Adnan AlSinan | 7075fe2 | 2021-07-05 13:12:52 +0100 | [diff] [blame] | 1 | /* |
| 2 | * Copyright (c) 2018-2021 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 | */ |
| 24 | #include "activation_float_helpers.h" |
| 25 | #include "helpers.h" |
| 26 | #include "tile_helpers.h" |
| 27 | |
| 28 | #if defined(NUM_TILES_X) && defined(OUTPUT_TILE_W) && defined(OUTPUT_TILE_H) |
| 29 | #if defined(VEC_SIZE) && VEC_SIZE == 2 |
| 30 | /** This OpenCL kernel performs Winograd output transform when the output tile is 2x2/2x1 or 1x2, the filter size 7x7/7x1 or 1x7 and the data layout is NHWC |
| 31 | * |
| 32 | * @note The number of tiles along the X direction must be passed at compile time using -DNUM_TILES_X: e.g. -DNUM_TILES_X=16 |
| 33 | * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=2 |
| 34 | * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=2 |
| 35 | * @note The height of the input tensor must be passed at compile time using -DSRC_HEIGHT: e.g. -DSRC_HEIGHT=32 |
| 36 | * @note The width of the output tensor must be passed at compile time using -DDST_WIDTH: e.g. -DDST_WIDTH=24 |
| 37 | * @note The height of the output tensor must be passed at compile time using -DDST_HEIGHT: e.g. -DDST_HEIGHT=32 |
| 38 | * @note If this kernel is used to perform Winograd output transform 7x1, -DWINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL has to be passed at compile time |
| 39 | * @note If this kernel is used to perform Winograd output transform 1x7, -DWINOGRAD_OUTPUT_TRANSFORM_VERTICAL has to be passed at compile time |
| 40 | * @note The data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types: float/half. |
| 41 | * @note The number of output elements processed along the X direction must be passed at compile time using -DN0 e.g. -DN0=1 |
| 42 | * |
| 43 | * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32/F16 |
| 44 | * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) |
| 45 | * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) |
| 46 | * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) |
| 47 | * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) |
| 48 | * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) |
| 49 | * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) |
| 50 | * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes) |
| 51 | * @param[in] src_step_w src_stride_w * number of elements along W processed per workitem(in bytes) |
| 52 | * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor |
| 53 | * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr |
| 54 | * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) |
| 55 | * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) |
| 56 | * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) |
| 57 | * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) |
| 58 | * @param[in] dst_stride_z Stride of the source tensor in Z dimension (in bytes) |
| 59 | * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) |
| 60 | * @param[in] dst_stride_w Stride of the source tensor in W dimension (in bytes) |
| 61 | * @param[in] dst_step_w dst_stride_w * number of elements along W processed per workitem(in bytes) |
| 62 | * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor |
| 63 | */ |
| 64 | __kernel void winograd_output_transform_2x2_7x7_nhwc( |
| 65 | TENSOR4D(src, BUFFER), |
| 66 | TENSOR4D(dst, BUFFER), |
| 67 | #if defined(HAS_BIAS) |
| 68 | VECTOR_DECLARATION(bias), |
| 69 | #endif // defined(HAS_BIAS) |
| 70 | int dst_size) |
| 71 | { |
| 72 | #define _ISRC_HEIGHT SRC_HEIGHT |
| 73 | #define _IDST_WIDTH DST_WIDTH |
| 74 | #define _IDST_HEIGHT DST_HEIGHT |
| 75 | #define _INUM_TILES_X NUM_TILES_X |
| 76 | |
| 77 | const int cout = GET_SPATIAL_IDX(0, N0, 0); // OFM |
| 78 | const int mout = GET_SPATIAL_IDX(1, 1, 0); // WINOGRAD OUTPUT TILES |
| 79 | const int bout = GET_SPATIAL_IDX(2, 1, 0); // BATCH SIZE IDX |
| 80 | |
| 81 | int x_out = (mout % _INUM_TILES_X) * OUTPUT_TILE_W; |
| 82 | int y_out = (mout / _INUM_TILES_X) * OUTPUT_TILE_H; |
| 83 | |
| 84 | #if defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) |
| 85 | TILE(DATA_TYPE, 8, N0, in); |
| 86 | TILE(DATA_TYPE, 2, N0, out); |
| 87 | TILE(uint, 8, 1, src_indirect_y); |
| 88 | |
| 89 | // Calculate the indirect Y for the source tensor |
| 90 | LOOP_UNROLLING(int, i, 0, 1, 8, |
| 91 | { |
| 92 | src_indirect_y[i].v = mout + i *_ISRC_HEIGHT; |
| 93 | src_indirect_y[i].v += bout * (int)(_ISRC_HEIGHT * 8); |
| 94 | }) |
| 95 | |
| 96 | // Initialize the input tile |
| 97 | LOOP_UNROLLING(int, i, 0, 1, 8, |
| 98 | { |
| 99 | in[i].v = 0; |
| 100 | }) |
| 101 | |
| 102 | // Load the values across the 8 channels to compose the 8x1 tile |
| 103 | T_LOAD_INDIRECT(DATA_TYPE, 8, N0, BUFFER, src, cout, src_stride_y, src_indirect_y, in); |
| 104 | |
| 105 | // Compute out0 and out01 |
| 106 | out[0].v = in[0].v + in[1].v + in[2].v + in[3].v + in[4].v + in[5].v + in[6].v; |
| 107 | out[1].v = -in[1].v + in[2].v - 2.f * in[3].v + 2.0f * in[4].v - 3.0f * in[5].v + 3.0f * in[6].v + in[7].v; |
| 108 | |
| 109 | #if defined(HAS_BIAS) |
| 110 | // Add bias |
| 111 | TILE(DATA_TYPE, 1, N0, b); |
| 112 | |
| 113 | T_LOAD(DATA_TYPE, 1, N0, BUFFER, bias, cout, 0, 1, 0, b); |
| 114 | |
| 115 | T_ADD_BROADCAST_X(DATA_TYPE, 2, N0, out, b, out); |
| 116 | #endif // defined(HAS_BIAS) |
| 117 | |
| 118 | T_ACTIVATION(DATA_TYPE, 2, N0, ACTIVATION_TYPE, A_VAL, B_VAL, out, out); |
| 119 | |
| 120 | TILE(uint, 2, 1, dst_indirect_y); |
| 121 | |
| 122 | #if defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) |
| 123 | LOOP_UNROLLING(int, yk, 0, 1, 2, |
| 124 | { |
| 125 | int y_c = min(y_out + yk, ((int)_IDST_HEIGHT - 1)); |
| 126 | dst_indirect_y[yk].v = x_out + y_c * (int)(_IDST_WIDTH); |
| 127 | }) |
| 128 | #else // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) |
| 129 | LOOP_UNROLLING(int, xk, 0, 1, 2, |
| 130 | { |
| 131 | int x_c = min(x_out + xk, ((int)_IDST_WIDTH - 1)); |
| 132 | dst_indirect_y[xk].v = x_c + y_out * (int)(_IDST_WIDTH); |
| 133 | }) |
| 134 | #endif // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) |
| 135 | |
| 136 | // Store the tile in reverse order so the invalid values are overwritten with the valid ones |
| 137 | T_STORE_INDIRECT_WIDTH_SELECT(DATA_TYPE, 2, N0, 0, BUFFER, dst, cout, dst_stride_y, false, out, dst_indirect_y); |
