1/* 2 * Copyright (c) 2022-2023 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 25#include "helpers.h" 26#include "tile_helpers.h" 27 28//! @cond Doxygen_Suppress 29/** OpenCL kernel to compute the transposed convolution. 30 * 31 * @note Data layout supported: NHWC 32 * @note Data type supported: F32/F16/QASYMM8/QASYMM8_SIGNED 33 * @note The transposed convolution padding (left and top) must be passed at compile time using -DPAD_LEFT and -DPAD_TOP (e.g. -DPAD_LEFT=2, -DPAD_TOP=2) 34 * @note The transposed convolution strides must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y (e.g. -DSTRIDE_X=2, -DSTRIDE_Y=2) 35 * @note The spatial dimensions of the weights must be passed at compile time using -DWEI_WIDTH and -DWEI_HEIGHT (e.g. -DWEI_WIDTH=9, -DWEI_HEIGHT=9) 36 * @note The spatial dimensions of the source tensor must be passed at compile time using -DSRC_WIDTH and -DSRC_HEIGHT (e.g. -DSRC_WIDTH=96, -DSRC_HEIGHT=64) 37 * @note The spatial dimensions of the destination tensor must be passed at compile time using -DDST_WIDTH and -DDST_HEIGHT (e.g. -DDST_WIDTH=96, -DDST_HEIGHT=64) 38 * @note The channels of the source tensor must be passed at compile time using -DSRC_CHANNELS (e.g. -DSRC_CHANNELS=64) 39 * @note The channels of the destination tensor must be passed at compile time using -DDST_CHANNELS (e.g. -DDST_CHANNELS=64) 40 * @note The tensor type (currently only "BUFFER" is supported) of the source tensor must be passed at compile time using -DSRC_TENSOR_TYPE (e.g. -DSRC_TENSOR_TYPE=BUFFER) 41 * @note The tensor type (currently only "BUFFER" is supported) of the weights tensor must be passed at compile time using -DWEI_TENSOR_TYPE (e.g. -DWEI_TENSOR_TYPE=BUFFER) 42 * @note The tensor type (currently only "BUFFER" is supported) of the destination tensor must be passed at compile time using -DDST_TENSOR_TYPE (e.g. -DDST_TENSOR_TYPE=BUFFER) 43 * @note The data type of the source tensor must be passed at compile time using -DSRC_DATA_TYPE (e.g. -DSRC_DATA_TYPE=float) 44 * @note The data type of the weights tensor must be passed at compile time using -DWEI_DATA_TYPE (e.g. -DWEI_DATA_TYPE=float) 45 * @note The data type of the destination tensor must be passed at compile time using -DDST_DATA_TYPE (e.g. -DDST_DATA_TYPE=float) 46 * @note The data type of the destination tensor must be passed at compile time using -DBIA_DATA_TYPE (e.g. -DBIA_DATA_TYPE=float) 47 * @note The data type of the accumulators must be passed at compile time using -DACC_DATA_TYPE (e.g. -DACC_DATA_TYPE=float) 48 * @note The number of M0 rows (width*height) to process must be passed at compile time using -DM0 (e.g. -DM0=2) 49 * @note The number of N0 output channels to process must be passed at compile time using -DN0 (e.g. -DN0=2) 50 * @note The number of K0 inner accumulations must be passed at compile time using -DK0 (e.g. -DK0=2) 51 * @note The size of the partial store block in x must be passed at compile time using -DPARTIAL_N0 (e.g. -DPARTIAL_N0=1) 52 * @note If bias exists, the compile time argument -DHAS_BIAS should be passed 53 * @note Only the following configurations of M0, N0 and K0 are currently supported: 54 * - M0 = 1 55 * - N0 = 1, 2, 3, 4, 8, 16 56 * - K0 = 1, 2, 3, 4, 8, 16 57 * 58 * @note In case of QASYMM8/QASYMM8_SIGNED, the following extra information must be passed at compile time: 59 * - -DIS_QUANTIZED 60 * - The destination quantization multiplier e.g. -DDST_MULTIPLIER=1234 61 * - The destination quantization shift e.g. -DDST_SHIFT=4 62 * - The destination offset e.g. -DDST_OFFSET=4 63 * - The source offset e.g. -DSRC_OFFSET=4 64 * - The weights offset e.g. -DWEI_OFFSET=4 65 * - The quantized zero value e.g. -DZERO_VALUE=4 66 * 67 * @param[in] src_img (Not supported) Read only cl_image object for the source tensor. Included when SRC_TENSOR_TYPE=IMAGE 68 * @param[in] src_ptr Pointer to the source tensor. Supported data type: F16/F32 69 * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) 70 * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) 71 * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes) 72 * @param[in] src_c The size of the channels (IFM) dimension of the source tensor 73 * @param[in] src_w The size of the width dimension of the source tensor 74 * @param[in] src_h The size of the height dimension of the source tensor 75 * @param[in] src_n The size of the batches dimension of the source tensor 76 * @param[out] dst_img (Not supported) Write only cl_image object for the destination tensor. Included when DST_TENSOR_TYPE=IMAGE 77 * @param[out] dst_ptr Pointer to the destination tensor. Supported data type: same as @p src_ptr 78 * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) 79 * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) 80 * @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes) 81 * @param[in] dst_c The size of the channels (OFM) dimension of the destination tensor 82 * @param[in] dst_w The size of the width dimension of the destination tensor 83 * @param[in] dst_h The size of the height dimension of the destination tensor 84 * @param[in] dst_n The size of the batches dimension of the destination tensor 85 * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor 86 * @param[in] wei_img (Not supported) Read only cl_image object for the weights tensor. Included when WEI_TENSOR_TYPE=IMAGE 87 * @param[in] wei_ptr Pointer to the weights tensor. Supported data type: same as @p src_ptr 88 * @param[in] wei_stride_y Stride of the weights tensor in Y dimension (in bytes) 89 * @param[in] wei_stride_z Stride of the weights tensor in Z dimension (in bytes) 90 * @param[in] wei_stride_w Stride of the weights tensor in W dimension (in bytes) 91 * @param[in] wei_c The size of the channels (IFM) dimension of the weights tensor 92 * @param[in] wei_w The size of the width dimension of the weights tensor 93 * @param[in] wei_h The size of the height dimension of the weights tensor 94 * @param[in] wei_n The size of the batches (OFM) dimension of the weights tensor 95 * @param[in] wei_offset_first_element_in_bytes The offset of the first element in the bias matrix 96 * @param[in] bia_ptr (Optional) Pointer to the bias tensor Supported data type: same as @p src_ptr (if F32/F16) 97 * @param[in] bia_stride_x (Optional) Stride of the bias tensor in X dimension (in bytes) 98 * @param[in] bia_step_x (Optional) bia_stride_x * number of elements along X processed per workitem(in bytes) 99 * @param[in] bia_offset_first_element_in_bytes (Optional) The offset of the first element in the bias matrix 100 */ 101//! @endcond 102__kernel void transposed_convolution_nhwc( 103 TENSOR4D_RO_T(src, SRC_TENSOR_TYPE), 104 TENSOR4D_WO_T(dst, DST_TENSOR_TYPE), 105 TENSOR4D_RO_T(wei, WEI_TENSOR_TYPE) 106#if defined(HAS_BIAS) 107 , 108 VECTOR_DECLARATION(bia) 109#endif // defined(HAS_BIAS) 110) 111{ 112 // All the tensor dimensions are passed at compile time. 113 // In case of dynamic tensor support, the following dimensions should be passed as function argument. 