Georgios Pinitas | 529b5a2 | 2021-07-27 15:55:30 +0100 | [diff] [blame] | 1 | /* |
Jakub Sujak | 617ed50 | 2023-03-29 11:16:18 +0100 | [diff] [blame] | 2 | * Copyright (c) 2017-2021, 2023 Arm Limited. |
Georgios Pinitas | 529b5a2 | 2021-07-27 15:55:30 +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/ClFullyConnected.h" |
Georgios Pinitas | 529b5a2 | 2021-07-27 15:55:30 +0100 | [diff] [blame] | 25 | |
| 26 | #include "arm_compute/core/Size2D.h" |
| 27 | #include "arm_compute/core/Validate.h" |
| 28 | #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| 29 | #include "arm_compute/core/utils/quantization/AsymmHelpers.h" |
| 30 | #include "arm_compute/runtime/CL/CLScheduler.h" |
| 31 | #include "src/core/CL/kernels/CLFillBorderKernel.h" |
| 32 | |
| 33 | #include "src/core/helpers/MemoryHelpers.h" |
Georgios Pinitas | 7891a73 | 2021-08-20 21:39:25 +0100 | [diff] [blame] | 34 | #include "src/gpu/cl/operators/ClConvertFullyConnectedWeights.h" |
| 35 | #include "src/gpu/cl/operators/ClFlatten.h" |
| 36 | #include "src/gpu/cl/operators/ClGemm.h" |
| 37 | #include "src/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.h" |
| 38 | #include "src/gpu/cl/operators/ClTranspose.h" |
| 39 | #include "src/gpu/cl/utils/ClAuxTensorHandler.h" |
Georgios Pinitas | 529b5a2 | 2021-07-27 15:55:30 +0100 | [diff] [blame] | 40 | |
ramelg01 | 2e53f17 | 2021-09-22 10:48:25 +0100 | [diff] [blame] | 41 | #include "src/common/utils/Log.h" |
Georgios Pinitas | 529b5a2 | 2021-07-27 15:55:30 +0100 | [diff] [blame] | 42 | #include "support/Cast.h" |
| 43 | |
| 44 | #include <algorithm> |
| 45 | |
| 46 | namespace arm_compute |
| 47 | { |
| 48 | namespace opencl |
| 49 | { |
| 50 | using namespace arm_compute::experimental; |
| 51 | using namespace arm_compute::misc::shape_calculator; |
| 52 | |
| 53 | namespace |
| 54 | { |
| 55 | Status construct_gemmlowp_output_stage(const ITensorInfo &src, const ITensorInfo &weights, const ITensorInfo &dst, |
| 56 | GEMMLowpOutputStageInfo &gemmlowp_output_stage, ActivationLayerInfo activation_info) |
| 57 | { |
| 58 | gemmlowp_output_stage.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT; |
| 59 | gemmlowp_output_stage.gemmlowp_offset = 0; |
| 60 | gemmlowp_output_stage.gemmlowp_multiplier = 0; |
| 61 | gemmlowp_output_stage.gemmlowp_shift = 0; |
| 62 | |
| 63 | const auto data_type = src.data_type(); |
| 64 | |
| 65 | // Configure output stage for quantized case |
| 66 | if(is_data_type_quantized_asymmetric(data_type)) |
| 67 | { |
| 68 | const QuantizationInfo oq_info = dst.quantization_info(); |
| 69 | const UniformQuantizationInfo iq_unif = src.quantization_info().uniform(); |
| 70 | const UniformQuantizationInfo wq_unif = weights.quantization_info().uniform(); |
| 71 | const UniformQuantizationInfo oq_unif = oq_info.uniform(); |
| 72 | |
| 73 | const auto output_quant_info = (dst.total_size() == 0) ? iq_unif : oq_unif; |
| 74 | |
| 75 | const float multiplier = (iq_unif.scale * wq_unif.scale) / output_quant_info.scale; |
| 76 | int output_multiplier = 0; |
| 77 | int output_shift = 0; |
| 78 | ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier(multiplier, &output_multiplier, &output_shift)); |
| 79 | |
| 80 | PixelValue type_min{}; |
| 81 | PixelValue type_max{}; |
