run_korquad_hvd.py

# 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.

# Followed this code
# https://github.com/google-research/bert/pull/568

# """Run BERT on SQuAD 1.1 and SQuAD 2.0."""
"""Run BERT on KorQuAD 2.x"""
 
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
 
import collections
import json
import math
import os
import random
import modeling
import optimization
import tokenization
import six
import tensorflow as tf
 
import horovod.tensorflow as hvd
 
flags = tf.flags
 
FLAGS = flags.FLAGS
 
## Required parameters
flags.DEFINE_string(
"bert_config_file", None,
"The config json file corresponding to the pre-trained BERT model. "
"This specifies the model architecture.")
 
flags.DEFINE_string("vocab_file", None,
                "The vocabulary file that the BERT model was trained on.")
 
flags.DEFINE_string(
"output_dir", None,
"The output directory where the model checkpoints will be written.")
 
## Other parameters
flags.DEFINE_string("train_file", None,
                "SQuAD json for training. E.g., train-v1.1.json")
 
flags.DEFINE_string(
"predict_file", None,
"SQuAD json for predictions. E.g., dev-v1.1.json or test-v1.1.json")
 
flags.DEFINE_string(
"init_checkpoint", None,
"Initial checkpoint (usually from a pre-trained BERT model).")
 
flags.DEFINE_bool(
"do_lower_case", True,
"Whether to lower case the input text. Should be True for uncased "
"models and False for cased models.")
 
flags.DEFINE_integer(
"max_seq_length", 384,
"The maximum total input sequence length after WordPiece tokenization. "
"Sequences longer than this will be truncated, and sequences shorter "
"than this will be padded.")
 
flags.DEFINE_integer(
"doc_stride", 128,
"When splitting up a long document into chunks, how much stride to "
"take between chunks.")
 
flags.DEFINE_integer(
"max_query_length", 128,
"The maximum number of tokens for the question. Questions longer than "
"this will be truncated to this length.")
 
flags.DEFINE_bool("do_train", False, "Whether to run training.")
 
flags.DEFINE_bool("do_predict", False, "Whether to run eval on the dev set.")
 
flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.")
 
flags.DEFINE_integer("predict_batch_size", 8,
                 "Total batch size for predictions.")
 
flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.")
 
flags.DEFINE_float("num_train_epochs", 3.0,
               "Total number of training epochs to perform.")
 
flags.DEFINE_float(
"warmup_proportion", 0.1,
"Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10% of training.")
 
flags.DEFINE_integer("save_checkpoints_steps", 1000,
                 "How often to save the model checkpoint.")
 
flags.DEFINE_integer("iterations_per_loop", 1000,
                 "How many steps to make in each estimator call.")
 
flags.DEFINE_integer(
"n_best_size", 20,
"The total number of n-best predictions to generate in the "
"nbest_predictions.json output file.")
 
flags.DEFINE_integer(
"max_answer_length", 60,
"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.")
 
flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")
 
flags.DEFINE_bool("use_multi_gpu", False, "Use multiple GPUs for training "
                                      "using Horovod.")
tf.flags.DEFINE_string(
"tpu_name", None,
"The Cloud TPU to use for training. This should be either the name "
"used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 "
"url.")
 
tf.flags.DEFINE_string(
"tpu_zone", None,
"[Optional] GCE zone where the Cloud TPU is located in. If not "
"specified, we will attempt to automatically detect the GCE project from "
"metadata.")
 
tf.flags.DEFINE_string(
"gcp_project", None,
"[Optional] Project name for the Cloud TPU-enabled project. If not "
"specified, we will attempt to automatically detect the GCE project from "
"metadata.")
 
tf.flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.")
 
flags.DEFINE_integer(
"num_tpu_cores", 8,
"Only used if `use_tpu` is True. Total number of TPU cores to use.")
 
flags.DEFINE_bool(
"verbose_logging", False,
"If true, all of the warnings related to data processing will be printed. "
"A number of warnings are expected for a normal SQuAD evaluation.")
 
flags.DEFINE_float(
"null_score_diff_threshold", 0.0,
"If null_score - best_non_null is greater than the threshold predict null.")
 
 
class SquadExample(object):
    """A single training/test example for simple sequence classification.
 
     For examples without an answer, the start and end position are -1.
    """
 
    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.printable_text(self.qas_id))
        s += ", question_text: %s" % (
            tokenization.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 tf.gfile.Open(input_file, "r") as reader:
        #    input_data = json.load(reader)["data"]
 
        file_names = os.listdir(input_file)
        file_names = [a for a in file_names if a.endswith('.json')]
        dataset = []
        for file_name in file_names:
          data_file = os.path.join(input_file, file_name)
          with tf.io.gfile.GFile(data_file,'r') as f:
              dataset_json = json.load(f)
              dataset.extend(dataset_json['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 dataset:
            paragraph_text = entry["context"]
            doc_tokens = [] # c
            # context에서 white space 기준으로 토큰화
            char_to_word_offset = [] # 한 토큰 당 character 수 저장
            prev_is_whitespace = True
            for c in paragraph_text:  # context 한 글자씩 for loop
              if is_whitespace(c):
                prev_is_whitespace = True
              else:
                if prev_is_whitespace: # 이전 글자가 white space 이면
                  doc_tokens.append(c) # doc_tokens에 append
                else:
                  doc_tokens[-1] += c # 마지막에 append
                prev_is_whitespace = False
              char_to_word_offset.append(len(doc_tokens) - 1)
 
            for qa in entry["qas"]:
              qas_id = qa["id"]
              question_text = qa["question"]
              start_position = None
              end_position = None
              orig_answer_text = None
              if is_training:
                answer = qa["answer"]
                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.whitespace_tokenize(orig_answer_text))
                if actual_text.find(cleaned_answer_text) == -1:
                  tf.logging.warning("Could not find answer: '%s' vs. '%s'",
                                         actual_text, cleaned_answer_text)
                  continue
                else:
                  start_position = -1
                  end_position = -1
                  orig_answer_text = ""
 
              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,
                             output_fn):
    """Loads a data file into a list of `InputBatch`s."""
 
    unique_id = 1000000000
 
    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 = [] # 토큰들의 doc_tokens에서의 원래 인덱스 
        orig_to_tok_index = [] # 토큰화된 인덱스
        all_doc_tokens = [] # tokenizer로 토큰화된 doc_token 토큰들 ex) I'm -> I,',m
        for (i, token) in enumerate(example.doc_tokens): 
            orig_to_tok_index.append(len(all_doc_tokens))
            sub_tokens = tokenizer.tokenize(token) # doc_tokens 토큰화
            for sub_token in sub_tokens:
                tok_to_orig_index.append(i) # doc_token 인덱스 
                all_doc_tokens.append(sub_token) # sub_token 담음
 
        tok_start_position = None
        tok_end_position = None
        # if is_training and example.is_impossible:
        #   tok_start_position = -1
        #   tok_end_position = -1
        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
            # answer 토큰화해서 그에 맞는 새로운 start,end 포지션 리턴 
            (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
                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 = 0
                    end_position = 0
                    continue # out_of_span일 경우 skip
                else:
                    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 is_training and example.is_impossible:
          #   start_position = 0
          #   end_position = 0
 
            if example_index < 20:
                tf.logging.info("*** Example ***")
                tf.logging.info("unique_id: %s" % (unique_id))
                tf.logging.info("example_index: %s" % (example_index))
                tf.logging.info("doc_span_index: %s" % (doc_span_index))
                tf.logging.info("tokens: %s" % " ".join(
                    [tokenization.printable_text(x) for x in tokens]))
                tf.logging.info("token_to_orig_map: %s" % " ".join(
                    ["%d:%d" % (x, y) for (x, y) in six.iteritems(token_to_orig_map)]))
                tf.logging.info("token_is_max_context: %s" % " ".join([
                    "%d:%s" % (x, y) for (x, y) in six.iteritems(token_is_max_context)
                ]))
                tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
                tf.logging.info(
                    "input_mask: %s" % " ".join([str(x) for x in input_mask]))
                tf.logging.info(
                    "segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
                # if is_training and example.is_impossible:
                #   tf.logging.info("impossible example")
                if is_training:
                    answer_text = " ".join(tokens[start_position:(end_position + 1)])
                    tf.logging.info("start_position: %d" % (start_position))
                    tf.logging.info("end_position: %d" % (end_position))
                    tf.logging.info(
                      "answer: %s" % (tokenization.printable_text(answer_text)))
 
            feature = 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)
 
