src/core/CL/kernels/CLElementwiseOperationKernel.cpp - platform/external/ComputeLibrary - Git at Google

 /*
  * Copyright (c) 2018-2019 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 "arm_compute/core/CL/kernels/CLElementwiseOperationKernel.h"

 #include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/CLValidate.h"
 #include "arm_compute/core/CL/ICLTensor.h"
 #include <map>

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

 std::map<ArithmeticOperation, std::string> supported_arithmetic_ops =
 {
     { ArithmeticOperation::ADD, "ADD" },
     { ArithmeticOperation::SUB, "SUB" },
     { ArithmeticOperation::DIV, "DIV" },
     { ArithmeticOperation::SQUARED_DIFF, "SQUARED_DIFF" },
     { ArithmeticOperation::MIN, "MIN" },
     { ArithmeticOperation::MAX, "MAX" },
     { ArithmeticOperation::POWER, "POWER" },
     { ArithmeticOperation::PRELU, "PRELU" },
 };

 std::map<ArithmeticOperation, std::string> supported_sat_arithmetic_ops =
 {
     { ArithmeticOperation::ADD, "ADD" },
     { ArithmeticOperation::SUB, "SUB" },
 };

 std::string generate_id_for_tuning_common(const std::string &kernel_name, const ITensorInfo &input1, const ITensorInfo &output)
 {
     std::string config_id;
     // Set config_id for enabling LWS tuning
     config_id = kernel_name;
     config_id += "_";
     config_id += lower_string(string_from_data_type(input1.data_type()));
     config_id += "_";
     config_id += support::cpp11::to_string(output.dimension(0));
     config_id += "_";
     config_id += support::cpp11::to_string(output.dimension(1));
     return config_id;
 }

 Status validate_arguments_with_float_only_supported_rules(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(&input1, &input2, &output);
     ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input1);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1, 1, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &input2);

     const TensorShape out_shape = TensorShape::broadcast_shape(input1.tensor_shape(), input2.tensor_shape());

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

     // Validate in case of configured output
     if(output.total_size() > 0)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&output, 1, DataType::F16, DataType::F32);
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &output);
         ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output.tensor_shape(), 0),
                                         "Wrong shape for output");
     }

     return Status{};
 }

 Status validate_arguments_with_arithmetic_rules(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input1);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::QSYMM16, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input2);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input2, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::QSYMM16, DataType::F16, DataType::F32);

     const bool is_quantized = is_data_type_quantized(input1.data_type()) || is_data_type_quantized(input2.data_type());
     if(is_quantized)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &input2);

         if(is_data_type_quantized_symmetric(input1.data_type()))
         {
             const int32_t in1_offset = input1.quantization_info().uniform().offset;
             const int32_t in2_offset = input2.quantization_info().uniform().offset;
             ARM_COMPUTE_RETURN_ERROR_ON_MSG(in1_offset != 0, "For quantized symmetric, offset must be zero");
             ARM_COMPUTE_RETURN_ERROR_ON_MSG(in2_offset != 0, "For quantized symmetric, offset must be zero");
         }
     }

     const TensorShape out_shape = TensorShape::broadcast_shape(input1.tensor_shape(), input2.tensor_shape());

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

     // Validate in case of configured output
     if(output.total_size() > 0)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&output);
         ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&output, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::QSYMM16, DataType::F16, DataType::F32);
         ARM_COMPUTE_RETURN_ERROR_ON_MSG((output.data_type() == DataType::U8) && ((input1.data_type() != DataType::U8) || (input2.data_type() != DataType::U8)),
                                         "Output can only be U8 if both inputs are U8");
         ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output.tensor_shape(), 0),
                                         "Wrong shape for output");

         if(is_quantized)
         {
             ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &output);

             if(is_data_type_quantized_symmetric(output.data_type()))
             {
                 const int32_t offset = output.quantization_info().uniform().offset;
                 ARM_COMPUTE_RETURN_ERROR_ON_MSG(offset != 0, "For quantized symmetric, offset must be zero");
             }
         }
     }
     return Status{};
 }

