From 164a2727d3bbce0e575d24b7db787c85e2e2c203 Mon Sep 17 00:00:00 2001 From: giuros01 Date: Tue, 20 Nov 2018 18:34:46 +0000 Subject: COMPMID-1717: CL: Implement Maximum, Minimum, SquaredDifference Change-Id: Ice653e48211053bd3cd20a693bd76de6b4efc370 Reviewed-on: https://review.mlplatform.org/270 Reviewed-by: Georgios Pinitas Tested-by: Arm Jenkins --- src/core/CL/CLKernelLibrary.cpp | 33 +- src/core/CL/cl_kernels/arithmetic_op.cl | 190 ------------ src/core/CL/cl_kernels/arithmetic_op_quantized.cl | 168 ---------- src/core/CL/cl_kernels/elementwise_operation.cl | 98 ++++++ .../cl_kernels/elementwise_operation_quantized.cl | 107 +++++++ src/core/CL/kernels/CLArithmeticAdditionKernel.cpp | 233 -------------- src/core/CL/kernels/CLArithmeticDivisionKernel.cpp | 185 ----------- .../CL/kernels/CLArithmeticSubtractionKernel.cpp | 232 -------------- .../CL/kernels/CLElementwiseOperationKernel.cpp | 337 +++++++++++++++++++++ src/runtime/CL/functions/CLArithmeticAddition.cpp | 54 ---- src/runtime/CL/functions/CLArithmeticDivision.cpp | 54 ---- .../CL/functions/CLArithmeticSubtraction.cpp | 54 ---- .../CL/functions/CLElementwiseOperations.cpp | 127 ++++++++ .../CL/functions/CLGEMMConvolutionLayer.cpp | 16 +- src/runtime/CL/functions/CLLSTMLayer.cpp | 18 +- src/runtime/CL/functions/CLLaplacianPyramid.cpp | 4 +- src/runtime/CL/functions/CLRNNLayer.cpp | 6 +- 17 files changed, 711 insertions(+), 1205 deletions(-) delete mode 100644 src/core/CL/cl_kernels/arithmetic_op.cl delete mode 100644 src/core/CL/cl_kernels/arithmetic_op_quantized.cl create mode 100644 src/core/CL/cl_kernels/elementwise_operation.cl create mode 100644 src/core/CL/cl_kernels/elementwise_operation_quantized.cl delete mode 100644 src/core/CL/kernels/CLArithmeticAdditionKernel.cpp delete mode 100644 src/core/CL/kernels/CLArithmeticDivisionKernel.cpp delete mode 100644 src/core/CL/kernels/CLArithmeticSubtractionKernel.cpp create mode 100644 src/core/CL/kernels/CLElementwiseOperationKernel.cpp delete mode 100644 src/runtime/CL/functions/CLArithmeticAddition.cpp delete mode 100644 src/runtime/CL/functions/CLArithmeticDivision.cpp delete mode 100644 src/runtime/CL/functions/CLArithmeticSubtraction.cpp create mode 100644 src/runtime/CL/functions/CLElementwiseOperations.cpp (limited to 'src') diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp index f2b5d45e2c..ac1d4b349e 100644 --- a/src/core/CL/CLKernelLibrary.cpp +++ b/src/core/CL/CLKernelLibrary.cpp @@ -149,11 +149,6 @@ const std::map CLKernelLibrary::_kernel_program_map = { "accumulate_weighted", "accumulate.cl" }, { "activation_layer", "activation_layer.cl" }, { "activation_layer_qa8", "activation_layer_qa8.cl" }, - { "arithmetic_add_quantized", "arithmetic_op_quantized.cl" }, - { "arithmetic_add", "arithmetic_op.cl" }, - { "arithmetic_sub", "arithmetic_op.cl" }, - { "arithmetic_sub_quantized", "arithmetic_op_quantized.cl" }, - { "arithmetic_div", "arithmetic_op.cl" }, { "batch_to_space_nchw", "batch_to_space.cl" }, { "batch_to_space_static_nchw", "batch_to_space.cl" }, { "batch_to_space_nhwc", "batch_to_space.cl" }, @@ -246,6 +241,18 @@ const std::map CLKernelLibrary::_kernel_program_map = { "direct_convolution5x5_nhwc", "direct_convolution5x5.cl" }, { "direct_convolution5x5_f32_bifrost", "direct_convolution5x5.cl" }, { "direct_convolution_1x1_3x3_5x5_quantized", "direct_convolution_1x1_3x3_5x5_quantized.cl" }, + { "elementwise_operation_ADD", "elementwise_operation.cl" }, + { "elementwise_operation_SUB", "elementwise_operation.cl" }, + { "elementwise_operation_MAX", "elementwise_operation.cl" }, + { "elementwise_operation_MIN", "elementwise_operation.cl" }, + { "elementwise_operation_DIV", "elementwise_operation.cl" }, + { "elementwise_operation_SQUARED_DIFF", "elementwise_operation.cl" }, + { "elementwise_operation_ADD_quantized", "elementwise_operation_quantized.cl" }, + { "elementwise_operation_SUB_quantized", "elementwise_operation_quantized.cl" }, + { "elementwise_operation_MAX_quantized", "elementwise_operation_quantized.cl" }, + { "elementwise_operation_MIN_quantized", "elementwise_operation_quantized.cl" }, + { "elementwise_operation_DIV_quantized", "elementwise_operation_quantized.cl" }, + { "elementwise_operation_SQUARED_DIFF_quantized", "elementwise_operation_quantized.cl" }, { "erode", "erode.cl" }, { "fast_corners", "fast_corners.cl" }, { "flatten", "flatten.cl" }, @@ -508,14 +515,6 @@ const std::map CLKernelLibrary::_program_source_map = { "activation_layer_qa8.cl", #include "./cl_kernels/activation_layer_qa8.clembed" - }, - { - "arithmetic_op.cl", -#include "./cl_kernels/arithmetic_op.clembed" - }, - { - "arithmetic_op_quantized.cl", -#include "./cl_kernels/arithmetic_op_quantized.clembed" }, { "batch_to_space.cl", @@ -640,6 +639,14 @@ const std::map CLKernelLibrary::_program_source_map = { "direct_convolution_1x1_3x3_5x5_quantized.cl", #include "./cl_kernels/direct_convolution_1x1_3x3_5x5_quantized.clembed" + }, + { + "elementwise_operation.cl", +#include "./cl_kernels/elementwise_operation.clembed" + }, + { + "elementwise_operation_quantized.cl", +#include "./cl_kernels/elementwise_operation_quantized.clembed" }, { "erode.cl", diff --git a/src/core/CL/cl_kernels/arithmetic_op.cl b/src/core/CL/cl_kernels/arithmetic_op.cl deleted file mode 100644 index 557615e7f2..0000000000 --- a/src/core/CL/cl_kernels/arithmetic_op.cl +++ /dev/null @@ -1,190 +0,0 @@ -/* - * Copyright (c) 2016-2018 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" - -#ifdef SATURATE -#define ADD(x, y) add_sat((x), (y)) -#define SUB(x, y) sub_sat((x), (y)) -#else /* SATURATE */ -#define ADD(x, y) (x) + (y) -#define SUB(x, y) (x) - (y) -#endif /* SATURATE */ - -#define DIV(x, y) (x) / (y) - -#if defined(DATA_TYPE_IN1) && defined(DATA_TYPE_IN2) && defined(DATA_TYPE_OUT) && defined(VEC_SIZE) -/** This function adds two tensors. - * - * @attention The input and output data_types need to be passed at compile time using -DDATA_TYPE_IN1, -DDATA_TYPE_IN2 and -DDATA_TYPE_OUT: - * e.g. -DDATA_TYPE_IN1=uchar -DDATA_TYPE_IN2=uchar -DDATA_TYPE_OUT=short - * @attention To perform saturating operation -DSATURATE has to be passed to the compiler otherwise wrapping policy will be used. - * @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16 - * - * @param[in] in1_ptr Pointer to the source tensor. Supported data types: U8/S16/F16/F32 - * @param[in] in1_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] in1_step_x in1_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] in1_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] in1_step_y in1_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] in1_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] in1_step_z in1_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] in1_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[in] in2_ptr Pointer to the source tensor. Supported data types: U8/S16/F16/F32 - * @param[in] in2_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] in2_step_x in2_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] in2_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] in2_step_y in2_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] in2_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] in2_step_z in2_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] in2_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[out] out_ptr Pointer to the destination tensor. Supported data types: U8 (only if both inputs are U8), S16/F16/F32 - * @param[in] out_stride_x Stride of the destination tensor in X dimension (in bytes) - * @param[in] out_step_x out_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] out_stride_y Stride of the destination tensor in Y dimension (in bytes) - * @param[in] out_step_y out_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] out_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] out_step_z out_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] out_offset_first_element_in_bytes The offset of the first element in the destination tensor - */ -__kernel void arithmetic_add( - TENSOR3D_DECLARATION(in1), - TENSOR3D_DECLARATION(in2), - TENSOR3D_DECLARATION(out)) -{ - // Get pixels pointer - Tensor3D in1 = CONVERT_TO_TENSOR3D_STRUCT(in1); - Tensor3D in2 = CONVERT_TO_TENSOR3D_STRUCT(in2); - Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(out); - - // Load values - VEC_DATA_TYPE(DATA_TYPE_OUT, VEC_SIZE) - in_a = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE_IN1 *)in1.ptr), VEC_DATA_TYPE(DATA_TYPE_OUT, VEC_SIZE)); - VEC_DATA_TYPE(DATA_TYPE_OUT, VEC_SIZE) - in_b = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE_IN2 *)in2.ptr), VEC_DATA_TYPE(DATA_TYPE_OUT, VEC_SIZE)); - - // Calculate and store result - VSTORE(VEC_SIZE) - (ADD(in_a, in_b), 0, (__global DATA_TYPE_OUT *)out.ptr); -} -#endif /* defined(DATA_TYPE_IN1) && defined(DATA_TYPE_IN2) && defined(DATA_TYPE_OUT) && defined(VEC_SIZE) */ - -/** This function subtracts one tensor from another. - * - * @attention The input and output data_types need to be passed at compile time using -DDATA_TYPE_IN1, -DDATA_TYPE_IN2 and -DDATA_TYPE_OUT: - * e.g. -DDATA_TYPE_IN1=uchar -DDATA_TYPE_IN2=uchar -DDATA_TYPE_OUT=short - * @attention To perform saturating operation -DSATURATE has to be passed to the compiler otherwise wrapping policy will be used. - * - * @param[in] in1_ptr Pointer to the source tensor. Supported data types: U8, S16 - * @param[in] in1_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] in1_step_x in1_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] in1_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] in1_step_y in1_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] in1_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] in1_step_z in1_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] in1_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[in] in2_ptr Pointer to the source tensor. Supported data types: U8, S16 - * @param[in] in2_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] in2_step_x in2_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] in2_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] in2_step_y in2_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] in2_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] in2_step_z in2_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] in2_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[out] out_ptr Pointer to the destination tensor. Supported data types: U8, S16 - * @param[in] out_stride_x Stride of the destination tensor in X dimension (in bytes) - * @param[in] out_step_x out_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] out_stride_y Stride of the destination tensor in Y dimension (in bytes) - * @param[in] out_step_y out_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] out_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] out_step_z out_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] out_offset_first_element_in_bytes The offset of the first element in the destination tensor - */ -__kernel void arithmetic_sub( - TENSOR3D_DECLARATION(in1), - TENSOR3D_DECLARATION(in2), - TENSOR3D_DECLARATION(out)) -{ - // Get pixels pointer - Tensor3D in1 = CONVERT_TO_TENSOR3D_STRUCT(in1); - Tensor3D in2 = CONVERT_TO_TENSOR3D_STRUCT(in2); - Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(out); - - // Load values - VEC_DATA_TYPE(DATA_TYPE_OUT, 16) - in_a = CONVERT(vload16(0, (__global DATA_TYPE_IN1 *)in1.ptr), VEC_DATA_TYPE(DATA_TYPE_OUT, 16)); - VEC_DATA_TYPE(DATA_TYPE_OUT, 16) - in_b = CONVERT(vload16(0, (__global DATA_TYPE_IN2 *)in2.ptr), VEC_DATA_TYPE(DATA_TYPE_OUT, 16)); - - // Calculate and store result - vstore16(SUB(in_a, in_b), 0, (__global DATA_TYPE_OUT *)out.ptr); -} - -/** This function divides one tensor from another. - * - * @attention The input and output data_types need to be passed at compile time using -DDATA_TYPE_IN1, -DDATA_TYPE_IN2 and -DDATA_TYPE_OUT: - * e.g. -DDATA_TYPE_IN1=float -DDATA_TYPE_IN2=float -DDATA_TYPE_OUT=float - * - * @param[in] in1_ptr Pointer to the source tensor. Supported data types: F16/F32 - * @param[in] in1_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] in1_step_x in1_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] in1_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] in1_step_y in1_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] in1_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] in1_step_z in1_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] in1_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[in] in2_ptr Pointer to the source tensor. Supported data types: Same as @p in1_ptr - * @param[in] in2_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] in2_step_x in2_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] in2_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] in2_step_y in2_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] in2_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] in2_step_z in2_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] in2_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[out] out_ptr Pointer to the destination tensor. Supported data types: Same as @p in1_ptr - * @param[in] out_stride_x Stride of the destination tensor in X dimension (in bytes) - * @param[in] out_step_x out_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] out_stride_y Stride of the destination tensor in Y dimension (in bytes) - * @param[in] out_step_y out_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] out_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] out_step_z out_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] out_offset_first_element_in_bytes The offset of the first element in the destination tensor - */ -__kernel void arithmetic_div( - TENSOR3D_DECLARATION(in1), - TENSOR3D_DECLARATION(in2), - TENSOR3D_DECLARATION(out)) -{ - // Get pixels pointer - Tensor3D in1 = CONVERT_TO_TENSOR3D_STRUCT(in1); - Tensor3D in2 = CONVERT_TO_TENSOR3D_STRUCT(in2); - Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(out); - - // Load values - VEC_DATA_TYPE(DATA_TYPE_OUT, 16) - in_a = CONVERT(vload16(0, (__global DATA_TYPE_IN1 *)in1.ptr), VEC_DATA_TYPE(DATA_TYPE_OUT, 16)); - VEC_DATA_TYPE(DATA_TYPE_OUT, 16) - in_b = CONVERT(vload16(0, (__global DATA_TYPE_IN2 *)in2.ptr), VEC_DATA_TYPE(DATA_TYPE_OUT, 16)); - - // Calculate and store result - vstore16(DIV(in_a, in_b), 0, (__global DATA_TYPE_OUT *)out.ptr); -} diff --git a/src/core/CL/cl_kernels/arithmetic_op_quantized.cl b/src/core/CL/cl_kernels/arithmetic_op_quantized.cl deleted file mode 100644 index fc7fa771f3..0000000000 --- a/src/core/CL/cl_kernels/arithmetic_op_quantized.cl +++ /dev/null @@ -1,168 +0,0 @@ -/* - * Copyright (c) 2016-2018 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" - -#ifdef SATURATE -#define ADD(x, y) add_sat((x), (y)) -#define SUB(x, y) sub_sat((x), (y)) -#else /* SATURATE */ -#define ADD(x, y) (x) + (y) -#define SUB(x, y) (x) - (y) -#endif /* SATURATE */ - -#define CONVERT_RTE(x, type) (convert_##type##_rte((x))) -#define CONVERT_DOWN(x, type) CONVERT_RTE(x, type) - -#if defined(OFFSET_IN1) && defined(OFFSET_IN2) && defined(OFFSET_OUT) && defined(SCALE_IN1) && defined(SCALE_IN2) && defined(SCALE_OUT) - -#if defined(VEC_SIZE) - -#define VEC_FLOAT VEC_DATA_TYPE(float, VEC_SIZE) -#define VEC_INT VEC_DATA_TYPE(int, VEC_SIZE) -#define VEC_UCHAR VEC_DATA_TYPE(uchar, VEC_SIZE) - -/** This function adds two tensors. - * - * @note The quantization offset of the first operand must be passed at compile time using -DOFFSET_IN1, i.e. -DOFFSET_IN1=10 - * @note The quantization offset of the second operand must be passed at compile time using -DOFFSET_IN2, i.e. -DOFFSET_IN2=10 - * @note The quantization offset of the output must be passed at compile time using -DOFFSET_OUT, i.e. -DOFFSET_OUT=10 - * @note The quantization scale of the first operand must be passed at compile time using -DSCALE_IN1, i.e. -DSCALE_IN1=10 - * @note The quantization scale of the second operand must be passed at compile time using -DSCALE_IN2, i.e. -DSCALE_IN2=10 - * @note The quantization scale of the output must be passed at compile time using -DSCALE_OUT, i.e. -DSCALE_OUT=10 - * @note To perform saturating operation -DSATURATE has to be passed to the compiler otherwise wrapping policy will be used. - * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16 - * - * @param[in] in1_ptr Pointer to the source tensor. Supported data types: QASYMM8 - * @param[in] in1_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] in1_step_x in1_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] in1_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] in1_step_y in1_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] in1_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] in1_step_z in1_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] in1_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[in] in2_ptr Pointer to the source tensor. Supported data types: same as @p in1_ptr - * @param[in] in2_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] in2_step_x in2_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] in2_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] in2_step_y in2_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] in2_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] in2_step_z in2_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] in2_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[out] out_ptr Pointer to the destination tensor. Supported data types: same as @p in1_ptr - * @param[in] out_stride_x Stride of the destination tensor in X dimension (in bytes) - * @param[in] out_step_x out_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] out_stride_y Stride of the destination tensor in Y dimension (in bytes) - * @param[in] out_step_y out_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] out_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] out_step_z out_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] out_offset_first_element_in_bytes The offset of the first element in the destination tensor - */ -__kernel void arithmetic_add_quantized( - TENSOR3D_DECLARATION(in1), - TENSOR3D_DECLARATION(in2), - TENSOR3D_DECLARATION(out)) -{ - // Get pixels pointer - Tensor3D in1 = CONVERT_TO_TENSOR3D_STRUCT(in1); - Tensor3D in2 = CONVERT_TO_TENSOR3D_STRUCT(in2); - Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(out); - - VEC_INT in_a = CONVERT(VLOAD(VEC_SIZE)(0, (__global uchar *)in1.ptr), VEC_INT); - VEC_INT in_b = CONVERT(VLOAD(VEC_SIZE)(0, (__global uchar *)in2.ptr), VEC_INT); - - in_a = SUB(in_a, (VEC_INT)((int)OFFSET_IN1)); - in_b = SUB(in_b, (VEC_INT)((int)OFFSET_IN2)); - - const VEC_FLOAT in1f32 = CONVERT(in_a, VEC_FLOAT) * (VEC_FLOAT)((float)SCALE_IN1); - const VEC_FLOAT in2f32 = CONVERT(in_b, VEC_FLOAT) * (VEC_FLOAT)((float)SCALE_IN2); - - const VEC_FLOAT qresf32 = (in1f32 + in2f32) / ((VEC_FLOAT)(float)SCALE_OUT) + ((VEC_FLOAT)((float)OFFSET_OUT)); - const VEC_UCHAR res = CONVERT_SAT(CONVERT_DOWN(qresf32, VEC_INT), VEC_UCHAR); - - // Store result - VSTORE(VEC_SIZE) - (res, 0, (__global uchar *)out.ptr); -} -#endif /* defined(VEC_SIZE) */ - -/** This function subtracts two tensors. - * - * @note The quantization offset of the first operand must be passed at compile time using -DOFFSET_IN1, i.e. -DOFFSET_IN1=10 - * @note The quantization offset of the second operand must be passed at compile time using -DOFFSET_IN2, i.e. -DOFFSET_IN2=10 - * @note The quantization offset of the output must be passed at compile time using -DOFFSET_OUT, i.e. -DOFFSET_OUT=10 - * @note The quantization scale of the first operand must be passed at compile time using -DSCALE_IN1, i.e. -DSCALE_IN1=10 - * @note The quantization scale of the second operand must be passed at compile time using -DSCALE_IN2, i.e. -DSCALE_IN2=10 - * @note The quantization scale of the output must be passed at compile time using -DSCALE_OUT, i.e. -DSCALE_OUT=10 - * @note To perform saturating operation -DSATURATE has to be passed to the compiler otherwise wrapping policy will be used. - * - * @param[in] in1_ptr Pointer to the source tensor. Supported data types: QASYMM8 - * @param[in] in1_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] in1_step_x in1_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] in1_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] in1_step_y in1_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] in1_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] in1_step_z in1_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] in1_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[in] in2_ptr Pointer to the source tensor. Supported data types: same as @p in1_ptr - * @param[in] in2_stride_x Stride of the source tensor in X dimension (in bytes) - * @param[in] in2_step_x in2_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] in2_stride_y Stride of the source tensor in Y dimension (in bytes) - * @param[in] in2_step_y in2_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] in2_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] in2_step_z in2_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] in2_offset_first_element_in_bytes The offset of the first element in the source tensor - * @param[out] out_ptr Pointer to the destination tensor. Supported data types: same as @p in1_ptr - * @param[in] out_stride_x Stride of the destination tensor in X dimension (in bytes) - * @param[in] out_step_x out_stride_x * number of elements along X processed per workitem(in bytes) - * @param[in] out_stride_y Stride of the destination tensor in Y dimension (in bytes) - * @param[in] out_step_y out_stride_y * number of elements along Y processed per workitem(in bytes) - * @param[in] out_stride_z Stride of the source tensor in Z dimension (in bytes) - * @param[in] out_step_z out_stride_z * number of elements along Z processed per workitem(in bytes) - * @param[in] out_offset_first_element_in_bytes The offset of the first element in the destination tensor - */ -__kernel