Source code for torch.xpu.streams

import ctypes

import torch
from torch._streambase import _EventBase, _StreamBase
from .._utils import _dummy_type

if not hasattr(torch._C, "_XpuStreamBase"):
    # Define dummy base classes
    torch._C.__dict__["_XpuStreamBase"] = _dummy_type("_XpuStreamBase")
    torch._C.__dict__["_XpuEventBase"] = _dummy_type("_XpuEventBase")

[docs]class Stream(torch._C._XpuStreamBase, _StreamBase): 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 necessary if device is None or ("stream_id" in kwargs and "device_index" in kwargs): return super().__new__(cls, priority=priority, **kwargs) else: with torch.xpu.device(device): return super().__new__(cls, priority=priority, **kwargs)
[docs] def wait_event(self, event): 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] def wait_stream(self, stream): 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] def record_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. """ if event is None: event = Event() event.record(self) return event
[docs] def query(self): r"""Check if all the work submitted has been completed. Returns: A boolean indicating if all kernels in this stream are completed. """ return super().query()
[docs] def synchronize(self): r"""Wait for all the kernels in this stream to complete.""" super().synchronize()
@property def _as_parameter_(self): return ctypes.c_void_p(self.sycl_queue) def __eq__(self, o): if isinstance(o, Stream): return super().__eq__(o) return False def __hash__(self): return hash((self.sycl_queue, self.device)) def __repr__(self): return f"torch.xpu.Stream(device={self.device} sycl_queue={self.sycl_queue:#x})"
[docs]class Event(torch._C._XpuEventBase, _EventBase): 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): return super().__new__(cls, enable_timing=enable_timing)
[docs] def record(self, stream=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. """ if stream is None: stream = torch.xpu.current_stream() super().record(stream)
[docs] def wait(self, stream=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. """ if stream is None: stream = torch.xpu.current_stream() super().wait(stream)
[docs] def query(self): r"""Check if all work currently captured by event has completed. Returns: A boolean indicating if all work currently captured by event has completed. """ return super().query()
[docs] def elapsed_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. """ return super().elapsed_time(end_event)
[docs] def synchronize(self): 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()
@property def _as_parameter_(self): return ctypes.c_void_p(self.sycl_event) def __repr__(self): if self.sycl_event: return f"torch.xpu.Event(sycl_event={self.sycl_event:#x})" else: return "torch.xpu.Event(uninitialized)"


Access comprehensive developer documentation for PyTorch

View Docs


Get in-depth tutorials for beginners and advanced developers

View Tutorials


Find development resources and get your questions answered

View Resources