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Diffstat (limited to 'src/core/CL/cl_kernels/reduction_operation.cl')
| -rw-r--r-- | src/core/CL/cl_kernels/reduction_operation.cl | 507 |
1 files changed, 0 insertions, 507 deletions
diff --git a/src/core/CL/cl_kernels/reduction_operation.cl b/src/core/CL/cl_kernels/reduction_operation.cl deleted file mode 100644 index b9d33bdd98..0000000000 --- a/src/core/CL/cl_kernels/reduction_operation.cl +++ /dev/null @@ -1,507 +0,0 @@ -/* - * Copyright (c) 2016-2020 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" -#include "helpers_asymm.h" - -#if defined(FLOAT_DATA_TYPE) -#define ISGREATER(x, y) isgreater(x, y) -#define ISLESS(x, y) isless(x, y) -#else // !FLOAT_DATA_TYPE -#if defined(WIDTH) -#define ISGREATER(x, y) (x > y) ? 1 : 0 -#define ISLESS(x, y) (x < y) ? 1 : 0 -#else // !defined(WIDTH) -#define ISGREATER(x, y) select((int16)0, (int16)-1, x > y) -#define ISLESS(x, y) select((int16)0, (int16)-1, x < y) -#endif // defined(WIDTH) -#endif // defined(FLOAT_DATA_TYPE) - -/** Calculate square sum of a vector - * - * @param[in] input Pointer to the first pixel. - * - * @return square sum of vector. - */ -inline DATA_TYPE square_sum(__global const DATA_TYPE *input) -{ - VEC_DATA_TYPE(DATA_TYPE, 16) - in = vload16(0, input); - - in *= in; - - in.s01234567 += in.s89ABCDEF; - in.s0123 += in.s4567; - in.s01 += in.s23; - - return (in.s0 + in.s1); -} - -/** Calculate sum of a vector - * - * @param[in] input Pointer to the first pixel. - * - * @return sum of vector. - */ -inline DATA_TYPE sum(__global const DATA_TYPE *input) -{ - VEC_DATA_TYPE(DATA_TYPE, 16) - in = vload16(0, input); - - in.s01234567 += in.s89ABCDEF; - in.s0123 += in.s4567; - in.s01 += in.s23; - - return (in.s0 + in.s1); -} - -/** Calculate product of a vector - * - * @param[in] input Pointer to the first pixel. - * - * @return product of vector. - */ -inline DATA_TYPE product(__global const DATA_TYPE *input) -{ - VEC_DATA_TYPE(DATA_TYPE, 16) - in = vload16(0, input); - - in.s01234567 *= in.s89ABCDEF; - in.s0123 *= in.s4567; - in.s01 *= in.s23; - - return (in.s0 * in.s1); -} -#if defined(OPERATION) -/** This kernel performs parallel reduction given an operation on x-axis. - * - * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float - * @note The operation we want to perform must be passed at compile time using -DOPERATION e.g. -DOPERATION=square_sum - * @note The mean flag must be passed at compile time using -DMEAN if we want to compute the mean value - * @note The product flag must be passed at compile time using -DPROD if we want to compute the product, otherwise sum will be used - * @note The width size must be passed at compile time using -DWIDTH e.g. -DWIDTH=128 if we want to compute the mean value - * - * @param[in] src_ptr Pointer to the source tensor. 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_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[in] partial_res_ptr The local buffer to hold partial result values. Supported data types: same as @p src_ptr - * @param[in] partial_res_stride_x Stride of the output tensor in X dimension (in bytes) - * @param[in] partial_res_step_x partial_res_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] partial_res_stride_y Stride of the output tensor in Y dimension (in bytes) - * @param[in] partial_res_step_y partial_res_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] partial_res_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[in] local_results Local buffer for storing the partial result - */ -__kernel void reduction_operation_x( - IMAGE_DECLARATION(src), - IMAGE_DECLARATION(partial_res), - __local DATA_TYPE *local_results) -{ - Image src = CONVERT_TO_IMAGE_STRUCT(src); - Image partial_res = CONVERT_TO_IMAGE_STRUCT(partial_res); - - unsigned int lsize = get_local_size(0); - unsigned int lid = get_local_id(0); - - for(unsigned int y = 0; y < get_local_size(1); ++y) - { - local_results[lid] = OPERATION((__global DATA_TYPE *)offset(&src, 0, y)); - barrier(CLK_LOCAL_MEM_FENCE); - - // Perform parallel reduction - for(unsigned int i = lsize >> 1; i > 0; i >>= 1) - { - if(lid < i) - { -#if defined(PROD) - local_results[lid] *= local_results[lid + i]; -#else // !defined(PROD) - local_results[lid] += local_results[lid + i]; -#endif // defined(PROD) - } - barrier(CLK_LOCAL_MEM_FENCE); - } - - if(lid == 0) - { -#if defined(MEAN) && defined(WIDTH) - if(y == get_local_size(1) - 1) - { - local_results[0] /= WIDTH; - } -#endif // defined(MEAN) && defined(WIDTH) - ((__global DATA_TYPE *)offset(&partial_res, get_group_id(0), y))[0] = local_results[0]; - } - } -} -#endif // defined(OPERATION) - -#if defined(WIDTH) -/** This kernel performs reduction on x-axis. (Non parallel) - * - * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float - * @note The width size must be passed at compile time using -DWIDTH e.g. -DWIDTH=128 - * @note The product flag must be passed at compile time using -DPROD if we want to compute the product, otherwise sum will be used - * @note In case of MIN and MAX the condition data type must be passed at compile time using -DCOND_DATA_TYPE e.g. -DCOND_DATA_TYPE=short - * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: S32/F16/F32 and QASYMM8 for operation MEAN - * @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_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[in] output_ptr The local buffer to hold sumed values. Supported data types: same as @p src_ptt - * @param[in] output_stride_x Stride of the output tensor in X dimension (in bytes) - * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] output_offset_first_element_in_bytes The offset of the first element in the source tensor - */ -__kernel void reduction_operation_non_parallel_x( - VECTOR_DECLARATION(src), - VECTOR_DECLARATION(output)) -{ - Vector src = CONVERT_TO_VECTOR_STRUCT(src); - Vector output = CONVERT_TO_VECTOR_STRUCT(output); - - DATA_TYPE_PROMOTED res = CONVERT(*((__global DATA_TYPE *)vector_offset(&src, 0)), DATA_TYPE_PROMOTED); - - // Convert input into F32 in order to perform quantized multiplication -#if defined(PROD) && defined(OFFSET) && defined(SCALE) - float res_f = DEQUANTIZE(res, OFFSET, SCALE, DATA_TYPE_PROMOTED, 1); -#endif // defined(PROD) && defined(OFFSET) && defined(SCALE) - - for(unsigned int x = 1; x < WIDTH; ++x) - { - DATA_TYPE_PROMOTED in = CONVERT(*((__global DATA_TYPE *)vector_offset(&src, x)), DATA_TYPE_PROMOTED); -#if defined(MIN) - res = select(res, in, CONVERT(ISLESS(in, res), COND_DATA_TYPE)); -#elif defined(MAX) - res = select(res, in, CONVERT(ISGREATER(in, res), COND_DATA_TYPE)); -#elif defined(PROD) -#if defined(OFFSET) && defined(SCALE) - res_f *= DEQUANTIZE(in, OFFSET, SCALE, DATA_TYPE_PROMOTED, 1); -#else // !(defined(OFFSET) && defined(SCALE)) - res *= in; -#endif // defined(OFFSET) && defined(SCALE) -#else // defined(SUM)) - res += in; -#endif // defined(MAX) || defined(MIN) || defined(PROD) - } - - // Store result -#if defined(MEAN) - res /= WIDTH; -#endif // defined(MEAN) - - // Subtract the offsets in case of quantized SUM -#if defined(SUM) && defined(OFFSET) && defined(SCALE) - res -= (WIDTH - 1) * OFFSET; -#endif // defined(OFFSET) && defined(OFFSET) && defined(SCALE) - - // Re-quantize -#if defined(PROD) && defined(OFFSET) && defined(SCALE) - res = QUANTIZE(res_f, OFFSET, SCALE, DATA_TYPE_PROMOTED, 1); -#endif // defined(PROD) && defined(OFFSET) && defined(SCALE) - - *((__global DATA_TYPE *)output.ptr) = CONVERT_SAT(res, DATA_TYPE); -} -#endif // defined(WIDTH) - -#if defined(HEIGHT) -/** This kernel performs reduction on y-axis. - * - * @note The input data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float - * @note The height size must be passed at compile time using -DHEIGHT e.g. -DHEIGHT=128 - * - * @param[in] src_ptr Pointer to the source tensor. Supported data types: QASYMM8/S32/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_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[in] output_ptr The local buffer to hold sumed values. Supported data types: same as @p src_ptt - * @param[in] output_stride_x Stride of the output tensor in X dimension (in bytes) - * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] output_stride_y Stride of the output tensor in Y dimension (in bytes) - * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] output_offset_first_element_in_bytes The offset of the first element in the source tensor - */ -__kernel void reduction_operation_y( - IMAGE_DECLARATION(src), - IMAGE_DECLARATION(output)) -{ - Image src = CONVERT_TO_IMAGE_STRUCT(src); - Image output = CONVERT_TO_IMAGE_STRUCT(output); - - VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16) - res = CONVERT(vload16(0, (__global DATA_TYPE *)offset(&src, 0, 0)), VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16)); - - // Convert input into F32 in order to perform quantized multiplication -#if defined(PROD) && defined(OFFSET) && defined(SCALE) - float16 res_f = DEQUANTIZE(res, OFFSET, SCALE, DATA_TYPE_PROMOTED, 16); -#endif // defined(PROD) && defined(OFFSET) && defined(SCALE) - -#if defined(SUM_SQUARE) - res *= res; -#endif // defined(SUM_SQUARE) - - for(unsigned int y = 1; y < HEIGHT; ++y) - { - VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16) - in = CONVERT(vload16(0, (__global DATA_TYPE *)offset(&src, 0, y)), VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16)); -#if defined(MIN) - res = select(res, in, ISLESS(in, res)); -#elif defined(MAX) - res = select(res, in, ISGREATER(in, res)); -#else // !(defined(MAX) || defined(MIN)) -#if defined(SUM_SQUARE) - in *= in; -#endif // defined(SUM_SQUARE) -#if defined(PROD) - -#if defined(OFFSET) && defined(SCALE) - res_f *= DEQUANTIZE(in, OFFSET, SCALE, DATA_TYPE_PROMOTED, 16); -#else // !(defined(OFFSET) && defined(SCALE)) - res *= in; -#endif // defined(OFFSET) && defined(SCALE) - -#else // !defined(PROD) - res += in; -#endif // defined(PROD) -#endif // defined(MAX) || defined(MIN) - } - -#if defined(MEAN) - res /= HEIGHT; -#endif // defined(MEAN) - - // Subtract the offsets in case of quantized SUM -#if defined(SUM) && defined(OFFSET) && defined(SCALE) - res -= (HEIGHT - 1) * OFFSET; -#endif // defined(OFFSET) && defined(OFFSET) && defined(SCALE) - - // Re-quantize -#if defined(PROD) && defined(OFFSET) && defined(SCALE) - res = QUANTIZE(res_f, OFFSET, SCALE, DATA_TYPE_PROMOTED, 16); -#endif // defined(PROD) && defined(OFFSET) && defined(SCALE) - - // Store result - vstore16(CONVERT_SAT(res, VEC_DATA_TYPE(DATA_TYPE, 16)), 0, (__global DATA_TYPE *)output.ptr); -} -#endif // defined(HEIGHT) - -#if defined(DEPTH) -/** This kernel performs reduction on z-axis. - * - * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float - * @note The depth size must be passed at compile time using -DDEPTH e.g. -DDEPTH=128 - * - * @param[in] input_ptr Pointer to the source tensor. Supported data types: QASYMM8/S32/F16/F32 - * @param[in] input_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] input_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] input_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[in] output_ptr