generate_dense_embeddings.py

#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

# Modified by Microsoft Corporation. 
# Licensed under the MIT license. 
"""
 Command line tool that produces embeddings for a large documents base based on the pretrained ctx & question encoders
 Supposed to be used in a 'sharded' way to speed up the process.
"""
import os
import sys
import pathlib

import argparse
import csv
import logging
import pickle
from typing import List, Tuple

import numpy as np
import torch
from torch import nn

from dpr.models import init_biencoder_components
from dpr.options import add_encoder_params, setup_args_gpu, print_args, set_encoder_params_from_state, \
    add_tokenizer_params, add_cuda_params
from dpr.utils.data_utils import Tensorizer
from dpr.utils.model_utils import setup_for_distributed_mode, get_model_obj, load_states_from_checkpoint,move_to_device,load_states_from_checkpoint_only_model

csv.field_size_limit(sys.maxsize)

logger = logging.getLogger()
logger.setLevel(logging.INFO)
if (logger.hasHandlers()):
    logger.handlers.clear()
console = logging.StreamHandler()
logger.addHandler(console)

def gen_ctx_vectors(ctx_rows: List[Tuple[object, str, str]], model: nn.Module, tensorizer: Tensorizer,
                    args, insert_title: bool = False) -> List[Tuple[object, np.array]]:
    n = len(ctx_rows)
    bsz = args.batch_size
    total = 0
    results = []
    for j, batch_start in enumerate(range(0, n, bsz)):

        batch_token_tensors = [tensorizer.text_to_tensor(ctx[1], title=ctx[2] if insert_title else None) for ctx in
                            ctx_rows[batch_start:batch_start + bsz]]

        ctx_ids_batch = move_to_device(torch.stack(batch_token_tensors, dim=0),args.device)
        ctx_seg_batch = move_to_device(torch.zeros_like(ctx_ids_batch),args.device)
        ctx_attn_mask = move_to_device(tensorizer.get_attn_mask(ctx_ids_batch),args.device)
        with torch.no_grad():
            _, out, _ = model(ctx_ids_batch, ctx_seg_batch, ctx_attn_mask)
        out = out.cpu()

        ctx_ids = [r[0] for r in ctx_rows[batch_start:batch_start + bsz]]

        assert len(ctx_ids) == out.size(0)

        total += len(ctx_ids)

        results.extend([
            (ctx_ids[i], out[i].view(-1).numpy())
            for i in range(out.size(0))
        ])

        if total % 100000 == 0:
            logger.info('Encoded passages %d', total)

    return results

def main(args):
    if not hasattr(args, 'load_trained_model') or not args.load_trained_model:
        saved_state = load_states_from_checkpoint(args.model_file)
    else: 
        saved_state = load_states_from_checkpoint_only_model(args.model_file)

    set_encoder_params_from_state(saved_state.encoder_params, args)
    print_args(args)
    tensorizer, encoder, _ = init_biencoder_components(args.encoder_model_type, args, inference_only=True)

    encoder = encoder.ctx_model

    encoder, _ = setup_for_distributed_mode(encoder, None, args.device, args.n_gpu,
                                            args.local_rank,
                                            args.fp16,
                                            args.fp16_opt_level)
    encoder.eval()

    model_to_load = get_model_obj(encoder)
    logger.info('Loading saved model state ...')
    logger.debug('saved model keys =%s', saved_state.model_dict.keys())

    prefix_len = len('ctx_model.')
    ctx_state = {key[prefix_len:]: value for (key, value) in saved_state.model_dict.items() if
                 key.startswith('ctx_model.')}

    model_to_load.load_state_dict(ctx_state)

    logger.info('reading data from file=%s', args.ctx_file)

    rows = []
    with open(args.ctx_file) as tsvfile:
        reader = csv.reader(tsvfile, delimiter='\t')

        rows.extend([(row[0], row[1], row[2]) for row in reader if row[0] != 'id' and all([len(row[0])!=0,len(row[1])>=10,len(row[2])!=0])])

    if hasattr(args, 'change_id_over_card') and args.change_id_over_card:
        args.shard_id = args.local_rank if args.local_rank != -1 else 0

    shard_size = int(len(rows) / args.num_shards)
    start_idx = args.shard_id * shard_size
    end_idx = start_idx + shard_size

    logger.info('Producing encodings for passages range: %d to %d (out of total %d)', start_idx, end_idx, len(rows))
    rows = rows[start_idx:end_idx]

    data = gen_ctx_vectors(rows, encoder, tensorizer, args, False)

    file = args.out_file + '_' + str(args.shard_id)
    pathlib.Path(os.path.dirname(file)).mkdir(parents=True, exist_ok=True)
    logger.info('Writing results to %s' % file)
    with open(file, mode='wb') as f:
        pickle.dump(data, f)

    logger.info('Total passages processed %d. Written to %s', len(data), file)

if __name__ == '__main__':
    parser = argparse.ArgumentParser()

    add_encoder_params(parser)
    add_tokenizer_params(parser)
    add_cuda_params(parser)

    parser.add_argument('--ctx_file', type=str, default=None, help='Path to passages set .tsv file')
    parser.add_argument('--out_file', required=True, type=str, default=None,
                        help='output .tsv file path to write results to ')
    parser.add_argument('--shard_id', type=int, default=0, help="Number(0-based) of data shard to process")
    parser.add_argument('--num_shards', type=int, default=1, help="Total amount of data shards")
    parser.add_argument('--batch_size', type=int, default=32, help="Batch size for the passage encoder forward pass")
    args = parser.parse_args()

    assert args.model_file, 'Please specify --model_file checkpoint to init model weights'

    setup_args_gpu(args)

    main(args)