The torch package contains data structures for multi-dimensional tensors and mathematical operations over these are defined. 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
.. 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
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
.. autosummary::
:toctree: generated
:nosignatures:
cat
chunk
gather
index_select
masked_select
narrow
nonzero
reshape
split
squeeze
stack
t
take
transpose
unbind
unsqueeze
where
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:toctree: generated
:nosignatures:
Generator
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: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
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:toctree: generated
:nosignatures:
bernoulli
multinomial
normal
poisson
rand
rand_like
randint
randint_like
randn
randn_like
randperm
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
.. autosummary::
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:nosignatures:
:template: sobolengine.rst
quasirandom.SobolEngine
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:toctree: generated
:nosignatures:
save
load
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:toctree: generated
:nosignatures:
get_num_threads
set_num_threads
get_num_interop_threads
set_num_interop_threads
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
.. autosummary::
:toctree: generated
:nosignatures:
abs
absolute
acos
acosh
add
addcdiv
addcmul
angle
asin
asinh
atan
atanh
atan2
bitwise_not
bitwise_and
bitwise_or
bitwise_xor
ceil
clamp
conj
cos
cosh
deg2rad
div
digamma
erf
erfc
erfinv
exp
expm1
floor
floor_divide
fmod
frac
imag
lerp
lgamma
log
log10
log1p
log2
logaddexp
logaddexp2
logical_and
logical_not
logical_or
logical_xor
mul
mvlgamma
neg
polygamma
pow
rad2deg
real
reciprocal
remainder
round
rsqrt
sigmoid
sign
sin
sinh
sqrt
square
tan
tanh
true_divide
trunc
.. autosummary::
:toctree: generated
:nosignatures:
argmax
argmin
dist
logsumexp
mean
median
mode
norm
prod
std
std_mean
sum
unique
unique_consecutive
var
var_mean
count_nonzero
.. autosummary::
:toctree: generated
:nosignatures:
allclose
argsort
eq
equal
ge
gt
isclose
isfinite
isinf
isnan
kthvalue
le
lt
max
min
ne
sort
topk
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:toctree: generated
:nosignatures:
fft
ifft
rfft
irfft
stft
istft
bartlett_window
blackman_window
hamming_window
hann_window
.. autosummary::
:toctree: generated
:nosignatures:
bincount
block_diag
broadcast_tensors
bucketize
cartesian_prod
cdist
combinations
cross
cummax
cummin
cumprod
cumsum
diag
diag_embed
diagflat
diagonal
einsum
flatten
flip
fliplr
flipud
rot90
histc
meshgrid
logcumsumexp
renorm
repeat_interleave
roll
searchsorted
tensordot
trace
tril
tril_indices
triu
triu_indices
vander
view_as_real
view_as_complex
.. autosummary::
:toctree: generated
:nosignatures:
addbmm
addmm
addmv
addr
baddbmm
bmm
chain_matmul
cholesky
cholesky_inverse
cholesky_solve
dot
eig
geqrf
ger
inverse
det
logdet
slogdet
lstsq
lu
lu_solve
lu_unpack
matmul
matrix_power
matrix_rank
mm
mv
orgqr
ormqr
pinverse
qr
solve
svd
svd_lowrank
pca_lowrank
symeig
lobpcg
trapz
triangular_solve
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:toctree: generated
:nosignatures:
compiled_with_cxx11_abi
result_type
can_cast
promote_types