src/core/gpu/cl/kernels/ClSoftmaxKernel.cpp - ml/ComputeLibrary - Gitiles

 /*
  * Copyright (c) 2017-2021 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to
  * deal in the Software without restriction, including without limitation the
  * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
  * sell copies of the Software, and to permit persons to whom the Software is
  * furnished to do so, subject to the following conditions:
  *
  * The above copyright notice and this permission notice shall be included in all
  * copies or substantial portions of the Software.
  *
  * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
 #include "src/core/gpu/cl/kernels/ClSoftmaxKernel.h"
 #include "arm_compute/core/CL/ICLTensor.h"
 #include "arm_compute/core/Utils.h"
 #include "arm_compute/core/experimental/Types.h"
 #include "arm_compute/core/utils/quantization/AsymmHelpers.h"
 #include "src/core/CL/CLValidate.h"
 #include "src/core/helpers/AutoConfiguration.h"
 #include "src/core/helpers/WindowHelpers.h"
 #include "support/Cast.h"
 #include "support/StringSupport.h"

 namespace arm_compute
 {
 namespace opencl
 {
 namespace kernels
 {
 namespace
 {
 /** 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 &src, const ITensorInfo &max, const ITensorInfo &dst, const ITensorInfo &sum)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&src);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&src, &max);

     const bool is_quantized_asymmetric = is_data_type_quantized_asymmetric(src.data_type());

     // Checks performed when output is configured
     if(dst.total_size() != 0)
     {
         if(is_quantized_asymmetric)
         {
             ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&dst, 1, DataType::S32);
         }
         else
         {
             ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&src, &dst);
         }
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&src, &dst);
     }

     // 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 &src, const ITensorInfo &sum, const ITensorInfo &dst, const SoftmaxKernelInfo &info)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&src);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&src, 1, DataType::S32, DataType::F16, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&src, &sum);
     ARM_COMPUTE_RETURN_ERROR_ON(info.is_log && !is_data_type_float(info.input_data_type));

     // Note: output should always have a scale of 1/256 and offset 0
     const QuantizationInfo allowed_quantization_info = get_softmax_output_quantization_info(info.input_data_type, info.is_log);
     const bool             is_quantized_asymmetric   = is_data_type_quantized_asymmetric(info.input_data_type);

     // Checks performed when output is configured
     if(dst.total_size() != 0)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&src, &dst);
         if(!is_quantized_asymmetric)
         {
             ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&src, &dst);
         }
         else
         {
             ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&dst, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED);
             ARM_COMPUTE_RETURN_ERROR_ON(dst.quantization_info() != allowed_quantization_info);
         }
     }

     return Status{};
 }
 } // 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;

 void ClLogits1DMaxShiftExpSumKernel::configure(const CLCompileContext &compile_context, const ITensorInfo &src, ITensorInfo &max, ITensorInfo &dst, ITensorInfo &sum, const SoftmaxKernelInfo &info)
 {
     auto padding_info = get_padding_info({ &src, &max, &dst, &sum });

     // Output auto initialization if not yet initialized
     auto_init_if_empty(sum, src.clone()->set_tensor_shape(max.tensor_shape()));
     auto_init_if_empty(dst, *src.clone());

     // Perform validation step
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_1DMaxShiftExpSum(src, max, dst, sum));

     const DataType                dt                 = src.data_type();
     const UniformQuantizationInfo qinfo              = src.quantization_info().uniform();
     const size_t                  reduction_dim_size = src.dimension(0);
     const float                   beta               = info.beta;
     const auto                    is_signed_qasymm8  = is_data_type_quantized_asymmetric_signed(info.input_data_type);
     const int                     min_value          = is_signed_qasymm8 ? CL_SCHAR_MIN : 0;

     ParallelReductionInfo parallel_reduction_info = is_parallel_reduction(reduction_dim_size);
     const unsigned int    vector_size             = adjust_vec_size(std::get<1>(parallel_reduction_info), reduction_dim_size);

     // Set build options
     CLBuildOptions build_opts;
     build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(dt));
     build_opts.add_option("-DMIN_VALUE=" + support::cpp11::to_string(min_value));
     build_opts.add_option("-DVECTOR_SIZE=" + support::cpp11::to_string(vector_size));
     build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(reduction_dim_size));
     build_opts.add_option("-DVECTOR_SIZE_LEFTOVER=" + support::cpp11::to_string(reduction_dim_size % 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");
     build_opts.add_option_if(is_signed_qasymm8, "-DQASYMM8_SIGNED");
     build_opts.add_option_if(is_data_type_float(dt) && (beta != 1.0f), "-DBETA=" + float_to_string_with_full_precision(beta));
     build_opts.add_option_if(is_data_type_float(dt) && info.is_log, "-DLOG_SOFTMAX");
     build_opts.add_option_if(is_data_type_float(dt), "-DMINVAL=" + ((dt == DataType::F16) ? std::string("-HALF_MAX") : std::string("-FLT_MAX")));
     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 = std::string("softmax_layer_max_shift_exp_sum_") + (is_data_type_quantized_asymmetric(dt) ? "quantized_" : "");

     // Configure parallel kernel if needed
     if(std::get<0>(parallel_reduction_info))
     {
         kernel_name += "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);
     }
     else
     {
         kernel_name += "serial";
     }

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

     // Configure window
     Window win = calculate_max_window(src, Steps(reduction_dim_size));
     IClKernel::configure_internal(win, lws_hint);

     ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
 }

 Status ClLogits1DMaxShiftExpSumKernel::validate(const ITensorInfo &src, const ITensorInfo &max, const ITensorInfo &dst, const ITensorInfo &sum)
 {
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_1DMaxShiftExpSum(src, max, dst, sum));
     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_op(ITensorPack &tensors, const Window &window, ::cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);

     auto src = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC));
     auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
     auto max = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_INT_0));
     auto sum = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_INT_1));

     ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst, max, sum);

     // 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(src->info()->dimension(0));
     if(std::get<0>(parallel_reduction_info))
     {
         // Launch grid_size parallel work items
         window_collapsed.set(Window::DimX, Window::Dimension(0, _grid_size, 1));
     }

     // Get slices
     Window slice = window_collapsed.first_slice_window_3D();
     do
     {
         unsigned int idx = 0;
         // Set inputs
         add_3D_tensor_argument(idx, src, slice);
         add_3D_tensor_argument(idx, max, slice);
         add_3D_tensor_argument(idx, dst, slice);
         add_3D_tensor_argument(idx, sum, slice);
         enqueue(queue, *this, slice, lws_hint());
     }
     while(window_collapsed.slide_window_slice_3D(slice));
 }

 void ClLogits1DNormKernel::configure(const CLCompileContext &compile_context, const ITensorInfo &src, const ITensorInfo &sum, ITensorInfo &dst, const SoftmaxKernelInfo &info)
 {
     auto padding_info = get_padding_info({ &src, &dst, &sum });

     // Note: output should always have a scale of 1/256 and offset 0
     const bool                    is_quantized_asymmetric   = is_data_type_quantized_asymmetric(info.input_data_type);
     const DataType                output_data_type          = info.input_data_type;
     const QuantizationInfo        allowed_quantization_info = get_softmax_output_quantization_info(info.input_data_type, info.is_log);
     const UniformQuantizationInfo qinfo                     = src.quantization_info().uniform();

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

     // Perform validation step
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_1DNorm(src, sum, dst, info));

     const auto         is_signed_qasymm8 = is_data_type_quantized_asymmetric_signed(info.input_data_type);
     const int          min_value         = is_signed_qasymm8 ? CL_SCHAR_MIN : 0;
     const unsigned int vector_size       = adjust_vec_size(16, src.dimension(0));

