Megatron Sequence Parallel requires Tensor Parallel size > 1

Quick answer

Megatron Sequence Parallel requires Tensor Parallel size > 1 means Sequence parallelism was enabled while tensor parallel size remained 1. Megatron partitions sequence work across the tensor-parallel group, so there is no group to partition across in this configuration. Preserve the first preceding error, then run the targeted control below.

Distributed Training#megatron#sequence#parallel#assertion#required

What this failure is

The literal signature is "Megatron Sequence Parallel requires Tensor Parallel size > 1". It is a distributed training failure associated with Megatron Core tensor and pipeline parallelism. The line identifies the failing operation or subsystem, while the surrounding evidence decides whether it is the initiating fault or a downstream symptom.

Live diagnosis, no signup

Is this what broke your run? Paste your log.

You're reading about Megatron Sequence Parallel requires Tensor Parallel size > 1. Paste your own crash log or traceback below and get the real root cause for YOUR run, not this generic entry. No account, no card. Logs are masked at ingress and never saved to account history.

training_logs.txt

No log to hand? Try one:

3 free diagnoses/day

Want 14 days of full access?

Request a free trial code for unlimited diagnoses, alerts, history, and follow-up questions. No credit card.

Request 14-day trial

Why it happens (the mechanism)

Sequence parallelism was enabled while tensor parallel size remained 1. Megatron partitions sequence work across the tensor-parallel group, so there is no group to partition across in this configuration. 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.

What you'll observe

The workload stops or loses forward progress after emitting "Megatron Sequence Parallel requires Tensor Parallel size > 1".
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.

Common symptoms and what they mean

Symptom	Why it happens
Megatron Sequence Parallel requires Tensor Parallel size > 1	Sequence parallelism was enabled while tensor parallel size remained 1. Megatron partitions sequence work across the tensor-parallel group, so there is no group to partition across in this configuration.
The same operation fails at a consistent stage of Megatron Core tensor and pipeline parallelism.	The decisive evidence is the first log line that precedes "Megatron Sequence Parallel requires Tensor Parallel size > 1" and differs from a healthy run.
The first related warning appears before the final exception and names the causal subsystem.	A nearby failure remains a competing hypothesis until a control separates configuration, capacity, transport, and hardware causes.
A known-good control changes one variable and either reproduces or clears the failure.	Sequence parallelism was enabled while tensor parallel size remained 1. Megatron partitions sequence work across the tensor-parallel group, so there is no group to partition across in this configuration.

Which systems are affected

Megatron Core tensor and pipeline parallelism
production-shaped multi-accelerator workloads
containerized and bare-metal deployments of the same stack

How to confirm this is the problem

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 "Megatron Sequence Parallel requires Tensor Parallel size > 1" and preserve at least 100 lines before it.
✓Identify which rank, node, device, or process emitted the first related warning.
✓print the resolved Megatron arguments on every rank. Launcher defaults or a stale checkpoint configuration can restore sequence_parallel after a command-line change.
✓Repeat the same input after the targeted change and require the signature to disappear.
✓Resume from job start after correcting parallelism settings only after the control passes.

Example training logs (fingerprint)

training.log (synthetic fingerprint)

Megatron Sequence Parallel requires Tensor Parallel size > 1

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.

The fix and the prevention pattern

The root cause is on this page and stays free. A free account adds the exact remediation steps, keeps your diagnoses instead of discarding them, and unlocks the fix on every entry in the encyclopedia.

No credit card · 3 free diagnoses · Instant access

Why the recommended fix works

either set tensor-model-parallel-size to at least 2 or disable sequence parallelism. This changes the condition in the causal diagnosis instead of hiding the outer exception. The repeated control proves ownership before recovery from job start after correcting parallelism settings.

Code examples

snippet

# Preserve evidence before restarting
rg -n -i 'error|exception|timeout|failed' <log-file>
nvidia-smi
python -m torch.utils.collect_env

# Find the exact signature in the complete log
rg -n -F -- "Megatron Sequence Parallel requires Tensor Parallel size > 1" <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.

Best practices by model family

Model / Stack	Recommendation	Notes
First response	Preserve the first failure	Keep the context before "Megatron Sequence Parallel requires Tensor Parallel size > 1" so aggregation does not erase causality.
Confirmation	Change one variable	Use a known-good node, rank, input, or configuration as the control.
Recovery	Resume from job start after correcting parallelism settings	Resume only after the literal signature no longer appears in the same control.

With the fix vs without the fix

Dimension	With the fix	Without the fix
Evidence	First preceding error and one controlled comparison	Only the final aggregated exception
Fix	either set tensor-model-parallel-size to at least 2 or disable sequence parallelism.	Retrying the unchanged workload
Exit criterion	"Megatron Sequence Parallel requires Tensor Parallel size > 1" is absent in the repeated control	The job happened to run once

Real engineering notes

“Treat "Megatron Sequence Parallel requires Tensor Parallel size > 1" as a search key and an investigation checkpoint, not as proof of every cause associated with the phrase. The high-value evidence is what changed immediately before it and whether the failure follows the workload, node, or configuration.”

Visual fingerprint

Decision path for Megatron Sequence Parallel requires Tensor Parallel size > 1

literal error captured
        |
        v
find first preceding failure
        |
        v
run one known-good control
        |
        +-- follows workload --> inspect input or configuration
        +-- follows node ------> inspect hardware or platform
        +-- disappears --------> validate the targeted fix

The control separates workload, configuration, and node ownership before recovery from job start after correcting parallelism settings.

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.

Install the free VS Code extension

Related failures to investigate next

Root cause

Sequence parallelism was enabled while tensor parallel size remained 1. Megatron partitions sequence work across the tensor-parallel group, so there is no group to partition across in this configuration.
The decisive evidence is the first log line that precedes "Megatron Sequence Parallel requires Tensor Parallel size > 1" and differs from a healthy run.
A nearby failure remains a competing hypothesis until a control separates configuration, capacity, transport, and hardware causes.

The fix and how to prevent it

Unlock the full remediation runbook

14 days on the Scale plan, up to 50 diagnoses a day. Step-by-step remediation, the RMA evidence payload, and multi-node correlation on your own logs. No card, and it does not roll into a subscription.

Frequently asked questions

Twelve targeted questions that engineers and on-call staff most commonly ask about this failure.

What does "Megatron Sequence Parallel requires Tensor Parallel size > 1" mean?

Is this line always the root cause?

No. It can be the direct failure or the point where an earlier failure becomes visible. The first preceding error and a controlled comparison decide which.

What should I collect before restarting?

Collect complete log context, the emitting rank or node, component versions, resolved configuration, and the diagnostic output shown above.

What is the fastest confirmation?

print the resolved Megatron arguments on every rank. Launcher defaults or a stale checkpoint configuration can restore sequence_parallel after a command-line change.

How do I prevent it from recurring?

validate sequence_parallel implies tensor_model_parallel_size > 1 before process-group initialization, so the job fails before allocating model memory.

References

Don't just read the fix, diagnose your run

The encyclopedia tells you what went wrong. Denpex tells you what went wrong in YOUR training run. With your logs, your config, and your stack.

Diagnose your logs free Browse all failures

Related Distributed Training errors

NCCL Broadcast Hang

Distributed Training · high

DDP Hang at Epoch Boundary

Distributed Training · high

DDP Rank Stuck During Training

Distributed Training · high

FSDP Flat Parameter Error

Distributed Training · high