          # Run callback
            output_fn(feature)
 
            unique_id += 1
 
 
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
 
 
def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
             use_one_hot_embeddings):
    """Creates a classification model."""
    model = modeling.BertModel(
            config=bert_config,
        is_training=is_training,
        input_ids=input_ids,
        input_mask=input_mask,
        token_type_ids=segment_ids,
        use_one_hot_embeddings=use_one_hot_embeddings)
 
    final_hidden = model.get_sequence_output()
 
    final_hidden_shape = modeling.get_shape_list(final_hidden, expected_rank=3)
    batch_size = final_hidden_shape[0]
    seq_length = final_hidden_shape[1]
    hidden_size = final_hidden_shape[2]
 
    output_weights = tf.get_variable(
        "cls/squad/output_weights", [2, hidden_size],
        initializer=tf.truncated_normal_initializer(stddev=0.02))
 
    output_bias = tf.get_variable(
        "cls/squad/output_bias", [2], initializer=tf.zeros_initializer())
 
    final_hidden_matrix = tf.reshape(final_hidden,
                                    [batch_size * seq_length, hidden_size])
    logits = tf.matmul(final_hidden_matrix, output_weights, transpose_b=True)
    logits = tf.nn.bias_add(logits, output_bias)
 
    logits = tf.reshape(logits, [batch_size, seq_length, 2])
    logits = tf.transpose(logits, [2, 0, 1])
 
    unstacked_logits = tf.unstack(logits, axis=0)
 
    (start_logits, end_logits) = (unstacked_logits[0], unstacked_logits[1])
 
    return (start_logits, end_logits)
 
 
def model_fn_builder(bert_config, init_checkpoint, learning_rate,
                 num_train_steps, num_warmup_steps, use_tpu,
                 use_one_hot_embeddings, use_multi_gpu):
    """Returns `model_fn` closure for TPUEstimator."""
 
    def model_fn(features, labels, mode, params):  # pylint: disable=unused-argument
        """The `model_fn` for TPUEstimator."""
 
        tf.logging.info("*** Features ***")
        for name in sorted(features.keys()):
            tf.logging.info("  name = %s, shape = %s" % (name, features[name].shape))
 
        unique_ids = features["unique_ids"]
        input_ids = features["input_ids"]
        input_mask = features["input_mask"]
        segment_ids = features["segment_ids"]
 
        is_training = (mode == tf.estimator.ModeKeys.TRAIN)
 
        (start_logits, end_logits) = create_model(
            bert_config=bert_config,
            is_training=is_training,
            input_ids=input_ids,
            input_mask=input_mask,
            segment_ids=segment_ids,
            use_one_hot_embeddings=use_one_hot_embeddings)
 
        tvars = tf.trainable_variables()
 
        initialized_variable_names = {}
        scaffold_fn = None
        if init_checkpoint:
          (assignment_map, initialized_variable_names) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
            if use_tpu:
                def tpu_scaffold():
                    tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
                    return tf.train.Scaffold()
 
                scaffold_fn = tpu_scaffold
            else:
                tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
 
        tf.logging.info("**** Trainable Variables ****")
        for var in tvars:
            init_string = ""
            if var.name in initialized_variable_names:
                init_string = ", *INIT_FROM_CKPT*"
            tf.logging.info("  name = %s, shape = %s%s", var.name, var.shape, init_string)
 
        output_spec = None
        if mode == tf.estimator.ModeKeys.TRAIN:
            seq_length = modeling.get_shape_list(input_ids)[1]
 
            def compute_loss(logits, positions):
                one_hot_positions = tf.one_hot(positions, depth=seq_length, dtype=tf.float32)
                log_probs = tf.nn.log_softmax(logits, axis=-1)
                loss = -tf.reduce_mean(
                    tf.reduce_sum(one_hot_positions * log_probs, axis=-1))
                return loss
 
            start_positions = features["start_positions"]
            end_positions = features["end_positions"]
 
