[Whisper] patch float type on mps (#35295)

* fix float type on mps

* make

examples/flax/question-answering/run_qa.py

@@ -61,7 +61,7 @@ from transformers.utils import check_min_version, send_example_telemetry
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 Array = Any
 Dataset = datasets.arrow_dataset.Dataset

examples/flax/speech-recognition/run_flax_speech_recognition_seq2seq.py

@@ -60,7 +60,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risk.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=2.14.0", "To fix: pip install -r examples/flax/speech-recognition/requirements.txt")
 

examples/flax/text-classification/run_flax_glue.py

@@ -56,7 +56,7 @@ from transformers.utils import check_min_version, send_example_telemetry
 
 logger = logging.getLogger(__name__)
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 Array = Any
 Dataset = datasets.arrow_dataset.Dataset

examples/flax/token-classification/run_flax_ner.py

@@ -57,7 +57,7 @@ from transformers.utils.versions import require_version
 
 logger = logging.getLogger(__name__)
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/token-classification/requirements.txt")
 

examples/pytorch/audio-classification/run_audio_classification.py

@@ -45,7 +45,7 @@ from transformers.utils.versions import require_version
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=1.14.0", "To fix: pip install -r examples/pytorch/audio-classification/requirements.txt")
 

examples/pytorch/contrastive-image-text/run_clip.py

@@ -54,7 +54,7 @@ from transformers.utils.versions import require_version
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/contrastive-image-text/requirements.txt")
 

examples/pytorch/image-classification/run_image_classification.py

@@ -56,7 +56,7 @@ from transformers.utils.versions import require_version
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=2.14.0", "To fix: pip install -r examples/pytorch/image-classification/requirements.txt")
 

examples/pytorch/image-classification/run_image_classification_no_trainer.py

@@ -49,7 +49,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 logger = get_logger(__name__)
 

examples/pytorch/image-pretraining/run_mae.py

@@ -43,7 +43,7 @@ from transformers.utils.versions import require_version
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")
 

examples/pytorch/image-pretraining/run_mim.py

@@ -48,7 +48,7 @@ Any model supported by the AutoModelForMaskedImageModeling API can be used.
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")
 

examples/pytorch/image-pretraining/run_mim_no_trainer.py

@@ -53,7 +53,7 @@ Any model supported by the AutoModelForMaskedImageModeling API can be used.
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")
 

examples/pytorch/instance-segmentation/run_instance_segmentation.py

@@ -46,7 +46,7 @@ from transformers.utils.versions import require_version
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=2.0.0", "To fix: pip install -r examples/pytorch/instance-segmentation/requirements.txt")
 

examples/pytorch/instance-segmentation/run_instance_segmentation_no_trainer.py

@@ -52,7 +52,7 @@ from transformers.utils.versions import require_version
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=2.0.0", "To fix: pip install -r examples/pytorch/instance-segmentation/requirements.txt")
 

examples/pytorch/language-modeling/run_clm.py

@@ -55,7 +55,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=2.14.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
 

examples/pytorch/language-modeling/run_clm_no_trainer.py

@@ -57,7 +57,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 logger = get_logger(__name__)
 

examples/pytorch/language-modeling/run_fim.py

@@ -58,7 +58,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=2.14.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
 

examples/pytorch/language-modeling/run_fim_no_trainer.py

@@ -60,7 +60,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 logger = get_logger(__name__)
 

examples/pytorch/language-modeling/run_mlm.py

@@ -54,7 +54,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=2.14.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
 

examples/pytorch/language-modeling/run_mlm_no_trainer.py

@@ -57,7 +57,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 logger = get_logger(__name__)
 require_version("datasets>=2.14.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")

examples/pytorch/language-modeling/run_plm.py

@@ -47,7 +47,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=2.14.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
 

examples/pytorch/multiple-choice/run_swag.py

@@ -47,7 +47,7 @@ from transformers.utils import PaddingStrategy, check_min_version, send_example_
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 logger = logging.getLogger(__name__)
 