| 138 | |
| 139 | #else // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) |
| 140 | |
| 141 | TILE(DATA_TYPE, 64, N0, in); |
| 142 | TILE(DATA_TYPE, 4, N0, out); |
| 143 | TILE(DATA_TYPE, 16, N0, tmp); |
| 144 | TILE(uint, 64, 1, src_indirect_y); |
| 145 | |
| 146 | // Calculate the indirect Y for the source tensor |
| 147 | LOOP_UNROLLING(int, i, 0, 1, 64, |
| 148 | { |
| 149 | src_indirect_y[i].v = mout + i *_ISRC_HEIGHT; |
| 150 | src_indirect_y[i].v += bout * (int)(_ISRC_HEIGHT * 64); |
| 151 | }) |
| 152 | |
| 153 | // Initialize the input tile |
| 154 | LOOP_UNROLLING(int, i, 0, 1, 64, |
| 155 | { |
| 156 | in[i].v = 0; |
| 157 | }) |
| 158 | |
| 159 | // Load the values across the 64 channels to compose the 8x8 tile |
| 160 | T_LOAD_INDIRECT(DATA_TYPE, 64, N0, BUFFER, src, cout, src_stride_y, src_indirect_y, in); |
| 161 | |
| 162 | LOOP_UNROLLING(int, i, 0, 1, 8, |
| 163 | { |
| 164 | tmp[i * 2].v = in[0 + i].v + in[8 + i].v + in[16 + i].v + in[24 + i].v + in[32 + i].v + in[40 + i].v + in[48 + i].v; |
| 165 | tmp[i * 2 + 1].v = -in[8 + i].v + in[16 + i].v - 2 * in[24 + i].v + 2 * in[32 + i].v + -3 * in[40 + i].v + 3 * in[48 + i].v + in[56 + i].v; |
| 166 | }) |
| 167 | |
| 168 | // Compute the 2x2 output tile |
| 169 | LOOP_UNROLLING(int, i, 0, 1, 2, |
| 170 | { |
| 171 | out[i * 2].v = tmp[0 + i].v + tmp[2 + i].v + tmp[4 + i].v + tmp[6 + i].v + tmp[8 + i].v + tmp[10 + i].v + tmp[12 + i].v; |
| 172 | out[i * 2 + 1].v = -tmp[2 + i].v + tmp[4 + i].v - 2 * tmp[6 + i].v + 2 * tmp[8 + i].v - 3 * tmp[10 + i].v + 3 * tmp[12 + i].v + tmp[14 + i].v; |
| 173 | }) |
| 174 | |
| 175 | #if defined(HAS_BIAS) |
| 176 | // Add bias |
| 177 | TILE(DATA_TYPE, 1, N0, b); |
| 178 | |
| 179 | T_LOAD(DATA_TYPE, 1, N0, BUFFER, bias, cout, 0, 1, 0, b); |
| 180 | |
| 181 | T_ADD_BROADCAST_X(DATA_TYPE, 4, N0, out, b, out); |
| 182 | #endif // defined(HAS_BIAS) |
| 183 | |
| 184 | T_ACTIVATION(DATA_TYPE, 4, N0, ACTIVATION_TYPE, A_VAL, B_VAL, out, out); |
| 185 | |
| 186 | TILE(uint, 4, 1, dst_indirect_y); |
| 187 | |
| 188 | // Calculate the destination indirect Y |
| 189 | LOOP_UNROLLING(int, yk, 0, 1, 2, |
| 190 | { |
| 191 | LOOP_UNROLLING(int, xk, 0, 1, 2, |
| 192 | { |
| 193 | int x_c = min(x_out + xk, ((int)_IDST_WIDTH - 1)); |
| 194 | int y_c = min(y_out + yk, ((int)_IDST_HEIGHT - 1)); |
| 195 | dst_indirect_y[xk + yk * 2].v = x_c + y_c *_IDST_WIDTH; |
| 196 | dst_indirect_y[xk + yk * 2].v += bout * (int)(_IDST_WIDTH * _IDST_HEIGHT); |
| 197 | }) |
| 198 | }) |
| 199 | |
| 200 | // Store the tile in reverse order so the invalid values are overwritten with the valid ones |
| 201 | T_STORE_INDIRECT_WIDTH_SELECT(DATA_TYPE, 4, N0, 0, BUFFER, dst, cout, dst_stride_y, false, out, dst_indirect_y); |
| 202 | #endif // !defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) |
| 203 | } |
| 204 | #endif // defined(VEC_SIZE) && VEC_SIZE == 2 |
| 205 | |
| 206 | #if defined(VEC_SIZE) && VEC_SIZE == 4 |
| 207 | /** This OpenCL kernel performs Winograd output transform when the output tile is 4x4, 4x1 or 1x4, the filter size 3x3, 3x1 or 1x3 and the data layout is NHWC |
| 208 | * |
| 209 | * @note The number of tiles along the X direction must be passed at compile time using -DNUM_TILES_X: e.g. -DNUM_TILES_X=16 |
| 210 | * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=4 |
| 211 | * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=4 |
| 212 | * @note The height of the input tensor must be passed at compile time using -DSRC_HEIGHT: e.g. -DSRC_HEIGHT=32 |
| 213 | * @note The width of the output tensor must be passed at compile time using -DDST_WIDTH: e.g. -DDST_WIDTH=24 |
| 214 | * @note The height of the output tensor must be passed at compile time using -DDST_HEIGHT: e.g. -DDST_HEIGHT=32 |
| 215 | * @note If this kernel is used to perform Winograd output transform 3x1, -DWINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL has to be passed at compile time |
| 216 | * @note If this kernel is used to perform Winograd output transform 1x3, -DWINOGRAD_OUTPUT_TRANSFORM_VERTICAL has to be passed at compile time |
| 217 | * @note The data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types: float/half. |
| 218 | * @note The number of output elements processed along the X direction must be passed at compile time using -DN0 e.g. -DN0=1 |
| 219 | * |
| 220 | * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32/F16 |
| 221 | * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) |
| 222 | * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) |
| 223 | * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) |
| 224 | * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) |
| 225 | * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) |
| 226 | * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) |
| 227 | * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes) |
| 228 | * @param[in] src_step_w src_stride_w * number of elements along W processed per workitem(in bytes) |
| 229 | * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor |
| 230 | * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr |
| 231 | * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) |
| 232 | * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) |
| 233 | * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) |
| 234 | * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) |
| 235 | * @param[in] dst_stride_z Stride of the source tensor in Z dimension (in bytes) |
| 236 | * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) |
| 237 | * @param[in] dst_stride_w Stride of the source tensor in W dimension (in bytes) |
| 238 | * @param[in] dst_step_w dst_stride_w * number of elements along W processed per workitem(in bytes) |
| 239 | * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor |
| 240 | * @param[in] dst_size Size of the destination tensor, minus the last padding |
| 241 | */ |
| 242 | __kernel void winograd_output_transform_4x4_3x3_nhwc( |
| 243 | TENSOR4D(src, BUFFER), |
| 244 | TENSOR4D(dst, BUFFER), |
| 245 | #if defined(HAS_BIAS) |
| 246 | VECTOR_DECLARATION(bias), |
| 247 | #endif // defined(HAS_BIAS) |
| 248 | int dst_size) |
| 249 | { |
| 250 | const int cout = GET_SPATIAL_IDX(0, N0, 0); // OFM |
| 251 | const int mout = GET_SPATIAL_IDX(1, 1, 0); // WINOGRAD OUTPUT TILES |
