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

 /*
  * Copyright (c) 2017-2019 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/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/CLValidate.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/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;
     int 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;
 }

 Status validate_arguments_1DMaxShiftExpSum(const ITensorInfo *input, const ITensorInfo *max, const ITensorInfo *output, const ITensorInfo *sum)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(max, sum, output);

     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, max);

     const bool is_quantized_asymmetric = is_data_type_quantized_asymmetric(input->data_type());

     // Checks performed when output is configured
     if(output->total_size() != 0)
     {
         if(is_quantized_asymmetric)
         {
             ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32);
         }
         else
         {
             ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
         }
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
     }

     // Checks performed when sum is configured
     if(sum->total_size() != 0)
     {
         if(is_quantized_asymmetric)
         {
             ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(sum, 1, DataType::S32);
         }
         else
         {
             ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(max, sum);
         }
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(max, sum);
     }

     return Status{};
 }

 Status validate_arguments_1DNorm(const ITensorInfo *input, const ITensorInfo *sum, const ITensorInfo *output)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(sum, output);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(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->data_type() == DataType::S32);

     // Checks performed when output is configured
     if(output->total_size() != 0)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
         if(!is_quantized_asymmetric)
         {
             ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
         }
         else
         {
             ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8);
             ARM_COMPUTE_RETURN_ERROR_ON(output->quantization_info() != allowed_quantization_info);
         }
     }

     return Status{};
 }

 // Window validation

 std::pair<Status, Window> validate_and_configure_window_1DMaxShiftExpSum(ITensorInfo *input, ITensorInfo *max, ITensorInfo *output, ITensorInfo *sum)
 {
     // Output auto initialization if not yet initialized
     auto_init_if_empty(*sum, input->clone()->set_tensor_shape(max->tensor_shape()));
     auto_init_if_empty(*output, *input->clone());

     CLLogits1DMaxShiftExpSumKernel::ParallelReductionInfo parallel_reduction_info = CLLogits1DMaxShiftExpSumKernel::is_parallel_reduction(input->dimension(0));
     unsigned int                                          vector_size             = std::get<1>(parallel_reduction_info);
     const unsigned int                                    num_elems_x             = ceil_to_multiple(input->tensor_shape().x(), vector_size);
     Window                                                win                     = calculate_max_window(*input, Steps(num_elems_x));

     AccessWindowHorizontal input_access(input, 0, num_elems_x);
     AccessWindowHorizontal max_access(max, 0, 1);
     AccessWindowHorizontal output_access(output, 0, num_elems_x);
     AccessWindowHorizontal sum_access(sum, 0, 1);

     bool window_changed = update_window_and_padding(win, input_access, max_access, output_access, sum_access);

     output_access.set_valid_region(win, input->valid_region());
     sum_access.set_valid_region(win, ValidRegion(Coordinates(), sum->tensor_shape()));

     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_1DNorm(ITensorInfo *input, ITensorInfo *output, ITensorInfo *sum)
 {
     const QuantizationInfo allowed_quantization_info = QuantizationInfo(1.f / 256, 0);
     const bool             is_quantized_asymmetric   = (input->data_type() == DataType::S32);
     const DataType         output_data_type          = is_quantized_asymmetric ? DataType::QASYMM8 : input->data_type();

     // Output auto initialization if not yet initialized
     auto_init_if_empty(*output,
                        input->clone()->set_data_type(output_data_type).set_quantization_info(allowed_quantization_info));

     constexpr unsigned int num_elems_processed_per_iteration = 16;

     Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));

     AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration);
     AccessWindowStatic     sum_access(sum, 0, 0, 1, sum->dimension(1));
     AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);

     bool window_changed = update_window_and_padding(win, input_access, sum_access, output_access);

     output_access.set_valid_region(win, input->valid_region());

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

 } // namespace

 /**< 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_NULLPTR(input, max, sum, output);

     // Output auto initialization if not yet initialized
     auto_init_if_empty(*sum->info(), input->info()->clone()->set_tensor_shape(max->info()->tensor_shape()));
     auto_init_if_empty(*output->info(), *input->info()->clone());

     // Perform validation step
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_1DMaxShiftExpSum(input->info(), max->info(), output->info(), sum->info()));

     _input  = input;
     _max    = max;
     _output = output;
     _sum    = sum;

     const DataType                dt                 = input->info()->data_type();
     const UniformQuantizationInfo qinfo              = input->info()->quantization_info().uniform();
     const size_t                  reduction_dim_size = input->info()->dimension(0);

