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/*
* Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
/*
* 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_CLFULLYCONNECTEDLAYEREX_H__
#define __ARM_COMPUTE_CLFULLYCONNECTEDLAYEREX_H__
#include "arm_compute/runtime/CL/ICLSimpleFunction.h"
#include "arm_compute/core/CL/kernels/CLGEMMMatrixAccumulateBiasesKernel.h"
#include "arm_compute/core/CL/kernels/CLTransposeKernel.h"
#include "arm_compute/runtime/CL/CLTensor.h"
#include "arm_compute/runtime/CL/functions/CLConvertFullyConnectedWeights.h"
#include "arm_compute/runtime/CL/functions/CLFlattenLayer.h"
#include "arm_compute/runtime/CL/functions/CLGEMM.h"
#include "arm_compute/runtime/CL/functions/CLGEMMLowpMatrixMultiplyCore.h"
#include "arm_compute/runtime/CL/functions/CLGEMMLowpOutputStage.h"
#include "arm_compute/runtime/IWeightsManager.h"
#include "arm_compute/runtime/MemoryGroup.h"
namespace arm_compute
{
/** Basic function to reshape the weights of Fully Connected layer with OpenCL. This function calls
* the following kernels:
*
* -# @ref CLTransposeKernel
*
* @note The fully connected layer accepts "weights" tensors only with 2 dimensions.
*/
class CLFullyConnectedLayerReshapeWeightsEx : public ICLSimpleFunction
{
public:
/** Set the input and output tensors.
*
* @param[in] input Weights tensor. The weights must be 2 dimensional. Data types supported:
* QASYMM8/F16/F32.
* @param[out] output Destination tensor which stores the transposed input tensor. Data type
* supported: Same as @p input.
*/
void configure(const ICLTensor *input, ICLTensor *output);
/** Static function to check if given info will lead to a valid configuration of @ref
* CLFullyConnectedLayerReshapeWeightsEx
*
* @param[in] input Weights tensor. The weights must be 2 dimensional. Data types supported:
* QASYMM8/F16/F32.
* @param[in] output Destination tensor which stores the transposed input tensor. 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
* CLFullyConnectedLayerReshapeWeightsEx */
class CLFullyConnectedLayerReshapeWeightsExManaged : public ITransformWeights
{
public:
// Inherited method override
void run() override
{
_output.allocator()->allocate();
_func.run();
_reshape_run = true;
}
// Inherited method override
void release() override { _output.allocator()->free(); }
// Inherited method override
ICLTensor *get_weights() override { return &_output; }
// Inherited method override
uint32_t uid() override { return _uid; }
/** Configures the @ref CLFullyConnectedLayerReshapeWeightsEx function
*
* @param[in] input Source tensor. Data type supported: QASYMM8/F16/F32.
*/
void configure(const ICLTensor *input) { _func.configure(input, &_output); }
private:
static constexpr uint32_t _uid = 0x0;
CLTensor _output{};
CLFullyConnectedLayerReshapeWeightsEx _func{};
};
} // namespace weights_transformations
/** Basic function to compute a Fully Connected layer on OpenCL. This function calls the following
* OpenCL kernels:
*
* -# @ref CLIm2ColKernel (called when the input comes from a convolutional layer)
* -# @ref CLFullyConnectedLayerReshapeWeightsEx (if @p are_weights_reshaped is set to false and
* transpose_weights is set to true ) (called once)
* -# @ref CLGEMMMatrixMultiplyKernel or @ref CLGEMMLowpMatrixMultiplyCore (if quantized
* asymmetric)
* -# @ref CLGEMMMatrixAccumulateBiasesKernel or @ref
* CLGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint (if quantized asymmetric) (if @p biases is
* not equal to nullptr)
*
* @note The fully connected layer accepts "weights" tensors only with 2 dimensions.
*/
class CLFullyConnectedLayerEx : public IFunction
{
public:
/** Constructor */
CLFullyConnectedLayerEx(std::shared_ptr<IMemoryManager> memory_manager = nullptr,
IWeightsManager *weights_manager = nullptr);
/** Prevent instances of this class from being copied (As this class contains pointers) */
CLFullyConnectedLayerEx(const CLFullyConnectedLayerEx &) = delete;
/** Default move constructor */
CLFullyConnectedLayerEx(CLFullyConnectedLayerEx &&) = default;
/** Prevent instances of this class from being copied (As this class contains pointers) */
CLFullyConnectedLayerEx &operator=(const CLFullyConnectedLayerEx &) = delete;
/** Default move assignment operator */
CLFullyConnectedLayerEx &operator=(CLFullyConnectedLayerEx &&) = default;
/** Set the input and output tensors.
*
* @param[in] input Source tensor. Data type supported: QASYMM8/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 input.
* @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 ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases,
ICLTensor *output, FullyConnectedLayerInfo fc_info = FullyConnectedLayerInfo());
/** Static function to check if given info will lead to a valid configuration of @ref
* CLFullyConnectedLayerEx
*
* @param[in] input Source tensor info. Data type supported: QASYMM8/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 info. Can be nullptr. Data type supported:Same as @p input.
* @param[out] 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 ICLTensor *input, const ICLTensor *weights, const ICLTensor *bias,
ICLTensor *output, const FullyConnectedLayerInfo &fc_info);
void configure_conv_fc(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *bias,
ICLTensor *output, const FullyConnectedLayerInfo &fc_info);
void configure_mm(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *bias,
ICLTensor *output, const FullyConnectedLayerInfo &fc_info);
MemoryGroup _memory_group;
IWeightsManager *_weights_manager;
CLConvertFullyConnectedWeights _convert_weights;
weights_transformations::CLConvertFullyConnectedWeightsManaged _convert_weights_managed;
weights_transformations::CLFullyConnectedLayerReshapeWeightsExManaged
_reshape_weights_managed_function;
CLFlattenLayer _flatten_layer;
CLFullyConnectedLayerReshapeWeightsEx _reshape_weights_function;
CLGEMM _mm_gemm;
CLGEMMLowpMatrixMultiplyCore _mm_gemmlowp;
CLTensor _flatten_output;
CLTensor _converted_weights_output;
CLTensor _reshape_weights_output;
bool _are_weights_converted;
bool _are_weights_reshaped;
bool _is_fc_after_conv;
bool _is_quantized;
bool _is_prepared;
const ICLTensor *_original_weights;
};
} // namespace arm_compute
#endif /* __ARM_COMPUTE_CLFULLYCONNECTEDLAYEREX_H__ */
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