| 252 | const int bout = GET_SPATIAL_IDX(2, 1, 0); // BATCH SIZE IDX |
| 253 | |
| 254 | #if defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) |
| 255 | |
| 256 | TILE(DATA_TYPE, 6, N0, in); |
| 257 | TILE(DATA_TYPE, 4, N0, out); |
| 258 | TILE(uint, 6, 1, src_indirect_y); |
| 259 | |
| 260 | LOOP_UNROLLING(int, i, 0, 1, 6, |
| 261 | { |
| 262 | src_indirect_y[i].v = mout + i *SRC_HEIGHT; |
| 263 | src_indirect_y[i].v += bout * (int)(SRC_HEIGHT * 6); |
| 264 | }) |
| 265 | |
| 266 | // Initialize the input tile |
| 267 | LOOP_UNROLLING(int, i, 0, 1, 6, |
| 268 | { |
| 269 | in[i].v = 0; |
| 270 | }) |
| 271 | |
| 272 | // Load the values across the 36 channels to compose the 6x6 or 6x1 tile |
| 273 | T_LOAD_INDIRECT(DATA_TYPE, 6, N0, BUFFER, src, cout, src_stride_y, src_indirect_y, in); |
| 274 | |
| 275 | // Compute out00, out01, out02 and out03 |
| 276 | out[0].v = in[0].v + in[1].v + in[2].v + in[3].v + in[4].v; |
| 277 | out[1].v = in[1].v - in[2].v + 2.0f * in[3].v - 2.0f * in[4].v; |
| 278 | out[2].v = in[1].v + in[2].v + 4.0f * in[3].v + 4.0f * in[4].v; |
| 279 | out[3].v = in[1].v - in[2].v + 8.0f * in[3].v - 8.0f * in[4].v + in[5].v; |
| 280 | |
| 281 | #if defined(HAS_BIAS) |
| 282 | TILE(DATA_TYPE, 1, N0, b); |
| 283 | |
| 284 | T_LOAD(DATA_TYPE, 1, N0, BUFFER, bias, cout, 0, 1, 0, b); |
| 285 | |
| 286 | // c = c + bias[broadcasted] |
| 287 | T_ADD_BROADCAST_X(DATA_TYPE, 4, N0, out, b, out); |
| 288 | #endif // HAS_BIAS |
| 289 | |
| 290 | int x_out = (mout % NUM_TILES_X) * OUTPUT_TILE_W; |
| 291 | int y_out = (mout / NUM_TILES_X) * OUTPUT_TILE_H; |
| 292 | |
| 293 | T_ACTIVATION(DATA_TYPE, 4, N0, ACTIVATION_TYPE, A_VAL, B_VAL, out, out); |
| 294 | |
| 295 | TILE(uint, 4, 1, dst_indirect_y); |
| 296 | |
| 297 | // Calculate the destination indirect Y |
| 298 | #if defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) |
| 299 | LOOP_UNROLLING(int, yk, 0, 1, 4, |
| 300 | { |
| 301 | int y_c = min(y_out + yk, ((int)DST_HEIGHT - 1)); |
| 302 | dst_indirect_y[yk].v = x_out + y_c *DST_WIDTH; |
| 303 | dst_indirect_y[yk].v += bout * (int)(DST_WIDTH * DST_HEIGHT); |
| 304 | }) |
| 305 | #else // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) |
| 306 | LOOP_UNROLLING(int, xk, 0, 1, 4, |
| 307 | { |
| 308 | int x_c = min(x_out + xk, ((int)DST_WIDTH - 1)); |
| 309 | dst_indirect_y[xk].v = x_c + y_out *DST_WIDTH; |
| 310 | dst_indirect_y[xk].v += bout * (int)(DST_WIDTH * DST_HEIGHT); |
| 311 | }) |
| 312 | #endif // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) |
| 313 | |
| 314 | // Store the tile in reverse order so the invalid values are overwritten with the valid ones |
| 315 | T_STORE_INDIRECT_WIDTH_SELECT(DATA_TYPE, 4, N0, 0, BUFFER, dst, cout, dst_stride_y, false, out, dst_indirect_y); |
| 316 | |
| 317 | #else // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) |
| 318 | |
| 319 | // Calculate the indirect Y for the source tensor |
| 320 | TILE(DATA_TYPE, 36, N0, in); |
| 321 | TILE(DATA_TYPE, 4, N0, tmp); |
| 322 | TILE(uint, 36, 1, src_indirect_y); |
| 323 | |
| 324 | LOOP_UNROLLING(int, i, 0, 1, 36, |
| 325 | { |
| 326 | src_indirect_y[i].v = mout + i *SRC_HEIGHT; |
| 327 | src_indirect_y[i].v += bout * (int)(SRC_HEIGHT * 36); |
| 328 | }) |
| 329 | |
| 330 | // Initialize the input tile |
| 331 | LOOP_UNROLLING(int, i, 0, 1, 36, |
| 332 | { |
| 333 | in[i].v = 0; |
| 334 | }) |
| 335 | |
| 336 | // Load the values across the 36 channels to compose the 6x6 or 6x1 tile |
| 337 | T_LOAD_INDIRECT(DATA_TYPE, 36, N0, BUFFER, src, cout, src_stride_y, src_indirect_y, in); |
| 338 | |
| 339 | LOOP_UNROLLING(int, i, 0, 1, 6, |
| 340 | { |
| 341 | tmp[0].v = in[6 + i].v + in[12 + i].v; |
| 342 | tmp[1].v = in[6 + i].v - in[12 + i].v; |
| 343 | tmp[2].v = in[18 + i].v + in[24 + i].v; |
| 344 | tmp[3].v = in[18 + i].v - in[24 + i].v; |
| 345 | tmp[3].v = tmp[3].v + tmp[3].v; |
| 346 | in[i].v = in[i].v + tmp[0].v + tmp[2].v; |
| 347 | in[6 + i].v = tmp[3].v + tmp[1].v; |
| 348 | in[12 + i].v = fma(tmp[2].v, (VEC_DATA_TYPE(DATA_TYPE, N0))4.0f, tmp[0].v); |
| 349 | in[18 + i].v = fma(tmp[3].v, (VEC_DATA_TYPE(DATA_TYPE, N0))4.0f, tmp[1].v) + in[30 + i].v; |
| 350 | }) |
| 351 | |
| 352 | // Compute the output tile |
| 353 | TILE(DATA_TYPE, 16, N0, out); |
| 354 | |
| 355 | LOOP_UNROLLING(int, i, 0, 1, 4, |
| 356 | { |
| 357 | tmp[0].v = in[6 * i + 1].v + in[6 * i + 2].v; |
| 358 | tmp[1].v = in[6 * i + 1].v - in[6 * i + 2].v; |
| 359 | tmp[2].v = in[6 * i + 3].v + in[6 * i + 4].v; |
| 360 | tmp[3].v = in[6 * i + 3].v - in[6 * i + 4].v; |
| 361 | tmp[3].v = tmp[3].v + tmp[3].v; |
| 362 | out[4 * i + 0].v = in[6 * i + 0].v + tmp[0].v + tmp[2].v; |
| 363 | out[4 * i + 1].v = tmp[3].v + tmp[1].v; |
| 364 | out[4 * i + 2].v = fma(tmp[2].v, (VEC_DATA_TYPE(DATA_TYPE, N0))4.0f, tmp[0].v); |
| 365 | out[4 * i + 3].v = fma(tmp[3].v, (VEC_DATA_TYPE(DATA_TYPE, N0))4.0f, tmp[1].v) + in[6 * i + 5].v; |
| 366 | }) |
| 367 | |
| 368 | #if defined(HAS_BIAS) |
| 369 | TILE(DATA_TYPE, 1, N0, b); |
| 370 | |
| 371 | T_LOAD(DATA_TYPE, 1, N0, BUFFER, bias, cout, 0, 1, 0, b); |
| 372 | |
| 373 | // c = c + bias[broadcasted] |
| 374 | T_ADD_BROADCAST_X(DATA_TYPE, 16, N0, out, b, out); |
| 375 | #endif // HAS_BIAS |
| 376 | |
| 377 | int x_out = (mout % NUM_TILES_X) * OUTPUT_TILE_W; |
| 378 | int y_out = (mout / NUM_TILES_X) * OUTPUT_TILE_H; |
| 379 | |
| 380 | T_ACTIVATION(DATA_TYPE, 16, N0, ACTIVATION_TYPE, A_VAL, B_VAL, out, out); |
| 381 | |
| 382 | TILE(uint, 16, 1, dst_indirect_y); |
| 383 | |
| 384 | // Calculate the destination indirect Y |
| 385 | LOOP_UNROLLING(int, yk, 0, 1, 4, |
| 386 | { |
| 387 | LOOP_UNROLLING(int, xk, 0, 1, 4, |
| 388 | { |
| 389 | int x_c = min(x_out + xk, ((int)DST_WIDTH - 1)); |
| 390 | int y_c = min(y_out + yk, ((int)DST_HEIGHT - 1)); |
| 391 | dst_indirect_y[xk + yk * 4].v = x_c + y_c *DST_WIDTH; |
| 392 | dst_indirect_y[xk + yk * 4].v += bout * (int)(DST_WIDTH * DST_HEIGHT); |
| 393 | }) |
| 394 | }) |
| 395 | |
| 396 | // Store the tile in reverse order so the invalid values are overwritten with the valid ones |
| 397 | T_STORE_INDIRECT_WIDTH_SELECT(DATA_TYPE, 16, N0, 0, BUFFER, dst, cout, dst_stride_y, false, out, dst_indirect_y); |
| 398 | #endif // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) |
| 399 | } |
| 400 | |
