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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 Adagrad Optimizer | |
| # where we use these optimizer in a functional way. | |
| # Instead of using the `param.grad` when updating parameters, | |
| # we explicitly let the user pass gradients to the `step` function | |
| # this is so that 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 _FunctionalAdagrad: | |
| def __init__( | |
| self, | |
| params: List[Tensor], | |
| lr: float = 1e-2, | |
| lr_decay: float = 0.0, | |
| weight_decay: float = 0.0, | |
| initial_accumulator_value: float = 0.0, | |
| warmup_lr_multiplier: float = 1.0, | |
| warmup_num_iters: float = 0.0, | |
| eps: float = 1e-10, | |
| coalesce_grad: bool = True, | |
| foreach: bool = False, | |
| maximize: bool = False, | |
| _allow_empty_param_list: bool = False, | |
| ): | |
| self.defaults = { | |
| "lr": lr, | |
| "lr_decay": lr_decay, | |
| "eps": eps, | |
| "weight_decay": weight_decay, | |
| "initial_accumulator_value": initial_accumulator_value, | |
| "warmup_lr_multiplier": warmup_lr_multiplier, | |
| "warmup_num_iters": warmup_num_iters, | |
| } | |
| self.coalesce_grad = coalesce_grad | |
| 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} | |
| # TODO: no union or any types in TorchScript, make step a scalar tensor instead | |
| # This is also needed by if we want to share_memory on the step across processes | |
| for p in self.param_group["params"]: | |
| self.state[p] = { | |
| "sum": torch.full_like(p.data, initial_accumulator_value), | |
| "step": torch.tensor(0.0), | |
| } | |
| def step(self, gradients: List[Optional[Tensor]]): | |
| params = self.param_group["params"] | |
| params_with_grad = [] | |
| grads = [] | |
| state_sums = [] | |
| 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_sparse_grad, has_complex = False, False | |
| for param, gradient in zip(self.param_group["params"], gradients): | |
| if gradient is not None: | |
| has_sparse_grad |= gradient.is_sparse | |
| has_complex |= torch.is_complex(param) | |
| params_with_grad.append(param) | |
| grads.append(gradient) | |
| state = self.state[param] | |
| state_sums.append(state["sum"]) | |
| state_steps.append(state["step"]) | |
| with torch.no_grad(): | |
| F.adagrad( | |
| params, | |
| grads, | |
| state_sums, | |
| state_steps, | |
| lr=self.defaults["lr"], | |
| weight_decay=self.defaults["weight_decay"], | |
| lr_decay=self.defaults["lr_decay"], | |
| eps=self.defaults["eps"], | |
| has_sparse_grad=has_sparse_grad, | |
| foreach=self.foreach, | |
| maximize=self.maximize, | |
| has_complex=has_complex, | |
| ) | |