Add clone to various nn layers. (#910)

7 files changed, 11 insertions, 11 deletions

diff --git a/candle-nn/src/batch_norm.rs b/candle-nn/src/batch_norm.rs
index 2dac0758..27ef15f7 100644
--- a/candle-nn/src/batch_norm.rs
+++ b/candle-nn/src/batch_norm.rs
@@ -38,7 +38,7 @@ impl From<f64> for BatchNormConfig {
     }
 }
 
-#[derive(Debug)]
+#[derive(Clone, Debug)]
 pub struct BatchNorm {
     running_mean: Tensor,
     running_var: Tensor,
diff --git a/candle-nn/src/conv.rs b/candle-nn/src/conv.rs
index 31bf9af0..89e9f42d 100644
--- a/candle-nn/src/conv.rs
+++ b/candle-nn/src/conv.rs
@@ -20,7 +20,7 @@ impl Default for Conv1dConfig {
     }
 }
 
-#[derive(Debug)]
+#[derive(Clone, Debug)]
 pub struct Conv1d {
     weight: Tensor,
     bias: Option<Tensor>,
@@ -88,7 +88,7 @@ impl Default for Conv2dConfig {
     }
 }
 
-#[derive(Debug)]
+#[derive(Clone, Debug)]
 pub struct Conv2d {
     weight: Tensor,
     bias: Option<Tensor>,
@@ -157,7 +157,7 @@ impl Default for ConvTranspose2dConfig {
     }
 }
 
-#[derive(Debug)]
+#[derive(Clone, Debug)]
 pub struct ConvTranspose2d {
     weight: Tensor,
     bias: Option<Tensor>,
diff --git a/candle-nn/src/embedding.rs b/candle-nn/src/embedding.rs
index fccc8a17..52968bc2 100644
--- a/candle-nn/src/embedding.rs
+++ b/candle-nn/src/embedding.rs
@@ -1,7 +1,7 @@
 //! Embedding Layer.
 use candle::{Result, Tensor};
 
-#[derive(Debug)]
+#[derive(Clone, Debug)]
 pub struct Embedding {
     embeddings: Tensor,
     hidden_size: usize,
diff --git a/candle-nn/src/group_norm.rs b/candle-nn/src/group_norm.rs
index eb1b889f..5b80b970 100644
--- a/candle-nn/src/group_norm.rs
+++ b/candle-nn/src/group_norm.rs
@@ -4,7 +4,7 @@
 use candle::{DType, Result, Tensor};
 
 // This group norm version handles both weight and bias so removes the mean.
-#[derive(Debug)]
+#[derive(Clone, Debug)]
 pub struct GroupNorm {
     weight: Tensor,
     bias: Tensor,
diff --git a/candle-nn/src/layer_norm.rs b/candle-nn/src/layer_norm.rs
index d2e80a82..7617fc6c 100644
--- a/candle-nn/src/layer_norm.rs
+++ b/candle-nn/src/layer_norm.rs
@@ -60,7 +60,7 @@ impl From<f64> for LayerNormConfig {
 }
 
 // This layer norm version handles both weight and bias so removes the mean.
-#[derive(Debug)]
+#[derive(Clone, Debug)]
 pub struct LayerNorm {
     weight: Tensor,
     bias: Option<Tensor>,
@@ -143,7 +143,7 @@ pub fn layer_norm<C: Into<LayerNormConfig>>(
 }
 
 /// RmsNorm is a specialized version of the LayerNorm module.
-#[derive(Debug)]
+#[derive(Clone, Debug)]
 pub struct RmsNorm(LayerNorm);
 
 impl RmsNorm {
diff --git a/candle-nn/src/linear.rs b/candle-nn/src/linear.rs
index de335964..94632296 100644
--- a/candle-nn/src/linear.rs
+++ b/candle-nn/src/linear.rs
@@ -19,7 +19,7 @@
 //! ```
 use candle::{Result, Tensor};
 
-#[derive(Debug)]
+#[derive(Clone, Debug)]
 pub struct Linear {
     weight: Tensor,
     bias: Option<Tensor>,
diff --git a/candle-nn/src/rnn.rs b/candle-nn/src/rnn.rs
index d52a9082..18a4a71c 100644
--- a/candle-nn/src/rnn.rs
+++ b/candle-nn/src/rnn.rs
@@ -85,7 +85,7 @@ impl LSTMConfig {
 ///
 /// <https://en.wikipedia.org/wiki/Long_short-term_memory>
 #[allow(clippy::upper_case_acronyms, unused)]
-#[derive(Debug)]
+#[derive(Clone, Debug)]
 pub struct LSTM {
     w_ih: Tensor,
     w_hh: Tensor,
@@ -235,7 +235,7 @@ impl GRUConfig {
 ///
 /// <https://en.wikipedia.org/wiki/Gated_recurrent_unit>
 #[allow(clippy::upper_case_acronyms, unused)]
-#[derive(Debug)]
+#[derive(Clone, Debug)]
 pub struct GRU {
     w_ih: Tensor,
     w_hh: Tensor,