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 called the imaginary unit, which satisfies the equation $j^2 = -1$. Complex numbers frequently occur in mathematics and engineering, especially in topics like 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 while working with complex numbers. Operations on complex tensors (e.g., torch.mv(), 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 in the torch.fft module support native 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 torch.linspace(), torch.logspace(), and 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 torch.view_as_complex() and 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 real and 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 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

Many linear algebra operations, like torch.matmul(), torch.linalg.svd(), torch.linalg.solve() etc., support complex numbers. If you’d like to request an operation we don’t currently support, 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 gradient computed is the Conjugate Wirtinger derivative, the negative of which is precisely the direction of steepest descent used in Gradient Descent algorithm. Thus, all the existing optimizers can be implemented to work out of the box with complex parameters. For more details, check out the note Autograd for Complex Numbers.

Optimizers

Semantically, we define stepping through a PyTorch optimizer with complex parameters as being equivalent to stepping through the same optimizer on the torch.view_as_real() equivalent of the complex params. More concretely:

>>> params = [torch.rand(2, 3, dtype=torch.complex64) for _ in range(5)]
>>> real_params = [torch.view_as_real(p) for p in params]

>>> complex_optim = torch.optim.AdamW(params)
>>> real_optim = torch.optim.AdamW(real_params)

real_optim and complex_optim will compute the same updates on the parameters, though there may be slight numerical discrepancies between the two optimizers, similar to numerical discrepancies between foreach vs forloop optimizers and capturable vs default optimizers. For more details, see https://pytorch.org/docs/stable/notes/numerical_accuracy.html.

Specifically, while you can think of our optimizer’s handling of complex tensors as the same as optimizing over their p.real and p.imag pieces separately, the implementation details are not precisely that. Note that the torch.view_as_real() equivalent will convert a complex tensor to a real tensor with shape $(..., 2)$, whereas splitting a complex tensor into two tensors is 2 tensors of size $(...)$. This distinction has no impact on pointwise optimizers (like AdamW) but will cause slight discrepancy in optimizers that do global reductions (like LBFGS). We currently do not have optimizers that do per-Tensor reductions and thus do not yet define this behavior. Open an issue if you have a use case that requires precisely defining this behavior.

We do not fully 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.