Better filtering of the model outputs in Trainer (#8633)

* Better filtering of the model outputs in Trainer

* Fix examples tests

* Add test for Lysandre

examples/seq2seq/seq2seq_trainer.py

@@ -1,4 +1,4 @@
-from typing import Any, Dict, Optional, Tuple, Union
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import torch
 from torch import nn
@@ -153,7 +153,11 @@ class Seq2SeqTrainer(Trainer):
         return loss
 
     def prediction_step(
-        self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]], prediction_loss_only: bool
+        self,
+        model: nn.Module,
+        inputs: Dict[str, Union[torch.Tensor, Any]],
+        prediction_loss_only: bool,
+        ignore_keys: Optional[List[str]] = None,
     ) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
         """
         Perform an evaluation step on :obj:`model` using obj:`inputs`.

src/transformers/configuration_utils.py

@@ -43,6 +43,8 @@ class PretrainedConfig(object):
         - **is_composition** (:obj:`bool`): Whether the config class is composed of multiple sub-configs. In this case
           the config has to be initialized from two or more configs of type :class:`~transformers.PretrainedConfig`
           like: :class:`~transformers.EncoderDecoderConfig` or :class:`~RagConfig`.
+        - **keys_to_ignore_at_inference** (:obj:`List[str]`): A list of keys to ignore by default when looking at
+          dictionary outputs of the model during inference.
 
     Args:
         name_or_path (:obj:`str`, `optional`, defaults to :obj:`""`):

src/transformers/models/bart/configuration_bart.py

@@ -110,6 +110,7 @@ class BartConfig(PretrainedConfig):
             :obj:`True` for `bart-large-cnn`.
     """
     model_type = "bart"
+    keys_to_ignore_at_inference = ["past_key_values"]
 
     def __init__(
         self,

src/transformers/models/ctrl/configuration_ctrl.py

@@ -77,6 +77,7 @@ class CTRLConfig(PretrainedConfig):
     """
 
     model_type = "ctrl"
+    keys_to_ignore_at_inference = ["past_key_values"]
 
     def __init__(
         self,

src/transformers/models/gpt2/configuration_gpt2.py

@@ -120,6 +120,7 @@ class GPT2Config(PretrainedConfig):
     """
 
     model_type = "gpt2"
+    keys_to_ignore_at_inference = ["past_key_values"]
 
     def __init__(
         self,

src/transformers/models/marian/configuration_marian.py

@@ -97,3 +97,4 @@ class MarianConfig(BartConfig):
     """
 
     model_type = "marian"
+    keys_to_ignore_at_inference = ["past_key_values"]

src/transformers/models/mbart/configuration_mbart.py

@@ -102,3 +102,4 @@ class MBartConfig(BartConfig):
     """
 
     model_type = "mbart"
+    keys_to_ignore_at_inference = ["past_key_values"]

src/transformers/models/mt5/configuration_mt5.py

@@ -62,6 +62,7 @@ class MT5Config(PretrainedConfig):
             Type of feed forward layer to be used. Should be one of :obj:`"relu"` or :obj:`"gated-gelu"`.
     """
     model_type = "mt5"
+    keys_to_ignore_at_inference = ["past_key_values"]
 
     def __init__(
         self,

src/transformers/models/pegasus/configuration_pegasus.py

@@ -141,4 +141,5 @@ class PegasusConfig(BartConfig):
     """
 
     model_type = "pegasus"
+    keys_to_ignore_at_inference = ["past_key_values"]
     # The implementation of the config object is in BartConfig

src/transformers/models/prophetnet/configuration_prophetnet.py

@@ -92,6 +92,7 @@ class ProphetNetConfig(PretrainedConfig):
             smoothing is performed.
     """
     model_type = "prophetnet"
+    keys_to_ignore_at_inference = ["past_key_values"]
 
     def __init__(
         self,

src/transformers/models/reformer/configuration_reformer.py

@@ -153,6 +153,7 @@ class ReformerConfig(PretrainedConfig):
         >>> configuration = model.config
     """
     model_type = "reformer"
+    keys_to_ignore_at_inference = ["past_buckets_states"]
 
     def __init__(
         self,

src/transformers/models/t5/configuration_t5.py

@@ -71,6 +71,7 @@ class T5Config(PretrainedConfig):
             the :obj:`"gated-gelu"` feed forward projection. Original T5 uses :obj:`"relu"`.
     """
     model_type = "t5"
+    keys_to_ignore_at_inference = ["past_key_values"]
 
     def __init__(
         self,

src/transformers/models/transfo_xl/configuration_transfo_xl.py

@@ -105,6 +105,7 @@ class TransfoXLConfig(PretrainedConfig):
     """
 
     model_type = "transfo-xl"
+    keys_to_ignore_at_inference = ["mems"]
 
     def __init__(
         self,

src/transformers/models/xlnet/configuration_xlnet.py

@@ -128,6 +128,7 @@ class XLNetConfig(PretrainedConfig):
     """
 
     model_type = "xlnet"
+    keys_to_ignore_at_inference = ["mems"]
 
     def __init__(
         self,

src/transformers/trainer.py

@@ -1098,10 +1098,11 @@ class Trainer:
         """
         outputs = model(**inputs)
         # Save past state if it exists
+        # TODO: this needs to be fixed and made cleaner later.
         if self.args.past_index >= 0:
             self._past = outputs[self.args.past_index]
         # We don't use .loss here since the model may return tuples instead of ModelOutput.
-        return outputs[0]
+        return outputs["loss"] if isinstance(outputs, dict) else outputs[0]
 
     def is_local_process_zero(self) -> bool:
         """
@@ -1220,7 +1221,9 @@ class Trainer:
             logger.info("Deleting older checkpoint [{}] due to args.save_total_limit".format(checkpoint))
             shutil.rmtree(checkpoint)
 
