Rooba
/
lightning
mirror of https://github.com/Lightning-AI/lightning.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Loop Refactor 2/N - Remove Old Training Loop (#7985) 

Co-authored-by: Justus Schock <justus.schock@rwth-aachen.de>
Co-authored-by: Justus Schock <justus.schock@posteo.de>
Co-authored-by: Justus Schock <12886177+justusschock@users.noreply.github.com>
This commit is contained in:
Adrian Wälchli 2021-06-16 10:00:33 +02:00 committed by GitHub
parent b71aa55b9e
commit 341adad819
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 3 additions and 926 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
CHANGELOG.md
View File

@ -125,6 +125,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
* Simplified logic for updating the learning rate for schedulers ([#7682](https://github.com/PyTorchLightning/pytorch-lightning/pull/7682))
* Removed the `on_epoch` guard from the "should stop" validation check ([#7701](https://github.com/PyTorchLightning/pytorch-lightning/pull/7701))
* Refactored internal loop interface; added new classes `FitLoop`, `TrainingEpochLoop`, `TrainingBatchLoop` ([#7871](https://github.com/PyTorchLightning/pytorch-lightning/pull/7871))
* Removed `pytorch_lightning/trainer/training_loop.py` ([#7985](https://github.com/PyTorchLightning/pytorch-lightning/pull/7985))
- Refactored logging
* Renamed and moved `core/step_result.py` to `trainer/connectors/logger_connector/result.py` ([#7736](https://github.com/PyTorchLightning/pytorch-lightning/pull/7736))

4
pytorch_lightning/trainer/trainer.py
View File

@ -1229,10 +1229,10 @@ class Trainer(
model._current_dataloader_idx = None
def call_hook(self, hook_name: str, *args, **kwargs) -> Any:
# Note this implementation is copy/pasted into the TrainLoop class in TrainLoop._on_train_epoch_end_hook
# Note this implementation is copy/pasted into the TrainLoop class in TrainingEpochLoop._on_train_epoch_end_hook
# This was done to manage the deprecation of the `outputs` argument to on_train_epoch_end
# If making changes to this function, ensure that those changes are also made to
# TrainLoop._on_train_epoch_end_hook
# TrainingEpochLoop._on_train_epoch_end_hook
if self.lightning_module:
prev_fx_name = self.lightning_module._current_fx_name
self.lightning_module._current_fx_name = hook_name

923
pytorch_lightning/trainer/training_loop.py
View File

@ -1,923 +0,0 @@
# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections import OrderedDict
from contextlib import contextmanager, suppress
from functools import partial, update_wrapper
from typing import Any, Callable, Dict, List, Mapping, Optional, Tuple, Union
import numpy as np
import torch
from torch.optim import Optimizer
from pytorch_lightning.core.optimizer import LightningOptimizer
from pytorch_lightning.plugins import ParallelPlugin
from pytorch_lightning.trainer.connectors.logger_connector.result import ResultCollection
from pytorch_lightning.trainer.supporters import TensorRunningAccum
from pytorch_lightning.utilities import _TPU_AVAILABLE, AMPType, DeviceType
from pytorch_lightning.utilities.distributed import rank_zero_info
from pytorch_lightning.utilities.exceptions import MisconfigurationException
from pytorch_lightning.utilities.finite_checks import detect_nan_parameters
from pytorch_lightning.utilities.grads import grad_norm
from pytorch_lightning.utilities.model_helpers import is_overridden
from pytorch_lightning.utilities.parsing import AttributeDict
from pytorch_lightning.utilities.signature_utils import is_param_in_hook_signature
from pytorch_lightning.utilities.warnings import WarningCache
class TrainLoop:
def __init__(
self,
trainer,
max_epochs: Optional[int],
min_epochs: Optional[int],
max_steps: Optional[int],
min_steps: Optional[int],
num_sanity_val_steps: int,
):
self.trainer = trainer
self.accumulated_loss = None
self.warning_cache = WarningCache()
self.running_loss = TensorRunningAccum(window_length=20)
self._skip_backward = False
self._optimizer_freq_cumsum = None
self._hiddens = None
self.global_step = 0
self.current_epoch = 0
self.trainer.should_stop = False
# the total batch index across all epochs
self.total_batch_idx = 0
# the current batch index in the loop that runs over the dataloader(s)
self.batch_idx = 0
# the current split index when the batch gets split into chunks in truncated backprop through time
self.split_idx = None
self.trainer.num_training_batches = 0
self.trainer.train_dataloader = None
# If neither max_epochs or max_steps is set, then use existing default of max_epochs = 1000
self.max_epochs = 1000 if (max_epochs is None and max_steps is None) else max_epochs
# If neither min_epochs or min_steps is set, then use existing default of min_epochs = 1
self.min_epochs = 1 if (min_epochs is None and min_steps is None) else min_epochs
self.max_steps = max_steps
self.min_steps = min_steps
if num_sanity_val_steps == -1:
self.trainer.num_sanity_val_steps = float("inf")
else:
self.trainer.num_sanity_val_steps = num_sanity_val_steps
self.results = ResultCollection(training=True)
@property
def num_active_optimizers(self) -> int:
return len(self.get_active_optimizers())
@property
def optimizer_freq_cumsum(self):
if self._optimizer_freq_cumsum is None:
self._optimizer_freq_cumsum = np.cumsum(self.trainer.optimizer_frequencies)
return self._optimizer_freq_cumsum
def should_skip_training(self) -> bool:
should_by_max_steps = self.max_steps is not None and self.global_step >= self.max_steps
should_by_epoch = self.max_epochs is not None and self.current_epoch >= self.max_epochs
return should_by_max_steps or should_by_epoch or self.trainer.num_training_batches == 0
def on_train_start(self):
self.results.to(device=self.trainer.lightning_module.device)
self.trainer.call_hook("on_train_start")
def on_train_end(self):
# trigger checkpoint check. need to temporarily decrease the global step to avoid saving duplicates
# when a checkpoint was saved at the last step
self.global_step -= 1
self.check_checkpoint_callback(should_update=True, is_last=True)
self.global_step += 1
# hook
self.trainer.call_hook("on_train_end")
# todo: TPU 8 cores hangs in flush with TensorBoard. Might do for all loggers.
# It might be related to xla tensors blocked when moving the cpu
# kill loggers
if self.trainer.logger is not None:
self.trainer.logger.finalize("success")
# summarize profile results
self.trainer.profiler.describe()
# give accelerators a chance to finish
self.trainer.accelerator.on_train_end()
# reset bookkeeping
self.trainer.state.stage = None
def check_checkpoint_callback(self, should_update, is_last=False):
# TODO bake this logic into the ModelCheckpoint callback
if should_update and self.trainer.checkpoint_connector.has_trained:
callbacks = self.trainer.checkpoint_callbacks
if is_last and any(cb.save_last and cb.verbose for cb in callbacks):
rank_zero_info("Saving latest checkpoint...")
