Allow scalar all gather (pytorch#5797)

test/test_torch_distributed_xla_backend.py

@@ -97,6 +97,16 @@ def test_allgather(self):
     hlo = torch_xla._XLAC._get_xla_tensors_hlo(output_tensors)
     hlo_matches(hlo, all_gather_pattern)
 
+  @patch_world(rank=3, size=8)
+  def test_all_scalar_allgather(self):
+    device = xm.xla_device()
+    tensor = torch.zeros((), device=device) + 1 + 2 * dist.get_rank()
+    output_tensors = [torch.zeros_like(tensor, device=device) for _ in range(8)]
+    all_gather_pattern = r'%all\-gather\.\d+ = .+ all\-gather\('
+    dist.all_gather(output_tensors, tensor)
+    hlo = torch_xla._XLAC._get_xla_tensors_hlo(output_tensors)
+    hlo_matches(hlo, all_gather_pattern)
+
   @patch_world(rank=3, size=8)
   def test_allgather_coalesced(self):
     device = xm.xla_device()

torch_xla/distributed/xla_backend.py

@@ -73,10 +73,14 @@ def allreduce(self, tensors, all_reduce_options):
 
   def allgather(self, output_tensors_list, input_tensors, opts=None):
     for input_tensor, output_tensors in zip(input_tensors, output_tensors_list):
+      is_scalar = (input_tensor.dim() == 0)
+      if is_scalar:
+        input_tensor = torch.reshape(input_tensor, (1,))
       result = xm.all_gather(input_tensor, groups=self._mesh, pin_layout=False)
       for i, slice in enumerate(torch.split(result, input_tensor.shape[0])):
         with torch.no_grad():
-          output_tensors[i].copy_(slice)
+          output_tensors[i].copy_(
+              slice if not is_scalar else torch.reshape(slice, ()))
 
     return _ret_work([t for sublist in output_tensors_list for t in sublist])