Raise explicitly on Python methods that are incompatible with lazy variables

Notably changes the behavior of `__bool__` to always raise. Before there was a hack based on whether a variable had been compared to something before.

Author: ricardoV94

pytensor/compile/function/pfunc.py

@@ -569,7 +569,7 @@ def construct_pfunc_ins_and_outs(
     if not fgraph:
         # Extend the outputs with the updates on input variables so they are
         # also cloned
-        additional_outputs = [i.update for i in inputs if i.update]
+        additional_outputs = [i.update for i in inputs if i.update is not None]
         if outputs is None:
             out_list = []
         else:
@@ -608,7 +608,7 @@ def construct_pfunc_ins_and_outs(
             new_i.variable = iv
 
             # If needed, replace the input's update by its cloned equivalent
-            if i.update:
+            if i.update is not None:
                 new_i.update = clone_d[i.update]
 
             new_inputs.append(new_i)

pytensor/compile/function/types.py

@@ -198,7 +198,7 @@ def std_fgraph(
     update_mapping = {}
     out_idx = len(output_specs)
     for idx, input_spec in enumerate(input_specs):
-        if input_spec.update:
+        if input_spec.update is not None:
             updates.append(input_spec.update)
             update_mapping[out_idx] = idx
             out_idx += 1
@@ -1195,7 +1195,7 @@ def insert_deepcopy(fgraph, wrapped_inputs, wrapped_outputs):
     updated_fgraph_inputs = {
         fgraph_i
         for i, fgraph_i in zip(wrapped_inputs, fgraph.inputs, strict=True)
-        if getattr(i, "update", False)
+        if getattr(i, "update", None) is not None
     }
 
     # We can't use fgraph.inputs as this don't include Constant Value.
@@ -1351,7 +1351,11 @@ def check_unused_inputs(inputs, outputs, on_unused_input):
             ancestors(
                 (
                     [o.variable for o in outputs]
-                    + [i.update for i in inputs if getattr(i, "update", False)]
+                    + [
+                        i.update
+                        for i in inputs
+                        if getattr(i, "update", None) is not None
+                    ]
                 ),
                 blockers=[i.variable for i in inputs],
             )

pytensor/compile/nanguardmode.py

@@ -36,7 +36,7 @@ def _is_numeric_value(arr, var):
         return False
     elif isinstance(arr, np.random.mtrand.RandomState | np.random.Generator):
         return False
-    elif var and isinstance(var.type, RandomType):
+    elif var is not None and isinstance(var.type, RandomType):
         return False
     elif isinstance(arr, slice):
         return False

pytensor/scalar/basic.py

@@ -725,6 +725,37 @@ def get_scalar_type(dtype, cache: dict[str, ScalarType] = {}) -> ScalarType:
 
 
 class _scalar_py_operators:
+    # These can't work because Python requires native output types
+    def __bool__(self):
+        raise TypeError(
+            "ScalarVariable cannot be converted to Python boolean. "
+            "Call `.astype(bool)` for the symbolic equivalent."
+        )
+
+    def __index__(self):
+        raise TypeError(
+            "ScalarVariable cannot be converted to Python integer. "
+            "Call `.astype(int)` for the symbolic equivalent."
+        )
+
+    def __int__(self):
+        raise TypeError(
+            "ScalarVariable cannot be converted to Python integer. "
+            "Call `.astype(int)` for the symbolic equivalent."
+        )
+
+    def __float__(self):
+        raise TypeError(
+            "ScalarVariable cannot be converted to Python float. "
+            "Call `.astype(float)` for the symbolic equivalent."
+        )
+
+    def __complex__(self):
+        raise TypeError(
+            "ScalarVariable cannot be converted to Python complex number. "
+            "Call `.astype(complex)` for the symbolic equivalent."
+        )
+
     # So that we can simplify checking code when we have a mixture of ScalarType
     # variables and Tensor variables
     ndim = 0

pytensor/scalar/loop.py

@@ -60,12 +60,12 @@ def __init__(
             constant = []
         if not len(init) == len(update):
             raise ValueError("An update must be given for each init variable")
-        if until:
+        if until is not None:
             inputs, outputs = clone([*init, *constant], [*update, until])
         else:
             inputs, outputs = clone([*init, *constant], update)
 
-        self.is_while = bool(until)
+        self.is_while = until is not None
         self.inputs, self.outputs = self._cleanup_graph(inputs, outputs)
         self._validate_updates(self.inputs, self.outputs)
 

pytensor/scalar/math.py

@@ -942,7 +942,7 @@ def inner_loop_a(sum_a, delta, xpow, k_minus_one_minus_n, fac, dfac, x):
             dfac = k_minus_one_minus_n * dfac + fac
             fac *= k_minus_one_minus_n
             delta = dfac / xpow
-            return (sum_a, delta, xpow, k_minus_one_minus_n, fac, dfac), ()
+            return (sum_a, delta, xpow, k_minus_one_minus_n, fac, dfac), None
 
         init = [sum_a0, delta, xpow, k_minus_one_minus_n, fac, dfac]
         constant = [x]

pytensor/scan/basic.py

@@ -979,7 +979,7 @@ def wrap_into_list(x):
         # user-specified within the inner-function (e.g. by returning an update
         # `dict`) or the `SharedVariable.default_update`s of a shared variable
         # created in the inner-function.
-        if input.update and (is_local or input.variable in updates):
+        if input.update is not None and (is_local or input.variable in updates):
             # We need to remove the `default_update`s on the shared
             # variables created within the context of the loop function
             # (e.g. via use of `RandomStream`); otherwise, they'll get

pytensor/scan/op.py

@@ -2582,7 +2582,7 @@ def compute_all_gradients(known_grads):
 
         # mask inputs that get no gradients
         for dx in range(len(dC_dinps_t)):
-            if not dC_dinps_t[dx]:
+            if dC_dinps_t[dx] is None:
                 dC_dinps_t[dx] = pt.zeros_like(diff_inputs[dx])
             else:
                 disconnected_dC_dinps_t[dx] = False

pytensor/tensor/basic.py

@@ -3386,7 +3386,14 @@ def __getitem__(self, *args):
                 raise NotImplementedError(
                     "Not implemented for slices whose step is complex"
                 )
-        ranges = [arange(sl.start or 0, sl.stop, sl.step or 1) for sl in args[0]]
+        ranges = [
+            arange(
+                sl.start if sl.start is not None else 0,
+                sl.stop,
+                sl.step if sl.step is not None else 1,
+            )
+            for sl in args[0]
+        ]
         shapes = [
             tuple([1] * j + [r.shape[0]] + [1] * (ndim - 1 - j))
             for j, r in enumerate(ranges)

pytensor/tensor/conv/abstract_conv.py

@@ -2198,7 +2198,7 @@ def __init__(
         ):
             border_mode = "valid"
 
-        self.imshp = tuple(imshp) if imshp else (None,) * (2 + convdim)
+        self.imshp = tuple(imshp) if imshp is not None else (None,) * (2 + convdim)
         for imshp_i in self.imshp:
             if imshp_i is not None:
                 # Components of imshp should be constant or ints
@@ -2208,7 +2208,7 @@ def __init__(
                     raise ValueError(
                         "imshp should be None or a tuple of constant int values"
                     ).with_traceback(sys.exc_info()[2])
-        if kshp:
+        if kshp is not None:
             self.kshp = tuple(kshp)
         else:
             self.kshp = (None,) * ((2 + 2 * convdim) if unshared else (2 + convdim))