text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
conversation = [
{
"role": "user",
"content": [
{"type": "image", "image": "https://www.ilankelman.org/stopsigns/australia.jpg"},
{"type": "text", "text": "Please describe this image in detail."},
],
},
... | 123 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/processing_utils.py |
if chat_template is None:
if self.chat_template is not None:
chat_template = self.chat_template
else:
raise ValueError(
"No chat template is set for this processor. Please either set the `chat_template` attribute, "
"or prov... | 123 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/processing_utils.py |
# Pop kwargs that should not be used by tokenizer's `apply_chat_template`
tokenize = chat_template_kwargs.pop("tokenize")
return_dict = chat_template_kwargs.pop("return_dict")
num_frames = chat_template_kwargs.pop("num_frames")
video_load_backend = chat_template_kwargs.pop("video_load_ba... | 123 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/processing_utils.py |
# we will have to return all processed inputs in a dict
if tokenize:
images, videos = [], []
for message in conversation:
visuals = [content for content in message["content"] if content["type"] in ["image", "video"]]
for vision_info in visuals:
... | 123 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/processing_utils.py |
out = self(
text=prompt,
images=images if images else None,
videos=videos if videos else None,
**kwargs,
)
if return_dict:
return out
else:
return out["input_ids"]
return prompt
... | 123 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/processing_utils.py |
class BatchFeature(BaseBatchFeature):
r"""
Holds the output of the image processor specific `__call__` methods.
This class is derived from a python dictionary and can be used as a dictionary.
Args:
data (`dict`):
Dictionary of lists/arrays/tensors returned by the __call__ method ('... | 124 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py |
class ImageProcessingMixin(PushToHubMixin):
"""
This is an image processor mixin used to provide saving/loading functionality for sequential and image feature
extractors.
"""
_auto_class = None
def __init__(self, **kwargs):
"""Set elements of `kwargs` as attributes."""
# This k... | 125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py |
def _set_processor_class(self, processor_class: str):
"""Sets processor class as an attribute."""
self._processor_class = processor_class
@classmethod
def from_pretrained(
cls: Type[ImageProcessorType],
pretrained_model_name_or_path: Union[str, os.PathLike],
cache_dir: O... | 125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py |
- a string, the *model id* of a pretrained image_processor hosted inside a model repo on
huggingface.co.
- a path to a *directory* containing a image processor file saved using the
[`~image_processing_utils.ImageProcessingMixin.save_pretrained`] method, e.g.,
... | 125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py |
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
'http:/... | 125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py |
<Tip>
To test a pull request you made on the Hub, you can pass `revision="refs/pr/<pr_number>"`.
</Tip> | 125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py |
return_unused_kwargs (`bool`, *optional*, defaults to `False`):
If `False`, then this function returns just the final image processor object. If `True`, then this
functions returns a `Tuple(image_processor, unused_kwargs)` where *unused_kwargs* is a dictionary
consisting ... | 125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py |
loaded values. Behavior concerning key/value pairs whose keys are *not* image processor attributes is
controlled by the `return_unused_kwargs` keyword parameter. | 125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py |
Returns:
A image processor of type [`~image_processing_utils.ImageProcessingMixin`].
Examples: | 125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py |
```python
# We can't instantiate directly the base class *ImageProcessingMixin* so let's show the examples on a
# derived class: *CLIPImageProcessor*
image_processor = CLIPImageProcessor.from_pretrained(
"openai/clip-vit-base-patch32"
) # Download image_processing_config fro... | 125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py |
"openai/clip-vit-base-patch32", do_normalize=False, foo=False, return_unused_kwargs=True
)
assert image_processor.do_normalize is False
assert unused_kwargs == {"foo": False}
```"""
kwargs["cache_dir"] = cache_dir
kwargs["force_download"] = force_download
kwargs["... | 125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py |
use_auth_token = kwargs.pop("use_auth_token", None)
if use_auth_token is not None:
warnings.warn(
"The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.",
FutureWarning,
)
if token is... | 125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py |
def save_pretrained(self, save_directory: Union[str, os.PathLike], push_to_hub: bool = False, **kwargs):
"""
Save an image processor object to the directory `save_directory`, so that it can be re-loaded using the
[`~image_processing_utils.ImageProcessingMixin.from_pretrained`] class method. | 125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py |
Args:
save_directory (`str` or `os.PathLike`):
Directory where the image processor JSON file will be saved (will be created if it does not exist).
push_to_hub (`bool`, *optional*, defaults to `False`):
Whether or not to push your model to the Hugging Face model hu... | 125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py |
if use_auth_token is not None:
warnings.warn(
"The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.",
FutureWarning,
)
if kwargs.get("token", None) is not None:
raise ValueEr... | 125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py |
# If we have a custom config, we copy the file defining it in the folder and set the attributes so it can be
# loaded from the Hub.
if self._auto_class is not None:
custom_object_save(self, save_directory, config=self)
# If we save using the predefined names, we can load using `from... | 125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py |
@classmethod
def get_image_processor_dict(
cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs
) -> Tuple[Dict[str, Any], Dict[str, Any]]:
"""
From a `pretrained_model_name_or_path`, resolve to a dictionary of parameters, to be used for instantiating a
image pro... | 125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py |
Returns:
`Tuple[Dict, Dict]`: The dictionary(ies) that will be used to instantiate the image processor object.
"""
cache_dir = kwargs.pop("cache_dir", None)
force_download = kwargs.pop("force_download", False)
resume_download = kwargs.pop("resume_download", None)
prox... | 125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py |
if use_auth_token is not None:
warnings.warn(
"The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.",
FutureWarning,
)
if token is not None:
raise ValueError(
... | 125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py |
pretrained_model_name_or_path = str(pretrained_model_name_or_path)
is_local = os.path.isdir(pretrained_model_name_or_path)
if os.path.isdir(pretrained_model_name_or_path):
image_processor_file = os.path.join(pretrained_model_name_or_path, image_processor_filename)
if os.path.isfile(p... | 125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py |
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
token=token,
user_agent=user_agent,
revisio... | 125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py |
f" same name. Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a"
f" directory containing a {image_processor_filename} file"
) | 125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py |
try:
# Load image_processor dict
with open(resolved_image_processor_file, "r", encoding="utf-8") as reader:
text = reader.read()
image_processor_dict = json.loads(text)
except json.JSONDecodeError:
raise EnvironmentError(
f"It look... | 125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py |
if is_local:
logger.info(f"loading configuration file {resolved_image_processor_file}")
else:
logger.info(
f"loading configuration file {image_processor_file} from cache at {resolved_image_processor_file}"
)
if "auto_map" in image_processor_dict:
... | 125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py |
@classmethod
def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs):
"""
Instantiates a type of [`~image_processing_utils.ImageProcessingMixin`] from a Python dictionary of parameters.
Args:
image_processor_dict (`Dict[str, Any]`):
Dictionary that wil... | 125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py |
# The `size` parameter is a dict and was previously an int or tuple in feature extractors.
# We set `size` here directly to the `image_processor_dict` so that it is converted to the appropriate
# dict within the image processor and isn't overwritten if `size` is passed in as a kwarg.
if "size" i... | 125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py |
logger.info(f"Image processor {image_processor}")
if return_unused_kwargs:
return image_processor, kwargs
else:
return image_processor
def to_dict(self) -> Dict[str, Any]:
"""
Serializes this instance to a Python dictionary.
Returns:
`Dic... | 125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py |
Returns:
A image processor of type [`~image_processing_utils.ImageProcessingMixin`]: The image_processor object
instantiated from that JSON file.
"""
with open(json_file, "r", encoding="utf-8") as reader:
text = reader.read()
image_processor_dict = json.loads(... | 125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py |
# make sure private name "_processor_class" is correctly
# saved as "processor_class"
_processor_class = dictionary.pop("_processor_class", None)
if _processor_class is not None:
dictionary["processor_class"] = _processor_class
return json.dumps(dictionary, indent=2, sort_ke... | 125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py |
@classmethod
def register_for_auto_class(cls, auto_class="AutoImageProcessor"):
"""
Register this class with a given auto class. This should only be used for custom image processors as the ones
in the library are already mapped with `AutoImageProcessor `.
<Tip warning={true}>
... | 125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py |
def fetch_images(self, image_url_or_urls: Union[str, List[str]]):
"""
Convert a single or a list of urls into the corresponding `PIL.Image` objects. | 125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py |
If a single url is passed, the return value will be a single object. If a list is passed a list of objects is
returned.
"""
headers = {
"User-Agent": (
"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0"
... | 125 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/image_processing_base.py |
class Seq2SeqTrainingArguments(TrainingArguments):
"""
Args:
predict_with_generate (`bool`, *optional*, defaults to `False`):
Whether to use generate to calculate generative metrics (ROUGE, BLEU).
generation_max_length (`int`, *optional*):
The `max_length` to use on each ... | 126 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args_seq2seq.py |
- a string, the *model id* of a pretrained model configuration hosted inside a model repo on
huggingface.co.
