code: # 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 contextlib import contextmanager, suppress
  from copy import copy, deepcopy
  
  import numpy as np
  import torch
  
  from pytorch_lightning.callbacks import EarlyStopping
  from pytorch_lightning.core.memory import ModelSummary
  from pytorch_lightning.core.optimizer import LightningOptimizer
  from pytorch_lightning.core.step_result import Result
  from pytorch_lightning.plugins import ParallelPlugin
  from pytorch_lightning.trainer.states import RunningStage, TrainerState
  from pytorch_lightning.trainer.supporters import Accumulator, TensorRunningAccum
  from pytorch_lightning.utilities import _TPU_AVAILABLE, AMPType, DeviceType, parsing
  from pytorch_lightning.utilities.distributed import rank_zero_info, rank_zero_warn
  from pytorch_lightning.utilities.exceptions import MisconfigurationException
  from pytorch_lightning.utilities.memory import recursive_detach
  from pytorch_lightning.utilities.model_helpers import is_overridden
  from pytorch_lightning.utilities.parsing import AttributeDict
  from pytorch_lightning.utilities.warnings import WarningCache
  
  
  class TrainLoop:
  
      def __init__(self, trainer, multiple_trainloader_mode):
          self.trainer = trainer
          self.early_stopping_accumulator = None
          self.checkpoint_accumulator = None
          self.accumulated_loss = None
          self.warning_cache = WarningCache()
          self._teardown_already_run = False
          self.running_loss = TensorRunningAccum(window_length=20)
          self.automatic_optimization = True
          self._curr_step_result = None
          self._cur_grad_norm_dict = None
          self._multiple_trainloader_mode = multiple_trainloader_mode
          self._skip_backward = False
          self.trainer._multiple_trainloader_mode = multiple_trainloader_mode
  
      def on_trainer_init(
          self,
          max_epochs,
          min_epochs,
          max_steps,
          min_steps,
          num_sanity_val_steps,
          automatic_optimization,
          weights_summary,
      ):
          self.trainer.global_step = 0
          self.trainer.current_epoch = 0
          self.trainer.interrupted = False
          self.trainer.should_stop = False
          self.trainer._state = TrainerState.INITIALIZING
  
          self.trainer.total_batch_idx = 0
          self.trainer.batch_idx = 0
          self.trainer.num_training_batches = 0
          self.trainer.train_dataloader = None
          self.automatic_optimization = automatic_optimization
  
          # If neither max_epochs or max_steps is set, then use existing default of max_epochs = 1000
          self.trainer.max_epochs = 1000 if (max_epochs is None and max_steps is None) else max_epochs
          # If neither min_epochs or min_steps is set, then use existing default of min_epochs = 1
          self.trainer.min_epochs = 1 if (min_epochs is None and min_steps is None) else 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
  
          self.trainer.weights_summary = weights_summary
          if weights_summary is not None and weights_summary not in ModelSummary.MODES:
              raise MisconfigurationException(
                  f"`weights_summary` can be None, {', '.join(ModelSummary.MODES)}, got {weights_summary}"
              )
  
      @property
      def num_optimizers(self):
          num_optimizers = len(self.get_optimizers_iterable())
          return num_optimizers
  
      def should_skip_training(self):
          should_by_max_steps = self.trainer.max_steps is not None and self.trainer.global_step >= self.trainer.max_steps
          should_by_epoch = self.trainer.max_epochs is not None and self.trainer.current_epoch >= self.trainer.max_epochs
          return should_by_max_steps or should_by_epoch or self.trainer.num_training_batches == 0
  
      def on_train_start(self):
          # hook
          self.trainer.call_hook("on_train_start")
  
          # provide rank to profiler
          self.trainer.profile_connector.on_train_start(self.trainer)
  
      def setup_fit(self, model, train_dataloader, val_dataloaders, datamodule):
          # clean hparams
          if hasattr(model, "hparams"):
              parsing.clean_namespace(model.hparams)
  
          # links data to the trainer
          self.trainer.data_connector.attach_data(model, train_dataloader, val_dataloaders, datamodule)
  
          # check that model is configured correctly
          self.trainer.config_validator.verify_loop_configurations(model)
  
          # attach model log function to callback
          self.trainer.callback_connector.attach_model_logging_functions(model)
  
      def on_train_end(self):
          if self._teardown_already_run:
              return
  
          self._teardown_already_run = True
  
          # trigger checkpoint check. need to temporarily decrease the global step to avoid saving duplicates
          # when a checkpoint was saved at the last step
          self.trainer.global_step -= 1
          self.check_checkpoint_callback(should_update=True, is_last=True)
          self.trainer.global_step += 1
  