     // Set build options
     CLBuildOptions build_opts;
     build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(info.input_data_type));
     build_opts.add_option("-DMIN_VALUE=" + support::cpp11::to_string(min_value));
     build_opts.add_option("-DVECTOR_SIZE=" + support::cpp11::to_string(vector_size));
     build_opts.add_option("-DVECTOR_SIZE_LEFTOVER=" + support::cpp11::to_string(src.dimension(0) % vector_size));
     build_opts.add_option_if(is_data_type_quantized_asymmetric_signed(info.input_data_type), "-DQASYMM8_SIGNED");
     build_opts.add_options_if(is_quantized_asymmetric,
                               prepare_quantized_softmax_build_options(qinfo.scale, info.beta).options());
     build_opts.add_option_if(info.is_log, "-DLOG_SOFTMAX");

     // Create kernel
     std::string kernel_name = std::string("softmax_layer_norm") + (is_quantized_asymmetric ? "_quantized" : "");
     _kernel                 = create_kernel(compile_context, kernel_name, build_opts.options());

     // Configure window
     auto win = calculate_max_window(src, Steps(vector_size));
     ICLKernel::configure_internal(win);

     ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
 }

 Status ClLogits1DNormKernel::validate(const ITensorInfo &src, const ITensorInfo &sum, const ITensorInfo &dst, const SoftmaxKernelInfo &info)
 {
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_1DNorm(src, sum, dst, info));

     return Status{};
 }

 void ClLogits1DNormKernel::run_op(ITensorPack &tensors, const Window &window, ::cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);

     auto src = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC));
     auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
     auto sum = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_INT_0));

     ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst, sum);

     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, src, slice);
         add_3D_tensor_argument(idx, sum, sum_slice);
         add_3D_tensor_argument(idx, dst, slice);
         enqueue(queue, *this, slice, lws_hint());
     }
     while(window_collapsed.slide_window_slice_3D(slice));
 }
 } // namespace kernels
 } // namespace opencl
 } // namespace arm_compute
	/*
	* Copyright (c) 2017-2021 Arm Limited.
	*
	* SPDX-License-Identifier: MIT
	*
	* Permission is hereby granted, free of charge, to any person obtaining a copy
	* of this software and associated documentation files (the "Software"), to
	* deal in the Software without restriction, including without limitation the
	* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
	* sell copies of the Software, and to permit persons to whom the Software is
	* furnished to do so, subject to the following conditions:
	*
	* The above copyright notice and this permission notice shall be included in all
	* copies or substantial portions of the Software.
	*
	* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
	* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
	* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
	* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
	* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
	* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
	* SOFTWARE.
	*/
	#include "src/core/gpu/cl/kernels/ClSoftmaxKernel.h"
	#include "arm_compute/core/CL/ICLTensor.h"
	#include "arm_compute/core/Utils.h"
	#include "arm_compute/core/experimental/Types.h"
	#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
	#include "src/core/CL/CLValidate.h"
	#include "src/core/helpers/AutoConfiguration.h"
	#include "src/core/helpers/WindowHelpers.h"
	#include "support/Cast.h"
	#include "support/StringSupport.h"

	namespace arm_compute
	{
	namespace opencl
	{
	namespace kernels
	{
	namespace
	{
	/** 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 &src, const ITensorInfo &max, const ITensorInfo &dst, const ITensorInfo &sum)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&src);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&src, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&src, &max);

	const bool is_quantized_asymmetric = is_data_type_quantized_asymmetric(src.data_type());

	// Checks performed when output is configured
	if(dst.total_size() != 0)
	{
	if(is_quantized_asymmetric)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&dst, 1, DataType::S32);
	}
	else
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&src, &dst);
	}
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&src, &dst);
	}

	// 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 &src, const ITensorInfo &sum, const ITensorInfo &dst, const SoftmaxKernelInfo &info)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(&src);
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&src, 1, DataType::S32, DataType::F16, DataType::F32);
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&src, &sum);
	ARM_COMPUTE_RETURN_ERROR_ON(info.is_log && !is_data_type_float(info.input_data_type));

