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# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
import os
import torch
from megatron import get_retro_args, print_rank_0
from megatron.data.gpt_dataset import build_train_valid_test_datasets \
as build_gpt_train_valid_test_datasets
from megatron.training import (
build_train_valid_test_datasets as build_pretraining_train_valid_test_datasets,
update_train_iters,
)
from tools.retro.db.utils import get_indexed_dataset_infos
from tools.retro.utils import get_num_chunks_per_sample
from .utils import get_neighbor_dirname, get_query_workdir
class ChunkDataset(torch.utils.data.Dataset):
'''Pretraining chunk dataset wraps a standard GPT dataset.
This dataset conceptually divides each sample (e.g., length 2048)
into chunks (e.g., length 64) and restructures them into a list of
chunks (e.g., length num_samples * num_chunks_per_sample).
'''
def __init__(self, sample_dataset, chunk_length):
super().__init__()
self.sample_dataset = sample_dataset
self.chunk_length = chunk_length
self.n_chunks_per_sample = get_num_chunks_per_sample()
self.n_samples = len(sample_dataset)
self.n_chunks = self.n_samples * self.n_chunks_per_sample
def __len__(self):
return self.n_chunks
def __getitem__(self, idx):
# Convert global chunk index to global sample index & local chunk index.
sample_idx = idx // self.n_chunks_per_sample
chunk_idx = idx % self.n_chunks_per_sample
# Extract sample data.
sample = self.sample_dataset[sample_idx]
sample_token_ids = sample["text"]
sample_doc_ids = sample["doc_ids"]
# Chunk start/end token idxs.
token_start_idx = chunk_idx * self.chunk_length
token_end_idx = token_start_idx + self.chunk_length
chunk_token_ids = sample_token_ids[token_start_idx:token_end_idx]
# Sample.
return {
"doc_ids" : sample_doc_ids,
"text" : chunk_token_ids,
}
def verify_indexed_dataset_order():
'''Verify pretraining order same as DB order.'''
args = get_retro_args()
# DB dataset prefixes.
db_indexed_dataset_infos = get_indexed_dataset_infos()
db_prefixes = [ info["prefix"] for info in db_indexed_dataset_infos ]
# Verify order & prefixes.
assert len(args.data_path) >= 2, "blendable dataset supported only."
pretraining_prefixes = args.data_path[1:None:2]
if len(db_prefixes) != len(pretraining_prefixes):
raise Exception("inconsistent dataset count between db & pretraining.")
if db_prefixes != pretraining_prefixes:
raise Exception("inconsistent dataset order between db & pretraining.")
def train_valid_test_datasets_provider(train_val_test_num_samples):
"""Build train, valid, and test datasets."""
args = get_retro_args()
print_rank_0('> building train, validation, and test datasets '
'for GPT ...')
train_ds, valid_ds, test_ds = build_gpt_train_valid_test_datasets(
data_prefix=args.retro_gpt_data_path,
data_impl=args.retro_gpt_data_impl,
splits_string=args.retro_gpt_split,
train_valid_test_num_samples=train_val_test_num_samples,
seq_length=args.retro_gpt_seq_length,
seed=args.retro_gpt_seed,
skip_warmup=(not args.retro_gpt_mmap_warmup),
return_doc_ids=args.retro_return_doc_ids)
print_rank_0("> finished creating pretrained GPT datasets ...")
return train_ds, valid_ds, test_ds
def get_chunk_dataset_map():
'''Get train, valid, test chunk datasets.'''
args = get_retro_args()
# Update train iters.
update_train_iters(args)
args.iteration = 0
args.consumed_train_samples = 0
# Verify indexed dataset order.
verify_indexed_dataset_order()
# Datasets.
print_rank_0(" > datasets.")
train_ds, valid_ds, test_ds = build_pretraining_train_valid_test_datasets(
train_valid_test_datasets_provider)
sample_dataset_map = {
"train" : train_ds,
"valid" : valid_ds,
"test" : test_ds,
}
# Info dict.
chunk_dataset_map = {
key : {
"neighbor_dir" : get_neighbor_dirname(key, sample_ds),
"data" : ChunkDataset(sample_ds, args.retro_gpt_chunk_length),
}
for key, sample_ds in sample_dataset_map.items() if sample_ds
}
return chunk_dataset_map
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