317 lines
11 KiB
Python
317 lines
11 KiB
Python
import torch
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from torch import Tensor
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from .optimizer import (Optimizer, _use_grad_for_differentiable, _default_to_fused_or_foreach,
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_differentiable_doc, _foreach_doc, _maximize_doc, _view_as_real)
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from typing import List, Optional
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__all__ = ["Adadelta", "adadelta"]
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class Adadelta(Optimizer):
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def __init__(
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self,
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params,
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lr=1.0,
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rho=0.9,
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eps=1e-6,
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weight_decay=0,
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foreach: Optional[bool] = None,
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*,
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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(f"Invalid learning rate: {lr}")
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if not 0.0 <= rho <= 1.0:
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raise ValueError(f"Invalid rho value: {rho}")
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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 <= weight_decay:
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raise ValueError(f"Invalid weight_decay value: {weight_decay}")
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defaults = dict(
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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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maximize=maximize,
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foreach=foreach,
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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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def _init_group(self, group, params_with_grad, grads, square_avgs, acc_deltas):
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has_complex = False
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for p in group["params"]:
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if p.grad is None:
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continue
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has_complex |= torch.is_complex(p)
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params_with_grad.append(p)
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if p.grad.is_sparse:
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raise RuntimeError("Adadelta 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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# Lazy state initialization
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if len(state) == 0:
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state["step"] = 0
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state["square_avg"] = torch.zeros_like(
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p, memory_format=torch.preserve_format
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)
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state["acc_delta"] = torch.zeros_like(
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p, memory_format=torch.preserve_format
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)
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square_avgs.append(state["square_avg"])
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acc_deltas.append(state["acc_delta"])
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state["step"] += 1
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return has_complex
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@_use_grad_for_differentiable
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def step(self, closure=None):
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"""Perform 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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square_avgs = []
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acc_deltas = []
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lr, rho, eps, weight_decay, foreach, maximize, differentiable = (
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group["lr"],
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group["rho"],
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group["eps"],
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group["weight_decay"],
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group["foreach"],
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group["maximize"],
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group["differentiable"],
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)
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has_complex = self._init_group(group, params_with_grad, grads, square_avgs, acc_deltas)
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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=foreach,
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maximize=maximize,
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differentiable=differentiable,
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has_complex=has_complex,
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)
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return loss
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Adadelta.__doc__ = r"""Implements Adadelta algorithm.
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.. math::
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\begin{aligned}
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&\rule{110mm}{0.4pt} \\
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&\textbf{input} : \gamma \text{ (lr)}, \: \theta_0 \text{ (params)},
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\: f(\theta) \text{ (objective)}, \: \rho \text{ (decay)},
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\: \lambda \text{ (weight decay)} \\
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&\textbf{initialize} : v_0 \leftarrow 0 \: \text{ (square avg)},
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\: u_0 \leftarrow 0 \: \text{ (accumulate variables)} \\[-1.ex]
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&\rule{110mm}{0.4pt} \\
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&\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\
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&\hspace{5mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\
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&\hspace{5mm}if \: \lambda \neq 0 \\
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&\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\
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&\hspace{5mm} v_t \leftarrow v_{t-1} \rho + g^2_t (1 - \rho) \\
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&\hspace{5mm}\Delta x_t \leftarrow \frac{\sqrt{u_{t-1} +
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\epsilon }}{ \sqrt{v_t + \epsilon} }g_t \hspace{21mm} \\
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&\hspace{5mm} u_t \leftarrow u_{t-1} \rho +
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\Delta x^2_t (1 - \rho) \\
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&\hspace{5mm}\theta_t \leftarrow \theta_{t-1} - \gamma \Delta x_t \\
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&\rule{110mm}{0.4pt} \\[-1.ex]
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&\bf{return} \: \theta_t \\[-1.ex]
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&\rule{110mm}{0.4pt} \\[-1.ex]
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\end{aligned}
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For further details regarding the algorithm we refer to `ADADELTA: An Adaptive Learning Rate Method`_.
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""" + fr"""
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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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rho (float, optional): coefficient used for computing a running average
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of squared gradients (default: 0.9). A higher value of `rho` will
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result in a slower average, which can be helpful for preventing
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oscillations in the learning process.