| 401 | /** This OpenCL kernel performs Winograd output transform when the output tile is 4x4/4x1 or 1x4, the filter size 5x5/5x1 or 1x5 and the data layout is NHWC |
| 402 | * |
| 403 | * @note The number of tiles along the X direction must be passed at compile time using -DNUM_TILES_X: e.g. -DNUM_TILES_X=16 |
| 404 | * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=4 |
| 405 | * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=4 |
| 406 | * @note The height of the input tensor must be passed at compile time using -DSRC_HEIGHT: e.g. -DSRC_HEIGHT=32 |
| 407 | * @note The width of the output tensor must be passed at compile time using -DDST_WIDTH: e.g. -DDST_WIDTH=24 |
| 408 | * @note The height of the output tensor must be passed at compile time using -DDST_HEIGHT: e.g. -DDST_HEIGHT=32 |
| 409 | * @note If this kernel is used to perform Winograd output transform 5x1, -DWINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL has to be passed at compile time |
| 410 | * @note If this kernel is used to perform Winograd output transform 1x5, -DWINOGRAD_OUTPUT_TRANSFORM_VERTICAL has to be passed at compile time |
| 411 | * @note The data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types: float/half. |
| 412 | * @note The number of output elements processed along the X direction must be passed at compile time using -DN0 e.g. -DN0=1 |
| 413 | * |
| 414 | * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32/F16 |
| 415 | * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) |
| 416 | * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) |
| 417 | * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) |
| 418 | * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) |
| 419 | * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) |
| 420 | * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) |
| 421 | * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes) |
| 422 | * @param[in] src_step_w src_stride_w * number of elements along W processed per workitem(in bytes) |
| 423 | * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor |
| 424 | * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr |
| 425 | * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) |
| 426 | * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) |
| 427 | * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) |
| 428 | * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) |
| 429 | * @param[in] dst_stride_z Stride of the source tensor in Z dimension (in bytes) |
| 430 | * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) |
| 431 | * @param[in] dst_stride_w Stride of the source tensor in W dimension (in bytes) |
| 432 | * @param[in] dst_step_w dst_stride_w * number of elements along W processed per workitem(in bytes) |
| 433 | * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor |
| 434 | */ |
| 435 | __kernel void winograd_output_transform_4x4_5x5_nhwc( |
| 436 | TENSOR4D(src, BUFFER), |
| 437 | TENSOR4D(dst, BUFFER), |
| 438 | #if defined(HAS_BIAS) |
| 439 | VECTOR_DECLARATION(bias), |
| 440 | #endif // defined(HAS_BIAS) |
| 441 | int dst_size) |
| 442 | { |
| 443 | const int cout = GET_SPATIAL_IDX(0, N0, 0); // OFM |
| 444 | const int mout = GET_SPATIAL_IDX(1, 1, 0); // WINOGRAD OUTPUT TILES |
| 445 | const int bout = GET_SPATIAL_IDX(2, 1, 0); // BATCH SIZE IDX |
| 446 | |
| 447 | #if defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) |
| 448 | TILE(DATA_TYPE, 8, N0, in); |
| 449 | TILE(DATA_TYPE, 4, N0, out); |
| 450 | TILE(DATA_TYPE, 4, N0, tmp); |
| 451 | TILE(uint, 8, 1, src_indirect_y); |
| 452 | |
| 453 | LOOP_UNROLLING(int, i, 0, 1, 8, |
| 454 | { |
| 455 | src_indirect_y[i].v = mout + i *SRC_HEIGHT; |
| 456 | src_indirect_y[i].v += bout * (int)(SRC_HEIGHT * 8); |
| 457 | }) |
| 458 | |
| 459 | // Initialize the input tile |
| 460 | LOOP_UNROLLING(int, i, 0, 1, 8, |
| 461 | { |
| 462 | in[i].v = 0; |
| 463 | }) |
| 464 | |
| 465 | // "in" contains 1x8 or 8x1 tile here |
| 466 | T_LOAD_INDIRECT(DATA_TYPE, 8, N0, BUFFER, src, cout, src_stride_y, src_indirect_y, in); |
| 467 | |
| 468 | // A^T * in, and in this degenerate case out consists of 1 column/row |
| 469 | tmp[0].v = in[1].v - in[2].v; |
| 470 | tmp[1].v = 2.0f * (in[3].v - in[4].v); |
| 471 | tmp[2].v = 2.0f * (in[5].v + in[6].v); |
| 472 | tmp[3].v = in[3].v + in[4].v; |
| 473 | out[0].v = in[0].v + in[1].v + in[2].v + tmp[3].v + 4.0f * tmp[2].v; |
| 474 | out[1].v = tmp[0].v + tmp[1].v + 4.0f * (in[5].v - in[6].v); |
| 475 | out[2].v = in[1].v + in[2].v + 4.0f * tmp[3].v + tmp[2].v; |
| 476 | out[3].v = tmp[0].v + 4.0f * tmp[1].v + in[5].v - in[6].v + in[7].v; |
| 477 | |
| 478 | #if defined(HAS_BIAS) |
| 479 | TILE(DATA_TYPE, 1, N0, b); |
| 480 | |
| 481 | T_LOAD(DATA_TYPE, 1, N0, BUFFER, bias, cout, 0, 1, 0, b); |
| 482 | |
| 483 | // c = c + bias[broadcasted] |
| 484 | T_ADD_BROADCAST_X(DATA_TYPE, 4, N0, out, b, out); |
| 485 | #endif // HAS_BIAS |
| 486 | |
| 487 | int x_out = (mout % NUM_TILES_X) * OUTPUT_TILE_W; |
| 488 | int y_out = (mout / NUM_TILES_X) * OUTPUT_TILE_H; |
| 489 | |
| 490 | T_ACTIVATION(DATA_TYPE, 4, N0, ACTIVATION_TYPE, A_VAL, B_VAL, out, out); |
| 491 | |
| 492 | TILE(uint, 4, 1, dst_indirect_y); |
| 493 | |
| 494 | // Calculate the destination indirect Y |
| 495 | #if defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) |
| 496 | LOOP_UNROLLING(int, yk, 0, 1, 4, |
| 497 | { |
| 498 | int y_c = min(y_out + yk, ((int)DST_HEIGHT - 1)); |
| 499 | dst_indirect_y[yk].v = x_out + y_c *DST_WIDTH; |
| 500 | dst_indirect_y[yk].v += bout * (int)(DST_WIDTH * DST_HEIGHT); |
| 501 | }) |
| 502 | #else // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) |
| 503 | LOOP_UNROLLING(int, xk, 0, 1, 4, |
| 504 | { |
| 505 | int x_c = min(x_out + xk, ((int)DST_WIDTH - 1)); |
| 506 | dst_indirect_y[xk].v = x_c + y_out *DST_WIDTH; |
| 507 | dst_indirect_y[xk].v += bout * (int)(DST_WIDTH * DST_HEIGHT); |
| 508 | }) |
| 509 | #endif // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) |
| 510 | |
| 511 | // Store the tile in reverse order so the invalid values are overwritten with the valid ones |
| 512 | T_STORE_INDIRECT_WIDTH_SELECT(DATA_TYPE, 4, N0, 0, BUFFER, dst, cout, dst_stride_y, false, out, dst_indirect_y); |
| 513 | |
| 514 | #else // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) |
| 515 | // Calculate the indirect Y for the source tensor |
