src/core/gpu/cl/kernels/ClPixelWiseMultiplicationKernel.cpp - ml/ComputeLibrary - Gitiles

 /*
  * 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/gpu/cl/kernels/ClPixelWiseMultiplicationKernel.h"

 #include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/CLKernelLibrary.h"
 #include "arm_compute/core/CL/ICLTensor.h"
 #include "arm_compute/core/CL/OpenCL.h"
 #include "arm_compute/core/TensorInfo.h"
 #include "src/core/CL/CLValidate.h"
 #include "src/core/helpers/AutoConfiguration.h"
 #include "src/core/helpers/WindowHelpers.h"
 #include "support/Cast.h"
 #include "support/StringSupport.h"

 namespace arm_compute
 {
 namespace opencl
 {
 namespace kernels
 {
 namespace
 {
 constexpr unsigned int num_elems_processed_per_iteration = 16;

 Status validate_arguments(const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float scale,
                           ConvertPolicy overflow_policy, RoundingPolicy rounding_policy, const ActivationLayerInfo &act_info)
 {
     ARM_COMPUTE_UNUSED(overflow_policy);
     ARM_COMPUTE_UNUSED(rounding_policy);

     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src1, src2, dst);
     ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src1);
     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::F32);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src2,
                                                          1,
                                                          DataType::U8, DataType::QASYMM8, DataType::QASYMM8_SIGNED,
                                                          DataType::S16, DataType::QSYMM16, DataType::F16,
                                                          DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(scale < 0, "Scale cannot be negative.");
     ARM_COMPUTE_RETURN_ERROR_ON(act_info.enabled() && !is_data_type_float(dst->data_type()));

     const TensorShape &out_shape = TensorShape::broadcast_shape(src1->tensor_shape(), src2->tensor_shape());

     ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible");

     // Validate in case of configured dst
     if(dst->total_size() > 0)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst,
                                                              1,
                                                              DataType::U8, DataType::QASYMM8, DataType::QASYMM8_SIGNED,
                                                              DataType::S16, DataType::QSYMM16, DataType::F16,
                                                              DataType::S32, DataType::F32);
         ARM_COMPUTE_RETURN_ERROR_ON_MSG(dst->data_type() == DataType::U8 && (src1->data_type() != DataType::U8 || src2->data_type() != DataType::U8),
                                         "Dst can only be U8 if both src are U8");
         ARM_COMPUTE_RETURN_ERROR_ON_MSG(dst->data_type() == DataType::QASYMM8 && (src1->data_type() != DataType::QASYMM8 || src2->data_type() != DataType::QASYMM8),
                                         "Dst can only be QASYMM8 if both src are QASYMM8");
         ARM_COMPUTE_RETURN_ERROR_ON_MSG(dst->data_type() == DataType::QASYMM8_SIGNED && (src1->data_type() != DataType::QASYMM8_SIGNED || src2->data_type() != DataType::QASYMM8_SIGNED),
                                         "Dst can only be QASYMM8_SIGNED if both src are QASYMM8_SIGNED");
         ARM_COMPUTE_RETURN_ERROR_ON_MSG(dst->data_type() == DataType::QSYMM16 && (src1->data_type() != DataType::QSYMM16 || src2->data_type() != DataType::QSYMM16),
                                         "Dst can only be QSYMM16 if both src are QSYMM16");
         ARM_COMPUTE_RETURN_ERROR_ON_MSG(dst->data_type() == DataType::S32 && (src1->data_type() != DataType::QSYMM16 || src2->data_type() != DataType::QSYMM16),
                                         "Dst can only be S32 if both src are QSYMM16");
         ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, dst->tensor_shape(), 0), "Wrong shape for dst");
     }

     return Status{};
 }

 std::pair<Status, Window> validate_and_configure_window(ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst)
 {
     const TensorShape &out_shape = TensorShape::broadcast_shape(src1->tensor_shape(), src2->tensor_shape());

     // Auto initialize dst if not initialized
     {
         set_shape_if_empty(*dst, out_shape);

         if(src1->data_type() == DataType::S16 || src2->data_type() == DataType::S16)
         {
             set_format_if_unknown(*dst, Format::S16);
         }
         else if(src1->data_type() == DataType::F32 || src2->data_type() == DataType::F32)
         {
             set_format_if_unknown(*dst, Format::F32);
         }
         else if(src1->data_type() == DataType::QASYMM8)
         {
             set_data_type_if_unknown(*dst, DataType::QASYMM8);
         }
         else if(src1->data_type() == DataType::QASYMM8_SIGNED)
         {
             set_data_type_if_unknown(*dst, DataType::QASYMM8_SIGNED);
         }
         else if(src1->data_type() == DataType::QSYMM16)
         {
             set_data_type_if_unknown(*dst, DataType::QSYMM16);
         }
     }

