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/*
* Copyright (c) 2021-2023 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 ACL_SRC_CPU_OPERATORS_CPUFULLYCONNECTED_H
#define ACL_SRC_CPU_OPERATORS_CPUFULLYCONNECTED_H
#include "arm_compute/core/TensorInfo.h"
#include "arm_compute/function_info/FullyConnectedLayerInfo.h"
#include "src/cpu/ICpuOperator.h"
#include <memory>
namespace arm_compute
{
namespace cpu
{
// Forward declarations
class CpuConvertFullyConnectedWeights;
class CpuFlatten;
class CpuGemm;
class CpuGemmLowpMatrixMultiplyCore;
namespace kernels
{
class CpuTransposeKernel;
} // namespace kernels
/** Basic function to compute a Fully Connected layer. This function calls the following kernels:
* -# @ref kernels::CpuIm2ColKernel (called when the input comes from a convolutional layer)
* -# @ref kernels::CpuTransposeKernel (if @p are_weights_reshaped is set to false and transpose_weights is set to true ) (called once)
* -# @ref CpuGemm or @ref CpuGemmLowpMatrixMultiplyCore (if quantized asymmetric)
* -# @ref kernels::CpuGemmMatrixAdditionKernel or @ref CpuGemmLowpOutputStage (if quantized asymmetric) (if @p biases is not equal to nullptr)
*
* @note The fully connected layer accepts "weights" tensors only with 2 dimensions.
*/
class CpuFullyConnected : public ICpuOperator
{
public:
/** Constructor */
CpuFullyConnected();
/** Destructor */
~CpuFullyConnected();
/** Set the input and output tensors.
*
* Valid data layouts:
* - NHWC
* - NCHW
*
* Valid data type configurations:
* |src0 |src1 |src2 |dst |
* |:--------------|:------------------|:------|:--------------|
* |F16 |F16 |F16 |F16 |
* |F32 |F32 |F32 |F32 |
* |QASYMM8 |QASYMM8 |S32 |QASYMM8 |
* |QASYMM8_SIGNED |QASYMM8_SIGNED |S32 |QASYMM8_SIGNED |
*
* @param[in] src 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 src.
* @param[in] biases Bias tensor info. Can be nullptr. Data type supported: Same as @p weights, S32 if @p weights is QASYMM8/QASYMM8_SIGNED.
* @param[out] dst 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 src.
* @param[in] fc_info (Optional) Fully connected layer additional info
* @param[in] weights_info (Optional) Stores neccessary compute information when weights are already reshaped
*/
void configure(const ITensorInfo *src,
const ITensorInfo *weights,
const ITensorInfo *biases,
ITensorInfo *dst,
FullyConnectedLayerInfo fc_info = FullyConnectedLayerInfo(),
const WeightsInfo &weights_info = WeightsInfo());
/** Static function to check if given info will lead to a valid configuration of @ref CpuFullyConnected
*
* Similar to @ref CpuFullyConnected::configure()
*
* @return a status
*/
static Status validate(const ITensorInfo *src,
const ITensorInfo *weights,
const ITensorInfo *biases,
const ITensorInfo *dst,
FullyConnectedLayerInfo fc_info = FullyConnectedLayerInfo(),
const WeightsInfo &weights_info = WeightsInfo());
/** Static function that queries whether there exists fixed-format kernel and if it exists it will return in the first argument in what format
* weights are expected to be reshaped as defined by WeightFormat class. Apart from the first argument the rest of the arguments are the same
* as in @ref CpuFullyConnectedLayer::validate() except that all arguments are required.
*
* @return a status
*/
static Status has_opt_impl(arm_compute::WeightFormat &expected_weight_format,
const ITensorInfo *src,
const ITensorInfo *weights,
const ITensorInfo *biases,
const ITensorInfo *dst,
FullyConnectedLayerInfo fc_info,
WeightsInfo weights_info);
//Inherited methods override
void run(ITensorPack &tensors) override;
void prepare(ITensorPack &tensors) override;
experimental::MemoryRequirements workspace() const override;
private:
void configure_fc_fc(const ITensorInfo *src,
const ITensorInfo *weights,
const ITensorInfo *biases,
ITensorInfo *dst,
const ActivationLayerInfo &act);
void configure_conv_fc(const ITensorInfo *src,
const ITensorInfo *weights,
const ITensorInfo *biases,
ITensorInfo *dst,
const ActivationLayerInfo &act);
void configure_mm(const ITensorInfo *src,
const ITensorInfo *weights,
const ITensorInfo *biases,
ITensorInfo *dst,
const ActivationLayerInfo &act);
enum AuxTensorIdx
{
AsmGemmWorkspace = 0,
Pretranspose,
GemmTemp1,
GemmTemp2,
GemmTemp3,
GemmTemp4,
GemmTemp5,
GemmTemp6,
GemmTemp7,
GemmTemp8,
// Slots above (0-9) reserved for either CpuGemm or CpuGemmLowpMatrixMultiplyCore
TransposedWeights,
ConvertedWeights,
FlattenedSrc,
Count
};
std::unique_ptr<CpuFlatten> _flatten;
std::unique_ptr<CpuConvertFullyConnectedWeights> _convert_weights;
std::unique_ptr<kernels::CpuTransposeKernel> _transpose_weights;
std::unique_ptr<CpuGemm> _mm_gemm;
std::unique_ptr<CpuGemmLowpMatrixMultiplyCore> _mm_gemmlowp;
TensorInfo _flattened_src;
TensorInfo _converted_weights;
TensorInfo _reshaped_weights;
TensorInfo _trans_weights;
AuxTensorIdx _trans_weights_idx;
experimental::MemoryRequirements _aux_mem;
bool _needs_weights_conversion;
bool _needs_weights_reshape;
bool _is_fc_after_conv;
bool _is_quantized_asymmetric;
bool _is_prepared;
bool _enable_fast_math;
bool _fixed_format;
arm_compute::WeightFormat _weight_format;
bool _dynamic_weights;
#ifdef ARM_COMPUTE_ASSERTS_ENABLED
int _asrt_run_count{};
int _asrt_prepare_count{};
#endif // ARM_COMPUTE_ASSERTS_ENABLED
};
} // namespace cpu
} // namespace arm_compute
#endif // ACL_SRC_CPU_OPERATORS_CPUFULLYCONNECTED_H
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