faster_predict.py

# Copyright (c) 2021 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.

import os
from functools import partial
import argparse
from pprint import pprint
import numpy as np

import paddle
import paddle.nn as nn
import paddle.nn.functional as F

from paddlenlp.transformers import ErnieTokenizer, ErnieModel
from paddlenlp.data import Pad, Tuple
from paddlenlp.datasets import load_dataset
from paddlenlp.ops import enable_faster_encoder, disable_faster_encoder

from data import read_text_pair, convert_example, create_dataloader


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--text_pair_file",
        type=str,
        required=True,
        help="The full path of input file")
    parser.add_argument(
        "--output_emb_size",
        default=None,
        type=int,
        help="output_embedding_size")
    parser.add_argument(
        "--params_path",
        type=str,
        required=True,
        help="The path to model parameters to be loaded.")
    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."
    )
    parser.add_argument(
        "--dropout", default=0.0, type=float, help="Dropout probability.")
    parser.add_argument(
        "--batch_size",
        default=32,
        type=int,
        help="Batch size per GPU/CPU for training.")
    parser.add_argument("--seed", default=42, type=int, help="Random seed.")
    parser.add_argument(
        "--pad_to_max_seq_len",
        action="store_true",
        help="Whether to pad to max_seq_len.")

    args = parser.parse_args()
    return args


class SemanticIndexingPredictor(nn.Layer):
    def __init__(self, pretrained_model, output_emb_size, bos_id=0, dropout=0):
        super(SemanticIndexingPredictor, self).__init__()
        self.bos_id = bos_id
        self.ptm = pretrained_model
        self.dropout = nn.Dropout(dropout if dropout is not None else 0.0)
        self.output_emb_size = output_emb_size
        if output_emb_size > 0:
            weight_attr = paddle.ParamAttr(
                initializer=paddle.nn.initializer.TruncatedNormal(std=0.02))
            self.emb_reduce_linear = paddle.nn.Linear(
                768, output_emb_size, weight_attr=weight_attr)

    def get_pooled_embedding(self,
                             input_ids,
                             token_type_ids=None,
                             position_ids=None,
                             attention_mask=None):
        src_mask = (input_ids != self.bos_id
                    ).astype(self.ptm.encoder.layers[0].norm1.bias.dtype)
        src_mask = paddle.unsqueeze(src_mask, axis=[1, 2])
        src_mask.stop_gradient = True

        ones = paddle.ones_like(input_ids, dtype="int64")
        seq_length = paddle.cumsum(ones, axis=1)
        position_ids = seq_length - ones
        position_ids.stop_gradient = True

        embedding_output = self.ptm.embeddings(
            input_ids=input_ids,
            position_ids=position_ids,
            token_type_ids=token_type_ids)
        sequence_output = self.ptm.encoder(embedding_output, src_mask)
        cls_embedding = self.ptm.pooler(sequence_output)

        if self.output_emb_size > 0:
            cls_embedding = self.emb_reduce_linear(cls_embedding)
        cls_embedding = self.dropout(cls_embedding)
        cls_embedding = F.normalize(cls_embedding, p=2, axis=-1)

        return cls_embedding

    def forward(self,
                query_input_ids,
                title_input_ids,
                query_token_type_ids=None,
                query_position_ids=None,
                query_attention_mask=None,
                title_token_type_ids=None,
                title_position_ids=None,
                title_attention_mask=None):
        query_cls_embedding = self.get_pooled_embedding(
            query_input_ids, query_token_type_ids, query_position_ids,
            query_attention_mask)
        title_cls_embedding = self.get_pooled_embedding(
            title_input_ids, title_token_type_ids, title_position_ids,
            title_attention_mask)
        cosine_sim = paddle.sum(query_cls_embedding * title_cls_embedding,
                                axis=-1)
        return cosine_sim

    def load(self, init_from_params):
        if init_from_params and os.path.isfile(init_from_params):
            state_dict = paddle.load(init_from_params)
            self.set_state_dict(state_dict)
            print("Loaded parameters from %s" % init_from_params)
        else:
            raise ValueError(
                "Please set --params_path with correct pretrained model file")


def do_predict(args):
    place = paddle.set_device("gpu")
    paddle.seed(args.seed)
    tokenizer = ErnieTokenizer.from_pretrained('ernie-1.0')

    trans_func = partial(
        convert_example,
        tokenizer=tokenizer,
        max_seq_length=args.max_seq_length,
        pad_to_max_seq_len=args.pad_to_max_seq_len)

    batchify_fn = lambda samples, fn=Tuple(
        Pad(axis=0, pad_val=tokenizer.pad_token_id),  # query_input
        Pad(axis=0, pad_val=tokenizer.pad_token_type_id),  # query_segment
        Pad(axis=0, pad_val=tokenizer.pad_token_id),  # title_input
        Pad(axis=0, pad_val=tokenizer.pad_token_type_id),  # tilte_segment
    ): [data for data in fn(samples)]

    valid_ds = load_dataset(
        read_text_pair, data_path=args.text_pair_file, lazy=False)

    valid_data_loader = create_dataloader(
        valid_ds,
        mode="predict",
        batch_size=args.batch_size,
        batchify_fn=batchify_fn,
        trans_fn=trans_func)

    pretrained_model = ErnieModel.from_pretrained("ernie-1.0")

    model = SemanticIndexingPredictor(
        pretrained_model, args.output_emb_size, dropout=args.dropout)
    model.eval()
    model.load(args.params_path)
    model = enable_faster_encoder(model)
    cosine_sims = []
    for batch_data in valid_data_loader:
        query_input_ids, query_token_type_ids, title_input_ids, title_token_type_ids = batch_data
        query_input_ids = paddle.to_tensor(query_input_ids)
        query_token_type_ids = paddle.to_tensor(query_token_type_ids)
        title_input_ids = paddle.to_tensor(title_input_ids)
        title_token_type_ids = paddle.to_tensor(title_token_type_ids)
        batch_cosine_sim = model(
            query_input_ids=query_input_ids,
            title_input_ids=title_input_ids,
            query_token_type_ids=query_token_type_ids,
            title_token_type_ids=title_token_type_ids).numpy()
        cosine_sims.append(batch_cosine_sim)

    cosine_sims = np.concatenate(cosine_sims, axis=0)
    for cosine in cosine_sims:
        print('{}'.format(cosine))
    model = disable_faster_encoder(model)


if __name__ == "__main__":
    args = parse_args()
    pprint(args)
    do_predict(args)