| 82 | std::tie(type_min, type_max) = get_min_max(data_type); |
| 83 | |
| 84 | if(activation_info.enabled()) |
| 85 | { |
| 86 | std::tie(type_min, type_max) = get_quantized_activation_min_max(activation_info, data_type, output_quant_info); |
| 87 | } |
| 88 | |
| 89 | // Set the GEMMLowp output stage info |
| 90 | gemmlowp_output_stage.gemmlowp_offset = output_quant_info.offset; |
| 91 | gemmlowp_output_stage.gemmlowp_multiplier = output_multiplier; |
| 92 | gemmlowp_output_stage.gemmlowp_shift = output_shift; |
| 93 | gemmlowp_output_stage.gemmlowp_multipliers.push_back(output_multiplier); |
| 94 | gemmlowp_output_stage.gemmlowp_shifts.push_back(output_shift); |
| 95 | type_min.get(gemmlowp_output_stage.gemmlowp_min_bound); |
| 96 | type_max.get(gemmlowp_output_stage.gemmlowp_max_bound); |
| 97 | } |
| 98 | |
| 99 | return Status{}; |
| 100 | } |
| 101 | |
| 102 | Status validate_mm(const ITensorInfo &src, const ITensorInfo &weights, const ITensorInfo *bias, const ITensorInfo &dst, const FullyConnectedLayerInfo &fc_info) |
| 103 | { |
| 104 | GEMMLowpOutputStageInfo gemmlowp_output_stage; |
| 105 | ARM_COMPUTE_RETURN_ON_ERROR(construct_gemmlowp_output_stage(src, weights, dst, gemmlowp_output_stage, fc_info.activation_info)); |
| 106 | |
| 107 | const GEMMInfo &gemm_info = GEMMInfo(false, // is_a_reshaped |
| 108 | false, // is_b_reshaped |
| 109 | true, // reshape_b_only_on_first_run |
| 110 | 0, // depth_output_gemm3d |
| 111 | false, // reinterpret_input_as_3d |
| 112 | fc_info.retain_internal_weights, // retain_internal_weights |
| 113 | gemmlowp_output_stage, // gemmlowp_output_stage |
| 114 | fc_info.fp_mixed_precision, // fp_mixed_precision |
| 115 | false, // fast_math |
| 116 | true, // broadcast_bias |
| 117 | ActivationLayerInfo()); // activation_info |
| 118 | |
| 119 | if(is_data_type_quantized_asymmetric(src.data_type())) |
| 120 | { |
| 121 | const UniformQuantizationInfo iq_info = src.quantization_info().uniform(); |
| 122 | const UniformQuantizationInfo wq_info = weights.quantization_info().uniform(); |
| 123 | |
| 124 | // Since we need negative offsets for computing convolution, we need to change QuantizationInfo() |
| 125 | // Extract and negate src and weights offset |
| 126 | const QuantizationInfo src_quantization_info(iq_info.scale, -iq_info.offset); |
| 127 | const QuantizationInfo weights_quantization_info(wq_info.scale, -wq_info.offset); |
| 128 | |
| 129 | // Validate gemmlowp function |
| 130 | ARM_COMPUTE_RETURN_ON_ERROR(ClGemmLowpMatrixMultiplyCore::validate(&src.clone()->set_quantization_info(src_quantization_info), |
| 131 | &weights.clone()->set_quantization_info(weights_quantization_info), |
| 132 | bias, |
| 133 | &dst, |
| 134 | gemm_info)); |
| 135 | } |
| 136 | else |
| 137 | { |
| 138 | ARM_COMPUTE_RETURN_ON_ERROR(ClGemm::validate(&src, &weights, bias, &dst, 1.f, 1.f, gemm_info)); |
| 139 | } |
| 140 | |
| 141 | return Status{}; |
| 142 | } |
| 143 | } // namespace |
| 144 | |
| 145 | ClFullyConnected::ClFullyConnected() |
| 146 | : _convert_weights(nullptr), |
| 147 | _flatten(nullptr), |
| 148 | _reshape_weights(nullptr), |
| 149 | _mm_gemm(nullptr), |
| 150 | _mm_gemmlowp(nullptr), |
| 151 | _aux_mem(Count) |
| 152 | { |
| 153 | } |
| 154 | |
| 155 | ClFullyConnected::~ClFullyConnected() = default; |
| 156 | |
| 157 | void ClFullyConnected::configure_mm(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *bias, ITensorInfo *dst, |
| 158 | const FullyConnectedLayerInfo &fc_info) |
| 159 | { |
| 160 | GEMMLowpOutputStageInfo gemmlowp_output_stage; |
| 161 | construct_gemmlowp_output_stage(*src, *weights, *dst, gemmlowp_output_stage, fc_info.activation_info); |
| 162 | |
| 163 | const GEMMInfo &gemm_info = GEMMInfo(false, // is_a_reshaped |
| 164 | false, // is_b_reshaped |
Jakub Sujak | 617ed50 | 2023-03-29 11:16:18 +0100 | [diff] [blame] | 165 | !_dynamic_weights, // reshape_b_only_on_first_run |
Georgios Pinitas | 529b5a2 | 2021-07-27 15:55:30 +0100 | [diff] [blame] | 166 | 0, // depth_output_gemm3d |
| 167 | false, // reinterpret_input_as_3d |
| 168 | fc_info.retain_internal_weights, // retain_internal_weights |
| 169 | gemmlowp_output_stage, // gemmlowp_output_stage |
| 170 | fc_info.fp_mixed_precision, // fp_mixed_precision |
| 171 | false, // fast_math |
| 172 | true, // broadcast_bias |
Giorgio Arena | 63e0beb | 2021-09-24 14:04:27 +0100 | [diff] [blame] | 173 | fc_info.activation_info); // activation_info |
Georgios Pinitas | 529b5a2 | 2021-07-27 15:55:30 +0100 | [diff] [blame] | 174 | |
| 175 | if(_is_quantized) |
| 176 | { |
| 177 | // Since we need negative offsets for computing convolution, we need to change QuantizationInfo() |
| 178 | // Extract and negate input and weights offset |
| 179 | const QuantizationInfo src_quantization_info = src->quantization_info(); |
| 180 | const QuantizationInfo weights_quantization_info = weights->quantization_info(); |
| 181 | |
| 182 | TensorInfo src_info = src->clone()->set_quantization_info(src_quantization_info); |
| 183 | TensorInfo weights_info = weights->clone()->set_quantization_info(weights_quantization_info); |
| 184 | |
| 185 | src_info.set_quantization_info(QuantizationInfo(src_quantization_info.uniform().scale, -src_quantization_info.uniform().offset)); |
| 186 | weights_info.set_quantization_info(QuantizationInfo(weights_quantization_info.uniform().scale, -weights_quantization_info.uniform().offset)); |
| 187 | |
| 188 | // Configure gemmlowp function |
| 189 | _mm_gemmlowp = std::make_unique<ClGemmLowpMatrixMultiplyCore>(); |
| 190 | _mm_gemmlowp->configure(compile_context, &src_info, &weights_info, bias, dst, gemm_info); |
| 191 | } |
| 192 | else |
| 193 | { |
| 194 | // Configure matrix multiply kernel |
| 195 | _mm_gemm = std::make_unique<ClGemm>(); |
| 196 | _mm_gemm->configure(compile_context, src, weights, bias, dst, 1.f, 1.f, gemm_info); |
| 197 | } |
| 198 | } |
| 199 | |
| 200 | void ClFullyConnected::configure_conv_fc(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *bias, ITensorInfo *dst, |
| 201 | const FullyConnectedLayerInfo &fc_info) |
| 202 | { |
| 203 | ARM_COMPUTE_ERROR_ON((weights->dimension(1) != (src->dimension(0) * src->dimension(1) * src->dimension(2)))); |
| 204 | |
| 205 | // If the fully connected layer is called after a convolution layer, the input tensor must be linearized |
| 206 | |
| 207 | // Initialize output tensor for flatten |
| 208 | _flattened_src = src->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_flatten_shape(src)).set_data_layout(DataLayout::NCHW); |
| 209 | |
