torch-hd 1.0.3

Optimized implementations of HD functions using pytorch with GPU support

Torch-HD lives here

Torch-HD is a library that provides optimized implementations of various Hyperdimensional Computing functions using both GPUs and CPUs. The package also provides HD based ML functions for classification tasks.

Get started now. View it on GitHub

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Getting started

Installation

Installation is straightforward. Simply use pip to install the pacakge.

pip3 install torch-hd

Requires python 3.6+ and PyTorch 1.8.2 or later.

To compile it locally clone this repo and run

python setup.py install

Quick start: Encode and decode a vector using ID-Level encoding

from torch_hd import hdlayers as hd

codec = hd.IDLevelCodec(dim_in = 5, D = 10000, qbins = 8, max_val = 8, min_val = 0)
testdata = torch.tensor([0, 4, 1, 3, 0]).type(torch.float)
out = codec(testdata)

print(out)
print(testdata)

Output

tensor([[0., 4., 1., 3., 0.]])
tensor([[0., 4., 1., 3., 0.]])

Checkout the Examples section for a classification example

Functionalities available

Currently Torch-HD supports 3 different encoding methodologies namely

• Random Projection Encoding
• ID-Level Encoding
• Selective Kanerva Coding
• Pact quantization

Apart from encoding functionalities, the library also provides a HD classifier which can be used for training and inference on classification tasks

Coming soon

• [] Implement fractional-binding
• [] Utility functions for training and validation
• Different VSA architectures
  • [] Multiply-Add-Permute (MAP) - real, binary and integer vector spaces
  • [] Holographic Reduced Representations (HRR)
  • [] HRR in Frequency domain (FHRR)
• Functional implementations of
  • [] binding
  • [] unbinding
  • [] bundling

Contributing

Contributions to help improve the implementation are welcome. Please create a pull request on the repo or report issues.