aptt 1.0.2

Comprehensive deep learning framework with state-of-the-art implementations: GPT, DeepSeek-V3 with MLA/MoE, YOLO, CenterNet, and specialized audio/vision models built on PyTorch Lightning

APTT – Antons PyTorch Tools

APTT (Antons PyTorch Tools) is a comprehensive deep learning framework built on PyTorch Lightning that provides production-ready implementations of state-of-the-art architectures including transformer language models (GPT, DeepSeek-V3), object detection (YOLO, CenterNet), and specialized neural networks for vision and audio tasks.

🚀 Features

Language Models & NLP

• ✅ GPT-2/GPT-3 Architecture: Full transformer implementation with configurable layers
• ✅ DeepSeek-V3: State-of-the-art LLM with Multi-Head Latent Attention (MLA) and Mixture-of-Experts (MoE)
  • Multi-Head Latent Attention with KV-Compression
  • Auxiliary-Loss-Free Load Balancing
  • Multi-Token Prediction (MTP)
  • Rotary Position Embeddings (RoPE)
• ✅ Text Dataset Loaders: Support for .txt, .jsonl, pre-tokenized data with sliding window

Computer Vision

• ✅ Object Detection: YOLO (v3/v4/v5), CenterNet, EfficientDet
• ✅ Feature Extractors: ResNet, DarkNet, EfficientNet, MobileNet, FPN
• ✅ Tracking: RNN-based object tracking with ReID

Audio Processing

• ✅ Beamforming: Multi-channel audio processing
• ✅ Direction of Arrival (DOA): Acoustic source localization
• ✅ Feature Networks: WaveNet, Complex-valued networks

Training & Optimization

• 🧠 Continual Learning: Built-in knowledge distillation and LwF (Learning without Forgetting)
• 🧩 Pluggable Callbacks: TorchScript export, TensorRT optimization, t-SNE visualization
• ⚙️ Modular Design: Composable heads, losses, layers, and metrics
• 📊 Visualization Tools: Embedding analysis, training metrics, model profiling
• 🗂️ Flexible Dataset Loaders: Image, audio, text with augmentation support

🛠️ Installation

# Clone the repository
git clone https://github.com/afeldman/aptt.git
cd aptt

# Create virtual environment (recommended)
python -m venv .venv
source .venv/bin/activate  # or .venv\Scripts\activate on Windows

# Install for CPU
uv sync --extra cpu --extra dev

# Install for CUDA 12.4
uv sync --extra cu124 --extra dev

# For documentation building
apt-get install libgraphviz-dev  # Linux
brew install graphviz             # macOS

🎯 Quick Start

Language Model Training (DeepSeek-V3)

import pytorch_lightning as pl
from aptt.modules.deepseek import DeepSeekModule
from aptt.lightning_base.dataset import TextDataLoader

# Prepare dataset
datamodule = TextDataLoader(
    train_data_path="data/train.txt",
    val_data_path="data/val.txt",
    tokenizer=tokenizer,
    max_seq_len=512,
    batch_size=32,
    return_mtp=True,  # Enable Multi-Token Prediction
)

# Create model
model = DeepSeekModule(
    vocab_size=50000,
    d_model=2048,
    n_layers=24,
    n_heads=16,
    use_moe=True,           # Enable Mixture-of-Experts
    use_mtp=True,           # Enable Multi-Token Prediction
    n_routed_experts=256,
    n_expert_per_token=8,
)

# Train
trainer = pl.Trainer(max_steps=100000, accelerator="gpu")
trainer.fit(model, datamodule)

Object Detection (YOLO)

from aptt.modul.yolo import YOLOModule

model = YOLOModule(
    num_classes=80,
    model_size="yolov5s",
    pretrained=True,
)

trainer = pl.Trainer(max_epochs=100, accelerator="gpu")
trainer.fit(model, datamodule)

📚 Documentation

Core Modules

Language Models

• LLM Modules: GPT and DeepSeek-V3 architecture documentation
• LLM Loss & Heads: Language modeling losses and output heads
• Mixture-of-Experts: DeepSeek-V3 MoE implementation
• Text Datasets: Text data loading and preprocessing

Computer Vision

• Detection models (YOLO, CenterNet, EfficientDet)
• Feature extractors (ResNet, DarkNet, EfficientNet, FPN)
• Object tracking systems

Audio Processing

• Beamforming algorithms
• DOA estimation
• Complex-valued neural networks

Examples

# Language Models
python examples/llm_modules_example.py      # GPT & DeepSeek-V3
python examples/llm_loss_head_example.py    # Loss functions & heads
python examples/moe_example.py              # Mixture-of-Experts
python examples/text_dataset_simple.py      # Text data loading

# View all examples
ls examples/

Build Documentation Locally

cd docs
make html
# Open docs/_build/html/index.html

🏗️ Project Structure

aptt/
├── src/aptt/                      # Core source code
│   ├── callbacks/                 # Training callbacks (TensorRT, t-SNE, etc.)
│   ├── heads/                     # Output heads (classification, detection, LM)
│   ├── layers/                    # Neural network layers
│   │   ├── attention/             # Attention mechanisms (MLA, RoPE, KV-Compression)
│   │   └── moe.py                 # Mixture-of-Experts
│   ├── lightning_base/            # Lightning modules and utilities
│   │   └── dataset/               # Dataset loaders (image, audio, text)
│   ├── loss/                      # Loss functions
│   ├── metric/                    # Evaluation metrics
│   ├── model/                     # Model architectures
│   │   ├── beamforming/           # Audio beamforming
│   │   └── detection/             # Object detection
│   ├── modules/                   # Lightning modules
│   │   ├── deepseek.py            # DeepSeek-V3 module
│   │   ├── gpt.py                 # GPT module
│   │   ├── yolo.py                # YOLO module
│   │   └── ...
│   └── utils/                     # Utility functions
├── examples/                      # Usage examples
│   ├── llm_modules_example.py     # Language model examples
│   ├── moe_example.py             # MoE examples
│   └── text_dataset_simple.py     # Dataset examples
├── tests/                         # Unit tests
├── docs/                          # Sphinx documentation
│   ├── llm_modules.md             # LLM documentation
│   ├── moe.md                     # MoE documentation
│   └── text_dataset.md            # Dataset documentation
├── pyproject.toml                 # Project configuration
├── README.md                      # This file
#

🎓 Key Concepts

Multi-Head Latent Attention (MLA)

DeepSeek-V3's efficient attention mechanism with low-rank KV-compression:

from aptt.layers.attention.mla import MultiHeadLatentAttention

attention = MultiHeadLatentAttention(
d=2048, # Model dimension
n_h=16, # Number of heads
d_h_c=256, # Compressed KV dimension
d_h_r=64, # Per-head RoPE dimension
)

Mixture-of-Experts (MoE)

Sparse expert activation with auxiliary-loss-free load balancing:

from aptt.layers.moe import DeepSeekMoE

moe = DeepSeekMoE(
d_model=2048,
n_shared_experts=1, # Always active
n_routed_experts=256, # Selectively activated
n_expert_per_token=8, # Top-K experts per token
)

Multi-Token Prediction (MTP)

Predict multiple future tokens simultaneously:

# Dataset with MTP targets

dataset = TextDataset(
data_path="train.txt",
tokenizer=tokenizer,
return_mtp=True,
mtp_depth=3, # Predict 1, 2, 3 tokens ahead
)

# Model with MTP loss

model = DeepSeekModule(
vocab_size=50000,
use_mtp=True,
mtp_lambda=0.3, # MTP loss weight
)

📊 Model Zoo

Language Models

Model	Parameters	Config	Performance
GPT-Small	124M	`d_model=768, n_layers=12`	GPT-2 baseline
DeepSeek-Small	51M	`d_model=512, n_layers=4, use_moe=True`	Demo config
DeepSeek-Base	1.3B	`d_model=2048, n_layers=24, n_experts=256`	Production
DeepSeek-V3	685B	`d_model=7168, n_layers=60, n_experts=256`	Full scale

Object Detection

Model	Backbone	mAP	FPS
YOLOv5s	CSPDarknet	37.4	140
YOLOv5m	CSPDarknet	45.4	100
CenterNet	ResNet-50	42.1	45

🧪 Testing

# Run all tests

pytest

# Run specific test

pytest tests/test_tensor_rt_export_callback.py

# With coverage

pytest --cov=aptt

🛠️ Development

Code Quality

# Format code

ruff format .

# Lint

ruff check .

# Type checking

mypy src/aptt

Pre-commit Hooks

# Install pre-commit

pip install pre-commit

# Setup hooks

pre-commit install

# Run manually

pre-commit run --all-files

📖 Citation

If you use APTT in your research, please cite:

@software{aptt2025,
title = {APTT: Antons PyTorch Tools},
author = {Anton Feldmann},
year = {2025},
url = {https://github.com/afeldman/aptt}
}

For DeepSeek-V3:

@article{deepseekai2024deepseekv3,
title={DeepSeek-V3 Technical Report},
author={DeepSeek-AI},
journal={arXiv preprint arXiv:2412.19437},
year={2024}
}

🤝 Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request

Please ensure:

• Code follows the style guide (Ruff + MyPy)
• Tests pass (`pytest`)
• Documentation is updated

🙏 Acknowledgments

• PyTorch Lightning for the training framework
• DeepSeek-AI for the DeepSeek-V3 architecture
• Ultralytics for YOLO implementations
• The open-source community for various model implementations

📧 Contact

Anton Feldmann - [e-mail]

Project Link: https://github.com/afeldman/aptt

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Version: 0.2.0 | Python: >=3.11 | PyTorch: >=2.6.0 | Lightning: >=2.5.1