deepspeedai / deepspeedai/DeepSpeed
How to finetune certain portion of the whole parameter
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- Python
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Description
I have to add some LoRA layers by hand(without left) to a pre-trained Multi-modal model, to finetune the model for new data. I want Deepspeed to optimize ONLY the parameters from the LoRA layer rather than all the parameters. Like this
The platform is hugging face's transformers and Deepspeed.
Therefore I decorate the Trainer from HF's transformers, as below:
Unfortunately, it doesn't work, both LoRA and non-LoRa's weights are not changed during training. It seems that the optimizer in Deepspeed is not the same as that from Pytorch.
My question is, are there any ways that allow me to ONLY finetune certain subnet (LoRA) parameters with Deepspeed+Transformer's Trainer?
Contributor guide
First steps
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Research direction
No repository file or test is named. Start by tracing the Hugging Face Trainer and DeepSpeed optimizer integration, then verify how parameter groups and trainable parameters are passed through it. Done means a supported configuration updates only the manually added LoRA parameters while leaving the other model weights unchanged.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, pytorch
- Domain
- distributed-systems, machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100