wehub-resource-sync
192 lines
7.6 KiB
Python
Executable File
192 lines
7.6 KiB
Python
Executable File
# Copyright (c) 2022 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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import argparse
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import os
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import time
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from functools import partial
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import paddle
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from ance.model import SemanticIndexANCE
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from ann_util import build_index
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from data import (
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convert_example,
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create_dataloader,
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gen_id2corpus,
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gen_text_file,
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get_latest_ann_data,
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get_latest_checkpoint,
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)
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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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# yapf: disable
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parser = argparse.ArgumentParser()
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# Required parameters
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parser.add_argument("--similar_text_pair_file", default=None, type=str, required=True, help="The train_set tsv file that each line is similar text pair")
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parser.add_argument("--corpus_file", default=None, type=str, required=True, help="The corpus file that each line is a text for buinding indexing")
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parser.add_argument("--save_dir", default=None, type=str, required=True, help="Saved model dir, will look for latest checkpoint dir in here")
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parser.add_argument("--ann_data_dir", default=None, type=str, required=True, help="The output directory where the training data will be written")
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parser.add_argument("--init_from_ckpt", default=None, type=str, help="Initial model dir, will use this if no checkpoint is found in model_dir")
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parser.add_argument("--end_ann_step", default=1000000, type=int, help="Stop after this number of data versions has been generated, default run forever")
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parser.add_argument("--batch_size", default=128, type=int, help="Batch size for predicting embedding of texts")
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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("--max_seq_length", default=128, type=int, help="Batch size for predicting embedding of texts")
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parser.add_argument("--topk_training", default=500, type=int, help="top k from which negative samples are collected")
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parser.add_argument("--num_negative_sample", default=5, type=int, help="at each resample, how many negative samples per query do I use")
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# hnsw argument
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parser.add_argument("--hnsw_m", default=10, type=int, help="Recall number for each query from Ann index.")
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parser.add_argument("--hnsw_ef", default=10, 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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args = parser.parse_args()
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# yapf: enable
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def generate_new_ann(args, data_loader_dict, checkpoint_path, latest_step_num):
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pretrained_model = AutoModel.from_pretrained("ernie-3.0-medium-zh")
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model = SemanticIndexANCE(pretrained_model, output_emb_size=args.output_emb_size)
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logger.info("checkpoint_path:{}".format(checkpoint_path))
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state_dict = paddle.load(checkpoint_path)
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model.set_dict(state_dict)
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logger.info("load params from:{}".format(checkpoint_path))
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logger.info("***** inference of corpus *****")
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final_index = build_index(args, data_loader_dict["corpus_data_loader"], model)
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logger.info("***** inference of query *****")
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query_embedding = model.get_semantic_embedding(data_loader_dict["text_data_loader"])
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text_list = data_loader_dict["text_list"]
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id2corpus = data_loader_dict["id2corpus"]
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text2similar_text = data_loader_dict["text2similar_text"]
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new_ann_data_path = os.path.join(args.ann_data_dir, str(latest_step_num))
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if not os.path.exists(new_ann_data_path):
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os.mkdir(new_ann_data_path)
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with open(os.path.join(new_ann_data_path, "new_ann_data"), "w") 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, args.topk_training)
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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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hard_neg_samples = recalled_idx[row_index][-1 * args.num_negative_sample :]
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for idx, hard_neg_doc_idx in enumerate(hard_neg_samples):
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text = text_list[text_index]["text"]
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similar_text = text2similar_text[text]
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hard_neg_sample = id2corpus[hard_neg_doc_idx]
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f.write("{}\t{}\t{}\n".format(text, similar_text, hard_neg_sample))
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succeed_flag_file = os.path.join(new_ann_data_path, "succeed_flag_file")
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open(succeed_flag_file, "a").close()
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logger.info("finish generate ann data step:{}".format(latest_step_num))
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def build_data_loader(args, tokenizer):
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"""build corpus_data_loader and text_data_loader"""
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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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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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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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# build text data_loader
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text_list, text2similar_text = gen_text_file(args.similar_text_pair_file)
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text_ds = MapDataset(text_list)
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text_data_loader = create_dataloader(
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text_ds, mode="predict", batch_size=args.batch_size, batchify_fn=batchify_fn, trans_fn=trans_func
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)
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d = {
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"text_data_loader": text_data_loader,
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"corpus_data_loader": corpus_data_loader,
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"id2corpus": id2corpus,
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"text2similar_text": text2similar_text,
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"text_list": text_list,
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}
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return d
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def ann_data_gen(args):
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# use init_from_ckpt as last_checkpoint
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last_checkpoint = args.init_from_ckpt
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# get latest_ann_data_step to decide when stop gen_ann_data
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_, latest_ann_data_step = get_latest_ann_data(args.ann_data_dir)
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rank = paddle.distributed.get_rank()
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if rank == 0:
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if not os.path.exists(args.ann_data_dir):
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os.makedirs(args.ann_data_dir)
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tokenizer = AutoTokenizer.from_pretrained("ernie-3.0-medium-zh")
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data_load_dict = build_data_loader(args, tokenizer)
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while latest_ann_data_step <= args.end_ann_step:
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next_checkpoint, latest_step_num = get_latest_checkpoint(args)
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logger.info("next_checkpoint:{}".format(next_checkpoint))
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if next_checkpoint == last_checkpoint:
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logger.info("next_checkpoint == lase_checkpoint:{}".format(next_checkpoint))
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logger.info("sleep 10s")
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time.sleep(10)
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else:
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logger.info("start generate ann data using checkpoint:{}".format(next_checkpoint))
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generate_new_ann(args, data_load_dict, next_checkpoint, latest_step_num)
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logger.info("finished generating ann data step {}".format(latest_step_num))
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last_checkpoint = next_checkpoint
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def main():
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ann_data_gen(args)
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if __name__ == "__main__":
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main()
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