Fix multi-rank distributed-launch races and redundant LoRA merge in f…

[sagarneeldubey]

Summary

vla-scripts/finetune.py has three multi-rank issues that I hit while fine-tuning OpenVLA with torchrun / accelerate launch on more than one GPU on a single node. All three are fixed this PR

1. snapshot_download() race. All ranks call it concurrently, which produces one HF Hub API hit per rank and races the rank-0-only update_auto_map() rewrite that happens immediately after.

2. from_pretrained(..., trust_remote_code=True) race. transformers copies the model's custom modules (processing_prismatic.py, modeling_prismatic.py, etc.) into a process-local transformers_modules/<commit>/ cache. With N ranks importing concurrently, one rank can observe a partial file write and raise:

AttributeError: module 'transformers_modules.<commit>.processing_prismatic' has no attribute 'PrismaticProcessor'

This reproduces fairly reliably at ≥4 ranks on a fresh HF cache.

3. Redundant LoRA merge in save_training_checkpoint(). Every rank calls AutoModelForVision2Seq.from_pretrained(...) + PeftModel.from_pretrained(...) + merge_and_unload() on the 7B base model, but only rank 0 writes the result. The other ranks build merged_vla and immediately drop it on the floor. On 8 ranks that's 8× redundant 7B load + merge per checkpoint.

Changes (single commit, single file)

• snapshot_download race: wrapped in distributed_state.main_process_first() so rank 0 populates the cache and runs update_auto_map before other ranks read it.
• from_pretrained race: wrapped both AutoProcessor.from_pretrained and AutoModelForVision2Seq.from_pretrained in the same main_process_first() block so rank 0 finishes the dynamic-modules copy before peers import.
• Redundant merge: moved from_pretrained + PeftModel.from_pretrained + merge_and_unload() inside the existing if distributed_state.is_main_process: branch in save_training_checkpoint. The preceding dist.barrier() already guarantees the adapter is on disk before this block runs.
• The trailing dist.barrier() after the merge is intentionally kept outside the is_main_process guard so all ranks still reach it and stay in sync. Putting it inside the guard would deadlock.
• Added comments at each call site explaining the failure mode so the next reader doesn't accidentally undo the guards.

Single-process / single-rank runs hit the same code paths but with main_process_first() as a no-op, so behavior is unchanged.

Test plan

Smoke-tested on a single AWS p4d.24xlarge (8× A100 40GB) with 8 ranks via torchrun --nproc_per_node=8, fine-tuning openvla/openvla-7b on aloha_scoop_x_into_bowl. The short run exercises:

• snapshot_download + update_auto_map on a cold HF cache,
• trust_remote_code dynamic-modules copy across all 8 ranks,
• at least one checkpoint save, exercising the LoRA merge path.

Verified on the patched version:

• All 8 ranks reach the training loop without the PrismaticProcessor AttributeError previously seen on main.
• Training step loss decreases as expected.
• Checkpoint directory contains the merged model + adapter; only rank 0 logs Saved merged model for Step ....
• No deadlock at the post-merge dist.barrier(); all ranks proceed to the next training step.

Logs / screenshots

smoke test: 8 ranks past model load

smoke test: checkpoint save

Risk

Low. All changes are process-ordering / guard adjustments in vla-scripts/finetune.py. No change to model architecture, optimizer, scheduler, dataset pipeline, training loop semantics, or saved-checkpoint format. Single-rank users see no behavior change.

Notes for reviewers

• Happy to split the redundant-merge change into a separate commit if you'd prefer two commits in this PR (one for the races, one for the merge cleanup). They're independent fixes; I bundled them because they're both small and touch only finetune.py.
• The main_process_first() pattern is already used in accelerate examples for exactly this kind of cache-population race, so I went with that rather than a hand-rolled dist.barrier()-based gate.