Manuel Bottini | d87aded | 2021-07-16 10:23:31 +0100 | [diff] [blame] | 1 | /* |
| 2 | * Copyright (c) 2017-2021 Arm Limited. |
| 3 | * |
| 4 | * SPDX-License-Identifier: MIT |
| 5 | * |
| 6 | * Permission is hereby granted, free of charge, to any person obtaining a copy |
| 7 | * of this software and associated documentation files (the "Software"), to |
| 8 | * deal in the Software without restriction, including without limitation the |
| 9 | * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| 10 | * sell copies of the Software, and to permit persons to whom the Software is |
| 11 | * furnished to do so, subject to the following conditions: |
| 12 | * |
| 13 | * The above copyright notice and this permission notice shall be included in all |
| 14 | * copies or substantial portions of the Software. |
| 15 | * |
| 16 | * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| 17 | * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| 18 | * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| 19 | * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| 20 | * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| 21 | * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| 22 | * SOFTWARE. |
| 23 | */ |
Georgios Pinitas | 7891a73 | 2021-08-20 21:39:25 +0100 | [diff] [blame] | 24 | #include "src/gpu/cl/operators/ClGemmConv2d.h" |
Manuel Bottini | d87aded | 2021-07-16 10:23:31 +0100 | [diff] [blame] | 25 | |
| 26 | #include "arm_compute/core/CL/ICLTensor.h" |
| 27 | #include "arm_compute/core/PixelValue.h" |
| 28 | #include "arm_compute/core/Size2D.h" |
| 29 | #include "arm_compute/core/TensorInfo.h" |
| 30 | #include "arm_compute/core/Utils.h" |
| 31 | #include "arm_compute/core/Validate.h" |
| 32 | #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| 33 | #include "arm_compute/core/utils/quantization/AsymmHelpers.h" |
| 34 | #include "arm_compute/runtime/CL/CLScheduler.h" |
Manuel Bottini | d87aded | 2021-07-16 10:23:31 +0100 | [diff] [blame] | 35 | #include "src/core/helpers/AutoConfiguration.h" |
| 36 | #include "src/core/helpers/MemoryHelpers.h" |
Georgios Pinitas | 7891a73 | 2021-08-20 21:39:25 +0100 | [diff] [blame] | 37 | #include "src/gpu/cl/kernels/ClActivationKernel.h" |
| 38 | #include "src/gpu/cl/kernels/ClCol2ImKernel.h" |
| 39 | #include "src/gpu/cl/kernels/ClIm2ColKernel.h" |
| 40 | #include "src/gpu/cl/kernels/ClWeightsReshapeKernel.h" |
| 41 | #include "src/gpu/cl/operators/ClGemm.h" |
| 42 | #include "src/gpu/cl/operators/ClGemmLowpMatrixMultiplyCore.h" |
| 43 | #include "src/gpu/cl/utils/ClAuxTensorHandler.h" |
ramelg01 | 2e53f17 | 2021-09-22 10:48:25 +0100 | [diff] [blame] | 44 | |
| 45 | #include "src/common/utils/Log.h" |
Manuel Bottini | d87aded | 2021-07-16 10:23:31 +0100 | [diff] [blame] | 46 | #include "support/Cast.h" |
| 47 | |
| 48 | namespace arm_compute |
| 49 | { |
| 50 | using namespace experimental; |
| 51 | using namespace misc::shape_calculator; |
| 52 | using namespace utils::cast; |
| 53 | namespace opencl |
| 54 | { |
Georgios Pinitas | 1988463 | 2021-08-16 12:38:54 +0100 | [diff] [blame] | 55 | ClGemmConv2d::ClGemmConv2d() |
Manuel Bottini | d87aded | 2021-07-16 10:23:31 +0100 | [diff] [blame] | 56 | : _weights_reshape_kernel(nullptr), _im2col_kernel(nullptr), _mm_gemm(nullptr), _mm_gemmlowp(nullptr), _col2im_kernel(nullptr), _activation_kernel(nullptr), _im2col_output(), _weights_reshaped(), |
SiCongLi | 579ca84 | 2021-10-18 09:38:33 +0100 | [diff] [blame] | 57 | _gemm_output(), _skip_im2col(false), _skip_col2im(false), _is_quantized(false), _fuse_activation(true), _append_bias(false), _is_prepared(false), _use_post_ops(false), _aux_mem(AuxTensorIdx::Count) |
Manuel Bottini | d87aded | 2021-07-16 10:23:31 +0100 | [diff] [blame] | 58 | { |
| 59 | } |
Georgios Pinitas | 1988463 | 2021-08-16 12:38:54 +0100 | [diff] [blame] | 60 | ClGemmConv2d::~ClGemmConv2d() = default; |
Manuel Bottini | d87aded | 2021-07-16 10:23:31 +0100 | [diff] [blame] | 61 | |
Georgios Pinitas | 1988463 | 2021-08-16 12:38:54 +0100 | [diff] [blame] | 62 | void ClGemmConv2d::configure_mm(const ClCompileContext &compile_context, const ITensorInfo *src, ITensorInfo *weights, ITensorInfo *biases, ITensorInfo *dst, |
| 63 | const GEMMLowpOutputStageInfo &gemmlowp_output_stage, |
SiCongLi | 579ca84 | 2021-10-18 09:38:33 +0100 | [diff] [blame] | 64 | int gemm_3d_depth, const ActivationLayerInfo &act_info, const experimental::PostOpList<ITensorInfo *> &post_ops) |
