562 lines
No EOL
21 KiB
Python
562 lines
No EOL
21 KiB
Python
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import collections
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import json
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import math
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import os
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import random
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import modeling
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import optimization
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import tokenization
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import six
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import tensorflow as tf
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import horovod.tensorflow as hvd
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import time
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flags = tf.flags
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FLAGS = None
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def extract_flags():
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flags.DEFINE_integer(
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"max_seq_length", 384,
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"The maximum total input sequence length after WordPiece tokenization. "
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"Sequences longer than this will be truncated, and sequences shorter "
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"than this will be padded.")
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flags.DEFINE_integer(
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"doc_stride", 128,
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"When splitting up a long document into chunks, how much stride to "
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"take between chunks.")
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flags.DEFINE_integer(
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"max_query_length", 64,
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"The maximum number of tokens for the question. Questions longer than "
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"this will be truncated to this length.")
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flags.DEFINE_bool(
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"version_2_with_negative", False,
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"If true, the SQuAD examples contain some that do not have an answer.")
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flags.DEFINE_string("train_file", None,
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"SQuAD json for training. E.g., train-v1.1.json")
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flags.DEFINE_string(
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"predict_file", None,
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"SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json")
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flags.DEFINE_string(
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"squad_dir", None,
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"The output directory where the model checkpoints will be written.")
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flags.DEFINE_string("vocab_file", None,
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"The vocabulary file that the BERT model was trained on.")
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flags.DEFINE_bool(
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"do_lower_case", True,
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"Whether to lower case the input text. Should be True for uncased "
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"models and False for cased models.")
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flags.DEFINE_bool(
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"verbose_logging", False,
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"If true, all of the warnings related to data processing will be printed. "
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"A number of warnings are expected for a normal SQuAD evaluation.")
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flags.mark_flag_as_required("train_file")
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flags.mark_flag_as_required("predict_file")
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flags.mark_flag_as_required("squad_dir")
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flags.mark_flag_as_required("vocab_file")
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return flags.FLAGS
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class SquadExample(object):
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"""A single training/test example for simple sequence classification.
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For examples without an answer, the start and end position are -1.
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"""
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def __init__(self,
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qas_id,
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question_text,
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doc_tokens,
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orig_answer_text=None,
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start_position=None,
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end_position=None,
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is_impossible=False):
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self.qas_id = qas_id
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self.question_text = question_text
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self.doc_tokens = doc_tokens
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self.orig_answer_text = orig_answer_text
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self.start_position = start_position
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self.end_position = end_position
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self.is_impossible = is_impossible
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def __str__(self):
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return self.__repr__()
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def __repr__(self):
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s = ""
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s += "qas_id: %s" % (tokenization.printable_text(self.qas_id))
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s += ", question_text: %s" % (
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tokenization.printable_text(self.question_text))
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s += ", doc_tokens: [%s]" % (" ".join(self.doc_tokens))
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if self.start_position:
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s += ", start_position: %d" % (self.start_position)
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if self.start_position:
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s += ", end_position: %d" % (self.end_position)
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if self.start_position:
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s += ", is_impossible: %r" % (self.is_impossible)
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return s
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class InputFeatures(object):
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"""A single set of features of data."""