-    def evaluate(self, eval_dataset: Optional[Dataset] = None) -> Dict[str, float]:
+    def evaluate(
+        self, eval_dataset: Optional[Dataset] = None, ignore_keys: Optional[List[str]] = None
+    ) -> Dict[str, float]:
         """
         Run evaluation and returns metrics.
 
@@ -1234,6 +1237,9 @@ class Trainer:
                 Pass a dataset if you wish to override :obj:`self.eval_dataset`. If it is an :obj:`datasets.Dataset`,
                 columns not accepted by the ``model.forward()`` method are automatically removed. It must implement the
                 :obj:`__len__` method.
+            ignore_keys (:obj:`Lst[str]`, `optional`):
+                A list of keys in the output of your model (if it is a dictionary) that should be ignored when
+                gathering predictions.
 
         Returns:
             A dictionary containing the evaluation loss and the potential metrics computed from the predictions. The
@@ -1250,6 +1256,7 @@ class Trainer:
             # No point gathering the predictions if there are no metrics, otherwise we defer to
             # self.args.prediction_loss_only
             prediction_loss_only=True if self.compute_metrics is None else None,
+            ignore_keys=ignore_keys,
         )
 
         self.log(output.metrics)
@@ -1261,7 +1268,7 @@ class Trainer:
         self.control = self.callback_handler.on_evaluate(self.args, self.state, self.control, output.metrics)
         return output.metrics
 
-    def predict(self, test_dataset: Dataset) -> PredictionOutput:
+    def predict(self, test_dataset: Dataset, ignore_keys: Optional[List[str]] = None) -> PredictionOutput:
         """
         Run prediction and returns predictions and potential metrics.
 
@@ -1272,6 +1279,9 @@ class Trainer:
             test_dataset (:obj:`Dataset`):
                 Dataset to run the predictions on. If it is an :obj:`datasets.Dataset`, columns not accepted by the
                 ``model.forward()`` method are automatically removed. Has to implement the method :obj:`__len__`
+            ignore_keys (:obj:`Lst[str]`, `optional`):
+                A list of keys in the output of your model (if it is a dictionary) that should be ignored when
+                gathering predictions.
 
         .. note::
 
@@ -1291,10 +1301,14 @@ class Trainer:
 
         test_dataloader = self.get_test_dataloader(test_dataset)
 
-        return self.prediction_loop(test_dataloader, description="Prediction")
+        return self.prediction_loop(test_dataloader, description="Prediction", ignore_keys=ignore_keys)
 
     def prediction_loop(
-        self, dataloader: DataLoader, description: str, prediction_loss_only: Optional[bool] = None
+        self,
+        dataloader: DataLoader,
+        description: str,
+        prediction_loss_only: Optional[bool] = None,
+        ignore_keys: Optional[List[str]] = None,
     ) -> PredictionOutput:
         """
         Prediction/evaluation loop, shared by :obj:`Trainer.evaluate()` and :obj:`Trainer.predict()`.
@@ -1346,7 +1360,7 @@ class Trainer:
         self.callback_handler.eval_dataloader = dataloader
 
         for step, inputs in enumerate(dataloader):
-            loss, logits, labels = self.prediction_step(model, inputs, prediction_loss_only)
+            loss, logits, labels = self.prediction_step(model, inputs, prediction_loss_only, ignore_keys=ignore_keys)
             if loss is not None:
                 losses = loss.repeat(batch_size)
                 losses_host = losses if losses_host is None else torch.cat((losses_host, losses), dim=0)
@@ -1410,7 +1424,11 @@ class Trainer:
         return nested_numpify(tensors)
 
     def prediction_step(
-        self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]], prediction_loss_only: bool
+        self,
+        model: nn.Module,
+        inputs: Dict[str, Union[torch.Tensor, Any]],
+        prediction_loss_only: bool,
+        ignore_keys: Optional[List[str]] = None,
     ) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
         """
         Perform an evaluation step on :obj:`model` using obj:`inputs`.
@@ -1427,6 +1445,9 @@ class Trainer:
                 argument :obj:`labels`. Check your model's documentation for all accepted arguments.
             prediction_loss_only (:obj:`bool`):
                 Whether or not to return the loss only.
+            ignore_keys (:obj:`Lst[str]`, `optional`):
+                A list of keys in the output of your model (if it is a dictionary) that should be ignored when
+                gathering predictions.
 
         Return:
             Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]: A tuple with the loss, logits and
@@ -1434,6 +1455,11 @@ class Trainer:
         """
         has_labels = all(inputs.get(k) is not None for k in self.label_names)
         inputs = self._prepare_inputs(inputs)
+        if ignore_keys is None:
+            if hasattr(self.model, "config"):
+                ignore_keys = getattr(self.model.config, "keys_to_ignore_at_inference", [])
+            else:
+                ignore_keys = []
 
         with torch.no_grad():
             if self.args.fp16 and _use_native_amp:
@@ -1442,16 +1468,21 @@ class Trainer:
             else:
                 outputs = model(**inputs)
             if has_labels:
-                loss = outputs[0].mean().detach()
-                logits = outputs[1:]
+                if isinstance(outputs, dict):
+                    loss = outputs["loss"].mean().detach()
+                    logits = tuple(v for k, v in outputs.items() if k not in ignore_keys + ["loss"])
+                else:
+                    loss = outputs[0].mean().detach()
+                    logits = outputs[1:]
             else:
                 loss = None
-                # Slicing so we get a tuple even if `outputs` is a `ModelOutput`.
-                logits = outputs[:]
+                if isinstance(outputs, dict):
+                    logits = tuple(v for k, v in outputs.items() if k not in ignore_keys)
+                else:
+                    logits = outputs
+            # TODO: this needs to be fixed and made cleaner later.
             if self.args.past_index >= 0:
                 self._past = outputs[self.args.past_index if has_labels else self.args.past_index - 1]
-                # Remove the past from the logits.
-                logits = logits[: self.args.past_index - 1] + logits[self.args.past_index :]
 
         if prediction_loss_only:
             return (loss, None, None)

tests/test_trainer.py

@@ -44,6 +44,8 @@ if is_torch_available():
         DataCollatorForLanguageModeling,
         GlueDataset,
         GlueDataTrainingArguments,
+        GPT2Config,
+        GPT2LMHeadModel,
         LineByLineTextDataset,
         PreTrainedModel,
         TextDataset,
@@ -73,6 +75,18 @@ class RegressionDataset:
         return result
 
 
+class RepeatDataset:
+    def __init__(self, x, length=64):
+        self.x = x
+        self.length = length
+
+    def __len__(self):
+        return self.length
+
+    def __getitem__(self, i):
+        return {"input_ids": self.x, "labels": self.x}
+
+
 class DynamicShapesDataset:
     def __init__(self, length=64, seed=42, batch_size=8):
         self.length = length
@@ -136,6 +150,20 @@ if is_torch_available():
             loss = torch.nn.functional.mse_loss(y, labels)
             return (loss, y, y) if self.double_output else (loss, y)
 
+    class RegressionDictModel(torch.nn.Module):
+        def __init__(self, a=0, b=0):
+            super().__init__()
+            self.a = torch.nn.Parameter(torch.tensor(a).float())
+            self.b = torch.nn.Parameter(torch.tensor(b).float())
+            self.config = None
+
+        def forward(self, input_x=None, labels=None, **kwargs):
+            y = input_x * self.a + self.b
+            result = {"output": y}
+            if labels is not None:
+                result["loss"] = torch.nn.functional.mse_loss(y, labels)
+            return result
+
     class RegressionPreTrainedModel(PreTrainedModel):
         config_class = RegressionModelConfig
         base_model_prefix = "regression"
@@ -236,6 +264,33 @@ class TrainerIntegrationTest(unittest.TestCase):
         metrics = trainer.evaluate()
         self.assertEqual(metrics[metric], best_value)
 
+    def test_trainer_works_with_dict(self):
+        # Edge case because Apex with mode O2 will change our models to return dicts. This test checks it doesn't break
+        # anything.
+        train_dataset = RegressionDataset()
+        eval_dataset = RegressionDataset()
+        model = RegressionDictModel()
+        args = TrainingArguments("./regression")
+        trainer = Trainer(model, args, train_dataset=train_dataset, eval_dataset=eval_dataset)
+        trainer.train()
+        _ = trainer.evaluate()
+        _ = trainer.predict(eval_dataset)
+
+    def test_evaluation_with_keys_to_drop(self):
+        config = GPT2Config(vocab_size=100, n_positions=128, n_ctx=128, n_embd=32, n_layer=3, n_head=4)
+        tiny_gpt2 = GPT2LMHeadModel(config)
+        x = torch.randint(0, 100, (128,))
+        eval_dataset = RepeatDataset(x)
+        args = TrainingArguments("./test")
+        trainer = Trainer(tiny_gpt2, args, eval_dataset=eval_dataset)
+        # By default the past_key_values are removed
+        result = trainer.predict(eval_dataset)
+        self.assertTrue(isinstance(result.predictions, np.ndarray))
+        # We can still get them by setting ignore_keys to []
+        result = trainer.predict(eval_dataset, ignore_keys=[])
+        self.assertTrue(isinstance(result.predictions, tuple))
+        self.assertEqual(len(result.predictions), 2)
+
     def test_training_arguments_are_left_untouched(self):
         trainer = get_regression_trainer()
         trainer.train()