Merge branch 'master' into optimizer_step/training_step

CHANGELOG.md

@@ -39,6 +39,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Fixed error using `auto_select_gpus=True` with `gpus=-1` ([#4209](https://github.com/PyTorchLightning/pytorch-lightning/pull/4209))
 
+- Fixed that metrics do not store computational graph for all seen data ([#4313](https://github.com/PyTorchLightning/pytorch-lightning/pull/4313))
+
 - Fixed AMP unscale for `on_after_backward` ([#4439](https://github.com/PyTorchLightning/pytorch-lightning/pull/4439))
 
 ## [1.0.4] - 2020-10-27

docs/source/metrics.rst

@@ -150,6 +150,19 @@ Example implementation:
         def compute(self):
             return self.correct.float() / self.total
 
+Metrics support backpropagation, if all computations involved in the metric calculation
+are differentiable. However, note that the cached state is detached from the computational
+graph and cannot be backpropagated. Not doing this would mean storing the computational
+graph for each update call, which can lead to out-of-memory errors.
+In practise this means that:
+
+.. code-block:: python
+
+    metric = MyMetric()
+    val = metric(pred, target) # this value can be backpropagated
+    val = metric.compute() # this value cannot be backpropagated
+
+
 **********
 Metric API
 **********
@@ -453,4 +466,3 @@ embedding_similarity [func]
 
 .. autofunction:: pytorch_lightning.metrics.functional.self_supervised.embedding_similarity
     :noindex:
-

pytorch_lightning/core/step_result.py

@@ -259,7 +259,7 @@ def get_batch_log_metrics(self, include_forked_originals=True) -> dict:
 
             if options['logger'] and options['on_step']:
                 if isinstance(self[k], Metric):
-                    result[k] = self[k]._forward_cache
+                    result[k] = self[k]._forward_cache.detach()
                 else:
                     result[k] = self[k]
 
@@ -281,7 +281,7 @@ def get_epoch_log_metrics(self) -> dict:
 
             if options['logger'] and options['on_epoch']:
                 if isinstance(self[k], Metric):
-                    result[k] = self[k].compute()
+                    result[k] = self[k].compute().detach()
                 else:
                     result[k] = self[k]
 
@@ -307,7 +307,7 @@ def get_epoch_pbar_metrics(self):
 
             if options['prog_bar'] and options['on_epoch']:
                 if isinstance(self[k], Metric):
-                    result[k] = self[k].compute()
+                    result[k] = self[k].compute().detach()
                 else:
                     result[k] = self[k]
 

pytorch_lightning/metrics/metric.py

@@ -150,7 +150,8 @@ def forward(self, *args, **kwargs):
         Automatically calls ``update()``. Returns the metric value over inputs if ``compute_on_step`` is True.
         """
         # add current step
-        self.update(*args, **kwargs)
+        with torch.no_grad():
+            self.update(*args, **kwargs)
         self._forward_cache = None
 
         if self.compute_on_step: