code: # 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 masked LM/next sentence masked_lm pre-training for BERT."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os
  import modeling
  import optimization
  import tensorflow as tf
  
  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(
      "input_file", None,
      "Input TF example files (can be a glob or comma separated).")
  
  flags.DEFINE_string(
      "output_dir", None,
      "The output directory where the model checkpoints will be written.")
  
  ## Other parameters
  flags.DEFINE_string(
      "init_checkpoint", None,
      "Initial checkpoint (usually from a pre-trained BERT model).")
  
  flags.DEFINE_integer(
      "max_seq_length", 128,
      "The maximum total input sequence length after WordPiece tokenization. "
      "Sequences longer than this will be truncated, and sequences shorter "
      "than this will be padded. Must match data generation.")
  
  flags.DEFINE_integer(
      "max_predictions_per_seq", 20,
      "Maximum number of masked LM predictions per sequence. "
      "Must match data generation.")
  
  flags.DEFINE_bool("do_train", False, "Whether to run training.")
  
  flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.")
  
  flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.")
  
  flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.")
  
  flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.")
  
  flags.DEFINE_integer("num_train_steps", 100000, "Number of training steps.")
  
  flags.DEFINE_integer("num_warmup_steps", 10000, "Number of warmup steps.")
  
  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("max_eval_steps", 100, "Maximum number of eval steps.")
  
  flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")
  
  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.")
  
  
  def model_fn_builder(bert_config, init_checkpoint, learning_rate,
                       num_train_steps, num_warmup_steps, use_tpu,
                       use_one_hot_embeddings):
      """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))
  
          input_ids = features["input_ids"]
          input_mask = features["input_mask"]
          segment_ids = features["segment_ids"]
          masked_lm_positions = features["masked_lm_positions"]
          masked_lm_ids = features["masked_lm_ids"]
          masked_lm_weights = features["masked_lm_weights"]
          next_sentence_labels = features["next_sentence_labels"]
  
          is_training = (mode == tf.estimator.ModeKeys.TRAIN)
  
          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)
  
          (masked_lm_loss,
           masked_lm_example_loss, masked_lm_log_probs) = get_masked_lm_output(
              bert_config, model.get_sequence_output(), model.get_embedding_table(), model.get_embedding_table_2(),
              masked_lm_positions, masked_lm_ids, masked_lm_weights)
  
          (next_sentence_loss, next_sentence_example_loss,
           next_sentence_log_probs) = get_next_sentence_output(
              bert_config, model.get_pooled_output(), next_sentence_labels)
  
          total_loss = masked_lm_loss + next_sentence_loss
  
          tvars = tf.trainable_variables()
  
          initialized_variable_names = {}
          print("init_checkpoint:", init_checkpoint)
          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:
              train_op = optimization.create_optimizer(
                  total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)
  
              output_spec = tf.contrib.tpu.TPUEstimatorSpec(
                  mode=mode,
                  loss=total_loss,
                  train_op=train_op,
                  scaffold_fn=scaffold_fn)
          elif mode == tf.estimator.ModeKeys.EVAL:
  
              def metric_fn(masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids,
                            masked_lm_weights, next_sentence_example_loss,
                            next_sentence_log_probs, next_sentence_labels):
                  """Computes the loss and accuracy of the model."""
                  masked_lm_log_probs = tf.reshape(masked_lm_log_probs, [-1, masked_lm_log_probs.shape[-1]])
                  masked_lm_predictions = tf.argmax(masked_lm_log_probs, axis=-1, output_type=tf.int32)
                  masked_lm_example_loss = tf.reshape(masked_lm_example_loss, [-1])
                  masked_lm_ids = tf.reshape(masked_lm_ids, [-1])
                  masked_lm_weights = tf.reshape(masked_lm_weights, [-1])
                  masked_lm_accuracy = tf.metrics.accuracy(
                      labels=masked_lm_ids,
                      predictions=masked_lm_predictions,
                      weights=masked_lm_weights)
                  masked_lm_mean_loss = tf.metrics.mean(
                      values=masked_lm_example_loss, weights=masked_lm_weights)
  
                  next_sentence_log_probs = tf.reshape(
                      next_sentence_log_probs, [-1, next_sentence_log_probs.shape[-1]])
                  next_sentence_predictions = tf.argmax(
                      next_sentence_log_probs, axis=-1, output_type=tf.int32)
                  next_sentence_labels = tf.reshape(next_sentence_labels, [-1])
                  next_sentence_accuracy = tf.metrics.accuracy(
                      labels=next_sentence_labels, predictions=next_sentence_predictions)
                  next_sentence_mean_loss = tf.metrics.mean(
                      values=next_sentence_example_loss)
  
                  return {
                      "masked_lm_accuracy": masked_lm_accuracy,
                      "masked_lm_loss": masked_lm_mean_loss,
                      "next_sentence_accuracy": next_sentence_accuracy,
                      "next_sentence_loss": next_sentence_mean_loss,
                  }
  
              # next_sentence_example_loss=0.0 TODO
              # next_sentence_log_probs=0.0 # TODO
              eval_metrics = (metric_fn, [
                  masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids,
                  masked_lm_weights, next_sentence_example_loss,
                  next_sentence_log_probs, next_sentence_labels
              ])
              output_spec = tf.contrib.tpu.TPUEstimatorSpec(
                  mode=mode,
                  loss=total_loss,
                  eval_metrics=eval_metrics,
                  scaffold_fn=scaffold_fn)
          else:
              raise ValueError("Only TRAIN and EVAL modes are supported: %s" % (mode))
  
          return output_spec
  
      return model_fn
  
  
  def get_masked_lm_output(bert_config, input_tensor, output_weights, project_weights, positions,
                           label_ids, label_weights):
      """Get loss and log probs for the masked LM."""
      input_tensor = gather_indexes(input_tensor, positions)
  
      with tf.variable_scope("cls/predictions"):
          # We apply one more non-linear transformation before the output layer.
          # This matrix is not used after pre-training.
          with tf.variable_scope("transform"):
              input_tensor = tf.layers.dense(
                  input_tensor,
                  units=bert_config.hidden_size,
                  activation=modeling.get_activation(bert_config.hidden_act),
                  kernel_initializer=modeling.create_initializer(
                      bert_config.initializer_range))
              input_tensor = modeling.layer_norm(input_tensor)
  
          # The output weights are the same as the input embeddings, but there is
          # an output-only bias for each token.
          output_bias = tf.get_variable(
              "output_bias",
              shape=[bert_config.vocab_size],
              initializer=tf.zeros_initializer())
          # logits = tf.matmul(input_tensor, output_weights, transpose_b=True)
          # input_tensor=[-1,hidden_size], project_weights=[embedding_size, hidden_size], project_weights_transpose=[hidden_size, embedding_size]--->[-1, embedding_size]
          input_project = tf.matmul(input_tensor, project_weights, transpose_b=True)
          logits = tf.matmul(input_project, output_weights, transpose_b=True)
          #  # input_project=[-1, embedding_size], output_weights=[vocab_size, embedding_size], output_weights_transpose=[embedding_size, vocab_size] ---> [-1, vocab_size]
  
          logits = tf.nn.bias_add(logits, output_bias)
          log_probs = tf.nn.log_softmax(logits, axis=-1)
  
          label_ids = tf.reshape(label_ids, [-1])
          label_weights = tf.reshape(label_weights, [-1])
  
          one_hot_labels = tf.one_hot(label_ids, depth=bert_config.vocab_size, dtype=tf.float32)
  
          # The `positions` tensor might be zero-padded (if the sequence is too
          # short to have the maximum number of predictions). The `label_weights`
          # tensor has a value of 1.0 for every real prediction and 0.0 for the
          # padding predictions.
          per_example_loss = -tf.reduce_sum(log_probs * one_hot_labels, axis=[-1])
          numerator = tf.reduce_sum(label_weights * per_example_loss)
          denominator = tf.reduce_sum(label_weights) + 1e-5
          loss = numerator / denominator
  
      return (loss, per_example_loss, log_probs)
  
  
  def get_next_sentence_output(bert_config, input_tensor, labels):
      """Get loss and log probs for the next sentence prediction."""
  
