Make Add kernel and operator stateless
- Rename NEArithmeticAdditionKernel to CpuAddKernel Cpu and move files appropriately
- Add CpuAdd under src/runtime/cpu/operators
Partially resolves: COMPMID-4005
Signed-off-by: Sheri Zhang <sheri.zhang@arm.com>
Change-Id: I1d8d406df9773fea198899f50327407d7125c38d
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/4867
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
diff --git a/Android.bp b/Android.bp
index 5ebcb30..e686bdf 100644
--- a/Android.bp
+++ b/Android.bp
@@ -226,7 +226,6 @@
"src/core/MultiImageInfo.cpp",
"src/core/NEON/kernels/NEAbsoluteDifferenceKernel.cpp",
"src/core/NEON/kernels/NEAccumulateKernel.cpp",
- "src/core/NEON/kernels/NEArithmeticAdditionKernel.cpp",
"src/core/NEON/kernels/NEArithmeticSubtractionKernel.cpp",
"src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp",
"src/core/NEON/kernels/NEBatchToSpaceLayerKernel.cpp",
@@ -337,14 +336,6 @@
"src/core/NEON/kernels/NEWarpKernel.cpp",
"src/core/NEON/kernels/NEWeightsReshapeKernel.cpp",
"src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp",
- "src/core/NEON/kernels/arithmetic_addition/impl/NEON/integer.cpp",
- "src/core/NEON/kernels/arithmetic_addition/impl/NEON/qasymm8.cpp",
- "src/core/NEON/kernels/arithmetic_addition/impl/NEON/qasymm8_signed.cpp",
- "src/core/NEON/kernels/arithmetic_addition/impl/NEON/qsymm16.cpp",
- "src/core/NEON/kernels/arithmetic_addition/impl/SVE/integer.cpp",
- "src/core/NEON/kernels/arithmetic_addition/impl/SVE/qasymm8.cpp",
- "src/core/NEON/kernels/arithmetic_addition/impl/SVE/qasymm8_signed.cpp",
- "src/core/NEON/kernels/arithmetic_addition/impl/SVE/qsymm16.cpp",
"src/core/NEON/kernels/arm_gemm/gemm_bf16.cpp",
"src/core/NEON/kernels/arm_gemm/gemm_fp16.cpp",
"src/core/NEON/kernels/arm_gemm/gemm_fp32.cpp",
@@ -420,6 +411,7 @@
"src/core/Validate.cpp",
"src/core/Version.cpp",
"src/core/cpu/kernels/CpuActivationKernel.cpp",
+ "src/core/cpu/kernels/CpuAddKernel.cpp",
"src/core/cpu/kernels/CpuConcatenateBatchKernel.cpp",
"src/core/cpu/kernels/CpuConcatenateDepthKernel.cpp",
"src/core/cpu/kernels/CpuConcatenateHeightKernel.cpp",
@@ -435,6 +427,14 @@
"src/core/cpu/kernels/activation/SVE/qasymm8.cpp",
"src/core/cpu/kernels/activation/SVE/qasymm8_signed.cpp",
"src/core/cpu/kernels/activation/SVE/qsymm16.cpp",
+ "src/core/cpu/kernels/add/neon/integer.cpp",
+ "src/core/cpu/kernels/add/neon/qasymm8.cpp",
+ "src/core/cpu/kernels/add/neon/qasymm8_signed.cpp",
+ "src/core/cpu/kernels/add/neon/qsymm16.cpp",
+ "src/core/cpu/kernels/add/sve/integer.cpp",
+ "src/core/cpu/kernels/add/sve/qasymm8.cpp",
+ "src/core/cpu/kernels/add/sve/qasymm8_signed.cpp",
+ "src/core/cpu/kernels/add/sve/qsymm16.cpp",
"src/core/cpu/kernels/floor/NEON/fp16.cpp",
"src/core/cpu/kernels/floor/NEON/fp32.cpp",
"src/core/helpers/SoftmaxHelpers.cpp",
@@ -774,6 +774,7 @@
"src/runtime/TensorAllocator.cpp",
"src/runtime/Utils.cpp",
"src/runtime/cpu/operators/CpuActivation.cpp",
+ "src/runtime/cpu/operators/CpuAdd.cpp",
"src/runtime/cpu/operators/CpuConcatenate.cpp",
"src/runtime/cpu/operators/CpuFloor.cpp",
"utils/CommonGraphOptions.cpp",
diff --git a/SConscript b/SConscript
index ffea1b8..8b8e504 100644
--- a/SConscript
+++ b/SConscript
@@ -277,6 +277,8 @@
core_files += Glob('src/core/cpu/kernels/*/*/qasymm8_signed.cpp')
if any(i in env['data_type_support'] for i in ['all', 'qsymm16']):
core_files += Glob('src/core/cpu/kernels/*/*/qsymm16.cpp')
+ if any(i in env['data_type_support'] for i in ['all', 'integer']):
+ core_files += Glob('src/core/cpu/kernels/*/*/integer.cpp')
runtime_files += Glob('src/runtime/cpu/*.cpp')
runtime_files += Glob('src/runtime/cpu/operators/*.cpp')
diff --git a/arm_compute/runtime/NEON/functions/NEArithmeticAddition.h b/arm_compute/runtime/NEON/functions/NEArithmeticAddition.h
index 6aaa5ff..6648e46 100644
--- a/arm_compute/runtime/NEON/functions/NEArithmeticAddition.h
+++ b/arm_compute/runtime/NEON/functions/NEArithmeticAddition.h
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2016-2020 Arm Limited.
+ * Copyright (c) 2016-2021 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -26,68 +26,14 @@
#include "arm_compute/core/Types.h"
#include "arm_compute/runtime/IFunction.h"
-#include "arm_compute/runtime/NEON/INEOperator.h"
+#include <memory>
namespace arm_compute
{
class ITensor;
+class ITensorInfo;
-namespace experimental
-{
-/** Basic function to run @ref NEArithmeticAdditionKernel */
-class NEArithmeticAddition : public INEOperator
-{
-public:
- /** Constructor */
- NEArithmeticAddition() = default;
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- NEArithmeticAddition(const NEArithmeticAddition &) = delete;
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- NEArithmeticAddition &operator=(const NEArithmeticAddition &) = delete;
- /** Prevent instances of this class from being moved (As this class contains non movable objects) */
- NEArithmeticAddition(NEArithmeticAddition &&) = delete;
- /** Prevent instances of this class from being moved (As this class contains non movable objects) */
- NEArithmeticAddition &operator=(NEArithmeticAddition &&) = delete;
- /** Default destructor */
- ~NEArithmeticAddition();
- /** Initialise the kernel's inputs, output and conversion policy.
- *
- * Valid configurations (Input1,Input2) -> Output :
- *
- * - (U8,U8) -> U8
- * - (U8,U8) -> S16
- * - (S16,U8) -> S16
- * - (U8,S16) -> S16
- * - (S16,S16) -> S16
- * - (S32,S32) -> S32
- * - (F16,F16) -> F16
- * - (F32,F32) -> F32
- * - (QASYMM8,QASYMM8) -> QASYMM8
- * - (QASYMM8_SIGNED,QASYMM8_SIGNED) -> QASYMM8_SIGNED
- * - (QSYMM16,QSYMM16) -> QSYMM16
- *
- * @param[in] input1 First tensor input info. Data types supported: U8/QASYMM8/QASYMM8_SIGNED/S16/QSYMM16/F16/S32/F32
- * @param[in] input2 Second tensor input info. Data types supported: U8/QASYMM8/QASYMM8_SIGNED/S16/QSYMM16/F16/S32/F32
- * @param[out] output Output tensor info. Data types supported: U8/QASYMM8/QASYMM8_SIGNED/S16/QSYMM16/F16/S32/F32
- * @param[in] policy Policy to use to handle overflow.
- * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Currently not supported.
- */
- void configure(const ITensorInfo *input1, const ITensorInfo *input2, ITensorInfo *output, ConvertPolicy policy, const ActivationLayerInfo &act_info = ActivationLayerInfo());
- /** Static function to check if given info will lead to a valid configuration of @ref NEArithmeticAddition
- *
- * @param[in] input1 First tensor input info. Data types supported: U8/QASYMM8/QASYMM8_SIGNED/S16/QSYMM16/F16/S32/F32
- * @param[in] input2 Second tensor input info. Data types supported: U8/QASYMM8/QASYMM8_SIGNED/S16/QSYMM16/F16/S32/F32
- * @param[in] output Output tensor info. Data types supported: U8/SQASYMM8/QASYMM8_SIGNED/S16/QSYMM16/F16/S32/F32
- * @param[in] policy Policy to use to handle overflow
- * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Currently not supported.
- *
- * @return a status
- */
- static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy, const ActivationLayerInfo &act_info = ActivationLayerInfo());
-};
-} // namespace experimental
-
-/** Basic function to run @ref NEArithmeticAdditionKernel */
+/** Basic function to run @ref CpuAddKernel */
class NEArithmeticAddition : public IFunction
{
public:
@@ -146,4 +92,4 @@
std::unique_ptr<Impl> _impl;
};
} // namespace arm_compute
-#endif /*ARM_COMPUTE_NEARITHMETICADDITION_H */
+#endif /* ARM_COMPUTE_NEARITHMETICADDITION_H */
diff --git a/src/core/NEON/NEKernels.h b/src/core/NEON/NEKernels.h
index 64c1c8f..6c31a73 100644
--- a/src/core/NEON/NEKernels.h
+++ b/src/core/NEON/NEKernels.h
@@ -27,7 +27,6 @@
/* Header regrouping all the NEON kernels */
#include "src/core/NEON/kernels/NEAbsoluteDifferenceKernel.h"
#include "src/core/NEON/kernels/NEAccumulateKernel.h"
-#include "src/core/NEON/kernels/NEArithmeticAdditionKernel.h"
#include "src/core/NEON/kernels/NEArithmeticSubtractionKernel.h"
#include "src/core/NEON/kernels/NEBatchNormalizationLayerKernel.h"
#include "src/core/NEON/kernels/NEBatchToSpaceLayerKernel.h"
diff --git a/src/core/NEON/kernels/NEArithmeticAdditionKernel.cpp b/src/core/NEON/kernels/NEArithmeticAdditionKernel.cpp
deleted file mode 100644
index 4b53d26..0000000
--- a/src/core/NEON/kernels/NEArithmeticAdditionKernel.cpp
+++ /dev/null
@@ -1,331 +0,0 @@
-/*
- * Copyright (c) 2016-2021 Arm Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#include "src/core/NEON/kernels/NEArithmeticAdditionKernel.h"
-
-#include "arm_compute/core/Error.h"
-#include "arm_compute/core/Helpers.h"
-#include "arm_compute/core/ITensor.h"
-#include "arm_compute/core/Validate.h"
-#include "src/core/CPP/Validate.h"
-#include "src/core/NEON/kernels/arithmetic_addition/impl/NEON/list.h"
-#include "src/core/NEON/kernels/arithmetic_addition/impl/SVE/list.h"
-#include "src/core/NEON/wrapper/wrapper.h"
-#include "src/core/common/Registrars.h"
-#include "src/core/helpers/AutoConfiguration.h"
-#include "src/core/helpers/WindowHelpers.h"
-
-#include <map>
-#include <string>
-
-namespace arm_compute
-{
-namespace
-{
-struct ArithmeticAdditionSelectorData
-{
- DataType dt1;
- DataType dt2;
- DataType dt3;
-};
-
-using ArithmeticAdditionSelectorPtr = std::add_pointer<bool(const ArithmeticAdditionSelectorData &data)>::type;
-
-struct ArithmeticAdditionKernel
-{
- const char *name;
- const ArithmeticAdditionSelectorPtr is_selected;
- NEArithmeticAdditionKernel::ArithmeticAdditionKernelPtr ukernel;
-};
-
-static const ArithmeticAdditionKernel available_kernels[] =
-{
-#if defined(__ARM_FEATURE_SVE)
- {
- "arithmetic_addition_same_sve",
- [](const ArithmeticAdditionSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt1 == DataType::F32)); },
- REGISTER_FP32_SVE(arm_compute::cpu::arithmetic_addition_same_sve<float>)
- },
- {
- "arithmetic_addition_same_sve",
- [](const ArithmeticAdditionSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt1 == DataType::F16)); },
- REGISTER_FP16_SVE(arm_compute::cpu::arithmetic_addition_same_sve<float16_t>)
- },
- {
- "arithmetic_addition_same_sve",
- [](const ArithmeticAdditionSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt1 == data.dt3) && (data.dt1 == DataType::U8)); },
- REGISTER_INTEGER_SVE(arm_compute::cpu::arithmetic_addition_same_sve<uint8_t>)
- },
- {
- "arithmetic_addition_same_sve",
- [](const ArithmeticAdditionSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt1 == data.dt3) && (data.dt1 == DataType::S16)); },
- REGISTER_INTEGER_SVE(arm_compute::cpu::arithmetic_addition_same_sve<int16_t>)
- },
- {
- "arithmetic_addition_same_sve",
- [](const ArithmeticAdditionSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt1 == data.dt3) && (data.dt1 == DataType::S32)); },
- REGISTER_INTEGER_SVE(arm_compute::cpu::arithmetic_addition_same_sve<int32_t>)
- },
- {
- "arithmetic_addition_U8_S16_S16_sve",
- [](const ArithmeticAdditionSelectorData & data) { return ((data.dt1 == DataType::U8) && (data.dt2 == DataType::S16)); },
- REGISTER_INTEGER_SVE(arm_compute::cpu::arithmetic_addition_U8_S16_S16_sve)
- },
- {
- "arithmetic_addition_S16_U8_S16_sve",
- [](const ArithmeticAdditionSelectorData & data) { return ((data.dt1 == DataType::S16) && (data.dt2 == DataType::U8)); },
- REGISTER_INTEGER_SVE(arm_compute::cpu::arithmetic_addition_S16_U8_S16_sve)
- },
- {
- "arithmetic_addition_U8_U8_S16_sve",
- [](const ArithmeticAdditionSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt3 == DataType::S16)); },
- REGISTER_INTEGER_SVE(arm_compute::cpu::arithmetic_addition_U8_U8_S16_sve)
- },
-#else /* !defined(__ARM_FEATURE_SVE) */
- {
- "arithmetic_addition_same_neon",
- [](const ArithmeticAdditionSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt1 == DataType::F32)); },
