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Diffstat (limited to 'arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h')
| -rw-r--r-- | arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h | 351 |
1 files changed, 128 insertions, 223 deletions
diff --git a/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h b/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h index e89eae1d31..3e84c3e2cf 100644 --- a/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h +++ b/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2021 Arm Limited. + * Copyright (c) 2017-2023 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -24,147 +24,31 @@ #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/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 "arm_compute/runtime/NEON/functions/NEGEMM.h" -#include "arm_compute/runtime/NEON/functions/NEGEMMLowpMatrixMultiplyCore.h" -#include "arm_compute/runtime/NEON/functions/NEGEMMLowpOutputStage.h" -#include "arm_compute/runtime/NEON/functions/NEReshapeLayer.h" -#include "arm_compute/runtime/Tensor.h" #include <memory> namespace arm_compute { class ITensor; -class NECol2ImKernel; -class NEIm2ColKernel; -class NEWeightsReshapeKernel; - -/** Function to reshape the weights. This function calls the following kernel: - * -# @ref NEWeightsReshapeKernel - */ -class NEConvolutionLayerReshapeWeights : public IFunction -{ -public: - /** Constructor */ - NEConvolutionLayerReshapeWeights() noexcept; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - NEConvolutionLayerReshapeWeights(const NEConvolutionLayerReshapeWeights &) = delete; - /** Prevent instances of this class from being moved (As this class contains non movable objects) */ - NEConvolutionLayerReshapeWeights(NEConvolutionLayerReshapeWeights &&) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - NEConvolutionLayerReshapeWeights &operator=(const NEConvolutionLayerReshapeWeights &) = delete; - /** Prevent instances of this class from being moved (As this class contains non movable objects) */ - NEConvolutionLayerReshapeWeights &operator=(NEConvolutionLayerReshapeWeights &&) = delete; - /** Default destructor */ - ~NEConvolutionLayerReshapeWeights(); - /** Set the input and output tensors. - * - * @param[in] weights Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. - * Data type supported: All. - * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. - * Data type supported: same as @p weights. - * @warning Appending biases to weights reshaped matrix is not supported for quantized asymmetric types. - * @param[out] output Destination tensor. Data types supported: same as @p weights. - */ - void configure(const ITensor *weights, const ITensor *biases, ITensor *output); - /** Static function to check if given info will lead to a valid configuration of @ref NEConvolutionLayerReshapeWeights - * - * @param[in] weights Weights tensor info. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. - * Data type supported: All. - * @param[in] biases Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. - * Data type supported: same as @p weights. - * @warning Appending biases to weights reshaped matrix is not supported for quantized asymmetric types. - * @param[in] output Destination tensor. Data types supported: same as @p weights. - * - * @return an error status - */ - static Status validate(const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output); - - // Inherited methods overridden: - void run() override; - -private: - std::unique_ptr<NEWeightsReshapeKernel> _weights_reshape_kernel; -}; - -namespace weights_transformations -{ -/** Basic function to manage the reshape weights generated from @ref NEConvolutionLayerReshapeWeights */ -class NEConvolutionLayerReshapeWeightsTransform : public ITransformWeights -{ -public: - /** Constructor */ - NEConvolutionLayerReshapeWeightsTransform() = default; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - NEConvolutionLayerReshapeWeightsTransform(const NEConvolutionLayerReshapeWeightsTransform &) = delete; - /** Prevent instances of this class from being copied (As this class contains pointers) */ - NEConvolutionLayerReshapeWeightsTransform &operator=(const NEConvolutionLayerReshapeWeightsTransform &) = delete; - /** Prevent instances of this class from being moved (As this class contains non movable objects) */ - NEConvolutionLayerReshapeWeightsTransform(NEConvolutionLayerReshapeWeightsTransform &&) = delete; - /** Prevent instances of this class from being moved (As this class contains non movable objects) */ - NEConvolutionLayerReshapeWeightsTransform &operator=(NEConvolutionLayerReshapeWeightsTransform &&) = delete; - /** Default destructor */ - ~NEConvolutionLayerReshapeWeightsTransform() = default; - void configure(const ITensor *input, const ITensor *biases) - { - _bias_bit = (biases != nullptr) ? 