Is this a hardware or a software issue?

Precision 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 precision 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 #Mixed Precision (AMP). 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 Mixed Precision (AMP) is upgraded. Pin compatible versions and rerun the prevention smoke test on every dependency change.

Is there a fingerprint I can monitor?

Yes. Model trains normally for a few steps and then loss suddenly jumps to NaN. 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.

Missing GradScaler Update in AMP Loop

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 when using `torch.cuda.amp.autocast()`, gradients are computed in float16. the dynamic range of float16 is narrow, causing gradients to overflow to infinity. if a `gradscaler` is not used, or if `scaler.update()` is omitted, the scale factor isn't adjusted to prevent these overflows, leading to nan gradients and subsequently nan loss.. 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 when using `torch.cuda.amp.autocast()`, gradients are computed in float16. the dynamic range of float16 is narrow, causing gradients to overflow to infinity. if a `gradscaler` is not used, or if `scaler.update()` is omitted, the scale factor isn't adjusted to prevent these overflows, leading to nan gradients and subsequently nan loss. 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.

When using `torch.cuda.amp.autocast()`, gradients are computed in float16. The dynamic range of float16 is narrow, causing gradients to overflow to infinity. If a `GradScaler` is not used, or if `scaler.update()` is omitted, the scale factor isn't adjusted to prevent these overflows, leading to NaN gradients and subsequently NaN loss.

Quick answer

When using `torch.

Symptom: RuntimeError: Function 'LogSoftmaxBackward' returned nan values in its 0th output
Root cause: When using `torch.cuda.amp.
Recommended fix: Wrap optimizer step and loss backward with GradScaler scaler = torch.cuda.amp.GradScaler() scaler.scale(loss).backward() scaler.step(optimizer) scaler.update() GradScaler multiplies the loss by a scale factor to prevent float16 underflow/overflow, then un-scales gradients before applying updates.
How Denpex helps: Denpex investigates Missing GradScaler Update in AMP Loop 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.

Precision#Mixed Precision (AMP)

What this failure is

Missing GradScaler Update in AMP Loop is a Precision failure seen during ML training runs. When using `torch.cuda.amp.autocast()`, gradients are computed in float16. The dynamic range of float16 is narrow, causing gradients to overflow to infinity. If a `GradScaler` is not used, or if `scaler.update()` is omitted, the scale factor isn't adjusted to prevent these overflows, leading to NaN gradients and subsequently NaN loss. Common tags: Mixed Precision (AMP).

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Why it happens (the mechanism)

The crash often happens randomly during training, making it look like a data issue or a learning rate explosion, when it's purely a numerical precision limitation of FP16.

What you'll observe

Loss goes to NaN
RuntimeError: Function 'LogSoftmaxBackward' returned nan values in its 0th output

Common symptoms and what they mean

Symptom	Why it happens
Model trains normally for a few steps and then loss suddenly jumps to NaN.	When using `torch.cuda.amp.autocast()`, gradients are computed in float16. The dynamic range of float16 is narrow, causing gradients to overflow to infinity. If a `GradScaler` is not used, or if `scaler.update()` is omitted, the scale factor isn't adjusted to prevent these overflows, leading to NaN gradients and subsequently NaN loss.
Weights become filled with NaN.	When using `torch.cuda.amp.autocast()`, gradients are computed in float16. The dynamic range of float16 is narrow, causing gradients to overflow to infinity. If a `GradScaler` is not used, or if `scaler.update()` is omitted, the scale factor isn't adjusted to prevent these overflows, leading to NaN gradients and subsequently NaN loss.

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.

✓Check if `torch.cuda.amp.autocast()` is used without `torch.cuda.amp.GradScaler()`.
✓Print `scaler.get_scale()` to see if it drops to 0 or explodes.

Root cause

When using `torch.cuda.amp.autocast()`, gradients are computed in float16. The dynamic range of float16 is narrow, causing gradients to overflow to infinity. If a `GradScaler` is not used, or if `scaler.update()` is omitted, the scale factor isn't adjusted to prevent these overflows, leading to NaN gradients and subsequently NaN loss.

The fix and how to prevent it

Searchable error signature

search key

RuntimeError: Function 'LogSoftmaxBackward' returned nan values in its 0th output

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

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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References

↗PyTorch AMP Documentation

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