wehub-resource-sync
185 lines
7.1 KiB
Python
185 lines
7.1 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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from pprint import pprint
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import numpy as np
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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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 disable_fast_encoder, enable_fast_encoder
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from paddlenlp.transformers import ErnieModel, ErnieTokenizer
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def parse_args():
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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("--output_emb_size", default=None, type=int, help="output_embedding_size")
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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(
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"--max_seq_length",
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default=64,
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type=int,
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help="The maximum total input sequence length after tokenization. "
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"Sequences longer than this will be truncated, sequences shorter will be padded.",
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)
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parser.add_argument("--dropout", default=0.0, type=float, help="Dropout probability.")
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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("--seed", default=42, type=int, help="Random seed.")
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parser.add_argument("--pad_to_max_seq_len", action="store_true", help="Whether to pad to max_seq_len.")
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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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return args
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class SemanticIndexingPredictor(nn.Layer):
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def __init__(self, pretrained_model, output_emb_size, bos_id=0, dropout=0, use_fp16=False):
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super(SemanticIndexingPredictor, self).__init__()
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self.bos_id = bos_id
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self.ptm = pretrained_model
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self.dropout = nn.Dropout(dropout if dropout is not None else 0.0)
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self.output_emb_size = output_emb_size
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if output_emb_size > 0:
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weight_attr = paddle.ParamAttr(initializer=paddle.nn.initializer.TruncatedNormal(std=0.02))
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self.emb_reduce_linear = paddle.nn.Linear(768, output_emb_size, weight_attr=weight_attr)
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self.use_fp16 = use_fp16
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def get_pooled_embedding(self, input_ids, token_type_ids=None, position_ids=None):
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src_mask = input_ids == self.bos_id
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src_mask = paddle.cast(src_mask, "float32")
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# [bs, 1, 1, max_len]
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src_mask = paddle.unsqueeze(src_mask, axis=[1, 2])
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src_mask.stop_gradient = True
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ones = paddle.ones_like(input_ids, dtype="int64")
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seq_length = paddle.cumsum(ones, axis=1)
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position_ids = seq_length - ones
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position_ids.stop_gradient = True
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embedding_output = self.ptm.embeddings(
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input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids
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)
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if self.use_fp16:
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embedding_output = paddle.cast(embedding_output, "float16")
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sequence_output = self.ptm.encoder(embedding_output, src_mask)
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if self.use_fp16:
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sequence_output = paddle.cast(sequence_output, "float32")
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cls_embedding = self.ptm.pooler(sequence_output)
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if self.output_emb_size > 0:
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cls_embedding = self.emb_reduce_linear(cls_embedding)
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cls_embedding = self.dropout(cls_embedding)
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cls_embedding = F.normalize(cls_embedding, p=2, axis=-1)
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return cls_embedding
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def forward(
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self,
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query_input_ids,
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title_input_ids,
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query_token_type_ids=None,
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query_position_ids=None,
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title_token_type_ids=None,
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title_position_ids=None,
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):
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query_cls_embedding = self.get_pooled_embedding(query_input_ids, query_token_type_ids, query_position_ids)
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title_cls_embedding = self.get_pooled_embedding(title_input_ids, title_token_type_ids, title_position_ids)
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cosine_sim = paddle.sum(query_cls_embedding * title_cls_embedding, axis=-1)
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return cosine_sim
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def load(self, init_from_params):
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if init_from_params and os.path.isfile(init_from_params):
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state_dict = paddle.load(init_from_params)
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self.set_state_dict(state_dict)
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print("Loaded parameters from %s" % init_from_params)
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else:
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raise ValueError("Please set --params_path with correct pretrained model file")
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def do_predict(args):
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paddle.set_device("gpu")
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paddle.seed(args.seed)
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tokenizer = ErnieTokenizer.from_pretrained("ernie-1.0")
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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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def batchify_fn(samples):
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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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)
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return [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 = ErnieModel.from_pretrained("ernie-1.0")
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model = SemanticIndexingPredictor(
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pretrained_model, args.output_emb_size, dropout=args.dropout, use_fp16=args.use_fp16
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)
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model.eval()
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model.load(args.params_path)
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model = enable_fast_encoder(model, use_fp16=args.use_fp16)
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cosine_sims = []
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for batch_data in valid_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(
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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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for cosine in cosine_sims:
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print("{}".format(cosine))
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model = disable_fast_encoder(model)
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if __name__ == "__main__":
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args = parse_args()
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pprint(args)
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do_predict(args)
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