meta-pytorch / float8_experimentalLinks
This repository contains the experimental PyTorch native float8 training UX
☆224Updated last year
Alternatives and similar repositories for float8_experimental
Users that are interested in float8_experimental are comparing it to the libraries listed below
Sorting:
- Applied AI experiments and examples for PyTorch☆292Updated last week
- Fast low-bit matmul kernels in Triton☆356Updated this week
- 🚀 Collection of components for development, training, tuning, and inference of foundation models leveraging PyTorch native components.☆208Updated last week
- Triton-based implementation of Sparse Mixture of Experts.☆233Updated last week
- ☆234Updated last week
- 🚀 Efficiently (pre)training foundation models with native PyTorch features, including FSDP for training and SDPA implementation of Flash…☆262Updated last month
- Tritonbench is a collection of PyTorch custom operators with example inputs to measure their performance.☆214Updated this week
- ☆159Updated last year
- extensible collectives library in triton☆88Updated 5 months ago
- A Quirky Assortment of CuTe Kernels☆435Updated this week
- ☆110Updated last year
- ☆328Updated this week
- Cataloging released Triton kernels.☆252Updated 7 months ago
- ring-attention experiments☆149Updated 10 months ago
- A library for unit scaling in PyTorch☆129Updated last month
- Load compute kernels from the Hub☆258Updated this week
- A Python-embedded DSL that makes it easy to write fast, scalable ML kernels with minimal boilerplate.☆274Updated this week
- PyTorch bindings for CUTLASS grouped GEMM.☆110Updated 3 months ago
- Collection of kernels written in Triton language☆152Updated 4 months ago
- ☆118Updated last year
- A subset of PyTorch's neural network modules, written in Python using OpenAI's Triton.☆571Updated 2 weeks ago
- ☆163Updated last year
- A safetensors extension to efficiently store sparse quantized tensors on disk☆153Updated this week
- A bunch of kernels that might make stuff slower 😉☆58Updated last week
- Fast Hadamard transform in CUDA, with a PyTorch interface☆224Updated last year
- QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference☆119Updated last year
- An efficient GPU support for LLM inference with x-bit quantization (e.g. FP6,FP5).☆261Updated last month
- Implementation of a Transformer, but completely in Triton☆273Updated 3 years ago
- ☆149Updated 2 years ago
- Flash-Muon: An Efficient Implementation of Muon Optimizer☆174Updated 2 months ago