Module julius.fftconv

Implementation of a FFT based 1D convolution in PyTorch. While FFT is used in CUDNN for small kernel sizes, it is not the case for long ones, e.g. 512. This module implements efficient FFT based convolutions for such convolutions. A typical application is for evaluationg FIR filters with a long receptive field, typically evaluated with a stride of 1.

Functions

def fft_conv1d(input: torch.Tensor, weight: torch.Tensor, bias: Optional[torch.Tensor] = None, stride: int = 1, padding: int = 0, block_ratio: float = 5)

Same as torch.nn.functional.conv1d but using FFT for the convolution. Please check PyTorch documentation for more information.

Args

input : Tensor

input signal of shape [B, C, T].

weight : Tensor

weight of the convolution [D, C, K] with D the number of output channels.

bias : Tensor or None

if not None, bias term for the convolution.

stride : int

stride of convolution.

padding : int

padding to apply to the input.

block_ratio : float

can be tuned for speed. The input is splitted in chunks with a size of int(block_ratio * kernel_size).

Shape

• Inputs: input is [B, C, T], weight is [D, C, K] and bias is [D].
• Output: (*, T)

Note

This function is faster than torch.nn.functional.conv1d only in specific cases. Typically, the kernel size should be of the order of 256 to see any real gain, for a stride of 1.

Warning

Dilation and groups are not supported at the moment. This function might use more memory than the default Conv1d implementation.

Classes

class FFTConv1d (in_channels: int, out_channels: int, kernel_size: int, stride: int = 1, padding: int = 0, bias: bool = True)

Same as torch.nn.Conv1d but based on fft_conv1d(). Please check PyTorch documentation for more information.

Args

in_channels : int

number of input channels.

out_channels : int

number of output channels.

kernel_size : int

kernel size of convolution.

stride : int

stride of convolution.

padding : int

padding to apply to the input.

bias : bool

if True, use a bias term.

Note

This module is faster than torch.nn.Conv1d only in specific cases. Typically, kernel_size should be of the order of 256 to see any real gain, for a stride of 1.

Warning

Dilation and groups are not supported at the moment. This module might use more memory than the default Conv1d implementation.

>>> fftconv = FFTConv1d(12, 24, 128, 4)
>>> x = torch.randn(4, 12, 1024)
>>> print(list(fftconv(x).shape))
[4, 24, 225]

Initialize internal Module state, shared by both nn.Module and ScriptModule.

Expand source code Browse git

class FFTConv1d(torch.nn.Module):
    """
    Same as `torch.nn.Conv1d` but based on `fft_conv1d`.
    Please check PyTorch documentation for more information.

    Args:
        in_channels (int): number of input channels.
        out_channels (int): number of output channels.
        kernel_size (int): kernel size of convolution.
        stride (int): stride of convolution.
        padding (int): padding to apply to the input.
        bias (bool): if True, use a bias term.

    ..note::
        This module is faster than `torch.nn.Conv1d` only in specific cases.
        Typically, `kernel_size` should be of the order of 256 to see any real gain,
        for a stride of 1.

    ..warning::
        Dilation and groups are not supported at the moment. This module might use
        more memory than the default Conv1d implementation.

    >>> fftconv = FFTConv1d(12, 24, 128, 4)
    >>> x = torch.randn(4, 12, 1024)
    >>> print(list(fftconv(x).shape))
    [4, 24, 225]
    """
    def __init__(self, in_channels: int, out_channels: int, kernel_size: int,
                 stride: int = 1, padding: int = 0, bias: bool = True):
        super().__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = kernel_size
        self.stride = stride
        self.padding = padding

        conv = torch.nn.Conv1d(in_channels, out_channels, kernel_size, bias=bias)
        self.weight = conv.weight
        self.bias = conv.bias

    def forward(self, input: torch.Tensor):
        return fft_conv1d(
            input, self.weight, self.bias, self.stride, self.padding)

    def __repr__(self):
        return simple_repr(self, overrides={"bias": self.bias is not None})

Ancestors

• torch.nn.modules.module.Module

Class variables

var call_super_init : bool

var dump_patches : bool

var training : bool

Methods

def forward(self, input: torch.Tensor) ‑> Callable[..., Any]

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the :class:Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.