Diana Bite | b7f4a95 | 2020-02-06 22:12:07 +0000 | [diff] [blame] | 1 | /* |
Manuel Bottini | ae58bdf | 2021-06-17 17:18:45 +0100 | [diff] [blame] | 2 | * Copyright (c) 2020-2021 Arm Limited. |
Diana Bite | b7f4a95 | 2020-02-06 22:12:07 +0000 | [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 | */ |
| 24 | #include "arm_compute/core/Types.h" |
| 25 | #include "arm_compute/core/WindowIterator.h" |
| 26 | #include "arm_compute/core/utils/quantization/AsymmHelpers.h" |
| 27 | #include "arm_compute/runtime/NEON/NEFunctions.h" |
| 28 | #include "arm_compute/runtime/NEON/NEScheduler.h" |
Diana Bite | b7f4a95 | 2020-02-06 22:12:07 +0000 | [diff] [blame] | 29 | #include "support/ToolchainSupport.h" |
Manuel Bottini | ae58bdf | 2021-06-17 17:18:45 +0100 | [diff] [blame] | 30 | #include "utils/Utils.h" |
Diana Bite | b7f4a95 | 2020-02-06 22:12:07 +0000 | [diff] [blame] | 31 | |
| 32 | #include <cstdlib> |
| 33 | |
| 34 | using namespace arm_compute; |
| 35 | using namespace utils; |
| 36 | |
| 37 | // Find min and max value in a float array |
| 38 | void find_min_max(int size, const float *data, float *min, float *max) |
| 39 | { |
| 40 | *min = *max = data[0]; |
| 41 | for(int i = 0; i < size; i++) |
| 42 | { |
| 43 | const float val = data[i]; |
| 44 | *min = std::min(*min, val); |
| 45 | *max = std::max(*max, val); |
| 46 | } |
| 47 | } |
| 48 | |
| 49 | // Return reasonable quantisation parameters to use for an array of floats |
| 50 | // based on min and max values |
| 51 | QuantizationInfo choose_quantization_params(float min, float max) |
| 52 | { |
| 53 | // Extend the [min,max] interval to contain 0 so we can represent it exactly |
| 54 | min = std::min(min, 0.f); |
| 55 | max = std::max(max, 0.f); |
| 56 | |
| 57 | // Set the quantized min and max in float values |
| 58 | const float qmin = 0; |
| 59 | const float qmax = 255; |
| 60 | |
| 61 | // Determine the scale |
| 62 | const float scale = (max - min) / (qmax - qmin); |
| 63 | |
| 64 | // Determine the zero-point; using affine equation val = (qval-zerop) * scale |
| 65 | const float zero_point_real = qmin - min / scale; |
| 66 | |
| 67 | // But we need to nudge the zero_point to an integer (exact quantized value) |
| 68 | std::uint8_t zero_point_nudged = 0; |
| 69 | if(zero_point_real < qmin) |
| 70 | { |
| 71 | zero_point_nudged = qmin; |
| 72 | } |
| 73 | else if(zero_point_real > qmax) |
| 74 | { |
| 75 | zero_point_nudged = qmax; |
| 76 | } |
| 77 | else |
| 78 | { |
| 79 | zero_point_nudged = static_cast<std::uint8_t>(support::cpp11::round(zero_point_real)); |
| 80 | } |
| 81 | |
| 82 | QuantizationInfo qinfo = QuantizationInfo(scale, zero_point_nudged); |
| 83 | return qinfo; |
| 84 | } |
| 85 | |
| 86 | void quantize_values(int size, qasymm8_t *output, float *input, const QuantizationInfo qinfo) |
| 87 | { |
| 88 | for(int i = 0; i < size; i++) |
| 89 | { |
| 90 | output[i] = quantize_qasymm8(input[i], qinfo); |
| 91 | } |
| 92 | std::cout << "\n"; |
| 93 | } |
| 94 | |
| 95 | int main(int argc, char **argv) |
| 96 | { |
| 97 | Tensor src1; |
| 98 | Tensor src2; |
| 99 | Tensor dst0; |
| 100 | Tensor q_src1; |
| 101 | Tensor q_src2; |
| 102 | Tensor q_dst0; |
| 103 | Tensor q_res; |
| 104 | Tensor q_res_output; |
Manuel Bottini | ae58bdf | 2021-06-17 17:18:45 +0100 | [diff] [blame] | 105 | size_t M = 4; |
| 106 | size_t N = 4; |
| 107 | size_t K = 4; |
| 108 | bool default_input = true; |
Diana Bite | b7f4a95 | 2020-02-06 22:12:07 +0000 | [diff] [blame] | 109 | |
| 110 | // Parse args |
| 111 | if(argc < 3) /* case default matrix sizes */ |
| 112 | { |
| 113 | // Print help |
| 114 | std::cout << "Usage: ./build/neon_gemm_qasymm8 M N K\n"; |
| 115 | std::cout << "Too few or no inputs provided. Using default M=4, N=4, K=4\n\n"; |
| 116 | } |
