extract manual optimization loop

CHANGELOG.md

@@ -68,6 +68,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
     * Added `Closure` and `AbstractClosure` classes ([#8642](https://github.com/PyTorchLightning/pytorch-lightning/pull/8642))
     * Refactored `TrainingBatchLoop` and extracted `OptimizerLoop`, splitting off automatic optimization into its own loop ([#9191](https://github.com/PyTorchLightning/pytorch-lightning/pull/9191))
     * Removed `TrainingBatchLoop.backward()`; manual optimization now calls directly into `Accelerator.backward()` and automatic optimization handles backward in new `OptimizerLoop` ([#9265](https://github.com/PyTorchLightning/pytorch-lightning/pull/9265))
+    * Extracted `ManualOptimization` logic from `TrainingBatchLoop` into its own separate loop class ([#9266](https://github.com/PyTorchLightning/pytorch-lightning/pull/9266))
 
 - Added support for saving and loading state of multiple callbacks of the same type ([#7187](https://github.com/PyTorchLightning/pytorch-lightning/pull/7187))
 

pyproject.toml

@@ -63,6 +63,7 @@ ignore_errors = "True"
 module = [
     "pytorch_lightning.callbacks.pruning",
     "pytorch_lightning.loops.closure",
+    "pytorch_lightning.loops.batch.manual",
     "pytorch_lightning.trainer.evaluation_loop",
     "pytorch_lightning.trainer.connectors.logger_connector.logger_connector",
     "pytorch_lightning.utilities.apply_func",

pytorch_lightning/loops/__init__.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 from pytorch_lightning.loops.base import Loop  # noqa: F401
+from pytorch_lightning.loops.batch import ManualOptimization  # noqa: F401
 from pytorch_lightning.loops.batch import TrainingBatchLoop  # noqa: F401
 from pytorch_lightning.loops.dataloader import DataLoaderLoop, EvaluationLoop, PredictionLoop  # noqa: F401
 from pytorch_lightning.loops.epoch import EvaluationEpochLoop, PredictionEpochLoop, TrainingEpochLoop  # noqa: F401

pytorch_lightning/loops/batch/__init__.py

@@ -12,4 +12,5 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from pytorch_lightning.loops.batch.manual import ManualOptimization  # noqa: F401
 from pytorch_lightning.loops.batch.training_batch_loop import TrainingBatchLoop  # noqa: F401

pytorch_lightning/loops/batch/manual.py

@@ -0,0 +1,93 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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, Optional, Tuple
+
+from pytorch_lightning.loops import Loop
+from pytorch_lightning.loops.utilities import (
+    _build_training_step_kwargs,
+    _check_training_step_output,
+    _process_training_step_output,
+)
+from pytorch_lightning.trainer.connectors.logger_connector.result import ResultCollection
+
+
+class ManualOptimization(Loop):
+    """
+    A special loop implementing what is known in Lightning as Manual Optimization where the optimization happens
+    entirely in the :meth:`~pytorch_lightning.core.lightning.LightningModule.training_step` and therefore the user
+    is responsible for back-propagating gradients and making calls to the optimizers.
+
+    This loop is a trivial case because it performs only a single iteration (calling directly into the module's
+    :meth:`~pytorch_lightning.core.lightning.LightningModule.training_step`) and passing through the output(s).
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+        self._done: bool = False
+        self._hiddens: Optional[Any] = None
+        self._output: Optional[ResultCollection] = None
+
+    @property
+    def done(self) -> bool:
+        return self._done
+
+    def reset(self) -> None:
+        self._done = False
+
+    def advance(self, batch: Any, batch_idx: int, hiddens: Optional[Any] = None) -> None:  # type: ignore[override]
+        """Performs the training step for manual optimization.
+
+        Args:
+            batch: the current tbptt split of the current batch
+            batch_idx: the index of the current batch
+            hiddens: the model's hidden state of the previous iteration
+
+        Returns:
+            post-processed outputs from the training step, or ``None`` if training step returned nothing
+        """
+        assert self.trainer is not None
+        ligtning_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=hiddens
+            )
+
+            # manually capture logged metrics
+            ligtning_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()
+
+            del step_kwargs
+
+            training_step_output = self.trainer.call_hook("training_step_end", training_step_output)
+
+            _check_training_step_output(ligtning_module, training_step_output)
+
+            result_collection, hiddens = _process_training_step_output(self.trainer, training_step_output)
+
+        self._done = True
+        self._hiddens = hiddens
+        self._output = result_collection
+
+    def on_run_end(self) -> Optional[Tuple[ResultCollection, Optional[Any]]]:
+        hiddens = self._hiddens
+        output = self._output
+        self._hiddens, self._output = None, None  # free memory
+        if output is None:
+            return None
+        return output, hiddens

