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# coding=utf-8
# Copyright (c) 2023 Habana Labs, Ltd. an Intel Company.
# 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
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 if get_args.device.type=="cuda" else torch.int32
# Broadcast data.
if data_iterator is not None:
data = next(data_iterator)
# TODO (SW-62395): Implement proper Long -> Int casting
for key, val in data.items():
data[key] = val.to(datatype)
else:
data = None
data_b = mpu.broadcast_data(keys, data, datatype)
# Unpack.
if (datatype == torch.int64):
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()
else:
tokens = data_b['text'].int()
types = data_b['types'].int()
sentence_order = data_b['is_random'].int()
loss_mask = data_b['loss_mask'].float()
lm_labels = data_b['labels'].int()
padding_mask = data_b['padding_mask'].int()
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'})
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