def transform(self, X, y=None, **fit_params):
"""Transforms a list of strings to a list of BERT inputs."""
exs = []
for text in X:
exs.append(InputExample(guid=None, text_a=text, text_b=None, label="attack"))
visu_features = convert_examples_to_features(exs, label_list, args.max_seq_length, tokenizer)
all_input_ids = torch.tensor([f.input_ids for f in visu_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in visu_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in visu_features], dtype=torch.long)
return [all_input_ids, all_segment_ids, all_input_mask]
def convert(split, modus, exs):
"""Converts the examples or load them from cache."""
cached_features_file = os.path.join(args.data_dir, 'cache', '{0}_{1}_{2}_{3}_{4}_{5}'.format(modus,
list(filter(None, args.bert_model.split('/'))).pop(),
str(args.max_seq_length),
str(task_name), str(args.input_to_use), split))
# Try to load the cached features.
try:
with open(cached_features_file, "rb") as reader:
fs = pickle.load(reader)
# Creates and cache the features.
except FileNotFoundError:
if not os.path.exists(os.path.join(args.data_dir, 'cache')):
os.makedirs(os.path.join(args.data_dir, 'cache'))
fs = convert_examples_to_features(
exs, label_list, args.max_seq_length, tokenizer)
logger.info('Saving {0} features into cached file {1}'.format(mode, cached_features_file))
with open(cached_features_file, "wb") as writer:
pickle.dump(fs, writer)
return fs
def run_model():
parser = argparse.ArgumentParser()
parser.add_argument(
'--model_name_or_path',
type=str,
default='bert-base-cased-finetuned-mrpc',
help='pretrained model name or path to local checkpoint')
parser.add_argument("--task_name",
type=str,
default='mrpc',
help="The name of the task to train.")
parser.add_argument(
"--data_dir",
type=str,
required=True,
help=
"The input data dir. Should contain the .tsv files (or other data files) for the task."
)
parser.add_argument(
"--output_dir",
type=str,
required=True,
help=
"The output directory where the model predictions and checkpoints will be written."
)
parser.add_argument(
"--data_subset",
type=int,
default=-1,
help="If > 0: limit the data to a subset of data_subset instances.")
parser.add_argument("--overwrite_output_dir",
action='store_true',
help="Whether to overwrite data in output directory")
parser.add_argument("--dont_normalize_importance_by_layer",
action='store_true',
help="Don't normalize importance score by layers")
parser.add_argument(
"--dont_normalize_global_importance",
action='store_true',
help="Don't normalize all importance scores between 0 and 1")
parser.add_argument(
"--try_masking",
action='store_true',
help="Whether to try to mask head until a threshold of accuracy.")
parser.add_argument(
"--masking_threshold",
default=0.9,
type=float,
help="masking threshold in term of metrics"
"(stop masking when metric < threshold * original metric value).")
parser.add_argument(
"--masking_amount",
default=0.1,
type=float,
help="Amount to heads to masking at each masking step.")
parser.add_argument("--metric_name",
default="acc",
type=str,
help="Metric to use for head masking.")
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("--batch_size",
default=1,
type=int,
help="Batch size.")
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
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()
# Setup devices and distributed training
if args.local_rank == -1 or args.no_cuda:
args.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)
args.device = torch.device("cuda", args.local_rank)
n_gpu = 1
torch.distributed.init_process_group(
backend='nccl') # Initializes the distributed backend
# Setup logging
logging.basicConfig(
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
logger.info("device: {} n_gpu: {}, distributed: {}".format(
args.device, n_gpu, bool(args.local_rank != -1)))
# Set seeds
np.random.seed(args.seed)
torch.random.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed(args.seed)
# Prepare GLUE task
task_name = args.task_name.lower()
processor = processors[task_name]()
label_list = processor.get_labels()
args.output_mode = output_modes[task_name]
args.num_labels = len(label_list)
# Prepare output directory
if os.path.exists(args.output_dir) and os.listdir(
args.output_dir) and not args.overwrite_output_dir:
raise ValueError(
"Output directory ({}) already exists and is not empty.".format(
args.output_dir))
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
# Load model & tokenizer
if args.local_rank not in [-1, 0]:
torch.distributed.barrier(
) # Make sure only one distributed process download model & vocab
tokenizer = BertTokenizer.from_pretrained(args.model_name_or_path)
# Load a model with all BERTology options on:
# output_attentions => will output attention weights
# keep_multihead_output => will store gradient of attention head outputs for head importance computation
# see: http://arxiv.org/abs/1905.10650
model = BertForSequenceClassification.from_pretrained(
args.model_name_or_path,
num_labels=args.num_labels,
output_attentions=True,
keep_multihead_output=True)
if args.local_rank == 0:
torch.distributed.barrier(
) # Make sure only one distributed process download model & vocab
model.to(args.device)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
model.eval()
# Prepare dataset for the GLUE task
eval_examples = processor.get_dev_examples(args.data_dir)
cached_eval_features_file = os.path.join(
args.data_dir, 'dev_{0}_{1}_{2}'.format(
list(filter(None, args.model_name_or_path.split('/'))).pop(),
str(args.max_seq_length), str(task_name)))
try:
eval_features = torch.load(cached_eval_features_file)
except:
eval_features = convert_examples_to_features(eval_examples, label_list,
args.max_seq_length,
tokenizer,
args.output_mode)
if args.local_rank in [-1, 0]:
logger.info("Saving eval features to cache file %s",
cached_eval_features_file)
torch.save(eval_features, cached_eval_features_file)
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 if args.output_mode
== "classification" else torch.float)
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
all_label_ids)
if args.data_subset > 0:
eval_data = Subset(eval_data,
list(range(min(args.data_subset, len(eval_data)))))
eval_sampler = SequentialSampler(
eval_data) if args.local_rank == -1 else DistributedSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.batch_size)
# Print/save training arguments
print(args)
torch.save(args, os.path.join(args.output_dir, 'run_args.bin'))
# Compute head entropy and importance score
attn_entropy, head_importance, _, _ = compute_heads_importance(
args, model, eval_dataloader)
# Print/save matrices
np.save(os.path.join(args.output_dir, 'attn_entropy.npy'),
attn_entropy.detach().cpu().numpy())
np.save(os.path.join(args.output_dir, 'head_importance.npy'),
head_importance.detach().cpu().numpy())
logger.info("Attention entropies")
print_2d_tensor(attn_entropy)
logger.info("Head importance scores")
print_2d_tensor(head_importance)
logger.info("Head ranked by importance scores")
head_ranks = torch.zeros(head_importance.numel(),
dtype=torch.long,
device=args.device)
head_ranks[head_importance.view(-1).sort(
descending=True)[1]] = torch.arange(head_importance.numel(),
device=args.device)
head_ranks = head_ranks.view_as(head_importance)
print_2d_tensor(head_ranks)
# Do masking if we want to
if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
_, head_importance, preds, labels = compute_heads_importance(
args, model, eval_dataloader, compute_entropy=False)
preds = np.argmax(
preds, axis=1
) if args.output_mode == "classification" else np.squeeze(preds)
original_score = compute_metrics(task_name, preds,
labels)[args.metric_name]
logger.info("Pruning: original score: %f, threshold: %f",
original_score, original_score * args.masking_threshold)
