_vocab_size, config_path=args.config_path, task_idx=3, num_sentlvl_labels=num_sentlvl_labels, max_position_embeddings=args.max_position_embeddings, label_smoothing=args.label_smoothing, fp32_embedding=args.fp32_embedding, relax_projection=relax_projection, new_pos_ids=args.new_pos_ids, ffn_type=args.ffn_type, hidden_dropout_prob=args.hidden_dropout_prob, attention_probs_dropout_prob=args.attention_probs_dropout_prob, num_qkv=args.num_qkv, seg_emb=args.seg_emb)
if args.local_rank == 0:
dist.barrier()
if args.fp16:
model.half()
if args.fp32_embedding:
model.bert.embeddings.word_embeddings.float()
model.bert.embeddings.position_embeddings.float()
model.bert.embeddings.token_type_embeddings.float()
model.to(device)
if args.local_rank != -1:
try:
from torch.nn.parallel import DistributedDataParallel as DDP
except ImportError:
raise ImportError("DistributedDataParallel")
model = DDP(model, device_ids=[
args.local_rank], output_device=args.local_rank, find_unused_parameters=True)
elif n_gpu > 1:
# model = torch.nn.DataParallel(model)
model = DataParallelImbalance(model)
# 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 optimization_fp16 import FP16_Optimizer_State
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_State(
optimizer, dynamic_loss_scale=True)
else:
optimizer = FP16_Optimizer_State(
optimizer, static_loss_scale=args.loss_scale)
else:
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=t_total)
if recover_step:
logger.info("***** Recover optimizer: %d *****", recover_step)
optim_recover = torch.load(os.path.join(
args.output_dir, "optim.{0}.bin".format(recover_step)), map_location='cpu')
if hasattr(optim_recover, 'state_dict'):
optim_recover = optim_recover.state_dict()
optimizer.load_state_dict(optim_recover)
if args.loss_scale == 0:
logger.info("***** Recover optimizer: dynamic_loss_scale *****")
optimizer.dynamic_loss_scale = True
logger.info("***** CUDA.empty_cache() *****")
torch.cuda.empty_cache()
if args.do_train:
KL_weight = 0.0
logger.info("***** Running training *****")
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", t_total)
model.train()
if recover_step:
start_epoch = recover_step+1
else:
start_epoch = 1
for i_epoch in trange(start_epoch, int(args.num_train_epochs)+1, desc="Epoch", disable=args.local_rank not in (-1, 0)):
if args.local_rank != -1:
train_sampler.set_epoch(i_epoch)
step = 0
for batch, ks_batch in zip(train_dataloader,ks_train_dataloader):
batch = [
t.to(device) if t is not None else None for t in batch]
input_ids, segment_ids, input_mask, mask_qkv, lm_label_ids, masked_pos, masked_weights, is_next, task_idx,labels, ks_labels = batch
oracle_pos, oracle_weights, oracle_labels = None, None, None
loss_tuple = model(input_ids, segment_ids, input_mask, lm_label_ids, is_next, masked_pos=masked_pos, masked_weights=masked_weights, task_idx=task_idx, masked_pos_2=oracle_pos, masked_weights_2=oracle_weights,
masked_labels_2=oracle_labels, mask_qkv=mask_qkv,labels=labels,ks_labels=ks_labels,train_vae=args.train_vae)
if args.train_vae:
masked_lm_loss, next_sentence_loss, KL_loss = loss_tuple
if n_gpu > 1: # mean() to average on multi-gpu.
masked_lm_loss = masked_lm_loss.mean()
next_sentence_loss = next_sentence_loss.mean()
KL_loss = KL_loss.mean()
else:
masked_lm_loss, next_sentence_loss, _ = loss_tuple
if n_gpu > 1: # mean() to average on multi-gpu.
masked_lm_loss = masked_lm_loss.mean()
next_sentence_loss = next_sentence_loss.mean()
KL_weight += 1.0 / float(len(ks_train_dataloader))
if args.train_vae:
loss = masked_lm_loss + next_sentence_loss + KL_weight * KL_loss
else:
loss = masked_lm_loss + next_sentence_loss
logger.info("In{}step, masked_lm_loss:{}".format(step, masked_lm_loss))
logger.info("In{}step, KL_weight:{}".format(step, KL_weight))
#logger.info("In{}step, KL_loss:{}".format(step, KL_loss))
logger.info("******************************************* ")
# ensure that accumlated gradients are normalized
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
optimizer.backward(loss)
if amp_handle:
amp_handle._clear_cache()
else:
loss.backward()
if (step + 1) % args.gradient_accumulation_steps == 0:
lr_this_step = args.learning_rate * \
warmup_linear(global_step/t_total,
args.warmup_proportion)
if args.fp16:
# modify learning rate with special warm up BERT uses
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step()
optimizer.zero_grad()
global_step += 1
if random.randint(0,0) == 0:
ks_batch = [
t.to(device) if t is not None else None for t in ks_batch]
input_ids, segment_ids, input_mask, mask_qkv, lm_label_ids, masked_pos, masked_weights, is_next, task_idx, labels, ks_labels = ks_batch
oracle_pos, oracle_weights, oracle_labels = None, None, None
loss_tuple = model(input_ids, segment_ids, input_mask, lm_label_ids, is_next, masked_pos=masked_pos,
masked_weights=masked_weights, task_idx=task_idx, masked_pos_2=oracle_pos,
masked_weights_2=oracle_weights,
masked_labels_2=oracle_labels, mask_qkv=mask_qkv, labels=labels, ks_labels=ks_labels,train_ks=True,train_vae=args.train_vae)
if args.train_vae:
ks_loss, KS_KL_loss = loss_tuple
if n_gpu > 1: # mean() to average on multi-gpu.
ks_loss = ks_loss.mean()
KS_KL_loss = KS_KL_loss.mean()
loss = ks_loss + KL_weight * KS_KL_loss
else:
ks_loss, _ = loss_tuple
if n_gpu > 1: # mean() to average on multi-gpu.
ks_loss = ks_loss.mean()
loss = ks_loss
logger.info("In{}step, ks_loss:{}".format(step, ks_loss))
#logger.info("In{}step, KS_KL_loss:{}".format(step, KS_KL_loss))
logger.info("******************************************* ")
# ensure that accumlated gradients are normalized
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
optimizer.backward(loss)
if amp_handle:
amp_handle._clear_cache()
else:
loss.backward()
if (step + 1) % args.gradient_accumulation_steps == 0:
lr_this_step = args.learning_rate * \
warmup_linear(global_step / t_total,
args.warmup_proportion)
if args.fp16:
# modify learning rate with special warm up BERT uses
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step()
optimizer.zero_grad()
global_step += 1
step += 1
if (step + 1) % 200 == 0:
logger.info("***** Running QKR evaling *****")
logger.info(" Batch size = %d", args.eval_batch_size)
if args.local_rank != -1:
train_sampler.set_epoch(i_epoch)
dev_iter_bar = tqdm(dev_reddit_dataloader, desc='Iter (loss=X.XXX)',
disable=args.local_rank not in (-1, 0))
total_lm_loss = 0
total_kl_loss = 0
for qkr_dev_step, batch in enumerate(dev_iter_bar):
batch = [
t.to(device) if t is not None else None for t in batch]
if args.has_sentence_oracle:
input_ids, segment_ids, input_mask, mask_qkv, lm_label_ids, masked_pos, masked_weights, is_next, task_idx, oracle_pos, oracle_weights, oracle_labels = batch
else:
input_ids, segment_ids, input_mask, mask_qkv, lm_label_ids, masked_pos, masked_weights, is_next, task_idx, labels, ks_labels = batch
oracle_pos, oracle_weights, oracle_labels = None, None, None
with torch.no_grad():
loss_tuple = model(input_ids, segment_ids, input_mask, lm_label_ids, is_next,
masked_pos=masked_pos, masked_weights=masked_weights, task_idx=task_idx,
masked_pos_2=oracle_pos, masked_weights_2=oracle_weights,
masked_labels_2=oracle_labels, mask_qkv=mask_qkv,labels=labels,ks_labels=ks_labels,train_vae=args.train_vae)
masked_lm_loss, next_sentence_loss, KL_loss = loss_tuple
if n_gpu > 1: # mean() to average on multi-gpu.
# loss = loss.mean()
masked_lm_loss = masked_lm_loss.mean()
next_sentence_loss = next_sentence_loss.mean()
KL_loss = KL_loss.mean()
# logging for each step (i.e., before normalization by args.gradient_accumulation_steps)
dev_iter_bar.set_description('Iter (loss=%5.3f)' % masked_lm_loss.item())
total_lm_loss += masked_lm_loss.item()
total_kl_loss += KL_loss.item()
# ensure that accumlated gradients are normalized
total_mean_lm_loss = total_lm_loss /(qkr_dev_step + 1)
total_mean_kl_loss = total_kl_loss / (qkr_dev_step + 1)
logger.info("** ** * Evaling mean loss ** ** * ")
logger.info("In{}epoch,dev_lm_loss:{}".format(i_epoch, total_mean_lm_loss))
logger.info("In{}epoch,dev_kl_loss:{}".format(i_epoch, total_mean_kl_loss))
logger.info("******************************************* ")
logger.info("***** Running KS evaling *****")
logger.info(" Batch size = %d", args.eval_batch_size)
ks_dev_iter_bar = tqdm(ks_dev_reddit_dataloader, desc='Iter (loss=X.XXX)',
disable=args.local_rank not in (-1, 0))
total_ks_loss = 0
total_ks_kl_loss = 0
for ks_dev_step, batch in enumerate(ks_dev_iter_bar):
batch = [
t.to(device) if t is not None else None for t in batch]
input_ids, segment_ids, input_mask, mask_qkv, lm_label_ids, masked_pos, masked_weights, is_next, task_idx, labels, ks_labels = batch
oracle_pos, oracle_weights, oracle_labels = None, None, None
with torch.no_grad():
loss_tuple = model(input_ids, segment_ids, input_mask, lm_label_ids, is_next,
masked_pos=masked_pos, masked_weights=masked_weights, task_idx=task_idx,
masked_pos_2=oracle_pos, masked_weights_2=oracle_weights,
masked_labels_2=oracle_labels, mask_qkv=mask_qkv,labels=labels,ks_labels=ks_labels, train_ks=True,train_vae=args.train_vae)
ks_loss, KS_KL_loss = loss_tuple
if n_gpu > 1: # mean() to average on multi-gpu.
# loss = loss.mean()
ks_loss = ks_loss.mean()
KS_KL_loss = KS_KL_loss.mean()
# logging for each step (i.e., before normalization by args.gradient_accumulation_steps)
ks_dev_iter_bar.set_description('Iter (loss=%5.3f)' % ks_loss.item())
total_ks_loss += ks_loss.item()
total_ks_kl_loss += KS_KL_loss.item()
total_mean_ks_loss = total_ks_loss / (ks_dev_step + 1)
total_mean_ks_kl_loss = total_ks_kl_loss / (ks_dev_step + 1)
logger.info("** ** * Evaling mean loss ** ** * ")
logger.info("In{}epoch,dev_ks_loss:{}".format(i_epoch, total_mean_ks_loss))
logger.info("In{}epoch,dev_ks_kl_loss:{}".format(i_epoch, total_mean_ks_kl_loss))
total_mean_loss = total_mean_lm_loss + total_mean_kl_loss + total_mean_ks_loss + total_mean_ks_kl_loss
logger.info("In{}epoch,dev_loss:{}".format(i_epoch, total_mean_loss))
logger.info("******************************************* ")
# Save a trained model
if (args.local_rank == -1 or torch.distributed.get_rank() == 0):
logger.info(
"** ** * Saving fine-tuned model and optimizer ** ** * ")
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, "model.{}_{}_{}.bin".format(i_epoch,step,round(total_mean_loss,4)))
torch.save(model_to_save.state_dict(), output_model_file)
output_optim_file = os.path.join(
args.output_dir, "optim.bin")
torch.save(optimizer.state_dict(), output_optim_file)
logger.info("***** CUDA.empty_cache() *****")
torch.cuda.empty_cache()
if args.do_predict:
bi_uni_pipeline = [
seq2seq_loader.Preprocess4Seq2seq_predict(args.max_pred, args.mask_prob, list(tokenizer.vocab.keys(
)), tokenizer.convert_tokens_to_ids, args.max_seq_length, new_segment_ids=args.new_segment_ids,
truncate_config={'max_len_a': args.max_len_a,
'max_len_b': args.max_len_b,
'trunc_seg': args.trunc_seg,
'always_truncate_tail': args.always_truncate_tail},
mask_source_words=args.mask_source_words,
skipgram_prb=args.skipgram_prb, skipgram_size=args.skipgram_size,
mask_whole_word=args.mask_whole_word, mode="s2s",
has_oracle=args.has_sentence_oracle, num_qkv=args.num_qkv,
s2s_special_token=args.s2s_special_token,
s2s_add_segment=args.s2s_add_segment,
s2s_share_segment=args.s2s_share_segment,
pos_shift=args.pos_shift)]
next_i = 0
model.eval()
with open(os.path.join(args.data_dir, args.predict_input_file), "r", encoding="utf-8") as file:
src_file = file.readlines()
with open("train_tgt_pad.empty", "r", encoding="utf-8") as file:
tgt_file = file.readlines()
with open(os.path.join(args.data_dir, args.predict_output_file), "w", encoding="utf-8") as out:
while next_i < len(src_file):
print(next_i)
batch_src = src_file[next_i:next_i + args.eval_batch_size]
batch_tgt = tgt_file[next_i:next_i + args.eval_batch_size]
next_i += args.eval_batch_size
ex_list = []
for src, tgt in zip(batch_src, batch_tgt):
src_tk = data_tokenizer.tokenize(src.strip())
tgt_tk = data_tokenizer.tokenize(tgt.strip())
ex_list.append((src_tk, tgt_tk))
batch = []
for idx in range(len(ex_list)):
instance = ex_list[idx]
for proc in bi_uni_pipeline:
instance = proc(instance)
batch.append(instance)
batch_tensor = seq2seq_loader.batch_list_to_batch_tensors(batch)
batch = [
t.to(device) if t is not None else None for t in batch_tensor]
input_ids, segment_ids, input_mask, mask_qkv, lm_label_ids, masked_pos, masked_weights, is_next, task_idx = batch
predict_bleu = args.predict_bleu * torch.ones([input_ids.shape[0]], device=input_ids.device) # B
oracle_pos, oracle_weights, oracle_labels = None, None, None
with torch.no_grad():
logits = model(input_ids, segment_ids, input_mask, lm_label_ids, is_next,
masked_pos=masked_pos, masked_weights=masked_weights, task_idx=task_idx,
masked_pos_2=oracle_pos, masked_weights_2=oracle_weights,
masked_labels_2=oracle_labels, mask_qkv=mask_qkv, labels=predict_bleu, train_ks=True,train_vae=args.train_vae)
logits = torch.nn.functional.softmax(logits, dim=1)
labels = logits[:, 1].cpu().numpy()
# print(labels)
for i in range(len(labels)):
line = batch_src[i].strip()
line += "\t"
line += str(labels[i])
out.write(line)
out.write("\n")
def init_optimizer_and_amp(model, learning_rate, loss_scale, warmup_proportion,
num_train_optimization_steps, use_fp16):
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
},
]
optimizer, scheduler = None, None
if use_fp16:
logger.info("using fp16")
try:
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.")
if num_train_optimization_steps is not None:
optimizer = FusedAdam(
optimizer_grouped_parameters,
lr=learning_rate,
bias_correction=False,
)
amp_inits = amp.initialize(
model,
optimizers=optimizer,
opt_level="O2",
keep_batchnorm_fp32=False,
loss_scale="dynamic" if loss_scale == 0 else loss_scale,
)
model, optimizer = (amp_inits if num_train_optimization_steps
is not None else (amp_inits, None))
if num_train_optimization_steps is not None:
scheduler = LinearWarmUpScheduler(
optimizer,
warmup=warmup_proportion,
total_steps=num_train_optimization_steps,
)
else:
logger.info("using fp32")
if num_train_optimization_steps is not None:
optimizer = BertAdam(
optimizer_grouped_parameters,
lr=learning_rate,
warmup=warmup_proportion,
t_total=num_train_optimization_steps,
)
return model, optimizer, scheduler
def main():
parser = argparse.ArgumentParser()
# # 必要参数
parser.add_argument('--task',
default='multi',
type=str,
help='Task affecting load data and vectorize feature')
parser.add_argument(
'--loss_type',
default='double',
type=str,
help='Select loss double or single, only for multi task'
) # 针对multi任务才有效
parser.add_argument(
"--bert_model",
default="bert-base-uncased",
type=str,
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-chinese,"
"bert-base-multilingual-cased.") # 选择预训练模型参数
parser.add_argument("--debug",
default=False,
help="Whether run on small dataset") # 正常情况下都应该选择false
parser.add_argument(
"--output_dir",
default="./SQuAD/output/",
type=str,
help=
"The output directory where the model checkpoints and predictions will be written."
)
# # 其他参数
parser.add_argument("--train_file",
default="./SQuAD/version/train.json",
type=str,
help="SQuAD json for training. E.g., train-v1.1.json")
parser.add_argument(
"--predict_file",
default="./SQuAD/version/prediction.json",
type=str,
help=
"SQuAD json for predictio ns. E.g., dev-v1.1.json or test-v1.1.json")
parser.add_argument(
"--max_seq_length",
default=384,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. Sequences "
"longer than this will be truncated, and sequences shorter than this will be padded."
)
parser.add_argument(
"--doc_stride",
default=128,
type=int,
help=
"When splitting up a long document into chunks, how much stride to take between chunks."
)
parser.add_argument(
"--max_query_length",
default=64,
type=int,
help=
"The maximum number of tokens for the question. Questions longer than this will be "
"truncated to this length.")
# # 控制参数
parser.add_argument("--do_train",
default=True,
help="Whether to run training.")
parser.add_argument("--do_predict",
default=True,
help="Whether to run eval on the dev set.")
parser.add_argument("--train_batch_size",
default=18,
type=int,
help="Total batch size for training.")
parser.add_argument("--predict_batch_size",
default=18,
type=int,
help="Total batch size for predictions.")
parser.add_argument("--learning_rate",
default=3e-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.")
parser.add_argument(
"--n_best_size",
default=20,
type=int,
help=
"The total number of n-best predictions to generate in the nbest_predictions.json file."
)
parser.add_argument(
"--max_answer_length",
default=30,
type=int,
help=
"The maximum length of an answer that can be generated.This is needed because the start "
"and end predictions are not conditioned on one another.")
parser.add_argument(
"--verbose_logging",
default=False,
help=
"If true, all of the warnings related to data processing will be printed.A number of "
"warnings are expected for a normal SQuAD evaluation.")
parser.add_argument("--no_cuda",
default=False,
help="Whether not to use CUDA when available")
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(
"--do_lower_case",
default=True,
help=
"Whether to lower case the input text. True for uncased models, False for cased models."
)
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument(
'--fp16',
default=False,
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.Positive power of 2: static loss scaling value.\n"
)
parser.add_argument(
'--version_2_with_negative',
default=False,
help=
'If true, the SQuAD examples contain some that do not have an answer.')
parser.add_argument(
'--null_score_diff_threshold',
type=float,
default=0.0,
help=
"If null_score - best_non_null is greater than the threshold predict null."
)
args = parser.parse_args()
# if是采用单机形式，else采用的是分布式形式；因为我们没有分布式系统，所以采用单机多GPU的方式进行训练10.24
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='hierarchical_copy')
# 以下三句话的意义不是很大，基本操作这一部分是日志的输出形式10.24
logging.basicConfig(
format='%(asctime)s-%(levelname)s-%(name)s-%(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
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))
# 以下几行均是用来设置参数10.24
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
random.seed(args.seed) # 设置随机种子
np.random.seed(args.seed) # 设置随机种子
torch.manual_seed(args.seed) # 为CPU设置种子用于生成随机数，以使得结果是确定的
if n_gpu > 0: # 如果使用多个GPU，应该使用torch.cuda.manual_seed_all()为所有的GPU设置种子
torch.cuda.manual_seed_all(args.seed)
# 以下三句又是基本操作，意义不大10.24
if not args.do_train and not args.do_predict:
raise ValueError(
"At least one of `do_train` or `do_predict` must be True.")
if args.do_train:
if not args.train_file:
raise ValueError(
"If `do_train` is True, then `train_file` must be specified.")
if args.do_predict:
if not args.predict_file:
raise ValueError(
"If `do_predict` is True, then `predict_file` must be specified."
)
# 以下2句是用来判断output_dir是否存在，若不存在，则创建即可（感觉有这个东西反而不太好，因为需要空文件夹）10.24
# 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.")
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
# 这个东西是用来干啥的（从tokenization中读取，对Tokenizer进行初始化操作）10.24
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=args.do_lower_case)
# 从data中读取数据的方式，一种是单队列的读取方式，另一种是多通道读取方式10.24
if args.task == 'squad':
read_examples = read_squad_examples
elif args.task == 'multi':
read_examples = read_multi_examples
# 用来加载训练样例以及优化的步骤10.24
train_examples = None
num_train_optimization_steps = None
if args.do_train:
train_examples = read_examples(
input_file=args.train_file,
is_training=True,
version_2_with_negative=args.version_2_with_negative)
if args.debug:
train_examples = train_examples[:100]
num_train_optimization_steps = \
int(len(train_examples)/args.train_batch_size/args.gradient_accumulation_steps) * args.num_train_epochs
if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size(
)
# 模型准备中ing10.24
model = BertForQuestionAnswering.from_pretrained(
args.bert_model,
cache_dir=os.path.join(str(PYTORCH_PRETRAINED_BERT_CACHE),
'distributed_{}'.format(args.local_rank)))
# model = torch.nn.DataParallel(model).cuda()
# 判断是否使用float16编码10.24
if args.fp16:
# model.half().cuda()
model.half()
# 将模型加载到相应的CPU或者GPU中10.24
model.to(device)
# 配置优化器等函数10.24
if args.do_train:
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
}]
if args.fp16:
try:
# from apex.optimizers import FP16_Optimizer
from apex.fp16_utils 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=True)
if args.loss_scale == 0:
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)
# 进行模型的拟合训练10.24
global_step = 0
if args.do_train:
# 训练语料的特征提取
train_features = convert_examples_to_features(
examples=train_examples,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length,
is_training=True)
logger.info("***** Running training *****")
logger.info(" Num orig examples = %d", len(train_examples))
logger.info(" Num split examples = %d", len(train_features))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps)
all_input_ids = torch.tensor([f.input_ids for f in train_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in train_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in train_features],
dtype=torch.long)
all_start_positions = torch.tensor(
[f.start_position for f in train_features], dtype=torch.long)
all_end_positions = torch.tensor(
[f.end_position for f in train_features], dtype=torch.long)
all_start_vector = torch.tensor(
[f.start_vector for f in train_features], dtype=torch.float)
all_end_vector = torch.tensor([f.end_vector for f in train_features],
dtype=torch.float)
all_content_vector = torch.tensor(
[f.content_vector for f in train_features], dtype=torch.float)
# # 替换的内容all_start_positions以及all_end_positions
# all1_start_positions = []
# for i in range(len(train_features)):
# for j in range(len(train_features[i].start_position)):
# all1_start_positions.append(train_features[i].start_position[j])
# all_start_positions = torch.tensor([k for k in all1_start_positions], dtype=torch.long)
# all1_end_positions = []
# for i in range(len(train_features)):
# for j in range(len(train_features[i].end_position)):
# all1_end_positions.append(train_features[i].end_position[j])
# all_end_positions = torch.tensor([k for k in all1_end_positions], dtype=torch.long)
# ####################################################################
train_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_start_positions,
all_end_positions, all_start_vector,
all_end_vector, all_content_vector)
if args.local_rank == -1:
train_sampler = RandomSampler(train_data) # 随机采样器
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 trange(int(args.num_train_epochs), desc="Epoch"):
# 每次都叫他进行分发，这样的话，就可以进行多GPU训练
model = torch.nn.DataParallel(model).cuda()
for step, batch in enumerate(
tqdm(train_dataloader,
desc="Iteration",
disable=args.local_rank not in [-1, 0])):
if n_gpu == 1:
batch = tuple(
t.to(device)
for t in batch) # multi-gpu does scattering it-self
input_ids, input_mask, segment_ids, start_positions, end_positions, start_vector, end_vector, content_vector = batch
loss = model(input_ids, segment_ids, input_mask,
start_positions, end_positions, start_vector,
end_vector, content_vector, args.loss_type)
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
print("loss率为：{}".format(loss))
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
optimizer.backward(loss)
else:
loss.backward()
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 and 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
optimizer.step()
optimizer.zero_grad()
global_step += 1
print("\n")
print(ep)
output_model_file = os.path.join(args.output_dir,
str(ep) + WEIGHTS_NAME)
output_config_file = os.path.join(args.output_dir,
str(ep) + CONFIG_NAME)
torch.save(model.state_dict(), output_model_file)
if isinstance(model, torch.nn.DataParallel):
model = model.module
model.config.to_json_file(output_config_file)
tokenizer.save_vocabulary(args.output_dir)
# 这个是用来加载进行微调调好后的代码以方便进行预测10.25
if args.do_train and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
# Save a trained model, configuration and tokenizer
model_to_save = model.module if hasattr(
model, 'module') else model # Only save the model it-self
# If we save using the predefined names, we can load using `from_pretrained`
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)
# Load a trained model and vocabulary that you have fine-tuned
model = BertForQuestionAnswering.from_pretrained(args.output_dir)
tokenizer = BertTokenizer.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
else:
model = BertForQuestionAnswering.from_pretrained(args.output_dir)
tokenizer = BertTokenizer.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
# 再次将GPU加入10.25
model.to(device)
# 这部分就是进行相应的预测（用于生成预测文件）
if args.do_predict and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
eval_examples = \
read_examples(input_file=args.predict_file, is_training=False, version_2_with_negative=args.version_2_with_negative)
if args.debug:
eval_examples = eval_examples[:100]
eval_features = convert_examples_to_features(
examples=eval_examples,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length,
is_training=False)
logger.info("***** Running predictions *****")
logger.info(" Num orig examples = %d", len(eval_examples))
logger.info(" Num split examples = %d", len(eval_features))
logger.info(" Batch size = %d", args.predict_batch_size)
all_input_ids = torch.tensor([f.input_ids for f in eval_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features],
dtype=torch.long)
all_example_index = torch.arange(all_input_ids.size(0),
dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_example_index)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.predict_batch_size)
model.eval()
all_results = []
logger.info("Start evaluating")
for input_ids, input_mask, segment_ids, example_indices in tqdm(
eval_dataloader,
desc="Evaluating",
disable=args.local_rank not in [-1, 0]):
if len(all_results) % 1000 == 0:
logger.info("Processing example: %d" % (len(all_results)))
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
with torch.no_grad():
batch_start_logits, batch_end_logits = model(
input_ids, segment_ids, input_mask)
for i, example_index in enumerate(example_indices):
start_logits = batch_start_logits[i].detach().cpu().tolist()
end_logits = batch_end_logits[i].detach().cpu().tolist()
eval_feature = eval_features[example_index.item()]
unique_id = int(eval_feature.unique_id)
all_results.append(
RawResult(unique_id=unique_id,
start_logits=start_logits,
end_logits=end_logits))
middle_result = os.path.join(args.output_dir, 'middle_result.pkl')
pickle.dump([eval_examples, eval_features, all_results],
open(middle_result, 'wb'))
output_prediction_file = os.path.join(args.output_dir,
"predictions.json")
output_nbest_file = os.path.join(args.output_dir,
"nbest_predictions.json")
output_null_log_odds_file = os.path.join(args.output_dir,
"null_odds.json")
if (args.loss_type == 'double'):
write_predictions_couple_labeling(
eval_examples, eval_features, all_results, args.n_best_size,
args.max_answer_length, args.do_lower_case,
output_prediction_file, output_nbest_file,
output_null_log_odds_file, args.verbose_logging,
args.version_2_with_negative, args.null_score_diff_threshold)
elif (args.loss_type == 'single'):
write_predictions_single_labeling(
eval_examples, eval_features, all_results, args.n_best_size,
args.max_answer_length, args.do_lower_case,
output_prediction_file, output_nbest_file,
output_null_log_odds_file, args.verbose_logging,
args.version_2_with_negative, args.null_score_diff_threshold)
elif (args.loss_type == 'origin') or (args.task == 'multi'
and args.loss_type == 'squad'):
write_predictions(eval_examples, eval_features, all_results,
args.n_best_size, args.max_answer_length,
args.do_lower_case, output_prediction_file,
output_nbest_file, output_null_log_odds_file,
args.verbose_logging,
args.version_2_with_negative,
args.null_score_diff_threshold)
else:
raise ValueError('{} dataset and {} loss is not support'.format(
args.task, args.loss_type))
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."
)
parser.add_argument("--word_embedding_file",
default='./emb/wiki-news-300d-1M.vec',
type=str,
help="The input directory of word embeddings.")
parser.add_argument("--index_path",
default='./emb/p_index.bin',
type=str,
help="The input directory of word embedding index.")
parser.add_argument("--word_embedding_info",
default='./emb/vocab_info.txt',
type=str,
help="The input directory of word embedding info.")
parser.add_argument("--data_file",
default='',
type=str,
help="The input directory of input data file.")
## 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=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("--max_ngram_length",
default=16,
type=int,
help="The maximum total ngram sequence")
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_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("--embedding_size",
default=300,
type=int,
help="Total batch size for embeddings.")
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("--num_eval_epochs",
default=3.0,
type=float,
help="Total number of eval 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('--single',
action='store_true',
help="Whether only evaluate a single epoch")
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.")
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()
# Comment the if else block for no CUDA
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')
#device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
#device = torch.device("cpu") # uncomment this for no GPU
logger.info(
"device: {} , distributed training: {}, 16-bits training: {}".format(
device, 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: # Comment this to No GPU
torch.cuda.manual_seed_all(args.seed) # Comment this for No GPU
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 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]()
num_labels = num_labels_task[task_name]
label_list = processor.get_labels()
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=args.do_lower_case)
train_examples = None
num_train_optimization_steps = None
w2i, i2w, vocab_size = {}, {}, 1
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
if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size(
)
train_features, w2i, i2w, vocab_size = convert_examples_to_features_disc_train(
train_examples, label_list, args.max_seq_length, tokenizer)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples))
logger.info(" Num token vocab = %d", vocab_size)
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps)
all_tokens = torch.tensor([f.token_ids for f in train_features],
dtype=torch.long)
all_label_id = torch.tensor([f.label_id for f in train_features],
dtype=torch.long)
# load embeddings sa
if args.do_train:
logger.info("Loading word embeddings ... ")
emb_dict, emb_vec, vocab_list, emb_vocab_size = load_vectors(
args.word_embedding_file)
if not os.path.exists(args.index_path):
write_vocab_info(args.word_embedding_info, emb_vocab_size,
vocab_list)
p = load_embeddings_and_save_index(range(emb_vocab_size), emb_vec,
args.index_path)
else:
#emb_vocab_size, vocab_list = load_vocab_info(args.word_embedding_info)
p = load_embedding_index(args.index_path,
emb_vocab_size,
num_dim=args.embedding_size)
#emb_dict, emb_vec, vocab_list, emb_vocab_size, p = None, None, None, None, None
# Prepare model
cache_dir = args.cache_dir if args.cache_dir else os.path.join(
PYTORCH_PRETRAINED_BERT_CACHE, 'distributed_{}'.format(
args.local_rank))
model = BertForDiscriminator.from_pretrained(args.bert_model,
cache_dir=cache_dir,
num_labels=num_labels)
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: # Comment this for NO GPU
model = torch.nn.DataParallel(model) # Comment this for NO GPU
# 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
}]
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 = 1
tr_loss = 0
if args.do_train:
train_data = TensorDataset(all_tokens, all_label_id)
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 ind in trange(int(args.num_train_epochs), desc="Epoch"):
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
flaw_eval_f1 = []
flaw_eval_recall = []
flaw_eval_precision = []
for step, batch in enumerate(
tqdm(train_dataloader, desc="Iteration")):
batch = tuple(t.to(device) for t in batch)
tokens, _ = batch #, label_id, ngram_ids, ngram_labels, ngram_masks
# module1: learn a discriminator
tokens = tokens.to('cpu').numpy()
#print("PRINTING TOKENS!!!!!!!!! ", len(tokens[0]))
train_features = convert_examples_to_features_flaw(
tokens, args.max_seq_length, args.max_ngram_length,
tokenizer, i2w, emb_dict, p, vocab_list)
flaw_mask = torch.tensor([f.flaw_mask for f in train_features],
dtype=torch.long).to(
device) # [1, 1, 1, 1, 0,0,0,0]
flaw_ids = torch.tensor([f.flaw_ids for f in train_features],
dtype=torch.long).to(
device) # [12,25,37,54,0,0,0,0]
flaw_labels = torch.tensor(
[f.flaw_labels for f in train_features],
dtype=torch.long).to(device) # [0, 1, 1, 1, 0,0,0,0]
loss, logits = model(flaw_ids, flaw_mask, flaw_labels)
logits = logits.detach().cpu().numpy()
if n_gpu > 1: # Comment this for NO GPU
loss = loss.mean() # Comment this for NO GPU
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
loss.backward()
tr_loss += loss.item()
nb_tr_examples += flaw_ids.size(0)
nb_tr_steps += 1
if (step + 1) % args.gradient_accumulation_steps == 0:
optimizer.step()
optimizer.zero_grad()
global_step += 1
# eval during training
flaw_labels = flaw_labels.to('cpu').numpy()
flaw_tmp_eval_f1, flaw_tmp_eval_recall, flaw_tmp_eval_precision = f1_3d(
logits, flaw_labels)
flaw_eval_f1.append(flaw_tmp_eval_f1)
flaw_eval_recall.append(flaw_tmp_eval_recall)
flaw_eval_precision.append(flaw_tmp_eval_precision)
nb_eval_examples += flaw_ids.size(0)
nb_eval_steps += 1
flaw_f1 = sum(flaw_eval_f1) / len(flaw_eval_f1)
flaw_recall = sum(flaw_eval_recall) / len(flaw_eval_recall)
flaw_precision = sum(flaw_eval_precision) / len(
flaw_eval_precision)
loss = tr_loss / nb_tr_steps if args.do_train else None
result = {
'flaw_f1': flaw_f1,
"flaw_recall": flaw_recall,
"flaw_precision": flaw_precision,
'loss': loss,
}
output_eval_file = os.path.join(args.output_dir,
"train_results.txt")
with open(output_eval_file, "a") as writer:
#logger.info("***** Training results *****")
writer.write("epoch" + str(ind) + '\n')
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
writer.write('\n')
model_to_save = model.module if hasattr(model, 'module') else model
output_model_file = os.path.join(args.output_dir,
"epoch" + str(ind) + 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())
os.rename(
output_model_file,
os.path.join(args.output_dir, "disc_trained_" + WEIGHTS_NAME))
current_path = os.path.join(args.output_dir,
"disc_trained_" + WEIGHTS_NAME)
new_path = os.path.join('./models', "disc_trained_" + WEIGHTS_NAME)
new_path_config = os.path.join('./models' + CONFIG_NAME)
shutil.move(current_path, new_path)
shutil.move(output_config_file, new_path_config)
if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank()
== 0): # for trouble-shooting
eval_examples = processor.get_disc_dev_examples(args.data_file)
eval_features, w2i, i2w, vocab_size = convert_examples_to_features_disc_eval(
eval_examples, label_list, args.max_seq_length, tokenizer, w2i,
i2w, vocab_size)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Num token vocab = %d", vocab_size)
logger.info(" Batch size = %d", args.eval_batch_size)
all_token_ids = torch.tensor([f.token_ids for f in eval_features],
dtype=torch.long)
all_input_ids = torch.tensor([f.input_ids for f in eval_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features],
dtype=torch.long)
all_flaw_labels = torch.tensor([f.flaw_labels for f in eval_features],
dtype=torch.long)
all_flaw_ids = torch.tensor([f.flaw_ids for f in eval_features],
dtype=torch.long)
all_label_id = torch.tensor([f.label_id for f in eval_features],
dtype=torch.long)
all_chunks = torch.tensor([f.chunks for f in eval_features],
dtype=torch.long)
#print("flaw ids in eval_features: ", all_flaw_ids)
eval_data = TensorDataset(all_token_ids, all_input_ids, all_input_mask,
all_flaw_ids, all_flaw_labels, all_label_id,
all_chunks)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
# Load a trained model and config that you have fine-tuned
if args.single:
eval_range = trange(int(args.num_eval_epochs),
int(args.num_eval_epochs + 1),
desc="Epoch")
else:
eval_range = trange(int(args.num_eval_epochs), desc="Epoch")
attack_type = 'rand'
for epoch in eval_range:
output_file = os.path.join(
args.data_dir, "epoch" + str(epoch) + "disc_eval_outputs_" +
attack_type + ".tsv")
with open(output_file, "w") as csv_file:
writer = csv.writer(csv_file, delimiter='\t')
writer.writerow(["sentence", "label", "ids"])
#output_model_file = os.path.join(args.output_dir, "epoch"+str(epoch)+WEIGHTS_NAME)
output_model_file = os.path.join(args.output_dir,
"disc_trained_" + WEIGHTS_NAME)
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
#print("output_model_file: ", output_model_file)
config = BertConfig(output_config_file)
model = BertForDiscriminator(config, num_labels=num_labels)
model.load_state_dict(torch.load(output_model_file))
model.to(device)
model.eval()
predictions, truths = [], []
eval_loss, nb_eval_steps, nb_eval_examples = 0, 0, 0
eval_accuracy = 0
for token_ids, input_ids, input_mask, flaw_ids, flaw_labels, label_id, chunks in tqdm(
eval_dataloader, desc="Evaluating"):
token_ids = token_ids.to(device)
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
flaw_labels = flaw_labels.to(device)
flaw_ids = flaw_ids.to(device)
#print("flaw ids in eval_dataloader: ", flaw_ids)
with torch.no_grad():
tmp_eval_loss, s = model(input_ids, input_mask,
flaw_labels)
# print("tmp_eval_loss: ",tmp_eval_loss)
# print("s: ",s)
logits = model(input_ids, input_mask)
print("len of logits: ", len(logits))
print("shape of logits: ", logits.size())
print("type of logits: ", type(logits))
print("type of logits: ", logits)
flaw_logits = torch.argmax(logits, dim=2)
print("Type of flaw_logits: ", type(flaw_logits))
print("shape of flaw_logits: ", flaw_logits.size())
print("Length of flaw_logits: ", len(flaw_logits))
print("flaw_logits: ", flaw_logits)
logits = logits.detach().cpu().numpy()
flaw_logits = flaw_logits.detach().cpu().numpy()
flaw_ids = flaw_ids.to('cpu').numpy()
label_id = label_id.to('cpu').numpy()
chunks = chunks.to('cpu').numpy()
token_ids = token_ids.to('cpu').numpy()
flaw_logits = logit_converter(
flaw_logits, chunks) # each word only has one '1'
print("Type of flaw_logits logit_converter: ",
type(flaw_logits))
#print("shape of flaw_logits logit_converter : ",flaw_logits.size())
print("Length of flaw_logits logit_converter : ",
len(flaw_logits))
print("flaw_logits logit_converter : ", flaw_logits)
true_logits = []
#print("length of flaw_ids: ",len(flaw_ids))
for i in range(len(flaw_ids)):
tmp = [0] * len(flaw_logits[i])
#print("tmp: ",tmp) # ne line
#print("printing i:",i)
#print("len of tmp: ",len(tmp))
#print("length of flaw_ids of i : ",len(flaw_ids[i]))
#print("flaw_ids[i]: ",flaw_ids[i])
for j in range(len(flaw_ids[0])):
#print("flaw_ids[i][j] : ",flaw_ids[i][j])
#print("tmp value: ", tmp)
#print("tmp len: ", len(tmp))
if flaw_ids[i][j] == 0: break
if flaw_ids[i][j] >= len(tmp): continue
tmp[flaw_ids[i][j]] = 1
true_logits.append(tmp)
#print('true_logits: ', true_logits)
tmp_eval_accuracy = accuracy_2d(flaw_logits, true_logits)
eval_accuracy += tmp_eval_accuracy
predictions += true_logits # Original
truths += flaw_logits # Original
#predictions += flaw_logits # for trouble-shooting
#truths += true_logits # for trouble-shooting
eval_loss += tmp_eval_loss.mean().item()
nb_eval_examples += input_ids.size(0)
nb_eval_steps += 1
with open(output_file, "a") as csv_file:
for i in range(len(label_id)):
#print("i in write output file:",i)
token = ' '.join(
[i2w[x] for x in token_ids[i] if x != 0])
flaw_logit = flaw_logits[i]
#print("flaw_logit in write output file: ",flaw_logit)
label = str(label_id[i])
logit = ','.join([
str(i) for i, x in enumerate(flaw_logit) if x == 1
]) # for trouble-shooting
logit = '-1' if logit == '' else logit # for trouble-shooting
writer = csv.writer(csv_file, delimiter='\t')
writer.writerow([token, label, logit])
# Renaming and moving the file for Embedding Estimator
eval_loss = eval_loss / nb_eval_steps
eval_accuracy = eval_accuracy / nb_eval_steps
eval_f1_score, eval_recall_score, eval_precision_score = f1_2d(
truths, predictions)
loss = tr_loss / nb_tr_steps if args.do_train else None
result = {
'eval_loss': eval_loss,
'eval_f1': eval_f1_score,
'eval_recall': eval_recall_score,
'eval_precision': eval_precision_score,
'eval_acc': eval_accuracy
}
output_eval_file = os.path.join(
args.output_dir,
"disc_eval_results_" + attack_type + "_attacks.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])))
#attack_type='drop'
new_path = os.path.join(
args.data_dir, "disc_eval_outputs_" + attack_type + ".tsv")
current_path = os.path.join(
args.data_dir, "epoch" + str(epoch) + "disc_eval_outputs_" +
attack_type + ".tsv")
os.rename(current_path, new_path)
def main():
parser = argparse.ArgumentParser()
## Required parameters
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(
"--output_dir",
default=None,
type=str,
required=True,
help=
"The output directory where the model checkpoints and predictions will be written."
)
## Other parameters
parser.add_argument("--train_file",
default=None,
type=str,
help="SQuAD json for training. E.g., train-v1.1.json")
parser.add_argument(
"--predict_file",
default=None,
type=str,
help="SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json"
)
parser.add_argument(
"--max_seq_length",
default=384,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. Sequences "
"longer than this will be truncated, and sequences shorter than this will be padded."
)
parser.add_argument(
"--doc_stride",
default=128,
type=int,
help=
"When splitting up a long document into chunks, how much stride to take between chunks."
)
parser.add_argument(
"--max_query_length",
default=64,
type=int,
help=
"The maximum number of tokens for the question. Questions longer than this will "
"be truncated to this length.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_predict",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--predict_batch_size",
default=16,
type=int,
help="Total batch size for predictions.")
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(
"--n_best_size",
default=20,
type=int,
help=
"The total number of n-best predictions to generate in the nbest_predictions.json "
"output file.")
parser.add_argument(
"--max_answer_length",
default=30,
type=int,
help=
"The maximum length of an answer that can be generated. This is needed because the start "
"and end predictions are not conditioned on one another.")
parser.add_argument(
"--verbose_logging",
action='store_true',
help=
"If true, all of the warnings related to data processing will be printed. "
"A number of warnings are expected for a normal SQuAD evaluation.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
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(
"--do_lower_case",
action='store_true',
help=
"Whether to lower case the input text. True for uncased models, False for cased models."
)
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument(
'--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument(
'--pretrain',
action='store_true',
help="Whether to load a pre-trained model for continuing training")
parser.add_argument('--pretrained_model_file',
type=str,
help="The path of the pretrained_model_file")
parser.add_argument(
"--percent",
default=100,
type=float,
help="The percentage of examples used in the training data.\n")
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")
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: {}, 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 = 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_predict:
raise ValueError(
"At least one of `do_train` or `do_predict` must be True.")
if args.do_train:
if not args.train_file:
raise ValueError(
"If `do_train` is True, then `train_file` must be specified.")
if args.do_predict:
if not args.predict_file:
raise ValueError(
"If `do_predict` is True, then `predict_file` must be specified."
)
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.")
os.makedirs(args.output_dir, exist_ok=True)
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=args.do_lower_case)
train_examples = None
num_train_steps = None
if args.do_train:
train_examples = read_squad_examples(input_file=args.train_file,
is_training=True)
train_examples = train_examples[:int(
len(train_examples) * args.percent / 100)]
num_train_steps = int(
len(train_examples) / args.train_batch_size /
args.gradient_accumulation_steps * args.num_train_epochs)
# Prepare model
model = ConditionalSemanticBertForQuestionAnswering.from_pretrained(
args.bert_model,
cache_dir=PYTORCH_PRETRAINED_BERT_CACHE /
'distributed_{}'.format(args.local_rank))
model.QABert = BertModel.from_pretrained(
args.bert_model,
cache_dir=PYTORCH_PRETRAINED_BERT_CACHE /
'distributed_{}'.format(args.local_rank))
if args.pretrain:
# Load a pre-trained model
print('load a pre-trained model from ' + args.pretrained_model_file)
pretrained_state_dict = torch.load(args.pretrained_model_file)
model_state_dict = model.state_dict()
print(
'QABert', model_state_dict[
'QABert.encoder.layer.4.attention.self.value.weight'])
print(
'bert', model_state_dict[
'bert.encoder.layer.4.attention.self.value.weight'])
print('pretrained_state_dict', pretrained_state_dict.keys())
print('model_state_dict', model_state_dict.keys())
pretrained_state = {
k: v
for k, v in pretrained_state_dict.items() if k in model_state_dict
and v.size() == model_state_dict[k].size()
}
print('stored pretrained dict', pretrained_state.keys())
model_state_dict.update(pretrained_state)
print('updated_state_dict', model_state_dict.keys())
model.load_state_dict(model_state_dict)
model.to(device)
# for param in model.bert.parameters():
# param.requires_grad = False
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)
# 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()
if t_total is None:
t_total = -1
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=t_total)
global_step = 0
if args.do_train:
cached_train_features_file = args.train_file + '_{0}_{1}_{2}_{3}'.format(
list(filter(None, args.bert_model.split('/'))).pop(),
str(args.max_seq_length), str(args.doc_stride),
str(args.max_query_length))
train_features = None
try:
# with open(cached_train_features_file, "rb") as reader:
with open('not load features', "rb") as reader:
train_features = pickle.load(reader)
print('not load stored features, but generate itself')
except:
train_features = convert_examples_to_features(
examples=train_examples,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length,
is_training=True)
if args.local_rank == -1 or torch.distributed.get_rank() == 0:
logger.info(" Saving train features into cached file %s",
cached_train_features_file)
with open(cached_train_features_file, "wb") as writer:
pickle.dump(train_features, writer)
logger.info("***** Running training *****")
logger.info(" Num orig examples = %d", len(train_examples))
logger.info(" Num split examples = %d", len(train_features))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_steps)
all_input_ids = torch.tensor([f.input_ids for f in train_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in train_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in train_features],
dtype=torch.long)
all_start_positions = torch.tensor(
[f.start_position for f in train_features], dtype=torch.long)
all_end_positions = torch.tensor(
[f.end_position for f in train_features], dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_start_positions,
all_end_positions)
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 _ in trange(int(args.num_train_epochs), desc="Epoch"):
total_loss = 0
for step, batch in enumerate(
tqdm(train_dataloader, desc="Iteration")):
if n_gpu == 1:
batch = tuple(
t.to(device)
for t in batch) # multi-gpu does scattering it-self
input_ids, input_mask, segment_ids, start_positions, end_positions = batch
loss = model(input_ids, segment_ids, input_mask,
start_positions, end_positions)
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
total_loss += loss
if args.fp16:
optimizer.backward(loss)
else:
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
print('loss', total_loss)
# 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")
if args.do_train:
torch.save(model_to_save.state_dict(), output_model_file)
# Load a trained model that you have fine-tuned
model_state_dict = torch.load(output_model_file)
model = ConditionalSemanticBertForQuestionAnswering.from_pretrained(
args.bert_model, state_dict=model_state_dict)
model.to(device)
if args.do_predict and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
eval_examples = read_squad_examples(input_file=args.predict_file,
is_training=False)
eval_features = convert_examples_to_features(
examples=eval_examples,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length,
is_training=False)
logger.info("***** Running predictions *****")
logger.info(" Num orig examples = %d", len(eval_examples))
logger.info(" Num split examples = %d", len(eval_features))
logger.info(" Batch size = %d", args.predict_batch_size)
all_input_ids = torch.tensor([f.input_ids for f in eval_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features],
dtype=torch.long)
all_example_index = torch.arange(all_input_ids.size(0),
dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_example_index)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.predict_batch_size)
model.eval()
all_results = []
logger.info("Start evaluating")
for input_ids, input_mask, segment_ids, example_indices in tqdm(
eval_dataloader, desc="Evaluating"):
if len(all_results) % 1000 == 0:
logger.info("Processing example: %d" % (len(all_results)))
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
with torch.no_grad():
batch_start_logits, batch_end_logits = model(
input_ids, segment_ids, input_mask)
for i, example_index in enumerate(example_indices):
start_logits = batch_start_logits[i].detach().cpu().tolist()
end_logits = batch_end_logits[i].detach().cpu().tolist()
eval_feature = eval_features[example_index.item()]
unique_id = int(eval_feature.unique_id)
all_results.append(
RawResult(unique_id=unique_id,
start_logits=start_logits,
end_logits=end_logits))
output_prediction_file = os.path.join(args.output_dir,
"predictions.json")
output_nbest_file = os.path.join(args.output_dir,
"nbest_predictions.json")
write_predictions(eval_examples, eval_features, all_results,
args.n_best_size, args.max_answer_length,
args.do_lower_case, output_prediction_file,
output_nbest_file, args.verbose_logging)
def main():
parser = argparse.ArgumentParser()
## Required parameters
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("--pretrained_model_path",
default=None,
type=str,
help="Pretrained basic Bert model")
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help=
"The output directory where the model checkpoints and predictions will be written."
