[Metrics] Detach bugfix

CHANGELOG.md

@@ -21,6 +21,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Fixed
 
+- Fixed that metrics do not store computational graph for all seen data ([#4313](https://github.com/PyTorchLightning/pytorch-lightning/pull/4313))
+
 
 ## [1.0.2] - 2020-10-15
 
@@ -97,7 +99,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 ### Fixed
 
 - Fixed `current_epoch` property update to reflect true epoch number inside `LightningDataModule`, when `reload_dataloaders_every_epoch=True`. ([#3974](https://github.com/PyTorchLightning/pytorch-lightning/pull/3974))
-- Fixed to print scaler value in progress bar ([#4053](https://github.com/PyTorchLightning/pytorch-lightning/pull/4053)) 
+- Fixed to print scaler value in progress bar ([#4053](https://github.com/PyTorchLightning/pytorch-lightning/pull/4053))
 - Fixed mismatch between docstring and code regarding when `on_load_checkpoint` hook is called ([#3996](https://github.com/PyTorchLightning/pytorch-lightning/pull/3996))
 
 
@@ -442,7 +444,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed adding val step argument to metrics ([#2986](https://github.com/PyTorchLightning/pytorch-lightning/pull/2986))
 - Fixed an issue that caused `Trainer.test()` to stall in ddp mode ([#2997](https://github.com/PyTorchLightning/pytorch-lightning/pull/2997))
 - Fixed gathering of results with tensors of varying shape ([#3020](https://github.com/PyTorchLightning/pytorch-lightning/pull/3020))
-- Fixed batch size auto-scaling feature to set the new value on the correct model attribute ([#3043](https://github.com/PyTorchLightning/pytorch-lightning/pull/3043)) 
+- Fixed batch size auto-scaling feature to set the new value on the correct model attribute ([#3043](https://github.com/PyTorchLightning/pytorch-lightning/pull/3043))
 - Fixed automatic batch scaling not working with half precision ([#3045](https://github.com/PyTorchLightning/pytorch-lightning/pull/3045))
 - Fixed setting device to root gpu ([#3042](https://github.com/PyTorchLightning/pytorch-lightning/pull/3042))
 

docs/source/metrics.rst

@@ -31,14 +31,14 @@ The example below shows how to use a metric in your ``LightningModule``:
     def __init__(self):
         ...
         self.accuracy = pl.metrics.Accuracy()
-   
+
     def training_step(self, batch, batch_idx):
         logits = self(x)
         ...
         # log step metric
         self.log('train_acc_step', self.accuracy(logits, y))
         ...
-   
+
     def training_epoch_end(self, outs):
         # log epoch metric
         self.log('train_acc_epoch', self.accuracy.compute())
@@ -57,15 +57,15 @@ If ``on_epoch`` is True, the logger automatically logs the end of epoch metric v
     This however is only true for metrics that inherit the base class ``Metric``,
     and thus the functional metric API provides no support for in-built distributed synchronization
     or reduction functions.
-    
+
 
 .. code-block:: python
 
     def __init__(self):
         ...
         self.train_acc = pl.metrics.Accuracy()
         self.valid_acc = pl.metrics.Accuracy()
-   
+
     def training_step(self, batch, batch_idx):
         logits = self(x)
         ...
@@ -91,17 +91,17 @@ This metrics API is independent of PyTorch Lightning. Metrics can directly be us
     for epoch in range(epochs):
         for x, y in train_data:
             y_hat = model(x)
-            
+
             # training step accuracy
             batch_acc = train_accuracy(y_hat, y)
-            
+
         for x, y in valid_data:
             y_hat = model(x)
             valid_accuracy(y_hat, y)
-            
+
     # total accuracy over all training batches
     total_train_accuracy = train_accuracy.compute()
-    
+
     # total accuracy over all validation batches
     total_valid_accuracy = valid_accuracy.compute()
 
@@ -144,6 +144,18 @@ 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 cashed 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
 **********

pytorch_lightning/metrics/metric.py

@@ -142,7 +142,8 @@ 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: