FM / torch

/usr/local/lib64/python3.6/site-packages/torch/

/usr/local/lib64/python3.6/site-packages/torch

Name	Size	Mode	Actions
ao/	-	0755	rm
autograd/	-	0755	rm
backends/	-	0755	rm
bin/	-	0755	rm
contrib/	-	0755	rm
cpu/	-	0755	rm
cuda/	-	0755	rm
distributed/	-	0755	rm
distributions/	-	0755	rm
fft/	-	0755	rm
for_onnx/	-	0755	rm
futures/	-	0755	rm
fx/	-	0755	rm
include/	-	0755	rm
jit/	-	0755	rm
lib/	-	0755	rm
linalg/	-	0755	rm
multiprocessing/	-	0755	rm
nn/	-	0755	rm
onnx/	-	0755	rm
optim/	-	0755	rm
package/	-	0755	rm
profiler/	-	0755	rm
quantization/	-	0755	rm
share/	-	0755	rm
sparse/	-	0755	rm
special/	-	0755	rm
testing/	-	0755	rm
utils/	-	0755	rm
_C/	-	0755	rm
__pycache__/	-	0755	rm
autocast_mode.py	9663	0644	edit dl rm
functional.py	71257	0644	edit dl rm
hub.py	23371	0644	edit dl rm
overrides.py	84146	0644	edit dl rm
py.typed	0	0644	edit dl rm
quasirandom.py	7489	0644	edit dl rm
random.py	4828	0644	edit dl rm
serialization.py	36544	0644	edit dl rm
storage.py	5762	0644	edit dl rm
torch_version.py	3468	0644	edit dl rm
types.py	1552	0644	edit dl rm
version.py	125	0644	edit dl rm
_appdirs.py	26245	0644	edit dl rm
_C.cpython-36m-x86_64-linux-gnu.so	29296	0755	edit dl rm
_classes.py	1717	0644	edit dl rm
_deploy.py	3100	0644	edit dl rm
_dl.cpython-36m-x86_64-linux-gnu.so	29832	0755	edit dl rm
_jit_internal.py	46910	0644	edit dl rm
_linalg_utils.py	2373	0644	edit dl rm
_lobpcg.py	44076	0644	edit dl rm
_lowrank.py	11031	0644	edit dl rm
_namedtensor_internals.py	5351	0644	edit dl rm
_ops.py	4457	0644	edit dl rm
_python_dispatcher.py	7011	0644	edit dl rm
_six.py	1858	0644	edit dl rm
_sources.py	3882	0644	edit dl rm
_storage_docs.py	1298	0644	edit dl rm
_tensor.py	50854	0644	edit dl rm
_tensor_docs.py	113474	0644	edit dl rm
_tensor_str.py	18091	0644	edit dl rm
_torch_docs.py	364678	0644	edit dl rm
_utils.py	21617	0644	edit dl rm
_utils_internal.py	1687	0644	edit dl rm
_VF.py	656	0644	edit dl rm
_vmap_internals.py	13219	0644	edit dl rm
__config__.py	551	0644	edit dl rm
__future__.py	813	0644	edit dl rm
__init__.py	31199	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/torch/_linalg_utils.py (2373B)

"""Various linear algebra utility methods for internal use.

"""

from torch import Tensor
import torch

from typing import Optional, Tuple

def is_sparse(A):
    """Check if tensor A is a sparse tensor"""
    if isinstance(A, torch.Tensor):
        return A.layout == torch.sparse_coo

error_str = "expected Tensor"
    if not torch.jit.is_scripting():
        error_str += " but got {}".format(type(A))
    raise TypeError(error_str)

def get_floating_dtype(A):
    """Return the floating point dtype of tensor A.

Integer types map to float32.
    """
    dtype = A.dtype
    if dtype in (torch.float16, torch.float32, torch.float64):
        return dtype
    return torch.float32

def matmul(A: Optional[Tensor], B: Tensor) -> Tensor:
    """Multiply two matrices.

If A is None, return B. A can be sparse or dense. B is always
    dense.
    """
    if A is None:
        return B
    if is_sparse(A):
        return torch.sparse.mm(A, B)
    return torch.matmul(A, B)

def conjugate(A):
    """Return conjugate of tensor A.

.. note:: If A's dtype is not complex, A is returned.
    """
    if A.is_complex():
        return A.conj()
    return A

def transpose(A):
    """Return transpose of a matrix or batches of matrices.
    """
    ndim = len(A.shape)
    return A.transpose(ndim - 1, ndim - 2)

def transjugate(A):
    """Return transpose conjugate of a matrix or batches of matrices.
    """
    return conjugate(transpose(A))

def bform(X: Tensor, A: Optional[Tensor], Y: Tensor) -> Tensor:
    """Return bilinear form of matrices: :math:`X^T A Y`.
    """
    return matmul(transpose(X), matmul(A, Y))

def qform(A: Optional[Tensor], S: Tensor):
    """Return quadratic form :math:`S^T A S`.
    """
    return bform(S, A, S)

def basis(A):
    """Return orthogonal basis of A columns.
    """
    if A.is_cuda:
        # torch.orgqr is not available in CUDA
        Q = torch.linalg.qr(A).Q
    else:
        Q = torch.orgqr(*torch.geqrf(A))
    return Q

def symeig(A: Tensor, largest: Optional[bool] = False) -> Tuple[Tensor, Tensor]:
    """Return eigenpairs of A with specified ordering.
    """
    if largest is None:
        largest = False
    E, Z = torch.linalg.eigh(A, UPLO='U')
    # assuming that E is ordered
    if largest:
        E = torch.flip(E, dims=(-1,))
        Z = torch.flip(Z, dims=(-1,))
    return E, Z

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