# 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)