# Copyright (c) 2019 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 numpy import paddle.fluid as fluid import paddle.fluid.layers as layers import paddle.fluid.core as core from paddle.fluid.contrib.layers import basic_gru from paddle.fluid.executor import Executor from paddle.fluid import framework import numpy as np SIGMOID_THRESHOLD_MIN = -40.0 SIGMOID_THRESHOLD_MAX = 13.0 EXP_MAX_INPUT = 40.0 def sigmoid(x): y = np.copy(x) y[x < SIGMOID_THRESHOLD_MIN] = SIGMOID_THRESHOLD_MIN y[x > SIGMOID_THRESHOLD_MAX] = SIGMOID_THRESHOLD_MAX return 1. / (1. + np.exp(-y)) def tanh(x): y = -2. * x y[y > EXP_MAX_INPUT] = EXP_MAX_INPUT return (2. / (1. + np.exp(y))) - 1. def gru_np(input, init_h, hidden_size, gate_weight, gate_bias, candidate_weight, candidate_bias, num_layers=1, batch_first=False, is_bidirect=False, sequence_length=None): def step(step_in, pre_hidden, gate_w, gate_b, candidate_w, candidate_b): concat_1 = np.concatenate([step_in, pre_hidden], 1) gate_input = np.matmul(concat_1, gate_w) gate_input += gate_b gate_input = sigmoid(gate_input) r, u = np.split(gate_input, indices_or_sections=2, axis=1) r_hidden = r * pre_hidden candidate = np.matmul( np.concatenate([step_in, pre_hidden], 1), candidate_w) candidate += candidate_b c = tanh(candidate) new_hidden = u * pre_hidden + (1 - u) * c return new_hidden if batch_first: input = np.tranpose(input, [1, 0, 2]) batch_size = input.shape[1] mask = None if sequence_length is not None: max_seq_len = input.shape[0] mask = np.zeros([batch_size, max_seq_len]) for i, len in enumerate(sequence_length): mask[i, :len] = 1.0 mask = np.transpose(mask, [1, 0]) direc_num = 1 if is_bidirect: direc_num = 2 if init_h: init_h = np.reshape( init_h, shape=[num_layers, direc_num, -1, hidden_size]) else: init_h = np.zeros([num_layers, direc_num, batch_size, hidden_size]) def get_single_direction_output(rnn_input, mask=None, direc_index=0): seq_len = rnn_input.shape[0] output = [] # init pre hidden pre_hidden_array = [] for i in range(num_layers): pre_hidden_array.append(init_h[i, direc_index]) for i in range(seq_len): step_input = rnn_input[i] if mask is not None: step_mask = mask[i] step_mask = np.reshape(step_mask, [-1, 1]) for i in range(num_layers): new_hidden = step( step_input, pre_hidden_array[i], gate_weight[direc_index * num_layers + i], gate_bias[direc_index * num_layers + i], candidate_weight[direc_index * num_layers + i], candidate_bias[direc_index * num_layers + i]) if mask is not None: new_hidden = new_hidden * step_mask + ( 1 - step_mask) * pre_hidden_array[i] pre_hidden_array[i] = new_hidden step_input = new_hidden output.append(step_input) rnn_out = np.concatenate(output, 0) rnn_out = np.reshape(rnn_out, [seq_len, -1, hidden_size]) last_hidden_out = np.concatenate(pre_hidden_array, 0) last_hidden_out = np.reshape(last_hidden_out, [num_layers, -1, hidden_size]) return rnn_out, last_hidden_out fw_rnn_out, fw_last_hidden = get_single_direction_output( input, mask, direc_index=0) if is_bidirect: bw_input = input[::-1] bw_mask = None if mask is not None: bw_mask = mask[::-1] bw_rnn_out, bw_last_hidden = get_single_direction_output( bw_input, bw_mask, direc_index=1) bw_rnn_out = bw_rnn_out[::-1] rnn_out = np.concatenate([fw_rnn_out, bw_rnn_out], 2) last_hidden = np.concatenate([fw_last_hidden, bw_last_hidden], 1) last_hidden = np.reshape(last_hidden, [num_layers * direc_num, -1, hidden_size]) if batch_first: rnn_out = np.transpose(rnn_out, [1, 0, 2]) return rnn_out, last_hidden else: rnn_out = fw_rnn_out last_hidden = fw_last_hidden if batch_first: rnn_out = np.transpose(rnn_out, [1, 0, 2]) return rnn_out, last_hidden class TestBasicGRUApi(unittest.TestCase): def setUp(self): self.hidden_size = 10 self.batch_size = 5 self.seq_len = 6 self.num_layers = 2 self.is_bidirect = True self.batch_first = False def test_run(self): x = layers.data( name='x', shape=[-1, self.batch_size, self.hidden_size], dtype='float32') sequence_length = layers.data( name="sequence_length", shape=[-1], dtype='float32') rnn_out, last_hidden = basic_gru( x, None, self.hidden_size, num_layers=self.num_layers, \ batch_first = self.batch_first, bidirectional=self.is_bidirect, sequence_length=sequence_length ) last_hidden.persisbale = True rnn_out.persisbale = True if core.is_compiled_with_cuda(): place = core.CUDAPlace(0) else: place = core.CPUPlace() exe = Executor(place) exe.run(framework.default_startup_program()) param_list = fluid.default_main_program().block(0).all_parameters() # process weight and bias gate_weight = [] gate_bias = [] candidate_weight = [] candidate_bias = [] for i in range(self.num_layers): gate_w_name = "basic_gru_layers_" + str(i) + "/BasicGRUUnit_0.w_0" gate_b_name = "basic_gru_layers_" + str(i) + "/BasicGRUUnit_0.b_0" candidate_w_name = "basic_gru_layers_" + str( i) + "/BasicGRUUnit_0.w_1" candidate_b_name = "basic_gru_layers_" + str( i) + "/BasicGRUUnit_0.b_1" gate_w = np.array(fluid.global_scope().find_var(gate_w_name) .get_tensor()) gate_w = np.random.uniform( -0.1, 0.1, size=gate_w.shape).astype('float32') fluid.global_scope().find_var(gate_w_name).get_tensor().set(gate_w, place) gate_b = np.array(fluid.global_scope().find_var(gate_b_name) .get_tensor()) gate_b = np.random.uniform( -0.1, 0.1, size=gate_b.shape).astype('float32') fluid.global_scope().find_var(gate_b_name).get_tensor().set(gate_b, place) candidate_w = np.array(fluid.global_scope().find_var( candidate_w_name).get_tensor()) candidate_w = np.random.uniform( -0.1, 0.1, size=candidate_w.shape).astype('float32') fluid.global_scope().find_var(candidate_w_name).get_tensor().set( candidate_w, place) candidate_b = np.array(fluid.global_scope().find_var( candidate_b_name).get_tensor()) candidate_b = np.random.uniform( -0.1, 0.1, size=candidate_b.shape).astype('float32') fluid.global_scope().find_var(candidate_b_name).get_tensor().set( candidate_b, place) gate_weight.append(gate_w) gate_bias.append(gate_b) candidate_weight.append(candidate_w) candidate_bias.append(candidate_b) if self.is_bidirect: for i in range(self.num_layers): gate_w_name = "basic_gru_reverse_layers_" + str( i) + "/BasicGRUUnit_0.w_0" gate_b_name = "basic_gru_reverse_layers_" + str( i) + "/BasicGRUUnit_0.b_0" candidate_w_name = "basic_gru_reverse_layers_" + str( i) + "/BasicGRUUnit_0.w_1" candidate_b_name = "basic_gru_reverse_layers_" + str( i) + "/BasicGRUUnit_0.b_1" gate_w = np.array(fluid.global_scope().find_var(gate_w_name) .get_tensor()) gate_w = np.random.uniform( -0.1, 0.1, size=gate_w.shape).astype('float32') fluid.global_scope().find_var(gate_w_name).get_tensor().set( gate_w, place) gate_b = np.array(fluid.global_scope().find_var(gate_b_name) .get_tensor()) gate_b = np.random.uniform( -0.1, 0.1, size=gate_b.shape).astype('float32') fluid.global_scope().find_var(gate_b_name).get_tensor().set( gate_b, place) candidate_w = np.array(fluid.global_scope().find_var( candidate_w_name).get_tensor()) candidate_w = np.random.uniform( -0.1, 0.1, size=candidate_w.shape).astype('float32') fluid.global_scope().find_var(candidate_w_name).get_tensor( ).set(candidate_w, place) candidate_b = np.array(fluid.global_scope().find_var( candidate_b_name).get_tensor()) candidate_b = np.random.uniform( -0.1, 0.1, size=candidate_b.shape).astype('float32') fluid.global_scope().find_var(candidate_b_name).get_tensor( ).set(candidate_b, place) gate_weight.append(gate_w) gate_bias.append(gate_b) candidate_weight.append(candidate_w) candidate_bias.append(candidate_b) step_input_np = np.random.uniform(-0.1, 0.1, ( self.seq_len, self.batch_size, self.hidden_size)).astype('float32') sequence_length_np = np.random.randint( self.seq_len // 2, self.seq_len, size=(self.batch_size)).astype('int64') out = exe.run( feed={'x': step_input_np, 'sequence_length': sequence_length_np}, fetch_list=[rnn_out, last_hidden]) api_rnn_out = out[0] api_last_hidden = out[1] np_out = gru_np( step_input_np, None, self.hidden_size, gate_weight, gate_bias, candidate_weight, candidate_bias, num_layers=self.num_layers, batch_first=self.batch_first, is_bidirect=self.is_bidirect, sequence_length=sequence_length_np) self.assertTrue(np.allclose(api_rnn_out, np_out[0], rtol=1e-4, atol=0)) self.assertTrue( np.allclose( api_last_hidden, np_out[1], rtol=1e-4, atol=0)) if __name__ == '__main__': unittest.main()