| 516 | TILE(DATA_TYPE, 64, N0, in); |
| 517 | TILE(DATA_TYPE, 6, N0, tmp); |
| 518 | TILE(uint, 64, 1, src_indirect_y); |
| 519 | |
| 520 | LOOP_UNROLLING(int, i, 0, 1, 64, |
| 521 | { |
| 522 | src_indirect_y[i].v = mout + i *SRC_HEIGHT; |
| 523 | src_indirect_y[i].v += bout * (int)(SRC_HEIGHT * 64); |
| 524 | }) |
| 525 | |
| 526 | // Initialize the input tile |
| 527 | LOOP_UNROLLING(int, i, 0, 1, 64, |
| 528 | { |
| 529 | in[i].v = 0; |
| 530 | }) |
| 531 | |
| 532 | // "in" here is 8x8 tile |
| 533 | T_LOAD_INDIRECT(DATA_TYPE, 64, N0, BUFFER, src, cout, src_stride_y, src_indirect_y, in); |
| 534 | |
| 535 | // A^T * in |
| 536 | LOOP_UNROLLING(int, i, 0, 1, 8, |
| 537 | { |
| 538 | tmp[0].v = in[8 + i].v + in[16 + i].v; |
| 539 | tmp[1].v = in[8 + i].v - in[16 + i].v; |
| 540 | tmp[2].v = in[24 + i].v + in[32 + i].v; |
| 541 | tmp[3].v = in[24 + i].v - in[32 + i].v; |
| 542 | tmp[3].v = tmp[3].v + tmp[3].v; |
| 543 | tmp[4].v = in[40 + i].v + in[48 + i].v; |
| 544 | tmp[4].v = tmp[4].v + tmp[4].v; |
| 545 | tmp[5].v = in[40 + i].v - in[48 + i].v; |
| 546 | |
| 547 | // 4x8 matrix as a result |
| 548 | in[i].v = in[i].v + tmp[0].v + fma((VEC_DATA_TYPE(DATA_TYPE, N0))4.0f, tmp[4].v, tmp[2].v); |
| 549 | in[8 + i].v = tmp[1].v + fma((VEC_DATA_TYPE(DATA_TYPE, N0))4.0f, tmp[5].v, tmp[3].v); |
| 550 | in[16 + i].v = tmp[0].v + fma((VEC_DATA_TYPE(DATA_TYPE, N0))4.0f, tmp[2].v, tmp[4].v); |
| 551 | in[24 + i].v = tmp[1].v + fma((VEC_DATA_TYPE(DATA_TYPE, N0))4.0f, tmp[3].v, tmp[5].v) + in[56 + i].v; |
| 552 | }) |
| 553 | |
| 554 | // Compute the output tile |
| 555 | TILE(DATA_TYPE, 16, N0, out); |
| 556 | |
| 557 | // in * A, with in = A^T * in as above |
| 558 | LOOP_UNROLLING(int, i, 0, 1, 4, |
| 559 | { |
| 560 | tmp[0].v = in[8 * i + 1].v + in[8 * i + 2].v; |
| 561 | tmp[1].v = in[8 * i + 1].v - in[8 * i + 2].v; |
| 562 | tmp[2].v = in[8 * i + 3].v + in[8 * i + 4].v; |
| 563 | tmp[3].v = in[8 * i + 3].v - in[8 * i + 4].v; |
| 564 | tmp[3].v = tmp[3].v + tmp[3].v; |
| 565 | tmp[4].v = in[8 * i + 5].v + in[8 * i + 6].v; |
| 566 | tmp[4].v = tmp[4].v + tmp[4].v; |
| 567 | tmp[5].v = in[8 * i + 5].v - in[8 * i + 6].v; |
| 568 | |
| 569 | // 4x4 tile |
| 570 | out[4 * i].v = in[8 * i].v + tmp[0].v + fma((VEC_DATA_TYPE(DATA_TYPE, N0))4.0f, tmp[4].v, tmp[2].v); |
| 571 | out[4 * i + 1].v = tmp[1].v + fma((VEC_DATA_TYPE(DATA_TYPE, N0))4.0f, tmp[5].v, tmp[3].v); |
| 572 | out[4 * i + 2].v = fma((VEC_DATA_TYPE(DATA_TYPE, N0))4.0f, tmp[2].v, tmp[0].v) + tmp[4].v; |
| 573 | out[4 * i + 3].v = fma((VEC_DATA_TYPE(DATA_TYPE, N0))4.0f, tmp[3].v, tmp[1].v) + tmp[5].v + in[8 * i + 7].v; |
| 574 | }) |
| 575 | |
| 576 | #if defined(HAS_BIAS) |
| 577 | TILE(DATA_TYPE, 1, N0, b); |
| 578 | |
| 579 | T_LOAD(DATA_TYPE, 1, N0, BUFFER, bias, cout, 0, 1, 0, b); |
| 580 | |
| 581 | // c = c + bias[broadcasted] |
| 582 | T_ADD_BROADCAST_X(DATA_TYPE, 16, N0, out, b, out); |
| 583 | #endif // HAS_BIAS |
| 584 | |
| 585 | int x_out = (mout % NUM_TILES_X) * OUTPUT_TILE_W; |
| 586 | int y_out = (mout / NUM_TILES_X) * OUTPUT_TILE_H; |
| 587 | |
| 588 | T_ACTIVATION(DATA_TYPE, 16, N0, ACTIVATION_TYPE, A_VAL, B_VAL, out, out); |
| 589 | |
| 590 | TILE(uint, 16, 1, dst_indirect_y); |
| 591 | |
| 592 | // Calculate the destination indirect Y |
| 593 | LOOP_UNROLLING(int, yk, 0, 1, 4, |
| 594 | { |
| 595 | LOOP_UNROLLING(int, xk, 0, 1, 4, |
| 596 | { |
| 597 | int x_c = min(x_out + xk, ((int)DST_WIDTH - 1)); |
| 598 | int y_c = min(y_out + yk, ((int)DST_HEIGHT - 1)); |
| 599 | dst_indirect_y[xk + yk * 4].v = x_c + y_c *DST_WIDTH; |
| 600 | dst_indirect_y[xk + yk * 4].v += bout * (int)(DST_WIDTH * DST_HEIGHT); |
| 601 | }) |
| 602 | }) |
| 603 | |
| 604 | // Store the tile in reverse order so the invalid values are overwritten with the valid ones |
| 605 | T_STORE_INDIRECT_WIDTH_SELECT(DATA_TYPE, 16, N0, 0, BUFFER, dst, cout, dst_stride_y, false, out, dst_indirect_y); |
| 606 | #endif // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) |
| 607 | } |
| 608 | #endif // defined(VEC_SIZE) && VEC_SIZE == 4 |
| 609 | |
| 610 | #if defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) |
| 611 | #if defined(VEC_SIZE) && VEC_SIZE == 2 |
| 612 | /** This OpenCL kernel performs Winograd output transform when the output tile is 2x1, the filter size 7x1 and the data layout is NHWC |
| 613 | * |
| 614 | * @note The number of tiles along the X direction must be passed at compile time using -DNUM_TILES_X: e.g. -DNUM_TILES_X=16 |
| 615 | * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=2 |
| 616 | * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=1 |
| 617 | * @note The width of the output tensor must be passed at compile time using -DDST_WIDTH: e.g. -DDST_WIDTH=24 |
| 618 | * @note The height of the output tensor must be passed at compile time using -DDST_HEIGHT: e.g. -DDST_HEIGHT=32 |
| 619 | * @note -DWINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL has to be passed at compile time |
| 620 | * @note The data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types: float/half. |
| 621 | * |
| 622 | * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32/F16 |
| 623 | * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) |
| 624 | * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) |
| 625 | * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) |
| 626 | * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) |
| 627 | * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) |
| 628 | * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) |
| 629 | * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes) |
| 630 | * @param[in] src_step_w src_stride_w * number of elements along W processed per workitem(in bytes) |
| 631 | * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor |
| 632 | * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr |
| 633 | * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) |
| 634 | * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) |
| 635 | * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) |
| 636 | * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) |
| 637 | * @param[in] dst_stride_z Stride of the source tensor in Z dimension (in bytes) |
| 638 | * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) |
| 639 | * @param[in] dst_stride_w Stride of the source tensor in W dimension (in bytes) |
