def load_model(self):
self.tokenizer = BertTokenizer.from_pretrained(
self.args.pretrained_path, do_lower_case=self.args.do_lower_case)
self.config = BertConfig.from_pretrained(
self.args.pretrained_path, num_labels=self.args.num_labels)
if self.args.resume_model:
self.model = BertForSequenceClassification.from_pretrained(
self.args.resume_model_path, config=self.config)
else:
self.model = BertForSequenceClassification.from_pretrained(
self.args.pretrained_path, config=self.config)
if self.args.cuda:
self.model.cuda()
if self.args.n_gpus > 1:
self.model = DataParallel(self.model)
def __init__(self, n_bert_layers=N_LAYERS, n_features=N_FEATURES, extract_features=False):
super(BERTForFeatures, self).__init__()
self.extract_features = extract_features
self.bert = BertForSequenceClassification.from_pretrained("bert-base-uncased")
self.bert.bert.encoder.layer = self.bert.bert.encoder.layer[:n_bert_layers]
self.bert.classifier = nn.Linear(768, n_features)
self.cls = nn.Linear(n_features, 1)
def setup_class(self):
self.use_gpu = torch.cuda.is_available()
self.test_dir = Path(tempfile.mkdtemp())
self.base_tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True,
cache_dir=self.test_dir)
self.rust_tokenizer = PyBertTokenizer(
get_from_cache(self.base_tokenizer.pretrained_vocab_files_map['vocab_file']['bert-base-uncased']))
self.model = BertForSequenceClassification.from_pretrained('bert-base-uncased', output_attentions=False).eval()
if self.use_gpu:
self.model.cuda()
self.sentence_list = ['For instance, on the planet Earth, man had always assumed that he was more intelligent '
'than dolphins because he had achieved so much—the wheel, New York, wars and so on—whilst'
' all the dolphins had ever done was muck about in the water having a good time. But '
'conversely, the dolphins had always believed that they were far more intelligent than '
'man—for precisely the same reasons.'] * 64
# Pre-allocate GPU memory
tokens_list = [self.base_tokenizer.tokenize(sentence) for sentence in self.sentence_list]
features = [self.base_tokenizer.convert_tokens_to_ids(tokens) for tokens in tokens_list]
features = [self.base_tokenizer.prepare_for_model(input, None, add_special_tokens=True, max_length=128) for
input
in features]
all_input_ids = torch.tensor([f['input_ids'] for f in features], dtype=torch.long)
if self.use_gpu:
all_input_ids = all_input_ids.cuda()
with torch.no_grad():
_ = self.model(all_input_ids)[0].cpu().numpy()
def load_model(model_name, data_dir):
processors = {
"rte": RteProcessor
}
output_modes = {
"rte": "classification"
}
# task_name = args.task_name.lower()
task_name = 'rte'
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() # [0,1]
num_labels = len(label_list)
pretrain_model_dir = '{}/FineTuneOn{}'.format(data_dir, model_name)
# pretrain_model_dir = 'please enter your pretrain models path here/FineTuneOn{}'.format(model_name)
# Prepare model
# cache_dir = os.path.join(str(PYTORCH_TRANSFORMERS_CACHE), '{} model distributed_{}'.format(model_name, -1))
# # cache_dir = os.path.join(str(PYTORCH_TRANSFORMERS_CACHE), '{} model distributed_{}'.format(model_name, -1))
model = BertForSequenceClassification.from_pretrained(pretrain_model_dir, num_labels=num_labels)
tokenizer = BertTokenizer.from_pretrained(pretrain_model_dir)
# model = BertForSequenceClassification.from_pretrained('bert-base-uncased',
# cache_dir=cache_dir,
# num_labels=num_labels)
# tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)
# print(tokenizer)
return model, tokenizer
def __init__(self, model_path, num_labels=2):
model_file_path = utils.download_if_needed(model_path)
self.model = BertForSequenceClassification.from_pretrained(
model_file_path, num_labels=num_labels)
self.model.to(utils.device)
self.model.eval()
self.tokenizer = BERTTokenizer(model_file_path)
def __init__(self, hparams):
super().__init__(hparams)
# super light BERT model
config = BertConfig(hidden_size=12,
num_hidden_layers=1,
num_attention_heads=1,
intermediate_size=12)
self.model = BertForSequenceClassification(config)
def from_pretrained(model_id_or_path: str,
device: Optional[torch.device] = None):
# First, Use the function of from_pretrained to load the model you trained.
torch_model = TorchBertForSequenceClassification.from_pretrained(
model_id_or_path)
# Then, Use the init function of the acceleration model to get it.
model = BertForSequenceClassification.from_torch(torch_model, device)
model._torch_model = torch_model # prevent destroy torch model.
return model
def __init__(self):
# Googleの公開している事前学習済みのトークナイザとモデルをロード
self.tokenizer = BertTokenizer.from_pretrained(
"bert-base-multilingual-cased", do_lower_case=False)
self.model = BertForSequenceClassification.from_pretrained(
"bert-base-multilingual-cased", num_labels=2)
# Google Colabでファインチューニングしたモデルをロード
self.model.load_state_dict(
torch.load("bert_evaluator.bin", map_location='cpu'))
def __init__(self, device, serial_model_path, par_model_path):
self.device = device
pretrained_path = 'cl-tohoku/bert-base-japanese-whole-word-masking'
self.tokenizer = BertTokenizer.from_pretrained(pretrained_path,
do_lower_case=False)
config = BertConfig.from_pretrained(pretrained_path)
config.num_labels = 4
self.serial_model = BertForSequenceClassification(config)
config.num_labels = 2
self.par_model = BertForSequenceClassification(config)
self.serial_model.load_state_dict(torch.load(serial_model_path))
self.serial_model.to(self.device)
self.serial_model.eval()
self.par_model.load_state_dict(torch.load(par_model_path))
self.par_model.to(self.device)
self.par_model.eval()
def _create_task_model(self):
bert_config = BertConfig(vocab_size=21128,
hidden_size=312,
num_hidden_layers=1,
num_attention_heads=4,
intermediate_size=1536,
max_position_embeddings=128)
bert_tokenizer = BertTokenizer('../dataset/vocab.txt')
bert_model = BertForSequenceClassification(bert_config)
return TaskModel(bert_config, bert_tokenizer, bert_model)
def __init__(self, model_path, num_labels=2, entailment=False):
model_file_path = utils.download_if_needed(model_path)
self.model = BertForSequenceClassification.from_pretrained(
model_file_path, num_labels=num_labels)
self.model.to(utils.get_device())
self.model.eval()
if entailment:
self.tokenizer = BERTEntailmentTokenizer()
else:
self.tokenizer = BERTTokenizer(model_file_path)
def __init__(self, hparams):
super().__init__(hparams)
# super light BERT model
config = BertConfig(hidden_size=12,
num_hidden_layers=1,
num_attention_heads=1,
intermediate_size=12)
self.model = BertForSequenceClassification(config)
self.tokenizer = BertTokenizer.from_pretrained(
"bert-base-cased", config=config, cache_dir=hparams.cache_dir)
def get_bert_classifier(model_type: str,
num_labels: int,
model_file: str = None,
device: str = "cpu") -> BertForSequenceClassification:
"""
Load a BertForSequenceClassification model, either from a model file with a finetuned
model, or as a simple pretrained model.
