Patch release

* Fix float8_e4m3fn in modeling_utils

* style

* fix

* comment

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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 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("4.43.0.dev0")
+check_min_version("4.43.0")
 
 require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/summarization/requirements.txt")
 

setup.py

@@ -430,7 +430,7 @@ install_requires = [
 
 setup(
     name="transformers",
-    version="4.43.0.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="4.43.2",  # 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__ = "4.43.0.dev0"
+__version__ = "4.43.2"
 
 from typing import TYPE_CHECKING
 

src/transformers/modeling_rope_utils.py

@@ -129,6 +129,7 @@ def _compute_dynamic_ntk_parameters(
         Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
         post-processing scaling factor applied to the computed cos/sin (unused in this type of RoPE).
     """
+    # TODO (joao): use the new `original_max_position_embeddings` from rope_scaling
     if config is not None and len(rope_kwargs) > 0:
         raise ValueError(
             "Unexpected arguments: `**rope_kwargs` and `config` are mutually exclusive in "
@@ -249,6 +250,7 @@ def _compute_longrope_parameters(
         Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
         post-processing scaling factor applied to the computed cos/sin.
     """
+    # TODO (joao): use the new `original_max_position_embeddings` from rope_scaling
     # No need to keep BC with longrope, unreleased when this new pattern was created.
     if len(rope_kwargs) > 0:
         raise ValueError(
@@ -293,6 +295,50 @@ def _compute_longrope_parameters(
     return inv_freq, attention_factor
 
 
+def _compute_llama3_parameters(
+    config: PretrainedConfig, device: "torch.device", seq_len: Optional[int] = None, **rope_kwargs
+) -> Tuple["torch.Tensor", float]:
+    """
+    Computes the inverse frequencies for llama 3.1.
+
+    Args:
+        config ([`~transformers.PretrainedConfig`]):
+            The model configuration.
+        device (`torch.device`):
+            The device to use for initialization of the inverse frequencies.
+        seq_len (`int`, *optional*):
+            The current sequence length. Unused for this type of RoPE.
+        rope_kwargs (`Dict`, *optional*):
+            BC compatibility with the previous RoPE class instantiation, will be removed in v4.45.
+    Returns:
+        Tuple of (`torch.Tensor`, `float`), containing the inverse frequencies for the RoPE embeddings and the
+        post-processing scaling factor applied to the computed cos/sin.
+    """
+    # Gets the default RoPE parameters
+    inv_freq, attention_factor = _compute_default_rope_parameters(config, device, seq_len, **rope_kwargs)
+
+    factor = config.rope_scaling["factor"]  # `8` in the original implementation
+    low_freq_factor = config.rope_scaling["low_freq_factor"]  # `1` in the original implementation
+    high_freq_factor = config.rope_scaling["high_freq_factor"]  # `4` in the original implementation
+    old_context_len = config.rope_scaling["original_max_position_embeddings"]  # `8192` in the original implementation
+
+    low_freq_wavelen = old_context_len / low_freq_factor
+    high_freq_wavelen = old_context_len / high_freq_factor
+    new_freqs = []
+    for freq in inv_freq:
+        wavelen = 2 * math.pi / freq
+        if wavelen < high_freq_wavelen:
+            new_freqs.append(freq)
+        elif wavelen > low_freq_wavelen:
+            new_freqs.append(freq / factor)
+        else:
+            assert low_freq_wavelen != high_freq_wavelen
+            smooth = (old_context_len / wavelen - low_freq_factor) / (high_freq_factor - low_freq_factor)
+            new_freqs.append((1 - smooth) * freq / factor + smooth * freq)
+    inv_freq = torch.tensor(new_freqs, dtype=inv_freq.dtype, device=inv_freq.device)
+    return inv_freq, attention_factor
+
+
 # This maps the "rope_type" string field in rope config to the corresponding function to compute the RoPE parameters
 # from the model config. You can append new {'rope_type': callable} pairs to this dictionary to enable custom RoPE
 # parameterizations, as long as the callable has the same signature.
@@ -302,11 +348,17 @@ ROPE_INIT_FUNCTIONS = {
     "dynamic": _compute_dynamic_ntk_parameters,
     "yarn": _compute_yarn_parameters,
     "longrope": _compute_longrope_parameters,
+    "llama3": _compute_llama3_parameters,
 }
 
 
 def _check_received_keys(rope_type: str, received_keys: set, required_keys: set, optional_keys: Optional[set] = None):
     """Compare the received keys in `config.rope_scaling` against the expected and optional keys"""
+    # BC: "rope_type" was originally "type" -- let's gracefully handle it
+    if "rope_type" not in received_keys and "type" in received_keys:
+        received_keys -= {"type"}
+        received_keys.add("rope_type")
+
     missing_keys = required_keys - received_keys
     if missing_keys:
         raise KeyError(f"Missing required keys in `rope_scaling` for 'rope_type'='{rope_type}': {missing_keys}")
@@ -314,14 +366,14 @@ def _check_received_keys(rope_type: str, received_keys: set, required_keys: set,
     if optional_keys is not None:
         unused_keys = received_keys - required_keys - optional_keys
     else:
-        unused_keys = received_keys - received_keys
+        unused_keys = received_keys - required_keys
     if unused_keys:
-        raise KeyError(f"Unrecognized keys in `rope_scaling` for 'rope_type'='{rope_type}': {unused_keys}")
+        logger.warning(f"Unrecognized keys in `rope_scaling` for 'rope_type'='{rope_type}': {unused_keys}")
 
 
 def _validate_default_rope_parameters(config: PretrainedConfig):
     rope_scaling = config.rope_scaling
-    rope_type = rope_scaling["rope_type"]
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
     required_keys = {"rope_type"}
     received_keys = set(rope_scaling.keys())
     _check_received_keys(rope_type, received_keys, required_keys)
@@ -329,19 +381,33 @@ def _validate_default_rope_parameters(config: PretrainedConfig):
 
