Segment Anything ... Fast
Segment Anything ... Fast is a novel project aimed at enhancing the efficiency and speed of the existing segment-anything model developed by Facebook Research. More specifically, it leverages advanced techniques to accelerate generative AI applications based on the PyTorch framework. The project is openly available and can be explored further through the relevant GitHub repository and blog posts.
Installation
To get started with Segment Anything ... Fast, users need to follow a simple two-step installation process involving PyTorch and GitHub.
Step 1: Install PyTorch Nightly
The initial step involves obtaining the latest PyTorch nightly version. Depending on the hardware configuration (GPU or CPU), users can execute one of the following commands:
pip3 install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu121
for a GPU setup or,
pip3 install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cpu
for a CPU setup. More detailed instructions tailored to different platforms can be found on the PyTorch website.
Step 2: Install the Segment Anything ... Fast Package
After setting up PyTorch Nightly, the next step is to install the segment-anything-fast package using the following command:
pip install git+https://github.com/pytorch-labs/segment-anything-fast.git
Usage
The segment-anything-fast package is designed as a direct replacement for the original segment-anything system. For users already employing segment-anything, this transition should be seamless, allowing them to substitute imports as follows:
from segment_anything import sam_model_registry
should be replaced with
from segment_anything_fast import sam_model_registry
The primary attraction of this project, however, is its fast inference capabilities facilitated by a sam_model_fast_registry. This registry introduces several optimizations like:
- Eval Mode: Automatically sets the model to evaluation mode, ensuring optimized performance.
- bfloat16 Usage: Performs computations using bfloat16 precision to boost speed without compromising much on accuracy.
- Torch Compile with Max-Autotune: Leverages PyTorch's compilation and autotuning features for improved performance.
- Custom Triton Kernel: Implemented for better handling of scaled dot-product attention (SDPA) for long sequence lengths, particularly optimized for A100 GPUs.
For those working with different hardware, the system is designed to re-tune performance settings automatically. If problems are encountered, users are able to disable specific optimizations like the custom kernel by setting environment variables accordingly.
Results
The project showcases a variety of techniques that can be combined to optimize generative AI tasks. These techniques include bfloat16 precision, scaled dot-product attention via PyTorch, custom Triton kernels, NestedTensors, dynamic int8 quantization, and sparse formats like 2:4. The combination of these features results in significant performance enhancements, visualized in an illustrative bar chart within the project's directory.
License
The Segment Anything ... Fast project is shared under the Apache 2.0 open-source license, encouraging collaborative development and sharing within the community.
In conclusion, Segment Anything ... Fast stands out not only as a faster alternative to the standard segment-anything model but also as a richly featured tool adept at catering to the modern needs of generative AI based on advanced PyTorch capabilities.