src/cpu/kernels/elementwise_binary/generic/sve/impl.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2021-2022 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/cpu/kernels/elementwise_binary/generic/sve/impl.h"
 #include "src/core/NEON/SVEMath.h"
 #include <arm_sve.h>

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

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

     const auto all_true_pg = svptrue<ScalarType>();

     // 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<ScalarType *>(output.ptr());
             const auto       non_broadcast_input_ptr = reinterpret_cast<const ScalarType *>(non_broadcast_input.ptr());
             const ScalarType broadcast_value         = *reinterpret_cast<const ScalarType *>(broadcast_input.ptr());
             const auto       broadcast_vector        = svdup_n(broadcast_value);

             int x = window_start_x;

             svbool_t pg = svwhilelt<ScalarType>(x, window_end_x);
             do
             {
                 const auto non_broadcast_vector = svld1(pg, non_broadcast_input_ptr + x);
                 VectorType res{};

                 if(is_broadcast_input_2)
                 {
                     res = elementwise_arithmetic_op<typename sve_vector<ScalarType>::type>(pg, non_broadcast_vector, broadcast_vector, op);
                 }
                 else
                 {
                     res = elementwise_arithmetic_op<typename sve_vector<ScalarType>::type>(pg, broadcast_vector, non_broadcast_vector, op);
                 }
                 svst1(pg, output_ptr + x, res);

                 x += svcnt<ScalarType>();
                 pg = svwhilelt<ScalarType>(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<ScalarType *>(output.ptr());
             const auto input1_ptr = reinterpret_cast<const ScalarType *>(input1.ptr());
             const auto input2_ptr = reinterpret_cast<const ScalarType *>(input2.ptr());

             int x = window_start_x;

             svbool_t pg = svwhilelt<ScalarType>(x, window_end_x);
             do
             {
                 const auto in1 = svld1(pg, input1_ptr + x);
                 const auto in2 = svld1(pg, input2_ptr + x);
                 const auto res = elementwise_arithmetic_op<typename sve_vector<ScalarType>::type>(pg, in1, in2, op);
                 svst1(pg, output_ptr + x, res);

                 x += svcnt<ScalarType>();
                 pg = svwhilelt<ScalarType>(x, window_end_x);
             }
             while(svptest_any(all_true_pg, pg));
         },
         input1, input2, output);
     }
 }
 template void elementwise_arithmetic_op<float32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const ArithmeticOperation op, const Window &window);
 template void elementwise_arithmetic_op<float16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const ArithmeticOperation op, const Window &window);
 template void elementwise_arithmetic_op<int16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const ArithmeticOperation op, const Window &window);
 template void elementwise_arithmetic_op<int32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const ArithmeticOperation op, const Window &window);

 template <typename InputScalarType, typename OutputScalarType>
 void elementwise_comparison_op(const ITensor *in1, const ITensor *in2, ITensor *out, ComparisonOperation op, 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 OutputVectorType = typename sve_vector<OutputScalarType>::type;
     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());
             const auto            broadcast_vector        = svdup_n(broadcast_value);

             int x = window_start_x;

             svbool_t pg = svwhilelt<InputScalarType>(x, window_end_x);
             do
             {
                 const auto       non_broadcast_vector = svld1(pg, non_broadcast_input_ptr + x);
                 const svbool_t   output_pg            = narrow_to_byte_predicate<sizeof(InputScalarType)>(pg);
                 OutputVectorType res{};
                 if(is_broadcast_input_2)
                 {
                     res = elementwise_comparison_op<typename sve_vector<InputScalarType>::type, typename sve_vector<OutputScalarType>::type>(pg, non_broadcast_vector, broadcast_vector, op);
                 }
                 else
                 {
                     res = elementwise_comparison_op<typename sve_vector<InputScalarType>::type, typename sve_vector<OutputScalarType>::type>(pg, broadcast_vector, non_broadcast_vector, op);
                 }
                 svst1(output_pg, output_ptr + x, res);

                 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
             {
                 const auto     in1       = svld1(pg, input1_ptr + x);
                 const auto     in2       = svld1(pg, input2_ptr + x);
                 const auto     res       = elementwise_comparison_op<typename sve_vector<InputScalarType>::type, typename sve_vector<OutputScalarType>::type>(pg, in1, in2, op);
                 const svbool_t output_pg = narrow_to_byte_predicate<sizeof(InputScalarType)>(pg);
                 svst1(output_pg, output_ptr + x, res);

                 x += svcnt<InputScalarType>();
                 pg = svwhilelt<InputScalarType>(x, window_end_x);
             }
             while(svptest_any(all_true_pg, pg));
         },
         input1, input2, output);
     }
 }

 template void elementwise_comparison_op<float32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const ComparisonOperation op, const Window &window);
 template void elementwise_comparison_op<float16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const ComparisonOperation op, const Window &window);
 template void elementwise_comparison_op<uint8_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const ComparisonOperation op, const Window &window);
 template void elementwise_comparison_op<int16_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const ComparisonOperation op, const Window &window);
 template void elementwise_comparison_op<int32_t>(const ITensor *in1, const ITensor *in2, ITensor *out, const ComparisonOperation op, const Window &window);

 template <>
 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 <>
 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 <>
 svint16_t elementwise_div<svint16_t>(svbool_t &pg, const svint16_t &a, const svint16_t &b)
 {
     ARM_COMPUTE_UNUSED(pg, a, b);
     ARM_COMPUTE_ERROR("Not supported");
 }

 } // namespace cpu
 } // namespace arm_compute
	/*
	* Copyright (c) 2021-2022 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/cpu/kernels/elementwise_binary/generic/sve/impl.h"
	#include "src/core/NEON/SVEMath.h"
	#include <arm_sve.h>

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

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

	const auto all_true_pg = svptrue<ScalarType>();

	// 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<ScalarType *>(output.ptr());
	const auto non_broadcast_input_ptr = reinterpret_cast<const ScalarType *>(non_broadcast_input.ptr());
	const ScalarType broadcast_value = reinterpret_cast<const ScalarType >(broadcast_input.ptr());
	const auto broadcast_vector = svdup_n(broadcast_value);

	int x = window_start_x;

	svbool_t pg = svwhilelt<ScalarType>(x, window_end_x);
	do
	{
	const auto non_broadcast_vector = svld1(pg, non_broadcast_input_ptr + x);
	VectorType res{};

	if(is_broadcast_input_2)
	{
	res = elementwise_arithmetic_op<typename sve_vector<ScalarType>::type>(pg, non_broadcast_vector, broadcast_vector, op);
	}
	else
	{
	res = elementwise_arithmetic_op<typename sve_vector<ScalarType>::type>(pg, broadcast_vector, non_broadcast_vector, op);
	}
	svst1(pg, output_ptr + x, res);

	x += svcnt<ScalarType>();
	pg = svwhilelt<ScalarType>(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<ScalarType *>(output.ptr());
	const auto input1_ptr = reinterpret_cast<const ScalarType *>(input1.ptr());
	const auto input2_ptr = reinterpret_cast<const ScalarType *>(input2.ptr());

	int x = window_start_x;

	svbool_t pg = svwhilelt<ScalarType>(x, window_end_x);
	do
	{
	const auto in1 = svld1(pg, input1_ptr + x);
	const auto in2 = svld1(pg, input2_ptr + x);
	const auto res = elementwise_arithmetic_op<typename sve_vector<ScalarType>::type>(pg, in1, in2, op);
	svst1(pg, output_ptr + x, res);

	x += svcnt<ScalarType>();
	pg = svwhilelt<ScalarType>(x, window_end_x);
	}
	while(svptest_any(all_true_pg, pg));
	},
	input1, input2, output);
	}
	}
	template void elementwise_arithmetic_op<float32_t>(const ITensor in1, const ITensor in2, ITensor *out, const ArithmeticOperation op, const Window &window);
	template void elementwise_arithmetic_op<float16_t>(const ITensor in1, const ITensor in2, ITensor *out, const ArithmeticOperation op, const Window &window);
	template void elementwise_arithmetic_op<int16_t>(const ITensor in1, const ITensor in2, ITensor *out, const ArithmeticOperation op, const Window &window);
	template void elementwise_arithmetic_op<int32_t>(const ITensor in1, const ITensor in2, ITensor *out, const ArithmeticOperation op, const Window &window);

	template <typename InputScalarType, typename OutputScalarType>
	void elementwise_comparison_op(const ITensor in1, const ITensor in2, ITensor *out, ComparisonOperation op, 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 OutputVectorType = typename sve_vector<OutputScalarType>::type;
	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());
	const auto broadcast_vector = svdup_n(broadcast_value);

	int x = window_start_x;

	svbool_t pg = svwhilelt<InputScalarType>(x, window_end_x);
	do
	{
	const auto non_broadcast_vector = svld1(pg, non_broadcast_input_ptr + x);
	const svbool_t output_pg = narrow_to_byte_predicate<sizeof(InputScalarType)>(pg);
	OutputVectorType res{};
	if(is_broadcast_input_2)
	{
	res = elementwise_comparison_op<typename sve_vector<InputScalarType>::type, typename sve_vector<OutputScalarType>::type>(pg, non_broadcast_vector, broadcast_vector, op);
	}
	else
	{
	res = elementwise_comparison_op<typename sve_vector<InputScalarType>::type, typename sve_vector<OutputScalarType>::type>(pg, broadcast_vector, non_broadcast_vector, op);
	}
	svst1(output_pg, output_ptr + x, res);

	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
	{
	const auto in1 = svld1(pg, input1_ptr + x);
	const auto in2 = svld1(pg, input2_ptr + x);
	const auto res = elementwise_comparison_op<typename sve_vector<InputScalarType>::type, typename sve_vector<OutputScalarType>::type>(pg, in1, in2, op);
	const svbool_t output_pg = narrow_to_byte_predicate<sizeof(InputScalarType)>(pg);
	svst1(output_pg, output_ptr + x, res);

	x += svcnt<InputScalarType>();
	pg = svwhilelt<InputScalarType>(x, window_end_x);
	}
	while(svptest_any(all_true_pg, pg));
	},
	input1, input2, output);
	}
	}

	template void elementwise_comparison_op<float32_t>(const ITensor in1, const ITensor in2, ITensor *out, const ComparisonOperation op, const Window &window);
	template void elementwise_comparison_op<float16_t>(const ITensor in1, const ITensor in2, ITensor *out, const ComparisonOperation op, const Window &window);
	template void elementwise_comparison_op<uint8_t>(const ITensor in1, const ITensor in2, ITensor *out, const ComparisonOperation op, const Window &window);
	template void elementwise_comparison_op<int16_t>(const ITensor in1, const ITensor in2, ITensor *out, const ComparisonOperation op, const Window &window);
	template void elementwise_comparison_op<int32_t>(const ITensor in1, const ITensor in2, ITensor *out, const ComparisonOperation op, const Window &window);

	template <>
	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 <>
	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 <>
	svint16_t elementwise_div<svint16_t>(svbool_t &pg, const svint16_t &a, const svint16_t &b)
	{
	ARM_COMPUTE_UNUSED(pg, a, b);
	ARM_COMPUTE_ERROR("Not supported");
	}

	} // namespace cpu
	} // namespace arm_compute