     Window win        = calculate_max_window(out_shape, Steps(num_elems_processed_per_iteration));
     Window win_input1 = win.broadcast_if_dimension_le_one(*src1);
     Window win_input2 = win.broadcast_if_dimension_le_one(*src2);

     AccessWindowHorizontal input1_access(src1, 0, num_elems_processed_per_iteration);
     AccessWindowHorizontal input2_access(src2, 0, num_elems_processed_per_iteration);
     AccessWindowHorizontal output_access(dst, 0, num_elems_processed_per_iteration);

     bool window_changed = update_window_and_padding(win_input1, input1_access)
                           || update_window_and_padding(win_input2, input2_access)
                           || update_window_and_padding(win, output_access);

     Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
     return std::make_pair(err, win);
 }

 BorderSize calc_border_size(ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst)
 {
     const unsigned int replicateSize = dst->dimension(0) - std::min(src1->dimension(0), src2->dimension(0));
     const unsigned int border        = std::min<unsigned int>(num_elems_processed_per_iteration - 1U, replicateSize);

     return BorderSize{ 0, border, 0, 0 };
 }
 } // namespace

 void ClPixelWiseMultiplicationKernel::configure(const CLCompileContext &compile_context, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, float scale,
                                                 ConvertPolicy overflow_policy, RoundingPolicy rounding_policy, const ActivationLayerInfo &act_info)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(src1, src2, dst);
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src1, src2, dst,
                                                   scale, overflow_policy, rounding_policy, act_info));

     // Calculate border size
     _border_size = calc_border_size(src1, src2, dst);

     // Configure kernel window
     auto win_config = validate_and_configure_window(src1, src2, dst);
     ARM_COMPUTE_ERROR_THROW_ON(win_config.first);

     int scale_int = -1;
     // Extract sign, exponent and mantissa
     int   exponent            = 0;
     float normalized_mantissa = std::frexp(scale, &exponent);
     // Use int scaling if factor is equal to 1/2^n for 0 <= n <= 15
     // frexp returns 0.5 as mantissa which means that the exponent will be in the range of -1 <= e <= 14
     // Moreover, it will be negative as we deal with 1/2^n
     if((normalized_mantissa == 0.5f) && (-14 <= exponent) && (exponent <= 1))
     {
         // Store the positive exponent. We know that we compute 1/2^n
         // Additionally we need to subtract 1 to compensate that frexp used a mantissa of 0.5
         scale_int = std::abs(exponent - 1);
     }

     std::string acc_type;
     // Check if it has float src and dst
     if(is_data_type_float(src1->data_type()) || is_data_type_float(src2->data_type()))
     {
         scale_int = -1;
         acc_type  = (src1->data_type() == DataType::F32 || src2->data_type() == DataType::F32) ? "float" : "half";
     }
     else
     {
         if(src1->element_size() == 2 || src2->element_size() == 2)
         {
             // Use 32-bit accumulator for 16-bit input
             acc_type = "int";
         }
         else
         {
             // Use 16-bit accumulator for 8-bit input
             acc_type = "ushort";
         }
     }

     const bool is_quantized = is_data_type_quantized(src1->data_type());

     // Set kernel build options
     std::string    kernel_name = "pixelwise_mul";
     CLBuildOptions build_opts;
     build_opts.add_option("-DDATA_TYPE_IN1=" + get_cl_type_from_data_type(src1->data_type()));
     build_opts.add_option("-DDATA_TYPE_IN2=" + get_cl_type_from_data_type(src2->data_type()));
     build_opts.add_option("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(dst->data_type()));
     build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration));
     if(is_quantized && (dst->data_type() != DataType::S32))
     {
         const UniformQuantizationInfo iq1_info = src1->quantization_info().uniform();
         const UniformQuantizationInfo iq2_info = src2->quantization_info().uniform();
         const UniformQuantizationInfo oq_info  = dst->quantization_info().uniform();