new_head_mask = torch.ones_like(head_importance)
num_to_mask = max(1, int(new_head_mask.numel() * args.masking_amount))
current_score = original_score
while current_score >= original_score * args.masking_threshold:
head_mask = new_head_mask.clone() # save current head mask
# heads from least important to most - keep only not-masked heads
head_importance[head_mask == 0.0] = float('Inf')
current_heads_to_mask = head_importance.view(-1).sort()[1]
if len(current_heads_to_mask) <= num_to_mask:
break
# mask heads
current_heads_to_mask = current_heads_to_mask[:num_to_mask]
logger.info("Heads to mask: %s",
str(current_heads_to_mask.tolist()))
new_head_mask = new_head_mask.view(-1)
new_head_mask[current_heads_to_mask] = 0.0
new_head_mask = new_head_mask.view_as(head_mask)
print_2d_tensor(new_head_mask)
# Compute metric and head importance again
_, head_importance, preds, labels = compute_heads_importance(
args,
model,
eval_dataloader,
compute_entropy=False,
head_mask=new_head_mask)
preds = np.argmax(
preds, axis=1
) if args.output_mode == "classification" else np.squeeze(preds)
current_score = compute_metrics(task_name, preds,
labels)[args.metric_name]
logger.info(
"Masking: current score: %f, remaning heads %d (%.1f percents)",
current_score, new_head_mask.sum(),
new_head_mask.sum() / new_head_mask.numel() * 100)
logger.info("Final head mask")
print_2d_tensor(head_mask)
np.save(os.path.join(args.output_dir, 'head_mask.npy'),
head_mask.detach().cpu().numpy())
# Try pruning and test time speedup
# Pruning is like masking but we actually remove the masked weights
before_time = datetime.now()
_, _, preds, labels = compute_heads_importance(
args,
model,
eval_dataloader,
compute_entropy=False,
compute_importance=False,
head_mask=head_mask)
preds = np.argmax(
preds, axis=1
) if args.output_mode == "classification" else np.squeeze(preds)
score_masking = compute_metrics(task_name, preds,
labels)[args.metric_name]
original_time = datetime.now() - before_time
original_num_params = sum(p.numel() for p in model.parameters())
heads_to_prune = dict(
(layer, (1 - head_mask[layer].long()).nonzero().tolist())
for layer in range(len(head_mask)))
assert sum(len(h) for h in heads_to_prune.values()) == (
1 - head_mask.long()).sum().item()
model.bert.prune_heads(heads_to_prune)
pruned_num_params = sum(p.numel() for p in model.parameters())
before_time = datetime.now()
_, _, preds, labels = compute_heads_importance(
args,
model,
eval_dataloader,
compute_entropy=False,
compute_importance=False,
head_mask=None)
preds = np.argmax(
preds, axis=1
) if args.output_mode == "classification" else np.squeeze(preds)
score_pruning = compute_metrics(task_name, preds,
labels)[args.metric_name]
new_time = datetime.now() - before_time
logger.info(
"Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)",
original_num_params, pruned_num_params,
pruned_num_params / original_num_params * 100)
logger.info("Pruning: score with masking: %f score with pruning: %f",
score_masking, score_pruning)
logger.info(
"Pruning: speed ratio (new timing / original timing): %f percents",
original_time / new_time * 100)
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--data_dir",
default='/hdd/user4/gpt_classification/dataset/ag_news',
type=str,
help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
parser.add_argument('--model_name', type=str, default='openai-gpt',
help='pretrained model name')
parser.add_argument("--task_name",
default='ag_news',
type=str,
help="The name of the task to train.")
parser.add_argument("--output_dir",
default='/hdd/user4/gpt_classification/experiment/ag_news',
type=str,
help="The output directory where the model predictions and checkpoints will be written.")
parser.add_argument("--max_grad_norm",
default=1)
parser.add_argument('--weight_decay', type=float, default=0.0)
## Other parameters
parser.add_argument("--cache_dir",
default='/hdd/user4/gpt_classification/pretrained',
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",
default=True,
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
default=True,
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument("--train_batch_size",
default=16,
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=9.0,
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('--lr_schedule', type=str, default='warmup_linear')
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument('--overwrite_output_dir',
default=True,
action='store_true',
help="Overwrite the content of the output directory")
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()
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()
n_gpu = 1
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')
args.device = device
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
device, n_gpu, bool(args.local_rank != -1), args.fp16))
if args.gradient_accumulation_steps < 1:
raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
args.gradient_accumulation_steps))
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if not args.do_train and not args.do_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True.")
if os.path.exists(args.output_dir) and os.listdir(
args.output_dir) and args.do_train and not args.overwrite_output_dir:
raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
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](args.data_dir)
output_mode = output_modes[task_name]
label_list = processor.get_labels()
num_labels = len(label_list)
if args.local_rank not in [-1, 0]:
torch.distributed.barrier() # Make sure only the first process in distributed training will download model & vocab
special_tokens = ['_start_', '_delimiter_', '_classify_']
tokenizer = OpenAIGPTTokenizer.from_pretrained(args.model_name, special_tokens=special_tokens)
model = OpenAIGPTForClassification.from_pretrained(args.model_name,
num_special_tokens=len(special_tokens),
num_labels=num_labels)
if args.local_rank == 0:
torch.distributed.barrier()
model.to(device)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
elif n_gpu > 1:
model = torch.nn.DataParallel(model)
global_step = 0
tr_loss = 0
if args.do_train:
if args.local_rank in [-1, 0]:
tb_writer = SummaryWriter()
# Prepare data loader
train_examples = processor.get_train_examples()
cached_train_features_file = os.path.join(args.data_dir, 'train_{0}_{1}_{2}'.format(
list(filter(None, args.model_name.split('/'))).pop(),
str(args.max_seq_length),
str(task_name)))
try:
with open(cached_train_features_file, "rb") as reader:
train_features = pickle.load(reader)
except:
train_features = convert_examples_to_features(
train_examples, label_list, args.max_seq_length, tokenizer, output_mode)
if args.local_rank == -1 or torch.distributed.get_rank() == 0:
logger.info(" Saving train features into cached file %s", cached_train_features_file)
with open(cached_train_features_file, "wb") as writer:
pickle.dump(train_features, writer)
all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long)
if output_mode == "classification":
all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.long)
elif output_mode == "regression":
all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.float)
train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
if args.local_rank == -1:
train_sampler = RandomSampler(train_data)
else:
train_sampler = DistributedSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
# 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}
]
num_train_optimization_steps = len(train_dataloader) * args.num_train_epochs
optimizer = OpenAIAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
max_grad_norm=args.max_grad_norm,
weight_decay=args.weight_decay,
t_total=num_train_optimization_steps)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps)
model.train()
