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

Tensors

is_tensor	Returns True if obj is a PyTorch tensor.
is_storage	Returns True if obj is a PyTorch storage object.
is_complex	Returns True if the data type of input is a complex data type i.e., one of torch.complex64, and torch.complex128.
is_floating_point	Returns True if the data type of input is a floating point data type i.e., one of torch.float64, torch.float32, torch.float16, and torch.bfloat16.
is_nonzero	Returns True if the input is a single element tensor which is not equal to zero after type conversions.
set_default_dtype	Sets the default floating point dtype to d.
get_default_dtype	Get the current default floating point torch.dtype.
set_default_tensor_type	Sets the default torch.Tensor type to floating point tensor type t.
numel	Returns the total number of elements in the input tensor.
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 In-place random sampling methods to create torch.Tensor s with values sampled from a broader range of distributions.

tensor	Constructs a tensor with data.
sparse_coo_tensor	Constructs a sparse tensor in COO(rdinate) format with specified values at the given indices.
as_tensor	Convert the data into a torch.Tensor.
as_strided	Create a view of an existing torch.Tensor input with specified size, stride and storage_offset.
from_numpy	Creates a Tensor from a numpy.ndarray.
zeros	Returns a tensor filled with the scalar value 0, with the shape defined by the variable argument size.
zeros_like	Returns a tensor filled with the scalar value 0, with the same size as input.
ones	Returns a tensor filled with the scalar value 1, with the shape defined by the variable argument size.
ones_like	Returns a tensor filled with the scalar value 1, with the same size as input.
arange	Returns a 1-D tensor of size $\left\lceil \frac{\text{end} - \text{start}}{\text{step}} \right\rceil$ with values from the interval [start, end) taken with common difference step beginning from start.
range	Returns a 1-D tensor of size $\left\lfloor \frac{\text{end} - \text{start}}{\text{step}} \right\rfloor + 1$ with values from start to end with step step.
linspace	Creates a one-dimensional tensor of size steps whose values are evenly spaced from start to end, inclusive.
logspace	Creates a one-dimensional tensor of size steps whose values are evenly spaced from ${{\text{{base}}}}^{{\text{{start}}}}$ to ${{\text{{base}}}}^{{\text{{end}}}}$ , inclusive, on a logarithmic scale with base base.
eye	Returns a 2-D 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 input.
empty_strided	Returns a tensor filled with uninitialized data.
full	Creates a tensor of size size filled with fill_value.
full_like	Returns a tensor with the same size as input filled with fill_value.
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 per-channel 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 real and its imaginary part equal to imag.
polar	Constructs a complex tensor whose elements are Cartesian coordinates corresponding to the polar coordinates with absolute value abs and angle angle.
heaviside	Computes the Heaviside step function for each element in input.

Indexing, Slicing, Joining, Mutating Ops

cat	Concatenates the given sequence of seq tensors in the given dimension.
chunk	Splits a tensor into a specific number of chunks.
column_stack	Creates a new tensor by horizontally stacking the tensors in tensors.
dstack	Stack tensors in sequence depthwise (along third axis).
gather	Gathers values along an axis specified by dim.
hstack	Stack tensors in sequence horizontally (column wise).
index_select	Returns a new tensor which indexes the input tensor along dimension dim using the entries in index which is a LongTensor.
masked_select	Returns a new 1-D tensor which indexes the input tensor according to the boolean mask mask which is a BoolTensor.
movedim	Moves the dimension(s) of input at the position(s) in source to the position(s) in destination.
moveaxis	Alias for torch.movedim().
narrow	Returns a new tensor that is a narrowed version of input tensor.
nonzero
reshape	Returns a tensor with the same data and number of elements as input, but with the specified shape.
row_stack	Alias of torch.vstack().
scatter	Out-of-place version of torch.Tensor.scatter_()
scatter_add	Out-of-place version of torch.Tensor.scatter_add_()
split	Splits the tensor into chunks.
squeeze	Returns a tensor with all the dimensions of input of size 1 removed.
stack	Concatenates a sequence of tensors along a new dimension.
swapaxes	Alias for torch.transpose().
swapdims	Alias for torch.transpose().
t	Expects input to be <= 2-D tensor and transposes dimensions 0 and 1.
take	Returns a new tensor with the elements of input at the given indices.
tensor_split	Splits a tensor into multiple sub-tensors, all of which are views of input, along dimension dim according to the indices or number of sections specified by indices_or_sections.
tile	Constructs a tensor by repeating the elements of input.
transpose	Returns a tensor that is a transposed version of input.
unbind	Removes a tensor dimension.
unsqueeze	Returns a new tensor with a dimension of size one inserted at the specified position.
vstack	Stack tensors in sequence vertically (row wise).
where	Return a tensor of elements selected from either x or y, depending on condition.

