fix trainer

optimum/intel/neural_compressor/trainer.py

@@ -39,6 +39,7 @@
 from transformers import Trainer
 from transformers.data.data_collator import DataCollator
 from transformers.debug_utils import DebugOption, DebugUnderflowOverflow
+from transformers.feature_extraction_utils import FeatureExtractionMixin
 from transformers.modeling_utils import PreTrainedModel, get_parameter_dtype, unwrap_model
 from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
 from transformers.tokenization_utils_base import PreTrainedTokenizerBase
@@ -104,7 +105,7 @@
     from neural_compressor.config import _BaseQuantizationConfig
 
 
-__version__ = "4.22.2"
+__version__ = "4.46.0"
 
 
 logger = logging.get_logger(__name__)
@@ -122,8 +123,9 @@ def __init__(
         data_collator: Optional[DataCollator] = None,
         train_dataset: Optional[Dataset] = None,
         eval_dataset: Optional[Dataset] = None,
-        tokenizer: Optional[PreTrainedTokenizerBase] = None,
+        processing_class: Optional[Union[PreTrainedTokenizerBase, FeatureExtractionMixin]] = None,
         model_init: Callable[[], PreTrainedModel] = None,
+        compute_loss_func: Optional[Callable] = None,
         compute_metrics: Optional[Callable[[EvalPrediction], Dict]] = None,
         callbacks: Optional[List[TrainerCallback]] = None,
         optimizers: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None),
@@ -132,6 +134,7 @@ def __init__(
         pruning_config: Optional[_BaseQuantizationConfig] = None,
         distillation_config: Optional[_BaseQuantizationConfig] = None,
         task: Optional[str] = None,
+        **kwargs,
     ):
         self.neftune_noise_alpha = None
 
@@ -141,12 +144,12 @@ def __init__(
             data_collator,
             train_dataset,
             eval_dataset,
-            tokenizer,
-            model_init,
-            compute_metrics,
-            callbacks,
-            optimizers,
-            preprocess_logits_for_metrics,
+            processing_class or kwargs.get("tokenizer", None),
+            model_init=model_init,
+            compute_metrics=compute_metrics,
+            callbacks=callbacks,
+            optimizers=optimizers,
+            preprocess_logits_for_metrics=preprocess_logits_for_metrics,
         )
 
         if self.args.device.type == "cuda" and not is_neural_compressor_version(">", "2.0.0"):
@@ -766,7 +769,7 @@ def _get_logits(model_outputs):
         output_names = ["logits", "start_logits", "end_logits"]
         return tuple(model_outputs.get(name) for name in output_names if name in model_outputs)
 
-    def compute_loss(self, model, inputs, return_outputs=False):
+    def compute_loss(self, model, inputs, return_outputs=False, num_items_in_batch=None):
         """
         How the loss is computed by Trainer. By default, all models return the loss in the first element.
         """

optimum/intel/openvino/trainer.py

@@ -216,6 +216,11 @@ def __init__(
         logger.warning("OVTrainer is deprecated and will be removed in optimum-intel v1.22.0.")
 
         if is_transformers_version(">=", "4.45.0"):
+            if is_transformers_version(">=", "4.46.0"):
+                raise ImportError(
+                    f"Unsupported transformers version found is {_transformers_version} which is not supported by the OVTrainer. Please downgrade to v4.44"
+                )
+
             logger.warning(
                 f"The transformers version found is {_transformers_version} which is not officially supported by the OVTrainer, use at your own risk"
             )

tests/openvino/test_quantization.py

@@ -771,6 +771,9 @@ class OVTrainerTest(unittest.TestCase):
     SUPPORTED_ARCHITECTURES_WITH_EXPECTED_QUANTIZED_MATMULS = (("albert", 64, 39),)
 
     @parameterized.expand(SUPPORTED_ARCHITECTURES_WITH_EXPECTED_QUANTIZED_MATMULS)
+    @unittest.skipIf(
+        is_transformers_version(">=", "4.46"), reason="OVTrainer is not compatible with transformers>=v4.46"
+    )
     def test_aware_training_quantization(self, model_name, expected_fake_quantize, expected_int8):
         model_id = MODEL_NAMES[model_name]
         model = AutoModelForSequenceClassification.from_pretrained(model_id, attn_implementation="eager")

tests/openvino/test_training.py

@@ -475,7 +475,10 @@ class OVTrainerTextClassificationTrainingTest(OVTrainerBaseTrainingTest):
     task = "sequence-classification"
 
     @parameterized.expand(OVTRAINER_TEXT_CLASSIFICATION_TEST_DESCRIPTORS.items())
-    @unittest.skipIf(is_transformers_version("<", "4.41.0"), reason="Mismatch in expected fake quantized op")
+    @unittest.skipIf(
+        is_transformers_version("<", "4.41") or is_transformers_version(">=", "4.46"),
+        reason="Mismatch in expected fake quantized op and incompatible with transformers v4.46",
+    )
     def test_training(self, _, desc: OVTrainerTestDescriptor):
         self.run_ovtrainer_training_checks(desc)
 
@@ -627,7 +630,10 @@ class OVTrainerImageClassificationTrainingTest(OVTrainerBaseTrainingTest):
     @parameterized.expand(OVTRAINER_IMAGE_CLASSIFICATION_TEST_DESCRIPTORS.items())
     @pytest.mark.run_slow
     @slow
-    @unittest.skipIf(is_transformers_version("<", "4.41.0"), reason="Mismatch in expected fake quantized op")
+    @unittest.skipIf(
+        is_transformers_version("<", "4.41") or is_transformers_version(">=", "4.46"),
+        reason="Mismatch in expected fake quantized op and incompatible with transformers v4.46",
+    )
     def test_training(self, _, desc: OVTrainerTestDescriptor):
         self.run_ovtrainer_training_checks(desc)
 
@@ -808,6 +814,9 @@ class OVTrainerAudioClassificationTrainingTest(OVTrainerBaseTrainingTest):
     @parameterized.expand(OVTRAINER_AUDIO_CLASSIFICATION_TEST_DESCRIPTORS.items())
     @pytest.mark.run_slow
     @slow
+    @unittest.skipIf(
+        is_transformers_version(">=", "4.46"), reason="OVTrainer is not compatible with transformers>=v4.46"
+    )
     def test_training(self, _, desc: OVTrainerTestDescriptor):
         self.run_ovtrainer_training_checks(desc)