pytorch_lightning/loops/batch/training_batch_loop.py

@@ -12,22 +12,16 @@
 # 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, List, Optional, Tuple
+from typing import Any, List, Optional, Tuple
 
 import numpy as np
 from deprecate import void
 from torch import Tensor
 from torch.optim import Optimizer
 
 from pytorch_lightning.loops.base import Loop
-from pytorch_lightning.loops.closure import Closure, ClosureResult
+from pytorch_lightning.loops.batch.manual import ManualOptimization
 from pytorch_lightning.loops.optimizer.optimizer_loop import OptimizerLoop
-from pytorch_lightning.loops.utilities import (
-    _build_training_step_kwargs,
-    _check_training_step_output,
-    _process_training_step_output,
-)
 from pytorch_lightning.trainer.supporters import TensorRunningAccum
 from pytorch_lightning.utilities import AttributeDict
 from pytorch_lightning.utilities.types import STEP_OUTPUT
@@ -45,6 +39,7 @@ def __init__(self) -> None:
         # the current split index when the batch gets split into chunks in truncated backprop through time
         self.split_idx: Optional[int] = None
         self.optimizer_loop = OptimizerLoop()
+        self.manual_loop = ManualOptimization()
 
         self._warning_cache: WarningCache = WarningCache()
         self._hiddens: Optional[Tensor] = None
@@ -63,8 +58,9 @@ def optimizer_freq_cumsum(self) -> int:
             self._optimizer_freq_cumsum = np.cumsum(self.trainer.optimizer_frequencies)
         return self._optimizer_freq_cumsum
 
-    def connect(self, optimizer_loop: "Loop") -> None:
+    def connect(self, optimizer_loop: "Loop", manual_loop: ManualOptimization) -> None:
         self.optimizer_loop = optimizer_loop
+        self.manual_loop = manual_loop
 
     def run(self, batch: Any, batch_idx: int) -> AttributeDict:
         """Runs all the data splits and the ``on_batch_start`` and ``on_train_batch_start`` hooks
@@ -132,10 +128,11 @@ def advance(self, batch, batch_idx):
             for k in range(len(batch_outputs)):
                 self.batch_outputs[k].extend(batch_outputs[k])
         else:
-            # in manual optimization, there is no looping over optimizers
-            result = self._run_optimization(batch_idx, split_batch)
-            if result:
-                self.batch_outputs[0].append(deepcopy(result.result_collection))
+            # in manual optimization, hand over execution to the ManualOptimization loop
+            output = self.manual_loop.run(split_batch, batch_idx, self._hiddens)
+            if output:
+                result, self._hiddens = output
+                self.batch_outputs[0].append(deepcopy(result))
 
     def teardown(self) -> None:
         # release memory
@@ -145,91 +142,6 @@ def num_active_optimizers(self, batch_idx: Optional[int] = None) -> int:
         """Gets the number of active optimizers based on their frequency"""
         return len(self.get_active_optimizers(batch_idx))
 
