1007 lines
42 KiB
Python
1007 lines
42 KiB
Python
# Copyright The PyTorch Lightning team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from contextlib import contextmanager, suppress
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from copy import copy, deepcopy
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from typing import Any, Dict, List, Optional, Union
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import numpy as np
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import torch
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from pytorch_lightning.core.optimizer import LightningOptimizer
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from pytorch_lightning.core.step_result import Result
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from pytorch_lightning.plugins import ParallelPlugin
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from pytorch_lightning.trainer.supporters import TensorRunningAccum
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from pytorch_lightning.utilities import _TPU_AVAILABLE, AMPType, DeviceType
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from pytorch_lightning.utilities.distributed import rank_zero_info
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from pytorch_lightning.utilities.finite_checks import detect_nan_parameters
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from pytorch_lightning.utilities.grads import grad_norm
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from pytorch_lightning.utilities.model_helpers import is_overridden
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from pytorch_lightning.utilities.parsing import AttributeDict
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from pytorch_lightning.utilities.signature_utils import is_param_in_hook_signature
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from pytorch_lightning.utilities.warnings import WarningCache
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class TrainLoop:
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def __init__(
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self,
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trainer,
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multiple_trainloader_mode: str,
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max_epochs: Optional[int],
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min_epochs: Optional[int],
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max_steps: Optional[int],
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min_steps: Optional[int],
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num_sanity_val_steps: int,
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):
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self.trainer = trainer
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self.accumulated_loss = None
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self.warning_cache = WarningCache()
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self._teardown_already_run = False
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self.running_loss = TensorRunningAccum(window_length=20)
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self._curr_step_result = None
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self._cur_grad_norm_dict = None
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self._multiple_trainloader_mode = multiple_trainloader_mode
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self._skip_backward = False
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self.trainer._multiple_trainloader_mode = multiple_trainloader_mode
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self._optimizer_freq_cumsum = None
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self.global_step = 0
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self.current_epoch = 0
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self.trainer.should_stop = False
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self.total_batch_idx = 0
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self.batch_idx = 0
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self.trainer.num_training_batches = 0
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self.trainer.train_dataloader = None
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# If neither max_epochs or max_steps is set, then use existing default of max_epochs = 1000
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self.max_epochs = 1000 if (max_epochs is None and max_steps is None) else max_epochs
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# If neither min_epochs or min_steps is set, then use existing default of min_epochs = 1
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self.min_epochs = 1 if (min_epochs is None and min_steps is None) else min_epochs
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self.max_steps = max_steps
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self.min_steps = min_steps
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if num_sanity_val_steps == -1:
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self.trainer.num_sanity_val_steps = float("inf")
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else:
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self.trainer.num_sanity_val_steps = num_sanity_val_steps
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@property
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def num_optimizers(self):
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num_optimizers = len(self.get_optimizers_iterable())
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return num_optimizers
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@property
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def optimizer_freq_cumsum(self):
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if self._optimizer_freq_cumsum is None:
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self._optimizer_freq_cumsum = np.cumsum(self.trainer.optimizer_frequencies)
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return self._optimizer_freq_cumsum
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def should_skip_training(self) -> bool:
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should_by_max_steps = self.max_steps is not None and self.global_step >= self.max_steps
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should_by_epoch = self.max_epochs is not None and self.current_epoch >= self.max_epochs
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return should_by_max_steps or should_by_epoch or self.trainer.num_training_batches == 0
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def on_train_start(self):
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# hook
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self.trainer.call_hook("on_train_start")
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def on_train_end(self):
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if self._teardown_already_run:
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return
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self._teardown_already_run = True
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# trigger checkpoint check. need to temporarily decrease the global step to avoid saving duplicates
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# when a checkpoint was saved at the last step
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self.global_step -= 1
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self.check_checkpoint_callback(should_update=True, is_last=True)
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self.global_step += 1
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# hook
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self.trainer.call_hook("on_train_end")
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# todo: TPU 8 cores hangs in flush with TensorBoard. Might do for all loggers.
