FM / native

/usr/local/lib64/python3.6/site-packages/torch/include/ATen/native/

/usr/local/lib64/python3.6/site-packages/torch/include/ATen/native

Name	Size	Mode	Actions
cpu/	-	0755	rm
cuda/	-	0755	rm
quantized/	-	0755	rm
Activation.h	3069	0644	edit dl rm
AdaptivePooling.h	1165	0644	edit dl rm
BatchLinearAlgebra.h	8246	0644	edit dl rm
batch_norm.h	1285	0644	edit dl rm
BinaryOps.h	4916	0644	edit dl rm
BucketizationUtils.h	4248	0644	edit dl rm
ComplexHelper.h	3797	0644	edit dl rm
CompositeRandomAccessor.h	888	0644	edit dl rm
CompositeRandomAccessorCommon.h	6713	0644	edit dl rm
ConvUtils.h	5350	0644	edit dl rm
Copy.h	356	0644	edit dl rm
CPUBlas.h	4199	0644	edit dl rm
CPUFallback.h	2404	0644	edit dl rm
Cross.h	262	0644	edit dl rm
DilatedConvolutionUtils.h	6416	0644	edit dl rm
DispatchStub.h	7672	0644	edit dl rm
Distance.h	732	0644	edit dl rm
Distributions.h	21654	0644	edit dl rm
DistributionTemplates.h	18623	0644	edit dl rm
EmbeddingBag.h	1320	0644	edit dl rm
Fill.h	384	0644	edit dl rm
ForeachUtils.h	5962	0644	edit dl rm
FunctionOfAMatrixUtils.h	436	0644	edit dl rm
GridSampler.h	10525	0644	edit dl rm
group_norm.h	896	0644	edit dl rm
Histogram.h	492	0644	edit dl rm
im2col.h	2838	0644	edit dl rm
im2col_shape_check.h	6181	0644	edit dl rm
IndexingUtils.h	5373	0644	edit dl rm
layer_norm.h	2892	0644	edit dl rm
Lerp.h	553	0644	edit dl rm
LinearAlgebra.h	603	0644	edit dl rm
LinearAlgebraUtils.h	25236	0644	edit dl rm
LossMulti.h	2197	0644	edit dl rm
Math.h	91356	0644	edit dl rm
MathBitFallThroughLists.h	4086	0644	edit dl rm
MathBitsFallback.h	7326	0644	edit dl rm
MaxPooling.h	1234	0644	edit dl rm
Normalization.h	302	0644	edit dl rm
PointwiseOps.h	749	0644	edit dl rm
Pool.h	10922	0644	edit dl rm
Pow.h	1694	0644	edit dl rm
ReduceAllOps.h	378	0644	edit dl rm
ReduceOps.h	1745	0644	edit dl rm
ReduceOpsUtils.h	12245	0644	edit dl rm
Repeat.h	1286	0644	edit dl rm
Resize.h	6501	0644	edit dl rm
ResizeCommon.h	1321	0644	edit dl rm
RNN.h	2467	0644	edit dl rm
ScatterGatherChecks.h	3641	0644	edit dl rm
SegmentReduce.h	685	0644	edit dl rm
SharedReduceOps.h	15785	0644	edit dl rm
SobolEngineOpsUtils.h	1723	0644	edit dl rm
Sorting.h	536	0644	edit dl rm
SortingUtils.h	5722	0644	edit dl rm
SpectralOpsUtils.h	3146	0644	edit dl rm
StridedRandomAccessor.h	6847	0644	edit dl rm
TensorAdvancedIndexing.h	3072	0644	edit dl rm
TensorCompare.h	1333	0644	edit dl rm
TensorDimApply.h	1832	0644	edit dl rm
TensorFactories.h	3382	0644	edit dl rm
TensorIterator.h	46	0644	edit dl rm
TensorIteratorDynamicCasting.h	2025	0644	edit dl rm
TensorShape.h	1049	0644	edit dl rm
TensorTransformations.h	938	0644	edit dl rm
TriangularOpsUtils.h	2000	0644	edit dl rm
TypeProperties.h	496	0644	edit dl rm
UnaryOps.h	4464	0644	edit dl rm
Unfold2d.h	551	0644	edit dl rm
Unfold3d.h	852	0644	edit dl rm
UnfoldBackward.h	5398	0644	edit dl rm
UpSample.h	13599	0644	edit dl rm
vol2col.h	3642	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/torch/include/ATen/native/BatchLinearAlgebra.h (8246B)