- REGISTER_FP32_NEON(arm_compute::cpu::arithmetic_addition_same_neon<float>)
- },
-#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
- {
- "arithmetic_addition_same_neon",
- [](const ArithmeticAdditionSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt1 == DataType::F16)); },
- REGISTER_FP16_NEON(arm_compute::cpu::arithmetic_addition_same_neon<float16_t>)
- },
-#endif /* defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) */
- {
- "arithmetic_addition_same_neon",
- [](const ArithmeticAdditionSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt1 == data.dt3) && (data.dt1 == DataType::U8)); },
- REGISTER_INTEGER_NEON(arm_compute::cpu::arithmetic_addition_same_neon<uint8_t>)
- },
- {
- "arithmetic_addition_same_neon",
- [](const ArithmeticAdditionSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt1 == data.dt3) && (data.dt1 == DataType::S16)); },
- REGISTER_INTEGER_NEON(arm_compute::cpu::arithmetic_addition_same_neon<int16_t>)
- },
- {
- "arithmetic_addition_same_neon",
- [](const ArithmeticAdditionSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt1 == data.dt3) && (data.dt1 == DataType::S32)); },
- REGISTER_INTEGER_NEON(arm_compute::cpu::arithmetic_addition_same_neon<int32_t>)
- },
- {
- "arithmetic_addition_U8_S16_S16_neon",
- [](const ArithmeticAdditionSelectorData & data) { return ((data.dt1 == DataType::U8) && (data.dt2 == DataType::S16)); },
- REGISTER_INTEGER_NEON(arm_compute::cpu::arithmetic_addition_U8_S16_S16_neon)
- },
- {
- "arithmetic_addition_S16_U8_S16_neon",
- [](const ArithmeticAdditionSelectorData & data) { return ((data.dt1 == DataType::S16) && (data.dt2 == DataType::U8)); },
- REGISTER_INTEGER_NEON(arm_compute::cpu::arithmetic_addition_S16_U8_S16_neon)
- },
- {
- "arithmetic_addition_U8_U8_S16_neon",
- [](const ArithmeticAdditionSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt3 == DataType::S16)); },
- REGISTER_INTEGER_NEON(arm_compute::cpu::arithmetic_addition_U8_U8_S16_neon)
- },
-#endif /* defined(__ARM_FEATURE_SVE) */
-
-#if defined(__ARM_FEATURE_SVE2)
- {
- "arithmetic_addition_qasymm8_sve",
- [](const ArithmeticAdditionSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt1 == DataType::QASYMM8)); },
- REGISTER_QASYMM8_SVE(arm_compute::cpu::arithmetic_addition_qasymm8_sve)
- },
- {
- "arithmetic_addition_qasymm8_signed_sve",
- [](const ArithmeticAdditionSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt1 == DataType::QASYMM8_SIGNED)); },
- REGISTER_QASYMM8_SIGNED_SVE(arm_compute::cpu::arithmetic_addition_qasymm8_signed_sve)
- },
- {
- "arithmetic_addition_qsymm16_sve",
- [](const ArithmeticAdditionSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt1 == DataType::QSYMM16)); },
- REGISTER_QSYMM16_SVE(arm_compute::cpu::arithmetic_addition_qsymm16_sve)
- },
-#else /* !defined(__ARM_FEATURE_SVE2) */
- {
- "arithmetic_addition_qasymm8_neon",
- [](const ArithmeticAdditionSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt1 == DataType::QASYMM8)); },
- REGISTER_QASYMM8_NEON(arm_compute::cpu::arithmetic_addition_qasymm8_neon)
- },
- {
- "arithmetic_addition_qasymm8_signed_neon",
- [](const ArithmeticAdditionSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt1 == DataType::QASYMM8_SIGNED)); },
- REGISTER_QASYMM8_SIGNED_NEON(arm_compute::cpu::arithmetic_addition_qasymm8_signed_neon)
- },
- {
- "arithmetic_addition_qsymm16_neon",
- [](const ArithmeticAdditionSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt1 == DataType::QSYMM16)); },
- REGISTER_QSYMM16_NEON(arm_compute::cpu::arithmetic_addition_qsymm16_neon)
- },
-#endif /* defined(__ARM_FEATURE_SVE2) */
-
-};
-
-const ArithmeticAdditionKernel *get_implementation(DataType dt1, DataType dt2, DataType dt3)
-{
- for(const auto &uk : available_kernels)
- {
- if(uk.is_selected({ dt1, dt2, dt3 }))
- {
- return &uk;
- }
- }
- return nullptr;
-}
-
-Status validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output, ConvertPolicy policy)
-{
- ARM_COMPUTE_UNUSED(policy);
-
- ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(&input1);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1, 1, DataType::U8, DataType::QASYMM8, DataType::QASYMM8_SIGNED,
- DataType::S16, DataType::QSYMM16, DataType::F16,
- DataType::S32, DataType::F32);
- ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input2, 1, DataType::U8, DataType::QASYMM8, DataType::QASYMM8_SIGNED,
- DataType::S16, DataType::QSYMM16, DataType::F16,
- DataType::S32, DataType::F32);
-
- 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");
- ARM_COMPUTE_RETURN_ERROR_ON_MSG((input1.tensor_shape().x() != input2.tensor_shape().x()) && ((input1.data_type() != input2.data_type()) || (input1.data_type() != output.data_type())
- || (input2.data_type() != output.data_type())),
- "Broadcasting across width is supported on configurations where all tensors have the same data type");
-
- // Validate in case of configured output
- if(output.total_size() > 0)
- {
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(
- !(input1.data_type() == DataType::U8 && input2.data_type() == DataType::U8 && output.data_type() == DataType::U8)
- && !(input1.data_type() == DataType::U8 && input2.data_type() == DataType::U8 && output.data_type() == DataType::S16)
- && !(input1.data_type() == DataType::U8 && input2.data_type() == DataType::S16 && output.data_type() == DataType::S16)
- && !(input1.data_type() == DataType::S16 && input2.data_type() == DataType::U8 && output.data_type() == DataType::S16)
- && !(input1.data_type() == DataType::S16 && input2.data_type() == DataType::S16 && output.data_type() == DataType::S16)
- && !(input1.data_type() == DataType::S32 && input2.data_type() == DataType::S32 && output.data_type() == DataType::S32)
- && !(input1.data_type() == DataType::F32 && input2.data_type() == DataType::F32 && output.data_type() == DataType::F32)
- && !(input1.data_type() == DataType::F16 && input2.data_type() == DataType::F16 && output.data_type() == DataType::F16)
- && !(input1.data_type() == DataType::QASYMM8 && input2.data_type() == DataType::QASYMM8 && output.data_type() == DataType::QASYMM8)
- && !(input1.data_type() == DataType::QASYMM8_SIGNED && input2.data_type() == DataType::QASYMM8_SIGNED && output.data_type() == DataType::QASYMM8_SIGNED)
- && !(input1.data_type() == DataType::QSYMM16 && input2.data_type() == DataType::QSYMM16 && output.data_type() == DataType::QSYMM16),
- "You called addition with the wrong image formats");
-
- ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output.tensor_shape(), 0),
- "Wrong shape for output");
- }
-
- return Status{};
-}
-
-std::pair<Status, Window> validate_and_configure_window(const ITensorInfo &input1, const ITensorInfo &input2, ITensorInfo &output)
-{
- const std::pair<TensorShape, ValidRegion> 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);
- }
- if(input1.data_type() == DataType::S32 || input2.data_type() == DataType::S32)
- {
- set_format_if_unknown(output, Format::S32);
- }
- 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);
- }
- else if(input1.data_type() == DataType::QASYMM8 || input2.data_type() == DataType::QASYMM8)
- {
- set_data_type_if_unknown(output, DataType::QASYMM8);
- }
- else if(input1.data_type() == DataType::QASYMM8_SIGNED || input2.data_type() == DataType::QASYMM8_SIGNED)
- {
- set_data_type_if_unknown(output, DataType::QASYMM8_SIGNED);
- }
- else if(input1.data_type() == DataType::QSYMM16 || input2.data_type() == DataType::QSYMM16)
- {
- set_data_type_if_unknown(output, DataType::QSYMM16);
- }
- }
-
- Window win = calculate_max_window(valid_region, Steps());
-
- // NEArithmeticAdditionKernel doesn't need padding so update_window_and_padding() can be skipped
- Coordinates coord;
- coord.set_num_dimensions(output.num_dimensions());
- output.set_valid_region(valid_region);
- return std::make_pair(Status{}, win);
-}
-} // namespace
-
-NEArithmeticAdditionKernel::NEArithmeticAdditionKernel()
- : _func(nullptr), _policy()
-{
-}
-
-void NEArithmeticAdditionKernel::configure(const ITensorInfo *input1, const ITensorInfo *input2, ITensorInfo *output, ConvertPolicy policy)
-{
- ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
- ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*input1, *input2, *output, policy));
-
- _policy = policy;
- _func = get_implementation(input1->data_type(), input2->data_type(), output->data_type())->ukernel;
-
- // Configure kernel window
- auto win_config = validate_and_configure_window(*input1, *input2, *output);
- ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
- INEKernel::configure(win_config.second);
-}
-
-Status NEArithmeticAdditionKernel::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 NEArithmeticAdditionKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info)
-{
- ARM_COMPUTE_UNUSED(info);
- ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
- ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
- // Dispatch kernel
- (*_func)(tensors.get_const_tensor(TensorType::ACL_SRC_0),
- tensors.get_const_tensor(TensorType::ACL_SRC_1),
- tensors.get_tensor(TensorType::ACL_DST),
- _policy,
- window);
-}
-} // namespace arm_compute
diff --git a/src/core/NEON/kernels/NEArithmeticAdditionKernel.h b/src/core/NEON/kernels/NEArithmeticAdditionKernel.h
deleted file mode 100644
index b88fc8a..0000000
--- a/src/core/NEON/kernels/NEArithmeticAdditionKernel.h
+++ /dev/null
@@ -1,107 +0,0 @@
-/*
- * Copyright (c) 2016-2021 Arm Limited.
- *
- * SPDX-License-Identifier: MIT
- *
- * Permission is hereby granted, free of charge, to any person obtaining a copy
- * of this software and associated documentation files (the "Software"), to
- * deal in the Software without restriction, including without limitation the
- * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
- * sell copies of the Software, and to permit persons to whom the Software is
- * furnished to do so, subject to the following conditions:
- *
- * The above copyright notice and this permission notice shall be included in all
- * copies or substantial portions of the Software.
- *
- * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
- * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
- * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
- * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
- * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
- * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
- * SOFTWARE.
- */
-#ifndef ARM_COMPUTE_NEARITHMETICADDITIONKERNEL_H
-#define ARM_COMPUTE_NEARITHMETICADDITIONKERNEL_H
-
-#include "arm_compute/core/Types.h"
-#include "src/core/NEON/INEKernel.h"
-
-namespace arm_compute
-{
-class ITensor;
-
-/** Interface for the kernel to perform addition between two tensors */
-class NEArithmeticAdditionKernel : public INEKernel
-{
-public:
- const char *name() const override
- {
- return "NEArithmeticAdditionKernel";
- }
- /** Default constructor */
- NEArithmeticAdditionKernel();
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- NEArithmeticAdditionKernel(const NEArithmeticAdditionKernel &) = delete;
- /** Prevent instances of this class from being copied (As this class contains pointers) */
- NEArithmeticAdditionKernel &operator=(const NEArithmeticAdditionKernel &) = delete;
- /** Allow instances of this class to be moved */
- NEArithmeticAdditionKernel(NEArithmeticAdditionKernel &&) = default;
- /** Allow instances of this class to be moved */
- NEArithmeticAdditionKernel &operator=(NEArithmeticAdditionKernel &&) = default;
- /** Default destructor */
- ~NEArithmeticAdditionKernel() = default;
-
- /** Initialise the kernel's input, output and border mode.