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def __init__(self,
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unique_id,
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example_index,
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doc_span_index,
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tokens,
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token_to_orig_map,
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token_is_max_context,
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input_ids,
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input_mask,
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segment_ids,
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start_position=None,
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end_position=None,
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is_impossible=None):
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self.unique_id = unique_id
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self.example_index = example_index
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self.doc_span_index = doc_span_index
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self.tokens = tokens
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self.token_to_orig_map = token_to_orig_map
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self.token_is_max_context = token_is_max_context
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self.input_ids = input_ids
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self.input_mask = input_mask
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self.segment_ids = segment_ids
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self.start_position = start_position
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self.end_position = end_position
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self.is_impossible = is_impossible
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def read_squad_examples(input_file, is_training, version_2_with_negative=False, input_data=None):
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"""Return list of SquadExample from input_data or input_file (SQuAD json file)"""
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if input_data is None:
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with tf.gfile.Open(input_file, "r") as reader:
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input_data = json.load(reader)["data"]
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def is_whitespace(c):
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if c == " " or c == "\t" or c == "\r" or c == "\n" or ord(c) == 0x202F:
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return True
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return False
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examples = []
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for entry in input_data:
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for paragraph in entry["paragraphs"]:
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paragraph_text = paragraph["context"]
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doc_tokens = []
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char_to_word_offset = []
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prev_is_whitespace = True
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for c in paragraph_text:
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if is_whitespace(c):
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prev_is_whitespace = True
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else:
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if prev_is_whitespace:
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doc_tokens.append(c)
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else:
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doc_tokens[-1] += c
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prev_is_whitespace = False
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char_to_word_offset.append(len(doc_tokens) - 1)
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for qa in paragraph["qas"]:
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qas_id = qa["id"]
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question_text = qa["question"]
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start_position = None
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end_position = None
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orig_answer_text = None
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is_impossible = False
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if is_training:
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if version_2_with_negative:
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is_impossible = qa["is_impossible"]
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if (len(qa["answers"]) != 1) and (not is_impossible):
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raise ValueError(
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"For training, each question should have exactly 1 answer.")
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if not is_impossible:
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answer = qa["answers"][0]
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orig_answer_text = answer["text"]
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answer_offset = answer["answer_start"]
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answer_length = len(orig_answer_text)
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start_position = char_to_word_offset[answer_offset]
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end_position = char_to_word_offset[answer_offset + answer_length -
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1]
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# Only add answers where the text can be exactly recovered from the
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# document. If this CAN'T happen it's likely due to weird Unicode
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# stuff so we will just skip the example.
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#
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# Note that this means for training mode, every example is NOT
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# guaranteed to be preserved.
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actual_text = " ".join(
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doc_tokens[start_position:(end_position + 1)])
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cleaned_answer_text = " ".join(
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tokenization.whitespace_tokenize(orig_answer_text))
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if actual_text.find(cleaned_answer_text) == -1:
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tf.logging.warning("Could not find answer: '%s' vs. '%s'",
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actual_text, cleaned_answer_text)
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continue
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else:
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start_position = -1
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end_position = -1
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orig_answer_text = ""
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example = SquadExample(
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qas_id=qas_id,
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question_text=question_text,
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doc_tokens=doc_tokens,
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orig_answer_text=orig_answer_text,
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start_position=start_position,
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end_position=end_position,
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is_impossible=is_impossible)
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examples.append(example)
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return examples
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def _check_is_max_context(doc_spans, cur_span_index, position):
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"""Check if this is the 'max context' doc span for the token."""
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# Because of the sliding window approach taken to scoring documents, a single
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# token can appear in multiple documents. E.g.
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# Doc: the man went to the store and bought a gallon of milk
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# Span A: the man went to the
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# Span B: to the store and bought
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# Span C: and bought a gallon of
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# ...
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#
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# Now the word 'bought' will have two scores from spans B and C. We only
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# want to consider the score with "maximum context", which we define as
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# the *minimum* of its left and right context (the *sum* of left and
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# right context will always be the same, of course).
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#
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# In the example the maximum context for 'bought' would be span C since
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# it has 1 left context and 3 right context, while span B has 4 left context
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# and 0 right context.
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best_score = None
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best_span_index = None
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for (span_index, doc_span) in enumerate(doc_spans):
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end = doc_span.start + doc_span.length - 1
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if position < doc_span.start:
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continue
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if position > end:
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continue
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num_left_context = position - doc_span.start
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num_right_context = end - position
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score = min(num_left_context, num_right_context) + 0.01 * doc_span.length
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if best_score is None or score > best_score:
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best_score = score
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best_span_index = span_index
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return cur_span_index == best_span_index
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def _improve_answer_span(doc_tokens, input_start, input_end, tokenizer,
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orig_answer_text):
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"""Returns tokenized answer spans that better match the annotated answer."""
