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/reduce.h (2672B)

#ifndef CAFFE2_UTILS_MATH_REDUCE_H_
#define CAFFE2_UTILS_MATH_REDUCE_H_

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

namespace caffe2 {

class Tensor;

namespace math {

template <typename T, class Context>
TORCH_API void
ReduceMin(const int N, const T* X, T* y, Tensor* scratch_ptr, Context* context);

template <typename T, class Context>
TORCH_API void
ReduceMax(const int N, const T* X, T* y, Tensor* scratch_ptr, Context* context);

// In all of the reduce functions, X_dims and Y_dims should have ndim elements.
// Each dimension of Y_dims must match the corresponding dimension of X_dims or
// must be equal to 1. The dimensions equal to 1 indicate the dimensions of X to
// be reduced.

// Y = alpha * ReduceMin(X)
template <typename T, class Context>
TORCH_API void ReduceMin(
    const int ndim,
    const int* X_dims,
    const int* Y_dims,
    const T alpha,
    const T* X,
    T* Y,
    Context* context,
    bool allow_broadcast_fastpath=false);

// Y = alpha * ReduceMax(X)
template <typename T, class Context>
TORCH_API void ReduceMax(
    const int ndim,
    const int* X_dims,
    const int* Y_dims,
    const T alpha,
    const T* X,
    T* Y,
    Context* context,
    bool allow_broadcast_fastpath=false);

// Y = alpha * ReduceSum(X)
template <typename T, class Context>
TORCH_API void ReduceSum(
    const int ndim,
    const int* X_dims,
    const int* Y_dims,
    const T alpha,
    const T* X,
    T* Y,
    Context* context,
    bool allow_broadcast_fastpath=false);

// Y = alpha * ReduceMean(X)
template <typename T, class Context>
TORCH_API void ReduceMean(
    const int ndim,
    const int* X_dims,
    const int* Y_dims,
    const T alpha,
    const T* X,
    T* Y,
    Context* context,
    bool allow_broadcast_fastpath=false);

// Y = alpha * ReduceL1(X)
template <typename T, class Context>
TORCH_API void ReduceL1(
    const int ndim,
    const int* X_dims,
    const int* Y_dims,
    const T alpha,
    const T* X,
    T* Y,
    Context* context,
    bool allow_broadcast_fastpath=false);

// Y = alpha * ReduceL2(X)
template <typename T, class Context>
TORCH_API void ReduceL2(
    const int ndim,
    const int* X_dims,
    const int* Y_dims,
    const T alpha,
    const T* X,
    T* Y,
    Context* context,
    bool allow_broadcast_fastpath=false);

// Computes mean and variance over axes.
template <typename T, class Context>
TORCH_API void Moments(
    const int ndims,
    const int* X_dims,
    const int* Y_dims,
    const T* X,
    T* mean,
    T* var,
    Context* context,
    bool allow_broadcast_fastpath=false);

} // namespace math

} // namespace caffe2

#endif // CAFFE2_UTILS_MATH_REDUCE_H_

#ifndef CAFFE2_UTILS_MATH_REDUCE_H_ #define CAFFE2_UTILS_MATH_REDUCE_H_ #include "caffe2/core/common.h" #include "caffe2/core/types.h" namespace caffe2 { class Tensor; namespace math { template TORCH_API void ReduceMin(const int N, const T* X, T* y, Tensor* scratch_ptr, Context* context); template TORCH_API void ReduceMax(const int N, const T* X, T* y, Tensor* scratch_ptr, Context* context); // In all of the reduce functions, X_dims and Y_dims should have ndim elements. // Each dimension of Y_dims must match the corresponding dimension of X_dims or // must be equal to 1. The dimensions equal to 1 indicate the dimensions of X to // be reduced. // Y = alpha * ReduceMin(X) template TORCH_API void ReduceMin( const int ndim, const int* X_dims, const int* Y_dims, const T alpha, const T* X, T* Y, Context* context, bool allow_broadcast_fastpath=false); // Y = alpha * ReduceMax(X) template TORCH_API void ReduceMax( const int ndim, const int* X_dims, const int* Y_dims, const T alpha, const T* X, T* Y, Context* context, bool allow_broadcast_fastpath=false); // Y = alpha * ReduceSum(X) template TORCH_API void ReduceSum( const int ndim, const int* X_dims, const int* Y_dims, const T alpha, const T* X, T* Y, Context* context, bool allow_broadcast_fastpath=false); // Y = alpha * ReduceMean(X) template TORCH_API void ReduceMean( const int ndim, const int* X_dims, const int* Y_dims, const T alpha, const T* X, T* Y, Context* context, bool allow_broadcast_fastpath=false); // Y = alpha * ReduceL1(X) template TORCH_API void ReduceL1( const int ndim, const int* X_dims, const int* Y_dims, const T alpha, const T* X, T* Y, Context* context, bool allow_broadcast_fastpath=false); // Y = alpha * ReduceL2(X) template TORCH_API void ReduceL2( const int ndim, const int* X_dims, const int* Y_dims, const T alpha, const T* X, T* Y, Context* context, bool allow_broadcast_fastpath=false); // Computes mean and variance over axes. template TORCH_API void Moments( const int ndims, const int* X_dims, const int* Y_dims, const T* X, T* mean, T* var, Context* context, bool allow_broadcast_fastpath=false); } // namespace math } // namespace caffe2 #endif // CAFFE2_UTILS_MATH_REDUCE_H_