examples/pytorch/multiple-choice/run_swag_no_trainer.py

@@ -56,7 +56,7 @@ from transformers.utils import PaddingStrategy, check_min_version, send_example_
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 logger = get_logger(__name__)
 # You should update this to your particular problem to have better documentation of `model_type`

examples/pytorch/object-detection/run_object_detection.py

@@ -48,7 +48,7 @@ from transformers.utils.versions import require_version
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=2.0.0", "To fix: pip install -r examples/pytorch/object-detection/requirements.txt")
 

examples/pytorch/object-detection/run_object_detection_no_trainer.py

@@ -51,7 +51,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 logging.basicConfig(level=logging.INFO)
 logger = get_logger(__name__)

examples/pytorch/question-answering/run_qa.py

@@ -50,7 +50,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/question-answering/requirements.txt")
 

examples/pytorch/question-answering/run_qa_beam_search.py

@@ -48,7 +48,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/question-answering/requirements.txt")
 

examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py

@@ -56,7 +56,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/question-answering/requirements.txt")
 

examples/pytorch/question-answering/run_qa_no_trainer.py

@@ -57,7 +57,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/question-answering/requirements.txt")
 

examples/pytorch/question-answering/run_seq2seq_qa.py

@@ -46,7 +46,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/question-answering/requirements.txt")
 

examples/pytorch/semantic-segmentation/run_semantic_segmentation.py

@@ -51,7 +51,7 @@ from transformers.utils.versions import require_version
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=2.0.0", "To fix: pip install -r examples/pytorch/semantic-segmentation/requirements.txt")
 

examples/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py

@@ -50,7 +50,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 logger = get_logger(__name__)
 

examples/pytorch/speech-recognition/run_speech_recognition_ctc.py

@@ -50,7 +50,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=1.18.0", "To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt")
 

examples/pytorch/speech-recognition/run_speech_recognition_ctc_adapter.py

@@ -53,7 +53,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=1.18.0", "To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt")
 

examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py

@@ -48,7 +48,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=1.18.0", "To fix: pip install -r examples/pytorch/speech-recognition/requirements.txt")
 

examples/pytorch/summarization/run_summarization.py

@@ -52,7 +52,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/summarization/requirements.txt")
 

examples/pytorch/summarization/run_summarization_no_trainer.py

@@ -56,7 +56,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 logger = get_logger(__name__)
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/summarization/requirements.txt")

examples/pytorch/text-classification/run_classification.py

@@ -47,7 +47,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
 

examples/pytorch/text-classification/run_glue.py

@@ -48,7 +48,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
 

examples/pytorch/text-classification/run_glue_no_trainer.py

@@ -49,7 +49,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 logger = get_logger(__name__)
 

examples/pytorch/text-classification/run_xnli.py

@@ -48,7 +48,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
 

examples/pytorch/token-classification/run_ner.py

@@ -49,7 +49,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/token-classification/requirements.txt")
 

examples/pytorch/token-classification/run_ner_no_trainer.py

@@ -56,7 +56,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 logger = get_logger(__name__)
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/token-classification/requirements.txt")

examples/pytorch/translation/run_translation.py

@@ -52,7 +52,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/translation/requirements.txt")
 

examples/pytorch/translation/run_translation_no_trainer.py

@@ -57,7 +57,7 @@ from transformers.utils.versions import require_version
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 logger = get_logger(__name__)
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/translation/requirements.txt")

examples/tensorflow/contrastive-image-text/run_clip.py

@@ -51,7 +51,7 @@ from transformers.utils.versions import require_version
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version(
     "datasets>=1.8.0", "To fix: pip install -r examples/tensorflow/contrastive-image-text/requirements.txt"

examples/tensorflow/image-classification/run_image_classification.py

@@ -55,7 +55,7 @@ from transformers.utils.versions import require_version
 logger = logging.getLogger(__name__)
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-classification/requirements.txt")
 

