1/* 2 * Copyright (c) 2021-2023 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// *INDENT-OFF* 29// clang-format off 30#define CALCULATE_WEIGHTS_OFFSET_CORRECTION(A_DATA_TYPE, B_DATA_TYPE) CALCULATE_WEIGHTS_OFFSET_CORRECTION_STR(A_DATA_TYPE, B_DATA_TYPE) 31#define CALCULATE_WEIGHTS_OFFSET_CORRECTION_STR(A_DATA_TYPE, B_DATA_TYPE) CALCULATE_WEIGHTS_OFFSET_CORRECTION_##A_DATA_TYPE##_##B_DATA_TYPE 32#define CALCULATE_WEIGHTS_OFFSET_CORRECTION_char_char (0) 33#define CALCULATE_WEIGHTS_OFFSET_CORRECTION_uchar_uchar (0) 34#define CALCULATE_WEIGHTS_OFFSET_CORRECTION_uchar_char (128) 35#define CALCULATE_WEIGHTS_OFFSET_CORRECTION_char_uchar (-128) 36 37#define T_LOAD_MULTIPLIERS_SHIFT_PER_TENSOR() \ 38 ({}) 39 40#define T_LOAD_MULTIPLIERS_SHIFT_PER_CHANNEL() \ 41 TILE(DST_MULTIPLIERS_DATA_TYPE, 1, N0, multipliers); \ 42 TILE(DST_SHIFTS_DATA_TYPE, 1, N0, shifts); \ 43 T_LOAD(DST_MULTIPLIERS_DATA_TYPE, 1, N0, BUFFER, dst_multipliers, cout, 0, 0, 0, multipliers); \ 44 T_LOAD(DST_SHIFTS_DATA_TYPE, 1, N0, BUFFER, dst_shifts, cout, 0, 0, 0, shifts); 45 46#define T_LOAD_MULTIPLIERS_SHIFT(QUANTIZATION_TYPE) T_LOAD_MULTIPLIERS_SHIFT_STR(QUANTIZATION_TYPE) 47#define T_LOAD_MULTIPLIERS_SHIFT_STR(QUANTIZATION_TYPE) T_LOAD_MULTIPLIERS_SHIFT_##QUANTIZATION_TYPE() 48 49#if defined(WEI_WIDTH) && defined(WEI_HEIGHT) && defined(N0) && defined(M0) && defined(DILATION_X) && defined(DILATION_Y) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(PAD_LEFT) && defined(PAD_TOP) 50//! @cond Doxygen_Suppress 51/** OpenCL kernel to compute the depthwise convolution for quantized data types 52 * 53 * @note Data layout supported: NHWC 54 * @note Data type supported: QSYMM8/QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL 55 * @note The 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) 56 * @note The convolution strides must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y (e.g. -DSTRIDE_X=2, -DSTRIDE_Y=2) 57 * @note The convolution dilations must be passed at compile time using -DDILATION_X and -DDILATION_Y (e.g. -DDILATION_X=2, -DDILATION_Y=2) 58 * @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) 59 * @note The tensor type ("BUFFER" or "IMAGE") of the source tensor must be passed at compile time using -DSRC_TENSOR_TYPE (e.g. -DSRC_TENSOR_TYPE=BUFFER) 60 * @note The tensor type ("BUFFER" or "IMAGE") of the weights tensor must be passed at compile time using -DWEI_TENSOR_TYPE (e.g. -DWEI_TENSOR_TYPE=BUFFER) 61 * @note The tensor type ("BUFFER" or "IMAGE") of the destination tensor must be passed at compile time using -DDST_TENSOR_TYPE (e.g. -DDST_TENSOR_TYPE=BUFFER) 62 * @note The data type of the source tensor must be passed at compile time using -DSRC_DATA_TYPE (e.g. -DSRC_DATA_TYPE=int8) 63 * @note The data type of the weights tensor must be passed at compile time using -DWEI_DATA_TYPE (e.g. -DWEI_DATA_TYPE=int8) 64 * @note The data type of the destination tensor must be passed at compile time using -DDST_DATA_TYPE (e.g. -DDST_DATA_TYPE=int8) 65 * @note The data type of the accumulators must be passed at compile time using -DACC_DATA_TYPE (e.g. -DACC_DATA_TYPE=int) 66 * @note The number of M0 rows (width) to process must be passed at compile time using -DM0 (e.g. -DM0=2) 67 * @note The number of N0 output channels to process must be passed at compile time using -DN0 (e.g. -DN0=2) 68 * @note The size of the partial store block in the first dimension must be passed at compile time using -DPARTIAL_N0 (e.g. -DPARTIAL_N0=1) 69 * @note The activation type must be passed at compile using -DACTIVATION_TYPE e.g. -DACTIVATION_TYPE=relu 70 * @note The A and B variables required by some activation functions must be passed at compile time using -DA_VAL= and -DB_VAL= respectively 71 * @note The quantization offset used for both the per-tensor and per-channel quantization must be passed at compile using -DDST_OFFSET (e.g., -DDST_OFFSET=3) 72 * @note The quantization shift for the per-tensor quantization must be passed at compile time using -DDST_SHIFT (e.g., -DDST_SHIFT=1) 73 * @note The quantization multiplier for the per-tensor quantization must be passed at compile using -DDST_MULTIPLIER (e.g., -DDST_MULTIPLER=121432) 74 * @note Only the following configurations of M0 and N0 are currently supported: 75 * - M0 = 1, 2, 3, 4, 5, .... n (M0 != 1 with STRIDE_X == 1 && DILATION_X == 1 only) 76 * - N0 = 2, 3, 4, 8, 16 77 * @note The number of rows to read from the src tensor must be passed at compile time using -DM0_A (e.g., -DM0_A=3). M0_A must be equal to WEI_WIDTH + (M0 - 1) 78 * @note The number of columns to read from the src tensor must be passed at compile time using -DN0_A. It can either be 1 (for DEPTH_MULTIPLIER > 1) or N0 (for DEPTH_MULTIPLIER == 1) 79 * 80 * @param[in] src_img (Not supported) Read only cl_image object for the source tensor. Included when SRC_TENSOR_TYPE=IMAGE 81 * @param[in] src_ptr Pointer to the source tensor. Supported data type: QSYMM8/QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL 82 * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) 83 * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) 84 * @param[in] src_stride_w Stride of the source tensor in W dimension (in bytes) 85 * @param[in] src_c The size of the channels dimension of the source tensor 86 * @param[in] src_w The size of the width dimension of the source tensor 87 * @param[in] src_h The size of the height dimension of the source tensor 88 * @param[in] src_n The size of the batches dimension of the source tensor 89 * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor 90 * @param[out] dst_img (Not supported) Write only cl_image object for the destination tensor. Included when DST_TENSOR_TYPE=IMAGE 91 * @param[out] dst_ptr Pointer to the destination tensor. Supported data type: same as @p src_ptr 92 * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) 93 * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) 94 * @param[in] dst_stride_w