Fix DDP "marked ready twice" for VLMs with CPU offload + TiledMLP

[nepfaff]

Improves the existing DDP compatibility block (from PR #3751) with two targeted fixes that preserve Unsloth's memory optimizations:

1. Non-reentrant checkpointing with CPU activation offloading via saved_tensors_hooks. PR Fix VLM + DDP checkpointing #3751 switched to non-reentrant but dropped CPU offloading entirely.

2. DDP-safe TiledMLP backward: uses functional torch.autograd.grad() for all-but-last sequence chunk (no DDP hooks fired), then .backward() for the final chunk (fires hooks exactly once).

Both fixes are gated behind is_distributed(), so single-GPU training is completely unaffected.

Tested on Qwen3-VL-4B + LoRA with 8x L40S GPUs. These changes successfully enabled multi-GPU training.

[gemini-code-assist]

Summary of Changes

Hello @nepfaff, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request resolves critical DDP compatibility issues for Vision Language Models (VLMs) that utilize CPU offloading and TiledMLP. The changes ensure that Unsloth's memory optimizations are maintained while enabling stable multi-GPU training by preventing the 'marked ready twice' error. These fixes are specifically applied when is_distributed() is true, ensuring no impact on single-GPU training setups.

Highlights

• Enhanced DDP Checkpointing with CPU Offload: Implemented non-reentrant checkpointing that leverages saved_tensors_hooks to offload activations to CPU, preserving memory optimizations while ensuring compatibility with Distributed Data Parallel (DDP) training.
• DDP-Safe TiledMLP Backward Pass: Patched the TiledMLP backward pass to correctly handle gradient accumulation in DDP. It now uses torch.autograd.grad() for all but the final sequence chunk and torch.autograd.backward() only for the last chunk, preventing multiple DDP hook firings.

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Changelog

• unsloth/models/vision.py
  • Modified the DDP compatibility block to incorporate CPU activation offloading for non-reentrant gradient checkpointing.
  • Introduced a new _ddp_safe_backward method for TiledMLP to manage gradient computation in a DDP-compatible manner, using torch.autograd.grad() and torch.autograd.backward() strategically.

Activity

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[gemini-code-assist]

Code Review

The pull request introduces two critical fixes for Distributed Data Parallel (DDP) compatibility in Vision-Language Models (VLMs). First, it implements non-reentrant gradient checkpointing with CPU activation offloading using saved_tensors_hooks. This restores memory efficiency that was previously lost when switching to non-reentrant checkpointing for DDP safety. Second, it patches the TiledMLP backward pass to be DDP-safe by using functional torch.autograd.grad for all but the last sequence chunk, ensuring that DDP hooks (which trigger gradient reduction) are only fired once during the final .backward() call. These changes are correctly gated behind is_distributed() to avoid impacting single-GPU performance.

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[mmathew23]

Hi @nepfaff thanks for the PR. We've been working on a non reentrant checkpointer which should perform similarly to the reentrant version. I haven't yet started the TiledMLP portion yet. It would be cool to collab on that piece if you're interested. That would be a change to unsloth-zoo/tiled_mlp.py and ultimately there would need to be some coordination to match checkpointing and tiled mlp reentrant approaches.

[nepfaff]

That sounds exciting! I'm unsure about how much time I will have, but I'd be happy to help out.

[mmathew23]

@nepfaff Are you on our discord? If you could ping me there I'm doublemathew and we can discuss further.

[Datta0]

Any updates on this? :)

[mmathew23]

@Datta0 haven't heard from him on Discord yet, but I'm actively working on a feature for use_reentrant=False with cpu offload so that we can run it with DDP. TiledMLP support will be a future release.

[nepfaff]

Hi @mmathew23, sorry for not joining Discord yet. I had some priorities and resources shift around. I'm no longer going the unsloth path and thus would struggle to help without having the framework running for my training jobs.