sunkx109 / My-Torch-Extension
A minimalist and extensible PyTorch extension for implementing custom backend operators in PyTorch.
☆30Updated 10 months ago
Alternatives and similar repositories for My-Torch-Extension:
Users that are interested in My-Torch-Extension are comparing it to the libraries listed below
- learning how CUDA works☆207Updated 6 months ago
- llm theoretical performance analysis tools and support params, flops, memory and latency analysis.☆79Updated last month
- A CUDA tutorial to make people learn CUDA program from 0☆212Updated 7 months ago
- flash attention tutorial written in python, triton, cuda, cutlass☆286Updated 2 months ago
- Implement Flash Attention using Cute.☆70Updated 2 months ago
- Examples of CUDA implementations by Cutlass CuTe☆141Updated last month
- A light llama-like llm inference framework based on the triton kernel.☆92Updated last week
- 📚FFPA(Split-D): Yet another Faster Flash Prefill Attention with O(1)⚡️GPU SRAM complexity for headdim > 256, ~2x↑🎉vs SDPA EA.☆123Updated last week
- ☆111Updated 11 months ago
- ☆101Updated 2 months ago
- Tutorials for writing high-performance GPU operators in AI frameworks.☆129Updated last year
- Puzzles for learning Triton, play it with minimal environment configuration!☆248Updated 3 months ago
- Triton Documentation in Chinese Simplified / Triton 中文文档☆56Updated last month
- 使用 CUDA C++ 实现的 llama 模型推理框架☆46Updated 3 months ago
- 使用 cutlass 实现 flash-attention 精简版,具有教学意义☆36Updated 6 months ago
- ☆116Updated this week
- ⚡️Write HGEMM from scratch using Tensor Cores with WMMA, MMA and CuTe API, Achieve Peak⚡️ Performance.☆53Updated last week
- Summary of some awesome work for optimizing LLM inference☆58Updated 3 weeks ago
- ☆18Updated 9 months ago
- Implement custom operators in PyTorch with cuda/c++☆54Updated 2 years ago
- A tutorial for CUDA&PyTorch☆127Updated last month
- A Easy-to-understand TensorOp Matmul Tutorial☆322Updated 5 months ago
- Performance of the C++ interface of flash attention and flash attention v2 in large language model (LLM) inference scenarios.☆35Updated this week
- ☆39Updated last month
- CUDA 算子手撕与面试指南☆174Updated last month
- 使用 cutlass 仓库在 ada 架构上实现 fp8 的 flash attention☆54Updated 6 months ago
- ☆58Updated 3 months ago
- Optimize softmax in triton in many cases☆18Updated 5 months ago
- Decoding Attention is specially optimized for multi head attention (MHA) using CUDA core for the decoding stage of LLM inference.☆29Updated this week
- ☆71Updated this week