On this page
torch
The torch package contains data structures for multidimensional tensors and defines mathematical operations over these tensors. Additionally, it provides many utilities for efficient serialization 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.
Tensors
is_tensor 
Returns True if 
is_storage 
Returns True if 
is_complex 
Returns True if the data type of 
is_conj 
Returns True if the 
is_floating_point 
Returns True if the data type of 
is_nonzero 
Returns True if the 
set_default_dtype 
Sets the default floating point dtype to 
get_default_dtype 
Get the current default floating point 
set_default_device 
Sets the default 
set_default_tensor_type 
Sets the default 
numel 
Returns the total number of elements in the 
set_printoptions 
Set options for printing. 
set_flush_denormal 
Disables denormal floating numbers on CPU. 
Creation Ops
Note
Random sampling creation ops are listed under Random sampling and include: torch.rand()
torch.rand_like()
torch.randn()
torch.randn_like()
torch.randint()
torch.randint_like()
torch.randperm()
You may also use torch.empty()
with the Inplace random sampling methods to create torch.Tensor
s with values sampled from a broader range of distributions.
tensor 
Constructs a tensor with no autograd history (also known as a "leaf tensor", see Autograd mechanics) by copying 
sparse_coo_tensor 
Constructs a sparse tensor in COO(rdinate) format with specified values at the given 
sparse_csr_tensor 
Constructs a sparse tensor in CSR (Compressed Sparse Row) with specified values at the given 
sparse_csc_tensor 
Constructs a sparse tensor in CSC (Compressed Sparse Column) with specified values at the given 
sparse_bsr_tensor 
Constructs a sparse tensor in BSR (Block Compressed Sparse Row)) with specified 2dimensional blocks at the given 
sparse_bsc_tensor 
Constructs a sparse tensor in BSC (Block Compressed Sparse Column)) with specified 2dimensional blocks at the given 
asarray 
Converts 
as_tensor 
Converts 
as_strided 
Create a view of an existing 
from_numpy 
Creates a 
from_dlpack 
Converts a tensor from an external library into a 
frombuffer 
Creates a 1dimensional 
zeros 
Returns a tensor filled with the scalar value 
zeros_like 
Returns a tensor filled with the scalar value 
ones 
Returns a tensor filled with the scalar value 
ones_like 
Returns a tensor filled with the scalar value 
arange 
Returns a 1D tensor of size $\left\lceil \frac{\text{end}  \text{start}}{\text{step}} \right\rceil$ with values from the interval 
range 
Returns a 1D tensor of size $\left\lfloor \frac{\text{end}  \text{start}}{\text{step}} \right\rfloor + 1$ with values from 
linspace 
Creates a onedimensional tensor of size 
logspace 
Creates a onedimensional tensor of size 
eye 
Returns a 2D tensor with ones on the diagonal and zeros elsewhere. 
empty 
Returns a tensor filled with uninitialized data. 
empty_like 
Returns an uninitialized tensor with the same size as 
empty_strided 
Creates a tensor with the specified 
full 
Creates a tensor of size 
full_like 
Returns a tensor with the same size as 
quantize_per_tensor 
Converts a float tensor to a quantized tensor with given scale and zero point. 
quantize_per_channel 
Converts a float tensor to a perchannel quantized tensor with given scales and zero points. 
dequantize 
Returns an fp32 Tensor by dequantizing a quantized Tensor 
complex 
Constructs a complex tensor with its real part equal to 
polar 
Constructs a complex tensor whose elements are Cartesian coordinates corresponding to the polar coordinates with absolute value 
heaviside 
Computes the Heaviside step function for each element in 
Indexing, Slicing, Joining, Mutating Ops
adjoint 
Returns a view of the tensor conjugated and with the last two dimensions transposed. 
argwhere 
Returns a tensor containing the indices of all nonzero elements of 
cat 
Concatenates the given sequence of 
concat 
Alias of 
concatenate 
Alias of 
conj 
Returns a view of 
chunk 
Attempts to split a tensor into the specified number of chunks. 
dsplit 
Splits 
column_stack 
Creates a new tensor by horizontally stacking the tensors in 
dstack 
Stack tensors in sequence depthwise (along third axis). 
