Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 1 | /* |
Gunes Bayir | 9d0c4de | 2023-04-13 18:22:58 +0100 | [diff] [blame] | 2 | * Copyright (c) 2017-2023 Arm Limited. |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 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 | */ |
Gunes Bayir | 9d0c4de | 2023-04-13 18:22:58 +0100 | [diff] [blame] | 24 | #ifndef ACL_TESTS_VALIDATION_HELPERS |
| 25 | #define ACL_TESTS_VALIDATION_HELPERS |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 26 | |
Georgios Pinitas | 7b7858d | 2017-06-21 16:44:24 +0100 | [diff] [blame] | 27 | #include "arm_compute/core/Types.h" |
Moritz Pflanzer | a09de0c | 2017-09-01 20:41:12 +0100 | [diff] [blame] | 28 | #include "arm_compute/core/Utils.h" |
Moritz Pflanzer | 6c6597c | 2017-09-24 12:09:41 +0100 | [diff] [blame] | 29 | #include "support/Half.h" |
John Richardson | 6f4d49f | 2017-09-07 11:21:10 +0100 | [diff] [blame] | 30 | #include "tests/Globals.h" |
Moritz Pflanzer | 6c6597c | 2017-09-24 12:09:41 +0100 | [diff] [blame] | 31 | #include "tests/SimpleTensor.h" |
Georgios Pinitas | 7b7858d | 2017-06-21 16:44:24 +0100 | [diff] [blame] | 32 | |
Michalis Spyrou | a3c9a3b | 2020-12-08 21:02:16 +0000 | [diff] [blame] | 33 | #include <math.h> |
Georgios Pinitas | 7b7858d | 2017-06-21 16:44:24 +0100 | [diff] [blame] | 34 | #include <random> |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 35 | #include <type_traits> |
| 36 | #include <utility> |
| 37 | |
| 38 | namespace arm_compute |
| 39 | { |
| 40 | namespace test |
| 41 | { |
| 42 | namespace validation |
| 43 | { |
Moritz Pflanzer | a09de0c | 2017-09-01 20:41:12 +0100 | [diff] [blame] | 44 | template <typename T> |
| 45 | struct is_floating_point : public std::is_floating_point<T> |
Pablo Tello | 8fda1cb | 2017-07-05 15:20:38 +0100 | [diff] [blame] | 46 | { |
Moritz Pflanzer | a09de0c | 2017-09-01 20:41:12 +0100 | [diff] [blame] | 47 | }; |
| 48 | |
| 49 | template <> |
Georgios Pinitas | 583137c | 2017-08-31 18:12:42 +0100 | [diff] [blame] | 50 | struct is_floating_point<half> : public std::true_type |
Moritz Pflanzer | a09de0c | 2017-09-01 20:41:12 +0100 | [diff] [blame] | 51 | { |
| 52 | }; |
Pablo Tello | 8fda1cb | 2017-07-05 15:20:38 +0100 | [diff] [blame] | 53 | |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 54 | /** Helper function to get the testing range for each activation layer. |
| 55 | * |
Vidhya Sudhan Loganathan | 014333d | 2018-07-02 09:13:49 +0100 | [diff] [blame] | 56 | * @param[in] activation Activation function to test. |
| 57 | * @param[in] data_type Data type. |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 58 | * |
| 59 | * @return A pair containing the lower upper testing bounds for a given function. |
| 60 | */ |
| 61 | template <typename T> |
Vidhya Sudhan Loganathan | 014333d | 2018-07-02 09:13:49 +0100 | [diff] [blame] | 62 | std::pair<T, T> get_activation_layer_test_bounds(ActivationLayerInfo::ActivationFunction activation, DataType data_type) |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 63 | { |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 64 | std::pair<T, T> bounds; |
| 65 | |
Moritz Pflanzer | a09de0c | 2017-09-01 20:41:12 +0100 | [diff] [blame] | 66 | switch(data_type) |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 67 | { |
