How to Overcome the Large Vocabulary Bottleneck Using an Adaptive Softmax Layer

How to Overcome the Large Vocabulary Bottleneck Using an Adaptive Softmax Layer

In this post, we'll a detailed guide to how a simple GPU-optimized layer substitution can offer 2x-10x speedups, with little to no loss in performance.

A detailed guide to how a simple GPU-optimized layer substitution can offer 2x-10x speedups, with little to no loss in performance

The goal of this post is to explain and provide a TensorFlow 2.0+ implementation of the adaptive softmax, outlined in Reference [1] (link):

Just by switching your softmax to an adaptive softmax, you can easily achieve anywhere from 2x-10x speedups in both training and inference. Before getting started, here’s the overview of what we’re going to be doing:

  1. Large vocabularies = bottlenecks. We break down the problems associated with using a regular softmax over large vocabularies.
  2. We survey other solutions to this issue, then explain how the adaptive softmax addresses the shortcomings of these other approaches.
  3. We dive into the actual implementation details.
  4. We conclude with ready to run code and implementation suggestions.

For an implementation in TensorFlow 2.0+, please see the following link available here and at the bottom of the post.

Let’s get started!

gpu machine-learning tensorflow deep-learning nlp

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