# 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 print_function import unittest import paddle.fluid as fluid from paddle.fluid.imperative.nn import EMBEDDING import paddle.fluid.framework as framework from paddle.fluid.optimizer import SGDOptimizer from paddle.fluid.imperative.base import to_variable import numpy as np from paddle.fluid.backward import append_backward class SimpleLSTMRNN(fluid.imperative.Layer): def __init__(self, hidden_size, num_steps, num_layers=2, init_scale=0.1, dropout=None): super(SimpleLSTMRNN, self).__init__() self._hidden_size = hidden_size self._num_layers = num_layers self._init_scale = init_scale self._dropout = dropout self.input = None self.num_steps = num_steps def _build_once(self, input_embedding, init_hidden=None, init_cell=None): self.weight_1_arr = [] self.weight_2_arr = [] self.bias_arr = [] self.hidden_array = [] self.cell_array = [] self.mask_array = [] for i in range(self._num_layers): weight_1 = fluid.layers.create_parameter( shape=[self._hidden_size * 2, self._hidden_size * 4], dtype="float32", name="fc_weight1_" + str(i), default_initializer=fluid.initializer.UniformInitializer( low=-self._init_scale, high=self._init_scale)) self.weight_1_arr.append(weight_1) bias_1 = fluid.layers.create_parameter( [self._hidden_size * 4], dtype="float32", name="fc_bias1_" + str(i), default_initializer=fluid.initializer.Constant(0.0)) self.bias_arr.append(bias_1) pre_hidden = fluid.layers.slice( init_hidden, axes=[0], starts=[i], ends=[i + 1]) pre_cell = fluid.layers.slice( init_cell, axes=[0], starts=[i], ends=[i + 1]) pre_hidden = fluid.layers.reshape( pre_hidden, shape=[-1, self._hidden_size]) pre_cell = fluid.layers.reshape( pre_cell, shape=[-1, self._hidden_size]) self.hidden_array.append(pre_hidden) self.cell_array.append(pre_cell) def forward(self, input_embedding, init_hidden=None, init_cell=None): res = [] for index in range(self.num_steps): self.input = fluid.layers.slice( input_embedding, axes=[1], starts=[index], ends=[index + 1]) self.input = fluid.layers.reshape( self.input, shape=[-1, self._hidden_size]) for k in range(self._num_layers): pre_hidden = self.hidden_array[k] print("pre_hidden shape is:{}".format(pre_hidden.shape)) print("input shape is:{}".format(self.input.shape)) pre_cell = self.cell_array[k] weight_1 = self.weight_1_arr[k] bias = self.bias_arr[k] nn = fluid.layers.concat([self.input, pre_hidden], 1) gate_input = fluid.layers.matmul(x=nn, y=weight_1) gate_input = fluid.layers.elementwise_add(gate_input, bias) print("gate_input shape is: {}".format(gate_input.shape)) print("gate_input value is :{}".format(gate_input._numpy())) print("gate_input desc is :{}".format(gate_input)) # i, j, f, o = fluid.layers.split(gate_input, num_or_sections=4, dim=-1) # # # # c = pre_cell * fluid.layers.sigmoid(f) + fluid.layers.sigmoid( # # i) * fluid.layers.tanh(j) # # m = fluid.layers.tanh(c) * fluid.layers.sigmoid(o) # # # # self.hidden_array[k] = m # # self.cell_array[k] = c # # self.input = m # # # # if self.dropout is not None and self.dropout > 0.0: # # self.input = fluid.layers.dropout( # # self.input, # # dropout_prob=self.dropout, # # dropout_implementation='upscale_in_train') # # # # res.append( # # fluid.layers.reshape( # # input, shape=[1, -1, self._hidden_size])) # # real_res = fluid.layers.concat(res, 0) # # real_res = fluid.layers.transpose(x=real_res, perm=[1, 0, 2]) # # last_hidden = fluid.layers.concat(self.hidden_array, 1) # # last_hidden = fluid.layers.reshape( # # last_hidden, shape=[-1, self._num_layers, self._hidden_size]) # # last_hidden = fluid.layers.transpose(x=last_hidden, perm=[1, 0, 2]) # # last_cell = fluid.layers.concat(self.cell_array, 1) # # last_cell = fluid.layers.reshape( # # last_cell, shape=[-1, self._num_layers, self._hidden_size]) # # last_cell = fluid.layers.transpose(x=last_cell, perm=[1, 0, 2]) # # # return real_res, last_hidden, last_cell return [1], [2], [3] class PtbModel(fluid.imperative.Layer): def __init__(self, hidden_size, vocab_size, num_layers=2, num_steps=20, init_scale=0.1, dropout=None): super(PtbModel, self).