math.h

#ifndef CAFFE2_UTILS_MATH_H_
#define CAFFE2_UTILS_MATH_H_
// This is a simple translation from the old Caffe math interfaces. We aim to
// still keep it simple, so all platforms would be able to support it fairly
// easily.

// We include the cblas header here so that we can obtain the macros from cblas.
extern "C" {
#include "caffe2/utils/cblas.h"
}

#ifdef CAFFE2_USE_ACCELERATE
#include <Accelerate/Accelerate.h>
#endif // CAFFE2_USE_ACCELERATE

#include "caffe2/core/common.h"
#include "caffe2/core/types.h"
#include "caffe2/utils/math/broadcast.h"
#include "caffe2/utils/math/elementwise.h"
#include "caffe2/utils/math/reduce.h"
#include "caffe2/utils/math/transpose.h"
#include "caffe2/utils/math/utils.h"

namespace caffe2 {

// TODO: Change dims related arguments to int64_t?
class Tensor;

// An empty class as a placeholder for a math function that has no specific
// engine specified.
class CAFFE2_API DefaultEngine {};

namespace math {

#define C10_DECLARE_COMPARE_OP(Comp)                               \
  template <typename T, class Context, bool kBroadcast1st = false> \
  void Rowwise##Comp(                                              \
      const int rows,                                              \
      const int cols,                                              \
      const T* A,                                                  \
      const T* B,                                                  \
      bool* C,                                                     \
      Context* context);                                           \
                                                                   \
  template <typename T, class Context, bool kBroadcast1st = false> \
  void Colwise##Comp(                                              \
      const int rows,                                              \
      const int cols,                                              \
      const T* A,                                                  \
      const T* B,                                                  \
      bool* C,                                                     \
      Context* context);                                           \
                                                                   \
  template <typename T, class Context>                             \
  void Comp(                                                       \
      const int A_ndim,                                            \
      const int* A_dims,                                           \
      const int B_ndim,                                            \
      const int* B_dims,                                           \
      const T* A,                                                  \
      const T* B,                                                  \
      bool* C,                                                     \
      Context* context);

C10_DECLARE_COMPARE_OP(EQ)
C10_DECLARE_COMPARE_OP(NE)
C10_DECLARE_COMPARE_OP(LT)
C10_DECLARE_COMPARE_OP(LE)
C10_DECLARE_COMPARE_OP(GT)
C10_DECLARE_COMPARE_OP(GE)

#undef C10_DECLARE_COMPARE_OP

#define C10_DECLARE_BINARY_OP(Func)                                \
  template <typename T, class Context, bool kBroadcast1st = false> \
  void Rowwise##Func(                                              \
      const int rows,                                              \
      const int cols,                                              \
      const T* A,                                                  \
      const T* B,                                                  \
      T* C,                                                        \
      Context* context);                                           \
                                                                   \
  template <typename T, class Context, bool kBroadcast1st = false> \
  void Colwise##Func(                                              \
      const int rows,                                              \
      const int cols,                                              \
      const T* A,                                                  \
      const T* B,                                                  \
      T* C,                                                        \
      Context* context);                                           \
                                                                   \
  template <typename T, class Context>                             \
  void Func(                                                       \
      const int A_ndim,                                            \
      const int* A_dims,                                           \
      const int B_ndim,                                            \
      const int* B_dims,                                           \
      const T* A,                                                  \
      const T* B,                                                  \
      T* C,                                                        \
      Context* context);

C10_DECLARE_BINARY_OP(Add)
C10_DECLARE_BINARY_OP(Sub)
C10_DECLARE_BINARY_OP(Mul)
C10_DECLARE_BINARY_OP(Div)

C10_DECLARE_BINARY_OP(And)
C10_DECLARE_BINARY_OP(Or)
C10_DECLARE_BINARY_OP(Xor)

C10_DECLARE_BINARY_OP(BitwiseAnd)
C10_DECLARE_BINARY_OP(BitwiseOr)
C10_DECLARE_BINARY_OP(BitwiseXor)

#undef C10_DECLARE_BINARY_OP

// Broadcasts X with X_dims to Y with Y_dims.
template <typename T, class Context>
CAFFE2_API void Broadcast(
    const int X_ndim,
    const int* X_dims,
    const int Y_ndim,
    const int* Y_dims,
    const T alpha,
    const T* X,
    T* Y,
    Context* context);

// Computes inv_std from variance.
template <typename T, class Context>
CAFFE2_API void InvStd(
    const int N,
    const T epsilon,
    const T* var,
    T* inv_std,
    Context* context);

// Adds batch sub-tensors elementwise to output. Stripe is the stripe length
// and N is the number of elements to add (size of Y).
template <typename T, class Context>
CAFFE2_API void AddStripedBatch(
    const int N,
    const T* first,
    T* y,
    const int stripe,
    const int batch,
    Context* context);

