Bruce-Lee-LY / flash_attention_inferenceLinks
Performance of the C++ interface of flash attention and flash attention v2 in large language model (LLM) inference scenarios.
☆41Updated 8 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:
- A standalone GEMM kernel for fp16 activation and quantized weight, extracted from FasterTransformer☆96Updated 2 months ago
- ☆101Updated last year
- 使用 cutlass 仓库在 ada 架构上实现 fp8 的 flash attention☆78Updated last year
- Standalone Flash Attention v2 kernel without libtorch dependency☆112Updated last year
- ☆143Updated last year
- ☆110Updated 5 months ago
- ☆152Updated 10 months ago
- ☆139Updated last year
- Benchmark code for the "Online normalizer calculation for softmax" paper☆102Updated 7 years ago
- Implement Flash Attention using Cute.☆96Updated 10 months ago
- ☆112Updated 7 months ago
- ☆47Updated last year
- llama INT4 cuda inference with AWQ☆55Updated 9 months ago
- ☆140Updated this week
- play gemm with tvm☆92Updated 2 years ago
- Several optimization methods of half-precision general matrix vector multiplication (HGEMV) using CUDA core.☆68Updated last year
- An easy-to-use package for implementing SmoothQuant for LLMs☆107Updated 7 months ago
- ⚡️Write HGEMM from scratch using Tensor Cores with WMMA, MMA and CuTe API, Achieve Peak⚡️ Performance.☆124Updated 6 months ago
- ☆33Updated 9 months ago
- ☆59Updated 3 months ago
- We invite you to visit and follow our new repository at https://github.com/microsoft/TileFusion. TiledCUDA is a highly efficient kernel …☆186Updated 9 months ago
- Decoding Attention is specially optimized for MHA, MQA, GQA and MLA using CUDA core for the decoding stage of LLM inference.☆45Updated 5 months ago
- High-speed GEMV kernels, at most 2.7x speedup compared to pytorch baseline.☆120Updated last year
- 使用 cutlass 实现 flash-attention 精简版,具有教学意义☆50Updated last year
- 🤖FFPA: Extend FlashAttention-2 with Split-D, ~O(1) SRAM complexity for large headdim, 1.8x~3x↑🎉 vs SDPA EA.☆226Updated 3 months ago
- ☆60Updated 11 months ago
- ☆243Updated last year
- QQQ is an innovative and hardware-optimized W4A8 quantization solution for LLMs.☆145Updated 2 months ago
- ☆130Updated 10 months ago
- Examples of CUDA implementations by Cutlass CuTe☆247Updated 4 months ago