| """Regression cases for the task policies used by both validation runners.""" |
|
|
| import sys |
| import unittest |
| from pathlib import Path |
|
|
| import torch |
|
|
| sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "scripts")) |
| from task_acceptance import compare_output |
|
|
|
|
| class TaskAcceptanceTests(unittest.TestCase): |
| def test_contest_or_is_not_additive_tolerance(self): |
| expected = torch.tensor([100.0]) |
| actual = torch.tensor([115.0]) |
| additive = dict(atol=10.0, rtol=0.1) |
| compare_output(actual, expected, additive) |
| with self.assertRaises(AssertionError): |
| compare_output( |
| actual, expected, dict(additive, comparison="absolute_or_relative") |
| ) |
|
|
| def test_strict_gdn_diagnostic_is_distinct_from_contest(self): |
| expected, actual = torch.tensor([0.05]), torch.tensor([0.05151]) |
| with self.assertRaises(AssertionError): |
| compare_output(actual, expected, dict(atol=2e-5, rtol=1e-4)) |
| compare_output( |
| actual, |
| expected, |
| dict(atol=0.01, rtol=0.01, comparison="absolute_or_relative"), |
| ) |
|
|
| def test_matched_ratio_boundary_uses_integer_counts(self): |
| expected = torch.zeros(10000) |
| actual = expected.clone() |
| actual[0] = 1.0 |
| metrics = compare_output( |
| actual, expected, dict(atol=0.0, rtol=0.0, required_matched_ratio=0.9999) |
| ) |
| self.assertEqual(metrics["mismatched_elements"], 1) |
| actual[1] = 1.0 |
| with self.assertRaises(AssertionError): |
| compare_output( |
| actual, |
| expected, |
| dict(atol=0.0, rtol=0.0, required_matched_ratio=0.9999), |
| ) |
| metrics = compare_output(expected, expected, dict(atol=0.0, rtol=0.0)) |
| self.assertEqual(metrics["matched_ratio"], 1.0) |
|
|
| def test_nonfinite_values_and_integer_mismatches_fail(self): |
| for value in [float("nan"), float("inf")]: |
| with self.assertRaises(AssertionError): |
| compare_output( |
| torch.tensor([value]), |
| torch.tensor([value]), |
| dict(atol=1.0, rtol=1.0), |
| ) |
| with self.assertRaises(AssertionError): |
| compare_output( |
| torch.tensor([1]), torch.tensor([2]), dict(atol=100.0, rtol=100.0) |
| ) |
| compare_output(torch.tensor([1]), torch.tensor([1]), dict(atol=0.0, rtol=0.0)) |
|
|
|
|
| if __name__ == "__main__": |
| unittest.main() |
|
|