Args:
model_type: the type of BERT model to load, e.g. "bert-base-uncased"
num_labels: the number of cells for the output layer of the classifier
model_file: if we load a finetuned model, this is the file where the model is saved
device: the device to load the model to ("cpu" or "cuda")
Returns: a BertForSequenceClassification model
"""
if model_file:
model_state_dict = torch.load(
model_file, map_location=lambda storage, loc: storage)
if "distilbert" in model_type:
model = DistilBertForSequenceClassification.from_pretrained(
model_type, state_dict=model_state_dict, num_labels=num_labels)
else:
model = BertForSequenceClassification.from_pretrained(
model_type, state_dict=model_state_dict, num_labels=num_labels)
else:
if "distilbert" in model_type:
model = DistilBertForSequenceClassification.from_pretrained(
model_type, num_labels=num_labels)
else:
model = BertForSequenceClassification.from_pretrained(
model_type, num_labels=num_labels)
model.to(device)
return model
def __init__(self, label_list, device, cache_dir):
self._label_list = label_list
self._device = device
self._tokenizer = BertTokenizer.from_pretrained(BERT_MODEL,
do_lower_case=True,
cache_dir=cache_dir)
self._model = BertForSequenceClassification.from_pretrained(
BERT_MODEL, num_labels=len(label_list), cache_dir=cache_dir)
self._model.to(device)
self._optimizer = None
self._dataset = {}
self._data_loader = {}
def bertForSequenceClassification(*args, **kwargs):
"""
BertForSequenceClassification is a fine-tuning model that includes
BertModel and a sequence-level (sequence or pair of sequences) classifier
on top of the BertModel. Note that the classification head is only initialized
and has to be trained.
The sequence-level classifier is a linear layer that takes as input the
last hidden state of the first character in the input sequence
(see Figures 3a and 3b in the BERT paper).
Args:
num_labels: the number (>=2) of classes for the classifier.
Example:
# Load the tokenizer
>>> import torch
>>> tokenizer = torch.hub.load('huggingface/pytorch-transformers', 'bertTokenizer', 'bert-base-cased', do_basic_tokenize=False)
# Prepare tokenized input
>>> text = "[CLS] Who was Jim Henson ? [SEP] Jim Henson was a puppeteer [SEP]"
>>> tokenized_text = tokenizer.tokenize(text)
>>> indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
>>> segments_ids = [0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1]
>>> tokens_tensor = torch.tensor([indexed_tokens])
>>> segments_tensors = torch.tensor([segments_ids])
# Load bertForSequenceClassification
>>> model = torch.hub.load('huggingface/pytorch-transformers', 'bertForSequenceClassification', 'bert-base-cased', num_labels=2)
>>> model.eval()
# Predict the sequence classification logits
>>> with torch.no_grad():
seq_classif_logits = model(tokens_tensor, segments_tensors)
# Or get the sequence classification loss
>>> labels = torch.tensor([1])
>>> seq_classif_loss = model(tokens_tensor, segments_tensors, labels=labels) # set model.train() before if training this loss
"""
model = BertForSequenceClassification.from_pretrained(*args, **kwargs)
return model
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument(
"--train_data_dir",
default='./',
type=str,
required=True,
help=
"The input data dir. Should contain the .tsv files (or other data files) for the task."
)
parser.add_argument(
"--test_data_dir",
default='./',
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='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("--task_name",
default='ci_evaluation',
type=str,
required=False,
help="The name of the task to train.")
parser.add_argument(
"--output_dir",
default="./saves/",
type=str,
required=True,
help=
"The output directory where the model predictions and checkpoints will be written."
)
## Other parameters
parser.add_argument(
"--load_model",
action='store_true',
help="Whether to load a fine-tuned model from output directory.")
parser.add_argument(
"--model_name",
default=None,
type=str,
help=
"The name of the model to load, relevant only in case that load_model is positive."
)
parser.add_argument("--load_model_path",
default='./saves/pytorch_model.bin',
type=str,
help="Path to directory containing fine-tuned model.")
parser.add_argument(
"--save_on_epoch_end",
action='store_true',
help="Whether to save the weights each time an epoch ends.")
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("--N_train",
type=int,
default=-1,
help="number of training examples")
parser.add_argument("--N_dev",
type=int,
default=-1,
help="number of development examples")
parser.add_argument(
"--save_best_weights",
type=bool,
default=True,
help="saves model weight each time epoch accuracy is maximum")
parser.add_argument(
"--max_seq_length",
default=128,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument(
"--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
default=64,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=64,
type=int,
help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--num_train_epochs",
default=5,
type=int,
help="Total number of training epochs to perform.")
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(
'--save_according_to',
type=str,
default='acc',
help="save results according to in domain dev acc or in domain dev loss"
)
parser.add_argument('--optimizer',
type=str,
default='adam',
help="which optimizer model to use: adam or sgd")
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('--data_type',
type=str,
default='sports',
help="Evaluate classifier on sports or wiki")
parser.add_argument(
"--soph_flag",
action='store_true',
help="Evaluate on sophisticated examples or not (unsophisticated).")
parser.add_argument(
"--generation_flag",
action='store_true',
help=
"Evaluate on original examples or caunterfactuals (generated ones).")
args = parser.parse_args()
processors = {
"sentiment": DiscoFuseProcessor,
"ci_evaluation": GeneratedDiscoFuseProcessor,
}
output_modes = {
"sentiment": "classification",
"ci_evaluation": "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')
print("device: {} n_gpu: {}, distributed training: {}".format(
device, n_gpu, bool(args.local_rank != -1)))
print("learning rate: {}, batch size: {}".format(args.learning_rate,
args.train_batch_size))
if args.gradient_accumulation_steps < 1:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
.format(args.gradient_accumulation_steps))
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 and not args.load_model:
raise ValueError(
"Output directory ({}) already exists and is not empty.".format(
args.output_dir))
if not os.path.exists(
args.output_dir) and args.task_name != "ci_evaluation":
os.makedirs(args.output_dir)
task_name = args.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
output_mode = output_modes[task_name]
label_list = processor.get_labels()
num_labels = len(label_list)
train_examples = None
num_train_optimization_steps = None
if args.do_train:
train_examples = processor.get_train_examples(args.train_data_dir)
train_examples = train_examples[:args.
