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164 lines
7.7 KiB
164 lines
7.7 KiB
/*
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* Copyright (c) 2017-2023 Arm Limited.
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*
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* SPDX-License-Identifier: MIT
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*
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* Permission is hereby granted, free of charge, to any person obtaining a copy
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* of this software and associated documentation files (the "Software"), to
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* deal in the Software without restriction, including without limitation the
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* rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
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* sell copies of the Software, and to permit persons to whom the Software is
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* furnished to do so, subject to the following conditions:
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*
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* The above copyright notice and this permission notice shall be included in all
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* copies or substantial portions of the Software.
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*
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* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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* SOFTWARE.
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*/
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#ifndef ARM_COMPUTE_NEFULLYCONNECTEDLAYER_H
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#define ARM_COMPUTE_NEFULLYCONNECTEDLAYER_H
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#include "arm_compute/runtime/IFunction.h"
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#include "arm_compute/runtime/IMemoryManager.h"
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#include "arm_compute/runtime/IWeightsManager.h"
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#include "arm_compute/runtime/NEON/functions/NETranspose.h"
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#include "arm_compute/runtime/Tensor.h"
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#include <memory>
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namespace arm_compute
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{
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namespace weights_transformations
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{
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/** Basic function to manage the reshape weights generated from @ref NETranspose */
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class NEFullyConnectedLayerReshapeWeightsManaged : public ITransformWeights
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{
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public:
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void run() override
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{
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_output.allocator()->allocate();
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_func.run();
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_reshape_run = true;
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}
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void release() override
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{
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_output.allocator()->free();
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}
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ITensor *get_weights() override
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{
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return &_output;
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}
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uint32_t uid() override
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{
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return _uid;
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}
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void configure(const ITensor *input)
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{
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_func.configure(input, &_output);
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}
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private:
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static constexpr uint32_t _uid = 0x0;
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Tensor _output{};
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NETranspose _func{};
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};
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} // namespace weights_transformations
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/** Basic function to compute a Fully Connected layer. This function calls the following kernels:
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* -# @ref cpu::kernels::CpuIm2ColKernel (called when the input comes from a convolutional layer)
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* -# @ref NETranspose (if @p are_weights_reshaped is set to false and transpose_weights is set to true ) (called once)
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* -# @ref NEGEMM or @ref NEGEMMLowpMatrixMultiplyCore (if quantized asymmetric)
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* -# @ref cpu::kernels::CpuGemmMatrixAdditionKernel or @ref NEGEMMLowpOutputStage (if quantized asymmetric) (if @p biases is not equal to nullptr)
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*
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* @note The fully connected layer accepts "weights" tensors only with 2 dimensions.
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*/
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class NEFullyConnectedLayer : public IFunction
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{
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public:
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/** Constructor */
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NEFullyConnectedLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr, IWeightsManager *weights_manager = nullptr);
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/** Prevent instances of this class from being copied (As this class contains pointers) */
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NEFullyConnectedLayer(const NEFullyConnectedLayer &) = delete;
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/** Prevent instances of this class from being moved (As this class contains pointers) */
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NEFullyConnectedLayer(NEFullyConnectedLayer &&) = delete;
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/** Prevent instances of this class from being copied (As this class contains pointers) */
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NEFullyConnectedLayer &operator=(const NEFullyConnectedLayer &) = delete;
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/** Prevent instances of this class from being moved (As this class contains pointers) */
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NEFullyConnectedLayer &operator=(NEFullyConnectedLayer &&) = delete;
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/** Default destructor */
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~NEFullyConnectedLayer();
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/** Set the input and output tensors.
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*
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* Valid data layouts:
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* - NHWC
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* - NCHW
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*
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* Valid data type configurations:
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* |src0 |src1 |src2 |dst |
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* |:--------------|:------------------|:------|:--------------|
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* |F16 |F16 |F16 |F16 |
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* |F32 |F32 |F32 |F32 |
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* |QASYMM8 |QASYMM8 |S32 |QASYMM8 |
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* |QASYMM8_SIGNED |QASYMM8_SIGNED |S32 |QASYMM8_SIGNED |
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*
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* @param[in] input Source tensor. Data type supported: QASYMM8/QASYMM8_SIGNED/F16/F32.
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* @param[in] weights Weights tensor. The weights must be 2 dimensional.
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* 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.
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* If it is called after another FullyConnected Layer, the (transposed) weights will have as many rows as the input's first dimension.
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* Data type supported: Same as @p input.
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* @param[in] biases Bias tensor. Can be nullptr. Data type supported: Same as @p weights, S32 if @p weights is QASYMM8/QASYMM8_SIGNED.
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* @param[out] output Destination tensor. Its shape should be equal to the output of a matrix multiplication between:
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* - The output of im2col on the input and the (transposed) 2D weights, if the function is called after a Convolution Layer
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* - The input tensor and the (transposed) 2D weights, if the function is called after another FullyConnected Layer.
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* Data type supported: Same as @p input.
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* @param[in] fc_info (Optional) Fully connected layer additional info
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* @param[in] weights_info (Optional) Stores neccessary compute information when weights are already reshaped
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*/
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void configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output,
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FullyConnectedLayerInfo fc_info = FullyConnectedLayerInfo(), const WeightsInfo &weights_info = WeightsInfo());
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/** Static function to check if given info will lead to a valid configuration of @ref NEFullyConnectedLayer
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*
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* Similar to @ref NEFullyConnectedLayer::configure()
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*
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* @return a status
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*/
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static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output,
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FullyConnectedLayerInfo fc_info = FullyConnectedLayerInfo(), const WeightsInfo &weights_info = WeightsInfo());
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/** Static function that queries whether fixed-format kernel exists for a given problem description
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*
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* @param[out] expected_weight_format Format in which weights should be for found fixed format kernel
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* @param[in] input Source tensor
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* @param[in] weights Weights tensor.
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* @param[in] biases Bias tensor. Can be nullptr. Data type supported: Same as @p weights, S32 if @p weights is QASYMM8/QASYMM8_SIGNED.
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* @param[in] output Destination tensor
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* @param[in] fc_info Fully connected layer additional info
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* @param[in] weights_info Describes weights shape
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*
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* @return a status
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*/
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static Status has_opt_impl(arm_compute::WeightFormat &expected_weight_format, const ITensorInfo *input, const ITensorInfo *weights,
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const ITensorInfo *biases, const ITensorInfo *output, const FullyConnectedLayerInfo &fc_info, const WeightsInfo &weights_info);
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//Inherited methods override
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void run() override;
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void prepare() override;
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private:
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struct Impl;
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std::unique_ptr<Impl> _impl;
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};
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} // namespace arm_compute
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#endif /* ARM_COMPUTE_NEFULLYCONNECTEDLAYER_H */
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