 CLBuildOptions generate_build_options_with_arithmetic_rules(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output, const std::string &operation_string)
 {
     CLBuildOptions build_opts;

     build_opts.add_option("-DDATA_TYPE_IN1=" + get_cl_type_from_data_type(input1.data_type()));
     build_opts.add_option("-DDATA_TYPE_IN2=" + get_cl_type_from_data_type(input2.data_type()));
     build_opts.add_option("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output.data_type()));
     build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration));
     build_opts.add_option("-DOP=" + operation_string);
     if(is_data_type_quantized(input1.data_type()))
     {
         const UniformQuantizationInfo iq1info = input1.quantization_info().uniform();
         const UniformQuantizationInfo iq2info = input2.quantization_info().uniform();
         const UniformQuantizationInfo oqinfo  = output.quantization_info().uniform();

         build_opts.add_option("-DOFFSET_IN1=" + support::cpp11::to_string(iq1info.offset));
         build_opts.add_option("-DOFFSET_IN2=" + support::cpp11::to_string(iq2info.offset));
         build_opts.add_option("-DOFFSET_OUT=" + support::cpp11::to_string(oqinfo.offset));
         build_opts.add_option("-DSCALE_IN1=" + float_to_string_with_full_precision(iq1info.scale));
         build_opts.add_option("-DSCALE_IN2=" + float_to_string_with_full_precision(iq2info.scale));
         build_opts.add_option("-DSCALE_OUT=" + float_to_string_with_full_precision(oqinfo.scale));
     }
     return build_opts;
 }

 std::pair<Status, Window> configure_window_arithmetic_common(const ValidRegion &valid_region, ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output)
 {
     Window win        = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration));
     Window win_input1 = win.broadcast_if_dimension_le_one(input1);
     Window win_input2 = win.broadcast_if_dimension_le_one(input2);

     AccessWindowHorizontal input1_access(&input1, 0, num_elems_processed_per_iteration);
     AccessWindowHorizontal input2_access(&input2, 0, num_elems_processed_per_iteration);
     AccessWindowHorizontal output_access(&output, 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);

     output_access.set_valid_region(win, valid_region);

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

 std::pair<Status, Window> validate_and_configure_window_for_arithmetic_operators(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output)
 {
     const std::pair<TensorShape, ValidRegion> broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(input1, input2);
     const TensorShape &out_shape    = broadcast_pair.first;
     const ValidRegion &valid_region = broadcast_pair.second;

     set_shape_if_empty(output, out_shape);

     if(input1.data_type() == DataType::S16 || input2.data_type() == DataType::S16)
     {
         set_format_if_unknown(output, Format::S16);
     }
     else if(input1.data_type() == DataType::F16 && input2.data_type() == DataType::F16)
     {
         set_format_if_unknown(output, Format::F16);
     }
     else if(input1.data_type() == DataType::F32 || input2.data_type() == DataType::F32)
     {
         set_format_if_unknown(output, Format::F32);
     }
     else if(input1.data_type() == DataType::QASYMM8 || input2.data_type() == DataType::QASYMM8)
     {
         set_data_type_if_unknown(output, DataType::QASYMM8);
     }
     else if(input1.data_type() == DataType::QSYMM16 || input2.data_type() == DataType::QSYMM16)
     {
         set_data_type_if_unknown(output, DataType::QSYMM16);
     }

     return configure_window_arithmetic_common(valid_region, input1, input2, output);
 }

 std::pair<Status, Window> validate_and_configure_window_for_division(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output)
 {
     const std::pair<TensorShape, ValidRegion> broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(input1, input2);
     const TensorShape &out_shape    = broadcast_pair.first;
     const ValidRegion &valid_region = broadcast_pair.second;
     auto_init_if_empty(output, out_shape, 1, input1.data_type());
     return configure_window_arithmetic_common(valid_region, input1, input2, output);
 }
 } // namespace

 CLElementwiseOperationKernel::CLElementwiseOperationKernel()
     : _input1(nullptr), _input2(nullptr), _output(nullptr)
 {
 }

 void CLElementwiseOperationKernel::configure_common(const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(*input1->info(), *input2->info(), *output->info()));

     // Configure kernel window
     auto win_config = validate_and_configure_window(*input1->info(), *input2->info(), *output->info());
     ARM_COMPUTE_ERROR_THROW_ON(win_config.first);

     _input1 = input1;
     _input2 = input2;
     _output = output;

     std::string kernel_name = "elementwise_operation_" + name();
     if(is_data_type_quantized(input1->info()->data_type()))
     {
         kernel_name += "_quantized";
     }

     // Set kernel build options
     CLBuildOptions build_opts = generate_build_options(*input1->info(), *input2->info(), *output->info());

     // Create kernel
     _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));

     ICLKernel::configure_internal(win_config.second);

     _config_id = generate_id_for_tuning(kernel_name, *input1->info(), *output->info());
 }

 void CLElementwiseOperationKernel::run(const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);

     const TensorShape &in_shape1 = _input1->info()->tensor_shape();
     const TensorShape &in_shape2 = _input2->info()->tensor_shape();
     const TensorShape &out_shape = _output->info()->tensor_shape();

     bool       can_collapse = true;
     const bool is_vector    = in_shape1.num_dimensions() == 1 || in_shape2.num_dimensions() == 1;
     if(std::min(in_shape1.total_size(), in_shape2.total_size()) > 1 && !is_vector)
     {
         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, _input1, slice_input1);
         add_3D_tensor_argument(idx, _input2, slice_input2);
         add_3D_tensor_argument(idx, _output, 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 CLElementwiseOperationKernel::border_size() const
 {
     const unsigned int replicateSize = _output->info()->dimension(0) - std::min(_input1->info()->dimension(0), _input2->info()->dimension(0));
     const unsigned int border        = std::min<unsigned int>(num_elems_processed_per_iteration - 1U, replicateSize);
     return BorderSize{ 0, border, 0, 0 };
 }

 /** Arithmetic operations with saturation*/

 void CLSaturatedArithmeticOperationKernel::configure(ArithmeticOperation op, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output, const ConvertPolicy &policy)
 {
     _policy = policy;
     _op     = op;
     configure_common(input1, input2, output);
 }

 Status CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation op, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, const ConvertPolicy &policy)
 {
     ARM_COMPUTE_UNUSED(op, policy);
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output);
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_with_arithmetic_rules(*input1, *input2, *output));
     ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_for_arithmetic_operators(*input1->clone(), *input2->clone(), *output->clone()).first);

     return Status{};
 }

 std::pair<Status, Window> CLSaturatedArithmeticOperationKernel::validate_and_configure_window(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output)
 {
     return validate_and_configure_window_for_arithmetic_operators(input1, input2, output);
 }

 Status CLSaturatedArithmeticOperationKernel::validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output)
 {
     return validate_arguments_with_arithmetic_rules(input1, input2, output);
 }

 CLBuildOptions CLSaturatedArithmeticOperationKernel::generate_build_options(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output)
 {
     const bool has_float_out = is_data_type_float(output.data_type());
     auto       build_options = generate_build_options_with_arithmetic_rules(input1, input2, output, name());
     build_options.add_option((_policy == ConvertPolicy::WRAP || has_float_out) ? "-DWRAP" : "-DSATURATE");
     return build_options;
 }
 std::string CLSaturatedArithmeticOperationKernel::generate_id_for_tuning(const std::string &kernel_name, const ITensorInfo &input1, const ITensorInfo &output)
 {
     auto config_id = generate_id_for_tuning_common(kernel_name, input1, output);
     config_id += (_policy == ConvertPolicy::WRAP) ? "_wrap_" : "_saturate_";
     config_id += lower_string(string_from_data_layout(input1.data_layout()));
     return config_id;
 }

 std::string CLSaturatedArithmeticOperationKernel::name()
 {
     return supported_sat_arithmetic_ops[_op];
 }

 /** Arithmetic operations*/

 void CLArithmeticOperationKernel::configure(ArithmeticOperation op, const ICLTensor *input1, const ICLTensor *input2, ICLTensor *output)
 {
     _op = op;
     configure_common(input1, input2, output);
 }

