1 /* 2 * Copyright (c) 2017-2021 Arm Limited. 3 * 4 * SPDX-License-Identifier: MIT 5 * 6 * Permission is hereby granted, free of charge, to any person obtaining a copy 7 * of this software and associated documentation files (the "Software"), to 8 * deal in the Software without restriction, including without limitation the 9 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or 10 * sell copies of the Software, and to permit persons to whom the Software is 11 * furnished to do so, subject to the following conditions: 12 * 13 * The above copyright notice and this permission notice shall be included in all 14 * copies or substantial portions of the Software. 15 * 16 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 17 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 18 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 19 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 20 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 21 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 22 * SOFTWARE. 23 */ 24 #ifndef ARM_COMPUTE_CLCONVOLUTIONLAYER_H 25 #define ARM_COMPUTE_CLCONVOLUTIONLAYER_H 26 27 #include "arm_compute/core/CL/CLCompileContext.h" 28 #include "arm_compute/core/Types.h" 29 #include "arm_compute/core/experimental/IPostOp.h" 30 #include "arm_compute/runtime/CL/CLTensor.h" 31 #include "arm_compute/runtime/IFunction.h" 32 #include "arm_compute/runtime/IMemoryManager.h" 33 34 #include <memory> 35 36 namespace arm_compute 37 { 38 class CLCompileContext; 39 class ICLTensor; 40 class ITensorInfo; 41 42 /** Basic function to compute the convolution layer. This function calls the following OpenCL kernels/functions: 43 * 44 * -# @ref opencl::ClGemmConv2d 45 * -# @ref opencl::ClWinogradConv2d 46 * -# @ref opencl::ClDirectConv2d 47 * -# @ref CLFFTConvolutionLayer 48 * 49 * The function selects one of the algorithms mentioned above based on: 50 * - The size of the kernel 51 * - Number of input/output feature maps 52 * - Amount of memory needed 53 * 54 * Generally GEMM-based convolution is executed when neither Winograd nor FFT nor Direct convolution can be performed. 55 * 56 * FP32 Algorithm| Filter Size | Input/Output feature maps | 57 * --------------|-------------------------------------------------------------|-------------------------------------------| 58 * Winograd | 3x3 1x3 3x1 5x1 1x5 5x5(fast maths) 7x1 1x7 | Input channels is greater than 3 | 59 * FFT | Squared kernels and greater than 9x9 | Input feature maps > Output feature maps | 60 * DirectConv | 9x9 | | 61 * GEMM | Any size | | 62 * 63 * Winograd 5x5 requires fast maths enabled. 64 * 65 * FP16 Algorithm| Filter Size | Input/Output feature maps | 66 * --------------|----------------------------|-------------------------------------------| 67 * Winograd | 3x3 1x3 3x1 5x1 1x5 5x5 | Input channels is greater than 3 | 68 * FFT | Not supported | | 69 * DirectConv | 9x9 | | 70 * GEMM | Any size | | 71 * 72 * Winograd FP16 requires fast maths enabled. 73 * 74 */ 75 class CLConvolutionLayer : public IFunction 76 { 77 public: 78 /** Default constructor */ 79 CLConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr); 80 /** Default Destructor */ 81 ~CLConvolutionLayer(); 82 /** Prevent instances of this class from being copied (As this class contains pointers) */ 83 CLConvolutionLayer(const CLConvolutionLayer &) = delete; 84 /** Default move constructor */ 85 CLConvolutionLayer(CLConvolutionLayer &&) = default; 86 /** Prevent instances of this class from being copied (As this class contains pointers) */ 87 CLConvolutionLayer &operator=(const CLConvolutionLayer &) = delete; 88 /** Default move assignment operator */ 89 CLConvolutionLayer &operator=(CLConvolutionLayer &&) = default; 90 /** Set the input and output tensors. 91 * 92 * Valid data layouts: 93 * - NHWC 94 * - NCHW 95 * 96 * Valid data type configurations: 97 * |src0 |src1 |src2 |dst | 98 * |:--------------|:------------------|:------|:--------------| 99 * |F16 |F16 |F16 |F16 | 100 * |F32 |F32 |F32 |F32 | 101 * |QASYMM8 |QASYMM8 |S32 |QASYMM8 | 102 * |QASYMM8 |QSYMM8_PER_CHANNEL |S32 |QASYMM8 | 103 * |QASYMM8_SIGNED |QASYMM8_SIGNED |S32 |QASYMM8_SIGNED | 104 * |QASYMM8_SIGNED |QSYMM8_PER_CHANNEL |S32 |QASYMM8_SIGNED | 105 * 106 * @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM], 107 * while every optional dimension from 4 and above represent a batch of inputs. 108 * Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32. 109 * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. 110 * Data type supported: Same as @p input, also could be QSYMM8_PER_CHANNEL if input is QASYMM8/QASYMM8_SIGNED. 111 * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. 112 * Data type supported: Same as @p input, except for input of QASYMM8/QASYMM8_SIGNED type where biases should be of S32 type. 113 * @param[out] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. 114 * Data types supported: Same as @p input. 115 * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. 116 * @param[in] weights_info Specifies if the weights tensor has been reshaped with CLWeightsReshapeKernel. Data type supported: Same as @p input. 117 * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). 118 * @param[in] act_info (Optional) Activation layer information in case of a fused activation. 119 * @param[in] enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation 120 * available which may introduce a drop of accuracy as well. Default is false 121 * @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout 122 * @param[in] post_ops (Optional) A sequence of post operations that are performed after the main operation. 