src/core/SVE/kernels/elementwise/impl/elementwise_list.h - ml/ComputeLibrary - Gitiles

 /*
  * 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 SRC_CORE_SVE_KERNELS_ELEMENTWISE_LIST_H
 #define SRC_CORE_SVE_KERNELS_ELEMENTWISE_LIST_H
 #if defined(__ARM_FEATURE_SVE)
 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/utils/misc/Traits.h"
 #include "src/core/NEON/SVEMath.h"
 #include "src/core/NEON/wrapper/intrinsics/intrinsics.h"
 #include "src/core/NEON/wrapper/svtraits.h"
 #include <arm_sve.h>

 namespace arm_compute
 {
 namespace cpu
 {
 namespace sve
 {
 using namespace arm_compute::wrapper;

 template <typename VectorType>
 inline VectorType elementwise_pow(svbool_t &pg, const VectorType &a, const VectorType &b)
 {
     return svpow_z(pg, a, b);
 }

 template <>
 inline svint32_t elementwise_pow<svint32_t>(svbool_t &pg, const svint32_t &a, const svint32_t &b)
 {
     return svcvt_s32_z(pg, svpow_z(pg, svcvt_f32_z(pg, a), svcvt_f32_z(pg, b)));
 }

 template <typename VectorType>
 inline VectorType elementwise_div(svbool_t &pg, const VectorType &a, const VectorType &b)
 {
     return svdiv_z(pg, a, b);
 }

 template <>
 inline svint32_t elementwise_div<svint32_t>(svbool_t &pg, const svint32_t &a, const svint32_t &b)
 {
     return svcvt_s32_z(pg, svdiv_z(pg, svcvt_f32_z(pg, a), svcvt_f32_z(pg, b)));
 }

 template <typename VectorType>
 inline VectorType elementwise_arithmetic_op(svbool_t &pg, const VectorType &a, const VectorType &b, ArithmeticOperation op)
 {
     using ScalarType = typename sve_scalar<VectorType>::type;
     VectorType res{};

     switch(op)
     {
         case ArithmeticOperation::MAX:
             res = svmax_z(pg, a, b);
             break;
         case ArithmeticOperation::MIN:
             res = svmin_z(pg, a, b);
             break;
         case ArithmeticOperation::SQUARED_DIFF:
         {
             const auto tmp = svsub_z(pg, a, b);
             res            = svmul_z(pg, tmp, tmp);
             break;
         }
         case ArithmeticOperation::PRELU:
         {
             const auto zero = svdup_n(ScalarType(0));
             const auto tmp  = svmul_z(pg, a, b);
             const auto gt   = svcmpgt(pg, a, zero);
             res             = svsel(gt, a, tmp);
             break;
         }
         case ArithmeticOperation::DIV:
         {
             res = elementwise_div(pg, a, b);
             break;
         }
         case ArithmeticOperation::POWER:
         {
             res = elementwise_pow(pg, a, b);
             break;
         }
         default:
             ARM_COMPUTE_ERROR("NOT_SUPPORTED!");
     }

     return res;
 }

 template <uint32_t bytewidth>
 inline svbool_t narrow_to_byte_predicate(svbool_t pg)
 {
     const auto all_false = svpfalse();

     switch(bytewidth)
     {
         case 8:
             pg = svuzp1_b32(pg, all_false);
         /* fall through */
         case 4:
             pg = svuzp1_b16(pg, all_false);
         /* fall through */
         case 2:
             pg = svuzp1_b8(pg, all_false);
         /* fall through */
         default:
             break;
     }
     return pg;
 }

 template <typename InputVectorType, typename OutputVectorType>
 inline OutputVectorType elementwise_comparison_op(svbool_t &pg, const InputVectorType &a, const InputVectorType &b, ComparisonOperation op)
 {
     svbool_t selection_vector{};

     switch(op)
     {
         case ComparisonOperation::Equal:
             selection_vector = svcmpeq(pg, a, b);
             break;
         case ComparisonOperation::NotEqual:
             selection_vector = svcmpne(pg, a, b);
             break;
         case ComparisonOperation::Greater:
             selection_vector = svcmpgt(pg, a, b);
             break;
         case ComparisonOperation::GreaterEqual:
             selection_vector = svcmpge(pg, a, b);
             break;
         case ComparisonOperation::Less:
             selection_vector = svcmplt(pg, a, b);
             break;
         case ComparisonOperation::LessEqual:
             selection_vector = svcmple(pg, a, b);
             break;
         default:
             ARM_COMPUTE_ERROR("NOT_SUPPORTED!");
     }

