cls.py

# Copyright (c) 2020 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.
"dygraph transformer layers"

import six
import json
import numpy as np

import paddle
import paddle.fluid as fluid
from paddle.fluid.dygraph import Linear, Layer

from hapi.text.bert import BertEncoder
from hapi.model import Model


class ClsModelLayer(Model):
    """
    classify model
    """

    def __init__(self,
                 args,
                 config,
                 num_labels,
                 is_training=True,
                 return_pooled_out=True,
                 use_fp16=False):
        super(ClsModelLayer, self).__init__()
        self.config = config
        self.is_training = is_training
        self.use_fp16 = use_fp16
        self.loss_scaling = args.loss_scaling

        self.bert_layer = BertEncoder(
            config=self.config, return_pooled_out=True, use_fp16=self.use_fp16)

        self.cls_fc = Linear(
            input_dim=self.config["hidden_size"],
            output_dim=num_labels,
            param_attr=fluid.ParamAttr(
                name="cls_out_w",
                initializer=fluid.initializer.TruncatedNormal(scale=0.02)),
            bias_attr=fluid.ParamAttr(
                name="cls_out_b", initializer=fluid.initializer.Constant(0.)))

    def forward(self, src_ids, position_ids, sentence_ids, input_mask):
        """
        forward
        """

        enc_output, next_sent_feat = self.bert_layer(src_ids, position_ids,
                                                     sentence_ids, input_mask)

        cls_feats = fluid.layers.dropout(
            x=next_sent_feat,
            dropout_prob=0.1,
            dropout_implementation="upscale_in_train")

        logits = self.cls_fc(cls_feats)

        return logits