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# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
"""Pretrain Retro."""
from functools import partial
import torch
from megatron import get_args, get_retro_args
from megatron import get_timers
from megatron import get_tokenizer
from megatron import print_rank_0
from megatron.core import mpu, tensor_parallel
from megatron.core.enums import ModelType
from megatron.model import GPTModel
from megatron.training import pretrain
from megatron.utils import get_ltor_masks_and_position_ids
from tools.retro.query.retro_dataset import get_retro_datasets
from pretrain_gpt import (
loss_func,
model_provider,
train_valid_test_datasets_provider as standard_datasets_provider,
)
def get_batch(data_iterator):
"""Generate a batch"""
args = get_args()
retro_args = get_retro_args()
tokenizer = get_tokenizer()
# Items and their type.
keys = ['text']
datatype = torch.int64
if args.retro_add_retriever:
keys += 'neighbor_tokens',
# Broadcast data.
if data_iterator is not None:
data = next(data_iterator)
else:
data = None
data_b = tensor_parallel.broadcast_data(keys, data, datatype)
# Unpack.
tokens_ = data_b['text'].long()
labels = tokens_[:, 1:].contiguous()
tokens = tokens_[:, :-1].contiguous()
if args.retro_add_retriever:
# note: [bs * l * k, r]
# note: 2x == neighbor, continuation
neighbor_tokens = data_b['neighbor_tokens'] \
.view(-1, retro_args.retro_gpt_retrieved_length).long()
# 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)
if args.retro_add_retriever:
_, _, neighbor_position_ids = get_ltor_masks_and_position_ids(
neighbor_tokens,
tokenizer.eod,
args.reset_position_ids,
args.reset_attention_mask,
args.eod_mask_loss)
neighbor_attention_mask = None
return tokens, labels, loss_mask, attention_mask, position_ids, \
neighbor_tokens, neighbor_attention_mask, neighbor_position_ids
else:
return tokens, labels, loss_mask, attention_mask, position_ids
def forward_step(data_iterator, model):
"""Forward step."""
args = get_args()
timers = get_timers()
# Get the batch.
timers('batch-generator').start()
if args.retro_add_retriever:
tokens, labels, loss_mask, attention_mask, position_ids, \
neighbor_tokens, neighbor_attention_mask, neighbor_position_ids = \
get_batch(data_iterator)
else:
tokens, labels, loss_mask, attention_mask, position_ids = get_batch(
data_iterator)
neighbor_tokens, neighbor_attention_mask, neighbor_position_ids = \
None, None, None
timers('batch-generator').stop()
output_tensor = model(tokens, position_ids, attention_mask,
retriever_input_ids=neighbor_tokens,
retriever_position_ids=neighbor_position_ids,
retriever_attn_mask=neighbor_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()
if args.retro_add_retriever:
return get_retro_datasets()
else:
return standard_datasets_provider(train_val_test_num_samples)
if __name__ == "__main__":
pretrain(train_valid_test_datasets_provider,
model_provider,
ModelType.retro_decoder,
forward_step,
args_defaults={'tokenizer_type': 'GPT2BPETokenizer',
'retro_add_retriever': True})
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