{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/0d491fee18cb459da598e6a99cf37185\" frameborder=\"0\" width=\"2320\" height=\"1740\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1740,"width":2320,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1740,"thumbnail_width":2320,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/0d491fee18cb459da598e6a99cf37185-800a90483b565cb5.gif","duration":149.8239,"title":"loopfuse demo","description":"In this video, I showcase a compiler I've developed that can efficiently fuse Triton kernels, like FlashAttention, from Torch code. I demonstrate how the compiler can handle various operations, such as adding logic for soft cupping, inlining functions, and reasoning about shapes and sparsity. Additionally, I highlight the performance benchmarks, showing that our implementation maintains high efficiency compared to existing solutions like Torch compile and FlexAttention. I encourage viewers to check the readme for more details and benchmarks. Your feedback on this tool would be greatly appreciated as we continue to refine its capabilities."}