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# The SQuAD annotations are character based. We first project them to
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# whitespace-tokenized words. But then after WordPiece tokenization, we can
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# often find a "better match". For example:
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#
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# Question: What year was John Smith born?
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# Context: The leader was John Smith (1895-1943).
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# Answer: 1895
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#
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# The original whitespace-tokenized answer will be "(1895-1943).". However
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# after tokenization, our tokens will be "( 1895 - 1943 ) .". So we can match
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# the exact answer, 1895.
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#
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# However, this is not always possible. Consider the following:
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#
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# Question: What country is the top exporter of electornics?
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# Context: The Japanese electronics industry is the lagest in the world.
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# Answer: Japan
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#
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# In this case, the annotator chose "Japan" as a character sub-span of
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# the word "Japanese". Since our WordPiece tokenizer does not split
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# "Japanese", we just use "Japanese" as the annotation. This is fairly rare
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# in SQuAD, but does happen.
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tok_answer_text = " ".join(tokenizer.tokenize(orig_answer_text))
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for new_start in range(input_start, input_end + 1):
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for new_end in range(input_end, new_start - 1, -1):
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text_span = " ".join(doc_tokens[new_start:(new_end + 1)])
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if text_span == tok_answer_text:
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return (new_start, new_end)
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return (input_start, input_end)
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def convert_examples_to_features(examples, tokenizer, max_seq_length,
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doc_stride, max_query_length, is_training,
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output_fn, verbose_logging=False):
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"""Loads a data file into a list of `InputBatch`s."""
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unique_id = 1000000000
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for (example_index, example) in enumerate(examples):
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query_tokens = tokenizer.tokenize(example.question_text)
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if len(query_tokens) > max_query_length:
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query_tokens = query_tokens[0:max_query_length]
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tok_to_orig_index = []
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orig_to_tok_index = []
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all_doc_tokens = []
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for (i, token) in enumerate(example.doc_tokens):
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orig_to_tok_index.append(len(all_doc_tokens))
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sub_tokens = tokenizer.tokenize(token)
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for sub_token in sub_tokens:
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tok_to_orig_index.append(i)
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all_doc_tokens.append(sub_token)
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tok_start_position = None
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tok_end_position = None
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if is_training and example.is_impossible:
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tok_start_position = -1
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tok_end_position = -1
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if is_training and not example.is_impossible:
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tok_start_position = orig_to_tok_index[example.start_position]
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if example.end_position < len(example.doc_tokens) - 1:
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tok_end_position = orig_to_tok_index[example.end_position + 1] - 1
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else:
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tok_end_position = len(all_doc_tokens) - 1
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(tok_start_position, tok_end_position) = _improve_answer_span(
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all_doc_tokens, tok_start_position, tok_end_position, tokenizer,
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example.orig_answer_text)
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# The -3 accounts for [CLS], [SEP] and [SEP]
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max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
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# We can have documents that are longer than the maximum sequence length.
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# To deal with this we do a sliding window approach, where we take chunks
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# of the up to our max length with a stride of `doc_stride`.
