src/core/NEON/kernels/NESoftmaxLayerKernel.cpp

/*
 * Copyright (c) 2017-2020 Arm Limited.
 *
 * SPDX-License-Identifier: MIT
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to
 * deal in the Software without restriction, including without limitation the
 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
 * sell copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in all
 * copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */
#include "src/core/NEON/kernels/NESoftmaxLayerKernel.h"

#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensor.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 "src/core/AccessWindowStatic.h"
#include "src/core/CPP/Validate.h"
#include "src/core/NEON/NEFixedPoint.h"
#include "src/core/NEON/NEMath.h"
#include "src/core/NEON/wrapper/wrapper.h"
#include "src/core/helpers/AutoConfiguration.h"
#include "src/core/helpers/WindowHelpers.h"
#include "support/SaturateCast.h"

#include <algorithm>
#include <arm_neon.h>
#include <cfloat>
#include <functional>

namespace arm_compute
{
template <typename float_vec_type, typename int_vec_type>
int_vec_type convert_float_to_int(const float_vec_type &in);

template <typename float_vec_type, typename int_vec_type>
float_vec_type convert_int_to_float(const int_vec_type &in);

template <>
uint8x16_t convert_float_to_int<float32x4x4_t, uint8x16_t>(const float32x4x4_t &in)
{
    uint8x16_t out;
    convert_float32x4x4_to_uint8x16(in, out);
    return out;
}

template <>
int8x16_t convert_float_to_int<float32x4x4_t, int8x16_t>(const float32x4x4_t &in)
{
    int8x16_t out;
    convert_float32x4x4_to_int8x16(in, out);
    return out;
}

template <>
float32x4x4_t convert_int_to_float<float32x4x4_t, uint8x16_t>(const uint8x16_t &in)
{
    return convert_uint8x16_to_float32x4x4(in);
}

template <>
float32x4x4_t convert_int_to_float<float32x4x4_t, int8x16_t>(const int8x16_t &in)
{
    return convert_int8x16_to_float32x4x4(in);
}

namespace
{
Status validate_arguments_logits_1d_max(const ITensorInfo &input, const ITensorInfo &output)
{
    ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(&input);
    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32);

    // Validate in case of configured output
    if(output.total_size() != 0)
    {
        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input, &output);
        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(&input, &output);
        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output.tensor_shape(), TensorShape(input.tensor_shape()).set(0, 1));
    }

    return Status{};
}

template <typename T>
void logits_1d_max(const ITensor &in, ITensor &out, const Window &window)
{
    /** NEON vector tag type. */
    using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>;

    constexpr int window_step_x  = 16 / sizeof(T);
    const auto    window_start_x = static_cast<int>(window.x().start());
    const auto    window_end_x   = static_cast<int>(window.x().end());

    Window win{ window };
    win.set(Window::DimX, Window::Dimension(0, 1, 1));
    Iterator input(&in, win);
    Iterator output(&out, win);

    const int sum_stages = log2(window_step_x / 2);
    execute_window_loop(win, [&](const Coordinates &)
    {
        // Get pointers
        const auto in_ptr  = reinterpret_cast<const T *>(input.ptr());
        const auto out_ptr = reinterpret_cast<T *>(output.ptr());

        // Init max value
        auto vec_max = wrapper::vdup_n(support::cpp11::lowest<T>(), ExactTagType{});
        int  x       = window_start_x;

        for(; x <= (window_end_x - window_step_x); x += window_step_x)
        {
            const auto current_value = wrapper::vloadq(in_ptr + x);
            vec_max                  = wrapper::vmax(vec_max, current_value);
        }
        auto carry_max = wrapper::vpmax(wrapper::vgethigh(vec_max), wrapper::vgetlow(vec_max));

        for(int i = 0; i < sum_stages; ++i)
        {
            carry_max = wrapper::vpmax(carry_max, carry_max);
        }
        T max_val = wrapper::vgetlane(carry_max, 0);

        // Compute left-over elements
        for(; x < window_end_x; ++x)
        {
            max_val = *(in_ptr + x) > max_val ? *(in_ptr + x) : max_val;
        }

