340 lines
10 KiB
Python
340 lines
10 KiB
Python
import torch
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from torch import Tensor
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from .optimizer import (Optimizer, _use_grad_for_differentiable, _get_value, _default_to_fused_or_foreach,
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_differentiable_doc, _foreach_doc, _maximize_doc)
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from torch._utils import is_compiling
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from torch.utils._foreach_utils import _group_tensors_by_device_and_dtype
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from typing import List, Optional
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__all__ = ["ASGD", "asgd"]
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def _to_tensor(x):
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if not isinstance(x, torch.Tensor):
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return torch.tensor(x)
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return x
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class ASGD(Optimizer):
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def __init__(
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self,
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params,
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lr=1e-2,
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lambd=1e-4,
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alpha=0.75,
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t0=1e6,
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weight_decay=0,
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foreach: Optional[bool] = None,
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maximize: bool = False,
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differentiable: bool = False,
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):
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if not 0.0 <= lr:
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raise ValueError("Invalid learning rate: {}".format(lr))
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if not 0.0 <= weight_decay:
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raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
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defaults = dict(
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lr=lr,
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lambd=lambd,
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alpha=alpha,
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t0=t0,
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weight_decay=weight_decay,
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foreach=foreach,
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maximize=maximize,
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differentiable=differentiable,
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)
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super().__init__(params, defaults)
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def __setstate__(self, state):
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super().__setstate__(state)
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for group in self.param_groups:
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group.setdefault("foreach", None)
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group.setdefault("maximize", False)
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group.setdefault("differentiable", False)
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state_values = list(self.state.values())
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step_is_tensor = (len(state_values) != 0) and torch.is_tensor(
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state_values[0]["step"]
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)
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if not step_is_tensor:
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for s in state_values:
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s["step"] = torch.tensor(float(s["step"]))
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eta_is_tensor = (len(state_values) != 0) and torch.is_tensor(
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state_values[0]["eta"]
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)
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if not eta_is_tensor:
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for s in state_values:
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s["eta"] = torch.tensor(s["eta"])
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mu_is_tensor = (len(state_values) != 0) and torch.is_tensor(
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state_values[0]["mu"]
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)
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if not mu_is_tensor:
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for s in state_values:
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s["mu"] = torch.tensor(float(s["mu"]))
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def _init_group(self, group, params_with_grad, grads, mus, axs, etas, state_steps):
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for p in group["params"]:
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if p.grad is not None:
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params_with_grad.append(p)
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if p.grad.is_sparse:
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raise RuntimeError("ASGD does not support sparse gradients")
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grads.append(p.grad)
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state = self.state[p]
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# State initialization
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if len(state) == 0:
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state["step"] = torch.tensor(0.0)
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state["eta"] = torch.tensor(group["lr"])
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state["mu"] = torch.tensor(1.0)
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state["ax"] = torch.zeros_like(
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p, memory_format=torch.preserve_format
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)
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mus.append(state["mu"])
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axs.append(state["ax"])
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etas.append(state["eta"])
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state_steps.append(state["step"])
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@_use_grad_for_differentiable
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def step(self, closure=None):
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"""Performs a single optimization step.
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Args:
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closure (Callable, optional): A closure that reevaluates the model
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and returns the loss.
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"""
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loss = None
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if closure is not None:
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with torch.enable_grad():
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loss = closure()
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for group in self.param_groups:
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params_with_grad = []
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grads = []
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mus = []
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axs = []
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etas = []
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state_steps = []
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self._init_group(group, params_with_grad, grads, mus, axs, etas, state_steps)
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asgd(
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params_with_grad,
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grads,
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axs,
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mus,
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etas,
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state_steps,
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lambd=group["lambd"],
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lr=group["lr"],
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t0=group["t0"],
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alpha=group["alpha"],
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weight_decay=group["weight_decay"],
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foreach=group["foreach"],
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maximize=group["maximize"],
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differentiable=group["differentiable"],
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)
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return loss
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ASGD.__doc__ = r"""Implements Averaged Stochastic Gradient Descent.
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It has been proposed in `Acceleration of stochastic approximation by
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averaging`_.
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Args:
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params (iterable): iterable of parameters to optimize or dicts defining
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parameter groups
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lr (float, optional): learning rate (default: 1e-2)
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lambd (float, optional): decay term (default: 1e-4)
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alpha (float, optional): power for eta update (default: 0.75)
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t0 (float, optional): point at which to start averaging (default: 1e6)
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weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
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{foreach}
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{maximize}
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{differentiable}
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.. _Acceleration of stochastic approximation by averaging:
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https://dl.acm.org/citation.cfm?id=131098
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""".format(foreach=_foreach_doc, maximize=_maximize_doc, differentiable=_differentiable_doc)
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def asgd(
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params: List[Tensor],
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grads: List[Tensor],
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axs: List[Tensor],
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mus: List[Tensor],
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etas: List[Tensor],
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state_steps: List[Tensor],
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# kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
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# setting this as kwarg for now as functional API is compiled by torch/distributed/optim
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foreach: Optional[bool] = None,
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maximize: bool = False,
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differentiable: bool = False,
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*,
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lambd: float,
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lr: float,
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t0: float,
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alpha: float,
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weight_decay: float,
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):
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r"""Functional API that performs asgd algorithm computation.
