118 lines
4.3 KiB
Python
118 lines
4.3 KiB
Python
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from typing import Dict, List, Optional, Tuple
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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 Adamax 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 _FunctionalAdamax:
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def __init__(
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self,
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params: List[Tensor],
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lr: float = 1e-3,
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betas: Tuple[float, float] = (0.9, 0.999),
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eps: float = 1e-8,
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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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if not 0.0 <= lr:
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raise ValueError(f"Invalid learning rate: {lr}")
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if not 0.0 <= eps:
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raise ValueError(f"Invalid epsilon value: {eps}")
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if not 0.0 <= betas[0] < 1.0:
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raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}")
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if not 0.0 <= betas[1] < 1.0:
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raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}")
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if not 0.0 <= weight_decay:
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raise ValueError(f"Invalid weight_decay value: {weight_decay}")
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self.defaults = {
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"lr": lr,
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"eps": eps,
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"beta1": betas[0],
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"beta2": betas[1],
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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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self.state = torch.jit.annotate(Dict[torch.Tensor, Dict[str, torch.Tensor]], {})
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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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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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exp_avgs = []
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exp_infs = []
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state_steps: List[Tensor] = []
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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(self.param_group["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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# Exponential moving average of gradient values
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state["exp_avg"] = torch.zeros_like(
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param, memory_format=torch.preserve_format
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)
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# Exponential moving average of squared gradient values
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state["exp_inf"] = 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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exp_avgs.append(state["exp_avg"])
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exp_infs.append(state["exp_inf"])
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state_steps.append(state["step"])
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with torch.no_grad():
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F.adamax(
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params_with_grad,
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grads,
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exp_avgs,
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exp_infs,
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state_steps,
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eps=self.defaults["eps"],
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beta1=self.defaults["beta1"],
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beta2=self.defaults["beta2"],
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lr=self.defaults["lr"],
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weight_decay=self.defaults["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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