            start_loss = compute_loss(start_logits, start_positions)
            end_loss = compute_loss(end_logits, end_positions)
 
            total_loss = (start_loss + end_loss) / 2.0
 
            train_op = optimization.create_optimizer(
                total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu, use_multi_gpu)
 
            output_spec = tf.contrib.tpu.TPUEstimatorSpec(
                mode=mode,
                loss=total_loss,
                train_op=train_op,
                scaffold_fn=scaffold_fn)
        elif mode == tf.estimator.ModeKeys.PREDICT:
            predictions = {
              "unique_ids": unique_ids,
              "start_logits": start_logits,
              "end_logits": end_logits,
            }
            output_spec = tf.contrib.tpu.TPUEstimatorSpec(
              mode=mode, predictions=predictions, scaffold_fn=scaffold_fn)
        else:
            raise ValueError(
                "Only TRAIN and PREDICT modes are supported: %s" % (mode))
 
        return output_spec
 
    return model_fn
 
 
def input_fn_builder(input_file, seq_length, is_training, drop_remainder):
    """Creates an `input_fn` closure to be passed to TPUEstimator."""
 
    name_to_features = {
      "unique_ids": tf.FixedLenFeature([], tf.int64),
      "input_ids": tf.FixedLenFeature([seq_length], tf.int64),
      "input_mask": tf.FixedLenFeature([seq_length], tf.int64),
      "segment_ids": tf.FixedLenFeature([seq_length], tf.int64),
    }
 
    if is_training:
        name_to_features["start_positions"] = tf.FixedLenFeature([], tf.int64)
        name_to_features["end_positions"] = tf.FixedLenFeature([], tf.int64)
 
    def _decode_record(record, name_to_features):
        """Decodes a record to a TensorFlow example."""
        example = tf.parse_single_example(record, name_to_features)
 
        # tf.Example only supports tf.int64, but the TPU only supports tf.int32.
        # So cast all int64 to int32.
        for name in list(example.keys()):
            t = example[name]
            if t.dtype == tf.int64:
                t = tf.to_int32(t)
            example[name] = t
 
        return example
 
    def input_fn(params):
        """The actual input function."""
        batch_size = params["batch_size"]
 
        # For training, we want a lot of parallel reading and shuffling.
        # For eval, we want no shuffling and parallel reading doesn't matter.
        d = tf.data.TFRecordDataset(input_file)
        if is_training:
            d = d.repeat()
            d = d.shuffle(buffer_size=100)
 
        d = d.apply(
                tf.contrib.data.map_and_batch(
                    lambda record: _decode_record(record, name_to_features),
                    batch_size=batch_size,
                    drop_remainder=drop_remainder))
 
        return d
 
    return input_fn
 
 
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, output_null_log_odds_file):
    """Write final predictions to the json file and log-odds of null if needed."""
    tf.logging.info("Writing predictions to: %s" % (output_prediction_file))
    tf.logging.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()
    scores_diff_json = collections.OrderedDict()
 
    for (example_index, example) in enumerate(all_examples):
        features = example_index_to_features[example_index]
 
        prelim_predictions = []
        # keep track of the minimum score of null start+end of position 0
        score_null = 1000000  # large and positive
        min_null_feature_index = 0  # the paragraph slice with min mull score
        null_start_logit = 0  # the start logit at the slice with min null score
        null_end_logit = 0  # the end logit at the slice with min null score
        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)
          # if we could have irrelevant answers, get the min score of irrelevant
        # if FLAGS.version_2_with_negative:
        #   feature_null_score = result.start_logits[0] + result.end_logits[0]
        #   if feature_null_score < score_null:
        #       score_null = feature_null_score
        #       min_null_feature_index = feature_index
        #       null_start_logit = result.start_logits[0]
        #       null_end_logit = result.end_logits[0]
            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]
            if pred.start_index > 0:  # this is a non-null prediction
                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
            else:
                final_text = ""
                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 = []
        best_non_null_entry = None
        for entry in nbest:
            total_scores.append(entry.start_logit + entry.end_logit)
            if not best_non_null_entry:
                if entry.text:
                    best_non_null_entry = entry
 
