Eidolon

eidolon: in ancient Greek mythology, an eidolon was the spirit-image of a living or deceased person.

Here, tensor networks aim to capture the spirit of language-like data: the Motzkin spin chains. This code was written for my PhD thesis project under the supervision of Dr. David Schwab and Dr. John Terilla.

Running the learning loops

1. Create a conda environment using conda [environment name] create -f environment.yml. Replace "[environment name]" with the desired environment name. Then, activate the environment with conda activate [environment name].
2. N.b. environment.yml uses the cpu version of Jax, and motzkin_gpu.yml uses the gpu version.
3. Configure the various parameters as desired in the conf/ directory.
4. From the src/ directory, run python main.py name=[run name], where "[run_name]" is the name the logger will save the logfiles to (output directory printed by the code). They should end up in an "experiments" directory; if an error arises, one might need to make that directory first.

Running tests

From the root directory, run pytest tests. To see printouts of the errors calculated in each test, run pytest -s tests.

The tests are run on sequences of length 8. Note that for each test, a scaled error is calculated. When comparing the outputs of two calculation methods, call them x and y, the scaled error is 2*|x+y|/(x+y); that is, the distance between outputs divided by their average value. The threshold to pass is that the scaled error should be belore 1e-6.

The tests are:

1. test_contract_einsum: compare the output of contraction of the dense model with a single input sequence to that calculated using jax numpy einsum.
2. test_john_contractr': compare the output of contraction of the dense model with a single input sequence to that calculated using John's function.
3. test_tests: compare the outputs of the einsum and John's methods from the previous tests as a sanity check.
4. test_einsum norm: compare the norm calculation of the dense model to one calculated using jax numpy einsum.
5. test_john_norm: compare the norm calculation of the dense model to one calculated using John's method.
6. test_dense_sum_contractions_norm: compare the norm calculation of the dense model to the sum of all unnormalized probabilities of the dense model.
7. test_factored_sum_contractions_norm: compare the norm calculation of the factored model to the sum of all unnormalized probabilities of the factored model.
8. test_grad_batching: test the parameter gradient output (for the dense model) from a pmap'ed batch to one calculated by adding the gradients from each element of the batch, then dividing by the total number of elements.