feat: allow broadcast_to operation on scalars

concrete/numpy/mlir/graph_converter.py

@@ -314,7 +314,7 @@ class GraphConverter:
         """
 
         # pylint: disable=invalid-name
-        OPS_TO_TENSORIZE = ["add", "dot", "multiply", "subtract"]
+        OPS_TO_TENSORIZE = ["add", "broadcast_to", "dot", "multiply", "subtract"]
         # pylint: enable=invalid-name
 
         tensorized_scalars: Dict[Node, Node] = {}
@@ -322,10 +322,14 @@ class GraphConverter:
         nx_graph = graph.graph
         for node in list(nx_graph.nodes):
             if node.operation == Operation.Generic and node.properties["name"] in OPS_TO_TENSORIZE:
-                assert_that(len(node.inputs) == 2)
+                assert len(node.inputs) in {1, 2}
 
-                if set(inp.is_scalar for inp in node.inputs) != {True, False}:
-                    continue
+                if len(node.inputs) == 2:
+                    if set(inp.is_scalar for inp in node.inputs) != {True, False}:
+                        continue
+                else:
+                    if not node.inputs[0].is_scalar:
+                        continue
 
                 pred_to_tensorize: Optional[Node] = None
                 pred_to_tensorize_index = 0

concrete/numpy/mlir/node_converter.py

@@ -256,7 +256,10 @@ class NodeConverter:
         resulting_type = NodeConverter.value_to_mlir_type(self.ctx, self.node.output)
 
         zeros = fhe.ZeroTensorOp(resulting_type).result
-        result = fhelinalg.AddEintOp(resulting_type, self.preds[0], zeros).result
+        if self.node.inputs[0].is_encrypted:
+            result = fhelinalg.AddEintOp(resulting_type, zeros, self.preds[0]).result
+        else:
+            result = fhelinalg.AddEintIntOp(resulting_type, zeros, self.preds[0]).result
 
         # TODO: convert this to a single operation once it can be done
         # (https://github.com/zama-ai/concrete-numpy-internal/issues/1610)

tests/execution/test_broadcast_to.py

@@ -11,6 +11,8 @@ import concrete.numpy as cnp
 @pytest.mark.parametrize(
     "from_shape,to_shape",
     [
+        pytest.param((), (2,)),
+        pytest.param((), (2, 3)),
         pytest.param((3,), (2, 3)),
         pytest.param((3,), (4, 2, 3)),
         pytest.param((1, 2), (4, 3, 2)),