CodeBERT

This repo provides the code for reproducing the experiments in CodeBERT: A Pre-Trained Model for Programming and Natural Languages.

Dependency

pip install torch==1.4.0
pip install transformers==2.5.0
pip install filelock more_itertools

Qiuck Tour

We use huggingface/transformers framework to train the model. You can use our model like the pre-trained Roberta base. Now, We give an example on how to load the model.

import torch
from transformers import RobertaTokenizer, RobertaConfig, RobertaModel

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = RobertaTokenizer.from_pretrained("microsoft/codebert-base")
model = RobertaModel.from_pretrained("microsoft/codebert-base")
model.to(device)

Code Search

Data Preprocess

Both training and validation datasets are created in a way that positive and negative samples are balanced. Negative samples consist of balanced number of instances with randomly replaced NL and PL.

We follow the official evaluation metric to calculate the Mean Reciprocal Rank (MRR) for each pair of test data (c, w) over a fixed set of 999 distractor codes.

You can use the following command to download the preprocessed training and validation dataset and preprocess the test dataset by yourself. The preprocessed testing dataset is very large, so only the preprocessing script is provided.

mkdir data data/codesearch
cd data/codesearch
gdown https://drive.google.com/uc?id=1xgSR34XO8xXZg4cZScDYj2eGerBE9iGo  
unzip codesearch_data.zip
rm  codesearch_data.zip
cd ../../codesearch
python process_data.py
cd ..

Fine-Tune

We fine-tuned the model on 2*P100 GPUs.

cd codesearch

lang=php #fine-tuning a language-specific model for each programming language 
pretrained_model=microsoft/codebert-base  #Roberta: roberta-base

python run_classifier.py \
--model_type roberta \
--task_name codesearch \
--do_train \
--do_eval \
--eval_all_checkpoints \
--train_file train.txt \
--dev_file valid.txt \
--max_seq_length 200 \
--per_gpu_train_batch_size 32 \
--per_gpu_eval_batch_size 32 \
--learning_rate 1e-5 \
--num_train_epochs 8 \
--gradient_accumulation_steps 1 \
--overwrite_output_dir \
--data_dir ../data/codesearch/train_valid/$lang \
--output_dir ./models/$lang  \
--model_name_or_path $pretrained_model

Inference and Evaluation

Inference

lang=php #programming language
idx=0 #test batch idx

python run_classifier.py \
--model_type roberta \
--model_name_or_path microsoft/codebert-base \
--task_name codesearch \
--do_predict \
--output_dir ../data/codesearch/test/$lang \
--data_dir ../data/codesearch/test/$lang \
--max_seq_length 200 \
--per_gpu_train_batch_size 32 \
--per_gpu_eval_batch_size 32 \
--learning_rate 1e-5 \
--num_train_epochs 8 \
--test_file batch_${idx}.txt \
--pred_model_dir ./models/$lang/checkpoint-best/ \
--test_result_dir ./results/$lang/${idx}_batch_result.txt

Evaluation

python mrr.py

Probing

As stated in the paper, CodeBERT is not suitable for mask prediction task, while CodeBERT (MLM) is suitable for mask prediction task.

We give an example on how to use CodeBERT(MLM) for mask prediction task.

from transformers import RobertaConfig, RobertaTokenizer, RobertaForMaskedLM, pipeline

model = RobertaForMaskedLM.from_pretrained("microsoft/codebert-base-mlm")
tokenizer = RobertaTokenizer.from_pretrained("microsoft/codebert-base-mlm")

CODE = "if (x is not None) <mask> (x>1)"
fill_mask = pipeline('fill-mask', model=model, tokenizer=tokenizer)

outputs = fill_mask(CODE)
print(outputs)

Results

'and', 'or', 'if', 'then', 'AND'

The detailed outputs are as follows:

{'sequence': '<s> if (x is not None) and (x>1)</s>', 'score': 0.6049249172210693, 'token': 8}
{'sequence': '<s> if (x is not None) or (x>1)</s>', 'score': 0.30680200457572937, 'token': 50}
{'sequence': '<s> if (x is not None) if (x>1)</s>', 'score': 0.02133703976869583, 'token': 114}
{'sequence': '<s> if (x is not None) then (x>1)</s>', 'score': 0.018607674166560173, 'token': 172}
{'sequence': '<s> if (x is not None) AND (x>1)</s>', 'score': 0.007619690150022507, 'token': 4248}

Code Documentation Generation

This repo provides the code for reproducing the experiments on CodeSearchNet dataset for code document generation tasks in six programming languages.

