torch

Tensors

torch.is_tensor(obj)

Returns True if obj is a PyTorch tensor.

Parameters:obj (Object) – Object to test

torch.is_storage(obj)

Returns True if obj is a PyTorch storage object.

Parameters:obj (Object) – Object to test

torch.set_default_dtype(d)

Sets the default floating point dtype to d. This type will be used as default floating point type for type inference in torch.tensor().

The default floating point dtype is initially torch.float32.

Parameters:d (torch.dtype) – the floating point dtype to make the default

Example:

>>> torch.tensor([1.2, 3]).dtype           # initial default for floating point is torch.float32
torch.float32
>>> torch.set_default_dtype(torch.float64)
>>> torch.tensor([1.2, 3]).dtype           # a new floating point tensor
torch.float64

torch.get_default_dtype() → :class:`torch.dtype`

Get the current default floating point torch.dtype.

Example:

>>> torch.get_default_dtype()  # initial default for floating point is torch.float32
torch.float32
>>> torch.set_default_dtype(torch.float64)
>>> torch.get_default_dtype()  # default is now changed to torch.float64
torch.float64
>>> torch.set_default_tensor_type(torch.FloatTensor)  # setting tensor type also affects this
>>> torch.get_default_dtype()  # changed to torch.float32, the dtype for torch.FloatTensor
torch.float32

torch.set_default_tensor_type(t)

Sets the default torch.Tensor type to floating point tensor type t. This type will also be used as default floating point type for type inference in torch.tensor().

The default floating point tensor type is initially torch.FloatTensor.

Parameters:t (type or string) – the floating point tensor type or its name

Example:

>>> torch.tensor([1.2, 3]).dtype    # initial default for floating point is torch.float32
torch.float32
>>> torch.set_default_tensor_type(torch.DoubleTensor)
>>> torch.tensor([1.2, 3]).dtype    # a new floating point tensor
torch.float64

torch.numel(input) → int

Returns the total number of elements in the input tensor.

Parameters:input (Tensor) – the input tensor

Example:

>>> a = torch.randn(1, 2, 3, 4, 5)
>>> torch.numel(a)
120
>>> a = torch.zeros(4,4)
>>> torch.numel(a)
16

torch.set_printoptions(precision=None, threshold=None, edgeitems=None, linewidth=None, profile=None)

Set options for printing. Items shamelessly taken from NumPy

Parameters:

• precision – Number of digits of precision for floating point output (default = 8).
• threshold – Total number of array elements which trigger summarization rather than full repr (default = 1000).
• edgeitems – Number of array items in summary at beginning and end of each dimension (default = 3).
• linewidth – The number of characters per line for the purpose of inserting line breaks (default = 80). Thresholded matrices will ignore this parameter.
• profile – Sane defaults for pretty printing. Can override with any of the above options. (any one of default, short, full)

torch.set_flush_denormal(mode) → bool

Disables denormal floating numbers on CPU.

Returns True if your system supports flushing denormal numbers and it successfully configures flush denormal mode. set_flush_denormal() is only supported on x86 architectures supporting SSE3.

Parameters:mode (bool) – Controls whether to enable flush denormal mode or not

Example:

>>> torch.set_flush_denormal(True)
True
>>> torch.tensor([1e-323], dtype=torch.float64)
tensor([ 0.], dtype=torch.float64)
>>> torch.set_flush_denormal(False)
True
>>> torch.tensor([1e-323], dtype=torch.float64)
tensor(9.88131e-324 *
       [ 1.0000], dtype=torch.float64)

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.

torch.tensor(data, dtype=None, device=None, requires_grad=False) → Tensor

Constructs a tensor with data.

Warning

torch.tensor() always copies data. If you have a Tensor data and want to avoid a copy, use torch.Tensor.requires_grad_() or torch.Tensor.detach(). If you have a NumPy ndarray and want to avoid a copy, use torch.from_numpy().

Parameters:

• data (array_like) – Initial data for the tensor. Can be a list, tuple, NumPy ndarray, scalar, and other types.
• dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: if None, infers data type from data.
• device (torch.device, optional) – the desired device of returned tensor. Default: if None, uses the current device for the default tensor type (see torch.set_default_tensor_type()). device will be the CPU for CPU tensor types and the current CUDA device for CUDA tensor types.
• requires_grad (bool, optional) – If autograd should record operations on the returned tensor. Default: False.

Example:

>>> torch.tensor([[0.1, 1.2], [2.2, 3.1], [4.9, 5.2]])
tensor([[ 0.1000,  1.2000],
        [ 2.2000,  3.1000],
        [ 4.9000,  5.2000]])

>>> torch.tensor([0, 1])  # Type inference on data
tensor([ 0,  1])

>>> torch.tensor([[0.11111, 0.222222, 0.3333333]],
                 dtype=torch.float64,
                 device=torch.device('cuda:0'))  # creates a torch.cuda.DoubleTensor
tensor([[ 0.1111,  0.2222,  0.3333]], dtype=torch.float64, device='cuda:0')

>>> torch.tensor(3.14159)  # Create a scalar (zero-dimensional tensor)
tensor(3.1416)

>>> torch.tensor([])  # Create an empty tensor (of size (0,))
tensor([])

torch.from_numpy(ndarray) → Tensor

Creates a Tensor from a numpy.ndarray.

The returned tensor and ndarray share the same memory. Modifications to the tensor will be reflected in the ndarray and vice versa. The returned tensor is not resizable.

Example:

>>> a = numpy.array([1, 2, 3])
>>> t = torch.from_numpy(a)
>>> t
tensor([ 1,  2,  3])
>>> t[0] = -1
>>> a
array([-1,  2,  3])

torch.zeros(*sizes, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

Returns a tensor filled with the scalar value 0, with the shape defined by the variable argument sizes.

Parameters:

• sizes (int...) – a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple.
• out (Tensor, optional) – the output tensor
• dtype (torch.dtype, optional) – the desired data type of returned tensor.
• layout (torch.layout, optional) – the desired layout of returned Tensor.
• device (torch.device, optional) – the desired device of returned tensor.
• requires_grad (bool, optional) – If autograd should record operations on the

Example:

>>> torch.zeros(2, 3)
tensor([[ 0.,  0.,  0.],
        [ 0.,  0.,  0.]])

>>> torch.zeros(5)
tensor([ 0.,  0.,  0.,  0.,  0.])

torch.zeros_like(input, dtype=None, layout=None, device=None, requires_grad=False) → Tensor

Returns a tensor filled with the scalar value 0, with the same size as input. torch.zeros_like(input) is equivalent to torch.zeros(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).

Warning

As of 0.4, this function does not support an out keyword. As an alternative, the old torch.zeros_like(input, out=output) is equivalent to torch.zeros(input.size(), out=output).

Parameters:

• input (Tensor) – the size of input will determine size of the output tensor
• dtype (torch.dtype, optional) – the desired data type of returned Tensor.
• layout (torch.layout, optional) – the desired layout of returned tensor.
• device (torch.device, optional) – the desired device of returned tensor.
• requires_grad (bool, optional) – If autograd should record operations on the

Example:

>>> input = torch.empty(2, 3)
>>> torch.zeros_like(input)
tensor([[ 0.,  0.,  0.],
        [ 0.,  0.,  0.]])

torch.ones(*sizes, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

Returns a tensor filled with the scalar value 1, with the shape defined by the variable argument sizes.

Parameters:

• sizes (int...) – a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple.
• out (Tensor, optional) – the output tensor
• dtype (torch.dtype, optional) – the desired data type of returned tensor.
• layout (torch.layout, optional) – the desired layout of returned Tensor.
• device (torch.device, optional) – the desired device of returned tensor.
• requires_grad (bool, optional) – If autograd should record operations on the

Example:

>>> torch.ones(2, 3)
tensor([[ 1.,  1.,  1.],
        [ 1.,  1.,  1.]])

>>> torch.ones(5)
tensor([ 1.,  1.,  1.,  1.,  1.])

torch.ones_like(input, dtype=None, layout=None, device=None, requires_grad=False) → Tensor

Returns a tensor filled with the scalar value 1, with the same size as input. torch.ones_like(input) is equivalent to torch.ones(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).

Warning

As of 0.4, this function does not support an out keyword. As an alternative, the old torch.ones_like(input, out=output) is equivalent to torch.ones(input.size(), out=output).

Parameters:

• input (Tensor) – the size of input will determine size of the output tensor
• dtype (torch.dtype, optional) – the desired data type of returned Tensor.
• layout (torch.layout, optional) – the desired layout of returned tensor.
• device (torch.device, optional) – the desired device of returned tensor.
• requires_grad (bool, optional) – If autograd should record operations on the

Example:

>>> input = torch.empty(2, 3)
>>> torch.ones_like(input)
tensor([[ 1.,  1.,  1.],
        [ 1.,  1.,  1.]])

torch.arange(start=0, end, step=1, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

Returns a 1-D tensor of size $\left\lfloor \frac{end - start}{step} \right\rfloor$ with values from the interval [start, end) taken with common difference step beginning from start.

Note that non-integer step is subject to floating point rounding errors when comparing against end; to avoid inconsistency, we advise adding a small epsilon to end in such cases.

$$\text{out}_{i+1} = \text{out}_{i} + \text{step}$$

Parameters:

• start (float) – the starting value for the set of points. Default: 0.
• end (float) – the ending value for the set of points
• step (float) – the gap between each pair of adjacent points. Default: 1.
• out (Tensor, optional) – the output tensor
• dtype (torch.dtype, optional) – the desired data type of returned tensor.
• layout (torch.layout, optional) – the desired layout of returned Tensor.
• device (torch.device, optional) – the desired device of returned tensor.
• requires_grad (bool, optional) – If autograd should record operations on the

Example:

>>> torch.arange(5)
tensor([ 0.,  1.,  2.,  3.,  4.])
>>> torch.arange(1, 4)
tensor([ 1.,  2.,  3.])
>>> torch.arange(1, 2.5, 0.5)
tensor([ 1.0000,  1.5000,  2.0000])

torch.range(start=0, end, step=1, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

Returns a 1-D tensor of size $\left\lfloor \frac{end - start}{step} \right\rfloor + 1$ with values from start to end with step step. Step is the gap between two values in the tensor.

$$\text{out}_{i+1} = \text{out}_i + step.$$

Warning

This function is deprecated in favor of torch.arange().

Parameters:

• start (float) – the starting value for the set of points. Default: 0.
• end (float) – the ending value for the set of points
• step (float) – the gap between each pair of adjacent points. Default: 1.
• out (Tensor, optional) – the output tensor
• dtype (torch.dtype, optional) – the desired data type of returned tensor.
• layout (torch.layout, optional) – the desired layout of returned Tensor.
• device (torch.device, optional) – the desired device of returned tensor.
• requires_grad (bool, optional) – If autograd should record operations on the

Example:

>>> torch.range(1, 4)
tensor([ 1.,  2.,  3.,  4.])
>>> torch.range(1, 4, 0.5)
tensor([ 1.0000,  1.5000,  2.0000,  2.5000,  3.0000,  3.5000,  4.0000])

torch.linspace(start, end, steps=100, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

Returns a one-dimensional tensor of steps equally spaced points between start and end.

The output tensor is 1-D of size steps.

Parameters:

• start (float) – the starting value for the set of points
• end (float) – the ending value for the set of points
• steps (int) – number of points to sample between start and end. Default: 100.
• out (Tensor, optional) – the output tensor
• dtype (torch.dtype, optional) – the desired data type of returned tensor.
• layout (torch.layout, optional) – the desired layout of returned Tensor.
• device (torch.device, optional) – the desired device of returned tensor.
• requires_grad (bool, optional) – If autograd should record operations on the

Example:

>>> torch.linspace(3, 10, steps=5)
tensor([  3.0000,   4.7500,   6.5000,   8.2500,  10.0000])
>>> torch.linspace(-10, 10, steps=5)
tensor([-10.,  -5.,   0.,   5.,  10.])
>>> torch.linspace(start=-10, end=10, steps=5)
tensor([-10.,  -5.,   0.,   5.,  10.])

torch.logspace(start, end, steps=100, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

Returns a one-dimensional tensor of steps points logarithmically spaced between $10^{\text{start}}$ and $10^{\text{end}}$.

The output tensor is 1-D of size steps.

Parameters:

• start (float) – the starting value for the set of points
• end (float) – the ending value for the set of points
• steps (int) – number of points to sample between start and end. Default: 100.
• out (Tensor, optional) – the output tensor
• dtype (torch.dtype, optional) – the desired data type of returned tensor.
• layout (torch.layout, optional) – the desired layout of returned Tensor.
• device (torch.device, optional) – the desired device of returned tensor.
• requires_grad (bool, optional) – If autograd should record operations on the

Example:

>>> torch.logspace(start=-10, end=10, steps=5)
tensor([ 1.0000e-10,  1.0000e-05,  1.0000e+00,  1.0000e+05,  1.0000e+10])
>>> torch.logspace(start=0.1, end=1.0, steps=5)
tensor([  1.2589,   2.1135,   3.5481,   5.9566,  10.0000])

torch.eye(n, m=None, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

Returns a 2-D tensor with ones on the diagonal and zeros elsewhere.

Parameters:

• n (int) – the number of rows
• m (int, optional) – the number of columns with default being n
• out (Tensor, optional) – the output tensor
• dtype (torch.dtype, optional) – the desired data type of returned tensor.
• layout (torch.layout, optional) – the desired layout of returned Tensor.
• device (torch.device, optional) – the desired device of returned tensor.
• requires_grad (bool, optional) – If autograd should record operations on the

Returns:

A 2-D tensor with ones on the diagonal and zeros elsewhere

Return type:

Tensor

Example:

>>> torch.eye(3)
tensor([[ 1.,  0.,  0.],
        [ 0.,  1.,  0.],
        [ 0.,  0.,  1.]])

torch.empty(*sizes, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

Returns a tensor filled with uninitialized data. The shape of the tensor is defined by the variable argument sizes.

Parameters:

• sizes (int...) – a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple.
• out (Tensor, optional) – the output tensor
• dtype (torch.dtype, optional) – the desired data type of returned tensor.
• layout (torch.layout, optional) – the desired layout of returned Tensor.
• device (torch.device, optional) – the desired device of returned tensor.
• requires_grad (bool, optional) – If autograd should record operations on the

Example:

>>> torch.empty(2, 3)
tensor(1.00000e-08 *
       [[ 6.3984,  0.0000,  0.0000],
        [ 0.0000,  0.0000,  0.0000]])

torch.empty_like(input, dtype=None, layout=None, device=None, requires_grad=False) → Tensor

Returns an uninitialized tensor with the same size as input. torch.empty_like(input) is equivalent to torch.empty(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).

Parameters:

• input (Tensor) – the size of input will determine size of the output tensor
• dtype (torch.dtype, optional) – the desired data type of returned Tensor.
• layout (torch.layout, optional) – the desired layout of returned tensor.
• device (torch.device, optional) – the desired device of returned tensor.
• requires_grad (bool, optional) – If autograd should record operations on the

Example:

>>> input = torch.empty((2,3), dtype=torch.int64)
>>> input.new(input.size())
tensor([[ 9.4064e+13,  2.8000e+01,  9.3493e+13],
        [ 7.5751e+18,  7.1428e+18,  7.5955e+18]])

torch.full(size, fill_value, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

Returns a tensor of size size filled with fill_value.

Parameters:

• size (int...) – a list, tuple, or torch.Size of integers defining the shape of the output tensor.
• fill_value – the number to fill the output tensor with.
• out (Tensor, optional) – the output tensor
• dtype (torch.dtype, optional) – the desired data type of returned tensor.
• layout (torch.layout, optional) – the desired layout of returned Tensor.
• device (torch.device, optional) – the desired device of returned tensor.
• requires_grad (bool, optional) – If autograd should record operations on the

Example:

>>> torch.full((2, 3), 3.141592)
tensor([[ 3.1416,  3.1416,  3.1416],
        [ 3.1416,  3.1416,  3.1416]])

torch.full_like(input, fill_value, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

Returns a tensor with the same size as input filled with fill_value. torch.full_like(input, fill_value) is equivalent to torch.full_like(input.size(), fill_value, dtype=input.dtype, layout=input.layout, device=input.device).

Parameters:

• input (Tensor) – the size of input will determine size of the output tensor
• fill_value – the number to fill the output tensor with.
• dtype (torch.dtype, optional) – the desired data type of returned Tensor.
• layout (torch.layout, optional) – the desired layout of returned tensor.
• device (torch.device, optional) – the desired device of returned tensor.
• requires_grad (bool, optional) – If autograd should record operations on the

Indexing, Slicing, Joining, Mutating Ops

torch.cat(seq, dim=0, out=None) → Tensor

Concatenates the given sequence of seq tensors in the given dimension. All tensors must either have the same shape (except in the concatenating dimension) or be empty.

torch.cat() can be seen as an inverse operation for torch.split() and torch.chunk().

torch.cat() can be best understood via examples.

Parameters:

• seq (sequence of Tensors) – any python sequence of tensors of the same type. Non-empty tensors provided must have the same shape, except in the cat dimension.
• dim (int, optional) – the dimension over which the tensors are concatenated
• out (Tensor, optional) – the output tensor

Example:

>>> x = torch.randn(2, 3)
>>> x
tensor([[ 0.6580, -1.0969, -0.4614],
        [-0.1034, -0.5790,  0.1497]])
>>> torch.cat((x, x, x), 0)
tensor([[ 0.6580, -1.0969, -0.4614],
        [-0.1034, -0.5790,  0.1497],
        [ 0.6580, -1.0969, -0.4614],
        [-0.1034, -0.5790,  0.1497],
        [ 0.6580, -1.0969, -0.4614],
        [-0.1034, -0.5790,  0.1497]])
>>> torch.cat((x, x, x), 1)
tensor([[ 0.6580, -1.0969, -0.4614,  0.6580, -1.0969, -0.4614,  0.6580,
         -1.0969, -0.4614],
        [-0.1034, -0.5790,  0.1497, -0.1034, -0.5790,  0.1497, -0.1034,
         -0.5790,  0.1497]])

torch.chunk(tensor, chunks, dim=0) → List of Tensors

Splits a tensor into a specific number of chunks.

Last chunk will be smaller if the tensor size along the given dimension dim is not divisible by chunks.

Parameters:

• tensor (Tensor) – the tensor to split
• chunks (int) – number of chunks to return
• dim (int) – dimension along which to split the tensor

torch.gather(input, dim, index, out=None) → Tensor

Gathers values along an axis specified by dim.

For a 3-D tensor the output is specified by:

out[i][j][k] = input[index[i][j][k]][j][k]  # if dim == 0
out[i][j][k] = input[i][index[i][j][k]][k]  # if dim == 1
out[i][j][k] = input[i][j][index[i][j][k]]  # if dim == 2

If input is an n-dimensional tensor with size $(x_0, x_1..., x_{i-1}, x_i, x_{i+1}, ..., x_{n-1})$ and dim $= i$, then index must be an $n$-dimensional tensor with size $(x_0, x_1, ..., x_{i-1}, y, x_{i+1}, ..., x_{n-1})$ where $y \geq 1$ and out will have the same size as index.

Parameters:

• input (Tensor) – the source tensor
• dim (int) – the axis along which to index
• index (LongTensor) – the indices of elements to gather
• out (Tensor, optional) – the destination tensor

Example:

>>> t = torch.tensor([[1,2],[3,4]])
>>> torch.gather(t, 1, torch.tensor([[0,0],[1,0]]))
tensor([[ 1,  1],
        [ 4,  3]])

torch.index_select(input, dim, index, out=None) → Tensor

Returns a new tensor which indexes the input tensor along dimension dim using the entries in index which is a LongTensor.

The returned tensor has the same number of dimensions as the original tensor (input). The dimth dimension has the same size as the length of index; other dimensions have the same size as in the original tensor.

Note

The returned tensor does not use the same storage as the original tensor. If out has a different shape than expected, we silently change it to the correct shape, reallocating the underlying storage if necessary.

Parameters:

• input (Tensor) – the input tensor
• dim (int) – the dimension in which we index
• index (LongTensor) – the 1-D tensor containing the indices to index
• out (Tensor, optional) – the output tensor

Example:

>>> x = torch.randn(3, 4)
>>> x
tensor([[ 0.1427,  0.0231, -0.5414, -1.0009],
        [-0.4664,  0.2647, -0.1228, -1.1068],
        [-1.1734, -0.6571,  0.7230, -0.6004]])
>>> indices = torch.tensor([0, 2])
>>> torch.index_select(x, 0, indices)
tensor([[ 0.1427,  0.0231, -0.5414, -1.0009],
        [-1.1734, -0.6571,  0.7230, -0.6004]])
>>> torch.index_select(x, 1, indices)
tensor([[ 0.1427, -0.5414],
        [-0.4664, -0.1228],
        [-1.1734,  0.7230]])

torch.masked_select(input, mask, out=None) → Tensor

Returns a new 1-D tensor which indexes the input tensor according to the binary mask mask which is a ByteTensor.