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# It might be related to xla tensors blocked when moving the cpu
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# kill loggers
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if self.trainer.logger is not None:
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self.trainer.logger.finalize("success")
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# summarize profile results
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self.trainer.profiler.describe()
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# give accelerators a chance to finish
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self.trainer.accelerator.on_train_end()
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# reset bookkeeping
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self.trainer.state.stage = None
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def check_checkpoint_callback(self, should_update, is_last=False):
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# TODO bake this logic into the ModelCheckpoint callback
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if should_update and self.trainer.checkpoint_connector.has_trained:
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callbacks = self.trainer.checkpoint_callbacks
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if is_last and any(cb.save_last and cb.verbose for cb in callbacks):
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rank_zero_info("Saving latest checkpoint...")
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model = self.trainer.lightning_module
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for cb in callbacks:
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cb.on_validation_end(self.trainer, model)
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def on_train_epoch_start(self, epoch):
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# update training progress in trainer
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self.current_epoch = epoch
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model = self.trainer.lightning_module
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# reset train dataloader
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if epoch != 0 and self.trainer.reload_dataloaders_every_epoch:
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self.trainer.reset_train_dataloader(model)
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# todo: specify the possible exception
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with suppress(Exception):
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# set seed for distributed sampler (enables shuffling for each epoch)
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self.trainer.train_dataloader.sampler.set_epoch(epoch)
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# changing gradient according accumulation_scheduler
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self.trainer.accumulation_scheduler.on_train_epoch_start(self.trainer, self.trainer.lightning_module)
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# stores accumulated grad fractions per batch
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self.accumulated_loss = TensorRunningAccum(window_length=self.trainer.accumulate_grad_batches)
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# hook
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self.trainer.call_hook("on_epoch_start")
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self.trainer.call_hook("on_train_epoch_start")
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def on_train_batch_end(self, epoch_output, batch_end_outputs, batch, batch_idx, dataloader_idx):
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batch_end_outputs = [opt_idx_out for opt_idx_out in batch_end_outputs if len(opt_idx_out)]
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processed_batch_end_outputs = TrainLoop._prepare_outputs(batch_end_outputs, batch_mode=True)
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# hook
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self.trainer.call_hook('on_train_batch_end', processed_batch_end_outputs, batch, batch_idx, dataloader_idx)
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self.trainer.call_hook('on_batch_end')
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# figure out what to track for epoch end
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self.track_epoch_end_reduce_metrics(epoch_output, batch_end_outputs)
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# reset batch logger internals
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self.trainer.logger_connector.on_train_batch_end()
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def reset_train_val_dataloaders(self, model) -> None:
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"""
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Resets train and val dataloaders if none are attached to the trainer.
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The val dataloader must be initialized before training loop starts, as the training loop
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inspects the val dataloader to determine whether to run the evaluation loop.
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"""
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if self.trainer.train_dataloader is None:
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self.trainer.reset_train_dataloader(model)
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if self.trainer.val_dataloaders is None:
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self.trainer.reset_val_dataloader(model)
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def track_epoch_end_reduce_metrics(self, epoch_output, batch_end_outputs):
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hook_overridden = self._should_add_batch_output_to_epoch_output()
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# track the outputs to reduce at the end of the epoch
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for opt_idx, opt_outputs in enumerate(batch_end_outputs):
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sample_output = opt_outputs[-1]
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# decide if we need to reduce at the end of the epoch automatically
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auto_reduce_tng_result = isinstance(sample_output, Result) and sample_output.should_reduce_on_epoch_end
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# only track when a) it needs to be autoreduced OR b) the user wants to manually reduce on epoch end
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if not (hook_overridden or auto_reduce_tng_result):
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continue
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# with 1 step (no tbptt) don't use a sequence at epoch end
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if isinstance(opt_outputs, list) and len(opt_outputs) == 1 and not isinstance(opt_outputs[0], Result):
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opt_outputs = opt_outputs[0]
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epoch_output[opt_idx].append(opt_outputs)
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def _should_add_batch_output_to_epoch_output(self) -> bool:
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# We add to the epoch outputs if
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# 1. The model defines training_epoch_end OR
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# 2. The model overrides on_train_epoch_end which has `outputs` in the signature
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# TODO: in v1.5 this only needs to check if training_epoch_end is overridden
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lightning_module = self.trainer.lightning_module
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if is_overridden("training_epoch_end", model=lightning_module):
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return True
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if is_overridden("on_train_epoch_end", model=lightning_module):
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model_hook_fx = getattr(lightning_module, "on_train_epoch_end")
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if is_param_in_hook_signature(model_hook_fx, "outputs"):
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return True
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return False
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def get_optimizers_iterable(self, batch_idx=None):
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"""
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Generates an iterable with (idx, optimizer) for each optimizer.
