Source code for numpyro.contrib.optim

# Copyright Contributors to the Pyro project.
# SPDX-License-Identifier: Apache-2.0

This module provides a wrapper for Optax optimizers so that they can be used with
NumPyro inference algorithms.

from typing import Tuple, TypeVar

import optax

from numpyro.optim import _NumPyroOptim

_Params = TypeVar("_Params")
_State = Tuple[_Params, optax.OptState]

[docs]def optax_to_numpyro(transformation: optax.GradientTransformation) -> _NumPyroOptim: """ This function produces a ``numpyro.optim._NumPyroOptim`` instance from an ``optax.GradientTransformation`` so that it can be used with ``numpyro.infer.svi.SVI``. It is a lightweight wrapper that recreates the ``(init_fn, update_fn, get_params_fn)`` interface defined by :mod:`jax.experimental.optimizers`. :param transformation: An ``optax.GradientTransformation`` instance to wrap. :return: An instance of ``numpyro.optim._NumPyroOptim`` wrapping the supplied Optax optimizer. """ def init_fn(params: _Params) -> _State: opt_state = transformation.init(params) return params, opt_state def update_fn(step, grads: _Params, state: _State) -> _State: params, opt_state = state updates, opt_state = transformation.update(grads, opt_state, params) updated_params = optax.apply_updates(params, updates) return updated_params, opt_state def get_params_fn(state: _State) -> _Params: params, _ = state return params return _NumPyroOptim(lambda x, y, z: (x, y, z), init_fn, update_fn, get_params_fn)