The shapes of the mask tensor and the input tensor don’t need to match, but they must be broadcastable.

Note

The returned tensor does not use the same storage as the original tensor

Parameters:

• input (Tensor) – the input data
• mask (ByteTensor) – the tensor containing the binary mask to index with
• out (Tensor, optional) – the output tensor

Example:

>>> x = torch.randn(3, 4)
>>> x
tensor([[ 0.3552, -2.3825, -0.8297,  0.3477],
        [-1.2035,  1.2252,  0.5002,  0.6248],
        [ 0.1307, -2.0608,  0.1244,  2.0139]])
>>> mask = x.ge(0.5)
>>> mask
tensor([[ 0,  0,  0,  0],
        [ 0,  1,  1,  1],
        [ 0,  0,  0,  1]], dtype=torch.uint8)
>>> torch.masked_select(x, mask)
tensor([ 1.2252,  0.5002,  0.6248,  2.0139])

torch.nonzero(input, out=None) → LongTensor

Returns a tensor containing the indices of all non-zero elements of input. Each row in the result contains the indices of a non-zero element in input.

If input has n dimensions, then the resulting indices tensor out is of size $(z \times n)$, where $z$ is the total number of non-zero elements in the input tensor.

Parameters:

• input (Tensor) – the input tensor
• out (LongTensor, optional) – the output tensor containing indices

Example:

>>> torch.nonzero(torch.tensor([1, 1, 1, 0, 1]))
tensor([[ 0],
        [ 1],
        [ 2],
        [ 4]])
>>> torch.nonzero(torch.tensor([[0.6, 0.0, 0.0, 0.0],
                                [0.0, 0.4, 0.0, 0.0],
                                [0.0, 0.0, 1.2, 0.0],
                                [0.0, 0.0, 0.0,-0.4]]))
tensor([[ 0,  0],
        [ 1,  1],
        [ 2,  2],
        [ 3,  3]])

torch.reshape(input, shape) → Tensor

Returns a tensor with the same data and number of elements as input, but with the specified shape. When possible, the returned tensor will be a view of input. Otherwise, it will be a copy. Contiguous inputs and inputs with compatible strides can be reshaped without copying, but you should not depend on the copying vs. viewing behavior.

A single dimension may be -1, in which case it’s inferred from the remaining dimensions and the number of elements in input.

Parameters:

• input (Tensor) – the tensor to be reshaped
• shape (tuple of python:ints) – the new shape

Example:

>>> a = torch.arange(4)
>>> torch.reshape(a, (2, 2))
tensor([[ 0.,  1.],
        [ 2.,  3.]])
>>> b = torch.tensor([[0, 1], [2, 3]])
>>> torch.reshape(b, (-1,))
tensor([ 0,  1,  2,  3])

torch.split(tensor, split_size_or_sections, dim=0)

Splits the tensor into chunks.

If split_size_or_sections is an integer type, then tensor will be split into equally sized chunks (if possible). Last chunk will be smaller if the tensor size along the given dimension dim= is not divisible by :attr:`split_size.

If split_size_or_sections is a list, then tensor will be split into len(split_size_or_sections) chunks with sizes in dim according to split_size_or_sections.

Parameters:

• tensor (Tensor) – tensor to split.
• split_size_or_sections (int) or (list(int)) – size of a single chunk or
• of sizes for each chunk (list) –
• dim (int) – dimension along which to split the tensor.

torch.squeeze(input, dim=None, out=None) → Tensor

Returns a tensor with all the dimensions of input of size 1 removed.

For example, if input is of shape: $(A \times 1 \times B \times C \times 1 \times D)$ then the out tensor will be of shape: $(A \times B \times C \times D)$.

When dim is given, a squeeze operation is done only in the given dimension. If input is of shape: $(A \times 1 \times B)$, squeeze(input, 0) leaves the tensor unchanged, but squeeze(input, 1)() will squeeze the tensor to the shape $(A \times B)$.

Note

As an exception to the above, a 1-dimensional tensor of size 1 will not have its dimensions changed.

Note

The returned tensor shares the storage with the input tensor, so changing the contents of one will change the contents of the other.

Parameters:

• input (Tensor) – the input tensor
• dim (int, optional) – if given, the input will be squeezed only in this dimension
• out (Tensor, optional) – the output tensor

Example:

>>> x = torch.zeros(2, 1, 2, 1, 2)
>>> x.size()
torch.Size([2, 1, 2, 1, 2])
>>> y = torch.squeeze(x)
>>> y.size()
torch.Size([2, 2, 2])
>>> y = torch.squeeze(x, 0)
>>> y.size()
torch.Size([2, 1, 2, 1, 2])
>>> y = torch.squeeze(x, 1)
>>> y.size()
torch.Size([2, 2, 1, 2])

torch.stack(seq, dim=0, out=None) → Tensor

Concatenates sequence of tensors along a new dimension.

All tensors need to be of the same size.

Parameters:

• seq (sequence of Tensors) – sequence of tensors to concatenate
• dim (int) – dimension to insert. Has to be between 0 and the number of dimensions of concatenated tensors (inclusive)
• out (Tensor, optional) – the output tensor

torch.t(input, out=None) → Tensor

Expects input to be a matrix (2-D tensor) and transposes dimensions 0 and 1.

Can be seen as a short-hand function for transpose(input, 0, 1)()

Parameters:

• input (Tensor) – the input tensor
• out (Tensor, optional) – the output tensor

Example:

>>> x = torch.randn(2, 3)
>>> x
tensor([[ 0.4875,  0.9158, -0.5872],
        [ 0.3938, -0.6929,  0.6932]])
>>> torch.t(x)
tensor([[ 0.4875,  0.3938],
        [ 0.9158, -0.6929],
        [-0.5872,  0.6932]])

torch.take(input, indices) → Tensor

Returns a new tensor with the elements of input at the given indices. The input tensor is treated as if it were viewed as a 1-D tensor. The result takes the same shape as the indices.

Parameters:

• input (Tensor) – the input tensor
• indices (LongTensor) – the indices into tensor

Example:

>>> src = torch.tensor([[4, 3, 5],
                        [6, 7, 8]])
>>> torch.take(src, torch.tensor([0, 2, 5]))
tensor([ 4,  5,  8])

torch.transpose(input, dim0, dim1, out=None) → Tensor

Returns a tensor that is a transposed version of input. The given dimensions dim0 and dim1 are swapped.

The resulting out tensor shares it’s underlying storage with the input tensor, so changing the content of one would change the content of the other.

Parameters:

• input (Tensor) – the input tensor
• dim0 (int) – the first dimension to be transposed
• dim1 (int) – the second dimension to be transposed
• out (Tensor, optional) – the output tensor

Example:

>>> x = torch.randn(2, 3)
>>> x
tensor([[ 1.0028, -0.9893,  0.5809],
        [-0.1669,  0.7299,  0.4942]])
>>> torch.transpose(x, 0, 1)
tensor([[ 1.0028, -0.1669],
        [-0.9893,  0.7299],
        [ 0.5809,  0.4942]])

torch.unbind(tensor, dim=0)

Removes a tensor dimension.

Returns a tuple of all slices along a given dimension, already without it.

Parameters:

• tensor (Tensor) – the tensor to unbind
• dim (int) – dimension to remove

torch.unsqueeze(input, dim, out=None) → Tensor

Returns a new tensor with a dimension of size one inserted at the specified position.

The returned tensor shares the same underlying data with this tensor.

A negative dim value within the range [-input.dim(), input.dim()) can be used and will correspond to unsqueeze() applied at dim = dim + input.dim() + 1

Parameters:

• input (Tensor) – the input tensor
• dim (int) – the index at which to insert the singleton dimension
• out (Tensor, optional) – the output tensor

Example:

>>> x = torch.tensor([1, 2, 3, 4])
>>> torch.unsqueeze(x, 0)
tensor([[ 1,  2,  3,  4]])
>>> torch.unsqueeze(x, 1)
tensor([[ 1],
        [ 2],
        [ 3],
        [ 4]])

torch.where(condition, x, y) → Tensor

Return a tensor of elements selected from either x or y, depending on condition.

The operation is defined as:

$$\begin{split}out_i = \begin{cases} x_i & \text{if } condition_i \\ y_i & \text{otherwise} \\ \end{cases}\end{split}$$

Note

The tensors condition, x, y must be broadcastable.

Parameters:

• condition (ByteTensor) – When True (nonzero), yield x, otherwise yield y
• x (Tensor) – values selected at indices where condition is True
• y (Tensor) – values selected at indices where condition is False

Returns:

A tensor of shape equal to the broadcasted shape of condition, x, y

Return type:

Tensor

Example:

>>> x = torch.randn(3, 2)
>>> y = torch.ones(3, 2)
>>> x
tensor([[-0.4620,  0.3139],
        [ 0.3898, -0.7197],
        [ 0.0478, -0.1657]])
>>> torch.where(x > 0, x, y)
tensor([[ 1.0000,  0.3139],
        [ 0.3898,  1.0000],
        [ 0.0478,  1.0000]])

Random sampling

torch.manual_seed(seed)

Sets the seed for generating random numbers. Returns a torch._C.Generator object.

Parameters:seed (int) – The desired seed.

torch.initial_seed()

Returns the initial seed for generating random numbers as a Python long.

torch.get_rng_state()

Returns the random number generator state as a torch.ByteTensor.

torch.set_rng_state(new_state)

Sets the random number generator state.

Parameters:new_state (torch.ByteTensor) – The desired state

torch.default_generator = <torch._C.Generator object>

torch.bernoulli(input, out=None) → Tensor

Draws binary random numbers (0 or 1) from a Bernoulli distribution.

The input tensor should be a tensor containing probabilities to be used for drawing the binary random number. Hence, all values in input have to be in the range: $0 \leq \text{input}_i \leq 1$.

The $\text{i}^{th}$ element of the output tensor will draw a value 1 according to the $\text{i}^{th}$ probability value given in input.

$$\text{out}_{i} \sim \mathrm{Bernoulli}(p = \text{input}_{i})$$

The returned out tensor only has values 0 or 1 and is of the same shape as input

Parameters:

• input (Tensor) – the input tensor of probability values for the Bernoulli distribution
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.empty(3, 3).uniform_(0, 1) # generate a uniform random matrix with range [0, 1]
>>> a
tensor([[ 0.1737,  0.0950,  0.3609],
        [ 0.7148,  0.0289,  0.2676],
        [ 0.9456,  0.8937,  0.7202]])
>>> torch.bernoulli(a)
tensor([[ 1.,  0.,  0.],
        [ 0.,  0.,  0.],
        [ 1.,  1.,  1.]])

>>> a = torch.ones(3, 3) # probability of drawing "1" is 1
>>> torch.bernoulli(a)
tensor([[ 1.,  1.,  1.],
        [ 1.,  1.,  1.],
        [ 1.,  1.,  1.]])
>>> a = torch.zeros(3, 3) # probability of drawing "1" is 0
>>> torch.bernoulli(a)
tensor([[ 0.,  0.,  0.],
        [ 0.,  0.,  0.],
        [ 0.,  0.,  0.]])

torch.multinomial(input, num_samples, replacement=False, out=None) → LongTensor

Returns a tensor where each row contains num_samples indices sampled from the multinomial probability distribution located in the corresponding row of tensor input.

Note

The rows of input do not need to sum to one (in which case we use the values as weights), but must be non-negative and have a non-zero sum.

Indices are ordered from left to right according to when each was sampled (first samples are placed in first column).

If input is a vector, out is a vector of size num_samples.

If input is a matrix with m rows, out is an matrix of shape $(m \times num\_samples)$.

If replacement is True, samples are drawn with replacement.

If not, they are drawn without replacement, which means that when a sample index is drawn for a row, it cannot be drawn again for that row.

This implies the constraint that num_samples must be lower than input length (or number of columns of input if it is a matrix).

Parameters:

• input (Tensor) – the input tensor containing probabilities
• num_samples (int) – number of samples to draw
• replacement (bool, optional) – whether to draw with replacement or not
• out (Tensor, optional) – the output tensor

Example:

>>> weights = torch.tensor([0, 10, 3, 0], dtype=torch.float) # create a tensor of weights
>>> torch.multinomial(weights, 4)
tensor([ 1,  2,  0,  0])
>>> torch.multinomial(weights, 4, replacement=True)
tensor([ 2,  1,  1,  1])

torch.normal()

torch.normal(mean, std, out=None) → Tensor

Returns a tensor of random numbers drawn from separate normal distributions whose mean and standard deviation are given.

The mean is a tensor with the mean of each output element’s normal distribution

The std is a tensor with the standard deviation of each output element’s normal distribution

The shapes of mean and std don’t need to match, but the total number of elements in each tensor need to be the same.

Note

When the shapes do not match, the shape of mean is used as the shape for the returned output tensor

Parameters:

• mean (Tensor) – the tensor of per-element means
• std (Tensor) – the tensor of per-element standard deviations
• out (Tensor, optional) – the output tensor

Example:

>>> torch.normal(mean=torch.arange(1, 11), std=torch.arange(1, 0, -0.1))
tensor([  1.0425,   3.5672,   2.7969,   4.2925,   4.7229,   6.2134,
          8.0505,   8.1408,   9.0563,  10.0566])

torch.normal(mean=0.0, std, out=None) → Tensor

Similar to the function above, but the means are shared among all drawn elements.

Parameters:

• mean (float, optional) – the mean for all distributions
• std (Tensor) – the tensor of per-element standard deviations
• out (Tensor, optional) – the output tensor

Example:

>>> torch.normal(mean=0.5, std=torch.arange(1, 6))
tensor([-1.2793, -1.0732, -2.0687,  5.1177, -1.2303])

torch.normal(mean, std=1.0, out=None) → Tensor

Similar to the function above, but the standard-deviations are shared among all drawn elements.

Parameters:

• mean (Tensor) – the tensor of per-element means
• std (float, optional) – the standard deviation for all distributions
• out (Tensor, optional) – the output tensor

Example:

>>> torch.normal(mean=torch.arange(1, 6))
tensor([ 1.1552,  2.6148,  2.6535,  5.8318,  4.2361])

torch.rand(*sizes, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

Returns a tensor filled with random numbers from a uniform distribution on the interval $[0, 1)$

The shape of the tensor is defined by the variable argument sizes.

Parameters:

• sizes (int...) – a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple.
• {out} –
• {dtype} –
• {layout} –
• {device} –
• {requires_grad} –

Example:

>>> torch.rand(4)
tensor([ 0.5204,  0.2503,  0.3525,  0.5673])
>>> torch.rand(2, 3)
tensor([[ 0.8237,  0.5781,  0.6879],
        [ 0.3816,  0.7249,  0.0998]])

torch.rand_like(input, dtype=None, layout=None, device=None, requires_grad=False) → Tensor

Returns a tensor with the same size as input that is filled with random numbers from a uniform distribution on the interval $[0, 1)$. torch.rand_like(input) is equivalent to torch.rand(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).

Parameters:

• input (Tensor) – the size of input will determine size of the output tensor
• dtype (torch.dtype, optional) – the desired data type of returned Tensor.
• layout (torch.layout, optional) – the desired layout of returned tensor.
• device (torch.device, optional) – the desired device of returned tensor.
• requires_grad (bool, optional) – If autograd should record operations on the

torch.randint(low=0, high, size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

Returns a tensor filled with random integers generated uniformly between low (inclusive) and high (exclusive).

The shape of the tensor is defined by the variable argument size.

Parameters:

• low (int, optional) – Lowest integer to be drawn from the distribution. Default: 0.
• high (int) – One above the highest integer to be drawn from the distribution.
• size (tuple) – a tuple defining the shape of the output tensor.
• out (Tensor, optional) – the output tensor
• dtype (torch.dtype, optional) – the desired data type of returned tensor.
• layout (torch.layout, optional) – the desired layout of returned Tensor.
• device (torch.device, optional) – the desired device of returned tensor.
• requires_grad (bool, optional) – If autograd should record operations on the

Example:

>>> torch.randint(3, 5, (3,))
tensor([ 4.,  3.,  4.])


>>> torch.randint(3, 10, (2,2), dtype=torch.long)
tensor([[ 8,  3],
        [ 3,  9]])


>>> torch.randint(3, 10, (2,2))
tensor([[ 4.,  5.],
        [ 6.,  7.]])

torch.randint_like(input, low=0, high, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

Returns a tensor with the same shape as Tensor input filled with random integers generated uniformly between low (inclusive) and high (exclusive).

Parameters:

• input (Tensor) – the size of input will determine size of the output tensor
• low (int, optional) – Lowest integer to be drawn from the distribution. Default: 0.
• high (int) – One above the highest integer to be drawn from the distribution.
• dtype (torch.dtype, optional) – the desired data type of returned Tensor.
• layout (torch.layout, optional) – the desired layout of returned tensor.
• device (torch.device, optional) – the desired device of returned tensor.
• requires_grad (bool, optional) – If autograd should record operations on the

torch.randn(*sizes, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → Tensor

Returns a tensor filled with random numbers from a normal distribution with mean 0 and variance 1 (also called the standard normal distribution).

$$\text{out}_{i} \sim \mathcal{N}(0, 1)$$

The shape of the tensor is defined by the variable argument sizes.

Parameters:

• sizes (int...) – a sequence of integers defining the shape of the output tensor. Can be a variable number of arguments or a collection like a list or tuple.
• out (Tensor, optional) – the output tensor
• dtype (torch.dtype, optional) – the desired data type of returned tensor.
• layout (torch.layout, optional) – the desired layout of returned Tensor.
• device (torch.device, optional) – the desired device of returned tensor.
• requires_grad (bool, optional) – If autograd should record operations on the

Example:

>>> torch.randn(4)
tensor([-2.1436,  0.9966,  2.3426, -0.6366])
>>> torch.randn(2, 3)
tensor([[ 1.5954,  2.8929, -1.0923],
        [ 1.1719, -0.4709, -0.1996]])

torch.randn_like(input, dtype=None, layout=None, device=None, requires_grad=False) → Tensor

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. torch.randn_like(input) is equivalent to torch.randn(input.size(), dtype=input.dtype, layout=input.layout, device=input.device).

Parameters:

• input (Tensor) – the size of input will determine size of the output tensor
• dtype (torch.dtype, optional) – the desired data type of returned Tensor.
• layout (torch.layout, optional) – the desired layout of returned tensor.
• device (torch.device, optional) – the desired device of returned tensor.
• requires_grad (bool, optional) – If autograd should record operations on the

torch.randperm(n, out=None, dtype=torch.int64, layout=torch.strided, device=None, requires_grad=False) → LongTensor

Returns a random permutation of integers from 0 to n - 1.

Parameters:

• n (int) – the upper bound (exclusive)
• out (Tensor, optional) – the output tensor
• dtype (torch.dtype, optional) – the desired data type of returned tensor. Default: torch.int64.
• layout (torch.layout, optional) – the desired layout of returned Tensor.
• device (torch.device, optional) – the desired device of returned tensor.
• requires_grad (bool, optional) – If autograd should record operations on the

Example:

>>> torch.randperm(4)
tensor([ 2,  1,  0,  3])

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

Serialization

torch.save(obj, f, pickle_module=pickle, pickle_protocol=2)

Saves an object to a disk file.

See also: Recommended approach for saving a model

Parameters:

• obj – saved object
• f – a file-like object (has to implement write and flush) or a string containing a file name
• pickle_module – module used for pickling metadata and objects
• pickle_protocol – can be specified to override the default protocol

Warning

If you are using Python 2, torch.save does NOT support StringIO.StringIO as a valid file-like object. This is because the write method should return the number of bytes written; StringIO.write() does not do this.

Please use something like io.BytesIO instead.

Example

>>> # Save to file
>>> x = torch.tensor([0, 1, 2, 3, 4])
>>> torch.save(x, 'tensor.pt')
>>> # Save to io.BytesIO buffer
>>> buffer = io.BytesIO()
>>> torch.save(x, buffer)

torch.load(f, map_location=None, pickle_module=pickle)

Loads an object saved with torch.save() from a file.

torch.load() uses Python’s unpickling facilities but treats storages, which underlie tensors, specially. They are first deserialized on the CPU and are then moved to the device they were saved from. If this fails (e.g. because the run time system doesn’t have certain devices), an exception is raised. However, storages can be dynamically remapped to an alternative set of devices using the map_location argument.

If map_location is a callable, it will be called once for each serialized storage with two arguments: storage and location. The storage argument will be the initial deserialization of the storage, residing on the CPU. Each serialized storage has a location tag associated with it which identifies the device it was saved from, and this tag is the second argument passed to map_location. The builtin location tags are ‘cpu’ for CPU tensors and ‘cuda:device_id’ (e.g. ‘cuda:2’) for CUDA tensors. map_location should return either None or a storage. If map_location returns a storage, it will be used as the final deserialized object, already moved to the right device. Otherwise, $torch.load$ will fall back to the default behavior, as if map_location wasn’t specified.