void arithmetic_sub_quantized( - TENSOR3D_DECLARATION(in1), - TENSOR3D_DECLARATION(in2), - TENSOR3D_DECLARATION(out)) -{ - // Get pixels pointer - Tensor3D in1 = CONVERT_TO_TENSOR3D_STRUCT(in1); - Tensor3D in2 = CONVERT_TO_TENSOR3D_STRUCT(in2); - Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(out); - - int16 in_a = CONVERT(vload16(0, (__global uchar *)in1.ptr), int16); - int16 in_b = CONVERT(vload16(0, (__global uchar *)in2.ptr), int16); - - in_a = SUB(in_a, (int16)((int)OFFSET_IN1)); - in_b = SUB(in_b, (int16)((int)OFFSET_IN2)); - - const float16 in1f32 = convert_float16(in_a) * (float16)((float)SCALE_IN1); - const float16 in2f32 = convert_float16(in_b) * (float16)((float)SCALE_IN2); - const float16 qresf32 = (in1f32 - in2f32) / ((float16)(float)SCALE_OUT) + ((float16)((float16)OFFSET_OUT)); - const uchar16 res = convert_uchar16_sat(convert_int16_rte(qresf32)); - - // Store result - vstore16(res, 0, (__global uchar *)out.ptr); -} -#endif /* defined(OFFSET_IN1) && defined(OFFSET_IN2) && defined(OFFSET_OUT) && defined(SCALE_IN1) && defined(SCALE_IN2) && defined(SCALE_OUT) */ diff --git a/src/core/CL/cl_kernels/elementwise_operation.cl b/src/core/CL/cl_kernels/elementwise_operation.cl new file mode 100644 index 0000000000..00d7ed3ba1 --- /dev/null +++ b/src/core/CL/cl_kernels/elementwise_operation.cl @@ -0,0 +1,98 @@ +/* + * Copyright (c) 2018 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" + +/** List of all the operations supported by this kernel. + * @note ADD and SUB operations, when executed on integers, support saturation */ +#ifdef SATURATE +#define ADD(x, y) add_sat((x), (y)) +#define SUB(x, y) sub_sat((x), (y)) +#else /* SATURATE */ +#define ADD(x, y) (x) + (y) +#define SUB(x, y) (x) - (y) +#endif /* SATURATE */ + +#define MAX(x, y) max(x, y) +#define MIN(x, y) min(x, y) +#define SQUARED_DIFF(x, y) (x - y) * (x - y) +#define DIV(x, y) (x / y) + +#define OP_FUN_NAME_STR(op) elementwise_operation_##op +#define OP_FUN_NAME(op) OP_FUN_NAME_STR(op) + +#if defined(OP) && defined(DATA_TYPE_IN1) && defined(DATA_TYPE_IN2) && defined(DATA_TYPE_OUT) && defined(VEC_SIZE) +/** This function executes an element-wise operation among two tensors. + * + * @attention The input and output data_types need to be passed at compile time using -DDATA_TYPE_IN1, -DDATA_TYPE_IN2 and -DDATA_TYPE_OUT: + * e.g. -DDATA_TYPE_IN1=uchar -DDATA_TYPE_IN2=uchar -DDATA_TYPE_OUT=short + * @attention To perform saturating operation -DSATURATE has to be passed to the compiler otherwise wrapping policy will be used. + * @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16 + * @attention The element-wise operation to be executed has to be passed at compile time using -DOP (e.g., -DOP=ADD) + * + * @param[in] in1_ptr Pointer to the source tensor. Supported data types: U8/S16/F16/F32 + * @param[in] in1_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] in1_step_x in1_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] in1_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] in1_step_y in1_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] in1_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] in1_step_z in1_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] in1_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[in] in2_ptr Pointer to the source tensor. Supported data types: U8/S16/F16/F32 + * @param[in] in2_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] in2_step_x in2_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] in2_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] in2_step_y in2_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] in2_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] in2_step_z in2_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] in2_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[out] out_ptr Pointer to the destination tensor. Supported data types: U8 (only if both inputs are U8), S16/F16/F32 + * @param[in] out_stride_x Stride of the destination tensor in X dimension (in bytes) + * @param[in] out_step_x out_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] out_stride_y Stride of the destination tensor in Y dimension (in bytes) + * @param[in] out_step_y out_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] out_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] out_step_z out_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] out_offset_first_element_in_bytes The offset of the first element in the destination tensor + */ +__kernel void OP_FUN_NAME(OP)( + TENSOR3D_DECLARATION(in1), + TENSOR3D_DECLARATION(in2), + TENSOR3D_DECLARATION(out)) +{ + // Get pixels pointer + Tensor3D in1 = CONVERT_TO_TENSOR3D_STRUCT(in1); + Tensor3D in2 = CONVERT_TO_TENSOR3D_STRUCT(in2); + Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(out); + + // Load values + VEC_DATA_TYPE(DATA_TYPE_OUT, VEC_SIZE) + in_a = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE_IN1 *)in1.ptr), VEC_DATA_TYPE(DATA_TYPE_OUT, VEC_SIZE)); + VEC_DATA_TYPE(DATA_TYPE_OUT, VEC_SIZE) + in_b = CONVERT(VLOAD(VEC_SIZE)(0, (__global DATA_TYPE_IN2 *)in2.ptr), VEC_DATA_TYPE(DATA_TYPE_OUT, VEC_SIZE)); + + // Calculate and store result + VSTORE(VEC_SIZE) + (OP(in_a, in_b), 0, (__global DATA_TYPE_OUT *)out.ptr); +} +#endif /* defined(DATA_TYPE_IN1) && defined(DATA_TYPE_IN2) && defined(DATA_TYPE_OUT) && defined(VEC_SIZE) */ diff --git a/src/core/CL/cl_kernels/elementwise_operation_quantized.cl b/src/core/CL/cl_kernels/elementwise_operation_quantized.cl new file mode 100644 index 0000000000..1f0533be13 --- /dev/null +++ b/src/core/CL/cl_kernels/elementwise_operation_quantized.cl @@ -0,0 +1,107 @@ +/* + * Copyright (c) 2018 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" + +#define SUB(x, y) (x - y) +#define ADD(x, y) (x + y) +#define MAX(x, y) max((x), (y)) +#define MIN(x, y) min((x), (y)) +#define SQUARED_DIFF(x, y) (x - y) * (x - y) +#define DIV(x, y) (x / y) + +#define CONVERT_RTE(x, type) (convert_##type##_rte((x))) +#define CONVERT_DOWN(x, type) CONVERT_RTE(x, type) + +#define OP_FUN_NAME_STR(op) elementwise_operation_##op##_quantized +#define OP_FUN_NAME(op) OP_FUN_NAME_STR(op) + +#if defined(OP) && defined(VEC_SIZE) && defined(OFFSET_IN1) && defined(OFFSET_IN2) && defined(OFFSET_OUT) && defined(SCALE_IN1) && defined(SCALE_IN2) && defined(SCALE_OUT) + +#define VEC_FLOAT VEC_DATA_TYPE(float, VEC_SIZE) +#define VEC_INT VEC_DATA_TYPE(int, VEC_SIZE) +#define VEC_UCHAR VEC_DATA_TYPE(uchar, VEC_SIZE) + +/** This function executes an element-wise operation among two tensors. + * + * @attention The quantization offset of the first operand must be passed at compile time using -DOFFSET_IN1, i.e. -DOFFSET_IN1=10 + * @attention The quantization offset of the second operand must be passed at compile time using -DOFFSET_IN2, i.e. -DOFFSET_IN2=10 + * @attention The quantization offset of the output must be passed at compile time using -DOFFSET_OUT, i.e. -DOFFSET_OUT=10 + * @attention The quantization scale of the first operand must be passed at compile time using -DSCALE_IN1, i.e. -DSCALE_IN1=10 + * @attention The quantization scale of the second operand must be passed at compile time using -DSCALE_IN2, i.e. -DSCALE_IN2=10 + * @attention The quantization scale of the output must be passed at compile time using -DSCALE_OUT, i.e. -DSCALE_OUT=10 + * @attention To perform saturating operation -DSATURATE has to be passed to the compiler otherwise wrapping policy will be used. + * @attention Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. e.g. -DVEC_SIZE=16 + * @attention The element-wise operation to be executed has to be passed at compile time using -DOP (e.g., -DOP=ADD) + * + * @param[in] in1_ptr Pointer to the source tensor. Supported data types: QASYMM8 + * @param[in] in1_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] in1_step_x in1_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] in1_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] in1_step_y in1_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] in1_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] in1_step_z in1_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] in1_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[in] in2_ptr Pointer to the source tensor. Supported data types: same as @p in1_ptr + * @param[in] in2_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] in2_step_x in2_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] in2_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] in2_step_y in2_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] in2_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] in2_step_z in2_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] in2_offset_first_element_in_bytes The offset of the first element in the source tensor + * @param[out] out_ptr Pointer to the destination tensor. Supported data types: same as @p in1_ptr + * @param[in] out_stride_x Stride of the destination tensor in X dimension (in bytes) + * @param[in] out_step_x out_stride_x * number of elements along X processed per workitem(in bytes) + * @param[in] out_stride_y Stride of the destination tensor in Y dimension (in bytes) + * @param[in] out_step_y out_stride_y * number of elements along Y processed per workitem(in bytes) + * @param[in] out_stride_z Stride of the source tensor in Z dimension (in bytes) + * @param[in] out_step_z out_stride_z * number of elements along Z processed per workitem(in bytes) + * @param[in] out_offset_first_element_in_bytes The offset of the first element in the destination tensor + */ +__kernel void OP_FUN_NAME(OP)( + TENSOR3D_DECLARATION(in1), + TENSOR3D_DECLARATION(in2), + TENSOR3D_DECLARATION(out)) +{ + // Get pixels pointer + Tensor3D in1 = CONVERT_TO_TENSOR3D_STRUCT(in1); + Tensor3D in2 = CONVERT_TO_TENSOR3D_STRUCT(in2); + Tensor3D out = CONVERT_TO_TENSOR3D_STRUCT(out); + + VEC_INT in_a = CONVERT(VLOAD(VEC_SIZE)(0, (__global uchar *)in1.ptr), VEC_INT); + VEC_INT in_b = CONVERT(VLOAD(VEC_SIZE)(0, (__global uchar *)in2.ptr), VEC_INT); + + in_a = SUB(in_a, (VEC_INT)((int)OFFSET_IN1)); + in_b = SUB(in_b, (VEC_INT)((int)OFFSET_IN2)); + + const VEC_FLOAT in1f32 = CONVERT(in_a, VEC_FLOAT) * (VEC_FLOAT)((float)SCALE_IN1); + const VEC_FLOAT in2f32 = CONVERT(in_b, VEC_FLOAT) * (VEC_FLOAT)((float)SCALE_IN2); + const VEC_FLOAT qresf32 = OP(in1f32, in2f32) / ((VEC_FLOAT)(float)SCALE_OUT) + ((VEC_FLOAT)((float)OFFSET_OUT)); + const VEC_UCHAR res = CONVERT_SAT(CONVERT_DOWN(qresf32, VEC_INT), VEC_UCHAR); + + // Store result + VSTORE(VEC_SIZE) + (res, 0, (__global uchar *)out.ptr); +} +#endif /* defined(OFFSET_IN1) && defined(OFFSET_IN2) && defined(OFFSET_OUT) && defined(SCALE_IN1) && defined(SCALE_IN2) && defined(SCALE_OUT) */ diff --git a/src/core/CL/kernels/CLArithmeticAdditionKernel.cpp b/src/core/CL/kernels/CLArithmeticAdditionKernel.cpp deleted file mode 100644 index 10d7fd4f2c..0000000000 --- a/src/core/CL/kernels/CLArithmeticAdditionKernel.cpp +++ /dev/null @@ -1,233 +0,0 @@ -/* - * Copyright (c) 2016-2018 