          # hook
          self.trainer.call_hook("on_train_end")
  
          # todo: TPU 8 cores hangs in flush with TensorBoard. Might do for all loggers.
          # It might be related to xla tensors blocked when moving the cpu
          # kill loggers
          if self.trainer.logger is not None and self.trainer.training_type_plugin.should_finalize:
              self.trainer.logger.finalize("success")
  
          # summarize profile results
          if self.trainer.global_rank == 0:
              self.trainer.profiler.describe()
  
          # give accelerators a chance to finish
          self.trainer.accelerator_backend.on_train_end()
  
          # clear mem
          if self.trainer._device_type == DeviceType.GPU:
              model = self.trainer.get_model()
              model.cpu()
              torch.cuda.empty_cache()
  
      def check_checkpoint_callback(self, should_update, is_last=False):
          # TODO bake this logic into the ModelCheckpoint callback
          if should_update and self.trainer.checkpoint_connector.has_trained:
              callbacks = self.trainer.checkpoint_callbacks
  
              if is_last and any(cb.save_last for cb in callbacks):
                  rank_zero_info("Saving latest checkpoint...")
  
              model = self.trainer.get_model()
  
              for cb in callbacks:
                  cb.on_validation_end(self.trainer, model)
  
      def check_early_stopping_callback(self, should_update):
          # TODO bake this logic into the EarlyStopping callback
          if should_update and self.trainer.checkpoint_connector.has_trained:
              callbacks = [c for c in self.trainer.callbacks if isinstance(c, EarlyStopping)]
              model = self.trainer.get_model()
  
              for cb in callbacks:
                  cb.on_validation_end(self.trainer, model)
  
      def on_train_epoch_start(self, epoch):
  
          # update training progress in trainer
          self.trainer.current_epoch = epoch
  
          model = self.trainer.get_model()
  
          # reset train dataloader
          if epoch != 0 and self.trainer.reload_dataloaders_every_epoch:
              self.trainer.reset_train_dataloader(model)
  
          # todo: specify the possible exception
          with suppress(Exception):
              # set seed for distributed sampler (enables shuffling for each epoch)
              self.trainer.train_dataloader.sampler.set_epoch(epoch)
  
          # changing gradient according accumulation_scheduler
          self.trainer.accumulation_scheduler.on_epoch_start(self.trainer, self.trainer.get_model())
  
          # stores accumulated grad fractions per batch
          self.accumulated_loss = TensorRunningAccum(window_length=self.trainer.accumulate_grad_batches)
  
          # structured result accumulators for callbacks
          self.early_stopping_accumulator = Accumulator()
          self.checkpoint_accumulator = Accumulator()
  
          # hook
          self.trainer.call_hook("on_epoch_start")
          self.trainer.call_hook("on_train_epoch_start")
  
      def on_train_batch_end(self, epoch_output, batch_end_outputs, batch, batch_idx, dataloader_idx):
          # hook
          self.trainer.call_hook('on_train_batch_end', batch_end_outputs, batch, batch_idx, dataloader_idx)
          self.trainer.call_hook('on_batch_end')
  
          # figure out what to track for epoch end
          self.track_epoch_end_reduce_metrics(epoch_output, batch_end_outputs)
  
          # reset batch logger internals
          self.trainer.logger_connector.on_train_batch_end()
  
      def reset_train_val_dataloaders(self, model):
          if self.trainer.train_dataloader is None or not self.trainer.reload_dataloaders_every_epoch:
              self.trainer.reset_train_dataloader(model)
  
          if self.trainer.val_dataloaders is None and not self.trainer.reload_dataloaders_every_epoch:
              self.trainer.reset_val_dataloader(model)
  
      def track_epoch_end_reduce_metrics(self, epoch_output, batch_end_outputs):
  
          # track the outputs to reduce at the end of the epoch
          for opt_idx, opt_outputs in enumerate(batch_end_outputs):
              sample_output = opt_outputs[-1]
  
              # decide if we need to reduce at the end of the epoch automatically
              auto_reduce_tng_result = isinstance(sample_output, Result) and sample_output.should_reduce_on_epoch_end
              hook_overridden = (
                  is_overridden("training_epoch_end", model=self.trainer.get_model())
                  or is_overridden("on_train_epoch_end", model=self.trainer.get_model())
              )
  
              # only track when a) it needs to be autoreduced OR b) the user wants to manually reduce on epoch end
              if not (hook_overridden or auto_reduce_tng_result):
                  continue
  
