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# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
import numpy as np
import os
import shutil
import torch
from tqdm import tqdm
from megatron import get_retro_args, print_rank_0
from tools.bert_embedding import DiskDataParallelBertEmbedder
from tools.retro.db.utils import (
get_indexed_dataset_infos,
get_merged_sampled_dataset,
get_merged_train_dataset,
)
from tools.retro.external_libs import h5py
from tools.retro.index.factory import IndexFactory
from tools.retro.utils import GPTToTextDataset
from .utils import (
get_training_data_block_dir,
get_training_data_block_paths,
get_training_data_merged_path,
get_training_data_root_dir,
)
##################################################
# Train index.
##################################################
def get_empty_index_path():
'''Path of empty index.'''
args = get_retro_args()
index = IndexFactory.get_index(args.retro_index_type)
empty_index_path = index.get_empty_index_path()
return empty_index_path
def get_block_nload(block_path, load_fraction):
with h5py.File(block_path) as fi:
return int(load_fraction * fi["data"].shape[0])
def merge_embedding_blocks():
if torch.distributed.get_rank() != 0:
return
args = get_retro_args()
# Get block, merged paths.
load_fraction = args.retro_index_train_load_fraction
block_paths = get_training_data_block_paths()
bin_path = get_training_data_merged_path()
# Skip, if already built.
if os.path.exists(bin_path):
return
# Merge blocks.
with open(bin_path, "wb") as fo:
byte_offset = 0
for block_idx, block_path in \
enumerate(tqdm(block_paths, "merge train embeddings")):
with h5py.File(block_path) as fi:
nload = get_block_nload(block_path, load_fraction)
block = np.array(fi["data"][:nload], copy = False)
fo.write(block.tobytes())
byte_offset += block.size * block.itemsize
fo.seek(byte_offset)
def embed_db():
'''Embed DB chunks.
Store chunks in blocks on disk. These blocks will later be merged into
a single dataset for training the index.
'''
args = get_retro_args()
merged_train_data_path = get_training_data_merged_path()
if os.path.exists(merged_train_data_path):
return
# Get db dataset.
gpt_dataset = get_merged_sampled_dataset()
text_dataset = GPTToTextDataset(gpt_dataset)
# Embed dataset.
embedder = DiskDataParallelBertEmbedder(args.retro_bert_batch_size,
args.retro_bert_max_chunk_length,
args.retro_block_size,
args.bert_embedder_type)
embedder.embed_text_dataset("index",
get_training_data_block_dir(),
text_dataset)
# Merge embeddings.
merge_embedding_blocks()
def train_on_embeddings():
'''Train index on embedded DB chunks.'''
args = get_retro_args()
index = IndexFactory.get_index(args.retro_index_type)
index.train()
def remove_embeddings():
'''Remove embeddings after training.'''
torch.distributed.barrier()
if torch.distributed.get_rank() != 0:
return
empty_index_path = get_empty_index_path()
assert os.path.isfile(empty_index_path)
shutil.rmtree(get_training_data_root_dir(), ignore_errors=True)
def train_index():
'''Train index on DB chunks.'''
args = get_retro_args()
# Check if trained index already exists.
if not os.path.isfile(get_empty_index_path()):
# Embed training chunks.
embed_db()
# Train index on embeddings.
train_on_embeddings()
# Wait for (single-process) training to complete.
torch.distributed.barrier()
# Remove embeddings.
if args.retro_index_delete_training_embeddings:
remove_embeddings()
##################################################
# Add to index.
##################################################
def add_to_index():
'''Add DB chunks to index.'''
args = get_retro_args()
# Get index.
index = IndexFactory.get_index(args.retro_index_type)
# Get text dataset.
gpt_dataset = get_merged_train_dataset()
text_dataset = GPTToTextDataset(gpt_dataset)
# Add to index.
output_index_path = index.add(text_dataset)
return output_index_path
##################################################
# Build index (train + add).
##################################################
def build_index():
'''Build index.
Building index involves sequentially running stages above:
- Train index (on sampled training chunks).
- Add to index (on all training chunks).
'''
# Train index.
train_index()
# Add to index.
add_to_index()
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