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Commit 3e4e1ab2 authored by Mohammad Shoeybi's avatar Mohammad Shoeybi
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moved pretrain albert to pretrain bert

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pretrain_albert.py deleted 100644 → 0
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# coding=utf-8
# Copyright (c) 2019, 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 ALBERT"""
import torch
import torch.nn.functional as F
from megatron import mpu
from megatron.model import BertModel
from megatron.utils import print_rank_0
from megatron.utils import reduce_losses
from megatron.utils import vocab_size_with_padding
from megatron.training import run
from megatron.data.albert_dataset import build_train_valid_test_datasets
from megatron.data_utils.samplers import DistributedBatchSampler
def model_provider(args):
"""Build the model."""
print_rank_0('building BERT model ...')
model = BertModel(
num_layers=args.num_layers,
vocab_size=args.vocab_size,
hidden_size=args.hidden_size,
num_attention_heads=args.num_attention_heads,
embedding_dropout_prob=args.hidden_dropout,
attention_dropout_prob=args.attention_dropout,
output_dropout_prob=args.hidden_dropout,
max_sequence_length=args.max_position_embeddings,
checkpoint_activations=args.checkpoint_activations,
checkpoint_num_layers=args.checkpoint_num_layers,
add_binary_head=True,
layernorm_epsilon=args.layernorm_epsilon,
num_tokentypes=args.tokentype_size,
parallel_output=True,
apply_query_key_layer_scaling=args.apply_query_key_layer_scaling,
attention_softmax_in_fp32=args.attention_softmax_in_fp32)
return model
def get_batch(data_iterator, timers):
# Items and their type.
keys = ['text', 'types', 'labels', 'is_random', 'loss_mask', 'padding_mask']
datatype = torch.int64
# Broadcast data.
timers('data loader').start()
if data_iterator is not None:
data = next(data_iterator)
else:
data = None
timers('data loader').stop()
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, args, timers):
"""Forward step."""
# Get the batch.
timers('batch generator').start()
tokens, types, sentence_order, loss_mask, lm_labels, padding_mask \
= get_batch(data_iterator, timers)
timers('batch generator').stop()
# Forward model.
lm_logits, sop_logits = model(tokens, padding_mask, tokentype_ids=types)
sop_loss = F.cross_entropy(sop_logits.view(-1, 2).contiguous().float(),
sentence_order.view(-1).contiguous(),
ignore_index=-1)
lm_loss_ = mpu.vocab_parallel_cross_entropy(lm_logits.contiguous().float(),
lm_labels.contiguous())
lm_loss = torch.sum(
lm_loss_.view(-1) * loss_mask.reshape(-1)) / loss_mask.sum()
loss = lm_loss + sop_loss
reduced_losses = reduce_losses([lm_loss, sop_loss])
return loss, {'lm loss': reduced_losses[0], 'sop loss': reduced_losses[1]}
def get_train_val_test_data(args):
"""Load the data on rank zero and boradcast number of tokens to all GPUS."""
(train_data, valid_data, test_data) = (None, None, None)
# Data loader only on rank 0 of each model parallel group.
if mpu.get_model_parallel_rank() == 0:
print_rank_0('> building train, validation, and test datasets '
'for ALBERT ...')
if args.data_loader is None:
args.data_loader = 'binary'
if args.data_loader != 'binary':
print('Unsupported {} data loader for ALBERT.'.format(
args.data_loader))
exit(1)
if not args.data_path:
print('ALBERT only supports a unified dataset specified '
'with --data-path')
exit(1)
data_parallel_size = mpu.get_data_parallel_world_size()
data_parallel_rank = mpu.get_data_parallel_rank()
global_batch_size = args.batch_size * data_parallel_size
# Number of train/valid/test samples.
train_iters = args.train_iters
eval_iters = (train_iters // args.eval_interval + 1) * args.eval_iters
test_iters = args.eval_iters
train_val_test_num_samples = [args.train_iters * global_batch_size,
eval_iters * global_batch_size,
test_iters * global_batch_size]
print_rank_0(' > datasets target sizes (minimum size):')
print_rank_0(' train: {}'.format(train_val_test_num_samples[0]))
print_rank_0(' validation: {}'.format(train_val_test_num_samples[1]))
print_rank_0(' test: {}'.format(train_val_test_num_samples[2]))
assert len(args.data_path) == 1
train_ds, valid_ds, test_ds = build_train_valid_test_datasets(
vocab_file=args.vocab,
data_prefix=args.data_path[0],
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=args.skip_mmap_warmup)
print_rank_0("> finished creating ALBERT datasets ...")
