SiCong Li | f44bbc5 | 2022-08-29 18:25:51 +0100 | [diff] [blame^] | 1 | /* |
| 2 | * Copyright (c) 2022 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 | */ |
| 24 | #include "ClTemplateDirectConv2d.h" |
| 25 | |
| 26 | #include "src/dynamic_fusion/sketch/gpu/GpuKernelComponentGroup.h" |
| 27 | #include "src/dynamic_fusion/sketch/gpu/components/cl/ClComponentDirectConv2d.h" |
| 28 | |
| 29 | #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| 30 | #include "src/core/helpers/WindowHelpers.h" |
| 31 | |
| 32 | #include "support/StringSupport.h" |
| 33 | |
| 34 | namespace arm_compute |
| 35 | { |
| 36 | namespace experimental |
| 37 | { |
| 38 | namespace dynamic_fusion |
| 39 | { |
| 40 | ClTemplateDirectConv2d::ClTemplateDirectConv2d(ComponentId id, |
| 41 | const ArgumentPack<ITensorInfo> &tensors, |
| 42 | const Attributes &attributes, |
| 43 | const Settings &settings) |
| 44 | : IGpuTemplateComponentWriter{ id, tensors }, |
| 45 | _src{}, |
| 46 | _weight{}, |
| 47 | _bias{}, |
| 48 | _dst{}, |
| 49 | _attributes{ attributes }, |
| 50 | _settings{ settings } |
| 51 | { |
| 52 | _src = this->tensors().get_const_tensor(TensorType::ACL_SRC_0); |
| 53 | _weight = this->tensors().get_const_tensor(TensorType::ACL_SRC_1); |
| 54 | if(this->tensors().get_const_tensor(TensorType::ACL_SRC_2)) |
| 55 | { |
| 56 | _bias = this->tensors().get_const_tensor(TensorType::ACL_SRC_2); |
| 57 | } |
| 58 | _dst = this->tensors().get_const_tensor(TensorType::ACL_DST_0); |
| 59 | ARM_COMPUTE_ERROR_ON_NULLPTR(_src, _weight, _dst); |
| 60 | } |
| 61 | |
| 62 | std::string ClTemplateDirectConv2d::get_name() const |
| 63 | { |
| 64 | return "direct_conv2d"; |
| 65 | } |
| 66 | |
| 67 | std::string ClTemplateDirectConv2d::get_component_code(const ComponentGroup &comp_group) const |
| 68 | { |
| 69 | ARM_COMPUTE_UNUSED(comp_group); |
| 70 | |
| 71 | const auto channel_idx = get_data_layout_dimension_index(_src->data_layout(), DataLayoutDimension::CHANNEL); |
| 72 | const auto k0 = adjust_vec_size(is_data_type_quantized(_src->data_type()) ? 16u : 8u, _src->dimension(channel_idx)); |
| 73 | const bool leftover_loop = (_src->dimension(channel_idx) % k0) != 0; |
| 74 | |
| 75 | std::string code = R"_( |
| 76 | //------------------ START KERNEL {{meta_kernel_id}} --------------------- |
| 77 | // IN_0(src) {{src}} |
| 78 | // IN_1(wei) {{weight}} |
| 79 | )_"; |
| 80 | if(_bias && _bias->has_valid_id()) |
| 81 | { |
| 82 | code += R"_( |
| 83 | // IN_1(bia) {{bias}} |
| 84 | )_"; |
| 85 | } |
| 86 | code += R"_( |
| 87 | // OUT(dst, accum) {{dst}} |
| 88 | |
| 89 | // Initialize the accumulators |
| 90 | TILE({{ACC_DATA_TYPE}}, M0, N0, {{dst}}); |
| 91 | { |
| 92 | // All the tensor dimensions are passed at compile time. |
| 93 | // In case of dynamic tensor support, the following dimensions should be passed as function argument. |
| 94 | #define _IWEI_WIDTH {{WEI_WIDTH}} |
| 95 | #define _IWEI_HEIGHT {{WEI_HEIGHT}} |
| 96 | #define _ISRC_WIDTH {{src}}_w |
| 97 | #define _ISRC_HEIGHT {{src}}_h |