If map_location is a string, it should be a device tag, where all tensors should be loaded.

Otherwise, if map_location is a dict, it will be used to remap location tags appearing in the file (keys), to ones that specify where to put the storages (values).

User extensions can register their own location tags and tagging and deserialization methods using register_package.

Parameters:

• f – a file-like object (has to implement read, readline, tell, and seek), or a string containing a file name
• map_location – a function, string or a dict specifying how to remap storage locations
• pickle_module – module used for unpickling metadata and objects (has to match the pickle_module used to serialize file)

Example

>>> torch.load('tensors.pt')
# Load all tensors onto the CPU
>>> torch.load('tensors.pt', map_location='cpu')
# Load all tensors onto the CPU, using a function
>>> torch.load('tensors.pt', map_location=lambda storage, loc: storage)
# Load all tensors onto GPU 1
>>> torch.load('tensors.pt', map_location=lambda storage, loc: storage.cuda(1))
# Map tensors from GPU 1 to GPU 0
>>> torch.load('tensors.pt', map_location={'cuda:1':'cuda:0'})
# Load tensor from io.BytesIO object
>>> with open('tensor.pt') as f:
        buffer = io.BytesIO(f.read())
>>> torch.load(buffer)

Parallelism

torch.get_num_threads() → int

Gets the number of OpenMP threads used for parallelizing CPU operations

torch.set_num_threads(int)

Sets the number of OpenMP threads used for parallelizing CPU operations

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.

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

Math operations

Pointwise Ops

torch.abs(input, out=None) → Tensor

Computes the element-wise absolute value of the given input tensor.

$$\text{out}_{i} = |\text{input}_{i}|$$

Parameters:

• input (Tensor) – the input tensor
• out (Tensor, optional) – the output tensor

Example:

>>> torch.abs(torch.tensor([-1, -2, 3]))
tensor([ 1,  2,  3])

torch.acos(input, out=None) → Tensor

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

$$\text{out}_{i} = \cos^{-1}(\text{input}_{i})$$

Parameters:

• input (Tensor) – the input tensor
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4)
>>> a
tensor([ 0.3348, -0.5889,  0.2005, -0.1584])
>>> torch.acos(a)
tensor([ 1.2294,  2.2004,  1.3690,  1.7298])

torch.add()

torch.add(input, value, out=None)

Adds the scalar value to each element of the input input and returns a new resulting tensor.

$$out = input + value$$

If input is of type FloatTensor or DoubleTensor, value must be a real number, otherwise it should be an integer.

Parameters:

• input (Tensor) – the input tensor
• value (Number) – the number to be added to each element of input

Keyword Arguments:  

out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4)
>>> a
tensor([ 0.0202,  1.0985,  1.3506, -0.6056])
>>> torch.add(a, 20)
tensor([ 20.0202,  21.0985,  21.3506,  19.3944])

torch.add(input, value=1, other, out=None)

Each element of the tensor other is multiplied by the scalar value and added to each element of the tensor input. The resulting tensor is returned.

The shapes of input and other must be broadcastable.

$$out = input + value \times other$$

If other is of type FloatTensor or DoubleTensor, value must be a real number, otherwise it should be an integer.

Parameters:

• input (Tensor) – the first input tensor
• value (Number) – the scalar multiplier for other
• other (Tensor) – the second input tensor

Keyword Arguments:  

out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4)
>>> a
tensor([-0.9732, -0.3497,  0.6245,  0.4022])
>>> b = torch.randn(4, 1)
>>> b
tensor([[ 0.3743],
        [-1.7724],
        [-0.5811],
        [-0.8017]])
>>> torch.add(a, 10, b)
tensor([[  2.7695,   3.3930,   4.3672,   4.1450],
        [-18.6971, -18.0736, -17.0994, -17.3216],
        [ -6.7845,  -6.1610,  -5.1868,  -5.4090],
        [ -8.9902,  -8.3667,  -7.3925,  -7.6147]])

torch.addcdiv(tensor, value=1, tensor1, tensor2, out=None) → Tensor

Performs the element-wise division of tensor1 by tensor2, multiply the result by the scalar value and add it to tensor.

$$out_i = tensor_i + value \times \frac{tensor1_i}{tensor2_i}$$

The shapes of tensor, tensor1, and tensor2 must be broadcastable.

For inputs of type FloatTensor or DoubleTensor, value must be a real number, otherwise an integer.

Parameters:

• tensor (Tensor) – the tensor to be added
• value (Number, optional) – multiplier for $tensor1 ./ tensor2$
• tensor1 (Tensor) – the numerator tensor
• tensor2 (Tensor) – the denominator tensor
• out (Tensor, optional) – the output tensor

Example:

>>> t = torch.randn(1, 3)
>>> t1 = torch.randn(3, 1)
>>> t2 = torch.randn(1, 3)
>>> torch.addcdiv(t, 0.1, t1, t2)
tensor([[-0.2312, -3.6496,  0.1312],
        [-1.0428,  3.4292, -0.1030],
        [-0.5369, -0.9829,  0.0430]])

torch.addcmul(tensor, value=1, tensor1, tensor2, out=None) → Tensor

Performs the element-wise multiplication of tensor1 by tensor2, multiply the result by the scalar value and add it to tensor.

$$out_i = tensor_i + value \times tensor1_i \times tensor2_i$$

The shapes of tensor, tensor1, and tensor2 must be broadcastable.

For inputs of type FloatTensor or DoubleTensor, value must be a real number, otherwise an integer.

Parameters:

• tensor (Tensor) – the tensor to be added
• value (Number, optional) – multiplier for $tensor1 .* tensor2$
• tensor1 (Tensor) – the tensor to be multiplied
• tensor2 (Tensor) – the tensor to be multiplied
• out (Tensor, optional) – the output tensor

Example:

>>> t = torch.randn(1, 3)
>>> t1 = torch.randn(3, 1)
>>> t2 = torch.randn(1, 3)
>>> torch.addcmul(t, 0.1, t1, t2)
tensor([[-0.8635, -0.6391,  1.6174],
        [-0.7617, -0.5879,  1.7388],
        [-0.8353, -0.6249,  1.6511]])

torch.asin(input, out=None) → Tensor

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

$$\text{out}_{i} = \sin^{-1}(\text{input}_{i})$$

Parameters:

• input (Tensor) – the input tensor
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4)
>>> a
tensor([-0.5962,  1.4985, -0.4396,  1.4525])
>>> torch.asin(a)
tensor([-0.6387,     nan, -0.4552,     nan])

torch.atan(input, out=None) → Tensor

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

$$\text{out}_{i} = \tan^{-1}(\text{input}_{i})$$

Parameters:

• input (Tensor) – the input tensor
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4)
>>> a
tensor([ 0.2341,  0.2539, -0.6256, -0.6448])
>>> torch.atan(a)
tensor([ 0.2299,  0.2487, -0.5591, -0.5727])

torch.atan2(input1, input2, out=None) → Tensor

Returns a new tensor with the arctangent of the elements of input1 and input2.

The shapes of input1 and input2 must be broadcastable.

Parameters:

• input1 (Tensor) – the first input tensor
• input2 (Tensor) – the second input tensor
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4)
>>> a
tensor([ 0.9041,  0.0196, -0.3108, -2.4423])
>>> torch.atan2(a, torch.randn(4))
tensor([ 0.9833,  0.0811, -1.9743, -1.4151])

torch.ceil(input, out=None) → Tensor

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

$$\text{out}_{i} = \left\lceil \text{input}_{i} \right\rceil = \left\lfloor \text{input}_{i} \right\rfloor + 1$$

Parameters:

• input (Tensor) – the input tensor
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4)
>>> a
tensor([-0.6341, -1.4208, -1.0900,  0.5826])
>>> torch.ceil(a)
tensor([-0., -1., -1.,  1.])

torch.clamp(input, min, max, out=None) → Tensor

Clamp all elements in input into the range [ min, max ] and return a resulting tensor:

$$\begin{split}y_i = \begin{cases} \text{min} & \text{if } x_i < \text{min} \\ x_i & \text{if } \text{min} \leq x_i \leq \text{max} \\ \text{max} & \text{if } x_i > \text{max} \end{cases}\end{split}$$

If input is of type FloatTensor or DoubleTensor, args min and max must be real numbers, otherwise they should be integers.

Parameters:

• input (Tensor) – the input tensor
• min (Number) – lower-bound of the range to be clamped to
• max (Number) – upper-bound of the range to be clamped to
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4)
>>> a
tensor([-1.7120,  0.1734, -0.0478, -0.0922])
>>> torch.clamp(a, min=-0.5, max=0.5)
tensor([-0.5000,  0.1734, -0.0478, -0.0922])

torch.clamp(input, *, min, out=None) → Tensor

Clamps all elements in input to be larger or equal min.

If input is of type FloatTensor or DoubleTensor, value should be a real number, otherwise it should be an integer.

Parameters:

• input (Tensor) – the input tensor
• value (Number) – minimal value of each element in the output
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4)
>>> a
tensor([-0.0299, -2.3184,  2.1593, -0.8883])
>>> torch.clamp(a, min=0.5)
tensor([ 0.5000,  0.5000,  2.1593,  0.5000])

torch.clamp(input, *, max, out=None) → Tensor

Clamps all elements in input to be smaller or equal max.

If input is of type FloatTensor or DoubleTensor, value should be a real number, otherwise it should be an integer.

Parameters:

• input (Tensor) – the input tensor
• value (Number) – maximal value of each element in the output
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4)
>>> a
tensor([ 0.0753, -0.4702, -0.4599,  0.1899])
>>> torch.clamp(a, max=0.5)
tensor([ 0.0753, -0.4702, -0.4599,  0.1899])

torch.cos(input, out=None) → Tensor

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

$$\text{out}_{i} = \cos(\text{input}_{i})$$

Parameters:

• input (Tensor) – the input tensor
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4)
>>> a
tensor([ 1.4309,  1.2706, -0.8562,  0.9796])
>>> torch.cos(a)
tensor([ 0.1395,  0.2957,  0.6553,  0.5574])

torch.cosh(input, out=None) → Tensor

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

$$\text{out}_{i} = \cosh(\text{input}_{i})$$

Parameters:

• input (Tensor) – the input tensor
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4)
>>> a
tensor([ 0.1632,  1.1835, -0.6979, -0.7325])
>>> torch.cosh(a)
tensor([ 1.0133,  1.7860,  1.2536,  1.2805])

torch.div()

torch.div(input, value, out=None) → Tensor

Divides each element of the input input with the scalar value and returns a new resulting tensor.

$$out_i = \frac{input_i}{value}$$

If input is of type FloatTensor or DoubleTensor, value should be a real number, otherwise it should be an integer

Parameters:

• input (Tensor) – the input tensor
• value (Number) – the number to be divided to each element of input
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(5)
>>> a
tensor([ 0.3810,  1.2774, -0.2972, -0.3719,  0.4637])
>>> torch.div(a, 0.5)
tensor([ 0.7620,  2.5548, -0.5944, -0.7439,  0.9275])

torch.div(input, other, out=None) → Tensor

Each element of the tensor input is divided by each element of the tensor other. The resulting tensor is returned. The shapes of input and other must be broadcastable.

$$out_i = \frac{input_i}{other_i}$$

Parameters:

• input (Tensor) – the numerator tensor
• other (Tensor) – the denominator tensor
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4, 4)
>>> a
tensor([[-0.3711, -1.9353, -0.4605, -0.2917],
        [ 0.1815, -1.0111,  0.9805, -1.5923],
        [ 0.1062,  1.4581,  0.7759, -1.2344],
        [-0.1830, -0.0313,  1.1908, -1.4757]])
>>> b = torch.randn(4)
>>> b
tensor([ 0.8032,  0.2930, -0.8113, -0.2308])
>>> torch.div(a, b)
tensor([[-0.4620, -6.6051,  0.5676,  1.2637],
        [ 0.2260, -3.4507, -1.2086,  6.8988],
        [ 0.1322,  4.9764, -0.9564,  5.3480],
        [-0.2278, -0.1068, -1.4678,  6.3936]])

torch.erf(tensor, out=None) → Tensor

Computes the error function of each element. The error function is defined as follows:

$$\mathrm{erf}(x) = \frac{2}{\sqrt{\pi}} \int_{0}^{x} e^{-t^2} dt$$

Parameters:

• tensor (Tensor) – the input tensor
• out (Tensor, optional) – the output tensor

Example:

>>> torch.erf(torch.tensor([0, -1., 10.]))
tensor([ 0.0000, -0.8427,  1.0000])

torch.erfinv(tensor, out=None) → Tensor

Computes the inverse error function of each element. The inverse error function is defined in the range $(-1, 1)$ as:

$$\mathrm{erfinv}(\mathrm{erf}(x)) = x$$

Parameters:

• tensor (Tensor) – the input tensor
• out (Tensor, optional) – the output tensor

Example:

>>> torch.erfinv(torch.tensor([0, 0.5, -1.]))
tensor([ 0.0000,  0.4769,    -inf])

torch.exp(tensor, out=None) → Tensor

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

$$y_{i} = e^{x_{i}}$$

Parameters:

• input (Tensor) – the input tensor
• out (Tensor, optional) – the output tensor
• tensor (Tensor) – the input tensor
• out – the output tensor

Example:

>>> torch.exp(torch.tensor([0, math.log(2)]))
tensor([ 1.,  2.])

torch.expm1(tensor, out=None) → Tensor

Returns a new tensor with the exponential of the elements minus 1 of input.

$$y_{i} = e^{x_{i}} - 1$$

Parameters:

• input (Tensor) – the input tensor
• out (Tensor, optional) – the output tensor
• tensor (Tensor) – the input tensor
• out – the output tensor

Example:

>>> torch.expm1(torch.tensor([0, math.log(2)]))
tensor([ 0.,  1.])

torch.floor(input, out=None) → Tensor

Returns a new tensor with the floor of the elements of input, the largest integer less than or equal to each element.

$$\text{out}_{i} = \left\lfloor \text{input}_{i} \right\rfloor$$

Parameters:

• input (Tensor) – the input tensor
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4)
>>> a
tensor([-0.8166,  1.5308, -0.2530, -0.2091])
>>> torch.floor(a)
tensor([-1.,  1., -1., -1.])

torch.fmod(input, divisor, out=None) → Tensor

Computes the element-wise remainder of division.

The dividend and divisor may contain both for integer and floating point numbers. The remainder has the same sign as the dividend input.

When divisor is a tensor, the shapes of input and divisor must be broadcastable.

Parameters:

• input (Tensor) – the dividend
• divisor (Tensor or float) – the divisor, which may be either a number or a tensor of the same shape as the dividend
• out (Tensor, optional) – the output tensor

Example:

>>> torch.fmod(torch.tensor([-3., -2, -1, 1, 2, 3]), 2)
tensor([-1., -0., -1.,  1.,  0.,  1.])
>>> torch.fmod(torch.tensor([1., 2, 3, 4, 5]), 1.5)
tensor([ 1.0000,  0.5000,  0.0000,  1.0000,  0.5000])

torch.frac(tensor, out=None) → Tensor

Computes the fractional portion of each element in tensor.

$$\text{out}_{i} = \text{input}_{i} - \left\lfloor \text{input}_{i} \right\rfloor$$

Example:

>>> torch.frac(torch.tensor([1, 2.5, -3.2]))
tensor([ 0.0000,  0.5000, -0.2000])

torch.lerp(start, end, weight, out=None)

Does a linear interpolation of two tensors start and end based on a scalar weight and returns the resulting out tensor.

$$out_i = start_i + weight \times (end_i - start_i)$$

The shapes of start and end must be broadcastable.

Parameters:

• start (Tensor) – the tensor with the starting points
• end (Tensor) – the tensor with the ending points
• weight (float) – the weight for the interpolation formula
• out (Tensor, optional) – the output tensor

Example:

>>> start = torch.arange(1, 5)
>>> end = torch.empty(4).fill_(10)
>>> start
tensor([ 1.,  2.,  3.,  4.])
>>> end
tensor([ 10.,  10.,  10.,  10.])
>>> torch.lerp(start, end, 0.5)
tensor([ 5.5000,  6.0000,  6.5000,  7.0000])

torch.log(input, out=None) → Tensor

Returns a new tensor with the natural logarithm of the elements of input.

$$y_{i} = \log_{e} (x_{i})$$

Parameters:

• input (Tensor) – the input tensor
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(5)
>>> a
tensor([-0.7168, -0.5471, -0.8933, -1.4428, -0.1190])
>>> torch.log(a)
tensor([ nan,  nan,  nan,  nan,  nan])

torch.log10(input, out=None) → Tensor

Returns a new tensor with the logarithm to the base 10 of the elements of input.

$$y_{i} = \log_{10} (x_{i})$$

Parameters:

• input (Tensor) – the input tensor
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.rand(5)
>>> a
tensor([ 0.5224,  0.9354,  0.7257,  0.1301,  0.2251])


>>> torch.log10(a)
tensor([-0.2820, -0.0290, -0.1392, -0.8857, -0.6476])

torch.log1p(input, out=None) → Tensor

Returns a new tensor with the natural logarithm of (1 + input).

$$y_i = \log_{e} (x_i + 1)$$

Note

This function is more accurate than torch.log() for small values of input

Parameters:

• input (Tensor) – the input tensor
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(5)
>>> a
tensor([-1.0090, -0.9923,  1.0249, -0.5372,  0.2492])
>>> torch.log1p(a)
tensor([    nan, -4.8653,  0.7055, -0.7705,  0.2225])

torch.log2(input, out=None) → Tensor

Returns a new tensor with the logarithm to the base 2 of the elements of input.

$$y_{i} = \log_{2} (x_{i})$$

Parameters:

• input (Tensor) – the input tensor
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.rand(5)
>>> a
tensor([ 0.8419,  0.8003,  0.9971,  0.5287,  0.0490])


>>> torch.log2(a)
tensor([-0.2483, -0.3213, -0.0042, -0.9196, -4.3504])

torch.mul()

torch.mul(input, value, out=None)

Multiplies each element of the input input with the scalar value and returns a new resulting tensor.

$$out_i = value \times input_i$$

If input is of type FloatTensor or DoubleTensor, value should be a real number, otherwise it should be an integer

Parameters:

• input (Tensor) – the input tensor
• value (Number) – the number to be multiplied to each element of input
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(3)
>>> a
tensor([ 0.2015, -0.4255,  2.6087])
>>> torch.mul(a, 100)
tensor([  20.1494,  -42.5491,  260.8663])

torch.mul(input, other, out=None)

Each element of the tensor input is multiplied by each element of the Tensor other. The resulting tensor is returned.

The shapes of input and other must be broadcastable.

$$out_i = input_i \times other_i$$

Parameters:

• input (Tensor) – the first multiplicand tensor
• other (Tensor) – the second multiplicand tensor
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4, 1)
>>> a
tensor([[ 1.1207],
        [-0.3137],
        [ 0.0700],
        [ 0.8378]])
>>> b = torch.randn(1, 4)
>>> b
tensor([[ 0.5146,  0.1216, -0.5244,  2.2382]])
>>> torch.mul(a, b)
tensor([[ 0.5767,  0.1363, -0.5877,  2.5083],
        [-0.1614, -0.0382,  0.1645, -0.7021],
        [ 0.0360,  0.0085, -0.0367,  0.1567],
        [ 0.4312,  0.1019, -0.4394,  1.8753]])

torch.neg(input, out=None) → Tensor

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

$$out = -1 \times input$$

Parameters:

• input (Tensor) – the input tensor
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(5)
>>> a
tensor([ 0.0090, -0.2262, -0.0682, -0.2866,  0.3940])
>>> torch.neg(a)
tensor([-0.0090,  0.2262,  0.0682,  0.2866, -0.3940])

torch.pow()

torch.pow(input, exponent, out=None) → Tensor

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

exponent can be either a single float number or a Tensor with the same number of elements as input.

When exponent is a scalar value, the operation applied is:

$$out_i = x_i ^ {exponent}$$

When exponent is a tensor, the operation applied is:

$$out_i = x_i ^ {exponent_i}$$

When exponent is a tensor, the shapes of input and exponent must be broadcastable.

Parameters:

• input (Tensor) – the input tensor
• exponent (float or tensor) – the exponent value
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4)
>>> a
tensor([ 0.4331,  1.2475,  0.6834, -0.2791])
>>> torch.pow(a, 2)
tensor([ 0.1875,  1.5561,  0.4670,  0.0779])
>>> exp = torch.arange(1, 5)

>>> a = torch.arange(1, 5)
>>> a
tensor([ 1.,  2.,  3.,  4.])
>>> exp
tensor([ 1.,  2.,  3.,  4.])
>>> torch.pow(a, exp)
tensor([   1.,    4.,   27.,  256.])

torch.pow(base, input, out=None) → Tensor

base is a scalar float value, and input is a tensor. The returned tensor out is of the same shape as input

The operation applied is:

$$out_i = base ^ {input_i}$$

Parameters:

• base (float) – the scalar base value for the power operation
• input (Tensor) – the exponent tensor
• out (Tensor, optional) – the output tensor

Example:

>>> exp = torch.arange(1, 5)
>>> base = 2
>>> torch.pow(base, exp)
tensor([  2.,   4.,   8.,  16.])

torch.reciprocal(input, out=None) → Tensor

Returns a new tensor with the reciprocal of the elements of input

$$\text{out}_{i} = \frac{1}{\text{input}_{i}}$$

Parameters:

• input (Tensor) – the input tensor
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4)
>>> a
tensor([-0.4595, -2.1219, -1.4314,  0.7298])
>>> torch.reciprocal(a)
tensor([-2.1763, -0.4713, -0.6986,  1.3702])

torch.remainder(input, divisor, out=None) → Tensor

Computes the element-wise remainder of division.

