Bruce-Lee-LY / flash_attention_inference
Performance of the C++ interface of flash attention and flash attention v2 in large language model (LLM) inference scenarios.
☆34Updated 4 months ago
Alternatives and similar repositories for flash_attention_inference:
Users that are interested in flash_attention_inference are comparing it to the libraries listed below
- ☆140Updated 9 months ago
- ☆79Updated 4 months ago
- Standalone Flash Attention v2 kernel without libtorch dependency☆99Updated 4 months ago
- ☆142Updated 3 weeks ago
- A standalone GEMM kernel for fp16 activation and quantized weight, extracted from FasterTransformer☆88Updated 11 months ago
- 使用 cutlass 仓库在 ada 架构上实现 fp8 的 flash attention☆53Updated 5 months ago
- ☆80Updated last year
- ☆95Updated last month
- ☆106Updated 10 months ago
- ☆180Updated 6 months ago
- play gemm with tvm☆85Updated last year
- Examples of CUDA implementations by Cutlass CuTe☆132Updated 2 months ago
- Benchmark code for the "Online normalizer calculation for softmax" paper☆64Updated 6 years ago
- High-speed GEMV kernels, at most 2.7x speedup compared to pytorch baseline.☆94Updated 6 months ago
- We invite you to visit and follow our new repository at https://github.com/microsoft/TileFusion. TiledCUDA is a highly efficient kernel …☆175Updated this week
- A Easy-to-understand TensorOp Matmul Tutorial☆307Updated 4 months ago
- ☆57Updated 2 months ago
- QQQ is an innovative and hardware-optimized W4A8 quantization solution for LLMs.☆95Updated last month
- Implement Flash Attention using Cute.☆67Updated last month
- llama INT4 cuda inference with AWQ☆50Updated last week
- Several optimization methods of half-precision general matrix vector multiplication (HGEMV) using CUDA core.☆54Updated 4 months ago
- A collection of memory efficient attention operators implemented in the Triton language.☆233Updated 7 months ago
- An easy-to-use package for implementing SmoothQuant for LLMs☆89Updated 8 months ago
- ☆59Updated last month
- ☆127Updated last month
- ☆33Updated 3 months ago
- ⚡️Write HGEMM from scratch using Tensor Cores with WMMA, MMA and CuTe API, Achieve Peak⚡️ Performance.☆47Updated this week
- ☆73Updated 6 months ago
- Decoding Attention is specially optimized for multi head attention (MHA) using CUDA core for the decoding stage of LLM inference.☆27Updated 2 months ago
- flash attention tutorial written in python, triton, cuda, cutlass☆255Updated 3 weeks ago