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#pragma once
#include "caffe2/core/operator.h"
#include "caffe2/core/tensor.h"
namespace caffe2 {
constexpr size_t k2b1bXBits = 2;
struct ConvArgs {
int stride_w{1};
int stride_h{1};
int pad_l{0};
int pad_t{0};
int pad_b{0};
int pad_r{0};
};
using ParallelFor = std::function<void(size_t, std::function<void(size_t)>)>;
struct QConvState {
std::vector<std::unique_ptr<TensorCPU>> XQs;
std::vector<std::unique_ptr<TensorCPU>> YQs;
std::unique_ptr<TensorCPU> WQ;
// architecture-dependent whether packing is used.
std::unique_ptr<TensorCPU> WQPacked;
std::unique_ptr<TensorCPU> WQN;
std::unique_ptr<TensorCPU> WQL1Norm;
// Useful for e.g. incomplete tiles
std::unique_ptr<TensorCPU> scratch;
std::unique_ptr<TensorCPU> scratchColBuffer;
std::unique_ptr<TensorCPU> bias;
ParallelFor parallelFor{nullptr};
};
void uniformQuantize2b1b(const TensorCPU& X,
const std::vector<std::unique_ptr<TensorCPU>>& XQ,
float offset,
float inter_center_distance);
void qpad_zero(const ConvArgs& args, const TensorCPU& X, TensorCPU* Y);
inline size_t divRoundUp(size_t x, size_t d) { return (x + d - 1) / d; }
void signQuantize(const TensorCPU& X, TensorCPU* XQ);
void filterNormalization11(const TensorCPU& WQ, TensorCPU* WQN);
void filterNormalizationL1(const TensorCPU& W, TensorCPU* WL1);
std::unique_ptr<QConvState> create2b1bConvState(Workspace* ws,
const TensorCPU& W,
const TensorCPU* b);
void run2b1bConvGeneric(QConvState* state, const ConvArgs& args, const TensorCPU& X, TensorCPU* Y);
void qconv(
const ConvArgs& args, const TensorCPU& X, const TensorCPU& W, const TensorCPU* b, TensorCPU* Y);
void qim2col(const ConvArgs& args, const TensorCPU& XQ, const TensorCPU& WQ, TensorCPU* XQcol);
void run2b1bUnification(QConvState* state,
size_t N,
size_t C,
const float* WQNVdata,
const float* YQs0Vdata,
const float* YQs1Vdata,
size_t YQstride,
float* Ydata,
size_t Ystride,
const float* bias);
} // namespace caffe2
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