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See :class:`~torch.optim.ASGD` for details.
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"""
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if foreach is None:
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_, foreach = _default_to_fused_or_foreach(params, differentiable, use_fused=False)
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if foreach and torch.jit.is_scripting():
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raise RuntimeError("torch.jit.script not supported with foreach optimizers")
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if foreach and not torch.jit.is_scripting():
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func = _multi_tensor_asgd
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else:
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func = _single_tensor_asgd
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func(
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params,
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grads,
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axs,
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mus,
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etas,
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state_steps,
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lambd=lambd,
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lr=lr,
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t0=t0,
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alpha=alpha,
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weight_decay=weight_decay,
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maximize=maximize,
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differentiable=differentiable,
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)
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def _single_tensor_asgd(
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params: List[Tensor],
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grads: List[Tensor],
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axs: List[Tensor],
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mus: List[Tensor],
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etas: List[Tensor],
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state_steps: List[Tensor],
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*,
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lambd: float,
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lr: float,
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t0: float,
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alpha: float,
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weight_decay: float,
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maximize: bool,
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differentiable: bool,
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):
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def _to_tensor(x):
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if not isinstance(x, torch.Tensor):
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return torch.tensor(x)
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return x
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for i, param in enumerate(params):
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grad = grads[i]
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grad = grad if not maximize else -grad
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mu = mus[i]
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ax = axs[i]
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eta = etas[i]
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step_t = state_steps[i]
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if torch.is_complex(param):
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grad = torch.view_as_real(grad)
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param = torch.view_as_real(param)
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ax = torch.view_as_real(ax)
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# update step
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step_t += 1
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step = _get_value(step_t)
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if weight_decay != 0:
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grad = grad.add(param, alpha=weight_decay)
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eta_value = _get_value(eta)
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# decay term
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param.mul_(1 - lambd * eta_value)
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# update parameter
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param.add_(grad, alpha=-eta_value)
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# averaging
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if is_compiling() or mu.item() != 1:
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ax.add_(param.sub(ax).mul(mu))
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else:
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ax.copy_(param)
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new_eta = _to_tensor(lr / ((1 + lambd * lr * step) ** alpha))
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eta.copy_(new_eta)
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new_mu = _to_tensor(1 / max(1, step - t0))
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mu.copy_(new_mu)
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def _multi_tensor_asgd(
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params: List[Tensor],
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grads: List[Tensor],
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axs: List[Tensor],
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mus: List[Tensor],
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etas: List[Tensor],
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state_steps: List[Tensor],
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*,
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lambd: float,
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lr: float,
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t0: float,
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alpha: float,
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weight_decay: float,
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maximize: bool,
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differentiable: bool,
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):
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if len(params) == 0:
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return
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assert not differentiable, "_foreach ops don't support autograd"
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grouped_tensors = _group_tensors_by_device_and_dtype([params, grads, axs, mus, etas, state_steps])
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for (grouped_params, grouped_grads, grouped_axs, grouped_mus,
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grouped_etas, grouped_state_steps) in grouped_tensors.values():
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if maximize:
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grouped_grads = torch._foreach_neg(grouped_grads)
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def _view_complex_as_real(tensor_list):
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return [
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torch.view_as_real(t) if torch.is_complex(t) else t for t in tensor_list
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]
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grouped_grads = _view_complex_as_real(grouped_grads)
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grouped_params = _view_complex_as_real(grouped_params)
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grouped_axs = _view_complex_as_real(grouped_axs)
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# update step
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torch._foreach_add_(grouped_state_steps, 1)
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if weight_decay != 0:
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grouped_grads = torch._foreach_add(grouped_grads, grouped_params, alpha=weight_decay)
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# decay term
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eta = _get_value(grouped_etas[0])
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torch._foreach_mul_(grouped_params, 1 - lambd * eta)
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# update parameter
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torch._foreach_add_(grouped_params, grouped_grads, alpha=-eta)
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# averaging
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for i in range(len(grouped_axs)):
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if is_compiling() or grouped_mus[i].item() != 1:
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grouped_axs[i].add_(grouped_params[i].sub(grouped_axs[i]).mul(grouped_mus[i]))
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else:
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grouped_axs[i].copy_(grouped_params[i])
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# update eta and mu
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for i in range(len(grouped_mus)):
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new_eta = _to_tensor(
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lr / (1 + lambd * lr * _get_value(grouped_state_steps[i]) ** alpha)
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)
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grouped_etas[i].copy_(new_eta)
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new_mu = _to_tensor(1 / max(1, _get_value(grouped_state_steps[i]) - t0))
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grouped_mus[i].copy_(new_mu)
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