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# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
from collections import defaultdict
import glob
import json
import numpy as np
import os
from tqdm import tqdm
from megatron import get_retro_args, print_rank_0
from megatron.data.indexed_dataset import make_dataset as make_indexed_dataset
from tools.retro.external_libs import h5py
from .dataset import DBDataset
def get_base_db_workdir():
'''Sub-directory for DB data.'''
args = get_retro_args()
return os.path.join(args.retro_workdir, "db")
def get_indexed_dataset_infos_path():
'''Path to indexed dataset meta-infos.'''
return os.path.join(get_base_db_workdir(), "indexed_dataset_infos.json")
def save_indexed_dataset_infos(indexed_dataset_infos):
'''Save dataset order & meta-info.'''
# Remove 'dataset' field.
clean_infos = []
for info in indexed_dataset_infos:
info = dict(info)
del info["dataset"]
clean_infos.append(info)
# Save.
with open(get_indexed_dataset_infos_path(), "w") as f:
json.dump(clean_infos, f, indent=4)
def get_indexed_dataset_infos():
'''Load indexed dataset meta-infos.'''
# Load json.
path = get_indexed_dataset_infos_path()
with open(path) as f:
infos = json.load(f)
# Add indexed datasets.
for info in infos:
info["dataset"] = make_indexed_dataset(info["prefix"], "mmap", True)
return infos
def get_individual_db_dir(name):
'''Individual DB's directory.'''
return os.path.join(get_base_db_workdir(), "individual", name)
def get_individual_chunk_db(ds_id, ds_info):
'''Load individual dataset's chunk DB.'''
db_paths = sorted(glob.glob(ds_info["db_dir"] + "/*hdf5"))
# *Note*: convert to dataset, rather than copying to memory.
db = np.zeros((ds_info["n_chunks"], 5), dtype="uint32")
db[:, 0] = ds_id
start_idx = 0
for db_path in db_paths:
f = h5py.File(db_path, "r")
n_chunks_current = f["chunks_valid"].shape[0]
db[start_idx:(start_idx+n_chunks_current), 1:] = f["chunks_valid"]
start_idx += n_chunks_current
f.close()
assert start_idx == ds_info["n_chunks"]
return db
def get_individual_doc_offsets(ds_id, ds_info):
'''Load individual dataset's chunk DB.'''
paths = sorted(glob.glob(ds_info["db_dir"] + "/*hdf5"))
# *Note*: convert to dataset, rather than copying to memory.
doc_offsets = np.zeros((ds_info["n_docs"], 3), dtype="uint64")
doc_offsets[:, 0] = ds_id
start_idx = 0
start_offset = 0
for path in paths:
with h5py.File(path) as f:
current_doc_offsets = np.copy(f["doc_offsets"])
current_doc_offsets[:, 1] += start_offset
current_ndocs = current_doc_offsets.shape[0]
doc_offsets[start_idx:(start_idx+current_ndocs), 1:] = \
current_doc_offsets
start_idx += current_ndocs
start_offset = current_doc_offsets[-1, 1].item()
return doc_offsets
def get_merged_db_path_map():
'''Paths to merged datasets.'''
base_dir = get_base_db_workdir()
return {
"sampled" : os.path.join(base_dir, "merged", "sampled.hdf5"),
"train" : os.path.join(base_dir, "merged", "train.hdf5"),
"valid" : os.path.join(base_dir, "merged", "valid.hdf5"),
}
def get_merged_dataset(db_type, indexed_dataset_infos=None):
'''Get merged dataset.'''
args = get_retro_args()
if not indexed_dataset_infos:
indexed_dataset_infos = get_indexed_dataset_infos()
# Load chunks.
db_path = get_merged_db_path_map()[db_type]
f = h5py.File(db_path, "r")
chunks = f["chunks"]
# DB dataset.
indexed_datasets = [ info["dataset"] for info in indexed_dataset_infos ]
dataset = DBDataset(db_path, indexed_datasets, chunks,
args.retro_gpt_chunk_length)
return dataset
def get_merged_sampled_dataset(indexed_dataset_infos=None):
return get_merged_dataset("sampled", indexed_dataset_infos)
def get_merged_train_dataset(indexed_dataset_infos=None):
return get_merged_dataset("train", indexed_dataset_infos)
def get_merged_valid_dataset(indexed_dataset_infos=None):
return get_merged_dataset("valid", indexed_dataset_infos)
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