| 640 | * @param[in] dst_step_w dst_stride_w * number of elements along W processed per workitem(in bytes) |
| 641 | * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor |
| 642 | */ |
| 643 | __kernel void winograd_output_transform_2x1_7x1_nhwc( |
| 644 | TENSOR4D_DECLARATION(src), |
| 645 | TENSOR4D_DECLARATION(dst), |
| 646 | #if defined(HAS_BIAS) |
| 647 | VECTOR_DECLARATION(bias), |
| 648 | #endif // defined(HAS_BIAS) |
| 649 | int dst_size) |
| 650 | { |
| 651 | winograd_output_transform_2x2_7x7_nhwc(src_ptr, |
| 652 | src_stride_x, |
| 653 | src_step_x, |
| 654 | src_stride_y, |
| 655 | src_step_y, |
| 656 | src_stride_z, |
| 657 | src_step_z, |
| 658 | src_stride_w, |
| 659 | src_step_w, |
| 660 | src_offset_first_element_in_bytes, |
| 661 | dst_ptr, |
| 662 | dst_stride_x, |
| 663 | dst_step_x, |
| 664 | dst_stride_y, |
| 665 | dst_step_y, |
| 666 | dst_stride_z, |
| 667 | dst_step_z, |
| 668 | dst_stride_w, |
| 669 | dst_step_w, |
| 670 | dst_offset_first_element_in_bytes, |
| 671 | #if defined(HAS_BIAS) |
| 672 | bias_ptr, |
| 673 | bias_stride_x, |
| 674 | bias_step_x, |
| 675 | bias_offset_first_element_in_bytes, |
| 676 | #endif // defined(HAS_BIAS) |
| 677 | dst_size); |
| 678 | } |
| 679 | #endif // defined(VEC_SIZE) && VEC_SIZE == 2 |
| 680 | |
| 681 | #if defined(VEC_SIZE) && VEC_SIZE == 4 |
| 682 | |
| 683 | /** This OpenCL kernel performs Winograd output transform when the output tile is 4x1, the filter size 3x1 and the data layout is NHWC |
| 684 | * |
| 685 | * @note The number of tiles along the X direction must be passed at compile time using -DNUM_TILES_X: e.g. -DNUM_TILES_X=16 |
| 686 | * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=4 |
| 687 | * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=1 |
| 688 | * @note The width of the output tensor must be passed at compile time using -DDST_WIDTH: e.g. -DDST_WIDTH=24 |
| 689 | * @note The height of the output tensor must be passed at compile time using -DDST_HEIGHT: e.g. -DDST_HEIGHT=32 |
| 690 | * @note -DWINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL has to be passed at compile time |
| 691 | * @note The data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types: float/half. |
| 692 | * |
| 693 | * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32/F16 |
| 694 | * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) |
| 695 | * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) |
| 696 | * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) |
| 697 | * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) |
| 698 | * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) |
| 699 | * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) |
| 700 | * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes) |
| 701 | * @param[in] src_step_w src_stride_w * number of elements along W processed per workitem(in bytes) |
| 702 | * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor |
| 703 | * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr |
| 704 | * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) |
| 705 | * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) |
| 706 | * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) |
| 707 | * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) |
| 708 | * @param[in] dst_stride_z Stride of the source tensor in Z dimension (in bytes) |
| 709 | * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) |
| 710 | * @param[in] dst_stride_w Stride of the source tensor in W dimension (in bytes) |
| 711 | * @param[in] dst_step_w dst_stride_w * number of elements along W processed per workitem(in bytes) |
| 712 | * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor |
| 713 | */ |
| 714 | __kernel void winograd_output_transform_4x1_3x1_nhwc( |
| 715 | TENSOR4D_DECLARATION(src), |
| 716 | TENSOR4D_DECLARATION(dst), |
| 717 | #if defined(HAS_BIAS) |
| 718 | VECTOR_DECLARATION(bias), |
| 719 | #endif // defined(HAS_BIAS) |
| 720 | int dst_size) |
| 721 | { |
| 722 | winograd_output_transform_4x4_3x3_nhwc(src_ptr, |
| 723 | src_stride_x, |
| 724 | src_step_x, |
| 725 | src_stride_y, |
| 726 | src_step_y, |
| 727 | src_stride_z, |
| 728 | src_step_z, |
| 729 | src_stride_w, |
| 730 | src_step_w, |
| 731 | src_offset_first_element_in_bytes, |
| 732 | dst_ptr, |
| 733 | dst_stride_x, |
| 734 | dst_step_x, |
| 735 | dst_stride_y, |
| 736 | dst_step_y, |
| 737 | dst_stride_z, |
| 738 | dst_step_z, |
| 739 | dst_stride_w, |
| 740 | dst_step_w, |
| 741 | dst_offset_first_element_in_bytes, |
| 742 | #if defined(HAS_BIAS) |
| 743 | bias_ptr, |
| 744 | bias_stride_x, |
| 745 | bias_step_x, |
| 746 | bias_offset_first_element_in_bytes, |
| 747 | #endif // defined(HAS_BIAS) |
| 748 | dst_size); |
| 749 | } |
| 750 | |
| 751 | /** This OpenCL kernel performs Winograd output transform when the output tile is 4x1, the filter size 5x1 and the data layout is NHWC |
| 752 | * |
| 753 | * @note The number of tiles along the X direction must be passed at compile time using -DNUM_TILES_X: e.g. -DNUM_TILES_X=16 |
| 754 | * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=4 |
| 755 | * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=1 |
| 756 | * @note The width of the output tensor must be passed at compile time using -DDST_WIDTH: e.g. -DDST_WIDTH=24 |
| 757 | * @note The height of the output tensor must be passed at compile time using -DDST_HEIGHT: e.g. -DDST_HEIGHT=32 |
| 758 | * @note -DWINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL has to be passed at compile time |
| 759 | * @note The data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types: float/half. |
| 760 | * |
| 761 | * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32/F16 |
| 762 | * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) |
| 763 | * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) |
| 764 | * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) |
| 765 | * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) |
| 766 | * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) |
| 767 | * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) |
| 768 | * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes) |
| 769 | * @param[in] src_step_w src_stride_w * number of elements along W processed per workitem(in bytes) |