Moritz Pflanzer | a09de0c | 2017-09-01 20:41:12 +0100 | [diff] [blame] | 68 | case DataType::F16: |
Isabella Gottardi | 3b77e9d | 2017-06-22 11:05:41 +0100 | [diff] [blame] | 69 | { |
Moritz Pflanzer | a09de0c | 2017-09-01 20:41:12 +0100 | [diff] [blame] | 70 | using namespace half_float::literal; |
Isabella Gottardi | 3b77e9d | 2017-06-22 11:05:41 +0100 | [diff] [blame] | 71 | |
Moritz Pflanzer | a09de0c | 2017-09-01 20:41:12 +0100 | [diff] [blame] | 72 | switch(activation) |
Isabella Gottardi | 3b77e9d | 2017-06-22 11:05:41 +0100 | [diff] [blame] | 73 | { |
Georgios Pinitas | 3463a8b | 2018-08-23 13:11:53 +0100 | [diff] [blame] | 74 | case ActivationLayerInfo::ActivationFunction::TANH: |
Moritz Pflanzer | a09de0c | 2017-09-01 20:41:12 +0100 | [diff] [blame] | 75 | case ActivationLayerInfo::ActivationFunction::SQUARE: |
| 76 | case ActivationLayerInfo::ActivationFunction::LOGISTIC: |
| 77 | case ActivationLayerInfo::ActivationFunction::SOFT_RELU: |
| 78 | // Reduce range as exponent overflows |
Georgios Pinitas | 3463a8b | 2018-08-23 13:11:53 +0100 | [diff] [blame] | 79 | bounds = std::make_pair(-2._h, 2._h); |
Isabella Gottardi | 3b77e9d | 2017-06-22 11:05:41 +0100 | [diff] [blame] | 80 | break; |
Moritz Pflanzer | a09de0c | 2017-09-01 20:41:12 +0100 | [diff] [blame] | 81 | case ActivationLayerInfo::ActivationFunction::SQRT: |
| 82 | // Reduce range as sqrt should take a non-negative number |
Georgios Pinitas | 3463a8b | 2018-08-23 13:11:53 +0100 | [diff] [blame] | 83 | bounds = std::make_pair(0._h, 128._h); |
Isabella Gottardi | 3b77e9d | 2017-06-22 11:05:41 +0100 | [diff] [blame] | 84 | break; |
| 85 | default: |
Moritz Pflanzer | a09de0c | 2017-09-01 20:41:12 +0100 | [diff] [blame] | 86 | bounds = std::make_pair(-255._h, 255._h); |
| 87 | break; |
Isabella Gottardi | 3b77e9d | 2017-06-22 11:05:41 +0100 | [diff] [blame] | 88 | } |
Moritz Pflanzer | a09de0c | 2017-09-01 20:41:12 +0100 | [diff] [blame] | 89 | break; |
Isabella Gottardi | 3b77e9d | 2017-06-22 11:05:41 +0100 | [diff] [blame] | 90 | } |
Moritz Pflanzer | a09de0c | 2017-09-01 20:41:12 +0100 | [diff] [blame] | 91 | case DataType::F32: |
| 92 | switch(activation) |
| 93 | { |
Gunes Bayir | 01934e9 | 2022-11-02 11:50:37 +0000 | [diff] [blame] | 94 | case ActivationLayerInfo::ActivationFunction::SOFT_RELU: |
| 95 | // Reduce range as exponent overflows |
| 96 | bounds = std::make_pair(-40.f, 40.f); |
| 97 | break; |
Moritz Pflanzer | a09de0c | 2017-09-01 20:41:12 +0100 | [diff] [blame] | 98 | case ActivationLayerInfo::ActivationFunction::SQRT: |
| 99 | // Reduce range as sqrt should take a non-negative number |
| 100 | bounds = std::make_pair(0.f, 255.f); |
| 101 | break; |
| 102 | default: |
| 103 | bounds = std::make_pair(-255.f, 255.f); |
| 104 | break; |
| 105 | } |
| 106 | break; |
Moritz Pflanzer | a09de0c | 2017-09-01 20:41:12 +0100 | [diff] [blame] | 107 | default: |
| 108 | ARM_COMPUTE_ERROR("Unsupported data type"); |
Isabella Gottardi | 3b77e9d | 2017-06-22 11:05:41 +0100 | [diff] [blame] | 109 | } |
| 110 | |
Moritz Pflanzer | a09de0c | 2017-09-01 20:41:12 +0100 | [diff] [blame] | 111 | return bounds; |
Isabella Gottardi | 3b77e9d | 2017-06-22 11:05:41 +0100 | [diff] [blame] | 112 | } |
| 113 | |
Georgios Pinitas | ac4e873 | 2017-07-05 17:02:25 +0100 | [diff] [blame] | 114 | /** Calculate output tensor shape give a vector of input tensor to concatenate |
