923 lines
39 KiB
Python
923 lines
39 KiB
Python
# coding=utf-8
|
|
# Copyright 2018 The Google AI Language Team Authors.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
"""Run BERT on SQuAD."""
|
|
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import six
|
|
import argparse
|
|
import collections
|
|
import logging
|
|
import json
|
|
import math
|
|
import os
|
|
from tqdm import tqdm, trange
|
|
import random
|
|
import numpy as np
|
|
|
|
import torch
|
|
from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
|
|
from torch.utils.data.distributed import DistributedSampler
|
|
|
|
import tokenization_pytorch
|
|
from modeling_pytorch import BertConfig, BertForQuestionAnswering
|
|
from optimization_pytorch import BERTAdam
|
|
|
|
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
|
|
datefmt = '%m/%d/%Y %H:%M:%S',
|
|
level = logging.INFO)
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class SquadExample(object):
|
|
"""A single training/test example for simple sequence classification."""
|
|
|
|
def __init__(self,
|
|
qas_id,
|
|
question_text,
|
|
doc_tokens,
|
|
orig_answer_text=None,
|
|
start_position=None,
|
|
end_position=None):
|
|
self.qas_id = qas_id
|
|
self.question_text = question_text
|
|
self.doc_tokens = doc_tokens
|
|
self.orig_answer_text = orig_answer_text
|
|
self.start_position = start_position
|
|
self.end_position = end_position
|
|
|
|
def __str__(self):
|
|
return self.__repr__()
|
|
|
|
def __repr__(self):
|
|
s = ""
|
|
s += "qas_id: %s" % (tokenization_pytorch.printable_text(self.qas_id))
|
|
s += ", question_text: %s" % (
|
|
tokenization_pytorch.printable_text(self.question_text))
|
|
s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))
|
|
if self.start_position:
|
|
s += ", start_position: %d" % (self.start_position)
|
|
if self.start_position:
|
|
s += ", end_position: %d" % (self.end_position)
|
|
return s
|
|
|
|
|
|
class InputFeatures(object):
|
|
"""A single set of features of data."""
|
|
|
|
def __init__(self,
|
|
unique_id,
|
|
example_index,
|
|
doc_span_index,
|
|
tokens,
|
|
token_to_orig_map,
|
|
token_is_max_context,
|
|
input_ids,
|
|
input_mask,
|
|
segment_ids,
|
|
start_position=None,
|
|
end_position=None):
|
|
self.unique_id = unique_id
|
|
self.example_index = example_index
|
|
self.doc_span_index = doc_span_index
|
|
self.tokens = tokens
|
|
self.token_to_orig_map = token_to_orig_map
|
|
self.token_is_max_context = token_is_max_context
|
|
self.input_ids = input_ids
|
|
self.input_mask = input_mask
|
|
self.segment_ids = segment_ids
|
|
self.start_position = start_position
|
|
self.end_position = end_position
|
|
|
|
|
|
def read_squad_examples(input_file, is_training):
|
|
"""Read a SQuAD json file into a list of SquadExample."""
|
|
with open(input_file, "r") as reader:
|
|
input_data = json.load(reader)["data"]
|
|
|
|
def is_whitespace(c):
|
|
if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
|
|
return True
|
|
return False
|
|
|
|
examples = []
|
|
for entry in input_data:
|
|
for paragraph in entry["paragraphs"]:
|
|
paragraph_text = paragraph["context"]
|
|
doc_tokens = []
|
|
char_to_word_offset = []
|
|
prev_is_whitespace = True
|
|
for c in paragraph_text:
|
|
if is_whitespace(c):
|
|
prev_is_whitespace = True
|
|
else:
|
|
if prev_is_whitespace:
|
|
doc_tokens.append(c)
|
|
else:
|
|
doc_tokens[-1] += c
|
|
prev_is_whitespace = False
|
|
char_to_word_offset.append(len(doc_tokens) - 1)
|
|
|
|
for qa in paragraph["qas"]:
|
|
qas_id = qa["id"]
|
|
question_text = qa["question"]
|
|
start_position = None
|
|
end_position = None
|
|
orig_answer_text = None
|
|
if is_training:
|
|
if len(qa["answers"]) != 1:
|
|
raise ValueError(
|
|
"For training, each question should have exactly 1 answer.")