 def _validate_linear_scaling_rope_parameters(config: PretrainedConfig):
     rope_scaling = config.rope_scaling
-    rope_type = rope_scaling["rope_type"]
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
     required_keys = {"rope_type", "factor"}
     received_keys = set(rope_scaling.keys())
     _check_received_keys(rope_type, received_keys, required_keys)
 
     factor = rope_scaling["factor"]
     if factor is None or not isinstance(factor, float) or factor < 1.0:
-        raise ValueError(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
+        logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
+
+
+def _validate_dynamic_scaling_rope_parameters(config: PretrainedConfig):
+    rope_scaling = config.rope_scaling
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
+    required_keys = {"rope_type", "factor"}
+    # TODO (joao): update logic for the inclusion of `original_max_position_embeddings`
+    optional_keys = {"original_max_position_embeddings"}
+    received_keys = set(rope_scaling.keys())
+    _check_received_keys(rope_type, received_keys, required_keys, optional_keys)
+
+    factor = rope_scaling["factor"]
+    if factor is None or not isinstance(factor, float) or factor < 1.0:
+        logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
 
 
 def _validate_yarn_parameters(config: PretrainedConfig):
     rope_scaling = config.rope_scaling
-    rope_type = rope_scaling["rope_type"]
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
     required_keys = {"rope_type", "factor"}
     optional_keys = {"attention_factor", "beta_fast", "beta_slow"}
     received_keys = set(rope_scaling.keys())
@@ -349,22 +415,22 @@ def _validate_yarn_parameters(config: PretrainedConfig):
 
     factor = rope_scaling["factor"]
     if factor is None or not isinstance(factor, float) or factor < 1.0:
-        raise ValueError(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
+        logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
 
     attention_factor = rope_scaling.get("attention_factor")
     if attention_factor is not None and (not isinstance(attention_factor, float) or attention_factor < 0):
-        raise ValueError(
+        logger.warning(
             f"`rope_scaling`'s attention_factor field must be a float greater than 0, got {attention_factor}"
         )
     beta_fast = rope_scaling.get("beta_fast")
     if beta_fast is not None and not isinstance(beta_fast, float):
-        raise ValueError(f"`rope_scaling`'s beta_fast field must be a float, got {beta_fast}")
+        logger.warning(f"`rope_scaling`'s beta_fast field must be a float, got {beta_fast}")
     beta_slow = rope_scaling.get("beta_slow")
     if beta_slow is not None and not isinstance(beta_slow, float):
-        raise ValueError(f"`rope_scaling`'s beta_slow field must be a float, got {beta_slow}")
+        logger.warning(f"`rope_scaling`'s beta_slow field must be a float, got {beta_slow}")
 
     if (beta_fast or 32) < (beta_slow or 1):
-        raise ValueError(
+        logger.warning(
             f"`rope_scaling`'s beta_fast field must be greater than beta_slow, got beta_fast={beta_fast} "
             f"(defaults to 32 if None) and beta_slow={beta_slow} (defaults to 1 if None)"
         )
@@ -372,9 +438,10 @@ def _validate_yarn_parameters(config: PretrainedConfig):
 
 def _validate_longrope_parameters(config: PretrainedConfig):
     rope_scaling = config.rope_scaling
-    rope_type = rope_scaling["rope_type"]
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
     required_keys = {"rope_type", "short_factor", "long_factor"}
-    optional_keys = {"attention_factor", "factor"}
+    # TODO (joao): update logic for the inclusion of `original_max_position_embeddings`
+    optional_keys = {"attention_factor", "factor", "original_max_position_embeddings"}
     received_keys = set(rope_scaling.keys())
     _check_received_keys(rope_type, received_keys, required_keys, optional_keys)
 
@@ -383,15 +450,15 @@ def _validate_longrope_parameters(config: PretrainedConfig):
 
     short_factor = rope_scaling.get("short_factor")
     if not isinstance(short_factor, list) and all(isinstance(x, (int, float)) for x in short_factor):
-        raise ValueError(f"`rope_scaling`'s short_factor field must be a list of numbers, got {short_factor}")
+        logger.warning(f"`rope_scaling`'s short_factor field must be a list of numbers, got {short_factor}")
     if not len(short_factor) == dim // 2:
-        raise ValueError(f"`rope_scaling`'s short_factor field must have length {dim // 2}, got {len(short_factor)}")
+        logger.warning(f"`rope_scaling`'s short_factor field must have length {dim // 2}, got {len(short_factor)}")
 
     long_factor = rope_scaling.get("long_factor")
     if not isinstance(long_factor, list) and all(isinstance(x, (int, float)) for x in long_factor):
-        raise ValueError(f"`rope_scaling`'s long_factor field must be a list of numbers, got {long_factor}")
+        logger.warning(f"`rope_scaling`'s long_factor field must be a list of numbers, got {long_factor}")
     if not len(long_factor) == dim // 2:
-        raise ValueError(f"`rope_scaling`'s long_factor field must have length {dim // 2}, got {len(long_factor)}")
+        logger.warning(f"`rope_scaling`'s long_factor field must have length {dim // 2}, got {len(long_factor)}")
 
     # Handle Phi3 divergence: prefer the use of `attention_factor` and/or `factor` over
     # `original_max_position_embeddings` to compute internal variables. The latter lives outside `rope_scaling` and is
@@ -406,24 +473,61 @@ def _validate_longrope_parameters(config: PretrainedConfig):
     else:
         factor = rope_scaling.get("factor")
         if factor is None:
-            raise ValueError("Missing required keys in `rope_scaling`: 'factor'")
+            logger.warning("Missing required keys in `rope_scaling`: 'factor'")
         elif not isinstance(factor, float) or factor < 1.0:
-            raise ValueError(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
+            logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
 
         attention_factor = rope_scaling.get("attention_factor")
         if attention_factor is not None and not isinstance(attention_factor, float) or attention_factor < 0:
-            raise ValueError(
+            logger.warning(
                 f"`rope_scaling`'s attention_factor field must be a float greater than 0, got {attention_factor}"
             )
 
