thrml.th
thrml.th exposes THRML’s JAX-based samplers through a PyTorch-friendly API. The module supplies:
enable()– registers thejaxdevice type in PyTorch throughtorchax.compile(program, schedule, …)– turns a THRML sampling program into atorch.nn.Module.torch_view/jax_view– zero-copy conversions between JAX arrays and Torch tensors.- RNG helpers (
make_key,KeyStream) so repeated sampler calls stay deterministic when needed.
Everything necessary to use the package lives in this README and the example scripts.
Install
pip install thrml-th
Usage
import jax
import thrml
import thrml.th as thrml_th
from thrml.models import IsingEBM, IsingSamplingProgram, hinton_init
thrml_th.enable()
nodes = [thrml.SpinNode() for _ in range(4)]
program = IsingSamplingProgram(...)
schedule = thrml.SamplingSchedule(n_warmup=10, n_samples=16, steps_per_sample=2)
sampler = thrml_th.compile(program, schedule)
init_state = thrml_th.torch_view(tuple(hinton_init(
jax.random.key(0), program.model, program.gibbs_spec.free_blocks, ()
)))
samples = sampler(init_state)
Examples
examples/ising_sampler.py– builds an Ising chain, compiles it withthrml.th, prints magnetization stats.examples/pytorch_training_loop.py– drops a compiled sampler into a regular PyTorch training loop.examples/blockwise_sampler.py– compares contiguous vs. checkerboardnodes_to_samplepartitions.
Run any script with python examples/<name>.py after installing the package and torchax dependency.
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