Added args/kwargs to idist barrier

ignite/distributed/comp_models/base.py

@@ -276,7 +276,7 @@ def _do_broadcast(self, tensor: torch.Tensor, src: int) -> torch.Tensor:
         pass
 
     @abstractmethod
-    def barrier(self) -> None:
+    def barrier(self, *args: Any, **kwargs: Any) -> None:
         pass
 
 
@@ -357,5 +357,5 @@ def _do_all_gather(self, tensor: torch.Tensor) -> torch.Tensor:
     def _do_broadcast(self, tensor: torch.Tensor, src: int) -> torch.Tensor:
         return tensor
 
-    def barrier(self) -> None:
+    def barrier(self, *args: Any, **kwargs: Any) -> None:
         pass

ignite/distributed/comp_models/horovod.py

@@ -1,11 +1,13 @@
 import warnings
+from distutils.version import LooseVersion
 from typing import Any, Callable, Mapping, Optional, Tuple, cast
 
 import torch
 
 from ignite.distributed.comp_models.base import ComputationModel
 
 try:
+    import horovod
     import horovod.torch as hvd
 
     try:
@@ -23,6 +25,7 @@
 if has_hvd_support:
 
     HOROVOD = "horovod"
+    HOROVOD_VERSION = horovod.__version__
 
     class _HorovodDistModel(ComputationModel):
         """Private class for `Horovod <https://horovod.readthedocs.io/en/stable/>`_ distributed computation model."""
@@ -192,7 +195,15 @@ def _do_all_gather(self, tensor: torch.Tensor) -> torch.Tensor:
         def _do_broadcast(self, tensor: torch.Tensor, src: int) -> torch.Tensor:
             return hvd.broadcast(tensor, root_rank=src)
 
-        def barrier(self) -> None:
-            # https://github.com/horovod/horovod/issues/159#issuecomment-424834603
-            # hvd.allreduce(torch.tensor(0, device=self.device()), name="barrier")
-            hvd.allreduce(torch.tensor(0, device="cpu"), name="barrier")
+        def barrier(self, *args: Any, **kwargs: Any) -> None:
+            if LooseVersion(HOROVOD_VERSION) < LooseVersion("0.23.0"):
+                if len(args) or len(kwargs):
+                    warnings.warn(
+                        f"Arguments {list(args) + list(kwargs)} are not passed to horovod barrier method. "
+                        f"Please use horovod version>='0.23.0'"
+                    )
+                # https://github.com/horovod/horovod/issues/159#issuecomment-424834603
+                # hvd.allreduce(torch.tensor(0, device=self.device()), name="barrier")
+                hvd.allreduce(torch.tensor(0, device="cpu"), name="barrier")
+            else:
+                hvd.barrier(*args, **kwargs)

ignite/distributed/comp_models/native.py

@@ -437,8 +437,8 @@ def _do_broadcast(self, tensor: torch.Tensor, src: int) -> torch.Tensor:
             dist.broadcast(tensor, src=src)
             return tensor
 
-        def barrier(self) -> None:
-            dist.barrier()
+        def barrier(self, *args: Any, **kwargs: Any) -> None:
+            dist.barrier(*args, **kwargs)
 
     def _expand_hostlist(nodelist: str) -> List[str]:
         """Expand a compressed hostlist string and returns all hosts listed.

ignite/distributed/comp_models/xla.py

@@ -159,5 +159,5 @@ def _do_broadcast(self, tensor: torch.Tensor, src: int) -> torch.Tensor:
             xm.all_reduce("sum", [tensor])
             return tensor
 
-        def barrier(self) -> None:
-            xm.rendezvous("barrier")
+        def barrier(self, *args: Any, tag: str = "barrier", **kwargs: Any) -> None:
+            xm.rendezvous(tag, *args, **kwargs)

ignite/distributed/utils.py

@@ -418,12 +418,28 @@ def broadcast(
     return _model.broadcast(tensor, src=src, safe_mode=safe_mode)
 
 
-def barrier() -> None:
-    """Helper method to synchronize all processes."""
+def barrier(*args: Any, **kwargs: Any) -> None:
+    """Helper method to synchronize all processes.
+
+    Args:
+        args: acceptable args according to provided backend
+        kwargs: acceptable kwargs according to provided backend
+
+            - | "nccl" or "gloo" : ``group`` (default, GroupMember.WORLD), ``async_op`` (default, False),
+              | ``device_ids`` (default, None).
+
+            - | "horovod" : for version >= "0.23.0", ``process_set`` (default, global_process_set).
+
+            - | "xla-tpu" : ``tag``, ``payload`` (default, b""), ``replicas`` (default, []).
+
+    .. versionchanged:: 0.5.1
+        Method now accepts ``args`` and  ``kwargs`` for all supported backends.
+
+    """
     if _need_to_sync and isinstance(_model, _SerialModel):
         sync(temporary=True)
 
