1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
|
import ctypes
import torch
class Stream(torch._C._CudaStreamBase):
r"""Wrapper around a CUDA stream.
A CUDA stream is a linear sequence of execution that belongs to a specific
device, independent from other streams. See :ref:`cuda-semantics` for
details.
Arguments:
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. Lower numbers
represent higher priorities.
"""
def __new__(cls, device=None, priority=0, **kwargs):
with torch.cuda.device(device):
return super(Stream, cls).__new__(cls, priority=priority, **kwargs)
def wait_event(self, event):
r"""Makes all future work submitted to the stream wait for an event.
Arguments:
event (Event): an event to wait for.
.. note:: This is a wrapper around ``cudaStreamWaitEvent()``: see `CUDA
documentation`_ for more info.
This function returns without waiting for :attr:`event`: only future
operations are affected.
.. _CUDA documentation:
http://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__STREAM.html
"""
event.wait(self)
def wait_stream(self, stream):
r"""Synchronizes 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.
Arguments:
stream (Stream): a stream to synchronize.
.. note:: This function returns without waiting for currently enqueued
kernels in :attr:`stream`: only future operations are affected.
"""
self.wait_event(stream.record_event())
def record_event(self, event=None):
r"""Records an event.
Arguments:
event (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
def query(self):
r"""Checks if all the work submitted has been completed.
Returns:
A boolean indicating if all kernels in this stream are completed."""
return super(Stream, self).query()
def synchronize(self):
r"""Wait for all the kernels in this stream to complete.
.. note:: This is a wrapper around ``cudaStreamSynchronize()``: see
`CUDA documentation`_ for more info.
.. _CUDA documentation:
http://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__STREAM.html
"""
super(Stream, self).synchronize()
@property
def _as_parameter_(self):
return ctypes.c_void_p(self.cuda_stream)
def __eq__(self, o):
if isinstance(o, Stream):
return super(Stream, self).__eq__(o)
return False
def __hash__(self):
return hash((self.cuda_stream, self.device))
def __repr__(self):
return ('<torch.cuda.Stream device={0} cuda_stream={1:#x}>'
.format(self.device, self.cuda_stream))
class Event(torch._C._CudaEventBase):
r"""Wrapper around a CUDA event.
CUDA events are synchronization markers that can be used to monitor the
device's progress, to accurately measure timing, and to synchronize CUDA
streams.
The underlying CUDA events are lazily initialized when the event is first
recorded or exported to another process. After creation, only streams on the
same device may record the event. However, streams on any device can wait on
the event.
Arguments:
enable_timing (bool, optional): indicates if the event should measure time
(default: ``False``)
blocking (bool, optional): if ``True``, :meth:`wait` will be blocking (default: ``False``)
interprocess (bool): if ``True``, the event can be shared between processes
(default: ``False``)
.. _CUDA documentation:
https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__EVENT.html
"""
def __new__(cls, enable_timing=False, blocking=False, interprocess=False):
return super(Event, cls).__new__(
cls,
enable_timing=enable_timing, blocking=blocking, interprocess=interprocess)
@classmethod
def from_ipc_handle(cls, device, handle):
r"""Reconstruct an event from an IPC handle on the given device."""
return super(Event, cls).from_ipc_handle(device, handle)
def record(self, stream=None):
r"""Records the event in a given stream.
Uses ``torch.cuda.current_stream()`` if no stream is specified. The
stream's device must match the event's device."""
if stream is None:
stream = torch.cuda.current_stream()
super(Event, self).record(stream)
def wait(self, stream=None):
r"""Makes all future work submitted to the given stream wait for this
event.
Use ``torch.cuda.current_stream()`` if no stream is specified."""
if stream is None:
stream = torch.cuda.current_stream()
super(Event, self).wait(stream)
def query(self):
r"""Checks if all work currently captured by event has completed.
Returns:
A boolean indicating if all work currently captured by event has
completed.
"""
return super(Event, self).query()
def elapsed_time(self, end_event):
r"""Returns the time elapsed in milliseconds after the event was
recorded and before the end_event was recorded.
"""
return super(Event, self).elapsed_time(end_event)
def synchronize(self):
r"""Waits 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.
.. note:: This is a wrapper around ``cudaEventSynchronize()``: see `CUDA
documentation`_ for more info.
.. _CUDA documentation:
https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__EVENT.html
"""
super(Event, self).synchronize()
def ipc_handle(self):
r"""Returns an IPC handle of this event. If not recorded yet, the event
will use the current device. """
return super(Event, self).ipc_handle()
@property
def _as_parameter_(self):
return ctypes.c_void_p(self.cuda_event)
def __repr__(self):
if self.cuda_event:
return '<torch.cuda.Event {0:#x}>'.format(self._as_parameter_.value)
else:
return '<torch.cuda.Event uninitialized>'
|