|
|
answer = qa["answers"][0]
|
|
orig_answer_text = answer["text"]
|
|
answer_offset = answer["answer_start"]
|
|
answer_length = len(orig_answer_text)
|
|
start_position = char_to_word_offset[answer_offset]
|
|
end_position = char_to_word_offset[answer_offset + answer_length - 1]
|
|
# Only add answers where the text can be exactly recovered from the
|
|
# document. If this CAN'T happen it's likely due to weird Unicode
|
|
# stuff so we will just skip the example.
|
|
#
|
|
# Note that this means for training mode, every example is NOT
|
|
# guaranteed to be preserved.
|
|
actual_text = " ".join(doc_tokens[start_position:(end_position + 1)])
|
|
cleaned_answer_text = " ".join(
|
|
tokenization_pytorch.whitespace_tokenize(orig_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
|
|
|
|
example = SquadExample(
|
|
qas_id=qas_id,
|
|
question_text=question_text,
|
|
doc_tokens=doc_tokens,
|
|
orig_answer_text=orig_answer_text,
|
|
start_position=start_position,
|
|
end_position=end_position)
|
|
examples.append(example)
|
|
return examples
|
|
|
|
|
|
def convert_examples_to_features(examples, tokenizer, max_seq_length,
|
|
doc_stride, max_query_length, is_training):
|
|
"""Loads a data file into a list of `InputBatch`s."""
|
|
|
|
unique_id = 1000000000
|
|
|
|
features = []
|
|
for (example_index, example) in enumerate(examples):
|
|
query_tokens = tokenizer.tokenize(example.question_text)
|
|
|
|
if len(query_tokens) > max_query_length:
|
|
query_tokens = query_tokens[0:max_query_length]
|
|
|
|
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)
|
|
|
|
tok_start_position = None
|
|
tok_end_position = None
|
|
if is_training:
|
|
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.orig_answer_text)
|
|
|
|
# The -3 accounts for [CLS], [SEP] and [SEP]
|
|
max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
|
|
|
|
# We can have documents that are longer than the maximum sequence length.
|
|
# To deal with this we do a sliding window approach, where we take chunks
|
|
# of the up to our max length with a stride of `doc_stride`.
|
|
_DocSpan = collections.namedtuple( # pylint: disable=invalid-name
|
|
"DocSpan", ["start", "length"])
|
|
doc_spans = []
|
|
start_offset = 0
|
|
while start_offset < len(all_doc_tokens):
|
|
length = len(all_doc_tokens) - start_offset
|
|
if length > max_tokens_for_doc:
|
|
length = max_tokens_for_doc
|
|
doc_spans.append(_DocSpan(start=start_offset, length=length))
|
|
if start_offset + length == len(all_doc_tokens):
|
|
break
|
|
start_offset += min(length, doc_stride)
|
|
|
|
for (doc_span_index, doc_span) in enumerate(doc_spans):
|
|
tokens = []
|
|
token_to_orig_map = {}
|
|
token_is_max_context = {}
|
|
segment_ids = []
|
|
tokens.append("[CLS]")
|
|
segment_ids.append(0)
|
|
for token in query_tokens:
|
|
tokens.append(token)
|
|
segment_ids.append(0)
|
|
tokens.append("[SEP]")
|
|
segment_ids.append(0)
|
|
|
|
for i in range(doc_span.length):
|
|
split_token_index = doc_span.start + i
|
|
token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]
|
|
|
|
is_max_context = _check_is_max_context(doc_spans, doc_span_index,
|
|
split_token_index)
|
|
token_is_max_context[len(tokens)] = is_max_context
|
|
tokens.append(all_doc_tokens[split_token_index])
|
|
segment_ids.append(1)
|
|
tokens.append("[SEP]")
|
|
segment_ids.append(1)
|
|
|
|
input_ids = tokenizer.convert_tokens_to_ids(tokens)
