arm_compute/runtime/NEON/functions/NEDepthwiseConvolutionLayer.h

/*
 * Copyright (c) 2017-2019 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_NEDEPTHWISECONVOLUTION_H__
#define __ARM_COMPUTE_NEDEPTHWISECONVOLUTION_H__

#include "arm_compute/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.h"
#include "arm_compute/core/NEON/kernels/NEDepthwiseConvolutionLayerNativeKernel.h"
#include "arm_compute/core/NEON/kernels/NEDepthwiseIm2ColKernel.h"
#include "arm_compute/core/NEON/kernels/NEDepthwiseVectorToTensorKernel.h"
#include "arm_compute/core/NEON/kernels/NEDepthwiseWeightsReshapeKernel.h"
#include "arm_compute/core/NEON/kernels/NEDirectConvolutionLayerOutputStageKernel.h"
#include "arm_compute/core/NEON/kernels/NEFillBorderKernel.h"
#include "arm_compute/core/NEON/kernels/NEGEMMMatrixVectorMultiplyKernel.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/core/utils/misc/Macros.h"
#include "arm_compute/runtime/IFunction.h"
#include "arm_compute/runtime/IMemoryManager.h"
#include "arm_compute/runtime/MemoryGroup.h"
#include "arm_compute/runtime/NEON/functions/NEActivationLayer.h"
#include "arm_compute/runtime/NEON/functions/NEPermute.h"
#include "arm_compute/runtime/NEON/functions/assembly/NEDepthwiseConvolutionAssemblyDispatch.h"
#include "arm_compute/runtime/Tensor.h"

namespace arm_compute
{
// Forward declarations
class ITensor;

/** Basic function to execute a depthwise convolution for kernel size 3x3xC. This function calls the following NEON kernels:
 *
 * -# @ref NEDepthwiseConvolutionLayer3x3
 * -# @ref NEFillBorderKernel (if pad_x or pad_y > 0)
 *
 */
class NEDepthwiseConvolutionLayer3x3 : public IFunction
{
public:
    /** Default constructor */
    NEDepthwiseConvolutionLayer3x3(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
    /** Prevent instances of this class from being copied (As this class contains pointers) */
    NEDepthwiseConvolutionLayer3x3(const NEDepthwiseConvolutionLayer3x3 &) = delete;
    /** Default move constructor */
    NEDepthwiseConvolutionLayer3x3(NEDepthwiseConvolutionLayer3x3 &&) = default;
    /** Prevent instances of this class from being copied (As this class contains pointers) */
    NEDepthwiseConvolutionLayer3x3 &operator=(const NEDepthwiseConvolutionLayer3x3 &) = delete;
    /** Default move assignment operator */
    NEDepthwiseConvolutionLayer3x3 &operator=(NEDepthwiseConvolutionLayer3x3 &&) = default;
    /** Initialize the function's source, destination, kernels and border_size.
     *
     * @param[in, out] input            Source tensor. Data type supported: QASYMM8/F16/F32. (Written to only for border filling).
     * @param[in]      weights          Weights tensor. These are 3D tensors with shape [3, 3, IFM]. Data type supported: Same as @p input.
     * @param[in]      biases           Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed.
     *                                  Data type supported: Same as @p input.
     * @param[out]     output           Destination tensor. Data type supported: same as @p input.
     * @param[in]      conv_info        Padding and stride information to use for the convolution.
     * @param[in]      depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1.
     * @param[in]      act_info         (Optional) Activation layer information in case of a fused activation.
     * @param[in]      dilation         (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
     */
    ARM_COMPUTE_DEPRECATED_REL_REPLACE(19.08, NEDepthwiseConvolutionLayerOptimized)
    void configure(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info,
                   unsigned int depth_multiplier = 1, const ActivationLayerInfo &act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U));

    /** Static function to check if given info will lead to a valid configuration of @ref NEDepthwiseConvolutionLayer3x3
     *
     * @param[in] input            Source tensor. Data type supported: QASYMM8/F16/F32. (Written to only for border filling).
     * @param[in] weights          Weights tensor. These are 3D tensors with shape [3, 3, IFM]. Data type supported: Same as @p input.
     * @param[in] biases           Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed.
     *                             Data type supported: Same as @p input.
     * @param[in] output           Destination tensor. Data type supported: same as @p input.
     * @param[in] conv_info        Padding and stride information to use for the convolution.
     * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1.
     * @param[in] act_info         (Optional) Activation layer information in case of a fused activation.
     * @param[in] dilation         (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
     *
     * @return a status
     */
    static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
                           unsigned int depth_multiplier = 1, const ActivationLayerInfo &act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U));