gather 
Gathers values along an axis specified by 
hsplit 
Splits 
hstack 
Stack tensors in sequence horizontally (column wise). 
index_add 
See 
index_copy 
See 
index_reduce 
See 
index_select 
Returns a new tensor which indexes the 
masked_select 
Returns a new 1D tensor which indexes the 
movedim 
Moves the dimension(s) of 
moveaxis 
Alias for 
narrow 
Returns a new tensor that is a narrowed version of 
narrow_copy 
Same as 
nonzero 

permute 
Returns a view of the original tensor 
reshape 
Returns a tensor with the same data and number of elements as 
row_stack 
Alias of 
select 
Slices the 
scatter 
Outofplace version of 
diagonal_scatter 
Embeds the values of the 
select_scatter 
Embeds the values of the 
slice_scatter 
Embeds the values of the 
scatter_add 
Outofplace version of 
scatter_reduce 
Outofplace version of 
split 
Splits the tensor into chunks. 
squeeze 
Returns a tensor with all specified dimensions of 
stack 
Concatenates a sequence of tensors along a new dimension. 
swapaxes 
Alias for 
swapdims 
Alias for 
t 
Expects 
take 
Returns a new tensor with the elements of 
take_along_dim 
Selects values from 
tensor_split 
Splits a tensor into multiple subtensors, all of which are views of 
tile 
Constructs a tensor by repeating the elements of 
transpose 
Returns a tensor that is a transposed version of 
unbind 
Removes a tensor dimension. 
unsqueeze 
Returns a new tensor with a dimension of size one inserted at the specified position. 
vsplit 
Splits 
vstack 
Stack tensors in sequence vertically (row wise). 
where 
Return a tensor of elements selected from either 
Generators
Generator 
Creates and returns a generator object that manages the state of the algorithm which produces pseudo random numbers. 
Random sampling
seed 
Sets the seed for generating random numbers to a nondeterministic random number. 
manual_seed 
Sets the seed for generating random numbers. 
initial_seed 
Returns the initial seed for generating random numbers as a Python 
get_rng_state 
Returns the random number generator state as a 
set_rng_state 
Sets the random number generator state. 
torch.default_generator Returns the default CPU torch.Generator
bernoulli 
Draws binary random numbers (0 or 1) from a Bernoulli distribution. 
multinomial 
Returns a tensor where each row contains 
normal 
Returns a tensor of random numbers drawn from separate normal distributions whose mean and standard deviation are given. 
poisson 
Returns a tensor of the same size as 
rand 
Returns a tensor filled with random numbers from a uniform distribution on the interval $[0, 1)$ 
rand_like 
Returns a tensor with the same size as 
randint 
Returns a tensor filled with random integers generated uniformly between 
randint_like 
Returns a tensor with the same shape as Tensor 
randn 
Returns a tensor filled with random numbers from a normal distribution with mean 
randn_like 
Returns a tensor with the same size as 
randperm 
Returns a random permutation of integers from 
Inplace random sampling
There are a few more inplace random sampling functions defined on Tensors as well. Click through to refer to their documentation:
torch.Tensor.bernoulli_()
 inplace version oftorch.bernoulli()
torch.Tensor.cauchy_()
 numbers drawn from the Cauchy distributiontorch.Tensor.exponential_()
 numbers drawn from the exponential distributiontorch.Tensor.geometric_()
 elements drawn from the geometric distributiontorch.Tensor.log_normal_()
 samples from the lognormal distributiontorch.Tensor.normal_()
 inplace version oftorch.normal()
torch.Tensor.random_()
 numbers sampled from the discrete uniform distributiontorch.Tensor.uniform_()
 numbers sampled from the continuous uniform distribution
Quasirandom sampling
The 
Serialization
save 
Saves an object to a disk file. 
load 
Loads an object saved with 
Parallelism
get_num_threads 
Returns the number of threads used for parallelizing CPU operations 
set_num_threads 
Sets the number of threads used for intraop parallelism on CPU. 
get_num_interop_threads 
Returns the number of threads used for interop parallelism on CPU (e.g. 
set_num_interop_threads 
Sets the number of threads used for interop parallelism (e.g. 
