Fix reference issues during epoch end result collection

CHANGELOG.md

@@ -77,7 +77,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 -
 
 
--
+- Fixed reference issues during epoch end result collection ([#8621](https://github.com/PyTorchLightning/pytorch-lightning/pull/8621))
 
 
 - Fixed `trainer.fit_loop.split_idx` always returning `None` ([#8601](https://github.com/PyTorchLightning/pytorch-lightning/pull/8601))

pytorch_lightning/core/lightning.py

@@ -750,7 +750,7 @@ def training_step(self, batch, batch_idx):
 
                 out = self(x)
 
-                # softmax uses only a portion of the batch in the denomintaor
+                # softmax uses only a portion of the batch in the denominator
                 loss = self.softmax(out)
                 loss = nce_loss(loss)
                 return loss

pytorch_lightning/loops/batch/training_batch_loop.py

@@ -338,15 +338,16 @@ def _process_training_step_output(self, training_step_output: STEP_OUTPUT) -> Op
 
         loss = None
         hiddens = None
-        results.extra = {}
 
         # handle dict return
         if isinstance(training_step_output, dict):
-            loss = training_step_output.pop("loss", None)
-            hiddens = training_step_output.pop("hiddens", None)
+            # this should not modify the `training_step_output`, as the user could be using it after `training_step_end`
+            loss = training_step_output.get("loss")
+            hiddens = training_step_output.get("hiddens")
             # detach hiddens to avoid `RuntimeError: Trying to backward through the graph a second time`
             hiddens = apply_to_collection(hiddens, Tensor, lambda t: t.detach())
-            results.extra = training_step_output
+            # use the setter instead of `dict.update` because it calls `detach` on the tensor items
+            results.extra = {k: v for k, v in training_step_output.items() if k not in ("loss", "hiddens")}
 
         # handle scalar return
         elif isinstance(training_step_output, Tensor):

pytorch_lightning/loops/epoch/training_epoch_loop.py

@@ -11,7 +11,6 @@
 # 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 Any, Dict, Iterator, List, Optional, Union
 
 import torch
@@ -276,11 +275,7 @@ def _track_epoch_end_reduce_metrics(
         # track the outputs to reduce at the end of the epoch
         for opt_idx, opt_outputs in enumerate(batch_end_outputs):
             # with 1 step (no tbptt) don't use a sequence at epoch end
-            if (
-                isinstance(opt_outputs, list)
-                and len(opt_outputs) == 1
-                and not isinstance(opt_outputs[0], ResultCollection)
-            ):
+            if isinstance(opt_outputs, list) and len(opt_outputs) == 1:
                 opt_outputs = opt_outputs[0]
 
             epoch_output[opt_idx].append(opt_outputs)
@@ -320,9 +315,10 @@ def _prepare_outputs(
                     batch_outputs = [batch_outputs]
 
                 for tbptt_output in batch_outputs:
-                    out = tbptt_output.extra
+                    out = {}
                     if tbptt_output.minimize is not None:
                         out["loss"] = tbptt_output.minimize.detach()
+                    out.update(tbptt_output.extra)
                     processed_tbptt_outputs.append(out)
 
                 # if there was only one tbptt step then we can collapse that dimension

tests/trainer/loops/test_training_loop.py

@@ -163,3 +163,30 @@ def training_step_end(self, outputs):
     trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=1)
 
     trainer.fit(model)
+
+
+def test_prepare_outputs(tmpdir):
+    """
+    Test that the `extra` field of the saved `ResultCollection` objects for
+    `training_epoch_end` doesn't get accidentally modified by reference.
+    """
+
+    class TestModel(BoringModel):
+        on_train_batch_end_called = 0
+
+        def on_train_batch_end(self, outputs, *args, **kwargs):
+            epoch_outputs = self.trainer.fit_loop.epoch_loop._epoch_output
+            epoch_outputs = epoch_outputs[0]  # 1 optimizer
+            assert len(epoch_outputs) == self.on_train_batch_end_called
+            # `extra` should be empty for all `ResultCollection` objects
+            assert all(not out.extra for out in epoch_outputs)
+            self.on_train_batch_end_called += 1
+
+        def training_epoch_end(self, outputs) -> None:
+            # override so epoch outputs get stored
+            pass
+
+    model = TestModel()
+    trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=2)
+    trainer.fit(model)
+    assert model.on_train_batch_end_called == 2