Merge xm all_gather patch (#3416)

* Set proper shard_count for all_gather, when replica groups are non-empty.

* Update test_mp_all_gather.py

test/test_mp_all_gather.py

@@ -8,21 +8,41 @@
 
 def _mp_fn(index):
   device = xm.xla_device()
+  world_size = xm.xrt_world_size()
   if xm.xla_device_hw(device) in ('TPU', 'GPU'):
-    world_size = xm.xrt_world_size()
+    # Testing with a single replica group
     ordinal_tensor = torch.tensor([index], dtype=torch.float).to(device)
-    result = xm.all_gather(ordinal_tensor)
+    result = xm.all_gather(ordinal_tensor, dim=0)
 
     cpu_result = result.cpu()
     expected = torch.arange(0, world_size, dtype=torch.float)
     if not cpu_result.allclose(expected):
       print('xm.all_gather() produced wrong reductions', file=sys.stderr)
-      print('[{}] {}'.format(index, cpu_result), file=sys.stderr)
+      print(f'[{index}] {cpu_result}', file=sys.stderr)
       sys.exit(1)
+
+    # Testing with two replica groups
+    if world_size % 2 == 0 and world_size > 1:
+      mp_groups = [[n for n in range(world_size) if n % 2 == 0],
+                   [n for n in range(world_size) if n % 2 == 1]]
+      group_size = len(mp_groups[0])
+      replica_id = int(index % 2 == 1)
+
+      result = xm.all_gather(ordinal_tensor, dim=0, groups=mp_groups)
+
+      cpu_result = result.cpu()
+      expected = torch.arange(replica_id, world_size, step=2, dtype=torch.float)
+      if not cpu_result.allclose(expected):
+        print('xm.all_gather() produced wrong reductions', file=sys.stderr)
+        print(f'[{index}] {cpu_result}', file=sys.stderr)
+        sys.exit(1)
+    else:
+      print(
+          f'Failed to create two replica groups with {world_size} replicas',
+          file=sys.stderr)
+
   else:
-    print(
-        'Default device {} is not a TPU or GPU device'.format(device),
-        file=sys.stderr)
+    print(f'{device} is not a TPU or GPU device', file=sys.stderr)
 
 
 if __name__ == '__main__':

torch_xla/core/xla_model.py

@@ -603,7 +603,13 @@ def all_gather(value, dim=0, groups=None, output=None):
   if dim < 0:
     dim = value.dim() + dim
   token, devctx = _get_all_reduce_token()
-  shard_count = None if groups else xrt_world_size()
+  if groups:
+    shard_count = len(groups[0])
+    assert all(len(group) == shard_count for group in groups), \
+      "Replica groups must have the same number of replicas/shards."
+  else:
+    # All replicas belong to a single group
+    shard_count = xrt_world_size()
   if output != None:
     # Call the out of place version of the all_gather
     new_token = torch_xla._XLAC._xla_all_gather_out(output, value, token, dim,