[Docs] Note on running metric in dp (#4494)

* note

* Update docs/source/metrics.rst

Co-authored-by: chaton <thomas@grid.ai>
Co-authored-by: Sean Naren <sean.narenthiran@gmail.com>
Co-authored-by: Jeff Yang <ydcjeff@outlook.com>

docs/source/metrics.rst

@@ -78,6 +78,26 @@ If ``on_epoch`` is True, the logger automatically logs the end of epoch metric v
         self.valid_acc(logits, y)
         self.log('valid_acc', self.valid_acc, on_step=True, on_epoch=True)
 
+.. note::
+    If using metrics in data parallel mode (dp), the metric update/logging should be done
+    in the ``<mode>_step_end`` method (where ``<mode>`` is either ``training``, ``validation``
+    or ``test``). This is due to metric states else being destroyed after each forward pass,
+    leading to wrong accumulation. In practice do the following:
+
+    .. code-block:: python
+
+        def training_step(self, batch, batch_idx):
+            data, target = batch
+            pred = self(data)
+            ...
+            return {'loss' : loss, 'preds' : preds, 'target' : target}
+
+        def training_step_end(self, outputs):
+            #update and log
+            self.metric(outputs['preds'], outputs['target'])
+            self.log('metric', self.metric)
+
+
 This metrics API is independent of PyTorch Lightning. Metrics can directly be used in PyTorch as shown in the example:
 
 .. code-block:: python