| /* |
| * Copyright (c) 2017 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/CLSoftmaxLayerKernel.h" |
| |
| #include "arm_compute/core/AccessWindowStatic.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/Helpers.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/Window.h" |
| #include "arm_compute/core/utils/quantization/AsymmHelpers.h" |
| |
| #include <set> |
| #include <string> |
| |
| using namespace arm_compute; |
| namespace |
| { |
| /** Calculates softmax parameters from the quantized input scale and scaling factor for the exponent and places them as build options. |
| * |
| * Prepares these build options: |
| * -INPUT_BETA_MULTIPLIER, INPUT_BETA_LEFT_SHIFT - quantized representation of beta multiplier. |
| * -DIFF_MIN - threshold difference between maximum value of input data and current processed value, |
| * it defines whether the value will be taken into account or not. |
| * |
| * @param[in] build_opts Build options to extend |
| * @param[in] input_scale Input scaling factor |
| * @param[in] beta Exponent scaling factor beta |
| */ |
| CLBuildOptions prepare_quantized_softmax_build_options(float input_scale, float beta) |
| { |
| // Number of integer bits in temporary fixed-point representation of current-to-max difference |
| static const int scaled_diff_int_bits = 5; |
| // Number of integer bits used in temporary fixed-point representation of exponent accumulator |
| static const int exp_accumulation_in_bits = 12; |
| |
| const double beta_multiplier = std::min( |
| 1.0 * beta * input_scale * (1 << (31 - scaled_diff_int_bits)), |
| (1ll << 31) - 1.0); |
| int input_beta_multiplier, input_beta_left_shift; |
| quantization::calculate_quantized_multiplier_greater_than_one(beta_multiplier, &input_beta_multiplier, &input_beta_left_shift); |
| |
| const double max_input_rescaled = 1.0 * ((1 << scaled_diff_int_bits) - 1) * (1ll << (31 - scaled_diff_int_bits)) / (1ll << input_beta_left_shift); |
| const int diff_min = -1.f * std::floor(max_input_rescaled); |
| |
| CLBuildOptions build_opts; |
| build_opts.add_option("-DSCALED_DIFF_INT_BITS=" + support::cpp11::to_string(scaled_diff_int_bits)); |
| build_opts.add_option("-DEXP_ACCUMULATION_INT_BITS=" + support::cpp11::to_string(exp_accumulation_in_bits)); |
| build_opts.add_option("-DINPUT_BETA_MULTIPLIER=" + support::cpp11::to_string(input_beta_multiplier)); |
| build_opts.add_option("-DINPUT_BETA_LEFT_SHIFT=" + support::cpp11::to_string(input_beta_left_shift)); |
| build_opts.add_option("-DDIFF_MIN=" + support::cpp11::to_string(diff_min)); |
| |
| return build_opts; |
| } |
| } // namespace |
| |
| void CLLogits1DMaxKernel::configure(const ICLTensor *input, ICLTensor *output) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32); |
| ARM_COMPUTE_ERROR_ON_NULLPTR(output); |
| |
| // Softmax across the x dimension |
| TensorShape output_shape{ input->info()->tensor_shape() }; |
| output_shape.set(0, 1); |
| |
| // Output auto initialization if not yet initialized |
| auto_init_if_empty(*output->info(), |
| output_shape, |
| 1, |
| input->info()->data_type(), |
| input->info()->fixed_point_position(), |
| input->info()->quantization_info()); |
| |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, output); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape); |
| |
| _input = input; |
| _output = output; |
| |
| const DataType data_type = input->info()->data_type(); |
| // The kernel loops over all elements in steps of 16 |
| const unsigned int num_elems_processed_per_iteration = ceil_to_multiple(input->info()->dimension(0), 16); |
| |
| // Set build options |
| CLBuildOptions build_opts; |
| build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)); |
| build_opts.add_option_if(is_data_type_fixed_point(data_type), |
| "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position())); |
| build_opts.add_option_if(data_type == DataType::F16, "-DUSE_F16"); |
| // Tell the kernel that the width is not a multiple of 16 |
| build_opts.add_option_if((input->info()->dimension(0) % max_cl_vector_width) != 0, "-DNON_MULTIPLE_OF_16"); |
| |
| // Create kernel |
| std::string kernel_name = is_data_type_quantized_asymmetric(data_type) ? "softmax_layer_max_quantized" : "softmax_layer_max"; |
| _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); |
| |
| // Set fixed arguments |
| unsigned int idx = 2 * num_arguments_per_3D_tensor(); //Skip the input and output parameters |
| _kernel.setArg<cl_uint>(idx++, input->info()->dimension(0)); |
| |
| // Configure kernel window |
| constexpr unsigned int num_elems_written_per_iteration = 1; |
| |
| Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration)); |
| AccessWindowHorizontal input_access(input->info(), 0, num_elems_processed_per_iteration); |
| AccessWindowHorizontal output_access(output->info(), 0, num_elems_written_per_iteration); |
| |
| update_window_and_padding(win, input_access, output_access); |
| |
| output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape())); |
| |
| ICLKernel::configure(win); |
| } |
| |
| CLLogits1DShiftExpSumKernel::CLLogits1DShiftExpSumKernel() |
| : _input(nullptr), _max(nullptr), _output(nullptr), _sum(nullptr) |
| { |
| } |
| |
| void CLLogits1DShiftExpSumKernel::configure(const ICLTensor *input, const ICLTensor *max, ICLTensor *output, ICLTensor *sum, float beta) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32); |
| ARM_COMPUTE_ERROR_ON_NULLPTR(max, sum, output); |
| |
| const bool is_quantized_asymmetric = is_data_type_quantized_asymmetric(input->info()->data_type()); |
| const DataType tmp_data_type = is_quantized_asymmetric ? DataType::S32 : input->info()->data_type(); |
| |
| // Output auto initialization if not yet initialized |
| auto_init_if_empty(*sum->info(), max->info()->tensor_shape(), 1, tmp_data_type, input->info()->fixed_point_position()); |
| auto_init_if_empty(*output->info(), input->info()->tensor_shape(), 1, tmp_data_type, input->info()->fixed_point_position()); |
| |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(max, sum); |
| if(is_quantized_asymmetric) |
| { |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, max); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(output, sum); |
| } |
| else |
| { |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output, max, sum); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, output, max, sum); |
| } |
| |
| _input = input; |
| _max = max; |
| _output = output; |
| _sum = sum; |
| |
| const DataType dt = input->info()->data_type(); |
| auto beta_int = static_cast<int>(lround(beta * (1 << input->info()->fixed_point_position()))); |
| |
| // The kernel loops over all elements in steps of 16 |
| const unsigned int num_elems_processed_per_iteration = ceil_to_multiple(input->info()->dimension(0), 16); |
| |
| // Set build options |
| CLBuildOptions build_opts; |
| build_opts.add_option(std::string("-DDATA_TYPE=" + get_cl_type_from_data_type(dt))); |
| build_opts.add_option_if(is_data_type_fixed_point(dt), |
| std::string("-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position()))); |
| build_opts.add_option_if(dt == DataType::F16, std::string("-DUSE_F16")); |
| // Tell the kernel that the width is not a multiple of 16 |
| build_opts.add_option_if((input->info()->dimension(0) % max_cl_vector_width) != 0, std::string("-DNON_MULTIPLE_OF_16")); |
| build_opts.add_option_if(is_data_type_fixed_point(dt) && (beta != 1.0f), std::string("-DBETA=" + support::cpp11::to_string(beta_int))); |
| build_opts.add_option_if(is_data_type_float(dt) && (beta != 1.0f), std::string("-DBETA=" + float_to_string_with_full_precision(beta))); |
| build_opts.add_options_if(is_quantized_asymmetric, |
| prepare_quantized_softmax_build_options(input->info()->quantization_info().scale, beta).options()); |
