Add test for skip_unrolling in Loss

tests/ignite/metrics/test_loss.py

@@ -5,7 +5,7 @@
 import torch
 from numpy.testing import assert_almost_equal
 from torch import nn
-from torch.nn.functional import nll_loss
+from torch.nn.functional import mse_loss, nll_loss
 
 import ignite.distributed as idist
 from ignite.engine import State
@@ -314,3 +314,50 @@ def compute(self):
         (torch.rand(4, 10), torch.randint(0, 3, size=(4,))),
     ]
     evaluator.run(data)
+
+
+class CustomMultiMSELoss(nn.Module):
+    def __init__(self) -> None:
+        super().__init__()
+
+    def forward(
+        self, y_pred: list[torch.Tensor, torch.Tensor], y_true: list[torch.Tensor, torch.Tensor]
+    ) -> torch.Tensor:
+        a_true, b_true = y_true
+        a_pred, b_pred = y_pred
+        return mse_loss(a_pred, a_true) + mse_loss(b_pred, b_true)
+
+
+class DummyLoss3(Loss):
+    def __init__(self, loss_fn, expected_loss, output_transform=lambda x: x, skip_unrolling=False):
+        super(DummyLoss3, self).__init__(loss_fn, output_transform=output_transform, skip_unrolling=skip_unrolling)
+        self._expected_loss = expected_loss
+        self._loss_fn = loss_fn
+
+    def reset(self):
+        pass
+
+    def compute(self):
+        pass
+
+    def update(self, output):
+        y_pred, y_true = output
+        calculated_loss = self._loss_fn(y_pred=y_pred, y_true=y_true)
+        assert calculated_loss == self._expected_loss
+
+
+def test_skip_unrolling_loss():
+    a_pred = torch.rand(8, 1)
+    b_pred = torch.rand(8, 1)
+    y_pred = [a_pred, b_pred]
+    a_true = torch.rand(8, 1)
+    b_true = torch.rand(8, 1)
+    y_true = [a_true, b_true]
+
+    multi_output_mse_loss = CustomMultiMSELoss()
+    expected_loss = multi_output_mse_loss(y_pred=y_pred, y_true=y_true)
+
+    loss_metric = DummyLoss3(loss_fn=multi_output_mse_loss, expected_loss=expected_loss, skip_unrolling=True)
+    state = State(output=(y_pred, y_true))
+    engine = MagicMock(state=state)
+    loss_metric.iteration_completed(engine)