Introduction to Hidet: An Open-Source Deep Learning Compiler
Hidet is an innovative open-source deep learning compiler designed to bridge the gap between complex neural network models and efficient execution on NVIDIA GPUs. The project is written in Python and has gained attention for its ability to optimize deep learning models, particularly those developed using PyTorch and ONNX frameworks, transforming them into highly efficient CUDA kernels.
Key Features
One of Hidet's standout features is its optimization capabilities. Through both graph-level and operator-level optimizations, it significantly enhances the performance of neural network models during inference. This makes it particularly valuable for machine learning engineers and researchers working on optimizing computational workloads on NVIDIA GPUs.
System Requirements
To utilize Hidet, users need to operate in a Linux environment with CUDA Toolkit version 11.6 or higher, and Python version 3.8 or later. These requirements ensure that users can leverage the full capabilities of Hidet for deep learning model optimization.
Getting Started
Installation
Installing Hidet is straightforward and can be done using pip, the Python package manager. Simply execute the following command in your terminal:
pip install hidet
For those who wish to experiment with the latest features, there is also an option to try the nightly build version or build Hidet directly from the source.
Usage
To optimize a PyTorch model using Hidet, users must have PyTorch 2.0 installed. Here is a quick example of how it can be done:
import torch
# Define PyTorch model
model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).cuda().eval()
x = torch.rand(1, 3, 224, 224).cuda()
# Compile the model through Hidet
model_opt = torch.compile(model, backend='hidet')
# Run the optimized model
y = model_opt(x)
In this example, a ResNet-18 model is loaded from PyTorch's model hub, optimized using Hidet, and executed to produce outputs. For more detailed guidance and advanced options, users can refer to the official tutorials.
Research and Development
Hidet originated from a significant research initiative highlighted in the publication titled “Hidet: Task-Mapping Programming Paradigm for Deep Learning Tensor Programs," presented at ASPLOS '23 by Yaoyao Ding, Cody Hao Yu, Bojian Zheng, Yizhi Liu, Yida Wang, and Gennady Pekhimenko. The project is actively developed by a dedicated team at CentML Inc.
Contributing to Hidet
Hidet invites contributions from the broader community. Contributors can refer to the contribution guide available in the project's documentation to learn how they can participate and help the project grow.
License
Hidet is distributed under the Apache 2.0 license, ensuring that it remains open and accessible to developers worldwide who are keen to explore the potential of deep learning optimization.
In summary, Hidet is a robust tool that can greatly aid in optimizing deep learning models for those working with NVIDIA GPU infrastructures, providing a seamless transition from model definition to high-performance execution.