fix binary_op on operands with single element

CHANGELOG.md

@@ -3,9 +3,13 @@
 - [#826](https://github.com/helmholtz-analytics/heat/pull/826) Fixed `__setitem__` handling of distributed `DNDarray` values which have a different shape in the split dimension
 - [#846](https://github.com/helmholtz-analytics/heat/pull/846) Fixed an issue in `_reduce_op` when axis and keepdim were set.
 - [#846](https://github.com/helmholtz-analytics/heat/pull/846) Fixed an issue in `min`, `max` where DNDarrays with empty processes can't be computed.
+- [#868](https://github.com/helmholtz-analytics/heat/pull/868) Fixed an issue in `__binary_op` where data was falsely distributed if a DNDarray has single element.
 
 ## Feature Additions
 
+### Communication
+- [#868](https://github.com/helmholtz-analytics/heat/pull/868) New `MPICommunication` method `Split`
+
 ### DNDarray
 - [#856](https://github.com/helmholtz-analytics/heat/pull/856) New `DNDarray` method `__torch_proxy__`
 

heat/core/_operations.py

@@ -9,7 +9,6 @@
 
 from .communication import MPI, MPI_WORLD
 from . import factories
-from . import devices
 from . import stride_tricks
 from . import sanitation
 from . import statistics
@@ -111,28 +110,31 @@ def __binary_op(
             output_device = t1.device
             output_comm = t1.comm
 
-            # ToDo: Fine tuning in case of comm.size>t1.shape[t1.split]. Send torch tensors only to ranks, that will hold data.
             if t1.split is not None:
                 if t1.shape[t1.split] == 1 and t1.comm.is_distributed():
                     # warnings.warn(
                     #     "Broadcasting requires transferring data of first operator between MPI ranks!"
                     # )
-                    if t1.comm.rank > 0:
+                    color = 0 if t1.comm.rank < t2.shape[t1.split] else 1
+                    newcomm = t1.comm.Split(color, t1.comm.rank)
+                    if t1.comm.rank > 0 and color == 0:
                         t1.larray = torch.zeros(
                             t1.shape, dtype=t1.dtype.torch_type(), device=t1.device.torch_device
                         )
-                    t1.comm.Bcast(t1)
+                    newcomm.Bcast(t1)
 
             if t2.split is not None:
                 if t2.shape[t2.split] == 1 and t2.comm.is_distributed():
                     # warnings.warn(
                     #     "Broadcasting requires transferring data of second operator between MPI ranks!"
                     # )
-                    if t2.comm.rank > 0:
+                    color = 0 if t2.comm.rank < t1.shape[t2.split] else 1
+                    newcomm = t2.comm.Split(color, t2.comm.rank)
+                    if t2.comm.rank > 0 and color == 0:
                         t2.larray = torch.zeros(
                             t2.shape, dtype=t2.dtype.torch_type(), device=t2.device.torch_device
                         )
-                    t2.comm.Bcast(t2)
+                    newcomm.Bcast(t2)
 
         else:
             raise TypeError(

heat/core/communication.py

@@ -436,6 +436,19 @@ def alltoall_recvbuffer(
 
         return self.as_mpi_memory(obj), (recvcount, recvdispls), recvtypes
 
+    def Split(self, color: int = 0, key: int = 0):
+        """
+        Split communicator by color and key.
+
+        Parameters
+        ----------
+        color : int, optional
+            Determines the new communicator for a process.
+        key: int, optional
+            Ordering within the new communicator.
+        """
+        return MPICommunication(self.handle.Split(color, key))
+
     def Irecv(
         self,
         buf: Union[DNDarray, torch.Tensor, Any],

heat/core/tests/test_arithmetics.py

@@ -34,6 +34,17 @@ def test_add(self):
         self.assertTrue(ht.equal(ht.add(self.a_tensor, self.an_int_scalar), result))
         self.assertTrue(ht.equal(ht.add(self.a_split_tensor, self.a_tensor), result))
 
+        # Single element split
+        a = ht.array([1], split=0)
+        b = ht.array([1, 2], split=0)
+        c = ht.add(a, b)
+        self.assertTrue(ht.equal(c, ht.array([2, 3])))
+        if c.comm.size > 1:
+            if c.comm.rank < 2:
+                self.assertEqual(c.larray.size()[0], 1)
+            else:
+                self.assertEqual(c.larray.size()[0], 0)
+
         with self.assertRaises(ValueError):
             ht.add(self.a_tensor, self.another_vector)
         with self.assertRaises(TypeError):

heat/core/tests/test_communication.py

@@ -196,6 +196,19 @@ def test_default_comm(self):
         with self.assertRaises(TypeError):
             ht.use_comm("1")
 
+    def test_split(self):
+        a = ht.zeros((4, 5), split=0)
+
+        color = a.comm.rank % 2
+        newcomm = a.comm.Split(color, key=a.comm.rank)
+
+        self.assertIsInstance(newcomm, ht.MPICommunication)
+        if ht.MPI_WORLD.size == 1:
+            self.assertTrue(newcomm.size == a.comm.size)
+        else:
+            self.assertTrue(newcomm.size < a.comm.size)
+        self.assertIsNot(newcomm, a.comm)
+
     def test_allgather(self):
         # contiguous data
         data = ht.ones((1, 7))