def main():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--bert_model",
default=None,
type=str,
required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, "
"bert-base-multilingual-cased, bert-base-chinese.")
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help=
"The output directory where the model checkpoints and predictions will be written."
)
# Optimizer parameters
parser.add_argument("--adam_epsilon",
default=1e-6,
type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm",
default=1.0,
type=float,
help="Max gradient norm.")
parser.add_argument("--adam_betas", default="(0.9, 0.999)", type=str)
parser.add_argument("--no_bias_correction",
default=False,
action='store_true')
# Other parameters
parser.add_argument("--train_file",
default=None,
type=str,
help="SQuAD-format json file for training.")
parser.add_argument("--predict_file",
default=None,
type=str,
help="SQuAD-format json file for evaluation.")
parser.add_argument(
"--max_seq_length",
default=384,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. Sequences "
"longer than this will be truncated, and sequences shorter than this will be padded."
)
parser.add_argument(
"--doc_stride",
default=128,
type=int,
help=
"When splitting up a long document into chunks, how much stride to take between chunks."
)
parser.add_argument(
"--max_query_length",
default=64,
type=int,
help=
"The maximum number of tokens for the question. Questions longer than this will "
"be truncated to this length.")
parser.add_argument("--do_train",
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_label", action='store_true')
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--predict_batch_size",
default=8,
type=int,
help="Total batch size for predictions.")
parser.add_argument("--learning_rate",
default=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(
"--n_best_size",
default=20,
type=int,
help=
"The total number of n-best predictions to generate in the nbest_predictions.json "
"output file.")
parser.add_argument(
"--max_answer_length",
default=30,
type=int,
help=
"The maximum length of an answer that can be generated. This is needed because the start "
"and end predictions are not conditioned on one another.")
parser.add_argument(
"--verbose_logging",
action='store_true',
help=
"If true, all of the warnings related to data processing will be printed. "
"A number of warnings are expected for a normal SQuAD evaluation.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument('--seed',
type=int,
default=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(
"--do_lower_case",
default=False,
action='store_true',
help=
"Whether to lower case the input text. True for uncased models, False for cased models."
)
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument(
'--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument('--fp16_opt_level', default='O1', type=str)
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(
'--version_2_with_negative',
action='store_true',
help=
'If true, the SQuAD examples contain some that do not have an answer.')
parser.add_argument(
'--null_score_diff_threshold',
type=float,
default=0.0,
help=
"If null_score - best_non_null is greater than the threshold predict null."
)
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(
'--no_masking',
action='store_true',
help='If true, we do not mask the span loss for no-answer examples.')
parser.add_argument(
'--skip_negatives',
action='store_true',
help=
'If true, we skip negative examples during training; this is mainly for ablation.'
)
# For Natural Questions
parser.add_argument(
'--max_answer_len',
type=int,
default=1000000,
help=
"maximum length of answer tokens (might be set to 5 for Natural Questions!)"
)
# balance the two losses.
parser.add_argument(
'--lambda_scale',
type=float,
default=1.0,
help=
"If you would like to change the two losses, please change the lambda scale."
)
# Save checkpoints more
parser.add_argument(
'--save_gran',
type=str,
default="10,3",
help='"10,5" means saving a checkpoint every 1/10 of the total updates,'
'but start saving from the 5th attempt')
parser.add_argument('--oss_cache_dir', default=None, type=str)
parser.add_argument('--cache_dir', default=None, type=str)
parser.add_argument('--dist', default=False, action='store_true')
args = parser.parse_args()
print(args)
if args.dist:
dist.init_process_group(backend='nccl')
print(f"local rank: {args.local_rank}")
print(f"global rank: {dist.get_rank()}")
print(f"world size: {dist.get_world_size()}")
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
# synchronizing nodes/GPUs
dist.init_process_group(backend='nccl')
if args.dist:
global_rank = dist.get_rank()
world_size = dist.get_world_size()
if world_size > 1:
args.local_rank = global_rank
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO)
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 args.do_train:
if not args.train_file:
raise ValueError(
"If `do_train` is True, then `train_file` must be specified.")
if args.do_predict:
if not args.predict_file:
raise ValueError(
"If `do_predict` is True, then `predict_file` must be specified."
)
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.")