| |
| // Create kernel |
| std::string kernel_name = is_quantized_asymmetric ? "softmax_layer_shift_exp_sum_quantized" : "softmax_layer_shift_exp_sum"; |
| _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); |
| |
| // Set fixed arguments |
| unsigned int idx = 4 * num_arguments_per_3D_tensor(); //Skip the input and output parameters |
| _kernel.setArg<cl_uint>(idx++, input->info()->dimension(0)); |
| |
| // Configure window |
| Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration)); |
| |
| AccessWindowHorizontal input_access(input->info(), 0, num_elems_processed_per_iteration); |
| AccessWindowHorizontal max_access(max->info(), 0, 1); |
| AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration); |
| AccessWindowHorizontal sum_access(sum->info(), 0, 1); |
| |
| update_window_and_padding(win, input_access, max_access, output_access, sum_access); |
| |
| output_access.set_valid_region(win, input->info()->valid_region()); |
| sum_access.set_valid_region(win, ValidRegion(Coordinates(), sum->info()->tensor_shape())); |
| |
| ICLKernel::configure(win); |
| } |
| |
| void CLLogits1DShiftExpSumKernel::run(const Window &window, cl::CommandQueue &queue) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); |
| |
| Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ); |
| Window slice = window_collapsed.first_slice_window_3D(); |
| |
| do |
| { |
| unsigned int idx = 0; |
| // Set inputs |
| add_3D_tensor_argument(idx, _input, slice); |
| add_3D_tensor_argument(idx, _max, slice); |
| add_3D_tensor_argument(idx, _output, slice); |
| add_3D_tensor_argument(idx, _sum, slice); |
| enqueue(queue, *this, slice); |
| } |
| while(window_collapsed.slide_window_slice_3D(slice)); |
| } |
| |
| /**< Grid size (obtained through auto-tuning) */ |
| const unsigned int CLLogits1DMaxShiftExpSumKernel::_grid_size = 64; |
| /**< Vector size in the serial case (obtained through auto-tuning) */ |
| const unsigned int CLLogits1DMaxShiftExpSumKernel::_serial_vector_size = 8; |
| /**< Vector size in the parallel case (obtained through auto-tuning, enables the best memory access pattern for Bifrost) .*/ |
| const unsigned int CLLogits1DMaxShiftExpSumKernel::_parallel_vector_size = 4; |
| |
| CLLogits1DMaxShiftExpSumKernel::CLLogits1DMaxShiftExpSumKernel() |
| : _input(nullptr), _max(nullptr), _output(nullptr), _sum(nullptr) |
| { |
| } |
| |
| void CLLogits1DMaxShiftExpSumKernel::configure(const ICLTensor *input, ICLTensor *max, ICLTensor *output, ICLTensor *sum, float beta) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32); |
| ARM_COMPUTE_ERROR_ON_NULLPTR(max, sum, output); |
| |
| // Output auto initialization if not yet initialized |
| auto_init_if_empty(*sum->info(), max->info()->tensor_shape(), 1, input->info()->data_type(), input->info()->fixed_point_position()); |
| auto_init_if_empty(*output->info(), input->info()->tensor_shape(), 1, input->info()->data_type(), input->info()->fixed_point_position()); |
| |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output, max, sum); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, output, max, sum); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(max, sum); |
| |
| _input = input; |
| _max = max; |
| _output = output; |
| _sum = sum; |
| |
| const DataType dt = input->info()->data_type(); |
| const size_t reduction_dim_size = input->info()->dimension(0); |
| auto beta_int = static_cast<int>(lround(beta * (1 << input->info()->fixed_point_position()))); |
| |
| // Set build options |
| CLBuildOptions build_opts; |
| build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(dt)); |
| build_opts.add_option_if(is_data_type_fixed_point(dt), |
| "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position())); |
| build_opts.add_option_if(dt == DataType::F16, "-DUSE_F16"); |
| build_opts.add_option_if(is_data_type_fixed_point(dt) && (beta != 1.0f), "-DBETA=" + support::cpp11::to_string(beta_int)); |
