def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--model_dir",
default=None,
type=str,
required=True,
help="")
parser.add_argument("--my_config", default=None, type=str, required=True)
parser.add_argument("--feature_path",
default=None,
type=str,
required=True)
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_pattern",
default=None,
type=str,
help="training data path.")
parser.add_argument("--valid_pattern",
default=None,
type=str,
help="validation data path.")
parser.add_argument("--test_pattern",
default=None,
type=str,
help="test data path.")
parser.add_argument(
"--max_seq_length",
default=512,
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("--report_steps",
default=100,
type=int,
help="report steps when training.")
parser.add_argument("--train_batch_size",
default=4,
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_steps", default=-1, type=int)
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("--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('--train_size',
type=int,
default=10000,
help="Use how many train data")
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('--frame_name', type=str, default='albert-base-v2')
args = parser.parse_args()
print(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)
torch.distributed.init_process_group(backend='nccl')
n_gpu = 1
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_pattern:
raise ValueError(
"If `do_train` is True, then `train_pattern` must be specified."
)
if args.do_predict:
if not args.test_pattern:
raise ValueError(
"If `do_predict` is True, then `test_pattern` must be specified."
)
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
# Prepare model
my_config = Config(args.my_config)
my_config.num_edge_types = sum(EdgePosition.max_edge_types)
my_config.forward_edges = [
EdgeType.TOKEN_TO_SENTENCE, EdgeType.SENTENCE_TO_PARAGRAPH,
EdgeType.PARAGRAPH_TO_DOCUMENT
]
#print(my_config)
if args.do_train:
pretrained_config_file = os.path.join(args.model_dir, CONFIG_NAME)
bert_config = BertConfig(pretrained_config_file)
pretrained_model_file = os.path.join(args.model_dir, WEIGHTS_NAME)
model = NqModel(bert_config=bert_config, my_config=my_config)
#model_dict = model.state_dict()
#pretrained_model_dict = torch.load(pretrained_model_file, map_location=lambda storage, loc: storage)
#pretrained_model_dict = {k: v for k, v in pretrained_model_dict.items() if k in model_dict.keys()}
#model_dict.update(pretrained_model_dict)
#model.load_state_dict(model_dict)
else:
pretrained_config_file = os.path.join(args.model_dir, CONFIG_NAME)
bert_config = BertConfig(pretrained_config_file)
model = NqModel(bert_config=bert_config, my_config=my_config)
pretrained_model_file = os.path.join(args.model_dir, WEIGHTS_NAME)
model.load_state_dict(torch.load(pretrained_model_file))
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)
num_train_features = None
num_train_optimization_steps = None
#train_dataset = None
#train_features = None
if args.do_train:
num_train_features = 0
for data_path in glob(args.train_pattern):
train_dataset = NqDataset(args, data_path, is_training=True)
train_features = train_dataset.features
num_train_features += len(train_dataset.features)
num_train_optimization_steps = int(
num_train_features / 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 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.warmup_steps > 0:
args.warmup_proportion = min(
args.warmup_proportion,
args.warmup_steps / num_train_optimization_steps)
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:
logger.info("***** Running training *****")
logger.info(" Num split examples = %d", num_train_features)
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_optimization_steps)
model.train()
tr_loss, report_loss = 0.0, 0.0
nb_tr_examples = 0
for _ in trange(int(args.num_train_epochs), desc="Epoch"):
for data_path in glob(args.train_pattern):
#logging.info("Reading data from {}.".format(data_path))
if train_dataset == None:
train_dataset = NqDataset(args,
data_path,
is_training=True)
train_features = train_dataset.features
#logging.info("Data Load Done!")
if args.local_rank == -1:
train_sampler = RandomSampler(train_features)
else:
train_sampler = DistributedSampler(train_features)
train_dataloader = DataLoader(train_features,
sampler=train_sampler,
batch_size=args.train_batch_size,
collate_fn=batcher(
device, is_training=True),
num_workers=0)
train_features = train_dataset.features
logging.info("Data ready {} ".format(len(train_features)))
for step, batch in enumerate(train_dataloader):
loss = model(batch.input_ids, batch.input_mask,
batch.segment_ids, batch.st_mask,
batch.st_index,
(batch.edges_src, batch.edges_tgt,
batch.edges_type, batch.edges_pos),
batch.start_positions, batch.end_positions,
batch.answer_type)
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.local_rank != -1:
loss = loss + 0 * sum(
[x.sum() for x in model.parameters()])
if args.fp16:
optimizer.backward(loss)
else:
loss.backward()
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 and handles this automatically
lr_this_step = args.learning_rate * warmup_linear(
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
tr_loss += loss.item()
nb_tr_examples += 1
if (step + 1) % args.gradient_accumulation_steps == 0 and (
global_step + 1) % args.report_steps == 0 and (
args.local_rank == -1
or torch.distributed.get_rank() == 0):
lr_this_step = args.learning_rate * warmup_linear(
global_step / num_train_optimization_steps,
args.warmup_proportion)
logging.info(
"Epoch={} iter={} lr={:.6f} train_ave_loss={:.6f} ."
