-
Notifications
You must be signed in to change notification settings - Fork 255
Open
Description
I have to admit that I do not fully understand the necessity of hk.vmap
instead of jax.vmap
. Nevertheless, when I need to vmap something, I would use hk.vmap
whenever the inner function contains calls to haiku modules. This works OK, until I debug the bad performance of a transformer model. Things boils down to the following snippet
import jax, haiku as hk
jax.config.update("jax_platforms", "cpu")
def f1(x):
def g(x):
return hk.Linear(2)(x)
x = g(x)
x = g(x)
return x
def f2(x):
def g(x):
return hk.Linear(2)(x)
x = jax.vmap(g)(x)
x = jax.vmap(g)(x)
return x
key = jax.random.PRNGKey(42)
x = jax.random.normal(key, (1, 2))
w1 = hk.transform(f1).init(key, x)
w2 = hk.transform(f2).init(key, x)
print("w1:", w1.keys())
print("w2:", w2.keys())
# w1: dict_keys(['linear', 'linear_1'])
# w2: dict_keys(['linear'])
It turns out that when g
is vmapped, modules created inside g
would reuse a previously created module. In some cases, errors would happen immediately due to incompatible shape, but in other cases (for me, transformer layers have quite consistent shapes), things went wrong silently.
My question: Is this behavior intended? Could the documentation be improved on this topic? Or am I missing something?
Metadata
Metadata
Assignees
Labels
No labels