Update

[ghstack-poisoned]

Author: GregoryComer

backends/test/suite/operators/__init__.py

@@ -133,7 +133,9 @@ def _create_test_for_backend(
 
 
 class OperatorTest(unittest.TestCase):
-    def _test_op(self, model, inputs, flow: TestFlow):
+    def _test_op(
+        self, model, inputs, flow: TestFlow, generate_random_test_inputs: bool = True
+    ):
         context = get_active_test_context()
 
         # This should be set in the wrapped test. See _make_wrapped_test above.
@@ -145,6 +147,7 @@ def _test_op(self, model, inputs, flow: TestFlow):
             flow,
             context.test_name,
             context.params,
+            generate_random_test_inputs=generate_random_test_inputs,
         )
 
         log_test_summary(run_summary)

backends/test/suite/operators/test_abs.py

@@ -1,92 +1,106 @@
-# (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
+# 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.
 
-# pyre-strict
+# pyre-unsafe
 
-from typing import Callable
 
 import torch
+from executorch.backends.test.suite.flow import TestFlow
 
-from executorch.backends.test.compliance_suite import (
+from executorch.backends.test.suite.operators import (
     dtype_test,
     operator_test,
     OperatorTest,
 )
 
+
 class AbsModel(torch.nn.Module):
     def __init__(self):
         super().__init__()
-        
+
     def forward(self, x):
         return torch.abs(x)
 
+
 @operator_test
 class TestAbs(OperatorTest):
     @dtype_test
-    def test_abs_dtype(self, dtype, tester_factory: Callable) -> None:
+    def test_abs_dtype(self, flow: TestFlow, dtype) -> None:
         # Test with different dtypes
         model = AbsModel().to(dtype)
-        self._test_op(model, (torch.rand(10, 10).to(dtype) * 2 - 1,), tester_factory)
-        
-    def test_abs_basic(self, tester_factory: Callable) -> None:
+        self._test_op(model, (torch.rand(10, 10).to(dtype) * 2 - 1,), flow)
+
+    def test_abs_basic(self, flow: TestFlow) -> None:
         # Basic test with default parameters
         # Input: tensor with positive and negative values
-        self._test_op(AbsModel(), (torch.randn(10, 10),), tester_factory)
-        
-    def test_abs_shapes(self, tester_factory: Callable) -> None:
+        self._test_op(AbsModel(), (torch.randn(10, 10),), flow)
+
+    def test_abs_shapes(self, flow: TestFlow) -> None:
         # Test with different tensor shapes
-        
+
         # 1D tensor
-        self._test_op(AbsModel(), (torch.randn(20),), tester_factory)
-        
+        self._test_op(AbsModel(), (torch.randn(20),), flow)
+
         # 2D tensor
-        self._test_op(AbsModel(), (torch.randn(5, 10),), tester_factory)
-        
+        self._test_op(AbsModel(), (torch.randn(5, 10),), flow)
+
         # 3D tensor
-        self._test_op(AbsModel(), (torch.randn(3, 4, 5),), tester_factory)
-        
+        self._test_op(AbsModel(), (torch.randn(3, 4, 5),), flow)
+
         # 4D tensor
-        self._test_op(AbsModel(), (torch.randn(2, 3, 4, 5),), tester_factory)
-        
+        self._test_op(AbsModel(), (torch.randn(2, 3, 4, 5),), flow)
+
         # 5D tensor
-        self._test_op(AbsModel(), (torch.randn(2, 2, 3, 4, 5),), tester_factory)
-        
-    def test_abs_values(self, tester_factory: Callable) -> None:
+        self._test_op(AbsModel(), (torch.randn(2, 2, 3, 4, 5),), flow)
+
+    def test_abs_values(self, flow: TestFlow) -> None:
         # Test with different value ranges
-        
+
         # Small values
-        self._test_op(AbsModel(), (torch.randn(10, 10) * 0.01,), tester_factory)
-        
+        self._test_op(AbsModel(), (torch.randn(10, 10) * 0.01,), flow)
+
         # Large values
-        self._test_op(AbsModel(), (torch.randn(10, 10) * 1000,), tester_factory)
-        
+        self._test_op(AbsModel(), (torch.randn(10, 10) * 1000,), flow)
+
         # Mixed positive and negative values
-        self._test_op(AbsModel(), (torch.randn(10, 10) * 10,), tester_factory)
-        
+        self._test_op(AbsModel(), (torch.randn(10, 10) * 10,), flow)
+
         # All positive values
-        self._test_op(AbsModel(), (torch.rand(10, 10) * 10,), tester_factory)
-        
+        self._test_op(AbsModel(), (torch.rand(10, 10) * 10,), flow)
+
         # All negative values
-        self._test_op(AbsModel(), (torch.rand(10, 10) * -10,), tester_factory)
-        
+        self._test_op(AbsModel(), (torch.rand(10, 10) * -10,), flow)
+
         # Values close to zero
-        self._test_op(AbsModel(), (torch.randn(10, 10) * 1e-5,), tester_factory)
-        
-    def test_abs_edge_cases(self, tester_factory: Callable) -> None:
+        self._test_op(AbsModel(), (torch.randn(10, 10) * 1e-5,), flow)
+
+    def test_abs_edge_cases(self, flow: TestFlow) -> None:
         # Test edge cases
-        
+
         # Zero tensor
-        self._test_op(AbsModel(), (torch.zeros(10, 10),), tester_factory)
-        
+        self._test_op(
+            AbsModel(), (torch.zeros(10, 10),), flow, generate_random_test_inputs=False
+        )
+
         # Tensor with infinity
-        x = torch.tensor([float('inf'), float('-inf'), 1.0, -1.0])
-        self._test_op(AbsModel(), (x,), tester_factory)
-        
+        x = torch.tensor([float("inf"), float("-inf"), 1.0, -1.0])
+        self._test_op(AbsModel(), (x,), flow, generate_random_test_inputs=False)
+
         # Tensor with NaN
-        x = torch.tensor([float('nan'), 1.0, -1.0])
-        self._test_op(AbsModel(), (x,), tester_factory)
-        
-    def test_abs_scalar(self, tester_factory: Callable) -> None:
+        x = torch.tensor([float("nan"), 1.0, -1.0])
+        self._test_op(AbsModel(), (x,), flow, generate_random_test_inputs=False)
+
+    def test_abs_scalar(self, flow: TestFlow) -> None:
         # Test with scalar input (1-element tensor)
-        self._test_op(AbsModel(), (torch.tensor([-5.0]),), tester_factory)
-        self._test_op(AbsModel(), (torch.tensor([5.0]),), tester_factory)
-        self._test_op(AbsModel(), (torch.tensor([0.0]),), tester_factory)
+        self._test_op(
+            AbsModel(), (torch.tensor([-5.0]),), flow, generate_random_test_inputs=False
+        )
+        self._test_op(
+            AbsModel(), (torch.tensor([5.0]),), flow, generate_random_test_inputs=False
+        )
+        self._test_op(
+            AbsModel(), (torch.tensor([0.0]),), flow, generate_random_test_inputs=False
+        )