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/*
* 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
* 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_CPU_GEMM_CONV2D_H
#define ARM_COMPUTE_CPU_GEMM_CONV2D_H
#include "arm_compute/core/ActivationLayerInfo.h"
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/core/Types.h"
#include "src/cpu/ICpuOperator.h"
#include <memory>
namespace arm_compute
{
namespace cpu
{
class CpuGemm;
class CpuGemmLowpMatrixMultiplyCore;
class CpuGemmLowpOutputStage;
namespace kernels
{
class CpuWeightsReshapeKernel;
class CpuIm2ColKernel;
class CpuCol2ImKernel;
class CpuReshapeKernel;
} // namespace kernels
/** Basic function to compute the convolution layer. This function calls the following kernels/functions:
*
* -# @ref cpu::kernels::CpuIm2ColKernel
* -# @ref CpuGemm (if the data type is BFLOAT16/FP16/FP32)
* -# @ref CpuGemmLowpMatrixMultiplyCore (if the data type is QASYMM8/QASYMM8_SIGNED)
* -# @ref CpuGemmLowpOutputStage (if the data type is QASYMM8/QASYMM8_SIGNED)
* -# @ref cpu::kernels::CpuCol2ImKernel (if NCHW data layout)
* -# @ref kernels::CpuWeightsReshapeKernel
*
*/
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 NEWeightsReshapeKernel. 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 paramter 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
{
// CpuGemmLowpMatrixMultiplyCore has up to 8 internal tensors
Im2ColOutput = 9,
WeightsReshaped,
GemmOutput,
Count
};
std::unique_ptr<kernels::CpuWeightsReshapeKernel> _weights_reshape_kernel;
std::unique_ptr<cpu::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<kernels::CpuReshapeKernel> _reshape_kernel;
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;
experimental::MemoryRequirements _aux_mem{ Count };
};
} // namespace cpu
} // namespace arm_compute
#endif /* ARM_COMPUTE_CPU_GEMM_CONV2D_H */
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