Hacker News Reader

Comment by cinntaile | Hacker News Reader

Usually you just trivially have the model run on cpu or gpu by simply writing .cpu() at specific places, so he's wondering why this isn't the case here.

View on HN

that's literally all I did (plus switching the tensor type). I'd imagine people are posting and upvoting this not because it's actually interesting code but rather just because it runs unexpectedly fast on consumer CPUs and it's not something they considered feasible before.

That is vastly underestimating how tricky it is to make novel pieces of software run. There is a huge fringe of people who know how to click things but not use the terminal and a large fringe of people who know how to run "./execute.bat" but not how to write syntactically correct Python.

But a lot of those people want to play with LLMs.

How are you getting this to run fast? I'm on a top of the line M1 MBP and getting 1 token every 8 minutes.

probably pytorch is very optimized to x86. It's likely using lots of SIMD and whatnot. I'm sure it's possible to get similar performance on m1 macs, but not with the current version of pytorch.

Do you have enough ram? (not swapping to disk)?

Try switching all the .cuda() to .mps() I got a 100x speedup on a different language model on a Macbook M1 Air.

https://pytorch.org/docs/stable/notes/mps.html

dedicated fork: https://github.com/remixer-dec/llama-mps

Same experience for me, looks like it is only using one cpu core instead of all of them.

Or better yet, define a device = 'cpu', and use tensor.to(device).

Reply on news.ycombinator.com