/* * 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" #include "repeat.h" #include "tile_helpers.h" #if defined(POOL_AVG) || defined(POOL_L2) #define POOL_OP(x, y) ((x) + (y)) #else /* defined(POOL_AVG) || defined(POOL_L2) */ #define POOL_OP(x, y) (fmax((x), (y))) #endif /* defined(POOL_AVG) || defined(POOL_L2) */ #if defined(POOL_L2) #define POW2_OP(x, vec_size) ((x) * (x)) #else /* defined(POOL_L2) */ #define POW2_OP(x, vec_size) (x) #endif /* defined(POOL_L2) */ #define DIV_OP(x, y) (x * (1.f / y)) #define SQRT_OP(x) sqrt((x)) #if STRIDE_X == 1 #define POOLING3x3(res, input, output) POOLING3x3_STRIDE1(res, input, output) #elif STRIDE_X == 2 /* STRIDE_X == 1 */ #define POOLING3x3(res, input, output) POOLING3x3_STRIDE2(res, input, output) #elif STRIDE_X == 3 /* STRIDE_X not equals 1 or 2 */ #define POOLING3x3(res, input, output) POOLING3x3_STRIDE3(res, input, output) #endif /* STRIDE_X == 3 */ #if defined(FP_MIXED_PRECISION) #define CONVERT_TO_ACC_DATA_TYPE(x, n) CONVERT(x, VEC_DATA_TYPE(ACC_DATA_TYPE, n)) #define VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(n, offset, ptr) \ CONVERT_TO_ACC_DATA_TYPE(vload##n(offset, ptr), n) #else /* defined(FP_MIXED_PRECISION) */ #define VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(n, offset, ptr) vload##n(offset, ptr) #endif /* defined(FP_MIXED_PRECISION) */ #define POOLING3x3_STRIDE1(res, input, output) \ ({ \ VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \ data00 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(4, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0)); \ VEC_DATA_TYPE(ACC_DATA_TYPE, 2) \ data01 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(2, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0) + 4); \ VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \ data10 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(4, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0)); \ VEC_DATA_TYPE(ACC_DATA_TYPE, 2) \ data11 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(2, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0) + 4); \ VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \ data20 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(4, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 2, 0)); \ VEC_DATA_TYPE(ACC_DATA_TYPE, 2) \ data21 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(2, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 2, 0) + 4); \ data00 = POW2_OP(data00, 4); \ data01 = POW2_OP(data01, 2); \ data10 = POW2_OP(data10, 4); \ data11 = POW2_OP(data11, 2); \ data20 = POW2_OP(data20, 4); \ data21 = POW2_OP(data21, 2); \ \ VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \ values00 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 8))(data00.s01212323); \ VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \ values01 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(data01.s0, data00.s3, data01.s01); \ VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \ values10 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 8))(data10.s01212323); \ VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \ values11 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(data11.s0, data10.s3, data11.s01); \ VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \ values20 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 8))(data20.s01212323); \ VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \ values21 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(data21.s0, data20.s3, data21.s01); \ \ values00 = POOL_OP(values00, values10); \ values01 = POOL_OP(values01, values11); \ values00 = POOL_OP(values00, values20); \ values01 = POOL_OP(values01, values21); \ \ res = POOL_OP((VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(values00.s036, values01.s1), (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(values00.s147, values01.s2)); \ res = POOL_OP(res, (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(values00.s25, values01.s03)); \ }) #define POOLING3x3_STRIDE2(res, input, output) \ ({ \ VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \ data00 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(8, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0)); \ ACC_DATA_TYPE data01 = (ACC_DATA_TYPE)(*((__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0) + 8)); \ VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \ data10 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(8, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0)); \ ACC_DATA_TYPE data11 = (ACC_DATA_TYPE)(*((__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0) + 8)); \ VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \ data20 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(8, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 2, 0)); \ ACC_DATA_TYPE data21 = (ACC_DATA_TYPE)(*((__global DATA_TYPE *)tensor3D_offset(&input, 0, 2, 0) + 8)); \ data00 = POW2_OP(data00, 8); \ data01 = POW2_OP(data01, 1); \ data10 = POW2_OP(data10, 8); \ data11 = POW2_OP(data11, 1); \ data20 = POW2_OP(data20, 8); \ data21 = POW2_OP(data21, 1); \ \ VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \ values00 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 8))(data00.s01223445); \ VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \ values01 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(data00.s667, data01); \ VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \ values10 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 8))(data10.s01223445); \ VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \ values11 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(data10.s667, data11); \ VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \ values20 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 8))(data20.s01223445); \ VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \ values21 = (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(data20.s667, data21); \ \ values00 = POOL_OP(values00, values10); \ values01 = POOL_OP(values01, values11); \ values00 = POOL_OP(values00, values20); \ values01 = POOL_OP(values01, values21); \ \ res = POOL_OP((VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(values00.s036, values01.s1), (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(values00.s147, values01.s2)); \ res = POOL_OP(res, (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(values00.s25, values01.s03)); \ }) #define POOLING3x3_STRIDE3(res, input, output) \ ({ \ VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \ data00 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(8, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0)); \ VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \ data01 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(4, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0) + 8); \ VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \ data10 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(8, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0)); \ VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \ data11 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(4, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0) + 8); \ VEC_DATA_TYPE(ACC_DATA_TYPE, 8) \ data20 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(8, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 2, 0)); \ VEC_DATA_TYPE(ACC_DATA_TYPE, 4) \ data21 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(4, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 2, 0) + 8); \ data00 = POW2_OP(data00, 8); \ data01 = POW2_OP(data01, 4); \ data10 = POW2_OP(data10, 8); \ data11 = POW2_OP(data11, 4); \ data20 = POW2_OP(data20, 8); \ data21 = POW2_OP(data21, 4); \ \ data00 = POOL_OP(data00, data10); \ data01 = POOL_OP(data01, data11); \ data00 = POOL_OP(data00, data20); \ data01 = POOL_OP(data01, data21); \ \ res = POOL_OP((VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(data00.s036, data01.s1), (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(data00.s147, data01.s2)); \ res = POOL_OP(res, (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(data00.s25, data01.s03)); \ }) ACC_DATA_TYPE calculate_avg_scale(const int pool_size_x, const int pool_size_y, const int upper_bound_w, const int upper_bound_h, const int pad_x, const int pad_y, const int stride_x, const int stride_y) { int start_x = get_global_id(0) * stride_x - pad_x; int start_y = get_global_id(1) * stride_y - pad_y; const int end_x = min(start_x + pool_size_x, upper_bound_w); const int end_y = min(start_y + pool_size_y, upper_bound_h); #if defined(EXCLUDE_PADDING) start_x = max(0, start_x); start_y = max(0, start_y); #endif /* defined(EXCLUDE_PADDING) */ return ((end_y - start_y) * (end_x - start_x)); } /** Performs a pooling function of pool size equal to 2. * * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16/F32; * @note In case of average pooling the following information must be passed at compile time: * -DPOOL_AVG or -DPOOL_L2 must be provided otherwise max pooling will be performed. * -DMAX_WIDTH and -DMAX_HEIGHT which are the maximum accessible indeces in x and y dimensions (width + pad) * -DSTRIDE_X and -DSTRIDE_Y which are the steps of the window along the x and y directions * -DPAD_X and -DPAD_Y which are the pooling paddings in x and y dimension * * @param[in] input_ptr Pointer to the source tensor. Supported data types: 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[out] output_ptr Pointer to the destination tensor. Supported data types: same as @p input_ptr * @param[in] output_stride_x Stride of the destination 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 destination 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 source 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 destination tensor */ __kernel void pooling_layer_2( TENSOR3D_DECLARATION(input), TENSOR3D_DECLARATION(output)) { // Get pixels pointer Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input); Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); // Load data VEC_DATA_TYPE(ACC_DATA_TYPE, 2) data0 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(2, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0)); VEC_DATA_TYPE(ACC_DATA_TYPE, 2) data1 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(2, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0)); #if defined(POOL_L2) // Raise to power of 2 for L2 Pooling data0 = POW2_OP(data0, 2); data1 = POW2_OP(data1, 2); #endif /* defined(POOL_L2) */ // Perform calculations data0 = POOL_OP(data0, data1); ACC_DATA_TYPE res = POOL_OP(data0.s0, data0.s1); #if defined(POOL_AVG) || defined(POOL_L2) // Divide by pool region in case of average or l2 pooling res = DIV_OP(res, calculate_avg_scale(2, 2, MAX_WIDTH, MAX_HEIGHT, PAD_X, PAD_Y, STRIDE_X, STRIDE_Y)); #endif /* defined(POOL_AVG) || defined(POOL_L2) */ #if defined(POOL_L2) // Take square root of the result in L2 pooling res = SQRT_OP(res); #endif /* defined(POOL_L2) */ // Store result *(__global DATA_TYPE *)output.ptr = (DATA_TYPE)res; } /** Performs a pooling function of pool size equal to 3 * * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16/F32; * @note In case of average pooling the following information must be passed at compile time: * -DPOOL_AVG