Manuel Bottini | d87aded | 2021-07-16 10:23:31 +0100 | [diff] [blame] | 65 | { |
| 66 | ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights); |
| 67 | ARM_COMPUTE_ERROR_THROW_ON(validate_mm(src, weights, biases, dst, gemmlowp_output_stage, gemm_3d_depth, _skip_im2col, act_info)); |
| 68 | |
| 69 | const GEMMInfo &gemm_info = GEMMInfo(false, // is_a_reshaped |
| 70 | false, // is_b_reshaped |
| 71 | true, // reshape_b_only_on_first_run |
| 72 | gemm_3d_depth, // depth_output_gemm3d |
| 73 | _skip_im2col, // reinterpret_input_as_3d |
| 74 | false, // retain_internal_weights |
| 75 | gemmlowp_output_stage, // gemmlowp_output_stage |
| 76 | false, // fast_math |
| 77 | false, // fp_mixed_precision |
| 78 | true, // broadcast_bias |
SiCongLi | 579ca84 | 2021-10-18 09:38:33 +0100 | [diff] [blame] | 79 | act_info, // activation_info |
| 80 | post_ops // post ops |
| 81 | ); |
Manuel Bottini | d87aded | 2021-07-16 10:23:31 +0100 | [diff] [blame] | 82 | |
| 83 | TensorInfo tmp_src{ *src }; |
| 84 | if(_is_quantized) |
| 85 | { |
SiCongLi | 579ca84 | 2021-10-18 09:38:33 +0100 | [diff] [blame] | 86 | ARM_COMPUTE_ERROR_ON_MSG(post_ops.size() > 0, "ClGemmConv2d quantized types do not support post ops"); |
Manuel Bottini | d87aded | 2021-07-16 10:23:31 +0100 | [diff] [blame] | 87 | // Since we need negative offsets for computing convolution, we need to change QuantizationInfo() |
| 88 | // Extract and negate input and weights offset |
| 89 | const QuantizationInfo input_quantization_info = src->quantization_info(); |
| 90 | const QuantizationInfo weights_quantization_info = weights->quantization_info(); |
| 91 | |
| 92 | tmp_src.set_quantization_info(QuantizationInfo(input_quantization_info.uniform().scale, -input_quantization_info.uniform().offset)); |
| 93 | weights->set_quantization_info(QuantizationInfo(weights_quantization_info.uniform().scale, -weights_quantization_info.uniform().offset)); |
| 94 | |
| 95 | _mm_gemmlowp = std::make_unique<ClGemmLowpMatrixMultiplyCore>(); |
| 96 | _mm_gemmlowp->configure(compile_context, &tmp_src, weights, biases, dst, gemm_info); |
| 97 | |
| 98 | // Revert back QuantizatioInfo as weights could be used in other convolution layers |
| 99 | weights->set_quantization_info(weights_quantization_info); |
| 100 | |
| 101 | auto mm_mem_req = _mm_gemmlowp->workspace(); |
| 102 | for(unsigned int cont = 0; cont < mm_mem_req.size(); ++cont) |
| 103 | { |
| 104 | _aux_mem[cont] = mm_mem_req[cont]; |
| 105 | } |
| 106 | } |
| 107 | else |
| 108 | { |
| 109 | // Configure matrix multiply function |
| 110 | _mm_gemm = std::make_unique<ClGemm>(); |
| 111 | _mm_gemm->configure(compile_context, &tmp_src, weights, biases, dst, 1.0f, 1.0f, gemm_info); |
| 112 | auto mm_mem_req = _mm_gemm->workspace(); |
| 113 | for(unsigned int cont = 0; cont < mm_mem_req.size(); ++cont) |
| 114 | { |
| 115 | _aux_mem[cont] = mm_mem_req[cont]; |
| 116 | } |
| 117 | } |
| 118 | } |
| 119 | |
Georgios Pinitas | 1988463 | 2021-08-16 12:38:54 +0100 | [diff] [blame] | 120 | Status ClGemmConv2d::validate_mm(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, |
SiCongLi | 579ca84 | 2021-10-18 09:38:33 +0100 | [diff] [blame] | 121 | const GEMMLowpOutputStageInfo &gemmlowp_output_stage, int gemm_3d_depth, bool skip_im2col, const ActivationLayerInfo &act_info, const experimental::PostOpList<ITensorInfo *> &post_ops) |
Manuel Bottini | d87aded | 2021-07-16 10:23:31 +0100 | [diff] [blame] | 122 | { |
| 123 | const bool is_quantized = is_data_type_quantized_asymmetric(src->data_type()); |
| 124 | |
| 125 | const GEMMInfo &gemm_info = GEMMInfo(false, // is_a_reshaped |
| 126 | false, // is_b_reshaped |
| 127 | true, // reshape_b_only_on_first_run |
| 128 | gemm_3d_depth, // depth_output_gemm3d |
| 129 | skip_im2col, // reinterpret_input_as_3d |
| 130 | false, // retain_internal_weights |
| 131 | gemmlowp_output_stage, // gemmlowp_output_stage |
| 132 | false, // fast_math |
| 133 | false, // fp_mixed_precision |
| 134 | true, // broadcast_bias |
SiCongLi | 579ca84 | 2021-10-18 09:38:33 +0100 | [diff] [blame] | 135 | act_info, // activation_info |
| 136 | post_ops // post ops |
| 137 | ); |
Manuel Bottini | d87aded | 2021-07-16 10:23:31 +0100 | [diff] [blame] | 138 | |
| 139 | if(is_quantized) |
| 140 | { |
SiCongLi | 579ca84 | 2021-10-18 09:38:33 +0100 | [diff] [blame] | 141 | ARM_COMPUTE_RETURN_ERROR_ON_MSG(post_ops.size() > 0, "ClGemmConv2d quantized types do not support post ops"); |