N_train] if args.N_train > 0 else train_examples
num_train_optimization_steps = int(
len(train_examples) / 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(
)
# Load a trained model and vocabulary that you have fine-tuned
if args.load_model or args.load_model_path != '':
# path to directory to load from fine-tuned model
load_path = args.load_model_path if args.load_model_path != '' else args.output_dir
cache_dir = args.cache_dir if args.cache_dir else os.path.join(
str(PYTORCH_PRETRAINED_BERT_CACHE), 'distributed_{}'.format(
args.local_rank))
if task_name == 'sentiment' or task_name == 'ci_evaluation':
model = BertForSequenceClassification.from_pretrained(
args.bert_model,
cache_dir=args.cache_dir,
num_labels=num_labels)
else:
print('Error! no task named: {}'.format(task_name))
exit()
# load pre train modek weights
if args.load_model_path is not None:
print("--- Loading model:", args.load_model_path)
model.load_state_dict(torch.load(args.load_model_path),
strict=False)
else:
model.load_state_dict(torch.load(
os.path.join(load_path, "pytorch_model.bin")),
strict=False)
tokenizer = BertTokenizer.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
if not tokenizer:
tokenizer = BertTokenizer.from_pretrained(
args.bert_model, do_lower_case=args.do_lower_case)
model.to(device)
else:
cache_dir = args.cache_dir if args.cache_dir else os.path.join(
str(PYTORCH_PRETRAINED_BERT_CACHE), 'distributed_{}'.format(
args.local_rank))
if task_name == "sentiment":
model = BertForSequenceClassification.from_pretrained(
args.bert_model,
cache_dir=args.cache_dir,
num_labels=num_labels)
else:
print('Error! no task named: {}'.format(task_name))
exit()
tokenizer = BertTokenizer.from_pretrained(
args.bert_model, do_lower_case=args.do_lower_case)
model.to(device)
if args.local_rank != -1:
model = DDP(model)
elif n_gpu > 1:
model = torch.nn.DataParallel(model)
# Prepare optimizer
if args.do_train:
param_optimizer = model.named_parameters()
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params': [
p for n, p in param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
if args.optimizer == 'adam':
optimizer = AdamW(optimizer_grouped_parameters,
lr=args.learning_rate)
elif args.optimizer == 'sgd':
optimizer = torch.optim.sgd(model.parameters(),
lr=args.learning_rate,
weight_decay=1e-2)
# scheduler = ReduceLROnPlateau(optimizer, 'min',
# patience=hparams.reduce_lr_on_plateau_patience,
# factor=hparams.reduce_lr_on_plateau_factor, verbose=True)
global_step = 0
# prepare dev-set evaluation DataLoader
# if do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
if task_name == 'ci_evaluation':
eval_dataloader = make_DataLoader(data_dir=args.test_data_dir,
processor=processor,
tokenizer=tokenizer,
label_list=label_list,
max_seq_length=args.max_seq_length,
batch_size=args.eval_batch_size,
output_mode=output_mode,
local_rank=args.local_rank,
mode="dev",
N=args.N_dev,
data_type=args.data_type,
soph_flag=soph_flag,
generation_flag=generation_flag)
else:
eval_dataloader = make_DataLoader(data_dir=args.test_data_dir,
processor=processor,
tokenizer=tokenizer,
label_list=label_list,
max_seq_length=args.max_seq_length,
batch_size=args.eval_batch_size,
output_mode=output_mode,
local_rank=args.local_rank,
mode="dev",
N=args.N_dev)
if args.do_train:
# prepare training DataLoader
train_dataloader = make_DataLoader(data_dir=args.train_data_dir,
processor=processor,
tokenizer=tokenizer,
label_list=label_list,
max_seq_length=args.max_seq_length,
batch_size=train_batch_size,
output_mode=output_mode,
local_rank=args.local_rank,
mode="train",
N=args.N_train)
model.train()
# main training loop
best_dev_acc = 0.0
best_dev_loss = 100000.0
for epoch in trange(int(args.num_train_epochs), desc="Epoch"):
tr_loss = 0
tr_acc = 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[:4]
# define a new function to compute loss values for both output_modes
outputs = model(input_ids,
segment_ids,
input_mask,
labels=None)
logits = outputs[0]
if output_mode == "classification":
loss_fct = CrossEntropyLoss(ignore_index=-1)
loss = loss_fct(logits.view(-1, num_labels),
label_ids.view(-1))
preds = logits.detach().cpu().numpy()
preds = np.argmax(preds, axis=1)
tr_acc += compute_metrics(
task_name, preds,
label_ids.detach().cpu().numpy())["acc"]
elif output_mode == "regression":
loss_fct = MSELoss()
loss = loss_fct(logits.view(-1), label_ids.view(-1))
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
loss.backward()
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if (step + 1) % args.gradient_accumulation_steps == 0:
optimizer.step()
optimizer.zero_grad()
global_step += 1
# run evaluation on dev set
# dev-set loss
eval_results_dev = evaluate(eval_dataloader=eval_dataloader,
model=model,
device=device,
tokenizer=tokenizer,
output_mode=output_mode,
num_labels=num_labels)
dev_acc, dev_loss = eval_results_dev[:2]
# train-set loss
tr_loss /= nb_tr_steps
tr_acc /= nb_tr_steps
# print and save results
result = {
"acc": tr_acc,
"loss": tr_loss,
"dev_acc": dev_acc,
"dev_loss": dev_loss
}
print('Epoch {}'.format(epoch + 1))
for key, val in result.items():
print("{}: {}".format(key, val))
print("***** Evaluation results *****")
for key in sorted(result.keys()):
print(" %s = %s", key, str(result[key]))
# Save model, configuration and tokenizer on the first epoch
# If we save using the predefined names, we can load using `from_pretrained`
model_to_save = model.module if hasattr(
model, 'module') else model # Only save the model it-self
if epoch == 0:
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
model_to_save.config.to_json_file(output_config_file)
tokenizer.save_vocabulary(args.output_dir)
if args.save_on_epoch_end:
# Save a trained model
output_model_file = os.path.join(
args.output_dir,
WEIGHTS_NAME + '.Epoch_{}'.format(epoch + 1))
torch.save(model_to_save.state_dict(), output_model_file)
# save model with best performance on dev-set
if args.save_best_weights and dev_acc > best_dev_acc:
print("Saving model, accuracy improved from {} to {}".format(
best_dev_acc, dev_acc))
output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
torch.save(model_to_save.state_dict(), output_model_file)
best_dev_acc = dev_acc
if args.save_according_to == 'acc':
if dev_acc > best_dev_acc:
best_dev_acc = dev_acc
elif args.save_according_to == 'loss':
if dev_loss < best_dev_loss:
best_dev_loss = dev_loss
if args.save_according_to == 'acc':
print('Best results: Acc - {}'.format(best_dev_acc))
elif args.save_according_to == 'loss':
print('Best results: Loss - {}'.format(best_dev_loss))
if args.model_name is not None:
final_output_eval_file = os.path.join(
args.output_dir,
args.model_name + "-final_eval_results.txt")
else:
final_output_eval_file = os.path.join(
args.output_dir, "final_eval_results.txt")
elif args.do_eval and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
# dev-set loss
eval_results_dev = evaluate(eval_dataloader=eval_dataloader,
model=model,
device=device,
tokenizer=tokenizer,
output_mode=output_mode,
num_labels=num_labels)
print("eval_results_dev -", eval_results_dev)
dev_acc, dev_loss = eval_results_dev[:2]
# print results
print('Accuracy: {}'.format(dev_acc))
print('Loss: {}'.format(dev_loss))
else:
raise ValueError(
"At least one of `do_train` or `do_eval` must be True.")