or -DPOOL_L2 must be provided otherwise max pooling will be performed. * -DMAX_WIDTH and -DMAX_HEIGHT which are the maximum accessible indeces in x and y dimensions (width + pad) * -DSTRIDE_X and -DSTRIDE_Y which are the steps of the window along the x and y directions * -DPAD_X and -DPAD_Y which are the pooling paddings in x and y dimension * * @param[in] input_ptr Pointer to the source tensor. Supported data types: 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[out] output_ptr Pointer to the destination tensor. Supported data types: same as @p input_ptr * @param[in] output_stride_x Stride of the destination 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 destination 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 source 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 destination tensor */ __kernel void pooling_layer_3( TENSOR3D_DECLARATION(input), TENSOR3D_DECLARATION(output)) { // Get pixels pointer Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input); Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); // Load data VEC_DATA_TYPE(ACC_DATA_TYPE, 3) data0 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(3, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 0, 0)); VEC_DATA_TYPE(ACC_DATA_TYPE, 3) data1 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(3, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 1, 0)); VEC_DATA_TYPE(ACC_DATA_TYPE, 3) data2 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(3, 0, (__global DATA_TYPE *)tensor3D_offset(&input, 0, 2, 0)); #if defined(POOL_L2) // Raise to power of 2 for L2 Pooling data0 = POW2_OP(data0, 3); data1 = POW2_OP(data1, 3); data2 = POW2_OP(data2, 3); #endif /* defined(POOL_L2) */ // Perform calculations data0 = POOL_OP(data0, data1); data0 = POOL_OP(data0, data2); ACC_DATA_TYPE res = POOL_OP(POOL_OP(data0.s0, data0.s1), data0.s2); #if defined(POOL_AVG) || defined(POOL_L2) // Divide by pool region in case of average pooling res = DIV_OP(res, calculate_avg_scale(3, 3, MAX_WIDTH, MAX_HEIGHT, PAD_X, PAD_Y, STRIDE_X, STRIDE_Y)); #endif /* defined(POOL_AVG) || defined(POOL_L2) */ #if defined(POOL_L2) // Take square root of the result in L2 pooling res = SQRT_OP(res); #endif /* defined(POOL_L2) */ // Store result *(__global DATA_TYPE *)output.ptr = (DATA_TYPE)res; } #if defined(POOLING3x3) #define CONVERT_OP(data_type) convert_##data_type##4 #define CONVERT_VECTOR4(data_type) CONVERT_OP(data_type) VEC_DATA_TYPE(ACC_DATA_TYPE, 4) calculate_avg_scale4(const int pool_size, const int upper_bound_w, const int upper_bound_h, const int pad_x, const int pad_y, const int stride_x, const int stride_y) { int4 start_x = ((int4)get_global_id(0) * 4 + (int4)(0, 1, 2, 3)) * (int4)stride_x - (int4)pad_x; int start_y = get_global_id(1) * stride_y - pad_y; const int4 end_x = min(start_x + (int4)pool_size, (int4)upper_bound_w); const int end_y = min(start_y + pool_size, upper_bound_h); #if defined(EXCLUDE_PADDING) start_x = max((int4)0, start_x); start_y = max(0, start_y); #endif /* defined(EXCLUDE_PADDING) */ return (VEC_DATA_TYPE(ACC_DATA_TYPE, 4))(1.f) / CONVERT_VECTOR4(ACC_DATA_TYPE)(((int4)(end_y - start_y)) * (end_x - start_x)); } /** Performs an optimized pooling function of pool size equal to 3 when the stride_x is less equal than 3 * * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16/F32; * @note In case of average pooling the following information must be passed at compile time: * -DPOOL_AVG or -DPOOL_L2 must be provided otherwise max pooling will be performed. * -DMAX_WIDTH and -DMAX_HEIGHT which are the maximum accessible indeces in x and y dimensions (width + pad) * -DSTRIDE_X and -DSTRIDE_Y which are the steps of the window along the x and y directions * -DPAD_X and -DPAD_Y which are the pooling paddings in x and y dimension * * @param[in] input_ptr Pointer to the source tensor. Supported data types: 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[out] output_ptr Pointer to the destination tensor. Supported data types: same as @p input_ptr * @param[in] output_stride_x Stride of the destination 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 destination 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 source 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 destination tensor */ __kernel void pooling_layer_optimized_3( TENSOR3D_DECLARATION(input), TENSOR3D_DECLARATION(output)) { // Get pixels pointer Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input); Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); VEC_DATA_TYPE(ACC_DATA_TYPE, 4) res; // Perform pooling 3x3 for 4 output elements POOLING3x3(res, input, output); #if defined(POOL_AVG) || defined(POOL_L2) // Divide by pool region in case of average pooling res *= calculate_avg_scale4(3, MAX_WIDTH, MAX_HEIGHT, PAD_X, PAD_Y, STRIDE_X, STRIDE_Y); #endif /* defined(POOL_AVG) || defined(POOL_L2) */ #if defined(POOL_L2) // Take square root of the result in L2 pooling res = SQRT_OP(res); #endif /* defined(POOL_L2) */ vstore4(CONVERT(res, VEC_DATA_TYPE(DATA_TYPE, 4)), 0, (__global DATA_TYPE *)output.ptr); } #endif // defined(POOLING3x3) #if defined(POOL_SIZE_X) && defined(POOL_SIZE_Y) /** Performs a pooling function of pool size equal to N (NCHW) * * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16/F32; * @note Pool sizes must be passed using -DPOOL_SIZE_X and -DPOOL_SIZE_Y e.g. -DPOOL_SIZE_X=13; * @note In case of average pooling the following information must be passed at compile time: * -DPOOL_AVG must be provided otherwise max pooling will be performed. * -DMAX_WIDTH and -DMAX_HEIGHT which are the maximum accessible indeces in x and y dimensions (width + pad) * -DSTRIDE_X and -DSTRIDE_Y which are the steps of the window along the x and y directions * -DPAD_X and -DPAD_Y which are the pooling paddings in x and y dimension * @note The initial value for the pooling operation must be passed at compile time using -DINITIAL_VALUE e.g. -DINITIAL_VALUE=0 * * @param[in] input_ptr Pointer to the source tensor. Supported data types: 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[out] output_ptr Pointer to the destination tensor. Supported data types: same as @p input_ptr * @param[in] output_stride_x Stride of the destination 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 destination 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 source 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 destination tensor */ __kernel void pooling_layer_MxN_nchw( TENSOR3D_DECLARATION(input), TENSOR3D_DECLARATION(output)) { // Get pixels pointer Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input); Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); VEC_DATA_TYPE(ACC_DATA_TYPE, 8) vdata = INITIAL_VALUE; ACC_DATA_TYPE sdata = INITIAL_VALUE; // Load data for(int y = 0; y < POOL_SIZE_Y; y++) { int x = 0; for(; x <= ((int)POOL_SIZE_X - 8); x += 8) { VEC_DATA_TYPE(ACC_DATA_TYPE, 8) data0 = VLOAD_AND_CONVERT_TO_ACC_DATA_TYPE(8, 0, (__global DATA_TYPE *)tensor3D_offset(&input, x, y, 0)); #if defined(POOL_L2) // Raise to power of 2 for L2 Pooling data0 *= data0; #endif /* defined(POOL_L2) */ vdata = POOL_OP(vdata, data0); } // Leftover for(; x < (int)POOL_SIZE_X; ++x) { ACC_DATA_TYPE data0 = (ACC_DATA_TYPE)(*((__global DATA_TYPE *)tensor3D_offset(&input, x, y, 0))); #if defined(POOL_L2) // Raise to power of 2 for L2 Pooling data0 *= data0; #endif /* defined(POOL_L2) */ sdata = POOL_OP(sdata, data0); } } // Reduce result VEC_DATA_TYPE(ACC_DATA_TYPE, 4) reduce4 = POOL_OP(vdata.s0123, vdata.s4567); VEC_DATA_TYPE(ACC_DATA_TYPE, 2) reduce2 = POOL_OP(reduce4.s01, reduce4.s23); ACC_DATA_TYPE res = POOL_OP(reduce2.s0, reduce2.s1); res = POOL_OP(res, sdata); #if defined(POOL_AVG) || defined(POOL_L2) // Divide by pool region in case of average pooling res = DIV_OP(res, calculate_avg_scale(POOL_SIZE_X, POOL_SIZE_Y, MAX_WIDTH, MAX_HEIGHT, PAD_X, PAD_Y, STRIDE_X, STRIDE_Y)); #endif /* defined(POOL_AVG) || defined(POOL_L2) */ #if defined(POOL_L2) // Take square root of the result in L2 pooling res = SQRT_OP(res); #endif /* defined(POOL_L2) */ // Store result *(__global DATA_TYPE *)output.ptr = (DATA_TYPE)res; } #endif // defined(POOL_SIZE_X) && defined(POOL_SIZE_Y) #if defined(PAD_TENSOR_LEFT) && defined(PAD_TENSOR_RIGHT) && defined(PAD_TENSOR_TOP) && defined(PAD_TENSOR_BOTTOM) inline void offset_no_padding_nchw(const Tensor3D *input, uint *offset_top, uint *offset_bottom) { const int pad_horiz = PAD_TENSOR_LEFT + PAD_TENSOR_RIGHT; const int pad_vert = PAD_TENSOR_TOP + PAD_TENSOR_BOTTOM; const int x = get_global_id(0) * STRIDE_X; const int y = get_global_id(1) * STRIDE_Y; const int z = get_global_id(2); //x axis: width, y axis: height, z axis: component const uint padded_offset = input->offset_first_element_in_bytes + x * input->stride_x + y * input->stride_y + z * input->stride_z; const uint offset_base = padded_offset - y * pad_horiz * sizeof(DATA_TYPE) /* Horizontal padding for each row */ - PAD_TENSOR_TOP * input->stride_y /* top padding */ - z * MAX_HEIGHT * pad_horiz * sizeof(DATA_TYPE) - z * pad_vert * input->stride_y /* Z plane padding */ - PAD_TENSOR_LEFT * sizeof(DATA_TYPE); #if defined(TENSOR_CHANNEL) && defined(TENSOR_WIDTH) && defined(TENSOR_HEIGHT) *offset_top = (uint)((offset_base / sizeof(DATA_TYPE)) % (TENSOR_CHANNEL * TENSOR_WIDTH * TENSOR_HEIGHT)); #else /* defined(TENSOR_CHANNEL) && defined(TENSOR_WIDTH) && defined(TENSOR_HEIGHT) */ *offset_top = (uint)(offset_base / sizeof(DATA_TYPE)); #endif /* defined(TENSOR_CHANNEL) && defined(TENSOR_WIDTH) && defined(TENSOR_HEIGHT) */ *offset_bottom = *offset_top + input->stride_y / sizeof(DATA_TYPE) - pad_horiz; return; } #endif //defined(PAD_TENSOR_LEFT) && defined(PAD_TENSOR_RIGHT) && defined(PAD_TENSOR_TOP) && defined(PAD_TENSOR_BOTTOM) /** Performs a MAX pooling of pool size equal to 2, and record max value indices for NCHW. * * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=half. Supported data types are F32 * @note Pool sizes must be passed using -DPOOL_SIZE_X and -DPOOL_SIZE_Y e.g. -DPOOL_SIZE_X=13; * @note Tensors width and height must be passed at compile time using -DMAX_WIDTH and -DMAX_HEIGHT * @note Pool strides must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y which are the steps of the window along the x and y directions * @note Tensor padding values must be passed at compile time using PAD_TENSOR_LEFT, PAD_TENSOR_RIGHT, PAD_TENSOR_TOP and PAD_TENSOR_BOTTOM * * @param[in] input_ptr Pointer to the source tensor. Supported data types: 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[out] output_ptr Pointer to the destination tensor. Supported data types: same as @p input_ptr * @param[in] output_stride_x Stride of the destination 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 destination 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 source 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 destination tensor * @param[in] indices_ptr Pointer to the indices tensor. Supported data types: U32 * @param[in] indices_stride_x Stride of the indices tensor in X dimension (in bytes) * @param[in] indices_step_x indices_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] indices_stride_y Stride of the indices tensor in Y dimension (in bytes) * @param[in] indices_step_y