)
## Other parameters
parser.add_argument("--train_file",
default=None,
type=str,
help="triviaqa train file")
parser.add_argument("--predict_file",
default=None,
type=str,
help="triviaqa dev or test file in SQuAD format")
parser.add_argument("--predict_data_file",
default=None,
type=str,
help="triviaqa dev or test file in Triviaqa format")
# history queries parameters
parser.add_argument("--use_history", default=False, action="store_true")
parser.add_argument(
"--append_history",
default=False,
action="store_true",
help="Whether to append the previous queries to the current one.")
parser.add_argument(
"--n_history",
default=-1,
type=int,
help="The number of previous queries used in current query.")
parser.add_argument(
"--max_seq_length",
default=512,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. Sequences "
"longer than this will be truncated, and sequences shorter than this will be padded."
)
parser.add_argument(
"--doc_stride",
default=128,
type=int,
help=
"When splitting up a long document into chunks, how much stride to take between chunks."
)
parser.add_argument(
"--max_query_length",
default=64,
type=int,
help=
"The maximum number of tokens for the question. Questions longer than this will "
"be truncated to this length.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_validate",
action='store_true',
help="Whether to run validation when training")
parser.add_argument("--do_predict",
action='store_true',
help="Whether to run eval on the dev set.")
# supervised & reinforcement learning
parser.add_argument("--supervised_pretraining",
action='store_true',
help="Whether to do supervised pretraining.")
#parser.add_argument("--reload_model_path", type=str, help="Path of pretrained model.")
parser.add_argument("--recur_type",
type=str,
default="gated",
help="Recurrence model type.")
# model parameters
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--predict_batch_size",
default=8,
type=int,
help="Total batch size for predictions.")
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("--max_read_times",
default=6,
type=int,
help="Maximum read times of one document")
parser.add_argument("--stop_loss_weight",
default=1.0,
type=float,
help="The weight of stop_loss in training")
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(
"--n_best_size",
default=2,
type=int,
help=
"The total number of n-best predictions to generate in the nbest_predictions.json "
"output file.")
parser.add_argument(
"--max_answer_length",
default=30,
type=int,
help=
"The maximum length of an answer that can be generated. This is needed because the start "
"and end predictions are not conditioned on one another.")
parser.add_argument(
"--verbose_logging",
action='store_true',
help=
"If true, all of the warnings related to data processing will be printed. "
"A number of warnings are expected for a normal SQuAD evaluation.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
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(
"--do_lower_case",
action='store_true',
help=
"Whether to lower case the input text. True for uncased models, False for cased models."
)
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
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")
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: {}, 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 = 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_predict:
raise ValueError(
"At least one of `do_train` or `do_predict` must be True.")
if args.do_train:
if not args.train_file:
raise ValueError(
"If `do_train` is True, then `train_file` must be specified.")
if args.do_predict:
if not args.predict_file:
raise ValueError(
"If `do_predict` is True, then `predict_file` must be specified."
)
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.")
os.makedirs(args.output_dir, exist_ok=True)
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=args.do_lower_case)
# Prepare model
if args.pretrained_model_path is not None and os.path.isfile(
args.pretrained_model_path):
logger.info("Reloading pretrained model from {}".format(
args.pretrained_model_path))
model_state_dict = torch.load(args.pretrained_model_path)
model = RCMBert.from_pretrained(args.bert_model,
state_dict=model_state_dict,
action_num=len(stride_action_space),
recur_type=args.recur_type,
allow_yes_no=False)
else:
logger.info("Training a new model from scratch")
model = RCMBert.from_pretrained(
args.bert_model,
cache_dir=PYTORCH_PRETRAINED_BERT_CACHE /
'distributed_{}'.format(args.local_rank),
action_num=len(stride_action_space),
recur_type=args.recur_type,
allow_yes_no=False)
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)
# 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']
no_decay += ['recur_network', 'stop_network', 'move_stride_network']
logger.info("Parameter without decay: {}".format(no_decay))
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
}]
"""
examples & features
"""
train_examples = None
dev_examples = None
num_train_steps = None
if args.do_train:
cached_train_examples_file = args.train_file + '_train_examples'
cached_dev_examples_file = args.train_file + '_dev_examples'
try:
with open(cached_train_examples_file, "rb") as reader:
train_examples = pickle.load(reader)
with open(cached_dev_examples_file, "rb") as reader:
dev_examples = pickle.load(reader)
logger.info("Loading train and dev examples...")
except:
all_train_examples = read_quac_examples(
input_file=args.train_file,
is_training=True,
use_history=args.use_history,
n_history=args.n_history)
train_examples, dev_examples = split_train_dev_data(
all_train_examples)
with open(cached_train_examples_file, "wb") as writer:
pickle.dump(train_examples, writer)
with open(cached_dev_examples_file, "wb") as writer:
pickle.dump(dev_examples, writer)
num_train_steps = int(
len(train_examples) / args.train_batch_size /
args.gradient_accumulation_steps * args.num_train_epochs)
t_total = num_train_steps
if args.local_rank != -1:
t_total = t_total // torch.distributed.get_world_size()
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=t_total)
if args.do_train:
cached_train_features_file = args.train_file + '_{0}_{1}_RCM_train'.format(
list(filter(None, args.bert_model.split('/'))).pop(),
str(args.max_query_length))
cached_dev_features_file = args.train_file + '_{0}_{1}_RCM_dev'.format(
list(filter(None, args.bert_model.split('/'))).pop(),
str(args.max_query_length))
train_features = None
dev_features = None
try:
with open(cached_train_features_file, "rb") as reader:
train_features = pickle.load(reader)
with open(cached_dev_features_file, "rb") as reader:
dev_features = pickle.load(reader)
except:
train_features = convert_examples_to_features(
examples=train_examples,
tokenizer=tokenizer,
max_query_length=args.max_query_length,
is_training=True,
append_history=args.append_history)
dev_features = convert_examples_to_features(
examples=dev_examples,
tokenizer=tokenizer,
max_query_length=args.max_query_length,
is_training=True,
append_history=args.append_history)
if args.local_rank == -1 or torch.distributed.get_rank() == 0:
logger.info(" Saving train features into cached file %s",
cached_train_features_file)
logger.info(" Saving dev features into cached file %s",
cached_dev_features_file)
with open(cached_train_features_file, "wb") as writer:
pickle.dump(train_features, writer)
with open(cached_dev_features_file, "wb") as writer:
pickle.dump(dev_features, writer)
logger.info("***** Running training *****")
logger.info(" Num orig examples = %d", len(train_examples))
logger.info(" Num split examples = %d", len(train_features))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_steps)
if args.do_validate and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
logger.info("***** Dev data *****")
logger.info(" Num orig dev examples = %d", len(dev_examples))
logger.info(" Num split dev examples = %d", len(dev_features))
logger.info(" Batch size = %d", args.predict_batch_size)
dev_evaluator = QuACEvaluator(dev_examples)
train_model(args, model, tokenizer, optimizer, train_examples,
train_features, dev_examples, dev_features, dev_evaluator,
device, n_gpu, t_total)
if args.do_predict and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
# load model
output_model_file = os.path.join(args.output_dir, "best_RCM_model.bin")
model_state_dict = torch.load(output_model_file)
model = RCMBert.from_pretrained(args.bert_model,
state_dict=model_state_dict,
action_num=len(stride_action_space),
recur_type=args.recur_type,
allow_yes_no=False)
model.to(device)
# load data
test_examples = read_quac_examples(input_file=args.predict_file,
is_training=False,
use_history=args.use_history,
n_history=args.n_history)
cached_test_features_file = args.predict_file + '_{0}_{1}_RCM_test'.format(
list(filter(None, args.bert_model.split('/'))).pop(),
str(args.max_query_length))
test_features = None
try:
with open(cached_test_features_file, "rb") as reader:
test_features = pickle.load(reader)
except:
test_features = convert_examples_to_features(
examples=test_examples,
tokenizer=tokenizer,
max_query_length=args.max_query_length,
is_training=False,
append_history=args.append_history)
with open(cached_test_features_file, "wb") as writer:
pickle.dump(test_features, writer)
logger.info("***** Prediction data *****")
logger.info(" Num test orig examples = %d", len(test_examples))
logger.info(" Num test split examples = %d", len(test_features))
logger.info(" Batch size = %d", args.predict_batch_size)
test_model(args, model, tokenizer, test_examples, test_features,
device)
def run(self, repeats=1):
# Loss and Optimizer
criterion = nn.CrossEntropyLoss()
_params = filter(lambda p: p.requires_grad, self.model.parameters())
optimizer = self.opt.optimizer(_params,
lr=self.opt.learning_rate,
weight_decay=self.opt.l2reg)
if not os.path.exists('log/'):
os.mkdir('log/')
f_out = open('log/' + self.opt.model_name + '_' + self.opt.dataset +
'_val.txt',
'w',
encoding='utf-8')
max_test_acc_avg = 0
max_test_f1_avg = 0
for i in range(repeats):
print('repeat: ', (i + 1))
f_out.write('repeat: ' + str(i + 1))
self._reset_params()
self.model.bert = bert_model
self.model.cuda()
if self.opt.mode == "train":
parameters = [
p for name, p in self.model.named_parameters()
if 'bert' in name
]
parameters = [p for name, p in self.model.named_parameters()]
named_params = [(name, p)
for name, p in self.model.named_parameters()]
optimizer1 = torch.optim.Adam(parameters,
lr=0.00005,
weight_decay=0.00001)
optimizer_grouped_parameters = [{
'params': parameters,
'weight_decay': 0.01
}]
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params': [
p for n, p in named_params
if not any(nd in n for nd in no_decay)
],
'weight_decay':
0.01
}, {
'params': [
p for n, p in named_params
if any(nd in n for nd in no_decay)
],
'weight_decay':
0.0
}]
if self.opt.usebert:
optimizer1 = BertAdam(
optimizer_grouped_parameters,
lr=0.00005,
warmup=0.1,
t_total=self.train_data_loader.batch_len * 20)
else:
optimizer1 = torch.optim.Adam(parameters,
lr=0.001,
weight_decay=0.00001)
print('not use bert')
max_test_acc, max_test_f1 = self._train(
criterion, optimizer, optimizer1)
print('max_test_acc: {0} max_test_f1: {1}'.format(
max_test_acc, max_test_f1))
f_out.write('max_test_acc: {0}, max_test_f1: {1}'.format(
max_test_acc, max_test_f1))
max_test_acc_avg += max_test_acc
max_test_f1_avg += max_test_f1
print('#' * 100)
print("max_test_acc_avg:", max_test_acc_avg / repeats)
print("max_test_f1_avg:", max_test_f1_avg / repeats)
# torch.save(self.model.state_dict(),self.opt.model_name+'_'+self.opt.dataset+'.pth')
f_out.close()
else:
self.model.load_state_dict(
torch.load('state_dict/' + self.opt.model_name + '_' +
self.opt.dataset + '.pkl'))
test_acc, test_f1 = self._evaluate_acc_f1withmore()
print("max_test_acc_avg:", test_acc / repeats)
print("max_test_f1_avg:", test_f1 / repeats)
f_out.close()
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=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",
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_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=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",
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('--load_finetuned_model',
action='store_true',
default=False,
help="Load finetuned model.")
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 = {
"compq": COMPQProcessor,
}
output_modes = {
"compq": "classification",
}
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')
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
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]()
output_mode = output_modes[task_name]
label_list = processor.get_labels()
num_labels = len(label_list)
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
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))
if args.load_finetuned_model:
print("Loading finetuned model....")
model = BertForSequenceClassification.from_pretrained(
args.output_dir, num_labels=num_labels)
tokenizer = BertTokenizer.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
else:
model = BertForSequenceClassification.from_pretrained(
args.bert_model, num_labels=num_labels)
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)
global_step = 0
nb_tr_steps = 0
tr_loss = 0
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)
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)
train_features = convert_examples_to_features(train_examples,
label_list,
args.max_seq_length,
tokenizer, output_mode)
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)
all_input_ids = torch.tensor([f.input_ids for f in train_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in train_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in train_features],
dtype=torch.long)
if output_mode == "classification":
all_label_ids = torch.tensor([f.label_id for f in train_features],
dtype=torch.long)
elif output_mode == "regression":
all_label_ids = torch.tensor([f.label_id for f in train_features],
dtype=torch.float)
train_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
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 _ 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(train_dataloader, desc="Iteration")):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
# define a new function to compute loss values for both output_modes
logits = model(input_ids, segment_ids, input_mask, labels=None)
if output_mode == "classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, num_labels),
label_ids.view(-1))
elif output_mode == "regression":
loss_fct = MSELoss()
loss = loss_fct(logits.view(-1), label_ids.view(-1))
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.get_lr(
global_step, 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 and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
# Save a trained model, configuration and tokenizer
model_to_save = model.module if hasattr(
model, 'module') else model # Only save the model it-self
# If we save using the predefined names, we can load using `from_pretrained`
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)
model.to(device)
if args.do_eval and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
eval_examples = processor.get_dev_examples(args.data_dir)
eval_features = convert_examples_to_features(eval_examples, label_list,
args.max_seq_length,
tokenizer, output_mode)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
all_input_ids = torch.tensor([f.input_ids for f in eval_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features],
dtype=torch.long)
if output_mode == "classification":
all_label_ids = torch.tensor([f.label_id for f in eval_features],
dtype=torch.long)
elif output_mode == "regression":
all_label_ids = torch.tensor([f.label_id for f in eval_features],
dtype=torch.float)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
# 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 = 0
nb_eval_steps = 0
preds = []
softmax_preds = []
for input_ids, input_mask, segment_ids, label_ids in tqdm(
eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
logits = model(input_ids, segment_ids, input_mask, labels=None)
# create eval loss and other metric required by the task
if output_mode == "classification":
loss_fct = CrossEntropyLoss()
tmp_eval_loss = loss_fct(logits.view(-1, num_labels),
label_ids.view(-1))
elif output_mode == "regression":
loss_fct = MSELoss()
tmp_eval_loss = loss_fct(logits.view(-1), label_ids.view(-1))
eval_loss += tmp_eval_loss.mean().item()
nb_eval_steps += 1
if len(preds) == 0:
preds.append(logits.detach().cpu().numpy())
softmax_preds.append(Softmax(1)(logits).detach().cpu().numpy())
else:
preds[0] = np.append(preds[0],
logits.detach().cpu().numpy(),
axis=0)
softmax_preds[0] = np.append(
softmax_preds[0],
Softmax(1)(logits).detach().cpu().numpy(),
axis=0)
eval_loss = eval_loss / nb_eval_steps
preds = preds[0]
softmax_preds = softmax_preds[0]
output_prediction_file = os.path.join(args.output_dir,
"predictions.txt")
with open(output_prediction_file, 'w') as writer:
for i, pred in enumerate(softmax_preds):
writer.write(
str(pred[0]) + '\t' + str(pred[1]) + '\t' +
eval_examples[i].text_a + '\n')
if output_mode == "classification":
preds = np.argmax(preds, axis=1)
elif output_mode == "regression":
preds = np.squeeze(preds)
result = compute_metrics(task_name, preds, all_label_ids.numpy())
loss = tr_loss / nb_tr_steps if args.do_train else None
result['eval_loss'] = eval_loss
result['global_step'] = global_step
result['loss'] = loss
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "w") 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])))
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--task_name",
default=None,
type=str,
required=True,
choices=["WSD"],
help="The name of the task to train.")
parser.add_argument("--train_data_dir",
default=None,
type=str,
help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
parser.add_argument("--eval_data_dir",
default=None,
type=str,
help="The label data dir. (./wordnet)")
parser.add_argument("--label_data_dir",
default=None,
type=str,
required=True,
help="The label data dir. Should contain the .tsv files (or other data files) for the task.")
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_model", default=None, type=str, required=True,
help='''a path or url to a pretrained model archive containing:
'bert_config.json' a configuration file for the model
'pytorch_model.bin' a PyTorch dump of a BertForPreTraining instance''')
## 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("--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_test",
action='store_true',
help="Whether to run test on the test set.")
parser.add_argument("--do_lower_case",
default=False,
action='store_true',
help="Whether to lower case the input text. True for uncased models, False for cased models.")
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("--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 accumualte 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")
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')
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt = '%m/%d/%Y %H:%M:%S',
level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
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_test:
raise ValueError("At least one of `do_train` or `do_test` must be True.")
if args.do_train:
assert args.train_data_dir != None, "train_data_dir can not be None"
if args.do_eval:
assert args.eval_data_dir != None, "eval_data_dir can not be None"
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))
os.makedirs(args.output_dir, exist_ok=True)
# prepare dataloaders
processors = {
"WSD":WSDProcessor
}
output_modes = {
"WSD": "classification"
}
processor = processors[args.task_name]()
output_mode = output_modes[args.task_name]
label_list = processor.get_labels(args.label_data_dir)
num_labels = len(label_list)
tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
# training set
train_examples = None
num_train_optimization_steps = None
if args.do_train:
train_examples = processor.get_train_examples(args.train_data_dir, args.label_data_dir)
num_train_optimization_steps = int(
len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps) * args.num_train_epochs
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 = BertForTokenClassification.from_pretrained(args.bert_model,
cache_dir=cache_dir,
num_labels=num_labels)
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)
# 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)
# load data
if args.do_train:
train_features = convert_examples_to_features(
train_examples, label_list, args.max_seq_length, tokenizer, output_mode)
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)
all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long)
all_target_mask = torch.tensor([f.target_mask for f in train_features], dtype=torch.long)
all_index_start = torch.tensor([f.index_start for f in train_features], dtype=torch.long)
all_index_end = torch.tensor([f.index_end for f in train_features], dtype=torch.long)
if output_mode == "classification":
all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.long)
elif output_mode == "regression":
all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.float)
train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids, all_target_mask, all_index_start, all_index_end)
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)
if args.do_eval:
eval_examples = processor.get_dev_examples(args.eval_data_dir, args.label_data_dir)
eval_features = convert_examples_to_features(
eval_examples, label_list, args.max_seq_length, tokenizer, output_mode)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
all_target_mask = torch.tensor([f.target_mask for f in eval_features], dtype=torch.long)
all_label_mask = torch.tensor([f.label_mask for f in eval_features], dtype=torch.float)
if output_mode == "classification":
all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long)
elif output_mode == "regression":
all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.float)
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids, all_target_mask, all_label_mask)
eval_dataloader = DataLoader(eval_data, batch_size=args.eval_batch_size, shuffle=False)
# train
global_step = 0
nb_tr_steps = 0
tr_loss = 0
if args.do_train:
model.train()
epoch = 0
for _ in trange(int(args.num_train_epochs), desc="Epoch"):
epoch += 1
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
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, target_mask, index_start, index_end = batch
all_label_mask = []
for i in range(len(index_start)):
label_mask = [float("-inf")] * len(label_list)
for i in range(index_start[i][0].item(), index_end[i][0].item()):
label_mask[i] = 0
all_label_mask.append(label_mask)
all_label_mask = torch.tensor(all_label_mask, dtype=torch.float).to(device)
logits = model(input_ids=input_ids, token_type_ids=segment_ids, attention_mask=input_mask, labels=None, target_mask=target_mask)
logits = logits + all_label_mask
logits = F.softmax(logits, dim=-1)
if output_mode == "classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1))
elif output_mode == "regression":
loss_fct = MSELoss()
loss = loss_fct(logits.view(-1), label_ids.view(-1))
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
# Save a trained model, configuration and tokenizer
model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self
# If we save using the predefined names, we can load using `from_pretrained`
model_output_dir = os.path.join(args.output_dir, str(epoch))
if not os.path.exists(model_output_dir):
os.makedirs(model_output_dir)
output_model_file = os.path.join(model_output_dir, WEIGHTS_NAME)
output_config_file = os.path.join(model_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(model_output_dir)
if args.do_eval:
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
with open(os.path.join(args.output_dir, "results_"+str(epoch)+".txt"),"w") as f:
for input_ids, input_mask, segment_ids, label_ids, target_mask, label_mask in tqdm(eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
target_mask = target_mask.to(device)
label_mask = label_mask.to(device)
with torch.no_grad():
logits = model(input_ids=input_ids, token_type_ids=segment_ids, attention_mask=input_mask, labels=None, target_mask=target_mask)
logits = logits + label_mask
logits_ = F.softmax(logits, dim=-1)
logits_ = logits_.detach().cpu().numpy()
label_ids_ = label_ids.to('cpu').numpy()
outputs = np.argmax(logits_, axis=1)
for output_i in range(len(outputs)):
f.write(str(outputs[output_i]))
f.write("\n")
tmp_eval_accuracy = np.sum(outputs == label_ids_)
# create eval loss and other metric required by the task
if output_mode == "classification":
loss_fct = CrossEntropyLoss()
tmp_eval_loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1))
elif output_mode == "regression":
loss_fct = MSELoss()
tmp_eval_loss = loss_fct(logits.view(-1), label_ids.view(-1))
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
loss = tr_loss/nb_tr_steps if args.do_train else None
result = OrderedDict()
result['eval_loss'] = eval_loss
result['eval_accuracy'] = eval_accuracy
result['global_step'] = global_step
result['loss'] = loss
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "a+") as writer:
writer.write("epoch=%s\n"%str(epoch))
logger.info("***** Eval results *****")
for key in result.keys():
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
if args.do_test and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
eval_examples = processor.get_dev_examples(args.eval_data_dir, args.label_data_dir)
eval_features = convert_examples_to_features(
eval_examples, label_list, args.max_seq_length, tokenizer, output_mode)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
all_target_mask = torch.tensor([f.target_mask for f in eval_features], dtype=torch.long)
all_label_mask = torch.tensor([f.label_mask for f in eval_features], dtype=torch.float)
if output_mode == "classification":
all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long)
elif output_mode == "regression":
all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.float)
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids, all_target_mask, all_label_mask)
eval_dataloader = DataLoader(eval_data, batch_size=args.eval_batch_size, shuffle=False)
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
with open(os.path.join(args.output_dir, "results.txt"),"w") as f:
for input_ids, input_mask, segment_ids, label_ids, target_mask, label_mask in tqdm(eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
target_mask = target_mask.to(device)
label_mask = label_mask.to(device)
with torch.no_grad():
logits = model(input_ids=input_ids, token_type_ids=segment_ids, attention_mask=input_mask, labels=None, target_mask=target_mask)
logits = logits + label_mask
logits_ = F.softmax(logits, dim=-1)
logits_ = logits_.detach().cpu().numpy()
label_ids_ = label_ids.to('cpu').numpy()
outputs = np.argmax(logits_, axis=1)
for output_i in range(len(outputs)):
f.write(str(outputs[output_i]))
f.write("\n")
tmp_eval_accuracy = np.sum(outputs == label_ids_)
# create eval loss and other metric required by the task
if output_mode == "classification":
loss_fct = CrossEntropyLoss()
tmp_eval_loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1))
elif output_mode == "regression":
loss_fct = MSELoss()
tmp_eval_loss = loss_fct(logits.view(-1), label_ids.view(-1))
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
loss = tr_loss/nb_tr_steps if args.do_train else None
result = OrderedDict()
result['eval_loss'] = eval_loss
result['eval_accuracy'] = eval_accuracy
result['global_step'] = global_step
result['loss'] = loss
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 result.keys():
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
def train_and_evaluate(model, params, restore_file=None):
"""Train the model and evaluate every epoch."""
# load args
args = parser.parse_args()
# Load training data and val data
dataloader = NERDataLoader(params)
train_loader = dataloader.get_dataloader(data_sign='train')
val_loader = dataloader.get_dataloader(data_sign='val')
# 一个epoch的步数
params.train_steps = len(train_loader)
# Prepare optimizer
# fine-tuning
# 取模型权重
param_optimizer = list(model.named_parameters())
# pretrain model param
param_pre = [(n, p) for n, p in param_optimizer if 'bert' in n]
# middle model param
param_middle = [(n, p) for n, p in param_optimizer if 'bilstm' in n or 'dym_weight' in n]
# crf param
param_crf = [p for n, p in param_optimizer if 'crf' in n]
# 不进行衰减的权重
no_decay = ['bias', 'LayerNorm', 'dym_weight', 'layer_norm']
# 将权重分组
optimizer_grouped_parameters = [
# pretrain model param
# 衰减
{'params': [p for n, p in param_pre if not any(nd in n for nd in no_decay)],
'weight_decay': params.weight_decay_rate, 'lr': params.fin_tuning_lr
},
# 不衰减
{'params': [p for n, p in param_pre if any(nd in n for nd in no_decay)],
'weight_decay': 0.0, 'lr': params.fin_tuning_lr
},
# middle model
# 衰减
{'params': [p for n, p in param_middle if not any(nd in n for nd in no_decay)],
'weight_decay': params.weight_decay_rate, 'lr': params.middle_lr
},
# 不衰减
{'params': [p for n, p in param_middle if any(nd in n for nd in no_decay)],
'weight_decay': 0.0, 'lr': params.middle_lr
},
# crf,单独设置学习率
{'params': param_crf,
'weight_decay': 0.0, 'lr': params.crf_lr}
]
num_train_optimization_steps = len(train_loader) // params.gradient_accumulation_steps * args.epoch_num
optimizer = BertAdam(optimizer_grouped_parameters, warmup=params.warmup_prop, schedule="warmup_cosine",
t_total=num_train_optimization_steps, max_grad_norm=params.clip_grad)
# reload weights from restore_file if specified
if restore_file is not None:
restore_path = os.path.join(params.model_dir, args.restore_file + '.pth.tar')
logging.info("Restoring parameters from {}".format(restore_path))
# 读取checkpoint
utils.load_checkpoint(restore_path, model, optimizer)
# patience stage
best_val_f1 = 0.0
patience_counter = 0
for epoch in range(1, args.epoch_num + 1):
# Run one epoch
logging.info("Epoch {}/{}".format(epoch, args.epoch_num))
# Train for one epoch on training set
train(model, train_loader, optimizer, params)
# Evaluate for one epoch on training set and validation set
# train_metrics = evaluate(model, train_loader, params, mark='Train',
# verbose=True) # Dict['loss', 'f1']
val_metrics = evaluate(args, model, val_loader, params, mark='Val',
verbose=True) # Dict['loss', 'f1']
# 验证集f1-score
val_f1 = val_metrics['f1']
# 提升的f1-score
improve_f1 = val_f1 - best_val_f1
# Save weights of the network
model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self
optimizer_to_save = optimizer
utils.save_checkpoint({'epoch': epoch + 1,
'state_dict': model_to_save.state_dict(),
'optim_dict': optimizer_to_save.state_dict()},
is_best=improve_f1 > 0,
checkpoint=params.model_dir)
params.save(params.params_path / 'params.json')
# stop training based params.patience
if improve_f1 > 0:
logging.info("- Found new best F1")
best_val_f1 = val_f1
if improve_f1 < params.patience:
patience_counter += 1
else:
patience_counter = 0
else:
patience_counter += 1
# Early stopping and logging best f1
if (patience_counter > params.patience_num and epoch > params.min_epoch_num) or epoch == args.epoch_num:
logging.info("Best val f1: {:05.2f}".format(best_val_f1))
break
def main():
print("IN NEW MAIN XD\n")
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument(
"--input_dir",
default=None,
type=str,
required=True,
help="The input data dir. Should contain .hdf5 files for the task.")
parser.add_argument("--config_file",
default=None,
type=str,
required=True,
help="The BERT model config")
parser.add_argument(
"--bert_model",
default="bert-large-uncased",
type=str,
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."
)
## Other parameters
parser.add_argument(
"--max_seq_length",
default=512,
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(
"--max_predictions_per_seq",
default=80,
type=int,
help="The maximum total of masked tokens in input sequence")
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
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("--max_steps",
default=1000,
type=float,
help="Total number of training steps to perform.")
parser.add_argument(
"--warmup_proportion",
default=0.01,
type=float,
help=
"Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
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 accumualte before performing a backward/update pass."
)
parser.add_argument(
'--fp16',
default=False,
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument(
'--loss_scale',
type=float,
default=0.0,
help=
'Loss scaling, positive power of 2 values can improve fp16 convergence.'
)
parser.add_argument('--log_freq',
type=float,
default=50.0,
help='frequency of logging loss.')
parser.add_argument('--checkpoint_activations',
default=False,
action='store_true',
help="Whether to use gradient checkpointing")
parser.add_argument("--resume_from_checkpoint",
default=False,
action='store_true',
help="Whether to resume training from checkpoint.")
parser.add_argument('--resume_step',
type=int,
default=-1,
help="Step to resume training from.")
parser.add_argument(
'--num_steps_per_checkpoint',
type=int,
default=2000,
help="Number of update steps until a model checkpoint is saved to disk."
)
args = parser.parse_args()
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
assert (torch.cuda.is_available())
if args.local_rank == -1:
device = torch.device("cuda")
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',
init_method='env://')
logger.info("device %s n_gpu %d distributed training %r", device, n_gpu,
bool(args.local_rank != -1))
if args.gradient_accumulation_steps < 1:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
.format(args.gradient_accumulation_steps))
if args.train_batch_size % args.gradient_accumulation_steps != 0:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, batch size {} should be divisible"
.format(args.gradient_accumulation_steps, args.train_batch_size))
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
if not args.resume_from_checkpoint and os.path.exists(
args.output_dir) and (os.listdir(args.output_dir) and os.listdir(
args.output_dir) != ['logfile.txt']):
raise ValueError(
"Output directory ({}) already exists and is not empty.".format(
args.output_dir))
if not args.resume_from_checkpoint:
os.makedirs(args.output_dir, exist_ok=True)
# Prepare model
config = BertConfig.from_json_file(args.config_file)
model = BertForPreTraining(config)
if not args.resume_from_checkpoint:
global_step = 0
else:
if args.resume_step == -1:
model_names = [
f for f in os.listdir(args.output_dir) if f.endswith(".pt")
]
args.resume_step = max([
int(x.split('.pt')[0].split('_')[1].strip())
for x in model_names
])
global_step = args.resume_step
checkpoint = torch.load(os.path.join(args.output_dir,
"ckpt_{}.pt".format(global_step)),
map_location="cpu")
model.load_state_dict(checkpoint['model'], strict=False)
print("resume step from ", args.resume_step)
model.to(device)
# 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:
optimizer = FusedAdam(
optimizer_grouped_parameters,
lr=args.learning_rate,
#warmup=args.warmup_proportion,
#t_total=args.max_steps,
bias_correction=False,
weight_decay=0.01,
max_grad_norm=1.0)
if args.loss_scale == 0:
# optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
model, optimizer = amp.initialize(model,
optimizer,
opt_level="O2",
keep_batchnorm_fp32=False,
loss_scale="dynamic")
else:
# optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale)
model, optimizer = amp.initialize(model,
optimizer,
opt_level="O2",
keep_batchnorm_fp32=False,
loss_scale=args.loss_scale)
scheduler = LinearWarmUpScheduler(optimizer,
warmup=args.warmup_proportion,
total_steps=args.max_steps)
else:
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=args.max_steps)
if args.resume_from_checkpoint:
optimizer.load_state_dict(checkpoint['optimizer']) # , strict=False)
if args.local_rank != -1:
model = DDP(model)
elif n_gpu > 1:
model = torch.nn.DataParallel(model)
files = [
os.path.join(args.input_dir, f) for f in os.listdir(args.input_dir)
if os.path.isfile(os.path.join(args.input_dir, f))
]
files.sort()
num_files = len(files)
logger.info("***** Running training *****")
# logger.info(" Num examples = %d", len(train_data))
logger.info(" Batch size = %d", args.train_batch_size)
print(" LR = ", args.learning_rate)
model.train()
print("Training. . .")
most_recent_ckpts_paths = []
print("Training. . .")
tr_loss = 0.0 # total added training loss
average_loss = 0.0 # averaged loss every args.log_freq steps
epoch = 0
training_steps = 0
while True:
if not args.resume_from_checkpoint:
random.shuffle(files)
f_start_id = 0
else:
f_start_id = checkpoint['files'][0]
files = checkpoint['files'][1:]
args.resume_from_checkpoint = False
for f_id in range(f_start_id, len(files)):
data_file = files[f_id]
logger.info("file no %s file %s" % (f_id, data_file))
train_data = pretraining_dataset(
input_file=data_file,
max_pred_length=args.max_predictions_per_seq)
if args.local_rank == -1:
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(
train_data,
sampler=train_sampler,
batch_size=args.train_batch_size * n_gpu,
num_workers=4,
pin_memory=True)
else:
train_sampler = DistributedSampler(train_data)
train_dataloader = DataLoader(train_data,
sampler=train_sampler,
batch_size=args.train_batch_size,
num_workers=4,
pin_memory=True)
for step, batch in enumerate(
tqdm(train_dataloader, desc="File Iteration")):
training_steps += 1
batch = [t.to(device) for t in batch]
input_ids, segment_ids, input_mask, masked_lm_labels, next_sentence_labels = batch #\
loss = model(
input_ids=input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask,
masked_lm_labels=masked_lm_labels,
next_sentence_label=next_sentence_labels,
checkpoint_activations=args.checkpoint_activations)
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)
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
tr_loss += loss
average_loss += loss.item()
if training_steps % args.gradient_accumulation_steps == 0:
if args.fp16:
scheduler.step()
optimizer.step()
optimizer.zero_grad()
global_step += 1
if training_steps == 1 * args.gradient_accumulation_steps:
logger.info(
"Step:{} Average Loss = {} Step Loss = {} LR {}".
format(global_step, average_loss, loss.item(),
optimizer.param_groups[0]['lr']))
if training_steps % (args.log_freq *
args.gradient_accumulation_steps) == 0:
logger.info(
"Step:{} Average Loss = {} Step Loss = {} LR {}".
format(global_step, average_loss / args.log_freq,
loss.item(), optimizer.param_groups[0]['lr']))
average_loss = 0
if global_step >= args.max_steps or training_steps % (
args.num_steps_per_checkpoint *
args.gradient_accumulation_steps) == 0:
if (not torch.distributed.is_initialized()
or (torch.distributed.is_initialized()
and torch.distributed.get_rank() == 0)):
# Save a trained model
logger.info(
"** ** * Saving fine - tuned model ** ** * ")
model_to_save = model.module if hasattr(
model,
'module') else model # Only save the model it-self
output_save_file = os.path.join(
args.output_dir, "ckpt_{}.pt".format(global_step))
torch.save(
{
'model': model_to_save.state_dict(),
'optimizer': optimizer.state_dict(),
'files': [f_id] + files
}, output_save_file)
most_recent_ckpts_paths.append(output_save_file)
if len(most_recent_ckpts_paths) > 3:
ckpt_to_be_removed = most_recent_ckpts_paths.pop(0)
os.remove(ckpt_to_be_removed)
if global_step >= args.max_steps:
tr_loss = tr_loss * args.gradient_accumulation_steps / training_steps
if (torch.distributed.is_initialized()):
tr_loss /= torch.distributed.get_world_size()
torch.distributed.all_reduce(tr_loss)
logger.info("Total Steps:{} Final Loss = {}".format(
training_steps, tr_loss.item()))
return
del train_dataloader
del train_sampler
del train_data
#for obj in gc.get_objects():
# if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)):
# del obj
torch.cuda.empty_cache()
epoch += 1
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."
)
parser.add_argument("--init_checkpoint",
default=None,
type=str,
required=True,
help="The checkpoint file from pretraining")
## 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=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",
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_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=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("--google_pretrained",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument("--max_steps",
default=-1.0,
type=float,
help="Total number of training steps 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=1,
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',
default=False,
action='store_true',
help="Mixed precision training")
parser.add_argument('--amp',
default=False,
action='store_true',
help="Mixed precision training")
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("--old",
action='store_true',
help="use old fp16 optimizer")
parser.add_argument(
'--vocab_file',
type=str,
default=None,
required=True,
help="Vocabulary mapping/file BERT was pretrainined on")
parser.add_argument("--config_file",
default=None,
type=str,
required=True,
help="The BERT model config")
args = parser.parse_args()
args.fp16 = args.fp16 or args.amp
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 = {
"cola": ColaProcessor,
"mnli": MnliProcessor,
"mrpc": MrpcProcessor,
}
num_labels_task = {
"cola": 2,
"mnli": 3,
"mrpc": 2,
}
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:
print(
"WARNING: Output directory ({}) already exists and is not empty.".
format(args.output_dir))
if not os.path.exists(args.output_dir) and is_main_process():
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]()
num_labels = num_labels_task[task_name]
label_list = processor.get_labels()
#tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
tokenizer = BertTokenizer(args.vocab_file,
do_lower_case=args.do_lower_case,
max_len=512) # for bert large
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
if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size(
)
# Prepare model
config = modeling.BertConfig.from_json_file(args.config_file)
# Padding for divisibility by 8
if config.vocab_size % 8 != 0:
config.vocab_size += 8 - (config.vocab_size % 8)
modeling.ACT2FN["bias_gelu"] = modeling.bias_gelu_training
model = modeling.BertForSequenceClassification(config,
num_labels=num_labels)
print("USING CHECKPOINT from", args.init_checkpoint)
model.load_state_dict(torch.load(args.init_checkpoint,
map_location='cpu')["model"],
strict=False)
print("USED CHECKPOINT from", args.init_checkpoint)
model.to(device)
# 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:
print("using fp16")
try:
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)
if args.loss_scale == 0:
model, optimizer = amp.initialize(model,
optimizer,
opt_level="O2",
keep_batchnorm_fp32=False,
loss_scale="dynamic")
else:
model, optimizer = amp.initialize(model,
optimizer,
opt_level="O2",
keep_batchnorm_fp32=False,
loss_scale=args.loss_scale)
scheduler = LinearWarmUpScheduler(
optimizer,
warmup=args.warmup_proportion,
total_steps=num_train_optimization_steps)
else:
print("using fp32")
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
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)
global_step = 0
nb_tr_steps = 0
tr_loss = 0
if args.do_train:
print("data prep")
cached_train_features_file = args.data_dir + '_{0}_{1}_{2}'.format(
list(filter(None, args.bert_model.split('/'))).pop(),
str(args.max_seq_length), str(args.do_lower_case))
train_features = None
try:
with open(cached_train_features_file, "rb") as reader:
train_features = pickle.load(reader)
except:
train_features = convert_examples_to_features(
train_examples, label_list, args.max_seq_length, tokenizer)
if args.local_rank == -1 or torch.distributed.get_rank() == 0:
logger.info(" Saving train features into cached file %s",
cached_train_features_file)
with open(cached_train_features_file, "wb") as writer:
pickle.dump(train_features, writer)
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)
all_input_ids = torch.tensor([f.input_ids for f in train_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in train_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in train_features],
dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in train_features],
dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
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 _ 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(train_dataloader, desc="Iteration")):
if args.max_steps > 0 and global_step > args.max_steps:
break
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
loss = model(input_ids, segment_ids, input_mask, label_ids)
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:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
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 for BERT which FusedAdam doesn't do
scheduler.step()
optimizer.step()
optimizer.zero_grad()
global_step += 1
if args.do_eval and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
eval_examples = processor.get_dev_examples(args.data_dir)
eval_features = convert_examples_to_features(eval_examples, label_list,
args.max_seq_length,
tokenizer)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
all_input_ids = torch.tensor([f.input_ids for f in eval_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features],
dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in eval_features],
dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
# 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
preds = None
out_label_ids = None
for input_ids, input_mask, segment_ids, label_ids in tqdm(
eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
tmp_eval_loss = model(input_ids, segment_ids, input_mask,
label_ids)
logits = model(input_ids, segment_ids, input_mask)
eval_loss += tmp_eval_loss.mean().item()
nb_eval_steps += 1
if preds is None:
preds = logits.detach().cpu().numpy()
out_label_ids = label_ids.detach().cpu().numpy()
else:
preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)
out_label_ids = np.append(out_label_ids,
label_ids.detach().cpu().numpy(),
axis=0)
eval_loss = eval_loss / nb_eval_steps
preds = np.argmax(preds, axis=1)
eval_loss = eval_loss / nb_eval_steps
loss = tr_loss / nb_tr_steps if args.do_train else None
results = {
'eval_loss': eval_loss,
'global_step': global_step,
'loss': loss
}
result = compute_metrics(task_name, preds, out_label_ids)
results.update(result)
print(results)
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key in sorted(results.keys()):
logger.info(" %s = %s", key, str(results[key]))
writer.write("%s = %s\n" % (key, str(results[key])))
def main(args):
args.data_dir = os.path.join(args.data_dir, args.task_name)
args.output_dir = os.path.join(args.output_dir, args.task_name)
logger.info("args = %s", args)
processors = {
"cola": ColaProcessor,
"mnli": MnliProcessor,
"mrpc": MrpcProcessor,
"sst-2": Sst2Processor,
"sts-b": StsbProcessor,
"qqp": QqpProcessor,
"qnli": QnliProcessor,
"rte": RteProcessor,
"wnli": WnliProcessor,
"emo": EmoProcessor,
}
output_modes = {
"cola": "classification",
"mnli": "classification",
"mrpc": "classification",
"sst-2": "classification",
"sts-b": "regression",
"qqp": "classification",
"qnli": "classification",
"rte": "classification",
"wnli": "classification",
"emo": "classification"
}
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')
# device = torch.device('cpu')
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:
logger.info("Output directory already exists and is not empty.")
if not os.path.exists(args.output_dir):
try:
os.makedirs(args.output_dir)
except:
pass
logger.info("catch a error")
task_name = args.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
output_mode = output_modes[task_name]
label_list = processor.get_labels()
num_labels = len(label_list)
# tokenizer = BertTokenizer.from_pretrained(args.vocab_file, do_lower_case=args.do_lower_case)
tokenizer = BertTokenizer.from_pretrained(args.bert_model)
# Prepare model
cache_dir = args.cache_dir if args.cache_dir else os.path.join(
PYTORCH_PRETRAINED_BERT_CACHE, 'distributed_{}'.format(
args.local_rank))
# use bert to aug train_examples
ori_train_examples = processor.get_train_examples(args.data_dir)
eval_examples = processor.get_dev_examples(args.data_dir)
test_examples = processor.get_test_examples(args.data_dir)
num_train_optimization_steps = int(
len(ori_train_examples) / args.train_batch_size /
args.gradient_accumulation_steps) * args.num_train_epochs
if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size(
)
if args.use_saved == 1:
bert_saved_dir = args.ckpt
model = BertForNSPAug.from_pretrained(bert_saved_dir,
cache_dir=args.ckpt_cache_dir,
num_labels=num_labels,
args=args)
else:
model = BertForNSPAug.from_pretrained(args.bert_model,
cache_dir=cache_dir,
num_labels=num_labels,
args=args)
model.cuda()
if 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
}]
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
global_step = 0
best_val_acc = 0.0
first_time = time.time()
logger.info(
"*********************************** Running training ***********************************"
)
logger.info(" Num original examples = %d", len(ori_train_examples))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps)
model.train()
aug_ratio = 0.0
# aug_ratio = 0.2
aug_seed = np.random.randint(0, 1000)
for epoch in range(int(args.num_train_epochs)):
logger.info("epoch=%d, aug_ratio = %f, aug_seed=%d", epoch,
aug_ratio, aug_seed)
train_examples = Aug_each_ckpt(ori_train_examples,
label_list,
model,
tokenizer,
args=args,
num_show=args.num_show,
output_mode=output_mode,
seed=aug_seed,
aug_ratio=aug_ratio,
use_bert=False)
if aug_ratio + args.aug_ratio_each < 1.0:
aug_ratio += args.aug_ratio_each
aug_seed += 1
train_features = convert_examples_to_features(
train_examples,
label_list,
args.max_seq_length,
tokenizer,
num_show=args.num_show,
output_mode=output_mode,
args=args)
logger.info(
"*********************************** Done convert features ***********************************"
)
all_input_ids = torch.tensor([f.input_ids for f in train_features],
dtype=torch.long)
all_input_mask = torch.tensor(
[f.input_mask for f in train_features], dtype=torch.long)
all_segment_ids = torch.tensor(
[f.segment_ids for f in train_features], dtype=torch.long)
if output_mode == "classification":
all_label_ids = torch.tensor(
[f.label_id for f in train_features], dtype=torch.long)
elif output_mode == "regression":
all_label_ids = torch.tensor(
[f.label_id for f in train_features], dtype=torch.float)
token_real_label = torch.tensor(
[f.token_real_label for f in train_features], dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids,
token_real_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)
logger.info(
"*********************************** begin training ***********************************"
)
tr_loss, tr_seq_loss, tr_aug_loss, train_seq_accuracy, train_aug_accuracy = 0, 0, 0, 0, 0
nb_tr_examples, nb_tr_steps, nb_tr_tokens = 0, 0, 0
preds = []
all_labels = []
for step, batch in enumerate(train_dataloader):
batch = tuple(t.cuda() for t in batch)
input_ids, input_mask, segment_ids, label_ids, token_real_label = batch
seq_logits, aug_logits, aug_loss = model(
input_ids,
segment_ids,
input_mask,
labels=None,
token_real_label=token_real_label)
if output_mode == "classification":
# if task_name == "emo":
# loss_fct =
# else:
loss_fct = CrossEntropyLoss()
seq_loss = loss_fct(seq_logits.view(-1, num_labels),
label_ids.view(-1))
# print("[classification]label_ids: {}, size: {}".format(label_ids.view(-1), label_ids.view(-1).size()))
# print("[classification]seq_logits size: {}".format(seq_logits.view(-1, num_labels).size()))
elif output_mode == "regression":
loss_fct = MSELoss()
seq_loss = loss_fct(seq_logits.view(-1),
label_ids.view(-1))
token_real_label = token_real_label.detach().cpu().numpy()
w = args.aug_loss_weight
loss = (1 - w) * seq_loss + w * aug_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
loss.backward()
total_norm = torch.nn.utils.clip_grad_norm_(
model.parameters(), 10000.0)
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
batch_loss = seq_loss.mean().item()
tr_seq_loss += seq_loss.mean().item()
seq_logits = seq_logits.detach().cpu().numpy()
label_ids = label_ids.detach().cpu().numpy()
if len(preds) == 0:
preds.append(seq_logits)
all_labels.append(label_ids)
else:
preds[0] = np.append(preds[0], seq_logits, axis=0)
all_labels[0] = np.append(all_labels[0], label_ids, axis=0)
aug_logits = aug_logits.detach().cpu().numpy()
tmp_train_aug_accuracy, tmp_tokens = accuracy(aug_logits,
token_real_label,
type="aug")
train_aug_accuracy += tmp_train_aug_accuracy
nb_tr_tokens += tmp_tokens
tr_aug_loss += aug_loss.mean().item()
if global_step % 20 == 0:
loss = tr_loss / nb_tr_steps
seq_loss = tr_seq_loss / nb_tr_steps
aug_loss = tr_aug_loss / nb_tr_steps
tmp_pred = preds[0]
tmp_labels = all_labels[0]
if output_mode == "classification":
tmp_pred = np.argmax(tmp_pred, axis=1)
elif output_mode == "regression":
tmp_pred = np.squeeze(tmp_pred)
res = accuracy(tmp_pred, tmp_labels, task_name=task_name)
if nb_tr_tokens != 0:
aug_avg = train_aug_accuracy / nb_tr_tokens
else:
aug_avg = 0.0
log_string = ""
log_string += "epoch={:<5d}".format(epoch)
log_string += " step={:<9d}".format(global_step)
log_string += " total_loss={:<9.7f}".format(loss)
log_string += " seq_loss={:<9.7f}".format(seq_loss)
log_string += " aug_loss={:<9.7f}".format(aug_loss)
log_string += " batch_loss={:<9.7f}".format(batch_loss)
log_string += " lr={:<9.7f}".format(optimizer.get_lr()[0])
log_string += " |g|={:<9.7f}".format(total_norm)
#log_string += " tr_seq_acc={:<9.7f}".format(seq_avg)
log_string += " tr_aug_acc={:<9.7f}".format(aug_avg)
log_string += " mins={:<9.2f}".format(
float(time.time() - first_time) / 60)
for key in sorted(res.keys()):
log_string += " " + key + "= " + str(res[key])
logger.info(log_string)
if (step + 1) % args.gradient_accumulation_steps == 0:
optimizer.step()
optimizer.zero_grad()
global_step += 1
train_loss = tr_loss / nb_tr_steps
logger.info(
"*********************************** training epoch done ***********************************"
)
if args.do_eval and (args.local_rank == -1
or torch.distributed.get_rank()
== 0) and epoch % 1 == 0:
tot_time = float(time.time() - first_time) / 60
eval_loss, eval_seq_loss, eval_aug_loss, eval_res, eval_aug_accuracy, res_parts=\
do_evaluate(args, processor, label_list, tokenizer, model, epoch, output_mode, num_labels, task_name, eval_examples, type="dev")
eval_res["tot_time"] = tot_time
if "acc" in eval_res:
tmp_acc = eval_res["acc"]
elif "mcc" in eval_res:
tmp_acc = eval_res["mcc"]
else:
tmp_acc = eval_res["corr"]
result = {
'eval_total_loss': eval_loss,
'eval_seq_loss': eval_seq_loss,
'eval_aug_loss': eval_aug_loss,
'eval_aug_accuracy': eval_aug_accuracy,
'global_step': global_step,
'train_loss': train_loss,
'train_batch_size': args.train_batch_size,
'args': args
}
if tmp_acc >= best_val_acc:
best_val_acc = tmp_acc
dev_test = "dev"
result.update({'best_epoch': epoch})
model_to_save = model.module if hasattr(
model,
'module') else model # Only save the model it-self
output_model_dir = os.path.join(args.output_dir,
"dev_" + str(tmp_acc))
if not os.path.exists(output_model_dir):
os.makedirs(output_model_dir)
output_model_file = os.path.join(output_model_dir,
WEIGHTS_NAME)
torch.save(model_to_save.state_dict(), output_model_file)
output_config_file = os.path.join(output_model_dir,
CONFIG_NAME)
with open(output_config_file, 'w') as f:
f.write(model_to_save.config.to_json_string())
result.update(eval_res)
result.update(res_parts)
# output_eval_file = os.path.join(args.output_dir,
# dev_test + "_results_" + str(tmp_acc) + ".txt")
# with open(output_eval_file, "w") 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])))
else:
result = {
'eval_total_loss': eval_loss,
'eval_seq_loss': eval_seq_loss,
'eval_aug_loss': eval_aug_loss,
'eval_aug_accuracy': eval_aug_accuracy,
'global_step': global_step,
'train_loss': train_loss,
'train_batch_size': args.train_batch_size,
'args': args
}
result.update(eval_res)
result.update(res_parts)
logger.info(
"****************************** eval results ***********************************"
)
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
# write test results
if args.do_test:
# res_file = os.path.join(args.output_dir,
# "test_" + str(tmp_acc)+".tsv")
# idx, preds = do_test(args, label_list, task_name, processor, tokenizer, output_mode, model)
# dataframe = pd.DataFrame({'index': range(idx), 'prediction': preds})
# dataframe.to_csv(res_file, index=False, sep='\t')
# logger.info(" Num test length = %d", idx)
logger.info(
"*********************************** Running test ***********************************"
)
logger.info(" Num examples = %d", len(test_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
test_loss, test_seq_loss, test_aug_loss, test_res, test_aug_accuracy, res_parts=\
do_evaluate(args, processor, label_list, tokenizer, model, epoch, output_mode, num_labels, task_name, test_examples, type="test")
result = {
'test_total_loss': test_loss,
'test_seq_loss': test_seq_loss,
'test_aug_loss': test_aug_loss,
'test_aug_accuracy': test_aug_accuracy,
'global_step': global_step,
'args': args
}
result.update(test_res)
result.update(res_parts)
logger.info(
"****************************** test results ***********************************"
)
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
logger.info(
"*********************************** test done ***********************************"
)
def main():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--train_file",
default=None,
type=str,
required=True,
help="The train file path")
parser.add_argument("--eval_file",
default=None,
type=str,
required=True,
help="The dev file path")
parser.add_argument("--eval_train_file",
default=None,
type=str,
required=True,
help="The train eval file path")
parser.add_argument("--predict_file",
default=None,
type=str,
required=False,
help="The predict file path")
parser.add_argument("--top_n",
default=5,
type=float,
required=True,
help="higher than threshold is classify 1,")
parser.add_argument("--bert_config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the pre-trained BERT model. \n"
"This specifies the model architecture.")
parser.add_argument("--bert_model",
default=None,
type=str,
required=True,
help="The config json file corresponding to the pre-trained BERT model. \n"
"This specifies the model architecture.")
parser.add_argument("--result_file",
default=None,
type=str,
required=False,
help="The result file that the BERT model was trained on.")
parser.add_argument("--vocab_file",
default=None,
type=str,
required=True,
help="The vocabulary file that the BERT model was trained on.")
parser.add_argument("--output_dir",
default=None,
type=str,
required=True,
help="The output directory where the model checkpoints will be written.")
# Other parameters
parser.add_argument("--init_checkpoint",
default=None,
type=str,
help="Initial checkpoint (usually from a pre-trained BERT model).")
parser.add_argument("--do_lower_case",
default=False,
action='store_true',
help="Whether to lower case the input text.")
parser.add_argument("--max_seq_length",
default=180,
type=int,
help="maximum total input sequence length after WordPiece tokenization.")
parser.add_argument("--do_train",
default=False,
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_predict",
default=False,
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument("--do_eval",
default=False,
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument("--num_labels", default=1, type=int, help="mapping classify nums")
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=6.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument("--reduce_dim",
default=64,
type=int,
required=False,
help="from hidden size to this dimensions, reduce dim")
parser.add_argument("--gpu0_size",
default=1,
type=int,
help="maximum total input sequence length after WordPiece tokenization.")
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("--save_checkpoints_steps",
default=1000,
type=int,
help="How often to save the model checkpoint.")
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 accumualte before")
parser.add_argument('--optimize_on_cpu',
default=False,
action='store_true',
help="Whether to perform optimization and averages on CPU")
parser.add_argument('--fp16',
default=False,
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument('--loss_scale',
type=float,
default=128,
help='Loss scale, positive power of 2 can improve fp16 convergence.')
args = parser.parse_args()
data_processor = DataProcessor(args.num_labels)
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:
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')
if args.fp16:
logger.info("16-bits training currently not supported in distributed training")
args.fp16 = False # (see https://github.com/pytorch/pytorch/pull/13496)
logger.info("device %s n_gpu %d distributed training %r", device, n_gpu,
bool(args.local_rank != -1))
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 = int(args.train_batch_size / args.gradient_accumulation_steps)
print(f'args.train_batch_size = {args.train_batch_size}')
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 any([args.do_train, args.do_predict, args.do_eval]):
raise ValueError("At least one of `do_train` or `do_eval` or `do_predict` must be True.")
bert_config = BertConfig.from_json_file(args.bert_config_file)
bert_config.reduce_dim = args.reduce_dim
if args.max_seq_length > bert_config.max_position_embeddings:
raise ValueError(
"Cannot use sequence length {} because the BERT model was only trained up to sequence length {}"
.format(args.max_seq_length, bert_config.max_position_embeddings))
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 args.do_train:
os.makedirs(args.output_dir, exist_ok=True)
tokenizer = tokenization.FullTokenizer(vocab_file=args.vocab_file,
do_lower_case=args.do_lower_case)
def prepare_data(args, task_name='train'):
if task_name == 'train':
file_path = args.train_file
elif task_name == 'eval':
file_path = args.eval_file
elif task_name == 'train_eval':
file_path = args.eval_train_file
if os.path.isdir(file_path):
examples = data_processor.read_file_dir(file_path, top_n=args.top_n)
else:
examples, example_map_ids = data_processor.read_novel_examples(file_path,
top_n=args.top_n,
task_name=task_name)
features = convert_examples_to_features(examples, args.max_seq_length, tokenizer)
all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)
all_example_ids = torch.tensor([f.example_id for f in features], dtype=torch.long)
if task_name in ['train', 'eval', 'train_eval']:
all_label_ids = torch.tensor([f.label_id for f in features], dtype=torch.long)
datas = TensorDataset(all_example_ids, all_input_ids, all_input_mask, all_segment_ids,
all_label_ids)
else:
datas = TensorDataset(all_example_ids, all_input_ids, all_input_mask, all_segment_ids)
if task_name == 'train':
if args.local_rank == -1:
data_sampler = RandomSampler(datas)
else:
data_sampler = DistributedSampler(datas)
dataloader = DataLoader(datas,
sampler=data_sampler,
batch_size=args.train_batch_size,
drop_last=True)
else:
dataloader = DataLoader(datas, batch_size=args.eval_batch_size, drop_last=True)
return (dataloader, example_map_ids) if task_name != 'train' else dataloader
def accuracy(example_ids, logits, labels, probs=None, positive=False):
if positive:
# print(f'example_ids = {example_ids.shape}')
# print(f'logits = {logits.shape}')
# print(f'labels = {labels.shape}')
# print(f'probs = {probs.shape}')
logits = logits[labels > 0]
example_ids = example_ids[labels > 0]
probs = probs[labels > 0]
labels = labels[labels > 0]
if isinstance(logits, torch.Tensor):
logits = logits.tolist()
if isinstance(example_ids, torch.Tensor):
example_ids = example_ids.tolist()
if isinstance(labels, torch.Tensor):
labels = labels.tolist()
assert len(logits) == len(example_ids) == len(labels)
classify_name = ['part_same', 'full_same'] if positive else ['dif', 'same']
text_a, text_b, novel_names, persons = [], [], [], []
for i in example_ids:
example = example_map_ids[i]
# labels.append(example.label)
text_a.append("||".join(example.text_a))
text_b.append("||".join(example.text_b))
novel_names.append(example.name)
persons.append(example.person)
write_data = pd.DataFrame({
"text_a": text_a,
"text_b": text_b,
"labels": labels,
"logits": logits,
"novel_names": novel_names,
"persons": persons
})
write_data['yes_or_no'] = write_data['labels'] == write_data['logits']
if probs is not None:
if isinstance(probs, torch.Tensor):
probs = probs.tolist()
write_data['logits'] = probs
# write_data.to_csv(os.path.join(args.output_dir, f'{positive}.csv'), index=False)
assert len(labels) == len(logits)
try:
result = classification_report(labels, logits, target_names=classify_name)
except Exception:
result = 'label is not equal to 3'
print(f'\n{result}')
return result
def eval_model(model, eval_dataloader, device):
model.eval()
eval_loss = 0
all_first_logits, all_second_logits = [], []
all_example_ids = []
all_labels = []
all_first_probs, all_sencond_probs = [], []
for step, batch in enumerate(tqdm(eval_dataloader, desc="evaluating")):
example_ids, input_ids, input_mask, segment_ids, label_ids = batch
if not args.do_train and not args.do_eval:
label_ids = None
with torch.no_grad():
tmp_eval_loss, logits = model(input_ids, segment_ids, input_mask, labels=label_ids)
first_logits, second_logits = logits
first_prob, first_logits = torch.max(logits[0], dim=1)
second_prob, second_logits = torch.max(logits[1], dim=1)
all_labels.append(label_ids)
all_first_probs.append(first_prob)
all_sencond_probs.append(second_prob)
all_first_logits.append(first_logits)
all_second_logits.append(second_logits)
all_example_ids.append(example_ids)
eval_loss += tmp_eval_loss.mean().item()
all_first_logits = torch.cat(all_first_logits, dim=0)
all_second_logits = torch.cat(all_second_logits, dim=0)
all_first_probs = torch.cat(all_first_probs, dim=0)
all_sencond_probs = torch.cat(all_sencond_probs, dim=0)
all_labels = torch.cat(all_labels, dim=0)
all_first_labels, all_second_labels = [
label.view(-1) for label in torch.chunk(all_labels, dim=1, chunks=2)
]
all_example_ids = torch.cat(all_example_ids, dim=0)
accuracy(all_example_ids,
all_first_logits,
labels=all_first_labels,
probs=all_first_probs,
positive=False)
accuracy(all_second_logits,
all_second_logits,
labels=all_second_labels,
probs=all_sencond_probs,
positive=True)
eval_loss /= (step + 1)
return eval_loss
train_dataloader = None
num_train_steps = None
if args.do_train:
train_dataloader = prepare_data(args, task_name='train')
num_train_steps = int(
len(train_dataloader) / args.gradient_accumulation_steps * args.num_train_epochs)
model = ThreeCategoriesClassifier2(bert_config, num_labels=data_processor.num_labels)
new_state_dict = model.state_dict()
init_state_dict = torch.load(os.path.join(args.bert_model, 'pytorch_model.bin'))
for k, v in init_state_dict.items():
if k in new_state_dict:
print(f'k in = {k} v in shape = {v.shape}')
new_state_dict[k] = v
model.load_state_dict(new_state_dict)
if args.fp16:
model.half()
if args.do_predict or args.do_eval:
model_path = os.path.join(args.output_dir, WEIGHTS_NAME)
new_state_dict = torch.load(model_path)
new_state_dict = dict([
(k[7:], v) if k.startswith('module') else (k, v) for k, v in new_state_dict.items()
])
model.load_state_dict(new_state_dict)
model.to(device)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(model,
device_ids=[args.local_rank],
output_device=args.local_rank)
elif n_gpu > 1:
if args.gpu0_size > 0:
model = BalancedDataParallel(args.gpu0_size, model, dim=0).to(device)
else:
model = torch.nn.DataParallel(model)
if args.fp16:
param_optimizer = [(n, param.clone().detach().to('cpu').float().requires_grad_())
for n, param in model.named_parameters()]
elif args.optimize_on_cpu:
param_optimizer = [(n, param.clone().detach().to('cpu').requires_grad_())
for n, param in model.named_parameters()]
else:
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'gamma', 'beta']
optimizer_grouped_parameters = [{
'params': [p for n, p in param_optimizer if n not in no_decay],
'weight_decay': 0.01
}, {
'params': [p for n, p in param_optimizer if n in no_decay],
'weight_decay': 0.0
}]
eval_dataloader, example_map_ids = prepare_data(args, task_name='eval')
if args.do_train:
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_steps)
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
# eval_loss = eval_model(model, eval_dataloader, device)
# logger.info(f'初始开发集loss: {eval_loss}')
for epoch in trange(int(args.num_train_epochs), desc="Epoch"):
model.train()
torch.cuda.empty_cache()
model_save_path = os.path.join(args.output_dir, f"{WEIGHTS_NAME}.{epoch}")
tr_loss = 0
train_batch_count = 0
for step, batch in enumerate(tqdm(train_dataloader, desc="training")):
_, input_ids, input_mask, segment_ids, label_ids = batch
loss, _ = model(input_ids, segment_ids, input_mask, labels=label_ids)
if n_gpu > 1:
loss = loss.mean()
if args.fp16 and args.loss_scale != 1.0:
loss = loss * args.loss_scale
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
loss.backward()
tr_loss += loss.item()
if (step + 1) % args.gradient_accumulation_steps == 0:
optimizer.step()
model.zero_grad()
train_batch_count += 1
tr_loss /= train_batch_count
eval_loss = eval_model(model, eval_dataloader, device)
logger.info(
f'训练loss: {tr_loss}, 开发集loss：{eval_loss} 训练轮数：{epoch + 1}/{int(args.num_train_epochs)}'
)
model_to_save = model.module if hasattr(model, 'module') else model
torch.save(model.state_dict(), model_save_path)
if epoch == 0:
model_to_save.config.to_json_file(output_config_file)
tokenizer.save_vocabulary(args.output_dir)
elif args.do_eval:
eval_model(model, eval_dataloader, device)
if args.do_predict:
eval_model(model, eval_dataloader, device)
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("--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",
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_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=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",
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")
args = parser.parse_args()
processors = {
"cola": ColaProcessor,
"mnli": MnliProcessor,
"mrpc": MrpcProcessor,
"commonsenseqa": CommonsenseQaProcessor,
}
num_labels_task = {
"cola": 2,
"mnli": 3,
"mrpc": 2,
"commonsenseqa":4,
}
# 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')
device = "cuda:2"
n_gpu = 1
logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
device, n_gpu, bool(args.local_rank != -1), args.fp16))
print("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 = 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) and args.do_train:
raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
os.makedirs(args.output_dir, exist_ok=True)
task_name = args.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
print("current task is " + str(task_name))
processor = processors[task_name]()
num_labels = num_labels_task[task_name]
label_list = processor.get_labels()
tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
train_examples = None
num_train_steps = None
if args.do_train:
train_examples = processor.get_train_examples(args.data_dir)
num_train_steps = int(
len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs)
# Prepare model
# model = BertForSequenceClassification.from_pretrained(args.bert_model,
# cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(
# args.local_rank),
# num_labels=num_labels)
model = BertForMultipleChoice.from_pretrained(args.bert_model,
cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.
format(args.local_rank),
num_choices=num_labels)
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)
# 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}
]
t_total = num_train_steps
if args.local_rank != -1:
t_total = t_total // torch.distributed.get_world_size()
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=t_total)
global_step = 0
nb_tr_steps = 0
tr_loss = 0
best_eval_accuracy = 0.0
if args.do_train:
train_features = convert_examples_to_features_mc(
train_examples, label_list, args.max_seq_length, tokenizer)
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([f.input_ids for f in train_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
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()
# 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")
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(train_dataloader, desc="Iteration")):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
loss, logits = model(input_ids, segment_ids, input_mask, label_ids)
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:
# 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 args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
eval_examples = processor.get_dev_examples(args.data_dir)
eval_features = convert_examples_to_features_mc(
eval_examples, label_list, args.max_seq_length, tokenizer)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
# 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 input_ids, input_mask, segment_ids, label_ids in tqdm(eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
tmp_eval_loss, logits = model(input_ids, segment_ids, input_mask, label_ids)
# logits = model(input_ids, segment_ids, input_mask)
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_accuracy = eval_accuracy / nb_eval_examples
print("the current eval accuracy is: " + str(eval_accuracy))
if eval_accuracy > best_eval_accuracy:
best_eval_accuracy = eval_accuracy
if args.do_train:
torch.save(model_to_save.state_dict(), output_model_file)
model.train()
# # 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")
# if args.do_train:
# torch.save(model_to_save.state_dict(), output_model_file)
# Load a trained model that you have fine-tuned
model_state_dict = torch.load(output_model_file)
model = BertForMultipleChoice.from_pretrained(args.bert_model,
state_dict=model_state_dict,
num_choices=num_labels)
model.to(device)
if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
eval_examples = processor.get_dev_examples(args.data_dir)
eval_features = convert_examples_to_features_mc(
eval_examples, label_list, args.max_seq_length, tokenizer)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
# 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
all_pred_labels = []
all_anno_labels = []
all_logits = []
for input_ids, input_mask, segment_ids, label_ids in tqdm(eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
tmp_eval_loss, logits = model(input_ids, segment_ids, input_mask, label_ids)
# logits = model(input_ids, segment_ids, input_mask)
logits = logits.detach().cpu().numpy()
label_ids = label_ids.to('cpu').numpy()
output_labels = np.argmax(logits, axis=1)
all_pred_labels.extend(output_labels.tolist())
all_logits.extend(list(logits))
all_anno_labels.extend(list(label_ids))
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
loss = tr_loss / nb_tr_steps if args.do_train else None
result = {'eval_loss': eval_loss,
'eval_accuracy': eval_accuracy,
'best_eval_accuracy': best_eval_accuracy,
'global_step': global_step,
'loss': loss}
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "w") 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])))
for i in range(len(all_pred_labels)):
writer.write(str(i) + "\t" + str(all_anno_labels[i]) + "\t" +
str(all_pred_labels[i]) + "\t" + str(all_logits[i]) + "\n")
def main():
# args = parse_arguments()
# del args.local_rank
# print(args)
# args_to_yaml(args, 'config_finetune_train_glue_mrpc.yaml')
# exit(0)
config_yaml, local_rank = parse_my_arguments()
args = args_from_yaml(config_yaml)
args.local_rank = local_rank
""" Experiment Setup """
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()
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:
print(
"WARNING: 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)
processors = {
"cola": ColaProcessor,
"mnli": MnliProcessor,
"mrpc": MrpcProcessor,
}
num_labels_task = {
"cola": 2,
"mnli": 3,
"mrpc": 2,
}
task_name = args.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % task_name)
processor = processors[task_name]()
num_labels = num_labels_task[task_name]
label_list = processor.get_labels()
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
if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size(
)
""" Prepare Model """
# Prepare model
cache_dir = args.cache_dir if args.cache_dir else os.path.join(
PYTORCH_PRETRAINED_BERT_CACHE, 'distributed_{}'.format(
args.local_rank))
model = BertForSequenceClassification.from_pretrained(
args.bert_model, cache_dir=cache_dir, num_labels=num_labels)
state_dict = torch.load(args.init_checkpoint, map_location='cpu')
state_dict = state_dict.get(
'model', state_dict
) # in a full checkpoint weights are saved in state_dict['model']
model.load_state_dict(state_dict, strict=False)
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)
plain_model = getattr(model, 'module', model)
with open(args.sparsity_config, 'r') as f:
raw_dict = yaml.load(f, Loader=yaml.SafeLoader)
masks = dict.fromkeys(raw_dict['prune_ratios'].keys())
for param_name in list(masks.keys()):
if get_parameter_by_name(plain_model, param_name) is None:
print(f'[WARNING] Cannot find {param_name}')
del masks[param_name]
for param_name in masks:
param = get_parameter_by_name(plain_model, param_name)
non_zero_mask = torch.ne(param, 0).to(param.dtype)
masks[param_name] = non_zero_mask
""" Prepare Optimizer"""
# 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.fp16_utils.fp16_optimizer 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:
""" Prepare Dataset """
train_features = convert_examples_to_features(train_examples,
label_list,
args.max_seq_length,
tokenizer)
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)
all_input_ids = torch.tensor([f.input_ids for f in train_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in train_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in train_features],
dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in train_features],
dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
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)
""" Training Loop """
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(train_dataloader, desc="Iteration")):
if args.max_steps > 0 and global_step > args.max_steps:
break
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
loss = model(input_ids, segment_ids, input_mask, label_ids)
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
plain_model = getattr(model, 'module', model)
for param_name, mask in masks.items():
get_parameter_by_name(plain_model,
param_name).data *= mask
""" Load Model for Evaluation """
if args.do_train:
# Save a trained model and the associated configuration
output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
if is_main_process(
): # only the main process should save the trained model
model_to_save = model.module if hasattr(
model, 'module') else model # Only save the model it-self
torch.save(model_to_save.state_dict(), output_model_file)
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 = BertForSequenceClassification(config, num_labels=num_labels)
model.load_state_dict(torch.load(output_model_file))
else:
model = BertForSequenceClassification.from_pretrained(
args.bert_model, num_labels=num_labels)
state_dict = torch.load(args.init_checkpoint, map_location='cpu')
state_dict = state_dict.get('model', state_dict)
model.load_state_dict(state_dict, strict=False)
model.to(device)
""" Run Evaluation """
if args.do_eval and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
eval_examples = processor.get_dev_examples(args.data_dir)
eval_features = convert_examples_to_features(eval_examples, label_list,
args.max_seq_length,
tokenizer)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
all_input_ids = torch.tensor([f.input_ids for f in eval_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features],
dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in eval_features],
dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
# 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 input_ids, input_mask, segment_ids, label_ids in tqdm(
eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
tmp_eval_loss = model(input_ids, segment_ids, input_mask,
label_ids)
logits = model(input_ids, segment_ids, input_mask)
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
loss = tr_loss / nb_tr_steps if args.do_train else None
result = {
'eval_loss': eval_loss,
'eval_accuracy': eval_accuracy,
'global_step': global_step,
'loss': loss
}
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "w") 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])))
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--train_file",
default=None,
type=str,
required=True,
help="The train file path")
parser.add_argument("--eval_file",
default=None,
type=str,
required=True,
help="The dev file path")
parser.add_argument("--predict_file",
default=None,
type=str,
required=False,
help="The predict file path")
parser.add_argument("--predict_result_file",
default='datas/result.csv',
type=str,
required=False,
help="The predict result file path")
parser.add_argument(
"--bert_model",
default=None,
type=str,
required=True,
help=
"The config json file corresponding to the pre-trained BERT model. \n"
"This specifies the model architecture.")
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(
"--init_checkpoint",
default=None,
type=str,
help="Initial checkpoint (usually from a pre-trained BERT model).")
parser.add_argument(
"--do_lower_case",
default=False,
action='store_true',
help=
"Whether to lower case the input text. True for uncased models, False for cased models."
)
parser.add_argument(
"--max_seq_length",
default=250,
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_predict",
default=False,
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument("--do_eval",
default=False,
action='store_true',
help="Whether to run training.")
parser.add_argument("--load_checkpoint",
default=False,
action='store_true',
help="Whether to run load checkpoint.")
parser.add_argument("--num_labels",
default=1,
type=int,
help="mapping classify nums")
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--epoches",
default=6,
type=int,
help="Total epoch numbers 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=6.0,
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("--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 accumualte before performing a backward/update pass."
)
args = parser.parse_args()
vocab_path = os.path.join(args.bert_model, VOCAB_NAME)
# bert_config = BertConfig.from_json_file(vocab_path)
data_processor = DataProcessor()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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 args.do_train:
# 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))
# else:
# os.makedirs(args.output_dir, exist_ok=True)
tokenizer = tokenization.FullTokenizer(vocab_file=vocab_path,
do_lower_case=args.do_lower_case)
model = BertForSequenceClassification.from_pretrained(args.bert_model,
num_labels=3)
for k, v in model.state_dict().items():
print(f'k = {k}, v.grad = {v.grad}')
model.to(device)
# model = torch.nn.DataParallel(model)
def evaluating(model, eval_dataloader):
model.eval()
eval_loss = 0
logits, labels = [], []
for step, batch in enumerate(eval_dataloader):
input_ids, input_mask, segment_ids, label_ids = [
b.to(device) for b in batch
]
with torch.no_grad():
loss, logit = model(input_ids, segment_ids, input_mask,
label_ids)
loss = loss.mean()
eval_loss = loss * args.gradient_accumulation_steps if step == 0 else eval_loss + loss * args.gradient_accumulation_steps
logit = torch.argmax(logit, dim=-1)
logits.extend(logit.tolist())
labels.extend(label_ids.tolist())
return (eval_loss.item() / step, logits, labels)
def predicting(model, dataloader):
model.eval()
logits, example_ids = [], []
for step, batch in enumerate(dataloader):
if step % 100 == 0:
print(f'当前预测进度： {step}/{len(dataloader)}')
input_ids, input_mask, segment_ids, label_ids = [
b.to(device) for b in batch
]
with torch.no_grad():
logit = model(input_ids, segment_ids, input_mask)
logit = torch.argmax(logit, dim=-1)
logits.extend(logit.tolist())
example_ids.extend(label_ids.tolist())
return logits, example_ids
def eval_meric(model, data_loader):
eval_loss, all_logits, all_labels = evaluating(model, data_loader)
accuracy(all_labels, all_logits)
logger.info(f'Average eval loss = {eval_loss}')
return eval_loss
def write_predict_file(model, data_loader, file_path):
"""
写入预测文件： 格式：'五彩滨云-final.csv'
"""
logits, ids = predicting(model, data_loader)
assert len(ids) == len(logits)
logger.info(
f'zero nums {logits.count(0)}, one nums {logits.count(1)}, two nums {logits.count(2)}'
)
labels = [
data_processor.eval_dict[id][1] for id, logit in zip(ids, logits)
]
# if not args.do_eval:
# logits = [i - 1 for i in logits]
# data_df = pd.DataFrame({'id': ids, 'y': logits})
# else:
assert len(labels) == len(logits)
# accuracy(labels, logits)
passages = [
data_processor.eval_dict[id][0] for id, logit in zip(ids, logits)
]
autors = [
data_processor.eval_dict[id][2] for id, logit in zip(ids, logits)
]
like_counts = [
data_processor.eval_dict[id][3] for id, logit in zip(ids, logits)
]
times = [
data_processor.eval_dict[id][4] for id, logit in zip(ids, logits)
]
assert len(labels) == len(passages)
match_array = np.array((logits)) == np.array(labels)
match_list = match_array.tolist()
data_df = pd.DataFrame({
'id': ids,
'pred': logits,
'time': times,
'match': '',
'autors': autors,
'like_counts': like_counts,
'passage': passages
})
data_df.to_csv(file_path, index=None)
eval_examples = data_processor.get_examples(args.eval_file,
data_type='eval')
eval_features = convert_examples_to_features(args, eval_examples,
args.max_seq_length,
tokenizer)
eval_loader = ParaDataloader(eval_features)
eval_loader = DataLoader(eval_loader,
shuffle=False,
batch_size=args.eval_batch_size)
if 0:
# 数据读取
train_examples = data_processor.get_examples(args.train_file,
data_type='train')
# 特征转换
train_features = convert_examples_to_features(args, train_examples,
args.max_seq_length,
tokenizer)
num_train_steps = int(
len(train_features) // args.train_batch_size //
args.gradient_accumulation_steps * args.num_train_epochs)
# 数据loader
train_loader = ParaDataloader(train_features)
# 数据并行loader输入格式
train_loader = DataLoader(train_loader,
shuffle=True,
batch_size=args.train_batch_size)
model.zero_grad()
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
param_optimizer = list(model.named_parameters())
optimizer_grouped_parameters = [{
'params': [p for n, p in param_optimizer if n not in no_decay],
'weight_decay_rate':
0.01
}, {
'params': [p for n, p in param_optimizer if n in no_decay],
'weight_decay_rate':
0.0
}]
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_steps)
tr_loss = None
for epoch in range(args.epoches):
model.train()
min_eval_loss = 10000
for step, batch in enumerate(train_loader):
input_ids, input_mask, segment_ids, label_ids = [
b.to(device) for b in batch
]
loss, _ = model(input_ids, segment_ids, input_mask, label_ids)
loss = loss.mean()
print(f'loss = {loss}')
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
loss.backward()
tr_loss = loss * args.gradient_accumulation_steps if step == 0 else tr_loss + loss * args.gradient_accumulation_steps
optimizer.step()
optimizer.zero_grad()
if step % 1000 == 1:
eval_loss = eval_meric(model, eval_loader)
if eval_loss < min_eval_loss:
save_checkpoint(model, epoch, args.output_dir)
if args.do_predict:
if args.load_checkpoint:
state_dict = torch.load('output/pytorch_model-0004.bin')
model.load_state_dict(state_dict)
logger.info(f'Start to predict......')
if args.do_eval:
predict_examples = data_processor.get_eval_examples(args.eval_file)
else:
predict_examples = data_processor.get_predict_examples(
args.predict_file)
predict_features = convert_examples_to_features(
args, predict_examples, args.max_seq_length, tokenizer)
predict_loader = ParaDataloader(predict_features)
predict_loader = DataLoader(predict_loader,
shuffle=False,
batch_size=args.eval_batch_size)
write_predict_file(model, predict_loader, args.predict_result_file)