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 non-deterministic random number.
manual_seed	Sets the seed for generating random numbers.
initial_seed	Returns the initial seed for generating random numbers as a Python long.
get_rng_state	Returns the random number generator state as a torch.ByteTensor.
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 num_samples indices sampled from the multinomial probability distribution located in the corresponding row of tensor input.
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 input with each element sampled from a Poisson distribution with rate parameter given by the corresponding element in input i.e.,
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 input that is filled with random numbers from a uniform distribution on the interval $[0, 1)$ .
randint	Returns a tensor filled with random integers generated uniformly between low (inclusive) and high (exclusive).
randint_like	Returns a tensor with the same shape as Tensor input filled with random integers generated uniformly between low (inclusive) and high (exclusive).
randn	Returns a tensor filled with random numbers from a normal distribution with mean 0 and variance 1 (also called the standard normal distribution).
randn_like	Returns a tensor with the same size as input that is filled with random numbers from a normal distribution with mean 0 and variance 1.
randperm	Returns a random permutation of integers from 0 to n - 1.

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:

• torch.Tensor.bernoulli_() - in-place version of torch.bernoulli()

• torch.Tensor.cauchy_() - numbers drawn from the Cauchy distribution

• torch.Tensor.exponential_() - numbers drawn from the exponential distribution

• torch.Tensor.geometric_() - elements drawn from the geometric distribution

• torch.Tensor.log_normal_() - samples from the log-normal distribution

• torch.Tensor.normal_() - in-place version of torch.normal()

• torch.Tensor.random_() - numbers sampled from the discrete uniform distribution

• torch.Tensor.uniform_() - numbers sampled from the continuous uniform distribution

Quasi-random sampling

quasirandom.SobolEngine

The torch.quasirandom.SobolEngine is an engine for generating (scrambled) Sobol sequences.

Serialization

save	Saves an object to a disk file.
load	Loads an object saved with torch.save() from a file.

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 inter-op 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	Context-manager that disabled gradient calculation.
enable_grad	Context-manager that enables gradient calculation.
set_grad_enabled	Context-manager that sets gradient calculation to on or off.

