/*
 * Copyright (c) 2018-2021 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 "arm_compute/runtime/CL/functions/CLRNNLayer.h"

#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/runtime/CL/CLScheduler.h"

#include "src/common/utils/Log.h"
#include "src/core/CL/kernels/CLFillBorderKernel.h"

namespace arm_compute
{
using namespace arm_compute::misc::shape_calculator;

CLRNNLayer::CLRNNLayer(std::shared_ptr<IMemoryManager> memory_manager)
    : _memory_group(std::move(memory_manager)),
      _gemm_state_f(),
      _add_kernel(),
      _activation(),
      _fully_connected_kernel(),
      _copy(),
      _fully_connected_out(),
      _gemm_output(),
      _add_output(),
      _is_prepared(false)
{
}

CLRNNLayer::~CLRNNLayer() = default;

Status CLRNNLayer::validate(const ITensorInfo         *input,
                            const ITensorInfo         *weights,
                            const ITensorInfo         *recurrent_weights,
                            const ITensorInfo         *bias,
                            const ITensorInfo         *hidden_state,
                            const ITensorInfo         *output,
                            const ActivationLayerInfo &info)
{
    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, recurrent_weights, bias, hidden_state, output);
    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_NOT_IN(input, DataType::F16, DataType::F32);
    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, recurrent_weights, bias, hidden_state, output);

    const int idx_width  = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
    const int idx_height = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);

    ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(idx_width) != weights->dimension(idx_width));
    ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_height) != recurrent_weights->dimension(idx_width));
    ARM_COMPUTE_RETURN_ERROR_ON(recurrent_weights->dimension(idx_width) != recurrent_weights->dimension(1));
    ARM_COMPUTE_RETURN_ERROR_ON(bias->num_dimensions() != 1);
    ARM_COMPUTE_RETURN_ERROR_ON(bias->dimension(idx_width) != weights->dimension(idx_height));
    ARM_COMPUTE_RETURN_ERROR_ON(hidden_state->dimension(idx_width) != weights->dimension(idx_height));
    ARM_COMPUTE_RETURN_ERROR_ON(hidden_state->dimension(idx_height) != input->dimension(idx_height));
    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), hidden_state->tensor_shape());

    auto shape_info =
        TensorInfo(compute_rnn_shape(recurrent_weights, hidden_state->dimension(idx_height)), 1, input->data_type());

    ARM_COMPUTE_RETURN_ON_ERROR(CLFullyConnectedLayer::validate(input, weights, bias, &shape_info));
    ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(hidden_state, recurrent_weights, nullptr, &shape_info, 1.f, 0.f));
    ARM_COMPUTE_RETURN_ON_ERROR(
        CLArithmeticAddition::validate(&shape_info, &shape_info, &shape_info, ConvertPolicy::SATURATE));
    ARM_COMPUTE_RETURN_ON_ERROR(CLActivationLayer::validate(&shape_info, &shape_info, info));

    return Status{};
}

void CLRNNLayer::configure(const ICLTensor     *input,
                           const ICLTensor     *weights,
                           const ICLTensor     *recurrent_weights,
                           const ICLTensor     *bias,
                           ICLTensor           *hidden_state,
                           ICLTensor           *output,
                           ActivationLayerInfo &info)
{
    configure(CLKernelLibrary::get().get_compile_context(), input, weights, recurrent_weights, bias, hidden_state,
              output, info);
}

void CLRNNLayer::configure(const CLCompileContext &compile_context,
                           const ICLTensor        *input,
                           const ICLTensor        *weights,
                           const ICLTensor        *recurrent_weights,
                           const ICLTensor        *bias,
                           ICLTensor              *hidden_state,
                           ICLTensor              *output,
                           ActivationLayerInfo    &info)
{
    ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, recurrent_weights, bias, hidden_state, output);
    ARM_COMPUTE_ERROR_THROW_ON(CLRNNLayer::validate(input->info(), weights->info(), recurrent_weights->info(),
                                                    bias->info(), hidden_state->info(), output->info(), info));
    ARM_COMPUTE_LOG_PARAMS(input, weights, recurrent_weights, bias, hidden_state, output, info);

    const int   idx_height = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT);
    TensorShape shape      = compute_rnn_shape(recurrent_weights->info(), hidden_state->info()->dimension(idx_height));

    _is_prepared = false;

    _fully_connected_out.allocator()->init(TensorInfo(shape, 1, input->info()->data_type()));
    _gemm_output.allocator()->init(TensorInfo(shape, 1, input->info()->data_type()));

    // Manage intermediate buffers and configure
    _memory_group.manage(&_fully_connected_out);
    _fully_connected_kernel.configure(compile_context, input, weights, bias, &_fully_connected_out);

    _memory_group.manage(&_gemm_output);
    _gemm_state_f.configure(compile_context, hidden_state, recurrent_weights, nullptr, &_gemm_output, 1.f, 0.f);

    _add_output.allocator()->init(TensorInfo(shape, 1, input->info()->data_type()));
    _memory_group.manage(&_add_output);

    _add_kernel.configure(compile_context, &_fully_connected_out, &_gemm_output, &_add_output, ConvertPolicy::SATURATE);

    _fully_connected_out.allocator()->allocate();
    _gemm_output.allocator()->allocate();

    _activation.configure(compile_context, &_add_output, hidden_state, info);
    _add_output.allocator()->allocate();

    _copy.configure(compile_context, hidden_state, output);
}

void CLRNNLayer::run()
{
    prepare();

    MemoryGroupResourceScope scope_mg(_memory_group);

    _fully_connected_kernel.run();
    _gemm_state_f.run();
    _add_kernel.run();
    _activation.run();

    // copy hidden out to output
    _copy.run();
}

void CLRNNLayer::prepare()
{
    if (!_is_prepared)
    {
        _fully_connected_kernel.prepare();
        _gemm_state_f.prepare();

        _is_prepared = true;
    }
}
} // namespace arm_compute