The local buffer to hold sumed values. Supported data types: same as @p input_ptt - * @param[in] output_stride_x Stride of the output tensor in X dimension (in bytes) - * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] output_stride_y Stride of the output tensor in Y dimension (in bytes) - * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] output_stride_z Stride of the output tensor in Z dimension (in bytes) - * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] output_offset_first_element_in_bytes The offset of the first element in the source tensor - */ -__kernel void reduction_operation_z( - TENSOR3D_DECLARATION(input), - TENSOR3D_DECLARATION(output)) -{ - Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input); - Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); - - VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16) - res = CONVERT(vload16(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0)), VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16)); - - // Convert input into F32 in order to perform quantized multiplication -#if defined(PROD) && defined(OFFSET) && defined(SCALE) - float16 res_f = DEQUANTIZE(res, OFFSET, SCALE, DATA_TYPE_PROMOTED, 16); -#endif // defined(PROD) && defined(OFFSET) && defined(SCALE) - -#if defined(COMPLEX) - VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16) - res1 = CONVERT(vload16(0, (__global DATA_TYPE *)tensor3D_offset(&input, 8, 0, 0)), VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16)); -#endif // defined(COMPLEX) -#if defined(SUM_SQUARE) - res *= res; -#endif // defined(SUM_SQUARE) - - for(unsigned int z = 1; z < DEPTH; ++z) - { - VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16) - in = CONVERT(vload16(0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, z)), VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16)); - -#if defined(COMPLEX) - VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16) - in1 = CONVERT(vload16(0, (__global DATA_TYPE *)tensor3D_offset(&input, 8, 0, z)), VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16)); -#endif // defined(COMPLEX) - -#if defined(MIN) - res = select(res, in, ISLESS(in, res)); -#elif defined(MAX) - res = select(res, in, ISGREATER(in, res)); -#else // !(defined(MAX) || defined(MIN)) -#if defined(SUM_SQUARE) - in *= in; -#endif // defined(SUM_SQUARE) -#if defined(PROD) - -#if defined(OFFSET) && defined(SCALE) - res_f *= DEQUANTIZE(in, OFFSET, SCALE, DATA_TYPE_PROMOTED, 16); -#else // !(defined(OFFSET) && defined(SCALE)) - res *= in; -#endif // defined(OFFSET) && defined(SCALE) - -#else // !defined(PROD) - res += in; -#if defined(COMPLEX) - res1 += in1; -#endif // defined(COMPLEX) -#endif // defined(PROD) -#endif // defined(MAX) || defined(MIN) - } - -#if defined(MEAN) - res /= DEPTH; -#endif // defined(MEAN) - - // Subtract the offsets in case of quantized SUM -#if defined(SUM) && defined(OFFSET) && defined(SCALE) - res -= (DEPTH - 1) * OFFSET; -#endif // defined(OFFSET) && defined(OFFSET) && defined(SCALE) - - // Re-quantize -#if defined(PROD) && defined(OFFSET) && defined(SCALE) - res = QUANTIZE(res_f, OFFSET, SCALE, DATA_TYPE_PROMOTED, 16); -#endif // defined(PROD) && defined(OFFSET) && defined(SCALE) - - // Store result - vstore16(CONVERT_SAT(res, VEC_DATA_TYPE(DATA_TYPE, 16)), 0, (__global DATA_TYPE *)output.ptr); -#if defined(COMPLEX) - vstore16(CONVERT(res1, VEC_DATA_TYPE(DATA_TYPE, 16)), 0, (__global DATA_TYPE *)tensor3D_offset(&output, 8, 0, 0)); -#endif // defined(COMPLEX) -} -#endif /* defined(DEPTH) */ - -#if defined(BATCH) && defined(DEPTH) -/** This kernel performs reduction on w-axis. - * - * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float - * @note The batch size must be passed at compile time using -DBATCH e.g. -DBATCH=128 - * @note The depth size must be passed at compile time using -DBATCH e.g. -DDEPTH=128 - * - * @param[in] input_ptr Pointer to the source tensor. Supported data types: QASYMM8/S32/F16/F32 - * @param[in] input_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] input_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] input_stride_w Stride of the source tensor in W dimension (in bytes) - * @param[in] input_step_w input_stride_w * number of elements along W processed per workitem(in bytes) - * @param[in] input_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[in] output_ptr The local buffer to hold sumed values. Supported data types: same as @p input_ptt - * @param[in] output_stride_x Stride of the output tensor in X dimension (in bytes) - * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] output_stride_y Stride of the output tensor in Y dimension (in bytes) - * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] output_stride_z Stride of the output tensor in Z dimension (in bytes) - * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] output_stride_w Stride of the output tensor in W dimension (in bytes) - * @param[in] output_step_w output_stride_w * number of elements along W processed per workitem(in bytes) - * @param[in] output_offset_first_element_in_bytes The offset of the first element in the source tensor - */ -__kernel void reduction_operation_w( - TENSOR4D_DECLARATION(input), - TENSOR4D_DECLARATION(output)) -{ - Tensor4D input = CONVERT_TO_TENSOR4D_STRUCT(input, DEPTH); - Tensor4D output = CONVERT_TO_TENSOR4D_STRUCT(output, DEPTH); - - VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16) - res = CONVERT(vload16(0, (__global DATA_TYPE *)tensor4D_offset(&input, 0, 0, 0, 0)), VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16)); - - // Convert input into F32 in order to perform quantized multiplication -#if defined(PROD) && defined(OFFSET) && defined(SCALE) - float16 res_f = DEQUANTIZE(res, OFFSET, SCALE, DATA_TYPE_PROMOTED, 16); -#endif // defined(PROD) && defined(OFFSET) && defined(SCALE) - -#if defined(SUM_SQUARE) - res *= res; -#endif // defined(SUM_SQUARE) - - for(unsigned int w = 1; w < BATCH; ++w) - { - VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16) - in = CONVERT(vload16(0, (__global DATA_TYPE *)tensor4D_offset(&input, 0, 0, 0, w)), VEC_DATA_TYPE(DATA_TYPE_PROMOTED, 16)); - -#if defined(MIN) - res = select(res, in, ISLESS(in, res)); -#elif defined(MAX) - res = select(res, in, ISGREATER(in, res)); -#else // !(defined(MAX) || defined(MIN)) -#if defined(SUM_SQUARE) - in *= in; -#endif // defined(SUM_SQUARE) -#if defined(PROD) - -#if defined(OFFSET) && defined(SCALE) - res_f *= DEQUANTIZE(in, OFFSET, SCALE, DATA_TYPE_PROMOTED, 16); -#else // !(defined(OFFSET) && defined(SCALE)) - res *= in; -#endif // defined(OFFSET) && defined(SCALE) - -#else // !defined(PROD) - res += in; -#endif //defined(PROD) -#endif // defined(MAX) || defined(MIN) - } - -#if defined(MEAN) - res /= BATCH; -#endif // defined(MEAN) - - // Subtract the offsets in case of quantized SUM -#if defined(SUM) && defined(OFFSET) && defined(SCALE) - res -= (BATCH - 1) * OFFSET; -#endif // defined(OFFSET) && defined(OFFSET) && defined(SCALE) - - // Re-quantize -#if defined(PROD) && defined(OFFSET) && defined(SCALE) - res = QUANTIZE(res_f, OFFSET, SCALE, DATA_TYPE_PROMOTED, 16); -#endif // defined(PROD) && defined(OFFSET) && defined(SCALE) - - // Store result - vstore16(CONVERT_SAT(res, VEC_DATA_TYPE(DATA_TYPE, 16)), 0, (__global DATA_TYPE *)output.ptr); -} -#endif /* defined(BATCH) && defined(DEPTH) */ |