SYMBA_Pytorch

This is the repository for my Google Summer of Code Project Symbolic empirical representation of squared amplitudes in high-energy physics

Overview

In particle physics, a cross section is a measure of the likelihood that particles will interact or scatter with one another when they collide. It is a fundamental quantity that is used to describe the probability of certain interaction occurring between particles. The determination of cross-sectional area necessitates the computation of the squared amplitude, as well as the averaging and summation over the internal degrees of freedom of the particles involved. This project aims to apply symbolic deep learning techniques to predict the squared amplitudes and cross section for high energy physics.

Code Structure

SYMBA_Pytorch
|__ datasets
|          |__ Data.py # Code for dataset cleaning, tokenization etc.
|          |__ __init__.py
|          |__ registry.py # All the dataset must be registered
|          |__ utils.py # Helper modules
|__ engine
|         |__ __init__.py
|         |__ config.py # All the required configuration for training of models.
|         |__ plotter.py # Used for plotting the loss and accuracy
|         |__ predictor.py # Used for prediction 
|         |__ trainer.py # Used for training of models.
|         |__ utils.py # Helper modules
|__ models
|         |__ BART.py # Code for BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
|         |__ LED.py # Code for Longformer Encoder Decoder 
|         |__ __init__.py
|         |__ registery.py # All the models must be registered 
|         |__ seq2seq_transformer.py # Code for Sequence to sequence Transformer
|__ runs
|       |__ bart-base_trainer.sh # Script to run bart-base model using terminal
|       |__ seq2seq_trainer.sh # Script to run sequence to sequence transformer using terminal
|__ symba_trainer.py # Used inside bash script for training.
|__ symba_tuner.py # Used for hyperparameter optimization using Optuna
|__ symba_example.ipynb # Example Notebook.

Installation

Install required packages:

pip install torch
pip install timm
pip install transformers

After installing the required packages, clone this repository:

git clone https://github.com/ML4SCI/SYMBA_Pytorch.git

Training

Training can be done either using terminal or notebook.

Using Terminal

Use the following command to train the network on single GPU:

python symba_trainer.py --experiment_name="Example" --model_name="seq2seq_transformer" \
                        --dataset_name="QCD_Amplitude" --epochs=30 --learning_rate=0.0001 

Use the following command to train the network on multiple GPU:

torchrun symba_trainer.py --experiment_name="Example" --model_name="seq2seq_transformer" \
                          --dataset_name="QCD_Amplitude" --distributed=True --epochs=30 --learning_rate=0.0001 

Using Notebook

Refer to the symba_example.ipynb.