src/gpu/cl/kernels/ClMulKernel.cpp - platform/external/ComputeLibrary - Git at Google


            
            /*

            
             * 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/gpu/cl/kernels/ClMulKernel.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

            
            {

            
            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::S32,

            
                                                                     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::S32,

            
                                                                     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()));


                // Check whether it is in_place calculation

            
                const bool in_place      = (src1 == dst) || (src2 == dst);

            
                const bool src1_in_place = in_place && (src1 == dst);


                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((src1->data_type() == DataType::S32 || src2->data_type() == DataType::S32) && (dst->data_type() != DataType::S32),

            
                                                    "Dst must be S32 if source tensors are S32");

            
                    if(in_place)

            
                    {

            
                        ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, src1_in_place ? src1->tensor_shape() : src2->tensor_shape(), 0),

            
                                                        "Wrong shape for dst, cannot do in_place calculation");

            
                    }

            
                    else

            
                    {

            
                        ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, dst->tensor_shape(), 0),

            
                                                        "Wrong shape for dst");

            
                    }

            
                }


                return Status{};

            
            }

            
            } // namespace


            ClMulKernel::ClMulKernel()

            
            {

            
                _type = CLKernelType::ELEMENTWISE;

            
            }


            void ClMulKernel::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));


                auto padding_info = get_padding_info({ src1, src2, dst });


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

            
                auto_init_if_empty(*dst, src1->clone()->set_tensor_shape(out_shape));


                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() == 4 || src2->element_size() == 4)

            
                    {

            
                        // use 64 bit accumulator for 32-bit input

            
                        acc_type = "long";

            
                    }

            
                    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());

            
                const unsigned int vec_size          = adjust_vec_size(16 / dst->element_size(), dst->dimension(0));

            
                const unsigned int vec_size_leftover = dst->dimension(0) % vec_size;


                // 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_IN1=" + ((dst->dimension(0) != 1 && src1->dimension(0) == 1) ? "1" : support::cpp11::to_string(vec_size)));

            
                build_opts.add_option("-DVEC_SIZE_IN2=" + ((dst->dimension(0) != 1 && src2->dimension(0) == 1) ? "1" : support::cpp11::to_string(vec_size)));

            
                build_opts.add_option("-DVEC_SIZE_OUT=" + support::cpp11::to_string(vec_size));

            
                build_opts.add_option("-DVEC_SIZE_LEFTOVER=" + support::cpp11::to_string(vec_size_leftover));

            
                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()));

            
                    }

            
                }


                // Check whether it is in_place calculation

            
                const bool in_place      = (src1 == dst) || (src2 == dst);

            
                const bool src1_in_place = in_place && (src1 == dst);

            
                build_opts.add_option_if(in_place, "-DIN_PLACE");

            
                build_opts.add_option_if(src1_in_place, "-DSRC1_IN_PLACE");


                // Create kernel

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


                // Set scale argument

            
                unsigned int idx = (in_place ? 2 : 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);

            
                }


                Window win = calculate_max_window(*dst, Steps(vec_size));

            
                ICLKernel::configure_internal(win);


                ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));


                // Set config_id for enabling LWS tuning

            
                _config_id = kernel_name;

            
                _config_id += "_";

            
                _config_id += lower_string(string_from_data_type(dst->data_type()));

            
                _config_id += "_";

            
                _config_id += support::cpp11::to_string(src1->dimension(0));

            
                _config_id += "_";

            
                _config_id += support::cpp11::to_string(src1->dimension(1));

            
                _config_id += "_";

            
                _config_id += support::cpp11::to_string(src1->dimension(2));

            
                _config_id += "_";

            
                _config_id += support::cpp11::to_string(src2->dimension(0));

            
                _config_id += "_";

            
                _config_id += support::cpp11::to_string(src2->dimension(1));

            
                _config_id += "_";

            
                _config_id += support::cpp11::to_string(src2->dimension(2));

            
                _config_id += "_";

            
                _config_id += support::cpp11::to_string(dst->dimension(0));

            
                _config_id += "_";

            
                _config_id += support::cpp11::to_string(dst->dimension(1));

            
                _config_id += "_";

            
                _config_id += support::cpp11::to_string(dst->dimension(2));

            
            }


            Status ClMulKernel::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));


                return Status{};

            
            }


            void ClMulKernel::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));


                ARM_COMPUTE_ERROR_ON_NULLPTR(src_0, src_1, 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);


                // Check whether it is in_place calculation

            
                const bool in_place = (src_0 == dst) || (src_1 == dst);

            
                do

            
                {

            
                    unsigned int idx = 0;

            
                    add_3D_tensor_argument(idx, src_0, slice_input1);

            
                    add_3D_tensor_argument(idx, src_1, slice_input2);

            
                    if(!in_place)

            
                    {

            
                        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));

            
            }


            namespace

            
            {

            
            constexpr unsigned int vec_size_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{};

            
            }

            
            } // namespace


            ClComplexMulKernel::ClComplexMulKernel()

            
            {

            
                _type = CLKernelType::ELEMENTWISE;

            
            }


            void ClComplexMulKernel::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));


                auto padding_info = get_padding_info({ src1, src2, dst });


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

            
                auto_init_if_empty(*dst, src1->clone()->set_tensor_shape(out_shape));


                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());


                Window win = calculate_max_window(*dst, Steps(vec_size_complex));

