salesforce / CodeGen

CodeGen is a family of open-source model for program synthesis. Trained on TPU-v4. Competitive with OpenAI Codex.
Apache License 2.0
4.92k stars 380 forks source link

How to fine tune Codegen? #16

Open smith-co opened 2 years ago

smith-co commented 2 years ago

I would like to fine tune the Codegen model. Can you provide any documentation in this regard?

shmuelhizmi commented 2 years ago

+1

enijkamp commented 2 years ago

The converted PyTorch models can be fine-tuned similarly to other causal LMs in HuggingFace.

See tutorials like http://reyfarhan.com/posts/easy-gpt2-finetuning-huggingface/.

TheodoreGalanos commented 2 years ago

Would you be releasing training code for the original models? Would be nice to try some on v3s (if possible).

thisisanshgupta commented 2 years ago

I think this script might help in finetuning:

https://colab.research.google.com/drive/13dZVYEOMhXhkXWfvSMVM1TTtUDrT6Aeh?usp=sharing#scrollTo=vCPohrZ-CTWu

enijkamp commented 2 years ago

@TheodoreGalanos Working on a release for the JAX coding. I trained the models on TPU-v4 and have to resolve a blocker for v3.

smith-co commented 2 years ago

@enijkamp @thisisanshgupta I am checking the link you have shared.

Still I think it would greatly help everyone if it is possible to provide fine tuning steps in the repo. 🙏

Ontopic commented 2 years ago

I for one would appreciate any code/directions needed to run things on a TPU-v4. Great work all!

enijkamp commented 2 years ago

@thisisanshgupta @Ontopic Yes, I'm working on the release of my training library for TPU-v3/v4 and will keep you posted.

tlkh commented 2 years ago

Hello @enijkamp thank you for your work. Looking forward to some fine-tuning instructions and code.

Currently, I have tried to fine-tune as if it is GPT-2, but I am running into issues where the model's quality degrades significantly.

Is there any particular way the data has to be structured for fine-tuning? Currently, I am just concatenating together the prompts and code as follows:

def xyz():
    """abc"""
    code()

def xyz():
    """abc"""
    code()
enijkamp commented 2 years ago

@smith-co @thisisanshgupta @tlkh

For torch, I wrote up a minimal example in deepspeed, which can train the 16B on a ~24 GB gpu. You would need to sanity test this, optimize the configuration, plug in the data loader, and save the weights to disk: https://github.com/salesforce/CodeGen/blob/main/jaxformer/hf/train_deepspeed.py

For jax, the training library in is undergoing sanity checks on TPU-v3 and should be released soon.

enijkamp commented 2 years ago

@smith-co @thisisanshgupta @tlkh @Ontopic @TheodoreGalanos @shmuelhizmi A first release of the training code for TPU-v3/v4 is here:

https://github.com/salesforce/jaxformer

zhangybuaa commented 1 year ago

@enijkamp I want to fine-tune the model with my own code data, how should I build the dataset. Are there any requirements for the format of the dataset, whether the data needs to be labeled and what format should it be labeled in. Can some guidance or examples be given, thanks!

calix commented 1 year ago

@smith-co @thisisanshgupta @tlkh

For torch, I wrote up a minimal example in deepspeed, which can train the 16B on a ~24 GB gpu. You would need to sanity test this, optimize the configuration, plug in the data loader, and save the weights to disk: https://github.com/salesforce/CodeGen/blob/main/jaxformer/hf/train_deepspeed.py

For jax, the training library in is undergoing sanity checks on TPU-v3 and should be released soon.

Besides the VRAM, how much RAM would be required to train the model?

glicerico commented 1 year ago

@enijkamp , or anyone who has used jaxformer to fine-tune on TPU-v4, what is the approximate cost?

enijkamp commented 1 year ago

@glicerico Roughly speaking, cost is a function of the size of the model and data. How much data do you have? Which model do you want to fine-tune?

glicerico commented 1 year ago

@enijkamp , trying to reproduce the work by Shin and Van Durme, who used a few hundred (sentence, parse) pairs to fine tune codex for semantic parsing. I would like to do this with CodeGen. Seeing your results, I would probably want to fine tune the 16GB model.

srnsrn120 commented 1 year ago

@enijkamp : I want to finetune mono model , Can you please share dataset format for python and details steps or notebook .

watreyoung commented 1 year ago

@glicerico Roughly speaking, cost is a function of the size of the model and data. How much data do you have? Which model do you want to fine-tune?

Is there any more easier code script template withouth deep-speed to fine-tune CodeGen(350M)? Plus: Is the data format same as other pre-trained model like CodeT5 or CodeBERT? Looking forward to the reply.