Prevent crash if sync_dist=True on CPU

pytorch_lightning/accelerators/cpu_accelerator.py

@@ -11,9 +11,11 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+from typing import Optional, Union, Any
+
 import torch
 
-from pytorch_lightning.accelerators.accelerator import Accelerator
+from pytorch_lightning.accelerators.accelerator import Accelerator, ReduceOp
 from pytorch_lightning.utilities import AMPType, rank_zero_warn
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 
@@ -80,3 +82,9 @@ def test_step(self, args):
         else:
             output = self.trainer.model.test_step(*args)
         return output
+
+    def sync_tensor(self,
+                    tensor: Union[torch.Tensor],
+                    group: Optional[Any] = None,
+                    reduce_op: Optional[Union[ReduceOp, str]] = None) -> torch.Tensor:
+        return tensor

pytorch_lightning/accelerators/gpu_accelerator.py

@@ -11,10 +11,11 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+from typing import Union, Optional, Any
 
 import torch
 
-from pytorch_lightning.accelerators.accelerator import Accelerator
+from pytorch_lightning.accelerators.accelerator import Accelerator, ReduceOp
 from pytorch_lightning.utilities import AMPType
 from pytorch_lightning.distributed.dist import LightningDistributed
 
@@ -120,3 +121,9 @@ def to_device(self, batch):
         # be referenced from and if there are multiple optimizers the batch will
         # wind up copying it to the same device repeatedly.
         return self.batch_to_device(batch, gpu_id)
+
+    def sync_tensor(self,
+                    tensor: Union[torch.Tensor],
+                    group: Optional[Any] = None,
+                    reduce_op: Optional[Union[ReduceOp, str]] = None) -> torch.Tensor:
+        return tensor

pytorch_lightning/accelerators/tpu_accelerator.py

@@ -14,13 +14,13 @@
 import io
 import os
 import re
-from typing import Optional
+from typing import Optional, Union, Any
 
 import torch
 import torch.multiprocessing as mp
 
 from pytorch_lightning import _logger as log
-from pytorch_lightning.accelerators.accelerator import Accelerator
+from pytorch_lightning.accelerators.accelerator import Accelerator, ReduceOp
 from pytorch_lightning.core import LightningModule
 from pytorch_lightning.utilities import rank_zero_info, rank_zero_only, rank_zero_warn
 from pytorch_lightning.utilities.cloud_io import atomic_save
@@ -337,3 +337,9 @@ def broadcast(self, obj, src=0):
         buffer = io.BytesIO(data.cpu().byte().numpy())
         obj = torch.load(buffer)
         return obj
+
+    def sync_tensor(self,
+                    tensor: Union[torch.Tensor],
+                    group: Optional[Any] = None,
+                    reduce_op: Optional[Union[ReduceOp, str]] = None) -> torch.Tensor:
+        return tensor

tests/trainer/logging_tests/test_train_loop_logging_1_0.py

@@ -682,3 +682,69 @@ def get_expected_output(func_attr, original_values):
             assert func_name in trainer.logger_connector.progress_bar_metrics
         else:
             assert func_name not in trainer.logger_connector.progress_bar_metrics
+
+
+def test_logging_sync_dist_true_cpu(tmpdir):
+    """
+    Tests to ensure that the sync_dist flag works with CPU (should just return the original value)
+    """
+    fake_result = 1
+
+    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')
+            return acc
+
+        def validation_step(self, batch, batch_idx):
+            output = self.layer(batch)
+            loss = self.loss(batch, output)
+            self.log('bar', torch.tensor(fake_result), on_step=False, on_epoch=True, sync_dist=True, sync_dist_op='sum')
+            return {"x": loss}
+
+    model = TestModel()
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        limit_train_batches=1,
+        limit_val_batches=1,
+        max_epochs=2,
+        weights_summary=None,
+    )
+    trainer.fit(model)
+
+    assert trainer.logged_metrics['foo'] == fake_result
+    assert trainer.logged_metrics['bar'] == fake_result
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="test requires GPU machine")
+def test_logging_sync_dist_true_gpu(tmpdir):
+    """
+    Tests to ensure that the sync_dist flag works with GPU (should just return the original value)
+    """
+    fake_result = 1
+
+    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')
+            return acc
+
+        def validation_step(self, batch, batch_idx):
+            output = self.layer(batch)
+            loss = self.loss(batch, output)
+            self.log('bar', torch.tensor(fake_result), on_step=False, on_epoch=True, sync_dist=True, sync_dist_op='sum')
+            return {"x": loss}
+
+    model = TestModel()
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        limit_train_batches=1,
+        limit_val_batches=1,
+        max_epochs=2,
+        gpus=1,
+        weights_summary=None,
+    )
+    trainer.fit(model)
+
+    assert trainer.logged_metrics['foo'] == fake_result
+    assert trainer.logged_metrics['bar'] == fake_result