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 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(
"--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()
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)
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(
"--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 train(train_batch_size,
roberta_model,
hidden_size=768,
learning_rate=3e-5,
warmup_proportion=0.1,
seed=23):
device = torch.device("cuda" if torch.cuda.is_available() else 'cpu')
print(device)
n_gpu = torch.cuda.device_count()
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(seed)
print('loading train features.')
with open('preprocess/trainFeatures.pkl', 'rb') as f:
trainFeatures = pickle.load(f)
print('train features have been loaded.')
nums = len(trainFeatures)
print('训练集大小：', nums)
num_train_optimization_steps = 4 * int(
len(trainFeatures) / train_batch_size)
model = basemodel(roberta_model, hidden_size)
model.to(device)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
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=learning_rate,
warmup=warmup_proportion,
t_total=num_train_optimization_steps)
all_input_ids = torch.tensor([f.input_ids for f in trainFeatures],
dtype=torch.long)
all_input_masks = torch.tensor([f.input_mask for f in trainFeatures],
dtype=torch.long)
all_start_positions = torch.tensor(
[f.start_position for f in trainFeatures], dtype=torch.long)
all_end_positions = torch.tensor([f.end_position for f in trainFeatures],
dtype=torch.long)
all_answer_choices = torch.tensor([f.ans_choice for f in trainFeatures],
dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_masks,
all_start_positions, all_end_positions,
all_answer_choices)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data,
sampler=train_sampler,
batch_size=train_batch_size)
model.train()
print('training')
for epoch in range(4):
for step, batch in enumerate(
tqdm(train_dataloader, desc='Iteration', disable=False)):
if n_gpu == 1:
batch = tuple(t.to(device) for t in batch)
input_ids, input_masks, start_positions, end_positions, answer_choices = batch
loss = model(input_ids, input_masks, start_positions,
end_positions, answer_choices)
if n_gpu > 1:
loss = loss.mean()
loss.backward()
optimizer.step()
optimizer.zero_grad()
#print(loss)
#model_to_save = model.module if hasattr(model, 'module') else model
if (step % (int(nums / train_batch_size) // 3) == 0):
os.mkdir('model' + '/' + str(epoch) + '_' + str(step))
model_to_save = model.module if hasattr(
model, 'module') else model # Only save the model it-self
output_model_file = os.path.join(
'model' + '/' + str(epoch) + '_' + str(step),
'pytorch_model.bin')
output_config_file = os.path.join(
'model' + '/' + str(epoch) + '_' + str(step),
'config.json')
output_m_file = os.path.join(
'model' + '/' + str(epoch) + '_' + str(step), 'model.pt')
torch.save(model_to_save.state_dict(), output_m_file)
torch.save(model_to_save.roberta.state_dict(),
output_model_file)
model_to_save.roberta.config.to_json_file(output_config_file)
def train(train_iter, test_iter, config):
""""""
# Prepare model
# Prepare model
# reload weights from restore_file if specified
if config.pretrainning_model == 'nezha':
Bert_config = BertConfig.from_json_file(config.bert_config_file)
model = BertForQA(config=Bert_config, params=config)
nezha_utils.torch_init_model(model, config.bert_file)
elif config.pretrainning_model == 'albert':
Bert_config = AlbertConfig.from_pretrained(config.model_path)
model = BertForQA.from_pretrained(config.model_path,
config=Bert_config)
else:
Bert_config = RobertaConfig.from_pretrained(config.bert_config_file,
output_hidden_states=True)
model = BertForQA.from_pretrained(
config=Bert_config,
params=config,
pretrained_model_name_or_path=config.model_path)
if config.restore_file is not None:
logging.info("Restoring parameters from {}".format(
config.restore_file))
# 读取checkpoint
model, optimizer = load_checkpoint(config.restore_file)
model.to(device)
"""多卡训练"""
# if n_gpu > 1:
# model = torch.nn.DataParallel(model)
# optimizer
# 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 or 'electra' in n] # nezha的命名为bert
# middle model param
param_middle = [
(n, p) for n, p in param_optimizer
if not any([s in n for s in ('bert', 'crf', 'electra',
'albert')]) or 'dym_weight' in n
]
# crf param
# 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':
config.decay_rate,
'lr':
config.embed_learning_rate
},
# 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': config.embed_learning_rate
},
# 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':
config.decay_rate,
'lr':
config.learning_rate
},
# 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':
config.learning_rate
},
]
num_train_optimization_steps = train_iter.num_records // config.gradient_accumulation_steps * config.train_epoch
optimizer = BertAdam(optimizer_grouped_parameters,
warmup=config.warmup_proportion,
schedule="warmup_linear",
t_total=num_train_optimization_steps)
logger.info("***** Running training *****")
# logger.info(" Num examples = %d", len(train_examples))
logger.info(" Batch size = %d", config.batch_size)
logger.info(" Num epochs = %d", config.train_epoch)
logger.info(" Learning rate = %f", config.learning_rate)
cum_step = 0
best_acc = 0.0
timestamp = str(int(time.time()))
if device != 'cpu':
out_dir = os.path.abspath(
os.path.join(config.save_model, "runs_" + str(gpu_id), timestamp))
if device == 'cpu':
out_dir = os.path.abspath(
os.path.join(config.save_model, "runs_" + str('cpu_0'), timestamp))
if not os.path.exists(out_dir):
os.makedirs(out_dir)
print("Writing to {}\n".format(out_dir))
num_parameters = sum(torch.numel(param) for param in model.parameters())
print('total number of model parameters', num_parameters)
for i in range(config.train_epoch):
model.train()
for input_ids_list, input_mask_list, segment_ids_list, start_list, end_list, uid_list, \
answer_list, text_list, querylen_list, mapping_list, cls_list in tqdm(
train_iter):
# 转成张量
loss = model(list2ts2device(input_ids_list),
list2ts2device(input_mask_list),
list2ts2device(segment_ids_list),
list2ts2device(start_list), list2ts2device(end_list),
list2ts2device(cls_list))
# if n_gpu > 1:
# loss = loss.mean() # mean() to average on multi-gpu.
# 梯度累加
if config.gradient_accumulation_steps > 1:
loss = loss / config.gradient_accumulation_steps
if cum_step % 10 == 0:
format_str = 'step {}, loss {:.4f} lr {:.5f}'
print(format_str.format(cum_step, loss, config.learning_rate))
if config.flooding:
loss = (loss - config.flooding
).abs() + config.flooding # 让loss趋于某个值收敛
loss.backward() # 反向传播，得到正常的grad
if (cum_step + 1) % config.gradient_accumulation_steps == 0:
# performs updates using calculated gradients
optimizer.step()
model.zero_grad()
cum_step += 1
acc = set_test(model, test_iter, epoch=i)
# lr_scheduler学习率递减 step
print('dev set : step_{},ACC_{}'.format(cum_step, acc))
if acc > best_acc:
# Save a trained model
best_acc = acc
model_to_save = model.module if hasattr(
model, 'module') else model # Only save the model it-self
output_model_file = os.path.join(
os.path.join(out_dir,
'model_{:.4f}_{}.bin'.format(acc, str(cum_step))))
torch.save(model_to_save, output_model_file)
def train(args):
processor = data_utils.ABSAProcessor()
label_list = processor.get_labels('absa')
model = ABSABert.from_pretrained(
modelconfig.MODEL_ARCHIVE_MAP[args.bert_model],
num_labels=len(label_list))
tokenizer = ABSATokenizer.from_pretrained(
modelconfig.MODEL_ARCHIVE_MAP[args.bert_model])
model.cuda()
epoch_dataset = PregeneratedDataset(epoch=0,
training_path=args.data_dir,
tokenizer=tokenizer,
num_data_epochs=1)
unlabel_train_sampler = RandomSampler(epoch_dataset)
unlabel_train_dataloader = DataLoader(epoch_dataset,
sampler=unlabel_train_sampler,
batch_size=args.train_batch_size)
unlabel_iter = iter(unlabel_train_dataloader)
train_steps = len(unlabel_train_dataloader)
param_optimizer = [(k, v) for k, v in model.named_parameters()
if v.requires_grad == True]
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 = train_steps * args.num_train_epochs
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=t_total)
global_step = 0
model.train()
total_loss = 0
for e_ in range(args.num_train_epochs):
if e_ > 0:
epoch_dataset = PregeneratedDataset(epoch=e_,
training_path=args.data_dir,
tokenizer=tokenizer,
num_data_epochs=1)
unlabel_train_sampler = RandomSampler(epoch_dataset)
unlabel_train_dataloader = DataLoader(
epoch_dataset,
sampler=unlabel_train_sampler,
batch_size=args.train_batch_size)
unlabel_iter = iter(unlabel_train_dataloader)
train_steps = len(unlabel_train_dataloader)
logger.info('unlabel data number:{} steps：{}'.format(
len(epoch_dataset), train_steps))
for step in range(train_steps):
batch = unlabel_iter.next()
batch = tuple(t.cuda() for t in batch)
input_ids, tag_ids, head_tokens_index, rel_label_ids, input_mask, lm_label_ids, tag_label_ids, _ = batch
loss = model(input_ids,
input_tags=tag_ids,
head_tokens_index=head_tokens_index,
dep_relation_label=rel_label_ids,
masked_tag_labels=tag_label_ids,
attention_mask=input_mask)
loss.backward()
total_loss += loss.item()
optimizer.step()
optimizer.zero_grad()
global_step += 1
if global_step % 100 == 0:
logger.info('in step {} loss is: {}'.format(
global_step, total_loss / global_step))
# >>>> perform validation at the end of each epoch .
os.makedirs(args.output_dir + '/epoch' + str(e_), exist_ok=True)
torch.save(
model.state_dict(),
os.path.join(args.output_dir + '/epoch' + str(e_), "model.pt"))
class MIL:
def __init__(self, args):
self.args = args
self.raw_data = Raw_dataset(args.flickerfile, args.label_list)
self.transformer = get_transformer(network=args.network)
self.evaluator = evaluator(self.raw_data.label_list)
shuffle = False
if args.mode != 'test':
shuffle = True
self.model = ActionClassifier(self.raw_data.get_num_of_labels(),
head=args.head,
pos_dim=4,
hidden_size=512,
is_tsv_feat=False,
in_channels=args.num_boxes)
self.epoch = args.epochs
self.save_epoch = args.save_epoch
self.lr = args.learning_rate
if args.mode == 'train' or args.mode == 'small_data':
self.data_set = MIL_dataset(self.raw_data,
self.transformer,
img_path=args.img_root_dir,
tsv_path=args.tsv_path,
mode=args.mode,
use_tsv=False,
num_boxes=args.num_boxes)
self.data_loader = DataLoader(self.data_set,
batch_size=args.batch_size,
shuffle=shuffle,
num_workers=args.num_workers)
#params = list(self.decoder.parameters())
#params.extend(list(self.encoder.parameters()))
batch_per_epoch = len(self.data_loader)
t_total = int(batch_per_epoch * args.epochs)
if args.mode == 'dev':
self.eval_set = MIL_dataset(self.raw_data,
self.transformer,
img_path=args.img_root_dir,
tsv_path=args.tsv_path,
mode=args.mode,
use_tsv=False,
num_boxes=args.num_boxes)
self.eval_loader = DataLoader(self.eval_set,
batch_size=args.batch_size,
shuffle=shuffle,
num_workers=args.num_workers)
self.st_epoch = 0
if (args.load != None):
fname = os.path.join(args.model_dir, args.load)
self.st_epoch = self.load_model(fname)
self.model.cuda()
if args.multiGPU:
self.model = nn.DataParallel(self.model)
self.criterion = Mixture_loss(args.head)
#self.optimizer = torch.optim.SGD(self.model.parameters(), lr=0.05, momentum=0.9, weight_decay = 0.1)
if args.mode == 'train':
batch_per_epoch = len(self.data_loader)
print('batch per epoch ={}'.format(batch_per_epoch))
t_total = int(batch_per_epoch * args.epochs)
print('total iterations== {} ; warmup start = {}'.format(
t_total, t_total * args.wstep))
self.optimizer = BertAdam(
list(self.model.parameters()),
lr=args.learning_rate,
warmup=args.wstep,
t_total=t_total) #changing warmup from 0.1 to 0.3
def train(self):
print('training started')
self.model.train()
for epoch in range(self.epoch):
tr_loss = 0
nb_tr_steps = 0
em_loss_t = 0
cls_loss_t = 0
for imgs, subimgs, boxes, interaction_pattern, label_hot_vec in tqdm(
self.data_loader):
self.optimizer.zero_grad()
imgs, subimgs, boxes, interaction_pattern, label_hot_vec = imgs.cuda(
), subimgs.cuda(), boxes.cuda(), interaction_pattern.cuda(
), label_hot_vec.cuda()
g_x, p_yi_x = self.model(imgs, subimgs, boxes,
interaction_pattern, label_hot_vec)
loss, em_loss, class_loss = self.criterion(
g_x, p_yi_x,
label_hot_vec) #(h_x, g_x, p_y_x, p_yi_x,label_hot_vec)
if self.args.multiGPU:
loss = loss.mean()
tr_loss += loss.item()
em_loss_t += em_loss
cls_loss_t += class_loss
nb_tr_steps += 1
loss.backward()
nn.utils.clip_grad_norm_(self.model.parameters(), 1.)
#nn.utils.clip_grad_norm_(self.decoder.parameters(), 1.)
self.optimizer.step()
print(
"Train loss@epoch {}: total:{} emloss:{}, cls_loss:{}".format(
self.st_epoch + epoch + 1, tr_loss / nb_tr_steps,
em_loss_t / nb_tr_steps, cls_loss_t / nb_tr_steps))
if epoch == self.epoch - 1 or (epoch + 1) % self.save_epoch == 0:
filename = 'pascal_voc' + str(self.st_epoch + epoch +
1) + '.model'
filename = os.path.join(args.model_dir, filename)
self.save_model(filename, self.st_epoch + epoch + 1)
def evaluate(self, thresold=0.5):
print('evaluation started')
self.model.eval()
res = []
gold = []
for imgs, subimgs, boxes, interaction_pattern, label_hot_vec in tqdm(
self.eval_loader):
imgs, subimgs, boxes, interaction_pattern = imgs.cuda(
), subimgs.cuda(), boxes.cuda(), interaction_pattern.cuda()
g_x, p_yi_x = self.model(imgs, subimgs, boxes, interaction_pattern)
g_x = g_x.unsqueeze(dim=-1)
class_prob = torch.bernoulli(p_yi_x) * g_x
class_prob = torch.sum(class_prob, dim=1) - thresold
class_prob = class_prob.cpu()
target = [
torch.nonzero(t).squeeze(-1).numpy() for t in label_hot_vec
]
pred = [(t > 0).nonzero().squeeze(-1).numpy() for t in class_prob]
for gs in target:
gold.append(gs)
for pd in pred:
res.append(pd)
self.evaluator.evaluate(res, gold, self.args.dump)
def evaluate2(self, thresold=0.5):
print('evaluation started')
self.model.eval()
res = []
gold = []
for imgs, subimgs, boxes, interaction_pattern, label_hot_vec in tqdm(
self.eval_loader):
imgs, subimgs, boxes, interaction_pattern = imgs.cuda(
), subimgs.cuda(), boxes.cuda(), interaction_pattern.cuda()
g_x, p_yi_x = self.model(imgs, subimgs, boxes, interaction_pattern)
g_x = g_x.unsqueeze(dim=-1)
#class_prob = torch.bernoulli(p_yi_x) * g_x
#class_prob = torch.sum(class_prob,dim=1) - thresold
#class_prob = class_prob.cpu()
target = [
torch.nonzero(t).squeeze(-1).numpy() for t in label_hot_vec
]
pred = (p_yi_x >= thresold).float() * 1
pred = torch.prod(pred, dim=1)
#pred = torch.sum(pred, dim =1)
pred1 = [(t > 0).nonzero().squeeze(-1).numpy() for t in pred]
#pred1 = [(t>9).nonzero().squeeze(-1).numpy() for t in pred] #atleast 9 distribution among 16 says yes
for gs in target:
gold.append(gs)
for pd in pred1:
res.append(pd)
self.evaluator.evaluate(res, gold, self.args.dump)
def save_model(self, name, epoch):
#epoch = self.epoch
lr = self.lr
check_point = {}
check_point['model'] = self.model.state_dict()
#check_point['decoder'] = self.decoder.state_dict()
check_point['epoch'] = epoch
check_point['lr'] = lr
check_point['optimizer'] = None
torch.save(check_point, name)
print('model saved at {}'.format(name))
def load_model(self, path):
print("Load model from %s" % path)
check_point = torch.load(path)
model_dict = check_point['model']
#decoder_dict = check_point['decoder']
self.model.load_state_dict(model_dict, strict=False)
#self.decoder.load_state_dict(decoder_dict, strict=False)
if check_point['optimizer'] != None:
self.optimizer = optimizer
return check_point['epoch']
def train(args):
processor = data_utils.ABSAProcessor()
label_list = processor.get_labels(args.task_type)
model = BertForTokenClassification.from_pretrained(
modelconfig.MODEL_ARCHIVE_MAP[args.bert_model],
num_labels=len(label_list))
tokenizer = ABSATokenizer.from_pretrained(
modelconfig.MODEL_ARCHIVE_MAP[args.bert_model])
train_examples = processor.get_train_examples(args.data_dir,
args.task_type)
num_train_steps = int(
math.ceil(len(train_examples) /
args.train_batch_size)) * args.num_train_epochs
train_features = data_utils.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_steps)
all_input_ids = torch.tensor([f.input_ids 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_input_mask = torch.tensor([f.input_mask for f in train_features],
dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in train_features],
dtype=torch.long)
# all_tag_ids = torch.tensor([f.tag_id for f in train_features], dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_segment_ids, all_input_mask,
all_label_ids)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data,
sampler=train_sampler,
batch_size=args.train_batch_size)
if args.do_valid:
valid_examples = processor.get_dev_examples(args.data_dir,
args.task_type)
valid_features = data_utils.convert_examples_to_features(
valid_examples, label_list, args.max_seq_length, tokenizer)
valid_all_input_ids = torch.tensor(
[f.input_ids for f in valid_features], dtype=torch.long)
valid_all_segment_ids = torch.tensor(
[f.segment_ids for f in valid_features], dtype=torch.long)
valid_all_input_mask = torch.tensor(
[f.input_mask for f in valid_features], dtype=torch.long)
valid_all_label_ids = torch.tensor(
[f.label_id for f in valid_features], dtype=torch.long)
valid_data = TensorDataset(valid_all_input_ids, valid_all_segment_ids,
valid_all_input_mask, valid_all_label_ids)
logger.info("***** Running validations *****")
logger.info(" Num orig examples = %d", len(valid_examples))
logger.info(" Num split examples = %d", len(valid_features))
logger.info(" Batch size = %d", args.train_batch_size)
valid_sampler = SequentialSampler(valid_data)
valid_dataloader = DataLoader(valid_data,
sampler=valid_sampler,
batch_size=args.train_batch_size)
best_valid_loss = float('inf')
valid_losses = []
model.cuda()
param_optimizer = [(k, v) for k, v in model.named_parameters()
if v.requires_grad == True]
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 # num_train_steps
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=t_total)
global_step = 0
model.train()
train_steps = len(train_dataloader)
for e_ in range(args.num_train_epochs):
train_iter = iter(train_dataloader)
for step in range(train_steps):
batch = train_iter.next()
batch = tuple(t.cuda() for t in batch)
input_ids, segment_ids, input_mask, label_ids = batch
loss = model(input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask,
labels=label_ids)
loss.backward()
optimizer.step()
optimizer.zero_grad()
global_step += 1
if args.do_valid:
model.eval()
with torch.no_grad():
losses = []
valid_size = 0
for step, batch in enumerate(valid_dataloader):
batch = tuple(
t.cuda()
for t in batch) # multi-gpu does scattering it-self
input_ids, segment_ids, input_mask, label_ids = batch
loss = model(input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask,
labels=label_ids)
losses.append(loss.data.item() * input_ids.size(0))
valid_size += input_ids.size(0)
valid_loss = sum(losses) / valid_size
logger.info("validation loss: %f", valid_loss)
valid_losses.append(valid_loss)
if valid_loss < best_valid_loss:
torch.save(model, os.path.join(args.output_dir, "model.pt"))
best_valid_loss = valid_loss
model.train()
if args.do_valid:
with open(os.path.join(args.output_dir, "valid.json"), "w") as fw:
json.dump({"valid_losses": valid_losses}, fw)
else:
torch.save(model, os.path.join(args.output_dir, "model.pt"))
def train(args):
processor = data_utils.AscProcessor()
label_list = processor.get_labels()
tokenizer = ABSATokenizer.from_pretrained(
modelconfig.MODEL_ARCHIVE_MAP[args.bert_model])
train_examples = processor.get_train_examples(args.data_dir)
num_train_steps = int(
len(train_examples) / args.train_batch_size) * args.num_train_epochs
train_features = data_utils.convert_examples_to_features(
train_examples, label_list, args.max_seq_length, tokenizer, "asc")
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_segment_ids = torch.tensor([f.segment_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_label_ids = torch.tensor([f.label_id for f in train_features],
dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_segment_ids, all_input_mask,
all_label_ids)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data,
sampler=train_sampler,
batch_size=args.train_batch_size)
#>>>>> validation
if args.do_valid:
valid_examples = processor.get_dev_examples(args.data_dir)
valid_features = data_utils.convert_examples_to_features(
valid_examples, label_list, args.max_seq_length, tokenizer, "asc")
valid_all_input_ids = torch.tensor(
[f.input_ids for f in valid_features], dtype=torch.long)
valid_all_segment_ids = torch.tensor(
[f.segment_ids for f in valid_features], dtype=torch.long)
valid_all_input_mask = torch.tensor(
[f.input_mask for f in valid_features], dtype=torch.long)
valid_all_label_ids = torch.tensor(
[f.label_id for f in valid_features], dtype=torch.long)
valid_data = TensorDataset(valid_all_input_ids, valid_all_segment_ids,
valid_all_input_mask, valid_all_label_ids)
logger.info("***** Running validations *****")
logger.info(" Num orig examples = %d", len(valid_examples))
logger.info(" Num split examples = %d", len(valid_features))
logger.info(" Batch size = %d", args.train_batch_size)
valid_sampler = SequentialSampler(valid_data)
valid_dataloader = DataLoader(valid_data,
sampler=valid_sampler,
batch_size=args.train_batch_size)
best_valid_loss = float('inf')
valid_losses = []
#<<<<< end of validation declaration
model = BertForABSA.from_pretrained(
modelconfig.MODEL_ARCHIVE_MAP[args.bert_model],
num_labels=len(label_list))
model.cuda()
# Prepare optimizer
param_optimizer = [(k, v) for k, v in model.named_parameters()
if v.requires_grad == True]
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
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=t_total)
global_step = 0
model.train()
for epoch in range(args.num_train_epochs):
for step, batch in enumerate(train_dataloader):
batch = tuple(t.cuda() for t in batch)
input_ids, segment_ids, input_mask, label_ids = batch
optimizer.zero_grad()
loss = model(input_ids, segment_ids, input_mask, label_ids)
loss.backward()
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()
global_step += 1
print("training loss: ", loss.item(), epoch + 1)
#>>>> perform validation at the end of each epoch.
