# mypy: allow-untyped-defs from typing import Optional import torch import torch.optim._functional as F from torch import Tensor from torch.distributed.optim._deprecation_warning import ( _scripted_functional_optimizer_deprecation_warning, ) __all__: list[str] = [] # Define a TorchScript compatible Functional AdamW 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 _FunctionalAdamW: 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 = 1e-2, amsgrad: bool = False, maximize: bool = False, foreach: bool = False, fused: bool = False, _allow_empty_param_list: bool = False, ): _scripted_functional_optimizer_deprecation_warning(stacklevel=2) 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.amsgrad = amsgrad self.maximize = maximize self.foreach = foreach self.fused = fused 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_param(self, param: Tensor, grad: Optional[Tensor]): params_with_grad = [] grads = [] exp_avgs = [] exp_avg_sqs = [] max_exp_avg_sqs = [] state_steps: list[Tensor] = [] has_complex = torch.is_complex(param) if grad is not None: params_with_grad.append(param) grads.append(grad) # 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_avg_sq"] = torch.zeros_like( param, memory_format=torch.preserve_format ) if self.amsgrad: # Maintains max of all exp. moving avg. of sq. grad. values state["max_exp_avg_sq"] = torch.zeros_like( param, memory_format=torch.preserve_format ) state = self.state[param] exp_avgs.append(state["exp_avg"]) exp_avg_sqs.append(state["exp_avg_sq"]) if self.amsgrad: max_exp_avg_sqs.append(state["max_exp_avg_sq"]) state_steps.append(state["step"]) with torch.no_grad(): F.adamw( params_with_grad, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, amsgrad=self.amsgrad, maximize=self.maximize, beta1=self.defaults["beta1"], beta2=self.defaults["beta2"], lr=self.defaults["lr"], weight_decay=self.defaults["weight_decay"], eps=self.defaults["eps"], foreach=self.foreach, fused=self.fused, grad_scale=None, found_inf=None, has_complex=has_complex, ) def step(self, gradients: list[Optional[Tensor]]): params = self.param_group["params"] params_with_grad = [] grads = [] exp_avgs = [] exp_avg_sqs = [] max_exp_avg_sqs = [] 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_avg_sq"] = torch.zeros_like( param, memory_format=torch.preserve_format ) if self.amsgrad: # Maintains max of all exp. moving avg. of sq. grad. values state["max_exp_avg_sq"] = torch.zeros_like( param, memory_format=torch.preserve_format ) state = self.state[param] exp_avgs.append(state["exp_avg"]) exp_avg_sqs.append(state["exp_avg_sq"]) if self.amsgrad: max_exp_avg_sqs.append(state["max_exp_avg_sq"]) state_steps.append(state["step"]) with torch.no_grad(): F.adamw( params_with_grad, grads, exp_avgs, exp_avg_sqs, max_exp_avg_sqs, state_steps, amsgrad=self.amsgrad, maximize=self.maximize, beta1=self.defaults["beta1"], beta2=self.defaults["beta2"], lr=self.defaults["lr"], weight_decay=self.defaults["weight_decay"], eps=self.defaults["eps"], foreach=self.foreach, fused=self.fused, grad_scale=None, found_inf=None, has_complex=has_complex, )