def load_and_cache_examples(args, tokenizer):
# Load data features from cache or dataset file
input_file = args.train_file
if args.test is True:
logger.info("Test")
examples = read_squad_examples(input_file="data/korquad/sample.json",
eda_type=args.eda_type,
is_training=True,
version_2_with_negative=False,
args=args)
features = convert_examples_to_features(
examples=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)
# Convert to Tensors and build dataset
all_input_ids = torch.tensor([f.input_ids for f in features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in features],
dtype=torch.long)
all_start_positions = torch.tensor(
[f.start_position for f in features], dtype=torch.long)
all_end_positions = torch.tensor([f.end_position for f in features],
dtype=torch.long)
dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
all_start_positions, all_end_positions)
return dataset
logger.info("Creating features from dataset file at %s", input_file)
examples = read_squad_examples(input_file=args.train_file,
is_training=True,
version_2_with_negative=False,
args=args)
features = convert_examples_to_features(
examples=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)
# Convert to Tensors and build dataset
all_input_ids = torch.tensor([f.input_ids for f in features],
dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in features],
dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in features],
dtype=torch.long)
all_start_positions = torch.tensor([f.start_position for f in features],
dtype=torch.long)
all_end_positions = torch.tensor([f.end_position for f in features],
dtype=torch.long)
dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
all_start_positions, all_end_positions)
return dataset
def load_and_cache_examples(args, tokenizer):
# Load data features from cache or dataset file
input_file = args.train_file
cached_features_file = os.path.join(os.path.dirname(input_file), '_cached_{}_{}_{}'.format('train',
str(args.max_seq_length),
args.doc_stride))
if os.path.exists(cached_features_file):
logger.info("Loading features from cached file %s", cached_features_file)
features = torch.load(cached_features_file)
else:
logger.info("Creating features from dataset file at %s", input_file)
examples = read_squad_examples(input_file=args.train_file, is_training=True, version_2_with_negative=False)
features = convert_examples_to_features(examples=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]:
logger.info("Saving features into cached file %s", cached_features_file)
torch.save(features, cached_features_file, pickle_protocol=pickle.HIGHEST_PROTOCOL)
# Convert to Tensors and build dataset
all_input_ids = torch.tensor([f.input_ids for f in features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in features], dtype=torch.long)
all_start_positions = torch.tensor([f.start_position for f in features], dtype=torch.long)
all_end_positions = torch.tensor([f.end_position for f in features], dtype=torch.long)
dataset = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_start_positions, all_end_positions)
return dataset
def load_and_cache_examples(args, tokenizer):
# Load data features from cache or dataset file
examples = read_squad_examples(input_file=args.predict_file,
is_training=False,
version_2_with_negative=False)
features = convert_examples_to_features(
examples=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)
return examples, features
def load_and_cache_examples(args, tokenizer):
# Load data features from cache or dataset file
eval_cache_file = "evaluate_cache_file"
os.path.join(args.output_dir, eval_cache_file)
if os.path.exists(eval_cache_file):
examples_and_features = torch.load(eval_cache_file)
return (examples_and_features["examples"],
examples_and_features["features"])
else:
examples = read_squad_examples(input_file=args.predict_file,
is_training=False,
version_2_with_negative=False)
features = convert_examples_to_features(
examples=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)
torch.save({
"examples": examples,
"features": features
}, eval_cache_file)
return examples, features
def main():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--output_dir", default='output', type=str,
help="The output directory where the model checkpoints and predictions will be written.")
parser.add_argument("--checkpoint", default='pretrain_ckpt/bert_small_ckpt.bin',
type=str,
help="checkpoint")
parser.add_argument("--model_config", default='data/bert_small.json',
type=str)
# Other parameters
parser.add_argument("--train_file", default='data/KorQuAD_v1.0_train.json', type=str,
help="SQuAD json for training. E.g., train-v1.1.json")
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=96, type=int,
help="The maximum number of tokens for the question. Questions longer than this will "
"be truncated to this length.")
parser.add_argument("--train_batch_size", default=16, type=int, help="Total batch size for training.")
parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
parser.add_argument("--num_train_epochs", default=4.0, type=float,
help="Total number of training epochs to perform.")
parser.add_argument("--max_grad_norm", default=1.0, type=float,
help="Max gradient norm.")
parser.add_argument("--adam_epsilon", default=1e-6, type=float,
help="Epsilon for Adam optimizer.")
