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Issue: If we make a backward incompatible change or a regression, we don't have a mechanism to catch it. Also, if we start running jobs on a new platform we don't have an easy way to tell if the training will be identical to existing platforms.
Fix: Add training regression tests that run 2 steps of training and compare the model activations against an already-prepared set of model activations from beaker. The saved model activations can be updated by changing the flags passed to the tests.
The added tests also run on CPU, but this required making minor changes to OLMo training code. Autocast works differently on CPU, so the model activations are different for CPU compared to GPU.
The saved model-activations are about 26Mb total, which is a reasonable increase to repo size...