Pull request pytorch#69: Fix path to skipped test in test_integration.py

Merge in AITEC/executorch from hotfix/nxf93343/fix-skipped-integration-test to main-nxp

* commit 'c798a8661641fd61adfd8621a1924e7b0c886d06':
  Fix batch norm tests overwriting static context
  Fix path to skipped test in test_integration.py

Author: skywall

backends/nxp/tests/executors.py

@@ -206,3 +206,18 @@ def convert_run_compare(edge_program: ExportedProgram, input_data, rtol=1.e-5, a
                                               " number of outputs. Testing is not implemented for this case.")
 
     return tflite_executor, edge_program_executor
+
+
+class OverrideSupportedTargets:
+
+    def __init__(self, converter_class, *, new_targets):
+        self._converter_class = converter_class
+        self._new_targets = new_targets
+
+        self._old_targets = self._converter_class.supported_targets
+
+    def __enter__(self):
+        self._converter_class.supported_targets = self._new_targets
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self._converter_class.supported_targets = self._old_targets

backends/nxp/tests/test_batch_norm_fusion.py

@@ -11,6 +11,7 @@
 from executorch.backends.nxp.pytorch_passes.fuse_batch_norm_with_linear_pass import FuseBatchNormWithLinearPass
 from executorch.backends.nxp.pytorch_passes.nxp_pytorch_pass_manager import NXPPyTorchPassManager
 from executorch.backends.nxp.tests.executorch_pipeline import to_quantized_edge_program
+from executorch.backends.nxp.tests.executors import OverrideSupportedTargets
 
 
 @pytest.fixture(autouse=True)
@@ -150,11 +151,10 @@ def test_batch_norm_linear_fusing__full_pipeline(bias: bool):
 
     # Don't delegate the Linear node, because there seems to be a bug with the NeutronConverter/NeutronPartitioner.
     #  But that doesn't affect the validity of this test.
-    AddMMConverter.supported_targets = []
-    MMConverter.supported_targets = []
-
-    edge_program = to_quantized_edge_program(module, tuple(input_shape)).exported_program()
-    nodes = list(edge_program.graph.nodes)
+    with OverrideSupportedTargets(AddMMConverter, new_targets=[]):
+        with OverrideSupportedTargets(MMConverter, new_targets=[]):
+            edge_program = to_quantized_edge_program(module, tuple(input_shape)).exported_program()
+            nodes = list(edge_program.graph.nodes)
 
     assert len(nodes) == 14
     assert not any(node.op == 'call_function' and 'batch_norm' in node.target.__name__ for node in nodes)

backends/nxp/tests/test_integration.py

@@ -1,7 +1,6 @@
-import os.path
+import glob
 import pathlib
 
-import pytest
 import torch
 
 from executorch.backends.nxp.tests.executorch_pipeline import to_quantized_executorch_program
@@ -11,12 +10,20 @@
 _CURRENT_DIR = pathlib.Path(__file__).parent
 _PROJECT_DIR = _CURRENT_DIR.parent.parent.parent
 
-QUANTIZED_OPS_AOT_LIB_PATH = _PROJECT_DIR / "cmake-build-debug" / "kernels" / "quantized" / "libquantized_ops_aot_lib.so"
+
+def _get_quantized_ops_aot_lib_path(
+        default_path_pattern="pip-out/temp*/cmake-out/kernels/quantized/libquantized_ops_aot_lib.so"
+):
+    lib_path = glob.glob(f"{_PROJECT_DIR}/{default_path_pattern}")
+
+    if len(lib_path) < 1:
+        raise RuntimeError("Unable to find 'libquantized_ops_aot_lib'. Make sure you've built project "
+                           "with './install_requirements.sh' or provided correct path.")
+    return lib_path[0]
 
 
-@pytest.mark.skipif(not os.path.exists(QUANTIZED_OPS_AOT_LIB_PATH), reason="Quant OPS AoT library file not found.")
 def test_conv_fc_softmax__to_executorch_program():
-    torch.ops.load_library(str(QUANTIZED_OPS_AOT_LIB_PATH))
+    torch.ops.load_library(str(_get_quantized_ops_aot_lib_path()))
 
     model = ConvFCSoftmaxModule()
     input_shape = (1, 4, 5, 5)