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https://github.com/zebrajr/pytorch.git
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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
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:class: hidden-section
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torch.backends
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==============
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.. automodule:: torch.backends
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`torch.backends` controls the behavior of various backends that PyTorch supports.
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These backends include:
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- ``torch.backends.cuda``
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- ``torch.backends.cudnn``
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- ``torch.backends.mkl``
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- ``torch.backends.mkldnn``
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- ``torch.backends.openmp``
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torch.backends.cuda
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^^^^^^^^^^^^^^^^^^^
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.. automodule:: torch.backends.cuda
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.. autofunction:: torch.backends.cuda.is_built
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.. attribute:: torch.backends.cuda.matmul.allow_tf32
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A :class:`bool` that controls whether TensorFloat-32 tensor cores may be used in matrix
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multiplications on Ampere or newer GPUs. See :ref:`tf32_on_ampere`.
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.. attribute:: torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction
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A :class:`bool` that controls whether reduced precision reductions (e.g., with fp16 accumulation type) are allowed with fp16 GEMMs.
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.. attribute:: torch.backends.cuda.cufft_plan_cache
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``cufft_plan_cache`` caches the cuFFT plans
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.. attribute:: size
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A readonly :class:`int` that shows the number of plans currently in the cuFFT plan cache.
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.. attribute:: max_size
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A :class:`int` that controls cache capacity of cuFFT plan.
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.. method:: clear()
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Clears the cuFFT plan cache.
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.. autofunction:: torch.backends.cuda.preferred_linalg_library
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torch.backends.cudnn
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^^^^^^^^^^^^^^^^^^^^
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.. automodule:: torch.backends.cudnn
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.. autofunction:: torch.backends.cudnn.version
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.. autofunction:: torch.backends.cudnn.is_available
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.. attribute:: torch.backends.cudnn.enabled
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A :class:`bool` that controls whether cuDNN is enabled.
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.. attribute:: torch.backends.cudnn.allow_tf32
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A :class:`bool` that controls where TensorFloat-32 tensor cores may be used in cuDNN
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convolutions on Ampere or newer GPUs. See :ref:`tf32_on_ampere`.
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.. attribute:: torch.backends.cudnn.deterministic
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A :class:`bool` that, if True, causes cuDNN to only use deterministic convolution algorithms.
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See also :func:`torch.are_deterministic_algorithms_enabled` and
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:func:`torch.use_deterministic_algorithms`.
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.. attribute:: torch.backends.cudnn.benchmark
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A :class:`bool` that, if True, causes cuDNN to benchmark multiple convolution algorithms
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and select the fastest.
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torch.backends.mkl
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^^^^^^^^^^^^^^^^^^
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.. automodule:: torch.backends.mkl
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.. autofunction:: torch.backends.mkl.is_available
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torch.backends.mkldnn
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^^^^^^^^^^^^^^^^^^^^^
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.. automodule:: torch.backends.mkldnn
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.. autofunction:: torch.backends.mkldnn.is_available
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torch.backends.openmp
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^^^^^^^^^^^^^^^^^^^^^
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.. automodule:: torch.backends.openmp
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.. autofunction:: torch.backends.openmp.is_available
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.. Docs for other backends need to be added here.
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.. Automodules are just here to ensure checks run but they don't actually
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.. add anything to the rendered page for now.
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.. py:module:: torch.backends.quantized
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.. py:module:: torch.backends.xnnpack
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