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
# Prepare model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(args.bert_model,
do_lower_case=args.do_lower_case)
model = IterBertForQuestionAnsweringConfidence.from_pretrained(
args.bert_model,
num_labels=4,
no_masking=args.no_masking,
lambda_scale=args.lambda_scale)
model.to(device)
train_examples = None
train_features = None
num_train_optimization_steps = None
if args.do_train:
cached_train_features_file = args.train_file + '_{0}_{1}_{2}_{3}_{4}'.format(
model.base_model_prefix, str(args.max_seq_length),
str(args.doc_stride), str(args.max_query_length),
tokenizer.do_lower_case)
cached_train_features_file_name = cached_train_features_file.split(
'/')[-1]
_oss_feature_save_path = os.path.join(oss_features_cache_dir,
cached_train_features_file_name)
try:
if args.cache_dir is not None and os.path.exists(
os.path.join(args.cache_dir,
cached_train_features_file_name)):
logger.info(
f"Loading pre-processed features from {os.path.join(args.cache_dir, cached_train_features_file_name)}"
)
train_features = torch.load(
os.path.join(args.cache_dir,
cached_train_features_file_name))
else:
logger.info(
f"Loading pre-processed features from oss: {_oss_feature_save_path}"
)
train_features = torch.load(
load_buffer_from_oss(_oss_feature_save_path))
except:
train_examples = read_squad_examples(
input_file=args.train_file,
is_training=True,
version_2_with_negative=args.version_2_with_negative,
max_answer_len=args.max_answer_len,
skip_negatives=args.skip_negatives)
train_features = convert_examples_to_features_yes_no(
examples=train_examples,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length,
is_training=True)
if args.local_rank in [-1, 0]:
torch_save_to_oss(train_features, _oss_feature_save_path)
logger.info(
f"Saving train features into oss: {_oss_feature_save_path}"
)
num_train_optimization_steps = int(
len(train_features) / args.train_batch_size /
args.gradient_accumulation_steps) * args.num_train_epochs
if args.do_label:
logger.info("finished.")
return
if args.do_train:
# Prepare optimizer
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight', 'layer_norm']
optimizer_grouped_parameters = [{
'params': [
p for n, p in param_optimizer
if not any(nd in n for nd in no_decay)
],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
t_total = num_train_optimization_steps
if args.local_rank != -1:
t_total = t_total // dist.get_world_size()
optimizer = AdamW(optimizer_grouped_parameters,
lr=args.learning_rate,
betas=eval(args.adam_betas),
eps=args.adam_epsilon)
scheduler = get_linear_schedule_with_warmup(
optimizer, int(t_total * args.warmup_proportion),
num_train_optimization_steps)
if args.fp16:
from apex import amp
if args.fp16_opt_level == 'O1':
amp.register_half_function(torch, "einsum")
if args.loss_scale == 0:
model, optimizer = amp.initialize(
model, optimizer, opt_level=args.fp16_opt_level)
else:
model, optimizer = amp.initialize(
model,
optimizer,
opt_level=args.fp16_opt_level,
loss_scale=args.loss_scale)
if args.local_rank != -1:
if args.fp16_opt_level == 'O2':
try:
import apex
model = apex.parallel.DistributedDataParallel(
model, delay_allreduce=True)
except ImportError:
model = torch.nn.parallel.DistributedDataParallel(
model, find_unused_parameters=True)
else:
model = torch.nn.parallel.DistributedDataParallel(
model, find_unused_parameters=True)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
global_step = 0
logger.info("***** Running training *****")
if train_examples:
logger.info(" Num orig examples = %d", len(train_examples))
logger.info(" Num split examples = %d", len(train_features))
logger.info(" Instantaneous batch size per GPU = %d",
args.train_batch_size)
logger.info(
" Total train batch size (w. parallel, distributed & accumulation) = %d",
args.train_batch_size * args.gradient_accumulation_steps *
(dist.get_world_size() if args.local_rank != -1 else 1),
)
logger.info(" Gradient Accumulation steps = %d",
args.gradient_accumulation_steps)
logger.info(" Total optimization steps = %d", t_total)
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_start_positions = torch.tensor(
[f.start_position for f in train_features], dtype=torch.long)
all_end_positions = torch.tensor(
[f.end_position for f in train_features], dtype=torch.long)
all_switches = torch.tensor([f.switch for f in train_features],
dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_start_positions,
all_end_positions, all_switches)
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,
