add multiple processing
This commit is contained in:
erenup
@@ -360,7 +360,8 @@ def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=Fal
|
||||
doc_stride=args.doc_stride,
|
||||
max_query_length=args.max_query_length,
|
||||
is_training=not evaluate,
|
||||
return_dataset='pt'
|
||||
return_dataset='pt',
|
||||
threads=args.threads,
|
||||
)
|
||||
|
||||
if args.local_rank in [-1, 0]:
|
||||
@@ -478,6 +479,8 @@ def main():
|
||||
"See details at https://nvidia.github.io/apex/amp.html")
|
||||
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('--threads', type=int, default=1, help='multiple threads for converting example to features')
|
||||
args = parser.parse_args()
|
||||
|
||||
if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir:
|
||||
|
||||
@@ -4,6 +4,9 @@ import logging
|
||||
import os
|
||||
import json
|
||||
import numpy as np
|
||||
from multiprocessing import Pool
|
||||
from multiprocessing import cpu_count
|
||||
from functools import partial
|
||||
|
||||
from ...tokenization_bert import BasicTokenizer, whitespace_tokenize
|
||||
from .utils import DataProcessor, InputExample, InputFeatures
|
||||
@@ -76,9 +79,168 @@ def _is_whitespace(c):
|
||||
return True
|
||||
return False
|
||||
|
||||
def squad_convert_example_to_features(example, max_seq_length,
|
||||
doc_stride, max_query_length, is_training):
|
||||
features = []
|
||||
if is_training and not example.is_impossible:
|
||||
# Get start and end position
|
||||
start_position = example.start_position
|
||||
end_position = example.end_position
|
||||
|
||||
# If the answer cannot be found in the text, then skip this example.
|
||||
actual_text = " ".join(example.doc_tokens[start_position:(end_position + 1)])
|
||||
cleaned_answer_text = " ".join(whitespace_tokenize(example.answer_text))
|
||||
if actual_text.find(cleaned_answer_text) == -1:
|
||||
logger.warning("Could not find answer: '%s' vs. '%s'", actual_text, cleaned_answer_text)
|
||||
return []
|
||||
|
||||
tok_to_orig_index = []
|
||||
orig_to_tok_index = []
|
||||
all_doc_tokens = []
|
||||
for (i, token) in enumerate(example.doc_tokens):
|
||||
orig_to_tok_index.append(len(all_doc_tokens))
|
||||
sub_tokens = tokenizer.tokenize(token)
|
||||
for sub_token in sub_tokens:
|
||||
tok_to_orig_index.append(i)
|
||||
all_doc_tokens.append(sub_token)
|
||||
|
||||
if is_training and not example.is_impossible:
|
||||
tok_start_position = orig_to_tok_index[example.start_position]
|
||||
if example.end_position < len(example.doc_tokens) - 1:
|
||||
tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
|
||||
else:
|
||||
tok_end_position = len(all_doc_tokens) - 1
|
||||
|
||||
(tok_start_position, tok_end_position) = _improve_answer_span(
|
||||
all_doc_tokens, tok_start_position, tok_end_position, tokenizer, example.answer_text
|
||||
)
|
||||
|
||||
spans = []
|
||||
|
||||
truncated_query = tokenizer.encode(example.question_text, add_special_tokens=False, max_length=max_query_length)
|
||||
sequence_added_tokens = tokenizer.max_len - tokenizer.max_len_single_sentence + 1 \
|
||||
if 'roberta' in str(type(tokenizer)) else tokenizer.max_len - tokenizer.max_len_single_sentence
|
||||
sequence_pair_added_tokens = tokenizer.max_len - tokenizer.max_len_sentences_pair
|
||||
|
||||
span_doc_tokens = all_doc_tokens
|
||||
while len(spans) * doc_stride < len(all_doc_tokens):
|
||||
|
||||
encoded_dict = tokenizer.encode_plus(
|
||||
truncated_query if tokenizer.padding_side == "right" else span_doc_tokens,
|
||||
span_doc_tokens if tokenizer.padding_side == "right" else truncated_query,
|
||||
max_length=max_seq_length,
|
||||
return_overflowing_tokens=True,
|
||||
pad_to_max_length=True,
|
||||
stride=max_seq_length - doc_stride - len(truncated_query) - sequence_pair_added_tokens,
|
||||
truncation_strategy='only_second' if tokenizer.padding_side == "right" else 'only_first'
|
||||
)
|
||||
|
||||
paragraph_len = min(len(all_doc_tokens) - len(spans) * doc_stride,
|
||||
max_seq_length - len(truncated_query) - sequence_pair_added_tokens)
|
||||
|
||||
if tokenizer.pad_token_id in encoded_dict['input_ids']:
|
||||
non_padded_ids = encoded_dict['input_ids'][:encoded_dict['input_ids'].index(tokenizer.pad_token_id)]
|
||||
else:
|
||||
non_padded_ids = encoded_dict['input_ids']
|
||||
|
||||
tokens = tokenizer.convert_ids_to_tokens(non_padded_ids)
|
||||
|
||||
token_to_orig_map = {}
|
||||
for i in range(paragraph_len):
|
||||
index = len(truncated_query) + sequence_added_tokens + i if tokenizer.padding_side == "right" else i
|
||||
token_to_orig_map[index] = tok_to_orig_index[len(spans) * doc_stride + i]
|
||||
|
||||
encoded_dict["paragraph_len"] = paragraph_len
|
||||
encoded_dict["tokens"] = tokens
|
||||
encoded_dict["token_to_orig_map"] = token_to_orig_map
|
||||
encoded_dict["truncated_query_with_special_tokens_length"] = len(truncated_query) + sequence_added_tokens
|
||||
encoded_dict["token_is_max_context"] = {}
|
||||
encoded_dict["start"] = len(spans) * doc_stride
|
||||
encoded_dict["length"] = paragraph_len
|
||||
|
||||
spans.append(encoded_dict)
|
||||
|
||||
if "overflowing_tokens" not in encoded_dict:
|
||||
break
|
||||
span_doc_tokens = encoded_dict["overflowing_tokens"]
|
||||
|
||||
for doc_span_index in range(len(spans)):
|
||||
for j in range(spans[doc_span_index]["paragraph_len"]):
|
||||
is_max_context = _new_check_is_max_context(spans, doc_span_index, doc_span_index * doc_stride + j)
|
||||
index = j if tokenizer.padding_side == "left" else spans[doc_span_index][
|
||||
"truncated_query_with_special_tokens_length"] + j
|
||||
spans[doc_span_index]["token_is_max_context"][index] = is_max_context
|
||||
|
||||
for span in spans:
|
||||
# Identify the position of the CLS token
|
||||
cls_index = span['input_ids'].index(tokenizer.cls_token_id)
|
||||
|
||||
# p_mask: mask with 1 for token than cannot be in the answer (0 for token which can be in an answer)
|
||||
# Original TF implem also keep the classification token (set to 0) (not sure why...)
|
||||
p_mask = np.array(span['token_type_ids'])
|
||||
|
||||
p_mask = np.minimum(p_mask, 1)
|
||||
|
||||
if tokenizer.padding_side == "right":
|
||||
# Limit positive values to one
|
||||
p_mask = 1 - p_mask
|
||||
|
||||
p_mask[np.where(np.array(span["input_ids"]) == tokenizer.sep_token_id)[0]] = 1
|
||||
|
||||
# Set the CLS index to '0'
|
||||
p_mask[cls_index] = 0
|
||||
|
||||
span_is_impossible = example.is_impossible
|
||||
start_position = 0
|
||||
end_position = 0
|
||||
if is_training and not span_is_impossible:
|
||||
# For training, if our document chunk does not contain an annotation
|
||||
# we throw it out, since there is nothing to predict.
|
||||
doc_start = span["start"]
|
||||
doc_end = span["start"] + span["length"] - 1
|
||||
out_of_span = False
|
||||
|
||||
if not (tok_start_position >= doc_start and tok_end_position <= doc_end):
|
||||
out_of_span = True
|
||||
|
||||
if out_of_span:
|
||||
start_position = cls_index
|
||||
end_position = cls_index
|
||||
span_is_impossible = True
|
||||
else:
|
||||
if tokenizer.padding_side == "left":
|
||||
doc_offset = 0
|
||||
else:
|
||||
doc_offset = len(truncated_query) + sequence_added_tokens
|
||||
|
||||
start_position = tok_start_position - doc_start + doc_offset
|
||||
end_position = tok_end_position - doc_start + doc_offset
|
||||
|
||||
features.append(SquadFeatures(
|
||||
span['input_ids'],
|
||||
span['attention_mask'],
|
||||
span['token_type_ids'],
|
||||
cls_index,
|
||||
p_mask.tolist(),
|
||||
example_index=0,
|
||||
unique_id=0,
|
||||
paragraph_len=span['paragraph_len'],
|
||||
token_is_max_context=span["token_is_max_context"],
|
||||
tokens=span["tokens"],
|
||||
token_to_orig_map=span["token_to_orig_map"],
|
||||
|
||||
start_position=start_position,
|
||||
end_position=end_position
|
||||
))
|
||||
return features
|
||||
|
||||
def squad_convert_example_to_features_init(tokenizer_for_convert):
|
||||
global tokenizer
|
||||
tokenizer = tokenizer_for_convert
|
||||
|
||||
def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
|
||||
doc_stride, max_query_length, is_training,
|
||||
return_dataset=False):
|
||||
return_dataset=False, threads=1):
|
||||
"""
|
||||
Converts a list of examples into a list of features that can be directly given as input to a model.
|
||||
It is model-dependant and takes advantage of many of the tokenizer's features to create the model's inputs.
|
||||
@@ -93,6 +255,8 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
|
||||
return_dataset: Default False. Either 'pt' or 'tf'.
|
||||
if 'pt': returns a torch.data.TensorDataset,
|
||||
if 'tf': returns a tf.data.Dataset
|
||||
threads: multiple processing threadsa-smi
|
||||
|
||||
|
||||
Returns:
|
||||
list of :class:`~transformers.data.processors.squad.SquadFeatures`
|
||||
@@ -113,165 +277,26 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
|
||||
"""
|
||||
|
||||
# Defining helper methods
|
||||
unique_id = 1000000000
|
||||
|
||||
features = []
|
||||
for (example_index, example) in enumerate(tqdm(examples)):
|
||||
if is_training and not example.is_impossible:
|
||||
# Get start and end position
|
||||
start_position = example.start_position
|
||||
end_position = example.end_position
|
||||
|
||||
# If the answer cannot be found in the text, then skip this example.
|
||||
actual_text = " ".join(example.doc_tokens[start_position:(end_position + 1)])
|
||||
cleaned_answer_text = " ".join(whitespace_tokenize(example.answer_text))
|
||||
if actual_text.find(cleaned_answer_text) == -1:
|
||||
logger.warning("Could not find answer: '%s' vs. '%s'", actual_text, cleaned_answer_text)
|
||||
continue
|
||||
|
||||
|
||||
tok_to_orig_index = []
|
||||
orig_to_tok_index = []
|
||||
all_doc_tokens = []
|
||||
for (i, token) in enumerate(example.doc_tokens):
|
||||
orig_to_tok_index.append(len(all_doc_tokens))
|
||||
sub_tokens = tokenizer.tokenize(token)
|
||||
for sub_token in sub_tokens:
|
||||
tok_to_orig_index.append(i)
|
||||
all_doc_tokens.append(sub_token)
|
||||
|
||||
if is_training and not example.is_impossible:
|
||||
tok_start_position = orig_to_tok_index[example.start_position]
|
||||
if example.end_position < len(example.doc_tokens) - 1:
|
||||
tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
|
||||
else:
|
||||
tok_end_position = len(all_doc_tokens) - 1
|
||||
|
||||
(tok_start_position, tok_end_position) = _improve_answer_span(
|
||||
all_doc_tokens, tok_start_position, tok_end_position, tokenizer, example.answer_text
|
||||
)
|
||||
|
||||
spans = []
|
||||
|
||||
truncated_query = tokenizer.encode(example.question_text, add_special_tokens=False, max_length=max_query_length)
|
||||
sequence_added_tokens = tokenizer.max_len - tokenizer.max_len_single_sentence + 1 \
|
||||
if 'roberta' in str(type(tokenizer)) else tokenizer.max_len - tokenizer.max_len_single_sentence
|
||||
sequence_pair_added_tokens = tokenizer.max_len - tokenizer.max_len_sentences_pair
|
||||
|
||||
span_doc_tokens = all_doc_tokens
|
||||
while len(spans) * doc_stride < len(all_doc_tokens):
|
||||
|
||||
encoded_dict = tokenizer.encode_plus(
|
||||
truncated_query if tokenizer.padding_side == "right" else span_doc_tokens,
|
||||
span_doc_tokens if tokenizer.padding_side == "right" else truncated_query,
|
||||
max_length=max_seq_length,
|
||||
return_overflowing_tokens=True,
|
||||
pad_to_max_length=True,
|
||||
stride=max_seq_length - doc_stride - len(truncated_query) - sequence_pair_added_tokens,
|
||||
truncation_strategy='only_second' if tokenizer.padding_side == "right" else 'only_first'
|
||||
)
|
||||
|
||||
paragraph_len = min(len(all_doc_tokens) - len(spans) * doc_stride, max_seq_length - len(truncated_query) - sequence_pair_added_tokens)
|
||||
|
||||
if tokenizer.pad_token_id in encoded_dict['input_ids']:
|
||||
non_padded_ids = encoded_dict['input_ids'][:encoded_dict['input_ids'].index(tokenizer.pad_token_id)]
|
||||
else:
|
||||
non_padded_ids = encoded_dict['input_ids']
|
||||
|
||||
tokens = tokenizer.convert_ids_to_tokens(non_padded_ids)
|
||||
|
||||
token_to_orig_map = {}
|
||||
for i in range(paragraph_len):
|
||||
index = len(truncated_query) + sequence_added_tokens + i if tokenizer.padding_side == "right" else i
|
||||
token_to_orig_map[index] = tok_to_orig_index[len(spans) * doc_stride + i]
|
||||
|
||||
encoded_dict["paragraph_len"] = paragraph_len
|
||||
encoded_dict["tokens"] = tokens
|
||||
encoded_dict["token_to_orig_map"] = token_to_orig_map
|
||||
encoded_dict["truncated_query_with_special_tokens_length"] = len(truncated_query) + sequence_added_tokens
|
||||
encoded_dict["token_is_max_context"] = {}
|
||||
encoded_dict["start"] = len(spans) * doc_stride
|
||||
encoded_dict["length"] = paragraph_len
|
||||
|
||||
spans.append(encoded_dict)
|
||||
|
||||
if "overflowing_tokens" not in encoded_dict:
|
||||
break
|
||||
span_doc_tokens = encoded_dict["overflowing_tokens"]
|
||||
|
||||
for doc_span_index in range(len(spans)):
|
||||
for j in range(spans[doc_span_index]["paragraph_len"]):
|
||||
is_max_context = _new_check_is_max_context(spans, doc_span_index, doc_span_index * doc_stride + j)
|
||||
index = j if tokenizer.padding_side == "left" else spans[doc_span_index]["truncated_query_with_special_tokens_length"] + j
|
||||
spans[doc_span_index]["token_is_max_context"][index] = is_max_context
|
||||
|
||||
for span in spans:
|
||||
# Identify the position of the CLS token
|
||||
cls_index = span['input_ids'].index(tokenizer.cls_token_id)
