The code for two papers: Feedback Transformer and Expire-Span.

Related tags

Deep Learning transformer-sequential
Overview

transformer-sequential

This repo contains the code for two papers:

  • Feedback Transformer
  • Expire-Span

The training code is structured for long sequential modeling with Transformer-like architectures.

Requirements

You will need a CUDA-enabled GPU to run the code.

Setup

Run the following:

pip install -r requirements.txt

Feedback Transformer

Introduced in Addressing Some Limitations of Transformers with Feedback Memory.

Running Experiments from the Paper

enwik8

Model Params Valid Test
Feedback Transformer 77M 0.984 0.962

Numbers are Bits-Per-Character

bash experiments/feedback/enwik8.sh

Algorithmic

Model 3 Variable 5 Variable
Transformer 33.7 37.5
Feedback Transformer 99.1 92.6

Numbers are % Accuracy on Test

bash experiments/feedback/algorithmic_3var.sh
bash experiments/feedback/algorithmic_5var.sh

Expire-Span

Introduced in Not All Memories are Created Equal: Learning to Expire.

Running Experiments from the Paper

enwik8

Model Params Valid Test
Expire-Span 12L 38M 1.014 0.994

Numbers are Bits-Per-Character

bash experiments/expire_span/enwik8.sh

Object Collision

Model Maximum Span Test Error (%)
Expire-Span 16k 52.2
Expire-Span 32k 36.7
Expire-Span 64k 26.7 
bash experiments/expire_span/object_collision_16k.sh
bash experiments/expire_span/object_collision_32k.sh
bash experiments/expire_span/object_collision_64k.sh

License

The code is licensed under CC-BY-NC license. See the LICENSE file for more details.

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Comments
  • Unable to reproduce performance for the object collision task

    Unable to reproduce performance for the object collision task

    On running the following command - bash experiments/expire_span/object_collision_16k.sh, I get the following performance after the training, which is different from the 52.2% reported in the paper. Is there something that I am missing?

    100000	train: 68.22%	val: 75.97%	ms/batch: 346.1	gpu_mem: 9.7gb

    I reduced the batch size to 32 so that it could fit in my GPU, I wonder if that is causing the issue?

    opened by dido1998 0
Owner
Facebook Research
Facebook Research
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