README.md

word2ket

word2ket is an space-efficient embedding layer that can reduce the space required to store the embeddings by up to 100,000x.

This is a PyTorch implementaion of the embedding layer that is proposed in the paper word2ket: Space-efficient Word Embeddings inspired by Quantum Entanglement.

• word2ket
• Installation
  • Dependencies
  • Install word2ket
• Getting Started
• Running examples
  • Dependencies
  • Download Datasets
  • Run text summarization
  • Run German-English machine translation
• Reference
  • APA
  • BibTex
• License

Installation

Dependencies

# Install PyTorch
conda install pytorch torchvision -c pytorch
# Install GPyTorch
conda install gpytorch -c gpytorch

Install word2ket

pip install word2ket

Getting Started

You can directly use EmbeddingKet and EmbeddingKetXS layers in your model definition or you can use the ketify function to automatically replace all the nn.Embedding layers in your model. Below you can see a model that is using 7,680,000 parameters for its embedding could be ketify to just use 448 parameters.

# examples/demo.py 
from word2ket import EmbeddingKet, EmbeddingKetXS, ketify, summary
from torch import nn
import logging
logging.basicConfig(level=logging.INFO)


# Word2Ket Embedding Layer
w2v_embedding_layer = EmbeddingKetXS(num_embeddings=30000, embedding_dim=256, order=4, rank=1)
summary(w2v_embedding_layer)
"""
INFO:root:EmbeddingKetXS num_embeddings_leaf: 14
INFO:root:EmbeddingKetXS embedding_dim_leaf: 4
INFO:root:EmbeddingKetXS weight_leafs shape: torch.Size([4, 1, 14, 4])
Module Name                                                                           Total Parameters  Trainable Parameters # Elements in Trainable Parametrs       
EmbeddingKetXS(30000, 256)                                                            1                 1                    224                                     
Total number of trainable parameters elements 224
"""

# PyTorch Embedding Layer
class MyModel(nn.Module):
    def __init__(self, vocab_size, embedding_dim):
        super(MyModel, self).__init__()
        self.embedding = nn.Embedding(num_embeddings=vocab_size, embedding_dim=embedding_dim)
    
    def forward(self, x):
        self.embedding()
model = MyModel(vocab_size=30000, embedding_dim=256)
print("embedding.weight.shape: ", model.embedding.weight.shape)
"""
embedding.weight.shape:  torch.Size([30000, 256])
"""
summary(model)
"""
Module Name                                                                           Total Parameters  Trainable Parameters # Elements in Trainable Parametrs       
Embedding(30000, 256)                                                                 1                 1                    7,680,000                               
Total number of trainable parameters elements 7,680,000
"""

# Replace the nn.Embedding to EmbeddingKetXS automatically using the ketify function.
ketify(model, order=4, rank=2, use_EmbeddingKetXS=True)
summary(model)
"""
INFO:root:EmbeddingKetXS num_embeddings_leaf: 14
INFO:root:EmbeddingKetXS embedding_dim_leaf: 4
INFO:root:EmbeddingKetXS weight_leafs shape: torch.Size([4, 2, 14, 4])
INFO:root:Replaced embedding in MyModel
Module Name                                                                           Total Parameters  Trainable Parameters # Elements in Trainable Parametrs       
EmbeddingKetXS(30000, 256)                                                            1                 1                    448                                     
Total number of trainable parameters elements 448
"""

Running examples

Dependencies

# Install Texar-PyTorch
pip install texar-pytorch
# Install Rouge
pip install rouge

Download Datasets

cd ./examples/texar/
python ./prepare_data.py --data iwslt14
python ./prepare_data.py --data giga

Run text summarization

Using GIGAWORD dataset.

# Using Pytorch nn.Embedding Layer
python seq2seq_attn.py --embedding_type nn.Embedding --gpu 4 --runName G_000 --config-model config_model --config-data config_giga

# Using EmbeddingKet Layer
python seq2seq_attn.py --embedding_type EmbeddingKet   --gpu 0 --runName V2K_G_000       --config-model config_model --config-data config_giga --order 4 --rank 1

# Using EmbeddingKetXS Layer
python seq2seq_attn.py --embedding_type EmbeddingKetXS --gpu 0 --runName V2K_XS_G_000 --config-model config_model --config-data config_giga --order 4 --rank 1

Run German-English machine translation

Using IWSLT2014 (DE-EN) dataset

# Using Pytorch nn.Embedding Layer
python seq2seq_attn.py --embedding_type nn.Embedding --gpu 4 --runName I_000 --config-model config_model --config-data config_iwslt14

# Using EmbeddingKet Layer
python seq2seq_attn.py --embedding_type EmbeddingKet   --gpu 0 --runName V2K_I_000       --config-model config_model --config-data config_iwslt14 --order 4 --rank 1

# Using EmbeddingKetXS Layer
python seq2seq_attn.py --embedding_type EmbeddingKetXS --gpu 0 --runName V2K_XS_I_000 --config-model config_model --config-data config_iwslt14 --order 4 --rank 1

Reference

If you use word2ket, please cite our ICLR 2020 paper with the following BibTex entry:

APA

Panahi, A., Saeedi, S., & Arodz, T. (2019). word2ket: Space-efficient Word Embeddings inspired by Quantum Entanglement. In International Conference on Learning Representations 2020. Retrieved from https://openreview.net/forum?id=HkxARkrFwB

BibTex

@inproceedings{panahi2020wordket,
  title={word2ket: Space-efficient Word Embeddings inspired by Quantum Entanglement},
  author={Aliakbar Panahi and Seyran Saeedi and Tom Arodz},
  booktitle={International Conference on Learning Representations 2020},
  year={2019},
  url={https://openreview.net/forum?id=HkxARkrFwB}
}

License

BSD-3-Clause