Awesome

F-LM

Language modeling. This codebase contains implementation of G-LSTM and F-LSTM cells from [1]. It also might contain some ongoing experiments.

This code was forked from https://github.com/rafaljozefowicz/lm and contains "BIGLSTM" language model baseline from [2].

Current code runs on Tensorflow r1.5 and supports multi-GPU data parallelism using synchronized gradient updates.

Perplexity

~~On One Billion Words benchmark using 8 GPUs in one DGX-1, BIG G-LSTM G4 was able to achieve 24.29 after 2 weeks of training and 23.36 after 3 weeks.~~

On 02/06/2018 We found an issue with our experimental setup which makes perplexity numbers listed in the paper invalid.

See current numbers in the table below.

On DGX Station, after 1 week of training using all 4 GPUs (Tesla V100) and batch size of 256 per GPU:

Model	Perplexity	Steps	WPS
BIGLSTM	35.1	~0.99M	~33.8K
BIG F-LSTM F512	36.3	~1.67M	~56.5K
BIG G-LSTM G4	40.6	~1.65M	~56K
BIG G-LSTM G2	36	~1.37M	~47.1K
BIG G-LSTM G8	39.4	~1.7M	~58.5

Dependencies

TensorFlow r1.5
Python 2.7 (should work with Python 3 too)
1B Word Benchmark Dataset (https://github.com/ciprian-chelba/1-billion-word-language-modeling-benchmark to get data)

To run

Assuming the data directory is in: /raid/okuchaiev/Data/LM1B/1-billion-word-language-modeling-benchmark-r13output/, execute:

export CUDA_VISIBLE_DEVICES=0,1,2,3

SECONDS=604800
LOGSUFFIX=FLSTM-F512-1week

python /home/okuchaiev/repos/f-lm/single_lm_train.py --logdir=/raid/okuchaiev/Workspace/LM/GLSTM-G4/$LOGSUFFIX --num_gpus=4 --datadir=/raid/okuchaiev/Data/LM/LM1B/1-billion-word-language-modeling-benchmark-r13output/ --hpconfig run_profiler=False,float16_rnn=False,max_time=$SECONDS,num_steps=20,num_shards=8,num_layers=2,learning_rate=0.2,max_grad_norm=1,keep_prob=0.9,emb_size=1024,projected_size=1024,state_size=8192,num_sampled=8192,batch_size=256,fact_size=512  >> train_$LOGSUFFIX.log 2>&1

python /home/okuchaiev/repos/f-lm/single_lm_train.py --logdir=/raid/okuchaiev/Workspace/LM/GLSTM-G4/$LOGSUFFIX --num_gpus=1 --mode=eval_full --datadir=/raid/okuchaiev/Data/LM/LM1B/1-billion-word-language-modeling-benchmark-r13output/ --hpconfig run_profiler=False,float16_rnn=False,max_time=$SECONDS,num_steps=20,num_shards=8,num_layers=2,learning_rate=0.2,max_grad_norm=1,keep_prob=0.9,emb_size=1024,projected_size=1024,state_size=8192,num_sampled=8192,batch_size=1,fact_size=512

To use G-LSTM cell specify num_of_groups parameter.
To use F-LSTM cell specify fact_size parameter.

Note, that current data reader may miss some tokens when constructing mini-batches which can have a minor effect on final perplexity.

For most accurate results, use batch_size=1 and num_steps=1 in evaluation. Thanks to Ciprian for noticing this.

To change hyper-parameters

The command accepts and additional argument --hpconfig which allows to override various hyper-parameters, including:

batch_size=128 - batch size per GPU. Global batch size = batch_size*num_gpus
num_steps=20 - number of LSTM cell timesteps
num_shards=8 - embedding and softmax matrices are split into this many shards
num_layers=1 - numer of LSTM layers
learning_rate=0.2 - learning rate for optimizer
max_grad_norm=10.0 - maximum acceptable gradient norm for LSTM layers
keep_prob=0.9 - dropout keep probability
optimizer=0 - which optimizer to use: Adagrad(0), Momentum(1), Adam(2), RMSProp(3), SGD(4)
vocab_size=793470 - vocabluary size
emb_size=512 - size of the embedding (should be same as projected_size)
state_size=2048 - LSTM cell size
projected_size=512 - LSTM projection size
num_sampled=8192 - training uses sampled softmax, number of samples)
do_summaries=False - generate weight and grad stats for Tensorboard
max_time=180 - max time (in seconds) to run
fact_size - to use F-LSTM cell, this should be set to factor size
num_of_groups=0 - to use G-LSTM cell, this should be set to number of groups
save_model_every_min=30 - how often to checkpoint
save_summary_every_min=16 - how often to save summaries
use_residual=False - whether to use LSTM residual connections

Feedback

Forked code and GLSTM/FLSTM cells: okuchaiev@nvidia.com

References

[1] Factorization tricks for LSTM networks, ICLR 2017 workshop.
[2] Exploring the Limits of Language Modeling