torch
=====
The torch package contains data structures for multi-dimensional
tensors and defines mathematical operations over these tensors.
Additionally, it provides many utilities for efficient serializing of
Tensors and arbitrary types, and other useful utilities.
It has a CUDA counterpart, that enables you to run your tensor computations
on an NVIDIA GPU with compute capability >= 3.0
.. currentmodule:: torch
Tensors
-------
.. autosummary::
:toctree: generated
:nosignatures:
is_tensor
is_storage
is_complex
is_floating_point
is_nonzero
set_default_dtype
get_default_dtype
set_default_tensor_type
numel
set_printoptions
set_flush_denormal
.. _tensor-creation-ops:
Creation Ops
~~~~~~~~~~~~
.. note::
Random sampling creation ops are listed under :ref:`random-sampling` and
include:
:func:`torch.rand`
:func:`torch.rand_like`
:func:`torch.randn`
:func:`torch.randn_like`
:func:`torch.randint`
:func:`torch.randint_like`
:func:`torch.randperm`
You may also use :func:`torch.empty` with the :ref:`inplace-random-sampling`
methods to create :class:`torch.Tensor` s with values sampled from a broader
range of distributions.
.. autosummary::
:toctree: generated
:nosignatures:
tensor
sparse_coo_tensor
as_tensor
as_strided
from_numpy
zeros
zeros_like
ones
ones_like
arange
range
linspace
logspace
eye
empty
empty_like
empty_strided
full
full_like
quantize_per_tensor
quantize_per_channel
dequantize
complex
polar
heaviside
.. _indexing-slicing-joining:
Indexing, Slicing, Joining, Mutating Ops
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autosummary::
:toctree: generated
:nosignatures:
cat
chunk
dsplit
column_stack
dstack
gather
hsplit
hstack
index_select
masked_select
movedim
moveaxis
narrow
nonzero
reshape
row_stack
scatter
scatter_add
split
squeeze
stack
swapaxes
swapdims
t
take
take_along_dim
tensor_split
tile
transpose
unbind
unsqueeze
vsplit
vstack
where
.. _generators:
Generators
----------------------------------
.. autosummary::
:toctree: generated
:nosignatures:
Generator
.. _random-sampling:
Random sampling
----------------------------------
.. autosummary::
:toctree: generated
:nosignatures:
seed
manual_seed
initial_seed
get_rng_state
set_rng_state
.. autoattribute:: torch.default_generator
:annotation: Returns the default CPU torch.Generator
.. The following doesn't actually seem to exist.
https://github.com/pytorch/pytorch/issues/27780
.. autoattribute:: torch.cuda.default_generators
:annotation: If cuda is available, returns a tuple of default CUDA torch.Generator-s.
The number of CUDA torch.Generator-s returned is equal to the number of
GPUs available in the system.
.. autosummary::
:toctree: generated
:nosignatures:
bernoulli
multinomial
normal
poisson
rand
rand_like
randint
randint_like
randn
randn_like
randperm
.. _inplace-random-sampling:
In-place random sampling
~~~~~~~~~~~~~~~~~~~~~~~~
There are a few more in-place random sampling functions defined on Tensors as well. Click through to refer to their documentation:
- :func:`torch.Tensor.bernoulli_` - in-place version of :func:`torch.bernoulli`
- :func:`torch.Tensor.cauchy_` - numbers drawn from the Cauchy distribution
- :func:`torch.Tensor.exponential_` - numbers drawn from the exponential distribution
- :func:`torch.Tensor.geometric_` - elements drawn from the geometric distribution
- :func:`torch.Tensor.log_normal_` - samples from the log-normal distribution
- :func:`torch.Tensor.normal_` - in-place version of :func:`torch.normal`
- :func:`torch.Tensor.random_` - numbers sampled from the discrete uniform distribution
- :func:`torch.Tensor.uniform_` - numbers sampled from the continuous uniform distribution
Quasi-random sampling
~~~~~~~~~~~~~~~~~~~~~
.. autosummary::
:toctree: generated
:nosignatures:
:template: sobolengine.rst
quasirandom.SobolEngine
Serialization
----------------------------------
.. autosummary::
:toctree: generated
:nosignatures:
save
load
Parallelism
----------------------------------
.. autosummary::
:toctree: generated
:nosignatures:
get_num_threads
set_num_threads
get_num_interop_threads
set_num_interop_threads
Locally disabling gradient computation
--------------------------------------
The context managers :func:`torch.no_grad`, :func:`torch.enable_grad`, and
:func:`torch.set_grad_enabled` are helpful for locally disabling and enabling
gradient computation. See :ref:`locally-disable-grad` for more details on
their usage. These context managers are thread local, so they won't
work if you send work to another thread using the ``threading`` module, etc.