| 210 | // Configure flatten kernel |
| 211 | _flatten = std::make_unique<ClFlatten>(); |
| 212 | _flatten->configure(compile_context, src, &_flattened_src); |
| 213 | |
| 214 | // Configure matrix multiply kernel |
| 215 | configure_mm(compile_context, &_flattened_src, weights, bias, dst, fc_info); |
| 216 | } |
| 217 | |
| 218 | void ClFullyConnected::configure_fc_fc(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *bias, ITensorInfo *dst, |
| 219 | const FullyConnectedLayerInfo &fc_info) |
| 220 | { |
| 221 | ARM_COMPUTE_ERROR_ON(src->dimension(0) != weights->dimension(1)); |
| 222 | |
| 223 | // Configure matrix multiply kernel |
| 224 | configure_mm(compile_context, src, weights, bias, dst, fc_info); |
| 225 | } |
| 226 | |
| 227 | void ClFullyConnected::configure(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *biases, ITensorInfo *dst, |
| 228 | FullyConnectedLayerInfo fc_info) |
| 229 | { |
| 230 | ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst); |
| 231 | |
| 232 | // Perform validate step |
| 233 | ARM_COMPUTE_ERROR_THROW_ON(ClFullyConnected::validate(src, weights, biases, dst, fc_info)); |
ramelg01 | 2e53f17 | 2021-09-22 10:48:25 +0100 | [diff] [blame] | 234 | ARM_COMPUTE_LOG_PARAMS(src, weights, biases, dst, fc_info); |
Georgios Pinitas | 529b5a2 | 2021-07-27 15:55:30 +0100 | [diff] [blame] | 235 | |
| 236 | _are_weights_converted = true; |
| 237 | _are_weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true; |
| 238 | _is_fc_after_conv = true; |
| 239 | _is_quantized = is_data_type_quantized_asymmetric(src->data_type()); |
| 240 | _is_prepared = fc_info.retain_internal_weights; |
| 241 | _weights_to_use = TensorInfo(*weights); |
| 242 | _weights_to_use_idx = ACL_SRC_1; |
Jakub Sujak | 617ed50 | 2023-03-29 11:16:18 +0100 | [diff] [blame] | 243 | _dynamic_weights = !weights->are_values_constant() && !_are_weights_reshaped; |
Georgios Pinitas | 529b5a2 | 2021-07-27 15:55:30 +0100 | [diff] [blame] | 244 | |
| 245 | // With the Fully Connected layer we can have 4 different cases: |
| 246 | // 1) Convolution layer -> Fully Connected layer without batches |
| 247 | // 2) Fully Connected layer -> Fully Connected layer without batches |
| 248 | // 3) Convolution layer -> Fully Connected layer with batches |
| 249 | // 4) Fully Connected layer -> Fully Connected layer with batches |
| 250 | |
| 251 | // Check if we have a fully connected layer with batches |
| 252 | const bool is_batched_fc_layer = dst->dimension(1) > 1; |
| 253 | if(is_batched_fc_layer) |
| 254 | { |
| 255 | _is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) && (std::equal(src->tensor_shape().cbegin() + 3, |
| 256 | src->tensor_shape().cend(), |
| 257 | dst->tensor_shape().cbegin() + 1)); |
| 258 | } |
| 259 | else |
| 260 | { |
| 261 | _is_fc_after_conv = src->num_dimensions() > 1; |
| 262 | } |
| 263 | |
| 264 | ITensorInfo *weights_used = weights; |
| 265 | |
| 266 | // Reshape weights if needed |
| 267 | if(!_are_weights_reshaped) |
| 268 | { |
| 269 | // Reshape the weights |
| 270 | _reshape_weights = std::make_unique<ClTranspose>(); |
| 271 | _reshape_weights->configure(compile_context, weights, &_reshaped_weights); |
| 272 | weights_used = &_reshaped_weights; |
| 273 | _weights_to_use_idx = offset_int_vec(TransposedWeights); |
