Mixed Precision Overflow
Mixed precision overflow occurs in FP16 training when values exceed FP16 range (65504), causing inf/NaN.
Mixed precision overflow occurs in FP16 training when values exceed FP16 range (65504), causing inf/NaN.
What this failure is
Mixed Precision Overflow is a Training Stability failure seen during ML training runs. Mixed precision overflow occurs in FP16 training when values exceed FP16 range (65504), causing inf/NaN. Common tags: Mixed Precision, Fp16, Overflow, Gradscaler.
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Why it happens (the mechanism)
FP16 max value is 65504. Loss/activation values exceed FP16 range. No dynamic loss scaling. Softmax/log operations can overflow. 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
- FP16 loss becomes inf
- Gradients are inf in FP16
- Training crashes with overflow
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| RuntimeError: loss is inf | FP16 max value is 65504 |
| GradScaler inf check fails | Loss/activation values exceed FP16 range |
| Loss scale becomes very small | No dynamic loss scaling |
Which systems are affected
- FP16 training on Volta/Turing
- Training with very large model outputs
- Training with large losses
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: RuntimeError: loss is inf
- ✓Verified signal present: GradScaler inf check fails
- ✓Verified signal present: Loss scale becomes very small
- ✓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
- FP16 max value is 65504
- Loss/activation values exceed FP16 range
- No dynamic loss scaling
- Softmax/log operations can overflow
The fix and how to prevent it
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