#pragma once

#include <ATen/ATen.h>
#include <ATen/Config.h>
#include <ATen/native/DispatchStub.h>
#include <ATen/native/LinearAlgebraUtils.h>
#include <ATen/native/cpu/zmath.h>

namespace at { namespace native {

enum class LapackLstsqDriverType : int64_t { Gels, Gelsd, Gelsy, Gelss};

#if AT_BUILD_WITH_LAPACK()
// Define per-batch functions to be used in the implementation of batched
// linear algebra operations

template <class scalar_t>
void lapackCholesky(char uplo, int n, scalar_t *a, int lda, int *info);

template <class scalar_t>
void lapackCholeskyInverse(char uplo, int n, scalar_t *a, int lda, int *info);

template <class scalar_t, class value_t=scalar_t>
void lapackEig(char jobvl, char jobvr, int n, scalar_t *a, int lda, scalar_t *w, scalar_t* vl, int ldvl, scalar_t *vr, int ldvr, scalar_t *work, int lwork, value_t *rwork, int *info);

template <class scalar_t>
void lapackGeqrf(int m, int n, scalar_t *a, int lda, scalar_t *tau, scalar_t *work, int lwork, int *info);

template <class scalar_t>
void lapackOrgqr(int m, int n, int k, scalar_t *a, int lda, scalar_t *tau, scalar_t *work, int lwork, int *info);

template <class scalar_t>
void lapackOrmqr(char side, char trans, int m, int n, int k, scalar_t *a, int lda, scalar_t *tau, scalar_t *c, int ldc, scalar_t *work, int lwork, int *info);

template <class scalar_t, class value_t = scalar_t>
void lapackSyevd(char jobz, char uplo, int n, scalar_t* a, int lda, value_t* w, scalar_t* work, int lwork, value_t* rwork, int lrwork, int* iwork, int liwork, int* info);

template <class scalar_t>
void lapackGels(char trans, int m, int n, int nrhs,
    scalar_t *a, int lda, scalar_t *b, int ldb,
    scalar_t *work, int lwork, int *info);

template <class scalar_t, class value_t = scalar_t>
void lapackGelsd(int m, int n, int nrhs,
    scalar_t *a, int lda, scalar_t *b, int ldb,
    value_t *s, value_t rcond, int *rank,
    scalar_t* work, int lwork,
    value_t *rwork, int* iwork, int *info);

template <class scalar_t, class value_t = scalar_t>
void lapackGelsy(int m, int n, int nrhs,
    scalar_t *a, int lda, scalar_t *b, int ldb,
    int *jpvt, value_t rcond, int *rank,
    scalar_t *work, int lwork, value_t* rwork, int *info);

template <class scalar_t, class value_t = scalar_t>
void lapackGelss(int m, int n, int nrhs,
    scalar_t *a, int lda, scalar_t *b, int ldb,
    value_t *s, value_t rcond, int *rank,
    scalar_t *work, int lwork,
    value_t *rwork, int *info);

template <LapackLstsqDriverType, class scalar_t, class value_t = scalar_t>
struct lapackLstsq_impl;

