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/*
* Copyright (c) 2017-2021 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/runtime/NEON/INESimpleFunctionNoBorder.h"
#include "arm_compute/runtime/MemoryGroup.h"
#include "arm_compute/runtime/NEON/functions/NEConvertFullyConnectedWeights.h"
#include "arm_compute/runtime/NEON/functions/NEFlattenLayer.h"
#include "arm_compute/runtime/NEON/functions/NEGEMM.h"
#include "arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.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:
*
* @note The fully connected layer accepts "weights" tensors only with 2 dimensions.
*/
class NEFullyConnectedLayerReshapeWeights : public INESimpleFunctionNoBorder
{
public:
/** Constructor */
NEFullyConnectedLayerReshapeWeights() = default;
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEFullyConnectedLayerReshapeWeights(const NEFullyConnectedLayerReshapeWeights &) = delete;
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEFullyConnectedLayerReshapeWeights &operator=(const NEFullyConnectedLayerReshapeWeights &) = delete;
/** Prevent instances of this class from being moved (As this class contains non movable objects) */
NEFullyConnectedLayerReshapeWeights(NEFullyConnectedLayerReshapeWeights &&) = delete;
/** Prevent instances of this class from being moved (As this class contains non movable objects) */
NEFullyConnectedLayerReshapeWeights &operator=(NEFullyConnectedLayerReshapeWeights &&) = delete;
/** Default destructor */
~NEFullyConnectedLayerReshapeWeights() = default;
/** Set the input and output tensors.
*
* @param[in] input Weights tensor. The weights must be 2 dimensional. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
* @param[out] output Destination tensor. Data type supported: Same as @p input.
*/
void configure(const ITensor *input, ITensor *output);
/** Static function to check if given info will lead to a valid configuration of @ref NEFullyConnectedLayerReshapeWeights
*
* @param[in] input Weights tensor info. The weights must be 2 dimensional. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
* @param[in] output Destination tensor info. Data type supported: Same as @p input.
*
* @return a status
*/
static Status validate(const ITensorInfo *input, const ITensorInfo *output);
};
namespace weights_transformations
{
/** Basic function to manage the reshape weights generated from @ref NEFullyConnectedLayerReshapeWeights */
class NEFullyConnectedLayerReshapeWeightsManaged : public ITransformWeights
{
public:
void run() override
{
_output.allocator()->allocate();
_func.run();
_reshape_run = true;
}
void release() override
{
_output.allocator()->free();
}
ITensor *get_weights() override
{
return &_output;
}
uint32_t uid() override
{
return _uid;
}
void configure(const ITensor *input)
{
_func.configure(input, &_output);
}
private:
static constexpr uint32_t _uid = 0x0;
Tensor _output{};
NEFullyConnectedLayerReshapeWeights _func{};
};
} // namespace weights_transformations
/** 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 is set to false and transpose_weights is set to true ) (called once)
* -# @ref NEGEMMMatrixMultiplyKernel or @ref NEGEMMLowpMatrixMultiplyCore (if quantized asymmetric)
* -# @ref NEGEMMMatrixAdditionKernel or @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint (if quantized asymmetric) (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, IWeightsManager *weights_manager = nullptr);
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEFullyConnectedLayer(const NEFullyConnectedLayer &) = delete;
/** Prevent instances of this class from being moved (As this class contains pointers) */
NEFullyConnectedLayer(NEFullyConnectedLayer &&) = delete;
/** Prevent instances of this class from being copied (As this class contains pointers) */
NEFullyConnectedLayer &operator=(const NEFullyConnectedLayer &) = delete;
/** Prevent instances of this class from being moved (As this class contains pointers) */
NEFullyConnectedLayer &operator=(NEFullyConnectedLayer &&) = delete;
/** Default destructor */
~NEFullyConnectedLayer();
/** Set the input and output tensors.
*
* @param[in] input Source tensor. Data type supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
* @param[in] weights Weights tensor. The weights must be 2 dimensional.
* If this function is called after a Convolution Layer, the (transposed) weights will have as many rows as the product of the first 3 input's dimensions.
* If it is called after another FullyConnected Layer, the (transposed) weights will have as many rows as the input's first dimension.
* Data type supported: Same as @p input.
* @param[in] biases Bias tensor. Can be nullptr. Data type supported: Same as @p weights, S32 if @p weights is QASYMM8/QASYMM8_SIGNED.
* @param[out] output Destination tensor. Its shape should be equal to the output of a matrix multiplication between:
* - The output of im2col on the input and the (transposed) 2D weights, if the function is called after a Convolution Layer
* - The input tensor and the (transposed) 2D weights, if the function is called after another FullyConnected Layer.
* Data type supported: Same as @p input.
* @param[in] fc_info (Optional) Fully connected layer additional info
*/
void configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output,
FullyConnectedLayerInfo fc_info = FullyConnectedLayerInfo());
/** Static function to check if given info will lead to a valid configuration of @ref NEFullyConnectedLayer
*
* @param[in] input Source tensor info. Data type supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
* @param[in] weights Weights tensor info. The weights must be 2 dimensional.
* If this function is called after a Convolution Layer, the (transposed) weights will have as many rows as the product of the first 3 input's dimensions.
* If it is called after another FullyConnected Layer, the (transposed) weights will have as many rows as the input's first dimension.
* Data type supported: Same as @p input.
* @param[in] biases Bias tensor. Can be nullptr. Data type supported: Same as @p weights, S32 if @p weights is QASYMM8/QASYMM8_SIGNED.
* @param[in] output Destination tensor info. Its shape should be equal to the output of a matrix multiplication between:
* - The output of im2col on the input and the (transposed) 2D weights, if the function is called after a Convolution Layer
* - The input tensor and the (transposed) 2D weights, if the function is called after another FullyConnected Layer.
* Data type supported: Same as @p input.
* @param[in] fc_info (Optional) Fully connected layer additional info
*
* @return a status
*/
static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output,
FullyConnectedLayerInfo fc_info = FullyConnectedLayerInfo());
//Inherited methods override
void run() override;
void prepare() override;
private:
void configure_fc_fc(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const ActivationLayerInfo &act);
void configure_conv_fc(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const ActivationLayerInfo &act);
void configure_mm(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const ActivationLayerInfo &act);
MemoryGroup _memory_group;
IWeightsManager *_weights_manager;
NEFlattenLayer _flatten;
NEConvertFullyConnectedWeights _convert_weights;
weights_transformations::NEConvertFullyConnectedWeightsManaged _convert_weights_managed;
NEFullyConnectedLayerReshapeWeights _reshape_weights_function;
weights_transformations::NEFullyConnectedLayerReshapeWeightsManaged _reshape_weights_managed_function;
NEGEMM _mm_gemm;
NEGEMMLowpMatrixMultiplyCore _mm_gemmlowp;
Tensor _flatten_output;
Tensor _converted_weights_output;
Tensor _reshape_weights_output;
const ITensor *_original_weights;
bool _are_weights_converted;
bool _are_weights_reshaped;
bool _is_fc_after_conv;
bool _is_quantized_asymmetric;
bool _is_prepared;
};
} // namespace arm_compute
#endif /* ARM_COMPUTE_NEFULLYCONNECTEDLAYER_H */
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