1 : 0; - _func.configure(input, biases, &_output); - } - - void run() override - { - _output.allocator()->allocate(); - _func.run(); - _reshape_run = true; - } - - ITensor *get_weights() override - { - return &_output; - } - - void release() override - { - _output.allocator()->free(); - } - - uint32_t uid() override - { - return ((0x8) | (_bias_bit << 7)); - } - - bool is_reshape_run() - { - return _reshape_run; - } - -private: - Tensor _output{}; - NEConvolutionLayerReshapeWeights _func{}; - int32_t _bias_bit{ 0 }; -}; -} // namespace weights_transformations +class ITensorInfo; /** Basic function to compute the convolution layer. This function calls the following kernels/functions: * - * -# @ref NEIm2ColKernel - * -# @ref NEGEMM (if the data type is BFLOAT16/FP16/FP32) - * -# @ref NEGEMMLowpMatrixMultiplyCore (if the data type is QASYMM8/QASYMM8_SIGNED) - * -# @ref NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint (if the data type is QASYMM8/QASYMM8_SIGNED) - * -# @ref NEArithmeticAddition (if biases != nullptr and we have a 1x1 convolution with the NHWC data layout) - * -# @ref NECol2ImKernel (if NCHW data layout) + * -# @ref cpu::CpuGemmConv2d * */ class NEGEMMConvolutionLayer : public IFunction { public: /** Constructor */ - NEGEMMConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager = nullptr, IWeightsManager *weights_manager = nullptr); + 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) */ @@ -192,118 +76,139 @@ public: * |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 NEGEMMTranspose1xWKernel. 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] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is not supported + * @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(), unsigned int num_groups = 1); + 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 NEGEMMTranspose1xWKernel. 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] num_groups (Optional) Number of groups when performing a grouped convolution. num_groups != 1 is not supported + * @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(), unsigned int num_groups = 1); - - // Inherited methods overridden: - void run() override; - void prepare() override; - -private: - /** Configures the appropriate matrix multiply routine + 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. * - * @param[in] input Input tensor. Data types supported: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32. - * @param[in] weights Weights tensor. 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 Output tensor. Data types supported: Same as @p input, - * except for input of QASYMM8/QASYMM8_SIGNED type where output should be of S32 type. - * @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] gemm_3d_depth (Optional) Depth of GEMM 3D (Defaults to 1) - */ - void configure_mm(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const ActivationLayerInfo &act_info = ActivationLayerInfo(), int gemm_3d_depth = 1); - /** Static function to check if given info will lead to a valid configuration of @ref NEGEMMConvolutionLayer matrix multiply routines + * 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. * - * @param[in] input Input tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32. - * @param[in] weights Weights tensor info. 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 Output tensor info. Data types supported: Same as @p input, - * except for input of QASYMM8/QASYMM8_SIGNED type where output should be of S32 type. - * @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] gemm_3d_depth (Optional) Depth of GEMM 3D (Defaults to 1) - * @param[in] skip_im2col (Optional) Flag which specifies if im2col has to be skipped. i.e. 1x1 convolution with NHWC data layout. (Default to false) + * 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. * - * @return a status - */ - static Status validate_mm(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const ActivationLayerInfo &act_info = ActivationLayerInfo(), - int gemm_3d_depth = 1, bool skip_im2col = false); - /** Static function to check if GEMM3D is supported in @ref NEGEMM or in @ref NEGEMMLowpMatrixMultiplyCore + * Use case one - query for a specific format: * - * @param[in] input_info Input tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32. - * @param[in] weights_info Weights tensor info. Data types supported: QASYMM8/QASYMM8_SIGNED/BFLOAT16/F16/F32. - * @param[in] act_info Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU supported. - * @param[in] gemm_3d_depth Depth of GEMM 3D - * @param[in] skip_im2col Flag which specifies if im2col has to be skipped. i.e. 1x1 convolution with NHWC data layout + * 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<NEGEMMConvolutionlayer>(); + * conv->configure(..., weights_info, ...); // uses the same WeightFormat the user wanted originally, OHWYo4. + * conv->run(...); + * } * - * @return a status + * 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<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 validate_gemm3d(const ITensorInfo *input_info, const ITensorInfo *weights_info, const ActivationLayerInfo &act_info, int gemm_3d_depth, bool skip_im2col); + 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: - MemoryGroup _memory_group; - IWeightsManager *_weights_manager; - NEConvolutionLayerReshapeWeights _reshape_weights; - weights_transformations::NEConvolutionLayerReshapeWeightsTransform _reshape_weights_managed; - std::unique_ptr<NEIm2ColKernel> _im2col_kernel; - NEGEMM _mm_gemm; - NEGEMMLowpMatrixMultiplyCore _mm_gemmlowp; - std::unique_ptr<NECol2ImKernel> _col2im_kernel; - NEReshapeLayer _reshape_layer; - - const ITensor *_original_weights; - const ITensor *_original_output; - - Tensor _im2col_output; - Tensor _weights_reshaped; - Tensor _gemm_output; - Tensor _gemm_output_3d; - Tensor _tmp_output; - - DataLayout _data_layout; - - bool _skip_im2col; - bool _skip_col2im; - bool _is_quantized; - bool _is_prepared; + struct Impl; + std::unique_ptr<Impl> _impl; }; } // namespace arm_compute -#endif /* ARM_COMPUTE_NECONVOLUTIONGEMMLAYER_H */ +#endif /* ARM_COMPUTE_NEGEMMCONVOLUTIONLAYER_H */ |