"""
Jax models interface.
Adds an interface for the construction of loss functions
incorporating jax models. This permits computation of derivatives using a
combination of objective based methods and jax based autodiff.
"""
import copy
from functools import partial
from typing import Union
import numpy as np
from ...C import MODE_FUN, ModeType
from ..base import ObjectiveBase, ResultDict
try:
import jax
import jax.numpy as jnp
from jax import custom_jvp
except ImportError:
raise ImportError(
"Using a jax objective requires an installation of "
"the python package jax. Please install jax via "
"`pip install jax jaxlib`."
) from None
# jax compatible (jit-able) objective function using external callback, see
# https://jax.readthedocs.io/en/latest/notebooks/external_callbacks.html
@partial(custom_jvp, nondiff_argnums=(0,))
def _device_fun(base_objective: ObjectiveBase, x: jnp.array):
"""Jax compatible objective function execution using external callback.
Parameters
----------
obj:
The wrapped jax objective.
x:
jax computed input array.
"""
return jax.pure_callback(
partial(base_objective, sensi_orders=(0,)),
jax.ShapeDtypeStruct((), x.dtype),
x,
)
def _device_fun_value_and_grad(base_objective: ObjectiveBase, x: jnp.array):
"""Jax compatible objective gradient execution using external callback.
This function will be called when computing the gradient of the
`JaxObjective` using `jax.grad` or `jax.value_and_grad`. In the latter
case, the function will return both the function value and the gradient,
so no caching is necessary. For higher order derivatives, caching would
be advantageous, but unclear how to implement this.
Parameters
----------
obj:
The wrapped jax objective.
x:
jax computed input array.
"""
return jax.pure_callback(
partial(
base_objective,
sensi_orders=(
0,
1,
),
),
(
jax.ShapeDtypeStruct((), x.dtype),
jax.ShapeDtypeStruct(
x.shape, # bootstrap from cached value
x.dtype,
),
),
x,
)
# define custom jvp for device_fun to enable autodiff, see
# https://jax.readthedocs.io/en/latest/notebooks/Custom_derivative_rules_for_Python_code.html
@_device_fun.defjvp
def _device_fun_jvp(
obj: "JaxObjective", primals: jnp.array, tangents: jnp.array
):
"""JVP implementation for device_fun."""
(x,) = primals
(x_dot,) = tangents
value, grad = _device_fun_value_and_grad(obj, x)
return value, grad @ x_dot
[docs]
class JaxObjective(ObjectiveBase):
"""Objective function that enables use of pypesto objectives in jax models.
The generated function should generally be compatible with jax, but cannot
compute higher order derivatives and is not vectorized (but still
compatible with jax.vmap)
Parameters
----------
objective:
pyPESTO objective to be wrapped.
Note
----
Currently only implements MODE_FUN and sensi_orders=(0,). Support for
MODE_RES should be straightforward to add.
"""
[docs]
def __init__(
self,
objective: ObjectiveBase,
):
if not isinstance(objective, ObjectiveBase):
raise TypeError("objective must be an ObjectiveBase instance")
if not objective.check_mode(MODE_FUN):
raise NotImplementedError(
f"objective must support mode={MODE_FUN}"
)
self.base_objective = objective
# would be cleaner to also have this as class method, but not supported
# by signature inspection in bind call.
self.jax_objective = partial(_device_fun, self.base_objective)
[docs]
def check_mode(self, mode: ModeType) -> bool:
"""See `ObjectiveBase` documentation."""
return mode == MODE_FUN
[docs]
def check_sensi_orders(self, sensi_orders, mode: ModeType) -> bool:
"""See `ObjectiveBase` documentation."""
if not self.check_mode(mode):
return False
else:
return (
self.base_objective.check_sensi_orders(sensi_orders, mode)
and max(sensi_orders) == 0
)
[docs]
def __call__(
self,
x: jnp.ndarray,
sensi_orders: tuple[int, ...] = (0,),
mode: ModeType = MODE_FUN,
return_dict: bool = False,
**kwargs,
) -> Union[jnp.ndarray, tuple, ResultDict]:
"""
See :class:`ObjectiveBase` for more documentation.
Note that this function delegates pre- and post-processing as well as
history handling to the inner objective.
"""
if not self.check_mode(mode):
raise ValueError(
f"This Objective cannot be called with mode" f"={mode}."
)
if not self.check_sensi_orders(sensi_orders, mode):
raise ValueError(
f"This Objective cannot be called with "
f"sensi_orders= {sensi_orders} and mode={mode}."
)
# this computes all the results from the inner objective, rendering
# them accessible as cached values for device_fun, etc.
if kwargs.pop("return_dict", False):
raise ValueError(
"return_dict=True is not available for JaxObjective evaluation"
)
return self.jax_objective(x)
[docs]
def call_unprocessed(
self,
x: np.ndarray,
sensi_orders: tuple[int, ...],
mode: ModeType,
**kwargs,
) -> ResultDict:
"""
See :class:`ObjectiveBase` for more documentation.
This function is not implemented for JaxObjective as it is not called
in the override for __call__. However, it's marked as abstract so we
need to implement it.
"""
pass
def __deepcopy__(self, memodict=None):
other = JaxObjective(
copy.deepcopy(self.base_objective),
)
return other
@property
def history(self):
"""Exposes the history of the inner objective."""
return self.base_objective.history
@property
def pre_post_processor(self):
"""Exposes the pre_post_processor of inner objective."""
return self.base_objective.pre_post_processor
@property
def x_names(self):
"""Exposes the x_names of inner objective."""
return self.base_objective.x_names