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Five Checkpoints to Bitwise-Deterministic Training Runs
A five-checkpoint workflow for making large-scale training runs bitwise reproducible: pick the guarantee, fingerprint the run, and trace divergence down to the kernel.
Explanation
Bitwise determinism means two runs of the same job follow the same numerical trajectory, so a resumed run produces identical tensors, not merely similar metrics. This explainer turns it into five checkpoints: choose your guarantee, fingerprint GPU tensors with torch.hash_tensor, compare at full precision, then trace divergence from end-to-end metrics down to the responsible kernel. The last checkpoint is a fix plus a measurement: enforce a fixed reduction order, re-verify, and check the overhead, which can land near a 2% steady-state determinism tax at scale. Nondeterminism across thousands of GPUs makes failures hard to replay and fixes hard to validate.
FAQ
What is the difference between independent reproducibility and bitwise checkpoint resume?
Independent reproducibility means two runs launched from the same starting point follow the same numerical trajectory. Bitwise checkpoint resume means a run restarted from a checkpoint continues along the trajectory of the uninterrupted run. NVIDIA Megatron Core targets both guarantees, because either one can break at trillion-parameter scale across thousands of GPUs.
Why fingerprint tensors instead of just comparing loss or evaluation metrics?
End-to-end metrics can look close or even identical while the underlying trajectory has already diverged. Using torch.hash_tensor gives a GPU-resident fingerprint of a tensor, so you can compare states directly and bisect the difference. The workflow starts with full-precision metrics, then progressively narrows from end-to-end numbers to the layer and finally the responsible kernel.
The posts behind it
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