| /* |
| * Copyright (c) 2018-2020 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/ICLTensor.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" |
| #include <map> |
| |
| namespace arm_compute |
| { |
| namespace |
| { |
| constexpr unsigned int vector_size_byte_opencl = 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::QASYMM8_SIGNED, |
| DataType::S16, DataType::QSYMM16, DataType::F16, |
| DataType::S32, 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::QASYMM8_SIGNED, |
| DataType::S16, DataType::QSYMM16, DataType::F16, |
| DataType::S32, 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::QASYMM8_SIGNED, |
| DataType::S16, DataType::QSYMM16, DataType::F16, |
| DataType::S32, 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; |
| |
| const unsigned int num_elems_processed_per_iteration = adjust_vec_size(vector_size_byte_opencl / output.element_size(), output.dimension(0)); |
| |
| 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_IN1=" + support::cpp11::to_string(input1.dimension(0) == 1 ? 1 : num_elems_processed_per_iteration)); |
| build_opts.add_option("-DVEC_SIZE_IN2=" + support::cpp11::to_string(input2.dimension(0) == 1 ? 1 : num_elems_processed_per_iteration)); |
| build_opts.add_option("-DVEC_SIZE_OUT=" + support::cpp11::to_string(num_elems_processed_per_iteration)); |
| build_opts.add_option("-DVEC_SIZE_LEFTOVER=" + support::cpp11::to_string(output.dimension(0) % 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(ITensorInfo &output) |
| { |
| const unsigned int num_elems_processed_per_iteration = adjust_vec_size(vector_size_byte_opencl / output.element_size(), output.dimension(0)); |
| Window win = calculate_max_window(output, Steps(num_elems_processed_per_iteration)); |
| return std::make_pair(Status{}, 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; |
| |
| 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::QASYMM8_SIGNED || input2.data_type() == DataType::QASYMM8_SIGNED) |
| { |
| set_data_type_if_unknown(output, DataType::QASYMM8_SIGNED); |
| } |
| 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(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; |
| auto_init_if_empty(output, out_shape, 1, input1.data_type()); |
| return configure_window_arithmetic_common(output); |
| } |
| } // namespace |
| |
| CLElementwiseOperationKernel::CLElementwiseOperationKernel() |
| : _act_info(), _input1(nullptr), _input2(nullptr), _output(nullptr) |
| { |
| } |
| |
| void CLElementwiseOperationKernel::configure_common(ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output) |
| { |
| configure_common(CLKernelLibrary::get().get_compile_context(), input1, input2, output); |
| } |
| |
| void CLElementwiseOperationKernel::configure_common(const CLCompileContext &compile_context, ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output) |
| { |
| // Configure kernel window |
| auto win_config = validate_and_configure_window(*input1, *input2, *output); |
| 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->data_type())) |
| { |
| kernel_name += "_quantized"; |
| } |
| |
| // Set kernel build options |
| CLBuildOptions build_opts = generate_build_options(*input1, *input2, *output); |
| 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()); |
| |
| ICLKernel::configure_internal(win_config.second); |
| |
| _config_id = generate_id_for_tuning(kernel_name, *input1, *output); |
| } |
| |
| void CLElementwiseOperationKernel::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; |
| 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, 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)); |
| } |
| |
| /** Arithmetic operations with saturation*/ |
| |
| void CLSaturatedArithmeticOperationKernel::configure(ArithmeticOperation op, ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output, const ConvertPolicy &policy, |
| const ActivationLayerInfo &act_info) |
| { |
| configure(CLKernelLibrary::get().get_compile_context(), op, input1, input2, output, policy, act_info); |
| } |
| |
| void CLSaturatedArithmeticOperationKernel::configure(const CLCompileContext &compile_context, ArithmeticOperation op, ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output, |
| const ConvertPolicy &policy, |
| const ActivationLayerInfo &act_info) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output); |
| ARM_COMPUTE_ERROR_THROW_ON(CLSaturatedArithmeticOperationKernel::validate(op, input1, input2, output, policy, act_info)); |
| |
| _policy = policy; |
| _op = op; |
| _act_info = act_info; |
| configure_common(compile_context, input1, input2, output); |
| } |
| |
| Status CLSaturatedArithmeticOperationKernel::validate(ArithmeticOperation op, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, const ConvertPolicy &policy, |
| const ActivationLayerInfo &act_info) |
| { |
| 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); |
| ARM_COMPUTE_RETURN_ERROR_ON(act_info.enabled() && !is_data_type_float(output->data_type())); |
| |
| 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); |
| } |
| |
| 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, ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output, const ActivationLayerInfo &act_info) |
| { |
| configure(CLKernelLibrary::get().get_compile_context(), op, input1, input2, output, act_info); |
| } |
| |
| void CLArithmeticOperationKernel::configure(const CLCompileContext &compile_context, ArithmeticOperation op, ITensorInfo *input1, ITensorInfo *input2, ITensorInfo *output, |
| const ActivationLayerInfo &act_info) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input1, input2, output); |
| ARM_COMPUTE_ERROR_THROW_ON(CLArithmeticOperationKernel::validate(op, input1, input2, output, act_info)); |
| |
| _op = op; |
| _act_info = act_info; |
| configure_common(compile_context, input1, input2, output); |
| } |
| |
| Status CLArithmeticOperationKernel::validate(ArithmeticOperation op, const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output, const ActivationLayerInfo &act_info) |
| { |
| 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); |
| } |
| ARM_COMPUTE_RETURN_ERROR_ON(act_info.enabled() && !is_data_type_float(output->data_type())); |
| |
| 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); |
| } |
| } |
| |
| 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 |