// Compute the row-wise max of a N*D matrix X, and write it to a N
// dimensional vector y.
template <typename T, class Context>
CAFFE2_API void
RowwiseMax(const int N, const int D, const T* x, T* y, Context* context);

// Compute the column-wise max of a N*D matrix X, and write it to a D
// dimensional vector y.
template <typename T, class Context>
CAFFE2_API void
ColwiseMax(const int N, const int D, const T* x, T* y, Context* context);

// Elemwise maximum of vector x and scalar alpha. y[i] = max(x[i], alpha)
template <typename T, class Context>
CAFFE2_API void
Maximum(const int N, const float alpha, const T* x, T* y, Context* context);

// Decaf gemm provides a simpler interface to the gemm functions, with the
// limitation that the data has to be contiguous in memory.
template <typename T, class Context, class Engine = DefaultEngine>
CAFFE2_API void Gemm(
    const CBLAS_TRANSPOSE trans_A,
    const CBLAS_TRANSPOSE trans_B,
    const int M,
    const int N,
    const int K,
    const float alpha,
    const T* A,
    const T* B,
    const float beta,
    T* C,
    Context* context,
    TensorProto::DataType math_type = TensorProto_DataType_FLOAT);

// We also provide a gemm that has explicit lda, ldb and ldc specified.
// In most cases you probably want to use the function above, though.
template <typename T, class Context, class Engine = DefaultEngine>
CAFFE2_API void GemmEx(
    const CBLAS_TRANSPOSE trans_A,
    const CBLAS_TRANSPOSE trans_B,
    const int M,
    const int N,
    const int K,
    const T alpha,
    const T* A,
    const int lda,
    const T* B,
    const int ldb,
    const T beta,
    T* C,
    const int ldc,
    Context* context);

// GemmBatched provides a simple abstraction into library routines
template <typename T, class Context, class Engine = DefaultEngine>
CAFFE2_API void GemmBatched(
    const CBLAS_TRANSPOSE trans_A,
    const CBLAS_TRANSPOSE trans_B,
    const int batch_size,
    const int M,
    const int N,
    const int K,
    const float alpha,
    const T** A,
    const T** B,
    const float beta,
    T** C,
    Context* context,
    TensorProto::DataType math_type = TensorProto_DataType_FLOAT);

template <typename T, class Context, class Engine = DefaultEngine>
CAFFE2_API void GemmStridedBatched(
    const CBLAS_TRANSPOSE trans_A,
    const CBLAS_TRANSPOSE trans_B,
    const int batch_size,
    const int M,
    const int N,
    const int K,
    const float alpha,
    const T* A,
    const int A_stride,
    const T* B,
    const int B_stride,
    const float beta,
    T* C,
    const int C_stride,
    Context* context,
    TensorProto::DataType math_type = TensorProto_DataType_FLOAT);

// Gemv always takes in a M*N matrix A, and depending on whether we set TransA
// to Trans, the output is:
// CblasNoTrans: x is an N dim vector and y is an M dim vector.
// CblasTrans:   x is an M dim vector and y is an N dim vector.
template <typename T, class Context, class Engine = DefaultEngine>
CAFFE2_API void Gemv(
    const CBLAS_TRANSPOSE trans_A,
    const int M,
    const int N,
    const float alpha,
    const T* A,
    const T* x,
    const float beta,
    T* y,
    Context* context,
    TensorProto::DataType math_type = TensorProto_DataType_FLOAT);

template <typename T, class Context>
CAFFE2_API void
RandUniform(const size_t n, const T a, const T b, T* r, Context* context);

// Generate n values that sum up to a fixed sum
// and subject to a restriction a <= x <= b for each x generated
template <typename T, class Context>
CAFFE2_API void RandFixedSum(
    const size_t n,
    const T a,
    const T b,
    const T sum,
    T* r,
    Context* context);

template <typename T, class Context>
CAFFE2_API void RandUniformUnique(
    const size_t n,
    const T a,
    const T b,
    T* r,
    const size_t m,
    const T* avoid,
    Context* context);

// Generate n values from synthetic data distribution,
// define by unique accesses and stack distances
template <typename T, class Context>
CAFFE2_API void
RandSyntheticData(const size_t n, const T a, const T b, T* r, Context* context);

template <typename T, class Context>
CAFFE2_API void
RandGaussian(const size_t n, const T mean, const T std, T* r, Context* context);

// Dot matrix of vector a and b, and writes the result to a single value y.
template <typename T, class Context>
CAFFE2_API void
Dot(const int N, const T* a, const T* b, T* y, Context* context);

// Sum of vector x, and writes the result to a single value y.
template <typename T, class Context>
CAFFE2_API void Sum(
    const int N,
    const T* x,
    T* y,
    Context* context,
    Tensor* scratch_ptr = nullptr);

// Sum of squares of vector x, and writes the result to a single value y.
template <typename T, class Context>
CAFFE2_API void SumSqr(
    const int N,
    const T* x,
    T* y,
    Context* context,
    Tensor* scratch_ptr = nullptr);

