1 /* 2 * Copyright (c) 2017-2022 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_NEGEMMCONVOLUTIONLAYER_H 25 #define ARM_COMPUTE_NEGEMMCONVOLUTIONLAYER_H 26 27 #include "arm_compute/runtime/IFunction.h" 28 29 #include "arm_compute/core/Types.h" 30 #include "arm_compute/runtime/IFunction.h" 31 #include "arm_compute/runtime/IMemoryManager.h" 32 #include "arm_compute/runtime/IWeightsManager.h" 33 #include "arm_compute/runtime/MemoryGroup.h" 34 35 #include <memory> 36 37 namespace arm_compute 38 { 39 class ITensor; 40 class ITensorInfo; 41 42 /** Basic function to compute the convolution layer. This function calls the following kernels/functions: 43 * 44 * -# @ref cpu::CpuGemmConv2d 45 * 46 */ 47 class NEGEMMConvolutionLayer : public IFunction 48 { 49 public: 50 /** Constructor */ 51 NEGEMMConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager = nullptr, IWeightsManager *weights_manager = nullptr); 52 /** Prevent instances of this class from being copied (As this class contains pointers) */ 53 NEGEMMConvolutionLayer(const NEGEMMConvolutionLayer &) = delete; 54 /** Prevent instances of this class from being moved (As this class contains non movable objects) */ 55 NEGEMMConvolutionLayer(NEGEMMConvolutionLayer &&) = delete; 56 /** Prevent instances of this class from being copied (As this class contains pointers) */ 57 NEGEMMConvolutionLayer &operator=(const NEGEMMConvolutionLayer &) = delete; 58 /** Prevent instances of this class from being moved (As this class contains non movable objects) */ 59 NEGEMMConvolutionLayer &operator=(NEGEMMConvolutionLayer &&) = delete; 60 /** Default destructor */ 61 ~NEGEMMConvolutionLayer(); 62 /** Set the input and output tensors. 63 * 64 * Valid data layouts: 65 * - NHWC 66 * - NCHW 67 * 68 * Valid data type configurations: 69 * |src0 |src1 |src2 |dst | 70 * |:--------------|:------------------|:--------|:--------------| 71 * |F16 |F16 |F16 |F16 | 72 * |F32 |F32 |F32 |F32 | 73 * |BFLOAT16 |BFLOAT16 |BFLOAT16 |BFLOAT16 | 74 * |QASYMM8 |QASYMM8 |S32 |QASYMM8 | 75 * |QASYMM8 |QSYMM8_PER_CHANNEL |S32 |QASYMM8 | 76 * |QASYMM8_SIGNED |QASYMM8_SIGNED |S32 |QASYMM8_SIGNED | 77 * |QASYMM8_SIGNED |QSYMM8_PER_CHANNEL |S32 |QASYMM8_SIGNED | 78 * 79 * @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM], 80 * while every optional dimension from 4 and above represent a batch of inputs. 81 * Data types supported: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32. 82 * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. 83 * Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/BFLOAT16/F16/F32. 84 * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. 85 * Data type supported: Should match @p input data type, except for input of QASYMM8/QASYMM8_SIGNED type where biases should be of S32 type. 86 * @param[out] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. 87 * Data types supported: Same as @p input. 88 * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. 89 * @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 90 * tensor has also been transposed with cpu::kernels::CpuGemmTranspose1xWKernel. Data type supported: Same as @p input. 91 * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). 92 * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported. 93 * @param[in] enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation 94 * available which may introduce a drop of accuracy as well. Default is false 95 * @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is not supported 96 */ 97 void configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info = WeightsInfo(), 98 const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false, unsigned int num_groups = 1); 99 /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMConvolutionLayer 100 * 101 * @param[in] input Source tensor info. 3 lower dimensions represent a single input [width, height, IFM], 102 * while every optional dimension from 4 and above represent a batch of inputs. 103 * Data types supported: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32. 104 * @param[in] weights Weights tensor info. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. 105 * Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/BFLOAT16/F16/F32. 106 * @param[in] biases Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. 107 * Data type supported: Should match @p input data type, except for input of QASYMM8/QASYMM8_SIGNED type where biases should be of S32 type. 108 * @param[in] output Destination tensor info. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. 109 * Data types supported: Same as @p input. 110 * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. 111 * @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 112 * tensor has also been transposed with cpu::kernels::CpuGemmTranspose1xWKernel. Data type supported: Same as @p input. 113 * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). 114 * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported. 115 * @param[in] enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation 116 * available which may introduce a drop of accuracy as well. Default is false 117 * @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is not supported 118 * 119 * @return a status 120 */ 121 static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, 122 const WeightsInfo &weights_info = WeightsInfo(), const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), 123 bool enable_fast_math = false, unsigned int num_groups = 1); 124 125 /** Static function to check if there is an optimized version of 126 * GEMM available for the input parameters. 127 * 128 * The method is intended to be used to find out the optimal 129 * memory layout to be used for the weights tensor when running 130 * variable weights execution. 131 * 132 * The user can query the database of optimised kernels in 133 * arm_gemm by specifying one of the enumerations of 134 * arm_compute::WeightFormat in the weight_format field of the input 135 * parameter weights_info. In case of success, the method 136 * writes the expected format in the output parameter 137 * expected_weight_format. The expected_weight_format can than be 138 * used in the configure method of the class for retrieving the 139 * best optimal kernel. 140 * 141 * Use case one - query for a specific format: 142 * 143 * WeightInfo weights_info(..., arm_compute::WeightFormat::OHWIo4, ...); // Set the value of the input query. 144 * if (NEGEMMConvolutionlayer::has_opt_impl(WeightFormat(), ...., weights_info, ...)) 145 * { 146 * auto conv = std::unique_ptr<NEGEMMConvolutionlayer>(); 147 * conv->configure(..., weights_info, ...); // uses the same WeightFormat the user wanted originally, OHWYo4. 148 * conv->run(...); 149 * } 150 * 151 * Use case two - query for any format that would be optimal for the GEMM to execute: 152 * 153 * WeightInfo weights_info(..., arm_compute::WeightFormat::ANY, ...); // Set the value of the input query. 154 * arm_compute::WeightFormat expected_wf; 155 * if (NEGEMMConvolutionlayer::has_opt_impl(expected_wf, ...., weights_info, ...)) 156 * { 157 * auto conv = std::unique_ptr<NEGEMMConvolutionlayer>(); 158 * // ... code to convert the layout of the weights tensor to the layout returned by has_opt_impl 159 * WeightInfo new_weights_info(..., expected_wf, ...); // Set the value of the WeightFormat returned by has_opt_impl. 160 * conv->configure(..., new_weights_info, ...); 161 * conv->run(...); 162 * } 163 * 164 * Notice that a GEMM configured with a WeightFormat other than 165 * UNSPECIFIED will run GEMM with variable weights mode. 166 * 167 * @param[out] expected_weight_format The arm_compute::WeightFormat expected by the kernel. 168 * @param[in] src Source tensor info. 169 * @param[in] weights Weights tensor info. 170 * @param[in] biases Biases tensor info. Shared biases supported. 171 * @param[in] dst Destination tensor info. 172 * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. 173 * @param[in] weights_info (optional) Specifies additional configuration parameters for the weights of the GEMM computation. 174 * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). 175 * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported. And no activation (i.e. Linear) which is the default value. 176 * @param[in] enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation 177 * 178 * @return a Status 179 */ 180 static Status has_opt_impl(arm_compute::WeightFormat &expected_weight_format, const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, 181 const PadStrideInfo &conv_info, 182 const WeightsInfo &weights_info = WeightsInfo(), const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), 183 bool enable_fast_math = false); 184 // Inherited methods overridden: 185 void run() override; 186 void prepare() override; 187 188 private: 189 struct Impl; 190 std::unique_ptr<Impl> _impl; 191 }; 192 } // namespace arm_compute 193 #endif /* ARM_COMPUTE_NEGEMMCONVOLUTIONLAYER_H */ 194