- *
- * Valid configurations (Input1,Input2) -> Output :
- *
- * - (U8,U8) -> U8
- * - (U8,U8) -> S16
- * - (S16,U8) -> S16
- * - (U8,S16) -> S16
- * - (S16,S16) -> S16
- * - (S32,S32) -> S32
- * - (F16,F16) -> F16
- * - (F32,F32) -> F32
- * - (QASYMM8,QASYMM8) -> QASYMM8
- * - (QASYMM8_SIGNED,QASYMM8_SIGNED) -> QASYMM8_SIGNED
- * - (QSYMM16,QSYMM16) -> QSYMM16
- *
- * @param[in] input1 First input tensor info. Data types supported: U8/QASYMM8/QASYMM8_SIGNED/S16/QSYMM16/F16/S32/F32
- * @param[in] input2 Second input tensor info. Data types supported: U8/QASYMM8/QASYMM8_SIGNED/S16/QSYMM16/F16/S32/F32
- * @param[out] output The output tensor info. Data types supported: U8/QASYMM8/QASYMM8_SIGNED/S16/QSYMM16/F16/S32/F32.
- * @param[in] policy Overflow policy.
- */
- void configure(const ITensorInfo *input1, const ITensorInfo *input2, ITensorInfo *output, ConvertPolicy policy);
- /** Static function to check if given info will lead to a valid configuration of @ref NEArithmeticAdditionKernel
- *
- * @param[in] input1 First input tensor info. Data types supported: U8/QASYMM8/QASYMM8_SIGNED/S16/QSYMM16/F16/S32/F32
- * @param[in] input2 Second input tensor info. Data types supported: U8/QASYMM8/QASYMM8_SIGNED/S16/QSYMM16/F16/S32/F32
- * @param[in] output The output tensor info. Data types supported: U8/QASYMM8/QASYMM8_SIGNED/S16/QSYMM16/F16/S32/F32.
- * @param[in] policy Overflow policy.
- *
- * @return a status
- */
- static Status validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy);
-
- // Inherited methods overridden:
- void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override;
-
- /** Common signature for all the specialised add functions
- *
- * @param[in] input1 First input tensor. Data types supported: U8/QASYMM8/S16/QSYMM16/F16/S32/F32
- * @param[in] input2 Second input tensor. Data types supported: U8/QASYMM8/S16/QSYMM16/F16/S32/F32
- * @param[out] output The output tensor. Data types supported: U8/QASYMM8/S16/QSYMM16/F16/S32/F32.
- * @param[in] policy Overflow policy.
- * @param[in] window Region on which to execute the kernel.
- */
- using ArithmeticAdditionKernelPtr = std::add_pointer<void(const ITensor *, const ITensor *, ITensor *, const ConvertPolicy &, const Window &)>::type;
-
-private:
- /** Add function to use for the particular tensor types passed to configure() */
- ArithmeticAdditionKernelPtr _func;
- ConvertPolicy _policy;
-};
-} // namespace arm_compute
-#endif /*ARM_COMPUTE_NEARITHMETICADDITIONKERNEL_H */
diff --git a/src/core/cpu/kernels/CpuAddKernel.cpp b/src/core/cpu/kernels/CpuAddKernel.cpp
new file mode 100644
index 0000000..31c7b2a
--- /dev/null
+++ b/src/core/cpu/kernels/CpuAddKernel.cpp
@@ -0,0 +1,347 @@
+/*
+ * Copyright (c) 2021 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "src/core/cpu/kernels/CpuAddKernel.h"
+
+#include "arm_compute/core/ITensor.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Validate.h"
+#include "src/core/CPP/Validate.h"
+#include "src/core/common/Registrars.h"
+#include "src/core/cpu/kernels/add/neon/list.h"
+#include "src/core/cpu/kernels/add/sve/list.h"
+#include "src/core/helpers/AutoConfiguration.h"
+#include "src/core/helpers/WindowHelpers.h"
+
+#include <array>
+
+namespace arm_compute
+{
+namespace cpu
+{
+namespace kernels
+{
+namespace
+{
+struct AddSelectorData
+{
+ DataType dt1;
+ DataType dt2;
+ DataType dt3;
+};
+
+using AddSelectorPtr = std::add_pointer<bool(const AddSelectorData &data)>::type;
+using AddKernelPtr = std::add_pointer<void(const ITensor *, const ITensor *, ITensor *, const ConvertPolicy &, const Window &)>::type;
+struct AddKernel
+{
+ const char *name;
+ const AddSelectorPtr is_selected;
+ AddKernelPtr ukernel;
+};
+
+static const AddKernel available_kernels[] =
+{
+#if defined(__ARM_FEATURE_SVE)
+ {
+ "add_same_sve",
+ [](const AddSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt1 == DataType::F32)); },
+ REGISTER_FP32_SVE(arm_compute::cpu::add_same_sve<float>)
+ },
+ {
+ "add_same_sve",
+ [](const AddSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt1 == DataType::F16)); },
+ REGISTER_FP16_SVE(arm_compute::cpu::add_same_sve<float16_t>)
+ },
+ {
+ "add_same_sve",
+ [](const AddSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt1 == data.dt3) && (data.dt1 == DataType::U8)); },
+ REGISTER_INTEGER_SVE(arm_compute::cpu::add_same_sve<uint8_t>)
+ },
+ {
+ "add_same_sve",
+ [](const AddSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt1 == data.dt3) && (data.dt1 == DataType::S16)); },
+ REGISTER_INTEGER_SVE(arm_compute::cpu::add_same_sve<int16_t>)
+ },
+ {
+ "add_same_sve",
+ [](const AddSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt1 == data.dt3) && (data.dt1 == DataType::S32)); },
+ REGISTER_INTEGER_SVE(arm_compute::cpu::add_same_sve<int32_t>)
+ },
+ {
+ "add_u8_s16_s16_sve",
+ [](const AddSelectorData & data) { return ((data.dt1 == DataType::U8) && (data.dt2 == DataType::S16)); },
+ REGISTER_INTEGER_SVE(arm_compute::cpu::add_u8_s16_s16_sve)
+ },
+ {
+ "add_s16_u8_s16_sve",
+ [](const AddSelectorData & data) { return ((data.dt1 == DataType::S16) && (data.dt2 == DataType::U8)); },
+ REGISTER_INTEGER_SVE(arm_compute::cpu::add_s16_u8_s16_sve)
+ },
+ {
+ "add_u8_u8_s16_sve",
+ [](const AddSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt3 == DataType::S16)); },
+ REGISTER_INTEGER_SVE(arm_compute::cpu::add_u8_u8_s16_sve)
+ },
+#else /* !defined(__ARM_FEATURE_SVE) */
+ {
+ "add_same_neon",
+ [](const AddSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt1 == DataType::F32)); },
+ REGISTER_FP32_NEON(arm_compute::cpu::add_same_neon<float>)
+ },
+#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
+ {
+ "add_same_neon",
+ [](const AddSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt1 == DataType::F16)); },
+ REGISTER_FP16_NEON(arm_compute::cpu::add_same_neon<float16_t>)
+ },
+#endif /* defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) */
+ {
+ "add_same_neon",
+ [](const AddSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt1 == data.dt3) && (data.dt1 == DataType::U8)); },
+ REGISTER_INTEGER_NEON(arm_compute::cpu::add_same_neon<uint8_t>)
+ },
+ {
+ "add_same_neon",
+ [](const AddSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt1 == data.dt3) && (data.dt1 == DataType::S16)); },
+ REGISTER_INTEGER_NEON(arm_compute::cpu::add_same_neon<int16_t>)
+ },
+ {
+ "add_same_neon",
+ [](const AddSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt1 == data.dt3) && (data.dt1 == DataType::S32)); },
+ REGISTER_INTEGER_NEON(arm_compute::cpu::add_same_neon<int32_t>)
+ },
+ {
+ "add_u8_s16_s16_neon",
+ [](const AddSelectorData & data) { return ((data.dt1 == DataType::U8) && (data.dt2 == DataType::S16)); },
+ REGISTER_INTEGER_NEON(arm_compute::cpu::add_u8_s16_s16_neon)
+ },
+ {
+ "add_s16_u8_s16_neon",
+ [](const AddSelectorData & data) { return ((data.dt1 == DataType::S16) && (data.dt2 == DataType::U8)); },
+ REGISTER_INTEGER_NEON(arm_compute::cpu::add_s16_u8_s16_neon)
+ },
+ {
+ "add_u8_u8_s16_neon",
+ [](const AddSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt3 == DataType::S16)); },
+ REGISTER_INTEGER_NEON(arm_compute::cpu::add_u8_u8_s16_neon)
+ },
+#endif /* defined(__ARM_FEATURE_SVE) */
+
+#if defined(__ARM_FEATURE_SVE2)
+ {
+ "add_qasymm8_sve",
+ [](const AddSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt1 == DataType::QASYMM8)); },
+ REGISTER_QASYMM8_SVE(arm_compute::cpu::add_qasymm8_sve)
+ },
+ {
+ "add_qasymm8_signed_sve",
+ [](const AddSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt1 == DataType::QASYMM8_SIGNED)); },
+ REGISTER_QASYMM8_SIGNED_SVE(arm_compute::cpu::add_qasymm8_signed_sve)
+ },
+ {
+ "add_qsymm16_sve",
+ [](const AddSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt1 == DataType::QSYMM16)); },
+ REGISTER_QSYMM16_SVE(arm_compute::cpu::add_qsymm16_sve)
+ },
+#else /* !defined(__ARM_FEATURE_SVE2) */
+ {
+ "add_qasymm8_neon",
+ [](const AddSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt1 == DataType::QASYMM8)); },
+ REGISTER_QASYMM8_NEON(arm_compute::cpu::add_qasymm8_neon)
+ },
+ {
+ "add_qasymm8_signed_neon",
+ [](const AddSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt1 == DataType::QASYMM8_SIGNED)); },
+ REGISTER_QASYMM8_SIGNED_NEON(arm_compute::cpu::add_qasymm8_signed_neon)
+ },
+ {
+ "add_qsymm16_neon",
+ [](const AddSelectorData & data) { return ((data.dt1 == data.dt2) && (data.dt1 == DataType::QSYMM16)); },
+ REGISTER_QSYMM16_NEON(arm_compute::cpu::add_qsymm16_neon)
+ },
+#endif /* defined(__ARM_FEATURE_SVE2) */
+
+};
+
+/** Micro-kernel selector
+ *
+ * @param[in] data Selection data passed to help pick the appropriate micro-kernel
+ *
+ * @return A matching micro-kernel else nullptr
+ */
+const AddKernel *get_implementation(DataType dt1, DataType dt2, DataType dt3)
+{
+ for(const auto &uk : available_kernels)
+ {
+ if(uk.is_selected({ dt1, dt2, dt3 }))
+ {
+ return &uk;
+ }
+ }
+ return nullptr;
+}
+
+Status validate_arguments(const ITensorInfo &src0, const ITensorInfo &src1, const ITensorInfo &dst, ConvertPolicy policy)
+{
+ ARM_COMPUTE_UNUSED(policy);
+
+ ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(&src0);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&src0, 1, DataType::U8, DataType::QASYMM8, DataType::QASYMM8_SIGNED,
+ DataType::S16, DataType::QSYMM16, DataType::F16,
+ DataType::S32, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&src1, 1, DataType::U8, DataType::QASYMM8, DataType::QASYMM8_SIGNED,
+ DataType::S16, DataType::QSYMM16, DataType::F16,
+ DataType::S32, DataType::F32);
+
+ const TensorShape out_shape = TensorShape::broadcast_shape(src0.tensor_shape(), src1.tensor_shape());
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG((src0.tensor_shape().x() != src1.tensor_shape().x()) && ((src0.data_type() != src1.data_type()) || (src0.data_type() != dst.data_type())
+ || (src1.data_type() != dst.data_type())),
+ "Broadcasting across width is supported on configurations where all tensors have the same data type");
+
+ // Validate in case of configured dst
+ if(dst.total_size() > 0)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(
+ !(src0.data_type() == DataType::U8 && src1.data_type() == DataType::U8 && dst.data_type() == DataType::U8)
+ && !(src0.data_type() == DataType::U8 && src1.data_type() == DataType::U8 && dst.data_type() == DataType::S16)
+ && !(src0.data_type() == DataType::U8 && src1.data_type() == DataType::S16 && dst.data_type() == DataType::S16)
+ && !(src0.data_type() == DataType::S16 && src1.data_type() == DataType::U8 && dst.data_type() == DataType::S16)
+ && !(src0.data_type() == DataType::S16 && src1.data_type() == DataType::S16 && dst.data_type() == DataType::S16)
+ && !(src0.data_type() == DataType::S32 && src1.data_type() == DataType::S32 && dst.data_type() == DataType::S32)
+ && !(src0.data_type() == DataType::F32 && src1.data_type() == DataType::F32 && dst.data_type() == DataType::F32)
+ && !(src0.data_type() == DataType::F16 && src1.data_type() == DataType::F16 && dst.data_type() == DataType::F16)
+ && !(src0.data_type() == DataType::QASYMM8 && src1.data_type() == DataType::QASYMM8 && dst.data_type() == DataType::QASYMM8)
