All-Reduce Deadlock
All-reduce deadlocks occur when DDP/FSDP all-reduce operations are mismatched across ranks or with batch norm.
All-reduce deadlocks occur when DDP/FSDP all-reduce operations are mismatched across ranks or with batch norm.
What this failure is
All-Reduce Deadlock is a Communication failure seen during ML training runs. All-reduce deadlocks occur when DDP/FSDP all-reduce operations are mismatched across ranks or with batch norm. Common tags: All Reduce, Deadlock, Ddp, Distributed.
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Why it happens (the mechanism)
Different number of parameters across ranks. Batch norm sync missing. All-reduce in only some ranks. Inconsistent model state across ranks. Distributed all-reduce timeout not set. 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
- Distributed training hangs at all-reduce
- All-reduce never completes
- Deadlock with no error message
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| Training hangs at backward pass | Different number of parameters across ranks |
| DDP all-reduce hangs | Batch norm sync missing |
| FSDP all-gather hangs | All-reduce in only some ranks |
Which systems are affected
- DDP training
- FSDP training
- Pipeline parallel + DDP
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: Training hangs at backward pass
- ✓Verified signal present: DDP all-reduce hangs
- ✓Verified signal present: FSDP all-gather hangs
- ✓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
- Different number of parameters across ranks
- Batch norm sync missing
- All-reduce in only some ranks
- Inconsistent model state across ranks
- Distributed all-reduce timeout not set
The fix and how to prevent it
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