         build_opts.add_option_if(is_data_type_quantized_asymmetric(src1->data_type()),
                                  "-DOFFSET_IN1=" + support::cpp11::to_string(iq1_info.offset));
         build_opts.add_option_if(is_data_type_quantized_asymmetric(src2->data_type()),
                                  "-DOFFSET_IN2=" + support::cpp11::to_string(iq2_info.offset));
         build_opts.add_option_if(is_data_type_quantized_asymmetric(dst->data_type()),
                                  "-DOFFSET_OUT=" + support::cpp11::to_string(oq_info.offset));
         build_opts.add_option("-DSCALE_IN1=" + float_to_string_with_full_precision(iq1_info.scale));
         build_opts.add_option("-DSCALE_IN2=" + float_to_string_with_full_precision(iq2_info.scale));
         build_opts.add_option("-DSCALE_OUT=" + float_to_string_with_full_precision(oq_info.scale));
         kernel_name += "_quantized";
     }
     else
     {
         kernel_name += (scale_int >= 0) ? "_int" : "_float";
         build_opts.add_option_if_else(overflow_policy == ConvertPolicy::WRAP || is_data_type_float(dst->data_type()), "-DWRAP", "-DSATURATE");
         build_opts.add_option_if_else(rounding_policy == RoundingPolicy::TO_ZERO, "-DROUND=_rtz", "-DROUND=_rte");
         build_opts.add_option("-DACC_DATA_TYPE=" + acc_type);
         if(act_info.enabled())
         {
             build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(act_info.activation())));
             build_opts.add_option("-DA_VAL=" + float_to_string_with_full_precision(act_info.a()));
             build_opts.add_option("-DB_VAL=" + float_to_string_with_full_precision(act_info.b()));
         }
     }

     // Create kernel
     _kernel = create_kernel(compile_context, kernel_name, build_opts.options());

     // Set scale argument
     unsigned int idx = 3 * num_arguments_per_3D_tensor(); // Skip the src and dst parameters

     if(scale_int >= 0 && !is_quantized)
     {
         _kernel.setArg(idx++, scale_int);
     }
     else
     {
         _kernel.setArg(idx++, scale);
     }

     ICLKernel::configure_internal(win_config.second);
 }

 Status ClPixelWiseMultiplicationKernel::validate(const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, float scale,
                                                  ConvertPolicy overflow_policy, RoundingPolicy rounding_policy, const ActivationLayerInfo &act_info)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(src1, src2, dst);
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src1, src2, dst, scale, overflow_policy, rounding_policy, act_info));
     ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src1->clone().get(), src2->clone().get(), dst->clone().get()).first);

     return Status{};
 }

 void ClPixelWiseMultiplicationKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);

     const auto src_0 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
     const auto src_1 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
     auto       dst   = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));

     const TensorShape &in_shape1 = src_0->info()->tensor_shape();
     const TensorShape &in_shape2 = src_1->info()->tensor_shape();
     const TensorShape &out_shape = dst->info()->tensor_shape();

     bool can_collapse = true;
     if(std::min(in_shape1.total_size(), in_shape2.total_size()) > 1)
     {
         can_collapse = (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ);
         for(size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); ++d)
         {
             can_collapse = (in_shape1[d] == in_shape2[d]);
         }
     }

     bool   has_collapsed = false;
     Window collapsed     = can_collapse ? window.collapse_if_possible(ICLKernel::window(), Window::DimZ, &has_collapsed) : window;

     const TensorShape &in_shape1_collapsed = has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1;
     const TensorShape &in_shape2_collapsed = has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2;

     Window slice        = collapsed.first_slice_window_3D();
     Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed);
     Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed);

     do
     {
         unsigned int idx = 0;
         add_3D_tensor_argument(idx, src_0, slice_input1);
         add_3D_tensor_argument(idx, src_1, slice_input2);
         add_3D_tensor_argument(idx, dst, slice);
         enqueue(queue, *this, slice, lws_hint());

         ARM_COMPUTE_UNUSED(collapsed.slide_window_slice_3D(slice_input1));
         ARM_COMPUTE_UNUSED(collapsed.slide_window_slice_3D(slice_input2));
     }
     while(collapsed.slide_window_slice_3D(slice));
 }

 BorderSize ClPixelWiseMultiplicationKernel::border_size() const
 {
     return _border_size;
 }

 namespace
 {
 constexpr unsigned int num_elems_processed_per_iteration_complex = 1;

 Status validate_arguments_complex(const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, const ActivationLayerInfo &act_info)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src1, 2, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src2, 2, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src1, src2);

     const TensorShape &out_shape = TensorShape::broadcast_shape(src1->tensor_shape(), src2->tensor_shape());

     ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible");
     ARM_COMPUTE_RETURN_ERROR_ON(act_info.enabled() && !is_data_type_float(dst->data_type()));

