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

aesara/tensor/basic.py

@@ -28,7 +28,7 @@
 from aesara.graph.fg import FunctionGraph
 from aesara.graph.op import Op
 from aesara.graph.rewriting.utils import rewrite_graph
-from aesara.graph.type import Type
+from aesara.graph.type import HasShape, Type
 from aesara.link.c.op import COp
 from aesara.link.c.params_type import ParamsType
 from aesara.misc.safe_asarray import _asarray
@@ -348,8 +348,8 @@ def get_scalar_constant_value(
                 if isinstance(inp, Constant):
                     return np.asarray(np.shape(inp.data)[i])
                 # The shape of a broadcastable dimension is 1
-                if hasattr(inp.type, "broadcastable") and inp.type.broadcastable[i]:
-                    return np.asarray(1)
+                if isinstance(inp.type, HasShape) and inp.type.shape[i] is not None:
+                    return np.asarray(inp.type.shape[i])
 
             # Don't act as the constant_folding optimization here as this
             # fct is used too early in the optimization phase.  This would
@@ -502,21 +502,16 @@ def get_scalar_constant_value(
                             owner.inputs[1], max_recur=max_recur
                         )
                     grandparent = leftmost_parent.owner.inputs[0]
-                    gp_broadcastable = grandparent.type.broadcastable
+                    gp_shape = grandparent.type.shape
                     ndim = grandparent.type.ndim
                     if grandparent.owner and isinstance(
                         grandparent.owner.op, Unbroadcast
                     ):
-                        ggp_broadcastable = grandparent.owner.inputs[0].broadcastable
-                        l = [
-                            b1 or b2
-                            for b1, b2 in zip(ggp_broadcastable, gp_broadcastable)
-                        ]
-                        gp_broadcastable = tuple(l)
+                        ggp_shape = grandparent.owner.inputs[0].type.shape
+                        l = [s1 == 1 or s2 == 1 for s1, s2 in zip(ggp_shape, gp_shape)]
+                        gp_shape = tuple(l)
 
-                    assert ndim == len(gp_broadcastable)
-
-                    if not (idx < len(gp_broadcastable)):
+                    if not (idx < ndim):
                         msg = (
                             "get_scalar_constant_value detected "
                             f"deterministic IndexError: x.shape[{int(idx)}] "
@@ -528,8 +523,9 @@ def get_scalar_constant_value(
                             msg += f" x={v}"
                         raise ValueError(msg)
 
-                    if gp_broadcastable[idx]:
-                        return np.asarray(1)
+                    gp_shape_val = gp_shape[idx]
+                    if gp_shape_val is not None and gp_shape_val > -1:
+                        return np.asarray(gp_shape_val)
 
                     if isinstance(grandparent, Constant):
                         return np.asarray(np.shape(grandparent.data)[idx])
@@ -1511,15 +1507,16 @@ def grad(self, inputs, grads):
         axis_kept = []
         for i, (ib, gb) in enumerate(
             zip(
-                inputs[0].broadcastable,
+                inputs[0].type.shape,
                 # We need the dimensions corresponding to x
-                grads[0].broadcastable[-inputs[0].ndim :],
+                grads[0].type.shape[-inputs[0].ndim :],
             )
         ):
-            if ib and not gb:
+            if ib == 1 and gb != 1:
                 axis_broadcasted.append(i + n_axes_to_sum)
             else:
                 axis_kept.append(i)
+
         gx = gz.sum(axis=axis + axis_broadcasted)
         if axis_broadcasted:
             new_order = ["x"] * x.ndim
@@ -1865,11 +1862,14 @@ def transpose(x, axes=None):
 
     """
     _x = as_tensor_variable(x)
+
     if axes is None:
-        axes = list(range((_x.ndim - 1), -1, -1))
-    ret = DimShuffle(_x.broadcastable, axes)(_x)
-    if _x.name and axes == list(range((_x.ndim - 1), -1, -1)):
+        axes = list(range((_x.type.ndim - 1), -1, -1))
+    ret = DimShuffle(tuple(s == 1 for s in _x.type.shape), axes)(_x)
+
+    if _x.name and axes == list(range((_x.type.ndim - 1), -1, -1)):
         ret.name = _x.name + ".T"
+
     return ret
 
