Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 1 | /* |
Freddie Liardet | e572dff | 2022-05-16 14:09:10 +0100 | [diff] [blame] | 2 | * Copyright (c) 2017-2022 Arm Limited. |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +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 | */ |
Georgios Pinitas | 7891a73 | 2021-08-20 21:39:25 +0100 | [diff] [blame] | 24 | #include "src/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.h" |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 25 | |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 26 | #include "arm_compute/core/Log.h" |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 27 | #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 28 | |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 29 | #include "src/core/helpers/AutoConfiguration.h" |
| 30 | #include "src/core/helpers/MemoryHelpers.h" |
Georgios Pinitas | 7891a73 | 2021-08-20 21:39:25 +0100 | [diff] [blame] | 31 | #include "src/gpu/cl/kernels/ClCastKernel.h" |
| 32 | #include "src/gpu/cl/kernels/ClGemmLowpMatrixMultiplyNativeKernel.h" |
| 33 | #include "src/gpu/cl/kernels/ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel.h" |
Freddie Liardet | e572dff | 2022-05-16 14:09:10 +0100 | [diff] [blame] | 34 | #include "src/gpu/cl/kernels/ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel.h" |
Georgios Pinitas | 7891a73 | 2021-08-20 21:39:25 +0100 | [diff] [blame] | 35 | #include "src/gpu/cl/kernels/ClGemmLowpOffsetContributionKernel.h" |
| 36 | #include "src/gpu/cl/kernels/ClGemmLowpOffsetContributionOutputStageKernel.h" |
| 37 | #include "src/gpu/cl/kernels/ClGemmLowpReductionKernel.h" |
| 38 | #include "src/gpu/cl/kernels/ClGemmReshapeRhsMatrixKernel.h" |
| 39 | #include "src/gpu/cl/utils/ClAuxTensorHandler.h" |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 40 | #include "src/runtime/CL/gemm_auto_heuristics/CLGEMMAutoHeuristics.h" |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 41 | |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 42 | namespace arm_compute |
| 43 | { |
| 44 | namespace opencl |
| 45 | { |
| 46 | using namespace arm_compute::misc::shape_calculator; |
| 47 | using namespace arm_compute::cl_gemm; |
| 48 | using namespace arm_compute::opencl::kernels; |
| 49 | using namespace arm_compute::experimental; |
| 50 | |
| 51 | namespace |
| 52 | { |
| 53 | inline bool validate_gemm_kernel(CLGEMMKernelType kernel_type) |
| 54 | { |
| 55 | switch(kernel_type) |
| 56 | { |
| 57 | case CLGEMMKernelType::NATIVE: |
| 58 | case CLGEMMKernelType::RESHAPED_ONLY_RHS: |
Freddie Liardet | e572dff | 2022-05-16 14:09:10 +0100 | [diff] [blame] | 59 | case CLGEMMKernelType::RESHAPED_ONLY_RHS_MMUL: |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 60 | { |
| 61 | return true; |
| 62 | } |
| 63 | default: |
| 64 | { |
| 65 | return false; |
| 66 | } |
| 67 | } |
| 68 | } |
| 69 | |
| 70 | //Automatically select between mlgo (prioritized) and default heuristics for gemm kernel type |
| 71 | inline CLGEMMKernelType auto_select_gemm_kernel(auto_heuristics::CommonQuery query, bool reshape_b_only_on_first_run) |
| 72 | { |
| 73 | auto gemm_kernel = auto_heuristics::select_mlgo_gemm_kernel(query, reshape_b_only_on_first_run); |
| 74 | if(bool(gemm_kernel)) |
| 75 | { |
| 76 | if(validate_gemm_kernel(gemm_kernel.gemm_type)) |
| 77 | { |
| 78 | ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use gemm kernel from mlgo heuristics: %s.", to_string(gemm_kernel.gemm_type).c_str()); |
| 79 | return gemm_kernel.gemm_type; |
| 80 | } |
| 81 | } |
| 82 | gemm_kernel = auto_heuristics::select_default_gemm_kernel(query, reshape_b_only_on_first_run); |
| 83 | ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use gemm kernel from default heuristics: %s.", to_string(gemm_kernel.gemm_type).c_str()); |
| 84 | return gemm_kernel.gemm_type; |
| 85 | } |
| 86 | |
| 87 | // Validate lhs_info and rhs_info for native kernel |
| 88 | inline bool validate_lhs_rhs_info_native(const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const ITensorInfo *a, const ITensorInfo *b, const GEMMReshapeInfo &reshape_info) |
| 89 | { |
| 90 | // Validate GEMMLHSMatrixInfo and GEMMRHSMatrixInfo for reshaped only rhs kernel |
| 91 | TensorInfo mm_result_s32_info{}; |
| 92 | // Output tensor auto initialization if not yet initialized |
| 93 | auto_init_if_empty(mm_result_s32_info, a->clone()->set_tensor_shape(compute_mm_shape(*a, *b, false, reshape_info)).set_data_type(DataType::S32)); |
| 94 | // Validate mm kernel |
| 95 | // NOTE: Ignore all other parameters (eg. output stage etc.) and only validate lhs and rhs info |
| 96 | // NOTE: This assumes: |
| 97 | // 1. lhs and rhs info's validity does not depend on these other parameters and vice versa(in CLGEMMLowpMatrixMultiplyNativeKernel.cpp validate_arguments). |
| 98 | // 2. lhs and rhs info does not cause window and padding issues through side effects (in CLGEMMLowpMatrixMultiplyNativeKernel.cpp validate_and_configure_window). |
| 99 | if(!bool(ClGemmLowpMatrixMultiplyNativeKernel::validate(a, b, &mm_result_s32_info, lhs_info, rhs_info, reshape_info))) |
| 100 | { |
| 101 | return false; |
| 102 | } |
| 103 | return true; |
| 104 | } |
| 105 | |
| 106 | // Automatically select between mlgo (prioritized) and default heuristics for native kernel configs |
| 107 | std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> auto_select_gemm_config_native(auto_heuristics::CommonQuery query, const ITensorInfo *a, const ITensorInfo *b, const GEMMReshapeInfo &reshape_info) |
| 108 | { |
| 109 | auto config = auto_heuristics::select_mlgo_gemm_config_native(query); |
| 110 | if(config) |
| 111 | { |
| 112 | if(validate_lhs_rhs_info_native(config.lhs_info, config.rhs_info, a, b, reshape_info)) |
| 113 | { |
| 114 | ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use native config from mlgo heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str()); |
| 115 | return { config.lhs_info, config.rhs_info }; |
| 116 | } |
| 117 | } |
| 118 | config = auto_heuristics::select_default_gemm_config_native(query); |
| 119 | ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use native config from default heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str()); |