template <class scalar_t, class value_t>
struct lapackLstsq_impl<LapackLstsqDriverType::Gels, scalar_t, value_t> {
  static void call(
      char trans, int m, int n, int nrhs,
      scalar_t *a, int lda, scalar_t *b, int ldb,
      scalar_t *work, int lwork, int *info, // Gels flavor
      int *jpvt, value_t rcond, int *rank, value_t* rwork, // Gelsy flavor
      value_t *s, // Gelss flavor
      int *iwork // Gelsd flavor
      ) {
    lapackGels<scalar_t>(
        trans, m, n, nrhs,
        a, lda, b, ldb,
        work, lwork, info);
  }
};

template <class scalar_t, class value_t>
struct lapackLstsq_impl<LapackLstsqDriverType::Gelsy, scalar_t, value_t> {
  static void call(
      char trans, int m, int n, int nrhs,
      scalar_t *a, int lda, scalar_t *b, int ldb,
      scalar_t *work, int lwork, int *info, // Gels flavor
      int *jpvt, value_t rcond, int *rank, value_t* rwork, // Gelsy flavor
      value_t *s, // Gelss flavor
      int *iwork // Gelsd flavor
      ) {
    lapackGelsy<scalar_t, value_t>(
        m, n, nrhs,
        a, lda, b, ldb,
        jpvt, rcond, rank,
        work, lwork, rwork, info);
  }
};

template <class scalar_t, class value_t>
struct lapackLstsq_impl<LapackLstsqDriverType::Gelsd, scalar_t, value_t> {
  static void call(
      char trans, int m, int n, int nrhs,
      scalar_t *a, int lda, scalar_t *b, int ldb,
      scalar_t *work, int lwork, int *info, // Gels flavor
      int *jpvt, value_t rcond, int *rank, value_t* rwork, // Gelsy flavor
      value_t *s, // Gelss flavor
      int *iwork // Gelsd flavor
      ) {
    lapackGelsd<scalar_t, value_t>(
        m, n, nrhs,
        a, lda, b, ldb,
        s, rcond, rank,
        work, lwork,
        rwork, iwork, info);
  }
};

template <class scalar_t, class value_t>
struct lapackLstsq_impl<LapackLstsqDriverType::Gelss, scalar_t, value_t> {
  static void call(
      char trans, int m, int n, int nrhs,
      scalar_t *a, int lda, scalar_t *b, int ldb,
      scalar_t *work, int lwork, int *info, // Gels flavor
      int *jpvt, value_t rcond, int *rank, value_t* rwork, // Gelsy flavor
      value_t *s, // Gelss flavor
      int *iwork // Gelsd flavor
      ) {
    lapackGelss<scalar_t, value_t>(
        m, n, nrhs,
        a, lda, b, ldb,
        s, rcond, rank,
        work, lwork,
        rwork, info);
  }
};

template <LapackLstsqDriverType driver_type, class scalar_t, class value_t = scalar_t>
void lapackLstsq(
    char trans, int m, int n, int nrhs,
    scalar_t *a, int lda, scalar_t *b, int ldb,
    scalar_t *work, int lwork, int *info, // Gels flavor
    int *jpvt, value_t rcond, int *rank, value_t* rwork, // Gelsy flavor
    value_t *s, // Gelss flavor
    int *iwork // Gelsd flavor
    ) {
  lapackLstsq_impl<driver_type, scalar_t, value_t>::call(
      trans, m, n, nrhs,
      a, lda, b, ldb,
      work, lwork, info,
      jpvt, rcond, rank, rwork,
      s,
      iwork);
}

template <class scalar_t>
void lapackLuSolve(char trans, int n, int nrhs, scalar_t *a, int lda, int *ipiv, scalar_t *b, int ldb, int *info);

template <class scalar_t>
void lapackLu(int m, int n, scalar_t *a, int lda, int *ipiv, int *info);