Manuel Bottini | d87aded | 2021-07-16 10:23:31 +0100 | [diff] [blame] | 142 | // Since we need negative offsets for computing convolution, we need to change QuantizationInfo() |
| 143 | // Extract and negate input and weights offset |
| 144 | const QuantizationInfo input_quantization_info = src->quantization_info(); |
| 145 | const QuantizationInfo weights_quantization_info = weights->quantization_info(); |
| 146 | |
| 147 | std::unique_ptr<ITensorInfo> src_qa = src->clone(); |
| 148 | std::unique_ptr<ITensorInfo> weights_qa = weights->clone(); |
| 149 | src_qa->set_quantization_info(QuantizationInfo(input_quantization_info.uniform().scale, -input_quantization_info.uniform().offset)); |
| 150 | weights_qa->set_quantization_info(QuantizationInfo(weights_quantization_info.uniform().scale, -weights_quantization_info.uniform().offset)); |
| 151 | |
| 152 | // Perform validation step on GEMMLowp |
| 153 | return ClGemmLowpMatrixMultiplyCore::validate(src_qa.get(), weights_qa.get(), biases, dst, gemm_info); |
| 154 | } |
| 155 | else |
| 156 | { |
| 157 | // Perform validation step on Matrix multiply function |
| 158 | return ClGemm::validate(src, weights, biases, dst, 1.0f, 1.0f, gemm_info); |
| 159 | } |
| 160 | } |
| 161 | |
Georgios Pinitas | 1988463 | 2021-08-16 12:38:54 +0100 | [diff] [blame] | 162 | void ClGemmConv2d::configure(const CLCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *biases, ITensorInfo *dst, |
| 163 | const Conv2dInfo &conv2d_info, const WeightsInfo &weights_info) |
Manuel Bottini | d87aded | 2021-07-16 10:23:31 +0100 | [diff] [blame] | 164 | { |
| 165 | ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst); |
| 166 | |
Georgios Pinitas | 1988463 | 2021-08-16 12:38:54 +0100 | [diff] [blame] | 167 | ARM_COMPUTE_ERROR_THROW_ON(ClGemmConv2d::validate(src, weights, biases, dst, |
| 168 | conv2d_info, |
| 169 | weights_info)); |
ramelg01 | 2e53f17 | 2021-09-22 10:48:25 +0100 | [diff] [blame] | 170 | ARM_COMPUTE_LOG_PARAMS(src, weights, biases, dst, conv2d_info, weights_info); |
Manuel Bottini | d87aded | 2021-07-16 10:23:31 +0100 | [diff] [blame] | 171 | |
| 172 | const DataType data_type = src->data_type(); |
| 173 | const DataLayout data_layout = src->data_layout(); |
| 174 | const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); |
| 175 | const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); |
| 176 | const int idx_kernels = get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES); |
| 177 | |
| 178 | const unsigned int kernel_width = weights->dimension(idx_width); |
| 179 | const unsigned int kernel_height = weights->dimension(idx_height); |
| 180 | const unsigned int num_kernels = weights->dimension(idx_kernels); |
| 181 | |
| 182 | const UniformQuantizationInfo iq_info = src->quantization_info().uniform(); |
| 183 | const UniformQuantizationInfo oq_info = dst->quantization_info().uniform(); |
| 184 | |
| 185 | _is_prepared = weights_info.retain_internal_weights(); |
| 186 | _is_quantized = is_data_type_quantized_asymmetric(src->data_type()); |
| 187 | _skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv2d_info.conv_info.stride().first == 1 && conv2d_info.conv_info.stride().second == 1); |
| 188 | _skip_col2im = data_layout == DataLayout::NHWC; |
| 189 | |
| 190 | // Only for quantize there are few cases where we cannot fuse the activation function in GEMM |
| 191 | _fuse_activation = true; |
SiCongLi | 579ca84 | 2021-10-18 09:38:33 +0100 | [diff] [blame] | 192 | _use_post_ops = conv2d_info.post_ops.size() > 0; |
Manuel Bottini | d87aded | 2021-07-16 10:23:31 +0100 | [diff] [blame] | 193 | |
| 194 | const ITensorInfo *gemm_input_to_use = src; |
| 195 | ITensorInfo *gemm_output_to_use = dst; |
| 196 | |
| 197 | // Get parameters from conv_info |
| 198 | unsigned int stride_x = 0; |
| 199 | unsigned int stride_y = 0; |
| 200 | std::tie(stride_x, stride_y) = conv2d_info.conv_info.stride(); |
| 201 | |
| 202 | // Get convolved dimensions |
| 203 | unsigned int conv_w = 0; |
| 204 | unsigned int conv_h = 0; |
| 205 | std::tie(conv_w, conv_h) = scaled_dimensions(src->dimension(idx_width), |
| 206 | src->dimension(idx_height), |
| 207 | kernel_width, |
| 208 | kernel_height, |
| 209 | conv2d_info.conv_info, |
| 210 | conv2d_info.dilation); |
| 211 | |
| 212 | unsigned int mat_weights_cols = num_kernels / conv2d_info.num_groups; |
| 213 | |
| 214 | ITensorInfo *biases_to_use = biases; |
| 215 | _append_bias = false; |
| 216 | |
| 217 | _weights_reshape_kernel = std::make_unique<kernels::ClWeightsReshapeKernel>(); |
| 218 | if(conv2d_info.num_groups != 1 && biases != nullptr) |
| 219 | { |
| 220 | // num_groups != 1 can only be for NCHW |
| 221 | // Since it is missing an utility function to reshape the biases, we append the biases into the weights tensor |
| 222 | biases_to_use = nullptr; |
| 223 | _append_bias = true; |
| 224 | _weights_reshape_kernel->configure(compile_context, weights, biases, &_weights_reshaped, conv2d_info.num_groups); |
| 225 | } |
| 226 | else |
| 227 | { |
| 228 | _weights_reshape_kernel->configure(compile_context, weights, nullptr, &_weights_reshaped, conv2d_info.num_groups); |
| 229 | } |
| 230 | |
| 231 | // Create tensor to store im2col reshaped inputs |
| 232 | if(!_skip_im2col) |
| 233 | { |
| 234 | // Configure and tune im2col. im2col output shape is auto-initialized |
| 235 | _im2col_kernel = std::make_unique<opencl::kernels::ClIm2ColKernel>(); |
| 236 | |
| 237 | // Set the GPU target for im2col |
| 238 | _im2col_kernel->set_target(CLScheduler::get().target()); |
| 239 | _im2col_kernel->configure(compile_context, src, &_im2col_output, Size2D(kernel_width, kernel_height), conv2d_info.conv_info, _append_bias, conv2d_info.dilation, conv2d_info.num_groups); |
| 240 | |
| 241 | // Set quantization info |
| 242 | _im2col_output.set_quantization_info(src->quantization_info()); |
| 243 | CLScheduler::get().tune_kernel_static(*_im2col_kernel); |
| 244 | |
| 245 | // Update GEMM input |
| 246 | gemm_input_to_use = &_im2col_output; |
| 247 | } |
| 248 | |
| 249 | // Create GEMM output tensor |
| 250 | if(!_skip_col2im) |
| 251 | { |
| 252 | TensorShape shape_gemm; |
| 253 | |
| 254 | // If we cannot skip col2im it means we run im2col as well |
| 255 | shape_gemm = _im2col_output.tensor_shape(); |
| 256 | shape_gemm.set(0, mat_weights_cols); |
| 257 | shape_gemm.set(1, conv_w * conv_h); |
| 258 | |
| 259 | _gemm_output = TensorInfo(shape_gemm, 1, data_type); |
| 260 | _gemm_output.set_quantization_info(dst->quantization_info()).set_data_layout(src->data_layout()); |
| 261 | |
| 262 | // Update GEMM output |
| 263 | gemm_output_to_use = &_gemm_output; |
| 264 | } |
| 265 | |
| 266 | GEMMLowpOutputStageInfo gemmlowp_output_stage; |
| 267 | gemmlowp_output_stage.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT; |
| 268 | gemmlowp_output_stage.gemmlowp_offset = 0; |
| 269 | |
| 270 | // Configure output stage for quantized case |
| 271 | if(_is_quantized) |
| 272 | { |
| 273 | const auto output_quant_info = (dst->total_size() == 0) ? iq_info : oq_info; |
| 274 | const bool is_quantized_per_channel = is_data_type_quantized_per_channel(weights->data_type()); |
| 275 | const unsigned int num_filters = (is_quantized_per_channel) ? num_kernels : 1; |
| 276 | |
| 277 | gemmlowp_output_stage.is_quantized_per_channel = is_quantized_per_channel; |
| 278 | |
| 279 | gemmlowp_output_stage.gemmlowp_multipliers.resize(num_filters); |
| 280 | gemmlowp_output_stage.gemmlowp_shifts.resize(num_filters); |
| 281 | quantization::compute_quantized_multipliers_and_shifts(src, weights, dst, |
| 282 | gemmlowp_output_stage.gemmlowp_multipliers.data(), |
| 283 | gemmlowp_output_stage.gemmlowp_shifts.data()); |
| 284 | gemmlowp_output_stage.gemmlowp_multiplier = gemmlowp_output_stage.gemmlowp_multipliers[0]; |
| 285 | gemmlowp_output_stage.gemmlowp_shift = gemmlowp_output_stage.gemmlowp_shifts[0]; |
| 286 | |
| 287 | PixelValue min_val{}; |
| 288 | PixelValue max_val{}; |
| 289 | std::tie(min_val, max_val) = get_min_max(dst->data_type()); |
| 290 | |
| 291 | auto min_activation = min_val.get<int32_t>(); |
| 292 | auto max_activation = max_val.get<int32_t>(); |
| 293 | |
| 294 | const std::set<ActivationLayerInfo::ActivationFunction> supported_acts = { ActivationLayerInfo::ActivationFunction::RELU, |
| 295 | ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, |
| 296 | ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU |
| 297 | }; |
| 298 | |
| 299 | if(conv2d_info.act_info.enabled()) |
| 300 | { |
| 301 | if(supported_acts.count(conv2d_info.act_info.activation()) != 0) |
| 302 | { |
| 303 | std::tie(min_activation, max_activation) = get_quantized_activation_min_max(conv2d_info.act_info, data_type, output_quant_info); |
| 304 | } |
| 305 | else |
| 306 | { |
| 307 | _fuse_activation = false; |
| 308 | } |
| 309 | } |
| 310 | |
