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8b617f813d
Essentially the same change as #67946, except that the default is to disallow reduced precision reductions in `BFloat16` GEMMs (for now). If performance is severely regressed, we can change the default, but this option appears to be necessary to pass some `addmm` `BFloat16` tests on H100. CC @ptrblck @ngimel Pull Request resolved: https://github.com/pytorch/pytorch/pull/89172 Approved by: https://github.com/ngimel
180 lines
5.4 KiB
ReStructuredText
180 lines
5.4 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.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.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.matmul.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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.. 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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.. 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:: 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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.. attribute:: torch.backends.cudnn.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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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:: torch.backends.opt_einsum.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:: torch.backends.opt_einsum.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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