Megatron-LM Initialization Failed
Megatron-LM initialization errors prevent large model training from starting due to configuration mismatches.
Megatron-LM initialization errors prevent large model training from starting due to configuration mismatches.
What this failure is
Megatron-LM Initialization Failed is a Distributed Training failure seen during ML training runs. Megatron-LM initialization errors prevent large model training from starting due to configuration mismatches. Common tags: Megatron, Init, Tensor Parallel, Pipeline Parallel.
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Why it happens (the mechanism)
Hidden size not divisible by tensor parallel size. Number of layers not divisible by pipeline parallel size. TP*PP doesn't match world size. Sequence length not divisible by TP. 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
- Megatron training fails at init
- Tensor parallel setup fails
- Pipeline parallel pipeline layout invalid
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| Megatron-LM configuration error | Hidden size not divisible by tensor parallel size |
| TP/PP degree mismatch with model | Number of layers not divisible by pipeline parallel size |
| RuntimeError: invalid tensor parallel configuration | TP*PP doesn't match world size |
Which systems are affected
- Large model training with Megatron-LM
- New Megatron deployments
- Configuration changes
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: Megatron-LM configuration error
- ✓Verified signal present: TP/PP degree mismatch with model
- ✓Verified signal present: RuntimeError: invalid tensor parallel configuration
- ✓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
- Hidden size not divisible by tensor parallel size
- Number of layers not divisible by pipeline parallel size
- TP*PP doesn't match world size
- Sequence length not divisible by TP
The fix and how to prevent it
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