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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 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. | |
class _FunctionalAdadelta: | |
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)}" | |
) | |
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 | |
) | |
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, | |
has_complex=has_complex | |
) | |