#endif

#if AT_BUILD_WITH_BLAS()
template <class scalar_t>
void blasTriangularSolve(char side, char uplo, char trans, char diag, int n, int nrhs, scalar_t* a, int lda, scalar_t* b, int ldb);
#endif

using cholesky_fn = void (*)(const Tensor& /*input*/, const Tensor& /*info*/, bool /*upper*/);
DECLARE_DISPATCH(cholesky_fn, cholesky_stub);

using cholesky_inverse_fn = Tensor& (*)(Tensor& /*result*/, Tensor& /*infos*/, bool /*upper*/);

DECLARE_DISPATCH(cholesky_inverse_fn, cholesky_inverse_stub);

using eig_fn = std::tuple<Tensor, Tensor> (*)(const Tensor&, bool&);

DECLARE_DISPATCH(eig_fn, eig_stub);

using linalg_eig_fn = void (*)(Tensor& /*eigenvalues*/, Tensor& /*eigenvectors*/, Tensor& /*infos*/, const Tensor& /*input*/, bool /*compute_eigenvectors*/);

DECLARE_DISPATCH(linalg_eig_fn, linalg_eig_stub);

using geqrf_fn = void (*)(const Tensor& /*input*/, const Tensor& /*tau*/);
DECLARE_DISPATCH(geqrf_fn, geqrf_stub);

using orgqr_fn = Tensor& (*)(Tensor& /*result*/, const Tensor& /*tau*/);
DECLARE_DISPATCH(orgqr_fn, orgqr_stub);

using ormqr_fn = void (*)(const Tensor& /*input*/, const Tensor& /*tau*/, const Tensor& /*other*/, bool /*left*/, bool /*transpose*/);
DECLARE_DISPATCH(ormqr_fn, ormqr_stub);

using linalg_eigh_fn = void (*)(
    const Tensor& /*eigenvalues*/,
    const Tensor& /*eigenvectors*/,
    const Tensor& /*infos*/,
    bool /*upper*/,
    bool /*compute_eigenvectors*/);
DECLARE_DISPATCH(linalg_eigh_fn, linalg_eigh_stub);

using lstsq_fn = void (*)(
    const Tensor& /*a*/,
    Tensor& /*b*/,
    Tensor& /*rank*/,
    Tensor& /*singular_values*/,
    Tensor& /*infos*/,
    double /*rcond*/,
    std::string /*driver_name*/);
DECLARE_DISPATCH(lstsq_fn, lstsq_stub);

using triangular_solve_fn = void (*)(
    Tensor& /*A*/,
    Tensor& /*B*/,
    bool /*left*/,
    bool /*upper*/,
    TransposeType /*transpose*/,
    bool /*unitriangular*/);
DECLARE_DISPATCH(triangular_solve_fn, triangular_solve_stub);

using lu_fn = void (*)(
    const Tensor& /*input*/,
    const Tensor& /*pivots*/,
    const Tensor& /*infos*/,
    bool /*compute_pivots*/);
DECLARE_DISPATCH(lu_fn, lu_stub);

using lu_solve_fn = void (*)(
    const Tensor& /*b*/,
    const Tensor& /*lu*/,
    const Tensor& /*pivots*/);
DECLARE_DISPATCH(lu_solve_fn, lu_solve_stub);

using lu_solve_trans_fn = void (*)(
    const Tensor& /*b*/,
    const Tensor& /*lu*/,
    const Tensor& /*pivots*/,
    TransposeType /*trans*/);
DECLARE_DISPATCH(lu_solve_trans_fn, lu_solve_trans_stub);