| 117 | else /* case M N K arguments provided */ |
| 118 | { |
| 119 | M = strtol(argv[1], nullptr, 10); |
| 120 | N = strtol(argv[2], nullptr, 10); |
| 121 | K = strtol(argv[3], nullptr, 10); |
| 122 | default_input = false; |
| 123 | } |
| 124 | |
| 125 | /*** Floating point matrix multiplication ***/ |
| 126 | |
| 127 | // Initialise input matrices |
| 128 | NEGEMM fgemm{}; |
| 129 | |
| 130 | src1.allocator()->init(TensorInfo(TensorShape(K, M), 1, DataType::F32)); |
| 131 | src2.allocator()->init(TensorInfo(TensorShape(N, K), 1, DataType::F32)); |
| 132 | dst0.allocator()->init(TensorInfo(TensorShape(N, M), 1, DataType::F32)); |
| 133 | fgemm.configure(&src1, &src2, nullptr, &dst0, 1, 0); |
| 134 | |
| 135 | // Allocate matrices |
| 136 | src1.allocator()->allocate(); |
| 137 | src2.allocator()->allocate(); |
| 138 | dst0.allocator()->allocate(); |
| 139 | |
| 140 | // Fill in tensors, by default fill in with known data - for easy testing |
| 141 | auto *src1_ptr = reinterpret_cast<float *>(src1.buffer()); |
| 142 | auto *src2_ptr = reinterpret_cast<float *>(src2.buffer()); |
| 143 | auto *dst0_ptr = reinterpret_cast<float *>(dst0.buffer()); |
| 144 | |
| 145 | // Fill in: one is the identity matrix, other is sequential values |
| 146 | // src1: Identity matrix |
Manuel Bottini | ae58bdf | 2021-06-17 17:18:45 +0100 | [diff] [blame] | 147 | for(size_t i = 0; i < M * K; i++) |
| 148 | { |
Diana Bite | b7f4a95 | 2020-02-06 22:12:07 +0000 | [diff] [blame] | 149 | src1_ptr[i] = 0; |
| 150 | } |
Manuel Bottini | ae58bdf | 2021-06-17 17:18:45 +0100 | [diff] [blame] | 151 | for(size_t i = 0; i < M; i++) |
| 152 | { |
Diana Bite | b7f4a95 | 2020-02-06 22:12:07 +0000 | [diff] [blame] | 153 | src1_ptr[i * K + i] = 1.0f; |
| 154 | } |
| 155 | |
| 156 | // src2: Sequential values matrix |
Manuel Bottini | ae58bdf | 2021-06-17 17:18:45 +0100 | [diff] [blame] | 157 | for(size_t i = 0; i < K * N; i++) |
| 158 | { |
Diana Bite | b7f4a95 | 2020-02-06 22:12:07 +0000 | [diff] [blame] | 159 | src2_ptr[i] = i * 1.123f; |
| 160 | } |
| 161 | |
| 162 | // Otherwise if M, N, K is given, fill in with random values |
| 163 | if(!default_input) |
| 164 | { |
| 165 | fill_random_tensor(src1, 0.f, 1.f); |
| 166 | fill_random_tensor(src2, 0.f, 1.f); |
| 167 | } |
| 168 | |
| 169 | // Run single precision gemm and print result |
| 170 | fgemm.run(); |
| 171 | |
| 172 | #if ARM_COMPUTE_DEBUG_ENABLED |
| 173 | std::cout << "Result matrix:\n"; |
| 174 | src1.print(std::cout); |
| 175 | src2.print(std::cout); |
| 176 | dst0.print(std::cout); |
| 177 | #endif // ARM_COMPUTE_DEBUG_ENABLED |
| 178 | |
| 179 | /*** Quantised asymmetric 8bit matrix multiplication ***/ |
| 180 | |
| 181 | // Start by finding the quantisation parameters for each set of values |
| 182 | float src1_min; |
| 183 | float src1_max; |
| 184 | float src2_min; |
| 185 | float src2_max; |
| 186 | float dst0_min; |
| 187 | float dst0_max; |
| 188 | |
| 189 | find_min_max(M * K, src1_ptr, &src1_min, &src1_max); |
| 190 | find_min_max(K * N, src2_ptr, &src2_min, &src2_max); |
| 191 | find_min_max(M * N, dst0_ptr, &dst0_min, &dst0_max); |
| 192 | |
| 193 | const QuantizationInfo src1_qinfo = choose_quantization_params(src1_min, src1_max); |
| 194 | const QuantizationInfo src2_qinfo = choose_quantization_params(src2_min, src2_max); |
| 195 | const QuantizationInfo dst0_qinfo = choose_quantization_params(dst0_min, dst0_max); |
| 196 | |
| 197 | std::cout << "Matrix 1: min=" << src1_min << ", max=" << src1_max << ", "; |
| 198 | std::cout << "QuantisationInfo(" << src1_qinfo.scale()[0] << ", " << src1_qinfo.offset()[0] << ")\n"; |
| 199 | std::cout << "Matrix 2: min=" << src2_min << ", max=" << src2_max << ", "; |
| 200 | std::cout << "QuantisationInfo(" << src2_qinfo.scale()[0] << ", " << src2_qinfo.offset()[0] << ")\n"; |
