/* * Copyright (c) 2017-2021 Arm Limited. * * SPDX-License-Identifier: MIT * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the "Software"), to * deal in the Software without restriction, including without limitation the * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or * sell copies of the Software, and to permit persons to whom the Software is * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be included in all * copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ #include "helpers.h" #if defined(DATA_TYPE) && defined(MIN_VALUE) && defined(VECTOR_SIZE) && defined(VECTOR_SIZE_LEFTOVER) /** Divides all the values of the input tensor by the sum calculated from softmax_layer_shift_exp_sum kernel. * * @note Datatype must be given as a preprocessor argument using -DDATA_TYPE, e.g. -DDATA_TYPE=float * @note The zero value for the given data type must be given as a preprocessor argument using -DMIN_VALUE, e.g. -DMIN_VALUE=0 * @note Vector size should be given as a preprocessor argument using -DVECTOR_SIZE=size. e.g. -DVECTOR_SIZE=16 * @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 * @note In case of log softmax, -DLOG_SOFTMAX must be passed. * * @param[in] src_ptr Pointer to the source tensor slice. Supported data types: F16/F32 * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor * @param[in] sum_ptr Pointer to the sum values tensor slice. Supported data types: same as @p src_ptr * @param[in] sum_stride_x Stride of the sum values tensor in X dimension (in bytes) * @param[in] sum_step_x sum_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] sum_stride_y Stride of the sum values tensor in Y dimension (in bytes) * @param[in] sum_step_y sum_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] sum_stride_z Stride of the sum values tensor in Z dimension (in bytes) * @param[in] sum_step_z sum_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] sum_offset_first_element_in_bytes The offset of the first element in the sum values tensor * @param[out] dst_ptr Pointer to the destination tensor slice. Supported data types: same as @p src_ptr * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor */ __kernel void softmax_layer_norm( TENSOR3D_DECLARATION(src), TENSOR3D_DECLARATION(sum), TENSOR3D_DECLARATION(dst)) { const int x_offs = max((int)(get_global_id(0) * VECTOR_SIZE - (VECTOR_SIZE - VECTOR_SIZE_LEFTOVER) % VECTOR_SIZE), 0) * sizeof(DATA_TYPE); __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x_offs + get_global_id(1) * src_stride_y + get_global_id(2) * src_stride_z; __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x_offs + get_global_id(1) * dst_stride_y + get_global_id(2) * dst_stride_z; Image sum = CONVERT_TENSOR3D_TO_IMAGE_STRUCT_NO_STEP(sum); // Load max value of 1D logits vector (row) DATA_TYPE sum_val = *((__global DATA_TYPE *)offset(&sum, 0, get_global_id(1))); VEC_DATA_TYPE(DATA_TYPE, VECTOR_SIZE) data0 = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)src_addr); #if defined(LOG_SOFTMAX) sum_val = log(sum_val); data0 -= sum_val; #else // defined(LOG_SOFTMAX) data0 /= sum_val; #endif // defined(LOG_SOFTMAX) STORE_VECTOR_SELECT(data, DATA_TYPE, dst_addr, VECTOR_SIZE, VECTOR_SIZE_LEFTOVER, VECTOR_SIZE_LEFTOVER != 0 && get_global_id(0) == 0) } #if defined(SRC_WIDTH) && defined(LOG_VECTOR_SIZE) && defined(MINVAL) /* Number of workitems in dimension 0. */ #if !defined(GRID_SIZE) #define GRID_SIZE 1 #endif /* !defined(GRID_SIZE) */ #define VEC_TYPE VEC_DATA_TYPE(DATA_TYPE, VECTOR_SIZE) #define SELECT_TYPE SELECT_VEC_DATA_TYPE(DATA_TYPE, VECTOR_SIZE) /** Identifies the maximum value across the 1st dimension and shifts the values of the input tensor by this maximum value, * then gets the exponent of each element as sums all elements across each row. * * @note