for _ in range(int(args.num_train_epochs)):
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(
tqdm(train_dataloader, desc="Iteration", disable=args.local_rank not in [-1, 0])):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, _, label_ids = batch
# define a new function to compute loss values for both output_modes
logits = model.forward(input_ids, input_mask)
if output_mode == "classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1))
elif output_mode == "regression":
loss_fct = MSELoss()
loss = loss_fct(logits.view(-1), label_ids.view(-1))
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
optimizer.backward(loss)
else:
loss.backward()
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if (step + 1) % args.gradient_accumulation_steps == 0:
optimizer.step()
optimizer.zero_grad()
global_step += 1
if args.local_rank in [-1, 0]:
tb_writer.add_scalar('lr', optimizer.get_lr()[0], global_step)
tb_writer.add_scalar('loss', loss.item(), global_step)
tb_writer.close()
### Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
# Save a trained model, configuration and tokenizer
model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self
# If we save using the predefined names, we can load using `from_pretrained`
output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
torch.save(model_to_save.state_dict(), output_model_file)
model_to_save.config.to_json_file(output_config_file)
tokenizer.save_vocabulary(args.output_dir)
# Good practice: save your training arguments together with the trained model
output_args_file = os.path.join(args.output_dir, 'training_args.bin')
torch.save(args, output_args_file)
# Load a trained model and vocabulary that you have fine-tuned
model = OpenAIGPTForClassification.from_pretrained(args.output_dir,
num_labels=num_labels)
model.to(device)
### Evaluation
if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
eval_examples = processor.get_dev_examples()
cached_eval_features_file = os.path.join(args.data_dir, 'dev_{0}_{1}_{2}'.format(
list(filter(None, args.model_name.split('/'))).pop(),
str(args.max_seq_length),
str(task_name)))
try:
with open(cached_eval_features_file, "rb") as reader:
eval_features = pickle.load(reader)
except:
eval_features = convert_examples_to_features(
eval_examples, label_list, args.max_seq_length, tokenizer, output_mode)
if args.local_rank == -1 or torch.distributed.get_rank() == 0:
logger.info(" Saving eval features into cached file %s", cached_eval_features_file)
with open(cached_eval_features_file, "wb") as writer:
pickle.dump(eval_features, writer)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
if output_mode == "classification":
all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long)
elif output_mode == "regression":
all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.float)
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
# Run prediction for full data
if args.local_rank == -1:
eval_sampler = SequentialSampler(eval_data)
else:
eval_sampler = DistributedSampler(eval_data) # Note that this sampler samples randomly
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
model.eval()
eval_loss = 0
nb_eval_steps = 0
preds = []
out_label_ids = None
for input_ids, input_mask, segment_ids, label_ids in tqdm(eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
logits = model.forward(input_ids, input_mask)
# create eval loss and other metric required by the task
if output_mode == "classification":
loss_fct = CrossEntropyLoss()
tmp_eval_loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1))
elif output_mode == "regression":
loss_fct = MSELoss()
tmp_eval_loss = loss_fct(logits.view(-1), label_ids.view(-1))
eval_loss += tmp_eval_loss.mean().item()
nb_eval_steps += 1
if len(preds) == 0:
preds.append(logits.detach().cpu().numpy())
out_label_ids = label_ids.detach().cpu().numpy()
else:
preds[0] = np.append(
preds[0], logits.detach().cpu().numpy(), axis=0)
out_label_ids = np.append(
out_label_ids, label_ids.detach().cpu().numpy(), axis=0)
eval_loss = eval_loss / nb_eval_steps
preds = preds[0]
if output_mode == "classification":
output_odp = []
for arr in preds:
t = (-arr).argsort()[:5]
output_odp.append(t.tolist())
file_path = 'D:/바탕화면/(논문)multi-pretraining/NYT'
with open('gpt_top5.pkl','wb') as f:
pickle.dump(output_odp,f)
preds = np.argmax(preds, axis=1)
elif output_mode == "regression":
preds = np.squeeze(preds)
result = compute_metrics(task_name, preds, out_label_ids)
print('preds:',preds,'label:',out_label_ids)
loss = tr_loss / global_step if args.do_train else None
result['eval_loss'] = eval_loss
result['global_step'] = global_step
result['loss'] = loss
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
# hack for MNLI-MM
if task_name == "mnli":
task_name = "mnli-mm"
processor = processors[task_name]()
if os.path.exists(args.output_dir + '-MM') and os.listdir(args.output_dir + '-MM') 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 + '-MM'):
os.makedirs(args.output_dir + '-MM')
eval_examples = processor.get_dev_examples(args.data_dir)
eval_features = convert_examples_to_features(
eval_examples, label_list, args.max_seq_length, tokenizer, output_mode)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
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 = 0
nb_eval_steps = 0
preds = []
out_label_ids = None
for input_ids, input_mask, segment_ids, label_ids in tqdm(eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
logits = model(input_ids, token_type_ids=segment_ids, attention_mask=input_mask, labels=None)
loss_fct = CrossEntropyLoss()
tmp_eval_loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1))
eval_loss += tmp_eval_loss.mean().item()
nb_eval_steps += 1
if len(preds) == 0:
preds.append(logits.detach().cpu().numpy())
out_label_ids = label_ids.detach().cpu().numpy()
else:
preds[0] = np.append(
preds[0], logits.detach().cpu().numpy(), axis=0)
out_label_ids = np.append(
out_label_ids, label_ids.detach().cpu().numpy(), axis=0)
eval_loss = eval_loss / nb_eval_steps
preds = preds[0]
preds = np.argmax(preds, axis=1)
result = compute_metrics(task_name, preds, out_label_ids)
loss = tr_loss / global_step if args.do_train else None
result['eval_loss'] = eval_loss
result['global_step'] = global_step
result['loss'] = loss
output_eval_file = os.path.join(args.output_dir + '-MM', "eval_results.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
for idx, row in test_df.iterrows()
for c, i in enumerate(row.seqs)]
with test_path.open('wb') as f:
pickle.dump(examples_test, f)
print('Featurizing examples...', flush=True)
if not args.pregen_emb_path:
train_path = Path(
args.df_path).parents[1] / f'features_train_{path_str}.pkl'
if train_path.is_file():
print(f'Loading {train_path}...')
with train_path.open('rb') as f:
features_train = pickle.load(f)
else:
features_train = convert_examples_to_features(examples_train,
Constants.MAX_SEQ_LEN, tokenizer, output_mode=('regression' if args.task_type == 'regression' else 'classification'))
with train_path.open('wb') as f:
pickle.dump(features_train, f)
eval_path = Path(args.df_path).parents[1] / f'features_eval_{path_str}.pkl'
if eval_path.is_file():
print(f'Loading {eval_path}...')
with eval_path.open('rb') as f:
features_eval = pickle.load(f)
else:
features_eval = convert_examples_to_features(examples_eval,
Constants.MAX_SEQ_LEN, tokenizer, output_mode=('regression' if args.task_type == 'regression' else 'classification'))
with eval_path.open('wb') as f:
pickle.dump(features_eval, f)
test_path = Path(args.df_path).parents[1] / f'features_test_{path_str}.pkl'
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument(
"--data_dir",
default=None,
type=str,
required=True,
help=
"The input data dir. Should contain the .tsv files (or other data files) for the task."
