Replace use of broadcastable with shape in aesara.tensor.shape

aesara/tensor/rewriting/shape.py

@@ -364,8 +364,8 @@ class ShapeFeature(Feature):
                 else:
                     shape_vars.append(self.unpack(s[i], r))
             assert all(
-                not hasattr(r.type, "broadcastable")
-                or not r.type.broadcastable[i]
+                not hasattr(r.type, "shape")
+                or r.type.shape[i] != 1
                 or self.lscalar_one.equals(shape_vars[i])
                 or self.lscalar_one.equals(extract_constant(shape_vars[i]))
                 for i in range(r.type.ndim)
@@ -447,9 +447,9 @@ class ShapeFeature(Feature):
                 merged_shape.append(other_shape[i])
         assert all(
             (
-                not hasattr(r.type, "broadcastable")
-                or not r.type.broadcastable[i]
-                and not other_r.type.broadcastable[i]
+                not hasattr(r.type, "shape")
+                or r.type.shape[i] != 1
+                and other_r.type.shape[i] != 1
             )
             or self.lscalar_one.equals(merged_shape[i])
             or self.lscalar_one.equals(
@@ -474,8 +474,8 @@ class ShapeFeature(Feature):
             else:
                 new_shape.append(s_j)
         assert all(
-            not hasattr(r.type, "broadcastable")
-            or not r.type.broadcastable[idx]
+            not hasattr(r.type, "shape")
+            or r.type.shape[idx] != 1
             or self.lscalar_one.equals(new_shape[idx])
             or self.lscalar_one.equals(extract_constant(new_shape[idx]))
             for idx in range(r.type.ndim)
@@ -781,7 +781,11 @@ def local_reshape_chain(op):
         # We should try to figure out why we lost the information about this
         # constant value... but in the meantime, better not apply this
         # rewrite.
-        if rval.broadcastable == node.outputs[0].broadcastable:
+        if rval.type.ndim == node.outputs[0].type.ndim and all(
+            s1 == s1
+            for s1, s2 in zip(rval.type.shape, node.outputs[0].type.shape)
+            if s1 == 1 or s2 == 1
+        ):
             return [rval]
         else:
             return False
@@ -816,7 +820,11 @@ def local_useless_reshape(fgraph, node):
     if (
         inp.type.ndim == 1
         and output.type.ndim == 1
-        and inp.type.broadcastable == output.type.broadcastable
+        and all(
+            s1 == s2
+            for s1, s2 in zip(inp.type.shape, output.type.shape)
+            if s1 == 1 or s2 == 1
+        )
     ):
         return [inp]
 
@@ -862,7 +870,7 @@ def local_useless_reshape(fgraph, node):
                         shape_match[dim] = True
                         continue
 
-            # Match 1 if input.broadcastable[dim] is True
+            # Match 1 if input.type.shape[dim] == 1
             cst_outshp_i = extract_constant(outshp_i, only_process_constants=1)
             if inp.type.shape[dim] == 1 and cst_outshp_i == 1:
                 shape_match[dim] = True
@@ -931,7 +939,11 @@ def local_reshape_to_dimshuffle(fgraph, node):
     if index != output.type.ndim:
         inner = op.__class__(len(new_output_shape))(inp, new_output_shape)
         copy_stack_trace(output, inner)
-        new_node = [DimShuffle(inner.type.broadcastable, dimshuffle_new_order)(inner)]
+        new_node = [
+            DimShuffle(tuple(s == 1 for s in inner.type.shape), dimshuffle_new_order)(
+                inner
+            )
+        ]
         copy_stack_trace(output, new_node)
         return new_node
 
