123 lines
4.3 KiB
Python
123 lines
4.3 KiB
Python
|
from typing import Dict, List, Optional
|
||
|
|
|
||
|
|
import torch
|
||
|
|
import torch.optim._functional as F
|
||
|
|
|
||
|
|
from torch import Tensor
|
||
|
|
|
||
|
|
__all__: List[str] = []
|
||
|
|
|
||
|
|
# Define a TorchScript compatible Functional RMSprop 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 _FunctionalRMSprop:
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
params: List[Tensor],
|
||
|
|
lr: float = 1e-2,
|
||
|
|
alpha: float = 0.99,
|
||
|
|
eps: float = 1e-8,
|
||
|
|
weight_decay: float = 0.0,
|
||
|
|
momentum: float = 0.0,
|
||
|
|
centered: bool = False,
|
||
|
|
foreach: bool = False,
|
||
|
|
maximize: bool = False,
|
||
|
|
_allow_empty_param_list: bool = False,
|
||
|
|
):
|
||
|
|
self.defaults = {
|
||
|
|
"lr": lr,
|
||
|
|
"alpha": alpha,
|
||
|
|
"eps": eps,
|
||
|
|
"weight_decay": weight_decay,
|
||
|
|
"momentum": momentum,
|
||
|
|
}
|
||
|
|
self.centered = centered
|
||
|
|
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 = []
|
||
|
|
grad_avgs = []
|
||
|
|
momentum_buffer_list = []
|
||
|
|
lr = self.defaults["lr"]
|
||
|
|
alpha = self.defaults["alpha"]
|
||
|
|
eps = self.defaults["eps"]
|
||
|
|
momentum = self.defaults["momentum"]
|
||
|
|
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)}"
|
||
|
|
)
|
||
|
|
|
||
|
|
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["square_avg"] = torch.zeros_like(
|
||
|
|
param, memory_format=torch.preserve_format
|
||
|
|
)
|
||
|
|
if momentum > 0:
|
||
|
|
state["momentum_buffer"] = torch.zeros_like(
|
||
|
|
param, memory_format=torch.preserve_format
|
||
|
|
)
|
||
|
|
if self.centered:
|
||
|
|
state["grad_avg"] = torch.zeros_like(
|
||
|
|
param, memory_format=torch.preserve_format
|
||
|
|
)
|
||
|
|
|
||
|
|
state = self.state[param]
|
||
|
|
square_avgs.append(state["square_avg"])
|
||
|
|
if momentum > 0:
|
||
|
|
momentum_buffer_list.append(state["momentum_buffer"])
|
||
|
|
if self.centered:
|
||
|
|
grad_avgs.append(state["grad_avg"])
|
||
|
|
|
||
|
|
state["step"] += 1
|
||
|
|
|
||
|
|
with torch.no_grad():
|
||
|
|
F.rmsprop(
|
||
|
|
params_with_grad,
|
||
|
|
grads,
|
||
|
|
square_avgs,
|
||
|
|
grad_avgs,
|
||
|
|
momentum_buffer_list,
|
||
|
|
lr=lr,
|
||
|
|
alpha=alpha,
|
||
|
|
eps=eps,
|
||
|
|
weight_decay=weight_decay,
|
||
|
|
momentum=momentum,
|
||
|
|
centered=self.centered,
|
||
|
|
foreach=self.foreach,
|
||
|
|
maximize=self.maximize,
|
||
|
|
has_complex=has_complex,
|
||
|
|
)
|