def set_deterministic(d):
r"""Sets a global flag to force all operations to use a deterministic
implementation if available. If an operation that does not have a
deterministic implementation is called while this setting is True, the
operation will throw a RuntimeError.
Note that deterministic operations tend to have worse performance than
non-deterministic operations.
Args:
d (:class:`bool`): If True, force operations to be deterministic.
If False, allow non-deterministic operations.
"""
_C._set_deterministic(d)
def _set_deterministic(d):
r"""Sets a global flag to force all operations to use a deterministic
implementation if available. If an operation that does not have a
deterministic implementation is called while this setting is True, the
operation will throw a RuntimeError.
Note that deterministic operations tend to have worse performance than
non-deterministic operations.
Args:
d (:class:`bool`): If True, force operations to be deterministic.
If False, allow non-deterministic operations.
.. warning::
This feature is experimental and not complete. The above docstring
represents what the future behavior is intended to be. Right now,
`_set_deterministic` will only affect `torch.bmm` and convolution
operators.
"""
_C._set_deterministic(d)
def set_deterministic(d):
r""" Sets whether native PyTorch operations must use deterministic
algorithms. When True, operations without deterministic algorithms
will throw a :class:RuntimeError when called.
.. warning::
This feature is a beta feature, so it does not affect every
nondeterministic operation yet. The following operations are
affected by this flag.
The following normally-nondeterministic operations will act
deterministically when `d=True`:
* :class:`torch.nn.Conv1d` when called on CUDA tensor
* :class:`torch.nn.Conv2d` when called on CUDA tensor
* :class:`torch.nn.Conv3d` when called on CUDA tensor
* :class:`torch.nn.ConvTranspose1d` when called on CUDA tensor
* :class:`torch.nn.ConvTranspose2d` when called on CUDA tensor
* :class:`torch.nn.ConvTranspose3d` when called on CUDA tensor
* :func:`torch.bmm` when called on sparse-dense CUDA tensors
The following normally-nondeterministic operations will throw a
:class:`RuntimeError` when `d=True`:
* :class:`torch.nn.AvgPool3d` when called on a CUDA tensor that requires grad
* :class:`torch.nn.AdaptiveAvgPool2d` when called on a CUDA tensor that requires grad
* :class:`torch.nn.AdaptiveAvgPool3d` when called on a CUDA tensor that requires grad
* :class:`torch.nn.MaxPool3d` when called on a CUDA tensor that requires grad
* :class:`torch.nn.AdaptiveMaxPool2d` when called on a CUDA tensor that requires grad
* :class:`torch.nn.FractionalMaxPool2d` when called on a CUDA tensor that requires grad
* :class:`torch.nn.FractionalMaxPool3d` when called on a CUDA tensor that requires grad
* :func:`torch.nn.functional.interpolate` when called on a CUDA tensor that requires grad
and one of the following modes is used:
- `linear`
- `bilinear`
- `bicubic`
- `trilinear`
* :class:`torch.nn.ReflectionPad1d` when called on a CUDA tensor that requires grad
* :class:`torch.nn.ReflectionPad2d` when called on a CUDA tensor that requires grad
* :class:`torch.nn.ReplicationPad1d` when called on a CUDA tensor that requires grad
* :class:`torch.nn.ReplicationPad2d` when called on a CUDA tensor that requires grad
* :class:`torch.nn.ReplicationPad3d` when called on a CUDA tensor that requires grad
* :class:`torch.nn.NLLLoss` when called on a CUDA tensor that requires grad
* :class:`torch.nn.CTCLoss` when called on a CUDA tensor that requires grad
* :class:`torch.nn.EmbeddingBag` when called on a CUDA tensor that requires grad
* :func:`torch.scatter_add_` when called on a CUDA tensor
* :func:`torch.index_add_` when called on a CUDA tensor
* :func:`torch.index_select` when called on a CUDA tensor that requires grad
* :func:`torch.repeat_interleave` when called on a CUDA tensor that requires grad
* :func:`torch.histc` when called on a CUDA tensor
* :func:`torch.bincount` when called on a CUDA tensor
A handful of CUDA operations are nondeterministic if the CUDA version is
10.2 or greater, unless the environment variable `CUBLAS_WORKSPACE_CONFIG=:4096:8`
or `CUBLAS_WORKSPACE_CONFIG=:16:8` is set. See the CUDA documentation for more
details: `<https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility>`_
If one of these environment variable configurations is not set, a :class:`RuntimeError`
will be raised from these operations when called with CUDA tensors:
* :func:`torch.mm`
* :func:`torch.mv`
* :func:`torch.bmm`
Note that deterministic operations tend to have worse performance than
non-deterministic operations.
Args:
d (:class:`bool`): If True, force operations to be deterministic.
If False, allow non-deterministic operations.
"""
_C._set_deterministic(d)
