path: root/src/cpu/operators/CpuGemmConv2d.h

/*
 * Copyright (c) 2021-2023 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
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 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */
#ifndef ACL_SRC_CPU_OPERATORS_CPUGEMMCONV2D_H
#define ACL_SRC_CPU_OPERATORS_CPUGEMMCONV2D_H

#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/function_info/ActivationLayerInfo.h"

#include "src/cpu/ICpuOperator.h"

#include <memory>

namespace arm_compute
{
namespace cpu
{
class CpuGemm;
class CpuGemmLowpMatrixMultiplyCore;
class CpuGemmLowpOutputStage;
class CpuReshape;
namespace kernels
{
class CpuIm2ColKernel;
class CpuCol2ImKernel;
class CpuWeightsReshapeKernel;
} // namespace kernels

/** Basic function to compute the convolution layer. @ref note_CpuGemmConv2d_weight_transformation */
class CpuGemmConv2d : public ICpuOperator
{
public:
    /** Constructor */
    CpuGemmConv2d();
    /** Prevent instances of this class from being copied (As this class contains pointers) */
    CpuGemmConv2d(const CpuGemmConv2d &) = delete;
    /** Prevent instances of this class from being moved (As this class contains non movable objects) */
    CpuGemmConv2d(CpuGemmConv2d &&) = delete;
    /** Prevent instances of this class from being copied (As this class contains pointers) */
    CpuGemmConv2d &operator=(const CpuGemmConv2d &) = delete;
    /** Prevent instances of this class from being moved (As this class contains non movable objects) */
    CpuGemmConv2d &operator=(CpuGemmConv2d &&) = delete;
    /** Destructor */
    ~CpuGemmConv2d();
    /** 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            |
     * |BFLOAT16       |BFLOAT16           |BFLOAT16 |BFLOAT16       |
     * |QASYMM8        |QASYMM8            |S32      |QASYMM8        |
     * |QASYMM8        |QSYMM8_PER_CHANNEL |S32      |QASYMM8        |
     * |QASYMM8_SIGNED |QASYMM8_SIGNED     |S32      |QASYMM8_SIGNED |
     * |QASYMM8_SIGNED |QSYMM8_PER_CHANNEL |S32      |QASYMM8_SIGNED |
     *
     * @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: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32.
     * @param[in]  weights          Weights tensor info. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
     *                              Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/BFLOAT16/F16/F32.
     * @param[in]  biases           Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
     *                              Data type supported: Should match @p input data type, except for input of QASYMM8/QASYMM8_SIGNED type where biases should be of S32 type.
     * @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 input.
     * @param[in]  conv_info        Contains padding and stride information described in @ref PadStrideInfo.
     * @param[in]  weights_info     Specifies if the weights tensor has been reshaped with CpuWeightsReshapeKernel. If this is not part of the fully connected layer the weights
     *                              tensor has also been transposed with cpu::kernels::CpuGemmTranspose1xWKernel. Data type supported: Same as @p input.
     * @param[in]  dilation         (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
     * @param[in]  act_info         (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported.
     * @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
     * @param[in]  num_groups       (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is not supported
     */
    void configure(const ITensorInfo         *src,
                   const ITensorInfo         *weights,
                   const ITensorInfo         *biases,
                   ITensorInfo               *dst,
                   const PadStrideInfo       &conv_info,
                   const WeightsInfo         &weights_info     = WeightsInfo(),
                   const Size2D              &dilation         = Size2D(1U, 1U),
                   const ActivationLayerInfo &act_info         = ActivationLayerInfo(),
                   bool                       enable_fast_math = false,
                   unsigned int               num_groups       = 1);
    /** Static function to check if given info will lead to a valid configuration
     *
     * Similar to CpuGemmConvolution::configure()
     *
     * @return a status
     */
    static Status validate(const ITensorInfo         *src,
                           const ITensorInfo         *weights,
                           const ITensorInfo         *biases,
                           const ITensorInfo         *output,
                           const PadStrideInfo       &conv_info,
                           const WeightsInfo         &weights_info     = WeightsInfo(),
                           const Size2D              &dilation         = Size2D(1U, 1U),
                           const ActivationLayerInfo &act_info         = ActivationLayerInfo(),
                           bool                       enable_fast_math = false,
                           unsigned int               num_groups       = 1);

