1/* 2 * Copyright (c) 2017-2021 Arm Limited. 3 * 4 * SPDX-License-Identifier: MIT 5 * 6 * Permission is hereby granted, free of charge, to any person obtaining a copy 7 * of this software and associated documentation files (the "Software"), to 8 * deal in the Software without restriction, including without limitation the 9 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or 10 * sell copies of the Software, and to permit persons to whom the Software is 11 * furnished to do so, subject to the following conditions: 12 * 13 * The above copyright notice and this permission notice shall be included in all 14 * copies or substantial portions of the Software. 15 * 16 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 17 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 18 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 19 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 20 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 21 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 22 * SOFTWARE. 23 */ 24#include "helpers_asymm.h" 25 26#if defined(DATA_TYPE) && defined(MIN_VALUE) && defined(VECTOR_SIZE) && defined(VECTOR_SIZE_LEFTOVER) && defined(DIFF_MIN) 27 28#define VEC_BASE VEC_DATA_TYPE(DATA_TYPE, VECTOR_SIZE) 29#define VEC_INT VEC_DATA_TYPE(int, VECTOR_SIZE) 30#define VEC_FLOAT VEC_DATA_TYPE(float, VECTOR_SIZE) 31 32/** Divides all the values of the input tensor by the sum calculated from softmax_layer_shift_exp_sum kernel. 33 * 34 * @note Datatype must be given as a preprocessor argument using -DDATA_TYPE, e.g. -DDATA_TYPE=uchar 35 * @note The zero value for the given data type must be given as a preprocessor argument using -DMIN_VALUE, e.g. -DMIN_VALUE=-128 36 * @note Vector size should be given as a preprocessor argument using -DVECTOR_SIZE=size. e.g. -DVECTOR_SIZE=16 37 * @note Leftover vector size has to be passed at compile time using -DVECTOR_SIZE_LEFTOVER. e.g. -DVECTOR_SIZE_LEFTOVER=3. It is defined as the remainder between the input's first dimension and VECTOR_SIZE 38 * @note Quantized beta can be optionally passed at compile time using -DINPUT_BETA_MULTIPLIER and -DINPUT_BETA_LEFT_SHIFT (if undefined, assume beta equals 1.0) 39 * @note Additional quantization data must be passed at compile time using -DSCALED_DIFF_INT_BITS and -DEXP_ACCUMULATION_INT_BITS. 40 * @note -DDIFF_MIN must be passed at compile time. It is threshold difference between maximum value of input data and current processed value, it defines whether the value will be taken into account or not. 41 * @note In case the input's data type is QASYMM8_SIGNED, -DQASYMM8_SIGNED must be passed. 42 * 43 * @param[in] src_ptr Pointer to the source tensor slice. Supported data types: S32 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_offset_first_element_in_bytes The offset of the first element in the source tensor 51 * @param[in] sum_ptr Pointer to the sum values tensor slice. Supported data types: same as @p src_ptr 52 * @param[in] sum_stride_x Stride of the sum values tensor in X dimension (in bytes) 53 * @param[in] sum_step_x sum_stride_x * number of elements along X processed per workitem(in bytes) 54 * @param[in] sum_stride_y Stride of the sum values tensor in Y dimension (in bytes) 55 * @param[in] sum_step_y sum_stride_y * number of elements along Y processed per workitem(in bytes) 56 * @param[in] sum_stride_z Stride of the sum values tensor in Z dimension (in bytes) 57 * @param[in] sum_step_z sum_stride_z * number of elements along Z processed per workitem(in bytes) 58 * @param[in] sum_offset_first_element_in_bytes The offset of the first element in the sum values tensor 