def make_data_loader_(dataset):
if not dataset:
return None
# Use a simple sampler with distributed batch sampler.
sampler = torch.utils.data.SequentialSampler(dataset)
batch_sampler = DistributedBatchSampler(
sampler=sampler,
batch_size=global_batch_size,
drop_last=True,
rank=data_parallel_rank,
world_size=data_parallel_size)
# Torch dataloader.
return torch.utils.data.DataLoader(dataset,
batch_sampler=batch_sampler,
num_workers=args.num_workers,
pin_memory=True)
train_data = make_data_loader_(train_ds)
valid_data = make_data_loader_(valid_ds)
test_data = make_data_loader_(test_ds)
do_train = train_data is not None and args.train_iters > 0
do_valid = valid_data is not None and args.eval_iters > 0
do_test = test_data is not None and args.eval_iters > 0
# Need to broadcast num_tokens and num_type_tokens.
num_tokens = vocab_size_with_padding(train_ds.num_tokens(), args)
token_counts = torch.cuda.LongTensor([num_tokens,
2, # hard coded num_type_tokens
int(do_train),
int(do_valid),
int(do_test)])
else:
token_counts = torch.cuda.LongTensor([0, 0, 0, 0, 0])
# Broadcast num tokens.
torch.distributed.broadcast(token_counts,
mpu.get_model_parallel_src_rank(),
group=mpu.get_model_parallel_group())
args.vocab_size = token_counts[0].item()
args.tokentype_size = token_counts[1].item()
args.do_train = token_counts[2].item()
args.do_valid = token_counts[3].item()
args.do_test = token_counts[4].item()
return train_data, valid_data, test_data
if __name__ == "__main__":
run('Pretrain BERT model', get_train_val_test_data,
model_provider, forward_step)
pretrain_bert.py 100755 → 100644
View file @ 3e4e1ab2
......@@ -13,18 +13,19 @@
# See the License for the specific language governing permissions and
# limitations under the License.
"""Pretrain BERT"""
"""Pretrain ALBERT"""
import torch
import torch.nn.functional as F
from configure_data import configure_data
from megatron import mpu
from megatron.model import BertModel
from megatron.utils import print_rank_0
from megatron.utils import reduce_losses
from megatron.utils import vocab_size_with_padding
from megatron.training import run
from megatron.data.albert_dataset import build_train_valid_test_datasets
from megatron.data_utils.samplers import DistributedBatchSampler
def model_provider(args):
......@@ -56,7 +57,7 @@ def model_provider(args):
def get_batch(data_iterator, timers):
# Items and their type.
keys = ['text', 'types', 'is_random', 'mask', 'mask_labels', 'pad_mask']
keys = ['text', 'types', 'labels', 'is_random', 'loss_mask', 'padding_mask']
datatype = torch.int64
# Broadcast data.
......@@ -71,12 +72,12 @@ def get_batch(data_iterator, timers):
# Unpack.
tokens = data_b['text'].long()
types = data_b['types'].long()
next_sentence = data_b['is_random'].long()
loss_mask = data_b['mask'].float()
lm_labels = data_b['mask_labels'].long()
padding_mask = data_b['pad_mask'].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, next_sentence, loss_mask, lm_labels, padding_mask
return tokens, types, sentence_order, loss_mask, lm_labels, padding_mask
def forward_step(data_iterator, model, args, timers):
......@@ -84,15 +85,15 @@ def forward_step(data_iterator, model, args, timers):
# Get the batch.
timers('batch generator').start()
tokens, types, next_sentence, loss_mask, lm_labels, padding_mask \
tokens, types, sentence_order, loss_mask, lm_labels, padding_mask \
= get_batch(data_iterator, timers)
timers('batch generator').stop()
# Forward model.
lm_logits, nsp_logits = model(tokens, 1-padding_mask, tokentype_ids=types)
lm_logits, sop_logits = model(tokens, padding_mask, tokentype_ids=types)
nsp_loss = F.cross_entropy(nsp_logits.view(-1, 2).contiguous().float(),
next_sentence.view(-1).contiguous(),
sop_loss = F.cross_entropy(sop_logits.view(-1, 2).contiguous().float(),
sentence_order.view(-1).contiguous(),
ignore_index=-1)
lm_loss_ = mpu.vocab_parallel_cross_entropy(lm_logits.contiguous().float(),
......@@ -100,37 +101,95 @@ def forward_step(data_iterator, model, args, timers):
lm_loss = torch.sum(
lm_loss_.view(-1) * loss_mask.reshape(-1)) / loss_mask.sum()
loss = lm_loss + nsp_loss
loss = lm_loss + sop_loss
reduced_losses = reduce_losses([lm_loss, nsp_loss])
reduced_losses = reduce_losses([lm_loss, sop_loss])
return loss, {'lm loss': reduced_losses[0], 'nsp loss': reduced_losses[1]}
return loss, {'lm loss': reduced_losses[0], 'sop loss': reduced_losses[1]}
def get_train_val_test_data(args):
"""Load the data on rank zero and boradcast number of tokens to all GPUS."""
