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author | Adnan AlSinan <adnan.alsinan@arm.com> | 2021-07-05 13:12:52 +0100 |
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committer | Georgios Pinitas <georgios.pinitas@arm.com> | 2021-07-25 13:04:23 +0000 |
commit | 7075fe2c5ee6f7cfe7cfd9454d905235e70b9ac4 (patch) | |
tree | b65671bdf37eb1ef8cc30ef64ab572da795546fa /src/core/CL/cl_kernels/common/softmax_layer.cl | |
parent | 22f5ed51f1b01f7cf6993a556a0b763e437926fc (diff) | |
download | ComputeLibrary-7075fe2c5ee6f7cfe7cfd9454d905235e70b9ac4.tar.gz |
Reorganize the kernels into nhwc, nchw and common folders
The Following kernels have been split into nchw/nhwc kernels files:
- batchnormalization_layer
- batch_to_space
- channel_shuffle
- depth_to_space
- dequantization_layer
- im2col
- normalization_layer
- normalize_planar_yuv_layer
- normalize_planar_yuv_layer_quantized
- pooling_layer
- pooling_layer_quantized
- remap
- reorg_layer
- scale
- scale_quantized
- space_to_batch
- space_to_depth
- upsample_layer
- winograd_filter_transform
- winograd_input_transform
- winograd_output_transform
The following kernels have been moved to nchw folder:
- direct_convolution1x1
- direct_convolution3x3
- direct_convolution5x5
- direct_convolution_quantized
- prior_box_layer
The following kernels have been moved to nhwc folder:
- direct_convolution
- dwc_native_fp_nhwc
- dwc_native_quantized_nhwc
The following kernels have been removed:
- sobel_filter
While the rest kerenls have been moved to the common folder.
Partially resolves COMPMID-4453
Signed-off-by: Adnan AlSinan <adnan.alsinan@arm.com>
Change-Id: Ic327ac935687ec351c610c65a3c6357f364a5a58
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/5919
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'src/core/CL/cl_kernels/common/softmax_layer.cl')
-rw-r--r-- | src/core/CL/cl_kernels/common/softmax_layer.cl | 531 |
1 files changed, 531 insertions, 0 deletions
diff --git a/src/core/CL/cl_kernels/common/softmax_layer.cl b/src/core/CL/cl_kernels/common/softmax_layer.cl new file mode 100644 index 0000000000..4d2d89dd73 --- /dev/null +++ b/src/core/CL/cl_kernels/common/softmax_layer.cl @@ -0,0 +1,531 @@ +/* + * 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)
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