Library for Abelian symmetry preserving tensors.
abeliantensors is a Python 3 package that implements U(1) and Zn symmetry preserving tensors, as described by Singh et al. in arXiv: 0907.2994 and arXiv: 1008.4774. abeliantensors has been designed for use in tensor network algorithms, and works well with the ncon function.
If you just want to use the library:
pip install --user git+https://github.com/mhauru/abeliantensors
If you also want to modify and develop the library
git clone https://github.com/mhauru/abeliantensors cd abeliantensors pip install --user -e .[tests]
after which you can run the test suite by just calling
abeliantensors exports classes
cyclic groups Zn can be implemented with one-liners, see the file
symmetrytensors.py for examples. abeliantensors also exports a class called
Tensor, that is just a wrapper around regular numpy ndarrays, but that
implements the exact same interface as the symmetric tensor classes. This
allows for easy switching between utilizing and not utilizing the symmetry
preserving tensors by simply changing the class that is imported.
Each symmetric tensor has, in addition to its tensor elements, the following pieces of what we call form data:
shapedescribes the dimensions of the tensors, just like with numpy arrays. The difference is that for symmetric tensors the dimension of each index isn't just a number, but a list of numbers, that sets how the vector space is partitioned by the irreducible representations (irreps) of the symmetry. So for instance
shape=[[2,3], [5,4]]could be the shape of a Z2 symmetric matrix of dimensions 5 x 9, where the first 2 rows and 5 columns are associated with one of the two irreps of Z2, and the remaining 3 rows and 4 columns with the other.
shape, but lists the irrep charges instead of the dimensions. Irrep charges are often also called quantum numbers, hence the q. In the above example
qhape=[[0,1], [0,1]]would mark the first part of both the row and column space to belong to the trivial irrep of charge 0, and the second part to the irrep with charge 1. For Zn the possible charges are 0, 1, ..., n, for U(1) they are all positive and negative integers.
dirsis a list of 1s and -1s, that gives a direction to each index: either 1 for outgoing or -1 for ingoing.
chargeis an integer, the irrep charge associated to tensor. In most cases you want
charge=0, which is also the default when creating new tensors.
Note that each element of the tensor is associated with one irrep charge for
each of the indices. The symmetry property is then that an element can only be
non-zero if the charges from each index, multiplied by the direction of that
index, add up to the charge of the tensor. Addition of charges for Zn tensors
is modulo n. For instance for a
TensorZ2 object this means that
the charges on each leg must add up to an even number for an element to be
non-zero. The whole point of this library is to store and use such symmetric
tensors in an efficient way, where we don't waste memory or computation time on
the elements we know are zero by symmetry, and can't accidentally let them be
Here's a simple nonsense example of how abeliantensors can be used:
import numpy as np from abeliantensors import TensorZ2 # Create a symmetric tensor from an ndarray. All elements that should be zero # by symmetry are simply discarded, whether they are zero or not. sigmaz = np.array([[1, 0], [0, -1]]) sigmaz = TensorZ2.from_ndarray( sigmaz, shape=[[1, 1], [1, 1]], qhape=[[0, 1], [0, 1]], dirs=[1, -1] ) # Create a random symmetric tensor. a = TensorZ2.random( shape=[[3, 2], [2, 4], [4, 4], [1, 1]], qhape=[[0, 1]] * 4, dirs=[-1, 1, 1, -1], ) # Do a singular value decomposition of a tensor, thinking of it as a matrix # with some of the indices combined to a single matrix index, like one does # with numpy.reshape. Here we combine indices 0 and 2 to form the left matrix # index, and 1 and 3 to form the right one. The indices are reshaped back to # the original form after the SVD, so U and V are in this case order-3 tensors. U, S, V = a.svd([0, 2], [1, 3]) # You can also do a truncated SVD, in this case to truncating to dimension 4. U, S, V = a.svd([0, 2], [1, 3], chis=4) # We can contract tensors together easily using the ncon package. # Note that conjugation flips the direction of each index, as well as the # charge of the tensor, which in this case though is 0. from ncon import ncon aadg = ncon((a, a.conjugate()), ([1, 2, -1, -2], [1, 2, -11, -12])) # Finally, knowing that aadg is Hermitian, do an eigenvalue # decomposition of it, this time truncating not to a specific dimension, but # to a maximum relative truncation error of 1e-5. E, U = aadg.eig([0, 1], [2, 3], hermitian=True, eps=1e-5)
There are many other user-facing methods and features, for more, see the API docs.
Demo and performance
demo has an implementation of Levin and Nave's TRG
algorithm, and a script
that runs it on the square lattice Ising model, using both symmetric tensors
of the TensorZ2 class and dense Tensors, and compares the run times.
Below is a plot of how long it takes to run a single TRG step at various
bond dimensions for both of them.
Note that both axes are logarithmic.
At low bond dimensions the simple
Tensor class outperforms
keeping track of the symmetry structure imposes an overhead. The time
complexity of the overhead is subleading as a function of bond dimension, and
as one goes to higher bond dimensions the symmetric tensors become faster.
Asymptotically both have the same scaling as a function of bond dimension, but
the prefactor is smaller for
TensorZ2 by a factor of 1/4. This is because
instead of multiplying or decomposing an
m matrix at cost
are multiplying two
m/2 matrices, at a total cost of
2*(m/2)**3 = (m**3)/4. For larger symmetry groups, the asymptotic benefit would be
greater. For instance for
TensorZ3, we should see an approximately 9-fold
Similar results can be obtained for other algorithms, although the cross-over point in bond dimension will be different.
Design and structure
The implementation is built on top of numpy, and the block-wise sparse structure of the symmetry preserving tensors is implemented with Python dictionaries. Here's a quick summary of what each file does.
tensorcommon.py: A parent class of all the other classes,
that implements some higher-level features using the lower-level methods.
abeliantensor.py: All the fun is in here. Implements the class
AbelianTensor, that is the parent of all the symmetric tensor classes. This
includes implementations of various common tensor operations, such as
contractions and decompositions, preserving and making use of the block-wise
sparse structure these tensors have.
Tensor, the wrapper class for numpy arrays. It is designed so that
any call to a method of the
AbelianTensor class is also a valid call to a
similarly named method of the
Tensor class. All the symmetry-related
information is simply discarded and some underlying numpy function is called.
Even if one doesn't use symmetry preserving tensors, the
Tensor class provides
some neat convenience functions, such as an easy-to-read one-liner for the
transpose-reshape-decompose-reshape-transpose procedure for singular value and
eigenvalue decompositions of tensors.
symmetrytensors.py: A small file that simply creates subclasses of
AbelianTensor for specific symmetry groups. If you need something other than
Z2, Z3 and U(1), check this file to see how you could add what you need.
tests folder has plenty of tests for the various classes. They can be run
pytest, provided abeliantensors was installed with the extras option
Most of the tests are based on generating a random instance of one of the "fancy" tensor classes in this package, and confirming that the following diagram commutes:
Fancy tensor ─── map to numpy ndarray ───> ndarray │ │ │ │ Do the thing Do the thing │ │ │ │ V V Fancy tensor ─── map to numpy ndarray ───> ndarray
Two command line arguments can be provided,
--n_iters which sets how many
times each test is run, with different random tensors each time (100 by
--tensorclass which can be used to specify which
tensorclass(es) the tests are run on (by default all of them). Here's an
example of how one might run a specific test repeatedly:
pytest tests/test_tensors.py::test_to_and_from_ndarray --tensorclass TensorZ2 --n_iters 1000
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