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Complex Numbers

Complex numbers are numbers that can be expressed in the form a + bj, where a and b are real numbers, and j is a solution of the equation x^2 = -1. Complex numbers frequently occur in mathematics and engineering, especially in signal processing. Traditionally many users and libraries (e.g., TorchAudio) have handled complex numbers by representing the data in float tensors with shape (..., 2) where the last dimension contains the real and imaginary values.

Tensors of complex dtypes provide a more natural user experience for working with complex numbers. Operations on complex tensors (e.g., :func:`torch.mv`, :func:`torch.matmul`) are likely to be faster and more memory efficient than operations on float tensors mimicking them. Operations involving complex numbers in PyTorch are optimized to use vectorized assembly instructions and specialized kernels (e.g. LAPACK, cuBlas).

Note

Spectral operations (e.g., :func:`torch.fft`, :func:`torch.stft` etc.) currently don't use complex tensors but the API will be soon updated to use complex tensors.

Warning

Complex tensors is a beta feature and subject to change.

Creating Complex Tensors

We support two complex dtypes: torch.cfloat and torch.cdouble

>>> x = torch.randn(2,2, dtype=torch.cfloat)
>>> x
tensor([[-0.4621-0.0303j, -0.2438-0.5874j],
     [ 0.7706+0.1421j,  1.2110+0.1918j]])

Note

The default dtype for complex tensors is determined by the default floating point dtype. If the default floating point dtype is torch.float64 then complex numbers are inferred to have a dtype of torch.complex128, otherwise they are assumed to have a dtype of torch.complex64.

All factory functions apart from :func:`torch.linspace`, :func:`torch.logspace`, and :func:`torch.arange` are supported for complex tensors.

Transition from the old representation

Users who currently worked around the lack of complex tensors with real tensors of shape (..., 2) can easily to switch using the complex tensors in their code using :func:`torch.view_as_complex` and :func:`torch.view_as_real`. Note that these functions don’t perform any copy and return a view of the input tensor.

>>> x = torch.randn(3, 2)
>>> x
tensor([[ 0.6125, -0.1681],
     [-0.3773,  1.3487],
     [-0.0861, -0.7981]])
>>> y = torch.view_as_complex(x)
>>> y
tensor([ 0.6125-0.1681j, -0.3773+1.3487j, -0.0861-0.7981j])
>>> torch.view_as_real(y)
tensor([[ 0.6125, -0.1681],
     [-0.3773,  1.3487],
     [-0.0861, -0.7981]])

Accessing real and imag

The real and imaginary values of a complex tensor can be accessed using the :attr:`real` and :attr:`imag`.

Note

Accessing real and imag attributes doesn't allocate any memory, and in-place updates on the real and imag tensors will update the original complex tensor. Also, the returned real and imag tensors are not contiguous.

>>> y.real
tensor([ 0.6125, -0.3773, -0.0861])
>>> y.imag
tensor([-0.1681,  1.3487, -0.7981])

>>> y.real.mul_(2)
tensor([ 1.2250, -0.7546, -0.1722])
>>> y
tensor([ 1.2250-0.1681j, -0.7546+1.3487j, -0.1722-0.7981j])
>>> y.real.stride()
(2,)

Angle and abs

The angle and absolute values of a complex tensor can be computed using :func:`torch.angle` and torch.abs.

>>> x1=torch.tensor([3j, 4+4j])
>>> x1.abs()
tensor([3.0000, 5.6569])
>>> x1.angle()
tensor([1.5708, 0.7854])

Linear Algebra

Currently, there is very minimal linear algebra operation support for complex tensors. We currently support :func:`torch.mv`, :func:`torch.svd`, :func:`torch.qr`, and :func:`torch.inverse` (the latter three are only supported on CPU). However we are working to add support for more functions soon: :func:`torch.matmul`, :func:`torch.solve`, :func:`torch.eig`, :func:`torch.symeig`. If any of these would help your use case, please search if an issue has already been filed and if not, file one.

Serialization

Complex tensors can be serialized, allowing data to be saved as complex values.

>>> torch.save(y, 'complex_tensor.pt')
>>> torch.load('complex_tensor.pt')
tensor([ 0.6125-0.1681j, -0.3773+1.3487j, -0.0861-0.7981j])

Autograd

PyTorch supports autograd for complex tensors. The autograd APIs can be used for both holomorphic and non-holomorphic functions. For holomorphic functions, you get the regular complex gradient. For C \rightarrow R real-valued loss functions, grad.conj() gives a descent direction. For more details, check out the note :ref:`complex_autograd-doc`.

We do not support the following subsystems:

  • Quantization
  • JIT
  • Sparse Tensors
  • Distributed

If any of these would help your use case, please search if an issue has already been filed and if not, file one.