diff --git a/src/hl_ops/movement.rs b/src/hl_ops/movement.rs
index 410b90f9..e0a1e0e0 100644
--- a/src/hl_ops/movement.rs
+++ b/src/hl_ops/movement.rs
@@ -17,7 +17,14 @@ impl<S: Shape> GraphTensor<S> {
         S: BroadcastShapeTo<Dst, Ax>,
     {
         let new_dims = Dst::realized_shape();
-        if !new_dims.is_empty() {
+        let src_dims = S::realized_shape();
+        let is_noop = src_dims.len() == new_dims.len()
+            && src_dims
+                .iter()
+                .zip(new_dims.iter())
+                .all(|(src_dim, new_dim)| src_dim.as_num() == new_dim.as_num());
+
+        if !new_dims.is_empty() && !is_noop {
             for (i, dim) in Ax::as_array().into_iter().map(|i| (i, new_dims[i])) {
                 self.shape.expand(i, dim);
             }
@@ -514,4 +521,14 @@ mod tests {
 
         assert_close(&c.data(), &d_c.as_vec());
     }
+
+    #[test]
+    fn test_noop_expand() {
+        type S = R1<1>;
+        type Tensor = GraphTensor<S>;
+        let mut cx = Graph::new();
+        let a: Tensor = cx.tensor();
+        let noop_expanded: Tensor = a.expand::<S, LAxis<0>>();
+        assert_eq!(a.shape, noop_expanded.shape);
+    }
 }
diff --git a/src/hl_ops/reduction.rs b/src/hl_ops/reduction.rs
index 604a5aff..6a76f3f9 100644
--- a/src/hl_ops/reduction.rs
+++ b/src/hl_ops/reduction.rs
@@ -96,7 +96,7 @@ mod tests {
         let a_data = random_vec(6);
         let a = cx.tensor::<R2<2, 3>>();
         a.set(a_data.clone());
-        let b = a.sum_reduce::<_, LAxis<1>>();
+        let b = a.sum_reduce::<R1<2>, LAxis<1>>();
         b.retrieve();
 
         cx.execute();
@@ -114,7 +114,7 @@ mod tests {
         let a_data = random_vec(6);
         let a = cx.tensor::<R2<2, 3>>();
         a.set(a_data.clone());
-        let b = a.max_reduce::<_, LAxis<1>>();
+        let b = a.max_reduce::<R1<2>, LAxis<1>>();
         b.retrieve();
 
         cx.execute();
@@ -132,7 +132,7 @@ mod tests {
         let a_data = random_vec(6);
         let a = cx.tensor::<R2<2, 3>>();
         a.set(a_data.clone());
-        let b = a.mean_reduce::<_, LAxis<1>>();
+        let b = a.mean_reduce::<R1<2>, LAxis<1>>();
         b.retrieve();
 
         cx.execute();
diff --git a/src/shape/broadcast.rs b/src/shape/broadcast.rs
index 08b8649a..c8fd65eb 100644
--- a/src/shape/broadcast.rs
+++ b/src/shape/broadcast.rs
@@ -12,7 +12,7 @@ pub trait ReduceShape<Ax>: Sized + HasAxes<Ax> + ReduceShapeTo<Self::Reduced, Ax
     type Reduced: Shape + BroadcastShapeTo<Self, Ax>;
 }
 
-impl ReduceShapeTo<(), Axis<0>> for () {}
+impl<S: Shape + HasAxes<AnyAxes>, AnyAxes> ReduceShapeTo<S, AnyAxes> for S {}
 impl ReduceShape<Axis<0>> for () {
     type Reduced = ();
 }
diff --git a/src/tests/test_prim.rs b/src/tests/test_prim.rs
index 33116c62..5e895879 100644
--- a/src/tests/test_prim.rs
+++ b/src/tests/test_prim.rs
@@ -267,9 +267,15 @@ fn test_sum_reduce() {
     let a = cx
         .tensor::<R3<2, 2, 3>>()
         .set([[[1., 2., 3.], [1., 2., 3.]], [[1., 2., 3.], [1., 2., 3.]]]);
-    let b = a.sum_reduce::<_, crate::prelude::Axis<1>>().retrieve();
-    let c = a.sum_reduce::<_, crate::prelude::Axis<0>>().retrieve();
-    let d = a.sum_reduce::<_, crate::prelude::Axis<2>>().retrieve();
+    let b = a
+        .sum_reduce::<R2<2, 3>, crate::prelude::Axis<1>>()
+        .retrieve();
+    let c = a
+        .sum_reduce::<R2<2, 3>, crate::prelude::Axis<0>>()
+        .retrieve();
+    let d = a
+        .sum_reduce::<R2<2, 2>, crate::prelude::Axis<2>>()
+        .retrieve();
     cx.execute();
 
     let d_dev = Cpu::default();
@@ -290,7 +296,9 @@ fn test_sum_reduce2() {
         [[34.4, -96.0, 144.0], [43.0, 560.0, 180.0]],
         [[39.6, -120.0, 180.0], [49.5, 700.0, 225.0]],
     ]]);
-    let b = a.sum_reduce::<_, crate::prelude::Axis<3>>().retrieve();
+    let b = a
+        .sum_reduce::<R3<1, 2, 2>, crate::prelude::Axis<3>>()
+        .retrieve();
     cx.execute();
 
     let d_dev = Cpu::default();
@@ -309,9 +317,15 @@ fn test_max_reduce() {
     let a = cx
         .tensor::<R3<2, 2, 3>>()
         .set([[[1., 2., 3.], [1., 2., 3.]], [[1., 2., 3.], [1., 2., 3.]]]);
-    let b = a.max_reduce::<_, crate::prelude::Axis<1>>().retrieve();
-    let c = a.max_reduce::<_, crate::prelude::Axis<0>>().retrieve();
-    let d = a.max_reduce::<_, crate::prelude::Axis<2>>().retrieve();
+    let b = a
+        .max_reduce::<R2<2, 3>, crate::prelude::Axis<1>>()
+        .retrieve();
+    let c = a
+        .max_reduce::<R2<2, 3>, crate::prelude::Axis<0>>()
+        .retrieve();
+    let d = a
+        .max_reduce::<R2<2, 2>, crate::prelude::Axis<2>>()
+        .retrieve();
     cx.execute();
 
     let d_dev = Cpu::default();