new_dirs = os.path.join(args.output_dir, str(epoch + 1))
os.mkdir(new_dirs)
if args.do_valid:
model.eval()
with torch.no_grad():
losses = []
valid_size = 0
for step, batch in enumerate(valid_dataloader):
batch = tuple(
t.cuda()
for t in batch) # multi-gpu does scattering it-self
input_ids, segment_ids, input_mask, label_ids = batch
loss = model(input_ids, segment_ids, input_mask, label_ids)
losses.append(loss.data.item() * input_ids.size(0))
valid_size += input_ids.size(0)
valid_loss = sum(losses) / valid_size
logger.info("validation loss: %f, epoch: %d", valid_loss,
epoch + 1)
valid_losses.append(valid_loss)
torch.save(model, os.path.join(new_dirs, "model.pt"))
test(args, new_dirs, dev_as_test=True)
if epoch == args.num_train_epochs - 1:
torch.save(model, os.path.join(args.output_dir,
"model.pt"))
test(args, args.output_dir, dev_as_test=False)
os.remove(os.path.join(new_dirs, "model.pt"))
if valid_loss < best_valid_loss:
best_valid_loss = valid_loss
model.train()
if args.do_valid:
with open(os.path.join(args.output_dir, "valid.json"), "w") as fw:
json.dump({"valid_losses": valid_losses}, fw)
else:
torch.save(model, os.path.join(args.output_dir, "model.pt"))
def train(args):
processor = data_utils.ABSAProcessor()
label_list = processor.get_labels(args.task_type)
tokenizer = ABSATokenizer.from_pretrained(
modelconfig.MODEL_ARCHIVE_MAP[args.bert_model])
train_examples = processor.get_train_examples(args.data_dir,
args.task_type)
num_train_steps = int(
math.ceil(len(train_examples) /
args.train_batch_size)) * args.num_train_epochs
train_features = data_utils.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_steps)
all_input_ids = torch.tensor([f.input_ids 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_input_mask = torch.tensor([f.input_mask for f in train_features],
dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in train_features],
dtype=torch.long)
domain_dataset = PregeneratedDataset(epoch=0,
training_path=args.domain_dataset,
tokenizer=tokenizer,
num_data_epochs=1)
domain_train_sampler = RandomSampler(domain_dataset)
domain_train_dataloader = DataLoader(domain_dataset,
sampler=domain_train_sampler,
batch_size=16)
train_data = TensorDataset(all_input_ids, all_segment_ids, all_input_mask,
all_label_ids)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data,
sampler=train_sampler,
batch_size=args.train_batch_size)
# >>>>> validation
if args.do_valid:
valid_examples = processor.get_dev_examples(args.data_dir,
args.task_type)
valid_features = data_utils.convert_examples_to_features(
valid_examples, label_list, args.max_seq_length, tokenizer)
valid_all_input_ids = torch.tensor(
[f.input_ids for f in valid_features], dtype=torch.long)
valid_all_segment_ids = torch.tensor(
[f.segment_ids for f in valid_features], dtype=torch.long)
valid_all_input_mask = torch.tensor(
[f.input_mask for f in valid_features], dtype=torch.long)
valid_all_label_ids = torch.tensor(
[f.label_id for f in valid_features], dtype=torch.long)
# valid_all_tag_ids = torch.tensor([f.tag_id for f in valid_features], dtype=torch.long)
valid_data = TensorDataset(valid_all_input_ids, valid_all_segment_ids,
valid_all_input_mask, valid_all_label_ids)
logger.info("***** Running validations *****")
logger.info(" Num orig examples = %d", len(valid_examples))
logger.info(" Num split examples = %d", len(valid_features))
logger.info(" Batch size = %d", args.train_batch_size)
valid_sampler = SequentialSampler(valid_data)
valid_dataloader = DataLoader(valid_data,
sampler=valid_sampler,
batch_size=args.train_batch_size)
best_valid_loss = float('inf')
valid_losses = []
# <<<<< end of validation declaration
model = ABSABert.from_pretrained(
modelconfig.MODEL_ARCHIVE_MAP[args.bert_model],
num_labels=len(label_list))
if args.features_model != 'none':
state_dict = torch.load(args.features_model)
del state_dict['classifier.weight']
del state_dict['classifier.bias']
model.load_state_dict(state_dict, strict=False)
logger.info('load fine-tuned model from : {}'.format(
args.features_model))
model.cuda()
flag = True
if flag:
# bert-base
shared_param_optimizer = [(k, v)
for k, v in model.bert.named_parameters()
if v.requires_grad == True]
shared_param_optimizer = [
n for n in shared_param_optimizer if 'pooler' not in n[0]
]
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
shared_optimizer_grouped_parameters = [{
'params': [
p for n, p in shared_param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay':
0.01
}, {
'params': [
p for n, p in shared_param_optimizer
if any(nd in n for nd in no_decay)
],
'weight_decay':
0.0
}]
t_total = num_train_steps # num_train_steps
supervised_param_optimizer = model.classifier.parameters()
domain_classifier_param_optimizer = model.domain_cls.parameters()
shared_optimizer = BertAdam(shared_optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=t_total)
supervised_optimizer = BertAdam(supervised_param_optimizer,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=t_total)
domain_optimizer = BertAdam(domain_classifier_param_optimizer,
lr=3e-5,
warmup=args.warmup_proportion,
t_total=-1)
else:
param_optimizer = [(k, v) for k, v in model.named_parameters()
if v.requires_grad == True]
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 # num_train_steps
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=t_total)
global_step = 0
model.train()
train_steps = len(train_dataloader)
total_domain_loss = 0
for e_ in range(args.num_train_epochs):
train_iter = iter(train_dataloader)
domain_iter = iter(domain_train_dataloader)
for step in range(train_steps):
batch = train_iter.next()
batch = tuple(t.cuda() for t in batch)
input_ids, segment_ids, input_mask, label_ids = batch # all_input_ids, all_segment_ids, all_input_mask, all_label_ids, all_tag_ids
loss, _ = model(input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask,
labels=label_ids)
loss.backward()
if flag:
shared_optimizer.step()
shared_optimizer.zero_grad()
supervised_optimizer.step()
supervised_optimizer.zero_grad()
else:
optimizer.step()
optimizer.zero_grad()
dirt_n = 1 # 1 or 2
for _ in range(dirt_n):
try:
batch = domain_iter.next()
except:
domain_iter = iter(domain_train_dataloader)
batch = domain_iter.next()
batch = tuple(t.cuda() for t in batch)
input_ids, input_mask, domain_labels = batch[0], batch[
4], batch[-1]
d_loss = model(input_ids,
attention_mask=input_mask,
domain_label=domain_labels)
d_loss.backward()
total_domain_loss += d_loss.item()
domain_optimizer.step()
domain_optimizer.zero_grad()
shared_optimizer.zero_grad(
) # make sure to clear the gradients of encoder.