model = self.trainer.lightning_module
for cb in callbacks:
cb.on_validation_end(self.trainer, model)
def on_train_epoch_start(self, epoch):
# update training progress in trainer
self.current_epoch = epoch
model = self.trainer.lightning_module
# reset train dataloader
if epoch != 0 and self.trainer.reload_dataloaders_every_epoch:
self.trainer.reset_train_dataloader(model)
# todo: specify the possible exception
with suppress(Exception):
# set seed for distributed sampler (enables shuffling for each epoch)
self.trainer.train_dataloader.sampler.set_epoch(epoch)
# changing gradient according accumulation_scheduler
self.trainer.accumulation_scheduler.on_train_epoch_start(self.trainer, self.trainer.lightning_module)
# stores accumulated grad fractions per batch
self.accumulated_loss = TensorRunningAccum(window_length=self.trainer.accumulate_grad_batches)
# hook
self.trainer.logger_connector.on_epoch_start()
self.trainer.call_hook("on_epoch_start")
self.trainer.call_hook("on_train_epoch_start")
def on_train_batch_end(self, epoch_output, batch_end_outputs, batch, batch_idx, dataloader_idx):
batch_end_outputs = [opt_idx_out for opt_idx_out in batch_end_outputs if len(opt_idx_out)]
processed_batch_end_outputs = TrainLoop._prepare_outputs(batch_end_outputs, batch_mode=True)
# hook
self.trainer.call_hook('on_train_batch_end', processed_batch_end_outputs, batch, batch_idx, dataloader_idx)
self.trainer.call_hook('on_batch_end')
self.trainer.logger_connector.on_batch_end()
# figure out what to track for epoch end
self.track_epoch_end_reduce_metrics(epoch_output, batch_end_outputs)
def reset_train_val_dataloaders(self, model) -> None:
"""
Resets train and val dataloaders if none are attached to the trainer.
The val dataloader must be initialized before training loop starts, as the training loop
inspects the val dataloader to determine whether to run the evaluation loop.
"""
if self.trainer.train_dataloader is None:
self.trainer.reset_train_dataloader(model)
if self.trainer.val_dataloaders is None:
self.trainer.reset_val_dataloader(model)
def track_epoch_end_reduce_metrics(self, epoch_output, batch_end_outputs):
hook_overridden = self._should_add_batch_output_to_epoch_output()
if not hook_overridden:
return
# track the outputs to reduce at the end of the epoch
for opt_idx, opt_outputs in enumerate(batch_end_outputs):
# with 1 step (no tbptt) don't use a sequence at epoch end
if (
isinstance(opt_outputs, list) and len(opt_outputs) == 1
and not isinstance(opt_outputs[0], ResultCollection)
):
opt_outputs = opt_outputs[0]
epoch_output[opt_idx].append(opt_outputs)
def _should_add_batch_output_to_epoch_output(self) -> bool:
# We add to the epoch outputs if
# 1. The model defines training_epoch_end OR
# 2. The model overrides on_train_epoch_end which has `outputs` in the signature
# TODO: in v1.5 this only needs to check if training_epoch_end is overridden
lightning_module = self.trainer.lightning_module
if is_overridden("training_epoch_end", lightning_module):
return True
if is_overridden("on_train_epoch_end", lightning_module):
model_hook_fx = getattr(lightning_module, "on_train_epoch_end")
if is_param_in_hook_signature(model_hook_fx, "outputs"):
return True
return False
def get_active_optimizers(self, batch_idx: Optional[int] = None) -> List[Tuple[int, Optimizer]]:
"""
Returns the currently active optimizers. When multiple optimizers are used with different frequencies,
only one of the optimizers is active at a time.
Returns:
A list of tuples (opt_idx, optimizer) of currently active optimizers.
"""
if not self.trainer.optimizer_frequencies:
# call training_step once per optimizer
return list(enumerate(self.trainer.optimizers))
batch_idx = self.total_batch_idx if batch_idx is None else batch_idx
optimizers_loop_length = self.optimizer_freq_cumsum[-1]
current_place_in_loop = batch_idx % optimizers_loop_length
# find optimzier index by looking for the first {item > current_place} in the cumsum list
opt_idx = int(np.argmax(self.optimizer_freq_cumsum > current_place_in_loop))
return [(opt_idx, self.trainer.optimizers[opt_idx])]
def on_after_backward(self, batch_idx, untouched_loss):
# insert after step hook
self.trainer.call_hook("on_after_backward")
# when in dev debugging track the losses
self.trainer.dev_debugger.track_train_loss_history(batch_idx, untouched_loss.detach())
def _check_training_step_output(self, training_step_output):
if isinstance(training_step_output, torch.Tensor) and not self.trainer.lightning_module.automatic_optimization:
if training_step_output.grad_fn is None:
# TODO: Find why - RuntimeError: Expected to mark a variable ready only once ...
raise MisconfigurationException("In manual optimization, `training_step` should not return a Tensor")
elif self.trainer.lightning_module.automatic_optimization:
if not any((
isinstance(training_step_output, torch.Tensor),
(isinstance(training_step_output, Mapping)
and 'loss' in training_step_output), training_step_output is None
)):
raise MisconfigurationException(
"In automatic optimization, `training_step` must either return a Tensor, "
"a dict with key 'loss' or None (where the step will be skipped)."
)
def training_step(self, split_batch, batch_idx, opt_idx, hiddens):
# give the PL module a result for logging
model_ref = self.trainer.lightning_module
with self.trainer.profiler.profile("model_forward"):
step_kwargs = self._build_kwargs(split_batch, batch_idx, opt_idx, hiddens)
# manually capture logged metrics
model_ref._current_fx_name = 'training_step'
with self.trainer.profiler.profile("training_step"):
training_step_output = self.trainer.accelerator.training_step(step_kwargs)
self.trainer.accelerator.post_training_step()
training_step_output = self.trainer.call_hook("training_step_end", training_step_output)
self._check_training_step_output(training_step_output)
training_step_output = self._process_training_step_output(training_step_output)
if training_step_output is None:
return
closure_loss = None
loss = None
if self.trainer.lightning_module.automatic_optimization:
# accumulate loss. if accumulate_grad_batches==1, no effect
closure_loss = training_step_output.minimize / self.trainer.accumulate_grad_batches
# the loss will get scaled for amp. avoid any modifications to it
loss = closure_loss.detach().clone()
return AttributeDict(closure_loss=closure_loss, loss=loss, training_step_output=training_step_output)
def _process_training_step_output(self, training_step_output):
if training_step_output is None:
return None
results = self.results
loss = None
hiddens = None
results.extra = {}
# handle dict return
if isinstance(training_step_output, dict):
loss = training_step_output.pop("loss", None)
hiddens = training_step_output.pop("hiddens", None)
if hiddens is not None:
hiddens = hiddens.detach()
results.extra = training_step_output
# handle scalar return
elif isinstance(training_step_output, torch.Tensor):
loss = training_step_output
# map to results under the hood
results.minimize = loss
self._hiddens = hiddens
if self.trainer.move_metrics_to_cpu:
results.cpu()
return results
@staticmethod
def _prepare_outputs(
outputs: List[List[List['ResultCollection']]],
batch_mode: bool,
) -> Union[List[List[List[Dict]]], List[List[Dict]], List[Dict], Dict]:
"""
Extract required information from batch or epoch end results.
Args:
outputs: A 3-dimensional list of ``ResultCollection`` objects with dimensions:
``[optimizer outs][batch outs][tbptt steps]``.
batch_mode: If True, ignore the batch output dimension.
Returns:
The cleaned outputs with ``ResultCollection`` objects converted to dictionaries.
All list dimensions of size one will be collapsed.