(train_data, val_data, test_data) = (None, None, None)
(train_data, valid_data, test_data) = (None, None, None)
# Data loader only on rank 0 of each model parallel group.
if mpu.get_model_parallel_rank() == 0:
if (args.data_loader == 'raw'
or args.data_loader == 'lazy'
or args.data_loader == 'tfrecords'):
data_config = configure_data()
ds_type = 'BERT'
data_config.set_defaults(data_set_type=ds_type, transpose=False)
(train_data, val_data, test_data), tokenizer = data_config.apply(args)
num_tokens = vocab_size_with_padding(tokenizer.num_tokens, args)
# Need to broadcast num_tokens and num_type_tokens.
token_counts = torch.cuda.LongTensor([num_tokens,
tokenizer.num_type_tokens,
int(args.do_train),
int(args.do_valid),
int(args.do_test)])
else:
print("Unsupported data loader for BERT.")
print_rank_0('> building train, validation, and test datasets '
'for ALBERT ...')
if args.data_loader is None:
args.data_loader = 'binary'
if args.data_loader != 'binary':
print('Unsupported {} data loader for ALBERT.'.format(
args.data_loader))
exit(1)
if not args.data_path:
print('ALBERT only supports a unified dataset specified '
'with --data-path')
exit(1)
data_parallel_size = mpu.get_data_parallel_world_size()
data_parallel_rank = mpu.get_data_parallel_rank()
global_batch_size = args.batch_size * data_parallel_size
# Number of train/valid/test samples.
train_iters = args.train_iters
eval_iters = (train_iters // args.eval_interval + 1) * args.eval_iters
test_iters = args.eval_iters
train_val_test_num_samples = [args.train_iters * global_batch_size,
eval_iters * global_batch_size,
test_iters * global_batch_size]
print_rank_0(' > datasets target sizes (minimum size):')
print_rank_0(' train: {}'.format(train_val_test_num_samples[0]))
print_rank_0(' validation: {}'.format(train_val_test_num_samples[1]))
print_rank_0(' test: {}'.format(train_val_test_num_samples[2]))
assert len(args.data_path) == 1
train_ds, valid_ds, test_ds = build_train_valid_test_datasets(
vocab_file=args.vocab,
data_prefix=args.data_path[0],
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=args.skip_mmap_warmup)
print_rank_0("> finished creating ALBERT datasets ...")
def make_data_loader_(dataset):
if not dataset:
return None
# Use a simple sampler with distributed batch sampler.
sampler = torch.utils.data.SequentialSampler(dataset)
batch_sampler = DistributedBatchSampler(
sampler=sampler,
batch_size=global_batch_size,
drop_last=True,
rank=data_parallel_rank,
world_size=data_parallel_size)
# Torch dataloader.
return torch.utils.data.DataLoader(dataset,
batch_sampler=batch_sampler,
num_workers=args.num_workers,
pin_memory=True)
train_data = make_data_loader_(train_ds)
valid_data = make_data_loader_(valid_ds)
test_data = make_data_loader_(test_ds)
do_train = train_data is not None and args.train_iters > 0
do_valid = valid_data is not None and args.eval_iters > 0
do_test = test_data is not None and args.eval_iters > 0
# Need to broadcast num_tokens and num_type_tokens.
num_tokens = vocab_size_with_padding(train_ds.num_tokens(), args)
token_counts = torch.cuda.LongTensor([num_tokens,
2, # hard coded num_type_tokens
int(do_train),
int(do_valid),
int(do_test)])
else:
token_counts = torch.cuda.LongTensor([0, 0, 0, 0, 0])
......@@ -138,16 +197,13 @@ def get_train_val_test_data(args):
torch.distributed.broadcast(token_counts,
mpu.get_model_parallel_src_rank(),
group=mpu.get_model_parallel_group())
num_tokens = token_counts[0].item()
num_type_tokens = token_counts[1].item()
args.vocab_size = token_counts[0].item()
args.tokentype_size = token_counts[1].item()
args.do_train = token_counts[2].item()
args.do_valid = token_counts[3].item()
args.do_test = token_counts[4].item()
args.vocab_size = num_tokens
args.tokentype_size = num_type_tokens
return train_data, val_data, test_data
return train_data, valid_data, test_data
if __name__ == "__main__":
......
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