114#define _IWEI_WIDTH WEI_WIDTH 115#define _IWEI_HEIGHT WEI_HEIGHT 116#define _ISRC_WIDTH SRC_WIDTH 117#define _ISRC_HEIGHT SRC_HEIGHT 118#define _ISRC_CHANNELS SRC_CHANNELS 119#define _IDST_WIDTH DST_WIDTH 120#define _IDST_HEIGHT DST_HEIGHT 121#define _IDST_CHANNELS DST_CHANNELS 122#define _IY_MULTIPLIER (_IWEI_WIDTH * _IWEI_HEIGHT) 123 124#if defined(IS_QUANTIZED) 125#define _IOUTPUT_TILE cq 126#else // defined(IS_QUANTIZED) 127#define _IOUTPUT_TILE c 128#endif // defined(IS_QUANTIZED) 129 130 const int cout = GET_SPATIAL_IDX(0, N0, PARTIAL_N0); // OFM 131 const int mout = GET_SPATIAL_IDX(1, M0, 0); // WIDTH x HEIGHT 132 const int bout = GET_SPATIAL_IDX(2, 1, 0); // BATCH SIZE IDX 133 134 // .v = access the whole vector (OpenCL vector) 135 // .s[x] = access the vector element at position x (scalar access) 136 TILE(int, 1, M0, xi); 137 TILE(int, 1, M0, yi); 138 TILE(int, 1, M0, xu); 139 TILE(int, 1, M0, yu); 140 141 // Convert the linear index to coordinate 142 LOOP_UNROLLING(int, i, 0, 1, M0, 143 { 144 xu[0].s[i] = ((mout + i) % _IDST_WIDTH) - PAD_LEFT; 145 yu[0].s[i] = ((mout + i) / _IDST_WIDTH) - PAD_TOP; 146 xi[0].s[i] = ceil(xu[0].s[i] / (float)STRIDE_X); 147 yi[0].s[i] = ceil(yu[0].s[i] / (float)STRIDE_Y); 148 }) 149 150 // Initialize the accumulators 151 TILE(ACC_DATA_TYPE, M0, N0, c); 152 153 LOOP_UNROLLING(int, i, 0, 1, M0, 154 { 155 c[i].v = 0; 156 }) 157 158 // Flipped indices 159 const int x_start = _IWEI_WIDTH - (xi[0].s[0] * STRIDE_X - xu[0].s[0]) - 1; 160 const int y_start = _IWEI_HEIGHT - (yi[0].s[0] * STRIDE_Y - yu[0].s[0]) - 1; 161 162 for(int yk = y_start, yi_step = 0; yk >= 0; yk -= STRIDE_Y, ++yi_step) 163 { 164 for(int xk = x_start, xi_step = 0; xk >= 0; xk -= STRIDE_X, ++xi_step) 165 { 166 const int weights_y = cout * _IY_MULTIPLIER + yk * _IWEI_WIDTH + xk; 167 168 TILE(int, 1, M0, my); 169 170 LOOP_UNROLLING(int, i, 0, 1, M0, 171 { 172 int x_s = xi[0].s[i] + xi_step; 173 int y_s = yi[0].s[i] + yi_step; 174 my[0].s[i] = x_s + y_s *_ISRC_WIDTH; 175 my[0].s[i] = my[0].s[i] + bout * (int)(_ISRC_WIDTH * _ISRC_HEIGHT); 176 my[0].s[i] = select(-1, my[0].s[i], x_s >= 0); 177 my[0].s[i] = select(-1, my[0].s[i], x_s < _ISRC_WIDTH); 178 my[0].s[i] = select(-1, my[0].s[i], y_s >= 0); 179 my[0].s[i] = select(-1, my[0].s[i], y_s < _ISRC_HEIGHT); 180 }) 181 182 int ck = 0; 183 for(; ck <= (_ISRC_CHANNELS - K0); ck += K0) 184 { 185 TILE(SRC_DATA_TYPE, M0, K0, a); 186 TILE(WEI_DATA_TYPE, N0, K0, b); 187 188 // Initialize tiles 189 LOOP_UNROLLING(int, i, 0, 1, M0, 190 { 191 a[i].v = ZERO_VALUE; 192 }) 193 194 LOOP_UNROLLING(int, i, 0, 1, N0, 195 { 196 b[i].v = ZERO_VALUE; 197 }) 198 199 // Load tile from the src tensor 200 T_LOAD2D_INDIRECT(SRC_DATA_TYPE, M0, K0, SRC_TENSOR_TYPE, src, ck, src_stride_y, my, a); 201 202 // Load tile from the weights tensor 203 T_LOAD(WEI_DATA_TYPE, N0, K0, WEI_TENSOR_TYPE, wei, ck, weights_y, _IY_MULTIPLIER, wei_stride_y, b); 204 205 // Compute the matrix multiplication between two tiles 206 T_MMUL(SRC_DATA_TYPE, WEI_DATA_TYPE, ACC_DATA_TYPE, M0, N0, K0, NT, T, a, b, c); 207 