520 lines
21 KiB
Python
520 lines
21 KiB
Python
from typing import List, Optional
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import torch
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from torch import Tensor
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from .optimizer import (
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Optimizer,
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_default_to_fused_or_foreach,
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_differentiable_doc,
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_capturable_doc,
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_dispatch_sqrt,
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_foreach_doc,
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_get_scalar_dtype,
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_get_value,
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_use_grad_for_differentiable,
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_view_as_real,
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)
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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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decoupled_weight_decay: bool = False,
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*,
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foreach: Optional[bool] = None,
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capturable: 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 <= eps:
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raise ValueError(f"Invalid epsilon value: {eps}")
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if not 0.0 <= betas[0] < 1.0:
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raise ValueError(f"Invalid beta parameter at index 0: {betas[0]}")
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if not 0.0 <= betas[1] < 1.0:
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raise ValueError(f"Invalid beta parameter at index 1: {betas[1]}")
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if not 0.0 <= weight_decay:
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raise ValueError(f"Invalid weight_decay value: {weight_decay}")
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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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capturable=capturable,
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decoupled_weight_decay=decoupled_weight_decay,
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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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group.setdefault("decoupled_weight_decay", False)
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group.setdefault("capturable", False)
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for p in group["params"]:
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p_state = self.state.get(p, [])
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if len(p_state) != 0 and not torch.is_tensor(p_state['step']):
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step_val = float(p_state["step"])
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p_state["step"] = (torch.tensor(step_val, dtype=_get_scalar_dtype(), device=p.device) if group['capturable']
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else torch.tensor(step_val, dtype=_get_scalar_dtype()))
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def _init_group(self, group, params_with_grad, grads, exp_avgs, exp_avg_sqs, state_steps):
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has_complex = False
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for p in group["params"]:
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if p.grad is not None:
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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("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'] = (
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torch.zeros((), dtype=_get_scalar_dtype(), device=p.device)
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if group['capturable']
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else torch.tensor(0.0, dtype=_get_scalar_dtype())
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)
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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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return has_complex
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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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self._cuda_graph_capture_health_check()
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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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has_complex = 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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capturable=group["capturable"],
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differentiable=group["differentiable"],
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decoupled_weight_decay=group["decoupled_weight_decay"],
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has_complex=has_complex,
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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)}, \textit{decoupled\_weight\_decay} \\
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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{6mm} \theta_t \leftarrow \theta_{t-1} \\
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&\hspace{6mm} \textbf{if} \: \lambda \neq 0 \\
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&\hspace{12mm}\textbf{if} \: \textit{decoupled\_weight\_decay} \\
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&\hspace{18mm} \theta_t \leftarrow \theta_{t} - \gamma \lambda \theta_{t} \\
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&\hspace{12mm}\textbf{else} \\
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&\hspace{18mm} g_t \leftarrow g_t + \lambda \theta_{t} \\
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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 - \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 - \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 provides an option to use either the original weight_decay implementation as in Adam
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(where the weight_decay is applied to the gradient) or the one from AdamW (where weight_decay is applied
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to the weight) through the decoupled_weight_decay option. When decoupled_weight_decay is set to False
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(default), it uses the original Adam style weight decay, otherwise, it uses the AdamW style which
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corresponds more closely to the `author's implementation`_ in the RAdam paper. Further information
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about decoupled weight decay can be found in `Decoupled Weight Decay Regularization`_.
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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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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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decoupled_weight_decay (bool, optional): whether to use decoupled weight
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decay as in AdamW to obtain RAdamW (default: False)
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{_foreach_doc}
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{_differentiable_doc}
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{_capturable_doc}
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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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.. _Decoupled Weight Decay Regularization:
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https://arxiv.org/abs/1711.05101
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"""
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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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decoupled_weight_decay: bool = False,
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foreach: Optional[bool] = None,
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differentiable: bool = False,
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capturable: bool = False,
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has_complex: 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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decoupled_weight_decay=decoupled_weight_decay,
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differentiable=differentiable,
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capturable=capturable,
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has_complex=has_complex,
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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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decoupled_weight_decay: bool,
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capturable: bool,
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has_complex: 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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# If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
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if not torch._utils.is_compiling() and capturable:
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assert (param.is_cuda and step_t.is_cuda) or (
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param.is_xla and step_t.is_xla
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), "If capturable=True, params and state_steps must be CUDA or XLA tensors."
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if torch.is_complex(param):
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param = torch.view_as_real(param)
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grad = torch.view_as_real(grad)
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exp_avg = torch.view_as_real(exp_avg)
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exp_avg_sq = torch.view_as_real(exp_avg_sq)
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# update step
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step_t += 1
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step = step_t if capturable else _get_value(step_t)
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if weight_decay != 0:
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if decoupled_weight_decay:
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param.mul_(1 - lr * weight_decay)
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else:
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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.lerp_(grad, 1 - beta1)
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exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
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bias_correction1 = 1 - beta1 ** step
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bias_correction2 = 1 - beta2 ** step
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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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def _compute_rect():
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return (
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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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) ** 0.5
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def _compute_adaptive_lr():
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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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return (bias_correction2 ** 0.5) / exp_avg_sq_sqrt
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# Compute the variance rectification term and update parameters accordingly
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if capturable:
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update = torch.where(rho_t > 5.0, _compute_rect() * _compute_adaptive_lr(), 1.0)
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param.add_(bias_corrected_exp_avg * lr * update, alpha=-1.0)
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else:
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if rho_t > 5.0:
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param.add_(bias_corrected_exp_avg * lr * _compute_adaptive_lr() * _compute_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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decoupled_weight_decay: bool,
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differentiable: bool,
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capturable: bool,
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has_complex: 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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# If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
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if not torch._utils.is_compiling() and capturable:
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assert all(p.is_cuda and step.is_cuda for p, step in zip(params, state_steps)), \
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"If capturable=True, params and state_steps must be CUDA tensors."