 
+def _validate_llama3_parameters(config: PretrainedConfig):
+    rope_scaling = config.rope_scaling
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
+    required_keys = {"rope_type", "factor", "original_max_position_embeddings", "low_freq_factor", "high_freq_factor"}
+    received_keys = set(rope_scaling.keys())
+    _check_received_keys(rope_type, received_keys, required_keys)
+
+    factor = rope_scaling["factor"]
+    if factor is None or not isinstance(factor, float) or factor < 1.0:
+        logger.warning(f"`rope_scaling`'s factor field must be a float >= 1, got {factor}")
+
+    low_freq_factor = rope_scaling["low_freq_factor"]
+    high_freq_factor = rope_scaling["high_freq_factor"]
+    if low_freq_factor is None or not isinstance(low_freq_factor, float):
+        logger.warning(f"`rope_scaling`'s low_freq_factor field must be a float, got {low_freq_factor}")
+    if high_freq_factor is None or not isinstance(high_freq_factor, float):
+        logger.warning(f"`rope_scaling`'s high_freq_factor field must be a float, got {high_freq_factor}")
+    if high_freq_factor < low_freq_factor:
+        logger.warning(
+            "`rope_scaling`'s high_freq_factor field must be greater than low_freq_factor, got high_freq_factor="
+            f"{high_freq_factor} and low_freq_factor={low_freq_factor}"
+        )
+
+    original_max_position_embeddings = rope_scaling["original_max_position_embeddings"]
+    if original_max_position_embeddings is None or not isinstance(original_max_position_embeddings, int):
+        logger.warning(
+            "`rope_scaling`'s original_max_position_embeddings field must be an integer, got "
+            f"{original_max_position_embeddings}"
+        )
+    if original_max_position_embeddings >= config.max_position_embeddings:
+        logger.warning(
+            "`rope_scaling`'s original_max_position_embeddings field must be less than max_position_embeddings, got "
+            f"{original_max_position_embeddings} and max_position_embeddings={config.max_position_embeddings}"
+        )
+
+
 # Like `ROPE_INIT_FUNCTIONS`, this validation function mapping can be dynamically updated for custom RoPE types.
 ROPE_VALIDATION_FUNCTIONS = {
     "default": _validate_default_rope_parameters,
     "linear": _validate_linear_scaling_rope_parameters,
-    "dynamic": _validate_linear_scaling_rope_parameters,  # `dynamic` has the same validation pattern as `linear`
+    "dynamic": _validate_dynamic_scaling_rope_parameters,
     "yarn": _validate_yarn_parameters,
     "longrope": _validate_longrope_parameters,
+    "llama3": _validate_llama3_parameters,
 }
 
 
@@ -435,17 +539,12 @@ def rope_config_validation(config: PretrainedConfig):
     if rope_scaling is None:
         return
 
-    possible_rope_types = set(ROPE_INIT_FUNCTIONS.keys())
-    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", None))  # BC: "rope_type" was originally "type"
-    if rope_type is None:
-        raise ValueError(
-            f"rope_scaling must contain a non-None 'rope_type' field. Possible options are {possible_rope_types}"
-        )
-
+    # BC: "rope_type" was originally "type"
+    rope_type = rope_scaling.get("rope_type", rope_scaling.get("type", "default"))
     validation_fn = ROPE_VALIDATION_FUNCTIONS.get(rope_type)
     if validation_fn is not None:
         validation_fn(config)
     else:
-        raise ValueError(
+        logger.warning(
             f"Missing validation function mapping in `ROPE_VALIDATION_FUNCTIONS` for 'rope_type'='{rope_type}'"
         )

src/transformers/modeling_utils.py

@@ -855,6 +855,8 @@ def _load_state_dict_into_meta_model(
     for old_key, new_key in zip(old_keys, new_keys):
         state_dict[new_key] = state_dict.pop(old_key)
 
+    is_torch_e4m3fn_available = hasattr(torch, "float8_e4m3fn")
+
     for param_name, param in state_dict.items():
         # First part of the test is always true as load_state_dict_keys always contains state_dict keys.
         if param_name not in loaded_state_dict_keys or param_name not in expected_keys:
@@ -866,9 +868,10 @@ def _load_state_dict_into_meta_model(
         module_name = param_name
         set_module_kwargs = {}
 
-        # We convert floating dtypes to the `dtype` passed. We want to keep the buffers/params
+        # We convert floating dtypes to the `dtype` passed except for float8_e4m3fn type. We also want to keep the buffers/params
         # in int/uint/bool and not cast them.
-        if dtype is not None and torch.is_floating_point(param) and param.dtype != torch.float8_e4m3fn:
+        is_param_float8_e4m3fn = is_torch_e4m3fn_available and param.dtype == torch.float8_e4m3fn
+        if dtype is not None and torch.is_floating_point(param) and not is_param_float8_e4m3fn:
             if (
                 keep_in_fp32_modules is not None
                 and any(
@@ -2131,13 +2134,23 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
         # Replace weights in old_embeddings and return to maintain the same embedding type.
         # This ensures correct functionality when a Custom Embedding class is passed as input.
         # The input and output embedding types remain consistent. (c.f. https://github.com/huggingface/transformers/pull/31979)
-        old_embeddings.weight.data = new_embeddings.weight.data
-        old_embeddings.num_embeddings = new_embeddings.weight.data.shape[0]
+        if is_deepspeed_zero3_enabled() and not is_quantized:
+            import deepspeed
 
-        # If the new number of tokens is smaller than the original `padding_idx`, the `padding_idx`
-        # will be set to `None` in the resized embeddings.
-        if old_embeddings.padding_idx is not None and (new_num_tokens - 1) < old_embeddings.padding_idx:
-            old_embeddings.padding_idx = None
+            params = [old_embeddings.weight, new_embeddings.weight]
+            with deepspeed.zero.GatheredParameters(params, modifier_rank=0):
+                old_embeddings.weight.data = new_embeddings.weight.data
+                old_embeddings.num_embeddings = new_embeddings.weight.data.shape[0]
+
+                # If the new number of tokens is smaller than the original `padding_idx`, the `padding_idx`
+                # will be set to `None` in the resized embeddings.
+                if old_embeddings.padding_idx is not None and (new_num_tokens - 1) < old_embeddings.padding_idx:
+                    old_embeddings.padding_idx = None
+        else:
+            old_embeddings.weight.data = new_embeddings.weight.data
+            old_embeddings.num_embeddings = new_embeddings.weight.data.shape[0]
+            if old_embeddings.padding_idx is not None and (new_num_tokens - 1) < old_embeddings.padding_idx:
+                old_embeddings.padding_idx = None
 
         return old_embeddings
 

src/transformers/models/cohere/modeling_cohere.py

@@ -769,7 +769,9 @@ class CohereModel(CoherePreTrainedModel):
 
         past_seen_tokens = 0
         return_legacy_cache = False
-        if use_cache and not isinstance(past_key_values, Cache):  # kept for BC (non `Cache` `past_key_values` inputs)
+        if (
+            use_cache and not isinstance(past_key_values, Cache) and not self.training
+        ):  # kept for BC (non `Cache` `past_key_values` inputs)
             return_legacy_cache = True
             past_key_values = DynamicCache.from_legacy_cache(past_key_values)
 

src/transformers/models/dbrx/modeling_dbrx.py

@@ -1004,7 +1004,9 @@ class DbrxModel(DbrxPreTrainedModel):
         inputs_embeds = nn.functional.dropout(inputs_embeds, p=self.emb_pdrop, training=self.training)
 
         return_legacy_cache = False
-        if use_cache and not isinstance(past_key_values, Cache):  # kept for BC (non `Cache` `past_key_values` inputs)
+        if (
+            use_cache and not isinstance(past_key_values, Cache) and not self.training
+        ):  # kept for BC (non `Cache` `past_key_values` inputs)
             return_legacy_cache = True
             past_key_values = DynamicCache.from_legacy_cache(past_key_values)
             logger.warning_once(

src/transformers/models/gemma/diff_gemma.py

@@ -474,7 +474,9 @@ class GemmaModel(LlamaModel):
             inputs_embeds = self.embed_tokens(input_ids)
 
         return_legacy_cache = False  # noqa: F841
-        if use_cache and not isinstance(past_key_values, Cache):  # kept for BC (non `Cache` `past_key_values` inputs)
+        if (
+            use_cache and not isinstance(past_key_values, Cache) and not self.training
+        ):  # kept for BC (non `Cache` `past_key_values` inputs)
             return_legacy_cache = True  # noqa: F841
             past_key_values = DynamicCache.from_legacy_cache(past_key_values)
 

src/transformers/models/gemma/modeling_gemma.py

@@ -769,7 +769,9 @@ class GemmaModel(GemmaPreTrainedModel):
             inputs_embeds = self.embed_tokens(input_ids)
 
         return_legacy_cache = False  # noqa: F841
-        if use_cache and not isinstance(past_key_values, Cache):  # kept for BC (non `Cache` `past_key_values` inputs)
+        if (
+            use_cache and not isinstance(past_key_values, Cache) and not self.training
+        ):  # kept for BC (non `Cache` `past_key_values` inputs)
             return_legacy_cache = True  # noqa: F841
             past_key_values = DynamicCache.from_legacy_cache(past_key_values)
 
@@ -794,7 +796,9 @@ class GemmaModel(GemmaPreTrainedModel):
         # See https://github.com/huggingface/transformers/pull/29402
         normalizer = torch.tensor(self.config.hidden_size**0.5, dtype=hidden_states.dtype)
         hidden_states = hidden_states * normalizer
-        if use_cache and not isinstance(past_key_values, Cache):  # kept for BC (non `Cache` `past_key_values` inputs)
+        if (
+            use_cache and not isinstance(past_key_values, Cache) and not self.training
+        ):  # kept for BC (non `Cache` `past_key_values` inputs)
             return_legacy_cache = True
             past_key_values = DynamicCache.from_legacy_cache(past_key_values)
             logger.warning_once(

src/transformers/models/jetmoe/modeling_jetmoe.py

@@ -978,7 +978,9 @@ class JetMoeModel(JetMoePreTrainedModel):
             inputs_embeds = self.embed_tokens(input_ids)
 
         return_legacy_cache = False
-        if use_cache and not isinstance(past_key_values, Cache):  # kept for BC (non `Cache` `past_key_values` inputs)
+        if (
+            use_cache and not isinstance(past_key_values, Cache) and not self.training
+        ):  # kept for BC (non `Cache` `past_key_values` inputs)
             return_legacy_cache = True
             past_key_values = DynamicCache.from_legacy_cache(past_key_values)
 

