( model: InferenceSession config: PretrainedConfig vaip_config: Union = None model_save_dir: Union = None preprocessors: Optional = None **kwargs )
Base class for implementing models using ONNX Runtime.
The RyzenAIModel implements generic methods for interacting with the Hugging Face Hub as well as exporting vanilla
transformers models to ONNX using optimum.exporters.onnx
toolchain.
Class attributes:
str
, defaults to "onnx_model"
) — The name of the model type to use when
registering the RyzenAIModel classes.Type
, defaults to AutoModel
) — The “AutoModel” class to represented by the
current RyzenAIModel class.Common attributes:
ort.InferenceSession
) — The ONNX Runtime InferenceSession that is running the model.Path
) — The directory where the model exported to ONNX is saved.
By defaults, if the loaded model is local, the directory where the original model will be used. Otherwise, the
cache directory is used.( model_id: Union vaip_config: str = None export: bool = False force_download: bool = False use_auth_token: Optional = None cache_dir: Optional = None subfolder: str = '' config: Optional = None local_files_only: bool = False provider: str = 'VitisAIExecutionProvider' session_options: Optional = None provider_options: Optional = None trust_remote_code: bool = False revision: Optional = None library_name: Optional = None **kwargs ) → RyzenAIModel
Parameters
Union[str, Path]
) —
Can be either:
bert-base-uncased
, or namespaced under a
user or organization name, like dbmdz/bert-base-german-cased
.~OptimizedModel.save_pretrained
,
e.g., ./my_model_directory/
.bool
, defaults to False
) —
Defines whether the provided model_id
needs to be exported to the targeted format. bool
, defaults to True
) —
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist. Optional[Union[bool,str]]
, defaults to None
) —
Deprecated. Please use the token
argument instead. Optional[Union[bool,str]]
, defaults to None
) —
The token to use as HTTP bearer authorization for remote files. If True
, will use the token generated
when running huggingface-cli login
(stored in huggingface_hub.constants.HF_TOKEN_PATH
). Optional[str]
, defaults to None
) —
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standard cache should not be used. str
, defaults to ""
) —
In case the relevant files are located inside a subfolder of the model repo either locally or on huggingface.co, you can
specify the folder name here. Optional[transformers.PretrainedConfig]
, defaults to None
) —
The model configuration. Optional[bool]
, defaults to False
) —
Whether or not to only look at local files (i.e., do not try to download the model). bool
, defaults to False
) —
Whether or not to allow for custom code defined on the Hub in their own modeling. This option should only be set
to True
for repositories you trust and in which you have read the code, as it will execute code present on
the Hub on your local machine. Optional[str]
, defaults to None
) —
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so revision
can be any
identifier allowed by git. str
, defaults to "VitisAIExecutionProvider"
) —
ONNX Runtime provider to use for loading the model. See https://onnxruntime.ai/docs/execution-providers/ for
possible providers. Optional[onnxruntime.SessionOptions]
, defaults to None
), —
ONNX Runtime session options to use for loading the model. Optional[Dict[str, Any]]
, defaults to None
) —
Provider option dictionaries corresponding to the provider used. See available options
for each provider: https://onnxruntime.ai/docs/api/c/group___global.html . Dict[str, Any]
) —
Will be passed to the underlying model loading methods. Parameters for decoder models (RyzenAIForSpeechSeq2Seq)
Optional[bool]
, defaults to True
) —
Whether or not past key/values cache should be used. Defaults to True
. Returns
RyzenAIModel
The loaded RyzenAIModel model.
Instantiate a pretrained model from a pre-trained model configuration.
( save_directory: Union push_to_hub: bool = False **kwargs )
Parameters
Union[str, os.PathLike]
) —
Directory to which to save. Will be created if it doesn’t exist. bool
, optional, defaults to False
) —
Whether or not to push your model to the Hugging Face model hub after saving it.
Using push_to_hub=True
will synchronize the repository you are pushing to with save_directory
,
which requires save_directory
to be a local clone of the repo you are pushing to if it’s an existing
folder. Pass along temp_dir=True
to use a temporary directory instead.
Saves a model and its configuration file to a directory, so that it can be re-loaded using the
from_pretrained
class method.
( model_path: Union input_shape_dict: Dict output_shape_dict: Dict ) → Union[str, Path]
Parameters
Returns
Union[str, Path]
Path to the model after updating the input shapes.
Raises
ValueError
ValueError
— If the model provided has dynamic axes in input/output and no input/output shape is provided.Propagates the given input shapes on the model’s layers, fixing the input shapes of the model.
( model: InferenceSession config: PretrainedConfig vaip_config: Union = None model_save_dir: Union = None preprocessors: Optional = None **kwargs )
( model: InferenceSession config: PretrainedConfig vaip_config: Union = None model_save_dir: Union = None preprocessors: Optional = None **kwargs )
( model: InferenceSession config: PretrainedConfig vaip_config: Union = None model_save_dir: Union = None preprocessors: Optional = None **kwargs )
( model: InferenceSession config: PretrainedConfig vaip_config: Union = None model_save_dir: Union = None preprocessors: Optional = None **kwargs )
( model: InferenceSession config: PretrainedConfig vaip_config: Union = None model_save_dir: Union = None preprocessors: Optional = None **kwargs )