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 "arm_compute/core/CL/kernels/CLArithmeticAdditionKernel.h" - -#include "arm_compute/core/CL/CLHelpers.h" -#include "arm_compute/core/CL/CLValidate.h" -#include "arm_compute/core/CL/ICLTensor.h" - -using namespace arm_compute; - -namespace -{ -constexpr unsigned int num_elems_processed_per_iteration = 8; - -Status validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output, ConvertPolicy policy) -{ - ARM_COMPUTE_UNUSED(policy); - ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input1); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input2); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input2, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32); - - const bool is_qasymm = is_data_type_quantized_asymmetric(input1.data_type()) || is_data_type_quantized_asymmetric(input2.data_type()); - if(is_qasymm) - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &input2); - } - - const TensorShape out_shape = TensorShape::broadcast_shape(input1.tensor_shape(), input2.tensor_shape()); - - ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible"); - - // Validate in case of configured output - if(output.total_size() > 0) - { - ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&output, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_MSG((output.data_type() == DataType::U8) && ((input1.data_type() != DataType::U8) || (input2.data_type() != DataType::U8)), - "Output can only be U8 if both inputs are U8"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output.tensor_shape(), 0), - "Wrong shape for output"); - if(is_qasymm) - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &output); - } - } - - return Status{}; -} - -std::pair validate_and_configure_window(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output) -{ - const std::pair broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(input1, input2); - const TensorShape &out_shape = broadcast_pair.first; - const ValidRegion &valid_region = broadcast_pair.second; - - // Auto initialize output if not initialized - { - set_shape_if_empty(output, out_shape); - - if(input1.data_type() == DataType::S16 || input2.data_type() == DataType::S16) - { - set_format_if_unknown(output, Format::S16); - } - else if(input1.data_type() == DataType::F16 && input2.data_type() == DataType::F16) - { - set_format_if_unknown(output, Format::F16); - } - else if(input1.data_type() == DataType::F32 || input2.data_type() == DataType::F32) - { - set_format_if_unknown(output, Format::F32); - } - } - - Window win = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration)); - Window win_input1 = win.broadcast_if_dimension_le_one(input1); - Window win_input2 = win.broadcast_if_dimension_le_one(input2); - - AccessWindowHorizontal input1_access(&input1, 0, num_elems_processed_per_iteration); - AccessWindowHorizontal input2_access(&input2, 0, num_elems_processed_per_iteration); - AccessWindowHorizontal output_access(&output, 0, num_elems_processed_per_iteration); - - bool window_changed = update_window_and_padding(win_input1, input1_access) - || update_window_and_padding(win_input2, input2_access) - || update_window_and_padding(win, output_access); - - output_access.set_valid_region(win, valid_region); - - Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; - return std::make_pair(err, win); -} -} // namespace - -CLArithmeticAdditionKernel::CLArithmeticAdditionKernel() - : _input1(nullptr), _input2(nullptr), _output(nullptr) -{ -} - -void CLArithmeticAdditionKernel::configure(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, ConvertPolicy policy) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*input1->info(), *input2->info(), *output->info(), policy)); - - // Configure kernel window - auto win_config = validate_and_configure_window(*input1->info(), *input2->info(), *output->info()); - ARM_COMPUTE_ERROR_THROW_ON(win_config.first); - - _input1 = input1; - _input2 = input2; - _output = output; - - const bool has_float_out = is_data_type_float(output->info()->data_type()); - - std::string kernel_name = "arithmetic_add"; - - // Set kernel build options - std::set build_opts; - build_opts.emplace((policy == ConvertPolicy::WRAP || has_float_out) ? "-DWRAP" : "-DSATURATE"); - build_opts.emplace("-DDATA_TYPE_IN1=" + get_cl_type_from_data_type(input1->info()->data_type())); - build_opts.emplace("-DDATA_TYPE_IN2=" + get_cl_type_from_data_type(input2->info()->data_type())); - build_opts.emplace("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output->info()->data_type())); - build_opts.emplace("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration)); - if(is_data_type_quantized_asymmetric(input1->info()->data_type())) - { - build_opts.emplace("-DOFFSET_IN1=" + support::cpp11::to_string(input1->info()->quantization_info().offset)); - build_opts.emplace("-DOFFSET_IN2=" + support::cpp11::to_string(input2->info()->quantization_info().offset)); - build_opts.emplace("-DOFFSET_OUT=" + support::cpp11::to_string(output->info()->quantization_info().offset)); - build_opts.emplace("-DSCALE_IN1=" + support::cpp11::to_string(input1->info()->quantization_info().scale)); - build_opts.emplace("-DSCALE_IN2=" + support::cpp11::to_string(input2->info()->quantization_info().scale)); - build_opts.emplace("-DSCALE_OUT=" + support::cpp11::to_string(output->info()->quantization_info().scale)); - kernel_name += "_quantized"; - } - - // Create kernel - _kernel = static_cast(CLKernelLibrary::get().create_kernel(kernel_name, build_opts)); - - ICLKernel::configure_internal(win_config.second); - - // Set config_id for enabling LWS tuning - _config_id = kernel_name; - _config_id += "_"; - _config_id += lower_string(string_from_data_type(input1->info()->data_type())); - _config_id += "_"; - _config_id += support::cpp11::to_string(output->info()->dimension(0)); - _config_id += "_"; - _config_id += support::cpp11::to_string(output->info()->dimension(1)); - _config_id += (policy == ConvertPolicy::WRAP) ? "_wrap_" : "_saturate_"; - _config_id += lower_string(string_from_data_layout(input1->info()->data_layout())); -} - -Status CLArithmeticAdditionKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy) -{ - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output); - - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(*input1, *input2, *output, policy)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(*input1->clone(), *input2->clone(), *output->clone()).first); - - return Status{}; -} - -void CLArithmeticAdditionKernel::run(const Window &window, cl::CommandQueue &queue) -{ - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); - - const TensorShape &in_shape1 = _input1->info()->tensor_shape(); - const TensorShape &in_shape2 = _input2->info()->tensor_shape(); - const TensorShape &out_shape = _output->info()->tensor_shape(); - - bool can_collapse = true; - const bool is_vector = in_shape1.num_dimensions() == 1 || in_shape2.num_dimensions() == 1; - if(std::min(in_shape1.total_size(), in_shape2.total_size()) > 1 && !is_vector) - { - can_collapse = (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ); - for(size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); d++) - { - can_collapse = (in_shape1[d] == in_shape2[d]); - } - } - - bool has_collapsed = false; - Window collapsed = can_collapse ? window.collapse_if_possible(ICLKernel::window(), Window::DimZ, &has_collapsed) : window; - - const TensorShape &in_shape1_collapsed = has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1; - const TensorShape &in_shape2_collapsed = has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2; - - Window slice = collapsed.first_slice_window_3D(); - Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed); - Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed); - - do - { - unsigned int idx = 0; - - add_3D_tensor_argument(idx, _input1, slice_input1); - add_3D_tensor_argument(idx, _input2, slice_input2); - add_3D_tensor_argument(idx, _output, slice); - - enqueue(queue, *this, slice, lws_hint()); - - collapsed.slide_window_slice_3D(slice_input1); - collapsed.slide_window_slice_3D(slice_input2); - } - while(collapsed.slide_window_slice_3D(slice)); -} - -BorderSize CLArithmeticAdditionKernel::border_size() const -{ - const unsigned int replicateSize = _output->info()->dimension(0) - std::min(_input1->info()->dimension(0), _input2->info()->dimension(0)); - const unsigned int border = std::min(num_elems_processed_per_iteration - 1U, replicateSize); - return BorderSize(0, border, 0, 0); -} diff --git a/src/core/CL/kernels/CLArithmeticDivisionKernel.cpp b/src/core/CL/kernels/CLArithmeticDivisionKernel.cpp deleted file mode 100644 index e995ba1a41..0000000000 --- a/src/core/CL/kernels/CLArithmeticDivisionKernel.cpp +++ /dev/null @@ -1,185 +0,0 @@ -/* - * Copyright (c) 2018 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 "arm_compute/core/CL/kernels/CLArithmeticDivisionKernel.h" - -#include "arm_compute/core/CL/CLHelpers.h" -#include "arm_compute/core/CL/CLValidate.h" -#include "arm_compute/core/CL/ICLTensor.h" - -using namespace arm_compute; - -namespace -{ -constexpr unsigned int num_elems_processed_per_iteration = 16; - -Status validate_arguments(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output) -{ - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output); - ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input1); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input1, input2); - - const TensorShape out_shape = TensorShape::broadcast_shape(input1->tensor_shape(), input2->tensor_shape()); - - ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible"); - - // Validate in case of configured output - if(output->total_size() > 0) - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input1, output); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output->tensor_shape(), 0), - "Wrong shape for output"); - } - - return Status{}; -} - -std::pair validate_and_configure_window(ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output) -{ - const std::pair broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(*input1, *input2); - const TensorShape &out_shape = broadcast_pair.first; - const ValidRegion &valid_region = broadcast_pair.second; - - // Auto initialize output if not initialized - { - set_shape_if_empty(*output, out_shape); - - if(input1->data_type() == DataType::F16 && input2->data_type() == DataType::F16) - { - set_format_if_unknown(*output, Format::F16); - } - else if(input1->data_type() == DataType::F32 || input2->data_type() == DataType::F32) - { - set_format_if_unknown(*output, Format::F32); - } - } - - Window win = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration)); - Window win_input1 = win.broadcast_if_dimension_le_one(*input1); - Window win_input2 = win.broadcast_if_dimension_le_one(*input2); - - AccessWindowHorizontal input1_access(input1, 0, num_elems_processed_per_iteration); - AccessWindowHorizontal input2_access(input2, 