src/transformers/models/llama/configuration_llama.py

@@ -73,25 +73,28 @@ class LlamaConfig(PretrainedConfig):
             End of stream token id.
         pretraining_tp (`int`, *optional*, defaults to 1):
             Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
-            document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to understand more about it. This value is
-            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
-            issue](https://github.com/pytorch/pytorch/issues/76232).
+            document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to
+            understand more about it. This value is necessary to ensure exact reproducibility of the pretraining
+            results. Please refer to [this issue](https://github.com/pytorch/pytorch/issues/76232).
         tie_word_embeddings (`bool`, *optional*, defaults to `False`):
             Whether to tie weight embeddings
         rope_theta (`float`, *optional*, defaults to 10000.0):
             The base period of the RoPE embeddings.
         rope_scaling (`Dict`, *optional*):
-            Dictionary containing the scaling configuration for the RoPE embeddings. IMPORTANT: RoPE scaling expects
-            `max_position_embeddings` to remain unchanged -- some methods, like 'longrope', require the original value
-            to determine which scaling to apply.
+            Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
+            and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
+            accordingly.
             Expected contents:
                 `rope_type` (`str`):
-                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope'],
-                    with 'default' being the original RoPE implementation.
+                    The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
+                    'llama3'], with 'default' being the original RoPE implementation.
                 `factor` (`float`, *optional*):
                     Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
                     most scaling types, a `factor` of x will enable the model to handle sequences of length x *
-                    `max_position_embeddings`.
+                    original maximum pre-trained length.
+                `original_max_position_embeddings` (`int`, *optional*):
+                    Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
+                    pretraining.
                 `attention_factor` (`float`, *optional*):
                     Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
                     computation. If unspecified, it defaults to value recommended by the implementation, using the
@@ -104,12 +107,16 @@ class LlamaConfig(PretrainedConfig):
                     ramp function. If unspecified, it defaults to 1.
                 `short_factor` (`List[float]`, *optional*):
                     Only used with 'longrope'. The scaling factor to be applied to short contexts (<
-                    `max_position_embeddings` * `factor`). Must be a list of numbers with the same length as the hidden
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
                     size divided by the number of attention heads divided by 2
                 `long_factor` (`List[float]`, *optional*):
-                    Only used with 'longrope'. The scaling factor to be applied to short contexts (<
-                    `max_position_embeddings` * `factor`). Must be a list of numbers with the same length as the hidden
+                    Only used with 'longrope'. The scaling factor to be applied to long contexts (<
+                    `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
                     size divided by the number of attention heads divided by 2
+                `low_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
+                `high_freq_factor` (`float`, *optional*):
+                    Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
         attention_bias (`bool`, *optional*, defaults to `False`):
             Whether to use a bias in the query, key, value and output projection layers during self-attention.
         attention_dropout (`float`, *optional*, defaults to 0.0):
@@ -182,6 +189,9 @@ class LlamaConfig(PretrainedConfig):
         self.mlp_bias = mlp_bias
 
         # Validate the correctness of rotary position embeddings parameters
+        # BC: if there is a 'type' field, move it to 'rope_type'.
+        if self.rope_scaling is not None and "type" in self.rope_scaling:
+            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
         rope_config_validation(self)
 
         super().__init__(

src/transformers/models/llama/convert_llama_weights_to_hf.py

@@ -17,10 +17,11 @@ import json
 import os
 import shutil
 import warnings
+from typing import List
 
 import torch
 
-from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer, PreTrainedTokenizerFast
+from transformers import GenerationConfig, LlamaConfig, LlamaForCausalLM, LlamaTokenizer, PreTrainedTokenizerFast
 from transformers.convert_slow_tokenizer import TikTokenConverter
 
 
@@ -85,8 +86,12 @@ NUM_SHARDS = {
     "65B": 8,
     "70B": 8,
     "70Bf": 8,
+    "405B": 8,
+    "405B-MP16": 16,
 }
 
+CONTEXT_LENGTH_FOR_VERSION = {"3.1": 131072, "3": 8192, "2": 4096, "1": 2048}
+
 
 def compute_intermediate_size(n, ffn_dim_multiplier=1, multiple_of=256):
     return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3)) + multiple_of - 1) // multiple_of)
@@ -107,9 +112,10 @@ def write_model(
     input_base_path,
     model_size=None,
     safe_serialization=True,
-    llama_version=1,
+    llama_version="1",
     vocab_size=None,
     num_shards=None,
+    instruct=False,
 ):
     os.makedirs(model_path, exist_ok=True)
     tmp_model_path = os.path.join(model_path, "tmp")
@@ -125,18 +131,11 @@ def write_model(
     dims_per_head = dim // n_heads
     base = params.get("rope_theta", 10000.0)
     inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head))
-    if base > 10000.0 and llama_version != 3:
+    if base > 10000.0 and float(llama_version) < 3:
         max_position_embeddings = 16384
     else:
-        # Depending on the Llama version, the default max_position_embeddings has different values.
-        if llama_version == 1:
-            max_position_embeddings = 2048
-        elif llama_version == 2:
-            max_position_embeddings = 4096
-        elif llama_version == 3:
-            max_position_embeddings = 8192
+        max_position_embeddings = CONTEXT_LENGTH_FOR_VERSION[llama_version]
 
-    vocab_size = vocab_size if vocab_size is not None else 32000
     if params.get("n_kv_heads", None) is not None:
         num_key_value_heads = params["n_kv_heads"]  # for GQA / MQA
         num_key_value_heads_per_shard = num_key_value_heads // num_shards
@@ -144,8 +143,7 @@ def write_model(
     else:  # compatibility with other checkpoints
         num_key_value_heads = n_heads
         num_key_value_heads_per_shard = n_heads_per_shard
-        key_value_dim = dims_per_head * num_key_value_heads
-    print(num_shards, num_key_value_heads, num_key_value_heads_per_shard, key_value_dim)
+        key_value_dim = dim
 