@@ -1096,10 +1108,9 @@ def local_useless_dimshuffle_in_reshape(fgraph, node):
 
     new_order = node.inputs[0].owner.op.new_order
     inp = node.inputs[0].owner.inputs[0]
-    broadcastables = node.inputs[0].broadcastable
     new_order_of_nonbroadcast = []
-    for i, bd in zip(new_order, broadcastables):
-        if not bd:
+    for i, s in zip(new_order, node.inputs[0].type.shape):
+        if s != 1:
             new_order_of_nonbroadcast.append(i)
     no_change_in_order = all(
         new_order_of_nonbroadcast[i] <= new_order_of_nonbroadcast[i + 1]
@@ -1123,7 +1134,11 @@ def local_useless_unbroadcast(fgraph, node):
     """
     if isinstance(node.op, Unbroadcast):
         x = node.inputs[0]
-        if x.broadcastable == node.outputs[0].broadcastable:
+        if x.type.ndim == node.outputs[0].type.ndim and all(
+            s1 == s2
+            for s1, s2 in zip(x.type.shape, node.outputs[0].type.shape)
+            if s1 == 1 or s2 == 1
+        ):
             # No broadcastable flag was modified
             # No need to copy over stack trace,
             # because x should already have a stack trace.

tests/tensor/test_shape.py

@@ -55,13 +55,13 @@ from tests.test_rop import RopLopChecker
 
 def test_shape_basic():
     s = shape([])
-    assert s.type.broadcastable == (True,)
+    assert s.type.shape == (1,)
 
     s = shape([10])
-    assert s.type.broadcastable == (True,)
+    assert s.type.shape == (1,)
 
     s = shape(lscalar())
-    assert s.type.broadcastable == (False,)
+    assert s.type.shape == (0,)
 
     class MyType(Type):
         def filter(self, *args, **kwargs):
@@ -71,7 +71,7 @@ def test_shape_basic():
             return isinstance(other, MyType) and other.thingy == self.thingy
 
     s = shape(Variable(MyType(), None))
-    assert s.type.broadcastable == (False,)
+    assert s.type.shape == (None,)
 
     s = shape(np.array(1))
     assert np.array_equal(eval_outputs([s]), [])
@@ -119,15 +119,14 @@ class TestReshape(utt.InferShapeTester, utt.OptimizationTestMixin):
         b = dmatrix()
         d = dmatrix()
 
-        # basic to 1 dim(without list)
-        c = reshape(b, as_tensor_variable(6), ndim=1)
-        f = self.function([b], c)
-
         b_val1 = np.asarray([[0, 1, 2], [3, 4, 5]])
         c_val1 = np.asarray([0, 1, 2, 3, 4, 5])
         b_val2 = b_val1.T
         c_val2 = np.asarray([0, 3, 1, 4, 2, 5])
 
+        # basic to 1 dim(without list)
+        c = reshape(b, as_tensor_variable(6), ndim=1)
+        f = self.function([b], c)
         f_out1 = f(b_val1)
         f_out2 = f(b_val2)
         assert np.array_equal(f_out1, c_val1), (f_out1, c_val1)
@@ -191,10 +190,10 @@ class TestReshape(utt.InferShapeTester, utt.OptimizationTestMixin):
             f(np.asarray([[0, 1, 2], [3, 4, 5]])),
             np.asarray([[[0], [1], [2]], [[3], [4], [5]]]),
         )
-        assert f.maker.fgraph.toposort()[-1].outputs[0].type.broadcastable == (
-            False,
-            False,
-            True,
+        assert f.maker.fgraph.toposort()[-1].outputs[0].type.shape == (
+            None,
+            None,
+            1,
         )
 
         # test broadcast flag for constant value of 1 if it cannot be
@@ -205,10 +204,10 @@ class TestReshape(utt.InferShapeTester, utt.OptimizationTestMixin):
             f(np.asarray([[0, 1, 2], [3, 4, 5]])),
             np.asarray([[[0], [1]], [[2], [3]], [[4], [5]]]),
         )
-        assert f.maker.fgraph.toposort()[-1].outputs[0].type.broadcastable == (
-            False,
-            False,
-            True,
+        assert f.maker.fgraph.toposort()[-1].outputs[0].type.shape == (
+            None,
+            None,
+            1,
         )
 
     def test_m1(self):