ref: separate properties (#3432)

* ref: separate properties

* ref: separate properties

* ref: separate properties

* ref: separate properties

pytorch_lightning/trainer/model_connector.py

@@ -50,3 +50,8 @@ class ModelConnector:
             m.precision = self.trainer.precision
             m.global_rank = self.trainer.global_rank
             m.local_rank = self.trainer.local_rank
+
+    def get_model(self):
+        is_dp_module = isinstance(self.trainer.model, (LightningDistributedDataParallel, LightningDataParallel))
+        model = self.trainer.model.module if is_dp_module else self.trainer.model
+        return model

pytorch_lightning/trainer/properties.py

@@ -0,0 +1,152 @@
+from pytorch_lightning.utilities.cloud_io import get_filesystem
+from pytorch_lightning.trainer.logger_connector import LoggerConnector
+from pytorch_lightning.trainer.states import TrainerState
+from typing import List, Optional, Union
+from pytorch_lightning.utilities import argparse_utils
+from argparse import ArgumentParser, Namespace
+from abc import ABC
+import inspect
+import os
+from pytorch_lightning.utilities.model_utils import is_overridden
+from pytorch_lightning.core.lightning import LightningModule
+from pytorch_lightning.callbacks import ProgressBarBase
+from pytorch_lightning.trainer.model_connector import ModelConnector
+
+
+class TrainerProperties(ABC):
+
+    precision: int
+    logger_connector: LoggerConnector
+    _state: TrainerState
+    global_rank: int
+    fast_dev_run: bool
+    use_dp: bool
+    use_ddp: bool
+    use_ddp2: bool
+    model: LightningModule
+    data_parallel_device_ids: Optional[List[int]]
+    _progress_bar_callback: ProgressBarBase
+    limit_val_batches: int
+    _default_root_dir: str
+    _weights_save_path: str
+    model_connector: ModelConnector
+
+    @property
+    def use_amp(self) -> bool:
+        return self.precision == 16
+
+    @property
+    def callback_metrics(self):
+        return self.logger_connector.callback_metrics
+
+    @callback_metrics.setter
+    def callback_metrics(self, x):
+        self.logger_connector.callback_metrics = x
+
+    @property
+    def state(self) -> TrainerState:
+        return self._state
+
+    @property
+    def is_global_zero(self) -> bool:
+        return self.global_rank == 0
+
+    @property
+    def slurm_job_id(self) -> Optional[int]:
+        try:
+            job_id = os.environ['SLURM_JOB_ID']
+            job_id = int(job_id)
+
+            # in interactive mode, don't make logs use the same job id
+            in_slurm_interactive_mode = os.environ['SLURM_JOB_NAME'] == 'bash'
+            if in_slurm_interactive_mode:
+                job_id = None
+
+        except Exception:
+            job_id = None
+        return job_id
+
+    @classmethod
+    def default_attributes(cls):
+        init_signature = inspect.signature(cls)
+
+        args = {}
+        for param_name in init_signature.parameters:
+            value = init_signature.parameters[param_name].default
+            args[param_name] = value
+
+        return args
+
+    @classmethod
+    def get_deprecated_arg_names(cls) -> List:
+        """Returns a list with deprecated Trainer arguments."""
+        depr_arg_names = []
+        for name, val in cls.__dict__.items():
+            if name.startswith('DEPRECATED') and isinstance(val, (tuple, list)):
+                depr_arg_names.extend(val)
+        return depr_arg_names
+
+    @classmethod
+    def from_argparse_args(cls, args: Union[Namespace, ArgumentParser], **kwargs):
+        return argparse_utils.from_argparse_args(cls, args, **kwargs)
+
+    @classmethod
+    def parse_argparser(cls, arg_parser: Union[ArgumentParser, Namespace]) -> Namespace:
+        return argparse_utils.parse_argparser(cls, arg_parser)
+
+    @classmethod
+    def add_argparse_args(cls, parent_parser: ArgumentParser) -> ArgumentParser:
+        return argparse_utils.add_argparse_args(cls, parent_parser)
+
+    @property
+    def num_gpus(self) -> int:
+        gpus = self.data_parallel_device_ids
+        if gpus is None:
+            return 0
+        return len(gpus)
+
+    @property
+    def data_parallel(self) -> bool:
+        return self.use_dp or self.use_ddp or self.use_ddp2
+
+    @property
+    def progress_bar_callback(self):
+        return self._progress_bar_callback
+
+    @property
+    def progress_bar_dict(self) -> dict:
+        """ Read-only for progress bar metrics. """
+        ref_model = self.model if not self.data_parallel else self.model.module
+        return dict(**ref_model.get_progress_bar_dict(), **self.logger_connector.progress_bar_metrics)
+
+    @property
+    def disable_validation(self) -> bool:
+        """ Check if validation is disabled during training. """
+        return not self.enable_validation
+
+    @property
+    def enable_validation(self) -> bool:
+        """ Check if we should run validation during training. """
+        model_ref = self.model_connector.get_model()
+        val_loop_enabled = is_overridden('validation_step', model_ref) and self.limit_val_batches > 0
+        return val_loop_enabled or self.fast_dev_run
+
+    @property
+    def default_root_dir(self) -> str:
+        """
+        The default location to save artifacts of loggers, checkpoints etc.
+        It is used as a fallback if logger or checkpoint callback do not define specific save paths.
+        """
+        if get_filesystem(self._default_root_dir).protocol == "file":
+            return os.path.normpath(self._default_root_dir)
+        return self._default_root_dir
+
+    @property
+    def weights_save_path(self) -> str:
+        """
+        The default root location to save weights (checkpoints), e.g., when the
+        :class:`~pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint` does not define a file path.
+        """
+        if get_filesystem(self._weights_save_path).protocol == "file":
+            return os.path.normpath(self._weights_save_path)
+        return self._weights_save_path