)
parser.add_argument(
"--bert_model",
default=None,
type=str,
required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, "
"bert-base-multilingual-cased, bert-base-chinese.")
parser.add_argument("--task_name",
default=None,
type=str,
required=True,
help="The name of the task to train.")
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help=
"The output directory where the model predictions and checkpoints will be written."
)
## Other parameters
parser.add_argument(
"--cache_dir",
default="",
type=str,
help=
"Where do you want to store the pre-trained models downloaded from s3")
parser.add_argument(
"--max_seq_length",
default=128,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument(
"--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=8,
type=int,
help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument(
"--warmup_proportion",
default=0.1,
type=float,
help=
"Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument('--overwrite_output_dir',
action='store_true',
help="Overwrite the content of the output directory")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
'--gradient_accumulation_steps',
type=int,
default=1,
help=
"Number of updates steps to accumulate before performing a backward/update pass."
)
parser.add_argument(
'--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument(
'--loss_scale',
type=float,
default=0,
help=
"Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n")
parser.add_argument('--server_ip',
type=str,
default='',
help="Can be used for distant debugging.")
parser.add_argument('--server_port',
type=str,
default='',
help="Can be used for distant debugging.")
parser.add_argument('--input_text',
type=str,
default='',
help="Input text.")
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')
args.device = device
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
logger.info(
"device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".
format(device, n_gpu, bool(args.local_rank != -1), args.fp16))
if args.gradient_accumulation_steps < 1:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
.format(args.gradient_accumulation_steps))
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if not args.do_train and not args.do_eval:
raise ValueError(
"At least one of `do_train` or `do_eval` must be True.")
# if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir:
# raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
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)
if args.local_rank not in [-1, 0]:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=args.do_lower_case)
model = BertForSequenceClassification.from_pretrained(
args.bert_model, num_labels=num_labels)
if args.local_rank == 0:
torch.distributed.barrier()
if args.fp16:
model.half()
model.to(device)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
elif n_gpu > 1:
model = torch.nn.DataParallel(model)
global_step = 0
nb_tr_steps = 0
tr_loss = 0
# Load a trained model and vocabulary that you have fine-tuned
model = BertForSequenceClassification.from_pretrained(
args.output_dir, num_labels=num_labels)
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=args.do_lower_case)
# model = BertForSequenceClassification.from_pretrained(args.bert_model, num_labels=num_labels)
model.to(device)
### Evaluation
if args.do_eval and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
print("input text: ", args.input_text)
eval_examples = processor.get_test_example(args.input_text)
eval_features = convert_examples_to_features(eval_examples, label_list,
args.max_seq_length,
tokenizer, output_mode)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
all_input_ids = torch.tensor([f.input_ids for f in eval_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features],
dtype=torch.long)
if output_mode == "classification":
all_label_ids = torch.tensor([f.label_id for f in eval_features],
dtype=torch.long)
elif output_mode == "regression":
all_label_ids = torch.tensor([f.label_id for f in eval_features],
dtype=torch.float)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
# Run prediction for full data
if args.local_rank == -1:
eval_sampler = SequentialSampler(eval_data)
else:
eval_sampler = DistributedSampler(
eval_data) # Note that this sampler samples randomly
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
model.eval()
eval_loss = 0
nb_eval_steps = 0
preds = []
out_label_ids = None
for input_ids, input_mask, segment_ids, label_ids in tqdm(
eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
logits = model(input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask)
# create eval loss and other metric required by the task
if output_mode == "classification":
loss_fct = CrossEntropyLoss()
tmp_eval_loss = loss_fct(logits.view(-1, num_labels),
label_ids.view(-1))
elif output_mode == "regression":
loss_fct = MSELoss()
tmp_eval_loss = loss_fct(logits.view(-1), label_ids.view(-1))
eval_loss += tmp_eval_loss.mean().item()
nb_eval_steps += 1
if len(preds) == 0:
preds.append(logits.detach().cpu().numpy())
out_label_ids = label_ids.detach().cpu().numpy()
else:
preds[0] = np.append(preds[0],
logits.detach().cpu().numpy(),
axis=0)
out_label_ids = np.append(out_label_ids,
label_ids.detach().cpu().numpy(),
axis=0)
eval_loss = eval_loss / nb_eval_steps
preds = preds[0]
print(preds.shape)
print("preds", preds)
if output_mode == "classification":
preds = np.argmax(preds, axis=1)
elif output_mode == "regression":
preds = np.squeeze(preds)
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument(
"--data_dir",
default=None,
type=str,
required=True,
help=
"The input data dir. Should contain the .tsv files (or other data files) for the task."
)
parser.add_argument(
"--bert_model",
default=None,
type=str,
required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, "
"bert-base-multilingual-cased, bert-base-chinese.")
parser.add_argument("--task_name",
default=None,
type=str,
required=True,
help="The name of the task to train.")
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help=
"The output directory where the model predictions and checkpoints will be written."
