repo_description: TensorFlow code and pre-trained models for BERT
repo_topics: google natural-language-processing natural-language-understanding nlp tensorflow
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(),
           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 = {}
      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,
          }
  
        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, 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)
      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=4):
    """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:
      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)
  
    tf.gfile.MakeDirs(FLAGS.output_dir)
  
    input_files = []
    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(
          FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
  
    is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
    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))
  
    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()
  