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https://github.com/saymrwulf/pytorch.git
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71ca600af9
Summary: Since caffe2 and torch have been consolidated, CAFFE2_API should be merged with TORCH_API. Addresses a TODO. Manually edited some references of the removed `CAFFE2_API`: * `CONTRIBUTING.md` * `caffe2/proto/CMakeLists.txt` * `cmake/ProtoBuf.cmake` * `c10/macros/Export.h` * `torch/csrc/WindowsTorchApiMacro.h` Pull Request resolved: https://github.com/pytorch/pytorch/pull/49496 Reviewed By: malfet, samestep Differential Revision: D25600726 Pulled By: janeyx99 fbshipit-source-id: 7e068d959e397ac183c097d7e9a9afeca5ddd782
160 lines
6.3 KiB
C++
160 lines
6.3 KiB
C++
#ifndef CAFFE2_UTILS_MATH_ELEMENTWISE_H_
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#define CAFFE2_UTILS_MATH_ELEMENTWISE_H_
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#include "caffe2/core/common.h"
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#include "caffe2/core/types.h"
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namespace caffe2 {
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namespace math {
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template <typename T, class Context>
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TORCH_API void Exp(int N, const T* X, T* Y, Context* context);
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template <typename T, class Context>
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TORCH_API void Log(int N, const T* X, T* Y, Context* context);
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template <typename T, class Context>
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TORCH_API void Log1p(int N, const T* X, T* Y, Context* context);
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template <typename T, class Context>
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TORCH_API void Sin(int N, const T* X, T* Y, Context* context);
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template <typename T, class Context>
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TORCH_API void Asin(int N, const T* X, T* Y, Context* context);
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template <typename T, class Context>
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TORCH_API void Cos(int N, const T* X, T* Y, Context* context);
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template <typename T, class Context>
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TORCH_API void Acos(int N, const T* X, T* Y, Context* context);
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template <typename T, class Context>
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TORCH_API void Tan(int N, const T* X, T* Y, Context* context);
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template <typename T, class Context>
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TORCH_API void Atan(int N, const T* X, T* Y, Context* context);
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template <typename T, class Context>
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TORCH_API void Sinh(int N, const T* X, T* Y, Context* context);
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template <typename T, class Context>
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TORCH_API void Cosh(int N, const T* X, T* Y, Context* context);
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template <typename T, class Context>
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TORCH_API void SinCos(int N, const T* X, T* S, T* C, Context* context);
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template <typename T, class Context>
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TORCH_API void Tanh(int N, const T* X, T* Y, Context* context);
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template <typename T, class Context>
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TORCH_API void Abs(int N, const T* X, T* Y, Context* context);
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template <typename T, class Context>
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TORCH_API void Sqr(int N, const T* X, T* Y, Context* context);
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template <typename T, class Context>
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TORCH_API void Sqrt(int N, const T* X, T* Y, Context* context);
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template <typename T, class Context>
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TORCH_API void Rsqrt(int N, const T* X, T* Y, Context* context);
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template <typename T, class Context>
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TORCH_API void Cube(int N, const T* X, T* Y, Context* context);
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template <typename T, class Context>
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TORCH_API void Cbrt(int N, const T* X, T* Y, Context* context);
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template <typename T, class Context>
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TORCH_API void Neg(int N, const T* X, T* Y, Context* context);
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template <typename T, class Context>
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TORCH_API void Sign(int N, const T* X, T* Y, Context* context);
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template <typename T, class Context>
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TORCH_API void Not(int N, const T* X, T* Y, Context* context);
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template <typename T, class Context>
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TORCH_API void Powx(int N, const T* A, const T b, T* Y, Context* context);
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template <typename T, class Context>
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TORCH_API void Inv(int N, const T* X, T* Y, Context* context);
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template <typename T, class Context>
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TORCH_API void Erf(int N, const T* X, T* Y, Context* context);
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template <typename T, class Context>
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TORCH_API void CdfNorm(int N, const T* X, T* Y, Context* context);
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template <typename T, class Context>
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TORCH_API void Set(std::int64_t N, T alpha, T* X, Context* context);
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template <typename TAlpha, typename TData, class Context>
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TORCH_API void
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Scale(std::int64_t N, TAlpha alpha, const TData* X, TData* Y, Context* context);
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// Different from the Scale function above, if alpha is passed in as a pointer,
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// we will assume that it lives on the Context device, for example on GPU.
