Make Softmax kernels and operator stateless

COMPMID-3997

Change-Id: I3a3cc76d8247dd769d9a5e6e171d718ea909312c
Signed-off-by: Michalis Spyrou <michalis.spyrou@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/4986
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

4 files changed, 0 insertions, 1375 deletions

diff --git a/src/core/NEON/kernels/NESoftmaxLayerKernel.cpp b/src/core/NEON/kernels/NESoftmaxLayerKernel.cpp
deleted file mode 100644
index fe09f1ec59..0000000000
--- a/src/core/NEON/kernels/NESoftmaxLayerKernel.cpp
+++ /dev/null
@@ -1,380 +0,0 @@
-/*
- * 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/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/Validate.h"
-#include "arm_compute/core/Window.h"
-#include "src/core/CPP/Validate.h"
-#include "src/core/helpers/AutoConfiguration.h"
-#include "src/core/helpers/WindowHelpers.h"
-
-#include "src/core/NEON/kernels/softmax/impl/NEON/list.h"
-#include "src/core/NEON/kernels/softmax/impl/SVE/list.h"
-#include "src/core/common/Registrars.h"
-
-namespace arm_compute
-{
-namespace
-{
-struct SoftmaxSelectorData
-{
-    DataType dt;
-};
-using SoftmaxSelectorPtr          = std::add_pointer<bool(const SoftmaxSelectorData &data)>::type;
-using SoftmaxLogits1DMaxKernelPtr = std::add_pointer<void(const ITensor *, ITensor *, const Window &)>::type;
-using SoftmaxLogits1DKernelPtr    = std::add_pointer<void(const ITensor *, const ITensor *, void *const, ITensor *, float, bool, const Window &)>::type;
-
-struct SoftmaxLogits1DKernel
-{
-    const char              *name;
-    const SoftmaxSelectorPtr is_selected;
-    SoftmaxLogits1DKernelPtr ukernel;
-};
-
-struct SoftmaxLogits1DMaxKernel
-{
-    const char                 *name;
-    const SoftmaxSelectorPtr    is_selected;
-    SoftmaxLogits1DMaxKernelPtr ukernel;
-};
-
-static const SoftmaxLogits1DKernel available_logits_1d_kernels[] =
-{
-#if defined(__ARM_FEATURE_SVE)
-    {
-        "sve_softmax_logits_1d_float",
-        [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F32); },
-        REGISTER_FP32_SVE(arm_compute::cpu::sve_softmax_logits_1d_float<float>)
-    },
-    {
-        "sve_softmax_logits_1d_float",
-        [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F16); },
-        REGISTER_FP16_SVE(arm_compute::cpu::sve_softmax_logits_1d_float<float16_t>)
-    },
-#else /* !defined(__ARM_FEATURE_SVE) */
-    {
-        "neon_softmax_logits_1d_float",
-        [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F32); },
-        REGISTER_FP32_NEON(arm_compute::cpu::neon_softmax_logits_1d_float<float>)
-    },
-#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
-    {
-        "neon_softmax_logits_1d_float",
-        [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F16); },
-        REGISTER_FP16_NEON(arm_compute::cpu::neon_softmax_logits_1d_float<float16_t>)
-    },
-#endif /* defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) */
-#endif /* defined(__ARM_FEATURE_SVE) */
-
-#if defined(__ARM_FEATURE_SVE2)
-    {
-        "sve_softmax_logits_1d_quantized",
-        [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8); },
-        REGISTER_QASYMM8_SVE(arm_compute::cpu::sve_softmax_logits_1d_quantized<qasymm8_t>)
-    },
-    {
-        "sve_softmax_logits_1d_quantized",
-        [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8_SIGNED); },
-        REGISTER_QASYMM8_SIGNED_SVE(arm_compute::cpu::sve_softmax_logits_1d_quantized<qasymm8_signed_t>)
-    },
-#else  /* !defined(__ARM_FEATURE_SVE2) */
-    {
-        "neon_softmax_logits_1d_quantized",
-        [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8); },
-        REGISTER_QASYMM8_NEON(arm_compute::cpu::neon_softmax_logits_1d_quantized<qasymm8_t>)
-    },
-    {
-        "neon_softmax_logits_1d_quantized",
-        [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8_SIGNED); },
