support number for logging with sync_dist=True

pytorch_lightning/core/lightning.py

@@ -279,6 +279,7 @@ def log(
                 sync_dist_group,
                 accelerator.sync_tensor,
                 self._current_dataloader_idx,
+                self.device,
             )
 
     def log_dict(

pytorch_lightning/core/step_result.py

@@ -15,15 +15,15 @@
 """[Train, Eval]Result for easier logging, checkpointing, early stopping, epoch-wise reduction."""
 
 import numbers
+import os
 from copy import copy
-from typing import Optional, Dict, Union, Sequence, Callable, MutableMapping, Any, List, Tuple, Iterable
+from typing import Any, Callable, Dict, Iterable, List, MutableMapping, Optional, Sequence, Tuple, Union
 
 import torch
 from torch import Tensor
-import os
 
-from pytorch_lightning.utilities.distributed import sync_ddp_if_available
 from pytorch_lightning.metrics import Metric
+from pytorch_lightning.utilities.distributed import sync_ddp_if_available
 
 
 class Result(Dict):
@@ -128,6 +128,7 @@ def log(
         sync_dist_group: Optional[Any] = None,
         sync_fn: Callable = None,
         dataloader_idx: Optional[int] = None,
+        device: torch.device = None,
     ):
         # no metrics should be logged with graphs
         if not enable_graph and isinstance(value, torch.Tensor):
@@ -138,7 +139,10 @@ def log(
         if sync_dist and isinstance(value, (torch.Tensor, numbers.Number)):
             is_dist_initialized = torch.distributed.is_available() and torch.distributed.is_initialized()
             # TODO: Find a way to make the reduction only once, so we don't need to clone.
-            value = value.clone() if is_dist_initialized else value
+            if is_dist_initialized and isinstance(value, torch.Tensor):
+                value = value.clone()
+            else:
+                value = torch.tensor(value, device=device, dtype=torch.float)
             value = sync_fn(value, group=sync_dist_group, reduce_op=sync_dist_op)
 
         if 'meta' not in self:

pytorch_lightning/utilities/distributed.py

@@ -15,14 +15,14 @@
 import os
 import warnings
 from functools import wraps
+from typing import Any, Optional, Union
 
 import torch
+
 from pytorch_lightning import _logger as log
-from typing import Union, Optional, Any
 
 if torch.distributed.is_available():
-    from torch.distributed import ReduceOp
-    from torch.distributed import group
+    from torch.distributed import ReduceOp, group
 else:
     class ReduceOp:
         SUM = None
@@ -145,15 +145,14 @@ def sync_ddp(
     if group is None:
         group = torch.distributed.group.WORLD
 
-    if reduce_op is None:
-        reduce_op = torch.distributed.ReduceOp.SUM
-    elif isinstance(reduce_op, str) and reduce_op in ("avg", "mean"):
-        reduce_op = torch.distributed.ReduceOp.SUM
+    op = reduce_op if isinstance(reduce_op, ReduceOp) else ReduceOp.SUM
+
+    if isinstance(reduce_op, str) and reduce_op.lower() in ("avg", "mean"):
         divide_by_world_size = True
 
     # sync all processes before reduction
     torch.distributed.barrier(group=group)
-    torch.distributed.all_reduce(result, op=reduce_op, group=group, async_op=False)
+    torch.distributed.all_reduce(result, op=op, group=group, async_op=False)
 
     if divide_by_world_size:
         result = result / torch.distributed.get_world_size(group)

tests/special_tests.sh

@@ -19,4 +19,4 @@ python ${DEFAULTS} tests/plugins/test_rpc_plugin.py::test_rpc_function_calls_ddp
 python ${DEFAULTS} tests/plugins/test_ddp_sequential_plugin.py::test_ddp_sequential_plugin_ddp_rpc_manual
 python ${DEFAULTS} tests/plugins/test_ddp_sequential_plugin.py::test_ddp_sequential_plugin_ddp_rpc_manual_amp
 python ${DEFAULTS} tests/plugins/test_ddp_sequential_plugin.py::test_ddp_sequential_plugin_ddp_rpc_automatic
-# python ${DEFAULTS} tests/plugins/test_ddp_sequential_plugin.py::test_ddp_sequential_plugin_ddp_rpc_with_wrong_balance
+python ${DEFAULTS} tests/trainer/logging_tests/test_train_loop_logging_1_0.py::test_logging_sync_dist_true_ddp

tests/trainer/logging_tests/test_train_loop_logging_1_0.py

@@ -18,6 +18,7 @@
 import collections
 import itertools
 import os
+import platform
 from unittest import mock
 
 import numpy as np
@@ -685,6 +686,7 @@ class TestModel(BoringModel):
         def training_step(self, batch, batch_idx):
             acc = self.step(batch[0])
             self.log('foo', torch.tensor(fake_result), on_step=False, on_epoch=True, sync_dist=True, sync_dist_op='sum')
+            self.log('foo_2', 2, on_step=False, on_epoch=True, sync_dist=True, sync_dist_op='sum')
             return acc
 
         def validation_step(self, batch, batch_idx):
@@ -704,9 +706,46 @@ def validation_step(self, batch, batch_idx):
     trainer.fit(model)
 
     assert trainer.logged_metrics['foo'] == fake_result
+    assert trainer.logged_metrics['foo_2'] == 2
     assert trainer.logged_metrics['bar'] == fake_result
 
 
+@pytest.mark.skipif(torch.cuda.device_count() < 2, reason="test requires multi-GPU machine")
+@pytest.mark.skipif(not os.getenv("PL_RUNNING_SPECIAL_TESTS", '0') == '1',
+                    reason="test should be run outside of pytest")
+def test_logging_sync_dist_true_ddp(tmpdir):
+    """
+    Tests to ensure that the sync_dist flag works with ddp
+    """
+    class TestLoggingSyncDistModel(BoringModel):
+        def training_step(self, batch, batch_idx):
+            acc = self.step(batch[0])
+            self.log('foo', 1, on_step=False, on_epoch=True, sync_dist=True, sync_dist_op='SUM')
+            return acc
+
+        def validation_step(self, batch, batch_idx):
+            self.training_step_called = True
+            output = self.layer(batch)
+            loss = self.loss(batch, output)
+            self.log('bar', 2, on_step=False, on_epoch=True, sync_dist=True, sync_dist_op='AVG')
+            return {"x": loss}
+
+    model = TestLoggingSyncDistModel()
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        limit_train_batches=1,
+        limit_val_batches=1,
+        max_epochs=2,
+        weights_summary=None,
+        accelerator="ddp",
+        gpus=2,
+    )
+    trainer.fit(model)
+
+    assert trainer.logged_metrics['foo'] == 2
+    assert trainer.logged_metrics['bar'] == 2
+
+
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
 def test_logging_sync_dist_true_gpu(tmpdir):
     """