| 98 | #define _ISRC_CHANNELS {{src}}_c |
| 99 | #define _IDST_WIDTH {{arg_dst}}_w |
| 100 | #define _IDST_HEIGHT {{arg_dst}}_h |
| 101 | #define _IDST_CHANNELS {{arg_dst}}_c |
| 102 | #define _IY_MULTIPLIER (_IWEI_WIDTH * _IWEI_HEIGHT) |
| 103 | |
| 104 | // .v = access the whole vector (OpenCL vector) |
| 105 | // .s[x] = access the vector element at position x (scalar access) |
| 106 | TILE(int, M0, 1, xi); |
| 107 | TILE(int, M0, 1, yi); |
| 108 | |
| 109 | // Convert the linear index to coordinate |
| 110 | LOOP_UNROLLING(int, i, 0, 1, M0, |
| 111 | { |
| 112 | xi[i].v = ((g_ind_1 + i) % _IDST_WIDTH) * {{STRIDE_X}}; |
| 113 | yi[i].v = ((g_ind_1 + i) / _IDST_WIDTH) * {{STRIDE_Y}}; |
| 114 | xi[i].v -= {{PAD_LEFT}}; |
| 115 | yi[i].v -= {{PAD_TOP}}; |
| 116 | }) |
| 117 | |
| 118 | LOOP_UNROLLING(int, i, 0, 1, M0, |
| 119 | { |
| 120 | {{dst}}[i].v = 0; |
| 121 | }) |
| 122 | |
| 123 | for(int i = 0; i < (_IWEI_WIDTH * _IWEI_HEIGHT); ++i) |
| 124 | { |
| 125 | int ck = 0; |
| 126 | int xk = i % _IWEI_WIDTH; |
| 127 | int yk = i / _IWEI_WIDTH; |
| 128 | |
| 129 | int k = 0; |
| 130 | for(; k <= (_ISRC_CHANNELS - K0); k += K0) |
| 131 | { |
| 132 | TILE({{SRC_DATA_TYPE}}, M0, K0, a); |
| 133 | TILE({{WEI_DATA_TYPE}}, N0, K0, b); |
| 134 | |
| 135 | // Initialize tiles |
| 136 | LOOP_UNROLLING(int, i, 0, 1, M0, |
| 137 | { |
| 138 | a[i].v = {{ZERO_VALUE}}; |
| 139 | }) |
| 140 | |
| 141 | LOOP_UNROLLING(int, i, 0, 1, N0, |
| 142 | { |
| 143 | b[i].v = {{ZERO_VALUE}}; |
| 144 | }) |
| 145 | |
| 146 | // Load tile from the src tensor |
| 147 | T_LOAD_NHWC_INDIRECT({{SRC_DATA_TYPE}}, M0, K0, {{SRC_TENSOR_TYPE}}, {{src}}, g_ind_2, yk, xk, ck, _ISRC_WIDTH, _ISRC_HEIGHT, {{src}}_stride_y, xi, yi, a); |
| 148 | |
| 149 | // Load tile from the weights tensor |
| 150 | T_LOAD({{WEI_DATA_TYPE}}, N0, K0, {{WEI_TENSOR_TYPE}}, {{weight}}, ck, g_ind_0 * _IY_MULTIPLIER + i, _IY_MULTIPLIER, {{weight}}_stride_y, b); |
| 151 | |
| 152 | // Compute the matrix multiplication between two tiles |
| 153 | T_MMUL({{SRC_DATA_TYPE}}, {{WEI_DATA_TYPE}}, {{ACC_DATA_TYPE}}, M0, N0, K0, NT, T, a, b, {{dst}}); |
| 154 | |
| 155 | ck += K0; |
| 156 | } |
| 157 | |
| 158 | // We voluntarily use SRC_CHANNELS rather than _DSRC_CHANNELS |
| 159 | // This #if directive should be removed in case of dynamic tensor support |
| 160 | )_"; |
| 161 | |
| 162 | if(leftover_loop) |
| 163 | { |
| 164 | code += R"_( |
| 165 | // Left-over accumulations |
| 166 | for(; k < _ISRC_CHANNELS; ++k) |
| 167 | { |
| 168 | TILE({{SRC_DATA_TYPE}}, M0, 1, a); |
| 169 | TILE({{WEI_DATA_TYPE}}, N0, 1, b); |
| 170 | |
| 171 | // Initialize tiles |
| 172 | LOOP_UNROLLING(int, i, 0, 1, M0, |
| 173 | { |
| 174 | a[i].v = {{ZERO_VALUE}}; |
| 175 | }) |
| 176 | |
| 177 | LOOP_UNROLLING(int, i, 0, 1, N0, |
| 178 | { |
| 179 | b[i].v = {{ZERO_VALUE}}; |
| 180 | }) |
| 181 | |
| 182 | // Load tile from the src tensor |
| 183 | T_LOAD_NHWC_INDIRECT({{SRC_DATA_TYPE}}, M0, 1, {{SRC_TENSOR_TYPE}}, {{src}}, g_ind_2, yk, xk, ck, _ISRC_WIDTH, _ISRC_HEIGHT, {{src}}_stride_y, xi, yi, a); |
