Ë œÐVh"ãó<—dZddlmZddlmZddlZddlmZmZgd¢Z de fd „Zdefd „Z ddedeejfd„Zde fd „Zede¬«e«Zdeddfd„Zede¬«e«Zddedej*fd„Zdej*ddfd„Zddeddfd„Zy)z] This package introduces support for the current :ref:`accelerator` in python. é)ÚOptional)Ú deprecatedNé)Ú _device_tÚ_get_device_index) Úcurrent_acceleratorÚcurrent_device_idxÚcurrent_device_indexÚcurrent_streamÚdevice_countÚis_availableÚset_device_idxÚset_device_indexÚ set_streamÚsynchronizeÚreturncó>—tjj«S)zëReturn the number of current :ref:`accelerator` available. Returns: int: the number of the current :ref:`accelerator` available. If there is no available accelerators, return 0. )ÚtorchÚ_CÚ_accelerator_deviceCount©óúJ/home/dcms/DCMS/lib/python3.12/site-packages/torch/accelerator/__init__.pyrrs€ô8‰8×,Ñ,Ó.Ð.rcóf—t«}|€ytj|«}|j«S)a§Check if the current accelerator is available at runtime: it was build, all the required drivers are available and at least one device is visible. See :ref:`accelerator` for details. Returns: bool: A boolean indicating if there is an available :ref:`accelerator`. Example:: >>> assert torch.accelerator.is_available() "No available accelerators detected." F)rrÚget_device_moduler )ÚaccÚmods rr r %s3€ô Ó €CØ €{Øä × !Ñ ! #Ó &€CØ×ÑÓÐrÚcheck_availablecód—tjj«x}|r|rt«r|Sy)aäReturn the device of the accelerator available at compilation time. If no accelerator were available at compilation time, returns None. See :ref:`accelerator` for details. Args: check_available (bool, optional): if True, will also do a runtime check to see if the device :func:`torch.accelerator.is_available` on top of the compile-time check. Default: ``False`` Returns: torch.device: return the current accelerator as :class:`torch.device`. .. note:: The index of the returned :class:`torch.device` will be ``None``, please use :func:`torch.accelerator.current_device_index` to know the current index being used. Example:: >>> # xdoctest: >>> # If an accelerator is available, sent the model to it >>> model = torch.nn.Linear(2, 2) >>> if (current_device := current_accelerator(check_available=True)) is not None: >>> model.to(current_device) N)rrÚ_accelerator_getAcceleratorr )rrs rrr=s-€ô2x‰x×3Ñ3Ó5Ð5ˆÐBÙ¡_¼¼ØˆJØrcó>—tjj«S)z«Return the index of a currently selected device for the current :ref:`accelerator`. Returns: int: the index of a currently selected device. )rrÚ_accelerator_getDeviceIndexrrrr r \s€ô8‰8×/Ñ/Ó1Ð1rz#Use `current_device_index` instead.)ÚcategoryÚdevicecóX—t|«}tjj|«y)a Set the current device index to a given device. Args: device (:class:`torch.device`, str, int): a given device that must match the current :ref:`accelerator` device type. .. note:: This function is a no-op if this device index is negative. N)rrrÚ_accelerator_setDeviceIndex©r$Údevice_indexs rrrks €ô% VÓ,€LÜ ‡HH×(Ñ(¨Õ6rzUse `set_device_index` instead.cóX—t|d«}tjj|«S)a˜Return the currently selected stream for a given device. Args: device (:class:`torch.device`, str, int, optional): a given device that must match the current :ref:`accelerator` device type. If not given, use :func:`torch.accelerator.current_device_index` by default. Returns: torch.Stream: the currently selected stream for a given device. T)rrrÚ_accelerator_getStreamr's rrr~s%€ô% V¨TÓ2€LÜ8‰8×*Ñ*¨<Ó8Ð8rÚstreamcóB—tjj|«y)aSet the current stream to a given stream. Args: stream (torch.Stream): a given stream that must match the current :ref:`accelerator` device type. .. note:: This function will set the current device index to the device index of the given stream. N)rrÚ_accelerator_setStream)r+s rrrs€ô ‡HH×#Ñ# FÕ+rcóZ—t|d«}tjj|«y)aWait for all kernels in all streams on the given device to complete. Args: device (:class:`torch.device`, str, int, optional): device for which to synchronize. It must match the current :ref:`accelerator` device type. If not given, use :func:`torch.accelerator.current_device_index` by default. .. note:: This function is a no-op if the current :ref:`accelerator` is not initialized. Example:: >>> # xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA) >>> assert torch.accelerator.is_available() "No available accelerators detected." >>> start_event = torch.Event(enable_timing=True) >>> end_event = torch.Event(enable_timing=True) >>> start_event.record() >>> tensor = torch.randn(100, device=torch.accelerator.current_accelerator()) >>> sum = torch.sum(tensor) >>> end_event.record() >>> torch.accelerator.synchronize() >>> elapsed_time_ms = start_event.elapsed_time(end_event) TN)rrrÚ_accelerator_synchronizeDevicer's rrr˜s"€ô.% V¨TÓ2€LÜ ‡HH×+Ñ+¨LÕ9r)F)N)Ú__doc__ÚtypingrÚtyping_extensionsrrÚ_utilsrrÚ__all__ÚintrÚboolr r$rr Ú FutureWarningr rrÚStreamrrrrrrúr9sððñõÝ(ãç0ò€ð/có/ðdóñ0¨ð¸(À5Ç<Á<Ñ:Póð>2˜có2ð‘ZØ)Ø ôðóÐð 7˜Yð 7¨dó 7ð‘Ø%Ø ôðó€ñ9˜9ð9°5·<±<ó9ð,u—|‘|ð,¨ó,ñ:˜ ð:°ô:r