Is this a hardware or a software issue?

Memory 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 memory 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 #Out of Bounds Access. 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 Out of Bounds Access is upgraded. Pin compatible versions and rerun the prevention smoke test on every dependency change.

Is there a fingerprint I can monitor?

Yes. The training script crashes suddenly without warning, often pointing to an arbitrary line of code (like a loss function or forward pass of a different layer). 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.

nn.Embedding Out of Bounds Illegal Memory Access

Q: 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 an index passed to a pytorch `nn.embedding` layer exceeds the configured `num_embeddings` (e.g., passing token id 1000 to an embedding matrix of size 1000, where max valid is 999). because cuda executes asynchronously, the out-of-bounds kernel memory violation doesn't throw a python exception immediately. instead, the gpu's memory protection fault corrupts the cuda context, causing a subsequent operation to raise the generic 'illegal memory access' error.. Validating against the symptom list in this entry is the fastest way to confirm the diagnosis.

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 an index passed to a pytorch `nn.embedding` layer exceeds the configured `num_embeddings` (e.g., passing token id 1000 to an embedding matrix of size 1000, where max valid is 999). because cuda executes asynchronously, the out-of-bounds kernel memory violation doesn't throw a python exception immediately. instead, the gpu's memory protection fault corrupts the cuda context, causing a subsequent operation to raise the generic 'illegal memory access' error. 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.

An index passed to a PyTorch `nn.Embedding` layer exceeds the configured `num_embeddings` (e.g., passing token ID 1000 to an embedding matrix of size 1000, where max valid is 999). Because CUDA executes asynchronously, the out-of-bounds kernel memory violation doesn't throw a Python exception immediately. Instead, the GPU's memory protection fault corrupts the CUDA context, causing a subsequent operation to raise the generic 'illegal memory access' error.

Quick answer

An index passed to a PyTorch `nn.

Symptom: RuntimeError: CUDA error: an illegal memory access was encountered
Root cause: An index passed to a PyTorch `nn.Embedding` layer exceeds the configured `num_embeddings` (e.g.
Recommended fix: Clamp indices or fix the data pipeline input_ids = torch.clamp(input_ids, min=0, max=vocab_size - 1) Ensures all token IDs are within the valid range of the embedding matrix, preventing out-of-bounds memory access.
How Denpex helps: Denpex investigates nn.Embedding Out of Bounds Illegal Memory Access using the evidence you provide or your connected workload collects. Earlier rank, host or application evidence is needed to distinguish an initiating failure from a downstream report.

Memory#Out of Bounds Access

What this failure is

nn.Embedding Out of Bounds Illegal Memory Access is a Memory failure seen during ML training runs. An index passed to a PyTorch `nn.Embedding` layer exceeds the configured `num_embeddings` (e.g., passing token ID 1000 to an embedding matrix of size 1000, where max valid is 999). Because CUDA executes asynchronously, the out-of-bounds kernel memory violation doesn't throw a Python exception immediately. Instead, the GPU's memory protection fault corrupts the CUDA context, causing a subsequent operation to raise the generic 'illegal memory access' error. Common tags: Out Of Bounds Access.

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Why it happens (the mechanism)

The stack trace almost never points to the embedding layer itself unless `CUDA_LAUNCH_BLOCKING=1` is used. Users typically waste hours debugging the layer where the crash surfaced, assuming a hardware failure or driver bug.

What you'll observe

RuntimeError: CUDA error: an illegal memory access was encountered
block: [0,0,0], thread: [32,0,0] Assertion `index >= -sizes[i] && index < sizes[i]` failed

Common symptoms and what they mean

Symptom	Why it happens
The training script crashes suddenly without warning, often pointing to an arbitrary line of code (like a loss function or forward pass of a different layer).	An index passed to a PyTorch `nn.Embedding` layer exceeds the configured `num_embeddings` (e.g., passing token ID 1000 to an embedding matrix of size 1000, where max valid is 999). Because CUDA executes asynchronously, the out-of-bounds kernel memory violation doesn't throw a Python exception immediately. Instead, the GPU's memory protection fault corrupts the CUDA context, causing a subsequent operation to raise the generic 'illegal memory access' error.
When running in a multi-GPU setup, it might trigger an NCCL timeout because one GPU goes down.	An index passed to a PyTorch `nn.Embedding` layer exceeds the configured `num_embeddings` (e.g., passing token ID 1000 to an embedding matrix of size 1000, where max valid is 999). Because CUDA executes asynchronously, the out-of-bounds kernel memory violation doesn't throw a Python exception immediately. Instead, the GPU's memory protection fault corrupts the CUDA context, causing a subsequent operation to raise the generic 'illegal memory access' error.
The exact stack trace points to operations downstream of the embedding layer due to asynchronous CUDA execution.	An index passed to a PyTorch `nn.Embedding` layer exceeds the configured `num_embeddings` (e.g., passing token ID 1000 to an embedding matrix of size 1000, where max valid is 999). Because CUDA executes asynchronously, the out-of-bounds kernel memory violation doesn't throw a Python exception immediately. Instead, the GPU's memory protection fault corrupts the CUDA context, causing a subsequent operation to raise the generic 'illegal memory access' error.

Which systems are affected

PyTorch
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.

✓Run the script with `CUDA_LAUNCH_BLOCKING=1 python train.py` to get the synchronous stack trace.
✓Run with `TORCH_USE_CUDA_DSA=1` to enable device-side assertions for better error messages.
✓Add a check `assert inputs.max() < embedding_layer.num_embeddings` right before the embedding lookup.

Root cause

An index passed to a PyTorch `nn.Embedding` layer exceeds the configured `num_embeddings` (e.g., passing token ID 1000 to an embedding matrix of size 1000, where max valid is 999). Because CUDA executes asynchronously, the out-of-bounds kernel memory violation doesn't throw a Python exception immediately. Instead, the GPU's memory protection fault corrupts the CUDA context, causing a subsequent operation to raise the generic 'illegal memory access' error.

The fix and how to prevent it

Searchable error signature

search key

RuntimeError: CUDA error: an illegal memory access was encountered

Use this text as a lookup key in logs and upstream issue trackers. It is not presented as a captured customer log. Confirm the cause from your own preceding events, versions, configuration and the cited references.

The fix and the prevention pattern

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Diagnose this failure in VS Code

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References

↗CUDA error: an illegal memory access was encountered #32095

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