| 120 | return { config.lhs_info, config.rhs_info }; |
| 121 | } |
| 122 | |
| 123 | // Validate lhs_info and rhs_info for reshaped only rhs kernel |
| 124 | inline bool validate_lhs_rhs_info_reshaped_only_rhs(const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *output, |
| 125 | unsigned int m, unsigned int n, unsigned int k, bool reinterpret_input_as_3d, int depth_output_gemm3d) |
| 126 | { |
| 127 | // Validate GEMMLHSMatrixInfo and GEMMRHSMatrixInfo for reshaped only rhs kernel |
| 128 | TensorInfo tmp_b_info{}; |
| 129 | // Validate reshape RHS kernel |
| 130 | auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info))); |
| 131 | if(!bool(ClGemmReshapeRhsMatrixKernel::validate(b, &tmp_b_info, rhs_info))) |
| 132 | { |
| 133 | return false; |
| 134 | } |
| 135 | // Validate mm kernel |
| 136 | // NOTE: Ignore all other parameters (eg. depth_output_gemm3d, output stage etc.) and only validate lhs and rhs info |
| 137 | // NOTE: This assumes: |
| 138 | // 1. lhs and rhs info's validity does not depend on these other parameters and vice versa(in ClGemmLowpMatrixMultiplyReshapedOnlyRHSKernel.cpp validate_arguments). |
| 139 | // 2. lhs and rhs info does not cause window and padding issues through side effects (in ClGemmLowpMatrixMultiplyReshapedOnlyRHSKernel.cpp validate_and_configure_window). |
| 140 | GEMMKernelInfo gemm_kernel_info; |
| 141 | gemm_kernel_info.m = m; |
| 142 | gemm_kernel_info.n = n; |
| 143 | gemm_kernel_info.k = k; |
| 144 | gemm_kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d; |
| 145 | gemm_kernel_info.depth_output_gemm3d = depth_output_gemm3d; |
| 146 | gemm_kernel_info.lhs_info = lhs_info; |
| 147 | gemm_kernel_info.rhs_info = rhs_info; |
| 148 | // Since we ignore the output stage, output data type has to be S32 to pass the validation |
| 149 | TensorInfo output_info_copy(*output); |
| 150 | output_info_copy.set_data_type(DataType::S32); |
| 151 | if(!bool(ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel::validate(a, &tmp_b_info, &output_info_copy, gemm_kernel_info))) |
| 152 | { |
| 153 | return false; |
| 154 | } |
| 155 | return true; |
| 156 | } |
| 157 | |
Freddie Liardet | e572dff | 2022-05-16 14:09:10 +0100 | [diff] [blame] | 158 | // Validate lhs_info and rhs_info for reshaped only rhs kernel |
| 159 | inline bool validate_lhs_rhs_info_reshaped_only_rhs_mmul(const GEMMLHSMatrixInfo &lhs_info, const GEMMRHSMatrixInfo &rhs_info, const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *output, |
| 160 | unsigned int m, unsigned int n, unsigned int k, bool reinterpret_input_as_3d, int depth_output_gemm3d) |
| 161 | { |
| 162 | // Validate GEMMLHSMatrixInfo and GEMMRHSMatrixInfo for reshaped only rhs kernel |
| 163 | TensorInfo tmp_b_info{}; |
| 164 | // Validate reshape RHS kernel |
| 165 | auto_init_if_empty(tmp_b_info, b->clone()->set_tensor_shape(compute_rhs_reshaped_shape(*b, rhs_info))); |
| 166 | if(!bool(ClGemmReshapeRhsMatrixKernel::validate(b, &tmp_b_info, rhs_info))) |
| 167 | { |
| 168 | return false; |
| 169 | } |
| 170 | // Validate mm kernel |
| 171 | // NOTE: Ignore all other parameters (eg. depth_output_gemm3d, output stage etc.) and only validate lhs and rhs info |
| 172 | // NOTE: This assumes: |
| 173 | // 1. lhs and rhs info's validity does not depend on these other parameters and vice versa(in ClGemmLowpMatrixMultiplyReshapedOnlyRHSKernel.cpp validate_arguments). |
| 174 | // 2. lhs and rhs info does not cause window and padding issues through side effects (in ClGemmLowpMatrixMultiplyReshapedOnlyRHSKernel.cpp validate_and_configure_window). |
| 175 | GEMMKernelInfo gemm_kernel_info; |
| 176 | gemm_kernel_info.m = m; |
| 177 | gemm_kernel_info.n = n; |
| 178 | gemm_kernel_info.k = k; |
| 179 | gemm_kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d; |
| 180 | gemm_kernel_info.depth_output_gemm3d = depth_output_gemm3d; |
| 181 | gemm_kernel_info.lhs_info = lhs_info; |
| 182 | gemm_kernel_info.rhs_info = rhs_info; |
| 183 | // Since we ignore the output stage, output data type has to be S32 to pass the validation |
| 184 | TensorInfo output_info_copy(*output); |
| 185 | output_info_copy.set_data_type(DataType::S32); |
| 186 | if(!bool(ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel::validate(a, &tmp_b_info, &output_info_copy, gemm_kernel_info))) |
| 187 | { |
| 188 | return false; |
| 189 | } |
| 190 | return true; |
| 191 | } |
| 192 | |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 193 | // Automatically select between mlgo (prioritized) and default heuristics for reshaped only rhs kernel configs |
| 194 | std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> auto_select_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery query, bool reinterpret_input_as_3d, int depth_output_gemm3d, |
| 195 | const ITensorInfo *a, |
| 196 | const ITensorInfo *b, const ITensorInfo *output) |
| 197 | { |
| 198 | auto config = auto_heuristics::select_mlgo_gemm_config_reshaped_only_rhs(query); |
| 199 | if(config) |
| 200 | { |
| 201 | if(validate_lhs_rhs_info_reshaped_only_rhs(config.lhs_info, config.rhs_info, a, b, output, query.m, query.n, query.k, reinterpret_input_as_3d, depth_output_gemm3d)) |
| 202 | { |
| 203 | ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use reshaped_only_rhs config from mlgo heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str()); |
| 204 | return { config.lhs_info, config.rhs_info }; |
| 205 | } |
| 206 | } |
| 207 | config = auto_heuristics::select_default_gemm_config_reshaped_only_rhs(query); |
| 208 | ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use reshaped_only_rhs config from default heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), to_string(config.rhs_info).c_str()); |
| 209 | return { config.lhs_info, config.rhs_info }; |
| 210 | } |
| 211 | |
Freddie Liardet | e572dff | 2022-05-16 14:09:10 +0100 | [diff] [blame] | 212 | // Automatically select between mlgo (prioritized) and default heuristics for reshaped only rhs kernel configs |
| 213 | std::pair<GEMMLHSMatrixInfo, GEMMRHSMatrixInfo> auto_select_gemm_config_reshaped_only_rhs_mmul(auto_heuristics::CommonQuery query, bool reinterpret_input_as_3d, int depth_output_gemm3d, |