pin_memory=True,
num_workers=4)
if args.save_gran is not None:
save_chunk, save_start = args.save_gran.split(',')
save_chunk = t_total // int(save_chunk)
save_start = int(save_start)
model.train()
tr_loss = 0
for _epc in trange(int(args.num_train_epochs), desc="Epoch"):
if args.local_rank != -1:
train_dataloader.sampler.set_epoch(_epc)
for step, batch in enumerate(
tqdm(train_dataloader,
desc="Iteration",
disable=args.local_rank not in [-1, 0])):
if n_gpu == 1:
# multi-gpu does scattering it-self
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, start_positions, end_positions, switches = batch
loss = model(input_ids=input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask,
start_positions=start_positions,
end_positions=end_positions,
switch_list=switches)
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:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
tr_loss += loss.item()
if (step + 1) % args.gradient_accumulation_steps == 0:
optimizer.step()
scheduler.step()
optimizer.zero_grad()
global_step += 1
if global_step % 50 == 0:
logger.info(f"Training loss: {tr_loss / global_step}\t"
f"Learning rate: {scheduler.get_lr()[0]}\t"
f"Global step: {global_step}")
if args.save_gran is not None and args.local_rank in [
-1, 0
]:
if (global_step % save_chunk == 0) and (
global_step // save_chunk >= save_start):
logger.info('Saving a checkpoint....')
output_dir_per_epoch = os.path.join(
args.output_dir,
str(global_step) + 'steps')
os.makedirs(output_dir_per_epoch)
# 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 args.oss_cache_dir is not None:
_oss_model_save_path = os.path.join(
args.oss_cache_dir, f"{global_step}steps")
torch_save_to_oss(
model_to_save.state_dict(),
_oss_model_save_path +
"/pytorch_model.bin")
model_to_save.save_pretrained(output_dir_per_epoch)
tokenizer.save_pretrained(output_dir_per_epoch)
logger.info('Done')
if args.do_train and (args.local_rank == -1 or dist.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
model_to_save.save_pretrained(args.output_dir)
tokenizer.save_pretrained(args.output_dir)
torch_save_to_oss(
model_to_save.state_dict(),
os.path.join(args.oss_cache_dir, "pytorch_model.bin"))
# Load a trained model and vocabulary that you have fine-tuned
# model = IterBertForQuestionAnsweringConfidence.from_pretrained(
# args.output_dir, num_labels=4, no_masking=args.no_masking)
tokenizer = AutoTokenizer.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
if args.do_train is False and args.do_predict is True:
model = IterBertForQuestionAnsweringConfidence.from_pretrained(
args.output_dir, num_labels=4, no_masking=args.no_masking)
tokenizer = AutoTokenizer.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
elif args.do_train is True and args.do_predict is True:
model = IterBertForQuestionAnsweringConfidence.from_pretrained(
args.output_dir, num_labels=4, no_masking=args.no_masking)
tokenizer = AutoTokenizer.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
else:
model = IterBertForQuestionAnsweringConfidence.from_pretrained(
args.bert_model,
num_labels=4,
no_masking=args.no_masking,
lambda_scale=args.lambda_scale)
model.to(device)
if args.do_predict and (args.local_rank == -1 or dist.get_rank() == 0):
eval_examples = read_squad_examples(
input_file=args.predict_file,
is_training=False,
version_2_with_negative=args.version_2_with_negative,
max_answer_len=args.max_answer_length,
skip_negatives=args.skip_negatives)
eval_features = convert_examples_to_features_yes_no(
examples=eval_examples,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length,
is_training=False)
logger.info("***** Running predictions *****")
logger.info(" Num orig examples = %d", len(eval_examples))
logger.info(" Num split examples = %d", len(eval_features))
logger.info(" Batch size = %d", args.predict_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_example_index = torch.arange(all_input_ids.size(0),
dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_example_index)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.predict_batch_size)
model.eval()
all_results = []
logger.info("Start evaluating")
for input_ids, input_mask, segment_ids, example_indices in tqdm(
eval_dataloader,
desc="Evaluating",
disable=args.local_rank not in [-1, 0]):
if len(all_results) % 1000 == 0:
logger.info("Processing example: %d" % (len(all_results)))
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
with torch.no_grad():
batch_start_logits, batch_end_logits, batch_switch_logits = model(
input_ids, segment_ids, input_mask)
