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Remove deprecated (#8604)

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* Remove old deprecated arguments

Co-authored-by: LysandreJik <lysandre.debut@reseau.eseo.fr>

* Remove needless imports

* Fix tests

Co-authored-by: LysandreJik <lysandre.debut@reseau.eseo.fr>
This commit is contained in:
Sylvain Gugger
2020-11-17 15:11:29 -05:00
committed by GitHub
parent 3095ee9dab
commit dd52804f5f
37 changed files with 22 additions and 610 deletions
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76
src/transformers/trainer.py
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@@ -213,8 +213,6 @@ class Trainer:
containing the optimizer and the scheduler to use. Will default to an instance of
:class:`~transformers.AdamW` on your model and a scheduler given by
:func:`~transformers.get_linear_schedule_with_warmup` controlled by :obj:`args`.
kwargs:
Deprecated keyword arguments.
"""
def __init__(
@@ -229,7 +227,6 @@ class Trainer:
compute_metrics: Optional[Callable[[EvalPrediction], Dict]] = None,
callbacks: Optional[List[TrainerCallback]] = None,
optimizers: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
**kwargs,
):
if args is None:
logger.info("No `TrainingArguments` passed, using the current path as `output_dir`.")
@@ -262,27 +259,6 @@ class Trainer:
self.callback_handler = CallbackHandler(callbacks, self.model, self.optimizer, self.lr_scheduler)
self.add_callback(PrinterCallback if self.args.disable_tqdm else DEFAULT_PROGRESS_CALLBACK)
# Deprecated arguments
if "tb_writer" in kwargs:
warnings.warn(
"Passing `tb_writer` as a keyword argument is deprecated and won't be possible in a "
+ "future version. Use `TensorBoardCallback(tb_writer=...)` instead and pass it to the `callbacks`"
+ "argument",
FutureWarning,
)
tb_writer = kwargs.pop("tb_writer")
self.remove_callback(TensorBoardCallback)
self.add_callback(TensorBoardCallback(tb_writer=tb_writer))
if "prediction_loss_only" in kwargs:
warnings.warn(
"Passing `prediction_loss_only` as a keyword argument is deprecated and won't be possible in a "
+ "future version. Use `args.prediction_loss_only` instead. Setting "
+ f"`args.prediction_loss_only={kwargs['prediction_loss_only']}",
FutureWarning,
)
self.args.prediction_loss_only = kwargs.pop("prediction_loss_only")
assert kwargs == {}, f"Unexpected keyword arguments: {list(kwargs.keys())}."
# Will be set to True by `self._setup_loggers()` on first call to `self.log()`.
self._loggers_initialized = False
@@ -294,14 +270,7 @@ class Trainer:
# We'll find a more elegant and not need to do this in the future.
self.model.config.xla_device = True
if not callable(self.data_collator) and callable(getattr(self.data_collator, "collate_batch", None)):
self.data_collator = self.data_collator.collate_batch
warnings.warn(
(
"The `data_collator` should now be a simple callable (function, class with `__call__`), classes "
+ "with a `collate_batch` are deprecated and won't be supported in a future version."
),
FutureWarning,
)
raise ValueError("The `data_collator` should be a simple callable (function, class with `__call__`).")
if args.max_steps > 0:
logger.info("max_steps is given, it will override any value given in num_train_epochs")
@@ -1050,12 +1019,6 @@ class Trainer:
logs (:obj:`Dict[str, float]`):
The values to log.
"""
if hasattr(self, "_log"):
warnings.warn(
"The `_log` method is deprecated and won't be called in a future version, define `log` in your subclass.",
FutureWarning,
)
return self._log(logs)
if self.state.epoch is not None:
logs["epoch"] = self.state.epoch
@@ -1095,12 +1058,6 @@ class Trainer:
Return:
:obj:`torch.Tensor`: The tensor with training loss on this batch.
"""
if hasattr(self, "_training_step"):
warnings.warn(
"The `_training_step` method is deprecated and won't be called in a future version, define `training_step` in your subclass.",
FutureWarning,
)
return self._training_step(model, inputs, self.optimizer)
model.train()
inputs = self._prepare_inputs(inputs)
@@ -1140,18 +1097,6 @@ class Trainer:
# We don't use .loss here since the model may return tuples instead of ModelOutput.
return outputs[0]
def is_local_master(self) -> bool:
"""
Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on several
machines) main process.
.. warning::
This method is deprecated, use :meth:`~transformers.Trainer.is_local_process_zero` instead.
"""
warnings.warn("This method is deprecated, use `Trainer.is_local_process_zero()` instead.", FutureWarning)
return self.is_local_process_zero()
def is_local_process_zero(self) -> bool:
"""
Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on several
@@ -1162,18 +1107,6 @@ class Trainer:
else:
return self.args.local_rank in [-1, 0]
def is_world_master(self) -> bool:
"""
Whether or not this process is the global main process (when training in a distributed fashion on several
machines, this is only going to be :obj:`True` for one process).
.. warning::
This method is deprecated, use :meth:`~transformers.Trainer.is_world_process_zero` instead.
"""
warnings.warn("This method is deprecated, use `Trainer.is_world_process_zero()` instead.", FutureWarning)
return self.is_world_process_zero()
def is_world_process_zero(self) -> bool:
"""
Whether or not this process is the global main process (when training in a distributed fashion on several
@@ -1362,13 +1295,6 @@ class Trainer:
Works both with or without labels.
"""
if hasattr(self, "_prediction_loop"):
warnings.warn(
"The `_prediction_loop` method is deprecated and won't be called in a future version, define `prediction_loop` in your subclass.",
FutureWarning,
)
return self._prediction_loop(dataloader, description, prediction_loss_only=prediction_loss_only)
if not isinstance(dataloader.dataset, collections.abc.Sized):
raise ValueError("dataset must implement __len__")
prediction_loss_only = (