CLWinogradConvolutionLayer.h

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/*
 * Copyright (c) 2018-2021 Arm Limited.
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 * SPDX-License-Identifier: MIT
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#ifndef ARM_COMPUTE_CLWINOGRADCONVOLUTIONLAYER_H
#define ARM_COMPUTE_CLWINOGRADCONVOLUTIONLAYER_H

#include "arm_compute/core/Types.h"
#include "arm_compute/runtime/IFunction.h"
#include "arm_compute/runtime/IMemoryManager.h"

#include <memory>

namespace arm_compute
{
class CLCompileContext;
class ICLTensor;
class ITensorInfo;

/** Basic function to execute Winograd-based convolution on OpenCL. This function calls the following OpenCL functions/kernels:
 *
 *  -# @ref opencl::ClWinogradConv2d
 *
 */
class CLWinogradConvolutionLayer : public IFunction
{
public:
    /** Default Constructor */
    CLWinogradConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
    /** Default Destructor */
    ~CLWinogradConvolutionLayer();
    /** Prevent instances of this class from being copied (As this class contains pointers) */
    CLWinogradConvolutionLayer(const CLWinogradConvolutionLayer &) = delete;
    /** Default move constructor */
    CLWinogradConvolutionLayer(CLWinogradConvolutionLayer &&) = default;
    /** Prevent instances of this class from being copied (As this class contains pointers) */
    CLWinogradConvolutionLayer &operator=(const CLWinogradConvolutionLayer &) = delete;
    /** Default move assignment operator */
    CLWinogradConvolutionLayer &operator=(CLWinogradConvolutionLayer &&) = 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]  input            Source tensor. 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. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported:Same as @p input.
     * @param[in]  biases           Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].Data type supported: Same as @p input
     * @param[out] output           Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
     *                              Data types supported: Same as @p input.
     * @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(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info,
                   const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false);
    /** Set the input and output tensors.
     *
     * @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]  input            Source tensor. 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. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported:Same as @p input.
     * @param[in]  biases           Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].Data type supported: Same as @p input
     * @param[out] output           Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
     *                              Data types supported: Same as @p input.
     * @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, ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, 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 of @ref CLWinogradConvolutionLayer
     *
     * @note: This function only works with 3x3,3x1,1x3,5x5,5x1 and 1x5 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]  input            Source tensor. 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. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported:Same as @p input.
     * @param[in]  biases           Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].Data type supported: Same as @p input
     * @param[out] output           Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
     *                              Data types supported: Same as @p input.
     * @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
     *
     * @return a status
     */
    static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
                           const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false);

    // Inherited methods overridden:
    void run() override;
    void prepare() override;

private:
    struct Impl;
    std::unique_ptr<Impl> _impl;
};
} // namespace arm_compute
#endif /* ARM_COMPUTE_CLWINOGRADCONVOLUTIONLAYER_H */