Math operations

Pointwise Ops

abs	Computes the absolute value of each element in input.
absolute	Alias for torch.abs()
acos	Computes the inverse cosine of each element in input.
arccos	Alias for torch.acos().
acosh	Returns a new tensor with the inverse hyperbolic cosine of the elements of input.
arccosh	Alias for torch.acosh().
add	Adds the scalar other to each element of the input input and returns a new resulting tensor.
addcdiv	Performs the element-wise division of tensor1 by tensor2, multiply the result by the scalar value and add it to input.
addcmul	Performs the element-wise multiplication of tensor1 by tensor2, multiply the result by the scalar value and add it to input.
angle	Computes the element-wise angle (in radians) of the given input tensor.
asin	Returns a new tensor with the arcsine of the elements of input.
arcsin	Alias for torch.asin().
asinh	Returns a new tensor with the inverse hyperbolic sine of the elements of input.
arcsinh	Alias for torch.asinh().
atan	Returns a new tensor with the arctangent of the elements of input.
arctan	Alias for torch.atan().
atanh	Returns a new tensor with the inverse hyperbolic tangent of the elements of input.
arctanh	Alias for torch.atanh().
atan2	Element-wise arctangent of $\text{input}_{i} / \text{other}_{i}$ with consideration of the quadrant.
bitwise_not	Computes the bitwise NOT of the given input tensor.
bitwise_and	Computes the bitwise AND of input and other.
bitwise_or	Computes the bitwise OR of input and other.
bitwise_xor	Computes the bitwise XOR of input and other.
ceil	Returns a new tensor with the ceil of the elements of input, the smallest integer greater than or equal to each element.
clamp	Clamp all elements in input into the range [ min, max ].
clip	Alias for torch.clamp().
conj	Computes the element-wise conjugate of the given input tensor.
copysign	Create a new floating-point tensor with the magnitude of input and the sign of other, elementwise.
cos	Returns a new tensor with the cosine of the elements of input.
cosh	Returns a new tensor with the hyperbolic cosine of the elements of input.
deg2rad	Returns a new tensor with each of the elements of input converted from angles in degrees to radians.
div	Divides each element of the input input by the corresponding element of other.
divide	Alias for torch.div().
digamma	Computes the logarithmic derivative of the gamma function on input.
erf	Computes the error function of each element.
erfc	Computes the complementary error function of each element of input.
erfinv	Computes the inverse error function of each element of input.
exp	Returns a new tensor with the exponential of the elements of the input tensor input.
exp2	Computes the base two exponential function of input.
expm1	Returns a new tensor with the exponential of the elements minus 1 of input.
fix	Alias for torch.trunc()
float_power	Raises input to the power of exponent, elementwise, in double precision.
floor	Returns a new tensor with the floor of the elements of input, the largest integer less than or equal to each element.
floor_divide
fmod	Computes the element-wise remainder of division.
frac	Computes the fractional portion of each element in input.
imag	Returns a new tensor containing imaginary values of the self tensor.
ldexp	Multiplies input by 2**:attr:other.
lerp	Does a linear interpolation of two tensors start (given by input) and end based on a scalar or tensor weight and returns the resulting out tensor.
lgamma	Computes the logarithm of the gamma function on input.
log	Returns a new tensor with the natural logarithm of the elements of input.
log10	Returns a new tensor with the logarithm to the base 10 of the elements of input.
log1p	Returns a new tensor with the natural logarithm of (1 + input).
log2	Returns a new tensor with the logarithm to the base 2 of the elements of input.
logaddexp	Logarithm of the sum of exponentiations of the inputs.
logaddexp2	Logarithm of the sum of exponentiations of the inputs in base-2.
logical_and	Computes the element-wise logical AND of the given input tensors.
logical_not	Computes the element-wise logical NOT of the given input tensor.
logical_or	Computes the element-wise logical OR of the given input tensors.
logical_xor	Computes the element-wise logical XOR of the given input tensors.
logit	Returns a new tensor with the logit of the elements of input.
hypot	Given the legs of a right triangle, return its hypotenuse.
i0	Computes the zeroth order modified Bessel function of the first kind for each element of input.
igamma	Computes the regularized lower incomplete gamma function:
igammac	Computes the regularized upper incomplete gamma function:
mul	Multiplies each element of the input input with the scalar other and returns a new resulting tensor.
multiply	Alias for torch.mul().
mvlgamma	Computes the multivariate log-gamma function) with dimension $p$ element-wise, given by
nan_to_num	Replaces NaN, positive infinity, and negative infinity values in input with the values specified by nan, posinf, and neginf, respectively.
neg	Returns a new tensor with the negative of the elements of input.
negative	Alias for torch.neg()
nextafter	Return the next floating-point value after input towards other, elementwise.
polygamma	Computes the $n^{th}$ derivative of the digamma function on input.
pow	Takes the power of each element in input with exponent and returns a tensor with the result.
rad2deg	Returns a new tensor with each of the elements of input converted from angles in radians to degrees.
real	Returns a new tensor containing real values of the self tensor.
reciprocal	Returns a new tensor with the reciprocal of the elements of input
remainder	Computes the element-wise remainder of division.
round	Returns a new tensor with each of the elements of input rounded to the closest integer.
rsqrt	Returns a new tensor with the reciprocal of the square-root of each of the elements of input.
sigmoid	Returns a new tensor with the sigmoid of the elements of input.
sign	Returns a new tensor with the signs of the elements of input.
sgn	For complex tensors, this function returns a new tensor whose elemants have the same angle as that of the elements of input and absolute value 1.
signbit	Tests if each element of input has its sign bit set (is less than zero) or not.
sin	Returns a new tensor with the sine of the elements of input.
sinc	Computes the normalized sinc of input.
sinh	Returns a new tensor with the hyperbolic sine of the elements of input.
sqrt	Returns a new tensor with the square-root of the elements of input.
square	Returns a new tensor with the square of the elements of input.
sub	Subtracts other, scaled by alpha, from input.
subtract	Alias for torch.sub().
tan	Returns a new tensor with the tangent of the elements of input.
tanh	Returns a new tensor with the hyperbolic tangent of the elements of input.
true_divide	Alias for torch.div().
trunc	Returns a new tensor with the truncated integer values of the elements of input.
xlogy	Computes input * log(other) with the following cases.

