class TrainLoop:
def __init__(self, trainer, multiple_trainloader_mode: str):
self.trainer = trainer
self.accumulated_loss = None
self.warning_cache = WarningCache()
self._teardown_already_run = False
self.running_loss = TensorRunningAccum(window_length=20)
self.automatic_optimization = True
self._curr_step_result = None
self._cur_grad_norm_dict = None
self._multiple_trainloader_mode = multiple_trainloader_mode
self._skip_backward = False
self.trainer._multiple_trainloader_mode = multiple_trainloader_mode
def on_trainer_init(
self,
max_epochs: Optional[int],
min_epochs: Optional[int],
max_steps: Optional[int],
min_steps: Optional[int],
num_sanity_val_steps: int,
) -> None:
self.trainer.global_step = 0
self.trainer.current_epoch = 0
self.trainer.should_stop = False
self.trainer._state = TrainerState.INITIALIZING
self.trainer.total_batch_idx = 0
self.trainer.batch_idx = 0
self.trainer.num_training_batches = 0
self.trainer.train_dataloader = None
# If neither max_epochs or max_steps is set, then use existing default of max_epochs = 1000
self.trainer.max_epochs = 1000 if (max_epochs is None and max_steps is None) else max_epochs
# If neither min_epochs or min_steps is set, then use existing default of min_epochs = 1
self.trainer.min_epochs = 1 if (min_epochs is None and min_steps is None) else min_epochs
self.trainer.max_steps = max_steps
self.trainer.min_steps = min_steps
if num_sanity_val_steps == -1:
self.trainer.num_sanity_val_steps = float("inf")
else:
self.trainer.num_sanity_val_steps = num_sanity_val_steps
@property
def num_optimizers(self):
num_optimizers = len(self.get_optimizers_iterable())
return num_optimizers
def should_skip_training(self):
should_by_max_steps = self.trainer.max_steps is not None and self.trainer.global_step >= self.trainer.max_steps
should_by_epoch = self.trainer.max_epochs is not None and self.trainer.current_epoch >= self.trainer.max_epochs
return should_by_max_steps or should_by_epoch or self.trainer.num_training_batches == 0
def on_train_start(self):
# hook
self.trainer.call_hook("on_train_start")
def setup_fit(self, model, train_dataloader=None, val_dataloaders=None, datamodule=None):
# clean hparams
if hasattr(model, "hparams"):
parsing.clean_namespace(model.hparams)
# links data to the trainer
self.trainer.data_connector.attach_data(model, train_dataloader, val_dataloaders, datamodule)
# check that model is configured correctly
self.trainer.config_validator.verify_loop_configurations(model)
# attach model log function to callback
self.trainer.callback_connector.attach_model_logging_functions(model)
def on_train_end(self):
if self._teardown_already_run:
return
self._teardown_already_run = True
# trigger checkpoint check. need to temporarily decrease the global step to avoid saving duplicates
# when a checkpoint was saved at the last step
self.trainer.global_step -= 1
self.check_checkpoint_callback(should_update=True, is_last=True)
self.trainer.global_step += 1
# hook
self.trainer.call_hook("on_train_end")
# todo: TPU 8 cores hangs in flush with TensorBoard. Might do for all loggers.
# It might be related to xla tensors blocked when moving the cpu
# kill loggers
if self.trainer.logger is not None:
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._running_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 check_early_stopping_callback(self, should_update):
# TODO bake this logic into the EarlyStopping callback
if should_update and self.trainer.checkpoint_connector.has_trained:
callbacks = [c for c in self.trainer.callbacks if isinstance(c, EarlyStopping)]
model = self.trainer.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.trainer.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.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')
# figure out what to track for epoch end
self.track_epoch_end_reduce_metrics(epoch_output, batch_end_outputs)
# reset batch logger internals
self.trainer.logger_connector.on_train_batch_end()
def reset_train_val_dataloaders(self, model):
if self.trainer.train_dataloader is None or not self.trainer.reload_dataloaders_every_epoch:
self.trainer.reset_train_dataloader(model)
if self.trainer.val_dataloaders is None and not self.trainer.reload_dataloaders_every_epoch:
self.trainer.reset_val_dataloader(model)
def track_epoch_end_reduce_metrics(self, epoch_output, batch_end_outputs):
# track the outputs to reduce at the end of the epoch
for opt_idx, opt_outputs in enumerate(batch_end_outputs):
sample_output = opt_outputs[-1]
# decide if we need to reduce at the end of the epoch automatically
auto_reduce_tng_result = isinstance(sample_output, Result) and sample_output.should_reduce_on_epoch_end
hook_overridden = (
is_overridden("training_epoch_end", model=self.trainer.lightning_module)
or is_overridden("on_train_epoch_end", model=self.trainer.lightning_module)
)
# only track when a) it needs to be autoreduced OR b) the user wants to manually reduce on epoch end
if not (hook_overridden or auto_reduce_tng_result):
continue
# with 1 step (no tbptt) don't use a sequence at epoch end
if isinstance(opt_outputs, list) and len(opt_outputs) == 1 and not isinstance(opt_outputs[0], Result):
opt_outputs = opt_outputs[0]
epoch_output[opt_idx].append(opt_outputs)
def get_optimizers_iterable(self):
"""
Generates an iterable with (idx, optimizer) for each optimizer.
"""
if not self.trainer.optimizer_frequencies:
# call training_step once per optimizer
return list(enumerate(self.trainer.optimizers))
optimizer_freq_cumsum = np.cumsum(self.trainer.optimizer_frequencies)
optimizers_loop_length = optimizer_freq_cumsum[-1]
current_place_in_loop = self.trainer.total_batch_idx % optimizers_loop_length
# find optimzier index by looking for the first {item > current_place} in the cumsum list
opt_idx = np.argmax(optimizer_freq_cumsum > current_place_in_loop)
return [[opt_idx, self.trainer.optimizers[opt_idx]]]
def on_after_backward(self, training_step_output, batch_idx, untouched_loss):
training_step_output.detach()
# insert after step hook
self.trainer.call_hook("on_after_backward")
# when in dev debugging track the losses
self.trainer.dev_debugger.track_train_loss_history(batch_idx, untouched_loss.detach())
def _check_training_step_output(self, training_step_output):
if isinstance(training_step_output, torch.Tensor) and not self.automatic_optimization:
if training_step_output.grad_fn is None:
# TODO: Find why - RuntimeError: Expected to mark a variable ready only once ...
raise MisconfigurationException("In manual optimization, `training_step` should not return a Tensor")
def training_step(self, split_batch, batch_idx, opt_idx, hiddens):
# give the PL module a result for logging
model_ref = self.trainer.lightning_module
with self.trainer.profiler.profile("model_forward"):
args = self.build_train_args(split_batch, batch_idx, opt_idx, hiddens)
# manually capture logged metrics
model_ref._current_fx_name = 'training_step'
model_ref._results = Result()
with self.trainer.profiler.profile("training_step"):
training_step_output = self.trainer.accelerator.training_step(args)
self.trainer.accelerator.post_training_step()
self.trainer.logger_connector.cache_logged_metrics()
self._check_training_step_output(training_step_output)
training_step_output = self.trainer.call_hook("training_step_end", training_step_output)
training_step_output_for_epoch_end, training_step_output = self._process_training_step_output(
training_step_output, split_batch
)
if training_step_output_for_epoch_end is None:
return
# enable empty loss when using manual opt
closure_loss = None
untouched_loss = None
if self.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
untouched_loss = closure_loss.detach().clone()
# result
result = AttributeDict(
closure_loss=closure_loss,
loss=untouched_loss,
training_step_output=training_step_output,
training_step_output_for_epoch_end=training_step_output_for_epoch_end,
)
return result
def _process_training_step_output(self, training_step_output, split_batch):
training_step_output_for_epoch_end = training_step_output
# enable validation_step return None
if training_step_output_for_epoch_end is None:
return None, None
result = self.trainer.lightning_module._results
loss = None
hiddens = None
result["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()
result["extra"] = training_step_output
# handle scalar return
elif isinstance(training_step_output, torch.Tensor):
loss = training_step_output
# map to results under the hood
result.minimize = loss
self.trainer.hiddens = hiddens
# track batch for manual reduction with result
result.track_batch_size(len(split_batch))
# track metrics without grads for epoch reduction
training_step_output_for_epoch_end = copy(result)
training_step_output_for_epoch_end = training_step_output_for_epoch_end.detach()
if self.trainer.move_metrics_to_cpu:
training_step_output_for_epoch_end = training_step_output_for_epoch_end.cpu()
return training_step_output_for_epoch_end, result
@staticmethod
def _prepare_outputs(
outputs: List[List[List[Result]]],
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 ``Result`` objects with dimensions:
[optimizer outs][batch outs][tbptt steps].
batch_mode: If True, ignore the batch output dimension.
Returns:
The cleaned outputs with ``Result`` 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 = []
for tbptt_output in batch_outputs:
out = tbptt_output.extra
out['loss'] = tbptt_output.minimize
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):
# track gradient norms
grad_norm_dic = self._track_gradient_norm()
# clip gradients
self.trainer.accelerator.clip_gradients(
optimizer, self.trainer.gradient_clip_val, gradient_clip_algorithm=self.trainer.gradient_clip_algorithm
)
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.lightning_module
grad_norm_dict = model.grad_norm(self.trainer.track_grad_norm)
return grad_norm_dict
def tbptt_split_batch(self, batch):
splits = [batch]
if self.trainer.truncated_bptt_steps is not None:
model_ref = self.trainer.lightning_module
with self.trainer.profiler.profile("tbptt_split_batch"):
splits = model_ref.tbptt_split_batch(batch, self.trainer.truncated_bptt_steps)
return splits
def run_training_epoch(self):
# modify dataloader if needed (ddp, etc...)
train_dataloader = self.trainer.accelerator.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
for batch_idx, (batch, is_last_batch) in train_dataloader:
self.trainer.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 + CHECKPOINT CALLBACK
# -----------------------------------------
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.trainer.max_steps is not None and self.trainer.max_steps == self.trainer.global_step + 1
and self._accumulated_batches_reached()
):
break
# end epoch early
# stop when the flag is changed or we've gone past the amount
# requested in the batches
if self.trainer.should_stop:
break
self.trainer.total_batch_idx += 1
# stop epoch if we limited the number of training batches
if self._num_training_batches_reached(is_last_batch):
break
# progress global step according to grads progress
self.increment_accumulated_grad_global_step()
# 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)
self.check_early_stopping_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.trainer.call_hook('on_train_epoch_end', processed_epoch_output)
self.trainer.call_hook('on_epoch_end')
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:
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 optimizers:
# toggle model params + set info to logger_connector
self.run_train_split_start(split_idx, split_batch, opt_idx, optimizer)
if self.should_accumulate():
# For gradient accumulation
# -------------------
# calculate loss (train step + train step end)
# -------------------
# automatic_optimization=True: perform dpp sync only when performing optimizer_step
# automatic_optimization=False: don't block synchronization here
with self.block_ddp_sync_behaviour():
self.training_step_and_backward(
split_batch, batch_idx, opt_idx, optimizer, self.trainer.hiddens
)
batch_outputs = self._process_closure_result(
batch_outputs=batch_outputs,
opt_idx=opt_idx,
)
# ------------------------------
# BACKWARD PASS
# ------------------------------
# gradient update with accumulated gradients
else:
if self.automatic_optimization:
def train_step_and_backward_closure():
result = self.training_step_and_backward(
split_batch, batch_idx, opt_idx, optimizer, self.trainer.hiddens
)
return None if result is None else result.loss
# optimizer step
self.optimizer_step(optimizer, opt_idx, batch_idx, train_step_and_backward_closure)
else:
self._curr_step_result = self.training_step(
split_batch, batch_idx, opt_idx, self.trainer.hiddens
)
if self._curr_step_result is None:
# user decided to skip optimization
# make sure to zero grad.
continue
batch_outputs = self._process_closure_result(
batch_outputs=batch_outputs,
opt_idx=opt_idx,
)
# todo: Properly aggregate grad_norm accros opt_idx and split_idx
grad_norm_dic = self._cur_grad_norm_dict
self._cur_grad_norm_dict = None
# update running loss + reset accumulated loss
self.update_running_loss()
result = AttributeDict(
signal=0,
grad_norm_dic=grad_norm_dic,
training_step_output_for_epoch_end=batch_outputs,
)
return result
@contextmanager
def block_ddp_sync_behaviour(self, should_block_sync: bool = False):
"""
automatic_optimization = True
Blocks ddp sync gradients behaviour on backwards pass.
