Share the training step output data via ClosureResult

CHANGELOG.md

@@ -334,6 +334,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed `replace_sampler` missing the batch size under specific conditions ([#9367](https://github.com/PyTorchLightning/pytorch-lightning/pull/9367))
 
 
+- Fixed bug where the training step output needed to be `deepcopy`-ed ([#9349](https://github.com/PyTorchLightning/pytorch-lightning/pull/9349))
+
+
 - Fixed freeing data iterators in loop `on_run_end` ([#9386](https://github.com/PyTorchLightning/pytorch-lightning/pull/9386))
 
 

pytorch_lightning/loops/batch/manual.py

@@ -14,12 +14,13 @@
 from typing import Any, Optional
 
 from pytorch_lightning.loops import Loop
+from pytorch_lightning.loops.closure import ClosureResult
 from pytorch_lightning.loops.utilities import (
     _build_training_step_kwargs,
     _check_training_step_output,
-    _process_training_step_output,
+    _extract_hiddens,
+    check_finite_loss,
 )
-from pytorch_lightning.trainer.connectors.logger_connector.result import ResultCollection
 
 
 class ManualOptimization(Loop):
@@ -35,7 +36,7 @@ def __init__(self) -> None:
         super().__init__()
         self._done: bool = False
         self._hiddens: Optional[Any] = None
-        self._output: Optional[ResultCollection] = None
+        self._output: Optional[ClosureResult] = None
 
     @property
     def done(self) -> bool:
@@ -52,16 +53,16 @@ def advance(self, batch: Any, batch_idx: int) -> None:  # type: ignore[override]
             batch_idx: the index of the current batch
         """
         assert self.trainer is not None
-        ligtning_module = self.trainer.lightning_module
+        lightning_module = self.trainer.lightning_module
 
         with self.trainer.profiler.profile("model_forward"):
 
             step_kwargs = _build_training_step_kwargs(
-                ligtning_module, self.trainer.optimizers, batch, batch_idx, opt_idx=None, hiddens=self._hiddens
+                lightning_module, self.trainer.optimizers, batch, batch_idx, opt_idx=None, hiddens=self._hiddens
             )
 
             # manually capture logged metrics
-            ligtning_module._current_fx_name = "training_step"
+            lightning_module._current_fx_name = "training_step"
             with self.trainer.profiler.profile("training_step"):
                 training_step_output = self.trainer.accelerator.training_step(step_kwargs)
                 self.trainer.accelerator.post_training_step()
@@ -70,14 +71,28 @@ def advance(self, batch: Any, batch_idx: int) -> None:  # type: ignore[override]
 
             training_step_output = self.trainer.call_hook("training_step_end", training_step_output)
 
-            _check_training_step_output(ligtning_module, training_step_output)
+            _check_training_step_output(lightning_module, training_step_output)
 
-            result_collection, self._hiddens = _process_training_step_output(self.trainer, training_step_output)
+            self._hiddens = _extract_hiddens(training_step_output, lightning_module.truncated_bptt_steps)
+
+            # TODO: do not use `ClosureResult`
+            result = ClosureResult.from_training_step_output(training_step_output, self.trainer.accumulate_grad_batches)
+
+            if self.trainer.terminate_on_nan:
+                check_finite_loss(result.closure_loss)
+
+            if self.trainer.move_metrics_to_cpu:
+                # hiddens and the training step output are not moved as they are not considered "metrics"
+                # the user might need them on the correct device for an operation in `training_epoch_end`
+                assert self.trainer._results is not None
+                self.trainer._results.cpu()
 
         self._done = True
-        self._output = result_collection
+        self._output = result
 
-    def on_run_end(self) -> Optional[ResultCollection]:
+    def on_run_end(self) -> Optional[ClosureResult]:
         """Returns the result of this loop, i.e., the post-processed outputs from the training step."""
         output, self._output = self._output, None  # free memory
+        # #9052 added support for raising `StopIteration` in the `training_step`. If that happens, then `advance`
+        # doesn't finish and `self._output` stays as `None`. If #9415 happens then this would always return a result
         return output

pytorch_lightning/loops/batch/training_batch_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 copy import deepcopy
 from typing import Any, List, Optional, Tuple
 
 import numpy as np
@@ -131,9 +130,9 @@ def advance(self, batch, batch_idx):
                 self.batch_outputs[k].extend(batch_outputs[k])
         else:
             # in manual optimization, hand over execution to the ManualOptimization loop
-            output = self.manual_loop.run(split_batch, batch_idx)
-            if output is not None:
-                self.batch_outputs[0].append(deepcopy(output))
+            result = self.manual_loop.run(split_batch, batch_idx)
+            if result is not None and result.loss is not None:
+                self.batch_outputs[0].append(result.drop_closure_loss())
 
