101 lines
3.5 KiB
Python
101 lines
3.5 KiB
Python
|
from typing import Dict, List, Optional, Tuple
|
||
|
|
|
||
|
|
import torch
|
||
|
|
import torch.optim._functional as F
|
||
|
|
|
||
|
|
from torch import Tensor
|
||
|
|
|
||
|
|
__all__: List[str] = []
|
||
|
|
|
||
|
|
# Define a TorchScript compatible Functional Rprop 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 _FunctionalRprop:
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
params: List[Tensor],
|
||
|
|
lr: float = 1e-2,
|
||
|
|
etas: Tuple[float, float] = (0.5, 1.2),
|
||
|
|
step_sizes: Tuple[float, float] = (1e-6, 50),
|
||
|
|
foreach: bool = False,
|
||
|
|
maximize: bool = False,
|
||
|
|
_allow_empty_param_list: bool = False,
|
||
|
|
):
|
||
|
|
self.defaults = {
|
||
|
|
"lr": lr,
|
||
|
|
}
|
||
|
|
self.etas = etas
|
||
|
|
self.step_sizes = step_sizes
|
||
|
|
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 = []
|
||
|
|
prevs = []
|
||
|
|
step_sizes = []
|
||
|
|
lr = self.defaults["lr"]
|
||
|
|
etaminus, etaplus = self.etas
|
||
|
|
step_size_min, step_size_max = self.step_sizes
|
||
|
|
|
||
|
|
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)}"
|
||
|
|
)
|
||
|
|
|
||
|
|
has_complex = False
|
||
|
|
for param, gradient in zip(params, gradients):
|
||
|
|
if gradient is not None:
|
||
|
|
has_complex |= torch.is_complex(param)
|
||
|
|
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["prev"] = torch.zeros_like(
|
||
|
|
param, memory_format=torch.preserve_format
|
||
|
|
)
|
||
|
|
state["step_size"] = torch.full_like(gradient, lr)
|
||
|
|
|
||
|
|
state = self.state[param]
|
||
|
|
prevs.append(state["prev"])
|
||
|
|
step_sizes.append(state["step_size"])
|
||
|
|
|
||
|
|
state["step"] += 1
|
||
|
|
|
||
|
|
with torch.no_grad():
|
||
|
|
F.rprop(
|
||
|
|
params_with_grad,
|
||
|
|
grads,
|
||
|
|
prevs,
|
||
|
|
step_sizes,
|
||
|
|
step_size_min=step_size_min,
|
||
|
|
step_size_max=step_size_max,
|
||
|
|
etaminus=etaminus,
|
||
|
|
etaplus=etaplus,
|
||
|
|
foreach=self.foreach,
|
||
|
|
maximize=self.maximize,
|
||
|
|
has_complex=has_complex,
|
||
|
|
)
|