This is useful for skipping sync when accumulating gradients, reducing communication overhead
automatic_optimization = False
do not block ddp gradient sync when using manual optimization
as gradients are needed within the training step
Returns:
context manager with sync behaviour off
"""
if (
isinstance(self.trainer.training_type_plugin, ParallelPlugin)
and (self.automatic_optimization or should_block_sync)
):
with self.trainer.training_type_plugin.block_backward_sync():
yield None
else:
yield None
def _process_closure_result(self, batch_outputs: list, opt_idx: int) -> list:
opt_closure_result = self._curr_step_result
if opt_closure_result is not None:
# cache metrics
self.trainer.logger_connector.cache_training_step_metrics(opt_closure_result)
# 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.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.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 it 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)
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 = self.trainer.accelerator.update_global_step(
self.trainer.total_batch_idx, self.trainer.global_step
)
def _accumulated_batches_reached(self):
return (self.trainer.batch_idx + 1) % self.trainer.accumulate_grad_batches == 0
def _num_training_batches_reached(self, is_last_batch=False):
return (self.trainer.batch_idx + 1) == self.trainer.num_training_batches or is_last_batch
def should_accumulate(self):
# checks if backward or backward + optimizer step (via closure)
accumulation_done = self._accumulated_batches_reached()
is_final_batch = self._num_training_batches_reached()
return not (accumulation_done or is_final_batch)
def should_check_val_fx(self, batch_idx, is_last_batch, on_epoch=False):
# decide if we should run validation
is_val_check_batch = (batch_idx + 1) % self.trainer.val_check_batch == 0
is_val_check_epoch = (self.trainer.current_epoch + 1) % self.trainer.check_val_every_n_epoch == 0
can_check_val = self.trainer.enable_validation and is_val_check_epoch
is_last_batch_for_infinite_dataset = is_last_batch and self.trainer.val_check_batch == float("inf")
epoch_end_val_check = (batch_idx + 1) % self.trainer.num_training_batches == 0
should_check_val = ((is_val_check_batch and epoch_end_val_check) or self.trainer.should_stop
or is_last_batch_for_infinite_dataset
) if on_epoch else (is_val_check_batch and not epoch_end_val_check)
return should_check_val and can_check_val
def build_train_args(self, batch, batch_idx, opt_idx, hiddens):
# enable not needing to add opt_idx to training_step
args = [batch, batch_idx]
if len(self.trainer.optimizers) > 1:
if self.trainer.has_arg("training_step", "optimizer_idx"):
if not self.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 self.trainer.has_arg("training_step", "optimizer_idx") and self.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.trainer.truncated_bptt_steps is not None:
args.append(hiddens)
return args
def save_loggers_on_train_batch_end(self):
# when loggers should save to disk
should_flush_logs = self.trainer.logger_connector.should_flush_logs
if should_flush_logs and self.trainer.is_global_zero and self.trainer.logger is not None:
self.trainer.logger.save()
def 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.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()
standalone = os.getenv("PL_RUN_STANDALONE_TESTS", "0") == "1"
if standalone:
stderr = StringIO()
# recording
with redirect_stderr(stderr):
_warn("test1")
_warn("test2", category=DeprecationWarning)
rank_zero_warn("test3")
rank_zero_warn("test4", category=DeprecationWarning)
rank_zero_deprecation("test5")
cache = WarningCache()
cache.warn("test6")
cache.deprecation("test7")
output = stderr.getvalue()
assert "test_warnings.py:30: UserWarning: test1" in output
assert "test_warnings.py:31: DeprecationWarning: test2" in output
assert "test_warnings.py:33: UserWarning: test3" in output
assert "test_warnings.py:34: DeprecationWarning: test4" in output
assert "test_warnings.py:36: LightningDeprecationWarning: test5" in output
assert "test_warnings.py:39: UserWarning: test6" in output
assert "test_warnings.py:40: LightningDeprecationWarning: test7" in output
class PredictionEpochLoop(Loop):
"""Loop performing prediction on arbitrary sequentially used dataloaders."""
def __init__(self) -> None:
super().__init__()
self.return_predictions: bool = False
self.predictions: List[Any] = []
self.current_batch_indices: List[int] = []
self.batch_progress = Progress()
self._dl_max_batches: Optional[int] = None
self._num_dataloaders: Optional[int] = None
self._warning_cache = WarningCache()
self._all_batch_indices: List[int] = []
@property
def done(self) -> bool:
"""Ends prediction when the iteration count exceeds the total number of available batches"""
return self.batch_progress.current.completed >= self._dl_max_batches
@property
def should_store_predictions(self) -> bool:
"""Whether the predictions should be stored for later usage (e.g. aggregation or returning)"""
any_pred = any(cb.interval.on_epoch
for cb in self.trainer.prediction_writer_callbacks)
return self.return_predictions or any_pred
def connect(self, **kwargs: "Loop") -> None:
raise NotImplementedError(
f"{self.__class__.__name__} does not connect any child loops.")
def reset(self) -> None:
"""Resets the loops internal state"""
self._all_batch_indices: List[int] = []
self.predictions: List[Any] = []
self.batch_progress.current.reset()
def on_run_start(self,
dataloader_iter: Iterator,
dataloader_idx: int,
dl_max_batches: int,
num_dataloaders: int,
return_predictions: bool = False) -> None:
"""
Prepares the loops internal state
Args:
dataloader_iter: the iterator over the current dataloader
dataloader_idx: the index of the current dataloader
dl_max_batches: the maximum number of batches the current loader can produce
num_dataloaders: the total number of dataloaders
return_predictions: whether to return the obtained predictions
"""
void(dataloader_iter, dataloader_idx)
self._dl_max_batches = dl_max_batches
self._num_dataloaders = num_dataloaders
self.return_predictions = return_predictions
def advance(self,
dataloader_iter: Iterator,
dataloader_idx: int,
dl_max_batches: int,
num_dataloaders: int,
return_predictions: bool = False) -> None:
"""
Runs one prediction step.
Args:
dataloader_iter: the iterator over the current dataloader
dataloader_idx: the index of the current dataloader
dl_max_batches: the maximum number of batches the current loader can produce
num_dataloaders: the total number of dataloaders
return_predictions: whether to return the obtained predictions
"""
batch_idx, batch = next(dataloader_iter)
if batch is None:
raise StopIteration
with self.trainer.profiler.profile("predict_batch_to_device"):
batch = self.trainer.accelerator.batch_to_device(
batch, dataloader_idx=dataloader_idx)
self.batch_progress.increment_ready()
with self.trainer.profiler.profile("predict_step"):
self._predict_step(batch, batch_idx, dataloader_idx)
def on_run_end(self) -> Tuple[Any, Any]:
"""Returns the predictions and the corresponding batch indices"""
predictions = self.predictions
all_batch_indices = self._all_batch_indices
# free memory
self.predictions = []
self._all_batch_indices = []
return predictions, all_batch_indices
def _predict_step(self, batch: Any, batch_idx: int,
dataloader_idx: int) -> None:
"""Runs the actual predict step together with all the
necessary bookkeeping and the hooks tied to the predict step.
Args:
batch: the current batch to run the prediction on
batch_idx: the index of the current batch
dataloader_idx: the index of the dataloader producing the current batch
"""
# configure step_kwargs
step_kwargs = self._build_kwargs(batch, batch_idx, dataloader_idx)
# extract batch_indices and store them
self._store_batch_indices(dataloader_idx)
model_ref = self.trainer.lightning_module
self.trainer.call_hook("on_predict_batch_start", batch, batch_idx,
dataloader_idx)
self.batch_progress.increment_started()
model_ref._current_fx_name = "predict_step"
predictions = self.trainer.accelerator.predict_step(step_kwargs)
self.batch_progress.increment_processed()
if predictions is None:
self._warning_cache.warn(
"predict returned None if it was on purpose, ignore this warning..."
)
self.trainer.call_hook("on_predict_batch_end", predictions, batch,
batch_idx, dataloader_idx)
self.batch_progress.increment_completed()
if self.should_store_predictions:
self.predictions.append(predictions)
def _build_kwargs(self, batch: Any, batch_idx: int,
dataloader_idx: int) -> Dict[str, Any]:
"""
Assembles the keyword arguments for the ``predict_step``
Args:
batch: the current batch to run the prediction on
batch_idx: the index of the current batch
dataloader_idx: the index of the dataloader producing the current batch
Returns:
the dictionary containing all the keyboard arguments for the predict step
"""
step_kwargs = OrderedDict([('batch', batch), ('batch_idx', batch_idx)])
if self._num_dataloaders > 1:
step_kwargs['dataloader_idx'] = dataloader_idx
return step_kwargs
def _store_batch_indices(self, dataloader_idx: int) -> None:
"""Stores the batch indices if the predictions should be stored"""
batch_sampler = self.trainer.predict_dataloaders[
dataloader_idx].batch_sampler
if isinstance(batch_sampler, IndexBatchSamplerWrapper):
self.current_batch_indices = batch_sampler.batch_indices
if self.should_store_predictions:
self._all_batch_indices.append(batch_sampler.batch_indices)
class Closure(AbstractClosure[ClosureResult]):
"""An implementation of a :class:`AbstractClosure` for automatic optimization in Lightning that combines three
elementary closures into one: ``training_step``, ``backward`` and ``zero_grad``.
The Closure gets created by the training loop(s) and is then passed to the
:meth:`torch.optim.Optimizer.step` method. An optimizer is responsible for calling the closure and optionally
do something with the output.
Args:
step_fn: This is typically the :meth:`pytorch_lightning.core.lightning.LightningModule.training_step
wrapped with processing for its outputs
backward_fn: A function that takes a loss value as input, performs back-propagation and returns the loss value.
Can be set to ``None`` to skip the backward operation.
zero_grad_fn: A function that zeroes the gradients. Can be set to ``None`` to skip zero_grad, for example
when accumulating gradients.
profiler: A profiler for profiling the actions of the passed in closure functions.
Example:
closure = Closure()
optimizer = torch.optim.Adam(...)
optimizer.step(closure)
"""
warning_cache = WarningCache()
def __init__(
self,
step_fn: Callable[[], ClosureResult],
backward_fn: Optional[Callable[[Tensor], None]] = None,
zero_grad_fn: Optional[Callable[[], None]] = None,
profiler: Optional[BaseProfiler] = None,
):
super().__init__()
self._step_fn = step_fn
self._backward_fn = backward_fn
self._zero_grad_fn = zero_grad_fn
self._profiler = PassThroughProfiler() if profiler is None else profiler
def closure(self, *args: Any, **kwargs: Any) -> ClosureResult:
with self._profiler.profile("training_step_and_backward"):
step_output = self._step_fn()
if step_output.closure_loss is None:
self.warning_cache.warn(
"`training_step` returned `None`. If this was on purpose, ignore this warning..."
)
if self._zero_grad_fn is not None:
with self._profiler.profile("zero_grad"):
self._zero_grad_fn()
if self._backward_fn is not None and step_output.closure_loss is not None:
with self._profiler.profile("backward"):
self._backward_fn(step_output.closure_loss)
return step_output
def __call__(self, *args: Any, **kwargs: Any) -> Optional[Tensor]:
self._result = self.closure(*args, **kwargs)
return self._result.loss
class PredictLoop(object):
def __init__(self, trainer):
self.trainer = trainer
self.max_batches = None
self.num_dataloaders = None
self.warning_cache = WarningCache()
def on_trainer_init(self):
self.trainer.num_predict_batches = []
def get_predict_dataloaders(self):
self.trainer.reset_predict_dataloader(self.trainer.lightning_module)
dataloaders = self.trainer.predict_dataloaders
max_batches = self.trainer.num_predict_batches
return dataloaders, max_batches
def should_skip_predict(self, max_batches):
return sum(max_batches) == 0
def on_predict_model_eval(self, *_, **__):
model_ref = self.trainer.lightning_module
model_ref.on_predict_model_eval()
def setup(self, model, max_batches, dataloaders):
# copy properties for forward overrides
self.trainer.model_connector.copy_trainer_model_properties(model)
# convert max_batches to list
if isinstance(max_batches, int):
max_batches = [max_batches] * len(dataloaders)
self.max_batches = max_batches
self.num_dataloaders = self._get_num_dataloaders(dataloaders)
self._predictions = [[] for _ in range(self.num_dataloaders)]
self.trainer._progress_bar_callback.on_predict_start(
self.trainer, self.trainer.lightning_module)
def _get_num_dataloaders(self, dataloaders):
# case where user does:
# return dl1, dl2
length = len(dataloaders)
if len(dataloaders) > 0 and isinstance(dataloaders[0], (list, tuple)):
length = len(dataloaders[0])
return length
def predict(self, batch, batch_idx, dataloader_idx):
# configure args
args = [batch, batch_idx]
if self.num_dataloaders:
args.append(dataloader_idx)
model_ref = self.trainer.lightning_module
model_ref._current_fx_name = "predict"
predictions = self.trainer.accelerator.predict(args)
if predictions is None:
self.warning_cache.warn(
"predict returned None if it was on purpose, ignore this warning..."