     using InputScalarType = typename sve_scalar<InputVectorType>::type;
     selection_vector      = narrow_to_byte_predicate<sizeof(InputScalarType)>(selection_vector);

     using OutputScalarType  = typename sve_scalar<OutputVectorType>::type;
     const auto false_vector = svdup_n(static_cast<OutputScalarType>((uint32_t)0));
     const auto true_vector  = svdup_n(static_cast<OutputScalarType>(~(uint32_t)0));
     auto       ret          = svsel(selection_vector, true_vector, false_vector);

     return ret;
 }

 template <typename InputScalarType, typename OutputScalarType, typename OperatorType>
 struct LoopArguments
 {
     OperatorType           op;
     const InputScalarType *input1_ptr;
     const InputScalarType *input2_ptr;
     OutputScalarType      *output_ptr;
 };

 template <typename InputScalarType, typename OutputScalarType, typename OperatorType>
 struct BroadcastLoopArguments
 {
     OperatorType           op;
     const InputScalarType *input1_ptr;
     InputScalarType        broadcast_value;
     OutputScalarType      *output_ptr;
     bool                   reorder;
 };

 template <typename InputScalarType, typename OutputScalarType>
 inline void arithmetic_op_loop(svbool_t pg, const LoopArguments<InputScalarType, OutputScalarType, ArithmeticOperation> &args)
 {
     const auto in1 = svld1(pg, args.input1_ptr);
     const auto in2 = svld1(pg, args.input2_ptr);
     const auto res = elementwise_arithmetic_op<typename sve_vector<InputScalarType>::type>(pg, in1, in2, args.op);
     svst1(pg, args.output_ptr, res);
 }

 template <typename InputScalarType, typename OutputScalarType>
 inline void arithmetic_op_broadcast_loop(svbool_t pg, const BroadcastLoopArguments<InputScalarType, OutputScalarType, ArithmeticOperation> &args)
 {
     const auto non_broadcast_vector = svld1(pg, args.input1_ptr);
     const auto broadcast_vector     = svdup_n(args.broadcast_value);
     const auto in1                  = args.reorder ? broadcast_vector : non_broadcast_vector;
     const auto in2                  = args.reorder ? non_broadcast_vector : broadcast_vector;
     const auto res                  = elementwise_arithmetic_op<typename sve_vector<InputScalarType>::type>(pg, in1, in2, args.op);
     svst1(pg, args.output_ptr, res);
 }

 template <typename InputScalarType, typename OutputScalarType>
 inline void comparison_op_loop(svbool_t pg, const LoopArguments<InputScalarType, OutputScalarType, ComparisonOperation> &args)
 {
     const auto     in1       = svld1(pg, args.input1_ptr);
     const auto     in2       = svld1(pg, args.input2_ptr);
     const auto     res       = elementwise_comparison_op<typename sve_vector<InputScalarType>::type, typename sve_vector<OutputScalarType>::type>(pg, in1, in2, args.op);
     const svbool_t output_pg = narrow_to_byte_predicate<sizeof(InputScalarType)>(pg);
     svst1(output_pg, args.output_ptr, res);
 }

 template <typename InputScalarType, typename OutputScalarType>
 inline void comparison_op_broadcast_loop(svbool_t pg, const BroadcastLoopArguments<InputScalarType, OutputScalarType, ComparisonOperation> &args)
 {
     const auto     non_broadcast_vector = svld1(pg, args.input1_ptr);
     const auto     broadcast_vector     = svdup_n(args.broadcast_value);
     const auto     in1                  = args.reorder ? broadcast_vector : non_broadcast_vector;
     const auto     in2                  = args.reorder ? non_broadcast_vector : broadcast_vector;
     const auto     res                  = elementwise_comparison_op<typename sve_vector<InputScalarType>::type, typename sve_vector<OutputScalarType>::type>(pg, in1, in2, args.op);
     const svbool_t output_pg            = narrow_to_byte_predicate<sizeof(InputScalarType)>(pg);
     svst1(output_pg, args.output_ptr, res);
 }

 template <typename InputScalarType, typename OutputScalarType, typename OperatorType>
 using LoopFuncType = void (*)(svbool_t, const LoopArguments<InputScalarType, OutputScalarType, OperatorType> &);