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eps (float, optional): term added to the denominator to improve
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numerical stability (default: 1e-6).
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lr (float, optional): coefficient that scale delta before it is applied
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to the parameters (default: 1.0)
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weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
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{_foreach_doc}
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{_maximize_doc}
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{_differentiable_doc}
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.. _ADADELTA\: An Adaptive Learning Rate Method:
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https://arxiv.org/abs/1212.5701
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"""
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def adadelta(
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params: List[Tensor],
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grads: List[Tensor],
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square_avgs: List[Tensor],
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acc_deltas: 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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differentiable: bool = False,
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has_complex: bool = False,
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*,
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lr: float,
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rho: float,
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eps: float,
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weight_decay: float,
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maximize: bool,
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):
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r"""Functional API that performs Adadelta algorithm computation.
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See :class:`~torch.optim.Adadelta` for details.
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"""
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# We still respect when the user inputs False for foreach.
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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_adadelta
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else:
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func = _single_tensor_adadelta
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func(
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params,
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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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maximize=maximize,
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differentiable=differentiable,
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has_complex=has_complex,
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)
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def _single_tensor_adadelta(
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params: List[Tensor],
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grads: List[Tensor],
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square_avgs: List[Tensor],
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acc_deltas: List[Tensor],
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*,
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lr: float,
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rho: float,
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eps: float,
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weight_decay: float,
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maximize: bool,
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differentiable: bool,
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has_complex: bool,
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):
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for (param, grad, square_avg, acc_delta) in zip(
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params, grads, square_avgs, acc_deltas
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):
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grad = grad if not maximize else -grad
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if weight_decay != 0:
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grad = grad.add(param, alpha=weight_decay)
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if torch.is_complex(param):
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square_avg = torch.view_as_real(square_avg)
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acc_delta = torch.view_as_real(acc_delta)
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grad = torch.view_as_real(grad)
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square_avg.mul_(rho).addcmul_(grad, grad, value=1 - rho)
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std = square_avg.add(eps).sqrt_()
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delta = acc_delta.add(eps).sqrt_()
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if differentiable:
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delta = delta.clone()
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delta.div_(std).mul_(grad)
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acc_delta.mul_(rho).addcmul_(delta, delta, value=1 - rho)
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if torch.is_complex(param):
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delta = torch.view_as_complex(delta)
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param.add_(delta, alpha=-lr)
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def _multi_tensor_adadelta(
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params: List[Tensor],
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grads: List[Tensor],
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square_avgs: List[Tensor],
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acc_deltas: List[Tensor],
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*,
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lr: float,
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weight_decay: float,
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rho: float,
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eps: float,
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maximize: bool,
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differentiable: bool,
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has_complex: bool,
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):
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assert not differentiable, "_foreach ops don't support autograd"
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if len(params) == 0:
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return
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grouped_tensors = Optimizer._group_tensors_by_device_and_dtype([params, grads, square_avgs, acc_deltas])
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for ((device_params, device_grads, device_square_avgs, device_acc_deltas), _) in grouped_tensors.values():
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if has_complex:
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_view_as_real(device_params, device_grads, device_square_avgs, device_acc_deltas)
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if maximize:
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device_grads = torch._foreach_neg(device_grads)
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if weight_decay != 0:
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# Re-use the intermediate memory (device_grads) already allocated for maximize
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if maximize:
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torch._foreach_add_(device_grads, device_params, alpha=weight_decay)
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else:
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device_grads = torch._foreach_add(device_grads, device_params, alpha=weight_decay)
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torch._foreach_mul_(device_square_avgs, rho)
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torch._foreach_addcmul_(device_square_avgs, device_grads, device_grads, value=1 - rho)
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std = torch._foreach_add(device_square_avgs, eps)
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torch._foreach_sqrt_(std)
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deltas = torch._foreach_add(device_acc_deltas, eps)
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torch._foreach_sqrt_(deltas)
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torch._foreach_div_(deltas, std)
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torch._foreach_mul_(deltas, device_grads)
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torch._foreach_add_(device_params, deltas, alpha=-lr)
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torch._foreach_mul_(device_acc_deltas, rho)
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torch._foreach_addcmul_(device_acc_deltas, deltas, deltas, value=1 - rho)
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