Merge branch 'master' into chore/Lightning-AI#8728

.github/workflows/ci_test-base.yml

@@ -71,7 +71,7 @@ jobs:
 
     - name: Test Package [only]
       run: |
-        # NOTE: run coverage on tests does not propagare faler status for Win, https://github.com/nedbat/coveragepy/issues/1003
+        # NOTE: run coverage on tests does not propagate failure status for Win, https://github.com/nedbat/coveragepy/issues/1003
         coverage run --source pytorch_lightning -m pytest pytorch_lightning -v --junitxml=junit/test-results-${{ runner.os }}-${{ matrix.python-version }}-${{ matrix.requires }}.xml
 
     - name: Upload pytest test results

.github/workflows/ci_test-conda.yml

@@ -43,7 +43,7 @@ jobs:
 
     - name: Tests
       run: |
-        # NOTE: run coverage on tests does not propagare faler status for Win, https://github.com/nedbat/coveragepy/issues/1003
+        # NOTE: run coverage on tests does not propagate failure status for Win, https://github.com/nedbat/coveragepy/issues/1003
         coverage run --source pytorch_lightning -m pytest pytorch_lightning tests -v --durations=50 --junitxml=junit/test-results-${{ runner.os }}-torch${{ matrix.pytorch-version }}.xml
       shell: bash -l {0}
 

CHANGELOG.md

@@ -118,6 +118,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Removed the deprecated `sync_step` argument from `WandbLogger` ([#8763](https://github.com/PyTorchLightning/pytorch-lightning/pull/8763))
 
 
+- Removed the deprecated `Trainer.truncated_bptt_steps` in favor of `LightningModule.truncated_bptt_steps` ([#8826](https://github.com/PyTorchLightning/pytorch-lightning/pull/8826))
+
+
 ### Fixed
 
 - Fixed `trainer.fit_loop.split_idx` always returning `None` ([#8601](https://github.com/PyTorchLightning/pytorch-lightning/pull/8601))

docs/source/common/trainer.rst

@@ -1482,66 +1482,6 @@ Example::
     --env=XLA_USE_BF16=1
     -- python your_trainer_file.py
 
-truncated_bptt_steps
-^^^^^^^^^^^^^^^^^^^^
-
-.. raw:: html
-
-    <video width="50%" max-width="400px" controls
-    poster="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/trainer_flags/thumb/truncated_bptt_steps.jpg"
-    src="https://pl-bolts-doc-images.s3.us-east-2.amazonaws.com/pl_docs/trainer_flags/truncated_bptt_steps.mp4"></video>
-
-|
-
-Truncated back prop breaks performs backprop every k steps of
-a much longer sequence.
-
-If this is enabled, your batches will automatically get truncated
-and the trainer will apply Truncated Backprop to it.
-
-(`Williams et al. "An efficient gradient-based algorithm for on-line training of
-recurrent network trajectories."
-<http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.56.7941&rep=rep1&type=pdf>`_)
-
-.. testcode::
-
-    # default used by the Trainer (ie: disabled)
-    trainer = Trainer(truncated_bptt_steps=None)
-
-    # backprop every 5 steps in a batch
-    trainer = Trainer(truncated_bptt_steps=5)
-
-.. note::  Make sure your batches have a sequence dimension.
-
-Lightning takes care to split your batch along the time-dimension.
-
-.. code-block:: python
-
-    # we use the second as the time dimension
-    # (batch, time, ...)
-    sub_batch = batch[0, 0:t, ...]
-
-Using this feature requires updating your LightningModule's
-:meth:`pytorch_lightning.core.LightningModule.training_step` to include a `hiddens` arg
-with the hidden
-
-.. code-block:: python
-
-        # Truncated back-propagation through time
-        def training_step(self, batch, batch_idx, hiddens):
-            # hiddens are the hiddens from the previous truncated backprop step
-            out, hiddens = self.lstm(data, hiddens)
-            return {"loss": ..., "hiddens": hiddens}
-
-To modify how the batch is split,
-override :meth:`pytorch_lightning.core.LightningModule.tbptt_split_batch`:
-
-.. testcode::
-
-    class LitMNIST(LightningModule):
-        def tbptt_split_batch(self, batch, split_size):
-            # do your own splitting on the batch
-            return splits
 
 val_check_interval
 ^^^^^^^^^^^^^^^^^^

docs/source/starter/new-project.rst

@@ -728,7 +728,7 @@ Other cool features
 Once you define and train your first Lightning model, you might want to try other cool features like
 
 - :doc:`Automatic early stopping <../common/early_stopping>`
-- :ref:`Automatic truncated-back-propagation-through-time <common/trainer:truncated_bptt_steps>`
+- :ref:`Automatic truncated-back-propagation-through-time <common/lightning_module:truncated_bptt_steps>`
 - :ref:`Automatically scale your batch size <advanced/training_tricks:Auto scaling of batch size>`
 - :doc:`Automatically find a good learning rate <../advanced/lr_finder>`
 - :ref:`Load checkpoints directly from S3 <common/weights_loading:Checkpoint Loading>`

pytorch_lightning/core/lightning.py

@@ -1804,9 +1804,7 @@ def get_progress_bar_dict(self):
         if avg_training_loss is not None:
             tqdm_dict["loss"] = f"{avg_training_loss:.3g}"
 
-        module_tbptt_enabled = self.truncated_bptt_steps > 0
-        trainer_tbptt_enabled = self.trainer.truncated_bptt_steps is not None and self.trainer.truncated_bptt_steps > 0
-        if module_tbptt_enabled or trainer_tbptt_enabled:
+        if self.truncated_bptt_steps > 0:
             tqdm_dict["split_idx"] = self.trainer.fit_loop.split_idx
 
         if self.trainer.logger is not None and self.trainer.logger.version is not None:

pytorch_lightning/loops/batch/training_batch_loop.py

@@ -451,7 +451,7 @@ def _tbptt_split_batch(self, batch: Any) -> List[Any]:
         Args:
             batch: the current batch to split
         """
-        tbptt_steps = self._truncated_bptt_steps()
+        tbptt_steps = self.trainer.lightning_module.truncated_bptt_steps
         if tbptt_steps == 0:
             return [batch]
 
@@ -643,19 +643,7 @@ def _build_kwargs(self, batch: Any, batch_idx: int, opt_idx: int, hiddens: Optio
                 )
 
         # pass hiddens if using tbptt
-        if self._truncated_bptt_enabled():
+        if self.trainer.lightning_module.truncated_bptt_steps > 0:
             step_kwargs["hiddens"] = hiddens
 
         return step_kwargs
-
-    def _truncated_bptt_enabled(self) -> bool:
-        """Temporary tbptt utilities until this flag is fully migrated to the lightning module."""
-        return self._truncated_bptt_steps() > 0
-
-    def _truncated_bptt_steps(self) -> int:
-        """Returns the number of tbptt steps"""
-        lightning_module = self.trainer.lightning_module
-        # Give precedence to the LightningModule as the Trainer flag will be removed in v1.5
-        if lightning_module.truncated_bptt_steps > 0:
-            return lightning_module.truncated_bptt_steps
-        return self.trainer.truncated_bptt_steps or 0

pytorch_lightning/trainer/connectors/training_trick_connector.py

@@ -11,10 +11,10 @@
 # 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 Dict, List, Optional, Union
+from typing import Dict, List, Union
 
 from pytorch_lightning.callbacks import GradientAccumulationScheduler
-from pytorch_lightning.utilities import GradClipAlgorithmType, rank_zero_deprecation
+from pytorch_lightning.utilities import GradClipAlgorithmType
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 
 
@@ -28,7 +28,6 @@ def on_trainer_init(
         gradient_clip_algorithm: str,
         track_grad_norm: Union[int, float, str],
         accumulate_grad_batches: Union[int, Dict[int, int], List[list]],
-        truncated_bptt_steps: Optional[int],
         terminate_on_nan: bool,
     ):
 
@@ -49,13 +48,6 @@ def on_trainer_init(
         self.trainer.accumulate_grad_batches = accumulate_grad_batches
         self.configure_accumulated_gradients(accumulate_grad_batches)
 
-        if truncated_bptt_steps is not None and truncated_bptt_steps > 0:
-            rank_zero_deprecation(
-                "Trainer.truncated_bptt_steps is deprecated in v1.3 and will be removed in v1.5."
-                " Set truncated_bptt_steps directly on the LightningModule instead."
-            )
-        self.trainer.truncated_bptt_steps = truncated_bptt_steps
-
     def configure_accumulated_gradients(self, accumulate_grad_batches):
         if isinstance(accumulate_grad_batches, dict):
             self.trainer.accumulation_scheduler = GradientAccumulationScheduler(accumulate_grad_batches)

pytorch_lightning/trainer/trainer.py

@@ -140,7 +140,6 @@ def __init__(
         weights_summary: Optional[str] = "top",
         weights_save_path: Optional[str] = None,
         num_sanity_val_steps: int = 2,
-        truncated_bptt_steps: Optional[int] = None,
         resume_from_checkpoint: Optional[Union[Path, str]] = None,
         profiler: Optional[Union[BaseProfiler, str]] = None,
         benchmark: bool = False,
@@ -309,9 +308,6 @@ def __init__(
 
             track_grad_norm: -1 no tracking. Otherwise tracks that p-norm. May be set to 'inf' infinity-norm.
 
-            truncated_bptt_steps: Deprecated in v1.3 to be removed in 1.5.
-                Please use :paramref:`~pytorch_lightning.core.lightning.LightningModule.truncated_bptt_steps` instead.
-
             val_check_interval: How often to check the validation set. Use float to check within a training epoch,
                 use int to check every n steps (batches).
 
@@ -438,7 +434,6 @@ def __init__(
             gradient_clip_algorithm,
             track_grad_norm,
             accumulate_grad_batches,
-            truncated_bptt_steps,
             terminate_on_nan,
         )
         self._setup_on_init(num_sanity_val_steps)

tests/deprecated_api/test_remove_1-5.py

@@ -205,11 +205,6 @@ def test_v1_5_0_datamodule_setter():
     assert any("The `LightningModule.datamodule`" in w for w in warning_cache)
 
 
-def test_v1_5_0_trainer_tbptt_steps(tmpdir):
-    with pytest.deprecated_call(match="is deprecated in v1.3 and will be removed in v1.5"):
-        _ = Trainer(truncated_bptt_steps=1)
-
-
 @RunIf(deepspeed=True)
 @pytest.mark.parametrize(
     "params", [dict(cpu_offload=True), dict(cpu_offload_params=True), dict(cpu_offload_use_pin_memory=True)]

tests/models/test_truncated_bptt.py

@@ -20,8 +20,7 @@
 
 
 @pytest.mark.parametrize("n_hidden_states", (1, 2))
-@pytest.mark.parametrize("property_on_module", (False, True))
-def test_tbptt_cpu_model(tmpdir, n_hidden_states, property_on_module):
+def test_tbptt_cpu_model(tmpdir, n_hidden_states):
     """Test truncated back propagation through time works."""
     truncated_bptt_steps = 2
     sequence_size = 30
@@ -44,8 +43,7 @@ def __init__(self, batch_size, in_features, out_features, n_hidden_states, *args
             self.batch_size = batch_size
             self.layer = torch.nn.Linear(in_features, out_features)
             self.n_hidden_states = n_hidden_states
-            if property_on_module:
-                self.truncated_bptt_steps = truncated_bptt_steps
+            self.truncated_bptt_steps = truncated_bptt_steps
 
         def training_step(self, batch, batch_idx, hiddens):
             assert hiddens == self.test_hidden, "Hidden state not persistent between tbptt steps"
@@ -83,13 +81,10 @@ def train_dataloader(self):
     )
     model.example_input_array = torch.randn(5, truncated_bptt_steps)
 
-    trainer_tbptt_steps = None if property_on_module else truncated_bptt_steps
-
     # fit model
     trainer = Trainer(
         default_root_dir=tmpdir,
         max_epochs=1,
-        truncated_bptt_steps=trainer_tbptt_steps,
         limit_val_batches=0,
         weights_summary=None,
     )

tests/trainer/test_trainer_cli.py

@@ -141,7 +141,6 @@ def _raise():
                 "log_gpu_memory": None,
                 "accelerator": None,
                 "weights_save_path": None,
-                "truncated_bptt_steps": None,
                 "resume_from_checkpoint": None,
                 "profiler": None,
             },

tests/utilities/test_cli.py

@@ -126,7 +126,6 @@ def _raise():
                 log_gpu_memory=None,
                 distributed_backend=None,
                 weights_save_path=None,
-                truncated_bptt_steps=None,
                 resume_from_checkpoint=None,
                 profiler=None,
             ),