              # with 1 step (no tbptt) don't use a sequence at epoch end
              if isinstance(opt_outputs, list) and len(opt_outputs) == 1 and not isinstance(opt_outputs[0], Result):
                  opt_outputs = opt_outputs[0]
  
              epoch_output[opt_idx].append(opt_outputs)
  
      def get_optimizers_iterable(self):
          """
          Generates an iterable with (idx, optimizer) for each optimizer.
          """
          if not self.trainer.optimizer_frequencies:
              # call training_step once per optimizer
              return list(enumerate(self.trainer.optimizers))
  
          optimizer_freq_cumsum = np.cumsum(self.trainer.optimizer_frequencies)
          optimizers_loop_length = optimizer_freq_cumsum[-1]
          current_place_in_loop = self.trainer.total_batch_idx % optimizers_loop_length
  
          # find optimzier index by looking for the first {item > current_place} in the cumsum list
          opt_idx = np.argmax(optimizer_freq_cumsum > current_place_in_loop)
          return [[opt_idx, self.trainer.optimizers[opt_idx]]]
  
      def on_after_backward(self, training_step_output, batch_idx, untouched_loss):
          is_result_obj = isinstance(training_step_output, Result)
  
          if is_result_obj:
              training_step_output.detach()
          else:
              training_step_output.batch_loss = training_step_output.batch_loss.detach()
  
          # insert after step hook
          self.trainer.call_hook("on_after_backward")
  
          # when in dev debugging track the losses
          self.trainer.dev_debugger.track_train_loss_history(batch_idx, untouched_loss.detach())
  
      def _check_training_step_output(self, training_step_output):
          if isinstance(training_step_output, torch.Tensor) and not self.automatic_optimization:
              if training_step_output.grad_fn is None:
                  # TODO: Find why - RuntimeError: Expected to mark a variable ready only once ...
                  raise MisconfigurationException("In manual optimization, `training_step` should not return a Tensor")
  
      def training_step(self, split_batch, batch_idx, opt_idx, hiddens):
          # give the PL module a result for logging
          model_ref = self.trainer.get_model()
  
          with self.trainer.profiler.profile("model_forward"):
              args = self.build_train_args(split_batch, batch_idx, opt_idx, hiddens)
  
              # manually capture logged metrics
              model_ref._current_fx_name = 'training_step'
              model_ref._results = Result()
              with self.trainer.profiler.profile("training_step"):
                  training_step_output = self.trainer.accelerator_backend.training_step(args)
                  self.trainer.accelerator_backend.post_training_step()
  
              self.trainer.logger_connector.cache_logged_metrics()
  
              self._check_training_step_output(training_step_output)
  
              training_step_output = self.trainer.call_hook("training_step_end", training_step_output)
  
              training_step_output_for_epoch_end, training_step_output = self._process_training_step_output(
                  training_step_output, split_batch
              )
              is_result_obj = isinstance(training_step_output, Result)
  
              if training_step_output_for_epoch_end is None:
                  return None
  
          # enable empty loss when using manual opt
          closure_loss = None
          untouched_loss = None
  
          if self.trainer.train_loop.automatic_optimization:
              # accumulate loss
              # (if accumulate_grad_batches = 1 no effect)
              if is_result_obj:
                  closure_loss = training_step_output.minimize
              else:
                  closure_loss = training_step_output.batch_loss
  
              closure_loss = closure_loss / self.trainer.accumulate_grad_batches
  
              # the loss will get scaled for amp. avoid any modifications to it
              untouched_loss = closure_loss.detach().clone()
  
          # result
          result = AttributeDict(
              closure_loss=closure_loss,
              loss=untouched_loss,
              training_step_output=training_step_output,
              training_step_output_for_epoch_end=training_step_output_for_epoch_end,
              hiddens=training_step_output.hiddens,
          )
          return result
  
      def _process_training_step_output(self, training_step_output, split_batch):
          training_step_output_for_epoch_end = training_step_output
  
          # enable validation_step return None
          if training_step_output_for_epoch_end is None:
              return None, None
  
          # -----------------------------------------
          # process result return (DEPRECATE in 1.0)
          # -----------------------------------------
          if isinstance(training_step_output, Result):
              training_step_output_for_epoch_end = self._process_result(training_step_output, split_batch)
              return training_step_output_for_epoch_end, training_step_output
  
          # -----------------------------------------
          # process hybrid (1.0)
          # -----------------------------------------
          # no need for these checks in 1.0.0
          # TODO: remove checks in 1.0.0
          is_tensor = isinstance(training_step_output_for_epoch_end, torch.Tensor)
          is_1_0_output = is_tensor or ("log" not in training_step_output and "progress_bar" not in training_step_output)
          if is_1_0_output:
              return self._process_training_step_output_1_0(training_step_output, split_batch)
  
          # -----------------------------------------
          # process old dict (deprecate 1.0)
          # -----------------------------------------
          training_step_output = self.trainer.process_dict_result(training_step_output, train=True)
  
          training_step_output = AttributeDict(
              batch_loss=training_step_output[0],
              pbar_on_batch_end=training_step_output[1],
              log_metrics=training_step_output[2],
              callback_metrics=training_step_output[3],
              hiddens=training_step_output[4],
          )
          # if the user decides to finally reduce things in epoch_end, save raw output without graphs
          if isinstance(training_step_output_for_epoch_end, torch.Tensor):
              training_step_output_for_epoch_end = training_step_output_for_epoch_end.detach()
          else:
              training_step_output_for_epoch_end = recursive_detach(training_step_output_for_epoch_end)
  
          return training_step_output_for_epoch_end, training_step_output
  
      def _process_training_step_output_1_0(self, training_step_output, split_batch):
          result = self.trainer.get_model()._results
  
          loss = None
          hiddens = None
  
          # handle dict return
          if isinstance(training_step_output, dict):
              loss = training_step_output.pop("loss", None)
              hiddens = training_step_output.pop("hiddens", None)
              result["extra"] = training_step_output
  
          # handle scalar return
          elif isinstance(training_step_output, torch.Tensor):
              loss = training_step_output
              result["extra"] = {}
  
          # map to results under the hood
          result.minimize = loss
          result.hiddens = hiddens
  
          # track batch for manual reduction with result
          result.track_batch_size(len(split_batch))
  
          # track metrics without grads for epoch reduction
          training_step_output_for_epoch_end = copy(result)
          training_step_output_for_epoch_end.detach()
          if self.trainer.move_metrics_to_cpu:
              training_step_output_for_epoch_end.cpu()
  
          # what flows back into the system
          training_step_output = result
  
          return training_step_output_for_epoch_end, training_step_output
  
      def _process_result(self, training_step_output, split_batch):
          training_step_output.track_batch_size(len(split_batch))
          m = """
          TrainResult and EvalResult were deprecated in 0.9.1 and support will drop in 1.0.0.
          Use self.log and .write from the LightningModule to log metrics and write predictions.
          training_step can now only return a scalar (for the loss) or a dictionary with anything you want.
  
          Option 1:
          return loss
  
          Option 2:
          return {'loss': loss, 'anything_else': ...}
  
          Option 3:
          return {'loss': loss, 'hiddens': hiddens, 'anything_else': ...}
              """
          rank_zero_warn(m)
  
          training_step_output_for_epoch_end = copy(training_step_output)
          training_step_output_for_epoch_end.detach()
  
          return training_step_output_for_epoch_end
  
      def optimizer_step(self, optimizer, opt_idx, batch_idx, train_step_and_backward_closure):
          model_ref = self.trainer.get_model()
  
          is_lbfgs = isinstance(optimizer, torch.optim.LBFGS)
          using_native_amp = self.trainer.amp_backend == AMPType.NATIVE
  
          # native amp + lbfgs is a no go right now
          if using_native_amp and is_lbfgs:
              raise MisconfigurationException(
                  'native PyTorch amp and lbfgs are not compatible.'
                  ' To request, please file a Github issue in PyTorch and tag @mcarilli'
              )
  
          # wraps into LightningOptimizer only for running step
          optimizer = LightningOptimizer._to_lightning_optimizer(optimizer, self.trainer, opt_idx)
  
          # model hook
          model_ref.optimizer_step(
              self.trainer.current_epoch,
              batch_idx,
              optimizer,
              opt_idx,
              train_step_and_backward_closure,
              on_tpu=self.trainer._device_type == DeviceType.TPU and _TPU_AVAILABLE,
              using_native_amp=using_native_amp,
              using_lbfgs=is_lbfgs,
          )
  
      def on_before_zero_grad(self, optimizer):
          self.trainer.call_hook('on_before_zero_grad', optimizer)
  
      def optimizer_zero_grad(self, batch_idx, optimizer, opt_idx):
          self.trainer.accelerator_backend.optimizer_zero_grad(self.trainer.current_epoch, batch_idx, optimizer, opt_idx)
  
      def track_and_norm_grad(self, optimizer):
          # track gradient norms
          grad_norm_dic = self._track_gradient_norm()
  
          # clip gradients
          self.trainer.accelerator_backend.clip_gradients(optimizer, self.trainer.gradient_clip_val)
          self._cur_grad_norm_dict = grad_norm_dic
  
      def _track_gradient_norm(self):
          grad_norm_dict = {}
          if (self.trainer.global_step + 1) % self.trainer.log_every_n_steps == 0:
              if float(self.trainer.track_grad_norm) > 0:
                  model = self.trainer.get_model()
                  grad_norm_dict = model.grad_norm(self.trainer.track_grad_norm)
          return grad_norm_dict
  
      def process_hiddens(self, opt_closure_result):
          hiddens = opt_closure_result.hiddens
          if isinstance(opt_closure_result.training_step_output, Result):
              opt_closure_result.training_step_output_for_epoch_end.drop_hiddens()
          return hiddens
  
      def tbptt_split_batch(self, batch):
          splits = [batch]
          if self.trainer.truncated_bptt_steps is not None:
              model_ref = self.trainer.get_model()
              with self.trainer.profiler.profile("tbptt_split_batch"):
                  splits = model_ref.tbptt_split_batch(batch, self.trainer.truncated_bptt_steps)
          return splits
  
      def run_training_epoch(self):
          # modify dataloader if needed (ddp, etc...)
          train_dataloader = self.trainer.accelerator_backend.process_dataloader(self.trainer.train_dataloader)
  
          # track epoch output
          epoch_output = [[] for _ in range(self.num_optimizers)]
  
          train_dataloader = self.trainer.data_connector.get_profiled_train_dataloader(train_dataloader)
          dataloader_idx = 0
          should_check_val = False
  
          for batch_idx, (batch, is_last_batch) in train_dataloader:
  
              self.trainer.batch_idx = batch_idx
  
              # ------------------------------------
              # TRAINING_STEP + TRAINING_STEP_END
              # ------------------------------------
              with self.trainer.profiler.profile("run_training_batch"):
                  batch_output = self.run_training_batch(batch, batch_idx, dataloader_idx)
  
              # when returning -1 from train_step, we end epoch early
              if batch_output.signal == -1:
                  break
  
              batch_end_outputs = self.process_train_step_outputs(
                  batch_output.training_step_output_for_epoch_end,
                  self.early_stopping_accumulator,
                  self.checkpoint_accumulator,
              )
              # hook
              # TODO: add outputs to batches
              self.on_train_batch_end(epoch_output, batch_end_outputs, batch, batch_idx, dataloader_idx)
  
              # -----------------------------------------
              # SAVE METRICS TO LOGGERS
              # -----------------------------------------
              self.trainer.logger_connector.log_train_step_metrics(batch_output)
  
              # -----------------------------------------
              # VALIDATE IF NEEDED + CHECKPOINT CALLBACK
              # -----------------------------------------
              should_check_val = self.should_check_val_fx(batch_idx, is_last_batch)
              if should_check_val:
                  self.trainer.run_evaluation()
  
                  # reset stage to train
                  self.trainer._set_wide_running_stage(RunningStage.TRAINING)
  
              # -----------------------------------------
              # SAVE LOGGERS (ie: Tensorboard, etc...)
              # -----------------------------------------
              self.save_loggers_on_train_batch_end()
  
              # update LR schedulers
              monitor_metrics = deepcopy(self.trainer.logger_connector.callback_metrics)
              self.update_train_loop_lr_schedulers(monitor_metrics=monitor_metrics)
              self.trainer.checkpoint_connector.has_trained = True
  
              # max steps reached, end training
              if (
                  self.trainer.max_steps is not None and self.trainer.max_steps == self.trainer.global_step + 1
                  and self._accumulated_batches_reached()
              ):
                  break
  
              # end epoch early
              # stop when the flag is changed or we've gone past the amount
              # requested in the batches
              if self.trainer.should_stop:
                  break
  
              self.trainer.total_batch_idx += 1
  
              # stop epoch if we limited the number of training batches
              if self._num_training_batches_reached(is_last_batch):
                  break
  
              # progress global step according to grads progress
              self.increment_accumulated_grad_global_step()
  
          # epoch end hook
          self.run_on_epoch_end_hook(epoch_output)
  
          # log epoch metrics
          self.trainer.logger_connector.log_train_epoch_end_metrics(
              epoch_output, self.checkpoint_accumulator, self.early_stopping_accumulator, self.num_optimizers
          )
  
          should_check_val = self.should_check_val_fx(batch_idx, is_last_batch, on_epoch=True)
          if should_check_val:
              self.trainer.run_evaluation(on_epoch=True)
  
