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FSDP All Gather Timeout

FSDP all-gather operations can timeout when parameters are large or network is slow, causing training to fail.

Quick answer

FSDP all-gather operations can timeout when parameters are large or network is slow, causing training to fail.

Memory#fsdp#all-gather#timeout#distributed#memory

What this failure is

FSDP All Gather Timeout is a Memory failure seen during ML training runs. FSDP all-gather operations can timeout when parameters are large or network is slow, causing training to fail. Common tags: Fsdp, All Gather, Timeout, Distributed.

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Why it happens (the mechanism)

Parameter size larger than expected. Slow network between nodes. FSDP bucket size too large. Mixed precision FSDP issues. Taken together, these mechanisms explain why the failure is reproducible, why it tends to surface on specific workloads or scales, and why generic mitigation attempts often fall short without addressing the underlying cause.

What you'll observe

  • FSDP training times out at all-gather
  • FSDP hangs during forward/backward
  • FSDP all-gather is slow

Common symptoms and what they mean

SymptomWhy it happens
FSDP forward hangParameter size larger than expected
FSDP backward hangSlow network between nodes
FSDP collective timeoutFSDP bucket size too large

Which systems are affected

  • Large model training with FSDP
  • PyTorch FSDP training
  • Memory-efficient distributed training

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.

  • Reproduce the failure from a clean checkpoint/seed: the symptom must appear without warm-up state from a previous run.
  • Verified signal present: FSDP forward hang
  • Verified signal present: FSDP backward hang
  • Verified signal present: FSDP collective timeout
  • A targeted fix from the "How to fix it" section eliminates or substantially reduces the symptom within one validation pass.

The fix and the prevention pattern

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Root cause

  • Parameter size larger than expected
  • Slow network between nodes
  • FSDP bucket size too large
  • Mixed precision FSDP issues

The fix and how to prevent it

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