Force no split in `make_rng`

[cgarciae]

Discussed in https://github.com/google/flax/discussions/3113

^{Originally posted by zaccharieramzi May 24, 2023} I have the following situation: I am using a Dropout layer multiple times without a nn.scan or nn.while_loop, therefore I cannot use split_rngs={"dropout": False}. However, I would still like to use the same dropout mask twice.

Is it possible to specify "no split" to make rng for certain collections?

If I just take the original dropout example I would like to do something like:

# Setup.
import jax
import jax.numpy as jnp
import flax.linen as nn

# Randomness.
seed = 0
root_key = jax.random.PRNGKey(seed=seed)
main_key, params_key, dropout_key = jax.random.split(key=root_key, num=3)

# A simple network.
class MyModel(nn.Module):
  num_neurons: int
  training: bool
  @nn.compact
  def __call__(self, x):
    x = nn.Dense(self.num_neurons)(x)
    # Set the dropout layer with a rate of 50% .
    # When the `deterministic` flag is `True`, dropout is turned off.
    x = nn.Dropout(rate=0.5, deterministic=not self.training)(x)
    x = nn.Dropout(rate=0.5, deterministic=not self.training)(x)
    return x

# Instantiate `MyModel` (you don't need to set `training=True` to
# avoid performing the forward pass computation).
my_model = MyModel(num_neurons=3, training=False)

x = jax.random.uniform(key=main_key, shape=(3, 4, 4))

# Initialize with `flax.linen.init()`.
# The `params_key` is equivalent to a dictionary of PRNGs.
# (Here, you are providing only one PRNG key.) 
variables = my_model.init(params_key, x)

# Perform the forward pass with `flax.linen.apply()`.
my_model.training = True
y = my_model.apply(variables, x, rngs={'dropout': dropout_key})

and still have jnp.sum(y == 0.) / (3*4*3) == 0.5 approx.

For more context I am actually trying to implement Deep Equilibrium Models using jaxopt and flax, where the fixed point defining function uses dropout. I also tried to see if the split_rngs functionality could be extended to jaxopt but I think it's going to be difficult.

[cgarciae]

Hey @zaccharieramzi, I've converted the discussion into and issue as it seems something that we should improve. I've created #3114, which would allow you to optionally specify the rng key for each Dropout layer, e.g:

# A simple network.
class MyModel(nn.Module):
  num_neurons: int
  training: bool
  @nn.compact
  def __call__(self, x):
    x = nn.Dense(self.num_neurons)(x)
    # Set the dropout layer with a rate of 50% .
    # When the `deterministic` flag is `True`, dropout is turned off.
    key = self.make_rng('dropout')
    x = nn.Dropout(rate=0.5, deterministic=not self.training)(x, rng=key)
    x = nn.Dropout(rate=0.5, deterministic=not self.training)(x, rng=key)
    return 

This way both layers will produce the same mask.

[zaccharieramzi]

Would there be a way to propagate this information rather than having to pass it around to each dropout? Indeed, in my case I would need to do key = self.make_rng("dropout") and pass it down to the actual dropout layers which are nested deep in different nn.Modules.

Something like:

key = self.make_rng('dropout')
x = MyModule(...)(x, rng=key)

where originally MyModule does not have the rng parameter in its API.

[zaccharieramzi]

ofc I understand it might be way more complex to do, so it's really just a question

[zaccharieramzi]

@cgarciae I see that this was closed so maybe you missed my earlier question. Typically in modules like dot_product_attention the dropout is hardcoded without the possibility to set the rng. Do you think it's best then to reimplement all these modules with the possibility to pass the rng?

[chiamp]

FYI @zaccharieramzi, I added a dropout_arg to nn.MultiHeadDotProductAttention in #3384 so you can get the same dropout mask