        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 tf.gfile.GFile(output_prediction_file, "w") as writer:
        writer.write(json.dumps(all_predictions, indent=4) + "\n")
 
    with tf.gfile.GFile(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.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 FLAGS.verbose_logging:
            tf.logging.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 FLAGS.verbose_logging:
            tf.logging.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 FLAGS.verbose_logging:
            tf.logging.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 FLAGS.verbose_logging:
            tf.logging.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
 
 
class FeatureWriter(object):
    """Writes InputFeature to TF example file."""
 
    def __init__(self, filename, is_training):
        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)))
            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])
 
        tf_example = tf.train.Example(features=tf.train.Features(feature=features))
        self._writer.write(tf_example.SerializeToString())
 
    def close(self):
        self._writer.close()
 
 
def validate_flags_or_throw(bert_config):
    """Validate the input FLAGS or throw an exception."""
    tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case, FLAGS.init_checkpoint)
 
    if not FLAGS.do_train and not FLAGS.do_predict:
        raise ValueError("At least one of `do_train` or `do_predict` must be True.")
 
    if FLAGS.do_train:
        if not FLAGS.train_file:
            raise ValueError(
                "If `do_train` is True, then `train_file` must be specified.")
        if FLAGS.do_predict:
        if not FLAGS.predict_file:
            raise ValueError(
                "If `do_predict` is True, then `predict_file` must be specified.")
 
        if FLAGS.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" %
            (FLAGS.max_seq_length, bert_config.max_position_embeddings))
 
    if FLAGS.max_seq_length <= FLAGS.max_query_length + 3:
        raise ValueError(
            "The max_seq_length (%d) must be greater than max_query_length "
            "(%d) + 3" % (FLAGS.max_seq_length, FLAGS.max_query_length))
 
 
def main(_):
    if FLAGS.use_multi_gpu:
        hvd.init()
        FLAGS.output_dir = FLAGS.output_dir if hvd.rank() == 0 else os.path.join(FLAGS.output_dir, str(hvd.rank()))
 
    tf.logging.set_verbosity(tf.logging.INFO)

    bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)

    validate_flags_or_throw(bert_config)

    tf.gfile.MakeDirs(FLAGS.output_dir)

    tokenizer = tokenization.FullTokenizer(
    vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)

    tpu_cluster_resolver = None
    if FLAGS.use_tpu and FLAGS.tpu_name:
    tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
        FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)

    is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2

    if FLAGS.use_multi_gpu:
    # Horovod: pin GPU to be used to process local rank (one GPU per process)
        config = tf.ConfigProto()
        # config.gpu_options.allow_growth=True
        config.gpu_options.visible_device_list = str(hvd.local_rank())
        run_config = tf.contrib.tpu.RunConfig(
          cluster=tpu_cluster_resolver,
          master=FLAGS.master,
          model_dir=FLAGS.output_dir,
          save_checkpoints_steps=FLAGS.save_checkpoints_steps,
          tpu_config=tf.contrib.tpu.TPUConfig(
              iterations_per_loop=FLAGS.iterations_per_loop,
              num_shards=FLAGS.num_tpu_cores,
              per_host_input_for_training=is_per_host),
          log_step_count_steps=25,
          session_config=config)
        else:
        run_config = tf.contrib.tpu.RunConfig(
          cluster=tpu_cluster_resolver,
          master=FLAGS.master,
          model_dir=FLAGS.output_dir,
          save_checkpoints_steps=FLAGS.save_checkpoints_steps,
          tpu_config=tf.contrib.tpu.TPUConfig(
              iterations_per_loop=FLAGS.iterations_per_loop,
              num_shards=FLAGS.num_tpu_cores,
              per_host_input_for_training=is_per_host),
          log_step_count_steps=25)
 
    train_examples = None
    num_train_steps = None
    num_warmup_steps = None
    if FLAGS.do_train:
        train_examples = read_squad_examples(input_file=FLAGS.train_file, is_training=True)
        num_train_steps = int(len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs)
        num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
 
