Source code for torch.backends.mps

from functools import lru_cache as _lru_cache

import torch
from ...library import Library as _Library

__all__ = ["is_built", "is_available", "is_macos13_or_newer"]

[docs]def is_built() -> bool: r"""Returns whether PyTorch is built with MPS support. Note that this doesn't necessarily mean MPS is available; just that if this PyTorch binary were run a machine with working MPS drivers and devices, we would be able to use it.""" return torch._C._has_mps
[docs]@_lru_cache def is_available() -> bool: r"""Returns a bool indicating if MPS is currently available.""" return torch._C._mps_is_available()
@_lru_cache def is_macos13_or_newer(minor: int = 0) -> bool: r"""Returns a bool indicating whether MPS is running on MacOS 13 or newer.""" return torch._C._mps_is_on_macos_13_or_newer(minor) _lib = None def _init(): r"""Register prims as implementation of var_mean and group_norm""" global _lib if is_built() is False or _lib is not None: return from ..._decomp.decompositions import ( native_group_norm_backward as _native_group_norm_backward, ) from ..._refs import native_group_norm as _native_group_norm, var_mean as _var_mean _lib = _Library("aten", "IMPL") _lib.impl("var_mean.correction", _var_mean, "MPS") _lib.impl("native_group_norm", _native_group_norm, "MPS") _lib.impl("native_group_norm_backward", _native_group_norm_backward, "MPS")


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