953 lines
39 KiB
Python
953 lines
39 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
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from copy import copy, deepcopy
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import numpy as np
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import torch
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import torch.distributed as torch_distrib
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from pytorch_lightning.callbacks import ModelCheckpoint
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from pytorch_lightning.core.lightning import LightningModule
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from pytorch_lightning.core.memory import ModelSummary
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from pytorch_lightning.core.step_result import EvalResult, Result
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from pytorch_lightning.trainer.states import TrainerState
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from pytorch_lightning.trainer.supporters import TensorRunningAccum, Accumulator
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from pytorch_lightning.utilities import parsing, AMPType
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from pytorch_lightning.utilities.distributed import rank_zero_info, rank_zero_warn
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from pytorch_lightning.utilities.memory import recursive_detach
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from pytorch_lightning.utilities.model_utils import is_overridden
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from pytorch_lightning.utilities.parsing import AttributeDict
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from pytorch_lightning.utilities.warning_utils import WarningCache
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class TrainLoop:
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def __init__(self, trainer):
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self.trainer = trainer
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self.early_stopping_accumulator = None
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self.checkpoint_accumulator = None
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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.automatic_optimization = True
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self._curr_step_result = None
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self._cur_grad_norm_dict = None
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def on_trainer_init(
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self, max_epochs, min_epochs, max_steps, min_steps, num_sanity_val_steps, automatic_optimization
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):
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self.trainer.global_step = 0
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self.trainer.current_epoch = 0
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self.trainer.interrupted = False
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self.trainer.should_stop = False
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self.trainer._state = TrainerState.INITIALIZING
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self.trainer.total_batch_idx = 0
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self.trainer.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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self.automatic_optimization = automatic_optimization
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self.trainer.max_epochs = max_epochs
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self.trainer.min_epochs = min_epochs
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self.trainer.max_steps = max_steps
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self.trainer.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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def should_skip_training(self):
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if self.trainer.current_epoch >= self.trainer.max_epochs:
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return True
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if self.trainer.limit_train_batches == 0:
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return True
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return False
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def on_train_start(self):
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# clear cache before training
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if self.trainer.on_gpu and self.trainer.root_gpu is not None:
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# use context because of:
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# https://discuss.pytorch.org/t/out-of-memory-when-i-use-torch-cuda-empty-cache/57898
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with torch.cuda.device(f"cuda:{self.trainer.root_gpu}"):
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torch.cuda.empty_cache()
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# hook
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self.trainer.call_hook("on_train_start")
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def setup_fit(self, model, train_dataloader, val_dataloaders, datamodule):
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# bind logger and other properties
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self.trainer.model_connector.copy_trainer_model_properties(model)
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# clean hparams
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if hasattr(model, "hparams"):
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parsing.clean_namespace(model.hparams)
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# links data to the trainer
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self.trainer.data_connector.attach_data(model, train_dataloader, val_dataloaders, datamodule)
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# check that model is configured correctly
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self.trainer.config_validator.verify_loop_configurations(model)
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def setup_training(self, model: LightningModule):
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"""Sanity check a few things before starting actual training.
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Args:
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model: The model to run sanity test on.
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"""
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# --------------------------
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# Setup??