examples/tensorflow/multiple-choice/run_swag.py

@@ -50,7 +50,7 @@ from transformers.utils import PaddingStrategy, check_min_version, send_example_
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 logger = logging.getLogger(__name__)
 

examples/tensorflow/question-answering/run_qa.py

@@ -62,7 +62,7 @@ except (ModuleNotFoundError, ImportError):
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 logger = logging.getLogger(__name__)
 

examples/tensorflow/summarization/run_summarization.py

@@ -53,7 +53,7 @@ from transformers.utils.versions import require_version
 
 # region Checking dependencies
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/summarization/requirements.txt")
 

examples/tensorflow/text-classification/run_glue.py

@@ -47,7 +47,7 @@ from transformers.utils import check_min_version, send_example_telemetry
 
 
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 task_to_keys = {
     "cola": ("sentence", None),

examples/tensorflow/translation/run_translation.py

@@ -56,7 +56,7 @@ from transformers.utils.versions import require_version
 
 # region Dependencies and constants
 # Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.47.0.dev0")
+check_min_version("4.47.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/summarization/requirements.txt")
 

setup.py

@@ -435,7 +435,7 @@ install_requires = [
 
 setup(
     name="transformers",
-    version="4.47.0.dev0",  # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
+    version="4.47.1",  # expected format is one of x.y.z.dev0, or x.y.z.rc1 or x.y.z (no to dashes, yes to dots)
     author="The Hugging Face team (past and future) with the help of all our contributors (https://github.com/huggingface/transformers/graphs/contributors)",
     author_email="transformers@huggingface.co",
     description="State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow",

src/transformers/__init__.py

@@ -18,7 +18,7 @@
 # to defer the actual importing for when the objects are requested. This way `import transformers` provides the names
 # in the namespace without actually importing anything (and especially none of the backends).
 
-__version__ = "4.47.0.dev0"
+__version__ = "4.47.1"
 
 from typing import TYPE_CHECKING
 

src/transformers/models/bert/modeling_bert.py

@@ -1325,6 +1325,7 @@ class BertLMHeadModel(BertPreTrainedModel, GenerationMixin):
         output_attentions: Optional[bool] = None,
         output_hidden_states: Optional[bool] = None,
         return_dict: Optional[bool] = None,
+        **loss_kwargs,
     ) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
         r"""
         encoder_hidden_states  (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
@@ -1375,11 +1376,7 @@ class BertLMHeadModel(BertPreTrainedModel, GenerationMixin):
 
         lm_loss = None
         if labels is not None:
-            # we are doing next-token prediction; shift prediction scores and input ids by one
+            lm_loss = self.loss_function(prediction_scores, labels, self.config.vocab_size, **loss_kwargs)
-            shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
-            labels = labels[:, 1:].contiguous()
-            loss_fct = CrossEntropyLoss()
-            lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
 
         if not return_dict:
             output = (prediction_scores,) + outputs[2:]

src/transformers/models/speech_encoder_decoder/modeling_speech_encoder_decoder.py

@@ -491,6 +491,8 @@ class SpeechEncoderDecoderModel(PreTrainedModel, GenerationMixin):
         kwargs_decoder = {
             argument[len("decoder_") :]: value for argument, value in kwargs.items() if argument.startswith("decoder_")
         }
+        if "num_items_in_batch" in kwargs_encoder:
+            kwargs_decoder["num_items_in_batch"] = kwargs_encoder.pop("num_items_in_batch", None)
 
         if encoder_outputs is None:
             if inputs is None:

src/transformers/models/whisper/generation_whisper.py

@@ -632,7 +632,9 @@ class WhisperGenerationMixin(GenerationMixin):
                 cur_bsz=cur_bsz,
                 batch_idx_map=batch_idx_map,
             )
-            time_offset = seek.to(torch.float64) * time_precision / input_stride
+            time_offset = (
+                seek.to(torch.float32 if device.type == "mps" else torch.float64) * time_precision / input_stride
+            )
             seek_num_frames = (max_frames - seek).clamp(max=num_segment_frames)
 