if step % 50 == 0:
logger.info('in step {} domain loss: {}'.format(
dirt_n * (e_ * train_steps + step + 1), total_domain_loss /
(dirt_n * (e_ * train_steps + step + 1))))
global_step += 1
# >>>> perform validation at the end of each epoch .
if args.do_valid:
model.eval()
with torch.no_grad():
losses = []
valid_size = 0
for step, batch in enumerate(valid_dataloader):
batch = tuple(
t.cuda()
for t in batch) # multi-gpu does scattering it-self
input_ids, segment_ids, input_mask, label_ids = batch
loss, _ = model(input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask,
labels=label_ids)
loss = torch.mean(loss)
losses.append(loss.data.item() * input_ids.size(0))
valid_size += input_ids.size(0)
valid_loss = sum(losses) / valid_size
logger.info("validation loss: %f", valid_loss)
valid_losses.append(valid_loss)
if valid_loss < best_valid_loss:
torch.save(model.state_dict(),
os.path.join(args.output_dir, "model.pt"))
best_valid_loss = valid_loss
model.train()
if args.do_valid:
with open(os.path.join(args.output_dir, "valid.json"), "w") as fw:
json.dump({"valid_losses": valid_losses}, fw)
else:
torch.save(model.state_dict(), os.path.join(args.output_dir,
"model.pt"))
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 train(train_iter, test_iter, config):
""""""
# Prepare model
# Prepare model
# reload weights from restore_file if specified 如果指定就加载已经训练的权重
if config.pretrainning_model == 'nezha': #哪吒模型
Bert_config = BertConfig.from_json_file(config.bert_config_file)
model = BertForTokenClassification(config=Bert_config, params=config)
nezha_utils.torch_init_model(model, config.bert_file)
elif config.pretrainning_model == 'albert':
Bert_config = AlbertConfig.from_pretrained(config.model_path)
model = BertForTokenClassification.from_pretrained(config.model_path,
config=Bert_config)
else:
Bert_config = RobertaConfig.from_pretrained(config.bert_config_file,
output_hidden_states=True)
model = BertForTokenClassification.from_pretrained(
config=Bert_config,
params=config,
pretrained_model_name_or_path=config.model_path)
Bert_config.output_hidden_states = True # 获取每一层的输出
model.to(device)
"""多卡训练"""
if n_gpu > 1:
model = torch.nn.DataParallel(model)
# optimizer
# Prepare optimizer
# fine-tuning
# 取模型权重
param_optimizer = list(model.named_parameters())
# pretrain model param 预训练的参数
param_pre = [(n, p) for n, p in param_optimizer
if 'bert' in n or 'electra' in n] # nezha的命名为bert
# middle model param 中等参数
param_middle = [
(n, p) for n, p in param_optimizer
if not any([s in n for s in ('bert', 'crf', 'electra',
'albert')]) or 'dym_weight' in n
]
# crf param
# 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 预训练的参数
# 衰减
{
'params':
[p for n, p in param_pre if not any(nd in n for nd in no_decay)],
'weight_decay':
config.decay_rate,
'lr':
config.embed_learning_rate
},
# 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': config.embed_learning_rate
},
# middle model 中等参数
# 衰减
{
'params': [
p for n, p in param_middle
if not any(nd in n for nd in no_decay)
],
'weight_decay':
config.decay_rate,
'lr':
config.learning_rate
},
# 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':
config.learning_rate
},
]
num_train_optimization_steps = train_iter.num_records // config.gradient_accumulation_steps * config.train_epoch
optimizer = BertAdam(optimizer_grouped_parameters,
warmup=config.warmup_proportion,
schedule="warmup_cosine",
t_total=num_train_optimization_steps)
logger.info("***** Running training *****")
logger.info(" Batch size = %d", config.batch_size)
logger.info(" Num epochs = %d", config.train_epoch)
logger.info(" Learning rate = %f", config.learning_rate)
cum_step = 0
timestamp = str(int(time.time()))
out_dir = os.path.abspath(
os.path.join(config.save_model, "runs_" + str(gpu_id), timestamp))
if not os.path.exists(out_dir):
os.makedirs(out_dir)
print("Writing to {}\n".format(out_dir))
draw_step_list = []
draw_loss_list = []
for i in range(config.train_epoch):
model.train()
for input_ids_list, input_mask_list, segment_ids_list, label_ids_list, tokens_list in tqdm(
train_iter):
# 转成张量
loss = model(input_ids=list2ts2device(input_ids_list),
token_type_ids=list2ts2device(segment_ids_list),
attention_mask=list2ts2device(input_mask_list),
labels=list2ts2device(label_ids_list))
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
# 梯度累加
if config.gradient_accumulation_steps > 1:
loss = loss / config.gradient_accumulation_steps
if cum_step % 10 == 0:
draw_step_list.append(cum_step)
draw_loss_list.append(loss)
if cum_step % 100 == 0:
format_str = 'step {}, loss {:.4f} lr {:.5f}'
print(
format_str.format(cum_step, loss,
config.learning_rate))
loss.backward() # 反向传播，得到正常的grad
if (cum_step + 1) % config.gradient_accumulation_steps == 0:
# performs updates using calculated gradients
optimizer.step()
model.zero_grad()
cum_step += 1
p, r, f1 = set_test(model, test_iter)
# lr_scheduler学习率递减 step
print('dev set : step_{},precision_{}, recall_{}, F1_{}'.format(
cum_step, p, r, f1))
# 保存模型
model_to_save = model.module if hasattr(
model, 'module') else model # Only save the model it-self
output_model_file = os.path.join(
os.path.join(
out_dir, 'model_{:.4f}_{:.4f}_{:.4f}_{}.bin'.format(
p, r, f1, str(cum_step))))
torch.save(model_to_save, output_model_file)
with open(Config().processed_data + 'step_loss_data.pickle', 'wb') as mf:
draw_dict = {'step': draw_step_list, 'loss': draw_loss_list}
pickle.dump(draw_dict, mf)
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 ***********************************"
)
class Trainer:
def is_main_process(self):
return self.team_rank == 0
def parse_arguments(self):
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--input_file",
default=None,
type=str,
required=True,
help="The input data file. Should be zip file "
"containing .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("--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('--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=100,
help="Number of update steps until a model "
"checkpoint is saved to disk.")
