DeepLearningExamples/TensorFlow/LanguageModeling/BERT/run_pretraining.py

# coding=utf-8
# Copyright (c) 2019 NVIDIA CORPORATION. All rights reserved.
# 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 time
import modeling
import optimization
import tensorflow as tf
import glob
from utils.utils import LogEvalRunHook, setup_xla_flags
import utils.dllogger_class
from utils.gpu_affinity import set_affinity
from dllogger import Verbosity

from tensorflow.core.protobuf import rewriter_config_pb2

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_files_dir", None,
    "Directory with input files, comma separated or single directory.")

flags.DEFINE_string(
    "eval_files_dir", None,
    "Directory with eval files, comma separated or single directory. ")

flags.DEFINE_string(
    "output_dir", None,
    "The output directory where the model checkpoints will be written.")

## Other parameters
flags.DEFINE_string(
    "dllog_path", "/results/bert_dllog.json",
    "filename where dllogger writes to")

flags.DEFINE_string(
    "init_checkpoint", None,
    "Initial checkpoint (usually from a pre-trained BERT model).")

flags.DEFINE_string(
    "optimizer_type", "lamb",
    "Optimizer used for training - LAMB or ADAM")

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", 80,
    "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("display_loss_steps", 1,
                     "How often to print loss")

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_integer("num_accumulation_steps", 1,
                     "Number of accumulation steps before gradient update."
                      "Global batch size = num_accumulation_steps * train_batch_size")

flags.DEFINE_bool("allreduce_post_accumulation", False, "Whether to all reduce after accumulation of N steps or after each step")

flags.DEFINE_bool(
    "verbose_logging", False,
    "If true, all of the trainable parameters are printed")

flags.DEFINE_bool("horovod", False, "Whether to use Horovod for multi-gpu runs")

flags.DEFINE_bool("report_loss", True, "Whether to report total loss during training.")

flags.DEFINE_bool("manual_fp16", False, "Whether to use fp32 or fp16 arithmetic on GPU. "
                                        "Manual casting is done instead of using AMP")

flags.DEFINE_bool("amp", True, "Whether to enable AMP ops. When false, uses TF32 on A100 and FP32 on V100 GPUS.")
flags.DEFINE_bool("use_xla", True, "Whether to enable XLA JIT compilation.")
flags.DEFINE_integer("init_loss_scale", 2**32, "Initial value of loss scale if mixed precision training")

# report samples/sec, total loss and learning rate during training
class _LogSessionRunHook(tf.estimator.SessionRunHook):
  def __init__(self, global_batch_size, num_accumulation_steps, dllogging, display_every=10,
               save_ckpt_steps=1000, report_loss=True, hvd_rank=-1):
    self.global_batch_size = global_batch_size
    self.display_every = display_every
    self.save_ckpt_steps = save_ckpt_steps
    self.hvd_rank = hvd_rank
    self.num_accumulation_steps = num_accumulation_steps
    self.dllogging = dllogging
    self.report_loss = report_loss

  def after_create_session(self, session, coord):
    self.elapsed_secs = 0.0 #elapsed seconds between every print
    self.count = 0 # number of global steps between every print
    self.all_count = 0 #number of steps (including accumulation) between every print
    self.loss = 0.0 # accumulation of loss in each step between every print

    self.total_time = 0.0 # total time taken to train (excluding warmup + ckpt saving steps)
    self.step_time = 0.0 # time taken per step
    self.init_global_step = session.run(tf.train.get_global_step()) # training starts at init_global_step
    self.skipped = 0
    self.final_loss = 0

  def before_run(self, run_context):
    self.t0 = time.time()
    if self.num_accumulation_steps <= 1:
        if FLAGS.manual_fp16 or FLAGS.amp:
            return tf.estimator.SessionRunArgs(
                fetches=['step_update:0', 'total_loss:0',
                         'learning_rate:0', 'nsp_loss:0',
                         'mlm_loss:0', 'loss_scale:0'])
        else:
            return tf.estimator.SessionRunArgs(
                fetches=['step_update:0', 'total_loss:0',
                         'learning_rate:0', 'nsp_loss:0',
                         'mlm_loss:0'])
    else:
        if FLAGS.manual_fp16 or FLAGS.amp:
            return tf.estimator.SessionRunArgs(
                fetches=['step_update:0', 'update_step:0', 'total_loss:0',
                         'learning_rate:0', 'nsp_loss:0',
                         'mlm_loss:0', 'loss_scale:0'])
        else:
          return tf.estimator.SessionRunArgs(
              fetches=['step_update:0', 'update_step:0', 'total_loss:0',
                       'learning_rate:0', 'nsp_loss:0',
                       'mlm_loss:0'])
  def after_run(self, run_context, run_values):
    run_time = time.time() - self.t0

    if self.num_accumulation_steps <=1:
        if FLAGS.manual_fp16 or FLAGS.amp:
            self.global_step, total_loss, lr, nsp_loss, mlm_loss, loss_scaler = run_values.results
        else:
            self.global_step, total_loss, lr, nsp_loss, mlm_loss = run_values. \
                results
        update_step = True
    else:
        if FLAGS.manual_fp16 or FLAGS.amp:
          self.global_step, update_step, total_loss, lr, nsp_loss, mlm_loss, loss_scaler = run_values.results
        else:
          self.global_step, update_step, total_loss, lr, nsp_loss, mlm_loss = run_values.\
              results

    self.elapsed_secs += run_time
    self.step_time += run_time

    print_step = self.global_step + 1 # One-based index for printing.
    self.loss += total_loss
    self.all_count += 1
    if update_step:

        self.count += 1

        # Removing first six steps after every checkpoint save from timing
        if (self.global_step - self.init_global_step) % self.save_ckpt_steps < 6:
          print("Skipping time record for ", self.global_step, " due to checkpoint-saving/warmup overhead")
          self.skipped += 1
        else:
          self.total_time += self.step_time

        self.step_time = 0.0 #Reset Step Time

        if (print_step == 1 or print_step % self.display_every == 0):
            dt = self.elapsed_secs / self.count
            sent_per_sec = self.global_batch_size / dt
            avg_loss_step = self.loss / self.all_count
            if self.hvd_rank >= 0 and FLAGS.report_loss:
              if FLAGS.manual_fp16 or FLAGS.amp:
                self.dllogging.logger.log(step=(print_step),
                                     data={"Rank": int(self.hvd_rank), "throughput_train": float(sent_per_sec),
                                           "mlm_loss":float(mlm_loss), "nsp_loss":float(nsp_loss),
                                           "total_loss":float(total_loss), "avg_loss_step":float(avg_loss_step),
                                           "learning_rate": str(lr), "loss_scaler":int(loss_scaler)},
                                     verbosity=Verbosity.DEFAULT)
              else:
                self.dllogging.logger.log(step=int(print_step),
                                     data={"Rank": int(self.hvd_rank), "throughput_train": float(sent_per_sec),
                                           "mlm_loss":float(mlm_loss), "nsp_loss":float(nsp_loss),
                                           "total_loss":float(total_loss), "avg_loss_step":float(avg_loss_step),
                                           "learning_rate": str(lr)},
                                     verbosity=Verbosity.DEFAULT)
            else:
              if FLAGS.manual_fp16 or FLAGS.amp:
                self.dllogging.logger.log(step=int(print_step),
                                     data={"throughput_train": float(sent_per_sec),
                                           "mlm_loss":float(mlm_loss), "nsp_loss":float(nsp_loss),
                                           "total_loss":float(total_loss), "avg_loss_step":float(avg_loss_step),
                                           "learning_rate": str(lr), "loss_scaler":int(loss_scaler)},
                                     verbosity=Verbosity.DEFAULT)
              else:
                self.dllogging.logger.log(step=int(print_step),
                                     data={"throughput_train": float(sent_per_sec),
                                           "mlm_loss":float(mlm_loss), "nsp_loss":float(nsp_loss),
                                           "total_loss":float(total_loss), "avg_loss_step":float(avg_loss_step),
                                           "learning_rate": str(lr)},
                                     verbosity=Verbosity.DEFAULT)

            self.elapsed_secs = 0.0
            self.count = 0
            self.loss = 0.0
            self.all_count = 0
            self.final_loss = avg_loss_step

def model_fn_builder(bert_config, init_checkpoint, learning_rate,
                     num_train_steps, num_warmup_steps,
                     use_one_hot_embeddings, hvd=None):
  """Returns `model_fn` closure for TPUEstimator."""

  def model_fn(features, labels, mode, params):  # pylint: disable=unused-argument
    """The `model_fn` for TPUEstimator."""

    tf.compat.v1.logging.info("*** Features ***")
    for name in sorted(features.keys()):
      tf.compat.v1.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,
        compute_type=tf.float16 if FLAGS.manual_fp16 else tf.float32)

    (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)

    masked_lm_loss = tf.identity(masked_lm_loss, name="mlm_loss")
    next_sentence_loss = tf.identity(next_sentence_loss, name="nsp_loss")
    total_loss = masked_lm_loss + next_sentence_loss
    total_loss = tf.identity(total_loss, name='total_loss')

    tvars = tf.trainable_variables()

    initialized_variable_names = {}
    if init_checkpoint and (hvd is None or hvd.rank() == 0):
      print("Loading checkpoint", init_checkpoint)
      (assignment_map, initialized_variable_names
      ) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)

      tf.train.init_from_checkpoint(init_checkpoint, assignment_map)

    if FLAGS.verbose_logging:
        tf.compat.v1.logging.info("**** Trainable Variables ****")
        for var in tvars:
          init_string = ""
          if var.name in initialized_variable_names:
            init_string = ", *INIT_FROM_CKPT*"
          tf.compat.v1.logging.info("  %d :: name = %s, shape = %s%s", 0 if hvd is None else hvd.rank(), 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,
          hvd, FLAGS.manual_fp16, FLAGS.amp, FLAGS.num_accumulation_steps, FLAGS.optimizer_type, FLAGS.allreduce_post_accumulation, FLAGS.init_loss_scale)

      output_spec = tf.estimator.EstimatorSpec(
          mode=mode,
          loss=total_loss,
          train_op=train_op)
    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_metric_ops = 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.estimator.EstimatorSpec(
          mode=mode,
          loss=total_loss,
          eval_metric_ops=eval_metric_ops)
    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(tf.cast(input_tensor, tf.float32), 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(tf.cast(input_tensor, tf.float32), 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,
                     batch_size,
                     max_seq_length,
                     max_predictions_per_seq,
                     is_training,
                     num_cpu_threads=4,
                     hvd=None):
  """Creates an `input_fn` closure to be passed to Estimator."""

  def input_fn():
    """The actual input function."""

    name_to_features = {
        "input_ids":
            tf.io.FixedLenFeature([max_seq_length], tf.int64),
        "input_mask":
            tf.io.FixedLenFeature([max_seq_length], tf.int64),
        "segment_ids":
            tf.io.FixedLenFeature([max_seq_length], tf.int64),
        "masked_lm_positions":
            tf.io.FixedLenFeature([max_predictions_per_seq], tf.int64),
        "masked_lm_ids":
            tf.io.FixedLenFeature([max_predictions_per_seq], tf.int64),
        "masked_lm_weights":
            tf.io.FixedLenFeature([max_predictions_per_seq], tf.float32),
        "next_sentence_labels":
            tf.io.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))
      if hvd is not None: d = d.shard(hvd.size(), hvd.rank())
      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 if is_training else False))
    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(_):
  setup_xla_flags()

  tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
  dllogging = utils.dllogger_class.dllogger_class(FLAGS.dllog_path)

  if not FLAGS.do_train and not FLAGS.do_eval:
    raise ValueError("At least one of `do_train` or `do_eval` must be True.")

  if FLAGS.horovod:
    import horovod.tensorflow as hvd
    hvd.init()

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

  tf.io.gfile.makedirs(FLAGS.output_dir)

  input_files = []
  for input_file_dir in FLAGS.input_files_dir.split(","):
    input_files.extend(tf.io.gfile.glob(os.path.join(input_file_dir, "*")))

  if FLAGS.horovod and len(input_files) < hvd.size():
      raise ValueError("Input Files must be sharded")
  if FLAGS.amp and FLAGS.manual_fp16:
      raise ValueError("AMP and Manual Mixed Precision Training are both activated! Error")

  is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
  config = tf.compat.v1.ConfigProto()
  if FLAGS.horovod:
    config.gpu_options.visible_device_list = str(hvd.local_rank())
    set_affinity(hvd.local_rank())
    if hvd.rank() == 0:
      tf.compat.v1.logging.info("***** Configuaration *****")
      for key in FLAGS.__flags.keys():
          tf.compat.v1.logging.info('  {}: {}'.format(key, getattr(FLAGS, key)))
      tf.compat.v1.logging.info("**************************")

