| /* |
| * Copyright (c) 2017-2022 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/runtime/IFunction.h" |
| |
| #include "arm_compute/core/Types.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 <memory> |
| |
| 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<IMemoryManager> &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_gemm::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_gemm::WeightFormat::OHWIo4, ...); // Set the value of the input query. |
| * if (NEGEMMConvolutionlayer::has_opt_impl(WeightFormat(), ...., weights_info, ...)) |
| * { |
| * auto conv = std::unique_ptr<NEGEMMConvolutionlayer>(); |
| * 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_gemm::WeightFormat::ANY, ...); // Set the value of the input query. |
| * arm_gemm::WeightFormat expected_wf; |
| * if (NEGEMMConvolutionlayer::has_opt_impl(expected_wf, ...., weights_info, ...)) |
| * { |
| * auto conv = std::unique_ptr<NEGEMMConvolutionlayer>(); |
| * // ... 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_gemm::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> _impl; |
| }; |
| } // namespace arm_compute |
| #endif /* ARM_COMPUTE_NEGEMMCONVOLUTIONLAYER_H */ |