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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 SGD 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 _FunctionalSGD: | |
| def __init__( | |
| self, | |
| params: List[Tensor], | |
| lr: float = 1e-2, | |
| momentum: float = 0.0, | |
| dampening: float = 0.0, | |
| weight_decay: float = 0.0, | |
| nesterov: bool = False, | |
| maximize: bool = False, | |
| foreach: bool = False, | |
| _allow_empty_param_list: bool = False, | |
| ): | |
| self.defaults = { | |
| "lr": lr, | |
| "momentum": momentum, | |
| "dampening": dampening, | |
| "weight_decay": weight_decay, | |
| } | |
| self.nesterov = nesterov | |
| self.maximize = maximize | |
| self.foreach = foreach | |
| 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]): | |
| """Similar to self.step, but operates on a single parameter and | |
| its gradient. | |
| """ | |
| # TODO: Once step_param interface is robust, refactor step to call | |
| # step param on each param. | |
| weight_decay = self.defaults["weight_decay"] | |
| momentum = self.defaults["momentum"] | |
| dampening = self.defaults["dampening"] | |
| lr = self.defaults["lr"] | |
| params = [param] | |
| momentum_buffer_list: List[Optional[Tensor]] = [] | |
| grads = [] | |
| has_sparse_grad = False | |
| if grad is not None: | |
| grads.append(grad) | |
| if grad.is_sparse: | |
| has_sparse_grad = True | |
| if param not in self.state: | |
| self.state[param] = {} | |
| state = self.state[param] | |
| if "momentum_buffer" not in state: | |
| momentum_buffer_list.append(None) | |
| else: | |
| momentum_buffer_list.append(state["momentum_buffer"]) | |
| with torch.no_grad(): | |
| F.sgd( | |
| params, | |
| grads, | |
| momentum_buffer_list, | |
| weight_decay=weight_decay, | |
| momentum=momentum, | |
| lr=lr, | |
| dampening=dampening, | |
| nesterov=self.nesterov, | |
| maximize=self.maximize, | |
| has_sparse_grad=has_sparse_grad, | |
| foreach=self.foreach, | |
| ) | |
| # update momentum_buffer in state | |
| state = self.state[param] | |
| momentum_buffer = momentum_buffer_list[0] | |
| if momentum_buffer is not None: | |
| state["momentum_buffer"] = momentum_buffer | |
| def step(self, gradients: List[Optional[Tensor]]): | |
| params = self.param_group["params"] | |
| params_with_grad = [] | |
| grads = [] | |
| momentum_buffer_list: List[Optional[Tensor]] = [] | |
| lr = self.defaults["lr"] | |
| weight_decay = self.defaults["weight_decay"] | |
| momentum = self.defaults["momentum"] | |
| dampening = self.defaults["dampening"] | |
| 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 = False | |
| for param, gradient in zip(params, gradients): | |
| if gradient is not None: | |
| params_with_grad.append(param) | |
| grads.append(gradient) | |
| if gradient.is_sparse: | |
| has_sparse_grad = True | |
| if param not in self.state: | |
| self.state[param] = {} | |
| state = self.state[param] | |
| if "momentum_buffer" not in state: | |
| momentum_buffer_list.append(None) | |
| else: | |
| momentum_buffer_list.append(state["momentum_buffer"]) | |
| with torch.no_grad(): | |
| F.sgd( | |
| params_with_grad, | |
| grads, | |
| momentum_buffer_list, | |
| weight_decay=weight_decay, | |
| momentum=momentum, | |
| lr=lr, | |
| dampening=dampening, | |
| nesterov=self.nesterov, | |
| maximize=self.maximize, | |
| has_sparse_grad=has_sparse_grad, | |
| foreach=self.foreach, | |
| ) | |
| # update momentum_buffers in state | |
| for i, p in enumerate(params_with_grad): | |
| state = self.state[p] | |
| momentum_buffer = momentum_buffer_list[i] | |
| if momentum_buffer is not None: | |
| state["momentum_buffer"] = momentum_buffer | |