add ddp sync for logging in result step

pytorch_lightning/core/step_result.py

@@ -1,7 +1,9 @@
+import numbers
 from typing import Optional, Dict, Union, Sequence, Callable, MutableMapping, Any
 from torch import Tensor
 import torch
 from copy import copy
+from pytorch_lightning.metrics.converters import _sync_ddp_if_available
 
 
 class Result(Dict):
@@ -89,11 +91,18 @@ def log(
             on_epoch: bool = True,
             reduce_fx: Callable = torch.mean,
             enable_graph: bool = False,
+            sync_ddp: bool = False,
+            sync_ddp_op: Union[Any, str] = 'mean',
+            sync_ddp_group: Optional[Any] = None
     ):
         # no metrics should be logged with graphs
         if not enable_graph and isinstance(value, torch.Tensor):
             value = value.detach()
 
+        # sync across ddp
+        if sync_ddp and isinstance(value, (torch.Tensor, numbers.Number)):
+            value = _sync_ddp_if_available(value, group=sync_ddp_group, reduce_op=sync_ddp_op)
+
         if 'meta' not in self:
             self.__setitem__('meta', {})
 
@@ -338,6 +347,9 @@ def log(
             on_epoch: bool = False,
             reduce_fx: Callable = torch.mean,
             enable_graph: bool = False,
+            sync_ddp: bool = False,
+            sync_ddp_op: Union[Any, str] = 'mean',
+            sync_ddp_group: Optional[Any] = None
     ):
         """
         Log a key, value
@@ -369,7 +381,8 @@ def log(
             reduce_fx: Torch.mean by default
             enable_graph: if True, will not auto detach the graph
         """
-        super().log(name, value, prog_bar, logger, on_step, on_epoch, reduce_fx, enable_graph)
+        super().log(name, value, prog_bar, logger, on_step, on_epoch, reduce_fx, enable_graph,
+                    sync_ddp=sync_ddp, sync_ddp_group=sync_ddp_group, sync_ddp_op=sync_ddp_op)
 
     def log_dict(
             self,
@@ -380,6 +393,9 @@ def log_dict(
             on_epoch: bool = True,
             reduce_fx: Callable = torch.mean,
             enable_graph: bool = False,
+            sync_ddp: bool = False,
+            sync_ddp_op: Union[Any, str] = 'mean',
+            sync_ddp_group: Optional[Any] = None
     ):
         """
         Log a dictonary of values at once
@@ -399,7 +415,8 @@ def log_dict(
             enable_graph:
         """
         for k, v in dictionary.items():
-            self.log(k, v, prog_bar, logger, on_step, on_epoch, reduce_fx, enable_graph)
+            self.log(k, v, prog_bar, logger, on_step, on_epoch, reduce_fx, enable_graph,
+                     sync_ddp=sync_ddp, sync_ddp_group=sync_ddp_group, sync_ddp_op=sync_ddp_op)
 
 
 class EvalResult(Result):
@@ -446,6 +463,9 @@ def log(
             on_epoch: bool = True,
             reduce_fx: Callable = torch.mean,
             enable_graph: bool = False,
+            sync_ddp: bool = False,
+            sync_ddp_op: Union[Any, str] = 'mean',
+            sync_ddp_group: Optional[Any] = None
     ):
         """
         Log a key, value
@@ -476,7 +496,8 @@ def log(
             reduce_fx: Torch.mean by default
             enable_graph: if True, will not auto detach the graph :
         """
-        super().log(name, value, prog_bar, logger, on_step, on_epoch, reduce_fx, enable_graph)
+        super().log(name, value, prog_bar, logger, on_step, on_epoch, reduce_fx, enable_graph,
+                    sync_ddp=sync_ddp, sync_ddp_group=sync_ddp_group, sync_ddp_op=sync_ddp_op)
 
     def log_dict(
             self,
@@ -487,6 +508,9 @@ def log_dict(
             on_epoch: bool = True,
             reduce_fx: Callable = torch.mean,
             enable_graph: bool = False,
+            sync_ddp: bool = False,
+            sync_ddp_op: Union[Any, str] = 'mean',
+            sync_ddp_group: Optional[Any] = None
     ):
         """
         Log a dictonary of values at once
@@ -506,7 +530,8 @@ def log_dict(
             enable_graph:
         """
         for k, v in dictionary.items():
-            self.log(k, v, prog_bar, logger, on_step, on_epoch, reduce_fx, enable_graph)
+            self.log(k, v, prog_bar, logger, on_step, on_epoch, reduce_fx, enable_graph,
+                     sync_ddp=sync_ddp, sync_ddp_group=sync_ddp_group, sync_ddp_op=sync_ddp_op)
 
     def get_callback_metrics(self) -> dict:
         result = {

tests/core/test_results.py

@@ -0,0 +1,37 @@
+import pytest
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+from pytorch_lightning.core.step_result import Result, TrainResult, EvalResult
+import tests.base.develop_utils as tutils
+import sys
+
+
+def _setup_ddp(rank, worldsize):
+    import os
+
+    os.environ["MASTER_ADDR"] = "localhost"
+
+    # initialize the process group
+    dist.init_process_group("gloo", rank=rank, world_size=worldsize)
+
+
+def _ddp_test_fn(rank, worldsize, result_cls: Result):
+    _setup_ddp(rank, worldsize)
+    tensor = torch.tensor([1.0])
+
+    res = result_cls()
+    res.log("test_tensor", tensor, sync_ddp=True, sync_ddp_op=torch.distributed.ReduceOp.SUM)
+
+    assert res["test_tensor"].item() == dist.get_world_size(), "Result-Log does not work properly with DDP and Tensors"
+
+
+@pytest.mark.parametrize("result_cls", [Result, TrainResult, EvalResult])
+@pytest.mark.skipif(sys.platform == "win32", reason="DDP not available on windows")
+def test_result_reduce_ddp(result_cls):
+    """Make sure result logging works with DDP"""
+    tutils.reset_seed()
+    tutils.set_random_master_port()
+
+    worldsize = 2
+    mp.spawn(_ddp_test_fn, args=(worldsize, result_cls), nprocs=worldsize)