| | --- |
| | language: en |
| | license: apache-2.0 |
| | --- |
| | |
| | # CodeRosetta |
| | ## Pushing the Boundaries of Unsupervised Code Translation for Parallel Programming ([📃Paper](https://arxiv.org/abs/2410.20527), [🔗Website](https://coderosetta.com/)). |
| |
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| | CodeRosetta is an EncoderDecoder translation model. It supports the translation of C++, CUDA, and Fortran. \ |
| | This version of the model is fine-tuned on synthetic dataset for **C++ to CUDA translation.** |
| |
|
| | ### How to use |
| |
|
| | ```python |
| | from transformers import AutoTokenizer, EncoderDecoderModel |
| | |
| | # Load the CodeRosetta model and tokenizer |
| | model = EncoderDecoderModel.from_pretrained('CodeRosetta/CodeRosetta_cpp2cuda_ft') |
| | tokenizer = AutoTokenizer.from_pretrained('CodeRosetta/CodeRosetta_cpp2cuda_ft') |
| | |
| | # Encode the input C++ Code |
| | input_cpp_code = "void add_100 ( int numElements , int * data ) { for ( int idx = 0 ; idx < numElements ; idx ++ ) { data [ idx ] += 100 ; } }" |
| | input_ids = tokenizer.encode(input_cpp_code, return_tensors="pt") |
| | |
| | # Set the start token to <CUDA> |
| | start_token = "<CUDA>" |
| | decoder_start_token_id = tokenizer.convert_tokens_to_ids(start_token) |
| | |
| | # Generate the CUDA code |
| | output = model.generate( |
| | input_ids=input_ids, |
| | decoder_start_token_id=decoder_start_token_id, |
| | max_length=256 |
| | ) |
| | |
| | # Decode and print the generated output |
| | generated_code = tokenizer.decode(output[0], skip_special_tokens=True) |
| | print(generated_code) |
| | ``` |
| |
|
| | ### BibTeX |
| |
|
| | ```bibtex |
| | @inproceedings{coderosetta:neurips:2024, |
| | title = {CodeRosetta: Pushing the Boundaries of Unsupervised Code Translation for Parallel Programming}, |
| | author = {TehraniJamsaz, Ali and Bhattacharjee, Arijit and Chen, Le and Ahmed, Nesreen K and Yazdanbakhsh, Amir and Jannesari, Ali}, |
| | booktitle = {NeurIPS}, |
| | year = {2024}, |
| | } |
| | |