)
self._predictions[dataloader_idx].append(predictions)
self.trainer._progress_bar_callback.on_predict_batch_end(
self.trainer, model_ref, predictions, batch, batch_idx,
dataloader_idx)
return
def on_predict_epoch_end(self):
self.trainer._progress_bar_callback.on_predict_end(
self.trainer, self.trainer.lightning_module)
results = self._predictions
def _convert_to_numpy(v):
return v.cpu().numpy()
results = apply_to_collection(results, torch.Tensor, _convert_to_numpy)
if len(results) == 1:
return results[0]
return results
def __init__(self, trainer):
self.trainer = trainer
self.max_batches = None
self.num_dataloaders = None
self.warning_cache = WarningCache()
class WandbLogger(LightningLoggerBase):
r"""
Log using `Weights and Biases <https://www.wandb.com/>`_.
Install it with pip:
.. code-block:: bash
pip install wandb
Args:
name: Display name for the run.
save_dir: Path where data is saved (wandb dir by default).
offline: Run offline (data can be streamed later to wandb servers).
id: Sets the version, mainly used to resume a previous run.
version: Same as id.
anonymous: Enables or explicitly disables anonymous logging.
project: The name of the project to which this run will belong.
log_model: Save checkpoints in wandb dir to upload on W&B servers.
prefix: A string to put at the beginning of metric keys.
sync_step: Sync Trainer step with wandb step.
experiment: WandB experiment object. Automatically set when creating a run.
\**kwargs: Additional arguments like `entity`, `group`, `tags`, etc. used by
:func:`wandb.init` can be passed as keyword arguments in this logger.
Raises:
ImportError:
If required WandB package is not installed on the device.
MisconfigurationException:
If both ``log_model`` and ``offline``is set to ``True``.
Example::
from pytorch_lightning.loggers import WandbLogger
from pytorch_lightning import Trainer
wandb_logger = WandbLogger()
trainer = Trainer(logger=wandb_logger)
Note: When logging manually through `wandb.log` or `trainer.logger.experiment.log`,
make sure to use `commit=False` so the logging step does not increase.
See Also:
- `Tutorial <https://colab.research.google.com/drive/16d1uctGaw2y9KhGBlINNTsWpmlXdJwRW?usp=sharing>`__
on how to use W&B with PyTorch Lightning
- `W&B Documentation <https://docs.wandb.ai/integrations/lightning>`__
"""
LOGGER_JOIN_CHAR = '-'
def __init__(self,
name: Optional[str] = None,
save_dir: Optional[str] = None,
offline: Optional[bool] = False,
id: Optional[str] = None,
anonymous: Optional[bool] = False,
version: Optional[str] = None,
project: Optional[str] = None,
log_model: Optional[bool] = False,
experiment=None,
prefix: Optional[str] = '',
sync_step: Optional[bool] = True,
**kwargs):
if wandb is None:
raise ImportError(
'You want to use `wandb` logger which is not installed yet,' # pragma: no-cover
' install it with `pip install wandb`.')
if offline and log_model:
raise MisconfigurationException(
f'Providing log_model={log_model} and offline={offline} is an invalid configuration'
' since model checkpoints cannot be uploaded in offline mode.\n'
'Hint: Set `offline=False` to log your model.')
super().__init__()
self._name = name
self._save_dir = save_dir
self._offline = offline
self._id = version or id
self._anonymous = 'allow' if anonymous else None
self._project = project
self._log_model = log_model
self._prefix = prefix
self._sync_step = sync_step
self._experiment = experiment
self._kwargs = kwargs
# logging multiple Trainer on a single W&B run (k-fold, resuming, etc)
self._step_offset = 0
self.warning_cache = WarningCache()
def __getstate__(self):
state = self.__dict__.copy()
# args needed to reload correct experiment
state[
'_id'] = self._experiment.id if self._experiment is not None else None
# cannot be pickled
state['_experiment'] = None
return state
@property
@rank_zero_experiment
def experiment(self) -> Run:
r"""
Actual wandb object. To use wandb features in your
:class:`~pytorch_lightning.core.lightning.LightningModule` do the following.
Example::
self.logger.experiment.some_wandb_function()
"""
if self._experiment is None:
if self._offline:
os.environ['WANDB_MODE'] = 'dryrun'
self._experiment = wandb.init(
name=self._name,
dir=self._save_dir,
project=self._project,
anonymous=self._anonymous,
id=self._id,
resume='allow',
**self._kwargs) if wandb.run is None else wandb.run
# offset logging step when resuming a run
self._step_offset = self._experiment.step
# save checkpoints in wandb dir to upload on W&B servers
if self._save_dir is None:
self._save_dir = self._experiment.dir
return self._experiment
def watch(self,
model: nn.Module,
log: str = 'gradients',
log_freq: int = 100):
self.experiment.watch(model, log=log, log_freq=log_freq)
@rank_zero_only
def log_hyperparams(self, params: Union[Dict[str, Any],
Namespace]) -> None:
params = self._convert_params(params)
params = self._flatten_dict(params)
params = self._sanitize_callable_params(params)
self.experiment.config.update(params, allow_val_change=True)
@rank_zero_only
def log_metrics(self,
metrics: Dict[str, float],
step: Optional[int] = None) -> None:
assert rank_zero_only.rank == 0, 'experiment tried to log from global_rank != 0'
metrics = self._add_prefix(metrics)
if self._sync_step and step is not None and step + self._step_offset < self.experiment.step:
self.warning_cache.warn(
'Trying to log at a previous step. Use `WandbLogger(sync_step=False)`'
' or try logging with `commit=False` when calling manually `wandb.log`.'
)
if self._sync_step:
self.experiment.log(
metrics,
step=(step + self._step_offset) if step is not None else None)
elif step is not None:
self.experiment.log({
**metrics, 'trainer_step': (step + self._step_offset)
})
else:
self.experiment.log(metrics)
@property
def save_dir(self) -> Optional[str]:
return self._save_dir
@property
def name(self) -> Optional[str]:
# don't create an experiment if we don't have one
return self._experiment.project_name(
) if self._experiment else self._name
@property
def version(self) -> Optional[str]:
# don't create an experiment if we don't have one
return self._experiment.id if self._experiment else self._id
@rank_zero_only
def finalize(self, status: str) -> None:
# offset future training logged on same W&B run
if self._experiment is not None:
self._step_offset = self._experiment.step
# upload all checkpoints from saving dir
if self._log_model:
wandb.save(os.path.join(self.save_dir, "*.ckpt"))
class EvaluationLoop(object):
def __init__(self, trainer):
self.trainer = trainer
self.outputs = []
self.step_metrics = []
self.predictions = None
self.max_batches = None
self.warning_cache = WarningCache()
self.num_dataloaders = None
def on_trainer_init(self):
self.trainer.num_sanity_val_batches = []
self.trainer.num_test_batches = []
self.trainer.num_val_batches = []
self.trainer.test_dataloaders = None
self.trainer.val_dataloaders = None
# .validate() and .test() set this when they load a checkpoint
self.trainer.validated_ckpt_path = None
self.trainer.tested_ckpt_path = None
# when true, print evaluation results in .validate() and .test()
self.trainer.verbose_evaluate = True
def get_evaluation_dataloaders(self):
model = self.trainer.lightning_module
# select dataloaders
if self.trainer.testing:
self.trainer.reset_test_dataloader(model)
dataloaders = self.trainer.test_dataloaders
max_batches = self.trainer.num_test_batches
else:
# val
if self.trainer.val_dataloaders is None or self.trainer.reload_dataloaders_every_epoch:
self.trainer.reset_val_dataloader(model)
if self.trainer.sanity_checking:
self.trainer.num_sanity_val_batches = [
min(self.trainer.num_sanity_val_steps, val_batches)
for val_batches in self.trainer.num_val_batches
]
max_batches = self.trainer.num_sanity_val_batches
else:
max_batches = self.trainer.num_val_batches
dataloaders = self.trainer.val_dataloaders
return dataloaders, max_batches
def should_skip_evaluation(self, max_batches):
return sum(max_batches) == 0
def on_evaluation_start(self, *args, **kwargs):
if self.trainer.testing:
self.trainer.call_hook('on_test_start', *args, **kwargs)
else:
self.trainer.call_hook('on_validation_start', *args, **kwargs)
def on_evaluation_model_eval(self, *_, **__):
model_ref = self.trainer.lightning_module
if self.trainer.testing:
model_ref.on_test_model_eval()
else:
model_ref.on_validation_model_eval()
def on_evaluation_model_train(self, *_, **__):
model_ref = self.trainer.lightning_module
if self.trainer.testing:
model_ref.on_test_model_train()
else:
model_ref.on_validation_model_train()
def on_evaluation_end(self, *args, **kwargs):
if self.trainer.testing:
self.trainer.call_hook('on_test_end', *args, **kwargs)
else:
self.trainer.call_hook('on_validation_end', *args, **kwargs)
if self.trainer.state != TrainerState.FITTING:
# summarize profile results
self.trainer.profiler.describe()
def reload_evaluation_dataloaders(self):
model = self.trainer.lightning_module
if self.trainer.testing:
self.trainer.reset_test_dataloader(model)
else:
self.trainer.reset_val_dataloader(model)
def setup(self, model, max_batches, dataloaders):
# bookkeeping
self.outputs = []
self.predictions = PredictionCollection(self.trainer.global_rank,
self.trainer.world_size)
# convert max_batches to list
if isinstance(max_batches, int):
max_batches = [max_batches] * len(dataloaders)
self.max_batches = max_batches
self.num_dataloaders = self._get_num_dataloaders(dataloaders)
self._predictions = [[] for _ in range(self.num_dataloaders)]
def on_evaluation_epoch_start(self, *args, **kwargs):
self.trainer.call_hook('on_epoch_start', *args, **kwargs)
if self.trainer.testing:
self.trainer.call_hook('on_test_epoch_start', *args, **kwargs)
else:
self.trainer.call_hook('on_validation_epoch_start', *args,
**kwargs)
def _build_args(self, batch, batch_idx, dataloader_idx):
# make dataloader_idx arg in validation_step optional
args = [batch, batch_idx]
multiple_val_loaders = (
not self.trainer.testing
and self._get_num_dataloaders(self.trainer.val_dataloaders) > 1)
multiple_test_loaders = (
self.trainer.testing
and self._get_num_dataloaders(self.trainer.test_dataloaders) > 1)
if multiple_test_loaders or multiple_val_loaders:
args.append(dataloader_idx)
return args
def _get_num_dataloaders(self, dataloaders):
# case where user does:
# return dl1, dl2
length = len(dataloaders)
if len(dataloaders) > 0 and isinstance(dataloaders[0], (list, tuple)):
length = len(dataloaders[0])
return length
def evaluation_step(self, batch, batch_idx, dataloader_idx):
# configure args
args = self._build_args(batch, batch_idx, dataloader_idx)
model_ref = self.trainer.lightning_module
model_ref._results = Result()
if self.trainer.testing:
model_ref._current_fx_name = "test_step"
with self.trainer.profiler.profile("test_step"):
output = self.trainer.accelerator.test_step(args)
else:
model_ref._current_fx_name = "validation_step"
with self.trainer.profiler.profile("validation_step"):
output = self.trainer.accelerator.validation_step(args)
# capture any logged information
self.trainer.logger_connector.cache_logged_metrics()
# track batch size for weighted average
is_result_obj = isinstance(output, Result)
if is_result_obj:
output.track_batch_size(batch)
return output
def evaluation_step_end(self, *args, **kwargs):
if self.trainer.testing:
output = self.trainer.call_hook('test_step_end', *args, **kwargs)
else:
output = self.trainer.call_hook('validation_step_end', *args,
**kwargs)
return output
def evaluation_epoch_end(self, outputs):
# unset dataloder_idx in model
self.trainer.logger_connector.evaluation_epoch_end()
# call the model epoch end
model = self.trainer.lightning_module
if self.trainer.testing:
if is_overridden('test_epoch_end', model=model):
model._current_fx_name = 'test_epoch_end'
model.test_epoch_end(outputs)
else:
if is_overridden('validation_epoch_end', model=model):
model._current_fx_name = 'validation_epoch_end'
model.validation_epoch_end(outputs)
# capture logging
self.trainer.logger_connector.cache_logged_metrics()
def __gather_epoch_end_eval_results(self, outputs):
eval_results = []
for epoch_output in outputs:
result = epoch_output[0].__class__.gather(epoch_output)
eval_results.append(result)
# with 1 dataloader don't pass in a list
if len(eval_results) == 1:
eval_results = eval_results[0]
return eval_results
def __auto_reduce_result_objs(self, outputs):
# outputs has a list of results per dataloader
eval_results = []
for dl_output in outputs:
result = dl_output[0]
result = result.__class__.reduce_on_epoch_end(dl_output)
eval_results.append(result)
return eval_results
def on_predict_epoch_end(self):
if self.trainer._progress_bar_callback is not None:
self.trainer._progress_bar_callback.on_test_end(
self.trainer, self.trainer.lightning_module)
results = self._predictions
def _convert_to_numpy(v):
return v.cpu().numpy()
results = apply_to_collection(results, torch.Tensor, _convert_to_numpy)
return results, None
def on_evaluation_batch_start(self, batch, batch_idx, dataloader_idx):
# set dataloader_idx to model and track batch_size
self.trainer.logger_connector.on_evaluation_batch_start(
batch, dataloader_idx, self.num_dataloaders)
if self.trainer.testing:
self.trainer.call_hook('on_test_batch_start', batch, batch_idx,
dataloader_idx)
else:
self.trainer.call_hook('on_validation_batch_start', batch,
batch_idx, dataloader_idx)
def on_evaluation_batch_end(self, output, batch, batch_idx,
dataloader_idx):
if self.trainer.testing:
self.trainer.call_hook('on_test_batch_end', output, batch,
batch_idx, dataloader_idx)
else:
self.trainer.call_hook('on_validation_batch_end', output, batch,
batch_idx, dataloader_idx)
# store predicitons if do_write_predictions and track eval loss history
self.store_predictions(output, batch_idx, dataloader_idx)
def store_predictions(self, output, batch_idx, dataloader_idx):
# Add step predictions to prediction collection to write later
if output is not None:
do_write_predictions = isinstance(output,
Result) and self.trainer.testing
if do_write_predictions:
self.predictions.add(output.pop('predictions', None))
# track debug metrics
self.trainer.dev_debugger.track_eval_loss_history(
batch_idx, dataloader_idx, output)
def on_evaluation_epoch_end(
self, outputs: Union[List[List[Dict]], List[Dict]]) -> None:
model_ref = self.trainer.lightning_module
hook_name = "on_test_epoch_end" if self.trainer.testing else "on_validation_epoch_end"
self.trainer._reset_result_and_set_hook_fx_name(hook_name)
with self.trainer.profiler.profile(hook_name):
if hasattr(self.trainer, hook_name):
on_evaluation_epoch_end_hook = getattr(self.trainer, hook_name)
on_evaluation_epoch_end_hook(outputs)
if is_overridden(hook_name, model_ref):
model_hook_fx = getattr(model_ref, hook_name)
if is_param_in_hook_signature(model_hook_fx, "outputs"):
model_hook_fx(outputs)
else:
self.warning_cache.warn(
f"`ModelHooks.{hook_name}` signature has changed in v1.3. `outputs` parameter has been added."
