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/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/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/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/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))

pytensor/tensor/rewriting/math.py

@@ -1992,7 +1992,7 @@ def local_pow_canonicalize(fgraph, node):
         # x ** 1 = x
         new_out = broadcast_arrays(*node.inputs)[0]
 
-    if not new_out:
+    if new_out is None:
         return
 
     if new_out.dtype != node.out.dtype:

pytensor/tensor/variable.py

@@ -26,53 +26,54 @@
 
 
 class _tensor_py_operators:
+    # These can't work because Python requires native output types
+    def __bool__(self):
+        raise TypeError(
+            "TensorVariable cannot be converted to Python boolean. "
+            "Call `.astype(bool)` for the symbolic equivalent."
+        )
+
+    def __index__(self):
+        raise TypeError(
+            "TensorVariable cannot be converted to Python integer. "
+            "Call `.astype(int)` for the symbolic equivalent."
+        )
+
+    def __int__(self):
+        raise TypeError(
+            "TensorVariable cannot be converted to Python integer. "
+            "Call `.astype(int)` for the symbolic equivalent."
+        )
+
+    def __float__(self):
+        raise TypeError(
+            "TensorVariables cannot be converted to Python float. "
+            "Call `.astype(float)` for the symbolic equivalent."
+        )
+
+    def __complex__(self):
+        raise TypeError(
+            "TensorVariables cannot be converted to Python complex number. "
+            "Call `.astype(complex)` for the symbolic equivalent."
+        )
+
     def __abs__(self):
         return pt.math.abs(self)
 
     def __neg__(self):
         return pt.math.neg(self)
 
-    # These won't work because Python requires an int return value
-    # def __int__(self): return convert_to_int32(self)
-    # def __float__(self): return convert_to_float64(self)
-    # def __complex__(self): return convert_to_complex128(self)
-
-    _is_nonzero = True
-
     def __lt__(self, other):
-        rval = pt.math.lt(self, other)
-        rval._is_nonzero = False
-        return rval
+        return pt.math.lt(self, other)
 
     def __le__(self, other):
-        rval = pt.math.le(self, other)
-        rval._is_nonzero = False
-        return rval
+        return pt.math.le(self, other)
 
     def __gt__(self, other):
-        rval = pt.math.gt(self, other)
-        rval._is_nonzero = False
-        return rval
+        return pt.math.gt(self, other)
 
     def __ge__(self, other):
-        rval = pt.math.ge(self, other)
-        rval._is_nonzero = False
-        return rval
-
-    def __bool__(self):
-        # This is meant to prohibit stuff like a < b < c, which is internally
-        # implemented as (a < b) and (b < c). The trouble with this is the
-        # side-effect that checking for a non-NULL a by typing "if a: ..."
-        # uses the same __nonzero__ method.  We want these both to work, but
-        # it seems impossible.  Currently, all vars evaluate to nonzero except
-        # the return values of comparison operators, which raise this
-        # exception.  If you can think of a better solution, go for it!
-        #
-        # __bool__ is Python 3.x data model. __nonzero__ is Python 2.x.
-        if self._is_nonzero:
-            return True
-        else:
-            raise TypeError("Variables do not support boolean operations.")
+        return pt.math.ge(self, other)
 
     def __invert__(self):
         return pt.math.invert(self)