NVIDIA / online-softmaxLinks
Benchmark code for the "Online normalizer calculation for softmax" paper
☆98Updated 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:
- 使用 cutlass 仓库在 ada 架构上实现 fp8 的 flash attention☆75Updated last year
- High-speed GEMV kernels, at most 2.7x speedup compared to pytorch baseline.☆114Updated last year
- ☆98Updated last year
- Performance of the C++ interface of flash attention and flash attention v2 in large language model (LLM) inference scenarios.☆40Updated 6 months ago
- Several optimization methods of half-precision general matrix vector multiplication (HGEMV) using CUDA core.☆64Updated last year
- Standalone Flash Attention v2 kernel without libtorch dependency☆110Updated last year
- A standalone GEMM kernel for fp16 activation and quantized weight, extracted from FasterTransformer☆94Updated 2 weeks ago
- ☆231Updated last year
- llama INT4 cuda inference with AWQ☆54Updated 7 months ago
- We invite you to visit and follow our new repository at https://github.com/microsoft/TileFusion. TiledCUDA is a highly efficient kernel …☆185Updated 7 months ago
- ☆103Updated 4 months ago
- ⚡️Write HGEMM from scratch using Tensor Cores with WMMA, MMA and CuTe API, Achieve Peak⚡️ Performance.☆109Updated 4 months ago
- ☆115Updated 8 months ago
- ☆139Updated 4 months ago
- ☆108Updated 5 months ago
- play gemm with tvm☆91Updated 2 years ago
- ☆41Updated last year
- 使用 cutlass 实现 flash-attention 精简版,具有教学意义☆46Updated last year
- ☆88Updated 10 months ago
- ☆150Updated 8 months ago
- Implement Flash Attention using Cute.☆95Updated 8 months ago
- TileFusion is an experimental C++ macro kernel template library that elevates the abstraction level in CUDA C for tile processing.☆97Updated 2 months ago
- An efficient GPU support for LLM inference with x-bit quantization (e.g. FP6,FP5).☆265Updated 2 months ago
- CUDA Matrix Multiplication Optimization☆221Updated last year
- 🤖FFPA: Extend FlashAttention-2 with Split-D, ~O(1) SRAM complexity for large headdim, 1.8x~3x↑🎉 vs SDPA EA.☆212Updated last month
- A lightweight design for computation-communication overlap.☆165Updated this week
- ☆63Updated 4 months ago
- A collection of memory efficient attention operators implemented in the Triton language.☆277Updated last year
- Tritonbench is a collection of PyTorch custom operators with example inputs to measure their performance.☆221Updated this week
- ☆132Updated 9 months ago