bert_classifier.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.
"""BERT fine-tuning in Paddle Dygraph Mode."""

import paddle.fluid as fluid
from hapi.metrics import Accuracy
from hapi.configure import Config
from hapi.text.bert import BertEncoder
from paddle.fluid.dygraph import Linear, Layer
from hapi.loss import SoftmaxWithCrossEntropy
from hapi.model import set_device, Model, Input
import hapi.text.tokenizer.tokenization as tokenization
from hapi.text.bert import BertConfig, BertDataLoader, BertInputExample, make_optimizer


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

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

        pred = self.cls_fc(cls_feats)

        return pred


def main():

    config = Config(yaml_file="./bert.yaml")
    config.build()
    config.Print()

    device = set_device("gpu" if config.use_cuda else "cpu")
    fluid.enable_dygraph(device)

    bert_config = BertConfig(config.bert_config_path)
    bert_config.print_config()

    tokenizer = tokenization.FullTokenizer(
        vocab_file=config.vocab_path, do_lower_case=config.do_lower_case)

    def mnli_line_processor(line_id, line):
        if line_id == "0":
            return None
        uid = tokenization.convert_to_unicode(line[0])
        text_a = tokenization.convert_to_unicode(line[8])
        text_b = tokenization.convert_to_unicode(line[9])
        label = tokenization.convert_to_unicode(line[-1])
        if label not in ["contradiction", "entailment", "neutral"]:
            label = "contradiction"
        return BertInputExample(
            uid=uid, text_a=text_a, text_b=text_b, label=label)

    train_dataloader = BertDataLoader(
        "./data/glue_data/MNLI/train.tsv",
        tokenizer, ["contradiction", "entailment", "neutral"],
        max_seq_length=config.max_seq_len,
        batch_size=config.batch_size,
        line_processor=mnli_line_processor)

    test_dataloader = BertDataLoader(
        "./data/glue_data/MNLI/dev_matched.tsv",
        tokenizer, ["contradiction", "entailment", "neutral"],
        max_seq_length=config.max_seq_len,
        batch_size=config.batch_size,
        line_processor=mnli_line_processor,
        shuffle=False,
        phase="predict")

    trainer_count = fluid.dygraph.parallel.Env().nranks
    num_train_examples = len(train_dataloader.dataset)
    max_train_steps = config.epoch * num_train_examples // config.batch_size // trainer_count
    warmup_steps = int(max_train_steps * config.warmup_proportion)

    print("Trainer count: %d" % trainer_count)
    print("Num train examples: %d" % num_train_examples)
    print("Max train steps: %d" % max_train_steps)
    print("Num warmup steps: %d" % warmup_steps)

    inputs = [
        Input(
            [None, None], 'int64', name='src_ids'), Input(
                [None, None], 'int64', name='pos_ids'), Input(
                    [None, None], 'int64', name='sent_ids'), Input(
                        [None, None, 1], 'float32', name='input_mask')
    ]

    labels = [Input([None, 1], 'int64', name='label')]

    cls_model = ClsModelLayer(
        config,
        bert_config,
        len(["contradiction", "entailment", "neutral"]),
        return_pooled_out=True)

    optimizer = make_optimizer(
        warmup_steps=warmup_steps,
        num_train_steps=max_train_steps,
        learning_rate=config.learning_rate,
        weight_decay=config.weight_decay,
        scheduler=config.lr_scheduler,
        model=cls_model,
        loss_scaling=config.loss_scaling,
        parameter_list=cls_model.parameters())

    cls_model.prepare(
        optimizer,
        SoftmaxWithCrossEntropy(),
        Accuracy(topk=(1, 2)),
        inputs,
        labels,
        device=device)

    cls_model.bert_layer.load("./bert_small", reset_optimizer=True)

    # do train
    cls_model.fit(train_data=train_dataloader.dataloader,
                  epochs=config.epoch,
                  save_dir=config.checkpoints)

    # do eval
    cls_model.evaluate(
        eval_data=test_dataloader.dataloader, batch_size=config.batch_size)


if __name__ == '__main__':
    main()