src/core/NEON/kernels/NEBatchNormalizationLayerKernel.cpp

/*
 * Copyright (c) 2017-2021, 2023 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
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 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */
#include "src/core/NEON/kernels/NEBatchNormalizationLayerKernel.h"

#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/Window.h"

#include "src/core/common/Registrars.h"
#include "src/core/CPP/Validate.h"
#include "src/core/helpers/AutoConfiguration.h"
#include "src/core/helpers/WindowHelpers.h"
#include "src/core/NEON/kernels/batchnormalization/impl/list.h"
#include "src/core/NEON/kernels/detail/NEActivationFunctionDetail.h"
#include "src/core/NEON/NEFixedPoint.h"
#include "src/core/NEON/NEMath.h"
#include "src/core/NEON/wrapper/wrapper.h"

#include <map>

namespace arm_compute
{
namespace
{
struct BatchNormalizationSelectorData
{
    DataType       dt;
    const CPUInfo &ci;
};
using BatchNormalizationSelectorPtr = std::add_pointer<bool(const BatchNormalizationSelectorData &data)>::type;
using BatchNormalizationKernelPtr   = std::add_pointer<void(ITensor *,
                                                          ITensor *,
                                                          const ITensor *,
                                                          const ITensor *,
                                                          const ITensor *,
                                                          const ITensor *,
                                                          float,
                                                          ActivationLayerInfo &,
                                                          const Window &)>::type;

struct BatchNormalizationKernel
{
    const char                         *name;
    const BatchNormalizationSelectorPtr is_selected;
    BatchNormalizationKernelPtr         ukernel;
};

static const BatchNormalizationKernel available_kernels[] = {
#if defined(ARM_COMPUTE_ENABLE_SVE)
    {"sve_fp16_batch_normalization",
     [](const BatchNormalizationSelectorData &data) { return data.dt == DataType::F16 && data.ci.has_sve(); },
     REGISTER_FP16_SVE(arm_compute::cpu::fp16_sve_batch_normalization)},
    {"sve_fp32_batch_normalization",
     [](const BatchNormalizationSelectorData &data) { return data.dt == DataType::F32 && data.ci.has_sve(); },
     REGISTER_FP32_SVE(arm_compute::cpu::fp32_sve_batch_normalization)},
#endif /* !defined(ARM_COMPUTE_ENABLE_SVE) */
#if defined(ARM_COMPUTE_ENABLE_NEON)
#if defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
    {"neon_fp16_batch_normalization",
     [](const BatchNormalizationSelectorData &data) { return data.dt == DataType::F16; },
     REGISTER_FP16_NEON(arm_compute::cpu::fp16_neon_batch_normalization)},
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
    {"neon_fp32_batch_normalization",
     [](const BatchNormalizationSelectorData &data) { return data.dt == DataType::F32; },
     REGISTER_FP32_NEON(arm_compute::cpu::fp32_neon_batch_normalization)},
#endif /* !defined(ARM_COMPUTE_ENABLE_NEON) */
};

const BatchNormalizationKernel *get_implementation(const BatchNormalizationSelectorData &data)
{
    for (const auto &uk : available_kernels)
    {
        if (uk.is_selected(data))
        {
            return &uk;
        }
    }
    return nullptr;
}

Status validate_arguments(const ITensorInfo  *input,
                          const ITensorInfo  *output,
                          const ITensorInfo  *mean,
                          const ITensorInfo  *var,
                          const ITensorInfo  *beta,
                          const ITensorInfo  *gamma,
                          float               epsilon,
                          ActivationLayerInfo act_info)
{
    ARM_COMPUTE_UNUSED(epsilon);

    const auto *uk = get_implementation(BatchNormalizationSelectorData{input->data_type(), CPUInfo::get()});
    ARM_COMPUTE_RETURN_ERROR_ON(uk == nullptr || uk->ukernel == nullptr);

    if (act_info.enabled())
    {
        ActivationLayerInfo::ActivationFunction act = act_info.activation();
        ARM_COMPUTE_RETURN_ERROR_ON(act != ActivationLayerInfo::ActivationLayerInfo::ActivationFunction::RELU &&
                                    act != ActivationLayerInfo::ActivationLayerInfo::ActivationFunction::BOUNDED_RELU &&
                                    act !=
                                        ActivationLayerInfo::ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU);
        ARM_COMPUTE_RETURN_ERROR_ON(act_info.b() > act_info.a());
    }

    if (nullptr != output)
    {
        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output);
        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
    }