        *out_ptr = max_val;
    },
    input, output);
}
} // namespace

NELogits1DMaxKernel::NELogits1DMaxKernel()
    : _func(nullptr), _border_size()
{
}

BorderSize NELogits1DMaxKernel::border_size() const
{
    return _border_size;
}

void NELogits1DMaxKernel::configure(const ITensor *input, ITensor *output)
{
    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
    ARM_COMPUTE_ERROR_ON_NULLPTR(input->info(), output->info());
    // Perform validation step
    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_logits_1d_max(*input->info(), *output->info()));
    // Configure kernel window

    // Softmax across the x dimension
    const TensorShape output_shape = TensorShape(input->info()->tensor_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()->quantization_info());

    Window      win = calculate_max_window(*input->info(), Steps());
    Coordinates coord;
    coord.set_num_dimensions(output->info()->num_dimensions());
    output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape()));

    switch(input->info()->data_type())
    {
        case DataType::QASYMM8:
            _func = &logits_1d_max<qasymm8_t>;
            break;
        case DataType::QASYMM8_SIGNED:
            _func = &logits_1d_max<qasymm8_signed_t>;
            break;
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
        case DataType::F16:
            _func = &logits_1d_max<float16_t>;
            break;
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
        case DataType::F32:
            _func = &logits_1d_max<float>;
            break;
        default:
            ARM_COMPUTE_ERROR("Unsupported data type.");
    }

    _input  = input;
    _output = output;

    const int input_width                       = input->info()->valid_region().shape.x();
    const int num_elems_processed_per_iteration = 16U / data_size_from_type(input->info()->data_type());
    const int num_elems_read_per_iteration      = ceil_to_multiple(input_width, num_elems_processed_per_iteration);

    _border_size = BorderSize(0, num_elems_read_per_iteration - input_width, 0, 0);

    INEKernel::configure(win);
}

Status NELogits1DMaxKernel::validate(const ITensorInfo *input, const ITensorInfo *output)
{
    ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_logits_1d_max(*input, *output));

    return Status{};
}

void NELogits1DMaxKernel::run(const Window &window, const ThreadInfo &info)
{
    ARM_COMPUTE_UNUSED(info);
    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
    ARM_COMPUTE_ERROR_ON(_func == nullptr);

    (*_func)(*_input, *_output, window);
}

namespace
{
Status validate_arguments_logits_softmax(const ITensorInfo &input, const ITensorInfo &max,
                                         const ITensorInfo &output, const float beta, const ITensorInfo &tmp, bool is_log)
{
    ARM_COMPUTE_UNUSED(beta);
    // Check input
    ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(&input);
    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(&input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::F16, DataType::F32);

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

    // Check max
    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input, &max);
    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(TensorShape(input.tensor_shape()).set(0, 1), max.tensor_shape());
    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(&input, &max);

    // Check output if configured
    if(output.total_size() != 0)
    {
        const QuantizationInfo output_quantization = is_quantized_asymmetric ? arm_compute::get_softmax_output_quantization_info(input.data_type(), is_log) : output.quantization_info();
        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(&input, &output);
        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&input, &output);
        ARM_COMPUTE_RETURN_ERROR_ON(output.quantization_info() != output_quantization);
    }

    // Check tmp if configured
    if(tmp.total_size() != 0)
    {
        const DataType tmp_data_type = is_quantized_asymmetric ? DataType::F32 : input.data_type();
        ARM_COMPUTE_RETURN_ERROR_ON(tmp.data_type() != tmp_data_type);
        // We could potentially reduce tmp memory if we could predict or make an assumption
        // on the maximum number of threads that will run in parallel.
        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&input, &tmp);
    }

    return Status{};
}
template <typename T, bool is_log>
void logits_1d_softmax_qasymm8(const ITensor &in, const ITensor &max, void *const tmp, ITensor &out, const float beta, const Window &window)
{
    static_assert(std::is_same<T, qasymm8_t>::value
                  || std::is_same<T, qasymm8_signed_t>::value,
                  "quantized type should be either qasymm8_t or qasymm8_signed_t.");

    const int start_x     = in.info()->valid_region().anchor.x();
    const int input_width = in.info()->valid_region().shape.x();

    const float scale_beta     = -beta * in.info()->quantization_info().uniform().scale;
    const auto  scale_beta_vec = vdupq_n_f32(scale_beta);