parser.add_argument('--phase2',
default=False,
action='store_true',
help="Whether to train with seq len 512")
parser.add_argument('--phase1_end_step',
type=int,
default=7038,
help="Number of training steps in Phase1 - "
"seq len 128")
parser.add_argument('--online_distillation',
type=str,
default="none",
choices=["none", "original", "overlap", "logit"],
help="Settings for online distillation")
parser.add_argument('--burnin_steps', type=int, default=0)
parser.add_argument('--distillation_weight', type=float, default=1)
parser.add_argument('--distillation_loss',
type=str,
default="kl_divergence",
choices=["cross_entropy", "kl_divergence"])
parser.add_argument('--distillation_steps', type=int, default=50)
parser.add_argument('--optimizer',
type=str,
default="lamb",
choices=["lamb", "adam"])
self.args = parser.parse_args()
def setup_training(self):
assert (torch.cuda.is_available())
torch.cuda.set_device(self.args.local_rank)
self.device = torch.device("cuda", self.args.local_rank)
# Initializes the distributed backend which will take care of
# sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl',
init_method='env://')
self.rank = torch.distributed.get_rank()
self.size = torch.distributed.get_world_size()
if self.args.online_distillation == "none":
self.team = 0
self.team_masters = [0]
self.team_master = 0
self.local_group = torch.distributed.new_group(
ranks=list(range(0, self.size)))
self.team_rank = torch.distributed.get_rank()
self.team_size = torch.distributed.get_world_size()
else:
assert self.size % 2 == 0, \
'with distillation, world size must be a multiple of 2'
self.team = self.rank // (self.size // 2)
self.team_masters = [0, (self.size // 2)]
self.team_master = self.team_masters[self.team]
self.is_team_master = (self.rank % (self.size // 2) == 0)
local_group0 = torch.distributed.new_group(
ranks=list(range(0, self.size // 2)))
local_group1 = torch.distributed.new_group(
ranks=list(range(self.size // 2, self.size)))
self.local_groups = [local_group0, local_group1]
self.local_group = self.local_groups[self.team]
self.team_rank = self.rank % (self.size // 2)
self.team_size = self.size // 2
comm_model_group_rank0 = \
[0] + list(range(self.team_size, self.team_size * 2))
comm_model_group_rank1 = \
[self.team_size] + list(range(0, self.team_size))
self.comm_model_group_ranks = [
comm_model_group_rank0, comm_model_group_rank1
]
if self.args.online_distillation == "logit":
for i in range(0, self.size // 2):
ranks = [i, i + self.size // 2]
grp = torch.distributed.new_group(ranks=ranks)
if self.rank in ranks:
self.equalize_data_group = grp
# use different seeds in different teams
self.args.data_seed = 12345
self.args.seed += self.team * 12345
else:
# use different seeds in different teams
self.args.seed += self.team * 12345
self.args.train_batch_size //= self.team_size
if not self.args.resume_from_checkpoint:
chio.makedirs(self.args.output_dir, exist_ok=True)
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 prepare_snapshot(self):
self.snapshot = Snapshot(self.args, self.model, self.another_model,
self.optimizer, self.team)
flat_dist_call([param.data for param in self.model.parameters()],
torch.distributed.broadcast,
(self.team_master, self.local_group))
def forward(self, model, batch, calc_loss=True):
input_ids, segment_ids, input_mask, \
masked_lm_labels, next_sentence_labels = batch
if calc_loss:
return 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=self.args.checkpoint_activations)
else:
return model(
input_ids=input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask,
masked_lm_labels=None,
next_sentence_label=None,
checkpoint_activations=self.args.checkpoint_activations)
def backward(self, loss):
loss.backward()
def comm_model(self):
for i in range(2):
root = self.comm_model_group_ranks[i][0]
teams = set(range(root, root + self.team_size))
if self.rank in teams:
flat_dist_call(
[param.data for param in self.model.parameters()],
torch.distributed.broadcast, (i * self.team_size, ))
else:
flat_dist_call(
[param.data for param in self.another_model.parameters()],
torch.distributed.broadcast, (i * self.team_size, ))
def all_reduce(self, overflow_buf, accum=1):
scaler = amp.scaler.LossScaler(1.0)
# 1. allocate an uninitialized buffer for flattened gradient
master_grads = [
p.grad for p in amp.master_params(self.optimizer)
if p.grad is not None
]
flat_grad_size = sum(p.numel() for p in master_grads)
allreduce_dtype = torch.float32
flat_raw = torch.empty(flat_grad_size,
device='cuda',
dtype=allreduce_dtype)
# 2. combine unflattening and predivision of unscaled 'raw' gradient
allreduced_views = apex_C.unflatten(flat_raw, master_grads)
overflow_buf.zero_()
amp_C.multi_tensor_scale(
65536, overflow_buf, [master_grads, allreduced_views],
scaler.loss_scale() / (self.team_size * accum))
# 3. sum gradient across ranks. Because of the predivision,
# this averages the gradient
torch.distributed.all_reduce(flat_raw, group=self.local_group)
# 4. combine unscaling and unflattening of allreduced gradient
overflow_buf.zero_()
amp_C.multi_tensor_scale(65536, overflow_buf,
[allreduced_views, master_grads],
1. / scaler.loss_scale())
def take_optimizer_step(self, global_step):
# 1. call optimizer step function
self.optimizer.step()
global_step += 1
for param in self.model.parameters():
param.grad = None
return global_step
def init_dataloader(self, epoch, pool, rng=None):
rng = rng or random
if not self.args.resume_from_checkpoint or epoch > 0 or \
self.args.phase2:
with chio.open_as_container(self.args.input_file) as input_file:
files = [f for f in input_file.list() if "training" in f]
files.sort()
num_files = len(files)
rng.shuffle(files)
f_start_id = 0
else:
f_start_id = self.snapshot.f_id
files = self.snapshot.files
self.args.resume_from_checkpoint = False
num_files = len(files)
if torch.distributed.is_initialized() and \
self.team_size > num_files:
remainder = self.team_size % num_files
data_file = files[(f_start_id * self.team_size + self.team_rank +
remainder * f_start_id) % num_files]
else:
data_file = files[(f_start_id * self.team_size + self.team_rank) %
len(files)]
return pool.submit(create_pretraining_dataset, self.args.input_file,
data_file, self.args.max_predictions_per_seq,
self.args), f_start_id, files, data_file
def update_dataloader(self, pool, f_id, files):
if self.team_size > len(files):
remainder = self.team_size % len(files)
data_file = files[(f_id * self.team_size + self.team_rank +
remainder * f_id) % len(files)]
else:
data_file = files[(f_id * self.team_size + self.team_rank) %
len(files)]
dataset_future = pool.submit(create_pretraining_dataset,
self.args.input_file, data_file,
self.args.max_predictions_per_seq,
self.args)
return dataset_future, data_file
def loss(self, prediction_scores, seq_relationship_score, batch):
_, _, _, masked_lm_labels, next_sentence_labels = batch
loss_fct = torch.nn.CrossEntropyLoss(ignore_index=-1)
masked_lm_loss = loss_fct(
prediction_scores.view(-1, self.config.vocab_size),
masked_lm_labels.view(-1))
next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2),
next_sentence_labels.view(-1))
return masked_lm_loss + next_sentence_loss
def compute_distillation_loss(self, output, another_output, target=None):
c = output.shape[-1]
output = output.view(-1, c)
another_output = another_output.view(-1, c)
with torch.no_grad():
if target is None:
mask = torch.ones(len(output),
1,
device=output.device,
dtype=output.dtype)
else:
mask = (target != -1).long().view(-1, 1)
if self.args.distillation_loss == 'cross_entropy':
other_distr = torch.softmax(another_output, dim=1)
return -torch.sum(
mask *
(torch.log_softmax(output, dim=1) * other_distr)) / sum(mask)
elif self.args.distillation_loss == 'kl_divergence':
return torch.sum(
mask *
(torch.softmax(output, dim=1) *
(torch.log_softmax(output, dim=1) -
torch.log_softmax(another_output, dim=1)))) / sum(mask)
else:
raise ValueError('unknown distillation loss: {}'.format(
self.args.distillation_loss))
def train_simple(self):
global_step = self.snapshot.global_step or 0
if self.args.phase2:
self.args.accum = self.args.train_batch_size // 8
self.args.train_batch_size = 8
else:
self.args.accum = 1
if self.is_main_process():
print("SEED {}".format(self.args.seed))
logger.info("***** Running training *****")
# logger.info(" Num examples = %d", len(train_data))
logger.info(" Batch size = %d", self.args.train_batch_size)
logger.info(" Accum = %d", self.args.accum)
print(" LR = ", self.args.learning_rate)
print("Training. . .")