59 * @param[out] dst_ptr Pointer to the destination tensor slice. Supported data types: QASYMM8/QASYMM8_SIGNED 60 * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) 61 * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) 62 * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) 63 * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) 64 * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) 65 * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) 66 * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor 67 */ 68__kernel void softmax_layer_norm_quantized( 69 TENSOR3D_DECLARATION(src), 70 TENSOR3D_DECLARATION(sum), 71 TENSOR3D_DECLARATION(dst)) 72{ 73 const int x_offs = max((int)(get_global_id(0) * VECTOR_SIZE - (VECTOR_SIZE - VECTOR_SIZE_LEFTOVER) % VECTOR_SIZE), 0); 74 75 __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x_offs * sizeof(int) + get_global_id(1) * src_stride_y + get_global_id(2) * src_stride_z; 76 __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x_offs * sizeof(DATA_TYPE) + get_global_id(1) * dst_stride_y + get_global_id(2) * dst_stride_z; 77 78 Image sum = CONVERT_TENSOR3D_TO_IMAGE_STRUCT_NO_STEP(sum); 79 80#ifdef BETA 81 // Initialize beta 82 VEC_FLOAT beta = (VEC_FLOAT)BETA; 83 VEC_FLOAT scale_beta = -BETA * SCALE; 84#else /* BETA */ 85 VEC_FLOAT scale_beta = -SCALE; 86#endif /* BETA */ 87 88 // Load max value of 1D logits vector (row) 89 float sum_val = *((__global float *)offset(&sum, 0, get_global_id(1))); 90 float sum_val_inverse = 256.f / sum_val; 91 92 VEC_INT data_diff = VLOAD(VECTOR_SIZE)(0, (__global int *)src_addr); 93 VEC_FLOAT data_diff_f = CONVERT(data_diff, VEC_FLOAT); 94 95 data_diff_f *= scale_beta; 96 data_diff_f = exp(data_diff_f); 97 data_diff_f *= sum_val_inverse; 98 99#ifdef QASYMM8_SIGNED 100 data_diff_f -= 128.f; 101#endif /* QASYMM8_SIGNED */ 102 VEC_INT data = CONVERT(data_diff_f, VEC_INT); 103 VEC_BASE data0 = CONVERT_SAT(data, VEC_DATA_TYPE(DATA_TYPE, VECTOR_SIZE)); 104 STORE_VECTOR_SELECT(data, DATA_TYPE, dst_addr, VECTOR_SIZE, VECTOR_SIZE_LEFTOVER, VECTOR_SIZE_LEFTOVER != 0 && get_global_id(0) == 0); 105} 106 107#if defined(SRC_WIDTH) && defined(LOG_VECTOR_SIZE) 108 109/* Number of workitems in dimension 0. */ 110#if !defined(GRID_SIZE) 111#define GRID_SIZE 1 112#endif /* !defined(GRID_SIZE) */ 113 114#define VEC_UINT VEC_DATA_TYPE(uint, VECTOR_SIZE) 115 116VEC_INT mult_by_quantized_multiplier(VEC_INT data) 117{ 118#if defined(INPUT_BETA_MULTIPLIER) && defined(INPUT_BETA_LEFT_SHIFT) 119 if(INPUT_BETA_MULTIPLIER > 1) 120 { 121 return ASYMM_MULT(data * (1 << INPUT_BETA_LEFT_SHIFT), INPUT_BETA_MULTIPLIER, VECTOR_SIZE); 122 } 123#endif /* defined(INPUT_BETA_MULTIPLIER) && defined(INPUT_BETA_LEFT_SHIFT) */ 124 return data; 125} 126 127/** Shifts the values of the input tensor by the max calculated in softmax_layer_max kernel, 128 * then gets the exponent of each element as sums all elements across each row. 129 * 130 * @note Datatype must be given as a preprocessor argument using -DDATA_TYPE, e.g. -DDATA_TYPE=uchar 131 * @note The zero value for the given data type must be given as a preprocessor argument using -DMIN_VALUE, e.g. -DMIN_VALUE=-128 132 * @note Vector size should be given as a preprocessor argument using -DVECTOR_SIZE=size. e.g. -DVECTOR_SIZE=16 133 * @note Leftover vector size has to be passed at compile time using -DVECTOR_SIZE_LEFTOVER. e.g. -DVECTOR_SIZE_LEFTOVER=3. It is defined as the remainder between the input's first dimension and VECTOR_SIZE 134 * @note In case the input is not multiple of VECTOR_SIZE -DNON_MULTIPLE_OF_VECTOR_SIZE must be passed. 