PReLU activation implementation

[isaaccorley]

I wanted to gauge interest on adding a PReLU activation. I noticed that flax.linen.activations are simply aliasing jax.nn activation functions which also doesn't have a PReLU implementation.

To add some background, PReLU is simply Leaky ReLU where the alpha (slope) parameter is trainable and not fixed. This makes it simple to implement as a Module if desired.

Here's an example implementation from another project of mine.

from functools import partial
from typing import Any, Sequence

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


# This is nearly identical to jnp.ones however multiplies the output of jnp.ones by the constant value
def constant(key, shape: Sequence[int], value: Any, dtype: Any = jnp.float32) -> jnp.ndarray:
    value = jnp.asarray(value, dtype)
    return jnp.ones(shape, dtype) * value


class PReLU(nn.Module):
    negative_slope_init: float = 0.01
    dtype: Any = jnp.float32

    @nn.compact
    def __call__(self, x: jnp.ndarray) -> jnp.ndarray:
        x = jnp.asarray(x, self.dtype)
        negative_slope = self.param(
            "negative_slope",
            partial(constant, value=self.negative_slope_init, dtype=self.dtype),
            (1,)
        )
        return jnp.where(x >= 0, x, negative_slope * x)

[marcvanzee]

Given that all current activation functions reside in JAX, it seem more fitting to add this JAX. Do you want to file an issue against their repo?

[isaaccorley]

Thanks for the suggestion. The main reason I filed the issue here was because it seems like PReLU is a special case where it has a trainable param and, if I'm not mistaken, all other jax activations do not.

I'm not sure if this changes your suggestion, but it's something to consider.

[levskaya]

@isaaccorley - hey so sorry for the slow feedback on your suggestion here.

2 points:

• instead of defining a constant init func, we can just declare a jnp scalar array of the correct dtype.
• I think an -activation- "function" should strictly follow the dtype of its argument, so no dtype attribute, just derive it from x

So what if we added something like this?

class PReLU(nn.Module):
    negative_slope_init: float = 0.01
    @nn.compact
    def __call__(self, x: jnp.ndarray) -> jnp.ndarray:
        negative_slope = self.param(
            "negative_slope",
            lambda k: jnp.array(self.negative_slope_init, x.dtype)
        )
        return jnp.where(x >= 0, x, negative_slope * x)

[isaaccorley]

I'm indifferent on the implementation. I think the only thing to point out would be since we are inheriting from Module and other Modules have a dtype param, should we stray from that standard even though it is an activation function?

I created a constant init func because jax itself seemed to be lacking one, however I haven't received a response to the issue I posted in the jax repo requesting to add it so I'm fine with just using a lambda.

[levskaya]

• Other Modules have a dtype param to control the precision of their -intermediate- values, and a simple activation function like this doesn't have intermediates. We don't require modules to surface a dtype= attribute - it's just convention for the core layers to do so to give users the ability to control the floating-point types of the "insides"

• The "constant" functions you're looking for already exist: jnp.full and jnp.full_like

[isaaccorley]

1. Makes sense thanks for clarifying that.
2. Thanks for pointing me jnp.full. I wasn't aware of that.

Shall I make a PR then?

[levskaya]

Yeah if you'd like to make a PR we could add the above to activations.py I think (after all the passthrough function imports). (but no pressure - if you don't have time we can add it soon ourselves.)

[isaaccorley]

I'll try to take a first stab at it since it will be my first time contributing to flax.

[mfouesneau]

The current implementation of PReLU does not work as the other activation functions.

The following example code raises an error at initialization

class MLP(nn.Module):
    """ Definition of MLP 

    attributes
    ----------
    :param hidden_sizes: list of int corresponding to the number of neurons in each hidden layer.
    :param output_size: int corresponding to the number of neurons in the output layer.
    """
    hidden_sizes: Sequence[int]
    out_size: int
    
    @nn.compact
    def __call__(self, x, **kwargs):
        name = kwargs.pop('name', 'fc')
        for e, size in enumerate(self.hidden_sizes, 1):
            x = nn.Dense(size, name=name + str(e))(x)
            # x = nn.silu(x)
            x = nn.PReLU(x)
        x = nn.Dense(self.out_size, name=name + '_output')(x)
        return x 

    186 @compact
    187 def __call__(self, inputs: Array) -> Array:
    188   """Applies a linear transformation to the inputs along the last dimension.
    189 
    190   Args:
   (...)
    194     The transformed input.
    195   """
    196   kernel = self.param('kernel',
    197                       self.kernel_init,
--> 198                       (jnp.shape(inputs)[-1], self.features),
    199                       self.param_dtype)
    200   if self.use_bias:
    201     bias = self.param('bias', self.bias_init, (self.features,),
    202                       self.param_dtype)

IndexError: tuple index out of range

PReLU is not following the same definitions as the other activations.

In the previous example, it needs to be

x = nn.PReLU()(x) 

The documentation was not obvious to me.

[levskaya]

Because PReLU initializes a trained scalar parameter, it has to be treated as a layer. I've added clarification and example usage to the docs in #3122