     // Set build options
     CLBuildOptions build_opts;
     build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(dt));
     build_opts.add_option_if(dt == DataType::F16, "-DUSE_F16");
     build_opts.add_option_if(is_data_type_float(dt) && (beta != 1.0f), "-DBETA=" + float_to_string_with_full_precision(beta));
     build_opts.add_options_if(is_data_type_quantized_asymmetric(dt), prepare_quantized_softmax_build_options(qinfo.scale, beta).options());

     cl::NDRange lws_hint(cl::NullRange);
     std::string kernel_name = is_data_type_quantized_asymmetric(dt) ? std::string("softmax_layer_max_shift_exp_sum_quantized_serial") :
                               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            = is_data_type_quantized_asymmetric(dt) ? std::string("softmax_layer_max_shift_exp_sum_quantized_parallel") : 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");
         // 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::NDRange(_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
     auto win_config = validate_and_configure_window_1DMaxShiftExpSum(input->info(), max->info(), output->info(), sum->info());
     ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
     ICLKernel::configure_internal(win_config.second, lws_hint);
 }

 Status CLLogits1DMaxShiftExpSumKernel::validate(const ITensorInfo *input, const ITensorInfo *max, const ITensorInfo *output, const ITensorInfo *sum)
 {
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_1DMaxShiftExpSum(input, max, output, sum));
     ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_1DMaxShiftExpSum(input->clone().get(), max->clone().get(), output->clone().get(), sum->clone().get()).first);

     return Status{};
 }

 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_NULLPTR(input, sum, output);

     // 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();
     const UniformQuantizationInfo qinfo                     = input->info()->quantization_info().uniform();

     // Output auto initialization if not yet initialized
     auto_init_if_empty(*output->info(),
                        input->info()->clone()->set_data_type(output_data_type).set_quantization_info(allowed_quantization_info));

     // Perform validation step
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_1DNorm(input->info(), sum->info(), output->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_options_if(is_quantized_asymmetric,
                               prepare_quantized_softmax_build_options(qinfo.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
     auto win_config = validate_and_configure_window_1DNorm(input->info(), output->info(), sum->info());
     ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
     ICLKernel::configure_internal(win_config.second);
 }

 Status CLLogits1DNormKernel::validate(const ITensorInfo *input, const ITensorInfo *sum, const ITensorInfo *output)
 {
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_1DNorm(input, sum, output));
     ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_1DNorm(input->clone().get(), output->clone().get(), sum->clone().get()).first);

     return Status{};
 }

 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, lws_hint());
     }
     while(window_collapsed.slide_window_slice_3D(slice));
 }
	/*
	* Copyright (c) 2017-2019 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/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/CLValidate.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/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;
	int 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;
	}

	Status validate_arguments_1DMaxShiftExpSum(const ITensorInfo input, const ITensorInfo max, const ITensorInfo output, const ITensorInfo sum)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(max, sum, output);

	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, max);

	const bool is_quantized_asymmetric = is_data_type_quantized_asymmetric(input->data_type());

	// Checks performed when output is configured
	if(output->total_size() != 0)
	{
	if(is_quantized_asymmetric)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::S32);
	}
	else
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
	}
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
	}

	// Checks performed when sum is configured
	if(sum->total_size() != 0)
	{
	if(is_quantized_asymmetric)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(sum, 1, DataType::S32);
	}
	else
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(max, sum);
	}
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(max, sum);
	}

	return Status{};
	}

	Status validate_arguments_1DNorm(const ITensorInfo input, const ITensorInfo sum, const ITensorInfo *output)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::S32, DataType::F16, DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(sum, output);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(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->data_type() == DataType::S32);

	// Checks performed when output is configured
	if(output->total_size() != 0)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
	if(!is_quantized_asymmetric)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
	}
	else
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::QASYMM8);
	ARM_COMPUTE_RETURN_ERROR_ON(output->quantization_info() != allowed_quantization_info);
	}
	}

	return Status{};
	}

	// Window validation

	std::pair<Status, Window> validate_and_configure_window_1DMaxShiftExpSum(ITensorInfo input, ITensorInfo max, ITensorInfo output, ITensorInfo sum)
	{
	// Output auto initialization if not yet initialized
	auto_init_if_empty(*sum, input->clone()->set_tensor_shape(max->tensor_shape()));
	auto_init_if_empty(output, input->clone());

	CLLogits1DMaxShiftExpSumKernel::ParallelReductionInfo parallel_reduction_info = CLLogits1DMaxShiftExpSumKernel::is_parallel_reduction(input->dimension(0));
	unsigned int vector_size = std::get<1>(parallel_reduction_info);
	const unsigned int num_elems_x = ceil_to_multiple(input->tensor_shape().x(), vector_size);
	Window win = calculate_max_window(*input, Steps(num_elems_x));