    /** Indicates whether or not there is an optimal assembly implementation that can be used to process the given parameters.
     *
     * The parameter list is the same as @ref NEGEMMConvolutionLayer::has_opt_impl
     *
     * @return a status.
     */
    static Status has_opt_impl(arm_compute::WeightFormat &expected_weight_format,
                               const ITensorInfo         *src,
                               const ITensorInfo         *weights,
                               const ITensorInfo         *biases,
                               const ITensorInfo         *output,
                               const PadStrideInfo       &conv_info,
                               const WeightsInfo         &weights_info     = WeightsInfo(),
                               const Size2D              &dilation         = Size2D(1U, 1U),
                               const ActivationLayerInfo &act_info         = ActivationLayerInfo(),
                               const bool                 enable_fast_math = false);

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

private:
    /** Configures the appropriate matrix multiply routine
     *
     * @param[in]  src              Input tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32.
     * @param[in]  weights          Weights tensor info. Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/BFLOAT16/F16/F32.
     * @param[in]  biases           Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
     *                              Data type supported: Should match @p input data type, except for input of QASYMM8/QASYMM8_SIGNED type where biases should be of S32 type.
     * @param[out] dst              Output tensor info. Data types supported: Same as @p input,
     *                              except for input of QASYMM8/QASYMM8_SIGNED type where output should be of S32 type.
     * @param[in]  act_info         (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported.
     * @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
     * @param[in]  gemm_3d_depth    (Optional) Depth of GEMM 3D (Defaults to 1)
     * @param[in]  fixed_format     (Optional) Select GEMM execution with variable weights.
     * @param[in]  weight_format    (Optional) The layout to be used for the weights tensor when running GEMM with variable weights.
     */
    void configure_mm(const ITensorInfo         *src,
                      const ITensorInfo         *weights,
                      const ITensorInfo         *biases,
                      ITensorInfo               *output,
                      const ActivationLayerInfo &act_info         = ActivationLayerInfo(),
                      bool                       enable_fast_math = false,
                      int                        gemm_3d_depth    = 1,
                      bool                       fixed_format     = false,
                      arm_compute::WeightFormat  weight_format    = arm_compute::WeightFormat::UNSPECIFIED);
    /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMConvolutionLayer matrix multiply routines
     *
     * @param[in] src              Input tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32.
     * @param[in] weights          Weights tensor info. Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/BFLOAT16/F16/F32.
     * @param[in] biases           Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
     *                             Data type supported: Should match @p input data type, except for input of QASYMM8/QASYMM8_SIGNED type where biases should be of S32 type.
     * @param[in] dst              Output tensor info. Data types supported: Same as @p input,
     *                             except for input of QASYMM8/QASYMM8_SIGNED type where output should be of S32 type.
     * @param[in] act_info         (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported.
     * @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
     * @param[in] gemm_3d_depth    (Optional) Depth of GEMM 3D (Defaults to 1)
     * @param[in] skip_im2col      (Optional) Flag which specifies if im2col has to be skipped. i.e. 1x1 convolution with NHWC data layout. (Default to false)
     * @param[in] fixed_format     (Optional) Select GEMM execution with variable weights.
     * @param[in] weight_format    (Optional) The layout to be used for the weights tensor when running GEMM with variable weights.
     *
     * @return a status
     */
    static Status validate_mm(const ITensorInfo         *src,
                              const ITensorInfo         *weights,
                              const ITensorInfo         *biases,
                              const ITensorInfo         *dst,
                              const ActivationLayerInfo &act_info         = ActivationLayerInfo(),
                              bool                       enable_fast_math = false,
                              int                        gemm_3d_depth    = 1,
                              bool                       skip_im2col      = false,
                              bool                       fixed_format     = false,
                              arm_compute::WeightFormat  weight_format    = arm_compute::WeightFormat::UNSPECIFIED);
    /** Static function to check if GEMM3D is supported in @ref NEGEMM or in @ref CpuGemmMLowpMatrixMultiplyCore
     *
     * @param[in] src           Input tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32.
     * @param[in] weights       Weights tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32.
     * @param[in] act_info      Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported.
     * @param[in] gemm_3d_depth Depth of GEMM 3D
     * @param[in] skip_im2col   Flag which specifies if im2col has to be skipped. i.e. 1x1 convolution with NHWC data layout
     *
     * @return a status
     */
    static Status validate_gemm3d(const ITensorInfo         *src,
                                  const ITensorInfo         *weights,
                                  const ActivationLayerInfo &act_info,
                                  int                        gemm_3d_depth,
                                  bool                       skip_im2col);

