# 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 from copy import copy, deepcopy import numpy as np import torch import torch.distributed as torch_distrib from pytorch_lightning.callbacks import ModelCheckpoint from pytorch_lightning.core.lightning import LightningModule from pytorch_lightning.core.memory import ModelSummary from pytorch_lightning.core.step_result import EvalResult, Result from pytorch_lightning.trainer.states import TrainerState from pytorch_lightning.trainer.supporters import TensorRunningAccum, Accumulator from pytorch_lightning.utilities import parsing, AMPType 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_utils import is_overridden from pytorch_lightning.utilities.parsing import AttributeDict from pytorch_lightning.utilities.warning_utils import WarningCache class TrainLoop: def __init__(self, trainer): 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 def on_trainer_init( self, max_epochs, min_epochs, max_steps, min_steps, num_sanity_val_steps, automatic_optimization ): 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 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()) return num_optimizers def should_skip_training(self): if self.trainer.current_epoch >= self.trainer.max_epochs: return True if self.trainer.limit_train_batches == 0: return True return False def on_train_start(self): # clear cache before training if self.trainer.on_gpu and self.trainer.root_gpu is not None: # use context because of: # https://discuss.pytorch.org/t/out-of-memory-when-i-use-torch-cuda-empty-cache/57898 with torch.cuda.device(f"cuda:{self.trainer.root_gpu}"): torch.cuda.empty_cache() # hook self.trainer.call_hook("on_train_start") def setup_fit(self, model, train_dataloader, val_dataloaders, datamodule): # bind logger and other properties self.trainer.model_connector.copy_trainer_model_properties(model) # 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) def setup_training(self, model: LightningModule): """Sanity check a few things before starting actual training. Args: model: The model to run sanity test on. """ # -------------------------- # Setup?? # -------------------------- ref_model = model if self.trainer.data_parallel: ref_model = model.module # set the ranks and devices self.trainer.accelerator_backend.dist.rank = self.trainer.global_rank self.trainer.accelerator_backend.dist.device = ref_model.device # give model convenience properties ref_model.trainer = self.trainer # set local properties on the model self.trainer.model_connector.copy_trainer_model_properties(ref_model) # init amp. Must be done here instead of __init__ to allow ddp to work if self.trainer.amp_backend == AMPType.NATIVE and self.trainer.precision == 16 and not self.trainer.use_tpu: self.trainer.scaler = self.trainer.precision_connector.backend.scaler # log hyper-parameters if self.trainer.logger is not None: # save exp to get started (this is where the first experiment logs are written) self.trainer.logger.log_hyperparams(ref_model.hparams_initial) self.trainer.logger.log_graph(ref_model) self.trainer.logger.save() # wait for all to join if on distributed self.trainer.accelerator_backend.barrier("setup_training") # register auto-resubmit when on SLURM self.trainer.slurm_connector.register_slurm_signal_handlers() # -------------------------- # Pre-train # -------------------------- # on pretrain routine start self.trainer.on_pretrain_routine_start(ref_model) if self.trainer.is_function_implemented("on_pretrain_routine_start"): ref_model.on_pretrain_routine_start() # print model summary if self.trainer.is_global_zero and self.trainer.weights_summary is not None and not self.trainer.testing: if self.trainer.weights_summary in ModelSummary.MODES: ref_model.summarize(mode=self.trainer.weights_summary) else: raise MisconfigurationException("weights_summary can be None, " + ", ".join(ModelSummary.MODES)) # track model now. # if cluster resets state, the model will update with the saved weights self.trainer.model = model # restore training and model before hpc is called self.trainer.checkpoint_connector.restore_weights(model) # on pretrain routine end self.trainer.on_pretrain_routine_end(ref_model) if self.trainer.is_function_implemented("on_pretrain_routine_end"): ref_model.on_pretrain_routine_end() 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_save=True, is_last=True) self.trainer.global_step += 1 # hook self.trainer.call_hook("on_train_end") # kill loggers if self.trainer.logger is not None: 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.on_gpu: model = self.trainer.get_model() model.cpu() torch.cuda.empty_cache() def check_checkpoint_callback(self, should_save, is_last=False): # TODO bake this logic into the checkpoint callback if should_save and self.trainer.checkpoint_connector.has_trained: checkpoint_callbacks = [c for c in self.trainer.callbacks if isinstance(c, ModelCheckpoint)] if is_last and any(c.save_last for c in checkpoint_callbacks): rank_zero_info("Saving latest checkpoint...") model = self.trainer.get_model() [c.on_validation_end(self.trainer, model) for c in checkpoint_callbacks] 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 self.trainer.reload_dataloaders_every_epoch: self.trainer.reset_train_dataloader(model) # set seed for distributed sampler (enables shuffling for each epoch) try: self.trainer.train_dataloader.sampler.set_epoch(epoch) except Exception: pass # 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, epoch_end_outputs, batch, batch_idx, dataloader_idx): # hook self.trainer.call_hook('on_batch_end') self.trainer.call_hook('on_train_batch_end', epoch_end_outputs, batch, batch_idx, dataloader_idx) # figure out what to track for epoch end self.track_epoch_end_reduce_metrics(epoch_output, epoch_end_outputs) # reset batch logger internals self.trainer.logger_connector.on_train_batch_end() def reset_train_val_dataloaders(self, model): if 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, epoch_end_outputs): # track the outputs to reduce at the end of the epoch for opt_idx, opt_outputs in enumerate(epoch_end_outputs): # 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' training_step_output = self.trainer.accelerator_backend.training_step(args) 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) # don't allow EvalResult in the training_step if isinstance(training_step_output, EvalResult): raise MisconfigurationException( "training_step cannot return EvalResult, " "use a dict or TrainResult instead" ) 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, *args, **kwargs): with self.trainer.profiler.profile("optimizer_step"): # optimizer step lightningModule hook self.trainer.accelerator_backend.optimizer_step( optimizer, batch_idx, opt_idx, train_step_and_backward_closure, *args, **kwargs ) 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(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._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): # get model model = self.trainer.get_model() # 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)] # enable profiling for the dataloader 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 # ------------------------------------ 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 # only track outputs when user implements training_epoch_end # otherwise we will build up unnecessary memory epoch_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, epoch_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(test_mode=False) # reset stage to train self.trainer.logger_connector.set_stage("train") # ----------------------------------------- # 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: accumulation_done = self._accumulated_batches_reached() # Ensure accumulation across batches has completed before breaking loop if accumulation_done: 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 (batch_idx + 1) >= self.trainer.num_training_batches: 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 ) # when no val loop is present or fast-dev-run still need to call checkpoints self.check_checkpoint_callback(not (should_check_val or is_overridden('validation_step', model))) # 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 using_results_obj = False 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) # ------------------- # perform dpp sync only when performing optimizer_step 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. self.zero_grad_handler(batch_idx, optimizer, opt_idx) 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 # hook + clear gradients self.zero_grad_handler(batch_idx, optimizer, opt_idx) # 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): if isinstance(self.trainer.model, torch.nn.parallel.DistributedDataParallel): yield self.trainer.model.no_sync() else: yield 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 """ # lightning module hook result = self.training_step(split_batch, batch_idx, opt_idx, hiddens) self._curr_step_result = result if result is None: self.warning_cache.warn("training_step returned None if it was on purpose, ignore this warning...") return None if 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) return result def backward(self, result, optimizer, opt_idx, *args, **kwargs): self.trainer.dev_debugger.track_event("backward_call") # backward can be called manually in the training loop if isinstance(result, torch.Tensor): self.trainer.accelerator_backend.backward(result, optimizer, opt_idx, *args, **kwargs) else: result.closure_loss = self.trainer.accelerator_backend.backward( result.closure_loss, optimizer, opt_idx, *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): self.trainer.call_hook('on_epoch_end') self.trainer.call_hook('on_train_epoch_end', epoch_output) self.trainer.logger_connector.on_train_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): return (self.trainer.batch_idx + 1) == self.trainer.num_training_batches 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): # 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 should_check_val = is_val_check_batch or self.trainer.should_stop is_last_batch_for_infinite_dataset = is_last_batch and self.trainer.val_check_batch == float("inf") should_check_val = can_check_val and (should_check_val or is_last_batch_for_infinite_dataset) return should_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 or self.trainer.fast_dev_run: if 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) epoch_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"]) # 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 # only track when a) it needs to be autoreduced OR b) the user wants to manually reduce on epoch end if is_overridden("training_epoch_end", model=self.trainer.get_model()) or auto_reduce_tng_result: epoch_end_outputs.append(optimizer_idx_outputs) return epoch_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() def zero_grad_handler(self, batch_idx, optimizer, opt_idx): if self.automatic_optimization: # hook self.on_before_zero_grad(optimizer) optimizers = enumerate([optimizer]) else: # should be called handled in `manual_optimizer_step` optimizers = [] for idx, optimizer in optimizers: self.optimizer_zero_grad(batch_idx, optimizer, opt_idx)