 template <typename InputScalarType, typename OutputScalarType, typename OperatorType>
 using BroadcastLoopFuncType = void (*)(svbool_t, const BroadcastLoopArguments<InputScalarType, OutputScalarType, OperatorType> &);

 template <typename InputVectorType, typename OutputVectorType, typename OperatorType,
           typename InputScalarType  = typename sve_scalar<InputVectorType>::type,
           typename OutputScalarType = typename sve_scalar<OutputVectorType>::type>
 void elementwise_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window,
                     OperatorType op,
                     LoopFuncType<InputScalarType, OutputScalarType, OperatorType>          func,
                     BroadcastLoopFuncType<InputScalarType, OutputScalarType, OperatorType> broadcast_func)
 {
     const auto all_true_pg = svptrue<InputScalarType>();

     // 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());

     // Clear X Dimension on execution window as we handle manually
     Window win = window;
     win.set(Window::DimX, Window::Dimension(0, 1, 1));

     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();

     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;

         // 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);

         execute_window_loop(win, [&](const Coordinates &)
         {
             auto                  output_ptr              = reinterpret_cast<OutputScalarType *>(output.ptr());
             const auto            non_broadcast_input_ptr = reinterpret_cast<const InputScalarType *>(non_broadcast_input.ptr());
             const InputScalarType broadcast_value         = *reinterpret_cast<const InputScalarType *>(broadcast_input.ptr());

             int x = window_start_x;

             svbool_t pg = svwhilelt<InputScalarType>(x, window_end_x);
             do
             {
                 broadcast_func(pg,
                 {
                     op,
                     non_broadcast_input_ptr + x,
                     broadcast_value,
                     output_ptr + x,
                     !is_broadcast_input_2
                 });
                 x += svcnt<InputScalarType>();
                 pg = svwhilelt<InputScalarType>(x, window_end_x);
             }
             while(svptest_any(all_true_pg, pg));
         },
         broadcast_input, non_broadcast_input, output);
     }
     else
     {
         // Clear X Dimension on execution window as we handle manually
         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);

         execute_window_loop(win, [&](const Coordinates &)
         {
             auto       output_ptr = reinterpret_cast<OutputScalarType *>(output.ptr());
             const auto input1_ptr = reinterpret_cast<const InputScalarType *>(input1.ptr());
             const auto input2_ptr = reinterpret_cast<const InputScalarType *>(input2.ptr());

             int x = window_start_x;

             svbool_t pg = svwhilelt<InputScalarType>(x, window_end_x);
             do
             {
                 func(pg,
                 {
                     op,
                     input1_ptr + x,
                     input2_ptr + x,
                     output_ptr + x
                 });
                 x += svcnt<InputScalarType>();
                 pg = svwhilelt<InputScalarType>(x, window_end_x);
             }
             while(svptest_any(all_true_pg, pg));
         },
         input1, input2, output);
     }
 }

 template <ArithmeticOperation op, typename ScalarType>
 void elementwise_arithmetic_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
 {
     using VectorType = typename sve_vector<ScalarType>::type;

     elementwise_op<VectorType, VectorType, ArithmeticOperation>(in1, in2, out, window, op,
                                                                 &arithmetic_op_loop<ScalarType, ScalarType>,
                                                                 &arithmetic_op_broadcast_loop<ScalarType, ScalarType>);
 }

 template <ComparisonOperation op, typename InputScalarType, typename OutputScalarType = uint8_t>
 void elementwise_comparison_op(const ITensor *in1, const ITensor *in2, ITensor *out, const Window &window)
 {
     static_assert(sizeof(InputScalarType) >= sizeof(OutputScalarType), "input data type's width should be equal to or greater than output data type's width");
     using InputVectorType  = typename sve_vector<InputScalarType>::type;
     using OutputVectorType = typename sve_vector<OutputScalarType>::type;

     elementwise_op<InputVectorType, OutputVectorType, ComparisonOperation>(in1, in2, out, window, op,
                                                                            &comparison_op_loop<InputScalarType, OutputScalarType>,
                                                                            &comparison_op_broadcast_loop<InputScalarType, OutputScalarType>);
 }

