# 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 numpy as np import paddle.fluid.core as core from op_test import OpTest import paddle.fluid as fluid SIGMOID_THRESHOLD_MIN = -40.0 SIGMOID_THRESHOLD_MAX = 13.0 EXP_MAX_INPUT = 40.0 def lstm_naive( input, w, ): seq_len, batch_size, hidden_size = input.shape offset = 0 wi = w[offset:offset + hidden_size * hidden_size].reshape( (hidden_size, hidden_size)).transpose() offset += hidden_size * hidden_size wf = w[offset:offset + hidden_size * hidden_size].reshape( (hidden_size, hidden_size)).transpose() offset += hidden_size * hidden_size wc = w[offset:offset + hidden_size * hidden_size].reshape( (hidden_size, hidden_size)).transpose() offset += hidden_size * hidden_size wo = w[offset:offset + hidden_size * hidden_size].reshape( (hidden_size, hidden_size)).transpose() offset += hidden_size * hidden_size ri = w[offset:offset + hidden_size * hidden_size].reshape( (hidden_size, hidden_size)).transpose() offset += hidden_size * hidden_size rf = w[offset:offset + hidden_size * hidden_size].reshape( (hidden_size, hidden_size)).transpose() offset += hidden_size * hidden_size rc = w[offset:offset + hidden_size * hidden_size].reshape( (hidden_size, hidden_size)).transpose() offset += hidden_size * hidden_size ro = w[offset:offset + hidden_size * hidden_size].reshape( (hidden_size, hidden_size)).transpose() offset += hidden_size * hidden_size bi_1 = w[offset:offset + hidden_size] offset += hidden_size bf_1 = w[offset:offset + hidden_size] offset += hidden_size bc_1 = w[offset:offset + hidden_size] offset += hidden_size bo_1 = w[offset:offset + hidden_size] offset += hidden_size bi_2 = w[offset:offset + hidden_size] offset += hidden_size bf_2 = w[offset:offset + hidden_size] offset += hidden_size bc_2 = w[offset:offset + hidden_size] offset += hidden_size bo_2 = w[offset:offset + hidden_size] 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. output = [] pre_h = np.zeros((batch_size, hidden_size), dtype=input.dtype) pre_c = np.zeros((batch_size, hidden_size), dtype=input.dtype) for i in range(seq_len): emb_1 = input[i] input_gate = sigmoid( np.matmul(emb_1, wi) + np.matmul(pre_h, ri) + bi_1 + bi_2) forget_gate = sigmoid( np.matmul(emb_1, wf) + np.matmul(pre_h, rf) + bf_1 + bf_2) output_gate = sigmoid( np.matmul(emb_1, wo) + np.matmul(pre_h, ro) + bo_1 + bo_2) c_t_temp = tanh( np.matmul(emb_1, wc) + np.matmul(pre_h, rc) + bc_1 + bc_2) new_c = input_gate * c_t_temp + forget_gate * pre_c new_h = output_gate * tanh(new_c) pre_h = new_h pre_c = new_c output.append(new_h) output = np.concatenate(output, -1) output = output.reshape((batch_size, -1, hidden_size)) output = output.transpose((1, 0, 2)) return output, pre_h, pre_c class TestCUDNNLstmOp(OpTest): def setUp(self): self.op_type = "cudnn_lstm" self.dtype = np.float32 num_steps = 20 batch_size = 5 hidden_size = 20 input_weight_size = (hidden_size * hidden_size) * 4 hidden_weight_size = (hidden_size * hidden_size) * 4 weight_size = input_weight_size + hidden_weight_size weight_size += hidden_size * 8 input = np.random.uniform( low=-0.1, high=0.1, size=(num_steps, batch_size, hidden_size)).astype(self.dtype) flat_w = np.random.uniform( low=-0.1, high=0.1, size=(weight_size)).astype(self.dtype) output, last_hidden, last_cell = lstm_naive(input, flat_w) init_h = np.zeros((batch_size, hidden_size), dtype=np.float32) init_c = np.zeros((batch_size, hidden_size), dtype=np.float32) scope = core.Scope() program = fluid.Program() block = program.global_block() cache_temp = block.create_var( name="Cache", persistable=True, type=core.VarDesc.VarType.RAW, stop_gradient=True) self.inputs = { 'Input': OpTest.np_dtype_to_fluid_dtype(input), 'W': OpTest.np_dtype_to_fluid_dtype(flat_w), 'InitH': OpTest.np_dtype_to_fluid_dtype(init_h), 'InitC': OpTest.np_dtype_to_fluid_dtype(init_c), } self.cache_name_list = ['Cache'] self.attrs = { 'max_len': num_steps, 'dropout_prob': 0.0, 'is_bidirec': False, 'input_size': hidden_size, 'hidden_size': hidden_size, 'num_layers': 1, } self.outputs = { 'Out': output, "last_h": last_hidden, 'last_c': last_cell } def test_output_with_place(self): if self.has_cuda(): place = core.CUDAPlace(0) self.check_output_with_place(place, atol=1e-5) def test_grad_with_place(self): if core.is_compiled_with_cuda(): place = core.CUDAPlace(0) self.check_grad_with_place( place, set(['Input', 'W', 'InitH', 'InitC']), ['Out', 'last_h', 'last_c'], max_relative_error=0.02) def has_cuda(self): return core.is_compiled_with_cuda() if __name__ == '__main__': unittest.main()