| 770 | * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor |
| 771 | * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr |
| 772 | * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) |
| 773 | * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) |
| 774 | * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) |
| 775 | * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) |
| 776 | * @param[in] dst_stride_z Stride of the source tensor in Z dimension (in bytes) |
| 777 | * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) |
| 778 | * @param[in] dst_stride_w Stride of the source tensor in W dimension (in bytes) |
| 779 | * @param[in] dst_step_w dst_stride_w * number of elements along W processed per workitem(in bytes) |
| 780 | * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor |
| 781 | */ |
| 782 | __kernel void winograd_output_transform_4x1_5x1_nhwc( |
| 783 | TENSOR4D_DECLARATION(src), |
| 784 | TENSOR4D_DECLARATION(dst), |
| 785 | #if defined(HAS_BIAS) |
| 786 | VECTOR_DECLARATION(bias), |
| 787 | #endif // defined(HAS_BIAS) |
| 788 | int dst_size) |
| 789 | { |
| 790 | winograd_output_transform_4x4_5x5_nhwc(src_ptr, |
| 791 | src_stride_x, |
| 792 | src_step_x, |
| 793 | src_stride_y, |
| 794 | src_step_y, |
| 795 | src_stride_z, |
| 796 | src_step_z, |
| 797 | src_stride_w, |
| 798 | src_step_w, |
| 799 | src_offset_first_element_in_bytes, |
| 800 | dst_ptr, |
| 801 | dst_stride_x, |
| 802 | dst_step_x, |
| 803 | dst_stride_y, |
| 804 | dst_step_y, |
| 805 | dst_stride_z, |
| 806 | dst_step_z, |
| 807 | dst_stride_w, |
| 808 | dst_step_w, |
| 809 | dst_offset_first_element_in_bytes, |
| 810 | #if defined(HAS_BIAS) |
| 811 | bias_ptr, |
| 812 | bias_stride_x, |
| 813 | bias_step_x, |
| 814 | bias_offset_first_element_in_bytes, |
| 815 | #endif // defined(HAS_BIAS) |
| 816 | dst_size); |
| 817 | } |
| 818 | #endif // defined(VEC_SIZE) && VEC_SIZE == 4 |
| 819 | #endif // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) |
| 820 | |
| 821 | #if defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) |
| 822 | #if defined(VEC_SIZE) && VEC_SIZE == 2 |
| 823 | /** This OpenCL kernel performs Winograd output transform when the output tile is 1x2, the filter size 1x7 and the data layout is NHWC |
| 824 | * |
| 825 | * @note The number of tiles along the X direction must be passed at compile time using -DNUM_TILES_X: e.g. -DNUM_TILES_X=16 |
| 826 | * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=1 |
| 827 | * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=2 |
| 828 | * @note The width of the output tensor must be passed at compile time using -DDST_WIDTH: e.g. -DDST_WIDTH=24 |
| 829 | * @note The height of the output tensor must be passed at compile time using -DDST_HEIGHT: e.g. -DDST_HEIGHT=32 |
| 830 | * @note -DWINOGRAD_OUTPUT_TRANSFORM_VERTICAL has to be passed at compile time |
| 831 | * @note The data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types: float/half. |
| 832 | * |
| 833 | * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32/F16 |
| 834 | * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) |
| 835 | * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) |
| 836 | * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) |
| 837 | * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) |
| 838 | * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) |
| 839 | * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) |
| 840 | * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes) |
| 841 | * @param[in] src_step_w src_stride_w * number of elements along W processed per workitem(in bytes) |
| 842 | * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor |
| 843 | * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr |
| 844 | * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) |
| 845 | * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) |
| 846 | * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) |
| 847 | * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) |
| 848 | * @param[in] dst_stride_z Stride of the source tensor in Z dimension (in bytes) |
| 849 | * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) |
| 850 | * @param[in] dst_stride_w Stride of the source tensor in W dimension (in bytes) |
| 851 | * @param[in] dst_step_w dst_stride_w * number of elements along W processed per workitem(in bytes) |
| 852 | * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor |
| 853 | */ |
| 854 | __kernel void winograd_output_transform_1x2_1x7_nhwc( |
| 855 | TENSOR4D_DECLARATION(src), |
| 856 | TENSOR4D_DECLARATION(dst), |
| 857 | #if defined(HAS_BIAS) |
| 858 | VECTOR_DECLARATION(bias), |
| 859 | #endif // defined(HAS_BIAS) |
| 860 | int dst_size) |
| 861 | { |
| 862 | winograd_output_transform_2x2_7x7_nhwc(src_ptr, |
| 863 | src_stride_x, |
| 864 | src_step_x, |
| 865 | src_stride_y, |
| 866 | src_step_y, |
| 867 | src_stride_z, |
| 868 | src_step_z, |
| 869 | src_stride_w, |
| 870 | src_step_w, |
| 871 | src_offset_first_element_in_bytes, |
| 872 | dst_ptr, |
| 873 | dst_stride_x, |
| 874 | dst_step_x, |
| 875 | dst_stride_y, |
| 876 | dst_step_y, |
| 877 | dst_stride_z, |
| 878 | dst_step_z, |
| 879 | dst_stride_w, |
| 880 | dst_step_w, |
| 881 | dst_offset_first_element_in_bytes, |
| 882 | #if defined(HAS_BIAS) |
| 883 | bias_ptr, |
| 884 | bias_stride_x, |
| 885 | bias_step_x, |
| 886 | bias_offset_first_element_in_bytes, |
| 887 | #endif // defined(HAS_BIAS) |
| 888 | dst_size); |
| 889 | } |
| 890 | #endif // defined(VEC_SIZE) && VEC_SIZE == 2 |
| 891 | |
| 892 | #if defined(VEC_SIZE) && VEC_SIZE == 4 |
| 893 | /** This OpenCL kernel performs Winograd output transform when the output tile is 1x4, the filter size 1x3 and the data layout is NHWC |
| 894 | * |
| 895 | * @note The number of tiles along the X direction must be passed at compile time using -DNUM_TILES_X: e.g. -DNUM_TILES_X=16 |
| 896 | * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=1 |
| 897 | * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=4 |
| 898 | * @note The width of the output tensor must be passed at compile time using -DDST_WIDTH: e.g. -DDST_WIDTH=24 |
| 899 | * @note The height of the output tensor must be passed at compile time using -DDST_HEIGHT: e.g. -DDST_HEIGHT=32 |
| 900 | * @note -DWINOGRAD_OUTPUT_TRANSFORM_VERTICAL has to be passed at compile time |