Dependency

pip install torch==1.4.0
pip install transformers==2.5.0
pip install filelock

Data Preprocess

We clean CodeSearchNet dataset for this task by following steps:

Remove comments in the code
Remove examples that codes cannot be parsed into an abstract syntax tree.
Remove examples that #tokens of documents is < 3 or >256
Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
Remove examples that documents are not English.

Data statistic about the cleaned dataset for code document generation is shown in this Table. We release the cleaned dataset in this website.

PL	Training	Dev	Test
Python	251,820	13,914	14,918
PHP	241,241	12,982	14,014
Go	167,288	7,325	8,122
Java	164,923	5,183	10,955
JavaScript	58,025	3,885	3,291
Ruby	24,927	1,400	1,261

Data Download

You can download dataset from the website. Or use the following command.

pip install gdown
mkdir data data/code2nl
cd data/code2nl
gdown https://drive.google.com/uc?id=1rd2Tc6oUWBo7JouwexW3ksQ0PaOhUr6h
unzip Cleaned_CodeSearchNet.zip
rm Cleaned_CodeSearchNet.zip
cd ../..

Fine-Tune

Download pre-trained CodeBERT model from google drive. We fine-tuned the model on 4*P40 GPUs.

cd code2nl

lang=php #programming language
lr=5e-5
batch_size=64
beam_size=10
source_length=256
target_length=128
data_dir=../data/code2nl/CodeSearchNet
output_dir=model/$lang
train_file=$data_dir/$lang/train.jsonl
dev_file=$data_dir/$lang/valid.jsonl
eval_steps=1000 #400 for ruby, 600 for javascript, 1000 for others
train_steps=50000 #20000 for ruby, 30000 for javascript, 50000 for others
pretrained_model=microsoft/codebert-base #Roberta: roberta-base

python run.py --do_train --do_eval --model_type roberta --model_name_or_path $pretrained_model --train_filename $train_file --dev_filename $dev_file --output_dir $output_dir --max_source_length $source_length --max_target_length $target_length --beam_size $beam_size --train_batch_size $batch_size --eval_batch_size $batch_size --learning_rate $lr --train_steps $train_steps --eval_steps $eval_steps

Inference and Evaluation

After fine-tuning, inference and evaluation are as follows:

lang=php #programming language
beam_size=10
batch_size=128
source_length=256
target_length=128
output_dir=model/$lang
data_dir=../data/code2nl/CodeSearchNet
dev_file=$data_dir/$lang/valid.jsonl
test_file=$data_dir/$lang/test.jsonl
test_model=$output_dir/checkpoint-best-bleu/pytorch_model.bin #checkpoint for test

python run.py --do_test --model_type roberta --model_name_or_path microsoft/codebert-base --load_model_path $test_model --dev_filename $dev_file --test_filename $test_file --output_dir $output_dir --max_source_length $source_length --max_target_length $target_length --beam_size $beam_size --eval_batch_size $batch_size

The results on CodeSearchNet are shown in this Table:

Model	Ruby	Javascript	Go	Python	Java	PHP	Overall
Seq2Seq	9.64	10.21	13.98	15.93	15.09	21.08	14.32
Transformer	11.18	11.59	16.38	15.81	16.26	22.12	15.56
RoBERTa	11.17	11.90	17.72	18.14	16.47	24.02	16.57
CodeBERT	12.16	14.90	18.07	19.06	17.65	25.16	17.83

Contact

Feel free to contact Daya Guo (guody5@mail2.sysu.edu.cn) and Duyu Tang (dutang@microsoft.com) if you have any further questions.

xinzaifeixiang1992/CodeBERT

CodeBERT

Dependency

Qiuck Tour

Code Search

Data Preprocess

Fine-Tune

Inference and Evaluation

Probing

Code Documentation Generation

Dependency

Data Preprocess

Data Download

Fine-Tune

Inference and Evaluation

Contact