torch.compile Memory
torch.compile can use additional memory for graph compilation, guard evaluation, and dynamic shape handling.
torch.
What this failure is
torch.compile Memory is a Memory failure seen during ML training runs. torch.compile can use additional memory for graph compilation, guard evaluation, and dynamic shape handling. Common tags: Torch Compile, Dynamo, Compilation, Memory.
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Why it happens (the mechanism)
Torch.compile keeps compiled graphs in memory. Recompilation on dynamic shapes. Different configs for different model parts. Guard evaluation overhead. 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
- Memory grows with torch.compile
- First iterations are slow
- Memory is fragmented after compile
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| torch.compile causes OOM | torch.compile keeps compiled graphs in memory |
| Memory higher with torch.compile than eager mode | Recompilation on dynamic shapes |
| torch.compile recompiles on shape change | Different configs for different model parts |
Which systems are affected
- Training with torch.compile
- Inference with torch.compile
- Using dynamo for performance
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: torch.compile causes OOM
- ✓Verified signal present: Memory higher with torch.compile than eager mode
- ✓Verified signal present: torch.compile recompiles on shape change
- ✓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
- torch.compile keeps compiled graphs in memory
- Recompilation on dynamic shapes
- Different configs for different model parts
- Guard evaluation overhead
The fix and how to prevent it
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