-        REGISTER_QASYMM8_SIGNED_NEON(arm_compute::cpu::neon_softmax_logits_1d_quantized<qasymm8_signed_t>)
-    },
-#endif /* defined(__ARM_FEATURE_SVE2) */
-
-};
-
-static const SoftmaxLogits1DMaxKernel available_logits_1d_max_kernels[] =
-{
-#if defined(__ARM_FEATURE_SVE)
-    {
-        "sve_logits_1d_max",
-        [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F32); },
-        REGISTER_FP32_SVE(arm_compute::cpu::sve_logits_1d_max<float>)
-    },
-    {
-        "sve_logits_1d_max",
-        [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F16); },
-        REGISTER_FP16_SVE(arm_compute::cpu::sve_logits_1d_max<float16_t>)
-    },
-    {
-        "sve_logits_1d_max",
-        [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8); },
-        REGISTER_QASYMM8_SVE(arm_compute::cpu::sve_logits_1d_max<qasymm8_t>)
-    },
-    {
-        "sve_logits_1d_max",
-        [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8_SIGNED); },
-        REGISTER_QASYMM8_SIGNED_SVE(arm_compute::cpu::sve_logits_1d_max<qasymm8_signed_t>)
-    },
-#else /* !defined(__ARM_FEATURE_SVE) */
-    {
-        "neon_logits_1d_max",
-        [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F32); },
-        REGISTER_FP32_NEON(arm_compute::cpu::neon_logits_1d_max<float>)
-    },
-#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
-    {
-        "neon_logits_1d_max",
-        [](const SoftmaxSelectorData & data) { return (data.dt == DataType::F16); },
-        REGISTER_FP16_NEON(arm_compute::cpu::neon_logits_1d_max<float16_t>)
-    },
-#endif /* defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) */
-    {
-        "neon_logits_1d_max",
-        [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8); },
-        REGISTER_QASYMM8_NEON(arm_compute::cpu::neon_logits_1d_max<qasymm8_t>)
-    },
-    {
-        "neon_logits_1d_max",
-        [](const SoftmaxSelectorData & data) { return (data.dt == DataType::QASYMM8_SIGNED); },
-        REGISTER_QASYMM8_SIGNED_NEON(arm_compute::cpu::neon_logits_1d_max<qasymm8_signed_t>)
-    },
-#endif /* defined(__ARM_FEATURE_SVE) */
-};
-
-const SoftmaxLogits1DKernel *get_implementation_logits(const SoftmaxSelectorData &data)
-{
-    for(const auto &uk : available_logits_1d_kernels)
-    {
-        if(uk.is_selected({ data.dt }))
-        {
-            return &uk;
-        }
-    }
-    return nullptr;
-}
-
-const SoftmaxLogits1DMaxKernel *get_implementation_logits_max(const SoftmaxSelectorData &data)
-{
-    for(const auto &uk : available_logits_1d_max_kernels)
-    {
-        if(uk.is_selected({ data.dt }))
-        {
-            return &uk;
-        }
-    }
-    return nullptr;
-}
-
-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{};
-}
-
-} // namespace
-
-NELogits1DMaxKernel::NELogits1DMaxKernel()
-    : _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()));
-
-    _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);
-
-    const auto *uk = get_implementation_logits_max(SoftmaxSelectorData{ _input->info()->data_type() });
-    uk->ukernel(_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{};
-}
-} // namespace
-
-template <bool IS_LOG>
-NELogits1DSoftmaxKernel<IS_LOG>::NELogits1DSoftmaxKernel()
-    : _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()));
-
-    _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);
-
-    const auto *uk = get_implementation_logits(SoftmaxSelectorData{ _input->info()->data_type() });
-    uk->ukernel(_input, _max, tmp_for_thread, _output, _beta, IS_LOG, window);
-}
-
-template class NELogits1DSoftmaxKernel<true>;
-template class NELogits1DSoftmaxKernel<false>;
-
-} // namespace arm_compute
diff --git a/src/core/NEON/kernels/NESoftmaxLayerKernel.h b/src/core/NEON/kernels/NESoftmaxLayerKernel.h
deleted file mode 100644
index 70e2417fc2..0000000000
--- a/src/core/NEON/kernels/NESoftmaxLayerKernel.h
+++ /dev/null
@@ -1,141 +0,0 @@
-/*
- * 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.
