Is this a hardware or a software issue?

Software 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 software 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 #Collective API Desync. 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 Collective API Desync is upgraded. Pin compatible versions and rerun the prevention smoke test on every dependency change.

Is there a fingerprint I can monitor?

Yes. Training hangs randomly in the middle of an epoch. 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.

Watchdog Timeout from Inconsistent Tensor Shapes

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 ranks attempt to perform an all_gather or all_reduce on tensors that have different shapes across different ranks (e.g., dynamic sequence lengths in nlp). nccl expects the byte count of the transferred buffers to match perfectly. when they don't, the internal state machine of nccl gets deadlocked waiting for data that will never arrive.. 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 ranks attempt to perform an all_gather or all_reduce on tensors that have different shapes across different ranks (e.g., dynamic sequence lengths in nlp). nccl expects the byte count of the transferred buffers to match perfectly. when they don't, the internal state machine of nccl gets deadlocked waiting for data that will never arrive. 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.

Ranks attempt to perform an all_gather or all_reduce on tensors that have different shapes across different ranks (e.g., dynamic sequence lengths in NLP). NCCL expects the byte count of the transferred buffers to match perfectly. When they don't, the internal state machine of NCCL gets deadlocked waiting for data that will never arrive.

Quick answer

Ranks attempt to perform an all_gather or all_reduce on tensors that have different shapes across different ranks (e.

Symptom: NCCL WARN Cuda failure 'invalid argument'
Root cause: Ranks attempt to perform an all_gather or all_reduce on tensors that have different shapes across different ranks (e.g., dynamic sequence lengths in NLP).
Recommended fix: Pad tensors to a uniform size across all ranks. max_len = torch.tensor(local_tensor.shape[0], device=device)\ndist.all_reduce(max_len, op=dist.ReduceOp.MAX)\n# Pad local_tensor to max_len before collective Ensures the NCCL collective operation transfers the exact same number of bytes from every rank.
How Denpex helps: Denpex matches Watchdog Timeout from Inconsistent Tensor Shapes 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.

Software#Collective API Desync

What this failure is

Watchdog Timeout from Inconsistent Tensor Shapes is a Software failure seen during ML training runs. Ranks attempt to perform an all_gather or all_reduce on tensors that have different shapes across different ranks (e.g., dynamic sequence lengths in NLP). NCCL expects the byte count of the transferred buffers to match perfectly. When they don't, the internal state machine of NCCL gets deadlocked waiting for data that will never arrive. Common tags: Collective API Desync.

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Why it happens (the mechanism)

NCCL does not gracefully validate tensor shapes across ranks before initiating the transfer; it just hangs until the watchdog times out.

What you'll observe

Watchdog caught collective operation timeout
NCCL WARN Cuda failure 'invalid argument'

Common symptoms and what they mean

Symptom	Why it happens
Training hangs randomly in the middle of an epoch.	Ranks attempt to perform an all_gather or all_reduce on tensors that have different shapes across different ranks (e.g., dynamic sequence lengths in NLP). NCCL expects the byte count of the transferred buffers to match perfectly. When they don't, the internal state machine of NCCL gets deadlocked waiting for data that will never arrive.
Only happens with specific dynamic batch size configurations or sequence lengths.	Ranks attempt to perform an all_gather or all_reduce on tensors that have different shapes across different ranks (e.g., dynamic sequence lengths in NLP). NCCL expects the byte count of the transferred buffers to match perfectly. When they don't, the internal state machine of NCCL gets deadlocked waiting for data that will never arrive.

Which systems are affected

PyTorch
NCCL
FSDP

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.

✓Before the suspect collective operation, print tensor.shape on all ranks.
✓Check for dynamic batching or variable sequence lengths that might not be padded equally.

Searchable error signature

search key

NCCL WARN Cuda failure 'invalid argument'
Watchdog caught collective operation timeout

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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Root cause

Ranks attempt to perform an all_gather or all_reduce on tensors that have different shapes across different ranks (e.g., dynamic sequence lengths in NLP). NCCL expects the byte count of the transferred buffers to match perfectly. When they don't, the internal state machine of NCCL gets deadlocked waiting for data that will never arrive.

The fix and how to prevent it

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References

↗NCCL hangs when tensor sizes mismatch

Don't just read the fix, diagnose your run

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