1 /* 2 * Copyright (c) 2023 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 25 #ifndef TESTS_VALIDATION_FIXTURES_ADDMULADDFIXTURE 26 #define TESTS_VALIDATION_FIXTURES_ADDMULADDFIXTURE 27 28 #include "arm_compute/core/TensorShape.h" 29 #include "arm_compute/core/Types.h" 30 #include "tests/AssetsLibrary.h" 31 #include "tests/Globals.h" 32 #include "tests/IAccessor.h" 33 #include "tests/framework/Asserts.h" 34 #include "tests/framework/Fixture.h" 35 #include "tests/validation/Helpers.h" 36 #include "tests/validation/reference/ActivationLayer.h" 37 #include "tests/validation/reference/ArithmeticOperations.h" 38 #include "tests/validation/reference/DequantizationLayer.h" 39 #include "tests/validation/reference/PixelWiseMultiplication.h" 40 #include "tests/validation/reference/QuantizationLayer.h" 41 42 namespace arm_compute 43 { 44 namespace test 45 { 46 namespace validation 47 { 48 template <typename TensorType, typename AccessorType, typename FunctionType, typename T> 49 class AddMulAddGenericFixture : public framework::Fixture 50 { 51 public: 52 template <typename...> setup(const TensorShape & shape,DataType data_type,ActivationLayerInfo & act_info,bool interm_out)53 void setup(const TensorShape &shape, DataType data_type, ActivationLayerInfo &act_info, bool interm_out) 54 { 55 compute_target(shape, data_type, act_info, interm_out); 56 } 57 58 protected: 59 template <typename U> fill(U && tensor,int i,DataType data_type)60 void fill(U &&tensor, int i, DataType data_type) 61 { 62 switch(data_type) 63 { 64 case DataType::F32: 65 library->fill_tensor_uniform(tensor, i, -10.f, 10.f); 66 break; 67 case DataType::F16: 68 library->fill_tensor_uniform(tensor, i, -1.f, 1.f); 69 break; 70 default: 71 library->fill_tensor_uniform(tensor, i); 72 break; 73 } 74 } 75 compute_target(const TensorShape & shape,DataType data_type,ActivationLayerInfo & act_info,bool interm_out)76 void compute_target(const TensorShape &shape, DataType data_type, ActivationLayerInfo &act_info, bool interm_out) 77 { 78 TensorShape b_shape(shape.x()); 79 80 // Create tensors 81 TensorType input1 = create_tensor<TensorType>(shape, data_type, 1, _input1_qinfo); 82 TensorType input2 = create_tensor<TensorType>(shape, data_type, 1, _input2_qinfo); 83 TensorType bn_mul = create_tensor<TensorType>(b_shape, data_type, 1, _bn_mul_qinfo); 84 TensorType bn_add = create_tensor<TensorType>(b_shape, data_type, 1, _bn_add_qinfo); 85 TensorType add_output = create_tensor<TensorType>(shape, data_type, 1, _add_output_qinfo); 86 TensorType final_output = create_tensor<TensorType>(shape, data_type, 1, _final_output_qinfo); 87 88 // Create and configure function 89 FunctionType add_mul_add; 90 add_mul_add.configure(&input1, &input2, &bn_mul, &bn_add, interm_out ? &add_output : nullptr, &final_output, ConvertPolicy::SATURATE, act_info); 91 92 // Allocate tensors 93 input1.allocator()->allocate(); 94 input2.allocator()->allocate(); 95 bn_mul.allocator()->allocate(); 96 bn_add.allocator()->allocate(); 97 98 if(interm_out) 99 { 100 add_output.allocator()->allocate(); 101 } 102 103 final_output.allocator()->allocate(); 104 105 // Fill tensors 106 fill(AccessorType(input1), 0, data_type); 107 fill(AccessorType(input2), 1, data_type); 108 fill(AccessorType(bn_mul), 2, data_type); 109 fill(AccessorType(bn_add), 3, data_type); 110 111 // // Compute function 112 add_mul_add.run(); 113 114 _target = std::move(final_output); 115 116 if(interm_out) 117 { 118 _interm_target = std::move(add_output); 119 } 120 } 121 122 TensorType _target{}; 123 TensorType _interm_target{}; 124 SimpleTensor<T> _reference{}; 125 SimpleTensor<T> _interm_reference{}; 126 127 QuantizationInfo _input1_qinfo{}; 128 QuantizationInfo _input2_qinfo{}; 129 QuantizationInfo _bn_mul_qinfo{}; 130 QuantizationInfo _bn_add_qinfo{}; 131 QuantizationInfo _add_output_qinfo{}; 132 QuantizationInfo _final_output_qinfo{}; 133 }; 134 135 template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool interm_out> 136 class AddMulAddFloatValidationFixture : public AddMulAddGenericFixture<TensorType, AccessorType, FunctionType, T> 137 { 138 public: 139 using Parent = AddMulAddGenericFixture<TensorType, AccessorType, FunctionType, T>; 140 141 template <typename...> setup(const TensorShape & shape,DataType data_type,ActivationLayerInfo act_info)142 void setup(const TensorShape &shape, DataType data_type, ActivationLayerInfo act_info) 143 { 144 Parent::setup(shape, data_type, act_info, interm_out); 145 compute_reference(shape, data_type, act_info); 146 } 147 148 // Compute Reference is moved outside of the generic fixture because with the quantized data types, 149 // it becomes a very different implementation with intermediate tensors' data types being always float. 150 // This way the reference calculations are more readable and the size of the classes will be smaller 151 // due to unrepeated fill() and target() methods. compute_reference(const TensorShape & shape,DataType data_type,ActivationLayerInfo & act_info)152 void compute_reference(const TensorShape &shape, DataType data_type, ActivationLayerInfo &act_info) 153 { 154 TensorShape b_shape(shape.x()); 155 156 // Create reference 157 SimpleTensor<T> input1{ shape, data_type }; 158 SimpleTensor<T> input2{ shape, data_type }; 159 SimpleTensor<T> bn_mul{ b_shape, data_type }; 160 SimpleTensor<T> bn_add{ b_shape, data_type }; 161 SimpleTensor<T> add_output{ shape, data_type, 1 }; 162 163 SimpleTensor<T> bn_mul_out{ shape, data_type }; 164 SimpleTensor<T> bn_add_out{ shape, data_type }; 165 166 // Fill reference 167 Parent::fill(input1, 0, data_type); 168 Parent::fill(input2, 1, data_type); 169 Parent::fill(bn_mul, 2, data_type); 170 Parent::fill(bn_add, 3, data_type); 171 172 reference::arithmetic_operation<T>(reference::ArithmeticOperation::ADD, input1, input2, add_output, ConvertPolicy::SATURATE); 173 bn_mul_out = reference::pixel_wise_multiplication<T, T, T>(add_output, bn_mul, 1.f, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_UP, data_type); 174 reference::arithmetic_operation<T>(reference::ArithmeticOperation::ADD, bn_mul_out, bn_add, bn_add_out, ConvertPolicy::SATURATE); 175 176 if(interm_out) 177 { 178 Parent::_interm_reference = std::move(add_output); 179 } 180 181 if(act_info.enabled() && act_info.activation() != ActivationLayerInfo::ActivationFunction::IDENTITY) 182 { 183 Parent::_reference = reference::activation_layer(bn_add_out, act_info); 184 } 185 else 186 { 187 Parent::_reference = std::move(bn_add_out); 188 } 189 } 190 }; 191 192 template <typename TensorType, typename AccessorType, typename FunctionType, typename T, bool interm_out> 193 class AddMulAddQuantizedValidationFixture : public AddMulAddGenericFixture<TensorType, AccessorType, FunctionType, T> 194 { 195 public: 196 using Parent = AddMulAddGenericFixture<TensorType, AccessorType, FunctionType, T>; 197 198 template <typename...> setup(const TensorShape & shape,DataType data_type,ActivationLayerInfo act_info,QuantizationInfo input1_qinfo,QuantizationInfo input2_qinfo,QuantizationInfo bn_mul_qinfo,QuantizationInfo bn_add_qinfo,QuantizationInfo add_output_qinfo,QuantizationInfo final_output_qinfo)199 void setup(const TensorShape &shape, DataType data_type, ActivationLayerInfo act_info, 200 QuantizationInfo input1_qinfo, QuantizationInfo input2_qinfo, QuantizationInfo bn_mul_qinfo, 201 QuantizationInfo bn_add_qinfo, QuantizationInfo add_output_qinfo, QuantizationInfo final_output_qinfo) 202 { 203 // Quantization arguments moved to class attributes to prevent long function declerations 204 Parent::_input1_qinfo = input1_qinfo; 205 Parent::_input2_qinfo = input2_qinfo; 206 Parent::_bn_mul_qinfo = bn_mul_qinfo; 207 Parent::_bn_add_qinfo = bn_add_qinfo; 208 Parent::_add_output_qinfo = add_output_qinfo; 209 Parent::_final_output_qinfo = final_output_qinfo; 210 211 Parent::setup(shape, data_type, act_info, interm_out); 212 compute_reference(shape, data_type, act_info); 213 } 214 215 // Compute Reference is moved outside of the generic fixture because with the quantized data types, 216 // it becomes a very different implementation with intermediate tensors' data types being always float. 217 // This way the reference calculations are more readable and the size of the classes will be smaller 218 // due to unrepeated fill() and target() methods. compute_reference(const TensorShape & shape,DataType data_type,ActivationLayerInfo & act_info)219 void compute_reference(const TensorShape &shape, DataType data_type, ActivationLayerInfo &act_info) 220 { 221 TensorShape b_shape(shape.x()); 222 223 // Create reference 224 SimpleTensor<T> input1{ shape, data_type, 1, Parent::_input1_qinfo }; 225 SimpleTensor<T> input2{ shape, data_type, 1, Parent::_input2_qinfo }; 226 SimpleTensor<T> bn_mul{ b_shape, data_type, 1, Parent::_bn_mul_qinfo }; 227 SimpleTensor<T> bn_add{ b_shape, data_type, 1, Parent::_bn_add_qinfo }; 228 229 // Fill input tensors 230 Parent::fill(input1, 0, data_type); 231 Parent::fill(input2, 1, data_type); 232 Parent::fill(bn_mul, 2, data_type); 233 Parent::fill(bn_add, 3, data_type); 234 235 SimpleTensor<float> input1_dequantized = reference::dequantization_layer<float>(input1); 236 SimpleTensor<float> input2_dequantized = reference::dequantization_layer<float>(input2); 237 SimpleTensor<float> bn_mul_dequantized = reference::dequantization_layer<float>(bn_mul); 238 SimpleTensor<float> bn_add_dequantized = reference::dequantization_layer<float>(bn_add); 239 240 SimpleTensor<float> add_output_dequantized{ shape, DataType::F32 }; 241 SimpleTensor<float> bn_add_out_dequantized{ shape, DataType::F32 }; 242 243 reference::arithmetic_operation<float>(reference::ArithmeticOperation::ADD, input1_dequantized, input2_dequantized, add_output_dequantized, ConvertPolicy::SATURATE); 244 SimpleTensor<float> bn_mul_out_dequantized = reference::pixel_wise_multiplication<float, float, float>(add_output_dequantized, bn_mul_dequantized, 1.f, ConvertPolicy::SATURATE, 245 RoundingPolicy::TO_NEAREST_UP, DataType::F32); 246 reference::arithmetic_operation<float>(reference::ArithmeticOperation::ADD, bn_mul_out_dequantized, bn_add_dequantized, bn_add_out_dequantized, ConvertPolicy::SATURATE); 247 248 if(interm_out) 249 { 250 Parent::_interm_reference = reference::quantization_layer<float, T>(add_output_dequantized, data_type, Parent::_add_output_qinfo); 251 } 252 253 if(act_info.enabled() && act_info.activation() != ActivationLayerInfo::ActivationFunction::IDENTITY) 254 { 255 SimpleTensor<T> ref = reference::quantization_layer<float, T>(bn_add_out_dequantized, data_type, Parent::_final_output_qinfo); 256 Parent::_reference = reference::activation_layer(ref, act_info); 257 } 258 else 259 { 260 Parent::_reference = reference::quantization_layer<float, T>(bn_add_out_dequantized, data_type, Parent::_final_output_qinfo); 261 } 262 } 263 }; 264 } // namespace validation 265 } // namespace test 266 } // namespace arm_compute 267 268 #endif /* TESTS_VALIDATION_FIXTURES_ADDMULADDFIXTURE */ 269