finetune_classifier.py

#   Copyright (c) 2018 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
import re
import time
import logging
from random import random
from functools import reduce, partial

import numpy as np
import multiprocessing

import paddle
import paddle.fluid as F
import paddle.fluid.layers as L

from model.ernie import ErnieModel
from optimization import optimization
import utils.data

from propeller import log
import propeller.paddle as propeller
log.setLevel(logging.DEBUG)

class ClassificationErnieModel(propeller.train.Model):
    """propeller Model wraper for paddle-ERNIE """
    def __init__(self, hparam, mode, run_config):
        self.hparam = hparam
        self.mode = mode
        self.run_config = run_config

    def forward(self, features):
        src_ids, sent_ids = features
        zero = L.fill_constant([1], dtype='int64', value=0)
        input_mask = L.cast(L.logical_not(L.equal(src_ids, zero)), 'float32') # assume pad id == 0
        #input_mask = L.unsqueeze(input_mask, axes=[2])
        d_shape = L.shape(src_ids)
        seqlen = d_shape[1]
        batch_size = d_shape[0]
        pos_ids = L.unsqueeze(L.range(0, seqlen, 1, dtype='int32'), axes=[0])
        pos_ids = L.expand(pos_ids, [batch_size, 1])
        pos_ids = L.unsqueeze(pos_ids, axes=[2])
        pos_ids = L.cast(pos_ids, 'int64')
        pos_ids.stop_gradient = True
        input_mask.stop_gradient = True
        task_ids = L.zeros_like(src_ids) + self.hparam.task_id
        task_ids.stop_gradient = True

        ernie = ErnieModel(
            src_ids=src_ids,
            position_ids=pos_ids,
            sentence_ids=sent_ids,
            task_ids=task_ids,
            input_mask=input_mask,
            config=self.hparam,
            use_fp16=self.hparam['use_fp16']
        )

        cls_feats = ernie.get_pooled_output()

        cls_feats = L.dropout(
            x=cls_feats,
            dropout_prob=0.1,
            dropout_implementation="upscale_in_train"
        )

        logits = L.fc(
            input=cls_feats,
            size=self.hparam['num_label'],
            param_attr=F.ParamAttr(
                name="cls_out_w",
                initializer=F.initializer.TruncatedNormal(scale=0.02)),
            bias_attr=F.ParamAttr(
                name="cls_out_b", initializer=F.initializer.Constant(0.))
        )

        propeller.summary.histogram('pred', logits)

        if self.mode is propeller.RunMode.PREDICT:
            probs = L.softmax(logits)
            return probs
        else:
            return logits

    def loss(self, predictions, labels):
        ce_loss, probs = L.softmax_with_cross_entropy(
            logits=predictions, label=labels, return_softmax=True)
        #L.Print(ce_loss, message='per_example_loss')
        loss = L.mean(x=ce_loss)
        return loss

    def backward(self, loss):
        scheduled_lr, _ = optimization(
            loss=loss,
            warmup_steps=int(self.run_config.max_steps * self.hparam['warmup_proportion']),
            num_train_steps=self.run_config.max_steps,
            learning_rate=self.hparam['learning_rate'],
            train_program=F.default_main_program(), 
            startup_prog=F.default_startup_program(),
            weight_decay=self.hparam['weight_decay'],
            scheduler="linear_warmup_decay",)
        propeller.summary.scalar('lr', scheduled_lr)

    def metrics(self, predictions, label):
        predictions = L.argmax(predictions, axis=1)
        predictions = L.unsqueeze(predictions, axes=[1])
        acc = propeller.metrics.Acc(label, predictions)
        #auc = propeller.metrics.Auc(label, predictions)
        return {'acc': acc}


if __name__ == '__main__':
    parser = propeller.ArgumentParser('classify model with ERNIE')
    parser.add_argument('--max_seqlen', type=int, default=128)
    parser.add_argument('--data_dir', type=str, required=True)
    parser.add_argument('--vocab_file', type=str, required=True)
    parser.add_argument('--do_predict', action='store_true')
    parser.add_argument('--warm_start_from', type=str)
    parser.add_argument('--sentence_piece_model', type=str, default=None)
    parser.add_argument('--word_dict', type=str, default=None)
    args = parser.parse_args()
    run_config = propeller.parse_runconfig(args)
    hparams = propeller.parse_hparam(args)


