machine_translation.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.
"""seq2seq model for fluid."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np
import argparse
import time
import distutils.util

import paddle
import paddle.fluid as fluid
import paddle.fluid.core as core
import paddle.fluid.framework as framework
from paddle.fluid.executor import Executor


def lstm_step(x_t, hidden_t_prev, cell_t_prev, size):
    def linear(inputs):
        return fluid.layers.fc(input=inputs, size=size, bias_attr=True)

    forget_gate = fluid.layers.sigmoid(x=linear([hidden_t_prev, x_t]))
    input_gate = fluid.layers.sigmoid(x=linear([hidden_t_prev, x_t]))
    output_gate = fluid.layers.sigmoid(x=linear([hidden_t_prev, x_t]))
    cell_tilde = fluid.layers.tanh(x=linear([hidden_t_prev, x_t]))

    cell_t = fluid.layers.sums(input=[
        fluid.layers.elementwise_mul(
            x=forget_gate, y=cell_t_prev), fluid.layers.elementwise_mul(
                x=input_gate, y=cell_tilde)
    ])

    hidden_t = fluid.layers.elementwise_mul(
        x=output_gate, y=fluid.layers.tanh(x=cell_t))

    return hidden_t, cell_t


def seq_to_seq_net(embedding_dim, encoder_size, decoder_size, source_dict_dim,
                   target_dict_dim, is_generating, beam_size, max_length):
    """Construct a seq2seq network."""

    def bi_lstm_encoder(input_seq, gate_size):
        # Linear transformation part for input gate, output gate, forget gate
        # and cell activation vectors need be done outside of dynamic_lstm.
        # So the output size is 4 times of gate_size.
        input_forward_proj = fluid.layers.fc(input=input_seq,
                                             size=gate_size * 4,
                                             act=None,
                                             bias_attr=False)
        forward, _ = fluid.layers.dynamic_lstm(
            input=input_forward_proj, size=gate_size * 4, use_peepholes=False)
        input_reversed_proj = fluid.layers.fc(input=input_seq,
                                              size=gate_size * 4,
                                              act=None,
                                              bias_attr=False)
        reversed, _ = fluid.layers.dynamic_lstm(
            input=input_reversed_proj,
            size=gate_size * 4,
            is_reverse=True,
            use_peepholes=False)
        return forward, reversed

    src_word_idx = fluid.layers.data(
        name='source_sequence', shape=[1], dtype='int64', lod_level=1)

    src_embedding = fluid.layers.embedding(
        input=src_word_idx,
        size=[source_dict_dim, embedding_dim],
        dtype='float32')

    src_forward, src_reversed = bi_lstm_encoder(
        input_seq=src_embedding, gate_size=encoder_size)

    encoded_vector = fluid.layers.concat(
        input=[src_forward, src_reversed], axis=1)

    encoded_proj = fluid.layers.fc(input=encoded_vector,
                                   size=decoder_size,
                                   bias_attr=False)

    backward_first = fluid.layers.sequence_pool(
        input=src_reversed, pool_type='first')

    decoder_boot = fluid.layers.fc(input=backward_first,
                                   size=decoder_size,
                                   bias_attr=False,
                                   act='tanh')

    def lstm_decoder_with_attention(target_embedding, encoder_vec, encoder_proj,
                                    decoder_boot, decoder_size):
        def simple_attention(encoder_vec, encoder_proj, decoder_state):
            decoder_state_proj = fluid.layers.fc(input=decoder_state,
                                                 size=decoder_size,
                                                 bias_attr=False)
            decoder_state_expand = fluid.layers.sequence_expand(
                x=decoder_state_proj, y=encoder_proj)
            concated = fluid.layers.concat(
                input=[encoder_proj, decoder_state_expand], axis=1)
            attention_weights = fluid.layers.fc(input=concated,
                                                size=1,
                                                act='tanh',
                                                bias_attr=False)
            attention_weights = fluid.layers.sequence_softmax(
                input=attention_weights)
            weigths_reshape = fluid.layers.reshape(
                x=attention_weights, shape=[-1])
            scaled = fluid.layers.elementwise_mul(
                x=encoder_vec, y=weigths_reshape, axis=0)
            context = fluid.layers.sequence_pool(input=scaled, pool_type='sum')
            return context