| 214 | const ITensorInfo *a, |
| 215 | const ITensorInfo *b, const ITensorInfo *output) |
| 216 | { |
| 217 | ARM_COMPUTE_UNUSED(a, b, output, reinterpret_input_as_3d, depth_output_gemm3d); |
| 218 | auto config = auto_heuristics::select_default_gemm_config_reshaped_only_rhs(query); |
| 219 | validate_lhs_rhs_info_reshaped_only_rhs_mmul(config.lhs_info, config.rhs_info, a, b, output, query.m, query.n, query.k, reinterpret_input_as_3d, depth_output_gemm3d); |
| 220 | ARM_COMPUTE_LOG_INFO_MSG_WITH_FORMAT_CORE("Use reshaped_only_rhs_mmul config from default heuristics: LHS info: %s ; RHS info: %s ", to_string(config.lhs_info).c_str(), |
| 221 | to_string(config.rhs_info).c_str()); |
| 222 | return { config.lhs_info, config.rhs_info }; |
| 223 | } |
| 224 | |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 225 | inline bool is_gemm_reshaped(CLGEMMKernelType kernel_type) |
| 226 | { |
| 227 | switch(kernel_type) |
| 228 | { |
| 229 | case CLGEMMKernelType::NATIVE: |
| 230 | return false; |
| 231 | case CLGEMMKernelType::RESHAPED_ONLY_RHS: |
Freddie Liardet | e572dff | 2022-05-16 14:09:10 +0100 | [diff] [blame] | 232 | case CLGEMMKernelType::RESHAPED_ONLY_RHS_MMUL: |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 233 | return true; |
| 234 | default: |
| 235 | ARM_COMPUTE_ERROR("Not supported gemmlowp kernel!"); |
| 236 | } |
| 237 | } |
| 238 | } // namespace |
| 239 | |
| 240 | ClGemmLowpMatrixMultiplyCore::ClGemmLowpMatrixMultiplyCore() |
| 241 | : _weights_to_qasymm8(std::make_unique<ClCastKernel>()), |
| 242 | _mm_native_kernel(std::make_unique<ClGemmLowpMatrixMultiplyNativeKernel>()), |
| 243 | _mm_reshaped_only_rhs_kernel(std::make_unique<ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel>()), |
Freddie Liardet | e572dff | 2022-05-16 14:09:10 +0100 | [diff] [blame] | 244 | _mm_reshaped_only_rhs_mmul_kernel(std::make_unique<ClGemmLowpMatrixMultiplyReshapedOnlyRhsMMULKernel>()), |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 245 | _mtx_b_reshape_kernel(std::make_unique<ClGemmReshapeRhsMatrixKernel>()), |
| 246 | _mtx_a_reduction_kernel(std::make_unique<ClGemmLowpMatrixAReductionKernel>()), |
| 247 | _mtx_b_reduction_kernel(std::make_unique<ClGemmLowpMatrixBReductionKernel>()), |
| 248 | _offset_contribution_kernel(std::make_unique<ClGemmLowpOffsetContributionKernel>()), |
| 249 | _offset_contribution_output_stage_kernel(std::make_unique<ClGemmLowpOffsetContributionOutputStageKernel>()), |
| 250 | _aux_mem(AuxTensorIdx::Count) |
| 251 | { |
| 252 | } |
| 253 | |
| 254 | ClGemmLowpMatrixMultiplyCore::~ClGemmLowpMatrixMultiplyCore() = default; |
| 255 | |
| 256 | void ClGemmLowpMatrixMultiplyCore::configure(const CLCompileContext &compile_context, |
| 257 | ITensorInfo *a, ITensorInfo *b, ITensorInfo *c, ITensorInfo *output, |
| 258 | const GEMMInfo &gemm_info) |
| 259 | { |
| 260 | ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output); |
Freddie Liardet | e572dff | 2022-05-16 14:09:10 +0100 | [diff] [blame] | 261 | ARM_COMPUTE_ERROR_THROW_ON(ClGemmLowpMatrixMultiplyCore::validate(a, b, c, output, gemm_info)); |
ramelg01 | 2e53f17 | 2021-09-22 10:48:25 +0100 | [diff] [blame] | 262 | ARM_COMPUTE_LOG_PARAMS(a, b, c, output, gemm_info); |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 263 | |
| 264 | _reshape_b_only_on_first_run = gemm_info.reshape_b_only_on_first_run(); |
| 265 | _a_offset = a->quantization_info().uniform().offset; |
| 266 | _convert_to_qasymm8 = is_data_type_quantized_per_channel(b->data_type()) && is_data_type_quantized_symmetric(b->data_type()) |
| 267 | && a->data_type() == DataType::QASYMM8; |
| 268 | _b_offset = _convert_to_qasymm8 ? -128 : b->quantization_info().uniform().offset; |
| 269 | _gemm_info = gemm_info; |
| 270 | |
| 271 | // Get the GPU target |
| 272 | const GPUTarget gpu_target = CLScheduler::get().target(); |
| 273 | |
| 274 | // Set the target for the kernels |
| 275 | _mm_native_kernel->set_target(gpu_target); |
| 276 | _mm_reshaped_only_rhs_kernel->set_target(gpu_target); |
Freddie Liardet | e572dff | 2022-05-16 14:09:10 +0100 | [diff] [blame] | 277 | _mm_reshaped_only_rhs_mmul_kernel->set_target(gpu_target); |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 278 | |
| 279 | GEMMRHSMatrixInfo rhs_info; |
| 280 | GEMMLHSMatrixInfo lhs_info; |
| 281 | |
| 282 | // Arguments used by GEMMReshapeInfo |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 283 | // in order to know how the matrices have been reshaped |
| 284 | bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); |
| 285 | const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); |
| 286 | const unsigned int n = b->dimension(0); |
| 287 | const unsigned int k = a->dimension(0); |
| 288 | const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); |
| 289 | const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); |
| 290 | |
| 291 | const auto reshape_info = GEMMReshapeInfo(m, n, k, 1, 1, depth_output_gemm3d, reinterpret_input_as_3d); |
| 292 | |
Freddie Liardet | e572dff | 2022-05-16 14:09:10 +0100 | [diff] [blame] | 293 | _gemm_kernel_type = auto_select_gemm_kernel(auto_heuristics::CommonQuery{ gpu_target, a->data_type(), m, n, k, batch_size }, _reshape_b_only_on_first_run); |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 294 | |
| 295 | if(_convert_to_qasymm8) |
| 296 | { |
| 297 | // Set data type for converted weights |
| 298 | _qasymm8_weights = *b; |
| 299 | _qasymm8_weights.set_data_type(DataType::QASYMM8); |
| 300 | _weights_to_qasymm8->configure(compile_context, b, &_qasymm8_weights, ConvertPolicy::WRAP); |
| 301 | } |
| 302 | |
| 303 | ITensorInfo *matrix_b = _convert_to_qasymm8 ? &_qasymm8_weights : b; |
Freddie Liardet | e572dff | 2022-05-16 14:09:10 +0100 | [diff] [blame] | 304 | if(_gemm_kernel_type == CLGEMMKernelType::RESHAPED_ONLY_RHS) |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 305 | { |
| 306 | matrix_b = &_tmp_b; |
| 307 | |
| 308 | // Pick up the GEMM configuration |
| 309 | // It doesn't matter whether Datatype is DataType::QASYMM8 or DataType::QASYMM8_SIGNED, since it only affect the shape configuration |