     # permute for sliced rotary
     def permute(w, n_heads, dim1=dim, dim2=dim):
@@ -159,11 +157,9 @@ def write_model(
         loaded = torch.load(os.path.join(input_base_path, "consolidated.00.pth"), map_location="cpu")
     else:
         # Sharded
-        loaded = [
-            torch.load(os.path.join(input_base_path, file), map_location="cpu")
-            for file in sorted(os.listdir(input_base_path))
-            if file.endswith(".pth")
-        ]
+        checkpoint_list = sorted([file for file in os.listdir(input_base_path) if file.endswith(".pth")])
+        print("Loading in order:", checkpoint_list)
+        loaded = [torch.load(os.path.join(input_base_path, file), map_location="cpu") for file in checkpoint_list]
     param_count = 0
     index_dict = {"weight_map": {}}
     for layer_i in range(n_layers):
@@ -263,7 +259,7 @@ def write_model(
             "lm_head.weight": loaded["output.weight"],
         }
     else:
-        concat_dim = 0 if llama_version == 3 else 1
+        concat_dim = 0 if llama_version in ["3", "3.1"] else 1
         state_dict = {
             "model.norm.weight": loaded[0]["norm.weight"],
             "model.embed_tokens.weight": torch.cat(
@@ -282,6 +278,18 @@ def write_model(
     write_json(index_dict, os.path.join(tmp_model_path, "pytorch_model.bin.index.json"))
     ffn_dim_multiplier = params["ffn_dim_multiplier"] if "ffn_dim_multiplier" in params else 1
     multiple_of = params["multiple_of"] if "multiple_of" in params else 256
+
+    if llama_version in ["3", "3.1"]:
+        bos_token_id = 128000
+
+        if instruct:
+            eos_token_id = [128001, 128008, 128009]
+        else:
+            eos_token_id = 128001
+    else:
+        bos_token_id = 1
+        eos_token_id = 2
+
     config = LlamaConfig(
         hidden_size=dim,
         intermediate_size=compute_intermediate_size(dim, ffn_dim_multiplier, multiple_of),
@@ -292,11 +300,21 @@ def write_model(
         vocab_size=vocab_size,
         rope_theta=base,
         max_position_embeddings=max_position_embeddings,
-        bos_token_id=128000 if llama_version == 3 else 1,
-        eos_token_id=128001 if llama_version == 3 else 2,
+        bos_token_id=bos_token_id,
+        eos_token_id=eos_token_id,
     )
     config.save_pretrained(tmp_model_path)
 
+    if instruct:
+        generation_config = GenerationConfig(
+            do_sample=True,
+            temperature=0.6,
+            top_p=0.9,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+        )
+        generation_config.save_pretrained(tmp_model_path)
+
     # Make space so we can load the model properly now.
     del state_dict
     del loaded
@@ -313,7 +331,7 @@ def write_model(
 
 
 class Llama3Converter(TikTokenConverter):
-    def __init__(self, vocab_file, num_reserved_special_tokens=256, **kwargs):
+    def __init__(self, vocab_file, special_tokens=None, instruct=False, model_max_length=None, **kwargs):
         super().__init__(vocab_file, **kwargs)
         tokenizer = self.converted()
         chat_template = (
@@ -327,34 +345,24 @@ class Llama3Converter(TikTokenConverter):
             "{% endfor %}"
             "{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}"
         )
-        num_reserved_special_tokens = 256
-        special_tokens = [
-            "<|begin_of_text|>",
-            "<|end_of_text|>",
-            "<|reserved_special_token_0|>",
-            "<|reserved_special_token_1|>",
-            "<|reserved_special_token_2|>",
-            "<|reserved_special_token_3|>",
-            "<|start_header_id|>",
-            "<|end_header_id|>",
-            "<|reserved_special_token_4|>",
-            "<|eot_id|>",  # end of turn
-        ] + [f"<|reserved_special_token_{i}|>" for i in range(5, num_reserved_special_tokens - 5)]
         tokenizer.add_special_tokens(special_tokens)
 
         self.tokenizer = PreTrainedTokenizerFast(
             tokenizer_object=tokenizer,
             bos_token="<|begin_of_text|>",
-            eos_token="<|end_of_text|>",
-            chat_template=chat_template,
+            eos_token="<|end_of_text|>" if not instruct else "<|eot_id|>",
+            chat_template=chat_template if instruct else None,
             model_input_names=["input_ids", "attention_mask"],
+            model_max_length=model_max_length,
         )
 
 
-def write_tokenizer(tokenizer_path, input_tokenizer_path, llama_version=2):
+def write_tokenizer(tokenizer_path, input_tokenizer_path, llama_version="2", special_tokens=None, instruct=False):
     tokenizer_class = LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
-    if llama_version == 3:
-        tokenizer = Llama3Converter(input_tokenizer_path).tokenizer
+    if llama_version in ["3", "3.1"]:
+        tokenizer = Llama3Converter(
+            input_tokenizer_path, special_tokens, instruct, model_max_length=CONTEXT_LENGTH_FOR_VERSION[llama_version]
+        ).tokenizer
     else:
         tokenizer = tokenizer_class(input_tokenizer_path)
     print(f"Saving a {tokenizer_class.__name__} to {tokenizer_path}.")
@@ -362,6 +370,37 @@ def write_tokenizer(tokenizer_path, input_tokenizer_path, llama_version=2):
     return tokenizer
 