Stride of the destination tensor in W dimension (in bytes) 95 * @param[in] dst_c The size of the channels dimension of the destination tensor 96 * @param[in] dst_w The size of the width dimension of the destination tensor 97 * @param[in] dst_h The size of the height dimension of the destination tensor 98 * @param[in] dst_n The size of the batches dimension of the destination tensor 99 * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor 100 * @param[in] wei_img (Not supported) Read only cl_image object for the weights tensor. Included when WEI_TENSOR_TYPE=IMAGE 101 * @param[in] wei_ptr Pointer to the weights tensor. Supported data type: same as @p src_ptr 102 * @param[in] wei_stride_y Stride of the weights tensor in Y dimension (in bytes) 103 * @param[in] wei_stride_z Stride of the weights tensor in Z dimension (in bytes) 104 * @param[in] wei_stride_w Stride of the weights tensor in W dimension (in bytes) 105 * @param[in] wei_c The size of the channels dimension of the weights tensor 106 * @param[in] wei_w The size of the width dimension of the weights tensor 107 * @param[in] wei_h The size of the height dimension of the weights tensor 108 * @param[in] wei_n The size of the batches dimension of the weights tensor 109 * @param[in] wei_step_w wei_stride_w * number of elements along W processed per workitem(in bytes) 110 * @param[in] wei_offset_first_element_in_bytes The offset of the first element in the weights tensor 111 * @param[in] dst_multipliers_ptr Pointer to the destination multipliers tensor for the per-channel quantization. Supported data type: S32 112 * @param[in] dst_multipliers_stride_x Stride of the destination multipliers tensor in X dimension (in bytes) 113 * @param[in] dst_multipliers_step_x dst_multipliers_stride_x * number of elements along X processed per workitem(in bytes) 114 * @param[in] dst_multipliers_offset_first_element_in_bytes The offset of the first element in the destination multipliers tensor 115 * @param[in] dst_shifts_ptr Pointer to the destination shifts tensor for the per-channel quantization. Supported data type: S32 116 * @param[in] dst_shifts_stride_x Stride of the destination shifts tensor in X dimension (in bytes) 117 * @param[in] dst_shifts_step_x dst_shifts_stride_x * number of elements along X processed per workitem(in bytes) 118 * @param[in] dst_shifts_offset_first_element_in_bytes The offset of the first element in the destination shifts tensor 119 * @param[in] bia_ptr (Optional) Pointer to the bias tensor Supported data type: S32 120 * @param[in] bia_stride_x (Optional) Stride of the bias tensor in X dimension (in bytes) 121 * @param[in] bia_step_x (Optional) bia_stride_x * number of elements along X processed per workitem(in bytes) 122 * @param[in] bia_offset_first_element_in_bytes (Optional) The offset of the first element in the bias tensor 123 */ 124//! @endcond 125__kernel void dwc_native_quantized_nhwc( 126 TENSOR4D_RO_T(src, SRC_TENSOR_TYPE), 127 TENSOR4D_WO_T(dst, DST_TENSOR_TYPE), 128 TENSOR4D_RO_T(wei, WEI_TENSOR_TYPE), 129 VECTOR_DECLARATION(dst_multipliers), 130 VECTOR_DECLARATION(dst_shifts) 131#if defined(HAS_BIAS) 132 , 133 VECTOR_DECLARATION(bia) 134#endif // defined(HAS_BIAS) 135) 136{ 137 // Only the weight tensor dimensions are passed at compile time. 138 // In case of dynamic tensor support, the following dimensions should be passed as function argument. 