Trainer cleanup

pytorch_lightning/trainer/auto_mix_precision.py

@@ -12,8 +12,9 @@
 
 class TrainerAMPMixin(ABC):
 
-    def __init__(self):
-        self.use_amp = None
+    # this is just a summary on variables used in this abstract class,
+    #  the proper values/initialisation should be done in child class
+    use_amp: bool
 
     def init_amp(self, use_amp):
         self.use_amp = use_amp and APEX_AVAILABLE

pytorch_lightning/trainer/callback_config.py

@@ -1,17 +1,26 @@
 import os
-from abc import ABC
+from abc import ABC, abstractmethod
+from typing import Union
 
 from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
+from pytorch_lightning.loggers import LightningLoggerBase
 
 
 class TrainerCallbackConfigMixin(ABC):
 
-    def __init__(self):
-        # this is just a summary on variables used in this abstract class,
-        #  the proper values/initialisation should be done in child class
-        self.default_save_path = None
-        self.save_checkpoint = None
-        self.slurm_job_id = None
+    # this is just a summary on variables used in this abstract class,
+    #  the proper values/initialisation should be done in child class
+    default_save_path: str
+    logger: Union[LightningLoggerBase, bool]
+
+    @property
+    @abstractmethod
+    def slurm_job_id(self) -> int:
+        """Warning: this is just empty shell for code implemented in other class."""
+
+    @abstractmethod
+    def save_checkpoint(self, *args):
+        """Warning: this is just empty shell for code implemented in other class."""
 
     def configure_checkpoint_callback(self):
         """

pytorch_lightning/trainer/data_loading.py

@@ -1,4 +1,4 @@
-from abc import ABC
+from abc import ABC, abstractmethod
 
 import torch.distributed as dist
 from torch.utils.data import SequentialSampler, DataLoader
@@ -8,40 +8,43 @@
 
 try:
     from apex import amp
-
-    APEX_AVAILABLE = True
 except ImportError:
     APEX_AVAILABLE = False
+else:
+    APEX_AVAILABLE = True
 
 try:
     import torch_xla
     import torch_xla.core.xla_model as xm
     import torch_xla.distributed.xla_multiprocessing as xmp
-
-    XLA_AVAILABLE = True
 except ImportError:
     XLA_AVAILABLE = False
+else:
+    XLA_AVAILABLE = True
 
 
 class TrainerDataLoadingMixin(ABC):
 
-    def __init__(self):
-        # this is just a summary on variables used in this abstract class,
-        #  the proper values/initialisation should be done in child class
-        self.proc_rank = None
-        self.use_ddp = None
-        self.use_ddp2 = None
-        self.shown_warnings = None
-        self.val_check_interval = None
-        self.use_tpu = None
-        self.tpu_local_core_rank = None
-        self.train_dataloader = None
-        self.num_training_batches = None
-        self.val_check_batch = None
-        self.val_dataloaders = None
-        self.num_val_batches = None
-        self.test_dataloaders = None
-        self.num_test_batches = None
+    # this is just a summary on variables used in this abstract class,
+    #  the proper values/initialisation should be done in child class
+    proc_rank: int
+    use_ddp: bool
+    use_ddp2: bool
+    shown_warnings: ...
+    val_check_interval: float
+    use_tpu: bool
+    tpu_local_core_rank: int
+    train_dataloader: DataLoader
+    num_training_batches: int
+    val_check_batch: ...
+    val_dataloaders: DataLoader
+    num_val_batches: int
+    test_dataloaders: DataLoader
+    num_test_batches: int
+
+    @abstractmethod
+    def is_overriden(self, *args):
+        """Warning: this is just empty shell for code implemented in other class."""
 
     def _percent_range_check(self, name):
         value = getattr(self, name)

pytorch_lightning/trainer/distrib_data_parallel.py

@@ -118,48 +118,50 @@ def train_fx(trial_hparams, cluster_manager, _):
 import re
 import warnings
 from abc import ABC, abstractmethod
+from typing import Union
 
 import torch
+from pytorch_lightning.loggers import LightningLoggerBase
 
 from pytorch_lightning.utilities.debugging import MisconfigurationException
 
 try:
     from apex import amp
-
-    APEX_AVAILABLE = True
 except ImportError:
     APEX_AVAILABLE = False
+else:
+    APEX_AVAILABLE = True
 
 
 class TrainerDDPMixin(ABC):
 
