Start by explaining the numerical range differences between FP8 and BF16, then discuss specific techniques like scaling factors, loss scaling, and mixed-precision strategies to prevent overflow. Emphasize a systematic approach: monitor, detect, and mitigate overflow through dynamic adjustments and careful kernel design.
Pro tip: Mention that overflow often manifests as NaN/Inf in gradients or activations, so instrumenting training with checks for these values and logging the max absolute values per layer can catch issues early. Also, note that FP8's limited range makes per-tensor scaling less effective than per-channel or per-group scaling, which is a key insight for large models.
Compare the dynamic range and precision of FP8 (E4M3/E5M2) and BF16 to identify where overflow is likely to occur, especially in activations, gradients, and optimizer states.
Use loss scaling for gradients and per-tensor or per-channel scaling for FP8 tensors to shift values into representable ranges, adjusting scales dynamically based on observed maxima.
Add runtime checks for Inf/NaN in forward and backward passes, and track max absolute values per layer to identify overflow hotspots.
Apply techniques like gradient clipping, skipping updates on overflow, or falling back to higher precision for problematic layers, and consider using stochastic rounding to reduce bias.
Test the mixed-precision setup on a small scale, measure impact on convergence and accuracy, and iteratively refine scaling factors and precision assignments.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.