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grouped_tensors = Optimizer._group_tensors_by_device_and_dtype([params, grads, exp_avgs, exp_avg_sqs, state_steps])
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for ((
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grouped_params,
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grouped_grads,
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grouped_exp_avgs,
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grouped_exp_avg_sqs,
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grouped_state_steps,
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), _) in grouped_tensors.values():
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# Update steps
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# If steps are on CPU, foreach will fall back to the slow path, which is a for-loop calling t.add(1) over
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# and over. 1 will then be wrapped into a Tensor over and over again, which is slower than if we just
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# wrapped it once now. The alpha is required to assure we go to the right overload.
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if grouped_state_steps[0].is_cpu:
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torch._foreach_add_(grouped_state_steps, torch.tensor(1.0, device='cpu'), alpha=1.0)
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else:
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torch._foreach_add_(grouped_state_steps, 1)
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if has_complex:
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_view_as_real(grouped_params, grouped_grads, grouped_exp_avgs, grouped_exp_avg_sqs)
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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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if capturable:
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bias_correction1 = torch._foreach_pow(beta2, grouped_state_steps)
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torch._foreach_neg_(bias_correction1)
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torch._foreach_add_(bias_correction1, 1)
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bias_correction2 = torch._foreach_pow(beta2, grouped_state_steps)
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torch._foreach_mul_(bias_correction2, grouped_state_steps)
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torch._foreach_mul_(bias_correction2, 2)
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torch._foreach_div_(bias_correction2, bias_correction1)
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torch._foreach_neg_(bias_correction2)
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torch._foreach_add_(bias_correction2, rho_inf)
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rho_t_list = bias_correction2
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else:
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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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if weight_decay != 0:
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if decoupled_weight_decay:
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torch._foreach_mul_(grouped_params, 1 - lr * weight_decay)
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else:
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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_lerp_(grouped_exp_avgs, grouped_grads, 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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# Delete the local intermediate since it won't be used anymore to save on peak memory
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del grouped_grads
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if capturable:
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num = torch._foreach_sub(rho_t_list, 4)
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sub2 = torch._foreach_sub(rho_t_list, 2)
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torch._foreach_mul_(num, sub2)
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del sub2
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torch._foreach_mul_(num, rho_inf)
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rho_inf = ((rho_inf - 4) * (rho_inf - 2))
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denom = torch._foreach_mul(rho_t_list, rho_inf)
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torch._foreach_div_(num, denom)
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|
del denom
|
|
torch._foreach_sqrt_(num)
|
|
|
|
# TODO(mlazos): we should try and get a foreach_where op https://github.com/pytorch/pytorch/issues/117884
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|
rect = [torch.where(rho_t > 5.0, n, 0.0) for n, rho_t in zip(num, rho_t_list)]
|
|
del num
|
|
del rho_t_list
|
|
unrect_step_size = [torch.where(rect > 0, 0.0, 1.0) for rect in rect]
|
|
torch._foreach_mul_(unrect_step_size, lr)
|
|
|
|
bias_correction1 = torch._foreach_pow(beta1, grouped_state_steps)
|
|
torch._foreach_neg_(bias_correction1)
|
|
torch._foreach_add_(bias_correction1, 1)
|
|
|
|
torch._foreach_div_(unrect_step_size, bias_correction1)
|
|
torch._foreach_neg_(unrect_step_size)
|
|
|
|
bias_correction2 = torch._foreach_pow(beta2, grouped_state_steps)
|
|
torch._foreach_neg_(bias_correction2)
|
|
torch._foreach_add_(bias_correction2, 1)
|
|
torch._foreach_sqrt_(bias_correction2)
|
|
torch._foreach_mul_(bias_correction2, lr)
|
|
torch._foreach_mul_(bias_correction2, rect)
|
|
del rect
|
|
torch._foreach_neg_(bias_correction2)
|
|
torch._foreach_div_(bias_correction2, bias_correction1)
|
|
del bias_correction1
|
|
else:
|
|
rect = [
|
|
_dispatch_sqrt(
|
|
(rho_t - 4)
|
|
* (rho_t - 2)
|
|
* rho_inf
|
|
/ ((rho_inf - 4) * (rho_inf - 2) * rho_t)
|
|
)
|
|
if rho_t > 5
|
|
else 0
|
|
for rho_t in rho_t_list
|
|
]
|
|
unrectified = [0 if rect > 0 else 1.0 for rect in rect]
|
|
|
|
bias_correction1 = [1 - beta1 ** _get_value(step) for step in grouped_state_steps]
|
|
unrect_step_size = [(lr * rect / bc) * -1 for rect, bc in zip(unrectified, bias_correction1)]
|
|
bias_correction2 = [
|
|
_dispatch_sqrt(1 - beta2 ** _get_value(step)) * (lr * rect / bc) * -1
|
|
for step, rect, bc in zip(grouped_state_steps, rect, bias_correction1)
|
|
]
|
|
|
|
|
|
buffer = torch._foreach_sqrt(grouped_exp_avg_sqs)
|
|
torch._foreach_add_(buffer, eps)
|
|
torch._foreach_div_(buffer, bias_correction2)
|
|
torch._foreach_reciprocal_(buffer)
|
|
torch._foreach_add_(buffer, unrect_step_size)
|
|
|
|
# Here, buffer = sqrt(1 - beta2^t) * rect_step_size / (sqrt(v) + eps) + unrect_step_size
|
|
torch._foreach_addcmul_(grouped_params, grouped_exp_avgs, buffer)
|