def model_factory(bert_path, device=None, tokenizer=None, **kwargs):
if device is None:
device = get_default_device()
if tokenizer is None:
tokenizer = BertTokenizer('%s/vocab.txt' % (bert_path))
model = BertForSequenceClassification.from_pretrained(bert_path,
num_labels=2,
**kwargs).to(device)
return model, tokenizer, device
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.
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).
"""
model = BertForSequenceClassification.from_pretrained(*args, **kwargs)
return model
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument(
"--data_dir",
default=None,
type=str,
required=True,
help=
"The input data dir. Should contain the .tsv files (or other data files) for the task."
)
parser.add_argument(
"--bert_model",
default=None,
type=str,
required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, "
"bert-base-multilingual-cased, bert-base-chinese.")
parser.add_argument("--task_name",
default=None,
type=str,
required=True,
help="The name of the task to train.")
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help=
"The output directory where the model predictions and checkpoints will be written."
)
parser.add_argument("--negative_weight", default=1., type=float)
parser.add_argument("--neutral_words_file", default='data/identity.csv')
# if true, use test data instead of val data
parser.add_argument("--test", action='store_true')
# Explanation specific arguments below
# whether run explanation algorithms
parser.add_argument("--explain",
action='store_true',
help='if true, explain test set predictions')
parser.add_argument("--debug", action='store_true')
# which algorithm to run
parser.add_argument("--algo", choices=['soc'])
# the output filename without postfix
parser.add_argument("--output_filename", default='temp.tmp')
# see utils/config.py
parser.add_argument("--use_padding_variant", action='store_true')
parser.add_argument("--mask_outside_nb", action='store_true')
parser.add_argument("--nb_range", type=int)
parser.add_argument("--sample_n", type=int)
# whether use explanation regularization
parser.add_argument("--reg_explanations", action='store_true')
parser.add_argument("--reg_strength", type=float)
parser.add_argument("--reg_mse", action='store_true')
# whether discard other neutral words during regularization. default: False
parser.add_argument("--discard_other_nw",
action='store_false',
dest='keep_other_nw')
# whether remove neutral words when loading datasets
parser.add_argument("--remove_nw", action='store_true')
# if true, generate hierarchical explanations instead of word level outputs.
# Only useful when the --explain flag is also added.
parser.add_argument("--hiex", action='store_true')
parser.add_argument("--hiex_tree_height", default=5, type=int)
# whether add the sentence itself to the sample set in SOC
parser.add_argument("--hiex_add_itself", action='store_true')
# the directory where the lm is stored
parser.add_argument("--lm_dir", default='runs/lm')
# if configured, only generate explanations for instances with given line numbers
parser.add_argument("--hiex_idxs", default=None)
# if true, use absolute values of explanations for hierarchical clustering
parser.add_argument("--hiex_abs", action='store_true')
# if either of the two is true, only generate explanations for positive / negative instances
parser.add_argument("--only_positive", action='store_true')
parser.add_argument("--only_negative", action='store_true')
# stop after generating x explanation
parser.add_argument("--stop", default=100000000, type=int)
# early stopping with decreasing learning rate. 0: direct exit when validation F1 decreases
parser.add_argument("--early_stop", default=5, type=int)
# other external arguments originally here in pytorch_transformers
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=32,
type=int,
help="Total batch size for eval.")
parser.add_argument("--validate_steps",
default=200,
type=int,
help="validate once for how many steps")
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()
combine_args(configs, args)
args = configs
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 = {
'gab': GabProcessor,
'ws': WSProcessor,
'nyt': NytProcessor,
'MT': MTProcessor,
#'multi-label': multilabel_Processor,
}
output_modes = {
'gab': 'classification',
'ws': 'classification',
'nyt': 'classification'
}
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
logger.info(
"device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".
format(device, n_gpu, bool(args.local_rank != -1), args.fp16))
if args.gradient_accumulation_steps < 1:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
.format(args.gradient_accumulation_steps))
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if not args.do_train and not args.do_eval:
raise ValueError(
"At least one of `do_train` or `do_eval` must be True.")