| 901 | * @note The data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types: float/half. |
| 902 | * |
| 903 | * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32/F16 |
| 904 | * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) |
| 905 | * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) |
| 906 | * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) |
| 907 | * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) |
| 908 | * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) |
| 909 | * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) |
| 910 | * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes) |
| 911 | * @param[in] src_step_w src_stride_w * number of elements along W processed per workitem(in bytes) |
| 912 | * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor |
| 913 | * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr |
| 914 | * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) |
| 915 | * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) |
| 916 | * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) |
| 917 | * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) |
| 918 | * @param[in] dst_stride_z Stride of the source tensor in Z dimension (in bytes) |
| 919 | * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) |
| 920 | * @param[in] dst_stride_w Stride of the source tensor in W dimension (in bytes) |
| 921 | * @param[in] dst_step_w dst_stride_w * number of elements along W processed per workitem(in bytes) |
| 922 | * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor |
| 923 | */ |
| 924 | __kernel void winograd_output_transform_1x4_1x3_nhwc( |
| 925 | TENSOR4D_DECLARATION(src), |
| 926 | TENSOR4D_DECLARATION(dst), |
| 927 | #if defined(HAS_BIAS) |
| 928 | VECTOR_DECLARATION(bias), |
| 929 | #endif // defined(HAS_BIAS) |
| 930 | int dst_size) |
| 931 | { |
| 932 | winograd_output_transform_4x4_3x3_nhwc(src_ptr, |
| 933 | src_stride_x, |
| 934 | src_step_x, |
| 935 | src_stride_y, |
| 936 | src_step_y, |
| 937 | src_stride_z, |
| 938 | src_step_z, |
| 939 | src_stride_w, |
| 940 | src_step_w, |
| 941 | src_offset_first_element_in_bytes, |
| 942 | dst_ptr, |
| 943 | dst_stride_x, |
| 944 | dst_step_x, |
| 945 | dst_stride_y, |
| 946 | dst_step_y, |
| 947 | dst_stride_z, |
| 948 | dst_step_z, |
| 949 | dst_stride_w, |
| 950 | dst_step_w, |
| 951 | dst_offset_first_element_in_bytes, |
| 952 | #if defined(HAS_BIAS) |
| 953 | bias_ptr, |
| 954 | bias_stride_x, |
| 955 | bias_step_x, |
| 956 | bias_offset_first_element_in_bytes, |
| 957 | #endif // defined(HAS_BIAS) |
| 958 | dst_size); |
| 959 | } |
| 960 | |
| 961 | /** This OpenCL kernel performs Winograd output transform when the output tile is 1x4, the filter size 1x5 and the data layout is NHWC |
| 962 | * |
| 963 | * @note The number of tiles along the X direction must be passed at compile time using -DNUM_TILES_X: e.g. -DNUM_TILES_X=16 |
| 964 | * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=1 |
| 965 | * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=4 |
| 966 | * @note The width of the output tensor must be passed at compile time using -DDST_WIDTH: e.g. -DDST_WIDTH=24 |
| 967 | * @note The height of the output tensor must be passed at compile time using -DDST_HEIGHT: e.g. -DDST_HEIGHT=32 |
| 968 | * @note -DWINOGRAD_OUTPUT_TRANSFORM_VERTICAL has to be passed at compile time |
| 969 | * @note The data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types: float/half. |
| 970 | * |
| 971 | * @param[in] src_ptr Pointer to the source tensor. Supported data types: F32/F16 |
| 972 | * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) |
| 973 | * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) |
| 974 | * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) |
| 975 | * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) |
| 976 | * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) |
| 977 | * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) |
| 978 | * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes) |
| 979 | * @param[in] src_step_w src_stride_w * number of elements along W processed per workitem(in bytes) |
| 980 | * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor |
| 981 | * @param[out] dst_ptr Pointer to the destination tensor. Supported data types: same as @p src_ptr |
| 982 | * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) |
| 983 | * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) |
| 984 | * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) |
| 985 | * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) |
| 986 | * @param[in] dst_stride_z Stride of the source tensor in Z dimension (in bytes) |
| 987 | * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) |
| 988 | * @param[in] dst_stride_w Stride of the source tensor in W dimension (in bytes) |
| 989 | * @param[in] dst_step_w dst_stride_w * number of elements along W processed per workitem(in bytes) |
| 990 | * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor |
| 991 | */ |
| 992 | __kernel void winograd_output_transform_1x4_1x5_nhwc( |
| 993 | TENSOR4D_DECLARATION(src), |
| 994 | TENSOR4D_DECLARATION(dst), |
| 995 | #if defined(HAS_BIAS) |
| 996 | VECTOR_DECLARATION(bias), |
| 997 | #endif // defined(HAS_BIAS) |
| 998 | int dst_size) |
| 999 | { |
| 1000 | winograd_output_transform_4x4_5x5_nhwc(src_ptr, |
| 1001 | src_stride_x, |
| 1002 | src_step_x, |
| 1003 | src_stride_y, |
| 1004 | src_step_y, |
| 1005 | src_stride_z, |
| 1006 | src_step_z, |
| 1007 | src_stride_w, |
| 1008 | src_step_w, |
| 1009 | src_offset_first_element_in_bytes, |
| 1010 | dst_ptr, |
| 1011 | dst_stride_x, |
| 1012 | dst_step_x, |
| 1013 | dst_stride_y, |
| 1014 | dst_step_y, |
| 1015 | dst_stride_z, |
| 1016 | dst_step_z, |
| 1017 | dst_stride_w, |
| 1018 | dst_step_w, |
| 1019 | dst_offset_first_element_in_bytes, |
| 1020 | #if defined(HAS_BIAS) |
| 1021 | bias_ptr, |
| 1022 | bias_stride_x, |
| 1023 | bias_step_x, |
| 1024 | bias_offset_first_element_in_bytes, |
| 1025 | #endif // defined(HAS_BIAS) |
| 1026 | dst_size); |
| 1027 | } |
| 1028 | #endif // defined(VEC_SIZE) && VEC_SIZE == 4 |
| 1029 | #endif // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL) |
| 1030 | #endif // defined(NUM_TILES_X) && defined(OUTPUT_TILE_W) && defined(OUTPUT_TILE_H) |