" Support for the old signature will be removed in v1.5",
DeprecationWarning)
model_hook_fx()
self.trainer._cache_logged_metrics()
self.trainer.call_hook('on_epoch_end')
def log_evaluation_step_metrics(self, output, batch_idx):
if self.trainer.sanity_checking:
return
step_log_metrics = {}
step_pbar_metrics = {}
self.__log_result_step_metrics(step_log_metrics, step_pbar_metrics,
batch_idx)
def __log_result_step_metrics(self, step_log_metrics, step_pbar_metrics,
batch_idx):
cached_results = self.trainer.logger_connector.cached_results
cached_batch_pbar_metrics, cached_batch_log_metrics = cached_results.update_logger_connector(
)
step_log_metrics.update(cached_batch_log_metrics)
step_pbar_metrics.update(cached_batch_pbar_metrics)
if len(step_log_metrics) > 0:
# make the metrics appear as a different line in the same graph
metrics_by_epoch = {}
for k, v in step_log_metrics.items():
metrics_by_epoch[f'{k}/epoch_{self.trainer.current_epoch}'] = v
self.trainer.logger_connector.log_metrics(metrics_by_epoch, {},
step=batch_idx)
if len(step_pbar_metrics) > 0:
self.trainer.logger_connector.add_progress_bar_metrics(
step_pbar_metrics)
class PredictLoop(object):
def __init__(self, trainer):
self.trainer = trainer
self.max_batches = None
self.num_dataloaders = None
self.warning_cache = WarningCache()
self.batch_indices: Optional[List[int]] = None
self.epoch_batch_indices: Optional[List[List[int]]] = None
self.predictions: Optional[List[List[Any]]] = None
# `DDPSpawnPlugin` plugins and derivate don't support return predictions.
self._return_predictions: Optional[bool] = None
self._previous_grad_status: Optional[bool] = None
@property
def return_predictions(self) -> bool:
return self._return_predictions
@return_predictions.setter
def return_predictions(self,
return_predictions: Optional[bool] = None) -> None:
# ``DDPSpawnPlugin`` plugins and derivate don't support return predictions.
is_ddp_spawn = isinstance(self.trainer.training_type_plugin,
DDPSpawnPlugin)
if return_predictions and is_ddp_spawn:
raise MisconfigurationException(
"`return_predictions` should be set to `False` when using the `DDPSpawnPlugin` or children class. "
f"Found {return_predictions} with training_type_plugin {type(self.trainer.training_type_plugin)}."
)
# For non ``DDPSpawnPlugin`` plugin, the `return_predictions` is True by default unless user decide otherwise.
self._return_predictions = not is_ddp_spawn if return_predictions is None else return_predictions
@property
def should_store_predictions(self) -> bool:
any_pred = any(cb.interval.on_epoch
for cb in self.trainer.prediction_writer_callbacks)
return self.return_predictions or any_pred
def on_trainer_init(self):
self.trainer.num_predict_batches = []
self.trainer.predicted_ckpt_path = None
def get_predict_dataloaders(self):
self.trainer.reset_predict_dataloader(self.trainer.lightning_module)
dataloaders = self.trainer.predict_dataloaders
max_batches = self.trainer.num_predict_batches
return dataloaders, max_batches
def should_skip_predict(self, max_batches):
return sum(max_batches) == 0
def on_predict_model_eval(self):
model_ref = self.trainer.lightning_module
model_ref.on_predict_model_eval()
def setup(self, max_batches, dataloaders):
# convert max_batches to list
if isinstance(max_batches, int):
max_batches = [max_batches] * len(dataloaders)
self.max_batches = max_batches
self.num_dataloaders = self._get_num_dataloaders(dataloaders)
self.predictions = [[] for _ in range(self.num_dataloaders)]
self.epoch_batch_indices = [[] for _ in range(self.num_dataloaders)]
def _get_num_dataloaders(self, dataloaders: List[DataLoader]) -> int:
# case where user does:
# return dl1, dl2
length = len(dataloaders)
if len(dataloaders) > 0 and isinstance(dataloaders[0], (list, tuple)):
length = len(dataloaders[0])
return length
def _build_kwargs(self, batch, batch_idx, dataloader_idx):
step_kwargs = OrderedDict([('batch', batch), ('batch_idx', batch_idx)])
if self.num_dataloaders:
step_kwargs['dataloader_idx'] = dataloader_idx
return step_kwargs
def predict_step(self, batch: Any, batch_idx: int,
dataloader_idx: int) -> None:
# configure step_kwargs
step_kwargs = self._build_kwargs(batch, batch_idx, dataloader_idx)
# extract batch_indices and store them
self._store_batch_indices(dataloader_idx)
model_ref = self.trainer.lightning_module
self.trainer.call_hook("on_predict_batch_start", batch, batch_idx,
dataloader_idx)
model_ref._current_fx_name = "predict"
predictions = self.trainer.accelerator.predict_step(step_kwargs)
if predictions is None:
self.warning_cache.warn(
"predict returned None if it was on purpose, ignore this warning..."
)
self.trainer.call_hook("on_predict_batch_end", predictions, batch,
batch_idx, dataloader_idx)
if self.should_store_predictions:
self.predictions[dataloader_idx].append(predictions)
def _store_batch_indices(self, dataloader_idx: int) -> None:
batch_sampler = self.trainer.predict_dataloaders[
dataloader_idx].batch_sampler
if isinstance(batch_sampler, IndexBatchSamplerWrapper):
self.batch_indices = batch_sampler.batch_indices
if self.should_store_predictions:
self.epoch_batch_indices[dataloader_idx].append(
batch_sampler.batch_indices)
def on_predict_start(self) -> None:
# enable eval mode + no grads
self.on_predict_model_eval()
self.trainer.lightning_module.zero_grad()
self._previous_grad_status = torch.is_grad_enabled()
torch.set_grad_enabled(False)
# hook
self.trainer.call_hook("on_predict_start")
self.trainer.call_hook("on_predict_epoch_start")
def on_predict_epoch_end(self) -> Optional[_PREDICT_OUTPUT]:
self.trainer.profiler.describe()
results = self.predictions
self.trainer.call_hook("on_predict_epoch_end", results)
if self.return_predictions:
return results[0] if self.num_dataloaders == 1 else results
def on_predict_end(self):
# clear memory. the predictions are extracted in `on_predict_epoch_end`.
self.predictions = None
self.batch_indices = None
# reset grad to its previous status.
torch.set_grad_enabled(self._previous_grad_status)
# hook
self.trainer.call_hook("on_predict_end")
class TrainLoop:
def __init__(self, trainer, multiple_trainloader_mode):
self.trainer = trainer
self.early_stopping_accumulator = None
self.checkpoint_accumulator = None
self.accumulated_loss = None
self.warning_cache = WarningCache()
self._teardown_already_run = False
self.running_loss = TensorRunningAccum(window_length=20)
self.automatic_optimization = True
self._curr_step_result = None
self._cur_grad_norm_dict = None
self._multiple_trainloader_mode = multiple_trainloader_mode
self._skip_backward = False
self.trainer._multiple_trainloader_mode = multiple_trainloader_mode
def on_trainer_init(
self,
max_epochs,
min_epochs,
max_steps,
min_steps,
num_sanity_val_steps,
automatic_optimization,
weights_summary,
):
self.trainer.global_step = 0
self.trainer.current_epoch = 0
self.trainer.interrupted = False
self.trainer.should_stop = False
self.trainer._state = TrainerState.INITIALIZING
self.trainer.total_batch_idx = 0
self.trainer.batch_idx = 0
self.trainer.num_training_batches = 0
self.trainer.train_dataloader = None
self.automatic_optimization = automatic_optimization
# If neither max_epochs or max_steps is set, then use existing default of max_epochs = 1000
self.trainer.max_epochs = 1000 if (
max_epochs is None and max_steps is None) else max_epochs
# If neither max_epochs or max_steps is set, then use existing default of min_epochs = 1
self.trainer.min_epochs = 1 if (min_epochs is None
and min_steps is None) else min_epochs
self.trainer.max_steps = max_steps
self.trainer.min_steps = min_steps
if num_sanity_val_steps == -1:
self.trainer.num_sanity_val_steps = float("inf")
else:
self.trainer.num_sanity_val_steps = num_sanity_val_steps
self.trainer.weights_summary = weights_summary
if weights_summary is not None and weights_summary not in ModelSummary.MODES:
raise MisconfigurationException(
f"`weights_summary` can be None, {', '.join(ModelSummary.MODES)}, got {weights_summary}"
)
@property
def num_optimizers(self):
num_optimizers = len(self.get_optimizers_iterable())
return num_optimizers
def should_skip_training(self):
should_by_epoch = self.trainer.max_epochs is not None and self.trainer.current_epoch >= self.trainer.max_epochs
return should_by_epoch or self.trainer.num_training_batches == 0
def on_train_start(self):
# clear cache before training
if self.trainer._device_type == DeviceType.GPU and self.trainer.root_gpu is not None:
# use context because of:
# https://discuss.pytorch.org/t/out-of-memory-when-i-use-torch-cuda-empty-cache/57898
with torch.cuda.device(f"cuda:{self.trainer.root_gpu}"):
torch.cuda.empty_cache()
# hook
self.trainer.call_hook("on_train_start")
# provide rank to profiler
self.trainer.profile_connector.on_train_start(self.trainer)
def setup_fit(self, model, train_dataloader, val_dataloaders, datamodule):
# bind logger and other properties
self.trainer.model_connector.copy_trainer_model_properties(model)
# clean hparams
if hasattr(model, "hparams"):
parsing.clean_namespace(model.hparams)
# links data to the trainer
self.trainer.data_connector.attach_data(model, train_dataloader,
val_dataloaders, datamodule)
# check that model is configured correctly
self.trainer.config_validator.verify_loop_configurations(model)
# attach model log function to callback
self.trainer.callback_connector.attach_model_logging_functions(model)
def setup_training(self):
"""
Sanity check a few things before starting actual training.