#if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train:
# raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
# save configs
f = open(os.path.join(args.output_dir, 'args.json'), 'w')
json.dump(args.__dict__, f, indent=4)
f.close()
task_name = args.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
tokenizer = BertTokenizer.from_pretrained(args.bert_model,
do_lower_case=args.do_lower_case)
processor = processors[task_name](configs, tokenizer=tokenizer)
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.data_dir)
num_train_optimization_steps = int(
len(train_examples) / args.train_batch_size /
args.gradient_accumulation_steps) * args.num_train_epochs
if args.local_rank != -1:
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size(
)
# Prepare model
cache_dir = args.cache_dir if args.cache_dir else os.path.join(
str(PYTORCH_PRETRAINED_BERT_CACHE), 'distributed_{}'.format(
args.local_rank))
if args.do_train:
model = BertForSequenceClassification.from_pretrained(
args.bert_model, cache_dir=cache_dir, num_labels=num_labels)
else:
model = BertForSequenceClassification.from_pretrained(
args.output_dir, num_labels=num_labels)
model.to(device)
if args.fp16:
model.half()
if args.local_rank != -1:
try:
from apex.parallel import DistributedDataParallel as DDP
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
)
model = DDP(model)
# elif n_gpu > 1:
# model = torch.nn.DataParallel(model)
# Prepare optimizer
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
if args.fp16:
try:
from apex.optimizers import FP16_Optimizer
from apex.optimizers import FusedAdam
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
)
optimizer = FusedAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
bias_correction=False,
max_grad_norm=1.0)
if args.loss_scale == 0:
optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
else:
optimizer = FP16_Optimizer(optimizer,
static_loss_scale=args.loss_scale)
warmup_linear = WarmupLinearSchedule(
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
else:
if args.do_train:
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=num_train_optimization_steps)
global_step = 0
nb_tr_steps = 0
tr_loss, tr_reg_loss = 0, 0
tr_reg_cnt = 0
epoch = -1
val_best_f1 = -1
val_best_loss = 1e10
early_stop_countdown = args.early_stop
if args.reg_explanations:
train_lm_dataloder = processor.get_dataloader('train',
configs.train_batch_size)
dev_lm_dataloader = processor.get_dataloader('dev',
configs.train_batch_size)
explainer = SamplingAndOcclusionExplain(
model,
configs,
tokenizer,
device=device,
vocab=tokenizer.vocab,
train_dataloader=train_lm_dataloder,
dev_dataloader=dev_lm_dataloader,
lm_dir=args.lm_dir,
output_path=os.path.join(configs.output_dir,
configs.output_filename),
)
else:
explainer = None
if args.do_train:
epoch = 0
train_features = convert_examples_to_features(train_examples,
label_list,
args.max_seq_length,
tokenizer, output_mode,
configs)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps)
all_input_ids = torch.tensor([f.input_ids for f in train_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in train_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in train_features],
dtype=torch.long)
if output_mode == "classification":
all_label_ids = torch.tensor([f.label_id for f in train_features],
dtype=torch.long)
elif output_mode == "regression":
all_label_ids = torch.tensor([f.label_id for f in train_features],
dtype=torch.float)
train_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_label_ids)
if args.local_rank == -1:
train_sampler = RandomSampler(train_data)
else:
train_sampler = DistributedSampler(train_data)
train_dataloader = DataLoader(train_data,
sampler=train_sampler,
batch_size=args.train_batch_size)
class_weight = torch.FloatTensor([args.negative_weight, 1]).to(device)
model.train()
for _ in trange(int(args.num_train_epochs), desc="Epoch"):
tr_loss = 0
nb_tr_examples, nb_tr_steps = 0, 0
for step, batch in enumerate(
tqdm(train_dataloader, desc="Iteration")):
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, label_ids = batch
