wehub-resource-sync
144 lines
5.1 KiB
Python
144 lines
5.1 KiB
Python
#!/usr/bin/env python3
|
|
|
|
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
# Copyright GC-DPR authors.
|
|
# 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.
|
|
"""
|
|
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 argparse
|
|
import csv
|
|
import logging
|
|
import os
|
|
import pathlib
|
|
import pickle
|
|
from typing import List, Tuple
|
|
|
|
import numpy as np
|
|
import paddle
|
|
from biencoder_base_model import BiEncoder
|
|
from NQdataset import BertTensorizer
|
|
from paddle import nn
|
|
from paddle.io import DataLoader, Dataset
|
|
from tqdm import tqdm
|
|
|
|
from paddlenlp.transformers.bert.modeling import BertModel
|
|
|
|
logger = logging.getLogger()
|
|
logger.setLevel(logging.INFO)
|
|
if logger.hasHandlers():
|
|
logger.handlers.clear()
|
|
console = logging.StreamHandler()
|
|
logger.addHandler(console)
|
|
|
|
|
|
class CtxDataset(Dataset):
|
|
def __init__(self, ctx_rows: List[Tuple[object, str, str]], tensorizer: BertTensorizer, insert_title: bool = True):
|
|
self.rows = ctx_rows
|
|
self.tensorizer = tensorizer
|
|
self.insert_title = insert_title
|
|
|
|
def __len__(self):
|
|
return len(self.rows)
|
|
|
|
def __getitem__(self, item):
|
|
ctx = self.rows[item]
|
|
|
|
return self.tensorizer.text_to_tensor(ctx[1], title=ctx[2] if self.insert_title else None)
|
|
|
|
|
|
def no_op_collate(xx: List[object]):
|
|
return xx
|
|
|
|
|
|
def gen_ctx_vectors(
|
|
ctx_rows: List[Tuple[object, str, str]], model: nn.Layer, tensorizer: BertTensorizer, insert_title: bool = True
|
|
) -> List[Tuple[object, np.array]]:
|
|
bsz = args.batch_size
|
|
total = 0
|
|
results = []
|
|
|
|
dataset = CtxDataset(ctx_rows, tensorizer, insert_title)
|
|
loader = DataLoader(
|
|
dataset, shuffle=False, num_workers=2, collate_fn=no_op_collate, drop_last=False, batch_size=bsz
|
|
)
|
|
|
|
for batch_id, batch_token_tensors in enumerate(tqdm(loader)):
|
|
ctx_ids_batch = paddle.stack(batch_token_tensors, axis=0)
|
|
ctx_seg_batch = paddle.zeros_like(ctx_ids_batch)
|
|
with paddle.no_grad():
|
|
out = model.get_context_pooled_embedding(ctx_ids_batch, ctx_seg_batch)
|
|
|
|
out = out.astype("float32").cpu()
|
|
batch_start = batch_id * bsz
|
|
ctx_ids = [r[0] for r in ctx_rows[batch_start : batch_start + bsz]]
|
|
assert len(ctx_ids) == out.shape[0]
|
|
total += len(ctx_ids)
|
|
results.extend([(ctx_ids[i], out[i].reshape([-1]).numpy()) for i in range(out.shape[0])])
|
|
|
|
return results
|
|
|
|
|
|
def main(args):
|
|
|
|
tensorizer = BertTensorizer()
|
|
question_model = BertModel.from_pretrained(args.que_model_path)
|
|
context_model = BertModel.from_pretrained(args.con_model_path)
|
|
model = BiEncoder(question_encoder=question_model, context_encoder=context_model)
|
|
|
|
rows = []
|
|
with open(args.ctx_file) as tsvfile:
|
|
reader = csv.reader(tsvfile, delimiter="\t")
|
|
# file format: doc_id, doc_text, title
|
|
rows.extend([(row[0], row[1], row[2]) for row in reader if row[0] != "id"])
|
|
|
|
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, model, tensorizer, True)
|
|
file = args.out_file + "_" + str(args.shard_id) + ".pkl"
|
|
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()
|
|
|
|
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 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")
|
|
parser.add_argument("--que_model_path", type=str)
|
|
parser.add_argument("--con_model_path", type=str)
|
|
args = parser.parse_args()
|
|
|
|
main(args)
|