            
                ICLKernel::configure_internal(win);


                ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));

            
            }


            Status ClComplexMulKernel::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));


                return Status{};

            
            }


            void ClComplexMulKernel::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));

            
            }

            
            } // 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/gpu/cl/kernels/ClMulKernel.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
	{
	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::S32,
	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::S32,
	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()));

	// Check whether it is in_place calculation
	const bool in_place = (src1 == dst) \|\| (src2 == dst);
	const bool src1_in_place = in_place && (src1 == dst);

	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((src1->data_type() == DataType::S32 \|\| src2->data_type() == DataType::S32) && (dst->data_type() != DataType::S32),
	"Dst must be S32 if source tensors are S32");
	if(in_place)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, src1_in_place ? src1->tensor_shape() : src2->tensor_shape(), 0),
	"Wrong shape for dst, cannot do in_place calculation");
	}
	else
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, dst->tensor_shape(), 0),
	"Wrong shape for dst");
	}
	}

	return Status{};
	}
	} // namespace

	ClMulKernel::ClMulKernel()
	{
	_type = CLKernelType::ELEMENTWISE;
	}

	void ClMulKernel::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));

	auto padding_info = get_padding_info({ src1, src2, dst });

	const TensorShape &out_shape = TensorShape::broadcast_shape(src1->tensor_shape(), src2->tensor_shape());
	auto_init_if_empty(*dst, src1->clone()->set_tensor_shape(out_shape));

	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() == 4 \|\| src2->element_size() == 4)
	{
	// use 64 bit accumulator for 32-bit input
	acc_type = "long";
	}
	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());
	const unsigned int vec_size = adjust_vec_size(16 / dst->element_size(), dst->dimension(0));
	const unsigned int vec_size_leftover = dst->dimension(0) % vec_size;

	// 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_IN1=" + ((dst->dimension(0) != 1 && src1->dimension(0) == 1) ? "1" : support::cpp11::to_string(vec_size)));
	build_opts.add_option("-DVEC_SIZE_IN2=" + ((dst->dimension(0) != 1 && src2->dimension(0) == 1) ? "1" : support::cpp11::to_string(vec_size)));
	build_opts.add_option("-DVEC_SIZE_OUT=" + support::cpp11::to_string(vec_size));
	build_opts.add_option("-DVEC_SIZE_LEFTOVER=" + support::cpp11::to_string(vec_size_leftover));
	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()));
	}
	}

	// Check whether it is in_place calculation
	const bool in_place = (src1 == dst) \|\| (src2 == dst);
	const bool src1_in_place = in_place && (src1 == dst);
	build_opts.add_option_if(in_place, "-DIN_PLACE");
	build_opts.add_option_if(src1_in_place, "-DSRC1_IN_PLACE");

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

	// Set scale argument
	unsigned int idx = (in_place ? 2 : 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);
	}

	Window win = calculate_max_window(*dst, Steps(vec_size));
	ICLKernel::configure_internal(win);

	ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));

	// Set config_id for enabling LWS tuning
	_config_id = kernel_name;
	_config_id += "_";
	_config_id += lower_string(string_from_data_type(dst->data_type()));
	_config_id += "_";
	_config_id += support::cpp11::to_string(src1->dimension(0));
	_config_id += "_";
	_config_id += support::cpp11::to_string(src1->dimension(1));
	_config_id += "_";
	_config_id += support::cpp11::to_string(src1->dimension(2));
	_config_id += "_";
	_config_id += support::cpp11::to_string(src2->dimension(0));
	_config_id += "_";
	_config_id += support::cpp11::to_string(src2->dimension(1));
	_config_id += "_";
	_config_id += support::cpp11::to_string(src2->dimension(2));
	_config_id += "_";
	_config_id += support::cpp11::to_string(dst->dimension(0));
	_config_id += "_";
	_config_id += support::cpp11::to_string(dst->dimension(1));
	_config_id += "_";
	_config_id += support::cpp11::to_string(dst->dimension(2));
	}

	Status ClMulKernel::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));

	return Status{};
	}

	void ClMulKernel::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));

	ARM_COMPUTE_ERROR_ON_NULLPTR(src_0, src_1, 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);

	// Check whether it is in_place calculation
	const bool in_place = (src_0 == dst) \|\| (src_1 == dst);
	do
	{
	unsigned int idx = 0;
	add_3D_tensor_argument(idx, src_0, slice_input1);
	add_3D_tensor_argument(idx, src_1, slice_input2);
	if(!in_place)
	{
	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));
	}

	namespace
	{
	constexpr unsigned int vec_size_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{};
	}
	} // namespace

	ClComplexMulKernel::ClComplexMulKernel()
	{
	_type = CLKernelType::ELEMENTWISE;
	}

	void ClComplexMulKernel::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));

	auto padding_info = get_padding_info({ src1, src2, dst });

	const TensorShape &out_shape = TensorShape::broadcast_shape(src1->tensor_shape(), src2->tensor_shape());
	auto_init_if_empty(*dst, src1->clone()->set_tensor_shape(out_shape));

	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());

	Window win = calculate_max_window(*dst, Steps(vec_size_complex));
	ICLKernel::configure_internal(win);

	ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
	}

	Status ClComplexMulKernel::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));

	return Status{};
	}

	void ClComplexMulKernel::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));
	}
	} // namespace kernels
	} // namespace opencl
	} // namespace arm_compute