// Select does index selection of the rows a N*D matrix x, and gives the N
// dimensional vector y that contains the selected data.
template <typename T, class Context>
CAFFE2_API void Select(
    const int N,
    const int D,
    const T* x,
    const int* idx,
    T* y,
    Context* context);

template <typename T, class Context>
CAFFE2_API void
Axpy(const int N, const float alpha, const T* x, T* y, Context* context);

// Different from the Axpy function above, if alpha is passed in
// as a pointer, we will assume that it lives on the Context device,
// for example on GPU.
template <typename T, class Context>
CAFFE2_API void
Axpy(const int N, const float* alpha, const T* x, T* y, Context* context);

template <typename TCoeff, typename TData, class Context>
CAFFE2_API void Axpby(
    const int N,
    const TCoeff alpha,
    const TData* x,
    const TCoeff b,
    TData* y,
    Context* context);

template <typename TCoeff, typename TData, class Context>
CAFFE2_API void Axpby(
    const int N,
    const TCoeff* alpha,
    const TData* x,
    const TCoeff* b,
    TData* y,
    Context* context);

// groups must be 1 for GPU
// For NHWC order with groups > 1, the result will be layout in
// NHW G RS C/G order to make data within the same group to be contiguous.
// For NCHW order, groups doesn't make any difference because we're doing Im2Col
// for each N and C is the slowest moving dimension among CHW.
template <typename T, class Context, StorageOrder kOrder>
CAFFE2_API void Im2Col(
    const int channels,
    const int height,
    const int width,
    const int kernel_h,
    const int kernel_w,
    const int dilation_h,
    const int dilation_w,
    const int pad_t,
    const int pad_l,
    const int pad_b,
    const int pad_r,
    const int stride_h,
    const int stride_w,
    const T* img_data,
    T* col_data,
    Context* context,
    const int groups = 1);

// groups must be 1 for GPU
template <typename T, class Context, StorageOrder kOrder>
CAFFE2_API void Im2ColNd(
    const int N,
    const int img_size,
    const int col_size,
    const int* img_shape,
    const int* col_shape,
    const int* kernel_shape,
    const int* stride,
    const int* dilation,
    const int* pad,
    const T* img_data,
    T* col_data,
    Context* context,
    const int groups = 1);

// groups must be 1 for GPU
// For NHWC order with groups > 1, the result will be layout in
// NHW G RS C/G order to make data within the same group to be contiguous.
// For NCHW order, groups doesn't make any difference because we're doing Im2Col
// for each N and C is the slowest moving dimension among CHW.
template <typename T, class Context, StorageOrder kOrder>
CAFFE2_API void Col2Im(
    const int channels,
    const int height,
    const int width,
    const int patch_h,
    const int patch_w,
    const int dilation_h,
    const int dilation_w,
    const int pad_t,
    const int pad_l,
    const int pad_b,
    const int pad_r,
    const int stride_h,
    const int stride_w,
    const T* col_data,
    T* img_data,
    Context* context,
    const int groups = 1);

// groups must be 1 for GPU
// For NHWC order with groups > 1, the result will be layout in
// NHW G RS C/G order to make data within the same group to be contiguous.
// For NCHW order, groups doesn't make any difference because we're doing Im2Col
// for each N and C is the slowest moving dimension among CHW.
template <typename T, class Context, StorageOrder kOrder>
CAFFE2_API void Col2ImNd(
    const int N,
    const int img_size,
    const int col_size,
    const int* img_shape,
    const int* col_shape,
    const int* kernel_shape,
    const int* stride,
    const int* dilation,
    const int* pad,
    const T* col_data,
    T* img_data,
    Context* context,
    const int groups = 1);

// Applies a per-channel bias value to each channel of the input
// image. image_size is H * W
template <typename T, class Context>
CAFFE2_API void BiasCHW(
    const T* bias,
    const T* bias_multiplier,
    const int bias_channels,
    const int image_size,
    T* image,
    Context* context);

template <class Context>
CAFFE2_API void CopyMatrix(
    const size_t item_size,
    const int M,
    const int N,
    const void* A,
    const int lda,
    void* B,
    const int ldb,
    Context* context,
    TypeMeta::Copy copy = nullptr);

template <typename T, class Context>
CAFFE2_API void CopyMatrix(
    const int M,
    const int N,
    const T* A,
    const int lda,
    T* B,
    const int ldb,
    Context* context);

template <typename T, class Context>
CAFFE2_API void CopyMatrix(
    const int M,
    const int N,
    const T* A,
    const int A_outer_stride,
    const int A_inner_stride,
    T* B,
    const int B_outer_stride,
    const int B_inner_stride,
    Context* context);

template <typename T, class Context>
CAFFE2_API void CopyVector(const int N, const T* A, T* B, Context* context);


} // namespace math
} // namespace caffe2

#include "caffe2/utils/math-detail.h"
#endif // CAFFE2_UTILS_MATH_H_