for i, example_index in enumerate(example_indices):
start_logits = batch_start_logits[i].detach().cpu().tolist()
end_logits = batch_end_logits[i].detach().cpu().tolist()
switch_logits = batch_switch_logits[i].detach().cpu().tolist()
eval_feature = eval_features[example_index.item()]
unique_id = int(eval_feature.unique_id)
all_results.append(
RawResult(unique_id=unique_id,
start_logits=start_logits,
end_logits=end_logits,
switch_logits=switch_logits))
output_prediction_file = os.path.join(args.output_dir,
"predictions.json")
output_nbest_file = os.path.join(args.output_dir,
"nbest_predictions.json")
output_null_log_odds_file = os.path.join(args.output_dir,
"null_odds.json")
write_predictions_yes_no_no_empty_answer(
eval_examples, eval_features, all_results, args.n_best_size,
args.max_answer_length, args.do_lower_case, output_prediction_file,
output_nbest_file, output_null_log_odds_file, args.verbose_logging,
args.version_2_with_negative, args.null_score_diff_threshold,
args.no_masking)
def main():
reader_cfg = READER_CONFIG()
os.environ['CUDA_VISIBLE_DEVICES'] = ','.join(
str(x) for x in reader_cfg.visable_gpus)
if reader_cfg.local_rank == -1 or reader_cfg.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not reader_cfg.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(reader_cfg.local_rank)
device = torch.device("cuda", reader_cfg.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 reader_cfg.local_rank in [-1, 0] else logging.WARN)
logger.info(
"device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".
format(device, n_gpu, bool(reader_cfg.local_rank != -1),
reader_cfg.fp16))
if reader_cfg.gradient_accumulation_steps < 1:
raise ValueError(
"Invalid gradient_accumulation_steps parameter: {}, should be >= 1"
.format(reader_cfg.gradient_accumulation_steps))
reader_cfg.train_batch_size = reader_cfg.train_batch_size // reader_cfg.gradient_accumulation_steps
random.seed(reader_cfg.seed)
np.random.seed(reader_cfg.seed)
torch.manual_seed(reader_cfg.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(reader_cfg.seed)
if reader_cfg.do_predict:
if not reader_cfg.predict_file:
raise ValueError(
"If `do_predict` is True, then `predict_file` must be specified."
)
# if os.path.exists(reader_cfg.output_dir) and os.listdir(reader_cfg.output_dir) and reader_cfg.do_train:
# raise ValueError(
# "Output directory () already exists and is not empty.")
# if not os.path.exists(reader_cfg.output_dir):
# os.makedirs(reader_cfg.output_dir)
model_state_dict = torch.load(reader_cfg.load_ckpt_path)
model = BertForQuestionAnsweringConfidence.from_pretrained(
reader_cfg.output_dir, state_dict=model_state_dict)
tokenizer = BertTokenizer.from_pretrained(
reader_cfg.output_dir + 'vocab.txt',
do_lower_case=reader_cfg.do_lower_case)
# for name, param in model.named_parameters():
# print(name) # name, param.data
# import sys
# sys.exit()
model.to(device)
if reader_cfg.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)
if reader_cfg.do_predict and (reader_cfg.local_rank == -1
or torch.distributed.get_rank() == 0):
eval_examples = read_squad_examples(
input_file=reader_cfg.predict_file,
is_training=False,
version_2_with_negative=reader_cfg.version_2_with_negative,
max_answer_len=reader_cfg.max_answer_length,
skip_negatives=reader_cfg.skip_negatives)
eval_features = convert_examples_to_features_yes_no(
examples=eval_examples,
tokenizer=tokenizer,
max_seq_length=reader_cfg.max_seq_length,
doc_stride=reader_cfg.doc_stride,
max_query_length=reader_cfg.max_query_length,
is_training=False)
logger.info("***** Running predictions *****")
logger.info(" Num orig examples = %d", len(eval_examples))
logger.info(" Num split examples = %d", len(eval_features))
logger.info(" Batch size = %d", reader_cfg.predict_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_example_index = torch.arange(all_input_ids.size(0),
dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_example_index)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=reader_cfg.predict_batch_size)
model.eval()
all_results = []
logger.info("Start evaluating")
for input_ids, input_mask, segment_ids, example_indices in tqdm(
eval_dataloader,
desc="Evaluating",
disable=reader_cfg.local_rank not in [-1, 0]):
if len(all_results) % 1000 == 0:
logger.info("Processing example: %d" % (len(all_results)))
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
with torch.no_grad():
batch_start_logits, batch_end_logits, batch_switch_logits = model(
input_ids, segment_ids, input_mask)