Reduction Ops

argmax	Returns the indices of the maximum value of all elements in the input tensor.
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 input tensor in the given dimension(s) dim.
amin	Returns the minimum value of each slice of the input tensor in the given dimension(s) dim.
all	Tests if all elements in input evaluate to True.
any	param input the input tensor.
max	Returns the maximum value of all elements in the input tensor.
min	Returns the minimum value of all elements in the input tensor.
dist	Returns the p-norm of (input - other)
logsumexp	Returns the log of summed exponentials of each row of the input tensor in the given dimension dim.
mean	Returns the mean value of all elements in the input tensor.
median	Returns the median of the values in input.
nanmedian	Returns the median of the values in input, ignoring NaN values.
mode	Returns a namedtuple (values, indices) where values is the mode value of each row of the input tensor in the given dimension dim, i.e.
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 input tensor.
quantile	Returns the q-th quantiles of all elements in the input tensor, doing a linear interpolation when the q-th quantile lies between two data points.
nanquantile	This is a variant of torch.quantile() that “ignores” NaN values, computing the quantiles q as if NaN values in input did not exist.
std	Returns the standard-deviation of all elements in the input tensor.
std_mean	Returns the standard-deviation and mean of all elements in the input tensor.
sum	Returns the sum of all elements in the input tensor.
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	Returns the variance of all elements in the input tensor.
var_mean	Returns the variance and mean of all elements in the input tensor.
count_nonzero	Counts the number of non-zero values in the tensor input along the given dim.

Comparison Ops

allclose	This function checks if all input and other satisfy the condition:
argsort	Returns the indices that sort a tensor along a given dimension in ascending order by value.
eq	Computes element-wise equality
equal	True if two tensors have the same size and elements, False otherwise.
ge	Computes $\text{input} \geq \text{other}$ element-wise.
greater_equal	Alias for torch.ge().
gt	Computes $\text{input} > \text{other}$ element-wise.
greater	Alias for torch.gt().
isclose	Returns a new tensor with boolean elements representing if each element of input is “close” to the corresponding element of other.
isfinite	Returns a new tensor with boolean elements representing if each element is finite or not.
isinf	Tests if each element of input is infinite (positive or negative infinity) or not.
isposinf	Tests if each element of input is positive infinity or not.
isneginf	Tests if each element of input is negative infinity or not.
isnan	Returns a new tensor with boolean elements representing if each element of input is NaN or not.
isreal	Returns a new tensor with boolean elements representing if each element of input is real-valued or not.
kthvalue	Returns a namedtuple (values, indices) where values is the k th smallest element of each row of the input tensor in the given dimension dim.
le	Computes $\text{input} \leq \text{other}$ element-wise.
less_equal	Alias for torch.le().
lt	Computes $\text{input} < \text{other}$ element-wise.
less	Alias for torch.lt().
maximum	Computes the element-wise maximum of input and other.
minimum	Computes the element-wise minimum of input and other.
fmax	Computes the element-wise maximum of input and other.
fmin	Computes the element-wise minimum of input and other.
ne	Computes $\text{input} \neq \text{other}$ element-wise.
not_equal	Alias for torch.ne().
sort	Sorts the elements of the input tensor along a given dimension in ascending order by value.
topk	Returns the k largest elements of the given input tensor along a given dimension.
msort	Sorts the elements of the input tensor along its first dimension in ascending order by value.

Spectral Ops

stft	Short-time 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 window_length and shape parameter beta.

