mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-21 05:30:26 +08:00
8a35aafca6
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/12853 Differential Revision: D10458439 Pulled By: SsnL fbshipit-source-id: ebd259e598327b0c5d63de6b7c182781fe361fbd
57 lines
2.0 KiB
ReStructuredText
57 lines
2.0 KiB
ReStructuredText
Reproducibility
|
|
===============
|
|
|
|
Completely reproducible results are not guaranteed across PyTorch releases,
|
|
individual commits or different platforms. Furthermore, results need not be
|
|
reproducible between CPU and GPU executions, even when using identical seeds.
|
|
|
|
However, in order to make computations deterministic on your specific problem on
|
|
one specific platform and PyTorch release, there are a couple of steps to take.
|
|
|
|
There are two pseudorandom number generators involved in PyTorch, which you will
|
|
need to seed manually to make runs reproducible. Furthermore, you should ensure
|
|
that all other libraries your code relies on an which use random numbers also
|
|
use a fixed seed.
|
|
|
|
PyTorch
|
|
.......
|
|
You can use :meth:`torch.manual_seed()` to seed the RNG for all devices (both
|
|
CPU and CUDA)::
|
|
|
|
import torch
|
|
torch.manual_seed(0)
|
|
|
|
|
|
There are some PyTorch functions that use CUDA functions that can be a source
|
|
of non-determinism. One class of such CUDA functions are atomic operations,
|
|
in particular :attr:`atomicAdd`, where the order of parallel additions to the
|
|
same value is undetermined and, for floating-point variables, a source of
|
|
variance in the result. PyTorch functions that use :attr:`atomicAdd` in the forward
|
|
include :meth:`torch.Tensor.index_add_`, :meth:`torch.Tensor.scatter_add_`,
|
|
:meth:`torch.bincount`.
|
|
|
|
A number of operations have backwards that use :attr:`atomicAdd`, in particular
|
|
:meth:`torch.nn.functional.embedding_bag`,
|
|
:meth:`torch.nn.functional.ctc_loss` and many forms of pooling, padding, and sampling.
|
|
There currently is no simple way of avoiding non-determinism in these functions.
|
|
|
|
|
|
CuDNN
|
|
.....
|
|
When running on the CuDNN backend, two further options must be set::
|
|
|
|
torch.backends.cudnn.deterministic = True
|
|
torch.backends.cudnn.benchmark = False
|
|
|
|
.. warning::
|
|
|
|
Deterministic mode can have a performance impact, depending on your model.
|
|
|
|
Numpy
|
|
.....
|
|
If you or any of the libraries you are using rely on Numpy, you should seed the
|
|
Numpy RNG as well. This can be done with::
|
|
|
|
import numpy as np
|
|
np.random.seed(0)
|