-    def __init__(self):
-        # this is just a summary on variables used in this abstract class,
-        #  the proper values/initialisation should be done in child class
-        self.num_gpus = None
-        self.on_gpu = None
-        self.num_gpu_nodes = None
-        self.logger = None
-        self.data_parallel_device_ids = None
-        self.distributed_backend = None
-        self.use_amp = None
-        self.amp_level = None
-        self.use_tpu = None
+    # this is just a summary on variables used in this abstract class,
+    #  the proper values/initialisation should be done in child class
+    on_gpu: bool
+    num_gpu_nodes: int
+    logger: Union[LightningLoggerBase, bool]
+    data_parallel_device_ids: ...
+    distributed_backend: str
+    use_amp: bool
+    amp_level: str
+    use_tpu: bool
+
+    @property
+    @abstractmethod
+    def num_gpus(self) -> int:
+        """Warning: this is just empty shell for code implemented in other class."""
 
     @abstractmethod
-    def copy_trainer_model_properties(self, model):
-        # this is just empty shell for code from other class
-        pass
+    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, model):
-        # this is just empty shell for code from other class
-        pass
+    def run_pretrain_routine(self, *args):
+        """Warning: this is just empty shell for code implemented in other class."""
 
     @abstractmethod
-    def init_optimizers(self, optimizers):
-        # this is just empty shell for code from other class
-        pass
+    def init_optimizers(self, *args):
+        """Warning: this is just empty shell for code implemented in other class."""
 
     def init_tpu(self):
         # turn off all the GPU stuff

pytorch_lightning/trainer/distrib_parts.py

@@ -348,49 +348,47 @@
 
 try:
     from apex import amp
-
-    APEX_AVAILABLE = True
 except ImportError:
     APEX_AVAILABLE = False
+else:
+    APEX_AVAILABLE = True
 
 try:
     import torch_xla.core.xla_model as xm
-    XLA_AVAILABLE = True
-
 except ImportError:
     XLA_AVAILABLE = False
+else:
+    XLA_AVAILABLE = True
 
 
 class TrainerDPMixin(ABC):
 
-    def __init__(self):
-        # this is just a summary on variables used in this abstract class,
-        #  the proper values/initialisation should be done in child class
-        self.on_gpu = None
-        self.use_dp = None
-        self.use_ddp2 = None
-        self.use_ddp = None
-        self.use_amp = None
-        self.testing = None
-        self.single_gpu = None
-        self.root_gpu = None
-        self.amp_level = None
-        self.precision = None
-        self.current_tpu_idx = None
-        self.proc_rank = None
-        self.tpu_local_core_rank = None
-        self.tpu_global_core_rank = None
-        self.use_tpu = None
+    # this is just a summary on variables used in this abstract class,
+    #  the proper values/initialisation should be done in child class
+    on_gpu: bool
+    use_dp: bool
+    use_ddp2: bool
+    use_ddp: bool
+    use_amp: bool
+    testing: bool
+    single_gpu: bool
+    root_gpu: ...
+    amp_level: str
+    precision: ...
+    current_tpu_idx: ...
+    proc_rank: int
+    tpu_local_core_rank: int
+    tpu_global_core_rank: int
+    use_tpu: bool
+    data_parallel_device_ids: ...
 
     @abstractmethod
-    def run_pretrain_routine(self, model):
-        # this is just empty shell for code from other class
-        pass
+    def run_pretrain_routine(self, *args):
+        """Warning: this is just empty shell for code implemented in other class."""
 
     @abstractmethod
-    def init_optimizers(self, optimizers):
-        # this is just empty shell for code from other class
-        pass
+    def init_optimizers(self, *args):
+        """Warning: this is just empty shell for code implemented in other class."""
 
     def copy_trainer_model_properties(self, model):
         if isinstance(model, LightningDataParallel):