3.1.22.2. unit_scaling.functional.conv1d

unit_scaling.functional.conv1d(input: Tensor, weight: Tensor, bias: Tensor | None = None, stride: int = 1, padding: int = 0, dilation: int = 1, groups: int = 1, constraint: str | None = 'to_output_scale', scale_power: Tuple[float, float, float] = (0.5, 0.5, 0.5)) → Tensor[source]

Applies a unit-scaled 1D convolution.

Applies a 1D convolution over an input signal composed of several input planes.

This operator supports TensorFloat32.

See Conv1d for details and output shape.

Note

In some circumstances when given tensors on a CUDA device and using CuDNN, this operator may select a nondeterministic algorithm to increase performance. If this is undesirable, you can try to make the operation deterministic (potentially at a performance cost) by setting torch.backends.cudnn.deterministic = True. See /notes/randomness for more information.

Note

This operator supports complex data types i.e. complex32, complex64, complex128.

Parameters:

  • input – input tensor of shape \((\text{minibatch} , \text{in\_channels} , iW)\)

  • weight – filters of shape \((\text{out\_channels} , \frac{\text{in\_channels}}{\text{groups}} , kW)\)

  • bias – optional bias of shape \((\text{out\_channels})\). Default: None

  • stride – the stride of the convolving kernel. Can be a single number or a one-element tuple (sW,). Default: 1

  • padding –

    implicit paddings on both sides of the input. Can be a string {‘valid’, ‘same’}, single number or a one-element tuple (padW,). Default: 0 padding='valid' is the same as no padding. padding='same' pads the input so the output has the same shape as the input. However, this mode doesn’t support any stride values other than 1.

    Warning

    For padding='same', if the weight is even-length and dilation is odd in any dimension, a full pad() operation may be needed internally. Lowering performance.

  • dilation – the spacing between kernel elements. Can be a single number or a one-element tuple (dW,). Default: 1

  • groups – split input into groups, \(\text{in\_channels}\) should be divisible by the number of groups. Default: 1

  • constraint (Optional[str]?) – The name of the constraint function to be applied to the outputs & input gradient. In this case, the constraint name must be one of: [None, ‘gmean’, ‘hmean’, ‘amean’, ‘to_output_scale’, ‘to_grad_input_scale’] (see unit_scaling.constraints for details on these constraint functions). Defaults to gmean.

  • scale_power ((float, float, float)?) – scaling power for each of (output, grad(input), grad(weight|bias))

Examples

>>> inputs = torch.randn(33, 16, 30)
>>> filters = torch.randn(20, 16, 5)
>>> F.conv1d(inputs, filters)