# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved. import numpy as np import os import psutil import time import torch from tqdm import tqdm from megatron import get_retro_args, print_rank_0 from tools.bert_embedding import BertEmbedder from tools.bert_embedding.utils import get_missing_blocks_by_rank from tools.retro.db.utils import \ get_merged_train_dataset as get_db_merged_train_dataset from tools.retro.external_libs import faiss, h5py from tools.retro.index.factory import IndexFactory from tools.retro.index.utils import get_index_dir from tools.retro.utils import GPTToTextDataset from .chunk_dataset import get_chunk_dataset_map as get_query_dataset_map def get_index(ondisk=False): '''Read index from disk.''' args = get_retro_args() # Load index. index_wrapper = IndexFactory.get_index(args.retro_index_type) index_dir = get_index_dir() added_index_path = index_wrapper.get_added_index_path() if ondisk: index = faiss.read_index(added_index_path, faiss.IO_FLAG_MMAP) else: index = faiss.read_index(added_index_path) # Search parameters. faiss.ParameterSpace().set_index_parameter(index, "efSearch", args.retro_query_ef_search) faiss.ParameterSpace().set_index_parameter(index, "nprobe", args.retro_query_nprobe) return index def embed_block(gpt_dataset, block, embedder): '''Embed block of chunks.''' text_block_dataset = torch.utils.data.Subset( GPTToTextDataset(gpt_dataset), range(*block["range"]), ) return embedder.embed_text_dataset(text_block_dataset) def query_embeddings(db_dataset, index, embeddings, chunk_id_range, sample_map, n_chunks_per_sample, verbose=True): '''Query neighbors of a block of embeddings.''' args = get_retro_args() # Query neighbor ids. if verbose: print_rank_0("search.") t = time.time() assert index.ntotal > 0, "check we don't accidentally have an empty index." _, query_neighbor_ids = \ index.search(embeddings, args.retro_query_num_neighbors_query) if verbose: print_rank_0(" time : %.3f sec." % (time.time() - t)) # Filter banned neighbor ids. if verbose: print_rank_0("filter banned neighbor ids.") filtered_neighbor_ids = np.full( shape=(len(query_neighbor_ids), args.retro_query_num_neighbors_save), fill_value=-1, dtype="int64", ) min_chunk_id, max_chunk_id = chunk_id_range for chunk_id in range(min_chunk_id, max_chunk_id): sample_id = chunk_id // n_chunks_per_sample sample = sample_map[sample_id] sample_dataset_idx = sample["dataset_idx"].item() sample_doc_ids = sample["doc_ids"].tolist() sample_doc_tuples = [(sample_dataset_idx, d) for d in sample_doc_ids] # Get valid neighbors (!= -1). query_row = [ i for i in query_neighbor_ids[chunk_id-min_chunk_id] if i >= 0 ] # Filter row. filtered_row = [ i for i in query_row if tuple(db_dataset.doc_tuples[i].tolist()) not in sample_doc_tuples ] filtered_row = filtered_row[:args.retro_query_num_neighbors_save] filtered_row += \ [-1] * (args.retro_query_num_neighbors_save - len(filtered_row)) filtered_neighbor_ids[chunk_id-min_chunk_id] = filtered_row return query_neighbor_ids, filtered_neighbor_ids def query_embedding_block(db_dataset, index, embeddings, chunk_id_range, sample_map, n_chunks_per_sample): query_neighbor_ids = [] filtered_neighbor_ids = [] # Query in sub-blocks. partial_block_size = 1000 for partial_start_idx in tqdm( range(0, len(embeddings), partial_block_size), "search", ): partial_end_idx = min(len(embeddings), partial_start_idx + partial_block_size) partial_embeddings = embeddings[partial_start_idx:partial_end_idx] partial_chunk_id_range = ( chunk_id_range[0] + partial_start_idx, chunk_id_range[0] + partial_end_idx, ) partial_query_neighbor_ids, partial_filtered_neighbor_ids = \ query_embeddings(db_dataset, index, partial_embeddings, partial_chunk_id_range, sample_map, n_chunks_per_sample, verbose=False) query_neighbor_ids.append(partial_query_neighbor_ids) filtered_neighbor_ids.append(partial_filtered_neighbor_ids) # Concatenate. query_neighbor_ids = np.concatenate(query_neighbor_ids, axis=0) filtered_neighbor_ids = np.concatenate(filtered_neighbor_ids, axis=0) return query_neighbor_ids, filtered_neighbor_ids def query_block_neighbors(db_dataset, query_dataset, index, embedder, block): '''Query neighbors of a dataset block (i.e., range).''' args = get_retro_args() n_chunks_per_sample = query_dataset.n_chunks_per_sample # Sample map. sample_ids = sorted(list(set(chunk_id // n_chunks_per_sample for chunk_id in range(*block["range"])))) sample_map = {} for i in sample_ids: sample = query_dataset.sample_dataset[i] sample_map[i] = { "dataset_idx" : sample["dataset_idx"], "doc_ids" : sample["doc_ids"], } # Embed block. embeddings = embed_block(query_dataset, block, embedder) # Query embeddings. _, filtered_neighbor_ids = query_embedding_block( db_dataset, index, embeddings, block["range"], sample_map, n_chunks_per_sample) # Save neighbors. print_rank_0("save neighbors.") os.makedirs(os.path.dirname(block["path"]), exist_ok=True) f = h5py.File(block["path"], "w") f.create_dataset("neighbors", data=filtered_neighbor_ids) f.close() def query_dataset_neighbors(db_dataset, query_dataset, prefix, neighbor_dir, index, embedder): '''Query neighbors of each chunk within a dataset.''' args = get_retro_args() def validate(f): assert f["neighbors"].shape[1] == args.retro_query_num_neighbors_save, \ "neighbors.shape == %s; num_neighbors_target == %d." % ( str(f["neighbors"].shape), args.retro_num_neighbors_target, ) n_missing_blocks, missing_neighbor_blocks = get_missing_blocks_by_rank( neighbor_dir, len(query_dataset), args.retro_block_size, validate=validate, ) # Query each block. for block_index, block in enumerate(missing_neighbor_blocks): if block is not None: # Progress. print_rank_0("query '%s' block %d / %d ... %s ... mem %.3f gb, %.1f%%." % ( prefix, block_index, len(missing_neighbor_blocks), os.path.basename(block["path"]), psutil.virtual_memory()[3] / 1024**3, psutil.virtual_memory()[2], )) # Query block neighbors. query_block_neighbors(db_dataset, query_dataset, index, embedder, block) # Synchronize progress across all ranks. (for easier observation) print_rank_0(" > waiting for other ranks to finish block.") torch.distributed.barrier() def query_pretraining_neighbors(): '''Query pretraining datasets (train & valid).''' args = get_retro_args() # Num threads. faiss.omp_set_num_threads(64) # Load chunk db dataset. print_rank_0("load chunk db dataset.") db_dataset = get_db_merged_train_dataset() db_dataset.load_doc_tuples() # Load index. print_rank_0(" > get index.") index = get_index() # Load datasets. print_rank_0(" > get dataset map.") query_dataset_map = get_query_dataset_map() # Bert embedder. embedder = BertEmbedder(args.retro_bert_batch_size, args.retro_bert_max_chunk_length, args.bert_embedder_type) # Query each (i.e., train, valid, test) dataset. print_rank_0(" > query.") for prefix, info in query_dataset_map.items(): print_rank_0(" > query '%s' dataset ... %d samples." % (prefix, len(info["data"]))) query_dataset_neighbors(db_dataset, info["data"], prefix, info["neighbor_dir"], index, embedder)