vllm.utils.import_utils ¶
Contains helpers related to importing modules.
This is similar in concept to the importlib module.
LazyLoader ¶
Bases: ModuleType
LazyLoader module borrowed from [Tensorflow] (https://github.com/tensorflow/tensorflow/blob/main/tensorflow/python/util/lazy_loader.py) with an addition of "module caching".
Lazily import a module, mainly to avoid pulling in large dependencies. Modules such as xgrammar might do additional side effects, so we only want to use this when it is needed, delaying all eager effects.
Source code in vllm/utils/import_utils.py
__dir__ ¶
__getattr__ ¶
__init__ ¶
Source code in vllm/utils/import_utils.py
_load ¶
_load() -> ModuleType
Source code in vllm/utils/import_utils.py
PlaceholderModule ¶
Bases: _PlaceholderBase
A placeholder object to use when a module does not exist.
This enables more informative errors when trying to access attributes of a module that does not exist.
Source code in vllm/utils/import_utils.py
__getattr__ ¶
Source code in vllm/utils/import_utils.py
_PlaceholderBase ¶
Disallows downstream usage of placeholder modules.
We need to explicitly override each dunder method because __getattr__ is not called when they are accessed.
Source code in vllm/utils/import_utils.py
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__abs__ ¶
__bool__ ¶
__call__ ¶
__ceil__ ¶
__enter__ ¶
__exit__ ¶
__floor__ ¶
__getattr__ ¶
The main class should implement this to throw an error for attribute accesses representing downstream usage.
__hash__ ¶
__index__ ¶
__invert__ ¶
__len__ ¶
__neg__ ¶
__pos__ ¶
__pow__ ¶
__setitem__ ¶
__trunc__ ¶
_PlaceholderModuleAttr ¶
Bases: _PlaceholderBase
Source code in vllm/utils/import_utils.py
__getattr__ ¶
__init__ ¶
__init__(module: PlaceholderModule, attr_path: str) -> None
_has_module cached ¶
Return True if module_name can be found in the current environment.
The result is cached so that subsequent queries for the same module incur no additional overhead.
Source code in vllm/utils/import_utils.py
get_vllm_optional_dependencies cached ¶
Source code in vllm/utils/import_utils.py
import_from_path ¶
Import a Python file according to its file path.
Based on the official recipe: https://docs.python.org/3/library/importlib.html#importing-a-source-file-directly
Source code in vllm/utils/import_utils.py
import_pynvml ¶
Historical comments:
libnvml.so is the library behind nvidia-smi, and pynvml is a Python wrapper around it. We use it to get GPU status without initializing CUDA context in the current process. Historically, there are two packages that provide pynvml: - nvidia-ml-py (https://pypi.org/project/nvidia-ml-py/): The official wrapper. It is a dependency of vLLM, and is installed when users install vLLM. It provides a Python module named pynvml. - pynvml (https://pypi.org/project/pynvml/): An unofficial wrapper. Prior to version 12.0, it also provides a Python module pynvml, and therefore conflicts with the official one. What's worse, the module is a Python package, and has higher priority than the official one which is a standalone Python file. This causes errors when both of them are installed. Starting from version 12.0, it migrates to a new module named pynvml_utils to avoid the conflict. It is so confusing that many packages in the community use the unofficial one by mistake, and we have to handle this case. For example, nvcr.io/nvidia/pytorch:24.12-py3 uses the unofficial one, and it will cause errors, see the issue https://github.com/vllm-project/vllm/issues/12847 for example. After all the troubles, we decide to copy the official pynvml module to our codebase, and use it directly.
Source code in vllm/utils/import_utils.py
init_cached_hf_modules ¶
resolve_obj_by_qualname ¶
Resolve an object by its fully-qualified class name.