	AccessWindowHorizontal input_access(input, 0, num_elems_x);
	AccessWindowHorizontal max_access(max, 0, 1);
	AccessWindowHorizontal output_access(output, 0, num_elems_x);
	AccessWindowHorizontal sum_access(sum, 0, 1);

	bool window_changed = update_window_and_padding(win, input_access, max_access, output_access, sum_access);

	output_access.set_valid_region(win, input->valid_region());
	sum_access.set_valid_region(win, ValidRegion(Coordinates(), sum->tensor_shape()));

	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_1DNorm(ITensorInfo input, ITensorInfo output, ITensorInfo *sum)
	{
	const QuantizationInfo allowed_quantization_info = QuantizationInfo(1.f / 256, 0);
	const bool is_quantized_asymmetric = (input->data_type() == DataType::S32);
	const DataType output_data_type = is_quantized_asymmetric ? DataType::QASYMM8 : input->data_type();

	// Output auto initialization if not yet initialized
	auto_init_if_empty(*output,
	input->clone()->set_data_type(output_data_type).set_quantization_info(allowed_quantization_info));

	constexpr unsigned int num_elems_processed_per_iteration = 16;

	Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration));

	AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration);
	AccessWindowStatic sum_access(sum, 0, 0, 1, sum->dimension(1));
	AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);

	bool window_changed = update_window_and_padding(win, input_access, sum_access, output_access);

	output_access.set_valid_region(win, input->valid_region());

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

	} // namespace

	/*< 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_NULLPTR(input, max, sum, output);

	// Output auto initialization if not yet initialized
	auto_init_if_empty(*sum->info(), input->info()->clone()->set_tensor_shape(max->info()->tensor_shape()));
	auto_init_if_empty(output->info(), input->info()->clone());

	// Perform validation step
	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_1DMaxShiftExpSum(input->info(), max->info(), output->info(), sum->info()));

	_input = input;
	_max = max;
	_output = output;
	_sum = sum;

	const DataType dt = input->info()->data_type();
	const UniformQuantizationInfo qinfo = input->info()->quantization_info().uniform();
	const size_t reduction_dim_size = input->info()->dimension(0);

	// Set build options
	CLBuildOptions build_opts;
	build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(dt));
	build_opts.add_option_if(dt == DataType::F16, "-DUSE_F16");
	build_opts.add_option_if(is_data_type_float(dt) && (beta != 1.0f), "-DBETA=" + float_to_string_with_full_precision(beta));
	build_opts.add_options_if(is_data_type_quantized_asymmetric(dt), prepare_quantized_softmax_build_options(qinfo.scale, beta).options());

	cl::NDRange lws_hint(cl::NullRange);
	std::string kernel_name = is_data_type_quantized_asymmetric(dt) ? std::string("softmax_layer_max_shift_exp_sum_quantized_serial") :
	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 = is_data_type_quantized_asymmetric(dt) ? std::string("softmax_layer_max_shift_exp_sum_quantized_parallel") : 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");
	// 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::NDRange(_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
	auto win_config = validate_and_configure_window_1DMaxShiftExpSum(input->info(), max->info(), output->info(), sum->info());
	ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
	ICLKernel::configure_internal(win_config.second, lws_hint);
	}

	Status CLLogits1DMaxShiftExpSumKernel::validate(const ITensorInfo input, const ITensorInfo max, const ITensorInfo output, const ITensorInfo sum)
	{
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_1DMaxShiftExpSum(input, max, output, sum));
	ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_1DMaxShiftExpSum(input->clone().get(), max->clone().get(), output->clone().get(), sum->clone().get()).first);

	return Status{};
	}

	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_NULLPTR(input, sum, output);

	// 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();
	const UniformQuantizationInfo qinfo = input->info()->quantization_info().uniform();

	// Output auto initialization if not yet initialized
	auto_init_if_empty(*output->info(),
	input->info()->clone()->set_data_type(output_data_type).set_quantization_info(allowed_quantization_info));

	// Perform validation step
	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_1DNorm(input->info(), sum->info(), output->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_options_if(is_quantized_asymmetric,
	prepare_quantized_softmax_build_options(qinfo.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
	auto win_config = validate_and_configure_window_1DNorm(input->info(), output->info(), sum->info());
	ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
	ICLKernel::configure_internal(win_config.second);
	}

	Status CLLogits1DNormKernel::validate(const ITensorInfo input, const ITensorInfo sum, const ITensorInfo *output)
	{
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_1DNorm(input, sum, output));
	ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window_1DNorm(input->clone().get(), output->clone().get(), sum->clone().get()).first);

	return Status{};
	}

	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, lws_hint());
	}
	while(window_collapsed.slide_window_slice_3D(slice));
	}