"""
processed_outputs = []
for opt_outputs in outputs:
# handle an edge case where an optimizer output is the empty list
if len(opt_outputs) == 0:
continue
processed_batch_outputs = []
if batch_mode:
opt_outputs = [opt_outputs]
for batch_outputs in opt_outputs:
processed_tbptt_outputs = []
if isinstance(batch_outputs, ResultCollection):
batch_outputs = [batch_outputs]
for tbptt_output in batch_outputs:
out = tbptt_output.extra
if tbptt_output.minimize is not None:
out['loss'] = tbptt_output.minimize.detach()
processed_tbptt_outputs.append(out)
# if there was only one tbptt step then we can collapse that dimension
if len(processed_tbptt_outputs) == 1:
processed_tbptt_outputs = processed_tbptt_outputs[0]
processed_batch_outputs.append(processed_tbptt_outputs)
# batch_outputs should be just one dict (or a list of dicts if using tbptt) per optimizer
if batch_mode:
processed_batch_outputs = processed_batch_outputs[0]
processed_outputs.append(processed_batch_outputs)
# if there is only one optimiser then we collapse that dimension
if len(processed_outputs) == 1:
processed_outputs = processed_outputs[0]
return processed_outputs
def optimizer_step(self, optimizer, opt_idx, batch_idx, train_step_and_backward_closure):
model_ref = self.trainer.lightning_module
is_lbfgs = isinstance(optimizer, torch.optim.LBFGS)
using_native_amp = self.trainer.amp_backend == AMPType.NATIVE
# native amp + lbfgs is a no go right now
if using_native_amp and is_lbfgs:
raise MisconfigurationException(
'native PyTorch amp and lbfgs are not compatible.'
' To request, please file a Github issue in PyTorch and tag @mcarilli'
)
# wraps into LightningOptimizer only for running step
optimizer = LightningOptimizer._to_lightning_optimizer(optimizer, self.trainer, opt_idx)
# model hook
model_ref.optimizer_step(
self.trainer.current_epoch,
batch_idx,
optimizer,
opt_idx,
train_step_and_backward_closure,
on_tpu=self.trainer._device_type == DeviceType.TPU and _TPU_AVAILABLE,
using_native_amp=using_native_amp,
using_lbfgs=is_lbfgs,
)
def on_before_zero_grad(self, optimizer):
self.trainer.call_hook('on_before_zero_grad', optimizer)
def optimizer_zero_grad(self, batch_idx, optimizer, opt_idx):
self.trainer.accelerator.optimizer_zero_grad(self.trainer.current_epoch, batch_idx, optimizer, opt_idx)
def track_and_norm_grad(self, optimizer) -> dict:
# track gradient norms
grad_norm_dict = {}
if (self.global_step + 1) % self.trainer.log_every_n_steps == 0 and float(self.trainer.track_grad_norm) > 0:
grad_norm_dict = grad_norm(self.trainer.lightning_module, self.trainer.track_grad_norm)
# clip gradients
self.trainer.accelerator.clip_gradients(
optimizer, self.trainer.gradient_clip_val, gradient_clip_algorithm=self.trainer.gradient_clip_algorithm
)
return grad_norm_dict
def _tbptt_split_batch(self, batch: Any) -> List[Any]:
splits = [batch]
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_active_optimizers)]
train_dataloader = self.trainer.data_connector.get_profiled_train_dataloader(train_dataloader)
dataloader_idx = 0
batch_idx = None
for batch_idx, (batch, is_last_batch) in train_dataloader:
self.batch_idx = batch_idx
# ------------------------------------
# TRAINING_STEP + TRAINING_STEP_END
# ------------------------------------
with self.trainer.profiler.profile("run_training_batch"):
batch_output = self.run_training_batch(batch, batch_idx, dataloader_idx)
# when returning -1 from train_step, we end epoch early
if batch_output.signal == -1:
break
# hook
self.on_train_batch_end(
epoch_output,
batch_output.training_step_output,
batch,
batch_idx,
dataloader_idx,
)
# -----------------------------------------
# SAVE METRICS TO LOGGERS AND PROGRESS_BAR
# -----------------------------------------
self.trainer.logger_connector.update_train_step_metrics()
# -----------------------------------------
# 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
# -----------------------------------------
# SAVE LOGGERS (ie: Tensorboard, etc...)
# -----------------------------------------
self.save_loggers_on_train_batch_end()
# update LR schedulers
self.update_lr_schedulers('step')
self.trainer.checkpoint_connector.has_trained = True
self.total_batch_idx += 1
# progress global step according to grads progress
self.increment_accumulated_grad_global_step()
max_steps_reached = (self.max_steps is not None and self.max_steps <= self.global_step)
if max_steps_reached or self.trainer.should_stop or self._num_training_batches_reached(is_last_batch):
break
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)