    Iterator      in_it(&in, window);
    Iterator      max_it(&max, window);
    Iterator      out_it(&out, window);
    constexpr int vec_size = 16;

    execute_window_loop(window, [&](const Coordinates &)
    {
        /* Get pointers */
        const auto in_ptr  = reinterpret_cast<const T *>(in_it.ptr()) + start_x;
        const auto out_ptr = reinterpret_cast<T *>(out_it.ptr()) + start_x;
        const auto tmp_ptr = reinterpret_cast<float *>(tmp);

        float sum{};
        float sum_inversed{};

        /* Compute exponentials and sum */
        {
            /* Get max value */
            const auto max_val = *reinterpret_cast<const T *>(max_it.ptr());
            const auto vec_max = wrapper::vdup_n(max_val, wrapper::traits::vector_128_tag{});

            /* Init sum to zero */
            float32x4x4_t vec_sum =
            {
                vdupq_n_f32(0.f),
                vdupq_n_f32(0.f),
                vdupq_n_f32(0.f),
                vdupq_n_f32(0.f),
            };

            /* Loop over row and compute exponentials and sum */
            int x = 0;
            for(; x <= (input_width - vec_size); x += vec_size)
            {
                auto vec_elements     = wrapper::vloadq(in_ptr + x);
                vec_elements          = wrapper::vqsub(vec_max, vec_elements);
                auto vec_elements_flt = convert_int_to_float<float32x4x4_t>(vec_elements);

                if(is_log)
                {
                    vec_elements_flt.val[0] = vmulq_f32(vec_elements_flt.val[0], scale_beta_vec);
                    vec_elements_flt.val[1] = vmulq_f32(vec_elements_flt.val[1], scale_beta_vec);
                    vec_elements_flt.val[2] = vmulq_f32(vec_elements_flt.val[2], scale_beta_vec);
                    vec_elements_flt.val[3] = vmulq_f32(vec_elements_flt.val[3], scale_beta_vec);
                    vec_sum.val[0]          = vaddq_f32(vec_sum.val[0], vexpq_f32(vec_elements_flt.val[0]));
                    vec_sum.val[1]          = vaddq_f32(vec_sum.val[1], vexpq_f32(vec_elements_flt.val[1]));
                    vec_sum.val[2]          = vaddq_f32(vec_sum.val[2], vexpq_f32(vec_elements_flt.val[2]));
                    vec_sum.val[3]          = vaddq_f32(vec_sum.val[3], vexpq_f32(vec_elements_flt.val[3]));
                }
                else
                {
                    vec_elements_flt.val[0] = vexpq_f32(vmulq_f32(vec_elements_flt.val[0], scale_beta_vec));
                    vec_elements_flt.val[1] = vexpq_f32(vmulq_f32(vec_elements_flt.val[1], scale_beta_vec));
                    vec_elements_flt.val[2] = vexpq_f32(vmulq_f32(vec_elements_flt.val[2], scale_beta_vec));
                    vec_elements_flt.val[3] = vexpq_f32(vmulq_f32(vec_elements_flt.val[3], scale_beta_vec));
                    vec_sum.val[0]          = vaddq_f32(vec_sum.val[0], vec_elements_flt.val[0]);
                    vec_sum.val[1]          = vaddq_f32(vec_sum.val[1], vec_elements_flt.val[1]);
                    vec_sum.val[2]          = vaddq_f32(vec_sum.val[2], vec_elements_flt.val[2]);
                    vec_sum.val[3]          = vaddq_f32(vec_sum.val[3], vec_elements_flt.val[3]);
                }

                vst4q_f32(tmp_ptr + x, vec_elements_flt);
            }

            /* Reduce sum */
            const auto sum_16_byte = vaddq_f32(vaddq_f32(vec_sum.val[0], vec_sum.val[1]), vaddq_f32(vec_sum.val[2], vec_sum.val[3]));
            auto       sum_res     = vpadd_f32(vget_high_f32(sum_16_byte), vget_low_f32(sum_16_byte));
            sum_res                = vpadd_f32(sum_res, sum_res);
            sum                    = wrapper::vgetlane(sum_res, 0);