| 115 | * |
| 116 | * @param[in] input_shapes Shapes of the tensors to concatenate across depth. |
| 117 | * |
| 118 | * @return The shape of output concatenated tensor. |
| 119 | */ |
Moritz Pflanzer | a09de0c | 2017-09-01 20:41:12 +0100 | [diff] [blame] | 120 | TensorShape calculate_depth_concatenate_shape(const std::vector<TensorShape> &input_shapes); |
Sanghoon Lee | a7a5b7b | 2017-09-14 12:11:03 +0100 | [diff] [blame] | 121 | |
Pablo Tello | 3dd5b68 | 2019-03-04 14:14:02 +0000 | [diff] [blame] | 122 | /** Calculate output tensor shape for the concatenate operation along a given axis |
Michalis Spyrou | 55b3d12 | 2018-05-09 09:59:23 +0100 | [diff] [blame] | 123 | * |
| 124 | * @param[in] input_shapes Shapes of the tensors to concatenate across width. |
Pablo Tello | 3dd5b68 | 2019-03-04 14:14:02 +0000 | [diff] [blame] | 125 | * @param[in] axis Axis to use for the concatenate operation |
Michalis Spyrou | 55b3d12 | 2018-05-09 09:59:23 +0100 | [diff] [blame] | 126 | * |
| 127 | * @return The shape of output concatenated tensor. |
| 128 | */ |
Pablo Tello | 3dd5b68 | 2019-03-04 14:14:02 +0000 | [diff] [blame] | 129 | TensorShape calculate_concatenate_shape(const std::vector<TensorShape> &input_shapes, size_t axis); |
Michalis Spyrou | 55b3d12 | 2018-05-09 09:59:23 +0100 | [diff] [blame] | 130 | |
Sang-Hoon Park | 0779fec | 2019-11-13 17:08:12 +0000 | [diff] [blame] | 131 | /** Convert an asymmetric quantized simple tensor into float using tensor quantization information. |
Anton Lokhmotov | af6204c | 2017-11-08 09:34:19 +0000 | [diff] [blame] | 132 | * |
| 133 | * @param[in] src Quantized tensor. |
| 134 | * |
| 135 | * @return Float tensor. |
Anthony Barbier | f202e50 | 2017-11-23 18:02:04 +0000 | [diff] [blame] | 136 | */ |
Michalis Spyrou | ed7b27d | 2019-11-27 16:04:17 +0000 | [diff] [blame] | 137 | template <typename T> |
| 138 | SimpleTensor<float> convert_from_asymmetric(const SimpleTensor<T> &src); |
Michele Di Giorgio | 578a9fc | 2019-08-23 11:49:04 +0100 | [diff] [blame] | 139 | |
Anton Lokhmotov | af6204c | 2017-11-08 09:34:19 +0000 | [diff] [blame] | 140 | /** Convert float simple tensor into quantized using specified quantization information. |
| 141 | * |
| 142 | * @param[in] src Float tensor. |
| 143 | * @param[in] quantization_info Quantification information. |
| 144 | * |
| 145 | * @return Quantized tensor. |
Anthony Barbier | f202e50 | 2017-11-23 18:02:04 +0000 | [diff] [blame] | 146 | */ |
Michele Di Giorgio | 4aff98f | 2019-08-28 16:27:26 +0100 | [diff] [blame] | 147 | template <typename T> |
| 148 | SimpleTensor<T> convert_to_asymmetric(const SimpleTensor<float> &src, const QuantizationInfo &quantization_info); |
| 149 | |
| 150 | /** Convert quantized simple tensor into float using tensor quantization information. |
| 151 | * |
| 152 | * @param[in] src Quantized tensor. |
| 153 | * |
| 154 | * @return Float tensor. |
| 155 | */ |
Manuel Bottini | 3689fcd | 2019-06-14 17:18:12 +0100 | [diff] [blame] | 156 | template <typename T> |
| 157 | SimpleTensor<float> convert_from_symmetric(const SimpleTensor<T> &src); |
| 158 | |
| 159 | /** Convert float simple tensor into quantized using specified quantization information. |
| 160 | * |
| 161 | * @param[in] src Float tensor. |
| 162 | * @param[in] quantization_info Quantification information. |