             # 6.2 cut out next 30s segment from input features
@@ -1805,6 +1807,7 @@ class WhisperGenerationMixin(GenerationMixin):
         timestamp_segment_indices = torch.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[0]
         timestamp_segment_indices.add_(1)
         token_timestamps = seek_outputs[idx]["token_timestamps"] if return_token_timestamps else []
+        device = seek_sequence.device
 
         # If whisper predicted a "end of segment" via a timestep token, let's go ever each
         # "end of segment" prediction and slice the decoding into segments accordingly
@@ -1828,8 +1831,12 @@ class WhisperGenerationMixin(GenerationMixin):
                 end_timestamp_pos = sliced_tokens[idx_sliced_tokens] - timestamp_begin
                 segments.append(
                     {
-                        "start": time_offset[prev_idx] + start_timestamp_pos.to(torch.float64) * time_precision,
+                        "start": time_offset[prev_idx]
-                        "end": time_offset[prev_idx] + end_timestamp_pos.to(torch.float64) * time_precision,
+                        + start_timestamp_pos.to(torch.float32 if device.type == "mps" else torch.float64)
+                        * time_precision,
+                        "end": time_offset[prev_idx]
+                        + end_timestamp_pos.to(torch.float32 if device.type == "mps" else torch.float64)
+                        * time_precision,
                         "tokens": sliced_tokens,
                         "result": seek_outputs[idx],
                     }
@@ -1856,7 +1863,9 @@ class WhisperGenerationMixin(GenerationMixin):
             last_timestamp_pos = int(seek_num_frames[prev_idx] * time_precision_features / time_precision)
             if timestamps.numel() > 0 and timestamps[-1] != timestamp_begin:
                 # no consecutive timestamps but it has a timestamp; use the last one.
-                last_timestamp_pos = (timestamps[-1] - timestamp_begin).to(torch.float64)
+                last_timestamp_pos = (timestamps[-1] - timestamp_begin).to(
+                    torch.float32 if device.type == "mps" else torch.float64
+                )
             segments = [
                 {
                     "start": time_offset[prev_idx],

src/transformers/pytorch_utils.py

@@ -38,8 +38,10 @@ is_torch_greater_or_equal_than_2_0 = parsed_torch_version_base >= version.parse(
 is_torch_greater_or_equal_than_1_13 = parsed_torch_version_base >= version.parse("1.13")
 is_torch_greater_or_equal_than_1_12 = parsed_torch_version_base >= version.parse("1.12")
 
+# Cache this result has it's a C FFI call which can be pretty time-consuming
+_torch_distributed_available = torch.distributed.is_available()
 
-if is_torch_greater_or_equal("2.5"):
+if is_torch_greater_or_equal("2.5") and _torch_distributed_available:
     from torch.distributed.tensor import Replicate
     from torch.distributed.tensor.parallel import (
         ColwiseParallel,

src/transformers/trainer.py

@@ -2251,7 +2251,7 @@ class Trainer:
             else:
                 debug_overflow = DebugUnderflowOverflow(self.model)  # noqa
 
-        delay_optimizer_creation = is_sagemaker_mp_enabled() or self.is_fsdp_xla_enabled
+        delay_optimizer_creation = is_sagemaker_mp_enabled() or self.is_fsdp_xla_enabled or self.is_fsdp_enabled
 
         # We need to reset the scheduler, as its parameters may be different on subsequent calls
         if self._created_lr_scheduler:
@@ -2304,12 +2304,13 @@ class Trainer:
             # In case of auto_find_batch_size=True
             # Remove FSDP wrapping from sub-models.
             self.model = unwrap_model(self.model, recursive=True)
-            # configure fsdp plugin for qlora if any
-            self._fsdp_qlora_plugin_updates()
 
         if delay_optimizer_creation:
             if use_accelerator_prepare:
-                self.model = self.accelerator.prepare(self.model)
+                # configure fsdp plugin for qlora if any
+                self._fsdp_qlora_plugin_updates()
+                if self.accelerator.mixed_precision != "fp8":
+                    self.model = self.accelerator.prepare(self.model)
             self.create_optimizer_and_scheduler(num_training_steps=max_steps)
 