     // Validate in case of configured dst
     if(dst->total_size() > 0)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, 2, DataType::F16, DataType::F32);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src1, dst);
         ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, dst->tensor_shape(), 0), "Wrong shape for dst");
     }

     return Status{};
 }

 std::pair<Status, Window> validate_and_configure_window_complex(ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst)
 {
     const TensorShape &out_shape = TensorShape::broadcast_shape(src1->tensor_shape(), src2->tensor_shape());

     // Auto initialize dst if not initialized
     const TensorInfo out_info(out_shape, src1->num_channels(), src1->data_type());
     auto_init_if_empty(*dst, out_info);

     Window win        = calculate_max_window(out_shape, Steps(num_elems_processed_per_iteration_complex));
     Window win_input1 = win.broadcast_if_dimension_le_one(*src1);
     Window win_input2 = win.broadcast_if_dimension_le_one(*src2);

     AccessWindowHorizontal input1_access(src1, 0, num_elems_processed_per_iteration_complex);
     AccessWindowHorizontal input2_access(src2, 0, num_elems_processed_per_iteration_complex);
     AccessWindowHorizontal output_access(dst, 0, num_elems_processed_per_iteration_complex);

     bool window_changed = update_window_and_padding(win_input1, input1_access)
                           || update_window_and_padding(win_input2, input2_access)
                           || update_window_and_padding(win, output_access);

     Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
     return std::make_pair(err, win);
 }
 } // namespace

 void ClComplexPixelWiseMultiplicationKernel::configure(const CLCompileContext &compile_context, ITensorInfo *src1, ITensorInfo *src2, ITensorInfo *dst, const ActivationLayerInfo &act_info)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(src1, src2, dst);
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_complex(src1, src2, dst, act_info));

     // Calculate border size
     _border_size = calc_border_size(src1, src2, dst);

     // Configure kernel window
     auto win_config = validate_and_configure_window_complex(src1, src2, dst);
     ARM_COMPUTE_ERROR_THROW_ON(win_config.first);

     CLBuildOptions build_opts;
     build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(dst->data_type()));
     if(act_info.enabled())
     {
         build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(act_info.activation())));
         build_opts.add_option("-DA_VAL=" + float_to_string_with_full_precision(act_info.a()));
         build_opts.add_option("-DB_VAL=" + float_to_string_with_full_precision(act_info.b()));
     }

     // Create kernel
     _kernel = create_kernel(compile_context, "pixelwise_mul_complex", build_opts.options());

     ICLKernel::configure_internal(win_config.second);
 }

 Status ClComplexPixelWiseMultiplicationKernel::validate(const ITensorInfo *src1, const ITensorInfo *src2, const ITensorInfo *dst, const ActivationLayerInfo &act_info)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(src1, src2, dst);
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_complex(src1, src2, dst, act_info));
     ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_complex(src1->clone().get(), src2->clone().get(), dst->clone().get()).first);

     return Status{};
 }

 void ClComplexPixelWiseMultiplicationKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);

     const auto src_0 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
     const auto src_1 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
     auto       dst   = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));

     const TensorShape &in_shape1 = src_0->info()->tensor_shape();
     const TensorShape &in_shape2 = src_1->info()->tensor_shape();
     const TensorShape &out_shape = dst->info()->tensor_shape();

     bool can_collapse = true;
     if(std::min(in_shape1.total_size(), in_shape2.total_size()) > 1)
     {
         can_collapse = (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ);
         for(size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); ++d)
         {
             can_collapse = (in_shape1[d] == in_shape2[d]);
         }
     }

     bool   has_collapsed = false;
     Window collapsed     = can_collapse ? window.collapse_if_possible(ICLKernel::window(), Window::DimZ, &has_collapsed) : window;

     const TensorShape &in_shape1_collapsed = has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1;
     const TensorShape &in_shape2_collapsed = has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2;

     Window slice        = collapsed.first_slice_window_3D();
     Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed);
     Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed);

     do
     {
         unsigned int idx = 0;
         add_3D_tensor_argument(idx, src_0, slice_input1);
         add_3D_tensor_argument(idx, src_1, slice_input2);
         add_3D_tensor_argument(idx, dst, slice);
         enqueue(queue, *this, slice, lws_hint());

         ARM_COMPUTE_UNUSED(collapsed.slide_window_slice_3D(slice_input1));
         ARM_COMPUTE_UNUSED(collapsed.slide_window_slice_3D(slice_input2));
     }
     while(collapsed.slide_window_slice_3D(slice));
 }

 BorderSize ClComplexPixelWiseMultiplicationKernel::border_size() const
 {
     return _border_size;
 }
 } // namespace kernels
 } // namespace opencl
 } // namespace arm_compute
	/*
	* 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/gpu/cl/kernels/ClPixelWiseMultiplicationKernel.h"