Examples::
>>> x = torch.zeros(1, requires_grad=True)
>>> with torch.no_grad():
... y = x * 2
>>> y.requires_grad
False
>>> is_train = False
>>> with torch.set_grad_enabled(is_train):
... y = x * 2
>>> y.requires_grad
False
>>> torch.set_grad_enabled(True) # this can also be used as a function
>>> y = x * 2
>>> y.requires_grad
True
>>> torch.set_grad_enabled(False)
>>> y = x * 2
>>> y.requires_grad
False
.. autosummary::
:toctree: generated
:nosignatures:
no_grad
enable_grad
set_grad_enabled
is_grad_enabled
inference_mode
is_inference_mode_enabled
Math operations
---------------
Pointwise Ops
~~~~~~~~~~~~~~~~~~~~~~
.. autosummary::
:toctree: generated
:nosignatures:
abs
absolute
acos
arccos
acosh
arccosh
add
addcdiv
addcmul
angle
asin
arcsin
asinh
arcsinh
atan
arctan
atanh
arctanh
atan2
bitwise_not
bitwise_and
bitwise_or
bitwise_xor
ceil
clamp
clip
conj
copysign
cos
cosh
deg2rad
div
divide
digamma
erf
erfc
erfinv
exp
exp2
expm1
fake_quantize_per_channel_affine
fake_quantize_per_tensor_affine
fix
float_power
floor
floor_divide
fmod
frac
frexp
gradient
imag
ldexp
lerp
lgamma
log
log10
log1p
log2
logaddexp
logaddexp2
logical_and
logical_not
logical_or
logical_xor
logit
hypot
i0
igamma
igammac
mul
multiply
mvlgamma
nan_to_num
neg
negative
nextafter
polygamma
positive
pow
rad2deg
real
reciprocal
remainder
round
rsqrt
sigmoid
sign
sgn
signbit
sin
sinc
sinh
sqrt
square
sub
subtract
tan
tanh
true_divide
trunc
xlogy
Reduction Ops
~~~~~~~~~~~~~~~~~~~~~~
.. autosummary::
:toctree: generated
:nosignatures:
argmax
argmin
amax
amin
all
any
max
min
dist
logsumexp
mean
median
nanmedian
mode
norm
nansum
prod
quantile
nanquantile
std
std_mean
sum
unique
unique_consecutive
var
var_mean
count_nonzero
Comparison Ops
~~~~~~~~~~~~~~~~~~~~~~
.. autosummary::
:toctree: generated
:nosignatures:
allclose
argsort
eq
equal
ge
greater_equal
gt
greater
isclose
isfinite
isinf
isposinf
isneginf
isnan
isreal
kthvalue
le
less_equal
lt
less
maximum
minimum
fmax
fmin
ne
not_equal
sort
topk
msort
Spectral Ops
~~~~~~~~~~~~~~~~~~~~~~
.. autosummary::
:toctree: generated
:nosignatures:
stft
istft
bartlett_window
blackman_window
hamming_window
hann_window
kaiser_window
Other Operations
~~~~~~~~~~~~~~~~~~~~~~
.. autosummary::
:toctree: generated
:nosignatures:
atleast_1d
atleast_2d
atleast_3d
bincount
block_diag
broadcast_tensors
broadcast_to
broadcast_shapes
bucketize
cartesian_prod
cdist
clone
combinations
cross
cummax
cummin
cumprod
cumsum
diag
diag_embed
diagflat
diagonal
diff
einsum
flatten
flip
fliplr
flipud
kron
rot90
gcd
histc
meshgrid
lcm
logcumsumexp
ravel
renorm
repeat_interleave
roll
searchsorted
tensordot
trace
tril
tril_indices
triu
triu_indices
vander
view_as_real
view_as_complex
BLAS and LAPACK Operations
~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autosummary::
:toctree: generated
:nosignatures:
addbmm
addmm
addmv
addr
baddbmm
bmm
chain_matmul
cholesky
cholesky_inverse
cholesky_solve
dot
eig
geqrf
ger
inner
inverse
det
logdet
slogdet
lstsq
lu
lu_solve
lu_unpack
matmul
matrix_power
matrix_rank
matrix_exp
mm
mv
orgqr
ormqr
outer
pinverse
qr
solve
svd
svd_lowrank
pca_lowrank
symeig
lobpcg
trapz
triangular_solve
vdot
Utilities
----------------------------------
.. autosummary::
:toctree: generated
:nosignatures:
compiled_with_cxx11_abi
result_type
can_cast
promote_types
use_deterministic_algorithms
are_deterministic_algorithms_enabled
set_warn_always
is_warn_always_enabled
_assert