deprecate enable_pl_optimizer as it is not restored properly (#5244)

* update

* clean test

* still in progress

* udpdate test

* update

* update

* resolve flake

* add test for zero_grad

* update

* works without accumulated_grad

* update

* update

* resolve amp

* revert back to True

* update

* clean tests

* cleaned out

* typo

* update test

* git repare bug

* remove print

* udpate

* Fix formatting/optimizer imports

* Refactor the test for cleanliness

* Add vanilla model to the test, better var names

* Fixed var names, let's clean up these mock tests

* repare test

* update test

* resolve flake8

* add manual_optimization

* update tests

* resolve flake8

* add random accumulate_grad_batches

* improve test

* Update tests/trainer/optimization/test_parity_automatic_optimization.py

Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>

* Update tests/trainer/optimization/test_parity_automatic_optimization.py

Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>

* update

* clean tests

* correct bug

* Apply suggestions from code review

* format

* adress comments

* update on comments

* wip

* typo

* depreceate enable_pl_optimizer

* resolve latest bugs

* update

* resolve merge

* add comment

* Update pytorch_lightning/core/lightning.py

Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>

* Update tests/deprecated_api/test_remove_1-3.py

Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>

* Update pytorch_lightning/trainer/connectors/optimizer_connector.py

Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>

* Update pytorch_lightning/trainer/trainer.py

Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>

* Update pytorch_lightning/trainer/trainer.py

Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>

* Update tests/trainer/optimization/test_parity_automatic_optimization.py

Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>

* update on comments

* update restore

* add a property

* remove setstate as not needed anymore

* update test

* provide optimizer to on_before_zero_grad

* update on comments

* update on comments

* Update pytorch_lightning/trainer/trainer.py

Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>

* Update tests/trainer/optimization/test_parity_automatic_optimization.py

Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>

* Update tests/trainer/optimization/test_parity_automatic_optimization.py

Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>

* Update tests/trainer/optimization/test_parity_automatic_optimization.py

Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>

* mofidy import

* update changelog

* resolve flake8

* update

* update

* clean doc

Co-authored-by: SeanNaren <sean@grid.ai>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-62-109.ec2.internal>
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
Co-authored-by: Jirka Borovec <jirka.borovec@seznam.cz>
Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>
Co-authored-by: Sean Naren <sean.narenthiran@gmail.com>

(cherry picked from commit f2e99d6)

CHANGELOG.md

@@ -128,6 +128,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Changed
 
+- Changed depreceated `enable_pl_optimizer=True` ([#5244](https://github.com/PyTorchLightning/pytorch-lightning/pull/5244))
+
 
