142 lines
9.4 KiB
C++
142 lines
9.4 KiB
C++
/*
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* Copyright (c) 2018-2020 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_CLWINOGRADCONVOLUTIONLAYER_H
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#define ARM_COMPUTE_CLWINOGRADCONVOLUTIONLAYER_H
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#include "arm_compute/core/Types.h"
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#include "arm_compute/runtime/CL/functions/CLGEMM.h"
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#include "arm_compute/runtime/CL/functions/CLWinogradInputTransform.h"
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#include "arm_compute/runtime/IFunction.h"
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namespace arm_compute
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{
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class CLCompileContext;
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class CLWinogradFilterTransformKernel;
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class CLWinogradOutputTransformKernel;
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class ICLTensor;
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class ITensorInfo;
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/** Basic function to execute Winograd-based convolution on OpenCL. This function calls the following OpenCL functions/kernels:
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*
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* -# @ref CLWinogradInputTransform
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* -# @ref CLWinogradFilterTransformKernel (only once)
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* -# @ref CLGEMM
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* -# @ref CLWinogradOutputTransformKernel
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*
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*/
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class CLWinogradConvolutionLayer : public IFunction
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{
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public:
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/** Default constructor */
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CLWinogradConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
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/** Prevent instances of this class from being copied (As this class contains pointers) */
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CLWinogradConvolutionLayer(const CLWinogradConvolutionLayer &) = delete;
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/** Default move constructor */
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CLWinogradConvolutionLayer(CLWinogradConvolutionLayer &&) = default;
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/** Prevent instances of this class from being copied (As this class contains pointers) */
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CLWinogradConvolutionLayer &operator=(const CLWinogradConvolutionLayer &) = delete;
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/** Default move assignment operator */
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CLWinogradConvolutionLayer &operator=(CLWinogradConvolutionLayer &&) = default;
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/** Default destructor */
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~CLWinogradConvolutionLayer();
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/** Set the input and output tensors.
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*
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* @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
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* @note Some Winograd configurations (i.e. F(4x4, 5x5)) are supported only with enable_fast_math = true
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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: F16/F32.
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* @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported:Same as @p input.
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* @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].Data type supported: Same as @p input
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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] act_info (Optional) Activation layer information in case of a fused activation.
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* @param[in] enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation
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* available which may introduce a drop of accuracy as well. Default is false
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*/
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void configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info,
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const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false);
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/** Set the input and output tensors.
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*
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* @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
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* @note Some Winograd configurations (i.e. F(4x4, 5x5)) are supported only with enable_fast_math = true
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*
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* @param[in] compile_context The compile context to be used.
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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: F16/F32.
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* @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported:Same as @p input.
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* @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].Data type supported: Same as @p input
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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] act_info (Optional) Activation layer information in case of a fused activation.
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* @param[in] enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation
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* available which may introduce a drop of accuracy as well. Default is false
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*/
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void configure(const CLCompileContext &compile_context, ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info,
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const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false);
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/** Static function to check if given info will lead to a valid configuration of @ref CLWinogradConvolutionLayer
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*
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* @note: This function only works with 3x3,3x1,1x3,5x5,5x1 and 1x5 kernels along with unit strides for both NCHW and NHWC data layout
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* @note Some Winograd configurations (i.e. F(4x4, 5x5)) are supported only with enable_fast_math = true
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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: F16/F32.
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* @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported:Same as @p input.
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* @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM].Data type supported: Same as @p input
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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] act_info (Optional) Activation layer information in case of a fused activation.
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* @param[in] enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation
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* available which may introduce a drop of accuracy as well. Default is false
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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 ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false);
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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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MemoryGroup _memory_group;
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CLGEMM _batched_mm;
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CLWinogradInputTransform _input_transform;
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std::unique_ptr<CLWinogradFilterTransformKernel> _filter_transform;
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std::unique_ptr<CLWinogradOutputTransformKernel> _output_transform;
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CLTensor _input0;
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CLTensor _input1;
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CLTensor _batched_mm_output;
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const ICLTensor *_original_weights;
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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_CLWINOGRADCONVOLUTIONLAYER_H */
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