Fix list syncronization with partly empty lists

CHANGELOG.md

@@ -45,6 +45,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Fixed axis names with Precision-Recall curve ([#2462](https://github.com/Lightning-AI/torchmetrics/pull/2462))
 
 
+- Fixed list synchronization with partly empty lists ([#2468](https://github.com/Lightning-AI/torchmetrics/pull/2468))
+
+
 - Fixed memory leak in metrics using list states ([#2492](https://github.com/Lightning-AI/torchmetrics/pull/2492))
 
 

src/torchmetrics/metric.py

@@ -38,6 +38,7 @@
 )
 from torchmetrics.utilities.distributed import gather_all_tensors
 from torchmetrics.utilities.exceptions import TorchMetricsUserError
+from torchmetrics.utilities.imports import _TORCH_GREATER_EQUAL_2_1
 from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE, plot_single_or_multi_val
 from torchmetrics.utilities.prints import rank_zero_warn
 
@@ -438,6 +439,15 @@ def _sync_dist(self, dist_sync_fn: Callable = gather_all_tensors, process_group:
             if reduction_fn == dim_zero_cat and isinstance(input_dict[attr], list) and len(input_dict[attr]) > 1:
                 input_dict[attr] = [dim_zero_cat(input_dict[attr])]
 
+            # cornor case in distributed settings where a rank have not received any data, create empty to concatenate
+            if (
+                _TORCH_GREATER_EQUAL_2_1
+                and reduction_fn == dim_zero_cat
+                and isinstance(input_dict[attr], list)
+                and len(input_dict[attr]) == 0
+            ):
+                input_dict[attr] = [torch.tensor([], device=self.device, dtype=self.dtype)]
+
         output_dict = apply_to_collection(
             input_dict,
             Tensor,

tests/unittests/bases/test_ddp.py

@@ -22,6 +22,7 @@
 from torchmetrics import Metric
 from torchmetrics.utilities.distributed import gather_all_tensors
 from torchmetrics.utilities.exceptions import TorchMetricsUserError
+from torchmetrics.utilities.imports import _TORCH_GREATER_EQUAL_2_1
 
 from unittests import NUM_PROCESSES
 from unittests._helpers import seed_all
@@ -269,11 +270,28 @@ def test_sync_on_compute(sync_on_compute, test_func):
 def _test_sync_with_empty_lists(rank):
     dummy = DummyListMetric()
     val = dummy.compute()
-    assert val == []
+    assert torch.allclose(val, tensor([]))
 
 
 @pytest.mark.DDP()
+@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_2_1, reason="test only works on newer torch versions")
 @pytest.mark.skipif(sys.platform == "win32", reason="DDP not available on windows")
 def test_sync_with_empty_lists():
     """Test that synchronization of states can be enabled and disabled for compute."""
     pytest.pool.map(_test_sync_with_empty_lists, range(NUM_PROCESSES))
+
+
+def _test_sync_with_unequal_size_lists(rank):
+    """Test that synchronization of list states work even when some ranks have not received any data yet."""
+    dummy = DummyListMetric()
+    if rank == 0:
+        dummy.update(torch.zeros(2))
+    assert torch.all(dummy.compute() == tensor([0.0, 0.0]))
+
+
+@pytest.mark.DDP()
+@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_2_1, reason="test only works on newer torch versions")
+@pytest.mark.skipif(sys.platform == "win32", reason="DDP not available on windows")
+def test_sync_with_unequal_size_lists():
+    """Test that synchronization of states can be enabled and disabled for compute."""
+    pytest.pool.map(_test_sync_with_unequal_size_lists, range(NUM_PROCESSES))