#    config.gpu_options.per_process_gpu_memory_fraction = 0.7
  if FLAGS.use_xla:
      config.graph_options.optimizer_options.global_jit_level = tf.compat.v1.OptimizerOptions.ON_1
      config.graph_options.rewrite_options.memory_optimization = rewriter_config_pb2.RewriterConfig.NO_MEM_OPT
      if FLAGS.amp:
        tf.enable_resource_variables()

  run_config = tf.estimator.RunConfig(
      model_dir=FLAGS.output_dir,
      session_config=config,
      save_checkpoints_steps=FLAGS.save_checkpoints_steps if not FLAGS.horovod or hvd.rank() == 0 else None,
      save_summary_steps=FLAGS.save_checkpoints_steps if not FLAGS.horovod or hvd.rank() == 0 else None,
      # This variable controls how often estimator reports examples/sec.
      # Default value is every 100 steps.
      # When --report_loss is True, we set to very large value to prevent
      # default info reporting from estimator.
      # Ideally we should set it to None, but that does not work.
      log_step_count_steps=10000 if FLAGS.report_loss else 100)

  model_fn = model_fn_builder(
      bert_config=bert_config,
      init_checkpoint=FLAGS.init_checkpoint,
      learning_rate=FLAGS.learning_rate if not FLAGS.horovod else FLAGS.learning_rate*hvd.size(),
      num_train_steps=FLAGS.num_train_steps,
      num_warmup_steps=FLAGS.num_warmup_steps,
      use_one_hot_embeddings=False,
      hvd=None if not FLAGS.horovod else hvd)

  estimator = tf.estimator.Estimator(
      model_fn=model_fn,
      config=run_config)

  if FLAGS.do_train:

    training_hooks = []
    if FLAGS.horovod and hvd.size() > 1:
      training_hooks.append(hvd.BroadcastGlobalVariablesHook(0))
    if (not FLAGS.horovod or hvd.rank() == 0):
      global_batch_size = FLAGS.train_batch_size * FLAGS.num_accumulation_steps if not FLAGS.horovod else FLAGS.train_batch_size * FLAGS.num_accumulation_steps * hvd.size()
      log_hook = _LogSessionRunHook(global_batch_size, FLAGS.num_accumulation_steps, dllogging, FLAGS.display_loss_steps, FLAGS.save_checkpoints_steps, FLAGS.report_loss)
      training_hooks.append(log_hook)

    tf.compat.v1.logging.info("***** Running training *****")
    tf.compat.v1.logging.info("  Batch size = %d", FLAGS.train_batch_size)
    train_input_fn = input_fn_builder(
        input_files=input_files,
        batch_size=FLAGS.train_batch_size,
        max_seq_length=FLAGS.max_seq_length,
        max_predictions_per_seq=FLAGS.max_predictions_per_seq,
        is_training=True,
        hvd=None if not FLAGS.horovod else hvd)

    train_start_time = time.time()
    estimator.train(input_fn=train_input_fn, hooks=training_hooks, max_steps=FLAGS.num_train_steps)
    train_time_elapsed = time.time() - train_start_time

    if (not FLAGS.horovod or hvd.rank() == 0):
        train_time_wo_overhead = training_hooks[-1].total_time
        avg_sentences_per_second = FLAGS.num_train_steps * global_batch_size * 1.0 / train_time_elapsed
        ss_sentences_per_second = (FLAGS.num_train_steps - training_hooks[-1].skipped) * global_batch_size * 1.0 / train_time_wo_overhead

