tensorkrowch 1.1.5

Tensor Networks with PyTorch

Tensor Networks with PyTorch

TensorKrowch is a Python library built on top of PyTorch that simplifies the training of Tensor Networks as machine learning models and their integration into deep learning pipelines.

The primary goal of TensorKrowch is to offer an efficient and user-friendly framework for constructing and training diverse Tensor Networks. By providing essential components like Nodes, Edges, and TensorNetworks, TensorKrowch facilitates the creation and training of these models. Notably, even the included implementations of MPS or PEPS only rely on these fundamental components.

As a result, users who grasp the basic tools of TensorKrowch gain the ability to build a wide range of networks, ranging from simple Matrix Product States to more intricate architectures.

The true strength of TensorKrowch lies in its support for rapid experimentation, enabling users to create and train different models with just a few lines of code changes.

It's important to note that while TensorKrowch is a versatile library, it may not always be the fastest option in certain scenarios. However, it excels as a tool for exploration and identification of the most suitable Tensor Network. Once the ideal network is determined, users can develop further optimized code specifically tailored to that network.

Nevertheless, TensorKrowch incorporates various optimizations to ensure efficient training performance.

Documentation

For detailed usage instructions, API reference, and code examples, please refer to the official TensorKrowch documentation.

Requirements

• python >= 3.8
• torch >= 1.9
• opt_einsum >= 3.0

Installation

To install the package, run the following command:

pip install tensorkrowch

You can also install directly from GitHub with:

pip install git+https://github.com/joserapa98/tensorkrowch.git@master

or download the repository on your computer and run

pip install .

in the repository folder.

Tests are written outside the Python module, therefore they are not installed together with the package. To test the installation, clone the repository and run, in a Unix terminal

python -m pytest -v

inside the repository folder.

[!NOTE] Certain tests may experience failure as a result of statistical anomalies or hardware constraints. We advise reviewing the error messages to determine if these failures stem from such occurrences. Should this be the case, consider rerunning the tests to ascertain if the errors persist.

Example

With TensorKrowch you can experiment building Tensor Networks:

import torch
import tensorkrowch as tk

net = tk.TensorNetwork()

node1 = tk.randn(shape=(7, 5),
                 axes_names=('left', 'right'),
                 name='node1',
                 network=net,
                 param_node=True)
node2 = tk.randn(shape=(7, 5),
                 axes_names=('left', 'right'),
                 name='node2',
                 network=net,
                 param_node=True)

node1['left'] ^ node2['left']
node1['right'] ^ node2['right']

It is also quite easy to contract the network and compute gradients:

result = node1 @ node2
result.tensor.backward()

assert node1.grad is not None
assert node2.grad is not None

In TensorKrowch TensorNetworks work like PyTorch layers. Thus creating hybrid neural-tensor network models is straightforward:

import torch.nn as nn

my_model = nn.Sequential(
    tk.models.MPSLayer(n_features=101,
                       in_dim=3,
                       out_dim=10,
                       bond_dim=5),
    nn.ReLU(),
    nn.Linear(10, 10))

data = torch.randn(500, 100, 3)  # batch x n_features x in_dim
my_model(data)  # batch x out_dim

Tutorials

To fully grasp the basic components of TensorKrowch and harness its potential, it is highly recommended to explore the available tutorials. These tutorials provide a detailed introduction to the fundamental elements of the library and guide you through the process of constructing and training tensor networks.

By immersing yourself in the tutorials, you will become familiar with key concepts and best practices for using TensorKrowch. You will learn how to define Nodes, create connections between through their Edges, and configure the TensorNetwork structure. This hands-on approach will greatly enhance your understanding and proficiency with TensorKrowch.

• First Steps with TensorKrowch
• Creating a Tensor Network in TensorKrowch
• Contracting and Differentiating the Tensor Network
• How to save Memory and Time with TensorKrowch (ADVANCED)
• The different Types of Nodes (ADVANCED)
• How to subclass TensorNetwork to build Custom Models
• Creating a Hybrid Neural-Tensor Network Model

Example Notebooks

In addition to the informative tutorials, there is also a collection of examples that serve as practical demonstrations of how to apply TensorKrowch in various contexts, showcasing its versatility.

With the code provided in the examples, you will be able to reproduce key research findings that bridge the gap between tensor networks and machine learning. These examples provide a hands-on approach to understanding the intricacies of TensorKrowch, allowing you to explore its potential and adapt it to your specific needs.

• Training MPS in different ways
• Hybrid Tensorial Neural Network model
• Tensorizing Neural Networks
• DMRG-like training of MPS
• Hybrid DMRG-like training of MPS

License

TensorKrowch is licensed under the MIT License. Please see the LICENSE file for more information.

Citing

If you use TensorKrowch in your work, please cite TensorKrowch's paper:

• J. R. Pareja Monturiol, D. Pérez-García, and A. Pozas-Kerstjens, "TensorKrowch: Smooth integration of tensor networks in machine learning", Quantum 8, 1364 (2024), arXiv:2306.08595.

@article{pareja2024tensorkrowch,
  title={Tensor{K}rowch: {S}mooth integration of tensor networks in machine learning},
  author={Pareja Monturiol, Jos{\'e} Ram{\'o}n and P{\'e}rez-Garc{\'i}a, David and Pozas-Kerstjens, Alejandro},
  journal={Quantum},
  volume={8},
  pages={1364},
  year={2024},
  publisher={Verein zur F{\"o}rderung des Open Access Publizierens in den Quantenwissenschaften},
  doi = {10.22331/q-2024-06-11-1364},
  archivePrefix = {arXiv},
  eprint = {2306.08595}
}