+ && !(src0.data_type() == DataType::QASYMM8_SIGNED && src1.data_type() == DataType::QASYMM8_SIGNED && dst.data_type() == DataType::QASYMM8_SIGNED)
+ && !(src0.data_type() == DataType::QSYMM16 && src1.data_type() == DataType::QSYMM16 && dst.data_type() == DataType::QSYMM16),
+ "You called addition with the wrong image formats");
+
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, dst.tensor_shape(), 0),
+ "Wrong shape for dst");
+ }
+
+ const auto *uk = get_implementation(src0.data_type(), src1.data_type(), dst.data_type());
+ ARM_COMPUTE_RETURN_ERROR_ON(uk == nullptr || uk->ukernel == nullptr);
+
+ return Status{};
+}
+
+std::pair<Status, Window> validate_and_configure_window(const ITensorInfo &src0, const ITensorInfo &src1, ITensorInfo &dst)
+{
+ const std::pair<TensorShape, ValidRegion> broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(src0, src1);
+ const TensorShape &out_shape = broadcast_pair.first;
+ const ValidRegion &valid_region = broadcast_pair.second;
+
+ // Auto initialize dst if not initialized
+ {
+ set_shape_if_empty(dst, out_shape);
+
+ if(src0.data_type() == DataType::S16 || src1.data_type() == DataType::S16)
+ {
+ set_format_if_unknown(dst, Format::S16);
+ }
+ if(src0.data_type() == DataType::S32 || src1.data_type() == DataType::S32)
+ {
+ set_format_if_unknown(dst, Format::S32);
+ }
+ else if(src0.data_type() == DataType::F16 || src1.data_type() == DataType::F16)
+ {
+ set_format_if_unknown(dst, Format::F16);
+ }
+ else if(src0.data_type() == DataType::F32 || src1.data_type() == DataType::F32)
+ {
+ set_format_if_unknown(dst, Format::F32);
+ }
+ else if(src0.data_type() == DataType::QASYMM8 || src1.data_type() == DataType::QASYMM8)
+ {
+ set_data_type_if_unknown(dst, DataType::QASYMM8);
+ }
+ else if(src0.data_type() == DataType::QASYMM8_SIGNED || src1.data_type() == DataType::QASYMM8_SIGNED)
+ {
+ set_data_type_if_unknown(dst, DataType::QASYMM8_SIGNED);
+ }
+ else if(src0.data_type() == DataType::QSYMM16 || src1.data_type() == DataType::QSYMM16)
+ {
+ set_data_type_if_unknown(dst, DataType::QSYMM16);
+ }
+ }
+
+ Window win = calculate_max_window(valid_region, Steps());
+
+ // CpuAddKernel doesn't need padding so update_window_and_padding() can be skipped
+ Coordinates coord;
+ coord.set_num_dimensions(dst.num_dimensions());
+ dst.set_valid_region(valid_region);
+ return std::make_pair(Status{}, win);
+}
+} // namespace
+
+void CpuAddKernel::configure(const ITensorInfo *src0, const ITensorInfo *src1, ITensorInfo *dst, ConvertPolicy policy)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(src0, src1, dst);
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*src0, *src1, *dst, policy));
+
+ _policy = policy;
+
+ // Configure kernel window
+ auto win_config = validate_and_configure_window(*src0, *src1, *dst);
+ ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+ ICpuKernel::configure(win_config.second);
+}
+
+Status CpuAddKernel::validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *dst, ConvertPolicy policy)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src0, src1, dst);
+
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(*src0, *src1, *dst, policy));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(*src0->clone(), *src1->clone(), *dst->clone()).first);
+
+ return Status{};
+}
+
+void CpuAddKernel::run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info)
+{
+ ARM_COMPUTE_UNUSED(info);
+ ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+ ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICpuKernel::window(), window);
+
+ ARM_COMPUTE_ERROR_ON(tensors.empty());
+
+ const ITensor *src0 = tensors.get_const_tensor(TensorType::ACL_SRC_0);
+ const ITensor *src1 = tensors.get_const_tensor(TensorType::ACL_SRC_1);
+ ITensor *dst = tensors.get_tensor(TensorType::ACL_DST);
+
+ const auto *uk = get_implementation(src0->info()->data_type(), src1->info()->data_type(), dst->info()->data_type());
+ ARM_COMPUTE_ERROR_ON(uk == nullptr || uk->ukernel == nullptr);
+
+ uk->ukernel(src0, src1, dst, _policy, window);
+}
+
+const char *CpuAddKernel::name() const
+{
+ return "CpuAddKernel";
+}
+} // namespace kernels
+} // namespace cpu
+} // namespace arm_compute
diff --git a/src/core/cpu/kernels/CpuAddKernel.h b/src/core/cpu/kernels/CpuAddKernel.h
new file mode 100644
index 0000000..a36ec7a
--- /dev/null
+++ b/src/core/cpu/kernels/CpuAddKernel.h
@@ -0,0 +1,85 @@
+/*
+ * Copyright (c) 2016-2021 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef ARM_COMPUTE_CPUADDKERNEL_H
+#define ARM_COMPUTE_CPUADDKERNEL_H
+
+#include "src/core/common/Macros.h"
+#include "src/core/cpu/ICpuKernel.h"
+
+namespace arm_compute
+{
+namespace cpu
+{
+namespace kernels
+{
+/** Interface for the kernel to perform addition between two tensors */
+class CpuAddKernel : public ICpuKernel
+{
+public:
+ CpuAddKernel() = default;
+ ARM_COMPUTE_DISALLOW_COPY_ALLOW_MOVE(CpuAddKernel);
+ /** Initialise the kernel's input, dst and border mode.
+ *
+ * Valid configurations (src0,src1) -> dst :
+ *
+ * - (U8,U8) -> U8
+ * - (U8,U8) -> S16
+ * - (S16,U8) -> S16
+ * - (U8,S16) -> S16
+ * - (S16,S16) -> S16
+ * - (S32,S32) -> S32
+ * - (F16,F16) -> F16
+ * - (F32,F32) -> F32
+ * - (QASYMM8,QASYMM8) -> QASYMM8
+ * - (QASYMM8_SIGNED,QASYMM8_SIGNED) -> QASYMM8_SIGNED
+ * - (QSYMM16,QSYMM16) -> QSYMM16
+ *
+ * @param[in] src0 First input tensor info. Data types supported: U8/QASYMM8/QASYMM8_SIGNED/S16/QSYMM16/F16/S32/F32
+ * @param[in] src1 Second input tensor info. Data types supported: U8/QASYMM8/QASYMM8_SIGNED/S16/QSYMM16/F16/S32/F32
+ * @param[out] dst The dst tensor info. Data types supported: U8/QASYMM8/QASYMM8_SIGNED/S16/QSYMM16/F16/S32/F32.
+ * @param[in] policy Overflow policy.
+ */
+ void configure(const ITensorInfo *src0, const ITensorInfo *src1, ITensorInfo *dst, ConvertPolicy policy);
+ /** Static function to check if given info will lead to a valid configuration of @ref CpuAddKernel
+ *
+ * @param[in] src0 First input tensor info. Data types supported: U8/QASYMM8/QASYMM8_SIGNED/S16/QSYMM16/F16/S32/F32
+ * @param[in] src1 Second input tensor info. Data types supported: U8/QASYMM8/QASYMM8_SIGNED/S16/QSYMM16/F16/S32/F32
+ * @param[in] dst The dst tensor info. Data types supported: U8/QASYMM8/QASYMM8_SIGNED/S16/QSYMM16/F16/S32/F32.
+ * @param[in] policy Overflow policy.
+ *
+ * @return a status
+ */
+ static Status validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *dst, ConvertPolicy policy);
+
+ // Inherited methods overridden:
+ void run_op(ITensorPack &tensors, const Window &window, const ThreadInfo &info) override;
+ const char *name() const override;
+
+private:
+ ConvertPolicy _policy{};
+};
+} // namespace kernels
+} // namespace cpu
+} // namespace arm_compute
+#endif /*ARM_COMPUTE_CPUADDKERNEL_H */
diff --git a/src/core/NEON/kernels/arithmetic_addition/impl/NEON/integer.cpp b/src/core/cpu/kernels/add/neon/integer.cpp
similarity index 84%
rename from src/core/NEON/kernels/arithmetic_addition/impl/NEON/integer.cpp
rename to src/core/cpu/kernels/add/neon/integer.cpp
index 0aededf..24a0ac3 100644
--- a/src/core/NEON/kernels/arithmetic_addition/impl/NEON/integer.cpp
+++ b/src/core/cpu/kernels/add/neon/integer.cpp
@@ -32,21 +32,21 @@
{
namespace cpu
{
-void arithmetic_addition_U8_U8_S16_neon(const ITensor *in1, const ITensor *in2, ITensor *out, const ConvertPolicy &policy, const Window &window)
+void add_u8_u8_s16_neon(const ITensor *src0, const ITensor *src1, ITensor *dst, const ConvertPolicy &policy, const Window &window)
{
// Create input windows
Window win = window;
- Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
- Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
+ Window input1_win = window.broadcast_if_dimension_le_one(src0->info()->tensor_shape());
+ Window input2_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape());
// Clear X Dimension on execution window as we handle manually
win.set(Window::DimX, Window::Dimension(0, 1, 1));
input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
- Iterator input1(in1, input1_win);
- Iterator input2(in2, input2_win);
- Iterator output(out, win);
+ Iterator input1(src0, input1_win);
+ Iterator input2(src1, input2_win);
+ Iterator output(dst, win);
const int window_step_x = 8;
const auto window_start_x = static_cast<int>(window.x().start());
@@ -97,21 +97,21 @@
input1, input2, output);
}
-void arithmetic_addition_S16_U8_S16_neon(const ITensor *in1, const ITensor *in2, ITensor *out, const ConvertPolicy &policy, const Window &window)
+void add_s16_u8_s16_neon(const ITensor *src0, const ITensor *src1, ITensor *dst, const ConvertPolicy &policy, const Window &window)
{
// Create input windows
Window win = window;
- Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
- Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
+ Window input1_win = window.broadcast_if_dimension_le_one(src0->info()->tensor_shape());
+ Window input2_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape());
// Clear X Dimension on execution window as we handle manually
win.set(Window::DimX, Window::Dimension(0, 1, 1));
input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
- Iterator input1(in1, input1_win);
- Iterator input2(in2, input2_win);
- Iterator output(out, win);
+ Iterator input1(src0, input1_win);
+ Iterator input2(src1, input2_win);
+ Iterator output(dst, win);
const int window_step_x = 8;
const auto window_start_x = static_cast<int>(window.x().start());
@@ -161,10 +161,10 @@
input1, input2, output);
}
-void arithmetic_addition_U8_S16_S16_neon(const ITensor *input1, const ITensor *input2, ITensor *output, const ConvertPolicy &policy, const Window &window)
+void add_u8_s16_s16_neon(const ITensor *src0, const ITensor *src1, ITensor *dst, const ConvertPolicy &policy, const Window &window)
{
// Simply swap the two input buffers:
- arithmetic_addition_S16_U8_S16_neon(input2, input1, output, policy, window);
+ add_s16_u8_s16_neon(src1, src0, dst, policy, window);
}
} // namespace cpu
} // namespace arm_compute
\ No newline at end of file
diff --git a/src/core/NEON/kernels/arithmetic_addition/impl/NEON/list.h b/src/core/cpu/kernels/add/neon/list.h
similarity index 79%
rename from src/core/NEON/kernels/arithmetic_addition/impl/NEON/list.h
rename to src/core/cpu/kernels/add/neon/list.h
index a8ab091..53ea81e 100644
--- a/src/core/NEON/kernels/arithmetic_addition/impl/NEON/list.h
+++ b/src/core/cpu/kernels/add/neon/list.h
@@ -21,8 +21,8 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#ifndef SRC_CORE_NEON_KERNELS_ARITHMETIC_ADDITION_LIST_H
-#define SRC_CORE_NEON_KERNELS_ARITHMETIC_ADDITION_LIST_H
+#ifndef SRC_CORE_NEON_KERNELS_ADD_LIST_H
+#define SRC_CORE_NEON_KERNELS_ADD_LIST_H
#include "arm_compute/core/Types.h"
#include "arm_compute/core/utils/misc/Traits.h"
@@ -32,27 +32,27 @@
{
namespace cpu
{
-#define DECLARE_ARITHMETIC_ADDITION_KERNEL(func_name) \
- void func_name(const ITensor *in1, const ITensor *in2, ITensor *out, const ConvertPolicy &policy, const Window &window)
+#define DECLARE_ADD_KERNEL(func_name) \
+ void func_name(const ITensor *src0, const ITensor *src1, ITensor *dst, const ConvertPolicy &policy, const Window &window)