| 274 | } |
| 275 | |
| 276 | // Convert weights if needed |
| 277 | if(_is_fc_after_conv && (src->data_layout() != fc_info.weights_trained_layout)) |
| 278 | { |
| 279 | // Convert weights |
| 280 | _convert_weights = std::make_unique<ClConvertFullyConnectedWeights>(); |
| 281 | _convert_weights->configure(compile_context, |
| 282 | weights_used, |
| 283 | &_converted_weights, |
| 284 | src->tensor_shape(), |
| 285 | fc_info.weights_trained_layout); |
| 286 | |
| 287 | weights_used = &_converted_weights; |
| 288 | _weights_to_use_idx = offset_int_vec(ConvertedWeights); |
| 289 | _are_weights_converted = false; |
| 290 | } |
| 291 | |
| 292 | if(_is_fc_after_conv) |
| 293 | { |
| 294 | // Fully Connected layer after a Convolution Layer without batches |
| 295 | configure_conv_fc(compile_context, src, weights_used, biases, dst, fc_info); |
| 296 | } |
| 297 | else |
| 298 | { |
| 299 | // Fully Connected layer after a Fully Connected Layer without batches |
| 300 | configure_fc_fc(compile_context, src, weights_used, biases, dst, fc_info); |
| 301 | } |
| 302 | // Update TensorInfo of final weights used (Need to be done in the end due to padding expansion) |
| 303 | _weights_to_use = *weights_used; |
| 304 | |
| 305 | // Set auxiliary memory requirements |
| 306 | auto gemm_mem_req = (_is_quantized) ? _mm_gemmlowp->workspace() : _mm_gemm->workspace(); |
| 307 | for(unsigned int i = 0; i < gemm_mem_req.size(); ++i) |
| 308 | { |
| 309 | _aux_mem[i] = gemm_mem_req[i]; |
| 310 | } |
| 311 | if(_aux_mem[1].size > 0 || _aux_mem[2].size > 0) // Persistent weights memory on GEMMs |
| 312 | { |
| 313 | // Release permuted weights at the of prepare as they are further transposed by the assembly dispatch |
Jakub Sujak | 617ed50 | 2023-03-29 11:16:18 +0100 | [diff] [blame] | 314 | // Keep all the auxiliary tensors in case of dynamic weights as they are recalculated every time |
| 315 | _aux_mem[TransposedWeights] = MemoryInfo( |
| 316 | offset_int_vec(TransposedWeights), |
| 317 | _dynamic_weights ? MemoryLifetime::Temporary : MemoryLifetime::Prepare, |
| 318 | _reshaped_weights.total_size()); |
| 319 | _aux_mem[ConvertedWeights] = MemoryInfo( |
| 320 | offset_int_vec(ConvertedWeights), |
| 321 | _dynamic_weights ? MemoryLifetime::Temporary : MemoryLifetime::Prepare, |
| 322 | _converted_weights.total_size()); |
Georgios Pinitas | 529b5a2 | 2021-07-27 15:55:30 +0100 | [diff] [blame] | 323 | } |
| 324 | else |
| 325 | { |
| 326 | // Release permuted weights at the of prepare as they are further transposed by the assembly dispatch |
| 327 | const auto transposed_wei_lft = (_weights_to_use_idx == offset_int_vec(TransposedWeights)) ? MemoryLifetime::Persistent : MemoryLifetime::Prepare; |
| 328 | const auto converted_wei_lft = (_weights_to_use_idx == offset_int_vec(ConvertedWeights)) ? MemoryLifetime::Persistent : MemoryLifetime::Prepare; |
| 329 | |
Jakub Sujak | 617ed50 | 2023-03-29 11:16:18 +0100 | [diff] [blame] | 330 | _aux_mem[TransposedWeights] = MemoryInfo( |
| 331 | offset_int_vec(TransposedWeights), |
| 332 | _dynamic_weights ? MemoryLifetime::Temporary : transposed_wei_lft, |
| 333 | _reshaped_weights.total_size()); |
| 334 | _aux_mem[ConvertedWeights] = MemoryInfo( |
| 335 | offset_int_vec(ConvertedWeights), |