135 * @note Quantized beta can be optionally passed at compile time using -DINPUT_BETA_MULTIPLIER and -DINPUT_BETA_LEFT_SHIFT (if undefined, assume beta equals 1.0) 136 * @note Additional quantization data must be passed at compile time using -DSCALED_DIFF_INT_BITS and -DEXP_ACCUMULATION_INT_BITS. 137 * @note -DDIFF_MIN must be passed at compile time. It is threshold difference between maximum value of input data and current processed value, it defines whether the value will be taken into account or not. 138 * @note In case the input's data type is QASYMM8_SIGNED, -DQASYMM8_SIGNED must be passed. 139 * 140 * @param[in] src_ptr Pointer to the source tensor slice. Supported data types: QASYMM8/QASYMM8_SIGNED 141 * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) 142 * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) 143 * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) 144 * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) 145 * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) 146 * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) 147 * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor 148 * @param[in] max_ptr Pointer to the max values tensor slice. Supported data types: same as @p src_ptr 149 * @param[in] max_stride_x Stride of the max values tensor in X dimension (in bytes) 150 * @param[in] max_step_x max_stride_x * number of elements along X processed per workitem(in bytes) 151 * @param[in] max_stride_y Stride of the max values tensor in Y dimension (in bytes) 152 * @param[in] max_step_y max_stride_y * number of elements along Y processed per workitem(in bytes) 153 * @param[in] max_stride_z Stride of the max values tensor in Z dimension (in bytes) 154 * @param[in] max_step_z max_stride_z * number of elements along Z processed per workitem(in bytes) 155 * @param[in] max_offset_first_element_in_bytes The offset of the first element in the max values tensor 156 * @param[out] dst_ptr Pointer to the destination tensor slice. Supported data types: S32 157 * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) 158 * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) 159 * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) 160 * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) 161 * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) 162 * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) 163 * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor 164 * @param[out] sum_ptr Pointer to the sum values tensor slice. Supported data types: same as @p dst_ptr 165 * @param[in] sum_stride_x Stride of the sum values tensor in X dimension (in bytes) 166 * @param[in] sum_step_x sum_stride_x * number of elements along X processed per workitem(in bytes) 167 * @param[in] sum_stride_y Stride of the sum values tensor in Y dimension (in bytes) 168 * @param[in] sum_step_y sum_stride_z * number of elements along Z processed per workitem(in bytes) 169 * @param[in] sum_stride_z Stride of the sum values tensor in Z dimension (in bytes) 170 * @param[in] sum_step_z sum_stride_z * number of elements along Z processed per workitem(in bytes) 171 * @param[in] sum_offset_first_element_in_bytes The offset of the first element in the sum values tensor 172 */ 173__kernel void softmax_layer_max_shift_exp_sum_quantized_serial( 174 TENSOR3D_DECLARATION(src), 175 TENSOR3D_DECLARATION(maxo), 176 TENSOR3D_DECLARATION(dst), 177 TENSOR3D_DECLARATION(sum)) 178{ 179 __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + get_global_id(1) * src_stride_y + get_global_id(2) * src_stride_z; 180 __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + get_global_id(1) * dst_stride_y + get_global_id(2) * dst_stride_z; 