Awesome kan_gpt created by AdityaNG
Project description
KAN-GPT
The PyTorch implementation of Generative Pre-trained Transformers (GPTs) using Kolmogorov-Arnold Networks (KANs) for language modeling
Install it from PyPI
pip install kan_gpt
Usage
Refer to the KAN_GPT.ipynb and kan_gpt/prompt.py for usage examples. The following is an ourtine of how to use the model:
from kan_gpt.model import GPT
from transformers import GPT2Tokenizer
model_config = GPT.get_default_config()
model_config.model_type = "gpt2"
model_config.vocab_size = 50257
model_config.block_size = 1024
model = GPT(model_config)
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
prompt = "Bangalore is often described as the "
prompt_encoded = tokenizer.encode(
text=prompt, add_special_tokens=False
)
x = torch.tensor(prompt_encoded).unsqueeze(0)
model.eval()
y = model.generate(x, 50) # sample 50 tokens
result = tokenizer.decode(y)
print(result)
# Bangalore is often described as the Silicon Valley of India.
# The city has witnessed rapid growth in the past two decades.....
Setup for Development
# Download Repo
git clone https://github.com/AdityaNG/kan-gpt
cd kan-gpt
git pull
# Download Dataset
./scripts/download_webtext.sh
# Install dependencies for development
pip install -r requirements.txt
pip install -e .
Train
Use the following dummy script to make sure everything is working as expected
WANDB_MODE=offline CUDA_VISIBLE_DEVICE="" python3 -m kan_gpt.train --architecture MLP --batch_size 1 --dummy_dataset --device cpu
WANDB_MODE=offline CUDA_VISIBLE_DEVICE="" python3 -m kan_gpt.train --architecture KAN --batch_size 1 --dummy_dataset --device cpu
Then make use of the training script
python -m kan_gpt.train
Prompt
You can prompt the model to produce text as follows
python -m kan_gpt.prompt --prompt "Bangalore is often described as the " --model_path (checkpoint)
TODOs
- Integrate minGPT and pykan
- Dataset downloading script for WebText
- PyTorch Dataset parser for WebText
- Mini training POC for KAN-GPT
- Integrate KAN training logic from
KAN.train_kan
- Train a dummy batch w/o any memory issues
- Integrate KAN training logic from
- Mini training POC for MLP-GPT
- Train MLP-GPT on the webtext dataset as a baseline
- Auto Save checkpoints
- Auto Save checkpoints to W&B
- Auto Download model weights from git / huggingface
- Script to load checkpoint in interactive mode
- Training script to PyTorch Lighting
- Integrate with efficient-kan
- Test Cases
- KAN: Forward-Backward test
- GPT: Forward-Backward test
- KAN_GPT: Forward-Backward test
- EFFICIENT_KAN: Forward-Backward test
Development
Read the CONTRIBUTING.md file.
References
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
kan_gpt-0.2.0.tar.gz
(163.6 kB
view hashes)
Built Distribution
kan_gpt-0.2.0-py3-none-any.whl
(56.9 kB
view hashes)