Datatype must be given as a preprocessor argument using -DDATA_TYPE, e.g. -DDATA_TYPE=float * @note The zero value for the given data type must be given as a preprocessor argument using -DMIN_VALUE, e.g. -DMIN_VALUE=0 * @note Vector size should be given as a preprocessor argument using -DVECTOR_SIZE=size. e.g. -DVECTOR_SIZE=16 * @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 * @note In case the input is not a multiple of VECTOR_SIZE (2,4,8,16) -DNON_MULTIPLE_OF_VECTOR_SIZE must be passed. * @note Beta can be optionally passed at compile time using -DBETA (by default, it is 1.0). * @note In case of log softmax, -DLOG_SOFTMAX must be passed. * @note Based on the data type, the minimum possible value must be passed using -DMINVAL. For float it should be defined as -FLT_MAX, while for half it should be -HALF_MAX * * @param[in] src_ptr Pointer to the source tensor slice. Supported data types: F16/F32 * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor * @param[in] maxo_ptr Pointer to the max values tensor slice. Supported data types: same as @p src_ptr * @param[in] maxo_stride_x Stride of the max values tensor in X dimension (in bytes) * @param[in] maxo_step_x max_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] maxo_stride_y Stride of the max values tensor in Y dimension (in bytes) * @param[in] maxo_step_y max_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] maxo_stride_z Stride of the max values tensor in Z dimension (in bytes) * @param[in] maxo_step_z max_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] maxo_offset_first_element_in_bytes The offset of the first element in the max values tensor * @param[out] dst_ptr Pointer to the destination tensor slice. Supported data types: same as @p src_ptr * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor * @param[out] sum_ptr Pointer to the sum values tensor slice. Supported data types: same as @p src_ptr * @param[in] sum_stride_x Stride of the sum values tensor in X dimension (in bytes) * @param[in] sum_step_x sum_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] sum_stride_y Stride of the sum values tensor in Y dimension (in bytes) * @param[in] sum_step_y sum_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] sum_stride_z Stride of the sum values tensor in Z dimension (in bytes) * @param[in] sum_step_z sum_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] sum_offset_first_element_in_bytes The offset of the first element in the sum values tensor */ __kernel void softmax_layer_max_shift_exp_sum_serial( TENSOR3D_DECLARATION(src), TENSOR3D_DECLARATION(maxo), TENSOR3D_DECLARATION(dst), TENSOR3D_DECLARATION(sum)) { __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; __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; Image maxo = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(maxo); Image sum = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(sum); #ifdef BETA // Initialize beta VEC_TYPE beta = (VEC_TYPE)BETA; #endif /* BETA */ // Initialize local maximum VEC_TYPE max_val_vec = (VEC_TYPE)(MINVAL); #ifdef NON_MULTIPLE_OF_VECTOR_SIZE VEC_TYPE data = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)src_addr); SELECT_TYPE widx = (SELECT_TYPE)VECTOR_SIZE_LEFTOVER > VEC_OFFS(SELECT_DATA_TYPE(DATA_TYPE), VECTOR_SIZE); max_val_vec = max(max_val_vec, select((VEC_TYPE)(MINVAL), data, widx)); #endif /* NON_MULTIPLE_OF_VECTOR_SIZE */ for(uint i = VECTOR_SIZE_LEFTOVER; i < SRC_WIDTH; i += VECTOR_SIZE) { VEC_TYPE data = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(src_addr + i * sizeof(DATA_TYPE))); max_val_vec = max(data, max_val_vec); } // Perform max reduction DATA_TYPE max_val = MAX_REDUCE(max_val_vec, VECTOR_SIZE); *((__global DATA_TYPE *)maxo.ptr) = max_val; /* Second section */ // Set sum vector