Completely reproducible results are not guaranteed across PyTorch releases, individual commits, or different platforms. Furthermore, results may not be reproducible between CPU and GPU executions, even when using identical seeds.
However, there are some steps you can take to limit the number of sources of nondeterministic behavior for a specific platform, device, and PyTorch release. First, you can control sources of randomness that can cause multiple executions of your application to behave differently. Second, you can configure PyTorch to avoid using nondeterministic algorithms for some operations, so that multiple calls to those operations, given the same inputs, will produce the same result.
Deterministic operations are often slower than nondeterministic operations, so single-run performance may decrease for your model. However, determinism may save time in development by facilitating experimentation, debugging, and regression testing.
Controlling sources of randomness¶
PyTorch random number generator¶
You can use
torch.manual_seed() to seed the RNG for all devices (both
CPU and CUDA):
import torch torch.manual_seed(0)
Some PyTorch operations may use random numbers internally.
torch.svd_lowrank() does this, for instance. Consequently, calling it
multiple times back-to-back with the same input arguments may give different
results. However, as long as
torch.manual_seed() is set to a constant
at the beginning of an application and all other sources of nondeterminism have
been eliminated, the same series of random numbers will be generated each time
the application is run in the same environment.
It is also possible to obtain identical results from an operation that uses
random numbers by setting
torch.manual_seed() to the same value between
For custom operators, you might need to set python seed as well:
import random random.seed(0)
Random number generators in other libraries¶
If you or any of the libraries you are using rely on NumPy, you can seed the global NumPy RNG with:
import numpy as np np.random.seed(0)
However, some applications and libraries may use NumPy Random Generator objects, not the global RNG (https://numpy.org/doc/stable/reference/random/generator.html), and those will need to be seeded consistently as well.
If you are using any other libraries that use random number generators, refer to the documentation for those libraries to see how to set consistent seeds for them.
CUDA convolution benchmarking¶
The cuDNN library, used by CUDA convolution operations, can be a source of nondeterminism across multiple executions of an application. When a cuDNN convolution is called with a new set of size parameters, an optional feature can run multiple convolution algorithms, benchmarking them to find the fastest one. Then, the fastest algorithm will be used consistently during the rest of the process for the corresponding set of size parameters. Due to benchmarking noise and different hardware, the benchmark may select different algorithms on subsequent runs, even on the same machine.
Disabling the benchmarking feature with
torch.backends.cudnn.benchmark = False
causes cuDNN to deterministically select an algorithm, possibly at the cost of reduced
However, if you do not need reproducibility across multiple executions of your application,
then performance might improve if the benchmarking feature is enabled with
torch.backends.cudnn.benchmark = True.
Note that this setting is different from the
setting discussed below.
Avoiding nondeterministic algorithms¶
torch.use_deterministic_algorithms() lets you configure PyTorch to use
deterministic algorithms instead of nondeterministic ones where available, and
to throw an error if an operation is known to be nondeterministic (and without
a deterministic alternative).
Please check the documentation for
for a full list of affected operations. If an operation does not act correctly
according to the documentation, or if you need a deterministic implementation
of an operation that does not have one, please submit an issue:
For example, running the nondeterministic CUDA implementation of
will throw an error:
>>> import torch >>> torch.use_deterministic_algorithms(True) >>> torch.randn(2, 2).cuda().index_add_(0, torch.tensor([0, 1]), torch.randn(2, 2)) Traceback (most recent call last): File "<stdin>", line 1, in <module> RuntimeError: index_add_cuda_ does not have a deterministic implementation, but you set 'torch.use_deterministic_algorithms(True)'. ...
torch.bmm() is called with sparse-dense CUDA tensors it typically uses a
nondeterministic algorithm, but when the deterministic flag is turned on, its alternate
deterministic implementation will be used:
>>> import torch >>> torch.use_deterministic_algorithms(True) >>> torch.bmm(torch.randn(2, 2, 2).to_sparse().cuda(), torch.randn(2, 2, 2).cuda()) tensor([[[ 1.1900, -2.3409], [ 0.4796, 0.8003]], [[ 0.1509, 1.8027], [ 0.0333, -1.1444]]], device='cuda:0')
Furthermore, if you are using CUDA tensors, and your CUDA version is 10.2 or greater, you should set the environment variable CUBLAS_WORKSPACE_CONFIG according to CUDA documentation: https://docs.nvidia.com/cuda/cublas/index.html#cublasApi_reproducibility
CUDA convolution determinism¶
While disabling CUDA convolution benchmarking (discussed above) ensures that
CUDA selects the same algorithm each time an application is run, that algorithm
itself may be nondeterministic, unless either
torch.backends.cudnn.deterministic = True is set. The latter setting
controls only this behavior, unlike
which will make other PyTorch operations behave deterministically, too.
DataLoader will reseed workers following Randomness in multi-process data loading algorithm.
worker_init_fn() and generator to preserve reproducibility:
def seed_worker(worker_id): worker_seed = torch.initial_seed() % 2**32 numpy.random.seed(worker_seed) random.seed(worker_seed) g = torch.Generator() g.manual_seed(0) DataLoader( train_dataset, batch_size=batch_size, num_workers=num_workers, worker_init_fn=seed_worker, generator=g, )