)
## Other parameters
parser.add_argument(
"--cache_dir",
default="",
type=str,
help=
"Where do you want to store the pre-trained models downloaded from s3")
parser.add_argument(
"--max_seq_length",
default=128,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument(
"--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=8,
type=int,
help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument(
"--warmup_proportion",
default=0.1,
type=float,
help=
"Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument('--overwrite_output_dir',
action='store_true',
help="Overwrite the content of the output directory")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
args = parser.parse_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(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.overwrite_output_dir:
raise ValueError(
"Output directory ({}) already exists and is not empty.".format(
args.output_dir))
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
task_name = args.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
output_mode = output_modes[task_name]
label_list = processor.get_labels()
num_labels = len(label_list)
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=args.do_lower_case)
model = BertForSequenceClassification.from_pretrained(
args.bert_model, num_labels=num_labels).cuda()
if args.do_train:
# Prepare data loader
train_examples = processor.get_train_examples(args.data_dir)
cached_train_features_file = os.path.join(
args.data_dir, 'train_{0}_{1}_{2}'.format(
list(filter(None, args.bert_model.split('/'))).pop(),
str(args.max_seq_length), str(task_name)))
try:
with open(cached_train_features_file, "rb") as reader:
train_features = pickle.load(reader)
except:
train_features = convert_examples_to_features(
train_examples, label_list, args.max_seq_length, tokenizer,
output_mode)
with open(cached_train_features_file, "wb") as writer:
pickle.dump(train_features, writer)
label_dtype = None
if output_mode == "classification":
label_dtype = torch.long
elif output_mode == "regression":
label_dtype = torch.float
train_sampler = RandomSampler
all_input_ids = [f.input_ids for f in train_features]
all_segment_ids = [f.segment_ids for f in train_features]
all_label_ids = [f.label_id for f in train_features]
train_dataloader = SentPairClsDataLoader(
all_input_ids, all_segment_ids, all_label_ids,
args.train_batch_size, train_sampler, device, label_dtype)
num_train_optimization_steps = (
len(all_input_ids) //
args.train_batch_size) * args.num_train_epochs
# Prepare optimizer
optimizer = BertAdam(model.parameters(),
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps)
model.train()
for epoch in range(int(args.num_train_epochs)):
for step, batch in enumerate(train_dataloader):
graph, label_ids = batch
# define a new function to compute loss values for both output_modes
logits = model(graph)
if output_mode == "classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.view(-1, num_labels),
label_ids.view(-1))
elif output_mode == "regression":
loss_fct = MSELoss()
loss = loss_fct(logits.view(-1), label_ids.view(-1))
optimizer.zero_grad()
loss.backward()
optimizer.step()
if step % 20 == 0:
print(
f"Epoch {epoch}, step {step}, loss {loss.cpu().data.numpy()}, lr {optimizer.get_lr()[0]}"
)
# save model
output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
torch.save(model, output_model_file)
### Evaluation
if args.do_eval and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
model = torch.load(output_model_file)
eval_examples = processor.get_dev_examples(args.data_dir)
cached_eval_features_file = os.path.join(
args.data_dir, 'dev_{0}_{1}_{2}'.format(
list(filter(None, args.bert_model.split('/'))).pop(),
str(args.max_seq_length), str(task_name)))
try:
with open(cached_eval_features_file, "rb") as reader:
eval_features = pickle.load(reader)
except:
eval_features = convert_examples_to_features(
eval_examples, label_list, args.max_seq_length, tokenizer,
output_mode)
with open(cached_eval_features_file, "wb") as writer:
pickle.dump(eval_features, writer)
all_input_ids = [f.input_ids for f in eval_features]
all_segment_ids = [f.segment_ids for f in eval_features]
all_label_ids = [f.label_id for f in eval_features]
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
label_dtype = None
if output_mode == "classification":
label_dtype = torch.long
elif output_mode == "regression":
label_dtype = torch.float
# Run prediction for full data
if args.local_rank == -1:
eval_sampler = SequentialSampler
else:
eval_sampler = DistributedSampler # Note that this sampler samples randomly
eval_dataloader = SentPairClsDataLoader(all_input_ids, all_segment_ids,
all_label_ids,
args.eval_batch_size,
eval_sampler, device,
label_dtype)
model.eval()
preds = []
out_label_ids = None
for graph, label_ids in eval_dataloader:
with torch.no_grad():
logits = model(graph)
if len(preds) == 0:
preds.append(logits.detach().cpu().numpy())
out_label_ids = label_ids.detach().cpu().numpy()
else:
preds[0] = np.append(preds[0],
logits.detach().cpu().numpy(),
axis=0)
out_label_ids = np.append(out_label_ids,
label_ids.detach().cpu().numpy(),
axis=0)
preds = preds[0]
if output_mode == "classification":
preds = np.argmax(preds, axis=1)
elif output_mode == "regression":
preds = np.squeeze(preds)
result = compute_metrics(task_name, preds, out_label_ids)
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
def convert_input_example(note_id, text, seqIdx):
return InputExample(guid='%s-%s' % (note_id, seqIdx),
text_a=text,
text_b=None,
label=0,
group=0,
other_fields=[])
examples = [
convert_input_example(idx, i, c) for idx, row in df.iterrows()
for c, i in enumerate(row.seqs)
]
features = convert_examples_to_features(examples,
Constants.MAX_SEQ_LEN,
tokenizer,
output_mode='classification')
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = torch.cuda.device_count()
model.to(device)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
generator = data.DataLoader(MIMICDataset(features, 'train', 'classification'),
shuffle=True,
batch_size=n_gpu * 32)
EMB_SIZE = get_emb_size(args.emb_method)
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--train_data",
default=None,
type=str,
help="Train data path.")
parser.add_argument("--test_data",
default=None,
type=str,
help="Test data path.")
parser.add_argument("--eval_data",
default=None,
type=str,
help="Eval data path.")
parser.add_argument("--bert_model",
default=None,
type=str,
required=True,
help="PreTrained model path.")
parser.add_argument("--config",
default=None,
type=str,
required=True,
help="Model config path.")
parser.add_argument("--vocab",
default=None,
type=str,
required=True,
help="Vocabulary path.")
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(
"--result_file",
default=None,
type=str,
help=
"The output directory where the model predictions and checkpoints will be written."
)
parser.add_argument("--dic_dir",
default=None,
type=str,
required=True,
help="The dic directory which used by rule.")
## Other parameters
parser.add_argument(
"--max_seq_length",
default=128,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_predict",
action='store_true',
help="Whether to run 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("--pred_batch_size",
default=32,
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('--overwrite_output_dir',
action='store_true',
help="Overwrite the content of the output directory")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=2019,
help="random seed for initialization")
parser.add_argument(
'--gradient_accumulation_steps',
type=int,
default=1,
help=
"Number of updates steps to accumulate before performing a backward/update pass."
)
parser.add_argument(
'--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument(
'--loss_scale',
type=float,
default=0,
help=
"Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n")
args = parser.parse_args()
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
args.device = device
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
logger.info(
"device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".
format(device, n_gpu, bool(args.local_rank != -1), args.fp16))
if args.gradient_accumulation_steps < 1:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
.format(args.gradient_accumulation_steps))
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if not args.do_train and not args.do_predict:
raise ValueError(
"At least one of `do_train` or `do_predict` must be True.")