| 201 | std::cout << "Result : min=" << dst0_min << ", max=" << dst0_max << ", "; |
| 202 | std::cout << "QuantisationInfo(" << dst0_qinfo.scale()[0] << ", " << dst0_qinfo.offset()[0] << ")\n"; |
| 203 | |
| 204 | // We now have the quantisation info and can configure the quantised tensors |
| 205 | q_src1.allocator()->init(TensorInfo(TensorShape(K, M), 1, DataType::QASYMM8, src1_qinfo)); |
| 206 | q_src2.allocator()->init(TensorInfo(TensorShape(N, K), 1, DataType::QASYMM8, src2_qinfo)); |
| 207 | q_dst0.allocator()->init(TensorInfo(TensorShape(N, M), 1, DataType::QASYMM8, dst0_qinfo)); |
| 208 | |
| 209 | // In this approach we use the QuantizationLayer construct to perform quantization |
| 210 | NEQuantizationLayer q1; |
| 211 | NEQuantizationLayer q2; |
| 212 | NEQuantizationLayer q3; |
| 213 | q1.configure(&src1, &q_src1); |
| 214 | q2.configure(&src2, &q_src2); |
| 215 | q3.configure(&dst0, &q_dst0); |
| 216 | |
| 217 | // Configure low precision gemm and initialise result tensor (pre-output) |
| 218 | NEGEMMLowpMatrixMultiplyCore qgemm; |
| 219 | q_res.allocator()->init(TensorInfo(TensorShape(N, M), 1, DataType::S32)); |
| 220 | qgemm.configure(&q_src1, &q_src2, nullptr, &q_res); |
| 221 | |
| 222 | // Configure output stage after computing shift and multiplier parameters |
Manuel Bottini | ae58bdf | 2021-06-17 17:18:45 +0100 | [diff] [blame] | 223 | NEGEMMLowpOutputStage gemmlowp_output_stage; |
| 224 | int output_multiplier; |
| 225 | int output_shift; |
| 226 | float multiplier = (src1_qinfo.uniform().scale * src2_qinfo.uniform().scale) / dst0_qinfo.uniform().scale; |
Diana Bite | b7f4a95 | 2020-02-06 22:12:07 +0000 | [diff] [blame] | 227 | quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); |
| 228 | std::cout << "(q_multiplier, q_shift) = (" << output_multiplier << ", " << output_shift << ")\n\n"; |
Manuel Bottini | ae58bdf | 2021-06-17 17:18:45 +0100 | [diff] [blame] | 229 | |
| 230 | GEMMLowpOutputStageInfo info; |
| 231 | info.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT; |
| 232 | info.gemmlowp_multiplier = output_multiplier; |
| 233 | info.gemmlowp_shift = output_shift; |
| 234 | info.gemmlowp_offset = dst0_qinfo.uniform().offset; |
| 235 | info.output_data_type = DataType::QASYMM8; |
| 236 | q_res_output.info()->set_data_type(DataType::QASYMM8); |
| 237 | q_res_output.info()->set_num_channels(1); |
| 238 | gemmlowp_output_stage.configure(&q_res, nullptr, &q_res_output, info); |
Diana Bite | b7f4a95 | 2020-02-06 22:12:07 +0000 | [diff] [blame] | 239 | |
| 240 | // Allocate all tensors |
| 241 | q_src1.allocator()->allocate(); |
| 242 | q_src2.allocator()->allocate(); |
| 243 | q_dst0.allocator()->allocate(); |
| 244 | q_res.allocator()->allocate(); |
| 245 | q_res_output.allocator()->allocate(); |
| 246 | |
| 247 | // Run quantization layers (quantizes values of each tensor) |
| 248 | q1.run(); |
| 249 | q2.run(); |
| 250 | q3.run(); |
| 251 | // Run low precision matrix multiply kernel |
| 252 | qgemm.run(); |
| 253 | // Run output stage kernel |
| 254 | gemmlowp_output_stage.run(); |
| 255 | std::cout << "Done\n"; |
| 256 | |
| 257 | #if ARM_COMPUTE_DEBUG_ENABLED |
| 258 | // Print quantized source matrices |
| 259 | q_src1.print(std::cout); |
| 260 | q_src2.print(std::cout); |
| 261 | // Print result matrix in int32 form - before output stage processing |
| 262 | std::cout << "Lowp GEMM output (int32):\n"; |
| 263 | q_res.print(std::cout); |
| 264 | // Print QASYMM8 (quantized) matrix |
| 265 | std::cout << "Output pipeline result matrix:\n"; |
| 266 | q_res_output.print(std::cout); |
| 267 | |
| 268 | // Expected result |
| 269 | std::cout << "Expected result:\n"; |
| 270 | q_dst0.print(std::cout); |
| 271 | #endif // ARM_COMPUTE_DEBUG_ENABLED |
| 272 | } |