refactor 1/n for v1.0.0

.pyrightconfig.json

@@ -7,6 +7,7 @@
     "pytorch_lightning/__init__.py",
     "pytorch_lightning/callbacks",
     "pytorch_lightning/core",
+    "pytorch_lightning/accelerators",
     "pytorch_lightning/loggers",
     "pytorch_lightning/logging",
     "pytorch_lightning/metrics",

docs/source/conf.py

@@ -138,6 +138,7 @@
 exclude_patterns = [
     'api/pytorch_lightning.rst',
     'api/pl_examples.*',
+    'api/pytorch_lightning.accelerators.*',
     'api/modules.rst',
     'PULL_REQUEST_TEMPLATE.md',
 

pytorch_lightning/accelerators/__init__.py

@@ -0,0 +1 @@
+from pytorch_lightning.accelerators.gpu_accelerator import GPUAccelerator

pytorch_lightning/accelerators/gpu_accelerator.py

@@ -0,0 +1,46 @@
+# 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.
+
+import torch
+
+
+class GPUAccelerator(object):
+
+    def __init__(self, trainer):
+        self.trainer = trainer
+
+    def setup(self, model):
+        # call setup
+        if not self.trainer.testing:
+            self.trainer.setup('fit')
+            model.setup('fit')
+
+        model.cuda(self.trainer.root_gpu)
+
+        # CHOOSE OPTIMIZER
+        # allow for lr schedulers as well
+        optimizers, lr_schedulers, optimizer_frequencies = self.trainer.init_optimizers(model)
+        self.trainer.optimizers = optimizers
+        self.trainer.lr_schedulers = lr_schedulers
+        self.trainer.optimizer_frequencies = optimizer_frequencies
+
+        # TODO: remove with dropping NVIDIA AMP support
+        native_amp_available = hasattr(torch.cuda, "amp") and hasattr(torch.cuda.amp, "autocast")
+        if self.trainer.use_amp and not native_amp_available:
+            self._setup_nvidia_apex(model)
+
+    def _setup_nvidia_apex(self, model):
+        model, optimizers = model.configure_apex(amp, model, self.trainer.optimizers, self.trainer.amp_level)
+        self.trainer.optimizers = optimizers
+        self.trainer.reinit_scheduler_properties(self.trainer.optimizers, self.trainer.lr_schedulers)

pytorch_lightning/core/datamodule.py

@@ -1,3 +1,17 @@
+# 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.
+
 import inspect
 from abc import abstractmethod
 from argparse import ArgumentParser, Namespace

pytorch_lightning/trainer/distrib_data_parallel.py

@@ -1,3 +1,17 @@
+# 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.
+
 """
 Lightning supports model training on a cluster managed by SLURM in the following cases:
 

pytorch_lightning/trainer/distrib_parts.py

@@ -1,3 +1,17 @@
+# 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.
+
 """
 Root module for all distributed operations in Lightning.
 Currently supports training on CPU, GPU (dp, ddp, ddp2, horovod) and TPU.
@@ -165,28 +179,6 @@ def __transfer_batch_to_device(self, batch: Any, device: torch.device):
             return model.transfer_batch_to_device(batch, device)
         return move_data_to_device(batch, device)
 
-    def single_gpu_train(self, model):
-        # call setup
-        if not self.testing:
-            self.setup('fit')
-            model.setup('fit')
-
-        model.cuda(self.root_gpu)
-
-        # CHOOSE OPTIMIZER
-        # allow for lr schedulers as well
-        self.optimizers, self.lr_schedulers, self.optimizer_frequencies = self.init_optimizers(model)
-
-        # TODO: remove with dropping NVIDIA AMP support
-        if self.use_amp and not NATIVE_AMP_AVALAIBLE:
-            # An example
-            model, optimizers = model.configure_apex(amp, model, self.optimizers, self.amp_level)
-            self.optimizers = optimizers
-            self.reinit_scheduler_properties(self.optimizers, self.lr_schedulers)
-
-        results = self.run_pretrain_routine(model)
-        return results
-
     def tpu_train(self, tpu_core_idx, model):
         # call setup after the ddp process has connected
         if not self.testing:

pytorch_lightning/trainer/optimizers.py

@@ -1,3 +1,17 @@
+# 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 abc import ABC
 from typing import List, Tuple
 

pytorch_lightning/trainer/trainer.py

@@ -1,3 +1,17 @@
+# 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.
+
 import inspect
 import os
 import warnings
@@ -37,6 +51,7 @@
 from pytorch_lightning.utilities.debugging import InternalDebugger
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.trainer.configuration_validator import ConfigValidator
+from pytorch_lightning.accelerators.gpu_accelerator import GPUAccelerator
 
 # warnings to ignore in trainer
 warnings.filterwarnings(
@@ -646,6 +661,7 @@ def __init__(
         # tracks internal state for debugging
         self.dev_debugger = InternalDebugger(self)
         self.config_validator = ConfigValidator(self)
+        self.accelerator = None
 
         # Callback system
         self.on_init_end()
@@ -1057,7 +1073,9 @@ def fit(
             results = self.horovod_train(model)
 
         elif self.single_gpu:
-            results = self.single_gpu_train(model)
+            self.accelerator = GPUAccelerator(self)
+            self.accelerator.setup(model)
+            results = self.run_pretrain_routine(model)
 
         elif self.use_tpu:  # pragma: no-cover
             rank_zero_info(f'training on {self.tpu_cores} TPU cores')

pytorch_lightning/trainer/training_loop.py

@@ -1,3 +1,17 @@
+# 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.
+
 """
 The lightning training loop handles everything except the actual computations of your model.
  To decide what will happen in your training loop, define the `training_step` function.

pytorch_lightning/trainer/training_tricks.py

@@ -1,3 +1,17 @@
+# 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.
+
 import math
 import sys
 from abc import ABC, abstractmethod