              # reset stage to train
              self.trainer._set_wide_running_stage(RunningStage.TRAINING)
  
          should_skip_eval = self.trainer.evaluation_loop.should_skip_evaluation(self.trainer.num_val_batches)
          should_train_only = self.trainer.disable_validation or should_skip_eval
  
          if should_train_only:
              # update epoch level lr_schedulers
              self.trainer.optimizer_connector.update_learning_rates(interval='epoch')
              self.check_checkpoint_callback(True)
              self.check_early_stopping_callback(True)
  
          # increment the global step once
          # progress global step according to grads progress
          self.increment_accumulated_grad_global_step()
  
      def run_training_batch(self, batch, batch_idx, dataloader_idx):
          # track grad norms
          grad_norm_dic = {}
  
          # bookkeeping
          self.trainer.hiddens = None
  
          # track all outputs across time and num of optimizers
          batch_outputs = [[] for _ in range(len(self.get_optimizers_iterable()))]
  
          if batch is None:
              return AttributeDict(signal=0, grad_norm_dic=grad_norm_dic)
  
          # hook
          response = self.trainer.call_hook("on_batch_start")
          if response == -1:
              return AttributeDict(signal=-1, grad_norm_dic=grad_norm_dic)
  
          # hook
          response = self.trainer.call_hook("on_train_batch_start", batch, batch_idx, dataloader_idx)
          if response == -1:
              return AttributeDict(signal=-1, grad_norm_dic=grad_norm_dic)
  
          # lightning module hook
          splits = self.tbptt_split_batch(batch)
  
          for split_idx, split_batch in enumerate(splits):
  
              # create an iterable for optimizers and loop over them
              for opt_idx, optimizer in self.prepare_optimizers():
  
                  # toggle model params + set info to logger_connector
                  self.run_train_split_start(split_idx, split_batch, opt_idx, optimizer)
  
                  if self.should_accumulate():
                      # For gradient accumulation
  
                      # -------------------
                      # calculate loss (train step + train step end)
                      # -------------------
  
                      # automatic_optimization=True: perform dpp sync only when performing optimizer_step
                      # automatic_optimization=False: don't block synchronization here
                      with self.block_ddp_sync_behaviour():
                          self.training_step_and_backward(
                              split_batch, batch_idx, opt_idx, optimizer, self.trainer.hiddens
                          )
  
                      batch_outputs = self._process_closure_result(
                          batch_outputs=batch_outputs,
                          opt_idx=opt_idx,
                      )
  
                  # ------------------------------
                  # BACKWARD PASS
                  # ------------------------------
                  # gradient update with accumulated gradients
  
                  else:
                      if self.automatic_optimization:
  
                          def train_step_and_backward_closure():
                              result = self.training_step_and_backward(
                                  split_batch, batch_idx, opt_idx, optimizer, self.trainer.hiddens
                              )
                              return None if result is None else result.loss
  
                          # optimizer step
                          self.optimizer_step(optimizer, opt_idx, batch_idx, train_step_and_backward_closure)
  
                      else:
                          self._curr_step_result = self.training_step(
                              split_batch, batch_idx, opt_idx, self.trainer.hiddens
                          )
  
                      if self._curr_step_result is None:
                          # user decided to skip optimization
                          # make sure to zero grad.
                          continue
  
                      batch_outputs = self._process_closure_result(
                          batch_outputs=batch_outputs,
                          opt_idx=opt_idx,
                      )
  
                      # todo: Properly aggregate grad_norm accros opt_idx and split_idx
                      grad_norm_dic = self._cur_grad_norm_dict
                      self._cur_grad_norm_dict = None
  
                      # update running loss + reset accumulated loss
                      self.update_running_loss()
  
          result = AttributeDict(
              signal=0,
              grad_norm_dic=grad_norm_dic,
              training_step_output_for_epoch_end=batch_outputs,
          )
          return result
  
      @contextmanager
      def block_ddp_sync_behaviour(self, should_block_sync: bool = False):
          """
          automatic_optimization = True
          Blocks ddp sync gradients behaviour on backwards pass.
          This is useful for skipping sync when accumulating gradients, reducing communication overhead
  
          automatic_optimization = False
          do not block ddp gradient sync when using manual optimization
          as gradients are needed within the training step
  