| 184 | |
| 185 | // Load tile from the weights tensor |
| 186 | // The T_LOAD for the left-over elements can only use BUFFER because we load one element per iteration |
| 187 | T_LOAD({{WEI_DATA_TYPE}}, N0, 1, BUFFER, {{weight}}, ck, g_ind_0 * _IY_MULTIPLIER + i, _IY_MULTIPLIER, {{weight}}_stride_y, b); |
| 188 | |
| 189 | // Compute the matrix multiplication between two tiles |
| 190 | T_MMUL({{SRC_DATA_TYPE}}, {{WEI_DATA_TYPE}}, {{ACC_DATA_TYPE}}, M0, N0, 1, NT, T, a, b, {{dst}}); |
| 191 | |
| 192 | ++ck; |
| 193 | } |
| 194 | )_"; |
| 195 | } |
| 196 | |
| 197 | code += R"_( |
| 198 | #undef _I_WEI_WIDTH |
| 199 | #undef _I_WEI_HEIGHT |
| 200 | #undef _ISRC_WIDTH |
| 201 | #undef _ISRC_HEIGHT |
| 202 | #undef _ISRC_CHANNELS |
| 203 | #undef _IDST_WIDTH |
| 204 | #undef _IDST_HEIGHT |
| 205 | #undef _IDST_CHANNELS |
| 206 | #undef _IY_MULTIPLIER |
| 207 | |
| 208 | } |
| 209 | )_"; |
| 210 | |
| 211 | if(_bias && _bias->has_valid_id()) |
| 212 | { |
| 213 | code += R"_( |
| 214 | TILE({{BIA_DATA_TYPE}}, 1, N0, bias0); |
| 215 | |
| 216 | T_LOAD({{BIA_DATA_TYPE}}, 1, N0, BUFFER, {{bias}}, g_ind_0, 0, 1, 0, bias0); |
| 217 | |
| 218 | // c = c + bias[broadcasted] |
| 219 | T_ELTWISE_BROADCAST_ADD_X({{ACC_DATA_TYPE}}, M0, N0, {{dst}}, bias0, {{dst}}); |
| 220 | )_"; |
| 221 | } |
| 222 | |
| 223 | code += R"_( |
| 224 | } |
| 225 | //------------------ END KERNEL {{meta_kernel_id}} --------------------- |
| 226 | )_"; |
| 227 | return code; |
| 228 | } |
| 229 | |
| 230 | void ClTemplateDirectConv2d::declare_variables(GpuKernelVariableTable &vtable, const ComponentGroup &comp_group) const |
| 231 | { |
| 232 | vtable.declare_variable( |
| 233 | _src, |
| 234 | GpuKernelArgumentInfo(GpuKernelArgumentInfo::Type::Tensor_4D_t_Buffer), |
| 235 | comp_group.is_intermediate_tensor(_src), |
| 236 | "src"); |
| 237 | |
| 238 | const GpuKernelArgumentInfo::Type weight_type = _settings.export_to_cl_image() ? GpuKernelArgumentInfo::Type::Tensor_4D_t_Image : GpuKernelArgumentInfo::Type::Tensor_4D_t_Buffer; |
| 239 | vtable.declare_variable( |
| 240 | _weight, |
| 241 | GpuKernelArgumentInfo(weight_type), |
| 242 | comp_group.is_intermediate_tensor(_weight), |
| 243 | "weight"); |
| 244 | |
| 245 | if(_bias && _bias->has_valid_id()) // optional bias |
| 246 | { |
| 247 | vtable.declare_variable( |
| 248 | _bias, |
| 249 | GpuKernelArgumentInfo(GpuKernelArgumentInfo::Type::Vector), |
| 250 | comp_group.is_intermediate_tensor(_bias), |
| 251 | "bias"); |
| 252 | } |
| 253 | vtable.declare_variable( |
| 254 | _dst, |
| 255 | GpuKernelArgumentInfo(GpuKernelArgumentInfo::Type::Tensor_4D_t_Buffer), |
| 256 | comp_group.is_intermediate_tensor(_dst), |
| 257 | "dst"); |
| 258 | } |
| 259 | |
| 260 | TagLUT ClTemplateDirectConv2d::get_tag_lut(const GpuKernelVariableTable &vtable, const ComponentGroup &comp_group) const |
| 261 | { |
| 262 | TagLUT lut{}; |
| 263 | // Arguments and global shared variables |
| 264 | lut["src"] = vtable.get_variable(_src); |
| 265 | lut["weight"] = vtable.get_variable(_weight); |
| 266 | |
| 267 | if(_bias && _bias->has_valid_id()) // optional bias |
| 268 | { |
| 269 | lut["bias"] = vtable.get_variable(_bias); |
| 270 | lut["BIA_DATA_TYPE"] = get_cl_type_from_data_type(_bias->data_type()); |
| 271 | } |
| 272 | lut["dst"] = vtable.get_variable(_dst); |
| 273 | |
| 274 | const auto dst_argument = vtable.get_variable(comp_group.get_dst_tensors()[0]); |
| 275 | lut["arg_dst"] = dst_argument.uniq_name; |
| 276 | |
| 277 | // Local build options |
| 278 | lut["meta_kernel_id"] = id(); |
| 279 | lut["ACC_DATA_TYPE"] = _src->data_type(); |
| 280 | lut["SRC_DATA_TYPE"] = _src->data_type(); |
| 281 | lut["WEI_DATA_TYPE"] = _weight->data_type(); |
| 282 | |
| 283 | lut["SRC_TENSOR_TYPE"] = "BUFFER"; |
| 284 | switch(vtable.get_variable(_weight).kernel_argument_info.type) |
| 285 | { |
| 286 | case GpuKernelArgumentInfo::Type::Image_Export_To_ClImage2D: |
| 287 | case GpuKernelArgumentInfo::Type::Image_3D_Export_To_ClImage2D: |
| 288 | case GpuKernelArgumentInfo::Type::Tensor_4D_t_Image: |
| 289 | { |
| 290 | lut["WEI_TENSOR_TYPE"] = "IMAGE"; |
| 291 | break; |
| 292 | } |
| 293 | default: |
| 294 | { |
| 295 | lut["WEI_TENSOR_TYPE"] = "BUFFER"; |
| 296 | break; |
| 297 | } |
| 298 | } |
| 299 | const auto width_idx = 1; |
| 300 | const auto height_idx = 2; |
| 301 | lut["WEI_WIDTH"] = _weight->dimension(width_idx); |
| 302 | lut["WEI_HEIGHT"] = _weight->dimension(height_idx); |
| 303 | |
| 304 | lut["STRIDE_X"] = _attributes.stride().x(); |
| 305 | lut["STRIDE_Y"] = _attributes.stride().y(); |
| 306 | |
| 307 | lut["PAD_LEFT"] = _attributes.pad().left; |
| 308 | lut["PAD_TOP"] = _attributes.pad().top; |
| 309 | |
| 310 | lut["ZERO_VALUE"] = 0; |
| 311 | |
| 312 | return lut; |
| 313 | } |
| 314 | |
| 315 | CLBuildOptions ClTemplateDirectConv2d::get_build_options(const ComponentGroup &comp_group) const |
| 316 | { |
| 317 | const unsigned int channel_idx = get_data_layout_dimension_index(_src->data_layout(), DataLayoutDimension::CHANNEL); |
| 318 | const DataType data_type = _src->data_type(); |
| 319 | |
| 320 | /// NOTE: For now tile sizes (n0, m0, n0) are set by the execution window. This may change in the future |
| 321 | const auto root_window = comp_group.get_root_component()->template_writer()->get_window(); |
| 322 | const unsigned int n0 = root_window.x().step(); |
| 323 | const unsigned int m0 = root_window.y().step(); |
| 324 | const unsigned int k0 = adjust_vec_size(is_data_type_quantized(data_type) ? 16u : 8u, _src->dimension(channel_idx)); |
| 325 | const unsigned int partial_store_n0 = _dst->dimension(0) % n0; |
| 326 | |
| 327 | CLBuildOptions build_opts{}; |
| 328 | if(_settings.fast_relaxed_math()) |
| 329 | { |
| 330 | build_opts.add_option("-cl-fast-relaxed-math"); |
| 331 | } |
| 332 | else |
| 333 | { |
| 334 | // -cl-fast-relaxed-math also sets -cl-finite-math-only and -cl-unsafe-math-optimizations |
| 335 | // to disable -cl-finite-math-only, we only include -cl-unsafe-math-optimizations |
