# mypy: allow-untyped-defsimportctypesimporttorchfromtorch._utilsimport_dummy_typeifnothasattr(torch._C,"_XpuStreamBase"):# Define dummy base classestorch._C.__dict__["_XpuStreamBase"]=_dummy_type("_XpuStreamBase")torch._C.__dict__["_XpuEventBase"]=_dummy_type("_XpuEventBase")
[docs]classStream(torch._C._XpuStreamBase):r"""Wrapper around a XPU stream. A XPU stream is a linear sequence of execution that belongs to a specific device, independent from other streams. Args: device(torch.device or int, optional): a device on which to allocate the stream. If :attr:`device` is ``None`` (default) or a negative integer, this will use the current device. priority(int, optional): priority of the stream, should be 0 or negative, where negative numbers indicate higher priority. By default, streams have priority 0. """def__new__(cls,device=None,priority=0,**kwargs):# setting device manager is expensive, so we avoid it unless necessaryifdeviceisNoneor("stream_id"inkwargsand"device_index"inkwargs):returnsuper().__new__(cls,priority=priority,**kwargs)else:withtorch.xpu.device(device):returnsuper().__new__(cls,priority=priority,**kwargs)
[docs]defwait_event(self,event)->None:r"""Make all future work submitted to the stream wait for an event. Args: event (torch.xpu.Event): an event to wait for. """event.wait(self)
[docs]defwait_stream(self,stream)->None:r"""Synchronize with another stream. All future work submitted to this stream will wait until all kernels submitted to a given stream at the time of call complete. Args: stream (Stream): a stream to synchronize. """self.wait_event(stream.record_event())
[docs]defrecord_event(self,event=None):r"""Record an event. Args: event (torch.xpu.Event, optional): event to record. If not given, a new one will be allocated. Returns: Recorded event. """ifeventisNone:event=Event()event.record(self)returnevent
[docs]defquery(self)->bool:r"""Check if all the work submitted has been completed. Returns: A boolean indicating if all kernels in this stream are completed. """returnsuper().query()
[docs]defsynchronize(self)->None:r"""Wait for all the kernels in this stream to complete."""super().synchronize()
[docs]classEvent(torch._C._XpuEventBase):r"""Wrapper around a XPU event. XPU events are synchronization markers that can be used to monitor the device's progress, and to synchronize XPU streams. The underlying XPU events are lazily initialized when the event is first recorded. After creation, only streams on the same device may record the event. However, streams on any device can wait on the event. Args: enable_timing (bool, optional): indicates if the event should measure time (default: ``False``) """def__new__(cls,enable_timing=False):returnsuper().__new__(cls,enable_timing=enable_timing)
[docs]defrecord(self,stream=None)->None:r"""Record the event in a given stream. Uses ``torch.xpu.current_stream()`` if no stream is specified. The stream's device must match the event's device. """ifstreamisNone:stream=torch.xpu.current_stream()super().record(stream)
[docs]defwait(self,stream=None)->None:r"""Make all future work submitted to the given stream wait for this event. Use ``torch.xpu.current_stream()`` if no stream is specified. """ifstreamisNone:stream=torch.xpu.current_stream()super().wait(stream)
[docs]defquery(self)->bool:r"""Check if all work currently captured by event has completed. Returns: A boolean indicating if all work currently captured by event has completed. """returnsuper().query()
[docs]defelapsed_time(self,end_event):r"""Return the time elapsed. Time reported in milliseconds after the event was recorded and before the end_event was recorded. """returnsuper().elapsed_time(end_event)
[docs]defsynchronize(self)->None:r"""Wait for the event to complete. Waits until the completion of all work currently captured in this event. This prevents the CPU thread from proceeding until the event completes. """super().synchronize()
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