Flatten fetching abstract interface

src/lightning/pytorch/CHANGELOG.md

@@ -153,6 +153,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
   * Removed the `trainer.{fit,validate,test,predict}_loop` properties ([#16384](https://github.com/Lightning-AI/lightning/pull/16384))
   * Removed the default `Loop.run()` implementation ([#16384](https://github.com/Lightning-AI/lightning/pull/16384))
   * The loop classes are now marked as protected ([#16445](https://github.com/Lightning-AI/lightning/pull/16445))
+  * The fetching classes are now marked as protected ([#16664](https://github.com/Lightning-AI/lightning/pull/16664))
 
 - Removed special support for truncated backpropagation through time (TBPTT) ([#16172](https://github.com/Lightning-AI/lightning/pull/16172))
   * Removed the `LightningModule.truncated_bptt_steps` attribute

src/lightning/pytorch/loops/dataloader/evaluation_loop.py

@@ -15,22 +15,19 @@
 import shutil
 import sys
 from collections import ChainMap, OrderedDict
-from typing import Any, Iterable, List, Optional, Sequence, Tuple, Type, Union
+from typing import Any, Iterable, List, Optional, Sequence, Tuple, Union
 
 from lightning_utilities.core.apply_func import apply_to_collection
 from torch import Tensor
 from torch.utils.data.dataloader import DataLoader
 
-import lightning.pytorch as pl
 from lightning.pytorch.callbacks.progress.rich_progress import _RICH_AVAILABLE
 from lightning.pytorch.loops.dataloader import _DataLoaderLoop
 from lightning.pytorch.loops.epoch import _EvaluationEpochLoop
-from lightning.pytorch.loops.utilities import _set_sampler_epoch
+from lightning.pytorch.loops.utilities import _select_data_fetcher, _set_sampler_epoch
 from lightning.pytorch.trainer.connectors.logger_connector.result import _OUT_DICT, _ResultCollection
 from lightning.pytorch.trainer.states import TrainerFn
-from lightning.pytorch.utilities.fetching import AbstractDataFetcher, DataFetcher, DataLoaderIterDataFetcher
-from lightning.pytorch.utilities.rank_zero import rank_zero_warn
-from lightning.pytorch.utilities.signature_utils import is_param_in_hook_signature
+from lightning.pytorch.utilities.fetching import _DataFetcher
 
 if _RICH_AVAILABLE:
     from rich import get_console
@@ -53,7 +50,7 @@ def __init__(self, verbose: bool = True) -> None:
         self._logged_outputs: List[_OUT_DICT] = []
         self._max_batches: List[Union[int, float]] = []
         self._has_run: bool = False
-        self._data_fetcher: Optional[AbstractDataFetcher] = None
+        self._data_fetcher: Optional[_DataFetcher] = None
 
     @property
     def num_dataloaders(self) -> int:
@@ -125,8 +122,7 @@ def reset(self) -> None:
     def on_run_start(self) -> None:
         """Runs the ``_on_evaluation_model_eval``, ``_on_evaluation_start`` and ``_on_evaluation_epoch_start``
         hooks."""
-        data_fetcher_cls = _select_data_fetcher_type(self.trainer)
-        self._data_fetcher = data_fetcher_cls(prefetch_batches=self.prefetch_batches)
+        self._data_fetcher = _select_data_fetcher(self.trainer, prefetch_batches=self.prefetch_batches)
 
         # hook
         self._on_evaluation_model_eval()
@@ -373,16 +369,3 @@ def _print_results(results: List[_OUT_DICT], stage: str) -> None:
                         lines.append(row_format.format(metric, *row).rstrip())
                 lines.append(bar)
                 print(os.linesep.join(lines))
-
-
-def _select_data_fetcher_type(trainer: "pl.Trainer") -> Type[AbstractDataFetcher]:
-    lightning_module = trainer.lightning_module
-    step_fx_name = "test_step" if trainer.testing else "validation_step"
-    step_fx = getattr(lightning_module, step_fx_name)
-    if is_param_in_hook_signature(step_fx, "dataloader_iter", explicit=True):
-        rank_zero_warn(
-            f"Found `dataloader_iter` argument in the `{step_fx_name}`. Note that the support for "
-            "this signature is experimental and the behavior is subject to change."
-        )
-        return DataLoaderIterDataFetcher
-    return DataFetcher

src/lightning/pytorch/loops/epoch/evaluation_epoch_loop.py

@@ -19,7 +19,7 @@
 from lightning.pytorch.loops.progress import BatchProgress
 from lightning.pytorch.trainer.states import TrainerFn
 from lightning.pytorch.utilities.exceptions import SIGTERMException
-from lightning.pytorch.utilities.fetching import AbstractDataFetcher, DataLoaderIterDataFetcher
+from lightning.pytorch.utilities.fetching import _DataFetcher, _DataLoaderIterDataFetcher
 from lightning.pytorch.utilities.types import STEP_OUTPUT
 