    vocab = {j.strip().split(b'\t')[0].decode('utf8'): i for i, j in enumerate(open(args.vocab_file, 'rb'))}
    sep_id = vocab['[SEP]']
    cls_id = vocab['[CLS]']
    unk_id = vocab['[UNK]']

    if args.sentence_piece_model is not None:
        if args.word_dict is None:
            raise ValueError('--word_dict no specified in subword Model')
        tokenizer = utils.data.WSSPTokenizer(args.sentence_piece_model, args.word_dict, ws=True, lower=True)
    else:
        tokenizer = utils.data.CharTokenizer(vocab.keys())

    def tokenizer_func(inputs):
        '''avoid pickle error'''
        ret = tokenizer(inputs)
        return ret

    if not args.do_predict:
        feature_column = propeller.data.FeatureColumns([
            propeller.data.TextColumn('title',unk_id=unk_id, vocab_dict=vocab, tokenizer=tokenizer_func),
            propeller.data.LabelColumn('label'),
        ])

        def before(seg_a, label):
            sentence, segments = utils.data.build_1_pair(seg_a, max_seqlen=args.max_seqlen, cls_id=cls_id, sep_id=sep_id)
            return sentence, segments, label

        def after(sentence, segments, label):
            sentence, segments, label = utils.data.expand_dims(sentence, segments, label)
            return sentence, segments, label

        log.debug(os.path.join(args.data_dir, 'train'))
        train_ds = feature_column.build_dataset('train', data_dir=os.path.join(args.data_dir, 'train'), shuffle=True, repeat=True, use_gz=False) \
                                       .map(before) \
                                       .padded_batch(hparams.batch_size, (0, 0, 0)) \
                                       .map(after) 

        dev_ds = feature_column.build_dataset('dev', data_dir=os.path.join(args.data_dir, 'dev'), shuffle=False, repeat=False, use_gz=False) \
                                       .map(before) \
                                       .padded_batch(hparams.batch_size, (0, 0, 0)) \
                                       .map(after) 


        shapes = ([-1, args.max_seqlen, 1], [-1, args.max_seqlen, 1], [-1, 1])
        types = ('int64', 'int64', 'int64')

        train_ds.data_shapes = shapes
        train_ds.data_types = types
        dev_ds.data_shapes = shapes
        dev_ds.data_types = types

        varname_to_warmstart = re.compile(r'^encoder.*[wb]_0$|^.*embedding$|^.*bias$|^.*scale$|^pooled_fc.[wb]_0$')
        warm_start_dir = args.warm_start_from
        ws = propeller.WarmStartSetting(
                predicate_fn=lambda v: varname_to_warmstart.match(v.name) and os.path.exists(os.path.join(warm_start_dir, v.name)),
                from_dir=warm_start_dir
            )

        best_exporter = propeller.train.exporter.BestInferenceModelExporter(os.path.join(run_config.model_dir, 'best'), cmp_fn=lambda old, new: new['eval']['acc'] > old['eval']['acc'])
        propeller.train.train_and_eval(
                model_class_or_model_fn=ClassificationErnieModel, 
                params=hparams, 
                run_config=run_config, 
                train_dataset=train_ds, 
                eval_dataset=dev_ds,
                warm_start_setting=ws, 
                exporters=[best_exporter])
        print('dev_acc\t%.5f' % (best_exporter._best['eval']['acc']))
    else:
        feature_column = propeller.data.FeatureColumns([
            propeller.data.TextColumn('title',unk_id=unk_id, vocab_dict=vocab, tokenizer=tokenizer_func),
            propeller.data.LabelColumn('label'),
        ])
        def before(seg_a):
            sentence, segments = utils.data.build_1_pair(seg_a, max_seqlen=args.max_seqlen, cls_id=cls_id, sep_id=sep_id)
            return sentence, segments
        def after(sentence, segments):
            sentence, segments = utils.data.expand_dims(sentence, segments)
            return sentence, segments
        predict_ds = feature_column.build_dataset_from_stdin('predict') \
                               .map(before) \
                               .padded_batch(hparams.batch_size, (0, 0)) \
                               .map(after) 
        shapes = ([-1, args.max_seqlen, 1], [-1, args.max_seqlen, 1])
        types = ('int64', 'int64')

        predict_ds.data_shapes = shapes
        predict_ds.data_types = types
        finetuned_model = propeller.Learner(ClassificationErnieModel, run_config, hparams)
        for logits, in finetuned_model.predict(predict_ds, ckpt=-1): # ckpt=-1 means last step
            print(np.argmax(logits))