    // Inherited methods overriden:
    void run() override;
    void prepare() override;

private:
    /** Configure the kernels/functions for the generic pipeline.
     *
     * @param[in, out] input            Source tensor. Data type supported: QASYMM8/F16/F32. (Written to only for border filling).
     * @param[in]      weights          Weights tensor. These are 3D tensors with shape [3, 3, IFM]. Data type supported: Same as @p input.
     * @param[in]      biases           Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed.
     *                                  Data type supported: Same as @p input.
     * @param[out]     output           Destination tensor. Data type supported: same as @p input.
     * @param[in]      conv_info        Padding and stride information to use for the convolution.
     * @param[in]      depth_multiplier Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1.
     * @param[in]      act_info         Activation layer information in case of a fused activation.
     * @param[in]      dilation         (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
     *
     */
    void configure_generic(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info,
                           unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D &dilation = Size2D(1U, 1U));
    /** Configure the kernels/functions for the optimized pipeline.
     *
     * @param[in]  input            Source tensor. Data type supported: QASYMM8/F16/F32. (Written to only for border filling).
     * @param[in]  weights          Weights tensor. These are 3D tensors with shape [3, 3, IFM]. Data type supported: Same as @p input.
     * @param[in]  biases           Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed.
     *                              Data type supported: Same as @p input.
     * @param[out] output           Destination tensor. Data type supported: same as @p input.
     * @param[in]  conv_info        Padding and stride information to use for the convolution.
     * @param[in]  depth_multiplier Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1.
     * @param[in]  act_info         Activation layer information in case of a fused activation.
     */
    void configure_optimized(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info,
                             unsigned int depth_multiplier, const ActivationLayerInfo &act_info);
    /** Run generic kernel */
    void run_generic();
    /** Run optimized function */
    void run_optimized();

private:
    MemoryGroup                               _memory_group;
    NEDepthwiseConvolutionLayer3x3Kernel      _dwc_kernel;
    NEDepthwiseConvolutionAssemblyDispatch    _dwc_optimized_func;
    NEDirectConvolutionLayerOutputStageKernel _output_stage_kernel;
    NEFillBorderKernel                        _border_handler;
    NEPermute                                 _permute_input;
    NEPermute                                 _permute_weights;
    NEPermute                                 _permute_output;
    NEActivationLayer                         _activationlayer_function;
    Tensor                                    _accumulator;
    Tensor                                    _permuted_input;
    Tensor                                    _permuted_weights;
    Tensor                                    _permuted_output;
    const ITensor                            *_original_weights;
    bool                                      _has_bias;
    bool                                      _is_quantized;
    bool                                      _is_optimized;
    bool                                      _is_nchw;
    bool                                      _permute;
    bool                                      _is_activationlayer_enabled;
    bool                                      _is_prepared;
};

/** Basic function to execute optimized depthwise convolution routines. This function calls the following NEON kernels:
 *
 * @note At the moment 3x3 and 5x5 convolution of stride 1, 2 are supported
 *
 * -# @ref NEFillBorderKernel (if pad_x or pad_y > 0) and no assembly kernel implementation is present
 * -# @ref NEDepthwiseConvolutionLayer3x3Kernel if 3x3 and no assembly kernel implementation is present
 * -# @ref NEDepthwiseConvolutionAssemblyDispatch if assembly kernel implementation is present
 * -# @ref NEDirectConvolutionLayerOutputStageKernel if re-quantization of output is required
 * -# @ref NEActivationLayer if fused activation is required
 *
 */
class NEDepthwiseConvolutionLayerOptimized : public IFunction
{
public:
    /** Default constructor */
    NEDepthwiseConvolutionLayerOptimized(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
    /** Prevent instances of this class from being copied (As this class contains pointers) */
    NEDepthwiseConvolutionLayerOptimized(const NEDepthwiseConvolutionLayerOptimized &) = delete;
    /** Default move constructor */
    NEDepthwiseConvolutionLayerOptimized(NEDepthwiseConvolutionLayerOptimized &&) = default;
    /** Prevent instances of this class from being copied (As this class contains pointers) */
    NEDepthwiseConvolutionLayerOptimized &operator=(const NEDepthwiseConvolutionLayerOptimized &) = delete;
    /** Default move assignment operator */
    NEDepthwiseConvolutionLayerOptimized &operator=(NEDepthwiseConvolutionLayerOptimized &&) = default;
    /** Initialize the function's source, destination, kernels and border_size.
     *
     * @param[in, out] input            Source tensor. Data type supported: QASYMM8/F16/F32. (Written to only for border filling).
     * @param[in]      weights          Weights tensor. These are 3D tensors with shape [W, H, IFM]. Data type supported: Same as @p input.
     * @param[in]      biases           Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed.
     *                                  Data type supported: Same as @p input.
     * @param[out]     output           Destination tensor. Data type supported: same as @p input.
     * @param[in]      conv_info        Padding and stride information to use for the convolution.
     * @param[in]      depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1.
     * @param[in]      act_info         (Optional) Activation layer information in case of a fused activation.
     * @param[in]      dilation         (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
     */
    void configure(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info,
                   unsigned int depth_multiplier = 1, const ActivationLayerInfo &act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U));