123 */ 124 void configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info = WeightsInfo(), 125 const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false, unsigned int num_groups = 1, 126 const experimental::PostOpList<ICLTensor *> &post_ops = experimental::PostOpList<ICLTensor *> {}); 127 /** Set the input and output tensors. 128 * 129 * @param[in] compile_context The compile context to be used. 130 * @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM], 131 * while every optional dimension from 4 and above represent a batch of inputs. 132 * Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32. 133 * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. 134 * Data type supported: Same as @p input, also could be QSYMM8_PER_CHANNEL if input is QASYMM8/QASYMM8_SIGNED. 135 * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. 136 * Data type supported: Same as @p input, except for input of QASYMM8/QASYMM8_SIGNED type where biases should be of S32 type. 137 * @param[out] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. 138 * Data types supported: Same as @p input. 139 * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. 140 * @param[in] weights_info Specifies if the weights tensor has been reshaped with CLWeightsReshapeKernel. Data type supported: Same as @p input. 141 * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). 142 * @param[in] act_info (Optional) Activation layer information in case of a fused activation. 143 * @param[in] enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation 144 * available which may introduce a drop of accuracy as well. Default is false 145 * @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout 146 * @param[in] post_ops (Optional) A sequence of post operations that are performed after the main operation. 147 */ 148 void configure(const CLCompileContext &compile_context, ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, 149 const WeightsInfo &weights_info = WeightsInfo(), const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false, 150 unsigned int num_groups = 1, const experimental::PostOpList<ICLTensor *> &post_ops = experimental::PostOpList<ICLTensor *> {}); 151 /** Static function to check if given info will lead to a valid configuration of @ref CLConvolutionLayer 152 * 153 * @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM], 154 * while every optional dimension from 4 and above represent a batch of inputs. 155 * Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32. 156 * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. 157 * Data type supported: Same as @p input, also could be QSYMM8_PER_CHANNEL if input is QASYMM8/QASYMM8_SIGNED. 158 * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. 159 * Data type supported: Same as @p input, except for input of QASYMM8/QASYMM8_SIGNED type where biases should be of S32 type. 160 * @param[in] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. 161 * Data types supported: Same as @p input. 162 * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. 163 * @param[in] weights_info Specifies if the weights tensor has been reshaped with CLWeightsReshapeKernel. 164 * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). 165 * @param[in] act_info (Optional) Activation layer information in case of a fused activation. 166 * @param[in] enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation 167 * available which may introduce a drop of accuracy as well. Default is false 168 * @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is only supported for NCHW data layout 169 * @param[in] post_ops (Optional) A sequence of post operations that are performed after the main operation. 170 * 171 * @return a status 172 */ 173 static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, 174 const WeightsInfo &weights_info = WeightsInfo(), const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false, 175 unsigned int num_groups = 1, const experimental::PostOpList<ITensorInfo *> &post_ops = experimental::PostOpList<ITensorInfo *> {}); 176 /** Static function to check if given info will return the convolution called by @ref CLConvolutionLayer 177 * 178 * @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM], 179 * while every optional dimension from 4 and above represent a batch of inputs. 180 * Data types supported: QASYMM8/QASYMM8_SIGNED/F16/F32. 181 * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. 182 * Data type supported: Same as @p input, also could be QSYMM8_PER_CHANNEL if input is QASYMM8/QASYMM8_SIGNED. 183 * @param[in] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. 184 * Data types supported: Same as @p input. 185 * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. 186 * @param[in] weights_info Specifies if the weights tensor has been reshaped with CLWeightsReshapeKernel. 187 * @param[in] act_info (Optional) Activation layer information in case of a fused activation. 188 * @param[in] gpu_target Specifies the @p GPUTarget. 189 * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). 190 * @param[in] enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation 191 * available which may introduce a drop of accuracy as well. Default is false 192 * 193 * @return the Convolution Method Hint 194 */ 195 static ConvolutionMethod get_convolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info, 196 const WeightsInfo &weights_info, const ActivationLayerInfo &act_info, const GPUTarget gpu_target, const Size2D &dilation = Size2D(1U, 1U), bool enable_fast_math = false); 197 // Inherited methods overridden: 198 void run() override; 199 void prepare() override; 200 201 private: 202 struct Impl; 203 std::unique_ptr<Impl> _impl; 204 }; 205 } 206 #endif /* ARM_COMPUTE_CLCONVOLUTIONLAYER_H */ 207