- a path to a *directory* containing a configuration file saved using the
[`~GenerationConfig.save_pretrained`] method, e.g., `./my_model_directory/`.
- a [`~g... | 126 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args_seq2seq.py |
sortish_sampler: bool = field(default=False, metadata={"help": "Whether to use SortishSampler or not."})
predict_with_generate: bool = field(
default=False, metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}
)
generation_max_length: Optional[int] = field(
... | 126 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args_seq2seq.py |
default=None,
metadata={
"help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
},
) | 126 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args_seq2seq.py |
def to_dict(self):
"""
Serializes this instance while replace `Enum` by their values and `GenerationConfig` by dictionaries (for JSON
serialization support). It obfuscates the token values by removing their value.
"""
# filter out fields that are defined as field(init=False)
... | 126 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args_seq2seq.py |
class ModelCard:
r"""
Structured Model Card class. Store model card as well as methods for loading/downloading/saving model cards.
Please read the following paper for details and explanation on the sections: "Model Cards for Model Reporting" by
Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barn... | 127 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modelcard.py |
def __init__(self, **kwargs):
warnings.warn(
"The class `ModelCard` is deprecated and will be removed in version 5 of Transformers", FutureWarning
)
# Recommended attributes from https://arxiv.org/abs/1810.03993 (see papers)
self.model_details = kwargs.pop("model_details", {}... | 127 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modelcard.py |
# Open additional attributes
for key, value in kwargs.items():
try:
setattr(self, key, value)
except AttributeError as err:
logger.error(f"Can't set {key} with value {value} for {self}")
raise err
def save_pretrained(self, save_directo... | 127 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modelcard.py |
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
r"""
Instantiate a [`ModelCard`] from a pre-trained model model card.
Parameters:
pretrained_model_name_or_path: either:
- a string, the *model id* of a pretrained model card hosted ... | 127 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modelcard.py |
- The values in kwargs of any keys which are model card attributes will be used to override the loaded
values.
- Behavior concerning key/value pairs whose keys are *not* model card attributes is controlled by the
*return_unused_kwargs* keyword parameter.
... | 127 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modelcard.py |
- If False, then this function returns just the final model card object.
- If True, then this functions returns a tuple *(model card, unused_kwargs)* where *unused_kwargs* is a
dictionary consisting of the key/value pairs whose keys are not model card attributes: ie the part of
... | 127 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modelcard.py |
```python
# Download model card from huggingface.co and cache.
modelcard = ModelCard.from_pretrained("google-bert/bert-base-uncased")
# Model card was saved using *save_pretrained('./test/saved_model/')*
modelcard = ModelCard.from_pretrained("./test/saved_model/")
modelcard = Mod... | 127 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modelcard.py |
is_local = os.path.isdir(pretrained_model_name_or_path)
if os.path.isfile(pretrained_model_name_or_path):
resolved_model_card_file = pretrained_model_name_or_path
is_local = True
else:
try:
# Load from URL or cache if already cached
res... | 127 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modelcard.py |
except (EnvironmentError, json.JSONDecodeError):
# We fall back on creating an empty model card
modelcard = cls()
# Update model card with kwargs if needed
to_remove = []
for key, value in kwargs.items():
if hasattr(modelcard, key):
se... | 127 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modelcard.py |
@classmethod
def from_json_file(cls, json_file):
"""Constructs a `ModelCard` from a json file of parameters."""
with open(json_file, "r", encoding="utf-8") as reader:
text = reader.read()
dict_obj = json.loads(text)
return cls(**dict_obj)
def __eq__(self, other):
... | 127 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modelcard.py |
class TrainingSummary:
model_name: str
language: Optional[Union[str, List[str]]] = None
license: Optional[str] = None
tags: Optional[Union[str, List[str]]] = None
finetuned_from: Optional[str] = None
tasks: Optional[Union[str, List[str]]] = None
dataset: Optional[Union[str, List[str]]] = Non... | 128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modelcard.py |
def __post_init__(self):
