Ensure that tests have unique names

python/torch_mlir_e2e_test/registry.py

@@ -11,6 +11,8 @@ from .framework import Test
 
 # The global registry of tests.
 GLOBAL_TEST_REGISTRY = []
+# Ensure that there are no duplicate names in the global test registry.
+_SEEN_UNIQUE_NAMES = set()
 
 
 def register_test_case(module_factory: Callable[[], torch.nn.Module]):
@@ -22,6 +24,13 @@ def register_test_case(module_factory: Callable[[], torch.nn.Module]):
     `program_invoker` is the decorated function.
     """
     def decorator(f):
+        # Ensure that there are no duplicate names in the global test registry.
+        if f.__name__ in _SEEN_UNIQUE_NAMES:
+            raise Exception(
+                f"Duplicate test name: '{f.__name__}'. Please make sure that the function wrapped by `register_test_case` has a unique name.")
+        _SEEN_UNIQUE_NAMES.add(f.__name__)
+
+        # Store the test in the registry.
         GLOBAL_TEST_REGISTRY.append(
             Test(unique_name=f.__name__,
                  program_factory=module_factory,

python/torch_mlir_e2e_test/test_suite/conv.py

@@ -515,7 +515,7 @@ class _ConvolutionDeprecated2DCudnnModule(torch.nn.Module):
                                            cudnn_enabled=True)
 
 @register_test_case(module_factory=lambda: _ConvolutionDeprecated2DCudnnModule())
-def _Convolution2DCudnnModule_basic(module, tu: TestUtils):
+def _ConvolutionDeprecated2DCudnnModule_basic(module, tu: TestUtils):
     module.forward(torch.randn(3, 3, 10, 10), torch.randn(3, 3, 2, 2))
 
 class ConvolutionModule2DGroups(torch.nn.Module):