     def on_run_end(self) -> None:
         self.optimizer_loop._hiddens = None

pytorch_lightning/loops/closure.py

@@ -11,32 +11,84 @@
 # 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 abc import ABC, abstractmethod
-from dataclasses import dataclass
+from dataclasses import dataclass, field
 from typing import Any, Callable, Dict, Optional
 
 from torch import Tensor
 
 from pytorch_lightning.profiler import BaseProfiler, PassThroughProfiler
-from pytorch_lightning.trainer.connectors.logger_connector.result import ResultCollection
+from pytorch_lightning.utilities.apply_func import apply_to_collection
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
-from pytorch_lightning.utilities.warnings import WarningCache
+from pytorch_lightning.utilities.memory import recursive_detach
+from pytorch_lightning.utilities.types import STEP_OUTPUT
+from pytorch_lightning.utilities.warnings import rank_zero_deprecation, WarningCache
 
 
 @dataclass
 class ClosureResult:
     """A container to hold the result of a :class:`AbstractClosure` call.
 
+    It is created from the output of :meth:`~pytorch_lightning.core.lightning.LightningModule.training_step`.
+
     Attributes:
         closure_loss: The loss with a graph attached.
         loss: A detached copy of the closure loss.
-        result_collection: A collection of results returned by the closure.
+        extra: Any keys other than the loss returned.
     """
 
     closure_loss: Optional[Tensor]
-    loss: Optional[Tensor]
-    result_collection: Optional[ResultCollection]
+    loss: Optional[Tensor] = field(init=False, default=None)
+    extra: Dict[str, Tensor] = field(default_factory=dict)
+
+    def __post_init__(self) -> None:
+        # TODO: remove with the deprecation removal in v1.6
+        ClosureResult._check_extra_detach_deprecation(self.extra)
+        self.extra = recursive_detach(self.extra)
+
+        self._clone_loss()
+
+    def _clone_loss(self) -> None:
+        if self.closure_loss is not None:
+            # the loss will get scaled for amp. avoid any modifications to it
+            self.loss = self.closure_loss.detach().clone()
+
+    @classmethod
+    def from_training_step_output(
+        cls, training_step_output: Optional[STEP_OUTPUT], normalize: int = 1
+    ) -> "ClosureResult":
+        closure_loss, extra = None, {}
+
+        if isinstance(training_step_output, dict):
+            # this should not modify the `training_step_output`, as the user could be using it after `training_step_end`
+            closure_loss = training_step_output.get("loss")
+            extra = {k: v for k, v in training_step_output.items() if k not in ("loss", "hiddens")}
+        elif isinstance(training_step_output, Tensor):
+            closure_loss = training_step_output
+
+        if closure_loss is not None:
+            # accumulate the loss. If ``accumulate_grad_batches == 1``, no effect
+            closure_loss /= normalize
+
+        return cls(closure_loss, extra=extra)
+
+    @staticmethod
+    def _check_extra_detach_deprecation(extra: Dict[str, Any]) -> None:
+        def check_fn(v: Tensor) -> Tensor:
+            if v.grad_fn is not None:
+                rank_zero_deprecation(
+                    f"One of the returned values {set(extra.keys())} has a `grad_fn`. We will detach it automatically"
+                    " but this behaviour will change in v1.6. Please detach it manually:"
+                    " `return {'loss': ..., 'something': something.detach()}`"
+                )
+            return v
+
+        apply_to_collection(extra, Tensor, check_fn)
+
+    def drop_closure_loss(self) -> "ClosureResult":
+        """Return itself without the closure loss which could have a `grad_fn`"""
+        self.closure_loss = None
+        return self
 
 
 class AbstractClosure(ABC):
@@ -107,7 +159,7 @@ class Closure(AbstractClosure):
 
     def __init__(
         self,
-        step_fn: Callable[[], Optional[Dict]],
+        step_fn: Callable[[], ClosureResult],
         backward_fn: Optional[Callable[[Tensor], Tensor]] = None,
         zero_grad_fn: Optional[Callable[[], None]] = None,
         profiler: Optional[BaseProfiler] = None,
@@ -121,7 +173,6 @@ def __init__(
     def closure(self, *args: Any, **kwargs: Any) -> ClosureResult:
         with self._profiler.profile("training_step_and_backward"):
             step_output = self._step_fn()
-            step_output = ClosureResult(**step_output) if step_output else ClosureResult(None, None, None)
 