	#include "arm_compute/core/CL/CLHelpers.h"
	#include "arm_compute/core/CL/CLKernelLibrary.h"
	#include "arm_compute/core/CL/ICLTensor.h"
	#include "arm_compute/core/CL/OpenCL.h"
	#include "arm_compute/core/TensorInfo.h"
	#include "src/core/CL/CLValidate.h"
	#include "src/core/helpers/AutoConfiguration.h"
	#include "src/core/helpers/WindowHelpers.h"
	#include "support/Cast.h"
	#include "support/StringSupport.h"

	namespace arm_compute
	{
	namespace opencl
	{
	namespace kernels
	{
	namespace
	{
	constexpr unsigned int num_elems_processed_per_iteration = 16;

	Status validate_arguments(const ITensorInfo src1, const ITensorInfo src2, const ITensorInfo *dst, float scale,
	ConvertPolicy overflow_policy, RoundingPolicy rounding_policy, const ActivationLayerInfo &act_info)
	{
	ARM_COMPUTE_UNUSED(overflow_policy);
	ARM_COMPUTE_UNUSED(rounding_policy);

	ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src1, src2, dst);
	ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(src1);
	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::F32);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src2,
	1,
	DataType::U8, DataType::QASYMM8, DataType::QASYMM8_SIGNED,
	DataType::S16, DataType::QSYMM16, DataType::F16,
	DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(scale < 0, "Scale cannot be negative.");
	ARM_COMPUTE_RETURN_ERROR_ON(act_info.enabled() && !is_data_type_float(dst->data_type()));

	const TensorShape &out_shape = TensorShape::broadcast_shape(src1->tensor_shape(), src2->tensor_shape());

	ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible");

	// Validate in case of configured dst
	if(dst->total_size() > 0)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst,
	1,
	DataType::U8, DataType::QASYMM8, DataType::QASYMM8_SIGNED,
	DataType::S16, DataType::QSYMM16, DataType::F16,
	DataType::S32, DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(dst->data_type() == DataType::U8 && (src1->data_type() != DataType::U8 \|\| src2->data_type() != DataType::U8),
	"Dst can only be U8 if both src are U8");
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(dst->data_type() == DataType::QASYMM8 && (src1->data_type() != DataType::QASYMM8 \|\| src2->data_type() != DataType::QASYMM8),
	"Dst can only be QASYMM8 if both src are QASYMM8");
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(dst->data_type() == DataType::QASYMM8_SIGNED && (src1->data_type() != DataType::QASYMM8_SIGNED \|\| src2->data_type() != DataType::QASYMM8_SIGNED),
	"Dst can only be QASYMM8_SIGNED if both src are QASYMM8_SIGNED");
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(dst->data_type() == DataType::QSYMM16 && (src1->data_type() != DataType::QSYMM16 \|\| src2->data_type() != DataType::QSYMM16),
	"Dst can only be QSYMM16 if both src are QSYMM16");
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(dst->data_type() == DataType::S32 && (src1->data_type() != DataType::QSYMM16 \|\| src2->data_type() != DataType::QSYMM16),
	"Dst can only be S32 if both src are QSYMM16");
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, dst->tensor_shape(), 0), "Wrong shape for dst");
	}

	return Status{};
	}

	std::pair<Status, Window> validate_and_configure_window(ITensorInfo src1, ITensorInfo src2, ITensorInfo *dst)
	{
	const TensorShape &out_shape = TensorShape::broadcast_shape(src1->tensor_shape(), src2->tensor_shape());

	// Auto initialize dst if not initialized
	{
	set_shape_if_empty(*dst, out_shape);

	if(src1->data_type() == DataType::S16 \|\| src2->data_type() == DataType::S16)
	{
	set_format_if_unknown(*dst, Format::S16);
	}
	else if(src1->data_type() == DataType::F32 \|\| src2->data_type() == DataType::F32)
	{
	set_format_if_unknown(*dst, Format::F32);
	}
	else if(src1->data_type() == DataType::QASYMM8)
	{
	set_data_type_if_unknown(*dst, DataType::QASYMM8);
	}
	else if(src1->data_type() == DataType::QASYMM8_SIGNED)
	{
	set_data_type_if_unknown(*dst, DataType::QASYMM8_SIGNED);
	}
	else if(src1->data_type() == DataType::QSYMM16)
	{
	set_data_type_if_unknown(*dst, DataType::QSYMM16);
	}
	}

