wehub-resource-sync
122 lines
5.5 KiB
Python
122 lines
5.5 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# coding=UTF-8
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import argparse
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import os
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from functools import partial
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import paddle
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from ann_util import build_index
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from base_model import SemanticIndexBase
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from data import convert_example, create_dataloader, gen_id2corpus, gen_text_file
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from paddlenlp.data import Pad, Tuple
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from paddlenlp.datasets import MapDataset
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from paddlenlp.transformers import AutoModel, AutoTokenizer
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from paddlenlp.utils.log import logger
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# fmt: off
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parser = argparse.ArgumentParser()
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parser.add_argument("--corpus_file", type=str, required=True, help="The full path of input file")
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parser.add_argument("--similar_text_pair_file", type=str, required=True, help="The full path of similar text pair file")
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parser.add_argument("--recall_result_dir", type=str, default='recall_result', help="The full path of recall result file to save")
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parser.add_argument("--recall_result_file", type=str, default='recall_result_file', help="The file name of recall result")
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parser.add_argument("--params_path", type=str, required=True, help="The path to model parameters to be loaded.")
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parser.add_argument("--max_seq_length", default=64, type=int, help="The maximum total input sequence length after tokenization. Sequences longer than this will be truncated, sequences shorter will be padded.")
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parser.add_argument("--batch_size", default=32, type=int, help="Batch size per GPU/CPU for training.")
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parser.add_argument("--output_emb_size", default=None, type=int, help="output_embedding_size")
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parser.add_argument("--recall_num", default=10, type=int, help="Recall number for each query from Ann index.")
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parser.add_argument("--hnsw_m", default=100, type=int, help="Recall number for each query from Ann index.")
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parser.add_argument("--hnsw_ef", default=100, type=int, help="Recall number for each query from Ann index.")
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parser.add_argument("--hnsw_max_elements", default=1000000, type=int, help="Recall number for each query from Ann index.")
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parser.add_argument('--device', choices=['cpu', 'gpu'], default="gpu", help="Select which device to train model, defaults to gpu.")
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args = parser.parse_args()
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# fmt: on
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if __name__ == "__main__":
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paddle.set_device(args.device)
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rank = paddle.distributed.get_rank()
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if paddle.distributed.get_world_size() > 1:
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paddle.distributed.init_parallel_env()
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tokenizer = AutoTokenizer.from_pretrained("ernie-3.0-medium-zh")
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trans_func = partial(convert_example, tokenizer=tokenizer, max_seq_length=args.max_seq_length)
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batchify_fn = lambda samples, fn=Tuple(
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Pad(axis=0, pad_val=tokenizer.pad_token_id), # text_input
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Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # text_segment
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): [data for data in fn(samples)]
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pretrained_model = AutoModel.from_pretrained("ernie-3.0-medium-zh")
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model = SemanticIndexBase(pretrained_model, output_emb_size=args.output_emb_size)
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model = paddle.DataParallel(model)
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# Load pretrained semantic model
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if args.params_path and os.path.isfile(args.params_path):
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state_dict = paddle.load(args.params_path)
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model.set_dict(state_dict)
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logger.info("Loaded parameters from %s" % args.params_path)
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else:
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raise ValueError("Please set --params_path with correct pretrained model file")
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id2corpus = gen_id2corpus(args.corpus_file)
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# convert_example function's input must be dict
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corpus_list = [{idx: text} for idx, text in id2corpus.items()]
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corpus_ds = MapDataset(corpus_list)
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corpus_data_loader = create_dataloader(
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corpus_ds, mode="predict", batch_size=args.batch_size, batchify_fn=batchify_fn, trans_fn=trans_func
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)
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# Need better way to get inner model of DataParallel
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inner_model = model._layers
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final_index = build_index(args, corpus_data_loader, inner_model)
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text_list, text2similar_text = gen_text_file(args.similar_text_pair_file)
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query_ds = MapDataset(text_list)
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query_data_loader = create_dataloader(
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query_ds, mode="predict", batch_size=args.batch_size, batchify_fn=batchify_fn, trans_fn=trans_func
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)
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query_embedding = inner_model.get_semantic_embedding(query_data_loader)
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if not os.path.exists(args.recall_result_dir):
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os.mkdir(args.recall_result_dir)
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recall_result_file = os.path.join(args.recall_result_dir, args.recall_result_file)
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with open(recall_result_file, "w", encoding="utf-8") as f:
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for batch_index, batch_query_embedding in enumerate(query_embedding):
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recalled_idx, cosine_sims = final_index.knn_query(batch_query_embedding.numpy(), args.recall_num)
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batch_size = len(cosine_sims)
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for row_index in range(batch_size):
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text_index = args.batch_size * batch_index + row_index
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for idx, doc_idx in enumerate(recalled_idx[row_index]):
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f.write(
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"{}\t{}\t{}\n".format(
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text_list[text_index]["text"], id2corpus[doc_idx], 1.0 - cosine_sims[row_index][idx]
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)
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)
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