[Breaking] Add training to batchnorm with exponential moving average (#1504)

* Add training to batchnorm with exponential moving average

* Add more checks to batch norm

* Resolve some review comments

* Add with_momentum varients of `new` methods

* Add check for range of momentum variable; update batch norm test

* Run cargo fmt

* Add back num_features parameter

* Format; tiny simplification

candle-nn/src/batch_norm.rs

@@ -7,15 +7,22 @@
 //! running stats.
 //!
 //! [`Batch Normalization`]: https://arxiv.org/abs/1502.03167
-use candle::{DType, Result, Tensor};
+use crate::Init;
+use candle::{DType, Module, Result, Tensor, Var};
 
 #[derive(Debug, Clone, Copy, PartialEq)]
 pub struct BatchNormConfig {
     pub eps: f64,
     pub remove_mean: bool,
+
     /// The meaning of affine here is different from LayerNorm: when false there is no learnable
     /// parameter at all, 1 used for gamma and 0 for beta.
     pub affine: bool,
+
+    /// Controls exponential moving average of running stats. Defaults to 0.1
+    ///
+    /// `running_stat * (1.0 - momentum) + stat * momentum`.
+    pub momentum: f64,
 }
 
 impl Default for BatchNormConfig {
@@ -24,6 +31,7 @@ impl Default for BatchNormConfig {
             eps: 1e-5,
             remove_mean: true,
             affine: true,
+            momentum: 0.1,
         }
     }
 }
@@ -32,23 +40,62 @@ impl From<f64> for BatchNormConfig {
     fn from(eps: f64) -> Self {
         Self {
             eps,
-            remove_mean: true,
-            affine: true,
+            ..Default::default()
         }
     }
 }
 
 #[derive(Clone, Debug)]
 pub struct BatchNorm {
-    running_mean: Tensor,
-    running_var: Tensor,
+    running_mean: Var,
+    running_var: Var,
     weight_and_bias: Option<(Tensor, Tensor)>,
     remove_mean: bool,
     eps: f64,
-    num_features: usize,
+    momentum: f64,
 }
 
 impl BatchNorm {
+    fn check_validity(&self, num_features: usize) -> Result<()> {
+        if self.eps < 0. {
+            candle::bail!("batch-norm eps cannot be negative {}", self.eps)
+        }
+        if !(0.0..=1.0).contains(&self.momentum) {
+            candle::bail!(
+                "batch-norm momentum must be between 0 and 1, is {}",
+                self.momentum
+            )
+        }
+        if self.running_mean.dims() != [num_features] {
+            candle::bail!(
+                "batch-norm running mean has unexpected shape {:?} should have shape [{num_features}]",
+                self.running_mean.shape(),
+            )
+        }
+        if self.running_var.dims() != [num_features] {
+            candle::bail!(
+                "batch-norm running variance has unexpected shape {:?} should have shape [{num_features}]",
+                self.running_var.shape(),
+            )
+        }
+        if let Some((ref weight, ref bias)) = self.weight_and_bias.as_ref() {
+            if weight.dims() != [num_features] {
+                candle::bail!(
+                    "batch-norm weight has unexpected shape {:?} should have shape [{num_features}]",
+                    weight.shape(),
+                )
+            }
+            if bias.dims() != [num_features] {
+                candle::bail!(
+                    "batch-norm weight has unexpected shape {:?} should have shape [{num_features}]",
+                    bias.shape(),
+                )
+            }
+        }
+
+        Ok(())
+    }
+
     pub fn new(
         num_features: usize,
         running_mean: Tensor,
@@ -57,29 +104,16 @@ impl BatchNorm {
         bias: Tensor,
         eps: f64,
     ) -> Result<Self> {
-        if eps < 0. {
-            candle::bail!("batch-norm eps cannot be negative {eps}")
-        }
-        if weight.dims() != [num_features] {
-            candle::bail!(
-                "batch-norm unexpected weight shape {:?} {num_features}",
-                weight.shape()
-            )
-        }
-        if bias.dims() != [num_features] {
-            candle::bail!(
-                "batch-norm unexpected bias shape {:?} {num_features}",
-                bias.shape()
-            )
-        }
-        Ok(Self {
-            running_mean,
-            running_var,
+        let out = Self {
+            running_mean: Var::from_tensor(&running_mean)?,
+            running_var: Var::from_tensor(&running_var)?,
             weight_and_bias: Some((weight, bias)),
             remove_mean: true,
             eps,
-            num_features,
-        })
+            momentum: 0.1,
+        };
+        out.check_validity(num_features)?;
+        Ok(out)
     }
 