	// Note: output should always have a scale of 1/256 and offset 0
	const QuantizationInfo allowed_quantization_info = get_softmax_output_quantization_info(info.input_data_type, info.is_log);
	const bool is_quantized_asymmetric = is_data_type_quantized_asymmetric(info.input_data_type);

	// Checks performed when output is configured
	if(dst.total_size() != 0)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&src, &dst);
	if(!is_quantized_asymmetric)
	{
	ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&src, &dst);
	}
	else
	{
	ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&dst, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED);
	ARM_COMPUTE_RETURN_ERROR_ON(dst.quantization_info() != allowed_quantization_info);
	}
	}

	return Status{};
	}
	} // 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;

	void ClLogits1DMaxShiftExpSumKernel::configure(const CLCompileContext &compile_context, const ITensorInfo &src, ITensorInfo &max, ITensorInfo &dst, ITensorInfo &sum, const SoftmaxKernelInfo &info)
	{
	auto padding_info = get_padding_info({ &src, &max, &dst, &sum });

	// Output auto initialization if not yet initialized
	auto_init_if_empty(sum, src.clone()->set_tensor_shape(max.tensor_shape()));
	auto_init_if_empty(dst, *src.clone());

	// Perform validation step
	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_1DMaxShiftExpSum(src, max, dst, sum));

	const DataType dt = src.data_type();
	const UniformQuantizationInfo qinfo = src.quantization_info().uniform();
	const size_t reduction_dim_size = src.dimension(0);
	const float beta = info.beta;
	const auto is_signed_qasymm8 = is_data_type_quantized_asymmetric_signed(info.input_data_type);
	const int min_value = is_signed_qasymm8 ? CL_SCHAR_MIN : 0;

	ParallelReductionInfo parallel_reduction_info = is_parallel_reduction(reduction_dim_size);
	const unsigned int vector_size = adjust_vec_size(std::get<1>(parallel_reduction_info), reduction_dim_size);

	// Set build options
	CLBuildOptions build_opts;
	build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(dt));
	build_opts.add_option("-DMIN_VALUE=" + support::cpp11::to_string(min_value));
	build_opts.add_option("-DVECTOR_SIZE=" + support::cpp11::to_string(vector_size));
	build_opts.add_option("-DSRC_WIDTH=" + support::cpp11::to_string(reduction_dim_size));
	build_opts.add_option("-DVECTOR_SIZE_LEFTOVER=" + support::cpp11::to_string(reduction_dim_size % 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");
	build_opts.add_option_if(is_signed_qasymm8, "-DQASYMM8_SIGNED");
	build_opts.add_option_if(is_data_type_float(dt) && (beta != 1.0f), "-DBETA=" + float_to_string_with_full_precision(beta));
	build_opts.add_option_if(is_data_type_float(dt) && info.is_log, "-DLOG_SOFTMAX");
	build_opts.add_option_if(is_data_type_float(dt), "-DMINVAL=" + ((dt == DataType::F16) ? std::string("-HALF_MAX") : std::string("-FLT_MAX")));
	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 = std::string("softmax_layer_max_shift_exp_sum_") + (is_data_type_quantized_asymmetric(dt) ? "quantized_" : "");

	// Configure parallel kernel if needed
	if(std::get<0>(parallel_reduction_info))
	{
	kernel_name += "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);
	}
	else
	{
	kernel_name += "serial";
	}

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

	// Configure window
	Window win = calculate_max_window(src, Steps(reduction_dim_size));
	IClKernel::configure_internal(win, lws_hint);

	ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
	}

	Status ClLogits1DMaxShiftExpSumKernel::validate(const ITensorInfo &src, const ITensorInfo &max, const ITensorInfo &dst, const ITensorInfo &sum)
	{
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_1DMaxShiftExpSum(src, max, dst, sum));
	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_op(ITensorPack &tensors, const Window &window, ::cl::CommandQueue &queue)
	{
	ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
	ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);

	auto src = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC));
	auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
	auto max = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_INT_0));
	auto sum = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_INT_1));

	ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst, max, sum);

	// 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(src->info()->dimension(0));
	if(std::get<0>(parallel_reduction_info))
	{
	// Launch grid_size parallel work items
	window_collapsed.set(Window::DimX, Window::Dimension(0, _grid_size, 1));
	}

	// Get slices
	Window slice = window_collapsed.first_slice_window_3D();
	do
	{
	unsigned int idx = 0;
	// Set inputs
	add_3D_tensor_argument(idx, src, slice);
	add_3D_tensor_argument(idx, max, slice);
	add_3D_tensor_argument(idx, dst, slice);
	add_3D_tensor_argument(idx, sum, slice);
	enqueue(queue, *this, slice, lws_hint());
	}
	while(window_collapsed.slide_window_slice_3D(slice));
	}

	void ClLogits1DNormKernel::configure(const CLCompileContext &compile_context, const ITensorInfo &src, const ITensorInfo &sum, ITensorInfo &dst, const SoftmaxKernelInfo &info)
	{
	auto padding_info = get_padding_info({ &src, &dst, &sum });

	// Note: output should always have a scale of 1/256 and offset 0
	const bool is_quantized_asymmetric = is_data_type_quantized_asymmetric(info.input_data_type);
	const DataType output_data_type = info.input_data_type;
	const QuantizationInfo allowed_quantization_info = get_softmax_output_quantization_info(info.input_data_type, info.is_log);
	const UniformQuantizationInfo qinfo = src.quantization_info().uniform();

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

	// Perform validation step
	ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_1DNorm(src, sum, dst, info));

	const auto is_signed_qasymm8 = is_data_type_quantized_asymmetric_signed(info.input_data_type);
	const int min_value = is_signed_qasymm8 ? CL_SCHAR_MIN : 0;
	const unsigned int vector_size = adjust_vec_size(16, src.dimension(0));

	// Set build options
	CLBuildOptions build_opts;
	build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(info.input_data_type));
	build_opts.add_option("-DMIN_VALUE=" + support::cpp11::to_string(min_value));
	build_opts.add_option("-DVECTOR_SIZE=" + support::cpp11::to_string(vector_size));
	build_opts.add_option("-DVECTOR_SIZE_LEFTOVER=" + support::cpp11::to_string(src.dimension(0) % vector_size));
	build_opts.add_option_if(is_data_type_quantized_asymmetric_signed(info.input_data_type), "-DQASYMM8_SIGNED");
	build_opts.add_options_if(is_quantized_asymmetric,
	prepare_quantized_softmax_build_options(qinfo.scale, info.beta).options());
	build_opts.add_option_if(info.is_log, "-DLOG_SOFTMAX");

	// Create kernel
	std::string kernel_name = std::string("softmax_layer_norm") + (is_quantized_asymmetric ? "_quantized" : "");
	_kernel = create_kernel(compile_context, kernel_name, build_opts.options());

	// Configure window
	auto win = calculate_max_window(src, Steps(vector_size));
	ICLKernel::configure_internal(win);

	ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info));
	}

	Status ClLogits1DNormKernel::validate(const ITensorInfo &src, const ITensorInfo &sum, const ITensorInfo &dst, const SoftmaxKernelInfo &info)
	{
	ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_1DNorm(src, sum, dst, info));

	return Status{};
	}

	void ClLogits1DNormKernel::run_op(ITensorPack &tensors, const Window &window, ::cl::CommandQueue &queue)
	{
	ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
	ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);

	auto src = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC));
	auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST));
	auto sum = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_INT_0));

	ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst, sum);

	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, src, slice);
	add_3D_tensor_argument(idx, sum, sum_slice);
	add_3D_tensor_argument(idx, dst, slice);
	enqueue(queue, *this, slice, lws_hint());
	}
	while(window_collapsed.slide_window_slice_3D(slice));
	}
	} // namespace kernels
	} // namespace opencl
	} // namespace arm_compute