| 311 | // Set the GEMMLowp output stage info |
| 312 | gemmlowp_output_stage.gemmlowp_offset = output_quant_info.offset; |
| 313 | gemmlowp_output_stage.gemmlowp_min_bound = min_activation; |
| 314 | gemmlowp_output_stage.gemmlowp_max_bound = max_activation; |
| 315 | } |
| 316 | |
| 317 | // Configure and tune GEMM |
| 318 | // In case of NHWC, we need to run GEMM3D (gemm_3d_depth != 0) in order to avoid reshaping the output matrix |
| 319 | const unsigned int gemm_3d_depth = (data_layout == DataLayout::NHWC) ? conv_h : 0; |
| 320 | |
SiCongLi | 579ca84 | 2021-10-18 09:38:33 +0100 | [diff] [blame] | 321 | configure_mm(compile_context, gemm_input_to_use, &_weights_reshaped, biases_to_use, gemm_output_to_use, gemmlowp_output_stage, gemm_3d_depth, conv2d_info.act_info, conv2d_info.post_ops); |
Manuel Bottini | d87aded | 2021-07-16 10:23:31 +0100 | [diff] [blame] | 322 | |
| 323 | if(!_skip_col2im) |
| 324 | { |
SiCongLi | 579ca84 | 2021-10-18 09:38:33 +0100 | [diff] [blame] | 325 | ARM_COMPUTE_ERROR_ON_MSG(conv2d_info.post_ops.size() > 0, "ClGemmConv2d does not support post ops with col2im operation"); // Post ops must be performed after every other op |
Manuel Bottini | d87aded | 2021-07-16 10:23:31 +0100 | [diff] [blame] | 326 | // Set the GPU target for col2im |
| 327 | _col2im_kernel = std::make_unique<opencl::kernels::ClCol2ImKernel>(); |
| 328 | _col2im_kernel->set_target(CLScheduler::get().target()); |
| 329 | // Configure and tune Col2Im |
| 330 | _col2im_kernel->configure(compile_context, gemm_output_to_use, dst, Size2D(conv_w, conv_h), conv2d_info.num_groups); |
| 331 | CLScheduler::get().tune_kernel_static(*_col2im_kernel.get()); |
| 332 | } |
| 333 | |
| 334 | ARM_COMPUTE_ERROR_ON_MSG((dst->dimension(idx_width) != conv_w) || (dst->dimension(idx_height) != conv_h), |
| 335 | "Output shape does not match the expected one"); |
| 336 | |
SiCongLi | 579ca84 | 2021-10-18 09:38:33 +0100 | [diff] [blame] | 337 | // Disable running of activation kernel if post ops are used |
| 338 | if(!_fuse_activation && !_use_post_ops) |
Manuel Bottini | d87aded | 2021-07-16 10:23:31 +0100 | [diff] [blame] | 339 | { |
| 340 | _activation_kernel = std::make_unique<opencl::kernels::ClActivationKernel>(); |
| 341 | _activation_kernel->configure(compile_context, dst, nullptr, conv2d_info.act_info); |
| 342 | } |
| 343 | |
| 344 | _aux_mem[Im2ColOutput] = MemoryInfo(offset_int_vec(Im2ColOutput), MemoryLifetime::Temporary, _im2col_output.total_size()); |
| 345 | _aux_mem[WeightsReshaped] = MemoryInfo(offset_int_vec(WeightsReshaped), MemoryLifetime::Persistent, _weights_reshaped.total_size()); |
| 346 | _aux_mem[GemmOutput] = MemoryInfo(offset_int_vec(GemmOutput), MemoryLifetime::Temporary, _gemm_output.total_size()); |
| 347 | } |
| 348 | |
Georgios Pinitas | 1988463 | 2021-08-16 12:38:54 +0100 | [diff] [blame] | 349 | Status ClGemmConv2d::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const Conv2dInfo &conv2d_info, |
| 350 | const WeightsInfo &weights_info) |
Manuel Bottini | d87aded | 2021-07-16 10:23:31 +0100 | [diff] [blame] | 351 | { |
| 352 | ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, weights, dst); |
| 353 | ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights_info.are_reshaped(), "Weights already reshaped are not supported!"); |
| 354 | ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32); |
| 355 | const bool is_quantized_per_channel = is_data_type_quantized_per_channel(weights->data_type()); |
| 356 | |
| 357 | if(!is_quantized_per_channel) |
| 358 | { |
| 359 | ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, weights); |
| 360 | } |
| 361 | ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(src, weights); |
| 362 | ARM_COMPUTE_RETURN_ERROR_ON_MSG((conv2d_info.num_groups != 1) && (src->data_layout() != DataLayout::NCHW), "Grouping (num_groups != 1) with NHWC data layout is not supported"); |
| 363 | ARM_COMPUTE_RETURN_ERROR_ON_MSG((conv2d_info.num_groups != 1) && (src->data_type() == DataType::QASYMM8), "Grouping (num_groups != 1) is not supported with QASYMM8"); |
| 364 | ARM_COMPUTE_RETURN_ERROR_ON(((src->dimension(2) / weights->dimension(2)) != conv2d_info.num_groups) && (src->data_layout() == DataLayout::NCHW)); |
| 365 | |
| 366 | const DataLayout data_layout = src->data_layout(); |
| 367 | const DataType data_type = src->data_type(); |
| 368 | const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); |
