mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 00:21:07 +01:00
734281c3d6
Summary: Working towards https://docs.google.com/document/d/10yx2-4gs0gTMOimVS403MnoAWkqitS8TUHX73PN8EjE/edit?pli=1# This PR: - Ensure that all the submodules are listed in a rst file (that ensure they are considered by the coverage tool) - Remove some long deprecated code that just error out on import - Remove the allow list altogether to ensure nothing gets added back there Pull Request resolved: https://github.com/pytorch/pytorch/pull/73983 Reviewed By: anjali411 Differential Revision: D34787908 Pulled By: albanD fbshipit-source-id: 163ce61e133b12b2f2e1cbe374f979e3d6858db7 (cherry picked from commit c9edfead7a01dc45bfc24eaf7220d2a84ab1f62e)
107 lines
2.9 KiB
ReStructuredText
107 lines
2.9 KiB
ReStructuredText
.. role:: hidden
|
|
:class: hidden-section
|
|
|
|
torch.backends
|
|
==============
|
|
.. automodule:: torch.backends
|
|
|
|
`torch.backends` controls the behavior of various backends that PyTorch supports.
|
|
|
|
These backends include:
|
|
|
|
- ``torch.backends.cuda``
|
|
- ``torch.backends.cudnn``
|
|
- ``torch.backends.mkl``
|
|
- ``torch.backends.mkldnn``
|
|
- ``torch.backends.openmp``
|
|
|
|
|
|
torch.backends.cuda
|
|
^^^^^^^^^^^^^^^^^^^
|
|
.. automodule:: torch.backends.cuda
|
|
|
|
.. autofunction:: torch.backends.cuda.is_built
|
|
|
|
.. attribute:: torch.backends.cuda.matmul.allow_tf32
|
|
|
|
A :class:`bool` that controls whether TensorFloat-32 tensor cores may be used in matrix
|
|
multiplications on Ampere or newer GPUs. See :ref:`tf32_on_ampere`.
|
|
|
|
.. attribute:: torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction
|
|
|
|
A :class:`bool` that controls whether reduced precision reductions (e.g., with fp16 accumulation type) are allowed with fp16 GEMMs.
|
|
|
|
.. attribute:: torch.backends.cuda.cufft_plan_cache
|
|
|
|
``cufft_plan_cache`` caches the cuFFT plans
|
|
|
|
.. attribute:: size
|
|
|
|
A readonly :class:`int` that shows the number of plans currently in the cuFFT plan cache.
|
|
|
|
.. attribute:: max_size
|
|
|
|
A :class:`int` that controls cache capacity of cuFFT plan.
|
|
|
|
.. method:: clear()
|
|
|
|
Clears the cuFFT plan cache.
|
|
|
|
.. autofunction:: torch.backends.cuda.preferred_linalg_library
|
|
|
|
|
|
torch.backends.cudnn
|
|
^^^^^^^^^^^^^^^^^^^^
|
|
.. automodule:: torch.backends.cudnn
|
|
|
|
.. autofunction:: torch.backends.cudnn.version
|
|
|
|
.. autofunction:: torch.backends.cudnn.is_available
|
|
|
|
.. attribute:: torch.backends.cudnn.enabled
|
|
|
|
A :class:`bool` that controls whether cuDNN is enabled.
|
|
|
|
.. attribute:: torch.backends.cudnn.allow_tf32
|
|
|
|
A :class:`bool` that controls where TensorFloat-32 tensor cores may be used in cuDNN
|
|
convolutions on Ampere or newer GPUs. See :ref:`tf32_on_ampere`.
|
|
|
|
.. attribute:: torch.backends.cudnn.deterministic
|
|
|
|
A :class:`bool` that, if True, causes cuDNN to only use deterministic convolution algorithms.
|
|
See also :func:`torch.are_deterministic_algorithms_enabled` and
|
|
:func:`torch.use_deterministic_algorithms`.
|
|
|
|
.. attribute:: torch.backends.cudnn.benchmark
|
|
|
|
A :class:`bool` that, if True, causes cuDNN to benchmark multiple convolution algorithms
|
|
and select the fastest.
|
|
|
|
|
|
torch.backends.mkl
|
|
^^^^^^^^^^^^^^^^^^
|
|
.. automodule:: torch.backends.mkl
|
|
|
|
.. autofunction:: torch.backends.mkl.is_available
|
|
|
|
|
|
torch.backends.mkldnn
|
|
^^^^^^^^^^^^^^^^^^^^^
|
|
.. automodule:: torch.backends.mkldnn
|
|
|
|
.. autofunction:: torch.backends.mkldnn.is_available
|
|
|
|
|
|
torch.backends.openmp
|
|
^^^^^^^^^^^^^^^^^^^^^
|
|
.. automodule:: torch.backends.openmp
|
|
|
|
.. autofunction:: torch.backends.openmp.is_available
|
|
|
|
.. Docs for other backends need to be added here.
|
|
.. Automodules are just here to ensure checks run but they don't actually
|
|
.. add anything to the rendered page for now.
|
|
.. py:module:: torch.backends.quantized
|
|
.. py:module:: torch.backends.xnnpack
|