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 nesting all parameters deep within child fsdp modules leaves the outer fsdp root empty. when activation checkpointing triggers recomputation, the lack of parameters in the root module desynchronizes the fsdp state machine during the backward hooks.. Validating against the symptom list in this entry is the fastest way to confirm the diagnosis.

Is this a hardware or a software issue?

Model 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 model 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 #fsdp, #user-report. 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 fsdp is upgraded. Pin compatible versions and rerun the prevention smoke test on every dependency change.

Is there a fingerprint I can monitor?

Yes. AssertionError: APIs are called in the incorrect state 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.

Root FSDP Activation Checkpointing Backward Assert

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 nesting all parameters deep within child fsdp modules leaves the outer fsdp root empty. when activation checkpointing triggers recomputation, the lack of parameters in the root module desynchronizes the fsdp state machine during the backward hooks. 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.

Nesting all parameters deep within child FSDP modules leaves the outer FSDP root empty. When activation checkpointing triggers recomputation, the lack of parameters in the root module desynchronizes the FSDP state machine during the backward hooks.

Quick answer

Nesting all parameters deep within child FSDP modules leaves the outer FSDP root empty.

Symptom: AssertionError: APIs are called in the incorrect state
Root cause: Nesting all parameters deep within child FSDP modules leaves the outer FSDP root empty. When activation checkpointing triggers recomputation, the lack of parameters in the root module desynchronizes the FSDP state machine during the backward hooks.
Recommended fix: Ensure the root FSDP wrapper contains parameters, or use FairScale's explicit wrapper. - from fairscale.nn.checkpoint import checkpoint_wrapper
How Denpex helps: Denpex investigates Root FSDP Activation Checkpointing Backward Assert 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.

Model#fsdp#user-report

What this failure is

Root FSDP Activation Checkpointing Backward Assert is a Model failure seen during ML training runs. Nesting all parameters deep within child FSDP modules leaves the outer FSDP root empty. When activation checkpointing triggers recomputation, the lack of parameters in the root module desynchronizes the FSDP state machine during the backward hooks. Common tags: Fsdp, User Report.

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Why it happens (the mechanism)

The traceback points deep into PyTorch's autograd engine or FSDP hook registration, leading engineers to suspect a bug in the PyTorch binary or an incompatible CUDA version.

What you'll observe

The forward pass completes successfully.
The application crashes immediately upon calling `loss.backward()`.
The error only surfaces when activation checkpointing is explicitly enabled alongside FSDP.

Common symptoms and what they mean

Symptom	Why it happens
AssertionError: APIs are called in the incorrect state	Nesting all parameters deep within child FSDP modules leaves the outer FSDP root empty. When activation checkpointing triggers recomputation, the lack of parameters in the root module desynchronizes the FSDP state machine during the backward hooks.
trigger an assert on the backward pass	Nesting all parameters deep within child FSDP modules leaves the outer FSDP root empty. When activation checkpointing triggers recomputation, the lack of parameters in the root module desynchronizes the FSDP state machine during the backward hooks.

Which systems are affected

PyTorch
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.

✓Inspect the FSDP wrapping policy (`auto_wrap_policy`).
✓Iterate through the model parameters and verify if `model.parameters()` at the root level yields any non-wrapped weights.

Root cause

Nesting all parameters deep within child FSDP modules leaves the outer FSDP root empty. When activation checkpointing triggers recomputation, the lack of parameters in the root module desynchronizes the FSDP state machine during the backward hooks.

The fix and how to prevent it

Searchable error signature

search key

AssertionError: APIs are called in the incorrect state

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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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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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.

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