mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-06 12:20:52 +01:00
ce3094f5f6
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/70482
Test Plan: Imported from OSS
Reviewed By: anjali411
Differential Revision: D33767862
Pulled By: mikaylagawarecki
fbshipit-source-id: 8e2e9c986d5a3774093a79755940372945f1b3a9
(cherry picked from commit baea537277)
276 lines
12 KiB
Python
276 lines
12 KiB
Python
import torch
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from torch import Tensor
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from .optimizer import Optimizer
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from typing import List, Optional
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class RMSprop(Optimizer):
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r"""Implements RMSprop 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} : \alpha \text{ (alpha)},\: \gamma \text{ (lr)},
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\: \theta_0 \text{ (params)}, \: f(\theta) \text{ (objective)} \\
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&\hspace{13mm} \lambda \text{ (weight decay)},\: \mu \text{ (momentum)},\: centered\\
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&\textbf{initialize} : v_0 \leftarrow 0 \text{ (square average)}, \:
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\textbf{b}_0 \leftarrow 0 \text{ (buffer)}, \: g^{ave}_0 \leftarrow 0 \\[-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 \alpha v_{t-1} + (1 - \alpha) g^2_t
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\hspace{8mm} \\
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&\hspace{5mm} \tilde{v_t} \leftarrow v_t \\
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&\hspace{5mm}if \: centered \\
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&\hspace{10mm} g^{ave}_t \leftarrow g^{ave}_{t-1} \alpha + (1-\alpha) g_t \\
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&\hspace{10mm} \tilde{v_t} \leftarrow \tilde{v_t} - \big(g^{ave}_{t} \big)^2 \\
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&\hspace{5mm}if \: \mu > 0 \\
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&\hspace{10mm} \textbf{b}_t\leftarrow \mu \textbf{b}_{t-1} +
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g_t/ \big(\sqrt{\tilde{v_t}} + \epsilon \big) \\
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&\hspace{10mm} \theta_t \leftarrow \theta_{t-1} - \gamma \textbf{b}_t \\
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&\hspace{5mm} else \\
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&\hspace{10mm}\theta_t \leftarrow \theta_{t-1} -
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\gamma g_t/ \big(\sqrt{\tilde{v_t}} + \epsilon \big) \hspace{3mm} \\
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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
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`lecture notes <https://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf>`_ by G. Hinton.
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and centered version `Generating Sequences
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With Recurrent Neural Networks <https://arxiv.org/pdf/1308.0850v5.pdf>`_.
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The implementation here takes the square root of the gradient average before
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adding epsilon (note that TensorFlow interchanges these two operations). The effective
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learning rate is thus :math:`\gamma/(\sqrt{v} + \epsilon)` where :math:`\gamma`
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is the scheduled learning rate and :math:`v` is the weighted moving average
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of the squared gradient.
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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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momentum (float, optional): momentum factor (default: 0)
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alpha (float, optional): smoothing constant (default: 0.99)
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eps (float, optional): term added to the denominator to improve
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numerical stability (default: 1e-8)
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centered (bool, optional) : if ``True``, compute the centered RMSProp,
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the gradient is normalized by an estimation of its variance
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weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
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foreach (bool, optional): whether foreach implementation of optimizer
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is used (default: None)
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"""
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def __init__(self, params, lr=1e-2, alpha=0.99, eps=1e-8, weight_decay=0, momentum=0,
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centered=False, foreach: Optional[bool] = None):
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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 <= eps:
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raise ValueError("Invalid epsilon value: {}".format(eps))
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if not 0.0 <= momentum:
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raise ValueError("Invalid momentum value: {}".format(momentum))
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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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if not 0.0 <= alpha:
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raise ValueError("Invalid alpha value: {}".format(alpha))
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defaults = dict(lr=lr, momentum=momentum, alpha=alpha, eps=eps, centered=centered,
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weight_decay=weight_decay, foreach=foreach)
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super(RMSprop, self).__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('momentum', 0)
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group.setdefault('centered', False)
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group.setdefault('foreach', None)
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@torch.no_grad()
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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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square_avgs = []
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grad_avgs = []
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momentum_buffer_list = []
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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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params_with_grad.append(p)
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if p.grad.is_sparse:
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raise RuntimeError('RMSprop 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'] = 0
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state['square_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
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if group['momentum'] > 0:
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state['momentum_buffer'] = torch.zeros_like(p, memory_format=torch.preserve_format)
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if group['centered']:
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state['grad_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
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square_avgs.append(state['square_avg'])
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if group['momentum'] > 0:
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momentum_buffer_list.append(state['momentum_buffer'])
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if group['centered']:
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grad_avgs.append(state['grad_avg'])
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state['step'] += 1
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rmsprop(params_with_grad,
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grads,
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square_avgs,
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grad_avgs,
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momentum_buffer_list,
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lr=group['lr'],
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alpha=group['alpha'],
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eps=group['eps'],
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weight_decay=group['weight_decay'],
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momentum=group['momentum'],
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centered=group['centered'],
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foreach=group['foreach'])
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return loss
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def rmsprop(params: List[Tensor],
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grads: List[Tensor],
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square_avgs: List[Tensor],
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grad_avgs: List[Tensor],
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momentum_buffer_list: 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: bool = None,
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*,
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lr: float,
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alpha: float,
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eps: float,
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weight_decay: float,
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momentum: float,
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centered: bool):
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r"""Functional API that performs rmsprop algorithm computation.