__init__() self.hidden_size = hidden_size self.vocab_size = vocab_size self.init_scale = init_scale self.num_layers = num_layers self.num_steps = num_steps self.dropout = dropout self.simple_lstm_rnn = SimpleLSTMRNN( hidden_size, num_steps, num_layers=num_layers, init_scale=init_scale, dropout=dropout) self.embedding = EMBEDDING( size=[vocab_size, hidden_size], dtype='float32', is_sparse=False, param_attr=fluid.ParamAttr( name='embedding_para', initializer=fluid.initializer.UniformInitializer( low=-init_scale, high=init_scale))) def _build_once(self, input, label, init_hidden, init_cell): self.softmax_weight = fluid.layers.create_parameter( [self.hidden_size, self.vocab_size], dtype="float32", name="softmax_weight", default_initializer=fluid.initializer.UniformInitializer( low=-self.init_scale, high=self.init_scale)) self.softmax_bias = fluid.layers.create_parameter( [self.vocab_size], dtype="float32", name='softmax_bias', default_initializer=fluid.initializer.UniformInitializer( low=-self.init_scale, high=self.init_scale)) def forward(self, input, label, init_hidden, init_cell): init_h = fluid.layers.reshape( init_hidden, shape=[self.num_layers, -1, self.hidden_size]) init_c = fluid.layers.reshape( init_cell, shape=[self.num_layers, -1, self.hidden_size]) x_emb = self.embedding(input) x_emb = fluid.layers.reshape( x_emb, shape=[-1, self.num_steps, self.hidden_size]) if self.dropout is not None and self.dropout > 0.0: x_emb = fluid.layers.dropout( x_emb, dropout_prob=self.drop_out, dropout_implementation='upscale_in_train') print("init_c is {}".format(init_c)) rnn_out, last_hidden, last_cell = self.simple_lstm_rnn(x_emb, init_h, init_c) rnn_out = fluid.layers.reshape( rnn_out, shape=[-1, self.num_steps, self.hidden_size]) projection = fluid.layers.reshape(rnn_out, self.softmax_weight) projection = fluid.layers.elementwise_add(projection, self.softmax_bias) projection = fluid.layers.reshape( projection, shape=[-1, self.vocab_size]) projection = fluid.layers.reshape( projection, shape=[-1, self.vocab_size]) loss = fluid.layers.softmax_with_cross_entropy( logits=projection, label=label, soft_label=False) loss = fluid.layers.reshape(loss, shape=[-1, self.num_steps]) loss = fluid.layers.reduce_mean(loss, dim=[0]) loss = fluid.layers.reduce_sum(loss) loss.permissions = True return loss, last_hidden, last_cell class TestImperativePtbRnn(unittest.TestCase): def test_mnist_cpu_float32(self): seed = 90 hidden_size = 10 vocab_size = 1000 num_layers = 1 num_steps = 3 init_scale = 0.1 batch_size = 4 with fluid.imperative.guard(): fluid.default_startup_program().random_seed = seed fluid.default_main_program().random_seed = seed # TODO: marsyang1993 Change seed to ptb_model = PtbModel( hidden_size=hidden_size, vocab_size=vocab_size, num_layers=num_layers, num_steps=num_steps, init_scale=init_scale) sgd = SGDOptimizer(learning_rate=1e-3) print("q") for i in range(2): x_data = np.arange(12).reshape(4, 3).astype('int64') y_data = np.arange(1, 13).reshape(4, 3).astype('int64') x_data = x_data.reshape((-1, num_steps, 1)) y_data = y_data.reshape((-1, 1)) init_hidden_data = np.zeros( (num_layers, batch_size, hidden_size), dtype='float32') init_cell_data = np.zeros( (num_layers, batch_size, hidden_size), dtype='float32') x = to_variable(x_data) y = to_variable(y_data) init_hidden = to_variable(init_hidden_data) init_cell = to_variable(init_cell_data) dy_loss, last_hidden, last_cell = ptb_model(x, y, init_hidden, init_cell) dy_param_init = dict() if i == 0: for param in fluid.default_main_program().global_block( ).all_parameters(): dy_param_init[param.name] = param._numpy() dy_loss._backward() sgd.minimize(dy_loss) dy_param_updated = dict() for param in fluid.default_main_program().global_block( ).all_parameters(): dy_param_updated[param.name] = param._numpy() if __name__ == '__main__': unittest.main()