|
|
|
|
# The mask has 1 for real tokens and 0 for padding tokens. Only real
|
|
# tokens are attended to.
|
|
input_mask = [1] * len(input_ids)
|
|
|
|
# Zero-pad up to the sequence length.
|
|
while len(input_ids) < max_seq_length:
|
|
input_ids.append(0)
|
|
input_mask.append(0)
|
|
segment_ids.append(0)
|
|
|
|
assert len(input_ids) == max_seq_length
|
|
assert len(input_mask) == max_seq_length
|
|
assert len(segment_ids) == max_seq_length
|
|
|
|
start_position = None
|
|
end_position = None
|
|
if is_training:
|
|
# For training, if our document chunk does not contain an annotation
|
|
# we throw it out, since there is nothing to predict.
|
|
doc_start = doc_span.start
|
|
doc_end = doc_span.start + doc_span.length - 1
|
|
if (example.start_position < doc_start or
|
|
example.end_position < doc_start or
|
|
example.start_position > doc_end or example.end_position > doc_end):
|
|
continue
|
|
|
|
doc_offset = len(query_tokens) + 2
|
|
start_position = tok_start_position - doc_start + doc_offset
|
|
end_position = tok_end_position - doc_start + doc_offset
|
|
|
|
if example_index < 20:
|
|
logger.info("*** Example ***")
|
|
logger.info("unique_id: %s" % (unique_id))
|
|
logger.info("example_index: %s" % (example_index))
|
|
logger.info("doc_span_index: %s" % (doc_span_index))
|
|
logger.info("tokens: %s" % " ".join(
|
|
[tokenization_pytorch.printable_text(x) for x in tokens]))
|
|
logger.info("token_to_orig_map: %s" % " ".join(
|
|
["%d:%d" % (x, y) for (x, y) in six.iteritems(token_to_orig_map)]))
|
|
logger.info("token_is_max_context: %s" % " ".join([
|
|
"%d:%s" % (x, y) for (x, y) in six.iteritems(token_is_max_context)
|
|
]))
|
|
logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
|
|
logger.info(
|
|
"input_mask: %s" % " ".join([str(x) for x in input_mask]))
|
|
logger.info(
|
|
"segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
|
|
if is_training:
|
|
answer_text = " ".join(tokens[start_position:(end_position + 1)])
|
|
logger.info("start_position: %d" % (start_position))
|
|
logger.info("end_position: %d" % (end_position))
|
|
logger.info(
|
|
"answer: %s" % (tokenization_pytorch.printable_text(answer_text)))
|
|
|
|
features.append(
|
|
InputFeatures(
|
|
unique_id=unique_id,
|
|
example_index=example_index,
|
|
doc_span_index=doc_span_index,
|
|
tokens=tokens,
|
|
token_to_orig_map=token_to_orig_map,
|
|
token_is_max_context=token_is_max_context,
|
|
input_ids=input_ids,
|
|
input_mask=input_mask,
|
|
segment_ids=segment_ids,
|
|
start_position=start_position,
|
|
end_position=end_position))
|
|
unique_id += 1
|
|
|
|
return features
|
|
|
|
|
|
def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
|
|
orig_answer_text):
|
|
"""Returns tokenized answer spans that better match the annotated answer."""
|
|
|
|
# The SQuAD annotations are character based. We first project them to
|
|
# whitespace-tokenized words. But then after WordPiece tokenization, we can
|
|
# often find a "better match". For example:
|
|
#
|
|
# Question: What year was John Smith born?
|
|
# Context: The leader was John Smith (1895-1943).
|
|
# Answer: 1895
|
|
#
|
|
# The original whitespace-tokenized answer will be "(1895-1943).". However
|
|
# after tokenization, our tokens will be "( 1895 - 1943 ) .". So we can match
|
|
# the exact answer, 1895.
|
|
#
|
|
# However, this is not always possible. Consider the following:
|
|
#
|
|
# Question: What country is the top exporter of electornics?
|
|
# Context: The Japanese electronics industry is the lagest in the world.
|
|
# Answer: Japan
|
|
#
|
|
# In this case, the annotator chose "Japan" as a character sub-span of
|
|
# the word "Japanese". Since our WordPiece tokenizer does not split
|
|
# "Japanese", we just use "Japanese" as the annotation. This is fairly rare
|
|
# in SQuAD, but does happen.
|
|
tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
|
|
|
|
for new_start in range(input_start, input_end + 1):
|
|
for new_end in range(input_end, new_start - 1, -1):
|
|
text_span = " ".join(doc_tokens[new_start:(new_end + 1)])
|
|
if text_span == tok_answer_text:
|
|
return (new_start, new_end)
|
|
|
|
return (input_start, input_end)
|
|
|
|
|
|
def _check_is_max_context(doc_spans, cur_span_index, position):
|
|
"""Check if this is the 'max context' doc span for the token."""