# Infer default license from the checkpoint used, if possible.
if (
self.license is None
and not is_offline_mode()
and self.finetuned_from is not None
and len(self.finetuned_from) > 0
):
try:
inf... | 128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modelcard.py |
# Dataset mapping tag -> name
dataset_names = _listify(self.dataset)
dataset_tags = _listify(self.dataset_tags)
dataset_args = _listify(self.dataset_args)
dataset_metadata = _listify(self.dataset_metadata)
if len(dataset_args) < len(dataset_tags):
dataset_args = datas... | 128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modelcard.py |
if len(task_mapping) == 0 and len(dataset_mapping) == 0:
return [model_index]
if len(task_mapping) == 0:
task_mapping = {None: None}
if len(dataset_mapping) == 0:
dataset_mapping = {None: None}
# One entry per dataset and per task
all_possibilities = ... | 128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modelcard.py |
if len(metric_mapping) > 0:
result["metrics"] = []
for metric_tag, metric_name in metric_mapping.items():
result["metrics"].append(
{
"name": metric_name,
"type": metric_tag,
... | 128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modelcard.py |
metadata = {}
metadata = _insert_value(metadata, "library_name", "transformers")
metadata = _insert_values_as_list(metadata, "language", self.language)
metadata = _insert_value(metadata, "license", self.license)
if self.finetuned_from is not None and isinstance(self.finetuned_from, str) ... | 128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modelcard.py |
# Now the model card for realsies.
if self.source == "trainer":
model_card += AUTOGENERATED_TRAINER_COMMENT
else:
model_card += AUTOGENERATED_KERAS_COMMENT
model_card += f"\n# {self.model_name}\n\n"
if self.finetuned_from is None:
model_card += "This... | 128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modelcard.py |
if self.dataset is None:
model_card += "an unknown dataset."
else:
if isinstance(self.dataset, str):
model_card += f"the {self.dataset} dataset."
elif isinstance(self.dataset, (tuple, list)) and len(self.dataset) == 1:
model_card += f"the {self... | 128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modelcard.py |
model_card += "\n## Model description\n\nMore information needed\n"
model_card += "\n## Intended uses & limitations\n\nMore information needed\n"
model_card += "\n## Training and evaluation data\n\nMore information needed\n"
model_card += "\n## Training procedure\n"
model_card += "\n###... | 128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modelcard.py |
if self.source == "trainer" and is_torch_available():
import torch
model_card += f"- Pytorch {torch.__version__}\n"
elif self.source == "keras" and is_tf_available():
import tensorflow as tf
model_card += f"- TensorFlow {tf.__version__}\n"
if is_datasets... | 128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modelcard.py |
@classmethod
def from_trainer(
cls,
trainer,
language=None,
license=None,
tags=None,
model_name=None,
finetuned_from=None,
tasks=None,
dataset_tags=None,
dataset_metadata=None,
dataset=None,
dataset_args=None,
):
... | 128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modelcard.py |
dataset_tags = [default_tag]
if dataset_args is None:
dataset_args = [one_dataset.config_name] | 128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modelcard.py |
if dataset is None and dataset_tags is not None:
dataset = dataset_tags
# Infer default finetuned_from
if (
finetuned_from is None
and hasattr(trainer.model.config, "_name_or_path")
and not os.path.isdir(trainer.model.config._name_or_path)
):
... | 128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modelcard.py |
# Add `generated_from_trainer` to the tags
if tags is None:
tags = ["generated_from_trainer"]
elif isinstance(tags, str) and tags != "generated_from_trainer":
tags = [tags, "generated_from_trainer"]
elif "generated_from_trainer" not in tags:
tags.append("gener... | 128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modelcard.py |
@classmethod
def from_keras(
cls,
model,
model_name,
keras_history=None,
language=None,
license=None,
tags=None,
finetuned_from=None,
tasks=None,
dataset_tags=None,
dataset=None,
dataset_args=None,
):
# Infer... | 128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modelcard.py |
# Infer default finetuned_from
if (
finetuned_from is None
and hasattr(model.config, "_name_or_path")
and not os.path.isdir(model.config._name_or_path)
):
finetuned_from = model.config._name_or_path
# Infer default task tag:
if tasks is No... | 128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modelcard.py |
if keras_history is not None:
_, eval_lines, eval_results = parse_keras_history(keras_history)
else:
eval_lines = []
eval_results = {}
hyperparameters = extract_hyperparameters_from_keras(model)
return cls(
language=language,
license=l... | 128 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modelcard.py |
class AdamW(Optimizer):
"""
Implements Adam algorithm with weight decay fix as introduced in [Decoupled Weight Decay
Regularization](https://arxiv.org/abs/1711.05101). | 129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/optimization.py |
Parameters:
params (`Iterable[nn.parameter.Parameter]`):
Iterable of parameters to optimize or dictionaries defining parameter groups.
