text: # coding=utf-8
  # Copyright (c) 2020, NVIDIA CORPORATION.  All rights reserved.
  #
  # 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.
  
  """Pretrain BERT"""
  
  from functools import partial
  
  import torch
  import torch.nn.functional as F
  
  from megatron import get_args
  from megatron import print_rank_0
  from megatron import get_timers
  from megatron import mpu
  from megatron.data.dataset_utils import build_train_valid_test_datasets
  from megatron.model import BertModel
  from megatron.training import pretrain
  from megatron.utils import average_losses_across_data_parallel_group
  
  # HANS: Additional libraries
  import torch.cuda.nvtx as nvtx
  
  def model_provider(pre_process=True, post_process=True):
      """Build the model."""
  
      print_rank_0('building BERT model ...')
  
      args = get_args()
      num_tokentypes = 2 if args.bert_binary_head else 0
      model = BertModel(
          num_tokentypes=num_tokentypes,
          add_binary_head=args.bert_binary_head,
          parallel_output=True,
          pre_process=pre_process,
          post_process=post_process)
  
      return model
  
  
  def get_batch(data_iterator):
      """Build the batch."""
  
      # Items and their type.
      keys = ['text', 'types', 'labels', 'is_random', 'loss_mask', 'padding_mask']
      datatype = torch.int64
  
      # Broadcast data.
      if data_iterator is not None:
          data = next(data_iterator)
      else:
          data = None
      data_b = mpu.broadcast_data(keys, data, datatype)
  
      # Unpack.
      tokens = data_b['text'].long()
      types = data_b['types'].long()
      sentence_order = data_b['is_random'].long()
      loss_mask = data_b['loss_mask'].float()
      lm_labels = data_b['labels'].long()
      padding_mask = data_b['padding_mask'].long()
  
      return tokens, types, sentence_order, loss_mask, lm_labels, padding_mask
  
  
  def loss_func(loss_mask, sentence_order, output_tensor):
      lm_loss_, sop_logits = output_tensor
  
      lm_loss_ = lm_loss_.float()
      loss_mask = loss_mask.float()
      lm_loss = torch.sum(
          lm_loss_.view(-1) * loss_mask.reshape(-1)) / loss_mask.sum()
  
      if sop_logits is not None:
          sop_loss = F.cross_entropy(sop_logits.view(-1, 2).float(),
                                     sentence_order.view(-1),
                                     ignore_index=-1)
          sop_loss = sop_loss.float()
          loss = lm_loss + sop_loss
          averaged_losses = average_losses_across_data_parallel_group(
              [lm_loss, sop_loss])
          return loss, {'lm loss': averaged_losses[0],
                        'sop loss': averaged_losses[1]}
  
      else:
          loss = lm_loss
          averaged_losses = average_losses_across_data_parallel_group(
              [lm_loss])
          return loss, {'lm loss': averaged_losses[0]}
  
  
  def forward_step(data_iterator, model):
      """Forward step."""
      args = get_args()
      timers = get_timers()
  
      # Get the batch.
      timers('batch-generator').start()
      tokens, types, sentence_order, loss_mask, lm_labels, padding_mask = get_batch(
          data_iterator)
      timers('batch-generator').stop()
  
      if not args.bert_binary_head:
          types = None
  
      # Forward pass through the model.
      output_tensor = model(tokens, padding_mask, tokentype_ids=types,
                            lm_labels=lm_labels)
  
      return output_tensor, partial(loss_func, loss_mask, sentence_order)
  
  
  def train_valid_test_datasets_provider(train_val_test_num_samples):
      """Build train, valid, and test datasets."""
      args = get_args()
  
      print_rank_0('> building train, validation, and test datasets '
                   'for BERT ...')
      train_ds, valid_ds, test_ds = build_train_valid_test_datasets(
          data_prefix=args.data_path,
          data_impl=args.data_impl,
          splits_string=args.split,
          train_valid_test_num_samples=train_val_test_num_samples,
          max_seq_length=args.seq_length,
          masked_lm_prob=args.mask_prob,
          short_seq_prob=args.short_seq_prob,
          seed=args.seed,
          skip_warmup=(not args.mmap_warmup),
          binary_head=args.bert_binary_head)
      print_rank_0("> finished creating BERT datasets ...")
  
      return train_ds, valid_ds, test_ds
  
  
  if __name__ == "__main__":
  
      pretrain(train_valid_test_datasets_provider, model_provider, forward_step,
               args_defaults={'tokenizer_type': 'BertWordPieceLowerCase'})
  