-    def _run_optimization(
-        self,
-        batch_idx: int,
-        split_batch: Any,
-    ) -> Optional[ClosureResult]:
-        """Runs closure (train step + backward) together with optimization if necessary.
-
-        Args:
-            batch_idx: the index of the current batch
-            split_batch: the current tbptt split of the whole batch
-        """
-        # TODO: replace call through closure by direct call (manual optimization)
-        closure = self._make_closure(split_batch, batch_idx, self._hiddens)
-        closure()
-        result = closure.get_result()
-
-        if result:
-            # if no result, user decided to skip optimization
-            # otherwise update running loss + reset accumulated loss
-            self._update_running_loss(result.loss)
-
-        return result
-
-    def _make_closure(
-        self,
-        split_batch: Any,
-        batch_idx: int,
-        hiddens: Any,
-    ) -> Closure:
-        """
-        Build a closure object that captures the given arguments and runs the `training_step` function and optionally
-        other functions such as `backward` and `zero_grad`.
-        """
-        step_fn = self._make_step_fn(split_batch, batch_idx, hiddens)
-        backward_fn = None
-        zero_grad_fn = None
-
-        return Closure(
-            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, hiddens: Any) -> Callable[[], dict]:
-        """Build the step function that runs the `training_step` and processes its output."""
-        return partial(self._training_step, split_batch, batch_idx, hiddens)
-
-    def _training_step(self, split_batch: Any, batch_idx: int, hiddens: Tensor) -> Optional[AttributeDict]:
-        """Performs the training step for manual optimization.
-
-        Args:
-            split_batch: the current tbptt split of the current batch
-            batch_idx: the index of the current batch
-            hiddens: the model's hidden state of the previous iteration
-
-        Returns:
-            an AttributeDict containing the training step output.
-        """
-        # give the PL module a result for logging
-        model_ref = self.trainer.lightning_module
-
-        with self.trainer.profiler.profile("model_forward"):
-            step_kwargs = _build_training_step_kwargs(
-                model_ref, self.trainer.optimizers, split_batch, batch_idx, opt_idx=None, hiddens=hiddens
-            )
-
-            # manually capture logged metrics
-            model_ref._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()
-
-            del step_kwargs
-
-            training_step_output = self.trainer.call_hook("training_step_end", training_step_output)
-
-            _check_training_step_output(self.trainer.lightning_module, training_step_output)
-
-            result_collection, self._hiddens = _process_training_step_output(self.trainer, training_step_output)
-            if result_collection is None:
-                return
-
-        return AttributeDict(closure_loss=None, loss=None, result_collection=result_collection)
-
     def _tbptt_split_batch(self, batch: Any) -> List[Any]:
         """Splits a single batch into a list of sequence steps for tbptt.
 

pytorch_lightning/trainer/properties.py

@@ -163,7 +163,7 @@ def lightning_module(self) -> "pl.LightningModule":
         return self.accelerator.lightning_module
 
     @property
-    def optimizers(self) -> Optional[List[Optimizer]]:
+    def optimizers(self) -> List[Optimizer]:
         return self.accelerator.optimizers
 
     @optimizers.setter

tests/loops/test_loop_state_dict.py

@@ -56,8 +56,9 @@ def test_loops_state_dict_structure():
                 "total": {"ready": 0, "started": None, "processed": None, "completed": 0},
                 "current": {"ready": 0, "started": None, "processed": None, "completed": 0},
             },
-            "epoch_loop.batch_loop.optimizer_loop.state_dict": {},
             "epoch_loop.batch_loop.state_dict": {},
+            "epoch_loop.batch_loop.manual_loop.state_dict": {},
+            "epoch_loop.batch_loop.optimizer_loop.state_dict": {},
             "epoch_loop.batch_loop.optimizer_loop.optim_progress": {
                 "optimizer": {
                     "step": {

tests/loops/test_loops.py

@@ -461,6 +461,7 @@ def configure_optimizers_multiple(self):
             "current": {"ready": be_sch_steps, "started": None, "processed": None, "completed": be_sch_steps},
         },
         "epoch_loop.batch_loop.state_dict": ANY,
+        "epoch_loop.batch_loop.manual_loop.state_dict": ANY,
         "epoch_loop.batch_loop.optimizer_loop.state_dict": {},
         "epoch_loop.batch_loop.optimizer_loop.optim_progress": {
             "optimizer_idx": stop_optimizer,