|
|
|
|
# Because of the sliding window approach taken to scoring documents, a single
|
|
# token can appear in multiple documents. E.g.
|
|
# Doc: the man went to the store and bought a gallon of milk
|
|
# Span A: the man went to the
|
|
# Span B: to the store and bought
|
|
# Span C: and bought a gallon of
|
|
# ...
|
|
#
|
|
# Now the word 'bought' will have two scores from spans B and C. We only
|
|
# want to consider the score with "maximum context", which we define as
|
|
# the *minimum* of its left and right context (the *sum* of left and
|
|
# right context will always be the same, of course).
|
|
#
|
|
# In the example the maximum context for 'bought' would be span C since
|
|
# it has 1 left context and 3 right context, while span B has 4 left context
|
|
# and 0 right context.
|
|
best_score = None
|
|
best_span_index = None
|
|
for (span_index, doc_span) in enumerate(doc_spans):
|
|
end = doc_span.start + doc_span.length - 1
|
|
if position < doc_span.start:
|
|
continue
|
|
if position > end:
|
|
continue
|
|
num_left_context = position - doc_span.start
|
|
num_right_context = end - position
|
|
score = min(num_left_context, num_right_context) + 0.01 * doc_span.length
|
|
if best_score is None or score > best_score:
|
|
best_score = score
|
|
best_span_index = span_index
|
|
|
|
return cur_span_index == best_span_index
|
|
|
|
|
|
|
|
RawResult = collections.namedtuple("RawResult",
|
|
["unique_id", "start_logits", "end_logits"])
|
|
|
|
|
|
def write_predictions(all_examples, all_features, all_results, n_best_size,
|
|
max_answer_length, do_lower_case, output_prediction_file,
|
|
output_nbest_file):
|
|
"""Write final predictions to the json file."""
|
|
logger.info("Writing predictions to: %s" % (output_prediction_file))
|
|
logger.info("Writing nbest to: %s" % (output_nbest_file))
|
|
|
|
example_index_to_features = collections.defaultdict(list)
|
|
for feature in all_features:
|
|
example_index_to_features[feature.example_index].append(feature)
|
|
|
|
unique_id_to_result = {}
|
|
for result in all_results:
|
|
unique_id_to_result[result.unique_id] = result
|
|
|
|
_PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name
|
|
"PrelimPrediction",
|
|
["feature_index", "start_index", "end_index", "start_logit", "end_logit"])
|
|
|
|
all_predictions = collections.OrderedDict()
|
|
all_nbest_json = collections.OrderedDict()
|
|
for (example_index, example) in enumerate(all_examples):
|
|
features = example_index_to_features[example_index]
|
|
|
|
prelim_predictions = []
|
|
for (feature_index, feature) in enumerate(features):
|
|
result = unique_id_to_result[feature.unique_id]
|
|
|
|
start_indexes = _get_best_indexes(result.start_logits, n_best_size)
|
|
end_indexes = _get_best_indexes(result.end_logits, n_best_size)
|
|
for start_index in start_indexes:
|
|
for end_index in end_indexes:
|
|
# We could hypothetically create invalid predictions, e.g., predict
|
|
# that the start of the span is in the question. We throw out all
|
|
# invalid predictions.
|
|
if start_index >= len(feature.tokens):
|
|
continue
|
|
if end_index >= len(feature.tokens):
|
|
continue
|
|
if start_index not in feature.token_to_orig_map:
|
|
continue
|
|
if end_index not in feature.token_to_orig_map:
|
|
continue
|
|
if not feature.token_is_max_context.get(start_index, False):
|
|
continue
|
|
if end_index < start_index:
|
|
continue
|
|
length = end_index - start_index + 1
|
|
if length > max_answer_length:
|
|
continue
|
|
prelim_predictions.append(
|
|
_PrelimPrediction(
|
|
feature_index=feature_index,
|
|
start_index=start_index,
|
|
end_index=end_index,
|
|
start_logit=result.start_logits[start_index],
|
|
end_logit=result.end_logits[end_index]))
|
|
|
|
prelim_predictions = sorted(
|
|
prelim_predictions,
|
|
key=lambda x: (x.start_logit + x.end_logit),
|
|
reverse=True)
|
|
|
|
_NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name
|
|
"NbestPrediction", ["text", "start_logit", "end_logit"])
|
|
|
|
seen_predictions = {}
|
|
nbest = []
|
|
for pred in prelim_predictions:
|
|
if len(nbest) >= n_best_size:
|
|
break
|
|
feature = features[pred.feature_index]
|
|
|
|
tok_tokens = feature.tokens[pred.start_index:(pred.end_index + 1)]
|
|
orig_doc_start = feature.token_to_orig_map[pred.start_index]
|
|
orig_doc_end = feature.token_to_orig_map[pred.end_index]
|
|
orig_tokens = example.doc_tokens[orig_doc_start:(orig_doc_end + 1)]
|
|
tok_text = " ".join(tok_tokens)
|
|
|
|
# De-tokenize WordPieces that have been split off.
|
|
tok_text = tok_text.replace(" ##", "")
|
|
tok_text = tok_text.replace("##", "")
|
|
|
|
# Clean whitespace
|
|
tok_text = tok_text.strip()
|
|
tok_text = " ".join(tok_text.split())
|
|
orig_text = " ".join(orig_tokens)
|
|
|
|
final_text = get_final_text(tok_text, orig_text, do_lower_case)
|
|
if final_text in seen_predictions:
|
|
continue
|
|
|
|
seen_predictions[final_text] = True
|
|
nbest.append(
|
|
_NbestPrediction(
|
|
text=final_text,
|
|
start_logit=pred.start_logit,
|
|
end_logit=pred.end_logit))
|
|
|
|
# In very rare edge cases we could have no valid predictions. So we
|
|
# just create a nonce prediction in this case to avoid failure.
|
|
if not nbest:
|
|
nbest.append(
|
|
_NbestPrediction(text="empty", start_logit=0.0, end_logit=0.0))
|
|
|
|
assert len(nbest) >= 1
|
|
|
|
total_scores = []
|
|
for entry in nbest:
|
|
total_scores.append(entry.start_logit + entry.end_logit)
|
|
|
|
probs = _compute_softmax(total_scores)
|
|
|
|
nbest_json = []
|
|
for (i, entry) in enumerate(nbest):
|
|
output = collections.OrderedDict()
|
|
output["text"] = entry.text
|
|
output["probability"] = probs[i]
|
|
output["start_logit"] = entry.start_logit
|
|
output["end_logit"] = entry.end_logit
|
|
nbest_json.append(output)
|
|
|
|
assert len(nbest_json) >= 1
|
|
|
|
all_predictions[example.qas_id] = nbest_json[0]["text"]
|
|
all_nbest_json[example.qas_id] = nbest_json
|
|
|
|
with open(output_prediction_file, "w") as writer:
|
|
writer.write(json.dumps(all_predictions, indent=4) + "\n")
|
|
|
|
with open(output_nbest_file, "w") as writer:
|
|
writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
|
|
|
|
|
|
def get_final_text(pred_text, orig_text, do_lower_case):
|
|
"""Project the tokenized prediction back to the original text."""