# use 4 threads for BERT inference
turbo_transformers.set_num_threads(4)
model_id = os.path.join(os.path.dirname(__file__),
"bert_model") # the model of huggingface's path
tokenizer = BertTokenizer.from_pretrained(
model_id) # the initialization of tokenizer
turbo_model = BertForSequenceClassification.from_pretrained(
model_id,
torch.device("cpu:0")) # the initialization of the acceleration model
# predict after loading the model
text = "Sample input text"
inputs = tokenizer.encode_plus(text,
add_special_tokens=True,
return_tensors="pt")
# turbo_result holds the returned logits from TurboTransformers model
turbo_result = turbo_model(**inputs)
torch_model = TorchBertForSequenceClassification.from_pretrained(model_id)
# torch_result holds the returned logits from original Transformers model
torch_result = torch_model(**inputs)[0]
print(turbo_result)
# tensor([[0.2716, 0.0318]], grad_fn=<AddmmBackward>)
print(
torch_result) # torch_result and turbo_result should hold the same logits
# tensor([[0.2716, 0.0318]], grad_fn=<AddmmBackward>)
import torch
from keras.preprocessing.sequence import pad_sequences
if torch.cuda.is_available():
device = torch.device("cuda")
print('There are %d GPU(s) available.' % torch.cuda.device_count())
print('We will use the GPU:', torch.cuda.get_device_name(0))
else:
device = torch.device("cpu")
print('No GPU available, using the CPU instead.')
#모델 저장된 경로
output_dir = './model_save4/'
# Load a trained model and vocabulary that you have fine-tuned
model = BertForSequenceClassification.from_pretrained(output_dir)
tokenizer = BertTokenizer.from_pretrained(output_dir)
# Copy the model to the GPU.
model.to(device)
def convert_input_data(sentences):
# BERT의 토크나이저로 문장을 토큰으로 분리
tokenized_texts = [tokenizer.tokenize(sent) for sent in sentences]
# 입력 토큰의 최대 시퀀스 길이
MAX_LEN = 512
# 토큰을 숫자 인덱스로 변환
class BertEvaluator:
def __init__(self, device, serial_model_path, par_model_path):
self.device = device
pretrained_path = 'cl-tohoku/bert-base-japanese-whole-word-masking'
self.tokenizer = BertTokenizer.from_pretrained(pretrained_path,
do_lower_case=False)
config = BertConfig.from_pretrained(pretrained_path)
config.num_labels = 4
self.serial_model = BertForSequenceClassification(config)
config.num_labels = 2
self.par_model = BertForSequenceClassification(config)
self.serial_model.load_state_dict(torch.load(serial_model_path))
self.serial_model.to(self.device)
self.serial_model.eval()
self.par_model.load_state_dict(torch.load(par_model_path))
self.par_model.to(self.device)
self.par_model.eval()
def evaluate(self, user_input, candidate):
with torch.no_grad():
tokenized = self.tokenizer([[user_input, candidate]],
return_tensors="pt")
input_ids = tokenized["input_ids"].to(self.device)
token_type_ids = tokenized["token_type_ids"].to(self.device)
result_serial = self.serial_model.forward(
input_ids, token_type_ids=token_type_ids)
_, predicted = torch.max(result_serial[0], 1)
result_serial = predicted.cpu().numpy().tolist()
result_par = self.par_model.forward(input_ids,
token_type_ids=token_type_ids)
result_par = F.softmax(result_par[0], dim=1)
result_par = result_par[0].cpu().numpy().tolist()
return result_serial, result_par
def main():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--data_dir",
default='./data/input/',
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='bert-base-chinese', 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("--config_file", default='bert-base-chinese', 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='xgfy',
type=str,
required=True,
help="The name of the task to train.")
parser.add_argument("--vacab_root",
default='./data/model/',
type=str,
required=True,
help="The directory where the vocab file is saved.")
parser.add_argument("--output_dir",
default='./data/output/',
type=str,
required=True,
help="The output directory where the model predictions and checkpoints will be written.")
parser.add_argument("--weight_name",
default='net_weight_1.bin',
type=str,
)
parser.add_argument("--config_name",
default='config_name_1.bin',
type=str,
)
# Other parameters
parser.add_argument("--cache_dir",
default="./data/model/",
type=str,
help="Where do you want to store the pre-trained models downloaded from s3")
parser.add_argument("--max_seq_length",
default=128,
type=int,
help="The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument("--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=8,
type=int,
help="Total batch size for eval.")
parser.add_argument("--log_frq",
default=50,
type=int)
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--num_train_epochs",
default=1.0,
type=int,
help="Total number of training epochs to perform.")
parser.add_argument("--n_warmup",
default=1000,
type=int,
help="step of training to perform linear learning rate warmup for.")
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('--parall',
action='store_true')
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()
# 新冠肺炎
processors = {
"xgfy": SimProcessor
}
num_labels_task = {
"xgfy": 2,
}
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda:0" if torch.cuda.is_available() and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
# torch.distributed.init_process_group(backend='nccl')
logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
device, n_gpu, bool(args.local_rank != -1), args.fp16))
if args.gradient_accumulation_steps < 1:
raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
args.gradient_accumulation_steps))
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if not args.do_train and not args.do_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True.")