Locally disabling gradient computation
The context managers torch.no_grad()
, torch.enable_grad()
, and torch.set_grad_enabled()
are helpful for locally disabling and enabling gradient computation. See Locally disabling gradient computation 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
no_grad 
Contextmanager that disables gradient calculation. 
enable_grad 
Contextmanager that enables gradient calculation. 
set_grad_enabled 
Contextmanager that sets gradient calculation on or off. 
is_grad_enabled 
Returns True if grad mode is currently enabled. 
inference_mode 
Contextmanager that enables or disables inference mode 
is_inference_mode_enabled 
Returns True if inference mode is currently enabled. 
Math operations
Pointwise Ops
abs 
Computes the absolute value of each element in 
absolute 
Alias for 
acos 
Computes the inverse cosine of each element in 
arccos 
Alias for 
acosh 
Returns a new tensor with the inverse hyperbolic cosine of the elements of 
arccosh 
Alias for 
add 
Adds 
addcdiv 
Performs the elementwise division of 
addcmul 
Performs the elementwise multiplication of 
angle 
Computes the elementwise angle (in radians) of the given 
asin 
Returns a new tensor with the arcsine of the elements of 
arcsin 
Alias for 
asinh 
Returns a new tensor with the inverse hyperbolic sine of the elements of 
arcsinh 
Alias for 
atan 
Returns a new tensor with the arctangent of the elements of 
arctan 
Alias for 
atanh 
Returns a new tensor with the inverse hyperbolic tangent of the elements of 
arctanh 
Alias for 
atan2 
Elementwise arctangent of $\text{input}_{i} / \text{other}_{i}$ with consideration of the quadrant. 
arctan2 
Alias for 
bitwise_not 
Computes the bitwise NOT of the given input tensor. 
bitwise_and 
Computes the bitwise AND of 
bitwise_or 
Computes the bitwise OR of 
bitwise_xor 
Computes the bitwise XOR of 
bitwise_left_shift 
Computes the left arithmetic shift of 
bitwise_right_shift 
Computes the right arithmetic shift of 
ceil 
Returns a new tensor with the ceil of the elements of 
clamp 

clip 
Alias for 
conj_physical 
Computes the elementwise conjugate of the given 
copysign 
Create a new floatingpoint tensor with the magnitude of 
cos 
Returns a new tensor with the cosine of the elements of 
cosh 
Returns a new tensor with the hyperbolic cosine of the elements of 
deg2rad 
Returns a new tensor with each of the elements of 
div 
Divides each element of the input 
divide 
Alias for 
digamma 
Alias for 
erf 
Alias for 
erfc 
Alias for 
erfinv 
Alias for 
exp 
Returns a new tensor with the exponential of the elements of the input tensor 
exp2 
Alias for 
expm1 
Alias for 
fake_quantize_per_channel_affine 
Returns a new tensor with the data in 
fake_quantize_per_tensor_affine 
Returns a new tensor with the data in 
fix 
Alias for 
float_power 
Raises 
floor 
Returns a new tensor with the floor of the elements of 
floor_divide 

fmod 
Applies C++'s std::fmod entrywise. 
frac 
Computes the fractional portion of each element in 
frexp 
Decomposes 
gradient 
Estimates the gradient of a function $g : \mathbb{R}^n \rightarrow \mathbb{R}$ in one or more dimensions using the secondorder accurate central differences method and either first or second order estimates at the boundaries. 
imag 
Returns a new tensor containing imaginary values of the 
ldexp 
Multiplies 
lerp 
Does a linear interpolation of two tensors 
lgamma 
Computes the natural logarithm of the absolute value of the gamma function on 
log 
Returns a new tensor with the natural logarithm of the elements of 
log10 
Returns a new tensor with the logarithm to the base 10 of the elements of 
log1p 
Returns a new tensor with the natural logarithm of (1 + 
log2 
Returns a new tensor with the logarithm to the base 2 of the elements of 
logaddexp 
Logarithm of the sum of exponentiations of the inputs. 
logaddexp2 
Logarithm of the sum of exponentiations of the inputs in base2. 
logical_and 
Computes the elementwise logical AND of the given input tensors. 
logical_not 
Computes the elementwise logical NOT of the given input tensor. 
logical_or 
Computes the elementwise logical OR of the given input tensors. 