 Status CLArithmeticOperationKernel::validate(ArithmeticOperation op, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output);
     if(op == ArithmeticOperation::DIV || op == ArithmeticOperation::POWER)
     {
         // Division and Power operators don't support integer arithmetic
         ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_with_float_only_supported_rules(*input1, *input2, *output));
         ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_for_division(*input1->clone(), *input2->clone(), *output->clone()).first);
     }
     else
     {
         ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_with_arithmetic_rules(*input1, *input2, *output));
         ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_for_arithmetic_operators(*input1->clone(), *input2->clone(), *output->clone()).first);
     }

     return Status{};
 }
 std::pair<Status, Window> CLArithmeticOperationKernel::validate_and_configure_window(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output)
 {
     if(_op == ArithmeticOperation::DIV || _op == ArithmeticOperation::POWER)
     {
         // Division and Power operators don't support integer arithmetic
         return validate_and_configure_window_for_division(input1, input2, output);
     }
     else
     {
         return validate_and_configure_window_for_arithmetic_operators(input1, input2, output);
     }
 }
 Status CLArithmeticOperationKernel::validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output)
 {
     if(_op == ArithmeticOperation::DIV || _op == ArithmeticOperation::POWER)
     {
         // Division and Power operators don't support integer arithmetic
         return validate_arguments_with_float_only_supported_rules(input1, input2, output);
     }
     else
     {
         return validate_arguments_with_arithmetic_rules(input1, input2, output);
     }
 }

 CLBuildOptions CLArithmeticOperationKernel::generate_build_options(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output)
 {
     return generate_build_options_with_arithmetic_rules(input1, input2, output, name());
 }
 std::string CLArithmeticOperationKernel::generate_id_for_tuning(const std::string &kernel_name, const ITensorInfo &input1, const ITensorInfo &output)
 {
     return generate_id_for_tuning_common(kernel_name, input1, output);
 }

 std::string CLArithmeticOperationKernel::name()
 {
     return supported_arithmetic_ops[_op];
 }
 } // namespace arm_compute
	/*
	* Copyright (c) 2018-2019 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 "arm_compute/core/CL/kernels/CLElementwiseOperationKernel.h"

	#include "arm_compute/core/CL/CLHelpers.h"
	#include "arm_compute/core/CL/CLValidate.h"
	#include "arm_compute/core/CL/ICLTensor.h"
	#include <map>

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

	std::map<ArithmeticOperation, std::string> supported_arithmetic_ops =
	{
	{ ArithmeticOperation::ADD, "ADD" },
	{ ArithmeticOperation::SUB, "SUB" },
	{ ArithmeticOperation::DIV, "DIV" },
	{ ArithmeticOperation::SQUARED_DIFF, "SQUARED_DIFF" },
	{ ArithmeticOperation::MIN, "MIN" },
	{ ArithmeticOperation::MAX, "MAX" },
	{ ArithmeticOperation::POWER, "POWER" },
	{ ArithmeticOperation::PRELU, "PRELU" },
	};

	std::map<ArithmeticOperation, std::string> supported_sat_arithmetic_ops =
	{
	{ ArithmeticOperation::ADD, "ADD" },
	{ ArithmeticOperation::SUB, "SUB" },
	};

	std::string generate_id_for_tuning_common(const std::string &kernel_name, const ITensorInfo &input1, const ITensorInfo &output)
	{
	std::string config_id;
	// Set config_id for enabling LWS tuning
	config_id = kernel_name;
	config_id += "_";
	config_id += lower_string(string_from_data_type(input1.data_type()));
	config_id += "_";
	config_id += support::cpp11::to_string(output.dimension(0));
	config_id += "_";
	config_id += support::cpp11::to_string(output.dimension(1));
	return config_id;
	}

	Status validate_arguments_with_float_only_supported_rules(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(&input1, &input2, &output);
	ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input1);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1, 1, DataType::F16, DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &input2);

	const TensorShape out_shape = TensorShape::broadcast_shape(input1.tensor_shape(), input2.tensor_shape());