The divisor and dividend may contain both for integer and floating point numbers. The remainder has the same sign as the divisor.

When divisor is a tensor, the shapes of input and divisor must be broadcastable.

Parameters:

• input (Tensor) – the dividend
• divisor (Tensor or float) – the divisor that may be either a number or a Tensor of the same shape as the dividend
• out (Tensor, optional) – the output tensor

Example:

>>> torch.remainder(torch.tensor([-3., -2, -1, 1, 2, 3]), 2)
tensor([ 1.,  0.,  1.,  1.,  0.,  1.])
>>> torch.remainder(torch.tensor([1., 2, 3, 4, 5]), 1.5)
tensor([ 1.0000,  0.5000,  0.0000,  1.0000,  0.5000])

See also

torch.fmod(), which computes the element-wise remainder of division equivalently to the C library function fmod().

torch.round(input, out=None) → Tensor

Returns a new tensor with each of the elements of input rounded to the closest integer.

Parameters:

• input (Tensor) – the input tensor
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4)
>>> a
tensor([ 0.9920,  0.6077,  0.9734, -1.0362])
>>> torch.round(a)
tensor([ 1.,  1.,  1., -1.])

torch.rsqrt(input, out=None) → Tensor

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

$$\text{out}_{i} = \frac{1}{\sqrt{\text{input}_{i}}}$$

Parameters:

• input (Tensor) – the input tensor
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4)
>>> a
tensor([-0.0370,  0.2970,  1.5420, -0.9105])
>>> torch.rsqrt(a)
tensor([    nan,  1.8351,  0.8053,     nan])

torch.sigmoid(input, out=None) → Tensor

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

$$\text{out}_{i} = \frac{1}{1 + e^{-\text{input}_{i}}}$$

Parameters:

• input (Tensor) – the input tensor
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4)
>>> a
tensor([ 0.9213,  1.0887, -0.8858, -1.7683])
>>> torch.sigmoid(a)
tensor([ 0.7153,  0.7481,  0.2920,  0.1458])

torch.sign(input, out=None) → Tensor

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

Parameters:

• input (Tensor) – the input tensor
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4)
>>> a
tensor([ 1.0382, -1.4526, -0.9709,  0.4542])
>>> torch.sign(a)
tensor([ 1., -1., -1.,  1.])

torch.sin(input, out=None) → Tensor

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

$$\text{out}_{i} = \sin(\text{input}_{i})$$

Parameters:

• input (Tensor) – the input tensor
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4)
>>> a
tensor([-0.5461,  0.1347, -2.7266, -0.2746])
>>> torch.sin(a)
tensor([-0.5194,  0.1343, -0.4032, -0.2711])

torch.sinh(input, out=None) → Tensor

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

$$\text{out}_{i} = \sinh(\text{input}_{i})$$

Parameters:

• input (Tensor) – the input tensor
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4)
>>> a
tensor([ 0.5380, -0.8632, -0.1265,  0.9399])
>>> torch.sinh(a)
tensor([ 0.5644, -0.9744, -0.1268,  1.0845])

torch.sqrt(input, out=None) → Tensor

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

$$\text{out}_{i} = \sqrt{\text{input}_{i}}$$

Parameters:

• input (Tensor) – the input tensor
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4)
>>> a
tensor([-2.0755,  1.0226,  0.0831,  0.4806])
>>> torch.sqrt(a)
tensor([    nan,  1.0112,  0.2883,  0.6933])

torch.tan(input, out=None) → Tensor

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

$$\text{out}_{i} = \tan(\text{input}_{i})$$

Parameters:

• input (Tensor) – the input tensor
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4)
>>> a
tensor([-1.2027, -1.7687,  0.4412, -1.3856])
>>> torch.tan(a)
tensor([-2.5930,  4.9859,  0.4722, -5.3366])

torch.tanh(input, out=None) → Tensor

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

$$\text{out}_{i} = \tanh(\text{input}_{i})$$

Parameters:

• input (Tensor) – the input tensor
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4)
>>> a
tensor([ 0.8986, -0.7279,  1.1745,  0.2611])
>>> torch.tanh(a)
tensor([ 0.7156, -0.6218,  0.8257,  0.2553])

torch.trunc(input, out=None) → Tensor

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

Parameters:

• input (Tensor) – the input tensor
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4)
>>> a
tensor([ 3.4742,  0.5466, -0.8008, -0.9079])
>>> torch.trunc(a)
tensor([ 3.,  0., -0., -0.])

Reduction Ops

torch.argmax(input, dim=None, keepdim=False)

Returns the indices of the maximum values of a tensor across a dimension.

This is the second value returned by torch.max(). See its documentation for the exact semantics of this method.

Parameters:

• input (Tensor) – the input tensor
• dim (int) – the dimension to reduce. If None, the argmax of the flattened input is returned.
• keepdim (bool) – whether the output tensors have dim retained or not. Ignored if dim=None.

Example:

>>> a = torch.randn(4, 4)
>>> a
tensor([[ 1.3398,  0.2663, -0.2686,  0.2450],
        [-0.7401, -0.8805, -0.3402, -1.1936],
        [ 0.4907, -1.3948, -1.0691, -0.3132],
        [-1.6092,  0.5419, -0.2993,  0.3195]])


>>> torch.argmax(a, dim=1)
tensor([ 0,  2,  0,  1])

torch.argmin(input, dim=None, keepdim=False)

Returns the indices of the minimum values of a tensor across a dimension.

This is the second value returned by torch.min(). See its documentation for the exact semantics of this method.

Parameters:

• input (Tensor) – the input tensor
• dim (int) – the dimension to reduce. If None, the argmin of the flattened input is returned.
• keepdim (bool) – whether the output tensors have dim retained or not. Ignored if dim=None.

Example:

>>> a = torch.randn(4, 4)
>>> a
tensor([[ 0.1139,  0.2254, -0.1381,  0.3687],
        [ 1.0100, -1.1975, -0.0102, -0.4732],
        [-0.9240,  0.1207, -0.7506, -1.0213],
        [ 1.7809, -1.2960,  0.9384,  0.1438]])


>>> torch.argmin(a, dim=1)
tensor([ 2,  1,  3,  1])

torch.cumprod(input, dim, out=None) → Tensor

Returns the cumulative product of elements of input in the dimension dim.

For example, if input is a vector of size N, the result will also be a vector of size N, with elements.

$$y_i = x_1 \times x_2\times x_3\times \dots \times x_i$$

Parameters:

• input (Tensor) – the input tensor
• dim (int) – the dimension to do the operation over
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(10)
>>> a
tensor([ 0.6001,  0.2069, -0.1919,  0.9792,  0.6727,  1.0062,  0.4126,
        -0.2129, -0.4206,  0.1968])
>>> torch.cumprod(a, dim=0)
tensor([ 0.6001,  0.1241, -0.0238, -0.0233, -0.0157, -0.0158, -0.0065,
         0.0014, -0.0006, -0.0001])

>>> a[5] = 0.0
>>> torch.cumprod(a, dim=0)
tensor([ 0.6001,  0.1241, -0.0238, -0.0233, -0.0157, -0.0000, -0.0000,
         0.0000, -0.0000, -0.0000])

torch.cumsum(input, dim, out=None) → Tensor

Returns the cumulative sum of elements of input in the dimension dim.

For example, if input is a vector of size N, the result will also be a vector of size N, with elements.

$$y_i = x_1 + x_2 + x_3 + \dots + x_i$$

Parameters:

• input (Tensor) – the input tensor
• dim (int) – the dimension to do the operation over
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(10)
>>> a
tensor([-0.8286, -0.4890,  0.5155,  0.8443,  0.1865, -0.1752, -2.0595,
         0.1850, -1.1571, -0.4243])
>>> torch.cumsum(a, dim=0)
tensor([-0.8286, -1.3175, -0.8020,  0.0423,  0.2289,  0.0537, -2.0058,
        -1.8209, -2.9780, -3.4022])

torch.dist(input, other, p=2) → Tensor

Returns the p-norm of (input - other)

The shapes of input and other must be broadcastable.

Parameters:

• input (Tensor) – the input tensor
• other (Tensor) – the Right-hand-side input tensor
• p (float, optional) – the norm to be computed

Example:

>>> x = torch.randn(4)
>>> x
tensor([-1.5393, -0.8675,  0.5916,  1.6321])
>>> y = torch.randn(4)
>>> y
tensor([ 0.0967, -1.0511,  0.6295,  0.8360])
>>> torch.dist(x, y, 3.5)
tensor(1.6727)
>>> torch.dist(x, y, 3)
tensor(1.6973)
>>> torch.dist(x, y, 0)
tensor(inf)
>>> torch.dist(x, y, 1)
tensor(2.6537)

torch.mean()

torch.mean(input) → Tensor

Returns the mean value of all elements in the input tensor.

Parameters:input (Tensor) – the input tensor

Example:

>>> a = torch.randn(1, 3)
>>> a
tensor([[ 0.2294, -0.5481,  1.3288]])
>>> torch.mean(a)
tensor(0.3367)

torch.mean(input, dim, keepdim=False, out=None) → Tensor

Returns the mean value of each row of the input tensor in the given dimension dim.

If keepdim is True, the output tensor is of the same size as input except in the dimension dim where it is of size 1. Otherwise, dim is squeezed (see torch.squeeze()), resulting in the output tensor having 1 fewer dimension.

Parameters:

• input (Tensor) – the input tensor
• dim (int) – the dimension to reduce
• keepdim (bool, optional) – whether the output tensor has dim retained or not
• out (Tensor) – the output tensor

Example:

>>> a = torch.randn(4, 4)
>>> a
tensor([[-0.3841,  0.6320,  0.4254, -0.7384],
        [-0.9644,  1.0131, -0.6549, -1.4279],
        [-0.2951, -1.3350, -0.7694,  0.5600],
        [ 1.0842, -0.9580,  0.3623,  0.2343]])
>>> torch.mean(a, 1)
tensor([-0.0163, -0.5085, -0.4599,  0.1807])
>>> torch.mean(a, 1, True)
tensor([[-0.0163],
        [-0.5085],
        [-0.4599],
        [ 0.1807]])

torch.median()

torch.median(input) → Tensor

Returns the median value of all elements in the input tensor.

Parameters:input (Tensor) – the input tensor

Example:

>>> a = torch.randn(1, 3)
>>> a
tensor([[ 1.5219, -1.5212,  0.2202]])
>>> torch.median(a)
tensor(0.2202)

torch.median(input, dim=-1, keepdim=False, values=None, indices=None) -> (Tensor, LongTensor)

Returns the median value of each row of the input tensor in the given dimension dim. Also returns the index location of the median value as a LongTensor.

By default, dim is the last dimension of the input tensor.

If keepdim is True, the output tensors are of the same size as input except in the dimension dim where they are of size 1. Otherwise, dim is squeezed (see torch.squeeze()), resulting in the outputs tensor having 1 fewer dimension than input.

Parameters:

• input (Tensor) – the input tensor
• dim (int) – the dimension to reduce
• keepdim (bool) – whether the output tensors have dim retained or not
• values (Tensor, optional) – the output tensor
• indices (Tensor, optional) – the output index tensor

Example:

>>> a = torch.randn(4, 5)
>>> a
tensor([[ 0.2505, -0.3982, -0.9948,  0.3518, -1.3131],
        [ 0.3180, -0.6993,  1.0436,  0.0438,  0.2270],
        [-0.2751,  0.7303,  0.2192,  0.3321,  0.2488],
        [ 1.0778, -1.9510,  0.7048,  0.4742, -0.7125]])
>>> torch.median(a, 1)
(tensor([-0.3982,  0.2270,  0.2488,  0.4742]), tensor([ 1,  4,  4,  3]))

torch.mode(input, dim=-1, keepdim=False, values=None, indices=None) -> (Tensor, LongTensor)

Returns the mode value of each row of the input tensor in the given dimension dim. Also returns the index location of the mode value as a LongTensor.

By default, dim is the last dimension of the input tensor.

If keepdim is True, the output tensors are of the same size as input except in the dimension dim where they are of size 1. Otherwise, dim is squeezed (see torch.squeeze()), resulting in the output tensors having 1 fewer dimension than input.

Note

This function is not defined for torch.cuda.Tensor yet.

Parameters:

• input (Tensor) – the input tensor
• dim (int) – the dimension to reduce
• keepdim (bool) – whether the output tensors have dim retained or not
• values (Tensor, optional) – the output tensor
• indices (Tensor, optional) – the output index tensor

Example:

>>> a = torch.randn(4, 5)
>>> a
tensor([[-1.2808, -1.0966, -1.5946, -0.1148,  0.3631],
        [ 1.1395,  1.1452, -0.6383,  0.3667,  0.4545],
        [-0.4061, -0.3074,  0.4579, -1.3514,  1.2729],
        [-1.0130,  0.3546, -1.4689, -0.1254,  0.0473]])
>>> torch.mode(a, 1)
(tensor([-1.5946, -0.6383, -1.3514, -1.4689]), tensor([ 2,  2,  3,  2]))

torch.norm()

torch.norm(input, p=2) → Tensor

Returns the p-norm of the input tensor.

$$||x||_{p} = \sqrt[p]{x_{1}^{p} + x_{2}^{p} + \ldots + x_{N}^{p}}$$

Parameters:

• input (Tensor) – the input tensor
• p (float, optional) – the exponent value in the norm formulation

Example:

>>> a = torch.randn(1, 3)
>>> a
tensor([[-0.5192, -1.0782, -1.0448]])
>>> torch.norm(a, 3)
tensor(1.3633)

torch.norm(input, p, dim, keepdim=False, out=None) → Tensor

Returns the p-norm of each row of the input tensor in the given dimension dim.

If keepdim is True, the output tensor is of the same size as input except in the dimension dim where it is of size 1. Otherwise, dim is squeezed (see torch.squeeze()), resulting in the output tensor having 1 fewer dimension than input.

Parameters:

• input (Tensor) – the input tensor
• p (float) – the exponent value in the norm formulation
• dim (int) – the dimension to reduce
• keepdim (bool) – whether the output tensor has dim retained or not
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4, 2)
>>> a
tensor([[ 2.1983,  0.4141],
        [ 0.8734,  1.9710],
        [-0.7778,  0.7938],
        [-0.1342,  0.7347]])
>>> torch.norm(a, 2, 1)
tensor([ 2.2369,  2.1558,  1.1113,  0.7469])
>>> torch.norm(a, 0, 1, True)
tensor([[ 2.],
        [ 2.],
        [ 2.],
        [ 2.]])

torch.prod()

torch.prod(input) → Tensor

Returns the product of all elements in the input tensor.

Parameters:input (Tensor) – the input tensor

Example:

>>> a = torch.randn(1, 3)
>>> a
tensor([[-0.8020,  0.5428, -1.5854]])
>>> torch.prod(a)
tensor(0.6902)

torch.prod(input, dim, keepdim=False, out=None) → Tensor

Returns the product of each row of the input tensor in the given dimension dim.

If keepdim is True, the output tensor is of the same size as input except in the dimension dim where it is of size 1. Otherwise, dim is squeezed (see torch.squeeze()), resulting in the output tensor having 1 fewer dimension than input.

Parameters:

• input (Tensor) – the input tensor
• dim (int) – the dimension to reduce
• keepdim (bool) – whether the output tensor has dim retained or not
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4, 2)
>>> a
tensor([[ 0.5261, -0.3837],
        [ 1.1857, -0.2498],
        [-1.1646,  0.0705],
        [ 1.1131, -1.0629]])
>>> torch.prod(a, 1)
tensor([-0.2018, -0.2962, -0.0821, -1.1831])

torch.std()

torch.std(input, unbiased=True) → Tensor

Returns the standard-deviation of all elements in the input tensor.

If unbiased is False, then the standard-deviation will be calculated via the biased estimator. Otherwise, Bessel’s correction will be used.

Parameters:

• input (Tensor) – the input tensor
• unbiased (bool) – whether to use the unbiased estimation or not

Example:

>>> a = torch.randn(1, 3)
>>> a
tensor([[-0.8166, -1.3802, -0.3560]])
>>> torch.std(a)
tensor(0.5130)

torch.std(input, dim, keepdim=False, unbiased=True, out=None) → Tensor

Returns the standard-deviation of each row of the input tensor in the given dimension dim.

If keepdim is True, the output tensor is of the same size as input except in the dimension dim where it is of size 1. Otherwise, dim is squeezed (see torch.squeeze()), resulting in the output tensor having 1 fewer dimension than input.

If unbiased is False, then the standard-deviation will be calculated via the biased estimator. Otherwise, Bessel’s correction will be used.

Parameters:

• input (Tensor) – the input tensor
• dim (int) – the dimension to reduce
• keepdim (bool) – whether the output tensor has dim retained or not
• unbiased (bool) – whether to use the unbiased estimation or not
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4, 4)
>>> a
tensor([[ 0.2035,  1.2959,  1.8101, -0.4644],
        [ 1.5027, -0.3270,  0.5905,  0.6538],
        [-1.5745,  1.3330, -0.5596, -0.6548],
        [ 0.1264, -0.5080,  1.6420,  0.1992]])
>>> torch.std(a, dim=1)
tensor([ 1.0311,  0.7477,  1.2204,  0.9087])

torch.sum()

torch.sum(input) → Tensor

Returns the sum of all elements in the input tensor.

Parameters:input (Tensor) – the input tensor

Example:

>>> a = torch.randn(1, 3)
>>> a
tensor([[ 0.1133, -0.9567,  0.2958]])
>>> torch.sum(a)
tensor(-0.5475)

torch.sum(input, dim, keepdim=False, out=None) → Tensor

Returns the sum of each row of the input tensor in the given dimension dim.

If keepdim is True, the output tensor is of the same size as input except in the dimension dim where it is of size 1. Otherwise, dim is squeezed (see torch.squeeze()), resulting in the output tensor having 1 fewer dimension than input.

Parameters:

• input (Tensor) – the input tensor
• dim (int) – the dimension to reduce
• keepdim (bool) – whether the output tensor has dim retained or not
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4, 4)
>>> a
tensor([[ 0.0569, -0.2475,  0.0737, -0.3429],
        [-0.2993,  0.9138,  0.9337, -1.6864],
        [ 0.1132,  0.7892, -0.1003,  0.5688],
        [ 0.3637, -0.9906, -0.4752, -1.5197]])
>>> torch.sum(a, 1)
tensor([-0.4598, -0.1381,  1.3708, -2.6217])

torch.unique(input, sorted=False, return_inverse=False)

Returns the unique scalar elements of the input tensor as a 1-D tensor.

Parameters:

• input (Tensor) – the input tensor
• sorted (bool) – Whether to sort the unique elements in ascending order before returning as output.
• return_inverse (bool) – Whether to also return the indices for where elements in the original input ended up in the returned unique list.

Returns:

A tensor or a tuple of tensors containing

• output (Tensor): the output list of unique scalar elements.
• inverse_indices (Tensor): (optional) if return_inverse is True, there will be a 2nd returned tensor (same shape as input) representing the indices for where elements in the original input map to in the output; otherwise, this function will only return a single tensor.

Return type:

(Tensor, Tensor (optional))

Example:

>>> output = torch.unique(torch.tensor([1, 3, 2, 3], dtype=torch.long))
>>> output
tensor([ 2,  3,  1])

>>> output, inverse_indices = torch.unique(
        torch.tensor([1, 3, 2, 3], dtype=torch.long), sorted=True, return_inverse=True)
>>> output
tensor([ 1,  2,  3])
>>> inverse_indices
tensor([ 0,  2,  1,  2])

>>> output, inverse_indices = torch.unique(
        torch.tensor([[1, 3], [2, 3]], dtype=torch.long), sorted=True, return_inverse=True)
>>> output
tensor([ 1,  2,  3])
>>> inverse_indices
tensor([[ 0,  2],
        [ 1,  2]])

torch.var()

torch.var(input, unbiased=True) → Tensor

Returns the variance of all elements in the input tensor.

If unbiased is False, then the variance will be calculated via the biased estimator. Otherwise, Bessel’s correction will be used.

