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# Summary Simplification of Backend Selection This PR deprecates the `torch.backends/cuda/sdp_kernel` context manager and replaces it with a new context manager `torch.nn.attention.sdpa_kernel`. This context manager also changes the api for this context manager. For `sdp_kernel` one would specify the backend choice by taking the negation of what kernel they would like to run. The purpose of this backend manager was to only to be a debugging tool, "turn off the math backend" and see if you can run one of the fused implementations. Problems: - This pattern makes sense if majority of users don't care to know anything about the backends that can be run. However, if users are seeking to use this context manager then they are explicitly trying to run a specific backend. - This is not scalable. We are working on adding the cudnn backend and this API makes it so so that more implementations will need to be turned off if user wants to explicitly run a given backend. - Discoverability of the current context manager. It is somewhat un-intutive that this backend manager is in backends/cuda/init when this now also controls the CPU fused kernel behavior. I think centralizing to attention namespace will be helpful. Other concerns: - Typically backends (kernels) for operators are entirely hidden from users and implementation details of the framework. We have exposed this to users already, albeit not by default and with beta warnings. Does making backends choices even more explicit lead to problems when we potentially want to remove existing backends, (perhaps inputs shapes will get covered by newer backends). A nice side effect is now that we aren't using the `BACKEND_MAP` in test_transformers many, many dynamo failures are passing for CPU tests. Pull Request resolved: https://github.com/pytorch/pytorch/pull/114689 Approved by: https://github.com/cpuhrsch
216 lines
6.3 KiB
ReStructuredText
216 lines
6.3 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.mha``
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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.nnpack``
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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.SDPAParams
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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.can_use_flash_attention
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.. autofunction:: torch.backends.cuda.can_use_efficient_attention
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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.mha
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^^^^^^^^^^^^^^^^^^
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.. automodule:: torch.backends.mha
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.. autofunction:: torch.backends.mha.get_fastpath_enabled
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.. autofunction:: torch.backends.mha.set_fastpath_enabled
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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.nnpack
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^^^^^^^^^^^^^^^^^^^^^
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.. automodule:: torch.backends.nnpack
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.. autofunction:: torch.backends.nnpack.is_available
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.. autofunction:: torch.backends.nnpack.flags
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.. autofunction:: torch.backends.nnpack.set_flags
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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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