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"""
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if not self.trainer.optimizer_frequencies:
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# call training_step once per optimizer
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return list(enumerate(self.trainer.optimizers))
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if batch_idx is None:
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batch_idx = self.total_batch_idx
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optimizers_loop_length = self.optimizer_freq_cumsum[-1]
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current_place_in_loop = batch_idx % optimizers_loop_length
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# find optimzier index by looking for the first {item > current_place} in the cumsum list
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opt_idx = np.argmax(self.optimizer_freq_cumsum > current_place_in_loop)
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return [[opt_idx, self.trainer.optimizers[opt_idx]]]
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def on_after_backward(self, training_step_output, batch_idx, untouched_loss):
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training_step_output.detach()
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# insert after step hook
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self.trainer.call_hook("on_after_backward")
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# when in dev debugging track the losses
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self.trainer.dev_debugger.track_train_loss_history(batch_idx, untouched_loss.detach())
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def _check_training_step_output(self, training_step_output):
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if isinstance(training_step_output, torch.Tensor) and not self.trainer.lightning_module.automatic_optimization:
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if training_step_output.grad_fn is None:
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# TODO: Find why - RuntimeError: Expected to mark a variable ready only once ...
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raise MisconfigurationException("In manual optimization, `training_step` should not return a Tensor")
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def training_step(self, split_batch, batch_idx, opt_idx, hiddens):
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# give the PL module a result for logging
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model_ref = self.trainer.lightning_module
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with self.trainer.profiler.profile("model_forward"):
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args = self.build_train_args(split_batch, batch_idx, opt_idx, hiddens)
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# manually capture logged metrics
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model_ref._current_fx_name = 'training_step'
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model_ref._results = Result()
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with self.trainer.profiler.profile("training_step"):
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training_step_output = self.trainer.accelerator.training_step(args)
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self.trainer.accelerator.post_training_step()
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self.trainer.logger_connector.cache_logged_metrics()
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self._check_training_step_output(training_step_output)
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training_step_output = self.trainer.call_hook("training_step_end", training_step_output)
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training_step_output_for_epoch_end, training_step_output = self._process_training_step_output(
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training_step_output, split_batch
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)
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if training_step_output_for_epoch_end is None:
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return
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# enable empty loss when using manual opt
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closure_loss = None
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untouched_loss = None
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if self.trainer.lightning_module.automatic_optimization:
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# accumulate loss. if accumulate_grad_batches==1, no effect
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closure_loss = training_step_output.minimize / self.trainer.accumulate_grad_batches
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# the loss will get scaled for amp. avoid any modifications to it
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untouched_loss = closure_loss.detach().clone()
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# result
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result = AttributeDict(
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closure_loss=closure_loss,
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loss=untouched_loss,
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training_step_output=training_step_output,
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training_step_output_for_epoch_end=training_step_output_for_epoch_end,
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)
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return result
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def _process_training_step_output(self, training_step_output, split_batch):
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training_step_output_for_epoch_end = training_step_output
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# enable validation_step return None
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if training_step_output_for_epoch_end is None:
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return None, None
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result = self.trainer.lightning_module._results
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loss = None
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hiddens = None
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result["extra"] = {}
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# handle dict return
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if isinstance(training_step_output, dict):
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loss = training_step_output.pop("loss", None)
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hiddens = training_step_output.pop("hiddens", None)
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if hiddens is not None:
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hiddens = hiddens.detach()
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result["extra"] = training_step_output
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# handle scalar return
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elif isinstance(training_step_output, torch.Tensor):
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loss = training_step_output
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# map to results under the hood
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result.minimize = loss
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self.trainer.hiddens = hiddens
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# track batch for manual reduction with result
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result.track_batch_size(len(split_batch))
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# track metrics without grads for epoch reduction
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training_step_output_for_epoch_end = copy(result)
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training_step_output_for_epoch_end = training_step_output_for_epoch_end.detach()
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if self.trainer.move_metrics_to_cpu:
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training_step_output_for_epoch_end = training_step_output_for_epoch_end.cpu()
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return training_step_output_for_epoch_end, result
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@staticmethod
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def _prepare_outputs(
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outputs: List[List[List[Result]]],
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batch_mode: bool,
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) -> Union[List[List[List[Dict]]], List[List[Dict]], List[Dict], Dict]:
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"""
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Extract required information from batch or epoch end results.
