Minor optimization for idist.get_* (#1196)

* Minor optimization for idist.get_*

* Set overhead threshold to 1.9

* Keep only test_idist_methods_overhead_nccl

* Removed _sync_model_wrapper to implicitly check if we need to sync model
This also reduces time of idist.get_* method calls vs native calls

* Update test_native.py

* autopep8 fix

* Update test_native.py

Co-authored-by: AutoPEP8 <>
Co-authored-by: Sylvain Desroziers <sylvain.desroziers@gmail.com>

Author: vfdev-5

ignite/distributed/utils.py

@@ -4,7 +4,6 @@
 from typing import Callable, List, Mapping, Optional, Tuple, Union
 
 import torch
-import torch.distributed as dist
 
 from ignite.distributed.comp_models import (
     _SerialModel,
@@ -46,19 +45,13 @@
 _need_to_sync = True
 
 
-def _sync_model_wrapper(func):
-    @wraps(func)
-    def wrapper(*args, **kwargs):
-        if isinstance(_model, _SerialModel) and _need_to_sync:
-            sync()
-        return func(*args, **kwargs)
-
-    return wrapper
-
-
-def sync():
+def sync(temporary=False):
     """Helper method to force this module to synchronize with current distributed context.
     This method should be used when distributed context is manually created or destroyed.
+
+    Args:
+        temporary (bool): If True, distributed model synchronization is done every call of ``idist.get_*`` methods.
+            This may have performance negative impact.
     """
     global _model
 
@@ -67,13 +60,12 @@ def sync():
             continue
         model = comp_model_cls.create_from_context()
         if model is not None:
-            _model = model
+            _set_model(model, temporary=temporary)
             return
 
     _model = _SerialModel()
 
 
-@_sync_model_wrapper
 def device() -> torch.device:
     """Returns current device according to current distributed configuration.
 
@@ -84,10 +76,12 @@ def device() -> torch.device:
     Returns:
         torch.device
     """
+    if _need_to_sync and isinstance(_model, _SerialModel):
+        sync(temporary=True)
+
     return _model.device()
 
 
-@_sync_model_wrapper
 def backend() -> Optional[str]:
     """Returns computation model's backend.
 
@@ -98,6 +92,9 @@ def backend() -> Optional[str]:
     Returns:
         str or None
     """
+    if _need_to_sync and isinstance(_model, _SerialModel):
+        sync(temporary=True)
+
     return _model.backend()
 
 
@@ -110,7 +107,6 @@ def available_backends() -> Tuple[str]:
     return out
 
 
-@_sync_model_wrapper
 def model_name() -> str:
     """Returns distributed configuration name (given by ignite)
 
@@ -119,51 +115,66 @@ def model_name() -> str:
     - `xla-dist` for XLA distributed configuration
 
     """
+    if _need_to_sync and isinstance(_model, _SerialModel):
+        sync(temporary=True)
+
     return _model.name
 
 
-@_sync_model_wrapper
 def get_world_size() -> int:
     """Returns world size of current distributed configuration. Returns 1 if no distributed configuration.
     """
+    if _need_to_sync and isinstance(_model, _SerialModel):
+        sync(temporary=True)
+
     return _model.get_world_size()
 
 
-@_sync_model_wrapper
 def get_rank() -> int:
     """Returns process rank within current distributed configuration. Returns 0 if no distributed configuration.
     """
+    if _need_to_sync and isinstance(_model, _SerialModel):
+        sync(temporary=True)
+
     return _model.get_rank()
 
 
-@_sync_model_wrapper
 def get_local_rank() -> int:
     """Returns local process rank within current distributed configuration. Returns 0 if no distributed configuration.
     """
+    if _need_to_sync and isinstance(_model, _SerialModel):
+        sync(temporary=True)
+
     return _model.get_local_rank()
 
 
-@_sync_model_wrapper
 def get_nproc_per_node() -> int:
     """Returns number of processes (or tasks) per node within current distributed configuration.
     Returns 1 if no distributed configuration.
     """
+    if _need_to_sync and isinstance(_model, _SerialModel):
+        sync(temporary=True)
+
     return _model.get_nproc_per_node()
 
 
-@_sync_model_wrapper
 def get_nnodes() -> int:
     """Returns number of nodes within current distributed configuration.
     Returns 1 if no distributed configuration.
     """
+    if _need_to_sync and isinstance(_model, _SerialModel):
+        sync(temporary=True)
+
     return _model.get_nnodes()
 
 
-@_sync_model_wrapper
 def get_node_rank() -> int:
     """Returns node rank within current distributed configuration.
     Returns 0 if no distributed configuration.
     """
+    if _need_to_sync and isinstance(_model, _SerialModel):
+        sync(temporary=True)
+
     return _model.get_node_rank()
 
 
@@ -291,7 +302,6 @@ def train_fn(local_rank, a, b, c, d=12):
         )
 
 
-@_sync_model_wrapper
 def all_reduce(tensor: Union[torch.Tensor, Number], op: str = "SUM") -> Union[torch.Tensor, Number]:
     """Helper method to perform all reduce operation.
 
