ref: organize args 3/n (#3447)

* ref: organize args 2/n

* ref: organize args 2/n

* ref: organize args 2/n

* ref: organize args 2/n

pytorch_lightning/accelerators/accelerator_connector.py

@@ -82,6 +82,8 @@ class AcceleratorConnector:
         # NVIDIA setup
         self.trainer.set_nvidia_flags(self.trainer.is_slurm_managing_tasks, self.trainer.data_parallel_device_ids)
 
+        self.trainer.on_colab_kaggle = os.getenv('COLAB_GPU') or os.getenv('KAGGLE_URL_BASE')
+
     def select_accelerator(self):
         # SLURM ddp
         use_slurm_ddp = self.trainer.use_ddp and self.trainer.is_slurm_managing_tasks

pytorch_lightning/trainer/data_loading.py

@@ -345,10 +345,3 @@ class TrainerDataLoadingMixin(ABC):
             hvd.join()
 
         return dataloader
-
-    def determine_data_use_amount(self, overfit_batches: float) -> None:
-        """Use less data for debugging purposes"""
-        if overfit_batches > 0:
-            self.limit_train_batches = overfit_batches
-            self.limit_val_batches = overfit_batches
-            self.limit_test_batches = overfit_batches

pytorch_lightning/trainer/debugging_connector.py

@@ -0,0 +1,109 @@
+# Copyright The PyTorch Lightning team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
+from typing import Union
+from pytorch_lightning.utilities import rank_zero_warn, rank_zero_info
+
+
+class DebuggingConnector:
+
+    def __init__(self, trainer):
+        self.trainer = trainer
+
+    def on_init_start(
+            self,
+            overfit_pct,
+            val_percent_check,
+            test_percent_check,
+            train_percent_check,
+            limit_train_batches,
+            limit_val_batches,
+            limit_test_batches,
+            val_check_interval,
+            overfit_batches,
+            fast_dev_run
+    ):
+        self.trainer.fast_dev_run = fast_dev_run
+        if self.trainer.fast_dev_run:
+            limit_train_batches = 1
+            limit_val_batches = 1
+            limit_test_batches = 1
+            self.trainer.num_sanity_val_steps = 0
+            self.trainer.max_epochs = 1
+            rank_zero_info(
+                'Running in fast_dev_run mode: will run a full train,' ' val and test loop using a single batch'
+            )
+
+        # how much of the data to use
+        # TODO: remove in 0.10.0
+        if overfit_pct is not None:
+            rank_zero_warn(
+                "Argument `overfit_pct` is now set by `overfit_batches` since v0.8.0"
+                " and this argument will be removed in v0.10.0",
+                DeprecationWarning,
+            )
+            overfit_batches = overfit_pct
+
+        # TODO: remove in 0.10.0
+        if val_percent_check is not None:
+            rank_zero_warn(
+                "Argument `val_percent_check` is now set by `limit_val_batches` since v0.8.0"
+                " and this argument will be removed in v0.10.0",
+                DeprecationWarning,
+            )
+            limit_val_batches = val_percent_check
+
+        # TODO: remove in 0.10.0
+        if test_percent_check is not None:
+            rank_zero_warn(
+                "Argument `test_percent_check` is now set by `limit_test_batches` since v0.8.0"
+                " and this argument will be removed in v0.10.0",
+                DeprecationWarning,
+            )
+            limit_test_batches = test_percent_check
+
+        # TODO: remove in 0.10.0
+        if train_percent_check is not None:
+            rank_zero_warn(
+                "Argument `train_percent_check` is now set by `limit_train_batches` since v0.8.0"
+                " and this argument will be removed in v0.10.0",
+                DeprecationWarning,
+            )
+            limit_train_batches = train_percent_check
+
+        self.trainer.limit_train_batches = _determine_batch_limits(limit_train_batches, 'limit_train_batches')
+        self.trainer.limit_val_batches = _determine_batch_limits(limit_val_batches, 'limit_val_batches')
+        self.trainer.limit_test_batches = _determine_batch_limits(limit_test_batches, 'limit_test_batches')
+        self.trainer.val_check_interval = _determine_batch_limits(val_check_interval, 'val_check_interval')
+        self.trainer.overfit_batches = _determine_batch_limits(overfit_batches, 'overfit_batches')
+        self.determine_data_use_amount(self.trainer.overfit_batches)
+
+    def determine_data_use_amount(self, overfit_batches: float) -> None:
+        """Use less data for debugging purposes"""
+        if overfit_batches > 0:
+            self.trainer.limit_train_batches = overfit_batches
+            self.trainer.limit_val_batches = overfit_batches
+            self.trainer.limit_test_batches = overfit_batches
+
+
+def _determine_batch_limits(batches: Union[int, float], name: str) -> Union[int, float]:
+    if 0 <= batches <= 1:
+        return batches
+    elif batches > 1 and batches % 1.0 == 0:
+        return int(batches)
+    else:
+        raise MisconfigurationException(
+            f'You have passed invalid value {batches} for {name}, it has to be in [0.0, 1.0] or an int.'
+        )