lr (`float`, *optional*, defaults to 0.001):
The learning rate to use.
betas (`Tuple[float,float]`, *optional*, defaults to `(0.9, 0.999)... | 129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/optimization.py |
def __init__(
self,
params: Iterable[nn.parameter.Parameter],
lr: float = 1e-3,
betas: Tuple[float, float] = (0.9, 0.999),
eps: float = 1e-6,
weight_decay: float = 0.0,
correct_bias: bool = True,
no_deprecation_warning: bool = False,
):
if not ... | 129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/optimization.py |
raise ValueError(f"Invalid beta parameter: {betas[1]} - should be in [0.0, 1.0)")
if not 0.0 <= eps:
raise ValueError(f"Invalid epsilon value: {eps} - should be >= 0.0")
defaults = {"lr": lr, "betas": betas, "eps": eps, "weight_decay": weight_decay, "correct_bias": correct_bias}
supe... | 129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/optimization.py |
@torch.no_grad()
def step(self, closure: Callable = None):
"""
Performs a single optimization step.
Arguments:
closure (`Callable`, *optional*): A closure that reevaluates the model and returns the loss.
"""
loss = None
if closure is not None:
... | 129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/optimization.py |
# State initialization
if len(state) == 0:
state["step"] = 0
# Exponential moving average of gradient values
state["exp_avg"] = torch.zeros_like(p)
# Exponential moving average of squared gradient values
... | 129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/optimization.py |
step_size = group["lr"]
if group["correct_bias"]: # No bias correction for Bert
bias_correction1 = 1.0 - beta1 ** state["step"]
bias_correction2 = 1.0 - beta2 ** state["step"]
step_size = step_size * math.sqrt(bias_correction2) / bias_correcti... | 129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/optimization.py |
# Just adding the square of the weights to the loss function is *not*
# the correct way of using L2 regularization/weight decay with Adam,
# since that will interact with the m and v parameters in strange ways.
#
# Instead we want to decay the weights in a... | 129 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/optimization.py |
class Adafactor(Optimizer):
"""
AdaFactor pytorch implementation can be used as a drop in replacement for Adam original fairseq code:
https://github.com/pytorch/fairseq/blob/master/fairseq/optim/adafactor.py
Paper: *Adafactor: Adaptive Learning Rates with Sublinear Memory Cost* https://arxiv.org/abs/18... | 130 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/optimization.py |
Arguments:
params (`Iterable[nn.parameter.Parameter]`):
Iterable of parameters to optimize or dictionaries defining parameter groups.
lr (`float`, *optional*):
The external learning rate.
eps (`Tuple[float, float]`, *optional*, defaults to `(1e-30, 0.001)`):
R... | 130 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/optimization.py |
relative_step (`bool`, *optional*, defaults to `True`):
If True, time-dependent learning rate is computed instead of external learning rate
warmup_init (`bool`, *optional*, defaults to `False`):
Time-dependent learning rate computation depends on whether warm-up initialization is being u... | 130 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/optimization.py |
This implementation handles low-precision (FP16, bfloat) values, but we have not thoroughly tested.
Recommended T5 finetuning settings (https://discuss.huggingface.co/t/t5-finetuning-tips/684/3):
- Training without LR warmup or clip_threshold is not recommended.
- use scheduled LR warm-up to f... | 130 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/optimization.py |
When using `lr=None` with [`Trainer`] you will most likely need to use [`~optimization.AdafactorSchedule`]
scheduler as following:
```python
from transformers.optimization import Adafactor, AdafactorSchedule
optimizer = Adafactor(model.parameters(), scale_parameter=True, relative_step=True, warmup_ini... | 130 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/optimization.py |
def __init__(
self,
params,
lr=None,
eps=(1e-30, 1e-3),
clip_threshold=1.0,
decay_rate=-0.8,
beta1=None,
weight_decay=0.0,
scale_parameter=True,
relative_step=True,
warmup_init=False,
):
require_version("torch>=1.5.0") ... | 130 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/optimization.py |
@staticmethod
def _get_lr(param_group, param_state):
rel_step_sz = param_group["lr"]
if param_group["relative_step"]:
min_step = 1e-6 * param_state["step"] if param_group["warmup_init"] else 1e-2
rel_step_sz = min(min_step, 1.0 / math.sqrt(param_state["step"]))
param_... | 130 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/optimization.py |
@staticmethod
def _approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col):
# copy from fairseq's adafactor implementation:
# https://github.com/huggingface/transformers/blob/8395f14de6068012787d83989c3627c3df6a252b/src/transformers/optimization.py#L505
r_factor = (exp_avg_sq_row / exp_avg_sq_row.mea... | 130 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/optimization.py |
for group in self.param_groups:
for p in group["params"]:
if p.grad is None:
continue
grad = p.grad
if grad.dtype in {torch.float16, torch.bfloat16}:
grad = grad.float()
if grad.is_sparse:
... | 130 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/optimization.py |
if use_first_moment:
# Exponential moving average of gradient values
state["exp_avg"] = torch.zeros_like(grad)
if factored:
state["exp_avg_sq_row"] = torch.zeros(grad_shape[:-1]).to(grad)
state["exp_avg_s... | 130 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/optimization.py |
p_data_fp32 = p
if p.dtype in {torch.float16, torch.bfloat16}:
p_data_fp32 = p_data_fp32.float()
state["step"] += 1
state["RMS"] = self._rms(p_data_fp32)
lr = self._get_lr(group, state)
beta2t = 1.0 - math.pow(state["s... | 130 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/optimization.py |
# Approximation of exponential moving average of square of gradient
update = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col)
update.mul_(grad)
else:
exp_avg_sq = state["exp_avg_sq"]
exp_avg_sq.mul_(beta2t).add_(update,... | 130 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/optimization.py |
if p.dtype in {torch.float16, torch.bfloat16}:
p.copy_(p_data_fp32)
return loss | 130 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/optimization.py |
class AdafactorSchedule(LambdaLR):
"""
Since [`~optimization.Adafactor`] performs its own scheduling, if the training loop relies on a scheduler (e.g.,
for logging), this class creates a proxy object that retrieves the current lr values from the optimizer.
It returns `initial_lr` during startup and the... | 131 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/optimization.py |
class TFTrainingArguments(TrainingArguments):
"""
TrainingArguments is the subset of the arguments we use in our example scripts **which relate to the training loop
itself**.
Using [`HfArgumentParser`] we can turn this class into
[argparse](https://docs.python.org/3/library/argparse#module-argparse... | 132 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args_tf.py |
Parameters:
output_dir (`str`):
The output directory where the model predictions and checkpoints will be written.
overwrite_output_dir (`bool`, *optional*, defaults to `False`):
If `True`, overwrite the content of the output directory. Use this to continue training if `output_dir... | 132 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args_tf.py |
training/evaluation scripts instead. See the [example
scripts](https://github.com/huggingface/transformers/tree/main/examples) for more details.
do_predict (`bool`, *optional*, defaults to `False`):
Whether to run predictions on the test set or not. This argument is not directly used by ... | 132 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args_tf.py |
- `"no"`: No evaluation is done during training.
- `"steps"`: Evaluation is done (and logged) every `eval_steps`.
- `"epoch"`: Evaluation is done at the end of each epoch.
per_device_train_batch_size (`int`, *optional*, defaults to 8):
The batch size per GPU/TPU core... | 132 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args_tf.py |
learning_rate (`float`, *optional*, defaults to 5e-5):
The initial learning rate for Adam.
weight_decay (`float`, *optional*, defaults to 0):
The weight decay to apply (if not zero).
adam_beta1 (`float`, *optional*, defaults to 0.9):
The beta1 hyperparameter for the A... | 132 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args_tf.py |
For a finite dataset, training is reiterated through the dataset (if all data is exhausted) until
`max_steps` is reached.
warmup_ratio (`float`, *optional*, defaults to 0.0):
Ratio of total training steps used for a linear warmup from 0 to `learning_rate`.
warmup_steps (`int`, *o... | 132 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args_tf.py |
- `"no"`: No logging is done during training.
- `"epoch"`: Logging is done at the end of each epoch.
- `"steps"`: Logging is done every `logging_steps`.
logging_first_step (`bool`, *optional*, defaults to `False`):
Whether to log and evaluate the first `global_step` ... | 132 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args_tf.py |
save_steps (`int`, *optional*, defaults to 500):
Number of updates steps before two checkpoint saves if `save_strategy="steps"`.
save_total_limit (`int`, *optional*):
If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in
`output_di... | 132 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args_tf.py |
local_rank (`int`, *optional*, defaults to -1):
During distributed training, the rank of the process.
tpu_num_cores (`int`, *optional*):
When training on TPU, the number of TPU cores (automatically passed by launcher script).
debug (`bool`, *optional*, defaults to `False`):
... | 132 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/training_args_tf.py |
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