# the global step is manually decreased here due to backwards compatibility with existing loggers
# as they expect that the same step is used when logging epoch end metrics even when the batch loop has
# finished. this means the attribute does not exactly track the number of optimizer steps applied.
# TODO(@carmocca): deprecate and rename so users don't get confused
self.global_step -= 1
# log epoch metrics
self.trainer.logger_connector.update_train_epoch_metrics()
self.global_step += 1
self.update_lr_schedulers('epoch')
did_train_only = self.trainer.disable_validation or self.trainer.evaluation_loop.should_skip_evaluation(
self.trainer.num_val_batches
)
if did_train_only:
self.global_step -= 1
self.check_checkpoint_callback(True)
self.global_step += 1
def on_train_epoch_end(self, epoch_output: List[List[List['ResultCollection']]]) -> None:
# inform logger the batch loop has finished
self.trainer.logger_connector.epoch_end_reached()
# 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):
# run training_epoch_end
# refresh the result for custom logging at the epoch level
model._current_fx_name = 'training_epoch_end'
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'
)
# call train epoch end hooks
self._on_train_epoch_end_hook(processed_epoch_output)
self.trainer.call_hook('on_epoch_end')
self.trainer.logger_connector.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"
prev_fx_name = self.trainer.lightning_module._current_fx_name
self.trainer.lightning_module._current_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()
# call the accelerator hook
if hasattr(self.trainer.accelerator, hook_name):
accelerator_hook = getattr(self.trainer.accelerator, hook_name)
accelerator_hook()
# restore current_fx when nested context
self.trainer.lightning_module._current_fx_name = prev_fx_name
def run_training_batch(self, batch, batch_idx, dataloader_idx):
# bookkeeping
self._hiddens = None
optimizers = list(enumerate(self.trainer.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, training_step_output=batch_outputs)
# hook
self.trainer.logger_connector.on_batch_start()
response = self.trainer.call_hook("on_batch_start")
if response == -1:
return AttributeDict(signal=-1)
# hook
response = self.trainer.call_hook("on_train_batch_start", batch, batch_idx, dataloader_idx)
if response == -1:
return AttributeDict(signal=-1)
# lightning module hook
splits = self._tbptt_split_batch(batch)
for split_idx, split_batch in enumerate(splits):
self.split_idx = split_idx
# let logger connector extract batch size
self.trainer.logger_connector.on_train_split_start(batch_idx, split_idx, split_batch)
if self.trainer.lightning_module.automatic_optimization:
for opt_idx, optimizer in self.get_active_optimizers(batch_idx):
result = self._run_optimization(batch_idx, split_batch, opt_idx, optimizer)
if result:
batch_outputs[opt_idx].append(result.training_step_output)
else:
# in manual optimization, there is no looping over optimizers
result = self._run_optimization(batch_idx, split_batch)
if result:
batch_outputs[0].append(result.training_step_output)
return AttributeDict(signal=0, training_step_output=batch_outputs)
def _run_optimization(self, batch_idx, split_batch, opt_idx=0, optimizer=None):
# TODO: In v1.5, when optimizer_idx gets removed from training_step in manual_optimization, change
# opt_idx=0 to opt_idx=None in the signature here
# toggle model params
self.run_optimization_start(opt_idx, optimizer)
result = AttributeDict()
closure = self.make_closure(split_batch, batch_idx, opt_idx, optimizer, self._hiddens, result)
if self.should_accumulate():
# For gradient accumulation
# -------------------
# calculate loss (train step + train step end)
# -------------------
# automatic_optimization=True: perform ddp sync only when performing optimizer_step
# automatic_optimization=False: don't block synchronization here
with self.block_ddp_sync_behaviour():
closure()
# ------------------------------
# BACKWARD PASS
# ------------------------------
# gradient update with accumulated gradients
else:
if self.trainer.lightning_module.automatic_optimization:
self.optimizer_step(optimizer, opt_idx, batch_idx, closure)
