Remove the redundant precision attribute from LightningModule (#16203)

Author: awaelchli

src/pytorch_lightning/CHANGELOG.md

@@ -64,6 +64,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
   * Removed the `FitLoop.split_idx` property
   * Removed the `LoggerConnector.on_train_split_start` method
 
+- Removed the `LightningModule.precision` attribute ([#16203](https://github.com/Lightning-AI/lightning/pull/16203))
+
 
 ### Fixed
 

src/pytorch_lightning/core/module.py

@@ -108,9 +108,6 @@ def __init__(self, *args: Any, **kwargs: Any) -> None:
         # pointer to the trainer object
         self._trainer: Optional["pl.Trainer"] = None
 
-        # the precision used
-        self.precision: Union[int, str] = 32
-
         # optionally can be set by user
         self._example_input_array: Optional[Union[Tensor, Tuple, Dict]] = None
         self._current_fx_name: Optional[str] = None

src/pytorch_lightning/trainer/connectors/data_connector.py

@@ -144,13 +144,8 @@ def attach_data(
         elif self.trainer.state.fn == TrainerFn.PREDICTING:
             _check_dataloader_none(predict_dataloaders, self._predict_dataloader_source, self.trainer.state.fn)
 
-        # set local properties on the model
-        self._copy_trainer_model_properties(model)
-
-    def _copy_trainer_model_properties(self, model: "pl.LightningModule") -> None:
+        # Attach the trainer to the LightningModule
         model.trainer = proxy(self.trainer)
-        # for backward compatibility
-        model.precision = int(self.trainer.precision) if self.trainer.precision != "bf16" else "bf16"
 
     def attach_dataloaders(
         self,

src/pytorch_lightning/utilities/model_summary/model_summary.py

@@ -189,7 +189,8 @@ def __init__(self, model: "pl.LightningModule", max_depth: int = 1) -> None:
         self._layer_summary = self.summarize()
         # 1 byte -> 8 bits
         # TODO: how do we compute precision_megabytes in case of mixed precision?
-        precision = self._model.precision if isinstance(self._model.precision, int) else 32
+        precision_to_bits = {"64": 64, "32": 32, "16": 16, "bf16": 16}
+        precision = precision_to_bits.get(self._model.trainer.precision, 32) if self._model._trainer else 32
         self._precision_megabytes = (precision / 8.0) * 1e-6
 
     @property

tests/tests_pytorch/accelerators/test_ipu.py

@@ -189,7 +189,7 @@ def test_optimization(tmpdir):
 def test_half_precision(tmpdir):
     class TestCallback(Callback):
         def setup(self, trainer: Trainer, pl_module: LightningModule, stage: str) -> None:
-            assert trainer.strategy.model.precision == 16
+            assert trainer.precision == "16"
             raise SystemExit
 
     model = IPUModel()

tests/tests_pytorch/plugins/precision/hpu/test_hpu.py

@@ -42,7 +42,7 @@ def test_precision_plugin(hmp_params):
 def test_mixed_precision(tmpdir, hmp_params: dict):
     class TestCallback(Callback):
         def setup(self, trainer: Trainer, pl_module: LightningModule, stage: str) -> None:
-            assert trainer.strategy.model.precision == "bf16"
+            assert trainer.precision == "bf16"
             raise SystemExit
 
     model = BoringModel()
@@ -65,7 +65,7 @@ def setup(self, trainer: Trainer, pl_module: LightningModule, stage: str) -> Non
 def test_pure_half_precision(tmpdir, hmp_params: dict):
     class TestCallback(Callback):
         def on_train_start(self, trainer: Trainer, pl_module: LightningModule) -> None:
-            assert trainer.strategy.model.precision == "16"
+            assert trainer.precision == "16"
             for param in trainer.strategy.model.parameters():
                 assert param.dtype == torch.float16
             raise SystemExit

tests/tests_pytorch/strategies/test_ddp_fully_sharded_native.py

@@ -64,7 +64,7 @@ def on_predict_batch_end(self, outputs, batch, batch_idx, dataloader_idx) -> Non
     def _assert_layer_fsdp_instance(self) -> None:
         assert isinstance(self.layer, FullyShardedDataParallel)
         assert isinstance(self.trainer.strategy.precision_plugin, FullyShardedNativeNativeMixedPrecisionPlugin)
-        precision = torch.float16 if self.precision == 16 else torch.bfloat16
+        precision = torch.float16 if self.trainer.precision == "16" else torch.bfloat16
         assert self.layer.mixed_precision.param_dtype == precision
         assert self.layer.mixed_precision.reduce_dtype == precision
         assert self.layer.mixed_precision.buffer_dtype == precision
@@ -100,7 +100,7 @@ def _assert_layer_fsdp_instance(self) -> None:
         assert isinstance(self.layer, torch.nn.Sequential)
         assert isinstance(self.trainer.strategy.precision_plugin, FullyShardedNativeNativeMixedPrecisionPlugin)
 
-        precision = torch.float16 if self.precision == 16 else torch.bfloat16
+        precision = torch.float16 if self.trainer.precision == "16" else torch.bfloat16
         for layer_num in [0, 2]:
             assert isinstance(self.layer[layer_num], FullyShardedDataParallel)
             assert self.layer[layer_num].mixed_precision.param_dtype == precision