"""
Define functions and objects common to the ExplicitFuncComp and ImplicitFuncComp classes.
"""
import sys
import traceback
import re
from functools import partial
import numpy as np
try:
import jax
from jax import vmap
import jax.numpy as jnp
_HAS_JAX = True
except ImportError:
jax = None
_HAS_JAX = False
if _HAS_JAX:
try:
# 1. linear_util
try:
from jax.extend import linear_util
except ImportError:
from jax import linear_util
# 2. argnums_partial
try:
from jax._src.api_util import argnums_partial
except ImportError:
from jax.api_util import argnums_partial
# 3. _jvp and _vjp (Moved or removed in 0.4.28+)
try:
from jax._src.api import _jvp, _vjp
except ImportError:
try:
from jax._src.custom_derivatives import _jvp, _vjp
except ImportError:
# Fallback to public API. Public jax.vjp/jvp expect a standard callable,
# but OpenMDAO passes a linear_util.WrappedFun. We unwrap it to satisfy JAX.
from jax import jvp as _public_jvp
from jax import vjp as _public_vjp
def _jvp(fun, primals, tangents):
func = fun.call_wrapped if hasattr(fun, 'call_wrapped') else fun
return _public_jvp(func, primals, tangents)
def _vjp(fun, *primals):
func = fun.call_wrapped if hasattr(fun, 'call_wrapped') else fun
return _public_vjp(func, *primals)
# 4. api_util
try:
import jax._src.api_util as api_util
except ImportError:
try:
from jax._src.api import api_util
except ImportError:
from jax import api_util
jax.config.update("jax_enable_x64", True)
except Exception as e:
# If any internal import fails, print the EXACT error instead of silently masking it
print(f"\nOpenMDAO JAX Integration Warning: Failed to import internal JAX modules: {e}\n")
_, err, tb = sys.exc_info()
traceback.print_tb(tb)
jax = None
# regex to check for variable names.
namecheck_rgx = re.compile('[_a-zA-Z][_a-zA-Z0-9]*')
# Names that are not allowed for input or output variables (keywords for options)
_disallowed_varnames = {
'units', 'shape', 'shape_by_conn', 'run_root_only', 'distributed', 'assembled_jac_type'
}
def _copy_with_ignore(dct, keep, ignore=()):
"""
Copy the entries in the given dict whose keys are in keep.
Parameters
----------
dct : dict
The dictionary to be copied.
keep : set-like
Set of keys for entries we want to keep.
ignore : set or tuple
Don't issue a warning for these non-keeper keys.
Returns
-------
dict
A new dict containing 'keep' entries.
"""
return {k: v for k, v in dct.items() if k in keep and k not in ignore}
def _check_var_name(comp, name):
match = namecheck_rgx.match(name)
if match is None or match.group() != name:
raise NameError(f"{comp.msginfo}: '{name}' is not a valid variable name.")
if name in _disallowed_varnames:
raise NameError(f"{comp.msginfo}: cannot use variable name '{name}' because "
"it's a reserved keyword.")
[docs]
def jac_forward(fun, argnums, tangents):
"""
Similar to the jax.jacfwd function but allows specification of the tangent matrix.
This allows us to generate a compressed jacobian based on coloring.
Parameters
----------
fun : function
The function to be differentiated.
argnums : tuple of int or None
Specifies which positional args are dynamic. None means all positional args are dynamic.
tangents : ndarray
Array of 1.0's and 0's that is used to compute the value of the jacobian matrix.
Returns
-------
function
If there are multiple output variables, returns a function that returns rows of the
jacobian grouped by output variable, e.g., if there were 2 output variables of size 3 and 4,
the function would return a list with two entries. The first entry would contain the first 3
rows of J and the second would contain the next 4 rows of J. If there is only 1 output
variable, the values returned are grouped by input variable.
"""
try:
# Newer JAX versions (>= 0.4.x)
f = linear_util.wrap_init(fun, debug_info=api_util.debug_info('jac_forward', fun, (), {}))
except TypeError:
# Older JAX versions
f = linear_util.wrap_init(fun)
if argnums is None:
def jacfunf(*args):
return vmap(partial(_jvp, f, args), out_axes=(None, -1))(tangents)[1]
else:
def jacfunf(*args):
f_partial, dyn_args = argnums_partial(f, argnums, args)
return vmap(partial(_jvp, f_partial, dyn_args), out_axes=(None, -1))(tangents)[1]
return jacfunf
[docs]
def jac_reverse(fun, argnums, tangents):
"""
Similar to the jax.jacrev function but allows specification of the tangent matrix.