logical_xor 
Computes the elementwise logical XOR of the given input tensors. 
logit 
Alias for 
hypot 
Given the legs of a right triangle, return its hypotenuse. 
i0 
Alias for 
igamma 
Alias for 
igammac 
Alias for 
mul 
Multiplies 
multiply 
Alias for 
mvlgamma 
Alias for 
nan_to_num 
Replaces 
neg 
Returns a new tensor with the negative of the elements of 
negative 
Alias for 
nextafter 
Return the next floatingpoint value after 
polygamma 
Alias for 
positive 
Returns 
pow 
Takes the power of each element in 
quantized_batch_norm 
Applies batch normalization on a 4D (NCHW) quantized tensor. 
quantized_max_pool1d 
Applies a 1D max pooling over an input quantized tensor composed of several input planes. 
quantized_max_pool2d 
Applies a 2D max pooling over an input quantized tensor composed of several input planes. 
rad2deg 
Returns a new tensor with each of the elements of 
real 
Returns a new tensor containing real values of the 
reciprocal 
Returns a new tensor with the reciprocal of the elements of 
remainder 
Computes Python's modulus operation entrywise. 
round 
Rounds elements of 
rsqrt 
Returns a new tensor with the reciprocal of the squareroot of each of the elements of 
sigmoid 
Alias for 
sign 
Returns a new tensor with the signs of the elements of 
sgn 
This function is an extension of torch.sign() to complex tensors. 
signbit 
Tests if each element of 
sin 
Returns a new tensor with the sine of the elements of 
sinc 
Alias for 
sinh 
Returns a new tensor with the hyperbolic sine of the elements of 
softmax 
Alias for 
sqrt 
Returns a new tensor with the squareroot of the elements of 
square 
Returns a new tensor with the square of the elements of 
sub 
Subtracts 
subtract 
Alias for 
tan 
Returns a new tensor with the tangent of the elements of 
tanh 
Returns a new tensor with the hyperbolic tangent of the elements of 
true_divide 
Alias for 
trunc 
Returns a new tensor with the truncated integer values of the elements of 
xlogy 
Alias for 
Reduction Ops
argmax 
Returns the indices of the maximum value of all elements in the 
argmin 
Returns the indices of the minimum value(s) of the flattened tensor or along a dimension 
amax 
Returns the maximum value of each slice of the 
amin 
Returns the minimum value of each slice of the 
aminmax 
Computes the minimum and maximum values of the 
all 
Tests if all elements in 
any 
Tests if any element in 
max 
Returns the maximum value of all elements in the 
min 
Returns the minimum value of all elements in the 
dist 
Returns the pnorm of ( 
logsumexp 
Returns the log of summed exponentials of each row of the 
mean 
Returns the mean value of all elements in the 
nanmean 
Computes the mean of all 
median 
Returns the median of the values in 
nanmedian 
Returns the median of the values in 
mode 
Returns a namedtuple 
norm 
Returns the matrix norm or vector norm of a given tensor. 
nansum 
Returns the sum of all elements, treating Not a Numbers (NaNs) as zero. 
prod 
Returns the product of all elements in the 
quantile 
Computes the qth quantiles of each row of the 
nanquantile 
This is a variant of 
std 
Calculates the standard deviation over the dimensions specified by 
std_mean 
Calculates the standard deviation and mean over the dimensions specified by 
sum 
Returns the sum of all elements in the 
unique 
Returns the unique elements of the input tensor. 
unique_consecutive 
Eliminates all but the first element from every consecutive group of equivalent elements. 
var 
Calculates the variance over the dimensions specified by 
var_mean 
Calculates the variance and mean over the dimensions specified by 
count_nonzero 
Counts the number of nonzero values in the tensor 
Comparison Ops
allclose 
This function checks if 
argsort 
Returns the indices that sort a tensor along a given dimension in ascending order by value. 
eq 
Computes elementwise equality 
equal 

ge 
Computes $\text{input} \geq \text{other}$ elementwise. 
greater_equal 
Alias for 
gt 
Computes $\text{input} > \text{other}$ elementwise. 