     pub fn new_no_bias(
@@ -88,25 +122,64 @@ impl BatchNorm {
         running_var: Tensor,
         eps: f64,
     ) -> Result<Self> {
-        if eps < 0. {
-            candle::bail!("batch-norm eps cannot be negative {eps}")
-        }
-        Ok(Self {
-            running_mean,
-            running_var,
+        let out = Self {
+            running_mean: Var::from_tensor(&running_mean)?,
+            running_var: Var::from_tensor(&running_var)?,
             weight_and_bias: None,
             remove_mean: true,
             eps,
-            num_features,
-        })
+            momentum: 0.1,
+        };
+        out.check_validity(num_features)?;
+        Ok(out)
+    }
+
+    pub fn new_with_momentum(
+        num_features: usize,
+        running_mean: Tensor,
+        running_var: Tensor,
+        weight: Tensor,
+        bias: Tensor,
+        eps: f64,
+        momentum: f64,
+    ) -> Result<Self> {
+        let out = Self {
+            running_mean: Var::from_tensor(&running_mean)?,
+            running_var: Var::from_tensor(&running_var)?,
+            weight_and_bias: Some((weight, bias)),
+            remove_mean: true,
+            eps,
+            momentum,
+        };
+        out.check_validity(num_features)?;
+        Ok(out)
+    }
+
+    pub fn new_no_bias_with_momentum(
+        num_features: usize,
+        running_mean: Tensor,
+        running_var: Tensor,
+        eps: f64,
+        momentum: f64,
+    ) -> Result<Self> {
+        let out = Self {
+            running_mean: Var::from_tensor(&running_mean)?,
+            running_var: Var::from_tensor(&running_var)?,
+            weight_and_bias: None,
+            remove_mean: true,
+            eps,
+            momentum,
+        };
+        out.check_validity(num_features)?;
+        Ok(out)
     }
 
     pub fn running_mean(&self) -> &Tensor {
-        &self.running_mean
+        self.running_mean.as_tensor()
     }
 
     pub fn running_var(&self) -> &Tensor {
-        &self.running_var
+        self.running_var.as_tensor()
     }
 
     pub fn eps(&self) -> f64 {
@@ -117,7 +190,12 @@ impl BatchNorm {
         self.weight_and_bias.as_ref().map(|v| (&v.0, &v.1))
     }
 
+    pub fn momentum(&self) -> f64 {
+        self.momentum
+    }
+
     pub fn forward_learning(&self, x: &Tensor) -> Result<Tensor> {
+        let num_features = self.running_mean.as_tensor().dim(0)?;
         let x_dtype = x.dtype();
         let internal_dtype = match x_dtype {
             DType::F16 | DType::BF16 => DType::F32,
@@ -129,11 +207,11 @@ impl BatchNorm {
                 x.shape()
             )
         }
-        if x.dim(1)? != self.num_features {
+        if x.dim(1)? != num_features {
             candle::bail!(
                 "batch-norm input doesn't have the expected number of features ({:?} <> {})",
                 x.shape(),
-                self.num_features
+                num_features
             )
         }
         let x = x.to_dtype(internal_dtype)?;
@@ -142,26 +220,52 @@ impl BatchNorm {
         let x = x.flatten_from(1)?.contiguous()?;
         let x = if self.remove_mean {
             let mean_x = x.mean_keepdim(1)?;
+            {
+                // Update running mean
+                let new_mean = ((self.running_mean.as_tensor() * (1.0 - self.momentum))?
+                    + (mean_x.flatten_all()? * self.momentum)?)?;
+
+                self.running_mean.set(&new_mean)?;
+            }
             x.broadcast_sub(&mean_x)?
         } else {
             x
         };
         let norm_x = x.sqr()?.mean_keepdim(1)?;
+        {
+            // Update running variance
+            let batch_size = x.dim(1)? as f64;
+            let running_var_weight = 1.0 - self.momentum;
+            let norm_x_weight = self.momentum * batch_size / (batch_size - 1.0);
+
+            let new_var = ((self.running_var.as_tensor() * running_var_weight)?
+                + (&norm_x.flatten_all()? * norm_x_weight)?)?;
+
+            self.running_var.set(&new_var)?;
+        }
         let x_normed = x.broadcast_div(&(norm_x + self.eps)?.sqrt()?)?;
         let x = x_normed.to_dtype(x_dtype)?;
         let x = match &self.weight_and_bias {
             None => x,
             Some((weight, bias)) => {
-                let weight = weight.reshape((self.num_features, 1))?;
-                let bias = bias.reshape((self.num_features, 1))?;
+                let weight = weight.reshape(((), 1))?;
+                let bias = bias.reshape(((), 1))?;
                 x.broadcast_mul(&weight)?.broadcast_add(&bias)?
             }
         };
         x.reshape(x_dims_post_transpose)?.transpose(0, 1)
     }
+
+    pub fn forward_t(&self, x: &Tensor, train: bool) -> Result<Tensor> {
+        if train {
+            self.forward_learning(x)
+        } else {
+            self.forward(x)
+        }
+    }
 }
 
-impl crate::Module for BatchNorm {
+impl Module for BatchNorm {
     fn forward(&self, x: &Tensor) -> Result<Tensor> {
         let target_shape: Vec<usize> = x
             .dims()
@@ -170,9 +274,13 @@ impl crate::Module for BatchNorm {
             .map(|(idx, v)| if idx == 1 { *v } else { 1 })
             .collect();
         let target_shape = target_shape.as_slice();
+
         let x = x
-            .broadcast_sub(&self.running_mean.reshape(target_shape)?)?
-            .broadcast_div(&(self.running_var.reshape(target_shape)? + self.eps)?.sqrt()?)?;
+            .broadcast_sub(&self.running_mean.as_tensor().reshape(target_shape)?)?
+            .broadcast_div(
+                &(self.running_var.as_tensor().reshape(target_shape)? + self.eps)?.sqrt()?,
+            )?;
+
         match &self.weight_and_bias {
             None => Ok(x),
             Some((weight, bias)) => {
@@ -193,21 +301,21 @@ pub fn batch_norm<C: Into<BatchNormConfig>>(
     if config.eps < 0. {
         candle::bail!("batch-norm eps cannot be negative {}", config.eps)
     }
-    let running_mean = vb.get_with_hints(num_features, "running_mean", crate::Init::Const(0.))?;
-    let running_var = vb.get_with_hints(num_features, "running_var", crate::Init::Const(1.))?;
+    let running_mean = vb.get_with_hints(num_features, "running_mean", Init::Const(0.))?;
+    let running_var = vb.get_with_hints(num_features, "running_var", Init::Const(1.))?;
     let weight_and_bias = if config.affine {
-        let weight = vb.get_with_hints(num_features, "weight", crate::Init::Const(1.))?;
-        let bias = vb.get_with_hints(num_features, "bias", crate::Init::Const(0.))?;
+        let weight = vb.get_with_hints(num_features, "weight", Init::Const(1.))?;
+        let bias = vb.get_with_hints(num_features, "bias", Init::Const(0.))?;
         Some((weight, bias))
     } else {
         None
     };
     Ok(BatchNorm {
-        running_mean,
-        running_var,
+        running_mean: Var::from_tensor(&running_mean)?,
+        running_var: Var::from_tensor(&running_var)?,
         weight_and_bias,
         remove_mean: config.remove_mean,
         eps: config.eps,
-        num_features,
+        momentum: config.momentum,
     })
 }