          Returns:
              context manager with sync behaviour off
  
          """
          if (
              isinstance(self.trainer.training_type_plugin, ParallelPlugin)
              and (self.automatic_optimization or should_block_sync)
          ):
              with self.trainer.training_type_plugin.block_backward_sync():
                  yield None
          else:
              yield None
  
      def _process_closure_result(self, batch_outputs: list, opt_idx: int) -> list:
          opt_closure_result = self._curr_step_result
  
          if opt_closure_result is not None:
  
              # cache metrics
              self.trainer.logger_connector.cache_training_step_metrics(opt_closure_result)
  
              # track hiddens
              self.trainer.hiddens = self.process_hiddens(opt_closure_result)
  
              # check if loss or model weights are nan
              if self.trainer.terminate_on_nan:
                  self.trainer.detect_nan_tensors(opt_closure_result.loss)
  
              # track all the outputs across all steps
              batch_opt_idx = opt_idx if len(batch_outputs) > 1 else 0
              batch_outputs[batch_opt_idx].append(opt_closure_result.training_step_output_for_epoch_end)
  
              if self.automatic_optimization:
                  # track total loss for logging (avoid mem leaks)
                  self.accumulated_loss.append(opt_closure_result.loss)
  
          self._curr_step_result = None
  
          return batch_outputs
  
      def training_step_and_backward(self, split_batch, batch_idx, opt_idx, optimizer, hiddens):
          """
          wrap the forward step in a closure so second order methods work
          """
          with self.trainer.profiler.profile("training_step_and_backward"):
              # lightning module hook
              result = self.training_step(split_batch, batch_idx, opt_idx, hiddens)
              self._curr_step_result = result
  
              if result is None:
                  if self.automatic_optimization:
                      self.warning_cache.warn("training_step returned None if it was on purpose, ignore this warning...")
                  return None
  
              if not self._skip_backward and self.trainer.train_loop.automatic_optimization:
                  # backward pass
                  with self.trainer.profiler.profile("model_backward"):
                      self.backward(result, optimizer, opt_idx)
  
                  # hook - call this hook only
                  # when gradients have finished to accumulate
                  if not self.should_accumulate():
                      self.on_after_backward(result.training_step_output, batch_idx, result.loss)
  
                  # check if loss or model weights are nan
                  if self.trainer.terminate_on_nan:
                      self.trainer.detect_nan_tensors(result.loss)
  
                  if len(self.trainer.optimizers) > 1:
                      # revert back to previous state
                      self.trainer.get_model().untoggle_optimizer(opt_idx)
  
          return result
  
      def backward(self, result, optimizer, opt_idx, *args, **kwargs):
          self.trainer.dev_debugger.track_event("backward_call")
  
          should_accumulate = self.should_accumulate()
  
          # backward can be called manually in the training loop
          if isinstance(result, torch.Tensor):
              self.trainer.accelerator_backend.backward(result, optimizer, opt_idx, should_accumulate, *args, **kwargs)
          else:
              result.closure_loss = self.trainer.accelerator_backend.backward(
                  result.closure_loss, optimizer, opt_idx, should_accumulate, *args, **kwargs
              )
  
          if not self.should_accumulate():
              # track gradients
              self.track_and_norm_grad(optimizer=optimizer)
  
      def update_train_loop_lr_schedulers(self, monitor_metrics=None):
          num_accumulated_batches_reached = self._accumulated_batches_reached()
          num_training_batches_reached = self._num_training_batches_reached()
  
          if num_accumulated_batches_reached or num_training_batches_reached:
              # update lr
              self.trainer.optimizer_connector.update_learning_rates(interval="step", monitor_metrics=monitor_metrics)
  
      def run_on_epoch_end_hook(self, epoch_output):
          # inform logger the batch loop has finished
          self.trainer.logger_connector.on_train_epoch_end()
  
          self.trainer.call_hook('on_train_epoch_end', epoch_output)
          self.trainer.call_hook('on_epoch_end')
  
      def increment_accumulated_grad_global_step(self):
          num_accumulated_batches_reached = self._accumulated_batches_reached()
          num_training_batches_reached = self._num_training_batches_reached()
  