      # Simple binary classification. Note that 0 is "next sentence" and 1 is
      # "random sentence". This weight matrix is not used after pre-training.
      with tf.variable_scope("cls/seq_relationship"):
          output_weights = tf.get_variable(
              "output_weights",
              shape=[2, bert_config.hidden_size],
              initializer=modeling.create_initializer(bert_config.initializer_range))
          output_bias = tf.get_variable(
              "output_bias", shape=[2], initializer=tf.zeros_initializer())
  
          logits = tf.matmul(input_tensor, output_weights, transpose_b=True)
          logits = tf.nn.bias_add(logits, output_bias)
          log_probs = tf.nn.log_softmax(logits, axis=-1)
          labels = tf.reshape(labels, [-1])
          one_hot_labels = tf.one_hot(labels, depth=2, dtype=tf.float32)
          per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
          loss = tf.reduce_mean(per_example_loss)
          return (loss, per_example_loss, log_probs)
  
  
  def gather_indexes(sequence_tensor, positions):
      """Gathers the vectors at the specific positions over a minibatch."""
      sequence_shape = modeling.get_shape_list(sequence_tensor, expected_rank=3)
      batch_size = sequence_shape[0]
      seq_length = sequence_shape[1]
      width = sequence_shape[2]
  
      flat_offsets = tf.reshape(
          tf.range(0, batch_size, dtype=tf.int32) * seq_length, [-1, 1])
      flat_positions = tf.reshape(positions + flat_offsets, [-1])
      flat_sequence_tensor = tf.reshape(sequence_tensor,
                                        [batch_size * seq_length, width])
      output_tensor = tf.gather(flat_sequence_tensor, flat_positions)
      return output_tensor
  
  
  def input_fn_builder(input_files,
                       max_seq_length,
                       max_predictions_per_seq,
                       is_training,
                       num_cpu_threads=16):
      """Creates an `input_fn` closure to be passed to TPUEstimator."""
  
      def input_fn(params):
          """The actual input function."""
          batch_size = params["batch_size"]
  
          name_to_features = {
              "input_ids":
                  tf.FixedLenFeature([max_seq_length], tf.int64),
              "input_mask":
                  tf.FixedLenFeature([max_seq_length], tf.int64),
              "segment_ids":
                  tf.FixedLenFeature([max_seq_length], tf.int64),
              "masked_lm_positions":
                  tf.FixedLenFeature([max_predictions_per_seq], tf.int64),
              "masked_lm_ids":
                  tf.FixedLenFeature([max_predictions_per_seq], tf.int64),
              "masked_lm_weights":
                  tf.FixedLenFeature([max_predictions_per_seq], tf.float32),
              "next_sentence_labels":
                  tf.FixedLenFeature([1], tf.int64),
          }
  
          # For training, we want a lot of parallel reading and shuffling.
          # For eval, we want no shuffling and parallel reading doesn't matter.
          if is_training:
              d = tf.data.Dataset.from_tensor_slices(tf.constant(input_files))
              d = d.repeat()
              d = d.shuffle(buffer_size=len(input_files))
  
              # `cycle_length` is the number of parallel files that get read.
              cycle_length = min(num_cpu_threads, len(input_files))
  
              # `sloppy` mode means that the interleaving is not exact. This adds
              # even more randomness to the training pipeline.
              d = d.apply(
                  tf.contrib.data.parallel_interleave(
                      tf.data.TFRecordDataset,
                      sloppy=is_training,
                      cycle_length=cycle_length))
              d = d.shuffle(buffer_size=100)
          else:
              d = tf.data.TFRecordDataset(input_files)
              # Since we evaluate for a fixed number of steps we don't want to encounter
              # out-of-range exceptions.
              d = d.repeat()
  