for i, example_index in enumerate(example_indices):
start_logits = batch_start_logits[i].detach().cpu().tolist()
end_logits = batch_end_logits[i].detach().cpu().tolist()
switch_logits = batch_switch_logits[i].detach().cpu().tolist()
eval_feature = eval_features[example_index.item()]
unique_id = int(eval_feature.unique_id)
all_results.append(
RawResult(unique_id=unique_id,
start_logits=start_logits,
end_logits=end_logits,
switch_logits=switch_logits))
output_prediction_file = os.path.join(reader_cfg.output_dir,
"predictions.json")
output_nbest_file = os.path.join(reader_cfg.output_dir,
"nbest_predictions.json")
output_null_log_odds_file = os.path.join(reader_cfg.output_dir,
"null_odds.json")
write_predictions_yes_no_no_empty_answer(
eval_examples, eval_features, all_results, reader_cfg.n_best_size,
reader_cfg.max_answer_length, reader_cfg.do_lower_case,
output_prediction_file, output_nbest_file,
output_null_log_odds_file, reader_cfg.verbose_logging,
reader_cfg.version_2_with_negative,
reader_cfg.null_score_diff_threshold, reader_cfg.no_masking)
def main():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--bert_model",
default=None,
type=str,
required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, "
"bert-base-multilingual-cased, bert-base-chinese.")
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help=
"The output directory where the model checkpoints and predictions will be written."
)
# Other parameters
parser.add_argument("--train_file",
default=None,
type=str,
help="SQuAD-format json file for training.")
parser.add_argument("--predict_file",
default=None,
type=str,
help="SQuAD-format json file for evaluation.")
parser.add_argument(
"--max_seq_length",
default=384,
type=int,
help=
"The maximum total input sequence length after WordPiece tokenization. Sequences "
"longer than this will be truncated, and sequences shorter than this will be padded."
)
parser.add_argument(
"--doc_stride",
default=128,
type=int,
help=
"When splitting up a long document into chunks, how much stride to take between chunks."
)
parser.add_argument(
"--max_query_length",
default=64,
type=int,
help=
"The maximum number of tokens for the question. Questions longer than this will "
"be truncated to this length.")
parser.add_argument("--do_train",
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("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--predict_batch_size",
default=8,
type=int,
help="Total batch size for predictions.")
parser.add_argument("--learning_rate",
default=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(
"--n_best_size",
default=20,
type=int,
help=
"The total number of n-best predictions to generate in the nbest_predictions.json "
"output file.")
parser.add_argument(
"--max_answer_length",
default=30,
type=int,
help=
"The maximum length of an answer that can be generated. This is needed because the start "
"and end predictions are not conditioned on one another.")
parser.add_argument(
"--verbose_logging",
action='store_true',
help=
"If true, all of the warnings related to data processing will be printed. "
"A number of warnings are expected for a normal SQuAD evaluation.")
parser.add_argument("--no_cuda",
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument('--seed',
type=int,
default=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(
"--do_lower_case",
action='store_true',
help=
"Whether to lower case the input text. True for uncased models, False for cased models."
)
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument(
'--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(
'--version_2_with_negative',
action='store_true',
help=
'If true, the SQuAD examples contain some that do not have an answer.')
parser.add_argument(
'--null_score_diff_threshold',
type=float,
default=0.0,
help=
"If null_score - best_non_null is greater than the threshold predict null."
)
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(
'--no_masking',
action='store_true',
help='If true, we do not mask the span loss for no-answer examples.')
parser.add_argument(
'--skip_negatives',
action='store_true',
help=
'If true, we skip negative examples during training; this is mainly for ablation.'
)
# For Natural Questions
parser.add_argument(
'--max_answer_len',
type=int,
default=1000000,
help=
"maximum length of answer tokens (might be set to 5 for Natural Questions!)"
)
# balance the two losses.
parser.add_argument(
'--lambda_scale',
type=float,
default=1.0,
help=
"If you would like to change the two losses, please change the lambda scale."