|
|
|
|
# When we created the data, we kept track of the alignment between original
|
|
# (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
|
|
# now `orig_text` contains the span of our original text corresponding to the
|
|
# span that we predicted.
|
|
#
|
|
# However, `orig_text` may contain extra characters that we don't want in
|
|
# our prediction.
|
|
#
|
|
# For example, let's say:
|
|
# pred_text = steve smith
|
|
# orig_text = Steve Smith's
|
|
#
|
|
# We don't want to return `orig_text` because it contains the extra "'s".
|
|
#
|
|
# We don't want to return `pred_text` because it's already been normalized
|
|
# (the SQuAD eval script also does punctuation stripping/lower casing but
|
|
# our tokenizer does additional normalization like stripping accent
|
|
# characters).
|
|
#
|
|
# What we really want to return is "Steve Smith".
|
|
#
|
|
# Therefore, we have to apply a semi-complicated alignment heruistic between
|
|
# `pred_text` and `orig_text` to get a character-to-charcter alignment. This
|
|
# can fail in certain cases in which case we just return `orig_text`.
|
|
|
|
def _strip_spaces(text):
|
|
ns_chars = []
|
|
ns_to_s_map = collections.OrderedDict()
|
|
for (i, c) in enumerate(text):
|
|
if c == " ":
|
|
continue
|
|
ns_to_s_map[len(ns_chars)] = i
|
|
ns_chars.append(c)
|
|
ns_text = "".join(ns_chars)
|
|
return (ns_text, ns_to_s_map)
|
|
|
|
# We first tokenize `orig_text`, strip whitespace from the result
|
|
# and `pred_text`, and check if they are the same length. If they are
|
|
# NOT the same length, the heuristic has failed. If they are the same
|
|
# length, we assume the characters are one-to-one aligned.
|
|
tokenizer = tokenization_pytorch.BasicTokenizer(do_lower_case=do_lower_case)
|
|
|
|
tok_text = " ".join(tokenizer.tokenize(orig_text))
|
|
|
|
start_position = tok_text.find(pred_text)
|
|
if start_position == -1:
|
|
if args.verbose_logging:
|
|
logger.info(
|
|
"Unable to find text: '%s' in '%s'" % (pred_text, orig_text))
|
|
return orig_text
|
|
end_position = start_position + len(pred_text) - 1
|
|
|
|
(orig_ns_text, orig_ns_to_s_map) = _strip_spaces(orig_text)
|
|
(tok_ns_text, tok_ns_to_s_map) = _strip_spaces(tok_text)
|
|
|
|
if len(orig_ns_text) != len(tok_ns_text):
|
|
if args.verbose_logging:
|
|
logger.info("Length not equal after stripping spaces: '%s' vs '%s'",
|
|
orig_ns_text, tok_ns_text)
|
|
return orig_text
|
|
|
|
# We then project the characters in `pred_text` back to `orig_text` using
|
|
# the character-to-character alignment.
|
|
tok_s_to_ns_map = {}
|
|
for (i, tok_index) in six.iteritems(tok_ns_to_s_map):
|
|
tok_s_to_ns_map[tok_index] = i
|
|
|
|
orig_start_position = None
|
|
if start_position in tok_s_to_ns_map:
|
|
ns_start_position = tok_s_to_ns_map[start_position]
|
|
if ns_start_position in orig_ns_to_s_map:
|
|
orig_start_position = orig_ns_to_s_map[ns_start_position]
|
|
|
|
if orig_start_position is None:
|
|
if args.verbose_logging:
|
|
logger.info("Couldn't map start position")
|
|
return orig_text
|
|
|
|
orig_end_position = None
|
|
if end_position in tok_s_to_ns_map:
|
|
ns_end_position = tok_s_to_ns_map[end_position]
|
|
if ns_end_position in orig_ns_to_s_map:
|
|
orig_end_position = orig_ns_to_s_map[ns_end_position]
|
|
|
|
if orig_end_position is None:
|
|
if args.verbose_logging:
|
|
logger.info("Couldn't map end position")
|
|
return orig_text
|
|
|
|
output_text = orig_text[orig_start_position:(orig_end_position + 1)]
|
|
return output_text
|
|
|
|
|
|
def _get_best_indexes(logits, n_best_size):
|
|
"""Get the n-best logits from a list."""
|
|
index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
|
|
|
|
best_indexes = []
|
|
for i in range(len(index_and_score)):
|
|
if i >= n_best_size:
|
|
break
|
|
best_indexes.append(index_and_score[i][0])
|
|
return best_indexes
|
|
|
|
|
|
def _compute_softmax(scores):
|
|
"""Compute softmax probability over raw logits."""