181 182 Image maxo = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(maxo); 183 Image sum = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(sum); 184 185 VEC_BASE max_val_vec = (VEC_BASE)(MIN_VALUE); 186 187#ifdef BETA 188 // Initialize beta 189 VEC_FLOAT beta = (VEC_FLOAT)BETA; 190 VEC_FLOAT scale_beta = -BETA * SCALE; 191#else /* BETA */ 192 VEC_FLOAT scale_beta = -SCALE; 193#endif /* BETA */ 194 195 // Calculate max of row 196#ifdef NON_MULTIPLE_OF_VECTOR_SIZE 197 VEC_BASE vec_min_val = (VEC_BASE)(MIN_VALUE); 198 VEC_BASE data = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)src_addr); 199 VEC_INT widx = (VEC_INT)VECTOR_SIZE_LEFTOVER > VEC_OFFS(int, VECTOR_SIZE); 200 max_val_vec = max(max_val_vec, select(vec_min_val, data, CONVERT(widx, VEC_BASE))); 201#endif /* NON_MULTIPLE_OF_VECTOR_SIZE */ 202 203 for(uint i = VECTOR_SIZE_LEFTOVER; i < SRC_WIDTH; i += VECTOR_SIZE) 204 { 205 VEC_BASE data = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(src_addr + i * sizeof(DATA_TYPE))); 206 max_val_vec = max(data, max_val_vec); 207 } 208 209 // Perform max reduction 210 DATA_TYPE max_local = MAX_REDUCE(max_val_vec, VECTOR_SIZE); 211 *((__global DATA_TYPE *)maxo.ptr) = max_local; 212 213 // Second part 214 215 // Load max value of 1D logits vector (row) 216 int max_val = convert_int(max_local); 217 VEC_FLOAT sum1D_f = 0.f; 218 // Start with the leftover items 219#ifdef NON_MULTIPLE_OF_VECTOR_SIZE 220 VEC_INT data_fp = CONVERT(data, VEC_INT); 221 VEC_INT data_diff = max_val - data_fp; 222 VEC_FLOAT data_fp_f = CONVERT(data_diff, VEC_FLOAT); 223 data_fp_f *= scale_beta; 224 data_fp_f = exp(data_fp_f); 225 data_fp_f = select(0, data_fp_f, widx); 226 VSTORE_PARTIAL(VECTOR_SIZE, VECTOR_SIZE_LEFTOVER) 227 (data_diff, 0, (__global int *)dst_addr); 228 sum1D_f += data_fp_f; 229#endif /* NON_MULTIPLE_OF_VECTOR_SIZE */ 230 // Do the rest and compute exp and sum 231 for(uint i = VECTOR_SIZE_LEFTOVER; i < SRC_WIDTH; i += VECTOR_SIZE) 232 { 233 VEC_BASE data = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(src_addr + i * sizeof(DATA_TYPE))); 234 VEC_INT data_fp = CONVERT(data, VEC_INT); 235 VEC_INT data_diff = max_val - data_fp; 236 VEC_FLOAT data_fp_f = CONVERT(data_diff, VEC_FLOAT); 237 data_fp_f *= scale_beta; 238 data_fp_f = exp(data_fp_f); 239 sum1D_f += data_fp_f; 240 VSTORE(VECTOR_SIZE) 241 (data_diff, 0, (__global int *)(dst_addr + i * sizeof(int))); 242 } 243 // Perform sum reduction 244 *((__global float *)sum.ptr) = SUM_REDUCE(sum1D_f, VECTOR_SIZE); 245} 246 247/** Identifies the maximum value across the 1st dimension and shifts the values of the input tensor by this maximum value, 248 * then gets the exponent of each element as sums all elements across each row. 249 * 250 * @note Datatype must be given as a preprocessor argument using -DDATA_TYPE, e.g. -DDATA_TYPE=uchar 251 * @note The zero value for the given data type must be given as a preprocessor argument using -DMIN_VALUE, e.g. -DMIN_VALUE=-128 252 * @note Vector size should be given as a preprocessor argument using -DVECTOR_SIZE=size. e.g. -DVECTOR_SIZE=16 253 * @note Leftover vector size has to be passed at compile time using -DVECTOR_SIZE_LEFTOVER. e.g. -DVECTOR_SIZE_LEFTOVER=3. It is defined as the remainder between the input's first dimension and VECTOR_SIZE 254 * @note In case the input is not a multiple of VECTOR_SIZE (2,4,8,16) -DNON_MULTIPLE_OF_VECTOR_SIZE must be passed. 