 
@@ -3207,11 +3207,11 @@ def _rec_perform(self, node, x, y, inverse, out, curdim):
             if xs0 == ys0:
                 for i in range(xs0):
                     self._rec_perform(node, x[i], y[i], inverse, out[i], curdim + 1)
-            elif ys0 == 1 and node.inputs[1].type.broadcastable[curdim]:
+            elif ys0 == 1 and node.inputs[1].type.shape[curdim] == 1:
                 # Broadcast y
                 for i in range(xs0):
                     self._rec_perform(node, x[i], y[0], inverse, out[i], curdim + 1)
-            elif xs0 == 1 and node.inputs[0].type.broadcastable[curdim]:
+            elif xs0 == 1 and node.inputs[0].type.shape[curdim] == 1:
                 # Broadcast x
                 for i in range(ys0):
                     self._rec_perform(node, x[0], y[i], inverse, out[i], curdim + 1)
@@ -3270,7 +3270,7 @@ def grad(self, inp, grads):
         broadcasted_dims = [
             dim
             for dim in range(gz.type.ndim)
-            if x.type.broadcastable[dim] and not gz.type.broadcastable[dim]
+            if x.type.shape[dim] == 1 and gz.type.shape[dim] != 1
         ]
         gx = Sum(axis=broadcasted_dims)(gx)
 
@@ -3285,8 +3285,13 @@ def grad(self, inp, grads):
                 newdims.append(i)
                 i += 1
 
-        gx = DimShuffle(gx.type.broadcastable, newdims)(gx)
-        assert gx.type.broadcastable == x.type.broadcastable
+        gx = DimShuffle(tuple(s == 1 for s in gx.type.shape), newdims)(gx)
+        assert gx.type.ndim == x.type.ndim
+        assert all(
+            s1 == s2
+            for s1, s2 in zip(gx.type.shape, x.type.shape)
+            if s1 == 1 or s2 == 1
+        )
 
         # if x is an integer type, then so is the output.
         # this means f(x+eps) = f(x) so the gradient with respect

tests/tensor/test_basic.py

@@ -458,10 +458,10 @@ def test_make_vector_fail(self):
             res = MakeVector("int32")(a, b)
 
         res = MakeVector()(a)
-        assert res.broadcastable == (True,)
+        assert res.type.shape == (1,)
 
         res = MakeVector()()
-        assert res.broadcastable == (False,)
+        assert res.type.shape == (0,)
 
     def test_infer_shape(self):
         adscal = dscalar()
@@ -1665,18 +1665,18 @@ def test_broadcastable_flag_assignment_mixed_otheraxes(self):
         a = self.shared(a_val, shape=(None, None, 1))
         b = self.shared(b_val, shape=(1, None, 1))
         c = self.join_op(1, a, b)
-        assert c.type.broadcastable[0] and c.type.broadcastable[2]
-        assert not c.type.broadcastable[1]
+        assert c.type.shape[0] == 1 and c.type.shape[2] == 1
+        assert c.type.shape[1] != 1
 
         # Opt can remplace the int by an Aesara constant
         c = self.join_op(constant(1), a, b)
-        assert c.type.broadcastable[0] and c.type.broadcastable[2]
-        assert not c.type.broadcastable[1]
+        assert c.type.shape[0] == 1 and c.type.shape[2] == 1
+        assert c.type.shape[1] != 1
 
         # In case futur opt insert other useless stuff
         c = self.join_op(cast(constant(1), dtype="int32"), a, b)
-        assert c.type.broadcastable[0] and c.type.broadcastable[2]
-        assert not c.type.broadcastable[1]
+        assert c.type.shape[0] == 1 and c.type.shape[2] == 1
+        assert c.type.shape[1] != 1
 
         f = function([], c, mode=self.mode)
         topo = f.maker.fgraph.toposort()
@@ -1703,7 +1703,7 @@ def test_broadcastable_flag_assignment_mixed_thisaxes(self):
         a = self.shared(a_val, shape=(None, None, 1))
         b = self.shared(b_val, shape=(1, None, 1))
         c = self.join_op(0, a, b)
-        assert not c.type.broadcastable[0]
+        assert c.type.shape[0] != 1
 
         f = function([], c, mode=self.mode)
         topo = f.maker.fgraph.toposort()
@@ -1736,7 +1736,7 @@ def test_broadcastable_flags_all_broadcastable_on_joinaxis(self):
         a = self.shared(a_val, shape=(1, None, 1))
         b = self.shared(b_val, shape=(1, None, 1))
         c = self.join_op(0, a, b)
-        assert not c.type.broadcastable[0]
+        assert c.type.shape[0] != 1
 