 ### Deprecated
 

README.md

@@ -224,7 +224,8 @@ with tempfile.NamedTemporaryFile(suffix='.onnx', delete=False) as tmpfile:
 ```python
 class LitAutoEncoder(pl.LightningModule):
     def training_step(self, batch, batch_idx, opt_idx):
-        (opt_a, opt_b) = self.optimizers()
+        # access your optimizers with use_pl_optimizer=False. Default is True
+        (opt_a, opt_b) = self.optimizers(use_pl_optimizer=True)
 
         loss_a = ...
         self.manual_backward(loss_a, opt_a)

benchmarks/test_sharded_parity.py

@@ -175,7 +175,8 @@ def train_dataloader(self):
 class SeedTrainLoaderManualModel(SeedTrainLoaderModel):
     def training_step(self, batch, batch_idx, optimizer_idx):
         # manual
-        (opt_a, opt_b) = self.optimizers()
+        # access your optimizers with use_pl_optimizer=False. Default is True
+        (opt_a, opt_b) = self.optimizers(use_pl_optimizer=True)
         loss_1 = self.step(batch)
 
         self.manual_backward(loss_1, opt_a)

docs/source/new-project.rst

@@ -268,7 +268,8 @@ Now you own the train loop!
 .. code-block:: python
 
     def training_step(self, batch, batch_idx, opt_idx):
-        (opt_a, opt_b, opt_c) = self.optimizers()
+        # access your optimizers with use_pl_optimizer=False. Default is True
+        (opt_a, opt_b, opt_c) = self.optimizers(use_pl_optimizer=True)
 
         loss_a = self.generator(batch[0])
 

docs/source/optimizers.rst

@@ -28,8 +28,15 @@ to manually manage the optimization process. To do so, do the following:
 .. code-block:: python
 
     def training_step(self, batch, batch_idx, optimizer_idx):
-        # ignore optimizer_idx
-        (opt_g, opt_d) = self.optimizers()
+
+        # 1. ignore optimizer_idx
+        # 2. `use_pl_optimizer=True` means `opt_g` and `opt_d` will be of type `LightingOptimizer`
+        # `LightingOptimizer` simply wrapped your optimizer and behave the same way !
+        # When calling `optimizer.step`, `LightingOptimizer` will just handle TPU, AMP, accumulate_grad_batches, etc ... for you.
+
+        # access your optimizers with `use_pl_optimizer=False` or `optimizer.optimizer` when using use_pl_optimizer=True
+        # use_pl_optimizer=True is the default
+        (opt_g, opt_d) = self.optimizers(use_pl_optimizer=True)
 
         # do anything you want
         loss_a = ...
@@ -242,19 +249,29 @@ Here we add a learning-rate warm up
         # update params
         optimizer.step(closure=closure)
 
-The default ``optimizer_step`` is relying on the internal ``LightningOptimizer`` to properly perform a step.
+.. note:: The default ``optimizer_step`` is relying on the internal ``LightningOptimizer`` to properly perform a step. It handles TPUs, AMP, accumulate_grad_batches, zero_grad, and much more ...
 
 .. testcode::
 
-    from pytorch_lightning.core.optimizer import LightningOptimizer
+    # function hook in LightningModule
+    def optimizer_step(self, current_epoch, batch_nb, optimizer, optimizer_idx, closure, on_tpu=False, using_native_amp=False, using_lbfgs=False):
+      optimizer.step(closure=closure)
+
+.. note:: To access your wrapped Optimizer from ``LightningOptimizer``, do as follow.
+
+.. testcode::
 
     # function hook in LightningModule
     def optimizer_step(self, current_epoch, batch_nb, optimizer, optimizer_idx, closure, on_tpu=False, using_native_amp=False, using_lbfgs=False):
-      if not isinstance(optimizer, LightningOptimizer):
-         # wraps into LightingOptimizer only for running step
-         optimizer = LightningOptimizer.to_lightning_optimizer(optimizer, self.trainer)
+
+      # `optimizer is a ``LightningOptimizer`` wrapping the optimizer.
+      # To access it, do as follow:
+      optimizer = optimizer.optimizer
+
+      # run step. However, it won't work on TPU, AMP, etc...
       optimizer.step(closure=closure)
 
+
 ----------
 
 Using the closure functions for optimization

docs/source/trainer.rst

@@ -335,7 +335,8 @@ optimizer behavior
 Example::
 
     def training_step(self, batch, batch_idx):
-        opt = self.optimizers()
+        # access your optimizers with use_pl_optimizer=False. Default is True