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

	// Validate in case of configured output
	if(output.total_size() > 0)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&output, 1, DataType::F16, DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &output);
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output.tensor_shape(), 0),
	"Wrong shape for output");
	}

	return Status{};
	}

	Status validate_arguments_with_arithmetic_rules(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input1);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input1, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::QSYMM16, DataType::F16, DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&input2);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input2, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::QSYMM16, DataType::F16, DataType::F32);

	const bool is_quantized = is_data_type_quantized(input1.data_type()) \|\| is_data_type_quantized(input2.data_type());
	if(is_quantized)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &input2);

	if(is_data_type_quantized_symmetric(input1.data_type()))
	{
	const int32_t in1_offset = input1.quantization_info().uniform().offset;
	const int32_t in2_offset = input2.quantization_info().uniform().offset;
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(in1_offset != 0, "For quantized symmetric, offset must be zero");
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(in2_offset != 0, "For quantized symmetric, offset must be zero");
	}
	}

	const TensorShape out_shape = TensorShape::broadcast_shape(input1.tensor_shape(), input2.tensor_shape());

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

	// Validate in case of configured output
	if(output.total_size() > 0)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&output);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&output, 1, DataType::U8, DataType::QASYMM8, DataType::S16, DataType::QSYMM16, DataType::F16, DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON_MSG((output.data_type() == DataType::U8) && ((input1.data_type() != DataType::U8) \|\| (input2.data_type() != DataType::U8)),
	"Output can only be U8 if both inputs are U8");
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, output.tensor_shape(), 0),
	"Wrong shape for output");

	if(is_quantized)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input1, &output);

	if(is_data_type_quantized_symmetric(output.data_type()))
	{
	const int32_t offset = output.quantization_info().uniform().offset;
	ARM_COMPUTE_RETURN_ERROR_ON_MSG(offset != 0, "For quantized symmetric, offset must be zero");
	}
	}
	}
	return Status{};
	}

	CLBuildOptions generate_build_options_with_arithmetic_rules(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output, const std::string &operation_string)
	{
	CLBuildOptions build_opts;

	build_opts.add_option("-DDATA_TYPE_IN1=" + get_cl_type_from_data_type(input1.data_type()));
	build_opts.add_option("-DDATA_TYPE_IN2=" + get_cl_type_from_data_type(input2.data_type()));
	build_opts.add_option("-DDATA_TYPE_OUT=" + get_cl_type_from_data_type(output.data_type()));
	build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration));
	build_opts.add_option("-DOP=" + operation_string);
	if(is_data_type_quantized(input1.data_type()))
	{
	const UniformQuantizationInfo iq1info = input1.quantization_info().uniform();
	const UniformQuantizationInfo iq2info = input2.quantization_info().uniform();
	const UniformQuantizationInfo oqinfo = output.quantization_info().uniform();

	build_opts.add_option("-DOFFSET_IN1=" + support::cpp11::to_string(iq1info.offset));
	build_opts.add_option("-DOFFSET_IN2=" + support::cpp11::to_string(iq2info.offset));
	build_opts.add_option("-DOFFSET_OUT=" + support::cpp11::to_string(oqinfo.offset));
	build_opts.add_option("-DSCALE_IN1=" + float_to_string_with_full_precision(iq1info.scale));
	build_opts.add_option("-DSCALE_IN2=" + float_to_string_with_full_precision(iq2info.scale));
	build_opts.add_option("-DSCALE_OUT=" + float_to_string_with_full_precision(oqinfo.scale));
	}
	return build_opts;
	}

	std::pair<Status, Window> configure_window_arithmetic_common(const ValidRegion &valid_region, ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output)
	{
	Window win = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration));
	Window win_input1 = win.broadcast_if_dimension_le_one(input1);
	Window win_input2 = win.broadcast_if_dimension_le_one(input2);

	AccessWindowHorizontal input1_access(&input1, 0, num_elems_processed_per_iteration);
	AccessWindowHorizontal input2_access(&input2, 0, num_elems_processed_per_iteration);
	AccessWindowHorizontal output_access(&output, 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);

	output_access.set_valid_region(win, valid_region);