 
@@ -35,15 +35,15 @@ def __init__(self) -> None:
         self.batch_progress = BatchProgress()
 
         self._dl_max_batches: Union[int, float] = 0
-        self._data_fetcher: Optional[AbstractDataFetcher] = None
+        self._data_fetcher: Optional[_DataFetcher] = None
         self._dl_batch_idx = [0]
 
     @property
     def done(self) -> bool:
         """Returns ``True`` if the current iteration count reaches the number of dataloader batches."""
         return self.batch_progress.current.completed >= self._dl_max_batches
 
-    def run(self, data_fetcher: AbstractDataFetcher, dl_max_batches: Union[int, float], kwargs: OrderedDict) -> None:
+    def run(self, data_fetcher: _DataFetcher, dl_max_batches: Union[int, float], kwargs: OrderedDict) -> None:
         self.reset()
         self.on_run_start(data_fetcher, dl_max_batches, kwargs)
         while not self.done:
@@ -69,9 +69,7 @@ def reset(self) -> None:
         if self.done and self.trainer.state.fn != TrainerFn.FITTING:
             self.batch_progress.reset_on_run()
 
-    def on_run_start(
-        self, data_fetcher: AbstractDataFetcher, dl_max_batches: Union[int, float], kwargs: OrderedDict
-    ) -> None:
+    def on_run_start(self, data_fetcher: _DataFetcher, dl_max_batches: Union[int, float], kwargs: OrderedDict) -> None:
         """Adds the passed arguments to the loop's state if necessary.
 
         Args:
@@ -102,7 +100,7 @@ def _on_after_fetch(self) -> None:
 
     def advance(
         self,
-        data_fetcher: AbstractDataFetcher,
+        data_fetcher: _DataFetcher,
         kwargs: OrderedDict,
     ) -> None:
         """Calls the evaluation step with the corresponding hooks and updates the logger connector.
@@ -114,7 +112,7 @@ def advance(
         Raises:
             StopIteration: If the current batch is None
         """
-        if not isinstance(data_fetcher, DataLoaderIterDataFetcher):
+        if not isinstance(data_fetcher, _DataLoaderIterDataFetcher):
             batch_idx = self.batch_progress.current.ready
             batch = next(data_fetcher)
         else:

src/lightning/pytorch/loops/epoch/training_epoch_loop.py

@@ -25,7 +25,7 @@
 from lightning.pytorch.loops.utilities import _is_max_limit_reached
 from lightning.pytorch.trainer.connectors.logger_connector.result import _ResultCollection
 from lightning.pytorch.utilities.exceptions import MisconfigurationException, SIGTERMException
-from lightning.pytorch.utilities.fetching import AbstractDataFetcher, DataLoaderIterDataFetcher
+from lightning.pytorch.utilities.fetching import _DataFetcher, _DataLoaderIterDataFetcher
 from lightning.pytorch.utilities.rank_zero import rank_zero_warn, WarningCache
 from lightning.pytorch.utilities.signature_utils import is_param_in_hook_signature
 
@@ -125,7 +125,7 @@ def done(self) -> bool:
 
         return False
 
-    def run(self, data_fetcher: AbstractDataFetcher) -> None:
+    def run(self, data_fetcher: _DataFetcher) -> None:
         self.reset()
         self.on_run_start(data_fetcher)
         while not self.done:
@@ -160,7 +160,7 @@ def reset(self) -> None:
             # seen per epoch, this is useful for tracking when validation is run multiple times per epoch
             self.val_loop.epoch_loop.batch_progress.total.reset()
 
-    def on_run_start(self, data_fetcher: AbstractDataFetcher) -> None:
+    def on_run_start(self, data_fetcher: _DataFetcher) -> None:
         _ = iter(data_fetcher)  # creates the iterator inside the fetcher
         # add the previous `fetched` value to properly track `is_last_batch` with no prefetching
         data_fetcher.fetched += self.batch_progress.current.ready
@@ -174,7 +174,7 @@ def _on_before_fetch(self) -> None:
     def _on_after_fetch(self) -> None:
         self.trainer.profiler.stop(f"[{self.__class__.__name__}].train_dataloader_next")
 
-    def advance(self, data_fetcher: AbstractDataFetcher) -> None:
+    def advance(self, data_fetcher: _DataFetcher) -> None:
         """Runs a single training batch.
 