if os.path.exists(args.output_dir) and os.listdir(
args.output_dir
) and args.do_train and not args.overwrite_output_dir:
raise ValueError(
"Output directory ({}) already exists and is not empty.".format(
args.output_dir))
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
task_name = args.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
label_list = processor.get_labels()
domain_map = {}
for (i, label) in enumerate(label_list['domain']):
domain_map[label] = i
intent_map = {}
for (i, label) in enumerate(label_list['intent']):
intent_map[label] = i
slots_map = {}
for (i, label) in enumerate(label_list['slots']):
slots_map[label] = i
logger.info("***** label list *****")
for key, value in label_list.items():
logger.info("%s(%d): %s" % (key, len(value), ", ".join(value)))
if args.local_rank not in [-1, 0]:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
tokenizer = BertTokenizer.from_pretrained(args.vocab,
do_lower_case=args.do_lower_case)
model = BertForTaskNLU.from_pretrained(args.bert_model,
args.config,
label_list=label_list,
max_seq_len=args.max_seq_length)
if args.local_rank == 0:
torch.distributed.barrier()
if args.fp16:
model.half()
model.to(device)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
elif n_gpu > 1:
model = torch.nn.DataParallel(model)
global_step = 0
nb_tr_steps = 0
tr_loss = 0
if args.do_train:
# Prepare data loader
train_examples = processor.get_train_examples(args.train_data)
random.seed(args.seed)
random.shuffle(train_examples)
train_features = convert_examples_to_features(train_examples,
domain_map, intent_map,
slots_map,
args.max_seq_length,
tokenizer)
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_domain_ids = torch.tensor([f.domain_id for f in train_features],
dtype=torch.long)
all_intent_ids = torch.tensor([f.intent_id for f in train_features],
dtype=torch.long)
all_slots_ids = torch.tensor([f.slots_id for f in train_features],
dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_domain_ids,
all_intent_ids, all_slots_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)
num_train_optimization_steps = len(
train_dataloader
) // args.gradient_accumulation_steps * args.num_train_epochs
# Prepare optimizer
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params': [
p for n, p in param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
if args.fp16:
try:
from apex.optimizers import FP16_Optimizer
from apex.optimizers import FusedAdam
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
)
optimizer = FusedAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
bias_correction=False,
max_grad_norm=1.0)
if args.loss_scale == 0:
optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
else:
optimizer = FP16_Optimizer(optimizer,
static_loss_scale=args.loss_scale)
warmup_linear = WarmupLinearSchedule(
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
else:
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
logger.info("***** Running training *****")
logger.info("Num examples = %d", len(train_examples))
logger.info("Batch size = %d", args.train_batch_size)
logger.info("Num steps = %d", num_train_optimization_steps)
model.train()
for _ in range(int(args.num_train_epochs)):
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(train_dataloader):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, domain_id, intent_id, slots_id = batch
# define a new function to compute loss values for both output_modes
domain_logits, intent_logits, slots_logits = model(
input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask)
loss_fct = CrossEntropyLoss()
loss = loss_fct(domain_logits, domain_id) + loss_fct(
intent_logits, intent_id)
for i in range(len(slots_id)):
loss += loss_fct(slots_logits[i], slots_id[i])
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
optimizer.backward(loss)
else:
loss.backward()
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
# modify learning rate with special warm up BERT uses
# if args.fp16 is False, BertAdam is used that handles this automatically
lr_this_step = args.learning_rate * warmup_linear.get_lr(
global_step, args.warmup_proportion)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step()
optimizer.zero_grad()
global_step += 1
if args.local_rank in [-1, 0] and nb_tr_steps % 20 == 0:
# logger.info("lr = {}, global_step = {}".format(optimizer.get_lr()[0], global_step))
logger.info("loss = {:.6f}, global_step = {}".format(
tr_loss / global_step, global_step))
### Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
### Example:
if args.do_train and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
# Save a trained model, configuration and tokenizer
model_to_save = model.module if hasattr(
model, 'module') else model # Only save the model it-self
# If we save using the predefined names, we can load using `from_pretrained`
output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
torch.save(model_to_save.state_dict(), output_model_file)
else:
model = BertForTaskNLU.from_pretrained(args.bert_model,
args.config,
label_list=label_list,
max_seq_len=args.max_seq_length)
model.to(device)
### prediction
if args.do_predict and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
pred_examples = processor.get_test_examples(args.test_data)
pred_features = convert_examples_to_features(pred_examples, domain_map,
intent_map, slots_map,
args.max_seq_length,
tokenizer)
logger.info("***** Running prediction *****")
logger.info("Num examples = %d", len(pred_examples))
logger.info("Batch size = %d", args.pred_batch_size)
all_input_ids = torch.tensor([f.input_ids for f in pred_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in pred_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in pred_features],
dtype=torch.long)
pred_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids)
# Run prediction for full data
if args.local_rank == -1:
pred_sampler = SequentialSampler(pred_data)
else:
pred_sampler = DistributedSampler(
pred_data) # Note that this sampler samples randomly
pred_dataloader = DataLoader(pred_data,
sampler=pred_sampler,
batch_size=args.pred_batch_size)
model.eval()
preds = []
for input_ids, input_mask, segment_ids in pred_dataloader:
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
with torch.no_grad():
domain_logits, intent_logits, slots_logits = model(
input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask)
domain = domain_logits.detach().cpu().numpy()
intent = intent_logits.detach().cpu().numpy()
slots = slots_logits.detach().cpu().numpy()
for i in range(domain.shape[0]):
preds.append({
"domain": domain[i],
"intent": intent[i],
"slots": slots[i]
})
output_predict_file = os.path.join(args.output_dir, args.result_file)
write_result(output_predict_file, args.dic_dir, preds, pred_examples,
domain_map, intent_map, slots_map)
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("--loss_weight",
default=None,
type=str,
help="The Loss Weight.")
parser.add_argument("--pop_classifier_layer",
action='store_true',
help="pop classifier layer")
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_predict",
action='store_true',
help="Whether to run predict on the test 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("--predict_batch_size",
default=8,
type=int,
help="Total batch size for predict.")
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('--overwrite_output_dir',
action='store_true',
help="Overwrite the content of the output directory")
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()
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')
args.device = device
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
logger.info(
"device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".
format(device, n_gpu, bool(args.local_rank != -1), args.fp16))
if args.gradient_accumulation_steps < 1:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
.format(args.gradient_accumulation_steps))
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if not args.do_train and not args.do_eval and not args.do_predict:
raise ValueError(
"At least one of `do_train`, `do_eval` or `do_predict` must be True."
