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https://github.com/vardigroup/TensorOrder
A tool for weighted model counting through tensor network contraction
https://github.com/vardigroup/TensorOrder
Last synced: 3 months ago
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A tool for weighted model counting through tensor network contraction
- Host: GitHub
- URL: https://github.com/vardigroup/TensorOrder
- Owner: vardigroup
- License: mit
- Created: 2019-07-12T20:36:28.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2021-11-13T07:01:21.000Z (almost 3 years ago)
- Last Synced: 2024-07-04T02:13:27.730Z (4 months ago)
- Language: Python
- Size: 1.82 MB
- Stars: 20
- Watchers: 3
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# TensorOrder
A Python 3 tool for automatically contracting tensor networks for weighted model counting on multiple CPUs and on a GPU.
## Running with docker
Because of the variety of dependencies used in the various graph decomposition tools, it is recommended to use the docker container to run TensorOrder.
### Building the container
The docker container (for singlecore and multi-core) can be built with the following commands:
```
docker build --tag tensororder .
```
In order to leverage a GPU, you must compile the (larger) docker container with TensorFlow and gpu drivers:
```
docker build --tag tensororder-gpu -f Dockerfile-gpu .
```
### Using the container
Once built, docker containers can be used as follows to run TensorOrder:
```
docker run -i tensororder:latest python /src/tensororder.py --planner="line-Flow" --weights="unweighted" < "benchmarks/cubic_vertex_cover/cubic_vc_50_0.cnf"
```
By default, this runs the tensor network contraction on all available CPU cores with numpy. One can also choose to use the GPU to perform the contraction. This requires [nvidia-container-runtime](https://nvidia.github.io/nvidia-container-runtime/) to be installed.
```
docker run -i --gpus all tensororder-gpu:latest python /src/tensororder.py --planner="line-Flow" --weights="unweighted" --tensor_library="tensorflow-gpu" < "benchmarks/cubic_vertex_cover/cubic_vc_50_0.cnf"
```
It is also possible to connect to a TPU on Google Cloud to perform the tensor contractions:
```
docker run -i tensororder:latest python /src/tensororder.py --timeout="1000" --planner="line-Flow" --weights="unweighted" --tensor_library="jax-tpu" --entry_type="float32" --tpu="10.6.165.2" < "benchmarks/cubic_vertex_cover/cubic_vc_50_0.cnf"
```
Both docker containers are compatible with [Turbine](https://github.com/Kasekopf/Turbine) to run experiments on Google Cloud.
## Running with Singularity
There is also a [Singularity](https://sylabs.io/singularity/) container available for TensorOrder.
### Building the container
The Singularity container can be built with the following commands:
```
sudo singularity build tensororder Singularity
```
### Using the container
Once built, Singularity containers can be used as follows to run TensorOrder:
```
./tensororder --method="line-Flow" < "benchmarks/cubic_vertex_cover/cubic_vc_50_0.cnf"
```
## Running without containers
TensorOrder can also be used directly as a Python 3 tool. Since TensorOrder uses [Cython](https://cython.org/), it must be compiled:
```
make -C src
```
Moreover, the various tensor methods each require additional setup. Consult the [Docker file](Dockerfile) for an example set of installation commands.
* For KCMR-metis and KCMR-gn, METIS must be installed using the instructions [here](src/tensorcsp).
* For line-Tamaki and factor-Tamaki, the tree-decomposition solver Tamaki must be compiled using the `heuristic` instructions [here](solvers/TCS-Meiji).
* For line-Flow and factor-Flow, the tree-decomposition solver FlowCutter must be compiled using the instructions [here](solvers/flow-cutter-pace17).
* For line-htd and factor-htd, the tree-decomposition solver htd must be compiled using the instructions [here](solvers/htd-master).
* For factor-hicks, the branch-decomposition solver Hicks must be compiled using the Makefile [here](solvers/hicks).
* For line-portfolio3 and line-portfolio3, all tree-decompositions solvers must be compiled, and the portfolio must be compiled using the instructions [here](solvers/portfolio).
Once everything has been built, the primary script is located in `src/tensororder.py`. Example usage is
```
python src/tensororder.py --method="line-Flow" < "benchmarks/cubic_vertex_cover/cubic_vc_50_0.cnf"
```
TensorOrder requires the following python packages (see [requirements.txt](requirements.txt) for a working set of exact version information if needed):
1. `click`
2. `numpy`
3. `python-igraph`
4. `networkx`
5. `cython`
6. `threadpoolctl`
7. `tensorflow` (optional)
### Publications
Please cite the following article if you use our code in a publication:
* [Parallel Weighted Model Counting with Tensor Networks](https://arxiv.org/abs/2006.15512). Jeffrey M. Dudek and Moshe Y. Vardi. Proceedings of MCW'20.