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Args:
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outputs: A 3-dimensional list of ``Result`` objects with dimensions:
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[optimizer outs][batch outs][tbptt steps].
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batch_mode: If True, ignore the batch output dimension.
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Returns:
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The cleaned outputs with ``Result`` objects converted to dictionaries. All list dimensions of size one will
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be collapsed.
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"""
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processed_outputs = []
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for opt_outputs in outputs:
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# handle an edge case where an optimizer output is the empty list
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if len(opt_outputs) == 0:
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continue
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processed_batch_outputs = []
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if batch_mode:
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opt_outputs = [opt_outputs]
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for batch_outputs in opt_outputs:
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processed_tbptt_outputs = []
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for tbptt_output in batch_outputs:
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out = tbptt_output.extra
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out['loss'] = tbptt_output.minimize
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processed_tbptt_outputs.append(out)
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# if there was only one tbptt step then we can collapse that dimension
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if len(processed_tbptt_outputs) == 1:
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processed_tbptt_outputs = processed_tbptt_outputs[0]
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processed_batch_outputs.append(processed_tbptt_outputs)
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# batch_outputs should be just one dict (or a list of dicts if using tbptt) per optimizer
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if batch_mode:
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processed_batch_outputs = processed_batch_outputs[0]
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processed_outputs.append(processed_batch_outputs)
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# if there is only one optimiser then we collapse that dimension
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if len(processed_outputs) == 1:
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processed_outputs = processed_outputs[0]
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return processed_outputs
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def optimizer_step(self, optimizer, opt_idx, batch_idx, train_step_and_backward_closure):
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model_ref = self.trainer.lightning_module
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is_lbfgs = isinstance(optimizer, torch.optim.LBFGS)
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using_native_amp = self.trainer.amp_backend == AMPType.NATIVE
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# native amp + lbfgs is a no go right now
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if using_native_amp and is_lbfgs:
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raise MisconfigurationException(
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'native PyTorch amp and lbfgs are not compatible.'
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' To request, please file a Github issue in PyTorch and tag @mcarilli'
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)
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# wraps into LightningOptimizer only for running step
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optimizer = LightningOptimizer._to_lightning_optimizer(optimizer, self.trainer, opt_idx)
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# model hook
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model_ref.optimizer_step(
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self.trainer.current_epoch,
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batch_idx,
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optimizer,
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opt_idx,
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train_step_and_backward_closure,
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on_tpu=self.trainer._device_type == DeviceType.TPU and _TPU_AVAILABLE,
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using_native_amp=using_native_amp,
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using_lbfgs=is_lbfgs,
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)
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def on_before_zero_grad(self, optimizer):
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self.trainer.call_hook('on_before_zero_grad', optimizer)
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def optimizer_zero_grad(self, batch_idx, optimizer, opt_idx):
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self.trainer.accelerator.optimizer_zero_grad(self.trainer.current_epoch, batch_idx, optimizer, opt_idx)
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def track_and_norm_grad(self, optimizer):
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# track gradient norms
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grad_norm_dic = self._track_gradient_norm()
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# clip gradients
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self.trainer.accelerator.clip_gradients(
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optimizer, self.trainer.gradient_clip_val, gradient_clip_algorithm=self.trainer.gradient_clip_algorithm
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)
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self._cur_grad_norm_dict = grad_norm_dic
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def _track_gradient_norm(self):
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grad_norm_dict = {}
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if (self.global_step + 1) % self.trainer.log_every_n_steps == 0:
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if float(self.trainer.track_grad_norm) > 0:
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model = self.trainer.lightning_module
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grad_norm_dict = grad_norm(model, self.trainer.track_grad_norm)
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return grad_norm_dict
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def _tbptt_split_batch(self, batch: Any) -> List[Any]:
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splits = [batch]
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truncated_bptt_enabled = self._truncated_bptt_enabled()
|
|
if truncated_bptt_enabled:
|
|
model_ref = self.trainer.lightning_module
|
|
with self.trainer.profiler.profile("tbptt_split_batch"):
|
|
splits = model_ref.tbptt_split_batch(batch, self._truncated_bptt_steps())
|
|
return splits
|
|
|
|
def run_training_epoch(self):