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_DocSpan = collections.namedtuple( # pylint: disable=invalid-name
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"DocSpan", ["start", "length"])
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doc_spans = []
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start_offset = 0
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while start_offset < len(all_doc_tokens):
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length = len(all_doc_tokens) - start_offset
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if length > max_tokens_for_doc:
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length = max_tokens_for_doc
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doc_spans.append(_DocSpan(start=start_offset, length=length))
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if start_offset + length == len(all_doc_tokens):
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break
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start_offset += min(length, doc_stride)
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for (doc_span_index, doc_span) in enumerate(doc_spans):
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tokens = []
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token_to_orig_map = {}
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token_is_max_context = {}
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segment_ids = []
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tokens.append("[CLS]")
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segment_ids.append(0)
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for token in query_tokens:
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tokens.append(token)
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segment_ids.append(0)
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tokens.append("[SEP]")
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segment_ids.append(0)
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for i in range(doc_span.length):
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split_token_index = doc_span.start + i
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token_to_orig_map[len(tokens)] = tok_to_orig_index[split_token_index]
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is_max_context = _check_is_max_context(doc_spans, doc_span_index,
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split_token_index)
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token_is_max_context[len(tokens)] = is_max_context
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tokens.append(all_doc_tokens[split_token_index])
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segment_ids.append(1)
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tokens.append("[SEP]")
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segment_ids.append(1)
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input_ids = tokenizer.convert_tokens_to_ids(tokens)
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# The mask has 1 for real tokens and 0 for padding tokens. Only real
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# tokens are attended to.
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input_mask = [1] * len(input_ids)
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# Zero-pad up to the sequence length.
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while len(input_ids) < max_seq_length:
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input_ids.append(0)
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input_mask.append(0)
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segment_ids.append(0)
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assert len(input_ids) == max_seq_length
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assert len(input_mask) == max_seq_length
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assert len(segment_ids) == max_seq_length
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start_position = None
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end_position = None
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if is_training and not example.is_impossible:
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# For training, if our document chunk does not contain an annotation
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# we throw it out, since there is nothing to predict.
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doc_start = doc_span.start
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doc_end = doc_span.start + doc_span.length - 1
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out_of_span = False
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if not (tok_start_position >= doc_start and
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tok_end_position <= doc_end):
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out_of_span = True
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if out_of_span:
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start_position = 0
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end_position = 0
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else:
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doc_offset = len(query_tokens) + 2
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start_position = tok_start_position - doc_start + doc_offset
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end_position = tok_end_position - doc_start + doc_offset
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if is_training and example.is_impossible:
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start_position = 0
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end_position = 0
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if verbose_logging and example_index < 20:
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tf.compat.v1.logging.info("*** Example ***")
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tf.compat.v1.logging.info("unique_id: %s" % (unique_id))
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tf.compat.v1.logging.info("example_index: %s" % (example_index))
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tf.compat.v1.logging.info("doc_span_index: %s" % (doc_span_index))
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tf.compat.v1.logging.info("tokens: %s" % " ".join(
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[tokenization.printable_text(x) for x in tokens]))
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tf.compat.v1.logging.info("token_to_orig_map: %s" % " ".join(
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["%d:%d" % (x, y) for (x, y) in six.iteritems(token_to_orig_map)]))
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tf.compat.v1.logging.info("token_is_max_context: %s" % " ".join([
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"%d:%s" % (x, y) for (x, y) in six.iteritems(token_is_max_context)
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]))
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tf.compat.v1.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
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tf.compat.v1.logging.info(
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"input_mask: %s" % " ".join([str(x) for x in input_mask]))
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tf.compat.v1.logging.info(
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"segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
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if is_training and example.is_impossible:
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tf.compat.v1.logging.info("impossible example")
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if is_training and not example.is_impossible:
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answer_text = " ".join(tokens[start_position:(end_position + 1)])
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tf.compat.v1.logging.info("start_position: %d" % (start_position))
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tf.compat.v1.logging.info("end_position: %d" % (end_position))
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tf.compat.v1.logging.info(
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"answer: %s" % (tokenization.printable_text(answer_text)))
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feature = InputFeatures(
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unique_id=unique_id,
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example_index=example_index,
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doc_span_index=doc_span_index,
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tokens=tokens,
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token_to_orig_map=token_to_orig_map,
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token_is_max_context=token_is_max_context,
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input_ids=input_ids,
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input_mask=input_mask,
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segment_ids=segment_ids,
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start_position=start_position,
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end_position=end_position,
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is_impossible=example.is_impossible)
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# Run callback
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output_fn(feature)
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unique_id += 1
|
|
|
|
class FeatureWriter(object):
|
|
"""Writes InputFeature to TF example file."""
