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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, | |
| ) | |