          # progress global step according to grads progress
          if num_accumulated_batches_reached or num_training_batches_reached:
              self.trainer.global_step += 1
  
      def _accumulated_batches_reached(self):
          return (self.trainer.batch_idx + 1) % self.trainer.accumulate_grad_batches == 0
  
      def _num_training_batches_reached(self, is_last_batch=False):
          return (self.trainer.batch_idx + 1) == self.trainer.num_training_batches or is_last_batch
  
      def should_accumulate(self):
          # checks if backward or backward + optimizer step (via closure)
          accumulation_done = self._accumulated_batches_reached()
          is_final_batch = self._num_training_batches_reached()
          return not (accumulation_done or is_final_batch)
  
      def should_check_val_fx(self, batch_idx, is_last_batch, on_epoch=False):
          # decide if we should run validation
          is_val_check_batch = (batch_idx + 1) % self.trainer.val_check_batch == 0
          is_val_check_epoch = (self.trainer.current_epoch + 1) % self.trainer.check_val_every_n_epoch == 0
          can_check_val = self.trainer.enable_validation and is_val_check_epoch
          is_last_batch_for_infinite_dataset = is_last_batch and self.trainer.val_check_batch == float("inf")
          epoch_end_val_check = self.trainer.val_check_batch == self.trainer.num_training_batches
  
          should_check_val = ((is_val_check_batch and epoch_end_val_check) or self.trainer.should_stop
                              or is_last_batch_for_infinite_dataset
                              ) if on_epoch else (is_val_check_batch and not epoch_end_val_check)
  
          return should_check_val and can_check_val
  
      def build_train_args(self, batch, batch_idx, opt_idx, hiddens):
          # enable not needing to add opt_idx to training_step
          args = [batch, batch_idx]
  
          if len(self.trainer.optimizers) > 1:
              if self.trainer.has_arg("training_step", "optimizer_idx"):
                  args.append(opt_idx)
              else:
                  num_opts = len(self.trainer.optimizers)
                  raise ValueError(
                      f"Your LightningModule defines {num_opts} optimizers but "
                      f'training_step is missing the "optimizer_idx" argument.'
                  )
  
          # pass hiddens if using tbptt
          if self.trainer.truncated_bptt_steps is not None:
              args.append(hiddens)
  
          return args
  
      def save_loggers_on_train_batch_end(self):
          # when loggers should save to disk
          should_flush_logs = self.trainer.logger_connector.should_flush_logs
          if should_flush_logs and self.trainer.is_global_zero and self.trainer.logger is not None:
              self.trainer.logger.save()
  
      def process_train_step_outputs(self, all_train_step_outputs, early_stopping_accumulator, checkpoint_accumulator):
          """
          Figure out what needs to be tracked/logged at the end of the epoch
          """
  
          # the training step outputs a list per optimizer. The list contains the outputs at each time step
          # when no TBPTT is used, then the list has 1 item per batch
          # when TBPTT IS used, then the list has n items (1 per time step)
          batch_end_outputs = []
          for optimizer_idx_outputs in all_train_step_outputs:
              # extract one representative sample from each time step (1 if no tbptt) and 0th optimizer
              if len(optimizer_idx_outputs) == 0:
                  continue
  
              sample_output = optimizer_idx_outputs[-1]
  
              # pull out callback info if available (ie: Results object)
              if isinstance(sample_output, dict) and "early_stop_on" in sample_output:
                  early_stopping_accumulator.accumulate(sample_output["early_stop_on"])
  
              if isinstance(sample_output, dict) and "checkpoint_on" in sample_output:
                  checkpoint_accumulator.accumulate(sample_output["checkpoint_on"])
  
              batch_end_outputs.append(optimizer_idx_outputs)
  
          return batch_end_outputs
  
      def prepare_optimizers(self):
          # in manual optimization we loop over all optimizers at once
          optimizers = self.get_optimizers_iterable()
          if not self.automatic_optimization:
              optimizers = [optimizers[0]]
          return optimizers
  
      def run_train_split_start(self, split_idx, split_batch, opt_idx, optimizer):
          # set split_idx to trainer for tracking
          self.trainer.split_idx = split_idx
  
          # make sure only the gradients of the current optimizer's parameters are calculated
          # in the training step to prevent dangling gradients in multiple-optimizer setup.
          if self.automatic_optimization and len(self.trainer.optimizers) > 1:
              model = self.trainer.get_model()
              model.toggle_optimizer(optimizer, opt_idx)
  
          # use to track metrics internally
          self.trainer.logger_connector.on_train_split_start(split_idx, opt_idx, split_batch)
  
      def update_running_loss(self):
          accumulated_loss = self.accumulated_loss.mean()
  
          if accumulated_loss is not None:
              # calculate running loss for display
              self.running_loss.append(self.accumulated_loss.mean() * self.trainer.accumulate_grad_batches)
  
          # reset for next set of accumulated grads
          self.accumulated_loss.reset()
  
apis: torch.cuda.empty_cache numpy.cumsum numpy.argmax