            /* Run remaining elements */
            for(; x < input_width; ++x)
            {
                float element{};
                if(is_log)
                {
                    element = (max_val - in_ptr[x]) * scale_beta;
                    sum += std::exp(element);
                }
                else
                {
                    element = std::exp((max_val - in_ptr[x]) * scale_beta);
                    sum += element;
                }

                tmp_ptr[x] = element;
            }

            if(!is_log)
            {
                sum_inversed = 256.f / sum;
            }
            else
            {
                sum = std::log(sum);
            }
        }

        /* Normalize exponentials */
        {
            constexpr bool is_qasymm8_signed = std::is_same<T, qasymm8_signed_t>::value;
            /* Loop over row and compute softmax */
            int x = 0;
            for(; x <= (input_width - vec_size); x += vec_size)
            {
                using int_vec_type   = wrapper::traits::neon_vector_t<T, 16>;
                float32x4x4_t vec_in = vld4q_f32(tmp_ptr + x);
                int_vec_type  normalized_value{};
                if(is_log)
                {
                    const float32x4x4_t sub =
                    {
                        vsubq_f32(vec_in.val[0], vdupq_n_f32(sum)),
                        vsubq_f32(vec_in.val[1], vdupq_n_f32(sum)),
                        vsubq_f32(vec_in.val[2], vdupq_n_f32(sum)),
                        vsubq_f32(vec_in.val[3], vdupq_n_f32(sum)),
                    };
                    normalized_value = convert_float_to_int<float32x4x4_t, int_vec_type>(sub);
                }
                else
                {
                    float32x4x4_t mul =
                    {
                        vmulq_f32(vec_in.val[0], vdupq_n_f32(sum_inversed)),
                        vmulq_f32(vec_in.val[1], vdupq_n_f32(sum_inversed)),
                        vmulq_f32(vec_in.val[2], vdupq_n_f32(sum_inversed)),
                        vmulq_f32(vec_in.val[3], vdupq_n_f32(sum_inversed)),
                    };

                    if(is_qasymm8_signed)
                    {
                        const auto offset_vec = wrapper::vdup_n(128.f, wrapper::traits::vector_128_tag{});
                        mul.val[0]            = wrapper::vsub(mul.val[0], offset_vec);
                        mul.val[1]            = wrapper::vsub(mul.val[1], offset_vec);
                        mul.val[2]            = wrapper::vsub(mul.val[2], offset_vec);
                        mul.val[3]            = wrapper::vsub(mul.val[3], offset_vec);
                    }

                    normalized_value = convert_float_to_int<float32x4x4_t, int_vec_type>(mul);
                }
                wrapper::vstore(out_ptr + x, normalized_value);
            }
            /* Run remaining elements */
            for(; x < input_width; ++x)
            {
                if(is_log)
                {
                    out_ptr[x] = utils::cast::saturate_cast<T>(tmp_ptr[x] - sum);
                }
                else
                {
                    out_ptr[x] = utils::cast::saturate_cast<T>((tmp_ptr[x] * sum_inversed) - (is_qasymm8_signed ? 128.f : 0));
                }
            }
        }
    },
    in_it, max_it, out_it);
}

template <typename T, bool is_log = false>
void logits_1d_softmax_float(const ITensor &in, const ITensor &max, void *const tmp,
                             ITensor &out, const float beta, const Window &window)
{
    const int start_x     = in.info()->valid_region().anchor.x();
    const int input_width = in.info()->valid_region().shape.x();

    Iterator in_it(&in, window);
    Iterator max_it(&max, window);
    Iterator out_it(&out, window);

