Checkpoint Corruption
Corrupted checkpoints silently poison resumed training with bad weights. Denpex validates checkpoint integrity before resume.
Corrupted checkpoints silently poison resumed training with bad weights.
What this failure is
Checkpoint Corruption is a Data Integrity failure seen during ML training runs. Corrupted checkpoints silently poison resumed training with bad weights. Denpex validates checkpoint integrity before resume. Common tags: Checkpoint, Corruption, Data Integrity, Save.
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Why it happens (the mechanism)
Torn writes: the checkpoint save process is interrupted (node failure, OOM killer, NCCL timeout) mid-write, leaving an incomplete file that passes filesystem validation. Shard inconsistency: in distributed checkpointing, one rank's shard save succeeds while another's fails, leaving an incomplete sharded checkpoint. Bit rot / silent data corruption: storage media flips bits in the serialized tensor data without filesystem error detection. World size mismatch: checkpoint saved with N GPUs is loaded with M GPUs, causing optimizer state partition reassignment errors. 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
- Resumed training produces worse metrics than original
- Checkpoint load succeeds but model output is garbage
- Checkpoint file size differs between identical saves
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| Loading checkpoint succeeds but model.eval() produces constant output | Torn writes: the checkpoint save process is interrupted (node failure, OOM killer, NCCL timeout) mid-write, leaving an incomplete file that passes filesystem validation |
| RuntimeError: 'lengths' argument in 'EmbeddingBag' is empty after checkpoint load | Shard inconsistency: in distributed checkpointing, one rank's shard save succeeds while another's fails, leaving an incomplete sharded checkpoint |
| Optimizer state dictionary shape mismatch on resume with different world size | Bit rot / silent data corruption: storage media flips bits in the serialized tensor data without filesystem error detection |
| Checkpoint file hash differs after repeated saves of identical model state | World size mismatch: checkpoint saved with N GPUs is loaded with M GPUs, causing optimizer state partition reassignment errors |
Which systems are affected
- Distributed checkpointing with torch.distributed.checkpoint
- Hugging Face Trainer saving with save_safetensors=False
- NFS-mounted checkpoint directories with concurrent writers
- Cloud storage backed checkpoints (S3, GCS, Azure Blob) with partial uploads
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: Loading checkpoint succeeds but model.eval() produces constant output
- ✓Verified signal present: RuntimeError: 'lengths' argument in 'EmbeddingBag' is empty after checkpoint load
- ✓Verified signal present: Optimizer state dictionary shape mismatch on resume with different world size
- ✓Verified signal present: Checkpoint file hash differs after repeated saves of identical model state
- ✓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
- Torn writes: the checkpoint save process is interrupted (node failure, OOM killer, NCCL timeout) mid-write, leaving an incomplete file that passes filesystem validation
- Shard inconsistency: in distributed checkpointing, one rank's shard save succeeds while another's fails, leaving an incomplete sharded checkpoint
- Bit rot / silent data corruption: storage media flips bits in the serialized tensor data without filesystem error detection
- World size mismatch: checkpoint saved with N GPUs is loaded with M GPUs, causing optimizer state partition reassignment errors
The fix and how to prevent it
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