1 files changed, 59 insertions, 2 deletions
diff --git a/candle-nn/src/activation.rs b/candle-nn/src/activation.rs
index a2650634..80b750ed 100644
--- a/candle-nn/src/activation.rs
+++ b/candle-nn/src/activation.rs
@@ -1,4 +1,4 @@
-use candle::Tensor;
+use candle::{Result, Tensor};
 use serde::Deserialize;
 
 #[derive(Debug, Clone, Copy, PartialEq, Deserialize, Default)]
@@ -21,7 +21,7 @@ pub enum Activation {
 }
 
 impl super::Module for Activation {
-    fn forward(&self, xs: &Tensor) -> candle::Result<Tensor> {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
         match self {
             Self::Gelu => xs.gelu_erf(),
             // https://github.com/huggingface/transformers/blob/12f043eaeaabfef6f6efea411d98e6f6d3c094b7/src/transformers/activations.py#L49-L78
@@ -40,3 +40,60 @@ impl super::Module for Activation {
         }
     }
 }
+
+#[derive(Clone, Debug)]
+pub struct PReLU {
+    weight: Tensor,
+    is_scalar: bool,
+}
+
+impl PReLU {
+    pub fn new(weight: Tensor, is_scalar: bool) -> Self {
+        Self { weight, is_scalar }
+    }
+
+    pub fn weight(&self) -> &Tensor {
+        &self.weight
+    }
+
+    pub fn is_scalar(&self) -> bool {
+        self.is_scalar
+    }
+}
+
+impl candle::Module for PReLU {
+    fn forward(&self, xs: &Tensor) -> Result<Tensor> {
+        let weight = if self.is_scalar {
+            self.weight.reshape(())?
+        } else if xs.rank() >= 2 {
+            let num_channels = xs.dim(1)?;
+            let num_weights = self.weight.elem_count();
+            if num_weights != num_channels {
+                candle::bail!("error in prelu: unexpected number of channels for the input, got {num_channels}, weight dim is {num_weights}")
+            }
+            let mut s = vec![1; xs.rank()];
+            s[1] = self.weight.elem_count();
+            self.weight.reshape(s)?
+        } else {
+            self.weight.clone()
+        };
+        let zeros = xs.zeros_like()?;
+        xs.maximum(&zeros)? + xs.minimum(&zeros)?.broadcast_mul(&weight)?
+    }
+}
+
+/// Create or initialize a new PReLU layer.
+///
+/// This uses some default name for weights, namely `"weight"`.
+/// # Arguments
+///
+/// * `num_channels` - The number of channels. Use `None` to have as single trainable value and
+/// `Some` for a 1D vector with the appropriate number of channels. When applying the `forward`
+/// function, the input tensor shape `s` should either be one dimension with this number of
+/// channels or if `s.len() >= 2` it should have `s[1]` equal to this number.
+pub fn prelu(num_channels: Option<usize>, vs: crate::VarBuilder) -> Result<PReLU> {
+    let init_ws = crate::init::Init::Const(0.25);
+    // When using a scalar weight, the PyTorch encoding is to use a 1d vector of length 1.
+    let ws = vs.get_with_hints((num_channels.unwrap_or(1),), "weight", init_ws)?;
+    Ok(PReLU::new(ws, num_channels.is_none()))
+}