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.. 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.cpu
  • torch.backends.cuda
  • torch.backends.cudnn
  • torch.backends.cusparselt
  • torch.backends.mha
  • torch.backends.mps
  • torch.backends.mkl
  • torch.backends.mkldnn
  • torch.backends.nnpack
  • torch.backends.openmp
  • torch.backends.opt_einsum
  • torch.backends.python_native
  • torch.backends.xeon

torch.backends.cpu

.. automodule:: torch.backends.cpu

.. autofunction::  torch.backends.cpu.get_cpu_capability

torch.backends.cuda

.. automodule:: torch.backends.cuda

.. autofunction::  torch.backends.cuda.is_built

.. currentmodule:: torch.backends.cuda.matmul

.. attribute::  allow_tf32

    A :class:`bool` that controls whether TensorFloat-32 tensor cores may be used in matrix
    multiplications on Ampere or newer GPUs. allow_tf32 is going to be deprecated. See :ref:`tf32_on_ampere`.

.. attribute::  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.
    Assigning a tuple ``(allow_reduced_precision, allow_splitk)`` lets you also toggle whether
    split-K heuristics may be used when dispatching to cuBLASLt. ``allow_splitk`` defaults to ``True``.

.. attribute::  allow_bf16_reduced_precision_reduction

    A :class:`bool` that controls whether reduced precision reductions are allowed with bf16 GEMMs.
    Assigning a tuple ``(allow_reduced_precision, allow_splitk)`` lets you also toggle whether
    split-K heuristics may be used when dispatching to cuBLASLt. ``allow_splitk`` defaults to ``True``.

.. currentmodule:: torch.backends.cuda

.. attribute::  cufft_plan_cache

    ``cufft_plan_cache`` contains the cuFFT plan caches for each CUDA device.
    Query a specific device `i`'s cache via `torch.backends.cuda.cufft_plan_cache[i]`.

    .. currentmodule:: torch.backends.cuda.cufft_plan_cache
    .. attribute::  size

        A readonly :class:`int` that shows the number of plans currently in a cuFFT plan cache.

    .. attribute::  max_size

        A :class:`int` that controls the capacity of a cuFFT plan cache.

    .. method::  clear()

        Clears a cuFFT plan cache.

.. autofunction:: torch.backends.cuda.preferred_blas_library

.. autofunction:: torch.backends.cuda.preferred_rocm_fa_library

.. autofunction:: torch.backends.cuda.is_ck_sdpa_available

.. autofunction:: torch.backends.cuda.preferred_linalg_library

.. autoclass:: torch.backends.cuda.SDPAParams

.. autofunction:: torch.backends.cuda.flash_sdp_enabled

.. autofunction:: torch.backends.cuda.enable_mem_efficient_sdp

.. autofunction:: torch.backends.cuda.mem_efficient_sdp_enabled

.. autofunction:: torch.backends.cuda.enable_flash_sdp

.. autofunction:: torch.backends.cuda.math_sdp_enabled

.. autofunction:: torch.backends.cuda.enable_math_sdp

.. autofunction:: torch.backends.cuda.fp16_bf16_reduction_math_sdp_allowed

.. autofunction:: torch.backends.cuda.allow_fp16_bf16_reduction_math_sdp

.. autofunction:: torch.backends.cuda.cudnn_sdp_enabled

.. autofunction:: torch.backends.cuda.enable_cudnn_sdp

.. autofunction:: torch.backends.cuda.is_flash_attention_available

.. autofunction:: torch.backends.cuda.can_use_flash_attention

.. autofunction:: torch.backends.cuda.can_use_efficient_attention

.. autofunction:: torch.backends.cuda.can_use_cudnn_attention

.. autofunction:: torch.backends.cuda.sdp_kernel

torch.backends.cudnn

.. automodule:: torch.backends.cudnn

.. autofunction:: torch.backends.cudnn.version

.. autofunction:: torch.backends.cudnn.is_available

.. attribute::  enabled

    A :class:`bool` that controls whether cuDNN is enabled.

.. attribute::  allow_tf32

    A :class:`bool` that controls where TensorFloat-32 tensor cores may be used in cuDNN
    convolutions on Ampere or newer GPUs. allow_tf32 is going to be deprecated. See :ref:`tf32_on_ampere`.

.. attribute::  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::  benchmark

    A :class:`bool` that, if True, causes cuDNN to benchmark multiple convolution algorithms
    and select the fastest.

.. attribute::  benchmark_limit

    A :class:`int` that specifies the maximum number of cuDNN convolution algorithms to try when
    `torch.backends.cudnn.benchmark` is True. Set `benchmark_limit` to zero to try every
    available algorithm. Note that this setting only affects convolutions dispatched via the
    cuDNN v8 API.