VEC_TYPE sum1D = 0; #ifdef NON_MULTIPLE_OF_VECTOR_SIZE data -= max_val; #ifdef BETA data *= beta; #endif /* BETA */ #ifdef LOG_SOFTMAX VSTORE_PARTIAL(VECTOR_SIZE, VECTOR_SIZE_LEFTOVER) (data, 0, (__global DATA_TYPE *)dst_addr); data = exp(data); data = select(0, data, widx); #else /* LOG_SOFTMAX */ data = exp(data); data = select(0, data, widx); VSTORE_PARTIAL(VECTOR_SIZE, VECTOR_SIZE_LEFTOVER) (data, 0, (__global DATA_TYPE *)dst_addr); #endif /* LOG_SOFTMAX */ sum1D += data; #endif /* NON_MULTIPLE_OF_VECTOR_SIZE */ // Shift values, exp and sum for(uint i = VECTOR_SIZE_LEFTOVER; i < SRC_WIDTH; i += VECTOR_SIZE) { VEC_TYPE data = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(src_addr + i * sizeof(DATA_TYPE))); data -= max_val; #ifdef BETA data *= beta; #endif /* BETA */ #ifdef LOG_SOFTMAX VSTORE(VECTOR_SIZE) (data, 0, (__global DATA_TYPE *)(dst_addr + i * sizeof(DATA_TYPE))); data = exp(data); #else /* LOG_SOFTMAX */ data = exp(data); VSTORE(VECTOR_SIZE) (data, 0, (__global DATA_TYPE *)(dst_addr + i * sizeof(DATA_TYPE))); #endif /* LOG_SOFTMAX */ sum1D += data; } // Perform sum reduction *((__global DATA_TYPE *)sum.ptr) = SUM_REDUCE(sum1D, VECTOR_SIZE); } /** Identifies the maximum value across the 1st dimension and shifts the values of the input tensor by this maximum value, * then gets the exponent of each element as sums all elements across each row. * * @note Datatype must be given as a preprocessor argument using -DDATA_TYPE, e.g. -DDATA_TYPE=float * @note The zero value for the given data type must be given as a preprocessor argument using -DMIN_VALUE, e.g. -DMIN_VALUE=0 * @note Vector size should be given as a preprocessor argument using -DVECTOR_SIZE=size. e.g. -DVECTOR_SIZE=16 * @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 * @note In case the input is not a multiple of VECTOR_SIZE (2,4,8,16) -DNON_MULTIPLE_OF_VECTOR_SIZE must be passed. * @note Beta can be optionally passed at compile time using -DBETA (by default, it is 1.0). * @note In case of log softmax, -DLOG_SOFTMAX must be passed. * @note Based on the data type, the minimum possible value must be passed using -DMINVAL. For float it should be defined as -FLT_MAX, while for half it should be -HALF_MAX * * @param[in] src_ptr Pointer to the source tensor slice. Supported data types: F16/F32 * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) * @param[in] src_step_x src_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) * @param[in] src_step_y src_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] src_stride_z Stride of the source tensor in Z dimension (in bytes) * @param[in] src_step_z src_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source tensor * @param[in] maxo_ptr Pointer to the max values tensor slice. Supported data types: same as @p src_ptr * @param[in] maxo_stride_x Stride of the max values tensor in X dimension (in bytes) * @param[in] maxo_step_x max_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] maxo_stride_y Stride of the max values tensor in Y dimension (in bytes) * @param[in] maxo_step_y max_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] maxo_stride_z Stride of the max values tensor in Z dimension (in bytes) * @param[in] maxo_step_z max_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] maxo_offset_first_element_in_bytes The offset of the first element in the max values tensor * @param[out] dst_ptr Pointer to the destination tensor slice. Supported data types: same as @p src_ptr * @param[in] dst_stride_x Stride of the destination tensor in X dimension (in bytes) * @param[in] dst_step_x dst_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] dst_stride_y Stride of the destination tensor in Y dimension (in bytes) * @param[in] dst_step_y dst_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] dst_stride_z