| build_opts.add_option_if(is_data_type_float(dt) && (beta != 1.0f), "-DBETA=" + float_to_string_with_full_precision(beta)); |
| |
| // Setting _lws_hint in this way can also communicate grid_size to CLLogits1DMaxShiftExpSumKernel::run(). |
| // A single workgroup performs reduction in dimension 0 in the parallel case, hence lws[0]==gws[0]. |
| _lws_hint = cl::NullRange; |
| std::string kernel_name = std::string("softmax_layer_max_shift_exp_sum_serial"); |
| ParallelReductionInfo parallel_reduction_info = is_parallel_reduction(reduction_dim_size); |
| unsigned int vector_size = std::get<1>(parallel_reduction_info); |
| |
| build_opts.add_option("-DVECTOR_SIZE=" + support::cpp11::to_string(vector_size)); |
| build_opts.add_option("-DLOG_VECTOR_SIZE=" + support::cpp11::to_string(lround(log2(vector_size)))); |
| build_opts.add_option_if((reduction_dim_size % vector_size) != 0, "-DNON_MULTIPLE_OF_VECTOR_SIZE"); |
| |
| // Configure parallel kernel if needed |
| if(std::get<0>(parallel_reduction_info)) |
| { |
| kernel_name = std::string("softmax_layer_max_shift_exp_sum_parallel"); |
| bool is_grid_size_pow2 = (_grid_size != 0) && ((_grid_size & (_grid_size - 1)) == 0); |
| build_opts.add_option_if(is_grid_size_pow2 && _grid_size <= 256, "-DGRID_SIZE=" + support::cpp11::to_string(_grid_size)); |
| |
| // Handle boundary conditions. |
| const unsigned int multiple_grid_size = (reduction_dim_size / vector_size) % _grid_size; |
| build_opts.add_option_if((multiple_grid_size != 0) || ((reduction_dim_size % vector_size) != 0), "-DNON_MULTIPLE_OF_GRID_SIZE"); |
| } |
| |
| // Create kernel. |
| _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); |
| |
| // Set static arguments. Both the kernels use the same arguments |
| unsigned int idx = 4 * num_arguments_per_3D_tensor(); //Skip the input and output parameters |
| _kernel.setArg<cl_uint>(idx++, reduction_dim_size); |
| |
| // Configure window |
| const unsigned int num_elems_x = ceil_to_multiple(input->info()->tensor_shape().x(), vector_size); |
| Window win = calculate_max_window(*input->info(), Steps(num_elems_x)); |
| |
| AccessWindowHorizontal input_access(input->info(), 0, num_elems_x); |
| AccessWindowHorizontal max_access(max->info(), 0, 1); |
| AccessWindowHorizontal output_access(output->info(), 0, num_elems_x); |
| AccessWindowHorizontal sum_access(sum->info(), 0, 1); |
| |
| update_window_and_padding(win, input_access, max_access, output_access, sum_access); |
| |
| output_access.set_valid_region(win, input->info()->valid_region()); |
| sum_access.set_valid_region(win, ValidRegion(Coordinates(), sum->info()->tensor_shape())); |
| |
| ICLKernel::configure(win); |
| } |
| |
| CLLogits1DMaxShiftExpSumKernel::ParallelReductionInfo CLLogits1DMaxShiftExpSumKernel::is_parallel_reduction(size_t size) |
| { |
| bool is_parallel_reduction = (size >= (_grid_size * _serial_vector_size)) && (_grid_size > 1); |
| unsigned int vector_size = is_parallel_reduction ? _parallel_vector_size : _serial_vector_size; |
| return std::make_tuple(is_parallel_reduction, vector_size); |
| } |
| |
| void CLLogits1DMaxShiftExpSumKernel::run(const Window &window, cl::CommandQueue &queue) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); |
| |
| // Collapse window in Z dimension |
| Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ); |
| |
| // Reconfigure window in case of parallel reduction |
| ParallelReductionInfo parallel_reduction_info = is_parallel_reduction(_input->info()->dimension(0)); |
| if(std::get<0>(parallel_reduction_info)) |
| { |
| // To launch grid_size parallel workitems, steps.x should be modified as follows. |
| const unsigned int step = std::get<1>(parallel_reduction_info); |
| window_collapsed.set(Window::DimX, Window::Dimension(0, _grid_size * step, step)); |
| } |
| |
| // Get slices |
