Loop Refactor 1/N - Training Loop

CHANGELOG.md

@@ -110,8 +110,10 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
     * Moved attributes `hiddens` and `split_idx` to TrainLoop ([#7507](https://github.com/PyTorchLightning/pytorch-lightning/pull/7507))
     * Refactored the logic around manual and automatic optimization inside the optimizer loop ([#7526](https://github.com/PyTorchLightning/pytorch-lightning/pull/7526))
     * Simplified "should run validation" logic ([#7682](https://github.com/PyTorchLightning/pytorch-lightning/pull/7682))
-    * Refactored "should run validation" logic when the trainer is signaled to stop ([#7701](https://github.com/PyTorchLightning/pytorch-lightning/pull/7701))
-
+    * Simplified logic for updating the learning rate for schedulers ([#7682](https://github.com/PyTorchLightning/pytorch-lightning/pull/7682))
+    * Removed the `on_epoch` guard from the "should stop" validation check ([#7701](https://github.com/PyTorchLightning/pytorch-lightning/pull/7701))
+    ...
+    * Refactored internal loop interface; added new classes `EpochLoop`, `TrainingLoop`, `BatchLoop` ([#7871](https://github.com/PyTorchLightning/pytorch-lightning/pull/7871))
 
 - Refactored logging
     * Renamed and moved `core/step_result.py` to `trainer/connectors/logger_connector/result.py` ([#7736](https://github.com/PyTorchLightning/pytorch-lightning/pull/7736))

pytorch_lightning/core/lightning.py

@@ -1346,7 +1346,7 @@ def training_step(...):
 
         # backward
         self._running_manual_backward = True
-        self.trainer.train_loop.backward(loss, optimizer=None, opt_idx=None, *args, **kwargs)
+        self.trainer.fit_loop.training_loop.batch_loop.backward(loss, optimizer=None, opt_idx=None, *args, **kwargs)
         self._running_manual_backward = False
 
     def backward(self, loss: Tensor, optimizer: Optimizer, optimizer_idx: int, *args, **kwargs) -> None:
@@ -1445,7 +1445,7 @@ def optimizer_step(
             If you are overriding this method, make sure that you pass the ``optimizer_closure`` parameter
             to ``optimizer.step()`` function as shown in the examples. This ensures that
             ``training_step()``, ``optimizer.zero_grad()``, ``backward()`` are called within
-            :meth:`~pytorch_lightning.trainer.training_loop.TrainLoop.run_training_batch`.
+            :meth:`~pytorch_lightning.trainer.fit_loop.training_loop.batch_loop.TrainingBatchLoop.advance`.
 
         Args:
             epoch: Current epoch

pytorch_lightning/core/optimizer.py

@@ -120,7 +120,7 @@ def toggle_model(self, sync_grad: bool = True):
         during the accumulation phase.
         Setting `sync_grad` to False will block this synchronization and improve performance.
         """
-        with self._trainer.train_loop.block_ddp_sync_behaviour(not sync_grad):
+        with self._trainer.train_loop.training_loop.batch_loop.block_ddp_sync_behaviour(not sync_grad):
             self._toggle_model()
             yield
             self._untoggle_model()

pytorch_lightning/loops/__init__.py

@@ -0,0 +1,18 @@
+# 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 pytorch_lightning.loops.base import Loop  # noqa: F401
+from pytorch_lightning.loops.fit_loop import FitLoop  # noqa: F401
+from pytorch_lightning.loops.training_batch_loop import TrainingBatchLoop  # noqa: F401
+from pytorch_lightning.loops.training_epoch_loop import TrainingEpochLoop  # noqa: F401

pytorch_lightning/loops/base.py

@@ -0,0 +1,115 @@
+# 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 abc import ABC, abstractmethod
+from typing import Any, Optional
+from weakref import proxy
+
+from deprecate import void
+
+import pytorch_lightning as pl
+
+
+class Loop(ABC):
+    """
+    Basic Loops interface. All classes derived from this must implement the following properties and methods:
+
+        * :attr`done` (property): Condition to break the loop
+        * :attr`reset` (method): Resets the internal state between multiple calls of :attr`run`
+        * :attr`advance` (method): Implements one step of the loop
+
+    This class implements the following loop structure:
+
+    .. codeblock:: python
+
+        on_run_start()
+
+        while not done:
+            on_advance_start()
+            advance()
+            on_advance_end()
+
+        on_run_end()
+
+    """
+
+    def __init__(self) -> None:
+        self.iteration_count: int = 0
+        self.trainer: Optional['pl.Trainer'] = None
+
+    @property
+    @abstractmethod
+    def done(self) -> bool:
+        """Property indicating when loop is finished"""
+
+    def connect(self, trainer: 'pl.Trainer', *args: Any, **kwargs: Any) -> None:
+        """Connects Loop with all the necessary things like connectors and accelerators."""
+        self.trainer = proxy(trainer)
+
+    @abstractmethod
+    def reset(self) -> None:
+        """Resets the internal state of the loop at the beginning of each call to :attr:`run`."""
+
+    def run(self, *args: Any, **kwargs: Any) -> Any:
+        """
+        The main entry point to the loop.
+
+        Will frequently check the :attr:`done` condition and calls :attr:`advance`
+        until :attr`done` evaluates to ``True``.
+
+        Returns:
+            the output of :attr`on_run_end` (often outputs collected from each step of the loop)
+        """
+        self.reset()
+        self.on_run_start(*args, **kwargs)
+
+        while not self.done:
+            try:
+                self.on_advance_start(*args, **kwargs)
+                self.advance(*args, **kwargs)
+                self.on_advance_end()
+                self.iteration_count += 1
+            except StopIteration:
+                break
+
+        return self.on_run_end()
+
+    def on_run_start(self, *args: Any, **kwargs: Any) -> None:
+        """
+        Hook to be called as the first thing after entering :attr:`run` (except the state reset).
+
+        Accepts all arguments passed to :attr:`run`.
+
+        """
+        void(*args, **kwargs)
+
+    def on_advance_start(self, *args: Any, **kwargs: Any) -> None:
+        """
+        Hook to be called each time before :attr:`advance` is called. Accepts all arguments passed to :attr`run`.
+
+        """
+        void(*args, **kwargs)
+
+    @abstractmethod
+    def advance(self, *args: Any, **kwargs: Any) -> None:
+        """
+        Performs a single step. Accepts all arguments passed to :attr:`run`.
+
+        """
+
+    def on_advance_end(self) -> None:
+        """Hook to be called each time after :attr:`advance` is called."""
+
+    def on_run_end(self) -> Any:
+        """Hook to be called at the end of the run. Its return argument is returned from :attr:`run`."""