-DECLARE_ARITHMETIC_ADDITION_KERNEL(arithmetic_addition_qasymm8_neon);
-DECLARE_ARITHMETIC_ADDITION_KERNEL(arithmetic_addition_qasymm8_signed_neon);
-DECLARE_ARITHMETIC_ADDITION_KERNEL(arithmetic_addition_qsymm16_neon);
-DECLARE_ARITHMETIC_ADDITION_KERNEL(arithmetic_addition_S16_U8_S16_neon);
-DECLARE_ARITHMETIC_ADDITION_KERNEL(arithmetic_addition_U8_S16_S16_neon);
-DECLARE_ARITHMETIC_ADDITION_KERNEL(arithmetic_addition_U8_U8_S16_neon);
+DECLARE_ADD_KERNEL(add_qasymm8_neon);
+DECLARE_ADD_KERNEL(add_qasymm8_signed_neon);
+DECLARE_ADD_KERNEL(add_qsymm16_neon);
+DECLARE_ADD_KERNEL(add_s16_u8_s16_neon);
+DECLARE_ADD_KERNEL(add_u8_s16_s16_neon);
+DECLARE_ADD_KERNEL(add_u8_u8_s16_neon);
-#undef DECLARE_ARITHMETIC_ADDITION_KERNEL
+#undef DECLARE_ADD_KERNEL
template <typename ScalarType>
-void arithmetic_addition_same_neon(const ITensor *in1, const ITensor *in2, ITensor *out, const ConvertPolicy &policy, const Window &window)
+void add_same_neon(const ITensor *src0, const ITensor *src1, ITensor *dst, const ConvertPolicy &policy, const Window &window)
{
/** NEON vector tag type. */
using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<ScalarType, wrapper::traits::BitWidth::W128>;
// Create input windows
- Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
- Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
+ Window input1_win = window.broadcast_if_dimension_le_one(src0->info()->tensor_shape());
+ Window input2_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape());
// Clear X Dimension on execution window as we handle manually
Window win = window;
@@ -61,22 +61,22 @@
constexpr int window_step_x = 16 / sizeof(ScalarType);
const auto window_start_x = static_cast<int>(window.x().start());
const auto window_end_x = static_cast<int>(window.x().end());
- const bool is_broadcast_across_x = in1->info()->tensor_shape().x() != in2->info()->tensor_shape().x();
+ const bool is_broadcast_across_x = src0->info()->tensor_shape().x() != src1->info()->tensor_shape().x();
if(is_broadcast_across_x)
{
const bool is_broadcast_input_2 = input2_win.x().step() == 0;
Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win;
Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win;
- const ITensor *broadcast_tensor = is_broadcast_input_2 ? in2 : in1;
- const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1;
+ const ITensor *broadcast_tensor = is_broadcast_input_2 ? src1 : src0;
+ const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? src1 : src0;
// Clear X Dimension on execution window as we handle manually
non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1));
Iterator broadcast_input(broadcast_tensor, broadcast_win);
Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win);
- Iterator output(out, win);
+ Iterator output(dst, win);
execute_window_loop(win, [&](const Coordinates &)
{
@@ -110,9 +110,9 @@
input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
- Iterator input1(in1, input1_win);
- Iterator input2(in2, input2_win);
- Iterator output(out, win);
+ Iterator input1(src0, input1_win);
+ Iterator input2(src1, input2_win);
+ Iterator output(dst, win);
execute_window_loop(win, [&](const Coordinates &)
{
@@ -143,4 +143,4 @@
}
} // namespace cpu
} // namespace arm_compute
-#endif // SRC_CORE_NEON_KERNELS_ARITHMETIC_ADDITION_LIST_H
\ No newline at end of file
+#endif // SRC_CORE_NEON_KERNELS_ADD_LIST_H
\ No newline at end of file
diff --git a/src/core/NEON/kernels/arithmetic_addition/impl/NEON/qasymm8.cpp b/src/core/cpu/kernels/add/neon/qasymm8.cpp
similarity index 92%
rename from src/core/NEON/kernels/arithmetic_addition/impl/NEON/qasymm8.cpp
rename to src/core/cpu/kernels/add/neon/qasymm8.cpp
index 0b3a851..cc97f00 100644
--- a/src/core/NEON/kernels/arithmetic_addition/impl/NEON/qasymm8.cpp
+++ b/src/core/cpu/kernels/add/neon/qasymm8.cpp
@@ -32,13 +32,13 @@
{
namespace cpu
{
-void arithmetic_addition_qasymm8_neon(const ITensor *in1, const ITensor *in2, ITensor *out, const ConvertPolicy &policy, const Window &window)
+void add_qasymm8_neon(const ITensor *src0, const ITensor *src1, ITensor *dst, const ConvertPolicy &policy, const Window &window)
{
ARM_COMPUTE_UNUSED(policy);
// Create input windows
- Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
- Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
+ Window input1_win = window.broadcast_if_dimension_le_one(src0->info()->tensor_shape());
+ Window input2_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape());
// Clear X Dimension on execution window as we handle manually
Window win = window;
@@ -47,11 +47,11 @@
const int window_step_x = 16;
const auto window_start_x = static_cast<int>(window.x().start());
const auto window_end_x = static_cast<int>(window.x().end());
- const bool is_broadcast_across_x = in1->info()->tensor_shape().x() != in2->info()->tensor_shape().x();
+ const bool is_broadcast_across_x = src0->info()->tensor_shape().x() != src1->info()->tensor_shape().x();
- const UniformQuantizationInfo iq1_info = in1->info()->quantization_info().uniform();
- const UniformQuantizationInfo iq2_info = in2->info()->quantization_info().uniform();
- const UniformQuantizationInfo oq_info = out->info()->quantization_info().uniform();
+ const UniformQuantizationInfo iq1_info = src0->info()->quantization_info().uniform();
+ const UniformQuantizationInfo iq2_info = src1->info()->quantization_info().uniform();
+ const UniformQuantizationInfo oq_info = dst->info()->quantization_info().uniform();
const float32x4_t invvscaleo = vdupq_n_f32(1.f / oq_info.scale);
const float32x4_t voffseto = vdupq_n_f32(oq_info.offset);
@@ -61,8 +61,8 @@
const bool is_broadcast_input_2 = input2_win.x().step() == 0;
Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win;
Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win;
- const ITensor *broadcast_tensor = is_broadcast_input_2 ? in2 : in1;
- const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1;
+ const ITensor *broadcast_tensor = is_broadcast_input_2 ? src1 : src0;
+ const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? src1 : src0;
const UniformQuantizationInfo broadcast_qinfo = broadcast_tensor->info()->quantization_info().uniform();
const UniformQuantizationInfo non_broadcast_qinfo = non_broadcast_tensor->info()->quantization_info().uniform();
@@ -76,7 +76,7 @@
Iterator broadcast_input(broadcast_tensor, broadcast_win);
Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win);
- Iterator output(out, win);
+ Iterator output(dst, win);
execute_window_loop(win, [&](const Coordinates &)
{
@@ -140,9 +140,9 @@
input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
- Iterator input1(in1, input1_win);
- Iterator input2(in2, input2_win);
- Iterator output(out, win);
+ Iterator input1(src0, input1_win);
+ Iterator input2(src1, input2_win);
+ Iterator output(dst, win);
const float32x4_t vscale1 = vdupq_n_f32(iq1_info.scale);
const float32x4_t vscale2 = vdupq_n_f32(iq2_info.scale);
@@ -199,7 +199,7 @@
{
const float afs = static_cast<int32_t>((*(input1_ptr + x)) - iq1_info.offset) * iq1_info.scale;
const float bfs = static_cast<int32_t>((*(input2_ptr + x)) - iq2_info.offset) * iq2_info.scale;
- *(output_ptr + x) = quantize_qasymm8((afs + bfs), out->info()->quantization_info());
+ *(output_ptr + x) = quantize_qasymm8((afs + bfs), dst->info()->quantization_info());
}
},
input1, input2, output);
diff --git a/src/core/NEON/kernels/arithmetic_addition/impl/NEON/qasymm8_signed.cpp b/src/core/cpu/kernels/add/neon/qasymm8_signed.cpp
similarity index 91%
rename from src/core/NEON/kernels/arithmetic_addition/impl/NEON/qasymm8_signed.cpp
rename to src/core/cpu/kernels/add/neon/qasymm8_signed.cpp
index 18f5aab..d62d073 100644
--- a/src/core/NEON/kernels/arithmetic_addition/impl/NEON/qasymm8_signed.cpp
+++ b/src/core/cpu/kernels/add/neon/qasymm8_signed.cpp
@@ -32,13 +32,13 @@
{
namespace cpu
{
-void arithmetic_addition_qasymm8_signed_neon(const ITensor *in1, const ITensor *in2, ITensor *out, const ConvertPolicy &policy, const Window &window)
+void add_qasymm8_signed_neon(const ITensor *src0, const ITensor *src1, ITensor *dst, const ConvertPolicy &policy, const Window &window)
{
ARM_COMPUTE_UNUSED(policy);
// Create input windows
- Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
- Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
+ Window input1_win = window.broadcast_if_dimension_le_one(src0->info()->tensor_shape());
+ Window input2_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape());
// Clear X Dimension on execution window as we handle manually
Window win = window;
@@ -47,11 +47,11 @@
const int window_step_x = 16;
const auto window_start_x = static_cast<int>(window.x().start());
const auto window_end_x = static_cast<int>(window.x().end());
- const bool is_broadcast_across_x = in1->info()->tensor_shape().x() != in2->info()->tensor_shape().x();
+ const bool is_broadcast_across_x = src0->info()->tensor_shape().x() != src1->info()->tensor_shape().x();
- const UniformQuantizationInfo iq1_info = in1->info()->quantization_info().uniform();
- const UniformQuantizationInfo iq2_info = in2->info()->quantization_info().uniform();
- const UniformQuantizationInfo oq_info = out->info()->quantization_info().uniform();
+ const UniformQuantizationInfo iq1_info = src0->info()->quantization_info().uniform();
+ const UniformQuantizationInfo iq2_info = src1->info()->quantization_info().uniform();
+ const UniformQuantizationInfo oq_info = dst->info()->quantization_info().uniform();
const float32x4_t invvscaleo = vdupq_n_f32(1.f / oq_info.scale);
const float32x4_t voffseto = vdupq_n_f32(oq_info.offset);
@@ -61,8 +61,8 @@
const bool is_broadcast_input_2 = input2_win.x().step() == 0;
Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win;
Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win;
- const ITensor *broadcast_tensor = is_broadcast_input_2 ? in2 : in1;
- const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1;
+ const ITensor *broadcast_tensor = is_broadcast_input_2 ? src1 : src0;
+ const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? src1 : src0;
const UniformQuantizationInfo broadcast_qinfo = broadcast_tensor->info()->quantization_info().uniform();
const UniformQuantizationInfo non_broadcast_qinfo = non_broadcast_tensor->info()->quantization_info().uniform();
@@ -76,7 +76,7 @@
Iterator broadcast_input(broadcast_tensor, broadcast_win);
Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win);
- Iterator output(out, win);
+ Iterator output(dst, win);
execute_window_loop(win, [&](const Coordinates &)
{
@@ -140,9 +140,9 @@
input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
- Iterator input1(in1, input1_win);
- Iterator input2(in2, input2_win);
- Iterator output(out, win);
+ Iterator input1(src0, input1_win);
+ Iterator input2(src1, input2_win);
+ Iterator output(dst, win);
const float32x4_t vscale1 = vdupq_n_f32(iq1_info.scale);
const float32x4_t vscale2 = vdupq_n_f32(iq2_info.scale);
@@ -198,7 +198,7 @@
{
const float afs = static_cast<int32_t>((*(input1_ptr + x)) - iq1_info.offset) * iq1_info.scale;
