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

* Forward with training.

* Do not use dropout on vgg evaluation.

candle-examples/examples/mnist-training/main.rs

@@ -9,7 +9,7 @@ use clap::{Parser, ValueEnum};
 use rand::prelude::*;
 
 use candle::{DType, Result, Tensor, D};
-use candle_nn::{loss, ops, Conv2d, Linear, Module, Optimizer, VarBuilder, VarMap};
+use candle_nn::{loss, ops, Conv2d, Linear, Module, ModuleT, Optimizer, VarBuilder, VarMap};
 
 const IMAGE_DIM: usize = 784;
 const LABELS: usize = 10;
@@ -95,7 +95,7 @@ impl ConvNet {
             .flatten_from(1)?
             .apply(&self.fc1)?
             .relu()?;
-        self.dropout.forward(&xs, train)?.apply(&self.fc2)
+        self.dropout.forward_t(&xs, train)?.apply(&self.fc2)
     }
 }
 

candle-examples/examples/vgg/main.rs

@@ -5,7 +5,7 @@ extern crate intel_mkl_src;
 extern crate accelerate_src;
 
 use candle::{DType, IndexOp, D};
-use candle_nn::{Module, VarBuilder};
+use candle_nn::{ModuleT, VarBuilder};
 use candle_transformers::models::vgg::{Models, Vgg};
 use clap::{Parser, ValueEnum};
 
@@ -53,7 +53,7 @@ pub fn main() -> anyhow::Result<()> {
         Which::Vgg16 => Vgg::new(vb, Models::Vgg16)?,
         Which::Vgg19 => Vgg::new(vb, Models::Vgg19)?,
     };
-    let logits = model.forward(&image)?;
+    let logits = model.forward_t(&image, /*train=*/ false)?;
 
     let prs = candle_nn::ops::softmax(&logits, D::Minus1)?
         .i(0)?