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Import Utilities
This page goes through the transformers utilities to enable lazy and fast object import.
While we strive for minimal dependencies, some models have specific dependencies requirements that cannot be
worked around. We don't want for all users of transformers to have to install those dependencies to use other models,
we therefore mark those as soft dependencies rather than hard dependencies.
The transformers toolkit is not made to error-out on import of a model that has a specific dependency; instead, an
object for which you are lacking a dependency will error-out when calling any method on it. As an example, if
torchvision isn't installed, the fast image processors will not be available.
This object is still importable:
>>> from transformers import DetrImageProcessor
>>> print(DetrImageProcessor)
<class 'DetrImageProcessor'>
However, no method can be called on that object:
>>> DetrImageProcessor.from_pretrained()
ImportError:
DetrImageProcessor requires the Torchvision library but it was not found in your environment. Check out the instructions on the
installation page: https://pytorch.org/get-started/locally/ and follow the ones that match your environment.
Please note that you may need to restart your runtime after installation.
Let's see how to specify specific object dependencies.
Specifying Object Dependencies
Filename-based
All objects under a given filename have an automatic dependency to the tool linked to the filename
PyTorch: All files starting with modeling_ have an automatic PyTorch dependency
Tokenizers: All files starting with tokenization_ and ending with _fast have an automatic tokenizers dependency
Vision: All files starting with image_processing_ have an automatic dependency to the vision dependency group;
at the time of writing, this only contains the pillow dependency.
Vision + Torch + Torchvision: All files starting with image_processing_ and ending with _fast have an automatic
dependency to vision, torch, and torchvision.
All of these automatic dependencies are added on top of the explicit dependencies that are detailed below.
Explicit Object Dependencies
We add a method called requires that is used to explicitly specify the dependencies of a given object. As an
example, the Trainer class has two hard dependencies: torch and accelerate. Here is how we specify these
required dependencies:
from .utils.import_utils import requires
@requires(backends=("torch", "accelerate"))
class Trainer:
...
Backends that can be added here are all the backends that are available in the import_utils.py module.
Additionally, specific versions can be specified in each backend. For example, this is how you would specify
a requirement on torch>=2.6 on the Trainer class:
from .utils.import_utils import requires
@requires(backends=("torch>=2.6", "accelerate"))
class Trainer:
...
You can specify the following operators: ==, >, >=, <, <=, !=.
Methods
autodoc utils.import_utils.define_import_structure
autodoc utils.import_utils.requires
autodoc utils.import_utils.requires_backends