    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, mean, var);
    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, var);
    if (beta != nullptr)
    {
        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, beta);
        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, beta);
    }
    if (gamma != nullptr)
    {
        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, gamma);
        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(mean, gamma);
    }
    ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(get_data_layout_dimension_index(
                                    input->data_layout(), DataLayoutDimension::CHANNEL)) != mean->dimension(0));

    return Status{};
}
} //namespace

void NEBatchNormalizationLayerKernel::configure_non_fused()
{
    switch (_input->info()->data_type())
    {
        case DataType::F16:
            _func = REGISTER_FP16_NEON(cpu::fp16_batch_normalization_nchw_non_fused);
            break;
        case DataType::F32:
            _func = REGISTER_FP32_NEON(cpu::fp32_batch_normalization_nchw_non_fused);
            break;
        default:
            ARM_COMPUTE_ERROR("Element size not supported");
            break;
    }
}

void NEBatchNormalizationLayerKernel::configure_fused()
{
    // NCHW Fused Batched Normalization with activation functions : FP32
    static std::map<ActivationLayerInfo::ActivationFunction, BatchNormFunctionPtr> bn_fused_map_f32_nchw = {
        {ActivationLayerInfo::ActivationFunction::RELU,
         REGISTER_FP32_NEON(cpu::fp32_batch_normalization_nchw_non_fused_relu)},
        {ActivationLayerInfo::ActivationFunction::BOUNDED_RELU,
         REGISTER_FP32_NEON(cpu::fp32_batch_normalization_nchw_non_fused_brelu)},
        {ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU,
         REGISTER_FP32_NEON(cpu::fp32_batch_normalization_nchw_non_fused_lubrelu)}};

    // NCHW Fused Batched Normalization with activation functions : FP16
    static std::map<ActivationLayerInfo::ActivationFunction, BatchNormFunctionPtr> bn_fused_map_f16_nchw = {
        {ActivationLayerInfo::ActivationFunction::RELU,
         REGISTER_FP16_NEON(cpu::fp16_batch_normalization_nchw_non_fused_relu)},
        {ActivationLayerInfo::ActivationFunction::BOUNDED_RELU,
         REGISTER_FP16_NEON(cpu::fp16_batch_normalization_nchw_non_fused_brelu)},
        {ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU,
         REGISTER_FP16_NEON(cpu::fp16_batch_normalization_nchw_non_fused_lubrelu)}};

    switch (_input->info()->data_type())
    {
        case DataType::F16:
            _func = bn_fused_map_f16_nchw[_act_info.activation()];
            break;
        case DataType::F32:
            _func = bn_fused_map_f32_nchw[_act_info.activation()];
            break;
        default:
            ARM_COMPUTE_ERROR("Element size not supported");
            break;
    }
}

NEBatchNormalizationLayerKernel::NEBatchNormalizationLayerKernel()
    : _func(nullptr),
      _input(nullptr),
      _output(nullptr),
      _mean(nullptr),
      _var(nullptr),
      _gamma(nullptr),
      _beta(nullptr),
      _epsilon(),
      _act_info()
{
}

void NEBatchNormalizationLayerKernel::configure(ITensor            *input,
                                                ITensor            *output,
                                                const ITensor      *mean,
                                                const ITensor      *var,
                                                const ITensor      *beta,
                                                const ITensor      *gamma,
                                                float               epsilon,
                                                ActivationLayerInfo act_info)
{
    ARM_COMPUTE_ERROR_ON_NULLPTR(input, mean, var);

    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (output != nullptr) ? output->info() : nullptr,
                                                  mean->info(), var->info(), (beta != nullptr) ? beta->info() : nullptr,
                                                  (gamma != nullptr) ? gamma->info() : nullptr, epsilon, act_info));

    _input    = input;
    _output   = input;
    _mean     = mean;
    _var      = var;
    _gamma    = gamma;
    _beta     = beta;
    _epsilon  = epsilon;
    _act_info = act_info;

    const bool run_in_place = (output == nullptr) || (output == input);
    if (!run_in_place)
    {
        _output = output;
    }

    // Configure activation function to run
    const bool is_nchw = _input->info()->data_layout() == DataLayout::NCHW;
    if (is_nchw)
    {
        if (_act_info.enabled())
        {
            configure_fused();
        }
        else
        {
            configure_non_fused();
        }
    }

    // Configure kernel window
    Window win = calculate_max_window(*input->info(), Steps());
    INEKernel::configure(win);

    if (output != nullptr)
    {
        // Output auto initialization if not yet initialized
        auto_init_if_empty(*output->info(), *input->info()->clone());
    }
}

Status NEBatchNormalizationLayerKernel::validate(const ITensorInfo  *input,
                                                 const ITensorInfo  *output,
                                                 const ITensorInfo  *mean,
                                                 const ITensorInfo  *var,
                                                 const ITensorInfo  *beta,
                                                 const ITensorInfo  *gamma,
                                                 float               epsilon,
                                                 ActivationLayerInfo act_info)
{
    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, mean, var, beta, gamma, epsilon, act_info));

    return Status{};
}

void NEBatchNormalizationLayerKernel::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 && _input->info()->data_layout() == DataLayout::NCHW);

    const bool is_nchw = _input->info()->data_layout() == DataLayout::NCHW;
    if (is_nchw)
    {
        (*_func)(window, _input, _output, _mean, _var, _beta, _gamma, _epsilon, _act_info);
    }
    else
    {
        const auto *uk =
            get_implementation(BatchNormalizationSelectorData{_input->info()->data_type(), CPUInfo::get()});
        uk->ukernel(_input, _output, _mean, _var, _beta, _gamma, _epsilon, _act_info, window);
    }
}
} // namespace arm_compute