def f(x):
m = CountingModule(op=lambda x: x + 1)
if not base.params_frozen():
return m(x)
else:
stateful.while_loop(lambda _: base.next_rng_key(), lambda x: x,
x)
def f(x):
m = CountingModule(op=lambda x: x + 1)
if not base.params_frozen():
return m(x)
else:
_, y = stateful.while_loop(lambda a: a[0] < iters, lambda a:
(a[0] + 1, m(a[1])), (0, x))
return y
def f(): stateful.while_loop(lambda x: x.all(), lambda x: not x, 1)
("vmap", lambda f: stateful.vmap(f, split_rng=False)),
# Control flow.
# TODO(tomhennigan): Enable for associative_scan.
# ("associative_scan", lambda f:
# (lambda x: jax.lax.associative_scan(f, x))),
("cond", lambda f: (lambda x: stateful.cond(True, f, f, x))),
("fori_loop", lambda f:
(lambda x: stateful.fori_loop(0, 1, base_test.ignore_index(f), x))),
# ("map", lambda f: (lambda x: stateful.map(f, x))),
("scan", lambda f:
(lambda x: stateful.scan(base_test.identity_carry(f), None, x))),
("switch", lambda f: (lambda x: stateful.switch(0, [f, f], x))),
("while_loop", lambda f: toggle(
f, lambda x: stateful.while_loop(lambda xs: xs[0] == 0,
lambda xs: (1, f(xs[1])),
(0, x)))),
# Automatic differentiation.
# TODO(tomhennigan): Add missing features (e.g. custom_vjp, custom_jvp).
("grad", lambda f: stateful.grad(lambda x: f(x).sum())),
("value_and_grad", lambda f: stateful.value_and_grad(lambda x: f(x).sum())),
("checkpoint", stateful.remat),
)
# pylint: enable=g-long-lambda
class StatefulTest(parameterized.TestCase):
@test_utils.transform_and_run
