transformer

A PyTorch Implementation of Transformer in Attention Is All You Need.
This repository focused on implementing the contents of the paper as much as possible.

Intro

This repository focused on implementing the contents of the paper as much as possible,
while at the same time striving for a readable code. To improve readability,
I designed the model structure to fit as much as possible to the blocks in the above Transformers figure.

Installation

This project recommends Python 3.7 or higher. We recommend creating a new virtual environment for this project (using virtual env or conda).

Prerequisites

• Numpy: pip install numpy (Refer here for problem installing Numpy).
• Pytorch: Refer to PyTorch website to install the version w.r.t. your environment.

Install from source

Currently we only support installation from source code using setuptools. Checkout the source code and run the following commands:

pip install -e .

Usage

import torch
import torch.nn as nn
from transformer import Transformer

BATCH_SIZE, SEQ_LENGTH, D_MODEL = 3, 10, 64

cuda = torch.cuda.is_available()  
device = torch.device('cuda' if cuda else 'cpu')

inputs = torch.zeros(BATCH_SIZE, SEQ_LENGTH).long().to(device)
input_lengths = torch.LongTensor([12345, 12300, 12000])
targets = torch.LongTensor([[1, 3, 3, 3, 3, 3, 4, 5, 6, 2],
                            [1, 3, 3, 3, 3, 3, 4, 5, 2, 0],
                            [1, 3, 3, 3, 3, 3, 4, 2, 0, 0]]).to(device)
target_lengths = torch.LongTensor([9, 8, 7])

model = nn.DataParallel(Transformer(num_input_embeddings=30, num_output_embeddings=50, 
                                    d_model=64, 
                                    num_encoder_layers=3, num_decoder_layers=3)).to(device)

# Forward propagate
outputs = model(inputs, input_lengths, targets, target_lengths)

# Inference
outputs = model(inputs, input_lengths)

Troubleshoots and Contributing

If you have any questions, bug reports, and feature requests, please open an issue on github or
contacts [email protected] please.

I appreciate any kind of feedback or contribution. Feel free to proceed with small issues like bug fixes, documentation improvement. For major contributions and new features, please discuss with the collaborators in corresponding issues.

Code Style

I follow PEP-8 for code style. Especially the style of docstrings is important to generate documentation.

Author

• Soohwan Kim @sooftware
• Contacts: [email protected]