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