    /** NEON vector tag type. */
    using ExactTagType = typename wrapper::traits::neon_bitvector_tag_t<T, wrapper::traits::BitWidth::W128>;

    constexpr int vec_size   = 16 / sizeof(T);
    const int     sum_stages = log2(vec_size / 2);

    execute_window_loop(window, [&](const Coordinates &)
    {
        /* Get pointers */
        const auto in_ptr  = reinterpret_cast<const T *>(in_it.ptr()) + start_x;
        const auto out_ptr = reinterpret_cast<T *>(out_it.ptr()) + start_x;
        const auto tmp_ptr = reinterpret_cast<T *>(tmp);

        T sum{};
        T sum_inversed{};

        /* Compute exponentials and sum */
        {
            /* Get max value */
            const auto max_val = *reinterpret_cast<const T *>(max_it.ptr());
            const auto vec_max = wrapper::vdup_n(max_val, ExactTagType{});

            /* Init sum to zero */
            auto vec_sum = wrapper::vdup_n(static_cast<T>(0), ExactTagType{});

            /* Loop over row and compute exponentials and sum */
            int x = 0;
            for(; x <= (input_width - vec_size); x += vec_size)
            {
                auto vec_elements = wrapper::vloadq(in_ptr + x);
                vec_elements      = wrapper::vsub(vec_elements, vec_max);
                if(is_log)
                {
                    vec_elements = wrapper::vmul(vec_elements, wrapper::vdup_n(static_cast<T>(beta), ExactTagType{}));
                    vec_sum      = wrapper::vadd(vec_sum, wrapper::vexpq(vec_elements));
                }
                else
                {
                    vec_elements = wrapper::vexpq(wrapper::vmul(vec_elements, wrapper::vdup_n(static_cast<T>(beta), ExactTagType{})));
                    vec_sum      = wrapper::vadd(vec_sum, vec_elements);
                }
                wrapper::vstore(tmp_ptr + x, vec_elements);
            }

            /* Reduce sum */
            auto sum_res = wrapper::vpadd(wrapper::vgethigh(vec_sum), wrapper::vgetlow(vec_sum));
            for(int i = 0; i < sum_stages; ++i)
            {
                sum_res = wrapper::vpadd(sum_res, sum_res);
            }
            sum = wrapper::vgetlane(sum_res, 0);

            /* Run remaining elements */
            for(; x < input_width; ++x)
            {
                T element{};

                if(is_log)
                {
                    element = (in_ptr[x] - max_val) * beta;
                    sum += std::exp(element);
                }
                else
                {
                    element = std::exp((in_ptr[x] - max_val) * beta);
                    sum += element;
                }
                tmp_ptr[x] = element;
            }

            if(!is_log)
            {
                sum_inversed = T(1) / sum;
            }
            else
            {
                sum = static_cast<T>(std::log(sum));
            }
        }

        /* Normalize exponentials */
        {
            /* Loop over row and compute softmax */
            int x = 0;
            for(; x <= (input_width - vec_size); x += vec_size)
            {
                auto vec_in           = wrapper::vloadq(tmp_ptr + x);
                auto normalized_value = wrapper::vdup_n(static_cast<T>(0), ExactTagType{});
                if(is_log)
                {
                    normalized_value = wrapper::vsub(vec_in, wrapper::vdup_n(static_cast<T>(sum), ExactTagType{}));
                }
                else
                {
                    normalized_value = wrapper::vmul(vec_in, wrapper::vdup_n(static_cast<T>(sum_inversed), ExactTagType{}));
                }
                wrapper::vstore(out_ptr + x, normalized_value);
            }
            /* Run remaining elements */
            for(; x < input_width; ++x)
            {
                if(is_log)
                {
                    out_ptr[x] = tmp_ptr[x] - sum;
                }
                else
                {
                    out_ptr[x] = tmp_ptr[x] * sum_inversed;
                }
            }
        }
    },
    in_it, max_it, out_it);
}
} // namespace

