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0a94bb432e
Replace https://github.com/pytorch/pytorch/pull/138947 for re-import. Replaces https://github.com/ROCm/pytorch/pull/1592 This PR contains the initial implementation of SDPA with composable_kernel backend. The CK path can be forced by simply calling torch.backends.cuda.preferred_rocm_fa_library("ck"). Similarly, you can force the incumbent aotriton implementation by passing in "aotriton" or "default". As you'd expect, not setting this option will result in aotriton to be used as the backend. In the case of CK, if pytorch deems flash attention usable, then it will use the CK path in all the same places aotriton would have been used. This PR makes no changes to the heuristics which select which attention scheme to use (i.e. flash attention vs memory efficient attention vs math etc etc). It only gets called when flash attention is both enabled (via USE_FLASH_ATTENTION) and is selected at runtime by the existing heuristics. Files located in pytorch/aten/src/ATen/native/transformers/hip/flash_attn/ck/mha* have been pulled from https://github.com/Dao-AILab/flash-attention courtesy of @tridao's hard work who is the co-author NOTE: In order to use this backend, the user MUST set USE_CK_FLASH_ATTENTION=1 in their environment when they build PyTorch. Pull Request resolved: https://github.com/pytorch/pytorch/pull/143695 Approved by: https://github.com/malfet Co-authored-by: Andy Lugo <Andy.LugoReyes@amd.com> Co-authored-by: Jithun Nair <jithun.nair@amd.com>
241 lines
7.0 KiB
ReStructuredText
241 lines
7.0 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.cusparselt``
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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_blas_library
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.. autofunction:: torch.backends.cuda.preferred_rocm_fa_library
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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.fp16_bf16_reduction_math_sdp_allowed
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.. autofunction:: torch.backends.cuda.allow_fp16_bf16_reduction_math_sdp
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.. autofunction:: torch.backends.cuda.cudnn_sdp_enabled
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.. autofunction:: torch.backends.cuda.enable_cudnn_sdp
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.. autofunction:: torch.backends.cuda.is_flash_attention_available
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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.can_use_cudnn_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.cusparselt
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^^^^^^^^^^^^^^^^^^^^^^^^^
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.. automodule:: torch.backends.cusparselt
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.. autofunction:: torch.backends.cusparselt.version
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.. autofunction:: torch.backends.cusparselt.is_available
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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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.. py:module:: torch.backends.kleidiai
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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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