| 336 | build_opts.add_option("-cl-unsafe-math-optimizations"); |
| 337 | } |
| 338 | build_opts.add_option("-DIS_TILED"); |
| 339 | build_opts.add_option("-DN0=" + support::cpp11::to_string(n0)); |
| 340 | build_opts.add_option("-DM0=" + support::cpp11::to_string(m0)); |
| 341 | build_opts.add_option("-DK0=" + support::cpp11::to_string(k0)); |
| 342 | build_opts.add_option("-DPARTIAL_N0=" + support::cpp11::to_string(partial_store_n0)); |
| 343 | |
| 344 | return build_opts; |
| 345 | } |
| 346 | |
| 347 | std::string ClTemplateDirectConv2d::get_config_id() const |
| 348 | { |
| 349 | const DataType data_type = _src->data_type(); |
| 350 | const DataLayout data_layout = _src->data_layout(); |
| 351 | |
| 352 | const unsigned int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); |
| 353 | const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); |
| 354 | |
| 355 | const unsigned int kernel_size = _weight->dimension(width_idx); |
| 356 | |
| 357 | std::string config_id{}; |
| 358 | config_id += lower_string(string_from_data_type(data_type)); |
| 359 | config_id += "_"; |
| 360 | config_id += support::cpp11::to_string(kernel_size); |
| 361 | config_id += "_"; |
| 362 | config_id += support::cpp11::to_string(_attributes.stride().x()); |
| 363 | config_id += "_"; |
| 364 | config_id += support::cpp11::to_string(_attributes.stride().y()); |
| 365 | config_id += "_"; |
| 366 | config_id += support::cpp11::to_string(_dst->dimension(width_idx)); |
| 367 | config_id += "_"; |
| 368 | config_id += support::cpp11::to_string(_dst->dimension(height_idx)); |
| 369 | config_id += "_"; |
| 370 | config_id += lower_string(string_from_data_layout(data_layout)); |
| 371 | return config_id; |
| 372 | } |
| 373 | |
| 374 | std::set<std::string> ClTemplateDirectConv2d::get_headers_list() const |
| 375 | { |
| 376 | return std::set<std::string>{ "helpers.h", "tile_helpers.h" }; |
| 377 | } |
| 378 | |
| 379 | Window ClTemplateDirectConv2d::get_window() const |
| 380 | { |
| 381 | ARM_COMPUTE_ERROR_ON_MSG(_dst->tensor_shape().total_size() == 0U, "Destination tensor is not initialized"); |
| 382 | |
| 383 | const auto output_shape = _dst->tensor_shape(); |
| 384 | |
| 385 | const unsigned int vec_size = std::min(static_cast<unsigned int>(output_shape[0]), 4u); |
| 386 | const unsigned int num_rows = (_dst->tensor_shape()[0] > 16) ? ((_src->data_type() == DataType::F32) ? 2U : 4U) : 1U; |
| 387 | |
| 388 | // Create and configure kernel window |
| 389 | Window win = calculate_max_window(output_shape, Steps(vec_size, num_rows)); |
| 390 | |
| 391 | const size_t dim_y_collapsed = ceil_to_multiple(output_shape[1] * output_shape[2], num_rows); |
| 392 | win.set(Window::DimY, Window::Dimension(0, dim_y_collapsed, num_rows)); |
| 393 | win.set(Window::DimZ, Window::Dimension(0, output_shape.total_size_upper(3), 1)); |
| 394 | |
| 395 | return win; |
| 396 | } |
| 397 | |
| 398 | } // namespace dynamic_fusion |
| 399 | } // namespace experimental |
| 400 | } // namespace arm_compute |