const float bfs = static_cast<int32_t>((*(input2_ptr + x)) - iq2_info.offset) * iq2_info.scale;
- *(output_ptr + x) = quantize_qasymm8_signed((afs + bfs), out->info()->quantization_info());
+ *(output_ptr + x) = quantize_qasymm8_signed((afs + bfs), dst->info()->quantization_info());
}
},
input1, input2, output);
diff --git a/src/core/NEON/kernels/arithmetic_addition/impl/NEON/qsymm16.cpp b/src/core/cpu/kernels/add/neon/qsymm16.cpp
similarity index 88%
rename from src/core/NEON/kernels/arithmetic_addition/impl/NEON/qsymm16.cpp
rename to src/core/cpu/kernels/add/neon/qsymm16.cpp
index 650f25e..e76e408 100644
--- a/src/core/NEON/kernels/arithmetic_addition/impl/NEON/qsymm16.cpp
+++ b/src/core/cpu/kernels/add/neon/qsymm16.cpp
@@ -32,13 +32,13 @@
{
namespace cpu
{
-void arithmetic_addition_qsymm16_neon(const ITensor *in1, const ITensor *in2, ITensor *out, const ConvertPolicy &policy, const Window &window)
+void add_qsymm16_neon(const ITensor *src0, const ITensor *src1, ITensor *dst, const ConvertPolicy &policy, const Window &window)
{
ARM_COMPUTE_UNUSED(policy);
// Create input windows
- Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
- Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
+ Window input1_win = window.broadcast_if_dimension_le_one(src0->info()->tensor_shape());
+ Window input2_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape());
// Clear X Dimension on execution window as we handle manually
Window win = window;
@@ -47,11 +47,11 @@
const int window_step_x = 8;
const auto window_start_x = static_cast<int>(window.x().start());
const auto window_end_x = static_cast<int>(window.x().end());
- const bool is_broadcast_across_x = in1->info()->tensor_shape().x() != in2->info()->tensor_shape().x();
+ const bool is_broadcast_across_x = src0->info()->tensor_shape().x() != src1->info()->tensor_shape().x();
- const UniformQuantizationInfo iq1_info = in1->info()->quantization_info().uniform();
- const UniformQuantizationInfo iq2_info = in2->info()->quantization_info().uniform();
- const UniformQuantizationInfo oq_info = out->info()->quantization_info().uniform();
+ const UniformQuantizationInfo iq1_info = src0->info()->quantization_info().uniform();
+ const UniformQuantizationInfo iq2_info = src1->info()->quantization_info().uniform();
+ const UniformQuantizationInfo oq_info = dst->info()->quantization_info().uniform();
const float32x4_t vscale1 = vdupq_n_f32(iq1_info.scale);
const float32x4_t vscale2 = vdupq_n_f32(iq2_info.scale);
@@ -62,8 +62,8 @@
const bool is_broadcast_input_2 = input2_win.x().step() == 0;
Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win;
Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win;
- const ITensor *broadcast_tensor = is_broadcast_input_2 ? in2 : in1;
- const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1;
+ const ITensor *broadcast_tensor = is_broadcast_input_2 ? src1 : src0;
+ const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? src1 : src0;
const UniformQuantizationInfo broadcast_qinfo = broadcast_tensor->info()->quantization_info().uniform();
const UniformQuantizationInfo non_broadcast_qinfo = non_broadcast_tensor->info()->quantization_info().uniform();
@@ -72,7 +72,7 @@
Iterator broadcast_input(broadcast_tensor, broadcast_win);
Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win);
- Iterator output(out, win);
+ Iterator output(dst, win);
execute_window_loop(win, [&](const Coordinates &)
{
@@ -123,9 +123,9 @@
input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
- Iterator input1(in1, input1_win);
- Iterator input2(in2, input2_win);
- Iterator output(out, win);
+ Iterator input1(src0, input1_win);
+ Iterator input2(src1, input2_win);
+ Iterator output(dst, win);
execute_window_loop(win, [&](const Coordinates &)
{
@@ -164,7 +164,7 @@
{
const float afs = static_cast<int32_t>((*(input1_ptr + x))) * iq1_info.scale;
const float bfs = static_cast<int32_t>((*(input2_ptr + x))) * iq2_info.scale;
- *(output_ptr + x) = quantize_qsymm16((afs + bfs), out->info()->quantization_info());
+ *(output_ptr + x) = quantize_qsymm16((afs + bfs), dst->info()->quantization_info());
}
},
input1, input2, output);
diff --git a/src/core/NEON/kernels/arithmetic_addition/impl/SVE/integer.cpp b/src/core/cpu/kernels/add/sve/integer.cpp
similarity index 64%
rename from src/core/NEON/kernels/arithmetic_addition/impl/SVE/integer.cpp
rename to src/core/cpu/kernels/add/sve/integer.cpp
index c502a02..5bd2e12 100644
--- a/src/core/NEON/kernels/arithmetic_addition/impl/SVE/integer.cpp
+++ b/src/core/cpu/kernels/add/sve/integer.cpp
@@ -34,21 +34,21 @@
{
namespace cpu
{
-void arithmetic_addition_U8_U8_S16_sve(const ITensor *in1, const ITensor *in2, ITensor *out, const ConvertPolicy &policy, const Window &window)
+void add_u8_u8_s16_sve(const ITensor *src0, const ITensor *src1, ITensor *dst, const ConvertPolicy &policy, const Window &window)
{
// Create input windows
Window win = window;
- Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
- Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
+ Window input1_win = window.broadcast_if_dimension_le_one(src0->info()->tensor_shape());
+ Window input2_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape());
// Clear X Dimension on execution window as we handle manually
win.set(Window::DimX, Window::Dimension(0, 1, 1));
input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
- Iterator input1(in1, input1_win);
- Iterator input2(in2, input2_win);
- Iterator output(out, win);
+ Iterator input1(src0, input1_win);
+ Iterator input2(src1, input2_win);
+ Iterator output(dst, win);
const auto window_start_x = static_cast<int>(window.x().start());
const auto window_end_x = static_cast<int>(window.x().end());
@@ -68,15 +68,15 @@
svbool_t pg_1 = svwhilelt_b16(x, static_cast<int>(window_end_x + svcnth()));
do
{
- const auto vin1 = svld1(pg_u, input1_ptr + x);
- const auto vin2 = svld1(pg_u, input2_ptr + x);
+ const auto vsrc0 = svld1(pg_u, input1_ptr + x);
+ const auto vsrc1 = svld1(pg_u, input2_ptr + x);
- const auto vin1_lo = svreinterpret_s16_u16(svunpklo(vin1));
- const auto vin1_hi = svreinterpret_s16_u16(svunpkhi(vin1));
- const auto vin2_lo = svreinterpret_s16_u16(svunpklo(vin2));
- const auto vin2_hi = svreinterpret_s16_u16(svunpkhi(vin2));
- svst1(pg_0, output_ptr + x, svqadd(vin1_lo, vin2_lo));
- svst1(pg_1, output_ptr + x + svcnth(), svqadd(vin1_hi, vin2_hi));
+ const auto vsrc0_lo = svreinterpret_s16_u16(svunpklo(vsrc0));
+ const auto vsrc0_hi = svreinterpret_s16_u16(svunpkhi(vsrc0));
+ const auto vsrc1_lo = svreinterpret_s16_u16(svunpklo(vsrc1));
+ const auto vsrc1_hi = svreinterpret_s16_u16(svunpkhi(vsrc1));
+ svst1(pg_0, output_ptr + x, svqadd(vsrc0_lo, vsrc1_lo));
+ svst1(pg_1, output_ptr + x + svcnth(), svqadd(vsrc0_hi, vsrc1_hi));
x += svcntb();
pg_u = svwhilelt_b8(x, window_end_x);
@@ -93,15 +93,15 @@
svbool_t pg_1 = svwhilelt_b16(x, static_cast<int>(window_end_x + svcnth()));
do
{
- const auto vin1 = svld1(pg_u, input1_ptr + x);
- const auto vin2 = svld1(pg_u, input2_ptr + x);
+ const auto vsrc0 = svld1(pg_u, input1_ptr + x);
+ const auto vsrc1 = svld1(pg_u, input2_ptr + x);
- const auto vin1_lo = svreinterpret_s16_u16(svunpklo(vin1));
- const auto vin1_hi = svreinterpret_s16_u16(svunpkhi(vin1));
- const auto vin2_lo = svreinterpret_s16_u16(svunpklo(vin2));
- const auto vin2_hi = svreinterpret_s16_u16(svunpkhi(vin2));
- svst1(pg_0, output_ptr + x, svqadd(vin1_lo, vin2_lo));
- svst1(pg_1, output_ptr + x + svcnth(), svqadd(vin1_hi, vin2_hi));
+ const auto vsrc0_lo = svreinterpret_s16_u16(svunpklo(vsrc0));
+ const auto vsrc0_hi = svreinterpret_s16_u16(svunpkhi(vsrc0));
+ const auto vsrc1_lo = svreinterpret_s16_u16(svunpklo(vsrc1));
+ const auto vsrc1_hi = svreinterpret_s16_u16(svunpkhi(vsrc1));
+ svst1(pg_0, output_ptr + x, svqadd(vsrc0_lo, vsrc1_lo));
+ svst1(pg_1, output_ptr + x + svcnth(), svqadd(vsrc0_hi, vsrc1_hi));
x += svcntb();
pg_u = svwhilelt_b8(x, window_end_x);
@@ -114,21 +114,21 @@
input1, input2, output);
}
-void arithmetic_addition_S16_U8_S16_sve(const ITensor *in1, const ITensor *in2, ITensor *out, const ConvertPolicy &policy, const Window &window)
+void add_s16_u8_s16_sve(const ITensor *src0, const ITensor *src1, ITensor *dst, const ConvertPolicy &policy, const Window &window)
{
// Create input windows
Window win = window;
- Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
- Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
+ Window input1_win = window.broadcast_if_dimension_le_one(src0->info()->tensor_shape());
+ Window input2_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape());
// Clear X Dimension on execution window as we handle manually
win.set(Window::DimX, Window::Dimension(0, 1, 1));
input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
- Iterator input1(in1, input1_win);
- Iterator input2(in2, input2_win);
- Iterator output(out, win);
+ Iterator input1(src0, input1_win);
+ Iterator input2(src1, input2_win);
+ Iterator output(dst, win);
const auto window_start_x = static_cast<int>(window.x().start());
const auto window_end_x = static_cast<int>(window.x().end());
@@ -148,13 +148,13 @@
svbool_t pg_1 = svwhilelt_b16(x + static_cast<int>(svcnth()), window_end_x);
do
{
- const auto vin1_0 = svld1_s16(pg_0, input1_ptr + x);
- const auto vin1_1 = svld1_s16(pg_1, input1_ptr + x + svcnth());
- const auto vin2_u8 = svld1_u8(pg_u, input2_ptr + x);
- const auto vin2_0 = svreinterpret_s16_u16(svunpklo(vin2_u8));
- const auto vin2_1 = svreinterpret_s16_u16(svunpkhi(vin2_u8));
- svst1_s16(pg_0, output_ptr + x, svadd_s16_z(pg_0, vin1_0, vin2_0));
- svst1_s16(pg_1, output_ptr + x, svadd_s16_z(pg_1, vin1_1, vin2_1));
+ const auto vsrc0_0 = svld1_s16(pg_0, input1_ptr + x);
+ const auto vsrc0_1 = svld1_s16(pg_1, input1_ptr + x + svcnth());
+ const auto vsrc1_u8 = svld1_u8(pg_u, input2_ptr + x);
+ const auto vsrc1_0 = svreinterpret_s16_u16(svunpklo(vsrc1_u8));
+ const auto vsrc1_1 = svreinterpret_s16_u16(svunpkhi(vsrc1_u8));
+ svst1_s16(pg_0, output_ptr + x, svadd_s16_z(pg_0, vsrc0_0, vsrc1_0));
+ svst1_s16(pg_1, output_ptr + x, svadd_s16_z(pg_1, vsrc0_1, vsrc1_1));
x += svcnth();
pg_u = svwhilelt_b8(x, window_end_x);
@@ -171,14 +171,14 @@
svbool_t pg_1 = svwhilelt_b16(x + static_cast<int>(svcnth()), window_end_x);
do
{
- const auto vin1_0 = svld1_s16(pg_0, input1_ptr + x);
- const auto vin1_1 = svld1_s16(pg_1, input1_ptr + x);
- const auto vin2_u8 = svld1_u8(pg_u, input2_ptr + x);
- const auto vin2_0 = svreinterpret_s16_u16(svunpklo(vin2_u8));
- const auto vin2_1 = svreinterpret_s16_u16(svunpkhi(vin2_u8));
+ const auto vsrc0_0 = svld1_s16(pg_0, input1_ptr + x);
+ const auto vsrc0_1 = svld1_s16(pg_1, input1_ptr + x);
+ const auto vsrc1_u8 = svld1_u8(pg_u, input2_ptr + x);
+ const auto vsrc1_0 = svreinterpret_s16_u16(svunpklo(vsrc1_u8));
+ const auto vsrc1_1 = svreinterpret_s16_u16(svunpkhi(vsrc1_u8));
- svst1_s16(pg_0, output_ptr + x, svqadd(vin1_0, vin2_0));
- svst1_s16(pg_1, output_ptr + x, svqadd(vin1_1, vin2_1));
+ svst1_s16(pg_0, output_ptr + x, svqadd(vsrc0_0, vsrc1_0));
+ svst1_s16(pg_1, output_ptr + x, svqadd(vsrc0_1, vsrc1_1));
x += svcnth();
pg_u = svwhilelt_b8(x, window_end_x);
