ref: run_pretrain_routine -> setup_training

docs/source/trainer.rst

@@ -9,7 +9,7 @@ Trainer
    :members: fit, test
    :noindex:
    :exclude-members:
-        run_pretrain_routine,
+        setup_training,
         _abc_impl,
         set_random_port,
         _Trainer__set_root_gpu,

pytorch_lightning/accelerators/cpu_backend.py

@@ -40,7 +40,7 @@ def setup(self, model):
 
     def train(self):
         model = self.trainer.model
-        results = self.trainer.run_pretrain_routine(model)
+        results = self.trainer.setup_training(model)
         return results
 
     def training_step(self, args):

pytorch_lightning/accelerators/ddp2_backend.py

@@ -151,7 +151,7 @@ def ddp_train(self, process_idx, mp_queue, model, is_master=False, proc_offset=0
         model = model.configure_ddp(model, device_ids)
 
         # continue training routine
-        results = self.trainer.run_pretrain_routine(model)
+        results = self.trainer.setup_training(model)
 
         # get original model
         model = self.trainer.get_model()

pytorch_lightning/accelerators/ddp_backend.py

@@ -235,7 +235,7 @@ def ddp_train(self, process_idx, mp_queue, model, is_master=False, proc_offset=0
         model = model.configure_ddp(model, device_ids)
 
         # continue training routine
-        results = self.trainer.run_pretrain_routine(model)
+        results = self.trainer.setup_training(model)
 
         # get original model
         model = self.trainer.get_model()

pytorch_lightning/accelerators/ddp_spawn_backend.py

@@ -162,7 +162,7 @@ def ddp_train(self, process_idx, mp_queue, model):
         model = model.configure_ddp(model, device_ids)
 
         # continue training routine
-        results = self.trainer.run_pretrain_routine(model)
+        results = self.trainer.setup_training(model)
 
         # get original model
         model = self.trainer.get_model()

pytorch_lightning/accelerators/dp_backend.py

@@ -96,7 +96,7 @@ def __init_nvidia_apex(self, model):
 
     def train(self):
         model = self.trainer.model
-        results = self.trainer.run_pretrain_routine(model)
+        results = self.trainer.setup_training(model)
         return results
 
     def teardown(self):

pytorch_lightning/accelerators/gpu_backend.py

@@ -51,7 +51,7 @@ def setup(self, model):
 
     def train(self):
         model = self.trainer.model
-        results = self.trainer.run_pretrain_routine(model)
+        results = self.trainer.setup_training(model)
         return results
 
     def training_step(self, args):

pytorch_lightning/accelerators/horovod_backend.py

@@ -105,7 +105,7 @@ def train(self):
                 # Synchronization will be performed explicitly following backward()
                 stack.enter_context(optimizer.skip_synchronize())
 
-            result = self.trainer.run_pretrain_routine(self.trainer.model)
+            result = self.trainer.setup_training(self.trainer.model)
 
         # Make sure all workers have finished training before returning to the user
         hvd.join()

pytorch_lightning/accelerators/tpu_backend.py

@@ -115,7 +115,7 @@ def tpu_train_in_process(self, tpu_core_idx: int, model: LightningModule, traine
         self.__setup_tpu_training(model, trainer)
 
         # Run the pretrain routine
-        results = trainer.run_pretrain_routine(model)
+        results = trainer.setup_training(model)
 
         # save weights at the end of training
         self.__save_end_of_training_weights(model, trainer)

pytorch_lightning/trainer/distrib_data_parallel.py

@@ -204,10 +204,6 @@ def num_gpus(self) -> int:
     def copy_trainer_model_properties(self, *args):
         """Warning: this is just empty shell for code implemented in other class."""
 
-    @abstractmethod
-    def run_pretrain_routine(self, *args):
-        """Warning: this is just empty shell for code implemented in other class."""
-
     @abstractmethod
     def init_optimizers(self, *args) -> Tuple[List, List, List]:
         """Warning: this is just empty shell for code implemented in other class."""

pytorch_lightning/trainer/distrib_parts.py

@@ -86,10 +86,6 @@ class TrainerDPMixin(ABC):
     def call_setup_hook(self, *args):
         """Warning: this is just empty shell for code implemented in other class."""
 