)
# Save checkpoints more
parser.add_argument(
'--save_gran',
type=str,
default=None,
help=
'"10,5" means saving a checkpoint every 1/10 of the total updates, but start saving from the 5th attempt'
)
args = parser.parse_args()
print(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')
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 args.do_train:
if not args.train_file:
raise ValueError(
"If `do_train` is True, then `train_file` must be specified.")
if args.do_predict:
if not args.predict_file:
raise ValueError(
"If `do_predict` is True, then `predict_file` must be specified."
)
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.")
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
train_examples = None
num_train_optimization_steps = None
if args.do_train:
train_examples = read_squad_examples(
input_file=args.train_file,
is_training=True,
version_2_with_negative=args.version_2_with_negative,
max_answer_len=args.max_answer_len,
skip_negatives=args.skip_negatives)
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 and tokenizer
if args.bert_model != 'bert-large-uncased-whole-word-masking':
tokenizer = BertTokenizer.from_pretrained(
args.bert_model, do_lower_case=args.do_lower_case)
model = BertForQuestionAnsweringConfidence.from_pretrained(
args.bert_model,
cache_dir=os.path.join(str(PYTORCH_PRETRAINED_BERT_CACHE),
'distributed_{}'.format(args.local_rank)),
num_labels=4,
no_masking=args.no_masking,
lambda_scale=args.lambda_scale)
else:
model = BertForQuestionAnsweringConfidence.from_pretrained(
'bert-large-uncased',
cache_dir=os.path.join(str(PYTORCH_PRETRAINED_BERT_CACHE),
'distributed_{}'.format(args.local_rank)),
num_labels=4,
no_masking=args.no_masking,
lambda_scale=args.lambda_scale)
from modeling_utils import get_bert_model_from_pytorch_transformers
state_dict, vocab_file = get_bert_model_from_pytorch_transformers(
args.bert_model)
model.bert.load_state_dict(state_dict)
tokenizer = BertTokenizer.from_pretrained(
vocab_file, do_lower_case=args.do_lower_case)
print('The',
args.bert_model,
'model is successfully loaded!',
flush=True)
if args.fp16:
model.half()
model.to(device)
if args.local_rank != -1:
try:
from apex.parallel import DistributedDataParallel as DDP
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
)
model = DDP(model)
elif n_gpu > 1:
model = torch.nn.DataParallel(model)
# Prepare optimizer
param_optimizer = list(model.named_parameters())
# hack to remove pooler, which is not used
# thus it produce None grad that break apex
param_optimizer = [n for n in param_optimizer if 'pooler' not in n[0]]
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
if args.fp16:
try:
from apex.optimizers import FP16_Optimizer
from apex.optimizers import FusedAdam
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training."
)
optimizer = FusedAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
bias_correction=False,
max_grad_norm=1.0)
if args.loss_scale == 0:
optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
else:
optimizer = FP16_Optimizer(optimizer,
static_loss_scale=args.loss_scale)
else:
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
if args.do_train:
cached_train_features_file = args.train_file + '_{0}_{1}_{2}_{3}'.format(
list(filter(None, args.bert_model.split('/'))).pop(),
str(args.max_seq_length), str(args.doc_stride),
str(args.max_query_length))
train_features = None
try:
with open(cached_train_features_file, "rb") as reader:
train_features = pickle.load(reader)
except:
train_features = convert_examples_to_features_yes_no(
examples=train_examples,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length,
is_training=True)
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)
logger.info("***** Running training *****")
logger.info(" Num orig examples = %d", len(train_examples))
logger.info(" Num split examples = %d", len(train_features))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps)
all_input_ids = torch.tensor([f.input_ids for f in train_features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in train_features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in train_features],
dtype=torch.long)
all_start_positions = torch.tensor(
[f.start_position for f in train_features], dtype=torch.long)
all_end_positions = torch.tensor(
[f.end_position for f in train_features], dtype=torch.long)
all_switches = torch.tensor([f.switch for f in train_features],
dtype=torch.long)
train_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_start_positions,
all_end_positions, all_switches)
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)
if args.save_gran is not None:
save_chunk, save_start = args.save_gran.split(',')
save_chunk = num_train_optimization_steps // int(save_chunk)
save_start = int(save_start)
model.train()
for _ in trange(int(args.num_train_epochs), desc="Epoch"):
for step, batch in enumerate(
tqdm(train_dataloader,
desc="Iteration",
disable=args.local_rank not in [-1, 0])):
if n_gpu == 1:
# multi-gpu does scattering it-self
batch = tuple(t.to(device) for t in batch)
input_ids, input_mask, segment_ids, start_positions, end_positions, switches = batch
loss = model(input_ids=input_ids,
token_type_ids=segment_ids,
attention_mask=input_mask,
start_positions=start_positions,
end_positions=end_positions,
switch_list=switches)
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.mean().backward()
if (step + 1) % args.gradient_accumulation_steps == 0:
optimizer.step()
optimizer.zero_grad()
global_step += 1
if args.save_gran is not None:
if (global_step % save_chunk == 0) and (
global_step // save_chunk >= save_start):
logger.info('Saving a checkpoint....')
