text: # coding=utf-8
  # Copyright 2018 The Google AI 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.
  # Lint as: python2, python3
  """Run masked LM/next sentence masked_lm pre-training for ALBERT."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import time
  import modeling
  import optimization
  from six.moves import range
  import tensorflow.compat.v1 as tf
  from tensorflow.contrib import cluster_resolver as contrib_cluster_resolver
  from tensorflow.contrib import data as contrib_data
  from tensorflow.contrib import tpu as contrib_tpu
  
  flags = tf.flags
  
  FLAGS = flags.FLAGS
  
  ## Required parameters
  flags.DEFINE_string(
      "albert_config_file", None,
      "The config json file corresponding to the pre-trained ALBERT 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 ALBERT model).")
  
  flags.DEFINE_integer(
      "max_seq_length", 512,
      "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", True, "Whether to run training.")
  
  flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.")
  
  flags.DEFINE_integer("train_batch_size", 4096, "Total batch size for training.")
  
  flags.DEFINE_integer("eval_batch_size", 64, "Total batch size for eval.")
  
  flags.DEFINE_enum("optimizer", "lamb", ["adamw", "lamb"],
                    "The optimizer for training.")
  
  flags.DEFINE_float("learning_rate", 0.00176, "The initial learning rate.")
  
  flags.DEFINE_float("poly_power", 1.0, "The power of poly decay.")
  
  flags.DEFINE_integer("num_train_steps", 125000, "Number of training steps.")
  
  flags.DEFINE_integer("num_warmup_steps", 3125, "Number of warmup steps.")
  
  flags.DEFINE_integer("start_warmup_step", 0, "The starting step of warmup.")
  
  flags.DEFINE_integer("save_checkpoints_steps", 5000,
                       "How often to save the model checkpoint.")
  
  flags.DEFINE_integer("keep_checkpoint_max", 5,
                       "How many checkpoints to keep.")
  
  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.")
  
  flags.DEFINE_bool("init_from_group0", False, "Whether to initialize"
                    "parameters of other groups from group 0")
  
  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_float(
      "masked_lm_budget", 0,
      "If >0, the ratio of masked ngrams to unmasked ngrams. Default 0,"
      "for offline masking")
  
  
  def model_fn_builder(albert_config, init_checkpoint, learning_rate,
                       num_train_steps, num_warmup_steps, use_tpu,
                       use_one_hot_embeddings, optimizer, poly_power,
                       start_warmup_step):
    """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"]
      # Note: We keep this feature name `next_sentence_labels` to be compatible
      # with the original data created by lanzhzh@. However, in the ALBERT case
      # it does represent sentence_order_labels.
      sentence_order_labels = features["next_sentence_labels"]
  
      is_training = (mode == tf.estimator.ModeKeys.TRAIN)
  
      model = modeling.AlbertModel(
          config=albert_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(albert_config,
                                                   model.get_sequence_output(),
                                                   model.get_embedding_table(),
                                                   masked_lm_positions,
                                                   masked_lm_ids,
                                                   masked_lm_weights)
  
      (sentence_order_loss, sentence_order_example_loss,
       sentence_order_log_probs) = get_sentence_order_output(
           albert_config, model.get_pooled_output(), sentence_order_labels)
  
      total_loss = masked_lm_loss + sentence_order_loss
  
      tvars = tf.trainable_variables()
  
      initialized_variable_names = {}
      scaffold_fn = None
      if init_checkpoint:
        tf.logging.info("number of hidden group %d to initialize",
                        albert_config.num_hidden_groups)
        num_of_initialize_group = 1
        if FLAGS.init_from_group0:
          num_of_initialize_group = albert_config.num_hidden_groups
          if albert_config.net_structure_type > 0:
            num_of_initialize_group = albert_config.num_hidden_layers
        (assignment_map, initialized_variable_names
        ) = modeling.get_assignment_map_from_checkpoint(
                tvars, init_checkpoint, num_of_initialize_group)
        if use_tpu:
  
          def tpu_scaffold():
            for gid in range(num_of_initialize_group):
              tf.logging.info("initialize the %dth layer", gid)
              tf.logging.info(assignment_map[gid])
              tf.train.init_from_checkpoint(init_checkpoint, assignment_map[gid])
            return tf.train.Scaffold()
  
          scaffold_fn = tpu_scaffold
        else:
          for gid in range(num_of_initialize_group):
            tf.logging.info("initialize the %dth layer", gid)
            tf.logging.info(assignment_map[gid])
            tf.train.init_from_checkpoint(init_checkpoint, assignment_map[gid])
  
      #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, optimizer, poly_power, start_warmup_step)
  
        output_spec = 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(*args):
          """Computes the loss and accuracy of the model."""
          (masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids,
           masked_lm_weights, sentence_order_example_loss,
           sentence_order_log_probs, sentence_order_labels) = args[:7]
  
  
          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)
  
          metrics = {
              "masked_lm_accuracy": masked_lm_accuracy,
              "masked_lm_loss": masked_lm_mean_loss,
          }
  
          sentence_order_log_probs = tf.reshape(
              sentence_order_log_probs, [-1, sentence_order_log_probs.shape[-1]])
          sentence_order_predictions = tf.argmax(
              sentence_order_log_probs, axis=-1, output_type=tf.int32)
          sentence_order_labels = tf.reshape(sentence_order_labels, [-1])
          sentence_order_accuracy = tf.metrics.accuracy(
              labels=sentence_order_labels,
              predictions=sentence_order_predictions)
          sentence_order_mean_loss = tf.metrics.mean(
              values=sentence_order_example_loss)
          metrics.update({
              "sentence_order_accuracy": sentence_order_accuracy,
              "sentence_order_loss": sentence_order_mean_loss
          })
          return metrics
  
        metric_values = [
            masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids,
            masked_lm_weights, sentence_order_example_loss,
            sentence_order_log_probs, sentence_order_labels
        ]
  
        eval_metrics = (metric_fn, metric_values)
  
        output_spec = 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(albert_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=albert_config.embedding_size,
            activation=modeling.get_activation(albert_config.hidden_act),
            kernel_initializer=modeling.create_initializer(
                albert_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=[albert_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=albert_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_sentence_order_output(albert_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, albert_config.hidden_size],
          initializer=modeling.create_initializer(
              albert_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),
          # Note: We keep this feature name `next_sentence_labels` to be
          # compatible with the original data created by lanzhzh@. However, in
          # the ALBERT case it does represent sentence_order_labels.
          "next_sentence_labels": tf.FixedLenFeature([1], tf.int64),
      }
  
      if FLAGS.masked_lm_budget:
        name_to_features.update({
            "token_boundary":
                tf.FixedLenFeature([max_seq_length], tf.int64)})
      else:
        name_to_features.update({
            "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)})
  
      # 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(
            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.data.experimental.map_and_batch_with_legacy_function(
              lambda record: _decode_record(record, name_to_features),
              batch_size=batch_size,
              num_parallel_batches=num_cpu_threads,
              drop_remainder=True))
      #tf.logging.info(d)
      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.")
  
    albert_config = modeling.AlbertConfig.from_json_file(FLAGS.albert_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 = contrib_cluster_resolver.TPUClusterResolver(
          FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
  
    is_per_host = contrib_tpu.InputPipelineConfig.PER_HOST_V2
    run_config = contrib_tpu.RunConfig(
        cluster=tpu_cluster_resolver,
        master=FLAGS.master,
        model_dir=FLAGS.output_dir,
        save_checkpoints_steps=FLAGS.save_checkpoints_steps,
        keep_checkpoint_max=FLAGS.keep_checkpoint_max,
        tpu_config=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(
        albert_config=albert_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,
        optimizer=FLAGS.optimizer,
        poly_power=FLAGS.poly_power,
        start_warmup_step=FLAGS.start_warmup_step)
  
    # If TPU is not available, this will fall back to normal Estimator on CPU
    # or GPU.
    estimator = 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)
      global_step = -1
      output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
      writer = tf.gfile.GFile(output_eval_file, "w")
      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)
      best_perf = 0
      key_name = "masked_lm_accuracy"
      
      lc = None
      while global_step < FLAGS.num_train_steps:
        if estimator.latest_checkpoint() is None:
          tf.logging.info("No checkpoint found yet. Sleeping.")
          time.sleep(1)
        else:
          if lc == estimator.latest_checkpoint():
            time.sleep(5)
            continue
          lc = estimator.latest_checkpoint()
  
          result = estimator.evaluate(
              input_fn=eval_input_fn, steps=FLAGS.max_eval_steps)
          global_step = result["global_step"]
          tf.logging.info("***** Eval results *****")
          checkpoint_path = estimator.latest_checkpoint()
          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 result[key_name] > best_perf:
              best_perf = result[key_name]
              for ext in ["meta", "data-00000-of-00001", "index"]:
                src_ckpt = checkpoint_path + ".{}".format(ext)
                tgt_ckpt = checkpoint_path.rsplit(
                    "-", 1)[0] + "-best.{}".format(ext)
                tf.logging.info("saving {} to {}".format(src_ckpt, tgt_ckpt))
                tf.gfile.Copy(src_ckpt, tgt_ckpt, overwrite=True)
                writer.write("saved {} to {}\n".format(src_ckpt, tgt_ckpt))
  
  
  if __name__ == "__main__":
    flags.mark_flag_as_required("input_file")
    flags.mark_flag_as_required("albert_config_file")
    flags.mark_flag_as_required("output_dir")
    tf.app.run()
  