CodePLAN

Installation

Please follow the requirements.txt file to install the relevant dependencies or run:

pip install -r requirements.txt

Since our method modifies the transformers of huggingface, please make sure to install the same transformers as ours (we use transformers version 4.26.1):

cd transformers
pip install -e .

Datasets

We used two datasets to fine-tune our model:

APPS: You can download it here.
MBPP: This dataset can be found here.

Generating Plans from LLM

We use openai's gpt-3.5-turbo to generate the solution plans data for training.You can get it by running generate_plans_from_LLM\generate_apps_plan.py:

python generate_apps_plan.py --test_path ../data/apps/train --save_path ../data/apps/train --start 0 --end 5000

Finetuning with solution plans

Using CodeT5 as an example, you can run train_codet5.py to finetune the CodeT5 with solution plans:

python train_codet5.py --model codet5-large-ntp-py --save_dir ./models -- train_path ./data/appps/train --tuning_mode plan --clone_pl_head True --epochs 10

Generating codes with finetuned model

You can run generate_codet5.py to generate codes:

python generate_codet5.py --test_path data/apps/test -- output_path ./outputs/codes --model_path ./model --plan_head True --is_plan False --temperature

Generating solution plans with finetuned model

You can run generate_codet5_plan.py to generate solution plans:

python generate_codet5_plan.py --test_path ./data/apps/test --output_path ./outputs/plans --model_path ./model --plan_head True --is_plan True --temperature 0.6

Generating codes with solution plan

You can run 'generate_code_with_plan.py` to generate code with generated solution plan as prompt:

python generate_code_with_plan.py --test_path ./data/apps/test --output_path ./outputs/codes --model_path ./model --plan_head True --is_plan False

Evaluate generated codes

You can run test_one_solution.sh to evaluate generated codes:

bash test_one_solution.sh

sssszh/CodePLAN