mirror of
https://github.com/zebrajr/pytorch.git
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c4db607607
Add non-package python modules to the public API checks. The original change is to remove the `ispkg` check in this line https://github.com/pytorch/pytorch/blob/main/docs/source/conf.py#L518 Everything else is to add the appropriate modules to the rst files, make sure every module we provide can be imported (fixed by either making optional dependencies optional or just deleting files that have been un-importable for 3 years), make API that are both modules and functions (like torch.autograd.gradcheck) properly rendered on the docs website without confusion and add every non-documented API to the allow list (~3k of them). Next steps will be to try and fix these missing docs Pull Request resolved: https://github.com/pytorch/pytorch/pull/110568 Approved by: https://github.com/zou3519
192 lines
5.7 KiB
ReStructuredText
192 lines
5.7 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.cpu``
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- ``torch.backends.cuda``
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- ``torch.backends.cudnn``
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- ``torch.backends.mps``
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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.opt_einsum``
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- ``torch.backends.xeon``
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torch.backends.cpu
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^^^^^^^^^^^^^^^^^^^
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.. automodule:: torch.backends.cpu
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.. autofunction:: torch.backends.cpu.get_cpu_capability
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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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.. currentmodule:: torch.backends.cuda.matmul
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.. attribute:: 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:: 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:: allow_bf16_reduced_precision_reduction
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A :class:`bool` that controls whether reduced precision reductions are allowed with bf16 GEMMs.
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.. currentmodule:: torch.backends.cuda
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.. attribute:: cufft_plan_cache
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``cufft_plan_cache`` contains the cuFFT plan caches for each CUDA device.
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Query a specific device `i`'s cache via `torch.backends.cuda.cufft_plan_cache[i]`.
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.. currentmodule:: torch.backends.cuda.cufft_plan_cache
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.. attribute:: size
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A readonly :class:`int` that shows the number of plans currently in a cuFFT plan cache.
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.. attribute:: max_size
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A :class:`int` that controls the capacity of a cuFFT plan cache.
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.. method:: clear()
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Clears a cuFFT plan cache.
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.. autofunction:: torch.backends.cuda.preferred_linalg_library
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.. autoclass:: torch.backends.cuda.SDPBackend
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.. autofunction:: torch.backends.cuda.flash_sdp_enabled
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.. autofunction:: torch.backends.cuda.enable_mem_efficient_sdp
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.. autofunction:: torch.backends.cuda.mem_efficient_sdp_enabled
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.. autofunction:: torch.backends.cuda.enable_flash_sdp
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.. autofunction:: torch.backends.cuda.math_sdp_enabled
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.. autofunction:: torch.backends.cuda.enable_math_sdp
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.. autofunction:: torch.backends.cuda.sdp_kernel
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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:: enabled
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A :class:`bool` that controls whether cuDNN is enabled.
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.. attribute:: 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:: 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:: 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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.. attribute:: benchmark_limit
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A :class:`int` that specifies the maximum number of cuDNN convolution algorithms to try when
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`torch.backends.cudnn.benchmark` is True. Set `benchmark_limit` to zero to try every
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available algorithm. Note that this setting only affects convolutions dispatched via the
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cuDNN v8 API.
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.. py:module:: torch.backends.cudnn.rnn
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torch.backends.mps
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^^^^^^^^^^^^^^^^^^
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.. automodule:: torch.backends.mps
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.. autofunction:: torch.backends.mps.is_available
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.. autofunction:: torch.backends.mps.is_built
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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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.. autoclass:: torch.backends.mkl.verbose
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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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.. autoclass:: torch.backends.mkldnn.verbose
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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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torch.backends.opt_einsum
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^^^^^^^^^^^^^^^^^^^^^^^^^
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.. automodule:: torch.backends.opt_einsum
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.. autofunction:: torch.backends.opt_einsum.is_available
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.. autofunction:: torch.backends.opt_einsum.get_opt_einsum
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.. attribute:: enabled
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A :class:``bool`` that controls whether opt_einsum is enabled (``True`` by default). If so,
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torch.einsum will use opt_einsum (https://optimized-einsum.readthedocs.io/en/stable/path_finding.html)
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if available to calculate an optimal path of contraction for faster performance.
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If opt_einsum is not available, torch.einsum will fall back to the default contraction path
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of left to right.
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.. attribute:: strategy
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A :class:``str`` that specifies which strategies to try when ``torch.backends.opt_einsum.enabled``
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is ``True``. By default, torch.einsum will try the "auto" strategy, but the "greedy" and "optimal"
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strategies are also supported. Note that the "optimal" strategy is factorial on the number of
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inputs as it tries all possible paths. See more details in opt_einsum's docs
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(https://optimized-einsum.readthedocs.io/en/stable/path_finding.html).
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torch.backends.xeon
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^^^^^^^^^^^^^^^^^^^
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.. automodule:: torch.backends.xeon
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.. py:module:: torch.backends.xeon.run_cpu
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