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# 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 T5"""
from functools import partial
import torch
from megatron import (
get_args,
get_timers,
mpu,
print_rank_0
)
from megatron.data.dataset_utils import build_train_valid_test_datasets
from megatron.model import T5Model
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."""
assert pre_process and post_process, "T5 doesn't yet support pipelining"
print_rank_0('building T5 model ...')
model = T5Model(num_tokentypes=0,
parallel_output=True)
return model
def get_batch(data_iterator):
"""Build the batch."""
keys = ['text_enc', 'text_dec', 'labels', 'loss_mask',
'enc_mask', 'dec_mask', 'enc_dec_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_enc = data_b['text_enc'].long()
tokens_dec = data_b['text_dec'].long()
labels = data_b['labels'].long()
loss_mask = data_b['loss_mask'].float()
enc_mask = (data_b['enc_mask'] < 0.5)
dec_mask = (data_b['dec_mask'] < 0.5)
enc_dec_mask = (data_b['enc_dec_mask'] < 0.5)
return tokens_enc, tokens_dec, loss_mask, labels, \
enc_mask, dec_mask, enc_dec_mask
def loss_func(loss_mask, output_tensor):
lm_loss_, _ = output_tensor
lm_loss_ = lm_loss_.float()
lm_loss = torch.sum(
lm_loss_.view(-1) * loss_mask.reshape(-1)) / loss_mask.sum()
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_enc, tokens_dec, loss_mask, lm_labels, enc_mask, dec_mask, enc_dec_mask \
= get_batch(data_iterator)
timers('batch generator').stop()
# Forward model lm_labels
output_tensor = model(tokens_enc,
tokens_dec,
enc_mask,
dec_mask,
enc_dec_mask,
tokentype_ids=None,
lm_labels=lm_labels)
return output_tensor, partial(loss_func, loss_mask)
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 T5 ...')
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.encoder_seq_length,
max_seq_length_dec=args.decoder_seq_length,
masked_lm_prob=args.mask_prob,
short_seq_prob=args.short_seq_prob,
seed=args.seed,
skip_warmup=(not args.mmap_warmup),
dataset_type='t5')
print_rank_0("> finished creating T5 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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