Parameters:

• input (Tensor) – the input tensor
• unbiased (bool) – whether to use the unbiased estimation or not

Example:

>>> a = torch.randn(1, 3)
>>> a
tensor([[-0.3425, -1.2636, -0.4864]])
>>> torch.var(a)
tensor(0.2455)

torch.var(input, dim, keepdim=False, unbiased=True, out=None) → Tensor

Returns the variance of each row of the input tensor in the given dimension dim.

If keepdim is True, the output tensors are of the same size as input except in the dimension dim where they are of size 1. Otherwise, dim is squeezed (see torch.squeeze()), resulting in the outputs tensor having 1 fewer dimension than input.

If unbiased is False, then the variance will be calculated via the biased estimator. Otherwise, Bessel’s correction will be used.

Parameters:

• input (Tensor) – the input tensor
• dim (int) – the dimension to reduce
• keepdim (bool) – whether the output tensor has dim retained or not
• unbiased (bool) – whether to use the unbiased estimation or not
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4, 4)
>>> a
tensor([[-0.3567,  1.7385, -1.3042,  0.7423],
        [ 1.3436, -0.1015, -0.9834, -0.8438],
        [ 0.6056,  0.1089, -0.3112, -1.4085],
        [-0.7700,  0.6074, -0.1469,  0.7777]])
>>> torch.var(a, 1)
tensor([ 1.7444,  1.1363,  0.7356,  0.5112])

Comparison Ops

torch.eq(input, other, out=None) → Tensor

Computes element-wise equality

The second argument can be a number or a tensor whose shape is broadcastable with the first argument.

Parameters:

• input (Tensor) – the tensor to compare
• other (Tensor or float) – the tensor or value to compare
• out (Tensor, optional) – the output tensor. Must be a ByteTensor or the same type as input.

Returns:

A torch.ByteTensor containing a 1 at each location where comparison is true

Return type:

Tensor

Example:

>>> torch.eq(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]]))
tensor([[ 1,  0],
        [ 0,  1]], dtype=torch.uint8)

torch.equal(tensor1, tensor2) → bool

True if two tensors have the same size and elements, False otherwise.

Example:

>>> torch.equal(torch.tensor([1, 2]), torch.tensor([1, 2]))
True

torch.ge(input, other, out=None) → Tensor

Computes $input \geq other$ element-wise.

The second argument can be a number or a tensor whose shape is broadcastable with the first argument.

Parameters:

• input (Tensor) – the tensor to compare
• other (Tensor or float) – the tensor or value to compare
• out (Tensor, optional) – the output tensor that must be a ByteTensor or the same type as input

Returns:

A torch.ByteTensor containing a 1 at each location where comparison is true

Return type:

Tensor

Example:

>>> torch.ge(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]]))
tensor([[ 1,  1],
        [ 0,  1]], dtype=torch.uint8)

torch.gt(input, other, out=None) → Tensor

Computes $input > other$ element-wise.

The second argument can be a number or a tensor whose shape is broadcastable with the first argument.

Parameters:

• input (Tensor) – the tensor to compare
• other (Tensor or float) – the tensor or value to compare
• out (Tensor, optional) – the output tensor that must be a ByteTensor or the same type as input

Returns:

A torch.ByteTensor containing a 1 at each location where comparison is true

Return type:

Tensor

Example:

>>> torch.gt(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]]))
tensor([[ 0,  1],
        [ 0,  0]], dtype=torch.uint8)

torch.isnan(tensor)

Returns a new tensor with boolean elements representing if each element is NaN or not.

Parameters:tensor (Tensor) – A tensor to check Returns:A torch.ByteTensor containing a 1 at each location of NaN elements. Return type:Tensor

Example:

>>> torch.isnan(torch.tensor([1, float('nan'), 2]))
tensor([ 0,  1,  0], dtype=torch.uint8)

torch.kthvalue(input, k, dim=None, keepdim=False, out=None) -> (Tensor, LongTensor)

Returns the k th smallest element of the given input tensor along a given dimension.

If dim is not given, the last dimension of the input is chosen.

A tuple of (values, indices) is returned, where the indices is the indices of the kth-smallest element in the original input tensor in dimension dim.

If keepdim is True, both the values and indices tensors are the same size as input, except in the dimension dim where they are of size 1. Otherwise, dim is squeezed (see torch.squeeze()), resulting in both the values and indices tensors having 1 fewer dimension than the input tensor.

Parameters:

• input (Tensor) – the input tensor
• k (int) – k for the k-th smallest element
• dim (int, optional) – the dimension to find the kth value along
• keepdim (bool) – whether the output tensors have dim retained or not
• out (tuple, optional) – the output tuple of (Tensor, LongTensor) can be optionally given to be used as output buffers

Example:

>>> x = torch.arange(1, 6)
>>> x
tensor([ 1.,  2.,  3.,  4.,  5.])
>>> torch.kthvalue(x, 4)
(tensor(4.), tensor(3))

>>> x=torch.arange(1,7).resize_(2,3)
>>> x
tensor([[ 1.,  2.,  3.],
        [ 4.,  5.,  6.]])
>>> torch.kthvalue(x,2,0,True)
(tensor([[ 4.,  5.,  6.]]), tensor([[ 1,  1,  1]]))

torch.le(input, other, out=None) → Tensor

Computes $input \leq other$ element-wise.

The second argument can be a number or a tensor whose shape is broadcastable with the first argument.

Parameters:

• input (Tensor) – the tensor to compare
• other (Tensor or float) – the tensor or value to compare
• out (Tensor, optional) – the output tensor that must be a ByteTensor or the same type as input

Returns:

A torch.ByteTensor containing a 1 at each location where comparison is true

Return type:

Tensor

Example:

>>> torch.le(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]]))
tensor([[ 1,  0],
        [ 1,  1]], dtype=torch.uint8)

torch.lt(input, other, out=None) → Tensor

Computes $input < other$ element-wise.

The second argument can be a number or a tensor whose shape is broadcastable with the first argument.

Parameters:

• input (Tensor) – the tensor to compare
• other (Tensor or float) – the tensor or value to compare
• out (Tensor, optional) – the output tensor that must be a ByteTensor or the same type as input

Returns:

A torch.ByteTensor containing a 1 at each location where comparison is true

Return type:

Tensor

Example:

>>> torch.lt(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]]))
tensor([[ 0,  0],
        [ 1,  0]], dtype=torch.uint8)

torch.max()

torch.max(input) → Tensor

Returns the maximum value of all elements in the input tensor.

Parameters:input (Tensor) – the input tensor

Example:

>>> a = torch.randn(1, 3)
>>> a
tensor([[ 0.6763,  0.7445, -2.2369]])
>>> torch.max(a)
tensor(0.7445)

torch.max(input, dim, keepdim=False, out=None) -> (Tensor, LongTensor)

Returns the maximum value of each row of the input tensor in the given dimension dim. The second return value is the index location of each maximum value found (argmax).

If keepdim is True, the output tensors are of the same size as input except in the dimension dim where they are of size 1. Otherwise, dim is squeezed (see torch.squeeze()), resulting in the output tensors having 1 fewer dimension than input.

Parameters:

• input (Tensor) – the input tensor
• dim (int) – the dimension to reduce
• keepdim (bool) – whether the output tensors have dim retained or not
• out (tuple, optional) – the result tuple of two output tensors (max, max_indices)

Example:

>>> a = torch.randn(4, 4)
>>> a
tensor([[-1.2360, -0.2942, -0.1222,  0.8475],
        [ 1.1949, -1.1127, -2.2379, -0.6702],
        [ 1.5717, -0.9207,  0.1297, -1.8768],
        [-0.6172,  1.0036, -0.6060, -0.2432]])
>>> torch.max(a, 1)
(tensor([ 0.8475,  1.1949,  1.5717,  1.0036]), tensor([ 3,  0,  0,  1]))

torch.max(input, other, out=None) → Tensor

Each element of the tensor input is compared with the corresponding element of the tensor other and an element-wise maximum is taken.

The shapes of input and other don’t need to match, but they must be broadcastable.

$$out_i = \max(tensor_i, other_i)$$

Note

When the shapes do not match, the shape of the returned output tensor follows the broadcasting rules.

Parameters:

• input (Tensor) – the input tensor
• other (Tensor) – the second input tensor
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4)
>>> a
tensor([ 0.2942, -0.7416,  0.2653, -0.1584])
>>> b = torch.randn(4)
>>> b
tensor([ 0.8722, -1.7421, -0.4141, -0.5055])
>>> torch.max(a, b)
tensor([ 0.8722, -0.7416,  0.2653, -0.1584])

torch.min()

torch.min(input) → Tensor

Returns the minimum value of all elements in the input tensor.

Parameters:input (Tensor) – the input tensor

Example:

>>> a = torch.randn(1, 3)
>>> a
tensor([[ 0.6750,  1.0857,  1.7197]])
>>> torch.min(a)
tensor(0.6750)

torch.min(input, dim, keepdim=False, out=None) -> (Tensor, LongTensor)

Returns the minimum value of each row of the input tensor in the given dimension dim. The second return value is the index location of each minimum value found (argmin).

If keepdim is True, the output tensors are of the same size as input except in the dimension dim where they are of size 1. Otherwise, dim is squeezed (see torch.squeeze()), resulting in the output tensors having 1 fewer dimension than input.

Parameters:

• input (Tensor) – the input tensor
• dim (int) – the dimension to reduce
• keepdim (bool) – whether the output tensors have dim retained or not
• out (tuple, optional) – the tuple of two output tensors (min, min_indices)

Example:

>>> a = torch.randn(4, 4)
>>> a
tensor([[-0.6248,  1.1334, -1.1899, -0.2803],
        [-1.4644, -0.2635, -0.3651,  0.6134],
        [ 0.2457,  0.0384,  1.0128,  0.7015],
        [-0.1153,  2.9849,  2.1458,  0.5788]])
>>> torch.min(a, 1)
(tensor([-1.1899, -1.4644,  0.0384, -0.1153]), tensor([ 2,  0,  1,  0]))

torch.min(input, other, out=None) → Tensor

Each element of the tensor input is compared with the corresponding element of the tensor other and an element-wise minimum is taken. The resulting tensor is returned.

The shapes of input and other don’t need to match, but they must be broadcastable.

$$out_i = \min(tensor_i, other_i)$$

Note

When the shapes do not match, the shape of the returned output tensor follows the broadcasting rules.

Parameters:

• input (Tensor) – the input tensor
• other (Tensor) – the second input tensor
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4)
>>> a
tensor([ 0.8137, -1.1740, -0.6460,  0.6308])
>>> b = torch.randn(4)
>>> b
tensor([-0.1369,  0.1555,  0.4019, -0.1929])
>>> torch.min(a, b)
tensor([-0.1369, -1.1740, -0.6460, -0.1929])

torch.ne(input, other, out=None) → Tensor

Computes $input \neq other$ element-wise.

The second argument can be a number or a tensor whose shape is broadcastable with the first argument.

Parameters:

• input (Tensor) – the tensor to compare
• other (Tensor or float) – the tensor or value to compare
• out (Tensor, optional) – the output tensor that must be a ByteTensor or the same type as input

Returns:

A torch.ByteTensor containing a 1 at each location where comparison is true.

Return type:

Tensor

Example:

>>> torch.ne(torch.tensor([[1, 2], [3, 4]]), torch.tensor([[1, 1], [4, 4]]))
tensor([[ 0,  1],
        [ 1,  0]], dtype=torch.uint8)

torch.sort(input, dim=None, descending=False, out=None) -> (Tensor, LongTensor)

Sorts the elements of the input tensor along a given dimension in ascending order by value.

If dim is not given, the last dimension of the input is chosen.

If descending is True then the elements are sorted in descending order by value.

A tuple of (sorted_tensor, sorted_indices) is returned, where the sorted_indices are the indices of the elements in the original input tensor.

Parameters:

• input (Tensor) – the input tensor
• dim (int, optional) – the dimension to sort along
• descending (bool, optional) – controls the sorting order (ascending or descending)
• out (tuple, optional) – the output tuple of (Tensor, LongTensor) that can be optionally given to be used as output buffers

Example:

>>> x = torch.randn(3, 4)
>>> sorted, indices = torch.sort(x)
>>> sorted
tensor([[-0.2162,  0.0608,  0.6719,  2.3332],
        [-0.5793,  0.0061,  0.6058,  0.9497],
        [-0.5071,  0.3343,  0.9553,  1.0960]])
>>> indices
tensor([[ 1,  0,  2,  3],
        [ 3,  1,  0,  2],
        [ 0,  3,  1,  2]])

>>> sorted, indices = torch.sort(x, 0)
>>> sorted
tensor([[-0.5071, -0.2162,  0.6719, -0.5793],
        [ 0.0608,  0.0061,  0.9497,  0.3343],
        [ 0.6058,  0.9553,  1.0960,  2.3332]])
>>> indices
tensor([[ 2,  0,  0,  1],
        [ 0,  1,  1,  2],
        [ 1,  2,  2,  0]])

torch.topk(input, k, dim=None, largest=True, sorted=True, out=None) -> (Tensor, LongTensor)

Returns the k largest elements of the given input tensor along a given dimension.

If dim is not given, the last dimension of the input is chosen.

If largest is False then the k smallest elements are returned.

A tuple of (values, indices) is returned, where the indices are the indices of the elements in the original input tensor.

The boolean option sorted if True, will make sure that the returned k elements are themselves sorted

Parameters:

• input (Tensor) – the input tensor
• k (int) – the k in “top-k”
• dim (int, optional) – the dimension to sort along
• largest (bool, optional) – controls whether to return largest or smallest elements
• sorted (bool, optional) – controls whether to return the elements in sorted order
• out (tuple, optional) – the output tuple of (Tensor, LongTensor) that can be optionally given to be used as output buffers

Example:

>>> x = torch.arange(1, 6)
>>> x
tensor([ 1.,  2.,  3.,  4.,  5.])
>>> torch.topk(x, 3)
(tensor([ 5.,  4.,  3.]), tensor([ 4,  3,  2]))

Spectral Ops

torch.fft(input, signal_ndim, normalized=False) → Tensor

Complex-to-complex Discrete Fourier Transform

This method computes the complex-to-complex discrete Fourier transform. Ignoring the batch dimensions, it computes the following expression:

$$X[\omega_1, \dots, \omega_d] = \frac{1}{\prod_{i=1}^d N_i} \sum_{n_1=0}^{N_1} \dots \sum_{n_d=0}^{N_d} x[n_1, \dots, n_d] e^{-j\ 2 \pi \sum_{i=0}^d \frac{\omega_i n_i}{N_i}},$$

where $d$ = signal_ndim is number of dimensions for the signal, and $N_i$ is the size of signal dimension $i$.

This method supports 1D, 2D and 3D complex-to-complex transforms, indicated by signal_ndim. input must be a tensor with last dimension of size 2, representing the real and imaginary components of complex numbers, and should have at least signal_ndim + 1 dimensions with optionally arbitrary number of leading batch dimensions. If normalized is set to True, this normalizes the result by dividing it with $\sqrt{\prod_{i=1}^K N_i}$ so that the operator is unitary.

Returns the real and the imaginary parts together as one tensor of the same shape of input.

The inverse of this function is ifft().

Warning

For CPU tensors, this method is currently only available with MKL. Check torch.backends.mkl.is_available() to check if MKL is installed.

Parameters:

• input (Tensor) – the input tensor of at least signal_ndim + 1 dimensions
• signal_ndim (int) – the number of dimensions in each signal. signal_ndim can only be 1, 2 or 3
• normalized (bool, optional) – controls whether to return normalized results. Default: False

Returns:

A tensor containing the complex-to-complex Fourier transform result

Return type:

Tensor

Example:

>>> # unbatched 2D FFT
>>> x = torch.randn(4, 3, 2)
>>> torch.fft(x, 2)
tensor([[[-0.0876,  1.7835],
         [-2.0399, -2.9754],
         [ 4.4773, -5.0119]],

        [[-1.5716,  2.7631],
         [-3.8846,  5.2652],
         [ 0.2046, -0.7088]],

        [[ 1.9938, -0.5901],
         [ 6.5637,  6.4556],
         [ 2.9865,  4.9318]],

        [[ 7.0193,  1.1742],
         [-1.3717, -2.1084],
         [ 2.0289,  2.9357]]])
>>> # batched 1D FFT
>>> torch.fft(x, 1)
tensor([[[ 1.8385,  1.2827],
         [-0.1831,  1.6593],
         [ 2.4243,  0.5367]],

        [[-0.9176, -1.5543],
         [-3.9943, -2.9860],
         [ 1.2838, -2.9420]],

        [[-0.8854, -0.6860],
         [ 2.4450,  0.0808],
         [ 1.3076, -0.5768]],

        [[-0.1231,  2.7411],
         [-0.3075, -1.7295],
         [-0.5384, -2.0299]]])
>>> # arbitrary number of batch dimensions, 2D FFT
>>> x = torch.randn(3, 3, 5, 5, 2)
>>> y = torch.fft(x, 2)
>>> y.shape
torch.Size([3, 3, 5, 5, 2])

torch.ifft(input, signal_ndim, normalized=False) → Tensor

Complex-to-complex Inverse Discrete Fourier Transform

This method computes the complex-to-complex inverse discrete Fourier transform. Ignoring the batch dimensions, it computes the following expression:

$$X[\omega_1, \dots, \omega_d] = \frac{1}{\prod_{i=1}^d N_i} \sum_{n_1=0}^{N_1} \dots \sum_{n_d=0}^{N_d} x[n_1, \dots, n_d] e^{\ j\ 2 \pi \sum_{i=0}^d \frac{\omega_i n_i}{N_i}},$$

where $d$ = signal_ndim is number of dimensions for the signal, and $N_i$ is the size of signal dimension $i$.

The argument specifications are almost identical with fft(). However, if normalized is set to True, this instead returns the results multiplied by $\sqrt{\prod_{i=1}^d N_i}$, to become a unitary operator. Therefore, to invert a fft(), the normalized argument should be set identically for fft().

Returns the real and the imaginary parts together as one tensor of the same shape of input.

The inverse of this function is fft().

Warning

For CPU tensors, this method is currently only available with MKL. Check torch.backends.mkl.is_available() to check if MKL is installed.

Parameters:

• input (Tensor) – the input tensor of at least signal_ndim + 1 dimensions
• signal_ndim (int) – the number of dimensions in each signal. signal_ndim can only be 1, 2 or 3
• normalized (bool, optional) – controls whether to return normalized results. Default: False

Returns:

A tensor containing the complex-to-complex inverse Fourier transform result

Return type:

Tensor

Example:

>>> x = torch.randn(3, 3, 2)
>>> x
tensor([[[ 1.2766,  1.3680],
         [-0.8337,  2.0251],
         [ 0.9465, -1.4390]],

        [[-0.1890,  1.6010],
         [ 1.1034, -1.9230],
         [-0.9482,  1.0775]],

        [[-0.7708, -0.8176],
         [-0.1843, -0.2287],
         [-1.9034, -0.2196]]])
>>> y = torch.fft(x, 2)
>>> torch.ifft(y, 2)  # recover x
tensor([[[ 1.2766,  1.3680],
         [-0.8337,  2.0251],
         [ 0.9465, -1.4390]],

        [[-0.1890,  1.6010],
         [ 1.1034, -1.9230],
         [-0.9482,  1.0775]],

        [[-0.7708, -0.8176],
         [-0.1843, -0.2287],
         [-1.9034, -0.2196]]])

torch.rfft(input, signal_ndim, normalized=False, onesided=True) → Tensor

Real-to-complex Discrete Fourier Transform

This method computes the real-to-complex discrete Fourier transform. It is mathematically equivalent with fft() with differences only in formats of the input and output.

This method supports 1D, 2D and 3D real-to-complex transforms, indicated by signal_ndim. input must be a tensor with at least signal_ndim dimensions with optionally arbitrary number of leading batch dimensions. If normalized is set to True, this normalizes the result by multiplying it with $\sqrt{\prod_{i=1}^K N_i}$ so that the operator is unitary, where $N_i$ is the size of signal dimension $i$.

The real-to-complex Fourier transform results follow conjugate symmetry:

$$X[\omega_1, \dots, \omega_d] = X^*[N_1 - \omega_1, \dots, N_d - \omega_d],$$

where the index arithmetic is computed modulus the size of the corresponding dimension, $\ ^*$ is the conjugate operator, and $d$ = signal_ndim. onesided flag controls whether to avoid redundancy in the output results. If set to True (default), the output will not be full complex result of shape $(*, 2)$, where $*$ is the shape of input, but instead the last dimension will be halfed as of size $\lfloor \frac{N_d}{2} \rfloor + 1$.

The inverse of this function is irfft().

Warning

For CPU tensors, this method is currently only available with MKL. Check torch.backends.mkl.is_available() to check if MKL is installed.

