365 lines
14 KiB
Python
365 lines
14 KiB
Python
import math
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import torch
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from torch import Tensor
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from .optimizer import (Optimizer, _use_grad_for_differentiable, _get_value, _dispatch_sqrt, _stack_if_compiling,
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_default_to_fused_or_foreach, _differentiable_doc, _foreach_doc)
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from typing import List, Optional
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from torch.utils._foreach_utils import _group_tensors_by_device_and_dtype
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__all__ = ["RAdam", "radam"]
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class RAdam(Optimizer):
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def __init__(
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self,
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params,
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lr=1e-3,
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betas=(0.9, 0.999),
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eps=1e-8,
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weight_decay=0,
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*,
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foreach: Optional[bool] = None,
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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 <= eps:
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raise ValueError("Invalid epsilon value: {}".format(eps))
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if not 0.0 <= betas[0] < 1.0:
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raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
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if not 0.0 <= betas[1] < 1.0:
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raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
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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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betas=betas,
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eps=eps,
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weight_decay=weight_decay,
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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("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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def _init_group(self, group, params_with_grad, grads, exp_avgs, exp_avg_sqs, 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("RAdam 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"] = 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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p, 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_avg_sq"] = torch.zeros_like(
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p, memory_format=torch.preserve_format
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)
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exp_avgs.append(state["exp_avg"])
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exp_avg_sqs.append(state["exp_avg_sq"])
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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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exp_avgs = []
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exp_avg_sqs = []
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state_steps = []
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beta1, beta2 = group["betas"]
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self._init_group(group, params_with_grad, grads, exp_avgs, exp_avg_sqs, state_steps)
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radam(
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params_with_grad,
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grads,
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exp_avgs,
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exp_avg_sqs,
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state_steps,
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beta1=beta1,
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beta2=beta2,
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lr=group["lr"],
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weight_decay=group["weight_decay"],
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eps=group["eps"],
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foreach=group["foreach"],
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differentiable=group["differentiable"],
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)
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return loss
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RAdam.__doc__ = r"""Implements RAdam 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)}, \: \beta_1, \beta_2
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\text{ (betas)}, \: \theta_0 \text{ (params)}, \:f(\theta) \text{ (objective)}, \:
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\lambda \text{ (weightdecay)}, \\
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&\hspace{13mm} \epsilon \text{ (epsilon)} \\
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&\textbf{initialize} : m_0 \leftarrow 0 \text{ ( first moment)},
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v_0 \leftarrow 0 \text{ ( second moment)}, \\
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&\hspace{18mm} \rho_{\infty} \leftarrow 2/(1-\beta_2) -1 \\[-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{6mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\
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&\hspace{5mm} \textbf{if} \: \lambda \neq 0 \\
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&\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\
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&\hspace{6mm}m_t \leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t \\
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&\hspace{6mm}v_t \leftarrow \beta_2 v_{t-1} + (1-\beta_2) g^2_t \\
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&\hspace{6mm}\widehat{m_t} \leftarrow m_t/\big(1-\beta_1^t \big) \\
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&\hspace{6mm}\rho_t \leftarrow \rho_{\infty} -
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2 t \beta^t_2 /\big(1-\beta_2^t \big) \\[0.1.ex]
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&\hspace{6mm}\textbf{if} \: \rho_t > 5 \\
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&\hspace{12mm} l_t \leftarrow \frac{\sqrt{ (1-\beta^t_2) }}{ \sqrt{v_t} +\epsilon } \\
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&\hspace{12mm} r_t \leftarrow
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\sqrt{\frac{(\rho_t-4)(\rho_t-2)\rho_{\infty}}{(\rho_{\infty}-4)(\rho_{\infty}-2) \rho_t}} \\
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&\hspace{12mm}\theta_t \leftarrow \theta_{t-1} - \gamma \widehat{m_t} r_t l_t \\
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&\hspace{6mm}\textbf{else} \\
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&\hspace{12mm}\theta_t \leftarrow \theta_{t-1} - \gamma \widehat{m_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 `On the variance of the adaptive learning rate and beyond`_.
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This implementation uses the same weight_decay implementation as Adam (were the weight_decay is applied
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to the gradient) and not the one from AdamW (were weight_decay is applied to the update). This
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is different from the `author's implementation`_.
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""" + r"""
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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-3)
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betas (Tuple[float, float], optional): coefficients used for computing
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running averages of gradient and its square (default: (0.9, 0.999))
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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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weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
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{foreach}
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{differentiable}
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.. _On the variance of the adaptive learning rate and beyond:
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https://arxiv.org/abs/1908.03265
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.. _author's implementation:
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https://github.com/LiyuanLucasLiu/RAdam
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""".format(foreach=_foreach_doc, differentiable=_differentiable_doc)
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def radam(
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params: List[Tensor],
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grads: List[Tensor],
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exp_avgs: List[Tensor],
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exp_avg_sqs: 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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differentiable: bool = False,
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*,
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beta1: float,
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beta2: float,
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lr: float,
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weight_decay: float,
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eps: float,
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):
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r"""Functional API that performs RAdam algorithm computation.
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See :class:`~torch.optim.RAdam` for details.