output_dir_per_epoch = os.path.join(
args.output_dir,
str(global_step) + 'steps')
os.makedirs(output_dir_per_epoch)
# 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(
output_dir_per_epoch, WEIGHTS_NAME)
output_config_file = os.path.join(
output_dir_per_epoch, 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(output_dir_per_epoch)
logger.info('Done')
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 = BertForQuestionAnsweringConfidence.from_pretrained(
args.output_dir, num_labels=4, no_masking=args.no_masking)
tokenizer = BertTokenizer.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
if args.do_train is False and args.do_predict is True:
model = BertForQuestionAnsweringConfidence.from_pretrained(
args.output_dir, num_labels=4, no_masking=args.no_masking)
tokenizer = BertTokenizer.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
elif args.do_train is True and args.do_predict is True:
model = BertForQuestionAnsweringConfidence.from_pretrained(
args.output_dir, num_labels=4, no_masking=args.no_masking)
tokenizer = BertTokenizer.from_pretrained(
args.output_dir, do_lower_case=args.do_lower_case)
else:
model = BertForQuestionAnsweringConfidence.from_pretrained(
args.bert_model,
num_labels=4,
no_masking=args.no_masking,
lambda_scale=args.lambda_scale)
model.to(device)
if args.do_predict and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
eval_examples = read_squad_examples(
input_file=args.predict_file,
is_training=False,
version_2_with_negative=args.version_2_with_negative,
max_answer_len=args.max_answer_length,
skip_negatives=args.skip_negatives)
eval_features = convert_examples_to_features_yes_no(
examples=eval_examples,
tokenizer=tokenizer,
max_seq_length=args.max_seq_length,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length,
is_training=False)
logger.info("***** Running predictions *****")
logger.info(" Num orig examples = %d", len(eval_examples))
logger.info(" Num split examples = %d", len(eval_features))
logger.info(" Batch size = %d", args.predict_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_example_index = torch.arange(all_input_ids.size(0),
dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask,
all_segment_ids, all_example_index)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.predict_batch_size)
model.eval()
all_results = []
logger.info("Start evaluating")
for input_ids, input_mask, segment_ids, example_indices in tqdm(
eval_dataloader,
desc="Evaluating",
disable=args.local_rank not in [-1, 0]):
if len(all_results) % 1000 == 0:
logger.info("Processing example: %d" % (len(all_results)))
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
with torch.no_grad():
batch_start_logits, batch_end_logits, batch_switch_logits = model(
input_ids, segment_ids, input_mask)
for i, example_index in enumerate(example_indices):
start_logits = batch_start_logits[i].detach().cpu().tolist()
end_logits = batch_end_logits[i].detach().cpu().tolist()
switch_logits = batch_switch_logits[i].detach().cpu().tolist()
eval_feature = eval_features[example_index.item()]
unique_id = int(eval_feature.unique_id)
all_results.append(
RawResult(unique_id=unique_id,
start_logits=start_logits,
end_logits=end_logits,
switch_logits=switch_logits))
output_prediction_file = os.path.join(args.output_dir,
"predictions.json")
output_nbest_file = os.path.join(args.output_dir,
"nbest_predictions.json")
output_null_log_odds_file = os.path.join(args.output_dir,
"null_odds.json")
write_predictions_yes_no_no_empty_answer(
eval_examples, eval_features, all_results, args.n_best_size,
args.max_answer_length, args.do_lower_case, output_prediction_file,
output_nbest_file, output_null_log_odds_file, args.verbose_logging,
args.version_2_with_negative, args.null_score_diff_threshold,
args.no_masking)