flexi-hash-embedding 0.0.1

PyTorch Extension Library of Optimized Scatter Operations

Flexi Hash Embeddings

This PyTorch Module hashes and sums variably-sized dictionaries of features into a single fixed-size embedding. Feature keys are hashed, which is ideal for streaming contexts and online-learning such that we don't have to memorize a mapping between feature keys and indices.

So for example:

>>> X = [{'dog': 1, 'cat':2, 'elephant':4},
         {'dog': 2, 'run': 5}]
>>> from flexi_hash_embedding import FlexiHashEmbedding
>>> embed = FlexiHashEmbedding(dim=5)
>>> embed(X)
tensor([[ 2.5842e+00,  1.9553e+01,  1.0246e+00,  2.2797e+01,  1.7812e+01],
        [-6.2967e+00,  1.4947e+01, -2.6539e+01, -1.4348e+01, -6.7396e-01]])

Speed

A large batchsize of 4096 with on average 5 features per row equates to about 20,000 total features. This module will hash that many features in about 20ms on a modern MacBook Pro.

Installation

Install from PyPi do pip install flexi_hash_embedding

Install locally by doing [e-mail]:cemoody/flexi_hash_embedding.git.

Testing

>>> pip install -e .
>>> py.test