Source code for jaxopt._src.optax_wrapper

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"""Optax wrapper for JAXopt."""

from typing import Any
from typing import Callable
from typing import NamedTuple
from typing import Optional

from dataclasses import dataclass

import jax
import jax.numpy as jnp

from jaxopt._src import base
from jaxopt._src import tree_util


class OptaxState(NamedTuple):
  """Named tuple containing state information."""
  iter_num: int
  value: float
  error: float
  internal_state: NamedTuple
  aux: Any


[docs]@dataclass(eq=False) class OptaxSolver(base.StochasticSolver): """Optax solver. Attributes: fun: a function of the form ``fun(params, *args, **kwargs)``, where ``params`` are parameters of the model, ``*args`` and ``**kwargs`` are additional arguments. opt: the optimizer to use, an optax.GradientTransformation, which is just a NamedTuple with ``init`` and ``update`` functions. value_and_grad: whether ``fun`` just returns the value (False) or both the value and gradient (True). has_aux: whether ``fun`` outputs auxiliary data or not. If ``has_aux`` is False, ``fun`` is expected to be scalar-valued. If ``has_aux`` is True, then we have one of the following two cases. If ``value_and_grad`` is False, the output should be ``value, aux = fun(...)``. If ``value_and_grad == True``, the output should be ``(value, aux), grad = fun(...)``. At each iteration of the algorithm, the auxiliary outputs are stored in ``state.aux``. pre_update: a function to execute before Optax's update. The function signature must be ``params, state = pre_update(params, state, *args, **kwargs)``. maxiter: maximum number of solver iterations. tol: tolerance to use. verbose: whether to print error on every iteration or not. verbose=True will automatically disable jit. implicit_diff: whether to enable implicit diff or autodiff of unrolled iterations. implicit_diff_solve: the linear system solver to use. jit: whether to JIT-compile the optimization loop (default: "auto"). unroll: whether to unroll the optimization loop (default: "auto"). """ fun: Callable opt: NamedTuple value_and_grad: bool = False pre_update: Optional[Callable] = None maxiter: int = 500 tol: float = 1e-3 verbose: int = 0 implicit_diff: bool = False implicit_diff_solve: Optional[Callable] = None has_aux: bool = False jit: base.AutoOrBoolean = "auto" unroll: base.AutoOrBoolean = "auto"
[docs] def init_state(self, init_params: Any, *args, **kwargs) -> OptaxState: """Initialize the solver state. Args: init_params: pytree containing the initial parameters. *args: additional positional arguments to be passed to ``fun``. **kwargs: additional keyword arguments to be passed to ``fun``. Returns: state """ opt_state = self.opt.init(init_params) if self.has_aux: value, aux = self.fun(init_params, *args, **kwargs) else: value = self.fun(init_params, *args, **kwargs) aux = None params_dtype = tree_util.tree_single_dtype(init_params) return OptaxState(iter_num=jnp.asarray(0), value=jnp.asarray(jnp.inf, value.dtype), error=jnp.asarray(jnp.inf, dtype=params_dtype), aux=aux, internal_state=opt_state)
def _apply_updates(self, params, updates): update_fun = lambda p, u: jnp.asarray(p + u).astype(jnp.asarray(p).dtype) return jax.tree_map(update_fun, params, updates)
[docs] def update(self, params: Any, state: NamedTuple, *args, **kwargs) -> base.OptStep: """Performs one iteration of the optax solver. Args: params: pytree containing the parameters. state: named tuple containing the solver state. *args: additional positional arguments to be passed to ``fun``. **kwargs: additional keyword arguments to be passed to ``fun``. Returns: (params, state) """ if self.pre_update: params, state = self.pre_update(params, state, *args, **kwargs) (value, aux), grad = self._value_and_grad_fun(params, *args, **kwargs) delta, opt_state = self.opt.update(grad, state.internal_state, params) params = self._apply_updates(params, delta) # Computes optimality error before update to re-use grad evaluation. dtype = tree_util.tree_single_dtype(params) error = tree_util.tree_l2_norm(grad) new_state = OptaxState(iter_num=state.iter_num + 1, error=jnp.asarray(error, dtype=dtype), value=jnp.asarray(value), aux=aux, internal_state=opt_state) return base.OptStep(params=params, state=new_state)
[docs] def optimality_fun(self, params, *args, **kwargs): """Optimality function mapping compatible with ``@custom_root``.""" return self._grad_fun(params, *args, **kwargs)[0]
def __post_init__(self): _, self._grad_fun, self._value_and_grad_fun = \ base._make_funs_with_aux(fun=self.fun, value_and_grad=self.value_and_grad, has_aux=self.has_aux) self.reference_signature = self.fun