Raise inoperative error guards in ExportSession, XNNPACK node_visitor, and Samsung conv1d

backends/samsung/_passes/conv1d_to_conv2d.py

@@ -42,7 +42,9 @@ def update_kernel(self, weight_node: torch.Tensor):
                     weight_3d, -1
                 )
         else:
-            RuntimeError("Weight of 1d conv should be constant tensor or Parameter obj")
+            raise RuntimeError(
+                "Weight of 1d conv should be constant tensor or Parameter obj"
+            )
         weight_node.meta["val"] = weight_node.meta["val"].data.unsqueeze(dim=-1)
 
     def call(self, graph_module: torch.fx.GraphModule):

backends/xnnpack/operators/node_visitor.py

@@ -475,7 +475,10 @@ def define_tensor(  # noqa: C901
             elif quant_params.axis == 1:
                 quant_params.axis = 0
             else:
-                assert f"Unsupported weight per channel quantization axis for depthwise conv2d / conv_transpose2d : {quant_params.axis}, expecting 0 / 1."
+                check_or_raise(
+                    False,
+                    f"Unsupported weight per channel quantization axis for depthwise conv2d / conv_transpose2d : {quant_params.axis}, expecting 0 / 1.",
+                )
 
         # Serialize tensor value
         custom_meta = tensor.meta.get("custom", None)

export/export.py

@@ -714,7 +714,7 @@ def print_delegation_info(self) -> None:
         )
         if not lowering_stage:
             raise RuntimeError(
-                "No delegation info available, atleast one of the lowering stages should be present"
+                "No delegation info available, at least one of the lowering stages should be present"
             )
 
         stage_artifact = self._stage_to_artifacts.get(lowering_stage[0])

export/tests/test_print_delegation_info.py

@@ -0,0 +1,48 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+import unittest
+
+import torch
+from executorch.export import ExportRecipe, ExportSession
+from executorch.export.types import StageType
+
+
+class Tiny(torch.nn.Module):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return x + 1
+
+
+class TestPrintDelegationInfoGuards(unittest.TestCase):
+    def test_print_delegation_info_requires_a_lowering_stage(self) -> None:
+        session = ExportSession(
+            model=Tiny(),
+            example_inputs=[(torch.randn(2, 3),)],
+            export_recipe=ExportRecipe(
+                name="test",
+                pipeline_stages=[StageType.TORCH_EXPORT],
+            ),
+        )
+        with self.assertRaises(RuntimeError) as cm:
+            session.print_delegation_info()
+        self.assertIn("at least one of the lowering stages", str(cm.exception))
+
+    def test_print_delegation_info_requires_lowering_artifact(self) -> None:
+        session = ExportSession(
+            model=Tiny(),
+            example_inputs=[(torch.randn(2, 3),)],
+            export_recipe=ExportRecipe(
+                name="test",
+                pipeline_stages=[
+                    StageType.TORCH_EXPORT,
+                    StageType.TO_EDGE_TRANSFORM_AND_LOWER,
+                ],
+            ),
+        )
+        with self.assertRaises(RuntimeError) as cm:
+            session.print_delegation_info()
+        self.assertIn("run the lowering stage first", str(cm.exception))