Add deprecation warning for to_edge + to_backend workflow in XnnpackPartitioner

backends/xnnpack/partition/xnnpack_partitioner.py

@@ -4,8 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+import inspect
 import itertools
-
 import logging
 from typing import List, Optional, Type, Union
 
@@ -65,6 +65,37 @@ def __init__(
         self.per_op_mode = per_op_mode
         super().__init__(delegation_spec, initialized_configs)
 
+    def _check_if_called_from_to_backend(self) -> bool:
+        """
+        Check if the partition method is being called from the deprecated to_backend workflow.
+        Returns True if called from deprecated direct to_backend, False if called from to_edge_transform_and_lower.
+        """
+        stack = inspect.stack()
+
+        for frame_info in stack:
+            if frame_info.function == "to_edge_transform_and_lower":
+                return False
+
+        for frame_info in stack:
+            if frame_info.function == "to_backend":
+                filename = frame_info.filename
+                if "program/_program.py" in filename:
+                    return True
+        return False
+
+    def partition(self, exported_program):
+        """
+        Override partition to add deprecation warning when called from to_backend.
+        """
+        # Check if we're being called from the deprecated to_backend workflow
+        if self._check_if_called_from_to_backend():
+            logger.warning(
+                "\nDEPRECATION WARNING: You are using the deprecated 'to_edge() + to_backend()' workflow. "
+                "Please consider migrating to 'to_edge_transform_and_lower()' for better error handling and optimization. "
+            )
+
+        return super().partition(exported_program)
+
     def generate_partitions(self, ep: ExportedProgram) -> List[Partition]:
         """
         generate_partitions is different if partitioner is set to per_op_mode

backends/xnnpack/test/test_xnnpack_partitioner.py

@@ -0,0 +1,84 @@
+# 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 io
+import logging
+import unittest
+
+import torch
+from executorch.backends.xnnpack.partition.xnnpack_partitioner import XnnpackPartitioner
+from executorch.exir import to_edge, to_edge_transform_and_lower
+from torch.export import export
+
+
+class TestXnnpackPartitioner(unittest.TestCase):
+    """Test cases for XnnpackPartitioner functionality and deprecation warnings."""
+
+    class SimpleModel(torch.nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.linear = torch.nn.Linear(10, 5)
+
+        def forward(self, x):
+            return self.linear(x)
+
+    def test_deprecation_warning_for_to_backend_workflow(self):
+        """
+        Test that the deprecated to_edge + to_backend workflow shows a deprecation warning.
+        """
+        model = self.SimpleModel()
+        x = torch.randn(1, 10)
+
+        exported_model = export(model, (x,))
+
+        # Capture log output to check for deprecation warning
+        log_capture_string = io.StringIO()
+        ch = logging.StreamHandler(log_capture_string)
+        ch.setLevel(logging.WARNING)
+
+        logger = logging.getLogger(
+            "executorch.backends.xnnpack.partition.xnnpack_partitioner"
+        )
+        logger.addHandler(ch)
+        logger.setLevel(logging.WARNING)
+
+        edge = to_edge(exported_model)
+        partitioner = XnnpackPartitioner()
+
+        edge.to_backend(partitioner)
+
+        log_contents = log_capture_string.getvalue()
+        self.assertIn("DEPRECATION WARNING", log_contents)
+        self.assertIn("to_edge() + to_backend()", log_contents)
+        self.assertIn("to_edge_transform_and_lower()", log_contents)
+
+    def test_no_warning_for_to_edge_transform_and_lower_workflow(self):
+        """
+        Test that the recommended to_edge_transform_and_lower workflow does NOT show a deprecation warning.
+        """
+
+        model = self.SimpleModel()
+        x = torch.randn(1, 10)
+
+        exported_model = export(model, (x,))
+
+        # Capture log output to check for deprecation warning
+        log_capture_string = io.StringIO()
+        ch = logging.StreamHandler(log_capture_string)
+        ch.setLevel(logging.WARNING)
+
+        logger = logging.getLogger(
+            "executorch.backends.xnnpack.partition.xnnpack_partitioner"
+        )
+        logger.addHandler(ch)
+        logger.setLevel(logging.WARNING)
+
+        partitioner = XnnpackPartitioner()
+
+        to_edge_transform_and_lower(exported_model, partitioner=[partitioner])
+
+        log_contents = log_capture_string.getvalue()
+        self.assertNotIn("DEPRECATION WARNING", log_contents)