template <bool IS_LOG>
NELogits1DSoftmaxKernel<IS_LOG>::NELogits1DSoftmaxKernel()
    : _func(nullptr), _input(nullptr), _max(nullptr), _output(nullptr), _beta(1.0f), _tmp(nullptr)
{
}

template <bool IS_LOG>
void NELogits1DSoftmaxKernel<IS_LOG>::configure(const ITensor *input, const ITensor *max, ITensor *output, const float beta, ITensor *tmp)
{
    ARM_COMPUTE_ERROR_ON_NULLPTR(input, max, output, tmp);
    ARM_COMPUTE_ERROR_ON_NULLPTR(input->info(), max->info(), output->info(), tmp->info());
    // Perform validation step
    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments_logits_softmax(*input->info(), *max->info(), *output->info(), beta, *tmp->info(), IS_LOG));

    // Configure kernel window
    const bool is_quantized_asymmetric = is_data_type_quantized_asymmetric(input->info()->data_type());

    // Output auto initialization if not yet initialized
    const QuantizationInfo output_quantization = is_quantized_asymmetric ? arm_compute::get_softmax_output_quantization_info(input->info()->data_type(), IS_LOG) : output->info()->quantization_info();
    auto_init_if_empty(*output->info(), TensorInfo(*input->info()).set_quantization_info(output_quantization).reset_padding());

    // Tmp auto initialization if not yet initialized
    const DataType tmp_data_type = is_quantized_asymmetric ? DataType::F32 : input->info()->data_type();
    auto_init_if_empty(*tmp->info(), TensorInfo(*input->info()).set_data_type(tmp_data_type).reset_padding());

    // Configure kernel window
    Window      win = calculate_max_window(*max->info(), Steps());
    Coordinates coord;
    coord.set_num_dimensions(output->info()->num_dimensions());
    output->info()->set_valid_region(ValidRegion(coord, output->info()->tensor_shape()));

    switch(input->info()->data_type())
    {
        case DataType::QASYMM8:
            _func = &logits_1d_softmax_qasymm8<qasymm8_t, IS_LOG>;
            break;
        case DataType::QASYMM8_SIGNED:
            _func = &logits_1d_softmax_qasymm8<qasymm8_signed_t, IS_LOG>;
            break;
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
        case DataType::F16:
            _func = &logits_1d_softmax_float<float16_t, IS_LOG>;
            break;
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
        case DataType::F32:
            _func = &logits_1d_softmax_float<float, IS_LOG>;
            break;
        default:
            ARM_COMPUTE_ERROR("Unsupported data type.");
            break;
    }

    _input  = input;
    _max    = max;
    _output = output;
    _beta   = beta;
    _tmp    = tmp;

    INEKernel::configure(win);
}

template <bool IS_LOG>
Status NELogits1DSoftmaxKernel<IS_LOG>::validate(const ITensorInfo *input, const ITensorInfo *max,
                                                 const ITensorInfo *output, const float beta, const ITensorInfo *tmp)
{
    ARM_COMPUTE_ERROR_ON_NULLPTR(input, max, output, tmp);
    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments_logits_softmax(*input, *max, *output, beta, *tmp, IS_LOG));

    return Status{};
}

template <bool IS_LOG>
void NELogits1DSoftmaxKernel<IS_LOG>::run(const Window &window, const ThreadInfo &info)
{
    ARM_COMPUTE_UNUSED(info);
    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);

    const unsigned int num_elems_processed_per_iteration = _input->info()->valid_region().shape.x();
    const unsigned int tmp_size_for_thread               = _tmp->info()->element_size() * num_elems_processed_per_iteration;

    ARM_COMPUTE_ERROR_ON(_tmp->info()->total_size() < (info.num_threads * tmp_size_for_thread));

    void *tmp_for_thread = _tmp->buffer() + (info.thread_id * tmp_size_for_thread);

    (*_func)(*_input, *_max, tmp_for_thread, *_output, _beta, window);
}

template class NELogits1DSoftmaxKernel<true>;
template class NELogits1DSoftmaxKernel<false>;

} // namespace arm_compute