def finalize(f):
"""Decorate f with a custom gradient."""
if JAX_MODE:
# https://jax.readthedocs.io/en/latest/notebooks/Custom_derivative_rules_for_Python_code.html
# For JAX, we prefer to specify a custom JVP, as JAX can use a function
# transform to transpose a JVP (must be linear in the tangents) to a VJP.
if jvp_fn is not None:
f_jvp = custom_jvp(f, nondiff_argnums=nondiff_argnums)
f_jvp.defjvp(jvp_fn)
return f_jvp
else:
from jax import custom_vjp # pylint: disable=g-import-not-at-top
f_vjp = custom_vjp(f, nondiff_argnums=nondiff_argnums)
f_vjp.defvjp(vjp_fwd, vjp_bwd)
return f_vjp
else:
# TF custom gradients support only custom VJPs.
@tf.custom_gradient
def f_wrapped(*args, **kwargs):
val, aux = vjp_fwd(*args, **kwargs)
def vjp_bwd_wrapped(*g):
result = vjp_bwd(aux, tf.nest.pack_sequence_as(val, g))
for i in nondiff_argnums:
result = tuple(result[:i]) + (None,) + tuple(result[i:])
return result
return val, vjp_bwd_wrapped
return f_wrapped
def custom_gradient(self, f: Callable, gradient: Callable) -> Callable:
jax_fun = jax.custom_jvp(f)
@jax_fun.defjvp
def jax_grad(primals, tangents):
grad = gradient(*tangents)
return jax_fun(primals), grad
return jax_fun
def __init__(self,
fun: Callable[Concatenate[U, P], R_co],
static_argnums: Tuple[int, ...] = ()):
"""
Args:
fun: the function to decorate.
static_argnums: The indices of the static arguments.
"""
super().__init__()
static_argnums = tuple(sorted(static_argnums))
self.jvp = jax.custom_jvp(fun, nondiff_argnums=static_argnums)
def finalize(f):
"""Decorate f with a custom gradient."""
if JAX_MODE:
# https://jax.readthedocs.io/en/latest/notebooks/Custom_derivative_rules_for_Python_code.html
# For JAX, we prefer to specify a custom JVP, as JAX can use a function
# transform to transpose a JVP (must be linear in the tangents) to a VJP.
if jvp_fn is not None:
f_jvp = custom_jvp(f, nondiff_argnums=nondiff_argnums)
f_jvp.defjvp(jvp_fn)
return f_jvp
else:
from jax import custom_vjp # pylint: disable=g-import-not-at-top
f_vjp = custom_vjp(f, nondiff_argnums=nondiff_argnums)
f_vjp.defvjp(vjp_fwd, vjp_bwd)
return f_vjp
else:
# TF custom gradients support only custom VJPs.
def none_wrapper(*args, **kwargs): # custom_gradient can't handle None.
closure = {i: a for i, a in enumerate(args)
if i in nondiff_argnums or a is None}
trimmed_args = [a for i, a in enumerate(args) if i not in closure]
@tf.custom_gradient
def f_wrapped(*args, **kwargs):
reconstruct_args = []
args_structure = tf.nest.map_structure(lambda _: 0, args)
for i in range(len(args) + len(closure)):
if i in closure:
reconstruct_args.append(closure[i])
else:
reconstruct_args.append(args[0])
args = args[1:]
val, aux = vjp_fwd(*reconstruct_args, **kwargs)
def vjp_bwd_wrapped(*g):
result = tf.nest.flatten(
vjp_bwd(aux, tf.nest.pack_sequence_as(val, g)))
for i in nondiff_argnums:
result = tuple(result[:i]) + (None,) + tuple(result[i:])
result = [a for i, a in enumerate(result) if i not in closure]
return tf.nest.pack_sequence_as(args_structure, result)
return val, vjp_bwd_wrapped
return f_wrapped(*trimmed_args, **kwargs)
return none_wrapper
return partial_det[..., -1], x
def _det(a):
sign, logdet = slogdet(a)
return sign * jnp.exp(logdet)
def _det_jvp(primals, tangents, grad_type='fast'):
x, = primals
g, = tangents
y, z = _cofactor_solve(x, g, which=grad_type)
return y, jnp.trace(z, axis1=-1, axis2=-2)
_det_fast = custom_jvp(lambda a: _det(a))
_det_fast.defjvp(partial(_det_jvp, grad_type='fast'))
_det_safe = custom_jvp(lambda a: _det(a))
_det_safe.defjvp(partial(_det_jvp, grad_type='safe'))
@_wraps(np.linalg.det)
def det(a, grad_type='fast'):
if grad_type == 'fast':
return _det_fast(a)
elif grad_type == 'safe':
return _det_safe(a)
else:
raise ValueError(
"Unrecognized grad type for Det: {}".format(grad_type))
