/* * Copyright (c) 2017-2023 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 ARM_COMPUTE_NEFULLYCONNECTEDLAYER_H #define ARM_COMPUTE_NEFULLYCONNECTEDLAYER_H #include "arm_compute/function_info/FullyConnectedLayerInfo.h" #include "arm_compute/runtime/IFunction.h" #include "arm_compute/runtime/IMemoryManager.h" #include "arm_compute/runtime/IWeightsManager.h" #include "arm_compute/runtime/NEON/functions/NETranspose.h" #include "arm_compute/runtime/Tensor.h" #include namespace arm_compute { namespace weights_transformations { /** Basic function to manage the reshape weights generated from @ref NETranspose */ 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{}; NETranspose _func{}; }; } // namespace weights_transformations /** Basic function to compute a Fully Connected layer. This function calls the following kernels: * -# @ref cpu::kernels::CpuIm2ColKernel (called when the input comes from a convolutional layer) * -# @ref NETranspose (if @p are_weights_reshaped is set to false and transpose_weights is set to true ) (called once) * -# @ref NEGEMM or @ref NEGEMMLowpMatrixMultiplyCore (if quantized asymmetric) * -# @ref cpu::kernels::CpuGemmMatrixAdditionKernel or @ref NEGEMMLowpOutputStage (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 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. * * 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] 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 * @param[in] weights_info (Optional) Stores neccessary compute information when weights are already reshaped */ void configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, FullyConnectedLayerInfo fc_info = FullyConnectedLayerInfo(), const WeightsInfo &weights_info = WeightsInfo()); /** Static function to check if given info will lead to a valid configuration of @ref NEFullyConnectedLayer * * Similar to @ref NEFullyConnectedLayer::configure() * * @return a status */ static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, FullyConnectedLayerInfo fc_info = FullyConnectedLayerInfo(), const WeightsInfo &weights_info = WeightsInfo()); /** Static function that queries whether fixed-format kernel exists for a given problem description * * @param[out] expected_weight_format Format in which weights should be for found fixed format kernel * @param[in] input Source tensor * @param[in] weights Weights tensor. * @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 * @param[in] fc_info Fully connected layer additional info * @param[in] weights_info Describes weights shape * * @return a status */ static Status has_opt_impl(arm_compute::WeightFormat &expected_weight_format, const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const FullyConnectedLayerInfo &fc_info, const WeightsInfo &weights_info); //Inherited methods override void run() override; void prepare() override; private: struct Impl; std::unique_ptr _impl; }; } // namespace arm_compute #endif /* ARM_COMPUTE_NEFULLYCONNECTEDLAYER_H */