- */
-#ifndef ARM_COMPUTE_NESOFTMAXLAYERKERNEL_H
-#define ARM_COMPUTE_NESOFTMAXLAYERKERNEL_H
-
-#include "src/core/NEON/INEKernel.h"
-#include "src/core/NEON/INESimpleKernel.h"
-
-namespace arm_compute
-{
-class ITensor;
-
-/** Interface for the identifying the max value of 1D Logits */
-class NELogits1DMaxKernel : public INESimpleKernel
-{
-public:
-    const char *name() const override
-    {
-        return "NELogits1DMaxKernel";
-    }
-    /** Default constructor */
-    NELogits1DMaxKernel();
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    NELogits1DMaxKernel(const NELogits1DMaxKernel &) = delete;
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    NELogits1DMaxKernel &operator=(const NELogits1DMaxKernel &) = delete;
-    /** Allow instances of this class to be moved */
-    NELogits1DMaxKernel(NELogits1DMaxKernel &&) = default;
-    /** Allow instances of this class to be moved */
-    NELogits1DMaxKernel &operator=(NELogits1DMaxKernel &&) = default;
-    /** Default destructor */
-    ~NELogits1DMaxKernel() = default;
-    /** Set the input and output tensors.
-     *
-     * @param[in]  input  Source tensor. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
-     * @param[out] output Destination tensor. Data types supported: same as @p input
-     */
-    void configure(const ITensor *input, ITensor *output);
-    /** Static function to check if given info will lead to a valid configuration of @ref NELogits1DMaxKernel
-     *
-     * @param[in] input  Source tensor. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
-     * @param[in] output Destination tensor. Data types supported: same as @p input
-     *
-     * @return a status
-     */
-    static Status validate(const ITensorInfo *input, const ITensorInfo *output);
-
-    // Inherited methods overridden:
-    void run(const Window &window, const ThreadInfo &info) override;
-    BorderSize border_size() const override;
-
-private:
-    BorderSize _border_size;
-};
-
-/** Interface for softmax computation for QASYMM8 with pre-computed max. */
-template <bool IS_LOG = false>
-class NELogits1DSoftmaxKernel : public INEKernel
-{
-public:
-    const char *name() const override
-    {
-        if(IS_LOG)
-        {
-            return "NELogits1DSoftmaxKernel";
-        }
-        else
-        {
-            return "NELogits1DLogSoftmaxKernel";
-        }
-    }
-    /** Default constructor */
-    NELogits1DSoftmaxKernel();
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    NELogits1DSoftmaxKernel(const NELogits1DSoftmaxKernel &) = delete;
-    /** Prevent instances of this class from being copied (As this class contains pointers) */
-    NELogits1DSoftmaxKernel &operator=(const NELogits1DSoftmaxKernel &) = delete;
-    /** Allow instances of this class to be moved */
-    NELogits1DSoftmaxKernel(NELogits1DSoftmaxKernel &&) = default;
-    /** Allow instances of this class to be moved */
-    NELogits1DSoftmaxKernel &operator=(NELogits1DSoftmaxKernel &&) = default;
-    /** Default destructor */
-    ~NELogits1DSoftmaxKernel() = default;
-    /** Set the input and output tensors.
-     *
-     * @param[in]  input  Source tensor. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
-     * @param[in]  max    Max values tensor. Same shape as input with dimension 0 set to 1.
-     *                    Data types supported: same as @p input.
-     * @param[out] output Destination tensor. Data types supported: same as @p input.
-     * @param[in]  beta   A scaling factor for the exponent.
-     *
-     * @param      tmp    Auxiliary tensor. Must be type F32 and same shape as the input.
-     */
-    void configure(const ITensor *input, const ITensor *max, ITensor *output, const float beta, ITensor *tmp);
-    /** Static function to check if given info will lead to a valid configuration of @ref NELogits1DSoftmaxKernel
-     *
-     * @param[in] input  Source tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
-     * @param[in] max    Max values tensor info. Same shape as input with dimension 0 set to 1.
-     *                   Data types supported: same as @p input.
-     * @param[in] output Destination tensor info. Data types supported: same as @p input.
-     * @param[in] beta   A scaling factor for the exponent.
-     * @param[in] tmp    Tensor info of auxiliary. Must be type F32 and same shape as the input.