+        opt = self.optimizers(use_pl_optimizer=True)
 
         loss = ...
         self.manual_backward(loss, opt)
@@ -350,7 +351,8 @@ In the multi-optimizer case, ignore the optimizer_idx flag and use the optimizer
 Example::
 
     def training_step(self, batch, batch_idx, optimizer_idx):
-        (opt_a, opt_b) = self.optimizers()
+        # access your optimizers with use_pl_optimizer=False. Default is True
+        (opt_a, opt_b) = self.optimizers(use_pl_optimizer=True)
 
         gen_loss = ...
         self.manual_backward(gen_loss, opt_a)

pytorch_lightning/accelerators/cpu_accelerator.py

@@ -48,8 +48,6 @@ def setup(self, model):
         # allow for lr schedulers as well
         self.setup_optimizers(model)
 
-        self.trainer.convert_to_lightning_optimizers()
-
         self.trainer.model = model
 
     def train(self):

pytorch_lightning/accelerators/ddp2_accelerator.py

@@ -189,8 +189,6 @@ def ddp_train(self, process_idx, mp_queue, model):
         # 16-bit
         model = self.trainer.precision_connector.connect(model)
 
-        self.trainer.convert_to_lightning_optimizers()
-
         # device ids change depending on the DDP setup
         device_ids = self.get_device_ids()
 

pytorch_lightning/accelerators/ddp_accelerator.py

@@ -12,9 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License
 import os
+from os.path import abspath
 import subprocess
 import sys
-from os.path import abspath
 from time import sleep
 from typing import Any, List, Optional, Union
 
@@ -292,8 +292,6 @@ def ddp_train(self, process_idx, model):
         # 16-bit
         model = self.trainer.precision_connector.connect(model)
 
-        self.trainer.convert_to_lightning_optimizers()
-
         # device ids change depending on the DDP setup
         device_ids = self.get_device_ids()
 

pytorch_lightning/accelerators/ddp_cpu_spawn_accelerator.py

@@ -148,8 +148,6 @@ def ddp_train(self, process_idx, mp_queue, model):
         # 16-bit
         model = self.trainer.precision_connector.connect(model)
 
-        self.trainer.convert_to_lightning_optimizers()
-
         # DDP spawn already spawned off each process... no need to do anything
         device_ids = self.get_device_ids()
 

pytorch_lightning/accelerators/ddp_hpc_accelerator.py

@@ -14,8 +14,8 @@
 from typing import Any, List, Optional, Union
 
 import torch
-import torch.distributed as torch_distrib
 import torch.distributed as dist
+import torch.distributed as torch_distrib
 from torch.nn.parallel import DistributedDataParallel
 
 from pytorch_lightning import _logger as log
@@ -177,8 +177,6 @@ def ddp_train(self, process_idx, model):
         # 16-bit
         model = self.trainer.precision_connector.connect(model)
 
-        self.trainer.convert_to_lightning_optimizers()
-
         # device ids change depending on the DDP setup
         device_ids = self.get_device_ids()
 

pytorch_lightning/accelerators/ddp_spawn_accelerator.py

@@ -163,8 +163,6 @@ def ddp_train(self, process_idx, mp_queue, model, is_master: bool = False, proc_
         # 16-bit
         model = self.trainer.precision_connector.connect(model)
 
-        self.trainer.convert_to_lightning_optimizers()
-
         # device ids change depending on the DDP setup
         device_ids = self.get_device_ids()
 

pytorch_lightning/accelerators/dp_accelerator.py

@@ -64,8 +64,6 @@ def setup(self, model):
         if self.trainer.amp_backend:
             model = self.__init_half_precision(model)
 
-        self.trainer.convert_to_lightning_optimizers()
-
         self.trainer.model = model
 
     def __init_torch_data_parallel(self, model):

pytorch_lightning/accelerators/gpu_accelerator.py

@@ -53,8 +53,6 @@ def setup(self, model):
         # 16-bit
         model = self.trainer.precision_connector.connect(model)
 
-        self.trainer.convert_to_lightning_optimizers()
-
         self.trainer.model = model
 
     def train(self):

pytorch_lightning/accelerators/horovod_accelerator.py

@@ -90,8 +90,6 @@ def _filter_named_parameters(model, optimizer):
         # 16-bit
         model = self.trainer.precision_connector.connect(model)
 
