Trainer automatically drops unused columns in nlp datasets (huggingfa…

…ce#6449)

* Add a classmethod to easily build a Trainer from nlp dataset and metric

* Fix docstrings

* Split train/eval

* Formatting

* Log dropped columns + docs

* Authorize callable activations

* Poc for auto activation

* Be framework-agnostic

* Formatting

* Remove class method

* Remove unnecessary code

src/transformers/__init__.py

@@ -82,6 +82,7 @@
     add_start_docstrings,
     cached_path,
     is_apex_available,
+    is_nlp_available,
     is_psutil_available,
     is_py3nvml_available,
     is_tf_available,

src/transformers/file_utils.py

@@ -64,6 +64,14 @@
     _tf_available = False  # pylint: disable=invalid-name
 
 
+try:
+    import nlp  # noqa: F401
+
+    _nlp_available = True
+
+except ImportError:
+    _nlp_available = False
+
 try:
     from torch.hub import _get_torch_home
 
@@ -144,6 +152,10 @@ def is_torch_tpu_available():
     return _torch_tpu_available
 
 
+def is_nlp_available():
+    return _nlp_available
+
+
 def is_psutil_available():
     return _psutil_available
 

src/transformers/trainer.py

@@ -1,3 +1,4 @@
+import inspect
 import logging
 import math
 import os
@@ -19,7 +20,7 @@
 from tqdm.auto import tqdm, trange
 
 from .data.data_collator import DataCollator, default_data_collator
-from .file_utils import is_torch_tpu_available
+from .file_utils import is_nlp_available, is_torch_tpu_available
 from .integrations import is_comet_available, is_tensorboard_available, is_wandb_available
 from .modeling_utils import PreTrainedModel
 from .optimization import AdamW, get_linear_schedule_with_warmup
@@ -41,6 +42,8 @@
     _use_native_amp = True
     from torch.cuda.amp import autocast
 
+if is_nlp_available():
+    import nlp
 
 if is_torch_tpu_available():
     import torch_xla.core.xla_model as xm
@@ -140,19 +143,19 @@ class Trainer:
         model (:class:`~transformers.PreTrainedModel`):
             The model to train, evaluate or use for predictions.
         args (:class:`~transformers.TrainingArguments`):
-            The arguments to tweak training.
+            The arguments to tweak for training.
         data_collator (:obj:`DataCollator`, `optional`, defaults to :func:`~transformers.default_data_collator`):
-            The function to use to from a batch from a list of elements of :obj:`train_dataset` or
+            The function to use to form a batch from a list of elements of :obj:`train_dataset` or
             :obj:`eval_dataset`.
         train_dataset (:obj:`torch.utils.data.dataset.Dataset`, `optional`):
-            The dataset to use for training.
+            The dataset to use for training. If it is an :obj:`nlp.Dataset`, columns not accepted by the
+            ``model.forward()`` method are automatically removed.
         eval_dataset (:obj:`torch.utils.data.dataset.Dataset`, `optional`):
-            The dataset to use for evaluation.
+            The dataset to use for evaluation. If it is an :obj:`nlp.Dataset`, columns not accepted by the
+            ``model.forward()`` method are automatically removed.
         compute_metrics (:obj:`Callable[[EvalPrediction], Dict]`, `optional`):
             The function that will be used to compute metrics at evaluation. Must take a
             :class:`~transformers.EvalPrediction` and return a dictionary string to metric values.
-        prediction_loss_only (:obj:`bool`, `optional`, defaults to `False`):
-            When performing evaluation and predictions, only returns the loss.
         tb_writer (:obj:`SummaryWriter`, `optional`):
             Object to write to TensorBoard.
         optimizers (:obj:`Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR`, `optional`):
@@ -228,11 +231,32 @@ def __init__(
                 ),
                 FutureWarning,
             )
+
+        if is_nlp_available():
+            if isinstance(train_dataset, nlp.Dataset):
+                self._remove_unused_columns(self.train_dataset, description="training")
+            if isinstance(eval_dataset, nlp.Dataset):
+                self._remove_unused_columns(self.eval_dataset, description="evaluation")
+
         self.global_step = None
         self.epoch = None
         if self.args.fp16 and _use_native_amp:
             self.scaler = torch.cuda.amp.GradScaler()
 