|
|
# modify dataloader if needed (ddp, etc...)
|
|
train_dataloader = self.trainer.accelerator.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
|
|
val_loop_called = False
|
|
|
|
batch_idx = None
|
|
is_last_batch = None
|
|
|
|
for batch_idx, (batch, is_last_batch) in train_dataloader:
|
|
self.batch_idx = batch_idx
|
|
self.trainer.is_last_batch = is_last_batch
|
|
|
|
# ------------------------------------
|
|
# 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
|
|
|
|
# hook
|
|
# TODO: add outputs to batches
|
|
self.on_train_batch_end(
|
|
epoch_output,
|
|
batch_output.training_step_output_for_epoch_end,
|
|
batch,
|
|
batch_idx,
|
|
dataloader_idx,
|
|
)
|
|
|
|
# -----------------------------------------
|
|
# SAVE METRICS TO LOGGERS
|
|
# -----------------------------------------
|
|
self.trainer.logger_connector.log_train_step_metrics(batch_output)
|
|
|
|
# -----------------------------------------
|
|
# VALIDATE IF NEEDED
|
|
# -----------------------------------------
|
|
should_check_val = self._should_check_val_fx(batch_idx, is_last_batch)
|
|
if should_check_val:
|
|
self.trainer.validating = True
|
|
self.trainer._run_evaluation()
|
|
self.trainer.training = True
|
|
val_loop_called = True
|
|
|
|
# -----------------------------------------
|
|
# 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.max_steps is not None and self.max_steps <= self.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.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()
|
|
|
|
if batch_idx is None:
|
|
# dataloader/iterator did not produce a batch
|
|
return
|
|
|
|
# handle epoch_output on epoch end
|
|
self.on_train_epoch_end(epoch_output)
|
|
|
|
# log epoch metrics
|
|
self.trainer.logger_connector.log_train_epoch_end_metrics(epoch_output)
|
|
|
|
should_check_val = self._should_check_val_fx(batch_idx, is_last_batch, on_epoch=True)
|
|
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
|
|
|
|
# update epoch level lr_schedulers if no val loop outside train loop is triggered
|
|
if (val_loop_called and not should_check_val) or should_train_only:
|
|
self.trainer.optimizer_connector.update_learning_rates(interval='epoch')
|
|
|
|
if should_train_only:
|
|
self.check_checkpoint_callback(True)
|
|
|
|
if should_check_val:
|
|
self.trainer.validating = True
|
|
self.trainer._run_evaluation(on_epoch=True)
|
|
self.trainer.training = True
|
|
|
|
# increment the global step once
|
|
# progress global step according to grads progress
|
|
self.increment_accumulated_grad_global_step()
|
|
|
|
def on_train_epoch_end(self, epoch_output: List[List[List[Result]]]) -> None:
|
|
# inform logger the batch loop has finished
|
|
self.trainer.logger_connector.on_train_epoch_end()
|
|
|
|
# prepare epoch output
|
|
processed_epoch_output = TrainLoop._prepare_outputs(epoch_output, batch_mode=False)
|
|
|
|
# get the model and call model.training_epoch_end
|
|
model = self.trainer.lightning_module
|
|
|
|
if is_overridden('training_epoch_end', model=model):
|
|
# run training_epoch_end
|
|
# refresh the result for custom logging at the epoch level
|
|
model._current_fx_name = 'training_epoch_end'
|
|
|
|
# lightningmodule hook
|
|
training_epoch_end_output = model.training_epoch_end(processed_epoch_output)
|
|
|
|
if training_epoch_end_output is not None:
|
|
raise MisconfigurationException(
|
|
'training_epoch_end expects a return of None. '
|
|
'HINT: remove the return statement in training_epoch_end'
|
|
)
|
|
|
|
# capture logging
|
|
self.trainer.logger_connector.cache_logged_metrics()
|
|
|
|
# call train epoch end hooks
|
|
self._on_train_epoch_end_hook(processed_epoch_output)
|
|
self.trainer.call_hook('on_epoch_end')
|
|
|
|
def _on_train_epoch_end_hook(self, processed_epoch_output) -> None:
|
|
# We cannot rely on Trainer.call_hook because the signatures might be different across
|
|
# lightning module and callback
|
|
# As a result, we need to inspect if the module accepts `outputs` in `on_train_epoch_end`
|
|
|
|
# This implementation is copied from Trainer.call_hook
|
|
hook_name = "on_train_epoch_end"
|
|
|
|
# set hook_name to model + reset Result obj
|
|
skip = self.trainer._reset_result_and_set_hook_fx_name(hook_name)
|
|
|
|
# always profile hooks
|
|
with self.trainer.profiler.profile(hook_name):
|
|
|
|
# first call trainer hook
|
|
if hasattr(self.trainer, hook_name):
|
|
trainer_hook = getattr(self.trainer, hook_name)
|
|
trainer_hook(processed_epoch_output)
|
|
|
|
# next call hook in lightningModule
|
|
model_ref = self.trainer.lightning_module
|
|
if is_overridden(hook_name, model_ref):
|
|
hook_fx = getattr(model_ref, hook_name)
|
|
if is_param_in_hook_signature(hook_fx, "outputs"):
|
|
self.warning_cache.warn(
|
|
"The signature of `ModelHooks.on_train_epoch_end` has changed in v1.3."
