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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_NEFULLYCONNECTEDLAYER_H__
#define __ARM_COMPUTE_NEFULLYCONNECTEDLAYER_H__
#include "arm_compute/runtime/IFunction.h"
#include "arm_compute/core/NEON/kernels/NEGEMMInterleave4x4Kernel.h"
#include "arm_compute/core/NEON/kernels/NEGEMMMatrixAccumulateBiasesKernel.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/NETransposeKernel.h"
#include "arm_compute/runtime/MemoryGroup.h"
#include "arm_compute/runtime/Tensor.h"
namespace arm_compute
{
/** Basic function to reshape the weights of Fully Connected layer with NEON. This function calls the following kernels:
*
* -# @ref NETransposeKernel (if @p transpose_weights is set to true)
* -# @ref NEGEMMTranspose1xWKernel (if @p is_batched_fc_layer is set to true)
*
* @note The fully connected layer accepts "weights" tensors only with 2 dimensions.
*/
class NEFullyConnectedLayerReshapeWeights : public IFunction
{
public:
/** Constructor */
NEFullyConnectedLayerReshapeWeights(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
/** Set the input and output tensors.
*
* @param[in] input Weights tensor. The weights must be 2 dimensional. Data types supported: F32.
* @param[out] output Destination tensor. Data type supported: Same as @p input.
* @param[in] transpose_weights True if the weights must be transposed. Data types supported: Same as @p weights.
* @param[in] is_batched_fc_layer True if it is a batched fully connected layer
*/
void configure(const ITensor *input, ITensor *output, bool transpose_weights, bool is_batched_fc_layer);
/** Static function to check if given info will lead to a valid configuration of @ref CLFullyConnectedLayerReshapeWeights
*
* @param[in] input Weights tensor info. The weights must be 2 dimensional. Data types supported: F32.
* @param[in] output Destination tensor info. Data type supported: Same as @p input.
* @param[in] transpose_weights True if the weights must be transposed. Data types supported: Same as @p weights.
* @param[in] is_batched_fc_layer True if it is a batched fully connected layer
*
* @return a status
*/
static Status validate(const ITensorInfo *input, const ITensorInfo *output, bool transpose_weights, bool is_batched_fc_layer);
// Inherited methods overridden:
void run() override;
private:
MemoryGroup _memory_group;
NETransposeKernel _transpose_kernel;
NEGEMMTranspose1xWKernel _transpose1xW_kernel;
Tensor _transpose_output;
bool _transpose_weights;
bool _is_batched_fc_layer;
};
/** Basic function to compute a Fully Connected layer on NEON. This function calls the following NEON kernels:
* -# @ref NEIm2ColKernel (called when the input comes from a convolutional layer)
* -# @ref NEFullyConnectedLayerReshapeWeights (if @p are_weights_reshaped flag is set to false) (called once)
* -# @ref NEGEMMInterleave4x4Kernel (called if we have a multi-batch input)
* -# @ref NEGEMMMatrixMultiplyKernel
* -# @ref NEGEMMMatrixAccumulateBiasesKernel (if @p biases is not equal to nullptr)
*
* @note The fully connected layer accepts "weights" tensors only with 2 dimensions.
*/
class NEFullyConnectedLayer : public IFunction
{
public:
/** Constructor */
NEFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEFullyConnectedLayer(const NEFullyConnectedLayer &) = delete;
/** Default move constructor */
NEFullyConnectedLayer(NEFullyConnectedLayer &&) = default;
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEFullyConnectedLayer &operator=(const NEFullyConnectedLayer &) = delete;
/** Default move assignment operator */
NEFullyConnectedLayer &operator=(NEFullyConnectedLayer &&) = default;
/** Set the input and output tensors.
*
* @param[in] input Source tensor. Data type supported: F16/F32.
* @param[in] weights Weights tensor. The weights must be 2 dimensional. Data type supported: Same as @p input.
* @param[in] biases Bias tensor. Can be nullptr. Data type supported:Same as @p input.
* @param[out] output Destination tensor. Data type supported: Same as @p input.
* @param[in] transpose_weights (Optional) Transpose the weights tensor if true. Defaults to true.
* @param[in] are_weights_reshaped (Optional) Reshape the weights tensor if false. Defaults to false.
*/
void configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, bool transpose_weights = true, bool are_weights_reshaped = false);
/** Static function to check if given info will lead to a valid configuration of @ref CLFullyConnectedLayer
*
* @param[in] input Source tensor info. Data type supported: F16/F32.
* @param[in] weights Weights tensor info. The weights must be 2 dimensional. Data type supported: Same as @p input
* @param[in] biases Bias tensor info. It can be nullptr. Data type supported:Same as @p input.
* @param[in] output Destination tensor info. Data type supported: Same as @p input.
* @param[in] transpose_weights (Optional) Transpose weights if true. Defaults to true.
* @param[in] are_weights_reshaped (Optional) Reshape the weights tensor if false. Defaults to false.
*
* @return a status
*/
static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, bool transpose_weights = true, bool are_weights_reshaped = false);
//Inherited methods override
void run() override;
void prepare() override;
private:
MemoryGroup _memory_group;
NEIm2ColKernel _im2col_kernel;
NEFullyConnectedLayerReshapeWeights _reshape_weights_function;
NEGEMMInterleave4x4Kernel _interleave4x4_kernel;
NEGEMMMatrixMultiplyKernel _mm_kernel;
NEGEMMMatrixAccumulateBiasesKernel _accumulate_biases_kernel;
Tensor _im2col_output;
Tensor _interleave4x4_output;
Tensor _reshape_weights_output;
const ITensor *_original_weights;
bool _is_batched_fc_layer;
bool _linearize_input;
bool _accumulate_biases;
bool _is_prepared;
};
} // namespace arm_compute
#endif /* __ARM_COMPUTE_NEFULLYCONNECTEDLAYER_H__ */
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