|
- # Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
-
- """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.core import tensor_parallel
- from megatron.core.enums import ModelType
- 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
-
-
- 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 = tensor_parallel.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', log_level=2).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,
- ModelType.encoder_or_decoder,
- forward_step, args_defaults={'tokenizer_type': 'BertWordPieceLowerCase'})
|