| 369 | const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); |
| 370 | const int idx_channel = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); |
| 371 | const int idx_kernels = get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES); |
| 372 | |
| 373 | const unsigned int kernel_width = weights->dimension(idx_width); |
| 374 | const unsigned int kernel_height = weights->dimension(idx_height); |
| 375 | const unsigned int num_kernels = weights->dimension(idx_kernels); |
| 376 | |
| 377 | TensorInfo im2col_reshaped_info{}; |
| 378 | TensorInfo info_gemm{}; |
| 379 | TensorInfo weights_reshaped_info{}; |
| 380 | const ITensorInfo *gemm_input_to_use = src; |
| 381 | const ITensorInfo *gemm_output_to_use = dst; |
| 382 | const ITensorInfo *weights_to_use = weights; |
| 383 | const bool is_quantized = is_data_type_quantized_asymmetric(data_type); |
| 384 | const bool skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1 && conv2d_info.conv_info.stride().first == 1 |
| 385 | && conv2d_info.conv_info.stride().second == 1); |
Georgios Pinitas | 1988463 | 2021-08-16 12:38:54 +0100 | [diff] [blame] | 386 | const bool skip_col2im = data_layout == DataLayout::NHWC; |
| 387 | bool fuse_activation = true; |
SiCongLi | 579ca84 | 2021-10-18 09:38:33 +0100 | [diff] [blame] | 388 | bool use_post_ops = conv2d_info.post_ops.size() > 0; |
Manuel Bottini | d87aded | 2021-07-16 10:23:31 +0100 | [diff] [blame] | 389 | |
| 390 | ARM_COMPUTE_RETURN_ERROR_ON((weights->dimension(idx_channel) * conv2d_info.num_groups) != src->dimension(idx_channel)); |
| 391 | ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4); |
SiCongLi | d928735 | 2021-11-03 19:01:22 +0000 | [diff] [blame] | 392 | ARM_COMPUTE_RETURN_ERROR_ON_MSG(!skip_im2col |
| 393 | && conv2d_info.post_ops.size() > 0, |
| 394 | "ClGemmConv2d does not support post ops with col2im or im2col operation"); // Post ops must be performed after every other op |
Manuel Bottini | d87aded | 2021-07-16 10:23:31 +0100 | [diff] [blame] | 395 | |
| 396 | // Validate biases |
| 397 | if(biases != nullptr) |
| 398 | { |
| 399 | if(is_quantized) |
| 400 | { |
| 401 | ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32); |
| 402 | } |
| 403 | else |
| 404 | { |
| 405 | ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, biases); |
| 406 | } |
| 407 | ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(idx_kernels)); |
| 408 | ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); |
| 409 | } |
| 410 | |
| 411 | if(conv2d_info.act_info.enabled()) |
| 412 | { |
| 413 | ARM_COMPUTE_ERROR_ON(conv2d_info.act_info.b() > conv2d_info.act_info.a()); |
| 414 | } |
| 415 | |
| 416 | // Get convolved dimensions |
| 417 | unsigned int conv_w = 0; |
| 418 | unsigned int conv_h = 0; |
| 419 | |
| 420 | std::tie(conv_w, conv_h) = scaled_dimensions(src->dimension(idx_width), |
| 421 | src->dimension(idx_height), |
| 422 | kernel_width, |
| 423 | kernel_height, |
| 424 | conv2d_info.conv_info, |
| 425 | conv2d_info.dilation); |
| 426 | |
| 427 | unsigned int mat_weights_cols = num_kernels / conv2d_info.num_groups; |
| 428 | |
| 429 | const ITensorInfo *biases_to_use = biases; |
| 430 | bool append_bias = false; |
| 431 | |
| 432 | if(conv2d_info.num_groups != 1 && biases != nullptr) |
| 433 | { |
| 434 | // num_groups != 1 can only be for NCHW |
| 435 | // Since it is missing an utility function to reshape the biases, we append the biases into the weights tensor |
| 436 | biases_to_use = nullptr; |
| 437 | append_bias = true; |
| 438 | weights_reshaped_info = TensorInfo(compute_weights_reshaped_shape(*weights, true, conv2d_info.num_groups), 1, data_type); |
| 439 | } |
| 440 | else |
| 441 | { |
| 442 | weights_reshaped_info = TensorInfo(compute_weights_reshaped_shape(*weights, false, conv2d_info.num_groups), 1, data_type); |
| 443 | } |
| 444 | |
| 445 | weights_to_use = &weights_reshaped_info; |
| 446 | |
| 447 | if(!skip_im2col) |
| 448 | { |
| 449 | const Size2D kernel_dims(kernel_width, kernel_height); |
| 450 | |
| 451 | // Output tensor auto initialization if not yet initialized |
| 452 | TensorShape expected_output_shape = compute_im2col_conv_shape(src, kernel_dims, conv2d_info.conv_info, append_bias, conv2d_info.dilation, conv2d_info.num_groups == 1, conv2d_info.num_groups); |
| 453 | |
| 454 | auto_init_if_empty(im2col_reshaped_info, src->clone()->set_tensor_shape(expected_output_shape)); |
| 455 | |