| 310 | std::tie(lhs_info, rhs_info) = auto_select_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery{ gpu_target, DataType::QASYMM8, m, n, k, batch_size }, reinterpret_input_as_3d, |
| 311 | depth_output_gemm3d, |
| 312 | a, _convert_to_qasymm8 ? &_qasymm8_weights : b, output); |
| 313 | |
| 314 | // Configure reshape RHS kernel |
| 315 | _mtx_b_reshape_kernel->configure(compile_context, _convert_to_qasymm8 ? &_qasymm8_weights : b, &_tmp_b, rhs_info); |
| 316 | } |
Freddie Liardet | e572dff | 2022-05-16 14:09:10 +0100 | [diff] [blame] | 317 | if(_gemm_kernel_type == CLGEMMKernelType::RESHAPED_ONLY_RHS_MMUL) |
| 318 | { |
| 319 | matrix_b = &_tmp_b; |
| 320 | |
| 321 | // Pick up the GEMM configuration |
| 322 | // It doesn't matter whether Datatype is DataType::QASYMM8 or DataType::QASYMM8_SIGNED, since it only affect the shape configuration |
| 323 | std::tie(lhs_info, rhs_info) = auto_select_gemm_config_reshaped_only_rhs_mmul(auto_heuristics::CommonQuery{ gpu_target, DataType::QASYMM8, m, n, k, batch_size }, reinterpret_input_as_3d, |
| 324 | depth_output_gemm3d, |
| 325 | a, _convert_to_qasymm8 ? &_qasymm8_weights : b, output); |
| 326 | |
| 327 | // Configure reshape RHS kernel |
| 328 | _mtx_b_reshape_kernel->configure(compile_context, _convert_to_qasymm8 ? &_qasymm8_weights : b, &_tmp_b, rhs_info); |
| 329 | } |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 330 | |
| 331 | // Using default reduction info |
| 332 | const GEMMLowpReductionKernelInfo reduction_info {}; |
| 333 | |
| 334 | // Initialize matrix B reduction kernel only if _a_offset is not equal to 0 |
| 335 | if(_a_offset != 0) |
| 336 | { |
| 337 | _vector_sum_col = TensorInfo(compute_reductionA_shape(*b), 1, DataType::S32); |
| 338 | |
| 339 | // Configure Matrix B reduction kernel |
| 340 | _mtx_b_reduction_kernel->configure(compile_context, _convert_to_qasymm8 ? &_qasymm8_weights : b, &_vector_sum_col, reduction_info); |
| 341 | } |
| 342 | |
| 343 | // Initialize Matrix A reduction kernel only if _b_offset is not equal to 0 |
| 344 | if(_b_offset != 0) |
| 345 | { |
| 346 | _vector_sum_row = TensorInfo(compute_reductionB_shape(*a), 1, DataType::S32); |
| 347 | |
| 348 | // Configure matrix A reduction kernel |
| 349 | _mtx_a_reduction_kernel->configure(compile_context, a, &_vector_sum_row, reduction_info); |
| 350 | } |
| 351 | |
| 352 | GEMMKernelInfo gemm_kernel_info; |
| 353 | gemm_kernel_info.m = m; |
| 354 | gemm_kernel_info.n = n; |
| 355 | gemm_kernel_info.k = k; |
| 356 | gemm_kernel_info.depth_output_gemm3d = depth_output_gemm3d; |
| 357 | gemm_kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d; |
| 358 | gemm_kernel_info.lhs_info = lhs_info; |
| 359 | gemm_kernel_info.rhs_info = rhs_info; |
| 360 | gemm_kernel_info.a_offset = _a_offset; |
| 361 | gemm_kernel_info.b_offset = _b_offset; |
| 362 | // If GEMMLowpOutputStage != NONE, fuse the offset contribution with the output stage |
| 363 | if(gemm_info.gemmlowp_output_stage().type != GEMMLowpOutputStageType::NONE) |
| 364 | { |
| 365 | // Configure offset contribution kernel |
| 366 | const size_t num_filters = (gemm_info.gemmlowp_output_stage().is_quantized_per_channel) ? gemm_info.gemmlowp_output_stage().gemmlowp_multipliers.size() : 1; |
| 367 | |
| 368 | _gemm_output_stage_multipliers = TensorInfo(TensorShape(num_filters), 1, DataType::S32); |
| 369 | _gemm_output_stage_shifts = TensorInfo(TensorShape(num_filters), 1, DataType::S32); |
| 370 | |
| 371 | GEMMLowpOutputStageInfo gemmlowp_output_stage = gemm_info.gemmlowp_output_stage(); |
| 372 | gemmlowp_output_stage.output_data_type = a->data_type(); |
| 373 | if(num_filters == 1) |
| 374 | { |
| 375 | // Per-channel quantization with OFM == 1 is equivalent to uniform quantization. |
| 376 | // Setting this flag to false prevents the kernel from adding useless padding to the output multipliers and shifts |
| 377 | gemmlowp_output_stage.is_quantized_per_channel = false; |
| 378 | } |
| 379 | |
| 380 | gemm_kernel_info.output_stage = gemmlowp_output_stage; |
| 381 | |
Freddie Liardet | e572dff | 2022-05-16 14:09:10 +0100 | [diff] [blame] | 382 | if(_gemm_kernel_type == CLGEMMKernelType::RESHAPED_ONLY_RHS && gemmlowp_output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT) |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 383 | { |
| 384 | // Configure and tune matrix multiply kernel with fused output stage |
| 385 | _mm_reshaped_only_rhs_kernel->configure(compile_context, a, matrix_b, output, gemm_kernel_info, _a_offset == 0 ? nullptr : &_vector_sum_col, |
| 386 | _b_offset == 0 ? nullptr : &_vector_sum_row, c != nullptr ? c : nullptr, &_gemm_output_stage_multipliers, &_gemm_output_stage_shifts); |
| 387 | } |
Freddie Liardet | e572dff | 2022-05-16 14:09:10 +0100 | [diff] [blame] | 388 | else if(_gemm_kernel_type == CLGEMMKernelType::RESHAPED_ONLY_RHS_MMUL && gemmlowp_output_stage.type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT) |
| 389 | { |
| 390 | // Configure and tune matrix multiply kernel with fused output stage |
| 391 | _mm_reshaped_only_rhs_mmul_kernel->configure(compile_context, a, matrix_b, output, gemm_kernel_info, _a_offset == 0 ? nullptr : &_vector_sum_col, |
| 392 | _b_offset == 0 ? nullptr : &_vector_sum_row, c != nullptr ? c : nullptr, &_gemm_output_stage_multipliers, &_gemm_output_stage_shifts); |
| 393 | } |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 394 | else |
| 395 | { |
| 396 | _run_output_stage = true; |
| 397 | |
Freddie Liardet | e572dff | 2022-05-16 14:09:10 +0100 | [diff] [blame] | 398 | if(_gemm_kernel_type == CLGEMMKernelType::RESHAPED_ONLY_RHS) |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 399 | { |
| 400 | _mm_reshaped_only_rhs_kernel->configure(compile_context, a, matrix_b, &_mm_result_s32, gemm_kernel_info); |
| 401 | } |
Freddie Liardet | e572dff | 2022-05-16 14:09:10 +0100 | [diff] [blame] | 402 | if(_gemm_kernel_type == CLGEMMKernelType::RESHAPED_ONLY_RHS_MMUL) |
| 403 | { |
| 404 | _mm_reshaped_only_rhs_mmul_kernel->configure(compile_context, a, matrix_b, &_mm_result_s32, gemm_kernel_info); |
| 405 | } |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 406 | else |
| 407 | { |
| 408 | // Pick up the GEMM configuration |