}} // namespace at::native

#pragma once #include #include #include #include #include namespace at { namespace native { enum class LapackLstsqDriverType : int64_t { Gels, Gelsd, Gelsy, Gelss}; #if AT_BUILD_WITH_LAPACK() // Define per-batch functions to be used in the implementation of batched // linear algebra operations template void lapackCholesky(char uplo, int n, scalar_t *a, int lda, int *info); template void lapackCholeskyInverse(char uplo, int n, scalar_t *a, int lda, int *info); template void lapackEig(char jobvl, char jobvr, int n, scalar_t *a, int lda, scalar_t *w, scalar_t* vl, int ldvl, scalar_t *vr, int ldvr, scalar_t *work, int lwork, value_t *rwork, int *info); template void lapackGeqrf(int m, int n, scalar_t *a, int lda, scalar_t *tau, scalar_t *work, int lwork, int *info); template void lapackOrgqr(int m, int n, int k, scalar_t *a, int lda, scalar_t *tau, scalar_t *work, int lwork, int *info); template void lapackOrmqr(char side, char trans, int m, int n, int k, scalar_t *a, int lda, scalar_t *tau, scalar_t *c, int ldc, scalar_t *work, int lwork, int *info); template void lapackSyevd(char jobz, char uplo, int n, scalar_t* a, int lda, value_t* w, scalar_t* work, int lwork, value_t* rwork, int lrwork, int* iwork, int liwork, int* info); template void lapackGels(char trans, int m, int n, int nrhs, scalar_t *a, int lda, scalar_t *b, int ldb, scalar_t *work, int lwork, int *info); template void lapackGelsd(int m, int n, int nrhs, scalar_t *a, int lda, scalar_t *b, int ldb, value_t *s, value_t rcond, int *rank, scalar_t* work, int lwork, value_t *rwork, int* iwork, int *info); template void lapackGelsy(int m, int n, int nrhs, scalar_t *a, int lda, scalar_t *b, int ldb, int *jpvt, value_t rcond, int *rank, scalar_t *work, int lwork, value_t* rwork, int *info); template void lapackGelss(int m, int n, int nrhs, scalar_t *a, int lda, scalar_t *b, int ldb, value_t *s, value_t rcond, int *rank, scalar_t *work, int lwork, value_t *rwork, int *info); template struct lapackLstsq_impl; template struct lapackLstsq_impl { static void call( char trans, int m, int n, int nrhs, scalar_t *a, int lda, scalar_t *b, int ldb, scalar_t *work, int lwork, int *info, // Gels flavor int *jpvt, value_t rcond, int *rank, value_t* rwork, // Gelsy flavor value_t *s, // Gelss flavor int *iwork // Gelsd flavor ) { lapackGels( trans, m, n, nrhs, a, lda, b, ldb, work, lwork, info); } }; template struct lapackLstsq_impl { static void call( char trans, int m, int n, int nrhs, scalar_t *a, int lda, scalar_t *b, int ldb, scalar_t *work, int lwork, int *info, // Gels flavor int *jpvt, value_t rcond, int *rank, value_t* rwork, // Gelsy flavor value_t *s, // Gelss flavor int *iwork // Gelsd flavor ) { lapackGelsy( m, n, nrhs, a, lda, b, ldb, jpvt, rcond, rank, work, lwork, rwork, info); } }; template struct lapackLstsq_impl { static void call( char trans, int m, int n, int nrhs, scalar_t *a, int lda, scalar_t *b, int ldb, scalar_t *work, int lwork, int *info, // Gels flavor int *jpvt, value_t rcond, int *rank, value_t* rwork, // Gelsy flavor value_t *s, // Gelss flavor int *iwork // Gelsd flavor ) { lapackGelsd( m, n, nrhs, a, lda, b, ldb, s, rcond, rank, work, lwork, rwork, iwork, info); } }; template struct lapackLstsq_impl { static void call( char trans, int