pytorch_lightning/trainer/precision_connector.py

@@ -11,16 +11,26 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-
 from pytorch_lightning import _logger as log
 from pytorch_lightning.utilities import APEX_AVAILABLE, NATIVE_AMP_AVALAIBLE, rank_zero_warn, AMPType
 
 
-class Initializer:
+class PrecisionConnector:
 
     def __init__(self, trainer):
         self.trainer = trainer
 
+    def on_trainer_init(self, precision, amp_level, amp_backend):
+        # AMP init
+        # These are the only lines needed after v0.8.0
+        # we wrap the user's forward with autocast and give it back at the end of fit
+        self.trainer.autocast_original_forward = None
+        self.trainer.precision = precision
+        self.trainer.scaler = None
+
+        self.trainer.amp_level = amp_level
+        self.init_amp(amp_backend)
+
     def init_amp(self, amp_type: str):
         assert self.trainer.precision in (16, 32), 'only 32 or 16 bit precision supported'
         self.trainer.amp_backend = None

pytorch_lightning/trainer/trainer.py

@@ -43,15 +43,16 @@ from pytorch_lightning.utilities.debugging import InternalDebugger
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 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.trainer.logger_connector import LoggerConnector
 from pytorch_lightning.trainer.lr_scheduler_connector import LRSchedulerConnector
 from pytorch_lightning.trainer.callback_connector import CallbackConnector
 from pytorch_lightning.trainer.model_connector import ModelConnector
+from pytorch_lightning.trainer.debugging_connector import DebuggingConnector
 from pytorch_lightning import _logger as log
 from pytorch_lightning.tuner.tuning import Tuner
-from pytorch_lightning.trainer.initializer import Initializer
+from pytorch_lightning.trainer.precision_connector import PrecisionConnector
+from pytorch_lightning.trainer.data_connector import DataConnector
 from pytorch_lightning.utilities.model_utils import is_overridden
 from pytorch_lightning.trainer import docstrings
 from pytorch_lightning.trainer.properties import TrainerProperties
@@ -173,8 +174,10 @@ class Trainer(
         self.accelerator_connector = AcceleratorConnector(self)
         self.logger_connector = LoggerConnector(self)
         self.model_connector = ModelConnector(self)
-        self.initializer = Initializer(self)
+        self.precision_connector = PrecisionConnector(self)
         self.callback_connector = CallbackConnector(self)
+        self.debugging_connector = DebuggingConnector(self)
+
         self.tuner = Tuner(self)
         self.accelerator_backend = None
 
@@ -253,15 +256,8 @@ class Trainer(
         # -------------------
         self.weights_summary = weights_summary
 
-        self.max_epochs = max_epochs
-        self.min_epochs = min_epochs
-        self.max_steps = max_steps
-        self.min_steps = min_steps
-
-        if num_sanity_val_steps == -1:
-            self.num_sanity_val_steps = float('inf')
-        else:
-            self.num_sanity_val_steps = num_sanity_val_steps
+        # init train loop related flags
+        self.train_loop.on_init_start(max_epochs, min_epochs, max_steps, min_steps, num_sanity_val_steps)
 
         self.reload_dataloaders_every_epoch = reload_dataloaders_every_epoch
 
@@ -275,17 +271,6 @@ class Trainer(
         self.terminate_on_nan = terminate_on_nan
         self.shown_warnings = set()
 
-        self.fast_dev_run = fast_dev_run
-        if self.fast_dev_run:
-            limit_train_batches = 1
-            limit_val_batches = 1
-            limit_test_batches = 1
-            self.num_sanity_val_steps = 0
-            self.max_epochs = 1
-            rank_zero_info(
-                'Running in fast_dev_run mode: will run a full train,' ' val and test loop using a single batch'
-            )
-
         # configure profiler
         if profiler is True:
             profiler = SimpleProfiler()
@@ -300,61 +285,22 @@ class Trainer(
         self.log_save_interval = log_save_interval
         self.row_log_interval = row_log_interval
 
