Only use input shapes to compute output shape in Elemwise.infer_shape

aesara/tensor/elemwise.py

@@ -16,7 +16,6 @@ from aesara.misc.frozendict import frozendict
 from aesara.misc.safe_asarray import _asarray
 from aesara.printing import FunctionPrinter, Printer, pprint
 from aesara.scalar import get_scalar_type
-from aesara.scalar.basic import ScalarType
 from aesara.scalar.basic import bool as scalar_bool
 from aesara.scalar.basic import identity as scalar_identity
 from aesara.scalar.basic import transfer_type, upcast
@@ -804,37 +803,17 @@ class Elemwise(OpenMPOp):
                 storage[0] = variable
 
     def infer_shape(self, fgraph, node, i_shapes):
-        rval = []
-        for o in node.outputs:
-            oshp = []
-            for dim, b in enumerate(o.type.broadcastable):
-                b_dim = None
-                if b:
-                    # this is broadcastable
-                    b_dim = 1
-                else:
-                    # there must be some input that is not broadcastable in
-                    # dimension 'dim'
-                    for ishp, i in zip(i_shapes, node.inputs):
-                        if isinstance(i.type, ScalarType):
-                            continue  # we skip scalar
-                        if not i.type.broadcastable[dim]:
-                            # input i is not broadcastable in position dim
-                            # therefore if its shape is known, we can use it
-                            # as the output shape
-                            if ishp[dim]:
-                                b_dim = ishp[dim]
-                                break
-
-                # b_dim might still be None, if every input's shape was unknown
-                # in dimension 'dim'
-                oshp.append(b_dim)
-                # TODO: it would be interesting to return the constraining
-                # information that if one of the inputs shape[dim] is known
-                # and another input's shape[dim] is not, that we can now assume
-                # that the other input's shape[dim] is the same as the first.
-            rval.append(tuple(oshp))
-        return rval
+
+        if len(node.outputs) > 1:
+            from aesara.tensor.basic_opt import ShapeError
+
+            raise ShapeError(
+                "Multiple outputs are not supported by the default `Elemwise.infer_shape`"
+            )
+
+        out_shape = aesara.tensor.broadcast_shape(*i_shapes, arrays_are_shapes=True)
+
+        return [out_shape]
 
     def _c_all(self, node, nodename, inames, onames, sub):
         # Some `Op`s directly call `Elemwise._c_all` or `Elemwise.c_code`

tests/tensor/test_elemwise.py

@@ -11,12 +11,13 @@ import aesara.scalar as aes
 import tests.unittest_tools as utt
 from aesara.compile.mode import Mode
 from aesara.configdefaults import config
-from aesara.graph.basic import Variable
+from aesara.graph.basic import Apply, Variable
 from aesara.graph.fg import FunctionGraph
 from aesara.link.basic import PerformLinker
 from aesara.link.c.basic import CLinker, OpWiseCLinker
 from aesara.tensor import as_tensor_variable
 from aesara.tensor.basic import second
+from aesara.tensor.basic_opt import ShapeError
 from aesara.tensor.elemwise import CAReduce, CAReduceDtype, DimShuffle, Elemwise
 from aesara.tensor.math import all as at_all
 from aesara.tensor.math import any as at_any
@@ -800,6 +801,46 @@ class TestElemwise(unittest_tools.InferShapeTester):
         op = Elemwise(aes.add, inplace_pattern=None, name="my_op")
         assert str(op) == "my_op"
 
+    def test_partial_static_shape_info(self):
+        """Make sure that `Elemwise.infer_shape` can handle changes in the static shape information during rewriting."""
+
+        x = TensorType("floatX", shape=(None, None))()
+        z = Elemwise(aes.add)(x, x)
+
+        x_inferred_shape = (aes.constant(1), aes.constant(1))
+
+        res_shape = z.owner.op.infer_shape(
+            None, z.owner, [x_inferred_shape, x_inferred_shape]
+        )
+
+        assert len(res_shape) == 1
+        assert len(res_shape[0]) == 2
+        assert res_shape[0][0].data == 1
+        assert res_shape[0][1].data == 1
+
+    def test_multi_output(self):
+        class CustomElemwise(Elemwise):
+            def make_node(self, *args):
+                res = super().make_node(*args)
+                return Apply(
+                    self,
+                    res.inputs,
+                    # Return two outputs
+                    [
+                        TensorType(dtype="float64", shape=(None, None))()
+                        for i in range(2)
+                    ],
+                )
+
+        z_1, z_2 = CustomElemwise(aes.add)(
+            as_tensor_variable(np.eye(1)), as_tensor_variable(np.eye(1))
+        )
+
+        in_1_shape = (aes.constant(1), aes.constant(1))
+
+        with pytest.raises(ShapeError):
+            z_1.owner.op.infer_shape(None, z_1.owner, [in_1_shape, in_1_shape])
+
 
 def test_not_implemented_elemwise_grad():
     # Regression test for unimplemented gradient in an Elemwise Op.