/* * Copyright (c) 2017-2023 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_NEGEMMCONVOLUTIONLAYER_H #define ARM_COMPUTE_NEGEMMCONVOLUTIONLAYER_H #include "arm_compute/core/Types.h" #include "arm_compute/function_info/ActivationLayerInfo.h" #include "arm_compute/runtime/IFunction.h" #include "arm_compute/runtime/IMemoryManager.h" #include "arm_compute/runtime/IWeightsManager.h" #include "arm_compute/runtime/MemoryGroup.h" #include namespace arm_compute { class ITensor; class ITensorInfo; /** Basic function to compute the convolution layer. This function calls the following kernels/functions: * * -# @ref cpu::CpuGemmConv2d * */ class NEGEMMConvolutionLayer : public IFunction { public: /** Constructor */ NEGEMMConvolutionLayer(const std::shared_ptr &memory_manager = nullptr, IWeightsManager *weights_manager = nullptr); /** Prevent instances of this class from being copied (As this class contains pointers) */ NEGEMMConvolutionLayer(const NEGEMMConvolutionLayer &) = delete; /** Prevent instances of this class from being moved (As this class contains non movable objects) */ NEGEMMConvolutionLayer(NEGEMMConvolutionLayer &&) = delete; /** Prevent instances of this class from being copied (As this class contains pointers) */ NEGEMMConvolutionLayer &operator=(const NEGEMMConvolutionLayer &) = delete; /** Prevent instances of this class from being moved (As this class contains non movable objects) */ NEGEMMConvolutionLayer &operator=(NEGEMMConvolutionLayer &&) = delete; /** Default destructor */ ~NEGEMMConvolutionLayer(); /** Set the input and output tensors. * * Valid data layouts: * - NHWC * - NCHW * * Valid data type configurations: * |src0 |src1 |src2 |dst | * |:--------------|:------------------|:--------|:--------------| * |F16 |F16 |F16 |F16 | * |F32 |F32 |F32 |F32 | * |BFLOAT16 |BFLOAT16 |BFLOAT16 |BFLOAT16 | * |QASYMM8 |QASYMM8 |S32 |QASYMM8 | * |QASYMM8 |QSYMM8_PER_CHANNEL |S32 |QASYMM8 | * |QASYMM8_SIGNED |QASYMM8_SIGNED |S32 |QASYMM8_SIGNED | * |QASYMM8_SIGNED |QSYMM8_PER_CHANNEL |S32 |QASYMM8_SIGNED | * * @param[in] input Source tensor. 3 lower dimensions represent a single input [width, height, IFM], * while every optional dimension from 4 and above represent a batch of inputs. * Data types supported: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32. * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. * Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/BFLOAT16/F16/F32. * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. * Data type supported: Should match @p input data type, except for input of QASYMM8/QASYMM8_SIGNED type where biases should be of S32 type. * @param[out] output Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. * Data types supported: Same as @p input. * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. * @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 * tensor has also been transposed with cpu::kernels::CpuGemmTranspose1xWKernel. Data type supported: Same as @p input. * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported. * @param[in] enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation * available which may introduce a drop of accuracy as well. Default is false * @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is not supported */ void configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info = WeightsInfo(), const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false, unsigned int num_groups = 1); /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMConvolutionLayer * * @param[in] input Source tensor info. 3 lower dimensions represent a single input [width, height, IFM], * while every optional dimension from 4 and above represent a batch of inputs. * Data types supported: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32. * @param[in] weights Weights tensor info. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. * Data type supported: QASYMM8/QASYMM8_SIGNED/QSYMM8_PER_CHANNEL/BFLOAT16/F16/F32. * @param[in] biases Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. * Data type supported: Should match @p input data type, except for input of QASYMM8/QASYMM8_SIGNED type where biases should be of S32 type. * @param[in] output Destination tensor info. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs. * Data types supported: Same as @p input. * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. * @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 * tensor has also been transposed with cpu::kernels::CpuGemmTranspose1xWKernel. Data type supported: Same as @p input. * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported. * @param[in] enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation * available which may introduce a drop of accuracy as well. Default is false * @param[in] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is not supported * * @return a status */ static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, const WeightsInfo &weights_info = WeightsInfo(), const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false, unsigned int num_groups = 1); /** Static function to check if there is an optimized version of * GEMM available for the input parameters. * * The method is intended to be used to find out the optimal * memory layout to be used for the weights tensor when running * variable weights execution. * * The user can query the database of optimised kernels in * arm_gemm by specifying one of the enumerations of * arm_compute::WeightFormat in the weight_format field of the input * parameter weights_info. In case of success, the method * writes the expected format in the output parameter * expected_weight_format. The expected_weight_format can than be * used in the configure method of the class for retrieving the * best optimal kernel. * * Use case one - query for a specific format: * * WeightInfo weights_info(..., arm_compute::WeightFormat::OHWIo4, ...); // Set the value of the input query. * if (NEGEMMConvolutionlayer::has_opt_impl(WeightFormat(), ...., weights_info, ...)) * { * auto conv = std::unique_ptr(); * conv->configure(..., weights_info, ...); // uses the same WeightFormat the user wanted originally, OHWYo4. * conv->run(...); * } * * Use case two - query for any format that would be optimal for the GEMM to execute: * * WeightInfo weights_info(..., arm_compute::WeightFormat::ANY, ...); // Set the value of the input query. * arm_compute::WeightFormat expected_wf; * if (NEGEMMConvolutionlayer::has_opt_impl(expected_wf, ...., weights_info, ...)) * { * auto conv = std::unique_ptr(); * // ... code to convert the layout of the weights tensor to the layout returned by has_opt_impl * WeightInfo new_weights_info(..., expected_wf, ...); // Set the value of the WeightFormat returned by has_opt_impl. * conv->configure(..., new_weights_info, ...); * conv->run(...); * } * * Notice that a GEMM configured with a WeightFormat other than * UNSPECIFIED will run GEMM with variable weights mode. * * @param[out] expected_weight_format The arm_compute::WeightFormat expected by the kernel. * @param[in] src Source tensor info. * @param[in] weights Weights tensor info. * @param[in] biases Biases tensor info. Shared biases supported. * @param[in] dst Destination tensor info. * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. * @param[in] weights_info (optional) Specifies additional configuration parameters for the weights of the GEMM computation. * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). * @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. * @param[in] enable_fast_math (Optional) Enable fast math computation. In case this flag were set, the function could dispatch the fastest implementation * * @return a Status */ static Status has_opt_impl(arm_compute::WeightFormat &expected_weight_format, const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const PadStrideInfo &conv_info, const WeightsInfo &weights_info = WeightsInfo(), const Size2D &dilation = Size2D(1U, 1U), const ActivationLayerInfo &act_info = ActivationLayerInfo(), bool enable_fast_math = false); // Inherited methods overridden: void run() override; void prepare() override; private: struct Impl; std::unique_ptr _impl; }; } // namespace arm_compute #endif /* ARM_COMPUTE_NEGEMMCONVOLUTIONLAYER_H */