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

	std::pair<Status, Window> validate_and_configure_window_for_arithmetic_operators(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output)
	{
	const std::pair<TensorShape, ValidRegion> broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(input1, input2);
	const TensorShape &out_shape = broadcast_pair.first;
	const ValidRegion &valid_region = broadcast_pair.second;

	set_shape_if_empty(output, out_shape);

	if(input1.data_type() == DataType::S16 \|\| input2.data_type() == DataType::S16)
	{
	set_format_if_unknown(output, Format::S16);
	}
	else if(input1.data_type() == DataType::F16 && input2.data_type() == DataType::F16)
	{
	set_format_if_unknown(output, Format::F16);
	}
	else if(input1.data_type() == DataType::F32 \|\| input2.data_type() == DataType::F32)
	{
	set_format_if_unknown(output, Format::F32);
	}
	else if(input1.data_type() == DataType::QASYMM8 \|\| input2.data_type() == DataType::QASYMM8)
	{
	set_data_type_if_unknown(output, DataType::QASYMM8);
	}
	else if(input1.data_type() == DataType::QSYMM16 \|\| input2.data_type() == DataType::QSYMM16)
	{
	set_data_type_if_unknown(output, DataType::QSYMM16);
	}

	return configure_window_arithmetic_common(valid_region, input1, input2, output);
	}

	std::pair<Status, Window> validate_and_configure_window_for_division(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output)
	{
	const std::pair<TensorShape, ValidRegion> broadcast_pair = ITensorInfo::broadcast_shape_and_valid_region(input1, input2);
	const TensorShape &out_shape = broadcast_pair.first;
	const ValidRegion &valid_region = broadcast_pair.second;
	auto_init_if_empty(output, out_shape, 1, input1.data_type());
	return configure_window_arithmetic_common(valid_region, input1, input2, output);
	}
	} // namespace

	CLElementwiseOperationKernel::CLElementwiseOperationKernel()
	: _input1(nullptr), _input2(nullptr), _output(nullptr)
	{
	}

	void CLElementwiseOperationKernel::configure_common(const ICLTensor input1, const ICLTensor input2, ICLTensor *output)
	{
	ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output);
	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input1->info(), input2->info(), *output->info()));

	// Configure kernel window
	auto win_config = validate_and_configure_window(input1->info(), input2->info(), *output->info());
	ARM_COMPUTE_ERROR_THROW_ON(win_config.first);

	_input1 = input1;
	_input2 = input2;
	_output = output;

	std::string kernel_name = "elementwise_operation_" + name();
	if(is_data_type_quantized(input1->info()->data_type()))
	{
	kernel_name += "_quantized";
	}

	// Set kernel build options
	CLBuildOptions build_opts = generate_build_options(input1->info(), input2->info(), *output->info());

	// Create kernel
	_kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));

	ICLKernel::configure_internal(win_config.second);

	_config_id = generate_id_for_tuning(kernel_name, input1->info(), output->info());
	}

	void CLElementwiseOperationKernel::run(const Window &window, cl::CommandQueue &queue)
	{
	ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
	ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);

	const TensorShape &in_shape1 = _input1->info()->tensor_shape();
	const TensorShape &in_shape2 = _input2->info()->tensor_shape();
	const TensorShape &out_shape = _output->info()->tensor_shape();

	bool can_collapse = true;
	const bool is_vector = in_shape1.num_dimensions() == 1 \|\| in_shape2.num_dimensions() == 1;
	if(std::min(in_shape1.total_size(), in_shape2.total_size()) > 1 && !is_vector)
	{
	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, _input1, slice_input1);
	add_3D_tensor_argument(idx, _input2, slice_input2);
	add_3D_tensor_argument(idx, _output, 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 CLElementwiseOperationKernel::border_size() const
	{
	const unsigned int replicateSize = _output->info()->dimension(0) - std::min(_input1->info()->dimension(0), _input2->info()->dimension(0));
	const unsigned int border = std::min<unsigned int>(num_elems_processed_per_iteration - 1U, replicateSize);
	return BorderSize{ 0, border, 0, 0 };
	}

	/** Arithmetic operations with saturation*/

	void CLSaturatedArithmeticOperationKernel::configure(ArithmeticOperation op, const ICLTensor input1, const ICLTensor input2, ICLTensor *output, const ConvertPolicy &policy)
	{
	_policy = policy;
	_op = op;
	configure_common(input1, input2, output);
	}

