NVIDIA / online-softmaxLinks
Benchmark code for the "Online normalizer calculation for softmax" paper
☆95Updated 7 years ago
Alternatives and similar repositories for online-softmax
Users that are interested in online-softmax are comparing it to the libraries listed below
Sorting:
- ☆96Updated 10 months ago
- 使用 cutlass 仓库在 ada 架构上实现 fp8 的 flash attention☆74Updated 11 months ago
- Standalone Flash Attention v2 kernel without libtorch dependency☆111Updated 10 months ago
- A standalone GEMM kernel for fp16 activation and quantized weight, extracted from FasterTransformer☆94Updated 3 weeks ago
- High-speed GEMV kernels, at most 2.7x speedup compared to pytorch baseline.☆113Updated last year
- ☆227Updated last year
- Performance of the C++ interface of flash attention and flash attention v2 in large language model (LLM) inference scenarios.☆39Updated 5 months ago
- Several optimization methods of half-precision general matrix vector multiplication (HGEMV) using CUDA core.☆63Updated 10 months ago
- We invite you to visit and follow our new repository at https://github.com/microsoft/TileFusion. TiledCUDA is a highly efficient kernel …☆183Updated 6 months ago
- llama INT4 cuda inference with AWQ☆54Updated 6 months ago
- ☆89Updated 2 months ago
- ⚡️Write HGEMM from scratch using Tensor Cores with WMMA, MMA and CuTe API, Achieve Peak⚡️ Performance.☆93Updated 2 months ago
- ☆127Updated 2 months ago
- play gemm with tvm☆91Updated 2 years ago
- PyTorch bindings for CUTLASS grouped GEMM.☆106Updated 2 months ago
- ☆102Updated 7 months ago
- Implement Flash Attention using Cute.☆92Updated 7 months ago
- ☆85Updated 8 months ago
- ☆102Updated 4 months ago
- ☆60Updated 3 months ago
- ☆37Updated last year
- An extension library of WMMA API (Tensor Core API)☆99Updated last year
- TileFusion is an experimental C++ macro kernel template library that elevates the abstraction level in CUDA C for tile processing.☆93Updated last month
- Tritonbench is a collection of PyTorch custom operators with example inputs to measure their performance.☆193Updated this week
- A collection of memory efficient attention operators implemented in the Triton language.☆275Updated last year
- ☆128Updated 8 months ago
- An efficient GPU support for LLM inference with x-bit quantization (e.g. FP6,FP5).☆260Updated 3 weeks ago
- ☆149Updated 6 months ago
- CUDA Matrix Multiplication Optimization☆213Updated last year
- ⚡️FFPA: Extend FlashAttention-2 with Split-D, achieve ~O(1) SRAM complexity for large headdim, 1.8x~3x↑ vs SDPA.🎉☆194Updated 2 months ago