-    _model.barrier()
+    _model.barrier(*args, **kwargs)
 
 
 def set_local_rank(index: int) -> None:

tests/ignite/distributed/utils/__init__.py

@@ -215,14 +215,14 @@ def _test(data_src, data_others, safe_mode):
             idist.broadcast(None, src=0)
 
 
-def _test_distrib_barrier(device):
+def _test_distrib_barrier(device, *args, **kwargs):
 
     t = torch.tensor([idist.get_rank()], device=device, dtype=torch.float)
     true_res = sum([i for i in range(idist.get_world_size())])
 
     if idist.get_rank() == 0:
         t += 10.0
-    idist.barrier()
+    idist.barrier(*args, **kwargs)
 
     tt = idist.all_reduce(t)
     assert tt.item() == true_res + 10.0

tests/ignite/distributed/utils/test_native.py

@@ -255,21 +255,40 @@ def test_idist_broadcast_gloo(distributed_context_single_node_gloo):
     _test_distrib_broadcast(device)
 
 
+from torch.distributed import GroupMember
+
+
 @pytest.mark.distributed
 @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support")
 @pytest.mark.skipif(torch.cuda.device_count() < 1, reason="Skip if no GPU")
-def test_idist_barrier_nccl(distributed_context_single_node_nccl):
+@pytest.mark.parametrize(
+    "args,kwargs",
+    [
+        ([], {}),
+        ([GroupMember.WORLD, False], {}),
+        ([GroupMember.WORLD, True], {}),
+    ],
+)
+def test_idist_barrier_nccl(distributed_context_single_node_nccl, args, kwargs):
 
     device = idist.device()
-    _test_distrib_barrier(device)
+    _test_distrib_barrier(device, *args, **kwargs)
 
 
 @pytest.mark.distributed
 @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support")
-def test_idist_barrier_gloo(distributed_context_single_node_gloo):
+@pytest.mark.parametrize(
+    "args,kwargs",
+    [
+        ([], {}),
+        ([GroupMember.WORLD, False], {}),
+        ([GroupMember.WORLD, True], {}),
+    ],
+)
+def test_idist_barrier_gloo(distributed_context_single_node_gloo, args, kwargs):
 
     device = idist.device()
-    _test_distrib_barrier(device)
+    _test_distrib_barrier(device, *args, **kwargs)
 
 
 def _test_idist_methods_overhead(ok_factor):

tests/ignite/distributed/utils/test_xla.py

@@ -178,23 +178,41 @@ def test_idist_broadcast_xla_in_child_proc(xmp_executor):
 @pytest.mark.tpu
 @pytest.mark.skipif("NUM_TPU_WORKERS" in os.environ, reason="Skip if NUM_TPU_WORKERS is in env vars")
 @pytest.mark.skipif(not has_xla_support, reason="Skip if no PyTorch XLA package")
-def test_idist_barrier_xla():
+@pytest.mark.parametrize(
+    "args,kwargs",
+    [
+        ([], {}),
+        ([b"test_payload", []], {}),
+        ([b"test_payload", []], {"tag": "test_barrier"}),
+        ([], {"payload": b"test_payload", "replicas": [], "tag": "test_barrier"}),
+    ],
+)
+def test_idist_barrier_xla(args, kwargs):
 
     device = idist.device()
-    _test_distrib_barrier(device)
+    _test_distrib_barrier(device, *args, **kwargs)
 
 
-def _test_idist_barrier_xla_in_child_proc(index):
+def _test_idist_barrier_xla_in_child_proc(index, args, kwargs):
     device = idist.device()
-    _test_distrib_barrier(device)
+    _test_distrib_barrier(device, *args, **kwargs)
 
 
 @pytest.mark.tpu
 @pytest.mark.skipif("NUM_TPU_WORKERS" not in os.environ, reason="Skip if no NUM_TPU_WORKERS in env vars")
 @pytest.mark.skipif(not has_xla_support, reason="Skip if no PyTorch XLA package")
-def test_idist_barrier_xla_in_child_proc(xmp_executor):
+@pytest.mark.parametrize(
+    "args,kwargs",
+    [
+        ([], {}),
+        ([b"test_payload", []], {}),
+        ([b"test_payload", []], {"tag": "test_barrier"}),
+        ([], {"payload": b"test_payload", "replicas": [], "tag": "test_barrier"}),
+    ],
+)
+def test_idist_barrier_xla_in_child_proc(xmp_executor, args, kwargs):
     n = int(os.environ["NUM_TPU_WORKERS"])
-    xmp_executor(_test_idist_barrier_xla_in_child_proc, args=(), nprocs=n)
+    xmp_executor(_test_idist_barrier_xla_in_child_proc, args=(args, kwargs), nprocs=n)
 
 
 @pytest.mark.tpu