/*
 * Copyright (c) 2017-2018 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 "arm_compute/core/CL/kernels/CLGEMMMatrixAccumulateBiasesKernel.h"

#include "arm_compute/core/AccessWindowStatic.h"
#include "arm_compute/core/CL/CLHelpers.h"
#include "arm_compute/core/CL/CLKernelLibrary.h"
#include "arm_compute/core/CL/CLValidate.h"
#include "arm_compute/core/CL/ICLTensor.h"
#include "arm_compute/core/CL/OpenCL.h"
#include "arm_compute/core/Error.h"
#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Utils.h"

using namespace arm_compute;

namespace
{
Status validate_arguments(const ITensorInfo *accum, const ITensorInfo *biases)
{
    ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(accum);
    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(accum, 1, DataType::F16, DataType::F32);
    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(biases, accum);
    ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() != 1);

    return Status{};
}

std::pair<Status, Window> validate_and_configure_window(ITensorInfo *accum, ITensorInfo *biases, GPUTarget gpu_target,
                                                        unsigned int &num_elems_processed_per_iteration)
{
    // Select the vector size to use (8 for Bifrost; 16 for Midgard).
    num_elems_processed_per_iteration = gpu_target_is_in(gpu_target, GPUTarget::G71, GPUTarget::G72, GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT, GPUTarget::G76) ? 8 : 16;

    // Configure kernel window
    Window win = calculate_max_window(*accum, Steps(num_elems_processed_per_iteration));

    AccessWindowStatic     biases_access(biases, 0, 0, ceil_to_multiple(biases->dimension(0), num_elems_processed_per_iteration), biases->dimension(1));
    AccessWindowHorizontal accum_access(accum, 0, num_elems_processed_per_iteration);

    bool window_changed = update_window_and_padding(win, biases_access, accum_access);

    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
    return std::make_pair(err, win);
}
} // namespace

CLGEMMMatrixAccumulateBiasesKernel::CLGEMMMatrixAccumulateBiasesKernel()
    : _accum(nullptr), _biases(nullptr)
{
}

void CLGEMMMatrixAccumulateBiasesKernel::configure(ICLTensor *accum, const ICLTensor *biases)
{
    // Perform validate step
    ARM_COMPUTE_ERROR_ON_NULLPTR(accum, biases);
    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(accum->info(), biases->info()));

    _biases = biases;
    _accum  = accum;

    // Get the target gpu
    GPUTarget    gpu_target  = get_target();
    unsigned int vector_size = 0;

    // Configure kernel window
    auto win_config = validate_and_configure_window(accum->info(), biases->info(), gpu_target, vector_size);
    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
    ICLKernel::configure(win_config.second);

    // Add build options
    CLBuildOptions build_opts;
    build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(accum->info()->data_type()));
    build_opts.add_option("-DVECTOR_SIZE=" + support::cpp11::to_string(vector_size));

    // Create kernel
    _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("gemm_accumulate_biases", build_opts.options()));
}

Status CLGEMMMatrixAccumulateBiasesKernel::validate(const ITensorInfo *accum, const ITensorInfo *biases, GPUTarget gpu_target)
{
    unsigned int num_elems_processed_per_iteration = 0;
    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(accum, biases));
    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(accum->clone().get(), biases->clone().get(), gpu_target, num_elems_processed_per_iteration).first);

    return Status{};
}

void CLGEMMMatrixAccumulateBiasesKernel::run(const Window &window, cl::CommandQueue &queue)
{
    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
    ARM_COMPUTE_ERROR_ON_MISMATCHING_WINDOWS(ICLKernel::window(), window);

    Window accum_slice = window.first_slice_window_2D();

    Window biases_slice(accum_slice);
    biases_slice.set(Window::DimY, Window::Dimension(0, 1, 1));

    // Run kernel
    do
    {
        // Set arguments
        unsigned int idx = 0;
        add_2D_tensor_argument(idx, _accum, accum_slice);
        add_1D_tensor_argument(idx, _biases, biases_slice);

        enqueue(queue, *this, accum_slice, _lws_hint);
    }
    while(window.slide_window_slice_2D(accum_slice));
}