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https://github.com/facebookresearch/gtn
Automatic differentiation with weighted finite-state transducers.
https://github.com/facebookresearch/gtn
Last synced: 15 days ago
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Automatic differentiation with weighted finite-state transducers.
- Host: GitHub
- URL: https://github.com/facebookresearch/gtn
- Owner: facebookresearch
- License: mit
- Archived: true
- Created: 2020-09-29T17:32:06.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2021-09-20T16:32:36.000Z (about 3 years ago)
- Last Synced: 2024-08-01T03:28:56.101Z (3 months ago)
- Language: C++
- Size: 825 KB
- Stars: 452
- Watchers: 38
- Forks: 40
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
# Deprecated
This repository has been deprecated.
Future development will be in the [GTN org](https://github.com/gtn-org/gtn) repository.
# GTN: Automatic Differentiation with WFSTs
[**Quickstart**](#quickstart)
| [**Installation**](#installation)
| [**Documentation**](https://gtn.readthedocs.io/en/latest/)
[](https://circleci.com/gh/facebookresearch/gtn)
[](https://gtn.readthedocs.io/en/latest/)
## What is GTN?
GTN is a framework for automatic differentiation with weighted finite-state
transducers. The framework is written in C++ and has bindings to
Python.
The goal of GTN is to make adding and experimenting with structure in learning
algorithms much simpler. This structure is encoded as weighted automata, either
acceptors (WFSAs) or transducers (WFSTs). With `gtn` you can dynamically construct complex
graphs from operations on simpler graphs. Automatic differentiation gives gradients with respect to any input or intermediate graph
with a single call to `gtn.backward`.
Also checkout the repository [gtn_applications](https://github.com/facebookresearch/gtn_applications) which consists of GTN applications to Handwriting Recognition (HWR), Automatic Speech Recognition (ASR) etc.
## Quickstart
First [install](#installation) the python bindings.
The following is a minimal example of building two WFSAs with `gtn`, constructing a simple function on the graphs, and computing gradients. [](https://colab.research.google.com/github/facebookresearch/gtn/blob/master/examples/notebooks/quick-start.ipynb)
```python
import gtn
# Make some graphs:
g1 = gtn.Graph()
g1.add_node(True) # Add a start node
g1.add_node() # Add an internal node
g1.add_node(False, True) # Add an accepting node
# Add arcs with (src node, dst node, label):
g1.add_arc(0, 1, 1)
g1.add_arc(0, 1, 2)
g1.add_arc(1, 2, 1)
g1.add_arc(1, 2, 0)
g2 = gtn.Graph()
g2.add_node(True, True)
g2.add_arc(0, 0, 1)
g2.add_arc(0, 0, 0)
# Compute a function of the graphs:
intersection = gtn.intersect(g1, g2)
score = gtn.forward_score(intersection)
# Visualize the intersected graph:
gtn.draw(intersection, "intersection.pdf")
# Backprop:
gtn.backward(score)
# Print gradients of arc weights
print(g1.grad().weights_to_list()) # [1.0, 0.0, 1.0, 0.0]
```
## Installation
### Requirements
- A C++ compiler with good C++14 support (e.g. g++ >= 5)
- `cmake` >= 3.5.1, and `make`
### Python
Install the Python bindings with
```
pip install gtn
```
### Building C++ from source
First, clone the project:
```
git clone [email protected]:facebookresearch/gtn.git && cd gtn
```
Create a build directory and run CMake and make:
```
mkdir -p build && cd build
cmake ..
make -j $(nproc)
```
Run tests with:
```
make test
```
Run `make install` to install.
### Python bindings from source
Setting up your environment:
```
conda create -n gtn_env
conda activate gtn_env
```
Required dependencies:
```
cd bindings/python
conda install setuptools
```
Use one of the following commands for installation:
```
python setup.py install
```
or, to install in editable mode (for dev):
```
python setup.py develop
```
Python binding tests can be run with `make test`, or with
```
python -m unittest discover bindings/python/test
```
Run a simple example:
```
python bindings/python/examples/simple_graph.py
```
### License
GTN is licensed under a MIT license. See [LICENSE](LICENSE).