Implement saving FSDP with LoRA#295
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| buffer_dtype=torch.bfloat16, | ||
| ), | ||
| backward_prefetch=BackwardPrefetch.BACKWARD_PRE, | ||
| backward_prefetch=BackwardPrefetch.BACKWARD_POST, |
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what is the impact of making this change for non-lora usage?
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This is a performance/memory tradeoff. We should have it be configurable if possible, but I can limit it to only be this option when LoRA is used.
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Backward prefetch vs. postfetch shouldn't be impacting correctness of LoRA, but not using prefetch could hurt default training times. I thing prefetch should be the default for non-lora cases.
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+1 James, I'll create a follow-up issue to have this as a configurable setting.
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| # ############### Read-only parameters ############### | ||
| MODEL_NAME="instructlab/granite-7b-lab" | ||
| MODEL_NAME="/home/ec2-user/.cache/huggingface/hub/models--instructlab--granite-7b-lab/snapshots/4fb6a018d68ab813b95c7f470e424a70f2f7e561" |
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won't always be on ec2
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LoRA models when training with FSDP as the distributed backend. This is accomplished by creating a copy of the LoRA model on the CPU, loading in the state dict after gathering it from the distributed model, and saving after merging the adapters back into the original model. Afterwards, the CPU copy is discarded and training continues. Signed-off-by: Oleg S <97077423+RobotSail@users.noreply.github.com>
This commit adds a smoketest for testing LoRA + FSDP. Signed-off-by: Oleg S <97077423+RobotSail@users.noreply.github.com>
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LGTM, thanks for adding the test!
Additionally introuce a max_seq_len parameter to support testing on lower-end hardware. Signed-off-by: Oleg S <97077423+RobotSail@users.noreply.github.com>
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Currently we cannot save LoRA models with FSDP, this PR addresses this limitation by instantiating a copy of the model on CPU, loading in the LoRA settings, loading the state dict after it has been gathered, and finally performing the same save as we do elsewhere throughout the codebase.
Resolves #241