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('--fp16',
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument('--fp16_opt_level', type=str, default='O2',
help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html")
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.")
args = parser.parse_args()
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
logger.info("device: {} n_gpu: {}, 16-bits training: {}".format(
device, n_gpu, args.fp16))
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 os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
tokenizer = BertTokenizer('ko_vocab_32k.txt',
max_len=args.max_seq_length,
do_basic_tokenize=True)
# Prepare model
config = Config.from_json_file(args.model_config)
model = QuestionAnswering(config)
model.bert.load_state_dict(torch.load(args.checkpoint))
num_params = count_parameters(model)
logger.info("Total Parameter: %d" % num_params)
model.to(device)
cached_train_features_file = args.train_file + '_{0}_{1}_{2}'.format(str(args.max_seq_length), str(args.doc_stride),
str(args.max_query_length))
train_examples = read_squad_examples(input_file=args.train_file, is_training=True, version_2_with_negative=False)
try:
with open(cached_train_features_file, "rb") as reader:
train_features = pickle.load(reader)
except:
train_features = convert_examples_to_features(
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)
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)
num_train_optimization_steps = int(len(train_features) / args.train_batch_size) * args.num_train_epochs
# Prepare optimizer
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay': 0.01},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
optimizer = AdamW(optimizer_grouped_parameters,
lr=args.learning_rate,
eps=args.adam_epsilon)
scheduler = WarmupLinearSchedule(optimizer,
warmup_steps=num_train_optimization_steps*0.1,
t_total=num_train_optimization_steps)
if args.fp16:
try:
from apex import amp
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)
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)
num_train_step = 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)
train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids,
all_start_positions, all_end_positions)
train_sampler = RandomSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
model.train()
global_step = 0
epoch = 0
for _ in trange(int(args.num_train_epochs), desc="Epoch"):
iter_bar = tqdm(train_dataloader, desc="Train(XX Epoch) Step(XX/XX) (Mean loss=X.X) (loss=X.X)")
tr_step, total_loss, mean_loss = 0, 0., 0.
for step, batch in enumerate(iter_bar):
if n_gpu == 1:
batch = tuple(t.to(device) for t in batch) # multi-gpu does scattering it-self
input_ids, input_mask, segment_ids, start_positions, end_positions = batch
loss = model(input_ids, segment_ids, input_mask, start_positions, end_positions)
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args.fp16:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
scheduler.step()
optimizer.step()
optimizer.zero_grad()
global_step += 1
tr_step += 1
total_loss += loss
mean_loss = total_loss / tr_step
iter_bar.set_description("Train Step(%d / %d) (Mean loss=%5.5f) (loss=%5.5f)" %
(global_step, num_train_step, mean_loss, loss.item()))
logger.info("** ** * Saving file * ** **")
model_checkpoint = "korquad_%d.bin" % (epoch)
logger.info(model_checkpoint)
output_model_file = os.path.join(args.output_dir,model_checkpoint)
if n_gpu > 1:
torch.save(model.module.state_dict(), output_model_file)
else:
torch.save(model.state_dict(), output_model_file)
epoch += 1
def main(input, output):
max_seq_length = 512
doc_stride = 64
max_query_length = 64
batch_size = 16
n_best_size = 20
max_answer_length = 30
seed = 42
fp16 = False
device = torch.device("cpu")
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_gpu = torch.cuda.device_count()
logger.info("device: {} n_gpu: {}".format(device, n_gpu))
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(seed)
tokenizer = BertTokenizer(vocab_file=VOCAB_PATH,
max_len=max_seq_length,
do_basic_tokenize=True)
eval_examples = read_squad_examples(input_file=input,
is_training=False,
version_2_with_negative=False)
eval_features = convert_examples_to_features(
examples=eval_examples,
tokenizer=tokenizer,
max_seq_length=max_seq_length,
doc_stride=doc_stride,
max_query_length=max_query_length,
is_training=False)
# Prepare model
config = Config.from_json_file(CONFIG_PATH)
model = QuestionAnswering(config)
if fp16 is True:
model.half()
model.load_state_dict(torch.load(CHK_PATH, map_location=device))
model.to(device)
logger.info("***** Running training *****")
logger.info(" Num orig examples = %d", len(eval_examples))
logger.info(" Num split examples = %d", len(eval_features))
logger.info(" Batch size = %d", 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)
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=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"):
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 = 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()
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))
output_nbest_file = os.path.join("nbest_predictions.json")
write_predictions(eval_examples, eval_features, all_results, n_best_size,
max_answer_length, False, output, output_nbest_file,
None, False, False, 0.0)