| 336 | _dynamic_weights ? MemoryLifetime::Temporary : converted_wei_lft, |
| 337 | _converted_weights.total_size()); |
Georgios Pinitas | 529b5a2 | 2021-07-27 15:55:30 +0100 | [diff] [blame] | 338 | } |
| 339 | _aux_mem[FlattenedSrc] = MemoryInfo(offset_int_vec(FlattenedSrc), MemoryLifetime::Temporary, _flattened_src.total_size()); |
| 340 | } |
| 341 | |
| 342 | Status ClFullyConnected::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, |
| 343 | FullyConnectedLayerInfo fc_info) |
| 344 | { |
| 345 | ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, weights, dst); |
| 346 | ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32); |
| 347 | ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, weights, dst); |
| 348 | ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 2); |
| 349 | ARM_COMPUTE_RETURN_ERROR_ON(fc_info.activation_info.enabled() && is_data_type_quantized(src->data_type()) && fc_info.activation_info.activation() != ActivationLayerInfo::ActivationFunction::RELU |
| 350 | && fc_info.activation_info.activation() != ActivationLayerInfo::ActivationFunction::BOUNDED_RELU && fc_info.activation_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU); |
Georgios Pinitas | 529b5a2 | 2021-07-27 15:55:30 +0100 | [diff] [blame] | 351 | |
| 352 | bool weights_reshaped = fc_info.transpose_weights ? fc_info.are_weights_reshaped : true; |
| 353 | bool is_fc_after_conv = true; |
| 354 | |
| 355 | const ITensorInfo &flatten_src = TensorInfo(src->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_flatten_shape(src)).set_data_layout(DataLayout::NCHW)); |
| 356 | const ITensorInfo &reshaped_weights = TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_transposed_shape(*weights))); |
| 357 | const ITensorInfo &converted_weights = weights_reshaped ? TensorInfo(weights->clone()->set_is_resizable(true).reset_padding()) : TensorInfo(*reshaped_weights.clone()); |
| 358 | |
| 359 | // With the Fully Connected layer we can have 4 different cases: |
| 360 | // 1) Convolution layer -> Fully Connected layer without batches |
| 361 | // 2) Fully Connected layer -> Fully Connected layer without batches |
| 362 | // 3) Convolution layer -> Fully Connected layer with batches |
| 363 | // 4) Fully Connected layer -> Fully Connected layer with batches |
| 364 | |
| 365 | const ITensorInfo *src_to_use = src; |
| 366 | const ITensorInfo *weights_to_use = weights; |
| 367 | |
Giorgio Arena | 63e0beb | 2021-09-24 14:04:27 +0100 | [diff] [blame] | 368 | if(biases != nullptr) |
| 369 | { |
| 370 | ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); |
| 371 | if(is_data_type_quantized(src->data_type())) |
| 372 | { |
| 373 | ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32); |
| 374 | } |
| 375 | else |
| 376 | { |
| 377 | ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, biases); |
| 378 | } |
| 379 | } |
| 380 | |
Georgios Pinitas | 529b5a2 | 2021-07-27 15:55:30 +0100 | [diff] [blame] | 381 | // Check if we have a fully connected layer with batches |
| 382 | const bool is_batched_fc_layer = dst->dimension(1) > 1; |
| 383 | if(is_batched_fc_layer) |
| 384 | { |
| 385 | is_fc_after_conv = (TensorShape::num_max_dimensions >= 4) && (std::equal(src->tensor_shape().cbegin() + 3, |
