nqxpack 0.1.8

Library to serialise objects arising from scientific and Machine Learning libraries, including NetKet.

NQXPack

A library to save and load objects coming from Scientific Machine Learning libraries, with a special attention to Neural Quantum States from NetKet.

Goals:

• Simple format, possible to hand-edit and inspect manually;
• Compatibility among Python version;
• Allows to load Neural Networks with a single load command;

Usage

Install with

uv add git+https://github.com/NeuralQXLab/nqxpack.git

or (but seriously, stop using pip and start using uv)

pip install git+https://github.com/NeuralQXLab/nqxpack.git

With flax.linen

Save a dictionary containing the model and the parameters. Note that you cannot serialise jax arrays for the time-being but it could easily be added (I'd need to think about how to handle sharding...)

import nqxpack
import jax
from flax import linen as nn
import numpy as np

model = nn.Sequential((
    nn.Dense(features=2),
    nn.gelu,
    nn.Dense(features=1),
    jax.numpy.squeeze,
))

variables = model.init(jax.random.key(1), jax.numpy.ones((2,4)))
variables_np = jax.tree.map(np.asarray, variables)

# for the moment cannot serialise jax arrays.
# Could easily be implemented
nqxpack.save({'model':model, 'variables':jax.tree.map(np.asarray, variables)}, "mymodel.nk")

loaded_dict = nqxpack.load("mymodel.nk")
loaded_model, loaded_variables = loaded_dict['model'], loaded_dict['variables']

With flax.nnx (WIP, not working yet)

import nqxpack
import jax
from flax import nnx
import numpy as np

rngs = nnx.Rngs(0)
model = nnx.Sequential(
  nnx.Linear(1, 4, rngs=rngs),  # data
  nnx.Linear(4, 2, rngs=rngs),  # data
)

graphdef, variables = nnx.split(model)
variables_np = jax.tree.map(np.asarray, variables.to_pure_dict())

# for the moment cannot serialise jax arrays.
# Could easily be implemented
nqxpack.save({'graphdef':graphdef, 'variables':variables_np}, "mymodel.nk")

loaded_dict = nqxpack.load("mymodel.nk")
loaded_graphdef, loaded_variables = loaded_dict['model'], loaded_dict['variables']

loaded_model = nnx.merge(loaded_graphdef, loaded_variables)

With NetKet

import nqxpack
import netket as nk

hi = nk.hilbert.Spin(0.5, 10)
operator = nk.operator.spin.sigmax(nqs_state.hilbert, 1)

nqs_state = nk.vqs.MCState(nk.sampler.MetropolisLocal(hi), nk.models.RBM(alpha=4))
# print expectation value:
nqs_state.expect(operator)

nqxpack.save(nqs_state, "nqs_state.nk")
nqs_state_loaded = nqxpack.load("nqs_state.nk")

nqs_state_loaded.expect(operator)

The format

The format is a single zip file. You can decompress it yourself and look into it.

Feedback required

If you use this library, please let us know of any issue you might find.