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We....do use kernels and kernel generation in the ML field. Every day. I'm confused as to a few of the points made here, unless the first sentence is meaning '.

We....do use kernels and kernel generation in the ML field. Every day. I'm confused as to a few of the points made here, unless the first sentence is meaning '...doesn't use that many hand-written kernels'. All of the convolutions we have run on kernels, some pre-built and customized/chosen from a list based on performance, and some dynamically generated. PyTorch 2.0 for example decomposes and fuses operations, then uses OpenAI's Triton to dynamically generate a custom fused kernel that tends to be very efficient. There are still hand-written kernels, even, Flash-Attention and the Memory-Efficient attention papers both caused huge leaps forward because they manually went through a lot of the inefficiencies of naive matrix multiplies for attention w.r.t. the hardware design and optimized it quite a lot. I think generalized kernel generation though may have more life in it than you might suspect! It is a fascinating field and I do not know nearly enough about it. I hope someday to be able to write my own Triton kernels/get to know how it integrates as a dynamic compiler for PyTorch code. We certainly live in wild times. Crazy indeed.

brrrrrm 4y ago
My claim is pretty subjective, but the idea is that there aren't many distinct kernels used in machine learning. It's all tensor contractions and element-wise operations. I'd argue that this can be maintained by hand without need for automation or high level abstraction. Triton is used in a templated way for a very specific albeit pervasive hardware (PTX compatible GPUs), which is why it works so well. Here's some of the code: https://github.com/pytorch/pytorch/blob/a66625da3bcdf1e262ddf7bc7fedaa070147c78e/torch/_inductor/kernel/mm.py#L16-L71 https://github.com/pytorch/pytorch/blob/a66625da3bcdf1e262dd... Generalized kernel generation (i.e. synthesis of optimal performance from non-expert user defined kernels and novel hardware) would be fantastic to have, but it just doesn't seem particularly necessary in the field.

brrrrrm 4y ago

My claim is pretty subjective, but the idea is that there aren't many distinct kernels used in machine learning. It's all tensor contractions and element-wise operations. I'd argue that this can be maintained by hand without need for automation or high level abstraction. Triton is used in a templated way for a very specific albeit pervasive hardware (PTX compatible GPUs), which is why it works so well. Here's some of the code: https://github.com/pytorch/pytorch/blob/a66625da3bcdf1e262ddf7bc7fedaa070147c78e/torch/_inductor/kernel/mm.py#L16-L71 https://github.com/pytorch/pytorch/blob/a66625da3bcdf1e262dd... Generalized kernel generation (i.e. synthesis of optimal performance from non-expert user defined kernels and novel hardware) would be fantastic to have, but it just doesn't seem particularly necessary in the field.