indices_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] indices_stride_z Stride of the indices tensor in Z dimension (in bytes) * @param[in] indices_step_z indices_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] indices_offset_first_element_in_bytes The offset of the first element in the indices tensor */ __kernel void pooling_layer_2_nchw_indices_fp32( TENSOR3D_DECLARATION(input), TENSOR3D_DECLARATION(output), TENSOR3D_DECLARATION(indices)) { // Get pixels pointer Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input); Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); Tensor3D indices = CONVERT_TO_TENSOR3D_STRUCT(indices); // Load data float2 data0 = VLOAD(2)(0, (__global float *)tensor3D_offset(&input, 0, 0, 0)); float2 data1 = VLOAD(2)(0, (__global float *)tensor3D_offset(&input, 0, 1, 0)); // Perform calculations float data0_max = POOL_OP(data0.s0, data0.s1); float data1_max = POOL_OP(data1.s0, data1.s1); float res = POOL_OP(data0_max, data1_max); // Store result *(__global float *)output.ptr = res; #if defined(PAD_TENSOR_LEFT) && defined(PAD_TENSOR_RIGHT) && defined(PAD_TENSOR_TOP) && defined(PAD_TENSOR_BOTTOM) uint offset_top = 0; uint offset_bottom = 0; offset_no_padding_nchw(&input, &offset_top, &offset_bottom); uint index0 = select(offset_top + 1, offset_top, isgreaterequal(data0.s0, data0.s1)); uint index1 = select(offset_bottom + 1, offset_bottom, isgreaterequal(data1.s0, data1.s1)); uint index = select(index1, index0, isgreaterequal(data0_max, data1_max)); *(__global uint *)indices.ptr = index; #endif //defined(PAD_TENSOR_LEFT) && defined(PAD_TENSOR_RIGHT) && defined(PAD_TENSOR_TOP) && defined(PAD_TENSOR_BOTTOM) } /** Performs a MAX pooling of pool size equal to 2, and record max value indices for NCHW. * * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=half. Supported data types are F16 * @note Pool sizes must be passed using -DPOOL_SIZE_X and -DPOOL_SIZE_Y e.g. -DPOOL_SIZE_X=13; * @note Tensors width and height must be passed at compile time using -DMAX_WIDTH and -DMAX_HEIGHT * @note Pool strides must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y which are the steps of the window along the x and y directions * @note Tensor padding values must be passed at compile time using PAD_TENSOR_LEFT, PAD_TENSOR_RIGHT, PAD_TENSOR_TOP and PAD_TENSOR_BOTTOM * * @param[in] input_ptr Pointer to the source tensor. Supported data types: F16 * @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[out] output_ptr Pointer to the destination tensor. Supported data types: same as @p input_ptr * @param[in] output_stride_x Stride of the destination 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 destination 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 source 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 destination tensor * @param[in] indices_ptr Pointer to the indices tensor. Supported data types: U32 * @param[in] indices_stride_x Stride of the indices tensor in X dimension (in bytes) * @param[in] indices_step_x indices_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] indices_stride_y Stride of the indices tensor in Y dimension (in bytes) * @param[in] indices_step_y indices_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] indices_stride_z Stride of the indices tensor in Z dimension (in bytes) * @param[in] indices_step_z indices_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] indices_offset_first_element_in_bytes The offset of the first element in the indices tensor */ __kernel void pooling_layer_2_nchw_indices_fp16( TENSOR3D_DECLARATION(input), TENSOR3D_DECLARATION(output), TENSOR3D_DECLARATION(indices)) { // Get pixels pointer Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input); Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output); Tensor3D indices = CONVERT_TO_TENSOR3D_STRUCT(indices); // Load data half2 data0 = VLOAD(2)(0, (__global half *)tensor3D_offset(&input, 0, 0, 0)); half2 data1 = VLOAD(2)(0, (__global half *)tensor3D_offset(&input, 0, 1, 0)); // Perform calculations half data0_max = POOL_OP(data0.s0, data0.s1); half data1_max = POOL_OP(data1.s0, data1.s1); half res = POOL_OP(data0_max, data1_max); // Store result *(__global half *)output.ptr = res; #if defined(PAD_TENSOR_LEFT) && defined(PAD_TENSOR_RIGHT) && defined(PAD_TENSOR_TOP) && defined(PAD_TENSOR_BOTTOM) uint offset_top = 0; uint offset_bottom = 0; offset_no_padding_nchw(&input, &offset_top, &offset_bottom); uint index0 = select(offset_top + 1, offset_top, isgreaterequal(data0.s0, data0.s1)); uint index1 = select(offset_bottom + 1, offset_bottom, isgreaterequal(data1.s0, data1.s1)); uint index = select(index1, index0, isgreaterequal(data0_max, data1_max)); *(__global uint *)indices.ptr = index; #endif //defined(PAD_TENSOR_LEFT) && defined(PAD_TENSOR_RIGHT) && defined(PAD_TENSOR_TOP) && defined(PAD_TENSOR_BOTTOM) } #if defined(VEC_SIZE) && defined(VEC_SIZE_LEFTOVER) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(DST_CHANNELS) && defined(DST_HEIGHT) && defined(DST_BATCH_SIZE) && defined(ACC_DATA_TYPE) #if defined(POOL_SIZE_X) && defined(POOL_SIZE_Y) /** Performs pooling layer of size equal to MxN. This OpenCL kernel can perform the following pooling types: * -# max, -DPOOL_MAX must be passed at compile time * -# average, -DPOOL_AVG must be passed at compile time. If padding has to be expluded, -DEXCLUDE_PADDING should be passed at compile time * -# l2 normalisation, -DPOOL_L2 must be passed at compile time * * @note Datatype must be passed at compile type using -DDATA_TYPE e.g. -DDATA_TYPE=half. Supported data types are F32/F16 * @note Accumulation data type must be passed