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FunFact: Build Your Own Tensor Decomposition Model in a Breeze

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FunFact is a Python package that aims to simplify the design of matrix and tensor factorization algorithms. It features a powerful programming interface that augments the NumPy API with Einstein notations for writing concise tensor expressions. Given an arbitrary forward calculation scheme, the package will solve the corresponding inverse problem using stochastic gradient descent, automatic differentiation, and multi-replica vectorization. Its application areas include quantum circuit synthesis, tensor decomposition, and neural network compression. It is GPU- and parallelization-ready thanks to modern numerical linear algebra backends such as JAX/TensorFlow and PyTorch.

Quick start example: semi-nonnegative CP decomposition

Install from pip

pip install -U  "funfact[torch]"
Note: this installs FunFact with the PyTorch autograd backend. See the installation page for more installation options.

Package import

import funfact as ff
import numpy as np

Create target tensor

T = np.arange(60, dtype=np.float32).reshape(3, 4, 5); T

array([[[ 0.,  1.,  2.,  3.,  4.],
        [ 5.,  6.,  7.,  8.,  9.],
        [10., 11., 12., 13., 14.],
        [15., 16., 17., 18., 19.]],

    [[20., 21., 22., 23., 24.],
        [25., 26., 27., 28., 29.],
        [30., 31., 32., 33., 34.],
        [35., 36., 37., 38., 39.]],

    [[40., 41., 42., 43., 44.],
        [45., 46., 47., 48., 49.],
        [50., 51., 52., 53., 54.],
        [55., 56., 57., 58., 59.]]], dtype=float32)

Define abstract tensors and indices

R = 2
a = ff.tensor('a', T.shape[0], R, prefer=ff.conditions.NonNegative())
b = ff.tensor('b', T.shape[1], R)
c = ff.tensor('c', T.shape[2], R)
i, j, k, r = ff.indices('i, j, k, r')

Create a tensor expression

Note

This only specifies the algebra but does not carry out the computation immediately)

tsrex = (a[i, ~r] * b[j, r]) * c[k, r]; tsrex
\[{{{\boldsymbol{a}}_{{{i}}{{\widetilde{r}}}}} {{\boldsymbol{b}}_{{{j}}{{r}}}}} {{\boldsymbol{c}}_{{{k}}{{r}}}}\]

Find rank-2 approximation

fac = ff.factorize(tsrex, T, max_steps=1000, vec_size=8, penalty_weight=10)
fac.factors

100%|██████████| 1000/1000 [00:03<00:00, 304.00it/s]
<'data' fields of tensors a, b, c>

Reconstruction

fac()

DeviceArray([[[-0.234,  0.885,  2.004,  3.123,  4.243],
              [ 4.955,  5.979,  7.002,  8.025,  9.049],
              [10.145, 11.072, 12.   , 12.927, 13.855],
              [15.335, 16.167, 16.998, 17.83 , 18.661]],

             [[20.025, 21.014, 22.003, 22.992, 23.981],
              [25.019, 26.01 , 27.001, 27.992, 28.983],
              [30.013, 31.006, 31.999, 32.992, 33.985],
              [35.007, 36.002, 36.997, 37.992, 38.987]],

             [[40.281, 41.14 , 41.999, 42.858, 43.716],
              [45.082, 46.04 , 46.999, 47.958, 48.917],
              [49.882, 50.941, 51.999, 53.058, 54.117],
              [54.682, 55.841, 56.999, 58.158, 59.316]]], dtype=float32)

Factors

fac['a']

DeviceArray([[1.788, 1.156],
            [3.007, 0.582],
            [4.226, 0.008]], dtype=float32)

fac['b']

DeviceArray([[-2.923, -4.333],
            [-3.268, -3.541],
            [-3.614, -2.749],
            [-3.959, -1.957]], dtype=float32)

fac['c']

DeviceArray([[-3.271,  3.461],
            [-3.341,  3.309],
            [-3.41 ,  3.158],
            [-3.479,  3.006],
            [-3.548,  2.855]], dtype=float32)

Statement of Need

Tensor factorizations have numerous applications in various domains, such as tensor networks in quantum physics, tensor decompositions in machine learning and signal processing, and quantum computing. Most tensor factorization models are solved by special-purpose algorithms designed to factor the target data into a model with the prescribed structure. Furthermore, the models under consideration are often limited to linear contractions between the factor tensors, such as standard inner and outer products, elementwise multiplications, and matrix Kronecker products. Extending such a special-purpose solver to more generalized models can be daunting, especially if nonlinear operations are considered.

FunFact addresses this problem and fills the gap. It offers an embedded Domain Specific Language (eDSL) in Python for creating nonlinear tensor algebra expressions that use generalized Einstein operations. Using the eDSL, users can create custom tensor expressions and immediately use them to solve the corresponding inverse factorization problem. FunFact solves the inverse problem by combining stochastic gradient descent, automatic differentiation, and model vectorization for multi-replica learning. This combination achieves instantaneous time-to-algorithm for all conceivable tensor factorization models. It allows the user to explore the entire universe of nonlinear tensor factorization models. The software is designed to be accessible for everyone interested in tensor methods.

How to cite

If you use this package for a publication (either in-paper or electronically), please cite it using the following DOI: https://doi.org/10.11578/dc.20210922.1

Contributors

Current developers:

Previou contributors:

FunFact Copyright (c) 2021, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy). All rights reserved.

If you have questions about your rights to use or distribute this software, please contact Berkeley Lab's Intellectual Property Office at IPO@lbl.gov.

NOTICE. This Software was developed under funding from the U.S. Department of Energy and the U.S. Government consequently retains certain rights. As such, the U.S. Government has been granted for itself and others acting on its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the Software to reproduce, distribute copies to the public, prepare derivative works, and perform publicly and display publicly, and to permit others to do so.

Funding Acknowledgment

This work was supported by the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory under U.S. Department of Energy Contract No. DE-AC02-05CH11231.

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