-     *
-     * @return a status
-     */
-    static Status validate(const ITensorInfo *input, const ITensorInfo *max,
-                           const ITensorInfo *output, const float beta, const ITensorInfo *tmp);
-
-    // Inherited methods overridden:
-    void run(const Window &window, const ThreadInfo &info) override;
-
-private:
-    const ITensor *_input;
-    const ITensor *_max;
-    ITensor       *_output;
-    float          _beta;
-    ITensor       *_tmp; //Temporary. Used internally
-};
-} // namespace arm_compute
-#endif /*ARM_COMPUTE_NESOFTMAXLAYERKERNEL_H */
diff --git a/src/core/NEON/kernels/softmax/impl/NEON/list.h b/src/core/NEON/kernels/softmax/impl/NEON/list.h
deleted file mode 100644
index a8f781f439..0000000000
--- a/src/core/NEON/kernels/softmax/impl/NEON/list.h
+++ /dev/null
@@ -1,425 +0,0 @@
-/*
- * Copyright (c) 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.
- */
-#ifndef SRC_CORE_NEON_KERNELS_SOFTMAX_LIST_H
-#define SRC_CORE_NEON_KERNELS_SOFTMAX_LIST_H
-
-#include "src/core/NEON/wrapper/wrapper.h"
-#include "support/SaturateCast.h"
-#include "src/core/NEON/NEFixedPoint.h"
-#include "src/core/NEON/NEMath.h"
-
-namespace arm_compute
-{
-namespace cpu
-{
-namespace
-{
-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
-
-template <typename T>
-void neon_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);
-}
-
-template <typename T>
-void neon_softmax_logits_1d_quantized(const ITensor *in, const ITensor *max, void *const tmp,
-                                      ITensor *out, float beta, bool is_log, 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>
-void neon_softmax_logits_1d_float(const ITensor *in, const ITensor *max, void *const tmp,
-                                  ITensor *out, const float beta, bool is_log, 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 cpu
-} // namespace arm_compute
-
-#endif /* SRC_CORE_NEON_KERNELS_SOFTMAX_LIST_H */
diff --git a/src/core/NEON/kernels/softmax/impl/SVE/list.h b/src/core/NEON/kernels/softmax/impl/SVE/list.h
deleted file mode 100644
index 0936bd5a56..0000000000
--- a/src/core/NEON/kernels/softmax/impl/SVE/list.h
+++ /dev/null
@@ -1,429 +0,0 @@
-/*
- * Copyright (c) 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.
- */
-#ifndef SRC_CORE_SVE_KERNELS_SOFTMAX_LIST_H
-#define SRC_CORE_SVE_KERNELS_SOFTMAX_LIST_H
-
-#if defined(__ARM_FEATURE_SVE)
-#include "arm_compute/core/Types.h"
-#include "arm_compute/core/utils/misc/Traits.h"
-#include "src/core/NEON/SVEMath.h"
-#include "src/core/NEON/wrapper/intrinsics/intrinsics.h"
-#include <arm_sve.h>
-
-namespace arm_compute
-{
-namespace cpu
-{
-namespace
-{
-#if defined(__ARM_FEATURE_SVE2)
-template <typename int_vec_type>
-int_vec_type convert_float_to_int(const svfloat32_t &in_0, const svfloat32_t &in_1, const svfloat32_t &in_2, const svfloat32_t &in_3);
-
-template <>
-svuint8_t convert_float_to_int<svuint8_t>(const svfloat32_t &in_0, const svfloat32_t &in_1, const svfloat32_t &in_2, const svfloat32_t &in_3)
-{
-    svuint8_t  out;
-    const auto all_true_pg = svptrue_b32();
-    auto       tmp_0       = svcvt_u32_f32_z(all_true_pg, in_0);
-    auto       tmp_1       = svcvt_u32_f32_z(all_true_pg, in_1);
-    auto       tmp_2       = svcvt_u32_f32_z(all_true_pg, in_2);
-    auto       tmp_3       = svcvt_u32_f32_z(all_true_pg, in_3);
-
-    auto tmp_16_0 = svqxtnt_u32(svqxtnb_u32(tmp_0), tmp_1);
-    auto tmp_16_1 = svqxtnt_u32(svqxtnb_u32(tmp_2), tmp_3);
-
-    auto tmp_16_uzp_0 = svuzp1(tmp_16_0, tmp_16_0);
-    auto tmp_16_uzp_1 = svuzp2(tmp_16_0, tmp_16_0);
-    auto tmp_16_uzp_2 = svuzp1(tmp_16_1, tmp_16_1);
-    auto tmp_16_uzp_3 = svuzp2(tmp_16_1, tmp_16_1);
-
-    auto pg = svwhilelt_b16_s32(0, svcnth() / 2);