|
|
" `outputs` parameter has been deprecated."
|
|
" Support for the old signature will be removed in v1.5", DeprecationWarning
|
|
)
|
|
model_ref.on_train_epoch_end(processed_epoch_output)
|
|
else:
|
|
model_ref.on_train_epoch_end()
|
|
|
|
# if the PL module doesn't have the hook then call the accelerator
|
|
# used to auto-reduce things for the user with Results obj
|
|
elif hasattr(self.trainer.accelerator, hook_name):
|
|
accelerator_hook = getattr(self.trainer.accelerator, hook_name)
|
|
accelerator_hook()
|
|
|
|
if not skip:
|
|
self.trainer._cache_logged_metrics()
|
|
|
|
def run_training_batch(self, batch, batch_idx, dataloader_idx):
|
|
# track grad norms
|
|
grad_norm_dic = {}
|
|
|
|
# bookkeeping
|
|
self.trainer.hiddens = None
|
|
|
|
optimizers = self.prepare_optimizers()
|
|
|
|
# track all outputs across time and num of optimizers
|
|
batch_outputs = [[] for _ in range(len(optimizers))]
|
|
|
|
if batch is None:
|
|
self.warning_cache.warn("train_dataloader yielded None. If this was on purpose, ignore this warning...")
|
|
return AttributeDict(
|
|
signal=0,
|
|
grad_norm_dic=grad_norm_dic,
|
|
training_step_output_for_epoch_end=batch_outputs,
|
|
)
|
|
|
|
# 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 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.trainer.lightning_module.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.trainer.lightning_module.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)
|
|
|
|
# check if loss or model weights are nan
|
|
if self.trainer.terminate_on_nan:
|
|
self._check_finite(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.trainer.lightning_module.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 forward, zero_grad and backward 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 not self._skip_backward and self.trainer.lightning_module.automatic_optimization:
|
|
is_first_batch_to_accumulate = batch_idx % self.trainer.accumulate_grad_batches == 0
|
|
|
|
if is_first_batch_to_accumulate:
|
|
self.on_before_zero_grad(optimizer)
|
|
self.optimizer_zero_grad(batch_idx, optimizer, opt_idx)
|
|
|
|
# backward pass
|
|
if result is not None:
|
|
with self.trainer.profiler.profile("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._check_finite(result.loss)
|
|
|
|
else:
|
|
self.warning_cache.warn(
|
|
"training_step returned None. If this was on purpose, ignore this warning..."
|
|
)
|
|
|
|
if len(self.trainer.optimizers) > 1:
|
|
# revert back to previous state
|
|
self.trainer.lightning_module.untoggle_optimizer(opt_idx)
|
|
|
|
return result
|
|
|
|
def _check_finite(self, loss: torch.Tensor) -> None:
|
|
if not torch.isfinite(loss).all():
|
|
raise ValueError(f'The loss returned in `training_step` is {loss}.')