-    @abstractmethod
-    def run_pretrain_routine(self, *args):
-        """Warning: this is just empty shell for code implemented in other class."""
-
     @abstractmethod
     def init_optimizers(self, *args) -> Tuple[List, List, List]:
         """Warning: this is just empty shell for code implemented in other class."""

pytorch_lightning/trainer/trainer.py

@@ -1118,12 +1118,15 @@ def can_prepare_data(self):
         else:
             return self.node_rank == 0 and self.local_rank == 0 and should_call_dm_prepare_data
 
-    def run_pretrain_routine(self, model: LightningModule):
+    def setup_training(self, model: LightningModule):
         """Sanity check a few things before starting actual training.
 
         Args:
             model: The model to run sanity test on.
         """
+        # --------------------------
+        # Setup??
+        # --------------------------
         ref_model = model
         if self.data_parallel:
             ref_model = model.module
@@ -1151,7 +1154,7 @@ def run_pretrain_routine(self, model: LightningModule):
         # wait for all models to restore weights
         if self.on_tpu and XLA_AVAILABLE:
             # wait for all processes to catch up
-            torch_xla.core.xla_model.rendezvous("pl.Trainer.run_pretrain_routine")
+            torch_xla.core.xla_model.rendezvous("pl.Trainer.setup_training")
 
         elif self.use_horovod:
             # wait for all processes to catch up
@@ -1160,6 +1163,9 @@ def run_pretrain_routine(self, model: LightningModule):
         # register auto-resubmit when on SLURM
         self.register_slurm_signal_handlers()
 
+        # --------------------------
+        # Pre-train
+        # --------------------------
         # on pretrain routine start
         self.on_pretrain_routine_start(ref_model)
         if self.is_function_implemented('on_pretrain_routine_start'):
@@ -1179,6 +1185,14 @@ def run_pretrain_routine(self, model: LightningModule):
         # restore training and model before hpc is called
         self.restore_weights(model)
 
+        # on pretrain routine end
+        self.on_pretrain_routine_end(ref_model)
+        if self.is_function_implemented('on_pretrain_routine_end'):
+            ref_model.on_pretrain_routine_end()
+
+        # --------------------------
+        # if test
+        # --------------------------
         # when testing requested only run test and return
         if self.testing:
             # only load test dataloader for testing
@@ -1197,22 +1211,15 @@ def run_pretrain_routine(self, model: LightningModule):
 
             return eval_loop_results
 
+        # --------------------------
+        # sanity
+        # --------------------------
         # run a few val batches before training starts
         self._run_sanity_check(ref_model, model)
 
-        # clear cache before training
-        if self.on_gpu and self.root_gpu is not None:
-            # use context because of:
-            # https://discuss.pytorch.org/t/out-of-memory-when-i-use-torch-cuda-empty-cache/57898
-            with torch.cuda.device(f'cuda:{self.root_gpu}'):
-                torch.cuda.empty_cache()
-
-        # on pretrain routine end
-        self.on_pretrain_routine_end(ref_model)
-        if self.is_function_implemented('on_pretrain_routine_end'):
-            ref_model.on_pretrain_routine_end()
-
-        # CORE TRAINING LOOP
+        # --------------------------
+        # TRAIN
+        # --------------------------
         self.train()
 
     def _run_sanity_check(self, ref_model, model):

pytorch_lightning/trainer/training_loop.py

@@ -213,6 +213,7 @@ class TrainerTrainLoopMixin(ABC):
     max_epochs: int
     min_epochs: int
     on_gpu: bool
+    root_gpu: ...
     use_ddp: bool
     use_dp: bool
     use_ddp2: bool
@@ -330,14 +331,13 @@ def has_arg(self, *args):
         """Warning: this is just empty shell for code implemented in other class."""
 
     def train(self):
-        # add signal handlers for process kills
-        # def _signal_kill_handler(*args):
-        #     return TrainerTrainLoopMixin.run_training_teardown(self)
-        #
-        # orig_signal_handlers = {}
-        # for sig_name in SIGNAL_TERMINATE:
-        #     orig_signal_handlers[sig_name] = signal.signal(getattr(signal, sig_name),
-        #                                                    _signal_kill_handler)
+        # TODO: shrink
+        # clear cache before training
+        if self.on_gpu and self.root_gpu is not None:
+            # use context because of:
+            # https://discuss.pytorch.org/t/out-of-memory-when-i-use-torch-cuda-empty-cache/57898
+            with torch.cuda.device(f'cuda:{self.root_gpu}'):
+                torch.cuda.empty_cache()
 
         # get model
         model = self.get_model()