Pytorch implementation of Integrating Tree Path in Transformer for Code Representation

Related tags

Deep LearningTPTrans
Overview

This is an official Pytorch implementation of the approaches proposed in:

Han Peng, Ge Li, Wenhan Wang, Yunfei Zhao, Zhi Jin “Integrating Tree Path in Transformer for Code Representation”

which appeared at NeurIPS 2021[Paper Link][Poster][Slides].

In this paper, we investigate the interaction between the absolute and relative path encoding, and propose novel code representation model TPTrans and its variants, which introduce path encoding inductive bias into the attention module of Transformer and power Transformer to know the structure of source codes.

Please cite our paper if you use the model, experimental results, or our code in your own work.

1.1 Raw data

To run experiments with TPTrans and its variants, please first create datasets from raw code snippets of CodeSearchNet dataset. Download and unzip the raw jsonl data of CSN into the raw_data dir like that

├── raw_data        
│   ├── python         
│   │   ├── train    
│   │   │   ├── XXXX.jsonl...
│   │   ├── test    
│   │   ├── valid   
│   ├── ruby          
│   ├── go        
│   ├── javascript        

1.2 Tree-Sitter

The Tree-Sitter is a open-source parser for multi-language programming languages. Please install it and then download the grammer files into vendor dir for four different programming languages like that

├── vendor        
│   ├── tree-sitter-python  (from https://github.com/tree-sitter/tree-sitter-python)         
│   ├── tree-sitter-javascript  (from https://github.com/tree-sitter/tree-sitter-javascript)     
│   ├── tree-sitter-go  (from https://github.com/tree-sitter/tree-sitter-go)
│   ├── tree-sitter-ruby  (from https://github.com/tree-sitter/tree-sitter-ruby)

After that, run the multi_language_parse.py in parser dir to parse the raw code snippets into the data dir.

1.3 Training

After preprocessing, run the _main.py_ to train the model.

To run the TPTrans, please specify the relation_path=True and absolute_path=False.

To run the TPTrans-\alpha, please specify the relation_path=True and absolute_path=True.

For other command triggers, please refer the comment inline for details.

Contact If you have any questions, please contact me via email: [email protected] or open issue on Github.

Owner
Han Peng
Han Peng
ObjectDrawer-ToolBox: a graphical image annotation tool to generate ground plane masks for a 3D object reconstruction system

ObjectDrawer-ToolBox is a graphical image annotation tool to generate ground plane masks for a 3D object reconstruction system, Object Drawer.

77 Jan 05, 2023
A PyTorch Implementation of the Luna: Linear Unified Nested Attention

Unofficial PyTorch implementation of Luna: Linear Unified Nested Attention The quadratic computational and memory complexities of the Transformer’s at

Soohwan Kim 32 Nov 07, 2022
50-days-of-Statistics-for-Data-Science - This repository consist of a 50-day program

50-days-of-Statistics-for-Data-Science - This repository consist of a 50-day program. All the statistics required for the complete understanding of data science will be uploaded in this repository.

komal_lamba 22 Dec 09, 2022
The materials used in the SaxonJS tutorial presented at Declarative Amsterdam, 2021

SaxonJS-Tutorial-2021, version 1.0.4 Last updated on 4 November, 2021. Table of contents Background Prerequisites Starting a web server Running a Java

Saxonica 11 Oct 23, 2022
Negative Sample is Negative in Its Own Way: Tailoring Negative Sentences forImage-Text Retrieval

NSGDC Some codes in this repo are copied/modified from opensource implementations made available by UNITER, PyTorch, HuggingFace, OpenNMT, and Nvidia.

Zhihao Fan 2 Nov 07, 2022
Deep Reinforcement Learning for Multiplayer Online Battle Arena

MOBA_RL Deep Reinforcement Learning for Multiplayer Online Battle Arena Prerequisite Python 3 gym-derk Tensorflow 2.4.1 Dotaservice of TimZaman Seed R

Dohyeong Kim 32 Dec 18, 2022
Only works with the dashboard version / branch of jesse

Jesse optuna Only works with the dashboard version / branch of jesse. The config.yml should be self-explainatory. Installation # install from git pip

Markus K. 8 Dec 04, 2022
Federated Learning Based on Dynamic Regularization

Federated Learning Based on Dynamic Regularization This is implementation of Federated Learning Based on Dynamic Regularization. Requirements Please i

39 Jan 07, 2023
Python implementation of O-OFDMNet, a deep learning-based optical OFDM system,

O-OFDMNet This includes Python implementation of O-OFDMNet, a deep learning-based optical OFDM system, which uses neural networks for signal processin

Thien Luong 4 Sep 09, 2022
Super-Fast-Adversarial-Training - A PyTorch Implementation code for developing super fast adversarial training

Super-Fast-Adversarial-Training This is a PyTorch Implementation code for develo

LBK 26 Dec 02, 2022
SAPIEN Manipulation Skill Benchmark

ManiSkill Benchmark SAPIEN Manipulation Skill Benchmark (abbreviated as ManiSkill, pronounced as "Many Skill") is a large-scale learning-from-demonstr

Hao Su's Lab, UCSD 107 Jan 08, 2023
Example repository for custom C++/CUDA operators for TorchScript

Custom TorchScript Operators Example This repository contains examples for writing, compiling and using custom TorchScript operators. See here for the

106 Dec 14, 2022
HMLET (Hybrid-Method-of-Linear-and-non-linEar-collaborative-filTering-method)

Methods HMLET (Hybrid-Method-of-Linear-and-non-linEar-collaborative-filTering-method) Dynamically selecting the best propagation method for each node

Yong 7 Dec 18, 2022
pytorch implementation of ABC : Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning

ABC:Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning, NeurIPS 2021 pytorch implementation of ABC : Auxiliary Balanced Class

Hyuck Lee 25 Dec 22, 2022
Unofficial implementation of "TTNet: Real-time temporal and spatial video analysis of table tennis" (CVPR 2020)

TTNet-Pytorch The implementation for the paper "TTNet: Real-time temporal and spatial video analysis of table tennis" An introduction of the project c

Nguyen Mau Dung 438 Dec 29, 2022
Creating Artificial Life with Reinforcement Learning

Although Evolutionary Algorithms have shown to result in interesting behavior, they focus on learning across generations whereas behavior could also be learned during ones lifetime.

Maarten Grootendorst 49 Dec 21, 2022
Official implementation of deep Gaussian process (DGP)-based multi-speaker speech synthesis with PyTorch.

Multi-speaker DGP This repository provides official implementation of deep Gaussian process (DGP)-based multi-speaker speech synthesis with PyTorch. O

sarulab-speech 24 Sep 07, 2022
Deep Learning Theory

Deep Learning Theory 整理了一些深度学习的理论相关内容,持续更新。 Overview Recent advances in deep learning theory 总结了目前深度学习理论研究的六个方向的一些结果,概述型,没做深入探讨(2021)。 1.1 complexity

fq 103 Jan 04, 2023
Augmentation for Single-Image-Super-Resolution

SRAugmentation Augmentation for Single-Image-Super-Resolution Implimentation CutBlur Cutout CutMix Cutup CutMixup Blend RGBPermutation Identity OneOf

Yubo 6 Jun 27, 2022
an Evolutionary Algorithm assisted GAN

EvoGAN an Evolutionary Algorithm assisted GAN ckpts

3 Oct 09, 2022