| 456 | ARM_COMPUTE_RETURN_ON_ERROR(opencl::kernels::ClIm2ColKernel::validate(src, &im2col_reshaped_info, kernel_dims, conv2d_info.conv_info, append_bias, conv2d_info.dilation, conv2d_info.num_groups)); |
| 457 | gemm_input_to_use = &im2col_reshaped_info; |
| 458 | } |
| 459 | |
| 460 | // Create GEMM output tensor |
| 461 | if(!skip_col2im) |
| 462 | { |
| 463 | TensorShape shape_gemm; |
| 464 | |
| 465 | shape_gemm = gemm_input_to_use->tensor_shape(); |
| 466 | shape_gemm.set(0, mat_weights_cols); |
| 467 | shape_gemm.set(1, conv_w * conv_h); |
| 468 | |
| 469 | info_gemm = TensorInfo(shape_gemm, 1, data_type); |
| 470 | info_gemm.set_quantization_info(dst->quantization_info()).set_data_layout(src->data_layout()); |
| 471 | gemm_output_to_use = &info_gemm; |
| 472 | } |
| 473 | |
| 474 | GEMMLowpOutputStageInfo gemmlowp_output_stage; |
| 475 | gemmlowp_output_stage.type = GEMMLowpOutputStageType::QUANTIZE_DOWN_FIXEDPOINT; |
| 476 | gemmlowp_output_stage.gemmlowp_offset = 0; |
| 477 | gemmlowp_output_stage.is_quantized_per_channel = is_quantized_per_channel; |
| 478 | |
| 479 | if(is_quantized) |
| 480 | { |
| 481 | const UniformQuantizationInfo iq_info = src->quantization_info().uniform(); |
| 482 | const UniformQuantizationInfo oq_info = dst->quantization_info().uniform(); |
| 483 | const auto output_quant_info = (dst->total_size() == 0) ? iq_info : oq_info; |
| 484 | const unsigned int num_filters = (is_quantized_per_channel) ? num_kernels : 1; |
| 485 | |
| 486 | gemmlowp_output_stage.gemmlowp_multipliers.resize(num_filters); |
| 487 | gemmlowp_output_stage.gemmlowp_shifts.resize(num_filters); |
| 488 | quantization::compute_quantized_multipliers_and_shifts(src, weights, dst, |
| 489 | gemmlowp_output_stage.gemmlowp_multipliers.data(), |
| 490 | gemmlowp_output_stage.gemmlowp_shifts.data()); |
| 491 | gemmlowp_output_stage.gemmlowp_multiplier = gemmlowp_output_stage.gemmlowp_multipliers[0]; |
| 492 | gemmlowp_output_stage.gemmlowp_shift = gemmlowp_output_stage.gemmlowp_shifts[0]; |
| 493 | |
| 494 | int min_activation = 0; |
| 495 | int max_activation = 0; |
| 496 | |
| 497 | const std::set<ActivationLayerInfo::ActivationFunction> supported_acts = { ActivationLayerInfo::ActivationFunction::RELU, |
| 498 | ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, |
| 499 | ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU |
| 500 | }; |
| 501 | |
| 502 | if(conv2d_info.act_info.enabled()) |
| 503 | { |
| 504 | if(supported_acts.count(conv2d_info.act_info.activation()) != 0) |
| 505 | { |
| 506 | std::tie(min_activation, max_activation) = get_quantized_activation_min_max(conv2d_info.act_info, data_type, output_quant_info); |
| 507 | } |
| 508 | else |
| 509 | { |
| 510 | fuse_activation = false; |
| 511 | } |
| 512 | } |
| 513 | |
| 514 | // Set the GEMMLowp output stage info |
| 515 | gemmlowp_output_stage.gemmlowp_offset = output_quant_info.offset; |
| 516 | gemmlowp_output_stage.gemmlowp_min_bound = min_activation; |
| 517 | gemmlowp_output_stage.gemmlowp_max_bound = max_activation; |
| 518 | } |
| 519 | |
| 520 | // In case of NHWC, we need to run GEMM3D (gemm_3d_depth != 0) in order to avoid reshaping the output matrix |
| 521 | const unsigned int gemm_3d_depth = (data_layout == DataLayout::NHWC) ? conv_h : 0; |
| 522 | |
SiCongLi | 579ca84 | 2021-10-18 09:38:33 +0100 | [diff] [blame] | 523 | ARM_COMPUTE_RETURN_ON_ERROR(validate_mm(gemm_input_to_use, weights_to_use, biases_to_use, gemm_output_to_use, gemmlowp_output_stage, gemm_3d_depth, skip_im2col, conv2d_info.act_info, |
| 524 | conv2d_info.post_ops)); |
Manuel Bottini | d87aded | 2021-07-16 10:23:31 +0100 | [diff] [blame] | 525 | |
| 526 | // Validate Col2Im |
| 527 | if(!skip_col2im) |
| 528 | { |
| 529 | ARM_COMPUTE_RETURN_ON_ERROR(kernels::ClCol2ImKernel::validate(gemm_output_to_use, dst, Size2D(conv_w, conv_h), conv2d_info.num_groups)); |
| 530 | } |
| 531 | |
SiCongLi | 579ca84 | 2021-10-18 09:38:33 +0100 | [diff] [blame] | 532 | // Validate Activation Layer |
| 533 | // Disable running (thus validation) of activation kernel if post ops are used |
| 534 | if(!fuse_activation && !use_post_ops) |
Manuel Bottini | d87aded | 2021-07-16 10:23:31 +0100 | [diff] [blame] | 535 | { |
| 536 | ARM_COMPUTE_RETURN_ON_ERROR(kernels::ClActivationKernel::validate(dst, nullptr, conv2d_info.act_info)); |
| 537 | } |
| 538 | |
| 539 | return Status{}; |
| 540 | } |
| 541 | |
Georgios Pinitas | 1988463 | 2021-08-16 12:38:54 +0100 | [diff] [blame] | 542 | void ClGemmConv2d::run(ITensorPack &tensors) |