)
if os.path.exists(args.output_dir) and os.listdir(
args.output_dir
) and args.do_train and not args.overwrite_output_dir:
raise ValueError(
"Output directory ({}) already exists and is not empty.".format(
args.output_dir))
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
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)
if args.local_rank not in [-1, 0]:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=args.do_lower_case)
print("pop_classifier_layer", args.pop_classifier_layer)
model = BertForSequenceClassification.from_pretrained(
args.bert_model,
num_labels=num_labels,
pop_classifier_layer=args.pop_classifier_layer)
if args.local_rank == 0:
torch.distributed.barrier()
if args.fp16:
model.half()
model.to(device)
if args.local_rank != -1:
model = torch.nn.parallel.DistributedDataParallel(
model,
device_ids=[args.local_rank],
output_device=args.local_rank,
find_unused_parameters=True)
elif n_gpu > 1:
model = torch.nn.DataParallel(model)
print("loss_weight", args.loss_weight)
global_step = 0
nb_tr_steps = 0
tr_loss = 0
if args.do_train:
if args.local_rank in [-1, 0]:
tb_writer = SummaryWriter()
# Prepare data loader
train_examples = processor.get_train_examples(args.data_dir)
cached_train_features_file = os.path.join(
args.data_dir, 'train_{0}_{1}_{2}'.format(
list(filter(None, args.bert_model.split('/'))).pop(),
str(args.max_seq_length), str(task_name)))
try:
with open(cached_train_features_file, "rb") as reader:
train_features = pickle.load(reader)
except:
train_features = convert_examples_to_features(
train_examples, label_list, args.max_seq_length, tokenizer,
output_mode)
if args.local_rank == -1 or torch.distributed.get_rank() == 0:
logger.info(" Saving train features into cached file %s",
cached_train_features_file)
with open(cached_train_features_file, "wb") as writer:
pickle.dump(train_features, writer)
all_input_ids = torch.tensor([f.input_ids for f in train_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in train_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in train_features],
dtype=torch.long)
if output_mode == "classification":
all_label_ids = torch.tensor([f.label_id for f in train_features],
dtype=torch.long)
elif output_mode == "regression":
all_label_ids = torch.tensor([f.label_id for f in train_features],
dtype=torch.float)
train_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
if args.local_rank == -1:
train_sampler = RandomSampler(train_data)
else:
train_sampler = DistributedSampler(train_data)
train_dataloader = DataLoader(train_data,
sampler=train_sampler,
batch_size=args.train_batch_size)
num_train_optimization_steps = len(
train_dataloader
) // args.gradient_accumulation_steps * args.num_train_epochs
# Prepare optimizer
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params': [
p for n, p in param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
if args.fp16:
try:
from apex.optimizers import FP16_Optimizer
from apex.optimizers import FusedAdam
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
)
optimizer = FusedAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
bias_correction=False,
max_grad_norm=1.0)
if args.loss_scale == 0:
optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
else:
optimizer = FP16_Optimizer(optimizer,
static_loss_scale=args.loss_scale)
warmup_linear = WarmupLinearSchedule(
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
else:
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps)
model.train()
for _ in trange(int(args.num_train_epochs),
desc="Epoch",
disable=args.local_rank not in [-1, 0]):
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(
tqdm(train_dataloader,
desc="Iteration",
disable=args.local_rank not in [-1, 0])):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
# define a new function to compute loss values for both output_modes
logits = model(input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask)
# print(input_ids)
# print(logits)
# print(label_ids)
if output_mode == "classification":
if args.loss_weight == None:
loss_fct = CrossEntropyLoss()
else:
loss_weight = [
int(_) for _ in args.loss_weight.split(",")
]
loss_fct = CrossEntropyLoss(
torch.FloatTensor(loss_weight).cuda())
loss = loss_fct(logits.view(-1, num_labels),
label_ids.view(-1))
elif output_mode == "regression":
loss_fct = MSELoss()
loss = loss_fct(logits.view(-1), label_ids.view(-1))
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
optimizer.backward(loss)
else:
loss.backward()
tr_loss += loss.item()
nb_tr_examples += input_ids.size(0)
nb_tr_steps += 1
if (step + 1) % args.gradient_accumulation_steps == 0:
if args.fp16:
# modify learning rate with special warm up BERT uses
# if args.fp16 is False, BertAdam is used that handles this automatically
lr_this_step = args.learning_rate * warmup_linear.get_lr(
global_step, args.warmup_proportion)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step()
optimizer.zero_grad()
global_step += 1
if args.local_rank in [-1, 0]:
tb_writer.add_scalar('lr',
optimizer.get_lr()[0],
global_step)
tb_writer.add_scalar('loss', loss.item(), global_step)
### Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
### Example:
if args.do_train and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
# Save a trained model, configuration and tokenizer
model_to_save = model.module if hasattr(
model, 'module') else model # Only save the model it-self
# If we save using the predefined names, we can load using `from_pretrained`
output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
torch.save(model_to_save.state_dict(), output_model_file)
model_to_save.config.to_json_file(output_config_file)
tokenizer.save_vocabulary(args.output_dir)
# Load a trained model and vocabulary that you have fine-tuned
model = BertForSequenceClassification.from_pretrained(
args.output_dir, num_labels=num_labels)
tokenizer = BertTokenizer.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
# Good practice: save your training arguments together with the trained model
output_args_file = os.path.join(args.output_dir, 'training_args.bin')
torch.save(args, output_args_file)
else:
model = BertForSequenceClassification.from_pretrained(
args.bert_model, num_labels=num_labels)
model.to(device)
### Evaluation
if args.do_eval and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
eval_examples = processor.get_dev_examples(args.data_dir)
cached_eval_features_file = os.path.join(
args.data_dir, 'dev_{0}_{1}_{2}'.format(
list(filter(None, args.bert_model.split('/'))).pop(),
str(args.max_seq_length), str(task_name)))
try:
with open(cached_eval_features_file, "rb") as reader:
eval_features = pickle.load(reader)
except:
eval_features = convert_examples_to_features(
eval_examples, label_list, args.max_seq_length, tokenizer,
output_mode)
if args.local_rank == -1 or torch.distributed.get_rank() == 0:
logger.info(" Saving eval features into cached file %s",
cached_eval_features_file)
with open(cached_eval_features_file, "wb") as writer:
pickle.dump(eval_features, writer)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
all_input_ids = torch.tensor([f.input_ids for f in eval_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features],
dtype=torch.long)
if output_mode == "classification":
all_label_ids = torch.tensor([f.label_id for f in eval_features],
dtype=torch.long)
elif output_mode == "regression":
all_label_ids = torch.tensor([f.label_id for f in eval_features],
dtype=torch.float)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
# Run prediction for full data
if args.local_rank == -1:
eval_sampler = SequentialSampler(eval_data)
else:
eval_sampler = DistributedSampler(
eval_data) # Note that this sampler samples randomly
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
model.eval()
eval_loss = 0
nb_eval_steps = 0
preds = []
out_label_ids = None
for input_ids, input_mask, segment_ids, label_ids in tqdm(
eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
logits = model(input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask)
print(logits)
print(label_ids)
print(logits.view(-1, num_labels), label_ids.view(-1))
# create eval loss and other metric required by the task
if output_mode == "classification":