|
|
if not scores:
|
|
return []
|
|
|
|
max_score = None
|
|
for score in scores:
|
|
if max_score is None or score > max_score:
|
|
max_score = score
|
|
|
|
exp_scores = []
|
|
total_sum = 0.0
|
|
for score in scores:
|
|
x = math.exp(score - max_score)
|
|
exp_scores.append(x)
|
|
total_sum += x
|
|
|
|
probs = []
|
|
for score in exp_scores:
|
|
probs.append(score / total_sum)
|
|
return probs
|
|
|
|
|
|
def main():
|
|
parser = argparse.ArgumentParser()
|
|
|
|
## Required parameters
|
|
parser.add_argument("--bert_config_file", default=None, type=str, required=True,
|
|
help="The config json file corresponding to the pre-trained BERT model. "
|
|
"This specifies the model architecture.")
|
|
parser.add_argument("--vocab_file", default=None, type=str, required=True,
|
|
help="The vocabulary file that the BERT model was trained on.")
|
|
parser.add_argument("--output_dir", default=None, type=str, required=True,
|
|
help="The output directory where the model checkpoints will be written.")
|
|
|
|
## Other parameters
|
|
parser.add_argument("--train_file", default=None, type=str, help="SQuAD json for training. E.g., train-v1.1.json")
|
|
parser.add_argument("--predict_file", default=None, type=str,
|
|
help="SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json")
|
|
parser.add_argument("--init_checkpoint", default=None, type=str,
|
|
help="Initial checkpoint (usually from a pre-trained BERT model).")
|
|
parser.add_argument("--do_lower_case", default=True, action='store_true',
|
|
help="Whether to lower case the input text. Should be True for uncased "
|
|
"models and False for cased models.")
|
|
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", default=False, action='store_true', help="Whether to run training.")
|
|
parser.add_argument("--do_predict", default=False, 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("--save_checkpoints_steps", default=1000, type=int,
|
|
help="How often to save the model checkpoint.")
|
|
parser.add_argument("--iterations_per_loop", default=1000, type=int,
|
|
help="How many steps to make in each estimator call.")
|
|
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", default=False, 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",
|
|
default=False,
|
|
action='store_true',
|
|
help="Whether not to use CUDA when available")
|
|
parser.add_argument("--local_rank",
|
|
type=int,
|
|
default=-1,
|
|
help="local_rank for distributed training on gpus")
|
|
parser.add_argument('--seed',
|
|
type=int,
|
|
default=42,
|
|
help="random seed for initialization")
|
|
|
|
args = parser.parse_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:
|
|
device = torch.device("cuda", args.local_rank)
|
|
n_gpu = 1
|
|
# print("Initializing the distributed backend: NCCL")
|
|
print("device", device, "n_gpu", n_gpu)
|
|
|
|
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.")
|
|
|
|
bert_config = BertConfig.from_json_file(args.bert_config_file)
|
|
|
|
if args.max_seq_length > bert_config.max_position_embeddings:
|
|
raise ValueError(
|
|
"Cannot use sequence length %d because the BERT model "
|
|
"was only trained up to sequence length %d" %
|
|
(args.max_seq_length, bert_config.max_position_embeddings))
|
|
|
|
if os.path.exists(args.output_dir) and os.listdir(args.output_dir):
|
|
raise ValueError("Output directory () already exists and is not empty.")
