Skip to main content

Causal depthwise conv1d in CUDA, with a PyTorch interface

Project description

Causal depthwise conv1d in CUDA with a PyTorch interface

Features:

  • Support fp32, fp16, bf16.
  • Kernel size 2, 3, 4.

How to use

from causal_conv1d import causal_conv1d_fn
def causal_conv1d_fn(x, weight, bias=None, activation=None):
    """
    x: (batch, dim, seqlen)
    weight: (dim, width)
    bias: (dim,)
    activation: either None or "silu" or "swish"

    out: (batch, dim, seqlen)
    """

Equivalent to:

import torch.nn.functional as F

F.conv1d(x, weight.unsqueeze(1), bias, padding=width - 1, groups=dim)[..., :seqlen]

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

causal_conv1d-1.2.0.post1.tar.gz (7.1 kB view details)

Uploaded Source

File details

Details for the file causal_conv1d-1.2.0.post1.tar.gz.

File metadata

  • Download URL: causal_conv1d-1.2.0.post1.tar.gz
  • Upload date:
  • Size: 7.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.10.13

File hashes

Hashes for causal_conv1d-1.2.0.post1.tar.gz
Algorithm Hash digest
SHA256 51e0ff303383c5382e81da1b3ae781488ec86b761d803fbf7999b94ef5cadc5e
MD5 2622cadc868e061738532627f72f7b2a
BLAKE2b-256 5b60d0c1d2f15206406d67e60499172450c28d43abad6d45158242f9f4e2d443

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page