Manuel Bottini | d87aded | 2021-07-16 10:23:31 +0100 | [diff] [blame] | 543 | { |
| 544 | prepare(tensors); |
| 545 | |
| 546 | auto src = tensors.get_const_tensor(ACL_SRC_0); |
| 547 | auto biases = tensors.get_const_tensor(ACL_SRC_2); |
| 548 | auto dst = tensors.get_tensor(ACL_DST); |
| 549 | auto gemm_input_to_use = src; |
| 550 | auto gemm_output_to_use = dst; |
| 551 | |
| 552 | CLAuxTensorHandler im2col_output(offset_int_vec(Im2ColOutput), _im2col_output, tensors, false); |
| 553 | CLAuxTensorHandler gemm_output(offset_int_vec(GemmOutput), _gemm_output, tensors, false); |
| 554 | CLAuxTensorHandler weights_reshaped(offset_int_vec(WeightsReshaped), _weights_reshaped, tensors, false); |
| 555 | |
| 556 | // Run im2col |
| 557 | if(!_skip_im2col) |
| 558 | { |
| 559 | ITensorPack pack = |
| 560 | { |
| 561 | { TensorType::ACL_SRC, src }, |
| 562 | { TensorType::ACL_DST, im2col_output.get() } |
| 563 | }; |
| 564 | CLScheduler::get().enqueue_op(*_im2col_kernel, pack, false); |
| 565 | gemm_input_to_use = im2col_output.get(); |
| 566 | } |
| 567 | if(!_skip_col2im) |
| 568 | { |
| 569 | gemm_output_to_use = gemm_output.get(); |
| 570 | } |
| 571 | ITensorPack pack_mm = tensors; |
| 572 | pack_mm.add_const_tensor(TensorType::ACL_SRC_0, gemm_input_to_use); |
| 573 | pack_mm.add_const_tensor(TensorType::ACL_SRC_1, weights_reshaped.get()); |
| 574 | if(!_append_bias) |
| 575 | { |
| 576 | pack_mm.add_const_tensor(TensorType::ACL_SRC_2, biases); |
| 577 | } |
| 578 | pack_mm.add_tensor(TensorType::ACL_DST, gemm_output_to_use); |
| 579 | // Runs ClGemm or ClGemmLowpMatrixMultiplyCore functions |
| 580 | if(_is_quantized) |
| 581 | { |
| 582 | // Run gemmlowp |
| 583 | _mm_gemmlowp->run(pack_mm); |
| 584 | } |
| 585 | else |
| 586 | { |
| 587 | // Run gemm |
| 588 | _mm_gemm->run(pack_mm); |
| 589 | } |
| 590 | |
| 591 | // Reshape output matrix |
| 592 | if(!_skip_col2im) |
| 593 | { |
| 594 | ITensorPack pack = |
| 595 | { |
| 596 | { TensorType::ACL_SRC, gemm_output_to_use }, |
| 597 | { TensorType::ACL_DST, dst } |
| 598 | }; |
| 599 | CLScheduler::get().enqueue_op(*_col2im_kernel.get(), pack, false); |
| 600 | } |
| 601 | |
| 602 | //Run Activation Layer if we cannot fuse in GEMM |
SiCongLi | 579ca84 | 2021-10-18 09:38:33 +0100 | [diff] [blame] | 603 | // Disable running of activation kernel if post ops are used |
| 604 | if(!_fuse_activation && !_use_post_ops) |
Manuel Bottini | d87aded | 2021-07-16 10:23:31 +0100 | [diff] [blame] | 605 | { |
| 606 | ITensorPack pack = |
| 607 | { |
| 608 | { TensorType::ACL_SRC, dst }, |
| 609 | { TensorType::ACL_DST, dst } |
| 610 | }; |
| 611 | CLScheduler::get().enqueue_op(*_activation_kernel.get(), pack, false); |
| 612 | } |
| 613 | } |
| 614 | |
Georgios Pinitas | 1988463 | 2021-08-16 12:38:54 +0100 | [diff] [blame] | 615 | void ClGemmConv2d::prepare(ITensorPack &tensors) |
Manuel Bottini | d87aded | 2021-07-16 10:23:31 +0100 | [diff] [blame] | 616 | { |
| 617 | if(!_is_prepared) |
| 618 | { |
| 619 | // Run weights reshaping and mark original weights tensor as unused |
| 620 | ICLTensor *weights_reshaped_p = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(offset_int_vec(WeightsReshaped))); |
| 621 | CLAuxTensorHandler weights_reshaped(_weights_reshaped, *weights_reshaped_p); |
| 622 | auto weights = tensors.get_const_tensor(TensorType::ACL_SRC_1); |
| 623 | ITensorPack pack = |
| 624 | { |
| 625 | { TensorType::ACL_SRC, weights }, |
| 626 | { TensorType::ACL_DST, weights_reshaped.get() } |
| 627 | }; |
| 628 | |
| 629 | if(_append_bias) |
| 630 | { |
| 631 | const auto biases = tensors.get_const_tensor(TensorType::ACL_SRC_2); |
| 632 | pack.add_const_tensor(TensorType::ACL_BIAS, biases); |
| 633 | } |
| 634 | CLScheduler::get().enqueue_op(*_weights_reshape_kernel.get(), pack, true); |
| 635 | tensors.add_const_tensor(TensorType::ACL_SRC_1, weights_reshaped.get()); |
| 636 | |
| 637 | // Prepare GEMM |
| 638 | _is_quantized ? _mm_gemmlowp->prepare(tensors) : _mm_gemm->prepare(tensors); |
| 639 | _is_prepared = true; |
| 640 | } |
| 641 | } |
Georgios Pinitas | 1988463 | 2021-08-16 12:38:54 +0100 | [diff] [blame] | 642 | experimental::MemoryRequirements ClGemmConv2d::workspace() const |
Manuel Bottini | d87aded | 2021-07-16 10:23:31 +0100 | [diff] [blame] | 643 | { |
| 644 | return _aux_mem; |
| 645 | } |
| 646 | } // namespace opencl |
| 647 | } // namespace arm_compute |