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/*
* Copyright (c) 2017-2018 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_NEGEMMCONVOLUTIONLAYER_H__
#define __ARM_COMPUTE_NEGEMMCONVOLUTIONLAYER_H__
#include "arm_compute/runtime/IFunction.h"
#include "arm_compute/core/NEON/kernels/NEArithmeticAdditionKernel.h"
#include "arm_compute/core/NEON/kernels/NECol2ImKernel.h"
#include "arm_compute/core/NEON/kernels/NEFillBorderKernel.h"
#include "arm_compute/core/NEON/kernels/NEGEMMAssemblyBaseKernel.h"
#include "arm_compute/core/NEON/kernels/NEGEMMInterleave4x4Kernel.h"
#include "arm_compute/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.h"
#include "arm_compute/core/NEON/kernels/NEGEMMTranspose1xWKernel.h"
#include "arm_compute/core/NEON/kernels/NEIm2ColKernel.h"
#include "arm_compute/core/NEON/kernels/NEWeightsReshapeKernel.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/runtime/MemoryGroup.h"
#include "arm_compute/runtime/NEON/AssemblyHelper.h"
#include "arm_compute/runtime/NEON/functions/NEActivationLayer.h"
#include "arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h"
#include "arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h"
#include "arm_compute/runtime/Tensor.h"
#include <memory>
namespace arm_compute
{
class ITensor;
/** Function to reshape and perform 1xW transposition on the weights. This function calls the following kernels:
* -# @ref NEWeightsReshapeKernel
* -# @ref NEGEMMTranspose1xWKernel (executed in case GEMM is required for the operation)
*/
class NEConvolutionLayerReshapeWeights : public IFunction
{
public:
/** Constructor */
NEConvolutionLayerReshapeWeights(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
/** Set the input and output tensors.
*
* @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: QASYMM8/F32.
* @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights.
* @param[out] output Destination tensor. Data types supported: Same as @p weights.
* @param[in] transpose1xW True if the weights are to undergo a 1xW transposition after reshaping (in case of GEMM operation), false otherwise.
* Data types supported: Same as @p weights.
*/
void configure(const ITensor *weights, const ITensor *biases, ITensor *output, bool transpose1xW);
/** Static function to check if given info will lead to a valid configuration of @ref NEConvolutionLayerReshapeWeights
*
* @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: QASYMM8/F16/F32.
* @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights.
* @param[in] output Destination tensor. Data types supported: Same as @p weights.
* @param[in] transpose1xW True if the weights are to undergo a 1xW transposition after reshaping (in case of GEMM operation), false otherwise.
* Data types supported: Same as @p weights.
*
* @return an error status
*/
static Status validate(const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, bool transpose1xW);
// Inherited methods overridden:
void run() override;
private:
MemoryGroup _memory_group;
NEWeightsReshapeKernel _weights_reshape_kernel;
NEGEMMTranspose1xWKernel _weights_transposed_kernel;
Tensor _weights_reshaped;
bool _transpose1xW;
};
/** Basic function to simulate a convolution layer. This function calls the following NEON kernels:
* -# @ref NEWeightsReshapeKernel (executed only once for each configuration)
* -# @ref NEIm2ColKernel
* -# @ref NEGEMMInterleave4x4Kernel (executed only in case GEMM is required for the operation)
* -# @ref NEGEMMMatrixMultiplyKernel or @ref NEGEMMLowpMatrixMultiplyCore (if quantized asymmetric)
* -# @ref NEGEMMLowpQuantizeDownInt32ToUint8Scale (if quantized asymmetric)
* -# @ref NECol2ImKernel
* -# @ref NEActivationLayer (executed only if the activation layer is enabled)
*/
class NEGEMMConvolutionLayer : public IFunction
{
public:
/** Constructor */
NEGEMMConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager = nullptr);
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEGEMMConvolutionLayer(const NEGEMMConvolutionLayer &) = delete;
/** Default move constructor */
NEGEMMConvolutionLayer(NEGEMMConvolutionLayer &&) = default;
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEGEMMConvolutionLayer &operator=(const NEGEMMConvolutionLayer &) = delete;
/** Default move assignment operator */
NEGEMMConvolutionLayer &operator=(NEGEMMConvolutionLayer &&) = default;
/** Set the input and output tensors.
*
* @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: QASYMM8/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: Should match @p input data type, except for input of QASYMM8 type where biases should be of S32 type.
* @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] 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 NEGEMMTranspose1xWKernel. 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.
*/
void configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info = WeightsInfo(),
const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo());
/** Static function to check if given info will lead to a valid configuration of @ref NEGEMMConvolutionLayer
*
* @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: QASYMM8/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: Should match @p input data type, except for input of QASYMM8 type where biases should be of S32 type.
* @param[in] 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] 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 NEGEMMTranspose1xWKernel. 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.
*
* @return a status
*/
static Status validate(const ITensorInfo *input, 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());
// Inherited methods overridden:
void run() override;
void prepare() override;
private:
/** Configures the appropriate matrix multiply routine
*
* @param[in] input Input tensor. Data types supported: QASYMM8/F16/F32.
* @param[in] weights Weights tensor. Data type supported: Same as @p input.
* @param[out] output Output tensor. Data types supported: Same as @p input,
* except for input of QASYMM8 type where output should be of S32 type.
* @param[in] is_interleaved (Optional) True if input0 and input1 have been reshaped respectively using @ref CLGEMMInterleave4x4Kernel and @ref CLGEMMTranspose1xWKernel
* @param[in] reshape_info (Optional) GEMM reshape info. If is_interleaved_transposed = true, this object must contain the information to understand how the matrix A and matrix B have been reshaped
*/
void configure_mm(const ITensor *input, const ITensor *weights, ITensor *output, bool is_interleaved, const GEMMReshapeInfo &reshape_info = GEMMReshapeInfo());
private:
AssemblyKernelGlueF32 _asm_glue;
MemoryGroup _memory_group;
NEIm2ColKernel _input_im2col_kernel;
NEGEMMInterleave4x4Kernel _input_interleave_kernel;
NEConvolutionLayerReshapeWeights _reshape_weights;
NEGEMMMatrixMultiplyKernel _mm_kernel;
NEGEMMLowpMatrixMultiplyCore _mm_gemmlowp;
NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint _gemmlowp_output_stage;
NECol2ImKernel _output_col2im_kernel;
NEActivationLayer _activationlayer_function;
NEArithmeticAdditionKernel _add_bias_kernel;
const ITensor *_original_weights;
Tensor _input_im2col_reshaped;
Tensor _input_interleaved_reshaped;
Tensor _weights_reshaped;
Tensor _gemm_output;
Tensor _tmp_output;
Tensor _workspace;
Tensor _B_pretransposed;
DataLayout _data_layout;
bool _append_bias;
bool _is_fully_connected_convolution;
bool _are_weights_reshaped;
bool _is_quantized;
bool _is_interleaved;
bool _is_activationlayer_enabled;
bool _skip_im2col;
bool _is_prepared;
};
}
#endif /* __ARM_COMPUTE_NECONVOLUTIONGEMMLAYER_H__ */
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