Enable mypy's `warn_no_return` lint

pyproject.toml

@@ -156,17 +156,12 @@ lines-after-imports = 2
 [tool.mypy]
 python_version = "3.10"
 ignore_missing_imports = true
-no_implicit_optional = true
-check_untyped_defs = false
 strict_equality = true
 warn_redundant_casts = true
 warn_unused_configs = true
 warn_unused_ignores = true
 warn_return_any = true
-warn_no_return = false
 warn_unreachable = true
-show_error_codes = true
-allow_redefinition = false
 files = ["pytensor", "tests"]
 plugins = ["numpy.typing.mypy_plugin"]
 

pytensor/graph/basic.py

@@ -909,6 +909,7 @@ def ancestors(
     def expand(r: Variable) -> Iterator[Variable] | None:
         if r.owner and (not blockers or r not in blockers):
             return reversed(r.owner.inputs)
+        return None
 
     yield from cast(Generator[Variable, None, None], walk(graphs, expand, False))
 
@@ -1011,6 +1012,7 @@ def vars_between(
     def expand(r: Variable) -> Iterable[Variable] | None:
         if r.owner and r not in ins:
             return reversed(r.owner.inputs + r.owner.outputs)
+        return None
 
     yield from cast(Generator[Variable, None, None], walk(outs, expand))
 
@@ -2039,13 +2041,15 @@ def get_var_by_name(
     from pytensor.graph.op import HasInnerGraph
 
     def expand(r) -> list[Variable] | None:
-        if r.owner:
-            res = list(r.owner.inputs)
+        if not r.owner:
+            return None
+
+        res = list(r.owner.inputs)
 
-            if isinstance(r.owner.op, HasInnerGraph):
-                res.extend(r.owner.op.inner_outputs)
+        if isinstance(r.owner.op, HasInnerGraph):
+            res.extend(r.owner.op.inner_outputs)
 
-            return res
+        return res
 
     results: tuple[Variable, ...] = ()
     for var in walk(graphs, expand, False):

pytensor/tensor/rewriting/linalg.py

@@ -355,34 +355,37 @@ def local_lift_through_linalg(
     """
 
     # TODO: Simplify this if we end up Blockwising KroneckerProduct
-    if isinstance(node.op.core_op, MatrixInverse | Cholesky | MatrixPinv):
-        y = node.inputs[0]
-        outer_op = node.op
-
-        if y.owner and (
-            isinstance(y.owner.op, Blockwise)
-            and isinstance(y.owner.op.core_op, BlockDiagonal)
-            or isinstance(y.owner.op, KroneckerProduct)
-        ):
-            input_matrices = y.owner.inputs
-
-            if isinstance(outer_op.core_op, MatrixInverse):
-                outer_f = cast(Callable, inv)
-            elif isinstance(outer_op.core_op, Cholesky):
-                outer_f = cast(Callable, cholesky)
-            elif isinstance(outer_op.core_op, MatrixPinv):
-                outer_f = cast(Callable, pinv)
-            else:
-                raise NotImplementedError  # pragma: no cover
+    if not isinstance(node.op.core_op, MatrixInverse | Cholesky | MatrixPinv):
+        return None
 
-            inner_matrices = [cast(TensorVariable, outer_f(m)) for m in input_matrices]
+    y = node.inputs[0]
+    outer_op = node.op
 
-            if isinstance(y.owner.op, KroneckerProduct):
-                return [kron(*inner_matrices)]
-            elif isinstance(y.owner.op.core_op, BlockDiagonal):
-                return [block_diag(*inner_matrices)]
-            else:
-                raise NotImplementedError  # pragma: no cover
+    if y.owner and (
+        isinstance(y.owner.op, Blockwise)
+        and isinstance(y.owner.op.core_op, BlockDiagonal)
+        or isinstance(y.owner.op, KroneckerProduct)
+    ):
+        input_matrices = y.owner.inputs
+
+        if isinstance(outer_op.core_op, MatrixInverse):
+            outer_f = cast(Callable, inv)
+        elif isinstance(outer_op.core_op, Cholesky):
+            outer_f = cast(Callable, cholesky)
+        elif isinstance(outer_op.core_op, MatrixPinv):
+            outer_f = cast(Callable, pinv)
+        else:
+            raise NotImplementedError  # pragma: no cover
+
+        inner_matrices = [cast(TensorVariable, outer_f(m)) for m in input_matrices]
+
+        if isinstance(y.owner.op, KroneckerProduct):
+            return [kron(*inner_matrices)]
+        elif isinstance(y.owner.op.core_op, BlockDiagonal):
+            return [block_diag(*inner_matrices)]
+        else:
+            raise NotImplementedError  # pragma: no cover
+    return None
 
 
 def _find_diag_from_eye_mul(potential_mul_input):