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/*
* Copyright (c) 2017-2019 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/NECol2ImKernel.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/IWeightsManager.h"
#include "arm_compute/runtime/MemoryGroup.h"
#include "arm_compute/runtime/NEON/functions/NEGEMM.h"
#include "arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h"
#include "arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h"
#include "arm_compute/runtime/NEON/functions/NEReshapeLayer.h"
#include "arm_compute/runtime/Tensor.h"
#include <memory>
namespace arm_compute
{
class ITensor;
/** Function to reshape the weights. This function calls the following kernel:
* -# @ref NEWeightsReshapeKernel
*/
class NEConvolutionLayerReshapeWeights : public IFunction
{
public:
/** Constructor */
NEConvolutionLayerReshapeWeights();
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEConvolutionLayerReshapeWeights(const NEConvolutionLayerReshapeWeights &) = delete;
/** Default move constructor */
NEConvolutionLayerReshapeWeights(NEConvolutionLayerReshapeWeights &&) = default;
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEConvolutionLayerReshapeWeights &operator=(const NEConvolutionLayerReshapeWeights &) = delete;
/** Default move assignment operator */
NEConvolutionLayerReshapeWeights &operator=(NEConvolutionLayerReshapeWeights &&) = default;
/** 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/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[out] output Destination tensor. Data types supported: Same as @p weights.
*/
void configure(const ITensor *weights, const ITensor *biases, ITensor *output);
/** 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.
*
* @return an error status
*/
static Status validate(const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output);
// Inherited methods overridden:
void run() override;
private:
NEWeightsReshapeKernel _weights_reshape_kernel;
};
namespace weights_transformations
{
/** Basic function to manage the reshape weights generated from @ref NEConvolutionLayerReshapeWeights */
class NEConvolutionLayerReshapeWeightsTransform : public ITransformWeights
{
public:
void configure(const ITensor *input, const ITensor *biases)
{
_bias_bit = (biases != nullptr) ? 1 : 0;
_func.configure(input, biases, &_output);
}
void run() override
{
_output.allocator()->allocate();
_func.run();
_reshape_run = true;
}
ITensor *get_weights() override
{
return &_output;
}
void release() override
{
_output.allocator()->free();
}
uint32_t uid() override
{
return ((0x8) | (_bias_bit << 7));
}
bool is_reshape_run()
{
return _reshape_run;
}
private:
Tensor _output{};
NEConvolutionLayerReshapeWeights _func{};
int32_t _bias_bit{ 0 };
};
} // namespace weights_transformations
/** Basic function to compute the convolution layer. This function calls the following NEON kernels/functions:
*
* -# @ref NEIm2ColKernel
* -# @ref NEGEMM (if the data type is FP32 or FP16)
* -# @ref NEGEMMLowpMatrixMultiplyCore (if the data type is QASYMM8)
* -# @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint (if the data type is QASYMM8)
* -# @ref NEArithmeticAdditionKernel (if biases != nullptr and we have a 1x1 convolution with the NHWC data layout)
* -# @ref NECol2ImKernel (if NCHW data layout)
*
*/
class NEGEMMConvolutionLayer : public IFunction
{
public:
/** Constructor */
NEGEMMConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager = nullptr, IWeightsManager *weights_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.
* @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is not 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(), unsigned int num_groups = 1);
/** 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.
* @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is not 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(), unsigned int num_groups = 1);
// 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[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 Output tensor. Data types supported: Same as @p input,
* except for input of QASYMM8 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] gemm_3d_depth (Optional) Depth of GEMM 3D (Defaults to 1)
*/
void configure_mm(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const ActivationLayerInfo &act_info = ActivationLayerInfo(), int gemm_3d_depth = 1);
/** Static function to check if given info will lead to a valid configuration of @ref NEGEMMConvolutionLayer matrix multiply routines
*
* @param[in] input Input tensor. Data types supported: QASYMM8/F16/F32.
* @param[in] weights Weights tensor. 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 Output tensor. Data types supported: Same as @p input,
* except for input of QASYMM8 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] 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)
*
* @return a status
*/
static Status validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const ActivationLayerInfo &act_info = ActivationLayerInfo(),
int gemm_3d_depth = 1, bool skip_im2col = false);
/** Static function to check if GEMM3D is supported in @ref NEGEMM or in @ref NEGEMMLowpMatrixMultiplyCore
*
* @param[in] input_info Input tensor info. Data types supported: QASYMM8/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 *input_info, const ActivationLayerInfo &act_info, int gemm_3d_depth, bool skip_im2col);
private:
MemoryGroup _memory_group;
IWeightsManager *_weights_manager;
NEConvolutionLayerReshapeWeights _reshape_weights;
weights_transformations::NEConvolutionLayerReshapeWeightsTransform _reshape_weights_managed;
NEIm2ColKernel _im2col_kernel;
NEGEMM _mm_gemm;
NEGEMMLowpMatrixMultiplyCore _mm_gemmlowp;
NECol2ImKernel _col2im_kernel;
NEReshapeLayer _reshape_layer;
const ITensor *_original_weights;
Tensor _im2col_output;
Tensor _weights_reshaped;
Tensor _gemm_output;
Tensor _tmp_output;
DataLayout _data_layout;
bool _skip_im2col;
bool _skip_col2im;
bool _is_quantized;
bool _is_prepared;
};
} // namespace arm_compute
#endif /* __ARM_COMPUTE_NECONVOLUTIONGEMMLAYER_H__ */
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