	Window win = calculate_max_window(out_shape, Steps(num_elems_processed_per_iteration));
	Window win_input1 = win.broadcast_if_dimension_le_one(*src1);
	Window win_input2 = win.broadcast_if_dimension_le_one(*src2);

	AccessWindowHorizontal input1_access(src1, 0, num_elems_processed_per_iteration);
	AccessWindowHorizontal input2_access(src2, 0, num_elems_processed_per_iteration);
	AccessWindowHorizontal output_access(dst, 0, num_elems_processed_per_iteration);

	bool window_changed = update_window_and_padding(win_input1, input1_access)
	\|\| update_window_and_padding(win_input2, input2_access)
	\|\| update_window_and_padding(win, output_access);

	Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
	return std::make_pair(err, win);
	}

	BorderSize calc_border_size(ITensorInfo src1, ITensorInfo src2, ITensorInfo *dst)
	{
	const unsigned int replicateSize = dst->dimension(0) - std::min(src1->dimension(0), src2->dimension(0));
	const unsigned int border = std::min<unsigned int>(num_elems_processed_per_iteration - 1U, replicateSize);

	return BorderSize{ 0, border, 0, 0 };
	}
	} // namespace

	void ClPixelWiseMultiplicationKernel::configure(const CLCompileContext &compile_context, ITensorInfo src1, ITensorInfo src2, ITensorInfo *dst, float scale,
	ConvertPolicy overflow_policy, RoundingPolicy rounding_policy, const ActivationLayerInfo &act_info)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(src1, src2, dst);
	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src1, src2, dst,
	scale, overflow_policy, rounding_policy, act_info));

	// Calculate border size
	_border_size = calc_border_size(src1, src2, dst);

	// Configure kernel window
	auto win_config = validate_and_configure_window(src1, src2, dst);
	ARM_COMPUTE_ERROR_THROW_ON(win_config.first);

	int scale_int = -1;
	// Extract sign, exponent and mantissa
	int exponent = 0;
	float normalized_mantissa = std::frexp(scale, &exponent);
	// Use int scaling if factor is equal to 1/2^n for 0 <= n <= 15
	// frexp returns 0.5 as mantissa which means that the exponent will be in the range of -1 <= e <= 14
	// Moreover, it will be negative as we deal with 1/2^n
	if((normalized_mantissa == 0.5f) && (-14 <= exponent) && (exponent <= 1))
	{
	// Store the positive exponent. We know that we compute 1/2^n
	// Additionally we need to subtract 1 to compensate that frexp used a mantissa of 0.5
	scale_int = std::abs(exponent - 1);
	}

	std::string acc_type;
	// Check if it has float src and dst
	if(is_data_type_float(src1->data_type()) \|\| is_data_type_float(src2->data_type()))
	{
	scale_int = -1;
	acc_type = (src1->data_type() == DataType::F32 \|\| src2->data_type() == DataType::F32) ? "float" : "half";
	}
	else
	{
	if(src1->element_size() == 2 \|\| src2->element_size() == 2)
	{
	// Use 32-bit accumulator for 16-bit input
	acc_type = "int";
	}
	else
	{
	// Use 16-bit accumulator for 8-bit input
	acc_type = "ushort";
	}
	}

	const bool is_quantized = is_data_type_quantized(src1->data_type());

	// Set kernel build options
	std::string kernel_name = "pixelwise_mul";
	CLBuildOptions build_opts;
	build_opts.add_option("-DDATA_TYPE_IN1=" + get_cl_type_from_data_type(src1->data_type()));
	build_opts.add_option("-DDATA_TYPE_IN2=" + get_cl_type_from_data_type(src2->data_type()));
	build_opts.add_option("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(dst->data_type()));
	build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration));
	if(is_quantized && (dst->data_type() != DataType::S32))
	{
	const UniformQuantizationInfo iq1_info = src1->quantization_info().uniform();
	const UniformQuantizationInfo iq2_info = src2->quantization_info().uniform();
	const UniformQuantizationInfo oq_info = dst->quantization_info().uniform();

