ADELT: Transpilation between Deep Learning Frameworks
Authors: Linyuan Gong, Jiayi Wang, Alvin Cheung
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | It outperforms state-of-the-art transpilers, improving pass@1 rate by 16.2 pts and 15.0 pts for Py Torch-Keras and Py Torch-MXNet transpilation pairs respectively. We provide open access to our code at https://github.com/gonglinyuan/adelt. 3 Experiments We evaluate the effectiveness of ADELT on the task of transpilation between Py Torch, Keras, and MXNet and compare our method with baselines. Table 1: Comparison between ADELT and other methods on source-to-source transpilation. 3.7 Ablation Studies We conduct ablation studies on Py Torch-Keras transpilation to validate the contribution of each part of ADELT. |
| Researcher Affiliation | Academia | Linyuan Gong , Jiayi Wang , Alvin Cheung University of California, Berkeley {gly, jiayi_wang, akcheung}@berkeley.edu |
| Pseudocode | Yes | Algorithm 1 Pseudo-code for domain-adversarial training. |
| Open Source Code | Yes | We provide open access to our code at https://github.com/gonglinyuan/adelt. |
| Open Datasets | Yes | For training, we construct a Py Torch-Keras-MXNet corpus of deep learning code from various Internet sources, containing 49,705 Py Torch modules, 11,443 Keras layers/models, and 4,785 MXNet layers/models. ... We consider 3 data sources Git Hub, Jui Ce, Kaggle to build our DL corpus: Git Hub: The Git Hub public dataset available on Google Big Query.3 ... Jui Ce: A code generation dataset [Agashe et al., 2019]... Kaggle: All files in KGTorrent [Quaranta et al., 2021], a dataset of Jupyter Notebooks from Kaggle4... 3https://console.cloud.google.com/marketplace/details/github/github-repos 4https://kaggle.com |
| Dataset Splits | No | The paper mentions an “evaluation benchmark” and an “unsupervised validation criterion” but does not provide specific details (percentages or counts) for train/validation/test dataset splits of its main corpus. It describes an evaluation benchmark but not how its primary DL corpus is split for training, validation, and testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. It discusses software and models but no hardware specifications. |
| Software Dependencies | No | The paper mentions software like “Py BERT”, “Codex”, “GPT-3”, and “GPT-4” but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | Adversarial training. The generator and discriminator of ADELT are multilayer perceptrons. We search the learning rate and batch size according to the unsupervised validation criterion average cosine similarity [Conneau et al., 2018], which measures the consistency between learned API keyword embeddings and generated keyword translations. Other hyperparameters are set based on previous studies [Conneau et al., 2018] with details described in Appendix A.2. |