Stride of the destination tensor in Z dimension (in bytes) * @param[in] dst_step_z dst_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor * @param[out] sum_ptr Pointer to the sum values tensor slice. Supported data types: same as @p src_ptr * @param[in] sum_stride_x Stride of the sum values tensor in X dimension (in bytes) * @param[in] sum_step_x sum_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] sum_stride_y Stride of the sum values tensor in Y dimension (in bytes) * @param[in] sum_step_y sum_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] sum_stride_z Stride of the sum values tensor in Z dimension (in bytes) * @param[in] sum_step_z sum_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] sum_offset_first_element_in_bytes The offset of the first element in the sum values tensor */ __kernel void softmax_layer_max_shift_exp_sum_parallel( TENSOR3D_DECLARATION(src), TENSOR3D_DECLARATION(maxo), TENSOR3D_DECLARATION(dst), TENSOR3D_DECLARATION(sum)) { const uint lid = get_local_id(0); const uint x_offs = (VECTOR_SIZE_LEFTOVER + lid * VECTOR_SIZE) * sizeof(DATA_TYPE); __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x_offs + get_global_id(1) * src_stride_y + get_global_id(2) * src_stride_z; __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x_offs + get_global_id(1) * dst_stride_y + get_global_id(2) * dst_stride_z; Image maxo = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(maxo); Image sum = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(sum); #ifdef BETA // Initialize beta VEC_TYPE beta = (VEC_TYPE)BETA; #endif /* BETA */ // Define one temporary vector per work-item. __local VEC_TYPE tmp_local[GRID_SIZE]; __local DATA_TYPE max_local; VEC_TYPE max_val_vec = (VEC_TYPE)(MINVAL); // Number of iterations per work-item. const uint width = (SRC_WIDTH / GRID_SIZE) >> LOG_VECTOR_SIZE; // Calculate max of row uint i = 0; for(; i < width; ++i) { VEC_TYPE data_max = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(src_addr + (i * GRID_SIZE * VECTOR_SIZE) * sizeof(DATA_TYPE))); max_val_vec = max(data_max, max_val_vec); } #ifdef NON_MULTIPLE_OF_GRID_SIZE // How many work-items needed to complete the computation. int boundary_workitems = (SRC_WIDTH % (GRID_SIZE * VECTOR_SIZE)) / VECTOR_SIZE; if(lid < boundary_workitems) { VEC_TYPE data_max = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(src_addr + (i * GRID_SIZE * VECTOR_SIZE) * sizeof(DATA_TYPE))); max_val_vec = max(data_max, max_val_vec); } #ifdef NON_MULTIPLE_OF_VECTOR_SIZE SELECT_TYPE widx; if(lid == 0) { // Handle non multiple of 4 VEC_TYPE data_max = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(src_addr - VECTOR_SIZE_LEFTOVER * sizeof(DATA_TYPE))); widx = (SELECT_TYPE)VECTOR_SIZE_LEFTOVER > VEC_OFFS(SELECT_DATA_TYPE(DATA_TYPE), VECTOR_SIZE); max_val_vec = max(max_val_vec, select((VEC_TYPE)(MINVAL), data_max, widx)); } #endif /* NON_MULTIPLE_OF_VECTOR_SIZE */ #endif /* NON_MULTIPLE_OF_GRID_SIZE */ tmp_local[lid] = max_val_vec; barrier(CLK_LOCAL_MEM_FENCE); if(GRID_SIZE >= 256) { if(lid < 128) { tmp_local[lid] = max(tmp_local[lid + 128], tmp_local[lid]); } barrier(CLK_LOCAL_MEM_FENCE); } if(GRID_SIZE >= 128) { if(lid < 64) { tmp_local[lid] = max(tmp_local[lid + 64], tmp_local[lid]); } barrier(CLK_LOCAL_MEM_FENCE); } if(GRID_SIZE >= 64) { if(lid < 32) { tmp_local[lid] = max(tmp_local[lid + 32], tmp_local[lid]); } barrier(CLK_LOCAL_MEM_FENCE); } if(GRID_SIZE >= 32) { if(lid < 16) { tmp_local[lid] = max(tmp_local[lid + 16], tmp_local[lid]); } barrier(CLK_LOCAL_MEM_FENCE); } if(GRID_SIZE >= 16) { if(lid < 8) { tmp_local[lid] = max(tmp_local[lid + 8], tmp_local[lid]); } barrier(CLK_LOCAL_MEM_FENCE); } if(GRID_SIZE >= 8) { if(lid < 4) { tmp_local[lid] = max(tmp_local[lid + 