"""
# --------------------------
# Pre-train
# --------------------------
ref_model = self.trainer.get_model()
# on pretrain routine start
self.trainer.on_pretrain_routine_start(ref_model)
if self.trainer.is_function_implemented("on_pretrain_routine_start"):
ref_model.on_pretrain_routine_start()
# print model summary
if self.trainer.is_global_zero:
ref_model.summarize(mode=self.trainer.weights_summary)
# restore training state and model weights before hpc is called
self.trainer.checkpoint_connector.restore_weights()
# on pretrain routine end
self.trainer.on_pretrain_routine_end(ref_model)
if self.trainer.is_function_implemented("on_pretrain_routine_end"):
ref_model.on_pretrain_routine_end()
def on_train_end(self):
if self._teardown_already_run:
return
self._teardown_already_run = True
# trigger checkpoint check. need to temporarily decrease the global step to avoid saving duplicates
# when a checkpoint was saved at the last step
self.trainer.global_step -= 1
self.check_checkpoint_callback(should_update=True, is_last=True)
self.trainer.global_step += 1
# hook
self.trainer.call_hook("on_train_end")
# kill loggers
if self.trainer.logger is not None:
self.trainer.logger.finalize("success")
# summarize profile results
if self.trainer.global_rank == 0:
self.trainer.profiler.describe()
# give accelerators a chance to finish
self.trainer.accelerator_backend.on_train_end()
# clear mem
if self.trainer._device_type == DeviceType.GPU:
model = self.trainer.get_model()
model.cpu()
torch.cuda.empty_cache()
def check_checkpoint_callback(self, should_update, is_last=False):
# TODO bake this logic into the ModelCheckpoint callback
if should_update and self.trainer.checkpoint_connector.has_trained:
callbacks = self.trainer.checkpoint_callbacks
if is_last and any(cb.save_last for cb in callbacks):
rank_zero_info("Saving latest checkpoint...")
model = self.trainer.get_model()
for cb in callbacks:
cb.on_validation_end(self.trainer, model)
def check_early_stopping_callback(self, should_update):
# TODO bake this logic into the EarlyStopping callback
if should_update and self.trainer.checkpoint_connector.has_trained:
callbacks = [
c for c in self.trainer.callbacks
if isinstance(c, EarlyStopping)
]
model = self.trainer.get_model()
for cb in callbacks:
cb.on_validation_end(self.trainer, model)
def on_train_epoch_start(self, epoch):
# update training progress in trainer
self.trainer.current_epoch = epoch
model = self.trainer.get_model()
# reset train dataloader
if epoch != 0 and self.trainer.reload_dataloaders_every_epoch:
self.trainer.reset_train_dataloader(model)
# todo: specify the possible exception
with suppress(Exception):
# set seed for distributed sampler (enables shuffling for each epoch)
self.trainer.train_dataloader.sampler.set_epoch(epoch)
# changing gradient according accumulation_scheduler
self.trainer.accumulation_scheduler.on_epoch_start(
self.trainer, self.trainer.get_model())
# stores accumulated grad fractions per batch
self.accumulated_loss = TensorRunningAccum(
window_length=self.trainer.accumulate_grad_batches)
# structured result accumulators for callbacks
self.early_stopping_accumulator = Accumulator()
self.checkpoint_accumulator = Accumulator()
# hook
self.trainer.call_hook("on_epoch_start")
self.trainer.call_hook("on_train_epoch_start")
def on_train_batch_end(self, epoch_output, batch_end_outputs, batch,
batch_idx, dataloader_idx):
# hook
self.trainer.call_hook('on_train_batch_end', batch_end_outputs, batch,
batch_idx, dataloader_idx)
self.trainer.call_hook('on_batch_end')
# figure out what to track for epoch end
self.track_epoch_end_reduce_metrics(epoch_output, batch_end_outputs)
# reset batch logger internals
self.trainer.logger_connector.on_train_batch_end()
def reset_train_val_dataloaders(self, model):
if self.trainer.train_dataloader is None or not self.trainer.reload_dataloaders_every_epoch:
self.trainer.reset_train_dataloader(model)
if self.trainer.val_dataloaders is None and not self.trainer.reload_dataloaders_every_epoch:
self.trainer.reset_val_dataloader(model)
def track_epoch_end_reduce_metrics(self, epoch_output, batch_end_outputs):
# track the outputs to reduce at the end of the epoch
for opt_idx, opt_outputs in enumerate(batch_end_outputs):
sample_output = opt_outputs[-1]
# decide if we need to reduce at the end of the epoch automatically
auto_reduce_tng_result = isinstance(
sample_output,
Result) and sample_output.should_reduce_on_epoch_end
hook_overridden = (is_overridden("training_epoch_end",
model=self.trainer.get_model()) or
is_overridden("on_train_epoch_end",
model=self.trainer.get_model()))
# only track when a) it needs to be autoreduced OR b) the user wants to manually reduce on epoch end
if not (hook_overridden or auto_reduce_tng_result):
continue
# with 1 step (no tbptt) don't use a sequence at epoch end
if isinstance(opt_outputs,
list) and len(opt_outputs) == 1 and not isinstance(
opt_outputs[0], Result):
opt_outputs = opt_outputs[0]
epoch_output[opt_idx].append(opt_outputs)
def get_optimizers_iterable(self):
"""
Generates an iterable with (idx, optimizer) for each optimizer.
"""
if not self.trainer.optimizer_frequencies:
# call training_step once per optimizer
return list(enumerate(self.trainer.optimizers))
optimizer_freq_cumsum = np.cumsum(self.trainer.optimizer_frequencies)
optimizers_loop_length = optimizer_freq_cumsum[-1]
current_place_in_loop = self.trainer.total_batch_idx % optimizers_loop_length
# find optimzier index by looking for the first {item > current_place} in the cumsum list
opt_idx = np.argmax(optimizer_freq_cumsum > current_place_in_loop)
return [[opt_idx, self.trainer.optimizers[opt_idx]]]
def on_after_backward(self, training_step_output, batch_idx,
untouched_loss):
is_result_obj = isinstance(training_step_output, Result)
if is_result_obj:
training_step_output.detach()
else:
training_step_output.batch_loss = training_step_output.batch_loss.detach(
)
# insert after step hook
self.trainer.call_hook("on_after_backward")
# when in dev debugging track the losses
self.trainer.dev_debugger.track_train_loss_history(
batch_idx, untouched_loss.detach())
def _check_training_step_output(self, training_step_output):
if isinstance(training_step_output,
torch.Tensor) and not self.automatic_optimization:
if training_step_output.grad_fn is None:
# TODO: Find why - RuntimeError: Expected to mark a variable ready only once ...
raise MisconfigurationException(
"In manual optimization, `training_step` should not return a Tensor"
)
def training_step(self, split_batch, batch_idx, opt_idx, hiddens):
# give the PL module a result for logging
model_ref = self.trainer.get_model()
with self.trainer.profiler.profile("model_forward"):
args = self.build_train_args(split_batch, batch_idx, opt_idx,
hiddens)
# manually capture logged metrics
model_ref._current_fx_name = 'training_step'
model_ref._results = Result()
with self.trainer.profiler.profile("training_step"):
training_step_output = self.trainer.accelerator_backend.training_step(
args)
self.trainer.logger_connector.cache_logged_metrics()
self._check_training_step_output(training_step_output)
training_step_output = self.trainer.call_hook(
"training_step_end", training_step_output)
training_step_output_for_epoch_end, training_step_output = self._process_training_step_output(
training_step_output, split_batch)
is_result_obj = isinstance(training_step_output, Result)
if training_step_output_for_epoch_end is None:
return None
# enable empty loss when using manual opt
closure_loss = None
untouched_loss = None
if self.trainer.train_loop.automatic_optimization:
# accumulate loss
# (if accumulate_grad_batches = 1 no effect)
if is_result_obj:
closure_loss = training_step_output.minimize
else:
closure_loss = training_step_output.batch_loss
closure_loss = closure_loss / self.trainer.accumulate_grad_batches
# the loss will get scaled for amp. avoid any modifications to it
untouched_loss = closure_loss.detach().clone()
# result
result = AttributeDict(
closure_loss=closure_loss,
loss=untouched_loss,
training_step_output=training_step_output,
training_step_output_for_epoch_end=
training_step_output_for_epoch_end,
hiddens=training_step_output.hiddens,
)
return result
def _process_training_step_output(self, training_step_output, split_batch):
training_step_output_for_epoch_end = training_step_output
# enable validation_step return None
if training_step_output_for_epoch_end is None:
return None, None
# -----------------------------------------
# process result return (DEPRECATE in 1.0)
# -----------------------------------------
if isinstance(training_step_output, Result):
training_step_output_for_epoch_end = self._process_result(
training_step_output, split_batch)
return training_step_output_for_epoch_end, training_step_output
# -----------------------------------------
# process hybrid (1.0)
# -----------------------------------------
# no need for these checks in 1.0.0
# TODO: remove checks in 1.0.0
is_tensor = isinstance(training_step_output_for_epoch_end,
torch.Tensor)
is_1_0_output = is_tensor or ("log" not in training_step_output
and "progress_bar"
not in training_step_output)
if is_1_0_output:
return self._process_training_step_output_1_0(
training_step_output, split_batch)
# -----------------------------------------
# process old dict (deprecate 1.0)
# -----------------------------------------
training_step_output = self.trainer.process_dict_result(
training_step_output, train=True)
training_step_output = AttributeDict(
batch_loss=training_step_output[0],
pbar_on_batch_end=training_step_output[1],
log_metrics=training_step_output[2],
callback_metrics=training_step_output[3],
hiddens=training_step_output[4],
)
# if the user decides to finally reduce things in epoch_end, save raw output without graphs
if isinstance(training_step_output_for_epoch_end, torch.Tensor):
training_step_output_for_epoch_end = training_step_output_for_epoch_end.detach(
)
else:
training_step_output_for_epoch_end = recursive_detach(
training_step_output_for_epoch_end)
return training_step_output_for_epoch_end, training_step_output
def _process_training_step_output_1_0(self, training_step_output,
split_batch):
result = self.trainer.get_model()._results
loss = None
hiddens = None
# handle dict return
if isinstance(training_step_output, dict):
loss = training_step_output.pop("loss", None)
hiddens = training_step_output.pop("hiddens", None)
result["extra"] = training_step_output
# handle scalar return
elif isinstance(training_step_output, torch.Tensor):
loss = training_step_output
result["extra"] = {}
# map to results under the hood
result.minimize = loss
result.hiddens = hiddens
# track batch for manual reduction with result
result.track_batch_size(len(split_batch))
# track metrics without grads for epoch reduction
training_step_output_for_epoch_end = copy(result)
training_step_output_for_epoch_end.detach()
if self.trainer.move_metrics_to_cpu:
training_step_output_for_epoch_end.cpu()
# what flows back into the system
training_step_output = result
return training_step_output_for_epoch_end, training_step_output
def _process_result(self, training_step_output, split_batch):
training_step_output.track_batch_size(len(split_batch))
m = """
TrainResult and EvalResult were deprecated in 0.9.1 and support will drop in 1.0.0.
Use self.log and .write from the LightningModule to log metrics and write predictions.
training_step can now only return a scalar (for the loss) or a dictionary with anything you want.
Option 1:
return loss
Option 2:
return {'loss': loss, 'anything_else': ...}
Option 3:
return {'loss': loss, 'hiddens': hiddens, 'anything_else': ...}
"""
rank_zero_warn(m)
training_step_output_for_epoch_end = copy(training_step_output)
training_step_output_for_epoch_end.detach()
return training_step_output_for_epoch_end
def optimizer_step(self, optimizer, opt_idx, batch_idx,
train_step_and_backward_closure):
model_ref = self.trainer.get_model()
is_lbfgs = isinstance(optimizer, torch.optim.LBFGS)
using_native_amp = self.trainer.amp_backend == AMPType.NATIVE
# native amp + lbfgs is a no go right now
if using_native_amp and is_lbfgs:
raise MisconfigurationException(
'native PyTorch amp and lbfgs are not compatible.'