| 163 | * |
| 164 | * @return Quantized tensor. |
| 165 | */ |
| 166 | template <typename T> |
| 167 | SimpleTensor<T> convert_to_symmetric(const SimpleTensor<float> &src, const QuantizationInfo &quantization_info); |
| 168 | |
Giorgio Arena | 1f9ca1d | 2018-03-01 11:13:45 +0000 | [diff] [blame] | 169 | /** Matrix multiply between 2 float simple tensors |
| 170 | * |
| 171 | * @param[in] a Input tensor A |
| 172 | * @param[in] b Input tensor B |
| 173 | * @param[out] out Output tensor |
| 174 | * |
| 175 | */ |
Vidhya Sudhan Loganathan | 71ecf39 | 2018-08-31 16:10:16 +0100 | [diff] [blame] | 176 | template <typename T> |
| 177 | void matrix_multiply(const SimpleTensor<T> &a, const SimpleTensor<T> &b, SimpleTensor<T> &out); |
Giorgio Arena | 1f9ca1d | 2018-03-01 11:13:45 +0000 | [diff] [blame] | 178 | |
| 179 | /** Transpose matrix |
| 180 | * |
| 181 | * @param[in] in Input tensor |
| 182 | * @param[out] out Output tensor |
| 183 | * |
| 184 | */ |
Vidhya Sudhan Loganathan | 71ecf39 | 2018-08-31 16:10:16 +0100 | [diff] [blame] | 185 | template <typename T> |
| 186 | void transpose_matrix(const SimpleTensor<T> &in, SimpleTensor<T> &out); |
Giorgio Arena | 1f9ca1d | 2018-03-01 11:13:45 +0000 | [diff] [blame] | 187 | |
| 188 | /** Get a 2D tile from a tensor |
| 189 | * |
| 190 | * @note In case of out-of-bound reads, the tile will be filled with zeros |
| 191 | * |
| 192 | * @param[in] in Input tensor |
| 193 | * @param[out] tile Tile |
| 194 | * @param[in] coord Coordinates |
| 195 | */ |
| 196 | template <typename T> |
| 197 | void get_tile(const SimpleTensor<T> &in, SimpleTensor<T> &tile, const Coordinates &coord); |
Gian Marco Iodice | f1c2bf0 | 2018-06-13 14:05:54 +0100 | [diff] [blame] | 198 | |
| 199 | /** Fill with zeros the input tensor in the area defined by anchor and shape |
| 200 | * |
| 201 | * @param[in] in Input tensor to fill with zeros |
| 202 | * @param[out] anchor Starting point of the zeros area |
| 203 | * @param[in] shape Ending point of the zeros area |
| 204 | */ |
| 205 | template <typename T> |
| 206 | void zeros(SimpleTensor<T> &in, const Coordinates &anchor, const TensorShape &shape); |
Michele Di Giorgio | ed5a492 | 2018-09-13 16:22:01 +0100 | [diff] [blame] | 207 | |
| 208 | /** Helper function to compute quantized min and max bounds |
| 209 | * |
| 210 | * @param[in] quant_info Quantization info to be used for conversion |
| 211 | * @param[in] min Floating point minimum value to be quantized |
| 212 | * @param[in] max Floating point maximum value to be quantized |
| 213 | */ |
| 214 | std::pair<int, int> get_quantized_bounds(const QuantizationInfo &quant_info, float min, float max); |
Georgios Pinitas | dbdea0d | 2019-10-16 19:21:40 +0100 | [diff] [blame] | 215 | |
Georgios Pinitas | 6e1791b | 2019-12-02 19:01:25 +0000 | [diff] [blame] | 216 | /** Helper function to compute asymmetric quantized signed min and max bounds |
| 217 | * |
| 218 | * @param[in] quant_info Quantization info to be used for conversion |
| 219 | * @param[in] min Floating point minimum value to be quantized |
| 220 | * @param[in] max Floating point maximum value to be quantized |
| 221 | */ |
| 222 | std::pair<int, int> get_quantized_qasymm8_signed_bounds(const QuantizationInfo &quant_info, float min, float max); |
| 223 | |