	build_opts.add_option_if(is_data_type_quantized_asymmetric(src1->data_type()),
	"-DOFFSET_IN1=" + support::cpp11::to_string(iq1_info.offset));
	build_opts.add_option_if(is_data_type_quantized_asymmetric(src2->data_type()),
	"-DOFFSET_IN2=" + support::cpp11::to_string(iq2_info.offset));
	build_opts.add_option_if(is_data_type_quantized_asymmetric(dst->data_type()),
	"-DOFFSET_OUT=" + support::cpp11::to_string(oq_info.offset));
	build_opts.add_option("-DSCALE_IN1=" + float_to_string_with_full_precision(iq1_info.scale));
	build_opts.add_option("-DSCALE_IN2=" + float_to_string_with_full_precision(iq2_info.scale));
	build_opts.add_option("-DSCALE_OUT=" + float_to_string_with_full_precision(oq_info.scale));
	kernel_name += "_quantized";
	}
	else
	{
	kernel_name += (scale_int >= 0) ? "_int" : "_float";
	build_opts.add_option_if_else(overflow_policy == ConvertPolicy::WRAP \|\| is_data_type_float(dst->data_type()), "-DWRAP", "-DSATURATE");
	build_opts.add_option_if_else(rounding_policy == RoundingPolicy::TO_ZERO, "-DROUND=_rtz", "-DROUND=_rte");
	build_opts.add_option("-DACC_DATA_TYPE=" + acc_type);
	if(act_info.enabled())
	{
	build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(act_info.activation())));
	build_opts.add_option("-DA_VAL=" + float_to_string_with_full_precision(act_info.a()));
	build_opts.add_option("-DB_VAL=" + float_to_string_with_full_precision(act_info.b()));
	}
	}

	// Create kernel
	_kernel = create_kernel(compile_context, kernel_name, build_opts.options());

	// Set scale argument
	unsigned int idx = 3 * num_arguments_per_3D_tensor(); // Skip the src and dst parameters

	if(scale_int >= 0 && !is_quantized)
	{
	_kernel.setArg(idx++, scale_int);
	}
	else
	{
	_kernel.setArg(idx++, scale);
	}

	ICLKernel::configure_internal(win_config.second);
	}

	Status ClPixelWiseMultiplicationKernel::validate(const ITensorInfo src1, const ITensorInfo src2, const ITensorInfo *dst, float scale,
	ConvertPolicy overflow_policy, RoundingPolicy rounding_policy, const ActivationLayerInfo &act_info)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(src1, src2, dst);
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src1, src2, dst, scale, overflow_policy, rounding_policy, act_info));
	ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src1->clone().get(), src2->clone().get(), dst->clone().get()).first);

	return Status{};
	}

	void ClPixelWiseMultiplicationKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
	{
	ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
	ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);

	const auto src_0 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
	const auto src_1 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
	auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));

	const TensorShape &in_shape1 = src_0->info()->tensor_shape();
	const TensorShape &in_shape2 = src_1->info()->tensor_shape();
	const TensorShape &out_shape = dst->info()->tensor_shape();

	bool can_collapse = true;
	if(std::min(in_shape1.total_size(), in_shape2.total_size()) > 1)
	{
	can_collapse = (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ);
	for(size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); ++d)
	{
	can_collapse = (in_shape1[d] == in_shape2[d]);
	}
	}

	bool has_collapsed = false;
	Window collapsed = can_collapse ? window.collapse_if_possible(ICLKernel::window(), Window::DimZ, &has_collapsed) : window;

	const TensorShape &in_shape1_collapsed = has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1;
	const TensorShape &in_shape2_collapsed = has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2;

	Window slice = collapsed.first_slice_window_3D();
	Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed);
	Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed);

	do
	{
	unsigned int idx = 0;
	add_3D_tensor_argument(idx, src_0, slice_input1);
	add_3D_tensor_argument(idx, src_1, slice_input2);
	add_3D_tensor_argument(idx, dst, slice);
	enqueue(queue, *this, slice, lws_hint());

	ARM_COMPUTE_UNUSED(collapsed.slide_window_slice_3D(slice_input1));
	ARM_COMPUTE_UNUSED(collapsed.slide_window_slice_3D(slice_input2));
	}
	while(collapsed.slide_window_slice_3D(slice));
	}

	BorderSize ClPixelWiseMultiplicationKernel::border_size() const
	{
	return _border_size;
	}

	namespace
	{
	constexpr unsigned int num_elems_processed_per_iteration_complex = 1;

	Status validate_arguments_complex(const ITensorInfo src1, const ITensorInfo src2, const ITensorInfo *dst, const ActivationLayerInfo &act_info)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src1, 2, DataType::F16, DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src2, 2, DataType::F16, DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src1, src2);

	const TensorShape &out_shape = TensorShape::broadcast_shape(src1->tensor_shape(), src2->tensor_shape());

	ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible");
	ARM_COMPUTE_RETURN_ERROR_ON(act_info.enabled() && !is_data_type_float(dst->data_type()));

