/*
 * 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.
 */
#ifndef ARM_COMPUTE_CL_WINOGRADCONV2D_H
#define ARM_COMPUTE_CL_WINOGRADCONV2D_H

#include "arm_compute/runtime/CL/CLTensor.h"
#include "src/core/CL/kernels/CLFillBorderKernel.h"
#include "src/core/gpu/cl/ClCompileContext.h"
#include "src/runtime/gpu/cl/IClOperator.h"
#include "src/runtime/gpu/cl/operators/ClGemm.h"

namespace arm_compute
{
class CLCompileContext;
class ITensorInfo;
namespace opencl
{
namespace kernels
{
class ClWinogradInputTransformKernel;
class ClWinogradFilterTransformKernel;
class ClWinogradOutputTransformKernel;
} // kernels
/** Basic function to execute Winograd-based convolution on OpenCL. This function calls the following OpenCL functions/kernels:
 *
 *  -# @ref kernels::ClWinogradInputTransformKernel
 *  -# @ref kernels::ClWinogradFilterTransformKernel (only once)
 *  -# @ref ClGemm
 *  -# @ref kernels::ClWinogradOutputTransformKernel
 *
 */
class ClWinogradConv2d : public IClOperator
{
public:
    /** Default constructor */
    ClWinogradConv2d();
    /** Default destructor */
    ~ClWinogradConv2d();
    /** Prevent instances of this class from being copied (As this class contains pointers) */
    ClWinogradConv2d(const ClWinogradConv2d &) = delete;
    /** Default move constructor */
    ClWinogradConv2d(ClWinogradConv2d &&) = default;
    /** Prevent instances of this class from being copied (As this class contains pointers) */
    ClWinogradConv2d &operator=(const ClWinogradConv2d &) = delete;
    /** Default move assignment operator */
    ClWinogradConv2d &operator=(ClWinogradConv2d &&) = default;
    /** Set the input and output tensors.
     *
     * Valid data layouts:
     * - NHWC
     * - NCHW
     *
     * Valid data type configurations:
     * |src0           |src1           |src2   |dst            |
     * |:--------------|:--------------|:------|:--------------|
     * |F16            |F16            |F16    |F16            |
     * |F32            |F32            |F32    |F32            |
     *
     * @note: This function only works with 3x3,3x1,1x3,5x5,5x1,1x5,7x1 and 1x7 kernels along with unit strides for both NCHW and NHWC data layout
     * @note  Some Winograd configurations (i.e. F(4x4, 5x5)) are supported only with enable_fast_math = true
     *
     * @param[in]  compile_context  The compile context to be used.
     * @param[in]  src              Source tensor info. 3 lower dimensions represent a single input [width, height, IFM],
     *                              while every optional dimension from 4 and above represent a batch of inputs.
     *                              Data types supported: F16/F32.
     * @param[in]  weights          Weights tensor info. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported:Same as @p src.
     * @param[in]  biases           Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM].Data type supported: Same as @p src
     * @param[out] dst              Destination tensor info. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
     *                              Data types supported: Same as @p src.
     * @param[in]  conv_info        Contains padding and stride information described in @ref PadStrideInfo.
     * @param[in]  act_info         (Optional) Activation layer information in case of a fused activation.
     * @param[in]  enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation
     *                              available which may introduce a drop of accuracy as well. Default is false
     */
    void configure(const ClCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *biases, ITensorInfo *dst, const PadStrideInfo &conv_info,
                   const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false);
    /** Static function to check if given info will lead to a valid configuration
     *
     * Similar to ClWinogradConv2d::configure()
     *
     * @return a status
     */
    static Status validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const PadStrideInfo &conv_info,
                           const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false);

    // Inherited method overridden
    void run(ITensorPack &tensors) override;
    void prepare(ITensorPack &tensors) override;
    experimental::MemoryRequirements workspace() const override;

private:
    ClGemm                                                    _batched_mm;
    std::unique_ptr<kernels::ClWinogradInputTransformKernel>  _input_transform;
    std::unique_ptr<kernels::ClWinogradFilterTransformKernel> _filter_transform;
    std::unique_ptr<kernels::ClWinogradOutputTransformKernel> _output_transform;
    CLFillBorderKernel                                        _border_handler;
    TensorInfo                                                _input0;
    TensorInfo                                                _input1;
    TensorInfo                                                _batched_mm_output;
    bool                                                      _is_prepared;
    experimental::MemoryRequirements                          _aux_mem{};
};
} // namespace opencl
} // namespace arm_compute
#endif /* ARM_COMPUTE_CL_WINOGRADCONV2D_H */