Georgios Pinitas | dbdea0d | 2019-10-16 19:21:40 +0100 | [diff] [blame] | 224 | /** Helper function to compute symmetric quantized min and max bounds |
| 225 | * |
| 226 | * @param[in] quant_info Quantization info to be used for conversion |
| 227 | * @param[in] min Floating point minimum value to be quantized |
| 228 | * @param[in] max Floating point maximum value to be quantized |
| 229 | * @param[in] channel_id Channel id for per channel quantization info. |
| 230 | */ |
| 231 | std::pair<int, int> get_symm_quantized_per_channel_bounds(const QuantizationInfo &quant_info, float min, float max, size_t channel_id = 0); |
Giorgio Arena | 63825e8 | 2021-03-25 14:54:50 +0000 | [diff] [blame] | 232 | |
Giorgio Arena | ebbb6f9 | 2021-04-13 09:52:18 +0100 | [diff] [blame] | 233 | /** Add random padding along the X axis (between 1 and 16 columns per side) to all the input tensors. |
| 234 | * This is used in our validation suite in order to simulate implicit padding addition after configuring, but before allocating. |
Giorgio Arena | 63825e8 | 2021-03-25 14:54:50 +0000 | [diff] [blame] | 235 | * |
Manuel Bottini | f733e03 | 2021-05-19 16:15:36 +0100 | [diff] [blame] | 236 | * @param[in] tensors List of tensors to add padding to |
| 237 | * @param[in] data_layout (Optional) Data layout of the operator |
| 238 | * @param[in] only_right_pad (Optional) Only right padding testing, in case of cl image padding |
Giorgio Arena | 63825e8 | 2021-03-25 14:54:50 +0000 | [diff] [blame] | 239 | * |
| 240 | * @note This function adds padding to the input tensors only if data_layout == DataLayout::NHWC |
| 241 | */ |
Manuel Bottini | f733e03 | 2021-05-19 16:15:36 +0100 | [diff] [blame] | 242 | void add_padding_x(std::initializer_list<ITensor *> tensors, const DataLayout &data_layout = DataLayout::NHWC, bool only_right_pad = false); |
Gian Marco Iodice | 72b5687 | 2021-06-29 10:08:46 +0100 | [diff] [blame] | 243 | |
| 244 | /** Add random padding along the Y axis (between 1 and 4 rows per side) to all the input tensors. |
| 245 | * This is used in our validation suite in order to simulate implicit padding addition after configuring, but before allocating. |
| 246 | * |
| 247 | * @param[in] tensors List of tensors to add padding to |
| 248 | * @param[in] data_layout (Optional) Data layout of the operator |
| 249 | * |
| 250 | * @note This function adds padding to the input tensors only if data_layout == DataLayout::NHWC |
| 251 | */ |
| 252 | void add_padding_y(std::initializer_list<ITensor *> tensors, const DataLayout &data_layout = DataLayout::NHWC); |
Gunes Bayir | 9d0c4de | 2023-04-13 18:22:58 +0100 | [diff] [blame] | 253 | |
| 254 | /** For MatMulLowp, given the Lhs/Rhs matrix quantization informations and the matrix multiplication dimensions, |
| 255 | * calculate a suitable output quantization for obtaining non-saturated outputs with high probability. |
| 256 | */ |
| 257 | QuantizationInfo calculate_mat_mul_dst_q_info(const QuantizationInfo &lhs_q_info, const QuantizationInfo &rhs_q_info, int m, int n, int k, DataType data_type); |
Anthony Barbier | 6ff3b19 | 2017-09-04 18:44:23 +0100 | [diff] [blame] | 258 | } // namespace validation |
| 259 | } // namespace test |
| 260 | } // namespace arm_compute |
Gunes Bayir | 9d0c4de | 2023-04-13 18:22:58 +0100 | [diff] [blame] | 261 | #endif /* ACL_TESTS_VALIDATION_HELPERS */ |