	// Validate in case of configured dst
	if(dst->total_size() > 0)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(dst, 2, DataType::F16, DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src1, dst);
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, dst->tensor_shape(), 0), "Wrong shape for dst");
	}

	return Status{};
	}

	std::pair<Status, Window> validate_and_configure_window_complex(ITensorInfo src1, ITensorInfo src2, ITensorInfo *dst)
	{
	const TensorShape &out_shape = TensorShape::broadcast_shape(src1->tensor_shape(), src2->tensor_shape());

	// Auto initialize dst if not initialized
	const TensorInfo out_info(out_shape, src1->num_channels(), src1->data_type());
	auto_init_if_empty(*dst, out_info);

	Window win = calculate_max_window(out_shape, Steps(num_elems_processed_per_iteration_complex));
	Window win_input1 = win.broadcast_if_dimension_le_one(*src1);
	Window win_input2 = win.broadcast_if_dimension_le_one(*src2);

	AccessWindowHorizontal input1_access(src1, 0, num_elems_processed_per_iteration_complex);
	AccessWindowHorizontal input2_access(src2, 0, num_elems_processed_per_iteration_complex);
	AccessWindowHorizontal output_access(dst, 0, num_elems_processed_per_iteration_complex);

	bool window_changed = update_window_and_padding(win_input1, input1_access)
	\|\| update_window_and_padding(win_input2, input2_access)
	\|\| update_window_and_padding(win, output_access);

	Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
	return std::make_pair(err, win);
	}
	} // namespace

	void ClComplexPixelWiseMultiplicationKernel::configure(const CLCompileContext &compile_context, ITensorInfo src1, ITensorInfo src2, ITensorInfo *dst, const ActivationLayerInfo &act_info)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(src1, src2, dst);
	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_complex(src1, src2, dst, act_info));

	// Calculate border size
	_border_size = calc_border_size(src1, src2, dst);

	// Configure kernel window
	auto win_config = validate_and_configure_window_complex(src1, src2, dst);
	ARM_COMPUTE_ERROR_THROW_ON(win_config.first);

	CLBuildOptions build_opts;
	build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(dst->data_type()));
	if(act_info.enabled())
	{
	build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(act_info.activation())));
	build_opts.add_option("-DA_VAL=" + float_to_string_with_full_precision(act_info.a()));
	build_opts.add_option("-DB_VAL=" + float_to_string_with_full_precision(act_info.b()));
	}

	// Create kernel
	_kernel = create_kernel(compile_context, "pixelwise_mul_complex", build_opts.options());

	ICLKernel::configure_internal(win_config.second);
	}

	Status ClComplexPixelWiseMultiplicationKernel::validate(const ITensorInfo src1, const ITensorInfo src2, const ITensorInfo *dst, const ActivationLayerInfo &act_info)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(src1, src2, dst);
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_complex(src1, src2, dst, act_info));
	ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_complex(src1->clone().get(), src2->clone().get(), dst->clone().get()).first);

	return Status{};
	}

	void ClComplexPixelWiseMultiplicationKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue)
	{
	ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
	ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);

	const auto src_0 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0));
	const auto src_1 = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1));
	auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));

	const TensorShape &in_shape1 = src_0->info()->tensor_shape();
	const TensorShape &in_shape2 = src_1->info()->tensor_shape();
	const TensorShape &out_shape = dst->info()->tensor_shape();

	bool can_collapse = true;
	if(std::min(in_shape1.total_size(), in_shape2.total_size()) > 1)
	{
	can_collapse = (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ);
	for(size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); ++d)
	{
	can_collapse = (in_shape1[d] == in_shape2[d]);
	}
	}

	bool has_collapsed = false;
	Window collapsed = can_collapse ? window.collapse_if_possible(ICLKernel::window(), Window::DimZ, &has_collapsed) : window;

	const TensorShape &in_shape1_collapsed = has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1;
	const TensorShape &in_shape2_collapsed = has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2;

	Window slice = collapsed.first_slice_window_3D();
	Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed);
	Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed);

	do
	{
	unsigned int idx = 0;
	add_3D_tensor_argument(idx, src_0, slice_input1);
	add_3D_tensor_argument(idx, src_1, slice_input2);
	add_3D_tensor_argument(idx, dst, slice);
	enqueue(queue, *this, slice, lws_hint());

	ARM_COMPUTE_UNUSED(collapsed.slide_window_slice_3D(slice_input1));
	ARM_COMPUTE_UNUSED(collapsed.slide_window_slice_3D(slice_input2));
	}
	while(collapsed.slide_window_slice_3D(slice));
	}

	BorderSize ClComplexPixelWiseMultiplicationKernel::border_size() const
	{
	return _border_size;
	}
	} // namespace kernels
	} // namespace opencl
	} // namespace arm_compute