greater 
Alias for 
isclose 
Returns a new tensor with boolean elements representing if each element of 
isfinite 
Returns a new tensor with boolean elements representing if each element is 
isin 
Tests if each element of 
isinf 
Tests if each element of 
isposinf 
Tests if each element of 
isneginf 
Tests if each element of 
isnan 
Returns a new tensor with boolean elements representing if each element of 
isreal 
Returns a new tensor with boolean elements representing if each element of 
kthvalue 
Returns a namedtuple 
le 
Computes $\text{input} \leq \text{other}$ elementwise. 
less_equal 
Alias for 
lt 
Computes $\text{input} < \text{other}$ elementwise. 
less 
Alias for 
maximum 
Computes the elementwise maximum of 
minimum 
Computes the elementwise minimum of 
fmax 
Computes the elementwise maximum of 
fmin 
Computes the elementwise minimum of 
ne 
Computes $\text{input} \neq \text{other}$ elementwise. 
not_equal 
Alias for 
sort 
Sorts the elements of the 
topk 
Returns the 
msort 
Sorts the elements of the 
Spectral Ops
stft 
Shorttime Fourier transform (STFT). 
istft 
Inverse short time Fourier Transform. 
bartlett_window 
Bartlett window function. 
blackman_window 
Blackman window function. 
hamming_window 
Hamming window function. 
hann_window 
Hann window function. 
kaiser_window 
Computes the Kaiser window with window length 
Other Operations
atleast_1d 
Returns a 1dimensional view of each input tensor with zero dimensions. 
atleast_2d 
Returns a 2dimensional view of each input tensor with zero dimensions. 
atleast_3d 
Returns a 3dimensional view of each input tensor with zero dimensions. 
bincount 
Count the frequency of each value in an array of nonnegative ints. 
block_diag 
Create a block diagonal matrix from provided tensors. 
broadcast_tensors 
Broadcasts the given tensors according to Broadcasting semantics. 
broadcast_to 
Broadcasts 
broadcast_shapes 
Similar to 
bucketize 
Returns the indices of the buckets to which each value in the 
cartesian_prod 
Do cartesian product of the given sequence of tensors. 
cdist 
Computes batched the pnorm distance between each pair of the two collections of row vectors. 
clone 
Returns a copy of 
combinations 
Compute combinations of length $r$ of the given tensor. 
corrcoef 
Estimates the Pearson productmoment correlation coefficient matrix of the variables given by the 
cov 
Estimates the covariance matrix of the variables given by the 
cross 
Returns the cross product of vectors in dimension 
cummax 
Returns a namedtuple 
cummin 
Returns a namedtuple 
cumprod 
Returns the cumulative product of elements of 
cumsum 
Returns the cumulative sum of elements of 
diag 

diag_embed 
Creates a tensor whose diagonals of certain 2D planes (specified by 
diagflat 

diagonal 
Returns a partial view of 
diff 
Computes the nth forward difference along the given dimension. 
einsum 
Sums the product of the elements of the input 
flatten 
Flattens 
flip 
Reverse the order of an nD tensor along given axis in dims. 
fliplr 
Flip tensor in the left/right direction, returning a new tensor. 
flipud 
Flip tensor in the up/down direction, returning a new tensor. 
kron 
Computes the Kronecker product, denoted by $\otimes$, of 
rot90 
Rotate an nD tensor by 90 degrees in the plane specified by dims axis. 
gcd 
Computes the elementwise greatest common divisor (GCD) of 
histc 
Computes the histogram of a tensor. 
histogram 
Computes a histogram of the values in a tensor. 
histogramdd 
Computes a multidimensional histogram of the values in a tensor. 
meshgrid 
Creates grids of coordinates specified by the 1D inputs in 
lcm 
Computes the elementwise least common multiple (LCM) of 
logcumsumexp 
Returns the logarithm of the cumulative summation of the exponentiation of elements of 
ravel 
Return a contiguous flattened tensor. 
renorm 
Returns a tensor where each subtensor of 
repeat_interleave 
Repeat elements of a tensor. 
roll 
Roll the tensor 
searchsorted 
Find the indices from the innermost dimension of 
tensordot 
Returns a contraction of a and b over multiple dimensions. 
trace 
Returns the sum of the elements of the diagonal of the input 2D matrix. 