# if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train:
# raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
# if not os.path.exists(args.output_dir):
# os.makedirs(args.output_dir)
task_name = args.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]
num_labels = num_labels_task[task_name]
label_list = processor.get_labels()
tokenizer = BertTokenizer.from_pretrained(args.vacab_root, do_lower_case=args.do_lower_case)
train_examples = None
num_train_optimization_steps = None
if args.do_train:
train_examples = processor.get_train_examples(args.data_dir)
num_train_optimization_steps = int(
len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps) * args.num_train_epochs
if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size()
# Prepare model
cache_dir = args.cache_dir if args.cache_dir else os.path.join(str(PYTORCH_PRETRAINED_BERT_CACHE), 'distributed_{0}')
# cache_dir = args.cache_dir if args.cache_dir else os.path.join(PYTORCH_PRETRAINED_BERT_CACHE, 'distributed_{}'.format(str(args.local_rank)))
config = BertConfig.from_pretrained(args.config_file, num_labels=num_labels)
model = BertForSequenceClassification.from_pretrained(args.bert_model,
config=config,
cache_dir=cache_dir)
if args.fp16:
model.half()
model.to(device)
if args.local_rank != -1:
try:
from apex.parallel import DistributedDataParallel as DDP
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
model = DDP(model)
elif n_gpu > 1 and args.parall:
model = torch.nn.DataParallel(model)
# Prepare optimizer
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
if args.fp16:
try:
from apex.optimizers import FP16_Optimizer
from apex.optimizers import FusedAdam
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
optimizer = FusedAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
bias_correction=False,
max_grad_norm=1.0)
if args.loss_scale == 0:
optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
else:
optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale)
else:
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate)
global_step = 0
nb_tr_steps = 0
tr_loss = 0
if args.do_train:
train_features = convert_examples_to_features(
train_examples, label_list, args.max_seq_length, tokenizer)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps)
all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
if args.local_rank == -1:
train_sampler = RandomSampler(train_data)
else:
train_sampler = DistributedSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs
scheduler = get_linear_schedule_with_warmup(
optimizer, num_warmup_steps=args.n_warmup, num_training_steps=t_total
)
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
loss, _ = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask, labels=label_ids)
if n_gpu > 1 and args.parall:
loss = loss.mean() # mean() to average on multi-gpu.
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
optimizer.backward(loss)
else:
loss.backward()
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
# modify learning rate with special warm up BERT uses
# if args.fp16 is False, BertAdam is used that handles this automatically
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step()
scheduler.step()
optimizer.zero_grad()
global_step += 1
if (global_step) % args.log_frq == 0:
logger.info("TrLoss: {:.2f} | Loss: {:.2f} | Lr: {:.2f}".format(tr_loss, loss.item(), scheduler.get_lr()[0]))
if args.do_train:
# Save a trained model and the associated configuration
model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self
output_model_file = os.path.join(args.output_dir, args.weight_name)
torch.save(model_to_save.state_dict(), output_model_file)
output_config_file = os.path.join(args.output_dir, args.config_name)
with open(output_config_file, 'w') as f:
f.write(model_to_save.config.to_json_string())
# Load a trained model and config that you have fine-tuned
config = BertConfig(output_config_file)
model = BertForSequenceClassification(config)
model.load_state_dict(torch.load(output_model_file))
else:
output_model_file = os.path.join(args.output_dir, args.weight_name)
output_config_file = os.path.join(args.output_dir, args.config_name)
config = BertConfig(output_config_file)
model = BertForSequenceClassification(config)
model.load_state_dict(torch.load(output_model_file))
# model = BertForSequenceClassification.from_pretrained(args.bert_model)
model.to(device)
if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
eval_examples = processor.get_dev_examples(args.data_dir)
eval_features = convert_examples_to_features(
eval_examples, label_list, args.max_seq_length, tokenizer)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
model.eval()
eval_loss, eval_accuracy = 0, 0
nb_eval_steps, nb_eval_examples = 0, 0
for input_ids, input_mask, segment_ids, label_ids in tqdm(eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
tmp_eval_loss, logits = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask, labels=label_ids)
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}
logger.info(result)
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
def main():
parser = argparse.ArgumentParser()
## Required parameters
'''
python -u demo.py
'''
parser.add_argument("--premise_str",
default=None,
type=str,
required=True,
help="text to classify")
parser.add_argument("--hypo_list",
default=None,
type=str,
required=True,
help="sentences separated by |")
parser.add_argument("--task_name",
default='rte',
type=str,
help="The name of the task to train.")
parser.add_argument(
"--max_seq_length",
default=128,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval on the dev set.")
# parser.add_argument("--do_lower_case",
# action='store_true',
# help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=256,
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(
"--warmup_proportion",
default=0.1,
type=float,
help=
"Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
'--gradient_accumulation_steps',
type=int,
default=1,
help=
"Number of updates steps to accumulate before performing a backward/update pass."
)
parser.add_argument(
'--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument(
'--loss_scale',
type=float,
default=0,
help=
"Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n")
parser.add_argument('--server_ip',
type=str,
default='',
help="Can be used for distant debugging.")
parser.add_argument('--server_port',
type=str,
default='',
help="Can be used for distant debugging.")
args = parser.parse_args()
processors = {"rte": RteProcessor}
output_modes = {"rte": "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')
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)
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() #[0,1]
num_labels = len(label_list)
train_examples = None
# Prepare model
# cache_dir = args.cache_dir if args.cache_dir else os.path.join(str(PYTORCH_TRANSFORMERS_CACHE), 'distributed_{}'.format(args.local_rank))
# model = BertForSequenceClassification.from_pretrained(args.bert_model,
# cache_dir=cache_dir,
# num_labels=num_labels)
# tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
pretrain_model_dir = '/export/home/Dataset/fine_tune_Bert_stored/FineTuneOnRTE' #FineTuneOnCombined'# FineTuneOnMNLI
model = BertForSequenceClassification.from_pretrained(
pretrain_model_dir, num_labels=num_labels)
tokenizer = BertTokenizer.from_pretrained(pretrain_model_dir)
model.to(device)
if 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 = AdamW(optimizer_grouped_parameters,
# lr=args.learning_rate)
global_step = 0
nb_tr_steps = 0
tr_loss = 0
max_test_unseen_acc = 0.0
max_dev_unseen_acc = 0.0
max_dev_seen_acc = 0.0
max_overall_acc = 0.0
'''load test set'''
seen_types = set()
# test_examples, test_label_list, test_hypo_seen_str_indicator, test_hypo_2_type_index = processor.get_examples_Yahoo_test('/export/home/Dataset/YahooClassification/yahoo_answers_csv/zero-shot-split/test.txt', seen_types)
# test_examples = load_demo_input(premise_str, hypo_list)
# test_examples = load_demo_input('f**k why my email not come yet', ['anger', 'this text expresses anger', 'the guy is very unhappy'])
test_examples = load_demo_input(args.premise_str,
args.hypo_list.split(' | '))
test_features = convert_examples_to_features(test_examples, label_list,
args.max_seq_length,
tokenizer, output_mode)
test_all_input_ids = torch.tensor([f.input_ids for f in test_features],
dtype=torch.long)
test_all_input_mask = torch.tensor([f.input_mask for f in test_features],
dtype=torch.long)
test_all_segment_ids = torch.tensor([f.segment_ids for f in test_features],
dtype=torch.long)
test_all_label_ids = torch.tensor([f.label_id for f in test_features],
dtype=torch.long)
test_data = TensorDataset(test_all_input_ids, test_all_input_mask,
test_all_segment_ids, test_all_label_ids)
test_sampler = SequentialSampler(test_data)
test_dataloader = DataLoader(test_data,
sampler=test_sampler,
batch_size=args.eval_batch_size)
'''
start evaluate on test set after this epoch
'''
model.eval()
logger.info("***** Running testing *****")
logger.info(" Num examples = %d", len(test_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
test_loss = 0
nb_test_steps = 0
preds = []