          # We must `drop_remainder` on training because the TPU requires fixed
          # size dimensions. For eval, we assume we are evaluating on the CPU or GPU
          # and we *don't* want to drop the remainder, otherwise we wont cover
          # every sample.
          d = d.apply(
              tf.contrib.data.map_and_batch(
                  lambda record: _decode_record(record, name_to_features),
                  batch_size=batch_size,
                  num_parallel_batches=num_cpu_threads,
                  drop_remainder=True))
          return d
  
      return input_fn
  
  
  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 main(_):
      tf.logging.set_verbosity(tf.logging.INFO)
  
      if not FLAGS.do_train and not FLAGS.do_eval:  # 必须是训练或验证的类型
          raise ValueError("At least one of `do_train` or `do_eval` must be True.")
  
      bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)  # 从json文件中获得配置信息
  
      tf.gfile.MakeDirs(FLAGS.output_dir)
  
      input_files = []  # 输入可以是多个文件，以“逗号隔开”；可以是一个匹配形式的，如“input_x*”
      for input_pattern in FLAGS.input_file.split(","):
          input_files.extend(tf.gfile.Glob(input_pattern))
  
      tf.logging.info("*** Input Files ***")
      for input_file in input_files:
          tf.logging.info("  %s" % input_file)
  
      tpu_cluster_resolver = None
      if FLAGS.use_tpu and FLAGS.tpu_name:
          tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(  # TODO
              tpu=FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
  
      print("###tpu_cluster_resolver:", tpu_cluster_resolver, ";FLAGS.use_tpu:", FLAGS.use_tpu, ";FLAGS.tpu_name:",
            FLAGS.tpu_name, ";FLAGS.tpu_zone:", FLAGS.tpu_zone)
      # ###tpu_cluster_resolver: <tensorflow.python.distribute.cluster_resolver.tpu_cluster_resolver.TPUClusterResolver object at 0x7f4b387b06a0> ;FLAGS.use_tpu: True ;FLAGS.tpu_name: grpc://10.240.1.83:8470
  
      is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
      run_config = tf.contrib.tpu.RunConfig(
          keep_checkpoint_max=20,  # 10
          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))
  
      model_fn = model_fn_builder(
          bert_config=bert_config,
          init_checkpoint=FLAGS.init_checkpoint,
          learning_rate=FLAGS.learning_rate,
          num_train_steps=FLAGS.num_train_steps,
          num_warmup_steps=FLAGS.num_warmup_steps,
          use_tpu=FLAGS.use_tpu,
          use_one_hot_embeddings=FLAGS.use_tpu)
  
      # 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,
          eval_batch_size=FLAGS.eval_batch_size)
  
      if FLAGS.do_train:
          tf.logging.info("***** Running training *****")
          tf.logging.info("  Batch size = %d", FLAGS.train_batch_size)
          train_input_fn = input_fn_builder(
              input_files=input_files,
              max_seq_length=FLAGS.max_seq_length,
              max_predictions_per_seq=FLAGS.max_predictions_per_seq,
              is_training=True)
          estimator.train(input_fn=train_input_fn, max_steps=FLAGS.num_train_steps)
  
      if FLAGS.do_eval:
          tf.logging.info("***** Running evaluation *****")
          tf.logging.info("  Batch size = %d", FLAGS.eval_batch_size)
  
          eval_input_fn = input_fn_builder(
              input_files=input_files,
              max_seq_length=FLAGS.max_seq_length,
              max_predictions_per_seq=FLAGS.max_predictions_per_seq,
              is_training=False)
  
          result = estimator.evaluate(input_fn=eval_input_fn, steps=FLAGS.max_eval_steps)
  
          output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
          with tf.gfile.GFile(output_eval_file, "w") as writer:
              tf.logging.info("***** Eval results *****")
              for key in sorted(result.keys()):
                  tf.logging.info("  %s = %s", key, str(result[key]))
                  writer.write("%s = %s\n" % (key, str(result[key])))
  
  
  if __name__ == "__main__":
      flags.mark_flag_as_required("input_file")
      flags.mark_flag_as_required("bert_config_file")
      flags.mark_flag_as_required("output_dir")
      tf.app.run()
  
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