|
||||
|
||||
# p_mask: mask with 1 for token than cannot be in the answer (0 for token which can be in an answer)
|
||||
# Original TF implem also keep the classification token (set to 0) (not sure why...)
|
||||
p_mask = np.array(span['token_type_ids'])
|
||||
|
||||
p_mask = np.minimum(p_mask, 1)
|
||||
|
||||
if tokenizer.padding_side == "right":
|
||||
# Limit positive values to one
|
||||
p_mask = 1 - p_mask
|
||||
|
||||
p_mask[np.where(np.array(span["input_ids"]) == tokenizer.sep_token_id)[0]] = 1
|
||||
|
||||
# Set the CLS index to '0'
|
||||
p_mask[cls_index] = 0
|
||||
|
||||
|
||||
span_is_impossible = example.is_impossible
|
||||
start_position = 0
|
||||
end_position = 0
|
||||
if is_training and not span_is_impossible:
|
||||
# For training, if our document chunk does not contain an annotation
|
||||
# we throw it out, since there is nothing to predict.
|
||||
doc_start = span["start"]
|
||||
doc_end = span["start"] + span["length"] - 1
|
||||
out_of_span = False
|
||||
|
||||
if not (tok_start_position >= doc_start and tok_end_position <= doc_end):
|
||||
out_of_span = True
|
||||
|
||||
if out_of_span:
|
||||
start_position = cls_index
|
||||
end_position = cls_index
|
||||
span_is_impossible = True
|
||||
else:
|
||||
if tokenizer.padding_side == "left":
|
||||
doc_offset = 0
|
||||
else:
|
||||
doc_offset = len(truncated_query) + sequence_added_tokens
|
||||
|
||||
start_position = tok_start_position - doc_start + doc_offset
|
||||
end_position = tok_end_position - doc_start + doc_offset
|
||||
|
||||
|
||||
features.append(SquadFeatures(
|
||||
span['input_ids'],
|
||||
span['attention_mask'],
|
||||
span['token_type_ids'],
|
||||
cls_index,
|
||||
p_mask.tolist(),
|
||||
|
||||
example_index=example_index,
|
||||
unique_id=unique_id,
|
||||
paragraph_len=span['paragraph_len'],
|
||||
token_is_max_context=span["token_is_max_context"],
|
||||
tokens=span["tokens"],
|
||||
token_to_orig_map=span["token_to_orig_map"],
|
||||
|
||||
start_position=start_position,
|
||||
end_position=end_position
|
||||
))
|
||||
|
||||
threads = min(threads, cpu_count())
|
||||
with Pool(threads, initializer=squad_convert_example_to_features_init, initargs=(tokenizer,)) as p:
|
||||
annotate_ = partial(squad_convert_example_to_features, max_seq_length=max_seq_length,
|
||||
doc_stride=doc_stride, max_query_length=max_query_length, is_training=is_training)
|
||||
features = list(tqdm(p.imap(annotate_, examples, chunksize=32), total=len(examples), desc='convert squad examples to features'))
|
||||
new_features = []
|
||||
unique_id = 1000000000
|
||||
example_index = 0
|
||||
for example_features in tqdm(features, total=len(features), desc='add example index and unique id'):
|
||||
if not example_features:
|
||||
continue
|
||||
for example_feature in example_features:
|
||||
example_feature.example_index = example_index
|
||||
example_feature.unique_id = unique_id
|
||||
new_features.append(example_feature)
|
||||
unique_id += 1
|
||||
|
||||
example_index += 1
|
||||
features = new_features
|
||||
del new_features
|
||||
if return_dataset == 'pt':
|
||||
if not is_torch_available():
|
||||
raise ImportError("Pytorch must be installed to return a pytorch dataset.")
|
||||
@@ -296,7 +321,6 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
|
||||
|
||||
return features, dataset
|
||||
|
||||
|
||||
return features
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user