Skip to content
Implements an efficient softmax approximation as described in the paper "Efficient softmax approximation for GPUs" (http://arxiv.org/abs/1609.04309)
Branch: master
Clone or download
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
data Initial commit of adaptive-softmax Oct 7, 2016
utils Merge branch 'master' of github.com:facebookresearch/adaptive-softmax Aug 28, 2017
CONTRIBUTING.md Initial commit of adaptive-softmax Oct 7, 2016
LICENSE
PATENTS Initial commit of adaptive-softmax Oct 7, 2016
README.md Update README and train_big_lstm Oct 20, 2016
train_big_lstm.lua Add pre-trained word vectors option Aug 28, 2017

README.md

Adaptive Softmax

The adaptive-softmax project is a Torch implementation of the efficient softmax approximation for graphical processing units (GPU), described in the paper "Efficient softmax approximation for GPUs" (http://arxiv.org/abs/1609.04309).

This method is useful for training language models with large vocabularies. We provide a script to train large recurrent neural network language models, in order to reproduce the results of the paper.

Dependencies

This project depends on the following packages:

Examples

In order to train a recurrent neural network language model with default parameters, run

th train_big_lstm.lua -data DATA_DIR

where DATA_DIR is a directory containing three text files, train.txt, valid.txt and test.txt.

Penn TreeBank

In order to train a language model on PTB, run the command

th train_big_lstm.lua -data PATH/TO/PTB -nhid 512 -isz 512 -dropout 0.5 -usecudnn -cutoff 2000

Text8

In order to train a language model on text8, run the command

th train_big_lstm.lua -data PATH/TO/TEXT8 -nhid 512 -isz 512 -dropout 0.25 -batchsize 128 -usecudnn -cutoff 2000,10000

Billion word benchmark

In order to train a language model on the billion word benchmark, run the command

th train_big_lstm.lua -data PATH/TO/BILLION/WORD -nhid 2048 -isz 256 -dropout 0.01 -batchsize 128 -testbatchsize 128 -threshold 2 -usecudnn -cutoff 4000,40000,200000

Usage

We now briefly discuss how to use the adaptive softmax in your own projects. We provide a Torch layer called nn.AdaptiveSoftMax and a corresponding criterion, called nn.AdaptiveLoss, which must be used when training with the adaptive softmax. The vocabulary must be sorted by decreasing frequency, so that frequent words correspond to small indices.

The constructor of the nn.AdaptiveSoftMax layer takes two arguments: hidden_size, which is the size of the input of the adaptive softmax and cutoff, which is a table indicating the limits of the different clusters. The constructor of the nn.AdaptiveLoss criterion takes as only argument the cutoff table.

local nword       = 44372
local hidden_size = 256
local cutoff      = { 2000, 10000, nword }

local decoder   = nn.AdaptiveSoftMax( hidden_size, cutoff )
local criterion = nn.AdaptiveLoss( cutoff )

In the previous example, we created an adaptive softmax with three clusters. The first cluster contains the words from 1 to 2000, the second cluster contains the words from 2001 to 10,000 and finally, the last cluster contains the word from 10,001 to nword.

The forward method of the adaptive softmax takes a 2D tensor as input, and output a table of 2D tensors of scores for each cluster (one tensor per cluster). In order to be efficient, the nn.AdaptiveSoftMax does not compute the scores for all the word of the vocabulary for all the examples.It is thus necessary to call the method setTarget of the AdaptiveSoftMax layer before each forward pass:

decoder:setTarget( target )

where target is a 1D tensor. This ensure that the adaptive softmax will compute the scores for the corresponding targets. It is also possible to call the method getLogProb, which computes the log probabilities for all the words of the vocabulary, given a 2D tensor of hidden vectors.

Contributing

See the CONTRIBUTING file for how to help out.

License

adaptive-softmax is BSD-licensed. We also provide an additional patent grant.

You can’t perform that action at this time.