    # Pre-shuffle the input to avoid having to make a very large shuffle
    # buffer in in the `input_fn`.
        rng = random.Random(12345)
        rng.shuffle(train_examples)
 
    if FLAGS.use_multi_gpu:
        num_train_steps = num_train_steps // hvd.size()
        num_warmup_steps = num_warmup_steps // hvd.size()
 
    model_fn = model_fn_builder(
    bert_config=bert_config,
    init_checkpoint=FLAGS.init_checkpoint,
    learning_rate=FLAGS.learning_rate,
    num_train_steps=num_train_steps,
    num_warmup_steps=num_warmup_steps,
    use_tpu=FLAGS.use_tpu,
    use_one_hot_embeddings=FLAGS.use_tpu,
    use_multi_gpu=FLAGS.use_multi_gpu)
 
    # If TPU is not available, this will fall back to normal Estimator on CPU
    # or GPU.
    estimator = tf.contrib.tpu.TPUEstimator(
        use_tpu=FLAGS.use_tpu,
        model_fn=model_fn,
        config=run_config,
        train_batch_size=FLAGS.train_batch_size,
        predict_batch_size=FLAGS.predict_batch_size)
 
    if FLAGS.do_train:
        # We write to a temporary file to avoid storing very large constant tensors
        # in memory.
        train_writer = FeatureWriter(
            filename=os.path.join(FLAGS.output_dir, "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)
        train_writer.close()
 
        tf.logging.info("***** Running training *****")
        tf.logging.info("  Num orig examples = %d", len(train_examples))
        tf.logging.info("  Num split examples = %d", train_writer.num_features)
        tf.logging.info("  Batch size = %d", FLAGS.train_batch_size)
        tf.logging.info("  Num steps = %d", num_train_steps)
        del train_examples
 
        train_input_fn = input_fn_builder(
            input_file=train_writer.filename,
            seq_length=FLAGS.max_seq_length,
            is_training=True,
            drop_remainder=True)
 
        if FLAGS.use_multi_gpu:
            hooks = [hvd.BroadcastGlobalVariablesHook(0)]
        else:
            hooks = []
        estimator.train(input_fn=train_input_fn, max_steps=num_train_steps, hooks=hooks)
 
    if FLAGS.do_predict:
        eval_examples = read_squad_examples(
            input_file=FLAGS.predict_file, is_training=False)
 
        eval_writer = FeatureWriter(
            filename=os.path.join(FLAGS.output_dir, "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)
        eval_writer.close()
 
        tf.logging.info("***** Running predictions *****")
        tf.logging.info("  Num orig examples = %d", len(eval_examples))
        tf.logging.info("  Num split examples = %d", len(eval_features))
        tf.logging.info("  Batch size = %d", FLAGS.predict_batch_size)
 
        all_results = []
 
        predict_input_fn = input_fn_builder(
            input_file=eval_writer.filename,
            seq_length=FLAGS.max_seq_length,
            is_training=False,
            drop_remainder=False)
 
        # If running eval on the TPU, you will need to specify the number of
        # steps.
        all_results = []
        for result in estimator.predict(predict_input_fn, yield_single_examples=True):
            if len(all_results) % 1000 == 0:
                tf.logging.info("Processing example: %d" % (len(all_results)))
            unique_id = int(result["unique_ids"])
            start_logits = [float(x) for x in result["start_logits"].flat]
            end_logits = [float(x) for x in result["end_logits"].flat]
            all_results.append(
                RawResult(
                    unique_id=unique_id,
                    start_logits=start_logits,
                    end_logits=end_logits))
 
        output_prediction_file = os.path.join(FLAGS.output_dir, "predictions.json")
        output_nbest_file = os.path.join(FLAGS.output_dir, "nbest_predictions.json")
        output_null_log_odds_file = os.path.join(FLAGS.output_dir, "null_odds.json")
 
        write_predictions(eval_examples, eval_features, all_results,
                          FLAGS.n_best_size, FLAGS.max_answer_length,
                          FLAGS.do_lower_case, output_prediction_file,
                          output_nbest_file, output_null_log_odds_file)
 
# print("Total Training Time: " + str(end_train - start_train) + " seconds.")
 
 
if __name__ == "__main__":
    flags.mark_flag_as_required("vocab_file")
    flags.mark_flag_as_required("bert_config_file")
    flags.mark_flag_as_required("output_dir")
    tf.app.run()