@@ -191,10 +191,10 @@
input1, input2, output);
}
-void arithmetic_addition_U8_S16_S16_sve(const ITensor *input1, const ITensor *input2, ITensor *output, const ConvertPolicy &policy, const Window &window)
+void add_u8_s16_s16_sve(const ITensor *src0, const ITensor *src1, ITensor *dst, const ConvertPolicy &policy, const Window &window)
{
// Simply swap the two input buffers:
- arithmetic_addition_S16_U8_S16_sve(input2, input1, output, policy, window);
+ add_s16_u8_s16_sve(src1, src0, dst, policy, window);
}
} // namespace cpu
} // namespace arm_compute
diff --git a/src/core/NEON/kernels/arithmetic_addition/impl/SVE/list.h b/src/core/cpu/kernels/add/sve/list.h
similarity index 75%
rename from src/core/NEON/kernels/arithmetic_addition/impl/SVE/list.h
rename to src/core/cpu/kernels/add/sve/list.h
index 3e238c4..71dd875 100644
--- a/src/core/NEON/kernels/arithmetic_addition/impl/SVE/list.h
+++ b/src/core/cpu/kernels/add/sve/list.h
@@ -21,8 +21,8 @@
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-#ifndef SRC_CORE_SVE_KERNELS_ARITHMETIC_ADDITION_LIST_H
-#define SRC_CORE_SVE_KERNELS_ARITHMETIC_ADDITION_LIST_H
+#ifndef SRC_CORE_SVE_KERNELS_ADD_LIST_H
+#define SRC_CORE_SVE_KERNELS_ADD_LIST_H
#if defined(__ARM_FEATURE_SVE)
#include "arm_compute/core/Types.h"
@@ -35,25 +35,25 @@
{
namespace cpu
{
-#define DECLARE_ARITHMETIC_ADDITION_KERNEL(func_name) \
- void func_name(const ITensor *in1, const ITensor *in2, ITensor *out, const ConvertPolicy &policy, const Window &window)
+#define DECLARE_ADD_KERNEL(func_name) \
+ void func_name(const ITensor *src0, const ITensor *src1, ITensor *dst, const ConvertPolicy &policy, const Window &window)
-DECLARE_ARITHMETIC_ADDITION_KERNEL(arithmetic_addition_qasymm8_sve);
-DECLARE_ARITHMETIC_ADDITION_KERNEL(arithmetic_addition_qasymm8_signed_sve);
-DECLARE_ARITHMETIC_ADDITION_KERNEL(arithmetic_addition_qsymm16_sve);
-DECLARE_ARITHMETIC_ADDITION_KERNEL(arithmetic_addition_S16_U8_S16_sve);
-DECLARE_ARITHMETIC_ADDITION_KERNEL(arithmetic_addition_U8_S16_S16_sve);
-DECLARE_ARITHMETIC_ADDITION_KERNEL(arithmetic_addition_U8_U8_S16_sve);
+DECLARE_ADD_KERNEL(add_qasymm8_sve);
+DECLARE_ADD_KERNEL(add_qasymm8_signed_sve);
+DECLARE_ADD_KERNEL(add_qsymm16_sve);
+DECLARE_ADD_KERNEL(add_s16_u8_s16_sve);
+DECLARE_ADD_KERNEL(add_u8_s16_s16_sve);
+DECLARE_ADD_KERNEL(add_u8_u8_s16_sve);
-#undef DECLARE_ARITHMETIC_ADDITION_KERNEL
+#undef DECLARE_ADD_KERNEL
template <typename ScalarType>
-void arithmetic_addition_same_sve(const ITensor *in1, const ITensor *in2, ITensor *out, const ConvertPolicy &policy, const Window &window)
+void add_same_sve(const ITensor *src0, const ITensor *src1, ITensor *dst, const ConvertPolicy &policy, const Window &window)
{
const auto all_true_pg = wrapper::svptrue<ScalarType>();
const auto window_start_x = static_cast<int>(window.x().start());
const auto window_end_x = static_cast<int>(window.x().end());
- const bool is_broadcast_across_x = in1->info()->tensor_shape().x() != in2->info()->tensor_shape().x();
+ const bool is_broadcast_across_x = src0->info()->tensor_shape().x() != src1->info()->tensor_shape().x();
const bool is_sat = (policy == ConvertPolicy::SATURATE);
// Clear X Dimension on execution window as we handle manually
@@ -61,27 +61,27 @@
win.set(Window::DimX, Window::Dimension(0, 1, 1));
// Create input windows
- Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
- Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
+ Window input1_win = window.broadcast_if_dimension_le_one(src0->info()->tensor_shape());
+ Window input2_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape());
- Iterator input1(in1, window.broadcast_if_dimension_le_one(in1->info()->tensor_shape()));
- Iterator input2(in2, window.broadcast_if_dimension_le_one(in2->info()->tensor_shape()));
- Iterator output(out, window);
+ Iterator input1(src0, window.broadcast_if_dimension_le_one(src0->info()->tensor_shape()));
+ Iterator input2(src1, window.broadcast_if_dimension_le_one(src1->info()->tensor_shape()));
+ Iterator output(dst, window);
if(is_broadcast_across_x)
{
const bool is_broadcast_input_2 = input2_win.x().step() == 0;
Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win;
Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win;
- const ITensor *broadcast_tensor = is_broadcast_input_2 ? in2 : in1;
- const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1;
+ const ITensor *broadcast_tensor = is_broadcast_input_2 ? src1 : src0;
+ const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? src1 : src0;
// Clear X Dimension on execution window as we handle manually
non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1));
Iterator broadcast_input(broadcast_tensor, broadcast_win);
Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win);
- Iterator output(out, win);
+ Iterator output(dst, win);
execute_window_loop(win, [&](const Coordinates &)
{
@@ -112,9 +112,9 @@
input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
- Iterator input1(in1, input1_win);
- Iterator input2(in2, input2_win);
- Iterator output(out, win);
+ Iterator input1(src0, input1_win);
+ Iterator input2(src1, input2_win);
+ Iterator output(dst, win);
execute_window_loop(win, [&](const Coordinates &)
{
@@ -142,4 +142,4 @@
} // namespace cpu
} // namespace arm_compute
#endif // defined(__ARM_FEATURE_SVE)
-#endif // SRC_CORE_SVE_KERNELS_ARITHMETIC_ADDITION_LIST_H
\ No newline at end of file
+#endif // SRC_CORE_SVE_KERNELS_ADD_LIST_H
\ No newline at end of file
diff --git a/src/core/NEON/kernels/arithmetic_addition/impl/SVE/qasymm8.cpp b/src/core/cpu/kernels/add/sve/qasymm8.cpp
similarity index 91%
rename from src/core/NEON/kernels/arithmetic_addition/impl/SVE/qasymm8.cpp
rename to src/core/cpu/kernels/add/sve/qasymm8.cpp
index 871ee23..c47b5ab 100644
--- a/src/core/NEON/kernels/arithmetic_addition/impl/SVE/qasymm8.cpp
+++ b/src/core/cpu/kernels/add/sve/qasymm8.cpp
@@ -34,13 +34,13 @@
{
namespace cpu
{
-void arithmetic_addition_qasymm8_sve(const ITensor *in1, const ITensor *in2, ITensor *out, const ConvertPolicy &policy, const Window &window)
+void add_qasymm8_sve(const ITensor *src0, const ITensor *src1, ITensor *dst, const ConvertPolicy &policy, const Window &window)
{
ARM_COMPUTE_UNUSED(policy);
// Create input windows
- Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
- Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
+ Window input1_win = window.broadcast_if_dimension_le_one(src0->info()->tensor_shape());
+ Window input2_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape());
// Clear X Dimension on execution window as we handle manually
Window win = window;
@@ -48,12 +48,12 @@
const auto window_start_x = static_cast<int>(window.x().start());
const auto window_end_x = static_cast<int>(window.x().end());
- const bool is_broadcast_across_x = in1->info()->tensor_shape().x() != in2->info()->tensor_shape().x();
+ const bool is_broadcast_across_x = src0->info()->tensor_shape().x() != src1->info()->tensor_shape().x();
const auto all_true_pg = svptrue_b8();
- const UniformQuantizationInfo iq1_info = in1->info()->quantization_info().uniform();
- const UniformQuantizationInfo iq2_info = in2->info()->quantization_info().uniform();
- const UniformQuantizationInfo oq_info = out->info()->quantization_info().uniform();
+ const UniformQuantizationInfo iq1_info = src0->info()->quantization_info().uniform();
+ const UniformQuantizationInfo iq2_info = src1->info()->quantization_info().uniform();
+ const UniformQuantizationInfo oq_info = dst->info()->quantization_info().uniform();
const auto invvscaleo = svdup_n_f32(1.f / oq_info.scale);
const auto voffseto = svdup_n_f32(oq_info.offset);
@@ -63,8 +63,8 @@
const bool is_broadcast_input_2 = input2_win.x().step() == 0;
Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win;
Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win;
- const ITensor *broadcast_tensor = is_broadcast_input_2 ? in2 : in1;
- const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1;
+ const ITensor *broadcast_tensor = is_broadcast_input_2 ? src1 : src0;
+ const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? src1 : src0;
const svfloat32_t vscale1 = is_broadcast_input_2 ? svdup_n_f32(iq1_info.scale) : svdup_n_f32(iq2_info.scale);
const svfloat32_t vscale2 = is_broadcast_input_2 ? svdup_n_f32(iq2_info.scale) : svdup_n_f32(iq1_info.scale);
@@ -76,7 +76,7 @@
Iterator broadcast_input(broadcast_tensor, broadcast_win);
Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win);
- Iterator output(out, win);
+ Iterator output(dst, win);
execute_window_loop(win, [&](const Coordinates &)
{
@@ -127,9 +127,9 @@
input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
- Iterator input1(in1, input1_win);
- Iterator input2(in2, input2_win);
- Iterator output(out, win);
+ Iterator input1(src0, input1_win);
+ Iterator input2(src1, input2_win);
+ Iterator output(dst, win);
const auto vscale1 = svdup_n_f32(iq1_info.scale);
const auto vscale2 = svdup_n_f32(iq2_info.scale);
diff --git a/src/core/NEON/kernels/arithmetic_addition/impl/SVE/qasymm8_signed.cpp b/src/core/cpu/kernels/add/sve/qasymm8_signed.cpp
similarity index 91%
rename from src/core/NEON/kernels/arithmetic_addition/impl/SVE/qasymm8_signed.cpp
rename to src/core/cpu/kernels/add/sve/qasymm8_signed.cpp
index 2ba5d39..75d0f75 100644
--- a/src/core/NEON/kernels/arithmetic_addition/impl/SVE/qasymm8_signed.cpp
+++ b/src/core/cpu/kernels/add/sve/qasymm8_signed.cpp
@@ -34,13 +34,13 @@
{
namespace cpu
{
-void arithmetic_addition_qasymm8_signed_sve(const ITensor *in1, const ITensor *in2, ITensor *out, const ConvertPolicy &policy, const Window &window)
+void add_qasymm8_signed_sve(const ITensor *src0, const ITensor *src1, ITensor *dst, const ConvertPolicy &policy, const Window &window)
{
ARM_COMPUTE_UNUSED(policy);
// Create input windows
- Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
- Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
+ Window input1_win = window.broadcast_if_dimension_le_one(src0->info()->tensor_shape());
+ Window input2_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape());
// Clear X Dimension on execution window as we handle manually
Window win = window;
@@ -48,11 +48,11 @@
const auto window_start_x = static_cast<int>(window.x().start());
const auto window_end_x = static_cast<int>(window.x().end());
- const bool is_broadcast_across_x = in1->info()->tensor_shape().x() != in2->info()->tensor_shape().x();
+ const bool is_broadcast_across_x = src0->info()->tensor_shape().x() != src1->info()->tensor_shape().x();
- const UniformQuantizationInfo iq1_info = in1->info()->quantization_info().uniform();
- const UniformQuantizationInfo iq2_info = in2->info()->quantization_info().uniform();
- const UniformQuantizationInfo oq_info = out->info()->quantization_info().uniform();
+ const UniformQuantizationInfo iq1_info = src0->info()->quantization_info().uniform();
+ const UniformQuantizationInfo iq2_info = src1->info()->quantization_info().uniform();
+ const UniformQuantizationInfo oq_info = dst->info()->quantization_info().uniform();
const auto invvscaleo = svdup_n_f32(1.f / oq_info.scale);