' To request, please file a Github issue in PyTorch and tag @mcarilli'
)
# wraps into LightningOptimizer only for running step
optimizer = LightningOptimizer._to_lightning_optimizer(
optimizer, self.trainer, opt_idx)
# model hook
model_ref.optimizer_step(
self.trainer.current_epoch,
batch_idx,
optimizer,
opt_idx,
train_step_and_backward_closure,
on_tpu=self.trainer._device_type == DeviceType.TPU
and _TPU_AVAILABLE,
using_native_amp=using_native_amp,
using_lbfgs=is_lbfgs,
)
def on_before_zero_grad(self, optimizer):
self.trainer.call_hook('on_before_zero_grad', optimizer)
def 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):
# modify dataloader if needed (ddp, etc...)
train_dataloader = self.trainer.accelerator_backend.process_dataloader(
self.trainer.train_dataloader)
# track epoch output
epoch_output = [[] for _ in range(self.num_optimizers)]
train_dataloader = self.trainer.data_connector.get_profiled_train_dataloader(
train_dataloader)
dataloader_idx = 0
should_check_val = False
for batch_idx, (batch, is_last_batch) in train_dataloader:
self.trainer.batch_idx = batch_idx
# ------------------------------------
# TRAINING_STEP + TRAINING_STEP_END
# ------------------------------------
with self.trainer.profiler.profile("run_training_batch"):
batch_output = self.run_training_batch(batch, batch_idx,
dataloader_idx)
# when returning -1 from train_step, we end epoch early
if batch_output.signal == -1:
break
batch_end_outputs = self.process_train_step_outputs(
batch_output.training_step_output_for_epoch_end,
self.early_stopping_accumulator,
self.checkpoint_accumulator,
)
# hook
# TODO: add outputs to batches
self.on_train_batch_end(epoch_output, batch_end_outputs, batch,
batch_idx, dataloader_idx)
# -----------------------------------------
# SAVE METRICS TO LOGGERS
# -----------------------------------------
self.trainer.logger_connector.log_train_step_metrics(batch_output)
# -----------------------------------------
# VALIDATE IF NEEDED + CHECKPOINT CALLBACK
# -----------------------------------------
should_check_val = self.should_check_val_fx(
batch_idx, is_last_batch)
if should_check_val:
self.trainer.run_evaluation()
# reset stage to train
self.trainer._set_wide_running_stage(RunningStage.TRAINING)
# -----------------------------------------
# SAVE LOGGERS (ie: Tensorboard, etc...)
# -----------------------------------------
self.save_loggers_on_train_batch_end()
# update LR schedulers
monitor_metrics = deepcopy(
self.trainer.logger_connector.callback_metrics)
self.update_train_loop_lr_schedulers(
monitor_metrics=monitor_metrics)
self.trainer.checkpoint_connector.has_trained = True
# max steps reached, end training
if (self.trainer.max_steps is not None
and self.trainer.max_steps == self.trainer.global_step + 1
and self._accumulated_batches_reached()):
break
# end epoch early
# stop when the flag is changed or we've gone past the amount
# requested in the batches
if self.trainer.should_stop:
break
self.trainer.total_batch_idx += 1
# stop epoch if we limited the number of training batches
if self._num_training_batches_reached(is_last_batch):
break
# progress global step according to grads progress
self.increment_accumulated_grad_global_step()
# epoch end hook
self.run_on_epoch_end_hook(epoch_output)
# log epoch metrics
self.trainer.logger_connector.log_train_epoch_end_metrics(
epoch_output, self.checkpoint_accumulator,
self.early_stopping_accumulator, self.num_optimizers)
should_check_val = self.should_check_val_fx(batch_idx,
is_last_batch,
on_epoch=True)
if should_check_val:
self.trainer.run_evaluation(on_epoch=True)
# reset stage to train
self.trainer._set_wide_running_stage(RunningStage.TRAINING)
should_skip_eval = self.trainer.evaluation_loop.should_skip_evaluation(
self.trainer.num_val_batches)
should_train_only = self.trainer.disable_validation or should_skip_eval
if should_train_only:
# update epoch level lr_schedulers
self.trainer.optimizer_connector.update_learning_rates(
interval='epoch')
self.check_checkpoint_callback(True)
self.check_early_stopping_callback(True)
# increment the global step once
# progress global step according to grads progress
self.increment_accumulated_grad_global_step()
def run_training_batch(self, batch, batch_idx, dataloader_idx):
# track grad norms
grad_norm_dic = {}
# bookkeeping
self.trainer.hiddens = None
# track all outputs across time and num of optimizers
batch_outputs = [[]
for _ in range(len(self.get_optimizers_iterable()))]
if batch is None:
return AttributeDict(signal=0, grad_norm_dic=grad_norm_dic)
# hook
response = self.trainer.call_hook("on_batch_start")
if response == -1:
return AttributeDict(signal=-1, grad_norm_dic=grad_norm_dic)
# hook
response = self.trainer.call_hook("on_train_batch_start", batch,
batch_idx, dataloader_idx)
if response == -1:
return AttributeDict(signal=-1, grad_norm_dic=grad_norm_dic)
# lightning module hook
splits = self.tbptt_split_batch(batch)
for split_idx, split_batch in enumerate(splits):
# create an iterable for optimizers and loop over them
for opt_idx, optimizer in self.prepare_optimizers():
# toggle model params + set info to logger_connector
self.run_train_split_start(split_idx, split_batch, opt_idx,
optimizer)
if self.should_accumulate():
# For gradient accumulation
# -------------------
# calculate loss (train step + train step end)
# -------------------
# automatic_optimization=True: perform dpp sync only when performing optimizer_step
# automatic_optimization=False: don't block synchronization here
with self.block_ddp_sync_behaviour():
self.training_step_and_backward(
split_batch, batch_idx, opt_idx, optimizer,
self.trainer.hiddens)
batch_outputs = self._process_closure_result(
batch_outputs=batch_outputs,
opt_idx=opt_idx,
)
# ------------------------------
# BACKWARD PASS
# ------------------------------
# gradient update with accumulated gradients
else:
if self.automatic_optimization:
def train_step_and_backward_closure():
result = self.training_step_and_backward(
split_batch, batch_idx, opt_idx, optimizer,
self.trainer.hiddens)
return None if result is None else result.loss
# optimizer step
self.optimizer_step(optimizer, opt_idx, batch_idx,
train_step_and_backward_closure)
else:
self._curr_step_result = self.training_step(
split_batch, batch_idx, opt_idx,
self.trainer.hiddens)
if self._curr_step_result is None:
# user decided to skip optimization
# make sure to zero grad.
continue
batch_outputs = self._process_closure_result(
batch_outputs=batch_outputs,
opt_idx=opt_idx,
)
# todo: Properly aggregate grad_norm accros opt_idx and split_idx
grad_norm_dic = self._cur_grad_norm_dict
self._cur_grad_norm_dict = None
# update running loss + reset accumulated loss
self.update_running_loss()
result = AttributeDict(
signal=0,
grad_norm_dic=grad_norm_dic,
training_step_output_for_epoch_end=batch_outputs,
)
return result
@contextmanager
def block_ddp_sync_behaviour(self):
"""
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 self.trainer.accelerator_backend is not None and self.automatic_optimization:
yield self.trainer.accelerator_backend.block_ddp_plugin_sync_behaviour(
)
else:
yield None
def _process_closure_result(self, batch_outputs: list,
opt_idx: int) -> list:
opt_closure_result = self._curr_step_result
if opt_closure_result is not None:
# cache metrics
self.trainer.logger_connector.cache_training_step_metrics(
opt_closure_result)
# track hiddens
self.trainer.hiddens = self.process_hiddens(opt_closure_result)
# check if loss or model weights are nan
if self.trainer.terminate_on_nan:
self.trainer.detect_nan_tensors(opt_closure_result.loss)
# track all the outputs across all steps
batch_opt_idx = opt_idx if len(batch_outputs) > 1 else 0
batch_outputs[batch_opt_idx].append(
opt_closure_result.training_step_output_for_epoch_end)
if self.automatic_optimization:
# track total loss for logging (avoid mem leaks)
self.accumulated_loss.append(opt_closure_result.loss)
self._curr_step_result = None
return batch_outputs
def training_step_and_backward(self, split_batch, batch_idx, opt_idx,
optimizer, hiddens):
"""
wrap the forward step in a closure so second order methods work
"""
with self.trainer.profiler.profile("training_step_and_backward"):
# lightning module hook
result = self.training_step(split_batch, batch_idx, opt_idx,
hiddens)
self._curr_step_result = result
if result is None:
self.warning_cache.warn(
"training_step returned None if it was on purpose, ignore this warning..."
)
return None
if not self._skip_backward and self.trainer.train_loop.automatic_optimization:
# backward pass
with self.trainer.profiler.profile("model_backward"):
self.backward(result, optimizer, opt_idx)
# hook - call this hook only
# when gradients have finished to accumulate
if not self.should_accumulate():
self.on_after_backward(result.training_step_output,
batch_idx, result.loss)
# check if loss or model weights are nan
if self.trainer.terminate_on_nan:
self.trainer.detect_nan_tensors(result.loss)
if len(self.trainer.optimizers) > 1:
# revert back to previous state
self.trainer.get_model().untoggle_optimizer(opt_idx)
return result
def backward(self, result, optimizer, opt_idx, *args, **kwargs):
self.trainer.dev_debugger.track_event("backward_call")
# 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):
# inform logger the batch loop has finished
self.trainer.logger_connector.on_train_epoch_end()
self.trainer.call_hook('on_train_epoch_end', epoch_output)
self.trainer.call_hook('on_epoch_end')
def increment_accumulated_grad_global_step(self):
num_accumulated_batches_reached = self._accumulated_batches_reached()
num_training_batches_reached = self._num_training_batches_reached()
# progress global step according to grads progress
if num_accumulated_batches_reached or num_training_batches_reached:
self.trainer.global_step += 1
def _accumulated_batches_reached(self):
return (self.trainer.batch_idx +
1) % self.trainer.accumulate_grad_batches == 0
def _num_training_batches_reached(self, is_last_batch=False):
return (self.trainer.batch_idx +
1) == self.trainer.num_training_batches or is_last_batch
def should_accumulate(self):
# checks if backward or backward + optimizer step (via closure)
accumulation_done = self._accumulated_batches_reached()
is_final_batch = self._num_training_batches_reached()
return not (accumulation_done or is_final_batch)
def should_check_val_fx(self, batch_idx, is_last_batch, on_epoch=False):
# decide if we should run validation
is_val_check_batch = (batch_idx +
1) % self.trainer.val_check_batch == 0
is_val_check_epoch = (self.trainer.current_epoch +
1) % self.trainer.check_val_every_n_epoch == 0
can_check_val = self.trainer.enable_validation and is_val_check_epoch
is_last_batch_for_infinite_dataset = is_last_batch and self.trainer.val_check_batch == float(
"inf")
epoch_end_val_check = self.trainer.val_check_batch == self.trainer.num_training_batches
should_check_val = (
(is_val_check_batch and epoch_end_val_check)
or self.trainer.should_stop or is_last_batch_for_infinite_dataset
) if on_epoch else (is_val_check_batch and not epoch_end_val_check)
return should_check_val and can_check_val
def build_train_args(self, batch, batch_idx, opt_idx, hiddens):
# enable not needing to add opt_idx to training_step
args = [batch, batch_idx]
if len(self.trainer.optimizers) > 1:
if self.trainer.has_arg("training_step", "optimizer_idx"):
args.append(opt_idx)
else:
num_opts = len(self.trainer.optimizers)
raise ValueError(
f"Your LightningModule defines {num_opts} optimizers but "
f'training_step is missing the "optimizer_idx" argument.')
