check param is of nn.Parameter type for pruning sanitization

src/lightning/pytorch/callbacks/pruning.py

@@ -458,7 +458,10 @@ def sanitize_parameters_to_prune(
 
         if not parameters_to_prune:
             parameters_to_prune = [
-                (m, p) for p in parameters for m in current_modules if getattr(m, p, None) is not None
+                (m, p)
+                for p in parameters
+                for m in current_modules
+                if getattr(m, p, None) is not None and isinstance(getattr(m, p, None), nn.Parameter)
             ]
         elif (
             isinstance(parameters_to_prune, (list, tuple))

tests/tests_pytorch/callbacks/test_pruning.py

@@ -338,3 +338,80 @@ def on_save_checkpoint(self, trainer, pl_module, checkpoint):
     assert not hasattr(model.layer.mlp_3, "weight_orig")
     model = TestModel.load_from_checkpoint(trainer.checkpoint_callback.last_model_path)
     assert not hasattr(model.layer.mlp_3, "weight_orig")
+
+
+def test_sanitize_parameters_explicit_check():
+    """Test the sanitize_parameters_to_prune method with various attribute types."""
+
+    class TestModule(nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.weight = nn.Parameter(torch.randn(5, 5))
+            self.bias = nn.Parameter(torch.randn(5))
+            self.some_bool = True
+            self.some_tensor = torch.randn(3, 3)  # Regular tensor, not parameter
+            self.some_string = "test"
+            self.some_none = None
+
+    class TestModel(BoringModel):
+        def __init__(self):
+            super().__init__()
+            self.test_module = TestModule()
+
+    model = TestModel()
+
+    parameters_to_prune = ModelPruning.sanitize_parameters_to_prune(
+        model,
+        parameters_to_prune=(),
+        parameter_names=["weight", "bias", "some_bool", "some_tensor", "some_string", "some_none"],
+    )
+
+    param_names_found = set()
+    for module, param_name in parameters_to_prune:
+        param = getattr(module, param_name)
+        assert isinstance(param, nn.Parameter), f"Expected Parameter, got {type(param)}"
+        param_names_found.add(param_name)
+
+    assert "weight" in param_names_found
+    assert "bias" in param_names_found
+    assert "some_bool" not in param_names_found
+    assert "some_tensor" not in param_names_found
+    assert "some_string" not in param_names_found
+    assert "some_none" not in param_names_found
+
+
+def test_original_issue_reproduction():
+    """Issue: https://github.com/Lightning-AI/pytorch-lightning/issues/10835."""
+
+    class ProblematicModel(BoringModel):
+        def __init__(self):
+            super().__init__()
+            self.layer = Sequential(
+                OrderedDict([
+                    ("mlp_1", nn.Linear(32, 32)),
+                    ("mlp_2", nn.Linear(32, 2)),
+                ])
+            )
+            # Add boolean attributes that would cause the original error
+            self.layer.mlp_1.training = True
+            self.layer.mlp_2.requires_grad = True
+
+    model = ProblematicModel()
+
+    try:
+        parameters_to_prune = ModelPruning.sanitize_parameters_to_prune(
+            model, parameters_to_prune=(), parameter_names=["weight", "bias", "training", "requires_grad"]
+        )
+
+        for module, param_name in parameters_to_prune:
+            param = getattr(module, param_name)
+            assert isinstance(param, nn.Parameter), f"Non-parameter found: {type(param)}"
+
+        success = True
+    except AttributeError as e:
+        if "'bool' object has no attribute 'is_cuda'" in str(e):
+            success = False  # Original bug still present
+        else:
+            raise  # Different error
+
+    assert success, "The fix for issue #10835 is not working correctly"