Parameters:

• input (Tensor) – the input tensor of at least signal_ndim dimensions
• signal_ndim (int) – the number of dimensions in each signal. signal_ndim can only be 1, 2 or 3
• normalized (bool, optional) – controls whether to return normalized results. Default: False
• onesided (bool, optional) – controls whether to return half of results to avoid redundancy Default: True

Returns:

A tensor containing the real-to-complex Fourier transform result

Return type:

Tensor

Example:

>>> x = torch.randn(5, 5)
>>> torch.rfft(x, 2).shape
torch.Size([5, 3, 2])
>>> torch.rfft(x, 2, onesided=False).shape
torch.Size([5, 5, 2])

torch.irfft(input, signal_ndim, normalized=False, onesided=True, signal_sizes=None) → Tensor

Complex-to-real Inverse Discrete Fourier Transform

This method computes the complex-to-real inverse discrete Fourier transform. It is mathematically equivalent with ifft() with differences only in formats of the input and output.

The argument specifications are almost identical with ifft(). Similar to ifft(), if normalized is set to True, this normalizes the result by multiplying it with $\sqrt{\prod_{i=1}^K N_i}$ so that the operator is unitary, where $N_i$ is the size of signal dimension $i$.

Due to the conjugate symmetry, input do not need to contain the full complex frequency values. Roughly half of the values will be sufficient, as is the case when input is given by rfft() with rfft(signal, onesided=True). In such case, set the onesided argument of this method to True. Moreover, the original signal shape information can sometimes be lost, optionally set signal_sizes to be the size of the original signal (without the batch dimensions if in batched mode) to recover it with correct shape.

Therefore, to invert an rfft(), the normalized and onesided arguments should be set identically for irfft(), and preferrably a signal_sizes is given to avoid size mismatch. See the example below for a case of size mismatch.

See rfft() for details on conjugate symmetry.

The inverse of this function is rfft().

Warning

Generally speaking, the input of this function should contain values following conjugate symmetry. Note that even if onesided is True, often symmetry on some part is still needed. When this requirement is not satisfied, the behavior of irfft() is undefined. Since torch.autograd.gradcheck() estimates numerical Jacobian with point perturbations, irfft() will almost certainly fail the check.

Warning

For CPU tensors, this method is currently only available with MKL. Check torch.backends.mkl.is_available() to check if MKL is installed.

Parameters:

• input (Tensor) – the input tensor of at least signal_ndim + 1 dimensions
• signal_ndim (int) – the number of dimensions in each signal. signal_ndim can only be 1, 2 or 3
• normalized (bool, optional) – controls whether to return normalized results. Default: False
• onesided (bool, optional) – controls whether input was halfed to avoid redundancy, e.g., by rfft(). Default: True
• signal_sizes (list or torch.Size, optional) – the size of the original signal (without batch dimension). Default: None

Returns:

A tensor containing the complex-to-real inverse Fourier transform result

Return type:

Tensor

Example:

>>> x = torch.randn(4, 4)
>>> torch.rfft(x, 2, onesided=True).shape
torch.Size([4, 3, 2])
>>>
>>> # notice that with onesided=True, output size does not determine the original signal size
>>> x = torch.randn(4, 5)

>>> torch.rfft(x, 2, onesided=True).shape
torch.Size([4, 3, 2])
>>>
>>> # now we use the original shape to recover x
>>> x
tensor([[-0.8992,  0.6117, -1.6091, -0.4155, -0.8346],
        [-2.1596, -0.0853,  0.7232,  0.1941, -0.0789],
        [-2.0329,  1.1031,  0.6869, -0.5042,  0.9895],
        [-0.1884,  0.2858, -1.5831,  0.9917, -0.8356]])
>>> y = torch.rfft(x, 2, onesided=True)
>>> torch.irfft(y, 2, onesided=True, signal_sizes=x.shape)  # recover x
tensor([[-0.8992,  0.6117, -1.6091, -0.4155, -0.8346],
        [-2.1596, -0.0853,  0.7232,  0.1941, -0.0789],
        [-2.0329,  1.1031,  0.6869, -0.5042,  0.9895],
        [-0.1884,  0.2858, -1.5831,  0.9917, -0.8356]])

torch.stft(signal, frame_length, hop, fft_size=None, normalized=False, onesided=True, window=None, pad_end=0) → Tensor

Short-time Fourier transform (STFT).

Ignoring the batch dimension, this method computes the following expression:

$$X[m, \omega] = \sum_{k = 0}^{\text{frame_length}}% window[k]\ signal[m \times hop + k]\ e^{- j \frac{2 \pi \cdot \omega k}{\text{frame_length}}},$$

where $m$ is the index of the sliding window, and $\omega$ is the frequency that $0 \leq \omega <$ fft_size. When return_onsesided is the default value True, only values for $\omega$ in range $\left[0, 1, 2, \dots, \left\lfloor \frac{\text{fft_size}}{2} \right\rfloor + 1\right]$ are returned because the real-to-complex transform satisfies the Hermitian symmetry, i.e., $X[m, \omega] = X[m, \text{fft_size} - \omega]^*$.

The input signal must be 1-D sequence $(T)$ or 2-D a batch of sequences $(N \times T)$. If fft_size is None, it is default to same value as frame_length. window can be a 1-D tensor of size frame_length, e.g., see torch.hann_window(). If window is the default value None, it is treated as if having $1$ everywhere in the frame. pad_end indicates the amount of zero padding at the end of signal before STFT. If normalized is set to True, the function returns the normalized STFT results, i.e., multiplied by $(frame\_length)^{-0.5}$.

Returns the real and the imaginary parts together as one tensor of size $(* \times N \times 2)$, where $*$ is the shape of input signal, $N$ is the number of $\omega$ s considered depending on fft_size and return_onesided, and each pair in the last dimension represents a complex number as real part and imaginary part.

Parameters:

• signal (Tensor) – the input tensor
• frame_length (int) – the size of window frame and STFT filter
• hop (int) – the distance between neighboring sliding window frames
• fft_size (int, optional) – size of Fourier transform. Default: None
• normalized (bool, optional) – controls whether to return the normalized STFT results Default: False
• onesided (bool, optional) – controls whether to return half of results to avoid redundancy Default: True
• window (Tensor, optional) – the optional window function. Default: None
• pad_end (int, optional) – implicit zero padding at the end of signal. Default: 0

Returns:

A tensor containing the STFT result

Return type:

Tensor

torch.hann_window(window_length, periodic=True, dtype=torch.float32)

Hann window function.

This method computes the Hann window function:

$$w[n] = \frac{1}{2}\ \left[1 - \cos \left( \frac{2 \pi n}{N - 1} \right)\right] = \sin^2 \left( \frac{\pi n}{N - 1} \right),$$

where $N$ is the full window size.

The input window_length is a positive integer controlling the returned window size. periodic flag determines whether the returned window trims off the last duplicate value from the symmetric window and is ready to be used as a periodic window with functions like torch.stft(). Therefore, if periodic is true, the $N$ in above formula is in fact $\text{window_length} + 1$. Also, we always have torch.hann_window(L, periodic=True) equal to torch.hann_window(L + 1, periodic=False)[:-1]).

Note

If window_length $=1$, the returned window contains a single value 1.

Parameters:

• window_length (int) – the size of returned window
• periodic (bool, optional) – If True, returns a window to be used as periodic function. If False, return a symmetric window.
• dtype (torch.dtype, optional) – the desired type of returned window. Default: torch.float32

Returns:

A 1-D tensor of size $(\text{window_length},)$ containing the window

Return type:

Tensor

torch.hamming_window(window_length, periodic=True, alpha=0.54, beta=0.46, dtype=torch.float32)

Hamming window function.

This method computes the Hamming window function:

$$w[n] = \alpha - \beta\ \cos \left( \frac{2 \pi n}{N - 1} \right),$$

where $N$ is the full window size.

The input window_length is a positive integer controlling the returned window size. periodic flag determines whether the returned window trims off the last duplicate value from the symmetric window and is ready to be used as a periodic window with functions like torch.stft(). Therefore, if periodic is true, the $N$ in above formula is in fact $\text{window_length} + 1$. Also, we always have torch.hamming_window(L, periodic=True) equal to torch.hamming_window(L + 1, periodic=False)[:-1]).

Note

If window_length $=1$, the returned window contains a single value 1.

Note

This is a generalized version of torch.hann_window().

Parameters:

• window_length (int) – the size of returned window
• periodic (bool, optional) – If True, returns a window to be used as periodic function. If False, return a symmetric window.
• dtype (torch.dtype, optional) – the desired type of returned window. Default: torch.float32

Returns:

A 1-D tensor of size $(\text{window_length},)$ containing the window

Return type:

Tensor

torch.bartlett_window(window_length, periodic=True, dtype=torch.float32)

Bartlett window function.

This method computes the Bartlett window function:

$$\begin{split}w[n] = 1 - \left| \frac{2n}{N-1} - 1 \right| = \begin{cases} \frac{2n}{N - 1} & \text{if } 0 \leq n \leq \frac{N - 1}{2} \\ 2 - \frac{2n}{N - 1} & \text{if } \frac{N - 1}{2} < n < N \\ \end{cases},\end{split}$$

where $N$ is the full window size.

The input window_length is a positive integer controlling the returned window size. periodic flag determines whether the returned window trims off the last duplicate value from the symmetric window and is ready to be used as a periodic window with functions like torch.stft(). Therefore, if periodic is true, the $N$ in above formula is in fact $\text{window_length} + 1$. Also, we always have torch.bartlett_window(L, periodic=True) equal to torch.bartlett_window(L + 1, periodic=False)[:-1]).

Note

If window_length $=1$, the returned window contains a single value 1.

Parameters:

• window_length (int) – the size of returned window
• periodic (bool, optional) – If True, returns a window to be used as periodic function. If False, return a symmetric window.
• dtype (torch.dtype, optional) – the desired type of returned window. Default: torch.float32

Returns:

A 1-D tensor of size $(\text{window_length},)$ containing the window

Return type:

Tensor

Other Operations

torch.cross(input, other, dim=-1, out=None) → Tensor

Returns the cross product of vectors in dimension dim of input and other.

input and other must have the same size, and the size of their dim dimension should be 3.

If dim is not given, it defaults to the first dimension found with the size 3.

Parameters:

• input (Tensor) – the input tensor
• other (Tensor) – the second input tensor
• dim (int, optional) – the dimension to take the cross-product in.
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(4, 3)
>>> a
tensor([[-0.3956,  1.1455,  1.6895],
        [-0.5849,  1.3672,  0.3599],
        [-1.1626,  0.7180, -0.0521],
        [-0.1339,  0.9902, -2.0225]])
>>> b = torch.randn(4, 3)
>>> b
tensor([[-0.0257, -1.4725, -1.2251],
        [-1.1479, -0.7005, -1.9757],
        [-1.3904,  0.3726, -1.1836],
        [-0.9688, -0.7153,  0.2159]])
>>> torch.cross(a, b, dim=1)
tensor([[ 1.0844, -0.5281,  0.6120],
        [-2.4490, -1.5687,  1.9792],
        [-0.8304, -1.3037,  0.5650],
        [-1.2329,  1.9883,  1.0551]])
>>> torch.cross(a, b)
tensor([[ 1.0844, -0.5281,  0.6120],
        [-2.4490, -1.5687,  1.9792],
        [-0.8304, -1.3037,  0.5650],
        [-1.2329,  1.9883,  1.0551]])

torch.diag(input, diagonal=0, out=None) → Tensor

• If input is a vector (1-D tensor), then returns a 2-D square tensor with the elements of input as the diagonal.
• If input is a matrix (2-D tensor), then returns a 1-D tensor with the diagonal elements of input.

The argument diagonal controls which diagonal to consider:

• If diagonal = 0, it is the main diagonal.
• If diagonal > 0, it is above the main diagonal.
• If diagonal < 0, it is below the main diagonal.

Parameters:

• input (Tensor) – the input tensor
• diagonal (int, optional) – the diagonal to consider
• out (Tensor, optional) – the output tensor

See also

torch.diagonal() always returns the diagonal of its input.

torch.diagflat() always constructs a tensor with diagonal elements specified by the input.

Examples:

Get the square matrix where the input vector is the diagonal:

>>> a = torch.randn(3)
>>> a
tensor([ 0.5950,-0.0872, 2.3298])
>>> torch.diag(a)
tensor([[ 0.5950, 0.0000, 0.0000],
        [ 0.0000,-0.0872, 0.0000],
        [ 0.0000, 0.0000, 2.3298]])
>>> torch.diag(a, 1)
tensor([[ 0.0000, 0.5950, 0.0000, 0.0000],
        [ 0.0000, 0.0000,-0.0872, 0.0000],
        [ 0.0000, 0.0000, 0.0000, 2.3298],
        [ 0.0000, 0.0000, 0.0000, 0.0000]])

Get the k-th diagonal of a given matrix:

>>> a = torch.randn(3, 3)
>>> a
tensor([[-0.4264, 0.0255,-0.1064],
        [ 0.8795,-0.2429, 0.1374],
        [ 0.1029,-0.6482,-1.6300]])
>>> torch.diag(a, 0)
tensor([-0.4264,-0.2429,-1.6300])
>>> torch.diag(a, 1)
tensor([ 0.0255, 0.1374])

torch.diagflat(input, diagonal=0) → Tensor

• If input is a vector (1-D tensor), then returns a 2-D square tensor with the elements of input as the diagonal.
• If input is a tensor with more than one dimension, then returns a 2-D tensor with diagonal elements equal to a flattened input.

The argument offset controls which diagonal to consider:

• If offset = 0, it is the main diagonal.
• If offset > 0, it is above the main diagonal.
• If offset < 0, it is below the main diagonal.

Parameters:

• input (Tensor) – the input tensor
• offset (int, optional) – the diagonal to consider. Default: 0 (main diagonal).

Examples:

>>> a = torch.randn(3)
>>> a
tensor([-0.2956, -0.9068,  0.1695])
>>> torch.diagflat(a)
tensor([[-0.2956,  0.0000,  0.0000],
        [ 0.0000, -0.9068,  0.0000],
        [ 0.0000,  0.0000,  0.1695]])
>>> torch.diagflat(a, 1)
tensor([[ 0.0000, -0.2956,  0.0000,  0.0000],
        [ 0.0000,  0.0000, -0.9068,  0.0000],
        [ 0.0000,  0.0000,  0.0000,  0.1695],
        [ 0.0000,  0.0000,  0.0000,  0.0000]])

>>> a = torch.randn(2, 2)
>>> a
tensor([[ 0.2094, -0.3018],
        [-0.1516,  1.9342]])
>>> torch.diagflat(a)
tensor([[ 0.2094,  0.0000,  0.0000,  0.0000],
        [ 0.0000, -0.3018,  0.0000,  0.0000],
        [ 0.0000,  0.0000, -0.1516,  0.0000],
        [ 0.0000,  0.0000,  0.0000,  1.9342]])

torch.diagonal(input, offset=0) → Tensor

Returns a 1-D tensor with the diagonal elements of input.

The argument offset controls which diagonal to consider:

• If offset = 0, it is the main diagonal.
• If offset > 0, it is above the main diagonal.
• If offset < 0, it is below the main diagonal.

Parameters:

• input (Tensor) – the input tensor. Must be 2-dimensional.
• offset (int, optional) – which diagonal to consider. Default: 0 (main diagonal).

Examples:

>>> a = torch.randn(3, 3)
>>> a
tensor([[-1.0854,  1.1431, -0.1752],
        [ 0.8536, -0.0905,  0.0360],
        [ 0.6927, -0.3735, -0.4945]])


>>> torch.diagonal(a, 0)
tensor([-1.0854, -0.0905, -0.4945])


>>> torch.diagonal(a, 1)
tensor([ 1.1431,  0.0360])

torch.einsum(equation, operands) → Tensor

This function provides a way of computing multilinear expressions (i.e. sums of products) using the Einstein summation convention.

Parameters:

• equation (string) – The equation is given in terms of lower case letters (indices) to be associated with each dimension of the operands and result. The left hand side lists the operands dimensions, separated by commas. There should be one index letter per tensor dimension. The right hand side follows after -> and gives the indices for the output. If the -> and right hand side are omitted, it implicitly defined as the alphabetically sorted list of all indices appearing exactly once in the left hand side. The indices not apprearing in the output are summed over after multiplying the operands entries. einsum does not implement diagonals (multiple occurences of a single index for one tensor, e.g. ii->i) and ellipses (...).
• operands (list of Tensors) – The operands to compute the Einstein sum of. Note that the operands are passed as a list, not as individual arguments.

Examples:

>>> x = torch.randn(5)
>>> y = torch.randn(4)
>>> torch.einsum('i,j->ij', (x,y))  # outer product
tensor([[-0.0570, -0.0286, -0.0231,  0.0197],
        [ 1.2616,  0.6335,  0.5113, -0.4351],
        [ 1.4452,  0.7257,  0.5857, -0.4984],
        [-0.4647, -0.2333, -0.1883,  0.1603],
        [-1.1130, -0.5588, -0.4510,  0.3838]])


>>> A = torch.randn(3,5,4)
>>> l = torch.randn(2,5)
>>> r = torch.randn(2,4)
>>> torch.einsum('bn,anm,bm->ba', (l,A,r)) # compare torch.nn.functional.bilinear
tensor([[-0.3430, -5.2405,  0.4494],
        [ 0.3311,  5.5201, -3.0356]])


>>> As = torch.randn(3,2,5)
>>> Bs = torch.randn(3,5,4)
>>> torch.einsum('bij,bjk->bik', (As, Bs)) # batch matrix multiplication
tensor([[[-1.0564, -1.5904,  3.2023,  3.1271],
         [-1.6706, -0.8097, -0.8025, -2.1183]],

        [[ 4.2239,  0.3107, -0.5756, -0.2354],
         [-1.4558, -0.3460,  1.5087, -0.8530]],

        [[ 2.8153,  1.8787, -4.3839, -1.2112],
         [ 0.3728, -2.1131,  0.0921,  0.8305]]])

torch.histc(input, bins=100, min=0, max=0, out=None) → Tensor

Computes the histogram of a tensor.

The elements are sorted into equal width bins between min and max. If min and max are both zero, the minimum and maximum values of the data are used.

Parameters:

• input (Tensor) – the input tensor
• bins (int) – number of histogram bins
• min (int) – lower end of the range (inclusive)
• max (int) – upper end of the range (inclusive)
• out (Tensor, optional) – the output tensor

Returns:

Histogram represented as a tensor

Return type:

Tensor

Example:

>>> torch.histc(torch.tensor([1., 2, 1]), bins=4, min=0, max=3)
tensor([ 0.,  2.,  1.,  0.])

torch.renorm(input, p, dim, maxnorm, out=None) → Tensor

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

Note

If the norm of a row is lower than maxnorm, the row is unchanged

Parameters:

• input (Tensor) – the input tensor
• p (float) – the power for the norm computation
• dim (int) – the dimension to slice over to get the sub-tensors
• maxnorm (float) – the maximum norm to keep each sub-tensor under
• out (Tensor, optional) – the output tensor

Example:

>>> x = torch.ones(3, 3)
>>> x[1].fill_(2)
tensor([ 2.,  2.,  2.])
>>> x[2].fill_(3)
tensor([ 3.,  3.,  3.])
>>> x
tensor([[ 1.,  1.,  1.],
        [ 2.,  2.,  2.],
        [ 3.,  3.,  3.]])
>>> torch.renorm(x, 1, 0, 5)
tensor([[ 1.0000,  1.0000,  1.0000],
        [ 1.6667,  1.6667,  1.6667],
        [ 1.6667,  1.6667,  1.6667]])

torch.trace(input) → Tensor

Returns the sum of the elements of the diagonal of the input 2-D matrix.

Example:

>>> x = torch.arange(1, 10).view(3, 3)
>>> x
tensor([[ 1.,  2.,  3.],
        [ 4.,  5.,  6.],
        [ 7.,  8.,  9.]])
>>> torch.trace(x)
tensor(15.)

torch.tril(input, diagonal=0, out=None) → Tensor

Returns the lower triangular part of the matrix (2-D tensor) input, the other elements of the result tensor out are set to 0.

The lower triangular part of the matrix is defined as the elements on and below the diagonal.

The argument diagonal controls which diagonal to consider. If diagonal = 0, all elements on and below the main diagonal are retained. A positive value includes just as many diagonals above the main diagonal, and similarly a negative value excludes just as many diagonals below the main diagonal. The main diagonal are the set of indices $\lbrace (i, i) \rbrace$ for $i \in [0, \min\{d_{1}, d_{2}\} - 1]$ where $d_{1}, d_{2}$ are the dimensions of the matrix.