tril 
Returns the lower triangular part of the matrix (2D tensor) or batch of matrices 
tril_indices 
Returns the indices of the lower triangular part of a 
triu 
Returns the upper triangular part of a matrix (2D tensor) or batch of matrices 
triu_indices 
Returns the indices of the upper triangular part of a 
unflatten 
Expands a dimension of the input tensor over multiple dimensions. 
vander 
Generates a Vandermonde matrix. 
view_as_real 
Returns a view of 
view_as_complex 
Returns a view of 
resolve_conj 
Returns a new tensor with materialized conjugation if 
resolve_neg 
Returns a new tensor with materialized negation if 
BLAS and LAPACK Operations
addbmm 
Performs a batch matrixmatrix product of matrices stored in 
addmm 
Performs a matrix multiplication of the matrices 
addmv 
Performs a matrixvector product of the matrix 
addr 
Performs the outerproduct of vectors 
baddbmm 
Performs a batch matrixmatrix product of matrices in 
bmm 
Performs a batch matrixmatrix product of matrices stored in 
chain_matmul 
Returns the matrix product of the $N$ 2D tensors. 
cholesky 
Computes the Cholesky decomposition of a symmetric positivedefinite matrix $A$ or for batches of symmetric positivedefinite matrices. 
cholesky_inverse 
Computes the inverse of a symmetric positivedefinite matrix $A$ using its Cholesky factor $u$: returns matrix 
cholesky_solve 
Solves a linear system of equations with a positive semidefinite matrix to be inverted given its Cholesky factor matrix $u$. 
dot 
Computes the dot product of two 1D tensors. 
geqrf 
This is a lowlevel function for calling LAPACK's geqrf directly. 
ger 
Alias of 
inner 
Computes the dot product for 1D tensors. 
inverse 
Alias for 
det 
Alias for 
logdet 
Calculates log determinant of a square matrix or batches of square matrices. 
slogdet 
Alias for 
lu 
Computes the LU factorization of a matrix or batches of matrices 
lu_solve 
Returns the LU solve of the linear system $Ax = b$ using the partially pivoted LU factorization of A from 
lu_unpack 
Unpacks the LU decomposition returned by 
matmul 
Matrix product of two tensors. 
matrix_power 
Alias for 
matrix_exp 
Alias for 
mm 
Performs a matrix multiplication of the matrices 
mv 
Performs a matrixvector product of the matrix 
orgqr 
Alias for 
ormqr 
Computes the matrixmatrix multiplication of a product of Householder matrices with a general matrix. 
outer 
Outer product of 
pinverse 
Alias for 
qr 
Computes the QR decomposition of a matrix or a batch of matrices 
svd 
Computes the singular value decomposition of either a matrix or batch of matrices 
svd_lowrank 
Return the singular value decomposition 
pca_lowrank 
Performs linear Principal Component Analysis (PCA) on a lowrank matrix, batches of such matrices, or sparse matrix. 
lobpcg 
Find the k largest (or smallest) eigenvalues and the corresponding eigenvectors of a symmetric positive definite generalized eigenvalue problem using matrixfree LOBPCG methods. 
trapz 
Alias for 
trapezoid 
Computes the trapezoidal rule along 
cumulative_trapezoid 
Cumulatively computes the trapezoidal rule along 
triangular_solve 
Solves a system of equations with a square upper or lower triangular invertible matrix $A$ and multiple righthand sides $b$. 
vdot 
Computes the dot product of two 1D vectors along a dimension. 
Foreach Operations
Warning
This API is in beta and subject to future changes. Forwardmode AD is not supported.