         f = function([], c, mode=self.mode)
         topo = f.maker.fgraph.toposort()
@@ -1754,9 +1754,9 @@ def test_broadcastable_single_input_broadcastable_dimension(self):
         a_val = rng.random((1, 4, 1)).astype(self.floatX)
         a = self.shared(a_val, shape=(1, None, 1))
         b = self.join_op(0, a)
-        assert b.type.broadcastable[0]
-        assert b.type.broadcastable[2]
-        assert not b.type.broadcastable[1]
+        assert b.type.shape[0] == 1
+        assert b.type.shape[2] == 1
+        assert b.type.shape[1] != 1
 
         f = function([], b, mode=self.mode)
         topo = f.maker.fgraph.toposort()
@@ -1782,13 +1782,13 @@ def test_broadcastable_flags_many_dims_and_inputs(self):
         d = TensorType(dtype=self.floatX, shape=(1, None, 1, 1, None, 1))()
         e = TensorType(dtype=self.floatX, shape=(1, None, 1, None, None, 1))()
         f = self.join_op(0, a, b, c, d, e)
-        fb = f.type.broadcastable
+        fb = tuple(s == 1 for s in f.type.shape)
         assert not fb[0] and fb[1] and fb[2] and fb[3] and not fb[4] and fb[5]
         g = self.join_op(1, a, b, c, d, e)
-        gb = g.type.broadcastable
+        gb = tuple(s == 1 for s in g.type.shape)
         assert gb[0] and not gb[1] and gb[2] and gb[3] and not gb[4] and gb[5]
         h = self.join_op(4, a, b, c, d, e)
-        hb = h.type.broadcastable
+        hb = tuple(s == 1 for s in h.type.shape)
         assert hb[0] and hb[1] and hb[2] and hb[3] and not hb[4] and hb[5]
 
         f = function([a, b, c, d, e], f, mode=self.mode)
@@ -1981,8 +1981,8 @@ def test_TensorFromScalar():
     s = aes.constant(56)
     t = tensor_from_scalar(s)
     assert t.owner.op is tensor_from_scalar
-    assert t.type.broadcastable == (), t.type.broadcastable
-    assert t.type.ndim == 0, t.type.ndim
+    assert t.type.shape == ()
+    assert t.type.ndim == 0
     assert t.type.dtype == s.type.dtype
 
     v = eval_outputs([t])
@@ -2129,23 +2129,23 @@ def test_flatten_broadcastable():
 
     inp = TensorType("float64", shape=(None, None, None, None))()
     out = flatten(inp, ndim=2)
-    assert out.broadcastable == (False, False)
+    assert out.type.shape == (None, None)
 
     inp = TensorType("float64", shape=(None, None, None, 1))()
     out = flatten(inp, ndim=2)
-    assert out.broadcastable == (False, False)
+    assert out.type.shape == (None, None)
 
     inp = TensorType("float64", shape=(None, 1, None, 1))()
     out = flatten(inp, ndim=2)
-    assert out.broadcastable == (False, False)
+    assert out.type.shape == (None, None)
 
     inp = TensorType("float64", shape=(None, 1, 1, 1))()
     out = flatten(inp, ndim=2)
-    assert out.broadcastable == (False, True)
+    assert out.type.shape == (None, 1)
 
     inp = TensorType("float64", shape=(1, None, 1, 1))()
     out = flatten(inp, ndim=3)
-    assert out.broadcastable == (True, False, True)
+    assert out.type.shape == (1, None, 1)
 
 
 def test_flatten_ndim_invalid():
@@ -2938,8 +2938,8 @@ def permute_fixed(s_input):
 
     def test_3b_2(self):
         # Test permute_row_elements on a more complex broadcasting pattern:
-        # input.type.broadcastable = (False, True, False),
-        # p.type.broadcastable = (False, False).
+        # input.type.shape = (None, 1, None),
+        # p.type.shape = (None, None).
 
         input = TensorType("floatX", shape=(None, 1, None))()
         p = imatrix()
@@ -4046,7 +4046,7 @@ def test_broadcasted(self):
         B = np.asarray(np.random.random((4, 1)), dtype="float32")
         for m in self.modes:
             f = function([a, b], choose(a, b, mode=m))
-            assert choose(a, b, mode=m).broadcastable[0]
+            assert choose(a, b, mode=m).type.shape[0] == 1
             t_c = f(A, B)
             n_c = np.choose(A, B, mode=m)
             assert np.allclose(t_c, n_c)