.format(
# _, global_step, lr_this_step, tr_loss / nb_tr_examples))
_,
global_step,
lr_this_step,
(tr_loss - report_loss) / args.report_steps))
report_loss = tr_loss
if args.valid_pattern and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
valid_result = eval_model(args, device, model,
args.valid_pattern)
logging.info("valid_result = {}".format(valid_result))
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)
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
with open(output_config_file, 'w') as f:
f.write(model_to_save.config.to_json_string())
bert_config = BertConfig(output_config_file)
model = NqModel(bert_config=bert_config, my_config=my_config)
model.load_state_dict(torch.load(output_model_file))
if args.fp16:
model.half()
model.to(device)
if n_gpu > 1:
model = torch.nn.DataParallel(model)
if args.do_predict and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
test_result = eval_model(args, device, model, args.test_pattern)
logging.info("test_result = {}".format(test_result))
def eval_model(args, device, model, data_pattern):
all_predictions = []
raw_results = []
for data_path in glob(data_pattern):
eval_dataset = NqDataset(args, data_path, is_training=False)
eval_examples = eval_dataset.examples
eval_features = eval_dataset.features
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)
eval_sampler = SequentialSampler(eval_features)
eval_dataloader = DataLoader(eval_features,
sampler=eval_sampler,
batch_size=args.predict_batch_size,
collate_fn=batcher(device,
is_training=False),
num_workers=0)
model.eval()
part_results = []
logger.info("Start evaluating")
for batch in eval_dataloader:
if len(part_results) % 1000 == 0:
logger.info("Processing example: %d" % (len(part_results)))
with torch.no_grad():
batch_tok_start_logits, batch_tok_end_logits, batch_tok_ref_indexes, \
batch_para_logits, batch_para_ref_indexes, batch_doc_logits = \
model(batch.input_ids, batch.input_mask, batch.segment_ids, batch.st_mask, batch.st_index,
(batch.edges_src, batch.edges_tgt, batch.edges_type, batch.edges_pos))
for i, unique_id in enumerate(batch.unique_ids):
tok_start_logits = batch_tok_start_logits[i].detach().cpu(
).tolist()
tok_end_logits = batch_tok_end_logits[i].detach().cpu().tolist(
)
tok_ref_indexes = batch_tok_ref_indexes[i].detach().cpu(
).tolist()
para_logits = batch_para_logits[i].detach().cpu().tolist()
para_ref_indexes = batch_para_ref_indexes[i].detach().cpu(
).tolist()
doc_logits = batch_doc_logits[i].detach().cpu().tolist()
unique_id = int(unique_id)
part_results.append(
RawResult(unique_id=unique_id,
tok_start_logits=tok_start_logits,
tok_end_logits=tok_end_logits,
tok_ref_indexes=tok_ref_indexes,
para_logits=para_logits,
para_ref_indexes=para_ref_indexes,
doc_logits=doc_logits))
raw_results.extend(part_results)
part_predictions, part_nbest_predictions = get_predictions(
eval_examples, eval_features, part_results, args.n_best_size,
args.max_answer_length)
all_predictions += part_predictions
import pickle
raw_results_path = os.path.join(args.output_dir, "raw_results")
logger.info("Writing Raw results to: {}".format(raw_results_path))
with open(raw_results_path, "wb") as writer:
pickle.dump(raw_results, writer)
final_predictions = collections.OrderedDict()
final_predictions["predictions"] = all_predictions
predictions_path = os.path.join(args.output_dir, "tmp_predictions")
logger.info("Writing predictions to: {}".format(predictions_path))
with open(predictions_path, "w") as writer:
writer.write(json.dumps(final_predictions, indent=4) + "\n")
eval_results = nq_evaluate(gold_path=data_pattern,
predictions_path=predictions_path,
num_threads=16)
logger.info("***** Eval results *****".format())
for key in sorted(eval_results.keys()):
logger.info(" %s = %s", key, str(eval_results[key] * 100))
model.train()
return eval_results["Long Answer F1"], eval_results["Short Answer F1"]