    /** Static function to check if given info will lead to a valid configuration of @ref NEDepthwiseConvolutionLayer3x3
     *
     * @param[in] input            Source tensor. Data type supported: QASYMM8/F16/F32. (Written to only for border filling).
     * @param[in] weights          Weights tensor. These are 3D tensors with shape [W, H, IFM]. Data type supported: Same as @p input.
     * @param[in] biases           Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed.
     *                             Data type supported: Same as @p input.
     * @param[in] output           Destination tensor. Data type supported: same as @p input.
     * @param[in] conv_info        Padding and stride information to use for the convolution.
     * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1.
     * @param[in] act_info         (Optional) Activation layer information in case of a fused activation.
     * @param[in] dilation         (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
     *
     * @return a status
     */
    static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
                           unsigned int depth_multiplier = 1, const ActivationLayerInfo &act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U));

    // Inherited methods overriden:
    void run() override;
    void prepare() override;

private:
    /** Configure the kernels/functions for the generic pipeline.
     *
     * @param[in, out] input            Source tensor. Data type supported: QASYMM8/F16/F32. (Written to only for border filling).
     * @param[in]      weights          Weights tensor. These are 3D tensors with shape [W, H, IFM]. Data type supported: Same as @p input.
     * @param[in]      biases           Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed.
     *                                  Data type supported: Same as @p input.
     * @param[out]     output           Destination tensor. Data type supported: same as @p input.
     * @param[in]      conv_info        Padding and stride information to use for the convolution.
     * @param[in]      depth_multiplier Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1.
     * @param[in]      act_info         Activation layer information in case of a fused activation.
     * @param[in]      dilation         (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
     *
     */
    void configure_generic(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info,
                           unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D &dilation = Size2D(1U, 1U));
    /** Configure the kernels/functions for the optimized pipeline.
     *
     * @param[in]  input            Source tensor. Data type supported: QASYMM8/F16/F32. (Written to only for border filling).
     * @param[in]  weights          Weights tensor. These are 3D tensors with shape [W, H, IFM]. Data type supported: Same as @p input.
     * @param[in]  biases           Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed.
     *                              Data type supported: Same as @p input.
     * @param[out] output           Destination tensor. Data type supported: same as @p input.
     * @param[in]  conv_info        Padding and stride information to use for the convolution.
     * @param[in]  depth_multiplier Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1.
     * @param[in]  act_info         Activation layer information in case of a fused activation.
     */
    void configure_optimized(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info,
                             unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D &dilation = Size2D(1U, 1U));
    /** Run generic kernel */
    void run_generic();
    /** Run optimized function */
    void run_optimized();

private:
    MemoryGroup                               _memory_group;
    NEDepthwiseConvolutionLayer3x3Kernel      _dwc_kernel;
    NEDepthwiseConvolutionAssemblyDispatch    _dwc_optimized_func;
    NEDirectConvolutionLayerOutputStageKernel _output_stage_kernel;
    NEFillBorderKernel                        _border_handler;
    NEPermute                                 _permute_input;
    NEPermute                                 _permute_weights;
    NEPermute                                 _permute_output;
    NEActivationLayer                         _activationlayer_function;
    Tensor                                    _accumulator;
    Tensor                                    _permuted_input;
    Tensor                                    _permuted_weights;
    Tensor                                    _permuted_output;
    const ITensor                            *_original_weights;
    bool                                      _has_bias;
    bool                                      _is_quantized;
    bool                                      _is_optimized;
    bool                                      _is_nchw;
    bool                                      _permute;
    bool                                      _is_activationlayer_enabled;
    bool                                      _is_prepared;
};