if args.loss_weight == None:
loss_fct = CrossEntropyLoss()
else:
loss_weight = [int(_) for _ in args.loss_weight.split(",")]
loss_fct = CrossEntropyLoss(
torch.FloatTensor(loss_weight).cuda())
tmp_eval_loss = loss_fct(logits.view(-1, num_labels),
label_ids.view(-1))
elif output_mode == "regression":
loss_fct = MSELoss()
tmp_eval_loss = loss_fct(logits.view(-1), label_ids.view(-1))
eval_loss += tmp_eval_loss.mean().item()
nb_eval_steps += 1
if len(preds) == 0:
preds.append(logits.detach().cpu().numpy())
out_label_ids = label_ids.detach().cpu().numpy()
else:
preds[0] = np.append(preds[0],
logits.detach().cpu().numpy(),
axis=0)
out_label_ids = np.append(out_label_ids,
label_ids.detach().cpu().numpy(),
axis=0)
eval_loss = eval_loss / nb_eval_steps
preds = preds[0]
print(preds)
def swap_value(a):
temp = a[0]
a[0] = a[1]
a[1] = temp
if task_name == "copa":
preds = softmax(preds, axis=1)
print(preds)
for i in range(int(len(preds) / 2)):
if preds[2 * i][0] >= preds[2 * i + 1][0]:
if preds[2 * i][0] < preds[2 * i][1]:
# print(preds[2*i][0], preds[2*i][1])
swap_value(preds[2 * i])
# print(preds[2*i][0], preds[2*i][1])
if preds[2 * i + 1][0] > preds[2 * i + 1][1]:
swap_value(preds[2 * i + 1])
else:
if preds[2 * i][0] > preds[2 * i][1]:
swap_value(preds[2 * i])
if preds[2 * i + 1][0] < preds[2 * i + 1][1]:
swap_value(preds[2 * i + 1])
print(preds)
if output_mode == "classification":
preds = np.argmax(preds, axis=1)
elif output_mode == "regression":
preds = np.squeeze(preds)
print(preds, out_label_ids)
result = compute_metrics(task_name, preds, out_label_ids)
loss = tr_loss / global_step if args.do_train else None
result['eval_loss'] = eval_loss
result['global_step'] = global_step
result['loss'] = loss
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
### Prediction
if args.do_predict and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
predict_examples = processor.get_test_examples(args.data_dir)
cached_predict_features_file = os.path.join(
args.data_dir, 'predict_{0}_{1}_{2}'.format(
list(filter(None, args.bert_model.split('/'))).pop(),
str(args.max_seq_length), str(task_name)))
try:
with open(cached_predict_features_file, "rb") as reader:
predict_features = pickle.load(reader)
except:
predict_features = convert_examples_to_features(
predict_examples, label_list, args.max_seq_length, tokenizer,
output_mode)
if args.local_rank == -1 or torch.distributed.get_rank() == 0:
logger.info(" Saving predict features into cached file %s",
cached_predict_features_file)
with open(cached_predict_features_file, "wb") as writer:
pickle.dump(predict_features, writer)
logger.info("***** Running prediction *****")
logger.info(" Num examples = %d", len(predict_examples))
logger.info(" Batch size = %d", args.predict_batch_size)
all_input_ids = torch.tensor([f.input_ids for f in predict_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in predict_features],
dtype=torch.long)
all_segment_ids = torch.tensor(
[f.segment_ids for f in predict_features], dtype=torch.long)
if output_mode == "classification":
all_label_ids = torch.tensor(
[f.label_id for f in predict_features], dtype=torch.long)
elif output_mode == "regression":
all_label_ids = torch.tensor(
[f.label_id for f in predict_features], dtype=torch.float)
predict_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
# Run prediction for full data
if args.local_rank == -1:
predict_sampler = SequentialSampler(predict_data)
else:
predict_sampler = DistributedSampler(
predict_data) # Note that this sampler samples randomly
predict_dataloader = DataLoader(predict_data,
sampler=predict_sampler,
batch_size=args.predict_batch_size)
model.eval()
# predict_loss = 0
# nb_predict_steps = 0
preds = []
out_label_ids = None
for input_ids, input_mask, segment_ids, label_ids in tqdm(
predict_dataloader, desc="predicting"):
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,
token_type_ids=segment_ids,
attention_mask=input_mask)
print(logits)
print(label_ids)
# create eval loss and other metric required by the task
# if output_mode == "classification":
# loss_fct = CrossEntropyLoss()
# tmp_eval_loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1))
# elif output_mode == "regression":
# loss_fct = MSELoss()
# tmp_eval_loss = loss_fct(logits.view(-1), label_ids.view(-1))
#
# eval_loss += tmp_eval_loss.mean().item()
# nb_predict_steps += 1
if len(preds) == 0:
preds.append(logits.detach().cpu().numpy())
# out_label_ids = label_ids.detach().cpu().numpy()
else:
preds[0] = np.append(preds[0],
logits.detach().cpu().numpy(),
axis=0)
# out_label_ids = np.append(
# out_label_ids, label_ids.detach().cpu().numpy(), axis=0)
#
# eval_loss = eval_loss / nb_eval_steps
preds = preds[0]
print(preds)
if task_name == "copa":
preds = softmax(preds, axis=1)
print(preds)
results = []
for i in range(int(len(preds) / 2)):
if preds[2 * i][0] >= preds[2 * i + 1][0]:
results.append(0)
else:
results.append(1)
preds = results
label_map = {i: i for i in range(2)}
else:
if output_mode == "classification":
preds = np.argmax(preds, axis=1)
elif output_mode == "regression":
preds = np.squeeze(preds)
label_map = {i: label for i, label in enumerate(label_list)}
print(preds)
# result = compute_metrics(task_name, preds, out_label_ids)
# loss = tr_loss/global_step if args.do_train else None
# result['eval_loss'] = eval_loss
# result['global_step'] = global_step
# result['loss'] = loss
output_predict_file = os.path.join(args.output_dir,
"predict_results.txt")
with open(output_predict_file, "w") as writer:
logger.info("***** Predict results *****")
for i in range(len(preds)):
label_i = label_map[preds[i]]
# json_i= "\"idx: %d, \"label\": \"label_i\""
writer.write("{\"idx\": %d, \"label\": \"%s\"}\n" %
(i, label_i))
def predict(input_text, model, tokenizer, processor, output_mode, device):
# Parameter
max_seq_length = 128
eval_batch_size = 8
### Evaluation
label_list = processor.get_labels()
print("input text: ", input_text)
eval_examples = processor.get_test_example(input_text)
eval_features = convert_examples_to_features(eval_examples, label_list,
max_seq_length, tokenizer,
output_mode)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", 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=eval_batch_size)
model.eval()
preds = []
out_label_ids = None
for input_ids, input_mask, segment_ids, label_ids in tqdm(
eval_dataloader, desc="Evaluating"):
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
label_ids = label_ids.to(device)
with torch.no_grad():
logits = model(input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask)
if len(preds) == 0:
preds.append(logits.detach().cpu().numpy())
out_label_ids = label_ids.detach().cpu().numpy()
else:
preds[0] = np.append(preds[0],
logits.detach().cpu().numpy(),
axis=0)
out_label_ids = np.append(out_label_ids,
label_ids.detach().cpu().numpy(),
axis=0)
preds = preds[0]
print(preds.shape)
print("preds", preds)
scores = softmax(preds[0])
print("scores", scores)
arg_max = np.argmax(preds)
print("Argmax", arg_max)
return arg_max, scores[
arg_max] # returning the index of labels and its score
if args.do_train:
if args.local_rank in [-1, 0]:
tb_writer = SummaryWriter()
# Prepare data loader
train_examples = processor.get_train_examples(args.data_dir)
cached_train_features_file = os.path.join(args.data_dir, 'train_{0}_{1}_{2}'.format(
list(filter(None, args.bert_model.split('/'))).pop(),
str(args.max_seq_length),
str(task_name)))
try:
with open(cached_train_features_file, "rb") as reader:
train_features = pickle.load(reader)
except:
train_features = convert_examples_to_features(
train_examples, label_list, args.max_seq_length, tokenizer, output_mode)
if args.local_rank == -1 or torch.distributed.get_rank() == 0:
logger.info(" Saving train features into cached file %s", cached_train_features_file)
with open(cached_train_features_file, "wb") as writer:
pickle.dump(train_features, writer)
all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long)
if output_mode == "classification":
all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.long)
elif output_mode == "regression":
all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.float)
train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