-        self.trainer.convert_to_lightning_optimizers()
-
         # Update logger rank info from Horovod to avoid race conditions from  different ranks
         # creating directories / writing files in the same locations.
         self.trainer.global_rank = hvd.rank()

pytorch_lightning/accelerators/tpu_accelerator.py

@@ -30,6 +30,7 @@
     rank_zero_info,
     rank_zero_only,
     rank_zero_warn,
+    TPU_AVAILABLE,
 )
 from pytorch_lightning.utilities.cloud_io import atomic_save
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
@@ -229,8 +230,6 @@ def __setup_tpu_training(self, model: LightningModule, trainer):
                  f' global rank: {trainer.tpu_global_core_rank}'
                  f' with XLA_USE_BF16={os.environ.get("XLA_USE_BF16")}')
 
-        self.trainer.convert_to_lightning_optimizers()
-
     def backward(self, closure_loss, optimizer, opt_idx, *args, **kwargs):
         # do backward pass
         if self.trainer.train_loop.automatic_optimization:

pytorch_lightning/core/lightning.py

@@ -104,8 +104,11 @@ def __init__(self, *args, **kwargs):
         self._current_dataloader_idx = None
         self._automatic_optimization: bool = True
 
-    def optimizers(self):
-        opts = self.trainer.optimizers
+    def optimizers(self, use_pl_optimizer: bool = True) -> Union[Optimizer, List[Optimizer], List[LightningOptimizer]]:
+        if use_pl_optimizer:
+            opts = list(self.trainer.lightning_optimizers.values())
+        else:
+            opts = self.trainer.optimizers
 
         # single optimizer
         if isinstance(opts, list) and len(opts) == 1 and isinstance(opts[0], Optimizer):

pytorch_lightning/core/optimizer.py

@@ -63,6 +63,10 @@ def __init__(self,
         self._accumulate_grad_batches = accumulate_grad_batches
         self._optimizer_idx = None
 
+    @property
+    def optimizer(self):
+        return self._optimizer
+
     @property
     def defaults(self):
         return self._optimizer.defaults
@@ -103,9 +107,13 @@ def _on_trainer_init(self, trainer):
                 break
 
     @classmethod
-    def to_lightning_optimizer(cls, optimizer, trainer):
-        optimizer = cls(optimizer)
-        optimizer._on_trainer_init(trainer)
+    def _to_lightning_optimizer(cls, optimizer, trainer, opt_idx):
+        # apex overrides .step function and need to be wrapped on each step
+        if trainer.amp_backend == AMPType.APEX:
+            optimizer = cls(optimizer)
+            optimizer._on_trainer_init(trainer)
+        else:
+            optimizer = trainer.lightning_optimizers[opt_idx]
         return optimizer
 
     def _accumulated_batches_reached(self):
@@ -147,7 +155,7 @@ def __optimizer_step(self, *args, closure: Optional[Callable] = None, profiler_n
                     **kwargs
                 )
 
-        trainer.train_loop.on_before_zero_grad(self)
+        trainer.train_loop.on_before_zero_grad(optimizer)
 
         model.optimizer_zero_grad(
             trainer.current_epoch,

pytorch_lightning/plugins/ddp_sequential_plugin.py

@@ -15,8 +15,8 @@
 from typing import Any, List, Optional
 
 import torch
-import torch.distributed as torch_distrib
 from torch import nn
+import torch.distributed as torch_distrib
 from torch.nn.parallel import DistributedDataParallel
 
 from pytorch_lightning import _logger as log
@@ -28,6 +28,7 @@
 if _FAIRSCALE_PIPE_AVAILABLE:
     import fairscale.nn.model_parallel as mpu
     from fairscale.nn import PipeRPCWrapper
+    import fairscale.nn.model_parallel as mpu
     from fairscale.nn.pipe import balance as pipe_balance
     from fairscale.nn.pipe import rpc as rpc_pipe
     from fairscale.nn.pipe.pipeline import PipelineStyle
@@ -383,7 +384,6 @@ def register_optimizers(ctx, model):
     model.trainer.optimizers = optimizers
     model.trainer.lr_schedulers = lr_schedulers
     model.trainer.optimizer_frequencies = optimizer_frequencies
-    model.trainer.convert_to_lightning_optimizers()
 
 
 def run_optimizer(ctx, model):

pytorch_lightning/plugins/native_amp.py

@@ -52,7 +52,10 @@ def backward(self, closure_loss, optimizer, opt_idx, *args, **kwargs):
 
