Fix mask_where broadcasted input (#2381)

crates/burn-candle/src/ops/base.rs

@@ -134,3 +134,22 @@ pub fn expand<E: CandleElement>(tensor: CandleTensor<E>, shape: Shape) -> Candle
 pub fn sign<E: CandleElement>(tensor: CandleTensor<E>) -> CandleTensor<E> {
     CandleTensor::new(tensor.tensor.sign().unwrap())
 }
+
+pub fn mask_where_broadcasted<E: CandleElement>(
+    tensor: CandleTensor<E>,
+    mask: CandleTensor<u8>,
+    value: CandleTensor<E>,
+) -> CandleTensor<E> {
+    let shape = tensor
+        .tensor
+        .shape()
+        .broadcast_shape_binary_op(mask.tensor.shape(), "where_cond")
+        .unwrap();
+
+    let mut tensor = tensor.tensor;
+    if shape != *tensor.shape() {
+        tensor = tensor.broadcast_as(shape).unwrap();
+    }
+
+    CandleTensor::new(mask.tensor.where_cond(&value.tensor, &tensor).unwrap())
+}

crates/burn-candle/src/ops/int_tensor.rs

@@ -60,11 +60,7 @@ impl<F: FloatCandleElement, I: IntCandleElement> IntTensorOps<Self> for Candle<F
         mask: BoolTensor<Self>,
         source: IntTensor<Self>,
     ) -> IntTensor<Self> {
-        CandleTensor::new(
-            mask.tensor
-                .where_cond(&source.tensor, &tensor.tensor)
-                .unwrap(),
-        )
+        super::base::mask_where_broadcasted(tensor, mask, source)
     }
 
     fn int_mask_fill(

crates/burn-candle/src/ops/tensor.rs

@@ -203,11 +203,7 @@ impl<F: FloatCandleElement, I: IntCandleElement> FloatTensorOps<Self> for Candle
         mask: BoolTensor<Self>,
         value: FloatTensor<Self>,
     ) -> FloatTensor<Self> {
-        CandleTensor::new(
-            mask.tensor
-                .where_cond(&value.tensor, &tensor.tensor)
-                .unwrap(),
-        )
+        super::base::mask_where_broadcasted(tensor, mask, value)
     }
 
     fn float_mask_fill(

crates/burn-fusion/src/ops/float.rs

@@ -941,7 +941,7 @@ impl<B: FusionBackend> FloatTensorOps<Self> for Fusion<B> {
         let stream_1 = tensor.stream;
         let stream_2 = mask.stream;
         let stream_3 = value.stream;
-        let shape: Vec<usize> = tensor.shape.clone();
+        let shape = binary_ops_shape(&tensor.shape, &mask.shape);
         let out = tensor
             .client
             .tensor_uninitialized(shape, B::FloatElem::dtype());

crates/burn-fusion/src/ops/int.rs

@@ -217,7 +217,7 @@ impl<B: FusionBackend> IntTensorOps<Self> for Fusion<B> {
         let stream_1 = tensor.stream;
         let stream_2 = mask.stream;
         let stream_3 = value.stream;
-        let shape: Vec<usize> = tensor.shape.clone();
+        let shape = binary_ops_shape(&tensor.shape, &mask.shape);
         let out = tensor
             .client
             .tensor_uninitialized(shape, B::IntElem::dtype());

crates/burn-tensor/src/tests/ops/mask.rs

@@ -22,6 +22,60 @@ mod tests {
         output.into_data().assert_eq(&expected, false);
     }
 
+    #[test]
+    fn should_support_mask_where_broadcast_int() {
+        let device = Default::default();
+        // When broadcasted, the input [[2, 3], [4, 5]] is repeated 4 times
+        let tensor = Tensor::<TestBackend, 1, Int>::arange(2..6, &device).reshape([1, 2, 2]);
+        let mask = Tensor::<TestBackend, 3, Bool>::from_bool(
+            TensorData::from([
+                [[true, false], [false, true]],
+                [[false, true], [true, false]],
+                [[false, false], [false, false]],
+                [[true, true], [true, true]],
+            ]),
+            &device,
+        );
+        let value = Tensor::<TestBackend, 3, Int>::ones([4, 2, 2], &device);
+
+        let output = tensor.mask_where(mask, value);
+        let expected = TensorData::from([
+            [[1, 3], [4, 1]],
+            [[2, 1], [1, 5]],
+            [[2, 3], [4, 5]],
+            [[1, 1], [1, 1]],
+        ]);
+
+        output.into_data().assert_eq(&expected, false);
+    }
+
+    #[test]
+    fn should_support_mask_where_broadcast() {
+        let device = Default::default();
+        // When broadcasted, the input [[2, 3], [4, 5]] is repeated 4 times
+        let tensor = Tensor::<TestBackend, 1, Int>::arange(2..6, &device).reshape([1, 2, 2]);
+        let mask = Tensor::<TestBackend, 3, Bool>::from_bool(
+            TensorData::from([
+                [[true, false], [false, true]],
+                [[false, true], [true, false]],
+                [[false, false], [false, false]],
+                [[true, true], [true, true]],
+            ]),
+            &device,
+        );
+        let value = Tensor::<TestBackend, 3>::ones([4, 2, 2], &device);
+
+        let output = tensor.float().mask_where(mask, value);
+        let expected = TensorData::from([
+            [[1., 3.], [4., 1.]],
+            [[2., 1.], [1., 5.]],
+            [[2., 3.], [4., 5.]],
+            [[1., 1.], [1., 1.]],
+        ]);
+
+        output.into_data().assert_eq(&expected, false);
+    }
+
     #[test]
     fn should_handle_mask_where_nans() {
         let device = Default::default();