File size: 2,512 Bytes
addfd4b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | """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()
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