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# --------------------------
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ref_model = model
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if self.trainer.data_parallel:
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ref_model = model.module
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# set the ranks and devices
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self.trainer.accelerator_backend.dist.rank = self.trainer.global_rank
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self.trainer.accelerator_backend.dist.device = ref_model.device
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# give model convenience properties
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ref_model.trainer = self.trainer
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# set local properties on the model
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self.trainer.model_connector.copy_trainer_model_properties(ref_model)
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# init amp. Must be done here instead of __init__ to allow ddp to work
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if self.trainer.amp_backend == AMPType.NATIVE and self.trainer.precision == 16 and not self.trainer.use_tpu:
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self.trainer.scaler = self.trainer.precision_connector.backend.scaler
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# log hyper-parameters
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if self.trainer.logger is not None:
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# save exp to get started (this is where the first experiment logs are written)
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self.trainer.logger.log_hyperparams(ref_model.hparams_initial)
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self.trainer.logger.log_graph(ref_model)
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self.trainer.logger.save()
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# wait for all to join if on distributed
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self.trainer.accelerator_backend.barrier("setup_training")
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# register auto-resubmit when on SLURM
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self.trainer.slurm_connector.register_slurm_signal_handlers()
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# --------------------------
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# Pre-train
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# --------------------------
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# on pretrain routine start
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self.trainer.on_pretrain_routine_start(ref_model)
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if self.trainer.is_function_implemented("on_pretrain_routine_start"):
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ref_model.on_pretrain_routine_start()
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# print model summary
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if self.trainer.is_global_zero and self.trainer.weights_summary is not None and not self.trainer.testing:
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if self.trainer.weights_summary in ModelSummary.MODES:
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ref_model.summarize(mode=self.trainer.weights_summary)
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else:
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raise MisconfigurationException("weights_summary can be None, " + ", ".join(ModelSummary.MODES))
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# track model now.
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# if cluster resets state, the model will update with the saved weights
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self.trainer.model = model
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# restore training and model before hpc is called
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self.trainer.checkpoint_connector.restore_weights(model)
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# on pretrain routine end
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self.trainer.on_pretrain_routine_end(ref_model)
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if self.trainer.is_function_implemented("on_pretrain_routine_end"):
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ref_model.on_pretrain_routine_end()
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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.trainer.global_step -= 1
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self.check_checkpoint_callback(should_save=True, is_last=True)
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self.trainer.global_step += 1
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# hook
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self.trainer.call_hook("on_train_end")
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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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if self.trainer.global_rank == 0:
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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_backend.on_train_end()
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# clear mem
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if self.trainer.on_gpu:
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model = self.trainer.get_model()
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model.cpu()
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torch.cuda.empty_cache()
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def check_checkpoint_callback(self, should_save, is_last=False):
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# TODO bake this logic into the checkpoint callback
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if should_save and self.trainer.checkpoint_connector.has_trained:
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checkpoint_callbacks = [c for c in self.trainer.callbacks if isinstance(c, ModelCheckpoint)]
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if is_last and any(c.save_last for c in checkpoint_callbacks):
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rank_zero_info("Saving latest checkpoint...")
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model = self.trainer.get_model()
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[c.on_validation_end(self.trainer, model) for c in checkpoint_callbacks]
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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.trainer.current_epoch = epoch
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model = self.trainer.get_model()
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# reset train dataloader
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if self.trainer.reload_dataloaders_every_epoch:
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self.trainer.reset_train_dataloader(model)
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# set seed for distributed sampler (enables shuffling for each epoch)
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try:
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self.trainer.train_dataloader.sampler.set_epoch(epoch)
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except Exception:
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pass
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# changing gradient according accumulation_scheduler
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self.trainer.accumulation_scheduler.on_epoch_start(self.trainer, self.trainer.get_model())
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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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# structured result accumulators for callbacks
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self.early_stopping_accumulator = Accumulator()
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self.checkpoint_accumulator = Accumulator()
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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, epoch_end_outputs, batch, batch_idx, dataloader_idx):
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# hook
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self.trainer.call_hook('on_batch_end')
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self.trainer.call_hook('on_train_batch_end', epoch_end_outputs, batch, batch_idx, dataloader_idx)
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# figure out what to track for epoch end
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self.track_epoch_end_reduce_metrics(epoch_output, epoch_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):
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if not self.trainer.reload_dataloaders_every_epoch:
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self.trainer.reset_train_dataloader(model)
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if self.trainer.val_dataloaders is None and not self.trainer.reload_dataloaders_every_epoch:
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self.trainer.reset_val_dataloader(model)
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def track_epoch_end_reduce_metrics(self, epoch_output, epoch_end_outputs):
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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(epoch_end_outputs):
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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 get_optimizers_iterable(self):
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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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optimizer_freq_cumsum = np.cumsum(self.trainer.optimizer_frequencies)
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optimizers_loop_length = optimizer_freq_cumsum[-1]