255 * @note Quantized beta can be optionally passed at compile time using -DINPUT_BETA_MULTIPLIER and -DINPUT_BETA_LEFT_SHIFT (if undefined, assume beta equals 1.0) 256 * @note Additional quantization data must be passed at compile time using -DSCALED_DIFF_INT_BITS and -DEXP_ACCUMULATION_INT_BITS. 257 * @note -DDIFF_MIN must be passed at compile time. It is threshold difference between maximum value of input data and current processed value, it defines whether the value will be taken into account or not. 258 * @note In case the input's data type is QASYMM8_SIGNED, -DQASYMM8_SIGNED must be passed. 259 * 260 * @param[in] src_ptr Pointer to the source tensor slice. Supported data types: F16/F32 261 * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) 262 * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) 263 * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) 264 * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) 265 * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) 266 * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) 267 * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor 268 * @param[in] maxo_ptr Pointer to the max values tensor slice. Supported data types: same as @p src_ptr 269 * @param[in] maxo_stride_x Stride of the max values tensor in X dimension (in bytes) 270 * @param[in] maxo_step_x max_stride_x * number of elements along X processed per workitem(in bytes) 271 * @param[in] maxo_stride_y Stride of the max values tensor in Y dimension (in bytes) 272 * @param[in] maxo_step_y max_stride_y * number of elements along Y processed per workitem(in bytes) 273 * @param[in] maxo_stride_z Stride of the max values tensor in Z dimension (in bytes) 274 * @param[in] maxo_step_z max_stride_z * number of elements along Z processed per workitem(in bytes) 275 * @param[in] maxo_offset_first_element_in_bytes The offset of the first element in the max values tensor 276 * @param[out] dst_ptr Pointer to the destination tensor slice. Supported data types: same as @p src_ptr 277 * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) 278 * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) 279 * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) 280 * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) 281 * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) 282 * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) 283 * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor 284 * @param[out] sum_ptr Pointer to the sum values tensor slice. Supported data types: same as @p src_ptr 285 * @param[in] sum_stride_x Stride of the sum values tensor in X dimension (in bytes) 286 * @param[in] sum_step_x sum_stride_x * number of elements along X processed per workitem(in bytes) 287 * @param[in] sum_stride_y Stride of the sum values tensor in Y dimension (in bytes) 288 * @param[in] sum_step_y sum_stride_z * number of elements along Z processed per workitem(in bytes) 289 * @param[in] sum_stride_z Stride of the sum values tensor in Z dimension (in bytes) 290 * @param[in] sum_step_z sum_stride_z * number of elements along Z processed per workitem(in bytes) 291 * @param[in] sum_offset_first_element_in_bytes The offset of the first element in the sum values tensor 292 */ 293__kernel void softmax_layer_max_shift_exp_sum_quantized_parallel( 294 TENSOR3D_DECLARATION(src), 295 TENSOR3D_DECLARATION(maxo), 296 TENSOR3D_DECLARATION(dst), 297 TENSOR3D_DECLARATION(sum)) 298{ 299 const uint lid = get_local_id(0); 300 const uint x_offs = (VECTOR_SIZE_LEFTOVER + lid * VECTOR_SIZE); 301 302 __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x_offs * sizeof(DATA_TYPE) + get_global_id(1) * src_stride_y + get_global_id(2) * src_stride_z; 303 __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x_offs * sizeof(int) + get_global_id(1) * dst_stride_y + get_global_id(2) * dst_stride_z; 