wehub-resource-sync
122 lines
4.9 KiB
Python
122 lines
4.9 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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import argparse
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import os
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from functools import partial
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import numpy as np
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import paddle
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from base_model import SemanticIndexBase
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from data import convert_example, create_dataloader, read_text_pair
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from paddlenlp.data import Pad, Tuple
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from paddlenlp.datasets import load_dataset
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from paddlenlp.ops import convert_to_fp16
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from paddlenlp.transformers import AutoModel, AutoTokenizer
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# fmt: off
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parser = argparse.ArgumentParser()
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parser.add_argument("--text_pair_file", type=str, required=True, help="The full path of input file")
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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('--device', choices=['cpu', 'gpu'], default="gpu", help="Select which device to train model, defaults to gpu.")
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parser.add_argument("--pad_to_max_seq_len", action="store_true", help="Whether to pad to max seq length.")
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parser.add_argument("--use_fp16", action="store_true", help="Whether to use_fp16")
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args = parser.parse_args()
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# fmt: on
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def predict(model, data_loader):
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"""
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Predicts the data labels.
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Args:
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model (obj:`SemanticIndexBase`): A model to extract text embedding or calculate similarity of text pair.
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data_loader (obj:`List(Example)`): The processed data ids of text pair: [query_input_ids, query_token_type_ids, title_input_ids, title_token_type_ids]
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Returns:
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results(obj:`List`): cosine similarity of text pairs.
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"""
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cosine_sims = []
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model.eval()
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with paddle.no_grad():
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for batch_data in data_loader:
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query_input_ids, query_token_type_ids, title_input_ids, title_token_type_ids = batch_data
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query_input_ids = paddle.to_tensor(query_input_ids)
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query_token_type_ids = paddle.to_tensor(query_token_type_ids)
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title_input_ids = paddle.to_tensor(title_input_ids)
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title_token_type_ids = paddle.to_tensor(title_token_type_ids)
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batch_cosine_sim = model.cosine_sim(
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query_input_ids=query_input_ids,
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title_input_ids=title_input_ids,
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query_token_type_ids=query_token_type_ids,
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title_token_type_ids=title_token_type_ids,
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).numpy()
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cosine_sims.append(batch_cosine_sim)
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cosine_sims = np.concatenate(cosine_sims, axis=0)
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return cosine_sims
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if __name__ == "__main__":
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paddle.set_device(args.device)
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tokenizer = AutoTokenizer.from_pretrained("ernie-3.0-medium-zh")
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trans_func = partial(
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convert_example,
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tokenizer=tokenizer,
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max_seq_length=args.max_seq_length,
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pad_to_max_seq_len=args.pad_to_max_seq_len,
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)
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batchify_fn = lambda samples, fn=Tuple(
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Pad(axis=0, pad_val=tokenizer.pad_token_id), # query_input
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Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # query_segment
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Pad(axis=0, pad_val=tokenizer.pad_token_id), # title_input
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Pad(axis=0, pad_val=tokenizer.pad_token_type_id), # title_segment
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): [data for data in fn(samples)]
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valid_ds = load_dataset(read_text_pair, data_path=args.text_pair_file, lazy=False)
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valid_data_loader = create_dataloader(
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valid_ds, mode="predict", batch_size=args.batch_size, batchify_fn=batchify_fn, trans_fn=trans_func
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)
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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, use_fp16=args.use_fp16)
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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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print("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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if args.use_fp16:
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convert_to_fp16(model.ptm.encoder)
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cosine_sim = predict(model, valid_data_loader)
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for idx, cosine in enumerate(cosine_sim):
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print("{}".format(cosine))
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