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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 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 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_conj

Returns True if the input is a conjugated tensor, i.e. its conjugate bit is set to True.

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_device

Sets the default torch.Tensor to be allocated on device.

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 no autograd history (also known as a "leaf tensor", see Autograd mechanics) by copying data.

sparse_coo_tensor

Constructs a sparse tensor in COO(rdinate) format with specified values at the given indices.

sparse_csr_tensor

Constructs a sparse tensor in CSR (Compressed Sparse Row) with specified values at the given crow_indices and col_indices.

sparse_csc_tensor

Constructs a sparse tensor in CSC (Compressed Sparse Column) with specified values at the given ccol_indices and row_indices.

sparse_bsr_tensor

Constructs a sparse tensor in BSR (Block Compressed Sparse Row)) with specified 2-dimensional blocks at the given crow_indices and col_indices.

sparse_bsc_tensor

Constructs a sparse tensor in BSC (Block Compressed Sparse Column)) with specified 2-dimensional blocks at the given ccol_indices and row_indices.

asarray

Converts obj to a tensor.

as_tensor

Converts data into a tensor, sharing data and preserving autograd history if possible.

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.

from_dlpack

Converts a tensor from an external library into a torch.Tensor.

frombuffer

Creates a 1-dimensional Tensor from an object that implements the Python buffer protocol.

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 endstartstep\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 endstartstep+1\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 basestart{{\text{{base}}}}^{{\text{{start}}}} to baseend{{\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

Creates a tensor with the specified size and stride and filled with undefined 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

adjoint

Returns a view of the tensor conjugated and with the last two dimensions transposed.

argwhere

Returns a tensor containing the indices of all non-zero elements of input.

cat

Concatenates the given sequence of seq tensors in the given dimension.

concat

Alias of torch.cat().

concatenate

Alias of torch.cat().

conj

Returns a view of input with a flipped conjugate bit.

chunk

Attempts to split a tensor into the specified number of chunks.

dsplit

Splits input, a tensor with three or more dimensions, into multiple tensors depthwise according to indices_or_sections.

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.

hsplit

Splits input, a tensor with one or more dimensions, into multiple tensors horizontally according to indices_or_sections.

hstack

Stack tensors in sequence horizontally (column wise).

index_add

See index_add_() for function description.

index_copy

See index_add_() for function description.

index_reduce

See index_reduce_() for function description.

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.

narrow_copy

Same as Tensor.narrow() except this returns a copy rather than shared storage.

nonzero

permute

Returns a view of the original tensor input with its dimensions permuted.

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().

select

Slices the input tensor along the selected dimension at the given index.

scatter

Out-of-place version of torch.Tensor.scatter_()

diagonal_scatter

Embeds the values of the src tensor into input along the diagonal elements of input, with respect to dim1 and dim2.

select_scatter

Embeds the values of the src tensor into input at the given index.

slice_scatter

Embeds the values of the src tensor into input at the given dimension.

scatter_add

Out-of-place version of torch.Tensor.scatter_add_()

scatter_reduce

Out-of-place version of torch.Tensor.scatter_reduce_()

split

Splits the tensor into chunks.

squeeze

Returns a tensor with all specified 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.

take_along_dim

Selects values from input at the 1-dimensional indices from indices along the given dim.

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.

vsplit

Splits input, a tensor with two or more dimensions, into multiple tensors vertically according to indices_or_sections.

vstack

Stack tensors in sequence vertically (row wise).

where

Return a tensor of elements selected from either input or other, 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)[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)[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:

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 on or off.

is_grad_enabled

Returns True if grad mode is currently enabled.

inference_mode

Context-manager 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 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 other, scaled by alpha, to input.

addcdiv

Performs the element-wise division of tensor1 by tensor2, multiplies the result by the scalar value and adds it to input.

addcmul

Performs the element-wise multiplication of tensor1 by tensor2, multiplies the result by the scalar value and adds 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 inputi/otheri\text{input}_{i} / \text{other}_{i} with consideration of the quadrant.

arctan2

Alias for torch.atan2().