208#if defined(IS_QUANTIZED) 209 // Apply the offset correction (correction usually needed for asymmetric quantized computation) 210 // The computation is not performed if both SRC_OFFSET and WEI_OFFSET are zero 211 T_OFFSET_CORRECTION(ACC_DATA_TYPE, M0, N0, K0, SRC_OFFSET, WEI_OFFSET, a, b, c); 212#endif // defined(IS_QUANTIZED) 213 } 214 215 // This #if directive should be removed in case of dynamic tensor support 216#if defined(LEFTOVER_LOOP) 217 // Left-over accumulations 218 for(; ck < _ISRC_CHANNELS; ++ck) 219 { 220 TILE(SRC_DATA_TYPE, M0, 1, a); 221 TILE(WEI_DATA_TYPE, N0, 1, b); 222 223 // Initialize tiles 224 LOOP_UNROLLING(int, i, 0, 1, M0, 225 { 226 a[i].v = ZERO_VALUE; 227 }) 228 229 // Load tile from the src tensor 230 // The T_LOAD for the left-over elements can only use BUFFER because we load one element per iteration 231 T_LOAD2D_INDIRECT(SRC_DATA_TYPE, M0, 1, BUFFER, src, ck, src_stride_y, my, a); 232 233 // Load tile from the weights tensor 234 // The T_LOAD for the left-over elements can only use BUFFER because we load one element per iteration 235 T_LOAD(WEI_DATA_TYPE, N0, 1, BUFFER, wei, ck, weights_y, _IY_MULTIPLIER, wei_stride_y, b); 236 237 // Compute the matrix multiplication between two tiles 238 T_MMUL(SRC_DATA_TYPE, WEI_DATA_TYPE, ACC_DATA_TYPE, M0, N0, 1, NT, T, a, b, c); 239 240#if defined(IS_QUANTIZED) 241 // Apply the offset correction (correction usually needed for asymmetric quantized computation) 242 // The computation is not performed if both SRC_OFFSET and WEI_OFFSET are zero 243 T_OFFSET_CORRECTION(ACC_DATA_TYPE, M0, N0, 1, SRC_OFFSET, WEI_OFFSET, a, b, c); 244#endif // defined(IS_QUANTIZED) 245 } 246#endif // defined(LEFTOVER_LOOP) 247 } 248 } 249 250#if defined(IS_QUANTIZED) 251 const int total_pixels = floor((1 + y_start / (float)STRIDE_Y)) * floor(1 + x_start / (float)STRIDE_X); 252 253 T_ADD_CONSTANT(ACC_DATA_TYPE, M0, N0, c, (total_pixels * _ISRC_CHANNELS * SRC_OFFSET * WEI_OFFSET), c); 254#endif // defined(IS_QUANTIZED) 255 256#if defined(HAS_BIAS) 257 TILE(BIA_DATA_TYPE, 1, N0, bias0); 258 259 T_LOAD(BIA_DATA_TYPE, 1, N0, BUFFER, bia, cout, 0, 1, 0, bias0); 260 261 // c = c + bias[broadcasted] 262 T_ELTWISE_BROADCAST_ADD_X(ACC_DATA_TYPE, M0, N0, c, bias0, c); 263 264#endif // HAS_BIAS 265 266#if defined(IS_QUANTIZED) 267 268 TILE(DST_DATA_TYPE, M0, N0, cq); 269 270 // Quantize the tile 271 T_QUANTIZE8_ASYMMETRIC(ACC_DATA_TYPE, DST_DATA_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, c, cq); 272#endif // defined(IS_QUANTIZED) 273 274 TILE(uint, M0, 1, dst_indirect_y); 275 276 // Calculate the destination indirect Y 277 LOOP_UNROLLING(int, i, 0, 1, M0, 278 { 279 dst_indirect_y[i].v = (uint)min(mout + i, (int)(_IDST_WIDTH * _IDST_HEIGHT) - 1); 280 dst_indirect_y[i].v += bout * (int)(_IDST_WIDTH * _IDST_HEIGHT); 281 }) 282 283 bool x_cond = PARTIAL_N0 != 0 && get_global_id(0) == 0; 284 285 // Store the tile in reverse order so the invalid values are overwritten with the valid ones 286 T_STORE_INDIRECT_WIDTH_SELECT(DST_DATA_TYPE, M0, N0, PARTIAL_N0, DST_TENSOR_TYPE, dst, cout, dst_stride_y, x_cond, _IOUTPUT_TILE, dst_indirect_y); 287 288#undef _IWEI_WIDTH 289#undef _IWEI_HEIGHT 290#undef _ISRC_WIDTH 291#undef _ISRC_HEIGHT 292#undef _ISRC_CHANNELS 293#undef _IDST_WIDTH 294#undef _IDST_HEIGHT 295#undef _IDST_CHANNELS 296#undef _IY_MULTIPLIER 297} 298