|
|
model = self.trainer.lightning_module
|
|
detect_nan_parameters(model)
|
|
|
|
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.backward(result, optimizer, opt_idx, should_accumulate, *args, **kwargs)
|
|
else:
|
|
result.closure_loss = self.trainer.accelerator.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,
|
|
opt_indices=[opt_idx for opt_idx, _ in self.get_optimizers_iterable()],
|
|
)
|
|
|
|
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.global_step = self.trainer.accelerator.update_global_step(self.total_batch_idx, self.global_step)
|
|
|
|
def _accumulated_batches_reached(self):
|
|
return (self.batch_idx + 1) % self.trainer.accumulate_grad_batches == 0
|
|
|
|
def _num_training_batches_reached(self, is_last_batch=False):
|
|
return (self.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: int, is_last_batch: bool, on_epoch: bool = False) -> bool:
|
|
""" Decide if we should run validation. """
|
|
|
|
if not self.trainer.enable_validation:
|
|
return False
|
|
|
|
# check if this epoch is eligible to run validation
|
|
if (self.trainer.current_epoch + 1) % self.trainer.check_val_every_n_epoch != 0:
|
|
return False
|
|
|
|
# val_check_batch is inf for iterable datasets with no length defined
|
|
# TODO: let training/eval loop handle logic around limit_*_batches and val_check_batch
|
|
is_val_check_batch = False
|
|
if isinstance(self.trainer.limit_train_batches, int) and self.trainer.val_check_batch == float('inf'):
|
|
is_val_check_batch = (batch_idx + 1) % self.trainer.limit_train_batches == 0
|
|
elif self.trainer.val_check_batch != float('inf'):
|
|
is_val_check_batch = (batch_idx + 1) % self.trainer.val_check_batch == 0
|
|
|
|
# Note: num_training_batches is also inf for iterable datasets with no length defined
|
|
epoch_end_val_check = (batch_idx + 1) % self.trainer.num_training_batches == 0
|
|
is_last_batch_for_infinite_dataset = is_last_batch and self.trainer.val_check_batch == float("inf")
|
|
|
|
if on_epoch:
|
|
return (
|
|
is_val_check_batch and epoch_end_val_check
|
|
) or self.trainer.should_stop or is_last_batch_for_infinite_dataset
|
|
else:
|
|
return is_val_check_batch and not epoch_end_val_check
|
|
|
|
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]
|
|
|
|
lightning_module = self.trainer.lightning_module
|
|
|
|
if len(self.trainer.optimizers) > 1:
|
|
training_step_fx = getattr(lightning_module, "training_step")
|
|
has_opt_idx_in_train_step = is_param_in_hook_signature(training_step_fx, "optimizer_idx")
|
|
if has_opt_idx_in_train_step:
|
|
if not lightning_module.automatic_optimization:
|
|
self.warning_cache.warn(
|
|
"`training_step` hook signature has changed in v1.3."
|
|
" `optimizer_idx` argument has been removed in case of manual optimization. Support for"
|
|
" the old signature will be removed in v1.5", DeprecationWarning
|
|
)
|
|
args.append(opt_idx)
|
|
elif not has_opt_idx_in_train_step and lightning_module.automatic_optimization:
|
|
raise ValueError(
|
|
f"Your LightningModule defines {len(self.trainer.optimizers)} optimizers but"
|
|
' `training_step` is missing the `optimizer_idx` argument.'
|
|
)
|
|
|
|
# pass hiddens if using tbptt
|
|
if self._truncated_bptt_enabled():
|
|
args.append(hiddens)
|
|
|
|
return args
|
|
|
|
def _truncated_bptt_enabled(self) -> bool:
|
|
""" Temporary tbptt utilities until this flag is fully migrated to the lightning module. """
|
|
return self._truncated_bptt_steps() > 0
|
|
|
|
def _truncated_bptt_steps(self) -> int:
|
|
lightning_module = self.trainer.lightning_module
|
|
# Give precedence to the LightningModule as the Trainer flag will be removed in v1.5
|
|
if lightning_module.truncated_bptt_steps > 0:
|
|
return lightning_module.truncated_bptt_steps
|
|
return self.trainer.truncated_bptt_steps or 0
|
|
|
|
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 prepare_optimizers(self):
|
|
# in manual optimization we loop over all optimizers at once
|
|
optimizers = self.get_optimizers_iterable()
|
|
if not self.trainer.lightning_module.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.trainer.lightning_module.automatic_optimization and len(self.trainer.optimizers) > 1:
|
|
model = self.trainer.lightning_module
|
|
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()
|