103 lines
3.6 KiB
Python
103 lines
3.6 KiB
Python
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from typing import Dict, List, Optional
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import torch
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import torch.optim._functional as F
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from torch import Tensor
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__all__: List[str] = []
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# Define a TorchScript compatible Functional Adadelta Optimizer
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# where we use these optimizer in a functional way.
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# Instead of using the `param.grad` when updating parameters,
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# we explicitly allow the distributed optimizer pass gradients to
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# the `step` function. In this way, we could separate the gradients
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# and parameters and allow multithreaded trainer to update the
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# parameters without data traces on accumulating to the same .grad.
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# NOTE: This should be only used by distributed optimizer internals
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# and not meant to expose to the user.
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@torch.jit.script
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class _FunctionalAdadelta:
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def __init__(
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self,
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params: List[Tensor],
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lr: float = 1.0,
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rho: float = 0.9,
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eps: float = 1e-6,
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weight_decay: float = 0.0,
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foreach: bool = False,
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maximize: bool = False,
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_allow_empty_param_list: bool = False,
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):
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self.defaults = {
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"lr": lr,
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"rho": rho,
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"eps": eps,
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"weight_decay": weight_decay,
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}
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self.foreach = foreach
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self.maximize = maximize
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if len(params) == 0 and not _allow_empty_param_list:
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raise ValueError("optimizer got an empty parameter list")
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# NOTE: we only have one param_group and don't allow user to add additional
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# param group as it's not a common use case.
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self.param_group = {"params": params}
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self.state = torch.jit.annotate(Dict[torch.Tensor, Dict[str, torch.Tensor]], {})
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def step(self, gradients: List[Optional[Tensor]]):
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params = self.param_group["params"]
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params_with_grad = []
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grads = []
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square_avgs = []
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acc_deltas = []
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lr = self.defaults["lr"]
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rho = self.defaults["rho"]
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eps = self.defaults["eps"]
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weight_decay = self.defaults["weight_decay"]
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if len(params) != len(gradients):
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raise ValueError(
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"the gradients passed in does not equal to the size of the parameters!"
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+ f"Params length: {len(params)}. "
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+ f"Gradients length: {len(gradients)}"
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)
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has_complex = False
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for param, gradient in zip(params, gradients):
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if gradient is not None:
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has_complex |= torch.is_complex(param)
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params_with_grad.append(param)
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grads.append(gradient)
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# Lazy state initialization
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if param not in self.state:
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self.state[param] = {}
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state = self.state[param]
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state["step"] = torch.tensor(0.0)
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state["square_avg"] = torch.zeros_like(
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param, memory_format=torch.preserve_format
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)
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state["acc_delta"] = torch.zeros_like(
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param, memory_format=torch.preserve_format
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)
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state = self.state[param]
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square_avgs.append(state["square_avg"])
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acc_deltas.append(state["acc_delta"])
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with torch.no_grad():
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F.adadelta(
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params_with_grad,
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grads,
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square_avgs,
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acc_deltas,
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lr=lr,
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rho=rho,
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eps=eps,
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weight_decay=weight_decay,
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foreach=self.foreach,
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maximize=self.maximize,
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has_complex=has_complex
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)
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