Parameters:

• input (Tensor) – the input tensor
• diagonal (int, optional) – the diagonal to consider
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(3, 3)
>>> a
tensor([[-1.0813, -0.8619,  0.7105],
        [ 0.0935,  0.1380,  2.2112],
        [-0.3409, -0.9828,  0.0289]])
>>> torch.tril(a)
tensor([[-1.0813,  0.0000,  0.0000],
        [ 0.0935,  0.1380,  0.0000],
        [-0.3409, -0.9828,  0.0289]])

>>> b = torch.randn(4, 6)
>>> b
tensor([[ 1.2219,  0.5653, -0.2521, -0.2345,  1.2544,  0.3461],
        [ 0.4785, -0.4477,  0.6049,  0.6368,  0.8775,  0.7145],
        [ 1.1502,  3.2716, -1.1243, -0.5413,  0.3615,  0.6864],
        [-0.0614, -0.7344, -1.3164, -0.7648, -1.4024,  0.0978]])
>>> torch.tril(b, diagonal=1)
tensor([[ 1.2219,  0.5653,  0.0000,  0.0000,  0.0000,  0.0000],
        [ 0.4785, -0.4477,  0.6049,  0.0000,  0.0000,  0.0000],
        [ 1.1502,  3.2716, -1.1243, -0.5413,  0.0000,  0.0000],
        [-0.0614, -0.7344, -1.3164, -0.7648, -1.4024,  0.0000]])
>>> torch.tril(b, diagonal=-1)
tensor([[ 0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000],
        [ 0.4785,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000],
        [ 1.1502,  3.2716,  0.0000,  0.0000,  0.0000,  0.0000],
        [-0.0614, -0.7344, -1.3164,  0.0000,  0.0000,  0.0000]])

torch.triu(input, diagonal=0, out=None) → Tensor

Returns the upper triangular part of the matrix (2-D tensor) input, the other elements of the result tensor out are set to 0.

The upper triangular part of the matrix is defined as the elements on and above the diagonal.

The argument diagonal controls which diagonal to consider. If diagonal = 0, all elements on and below the main diagonal are retained. A positive value excludes just as many diagonals above the main diagonal, and similarly a negative value includes just as many diagonals below the main diagonal. The main diagonal are the set of indices $\lbrace (i, i) \rbrace$ for $i \in [0, \min\{d_{1}, d_{2}\} - 1]$ where $d_{1}, d_{2}$ are the dimensions of the matrix.

Parameters:

• input (Tensor) – the input tensor
• diagonal (int, optional) – the diagonal to consider
• out (Tensor, optional) – the output tensor

Example:

>>> a = torch.randn(3, 3)
>>> a
tensor([[ 0.2309,  0.5207,  2.0049],
        [ 0.2072, -1.0680,  0.6602],
        [ 0.3480, -0.5211, -0.4573]])
>>> torch.triu(a)
tensor([[ 0.2309,  0.5207,  2.0049],
        [ 0.0000, -1.0680,  0.6602],
        [ 0.0000,  0.0000, -0.4573]])
>>> torch.triu(a, diagonal=1)
tensor([[ 0.0000,  0.5207,  2.0049],
        [ 0.0000,  0.0000,  0.6602],
        [ 0.0000,  0.0000,  0.0000]])
>>> torch.triu(a, diagonal=-1)
tensor([[ 0.2309,  0.5207,  2.0049],
        [ 0.2072, -1.0680,  0.6602],
        [ 0.0000, -0.5211, -0.4573]])

>>> b = torch.randn(4, 6)
>>> b
tensor([[ 0.5876, -0.0794, -1.8373,  0.6654,  0.2604,  1.5235],
        [-0.2447,  0.9556, -1.2919,  1.3378, -0.1768, -1.0857],
        [ 0.4333,  0.3146,  0.6576, -1.0432,  0.9348, -0.4410],
        [-0.9888,  1.0679, -1.3337, -1.6556,  0.4798,  0.2830]])
>>> torch.tril(b, diagonal=1)
tensor([[ 0.5876, -0.0794,  0.0000,  0.0000,  0.0000,  0.0000],
        [-0.2447,  0.9556, -1.2919,  0.0000,  0.0000,  0.0000],
        [ 0.4333,  0.3146,  0.6576, -1.0432,  0.0000,  0.0000],
        [-0.9888,  1.0679, -1.3337, -1.6556,  0.4798,  0.0000]])
>>> torch.tril(b, diagonal=-1)
tensor([[ 0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000],
        [-0.2447,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000],
        [ 0.4333,  0.3146,  0.0000,  0.0000,  0.0000,  0.0000],
        [-0.9888,  1.0679, -1.3337,  0.0000,  0.0000,  0.0000]])

BLAS and LAPACK Operations

torch.addbmm(beta=1, mat, alpha=1, batch1, batch2, out=None) → Tensor

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). mat is added to the final result.

batch1 and batch2 must be 3-D tensors each containing the same number of matrices.

If batch1 is a $(b \times n \times m)$ tensor, batch2 is a $(b \times m \times p)$ tensor, mat must be broadcastable with a $(n \times p)$ tensor and out will be a $(n \times p)$ tensor.

$$out = \beta\ mat + \alpha\ (\sum_{i=0}^{b} batch1_i \mathbin{@} batch2_i)$$

For inputs of type FloatTensor or DoubleTensor, arguments beta and alpha must be real numbers, otherwise they should be integers.

Parameters:

• beta (Number, optional) – multiplier for mat ($\beta$)
• mat (Tensor) – matrix to be added
• alpha (Number, optional) – multiplier for batch1 @ batch2 ($\alpha$)
• batch1 (Tensor) – the first batch of matrices to be multiplied
• batch2 (Tensor) – the second batch of matrices to be multiplied
• out (Tensor, optional) – the output tensor

Example:

>>> M = torch.randn(3, 5)
>>> batch1 = torch.randn(10, 3, 4)
>>> batch2 = torch.randn(10, 4, 5)
>>> torch.addbmm(M, batch1, batch2)
tensor([[  6.6311,   0.0503,   6.9768, -12.0362,  -2.1653],
        [ -4.8185,  -1.4255,  -6.6760,   8.9453,   2.5743],
        [ -3.8202,   4.3691,   1.0943,  -1.1109,   5.4730]])

torch.addmm(beta=1, mat, alpha=1, mat1, mat2, out=None) → Tensor

Performs a matrix multiplication of the matrices mat1 and mat2. The matrix mat is added to the final result.

If mat1 is a $(n \times m)$ tensor, mat2 is a $(m \times p)$ tensor, then mat must be broadcastable with a $(n \times p)$ tensor and out will be a $(n \times p)$ tensor.

alpha and beta are scaling factors on matrix-vector product between mat1 and :attr`mat2` and the added matrix mat respectively.

$$out = \beta\ mat + \alpha\ (mat1_i \mathbin{@} mat2_i)$$

For inputs of type FloatTensor or DoubleTensor, arguments beta and alpha must be real numbers, otherwise they should be integers.

Parameters:

• beta (Number, optional) – multiplier for mat ($\beta$)
• mat (Tensor) – matrix to be added
• alpha (Number, optional) – multiplier for $mat1 @ mat2$ ($\alpha$)
• mat1 (Tensor) – the first matrix to be multiplied
• mat2 (Tensor) – the second matrix to be multiplied
• out (Tensor, optional) – the output tensor

Example:

>>> M = torch.randn(2, 3)
>>> mat1 = torch.randn(2, 3)
>>> mat2 = torch.randn(3, 3)
>>> torch.addmm(M, mat1, mat2)
tensor([[-4.8716,  1.4671, -1.3746],
        [ 0.7573, -3.9555, -2.8681]])

torch.addmv(beta=1, tensor, alpha=1, mat, vec, out=None) → Tensor

Performs a matrix-vector product of the matrix mat and the vector vec. The vector tensor is added to the final result.

If mat is a $(n \times m)$ tensor, vec is a 1-D tensor of size m, then tensor must be broadcastable with a 1-D tensor of size n and out will be 1-D tensor of size n.

alpha and beta are scaling factors on matrix-vector product between mat and vec and the added tensor tensor respectively.

$$out = \beta\ tensor + \alpha\ (mat \mathbin{@} vec)$$

For inputs of type FloatTensor or DoubleTensor, arguments beta and alpha must be real numbers, otherwise they should be integers

Parameters:

• beta (Number, optional) – multiplier for tensor ($\beta$)
• tensor (Tensor) – vector to be added
• alpha (Number, optional) – multiplier for $mat @ vec$ ($\alpha$)
• mat (Tensor) – matrix to be multiplied
• vec (Tensor) – vector to be multiplied
• out (Tensor, optional) – the output tensor

Example:

>>> M = torch.randn(2)
>>> mat = torch.randn(2, 3)
>>> vec = torch.randn(3)
>>> torch.addmv(M, mat, vec)
tensor([-0.3768, -5.5565])

torch.addr(beta=1, mat, alpha=1, vec1, vec2, out=None) → Tensor

Performs the outer-product of vectors vec1 and vec2 and adds it to the matrix mat.

Optional values beta and alpha are scaling factors on the outer product between vec1 and vec2 and the added matrix mat respectively.

$$out = \beta\ mat + \alpha\ (vec1 \otimes vec2)$$

If vec1 is a vector of size n and vec2 is a vector of size m, then mat must be broadcastable with a matrix of size $(n \times m)$ and out will be a matrix of size $(n \times m)$.

For inputs of type FloatTensor or DoubleTensor, arguments beta and alpha must be real numbers, otherwise they should be integers

Parameters:

• beta (Number, optional) – multiplier for mat ($\beta$)
• mat (Tensor) – matrix to be added
• alpha (Number, optional) – multiplier for $vec1 \otimes vec2$ ($\alpha$)
• vec1 (Tensor) – the first vector of the outer product
• vec2 (Tensor) – the second vector of the outer product
• out (Tensor, optional) – the output tensor

Example:

>>> vec1 = torch.arange(1, 4)
>>> vec2 = torch.arange(1, 3)
>>> M = torch.zeros(3, 2)
>>> torch.addr(M, vec1, vec2)
tensor([[ 1.,  2.],
        [ 2.,  4.],
        [ 3.,  6.]])

torch.baddbmm(beta=1, mat, alpha=1, batch1, batch2, out=None) → Tensor

Performs a batch matrix-matrix product of matrices in batch1 and batch2. mat is added to the final result.

batch1 and batch2 must be 3-D tensors each containing the same number of matrices.

If batch1 is a $(b \times n \times m)$ tensor, batch2 is a $(b \times m \times p)$ tensor, then mat must be broadcastable with a $(b \times n \times p)$ tensor and out will be a $(b \times n \times p)$ tensor. Both alpha and beta mean the same as the scaling factors used in torch.addbmm().

$$out_i = \beta\ mat_i + \alpha\ (batch1_i \mathbin{@} batch2_i)$$

For inputs of type FloatTensor or DoubleTensor, arguments beta and alpha must be real numbers, otherwise they should be integers.

Parameters:

• beta (Number, optional) – multiplier for mat ($\beta$)
• mat (Tensor) – the tensor to be added
• alpha (Number, optional) – multiplier for batch1 @ batch2 ($\alpha$)
• batch1 (Tensor) – the first batch of matrices to be multiplied
• batch2 (Tensor) – the second batch of matrices to be multiplied
• out (Tensor, optional) – the output tensor

Example:

>>> M = torch.randn(10, 3, 5)
>>> batch1 = torch.randn(10, 3, 4)
>>> batch2 = torch.randn(10, 4, 5)
>>> torch.baddbmm(M, batch1, batch2).size()
torch.Size([10, 3, 5])

torch.bmm(batch1, batch2, out=None) → Tensor

Performs a batch matrix-matrix product of matrices stored in batch1 and batch2.

batch1 and batch2 must be 3-D tensors each containing the same number of matrices.

If batch1 is a $(b \times n \times m)$ tensor, batch2 is a $(b \times m \times p)$ tensor, out will be a $(b \times n \times p)$ tensor.

$$out_i = batch1_i \mathbin{@} batch2_i$$

Note

This function does not broadcast. For broadcasting matrix products, see torch.matmul().

Parameters:

• batch1 (Tensor) – the first batch of matrices to be multiplied
• batch2 (Tensor) – the second batch of matrices to be multiplied
• out (Tensor, optional) – the output tensor

Example:

>>> batch1 = torch.randn(10, 3, 4)
>>> batch2 = torch.randn(10, 4, 5)
>>> res = torch.bmm(batch1, batch2)
>>> res.size()
torch.Size([10, 3, 5])

torch.btrifact(A, info=None, pivot=True)

Batch LU factorization.

Returns a tuple containing the LU factorization and pivots. Pivoting is done if pivot is set.

The optional argument info stores information if the factorization succeeded for each minibatch example. The info is provided as an IntTensor, its values will be filled from dgetrf and a non-zero value indicates an error occurred. Specifically, the values are from cublas if cuda is being used, otherwise LAPACK.

Warning

The info argument is deprecated in favor of torch.btrifact_with_info().

Parameters:

• A (Tensor) – the tensor to factor
• info (IntTensor, optional) – (deprecated) an IntTensor to store values indicating whether factorization succeeds
• pivot (bool, optional) – controls whether pivoting is done

Returns:

A tuple containing factorization and pivots.

Example:

>>> A = torch.randn(2, 3, 3)
>>> A_LU, pivots = torch.btrifact(A)
>>> A_LU
tensor([[[ 1.3506,  2.5558, -0.0816],
         [ 0.1684,  1.1551,  0.1940],
         [ 0.1193,  0.6189, -0.5497]],

        [[ 0.4526,  1.2526, -0.3285],
         [-0.7988,  0.7175, -0.9701],
         [ 0.2634, -0.9255, -0.3459]]])

>>> pivots
tensor([[ 3,  3,  3],
        [ 3,  3,  3]], dtype=torch.int32)

torch.btrifact_with_info(A, pivot=True) -> (Tensor, IntTensor, IntTensor)

Batch LU factorization with additional error information.

This is a version of torch.btrifact() that always creates an info IntTensor, and returns it as the third return value.

Parameters:

• A (Tensor) – the tensor to factor
• pivot (bool, optional) – controls whether pivoting is done

Returns:

A tuple containing factorization, pivots, and an IntTensor where non-zero values indicate whether factorization for each minibatch sample succeeds.

Example:

>>> A = torch.randn(2, 3, 3)
>>> A_LU, pivots, info = A.btrifact_with_info()
>>> if info.nonzero().size(0) == 0:
>>>   print('LU factorization succeeded for all samples!')
LU factorization succeeded for all samples!

torch.btrisolve(b, LU_data, LU_pivots) → Tensor

Batch LU solve.

Returns the LU solve of the linear system $Ax = b$.

Parameters:

• b (Tensor) – the RHS tensor
• LU_data (Tensor) – the pivoted LU factorization of A from btrifact().
• LU_pivots (IntTensor) – the pivots of the LU factorization

Example:

>>> A = torch.randn(2, 3, 3)
>>> b = torch.randn(2, 3)
>>> A_LU = torch.btrifact(A)
>>> x = torch.btrisolve(b, *A_LU)
>>> torch.norm(torch.bmm(A, x.unsqueeze(2)) - b.unsqueeze(2))
tensor(1.00000e-07 *
       2.8312)

torch.btriunpack(LU_data, LU_pivots, unpack_data=True, unpack_pivots=True)

Unpacks the data and pivots from a batched LU factorization (btrifact) of a tensor.

Returns a tuple of tensors as (the pivots, the L tensor, the U tensor).

Parameters:

• LU_data (Tensor) – the packed LU factorization data
• LU_pivots (Tensor) – the packed LU factorization pivots
• unpack_data (bool) – flag indicating if the data should be unpacked
• unpack_pivots (bool) – flag indicating if the pivots should be unpacked

Example:

>>> A = torch.randn(2, 3, 3)
>>> A_LU, pivots = A.btrifact()
>>> P, A_L, A_U = torch.btriunpack(A_LU, pivots)
>>>
>>> # can recover A from factorization
>>> A_ = torch.bmm(P, torch.bmm(A_L, A_U))

torch.dot(tensor1, tensor2) → Tensor

Computes the dot product (inner product) of two tensors.

Note

This function does not broadcast.

Example:

>>> torch.dot(torch.tensor([2, 3]), torch.tensor([2, 1]))
tensor(7)

torch.eig(a, eigenvectors=False, out=None) -> (Tensor, Tensor)

Computes the eigenvalues and eigenvectors of a real square matrix.

Parameters:

• a (Tensor) – the square matrix for which the eigenvalues and eigenvectors will be computed
• eigenvectors (bool) – True to compute both eigenvalues and eigenvectors; otherwise, only eigenvalues will be computed
• out (tuple, optional) – the output tensors

Returns:

A tuple containing

• e (Tensor): the right eigenvalues of a
• v (Tensor): the eigenvectors of a if eigenvectors is True; otherwise an empty tensor

Return type:

(Tensor, Tensor)

torch.gels(B, A, out=None) → Tensor

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 $(n \times k)$.

If $m \geq n$, gels() solves the least-squares problem:

$$\begin{array}{ll} \min_X & \|AX-B\|_2. \end{array}$$

If $m < n$, gels() solves the least-norm problem:

$$\begin{array}{ll} \min_X & \|X\|_2 & \mbox{subject to} & AX = B. \end{array}$$

Returned tensor $X$ has shape $(\max(m, n) \times k)$. The first $n$ rows of $X$ contains the solution. If :math`m geq n`, the residual sum of squares for the solution in each column is given by the sum of squares of elements in the remaining $m - n$ rows of that column.

Parameters:

• B (Tensor) – the matrix $B$
• A (Tensor) – the $m$ by $n$ matrix $A$
• out (tuple, optional) – the optional destination tensor

Returns:

A tuple containing:

• X (Tensor): the least squares solution
• qr (Tensor): the details of the QR factorization

Return type:

(Tensor, Tensor)

Note

The returned matrices will always be transposed, irrespective of the strides of the input matrices. That is, they will have stride (1, m) instead of (m, 1).

Example:

>>> A = torch.tensor([[1., 1, 1],
                      [2, 3, 4],
                      [3, 5, 2],
                      [4, 2, 5],
                      [5, 4, 3]])
>>> B = torch.tensor([[-10., -3],
                      [ 12, 14],
                      [ 14, 12],
                      [ 16, 16],
                      [ 18, 16]])
>>> X, _ = torch.gels(B, A)
>>> X
tensor([[  2.0000,   1.0000],
        [  1.0000,   1.0000],
        [  1.0000,   2.0000],
        [ 10.9635,   4.8501],
        [  8.9332,   5.2418]])

torch.geqrf(input, out=None) -> (Tensor, Tensor)

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

You’ll generally want to use torch.qr() instead.

Computes a QR decomposition of input, but without constructing $Q$ and $R$ as explicit separate matrices.

Rather, this directly calls the underlying LAPACK function ?geqrf which produces a sequence of ‘elementary reflectors’.

See LAPACK documentation for geqrf for further details.

Parameters:

• input (Tensor) – the input matrix
• out (tuple, optional) – the output tuple of (Tensor, Tensor)

torch.ger(vec1, vec2, out=None) → Tensor

Outer product of vec1 and vec2. If vec1 is a vector of size $n$ and vec2 is a vector of size $m$, then out must be a matrix of size $(n \times m)$.

Note

This function does not broadcast.

Parameters:

• vec1 (Tensor) – 1-D input vector
• vec2 (Tensor) – 1-D input vector
• out (Tensor, optional) – optional output matrix

Example:

>>> v1 = torch.arange(1, 5)
>>> v2 = torch.arange(1, 4)
>>> torch.ger(v1, v2)
tensor([[  1.,   2.,   3.],
        [  2.,   4.,   6.],
        [  3.,   6.,   9.],
        [  4.,   8.,  12.]])

torch.gesv(B, A, out=None) -> (Tensor, Tensor)

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 tuple X, LU.

LU contains L and U factors for LU factorization of A.

A has to be a square and non-singular matrix (2-D tensor).

If A is an $(m \times m)$ matrix and B is $(m \times k)$, the result LU is $(m \times m)$ and X is $(m \times k)$.

Note

Irrespective of the original strides, the returned matrices X and LU will be transposed, i.e. with strides (1, m) instead of (m, 1).

Parameters:

• B (Tensor) – input matrix of $(m \times k)$ dimensions
• A (Tensor) – input square matrix of $(m \times m)$ dimensions
• out (Tensor, optional) – optional output matrix

Example:

>>> A = torch.tensor([[6.80, -2.11,  5.66,  5.97,  8.23],
                      [-6.05, -3.30,  5.36, -4.44,  1.08],
                      [-0.45,  2.58, -2.70,  0.27,  9.04],
                      [8.32,  2.71,  4.35,  -7.17,  2.14],
                      [-9.67, -5.14, -7.26,  6.08, -6.87]]).t()
>>> B = torch.tensor([[4.02,  6.19, -8.22, -7.57, -3.03],
                      [-1.56,  4.00, -8.67,  1.75,  2.86],
                      [9.81, -4.09, -4.57, -8.61,  8.99]]).t()
>>> X, LU = torch.gesv(B, A)
>>> torch.dist(B, torch.mm(A, X))
tensor(1.00000e-06 *
       7.0977)

torch.inverse(input, out=None) → Tensor

Takes the inverse of the square matrix input.

Note

Irrespective of the original strides, the returned matrix will be transposed, i.e. with strides (1, m) instead of (m, 1)

Parameters:

• input (Tensor) – the input 2-D square tensor
• out (Tensor, optional) – the optional output tensor

Example:

>>> x = torch.rand(4, 4)
>>> y = torch.inverse(x)
>>> z = torch.mm(x, y)
>>> z
tensor([[ 1.0000, -0.0000, -0.0000,  0.0000],
        [ 0.0000,  1.0000,  0.0000,  0.0000],
        [ 0.0000,  0.0000,  1.0000,  0.0000],
        [ 0.0000, -0.0000, -0.0000,  1.0000]])
>>> torch.max(torch.abs(z - torch.eye(4))) # Max nonzero
tensor(1.00000e-07 *
       1.1921)

torch.det(A) → Tensor

Calculates determinant of a 2D square tensor.

Note

Backward through det() internally uses SVD results when A is not invertible. In this case, double backward through det() will be unstable in when A doesn’t have distinct singular values. See svd() for details.

Parameters:A (Tensor) – The input 2D square tensor

Example:

>>> A = torch.randn(3, 3)
>>> torch.det(A)
tensor(3.7641)

torch.logdet(A) → Tensor

Calculates log determinant of a 2D square tensor.

Note

Result is -inf if A has zero log determinant, and is nan if A has negative determinant.

Note

Backward through logdet() internally uses SVD results when A is not invertible. In this case, double backward through logdet() will be unstable in when A doesn’t have distinct singular values. See svd() for details.

Parameters:A (Tensor) – The input 2D square tensor

Example:

>>> A = torch.randn(3, 3)
>>> torch.det(A)
tensor(0.2611)
>>> torch.logdet(A)
tensor(-1.3430)

torch.slogdet(A) -> (Tensor, Tensor)

Calculates the sign and log value of a 2D square tensor’s determinant.

Note

If A has zero determinant, this returns (0, -inf).

Note

Backward through slogdet() internally uses SVD results when A is not invertible. In this case, double backward through slogdet() will be unstable in when A doesn’t have distinct singular values. See svd() for details.

Parameters:A (Tensor) – The input 2D square tensor Returns:A tuple containing the sign of the determinant, and the log value of the absolute determinant.

Example:

>>> A = torch.randn(3, 3)
>>> torch.det(A)
tensor(-4.8215)
>>> torch.logdet(A)
tensor(nan)
>>> torch.slogdet(A)
(tensor(-1.), tensor(1.5731))

torch.matmul(tensor1, tensor2, out=None) → Tensor

Matrix product of two tensors.

The behavior depends on the dimensionality of the tensors as follows:

• If both tensors are 1-dimensional, the dot product (scalar) is returned.
• If both arguments are 2-dimensional, the matrix-matrix product is returned.
• If the first argument is 1-dimensional and the second argument is 2-dimensional, a 1 is prepended to its dimension for the purpose of the matrix multiply. After the matrix multiply, the prepended dimension is removed.
• If the first argument is 2-dimensional and the second argument is 1-dimensional, the matrix-vector product is returned.
• If both arguments are at least 1-dimensional and at least one argument is N-dimensional (where N > 2), then a batched matrix multiply is returned. If the first argument is 1-dimensional, a 1 is prepended to its dimension for the purpose of the batched matrix multiply and removed after. If the second argument is 1-dimensional, a 1 is appended to its dimension for the purpose of the batched matrix multiple and removed after. The non-matrix (i.e. batch) dimensions are broadcasted (and thus must be broadcastable). For example, if tensor1 is a $(j \times 1 \times n \times m)$ tensor and tensor2 is a $(k \times m \times p)$ tensor, out will be an $(j \times k \times n \times p)$ tensor.

Note

The 1-dimensional dot product version of this function does not support an out parameter.

Parameters:

• tensor1 (Tensor) – the first tensor to be multiplied
• tensor2 (Tensor) – the second tensor to be multiplied
• out (Tensor, optional) – the output tensor

Example:

>>> # vector x vector
>>> tensor1 = torch.randn(3)
>>> tensor2 = torch.randn(3)
>>> torch.matmul(tensor1, tensor2).size()
torch.Size([])
>>> # matrix x vector
>>> tensor1 = torch.randn(3, 4)
>>> tensor2 = torch.randn(4)
>>> torch.matmul(tensor1, tensor2).size()
torch.Size([3])
>>> # batched matrix x broadcasted vector
>>> tensor1 = torch.randn(10, 3, 4)
>>> tensor2 = torch.randn(4)
>>> torch.matmul(tensor1, tensor2).size()
torch.Size([10, 3])
>>> # batched matrix x batched matrix
>>> tensor1 = torch.randn(10, 3, 4)
>>> tensor2 = torch.randn(10, 4, 5)
>>> torch.matmul(tensor1, tensor2).size()
torch.Size([10, 3, 5])
>>> # batched matrix x broadcasted matrix
>>> tensor1 = torch.randn(10, 3, 4)
>>> tensor2 = torch.randn(4, 5)
>>> torch.matmul(tensor1, tensor2).size()
torch.Size([10, 3, 5])

torch.mm(mat1, mat2, out=None) → Tensor

Performs a matrix multiplication of the matrices mat1 and mat2.

If mat1 is a $(n \times m)$ tensor, mat2 is a $(m \times p)$ tensor, out will be a $(n \times p)$ tensor.

Note

This function does not broadcast. For broadcasting matrix products, see torch.matmul().

Parameters:

• mat1 (Tensor) – the first matrix to be multiplied
• mat2 (Tensor) – the second matrix to be multiplied
• out (Tensor, optional) – the output tensor

Example:

>>> mat1 = torch.randn(2, 3)
>>> mat2 = torch.randn(3, 3)
>>> torch.mm(mat1, mat2)
tensor([[ 0.4851,  0.5037, -0.3633],
        [-0.0760, -3.6705,  2.4784]])

torch.mv(mat, vec, out=None) → Tensor

Performs a matrix-vector product of the matrix mat and the vector vec.

If mat is a $(n \times m)$ tensor, vec is a 1-D tensor of size $m$, out will be 1-D of size $n$.

Note

This function does not broadcast.

Parameters:

• mat (Tensor) – matrix to be multiplied
• vec (Tensor) – vector to be multiplied
• out (Tensor, optional) – the output tensor

Example:

>>> mat = torch.randn(2, 3)
>>> vec = torch.randn(3)
>>> torch.mv(mat, vec)
tensor([ 1.0404, -0.6361])

torch.orgqr(a, tau) → Tensor

Computes the orthogonal matrix Q of a QR factorization, from the (a, tau) tuple returned by torch.geqrf().

This directly calls the underlying LAPACK function ?orgqr. See LAPACK documentation for orgqr for further details.

Parameters:

• a (Tensor) – the a from torch.geqrf().
• tau (Tensor) – the tau from torch.geqrf().

torch.ormqr(a, tau, mat, left=True, transpose=False) -> (Tensor, Tensor)

Multiplies mat by the orthogonal Q matrix of the QR factorization formed by torch.geqrf() that is represented by (a, tau).

This directly calls the underlying LAPACK function ?ormqr. See LAPACK documentation for ormqr for further details.

Parameters:

• a (Tensor) – the a from torch.geqrf().
• tau (Tensor) – the tau from torch.geqrf().
• mat (Tensor) – the matrix to be multiplied.

torch.potrf(a, upper=True, out=None) → Tensor

Computes the Cholesky decomposition of a symmetric positive-definite matrix $A$.

If upper is True, the returned matrix U is upper-triangular, and the decomposition has the form:

$$A = U^TU$$

If upper is False, the returned matrix L is lower-triangular, and the decomposition has the form:

$$A = LL^T$$

Parameters:

• a (Tensor) – the input 2-D tensor, a symmetric positive-definite matrix
• upper (bool, optional) – flag that indicates whether to return the upper or lower triangular matrix
• out (Tensor, optional) – the output matrix

Example:

>>> a = torch.randn(3, 3)
>>> a = torch.mm(a, a.t()) # make symmetric positive definite
>>> u = torch.potrf(a)
>>> a
tensor([[ 2.4112, -0.7486,  1.4551],
        [-0.7486,  1.3544,  0.1294],
        [ 1.4551,  0.1294,  1.6724]])
>>> u
tensor([[ 1.5528, -0.4821,  0.9371],
        [ 0.0000,  1.0592,  0.5486],
        [ 0.0000,  0.0000,  0.7023]])
>>> torch.mm(u.t(), u)
tensor([[ 2.4112, -0.7486,  1.4551],
        [-0.7486,  1.3544,  0.1294],
        [ 1.4551,  0.1294,  1.6724]])

torch.potri(u, upper=True, out=None) → Tensor

Computes the inverse of a positive semidefinite matrix given its Cholesky factor u: returns matrix inv

If upper is True or not provided, u is upper triangular such that:

$$inv = (u^T u)^{-1}$$

If upper is False, u is lower triangular such that:

$$inv = (uu^{T})^{-1}$$

Parameters:

• u (Tensor) – the input 2-D tensor, a upper or lower triangular Cholesky factor
• upper (bool, optional) – whether to return a upper (default) or lower triangular matrix
• out (Tensor, optional) – the output tensor for inv

Example:

>>> a = torch.randn(3, 3)
>>> a = torch.mm(a, a.t()) # make symmetric positive definite
>>> u = torch.potrf(a)
>>> a
tensor([[  0.9935,  -0.6353,   1.5806],
        [ -0.6353,   0.8769,  -1.7183],
        [  1.5806,  -1.7183,  10.6618]])
>>> torch.potri(u)
tensor([[ 1.9314,  1.2251, -0.0889],
        [ 1.2251,  2.4439,  0.2122],
        [-0.0889,  0.2122,  0.1412]])
>>> a.inverse()
tensor([[ 1.9314,  1.2251, -0.0889],
        [ 1.2251,  2.4439,  0.2122],
        [-0.0889,  0.2122,  0.1412]])

torch.potrs(b, u, upper=True, out=None) → Tensor

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

If upper is True or not provided, u is upper triangular and c is returned such that:

$$c = (u^T u)^{-1} b$$

If upper is False, u is and lower triangular and c is returned such that:

$$c = (u u^T)^{-1} b$$

Note

b is always a 2-D tensor, use b.unsqueeze(1) to convert a vector.

Parameters:

• b (Tensor) – the right hand side 2-D tensor
• u (Tensor) – the input 2-D tensor, a upper or lower triangular Cholesky factor
• upper (bool, optional) – whether to return a upper (default) or lower triangular matrix
• out (Tensor, optional) – the output tensor for c

Example:

>>> a = torch.randn(3, 3)
>>> a = torch.mm(a, a.t()) # make symmetric positive definite
>>> u = torch.potrf(a)
>>> a
tensor([[ 0.7747, -1.9549,  1.3086],
        [-1.9549,  6.7546, -5.4114],
        [ 1.3086, -5.4114,  4.8733]])
>>> b = torch.randn(3, 2)
>>> b
tensor([[-0.6355,  0.9891],
        [ 0.1974,  1.4706],
        [-0.4115, -0.6225]])
>>> torch.potrs(b,u)
tensor([[ -8.1625,  19.6097],
        [ -5.8398,  14.2387],
        [ -4.3771,  10.4173]])
>>> torch.mm(a.inverse(),b)
tensor([[ -8.1626,  19.6097],
        [ -5.8398,  14.2387],
        [ -4.3771,  10.4173]])

torch.pstrf(a, upper=True, out=None) -> (Tensor, Tensor)

Computes the pivoted Cholesky decomposition of a positive semidefinite matrix a. returns matrices u and piv.

If upper is True or not provided, u is upper triangular such that $a = p^T u^T u p$, with p the permutation given by piv.

If upper is False, u is lower triangular such that $a = p^T u u^T p$.

Parameters:

• a (Tensor) – the input 2-D tensor
• upper (bool, optional) – whether to return a upper (default) or lower triangular matrix
• out (tuple, optional) – tuple of u and piv tensors

Example:

>>> a = torch.randn(3, 3)
>>> a = torch.mm(a, a.t()) # make symmetric positive definite
>>> a
tensor([[ 3.5405, -0.4577,  0.8342],
        [-0.4577,  1.8244, -0.1996],
        [ 0.8342, -0.1996,  3.7493]])
>>> u,piv = torch.pstrf(a)
>>> u
tensor([[ 1.9363,  0.4308, -0.1031],
        [ 0.0000,  1.8316, -0.2256],
        [ 0.0000,  0.0000,  1.3277]])
>>> piv
tensor([ 2,  0,  1], dtype=torch.int32)
>>> p = torch.eye(3).index_select(0,piv.long()).index_select(0,piv.long()).t() # make pivot permutation
>>> torch.mm(torch.mm(p.t(),torch.mm(u.t(),u)),p) # reconstruct
tensor([[ 3.5405, -0.4577,  0.8342],
        [-0.4577,  1.8244, -0.1996],
        [ 0.8342, -0.1996,  3.7493]])

torch.qr(input, out=None) -> (Tensor, Tensor)

Computes the QR decomposition of a matrix input, and returns matrices Q and R such that $\text{input} = Q R$, with $Q$ being an orthogonal matrix and $R$ being an upper triangular matrix.

This returns the thin (reduced) QR factorization.

Note

precision may be lost if the magnitudes of the elements of input are large

Note

While it should always give you a valid decomposition, it may not give you the same one across platforms - it will depend on your LAPACK implementation.

Note

Irrespective of the original strides, the returned matrix $Q$ will be transposed, i.e. with strides (1, m) instead of (m, 1).

Parameters:

• input (Tensor) – the input 2-D tensor
• out (tuple, optional) – tuple of Q and R tensors

Example:

>>> a = torch.tensor([[12., -51, 4], [6, 167, -68], [-4, 24, -41]])
>>> q, r = torch.qr(a)
>>> q
tensor([[-0.8571,  0.3943,  0.3314],
        [-0.4286, -0.9029, -0.0343],
        [ 0.2857, -0.1714,  0.9429]])
>>> r
tensor([[ -14.0000,  -21.0000,   14.0000],
        [   0.0000, -175.0000,   70.0000],
        [   0.0000,    0.0000,  -35.0000]])
>>> torch.mm(q, r).round()
tensor([[  12.,  -51.,    4.],
        [   6.,  167.,  -68.],
        [  -4.,   24.,  -41.]])
>>> torch.mm(q.t(), q).round()
tensor([[ 1.,  0.,  0.],
        [ 0.,  1., -0.],
        [ 0., -0.,  1.]])

torch.svd(input, some=True, out=None) -> (Tensor, Tensor, Tensor)

U, S, V = torch.svd(A) returns the singular value decomposition of a real matrix A of size (n x m) such that $A = USV^T$.

U is of shape $(n \times n)$.

S is a diagonal matrix of shape $(n \times m)$, represented as a vector of size $\min(n, m)$ containing the non-negative diagonal entries.

V is of shape $(m \times m)$.

If some is True (default), the returned U and V matrices will contain only $min(n, m)$ orthonormal columns.

Note

Irrespective of the original strides, the returned matrix U will be transposed, i.e. with strides (1, n) instead of (n, 1).

Note

Extra care needs to be taken when backward through U and V outputs. Such operation is really only stable when input is full rank with all distinct singular values. Otherwise, NaN can appear as the gradients are not properly defined. Also, notice that double backward will usually do an additional backward through U and V even if the original backward is only on S.

Note

When some = False, the gradients on U[:, min(n, m):] and V[:, min(n, m):] will be ignored in backward as those vectors can be arbitrary bases of the subspaces.

Parameters:

• input (Tensor) – the input 2-D tensor
• some (bool, optional) – controls the shape of returned U and V
• out (tuple, optional) – the output tuple of tensors

Example:

>>> a = torch.tensor([[8.79,  6.11, -9.15,  9.57, -3.49,  9.84],
                      [9.93,  6.91, -7.93,  1.64,  4.02,  0.15],
                      [9.83,  5.04,  4.86,  8.83,  9.80, -8.99],
                      [5.45, -0.27,  4.85,  0.74, 10.00, -6.02],
                      [3.16,  7.98,  3.01,  5.80,  4.27, -5.31]]).t()

>>> u, s, v = torch.svd(a)
>>> u
tensor([[-0.5911,  0.2632,  0.3554,  0.3143,  0.2299],
        [-0.3976,  0.2438, -0.2224, -0.7535, -0.3636],
        [-0.0335, -0.6003, -0.4508,  0.2334, -0.3055],
        [-0.4297,  0.2362, -0.6859,  0.3319,  0.1649],
        [-0.4697, -0.3509,  0.3874,  0.1587, -0.5183],
        [ 0.2934,  0.5763, -0.0209,  0.3791, -0.6526]])
>>> s
tensor([ 27.4687,  22.6432,   8.5584,   5.9857,   2.0149])
>>> v
tensor([[-0.2514,  0.8148, -0.2606,  0.3967, -0.2180],
        [-0.3968,  0.3587,  0.7008, -0.4507,  0.1402],
        [-0.6922, -0.2489, -0.2208,  0.2513,  0.5891],
        [-0.3662, -0.3686,  0.3859,  0.4342, -0.6265],
        [-0.4076, -0.0980, -0.4933, -0.6227, -0.4396]])
>>> torch.dist(a, torch.mm(torch.mm(u, torch.diag(s)), v.t()))
tensor(1.00000e-06 *
       9.3738)

torch.symeig(input, eigenvectors=False, upper=True, out=None) -> (Tensor, Tensor)

This function returns eigenvalues and eigenvectors of a real symmetric matrix input, represented by a tuple $(e, V)$.

input and $V$ are $(m \times m)$ matrices and $e$ is a $m$ dimensional vector.

This function calculates all eigenvalues (and vectors) of input such that $input = V diag(e) V^T$.

The boolean argument eigenvectors defines computation of eigenvectors or eigenvalues only.

If it is False, only eigenvalues are computed. If it is True, both eigenvalues and eigenvectors are computed.

Since the input matrix input is supposed to be symmetric, only the upper triangular portion is used by default.

If upper is False, then lower triangular portion is used.

Note: Irrespective of the original strides, the returned matrix V will be transposed, i.e. with strides (1, m) instead of (m, 1).

Parameters:

• input (Tensor) – the input symmetric matrix
• eigenvectors (boolean, optional) – controls whether eigenvectors have to be computed
• upper (boolean, optional) – controls whether to consider upper-triangular or lower-triangular region
• out (tuple, optional) – the output tuple of (Tensor, Tensor)

Examples:

>>> a = torch.tensor([[ 1.96,  0.00,  0.00,  0.00,  0.00],
                      [-6.49,  3.80,  0.00,  0.00,  0.00],
                      [-0.47, -6.39,  4.17,  0.00,  0.00],
                      [-7.20,  1.50, -1.51,  5.70,  0.00],
                      [-0.65, -6.34,  2.67,  1.80, -7.10]]).t()
>>> e, v = torch.symeig(a, eigenvectors=True)
>>> e
tensor([-11.0656,  -6.2287,   0.8640,   8.8655,  16.0948])
>>> v
tensor([[-0.2981, -0.6075,  0.4026, -0.3745,  0.4896],
        [-0.5078, -0.2880, -0.4066, -0.3572, -0.6053],
        [-0.0816, -0.3843, -0.6600,  0.5008,  0.3991],
        [-0.0036, -0.4467,  0.4553,  0.6204, -0.4564],
        [-0.8041,  0.4480,  0.1725,  0.3108,  0.1622]])

torch.trtrs(b, A, upper=True, transpose=False, unitriangular=False) -> (Tensor, Tensor)

Solves a system of equations with a triangular coefficient matrix A and multiple right-hand sides b.

In particular, solves $AX = b$ and assumes A is upper-triangular with the default keyword arguments.

This method is NOT implemented for CUDA tensors.

Parameters:

• A (Tensor) – the input triangular coefficient matrix
• b (Tensor) – multiple right-hand sides. Each column of b is a right-hand side for the system of equations.
• upper (bool, optional) – whether to solve the upper-triangular system of equations (default) or the lower-triangular system of equations. Default: True.
• transpose (bool, optional) – whether A should be transposed before being sent into the solver. Default: False.
• unitriangular (bool, optional) – whether A is unit triangular. If True, the diagonal elements of A are assumed to be 1 and not referenced from A. Default: False.

Returns:

A tuple (X, M) where M is a clone of A and X is the solution to AX = b (or whatever variant of the system of equations, depending on the keyword arguments.)

Shape:

• A: $(N, N)$
• b: $(N, C)$
• output[0]: $(N, C)$
• output[1]: $(N, N)$

Examples:

>>> A = torch.randn(2, 2).triu()
>>> A
tensor([[ 1.1527, -1.0753],
        [ 0.0000,  0.7986]])
>>> b = torch.randn(2, 3)
>>> b
tensor([[-0.0210,  2.3513, -1.5492],
        [ 1.5429,  0.7403, -1.0243]])
>>> torch.trtrs(b, A)
(tensor([[ 1.7840,  2.9045, -2.5405],
        [ 1.9319,  0.9269, -1.2826]]), tensor([[ 1.1527, -1.0753],
        [ 0.0000,  0.7986]]))