        rnn = fluid.layers.DynamicRNN()

        cell_init = fluid.layers.fill_constant_batch_size_like(
            input=decoder_boot,
            value=0.0,
            shape=[-1, decoder_size],
            dtype='float32')
        cell_init.stop_gradient = False

        with rnn.block():
            current_word = rnn.step_input(target_embedding)
            encoder_vec = rnn.static_input(encoder_vec)
            encoder_proj = rnn.static_input(encoder_proj)
            hidden_mem = rnn.memory(init=decoder_boot, need_reorder=True)
            cell_mem = rnn.memory(init=cell_init)
            context = simple_attention(encoder_vec, encoder_proj, hidden_mem)
            decoder_inputs = fluid.layers.concat(
                input=[context, current_word], axis=1)
            h, c = lstm_step(decoder_inputs, hidden_mem, cell_mem, decoder_size)
            rnn.update_memory(hidden_mem, h)
            rnn.update_memory(cell_mem, c)
            out = fluid.layers.fc(input=h,
                                  size=target_dict_dim,
                                  bias_attr=True,
                                  act='softmax')
            rnn.output(out)
        return rnn()

    if not is_generating:
        trg_word_idx = fluid.layers.data(
            name='target_sequence', shape=[1], dtype='int64', lod_level=1)

        trg_embedding = fluid.layers.embedding(
            input=trg_word_idx,
            size=[target_dict_dim, embedding_dim],
            dtype='float32')

        prediction = lstm_decoder_with_attention(trg_embedding, encoded_vector,
                                                 encoded_proj, decoder_boot,
                                                 decoder_size)
        label = fluid.layers.data(
            name='label_sequence', shape=[1], dtype='int64', lod_level=1)
        cost = fluid.layers.cross_entropy(input=prediction, label=label)
        avg_cost = fluid.layers.mean(x=cost)

        feeding_list = ["source_sequence", "target_sequence", "label_sequence"]

        return avg_cost, feeding_list


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 = np.concatenate(data, axis=0).astype("int64")
    flattened_data = flattened_data.reshape([len(flattened_data), 1])
    lod_t = core.LoDTensor()
    lod_t.set(flattened_data, place)
    lod_t.set_lod([lod])
    return lod_t, lod[-1]


def lodtensor_to_ndarray(lod_tensor):
    dims = lod_tensor.get_dims()
    ndarray = np.zeros(shape=dims).astype('float32')
    for i in xrange(np.product(dims)):
        ndarray.ravel()[i] = lod_tensor.get_float_element(i)
    return ndarray


def get_model(args):
    if args.use_reader_op:
        raise Exception("machine_translation do not support reader op for now.")
    embedding_dim = 512
    encoder_size = 512
    decoder_size = 512
    dict_size = 30000
    beam_size = 3
    max_length = 250
    avg_cost, feeding_list = seq_to_seq_net(
        embedding_dim,
        encoder_size,
        decoder_size,
        dict_size,
        dict_size,
        False,
        beam_size=beam_size,
        max_length=max_length)

    # clone from default main program
    inference_program = fluid.default_main_program().clone()

    optimizer = fluid.optimizer.Adam(learning_rate=args.learning_rate)

    train_batch_generator = paddle.batch(
        paddle.reader.shuffle(
            paddle.dataset.wmt14.train(dict_size), buf_size=1000),
        batch_size=args.batch_size)

    test_batch_generator = paddle.batch(
        paddle.reader.shuffle(
            paddle.dataset.wmt14.test(dict_size), buf_size=1000),
        batch_size=args.batch_size)

    return avg_cost, inference_program, optimizer, train_batch_generator, \
           test_batch_generator, None