o Zh"@sdZddlmZddlmZddlZddlmZmZgdZ de fd d Z de fd d Z d de deejfddZde fddZededeZdeddfddZededeZd!dedejfddZdejddfddZd!deddfddZdS)"z] This package introduces support for the current :ref:`accelerator` in python. )Optional) deprecatedN) _device_t_get_device_index) current_acceleratorcurrent_device_idxcurrent_device_indexcurrent_stream device_count is_availableset_device_idxset_device_index set_stream synchronizereturncC tjS)zReturn 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. )torch_CZ_accelerator_deviceCountrrI/var/www/auris/lib/python3.10/site-packages/torch/accelerator/__init__.pyr s r cCs$t}|dur dSt|}|S)aCheck 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." NF)rrZget_device_moduler )accmodrrrr %s  r Fcheck_availablecCs(tj}dur|r|rtr|SdS)aReturn 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)rrZ_accelerator_getAcceleratorr )rrrrrr=srcCr)zReturn the index of a currently selected device for the current :ref:`accelerator`. Returns: int: the index of a currently selected device. )rrZ_accelerator_getDeviceIndexrrrrr \s r z#Use `current_device_index` instead.)categorydevicecCst|}tj|dS)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)rrrZ_accelerator_setDeviceIndexrZ device_indexrrrrks rzUse `set_device_index` instead.cCst|d}tj|S)aReturn 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)rrrZ_accelerator_getStreamrrrrr ~s r streamcCstj|dS)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)rrZ_accelerator_setStream)rrrrrsrcCst|d}tj|dS)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)rrrZ_accelerator_synchronizeDevicerrrrrs r)F)N)__doc__typingrZtyping_extensionsrr_utilsrr__all__intr boolr rrr FutureWarningrrr ZStreamr rrrrrrs4