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"""
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if not all(isinstance(t, torch.Tensor) for t in state_steps):
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raise RuntimeError(
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"API has changed, `state_steps` argument must contain a list of singleton tensors"
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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_radam
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else:
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func = _single_tensor_radam
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func(
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params,
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grads,
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exp_avgs,
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exp_avg_sqs,
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state_steps,
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beta1=beta1,
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beta2=beta2,
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lr=lr,
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weight_decay=weight_decay,
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eps=eps,
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differentiable=differentiable,
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)
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def _single_tensor_radam(
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params: List[Tensor],
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grads: List[Tensor],
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exp_avgs: List[Tensor],
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exp_avg_sqs: List[Tensor],
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state_steps: List[Tensor],
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*,
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beta1: float,
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beta2: float,
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lr: float,
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weight_decay: float,
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eps: float,
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differentiable: bool,
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):
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for i, param in enumerate(params):
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grad = grads[i]
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exp_avg = exp_avgs[i]
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exp_avg_sq = exp_avg_sqs[i]
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step_t = state_steps[i]
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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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bias_correction1 = 1 - beta1 ** step
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bias_correction2 = 1 - beta2 ** step
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if weight_decay != 0:
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grad = grad.add(param, alpha=weight_decay)
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# Decay the first and second moment running average coefficient
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exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
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exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
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# correcting bias for the first moving moment
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bias_corrected_exp_avg = exp_avg / bias_correction1
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# maximum length of the approximated SMA
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rho_inf = 2 / (1 - beta2) - 1
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# compute the length of the approximated SMA
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rho_t = rho_inf - 2 * step * (beta2 ** step) / bias_correction2
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if rho_t > 5.0:
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# Compute the variance rectification term and update parameters accordingly
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rect = math.sqrt(
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(rho_t - 4)
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* (rho_t - 2)
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* rho_inf
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/ ((rho_inf - 4) * (rho_inf - 2) * rho_t)
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)
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exp_avg_sq_sqrt = exp_avg_sq.sqrt()
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if differentiable:
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exp_avg_sq_sqrt = exp_avg_sq_sqrt.add(eps)
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else:
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exp_avg_sq_sqrt = exp_avg_sq_sqrt.add_(eps)
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adaptive_lr = math.sqrt(bias_correction2) / exp_avg_sq_sqrt
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param.add_(bias_corrected_exp_avg * lr * adaptive_lr * rect, alpha=-1.0)
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else:
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param.add_(bias_corrected_exp_avg * lr, alpha=-1.0)
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def _multi_tensor_radam(
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params: List[Tensor],
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grads: List[Tensor],
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exp_avgs: List[Tensor],
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exp_avg_sqs: List[Tensor],
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state_steps: List[Tensor],
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*,
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beta1: float,
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beta2: float,
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lr: float,
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weight_decay: float,
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eps: float,
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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, exp_avgs, exp_avg_sqs, state_steps])
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for grouped_params, grouped_grads, grouped_exp_avgs, grouped_exp_avg_sqs, grouped_state_steps in grouped_tensors.values():
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# Update steps
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torch._foreach_add_(grouped_state_steps, 1)
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# maximum length of the approximated SMA
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rho_inf = 2 / (1 - beta2) - 1
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# compute the length of the approximated SMA
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rho_t_list = [rho_inf - 2 * _get_value(step) * (beta2 ** _get_value(step)) /
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(1 - beta2 ** _get_value(step)) for step in grouped_state_steps]
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bias_correction1 = [1 - beta1 ** _get_value(step) for step in grouped_state_steps]
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bias_correction2 = [1 - beta2 ** _get_value(step) for step in grouped_state_steps]
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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 the first and second moment running average coefficient
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torch._foreach_mul_(grouped_exp_avgs, beta1)
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torch._foreach_add_(grouped_exp_avgs, grouped_grads, alpha=1 - beta1)
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torch._foreach_mul_(grouped_exp_avg_sqs, beta2)
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torch._foreach_addcmul_(grouped_exp_avg_sqs, grouped_grads, grouped_grads, 1 - beta2)
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rect = [
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_dispatch_sqrt(
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(rho_t - 4)
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* (rho_t - 2)
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* rho_inf
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/ ((rho_inf - 4) * (rho_inf - 2) * rho_t)
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)
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if rho_t > 5
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else 0
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for rho_t in rho_t_list
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]
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unrectified = [0 if rect > 0 else 1.0 for rect in rect]
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exp_avg_sq_sqrt = torch._foreach_sqrt(grouped_exp_avg_sqs)
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torch._foreach_add_(exp_avg_sq_sqrt, eps)
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bias_correction_sqrt = [_dispatch_sqrt(bc) for bc in bias_correction2]
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denom = torch._foreach_div(exp_avg_sq_sqrt, bias_correction_sqrt)
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step_size = _stack_if_compiling([(lr * rect / bc) * -1 for rect, bc in zip(rect, bias_correction1)])
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torch._foreach_addcdiv_(grouped_params, grouped_exp_avgs, denom, step_size)
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denom = [torch.ones_like(exp_av, memory_format=torch.preserve_format) for exp_av in grouped_exp_avgs]
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step_size = _stack_if_compiling([(lr * rect / bc) * -1 for rect, bc in zip(unrectified, bias_correction1)])
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torch._foreach_addcdiv_(grouped_params, grouped_exp_avgs, denom, step_size)
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