Code and data for paper "Deep Painterly Harmonization": https://arxiv.org/abs/1804.03189

GPU Accelerated t-SNE for CUDA with Python bindings

RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing …

Causal depthwise conv1d in CUDA, with a PyTorch interface

Reference implementation of real-time autoregressive wavenet inference

[MLSys'24] Atom: Low-bit Quantization for Efficient and Accurate LLM Serving

Facebook's CUDA extensions.

Flash-LLM: Enabling Cost-Effective and Highly-Efficient Large Generative Model Inference with Unstructured Sparsity

Efficient reservoir sampling implementation for PyTorch