    struct SkipInfo
    {
        bool skip_im2col;
        bool skip_col2im;
    };

    /** Static function to provide skip_im2col and skip_col2im information.
     *
     * @param[in] src       Input tensor info.
     * @param[in] weights   Weights tensor info.
     * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo.
     * @param[in] dilation  Dilation, in elements, across x and y.
     * @param[in] act_info  Activation layer information in case of a fused activation.
     *
     * @return a SkipInfo instance.
     */
    static SkipInfo skip_im_col_info(const ITensorInfo         *src,
                                     const ITensorInfo         *weights,
                                     const PadStrideInfo       &conv_info,
                                     const Size2D              &dilation,
                                     const ActivationLayerInfo &act_info);

    /** Indicates if the convolution executes in variable weights mode.
     *
     * Similar to @ref CpuGemm::isVarWeightsKernel
     */
    bool isVarWeightsKernel() const;
    enum AuxTensorIdx
    {
        GemmAsmPretransposedRHS  = 2, // CpuGemmAssemblyDispatch::Pretranspose
        GemmTransposed1xWRHS     = 5, // CpuGemm::Transposed1xWRHS
        GemmLowpTransposed1xWRHS = 6, // CpuGemmLowpMatrixMultiplyCore::TmpB
        /* Slots 0 - 9 reserved and shared by CpuGemmLowpMatrixMultiplyCore and CpuGemm */
        Im2ColOutput = 10,
        WeightsReshaped,
        GemmOutput,
        Count
    };

    /** Weight transformation method. See @ref note_CpuGemmConv2d_weight_transformation */
    enum class WeightTransformMethod
    {
        ReinterpretThenTranspose,
        ReshapeThenTranspose,
        FusedReshapeAndTranspose,
    };

    /** Select weight transformation method
     *
     * @param[in] weights Input weights
     *
     * @return WeightTransformMethod
     */
    static WeightTransformMethod get_wt_method(const ITensorInfo &weights);

    std::unique_ptr<CpuReshape>                       _weights_reshape;
    std::unique_ptr<kernels::CpuWeightsReshapeKernel> _weights_reshape_and_transpose_kernel;
    std::unique_ptr<kernels::CpuIm2ColKernel>         _im2col_kernel;
    std::unique_ptr<CpuGemm>                          _mm_gemm;
    std::unique_ptr<CpuGemmLowpMatrixMultiplyCore>    _mm_gemmlowp;
    std::unique_ptr<kernels::CpuCol2ImKernel>         _col2im_kernel;
    std::unique_ptr<CpuReshape>                       _reshape;

    TensorInfo _im2col_output;
    TensorInfo _weights_reshaped;
    TensorInfo _gemm_output;
    TensorInfo _gemm_output_3d;

    DataLayout _data_layout;

    bool                  _skip_im2col;
    bool                  _skip_col2im;
    bool                  _is_quantized;
    bool                  _is_prepared;
    WeightTransformMethod _wt_method;
    bool                  _run_wt;

    experimental::MemoryRequirements _aux_mem{Count};
};
} // namespace cpu
} // namespace arm_compute
#endif // ACL_SRC_CPU_OPERATORS_CPUGEMMCONV2D_H