-
-    tmp_16_0 = svsplice(pg, tmp_16_uzp_0, tmp_16_uzp_1);
-    tmp_16_1 = svsplice(pg, tmp_16_uzp_2, tmp_16_uzp_3);
-
-    out = svqxtnt_u16(svqxtnb_u16(tmp_16_0), tmp_16_1);
-
-    auto out_uzp_0 = svuzp1(out, out);
-    auto out_uzp_1 = svuzp2(out, out);
-
-    pg  = svwhilelt_b8_s32(0, svcntb() / 2);
-    out = svsplice(pg, out_uzp_0, out_uzp_1);
-
-    return out;
-}
-
-template <>
-svint8_t convert_float_to_int<svint8_t>(const svfloat32_t &in_0, const svfloat32_t &in_1, const svfloat32_t &in_2, const svfloat32_t &in_3)
-{
-    svint8_t   out;
-    const auto all_true_pg = svptrue_b32();
-    auto       tmp_0       = svcvt_s32_f32_z(all_true_pg, in_0);
-    auto       tmp_1       = svcvt_s32_f32_z(all_true_pg, in_1);
-    auto       tmp_2       = svcvt_s32_f32_z(all_true_pg, in_2);
-    auto       tmp_3       = svcvt_s32_f32_z(all_true_pg, in_3);
-
-    auto tmp_16_0 = svqxtnt_s32(svqxtnb_s32(tmp_0), tmp_1);
-    auto tmp_16_1 = svqxtnt_s32(svqxtnb_s32(tmp_2), tmp_3);
-
-    auto tmp_16_uzp_0 = svuzp1(tmp_16_0, tmp_16_0);
-    auto tmp_16_uzp_1 = svuzp2(tmp_16_0, tmp_16_0);
-    auto tmp_16_uzp_2 = svuzp1(tmp_16_1, tmp_16_1);
-    auto tmp_16_uzp_3 = svuzp2(tmp_16_1, tmp_16_1);
-
-    auto pg = svwhilelt_b16_s32(0, svcnth() / 2);
-
-    tmp_16_0 = svsplice(pg, tmp_16_uzp_0, tmp_16_uzp_1);
-    tmp_16_1 = svsplice(pg, tmp_16_uzp_2, tmp_16_uzp_3);
-
-    out = svqxtnt_s16(svqxtnb_s16(tmp_16_0), tmp_16_1);
-
-    auto out_uzp_0 = svuzp1(out, out);
-    auto out_uzp_1 = svuzp2(out, out);
-
-    pg  = svwhilelt_b8_s32(0, svcntb() / 2);
-    out = svsplice(pg, out_uzp_0, out_uzp_1);
-
-    return out;
-}
-#endif /* defined(__ARM_FEATURE_SVE2) */
-} // namespace
-
-template <typename ScalarType>
-void sve_logits_1d_max(const ITensor *in, ITensor *out, const Window &window)
-{
-    const auto all_true_pg    = wrapper::svptrue<ScalarType>();
-    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);
-
-    execute_window_loop(win, [&](const Coordinates &)
-    {
-        // Get pointers
-        const auto in_ptr  = reinterpret_cast<const ScalarType *>(input.ptr());
-        const auto out_ptr = reinterpret_cast<ScalarType *>(output.ptr());
-
-        // Init max value
-        auto vec_max = wrapper::svdup_n(support::cpp11::lowest<ScalarType>());
-
-        int      x  = window_start_x;
-        svbool_t pg = wrapper::svwhilelt<ScalarType>(x, window_end_x);
-        do
-        {
-            const auto current_value = svld1(pg, in_ptr + x);
-            vec_max                  = svmax_m(pg, vec_max, current_value);
-
-            x += wrapper::svcnt<ScalarType>();
-            pg = wrapper::svwhilelt<ScalarType>(x, window_end_x);
-        }
-        while(svptest_any(all_true_pg, pg));
-
-        auto max_val = svmaxv(all_true_pg, vec_max);
-
-        *out_ptr = max_val;
-    },
-    input, output);
-}
-
-#if defined(__ARM_FEATURE_SVE2)
-template <typename ScalarType>
-void sve_softmax_logits_1d_quantized(const ITensor *in, const ITensor *max, void *const tmp,
-                                     ITensor *out, float beta, bool is_log, const Window &window)
-{
-    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 = svdup_n_f32(scale_beta);
-
-    Iterator   in_it(in, window);
-    Iterator   max_it(max, window);
-    Iterator   out_it(out, window);
-    const auto all_true_pg = wrapper::svptrue<ScalarType>();
-    using SVEType          = typename wrapper::traits::sve_vector<ScalarType>::type;
-
-    const int inc_1 = static_cast<int>(svcntw());
-    const int inc_2 = static_cast<int>(2 * svcntw());
-    const int inc_3 = static_cast<int>(3 * svcntw());
-
-    execute_window_loop(window, [&](const Coordinates &)
-    {
-        /* Get pointers */
