stacked_dynamic_lstm.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.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import cPickle
import os
import random
import time

import numpy
import paddle
import paddle.dataset.imdb as imdb
import paddle.fluid as fluid
import paddle.batch as batch
import paddle.fluid.profiler as profiler

word_dict = imdb.word_dict()


def crop_sentence(reader, crop_size):
    unk_value = word_dict['<unk>']

    def __impl__():
        for item in reader():
            if len([x for x in item[0] if x != unk_value]) < crop_size:
                yield item

    return __impl__


def get_model(args):
    if args.use_reader_op:
        raise Exception(
            "stacked_dynamic_lstm do not support reader op for now.")
    lstm_size = 512
    emb_dim = 512
    crop_size = 1500

    data = fluid.layers.data(
        name="words", shape=[1], lod_level=1, dtype='int64')
    sentence = fluid.layers.embedding(
        input=data, size=[len(word_dict), emb_dim])

    sentence = fluid.layers.fc(input=sentence, size=lstm_size, act='tanh')

    rnn = fluid.layers.DynamicRNN()
    with rnn.block():
        word = rnn.step_input(sentence)
        prev_hidden = rnn.memory(value=0.0, shape=[lstm_size])
        prev_cell = rnn.memory(value=0.0, shape=[lstm_size])

        def gate_common(
                ipt,
                hidden,
                size, ):
            gate0 = fluid.layers.fc(input=ipt, size=size, bias_attr=True)
            gate1 = fluid.layers.fc(input=hidden, size=size, bias_attr=False)
            gate = fluid.layers.sums(input=[gate0, gate1])
            return gate

        forget_gate = fluid.layers.sigmoid(
            x=gate_common(word, prev_hidden, lstm_size))
        input_gate = fluid.layers.sigmoid(
            x=gate_common(word, prev_hidden, lstm_size))
        output_gate = fluid.layers.sigmoid(
            x=gate_common(word, prev_hidden, lstm_size))
        cell_gate = fluid.layers.tanh(
            x=gate_common(word, prev_hidden, lstm_size))

        cell = fluid.layers.sums(input=[
            fluid.layers.elementwise_mul(
                x=forget_gate, y=prev_cell), fluid.layers.elementwise_mul(
                    x=input_gate, y=cell_gate)
        ])

        hidden = fluid.layers.elementwise_mul(
            x=output_gate, y=fluid.layers.tanh(x=cell))

        rnn.update_memory(prev_cell, cell)
        rnn.update_memory(prev_hidden, hidden)
        rnn.output(hidden)

    last = fluid.layers.sequence_pool(rnn(), 'last')
    logit = fluid.layers.fc(input=last, size=2, act='softmax')
    loss = fluid.layers.cross_entropy(
        input=logit,
        label=fluid.layers.data(
            name='label', shape=[1], dtype='int64'))
    loss = fluid.layers.mean(x=loss)

    # add acc
    batch_acc = fluid.layers.accuracy(input=logit, label=fluid.layers.data(name='label', \
                shape=[1], dtype='int64'))

    inference_program = fluid.default_main_program().clone()
    with fluid.program_guard(inference_program):
        inference_program = fluid.io.get_inference_program(
            target_vars=[batch_acc, batch_size_tensor])

    adam = fluid.optimizer.Adam()

    train_reader = batch(
        paddle.reader.shuffle(
            crop_sentence(imdb.train(word_dict), crop_size), buf_size=25000),
        batch_size=args.batch_size * args.gpus)
    test_reader = batch(
        paddle.reader.shuffle(
            crop_sentence(imdb.test(word_dict), crop_size), buf_size=25000),
        batch_size=args.batch_size)

    return loss, inference_program, adam, train_reader, test_reader, batch_acc


def to_lodtensor(data, place):
    seq_lens = [len(seq) for seq in data]
    cur_len = 0
    lod = [cur_len]
    for l in seq_lens:
        cur_len += l
        lod.append(cur_len)
    flattened_data = numpy.concatenate(data, axis=0).astype("int64")
    flattened_data = flattened_data.reshape([len(flattened_data), 1])
    res = fluid.LoDTensor()
    res.set(flattened_data, place)
    res.set_lod([lod])
    return res