4], tmp_local[lid]); } barrier(CLK_LOCAL_MEM_FENCE); } if(GRID_SIZE >= 4) { if(lid < 2) { tmp_local[lid] = max(tmp_local[lid + 2], tmp_local[lid]); } barrier(CLK_LOCAL_MEM_FENCE); } if(lid == 0) { max_val_vec = max(tmp_local[lid + 1], tmp_local[lid]); max_local = MAX_REDUCE(max_val_vec, VECTOR_SIZE); } barrier(CLK_LOCAL_MEM_FENCE); /* Second section */ // Set sum vector VEC_TYPE sum1D = 0; DATA_TYPE max_val = max_local; // Shift values, exp and sum for(i = 0; i < width; ++i) { VEC_TYPE data = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(src_addr + (i * GRID_SIZE * VECTOR_SIZE) * sizeof(DATA_TYPE))); data -= max_val; #ifdef BETA data *= beta; #endif /* BETA */ #ifdef LOG_SOFTMAX VSTORE(VECTOR_SIZE) (data, 0, (__global DATA_TYPE *)(dst_addr + (i * GRID_SIZE * VECTOR_SIZE) * sizeof(DATA_TYPE))); data = exp(data); #else /* LOG_SOFTMAX */ data = exp(data); VSTORE(VECTOR_SIZE) (data, 0, (__global DATA_TYPE *)(dst_addr + (i * GRID_SIZE * VECTOR_SIZE) * sizeof(DATA_TYPE))); #endif /* LOG_SOFTMAX */ sum1D += data; } #ifdef NON_MULTIPLE_OF_GRID_SIZE boundary_workitems = (SRC_WIDTH % (GRID_SIZE * VECTOR_SIZE)) / VECTOR_SIZE; if(lid < boundary_workitems) { VEC_TYPE data = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(__global DATA_TYPE *)(src_addr + (i * GRID_SIZE * VECTOR_SIZE) * sizeof(DATA_TYPE))); data -= max_val; #ifdef BETA data *= beta; #endif /* BETA */ #ifdef LOG_SOFTMAX VSTORE(VECTOR_SIZE) (data, 0, (__global DATA_TYPE *)(dst_addr + (i * GRID_SIZE * VECTOR_SIZE) * sizeof(DATA_TYPE))); data = exp(data); #else /* LOG_SOFTMAX */ data = exp(data); VSTORE(VECTOR_SIZE) (data, 0, (__global DATA_TYPE *)(dst_addr + (i * GRID_SIZE * VECTOR_SIZE) * sizeof(DATA_TYPE))); #endif /* LOG_SOFTMAX */ sum1D += data; } #ifdef NON_MULTIPLE_OF_VECTOR_SIZE if(lid == 0) { // Handle non multiple of vector size ((GRID_SIZE * i * 4) + 4, 0); move 4 float positions ahead, *4 is due to the stride VEC_TYPE data = VLOAD(VECTOR_SIZE)(0, (__global DATA_TYPE *)(src_addr - VECTOR_SIZE_LEFTOVER * sizeof(DATA_TYPE))); data -= max_val; #ifdef BETA data *= beta; #endif /* BETA */ #ifdef LOG_SOFTMAX VSTORE_PARTIAL(VECTOR_SIZE, VECTOR_SIZE_LEFTOVER) (data, 0, (__global DATA_TYPE *)(dst_addr - VECTOR_SIZE_LEFTOVER * sizeof(DATA_TYPE))); data = exp(data); data = select(0, data, widx); #else /* LOG_SOFTMAX */ data = exp(data); data = select(0, data, widx); VSTORE_PARTIAL(VECTOR_SIZE, VECTOR_SIZE_LEFTOVER) (data, 0, (__global DATA_TYPE *)(dst_addr - VECTOR_SIZE_LEFTOVER * sizeof(DATA_TYPE))); #endif /* LOG_SOFTMAX */ sum1D += data; } #endif /* NON_MULTIPLE_OF_VECTOR_SIZE */ #endif /* NON_MULTIPLE_OF_GRID_SIZE */ tmp_local[lid] = sum1D; barrier(CLK_LOCAL_MEM_FENCE); if(GRID_SIZE >= 256) { if(lid < 128) { tmp_local[lid] += tmp_local[lid + 128]; } barrier(CLK_LOCAL_MEM_FENCE); } if(GRID_SIZE >= 128) { if(lid < 64) { tmp_local[lid] += tmp_local[lid + 64]; } barrier(CLK_LOCAL_MEM_FENCE); } if(GRID_SIZE >= 64) { if(lid < 32) { tmp_local[lid] += tmp_local[lid + 32]; } barrier(CLK_LOCAL_MEM_FENCE); } if(GRID_SIZE >= 32) { if(lid < 16) { tmp_local[lid] += tmp_local[lid + 16]; } barrier(CLK_LOCAL_MEM_FENCE); } if(GRID_SIZE >= 16) { if(lid < 8) { tmp_local[lid] += tmp_local[lid + 8]; } barrier(CLK_LOCAL_MEM_FENCE); } if(GRID_SIZE >= 8) { if(lid < 4) { tmp_local[lid] += tmp_local[lid + 4]; } barrier(CLK_LOCAL_MEM_FENCE); } if(GRID_SIZE >= 4) { if(lid < 2) { tmp_local[lid] += tmp_local[lid + 2]; } barrier(CLK_LOCAL_MEM_FENCE); } if(lid == 0) { sum1D = (tmp_local[lid + 1] + tmp_local[lid]); // Perform sum reduction *((__global DATA_TYPE *)sum.ptr) = SUM_REDUCE(sum1D, VECTOR_SIZE); } } #endif // defined(SRC_WIDTH) && defined(LOG_VECTOR_SIZE) && defined(MINVAL) #endif // defined(DATA_TYPE) && defined(MIN_VALUE) && defined(VECTOR_SIZE) && defined(VECTOR_SIZE_LEFTOVER)