self.model.train()
average_loss = 0.0 # averaged loss every self.args.log_freq steps
epoch = 0
# Note: We loop infinitely over epochs, termination is handled via
# iteration count
begin = None
with ThreadPoolExecutor(1) as pool:
while True:
dataset_future, f_start_id, files, data_file = \
self.init_dataloader(epoch, pool)
previous_file = data_file
train_dataloader, _ = dataset_future.result(timeout=None)
overflow_buf = torch.cuda.IntTensor([0])
for f_id in range(f_start_id + 1, len(files)):
logger.info("file no %s file %s" % (f_id, previous_file))
dataset_future, data_file = \
self.update_dataloader(pool, f_id, files)
previous_file = data_file
it = 0
for batch in train_dataloader:
if begin is None:
begin = time.time()
it += 1
batch = [t.to(self.device) for t in batch]
loss = self.forward(self.model, batch)
self.backward(loss)
average_loss += loss.item()
if it % self.args.accum == 0:
self.all_reduce(overflow_buf, self.args.accum)
global_step = self.take_optimizer_step(global_step)
it = 0
if global_step % self.args.log_freq == 0:
divisor = self.args.log_freq * self.args.accum
if self.is_main_process():
print(
"Team: {} Step:{} Average Loss = {} ".
format(self.team, global_step,
average_loss / divisor))
average_loss = 0
if global_step >= self.args.max_steps or \
(global_step %
self.args.num_steps_per_checkpoint) == 0:
if self.team_rank == 0:
# Save a trained model
logger.info("** ** Saving model ** **")
self.snapshot.save(global_step, f_id,
files)
if global_step >= self.args.max_steps:
del train_dataloader
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print("Total time taken {}".format(
time.time() - begin))
return self.args
del train_dataloader
# Make sure pool has finished and switch train_dataloader
# NOTE: Will block until complete
train_dataloader, data_file = dataset_future.result(
timeout=None)
epoch += 1
def train_online_distillation_original(self):
global_step = self.snapshot.global_step or 0
if self.is_main_process():
print("SEED {}".format(self.args.seed))
logger.info("***** Running training *****")
# logger.info(" Num examples = %d", len(train_data))
logger.info(" Batch size = %d", self.args.train_batch_size)
print(" LR = ", self.args.learning_rate)
print(" Online Distillation")
print("Training. . .")
self.model.train()
average_loss = 0.0 # averaged loss every self.args.log_freq steps
average_dloss_0 = 0.0 # averaged loss every self.args.log_freq steps
average_dloss_1 = 0.0
epoch = 0
begin = None
# Note: We loop infinitely over epochs, termination is handled via
# iteration count
with ThreadPoolExecutor(1) as pool:
while True:
dataset_future, f_start_id, files, data_file = \
self.init_dataloader(epoch, pool)
previous_file = data_file
train_dataloader, _ = dataset_future.result(timeout=None)
overflow_buf = torch.cuda.IntTensor([0])
for f_id in range(f_start_id + 1, len(files)):
logger.info("file no %s file %s" % (f_id, previous_file))
dataset_future, data_file = \
self.update_dataloader(pool, f_id, files)
previous_file = data_file
for batch in train_dataloader:
if begin is None:
begin = time.time()
step = global_step
if self.args.phase2:
step += self.args.phase1_end_step
if step >= self.args.burnin_steps and \
(step % self.args.distillation_steps) == 0:
self.comm_model()
batch = [t.to(self.device) for t in batch]
_, _, _, masked_lm_labels, _ = batch
if step < self.args.burnin_steps:
loss = self.forward(self.model, batch)
dloss0 = torch.zeros(())
dloss1 = torch.zeros(())
else:
out0, out1 = self.forward(self.model,
batch,
calc_loss=False)
with torch.no_grad():
aout0, aout1 = self.forward(self.another_model,
batch,
calc_loss=False)
loss = self.loss(out0, out1, batch)
dloss0 = \
self.compute_distillation_loss(
out0, aout0, masked_lm_labels.view(-1))
dloss1 = \
self.compute_distillation_loss(out1, aout1)
dloss = dloss0 + dloss1
loss = loss + \
self.args.distillation_weight * dloss
self.backward(loss)
self.all_reduce(overflow_buf)
global_step = self.take_optimizer_step(global_step)
average_loss += loss.item()
average_dloss_0 += dloss0.item()
average_dloss_1 += dloss1.item()
if global_step % self.args.log_freq == 0:
divisor = self.args.log_freq
if self.is_main_process():
print(
"Team: {} Step:{} Average Loss = {} Average dLoss = {} {}"
.format(self.team, global_step,
average_loss / divisor,
average_dloss_0 / divisor,
average_dloss_1 / divisor))
average_loss = 0
average_dloss_0 = 0
average_dloss_1 = 0
if global_step >= self.args.max_steps or \
(global_step %
self.args.num_steps_per_checkpoint) == 0:
if self.team_rank == 0:
# Save a trained model
logger.info("** ** Saving model ** **")
self.snapshot.save(global_step, f_id, files)
if global_step >= self.args.max_steps:
del train_dataloader
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print("Total time taken {}".format(
time.time() - begin))
return self.args
del train_dataloader
# Make sure pool has finished and switch train_dataloader
# NOTE: Will block until complete
train_dataloader, data_file = dataset_future.result(
timeout=None)
epoch += 1
def train_online_distillation_overlap(self):
global_step = self.snapshot.global_step or 0
main_stream = torch.cuda.Stream()
another_model_fwd_stream = torch.cuda.Stream()
all_reduce_stream = torch.cuda.Stream()
distillation_stream = torch.cuda.Stream()
fwd_event = torch.cuda.Event()
bwd_event = torch.cuda.Event()
another_model_fwd_event = torch.cuda.Event()
all_reduce_event = torch.cuda.Event()
distillation_event = torch.cuda.Event()
if self.is_main_process():
print("SEED {}".format(self.args.seed))
logger.info("***** Running training *****")
# logger.info(" Num examples = %d", len(train_data))
logger.info(" Batch size = %d", self.args.train_batch_size)
print(" LR = ", self.args.learning_rate)
print(" Online Distillation")
print("Training. . .")
self.model.train()
average_loss = 0.0 # averaged loss every self.args.log_freq steps
average_dloss_0 = 0
average_dloss_1 = 0
epoch = 0
begin = None
# Note: We loop infinitely over epochs, termination is handled via
# iteration count
batch = None
another_output = None
with ThreadPoolExecutor(1) as pool:
while True:
dataset_future, f_start_id, files, data_file = \
self.init_dataloader(epoch, pool)
previous_file = data_file
train_dataloader, _ = dataset_future.result(timeout=None)
overflow_buf = torch.cuda.IntTensor([0])
for f_id in range(f_start_id + 1, len(files)):
logger.info("file no %s file %s" % (f_id, previous_file))
dataset_future, data_file = \
self.update_dataloader(pool, f_id, files)
previous_file = data_file
for next_batch in train_dataloader:
next_batch = [t.to(self.device) for t in next_batch]
if batch is None:
batch = next_batch
continue
if begin is None:
begin = time.time()
step = global_step
if self.args.phase2:
step += self.args.phase1_end_step
_, _, _, masked_lm_labels, _ = batch
fwd_event.record()
distillation_event.record()
if step >= self.args.burnin_steps:
with torch.cuda.stream(distillation_stream):
distillation_event.wait()
if (step % self.args.distillation_steps) \
== 0:
self.comm_model()
distillation_event.record()
with torch.cuda.stream(main_stream):
fwd_event.wait()
if another_output is None:
loss = self.forward(self.model, batch)
dloss0 = torch.zeros(())
dloss1 = torch.zeros(())
else:
out0, out1 = self.forward(self.model,
batch,
calc_loss=False)
aout0, aout1 = another_output
loss = self.loss(out0, out1, batch)
dloss0 = \
self.compute_distillation_loss(
out0, aout0,
masked_lm_labels.view(-1))
dloss1 = \
self.compute_distillation_loss(out1,
aout1)
dloss = dloss0 + dloss1
loss = loss + \
self.args.distillation_weight * dloss
fwd_event.record()
fwd_event.wait()
bwd_event.record()
with torch.cuda.stream(main_stream):
bwd_event.wait()
self.backward(loss)
bwd_event.record()
bwd_event.wait()
distillation_event.wait()
all_reduce_event.record()
another_model_fwd_event.record()
with torch.cuda.stream(all_reduce_stream):
all_reduce_event.wait()
self.all_reduce(overflow_buf)
all_reduce_event.record()
if step >= self.args.burnin_steps:
with torch.cuda.stream(another_model_fwd_stream):
another_model_fwd_event.wait()
with torch.no_grad():
another_output = self.forward(
self.another_model,
next_batch,
calc_loss=False)
another_model_fwd_event.record()
all_reduce_event.wait()
another_model_fwd_event.wait()
global_step = self.take_optimizer_step(global_step)
average_loss += loss.item()
average_dloss_0 += dloss0.item()
average_dloss_1 += dloss1.item()
if global_step % self.args.log_freq == 0:
divisor = self.args.log_freq
if self.is_main_process():
print(
"Team: {} Step:{} Average Loss = {} Average dLoss = {} {}"
.format(self.team, global_step,
average_loss / divisor,
average_dloss_0 / divisor,
average_dloss_1 / divisor))
average_loss = 0
average_dloss_0 = 0
average_dloss_1 = 0
if global_step >= self.args.max_steps or \
(global_step %
self.args.num_steps_per_checkpoint) == 0:
if self.team_rank == 0:
# Save a trained model
logger.info("** ** Saving model ** **")
self.snapshot.save(global_step, f_id, files)
if global_step >= self.args.max_steps:
del train_dataloader
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print(
"Total time taken {}".format(time.time() -
begin))
return self.args
batch = next_batch
del train_dataloader
# Make sure pool has finished and switch train_dataloader
# NOTE: Will block until complete
train_dataloader, data_file = dataset_future.result(
timeout=None)
epoch += 1
def train_online_distillation_logit(self):
global_step = self.snapshot.global_step or 0
if self.is_main_process():
print("SEED {}".format(self.args.seed))
logger.info("***** Running training *****")
# logger.info(" Num examples = %d", len(train_data))
logger.info(" Batch size = %d", self.args.train_batch_size)
print(" LR = ", self.args.learning_rate)
print(" Online Distillation")
print("Training. . .")
self.model.train()
average_loss = 0.0 # averaged loss every self.args.log_freq steps
average_dloss_0 = 0.0
average_dloss_1 = 0.0
epoch = 0
begin = None
# Note: We loop infinitely over epochs, termination is handled via
# iteration count
rng = random.Random(self.args.data_seed)
cnt = 0
with ThreadPoolExecutor(1) as pool:
while True:
cnt += 1
step = global_step
if self.args.phase2:
step += self.args.phase1_end_step
if step < self.args.burnin_steps:
dataset_future, f_start_id, files, data_file = \
self.init_dataloader(epoch, pool)
use_same_data = False
else:
torch.manual_seed(self.args.data_seed + cnt)
dataset_future, f_start_id, files, data_file = \
self.init_dataloader(epoch, pool, rng)
use_same_data = True
previous_file = data_file
train_dataloader, _ = dataset_future.result(timeout=None)
overflow_buf = torch.cuda.IntTensor([0])
for f_id in range(f_start_id + 1, len(files)):
logger.info("file no %s file %s" % (f_id, previous_file))
dataset_future, data_file = \
self.update_dataloader(pool, f_id, files)
previous_file = data_file
for batch in train_dataloader:
if begin is None:
begin = time.time()
step = global_step
if self.args.phase2:
step += self.args.phase1_end_step
if step == self.args.burnin_steps and \
not use_same_data:
break
batch = [t.to(self.device) for t in batch]
_, _, _, masked_lm_labels, _ = batch
aout0 = None
aout1 = None
if step < self.args.burnin_steps:
loss = self.forward(self.model, batch)
dloss0 = torch.zeros(())
dloss1 = torch.zeros(())
else:
out0, out1 = self.forward(self.model,
batch,
calc_loss=False)
mask = masked_lm_labels.view(-1)
c = out0.shape[-1]
# Send logit that are not maksed
dout0 = out0.view(-1, c)
dout0 = dout0[mask != -1]
with torch.no_grad():
aout0 = dout0.detach().clone()
aout1 = out1.detach().clone()
flat_dist_call([aout0, aout1],
torch.distributed.all_reduce,
(torch.distributed.ReduceOp.SUM,
self.equalize_data_group))
aout0 = aout0 * self.size - dout0
aout1 = aout1 * self.size - out1
loss = self.loss(out0, out1, batch)
dloss0 = \
self.compute_distillation_loss(dout0, aout0)
dloss1 = \
self.compute_distillation_loss(out1, aout1)
dloss = dloss0 + dloss1
loss = loss + \
self.args.distillation_weight * dloss
self.backward(loss)
self.all_reduce(overflow_buf)
global_step = self.take_optimizer_step(global_step)
average_loss += loss.item()
average_dloss_0 += dloss0.item()
average_dloss_1 += dloss1.item()
if global_step % self.args.log_freq == 0:
divisor = self.args.log_freq
if self.is_main_process():
print(
"Team: {} Step:{} Average Loss = {} Average dLoss = {} {}"
.format(self.team, global_step,
average_loss / divisor,
average_dloss_0 / divisor,
average_dloss_1 / divisor))
average_loss = 0
average_dloss_0 = 0
average_dloss_1 = 0
if global_step >= self.args.max_steps or \
(global_step %
self.args.num_steps_per_checkpoint) == 0:
if self.team_rank == 0:
# Save a trained model
logger.info("** ** Saving model ** **")
self.snapshot.save(global_step, f_id, files)
if global_step >= self.args.max_steps:
del train_dataloader
torch.distributed.barrier()
if torch.distributed.get_rank() == 0:
print(
"Total time taken {}".format(time.time() -
begin))
return self.args
del train_dataloader
# Make sure pool has finished and switch train_dataloader
# NOTE: Will block until complete
train_dataloader, data_file = dataset_future.result(
timeout=None)
if step == self.args.burnin_steps and not use_same_data:
break
epoch += 1