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.

bitwise_left_shift

Computes the left arithmetic shift of input by other bits.

bitwise_right_shift

Computes the right arithmetic shift of input by other bits.

ceil

Returns a new tensor with the ceil of the elements of input, the smallest integer greater than or equal to each element.

clamp

Clamps all elements in input into the range [ min, max ].

clip

Alias for torch.clamp().

conj_physical

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

Alias for torch.special.digamma().

erf

Alias for torch.special.erf().

erfc

Alias for torch.special.erfc().

erfinv

Alias for torch.special.erfinv().

exp

Returns a new tensor with the exponential of the elements of the input tensor input.

exp2

Alias for torch.special.exp2().

expm1

Alias for torch.special.expm1().

fake_quantize_per_channel_affine

Returns a new tensor with the data in input fake quantized per channel using scale, zero_point, quant_min and quant_max, across the channel specified by axis.

fake_quantize_per_tensor_affine

Returns a new tensor with the data in input fake quantized using scale, zero_point, quant_min and quant_max.

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

Applies C++'s std::fmod entrywise.

frac

Computes the fractional portion of each element in input.

frexp

Decomposes input into mantissa and exponent tensors such that input=mantissa×2exponent\text{input} = \text{mantissa} \times 2^{\text{exponent}}.

gradient

Estimates the gradient of a function g:RnRg : \mathbb{R}^n \rightarrow \mathbb{R} in one or more dimensions using the second-order accurate central differences method.

imag

Returns a new tensor containing imaginary values of the self tensor.

ldexp

Multiplies input by 2 ** 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 natural logarithm of the absolute value 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

Alias for torch.special.logit().

hypot

Given the legs of a right triangle, return its hypotenuse.

i0

Alias for torch.special.i0().

igamma

Alias for torch.special.gammainc().

igammac

Alias for torch.special.gammaincc().

mul

Multiplies input by other.

multiply

Alias for torch.mul().

mvlgamma

Alias for torch.special.multigammaln().

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

Alias for torch.special.polygamma().

positive

Returns input.

pow

Takes the power of each element in input with exponent and returns a tensor with the result.

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 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 Python's modulus operation entrywise.

round

Rounds elements of input to the nearest integer.

rsqrt

Returns a new tensor with the reciprocal of the square-root of each of the elements of input.

sigmoid

Alias for torch.special.expit().

sign

Returns a new tensor with the signs of the elements of input.

sgn

This function is an extension of torch.sign() to complex tensors.

signbit

Tests if each element of input has its sign bit set or not.

sin

Returns a new tensor with the sine of the elements of input.

sinc

Alias for torch.special.sinc().

sinh

Returns a new tensor with the hyperbolic sine of the elements of input.

softmax

Alias for torch.nn.functional.softmax().

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() with rounding_mode=None.

trunc

Returns a new tensor with the truncated integer values of the elements of input.

xlogy

Alias for torch.special.xlogy().

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.

aminmax

Computes the minimum and maximum values of the input tensor.

all

Tests if all elements in input evaluate to True.

any

Tests if any element in input evaluates to True.

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.

nanmean

Computes the mean of all non-NaN elements along the specified dimensions.

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. a value which appears most often in that row, and indices is the index location of each mode value found.

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

Computes the q-th quantiles of each row of the input tensor along the dimension dim.

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

Calculates the standard deviation over the dimensions specified by dim.

std_mean

Calculates the standard deviation and mean over the dimensions specified by dim.

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

Calculates the variance over the dimensions specified by dim.

var_mean

Calculates the variance and mean over the dimensions specified by dim.

count_nonzero

Counts the number of non-zero values in the tensor input along the given dim.

Comparison Ops

allclose

This function checks if 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 inputother\text{input} \geq \text{other} element-wise.

greater_equal

Alias for torch.ge().

gt

Computes input>other\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.

isin

Tests if each element of elements is in test_elements.

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 inputother\text{input} \leq \text{other} element-wise.

less_equal

Alias for torch.le().

lt

Computes input<other\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 inputother\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 input tensor with zero dimensions.

atleast_3d

Returns a 3-dimensional view of 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 rr of the given tensor.

corrcoef

Estimates the Pearson product-moment correlation coefficient matrix of the variables given by the input matrix, where rows are the variables and columns are the observations.

cov

Estimates the covariance matrix of the variables given by the input matrix, where rows are the variables and columns are the observations.