Other Operations

atleast_1d	Returns a 1-dimensional view of each input tensor with zero dimensions.
atleast_2d	Returns a 2-dimensional view of each each input tensor with zero dimensions.
atleast_3d	Returns a 3-dimensional view of each each input tensor with zero dimensions.
bincount	Count the frequency of each value in an array of non-negative ints.
block_diag	Create a block diagonal matrix from provided tensors.
broadcast_tensors	Broadcasts the given tensors according to Broadcasting semantics.
broadcast_to	Broadcasts input to the shape shape.
broadcast_shapes	Similar to broadcast_tensors() but for shapes.
bucketize	Returns the indices of the buckets to which each value in the input belongs, where the boundaries of the buckets are set by boundaries.
cartesian_prod	Do cartesian product of the given sequence of tensors.
cdist	Computes batched the p-norm distance between each pair of the two collections of row vectors.
clone	Returns a copy of input.
combinations	Compute combinations of length $r$ of the given tensor.
cross	Returns the cross product of vectors in dimension dim of input and other.
cummax	Returns a namedtuple (values, indices) where values is the cumulative maximum of elements of input in the dimension dim.
cummin	Returns a namedtuple (values, indices) where values is the cumulative minimum of elements of input in the dimension dim.
cumprod	Returns the cumulative product of elements of input in the dimension dim.
cumsum	Returns the cumulative sum of elements of input in the dimension dim.
diag	If input is a vector (1-D tensor), then returns a 2-D square tensor
diag_embed	Creates a tensor whose diagonals of certain 2D planes (specified by dim1 and dim2) are filled by input.
diagflat	If input is a vector (1-D tensor), then returns a 2-D square tensor
diagonal	Returns a partial view of input with the its diagonal elements with respect to dim1 and dim2 appended as a dimension at the end of the shape.
einsum	Sums the product of the elements of the input operands along dimensions specified using a notation based on the Einstein summation convention.
flatten	Flattens input by reshaping it into a one-dimensional tensor.
flip	Reverse the order of a n-D 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 input and other.
rot90	Rotate a n-D tensor by 90 degrees in the plane specified by dims axis.
gcd	Computes the element-wise greatest common divisor (GCD) of input and other.
histc	Computes the histogram of a tensor.
meshgrid	Take $N$ tensors, each of which can be either scalar or 1-dimensional vector, and create $N$ N-dimensional grids, where the $i$ th grid is defined by expanding the $i$ th input over dimensions defined by other inputs.
lcm	Computes the element-wise least common multiple (LCM) of input and other.
logcumsumexp	Returns the logarithm of the cumulative summation of the exponentiation of elements of input in the dimension dim.
ravel	Return a contiguous flattened tensor.
renorm	Returns a tensor where each sub-tensor of input along dimension dim is normalized such that the p-norm of the sub-tensor is lower than the value maxnorm
repeat_interleave	Repeat elements of a tensor.
roll	Roll the tensor along the given dimension(s).
searchsorted	Find the indices from the innermost dimension of sorted_sequence such that, if the corresponding values in values were inserted before the indices, the order of the corresponding innermost dimension within sorted_sequence would be preserved.
tensordot	Returns a contraction of a and b over multiple dimensions.
trace	Returns the sum of the elements of the diagonal of the input 2-D matrix.
tril	Returns the lower triangular part of the matrix (2-D tensor) or batch of matrices input, the other elements of the result tensor out are set to 0.
tril_indices	Returns the indices of the lower triangular part of a row-by- col matrix in a 2-by-N Tensor, where the first row contains row coordinates of all indices and the second row contains column coordinates.
triu	Returns the upper triangular part of a matrix (2-D tensor) or batch of matrices input, the other elements of the result tensor out are set to 0.
triu_indices	Returns the indices of the upper triangular part of a row by col matrix in a 2-by-N Tensor, where the first row contains row coordinates of all indices and the second row contains column coordinates.
vander	Generates a Vandermonde matrix.
view_as_real	Returns a view of input as a real tensor.
view_as_complex	Returns a view of input as a complex tensor.

