Allow for different behavior between training and eval (#1213)

* Forward with training.

* Do not use dropout on vgg evaluation.

candle-nn/src/func.rs

@@ -36,3 +36,38 @@ impl<'a> Func<'a> {
         Self { f: Arc::new(f) }
     }
 }
+
+/// A layer defined by a simple closure.
+#[derive(Clone)]
+pub struct FuncT<'a> {
+    #[allow(clippy::type_complexity)]
+    f: Arc<dyn 'a + Fn(&Tensor, bool) -> Result<Tensor> + Send + Sync>,
+}
+
+impl<'a> std::fmt::Debug for FuncT<'a> {
+    fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
+        write!(f, "func")
+    }
+}
+
+pub fn func_t<'a, F>(f: F) -> FuncT<'a>
+where
+    F: 'a + Fn(&Tensor, bool) -> Result<Tensor> + Send + Sync,
+{
+    FuncT { f: Arc::new(f) }
+}
+
+impl<'a> super::ModuleT for FuncT<'a> {
+    fn forward_t(&self, xs: &Tensor, train: bool) -> Result<Tensor> {
+        (*self.f)(xs, train)
+    }
+}
+
+impl<'a> FuncT<'a> {
+    pub fn new<F>(f: F) -> Self
+    where
+        F: 'a + Fn(&Tensor, bool) -> Result<Tensor> + Send + Sync,
+    {
+        Self { f: Arc::new(f) }
+    }
+}

candle-nn/src/lib.rs

@@ -22,7 +22,7 @@ pub use conv::{
     Conv1dConfig, Conv2d, Conv2dConfig, ConvTranspose2d, ConvTranspose2dConfig,
 };
 pub use embedding::{embedding, Embedding};
-pub use func::{func, Func};
+pub use func::{func, func_t, Func, FuncT};
 pub use group_norm::{group_norm, GroupNorm};
 pub use init::Init;
 pub use layer_norm::{layer_norm, rms_norm, LayerNorm, LayerNormConfig, RmsNorm};
@@ -34,4 +34,4 @@ pub use sequential::{seq, Sequential};
 pub use var_builder::VarBuilder;
 pub use var_map::VarMap;
 
-pub use candle::Module;
+pub use candle::{Module, ModuleT};

candle-nn/src/ops.rs

@@ -84,6 +84,12 @@ impl Dropout {
     }
 }
 
+impl candle::ModuleT for Dropout {
+    fn forward_t(&self, xs: &Tensor, train: bool) -> Result<Tensor> {
+        self.forward(xs, train)
+    }
+}
+
 struct SoftmaxLastDim;
 
 impl candle::CustomOp1 for SoftmaxLastDim {