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.

diff

Computes the n-th forward difference along the given dimension.

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 an 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 an 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.

histogram

Computes a histogram of the values in a tensor.

histogramdd

Computes a multi-dimensional histogram of the values in a tensor.

meshgrid

Creates grids of coordinates specified by the 1D inputs in attr:tensors.

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 input 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, when sorted, 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.

unflatten

Expands a dimension of the input tensor over multiple dimensions.

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.

resolve_conj

Returns a new tensor with materialized conjugation if input's conjugate bit is set to True, else returns input.

resolve_neg

Returns a new tensor with materialized negation if input's negative bit is set to True, else returns input.

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 NN 2-D tensors.

cholesky

Computes the Cholesky decomposition of a symmetric positive-definite matrix AA or for batches of symmetric positive-definite matrices.

cholesky_inverse

Computes the inverse of a symmetric positive-definite matrix AA using its Cholesky factor uu: returns matrix inv.

cholesky_solve

Solves a linear system of equations with a positive semidefinite matrix to be inverted given its Cholesky factor matrix uu.

dot

Computes the dot product of two 1D tensors.

geqrf

This is a low-level function for calling LAPACK's geqrf directly.

ger

Alias of torch.outer().

inner

Computes the dot product for 1D tensors.

inverse

Alias for torch.linalg.inv()

det

Alias for torch.linalg.det()

logdet

Calculates log determinant of a square matrix or batches of square matrices.

slogdet

Alias for torch.linalg.slogdet()

lu

Computes the LU factorization of a matrix or batches of matrices A.

lu_solve

Returns the LU solve of the linear system Ax=bAx = b using the partially pivoted LU factorization of A from lu_factor().

lu_unpack

Unpacks the LU decomposition returned by lu_factor() into the P, L, U matrices.

matmul

Matrix product of two tensors.

matrix_power

Alias for torch.linalg.matrix_power()

matrix_exp

Alias for torch.linalg.matrix_exp().

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

Alias for torch.linalg.householder_product().

ormqr

Computes the matrix-matrix multiplication of a product of Householder matrices with a general matrix.

outer

Outer product of input and vec2.

pinverse

Alias for torch.linalg.pinv()

qr

Computes the QR decomposition of a matrix or a batch of matrices input, and returns a namedtuple (Q, R) of tensors such that input=QR\text{input} = Q R with QQ being an orthogonal matrix or batch of orthogonal matrices and RR being an upper triangular matrix or batch of upper triangular matrices.

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 AA such that AUdiag(S)VTA \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.

lobpcg

Find the k largest (or smallest) eigenvalues and the corresponding eigenvectors of a symmetric positive definite generalized eigenvalue problem using matrix-free LOBPCG methods.

trapz

Alias for torch.trapezoid().

trapezoid

Computes the trapezoidal rule along dim.

cumulative_trapezoid

Cumulatively computes the trapezoidal rule along dim.

triangular_solve

Solves a system of equations with a square upper or lower triangular invertible matrix AA and multiple right-hand sides bb.

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. Forward-mode AD is not supported.

_foreach_abs

Apply torch.abs() to each Tensor of the input list.

_foreach_abs_

Apply torch.abs() to each Tensor of the input list.

_foreach_acos

Apply torch.acos() to each Tensor of the input list.

_foreach_acos_

Apply torch.acos() to each Tensor of the input list.

_foreach_asin

Apply torch.asin() to each Tensor of the input list.

_foreach_asin_

Apply torch.asin() to each Tensor of the input list.

_foreach_atan

Apply torch.atan() to each Tensor of the input list.

_foreach_atan_

Apply torch.atan() to each Tensor of the input list.

_foreach_ceil

Apply torch.ceil() to each Tensor of the input list.

_foreach_ceil_

Apply torch.ceil() to each Tensor of the input list.

_foreach_cos

Apply torch.cos() to each Tensor of the input list.

_foreach_cos_

Apply torch.cos() to each Tensor of the input list.

_foreach_cosh

Apply torch.cosh() to each Tensor of the input list.

_foreach_cosh_

Apply torch.cosh() to each Tensor of the input list.

_foreach_erf

Apply torch.erf() to each Tensor of the input list.

_foreach_erf_

Apply torch.erf() to each Tensor of the input list.

_foreach_erfc

Apply torch.erfc() to each Tensor of the input list.

_foreach_erfc_

Apply torch.erfc() to each Tensor of the input list.

_foreach_exp

Apply torch.exp() to each Tensor of the input list.

_foreach_exp_

Apply torch.exp() to each Tensor of the input list.

_foreach_expm1

Apply torch.expm1() to each Tensor of the input list.

_foreach_expm1_

Apply torch.expm1() to each Tensor of the input list.

_foreach_floor

Apply torch.floor() to each Tensor of the input list.

_foreach_floor_

Apply torch.floor() to each Tensor of the input list.

_foreach_log

Apply torch.log() to each Tensor of the input list.

_foreach_log_

Apply torch.log() to each Tensor of the input list.

_foreach_log10

Apply torch.log10() to each Tensor of the input list.

_foreach_log10_

Apply torch.log10() to each Tensor of the input list.

_foreach_log1p

Apply torch.log1p() to each Tensor of the input list.

_foreach_log1p_

Apply torch.log1p() to each Tensor of the input list.

_foreach_log2

Apply torch.log2() to each Tensor of the input list.

_foreach_log2_

Apply torch.log2() to each Tensor of the input list.

_foreach_neg

Apply torch.neg() to each Tensor of the input list.

_foreach_neg_

Apply torch.neg() to each Tensor of the input list.

_foreach_tan

Apply torch.tan() to each Tensor of the input list.

_foreach_tan_

Apply torch.tan() to each Tensor of the input list.

_foreach_sin

Apply torch.sin() to each Tensor of the input list.

_foreach_sin_

Apply torch.sin() to each Tensor of the input list.

_foreach_sinh

Apply torch.sinh() to each Tensor of the input list.

_foreach_sinh_

Apply torch.sinh() to each Tensor of the input list.

_foreach_round

Apply torch.round() to each Tensor of the input list.

_foreach_round_

Apply torch.round() to each Tensor of the input list.

_foreach_sqrt

Apply torch.sqrt() to each Tensor of the input list.

_foreach_sqrt_

Apply torch.sqrt() to each Tensor of the input list.

_foreach_lgamma

Apply torch.lgamma() to each Tensor of the input list.

_foreach_lgamma_

Apply torch.lgamma() to each Tensor of the input list.

_foreach_frac

Apply torch.frac() to each Tensor of the input list.

_foreach_frac_

Apply torch.frac() to each Tensor of the input list.

_foreach_reciprocal

Apply torch.reciprocal() to each Tensor of the input list.

_foreach_reciprocal_

Apply torch.reciprocal() to each Tensor of the input list.

_foreach_sigmoid

Apply torch.sigmoid() to each Tensor of the input list.

_foreach_sigmoid_

Apply torch.sigmoid() to each Tensor of the input list.

_foreach_trunc

Apply torch.trunc() to each Tensor of the input list.

_foreach_trunc_

Apply torch.trunc() to each Tensor of the input list.

_foreach_zero_

Apply torch.zero() to each Tensor of the input list.

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.

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; vmap(func) returns a new function that maps func over some dimension of the inputs.

_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

SymInt-aware utility for float casting.

sym_int

SymInt-aware utility for int casting.

sym_max

SymInt-aware utility for max().

sym_min

SymInt-aware utility for max().

sym_not

SymInt-aware utility for logical negation.

Optimizations

compile

Optimizes given model/function using TorchDynamo and specified backend.

Operator Tags

class torch.Tag

Members:

nondeterministic_bitwise

pointwise

view_copy

nondeterministic_seeded

dynamic_output_shape

core

inplace_view

generated

data_dependent_output

property name

torch.autograd provides classes and functions implementing automatic differentiation of arbitrary scalar valued functions. It requires minimal changes to the existing code - you only need to declare Tensor s for which gradients should be computed with the requires_grad=True keyword. As of now, we only support autograd for floating point Tensor types ( half, float, double and bfloat16) and complex Tensor types (cfloat, cdouble).

Engine Configuration

set_multithreading_enabled

Context-manager that sets multithreaded backwards on or off.

Docs

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Resources

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