Language modeling. This codebase contains implementation of G-LSTM and F-LSTM cells from . It also might contain some ongoing experiments.
This code was forked from https://github.com/rafaljozefowicz/lm and contains "BIGLSTM" language model baseline from .
Current code runs on Tensorflow r1.5 and supports multi-GPU data parallelism using synchronized gradient updates.
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:
|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|
- 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)
Assuming the data directory is in:
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
- To use F-LSTM cell specify
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
Forked code and GLSTM/FLSTM cells: email@example.com