.. py:module:: torch.backends.cudnn.rnn

torch.backends.cusparselt

.. automodule:: torch.backends.cusparselt

.. autofunction:: torch.backends.cusparselt.version

.. autofunction:: torch.backends.cusparselt.is_available

torch.backends.mha

.. automodule:: torch.backends.mha

.. autofunction::  torch.backends.mha.get_fastpath_enabled

.. autofunction::  torch.backends.mha.set_fastpath_enabled

torch.backends.miopen

.. automodule:: torch.backends.miopen

.. attribute::  immediate

    A :class:`bool` that, if True, causes MIOpen to use Immediate Mode
    (https://rocm.docs.amd.com/projects/MIOpen/en/latest/how-to/find-and-immediate.html).

torch.backends.mps

.. automodule:: torch.backends.mps

.. autofunction::  torch.backends.mps.is_available

.. autofunction::  torch.backends.mps.is_built

torch.backends.mkl

.. automodule:: torch.backends.mkl

.. autofunction::  torch.backends.mkl.is_available

.. autoclass::  torch.backends.mkl.verbose

torch.backends.mkldnn

.. automodule:: torch.backends.mkldnn

.. autofunction::  torch.backends.mkldnn.is_available

.. autoclass::  torch.backends.mkldnn.verbose

torch.backends.nnpack

.. automodule:: torch.backends.nnpack

.. autofunction::  torch.backends.nnpack.is_available

.. autofunction::  torch.backends.nnpack.flags

.. autofunction::  torch.backends.nnpack.set_flags

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

.. py:module:: torch.backends.kleidiai

.. autofunction:: torch.backends.kleidiai.is_available

torch.backends.opt_einsum

.. automodule:: torch.backends.opt_einsum

.. autofunction:: torch.backends.opt_einsum.is_available

.. autofunction:: torch.backends.opt_einsum.get_opt_einsum

.. attribute::  enabled

    A :class:`bool` that controls whether opt_einsum is enabled (``True`` by default). If so,
    torch.einsum will use opt_einsum (https://optimized-einsum.readthedocs.io/en/stable/path_finding.html)
    if available to calculate an optimal path of contraction for faster performance.

    If opt_einsum is not available, torch.einsum will fall back to the default contraction path
    of left to right.

.. attribute::  strategy

    A :class:`str` that specifies which strategies to try when ``torch.backends.opt_einsum.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).

torch.backends.python_native

.. automodule:: torch.backends.python_native

The torch.backends.python_native module provides user control over native operators implemented in python via. DSLs (Domain Specific Languages) that are defined in torch._native. This allows users to selectively enable or disable high-performance implementations from various DSLs like Triton and CuteDSL.

Module-level Functions

.. autofunction:: torch.backends.python_native.get_dsl_operations

.. autofunction:: torch.backends.python_native.disable_operations

.. autofunction:: torch.backends.python_native.enable_operations

.. autofunction:: torch.backends.python_native.disable_dispatch_keys

.. autofunction:: torch.backends.python_native.enable_dispatch_keys

.. autofunction:: torch.backends.python_native.operations_disabled

Module-level Properties

.. attribute:: available_dsls

    A :class:`list` of :class:`str` containing the names of DSLs that are available at runtime.
    This is a subset of :attr:`all_dsls` that have their runtime dependencies satisfied.

.. attribute:: all_dsls

    A :class:`list` of :class:`str` containing the names of all registered DSLs, whether
    available at runtime or not.

DSL Controllers

For each registered DSL (e.g., triton, cutedsl), auto-populated controller modules are available:

.. currentmodule:: torch.backends.python_native

DSL Properties

Each DSL controller (e.g., torch.backends.python_native.triton) provides the following properties:

Property Type Description
name str The name of the DSL
available bool Whether the DSL's runtime dependencies are available
enabled bool Controls whether all operations from this DSL are enabled. Setting to False disables all operations from the DSL, while True re-enables them
version Version or None The version of the DSL runtime, if available. Returns None if the DSL is not available

DSL Methods

Each DSL controller provides the following methods:

disable() Disable all operations from this DSL.

enable() Re-enable all operations from this DSL.

disabled() Context manager that temporarily disables all operations from this DSL. Operations are automatically re-enabled when exiting the context.

Example::

    with torch.backends.python_native.triton.disabled():
        # Triton operations are disabled here
        result = model(input)
    # Triton operations restored here

Usage Examples

.. code-block:: python

    import torch.backends.python_native as pn

    # Query available DSLs
    print(pn.available_dsls)  # ['triton', 'cutedsl']

    # Disable all Triton operations
    pn.triton.enabled = False

    # Temporarily disable CuteDSL operations
    with pn.cutedsl.disabled():
        result = model(input)  # CuteDSL ops disabled

    # Disable specific operations across all DSLs
    pn.disable_operations('scaled_mm', '_flash_attention_forward')

    # Query operations for a specific DSL
    triton_ops = pn.get_dsl_operations('triton')

torch.backends.xeon

.. automodule:: torch.backends.xeon

.. py:module:: torch.backends.xeon.run_cpu