File size: 4,893 Bytes
bbc9069 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 |
# coding=utf-8
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
"""Fine-tune GPT"""
import torch
from functools import partial
import os
import sys
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__),
os.path.pardir, os.path.pardir)))
from megatron import get_args
from megatron import get_timers
from megatron import get_tokenizer
from megatron import print_rank_0
from megatron.core import mpu
from megatron.data.blendable_dataset import BlendableDataset
from megatron.data.gpt_dataset import build_train_valid_test_datasets
from megatron.model import GPTModel
from megatron.arguments import core_transformer_config_from_args
from megatron.core.enums import ModelType
from megatron.training import pretrain
from megatron.utils import get_ltor_masks_and_position_ids
from megatron.utils import average_losses_across_data_parallel_group
def model_provider(pre_process=True, post_process=True):
"""Build the model."""
config = core_transformer_config_from_args(args)
print_rank_0('building GPT model ...')
model = GPTModel(
config=config,
num_tokentypes=0,
parallel_output=True,
pre_process=pre_process,
post_process=post_process
)
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)
return tokens, labels, loss_mask, attention_mask, position_ids
def loss_func(loss_mask, output_tensor):
losses = output_tensor.float()
loss_mask = loss_mask.view(-1).float()
loss = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum()
# Reduce loss for logging.
averaged_loss = average_losses_across_data_parallel_group([loss])
return loss, {'lm loss': averaged_loss[0]}
def forward_step(data_iterator, model):
"""Forward step."""
args = get_args()
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()
output_tensor = model(tokens, position_ids, attention_mask,
labels=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 GPT ...')
train_ds, valid_ds1, 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,
seq_length=args.seq_length,
seed=args.seed,
skip_warmup=(not args.mmap_warmup))
print_rank_0("> finished creating finetuning GPT datasets ...")
_, valid_ds, _ = build_train_valid_test_datasets(
data_prefix=args.data_path2,
data_impl="mmap",
splits_string="98,2,0",
train_valid_test_num_samples=train_val_test_num_samples,
seq_length=2048,
seed=1234,
skip_warmup=(not args.mmap_warmup))
print_rank_0("> finished creating pretrained GPT datasets ...")
return train_ds, valid_ds, test_ds
def add_validation_args(parser):
"""Text generation arguments."""
group = parser.add_argument_group(title='validation set')
group.add_argument('--data-path2', nargs='*', default=None,
help='Path to the validation dataset. Accepted format:'
'1) a single data path, 2) multiple datasets in the'
'form: dataset1-weight dataset1-path dataset2-weight '
'dataset2-path ...')
group.add_argument('--eval-ppl', action='store_true', default=False)
group.add_argument('--stored_params', type=dict, default=dict())
return parser
if __name__ == "__main__":
pretrain(train_valid_test_datasets_provider, model_provider,
ModelType.encoder_or_decoder,
forward_step, args_defaults={'tokenizer_type': 'GPT2BPETokenizer'},
extra_args_provider=add_validation_args,)
|