Warmup Missing
Missing warmup causes early training instability, especially with large learning rates or transformer models.
Missing warmup causes early training instability, especially with large learning rates or transformer models.
What this failure is
Warmup Missing is a Training Stability failure seen during ML training runs. Missing warmup causes early training instability, especially with large learning rates or transformer models. Common tags: Warmup, Scheduler, Transformer, Adamw.
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Why it happens (the mechanism)
Learning rate too high at start. Adam beta2 assumes warmup. LayerNorm outputs have high variance at init. Embeddings have high gradient magnitudes. 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
- Training is unstable at start
- Loss spikes in first epochs
- AdamW training diverges without warmup
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| Loss is very high in first iterations | Learning rate too high at start |
| First few epochs have NaN loss | Adam beta2 assumes warmup |
| Model performance is worse than expected | LayerNorm outputs have high variance at init |
Which systems are affected
- Large transformer training
- AdamW with high learning rate
- Training with mixed precision
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: Loss is very high in first iterations
- ✓Verified signal present: First few epochs have NaN loss
- ✓Verified signal present: Model performance is worse than expected
- ✓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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Diagnose this failure in VS Code
Select the traceback or open the failed terminal, then run Denpex locally to see the initiating rank, collateral failures, exact fix, and verification command without uploading the log.
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Root cause
- Learning rate too high at start
- Adam beta2 assumes warmup
- LayerNorm outputs have high variance at init
- Embeddings have high gradient magnitudes
The fix and how to prevent it
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