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TritonHub

About

TritonHub serves as a container for various PyTorch neural network components written in Triton, offering an easy way to access and integrate Triton-based functions from one centralized source. These components include custom implementations of activation functions, normalization layers, and other neural network operations designed for efficient execution on GPUs.

I hope this repository proves helpful to the community, making Triton-based neural network operations more accessible and convenient for developers and researchers alike.

Installation

You can install TritonHub through the following steps:

git clone https://github.com/AliYoussef97/TritonHub.git
cd TritonHub
python setup.py install

For development installation, you can use the following command:

python setup.py develop

TritonHub requires the following dependencies:

  • Linux
  • CUDA
  • NVIDIA GPU

Usage Example

import torch
from TritonHub.Normalization import LayerNorm
from TritonHub.Activation import GeLU

batch, length, dim = 2, 100, 128
device = "cuda"
dtype = torch.float32  # or torch.float16

x = torch.randn(batch, length, dim, device=device, dtype=dtype).to("cuda")

# Initialize LayerNorm and GeLU modules from TritonHub
layernorm = LayerNorm(128, eps=1e-6, elementwise_affine=True, bias=True, device=device, dtype=dtype)
gelu = GeLU(approximate='None') # or tanh

# Apply LayerNorm and GeLU on the input tensor
x = layernorm(x)
x = gelu(x)

Contributions

Contributions are welcome! If you'd like to add new functionalities, please:

  1. Create a pull request with your implementation.
  2. Ensure the pull request includes a unit test for the new module, similar to the tests found in the UnitTests folder.

If you encounter any bugs or issues with the current version, please feel free to report them by creating an issue or submitting a pull request.

TODO

Feature Status
Linear Layer Backward Pass ✔️
Include Triton Block Sizes in Autotune ❌
Convolution Layer ❌
BatchNorm ❌
Different Activation Functions ✔️

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Acknowledgments

Special thanks to the authors of Mamba, as parts of their Triton code was influential or some of the modules in TritonHub.