-        # how much of the data to use
-        # TODO: remove in 0.10.0
-        if overfit_pct is not None:
-            rank_zero_warn(
-                "Argument `overfit_pct` is now set by `overfit_batches` since v0.8.0"
-                " and this argument will be removed in v0.10.0",
-                DeprecationWarning,
-            )
-            overfit_batches = overfit_pct
+        # init debugging flags
+        self.debugging_connector.on_init_start(
+            overfit_pct,
+            val_percent_check,
+            test_percent_check,
+            train_percent_check,
+            limit_train_batches,
+            limit_val_batches,
+            limit_test_batches,
+            val_check_interval,
+            overfit_batches,
+            fast_dev_run
+        )
 
-        # TODO: remove in 0.10.0
-        if val_percent_check is not None:
-            rank_zero_warn(
-                "Argument `val_percent_check` is now set by `limit_val_batches` since v0.8.0"
-                " and this argument will be removed in v0.10.0",
-                DeprecationWarning,
-            )
-            limit_val_batches = val_percent_check
-
-        # TODO: remove in 0.10.0
-        if test_percent_check is not None:
-            rank_zero_warn(
-                "Argument `test_percent_check` is now set by `limit_test_batches` since v0.8.0"
-                " and this argument will be removed in v0.10.0",
-                DeprecationWarning,
-            )
-            limit_test_batches = test_percent_check
-
-        # TODO: remove in 0.10.0
-        if train_percent_check is not None:
-            rank_zero_warn(
-                "Argument `train_percent_check` is now set by `limit_train_batches` since v0.8.0"
-                " and this argument will be removed in v0.10.0",
-                DeprecationWarning,
-            )
-            limit_train_batches = train_percent_check
-
-        self.limit_train_batches = _determine_batch_limits(limit_train_batches, 'limit_train_batches')
-        self.limit_val_batches = _determine_batch_limits(limit_val_batches, 'limit_val_batches')
-        self.limit_test_batches = _determine_batch_limits(limit_test_batches, 'limit_test_batches')
-        self.val_check_interval = _determine_batch_limits(val_check_interval, 'val_check_interval')
-        self.overfit_batches = _determine_batch_limits(overfit_batches, 'overfit_batches')
-        self.determine_data_use_amount(self.overfit_batches)
-
-        # AMP init
-        # These are the only lines needed after v0.8.0
-        # we wrap the user's forward with autocast and give it back at the end of fit
-        self.autocast_original_forward = None
-        self.precision = precision
-        self.scaler = None
-
-        self.amp_level = amp_level
-        self.initializer.init_amp(amp_backend)
-
-        self.on_colab_kaggle = os.getenv('COLAB_GPU') or os.getenv('KAGGLE_URL_BASE')
+        # set precision
+        self.precision_connector.on_trainer_init(precision, amp_level, amp_backend)
 
         # Callback system
         self.on_init_end()
@@ -862,18 +808,6 @@ class Trainer(
 
             return output
 
-
-def _determine_batch_limits(batches: Union[int, float], name: str) -> Union[int, float]:
-    if 0 <= batches <= 1:
-        return batches
-    elif batches > 1 and batches % 1.0 == 0:
-        return int(batches)
-    else:
-        raise MisconfigurationException(
-            f'You have passed invalid value {batches} for {name}, it has to be in [0.0, 1.0] or an int.'
-        )
-
-
 # add docstrings
 Trainer.__init__.__doc__ = docstrings.trainer.init
 Trainer.fit.__doc__ = docstrings.trainer.fit

pytorch_lightning/trainer/training_loop.py

@@ -38,6 +38,17 @@ class TrainLoop:
         self._teardown_already_run = False
         self.running_loss = TensorRunningAccum(window_length=20)
 
+    def on_init_start(self, max_epochs, min_epochs, max_steps, min_steps, num_sanity_val_steps):
+        self.trainer.max_epochs = max_epochs
+        self.trainer.min_epochs = min_epochs
+        self.trainer.max_steps = max_steps
+        self.trainer.min_steps = min_steps
+
+        if num_sanity_val_steps == -1:
+            self.trainer.num_sanity_val_steps = float('inf')
+        else:
+            self.trainer.num_sanity_val_steps = num_sanity_val_steps
+
     @property
     def num_optimizers(self):
         num_optimizers = len(self.get_optimizers_iterable())