fsdp_checkpoint_corruptionTier 1 · 70% confidence
infrastructure-fsdp-checkpoint-corr-using-summon-full-params-for-inference-during-fsdp-fbbbc3aa
agent: infrastructure
When does this happen?
IF Using `summon_full_params` for inference during FSDP training causes checkpoint weights to diverge from the trained model, resulting in incorrect model after loading.
How others solved it
THEN Avoid calling `model.generate()` inside `fsdp.FullyShardedDataParallel.summon_full_params(model)` during training callbacks. Instead, perform inference on a separate copy of the model or use DDP if checkpoint integrity is required. If FSDP is necessary, consider using `writeback=False` and verify checkpoint correctness, or move inference to a separate process.
# Wrong: corrupts checkpoint
with fsdp.FullyShardedDataParallel.summon_full_params(model):
outputs = model.generate(...)
# Correct: use a separate model or do inference outside training checkpoint scopeRelated patterns
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