293 lines
17 KiB
C++
293 lines
17 KiB
C++
/*
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* Copyright (c) 2017-2020 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_NEGEMMCONVOLUTIONLAYER_H
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#define ARM_COMPUTE_NEGEMMCONVOLUTIONLAYER_H
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#include "arm_compute/runtime/IFunction.h"
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#include "arm_compute/core/Types.h"
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#include "arm_compute/runtime/IWeightsManager.h"
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#include "arm_compute/runtime/MemoryGroup.h"
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#include "arm_compute/runtime/NEON/functions/NEGEMM.h"
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#include "arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h"
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#include "arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h"
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#include "arm_compute/runtime/NEON/functions/NEReshapeLayer.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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class ITensor;
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class NECol2ImKernel;
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class NEIm2ColKernel;
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class NEWeightsReshapeKernel;
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/** Function to reshape the weights. This function calls the following kernel:
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* -# @ref NEWeightsReshapeKernel
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*/
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class NEConvolutionLayerReshapeWeights : public IFunction
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{
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public:
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/** Constructor */
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NEConvolutionLayerReshapeWeights();
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/** Prevent instances of this class from being copied (As this class contains pointers) */
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NEConvolutionLayerReshapeWeights(const NEConvolutionLayerReshapeWeights &) = delete;
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/** Prevent instances of this class from being moved (As this class contains non movable objects) */
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NEConvolutionLayerReshapeWeights(NEConvolutionLayerReshapeWeights &&) = delete;
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/** Prevent instances of this class from being copied (As this class contains pointers) */
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NEConvolutionLayerReshapeWeights &operator=(const NEConvolutionLayerReshapeWeights &) = delete;
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/** Prevent instances of this class from being moved (As this class contains non movable objects) */
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NEConvolutionLayerReshapeWeights &operator=(NEConvolutionLayerReshapeWeights &&) = delete;
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/** Default destructor */
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~NEConvolutionLayerReshapeWeights();
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/** Set the input and output tensors.
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*
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* @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
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* Data type supported: All.
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* @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
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* Data type supported: same as @p weights.
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* @warning Appending biases to weights reshaped matrix is not supported for quantized asymmetric types.
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* @param[out] output Destination tensor. Data types supported: same as @p weights.
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*/
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void configure(const ITensor *weights, const ITensor *biases, ITensor *output);
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/** Static function to check if given info will lead to a valid configuration of @ref NEConvolutionLayerReshapeWeights
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*
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* @param[in] weights Weights tensor info. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
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* Data type supported: All.
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* @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
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* Data type supported: same as @p weights.
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* @warning Appending biases to weights reshaped matrix is not supported for quantized asymmetric types.
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* @param[in] output Destination tensor. Data types supported: same as @p weights.
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*
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* @return an error status
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*/
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static Status validate(const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output);
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// Inherited methods overridden:
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void run() override;
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private:
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std::unique_ptr<NEWeightsReshapeKernel> _weights_reshape_kernel;
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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 NEConvolutionLayerReshapeWeights */
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class NEConvolutionLayerReshapeWeightsTransform : public ITransformWeights
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{
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public:
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/** Constructor */
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NEConvolutionLayerReshapeWeightsTransform() = default;
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/** Prevent instances of this class from being copied (As this class contains pointers) */
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NEConvolutionLayerReshapeWeightsTransform(const NEConvolutionLayerReshapeWeightsTransform &) = delete;
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/** Prevent instances of this class from being copied (As this class contains pointers) */
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NEConvolutionLayerReshapeWeightsTransform &operator=(const NEConvolutionLayerReshapeWeightsTransform &) = delete;
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/** Prevent instances of this class from being moved (As this class contains non movable objects) */
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NEConvolutionLayerReshapeWeightsTransform(NEConvolutionLayerReshapeWeightsTransform &&) = delete;
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/** Prevent instances of this class from being moved (As this class contains non movable objects) */
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NEConvolutionLayerReshapeWeightsTransform &operator=(NEConvolutionLayerReshapeWeightsTransform &&) = delete;
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/** Default destructor */
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~NEConvolutionLayerReshapeWeightsTransform() = default;
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void configure(const ITensor *input, const ITensor *biases)
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{
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_bias_bit = (biases != nullptr) ? 1 : 0;
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_func.configure(input, biases, &_output);
