NEMeanStdDevNormalizationKernel.cpp

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/*
 * Copyright (c) 2019-2022 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/NEMeanStdDevNormalizationKernel.h"

#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/ITensor.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Window.h"
#include "src/core/CPP/Validate.h"
#include "src/core/NEON/NEMath.h"
#include "src/core/NEON/wrapper/wrapper.h"
#include "src/core/common/Registrars.h"
#include "src/core/helpers/AutoConfiguration.h"
#include "src/core/helpers/WindowHelpers.h"
#include "src/cpu/kernels/meanstddevnorm/list.h"

namespace arm_compute
{
namespace
{
struct MeanStdDevNormSelectorData
{
    DataType dt;
};

using MeanStdDevNormSelctorPtr = std::add_pointer<bool(const MeanStdDevNormSelectorData &data)>::type;
using MeanStdDevNormUKernelPtr = std::add_pointer<void(ITensor *input, ITensor *output, float epsilon, const Window &window)>::type;

struct MeanStdDevNormKernel
{
    const char                    *name;
    const MeanStdDevNormSelctorPtr is_selected;
    MeanStdDevNormUKernelPtr       ukernel;
};

static const MeanStdDevNormKernel available_kernels[] =
{
    {
        "fp32_neon_meanstddevnorm",
        [](const MeanStdDevNormSelectorData & data) { return data.dt == DataType::F32; },
        REGISTER_FP32_NEON(arm_compute::cpu::neon_fp32_meanstddevnorm)
    },
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
    {
        "fp16_neon_meanstddevnorm",
        [](const MeanStdDevNormSelectorData & data) { return data.dt == DataType::F16; },
        REGISTER_FP16_NEON(arm_compute::cpu::neon_fp16_meanstddevnorm)
    },
#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
};

/** Micro-kernel selector
 *
 * @param[in] data Selection data passed to help pick the appropriate micro-kernel
 *
 * @return A matching micro-kernel else nullptr
 */
const MeanStdDevNormKernel *get_implementation(const MeanStdDevNormSelectorData &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, float epsilon)
{
    ARM_COMPUTE_UNUSED(epsilon);
    ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input);
    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
    ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->num_dimensions() > 2, "Input tensor cannot have more than 2 dimensions");
    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32);

    // Checks performed when output is configured
    if((output != nullptr) && (output->total_size() != 0))
    {
        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input, output);
        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
    }
    return Status{};
}

std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output)
{
    if(output != nullptr)
    {
        ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
        // Output auto inizialitation if not yet initialized
        auto_init_if_empty(*output, *input);
    }

    // This kernel doesn't need padding. A left-over for loop on dimension X, we cannot have any read or write out of memory
    // For this reason num_elems_processed_per_iteration is set to 1
    Window win = calculate_max_window(*input, Steps());

    return std::make_pair(Status{}, win);
}
} // namespace

NEMeanStdDevNormalizationKernel::NEMeanStdDevNormalizationKernel()
    : _input(nullptr), _output(nullptr), _epsilon(1e-8f)
{
}

void NEMeanStdDevNormalizationKernel::configure(ITensor *input, ITensor *output, float epsilon)
{
    ARM_COMPUTE_ERROR_ON_NULLPTR(input);

    ARM_COMPUTE_ERROR_THROW_ON(NEMeanStdDevNormalizationKernel::validate(input->info(), (output != nullptr) ? output->info() : nullptr, epsilon));

    _input   = input;
    _output  = (output == nullptr) ? input : output;
    _epsilon = epsilon;

    // Configure kernel window
    auto win_config = validate_and_configure_window(input->info(), (output == nullptr) ? nullptr : output->info());
    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
    ICPPKernel::configure(win_config.second);
}

Status NEMeanStdDevNormalizationKernel::validate(const ITensorInfo *input, const ITensorInfo *output, float epsilon)
{
    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, epsilon));
    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), (output != nullptr) ? output->clone().get() : nullptr).first);
    return Status{};
}

void NEMeanStdDevNormalizationKernel::run(const Window &window, const ThreadInfo &info)
{
    ARM_COMPUTE_UNUSED(info);
    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);

    const auto *uk = get_implementation(MeanStdDevNormSelectorData{ _output->info()->data_type() });
    ARM_COMPUTE_ERROR_ON(uk == nullptr || uk->ukernel == nullptr);

    uk->ukernel(_input, _output, _epsilon, window);
}
} // namespace arm_compute