+    def _remove_unused_columns(self, dataset: "nlp.Dataset", description: Optional[str] = None):
+        # Inspect model forward signature to keep only the arguments it accepts.
+        signature = inspect.signature(self.model.forward)
+        signature_columns = list(signature.parameters.keys())
+        # Labels may be named label or label_ids, the default data collator handles that.
+        signature_columns += ["label", "label_ids"]
+        columns = [k for k in signature_columns if k in dataset.column_names]
+        ignored_columns = list(set(dataset.column_names) - set(signature_columns))
+        dset_description = "" if description is None else f"in the {description} set "
+        logger.info(
+            f"The following columns {dset_description}don't have a corresponding argument in `{self.model.__class__.__name__}.forward` and have been ignored: {', '.join(ignored_columns)}."
+        )
+        dataset.set_format(columns=columns)
+
     def _get_train_sampler(self) -> Optional[torch.utils.data.sampler.Sampler]:
         if isinstance(self.train_dataset, torch.utils.data.IterableDataset):
             return None
@@ -287,11 +311,13 @@ def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoa
 
         Args:
             eval_dataset (:obj:`torch.utils.data.dataset.Dataset`, `optional`):
-                If provided, will override :obj:`self.eval_dataset`.
+                If provided, will override :obj:`self.eval_dataset`. If it is an :obj:`nlp.Dataset`, columns not
+                accepted by the ``model.forward()`` method are automatically removed.
         """
         if eval_dataset is None and self.eval_dataset is None:
             raise ValueError("Trainer: evaluation requires an eval_dataset.")
-
+        elif eval_dataset is not None and is_nlp_available() and isinstance(eval_dataset, nlp.Dataset):
+            self._remove_unused_columns(eval_dataset, description="evaluation")
         eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset
         eval_sampler = self._get_eval_sampler(eval_dataset)
 
@@ -314,8 +340,11 @@ def get_test_dataloader(self, test_dataset: Dataset) -> DataLoader:
 
         Args:
             eval_dataset (:obj:`torch.utils.data.dataset.Dataset`, `optional`):
-                The test dataset to use.
+                The test dataset to use. If it is an :obj:`nlp.Dataset`, columns not accepted by the
+                ``model.forward()`` method are automatically removed.
         """
+        if is_nlp_available() and isinstance(test_dataset, nlp.Dataset):
+            self._remove_unused_columns(test_dataset, description="test")
         test_sampler = self._get_eval_sampler(test_dataset)
 
         # We use the same batch_size as for eval.
@@ -903,7 +932,8 @@ def evaluate(self, eval_dataset: Optional[Dataset] = None) -> Dict[str, float]:
 
         Args:
             eval_dataset (:obj:`Dataset`, `optional`):
-                Pass a dataset if you wish to override :obj:`self.eval_dataset`.
+                Pass a dataset if you wish to override :obj:`self.eval_dataset`. If it is an :obj:`nlp.Dataset`,
+                columns not accepted by the ``model.forward()`` method are automatically removed.
 
         Returns:
             A dictionary containing the evaluation loss and the potential metrics computed from the predictions.
@@ -929,7 +959,8 @@ def predict(self, test_dataset: Dataset) -> PredictionOutput:
 
         Args:
             test_dataset (:obj:`Dataset`):
-                Dataset to run the predictions on.
+                Dataset to run the predictions on. If it is an :obj:`nlp.Dataset`, columns not accepted by the
+                ``model.forward()`` method are automatically removed.
 
         Returns:
             `NamedTuple`: