train_df = pd.read_csv(os.path.join(config.processed_train,
file_name + '.csv'),
index_col=0)
step_grad = config.gradient_accumulation_step_dict[file_name]
epoch_total += (len(train_df) // batch_size + 1) // step_grad + 1
print('epoch_total', epoch_total)
optimizer = BertAdam(optimizer_grouped_parameters,
lr=config.learning_rate,
warmup=config.warmup_proportion,
t_total=epoch_total * config.epochs)
# 下面这行根据实际需求是否注释
model, optimizer = train_valid(model, criterion, optimizer)
loss_weights = EpochLossWeight()
loss_weights_dict = loss_weights.run()
# 进行最后一次模型数据增强与结果测试
# if os.path.exists(config.checkpoint_file):
# print('正在加载最后一个模型', config.checkpoint_file)
# checkpoint = torch.load(config.checkpoint_file)
# model.load_state_dict(checkpoint['model_state'])
# optimizer.load_state_dict(checkpoint['optimizer_state'])
train_enhance(model, criterion, optimizer, loss_weights_dict)
test_model(model, 'last_predict', 'last_predict_submit.zip')
# 进行最佳模型的的数据增强与结果测试
if os.path.exists(config.best_checkpoint_file):
print('正在加载最佳模型', config.best_checkpoint_file)
checkpoint = torch.load(config.best_checkpoint_file)
model.load_state_dict(checkpoint['model_state'])
optimizer.load_state_dict(checkpoint['optimizer_state'])
train_enhance(model, criterion, optimizer, loss_weights_dict)
test_model(model, 'best_predict', 'best_predict_submit.zip')
param_optimizer = list(model.named_parameters())
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}
]
optimizer = BertAdam(optimizer_grouped_parameters, lr=lr)
if os.path.isfile(model_path):
print('=' * 80)
print('load training param, ', model_path)
print('=' * 80)
state = torch.load(model_path)
model.load_state_dict(state['best_model_state'])
optimizer.load_state_dict(state['best_opt_state'])
epoch_list = range(state['best_epoch'] + 1, state['best_epoch'] + 1 + EPOCH)
global_step = state['best_step']
state = {} ## FIXME: 临时的解决方案
else:
state = None
epoch_list = range(EPOCH)
global_step = 0
grade = 0
if on_windows:
print_every = 50
val_every = [50, 70, 50, 35]
else:
class QAModel(object):
"""
High level model that handles intializing the underlying network
architecture, saving, updating examples, and predicting examples.
"""
def __init__(self, opt, embedding=None, state_dict=None):
# Book-keeping.
self.opt = opt
self.updates = state_dict['updates'] if state_dict else 0
self.eval_embed_transfer = True
self.train_loss = AverageMeter()
# Building network.
self.network = FlowQA(opt, embedding)
if state_dict:
new_state = set(self.network.state_dict().keys())
for k in list(state_dict['network'].keys()):
if k not in new_state:
del state_dict['network'][k]
self.network.load_state_dict(state_dict['network'])
parameters = [p for p in self.network.parameters() if p.requires_grad]
self.total_param = sum([p.nelement() for p in parameters])
# Building optimizer.
if opt['finetune_bert'] != 0:
bert_params = [
p for p in self.network.bert.parameters() if p.requires_grad
]
self.bertadam = BertAdam(bert_params,
lr=opt['bert_lr'],
warmup=opt['bert_warmup'],
t_total=opt['bert_t_total'])
non_bert_params = []
for p in parameters:
for bp in bert_params:
if p is bp:
break
else:
non_bert_params.append(p)
parameters = non_bert_params
if opt['optimizer'] == 'sgd':
self.optimizer = optim.SGD(parameters,
opt['learning_rate'],
momentum=opt['momentum'],
weight_decay=opt['weight_decay'])
elif opt['optimizer'] == 'adamax':
self.optimizer = optim.Adamax(parameters,
weight_decay=opt['weight_decay'])
elif opt['optimizer'] == 'adadelta':
self.optimizer = optim.Adadelta(parameters,
rho=0.95,
weight_decay=opt['weight_decay'])
else:
raise RuntimeError('Unsupported optimizer: %s' % opt['optimizer'])
if state_dict:
self.optimizer.load_state_dict(state_dict['optimizer'])
if opt['cuda']:
for state in self.optimizer.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.cuda()
if opt['finetune_bert'] != 0 and 'bertadam' in state_dict:
self.bertadam.load_state_dict(state_dict['bertadam'])
if opt['cuda']:
for state in self.bertadam.state.values():
for k, v in state.items():
if isinstance(v, torch.Tensor):
state[k] = v.cuda()
if opt['fix_embeddings']:
wvec_size = 0
else:
wvec_size = (opt['vocab_size'] -
opt['tune_partial']) * opt['embedding_dim']
def update(self, batch):
# Train mode
self.network.train()
torch.set_grad_enabled(True)
if self.opt['use_bert']:
context_bertidx = batch[19]
context_bert_spans = batch[20]
question_bertidx = batch[21]
question_bert_spans = batch[22]
# Transfer to GPU
if self.opt['cuda']:
inputs = [e.cuda(non_blocking=True) for e in batch[:9]]
overall_mask = batch[9].cuda(non_blocking=True)
answer_s = batch[10].cuda(non_blocking=True)
answer_e = batch[11].cuda(non_blocking=True)
answer_c = batch[12].cuda(non_blocking=True)
rationale_s = batch[13].cuda(non_blocking=True)
rationale_e = batch[14].cuda(non_blocking=True)
if self.opt['use_bert']:
context_bertidx = [
x.cuda(non_blocking=True) for x in context_bertidx
]
else:
inputs = [e for e in batch[:9]]
overall_mask = batch[9]
answer_s = batch[10]
answer_e = batch[11]
answer_c = batch[12]
rationale_s = batch[13]
rationale_e = batch[14]
# Run forward
# output: [batch_size, question_num, context_len], [batch_size, question_num]
if self.opt['use_bert']:
score_s, score_e, score_c = self.network(*inputs, context_bertidx,
context_bert_spans,
question_bertidx,
question_bert_spans)
else:
score_s, score_e, score_c = self.network(*inputs)
# Compute loss and accuracies
if self.opt['use_elmo']:
loss = self.opt['elmo_lambda'] * (
self.network.elmo.scalar_mix_0.scalar_parameters[0]**2 +
self.network.elmo.scalar_mix_0.scalar_parameters[1]**2 +
self.network.elmo.scalar_mix_0.scalar_parameters[2]**2
) # ELMo L2 regularization
else:
loss = 0
all_no_span = (answer_c != 3)
answer_s.masked_fill_(all_no_span,
-100) # ignore_index is -100 in F.cross_entropy
answer_e.masked_fill_(all_no_span, -100)
rationale_s.masked_fill_(
all_no_span, -100) # ignore_index is -100 in F.cross_entropy
rationale_e.masked_fill_(all_no_span, -100)
for i in range(overall_mask.size(0)):
q_num = sum(overall_mask[i]
) # the true question number for this sampled context
target_s = answer_s[i, :q_num] # Size: q_num
target_e = answer_e[i, :q_num]
target_c = answer_c[i, :q_num]
target_s_r = rationale_s[i, :q_num]
target_e_r = rationale_e[i, :q_num]
target_no_span = all_no_span[i, :q_num]
# single_loss is averaged across q_num
single_loss = (F.cross_entropy(score_c[i, :q_num], target_c) *
q_num.item() / 15.0 +
F.cross_entropy(score_s[i, :q_num], target_s) *
(q_num - sum(target_no_span)).item() / 12.0 +
F.cross_entropy(score_e[i, :q_num], target_e) *
(q_num - sum(target_no_span)).item() / 12.0)
#+ self.opt['rationale_lambda'] * F.cross_entropy(score_s_r[i, :q_num], target_s_r) * (q_num - sum(target_no_span)).item() / 12.0
#+ self.opt['rationale_lambda'] * F.cross_entropy(score_e_r[i, :q_num], target_e_r) * (q_num - sum(target_no_span)).item() / 12.0)
loss = loss + (single_loss / overall_mask.size(0))
self.train_loss.update(loss.item(), overall_mask.size(0))
'''
# Clear gradients and run backward
self.optimizer.zero_grad()
loss.backward()
'''
loss = loss / self.opt['aggregate_grad_steps']
loss.backward()
'''
# Clip gradients
torch.nn.utils.clip_grad_norm_(self.network.parameters(),
self.opt['grad_clipping'])
# Update parameters
self.optimizer.step()
self.updates += 1
'''
return loss
def take_step(self):
# Clip Gradients
torch.nn.utils.clip_grad_norm_(self.network.parameters(),
self.opt['grad_clipping'])
# Update parameters
self.optimizer.step()
if self.opt['finetune_bert']:
self.bertadam.step()
self.updates += 1
# Reset any partially fixed parameters (e.g. rare words)
self.reset_embeddings()
self.eval_embed_transfer = True
# Clear gradients and run backward
self.optimizer.zero_grad()
if self.opt['finetune_bert']:
self.bertadam.zero_grad()
def predict(self, batch):
# Eval mode
self.network.eval()
torch.set_grad_enabled(False)
# Transfer trained embedding to evaluation embedding
if self.eval_embed_transfer:
self.update_eval_embed()
self.eval_embed_transfer = False
if self.opt['use_bert']:
context_bertidx = batch[19]
context_bert_spans = batch[20]
question_bertidx = batch[21]
question_bert_spans = batch[22]
# Transfer to GPU
if self.opt['cuda']:
inputs = [e.cuda(non_blocking=True) for e in batch[:9]]
if self.opt['use_bert']:
context_bertidx = [
x.cuda(non_blocking=True) for x in context_bertidx
]
else:
inputs = [e for e in batch[:9]]
# Run forward
# output: [batch_size, question_num, context_len], [batch_size, question_num]
if self.opt['use_bert']:
score_s, score_e, score_c = self.network(*inputs, context_bertidx,
context_bert_spans,
question_bertidx,
question_bert_spans)
else:
score_s, score_e, score_c = self.network(*inputs)
score_s = F.softmax(score_s, dim=2)
score_e = F.softmax(score_e, dim=2)
# Transfer to CPU/normal tensors for numpy ops
score_s = score_s.data.cpu()
score_e = score_e.data.cpu()
score_c = score_c.data.cpu()
# Get argmax text spans
text = batch[15]
spans = batch[16]
overall_mask = batch[9]
predictions = []
max_len = self.opt['max_len'] or score_s.size(2)
for i in range(overall_mask.size(0)):
for j in range(overall_mask.size(1)):
if overall_mask[i, j] == 0: # this dialog has ended
break
ans_type = np.argmax(score_c[i, j])
if ans_type == 0:
predictions.append("unknown")
elif ans_type == 1:
predictions.append("Yes")
elif ans_type == 2:
predictions.append("No")
else:
scores = torch.ger(score_s[i, j], score_e[i, j])
scores.triu_().tril_(max_len - 1)
scores = scores.numpy()
s_idx, e_idx = np.unravel_index(np.argmax(scores),
scores.shape)
s_offset, e_offset = spans[i][s_idx][0], spans[i][e_idx][1]
predictions.append(text[i][s_offset:e_offset])
return predictions # list of (list of strings)
# allow the evaluation embedding be larger than training embedding
# this is helpful if we have pretrained word embeddings
def setup_eval_embed(self, eval_embed, padding_idx=0):
# eval_embed should be a supermatrix of training embedding
self.network.eval_embed = nn.Embedding(eval_embed.size(0),
eval_embed.size(1),
padding_idx=padding_idx)
self.network.eval_embed.weight.data = eval_embed
for p in self.network.eval_embed.parameters():
p.requires_grad = False
self.eval_embed_transfer = True
if hasattr(self.network, 'CoVe'):
self.network.CoVe.setup_eval_embed(eval_embed)
def update_eval_embed(self):
# update evaluation embedding to trained embedding
if self.opt['tune_partial'] > 0:
offset = self.opt['tune_partial']
self.network.eval_embed.weight.data[0:offset] \
= self.network.embedding.weight.data[0:offset]
else:
offset = 10
self.network.eval_embed.weight.data[0:offset] \
= self.network.embedding.weight.data[0:offset]
def reset_embeddings(self):
# Reset fixed embeddings to original value
if self.opt['tune_partial'] > 0:
offset = self.opt['tune_partial']
if offset < self.network.embedding.weight.data.size(0):
self.network.embedding.weight.data[offset:] \
= self.network.fixed_embedding
def get_pretrain(self, state_dict):
own_state = self.network.state_dict()
for name, param in state_dict.items():
if name not in own_state:
continue
if isinstance(param, Parameter):
param = param.data
try:
own_state[name].copy_(param)
except:
print("Skip", name)
continue
def save(self, filename, epoch):
params = {
'state_dict': {
'network': self.network.state_dict(),
'optimizer': self.optimizer.state_dict(),
'updates': self.updates # how many updates
},
'config': self.opt,
'epoch': epoch
}
if self.opt['finetune_bert']:
params['state_dict']['bertadam'] = self.bertadam.state_dict()
try:
torch.save(params, filename)
logger.info('model saved to {}'.format(filename))
except BaseException:
logger.warn('[ WARN: Saving failed... continuing anyway. ]')
def save_for_predict(self, filename, epoch):
network_state = dict([(k, v)
for k, v in self.network.state_dict().items()
if k[0:4] != 'CoVe'])
if 'eval_embed.weight' in network_state:
del network_state['eval_embed.weight']
if 'fixed_embedding' in network_state:
del network_state['fixed_embedding']
params = {
'state_dict': {
'network': network_state
},
'config': self.opt,
}
try:
torch.save(params, filename)
logger.info('model saved to {}'.format(filename))
except BaseException:
logger.warn('[ WARN: Saving failed... continuing anyway. ]')
def cuda(self):
self.network.cuda()
class BertTrainer:
def __init__(self, hypers: Hypers, model_name, checkpoint,
**extra_model_args):
"""
initialize the BertOptimizer, with common logic for setting weight_decay_rate, doing gradient accumulation and
tracking loss
:param hypers: the core hyperparameters for the bert model
:param model_name: the fully qualified name of the bert model we will train
like pytorch_pretrained_bert.modeling.BertForQuestionAnswering
:param checkpoint: if resuming training,
this is the checkpoint that contains the optimizer state as checkpoint['optimizer']
"""
self.init_time = time.time()
self.model = self.get_model(hypers, model_name, checkpoint,
**extra_model_args)
self.step = 0
self.hypers = hypers
self.train_stats = TrainStats(hypers)
self.model.train()
logger.info('configured model for training')
# show parameter names
# logger.info(str([n for (n, p) in self.model.named_parameters()]))
# Prepare optimizer
if hasattr(hypers, 'exclude_pooler') and hypers.exclude_pooler:
# module.bert.pooler.dense.weight, module.bert.pooler.dense.bias
# see https://github.com/NVIDIA/apex/issues/131
self.param_optimizer = [
(n, p) for (n, p) in self.model.named_parameters()
if '.pooler.' not in n
]
else:
self.param_optimizer = list(self.model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params': [
p for n, p in self.param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay_rate':
0.01
}, {
'params': [
p for n, p in self.param_optimizer
if any(nd in n for nd in no_decay)
],
'weight_decay_rate':
0.0
}]
self.t_total = hypers.num_train_steps
self.global_step = hypers.global_step
if hypers.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=hypers.learning_rate,
bias_correction=False,
max_grad_norm=1.0)
if hypers.loss_scale == 0:
self.optimizer = FP16_Optimizer(
optimizer,
dynamic_loss_scale=True,
verbose=(hypers.global_rank == 0))
else:
self.optimizer = FP16_Optimizer(
optimizer,
static_loss_scale=hypers.loss_scale,
verbose=(hypers.global_rank == 0))
else:
self.optimizer = BertAdam(optimizer_grouped_parameters,
lr=hypers.learning_rate,
warmup=hypers.warmup_proportion,
t_total=self.t_total)
logger.info('created optimizer')
if checkpoint and type(
checkpoint) is dict and 'optimizer' in checkpoint:
self.optimizer.load_state_dict(checkpoint['optimizer'])
if hypers.fp16:
pass
else:
# if we load this state, we need to set the t_total to what we passed, not what was saved
self.optimizer.set_t_total(self.t_total)
# show state of optimizer
lrs = self.optimizer.get_lr()
logger.info('Min and max learn rate: %s',
str([min(lrs), max(lrs)]))
logger.info('Min and max step in state: %s',
str(self.optimizer.get_steps()))
instances_per_step = hypers.train_batch_size * hypers.gradient_accumulation_steps * hypers.world_size
if 'seen_instances' in checkpoint:
self.global_step = int(checkpoint['seen_instances'] /
instances_per_step)
self.train_stats.previous_instances = checkpoint[
'seen_instances']
logger.info('got global step from checkpoint = %i',
self.global_step)
logger.info('Loaded optimizer state:')
logger.info(repr(self.optimizer))
def reset(self):
"""
reset any gradient accumulation
:return:
"""
self.model.zero_grad()
self.step = 0
def should_continue(self):
"""
:return: True if training should continue
"""
if self.global_step >= self.t_total:
logger.info(
'stopping due to train step %i >= target train steps %i',
self.global_step, self.t_total)
return False
if 0 < self.hypers.time_limit <= (time.time() - self.init_time):
logger.info('stopping due to time out %i seconds',
self.hypers.time_limit)
return False
return True
def save_simple(self, filename):
if self.hypers.global_rank != 0:
logger.info('skipping save in %i', torch.distributed.get_rank())
return
model_to_save = self.model.module if hasattr(
self.model, 'module') else self.model # Only save the model itself
torch.save(model_to_save.state_dict(), filename)
logger.info(f'saved model only to {filename}')
def save(self, filename, **extra_checkpoint_info):
"""
save a checkpoint with the model parameters, the optimizer state and any additional checkpoint info
:param filename:
:param extra_checkpoint_info:
:return:
"""
# only local_rank 0, in fact only global rank 0
if self.hypers.global_rank != 0:
logger.info('skipping save in %i', torch.distributed.get_rank())
return
start_time = time.time()
checkpoint = extra_checkpoint_info
model_to_save = self.model.module if hasattr(
self.model, 'module') else self.model # Only save the model itself
os.makedirs(os.path.dirname(filename), exist_ok=True)
# also save the optimizer state, since we will likely resume from partial pre-training
checkpoint['state_dict'] = model_to_save.state_dict()
checkpoint['optimizer'] = self.optimizer.state_dict()
# include world size in instances_per_step calculation
instances_per_step = self.hypers.train_batch_size * \
self.hypers.gradient_accumulation_steps * \
self.hypers.world_size
checkpoint['seen_instances'] = self.global_step * instances_per_step
checkpoint['num_instances'] = self.t_total * instances_per_step
# CONSIDER: also save hypers?
torch.save(checkpoint, filename)
logger.info(
f'saved model to {filename} in {time.time()-start_time} seconds')
def get_instance_count(self):
instances_per_step = self.hypers.train_batch_size * \
self.hypers.gradient_accumulation_steps * \
self.hypers.world_size
return self.global_step * instances_per_step
def step_loss(self, loss):
"""
accumulates the gradient, tracks the loss and applies the gradient to the model
:param loss: the loss from evaluating the model
"""
if self.global_step == 0:
logger.info('first step_loss')
if self.hypers.n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
self.train_stats.note_loss(loss.item())
if self.hypers.gradient_accumulation_steps > 1:
loss = loss / self.hypers.gradient_accumulation_steps
if self.hypers.fp16:
self.optimizer.backward(loss)
else:
loss.backward()
if (self.step + 1) % self.hypers.gradient_accumulation_steps == 0:
lr_this_step = self.hypers.learning_rate * warmup_linear(
self.global_step / self.t_total, self.hypers.warmup_proportion)
for param_group in self.optimizer.param_groups:
param_group['lr'] = lr_this_step
self.optimizer.step()
self.model.zero_grad()
self.global_step += 1
self.step += 1
@classmethod
def get_files(cls, train_file, completed_files):
logger.info('completed files = %s, count = %i',
str(completed_files[:min(5, len(completed_files))]),
len(completed_files))
# multiple train files
if not os.path.isdir(train_file):
train_files = [train_file]
else:
if not train_file.endswith('/'):
train_file = train_file + '/'
train_files = glob.glob(train_file + '**', recursive=True)
train_files = [f for f in train_files if not os.path.isdir(f)]
# exclude completed files
if not set(train_files) == set(completed_files):
train_files = [f for f in train_files if f not in completed_files]
else:
completed_files = [] # new epoch
logger.info('train files = %s, count = %i',
str(train_files[:min(5, len(train_files))]),
len(train_files))
return train_files, completed_files
@classmethod
def get_model(cls, hypers, model_name, checkpoint, **extra_model_args):
override_state_dict = None
if checkpoint:
if type(checkpoint) is dict and 'state_dict' in checkpoint:
logger.info('loading from multi-part checkpoint')
override_state_dict = checkpoint['state_dict']
else:
logger.info('loading from saved model parameters')
override_state_dict = checkpoint
# create the model object by name
# https://stackoverflow.com/questions/4821104/python-dynamic-instantiation-from-string-name-of-a-class-in-dynamically-imported
import importlib
clsdot = model_name.rfind('.')
class_ = getattr(importlib.import_module(model_name[0:clsdot]),
model_name[clsdot + 1:])
model_args = {
'state_dict': override_state_dict,
'cache_dir': PYTORCH_PRETRAINED_BERT_CACHE
}
model_args.update(extra_model_args)
# logger.info(pprint.pformat(extra_model_args, indent=4))
model = class_.from_pretrained(hypers.bert_model, **model_args)
logger.info('built model')
# configure model for fp16, multi-gpu and/or distributed training
if hypers.fp16:
model.half()
logger.info('model halved')
logger.info('sending model to %s', str(hypers.device))
model.to(hypers.device)
logger.info('sent model to %s', str(hypers.device))
if hypers.local_rank != -1:
if not hypers.no_apex:
try:
from apex.parallel import DistributedDataParallel as DDP
model = DDP(model)
except ImportError:
raise ImportError("Please install apex")
else:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[hypers.local_rank],
output_device=hypers.local_rank)
logger.info('using DistributedDataParallel for world size %i',
hypers.world_size)
elif hypers.n_gpu > 1:
model = torch.nn.DataParallel(model)
return model
@classmethod
def get_base_parser(cls):
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-base-multilingual, bert-base-chinese."
)
# Other parameters
parser.add_argument(
"--num_instances",
default=-1,
type=int,
help="Total number of training instances to train over.")
parser.add_argument(
"--seen_instances",
default=-1,
type=int,
help=
"When resuming training, the number of instances we have already trained over."
)
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(
"--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("--no_apex",
default=False,
action='store_true',
help="Whether not to use apex 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(
'--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=0,
help=
'Loss scaling, positive power of 2 values can improve fp16 convergence. '
'Leave at zero to use dynamic loss scaling')
return parser
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--generation_dataset",
default='openi',
type=str,
help=["mimic-cxr, openi"])
parser.add_argument("--vqa_rad",
default="all",
type=str,
choices=["all", "chest", "head", "abd"])
parser.add_argument("--data_set",
default="train",
type=str,
help="train | valid")
parser.add_argument('--img_hidden_sz',
type=int,
default=2048,
help="Whether to use amp for fp16")
parser.add_argument(
"--bert_model",
default="bert-base-uncased",
type=str,
help=
"Bert pre-trained model selected in the list: bert-base-cased, bert-large-cased."
)
parser.add_argument(
"--mlm_task",
type=str,
default=True,
help="The model will train only mlm task!! | True | False")
parser.add_argument("--train_batch_size",
default=2,
type=int,
help="Total batch size for training.")
parser.add_argument("--num_train_epochs",
default=5,
type=int,
help="Total number of training epochs to perform.")
parser.add_argument(
'--from_scratch',
action='store_true',
default=False,
help=
"Initialize parameters with random values (i.e., training from scratch)."
)
parser.add_argument("--img_encoding",
type=str,
default='fully_use_cnn',
choices=['random_sample', 'fully_use_cnn'])
parser.add_argument(
'--len_vis_input',
type=int,
default=256,
help="The length of visual token input"
) #visual token의 fixed length를 100이라 하면, <Unknown> token 100개가 되고, 100개의 word 생성 가능.
parser.add_argument('--max_len_b',
type=int,
default=253,
help="Truncate_config: maximum length of segment B.")
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('--max_pred',
type=int,
default=10,
help="Max tokens of prediction.")
parser.add_argument(
'--s2s_prob',
default=1,
type=float,
help=
"Percentage of examples that are bi-uni-directional LM (seq2seq). This must be turned off!!!!!!! because this is not for seq2seq model!!!"
)
parser.add_argument(
'--bi_prob',
default=0,
type=float,
help="Percentage of examples that are bidirectional LM.")
parser.add_argument('--hidden_size', type=int, default=768)
parser.add_argument('--bar', default=False, type=str, help="True or False")
parser.add_argument("--config_path",
default='./pretrained_model/non_cross/config.json',
type=str,
help="Bert config file path.")
parser.add_argument(
"--model_recover_path",
default='./pretrained_model/non_cross/pytorch_model.bin',
type=str,
help="The file of fine-tuned pretraining model.") # model load
parser.add_argument(
"--output_dir",
default='./output_model/base_noncross_mimic_2',
type=str,
help=
"The output directory where the model predictions and checkpoints will be written."
)
parser.add_argument(
"--log_file",
default="training.log",
type=str,
help="The output directory where the log will be written.")
parser.add_argument('--img_postion',
default=True,
help="It will produce img_position.")
parser.add_argument(
"--do_train",
action='store_true',
default=True,
help="Whether to run training. This should ALWAYS be set to True.")
parser.add_argument(
"--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
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("--learning_rate",
default=1e-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(
"--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("--global_rank",
type=int,
default=-1,
help="global_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=123,
help="random seed for initialization")
parser.add_argument(
'--fp16',
action='store_true',
default=False,
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument(
'--fp32_embedding',
action='store_true',
default=False,
help=
"Whether to use 32-bit float precision instead of 32-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',
default=False,
help="Whether to use amp for fp16")
parser.add_argument('--new_segment_ids',
default=False,
action='store_true',
help="Use new segment ids for bi-uni-directional LM.")
parser.add_argument(
'--trunc_seg',
default='b',
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("--num_workers",
default=20,
type=int,
help="Number of workers for the data loader.")
parser.add_argument('--max_position_embeddings',
type=int,
default=None,
help="max position embeddings")
parser.add_argument(
'--image_root',
type=str,
default='/home/mimic-cxr/dataset/image_preprocessing/re_512_3ch/Train')
parser.add_argument('--split',
type=str,
nargs='+',
default=['train', 'valid'])
parser.add_argument('--world_size',
default=1,
type=int,
help='number of distributed processes')
parser.add_argument('--dist_url',
default='file://[PT_OUTPUT_DIR]/nonexistent_file',
type=str,
help='url used to set up distributed training')
parser.add_argument('--sche_mode',
default='warmup_linear',
type=str,
help="warmup_linear | warmup_constant | warmup_cosine")
parser.add_argument('--drop_prob', default=0.1, type=float)
parser.add_argument('--use_num_imgs', default=-1, type=int)
parser.add_argument('--max_drop_worst_ratio', default=0, type=float)
parser.add_argument('--drop_after', default=6, type=int)
parser.add_argument('--tasks',
default='report_generation',
help='report_generation | vqa')
parser.add_argument('--relax_projection',
action='store_true',
help="Use different projection layers for tasks.")
args = parser.parse_args()
print('global_rank: {}, local rank: {}'.format(args.global_rank,
args.local_rank))
args.max_seq_length = args.max_len_b + args.len_vis_input + 3 # +3 for 2x[SEP] and [CLS]
args.dist_url = args.dist_url.replace('[PT_OUTPUT_DIR]', args.output_dir)
if args.tasks == 'vqa':
wandb.init(config=args, project="VQA")
wandb.config["more"] = "custom"
args.src_file = '/home/mimic-cxr/dataset/data_RAD'
args.file_valid_jpgs = '/home/mimic-cxr/dataset/vqa_rad_original_set.json'
else:
if args.generation_dataset == 'mimic-cxr':
wandb.init(config=args, project="report_generation")
wandb.config["more"] = "custom"
args.src_file = '/home/mimic-cxr/new_dset/Train_253.jsonl'
args.file_valid_jpgs = '/home/mimic-cxr/new_dset/Train_253.jsonl'
else:
wandb.init(config=args, project="report_generation")
wandb.config["more"] = "custom"
args.src_file = '/home/mimic-cxr/dataset/open_i/Train_openi.jsonl'
args.file_valid_jpgs = '/home/mimic-cxr/dataset/open_i/Valid_openi.jsonl'
print(" # PID :", os.getpid())
os.makedirs(args.output_dir, exist_ok=True)
json.dump(args.__dict__,
open(os.path.join(args.output_dir, 'opt.json'), 'w'),
sort_keys=True,
indent=2)
logging.basicConfig(
filename=os.path.join(args.output_dir, args.log_file),
filemode='w',
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO)
logger = logging.getLogger(__name__)
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")
print("device", device)
n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
print("device", device)
n_gpu = 1
torch.distributed.init_process_group(backend='nccl',
init_method=args.dist_url,
world_size=args.world_size,
rank=args.global_rank)
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)
# fix random seed
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=True)
if args.do_train:
print("args.mask_prob", args.mask_prob)
print("args.train_batch_size", args.train_batch_size)
bi_uni_pipeline = [
data_loader.Preprocess4Seq2seq(
args,
args.max_pred,
args.mask_prob,
list(tokenizer.vocab.keys()),
tokenizer.convert_tokens_to_ids,
args.max_seq_length,
args.bar,
new_segment_ids=args.new_segment_ids,
truncate_config={
'max_len_b': args.max_len_b,
'trunc_seg': args.trunc_seg,
'always_truncate_tail': args.always_truncate_tail
},
mode="s2s",
len_vis_input=args.len_vis_input,
local_rank=args.local_rank,
load_vqa_set=(args.tasks == 'vqa'))
]
bi_uni_pipeline.append(
data_loader.Preprocess4Seq2seq(
args,
args.max_pred,
args.mask_prob,
list(tokenizer.vocab.keys()),
tokenizer.convert_tokens_to_ids,
args.max_seq_length,
args.bar,
new_segment_ids=args.new_segment_ids,
truncate_config={
'max_len_b': args.max_len_b,
'trunc_seg': args.trunc_seg,
'always_truncate_tail': args.always_truncate_tail
},
mode="bi",
len_vis_input=args.len_vis_input,
local_rank=args.local_rank,
load_vqa_set=(args.tasks == 'vqa')))
train_dataset = data_loader.Img2txtDataset(
args,
args.data_set,
args.src_file,
args.image_root,
args.split,
args.train_batch_size,
tokenizer,
args.max_seq_length,
file_valid_jpgs=args.file_valid_jpgs,
bi_uni_pipeline=bi_uni_pipeline,
use_num_imgs=args.use_num_imgs,
s2s_prob=args.s2s_prob, # this must be set to 1.
bi_prob=args.bi_prob,
tasks=args.tasks)
if args.world_size == 1:
train_sampler = RandomSampler(train_dataset, replacement=False)
else:
train_sampler = DistributedSampler(train_dataset)
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.train_batch_size,
sampler=train_sampler,
num_workers=args.num_workers,
collate_fn=batch_list_to_batch_tensors,
pin_memory=True)
t_total = int(
len(train_dataloader) * args.num_train_epochs * 1. /
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 if args.new_segment_ids else 2
relax_projection = 4 if args.relax_projection else 0
task_idx_proj = 3 if args.tasks == 'report_generation' else 0
mask_word_id, eos_word_ids, pad_word_ids = tokenizer.convert_tokens_to_ids(
["[MASK]", "[SEP]", "[PAD]"]) # index in BERT vocab: 103, 102, 0
# BERT model will be loaded! from scratch
if (args.model_recover_path is None):
_state_dict = {} if args.from_scratch else None
_state_dict = {}
model = BertForPreTrainingLossMask.from_pretrained(
args.bert_model,
state_dict=_state_dict,
args=args,
num_labels=cls_num_labels,
type_vocab_size=type_vocab_size,
relax_projection=relax_projection,
config_path=args.config_path,
task_idx=task_idx_proj,
max_position_embeddings=args.max_position_embeddings,
label_smoothing=args.label_smoothing,
fp32_embedding=args.fp32_embedding,
cache_dir=args.output_dir +
'/.pretrained_model_{}'.format(args.global_rank),
drop_prob=args.drop_prob,
len_vis_input=args.len_vis_input,
tasks=args.tasks)
print("scratch model's statedict : ")
for param_tensor in model.state_dict():
print(param_tensor, "\t", model.state_dict()[param_tensor].size())
global_step = 0
print("The model will train from scratch")
else:
print("Task :", args.tasks, args.s2s_prob)
print("Recoverd model :", args.model_recover_path)
for model_recover_path in glob.glob(args.model_recover_path.strip()):
logger.info("***** Recover model: %s *****",
args.model_recover_path)
model_recover = torch.load(model_recover_path)
for key in list(model_recover.keys()):
model_recover[key.replace('enc.', '').replace(
'mlm.', 'cls.')] = model_recover.pop(key)
global_step = 0
model = BertForPreTrainingLossMask.from_pretrained(
args.bert_model,
state_dict=model_recover,
args=args,
num_labels=cls_num_labels,
type_vocab_size=type_vocab_size,
relax_projection=relax_projection,
config_path=args.config_path,
task_idx=task_idx_proj,
max_position_embeddings=args.max_position_embeddings,
label_smoothing=args.label_smoothing,
fp32_embedding=args.fp32_embedding,
cache_dir=args.output_dir +
'/.pretrained_model_{}'.format(args.global_rank),
drop_prob=args.drop_prob,
len_vis_input=args.len_vis_input,
tasks=args.tasks)
model.load_state_dict(model_recover, strict=False)
print("The pretrained model loaded and fine-tuning.")
del model_recover
torch.cuda.empty_cache()
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(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
)
model = DDP(model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
elif n_gpu > 1:
model = DataParallelImbalance(model)
wandb.watch(model)
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,
schedule=args.sche_mode,
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)))
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:
logger.info("***** Running training *****")
model.train()
print("Total Parameters:",
sum([p.nelement() for p in model.parameters()]))
if recover_step:
start_epoch = recover_step + 1
print("start_epoch", start_epoch)
else:
start_epoch = 1
for i_epoch in trange(start_epoch,
args.num_train_epochs + 1,
desc="Epoch"):
if args.local_rank >= 0:
train_sampler.set_epoch(i_epoch - 1)
iter_bar = tqdm(train_dataloader, desc='Iter (loss=X.XXX)')
nbatches = len(train_dataloader)
train_loss = []
avg_loss = 0.0
batch_count = 0
for step, batch in enumerate(iter_bar):
batch = [t.to(device) for t in batch]
input_ids, segment_ids, input_mask, lm_label_ids, masked_pos, masked_weights, task_idx, img, vis_pe, ans_labels, ans_type, organ = batch
if args.fp16:
img = img.half()
vis_pe = vis_pe.half()
loss_tuple = model(img,
vis_pe,
input_ids,
segment_ids,
input_mask,
lm_label_ids,
ans_labels,
masked_pos=masked_pos,
masked_weights=masked_weights,
task_idx=task_idx,
drop_worst_ratio=args.max_drop_worst_ratio
if i_epoch > args.drop_after else 0,
ans_type=ans_type)
masked_lm_loss, vqa_loss = loss_tuple
batch_count += 1
if args.tasks == 'report_generation':
masked_lm_loss = masked_lm_loss.mean()
loss = masked_lm_loss
else:
vqa_loss = vqa_loss.mean()
loss = vqa_loss
iter_bar.set_description('Iter (loss=%5.3f)' % (loss.item()))
train_loss.append(loss.item())
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
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:
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step()
optimizer.zero_grad()
global_step += 1
wandb.log({"train_loss": np.mean(train_loss)})
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_config_file = os.path.join(args.output_dir, 'config.json')
with open(output_config_file, 'w') as f:
f.write(model_to_save.config.to_json_string())
output_model_file = os.path.join(args.output_dir,
"model.{0}.bin".format(i_epoch))
output_optim_file = os.path.join(args.output_dir,
"optim.{0}.bin".format(i_epoch))
if args.global_rank in (
-1, 0): # save model if the first device or no dist
torch.save(
copy.deepcopy(model_to_save).cpu().state_dict(),
output_model_file)
logger.info("***** CUDA.empty_cache() *****")
torch.cuda.empty_cache()
if args.world_size > 1:
torch.distributed.barrier()
def train_eval(args, train_data_path, valid_data_path):
index = read_pickle(args.index_path)
word2index, tag2index = index['word2id'], index['tag2id']
args.num_labels = len(tag2index)
args.vocab_size = len(word2index)+1
set_seed(args.seed_num)
train_dataloader, train_samples = get_dataloader(train_data_path, args.train_batch_size, True)
valid_dataloader, _ = get_dataloader(valid_data_path, args.valid_batch_size, False)
if args.model == 'bert':
bert_config = BertConfig(args.bert_config_path)
model = NERBert(bert_config, args)
model.load_state_dict(torch.load(args.bert_model_path), strict=False)
# model = NERBert.from_pretrained('bert_chinese',
# # cache_dir='/home/dutir/yuetianchi/.pytorch_pretrained_bert',
# num_labels=args.num_labels)
else:
if args.embedding:
word_embedding_matrix = read_pickle(args.embedding_data_path)
model = NERModel(args, word_embedding_matrix)
else:
model = NERModel(args)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = torch.cuda.device_count()
model.to(device)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
if args.model == 'bert':
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 'bert' not in n], 'lr': 5e-5, 'weight_decay': 0.01},
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay) and ('bert' in n)],
'weight_decay': 0.01},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay) and ('bert' in n)],
'weight_decay': 0.0}
]
warmup_proportion = 0.1
num_train_optimization_steps = int(
train_samples / args.train_batch_size / args.gradient_accumulation_steps) * args.epochs
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=warmup_proportion,
t_total=num_train_optimization_steps)
else:
current_learning_rate = args.learning_rate
optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, model.parameters()),
lr=current_learning_rate
)
if args.init_checkpoint:
# Restore model from checkpoint directory
logging.info('Loading checkpoint %s...' % args.init_checkpoint)
checkpoint = torch.load(os.path.join(args.init_checkpoint, 'checkpoint'))
init_step = checkpoint['step']
model.load_state_dict(checkpoint['model_state_dict'])
if args.do_train:
current_learning_rate = checkpoint['current_learning_rate']
warm_up_steps = checkpoint['warm_up_steps']
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
else:
logging.info('Ramdomly Initializing %s Model...' % args.model)
init_step = 0
global_step = init_step
best_score = 0.0
logging.info('Start Training...')
logging.info('init_step = %d' % global_step)
for epoch_id in range(int(args.epochs)):
tr_loss = 0
model.train()
for step, train_batch in enumerate(train_dataloader):
batch = tuple(t.to(device) for t in train_batch)
_, loss = model(batch[0], batch[1])
if n_gpu > 1:
loss = loss.mean()
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
tr_loss += loss.item()
loss.backward()
if (step + 1) % args.gradient_accumulation_steps == 0:
optimizer.step()
optimizer.zero_grad()
global_step += 1
if (step + 1) % 500 == 0:
print(loss.item())
if args.do_valid and global_step % args.valid_step == 1:
true_res = []
pred_res = []
len_res = []
model.eval()
for valid_step, valid_batch in enumerate(valid_dataloader):
valid_batch = tuple(t.to(device) for t in valid_batch)
with torch.no_grad():
logit = model(valid_batch[0])
if args.model == 'bert':
# 第一个token是‘cls’
len_res.extend(torch.sum(valid_batch[0].gt(0), dim=-1).detach().cpu().numpy()-1)
true_res.extend(valid_batch[1].detach().cpu().numpy()[:,1:])
pred_res.extend(logit.detach().cpu().numpy()[:,1:])
else:
len_res.extend(torch.sum(valid_batch[0].gt(0),dim=-1).detach().cpu().numpy())
true_res.extend(valid_batch[1].detach().cpu().numpy())
pred_res.extend(logit.detach().cpu().numpy())
acc, score = cal_score(true_res, pred_res, len_res, tag2index)
score = f1_score(true_res, pred_res, len_res, tag2index)
logging.info('Evaluation:step:{},acc:{},fscore:{}'.format(str(epoch_id), acc, score))
if score>=best_score:
best_score = score
if args.model == 'bert':
model_to_save = model.module if hasattr(model,
'module') else model # Only save the model it-self
output_dir = '{}_{}'.format('bert', str(global_step))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
output_model_file = os.path.join(output_dir, WEIGHTS_NAME)
torch.save(model_to_save.state_dict(), output_model_file)
output_config_file = os.path.join(output_dir, CONFIG_NAME)
with open(output_config_file, 'w') as f:
f.write(model_to_save.config.to_json_string())
else:
save_variable_list = {
'step': global_step,
'current_learning_rate': args.learning_rate,
'warm_up_steps': step
}
save_model(model, optimizer, save_variable_list, args)
model.train()
def main():
parser = argparse.ArgumentParser()
# Path parameters
parser.add_argument("--data_dir",
default=None,
type=str,
required=True,
help="The raw data dir.")
parser.add_argument("--vocab_path",
default=None,
type=str,
required=True,
help="bert vocab path")
parser.add_argument("--config_path",
default=None,
type=str,
help="Bert config file path.")
parser.add_argument(
"--model_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,
help="The param init of pretrain or finetune")
parser.add_argument("--optim_recover_path",
default=None,
type=str,
help="The file of pretraining optimizer.")
# Data Process 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('--max_position_embeddings',
type=int,
default=None,
help="max position embeddings")
parser.add_argument(
"--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
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('--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('--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('--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.")
parser.add_argument("--num_workers",
default=0,
type=int,
help="Number of workers for the data loader.")
# Model Paramters
parser.add_argument("--sop",
action='store_true',
help="whether use sop task.")
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("--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('--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>.")
# Train Eval Test Paramters
parser.add_argument("--checkpoint_steps",
required=True,
type=int,
help="save model eyery checkpoint_steps")
parser.add_argument("--total_steps",
required=True,
type=int,
help="all steps of training model")
parser.add_argument("--max_checkpoint",
required=True,
type=int,
help="max saved model in model_output_dir")
parser.add_argument(
"--examples_size_once",
type=int,
default=1000,
help="read how many examples every time in pretrain or finetune")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="process rank in local")
parser.add_argument("--local_debug",
action='store_true',
help="whether debug")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--fine_tune",
action='store_true',
help="Whether to run fine_tune.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval on the dev set.")
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("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument(
'--gradient_accumulation_steps',
type=int,
default=1,
help=
"Number of updates 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=str,
default='dynamic',
help=
'(float or str, optional, default=None): Optional property override. '
'If passed as a string,must be a string representing a number, e.g., "128.0", or the string "dynamic".'
)
parser.add_argument(
'--opt_level',
type=str,
default='O1',
help=
' (str, optional, default="O1"): Pure or mixed precision optimization level. '
'Accepted values are "O0", "O1", "O2", and "O3", explained in detail above.'
)
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)."
)
# Other Patameters
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument('--rank',
type=int,
default=0,
help="global rank of current process")
parser.add_argument("--world_size",
default=2,
type=int,
help="Number of process(显卡)")
args = parser.parse_args()
cur_env = os.environ
args.rank = int(cur_env.get('RANK', -1))
args.world_size = int(cur_env.get('WORLD_SIZE', -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)
assert args.train_batch_size >= 1, 'batch_size < 1 '
# 更新一次模型参数需要多少个样本
examples_per_update = args.world_size * args.train_batch_size * args.gradient_accumulation_steps
args.examples_size_once = args.examples_size_once // examples_per_update * examples_per_update
if args.fine_tune:
args.examples_size_once = examples_per_update
os.makedirs(args.model_output_dir, exist_ok=True)
os.makedirs(args.log_dir, exist_ok=True)
json.dump(args.__dict__,
open(os.path.join(args.model_output_dir, 'unilm_config.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 = torch.cuda.device_count()
dist.init_process_group(backend='nccl',
init_method='env://',
world_size=args.world_size,
rank=args.rank)
logger.info(
"world_size:{}, rank:{}, device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}"
.format(args.world_size, args.rank, device, n_gpu,
bool(args.world_size > 1), args.fp16))
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.fine_tune and not args.do_train and not args.do_eval:
raise ValueError(
"At least one of `do_train` or `do_eval` must be True.")
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.vocab_path,
do_lower_case=args.do_lower_case)
if args.max_position_embeddings:
tokenizer.max_len = args.max_position_embeddings
if args.local_rank == 0:
dist.barrier()
bi_uni_pipeline = [
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,
fine_tune=args.fine_tune)
]
file_oracle = None
if args.has_sentence_oracle:
file_oracle = os.path.join(args.data_dir, 'train.oracle')
# t_total表示模型参数更新的次数
# t_total = args.train_steps
# Prepare model
recover_step = _get_max_epoch_model(args.model_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,
local_debug=args.local_debug)
global_step = 0
else:
if recover_step:
logger.info("***** Recover model: %d *****", recover_step)
model_recover = torch.load(os.path.join(
args.output_model_dir, "model.{0}.bin".format(recover_step)),
map_location='cpu')
# recover_step == number of epochs
global_step = math.floor(recover_step * args.checkpoint_step)
# 预训练时模型的参数初始化，比如使用chinese-bert-base的模型参数进行初始化
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(
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,
local_debug=args.local_debug)
total_trainable_params = sum(p.numel() for p in model.parameters()
if p.requires_grad)
logger.info("模型参数： {}".format(total_trainable_params))
if args.local_rank == 0:
dist.barrier()
model.to(device)
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=args.total_steps)
if args.amp and args.fp16:
from apex import amp
model, optimizer = amp.initialize(model,
optimizer,
opt_level=args.opt_level,
loss_scale=args.loss_scale)
from apex.parallel import DistributedDataParallel as DDP
model = DDP(model)
else:
from torch.nn.parallel import DistributedDataParallel as DDP
model = DDP(model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
if recover_step:
logger.info("** ** * Recover optimizer: %d * ** **", recover_step)
optim_recover = torch.load(os.path.join(
args.model_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.fp16 and args.amp:
amp_recover = torch.load(os.path.join(
args.model_output_dir, "amp.{0}.bin".format(recover_step)),
map_location='cpu')
logger.info("** ** * Recover amp: %d * ** **", recover_step)
amp.load_state_dict(amp_recover)
logger.info("** ** * CUDA.empty_cache() * ** **")
torch.cuda.empty_cache()
if args.rank == 0:
writer = SummaryWriter(log_dir=args.log_dir)
logger.info("***** Running training *****")
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Param Update Num = %d", args.total_steps)
model.train()
PRE = "rank{},local_rank {},".format(args.rank, args.local_rank)
step = 1
start = time.time()
train_data_loader = TrainDataLoader(
bi_uni_pipline=bi_uni_pipeline,
examples_size_once=args.examples_size_once,
world_size=args.world_size,
train_batch_size=args.train_batch_size,
num_workers=args.num_workers,
data_dir=args.data_dir,
tokenizer=tokenizer,
max_len=args.max_seq_length)
best_result = -float('inf')
for global_step, batch in enumerate(train_data_loader, start=global_step):
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, task_idx, sop_label, oracle_pos, oracle_weights, oracle_labels = batch
else:
input_ids, segment_ids, input_mask, mask_qkv, lm_label_ids, masked_pos, masked_weights, task_idx, sop_label = batch
oracle_pos, oracle_weights, oracle_labels = None, None, None
if not args.sop:
# 不使用sop训练任务
sop_label = None
loss_tuple = model(input_ids,
segment_ids,
input_mask,
masked_lm_labels=lm_label_ids,
next_sentence_label=sop_label,
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)
masked_lm_loss, next_sentence_loss = loss_tuple
# mean() to average on multi-gpu.
if n_gpu > 1:
masked_lm_loss = masked_lm_loss.mean()
next_sentence_loss = next_sentence_loss.mean()
# ensure that accumlated gradients are normalized
if args.gradient_accumulation_steps > 1:
masked_lm_loss = masked_lm_loss / args.gradient_accumulation_steps
next_sentence_loss = next_sentence_loss / args.gradient_accumulation_steps
if not args.sop:
loss = masked_lm_loss
else:
loss = masked_lm_loss + next_sentence_loss
if args.fp16 and args.amp:
with amp.scale_loss(loss, optimizer) as scale_loss:
scale_loss.backward()
else:
loss.backward()
if (global_step + 1) % args.gradient_accumulation_steps == 0:
if args.rank == 0:
writer.add_scalar('unilm/mlm_loss', masked_lm_loss,
global_step)
writer.add_scalar('unilm/sop_loss', next_sentence_loss,
global_step)
lr_this_step = args.learning_rate * warmup_linear(
global_step / args.total_steps, 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
#更新一次模型参数花费的时间，单位:秒
cost_time_per_update = time.time() - start
# 更新完所有参数花费的时间，单位:小时
need_time = cost_time_per_update * (args.total_steps -
global_step) / 3600.0
cost_time_per_chectpoint = cost_time_per_update * args.checkpoint_steps / 3600.0
start = time.time()
if args.local_rank in [-1, 0]:
INFO = PRE + '当前/chcklpoint_steps/total:{}/{}/{},loss{}/{},更新一次参数{}秒,checkpoint_steps {}小时,' \
'训练完成{}小时\n'.format(global_step, args.checkpoint_steps, args.total_steps,
round(masked_lm_loss.item(), 5),
round(next_sentence_loss.item(), 5), round(cost_time_per_update, 4),
round(cost_time_per_chectpoint, 3), round(need_time, 3))
print(INFO)
# Save a trained model
if (global_step + 1) % args.checkpoint_steps == 0:
checkpoint_index = (global_step + 1) % args.checkpoint_steps
if args.rank >= 0:
train_data_loader.train_sampler.set_epoch(checkpoint_index)
# if args.eval:
# # 如果是pretrain，验证MLM；如果微调，验证评价指标
# result = None
#if best_result < result and _get_checkpont_num(args.model_output_num):
if args.rank in [0, -1]:
logger.info("** ** * Saving 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.model_output_dir,
"model.{0}.bin".format(checkpoint_index))
torch.save(model_to_save.state_dict(), output_model_file)
output_optim_file = os.path.join(
args.model_output_dir,
"optim.{0}.bin".format(checkpoint_index))
torch.save(optimizer.state_dict(), output_optim_file)
if args.fp16 and args.amp:
logger.info("** ** * Saving amp state * ** **")
output_amp_file = os.path.join(
args.model_output_dir,
"amp.{0}.bin".format(checkpoint_index))
torch.save(amp.state_dict(), output_amp_file)
logger.info("***** CUDA.empty_cache() *****")
torch.cuda.empty_cache()
if args.rank == 0:
writer.close()
print('** ** * train finished * ** **')
def train(config, model, train_iter, dev_iter):
start_time = time.time()
if os.path.exists(config.save_path):
model.load_state_dict(torch.load(config.save_path)['model_state_dict'])
model.train()
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 = torch.optim.Adam(model.parameters(), lr=config.learning_rate)
optimizer = BertAdam(optimizer_grouped_parameters,
lr=config.learning_rate,
warmup=0.05,
t_total=len(train_iter) * config.num_epochs)
scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, 0.5)
if os.path.exists(config.save_path):
optimizer.load_state_dict(
torch.load(config.save_path)['optimizer_state_dict'])
total_batch = 0
dev_best_loss = float('inf')
dev_last_loss = float('inf')
no_improve = 0
flag = False
model.train()
# plot_model(model, to_file= config.save_dic+'.png')
for epoch in range(config.num_epochs):
print('Epoch [{}/{}]'.format(epoch + 1, config.num_epochs))
for i, (trains, labels) in enumerate(train_iter):
outputs = model(trains)
model.zero_grad()
loss = F.cross_entropy(outputs, labels)
loss.backward()
optimizer.step()
if total_batch % 100 == 0:
true = labels.data.cpu()
predic = torch.max(outputs.data, 1)[1].cpu()
train_acc = metrics.accuracy_score(true, predic)
train_loss = loss.item()
dev_acc, dev_loss = evaluate(config, model, dev_iter)
if dev_loss < dev_best_loss:
state = {
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
}
dev_best_loss = dev_loss
torch.save(state,
config.save_dic + str(total_batch) + '.pth')
improve = '*'
del state
else:
improve = ''
if dev_last_loss > dev_loss:
no_improve = 0
elif no_improve % 2 == 0:
no_improve += 1
scheduler.step()
else:
no_improve += 1
dev_last_loss = dev_loss
time_dif = get_time_dif(start_time)
msg = 'Iter: {0:>6}, Train Loss: {1:>5.2}, Train Acc: {2:>6.2%}, Val Loss: {3:>5.2}, Val Acc: {4:>6.2%}, Time: {5} {6}'
print(
msg.format(total_batch, train_loss, train_acc, dev_loss,
dev_acc, time_dif, improve))
model.train()
total_batch += 1
if no_improve > config.require_improvement:
print("No optimization for a long time, auto-stopping...")
flag = True
break
if flag:
break
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)
if os.path.exists(state_save_path):
optimizer.load_state_dict(state['opt_state'])
device = torch.device("cuda")
tr_total = int(train_dataset.__len__() / args.train_batch_size /
args.gradient_accumulation_steps * args.num_train_epochs)
print_freq = args.print_freq
eval_freq = len(train_dataloader) // 4
print('Print freq:', print_freq, "Eval freq:", eval_freq)
for epoch in range(epoch_start, int(args.num_train_epochs) + 1):
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
with tqdm(total=len(train_dataloader)) as bar:
for step, batch in enumerate(train_dataloader, start=1):
model.train()
def train_eval(args):
set_seed(args.seed_num)
train_x_left, train_x_entity, train_x_right, train_y = read_pickle(args.train_data_path)
valid_x_left, valid_x_entity, valid_x_right, valid_y = read_pickle(args.valid_data_path)
args.num_labels = train_y.shape[1]
train_dataloader = get_dataloader(train_y, args.train_batch_size, True, train_x_left, train_x_entity, train_x_right)
valid_dataloader = get_dataloader(valid_y, args.valid_batch_size, False, valid_x_left, valid_x_entity, valid_x_right)
if args.model == 'bert':
return None
# bert_config = BertConfig(args.bert_config_path)
# model = NERBert(bert_config, args)
# model.load_state_dict(torch.load(args.bert_model_path), strict=False)
# model = NERBert.from_pretrained('bert_chinese',
# # cache_dir='/home/dutir/yuetianchi/.pytorch_pretrained_bert',
# num_labels=args.num_labels)
else:
if args.embedding:
word_embedding_matrix = read_pickle(args.embedding_data_path)
args.vocab_size = len(word_embedding_matrix)
model = AttentiveLSTM(args, word_embedding_matrix)
else:
logging.error("args.embedding should be true")
return None
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = torch.cuda.device_count()
model.to(device)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
if args.model == 'bert':
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 'bert' not in n], 'lr': 5e-5, 'weight_decay': 0.01},
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay) and ('bert' in n)],
'weight_decay': 0.01},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay) and ('bert' in n)],
'weight_decay': 0.0}
]
warmup_proportion = 0.1
num_train_optimization_steps = int(
train_samples / args.train_batch_size / args.gradient_accumulation_steps) * args.epochs
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=warmup_proportion,
t_total=num_train_optimization_steps)
else:
current_learning_rate = args.learning_rate
optimizer = torch.optim.Adam(
filter(lambda p: p.requires_grad, model.parameters()),
lr=current_learning_rate
)
if args.init_checkpoint:
# Restore model from checkpoint directory
logging.info('Loading checkpoint %s...' % args.init_checkpoint)
checkpoint = torch.load(os.path.join(args.init_checkpoint, 'checkpoint'))
init_step = checkpoint['step']
model.load_state_dict(checkpoint['model_state_dict'])
if args.do_train:
current_learning_rate = checkpoint['current_learning_rate']
warm_up_steps = checkpoint['warm_up_steps']
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
else:
logging.info('Ramdomly Initializing %s Model...' % args.model)
init_step = 0
global_step = init_step
best_score = 0.0
logging.info('Start Training...')
logging.info('init_step = %d' % global_step)
for epoch_id in range(int(args.epochs)):
train_loss = 0
for step, train_batch in enumerate(train_dataloader):
model.train()
batch = tuple(t.to(device) for t in train_batch)
train_x = (batch[0], batch[1], batch[2])
train_y = batch[3]
loss = model(train_x, train_y)
if n_gpu > 1:
loss = loss.mean()
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
train_loss += loss.item()
loss.backward()
if (step + 1) % args.gradient_accumulation_steps == 0:
optimizer.step()
optimizer.zero_grad()
global_step += 1
if args.do_valid and global_step % args.valid_step == 1:
true_res = []
pred_res = []
valid_losses = []
model.eval()
for valid_step, valid_batch in enumerate(valid_dataloader):
valid_batch = tuple(t.to(device) for t in valid_batch)
valid_x = (valid_batch[0], valid_batch[1], valid_batch[2])
valid_y = valid_batch[3]
with torch.no_grad():
valid_logit = model(valid_x)
valid_loss = F.binary_cross_entropy_with_logits(valid_logit, valid_y)
valid_logit = F.sigmoid(valid_logit)
if args.model == 'bert':
# 第一个token是‘cls’
valid_losses.append(valid_loss.item())
true_res.extend(valid_y.detach().cpu().numpy())
pred_res.extend(valid_logit.detach().cpu().numpy())
else:
valid_losses.append(valid_loss.item())
true_res.extend(valid_y.detach().cpu().numpy())
pred_res.extend(valid_logit.detach().cpu().numpy())
metric_res = acc_hook(pred_res, true_res)
logging.info('Evaluation:step:{},train_loss:{},valid_loss:{},microf1:{},macrof1:{}'.
format(str(global_step), train_loss / args.valid_step, np.average(valid_losses),
metric_res['loose_micro_f1'], metric_res['loose_macro_f1']))
if metric_res['loose_micro_f1'] >= best_score:
best_score = metric_res['loose_micro_f1']
if args.model == 'bert':
model_to_save = model.module if hasattr(model,
'module') else model # Only save the model it-self
output_dir = '{}_{}'.format('bert', str(global_step))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
output_model_file = os.path.join(output_dir, WEIGHTS_NAME)
torch.save(model_to_save.state_dict(), output_model_file)
output_config_file = os.path.join(output_dir, CONFIG_NAME)
with open(output_config_file, 'w') as f:
f.write(model_to_save.config.to_json_string())
else:
save_variable_list = {
'step': global_step,
'current_learning_rate': args.learning_rate,
'warm_up_steps': step
}
save_model(model, optimizer, save_variable_list, args)
train_loss = 0.0
def main(train_file,
matched_valid_file,
mismatched_valid_file,
test_file,
bert_path,
target_dir,
hidden_size=768,
dropout=0.5,
num_classes=2,
epochs=10,
batch_size=128,
learning_rate=5e-5,
patience=5,
max_grad_norm=10.0,
checkpoint=None):
"""
Train the ESIM model on the text_similarity dataset.
Args:
train_file: A path to some preprocessed data that must be used
to train the model.
valid_files: A dict containing the paths to the preprocessed matched
and mismatched datasets that must be used to validate the model.
embeddings_file: A path to some preprocessed word embeddings that
must be used to initialise the model.
target_dir: The path to a directory where the trained model must
be saved.
hidden_size: The size of the hidden layers in the model. Defaults
to 300.
dropout: The dropout rate to use in the model. Defaults to 0.5.
num_classes: The number of classes in the output of the model.
Defaults to 3.
epochs: The maximum number of epochs for training. Defaults to 64.
batch_size: The size of the batches for training. Defaults to 32.
lr: The learning rate for the optimizer. Defaults to 0.0004.
patience: The patience to use for early stopping. Defaults to 5.
checkpoint: A checkpoint from which to continue training. If None,
training starts from scratch. Defaults to None.
"""
device = torch.device("cuda:6" if torch.cuda.is_available() else "cpu")
print(20 * "=", " Preparing for training ", 20 * "=")
if not os.path.exists(target_dir):
os.makedirs(target_dir)
# -------------------- Data loading ------------------- #
print("\t* Loading training data...")
with open(train_file, "rb") as pkl:
train_data = TEXTSIMILARITYDataset(pickle.load(pkl))
train_loader = DataLoader(train_data, shuffle=True, batch_size=batch_size)
print("\t* Loading validation data...")
with open(matched_valid_file, "rb") as pkl:
matched_valid_data = TEXTSIMILARITYDataset(pickle.load(pkl))
with open(mismatched_valid_file, "rb") as pkl:
mismatched_valid_data = TEXTSIMILARITYDataset(pickle.load(pkl))
with open(test_file, "rb") as pkl:
test_raw_data = pickle.load(pkl)
test_data = TEXTSIMILARITYDataset(test_raw_data)
matched_valid_loader = DataLoader(matched_valid_data,
shuffle=False,
batch_size=batch_size)
mismatched_valid_loader = DataLoader(mismatched_valid_data,
shuffle=False,
batch_size=batch_size)
test_loader = DataLoader(test_data,
shuffle=False,
batch_size=batch_size)
# -------------------- Model definition ------------------- #
model = BERT(bert_path,
hidden_size,
num_classes=num_classes,
device=device).to(device)
# -------------------- Preparation for training ------------------- #
criterion = nn.CrossEntropyLoss()
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=learning_rate,
warmup=0.05,
t_total=len(train_loader) * epochs)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
mode="max",
factor=0.5,
patience=0)
best_score = 0.0
start_epoch = 1
# Data for loss curves plot.
epochs_count = []
train_losses = []
matched_valid_losses = []
mismatched_valid_losses = []
test_losses = []
# Continuing training from a checkpoint if one was given as argument.
if checkpoint:
checkpoint = torch.load(checkpoint)
start_epoch = checkpoint["epoch"] + 1
best_score = checkpoint["best_score"]
print("\t* Training will continue on existing model from epoch {}..."
.format(start_epoch))
model.load_state_dict(checkpoint["model"])
optimizer.load_state_dict(checkpoint["optimizer"])
epochs_count = checkpoint["epochs_count"]
train_losses = checkpoint["train_losses"]
matched_valid_losses = checkpoint["matched_valid_losses"]
mismatched_valid_losses = checkpoint["mismatched_valid_losses"]
test_losses = checkpoint["test_losses"]
# Compute loss and accuracy before starting (or resuming) training.
_, matched_valid_loss, matched_valid_accuracy, precisions, recalls, f1s = validate(model,
matched_valid_loader,
criterion)
print("\t* Validation loss before training on matched valid data: {:.4f}, accuracy: {:.4f}%"
.format(matched_valid_loss, (matched_valid_accuracy*100)))
_, mismatched_valid_loss, mismatched_valid_accuracy, precisions, recalls, f1s = validate(model,
mismatched_valid_loader,
criterion)
print("\t* Validation loss before training on mismatched valid data: {:.4f}, accuracy: {:.4f}%"
.format(mismatched_valid_loss, (mismatched_valid_accuracy*100)))
# -------------------- Training epochs ------------------- #
print("\n",
20 * "=",
"Training ESIM model on device: {}".format(device),
20 * "=")
patience_counter = 0
for epoch in range(start_epoch, epochs+1):
epochs_count.append(epoch)
print("* Training epoch {}:".format(epoch))
epoch_time, epoch_loss, epoch_accuracy = train(model,
train_loader,
optimizer,
criterion,
epoch,
max_grad_norm)
train_losses.append(epoch_loss)
print("-> Training time: {:.4f}s, loss = {:.4f}, accuracy: {:.4f}%\n"
.format(epoch_time, epoch_loss, (epoch_accuracy*100)))
print("* Validation for epoch {} on matched data:".format(epoch))
epoch_time, epoch_loss, epoch_accuracy, precisions, recalls, f1s = validate(model,
matched_valid_loader,
criterion)
matched_valid_losses.append(epoch_loss)
print("-> Valid. time: {:.4f}s, loss: {:.4f}, accuracy: {:.4f}%"
.format(epoch_time, epoch_loss, (epoch_accuracy*100)))
print("* Validation for epoch {} on mismatched data:".format(epoch))
epoch_time, epoch_loss, mis_epoch_accuracy, precisions, recalls, f1s = validate(model,
mismatched_valid_loader,
criterion)
mismatched_valid_losses.append(epoch_loss)
print("-> Valid. time: {:.4f}s, loss: {:.4f}, accuracy: {:.4f}%\n"
.format(epoch_time, epoch_loss, (mis_epoch_accuracy*100)))
print("* Validation for epoch {} on test data:".format(epoch))
print("test data size: ", len(test_data))
epoch_time, epoch_loss, test_epoch_accuracy = test(model,
test_loader,
test_raw_data,
criterion)
test_losses.append(epoch_loss)
print("-> Test. time: {:.4f}s, loss: {:.4f}, accuracy: {:.4f}%\n"
.format(epoch_time, epoch_loss, (test_epoch_accuracy*100)))
# Update the optimizer's learning rate with the scheduler.
scheduler.step(epoch_accuracy)
# Early stopping on validation accuracy.
if epoch_accuracy < best_score:
patience_counter += 1
else:
best_score = epoch_accuracy
patience_counter = 0
# Save the best model. The optimizer is not saved to avoid having
# a checkpoint file that is too heavy to be shared. To resume
# training from the best model, use the 'esim_*.pth.tar'
# checkpoints instead.
torch.save({"epoch": epoch,
"model": model.state_dict(),
"best_score": best_score,
"epochs_count": epochs_count,
"train_losses": train_losses,
"match_valid_losses": matched_valid_losses,
"mismatch_valid_losses": mismatched_valid_losses,
"test_losses": test_losses},
os.path.join(target_dir, "best.pth.tar"))
# Save the model at each epoch.
torch.save({"epoch": epoch,
"model": model.state_dict(),
"best_score": best_score,
"optimizer": optimizer.state_dict(),
"epochs_count": epochs_count,
"train_losses": train_losses,
"match_valid_losses": matched_valid_losses,
"mismatch_valid_losses": mismatched_valid_losses,
"test_losses": test_losses},
os.path.join(target_dir, "esim_{}.pth.tar".format(epoch)))
if patience_counter >= patience:
print("-> Early stopping: patience limit reached, stopping...")
break
print("* Validation on test1 data:")
epoch_time, epoch_loss, test_epoch_accuracy = test(model,
test_loader,
test_raw_data,
criterion)
print("-> Test. time: {:.4f}s, loss: {:.4f}, accuracy: {:.4f}%\n"
.format(epoch_time, epoch_loss, (test_epoch_accuracy*100)))
# Plotting of the loss curves for the train and validation sets.
plt.figure()
plt.plot(epochs_count, train_losses, "-r")
plt.plot(epochs_count, matched_valid_losses, "-b")
plt.plot(epochs_count, mismatched_valid_losses, "-g")
plt.xlabel("epoch")
plt.ylabel("loss")
plt.legend(["Training loss",
"Validation loss (matched set)",
"Validation loss (mismatched set)"])
plt.title("Cross entropy loss")
plt.savefig("loss.jpg")