| 409 | // It doesn't matter whether Datatype is DataType::QASYMM8 or DataType::QASYMM8_SIGNED, since it only affect the shape configuration |
| 410 | std::tie(lhs_info, rhs_info) = auto_select_gemm_config_native(auto_heuristics::CommonQuery{ gpu_target, DataType::QASYMM8, m, n, k, batch_size }, |
| 411 | a, _convert_to_qasymm8 ? &_qasymm8_weights : matrix_b, reshape_info); |
| 412 | |
| 413 | // Configure matrix multiply kernel |
| 414 | _mm_native_kernel->configure(compile_context, a, matrix_b, &_mm_result_s32, lhs_info, rhs_info, reshape_info); |
| 415 | |
| 416 | _offset_contribution_output_stage_kernel->configure(compile_context, &_mm_result_s32, _a_offset == 0 ? nullptr : &_vector_sum_col, _b_offset == 0 ? nullptr : &_vector_sum_row, |
| 417 | c != nullptr ? c : nullptr, output, a->dimension(0), _a_offset, _b_offset, gemmlowp_output_stage, |
| 418 | &_gemm_output_stage_multipliers, &_gemm_output_stage_shifts); |
| 419 | } |
| 420 | } |
| 421 | } |
| 422 | else |
| 423 | { |
| 424 | _run_offset_contribution = true; |
Freddie Liardet | e572dff | 2022-05-16 14:09:10 +0100 | [diff] [blame] | 425 | if(_gemm_kernel_type == CLGEMMKernelType::RESHAPED_ONLY_RHS) |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 426 | { |
| 427 | // Configure and tune matrix multiply kernel |
| 428 | _mm_reshaped_only_rhs_kernel->configure(compile_context, a, matrix_b, output, gemm_kernel_info); |
| 429 | } |
Freddie Liardet | e572dff | 2022-05-16 14:09:10 +0100 | [diff] [blame] | 430 | else if(_gemm_kernel_type == CLGEMMKernelType::RESHAPED_ONLY_RHS_MMUL) |
| 431 | { |
| 432 | // Configure and tune matrix multiply kernel |
| 433 | _mm_reshaped_only_rhs_mmul_kernel->configure(compile_context, a, matrix_b, output, gemm_kernel_info); |
| 434 | } |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 435 | else |
| 436 | { |
| 437 | // Pick up the GEMM configuration |
| 438 | // It doesn't matter whether Datatype is DataType::QASYMM8 or DataType::QASYMM8_SIGNED, since it only affect the shape configuration |
| 439 | std::tie(lhs_info, rhs_info) = auto_select_gemm_config_native(auto_heuristics::CommonQuery{ gpu_target, DataType::QASYMM8, m, n, k, batch_size }, |
| 440 | a, _convert_to_qasymm8 ? &_qasymm8_weights : b, reshape_info); |
| 441 | |
| 442 | // Configure matrix multiply kernel |
| 443 | _mm_native_kernel->configure(compile_context, a, matrix_b, output, lhs_info, rhs_info, reshape_info); |
| 444 | } |
| 445 | |
| 446 | // Configure offset contribution kernel |
| 447 | _offset_contribution_kernel->configure(compile_context, output, _a_offset == 0 ? nullptr : &_vector_sum_col, _b_offset == 0 ? nullptr : &_vector_sum_row, |
| 448 | c != nullptr ? c : nullptr, a->dimension(0), _a_offset, _b_offset); |
| 449 | } |
| 450 | |
| 451 | // Request memory |
| 452 | _aux_mem[RhsQAsymm8] = MemoryInfo(offset_int_vec(RhsQAsymm8), _reshape_b_only_on_first_run ? MemoryLifetime::Persistent : MemoryLifetime::Temporary, _qasymm8_weights.total_size()); |
Freddie Liardet | e572dff | 2022-05-16 14:09:10 +0100 | [diff] [blame] | 453 | if(is_gemm_reshaped(_gemm_kernel_type)) |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 454 | { |
| 455 | // Overwrite Rhs as prepare if gemm is reshaped as there will be a two-step transformation |
| 456 | _aux_mem[RhsQAsymm8] = MemoryInfo(offset_int_vec(RhsQAsymm8), _reshape_b_only_on_first_run ? MemoryLifetime::Prepare : MemoryLifetime::Temporary, _qasymm8_weights.total_size()); |
| 457 | _aux_mem[RhsReshape] = MemoryInfo(offset_int_vec(RhsReshape), _reshape_b_only_on_first_run ? MemoryLifetime::Persistent : MemoryLifetime::Temporary, _tmp_b.total_size()); |
| 458 | } |
| 459 | if(_a_offset != 0) |
| 460 | { |
| 461 | _aux_mem[VecSumCol] = MemoryInfo(offset_int_vec(VecSumCol), _reshape_b_only_on_first_run ? MemoryLifetime::Persistent : MemoryLifetime::Temporary, _vector_sum_col.total_size()); |
| 462 | } |
| 463 | if(_b_offset != 0) |
| 464 | { |
| 465 | _aux_mem[VecSumRow] = MemoryInfo(offset_int_vec(VecSumRow), MemoryLifetime::Temporary, _vector_sum_row.total_size()); |
| 466 | } |
| 467 | _aux_mem[ResultS32] = MemoryInfo(offset_int_vec(ResultS32), MemoryLifetime::Temporary, _mm_result_s32.total_size()); |
| 468 | _aux_mem[Multipliers] = MemoryInfo(offset_int_vec(Multipliers), MemoryLifetime::Persistent, _gemm_output_stage_multipliers.total_size()); |
| 469 | _aux_mem[Shifts] = MemoryInfo(offset_int_vec(Shifts), MemoryLifetime::Persistent, _gemm_output_stage_shifts.total_size()); |
| 470 | } |
| 471 | |
| 472 | Status ClGemmLowpMatrixMultiplyCore::validate(const ITensorInfo *a, const ITensorInfo *b, const ITensorInfo *c, const ITensorInfo *output, const GEMMInfo &gemm_info) |
| 473 | { |
| 474 | ARM_COMPUTE_ERROR_ON_NULLPTR(a, b, output); |
| 475 | ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(a, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED); |
| 476 | ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(b, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::QSYMM8, DataType::QSYMM8_PER_CHANNEL); |
| 477 | ARM_COMPUTE_RETURN_ERROR_ON(a->data_type() == DataType::QASYMM8 && b->data_type() == DataType::QASYMM8_SIGNED); |
| 478 | ARM_COMPUTE_RETURN_ERROR_ON(a->data_type() == DataType::QASYMM8_SIGNED && b->data_type() == DataType::QASYMM8); |
| 479 | ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_a_reshaped(), "Matrix A already reshaped is not supported"); |
| 480 | ARM_COMPUTE_RETURN_ERROR_ON_MSG(gemm_info.is_b_reshaped(), "Matrix B already reshaped is not supported"); |
| 481 | |
| 482 | int32_t a_offset = a->quantization_info().uniform().offset; |
| 483 | int32_t b_offset = b->quantization_info().uniform().offset; |
| 484 | |
| 485 | const ITensorInfo *matrix_a_info = a; |
| 486 | |
| 487 | TensorInfo tmp_b_info{}; |
| 488 | GEMMRHSMatrixInfo rhs_info; |
| 489 | GEMMLHSMatrixInfo lhs_info; |
| 490 | |
| 491 | // Get the GPU target |
| 492 | const GPUTarget gpu_target = CLScheduler::get().target(); |
| 493 | |
| 494 | bool reinterpret_input_as_3d = gemm_info.reinterpret_input_as_3d(); |
| 495 | const unsigned int m = reinterpret_input_as_3d ? (a->dimension(1) * a->dimension(2)) : a->dimension(1); |
| 496 | const unsigned int n = b->dimension(0); |
| 497 | const unsigned int k = a->dimension(0); |
| 498 | const unsigned int batch_size = reinterpret_input_as_3d ? a->dimension(3) : a->dimension(2); |
| 499 | const int depth_output_gemm3d = gemm_info.depth_output_gemm3d(); |
| 500 | |
| 501 | bool reshape_matrix_b = is_gemm_reshaped(auto_select_gemm_kernel(auto_heuristics::CommonQuery{ gpu_target, a->data_type(), m, n, k, batch_size }, gemm_info.reshape_b_only_on_first_run())); |
| 502 | |
| 503 | const GEMMReshapeInfo reshape_info = GEMMReshapeInfo(m, n, k, 1, 1, depth_output_gemm3d, reinterpret_input_as_3d); |
| 504 | |
| 505 | bool convert_to_qasymm8 = is_data_type_quantized_per_channel(b->data_type()) && is_data_type_quantized_symmetric(b->data_type()) |
| 506 | && is_data_type_quantized_asymmetric(a->data_type()); |
| 507 | TensorInfo weights_info(*b); |
| 508 | if(convert_to_qasymm8) |
| 509 | { |
| 510 | b_offset = -128; |
| 511 | weights_info.set_data_type(DataType::QASYMM8); |
| 512 | ARM_COMPUTE_RETURN_ON_ERROR(ClCastKernel::validate(b, &weights_info, ConvertPolicy::WRAP)); |
| 513 | } |
| 514 | const ITensorInfo *matrix_b_info = &weights_info; |
| 515 | if(reshape_matrix_b) |
| 516 | { |
| 517 | matrix_b_info = &tmp_b_info; |
| 518 | |
| 519 | // Pick up the GEMM configuration |
| 520 | // NOTE: No need to validate mlgo configurations as they automatically fall back to default heuristics if validation fails |
| 521 | // It doesn't matter whether Datatype is DataType::QASYMM8 or DataType::QASYMM8_SIGNED, since it only affect the shape configuration |
| 522 | const auto res = select_default_gemm_config_reshaped_only_rhs(auto_heuristics::CommonQuery{ gpu_target, DataType::QASYMM8, m, n, k, batch_size }); |
| 523 | lhs_info = res.lhs_info; |
| 524 | rhs_info = res.rhs_info; |
| 525 | |
| 526 | // Validate reshape RHS kernel |
| 527 | auto_init_if_empty(tmp_b_info, weights_info.clone()->set_tensor_shape(compute_rhs_reshaped_shape(weights_info, rhs_info))); |
| 528 | ARM_COMPUTE_RETURN_ON_ERROR(ClGemmReshapeRhsMatrixKernel::validate(&weights_info, &tmp_b_info, rhs_info)); |
| 529 | } |
| 530 | |
| 531 | TensorInfo info_vector_sum_col{}; |
| 532 | TensorInfo info_vector_sum_row{}; |
| 533 | |
| 534 | const GEMMLowpReductionKernelInfo reduction_info; |
| 535 | // Validate matrix B reduction kernel only if _a_offset is not equal to 0 |
| 536 | if(a_offset != 0) |
| 537 | { |
| 538 | info_vector_sum_col = TensorInfo(compute_reductionA_shape(weights_info), 1, DataType::S32); |
| 539 | |
| 540 | // Configure Matrix B reduction kernel |
| 541 | ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixBReductionKernel::validate(&weights_info, &info_vector_sum_col, reduction_info)); |
| 542 | } |
| 543 | |
| 544 | // Validate Matrix A reduction kernel only if _b_offset is not equal to 0 |
| 545 | if(b_offset != 0) |
| 546 | { |
| 547 | info_vector_sum_row = TensorInfo(compute_reductionB_shape(*a), 1, DataType::S32); |
| 548 | |
| 549 | // Configure matrix A reduction kernel |
| 550 | ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixAReductionKernel::validate(a, &info_vector_sum_row, reduction_info)); |
| 551 | } |
| 552 | |
| 553 | GEMMKernelInfo gemm_kernel_info; |
| 554 | gemm_kernel_info.m = m; |
| 555 | gemm_kernel_info.n = n; |
| 556 | gemm_kernel_info.k = k; |
| 557 | gemm_kernel_info.depth_output_gemm3d = depth_output_gemm3d; |
| 558 | gemm_kernel_info.reinterpret_input_as_3d = reinterpret_input_as_3d; |
| 559 | gemm_kernel_info.lhs_info = lhs_info; |
| 560 | gemm_kernel_info.rhs_info = rhs_info; |
| 561 | gemm_kernel_info.a_offset = a_offset; |
| 562 | gemm_kernel_info.b_offset = b_offset; |
| 563 | if(gemm_info.gemmlowp_output_stage().type != GEMMLowpOutputStageType::NONE) |
| 564 | { |
| 565 | const size_t num_filters = (gemm_info.gemmlowp_output_stage().is_quantized_per_channel) ? gemm_info.gemmlowp_output_stage().gemmlowp_multipliers.size() : 1; |
| 566 | |
| 567 | const TensorInfo gemm_output_stage_multipliers_shifts_info(TensorInfo(TensorShape(num_filters), 1, DataType::S32)); |
| 568 | |
| 569 | GEMMLowpOutputStageInfo gemmlowp_output_stage = gemm_info.gemmlowp_output_stage(); |
| 570 | gemmlowp_output_stage.output_data_type = a->data_type(); |
| 571 | |
| 572 | gemm_kernel_info.output_stage = gemmlowp_output_stage; |
| 573 | if(reshape_matrix_b && gemm_info.gemmlowp_output_stage().type == GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT) |
| 574 | { |
| 575 | ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel::validate(matrix_a_info, matrix_b_info, output, gemm_kernel_info, |
| 576 | a_offset == 0 ? nullptr : &info_vector_sum_col, |
| 577 | b_offset == 0 ? nullptr : &info_vector_sum_row, |
| 578 | c, |
| 579 | &gemm_output_stage_multipliers_shifts_info, |
| 580 | &gemm_output_stage_multipliers_shifts_info)); |
| 581 | } |
| 582 | else |
| 583 | { |
| 584 | TensorInfo mm_result_s32_info{}; |
| 585 | |
| 586 | if(reshape_matrix_b) |
| 587 | { |
| 588 | // Output tensor auto inizialitation if not yet initialized |
| 589 | auto_init_if_empty(mm_result_s32_info, a->clone()->set_tensor_shape(compute_mm_shape(*matrix_a_info, *matrix_b_info, reshape_info)).set_data_type(DataType::S32)); |
| 590 | |
| 591 | // Validate matrix multiply |
| 592 | ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel::validate(matrix_a_info, matrix_b_info, &mm_result_s32_info, gemm_kernel_info)); |
| 593 | } |
| 594 | else |
| 595 | { |
| 596 | // Output tensor auto inizialitation if not yet initialized |
| 597 | auto_init_if_empty(mm_result_s32_info, a->clone()->set_tensor_shape(compute_mm_shape(*matrix_a_info, *matrix_b_info, false, reshape_info)).set_data_type(DataType::S32)); |
| 598 | |
| 599 | // Pick up the GEMM configuration |
| 600 | // NOTE: No need to validate mlgo configurations as they automatically fall back to default heuristics if validation fails |
| 601 | // It doesn't matter whether Datatype is DataType::QASYMM8 or DataType::QASYMM8_SIGNED, since it only affect the shape configuration |
| 602 | const auto res = select_default_gemm_config_native(auto_heuristics::CommonQuery{ gpu_target, DataType::QASYMM8, m, n, k, batch_size }); |
| 603 | lhs_info = res.lhs_info; |
| 604 | rhs_info = res.rhs_info; |
| 605 | |