 
+DEFAULT_LLAMA_SPECIAL_TOKENS = {
+    "3": [
+        "<|begin_of_text|>",
+        "<|end_of_text|>",
+        "<|reserved_special_token_0|>",
+        "<|reserved_special_token_1|>",
+        "<|reserved_special_token_2|>",
+        "<|reserved_special_token_3|>",
+        "<|start_header_id|>",
+        "<|end_header_id|>",
+        "<|reserved_special_token_4|>",
+        "<|eot_id|>",  # end of turn
+    ]
+    + [f"<|reserved_special_token_{i}|>" for i in range(5, 256 - 5)],
+    "3.1": [
+        "<|begin_of_text|>",
+        "<|end_of_text|>",
+        "<|reserved_special_token_0|>",
+        "<|reserved_special_token_1|>",
+        "<|finetune_right_pad_id|>",
+        "<|reserved_special_token_2|>",
+        "<|start_header_id|>",
+        "<|end_header_id|>",
+        "<|eom_id|>",  # end of message
+        "<|eot_id|>",  # end of turn
+        "<|python_tag|>",
+    ]
+    + [f"<|reserved_special_token_{i}|>" for i in range(3, 256 - 8)],
+}
+
+
 def main():
     parser = argparse.ArgumentParser()
     parser.add_argument(
@@ -383,9 +422,9 @@ def main():
     # Different Llama versions used different default values for max_position_embeddings, hence the need to be able to specify which version is being used.
     parser.add_argument(
         "--llama_version",
-        choices=[1, 2, 3],
-        default=1,
-        type=int,
+        choices=["1", "2", "3", "3.1"],
+        default="1",
+        type=str,
         help="Version of the Llama model to convert. Currently supports Llama1 and Llama2. Controls the context size",
     )
     parser.add_argument(
@@ -394,11 +433,34 @@ def main():
         type=int,
         help="The number of individual shards used for the model. Does not have to be the same as the number of consolidated_xx.pth",
     )
+    parser.add_argument(
+        "--special_tokens",
+        default=None,
+        type=List[str],
+        help="The list of special tokens that should be added to the model.",
+    )
+    parser.add_argument(
+        "--instruct",
+        default=False,
+        type=bool,
+        help="Whether the model is an instruct model or not. Will affect special tokens for llama 3.1.",
+    )
     args = parser.parse_args()
     if args.model_size is None and args.num_shards is None:
         raise ValueError("You have to set at least `num_shards` if you are not giving the `model_size`")
+    if args.special_tokens is None:
+        args.special_tokens = DEFAULT_LLAMA_SPECIAL_TOKENS[str(args.llama_version)]
+
     spm_path = os.path.join(args.input_dir, "tokenizer.model")
-    vocab_size = len(write_tokenizer(args.output_dir, spm_path, llama_version=args.llama_version))
+    vocab_size = len(
+        write_tokenizer(
+            args.output_dir,
+            spm_path,
+            llama_version=args.llama_version,
+            special_tokens=args.special_tokens,
+            instruct=args.instruct,
+        )
+    )
     if args.model_size != "tokenizer_only":
         write_model(
             model_path=args.output_dir,
@@ -408,6 +470,7 @@ def main():
             llama_version=args.llama_version,
             vocab_size=vocab_size,
             num_shards=args.num_shards,
+            instruct=args.instruct,
         )
 
 

src/transformers/models/llama/modeling_llama.py

@@ -107,7 +107,7 @@ class LlamaRotaryEmbedding(nn.Module):
         else:
             # BC: "rope_type" was originally "type"
             if config.rope_scaling is not None:
-                self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling["type"])
+                self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type"))
             else:
                 self.rope_type = "default"
             self.max_seq_len_cached = config.max_position_embeddings
@@ -893,7 +893,9 @@ class LlamaModel(LlamaPreTrainedModel):
             inputs_embeds = self.embed_tokens(input_ids)
 
         return_legacy_cache = False
-        if use_cache and not isinstance(past_key_values, Cache):  # kept for BC (non `Cache` `past_key_values` inputs)
+        if (
+            use_cache and not isinstance(past_key_values, Cache) and not self.training
+        ):  # kept for BC (non `Cache` `past_key_values` inputs)
             return_legacy_cache = True
             past_key_values = DynamicCache.from_legacy_cache(past_key_values)
             logger.warning_once(

src/transformers/models/mistral/modeling_mistral.py

@@ -757,7 +757,7 @@ class MistralModel(MistralPreTrainedModel):
             inputs_embeds = self.embed_tokens(input_ids)
 
         return_legacy_cache = False
-        if use_cache and not isinstance(past_key_values, Cache):
+        if use_cache and not isinstance(past_key_values, Cache) and not self.training:
             past_key_values = DynamicCache.from_legacy_cache(past_key_values)
             return_legacy_cache = True
             logger.warning_once(

src/transformers/models/mixtral/modeling_mixtral.py

@@ -959,7 +959,7 @@ class MixtralModel(MixtralPreTrainedModel):
                 use_cache = False
 
         use_legacy_cache = False
-        if use_cache and not isinstance(past_key_values, Cache):
+        if use_cache and not isinstance(past_key_values, Cache) and not self.training:
             use_legacy_cache = True
             past_key_values = DynamicCache.from_legacy_cache(past_key_values)
             logger.warning_once(

src/transformers/models/olmo/modeling_olmo.py

@@ -810,7 +810,9 @@ class OlmoModel(OlmoPreTrainedModel):
             inputs_embeds = self.embed_tokens(input_ids)
 
         return_legacy_cache = False
-        if use_cache and not isinstance(past_key_values, Cache):  # kept for BC (non `Cache` `past_key_values` inputs)
+        if (
+            use_cache and not isinstance(past_key_values, Cache) and not self.training
+        ):  # kept for BC (non `Cache` `past_key_values` inputs)
             return_legacy_cache = True
             past_key_values = DynamicCache.from_legacy_cache(past_key_values)
             logger.warning_once(

src/transformers/models/persimmon/modeling_persimmon.py

@@ -626,7 +626,7 @@ class PersimmonModel(PersimmonPreTrainedModel):
                 use_cache = False
 
         use_legacy_cache = False
-        if use_cache and not isinstance(past_key_values, Cache):
+        if use_cache and not isinstance(past_key_values, Cache) and not self.training:
             use_legacy_cache = True
             past_key_values = DynamicCache.from_legacy_cache(past_key_values)
             logger.warning_once(

src/transformers/models/phi/modeling_phi.py

@@ -908,7 +908,7 @@ class PhiModel(PhiPreTrainedModel):
                 use_cache = False
 
         use_legacy_cache = False
-        if use_cache and not isinstance(past_key_values, Cache):
+        if use_cache and not isinstance(past_key_values, Cache) and not self.training:
             use_legacy_cache = True
             past_key_values = DynamicCache.from_legacy_cache(past_key_values)
             logger.warning_once(