# print('Testing...')
for input_ids, input_mask, segment_ids, label_ids in test_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():
logits = model(input_ids, segment_ids, input_mask, labels=None)
logits = logits[0]
if len(preds) == 0:
preds.append(logits.detach().cpu().numpy())
else:
preds[0] = np.append(preds[0],
logits.detach().cpu().numpy(),
axis=0)
# eval_loss = eval_loss / nb_eval_steps
preds = preds[0]
pred_probs = softmax(preds, axis=1)[:, 0]
return max(pred_probs)
test_papers = papers[-200:]
print("BUILDING TRAIN DATA...")
train_iterator, num_train_examples = get_dataloader(
ARGS.people, train_papers, tokenizer, ARGS.batch_size,
ARGS.working_dir + '/data_cache.train.pkl', test=False)
print('DONE. %d EXAMPLES' % num_train_examples)
print("BUILDING TEST DATA...")
test_iterator, num_test_examples = get_dataloader(
ARGS.people, test_papers, tokenizer, ARGS.batch_size,
ARGS.working_dir + '/data_cache.test.pkl',
test=True)
print('DONE. %d EXAMPLES' % num_test_examples)
model = BertForSequenceClassification.from_pretrained(
'bert-base-uncased',
cache_dir=ARGS.working_dir + '/cache')
if CUDA:
model = model.cuda()
optimizer, scheduler = build_optimizer_scheduler(
model, int((num_train_examples * ARGS.epochs) / ARGS.batch_size),
ARGS.learning_rate)
loss_fn = build_loss_fn()
for epoch in range(ARGS.epochs):
while True:
print('TRAIN %d' % epoch)
model.train()
def predict(options):
qid_lst, x_row, y_lst = [], [], []
read_file = open(options.data_file, 'rb')
read_file.readline()
for line in read_file:
line = line.decode('utf-8', 'ignore')
line_split = line.strip('\n').split('|')
x_row.append(line_split[1])
y_lst.append(int(line_split[0]))
# for line in read_file:
# line = line.decode('utf-8', 'ignore')
# line_split = line.strip('\n').strip('\r').split('\t')
# qid_lst.append(line_split[0])
# x_row.append(line_split[1])
# if len(line_split) >=3:
# y_lst.append(int(line_split[-1]))
read_file.close()
x_test_text, y_test = np.array(x_row), np.array(y_lst)
print("test text shape is ", x_test_text.shape)
print('Loading BERT tokenizer...')
tokenizer = BertTokenizer.from_pretrained('bert-base-chinese')
MAX_LEN = 180
test_input_ids = [
tokenizer.encode(sent, add_special_tokens=True, max_length=MAX_LEN)
for sent in x_test_text
]
test_input_ids = pad_sequences(test_input_ids,
maxlen=MAX_LEN,
dtype="long",
value=0,
truncating="post",
padding="post")
test_attention_masks = []
for sent in test_input_ids:
att_mask = [int(token_id > 0) for token_id in sent]
test_attention_masks.append(att_mask)
test_inputs = torch.tensor(test_input_ids)
test_masks = torch.tensor(test_attention_masks)
batch_size = 2
# Create the DataLoader for our test set.
test_data = TensorDataset(test_inputs, test_masks)
test_sampler = SequentialSampler(test_data)
test_dataloader = DataLoader(test_data,
sampler=test_sampler,
batch_size=batch_size)
model = BertForSequenceClassification.from_pretrained(
"bert-base-chinese",
num_labels=5,
output_attentions=False,
output_hidden_states=False,
)
# Tell pytorch to run this model on the GPU.
"""加载模型"""
model.load_state_dict(torch.load(options.model_file))
model.cuda()
all_predictions = []
model.eval()
for step, batch in enumerate(test_dataloader):
if step % 40 == 0 and not step == 0:
print(' Batch {:>5,} of {:>5,}.'.format(step,
len(test_dataloader)))
batch = tuple(t.to(device) for t in batch)
b_input_ids, b_input_mask = batch
with torch.no_grad():
outputs = model(b_input_ids,
token_type_ids=None,
attention_mask=b_input_mask)
logits = outputs[0]
logits = logits.detach().cpu().numpy()
pred_flat = np.argmax(logits, axis=1).flatten()
all_predictions.extend(pred_flat)
if len(y_test) > 0:
eps = 1e-3
TP, FP, FN = 0, 0, 0
for i in range(len(all_predictions)):
if all_predictions[i] == y_test[i] and y_test[i] != 0:
TP += 1
elif all_predictions[i] == 0 and y_test[i] != 0:
FN += 1
elif all_predictions[i] != 0 and y_test[i] == 0:
FP += 1
if abs(TP + FP) < eps:
P = 0.
else:
P = float(TP) / float(TP + FP)
if abs(TP + FP) < eps:
R = 0.
else:
R = float(TP) / float(TP + FN)
if abs(P) < eps and abs(R) < eps:
F1 = 0.
else:
F1 = 4 * P * R / (P + 3 * R)
print("TP is ", TP)
print("FP is ", FP)
print("FN is ", FN)
print("Total number of test examples: {}".format(len(y_test)))
print("P is ", P)
print("R is ", R)
print("Score is ", F1)
print("predict start.......")