BLAS and LAPACK Operations

addbmm	Performs a batch matrix-matrix product of matrices stored in batch1 and batch2, with a reduced add step (all matrix multiplications get accumulated along the first dimension).
addmm	Performs a matrix multiplication of the matrices mat1 and mat2.
addmv	Performs a matrix-vector product of the matrix mat and the vector vec.
addr	Performs the outer-product of vectors vec1 and vec2 and adds it to the matrix input.
baddbmm	Performs a batch matrix-matrix product of matrices in batch1 and batch2.
bmm	Performs a batch matrix-matrix product of matrices stored in input and mat2.
chain_matmul	Returns the matrix product of the $N$ 2-D tensors.
cholesky	Computes the Cholesky decomposition of a symmetric positive-definite matrix $A$ or for batches of symmetric positive-definite matrices.
cholesky_inverse	Computes the inverse of a symmetric positive-definite matrix $A$ using its Cholesky factor $u$ : returns matrix inv.
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.
eig	Computes the eigenvalues and eigenvectors of a real square matrix.
geqrf	This is a low-level function for calling LAPACK directly.
ger	Alias of torch.outer().
inner	Computes the dot product for 1D tensors.
inverse	Takes the inverse of the square matrix input.
det	Calculates determinant of a square matrix or batches of square matrices.
logdet	Calculates log determinant of a square matrix or batches of square matrices.
slogdet	Calculates the sign and log absolute value of the determinant(s) of a square matrix or batches of square matrices.
lstsq	Computes the solution to the least squares and least norm problems for a full rank matrix $A$ of size $(m \times n)$ and a matrix $B$ of size $(m \times k)$ .
lu	Computes the LU factorization of a matrix or batches of matrices A.
lu_solve	Returns the LU solve of the linear system $Ax = b$ using the partially pivoted LU factorization of A from torch.lu().
lu_unpack	Unpacks the data and pivots from a LU factorization of a tensor.
matmul	Matrix product of two tensors.
matrix_power	Returns the matrix raised to the power n for square matrices.
matrix_rank	Returns the numerical rank of a 2-D tensor.
matrix_exp	Returns the matrix exponential.
mm	Performs a matrix multiplication of the matrices input and mat2.
mv	Performs a matrix-vector product of the matrix input and the vector vec.
orgqr	Computes the orthogonal matrix Q of a QR factorization, from the (input, input2) tuple returned by torch.geqrf().
ormqr	Multiplies mat (given by input3) by the orthogonal Q matrix of the QR factorization formed by torch.geqrf() that is represented by (a, tau) (given by (input, input2)).
outer	Outer product of input and vec2.
pinverse	Calculates the pseudo-inverse (also known as the Moore-Penrose inverse) of a 2D tensor.
qr	Computes the QR decomposition of a matrix or a batch of matrices input, and returns a namedtuple (Q, R) of tensors such that $\text{input} = Q R$ with $Q$ being an orthogonal matrix or batch of orthogonal matrices and $R$ being an upper triangular matrix or batch of upper triangular matrices.
solve	This function returns the solution to the system of linear equations represented by $AX = B$ and the LU factorization of A, in order as a namedtuple solution, LU.
svd	Computes the singular value decomposition of either a matrix or batch of matrices input.
svd_lowrank	Return the singular value decomposition (U, S, V) of a matrix, batches of matrices, or a sparse matrix $A$ such that $A \approx U diag(S) V^T$ .
pca_lowrank	Performs linear Principal Component Analysis (PCA) on a low-rank matrix, batches of such matrices, or sparse matrix.
symeig	This function returns eigenvalues and eigenvectors of a real symmetric matrix input or a batch of real symmetric matrices, represented by a namedtuple (eigenvalues, eigenvectors).
lobpcg	Find the k largest (or smallest) eigenvalues and the corresponding eigenvectors of a symmetric positive defined generalized eigenvalue problem using matrix-free LOBPCG methods.
trapz	Estimate $\int y\,dx$ along dim, using the trapezoid rule.
triangular_solve	Solves a system of equations with a triangular coefficient matrix $A$ and multiple right-hand sides $b$ .
vdot	Computes the dot product of two 1D tensors.

Utilities

compiled_with_cxx11_abi	Returns whether PyTorch was built with _GLIBCXX_USE_CXX11_ABI=1
result_type	Returns the torch.dtype that would result from performing an arithmetic operation on the provided input tensors.
can_cast	Determines if a type conversion is allowed under PyTorch casting rules described in the type promotion documentation.
promote_types	Returns the torch.dtype with the smallest size and scalar kind that is not smaller nor of lower kind than either type1 or type2.
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.
vmap	vmap is the vectorizing map.
_assert	A wrapper around Python’s assert which is symbolically traceable.