             if step_output.closure_loss is None:
                 self.warning_cache.warn(

pytorch_lightning/loops/epoch/training_epoch_loop.py

@@ -17,6 +17,7 @@
 
 from pytorch_lightning import loops  # import as loops to avoid circular imports
 from pytorch_lightning.loops.batch import TrainingBatchLoop
+from pytorch_lightning.loops.closure import ClosureResult
 from pytorch_lightning.loops.utilities import _prepare_dataloader_iter
 from pytorch_lightning.trainer.connectors.logger_connector.result import ResultCollection
 from pytorch_lightning.trainer.progress import Progress, SchedulerProgress
@@ -283,18 +284,18 @@ def _track_epoch_end_reduce_metrics(
 
     @staticmethod
     def _prepare_outputs(
-        outputs: List[List[List["ResultCollection"]]], batch_mode: bool
+        outputs: List[List[List[ClosureResult]]], batch_mode: bool
     ) -> Union[List[List[List[Dict]]], List[List[Dict]], List[Dict], Dict]:
         """Extract required information from batch or epoch end results.
 
         Args:
-            outputs: A 3-dimensional list of ``ResultCollection`` objects with dimensions:
+            outputs: A 3-dimensional list of ``ClosureResult`` objects with dimensions:
                 ``[optimizer outs][batch outs][tbptt steps]``.
 
             batch_mode: If True, ignore the batch output dimension.
 
         Returns:
-            The cleaned outputs with ``ResultCollection`` objects converted to dictionaries.
+            The cleaned outputs with ``ClosureResult`` objects converted to dictionaries.
             All list dimensions of size one will be collapsed.
         """
         processed_outputs = []
@@ -311,13 +312,13 @@ def _prepare_outputs(
             for batch_outputs in opt_outputs:
                 processed_tbptt_outputs = []
 
-                if isinstance(batch_outputs, ResultCollection):
+                if isinstance(batch_outputs, ClosureResult):
                     batch_outputs = [batch_outputs]
 
                 for tbptt_output in batch_outputs:
                     out = {}
-                    if tbptt_output.minimize is not None:
-                        out["loss"] = tbptt_output.minimize.detach()
+                    if tbptt_output.loss is not None:
+                        out["loss"] = tbptt_output.loss
                     out.update(tbptt_output.extra)
                     processed_tbptt_outputs.append(out)
 

pytorch_lightning/loops/optimizer/optimizer_loop.py

@@ -11,8 +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 copy import deepcopy
 from functools import partial
 from typing import Any, Callable, Dict, List, Optional
 
@@ -27,16 +25,16 @@
     _block_parallel_sync_behavior,
     _build_training_step_kwargs,
     _check_training_step_output,
-    _process_training_step_output,
+    _extract_hiddens,
+    check_finite_loss,
 )
-from pytorch_lightning.trainer.connectors.logger_connector.result import ResultCollection
 from pytorch_lightning.trainer.progress import OptimizationProgress
-from pytorch_lightning.utilities import AMPType, AttributeDict, DeviceType, grad_norm
+from pytorch_lightning.utilities import AMPType, DeviceType, grad_norm
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.finite_checks import detect_nan_parameters
 from pytorch_lightning.utilities.imports import _TPU_AVAILABLE
 
-_OUTPUTS_TYPE = List[List[Optional[ResultCollection]]]
+_OUTPUTS_TYPE = List[List[ClosureResult]]
 
 
 class OptimizerLoop(Loop):
@@ -80,8 +78,8 @@ def advance(self, batch: Any, *args, **kwargs) -> None:  # type: ignore[override
             self._optimizers[self.optim_progress.optimizer_idx],
             self.optim_progress.optimizer_idx,
         )
-        if result.result_collection is not None:
-            self.outputs[self.optim_progress.optimizer_idx].append(deepcopy(result.result_collection))
+        if result.loss is not None:
+            self.outputs[self.optim_progress.optimizer_idx].append(result.drop_closure_loss())
 
         self.optim_progress.optimizer_idx += 1
 
@@ -168,7 +166,7 @@ def _make_closure(self, split_batch: Any, batch_idx: int, opt_idx: int, optimize
             step_fn=step_fn, backward_fn=backward_fn, zero_grad_fn=zero_grad_fn, profiler=self.trainer.profiler
         )
 
