sudden gradient norm spike without learning rate change transient DRAM parity error

The gradient norm jumped by orders of magnitude with no change to the schedule. Most such spikes are a data or numerical problem, but a spike with no corresponding loss anomaly, on one rank only, is one of the few observable signatures of silent data corruption. This entry explains how to confirm the cause, apply the fix, and separate it from adjacent sdc failures.

The literal signature is "sudden gradient norm spike without learning rate change transient DRAM parity error". It is a data integrity failure associated with silent data corruption and redundant computation checks. The line identifies the failing operation or subsystem, while the surrounding evidence decides whether it is the initiating fault or a downstream symptom.

The gradient norm jumped by orders of magnitude with no change to the schedule. Most such spikes are a data or numerical problem, but a spike with no corresponding loss anomaly, on one rank only, is one of the few observable signatures of silent data corruption. The failure becomes visible at this call site because the operation first requires the missing resource, valid state, healthy peer, or correct result. Earlier log lines and a known-good control carry more causal value than the final wrapper exception.

• The workload stops or loses forward progress after emitting "sudden gradient norm spike without learning rate change transient DRAM parity error".
• A retry on the same configuration reproduces the failure because the causal state has not changed.
• The outer framework exception can hide the rank, node, allocation, or dependency that failed first.
• Increasing timeouts or reducing workload size can suppress the symptom without correcting the cause.

• silent data corruption and redundant computation checks
• production-shaped multi-accelerator workloads
• containerized and bare-metal deployments of the same stack

Apply the following checklist to a small reproduction: each box below is a positive signal that you are looking at this exact failure rather than a sibling in the same taxonomy.

• ✓Find the first occurrence of "sudden gradient norm spike without learning rate change transient DRAM parity error" and preserve at least 100 lines before it.
• ✓Identify which rank, node, device, or process emitted the first related warning.
• ✓correlate the step timestamp against that node's ECC counters (nvidia-smi -q -d ECC) and dmesg Xid events. A correctable-error ramp at the same moment is strong evidence of hardware. Re-run the exact batch on a different node, reproducing means data, not reproducing means the node.
• ✓Repeat the same input after the targeted change and require the signature to disappear.
• ✓Resume from checkpoint predating the spike only after the control passes.

sudden gradient norm spike without learning rate change transient DRAM parity error

Timestamps and exact values vary across runs, but the pattern. An info-level start, an early WARN, an ERROR carrying the symptom. Is the actual fingerprint you should alert on. The Denpex platform flags this combination automatically.

check whether it is ONE rank or all of them. All ranks means data or optimizer; a single rank means that node, and it should be drained before it poisons the next checkpoint. This changes the condition in the causal diagnosis instead of hiding the outer exception. The repeated control proves ownership before recovery from checkpoint predating the spike.

# Preserve evidence before restarting
dmesg -T | grep -iE 'NVRM|Xid|AER|NVLink|ECC'
nvidia-smi -q
dcgmi diag -r 3

# Find the exact signature in the complete log
rg -n -F -- "sudden gradient norm spike without learning rate change transient DRAM parity error" <log-file>

Adapt the snippet to your framework. The same pattern holds for PyTorch Lightning, Hugging Face Trainer, DeepSpeed, Megatron-LM, and vLLM training wrappers. Where the wrapper exposes a config flag (for examplelr_scheduler_type in Trainer), prefer the flag over the imperative API to keep the schedule declarative and reproducible.

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.

• The gradient norm jumped by orders of magnitude with no change to the schedule. Most such spikes are a data or numerical problem, but a spike with no corresponding loss anomaly, on one rank only, is one of the few observable signatures of silent data corruption.
• The decisive evidence is the first log line that precedes "sudden gradient norm spike without learning rate change transient DRAM parity error" and differs from a healthy run.
• A nearby failure remains a competing hypothesis until a control separates configuration, capacity, transport, and hardware causes.