        tf.compat.v1.logging.info("-----------------------------")
        tf.compat.v1.logging.info("Total Training Time = %0.2f for Sentences = %d", train_time_elapsed,
                        FLAGS.num_train_steps * global_batch_size)
        tf.compat.v1.logging.info("Total Training Time W/O Overhead = %0.2f for Sentences = %d", train_time_wo_overhead,
                        (FLAGS.num_train_steps - training_hooks[-1].skipped) * global_batch_size)
        tf.compat.v1.logging.info("Throughput Average (sentences/sec) with overhead = %0.2f", avg_sentences_per_second)
        tf.compat.v1.logging.info("Throughput Average (sentences/sec) = %0.2f", ss_sentences_per_second)
        dllogging.logger.log(step=(), data={"throughput_train": ss_sentences_per_second}, verbosity=Verbosity.DEFAULT)
        if log_hook.final_loss != 0:
          dllogging.logger.log(step=(), data={"total_loss": log_hook.final_loss}, verbosity=Verbosity.DEFAULT)
        tf.compat.v1.logging.info("-----------------------------")

  if FLAGS.do_eval and (not FLAGS.horovod or hvd.rank() == 0):
    tf.compat.v1.logging.info("***** Running evaluation *****")
    tf.compat.v1.logging.info("  Batch size = %d", FLAGS.eval_batch_size)

    eval_files = []
    for eval_file_dir in FLAGS.eval_files_dir.split(","):
        eval_files.extend(tf.io.gfile.glob(os.path.join(eval_file_dir, "*")))

    eval_input_fn = input_fn_builder(
        input_files=eval_files,
        batch_size=FLAGS.eval_batch_size,
        max_seq_length=FLAGS.max_seq_length,
        max_predictions_per_seq=FLAGS.max_predictions_per_seq,
        is_training=False,
        hvd=None if not FLAGS.horovod else hvd)

    eval_hooks = [LogEvalRunHook(FLAGS.eval_batch_size)]
    eval_start_time = time.time()
    result = estimator.evaluate(
        input_fn=eval_input_fn, steps=FLAGS.max_eval_steps, hooks=eval_hooks)

    eval_time_elapsed = time.time() - eval_start_time
    time_list = eval_hooks[-1].time_list
    time_list.sort()
    # Removing outliers (init/warmup) in throughput computation.
    eval_time_wo_overhead = sum(time_list[:int(len(time_list) * 0.99)])
    num_sentences = (int(len(time_list) * 0.99)) * FLAGS.eval_batch_size

    ss_sentences_per_second = num_sentences * 1.0 / eval_time_wo_overhead

    tf.compat.v1.logging.info("-----------------------------")
    tf.compat.v1.logging.info("Total Inference Time = %0.2f for Sentences = %d", eval_time_elapsed,
                    eval_hooks[-1].count * FLAGS.eval_batch_size)
    tf.compat.v1.logging.info("Total Inference Time W/O Overhead = %0.2f for Sentences = %d", eval_time_wo_overhead,
                    num_sentences)
    tf.compat.v1.logging.info("Summary Inference Statistics on EVAL set")
    tf.compat.v1.logging.info("Batch size = %d", FLAGS.eval_batch_size)
    tf.compat.v1.logging.info("Sequence Length = %d", FLAGS.max_seq_length)
    tf.compat.v1.logging.info("Precision = %s", "fp16" if FLAGS.amp else "fp32")
    tf.compat.v1.logging.info("Throughput Average (sentences/sec) = %0.2f", ss_sentences_per_second)
    dllogging.logger.log(step=(), data={"throughput_val": ss_sentences_per_second}, verbosity=Verbosity.DEFAULT)
    tf.compat.v1.logging.info("-----------------------------")

    output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
    with tf.io.gfile.GFile(output_eval_file, "w") as writer:
      tf.compat.v1.logging.info("***** Eval results *****")
      for key in sorted(result.keys()):
        tf.compat.v1.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_files_dir")
  if FLAGS.do_eval:
    flags.mark_flag_as_required("eval_files_dir")
  flags.mark_flag_as_required("bert_config_file")
  flags.mark_flag_as_required("output_dir")
  if FLAGS.use_xla and FLAGS.manual_fp16:
    print('WARNING! Combining --use_xla with --manual_fp16 may prevent convergence.')
    print('         This warning message will be removed when the underlying')
    print('         issues have been fixed and you are running a TF version')
    print('         that has that fix.')
  tf.compat.v1.app.run()