{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"}],"enrichment":{"capability":"Implements a CUDA-optimized causal depthwise 1D convolution operation with PyTorch bindings, supporting fp32, fp16, and bf16 precision for kernel sizes 2, 3, and 4.","skillfed_tags":["gpu-acceleration","cuda-kernel","time-series"],"use_cases":["Accelerating causal convolution layers in transformer-based language models during training and inference.","Implementing efficient streaming time-series models that process sequences incrementally without lookahead.","Reducing latency in real-time audio or signal processing pipelines that depend on causal filtering.","Mixed-precision inference on edge GPUs where fp16 or bf16 precision reduces memory and bandwidth.","Building state-space models (e.g., Mamba, S4) that use causal convolutions as a core operation."],"what_it_does":"Causal-conv1d provides a CUDA kernel for depthwise 1D convolution that maintains causality\u2014the output at each position depends only on current and past inputs, never future ones. It wraps this kernel with a PyTorch interface, accepting batched sequences and returning convolved outputs in the same shape. The operation is mathematically equivalent to torch.nn.functional.conv1d with causal padding, but implemented as a fused CUDA kernel for lower latency and memory overhead.\n\nThe package targets machine learning workloads where causal convolutions are essential, such as autoregressive language models, time-series processing, and streaming inference. It supports mixed precision (fp32, fp16, bf16) and optional SiLU/Swish activation. Installation requires a working CUDA environment and build tools; ROCm users on version 6.0 must patch their installation first.","worth_installing":"Yes, if you are building or fine-tuning models that use causal convolutions and need lower latency than PyTorch's standard conv1d. The high install friction (CUDA, build tools, ROCm patching for some users) is a real cost; install only if you have a GPU environment already set up and the performance gain justifies the build complexity. No known vulnerabilities and active maintenance are positive signals."},"id":"causal-conv1d","links":{"html":"https://skillfed.io/packages/causal-conv1d","md":"https://skillfed.io/packages/causal-conv1d.md","pypi":"https://pypi.org/project/causal-conv1d/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-05-09","license_spdx":null,"license_treatment":"permissive","name":"causal-conv1d","python_support":"supports_current","summary":"Causal depthwise conv1d in CUDA, with a PyTorch interface"},"popularity":{"monthly_downloads":121015,"position":12000,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.6.2.post1"}