test: Add L0_pytorch_python_runtime coverage for default_model_filename autofill

qa/L0_pytorch_python_runtime/test.sh

@@ -1,5 +1,5 @@
 #!/bin/bash
-# Copyright 2023-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# Copyright 2023-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 #
 # Redistribution and use in source and binary forms, with or without
 # modification, are permitted provided that the following conditions
@@ -122,6 +122,24 @@ else
         fi
     done
 
+    # Regression guard: core auto-complete must not fill in
+    # default_model_filename for a model using the Python-based runtime.
+    # If it does, 'neuralnet' is selected as TorchScript and torch.jit.load()
+    # fails on its weights file with a "constants.pkl not found" error.
+    grep "Loading 'neuralnet' as TorchScript" $SERVER_LOG
+    if [ $? -eq 0 ]; then
+        echo -e "\n***\n*** 'neuralnet' took the TorchScript load path: core autofilled default_model_filename for a runtime \"model.py\" model. \n***"
+        RET=1
+    fi
+
+    if ! NEURALNET_DEFAULT_FILENAME=$(curl -sf localhost:8000/v2/models/neuralnet/config | python3 -c "import json,sys; c=json.load(sys.stdin); assert c.get('name')=='neuralnet', c; print(c.get('default_model_filename', ''))"); then
+        echo -e "\n***\n*** Failed to retrieve or parse the configuration for 'neuralnet'. \n***"
+        RET=1
+    elif [ "$NEURALNET_DEFAULT_FILENAME" != "" ]; then
+        echo -e "\n***\n*** Expected empty default_model_filename for 'neuralnet', got \"$NEURALNET_DEFAULT_FILENAME\". \n***"
+        RET=1
+    fi
+
     # Infer TorchScript model
     CLIENT_LOG="./infer.torchscript.log"
     python $IMAGE_CLIENT -m "resnet50_libtorch" -s INCEPTION -c 1 -b 2 "$IMAGE_DIR/vulture.jpeg" > $CLIENT_LOG 2>&1

qa/L0_pytorch_python_runtime/unit_test.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 
-# Copyright 2023-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# Copyright 2023-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
 #
 # Redistribution and use in source and binary forms, with or without
 # modification, are permitted provided that the following conditions
@@ -26,15 +26,22 @@
 # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
+import os
+import shutil
 import sys
+import tempfile
 import unittest
 
 import torch
 
 # satisfy Python runtime import requirements
 sys.modules["triton_python_backend_utils"] = unittest.mock.MagicMock()
 # import modules from Python runtime to be tested
-from py_runtime import _gather_torch_tensors, _scatter_torch_tensors
+from py_runtime import (  # noqa: E402
+    _gather_torch_tensors,
+    _get_model_path,
+    _scatter_torch_tensors,
+)
 
 
 class PyTorchPythonBackendRuntimeUnittest(unittest.TestCase):
@@ -150,5 +157,41 @@ def test_scatter_torch_tensors(self):
                     self.assertTrue(torch.equal(tensor, expected_tensor))
 
 
+class PyTorchPythonRuntimeModelPathTest(unittest.TestCase):
+    """For a Python-class model, 'model.py' must be selected over a
+    sibling weights file 'model.pt' unless the user explicitly requests the
+    weights file via default_model_filename."""
+
+    def setUp(self):
+        self._model_dir = tempfile.mkdtemp()
+        pb_utils = sys.modules["triton_python_backend_utils"]
+        pb_utils.get_model_dir.return_value = self._model_dir
+
+    def tearDown(self):
+        shutil.rmtree(self._model_dir)
+
+    def _touch(self, filename):
+        path = os.path.join(self._model_dir, filename)
+        open(path, "w").close()
+        return path
+
+    def test_model_py_preferred_over_sibling_weights(self):
+        expected = self._touch("model.py")
+        self._touch("model.pt")
+        config = {"default_model_filename": ""}
+        self.assertEqual(_get_model_path(config), expected)
+
+    def test_explicit_default_model_filename_wins(self):
+        self._touch("model.py")
+        expected = self._touch("model.pt")
+        config = {"default_model_filename": "model.pt"}
+        self.assertEqual(_get_model_path(config), expected)
+
+    def test_torchscript_only_model(self):
+        expected = self._touch("model.pt")
+        config = {"default_model_filename": ""}
+        self.assertEqual(_get_model_path(config), expected)
+
+
 if __name__ == "__main__":
     unittest.main()