What does this failure look like in production?

In production this failure typically appears during the first tens-to-hundreds of training steps: loss curves become non-monotonic, validation metrics plateau above the baseline, and downstream checkpoints either diverge or fail to improve. Engineers frequently misread it as data drift when the underlying mechanism is a secondary memory allocator (like cupy's memory pool) forcibly freed a block of memory that was zero-copied from pytorch. pytorch attempts to access the now-unmapped virtual address.. Validating against the symptom list in this entry is the fastest way to confirm the diagnosis.

Is this a hardware or a software issue?

CUDA failures can look like either layer depending on the symptom. The discriminator is the resolution of the symptom to the recommended fix: if applying the steps under "How to fix it" eliminates the symptom, this is a software/configuration class issue regardless of the hardware it surfaced on. Repeat the fix on a fresh node to confirm.

How long does the fix typically take?

Most resolutions from this entry take 10 to 30 minutes of engineer time once the diagnosis is confirmed. Long fixes come from skipping the diagnosis step and chasing apparent symptoms (validation drift, memory growth, slow throughput) instead of the actual root cause listed above. Apply the fix in a small experiment first, then roll it forward.

Does this affect inference or only training?

Primarily this is a cuda failure observed during training, but the same misconfiguration can leak into downstream evaluation and serving. Treat the prevention checklist as a guardrail: any pipeline that touches a trained checkpoint should re-validate the failure mode before promoting the model.

What's the cheapest way to confirm this is the problem?

Cheapest signal: reproduce the symptom on a 1-GPU toy run with the same configuration. If the toy run exhibits the same behavior, the cause is the configuration rather than the cluster. If only the multi-GPU run is affected, the failure is a distributed interaction, so PyTorch is the most likely layer to inspect.

Can the Denpex platform detect this automatically?

Yes. Denpex correlates the symptoms in this entry against your training logs in real time and surfaces the failure taxonomy, root cause, and exact remediation command on the console. Three full diagnoses are included free during signup, and the encyclopedia entry you are reading is generated from the same signal library that powers the platform.

What related failures should I check next?

Closely related failures cluster around #memory allocator, #user-report. After applying this fix, validate against the related entries below to confirm no overlapping cause is masking as this one. Engineers routinely discover that the dominant failure mode is actually a second, sibling failure compounded on top.

Will this recur after the fix?

Applied once with the prevention checklist, this failure does not recur under normal operation. The recurring variant most engineers encounter is the re-emergence of the same symptom on a different model or stack after memory allocator is upgraded. Pin compatible versions and rerun the prevention smoke test on every dependency change.

Is there a fingerprint I can monitor?

Yes. NVRM: Xid (PCI:0000:XX:00): 31, pid=XXXX, name=python, MMU Fault: ENGINE GRAPHICS is the highest-signal fingerprint. Wire a metric or alert on that signal (or its absence after the fix) so you can detect the recurrence automatically. The Denpex platform encodes this exact fingerprint and surfaces it directly in the dashboard.

What is the official documentation that covers this?

Reference documentation is listed in the References section at the bottom of this entry. The most authoritative URLs are the upstream framework docs and the issue trackers cited; the Denpex entry synthesizes both into the executive, engineer, and on-call perspectives so each audience can act without reading every linked source.

Does this entry include working code?

Yes. The "Code Examples" section contains copy-paste-ready snippets that target the most common stacks (PyTorch, Hugging Face Trainer, DeepSpeed, Megatron-LM, vLLM/Transformers). Each snippet is annotated with why it works so you can adapt the pattern to your own framework without losing fidelity.

Multi-Allocator Zero-Copy MMU Fault

Q: How does this scale from 1 GPU to 1000 GPUs?

At single-GPU scale the symptom usually looks like slow convergence or a single anomalous loss value. At 64+ GPU scale, distributed interactions amplify a secondary memory allocator (like cupy's memory pool) forcibly freed a block of memory that was zero-copied from pytorch. pytorch attempts to access the now-unmapped virtual address. through collectives, and the failure can manifest as a full job abort. Always reproduce the fix at the smallest cluster configuration that still exhibits the symptom before promoting to production scale.

A secondary memory allocator (like CuPy's memory pool) forcibly freed a block of memory that was zero-copied from PyTorch. PyTorch attempts to access the now-unmapped virtual address.

Quick answer

A secondary memory allocator (like CuPy's memory pool) forcibly freed a block of memory that was zero-copied from PyTorch.

Root cause: A secondary memory allocator (like CuPy's memory pool) forcibly freed a block of memory that was zero-copied from PyTorch. PyTorch attempts to access the now-unmapped virtual address.
Recommended fix: Enforce explicit tensor cloning across allocator boundaries. - tensor_cupy = cp.asarray(tensor_torch.clone().detach())
How Denpex helps: Denpex matches Multi-Allocator Zero-Copy MMU Fault across every rank in a distributed run and reports which rank failed first, so you act on the initiating node instead of the loudest one.

CUDA#memory allocator#user-report

What this failure is

Multi-Allocator Zero-Copy MMU Fault is a CUDA failure seen during ML training runs. A secondary memory allocator (like CuPy's memory pool) forcibly freed a block of memory that was zero-copied from PyTorch. PyTorch attempts to access the now-unmapped virtual address. Common tags: Memory Allocator, User Report.

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Why it happens (the mechanism)

An Xid 31 MMU fault is often blamed on physical hardware defects, bad VRAM, or driver bugs. In reality, it is a strict software-level use-after-free vulnerability across library boundaries.

What you'll observe

The entire CUDA context dies instantly.
The training pipeline resets or crashes completely.
Memory dumps show the faulting address in high-aligned memory spaces (e.g., 0x725f_fb800000).

Common symptoms and what they mean

Symptom	Why it happens
NVRM: Xid (PCI:0000:XX:00): 31, pid=XXXX, name=python, MMU Fault: ENGINE GRAPHICS	A secondary memory allocator (like CuPy's memory pool) forcibly freed a block of memory that was zero-copied from PyTorch. PyTorch attempts to access the now-unmapped virtual address.
Fault is of type FAULT_PDE	A secondary memory allocator (like CuPy's memory pool) forcibly freed a block of memory that was zero-copied from PyTorch. PyTorch attempts to access the now-unmapped virtual address.

Which systems are affected

PyTorch
CuPy
TensorRT
CUDA

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.

✓Decode the Xid 31 payload to identify if it is a FAULT_PDE (wholesale deallocation) or FAULT_PTE (single page issue).
✓Trace the tensor lifecycle using cuda-memcheck or Compute Sanitizer.
✓Identify zero-copy handoffs (e.g., DLPack operations) in the code.

The fix and the prevention pattern

The root cause is on this page and stays free. A free account adds the exact remediation steps, saved history, and the fix on every entry in the encyclopedia.

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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

A secondary memory allocator (like CuPy's memory pool) forcibly freed a block of memory that was zero-copied from PyTorch. PyTorch attempts to access the now-unmapped virtual address.

The fix and how to prevent it

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References

Don't just read the fix, diagnose your run

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Related CUDA errors

Xid 43 (GPU Stopped Processing)

CUDA · high

Hopper mma.sp Silent Data Corruption (SDC)

CUDA · critical