	Status CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation op, const ITensorInfo input1, const ITensorInfo input2, const ITensorInfo *output, const ConvertPolicy &policy)
	{
	ARM_COMPUTE_UNUSED(op, policy);
	ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output);
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_with_arithmetic_rules(input1, input2, *output));
	ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_for_arithmetic_operators(input1->clone(), input2->clone(), *output->clone()).first);

	return Status{};
	}

	std::pair<Status, Window> CLSaturatedArithmeticOperationKernel::validate_and_configure_window(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output)
	{
	return validate_and_configure_window_for_arithmetic_operators(input1, input2, output);
	}

	Status CLSaturatedArithmeticOperationKernel::validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output)
	{
	return validate_arguments_with_arithmetic_rules(input1, input2, output);
	}

	CLBuildOptions CLSaturatedArithmeticOperationKernel::generate_build_options(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output)
	{
	const bool has_float_out = is_data_type_float(output.data_type());
	auto build_options = generate_build_options_with_arithmetic_rules(input1, input2, output, name());
	build_options.add_option((_policy == ConvertPolicy::WRAP \|\| has_float_out) ? "-DWRAP" : "-DSATURATE");
	return build_options;
	}
	std::string CLSaturatedArithmeticOperationKernel::generate_id_for_tuning(const std::string &kernel_name, const ITensorInfo &input1, const ITensorInfo &output)
	{
	auto config_id = generate_id_for_tuning_common(kernel_name, input1, output);
	config_id += (_policy == ConvertPolicy::WRAP) ? "_wrap_" : "_saturate_";
	config_id += lower_string(string_from_data_layout(input1.data_layout()));
	return config_id;
	}

	std::string CLSaturatedArithmeticOperationKernel::name()
	{
	return supported_sat_arithmetic_ops[_op];
	}

	/** Arithmetic operations*/

	void CLArithmeticOperationKernel::configure(ArithmeticOperation op, const ICLTensor input1, const ICLTensor input2, ICLTensor *output)
	{
	_op = op;
	configure_common(input1, input2, output);
	}

	Status CLArithmeticOperationKernel::validate(ArithmeticOperation op, const ITensorInfo input1, const ITensorInfo input2, const ITensorInfo *output)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input1, input2, output);
	if(op == ArithmeticOperation::DIV \|\| op == ArithmeticOperation::POWER)
	{
	// Division and Power operators don't support integer arithmetic
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_with_float_only_supported_rules(input1, input2, *output));
	ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_for_division(input1->clone(), input2->clone(), *output->clone()).first);
	}
	else
	{
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_with_arithmetic_rules(input1, input2, *output));
	ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_for_arithmetic_operators(input1->clone(), input2->clone(), *output->clone()).first);
	}

	return Status{};
	}
	std::pair<Status, Window> CLArithmeticOperationKernel::validate_and_configure_window(ITensorInfo &input1, ITensorInfo &input2, ITensorInfo &output)
	{
	if(_op == ArithmeticOperation::DIV \|\| _op == ArithmeticOperation::POWER)
	{
	// Division and Power operators don't support integer arithmetic
	return validate_and_configure_window_for_division(input1, input2, output);
	}
	else
	{
	return validate_and_configure_window_for_arithmetic_operators(input1, input2, output);
	}
	}
	Status CLArithmeticOperationKernel::validate_arguments(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output)
	{
	if(_op == ArithmeticOperation::DIV \|\| _op == ArithmeticOperation::POWER)
	{
	// Division and Power operators don't support integer arithmetic
	return validate_arguments_with_float_only_supported_rules(input1, input2, output);
	}
	else
	{
	return validate_arguments_with_arithmetic_rules(input1, input2, output);
	}
	}

	CLBuildOptions CLArithmeticOperationKernel::generate_build_options(const ITensorInfo &input1, const ITensorInfo &input2, const ITensorInfo &output)
	{
	return generate_build_options_with_arithmetic_rules(input1, input2, output, name());
	}
	std::string CLArithmeticOperationKernel::generate_id_for_tuning(const std::string &kernel_name, const ITensorInfo &input1, const ITensorInfo &output)
	{
	return generate_id_for_tuning_common(kernel_name, input1, output);
	}

	std::string CLArithmeticOperationKernel::name()
	{
	return supported_arithmetic_ops[_op];
	}
	} // namespace arm_compute