FM / math

/usr/local/lib64/python3.6/site-packages/torch/include/caffe2/utils/math/

/usr/local/lib64/python3.6/site-packages/torch/include/caffe2/utils/math

Name	Size	Mode	Actions
broadcast.h	555	0644	edit dl rm
elementwise.h	6455	0644	edit dl rm
half_utils.h	1329	0644	edit dl rm
reduce.h	2672	0644	edit dl rm
transpose.h	785	0644	edit dl rm
utils.h	4643	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/torch/include/caffe2/utils/math/elementwise.h (6455B)

#ifndef CAFFE2_UTILS_MATH_ELEMENTWISE_H_
#define CAFFE2_UTILS_MATH_ELEMENTWISE_H_

#include "caffe2/core/common.h"
#include "caffe2/core/types.h"

namespace caffe2 {
namespace math {

template <typename T, class Context>
TORCH_API void Exp(int N, const T* X, T* Y, Context* context);
template <typename T, class Context>
TORCH_API void Log(int N, const T* X, T* Y, Context* context);
template <typename T, class Context>
TORCH_API void Log1p(int N, const T* X, T* Y, Context* context);
template <typename T, class Context>
TORCH_API void Sin(int N, const T* X, T* Y, Context* context);
template <typename T, class Context>
TORCH_API void Asin(int N, const T* X, T* Y, Context* context);
template <typename T, class Context>
TORCH_API void Cos(int N, const T* X, T* Y, Context* context);
template <typename T, class Context>
TORCH_API void Acos(int N, const T* X, T* Y, Context* context);
template <typename T, class Context>
TORCH_API void Tan(int N, const T* X, T* Y, Context* context);
template <typename T, class Context>
TORCH_API void Atan(int N, const T* X, T* Y, Context* context);
template <typename T, class Context>
TORCH_API void Sinh(int N, const T* X, T* Y, Context* context);
template <typename T, class Context>
TORCH_API void Cosh(int N, const T* X, T* Y, Context* context);
template <typename T, class Context>
TORCH_API void SinCos(int N, const T* X, T* S, T* C, Context* context);
template <typename T, class Context>
TORCH_API void Tanh(int N, const T* X, T* Y, Context* context);
template <typename T, class Context>
TORCH_API void Abs(int N, const T* X, T* Y, Context* context);
template <typename T, class Context>
TORCH_API void Sqr(int N, const T* X, T* Y, Context* context);
template <typename T, class Context>
TORCH_API void Sqrt(int N, const T* X, T* Y, Context* context);
template <typename T, class Context>
TORCH_API void Rsqrt(int N, const T* X, T* Y, Context* context);
template <typename T, class Context>
TORCH_API void Cube(int N, const T* X, T* Y, Context* context);
template <typename T, class Context>
TORCH_API void Cbrt(int N, const T* X, T* Y, Context* context);
template <typename T, class Context>
TORCH_API void Neg(int N, const T* X, T* Y, Context* context);
template <typename T, class Context>
TORCH_API void Sign(int N, const T* X, T* Y, Context* context);
template <typename T, class Context>
TORCH_API void Not(int N, const T* X, T* Y, Context* context);
template <typename T, class Context>
TORCH_API void Powx(int N, const T* A, const T b, T* Y, Context* context);
template <typename T, class Context>
TORCH_API void Inv(int N, const T* X, T* Y, Context* context);
template <typename T, class Context>
TORCH_API void Erf(int N, const T* X, T* Y, Context* context);
template <typename T, class Context>
TORCH_API void CdfNorm(int N, const T* X, T* Y, Context* context);

template <typename T, class Context>
TORCH_API void Set(std::int64_t N, T alpha, T* X, Context* context);

template <typename TAlpha, typename TData, class Context>
TORCH_API void
Scale(std::int64_t N, TAlpha alpha, const TData* X, TData* Y, Context* context);

// Different from the Scale 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 TAlpha, typename TData, class Context>
TORCH_API void Scale(
    std::int64_t N,
    const TAlpha* alpha,
    const TData* X,
    TData* Y,
    Context* context);

template <typename T, class Context>
TORCH_API void Add(int N, const T* A, const T* B, T* C, Context* context);
template <typename T, class Context>
TORCH_API void Sub(int N, const T* A, const T* B, T* C, Context* context);
template <typename T, class Context>
TORCH_API void Mul(int N, const T* A, const T* B, T* C, Context* context);
template <typename T, class Context>
TORCH_API void Div(int N, const T* A, const T* B, T* C, Context* context);

template <typename T, class Context>
TORCH_API void Min(int N, const T* A, const T* B, T* C, Context* context);
template <typename T, class Context>
TORCH_API void Max(int N, const T* A, const T* B, T* C, Context* context);

template <typename T, class Context>
TORCH_API void And(int N, const T* A, const T* B, T* C, Context* context);
template <typename T, class Context>
TORCH_API void Or(int N, const T* A, const T* B, T* C, Context* context);
template <typename T, class Context>
TORCH_API void Xor(int N, const T* A, const T* B, T* C, Context* context);

template <typename T, class Context>
TORCH_API void
BitwiseAnd(int N, const T* A, const T* B, T* C, Context* context);
template <typename T, class Context>
TORCH_API void
BitwiseOr(int N, const T* A, const T* B, T* C, Context* context);
template <typename T, class Context>
TORCH_API void
BitwiseXor(int N, const T* A, const T* B, T* C, Context* context);

template <typename T, class Context>
TORCH_API void EQ(int N, const T* A, const T* B, bool* C, Context* context);
template <typename T, class Context>
TORCH_API void NE(int N, const T* A, const T* B, bool* C, Context* context);
template <typename T, class Context>
TORCH_API void LT(int N, const T* A, const T* B, bool* C, Context* context);
template <typename T, class Context>
TORCH_API void LE(int N, const T* A, const T* B, bool* C, Context* context);
template <typename T, class Context>
TORCH_API void GT(int N, const T* A, const T* B, bool* C, Context* context);
template <typename T, class Context>
TORCH_API void GE(int N, const T* A, const T* B, bool* C, Context* context);

template <typename TAlpha, typename TData, class Context>
TORCH_API void
Axpy(std::int64_t N, TAlpha alpha, const TData* X, TData* 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 TAlpha, typename TData, class Context>
TORCH_API void Axpy(
    std::int64_t N,
    const TAlpha* alpha,
    const TData* X,
    TData* Y,
    Context* context);

template <typename TAlpha, typename TData, class Context>
TORCH_API void Axpby(
    std::int64_t N,
    TAlpha alpha,
    const TData* X,
    TAlpha beta,
    TData* Y,
    Context* context);

template <typename TAlpha, typename TData, class Context>
TORCH_API void Axpby(
    std::int64_t N,
    const TAlpha* alpha,
    const TData* X,
    const TAlpha* beta,
    TData* Y,
    Context* context);

} // namespace math
} // namespace caffe2

#endif // CAFFE2_UTILS_MATH_ELEMENTWISE_H_

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