FSDP Failed to allocate unpartitioned full weights during forward pre-hook

FSDP ran out of memory gathering a layer's full weights for the forward pass. Sharding reduces STEADY-STATE memory, but each layer is briefly unsharded to compute, so the peak is set by the largest single layer, not by the sharded average. This entry explains how to confirm the cause, apply the fix, and separate it from adjacent pytorch-fsdp-ddp failures.

Quick answer

FSDP Failed to allocate unpartitioned full weights during forward pre-hook means FSDP ran out of memory gathering a layer's full weights for the forward pass. Sharding reduces STEADY-STATE memory, but each layer is briefly unsharded to compute, so the peak is set by the largest single layer, not by the sharded average. Preserve the first preceding error, then run the targeted control below.

Symptom: FSDP Failed to allocate unpartitioned full weights during forward pre-hook
Root cause: FSDP ran out of memory gathering a layer's full weights for the forward pass. Sharding reduces STEADY-STATE memory, but each layer is briefly unsharded to compute, so the peak is set by the largest single layer, not by the sharded average. The decisive evidence is the first log line that precedes "FSDP Failed to allocate unpartitioned full weights during forward pre-hook" and differs from a healthy run.
Recommended fix: reduce the unshard peak rather than the model size. Enable CPU offload for parameters, or wrap at a finer granularity so fewer parameters are gathered at once.
How Denpex helps: Denpex investigates FSDP Failed to allocate unpartitioned full weights during forward pre-hook 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.

Distributed Training#pytorch-fsdp-ddp#fsdp#unshard#forward#oom

What this failure is

The literal signature is "FSDP Failed to allocate unpartitioned full weights during forward pre-hook". It is a distributed training failure associated with PyTorch Distributed, FSDP, DDP, and TorchElastic. The line identifies the failing operation or subsystem, while the surrounding evidence decides whether it is the initiating fault or a downstream symptom.

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Why it happens (the mechanism)

FSDP ran out of memory gathering a layer's full weights for the forward pass. Sharding reduces STEADY-STATE memory, but each layer is briefly unsharded to compute, so the peak is set by the largest single layer, not by the sharded average. 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 "FSDP Failed to allocate unpartitioned full weights during forward pre-hook".
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
FSDP Failed to allocate unpartitioned full weights during forward pre-hook	FSDP ran out of memory gathering a layer's full weights for the forward pass. Sharding reduces STEADY-STATE memory, but each layer is briefly unsharded to compute, so the peak is set by the largest single layer, not by the sharded average.
The same operation fails at a consistent stage of PyTorch Distributed, FSDP, DDP, and TorchElastic.	The decisive evidence is the first log line that precedes "FSDP Failed to allocate unpartitioned full weights during forward pre-hook" 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.	FSDP ran out of memory gathering a layer's full weights for the forward pass. Sharding reduces STEADY-STATE memory, but each layer is briefly unsharded to compute, so the peak is set by the largest single layer, not by the sharded average.

Which systems are affected

PyTorch Distributed, FSDP, DDP, and TorchElastic
production-shaped multi-accelerator workloads
containerized and bare-metal deployments of the same stack

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.

✓Find the first occurrence of "FSDP Failed to allocate unpartitioned full weights during forward pre-hook" and preserve at least 100 lines before it.
✓Identify which rank, node, device, or process emitted the first related warning.
✓the message names the layer. Look at its parameter count, a single fat embedding or MLP projection is usually the whole peak. torch.cuda.memory_summary() right before the failure shows how much headroom was actually left.
✓Repeat the same input after the targeted change and require the signature to disappear.
✓Resume from latest checkpoint only after the control passes.

Root cause

FSDP ran out of memory gathering a layer's full weights for the forward pass. Sharding reduces STEADY-STATE memory, but each layer is briefly unsharded to compute, so the peak is set by the largest single layer, not by the sharded average.
The decisive evidence is the first log line that precedes "FSDP Failed to allocate unpartitioned full weights during forward pre-hook" 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

Searchable error signature

search key

FSDP Failed to allocate unpartitioned full weights during forward pre-hook

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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Why the recommended fix works

reduce the unshard peak rather than the model size. Enable CPU offload for parameters, or wrap at a finer granularity so fewer parameters are gathered at once. This changes the condition in the causal diagnosis instead of hiding the outer exception. The repeated control proves ownership before recovery from latest checkpoint.

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 -- "FSDP Failed to allocate unpartitioned full weights during forward pre-hook" <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 "FSDP Failed to allocate unpartitioned full weights during forward pre-hook" 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 latest checkpoint	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	reduce the unshard peak rather than the model size. Enable CPU offload for parameters, or wrap at a finer granularity so fewer parameters are gathered at once.	Retrying the unchanged workload
Exit criterion	"FSDP Failed to allocate unpartitioned full weights during forward pre-hook" is absent in the repeated control	The job happened to run once

Diagnostic note

“Treat "FSDP Failed to allocate unpartitioned full weights during forward pre-hook" 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 FSDP Failed to allocate unpartitioned full weights during forward pre-hook

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 latest checkpoint.

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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Related failures to investigate next

Frequently asked questions

Questions engineers and on-call staff commonly ask about this failure.

What does "FSDP Failed to allocate unpartitioned full weights during forward pre-hook" 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?

the message names the layer. Look at its parameter count, a single fat embedding or MLP projection is usually the whole peak. torch.cuda.memory_summary() right before the failure shows how much headroom was actually left.

How do I prevent it from recurring?

set an explicit auto_wrap_policy (transformer_auto_wrap_policy on the block class) instead of wrapping the whole model as one unit, an unwrapped model gathers everything simultaneously and defeats sharding at exactly this moment.

References

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