# pass hiddens if using tbptt
if self.trainer.truncated_bptt_steps is not None:
args.append(hiddens)
return args
def save_loggers_on_train_batch_end(self):
# when loggers should save to disk
should_flush_logs = self.trainer.logger_connector.should_flush_logs
if should_flush_logs and self.trainer.is_global_zero and self.trainer.logger is not None:
self.trainer.logger.save()
def process_train_step_outputs(self, all_train_step_outputs,
early_stopping_accumulator,
checkpoint_accumulator):
"""
Figure out what needs to be tracked/logged at the end of the epoch
"""
# the training step outputs a list per optimizer. The list contains the outputs at each time step
# when no TBPTT is used, then the list has 1 item per batch
# when TBPTT IS used, then the list has n items (1 per time step)
batch_end_outputs = []
for optimizer_idx_outputs in all_train_step_outputs:
# extract one representative sample from each time step (1 if no tbptt) and 0th optimizer
if len(optimizer_idx_outputs) == 0:
continue
sample_output = optimizer_idx_outputs[-1]
# pull out callback info if available (ie: Results object)
if isinstance(sample_output,
dict) and "early_stop_on" in sample_output:
early_stopping_accumulator.accumulate(
sample_output["early_stop_on"])
if isinstance(sample_output,
dict) and "checkpoint_on" in sample_output:
checkpoint_accumulator.accumulate(
sample_output["checkpoint_on"])
batch_end_outputs.append(optimizer_idx_outputs)
return batch_end_outputs
def prepare_optimizers(self):
# in manual optimization we loop over all optimizers at once
optimizers = self.get_optimizers_iterable()
if not self.automatic_optimization:
optimizers = [optimizers[0]]
return optimizers
def run_train_split_start(self, split_idx, split_batch, opt_idx,
optimizer):
# set split_idx to trainer for tracking
self.trainer.split_idx = split_idx
# make sure only the gradients of the current optimizer's parameters are calculated
# in the training step to prevent dangling gradients in multiple-optimizer setup.
if self.automatic_optimization and len(self.trainer.optimizers) > 1:
model = self.trainer.get_model()
model.toggle_optimizer(optimizer, opt_idx)
# use to track metrics internally
self.trainer.logger_connector.on_train_split_start(
split_idx, opt_idx, split_batch)
def update_running_loss(self):
accumulated_loss = self.accumulated_loss.mean()
if accumulated_loss is not None:
# calculate running loss for display
self.running_loss.append(self.accumulated_loss.mean() *
self.trainer.accumulate_grad_batches)
# reset for next set of accumulated grads
self.accumulated_loss.reset()
class PredictionEpochLoop(Loop):
"""Loop performing prediction on arbitrary sequentially used dataloaders."""
def __init__(self) -> None:
super().__init__()
self.return_predictions = False
self.predictions: List[Any] = []
self.current_batch_indices: List[int] = []
self.batch_progress = Progress()
self._dl_max_batches = 0
self._num_dataloaders = 0
self._warning_cache = WarningCache()
self._seen_batch_indices: List[List[int]] = []
@property
def done(self) -> bool:
"""Ends prediction when the iteration count exceeds the total number of available batches."""
return self.batch_progress.current.completed >= self._dl_max_batches
@property
def should_store_predictions(self) -> bool:
"""Whether the predictions should be stored for later usage (e.g. aggregation or returning)"""
any_pred = any(cb.interval.on_epoch
for cb in self.trainer.prediction_writer_callbacks)
return self.return_predictions or any_pred
def connect(self, **kwargs: "Loop") -> None:
raise NotImplementedError(
f"{self.__class__.__name__} does not connect any child loops.")
def reset(self) -> None:
"""Resets the loops internal state."""
self._seen_batch_indices = []
self.predictions = []
self.batch_progress.reset_on_run()
def on_run_start( # type: ignore[override]
self,
dataloader_iter: Iterator,
dataloader_idx: int,
dl_max_batches: int,
num_dataloaders: int,
return_predictions: bool = False,
) -> None:
"""Prepares the loops internal state.
Args:
dataloader_iter: the iterator over the current dataloader
dataloader_idx: the index of the current dataloader
dl_max_batches: the maximum number of batches the current loader can produce
num_dataloaders: the total number of dataloaders
return_predictions: whether to return the obtained predictions
"""
void(dataloader_iter, dataloader_idx)
self._dl_max_batches = dl_max_batches
self._num_dataloaders = num_dataloaders
self.return_predictions = return_predictions
# this call requires that `self.return_predictions` is set
self._seen_batch_indices = self._get_batch_indices(dataloader_idx)
def advance( # type: ignore[override]
self,
dataloader_iter: Iterator,
dataloader_idx: int,
dl_max_batches: int,
num_dataloaders: int,
return_predictions: bool = False,
) -> None:
"""Runs one prediction step.
Args:
dataloader_iter: the iterator over the current dataloader
dataloader_idx: the index of the current dataloader
dl_max_batches: the maximum number of batches the current loader can produce
num_dataloaders: the total number of dataloaders
return_predictions: whether to return the obtained predictions
"""
batch_idx, batch = next(dataloader_iter)
self._seen_batch_indices = self._get_batch_indices(dataloader_idx)
# we need to truncate the list of batch indices due to prefetching in the dataloader and Lightning
self._seen_batch_indices = self._seen_batch_indices[:(
self.batch_progress.current.completed + 1)]
if batch is None:
raise StopIteration
batch = self.trainer._call_strategy_hook("batch_to_device",
batch,
dataloader_idx=dataloader_idx)
self.batch_progress.increment_ready()
self._predict_step(batch, batch_idx, dataloader_idx)
def on_run_end(self) -> Tuple[List[Any], List[List[int]]]:
"""Returns the predictions and the corresponding batch indices."""
predictions, all_batch_indices = self.predictions, self._seen_batch_indices
self.predictions, self._seen_batch_indices = [], [] # free memory
return predictions, all_batch_indices
def _predict_step(self, batch: Any, batch_idx: int,
dataloader_idx: int) -> None:
"""Runs the actual predict step together with all the necessary bookkeeping and the hooks tied to the
predict step.
Args:
batch: the current batch to run the prediction on
batch_idx: the index of the current batch
dataloader_idx: the index of the dataloader producing the current batch
"""
# configure step_kwargs
step_kwargs = self._build_kwargs(batch, batch_idx, dataloader_idx)
# extract batch_indices and store them
self.current_batch_indices = self._seen_batch_indices[
batch_idx] if self._seen_batch_indices else []
self.trainer._call_callback_hooks("on_predict_batch_start", batch,
batch_idx, dataloader_idx)
self.trainer._call_lightning_module_hook("on_predict_batch_start",
batch, batch_idx,
dataloader_idx)
self.batch_progress.increment_started()
predictions = self.trainer._call_strategy_hook("predict_step",
*step_kwargs.values())
self.batch_progress.increment_processed()
if predictions is None:
self._warning_cache.warn(
"predict returned None if it was on purpose, ignore this warning..."
)
self.trainer._call_callback_hooks("on_predict_batch_end", predictions,
batch, batch_idx, dataloader_idx)
self.trainer._call_lightning_module_hook("on_predict_batch_end",
predictions, batch, batch_idx,
dataloader_idx)
self.batch_progress.increment_completed()
if self.should_store_predictions:
self.predictions.append(
move_data_to_device(predictions, torch.device("cpu")))
def _build_kwargs(self, batch: Any, batch_idx: int,
dataloader_idx: int) -> Dict[str, Any]:
"""Assembles the keyword arguments for the ``predict_step``
Args:
batch: the current batch to run the prediction on
batch_idx: the index of the current batch
dataloader_idx: the index of the dataloader producing the current batch
Returns:
the dictionary containing all the keyboard arguments for the predict step
"""
step_kwargs = OrderedDict([("batch", batch), ("batch_idx", batch_idx)])
if self._num_dataloaders > 1:
step_kwargs["dataloader_idx"] = dataloader_idx
return step_kwargs
def _get_batch_indices(self, dataloader_idx: int) -> List[List[int]]:
"""Returns a reference to the seen batch indices if the dataloader has a batch sampler wrapped by our
:class:`~pytorch_lightning.overrides.distributed.IndexBatchSamplerWrapper`."""
# the batch_sampler is not be defined in case of CombinedDataLoaders
batch_sampler = getattr(
self.trainer.
predict_dataloaders[dataloader_idx], # type: ignore[has-type]
"batch_sampler",
None,
)
if isinstance(
batch_sampler,
IndexBatchSamplerWrapper) and self.should_store_predictions:
return batch_sampler.seen_batch_indices
warning_cache.warn(
"Lightning couldn't infer the indices fetched for your dataloader."
)
return []
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._teardown_already_run = False
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
@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):
# hook
self.trainer.call_hook("on_train_start")
def on_train_end(self):
if self._teardown_already_run:
return
self._teardown_already_run = True
# trigger checkpoint check. need to temporarily decrease the global step to avoid saving duplicates
# when a checkpoint was saved at the last step
self.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.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')
# figure out what to track for epoch end
self.track_epoch_end_reduce_metrics(epoch_output, batch_end_outputs)
# reset batch logger internals
self.trainer.logger_connector.on_train_batch_end()
def reset_train_val_dataloaders(self, model) -> 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()
# track the outputs to reduce at the end of the epoch
for opt_idx, opt_outputs in enumerate(batch_end_outputs):
sample_output = opt_outputs[-1]
# decide if we need to reduce at the end of the epoch automatically
auto_reduce_tng_result = isinstance(
sample_output,
Result) and sample_output.should_reduce_on_epoch_end
# only track when a) it needs to be autoreduced OR b) the user wants to manually reduce on epoch end
if not (hook_overridden or auto_reduce_tng_result):
continue
# with 1 step (no tbptt) don't use a sequence at epoch end
if isinstance(opt_outputs,
list) and len(opt_outputs) == 1 and not isinstance(
opt_outputs[0], Result):
opt_outputs = opt_outputs[0]
epoch_output[opt_idx].append(opt_outputs)
def _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", model=lightning_module):
return True
if is_overridden("on_train_epoch_end", model=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, training_step_output, batch_idx,
untouched_loss):
training_step_output.detach()
# insert after step hook
self.trainer.call_hook("on_after_backward")
# when in dev debugging track the losses
self.trainer.dev_debugger.track_train_loss_history(
batch_idx, untouched_loss.detach())
def _check_training_step_output(self, training_step_output):
if isinstance(
training_step_output, torch.Tensor
) and not self.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"
)
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'
model_ref._results = Result()
with self.trainer.profiler.profile("training_step"):
training_step_output = self.trainer.accelerator.training_step(
step_kwargs)
self.trainer.accelerator.post_training_step()
self.trainer.logger_connector.cache_logged_metrics()
self._check_training_step_output(training_step_output)
training_step_output = self.trainer.call_hook(
"training_step_end", training_step_output)
training_step_output_for_epoch_end, training_step_output = self._process_training_step_output(
training_step_output, split_batch)
if training_step_output_for_epoch_end is None:
return
# enable empty loss when using manual opt
closure_loss = None
untouched_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
untouched_loss = closure_loss.detach().clone()
# result
result = AttributeDict(
closure_loss=closure_loss,
loss=untouched_loss,
training_step_output=training_step_output,
training_step_output_for_epoch_end=
training_step_output_for_epoch_end,
)
return result
def _process_training_step_output(self, training_step_output, split_batch):
training_step_output_for_epoch_end = training_step_output
# enable validation_step return None
if training_step_output_for_epoch_end is None:
return None, None
result = self.trainer.lightning_module._results
loss = None
hiddens = None
result["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()
result["extra"] = training_step_output
# handle scalar return
elif isinstance(training_step_output, torch.Tensor):
loss = training_step_output
# map to results under the hood
result.minimize = loss
self._hiddens = hiddens
# track batch for manual reduction with result
result.track_batch_size(len(split_batch))
# track metrics without grads for epoch reduction
training_step_output_for_epoch_end = copy(result)
training_step_output_for_epoch_end = training_step_output_for_epoch_end.detach(
)
if self.trainer.move_metrics_to_cpu:
training_step_output_for_epoch_end = training_step_output_for_epoch_end.cpu(
)
return training_step_output_for_epoch_end, result
@staticmethod
def _prepare_outputs(
outputs: List[List[List[Result]]],
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 ``Result`` objects with dimensions:
[optimizer outs][batch outs][tbptt steps].
batch_mode: If True, ignore the batch output dimension.