-    def _make_step_fn(self, split_batch: Any, batch_idx: int, opt_idx: int) -> Callable[[], Optional[AttributeDict]]:
+    def _make_step_fn(self, split_batch: Any, batch_idx: int, opt_idx: int) -> Callable[[], ClosureResult]:
         """Build the step function that runs the `training_step` and processes its output."""
         return partial(self._training_step, split_batch, batch_idx, opt_idx)
 
@@ -241,7 +239,7 @@ def _optimizer_step(
             train_step_and_backward_closure: the closure function performing the train step and computing the
                 gradients. By default called by the optimizer (if possible)
         """
-        model_ref = self.trainer.lightning_module
+        lightning_module = self.trainer.lightning_module
 
         is_lbfgs = isinstance(optimizer, torch.optim.LBFGS)
         using_native_amp = self.trainer.amp_backend is not None and self.trainer.amp_backend == AMPType.NATIVE
@@ -259,7 +257,7 @@ def _optimizer_step(
         self.optim_progress.optimizer.step.increment_ready()
 
         # model hook
-        model_ref.optimizer_step(
+        lightning_module.optimizer_step(
             self.trainer.current_epoch,
             batch_idx,
             optimizer,
@@ -293,7 +291,7 @@ def _optimizer_zero_grad(self, batch_idx: int, optimizer: torch.optim.Optimizer,
         self.trainer.accelerator.optimizer_zero_grad(self.trainer.current_epoch, batch_idx, optimizer, opt_idx)
         self.optim_progress.optimizer.zero_grad.increment_completed()
 
-    def _training_step(self, split_batch: Any, batch_idx: int, opt_idx: int) -> Optional[AttributeDict]:
+    def _training_step(self, split_batch: Any, batch_idx: int, opt_idx: int) -> ClosureResult:
         """Performs the actual train step with the tied hooks.
 
         Args:
@@ -302,19 +300,19 @@ def _training_step(self, split_batch: Any, batch_idx: int, opt_idx: int) -> Opti
             opt_idx: the index of the current optimizer
 
         Returns:
-            an AttributeDict containing the loss value and the training step output.
+            A ``ClosureResult`` containing the training step output.
         """
         # give the PL module a result for logging
-        model_ref = self.trainer.lightning_module
+        lightning_module = self.trainer.lightning_module
 
         with self.trainer.profiler.profile("model_forward"):
 
             step_kwargs = _build_training_step_kwargs(
-                self.trainer.lightning_module, self.trainer.optimizers, split_batch, batch_idx, opt_idx, self._hiddens
+                lightning_module, self.trainer.optimizers, split_batch, batch_idx, opt_idx, self._hiddens
             )
 
             # manually capture logged metrics
-            model_ref._current_fx_name = "training_step"
+            lightning_module._current_fx_name = "training_step"
             with self.trainer.profiler.profile("training_step"):
                 training_step_output = self.trainer.accelerator.training_step(step_kwargs)
                 self.trainer.accelerator.post_training_step()
@@ -323,20 +321,20 @@ def _training_step(self, split_batch: Any, batch_idx: int, opt_idx: int) -> Opti
 
             training_step_output = self.trainer.call_hook("training_step_end", training_step_output)
 
-            _check_training_step_output(self.trainer.lightning_module, training_step_output)
+            _check_training_step_output(lightning_module, training_step_output)
+
+            self._hiddens = _extract_hiddens(training_step_output, lightning_module.truncated_bptt_steps)
 
-            result_collection, self._hiddens = _process_training_step_output(self.trainer, training_step_output)
-            if result_collection is None:
-                return None
+            result = ClosureResult.from_training_step_output(training_step_output, self.trainer.accumulate_grad_batches)
 
-        # output validation already done, here loss can't be None
-        assert result_collection.minimize is not None
+            if self.trainer.terminate_on_nan:
+                check_finite_loss(result.closure_loss)
+
+            if self.trainer.move_metrics_to_cpu:
+                # hiddens and the training step output are not moved as they are not considered "metrics"
+                self.trainer._results.cpu()
 
-        # accumulate loss. if accumulate_grad_batches==1, no effect
-        closure_loss = result_collection.minimize / self.trainer.accumulate_grad_batches
-        # the loss will get scaled for amp. avoid any modifications to it
-        loss = closure_loss.detach().clone()
-        return AttributeDict(closure_loss=closure_loss, loss=loss, result_collection=result_collection)
+        return result
 
     def _track_and_norm_grad(self, optimizer: torch.optim.Optimizer) -> Dict[str, float]:
         """Tracks gradient norms and clips the gradients of all parameters optimized by the current optimizer.