src/transformers/models/phi3/modeling_phi3.py

@@ -949,7 +949,7 @@ class Phi3Model(Phi3PreTrainedModel):
                 use_cache = False
 
         use_legacy_cache = False
-        if use_cache and not isinstance(past_key_values, Cache):
+        if use_cache and not isinstance(past_key_values, Cache) and not self.training:
             use_legacy_cache = True
             past_key_values = DynamicCache.from_legacy_cache(past_key_values)
             logger.warning_once(

src/transformers/models/qwen2/modeling_qwen2.py

@@ -807,7 +807,7 @@ class Qwen2Model(Qwen2PreTrainedModel):
                 use_cache = False
 
         use_legacy_cache = False
-        if use_cache and not isinstance(past_key_values, Cache):
+        if use_cache and not isinstance(past_key_values, Cache) and not self.training:
             use_legacy_cache = True
             past_key_values = DynamicCache.from_legacy_cache(past_key_values)
             logger.warning_once(

src/transformers/models/qwen2_moe/modeling_qwen2_moe.py

@@ -969,7 +969,7 @@ class Qwen2MoeModel(Qwen2MoePreTrainedModel):
                 use_cache = False
 
         use_legacy_cache = False
-        if use_cache and not isinstance(past_key_values, Cache):
+        if use_cache and not isinstance(past_key_values, Cache) and not self.training:
             use_legacy_cache = True
             past_key_values = DynamicCache.from_legacy_cache(past_key_values)
             logger.warning_once(

src/transformers/models/stablelm/modeling_stablelm.py

@@ -901,7 +901,7 @@ class StableLmModel(StableLmPreTrainedModel):
                 use_cache = False
 
         use_legacy_cache = False
-        if use_cache and not isinstance(past_key_values, Cache):
+        if use_cache and not isinstance(past_key_values, Cache) and not self.training:
             use_legacy_cache = True
             past_key_values = DynamicCache.from_legacy_cache(past_key_values)
             logger.warning_once(

src/transformers/models/starcoder2/modeling_starcoder2.py

@@ -783,7 +783,7 @@ class Starcoder2Model(Starcoder2PreTrainedModel):
                 use_cache = False
 
         use_legacy_cache = False
-        if use_cache and not isinstance(past_key_values, Cache):
+        if use_cache and not isinstance(past_key_values, Cache) and not self.training:
             use_legacy_cache = True
             past_key_values = DynamicCache.from_legacy_cache(past_key_values)
             logger.warning_once(

tests/models/llama/test_modeling_llama.py

@@ -526,6 +526,60 @@ class LlamaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixi
         torch.testing.assert_close(old_cos_long, new_cos_long)
         torch.testing.assert_close(old_sin_long, new_sin_long)
 
+    def test_model_loading_old_rope_configs(self):
+        def _reinitialize_config(base_config, new_kwargs):
+            # Reinitialize the config with the new kwargs, forcing the config to go through its __init__ validation
+            # steps.
+            base_config_dict = base_config.to_dict()
+            new_config = LlamaConfig.from_dict(config_dict={**base_config_dict, **new_kwargs})
+            return new_config
+
+        # from untouched config -> ✅
+        base_config, model_inputs = self.model_tester.prepare_config_and_inputs_for_common()
+        original_model = LlamaForCausalLM(base_config).to(torch_device)
+        original_model(**model_inputs)
+
+        # from a config with the expected rope configuration -> ✅
+        config = _reinitialize_config(base_config, {"rope_scaling": {"rope_type": "linear", "factor": 10.0}})
+        original_model = LlamaForCausalLM(config).to(torch_device)
+        original_model(**model_inputs)
+
+        # from a config with the old rope configuration ('type' instead of 'rope_type')  -> ✅ we gracefully handle BC
+        config = _reinitialize_config(base_config, {"rope_scaling": {"type": "linear", "factor": 10.0}})
+        original_model = LlamaForCausalLM(config).to(torch_device)
+        original_model(**model_inputs)
+
+        # from a config with both 'type' and 'rope_type'  -> ✅ they can coexist (and both are present in the config)
+        config = _reinitialize_config(
+            base_config, {"rope_scaling": {"type": "linear", "rope_type": "linear", "factor": 10.0}}
+        )
+        self.assertTrue(config.rope_scaling["type"] == "linear")
+        self.assertTrue(config.rope_scaling["rope_type"] == "linear")
+        original_model = LlamaForCausalLM(config).to(torch_device)
+        original_model(**model_inputs)
+
+        # from a config with parameters in a bad range ('factor' should be >= 1.0) -> ⚠️ throws a warning
+        with self.assertLogs("transformers.modeling_rope_utils", level="WARNING") as logs:
+            config = _reinitialize_config(base_config, {"rope_scaling": {"rope_type": "linear", "factor": -999.0}})
+            original_model = LlamaForCausalLM(config).to(torch_device)
+            original_model(**model_inputs)
+            self.assertEqual(len(logs.output), 1)
+            self.assertIn("factor field", logs.output[0])
+
+        # from a config with unknown parameters ('foo' isn't a rope option) -> ⚠️ throws a warning
+        with self.assertLogs("transformers.modeling_rope_utils", level="WARNING") as logs:
+            config = _reinitialize_config(
+                base_config, {"rope_scaling": {"rope_type": "linear", "factor": 10.0, "foo": "bar"}}
+            )
+            original_model = LlamaForCausalLM(config).to(torch_device)
+            original_model(**model_inputs)
+            self.assertEqual(len(logs.output), 1)
+            self.assertIn("Unrecognized keys", logs.output[0])
+
+        # from a config with specific rope type but missing one of its mandatory parameters -> ❌ throws exception
+        with self.assertRaises(KeyError):
+            config = _reinitialize_config(base_config, {"rope_scaling": {"rope_type": "linear"}})  # missing "factor"
+
     @require_flash_attn
     @require_torch_gpu
     @require_bitsandbytes