_foreach_abs 
Apply 
_foreach_abs_ 
Apply 
_foreach_acos 
Apply 
_foreach_acos_ 
Apply 
_foreach_asin 
Apply 
_foreach_asin_ 
Apply 
_foreach_atan 
Apply 
_foreach_atan_ 
Apply 
_foreach_ceil 
Apply 
_foreach_ceil_ 
Apply 
_foreach_cos 
Apply 
_foreach_cos_ 
Apply 
_foreach_cosh 
Apply 
_foreach_cosh_ 
Apply 
_foreach_erf 
Apply 
_foreach_erf_ 
Apply 
_foreach_erfc 
Apply 
_foreach_erfc_ 
Apply 
_foreach_exp 
Apply 
_foreach_exp_ 
Apply 
_foreach_expm1 
Apply 
_foreach_expm1_ 
Apply 
_foreach_floor 
Apply 
_foreach_floor_ 
Apply 
_foreach_log 
Apply 
_foreach_log_ 
Apply 
_foreach_log10 
Apply 
_foreach_log10_ 
Apply 
_foreach_log1p 
Apply 
_foreach_log1p_ 
Apply 
_foreach_log2 
Apply 
_foreach_log2_ 
Apply 
_foreach_neg 
Apply 
_foreach_neg_ 
Apply 
_foreach_tan 
Apply 
_foreach_tan_ 
Apply 
_foreach_sin 
Apply 
_foreach_sin_ 
Apply 
_foreach_sinh 
Apply 
_foreach_sinh_ 
Apply 
_foreach_round 
Apply 
_foreach_round_ 
Apply 
_foreach_sqrt 
Apply 
_foreach_sqrt_ 
Apply 
_foreach_lgamma 
Apply 
_foreach_lgamma_ 
Apply 
_foreach_frac 
Apply 
_foreach_frac_ 
Apply 
_foreach_reciprocal 
Apply 
_foreach_reciprocal_ 
Apply 
_foreach_sigmoid 
Apply 
_foreach_sigmoid_ 
Apply 
_foreach_trunc 
Apply 
_foreach_trunc_ 
Apply 
_foreach_zero_ 
Apply 
Utilities
compiled_with_cxx11_abi 
Returns whether PyTorch was built with _GLIBCXX_USE_CXX11_ABI=1 
result_type 
Returns the 
can_cast 
Determines if a type conversion is allowed under PyTorch casting rules described in the type promotion documentation. 
promote_types 
Returns the 
use_deterministic_algorithms 
Sets whether PyTorch operations must use "deterministic" algorithms. 
are_deterministic_algorithms_enabled 
Returns True if the global deterministic flag is turned on. 
is_deterministic_algorithms_warn_only_enabled 
Returns True if the global deterministic flag is set to warn only. 
set_deterministic_debug_mode 
Sets the debug mode for deterministic operations. 
get_deterministic_debug_mode 
Returns the current value of the debug mode for deterministic operations. 
set_float32_matmul_precision 
Sets the internal precision of float32 matrix multiplications. 
get_float32_matmul_precision 
Returns the current value of float32 matrix multiplication precision. 
set_warn_always 
When this flag is False (default) then some PyTorch warnings may only appear once per process. 
is_warn_always_enabled 
Returns True if the global warn_always flag is turned on. 
vmap 
vmap is the vectorizing map; 
_assert 
A wrapper around Python's assert which is symbolically traceable. 
Symbolic Numbers
class torch.SymInt(node)
[source]
Like an int (including magic methods), but redirects all operations on the wrapped node. This is used in particular to symbolically record operations in the symbolic shape workflow.
class torch.SymFloat(node)
[source]
Like an float (including magic methods), but redirects all operations on the wrapped node. This is used in particular to symbolically record operations in the symbolic shape workflow.
class torch.SymBool(node)
[source]
Like an bool (including magic methods), but redirects all operations on the wrapped node. This is used in particular to symbolically record operations in the symbolic shape workflow.
Unlike regular bools, regular boolean operators will force extra guards instead of symbolically evaluate. Use the bitwise operators instead to handle this.
sym_float 
SymIntaware utility for float casting. 
sym_int 
SymIntaware utility for int casting. 
sym_max 
SymIntaware utility for max(). 
sym_min 
SymIntaware utility for max(). 
sym_not 
SymIntaware utility for logical negation. 
Export Path
Warning
This feature is a prototype and may have compatibility breaking changes in the future.
export generated/exportdb/index
Optimizations
compile 
Optimizes given model/function using TorchDynamo and specified backend. 
Operator Tags
class torch.Tag

Members:
core
data_dependent_output
dynamic_output_shape
generated
inplace_view
nondeterministic_bitwise
nondeterministic_seeded
pointwise
view_copy
property name
© 2024, PyTorch Contributors
PyTorch has a BSDstyle license, as found in the LICENSE file.
https://pytorch.org/docs/2.1/torch.html