pretrain_bert.py

# 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
import megatron.model
from megatron.core.models.bert.bert_model import BertModel
from megatron.training import pretrain
from megatron.utils import average_losses_across_data_parallel_group
from megatron.arguments import core_transformer_config_from_args
from megatron.core.transformer.spec_utils import import_module
from megatron.core.models.bert.bert_layer_specs import bert_layer_with_transformer_engine_spec

def model_provider(pre_process=True, post_process=True):
    """Build the model."""

    print_rank_0('building BERT model ...')

    args = get_args()
    config = core_transformer_config_from_args(args)
    num_tokentypes = 2 if args.bert_binary_head else 0

    if args.use_mcore_models:

        if args.spec is not None:
            transformer_layer_spec = import_module(args.spec)
        else:
            transformer_layer_spec = bert_layer_with_transformer_engine_spec 

        model = BertModel(
            config=config,
            transformer_layer_spec=transformer_layer_spec,
            vocab_size=args.padded_vocab_size,
            max_sequence_length=args.max_position_embeddings,
            num_tokentypes=num_tokentypes, 
            add_binary_head=args.bert_binary_head,
            share_embeddings_and_output_weights=not args.untie_embeddings_and_output_weights,
            parallel_output=True,
            pre_process=pre_process,
            post_process=post_process)
    else:
        model = megatron.model.BertModel(
            config=config,
            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. Only rank 0 tensor parallel processes gets the 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,
        splits_string=args.split,
        train_valid_test_num_samples=train_val_test_num_samples,
        max_seq_length=args.seq_length,
        seed=args.seed,
        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'})