1 /* 2 * Copyright (c) 2018-2021 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_CL_WINOGRADCONV2D_H 25 #define ARM_COMPUTE_CL_WINOGRADCONV2D_H 26 27 #include "arm_compute/runtime/CL/CLTensor.h" 28 #include "src/core/CL/kernels/CLFillBorderKernel.h" 29 #include "src/gpu/cl/ClCompileContext.h" 30 #include "src/gpu/cl/IClOperator.h" 31 #include "src/gpu/cl/operators/ClGemm.h" 32 33 namespace arm_compute 34 { 35 class CLCompileContext; 36 class ITensorInfo; 37 namespace opencl 38 { 39 namespace kernels 40 { 41 class ClWinogradInputTransformKernel; 42 class ClWinogradFilterTransformKernel; 43 class ClWinogradOutputTransformKernel; 44 } // kernels 45 /** Basic function to execute Winograd-based convolution on OpenCL. This function calls the following OpenCL functions/kernels: 46 * 47 * -# @ref kernels::ClWinogradInputTransformKernel 48 * -# @ref kernels::ClWinogradFilterTransformKernel (only once) 49 * -# @ref ClGemm 50 * -# @ref kernels::ClWinogradOutputTransformKernel 51 * 52 */ 53 class ClWinogradConv2d : public IClOperator 54 { 55 public: 56 /** Default constructor */ 57 ClWinogradConv2d(); 58 /** Default destructor */ 59 ~ClWinogradConv2d(); 60 /** Prevent instances of this class from being copied (As this class contains pointers) */ 61 ClWinogradConv2d(const ClWinogradConv2d &) = delete; 62 /** Default move constructor */ 63 ClWinogradConv2d(ClWinogradConv2d &&) = default; 64 /** Prevent instances of this class from being copied (As this class contains pointers) */ 65 ClWinogradConv2d &operator=(const ClWinogradConv2d &) = delete; 66 /** Default move assignment operator */ 67 ClWinogradConv2d &operator=(ClWinogradConv2d &&) = default; 68 /** Set the input and output tensors. 69 * 70 * Valid data layouts: 71 * - NHWC 72 * - NCHW 73 * 74 * Valid data type configurations: 75 * |src0 |src1 |src2 |dst | 76 * |:--------------|:--------------|:------|:--------------| 77 * |F16 |F16 |F16 |F16 | 78 * |F32 |F32 |F32 |F32 | 79 * 80 * @note: This function only works with 3x3,3x1,1x3,5x5,5x1,1x5,7x1 and 1x7 kernels along with unit strides for both NCHW and NHWC data layout 81 * @note Some Winograd configurations (i.e. F(4x4, 5x5)) are supported only with enable_fast_math = true 82 * 83 * @param[in] compile_context The compile context to be used. 84 * @param[in] src Source tensor info. 3 lower dimensions represent a single input [width, height, IFM], 85 * while every optional dimension from 4 and above represent a batch of inputs. 86 * Data types supported: F16/F32. 87 * @param[in] weights Weights tensor info. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported:Same as @p src. 88 * @param[in] biases Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM].Data type supported: Same as @p src 89 * @param[out] dst Destination tensor info. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. 90 * Data types supported: Same as @p src. 91 * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. 92 * @param[in] act_info (Optional) Activation layer information in case of a fused activation. 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 */ 96 void configure(const ClCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *biases, ITensorInfo *dst, const PadStrideInfo &conv_info, 97 const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false); 98 /** Static function to check if given info will lead to a valid configuration 99 * 100 * Similar to ClWinogradConv2d::configure() 101 * 102 * @return a status 103 */ 104 static Status validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const PadStrideInfo &conv_info, 105 const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false); 106 107 // Inherited method overridden 108 void run(ITensorPack &tensors) override; 109 void prepare(ITensorPack &tensors) override; 110 experimental::MemoryRequirements workspace() const override; 111 112 private: 113 ClGemm _batched_mm; 114 std::unique_ptr<kernels::ClWinogradInputTransformKernel> _input_transform; 115 std::unique_ptr<kernels::ClWinogradFilterTransformKernel> _filter_transform; 116 std::unique_ptr<kernels::ClWinogradOutputTransformKernel> _output_transform; 117 CLFillBorderKernel _border_handler; 118 TensorInfo _input0; 119 TensorInfo _input1; 120 TensorInfo _batched_mm_output; 121 bool _is_prepared; 122 experimental::MemoryRequirements _aux_mem{}; 123 }; 124 } // namespace opencl 125 } // namespace arm_compute 126 #endif /* ARM_COMPUTE_CL_WINOGRADCONV2D_H */ 127