         # prepare using `accelerator` prepare
@@ -2516,6 +2517,7 @@ class Trainer:
                     context = (
                         functools.partial(self.accelerator.no_sync, model=model)
                         if i != len(batch_samples) - 1
+                        and self.accelerator.distributed_type != DistributedType.DEEPSPEED
                         else contextlib.nullcontext
                     )
                     with context():
@@ -3649,10 +3651,7 @@ class Trainer:
             return loss_mb.reduce_mean().detach().to(self.args.device)
 
         with self.compute_loss_context_manager():
-            if self.model_accepts_loss_kwargs:
+            loss = self.compute_loss(model, inputs, num_items_in_batch=num_items_in_batch)
-                loss = self.compute_loss(model, inputs)
-            else:
-                loss = self.compute_loss(model, inputs, num_items_in_batch=num_items_in_batch)
 
         del inputs
         if (
@@ -4175,7 +4174,7 @@ class Trainer:
             start_time = time.time()
             model = (
                 self.accelerator.prepare(model)
-                if self.is_deepspeed_enabled or self.is_fsdp_enabled
+                if self.is_deepspeed_enabled or (self.is_fsdp_enabled and self.accelerator.mixed_precision != "fp8")
                 else self.accelerator.prepare_model(model, evaluation_mode=True)
             )
             self.model_preparation_time = round(time.time() - start_time, 4)
@@ -5132,10 +5131,6 @@ class Trainer:
             except StopIteration:
                 break
 
-        # Keep default behavior the same
-        if not self.model_accepts_loss_kwargs:
-            return batch_samples, None
-
         if len(batch_samples) > 0 and "labels" in batch_samples[0]:
             # For now we don't support object detection
             try:
@@ -5145,4 +5140,8 @@ class Trainer:
 
         if self.args.average_tokens_across_devices:
             num_items_in_batch = self.accelerator.gather(num_items_in_batch).sum().item()
+
+        if torch.is_tensor(num_items_in_batch):
+            num_items_in_batch = num_items_in_batch.item()
+
         return batch_samples, num_items_in_batch

tests/trainer/test_trainer.py

@@ -750,11 +750,102 @@ class TrainerIntegrationPrerunTest(TestCasePlus, TrainerIntegrationCommon):
         self.check_trained_model(trainer.model, alternate_seed=True)
 
     @slow
-    def test_gradient_accumulation_loss_alignment(self):
+    def test_gradient_accumulation_loss_alignment_with_model_loss(self):
         set_seed(42)
         import datasets
 
