Fix sigmoid overflow for large logits causing incorrect AUROC results

src/torchmetrics/utilities/compute.py

@@ -234,13 +234,34 @@ def normalize_logits_if_needed(tensor: Tensor, normalization: Optional[Literal["
     # decrease sigmoid on cpu .
     if tensor.device == torch.device("cpu"):
         if not torch.all((tensor >= 0) * (tensor <= 1)):
-            tensor = tensor.sigmoid() if normalization == "sigmoid" else torch.softmax(tensor, dim=1)
+            if normalization == "sigmoid":
+                # Apply numerically stable sigmoid by subtracting max to prevent overflow
+                # For large positive logits (>16.7 for float32, >36.7 for float64), sigmoid(x) overflows to 1.0
+                # Only apply stabilization when min value is also large (indicating all values will overflow)
+                # This avoids the issue where subtracting max creates artificial ties for widely spread values
+                min_val = tensor.min()
+                max_val = tensor.max()
+                if min_val > 15:  # All values are large enough to potentially overflow
+                    tensor = (tensor - max_val).sigmoid()
+                else:
+                    tensor = tensor.sigmoid()
+            else:
+                tensor = torch.softmax(tensor, dim=1)
         return tensor
 
     # decrease device-host sync on device .
     condition = ((tensor < 0) | (tensor > 1)).any()
-    return torch.where(
-        condition,
-        torch.sigmoid(tensor) if normalization == "sigmoid" else torch.softmax(tensor, dim=1),
-        tensor,
-    )
+    if normalization == "sigmoid":
+        # Apply numerically stable sigmoid by subtracting max to prevent overflow
+        # Only stabilize when all values are large to avoid creating artificial ties
+        min_val = tensor.min()
+        max_val = tensor.max()
+        # Use stable sigmoid only when minimum value is also large (all values will overflow)
+        needs_stabilization = min_val > 15
+        if needs_stabilization:
+            tensor_stable = tensor - max_val
+            return torch.where(condition, tensor_stable.sigmoid(), tensor)
+        else:
+            return torch.where(condition, tensor.sigmoid(), tensor)
+    else:
+        return torch.where(condition, torch.softmax(tensor, dim=1), tensor)

tests/unittests/classification/test_auroc.py

@@ -140,6 +140,41 @@ def test_binary_auroc_threshold_arg(self, inputs, threshold_fn):
             assert torch.allclose(ap1, ap2)
 
 
+def test_binary_auroc_large_logits():
+    """Test that large logits don't cause numerical overflow in sigmoid.
+
+    Regression test for issue where very large logits (>16.7 for float32) cause
+    naive sigmoid to overflow to 1.0 for all values, losing ranking information
+    needed for AUROC.
+    """
+    # Test case from the issue: all logits in range 97-100
+    preds = torch.tensor([98.0950, 98.4612, 98.1145, 98.1506, 97.6037, 98.9425, 99.2644,
+                          99.5014, 99.7280, 99.6595, 99.6931, 99.4667, 99.9623, 99.8949, 99.8768])
+    target = torch.tensor([0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
+
+    result = binary_auroc(preds, target, thresholds=None)
+
+    # Expected AUROC is 0.9286 (as computed by sklearn)
+    # The ranking is preserved: lowest value (97.6037) corresponds to label 0,
+    # all others are higher and correspond to label 1
+    expected_sklearn = sk_roc_auc_score(target.numpy(), preds.numpy())
+    assert torch.allclose(result, torch.tensor(expected_sklearn), atol=1e-4)
+
+    # Test with even larger logits
+    preds_huge = torch.tensor([200.0, 201.0, 202.0, 203.0])
+    target_huge = torch.tensor([0, 0, 1, 1])
+    result_huge = binary_auroc(preds_huge, target_huge, thresholds=None)
+    expected_huge = sk_roc_auc_score(target_huge.numpy(), preds_huge.numpy())
+    assert torch.allclose(result_huge, torch.tensor(expected_huge), atol=1e-4)
+
+    # Test with mixed large and normal logits
+    preds_mixed = torch.tensor([-5.0, 0.0, 5.0, 50.0, 100.0])
+    target_mixed = torch.tensor([0, 0, 1, 1, 1])
+    result_mixed = binary_auroc(preds_mixed, target_mixed, thresholds=None)
+    expected_mixed = sk_roc_auc_score(target_mixed.numpy(), preds_mixed.numpy())
+    assert torch.allclose(result_mixed, torch.tensor(expected_mixed), atol=1e-4)
+
+
 def _reference_sklearn_auroc_multiclass(preds, target, average="macro", ignore_index=None):
     preds = np.moveaxis(preds.numpy(), 1, -1).reshape((-1, preds.shape[1]))
     target = target.numpy().flatten()