@@ -303,10 +313,12 @@ def all_reduce(tensor: Union[torch.Tensor, Number], op: str = "SUM") -> Union[to
         torch.Tensor or number
 
     """
+    if _need_to_sync and isinstance(_model, _SerialModel):
+        sync(temporary=True)
+
     return _model.all_reduce(tensor, op)
 
 
-@_sync_model_wrapper
 def all_gather(tensor: Union[torch.Tensor, Number, str]) -> Union[torch.Tensor, Number, List[str]]:
     """Helper method to perform all gather operation.
 
@@ -318,13 +330,18 @@ def all_gather(tensor: Union[torch.Tensor, Number, str]) -> Union[torch.Tensor,
         List of strings
 
     """
+    if _need_to_sync and isinstance(_model, _SerialModel):
+        sync(temporary=True)
+
     return _model.all_gather(tensor)
 
 
-@_sync_model_wrapper
 def barrier():
     """Helper method to synchronize all processes.
     """
+    if _need_to_sync and isinstance(_model, _SerialModel):
+        sync(temporary=True)
+
     _model.barrier()
 
 
@@ -356,11 +373,11 @@ def run(local_rank, *args, **kwargs):
     ComputationModel._ext_local_rank = index
 
 
-def _set_model(model):
+def _set_model(model, temporary=False):
     global _model, _need_to_sync
     _model = model
     _need_to_sync = True
-    if not isinstance(_model, _SerialModel):
+    if not isinstance(_model, _SerialModel) and not temporary:
         _need_to_sync = False
 
 
@@ -408,7 +425,7 @@ def train_fn(local_rank, a, b, c):
 
 
     """
-    if not (has_xla_support or dist.is_available()):
+    if not (has_xla_support or has_native_dist_support):
         # nothing to do => serial model
         # maybe warn about this
         return

tests/ignite/distributed/utils/test_native.py

@@ -202,51 +202,63 @@ def test_idist_barrier_gloo(distributed_context_single_node_gloo):
     _test_distrib_barrier(device)
 
 
+def _test_idist_methods_overhead(ok_factor):
+    import time
+
+    n = 100000
+    m = 5
+
+    t2 = 0.0
+    t1 = 0.0
+    for j in range(m):
+        start = time.time()
+        for _ in range(n):
+            _ = dist.get_world_size()
+            _ = dist.get_rank()
+        elapsed = time.time() - start
+        t2 += elapsed / n / m
+
+        start = time.time()
+        for _ in range(n):
+            _ = idist.get_world_size()
+            _ = idist.get_rank()
+        elapsed = time.time() - start
+        t1 += elapsed / n / m
+
+    overhead_factor = t1 / t2
+    assert overhead_factor < ok_factor, "{} vs {} | {} vs {}".format(overhead_factor, ok_factor, t2, t1)
+
+
 @pytest.mark.distributed
 @pytest.mark.skipif(not has_native_dist_support, reason="Skip if no native dist support")
+@pytest.mark.skipif(
+    not torch.cuda.is_available(), reason="Do not want to run this test on Github or Travis, but CircleCI"
+)
 def test_idist_methods_overhead_gloo(distributed_context_single_node_gloo):
-    import time
+    _test_idist_methods_overhead(2.5)
 
-    n = 100000
-    start = time.time()
-    for _ in range(n):
-        _ = idist.get_world_size()
-        _ = idist.get_rank()
-    elapsed = time.time() - start
-    t1 = elapsed / n
+    idist.sync()
+    from ignite.distributed.utils import _model
+    from ignite.distributed.comp_models.native import _NativeDistModel
 
-    start = time.time()
-    for _ in range(n):
-        _ = dist.get_world_size()
-        _ = idist.get_rank()
-    elapsed = time.time() - start
-    t2 = elapsed / n
+    assert isinstance(_model, _NativeDistModel)
 
-    assert t2 * 6 > t1, "{} * 6 vs {}".format(t2, t1)
+    _test_idist_methods_overhead(1.7)
 
 
 @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_methods_overhead_nccl(distributed_context_single_node_nccl):
-    import time
+    _test_idist_methods_overhead(2.5)
 
-    n = 100000
-    start = time.time()
-    for _ in range(n):
-        _ = idist.get_world_size()
-        _ = idist.get_rank()
-    elapsed = time.time() - start
-    t1 = elapsed / n
-
-    start = time.time()
-    for _ in range(n):
-        _ = dist.get_world_size()
-        _ = idist.get_rank()
-    elapsed = time.time() - start
-    t2 = elapsed / n
-
-    assert t2 * 3 > t1, "{} * 3 vs {}".format(t2, t1)
+    idist.sync()
+    from ignite.distributed.utils import _model
+    from ignite.distributed.comp_models.native import _NativeDistModel
+
+    assert isinstance(_model, _NativeDistModel)
+
+    _test_idist_methods_overhead(1.7)
 
 
 @pytest.mark.distributed