Source code for torch.backends.mkl

import torch

[docs]def is_available(): r"""Returns whether PyTorch is built with MKL support.""" return torch._C.has_mkl
[docs]class verbose: """ On-demand oneMKL verbosing functionality To make it easier to debug performance issues, oneMKL can dump verbose messages containing execution information like duration while executing the kernel. The verbosing functionality can be invoked via an environment variable named `MKL_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.mkl.verbose(torch.backends.mkl.VERBOSE_ON): model(data) Args: level: Verbose level - ``VERBOSE_OFF``: Disable verbosing - ``VERBOSE_ON``: Enable verbosing """ def __init__(self, enable): self.enable = enable def __enter__(self): if self.enable == VERBOSE_OFF: return st = torch._C._verbose.mkl_set_verbose(self.enable) assert st, "Failed to set MKL into verbose mode. Please consider to disable this verbose scope." return self def __exit__(self, exc_type, exc_val, exc_tb): torch._C._verbose.mkl_set_verbose(VERBOSE_OFF) return False


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