         Raises:
@@ -186,7 +186,7 @@ def advance(self, data_fetcher: AbstractDataFetcher) -> None:
         # we are going to train first so the val loop does not need to restart
         self.val_loop.restarting = False
 
-        if not isinstance(data_fetcher, DataLoaderIterDataFetcher):
+        if not isinstance(data_fetcher, _DataLoaderIterDataFetcher):
             batch_idx = self.batch_idx + 1
             batch = next(data_fetcher)
         else:

src/lightning/pytorch/loops/fit_loop.py

@@ -12,19 +12,17 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import logging
-from typing import Any, Optional, Type
+from typing import Any, Optional
 
-import lightning.pytorch as pl
 from lightning.pytorch.loops import _Loop
 from lightning.pytorch.loops.epoch import _TrainingEpochLoop
 from lightning.pytorch.loops.progress import Progress
-from lightning.pytorch.loops.utilities import _is_max_limit_reached, _set_sampler_epoch
+from lightning.pytorch.loops.utilities import _is_max_limit_reached, _select_data_fetcher, _set_sampler_epoch
 from lightning.pytorch.trainer.connectors.logger_connector.result import _ResultCollection
 from lightning.pytorch.trainer.supporters import CombinedLoader
 from lightning.pytorch.utilities.exceptions import MisconfigurationException, SIGTERMException
-from lightning.pytorch.utilities.fetching import AbstractDataFetcher, DataFetcher, DataLoaderIterDataFetcher
-from lightning.pytorch.utilities.rank_zero import rank_zero_debug, rank_zero_info, rank_zero_warn
-from lightning.pytorch.utilities.signature_utils import is_param_in_hook_signature
+from lightning.pytorch.utilities.fetching import _DataFetcher
+from lightning.pytorch.utilities.rank_zero import rank_zero_debug, rank_zero_info
 
 log = logging.getLogger(__name__)
 
@@ -74,7 +72,7 @@ def __init__(
         self.epoch_progress = Progress()
 
         self._is_fresh_start_epoch: bool = True
-        self._data_fetcher: Optional[AbstractDataFetcher] = None
+        self._data_fetcher: Optional[_DataFetcher] = None
 
     @property
     def total_batch_idx(self) -> int:
@@ -217,8 +215,7 @@ def on_run_start(self) -> None:
         if self.epoch_loop._should_check_val_epoch():
             self.epoch_loop.val_loop._reload_evaluation_dataloaders()
 
-        data_fetcher_cls = _select_data_fetcher(self.trainer)
-        self._data_fetcher = data_fetcher_cls(prefetch_batches=self.prefetch_batches)
+        self._data_fetcher = _select_data_fetcher(self.trainer, self.prefetch_batches)
 
         self._is_fresh_start_epoch = True
         self._results.to(device=self.trainer.lightning_module.device)
@@ -324,14 +321,3 @@ def _should_accumulate(self) -> bool:
 
     def _iteration_based_training(self) -> bool:
         return self.trainer.max_steps != -1
-
-
-def _select_data_fetcher(trainer: "pl.Trainer") -> Type[AbstractDataFetcher]:
-    training_step_fx = getattr(trainer.lightning_module, "training_step")
-    if is_param_in_hook_signature(training_step_fx, "dataloader_iter", explicit=True):
-        rank_zero_warn(
-            "Found `dataloader_iter` argument in the `training_step`. Note that the support for "
-            "this signature is experimental and the behavior is subject to change."
-        )
-        return DataLoaderIterDataFetcher
-    return DataFetcher

src/lightning/pytorch/loops/utilities.py

@@ -24,7 +24,9 @@
 from lightning.pytorch.loops.progress import BaseProgress
 from lightning.pytorch.strategies.parallel import ParallelStrategy
 from lightning.pytorch.strategies.strategy import Strategy
+from lightning.pytorch.utilities.fetching import _DataFetcher, _DataLoaderIterDataFetcher, _PrefetchDataFetcher
 from lightning.pytorch.utilities.rank_zero import rank_zero_warn
+from lightning.pytorch.utilities.signature_utils import is_param_in_hook_signature
 
 
 def check_finite_loss(loss: Optional[Tensor]) -> None:
@@ -130,3 +132,23 @@ def _set_sampler_epoch(dataloader: Iterable, epoch: int) -> None:
         sampler = getattr(dataloader, sampler_name, None)
         if sampler is not None and callable(getattr(sampler, "set_epoch", None)):
             sampler.set_epoch(epoch)
+
+
+def _select_data_fetcher(trainer: "pl.Trainer", prefetch_batches: int = 0) -> _DataFetcher:
+    lightning_module = trainer.lightning_module
+    if trainer.testing:
+        step_fx_name = "test_step"
+    elif trainer.training:
+        step_fx_name = "training_step"
+    elif trainer.validating or trainer.sanity_checking:
+        step_fx_name = "validation_step"
+    else:
+        raise RuntimeError(f"DataFetcher is unsupported for {trainer.state.stage}")
+    step_fx = getattr(lightning_module, step_fx_name)
+    if is_param_in_hook_signature(step_fx, "dataloader_iter", explicit=True):
+        rank_zero_warn(
+            f"Found `dataloader_iter` argument in the `{step_fx_name}`. Note that the support for "
+            "this signature is experimental and the behavior is subject to change."
+        )
+        return _DataLoaderIterDataFetcher()
+    return _PrefetchDataFetcher(prefetch_batches=prefetch_batches)