###################################
# 预测逻辑和结果输出，("%d\t%s\t%d", qid, content, predict_label)
###################################
print("Saving evaluation to {0}".format(options.out_put_file))
output_file = open(options.out_put_file, 'wb')
output_file.write("qid\ttext\tlabel\n".encode("utf-8"))
for i in range(len(all_predictions)):
# output_file.write((qid_lst[i]+'\t'+x_row[i]+'\t'+str(int(all_predictions[i]))+'\n').encode('utf-8', 'ignore'))
output_file.write(
(x_row[i] + '\t' + str(int(all_predictions[i])) + '\n').encode(
'utf-8', 'ignore'))
output_file.close()
print("predict end.......")
return None
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument(
"--data_dir",
default=None,
type=str,
required=True,
help=
"The input data dir. Should contain the .tsv files (or other data files) for the task."
)
parser.add_argument(
"--bert_model",
default=None,
type=str,
required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, "
"bert-base-multilingual-cased, bert-base-chinese.")
parser.add_argument("--task_name",
default=None,
type=str,
required=True,
help="The name of the task to train.")
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help=
"The output directory where the model predictions and checkpoints will be written."
)
## Other parameters
parser.add_argument(
"--cache_dir",
default="",
type=str,
help=
"Where do you want to store the pre-trained models downloaded from s3")
parser.add_argument(
"--max_seq_length",
default=128,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument(
"--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
default=64,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=256,
type=int,
help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument(
"--warmup_proportion",
default=0.1,
type=float,
help=
"Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
'--gradient_accumulation_steps',
type=int,
default=1,
help=
"Number of updates steps to accumulate before performing a backward/update pass."
)
parser.add_argument(
'--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument(
'--loss_scale',
type=float,
default=0,
help=
"Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n")
parser.add_argument('--server_ip',
type=str,
default='',
help="Can be used for distant debugging.")
parser.add_argument('--server_port',
type=str,
default='',
help="Can be used for distant debugging.")
args = parser.parse_args()
# if args.server_ip and args.server_port:
# # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
# import ptvsd
# print("Waiting for debugger attach")
# ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
# ptvsd.wait_for_attach()
processors = {
# "cola": ColaProcessor,
# "mnli": MnliProcessor,
# "mnli-mm": MnliMismatchedProcessor,
# "mrpc": MrpcProcessor,
# "sst-2": Sst2Processor,
# "sts-b": StsbProcessor,
# "qqp": QqpProcessor,
# "qnli": QnliProcessor,
"rte": RteProcessor
# "wnli": WnliProcessor,
}
output_modes = {
# "cola": "classification",
# "mnli": "classification",
# "mrpc": "classification",
# "sst-2": "classification",
# "sts-b": "regression",
# "qqp": "classification",
# "qnli": "classification",
"rte": "classification"
# "wnli": "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')
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.")
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() #[0,1]
num_labels = len(label_list)
train_examples = None
# num_train_optimization_steps = None
# if args.do_train:
# # train_examples = processor.get_train_examples_wenpeng('/home/wyin3/Datasets/glue_data/RTE/train.tsv')
# train_examples, seen_types = processor.get_examples_situation_train('/export/home/Dataset/LORELEI/zero-shot-split/train_pu_half_v0.txt') #train_pu_half_v1.txt
# # seen_classes=[0,2,4,6,8]
#
# num_train_optimization_steps = int(
# len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps) * args.num_train_epochs
# if args.local_rank != -1:
# num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size()
# Prepare model
cache_dir = args.cache_dir if args.cache_dir else os.path.join(
str(PYTORCH_TRANSFORMERS_CACHE), 'distributed_{}'.format(
args.local_rank))
pretrain_model_dir = '/export/home/Dataset/fine_tune_Bert_stored/FineTuneOnMNLI' #FineTuneOnCombined'# FineTuneOnMNLI, FineTuneOnFEVER, FineTuneOnRTE
model = BertForSequenceClassification.from_pretrained(
pretrain_model_dir, num_labels=num_labels)
tokenizer = BertTokenizer.from_pretrained(pretrain_model_dir,
do_lower_case=args.do_lower_case)
if args.fp16:
model.half()
model.to(device)
if 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 = AdamW(optimizer_grouped_parameters, lr=args.learning_rate)
global_step = 0
nb_tr_steps = 0
tr_loss = 0
max_test_unseen_acc = 0.0
max_dev_unseen_acc = 0.0
max_dev_seen_acc = 0.0
max_overall_acc = 0.0
'''load test set'''
seen_types = set()
test_examples, test_label_list, test_hypo_seen_str_indicator, test_hypo_2_type_index = processor.get_examples_situation_test(
'/export/home/Dataset/LORELEI/zero-shot-split/test.txt', seen_types)
test_features = convert_examples_to_features(test_examples, label_list,
args.max_seq_length,
tokenizer, output_mode)
test_all_input_ids = torch.tensor([f.input_ids for f in test_features],
dtype=torch.long)
test_all_input_mask = torch.tensor([f.input_mask for f in test_features],
dtype=torch.long)
test_all_segment_ids = torch.tensor([f.segment_ids for f in test_features],
dtype=torch.long)
test_all_label_ids = torch.tensor([f.label_id for f in test_features],
dtype=torch.long)
test_data = TensorDataset(test_all_input_ids, test_all_input_mask,
test_all_segment_ids, test_all_label_ids)
test_sampler = SequentialSampler(test_data)
test_dataloader = DataLoader(test_data,
sampler=test_sampler,
batch_size=args.eval_batch_size)
'''
start evaluate on test set after this epoch
'''
model.eval()
logger.info("***** Running testing *****")
logger.info(" Num examples = %d", len(test_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
test_loss = 0
nb_test_steps = 0
preds = []
print('Testing...')
for input_ids, input_mask, segment_ids, label_ids in test_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():
logits = model(input_ids, input_mask, segment_ids, labels=None)
logits = logits[0]
if len(preds) == 0:
preds.append(logits.detach().cpu().numpy())
else:
preds[0] = np.append(preds[0],
logits.detach().cpu().numpy(),
axis=0)
preds = preds[0]
pred_probs = softmax(preds, axis=1)[:, 0]
pred_binary_labels_harsh = []
pred_binary_labels_loose = []
for i in range(preds.shape[0]):
if preds[i][0] > preds[i][1] + 0.1:
pred_binary_labels_harsh.append(0)
else:
pred_binary_labels_harsh.append(1)
if preds[i][0] > preds[i][1]:
pred_binary_labels_loose.append(0)
else:
pred_binary_labels_loose.append(1)
seen_acc, unseen_acc = evaluate_situation_zeroshot_TwpPhasePred(
pred_probs, pred_binary_labels_harsh, pred_binary_labels_loose,
test_label_list, test_hypo_seen_str_indicator, test_hypo_2_type_index,
seen_types)
if unseen_acc > max_test_unseen_acc:
max_test_unseen_acc = unseen_acc
print('\n\n\t test seen_f1 & unseen_f1:', seen_acc, unseen_acc,
' max_test_unseen_f1:', max_test_unseen_acc, '\n')
def main(train_file,
valid_file,
test_file,
target_dir,
embedding_size=512,
hidden_size=512,
dropout=0.5,
num_classes=3,
epochs=64,
batch_size=32,
lr=0.0004,
patience=5,
max_grad_norm=1.0,
checkpoint=None):
"""
Train the ESIM model on the Quora dataset.
Args:
train_file: A path to some preprocessed data that must be used
to train the model.
valid_file: A path to some preprocessed data that must be used
to validate the model.
embeddings_file: A path to some preprocessed word embeddings that
must be used to initialise the model.
target_dir: The path to a directory where the trained model must
be saved.
hidden_size: The size of the hidden layers in the model. Defaults
to 300.
dropout: The dropout rate to use in the model. Defaults to 0.5.
num_classes: The number of classes in the output of the model.
Defaults to 3.
epochs: The maximum number of epochs for training. Defaults to 64.
batch_size: The size of the batches for training. Defaults to 32.
lr: The learning rate for the optimizer. Defaults to 0.0004.
patience: The patience to use for early stopping. Defaults to 5.
checkpoint: A checkpoint from which to continue training. If None,
training starts from scratch. Defaults to None.
"""
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(20 * "=", " Preparing for training ", 20 * "=")
if not os.path.exists(target_dir):
os.makedirs(target_dir)
# -------------------- Data loading ------------------- #
print("\t* Loading training data...")
with open(train_file, "rb") as pkl:
train_data = pickle.load(pkl)
print("\t* Loading validation data...")
with open(valid_file, "rb") as pkl:
valid_data = pickle.load(pkl)
valid_dataloader = transform_batch_data(valid_data, batch_size=batch_size, shuffle=False)
print("\t* Loading test data...")
with open(test_file, "rb") as pkl:
test_data = pickle.load(pkl)
test_dataloader = transform_batch_data(test_data, batch_size=batch_size, shuffle=False)
# -------------------- Model definition ------------------- #
print("\t* Building model...")
pretrained_weights = 'bert-base-uncased'
if checkpoint:
tokenizer = BertTokenizer.from_pretrained(target_dir+'/transformer/')
model = BertForSequenceClassification.from_pretrained(target_dir+'/transformer/')
else:
tokenizer = BertTokenizer.from_pretrained(pretrained_weights)
model = BertForSequenceClassification.from_pretrained(pretrained_weights)
print("\t* Building model success...")
model.to(device)
# -------------------- Preparation for training ------------------- #
# Parameters:
lr = 1e-3
max_grad_norm = 1.0
num_total_steps = 1000
num_warmup_steps = 100
warmup_proportion = float(num_warmup_steps) / float(num_total_steps) # 0.1
optimizer = AdamW(model.parameters(), lr=lr,
correct_bias=False) # To reproduce BertAdam specific behavior set correct_bias=False
scheduler = WarmupLinearSchedule(optimizer, warmup_steps=num_warmup_steps,
t_total=num_total_steps) # PyTorch scheduler
best_score = 0.0
start_epoch = 1
# Data for loss curves plot.
epochs_count = []
train_losses = []
valid_losses = []
# Compute loss and accuracy before starting (or resuming) training.
_, valid_loss, valid_accuracy = validate(model,
tokenizer,
valid_dataloader
)
print("\t* Validation loss before training: {:.4f}, accuracy: {:.4f}%"
.format(valid_loss, (valid_accuracy*100)))
_, test_loss, test_accuracy = validate(model,
tokenizer,
test_dataloader)
print("\t* test loss before training: {:.4f}, accuracy: {:.4f}%"
.format(test_loss, (test_accuracy*100)))
# -------------------- Training epochs ------------------- #
print("\n",
20 * "=",
"Training transformer model on device: {}".format(device),
20 * "=")
patience_counter = 0
for epoch in range(start_epoch, epochs+1):
train_dataloader = transform_batch_data(train_data, batch_size=batch_size, shuffle=True)
epochs_count.append(epoch)
print("* Training epoch {}:".format(epoch))
epoch_time, epoch_loss, epoch_accuracy = train(model,
tokenizer,
train_dataloader,
optimizer,
scheduler,
max_grad_norm)
train_losses.append(epoch_loss)
print("-> Training time: {:.4f}s, loss = {:.4f}, accuracy: {:.4f}%"
.format(epoch_time, epoch_loss, (epoch_accuracy*100)))
print("* Validation for epoch {}:".format(epoch))
epoch_time, epoch_loss, epoch_accuracy = validate(model,
tokenizer,
valid_dataloader)
valid_losses.append(epoch_loss)
print("-> Valid. time: {:.4f}s, loss: {:.4f}, accuracy: {:.4f}%\n"
.format(epoch_time, epoch_loss, (epoch_accuracy*100)))
print("* Test for epoch {}:".format(epoch))
epoch_time, epoch_loss, test_accuracy = validate(model,
tokenizer,
test_dataloader)
print("-> Test. time: {:.4f}s, loss: {:.4f}, accuracy: {:.4f}%\n"
.format(epoch_time, epoch_loss, (test_accuracy*100)))
sys.stdout.flush() #刷新输出
# Update the optimizer's learning rate with the scheduler.
scheduler.step(epoch_accuracy)
# Early stopping on validation accuracy.
if epoch_accuracy < best_score:
patience_counter += 1
else:
best_score = epoch_accuracy
patience_counter = 0
# Save the best model. The optimizer is not saved to avoid having
# a checkpoint file that is too heavy to be shared. To resume
# training from the best model, use the 'esim_*.pth.tar'
# checkpoints instead.
torch.save({"epoch": epoch,
"model": model.state_dict(),
"best_score": best_score,
"epochs_count": epochs_count,
"train_losses": train_losses,
"valid_losses": valid_losses},
os.path.join(target_dir, "best.pth.tar"))
# Save the model at each epoch.
torch.save({"epoch": epoch,
"model": model.state_dict(),
"best_score": best_score,
"optimizer": optimizer.state_dict(),
"epochs_count": epochs_count,
"train_losses": train_losses,
"valid_losses": valid_losses},
os.path.join(target_dir, "esim_{}.pth.tar".format(epoch)))
if patience_counter >= patience:
print("-> Early stopping: patience limit reached, stopping...")
break
# Plotting of the loss curves for the train and validation sets.
fig = plt.figure()
plt.plot(epochs_count, train_losses, "-r")
plt.plot(epochs_count, valid_losses, "-b")
plt.xlabel("epoch")
plt.ylabel("loss")
plt.legend(["Training loss", "Validation loss"])
plt.title("Cross entropy loss")
fig.savefig('quora_loss.png')
