A suitable conda environment named qsr can be created
and activated with:
conda create --name qsr
conda install -n qsr pip
conda activate qsr
pip install -r requirements.txt
This script is used to train new models from scratch. This is an example of a command
to train an
python train.py --train L=64 NLOOPS=16 K=1024 sub_directory=2x2 --ptf patch=2x2 --rydberg V=7 delta=1 Omega=1
Training parameters are shown when running:
python train.py --help --train
These are all possible training arguments:
Training Arguments:
L (int) -- Total lattice size (8x8 would be L=64).
Q (int) -- Number of minibatches per batch.
K (int) -- size of each minibatch.
B (int) -- Total batch size (should be Q*K).
NLOOPS (int) -- Number of loops within the off_diag_labels function. Higher values save ram and
generally makes the code run faster (up to 2x). Note, you can only set this
as high as your effective sequence length. (Take L and divide by your patch size).
steps (int) -- Number of training steps.
dir (str) -- Output directory, set to <NONE> for no output.
lr (float) -- Learning rate.
seed (int) -- Random seed for the run.
sgrad (bool) -- Whether or not to sample with gradients, otherwise create gradients in extra network run.
(Uses less ram when but slightly slower)
true_grad (bool) -- Set to false to approximate the gradients, more efficient but approximate.
sub_directory (str) -- String to add to the end of the output directory (inside a subfolder).
All optional rnn parameters can be viewed by running
python train.py --help --rnn
These are the RNN parameters:
RNN Optional arguments:
L (int) -- The total number of atoms in your lattice.
Nh (int) -- RNN hidden size.
patch (str) -- Number of atoms input/predicted at once (patch size).
The Input sequence will have an effective length of L/prod(patch).
Example values: 2x2, 2x3, 2, 4
rnntype (string) -- Which type of RNN cell to use. Only ELMAN and GRU are valid options at the moment.
All optional ptf parameters can be viewed by running
python train.py --help --ptf
These are your PTF parameters:
PTF Optional arguments:
L (int) -- The total number of atoms in your lattice.
Nh (int) -- Transformer token size. Input patches are projected to match the token size.
patch (str) -- Number of atoms input/predicted at once (patch size).
The Input sequence will have an effective length of L/prod(patch).
Example values: 2x2, 2x3, 2, 4
dropout (float) -- The amount of dropout to use in the transformer layers.
num_layers (int) -- The number of transformer layers to use.
nhead (int) -- The number of heads to use in Multi-headed Self-Attention. This should divide Nh.
repeat_pre (bool) -- Repeat the precondition (input) instead of projecting it out to match the token size.
All optional LPTF parameters can be viewed by running
python train.py --help --lptf
LPTF parameters must be followed by the sub-model (e.g. --rnn) and the corresponding parameters, where the L parameter needs to match the patch parameter of the LPTF (e.g. --lptf path=2x3 --rnn L=2x3).
These are your LPTF parameters:
LPTF Optional arguments:
L (int) -- The total number of atoms in your lattice.
Nh (int) -- Transformer token size. Input patches are projected to match the token size.
Note: When using an RNN subsampler this Nh MUST match the rnn's Nh.
patch (int) -- Number of atoms input/predicted at once (patch size).
The Input sequence will have an effective length of L/patch.
dropout (float) -- The amount of dropout to use in the transformer layers.
num_layers (int) -- The number of transformer layers to use.
nhead (int) -- The number of heads to use in Multi-headed Self-Attention. This should divide Nh.
subsampler (Sampler) -- The inner model to use for probability factorization. This is set implicitly
by including --rnn or --ptf arguments.
The following parameters can be chosen for the Rydberg Hamiltonian:
Lx 4
Ly 4
V 7.0
Omega 1.0
delta 1.0