| 386 | src->tensor_shape().cend(), |
| 387 | dst->tensor_shape().cbegin() + 1)); |
| 388 | } |
| 389 | else |
| 390 | { |
| 391 | is_fc_after_conv = src->num_dimensions() > 1; |
| 392 | } |
| 393 | |
| 394 | if(!weights_reshaped) |
| 395 | { |
| 396 | // Validate reshape weights kernel |
| 397 | ARM_COMPUTE_RETURN_ON_ERROR(ClTranspose::validate(weights, &reshaped_weights)); |
| 398 | weights_to_use = &reshaped_weights; |
| 399 | } |
| 400 | |
| 401 | if(is_fc_after_conv && (src->data_layout() != fc_info.weights_trained_layout)) |
| 402 | { |
| 403 | // Validate convert weights kernel |
| 404 | ARM_COMPUTE_RETURN_ON_ERROR(ClConvertFullyConnectedWeights::validate(weights_to_use, |
| 405 | &converted_weights, |
| 406 | src->tensor_shape(), |
| 407 | fc_info.weights_trained_layout)); |
| 408 | weights_to_use = &converted_weights; |
| 409 | } |
| 410 | |
| 411 | if(is_fc_after_conv) |
| 412 | { |
| 413 | // Fully Connected layer after a Convolution Layer without batches |
| 414 | ARM_COMPUTE_RETURN_ERROR_ON((weights_to_use->dimension(1) != (src->dimension(0) * src->dimension(1) * src->dimension(2)))); |
| 415 | |
| 416 | // Validate flatten kernel |
| 417 | ARM_COMPUTE_RETURN_ON_ERROR(ClFlatten::validate(src, &flatten_src)); |
| 418 | src_to_use = &flatten_src; |
| 419 | } |
| 420 | else |
| 421 | { |
| 422 | // Fully Connected layer after a Fully Connected Layer without batches |
| 423 | ARM_COMPUTE_RETURN_ERROR_ON(src->dimension(0) != weights_to_use->dimension(1)); |
| 424 | } |
| 425 | |
| 426 | // Validate matrix multiply kernel |
| 427 | ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(*src_to_use, *weights_to_use, biases, *dst, fc_info)); |
| 428 | |
| 429 | return Status{}; |
| 430 | } |
| 431 | |
| 432 | void ClFullyConnected::run(ITensorPack &tensors) |
| 433 | { |
| 434 | prepare(tensors); |
| 435 | |
Jakub Sujak | 617ed50 | 2023-03-29 11:16:18 +0100 | [diff] [blame] | 436 | #ifdef ARM_COMPUTE_ASSERTS_ENABLED |
| 437 | ++_asrt_run_count; |
| 438 | ARM_COMPUTE_ERROR_ON(_dynamic_weights && _asrt_prepare_count != _asrt_run_count); |
| 439 | #endif // ARM_COMPUTE_ASSERTS_ENABLED |
| 440 | |
Georgios Pinitas | 529b5a2 | 2021-07-27 15:55:30 +0100 | [diff] [blame] | 441 | auto src = tensors.get_const_tensor(ACL_SRC_0); |
| 442 | |
| 443 | CLAuxTensorHandler flattened_src(offset_int_vec(FlattenedSrc), _flattened_src, tensors, false); |
| 444 | CLAuxTensorHandler weights(_weights_to_use_idx, _weights_to_use, tensors, false); |
| 445 | |
| 446 | // Linearize input if it comes from a convolutional layer |
| 447 | if(_is_fc_after_conv) |
| 448 | { |
| 449 | ITensorPack flatten_pack{ { ACL_SRC, src }, { ACL_DST, flattened_src.get() } }; |
| 450 | _flatten->run(flatten_pack); |
| 451 | } |
| 452 | |
| 453 | ITensorPack gemm_pack = tensors; |
| 454 | gemm_pack.add_const_tensor(ACL_SRC_0, (_is_fc_after_conv) ? flattened_src.get() : src); |
| 455 | if(_weights_to_use_idx != ACL_SRC_1) |
| 456 | { |
| 457 | gemm_pack.add_const_tensor(ACL_SRC_1, weights.get()); |
| 458 | } |
| 459 | |
| 460 | // Run matrix multiply |
| 461 | if(_is_quantized) |
| 462 | { |
| 463 | _mm_gemmlowp->run(gemm_pack); |
| 464 | } |
| 465 | else |
| 466 | { |
| 467 | _mm_gemm->run(gemm_pack); |
| 468 | } |
| 469 | } |
| 470 | |
