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
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
|
from typing import List, Dict, Optional
import torch
import torch.optim._functional as F
from torch import Tensor
__all__ : List[str] = []
# Define a TorchScript compatible Functional Adadelta Optimizer
# where we use these optimizer in a functional way.
# Instead of using the `param.grad` when updating parameters,
# we explicitly allow the distributed optimizer pass gradients to
# the `step` function. In this way, we could separate the gradients
# and parameters and allow multithreaded trainer to update the
# parameters without data traces on accumulating to the same .grad.
# NOTE: This should be only used by distributed optimizer internals
# and not meant to expose to the user.
@torch.jit.script
class _FunctionalAdadelta(object):
def __init__(
self,
params: List[Tensor],
lr: float = 1.0,
rho: float = 0.9,
eps: float = 1e-6,
weight_decay: float = 0.0,
foreach: bool = False,
maximize: bool = False,
_allow_empty_param_list: bool = False,
):
self.defaults = {
"lr": lr,
"rho": rho,
"eps": eps,
"weight_decay": weight_decay,
}
self.foreach = foreach
self.maximize = maximize
if len(params) == 0 and not _allow_empty_param_list:
raise ValueError("optimizer got an empty parameter list")
# NOTE: we only have one param_group and don't allow user to add additional
# param group as it's not a common use case.
self.param_group = {"params": params}
self.state = torch.jit.annotate(Dict[torch.Tensor, Dict[str, torch.Tensor]], {})
def step(self, gradients: List[Optional[Tensor]]):
params = self.param_group['params']
params_with_grad = []
grads = []
square_avgs = []
acc_deltas = []
lr = self.defaults['lr']
rho = self.defaults['rho']
eps = self.defaults['eps']
weight_decay = self.defaults['weight_decay']
if len(params) != len(gradients):
raise ValueError(
"the gradients passed in does not equal to the size of the parameters!"
+ f"Params length: {len(params)}. "
+ f"Gradients length: {len(gradients)}"
)
for param, gradient in zip(params, gradients):
if gradient is not None:
params_with_grad.append(param)
grads.append(gradient)
# Lazy state initialization
if param not in self.state:
self.state[param] = {}
state = self.state[param]
state['step'] = torch.tensor(0.0)
state['square_avg'] = torch.zeros_like(param, memory_format=torch.preserve_format)
state['acc_delta'] = torch.zeros_like(param, memory_format=torch.preserve_format)
state = self.state[param]
square_avgs.append(state['square_avg'])
acc_deltas.append(state['acc_delta'])
with torch.no_grad():
F.adadelta(params_with_grad,
grads,
square_avgs,
acc_deltas,
lr=lr,
rho=rho,
eps=eps,
weight_decay=weight_decay,
foreach=self.foreach,
maximize=self.maximize)
|
