Transformer-based Large Action Model for Code Understanding

Overview

This repository contains a PyTorch implementation of a Transformer-based model designed for understanding and generating code. The model learns rich representations of source code that can be used for tasks like code completion, code summarization, and code generation.

Key Features

Complete Transformer Architecture: Implements both encoder and decoder with multi-head attention
Positional Encoding: Captures sequential information in code
Code-specific Dataset Handling: Preprocesses and batches code sequences
Training Pipeline: Includes masked training and evaluation
Code Generation: Can generate new code based on prompts

Model Architecture

The model follows the standard Transformer architecture with:

Embedding layer with positional encoding
Multiple encoder and decoder layers
Multi-head attention mechanisms
Position-wise feedforward networks
Layer normalization and dropout

Requirements

Python 3.7+
PyTorch 1.8+
NumPy

Installation

git clone https://github.com/yourusername/code-transformer.git
cd code-transformer
pip install -r requirements.txt

Usage

Training the Model

from model import Transformer
from dataset import CodeDataset
from train import train_model

# Initialize model
model = Transformer(
    src_vocab_size=10000,
    tgt_vocab_size=10000,
    d_model=512,
    num_heads=8,
    num_layers=6
)

# Prepare dataset
dataset = CodeDataset(your_code_sequences, max_len=100)
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)

# Train
train_model(model, dataloader, epochs=10)

Generating Code

from generate import generate_code

# Generate code from prompt
prompt = torch.tensor([your_start_tokens])  # Shape: [1, seq_len]
generated = generate_code(
    model,
    prompt,
    max_len=100,
    start_symbol=1,  # Your start token
    end_symbol=2     # Your end token
)

Data Preparation

Prepare your code data as sequences of tokens. The dataset should be:

Tokenized (using your preferred tokenizer)
Converted to numerical indices
Padded to consistent lengths

Example format:

[
    [1, 45, 23, 67, 2],  # First code sample
    [1, 89, 12, 34, 56, 2],  # Second code sample
    ...
]

Configuration

Here are the key hyperparameters you can configure:

| Parameter      | Description                  | Recommended Value |
|---------------|-----------------------------|------------------|
| `d_model`     | Embedding dimension         | `256-1024`       |
| `num_heads`   | Attention heads             | `4-16`           |
| `num_layers`  | Encoder/decoder layers      | `4-12`           |
| `d_ff`        | Feedforward dimension       | `2048-4096`      |
| `dropout`     | Dropout rate                | `0.1-0.3`        |
| `batch_size`  | Training batch size         | `16-64`          |

Evaluation

The model can be evaluated on:

Code completion accuracy
Generation quality (BLEU score, etc.)
Downstream task performance

Pretrained Models

Coming soon! We plan to release pretrained models for:

Python code understanding
JavaScript code generation
Multi-language embeddings

Contributing

Contributions are welcome! Please open an issue or pull request for:

Bug fixes
Performance improvements
Additional features

License

MIT License

Citation

If you use this code in your research, please cite:

@misc{code-transformer,
  author = {Your Name},
  title = {Transformer-based Code Understanding Model},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/yourusername/code-transformer}}
}

ankitkushwaha90
/

Large_Action_Model