-        model_name = "distilgpt2"
+        model_name = "nickypro/tinyllama-110M"
+        dataset_name = "wikitext"
+        dataset_config = "wikitext-2-raw-v1"
+        dataset = datasets.load_dataset(dataset_name, dataset_config, split="train[:500]")
+        dataset = dataset.train_test_split(test_size=0.2)
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+        tokenizer.pad_token = tokenizer.eos_token
+
+        def tokenize_function(examples):
+            return tokenizer(examples["text"], max_length=128, padding="max_length", truncation=True)
+
+        tokenized_dataset = dataset.map(tokenize_function, batched=True, remove_columns=dataset["train"].column_names)
+
+        data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
+
+        model = AutoModelForCausalLM.from_pretrained(model_name)
+
+        base_loss_callback = StoreLossCallback()
+
+        args_kwargs = {
+            "report_to": "none",
+            "logging_steps": 1,
+            "max_steps": 20,
+            "learning_rate": 3e-4,
+            "disable_tqdm": True,
+        }
+
+        args = TrainingArguments(
+            "./generation",
+            **args_kwargs,
+        )
+        trainer = Trainer(
+            model,
+            args,
+            train_dataset=tokenized_dataset["train"],
+            callbacks=[base_loss_callback],
+            data_collator=data_collator,
+        )
+        assert trainer.model_accepts_loss_kwargs
+        trainer.train()
+
+        grad_accum_loss_callback = StoreLossCallback()
+        args = TrainingArguments(
+            "./generation",
+            **args_kwargs,
+            gradient_accumulation_steps=2,
+            per_device_train_batch_size=4,
+        )
+        set_seed(42)
+        model = AutoModelForCausalLM.from_pretrained(model_name)
+        trainer = Trainer(
+            model,
+            args,
+            train_dataset=tokenized_dataset["train"],
+            callbacks=[grad_accum_loss_callback],
+            data_collator=data_collator,
+        )
+        trainer.train()
+
+        set_seed(42)
+        model = AutoModelForCausalLM.from_pretrained(model_name)
+        broken_loss_callback = StoreLossCallback()
+        trainer = Trainer(
+            model,
+            args,
+            train_dataset=tokenized_dataset["train"],
+            callbacks=[broken_loss_callback],
+            data_collator=data_collator,
+        )
+        # disable model_accepts_loss_kwargs
+        trainer.model_accepts_loss_kwargs = False
+        trainer.train()
+
+        # Calculate the difference between the base loss and the grad_accum loss
+        diff_truth = [
+            abs(base - grad) for base, grad in zip(base_loss_callback.losses, grad_accum_loss_callback.losses)
+        ]
+        diff_broken = [abs(base - grad) for base, grad in zip(base_loss_callback.losses, broken_loss_callback.losses)]
+
+        # all diff truth should be quite close
+        self.assertLess(max(diff_truth), 0.01, f"Difference {max(diff_truth)} is not within 0.01")
+
+        # max diff broken should be very off
+        self.assertGreater(max(diff_broken), 3, f"Difference {max(diff_broken)} is not greater than 3")
+
+    @slow
+    def test_gradient_accumulation_loss_alignment_with_loss_func(self):
+        set_seed(42)
+        import datasets
+
+        model_name = "roneneldan/TinyStories-33M"
         dataset_name = "wikitext"
         dataset_config = "wikitext-2-raw-v1"
         dataset = datasets.load_dataset(dataset_name, dataset_config, split="train[:500]")
@@ -836,15 +927,16 @@ class TrainerIntegrationPrerunTest(TestCasePlus, TrainerIntegrationCommon):
         trainer.train()
 
         # Calculate the difference between the base loss and the grad_accum loss
-        diff_truth = [base - grad for base, grad in zip(base_loss_callback.losses, grad_accum_loss_callback.losses)]
+        diff_truth = [
-        diff_broken = [base - grad for base, grad in zip(base_loss_callback.losses, broken_loss_callback.losses)]
+            abs(base - grad) for base, grad in zip(base_loss_callback.losses, grad_accum_loss_callback.losses)
-        # These should be quite close
+        ]
-        for diff in diff_truth:
+        diff_broken = [abs(base - grad) for base, grad in zip(base_loss_callback.losses, broken_loss_callback.losses)]
-            self.assertLess(abs(diff), 0.1, f"Difference {diff} is not within 0.1")
 
-        # These should be very off
+        # all diff truth should be quite close
-        for diff in diff_broken:
+        self.assertLess(max(diff_truth), 0.01, f"Difference {max(diff_truth)} is not within 0.01")
-            self.assertGreater(abs(diff), 0.1, f"Difference {diff} is not greater than 0.1")
+
+        # max diff broken should be very off
+        self.assertGreater(max(diff_broken), 3, f"Difference {max(diff_broken)} is not greater than 3")
 
     def test_gradient_accumulation(self):
         # Training with half the batch size but accumulation steps as 2 should give the same training losses.