at compile time using -DACC_DATA_TYPE e.g. -DACC_DATA_TYPE=float * @note If -DFP_MIXED_PRECISION is passed at compile time, the kernel will use F32 for the partial result * @note Pool size must be passed at compile time using -DPOOL_SIZE_X and -DPOOL_SIZE_Y. e.g. -DPOOL_SIZE_X=4, -DPOOL_SIZE_Y=4 * @note Input tensor width and height must be passed at compile time using -DSRC_WIDTH and -DSRC_HEIGHT * @note Output tensor height, channels and batch size must be passed at compile time using -DDST_HEIGHT, -DDST_CHANNELS and -DDST_BATCH_SIZE * @note Pool strides must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y which are the steps of the window along the x and y directions * @note Pool pads must be passed at compile time using -DPAD_X and -DPAD_Y * @note Vector size must be passed at compile time using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16 * @note Leftover vector size must be passed at compile time using -DVEC_SIZE_LEFTOVER. e.g. -DVEC_SIZE_LEFTOVER=3. It is defined as the remainder between the input's first dimension and VEC_SIZE * @note The initial value for the pooling operation must be passed at compile time using -DINITIAL_VALUE e.g. -DINITIAL_VALUE=0 * * @param[in] input_ptr Pointer to the source tensor. Supported data types: F32/F16 * @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[out] output_ptr Pointer to the destination tensor. Supported data types: same as @p input_ptr * @param[in] output_stride_x Stride of the destination 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 destination 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 destination 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 destination 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 destination tensor */ __kernel void pooling_layer_MxN_nhwc( TENSOR4D_DECLARATION(input), TENSOR4D_DECLARATION(output)) { // Note: If C is not multiple of VEC_SIZE, we shift back of VEC_SIZE_LEFTOVER elements to compute the leftover elements for get_global_id(0) == 0 // Note: If C is less than VEC_SIZE, VEC_SIZE should be SHRINKED to the closest smaller VEC_SIZE. This operation is performed on the host side int idx_out_c = GET_SPATIAL_IDX(0, VEC_SIZE, VEC_SIZE_LEFTOVER); int idx_out_w = GET_SPATIAL_IDX(1, 1, 0); #if DST_BATCH_SIZE != 1 // If batch size != 1, the batch size dimension is collapsed over the height dimension int idx_out_h = GET_SPATIAL_IDX(2, 1, 0) % DST_HEIGHT; int idx_out_n = GET_SPATIAL_IDX(2, 1, 0) / DST_HEIGHT; #else //DST_BATCH_SIZE != 1 int idx_out_h = GET_SPATIAL_IDX(2, 1, 0); int idx_out_n = 0; #endif // DST_BATCH_SIZE != 1 __global unsigned char *in_base_ptr = input_ptr + input_offset_first_element_in_bytes + idx_out_c * sizeof(DATA_TYPE) + idx_out_n * input_stride_w; __global unsigned char *out_base_ptr = output_ptr + output_offset_first_element_in_bytes + idx_out_c * sizeof(DATA_TYPE) + idx_out_w * output_stride_y + idx_out_h * output_stride_z + idx_out_n * output_stride_w; VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE) res0 = INITIAL_VALUE; #if POOL_SIZE_X == SRC_WIDTH && POOL_SIZE_Y == SRC_HEIGHT && PAD_X == 0 && PAD_Y == 0 // Global pooling path int filter_size = POOL_SIZE_X * POOL_SIZE_Y; #pragma unroll 8 for(int y = 0; y < POOL_SIZE_X * POOL_SIZE_Y; ++y) { VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE) data0; #if defined(FP_MIXED_PRECISION) // In case of FP_MIXED_PRECISION, ACC_DATA_TYPE is != DATA_TYPE data0 = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr), VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE)); #else // defined(FP_MIXED_PRECISION) data0 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr)); #endif // defined(FP_MIXED_PRECISION) #if defined(POOL_L2) // Raise to power of 2 for L2 Pooling data0 *= data0; #endif // defined(POOL_L2) res0 = POOL_OP(res0, data0); in_base_ptr += input_stride_y; } #else // POOL_SIZE_X == SRC_WIDTH && POOL_SIZE_Y == SRC_HEIGHT && PAD_X == 0 && PAD_Y == 0 int idx_in_w = idx_out_w * STRIDE_X - PAD_X; int idx_in_h = idx_out_h * STRIDE_Y - PAD_Y; int pool_x_s = max((int)0, -idx_in_w); int pool_x_e = min((int)POOL_SIZE_X, (int)SRC_WIDTH - idx_in_w); int pool_y_s = max((int)0, -idx_in_h); int pool_y_e = min((int)POOL_SIZE_Y, (int)SRC_HEIGHT - idx_in_h); #if defined(EXCLUDE_PADDING) int filter_size = (pool_y_e - pool_y_s) * (pool_x_e - pool_x_s); #else // defined(EXCLUDE_PADDING) int filter_size = POOL_SIZE_X * POOL_SIZE_Y; #endif // defined(EXCLUDE_PADDING) for(int y = pool_y_s; y < pool_y_e; ++y) { for(int x = pool_x_s; x < pool_x_e; ++x) { VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE) data0; #if defined(FP_MIXED_PRECISION) // In case of FP_MIXED_PRECISION, ACC_DATA_TYPE is != DATA_TYPE data0 = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr + (x + idx_in_w) * input_stride_y + (y + idx_in_h) * input_stride_z)), VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE)); #else // defined(FP_MIXED_PRECISION) data0 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr + (x + idx_in_w) * input_stride_y + (y + idx_in_h) * input_stride_z)); #endif // defined(FP_MIXED_PRECISION) #if defined(POOL_L2) // Raise to power of 2 for L2 Pooling data0 *= data0; #endif // defined(POOL_L2) res0 = POOL_OP(res0, data0); } } #endif // POOL_SIZE_X == SRC_WIDTH && POOL_SIZE_Y == SRC_HEIGHT && PAD_X == 0 && PAD_Y == 0 #if defined(POOL_AVG) || defined(POOL_L2) res0 /= (VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE))filter_size; #endif // defined(POOL_AVG) || defined(POOL_L2) #if defined(POOL_L2) // Take square root of the result in L2 pooling res0 = SQRT_OP(res0); #endif // defined(POOL_L2) // Store result #if defined(FP_MIXED_PRECISION) VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) res_converted0 = CONVERT(res0, VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)); STORE_VECTOR_SELECT(res_converted, DATA_TYPE, out_base_ptr, VEC_SIZE, VEC_SIZE_LEFTOVER, (VEC_SIZE_LEFTOVER != 0) && get_global_id(0) == 0); #else // defined(FP_MIXED_PRECISION) STORE_VECTOR_SELECT(res, DATA_TYPE, out_base_ptr, VEC_SIZE, VEC_SIZE_LEFTOVER, (VEC_SIZE_LEFTOVER != 0) && get_global_id(0) == 0); #endif // defined(FP_MIXED_PRECISION) } #endif // defined(POOL_SIZE_X) && defined(POOL_SIZE_Y) #define SELECT_TYPE SELECT_VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE) /** Performs pooling layer of size equal to 2. This OpenCL kernel can perform the following pooling types: * -# max, -DPOOL_MAX must be passed at compile time * -# max extracting the max index, -DPOOL_MAX and -DEXTRACT_MAX_INDEX must be passed at compile time * -# average, -DPOOL_AVG must be passed at compile time. If padding has to be expluded, -DEXCLUDE_PADDING should be passed at compile time * -# l2 normalisation, -DPOOL_L2 must be passed at compile time * * @note Datatype must be passed at compile type using -DDATA_TYPE e.g. -DDATA_TYPE=half. Supported data types are F32/F16 * @note Accumulation data type must be passed at compile time using -DACC_DATA_TYPE e.g. -DACC_DATA_TYPE=float * @note If -DFP_MIXED_PRECISION is passed at compile time, the kernel will use F32 for the partial result * @note Input tensor width and height must be passed at compile time using -DSRC_WIDTH and -DSRC_HEIGHT * @note Output tensor height, channels and batch size must be passed at compile time using -DDST_HEIGHT, -DDST_CHANNELS and -DDST_BATCH_SIZE * @note Pool strides must be passed at compile time using -DSTRIDE_X and -DSTRIDE_Y which are the steps of the window along the x and y directions * @note Pool pads must be passed at compile time using -DPAD_X and -DPAD_Y * @note Vector size must be passed at compile time using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16 * @note Leftover vector size must be passed at compile time using -DVEC_SIZE_LEFTOVER. e.g. -DVEC_SIZE_LEFTOVER=3. It is defined as the remainder between the input's first dimension and VEC_SIZE * @note The initial value for the pooling operation must be passed at compile time using -DINITIAL_VALUE e.g. -DINITIAL_VALUE=0 * * @param[in] input_ptr Pointer to the source tensor. Supported data types: F32/F16 * @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[out] output_ptr Pointer to the destination tensor. Supported data types: same as @p input_ptr * @param[in] output_stride_x Stride of the destination 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 destination 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 destination 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 destination 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 destination tensor * @param[in] indices_ptr (Optional) Pointer to the indices tensor. Supported data types: U32 * @param[in] indices_stride_x (Optional) Stride of the indices tensor in X dimension (in bytes) * @param[in] indices_step_x (Optional) indices_stride_x * number of elements along X processed per workitem(in bytes) * @param[in] indices_stride_y (Optional) Stride of the indices tensor in Y dimension (in bytes) * @param[in] indices_step_y (Optional) indices_stride_y * number of elements along Y processed per workitem(in bytes) * @param[in] indices_stride_z (Optional) Stride of the indices tensor in Z dimension (in bytes) * @param[in] indices_step_z (Optional) indices_stride_z * number of elements along Z processed per workitem(in bytes) * @param[in] indices_stride_w (Optional) Stride of the indices tensor in W dimension (in bytes) * @param[in] indices_step_w (Optional) indices_stride_w * number of elements along W processed per workitem(in bytes) * @param[in] indices_offset_first_element_in_bytes (Optional) The offset of the first element in the indices tensor */ __kernel void pooling_layer_2x2_nhwc( TENSOR4D_DECLARATION(input), TENSOR4D_DECLARATION(output) #if defined(EXTRACT_MAX_INDEX) && defined(POOL_MAX) , TENSOR4D_DECLARATION(indices) #endif // defined(EXTRACT_MAX_INDEX) && defined(POOL_MAX) ) { // Note: If C is not multiple of VEC_SIZE, we shift back of VEC_SIZE_LEFTOVER elements to compute the leftover elements for get_global_id(0) == 0 // Note: If C is less than VEC_SIZE, VEC_SIZE should be SHRINKED to the closest smaller VEC_SIZE. This operation is performed on the host side int idx_out_c = max((int)(get_global_id(0) * VEC_SIZE - (VEC_SIZE - VEC_SIZE_LEFTOVER) % VEC_SIZE), 0); int idx_out_w = get_global_id(1); #if DST_BATCH_SIZE != 1 // If batch size != 1, the batch size dimension is collapsed over the height dimension int idx_out_h = get_global_id(2) % DST_HEIGHT; int idx_out_n = get_global_id(2) / DST_HEIGHT; #else //SRC_BATCH_SIZE != 1 int idx_out_h = get_global_id(2); int idx_out_n = 0; #endif // SRC_BATCH_SIZE != 1 int idx_in_w = idx_out_w * STRIDE_X - PAD_X; int idx_in_h = idx_out_h * STRIDE_Y - PAD_Y; __global unsigned char *in_base_ptr = input_ptr + input_offset_first_element_in_bytes + idx_out_c * sizeof(DATA_TYPE) + idx_out_n * input_stride_w; __global unsigned char *out_base_ptr = output_ptr + output_offset_first_element_in_bytes + idx_out_c * sizeof(DATA_TYPE) + idx_out_w * output_stride_y + idx_out_h * output_stride_z + idx_out_n * output_stride_w; int pool_x_s = max((int)0, -idx_in_w); int pool_x_e = min((int)2, (int)SRC_WIDTH - idx_in_w); int pool_y_s = max((int)0, -idx_in_h); int pool_y_e = min((int)2, (int)SRC_HEIGHT - idx_in_h); int filter_size = (pool_x_e - pool_x_s) * (pool_y_e - pool_y_s); int x0 = pool_x_s + idx_in_w; int y0 = pool_y_s + idx_in_h; int x1 = pool_x_e - 1 + idx_in_w; int y1 = pool_y_e - 1 + idx_in_h; REPEAT_VAR_INIT_TO_CONST(4, VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE), data, 0); #if defined(FP_MIXED_PRECISION) // In case of FP_MIXED_PRECISION, ACC_DATA_TYPE is != DATA_TYPE data0 = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr + x0 * input_stride_y + y0 * input_stride_z)), VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE)); data1 = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr + x1 * input_stride_y + y0 * input_stride_z)), VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE)); data2 = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr + x0 * input_stride_y + y1 * input_stride_z)), VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE)); data3 = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr + x1 * input_stride_y + y1 * input_stride_z)), VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE)); #else // defined(FP_MIXED_PRECISION) data0 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr + x0 * input_stride_y + y0 * input_stride_z)); data1 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr + x1 * input_stride_y + y0 * input_stride_z)); data2 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr + x0 * input_stride_y + y1 * input_stride_z)); data3 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(in_base_ptr + x1 * input_stride_y + y1 * input_stride_z)); #endif // defined(FP_MIXED_PRECISION) #if !defined(POOL_MAX) if(filter_size != 4) { SELECT_TYPE cond_w_s = (SELECT_TYPE)idx_in_w < (SELECT_TYPE)0; SELECT_TYPE cond_w_e = (SELECT_TYPE)idx_in_w >= (SELECT_TYPE)(SRC_WIDTH - 1); SELECT_TYPE cond_h_s = (SELECT_TYPE)idx_in_h < (SELECT_TYPE)0; SELECT_TYPE cond_h_e = (SELECT_TYPE)idx_in_h >= (SELECT_TYPE)(SRC_HEIGHT - 1); // Make invalid the values loaded if the x or y coordinate was clamped (out-of-bound) data0 = select(data0, (VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE))INITIAL_VALUE, (SELECT_TYPE)(cond_w_s | cond_h_s)); data1 = select(data1, (VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE))INITIAL_VALUE, (SELECT_TYPE)(cond_w_e | cond_h_s)); data2 = select(data2, (VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE))INITIAL_VALUE, (SELECT_TYPE)(cond_w_s | cond_h_e)); data3 = select(data3, (VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE))INITIAL_VALUE, (SELECT_TYPE)(cond_w_e | cond_h_e)); } #endif // !defined(POOL_MAX) #if defined(POOL_L2) // Raise to power of 2 for L2 Pooling data0 *= data0; data1 *= data1; data2 *= data2; data3 *= data3; #endif /* defined(POOL_L2) */ VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE) res0 = data0; res0 = POOL_OP(res0, data1); res0 = POOL_OP(res0, data2); res0 = POOL_OP(res0, data3); #if defined(POOL_AVG) || defined(POOL_L2) #if defined(EXCLUDE_PADDING) res0 /= (VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE))filter_size; #else // !defined(EXCLUDE_PADDING) res0 /= (VEC_DATA_TYPE(ACC_DATA_TYPE, VEC_SIZE))4; #endif // defined(EXCLUDE_PADDING) #endif // defined(POOL_AVG) || defined(POOL_L2) #if defined(POOL_L2) // Take square root of the result in L2 pooling res0 = SQRT_OP(res0); #endif // defined(POOL_L2) // Store result #if defined(FP_MIXED_PRECISION) VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE) res_converted0 = CONVERT(res0, VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)); STORE_VECTOR_SELECT(res_converted, DATA_TYPE, out_base_ptr, VEC_SIZE, VEC_SIZE_LEFTOVER, (VEC_SIZE_LEFTOVER != 0) && get_global_id(0) == 0); #else // defined(FP_MIXED_PRECISION) STORE_VECTOR_SELECT(res, DATA_TYPE, out_base_ptr, VEC_SIZE, VEC_SIZE_LEFTOVER, (VEC_SIZE_LEFTOVER != 0) && get_global_id(0) == 0); #endif // defined(FP_MIXED_PRECISION) #if defined(EXTRACT_MAX_INDEX) && defined(POOL_MAX) // This part is used to return the index of the maximum value // Note: DST_CHANNELS and DST_BATCH_SIZE can be used for either the input and output tensor // note: Batch dimension does not contribute in the offset contribution VEC_DATA_TYPE(uint, VEC_SIZE) base_index = (uint)idx_out_c; base_index += VEC_OFFS(uint, VEC_SIZE); VEC_DATA_TYPE(uint, VEC_SIZE) index0 = base_index + (uint)x0 * DST_CHANNELS + (uint)y0 * (DST_CHANNELS * SRC_WIDTH); VEC_DATA_TYPE(uint, VEC_SIZE) index1 = base_index + (uint)x1 * DST_CHANNELS + (uint)y0 * (DST_CHANNELS * SRC_WIDTH); VEC_DATA_TYPE(uint, VEC_SIZE) index2 = base_index + (uint)x0 * DST_CHANNELS + (uint)y1 * (DST_CHANNELS * SRC_WIDTH); VEC_DATA_TYPE(uint, VEC_SIZE) index3 = base_index + (uint)x1 * DST_CHANNELS + (uint)y1 * (DST_CHANNELS * SRC_WIDTH); index0 = select(index1, index0, CONVERT(isgreaterequal(data0, data1), VEC_DATA_TYPE(int, VEC_SIZE))); index1 = select(index3, index2, CONVERT(isgreaterequal(data2, data3), VEC_DATA_TYPE(int, VEC_SIZE))); index0 = select(index1, index0, CONVERT(isgreaterequal(max(data0, data1), max(data2, data3)), VEC_DATA_TYPE(int, VEC_SIZE))); __global unsigned char *idx_base_ptr = indices_ptr + indices_offset_first_element_in_bytes + idx_out_c * sizeof(uint) + idx_out_w * indices_stride_y + idx_out_h * indices_stride_z + idx_out_n * indices_stride_w; // Store result STORE_VECTOR_SELECT(index, uint, idx_base_ptr, VEC_SIZE, VEC_SIZE_LEFTOVER, ((VEC_SIZE_LEFTOVER != 0) && get_global_id(0) == 0)); #endif // defined(EXTRACT_MAX_INDEX) && defined(POOL_MAX) } #endif // defined(VEC_SIZE) && defined(VEC_SIZE_LEFTOVER) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(DST_CHANNELS) && defined(DST_HEIGHT) && defined(DST_BATCH_SIZE) && defined(ACC_DATA_TYPE)