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}
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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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ITensor *get_weights() override
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{
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return &_output;
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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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uint32_t uid() override
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{
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return ((0x8) | (_bias_bit << 7));
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}
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bool is_reshape_run()
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{
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return _reshape_run;
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}
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private:
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Tensor _output{};
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NEConvolutionLayerReshapeWeights _func{};
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int32_t _bias_bit{ 0 };
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};
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} // namespace weights_transformations
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/** Basic function to compute the convolution layer. This function calls the following NEON kernels/functions:
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*
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* -# @ref NEIm2ColKernel
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* -# @ref NEGEMM (if the data type is BFLOAT16/FP16/FP32)
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* -# @ref NEGEMMLowpMatrixMultiplyCore (if the data type is QASYMM8/QASYMM8_SIGNED)
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* -# @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint (if the data type is QASYMM8/QASYMM8_SIGNED)
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* -# @ref NEArithmeticAdditionKernel (if biases != nullptr and we have a 1x1 convolution with the NHWC data layout)
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* -# @ref NECol2ImKernel (if NCHW data layout)
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*
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*/
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class NEGEMMConvolutionLayer : public IFunction
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{
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public:
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/** Constructor */
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NEGEMMConvolutionLayer(const 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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NEGEMMConvolutionLayer(const NEGEMMConvolutionLayer &) = delete;
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/** Prevent instances of this class from being moved (As this class contains non movable objects) */
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NEGEMMConvolutionLayer(NEGEMMConvolutionLayer &&) = delete;
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/** Prevent instances of this class from being copied (As this class contains pointers) */
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NEGEMMConvolutionLayer &operator=(const NEGEMMConvolutionLayer &) = delete;
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/** Prevent instances of this class from being moved (As this class contains non movable objects) */
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NEGEMMConvolutionLayer &operator=(NEGEMMConvolutionLayer &&) = delete;
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/** Default destructor */
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~NEGEMMConvolutionLayer();
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/** Set the input and output tensors.
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*
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* @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM],
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* while every optional dimension from 4 and above represent a batch of inputs.
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* Data types supported: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32.
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* @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
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* Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/BFLOAT16/F16/F32.
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* @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
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* Data type supported: Should match @p input data type, except for input of QASYMM8/QASYMM8_SIGNED type where biases should be of S32 type.
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* @param[out] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
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* Data types supported: Same as @p input.
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* @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo.
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* @param[in] weights_info Specifies if the weights tensor has been reshaped with NEWeightsReshapeKernel. If this is not part of the fully connected layer the weights
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* tensor has also been transposed with NEGEMMTranspose1xWKernel. Data type supported: Same as @p input.
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* @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
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* @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported.
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* @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is not supported
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*/
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void configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info = WeightsInfo(),
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const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), unsigned int num_groups = 1);
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/** Static function to check if given info will lead to a valid configuration of @ref NEGEMMConvolutionLayer
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*
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* @param[in] input Source tensor info. 3 lower dimensions represent a single input [width, height, IFM],
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* while every optional dimension from 4 and above represent a batch of inputs.
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* Data types supported: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32.
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* @param[in] weights Weights tensor info. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM].
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* Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/BFLOAT16/F16/F32.
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* @param[in] biases Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
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* Data type supported: Should match @p input data type, except for input of QASYMM8/QASYMM8_SIGNED type where biases should be of S32 type.
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* @param[in] output Destination tensor info. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
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* Data types supported: Same as @p input.
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* @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo.
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* @param[in] weights_info Specifies if the weights tensor has been reshaped with NEWeightsReshapeKernel. If this is not part of the fully connected layer the weights
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* tensor has also been transposed with NEGEMMTranspose1xWKernel. Data type supported: Same as @p input.
