iclementine / optimize_softmaxLinks
Optimize softmax in triton in many cases
☆21Updated last year
Alternatives and similar repositories for optimize_softmax
Users that are interested in optimize_softmax are comparing it to the libraries listed below
Sorting:
- Examples of CUDA implementations by Cutlass CuTe☆229Updated 2 months ago
- ☆103Updated 3 months ago
- 使用 cutlass 仓库在 ada 架构上实现 fp8 的 flash attention☆75Updated last year
- ☆138Updated last year
- Implement Flash Attention using Cute.☆95Updated 8 months ago
- ☆41Updated last year
- ☆132Updated 9 months ago
- llm theoretical performance analysis tools and support params, flops, memory and latency analysis.☆106Updated 2 months ago
- ☆55Updated last month
- ☆150Updated 8 months ago
- Optimize GEMM with tensorcore step by step☆32Updated last year
- This project is about convolution operator optimization on GPU, include GEMM based (Implicit GEMM) convolution.☆37Updated 8 months ago
- A Easy-to-understand TensorOp Matmul Tutorial☆376Updated 11 months ago
- A standalone GEMM kernel for fp16 activation and quantized weight, extracted from FasterTransformer☆94Updated 2 weeks ago
- ☆108Updated 5 months ago
- ⚡️Write HGEMM from scratch using Tensor Cores with WMMA, MMA and CuTe API, Achieve Peak⚡️ Performance.☆109Updated 4 months ago
- 使用 cutlass 实现 flash-attention 精简版,具有教学意义☆46Updated last year
- ☆98Updated last year
- ☆153Updated 8 months ago
- ☆69Updated 8 months ago
- Performance of the C++ interface of flash attention and flash attention v2 in large language model (LLM) inference scenarios.☆40Updated 6 months ago
- flash attention tutorial written in python, triton, cuda, cutlass☆417Updated 4 months ago
- 使用 CUDA C++ 实现的 llama 模型推理框架☆61Updated 10 months ago
- ☆141Updated last year
- Several optimization methods of half-precision general matrix vector multiplication (HGEMV) using CUDA core.☆64Updated last year
- hands on model tuning with TVM and profile it on a Mac M1, x86 CPU, and GTX-1080 GPU.☆49Updated 2 years ago
- ☆15Updated last year
- A GPU-optimized system for efficient long-context LLMs decoding with low-bit KV cache.☆59Updated 2 weeks ago
- [ICML 2025] Official PyTorch implementation of "FlatQuant: Flatness Matters for LLM Quantization"☆160Updated last month
- A tutorial for CUDA&PyTorch☆154Updated 7 months ago