Rename ttp -> strategy (#11312)

pytorch_lightning/loops/epoch/training_epoch_loop.py

@@ -368,9 +368,9 @@ class TrainingEpochLoop(loops.Loop[_OUTPUTS_TYPE]):
         accumulation_done = self._accumulated_batches_reached()
         # Lightning steps on the final batch
         is_final_batch = self._num_ready_batches_reached()
-        # but the TTP might not
-        ttp_accumulates_on_final_batch = self.trainer.strategy.handles_gradient_accumulation or not is_final_batch
-        return not accumulation_done and ttp_accumulates_on_final_batch
+        # but the strategy might not
+        strategy_accumulates_on_final_batch = self.trainer.strategy.handles_gradient_accumulation or not is_final_batch
+        return not accumulation_done and strategy_accumulates_on_final_batch
 
     @staticmethod
     def _prepare_outputs_training_batch_end(

pytorch_lightning/trainer/trainer.py

@@ -1504,11 +1504,11 @@ class Trainer(
                 # todo: move this data parallel logic into the data parallel strategy
                 output = accelerator_output if output is None else output
 
-            # call the ttp hook
+            # call the strategy hook
             if hook_name not in ("setup", "teardown", "on_train_start") and hasattr(self.strategy, hook_name):
-                ttp_hook = getattr(self.strategy, hook_name)
-                ttp_output = ttp_hook(*args, **kwargs)
-                output = ttp_output if output is None else output
+                strategy_hook = getattr(self.strategy, hook_name)
+                strategy_output = strategy_hook(*args, **kwargs)
+                output = strategy_output if output is None else output
 
         if pl_module:
             # restore current_fx when nested context
@@ -1791,8 +1791,10 @@ class Trainer(
         rank_zero_deprecation(
             "`Trainer.should_rank_save_checkpoint` is deprecated in v1.6 and will be removed in v1.8.", stacklevel=5
         )
-        ttp = self.strategy
-        return isinstance(ttp, pl.strategies.TPUSpawnStrategy) and ttp.local_rank == 0 or ttp.is_global_zero
+        strategy = self.strategy
+        return (
+            isinstance(strategy, pl.strategies.TPUSpawnStrategy) and strategy.local_rank == 0 or strategy.is_global_zero
+        )
 
     @property
     def _distrib_type(self) -> _StrategyType:

tests/accelerators/test_accelerator_connector.py

@@ -341,25 +341,25 @@ def test_custom_accelerator(device_count_mock, setup_distributed_mock):
     class Prec(PrecisionPlugin):
         pass
 
-    class TrainTypePlugin(SingleDeviceStrategy):
+    class Strat(SingleDeviceStrategy):
         pass
 
-    ttp = TrainTypePlugin(device=torch.device("cpu"), accelerator=Accel(), precision_plugin=Prec())
-    trainer = Trainer(strategy=ttp, fast_dev_run=True, num_processes=2)
+    strategy = Strat(device=torch.device("cpu"), accelerator=Accel(), precision_plugin=Prec())
+    trainer = Trainer(strategy=strategy, fast_dev_run=True, num_processes=2)
     assert isinstance(trainer.accelerator, Accel)
-    assert isinstance(trainer.strategy, TrainTypePlugin)
+    assert isinstance(trainer.strategy, Strat)
     assert isinstance(trainer.precision_plugin, Prec)
-    assert trainer._accelerator_connector.strategy is ttp
+    assert trainer._accelerator_connector.strategy is strategy
 
-    class DistributedPlugin(DDPStrategy):
+    class Strat(DDPStrategy):
         pass
 
-    ttp = DistributedPlugin(accelerator=Accel(), precision_plugin=Prec())
-    trainer = Trainer(strategy=ttp, fast_dev_run=True, num_processes=2)
+    strategy = Strat(accelerator=Accel(), precision_plugin=Prec())
+    trainer = Trainer(strategy=strategy, fast_dev_run=True, num_processes=2)
     assert isinstance(trainer.accelerator, Accel)
-    assert isinstance(trainer.strategy, DistributedPlugin)
+    assert isinstance(trainer.strategy, Strat)
     assert isinstance(trainer.precision_plugin, Prec)
-    assert trainer._accelerator_connector.strategy is ttp
+    assert trainer._accelerator_connector.strategy is strategy
 
 
 @mock.patch.dict(