| 471 | void ClFullyConnected::prepare(ITensorPack &tensors) |
| 472 | { |
Jakub Sujak | 617ed50 | 2023-03-29 11:16:18 +0100 | [diff] [blame] | 473 | if(!_is_prepared || _dynamic_weights) |
Georgios Pinitas | 529b5a2 | 2021-07-27 15:55:30 +0100 | [diff] [blame] | 474 | { |
Jakub Sujak | 617ed50 | 2023-03-29 11:16:18 +0100 | [diff] [blame] | 475 | #ifdef ARM_COMPUTE_ASSERTS_ENABLED |
| 476 | ++_asrt_prepare_count; |
| 477 | ARM_COMPUTE_ERROR_ON(!_dynamic_weights && _asrt_prepare_count > 1); |
| 478 | #endif // ARM_COMPUTE_ASSERTS_ENABLED |
| 479 | |
Georgios Pinitas | 529b5a2 | 2021-07-27 15:55:30 +0100 | [diff] [blame] | 480 | auto weights = tensors.get_const_tensor(ACL_SRC_1); |
| 481 | |
| 482 | CLAuxTensorHandler reshaped_weights(offset_int_vec(TransposedWeights), _reshaped_weights, tensors, false); |
| 483 | CLAuxTensorHandler converted_weights(offset_int_vec(ConvertedWeights), _converted_weights, tensors, false); |
| 484 | |
| 485 | // Pointer to current weights |
| 486 | const ITensor *cur_weights = weights; |
| 487 | |
Jakub Sujak | 617ed50 | 2023-03-29 11:16:18 +0100 | [diff] [blame] | 488 | // Reshape of the weights if needed |
Georgios Pinitas | 529b5a2 | 2021-07-27 15:55:30 +0100 | [diff] [blame] | 489 | if(!_are_weights_reshaped) |
| 490 | { |
| 491 | // Run reshape weights kernel and mark weights as unused |
| 492 | ITensorPack transpose_pack{ { ACL_SRC, weights }, { ACL_DST, reshaped_weights.get() } }; |
| 493 | _reshape_weights->run(transpose_pack); |
| 494 | |
| 495 | cur_weights->mark_as_unused(); |
| 496 | cur_weights = reshaped_weights.get(); |
Georgios Pinitas | 529b5a2 | 2021-07-27 15:55:30 +0100 | [diff] [blame] | 497 | } |
| 498 | |
Jakub Sujak | 617ed50 | 2023-03-29 11:16:18 +0100 | [diff] [blame] | 499 | // Convert weights if needed |
Georgios Pinitas | 529b5a2 | 2021-07-27 15:55:30 +0100 | [diff] [blame] | 500 | if(!_are_weights_converted) |
| 501 | { |
| 502 | ITensorPack convert_pack{ { ACL_SRC, cur_weights }, { ACL_DST, converted_weights.get() } }; |
| 503 | _convert_weights->run(convert_pack); |
| 504 | |
| 505 | cur_weights->mark_as_unused(); |
| 506 | cur_weights = converted_weights.get(); |
Georgios Pinitas | 529b5a2 | 2021-07-27 15:55:30 +0100 | [diff] [blame] | 507 | } |
| 508 | |
Jakub Sujak | 617ed50 | 2023-03-29 11:16:18 +0100 | [diff] [blame] | 509 | ITensorPack gemm_pack = tensors; |
| 510 | gemm_pack.add_const_tensor(ACL_SRC_1, cur_weights); |
Georgios Pinitas | 529b5a2 | 2021-07-27 15:55:30 +0100 | [diff] [blame] | 511 | |
| 512 | // Prepare GEMM prepare and release unused weights |
| 513 | if(!_is_quantized) |
| 514 | { |
Jakub Sujak | 617ed50 | 2023-03-29 11:16:18 +0100 | [diff] [blame] | 515 | _mm_gemm->prepare(gemm_pack); |
Georgios Pinitas | 529b5a2 | 2021-07-27 15:55:30 +0100 | [diff] [blame] | 516 | } |
| 517 | else |
| 518 | { |
Jakub Sujak | 617ed50 | 2023-03-29 11:16:18 +0100 | [diff] [blame] | 519 | _mm_gemmlowp->prepare(gemm_pack); |
Georgios Pinitas | 529b5a2 | 2021-07-27 15:55:30 +0100 | [diff] [blame] | 520 | } |
| 521 | _is_prepared = true; |
| 522 | } |
| 523 | } |
| 524 | |
| 525 | experimental::MemoryRequirements ClFullyConnected::workspace() const |
| 526 | { |
| 527 | return _aux_mem; |
| 528 | } |
| 529 | } // namespace opencl |
| 530 | } // namespace arm_compute |