NaN Loss from TransformerEngine FP8 Scaling Saturation
FP8 training with delayed scaling produces NaNs when an activation outlier saturates the scaling factor (amax history too short / margin too small). Loss and gradients are healthy until the exact step the scale saturates. Distinguishing it from LR- or data-driven NaN.
FP8 training with delayed scaling produces NaNs when an activation outlier saturates the scaling factor (amax history too short / margin too small).
What this failure is
NaN Loss from TransformerEngine FP8 Scaling Saturation is a Training Stability failure seen during ML training runs. FP8 training with delayed scaling produces NaNs when an activation outlier saturates the scaling factor (amax history too short / margin too small). Loss and gradients are healthy until the exact step the scale saturates. Distinguishing it from LR- or data-driven NaN. Common tags: Nan Loss, Fp8, Transformer Engine, Amax.
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Why it happens (the mechanism)
DelayedScaling margin=0 with short amax_history on outlier-prone layers. Per-tensor scaling on layers with heavy-tailed activations. Skipped amax sync across TP ranks after topology change. Taken together, these mechanisms explain why the failure is reproducible, why it tends to surface on specific workloads or scales, and why generic mitigation attempts often fall short without addressing the underlying cause.
What you'll observe
- loss=nan appears with stable prior grad_norm history
- TE logs an amax/scaling warning for a specific layer shortly before
- bf16 rerun of the same steps is clean
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| amax_history overflow / scaling factor saturated messages | DelayedScaling margin=0 with short amax_history on outlier-prone layers |
| NaN first appears in one layer's activations then propagates | per-tensor scaling on layers with heavy-tailed activations |
| per-step numerics fine with fp8 disabled | skipped amax sync across TP ranks after topology change |
Which systems are affected
- H100/H200 FP8 recipes (TransformerEngine)
- Megatron/NeMo FP8 configs
- custom DelayedScaling recipes
How to confirm this is the problem
Use this checklist to test the hypothesis against a small reproduction. No single line proves the root cause, so preserve the preceding events and compare one variable at a time.
- ✓Reproduce the failure from a clean checkpoint/seed: the symptom must appear without warm-up state from a previous run.
- ✓Verified signal present: amax_history overflow / scaling factor saturated messages
- ✓Verified signal present: NaN first appears in one layer's activations then propagates
- ✓Verified signal present: per-step numerics fine with fp8 disabled
- ✓A targeted fix from the "How to fix it" section eliminates or substantially reduces the symptom within one validation pass.
The fix and the prevention pattern
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Root cause
- DelayedScaling margin=0 with short amax_history on outlier-prone layers
- per-tensor scaling on layers with heavy-tailed activations
- skipped amax sync across TP ranks after topology change
The fix and how to prevent it
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