if len(self.trainer.optimizers) > 1:
# revert back to previous state
self.trainer.lightning_module.untoggle_optimizer(opt_idx)
else:
result = self.training_step(split_batch, batch_idx, opt_idx, self._hiddens)
if not result:
# user decided to skip optimization
return result
# update running loss + reset accumulated loss
self.update_running_loss(result.loss)
self._process_closure_result(result)
return result
def training_step_and_backward_closure(
self,
split_batch: Any,
batch_idx: int,
opt_idx: int,
optimizer: Optimizer,
hiddens,
return_result: AttributeDict,
) -> Optional[torch.Tensor]:
result = self.training_step_and_backward(split_batch, batch_idx, opt_idx, optimizer, hiddens)
if result is not None:
return_result.update(result)
return return_result.loss
def make_closure(self, *closure_args, **closure_kwargs: Any) -> Callable:
""" Wraps the training step closure into a partial object which will be called within ``optimizer.step``. """
partial_func = partial(self.training_step_and_backward_closure, *closure_args, **closure_kwargs)
return update_wrapper(partial_func, self.training_step_and_backward_closure)
@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, opt_closure_result: Optional[AttributeDict]) -> None:
if not opt_closure_result:
return
# check if loss or model weights are nan
if self.trainer.terminate_on_nan:
self._check_finite(opt_closure_result.loss)
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)
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(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..."
)
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
grad_norm_dict = self.track_and_norm_grad(optimizer=optimizer)
if grad_norm_dict:
self.trainer.lightning_module._current_fx_name = "on_after_backward"
self.trainer.lightning_module.log_grad_norm(grad_norm_dict)
def update_lr_schedulers(self, interval: str) -> None:
if interval == "step":
finished_accumulation = self._accumulated_batches_reached()
finished_epoch = self._num_training_batches_reached()
if not finished_accumulation and not finished_epoch:
return
self.trainer.optimizer_connector.update_learning_rates(
interval=interval,
opt_indices=[opt_idx for opt_idx, _ in self.get_active_optimizers()],
)
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) -> bool:
""" Decide if we should run validation. """
if not self.trainer.enable_validation:
return False
is_val_check_epoch = (self.trainer.current_epoch + 1) % self.trainer.check_val_every_n_epoch == 0
if not is_val_check_epoch:
return False
# val_check_batch is inf for iterable datasets with no length defined
is_infinite_dataset = self.trainer.val_check_batch == float('inf')
if is_last_batch and is_infinite_dataset:
return True
if self.trainer.should_stop:
return True
# TODO: let training/eval loop handle logic around limit_*_batches and val_check_batch
is_val_check_batch = is_last_batch
if isinstance(self.trainer.limit_train_batches, int) and is_infinite_dataset:
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
return is_val_check_batch
def _build_kwargs(self, batch, batch_idx, opt_idx, hiddens):
# enable not needing to add opt_idx to training_step
step_kwargs = OrderedDict([('batch', batch), ('batch_idx', 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
)
step_kwargs['optimizer_idx'] = opt_idx
elif not has_opt_idx_in_train_step and self.trainer.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():
step_kwargs['hiddens'] = hiddens
return step_kwargs
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 run_optimization_start(self, opt_idx, optimizer):
# 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)
def update_running_loss(self, current_loss: torch.Tensor) -> None:
if self.trainer.lightning_module.automatic_optimization:
# track total loss for logging (avoid mem leaks)
self.accumulated_loss.append(current_loss)
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()

1
setup.cfg
View File

@ -45,7 +45,6 @@ exclude_lines =
omit =
pytorch_lightning/cluster_environments/*.py
pytorch_lightning/utilities/distributed.py
pytorch_lightning/trainer/training_loop.py
pytorch_lightning/tuner/auto_gpu_select.py