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* @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
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* @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported.
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* @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is not supported
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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, const PadStrideInfo &conv_info,
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const WeightsInfo &weights_info = WeightsInfo(), const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), unsigned int num_groups = 1);
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// Inherited methods overridden:
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void run() override;
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void prepare() override;
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private:
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/** Configures the appropriate matrix multiply routine
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*
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* @param[in] input Input tensor. Data types supported: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32.
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* @param[in] weights Weights tensor. Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/BFLOAT16/F16/F32.
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* @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
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* Data type supported: Should match @p input data type, except for input of QASYMM8/QASYMM8_SIGNED type where biases should be of S32 type.
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* @param[out] output Output tensor. Data types supported: Same as @p input,
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* except for input of QASYMM8/QASYMM8_SIGNED type where output should be of S32 type.
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* @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported.
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* @param[in] gemm_3d_depth (Optional) Depth of GEMM 3D (Defaults to 1)
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*/
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void configure_mm(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const ActivationLayerInfo &act_info = ActivationLayerInfo(), int gemm_3d_depth = 1);
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/** Static function to check if given info will lead to a valid configuration of @ref NEGEMMConvolutionLayer matrix multiply routines
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*
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* @param[in] input Input tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32.
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* @param[in] weights Weights tensor info. Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/BFLOAT16/F16/F32.
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* @param[in] biases Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM].
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* Data type supported: Should match @p input data type, except for input of QASYMM8/QASYMM8_SIGNED type where biases should be of S32 type.
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* @param[in] output Output tensor info. Data types supported: Same as @p input,
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* except for input of QASYMM8/QASYMM8_SIGNED type where output should be of S32 type.
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* @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported.
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* @param[in] gemm_3d_depth (Optional) Depth of GEMM 3D (Defaults to 1)
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* @param[in] skip_im2col (Optional) Flag which specifies if im2col has to be skipped. i.e. 1x1 convolution with NHWC data layout. (Default to false)
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*
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* @return a status
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*/
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static Status validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const ActivationLayerInfo &act_info = ActivationLayerInfo(),
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int gemm_3d_depth = 1, bool skip_im2col = false);
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/** Static function to check if GEMM3D is supported in @ref NEGEMM or in @ref NEGEMMLowpMatrixMultiplyCore
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*
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* @param[in] input_info Input tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32.
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* @param[in] weights_info Weights tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32.
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* @param[in] act_info Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported.
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* @param[in] gemm_3d_depth Depth of GEMM 3D
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* @param[in] skip_im2col Flag which specifies if im2col has to be skipped. i.e. 1x1 convolution with NHWC data layout
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*
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* @return a status
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*/
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static Status validate_gemm3d(const ITensorInfo *input_info, const ITensorInfo *weights_info, const ActivationLayerInfo &act_info, int gemm_3d_depth, bool skip_im2col);
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private:
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MemoryGroup _memory_group;
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IWeightsManager *_weights_manager;
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NEConvolutionLayerReshapeWeights _reshape_weights;
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weights_transformations::NEConvolutionLayerReshapeWeightsTransform _reshape_weights_managed;
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std::unique_ptr<NEIm2ColKernel> _im2col_kernel;
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NEGEMM _mm_gemm;
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NEGEMMLowpMatrixMultiplyCore _mm_gemmlowp;
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std::unique_ptr<NECol2ImKernel> _col2im_kernel;
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NEReshapeLayer _reshape_layer;
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const ITensor *_original_weights;
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Tensor _im2col_output;
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Tensor _weights_reshaped;
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Tensor _gemm_output;
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Tensor _tmp_output;
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DataLayout _data_layout;
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bool _skip_im2col;
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bool _skip_col2im;
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bool _is_quantized;
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bool _is_prepared;
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};
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} // namespace arm_compute
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#endif /* ARM_COMPUTE_NECONVOLUTIONGEMMLAYER_H */
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