pytorch_lightning/trainer/trainer.py

@@ -12,10 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import inspect
 import os
 import warnings
-from argparse import ArgumentParser, Namespace
 from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
 
 import torch
@@ -47,12 +45,10 @@ from pytorch_lightning.trainer.training_tricks import TrainerTrainingTricksMixin
 from pytorch_lightning.utilities import parsing, rank_zero_info, rank_zero_only, rank_zero_warn, AMPType
 from pytorch_lightning.utilities.debugging import InternalDebugger
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
-from pytorch_lightning.utilities.cloud_io import get_filesystem
 from pytorch_lightning.trainer.evaluation_loop import EvaluationLoop
 from pytorch_lightning.trainer.training_loop import TrainLoop
 from pytorch_lightning.trainer.data_connector import DataConnector
 from pytorch_lightning.accelerators.accelerator_connector import AcceleratorConnector
-from pytorch_lightning.utilities import argparse_utils
 from pytorch_lightning.trainer.logger_connector import LoggerConnector
 from pytorch_lightning.trainer.lr_scheduler_connector import LRSchedulerConnector
 from pytorch_lightning.trainer.model_connector import ModelConnector
@@ -61,6 +57,7 @@ from pytorch_lightning.tuner.tuning import Tuner
 from pytorch_lightning.trainer.initializer import Initializer
 from pytorch_lightning.utilities.model_utils import is_overridden
 from pytorch_lightning.trainer import docstrings
+from pytorch_lightning.trainer.properties import TrainerProperties
 
 # warnings to ignore in trainer
 warnings.filterwarnings(
@@ -90,6 +87,7 @@ else:
 
 
 class Trainer(
+    TrainerProperties,
     TrainerIOMixin,
     TrainerCallbackHookMixin,
     TrainerModelHooksMixin,
@@ -423,125 +421,6 @@ class Trainer(
         # Callback system
         self.on_init_end()
 
