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.
☆36Updated 2 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
Sorting:
- ☆94Updated 8 months ago
- Standalone Flash Attention v2 kernel without libtorch dependency☆108Updated 8 months ago
- ☆139Updated last year
- ☆68Updated 3 weeks ago
- 使用 cutlass 仓库在 ada 架构上实现 fp8 的 flash attention☆64Updated 9 months ago
- A standalone GEMM kernel for fp16 activation and quantized weight, extracted from FasterTransformer☆92Updated last week
- ☆148Updated 4 months ago
- Benchmark code for the "Online normalizer calculation for softmax" paper☆91Updated 6 years ago
- ☆123Updated last year
- High-speed GEMV kernels, at most 2.7x speedup compared to pytorch baseline.☆109Updated 10 months ago
- ☆91Updated last month
- ☆119Updated 5 months ago
- ⚡️Write HGEMM from scratch using Tensor Cores with WMMA, MMA and CuTe API, Achieve Peak⚡️ Performance.☆76Updated this week
- We invite you to visit and follow our new repository at https://github.com/microsoft/TileFusion. TiledCUDA is a highly efficient kernel …☆181Updated 3 months ago
- ☆58Updated 5 months ago
- Implement Flash Attention using Cute.☆82Updated 4 months ago
- ☆84Updated last month
- QQQ is an innovative and hardware-optimized W4A8 quantization solution for LLMs.☆121Updated last month
- 使用 cutlass 实现 flash-attention 精简版,具有教学意义☆41Updated 9 months ago
- play gemm with tvm☆91Updated last year
- ☆202Updated 10 months ago
- A lightweight design for computation-communication overlap.☆92Updated last week
- ☆36Updated 7 months ago
- ☆109Updated last week
- Several optimization methods of half-precision general matrix vector multiplication (HGEMV) using CUDA core.☆61Updated 8 months ago
- An easy-to-use package for implementing SmoothQuant for LLMs☆97Updated last month
- Decoding Attention is specially optimized for MHA, MQA, GQA and MLA using CUDA core for the decoding stage of LLM inference.☆36Updated last month
- ☆33Updated last year
- Examples of CUDA implementations by Cutlass CuTe☆177Updated 3 months ago
- ☆127Updated 4 months ago