1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
|
"""
:mod:`torch.distributed.optim` exposes DistributedOptimizer, which takes a list
of remote parameters (:class:`~torch.distributed.rpc.RRef`) and runs the
optimizer locally on the workers where the parameters live. The distributed
optimizer can use any of the local optimizer :ref:`optimizer-algorithms` to
apply the gradients on each worker.
"""
import warnings
import torch
from torch import optim
from .apply_optimizer_in_backward import (
_apply_optimizer_in_backward,
_get_in_backward_optimizers,
)
from .functional_adadelta import _FunctionalAdadelta
from .functional_adagrad import _FunctionalAdagrad
from .functional_adam import _FunctionalAdam
from .functional_adamax import _FunctionalAdamax
from .functional_adamw import _FunctionalAdamW
from .functional_rmsprop import _FunctionalRMSprop
from .functional_rprop import _FunctionalRprop
from .functional_sgd import _FunctionalSGD
from .named_optimizer import _NamedOptimizer
from .utils import as_functional_optim
# DistributedOptimizer imports torch.distributed.rpc names, so gate availability
# based on RPC being available.
if hasattr(torch._C, "_rpc_init"):
from .optimizer import DistributedOptimizer
from .post_localSGD_optimizer import PostLocalSGDOptimizer
from .zero_redundancy_optimizer import ZeroRedundancyOptimizer
__all__ = [
"as_functional_optim",
"DistributedOptimizer",
"PostLocalSGDOptimizer",
"ZeroRedundancyOptimizer",
]
|