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current_place_in_loop = self.trainer.total_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(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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is_result_obj = isinstance(training_step_output, Result)
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if is_result_obj:
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training_step_output.detach()
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else:
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training_step_output.batch_loss = training_step_output.batch_loss.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.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.get_model()
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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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training_step_output = self.trainer.accelerator_backend.training_step(args)
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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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is_result_obj = isinstance(training_step_output, Result)
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if training_step_output_for_epoch_end is None:
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return None
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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.train_loop.automatic_optimization:
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# accumulate loss
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# (if accumulate_grad_batches = 1 no effect)
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if is_result_obj:
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closure_loss = training_step_output.minimize
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else:
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closure_loss = training_step_output.batch_loss
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closure_loss = closure_loss / 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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hiddens=training_step_output.hiddens,
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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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# -----------------------------------------
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# process result return (DEPRECATE in 1.0)
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# -----------------------------------------
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if isinstance(training_step_output, Result):
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training_step_output_for_epoch_end = self._process_result(training_step_output, split_batch)
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return training_step_output_for_epoch_end, training_step_output
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# -----------------------------------------
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# process hybrid (1.0)
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# -----------------------------------------
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# no need for these checks in 1.0.0
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# TODO: remove checks in 1.0.0
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is_tensor = isinstance(training_step_output_for_epoch_end, torch.Tensor)
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is_1_0_output = is_tensor or ("log" not in training_step_output and "progress_bar" not in training_step_output)
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if is_1_0_output:
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return self._process_training_step_output_1_0(training_step_output, split_batch)
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# -----------------------------------------
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# process old dict (deprecate 1.0)
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# -----------------------------------------
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training_step_output = self.trainer.process_dict_result(training_step_output, train=True)
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training_step_output = AttributeDict(
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batch_loss=training_step_output[0],
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pbar_on_batch_end=training_step_output[1],
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log_metrics=training_step_output[2],
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callback_metrics=training_step_output[3],
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hiddens=training_step_output[4],
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)
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# if the user decides to finally reduce things in epoch_end, save raw output without graphs
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if isinstance(training_step_output_for_epoch_end, torch.Tensor):
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training_step_output_for_epoch_end = training_step_output_for_epoch_end.detach()
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else:
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training_step_output_for_epoch_end = recursive_detach(training_step_output_for_epoch_end)
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return training_step_output_for_epoch_end, training_step_output
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def _process_training_step_output_1_0(self, training_step_output, split_batch):
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result = self.trainer.get_model()._results
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loss = None
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hiddens = None
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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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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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result["extra"] = {}
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# map to results under the hood
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result.minimize = loss
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result.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.detach()
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if self.trainer.move_metrics_to_cpu:
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training_step_output_for_epoch_end.cpu()
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# what flows back into the system
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training_step_output = result
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return training_step_output_for_epoch_end, training_step_output
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def _process_result(self, training_step_output, split_batch):
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training_step_output.track_batch_size(len(split_batch))
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m = """
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TrainResult and EvalResult were deprecated in 0.9.1 and support will drop in 1.0.0.
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Use self.log and .write from the LightningModule to log metrics and write predictions.
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training_step can now only return a scalar (for the loss) or a dictionary with anything you want.
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Option 1:
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return loss
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Option 2:
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return {'loss': loss, 'anything_else': ...}
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Option 3:
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return {'loss': loss, 'hiddens': hiddens, 'anything_else': ...}
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"""
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rank_zero_warn(m)
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|
|
|
# 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):
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accumulated_loss = self.accumulated_loss.mean()
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if accumulated_loss is not None:
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# calculate running loss for display
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self.running_loss.append(self.accumulated_loss.mean() * self.trainer.accumulate_grad_batches)
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# reset for next set of accumulated grads
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self.accumulated_loss.reset()
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def zero_grad_handler(self, batch_idx, optimizer, opt_idx):
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if self.automatic_optimization:
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# hook
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self.on_before_zero_grad(optimizer)
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optimizers = enumerate([optimizer])
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else:
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# should be called handled in `manual_optimizer_step`
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optimizers = []
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|
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for idx, optimizer in optimizers:
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self.optimizer_zero_grad(batch_idx, optimizer, opt_idx)
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