-        const auto in_ptr  = reinterpret_cast<const ScalarType *>(in_it.ptr()) + start_x;
-        const auto out_ptr = reinterpret_cast<ScalarType *>(out_it.ptr()) + start_x;
-        const auto tmp_ptr = reinterpret_cast<float *>(tmp);
-
-        float sum{};
-
-        /* Compute exponentials and sum */
-        {
-            /* Get max value */
-            const auto max_val = *reinterpret_cast<const ScalarType *>(max_it.ptr());
-            const auto vec_max = wrapper::svdup_n(max_val);
-
-            /* Init sum to zero */
-            auto vec_sum_0 = svdup_n_f32(0.f);
-            auto vec_sum_1 = svdup_n_f32(0.f);
-            auto vec_sum_2 = svdup_n_f32(0.f);
-            auto vec_sum_3 = svdup_n_f32(0.f);
-
-            /* Loop over row and compute exponentials and sum */
-            int      x    = 0;
-            svbool_t pg   = wrapper::svwhilelt<ScalarType>(x, input_width);
-            svbool_t pg_0 = svunpklo(svunpklo(pg));
-            svbool_t pg_1 = svunpkhi(svunpklo(pg));
-            svbool_t pg_2 = svunpklo(svunpkhi(pg));
-            svbool_t pg_3 = svunpkhi(svunpkhi(pg));
-            do
-            {
-                auto vec_elements = svld1(pg, in_ptr + x);
-                vec_elements      = svsub_z(pg, vec_max, vec_elements);
-
-                auto vec_elements_flt_0 = svcvt_f32_z(pg_0, svunpklo(svunpklo(vec_elements)));
-                auto vec_elements_flt_1 = svcvt_f32_z(pg_1, svunpkhi(svunpklo(vec_elements)));
-                auto vec_elements_flt_2 = svcvt_f32_z(pg_2, svunpklo(svunpkhi(vec_elements)));
-                auto vec_elements_flt_3 = svcvt_f32_z(pg_3, svunpkhi(svunpkhi(vec_elements)));
-
-                if(is_log)
-                {
-                    vec_elements_flt_0 = svmul_f32_z(pg_0, vec_elements_flt_0, scale_beta_vec);
-                    vec_elements_flt_1 = svmul_f32_z(pg_1, vec_elements_flt_1, scale_beta_vec);
-                    vec_elements_flt_2 = svmul_f32_z(pg_2, vec_elements_flt_2, scale_beta_vec);
-                    vec_elements_flt_3 = svmul_f32_z(pg_3, vec_elements_flt_3, scale_beta_vec);
-                    vec_sum_0          = svadd_f32_m(pg_0, vec_sum_0, svexp_f32_z(pg_0, vec_elements_flt_0));
-                    vec_sum_1          = svadd_f32_m(pg_1, vec_sum_1, svexp_f32_z(pg_1, vec_elements_flt_1));
-                    vec_sum_2          = svadd_f32_m(pg_2, vec_sum_2, svexp_f32_z(pg_2, vec_elements_flt_2));
-                    vec_sum_3          = svadd_f32_m(pg_3, vec_sum_3, svexp_f32_z(pg_3, vec_elements_flt_3));
-                }
-                else
-                {
-                    vec_elements_flt_0 = svexp_f32_z(pg_0, svmul_f32_z(pg_0, vec_elements_flt_0, scale_beta_vec));
-                    vec_elements_flt_1 = svexp_f32_z(pg_1, svmul_f32_z(pg_1, vec_elements_flt_1, scale_beta_vec));
-                    vec_elements_flt_2 = svexp_f32_z(pg_2, svmul_f32_z(pg_2, vec_elements_flt_2, scale_beta_vec));
-                    vec_elements_flt_3 = svexp_f32_z(pg_3, svmul_f32_z(pg_3, vec_elements_flt_3, scale_beta_vec));
-                    vec_sum_0          = svadd_f32_m(pg_0, vec_sum_0, vec_elements_flt_0);
-                    vec_sum_1          = svadd_f32_m(pg_1, vec_sum_1, vec_elements_flt_1);
-                    vec_sum_2          = svadd_f32_m(pg_2, vec_sum_2, vec_elements_flt_2);
-                    vec_sum_3          = svadd_f32_m(pg_3, vec_sum_3, vec_elements_flt_3);
-                }
-
-                svst1_f32(pg_0, tmp_ptr + x, vec_elements_flt_0);
-                svst1_f32(pg_1, tmp_ptr + x + inc_1, vec_elements_flt_1);
-                svst1_f32(pg_2, tmp_ptr + x + inc_2, vec_elements_flt_2);
-                svst1_f32(pg_3, tmp_ptr + x + inc_3, vec_elements_flt_3);
-
-                x += wrapper::svcnt<ScalarType>();
-                pg   = wrapper::svwhilelt<ScalarType>(x, input_width);
-                pg_0 = svunpklo(svunpklo(pg));
-                pg_1 = svunpkhi(svunpklo(pg));
-                pg_2 = svunpklo(svunpkhi(pg));
-                pg_3 = svunpkhi(svunpkhi(pg));
-            }
-            while(svptest_any(all_true_pg, pg));
-
-            /* Reduce sum */
-            const auto vec_sum = svadd_f32_z(all_true_pg, svadd_f32_z(all_true_pg, vec_sum_0, vec_sum_1), svadd_f32_z(all_true_pg, vec_sum_2, vec_sum_3));
-            sum                = svaddv_f32(all_true_pg, vec_sum);
-
-            /* Run remaining elements */
-            x = 0;
-            if(is_log)
-            {
-                sum = std::log(sum);
-            }
-            else
-            {
-                sum = 256.f / sum;
-            }
-        }
-
-        /* Normalize exponentials */
-        {
-            constexpr bool is_qasymm8_signed = std::is_same<ScalarType, qasymm8_signed_t>::value;
-            /* Loop over row and compute softmax */
-            int      x    = 0;
-            svbool_t pg   = wrapper::svwhilelt<ScalarType>(x, input_width);
-            svbool_t pg_0 = svunpklo(svunpklo(pg));
-            svbool_t pg_1 = svunpkhi(svunpklo(pg));
-            svbool_t pg_2 = svunpklo(svunpkhi(pg));
-            svbool_t pg_3 = svunpkhi(svunpkhi(pg));
-            do
-            {
-                auto vec_in_0 = svld1_f32(pg_0, tmp_ptr + x);
-                auto vec_in_1 = svld1_f32(pg_1, tmp_ptr + x + inc_1);
-                auto vec_in_2 = svld1_f32(pg_2, tmp_ptr + x + inc_2);
-                auto vec_in_3 = svld1_f32(pg_3, tmp_ptr + x + inc_3);
-
-                svfloat32_t res_0{};
-                svfloat32_t res_1{};
-                svfloat32_t res_2{};
-                svfloat32_t res_3{};
-
-                if(is_log)
-                {
-                    res_0 = svsub_f32_z(pg_0, vec_in_0, svdup_n_f32(sum));
-                    res_1 = svsub_f32_z(pg_1, vec_in_1, svdup_n_f32(sum));
-                    res_2 = svsub_f32_z(pg_2, vec_in_2, svdup_n_f32(sum));
-                    res_3 = svsub_f32_z(pg_3, vec_in_3, svdup_n_f32(sum));
-                }
-                else
-                {
-                    res_0 = svmul_f32_z(pg_0, vec_in_0, svdup_n_f32(sum));
-                    res_1 = svmul_f32_z(pg_1, vec_in_1, svdup_n_f32(sum));
-                    res_2 = svmul_f32_z(pg_2, vec_in_2, svdup_n_f32(sum));
-                    res_3 = svmul_f32_z(pg_3, vec_in_3, svdup_n_f32(sum));
-
-                    if(is_qasymm8_signed)
-                    {
-                        const auto offset_vec = svdup_n_f32(128.f);
-                        res_0                 = svsub_z(pg_0, vec_in_0, offset_vec);
-                        res_1                 = svsub_z(pg_1, vec_in_1, offset_vec);
-                        res_2                 = svsub_z(pg_2, vec_in_2, offset_vec);
-                        res_3                 = svsub_z(pg_3, vec_in_3, offset_vec);
-                    }
-                }
-
-                // Store value
-                const auto out = convert_float_to_int<SVEType>(res_0, res_1, res_2, res_3);
-                svst1(pg, out_ptr + x, out);
-                x += wrapper::svcnt<ScalarType>();
-                pg   = wrapper::svwhilelt<ScalarType>(x, input_width);
-                pg_0 = svunpklo(svunpklo(pg));
-                pg_1 = svunpkhi(svunpklo(pg));
-                pg_2 = svunpklo(svunpkhi(pg));
-                pg_3 = svunpkhi(svunpkhi(pg));
-            }
-            while(svptest_any(all_true_pg, pg));
-        }
