Source code for torch.backends.mkldnn
# mypy: allow-untyped-defs
import sys
from contextlib import contextmanager
from typing import TYPE_CHECKING
import torch
from torch.backends import __allow_nonbracketed_mutation, ContextProp, PropModule
[docs]def is_available():
r"""Return whether PyTorch is built with MKL-DNN support."""
return torch._C._has_mkldnn
VERBOSE_OFF = 0
VERBOSE_ON = 1
VERBOSE_ON_CREATION = 2
[docs]class verbose:
"""
On-demand oneDNN (former MKL-DNN) verbosing functionality.
To make it easier to debug performance issues, oneDNN can dump verbose
messages containing information like kernel size, input data size and
execution duration while executing the kernel. The verbosing functionality
can be invoked via an environment variable named `DNNL_VERBOSE`. However,
this methodology dumps messages in all steps. Those are a large amount of
verbose messages. Moreover, for investigating the performance issues,
generally taking verbose messages for one single iteration is enough.
This on-demand verbosing functionality makes it possible to control scope
for verbose message dumping. In the following example, verbose messages
will be dumped out for the second inference only.
.. highlight:: python
.. code-block:: python
import torch
model(data)
with torch.backends.mkldnn.verbose(torch.backends.mkldnn.VERBOSE_ON):
model(data)
Args:
level: Verbose level
- ``VERBOSE_OFF``: Disable verbosing
- ``VERBOSE_ON``: Enable verbosing
- ``VERBOSE_ON_CREATION``: Enable verbosing, including oneDNN kernel creation
"""
def __init__(self, level):
self.level = level
def __enter__(self):
if self.level == VERBOSE_OFF:
return
st = torch._C._verbose.mkldnn_set_verbose(self.level)
assert (
st
), "Failed to set MKLDNN into verbose mode. Please consider to disable this verbose scope."
return self
def __exit__(self, exc_type, exc_val, exc_tb):
torch._C._verbose.mkldnn_set_verbose(VERBOSE_OFF)
return False
def set_flags(_enabled):
orig_flags = (torch._C._get_mkldnn_enabled(),)
torch._C._set_mkldnn_enabled(_enabled)
return orig_flags
@contextmanager
def flags(enabled=False):
with __allow_nonbracketed_mutation():
orig_flags = set_flags(enabled)
try:
yield
finally:
with __allow_nonbracketed_mutation():
set_flags(orig_flags[0])
class MkldnnModule(PropModule):
def __init__(self, m, name):
super().__init__(m, name)
enabled = ContextProp(torch._C._get_mkldnn_enabled, torch._C._set_mkldnn_enabled)
if TYPE_CHECKING:
enabled: ContextProp
# Cool stuff from torch/backends/cudnn/__init__.py and
# https://stackoverflow.com/questions/2447353/getattr-on-a-module/7668273#7668273
sys.modules[__name__] = MkldnnModule(sys.modules[__name__], __name__)