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351 lines
15 KiB
351 lines
15 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import unittest
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import paddle.fluid as fluid
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from paddle.fluid.imperative.nn import Embedding
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import paddle.fluid.framework as framework
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from paddle.fluid.optimizer import SGDOptimizer
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from paddle.fluid.imperative.base import to_variable
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from test_imperative_base import new_program_scope
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import numpy as np
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import six
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from paddle.fluid.backward import append_backward
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class SimpleLSTMRNN(fluid.imperative.Layer):
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def __init__(self,
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hidden_size,
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num_steps,
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num_layers=2,
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init_scale=0.1,
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dropout=None):
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super(SimpleLSTMRNN, self).__init__()
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self._hidden_size = hidden_size
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self._num_layers = num_layers
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self._init_scale = init_scale
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self._dropout = dropout
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self._input = None
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self._num_steps = num_steps
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def _build_once(self, input_embedding, init_hidden=None, init_cell=None):
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self.weight_1_arr = []
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self.weight_2_arr = []
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self.bias_arr = []
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self.hidden_array = []
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self.cell_array = []
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self.mask_array = []
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for i in range(self._num_layers):
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weight_1 = fluid.layers.create_parameter(
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shape=[self._hidden_size * 2, self._hidden_size * 4],
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dtype="float32",
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name="fc_weight1_" + str(i),
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default_initializer=fluid.initializer.UniformInitializer(
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low=-self._init_scale, high=self._init_scale))
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self.weight_1_arr.append(weight_1)
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bias_1 = fluid.layers.create_parameter(
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[self._hidden_size * 4],
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dtype="float32",
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name="fc_bias1_" + str(i),
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default_initializer=fluid.initializer.Constant(0.0))
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self.bias_arr.append(bias_1)
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pre_hidden = fluid.layers.slice(
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init_hidden, axes=[0], starts=[i], ends=[i + 1])
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pre_cell = fluid.layers.slice(
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init_cell, axes=[0], starts=[i], ends=[i + 1])
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pre_hidden = fluid.layers.reshape(
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pre_hidden, shape=[-1, self._hidden_size])
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pre_cell = fluid.layers.reshape(
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pre_cell, shape=[-1, self._hidden_size])
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self.hidden_array.append(pre_hidden)
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self.cell_array.append(pre_cell)
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def parameters(self):
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parameters = list()
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for param in self.weight_1_arr:
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parameters.append(param)
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for param in self.weight_2_arr:
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parameters.append(param)
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for bias in self.bias_arr:
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parameters.append(bias)
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return parameters
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def forward(self, input_embedding, init_hidden=None, init_cell=None):
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res = []
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for index in range(self._num_steps):
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self._input = fluid.layers.slice(
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input_embedding, axes=[1], starts=[index], ends=[index + 1])
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self._input = fluid.layers.reshape(
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self._input, shape=[-1, self._hidden_size])
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for k in range(self._num_layers):
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pre_hidden = self.hidden_array[k]
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pre_cell = self.cell_array[k]
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weight_1 = self.weight_1_arr[k]
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bias = self.bias_arr[k]
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nn = fluid.layers.concat([self._input, pre_hidden], 1)
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gate_input = fluid.layers.matmul(x=nn, y=weight_1)
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gate_input = fluid.layers.elementwise_add(gate_input, bias)
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i, j, f, o = fluid.layers.split(
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gate_input, num_or_sections=4, dim=-1)
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c = pre_cell * fluid.layers.sigmoid(f) + fluid.layers.sigmoid(
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i) * fluid.layers.tanh(j)
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m = fluid.layers.tanh(c) * fluid.layers.sigmoid(o)
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self.hidden_array[k] = m
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self.cell_array[k] = c
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self._input = m
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if self._dropout is not None and self._dropout > 0.0:
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self._input = fluid.layers.dropout(
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self._input,
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dropout_prob=self._dropout,
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dropout_implementation='upscale_in_train')
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res.append(
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fluid.layers.reshape(
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self._input, shape=[1, -1, self._hidden_size]))
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real_res = fluid.layers.concat(res, 0)
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real_res = fluid.layers.transpose(x=real_res, perm=[1, 0, 2])
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last_hidden = fluid.layers.concat(self.hidden_array, 1)
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last_hidden = fluid.layers.reshape(
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last_hidden, shape=[-1, self._num_layers, self._hidden_size])
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last_hidden = fluid.layers.transpose(x=last_hidden, perm=[1, 0, 2])
