Сегодня выложили кернелы для матричных операций в FP8 https://github.com/deepseek-ai/DeepGEMM DeepGEMM is a library designed for clean and efficient FP8 General Matrix Multiplications (GEMMs) with fine-grained scaling, as proposed in DeepSeek-V3. It supports both normal and Mix-of-Experts (MoE) grouped GEMMs. Written in CUDA, the library has no compilation need during installation, by compiling all kernels at runtime using a lightweight Just-In-Time (JIT) module. Currently, DeepGEMM exclusively supports NVIDIA Hopper tensor cores. To address the imprecise FP8 tensor core accumulation, it employs CUDA-core two-level accumulation (promotion). While it leverages some concepts from CUTLASS and CuTe, it avoids heavy reliance on their templates or algebras. Instead, the library is designed for simplicity, with only one core kernel function comprising around ~300 lines of code. This makes it a clean and accessible resource for learning Hopper FP8 matrix multiplication and optimization techniques. Despite its lightweight design, DeepGEMM's performance matches or exceeds expert-tuned libraries across various matrix shapes. Неделя опенсорса продолжается!