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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 Adamax 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 _FunctionalAdamax: | |
def __init__( | |
self, | |
params: List[Tensor], | |
lr: float = 1e-3, | |
betas: Tuple[float, float] = (0.9, 0.999), | |
eps: float = 1e-8, | |
weight_decay: float = 0.0, | |
foreach: bool = False, | |
maximize: bool = False, | |
_allow_empty_param_list: bool = False, | |
): | |
if not 0.0 <= lr: | |
raise ValueError(f"Invalid learning rate: {lr}") | |
if not 0.0 <= eps: | |
raise ValueError(f"Invalid epsilon value: {eps}") | |
if not 0.0 <= betas[0] < 1.0: | |
raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}") | |
if not 0.0 <= betas[1] < 1.0: | |
raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}") | |
if not 0.0 <= weight_decay: | |
raise ValueError(f"Invalid weight_decay value: {weight_decay}") | |
self.defaults = { | |
"lr": lr, | |
"eps": eps, | |
"beta1": betas[0], | |
"beta2": betas[1], | |
"weight_decay": weight_decay, | |
} | |
self.foreach = foreach | |
self.maximize = maximize | |
self.state = torch.jit.annotate(Dict[torch.Tensor, Dict[str, torch.Tensor]], {}) | |
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} | |
def step(self, gradients: List[Optional[Tensor]]): | |
params = self.param_group["params"] | |
params_with_grad = [] | |
grads = [] | |
exp_avgs = [] | |
exp_infs = [] | |
state_steps: List[Tensor] = [] | |
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(self.param_group["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) | |
# Exponential moving average of gradient values | |
state["exp_avg"] = torch.zeros_like( | |
param, memory_format=torch.preserve_format | |
) | |
# Exponential moving average of squared gradient values | |
state["exp_inf"] = torch.zeros_like( | |
param, memory_format=torch.preserve_format | |
) | |
state = self.state[param] | |
exp_avgs.append(state["exp_avg"]) | |
exp_infs.append(state["exp_inf"]) | |
state_steps.append(state["step"]) | |
with torch.no_grad(): | |
F.adamax( | |
params_with_grad, | |
grads, | |
exp_avgs, | |
exp_infs, | |
state_steps, | |
eps=self.defaults["eps"], | |
beta1=self.defaults["beta1"], | |
beta2=self.defaults["beta2"], | |
lr=self.defaults["lr"], | |
weight_decay=self.defaults["weight_decay"], | |
foreach=self.foreach, | |
maximize=self.maximize, | |
has_complex=has_complex, | |
) | |