m, int n, int nrhs, scalar_t *a, int lda, scalar_t *b, int ldb, scalar_t *work, int lwork, int *info, // Gels flavor int *jpvt, value_t rcond, int *rank, value_t* rwork, // Gelsy flavor value_t *s, // Gelss flavor int *iwork // Gelsd flavor ) { lapackGelss( m, n, nrhs, a, lda, b, ldb, s, rcond, rank, work, lwork, rwork, info); } }; template void lapackLstsq( char trans, int m, int n, int nrhs, scalar_t *a, int lda, scalar_t *b, int ldb, scalar_t *work, int lwork, int *info, // Gels flavor int *jpvt, value_t rcond, int *rank, value_t* rwork, // Gelsy flavor value_t *s, // Gelss flavor int *iwork // Gelsd flavor ) { lapackLstsq_impl::call( trans, m, n, nrhs, a, lda, b, ldb, work, lwork, info, jpvt, rcond, rank, rwork, s, iwork); } template void lapackLuSolve(char trans, int n, int nrhs, scalar_t *a, int lda, int *ipiv, scalar_t *b, int ldb, int *info); template void lapackLu(int m, int n, scalar_t *a, int lda, int *ipiv, int *info); #endif #if AT_BUILD_WITH_BLAS() template void blasTriangularSolve(char side, char uplo, char trans, char diag, int n, int nrhs, scalar_t* a, int lda, scalar_t* b, int ldb); #endif using cholesky_fn = void (*)(const Tensor& /*input*/, const Tensor& /*info*/, bool /*upper*/); DECLARE_DISPATCH(cholesky_fn, cholesky_stub); using cholesky_inverse_fn = Tensor& (*)(Tensor& /*result*/, Tensor& /*infos*/, bool /*upper*/); DECLARE_DISPATCH(cholesky_inverse_fn, cholesky_inverse_stub); using eig_fn = std::tuple (*)(const Tensor&, bool&); DECLARE_DISPATCH(eig_fn, eig_stub); using linalg_eig_fn = void (*)(Tensor& /*eigenvalues*/, Tensor& /*eigenvectors*/, Tensor& /*infos*/, const Tensor& /*input*/, bool /*compute_eigenvectors*/); DECLARE_DISPATCH(linalg_eig_fn, linalg_eig_stub); using geqrf_fn = void (*)(const Tensor& /*input*/, const Tensor& /*tau*/); DECLARE_DISPATCH(geqrf_fn, geqrf_stub); using orgqr_fn = Tensor& (*)(Tensor& /*result*/, const Tensor& /*tau*/); DECLARE_DISPATCH(orgqr_fn, orgqr_stub); using ormqr_fn = void (*)(const Tensor& /*input*/, const Tensor& /*tau*/, const Tensor& /*other*/, bool /*left*/, bool /*transpose*/); DECLARE_DISPATCH(ormqr_fn, ormqr_stub); using linalg_eigh_fn = void (*)( const Tensor& /*eigenvalues*/, const Tensor& /*eigenvectors*/, const Tensor& /*infos*/, bool /*upper*/, bool /*compute_eigenvectors*/); DECLARE_DISPATCH(linalg_eigh_fn, linalg_eigh_stub); using lstsq_fn = void (*)( const Tensor& /*a*/, Tensor& /*b*/, Tensor& /*rank*/, Tensor& /*singular_values*/, Tensor& /*infos*/, double /*rcond*/, std::string /*driver_name*/); DECLARE_DISPATCH(lstsq_fn, lstsq_stub); using triangular_solve_fn = void (*)( Tensor& /*A*/, Tensor& /*B*/, bool /*left*/, bool /*upper*/, TransposeType /*transpose*/, bool /*unitriangular*/); DECLARE_DISPATCH(triangular_solve_fn, triangular_solve_stub); using lu_fn = void (*)( const Tensor& /*input*/, const Tensor& /*pivots*/, const Tensor& /*infos*/, bool /*compute_pivots*/); DECLARE_DISPATCH(lu_fn, lu_stub); using lu_solve_fn = void (*)( const Tensor& /*b*/, const Tensor& /*lu*/, const Tensor& /*pivots*/); DECLARE_DISPATCH(lu_solve_fn, lu_solve_stub); using lu_solve_trans_fn = void (*)( const Tensor& /*b*/, const Tensor& /*lu*/, const Tensor& /*pivots*/, TransposeType /*trans*/); DECLARE_DISPATCH(lu_solve_trans_fn, lu_solve_trans_stub); }} // namespace at::native