 } // namespace sve
 } // namespace cpu
 } // namespace arm_compute
 #endif // defined(__ARM_FEATURE_SVE)
 #endif /* SRC_CORE_SVE_KERNELS_ELEMENTWISE_LIST_H */
	/*
	* 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 SRC_CORE_SVE_KERNELS_ELEMENTWISE_LIST_H
	#define SRC_CORE_SVE_KERNELS_ELEMENTWISE_LIST_H
	#if defined(__ARM_FEATURE_SVE)
	#include "arm_compute/core/Types.h"
	#include "arm_compute/core/utils/misc/Traits.h"
	#include "src/core/NEON/SVEMath.h"
	#include "src/core/NEON/wrapper/intrinsics/intrinsics.h"
	#include "src/core/NEON/wrapper/svtraits.h"
	#include <arm_sve.h>

	namespace arm_compute
	{
	namespace cpu
	{
	namespace sve
	{
	using namespace arm_compute::wrapper;

	template <typename VectorType>
	inline VectorType elementwise_pow(svbool_t &pg, const VectorType &a, const VectorType &b)
	{
	return svpow_z(pg, a, b);
	}

	template <>
	inline svint32_t elementwise_pow<svint32_t>(svbool_t &pg, const svint32_t &a, const svint32_t &b)
	{
	return svcvt_s32_z(pg, svpow_z(pg, svcvt_f32_z(pg, a), svcvt_f32_z(pg, b)));
	}

	template <typename VectorType>
	inline VectorType elementwise_div(svbool_t &pg, const VectorType &a, const VectorType &b)
	{
	return svdiv_z(pg, a, b);
	}

	template <>
	inline svint32_t elementwise_div<svint32_t>(svbool_t &pg, const svint32_t &a, const svint32_t &b)
	{
	return svcvt_s32_z(pg, svdiv_z(pg, svcvt_f32_z(pg, a), svcvt_f32_z(pg, b)));
	}

	template <typename VectorType>
	inline VectorType elementwise_arithmetic_op(svbool_t &pg, const VectorType &a, const VectorType &b, ArithmeticOperation op)
	{
	using ScalarType = typename sve_scalar<VectorType>::type;
	VectorType res{};

	switch(op)
	{
	case ArithmeticOperation::MAX:
	res = svmax_z(pg, a, b);
	break;
	case ArithmeticOperation::MIN:
	res = svmin_z(pg, a, b);
	break;
	case ArithmeticOperation::SQUARED_DIFF:
	{
	const auto tmp = svsub_z(pg, a, b);
	res = svmul_z(pg, tmp, tmp);
	break;
	}
	case ArithmeticOperation::PRELU:
	{
	const auto zero = svdup_n(ScalarType(0));
	const auto tmp = svmul_z(pg, a, b);
	const auto gt = svcmpgt(pg, a, zero);
	res = svsel(gt, a, tmp);
	break;
	}
	case ArithmeticOperation::DIV:
	{
	res = elementwise_div(pg, a, b);
	break;
	}
	case ArithmeticOperation::POWER:
	{
	res = elementwise_pow(pg, a, b);
	break;
	}
	default:
	ARM_COMPUTE_ERROR("NOT_SUPPORTED!");
	}

	return res;
	}

	template <uint32_t bytewidth>
	inline svbool_t narrow_to_byte_predicate(svbool_t pg)
	{
	const auto all_false = svpfalse();

	switch(bytewidth)
	{
	case 8:
	pg = svuzp1_b32(pg, all_false);
	/* fall through */
	case 4:
	pg = svuzp1_b16(pg, all_false);
	/* fall through */
	case 2:
	pg = svuzp1_b8(pg, all_false);
	/* fall through */
	default:
	break;
	}
	return pg;
	}

	template <typename InputVectorType, typename OutputVectorType>
	inline OutputVectorType elementwise_comparison_op(svbool_t &pg, const InputVectorType &a, const InputVectorType &b, ComparisonOperation op)
	{
	svbool_t selection_vector{};

	switch(op)
	{
	case ComparisonOperation::Equal:
	selection_vector = svcmpeq(pg, a, b);
	break;
	case ComparisonOperation::NotEqual:
	selection_vector = svcmpne(pg, a, b);
	break;
	case ComparisonOperation::Greater:
	selection_vector = svcmpgt(pg, a, b);
	break;
	case ComparisonOperation::GreaterEqual:
	selection_vector = svcmpge(pg, a, b);
	break;
	case ComparisonOperation::Less:
	selection_vector = svcmplt(pg, a, b);
	break;
	case ComparisonOperation::LessEqual:
	selection_vector = svcmple(pg, a, b);
	break;
	default:
	ARM_COMPUTE_ERROR("NOT_SUPPORTED!");
	}

