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a348975e00
This achieves the same things as https://github.com/pytorch/pytorch/pull/85908 but using backends instead of kwargs (which breaks torchscript unfortunately). This also does mean we let go of numpy compatibility BUT the wins here are that users can control what opt einsum they wanna do! The backend allows for..well you should just read the docs: ``` .. attribute:: torch.backends.opteinsum.enabled A :class:`bool` that controls whether opt_einsum is enabled (on by default). If so, torch.einsum will use opt_einsum (https://optimized-einsum.readthedocs.io/en/stable/path_finding.html) to calculate an optimal path of contraction for faster performance. .. attribute:: torch.backends.opteinsum.strategy A :class:`str` that specifies which strategies to try when `torch.backends.opteinsum.enabled` is True. By default, torch.einsum will try the "auto" strategy, but the "greedy" and "optimal" strategies are also supported. Note that the "optimal" strategy is factorial on the number of inputs as it tries all possible paths. See more details in opt_einsum's docs (https://optimized-einsum.readthedocs.io/en/stable/path_finding.html). ``` In trying (and failing) to land 85908, I discovered that jit script does NOT actually pull from python's version of einsum (because it cannot support variadic args nor kwargs). Thus I learned that jitted einsum does not subscribe to the new opt_einsum path calculation. Overall, this is fine since jit script is getting deprecated, but where is the best place to document this? ## Test plan: - added tests to CI - locally tested that trying to set the strategy to something invalid will error properly - locally tested that tests will pass even if you don't have opt-einsum - locally tested that setting the strategy when opt-einsum is not there will also error properly Pull Request resolved: https://github.com/pytorch/pytorch/pull/86219 Approved by: https://github.com/soulitzer, https://github.com/malfet
169 lines
5.1 KiB
ReStructuredText
169 lines
5.1 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.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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.. autofunction:: torch.backends.cuda.flash_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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