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 $\left\lceil \frac{\text{end} - \text{start}}{\text{step}} \right\rceil$ 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	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 $[0, 1)$
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 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 $\text{input}_{i} / \text{other}_{i}$ 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 $\text{input} = \text{mantissa} \times 2^{\text{exponent}}$.
gradient	Estimates the gradient of a function $g : \mathbb{R}^n \rightarrow \mathbb{R}$ 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 $\text{input} \geq \text{other}$ 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.
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 $\text{input} \leq \text{other}$ 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 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 $r$ 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 $N$ 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.
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 $Ax = b$ 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 $\text{input} = Q R$ with $Q$ being an orthogonal matrix or batch of orthogonal matrices and $R$ being an upper triangular matrix or batch of upper triangular matrices.
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.
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 $A$ and multiple right-hand 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. 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)

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)

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)

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.