Author here. Hey, My students and I could really use your help!

Can anyone help shed light on why the MPS backend for PyTorch produces different numbers compared to the CUDA & CPU devices? I don't mean unsupported ops & CPU fallback, I mean fast, garbage numbers coming out of MPS. This PR references numerous other PyTorch issues related to MPS inaccuracy: https://github.com/Stability-AI/stable-audio-tools/pull/225

The story:

This tutorial post was a lesson I wrote for my undergrad "Deep Learning & AI Ethics" class (https://github.com/drscotthawley/DLAIE).

The plan for the semester was to abandon the standard lesson+assignment format (since LLMs make coding assignments moot) in favor of a project-based learning approach: We would, as a class, build a text-conditioned latent flow matching generative model from scratch, because in so doing we'd cover essentially all the key topics of a "normal" course.

We pivoted to adding guidance to pretrained models for logistical reasons, specifically working with Stable Audio Open Small, but we hit a snag re. our MPS outputs and I wonder if any readers here can help.

(Students are overwhelmingly Mac users, my small college doesn't provide GPUs, CPU execution is too slow, Colab takes too long to setup and then kicks us off. Waiting on an NSF NAIRR Pilot education allocation for some remote GPU access.)