Acceptance tests (GPU-gated not(no_cuda), run on dash5):
- adamw_parity_dump.rs + adamw_parity.py: build the tiny model with fixed init,
run N AdamW steps on a fixed batch, dump the loss trajectory + final params;
the Python side rebuilds the identical model and runs torch.optim.AdamW with
matched lr/wd/betas/eps, comparing trajectory + final params within rtol.
- checkpoint_roundtrip.rs: train a few steps, save, load into a fresh model with
a DIFFERENT init, assert identical logits/loss on a fixed input.
- real_training.rs (#[ignore], --release): train on TinyStories for a bounded
budget; assert loss drops substantially and print greedy samples.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Gahow Wang
22b7434b23