1 /* 2 * Copyright (c) 2018-2021 Arm Limited. 3 * 4 * SPDX-License-Identifier: MIT 5 * 6 * Permission is hereby granted, free of charge, to any person obtaining a copy 7 * of this software and associated documentation files (the "Software"), to 8 * deal in the Software without restriction, including without limitation the 9 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or 10 * sell copies of the Software, and to permit persons to whom the Software is 11 * furnished to do so, subject to the following conditions: 12 * 13 * The above copyright notice and this permission notice shall be included in all 14 * copies or substantial portions of the Software. 15 * 16 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 17 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 18 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 19 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 20 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 21 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 22 * SOFTWARE. 23 */ 24 #ifndef ARM_COMPUTE_TEST_RNN_LAYER_FIXTURE 25 #define ARM_COMPUTE_TEST_RNN_LAYER_FIXTURE 26 27 #include "tests/Globals.h" 28 #include "tests/framework/Asserts.h" 29 #include "tests/framework/Fixture.h" 30 #include "tests/validation/reference/ActivationLayer.h" 31 #include "tests/validation/reference/ArithmeticOperations.h" 32 #include "tests/validation/reference/FullyConnectedLayer.h" 33 #include "tests/validation/reference/GEMM.h" 34 35 namespace arm_compute 36 { 37 namespace test 38 { 39 namespace validation 40 { 41 template <typename TensorType, typename AccessorType, typename FunctionType, typename T> 42 class RNNLayerValidationFixture : public framework::Fixture 43 { 44 public: 45 template <typename...> setup(TensorShape input_shape,TensorShape weights_shape,TensorShape recurrent_weights_shape,TensorShape bias_shape,TensorShape output_shape,ActivationLayerInfo info,DataType data_type)46 void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape recurrent_weights_shape, TensorShape bias_shape, TensorShape output_shape, ActivationLayerInfo info, 47 DataType data_type) 48 { 49 _target = compute_target(input_shape, weights_shape, recurrent_weights_shape, bias_shape, output_shape, info, data_type); 50 _reference = compute_reference(input_shape, weights_shape, recurrent_weights_shape, bias_shape, output_shape, info, data_type); 51 } 52 53 protected: 54 template <typename U> fill(U && tensor,int i)55 void fill(U &&tensor, int i) 56 { 57 static_assert(std::is_floating_point<T>::value || std::is_same<T, half>::value, "Only floating point data types supported."); 58 using DistributionType = typename std::conditional<std::is_same<T, half>::value, arm_compute::utils::uniform_real_distribution_16bit<T>, std::uniform_real_distribution<T>>::type; 59 60 DistributionType distribution{ T(-1.0f), T(1.0f) }; 61 library->fill(tensor, distribution, i); 62 } 63 compute_target(const TensorShape & input_shape,const TensorShape & weights_shape,const TensorShape & recurrent_weights_shape,const TensorShape & bias_shape,const TensorShape & output_shape,ActivationLayerInfo info,DataType data_type)64 TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &recurrent_weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, 65 ActivationLayerInfo info, DataType data_type) 66 { 67 // Create tensors 68 TensorType input = create_tensor<TensorType>(input_shape, data_type); 69 TensorType weights = create_tensor<TensorType>(weights_shape, data_type); 70 TensorType recurrent_weights = create_tensor<TensorType>(recurrent_weights_shape, data_type); 71 TensorType bias = create_tensor<TensorType>(bias_shape, data_type); 72 TensorType hidden_state = create_tensor<TensorType>(output_shape, data_type); 73 TensorType output = create_tensor<TensorType>(output_shape, data_type); 74 75 // Create and configure function 76 FunctionType rnn; 77 rnn.configure(&input, &weights, &recurrent_weights, &bias, &hidden_state, &output, info); 78 79 ARM_COMPUTE_ASSERT(input.info()->is_resizable()); 80 ARM_COMPUTE_ASSERT(weights.info()->is_resizable()); 81 ARM_COMPUTE_ASSERT(recurrent_weights.info()->is_resizable()); 82 ARM_COMPUTE_ASSERT(bias.info()->is_resizable()); 83 ARM_COMPUTE_ASSERT(hidden_state.info()->is_resizable()); 84 ARM_COMPUTE_ASSERT(output.info()->is_resizable()); 85 86 // Allocate tensors 87 input.allocator()->allocate(); 88 weights.allocator()->allocate(); 89 recurrent_weights.allocator()->allocate(); 90 bias.allocator()->allocate(); 91 hidden_state.allocator()->allocate(); 92 output.allocator()->allocate(); 93 94 ARM_COMPUTE_ASSERT(!input.info()->is_resizable()); 95 ARM_COMPUTE_ASSERT(!weights.info()->is_resizable()); 96 ARM_COMPUTE_ASSERT(!recurrent_weights.info()->is_resizable()); 97 ARM_COMPUTE_ASSERT(!bias.info()->is_resizable()); 98 ARM_COMPUTE_ASSERT(!hidden_state.info()->is_resizable()); 99 ARM_COMPUTE_ASSERT(!output.info()->is_resizable()); 100 101 // Fill tensors 102 fill(AccessorType(input), 0); 103 fill(AccessorType(weights), 0); 104 fill(AccessorType(recurrent_weights), 0); 105 fill(AccessorType(bias), 0); 106 fill(AccessorType(hidden_state), 0); 107 108 // Compute function 109 rnn.run(); 110 111 return output; 112 } 113 compute_reference(const TensorShape & input_shape,const TensorShape & weights_shape,const TensorShape & recurrent_weights_shape,const TensorShape & bias_shape,const TensorShape & output_shape,ActivationLayerInfo info,DataType data_type)114 SimpleTensor<T> compute_reference(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &recurrent_weights_shape, const TensorShape &bias_shape, 115 const TensorShape &output_shape, ActivationLayerInfo info, DataType data_type) 116 { 117 // Create reference 118 SimpleTensor<T> input{ input_shape, data_type }; 119 SimpleTensor<T> weights{ weights_shape, data_type }; 120 SimpleTensor<T> recurrent_weights{ recurrent_weights_shape, data_type }; 121 SimpleTensor<T> bias{ bias_shape, data_type }; 122 SimpleTensor<T> hidden_state{ output_shape, data_type }; 123 124 // Fill reference 125 fill(input, 0); 126 fill(weights, 0); 127 fill(recurrent_weights, 0); 128 fill(bias, 0); 129 fill(hidden_state, 0); 130 131 TensorShape out_shape = recurrent_weights_shape; 132 out_shape.set(1, output_shape.y()); 133 134 // Compute reference 135 SimpleTensor<T> out_w{ out_shape, data_type }; 136 SimpleTensor<T> fully_connected = reference::fully_connected_layer(input, weights, bias, out_shape); 137 SimpleTensor<T> gemm = reference::gemm(hidden_state, recurrent_weights, out_w, 1.f, 0.f); 138 SimpleTensor<T> add_res = reference::arithmetic_operation(reference::ArithmeticOperation::ADD, fully_connected, gemm, data_type, ConvertPolicy::SATURATE); 139 return reference::activation_layer(add_res, info); 140 } 141 142 TensorType _target{}; 143 SimpleTensor<T> _reference{}; 144 }; 145 } // namespace validation 146 } // namespace test 147 } // namespace arm_compute 148 #endif /* ARM_COMPUTE_TEST_RNN_LAYER_FIXTURE */ 149