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Commit 8e9526b4 authored by erenup's avatar erenup
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add multiple processing

parent 9b312f9d
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Showing with 187 additions and 160 deletions
examples/run_squad.py
View file @ 8e9526b4
...@@ -360,7 +360,8 @@ def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=Fal ...@@ -360,7 +360,8 @@ def load_and_cache_examples(args, tokenizer, evaluate=False, output_examples=Fal
doc_stride=args.doc_stride, doc_stride=args.doc_stride,
max_query_length=args.max_query_length, max_query_length=args.max_query_length,
is_training=not evaluate, is_training=not evaluate,
return_dataset='pt' return_dataset='pt',
threads=args.threads,
) )
if args.local_rank in [-1, 0]: if args.local_rank in [-1, 0]:
...@@ -478,6 +479,8 @@ def main(): ...@@ -478,6 +479,8 @@ def main():
"See details at https://nvidia.github.io/apex/amp.html") "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_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('--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() 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: if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train and not args.overwrite_output_dir:
......
transformers/data/processors/squad.py
View file @ 8e9526b4
...@@ -4,6 +4,9 @@ import logging ...@@ -4,6 +4,9 @@ import logging
import os import os
import json import json
import numpy as np 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 ...tokenization_bert import BasicTokenizer, whitespace_tokenize
from .utils import DataProcessor, InputExample, InputFeatures from .utils import DataProcessor, InputExample, InputFeatures
...@@ -76,9 +79,168 @@ def _is_whitespace(c): ...@@ -76,9 +79,168 @@ def _is_whitespace(c):
return True return True
return False 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, def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
doc_stride, max_query_length, is_training, 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. 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. 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, ...@@ -93,6 +255,8 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
return_dataset: Default False. Either 'pt' or 'tf'. return_dataset: Default False. Either 'pt' or 'tf'.
if 'pt': returns a torch.data.TensorDataset, if 'pt': returns a torch.data.TensorDataset,
if 'tf': returns a tf.data.Dataset if 'tf': returns a tf.data.Dataset
threads: multiple processing threadsa-smi
Returns: Returns:
list of :class:`~transformers.data.processors.squad.SquadFeatures` list of :class:`~transformers.data.processors.squad.SquadFeatures`
...@@ -113,165 +277,26 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length, ...@@ -113,165 +277,26 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
""" """
# Defining helper methods # Defining helper methods
unique_id = 1000000000
features = [] features = []
for (example_index, example) in enumerate(tqdm(examples)): threads = min(threads, cpu_count())
if is_training and not example.is_impossible: with Pool(threads, initializer=squad_convert_example_to_features_init, initargs=(tokenizer,)) as p:
# Get start and end position annotate_ = partial(squad_convert_example_to_features, max_seq_length=max_seq_length,
start_position = example.start_position doc_stride=doc_stride, max_query_length=max_query_length, is_training=is_training)
end_position = example.end_position features = list(tqdm(p.imap(annotate_, examples, chunksize=32), total=len(examples), desc='convert squad examples to features'))
new_features = []
# If the answer cannot be found in the text, then skip this example. unique_id = 1000000000
actual_text = " ".join(example.doc_tokens[start_position:(end_position + 1)]) example_index = 0
cleaned_answer_text = " ".join(whitespace_tokenize(example.answer_text)) for example_features in tqdm(features, total=len(features), desc='add example index and unique id'):
if actual_text.find(cleaned_answer_text) == -1: if not example_features:
logger.warning("Could not find answer: '%s' vs. '%s'", actual_text, cleaned_answer_text) continue
continue for example_feature in example_features:
example_feature.example_index = example_index
example_feature.unique_id = unique_id
tok_to_orig_index = [] new_features.append(example_feature)
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
))
unique_id += 1 unique_id += 1
example_index += 1
features = new_features
del new_features
if return_dataset == 'pt': if return_dataset == 'pt':
if not is_torch_available(): if not is_torch_available():
raise ImportError("Pytorch must be installed to return a pytorch dataset.") raise ImportError("Pytorch must be installed to return a pytorch dataset.")
...@@ -295,7 +320,6 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length, ...@@ -295,7 +320,6 @@ def squad_convert_examples_to_features(examples, tokenizer, max_seq_length,
all_cls_index, all_p_mask) all_cls_index, all_p_mask)
return features, dataset return features, dataset
return features return features
......
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