139#define _IWEI_WIDTH WEI_WIDTH 140#define _IWEI_HEIGHT WEI_HEIGHT 141#define _IM0_A M0_A // _IWEI_WIDTH + (M0 - 1) Rows tile A (If M0 != 1, the tiles overlap of 1 element on the X dimension) 142#define _IN0_A N0_A // Cols tile A. It can be either 1 (for DEPTH_MULTIPLIER > 1) or N0 (for DEPTH_MULTIPLIER == 1) 143#define _IM0_B _IWEI_WIDTH // Rows tile B 144#define _IN0_B N0 // Cols tile B 145#define _IBOUNDARY_CHECK (!((WEI_WIDTH == 1 && WEI_HEIGHT == 1 && PAD_LEFT == 0 && PAD_TOP == 0 && M0 == 1))) 146 147 const int cout = GET_SPATIAL_IDX(0, N0, PARTIAL_N0); // OFM 148 const int xo = GET_SPATIAL_IDX(1, M0, 0); // WIDTH 149#if defined(BATCHED_EXECUTION) 150 const int yo = GET_SPATIAL_IDX(2, 1, 0) % dst_h; // HEIGHT 151 const int bout = GET_SPATIAL_IDX(2, 1, 0) / dst_h; // BATCH SIZE IDX 152#else // defined(BATCHED_EXECUTION) 153 const int yo = GET_SPATIAL_IDX(2, 1, 0); // HEIGHT 154 const int bout = 0; // BATCH SIZE IDX 155#endif // defined(BATCHED_EXECUTION) 156 157 int xi = xo * STRIDE_X; 158 int yi = yo * STRIDE_Y; 159 xi -= PAD_LEFT; 160 yi -= PAD_TOP; 161 162 TILE(ACC_DATA_TYPE, M0, N0, c); 163 164 // Reset accumulators 165 LOOP_UNROLLING(int, i, 0, 1, M0, 166 { 167 c[i].v = 0; 168 }) 169 170#if _IWEI_HEIGHT <= 5 171 LOOP_UNROLLING(int, yk, 0, 1, _IWEI_HEIGHT, 172#else // _IWEI_HEIGHT <= 5 173 for(int yk = 0; yk < _IWEI_HEIGHT; yk++) 174#endif // _IWEI_HEIGHT <= 5 175 { 176 TILE(SRC_DATA_TYPE, _IM0_A, _IN0_A, a); 177 178 LOOP_UNROLLING(int, i, 0, 1, _IM0_A, 179 { 180 a[i].v = ZERO_VALUE; 181 }) 182 183 // Load tile from the src tensor (TILE A) 184 T_LOAD_NHWC_WITH_DILATION(SRC_DATA_TYPE, 1, _IM0_A, _IN0_A, SRC_TENSOR_TYPE, src, bout, yi + yk * DILATION_Y, xi, (cout / DEPTH_MULTIPLIER), src_w, src_h, DILATION_X, 1, _IBOUNDARY_CHECK, a); 185 186 TILE(WEI_DATA_TYPE, _IM0_B, _IN0_B, b); 187 188 // Load tile from the weights tensor (TILE B) 189 T_LOAD(WEI_DATA_TYPE, _IM0_B, _IN0_B, WEI_TENSOR_TYPE, wei, cout, yk * _IM0_B, 1, wei_stride_y, b); 190 191 // Optimized path for STRIDE_X == 1 192 // If M0 != 1, we can skip the common loads between the two applied kernels on the X (WIDTH) dimension 193 LOOP_UNROLLING(int, m0, 0, 1, M0, 194 { 195 LOOP_UNROLLING(int, n0, 0, 1, N0, 196 { 197#if _IWEI_WIDTH <= 16 198#define DOT_DATA_TYPE SRC_DATA_TYPE 199#define WEI_OFFSET_CORRECTION (CALCULATE_WEIGHTS_OFFSET_CORRECTION(SRC_DATA_TYPE, WEI_DATA_TYPE)) 200 201 // Optimized path for the dot instruction 202 TILE(DOT_DATA_TYPE, 1, _IWEI_WIDTH, x0); 203 TILE(DOT_DATA_TYPE, 1, _IWEI_WIDTH, y0); 204 ACC_DATA_TYPE offset_a = 0; 205 ACC_DATA_TYPE offset_b = 0; 206 207 LOOP_UNROLLING(int, xk, 0, 1, _IWEI_WIDTH, 208 { 209 x0[0].s[xk] = a[xk + m0].s[n0]; 210 y0[0].s[xk] = b[xk].s[n0] + (int)WEI_OFFSET_CORRECTION; 211 }) 212 DOT_PRODUCT_INTEGER8(DOT_DATA_TYPE, DOT_DATA_TYPE, ACC_DATA_TYPE, _IWEI_WIDTH, x0[0].v, y0[0].v, c[m0].s[n0]); 213 REDUCE_INTEGER8(DOT_DATA_TYPE, DOT_DATA_TYPE, ACC_DATA_TYPE, _IWEI_WIDTH, x0[0].v, offset_a); 214 REDUCE_INTEGER8(DOT_DATA_TYPE, DOT_DATA_TYPE, ACC_DATA_TYPE, _IWEI_WIDTH, y0[0].v, offset_b); 215 c[m0].s[n0] += offset_a * (ACC_DATA_TYPE)(WEI_OFFSET - (ACC_DATA_TYPE)WEI_OFFSET_CORRECTION) + offset_b * (ACC_DATA_TYPE)SRC_OFFSET; 216#else // _IWEI_WIDTH <= 16 217 LOOP_UNROLLING(int, xk, 0, 1, _IWEI_WIDTH, 218 { 219 c[m0].s[n0] += ((ACC_DATA_TYPE)a[xk + m0].s[n0] + (ACC_DATA_TYPE)(SRC_OFFSET)) * ((ACC_DATA_TYPE)b[xk].s[n0] + (ACC_DATA_TYPE)(WEI_OFFSET)); 220 }) 221#endif // _IWEI_WIDTH <= 16 222 }) 223 }) 224 } 225#if _IWEI_HEIGHT <= 5 226 ) 227#endif // _IWEI_HEIGHT <= 5 228 229#if _IWEI_WIDTH <= 16 230 T_ADD_CONSTANT(ACC_DATA_TYPE, M0, N0, c, (_IWEI_WIDTH * _IWEI_HEIGHT * SRC_OFFSET * (ACC_DATA_TYPE)(WEI_OFFSET - (ACC_DATA_TYPE)WEI_OFFSET_CORRECTION)), c); 231#endif // _IWEI_WIDTH <= 16 232 233#if defined(HAS_BIAS) 234 TILE(BIA_DATA_TYPE, 1, N0, bias0); 235 236 // Load bias 237 T_LOAD(BIA_DATA_TYPE, 1, N0, BUFFER, bia, cout, 0, 0, 0, bias0); 238 239 // c = c + bias[broadcasted] 240 T_ELTWISE_BROADCAST_ADD_X(ACC_DATA_TYPE, M0, N0, c, bias0, c); 241#endif // HAS_BIAS 242 243 T_LOAD_MULTIPLIERS_SHIFT(QUANTIZATION_TYPE); 244 245 // Quantize the tile 246 TILE(DST_DATA_TYPE, M0, N0, cq); 247 T_QUANTIZE8(ACC_DATA_TYPE, DST_DATA_TYPE, QUANTIZATION_TYPE, M0, N0, DST_OFFSET, DST_SHIFT, DST_MULTIPLIER, c, multipliers, shifts, cq); 248 249 // Perform activation 250 T_ACTIVATION_QUANTIZED(DST_DATA_TYPE, M0, N0, ACTIVATION_TYPE, DST_OFFSET, A_VAL, B_VAL, cq, cq); 251 252 bool x_cond = PARTIAL_N0 != 0 && get_global_id(0) == 0; 253 254 if(x_cond) 255 { 256 LOOP_UNROLLING(int, m0, 0, 1, M0, 257 { 258 int xi_out = min(xo + M0 - 1 - m0, (int)(dst_w) - 1); 259 VSTORE_PARTIAL(N0, PARTIAL_N0) 260 (cq[M0 - 1 - m0].v, 0, (__global DST_DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + (uint)cout * sizeof(DST_DATA_TYPE) + (uint)xi_out * dst_stride_y + (uint)yo * dst_stride_z + (uint)bout * dst_stride_w)); 261 }) 262 } 263 else 264 { 265 LOOP_UNROLLING(int, m0, 0, 1, M0, 266 { 267 int xi_out = min(xo + M0 - 1 - m0, (int)(dst_w) - 1); 268 VSTORE(N0) 269 (cq[M0 - 1 - m0].v, 0, (__global DST_DATA_TYPE *)(dst_ptr + dst_offset_first_element_in_bytes + (uint)cout * sizeof(DST_DATA_TYPE) + (uint)xi_out * dst_stride_y + (uint)yo * dst_stride_z + (uint)bout * dst_stride_w)); 270 }) 271 } 272} 273#endif // defined(WEI_WIDTH) && defined(WEI_HEIGHT) && defined(N0) && defined(M0) && defined(DILATION_X) && defined(DILATION_Y) && defined(STRIDE_X) && defined(STRIDE_Y) && defined(PAD_LEFT) && defined(PAD_TOP) 274// *INDENT-ON* 275// clang-format on 276