|
|
os.makedirs(args.output_dir, exist_ok=True)
|
|
|
|
tokenizer = tokenization_pytorch.FullTokenizer(
|
|
vocab_file=args.vocab_file, do_lower_case=args.do_lower_case)
|
|
|
|
train_examples = None
|
|
num_train_steps = None
|
|
if args.do_train:
|
|
train_examples = read_squad_examples(
|
|
input_file=args.train_file, is_training=True)
|
|
num_train_steps = int(
|
|
len(train_examples) / args.train_batch_size * args.num_train_epochs)
|
|
|
|
model = BertForQuestionAnswering(bert_config)
|
|
if args.init_checkpoint is not None:
|
|
model.bert.load_state_dict(torch.load(args.init_checkpoint, map_location='cpu'))
|
|
model.to(device)
|
|
|
|
if n_gpu > 1:
|
|
model = torch.nn.DataParallel(model)
|
|
|
|
no_decay = ['bias', 'gamma', 'beta']
|
|
optimizer_parameters = [
|
|
{'params': [p for n, p in model.named_parameters() if n not in no_decay], 'weight_decay_rate': 0.01},
|
|
{'params': [p for n, p in model.named_parameters() if n in no_decay], 'weight_decay_rate': 0.0}
|
|
]
|
|
|
|
optimizer = BERTAdam(optimizer_parameters,
|
|
lr=args.learning_rate,
|
|
warmup=args.warmup_proportion,
|
|
t_total=num_train_steps)
|
|
|
|
global_step = 0
|
|
if args.do_train:
|
|
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("***** 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_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)
|
|
if args.local_rank == -1:
|
|
train_sampler = RandomSampler(train_data)
|
|
else:
|
|
train_sampler = DistributedSampler(train_data)
|
|
logger.info("HHHHH Dataloader")
|
|
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
|
|
|
|
logger.info("HHHHH Starting Traing")
|
|
model.train()
|
|
for epoch in trange(int(args.num_train_epochs), desc="Epoch"):
|
|
for input_ids, input_mask, segment_ids, start_positions, end_positions in tqdm(train_dataloader,
|
|
desc="Iteration"):
|
|
input_ids = input_ids.to(device)
|
|
input_mask = input_mask.float().to(device)
|
|
segment_ids = segment_ids.to(device)
|
|
#label_ids = label_ids.to(device)
|
|
start_positions = start_positions.to(device)
|
|
end_positions = start_positions.to(device)
|
|
|
|
start_positions = start_positions.view(-1, 1)
|
|
end_positions = end_positions.view(-1, 1)
|
|
|
|
loss, _ = model(input_ids, segment_ids, input_mask, start_positions, end_positions)
|
|
model.zero_grad()
|
|
loss.mean().backward()
|
|
optimizer.step()
|
|
global_step += 1
|
|
|
|
if args.do_predict:
|
|
eval_examples = read_squad_examples(
|
|
input_file=args.predict_file, is_training=False)
|
|
eval_features = convert_examples_to_features(
|
|
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)
|
|
if args.local_rank == -1:
|
|
eval_sampler = SequentialSampler(eval_data)
|
|
else:
|
|
eval_sampler = DistributedSampler(eval_data)
|
|
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.predict_batch_size)
|
|
|
|
model.eval()
|
|
all_results = []
|
|
logger.info("Start evaulating")
|
|
for input_ids, input_mask, segment_ids, example_index in eval_dataloader:
|
|
if len(all_results) % 1000 == 0:
|
|
logger.info("Processing example: %d" % (len(all_results)))
|
|
|
|
input_ids = input_ids.to(device)
|
|
input_mask = input_mask.float().to(device)
|
|
segment_ids = segment_ids.to(device)
|
|
|
|
start_logits, end_logits = model(input_ids, segment_ids, input_mask)
|
|
|
|
unique_id = [int(eval_features[e.item()].unique_id) for e in example_index]
|
|
start_logits = [x.view(-1).detach().cpu().numpy() for x in start_logits]
|
|
end_logits = [x.view(-1).detach().cpu().numpy() for x in end_logits]
|
|
for idx, i in enumerate(unique_id):
|
|
s = [float(x) for x in start_logits[idx]]
|
|
e = [float(x) for x in end_logits[idx]]
|
|
all_results.append(
|
|
RawResult(
|
|
unique_id=i,
|
|
start_logits=s,
|
|
end_logits=e
|
|
)
|
|
)
|
|
|
|
output_prediction_file = os.path.join(args.output_dir, "predictions.json")
|
|
output_nbest_file = os.path.join(args.output_dir, "nbest_predictions.json")
|
|
write_predictions(eval_examples, eval_features, all_results,
|
|
args.n_best_size, args.max_answer_length,
|
|
args.do_lower_case, output_prediction_file,
|
|
output_nbest_file)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|