|
|
|
|
def __init__(self, filename, is_training):
|
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self.filename = filename
|
|
self.is_training = is_training
|
|
self.num_features = 0
|
|
self._writer = tf.python_io.TFRecordWriter(filename)
|
|
|
|
def process_feature(self, feature):
|
|
"""Write a InputFeature to the TFRecordWriter as a tf.train.Example."""
|
|
self.num_features += 1
|
|
|
|
def create_int_feature(values):
|
|
feature = tf.train.Feature(
|
|
int64_list=tf.train.Int64List(value=list(values)))
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|
return feature
|
|
|
|
features = collections.OrderedDict()
|
|
features["unique_ids"] = create_int_feature([feature.unique_id])
|
|
features["input_ids"] = create_int_feature(feature.input_ids)
|
|
features["input_mask"] = create_int_feature(feature.input_mask)
|
|
features["segment_ids"] = create_int_feature(feature.segment_ids)
|
|
|
|
if self.is_training:
|
|
features["start_positions"] = create_int_feature([feature.start_position])
|
|
features["end_positions"] = create_int_feature([feature.end_position])
|
|
impossible = 0
|
|
if feature.is_impossible:
|
|
impossible = 1
|
|
features["is_impossible"] = create_int_feature([impossible])
|
|
|
|
tf_example = tf.train.Example(features=tf.train.Features(feature=features))
|
|
self._writer.write(tf_example.SerializeToString())
|
|
|
|
def close(self):
|
|
self._writer.close()
|
|
|
|
def main():
|
|
|
|
FLAGS = extract_flags()
|
|
tokenizer = tokenization.FullTokenizer(
|
|
vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)
|
|
tf.gfile.MakeDirs(FLAGS.squad_dir + "/final_tfrecords_sharded")
|
|
# We write to a temporary file to avoid storing very large constant tensors
|
|
# in memory.
|
|
train_examples = read_squad_examples(
|
|
input_file=FLAGS.train_file, is_training=True,
|
|
version_2_with_negative=FLAGS.version_2_with_negative)
|
|
train_writer = FeatureWriter(
|
|
filename=os.path.join(FLAGS.squad_dir, "final_tfrecords_sharded/train.tf_record"),
|
|
is_training=True)
|
|
convert_examples_to_features(
|
|
examples=train_examples,
|
|
tokenizer=tokenizer,
|
|
max_seq_length=FLAGS.max_seq_length,
|
|
doc_stride=FLAGS.doc_stride,
|
|
max_query_length=FLAGS.max_query_length,
|
|
is_training=True,
|
|
output_fn=train_writer.process_feature,
|
|
verbose_logging=FLAGS.verbose_logging)
|
|
train_writer.close()
|
|
|
|
|
|
eval_examples = read_squad_examples(
|
|
input_file=FLAGS.predict_file, is_training=False,
|
|
version_2_with_negative=FLAGS.version_2_with_negative)
|
|
|
|
eval_writer = FeatureWriter(
|
|
filename=os.path.join(FLAGS.squad_dir, "final_tfrecords_sharded/eval.tf_record"),
|
|
is_training=False)
|
|
eval_features = []
|
|
|
|
def append_feature(feature):
|
|
eval_features.append(feature)
|
|
eval_writer.process_feature(feature)
|
|
|
|
convert_examples_to_features(
|
|
examples=eval_examples,
|
|
tokenizer=tokenizer,
|
|
max_seq_length=FLAGS.max_seq_length,
|
|
doc_stride=FLAGS.doc_stride,
|
|
max_query_length=FLAGS.max_query_length,
|
|
is_training=False,
|
|
output_fn=append_feature,
|
|
verbose_logging=FLAGS.verbose_logging)
|
|
eval_writer.close()
|
|
|
|
if __name__ == "__main__":
|
|
main() |