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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 Rprop 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 _FunctionalRprop: | |
| def __init__( | |
| self, | |
| params: List[Tensor], | |
| lr: float = 1e-2, | |
| etas: Tuple[float, float] = (0.5, 1.2), | |
| step_sizes: Tuple[float, float] = (1e-6, 50), | |
| foreach: bool = False, | |
| maximize: bool = False, | |
| _allow_empty_param_list: bool = False, | |
| ): | |
| self.defaults = { | |
| "lr": lr, | |
| } | |
| self.etas = etas | |
| self.step_sizes = step_sizes | |
| 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 = [] | |
| prevs = [] | |
| step_sizes = [] | |
| lr = self.defaults["lr"] | |
| etaminus, etaplus = self.etas | |
| step_size_min, step_size_max = self.step_sizes | |
| 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["prev"] = torch.zeros_like( | |
| param, memory_format=torch.preserve_format | |
| ) | |
| state["step_size"] = torch.full_like(gradient, lr) | |
| state = self.state[param] | |
| prevs.append(state["prev"]) | |
| step_sizes.append(state["step_size"]) | |
| state["step"] += 1 | |
| with torch.no_grad(): | |
| F.rprop( | |
| params_with_grad, | |
| grads, | |
| prevs, | |
| step_sizes, | |
| step_size_min=step_size_min, | |
| step_size_max=step_size_max, | |
| etaminus=etaminus, | |
| etaplus=etaplus, | |
| foreach=self.foreach, | |
| maximize=self.maximize, | |
| has_complex=has_complex, | |
| ) | |