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483f748dd5
This PR enables `-Winconsistent-missing-destructor-override` and `-Winconsistent-missing-override` and fixes violations. <!-- copilot:summary --> ### <samp>🤖 Generated by Copilot at 47e904e</samp> This pull request updates the code of various classes and operators in the `caffe2` and `aten` subdirectories to use the `override` specifier instead of the `virtual` keyword for destructors and other virtual functions that override a base class function. This improves the code readability, quality, and consistency with C++ best practices. It also modifies the `./CMakeLists.txt` file to enable warnings for these specifiers, but disable errors. Pull Request resolved: https://github.com/pytorch/pytorch/pull/104032 Approved by: https://github.com/malfet
138 lines
4.0 KiB
C++
138 lines
4.0 KiB
C++
#pragma once
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#include <fbgemm/Fbgemm.h>
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#include "caffe2/operators/conv_op.h"
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#include "caffe2/operators/conv_pool_op_base.h"
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#include "caffe2/quantization/server/caffe2_dnnlowp_utils.h"
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#include "caffe2/quantization/server/conv_pool_dnnlowp_op_base.h"
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#include "caffe2/quantization/server/dnnlowp.h"
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#include "caffe2/quantization/server/op_wrapper.h"
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namespace caffe2 {
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using ConvFp32Op = ConvOp<float, CPUContext>;
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// Convolutional layer computed in integer with quantization
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template <typename T, bool ReluFused = false>
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class ConvDNNLowPOp : public ConvPoolDNNLowPOpBase<T, ConvFp32Op> {
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public:
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USE_CONV_POOL_BASE_FUNCTIONS(CPUContext);
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USE_CONV_POOL_DNNLOWP_OPERATOR_BASE_FUNCTIONS(T, ConvFp32Op);
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ConvDNNLowPOp(const OperatorDef& operator_def, Workspace* ws);
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~ConvDNNLowPOp() override;
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protected:
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bool RunOnDeviceWithOrderNCHW() override;
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bool RunOnDeviceWithOrderNHWC() override;
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bool GetQuantizationParameters_();
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/**
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* @return true if convolution is basically a GEMM point-wise (e.g., 1x1)
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* convolution, no stride/dilation/pad
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*/
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bool IsConvGEMM_() const;
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bool NoIm2ColNHWC_();
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int KernelDim_();
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const T* Im2ColNHWC_(Tensor* col_buffer);
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dnnlowp::TensorQuantizationParams& FilterQuantizationParams(int group_id);
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dnnlowp::RequantizationParams& RequantizationParams(int group_id);
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static void PartitionGroupedNHWCConv_(
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int* group_begin,
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int* group_end,
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int* i_begin,
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int* i_end,
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int num_groups,
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int m,
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int nthreads,
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int thread_id);
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virtual bool Acc16() const {
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return false;
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}
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Tensor col_buffer_{CPU};
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Tensor img_shape_device_{CPU};
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Tensor col_buffer_shape_device_{CPU};
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// Input: X, W, b
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// Output: Y
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INPUT_TAGS(INPUT, FILTER, BIAS);
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// x86 only provides SIMD instructions that multiply a signed integer with an
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// unsigned integer. We use signed for weights.
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using T_signed = typename std::make_signed<T>::type;
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// used in slow path for T != uint8_t
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std::vector<T_signed> W_quantized_;
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// pre-computed biases and offsets
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std::shared_ptr<std::vector<std::int32_t>> column_offsets_;
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std::vector<std::int32_t> row_offsets_;
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const std::int32_t* b_quantized_data_{nullptr};
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std::vector<std::uint8_t> X_pack_buf_;
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void RunOnDeviceEpilogueNCHW_(
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const T* col_buffer_data,
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std::int32_t* Y_int32,
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T* Y_data,
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std::size_t i_offset,
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int group_id);
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void RunOnDeviceEpilogueNHWC_(
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const T* col_buffer_data,
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std::int32_t* Y_int32);
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std::vector<std::int32_t> Y_int32_;
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std::vector<dnnlowp::TensorQuantizationParams> filter_qparams_;
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std::vector<std::int32_t> filter_zero_points_;
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std::vector<float> requantization_multipliers_;
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bool quantize_groupwise_;
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private:
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void QuantizeWeight_();
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void PreComputeRowColumnOffsets_();
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void QuantizeBias_();
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bool TakeDepthWise3x3FastPath_();
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bool TakeDepthWise3x3x3FastPath_();
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bool TakeGConvFastPath_();
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template <typename PackAMatrix, fbgemm::QuantizationGranularity Q_GRAN>
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void DispatchFBGEMM_(
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PackAMatrix& packA,
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vector<std::int32_t>* Y_int32,
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uint8_t* Y_uint8_data);
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void ConvNHWCCore_(const T* col_buffer_data, vector<std::int32_t>* Y_int32);
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fbgemm::conv_param_t<> GetConvParam_();
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fbgemm::conv_param_t<3> GetConv3DParam_();
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std::vector<dnnlowp::RequantizationParams> requantization_params_;
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// used in fast path for T == uint8_t
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std::shared_ptr<fbgemm::PackBMatrix<std::int8_t>> Wq_packed_;
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// For depthwise conv
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std::shared_ptr<fbgemm::PackedDepthWiseConvMatrix> Wq_depthwise_packed_;
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// For small gconv
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std::shared_ptr<fbgemm::PackWeightMatrixForGConv<std::int8_t>>
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Wq_gconv_packed_;
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std::shared_ptr<
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fbgemm::PackWeightMatrixForGConv<std::int8_t, std::int32_t, 3>>
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Wq_gconv3d_packed_;
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// pre-computed biases and offsets
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std::shared_ptr<std::vector<std::int32_t>> b_quantized_;
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float in_qparams_scale_old_{0};
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std::int32_t in_qparams_zero_point_old_{0};
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}; // class ConvDNNLowPOp
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} // namespace caffe2
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