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pretrain_gpt2.py
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pretrain_gpt2.py
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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
#
# https://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 GPT2"""
import os
import torch
from megatron import get_args
from megatron import get_timers
from megatron import get_tokenizer
from megatron import mpu
from megatron import print_rank_0
from megatron.data.gpt2_dataset import GPT2Dataset
from megatron.model import GPT2Model
from megatron.training import pretrain
from megatron.utils import get_ltor_masks_and_position_ids
from megatron.utils import make_data_loader
from megatron.utils import reduce_losses
def model_provider():
"""Build the model."""
args = get_args()
print_rank_0('building GPT2 model ...')
model = GPT2Model(num_tokentypes=0, parallel_output=True)
return model
def get_batch(data_iterator):
"""Generate a batch"""
args = get_args()
tokenizer = get_tokenizer()
# Items and their type.
keys = ['text']
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()
labels = tokens_[:, 1:].contiguous()
tokens = tokens_[:, :-1].contiguous()
# Get the masks and postition ids.
attention_mask, loss_mask, position_ids = get_ltor_masks_and_position_ids(
tokens,
tokenizer.eod,
args.reset_position_ids,
args.reset_attention_mask,
args.eod_mask_loss,
args.fp16)
return tokens, labels, loss_mask, attention_mask, position_ids
def forward_step(data_iterator, model):
"""Forward step."""
timers = get_timers()
# Get the batch.
timers('batch generator').start()
tokens, labels, loss_mask, attention_mask, position_ids = get_batch(
data_iterator)
timers('batch generator').stop()
# Forward model.
output = model(tokens, position_ids, attention_mask)
losses = mpu.vocab_parallel_cross_entropy(output.contiguous().float(),
labels)
loss_mask = loss_mask.view(-1)
loss = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum()
# Reduce loss for logging.
reduced_loss = reduce_losses([loss])
return loss, {'lm loss': reduced_loss[0]}
def make_gpt2_dataloaders():
"""Build gpt2 dataloders."""
args = get_args()
# Input parameters.
input_data_sizes_file = args.input_data_sizes_file
seq_length = args.seq_length
initial_seed = args.seed
# Build the datasets.
def _build_dataset(name):
return GPT2Dataset(os.path.join(args.data_path, name),
args.input_data_sizes_file,
args.seq_length, args.seed)
train_ds = _build_dataset('train')
valid_ds = _build_dataset('valid')
test_ds = _build_dataset('test')
# Dataloaders
train = make_data_loader(train_ds)
valid = make_data_loader(valid_ds)
test = make_data_loader(test_ds)
args.do_train = False
args.do_valid = False
args.do_test = False
if train is not None:
args.do_train = True
if valid is not None:
args.do_valid = True
if test is not None:
args.do_test = True
return (train, valid, test)
def get_train_val_test_data():
"""Load the data on rank zero and boradcast number of tokens to all GPUS."""
args = get_args()
(train_data, val_data, test_data) = (None, None, None)
# Data loader only on rank 0 of each model parallel group.
if mpu.get_model_parallel_rank() == 0:
(train_data, val_data, test_data) = make_gpt2_dataloaders()
flags = torch.cuda.LongTensor([int(args.do_train),
int(args.do_valid),
int(args.do_test)])
else:
flags = torch.cuda.LongTensor([0, 0, 0])
# Broadcast num tokens.
torch.distributed.broadcast(flags,
mpu.get_model_parallel_src_rank(),
group=mpu.get_model_parallel_group())
args.do_train = flags[0].item()
args.do_valid = flags[1].item()
args.do_test = flags[2].item()
return train_data, val_data, test_data
if __name__ == "__main__":
pretrain(get_train_val_test_data, model_provider, forward_step,
args_defaults={'tokenizer_type': 'GPT2BPETokenizer'})