CUSIM

Superfast CUDA implementation of Word2Vec and Latent Dirichlet Allocation (LDA)

Introduction

This project is to speed up various ML models (e.g. topic modeling, word embedding, etc) by CUDA. It would be nice to think of it as gensim's GPU version project. As a starting step, I implemented the most widely used word embedding model, the word2vec model, and the most representative topic model, the LDA (Latent Dirichlet Allocation) model.

Requirements

• Python3.6+
• gcc / g++ (>= 5.1 for c++14)
• cuda >= 7.0
• Tested on Ubuntu 18.04 / GCC 7.5 / CUDA 11.1 / Python 3.6

How to install

• install from pypi

pip install cusim

• install from source

# clone repo and submodules
git clone [email protected]:js1010/cusim.git && cd cusim && git submodule update --init

# install requirements
pip install -r requirements.txt

# generate proto
python -m grpc_tools.protoc --python_out cusim/ --proto_path cusim/proto/ config.proto

# install
python setup.py install

How to use

• examples/example_w2v.py, examples/example_lda.py and examples/README.md will be very helpful to understand the usage.
• paremeter description can be seen in cusim/proto/config.proto

Performance

• AWS g4dn 2xlarge instance is used to the experiment. (One NVIDIA T4 GPU with 8 vcpus, Intel(R) Xeon(R) Platinum 8259CL CPU @ 2.50GHz)
• results can be reproduced by simply running examples/example_w2v.py and examples/example_lda.py
• To evaluate w2v model, I used evaluate_word_pairs function (ref link) in gensim, note that better performance on WS-353 test set does not necessarily mean that the model will workbetter in application as desribed on the link. However, it is good to be measured quantitively and fast training time will be at least very objective measure of the performaance.
  • I trained W2V model on quora-duplicate-questions dataset from gensim downloader api on GPU with cusim and compare the performance (both speed and model quality) with gensim.
• To evaluate LDA model, I found there is no good way to measure the quality of traing results quantitatively. But we can check the model by looking at the top words of each topic. Also, we can compare the training time quantitatively.
• W2V (skip gram, hierarchical softmax)

attr	1 workers (gensim)	2 workers (gensim)	4 workers (gensim)	8 workers (gensim)	NVIDIA T4 (cusim)
training time (sec)	892.596	544.212	310.727	226.472	16.162
pearson	0.487832	0.487696	0.482821	0.487136	0.492101
spearman	0.500846	0.506214	0.501048	0.506718	0.479468

• W2V (skip gram, negative sampling)

attr	1 workers (gensim)	2 workers (gensim)	4 workers (gensim)	8 workers (gensim)	NVIDIA T4 (cusim)
training time (sec)	586.545	340.489	220.804	146.23	33.9173
pearson	0.354448	0.353952	0.352398	0.352925	0.360436
spearman	0.369146	0.369365	0.370565	0.365822	0.355204

• W2V (CBOW, hierarchical softmax)

attr	1 workers (gensim)	2 workers (gensim)	4 workers (gensim)	8 workers (gensim)	NVIDIA T4 (cusim)
training time (sec)	250.135	155.121	103.57	73.8073	6.20787
pearson	0.309651	0.321803	0.324854	0.314255	0.480298
spearman	0.294047	0.308723	0.318293	0.300591	0.480971

• W2V (CBOW, negative sampling)

attr	1 workers (gensim)	2 workers (gensim)	4 workers (gensim)	8 workers (gensim)	NVIDIA T4 (cusim)
training time (sec)	176.923	100.369	69.7829	49.9274	9.90391
pearson	0.18772	0.193152	0.204509	0.187924	0.368202
spearman	0.243975	0.24587	0.260531	0.237441	0.358042

• LDA (nytimes dataset from https://archive.ics.uci.edu/ml/datasets/bag+of+words)
  • I found that setting workers variable in gensim LdaMulticore does not work properly (it uses all cores in instance anyway), so I just compared the speed between cusim with single GPU and gensim with 8 vcpus.
  • One can compare the quality of modeling by looking at examples/cusim.topics.txt and examples/gensim.topics.txt.

attr	gensim (8 vpus)	cusim (NVIDIA T4)
training time (sec)	447.376	76.6972

Future tasks

• support half precision
• support multi device (multi device implementation on LDA model will not be that hard, while multi device training on w2v may require some considerations)
• implement other models such as FastText, BERT, etc
• contribution is always welcome