Is this a hardware or a software issue?

Synchronization 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 synchronization 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 #Shape Mismatch. 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 Shape Mismatch is upgraded. Pin compatible versions and rerun the prevention smoke test on every dependency change.

Is there a fingerprint I can monitor?

Yes. The script hangs during an explicit dist.all_gather() call. 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.

dist.all_gather Timeout from Dynamic 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 dist.all_gather requires every participating rank to pass a tensor of the exact same dimensions. if rank 0 detects 5 bounding boxes (shape [5, 4]) and rank 1 detects 3 bounding boxes (shape [3, 4]), the underlying nccl backend gets mismatched buffer sizes. rather than throwing an immediate shape error, nccl hangs indefinitely, waiting for matching sizes.. 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 dist.all_gather requires every participating rank to pass a tensor of the exact same dimensions. if rank 0 detects 5 bounding boxes (shape [5, 4]) and rank 1 detects 3 bounding boxes (shape [3, 4]), the underlying nccl backend gets mismatched buffer sizes. rather than throwing an immediate shape error, nccl hangs indefinitely, waiting for matching sizes. 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.

dist.all_gather requires every participating rank to pass a tensor of the exact same dimensions. If Rank 0 detects 5 bounding boxes (shape [5, 4]) and Rank 1 detects 3 bounding boxes (shape [3, 4]), the underlying NCCL backend gets mismatched buffer sizes. Rather than throwing an immediate shape error, NCCL hangs indefinitely, waiting for matching sizes.

Quick answer

dist.

Symptom: RuntimeError: NCCL error in: ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp
Root cause: dist.all_gather requires every participating rank to pass a tensor of the exact same dimensions. If Rank 0 detects 5 bounding boxes (shape [5, 4]) and Rank 1 detects 3 bounding boxes (shape [3, 4]), the underlying NCCL backend gets mismatched buffer sizes.
Recommended fix: Pad tensors to a common maximum shape before gathering. max_len = max(gathered_lengths); padded_tensor = F.pad(local_tensor, (0, 0, 0, max_len - local_tensor.size(0))) Ensures all tensors passed to NCCL have identical shapes, satisfying the backend requirements.
How Denpex helps: Denpex matches dist.all_gather Timeout from Dynamic 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.

Synchronization#Shape Mismatch

What this failure is

dist.all_gather Timeout from Dynamic Tensor Shapes is a Synchronization failure seen during ML training runs. dist.all_gather requires every participating rank to pass a tensor of the exact same dimensions. If Rank 0 detects 5 bounding boxes (shape [5, 4]) and Rank 1 detects 3 bounding boxes (shape [3, 4]), the underlying NCCL backend gets mismatched buffer sizes. Rather than throwing an immediate shape error, NCCL hangs indefinitely, waiting for matching sizes. Common tags: Shape Mismatch.

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Why it happens (the mechanism)

Instead of raising a ValueError for mismatched tensor dimensions, the underlying C++ backend blocks. The developer assumes a networking timeout, not a data dimension bug.

What you'll observe

RuntimeError: NCCL error in: ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp
Hang at dist.all_gather(tensor_list, local_tensor)

Common symptoms and what they mean

Symptom	Why it happens
The script hangs during an explicit dist.all_gather() call.	dist.all_gather requires every participating rank to pass a tensor of the exact same dimensions. If Rank 0 detects 5 bounding boxes (shape [5, 4]) and Rank 1 detects 3 bounding boxes (shape [3, 4]), the underlying NCCL backend gets mismatched buffer sizes. Rather than throwing an immediate shape error, NCCL hangs indefinitely, waiting for matching sizes.
Usually happens when gathering outputs like variable-length sequences or dynamically detected bounding boxes.	dist.all_gather requires every participating rank to pass a tensor of the exact same dimensions. If Rank 0 detects 5 bounding boxes (shape [5, 4]) and Rank 1 detects 3 bounding boxes (shape [3, 4]), the underlying NCCL backend gets mismatched buffer sizes. Rather than throwing an immediate shape error, NCCL hangs indefinitely, waiting for matching sizes.

Which systems are affected

PyTorch
NCCL

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.

✓Print tensor.shape before the all_gather call on every rank.
✓Enable TORCH_DISTRIBUTED_DEBUG=DETAIL.

Searchable error signature

search key

RuntimeError: NCCL error in: ../torch/csrc/distributed/c10d/ProcessGroupNCCL.cpp

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

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

dist.all_gather requires every participating rank to pass a tensor of the exact same dimensions. If Rank 0 detects 5 bounding boxes (shape [5, 4]) and Rank 1 detects 3 bounding boxes (shape [3, 4]), the underlying NCCL backend gets mismatched buffer sizes. Rather than throwing an immediate shape error, NCCL hangs indefinitely, waiting for matching sizes.

The fix and how to prevent it

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References

↗Handling varying sequence lengths in DDP

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Related Synchronization errors

DDP Hang Due to Unused Parameters in Forward Pass

Synchronization · high

DDP Hang from Uneven Dataset Sizes Across Ranks

Synchronization · critical

SyncBatchNorm Deadlock During Single-Rank Evaluation

Synchronization · high