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template <typename TAlpha, typename TData, class Context>
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TORCH_API void Scale(
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std::int64_t N,
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const TAlpha* alpha,
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const TData* X,
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TData* Y,
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Context* context);
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template <typename T, class Context>
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TORCH_API void Add(int N, const T* A, const T* B, T* C, Context* context);
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template <typename T, class Context>
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TORCH_API void Sub(int N, const T* A, const T* B, T* C, Context* context);
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template <typename T, class Context>
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TORCH_API void Mul(int N, const T* A, const T* B, T* C, Context* context);
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template <typename T, class Context>
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TORCH_API void Div(int N, const T* A, const T* B, T* C, Context* context);
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template <typename T, class Context>
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TORCH_API void Min(int N, const T* A, const T* B, T* C, Context* context);
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template <typename T, class Context>
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TORCH_API void Max(int N, const T* A, const T* B, T* C, Context* context);
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template <typename T, class Context>
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TORCH_API void And(int N, const T* A, const T* B, T* C, Context* context);
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template <typename T, class Context>
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TORCH_API void Or(int N, const T* A, const T* B, T* C, Context* context);
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template <typename T, class Context>
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TORCH_API void Xor(int N, const T* A, const T* B, T* C, Context* context);
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template <typename T, class Context>
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TORCH_API void
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BitwiseAnd(int N, const T* A, const T* B, T* C, Context* context);
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template <typename T, class Context>
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TORCH_API void
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BitwiseOr(int N, const T* A, const T* B, T* C, Context* context);
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template <typename T, class Context>
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TORCH_API void
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BitwiseXor(int N, const T* A, const T* B, T* C, Context* context);
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template <typename T, class Context>
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TORCH_API void EQ(int N, const T* A, const T* B, bool* C, Context* context);
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template <typename T, class Context>
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TORCH_API void NE(int N, const T* A, const T* B, bool* C, Context* context);
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template <typename T, class Context>
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TORCH_API void LT(int N, const T* A, const T* B, bool* C, Context* context);
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template <typename T, class Context>
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TORCH_API void LE(int N, const T* A, const T* B, bool* C, Context* context);
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template <typename T, class Context>
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TORCH_API void GT(int N, const T* A, const T* B, bool* C, Context* context);
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template <typename T, class Context>
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TORCH_API void GE(int N, const T* A, const T* B, bool* C, Context* context);
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template <typename TAlpha, typename TData, class Context>
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TORCH_API void
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Axpy(std::int64_t N, TAlpha alpha, const TData* X, TData* Y, Context* context);
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// Different from the Axpy function above, if alpha is passed in
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// as a pointer, we will assume that it lives on the Context device,
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// for example on GPU.
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template <typename TAlpha, typename TData, class Context>
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TORCH_API void Axpy(
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std::int64_t N,
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const TAlpha* alpha,
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const TData* X,
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TData* Y,
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Context* context);
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template <typename TAlpha, typename TData, class Context>
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TORCH_API void Axpby(
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std::int64_t N,
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TAlpha alpha,
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const TData* X,
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TAlpha beta,
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TData* Y,
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Context* context);
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template <typename TAlpha, typename TData, class Context>
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TORCH_API void Axpby(
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std::int64_t N,
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const TAlpha* alpha,
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const TData* X,
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const TAlpha* beta,
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TData* Y,
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Context* context);
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} // namespace math
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} // namespace caffe2
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#endif // CAFFE2_UTILS_MATH_ELEMENTWISE_H_
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