         # unscale gradient to allow analyze within `on_after_backward`
         if not self.trainer.train_loop.should_accumulate() and automatic_optimization:
-            self.trainer.scaler.unscale_(optimizer)
+            if isinstance(optimizer, LightningOptimizer):
+                self.trainer.scaler.unscale_(optimizer.optimizer)
+            else:
+                self.trainer.scaler.unscale_(optimizer)
 
         return closure_loss
 

pytorch_lightning/plugins/sharded_plugin.py

@@ -63,7 +63,7 @@ def _reinit_with_fairscale_oss(self, trainer):
         optimizers = trainer.optimizers
         for x, optimizer in enumerate(optimizers):
             if is_lightning_optimizer(optimizer):
-                optimizer = optimizer._optimizer
+                optimizer = optimizer.optimizer
             if not isinstance(optimizer, OSS):
                 optim_class = type(optimizer)
                 zero_optimizer = OSS(
@@ -73,7 +73,6 @@ def _reinit_with_fairscale_oss(self, trainer):
                 )
                 optimizers[x] = zero_optimizer
                 del optimizer
-        trainer.convert_to_lightning_optimizers()
 
     def get_model_from_plugin(
             self,

pytorch_lightning/trainer/configuration_validator.py

@@ -72,17 +72,7 @@ def __verify_train_loop_configuration(self, model):
 
         trainer.overriden_optimizer_step = is_overridden('optimizer_step', model)
         trainer.overriden_optimizer_zero_grad = is_overridden('optimizer_zero_grad', model)
-
-        enable_pl_optimizer = trainer._enable_pl_optimizer
         automatic_optimization = trainer.train_loop.automatic_optimization
-        if trainer.overriden_optimizer_step and not enable_pl_optimizer and automatic_optimization:
-            rank_zero_warn(
-                "When overriding `LightningModule` optimizer_step with"
-                " `Trainer(..., enable_pl_optimizer=False, ...)`,"
-                " we won't be calling `.zero_grad` we can't assume when you call your `optimizer.step()`."
-                " For Lightning to take care of it, please use `Trainer(enable_pl_optimizer=True)`."
-            )
-
         going_to_accumulate_grad_batches = trainer.accumulation_scheduler.going_to_accumulate_grad_batches()
 
         has_overriden_optimization_functions = trainer.overriden_optimizer_step or trainer.overriden_optimizer_zero_grad
@@ -93,13 +83,6 @@ def __verify_train_loop_configuration(self, model):
                 ' It ensures optimizer_step or optimizer_zero_grad are called on every batch.'
             )
 
-        if (enable_pl_optimizer) and trainer.overriden_optimizer_zero_grad and not automatic_optimization:
-            raise MisconfigurationException(
-                'When overriding `LightningModule` optimizer_zero_grad'
-                ' and preserving model property `automatic_optimization` as True with'
-                ' `Trainer(enable_pl_optimizer=True, ...) is not supported'
-            )
-
     def __verify_eval_loop_configuration(self, model, eval_loop_name):
         step_name = f'{eval_loop_name}_step'
 

pytorch_lightning/trainer/connectors/optimizer_connector.py

@@ -20,7 +20,11 @@ def __init__(self, trainer):
         self.trainer = trainer
 
     def on_trainer_init(self, enable_pl_optimizer):
-        self.trainer._enable_pl_optimizer = enable_pl_optimizer
+        if enable_pl_optimizer is not None:
+            rank_zero_warn(
+                "Trainer argument `enable_pl_optimizer` is deprecated in v1.1.3. It will be removed in v1.3.0",
+                DeprecationWarning
+            )
         self.trainer.lr_schedulers = []
         self.trainer.optimizers = []
         self.trainer.optimizer_frequencies = []

pytorch_lightning/trainer/connectors/precision_connector.py

@@ -67,7 +67,6 @@ def _setup_amp_backend(self, amp_type: str):
                 self.trainer.amp_backend = AMPType.APEX
                 self.backend = ApexPlugin(self.trainer)
                 log.warn("LightningOptimizer doesn't support Apex")
-                self.trainer._enable_pl_optimizer = False
 
         if not self.trainer.amp_backend:
             raise ModuleNotFoundError(