0, num_elems_processed_per_iteration); - AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration); - - bool window_changed = update_window_and_padding(win_input1, input1_access) - || update_window_and_padding(win_input2, input2_access) - || update_window_and_padding(win, output_access); - - output_access.set_valid_region(win, valid_region); - - Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; - return std::make_pair(err, win); -} -} // namespace - -CLArithmeticDivisionKernel::CLArithmeticDivisionKernel() - : _input1(nullptr), _input2(nullptr), _output(nullptr) -{ -} - -void CLArithmeticDivisionKernel::configure(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input1->info(), input2->info(), output->info())); - - // Configure kernel window - auto win_config = validate_and_configure_window(input1->info(), input2->info(), output->info()); - ARM_COMPUTE_ERROR_THROW_ON(win_config.first); - - _input1 = input1; - _input2 = input2; - _output = output; - - // Set kernel build options - std::set build_opts; - build_opts.emplace("-DDATA_TYPE_IN1=" + get_cl_type_from_data_type(input1->info()->data_type())); - build_opts.emplace("-DDATA_TYPE_IN2=" + get_cl_type_from_data_type(input2->info()->data_type())); - build_opts.emplace("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output->info()->data_type())); - - // Create kernel - _kernel = static_cast(CLKernelLibrary::get().create_kernel("arithmetic_div", build_opts)); - - ICLKernel::configure_internal(win_config.second); -} - -Status CLArithmeticDivisionKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output) -{ - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input1, input2, output)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input1->clone().get(), input2->clone().get(), output->clone().get()).first); - - return Status{}; -} - -void CLArithmeticDivisionKernel::run(const Window &window, cl::CommandQueue &queue) -{ - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); - - const TensorShape &in_shape1 = _input1->info()->tensor_shape(); - const TensorShape &in_shape2 = _input2->info()->tensor_shape(); - const TensorShape &out_shape = _output->info()->tensor_shape(); - - bool can_collapse = true; - if(std::min(in_shape1.total_size(), in_shape2.total_size()) > 1) - { - can_collapse = (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ); - for(size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); d++) - { - can_collapse = (in_shape1[d] == in_shape2[d]); - } - } - - bool has_collapsed = false; - Window collapsed = can_collapse ? window.collapse_if_possible(ICLKernel::window(), Window::DimZ, &has_collapsed) : window; - - const TensorShape &in_shape1_collapsed = has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1; - const TensorShape &in_shape2_collapsed = has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2; - - Window slice = collapsed.first_slice_window_3D(); - Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed); - Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed); - - do - { - unsigned int idx = 0; - - add_3D_tensor_argument(idx, _input1, slice_input1); - add_3D_tensor_argument(idx, _input2, slice_input2); - add_3D_tensor_argument(idx, _output, slice); - - enqueue(queue, *this, slice); - - collapsed.slide_window_slice_3D(slice_input1); - collapsed.slide_window_slice_3D(slice_input2); - } - while(collapsed.slide_window_slice_3D(slice)); -} - -BorderSize CLArithmeticDivisionKernel::border_size() const -{ - const unsigned int replicateSize = _output->info()->dimension(0) - std::min(_input1->info()->dimension(0), _input2->info()->dimension(0)); - const unsigned int border = std::min(num_elems_processed_per_iteration - 1U, replicateSize); - return BorderSize(0, border, 0, 0); -} diff --git a/src/core/CL/kernels/CLArithmeticSubtractionKernel.cpp b/src/core/CL/kernels/CLArithmeticSubtractionKernel.cpp deleted file mode 100644 index 95d201104d..0000000000 --- a/src/core/CL/kernels/CLArithmeticSubtractionKernel.cpp +++ /dev/null @@ -1,232 +0,0 @@ -/* - * Copyright (c) 2016-2018 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 "arm_compute/core/CL/kernels/CLArithmeticSubtractionKernel.h" - -#include "arm_compute/core/CL/CLHelpers.h" -#include "arm_compute/core/CL/CLKernelLibrary.h" -#include "arm_compute/core/CL/CLValidate.h" -#include "arm_compute/core/CL/ICLTensor.h" -#include "arm_compute/core/CL/OpenCL.h" -#include "arm_compute/core/Helpers.h" -#include "arm_compute/core/IAccessWindow.h" -#include "arm_compute/core/TensorInfo.h" -#include "arm_compute/core/Window.h" - -#include -#include - -namespace arm_compute -{ -namespace -{ -constexpr unsigned int num_elems_processed_per_iteration = 16; - -Status validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output, ConvertPolicy policy) -{ - ARM_COMPUTE_UNUSED(policy); - ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input1); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input2); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input2, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32); - const bool is_qasymm = is_data_type_quantized_asymmetric(input1.data_type()) || is_data_type_quantized_asymmetric(input2.data_type()); - if(is_qasymm) - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &input2); - } - - const TensorShape out_shape = TensorShape::broadcast_shape(input1.tensor_shape(), input2.tensor_shape()); - - ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible"); - - // Validate in case of configured output - if(output.total_size() > 0) - { - ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&output); - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&output, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32); - ARM_COMPUTE_RETURN_ERROR_ON_MSG((output.data_type() == DataType::U8) && ((input1.data_type() != DataType::U8) || (input2.data_type() != DataType::U8)), - "Output can only be U8 if both inputs are U8"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output.tensor_shape(), 0), - "Wrong shape for output"); - if(is_qasymm) - { - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &output); - } - } - - return Status{}; -} - -std::pair validate_and_configure_window(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output) -{ - const std::pair broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(input1, input2); - const TensorShape &out_shape = broadcast_pair.first; - const ValidRegion &valid_region = broadcast_pair.second; - - // Auto initialize output if not initialized - { - set_shape_if_empty(output, out_shape); - - if(input1.data_type() == DataType::S16 || input2.data_type() == DataType::S16) - { - set_format_if_unknown(output, Format::S16); - } - else if(input1.data_type() == DataType::F16 && input2.data_type() == DataType::F16) - { - set_format_if_unknown(output, Format::F16); - } - else if(input1.data_type() == DataType::F32 || input2.data_type() == DataType::F32) - { - set_format_if_unknown(output, Format::F32); - } - } - - Window win = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration)); - Window win_input1 = win.broadcast_if_dimension_le_one(input1); - Window win_input2 = win.broadcast_if_dimension_le_one(input2); - - AccessWindowHorizontal input1_access(&input1, 0, num_elems_processed_per_iteration); - AccessWindowHorizontal input2_access(&input2, 0, num_elems_processed_per_iteration); - AccessWindowHorizontal output_access(&output, 0, num_elems_processed_per_iteration); - - bool window_changed = update_window_and_padding(win_input1, input1_access) - || update_window_and_padding(win_input2, input2_access) - || update_window_and_padding(win, output_access); - - output_access.set_valid_region(win, valid_region); - - Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; - return std::make_pair(err, win); -} -} // namespace - -CLArithmeticSubtractionKernel::CLArithmeticSubtractionKernel() - : _input1(nullptr), _input2(nullptr), _output(nullptr) -{ -} - -void CLArithmeticSubtractionKernel::configure(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, ConvertPolicy policy) -{ - ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*input1->info(), *input2->info(), *output->info(), policy)); - - // Configure kernel window - auto win_config = validate_and_configure_window(*input1->info(), *input2->info(), *output->info()); - ARM_COMPUTE_ERROR_THROW_ON(win_config.first); - - _input1 = input1; - _input2 = input2; - _output = output; - - bool has_float_out = is_data_type_float(output->info()->data_type()); - - // Setup kernel - std::string kernel_name = "arithmetic_sub"; - - // Set kernel build options - CLBuildOptions build_opts; - build_opts.add_option_if_else(policy == ConvertPolicy::WRAP || has_float_out, "-DWRAP", "-DSATURATE"); - build_opts.add_option("-DDATA_TYPE_IN1=" + get_cl_type_from_data_type(input1->info()->data_type())); - build_opts.add_option("-DDATA_TYPE_IN2=" + get_cl_type_from_data_type(input2->info()->data_type())); - build_opts.add_option("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output->info()->data_type())); - if(is_data_type_quantized_asymmetric(input1->info()->data_type())) - { - build_opts.add_option("-DOFFSET_IN1=" + support::cpp11::to_string(input1->info()->quantization_info().offset)); - build_opts.add_option("-DOFFSET_IN2=" + support::cpp11::to_string(input2->info()->quantization_info().offset)); - build_opts.add_option("-DOFFSET_OUT=" + support::cpp11::to_string(output->info()->quantization_info().offset)); - build_opts.add_option("-DSCALE_IN1=" + support::cpp11::to_string(input1->info()->quantization_info().scale)); - build_opts.add_option("-DSCALE_IN2=" + support::cpp11::to_string(input2->info()->quantization_info().scale)); - build_opts.add_option("-DSCALE_OUT=" + support::cpp11::to_string(output->info()->quantization_info().scale)); - kernel_name += "_quantized"; - } - - // Create kernel - _kernel = static_cast(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); - - // Configure kernel window - ICLKernel::configure_internal(win_config.second); -} - -Status CLArithmeticSubtractionKernel::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy) -{ - ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output); - - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(*input1, *input2, *output, policy)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(*input1->clone(), *input2->clone(), *output->clone()).first); - - return Status{}; -} - -void CLArithmeticSubtractionKernel::run(const Window &window, cl::CommandQueue &queue) -{ - ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); - ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); - - const TensorShape &in_shape1 = _input1->info()->tensor_shape(); - const TensorShape &in_shape2 = _input2->info()->tensor_shape(); - const TensorShape &out_shape = _output->info()->tensor_shape(); - - // Collapse only if broadcast dimensions is less than 2, or in case of no broadcasting - bool can_collapse = true; - if(std::min(in_shape1.total_size(), in_shape2.total_size()) > 1) - { - can_collapse = (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ); - for(size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); d++) - { - can_collapse = (in_shape1[d] == in_shape2[d]); - } - } - - bool has_collapsed = false; - Window collapsed = can_collapse ? window.collapse_if_possible(ICLKernel::window(), Window::DimZ, &has_collapsed) : window; - - const TensorShape &in_shape1_collapsed = has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1; - const TensorShape &in_shape2_collapsed = has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2; - - Window slice = collapsed.first_slice_window_3D(); - Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed); - Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed); - - do - { - unsigned int idx = 0; - - add_3D_tensor_argument(idx, _input1, slice_input1); - add_3D_tensor_argument(idx, _input2, slice_input2); - add_3D_tensor_argument(idx, _output, slice); - - enqueue(queue, *this, slice); - - collapsed.slide_window_slice_3D(slice_input1); - collapsed.slide_window_slice_3D(slice_input2); - } - while(collapsed.slide_window_slice_3D(slice)); -} - -BorderSize CLArithmeticSubtractionKernel::border_size() const -{ - const unsigned int replicateSize = _output->info()->dimension(0) - std::min(_input1->info()->dimension(0), _input2->info()->dimension(0)); - const unsigned int border = std::min(num_elems_processed_per_iteration - 1U, replicateSize); - return BorderSize(0, border, 0, 0); -} -} // namespace arm_compute \ No newline at end of file diff --git a/src/core/CL/kernels/CLElementwiseOperationKernel.cpp b/src/core/CL/kernels/CLElementwiseOperationKernel.cpp new file mode 100644 index 0000000000..5dc5b7e13f --- /dev/null +++ b/src/core/CL/kernels/CLElementwiseOperationKernel.cpp @@ -0,0 +1,337 @@ +/* + * Copyright (c) 2018 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 "arm_compute/core/CL/kernels/CLElementwiseOperationKernel.h" + +#include "arm_compute/core/CL/CLHelpers.h" +#include "arm_compute/core/CL/CLValidate.h" +#include "arm_compute/core/CL/ICLTensor.h" +#include + +namespace arm_compute +{ +namespace +{ +constexpr unsigned int num_elems_processed_per_iteration = 16; + +std::map supported_arithmetic_ops = +{ + { ArithmeticOperation::ADD, "ADD" }, + { ArithmeticOperation::SUB, "SUB" }, + { ArithmeticOperation::DIV, "DIV" }, + { ArithmeticOperation::SQUARED_DIFF, "SQUARED_DIFF" }, + { ArithmeticOperation::MIN, "MIN" }, + { ArithmeticOperation::MAX, "MAX" }, +}; + +std::map supported_sat_arithmetic_ops = +{ + { ArithmeticOperation::ADD, "ADD" }, + { ArithmeticOperation::SUB, "SUB" }, +}; + +std::string generate_id_for_tuning_common(const std::string &kernel_name, const ITensorInfo &input1, const ITensorInfo &output) +{ + std::string config_id; + // Set config_id for enabling LWS tuning + config_id = kernel_name; + config_id += "_"; + config_id += lower_string(string_from_data_type(input1.data_type())); + config_id += "_"; + config_id += support::cpp11::to_string(output.dimension(0)); + config_id += "_"; + config_id += support::cpp11::to_string(output.dimension(1)); + return config_id; +} + +Status validate_arguments_with_arithmetic_rules(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output) +{ + ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input1); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input2); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input2, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32); + + const bool is_qasymm = is_data_type_quantized_asymmetric(input1.data_type()) || is_data_type_quantized_asymmetric(input2.data_type()); + if(is_qasymm) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &input2); + } + + const TensorShape out_shape = TensorShape::broadcast_shape(input1.tensor_shape(), input2.tensor_shape()); + + ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible"); + + // Validate in case of configured output + if(output.total_size() > 0) + { + ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&output); + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&output, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::F16, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MSG((output.data_type() == DataType::U8) && ((input1.data_type() != DataType::U8) || (input2.data_type() != DataType::U8)), + "Output can only be U8 if both inputs are U8"); + ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output.tensor_shape(), 0), + "Wrong shape for output"); + if(is_qasymm) + { + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &output); + } + } + return Status{}; +} + +CLBuildOptions generate_build_options_with_arithmetic_rules(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output, const std::string &operation_string) +{ + CLBuildOptions build_opts; + + build_opts.add_option("-DDATA_TYPE_IN1=" + get_cl_type_from_data_type(input1.data_type())); + build_opts.add_option("-DDATA_TYPE_IN2=" + get_cl_type_from_data_type(input2.data_type())); + build_opts.add_option("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output.data_type())); + build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration)); + build_opts.add_option("-DOP=" + operation_string); + if(is_data_type_quantized_asymmetric(input1.data_type())) + { + build_opts.add_option("-DOFFSET_IN1=" + support::cpp11::to_string(input1.quantization_info().offset)); + build_opts.add_option("-DOFFSET_IN2=" + support::cpp11::to_string(input2.quantization_info().offset)); + build_opts.add_option("-DOFFSET_OUT=" + support::cpp11::to_string(output.quantization_info().offset)); + build_opts.add_option("-DSCALE_IN1=" + float_to_string_with_full_precision(input1.quantization_info().scale)); + build_opts.add_option("-DSCALE_IN2=" + float_to_string_with_full_precision(input2.quantization_info().scale)); + build_opts.add_option("-DSCALE_OUT=" + float_to_string_with_full_precision(output.quantization_info().scale)); + } + return build_opts; +} + +std::pair validate_and_configure_window_for_arithmetic_operators(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output) +{ + const std::pair broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(input1, input2); + const TensorShape &out_shape = broadcast_pair.first; + const ValidRegion &valid_region = broadcast_pair.second; + + set_shape_if_empty(output, out_shape); + + if(input1.data_type() == DataType::S16 || input2.data_type() == DataType::S16) + { + set_format_if_unknown(output, Format::S16); + } + else if(input1.data_type() == DataType::F16 && input2.data_type() == DataType::F16) + { + set_format_if_unknown(output, Format::F16); + } + else if(input1.data_type() == DataType::F32 || input2.data_type() == DataType::F32) + { + set_format_if_unknown(output, Format::F32); + } + + Window win = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration)); + Window win_input1 = win.broadcast_if_dimension_le_one(input1); + Window win_input2 = win.broadcast_if_dimension_le_one(input2); + + AccessWindowHorizontal input1_access(&input1, 0, num_elems_processed_per_iteration); + AccessWindowHorizontal input2_access(&input2, 0, num_elems_processed_per_iteration); + AccessWindowHorizontal output_access(&output, 0, num_elems_processed_per_iteration); + + bool window_changed = update_window_and_padding(win_input1, input1_access) + || update_window_and_padding(win_input2, input2_access) + || update_window_and_padding(win, output_access); + + output_access.set_valid_region(win, valid_region); + + Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; + return std::make_pair(err, win); +} +} // namespace + +CLElementwiseOperationKernel::CLElementwiseOperationKernel() + : _input1(nullptr), _input2(nullptr), _output(nullptr) +{ +} + +void CLElementwiseOperationKernel::configure_common(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output) +{ + ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*input1->info(), *input2->info(), *output->info())); + + // Configure kernel window + auto win_config = validate_and_configure_window(*input1->info(), *input2->info(), *output->info()); + ARM_COMPUTE_ERROR_THROW_ON(win_config.first); + + _input1 = input1; + _input2 = input2; + _output = output; + + std::string kernel_name = "elementwise_operation_" + name(); + if(is_data_type_quantized_asymmetric(input1->info()->data_type())) + { + kernel_name += "_quantized"; + } + + // Set kernel build options + CLBuildOptions build_opts = generate_build_options(*input1->info(), *input2->info(), *output->info()); + + // Create kernel + _kernel = static_cast(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); + + ICLKernel::configure_internal(win_config.second); + + _config_id = generate_id_for_tuning(kernel_name, *input1->info(), *output->info()); +} + +void CLElementwiseOperationKernel::run(const Window &window, cl::CommandQueue &queue) +{ + ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); + ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window); + + const TensorShape &in_shape1 = _input1->info()->tensor_shape(); + const TensorShape &in_shape2 = _input2->info()->tensor_shape(); + const TensorShape &out_shape = _output->info()->tensor_shape(); + + bool can_collapse = true; + const bool is_vector = in_shape1.num_dimensions() == 1 || in_shape2.num_dimensions() == 1; + if(std::min(in_shape1.total_size(), in_shape2.total_size()) > 1 && !is_vector) + { + can_collapse = (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ); + for(size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); d++) + { + can_collapse = (in_shape1[d] == in_shape2[d]); + } + } + + bool has_collapsed = false; + Window collapsed = can_collapse ? window.collapse_if_possible(ICLKernel::window(), Window::DimZ, &has_collapsed) : window; + + const TensorShape &in_shape1_collapsed = has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1; + const TensorShape &in_shape2_collapsed = has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2; + + Window slice = collapsed.first_slice_window_3D(); + Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed); + Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed); + + do + { + unsigned int idx = 0; + + add_3D_tensor_argument(idx, _input1, slice_input1); + add_3D_tensor_argument(idx, _input2, slice_input2); + add_3D_tensor_argument(idx, _output, slice); + + enqueue(queue, *this, slice, lws_hint()); + + collapsed.slide_window_slice_3D(slice_input1); + collapsed.slide_window_slice_3D(slice_input2); + } + while(collapsed.slide_window_slice_3D(slice)); +} + +BorderSize CLElementwiseOperationKernel::border_size() const +{ + const unsigned int replicateSize = _output->info()->dimension(0) - std::min(_input1->info()->dimension(0), _input2->info()->dimension(0)); + const unsigned int border = std::min(num_elems_processed_per_iteration - 1U, replicateSize); + return BorderSize(0, border, 0, 0); +} + +/** Arithmetic operations with saturation*/ + +void CLSaturatedArithmeticOperationKernel::configure(ArithmeticOperation op, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, const ConvertPolicy &policy) +{ + _policy = policy; + _op = op; + configure_common(input1, input2, output); +} + +Status CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation op, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, const ConvertPolicy &policy) +{ + ARM_COMPUTE_UNUSED(op, policy); + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_with_arithmetic_rules(*input1, *input2, *output)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_for_arithmetic_operators(*input1->clone(), *input2->clone(), *output->clone()).first); + + return Status{}; +} + +std::pair CLSaturatedArithmeticOperationKernel::validate_and_configure_window(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output) +{ + return validate_and_configure_window_for_arithmetic_operators(input1, input2, output); +} + +Status CLSaturatedArithmeticOperationKernel::validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output) +{ + return validate_arguments_with_arithmetic_rules(input1, input2, output); +} + +CLBuildOptions CLSaturatedArithmeticOperationKernel::generate_build_options(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output) +{ + const bool has_float_out = is_data_type_float(output.data_type()); + auto build_options = generate_build_options_with_arithmetic_rules(input1, input2, output, name()); + build_options.add_option((_policy == ConvertPolicy::WRAP || has_float_out) ? "-DWRAP" : "-DSATURATE"); + return build_options; +} +std::string CLSaturatedArithmeticOperationKernel::generate_id_for_tuning(const std::string &kernel_name, const ITensorInfo &input1, const ITensorInfo &output) +{ + auto config_id = generate_id_for_tuning_common(kernel_name, input1, output); + config_id += (_policy == ConvertPolicy::WRAP) ? "_wrap_" : "_saturate_"; + config_id += lower_string(string_from_data_layout(input1.data_layout())); + return config_id; +} + +std::string CLSaturatedArithmeticOperationKernel::name() +{ + return supported_sat_arithmetic_ops[_op]; +} + +/** Arithmetic operations*/ + +void CLArithmeticOperationKernel::configure(ArithmeticOperation op, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output) +{ + _op = op; + configure_common(input1, input2, output); +} + +Status CLArithmeticOperationKernel::validate(ArithmeticOperation op, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output) +{ + ARM_COMPUTE_UNUSED(op); + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_with_arithmetic_rules(*input1, *input2, *output)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_for_arithmetic_operators(*input1->clone(), *input2->clone(), *output->clone()).first); + return Status{}; +} +std::pair CLArithmeticOperationKernel::validate_and_configure_window(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output) +{ + return validate_and_configure_window_for_arithmetic_operators(input1, input2, output); +} +Status CLArithmeticOperationKernel::validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output) +{ + return validate_arguments_with_arithmetic_rules(input1, input2, output); +} + +CLBuildOptions CLArithmeticOperationKernel::generate_build_options(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output) +{ + return generate_build_options_with_arithmetic_rules(input1, input2, output, name()); +} +std::string CLArithmeticOperationKernel::generate_id_for_tuning(const std::string &kernel_name, const ITensorInfo &input1, const ITensorInfo &output) +{ + return generate_id_for_tuning_common(kernel_name, input1, output); +} + +std::string CLArithmeticOperationKernel::name() +{ + return supported_arithmetic_ops[_op]; +} +} // namespace arm_compute diff --git a/src/runtime/CL/functions/CLArithmeticAddition.cpp b/src/runtime/CL/functions/CLArithmeticAddition.cpp deleted file mode 100644 index 0b05058c4d..0000000000 --- a/src/runtime/CL/functions/CLArithmeticAddition.cpp +++ /dev/null @@ -1,54 +0,0 @@ -/* - * Copyright (c) 2016-2018 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 "arm_compute/runtime/CL/functions/CLArithmeticAddition.h" - -#include "arm_compute/core/CL/ICLTensor.h" -#include "arm_compute/core/CL/kernels/CLArithmeticAdditionKernel.h" -#include "support/ToolchainSupport.h" - -#include - -using namespace arm_compute; - -void CLArithmeticAddition::configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output, ConvertPolicy policy) -{ - auto k = arm_compute::support::cpp14::make_unique(); - k->configure(input1, input2, output, policy); - _kernel = std::move(k); - - if(output->info()->dimension(0) > 1) - { - ICLTensor *broadcasted_info = (input1->info()->dimension(0) == 1) ? input1 : input2; - - if(broadcasted_info->info()->dimension(0) == 1) - { - _border_handler.configure(broadcasted_info, _kernel->border_size(), BorderMode::REPLICATE); - } - } -} - -Status CLArithmeticAddition::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy) -{ - return CLArithmeticAdditionKernel::validate(input1, input2, output, policy); -} diff --git a/src/runtime/CL/functions/CLArithmeticDivision.cpp b/src/runtime/CL/functions/CLArithmeticDivision.cpp deleted file mode 100644 index 1c2849cee9..0000000000 --- a/src/runtime/CL/functions/CLArithmeticDivision.cpp +++ /dev/null @@ -1,54 +0,0 @@ -/* - * Copyright (c) 2018 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 "arm_compute/runtime/CL/functions/CLArithmeticDivision.h" - -#include "arm_compute/core/CL/ICLTensor.h" -#include "arm_compute/core/CL/kernels/CLArithmeticDivisionKernel.h" -#include "support/ToolchainSupport.h" - -#include - -using namespace arm_compute; - -void CLArithmeticDivision::configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output) -{ - auto k = arm_compute::support::cpp14::make_unique(); - k->configure(input1, input2, output); - _kernel = std::move(k); - - if(output->info()->dimension(0) > 1) - { - ICLTensor *broadcasted_info = (input1->info()->dimension(0) == 1) ? input1 : input2; - - if(broadcasted_info->info()->dimension(0) == 1) - { - _border_handler.configure(broadcasted_info, _kernel->border_size(), BorderMode::REPLICATE); - } - } -} - -Status CLArithmeticDivision::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output) -{ - return CLArithmeticDivisionKernel::validate(input1, input2, output); -} diff --git a/src/runtime/CL/functions/CLArithmeticSubtraction.cpp b/src/runtime/CL/functions/CLArithmeticSubtraction.cpp deleted file mode 100644 index e661f6adc1..0000000000 --- a/src/runtime/CL/functions/CLArithmeticSubtraction.cpp +++ /dev/null @@ -1,54 +0,0 @@ -/* - * Copyright (c) 2016-2018 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 "arm_compute/runtime/CL/functions/CLArithmeticSubtraction.h" - -#include "arm_compute/core/CL/ICLTensor.h" -#include "arm_compute/core/CL/kernels/CLArithmeticSubtractionKernel.h" -#include "support/ToolchainSupport.h" - -#include - -using namespace arm_compute; - -void CLArithmeticSubtraction::configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output, ConvertPolicy policy) -{ - auto k = arm_compute::support::cpp14::make_unique(); - k->configure(input1, input2, output, policy); - _kernel = std::move(k); - - if(output->info()->dimension(0) > 1) - { - ICLTensor *broadcasted_info = (input1->info()->dimension(0) == 1) ? input1 : input2; - - if(broadcasted_info->info()->dimension(0) == 1) - { - _border_handler.configure(broadcasted_info, _kernel->border_size(), BorderMode::REPLICATE); - } - } -} - -Status CLArithmeticSubtraction::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy) -{ - return CLArithmeticSubtractionKernel::validate(input1, input2, output, policy); -} diff --git a/src/runtime/CL/functions/CLElementwiseOperations.cpp b/src/runtime/CL/functions/CLElementwiseOperations.cpp new file mode 100644 index 0000000000..28f4b13f22 --- /dev/null +++ b/src/runtime/CL/functions/CLElementwiseOperations.cpp @@ -0,0 +1,127 @@ +/* + * Copyright (c) 2018 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 "arm_compute/core/CL/ICLTensor.h" +#include "arm_compute/core/CL/kernels/CLElementwiseOperationKernel.h" +#include "support/ToolchainSupport.h" +#include + +#include + +namespace arm_compute +{ +namespace +{ +void configure_border_handler(CLFillBorderKernel &border_handler, BorderSize border_size, ICLTensor *input1, ICLTensor *input2, const ICLTensor *output) +{ + if(output->info()->dimension(0) > 1) + { + ICLTensor *broadcasted_info = (input1->info()->dimension(0) == 1) ? input1 : input2; + + if(broadcasted_info->info()->dimension(0) == 1) + { + border_handler.configure(broadcasted_info, border_size, BorderMode::REPLICATE); + } + } +} +} // namespace + +void CLArithmeticAddition::configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output, ConvertPolicy policy) +{ + auto k = arm_compute::support::cpp14::make_unique(); + k->configure(ArithmeticOperation::ADD, input1, input2, output, policy); + _kernel = std::move(k); + configure_border_handler(_border_handler, _kernel->border_size(), input1, input2, output); +} + +Status CLArithmeticAddition::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy) +{ + return CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation::ADD, input1, input2, output, policy); +} + +void CLArithmeticSubtraction::configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output, ConvertPolicy policy) +{ + auto k = arm_compute::support::cpp14::make_unique(); + k->configure(ArithmeticOperation::SUB, input1, input2, output, policy); + _kernel = std::move(k); + configure_border_handler(_border_handler, _kernel->border_size(), input1, input2, output); +} + +Status CLArithmeticSubtraction::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy) +{ + ARM_COMPUTE_UNUSED(policy); + return CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation::SUB, input1, input2, output, policy); +} + +void CLArithmeticDivision::configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output) +{ + auto k = arm_compute::support::cpp14::make_unique(); + k->configure(ArithmeticOperation::DIV, input1, input2, output); + _kernel = std::move(k); + configure_border_handler(_border_handler, _kernel->border_size(), input1, input2, output); +} + +Status CLArithmeticDivision::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output) +{ + return CLArithmeticOperationKernel::validate(ArithmeticOperation::DIV, input1, input2, output); +} + +void CLElementwiseMax::configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output) +{ + auto k = arm_compute::support::cpp14::make_unique(); + k->configure(ArithmeticOperation::MAX, input1, input2, output); + _kernel = std::move(k); + configure_border_handler(_border_handler, _kernel->border_size(), input1, input2, output); +} + +Status CLElementwiseMax::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output) +{ + return CLArithmeticOperationKernel::validate(ArithmeticOperation::MAX, input1, input2, output); +} + +void CLElementwiseMin::configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output) +{ + auto k = arm_compute::support::cpp14::make_unique(); + k->configure(ArithmeticOperation::MIN, input1, input2, output); + _kernel = std::move(k); + configure_border_handler(_border_handler, _kernel->border_size(), input1, input2, output); +} + +Status CLElementwiseMin::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output) +{ + return CLArithmeticOperationKernel::validate(ArithmeticOperation::MIN, input1, input2, output); +} + +void CLElementwiseSquaredDiff::configure(ICLTensor *input1, ICLTensor *input2, ICLTensor *output) +{ + auto k = arm_compute::support::cpp14::make_unique(); + k->configure(ArithmeticOperation::SQUARED_DIFF, input1, input2, output); + _kernel = std::move(k); + configure_border_handler(_border_handler, _kernel->border_size(), input1, input2, output); +} + +Status CLElementwiseSquaredDiff::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output) +{ + return CLArithmeticOperationKernel::validate(ArithmeticOperation::SQUARED_DIFF, input1, input2, output); +} +} // namespace arm_compute diff --git a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp index 4694aa7f37..3a8b1a5891 100644 --- a/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLGEMMConvolutionLayer.cpp @@ -242,7 +242,7 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor * else if(_append_bias) { // Configure add bias kernel - _add_bias_kernel.configure(output, biases, output, ConvertPolicy::SATURATE); + _add_bias_kernel.configure(ArithmeticOperation::ADD, output, biases, output, ConvertPolicy::SATURATE); } // Create GEMM output tensor @@ -276,9 +276,9 @@ void CLGEMMConvolutionLayer::configure(const ICLTensor *input, const ICLTensor * { const QuantizationInfo output_quant_info = (output->info()->total_size() == 0) ? input->info()->quantization_info() : output->info()->quantization_info(); - const float multiplier = (input->info()->quantization_info().scale * weights->info()->quantization_info().scale) / output_quant_info.scale; - int output_multiplier = 0; - int output_shift = 0; + const float multiplier = (input->info()->quantization_info().scale * weights->info()->quantization_info().scale) / output_quant_info.scale; + int output_multiplier = 0; + int output_shift = 0; quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); int min_activation = 0; @@ -432,7 +432,7 @@ Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI else if(append_bias) { // Validate add bias kernel - ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAdditionKernel::validate(output, biases, output, ConvertPolicy::SATURATE)); + ARM_COMPUTE_RETURN_ON_ERROR(CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation::ADD, output, biases, output, ConvertPolicy::SATURATE)); } // Create GEMM output tensor @@ -459,9 +459,9 @@ Status CLGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorI { const QuantizationInfo output_quant_info = (output->total_size() == 0) ? input->quantization_info() : output->quantization_info(); - const float multiplier = (input->quantization_info().scale * weights->quantization_info().scale) / output_quant_info.scale; - int output_multiplier = 0; - int output_shift = 0; + const float multiplier = (input->quantization_info().scale * weights->quantization_info().scale) / output_quant_info.scale; + int output_multiplier = 0; + int output_shift = 0; ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift)); diff --git a/src/runtime/CL/functions/CLLSTMLayer.cpp b/src/runtime/CL/functions/CLLSTMLayer.cpp index a89c4e3dbf..8a27d68d4a 100644 --- a/src/runtime/CL/functions/CLLSTMLayer.cpp +++ b/src/runtime/CL/functions/CLLSTMLayer.cpp @@ -110,7 +110,7 @@ void CLLSTMLayer::configure(const ICLTensor *input, _gemm_forget_gate.configure(output_state_in, &_forget_gate_out2, nullptr, &_forget_gate_out3, 1.f, 0.f); _forget_gate_out2.allocator()->allocate(); _memory_group.manage(&_forget_gate_out5); - _accum_forget_gate1.configure(&_forget_gate_out1, &_forget_gate_out3, &_forget_gate_out5, ConvertPolicy::SATURATE); + _accum_forget_gate1.configure(ArithmeticOperation::ADD, &_forget_gate_out1, &_forget_gate_out3, &_forget_gate_out5, ConvertPolicy::SATURATE); CLTensor *forget_gate_out = &_forget_gate_out5; if(lstm_params.has_peephole_opt()) @@ -139,7 +139,7 @@ void CLLSTMLayer::configure(const ICLTensor *input, { _memory_group.manage(&_input_gate_out1); _ones.allocator()->init(TensorInfo(cell_state_shape, 1, input->info()->data_type())); - _subtract_input_gate.configure(&_ones, &_forget_gate_out1, &_input_gate_out1, ConvertPolicy::SATURATE); + _subtract_input_gate.configure(ArithmeticOperation::SUB, &_ones, &_forget_gate_out1, &_input_gate_out1, ConvertPolicy::SATURATE); _ones.allocator()->allocate(); _run_cifg_opt = true; } @@ -160,7 +160,7 @@ void CLLSTMLayer::configure(const ICLTensor *input, _gemm_input_gate.configure(output_state_in, &_input_gate_out2, nullptr, &_input_gate_out3, 1.f, 0.f); _input_gate_out2.allocator()->allocate(); _memory_group.manage(&_input_gate_out4); - _accum_input_gate1.configure(&_input_gate_out1, &_input_gate_out3, &_input_gate_out4, ConvertPolicy::SATURATE); + _accum_input_gate1.configure(ArithmeticOperation::ADD, &_input_gate_out1, &_input_gate_out3, &_input_gate_out4, ConvertPolicy::SATURATE); if(_run_peephole_opt) { _memory_group.manage(&_input_gate_out5); @@ -190,14 +190,14 @@ void CLLSTMLayer::configure(const ICLTensor *input, _gemm_cell_state1.configure(output_state_in, &_cell_state_out2, nullptr, &_cell_state_out3, 1.f, 0.f); _cell_state_out2.allocator()->allocate(); _memory_group.manage(&_cell_state_out4); - _accum_cell_state1.configure(&_cell_state_out1, &_cell_state_out3, &_cell_state_out4, ConvertPolicy::SATURATE); + _accum_cell_state1.configure(ArithmeticOperation::ADD, &_cell_state_out1, &_cell_state_out3, &_cell_state_out4, ConvertPolicy::SATURATE); _activation_cell_state.configure(&_cell_state_out4, nullptr, activation_info); _memory_group.manage(&_cell_state_out5); _pixelwise_mul_cell_state1.configure(&_cell_state_out4, &_input_gate_out1, &_cell_state_out5, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN); _cell_state_out4.allocator()->allocate(); _pixelwise_mul_cell_state2.configure(&_forget_gate_out1, cell_state_in, &_cell_state_out3, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN); _forget_gate_out1.allocator()->allocate(); - _accum_cell_state2.configure(&_cell_state_out5, &_cell_state_out3, &_cell_state_out1, ConvertPolicy::SATURATE); + _accum_cell_state2.configure(ArithmeticOperation::ADD, &_cell_state_out5, &_cell_state_out3, &_cell_state_out1, ConvertPolicy::SATURATE); _cell_state_out3.allocator()->allocate(); _cell_state_out5.allocator()->allocate(); // Perform clipping @@ -223,7 +223,7 @@ void CLLSTMLayer::configure(const ICLTensor *input, _gemm_output.configure(output_state_in, &_output2, nullptr, &_output3, 1.f, 0.f); _output2.allocator()->allocate(); _memory_group.manage(&_output5); - _accum_output1.configure(&_output1, &_output3, &_output5, ConvertPolicy::SATURATE); + _accum_output1.configure(ArithmeticOperation::ADD, &_output1, &_output3, &_output5, ConvertPolicy::SATURATE); _output3.allocator()->allocate(); CLTensor *output_gate_out = &_output5; if(lstm_params.has_peephole_opt()) @@ -364,7 +364,7 @@ Status CLLSTMLayer::validate(const ITensorInfo *input, // Validate forget gate ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, input_to_forget_weights, forget_gate_bias, &forget_gate)); ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(output_state_in, &units_out_transposed_info, nullptr, &forget_gate, 1.f, 0.f, GEMMInfo())); - ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAdditionKernel::validate(&forget_gate, &forget_gate, &forget_gate, ConvertPolicy::SATURATE)); + ARM_COMPUTE_RETURN_ON_ERROR(CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation::ADD, &forget_gate, &forget_gate, &forget_gate, ConvertPolicy::SATURATE)); if(lstm_params.has_peephole_opt()) { ARM_COMPUTE_RETURN_ON_ERROR(CLPixelWiseMultiplicationKernel::validate(cell_state_in, lstm_params.cell_to_forget_weights(), &forget_gate, 1, ConvertPolicy::SATURATE, RoundingPolicy::TO_NEAREST_EVEN)); @@ -396,7 +396,7 @@ Status CLLSTMLayer::validate(const ITensorInfo *input, } else { - ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticSubtractionKernel::validate(&forget_gate, &forget_gate, &forget_gate, ConvertPolicy::SATURATE)); + ARM_COMPUTE_RETURN_ON_ERROR(CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation::SUB, &forget_gate, &forget_gate, &forget_gate, ConvertPolicy::SATURATE)); } // Validate cell state @@ -544,4 +544,4 @@ void CLLSTMLayer::run() _concat_scratch_buffer.run(); _memory_group.release(); -} \ No newline at end of file +} diff --git a/src/runtime/CL/functions/CLLaplacianPyramid.cpp b/src/runtime/CL/functions/CLLaplacianPyramid.cpp index 7e5278f380..559b57fd8d 100644 --- a/src/runtime/CL/functions/CLLaplacianPyramid.cpp +++ b/src/runtime/CL/functions/CLLaplacianPyramid.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017 ARM Limited. + * Copyright (c) 2017-2018 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -28,8 +28,8 @@ #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/Validate.h" #include "arm_compute/runtime/CL/CLTensor.h" -#include "arm_compute/runtime/CL/functions/CLArithmeticSubtraction.h" #include "arm_compute/runtime/CL/functions/CLDepthConvertLayer.h" +#include "arm_compute/runtime/CL/functions/CLElementwiseOperations.h" #include "arm_compute/runtime/CL/functions/CLGaussian5x5.h" #include "arm_compute/runtime/CL/functions/CLGaussianPyramid.h" #include "support/ToolchainSupport.h" diff --git a/src/runtime/CL/functions/CLRNNLayer.cpp b/src/runtime/CL/functions/CLRNNLayer.cpp index 1809e6e64e..63f00ac8ef 100644 --- a/src/runtime/CL/functions/CLRNNLayer.cpp +++ b/src/runtime/CL/functions/CLRNNLayer.cpp @@ -60,7 +60,7 @@ Status CLRNNLayer::validate(const ITensorInfo *input, const ITensorInfo *weights ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, weights, bias, &shape_info)); ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(hidden_state, recurrent_weights, nullptr, &shape_info, 1.f, 0.f)); - ARM_COMPUTE_RETURN_ON_ERROR(CLArithmeticAdditionKernel::validate(&shape_info, &shape_info, &shape_info, ConvertPolicy::SATURATE)); + ARM_COMPUTE_RETURN_ON_ERROR(CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation::ADD, &shape_info, &shape_info, &shape_info, ConvertPolicy::SATURATE)); ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayerKernel::validate(&shape_info, &shape_info, info)); return Status{}; @@ -90,7 +90,7 @@ void CLRNNLayer::configure(const ICLTensor *input, const ICLTensor *weights, con _add_output.allocator()->init(TensorInfo(shape, 1, input->info()->data_type())); _memory_group.manage(&_add_output); - _add_kernel.configure(&_fully_connected_out, &_gemm_output, &_add_output, ConvertPolicy::SATURATE); + _add_kernel.configure(ArithmeticOperation::ADD, &_fully_connected_out, &_gemm_output, &_add_output, ConvertPolicy::SATURATE); _fully_connected_out.allocator()->allocate(); _gemm_output.allocator()->allocate(); @@ -127,4 +127,4 @@ void CLRNNLayer::prepare() _is_prepared = true; } -} \ No newline at end of file +} -- cgit v1.2.1