304 305 Image maxo = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(maxo); 306 Image sum = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(sum); 307 308 // Define one temporary vector per work-item. 309 __local VEC_INT tmp_local[GRID_SIZE]; 310 __local DATA_TYPE max_local; 311 312 VEC_BASE vec_min_val = (VEC_BASE)(MIN_VALUE); 313 VEC_BASE max_val_vec = vec_min_val; 314 315 // Number of iterations per work-item. 316 const uint width = (SRC_WIDTH / GRID_SIZE) >> LOG_VECTOR_SIZE; 317 // Calculate max of row 318 uint i = 0; 319 for(; i < width; ++i) 320 { 321 VEC_BASE data_max = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(src_addr + (i * GRID_SIZE * VECTOR_SIZE) * sizeof(DATA_TYPE))); 322 max_val_vec = max(data_max, max_val_vec); 323 } 324#ifdef NON_MULTIPLE_OF_GRID_SIZE 325 // How many work-items needed to complete the computation. 326 int boundary_workitems = (SRC_WIDTH % (GRID_SIZE * VECTOR_SIZE)) / VECTOR_SIZE; 327 if(lid < boundary_workitems) 328 { 329 VEC_BASE data_max = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(src_addr + (i * GRID_SIZE * VECTOR_SIZE) * sizeof(DATA_TYPE))); 330 max_val_vec = max(data_max, max_val_vec); 331 } 332#ifdef NON_MULTIPLE_OF_VECTOR_SIZE 333 VEC_INT widx; 334 if(lid == 0) 335 { 336 // Handle non multiple of 4 337 VEC_BASE data_max = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(src_addr - VECTOR_SIZE_LEFTOVER * sizeof(DATA_TYPE))); 338 widx = (VEC_INT)VECTOR_SIZE_LEFTOVER > VEC_OFFS(int, VECTOR_SIZE); 339 max_val_vec = max(max_val_vec, select(vec_min_val, data_max, CONVERT(widx, VEC_BASE))); 340 } 341#endif /* NON_MULTIPLE_OF_VECTOR_SIZE */ 342#endif /* NON_MULTIPLE_OF_GRID_SIZE */ 343 tmp_local[lid] = CONVERT(max_val_vec, VEC_INT); 344 345 barrier(CLK_LOCAL_MEM_FENCE); 346 347 if(GRID_SIZE >= 256) 348 { 349 if(lid < 128) 350 { 351 tmp_local[lid] = max(tmp_local[lid + 128], tmp_local[lid]); 352 } 353 barrier(CLK_LOCAL_MEM_FENCE); 354 } 355 if(GRID_SIZE >= 128) 356 { 357 if(lid < 64) 358 { 359 tmp_local[lid] = max(tmp_local[lid + 64], tmp_local[lid]); 360 } 361 barrier(CLK_LOCAL_MEM_FENCE); 362 } 363 if(GRID_SIZE >= 64) 364 { 365 if(lid < 32) 366 { 367 tmp_local[lid] = max(tmp_local[lid + 32], tmp_local[lid]); 368 } 369 barrier(CLK_LOCAL_MEM_FENCE); 370 } 371 if(GRID_SIZE >= 32) 372 { 373 if(lid < 16) 374 { 375 tmp_local[lid] = max(tmp_local[lid + 16], tmp_local[lid]); 376 } 377 barrier(CLK_LOCAL_MEM_FENCE); 378 } 379 if(GRID_SIZE >= 16) 380 { 381 if(lid < 8) 382 { 383 tmp_local[lid] = max(tmp_local[lid + 8], tmp_local[lid]); 384 } 385 barrier(CLK_LOCAL_MEM_FENCE); 386 } 387 if(GRID_SIZE >= 8) 388 { 389 if(lid < 4) 390 { 391 tmp_local[lid] = max(tmp_local[lid + 4], tmp_local[lid]); 392 } 393 barrier(CLK_LOCAL_MEM_FENCE); 394 } 395 if(GRID_SIZE >= 4) 396 { 397 if(lid < 2) 398 { 399 tmp_local[lid] = max(tmp_local[lid + 2], tmp_local[lid]); 400 } 401 barrier(CLK_LOCAL_MEM_FENCE); 402 } 403 if(lid == 0) 404 { 405 max_val_vec = max(CONVERT((tmp_local[lid + 1]), VEC_BASE), CONVERT((tmp_local[lid]), VEC_BASE)); 406 max_local = MAX_REDUCE(max_val_vec, VECTOR_SIZE); 407 } 408 barrier(CLK_LOCAL_MEM_FENCE); 409 410 /* Second section */ 411 412 // Set sum vector 413 VEC_INT sum1D = 0; 414 int max_val = convert_int(max_local); 415 416 // Shift values, exp and sum 417 for(i = 0; i < width; ++i) 418 { 419 VEC_BASE data = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(src_addr + (i * GRID_SIZE * VECTOR_SIZE) * sizeof(DATA_TYPE))); 420 VEC_INT data_fp = CONVERT(data, VEC_INT); 421 VEC_INT data_diff = data_fp - max_val; 422 VEC_INT data_diff_mult = mult_by_quantized_multiplier(data_diff); 423 data_fp = ASYMM_EXP_ON_NEGATIVE_VALUES(data_diff_mult, SCALED_DIFF_INT_BITS, VECTOR_SIZE); 424 data_fp = ASYMM_RESCALE(data_fp, 0, EXP_ACCUMULATION_INT_BITS, VECTOR_SIZE); 425 VSTORE(VECTOR_SIZE) 426 (data_diff, 0, (__global int *)(dst_addr + (i * GRID_SIZE * VECTOR_SIZE) * sizeof(int))); 427 sum1D = sum1D + select(0, data_fp, data_diff >= (VEC_INT)(DIFF_MIN)); 428 } 429#ifdef NON_MULTIPLE_OF_GRID_SIZE 430 boundary_workitems = (SRC_WIDTH % (GRID_SIZE * VECTOR_SIZE)) / VECTOR_SIZE; 431 if(lid < boundary_workitems) 432 { 433 VEC_BASE data = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(src_addr + (i * GRID_SIZE * VECTOR_SIZE) * sizeof(DATA_TYPE))); 434 VEC_INT data_fp = CONVERT(data, VEC_INT); 435 VEC_INT data_diff = data_fp - max_val; 436 VEC_INT data_diff_mult = mult_by_quantized_multiplier(data_diff); 437 data_fp = ASYMM_EXP_ON_NEGATIVE_VALUES(data_diff_mult, SCALED_DIFF_INT_BITS, VECTOR_SIZE); 438 data_fp = ASYMM_RESCALE(data_fp, 0, EXP_ACCUMULATION_INT_BITS, VECTOR_SIZE); 439 VSTORE(VECTOR_SIZE) 440 (data_diff, 0, (__global int *)(dst_addr + (i * GRID_SIZE * VECTOR_SIZE) * sizeof(int))); 441 sum1D = sum1D + select(0, data_fp, data_diff >= (VEC_INT)(DIFF_MIN)); 442 } 443#ifdef NON_MULTIPLE_OF_VECTOR_SIZE 444 if(lid == 0) 445 { 446 // Handle non multiple of vector size ((GRID_SIZE * i * 4) + 4, 0); move 4 float positions ahead, *4 is due to the stride 447 VEC_BASE data = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(src_addr - VECTOR_SIZE_LEFTOVER * sizeof(DATA_TYPE))); 448 VEC_INT data_fp = CONVERT(data, VEC_INT); 449 VEC_INT data_diff = data_fp - max_val; 450 VEC_INT data_diff_mult = mult_by_quantized_multiplier(data_diff); 451 data_fp = ASYMM_EXP_ON_NEGATIVE_VALUES(data_diff_mult, SCALED_DIFF_INT_BITS, VECTOR_SIZE); 452 data_fp = ASYMM_RESCALE(data_fp, 0, EXP_ACCUMULATION_INT_BITS, VECTOR_SIZE); 453 VSTORE_PARTIAL(VECTOR_SIZE, VECTOR_SIZE_LEFTOVER) 454 (data_diff, 0, (__global int *)(dst_addr - VECTOR_SIZE_LEFTOVER * sizeof(int))); 455 data_fp = select(MIN_VALUE, data_fp, data_diff >= (VEC_INT)(DIFF_MIN)); 456 data_fp = select(0, data_fp, widx); 457 sum1D = sum1D + data_fp; 458 } 459#endif /* NON_MULTIPLE_OF_VECTOR_SIZE */ 460#endif /* NON_MULTIPLE_OF_GRID_SIZE */ 461 tmp_local[lid] = sum1D; 462 463 barrier(CLK_LOCAL_MEM_FENCE); 464 465 if(GRID_SIZE >= 256) 466 { 467 if(lid < 128) 468 { 469 tmp_local[lid] += tmp_local[lid + 128]; 470 } 471 barrier(CLK_LOCAL_MEM_FENCE); 472 } 473 if(GRID_SIZE >= 128) 474 { 475 if(lid < 64) 476 { 477 tmp_local[lid] += tmp_local[lid + 64]; 478 } 479 barrier(CLK_LOCAL_MEM_FENCE); 480 } 481 if(GRID_SIZE >= 64) 482 { 483 if(lid < 32) 484 { 485 tmp_local[lid] += tmp_local[lid + 32]; 486 } 487 barrier(CLK_LOCAL_MEM_FENCE); 488 } 489 if(GRID_SIZE >= 32) 490 { 491 if(lid < 16) 492 { 493 tmp_local[lid] += tmp_local[lid + 16]; 494 } 495 barrier(CLK_LOCAL_MEM_FENCE); 496 } 497 if(GRID_SIZE >= 16) 498 { 499 if(lid < 8) 500 { 501 tmp_local[lid] += tmp_local[lid + 8]; 502 } 503 barrier(CLK_LOCAL_MEM_FENCE); 504 } 505 if(GRID_SIZE >= 8) 506 { 507 if(lid < 4) 508 { 509 tmp_local[lid] += tmp_local[lid + 4]; 510 } 511 barrier(CLK_LOCAL_MEM_FENCE); 512 } 513 if(GRID_SIZE >= 4) 514 { 515 if(lid < 2) 516 { 517 tmp_local[lid] += tmp_local[lid + 2]; 518 } 519 barrier(CLK_LOCAL_MEM_FENCE); 520 } 521 if(lid == 0) 522 { 523 sum1D = (tmp_local[lid + 1] + tmp_local[lid]); 524 // Perform sum reduction 525 *((__global int *)sum.ptr) = SUM_REDUCE(sum1D, VECTOR_SIZE); 526 } 527} 528#endif // #if defined(SRC_WIDTH) && defined(LOG_VECTOR_SIZE) 529#endif /* defined(DATA_TYPE) && defined(DIFF_MIN) && defined(VECTOR_SIZE) && defined(VECTOR_SIZE_LEFTOVER) && defined(MIN_VALUE) */ 530