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/*
* Copyright (c) 2017-2020 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_CLFULLYCONNECTEDLAYER_H
#define ARM_COMPUTE_CLFULLYCONNECTEDLAYER_H
#include "arm_compute/runtime/CL/ICLSimpleFunction.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/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 CLFullyConnectedLayerReshapeWeights : public ICLSimpleFunction
{
public:
/** 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 which stores the transposed input tensor. Data type supported: Same as @p input.
*/
void configure(const ICLTensor *input, ICLTensor *output);
/** Set the input and output tensors.
*
* @param[in] compile_context The compile context to be used.
* @param[in] input Weights tensor. The weights must be 2 dimensional. Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
* @param[out] output Destination tensor which stores the transposed input tensor. Data type supported: Same as @p input.
*/
void configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output);
/** Static function to check if given info will lead to a valid configuration of @ref CLFullyConnectedLayerReshapeWeights
*
* @param[in] input Weights tensor. The weights must be 2 dimensional. Data types supported: QASYMM8/QASYMM8_SIGNED/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 CLFullyConnectedLayerReshapeWeights */
class CLFullyConnectedLayerReshapeWeightsManaged : 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 CLFullyConnectedLayerReshapeWeights function
*
* @param[in] input Source tensor. Data type supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
*/
void configure(const ICLTensor *input)
{
configure(CLKernelLibrary::get().get_compile_context(), input);
}
/** Configures the @ref CLFullyConnectedLayerReshapeWeights function
*
* @param[in] compile_context The compile context to be used.
* @param[in] input Source tensor. Data type supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
*/
void configure(const CLCompileContext &compile_context, const ICLTensor *input)
{
_func.configure(compile_context, input, &_output);
}
private:
static constexpr uint32_t _uid = 0x0;
CLTensor _output{};
CLFullyConnectedLayerReshapeWeights _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 CLFullyConnectedLayerReshapeWeights (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)
*
* @note The fully connected layer accepts "weights" tensors only with 2 dimensions.
*/
class CLFullyConnectedLayer : public IFunction
{
public:
/** Constructor */
CLFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr, IWeightsManager *weights_manager = nullptr);
/** Prevent instances of this class from being copied (As this class contains pointers) */
CLFullyConnectedLayer(const CLFullyConnectedLayer &) = delete;
/** Default move constructor */
CLFullyConnectedLayer(CLFullyConnectedLayer &&) = default;
/** Prevent instances of this class from being copied (As this class contains pointers) */
CLFullyConnectedLayer &operator=(const CLFullyConnectedLayer &) = delete;
/** Default move assignment operator */
CLFullyConnectedLayer &operator=(CLFullyConnectedLayer &&) = default;
/** 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 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());
/** Set the input and output tensors.
*
* @param[in] compile_context The compile context to be used.
* @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 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 CLCompileContext &compile_context, 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 CLFullyConnectedLayer
*
* @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 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 CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const FullyConnectedLayerInfo &fc_info);
void configure_conv_fc(const CLCompileContext &compile_context, const ICLTensor *input, const ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const FullyConnectedLayerInfo &fc_info);
void configure_mm(const CLCompileContext &compile_context, 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::CLFullyConnectedLayerReshapeWeightsManaged _reshape_weights_managed_function;
CLFlattenLayer _flatten_layer;
CLFullyConnectedLayerReshapeWeights _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_CLFULLYCONNECTEDLAYER_H */
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