-    },
-    in_it, max_it, out_it);
-}
-#endif /* defined(__ARM_FEATURE_SVE2) */
-
-template <typename ScalarType>
-void sve_softmax_logits_1d_float(const ITensor *in, const ITensor *max, void *const tmp,
-                                 ITensor *out, const float beta, bool is_log, 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);
-
-    const auto all_true_pg = wrapper::svptrue<ScalarType>();
-
-    execute_window_loop(window, [&](const Coordinates &)
-    {
-        /* Get pointers */
-        const auto in_ptr  = reinterpret_cast<const ScalarType *>(in_it.ptr()) + start_x;
-        const auto out_ptr = reinterpret_cast<ScalarType *>(out_it.ptr()) + start_x;
-        const auto tmp_ptr = reinterpret_cast<ScalarType *>(tmp);
-
-        ScalarType sum{ 0 };
-
-        /* Compute exponentials and sum */
-        {
-            /* Get max value */
-            const auto max_val = *reinterpret_cast<const ScalarType *>(max_it.ptr());
-            const auto vec_max = wrapper::svdup_n(max_val);
-
-            /* Init sum to zero */
-            auto vec_sum = wrapper::svdup_n(static_cast<ScalarType>(0));
-
-            /* Loop over row and compute exponentials and sum */
-            int      x  = 0;
-            svbool_t pg = wrapper::svwhilelt<ScalarType>(x, input_width);
-            do
-            {
-                auto vec_elements = svld1(pg, in_ptr + x);
-                vec_elements      = svsub_z(pg, vec_elements, vec_max);
-                if(is_log)
-                {
-                    vec_elements = svmul_z(pg, vec_elements, wrapper::svdup_n(static_cast<ScalarType>(beta)));
-                    vec_sum      = svadd_m(pg, vec_sum, wrapper::svexp_z(pg, vec_elements));
-                }
-                else
-                {
-                    vec_elements = wrapper::svexp_z(pg, svmul_z(pg, vec_elements, wrapper::svdup_n(static_cast<ScalarType>(beta))));
-                    vec_sum      = svadd_m(pg, vec_sum, vec_elements);
-                }
-                svst1(pg, tmp_ptr + x, vec_elements);
-
-                x += wrapper::svcnt<ScalarType>();
-                pg = wrapper::svwhilelt<ScalarType>(x, input_width);
-            }
-            while(svptest_any(all_true_pg, pg));
-
-            /* Reduce sum */
-            sum = svaddv(all_true_pg, vec_sum);
-
-            if(is_log)
-            {
-                sum = static_cast<ScalarType>(std::log(sum));
-            }
-            else
-            {
-                sum = ScalarType(1) / sum;
-            }
-        }
-
-        /* Normalize exponentials */
-        {
-            /* Loop over row and compute softmax */
-            int      x  = 0;
-            svbool_t pg = wrapper::svwhilelt<ScalarType>(x, input_width);
-            do
-            {
-                auto vec_in           = svld1(pg, tmp_ptr + x);
-                auto normalized_value = wrapper::svdup_n(static_cast<ScalarType>(0));
-                if(is_log)
-                {
-                    normalized_value = svsub_z(pg, vec_in, wrapper::svdup_n(static_cast<ScalarType>(sum)));
-                }
-                else
-                {
-                    normalized_value = svmul_z(pg, vec_in, wrapper::svdup_n(static_cast<ScalarType>(sum)));
-                }
-                svst1(pg, out_ptr + x, normalized_value);
-
-                x += wrapper::svcnt<ScalarType>();
-                pg = wrapper::svwhilelt<ScalarType>(x, input_width);
-            }
-            while(svptest_any(all_true_pg, pg));
-        }
-    },
-    in_it, max_it, out_it);
-}
-
-} // namespace cpu
-} // namespace arm_compute
-#endif /* defined(__ARM_FEATURE_SVE) */
-
-#endif /* SRC_CORE_SVE_KERNELS_SOFTMAX_LIST_H */