| Window slice = window_collapsed.first_slice_window_3D(); |
| do |
| { |
| unsigned int idx = 0; |
| // Set inputs |
| add_3D_tensor_argument(idx, _input, slice); |
| add_3D_tensor_argument(idx, _max, slice); |
| add_3D_tensor_argument(idx, _output, slice); |
| add_3D_tensor_argument(idx, _sum, slice); |
| enqueue(queue, *this, slice, _lws_hint); |
| } |
| while(window_collapsed.slide_window_slice_3D(slice)); |
| } |
| |
| CLLogits1DNormKernel::CLLogits1DNormKernel() |
| : _input(nullptr), _sum(nullptr), _output(nullptr) |
| { |
| } |
| |
| void CLLogits1DNormKernel::configure(const ICLTensor *input, const ICLTensor *sum, ICLTensor *output, float beta) |
| { |
| ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::S32, DataType::F16, DataType::F32); |
| ARM_COMPUTE_ERROR_ON_NULLPTR(sum, output); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, sum); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, sum); |
| |
| // Note: output should always have a scale of 1/256 and offset 0 |
| const QuantizationInfo allowed_quantization_info = QuantizationInfo(1.f / 256, 0); |
| const bool is_quantized_asymmetric = (input->info()->data_type() == DataType::S32); |
| const DataType output_data_type = is_quantized_asymmetric ? DataType::QASYMM8 : input->info()->data_type(); |
| |
| // Output auto initialization if not yet initialized |
| auto_init_if_empty(*output->info(), |
| input->info()->tensor_shape(), |
| 1, |
| output_data_type, |
| input->info()->fixed_point_position(), |
| allowed_quantization_info); |
| |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_SHAPES(input, output); |
| if(!is_quantized_asymmetric) |
| { |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, output); |
| } |
| else |
| { |
| ARM_COMPUTE_ERROR_ON(output->info()->quantization_info() != allowed_quantization_info); |
| } |
| |
| _input = input; |
| _sum = sum; |
| _output = output; |
| |
| // Set build options |
| CLBuildOptions build_opts; |
| build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); |
| build_opts.add_option_if(is_data_type_fixed_point(input->info()->data_type()), |
| "-DFIXED_POINT_POSITION=" + support::cpp11::to_string(input->info()->fixed_point_position())); |
| build_opts.add_options_if(is_quantized_asymmetric, |
| prepare_quantized_softmax_build_options(input->info()->quantization_info().scale, beta).options()); |
| |
| // Create kernel |
| std::string kernel_name = is_quantized_asymmetric ? "softmax_layer_norm_quantized" : "softmax_layer_norm"; |
| _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); |
| |
| // Configure window |
| constexpr unsigned int num_elems_processed_per_iteration = 16; |
| |
| Window win = calculate_max_window(*input->info(), Steps(num_elems_processed_per_iteration)); |
| |
| AccessWindowHorizontal input_access(input->info(), 0, num_elems_processed_per_iteration); |
| AccessWindowStatic sum_access(sum->info(), 0, 0, 1, sum->info()->dimension(1)); |
| AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration); |
| |
| update_window_and_padding(win, input_access, sum_access, output_access); |
| |
| output_access.set_valid_region(win, input->info()->valid_region()); |
| |
| ICLKernel::configure(win); |
| } |
| |
| void CLLogits1DNormKernel::run(const Window &window, cl::CommandQueue &queue) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); |
| |
| Window window_collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ); |
| Window slice = window_collapsed.first_slice_window_3D(); |
| |
| do |
| { |
| Window sum_slice = slice; |
| sum_slice.set(Window::DimX, Window::Dimension(0, 1, 1)); |
| |
| unsigned int idx = 0; |
| // Set inputs |
| add_3D_tensor_argument(idx, _input, slice); |
| add_3D_tensor_argument(idx, _sum, sum_slice); |
| add_3D_tensor_argument(idx, _output, slice); |
| enqueue(queue, *this, slice); |
| } |
| while(window_collapsed.slide_window_slice_3D(slice)); |
| } |