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See :class:`~torch.optim.RMSProp` for details.
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"""
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if foreach is None:
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# Placeholder for more complex foreach logic to be added when value is not set
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foreach = 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_rmsprop
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else:
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func = _single_tensor_rmsprop
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func(params,
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grads,
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square_avgs,
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grad_avgs,
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momentum_buffer_list,
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lr=lr,
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alpha=alpha,
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eps=eps,
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weight_decay=weight_decay,
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momentum=momentum,
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centered=centered)
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def _single_tensor_rmsprop(params: List[Tensor],
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grads: List[Tensor],
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square_avgs: List[Tensor],
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grad_avgs: List[Tensor],
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momentum_buffer_list: List[Tensor],
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*,
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lr: float,
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alpha: float,
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eps: float,
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weight_decay: float,
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momentum: float,
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centered: bool):
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for i, param in enumerate(params):
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grad = grads[i]
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square_avg = square_avgs[i]
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if weight_decay != 0:
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grad = grad.add(param, alpha=weight_decay)
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square_avg.mul_(alpha).addcmul_(grad, grad, value=1 - alpha)
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if centered:
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grad_avg = grad_avgs[i]
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grad_avg.mul_(alpha).add_(grad, alpha=1 - alpha)
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avg = square_avg.addcmul(grad_avg, grad_avg, value=-1).sqrt_().add_(eps)
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else:
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avg = square_avg.sqrt().add_(eps)
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if momentum > 0:
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buf = momentum_buffer_list[i]
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buf.mul_(momentum).addcdiv_(grad, avg)
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param.add_(buf, alpha=-lr)
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else:
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param.addcdiv_(grad, avg, value=-lr)
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def _multi_tensor_rmsprop(params: List[Tensor],
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grads: List[Tensor],
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square_avgs: List[Tensor],
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grad_avgs: List[Tensor],
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momentum_buffer_list: List[Tensor],
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*,
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lr: float,
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alpha: float,
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eps: float,
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weight_decay: float,
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momentum: float,
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centered: bool):
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if len(params) == 0:
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return
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if weight_decay != 0:
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torch._foreach_add_(grads, params, alpha=weight_decay)
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torch._foreach_mul_(square_avgs, alpha)
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torch._foreach_addcmul_(square_avgs, grads, grads, value=1 - alpha)
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if centered:
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torch._foreach_mul_(grad_avgs, alpha)
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torch._foreach_add_(grad_avgs, grads, alpha=1 - alpha)
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avg = torch._foreach_addcmul(square_avgs, grad_avgs, grad_avgs, value=-1)
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torch._foreach_sqrt_(avg)
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torch._foreach_add_(avg, eps)
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else:
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avg = torch._foreach_sqrt(square_avgs)
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torch._foreach_add_(avg, eps)
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if momentum > 0:
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torch._foreach_mul_(momentum_buffer_list, momentum)
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torch._foreach_addcdiv_(momentum_buffer_list, grads, avg)
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torch._foreach_add_(params, momentum_buffer_list, alpha=-lr)
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else:
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torch._foreach_addcdiv_(params, grads, avg, value=-lr)
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