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964732fa8d
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/16020 Needs to go over more iterations. For conv, I think we need a high level interface that abstracts out low-level details of which code path will be taken (acc16, outlier-aware, depth-wise, group conv, ...) otherwise the client code will be complex as can be seen from DNNLOWP Conv ops. This will also help us to make interface more stable. Reviewed By: dskhudia, jianyuh Differential Revision: D13588996 fbshipit-source-id: 9afce9e441bcaf20437fcc2874fb9d4165a46bcb
46 lines
1.3 KiB
C++
46 lines
1.3 KiB
C++
#pragma once
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#include <memory>
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#include <fbgemm/Fbgemm.h>
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#include <fbgemm/src/FbgemmI8DepthwiseAvx2.h>
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#include "caffe2/quantization/server/dnnlowp.h"
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namespace caffe2 {
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/**
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* Packed weight matrix for DNNLOWP Int8FC operator
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*/
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struct Int8FCDNNLowPPackedWeightBlob {
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std::vector<dnnlowp::TensorQuantizationParams> qparams;
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std::shared_ptr<std::vector<std::int32_t>> column_offsets;
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// The original tensor before packing
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Tensor original_tensor{CPU};
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std::shared_ptr<std::vector<std::int32_t>> bias;
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// Only for 32-bit accumulation
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std::shared_ptr<fbgemm::PackBMatrix<std::int8_t>> W;
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// Only for 16-bit accumulation
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// Dense matrix holding common values
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std::shared_ptr<fbgemm::PackBMatrix<std::int8_t, std::int16_t>> W_acc16;
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// Sparse matrix holding outliers
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std::shared_ptr<fbgemm::CompressedSparseColumn> W_outlier;
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int nbits_in_non_outlier;
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};
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/**
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* Packed weight matrix for DNNLOWP Int8Conv operator
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*/
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struct Int8ConvDNNLowPPackedWeightBlob : public Int8FCDNNLowPPackedWeightBlob {
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// Only for 32-bit accumulation
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std::shared_ptr<fbgemm::Packed3x3ConvMatrix> W_depthwise_3x3;
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std::shared_ptr<fbgemm::Packed3x3x3ConvMatrix> W_depthwise_3x3x3;
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std::shared_ptr<fbgemm::PackWeightMatrixForGConv<std::int8_t>> W_gconv;
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};
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} // namespace caffe2
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