Do not force sync_dist=True on epoch end

src/pytorch_lightning/CHANGELOG.md

@@ -139,7 +139,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - The `WandbLogger` will now use the run name in the logs folder if it is provided, and otherwise the project name  ([#12604](https://github.com/PyTorchLightning/pytorch-lightning/pull/12604))
 
 
--
+- Raised a warning instead of forcing `sync_dist=True` on epoch end ([13364](https://github.com/Lightning-AI/lightning/pull/13364))
 
 
 ### Deprecated

src/pytorch_lightning/trainer/connectors/logger_connector/result.py

@@ -522,12 +522,15 @@ def _get_cache(result_metric: _ResultMetric, on_step: bool) -> Optional[Tensor]:
             cache = result_metric._forward_cache
         elif not on_step and result_metric.meta.on_epoch:
             if result_metric._computed is None:
-                # always reduce on epoch end
-                should = result_metric.meta.sync.should
-                result_metric.meta.sync.should = True
+                if not result_metric.meta.sync.should:
+                    warning_cache.warn(
+                        f"It is recommended to use `self.log({result_metric.meta.name!r}, sync_dist=True)` when"
+                        " logging on epoch level in distributed setting to accumulate the metric across devices."
+                    )
                 result_metric.compute()
-                result_metric.meta.sync.should = should
+
             cache = result_metric._computed
+
         if cache is not None:
             if not isinstance(cache, torch.Tensor):
                 raise ValueError(
@@ -536,6 +539,7 @@ def _get_cache(result_metric: _ResultMetric, on_step: bool) -> Optional[Tensor]:
                 )
             if not result_metric.meta.enable_graph:
                 return cache.detach()
+
         return cache
 
     def valid_items(self) -> Generator:

tests/tests_pytorch/trainer/logging_/test_distributed_logging.py

@@ -15,6 +15,8 @@
 from typing import Any, Dict, Optional, Union
 from unittest.mock import Mock
 
+import pytest
+
 import pytorch_lightning as pl
 from pytorch_lightning import Callback, Trainer
 from pytorch_lightning.demos.boring_classes import BoringModel
@@ -194,3 +196,18 @@ def on_test_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") ->
     assert trainer.logger.logs == {"fit": 1, "validate": 1, "test": 1}
     trainer.predict(model)
     assert trainer.logger.logs == {"fit": 1, "validate": 1, "test": 1, "predict": 1}
+
+
+def test_logger_sync_dist():
+    class CustomBoringModel(BoringModel):
+        def training_epoch_end(self, *args, **kwargs):
+            super().training_epoch_end(*args, **kwargs)
+            self.log("global_rank", self.global_rank, sync_dist=False)
+
+    model = CustomBoringModel()
+    trainer = Trainer(fast_dev_run=1, accelerator="cpu", strategy="ddp", devices=2)
+
+    with pytest.warns(UserWarning, match="It is recommended to use .* sync_dist=True"):
+        trainer.fit(model)
+
+    assert trainer.callback_metrics["global_rank"] == 0