/** Basic function to execute a generic depthwise convolution. This function calls the following NEON kernels:
 *
 * If data type is F32 and data layout is NHWC:
 * -# @ref NEDepthwiseConvolutionLayerNativeKernel
 *
 * Otherwise:
 * -# @ref NEDepthwiseIm2ColKernel
 * -# @ref NEDepthwiseWeightsReshapeKernel
 * -# @ref NEGEMMMatrixVectorMultiplyKernel
 * -# @ref NEFillBorderKernel (if pad_x or pad_y > 0)
 *
 */
class NEDepthwiseConvolutionLayer : public IFunction
{
public:
    /** Default constructor */
    NEDepthwiseConvolutionLayer();
    /** Prevent instances of this class from being copied (As this class contains pointers) */
    NEDepthwiseConvolutionLayer(const NEDepthwiseConvolutionLayer &) = delete;
    /** Default move constructor */
    NEDepthwiseConvolutionLayer(NEDepthwiseConvolutionLayer &&) = default;
    /** Prevent instances of this class from being copied (As this class contains pointers) */
    NEDepthwiseConvolutionLayer &operator=(const NEDepthwiseConvolutionLayer &) = delete;
    /** Default move assignment operator */
    NEDepthwiseConvolutionLayer &operator=(NEDepthwiseConvolutionLayer &&) = default;
    /** Initialize the function's source, destination, weights and convolution information.
     *
     * @param[in, out] input            Source tensor. Data type supported: QASYMM8/F16/F32. (Written to only for border filling).
     * @param[out]     output           Destination tensor. Data type supported: same as @p input.
     * @param[in]      weights          Weights tensor. These are 3D tensors with shape [kernel_x, kernel_y, IFM]. Data type supported: Same as @p input.
     * @param[in]      biases           (Optional) Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed.
     *                                  Data type supported: Same as @p input, S32 when input is QASYMM8.
     * @param[in]      conv_info        Padding and stride information to use for the convolution.
     * @param[in]      depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1.
     * @param[in]      act_info         (Optional) Activation layer information in case of a fused activation.
     * @param[in]      dilation         (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
     */
    void configure(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info,
                   unsigned int depth_multiplier = 1, const ActivationLayerInfo &act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U));

    /** Static function to check if given info will lead to a valid configuration of @ref NEDepthwiseConvolutionLayer
     *
     * @param[in] input            Source tensor. Data type supported: QASYMM8/F16/F32. (Written to only for border filling).
     * @param[in] output           Destination tensor. Data type supported: same as @p input.
     * @param[in] weights          Weights tensor. These are 3D tensors with shape [kernel_x, kernel_y, IFM]. Data type supported: Same as @p input.
     * @param[in] biases           (Optional) Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed.
     *                             Data type supported: Same as @p input, S32 when input is QASYMM8.
     * @param[in] conv_info        Padding and stride information to use for the convolution.
     * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1.
     * @param[in] act_info         (Optional) Activation layer information in case of a fused activation.
     * @param[in] dilation         (Optional) Dilation, in elements, across x and y. Defaults to (1, 1).
     *
     * @return a status
     */
    static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
                           unsigned int depth_multiplier = 1, const ActivationLayerInfo &act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U));

    // Inherited methods overriden:
    void run() override;
    void prepare() override;

private:
    NEDepthwiseIm2ColKernel                   _im2col_kernel;
    NEDepthwiseWeightsReshapeKernel           _weights_reshape_kernel;
    NEGEMMMatrixVectorMultiplyKernel          _v2mm_kernel;
    NEDepthwiseConvolutionLayerNativeKernel   _depthwise_conv_kernel;
    NEDepthwiseVectorToTensorKernel           _vector_to_tensor_kernel;
    NEDirectConvolutionLayerOutputStageKernel _output_stage_kernel;
    NEFillBorderKernel                        _fill_border;
    NEFillBorderKernel                        _v2mm_input_fill_border;
    NEFillBorderKernel                        _v2mm_weights_fill_border;
    NEPermute                                 _permute_input;
    NEPermute                                 _permute_weights;
    NEPermute                                 _permute_output;
    NEActivationLayer                         _activationlayer_function;
    Tensor                                    _input_reshaped;
    Tensor                                    _weights_reshaped;
    Tensor                                    _v2mm_output;
    Tensor                                    _output_reshaped;
    Tensor                                    _permuted_input;
    Tensor                                    _permuted_weights;
    Tensor                                    _permuted_output;
    bool                                      _is_prepared;
    bool                                      _is_quantized;
    bool                                      _is_nhwc;
    bool                                      _is_activationlayer_enabled;
    bool                                      _is_optimized;
    const ITensor                            *_original_weights;
};
} // namespace arm_compute
#endif /* __ARM_COMPUTE_NEDEPTHWISECONVOLUTION_H__ */