Returns:
The cleaned outputs with ``Result`` 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 = []
for tbptt_output in batch_outputs:
out = tbptt_output.extra
out['loss'] = tbptt_output.minimize
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 = self._track_gradient_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 _track_gradient_norm(self):
grad_norm_dict = {}
if (self.global_step + 1) % self.trainer.log_every_n_steps == 0:
if float(self.trainer.track_grad_norm) > 0:
model = self.trainer.lightning_module
grad_norm_dict = grad_norm(model, self.trainer.track_grad_norm)
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
# 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
# -----------------------------------------
# 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.log_train_epoch_end_metrics(epoch_output)
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[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'
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_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_dict = {}
# 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,
grad_norm_dict={},
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_dict={})
# hook
response = self.trainer.call_hook("on_train_batch_start", batch,
batch_idx, dataloader_idx)
if response == -1:
return AttributeDict(signal=-1, grad_norm_dict={})
# lightning module hook
splits = self._tbptt_split_batch(batch)
for split_idx, split_batch in enumerate(splits):
self.split_idx = split_idx
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_idx,
split_batch, opt_idx,
optimizer)
if result:
batch_outputs[opt_idx].append(
result.training_step_output_for_epoch_end)
grad_norm_dict = result.get("grad_norm_dict", {})
else:
# in manual optimization, there is no looping over optimizers
result = self._run_optimization(batch_idx, split_idx,
split_batch)
if result:
batch_outputs[0].append(
result.training_step_output_for_epoch_end)
output = AttributeDict(
signal=0,
# todo: Properly aggregate grad_norm accros opt_idx and split_idx
grad_norm_dict=grad_norm_dict,
training_step_output_for_epoch_end=batch_outputs,
)
return output
def _run_optimization(self,
batch_idx,
split_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 + set info to logger_connector
self.run_train_split_start(split_idx, split_batch, 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
# 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)
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(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..."
)
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
result.grad_norm_dict = self.track_and_norm_grad(
optimizer=optimizer)
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_train_split_start(self, split_idx, split_batch, 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)
# use to track metrics internally
self.trainer.logger_connector.on_train_split_start(
split_idx, opt_idx, split_batch)
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()
class EvaluationLoop(object):
def __init__(self, trainer):
self.trainer = trainer
self.outputs = []
self.step_metrics = []
self.predictions = None
self.max_batches = None
self.warning_cache = WarningCache()
self.num_dataloaders = None
def on_trainer_init(self):
self.trainer.num_val_batches = []
self.trainer.num_sanity_val_batches = []
self.trainer.num_test_batches = []
self.trainer.test_dataloaders = None
self.trainer.val_dataloaders = None
self.trainer.running_sanity_check = False
# when .test() is called, it sets this
self.trainer.tested_ckpt_path = None
# when true, prints test results
self.trainer.verbose_test = True
def get_evaluation_dataloaders(self, max_batches):
# select dataloaders
model = self.trainer.get_model()
# select dataloaders
if self.trainer.testing:
self.trainer.reset_test_dataloader(model)
dataloaders = self.trainer.test_dataloaders
new_max_batches = self.trainer.num_test_batches
else:
# val
in_sanity_check = self.trainer.running_sanity_check
should_reload_every_epoch = self.trainer.reload_dataloaders_every_epoch
if (self.trainer.val_dataloaders is None
or should_reload_every_epoch) and not in_sanity_check:
self.trainer.reset_val_dataloader(model)
dataloaders = self.trainer.val_dataloaders
new_max_batches = self.trainer.num_val_batches
if max_batches is None:
max_batches = new_max_batches
return dataloaders, max_batches
def should_skip_evaluation(self, max_batches):
return sum(max_batches) == 0
def on_evaluation_start(self, *args, **kwargs):
if self.trainer.testing:
self.trainer.call_hook('on_test_start', *args, **kwargs)
else:
self.trainer.call_hook('on_validation_start', *args, **kwargs)
def on_evaluation_model_eval(self, *_, **__):
model_ref = self.trainer.get_model()
if self.trainer.testing:
model_ref.on_test_model_eval()
else:
model_ref.on_validation_model_eval()
def on_evaluation_model_train(self, *_, **__):
model_ref = self.trainer.get_model()
if self.trainer.testing:
model_ref.on_test_model_train()
else:
model_ref.on_validation_model_train()
def on_evaluation_end(self, *args, **kwargs):
if self.trainer.testing:
self.trainer.call_hook('on_test_end', *args, **kwargs)
else:
self.trainer.call_hook('on_validation_end', *args, **kwargs)
def reload_evaluation_dataloaders(self):
model = self.trainer.get_model()
if self.trainer.testing:
self.trainer.reset_test_dataloader(model)
else:
self.trainer.reset_val_dataloader(model)
def setup(self, model, max_batches, dataloaders):
# bookkeeping
self.outputs = []
self.predictions = PredictionCollection(self.trainer.global_rank,
self.trainer.world_size)
# convert max_batches to list
if isinstance(max_batches, int):
max_batches = [max_batches] * len(dataloaders)
self.max_batches = max_batches
self.num_dataloaders = self._get_num_dataloaders(dataloaders)
self._predictions = [[] for _ in range(self.num_dataloaders)]
def on_evaluation_epoch_start(self, *args, **kwargs):
if self.trainer.testing:
self.trainer.call_hook('on_test_epoch_start', *args, **kwargs)
else:
self.trainer.call_hook('on_validation_epoch_start', *args,
**kwargs)
def _build_args(self, batch, batch_idx, dataloader_idx):
# make dataloader_idx arg in validation_step optional
args = [batch, batch_idx]
multiple_val_loaders = (
not self.trainer.testing
and self._get_num_dataloaders(self.trainer.val_dataloaders) > 1)
multiple_test_loaders = (
self.trainer.testing
and self._get_num_dataloaders(self.trainer.test_dataloaders) > 1)
if multiple_test_loaders or multiple_val_loaders:
args.append(dataloader_idx)
return args
def _get_num_dataloaders(self, dataloaders):
# case where user does:
# return dl1, dl2
length = len(dataloaders)
if len(dataloaders) > 0 and isinstance(dataloaders[0], (list, tuple)):
length = len(dataloaders[0])
return length
def evaluation_step(self, batch, batch_idx, dataloader_idx):
# configure args
args = self._build_args(batch, batch_idx, dataloader_idx)
model_ref = self.trainer.get_model()
model_ref._results = Result()
if self.trainer._predicting:
model_ref._current_fx_name = "predict"
predictions = self.trainer.accelerator_backend.predict(args)
self._predictions[dataloader_idx].append(predictions)
self.trainer._progress_bar_callback.on_test_batch_end(
self.trainer, model_ref, predictions, batch, batch_idx,
dataloader_idx)
return
elif self.testing:
model_ref._current_fx_name = "test_step"
with self.trainer.profiler.profile("test_step"):
output = self.trainer.accelerator_backend.test_step(args)
else:
model_ref._current_fx_name = "validation_step"
with self.trainer.profiler.profile("validation_step"):
output = self.trainer.accelerator_backend.validation_step(args)
# capture any logged information
self.trainer.logger_connector.cache_logged_metrics()
# track batch size for weighted average
is_result_obj = isinstance(output, Result)
if is_result_obj:
output.track_batch_size(batch)
return output
def evaluation_step_end(self, *args, **kwargs):
if self.trainer.testing:
output = self.trainer.call_hook('test_step_end', *args, **kwargs)
else:
output = self.trainer.call_hook('validation_step_end', *args,
**kwargs)
return output
def evaluation_epoch_end(self):
# unset dataloder_idx in model
self.trainer.logger_connector.evaluation_epoch_end(
self.trainer.testing)
# call the model epoch end
deprecated_results = self.__run_eval_epoch_end(self.num_dataloaders)
# enable returning anything
for i, r in enumerate(deprecated_results):
if not isinstance(r, (dict, Result, torch.Tensor)):
deprecated_results[i] = []
return deprecated_results
def log_epoch_metrics_on_evaluation_end(self):
# get the final loop results
eval_loop_results = self.trainer.logger_connector.get_evaluate_epoch_results(
)
return eval_loop_results
def __run_eval_epoch_end(self, num_dataloaders):
model = self.trainer.get_model()
# with a single dataloader don't pass an array
outputs = self.outputs
eval_results = outputs
if num_dataloaders == 1:
eval_results = outputs[0]
user_reduced = False
if self.trainer.testing:
if is_overridden('test_epoch_end', model=model):
model._current_fx_name = 'test_epoch_end'
eval_results = model.test_epoch_end(eval_results)
user_reduced = True
else:
if is_overridden('validation_epoch_end', model=model):
model._current_fx_name = 'validation_epoch_end'
eval_results = model.validation_epoch_end(eval_results)
user_reduced = True
# capture logging
self.trainer.logger_connector.cache_logged_metrics()
# depre warning
if eval_results is not None and user_reduced:
step = 'testing_epoch_end' if self.trainer.testing else 'validation_epoch_end'
self.warning_cache.warn(
f'The {step} should not return anything as of 9.1.'
' To log, use self.log(...) or self.write(...) directly in the LightningModule'
)
if not isinstance(eval_results, list):
eval_results = [eval_results]
# track depreceated metrics
self.trainer.logger_connector.track_metrics_deprecated(eval_results)
return eval_results
def __gather_epoch_end_eval_results(self, outputs):
eval_results = []
for epoch_output in outputs:
result = epoch_output[0].__class__.gather(epoch_output)
if 'checkpoint_on' in result:
result.checkpoint_on = result.checkpoint_on.mean()
if 'early_stop_on' in result:
result.early_stop_on = result.early_stop_on.mean()
eval_results.append(result)
# with 1 dataloader don't pass in a list
if len(eval_results) == 1:
eval_results = eval_results[0]
return eval_results
def __auto_reduce_result_objs(self, outputs):
# outputs has a list of results per dataloader
eval_results = []
for dl_output in outputs:
result = dl_output[0]
result = result.__class__.reduce_on_epoch_end(dl_output)
if 'checkpoint_on' in result:
result.checkpoint_on = result.checkpoint_on.mean()
if 'early_stop_on' in result:
result.early_stop_on = result.early_stop_on.mean()
eval_results.append(result)
return eval_results
def on_predict_epoch_end(self):
self.trainer._progress_bar_callback.on_test_end(
self.trainer, self.trainer.get_model())
results = self._predictions
def _convert_to_numpy(v):
return v.cpu().numpy()
results = apply_to_collection(results, torch.Tensor, _convert_to_numpy)
return results, None
def on_evaluation_batch_start(self, batch, batch_idx, dataloader_idx):
# set dataloader_idx to model and track batch_size
self.trainer.logger_connector.on_evaluation_batch_start(
self.trainer.testing, batch, dataloader_idx, self.num_dataloaders)
if self.trainer.testing:
self.trainer.call_hook('on_test_batch_start', batch, batch_idx,
dataloader_idx)
else:
self.trainer.call_hook('on_validation_batch_start', batch,
batch_idx, dataloader_idx)
def on_evaluation_batch_end(self, output, batch, batch_idx,
dataloader_idx):
if self.trainer.testing:
self.trainer.call_hook('on_test_batch_end', output, batch,
batch_idx, dataloader_idx)
else:
self.trainer.call_hook('on_validation_batch_end', output, batch,
batch_idx, dataloader_idx)
# store predicitons if do_write_predictions and track eval loss history
self.store_predictions(output, batch_idx, dataloader_idx)
def store_predictions(self, output, batch_idx, dataloader_idx):
# Add step predictions to prediction collection to write later
if output is not None:
do_write_predictions = isinstance(output,
Result) and self.trainer.testing
if do_write_predictions:
self.predictions.add(output.pop('predictions', None))
# track debug metrics
self.trainer.dev_debugger.track_eval_loss_history(
batch_idx, dataloader_idx, output)
def on_evaluation_epoch_end(self, *args, **kwargs):
# call the callback hook
if self.trainer.testing:
self.trainer.call_hook('on_test_epoch_end', *args, **kwargs)
else:
self.trainer.call_hook('on_validation_epoch_end', *args, **kwargs)
def log_evaluation_step_metrics(self, output, batch_idx):
if self.trainer.running_sanity_check:
return
step_log_metrics = {}
step_pbar_metrics = {}
self.__log_result_step_metrics(step_log_metrics, step_pbar_metrics,
batch_idx)
def __log_result_step_metrics(self, step_log_metrics, step_pbar_metrics,
batch_idx):
cached_results = self.trainer.logger_connector.cached_results
cached_batch_pbar_metrics, cached_batch_log_metrics = cached_results.update_logger_connector(
)
step_log_metrics.update(cached_batch_log_metrics)
step_pbar_metrics.update(cached_batch_pbar_metrics)
if len(step_log_metrics) > 0:
# make the metrics appear as a different line in the same graph
metrics_by_epoch = {}
for k, v in step_log_metrics.items():
metrics_by_epoch[f'{k}/epoch_{self.trainer.current_epoch}'] = v
self.trainer.logger_connector.log_metrics(metrics_by_epoch, {},
step=batch_idx)
if len(step_pbar_metrics) > 0:
self.trainer.logger_connector.add_progress_bar_metrics(
step_pbar_metrics)
