def config(self):
return ProfilerConfig(
self.profiler_kind,
self.record_shapes,
self.profile_memory,
self.with_stack,
self.with_flops,
self.with_modules,
self.experimental_config)
def config(self):
return ProfilerConfig(
self.profiler_kind,
self.record_shapes,
self.profile_memory,
self.with_stack,
self.with_flops,
self.with_modules,
# avoid exposing _ExperimentalConfig this in legacy public API
torch._C._autograd._ExperimentalConfig(),
)
def __enter__(self):
if not self.enabled:
return
if self.entered:
raise RuntimeError(
"ITT annotation context manager is not reentrant")
self.entered = True
_enable_profiler(
ProfilerConfig(ProfilerState.ITT, self.record_shapes, False, False,
False, False, _ExperimentalConfig()), set())
return self
def __enter__(self):
if not self.enabled:
return
if self.entered:
raise RuntimeError(
"NVTX annotation context manager is not reentrant")
self.entered = True
torch.cuda.synchronize()
_enable_profiler(
ProfilerConfig(ProfilerState.NVTX, self.record_shapes, False,
False, False, False), set())
return self
def config(self):
return ProfilerConfig(self.profiler_kind, self.record_shapes,
self.profile_memory, self.with_stack,
self.with_flops)