# define a new function to compute loss values for both output_modes
logits = model(input_ids, segment_ids, input_mask, labels=None)
if output_mode == "classification":
loss_fct = CrossEntropyLoss(class_weight)
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
tr_loss += loss.item()
if args.fp16:
optimizer.backward(loss)
else:
loss.backward()
# regularize explanations
# NOTE: backward performed inside this function to prevent OOM
if args.reg_explanations:
reg_loss, reg_cnt = explainer.compute_explanation_loss(
input_ids,
input_mask,
segment_ids,
label_ids,
do_backprop=True)
tr_reg_loss += reg_loss # float
tr_reg_cnt += reg_cnt
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 / num_train_optimization_steps,
args.warmup_proportion)
for param_group in optimizer.param_groups:
param_group['lr'] = lr_this_step
optimizer.step()
optimizer.zero_grad()
global_step += 1
if global_step % args.validate_steps == 0:
val_result = validate(args, model, processor, tokenizer,
output_mode, label_list, device,
num_labels, task_name, tr_loss,
global_step, epoch, explainer)
val_acc, val_f1 = val_result['acc'], val_result['f1']
if val_f1 > val_best_f1:
val_best_f1 = val_f1
if args.local_rank == -1 or torch.distributed.get_rank(
) == 0:
save_model(args, model, tokenizer, num_labels)
else:
# halve the learning rate
for param_group in optimizer.param_groups:
param_group['lr'] *= 0.5
early_stop_countdown -= 1
logger.info(
"Reducing learning rate... Early stop countdown %d"
% early_stop_countdown)
if early_stop_countdown < 0:
break
if early_stop_countdown < 0:
break
epoch += 1
# training finish ############################
# if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
# if not args.explain:
# args.test = True
# validate(args, model, processor, tokenizer, output_mode, label_list, device, num_labels,
# task_name, tr_loss, global_step=0, epoch=-1, explainer=explainer)
# else:
# args.test = True
# explain(args, model, processor, tokenizer, output_mode, label_list, device)
if not args.explain:
args.test = True
print('--Test_args.test: %s' % str(args.test)) #Test_args.test: True
validate(args,
model,
processor,
tokenizer,
output_mode,
label_list,
device,
num_labels,
task_name,
tr_loss,
global_step=888,
epoch=-1,
explainer=explainer)
args.test = False
else:
print('--Test_args.test: %s' % str(args.test)) # Test_args.test: True
args.test = True
explain(args, model, processor, tokenizer, output_mode, label_list,
device)
args.test = False
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--device",
default=None,
type=str,
required=True,
help="The device you will run on.")
parser.add_argument("--model_dir",
default=None,
type=str,
required=True,
help="The model for inference.")
parser.add_argument(
"--features_file",
default=None,
type=str,
required=True,
help="The file contains the features of query-passage pairs."
"Format: example_id,input_ids,input_mask,segment_ids,label\n")
parser.add_argument("--output_scores_file",
default=None,
type=str,
required=True,
help="The output prediction from BERT ranker."
"Format: query_id\tpassage_id\tscore\n")
parser.add_argument("--cache_file_dir",
default='./cache',
type=str,
required=True,
help="The directory where cache the features.")
parser.add_argument("--eval_batch_size",
default=100,
type=int,
help="Batch size for eval.")
parser.add_argument(
"--max_seq_length",
default=256,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization."
)
args = parser.parse_args()
logger.info('The args: {}'.format(args))
if not os.path.exists(args.cache_file_dir):
os.makedirs(args.cache_file_dir)
os.environ["CUDA_VISIBLE_DEVICES"] = args.device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = BertForSequenceClassification.from_pretrained(args.model_dir,
num_labels=2)
model.to(device)
_, dataloader = distill_dataloader(args, SequentialSampler,
args.eval_batch_size)
scores = do_eval(model, dataloader, device)
save_scores(args, scores)
if os.path.exists(args.cache_file_dir):
import shutil
shutil.rmtree(args.cache_file_dir)