| 606 | // Validate matrix multiply |
| 607 | ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixMultiplyNativeKernel::validate(matrix_a_info, matrix_b_info, &mm_result_s32_info, lhs_info, rhs_info, reshape_info)); |
| 608 | } |
| 609 | |
| 610 | // Validate offset contribution kernel |
| 611 | ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpOffsetContributionOutputStageKernel::validate(&mm_result_s32_info, |
| 612 | a_offset == 0 ? nullptr : &info_vector_sum_col, |
| 613 | b_offset == 0 ? nullptr : &info_vector_sum_row, |
| 614 | c, |
| 615 | output, |
| 616 | a_offset, b_offset, |
| 617 | gemmlowp_output_stage, |
| 618 | &gemm_output_stage_multipliers_shifts_info, |
| 619 | &gemm_output_stage_multipliers_shifts_info)); |
| 620 | } |
| 621 | } |
| 622 | else |
| 623 | { |
| 624 | if(reshape_matrix_b) |
| 625 | { |
| 626 | // Validate matrix multiply |
| 627 | ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixMultiplyReshapedOnlyRhsKernel::validate(matrix_a_info, matrix_b_info, output, gemm_kernel_info)); |
| 628 | } |
| 629 | else |
| 630 | { |
| 631 | // Pick up the GEMM configuration |
| 632 | // It doesn't matter whether Datatype is DataType::QASYMM8 or DataType::QASYMM8_SIGNED, since it only affect the shape configuration |
| 633 | const auto res = select_default_gemm_config_native(auto_heuristics::CommonQuery{ gpu_target, DataType::QASYMM8, m, n, k, batch_size }); |
| 634 | lhs_info = res.lhs_info; |
| 635 | rhs_info = res.rhs_info; |
| 636 | |
| 637 | // Validate matrix multiply |
| 638 | ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixMultiplyNativeKernel::validate(matrix_a_info, matrix_b_info, output, lhs_info, rhs_info, reshape_info)); |
| 639 | } |
| 640 | |
| 641 | if(output->total_size() != 0) |
| 642 | { |
| 643 | // Validate offset contribution kernel |
| 644 | ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpOffsetContributionKernel::validate(output, |
| 645 | a_offset == 0 ? nullptr : &info_vector_sum_col, |
| 646 | b_offset == 0 ? nullptr : &info_vector_sum_row, |
| 647 | c, |
| 648 | a_offset, b_offset)); |
| 649 | } |
| 650 | } |
| 651 | |
| 652 | return Status{}; |
| 653 | } |
| 654 | |
| 655 | void ClGemmLowpMatrixMultiplyCore::run(ITensorPack &tensors) |
| 656 | { |
| 657 | const ITensor *a = tensors.get_const_tensor(ACL_SRC_0); |
| 658 | const ITensor *b = tensors.get_const_tensor(ACL_SRC_1); |
| 659 | const ITensor *c = tensors.get_const_tensor(ACL_SRC_2); |
| 660 | ITensor *dst = tensors.get_tensor(ACL_DST); |
| 661 | |
| 662 | ARM_COMPUTE_ERROR_ON_NULLPTR(a, dst); |
| 663 | |
| 664 | CLAuxTensorHandler vec_sum_col(offset_int_vec(VecSumCol), _vector_sum_col, tensors, true); |
| 665 | CLAuxTensorHandler vec_sum_row(offset_int_vec(VecSumRow), _vector_sum_row, tensors, true); |
| 666 | CLAuxTensorHandler rhs_qasymm8(offset_int_vec(RhsQAsymm8), _qasymm8_weights, tensors, true); |
| 667 | CLAuxTensorHandler tmp_b(offset_int_vec(RhsReshape), _tmp_b, tensors, true); |
| 668 | CLAuxTensorHandler res32(offset_int_vec(ResultS32), _mm_result_s32, tensors, true); |
| 669 | CLAuxTensorHandler shifts(offset_int_vec(Shifts), _gemm_output_stage_shifts, tensors, true); |
| 670 | CLAuxTensorHandler multipliers(offset_int_vec(Multipliers), _gemm_output_stage_multipliers, tensors, true); |
| 671 | |
| 672 | // Prepare the consts if needed |
| 673 | prepare(tensors); |
| 674 | |
| 675 | const ITensor *matrix_a = a; |
| 676 | const ITensor *matrix_b = _convert_to_qasymm8 ? rhs_qasymm8.get() : b; |
| 677 | |
Freddie Liardet | e572dff | 2022-05-16 14:09:10 +0100 | [diff] [blame] | 678 | if(is_gemm_reshaped(_gemm_kernel_type)) |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 679 | { |
| 680 | matrix_b = tmp_b.get(); |
| 681 | if(!_reshape_b_only_on_first_run) |
| 682 | { |
| 683 | // Run reshape matrix B |
| 684 | ITensorPack mtx_b_reshape_pack = |
| 685 | { |
| 686 | { TensorType::ACL_SRC, _convert_to_qasymm8 ? rhs_qasymm8.get() : b }, |
| 687 | { TensorType::ACL_DST, tmp_b.get() } |
| 688 | }; |
| 689 | CLScheduler::get().enqueue_op(*_mtx_b_reshape_kernel, mtx_b_reshape_pack, false); |
| 690 | } |
| 691 | } |
| 692 | |
| 693 | // Run matrix B reduction kernel only if _a_offset is not equal to 0 |
| 694 | if(_a_offset != 0 && !_reshape_b_only_on_first_run) |
| 695 | { |
| 696 | ITensorPack mtx_b_red_pack = |
| 697 | { |
| 698 | { TensorType::ACL_SRC, _convert_to_qasymm8 ? rhs_qasymm8.get() : b }, |
| 699 | { TensorType::ACL_DST, vec_sum_col.get() } |
| 700 | }; |
| 701 | CLScheduler::get().enqueue_op(*_mtx_b_reduction_kernel, mtx_b_red_pack, false); |
| 702 | } |
| 703 | |
| 704 | // Run matrix A reduction kernel only if _b_offset is not equal to 0 |
| 705 | if(_b_offset != 0) |
| 706 | { |
| 707 | ITensorPack mtx_a_red_pack = |
| 708 | { |
| 709 | { TensorType::ACL_SRC, matrix_a }, |
| 710 | { TensorType::ACL_DST, vec_sum_row.get() } |
| 711 | }; |
| 712 | CLScheduler::get().enqueue_op(*_mtx_a_reduction_kernel, mtx_a_red_pack, false); |
| 713 | } |
| 714 | |
| 715 | // Run matrix multiply |
Freddie Liardet | e572dff | 2022-05-16 14:09:10 +0100 | [diff] [blame] | 716 | if(is_gemm_reshaped(_gemm_kernel_type)) |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 717 | { |
| 718 | ITensorPack gemm_reshaped_pack; |
| 719 | if(_run_offset_contribution) |
| 720 | { |
| 721 | gemm_reshaped_pack = ITensorPack({ { TensorType::ACL_SRC_0, matrix_a }, |
| 722 | { TensorType::ACL_SRC_1, matrix_b }, |
| 723 | { TensorType::ACL_DST, _run_output_stage ? res32.get() : dst } |
| 724 | }); |
| 725 | } |
| 726 | else |
| 727 | { |
| 728 | gemm_reshaped_pack = ITensorPack( |
| 729 | { |
| 730 | { TensorType::ACL_SRC, matrix_a }, |
| 731 | { TensorType::ACL_SRC_1, matrix_b }, |
| 732 | { TensorType::ACL_BIAS, c }, |
| 733 | { TensorType::ACL_VEC_ROW_SUM, _b_offset == 0 ? nullptr : vec_sum_row.get() }, |
| 734 | { TensorType::ACL_VEC_COL_SUM, _a_offset == 0 ? nullptr : vec_sum_col.get() }, |
| 735 | { TensorType::ACL_SHIFTS, shifts.get() }, |
| 736 | { TensorType::ACL_MULTIPLIERS, multipliers.get() }, |
| 737 | { TensorType::ACL_DST, dst }, |
| 738 | }); |
| 739 | } |
Freddie Liardet | e572dff | 2022-05-16 14:09:10 +0100 | [diff] [blame] | 740 | if(_gemm_kernel_type == CLGEMMKernelType::RESHAPED_ONLY_RHS) |
| 741 | { |
| 742 | CLScheduler::get().enqueue_op(*_mm_reshaped_only_rhs_kernel, gemm_reshaped_pack, false); |
| 743 | } |
| 744 | else if(_gemm_kernel_type == CLGEMMKernelType::RESHAPED_ONLY_RHS_MMUL) |
| 745 | { |
| 746 | CLScheduler::get().enqueue_op(*_mm_reshaped_only_rhs_mmul_kernel, gemm_reshaped_pack, false); |
| 747 | } |
| 748 | else |
| 749 | { |
| 750 | ARM_COMPUTE_ERROR("Invalid reshaped kernel"); |
| 751 | } |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 752 | } |
| 753 | else |
| 754 | { |
| 755 | ITensorPack gemm_native_pack = |
| 756 | { |
| 757 | { TensorType::ACL_SRC_0, matrix_a }, |
| 758 | { TensorType::ACL_SRC_1, matrix_b }, |
| 759 | { TensorType::ACL_DST, _run_offset_contribution ? dst : res32.get() } |
| 760 | }; |
| 761 | CLScheduler::get().enqueue_op(*_mm_native_kernel, gemm_native_pack, false); |
| 762 | } |
| 763 | if(_run_output_stage) |
| 764 | { |
| 765 | // Run offset contribution/output stage kernel |
| 766 | ITensorPack output_stage_pack = |
| 767 | { |
| 768 | { TensorType::ACL_SRC, res32.get() }, |
| 769 | { TensorType::ACL_BIAS, c }, |
| 770 | { TensorType::ACL_VEC_ROW_SUM, _b_offset == 0 ? nullptr : vec_sum_row.get() }, |
| 771 | { TensorType::ACL_VEC_COL_SUM, _a_offset == 0 ? nullptr : vec_sum_col.get() }, |
| 772 | { TensorType::ACL_SHIFTS, shifts.get() }, |
| 773 | { TensorType::ACL_MULTIPLIERS, multipliers.get() }, |
| 774 | { TensorType::ACL_DST, dst }, |
| 775 | }; |
| 776 | CLScheduler::get().enqueue_op(*_offset_contribution_output_stage_kernel, output_stage_pack, true); |
| 777 | } |
| 778 | if(_run_offset_contribution) |
| 779 | { |
| 780 | // Run offset contribution kernel |
| 781 | ITensorPack offset_contrib_pack = |
| 782 | { |
| 783 | { TensorType::ACL_SRC_DST, dst }, |
| 784 | { TensorType::ACL_BIAS, c }, |
| 785 | { TensorType::ACL_VEC_ROW_SUM, _b_offset == 0 ? nullptr : vec_sum_row.get() }, |
| 786 | { TensorType::ACL_VEC_COL_SUM, _a_offset == 0 ? nullptr : vec_sum_col.get() } |
| 787 | }; |
| 788 | CLScheduler::get().enqueue_op(*_offset_contribution_kernel, offset_contrib_pack, true); |
| 789 | } |
| 790 | } |
| 791 | |
| 792 | void ClGemmLowpMatrixMultiplyCore::prepare(ITensorPack &tensors) |
| 793 | { |
| 794 | if(!_is_prepared) |
| 795 | { |
| 796 | auto b = tensors.get_const_tensor(TensorType::ACL_SRC_1); |
| 797 | CLAuxTensorHandler tmp_b(offset_int_vec(RhsReshape), _tmp_b, tensors, true); |
| 798 | CLAuxTensorHandler vec_sum_col(offset_int_vec(VecSumCol), _vector_sum_col, tensors, true); |
| 799 | CLAuxTensorHandler rhs_qasymm8(offset_int_vec(RhsQAsymm8), _qasymm8_weights, tensors, false); |
| 800 | |
| 801 | ARM_COMPUTE_ERROR_ON_NULLPTR(b); |
| 802 | |
| 803 | if(_convert_to_qasymm8) |
| 804 | { |
| 805 | ITensorPack convert_to_qs8_pack = { { ACL_SRC, b }, { ACL_DST, rhs_qasymm8.get() } }; |
| 806 | CLScheduler::get().enqueue_op(*_weights_to_qasymm8, convert_to_qs8_pack, false); |
Georgios Pinitas | 9805583 | 2021-07-27 10:34:59 +0100 | [diff] [blame] | 807 | b->mark_as_unused(); |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 808 | } |
| 809 | |
Freddie Liardet | e572dff | 2022-05-16 14:09:10 +0100 | [diff] [blame] | 810 | if(is_gemm_reshaped(_gemm_kernel_type) && _reshape_b_only_on_first_run) |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 811 | { |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 812 | // Run reshape kernel and mark original weights tensor as unused |
| 813 | ITensorPack mtx_b_pack = |
| 814 | { |
| 815 | { TensorType::ACL_SRC, _convert_to_qasymm8 ? rhs_qasymm8.get() : b }, |
| 816 | { TensorType::ACL_DST, tmp_b.get() } |
| 817 | }; |
| 818 | CLScheduler::get().enqueue_op(*_mtx_b_reshape_kernel, mtx_b_pack, false); |
| 819 | b->mark_as_unused(); |
| 820 | } |
| 821 | |
| 822 | // Run matrix B reduction kernel only if _a_offset is not equal to 0 |
| 823 | if(_a_offset != 0 && _reshape_b_only_on_first_run) |
| 824 | { |
| 825 | ITensorPack mtx_b_red_pack = |
| 826 | { |
| 827 | { TensorType::ACL_SRC, _convert_to_qasymm8 ? rhs_qasymm8.get() : b }, |
| 828 | { TensorType::ACL_DST, vec_sum_col.get() } |
| 829 | }; |
| 830 | CLScheduler::get().enqueue_op(*_mtx_b_reduction_kernel, mtx_b_red_pack, false); |
| 831 | } |
| 832 | |
| 833 | // Compute GEMM output multipliers and shifts for output stage |
| 834 | { |
| 835 | const size_t num_filters = (_gemm_info.gemmlowp_output_stage().is_quantized_per_channel) ? _gemm_info.gemmlowp_output_stage().gemmlowp_multipliers.size() : 1; |
| 836 | |
| 837 | CLAuxTensorHandler multipliers(offset_int_vec(Multipliers), _gemm_output_stage_multipliers, tensors, false); |
| 838 | CLAuxTensorHandler shifts(offset_int_vec(Shifts), _gemm_output_stage_shifts, tensors, false); |
| 839 | |
| 840 | ICLTensor *multiplier_tensor = multipliers.get(); |
| 841 | if(multiplier_tensor != nullptr && multiplier_tensor->info()->total_size() > 0) |
| 842 | { |
| 843 | multiplier_tensor->map(CLScheduler::get().queue(), true); |
| 844 | std::memcpy(multiplier_tensor->ptr_to_element(Coordinates(0)), _gemm_info.gemmlowp_output_stage().gemmlowp_multipliers.data(), num_filters * sizeof(int32_t)); |
| 845 | multiplier_tensor->unmap(CLScheduler::get().queue()); |
| 846 | } |
| 847 | |
| 848 | ICLTensor *shifts_tensor = shifts.get(); |
| 849 | if(shifts.get() != nullptr && shifts_tensor->info()->total_size() > 0) |
| 850 | { |
| 851 | shifts_tensor->map(CLScheduler::get().queue(), true); |
| 852 | std::memcpy(shifts_tensor->ptr_to_element(Coordinates(0)), _gemm_info.gemmlowp_output_stage().gemmlowp_shifts.data(), num_filters * sizeof(int32_t)); |
| 853 | shifts_tensor->unmap(CLScheduler::get().queue()); |
| 854 | } |
| 855 | } |
Gian Marco Iodice | d761a3e | 2021-08-11 14:06:28 +0100 | [diff] [blame] | 856 | CLScheduler::get().queue().finish(); |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 857 | _is_prepared = true; |
| 858 | } |
Georgios Pinitas | f4e84fb | 2021-07-08 15:36:07 +0100 | [diff] [blame] | 859 | } |
| 860 | |
| 861 | experimental::MemoryRequirements ClGemmLowpMatrixMultiplyCore::workspace() const |
| 862 | { |
| 863 | return _aux_mem; |
| 864 | } |
| 865 | } // namespace opencl |
| 866 | } // namespace arm_compute |