This allows us to generate a compressed jacobian based on coloring.
Parameters
----------
fun : function
The function to be differentiated.
argnums : tuple of int or None
Specifies which positional args are dynamic. None means all positional args are dynamic.
tangents : ndarray
Array of 1.0's and 0's that is used to compute the value of the jacobian matrix.
Returns
-------
function
A function that returns rows of the jacobian grouped by function input variable, e.g., if
there were 3 input variables of size 5 and 7 and 9, the function
would return a list with 3 entries. The first entry would contain the first 5 columns of J,
the second the next 7 columns of J, and the third the next 9 columns of J. Note that for
implicit systems, the function inputs will contain both inputs and outputs in the context
of OpenMDAO.
"""
try:
# Newer JAX versions (>= 0.4.x)
f = linear_util.wrap_init(fun, debug_info=api_util.debug_info('jac_reverse', fun, (), {}))
except TypeError:
# Older JAX versions
f = linear_util.wrap_init(fun)
if argnums is None:
def jacfunr(*args):
return vmap(_vjp(f, *args)[1])(tangents)
else:
def jacfunr(*args):
f_partial, dyn_args = argnums_partial(f, argnums, args)
return vmap(_vjp(f_partial, *dyn_args)[1])(tangents)
return jacfunr
[docs]
def jacvec_prod(fun, argnums, invals, tangent):
"""
Similar to the jvp function but gives back a flat column.
Note: this is significantly slower (when producing a full jacobian) than jac_forward.
Parameters
----------
fun : function
The function to be differentiated.
argnums : tuple of int or None
Specifies which positional args are dynamic. None means all positional args are dynamic.
invals : tuple of float or ndarray
Dynamic function input values.
tangent : ndarray
Array of 1.0's and 0's that is used to compute a column of the jacobian matrix.
Returns
-------
function
A function to compute the jacobian vector product.
"""
try:
# Newer JAX versions (>= 0.4.x)
f = linear_util.wrap_init(fun, debug_info=api_util.debug_info('jacvec_prod', fun, (), {}))
except TypeError:
# Older JAX versions
f = linear_util.wrap_init(fun)
if argnums is not None:
invals = list(argnums_partial(f, argnums, invals)[1])
# compute shaped tangents to use later
sizes = np.array([jnp.size(a) for a in invals])
inds = np.cumsum(sizes[:-1])
shaped_tangents = [a.reshape(s.shape) for a, s in zip(np.split(tangent, inds, axis=0), invals)]
if argnums is None:
def jvfun(inps):
return _jvp(f, inps, shaped_tangents)[1]
else:
def jvfun(inps):
f_partial, dyn_args = argnums_partial(f, argnums, inps)
return _jvp(f_partial, list(dyn_args), shaped_tangents)[1]
return jvfun
def _get_tangents(vals, direction, coloring=None, argnums=None, trans=None):
"""
Return a tuple of tangents values for use with vmap.
Parameters
----------
vals : list
List of function input values.
direction : str
Derivative computation direction ('fwd' or 'rev').
coloring : Coloring or None
If not None, the Coloring object used to compute a compressed tangent array.
argnums : list of int or None
Indices of dynamic (differentiable) function args.
trans : ndarray
Translation array from jacobian indices into function arg indices. This is needed
because OpenMDAO expects ordering to be outputs first, then inputs, but function args
could be in any order.
Returns
-------
tuple of ndarray or ndarray
The tangents values to be passed to vmap.
"""
if argnums is None:
leaves = vals
else:
leaves = [vals[i] for i in argnums]
sizes = [np.size(a) for a in leaves]
inds = np.cumsum(sizes[:-1])
if coloring is None:
tangent = np.eye(np.sum(sizes))
if trans is not None:
tangent = tangent[:, trans]
else:
tangent = coloring.tangent_matrix(direction, trans=trans)
shapes = [tangent.shape[:1] + np.shape(v) for v in leaves]
tangents = tuple([np.reshape(a, shp) for a, shp in zip(np.split(tangent, inds, axis=1),
shapes)])
if len(leaves) == 1:
tangents = tangents[0]
return tangents
def _ensure_iter(val):
"""
Turn the given value into an iterator if it is not already.
Parameters
----------
val : object
The value to be iterated over.
Returns
-------
tuple or iterable
"""
if isinstance(val, tuple):
return val
return val,