	using InputScalarType = typename sve_scalar<InputVectorType>::type;
	selection_vector = narrow_to_byte_predicate<sizeof(InputScalarType)>(selection_vector);

	using OutputScalarType = typename sve_scalar<OutputVectorType>::type;
	const auto false_vector = svdup_n(static_cast<OutputScalarType>((uint32_t)0));
	const auto true_vector = svdup_n(static_cast<OutputScalarType>(~(uint32_t)0));
	auto ret = svsel(selection_vector, true_vector, false_vector);

	return ret;
	}

	template <typename InputScalarType, typename OutputScalarType, typename OperatorType>
	struct LoopArguments
	{
	OperatorType op;
	const InputScalarType *input1_ptr;
	const InputScalarType *input2_ptr;
	OutputScalarType *output_ptr;
	};

	template <typename InputScalarType, typename OutputScalarType, typename OperatorType>
	struct BroadcastLoopArguments
	{
	OperatorType op;
	const InputScalarType *input1_ptr;
	InputScalarType broadcast_value;
	OutputScalarType *output_ptr;
	bool reorder;
	};

	template <typename InputScalarType, typename OutputScalarType>
	inline void arithmetic_op_loop(svbool_t pg, const LoopArguments<InputScalarType, OutputScalarType, ArithmeticOperation> &args)
	{
	const auto in1 = svld1(pg, args.input1_ptr);
	const auto in2 = svld1(pg, args.input2_ptr);
	const auto res = elementwise_arithmetic_op<typename sve_vector<InputScalarType>::type>(pg, in1, in2, args.op);
	svst1(pg, args.output_ptr, res);
	}

	template <typename InputScalarType, typename OutputScalarType>
	inline void arithmetic_op_broadcast_loop(svbool_t pg, const BroadcastLoopArguments<InputScalarType, OutputScalarType, ArithmeticOperation> &args)
	{
	const auto non_broadcast_vector = svld1(pg, args.input1_ptr);
	const auto broadcast_vector = svdup_n(args.broadcast_value);
	const auto in1 = args.reorder ? broadcast_vector : non_broadcast_vector;
	const auto in2 = args.reorder ? non_broadcast_vector : broadcast_vector;
	const auto res = elementwise_arithmetic_op<typename sve_vector<InputScalarType>::type>(pg, in1, in2, args.op);
	svst1(pg, args.output_ptr, res);
	}

	template <typename InputScalarType, typename OutputScalarType>
	inline void comparison_op_loop(svbool_t pg, const LoopArguments<InputScalarType, OutputScalarType, ComparisonOperation> &args)
	{
	const auto in1 = svld1(pg, args.input1_ptr);
	const auto in2 = svld1(pg, args.input2_ptr);
	const auto res = elementwise_comparison_op<typename sve_vector<InputScalarType>::type, typename sve_vector<OutputScalarType>::type>(pg, in1, in2, args.op);
	const svbool_t output_pg = narrow_to_byte_predicate<sizeof(InputScalarType)>(pg);
	svst1(output_pg, args.output_ptr, res);
	}

	template <typename InputScalarType, typename OutputScalarType>
	inline void comparison_op_broadcast_loop(svbool_t pg, const BroadcastLoopArguments<InputScalarType, OutputScalarType, ComparisonOperation> &args)
	{
	const auto non_broadcast_vector = svld1(pg, args.input1_ptr);
	const auto broadcast_vector = svdup_n(args.broadcast_value);
	const auto in1 = args.reorder ? broadcast_vector : non_broadcast_vector;
	const auto in2 = args.reorder ? non_broadcast_vector : broadcast_vector;
	const auto res = elementwise_comparison_op<typename sve_vector<InputScalarType>::type, typename sve_vector<OutputScalarType>::type>(pg, in1, in2, args.op);
	const svbool_t output_pg = narrow_to_byte_predicate<sizeof(InputScalarType)>(pg);
	svst1(output_pg, args.output_ptr, res);
	}

	template <typename InputScalarType, typename OutputScalarType, typename OperatorType>
	using LoopFuncType = void (*)(svbool_t, const LoopArguments<InputScalarType, OutputScalarType, OperatorType> &);

