pretrain_bert.py

# 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"""

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, BertModelFirstStage, BertModelIntermediateStage, BertModelLastStage
from megatron.training import pretrain
from megatron.utils import average_losses_across_data_parallel_group


def model_provider():
    """Build the model."""

    print_rank_0('building BERT model ...')

    args = get_args()
    if args.inter_layer_model_parallel_size > 1:
        # Determine model based on position of stage in pipeline.
        if mpu.is_inter_layer_first_stage():
            model = BertModelFirstStage(
                num_tokentypes=2)
        elif mpu.is_inter_layer_last_stage():
            model = BertModelLastStage(
                num_tokentypes=2,
                add_binary_head=True,
                parallel_output=True)
        else:
            model = BertModelIntermediateStage(
                num_tokentypes=2)
    else:
        model = BertModel(
            num_tokentypes=2,
            add_binary_head=True,
            parallel_output=True)

    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 forward_step(data_iterator, model, input_tensor):
    """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()

    # Forward pass through the model.
    if mpu.is_inter_layer_first_stage():
        assert input_tensor is None
        if mpu.is_inter_layer_last_stage():
            output_tensor = model(tokens, padding_mask, tokentype_ids=types,
                                  lm_labels=lm_labels)
        else:
            output_tensor = model(tokens, padding_mask, tokentype_ids=types)
    elif mpu.is_inter_layer_last_stage():
        assert input_tensor is not None
        output_tensor = model(input_tensor, padding_mask, lm_labels=lm_labels)
    else:
        assert input_tensor is not None
        output_tensor = model(input_tensor, padding_mask)

    if mpu.is_inter_layer_last_stage():
        lm_loss_, sop_logits = output_tensor

        sop_loss = F.cross_entropy(sop_logits.view(-1, 2).float(),
                                   sentence_order.view(-1),
                                   ignore_index=-1)
        sop_loss = sop_loss.float()

        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()

        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]}
    return output_tensor


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))
    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'})