const auto voffseto = svdup_n_f32(oq_info.offset);
@@ -62,8 +62,8 @@
const bool is_broadcast_input_2 = input2_win.x().step() == 0;
Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win;
Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win;
- const ITensor *broadcast_tensor = is_broadcast_input_2 ? in2 : in1;
- const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1;
+ const ITensor *broadcast_tensor = is_broadcast_input_2 ? src1 : src0;
+ const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? src1 : src0;
const auto all_true_pg = svptrue_b8();
const auto vscale1 = is_broadcast_input_2 ? svdup_n_f32(iq1_info.scale) : svdup_n_f32(iq2_info.scale);
@@ -76,7 +76,7 @@
Iterator broadcast_input(broadcast_tensor, broadcast_win);
Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win);
- Iterator output(out, win);
+ Iterator output(dst, win);
execute_window_loop(win, [&](const Coordinates &)
{
@@ -125,9 +125,9 @@
input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
- Iterator input1(in1, input1_win);
- Iterator input2(in2, input2_win);
- Iterator output(out, win);
+ Iterator input1(src0, input1_win);
+ Iterator input2(src1, input2_win);
+ Iterator output(dst, win);
const auto vscale1 = svdup_n_f32(iq1_info.scale);
const auto vscale2 = svdup_n_f32(iq2_info.scale);
diff --git a/src/core/NEON/kernels/arithmetic_addition/impl/SVE/qsymm16.cpp b/src/core/cpu/kernels/add/sve/qsymm16.cpp
similarity index 87%
rename from src/core/NEON/kernels/arithmetic_addition/impl/SVE/qsymm16.cpp
rename to src/core/cpu/kernels/add/sve/qsymm16.cpp
index c072cdb..c3b72a5 100644
--- a/src/core/NEON/kernels/arithmetic_addition/impl/SVE/qsymm16.cpp
+++ b/src/core/cpu/kernels/add/sve/qsymm16.cpp
@@ -34,13 +34,13 @@
{
namespace cpu
{
-void arithmetic_addition_qsymm16_sve(const ITensor *in1, const ITensor *in2, ITensor *out, const ConvertPolicy &policy, const Window &window)
+void add_qsymm16_sve(const ITensor *src0, const ITensor *src1, ITensor *dst, const ConvertPolicy &policy, const Window &window)
{
ARM_COMPUTE_UNUSED(policy);
// Create input windows
- Window input1_win = window.broadcast_if_dimension_le_one(in1->info()->tensor_shape());
- Window input2_win = window.broadcast_if_dimension_le_one(in2->info()->tensor_shape());
+ Window input1_win = window.broadcast_if_dimension_le_one(src0->info()->tensor_shape());
+ Window input2_win = window.broadcast_if_dimension_le_one(src1->info()->tensor_shape());
// Clear X Dimension on execution window as we handle manually
Window win = window;
@@ -48,11 +48,11 @@
const auto window_start_x = static_cast<int>(window.x().start());
const auto window_end_x = static_cast<int>(window.x().end());
- const bool is_broadcast_across_x = in1->info()->tensor_shape().x() != in2->info()->tensor_shape().x();
+ const bool is_broadcast_across_x = src0->info()->tensor_shape().x() != src1->info()->tensor_shape().x();
- const UniformQuantizationInfo iq1_info = in1->info()->quantization_info().uniform();
- const UniformQuantizationInfo iq2_info = in2->info()->quantization_info().uniform();
- const UniformQuantizationInfo oq_info = out->info()->quantization_info().uniform();
+ const UniformQuantizationInfo iq1_info = src0->info()->quantization_info().uniform();
+ const UniformQuantizationInfo iq2_info = src1->info()->quantization_info().uniform();
+ const UniformQuantizationInfo oq_info = dst->info()->quantization_info().uniform();
const auto vscale1 = svdup_n_f32(iq1_info.scale);
const auto vscale2 = svdup_n_f32(iq2_info.scale);
@@ -64,15 +64,15 @@
const bool is_broadcast_input_2 = input2_win.x().step() == 0;
Window broadcast_win = is_broadcast_input_2 ? input2_win : input1_win;
Window non_broadcast_win = !is_broadcast_input_2 ? input2_win : input1_win;
- const ITensor *broadcast_tensor = is_broadcast_input_2 ? in2 : in1;
- const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? in2 : in1;
+ const ITensor *broadcast_tensor = is_broadcast_input_2 ? src1 : src0;
+ const ITensor *non_broadcast_tensor = !is_broadcast_input_2 ? src1 : src0;
// Clear X Dimension on execution window as we handle manually
non_broadcast_win.set(Window::DimX, Window::Dimension(0, 1, 1));
Iterator broadcast_input(broadcast_tensor, broadcast_win);
Iterator non_broadcast_input(non_broadcast_tensor, non_broadcast_win);
- Iterator output(out, win);
+ Iterator output(dst, win);
execute_window_loop(win, [&](const Coordinates &)
{
@@ -114,9 +114,9 @@
input1_win.set(Window::DimX, Window::Dimension(0, 1, 1));
input2_win.set(Window::DimX, Window::Dimension(0, 1, 1));
- Iterator input1(in1, input1_win);
- Iterator input2(in2, input2_win);
- Iterator output(out, win);
+ Iterator input1(src0, input1_win);
+ Iterator input2(src1, input2_win);
+ Iterator output(dst, win);
execute_window_loop(win, [&](const Coordinates &)
{
diff --git a/src/runtime/NEON/functions/NEArithmeticAddition.cpp b/src/runtime/NEON/functions/NEArithmeticAddition.cpp
index 1eaccf3..2e4755b 100644
--- a/src/runtime/NEON/functions/NEArithmeticAddition.cpp
+++ b/src/runtime/NEON/functions/NEArithmeticAddition.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017-2020 Arm Limited.
+ * Copyright (c) 2017-2021 Arm Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -23,37 +23,19 @@
*/
#include "arm_compute/runtime/NEON/functions/NEArithmeticAddition.h"
-#include "arm_compute/core/ITensor.h"
-#include "src/core/NEON/kernels/NEArithmeticAdditionKernel.h"
+#include "arm_compute/core/Validate.h"
+#include "src/runtime/cpu/operators/CpuAdd.h"
#include <utility>
namespace arm_compute
{
-namespace experimental
-{
-NEArithmeticAddition::~NEArithmeticAddition() = default;
-
-void NEArithmeticAddition::configure(const ITensorInfo *input1, const ITensorInfo *input2, ITensorInfo *output, ConvertPolicy policy, const ActivationLayerInfo &act_info)
-{
- ARM_COMPUTE_UNUSED(act_info);
- auto k = std::make_unique<NEArithmeticAdditionKernel>();
- k->configure(input1, input2, output, policy);
- _kernel = std::move(k);
-}
-Status NEArithmeticAddition::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy, const ActivationLayerInfo &act_info)
-{
- ARM_COMPUTE_RETURN_ERROR_ON(act_info.enabled());
- return NEArithmeticAdditionKernel::validate(input1, input2, output, policy);
-}
-} // namespace experimental
-
struct NEArithmeticAddition::Impl
{
- const ITensor *src_0{ nullptr };
- const ITensor *src_1{ nullptr };
- ITensor *dst{ nullptr };
- std::unique_ptr<experimental::NEArithmeticAddition> op{ nullptr };
+ const ITensor *src_0{ nullptr };
+ const ITensor *src_1{ nullptr };
+ ITensor *dst{ nullptr };
+ std::unique_ptr<cpu::CpuAdd> op{ nullptr };
};
NEArithmeticAddition::NEArithmeticAddition()
@@ -66,7 +48,7 @@
Status NEArithmeticAddition::validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, ConvertPolicy policy, const ActivationLayerInfo &act_info)
{
- return experimental::NEArithmeticAddition::validate(input1, input2, output, policy, act_info);
+ return cpu::CpuAdd::validate(input1, input2, output, policy, act_info);
}
void NEArithmeticAddition::configure(const ITensor *input1, const ITensor *input2, ITensor *output, ConvertPolicy policy, const ActivationLayerInfo &act_info)
@@ -74,8 +56,8 @@
_impl->src_0 = input1;
_impl->src_1 = input2;
_impl->dst = output;
- _impl->op = std::make_unique<experimental::NEArithmeticAddition>();
- _impl->op->configure(input1->info(), input2->info(), output->info(), policy, act_info);
+ _impl->op = std::make_unique<cpu::CpuAdd>();
+ _impl->op->configure(_impl->src_0->info(), _impl->src_1->info(), _impl->dst->info(), policy, act_info);
}
void NEArithmeticAddition::run()
diff --git a/src/runtime/cpu/operators/CpuAdd.cpp b/src/runtime/cpu/operators/CpuAdd.cpp
new file mode 100644
index 0000000..280350f
--- /dev/null
+++ b/src/runtime/cpu/operators/CpuAdd.cpp
@@ -0,0 +1,46 @@
+/*
+ * Copyright (c) 2021 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "src/runtime/cpu/operators/CpuAdd.h"
+
+#include "src/core/cpu/kernels/CpuAddKernel.h"
+
+namespace arm_compute
+{
+namespace cpu
+{
+void CpuAdd::configure(const ITensorInfo *src0, const ITensorInfo *src1, ITensorInfo *dst, ConvertPolicy policy, const ActivationLayerInfo &act_info)
+{
+ ARM_COMPUTE_UNUSED(act_info);
+ auto k = std::make_unique<kernels::CpuAddKernel>();
+ k->configure(src0, src1, dst, policy);
+ _kernel = std::move(k);
+}
+
+Status CpuAdd::validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *dst, ConvertPolicy policy, const ActivationLayerInfo &act_info)
+{
+ ARM_COMPUTE_UNUSED(act_info);
+ return kernels::CpuAddKernel::validate(src0, src1, dst, policy);
+}
+} // namespace cpu
+} // namespace arm_compute
diff --git a/src/runtime/cpu/operators/CpuAdd.h b/src/runtime/cpu/operators/CpuAdd.h
new file mode 100644
index 0000000..7ddc69b
--- /dev/null
+++ b/src/runtime/cpu/operators/CpuAdd.h
@@ -0,0 +1,77 @@
+/*
+ * Copyright (c) 2021 Arm Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef ARM_COMPUTE_CPU_ADD_H
+#define ARM_COMPUTE_CPU_ADD_H
+
+#include "src/runtime/cpu/ICpuOperator.h"
+
+namespace arm_compute
+{
+namespace cpu
+{
+/** Basic function to run @ref CpuAddKernel */
+class CpuAdd : public ICpuOperator
+{
+public:
+ /** Constructor */
+ CpuAdd() = default;
+ /** Initialise the kernel's input, dst and border mode.
+ *
+ * Valid configurations (src0,src1) -> dst :
+ *
+ * - (U8,U8) -> U8
+ * - (U8,U8) -> S16
+ * - (S16,U8) -> S16
+ * - (U8,S16) -> S16
+ * - (S16,S16) -> S16
+ * - (S32,S32) -> S32
+ * - (F16,F16) -> F16
+ * - (F32,F32) -> F32
+ * - (QASYMM8,QASYMM8) -> QASYMM8
+ * - (QASYMM8_SIGNED,QASYMM8_SIGNED) -> QASYMM8_SIGNED
+ * - (QSYMM16,QSYMM16) -> QSYMM16
+ *
+ * @param[in] src0 First input tensor info. Data types supported: U8/QASYMM8/QASYMM8_SIGNED/S16/QSYMM16/F16/S32/F32
+ * @param[in] src1 Second input tensor info. Data types supported: U8/QASYMM8/QASYMM8_SIGNED/S16/QSYMM16/F16/S32/F32
+ * @param[out] dst The dst tensor info. Data types supported: U8/QASYMM8/QASYMM8_SIGNED/S16/QSYMM16/F16/S32/F32.
+ * @param[in] policy Overflow policy.
+ * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Currently not supported.
+ *
+ */
+ void configure(const ITensorInfo *src0, const ITensorInfo *src1, ITensorInfo *dst, ConvertPolicy policy, const ActivationLayerInfo &act_info = ActivationLayerInfo());
+ /** Static function to check if given info will lead to a valid configuration of @ref CpuAddKernel
+ *
+ * @param[in] src0 First input tensor info. Data types supported: U8/QASYMM8/QASYMM8_SIGNED/S16/QSYMM16/F16/S32/F32
+ * @param[in] src1 Second input tensor info. Data types supported: U8/QASYMM8/QASYMM8_SIGNED/S16/QSYMM16/F16/S32/F32
+ * @param[in] dst The dst tensor info. Data types supported: U8/QASYMM8/QASYMM8_SIGNED/S16/QSYMM16/F16/S32/F32.
+ * @param[in] policy Overflow policy.
+ * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Currently not supported.
+ *
+ * @return a status
+ */
+ static Status validate(const ITensorInfo *src0, const ITensorInfo *src1, const ITensorInfo *dst, ConvertPolicy policy, const ActivationLayerInfo &act_info = ActivationLayerInfo());
+};
+} // namespace cpu
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_CPU_ADD_H */