	template <typename InputScalarType, typename OutputScalarType, typename OperatorType>
	using BroadcastLoopFuncType = void (*)(svbool_t, const BroadcastLoopArguments<InputScalarType, OutputScalarType, OperatorType> &);

	template <typename InputVectorType, typename OutputVectorType, typename OperatorType,
	typename InputScalarType = typename sve_scalar<InputVectorType>::type,
	typename OutputScalarType = typename sve_scalar<OutputVectorType>::type>
	void elementwise_op(const ITensor in1, const ITensor in2, ITensor *out, const Window &window,
	OperatorType op,
	LoopFuncType<InputScalarType, OutputScalarType, OperatorType> func,
	BroadcastLoopFuncType<InputScalarType, OutputScalarType, OperatorType> broadcast_func)
	{
	const auto all_true_pg = svptrue<InputScalarType>();

	// 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());

	// Clear X Dimension on execution window as we handle manually
	Window win = window;
	win.set(Window::DimX, Window::Dimension(0, 1, 1));

	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();

	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;

	// 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);

	execute_window_loop(win, [&](const Coordinates &)
	{
	auto output_ptr = reinterpret_cast<OutputScalarType *>(output.ptr());
	const auto non_broadcast_input_ptr = reinterpret_cast<const InputScalarType *>(non_broadcast_input.ptr());
	const InputScalarType broadcast_value = reinterpret_cast<const InputScalarType >(broadcast_input.ptr());

	int x = window_start_x;

	svbool_t pg = svwhilelt<InputScalarType>(x, window_end_x);
	do
	{
	broadcast_func(pg,
	{
	op,
	non_broadcast_input_ptr + x,
	broadcast_value,
	output_ptr + x,
	!is_broadcast_input_2
	});
	x += svcnt<InputScalarType>();
	pg = svwhilelt<InputScalarType>(x, window_end_x);
	}
	while(svptest_any(all_true_pg, pg));
	},
	broadcast_input, non_broadcast_input, output);
	}
	else
	{
	// Clear X Dimension on execution window as we handle manually
	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);

	execute_window_loop(win, [&](const Coordinates &)
	{
	auto output_ptr = reinterpret_cast<OutputScalarType *>(output.ptr());
	const auto input1_ptr = reinterpret_cast<const InputScalarType *>(input1.ptr());
	const auto input2_ptr = reinterpret_cast<const InputScalarType *>(input2.ptr());

	int x = window_start_x;

	svbool_t pg = svwhilelt<InputScalarType>(x, window_end_x);
	do
	{
	func(pg,
	{
	op,
	input1_ptr + x,
	input2_ptr + x,
	output_ptr + x
	});
	x += svcnt<InputScalarType>();
	pg = svwhilelt<InputScalarType>(x, window_end_x);
	}
	while(svptest_any(all_true_pg, pg));
	},
	input1, input2, output);
	}
	}

	template <ArithmeticOperation op, typename ScalarType>
	void elementwise_arithmetic_op(const ITensor in1, const ITensor in2, ITensor *out, const Window &window)
	{
	using VectorType = typename sve_vector<ScalarType>::type;

	elementwise_op<VectorType, VectorType, ArithmeticOperation>(in1, in2, out, window, op,
	&arithmetic_op_loop<ScalarType, ScalarType>,
	&arithmetic_op_broadcast_loop<ScalarType, ScalarType>);
	}

	template <ComparisonOperation op, typename InputScalarType, typename OutputScalarType = uint8_t>
	void elementwise_comparison_op(const ITensor in1, const ITensor in2, ITensor *out, const Window &window)
	{
	static_assert(sizeof(InputScalarType) >= sizeof(OutputScalarType), "input data type's width should be equal to or greater than output data type's width");
	using InputVectorType = typename sve_vector<InputScalarType>::type;
	using OutputVectorType = typename sve_vector<OutputScalarType>::type;

	elementwise_op<InputVectorType, OutputVectorType, ComparisonOperation>(in1, in2, out, window, op,
	&comparison_op_loop<InputScalarType, OutputScalarType>,
	&comparison_op_broadcast_loop<InputScalarType, OutputScalarType>);
	}

	} // namespace sve
	} // namespace cpu
	} // namespace arm_compute
	#endif // defined(__ARM_FEATURE_SVE)
	#endif /* SRC_CORE_SVE_KERNELS_ELEMENTWISE_LIST_H */