Adafactor Optimizer Issues
Adafactor optimizer issues arise from incorrect epsilon, scaling factor, or relative step handling.
Adafactor optimizer issues arise from incorrect epsilon, scaling factor, or relative step handling.
What this failure is
Adafactor Optimizer Issues is a Training Stability failure seen during ML training runs. Adafactor optimizer issues arise from incorrect epsilon, scaling factor, or relative step handling. Common tags: Adafactor, Optimizer, Transformer, T5.
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Why it happens (the mechanism)
Adafactor epsilon too high (1e-30) or too low (1e-3). Adafactor relative_step disabled incorrectly. Adafactor warmup is missing. Adafactor weight decay parameter is different. 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
- Adafactor converges slower than Adam
- Adafactor loss is unstable
- Adafactor model performs worse than Adam
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| Loss spikes with Adafactor | Adafactor epsilon too high (1e-30) or too low (1e-3) |
| Adafactor uses more memory than expected | Adafactor relative_step disabled incorrectly |
| Relative step parameter is wrong | Adafactor warmup is missing |
Which systems are affected
- Training large transformers with Adafactor
- T5 fine-tuning
- Memory-constrained training
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 spikes with Adafactor
- ✓Verified signal present: Adafactor uses more memory than expected
- ✓Verified signal present: Relative step parameter is wrong
- ✓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
- Adafactor epsilon too high (1e-30) or too low (1e-3)
- Adafactor relative_step disabled incorrectly
- Adafactor warmup is missing
- Adafactor weight decay parameter is different
The fix and how to prevent it
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