Transformers Model Integration Contracts

On July 30, 2026, Transformers enabled PyTorch's scaled dot-product attention (SDPA) for T5. As a side effect, UMT5's decoder could attend to future tokens. During training, it could peek at the answer it was supposed to predict. Nothing crashed, and the bug stayed in main for almost two months.

When we later replayed those commits, a small UMT5 model running on CPU flagged it on every one of them. This post is about that model and the tests built around it.

Why bugs reach main

Transformers supports over 750 model types, and its test suite is big: 88,000+ CPU tests on main, plus 200,000+ GPU tests in a daily run. Model tests are only part of it: we also test pipelines, training, distributed execution, quantization and attention kernels.

The integration tests load real checkpoints and exercise the model's API on real hardware. Running all of that on every PR would mean keeping a lot of GPUs busy all day long, which isn't practical. By the time a scheduled run finds a regression, it may already be in main.

Our approach is to shrink the model instead. We build a fixture: a small model made from the real checkpoint's configuration, with fewer and narrower layers but every kind of layer it has, and its tokenizer and processor files unchanged. It is published once, then reused. We run "fast integration tests" against it on a cheap CPU runner. In our CPU study, the experimental UMT5 fixture used 66 MB of weights; the source checkpoint is 1,227 MB.

This also covers very large models: some don't fit on our standard GPU runners, but their fixtures run on CPU.

Model integrators already build tiny test models (Transformers' tiny-random-* checkpoints). We wanted a common recipe, and checks that go beyond loading the model and running a forward pass. The UMT5 bug would have passed a forward pass just fine.

Fixtures won't replace full integration tests, but on PRs they give us a chance to catch bugs before they reach main.