-    @property
-    def use_amp(self) -> bool:
-        return self.precision == 16
-
-    @property
-    def callback_metrics(self):
-        return self.logger_connector.callback_metrics
-
-    @callback_metrics.setter
-    def callback_metrics(self, x):
-        self.logger_connector.callback_metrics = x
-
-    @property
-    def state(self) -> TrainerState:
-        return self._state
-
-    @property
-    def is_global_zero(self) -> bool:
-        return self.global_rank == 0
-
-    @property
-    def slurm_job_id(self) -> Optional[int]:
-        try:
-            job_id = os.environ['SLURM_JOB_ID']
-            job_id = int(job_id)
-
-            # in interactive mode, don't make logs use the same job id
-            in_slurm_interactive_mode = os.environ['SLURM_JOB_NAME'] == 'bash'
-            if in_slurm_interactive_mode:
-                job_id = None
-
-        except Exception:
-            job_id = None
-        return job_id
-
-    @classmethod
-    def default_attributes(cls):
-        init_signature = inspect.signature(Trainer)
-
-        args = {}
-        for param_name in init_signature.parameters:
-            value = init_signature.parameters[param_name].default
-            args[param_name] = value
-
-        return args
-
-    @classmethod
-    def get_deprecated_arg_names(cls) -> List:
-        """Returns a list with deprecated Trainer arguments."""
-        depr_arg_names = []
-        for name, val in cls.__dict__.items():
-            if name.startswith('DEPRECATED') and isinstance(val, (tuple, list)):
-                depr_arg_names.extend(val)
-        return depr_arg_names
-
-    @classmethod
-    def from_argparse_args(cls, args: Union[Namespace, ArgumentParser], **kwargs) -> 'Trainer':
-        return argparse_utils.from_argparse_args(cls, args, **kwargs)
-
-    @classmethod
-    def parse_argparser(cls, arg_parser: Union[ArgumentParser, Namespace]) -> Namespace:
-        return argparse_utils.parse_argparser(cls, arg_parser)
-
-    @classmethod
-    def add_argparse_args(cls, parent_parser: ArgumentParser) -> ArgumentParser:
-        return argparse_utils.add_argparse_args(cls, parent_parser)
-
-    @property
-    def num_gpus(self) -> int:
-        gpus = self.data_parallel_device_ids
-        if gpus is None:
-            return 0
-        return len(gpus)
-
-    @property
-    def data_parallel(self) -> bool:
-        return self.use_dp or self.use_ddp or self.use_ddp2
-
-    @property
-    def progress_bar_callback(self):
-        return self._progress_bar_callback
-
-    @property
-    def progress_bar_dict(self) -> dict:
-        """ Read-only for progress bar metrics. """
-        ref_model = self.model if not self.data_parallel else self.model.module
-        return dict(**ref_model.get_progress_bar_dict(), **self.logger_connector.progress_bar_metrics)
-
-    @property
-    def disable_validation(self) -> bool:
-        """ Check if validation is disabled during training. """
-        return not self.enable_validation
-
-    @property
-    def enable_validation(self) -> bool:
-        """ Check if we should run validation during training. """
-        val_loop_enabled = is_overridden('validation_step', self.get_model()) and self.limit_val_batches > 0
-        return val_loop_enabled or self.fast_dev_run
-
-    @property
-    def default_root_dir(self) -> str:
-        """
-        The default location to save artifacts of loggers, checkpoints etc.
-        It is used as a fallback if logger or checkpoint callback do not define specific save paths.
-        """
-        if get_filesystem(self._default_root_dir).protocol == "file":
-            return os.path.normpath(self._default_root_dir)
-        return self._default_root_dir
-
-    @property
-    def weights_save_path(self) -> str:
-        """
-        The default root location to save weights (checkpoints), e.g., when the
-        :class:`~pytorch_lightning.callbacks.model_checkpoint.ModelCheckpoint` does not define a file path.
-        """
-        if get_filesystem(self._weights_save_path).protocol == "file":
-            return os.path.normpath(self._weights_save_path)
-        return self._weights_save_path
-
     def tune(
         self,
         model: LightningModule,