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last_cell = fluid.layers.concat(self.cell_array, 1)
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last_cell = fluid.layers.reshape(
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last_cell, shape=[-1, self._num_layers, self._hidden_size])
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last_cell = fluid.layers.transpose(x=last_cell, perm=[1, 0, 2])
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return real_res, last_hidden, last_cell
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class PtbModel(fluid.imperative.Layer):
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def __init__(self,
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hidden_size,
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vocab_size,
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num_layers=2,
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num_steps=20,
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init_scale=0.1,
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dropout=None):
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super(PtbModel, self).__init__()
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self.hidden_size = hidden_size
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self.vocab_size = vocab_size
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self.init_scale = init_scale
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self.num_layers = num_layers
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self.num_steps = num_steps
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self.dropout = dropout
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self.simple_lstm_rnn = SimpleLSTMRNN(
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hidden_size,
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num_steps,
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num_layers=num_layers,
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init_scale=init_scale,
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dropout=dropout)
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self.embedding = Embedding(
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size=[vocab_size, hidden_size],
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dtype='float32',
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is_sparse=False,
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param_attr=fluid.ParamAttr(
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name='embedding_para',
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initializer=fluid.initializer.UniformInitializer(
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low=-init_scale, high=init_scale)))
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self.softmax_weight = fluid.layers.create_parameter(
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[self.hidden_size, self.vocab_size],
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dtype="float32",
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name="softmax_weight",
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default_initializer=fluid.initializer.UniformInitializer(
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low=-self.init_scale, high=self.init_scale))
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self.softmax_bias = fluid.layers.create_parameter(
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[self.vocab_size],
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dtype="float32",
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name='softmax_bias',
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default_initializer=fluid.initializer.UniformInitializer(
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low=-self.init_scale, high=self.init_scale))
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def _build_once(self, input, label, init_hidden, init_cell):
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pass
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def parameters(self):
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parameters = self.simple_lstm_rnn.parameters() + [
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self.softmax_weight, self.softmax_bias
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] + self.embedding.parameters()
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return parameters
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def forward(self, input, label, init_hidden, init_cell):
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init_h = fluid.layers.reshape(
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init_hidden, shape=[self.num_layers, -1, self.hidden_size])
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init_c = fluid.layers.reshape(
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init_cell, shape=[self.num_layers, -1, self.hidden_size])
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x_emb = self.embedding(input)
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x_emb = fluid.layers.reshape(
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x_emb, shape=[-1, self.num_steps, self.hidden_size])
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if self.dropout is not None and self.dropout > 0.0:
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x_emb = fluid.layers.dropout(
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x_emb,
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dropout_prob=self.drop_out,
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dropout_implementation='upscale_in_train')
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rnn_out, last_hidden, last_cell = self.simple_lstm_rnn(x_emb, init_h,
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init_c)
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rnn_out = fluid.layers.reshape(
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rnn_out, shape=[-1, self.num_steps, self.hidden_size])
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projection = fluid.layers.matmul(rnn_out, self.softmax_weight)
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projection = fluid.layers.elementwise_add(projection, self.softmax_bias)
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projection = fluid.layers.reshape(
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projection, shape=[-1, self.vocab_size])
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projection = fluid.layers.reshape(
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projection, shape=[-1, self.vocab_size])
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loss = fluid.layers.softmax_with_cross_entropy(
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logits=projection, label=label, soft_label=False)
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loss = fluid.layers.reshape(loss, shape=[-1, self.num_steps])
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loss = fluid.layers.reduce_mean(loss, dim=[0])
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loss = fluid.layers.reduce_sum(loss)
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loss.permissions = True
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return loss, last_hidden, last_cell
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class TestImperativePtbRnn(unittest.TestCase):
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def test_ptb_rnn_cpu_float32(self):
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seed = 90
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hidden_size = 10
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vocab_size = 1000
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num_layers = 1
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num_steps = 3
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init_scale = 0.1
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batch_size = 4
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with fluid.imperative.guard():
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fluid.default_startup_program().random_seed = seed
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fluid.default_main_program().random_seed = seed
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# TODO: marsyang1993 Change seed to
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ptb_model = PtbModel(
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hidden_size=hidden_size,
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vocab_size=vocab_size,
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num_layers=num_layers,
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num_steps=num_steps,
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init_scale=init_scale)
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sgd = SGDOptimizer(learning_rate=1e-3)
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dy_param_updated = dict()
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dy_param_init = dict()
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dy_loss = None
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last_hidden = None
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last_cell = None
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for i in range(2):
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x_data = np.arange(12).reshape(4, 3).astype('int64')
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y_data = np.arange(1, 13).reshape(4, 3).astype('int64')
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x_data = x_data.reshape((-1, num_steps, 1))
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y_data = y_data.reshape((-1, 1))
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init_hidden_data = np.zeros(
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(num_layers, batch_size, hidden_size), dtype='float32')
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init_cell_data = np.zeros(
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(num_layers, batch_size, hidden_size), dtype='float32')
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x = to_variable(x_data)
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y = to_variable(y_data)
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init_hidden = to_variable(init_hidden_data)
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init_cell = to_variable(init_cell_data)
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dy_loss, last_hidden, last_cell = ptb_model(x, y, init_hidden,
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init_cell)
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if i == 0:
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for param in ptb_model.parameters():
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dy_param_init[param.name] = param._numpy()
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dy_loss._backward()
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sgd.minimize(dy_loss)
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for param in ptb_model.parameters():
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dy_param_updated[param.name] = param._numpy()
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with new_program_scope():
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fluid.default_startup_program().random_seed = seed
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fluid.default_main_program().random_seed = seed
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# TODO: marsyang1993 Change seed to
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ptb_model = PtbModel(
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hidden_size=hidden_size,
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vocab_size=vocab_size,
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num_layers=num_layers,
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num_steps=num_steps,
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init_scale=init_scale)
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exe = fluid.Executor(fluid.CPUPlace())
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sgd = SGDOptimizer(learning_rate=1e-3)
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x = fluid.layers.data(name="x", shape=[-1, 3, 1], dtype='int64')
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y = fluid.layers.data(name="y", shape=[-1, 1], dtype='float32')
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init_hidden = fluid.layers.data(
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name="init_hidden", shape=[1], dtype='float32')
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init_cell = fluid.layers.data(
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name="init_cell", shape=[1], dtype='float32')
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static_loss, static_last_hidden, static_last_cell = ptb_model(
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x, y, init_hidden, init_cell)
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sgd.minimize(static_loss)
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static_param_updated = dict()
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static_param_init = dict()
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static_param_name_list = list()
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for param in ptb_model.parameters():
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static_param_name_list.append(param.name)
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out = exe.run(framework.default_startup_program(),
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fetch_list=static_param_name_list)
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for i in range(len(static_param_name_list)):
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static_param_init[static_param_name_list[i]] = out[i]
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static_loss_value = None
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static_last_cell_value = None
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static_last_hidden_value = None
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for i in range(2):
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x_data = np.arange(12).reshape(4, 3).astype('int64')
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y_data = np.arange(1, 13).reshape(4, 3).astype('int64')
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x_data = x_data.reshape((-1, num_steps, 1))
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y_data = y_data.reshape((-1, 1))
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init_hidden_data = np.zeros(
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(num_layers, batch_size, hidden_size), dtype='float32')
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init_cell_data = np.zeros(
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(num_layers, batch_size, hidden_size), dtype='float32')
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fetch_list = [static_loss, static_last_hidden, static_last_cell]
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fetch_list.extend(static_param_name_list)
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out = exe.run(fluid.default_main_program(),
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feed={
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"x": x_data,
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"y": y_data,
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"init_hidden": init_hidden_data,
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"init_cell": init_cell_data
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},
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fetch_list=fetch_list)
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static_loss_value = out[0]
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static_last_cell_value = out[1]
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static_last_hidden_value = out[2]
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for k in range(3, len(out)):
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static_param_updated[static_param_name_list[k - 3]] = out[k]
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self.assertTrue(
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np.allclose(static_loss_value.all(), dy_loss._numpy().all()))
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self.assertTrue(
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np.allclose(static_last_cell_value.all(),
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last_cell._numpy().all()))
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self.assertTrue(
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np.allclose(static_last_hidden_value.all(),
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last_hidden._numpy().all()))
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for key, value in six.iteritems(static_param_init):
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self.assertTrue(
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np.allclose(value.all(), dy_param_init[key].all()))
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for key, value in six.iteritems(static_param_updated):
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self.assertTrue(
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np.allclose(value.all(), dy_param_updated[key].all()))
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if __name__ == '__main__':
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unittest.main()
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