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280 lines
11 KiB
Python
280 lines
11 KiB
Python
"""
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---
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title: Rectified Adam (RAdam) optimizer
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summary: A simple PyTorch implementation/tutorial of RAdam optimizer.
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---
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# Rectified Adam (RAdam) optimizer
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This implementation is based on
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[the official implementation](https://github.com/LiyuanLucasLiu/RAdam)
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of the paper
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[On the Variance of the Adaptive Learning Rate and Beyond](https://arxiv.org/abs/1908.03265).
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We have implemented it as an extension to [our AMSGrad implementation](amsgrad.html)
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thus requiring only the modifications to be implemented.
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Adam optimizer sometimes converges to a bad local optima during the initial stages of the training;
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especially when training transformers.
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Researches use warmups to counter this; for the the initial training steps (warm-up stage)
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they use a low learning rate.
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This paper identifies the problem to be the high variance of adaptive learning rate
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during initial stages of training, and counters it using a new rectification term to
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reduce variance.
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The paper also evaluates two variance reduction mechanisms:
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* **Adam-2k**: Only compute the adaptive learning rate ($v_t$ in [Adam](adam.html)) during the first 2k steps,
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without changing parameters or calculating momentum ($m_t$).
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* **Adam-eps**: Adam with large $\epsilon \approx 10^{-4}$.
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## Rectified Adam
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Let $\sigma(g_1, ..., g_t)$ and $\psi(g_1, ..., g_t)$ be the functions to calculate
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momentum and adaptive learning rate.
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For Adam, they are
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\begin{align}
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\sigma(g_1, ..., g_t) &= \frac{(1 - \beta_1)\sum_{i=1}^t \beta_1^{t-i} g_i}{1 - \beta_1^t} \\
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\psi(g_1, ..., g_t) &= \sqrt \frac{1 - \beta_2^t}{(1 - \beta_2)\sum_{i=1}^t \beta_2^{t-i} g_i^2}
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\end{align}
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### Exponential moving average as simple moving average
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The distribution of exponential moving average can be approximated as a simple moving average.
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\begin{align}
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p\Bigg(\frac{(1-\beta_2) \sum_{i=1}^t \beta_2^{t-i} g_i^2}{1 - \beta_2^t} \Bigg) \approx
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p\Bigg(\frac{\sum_{i=1}^{f(t,\beta_2)} g_{t+1-i}^2}{f(t,\beta_2)} \Bigg)
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\end{align}
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Here we are taking the simple moving average of the last $f(t,\beta_2)$ gradients.
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$f(t,\beta_2)$ satisfies the following,
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\begin{align}
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\frac{(1-\beta_2) \sum_{i=1}^t \beta_2^{t-i} \cdot i}{1 - \beta_2^t} =
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\frac{\sum_{i=1}^{f(t,\beta_2)} (t+1-i)}{f(t,\beta_2)}
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\end{align}
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which gives,
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$$f(t,\beta_2) = \frac{2}{1-\beta_2} - 1 - \frac{2 t \beta_2^t}{1 - \beta_2^t}$$
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### Scaled inverse chi-squared
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From above we have
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$$
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p\Big( \psi^2(g_1, ..., g_t) \Big) \approx
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p\Bigg(\frac{\sum_{i=1}^{f(t,\beta_2)} g_{t+1-i}^2}{f(t,\beta_2)} \Bigg)
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$$
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where $g_i \sim \mathcal{N}(0, \sigma^2)$.
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Note that $sigma$ here is the standard deviation and different from $\sigma(.)$ for momentum.
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[Scaled inverse chi-squared](https://en.wikipedia.org/wiki/Scaled_inverse_chi-squared_distribution)
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is the distribution of squared inverse of mean of $p$ normal distributions.
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$$
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p\Bigg(\frac{\sum_{i=1}^{f(t,\beta_2)} g_{t+1-i}^2}{f(t,\beta_2)} \Bigg)
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\sim
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\text{Scale-inv} \mathcal{X}^2(\rho,\frac{1}{\sigma^2})
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$$
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where $\rho = f(t,\beta_2)$.
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### Rectification
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They prove that variance of $\psi(.)$ decreases with $\rho$ when
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$\psi^2(.) \sim \text{Scale-inv} \mathcal{X}^2(\rho,\frac{1}{\sigma^2})$.
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Therefore the variance is minimized at maximal $\rho$ which is
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$\rho_{\infty} = \frac{2}{1-\beta_2} - 1$. Let the minimum variance be $C_{\text{var}}$
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In order to ensure that the adaptive learning
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rate $\psi(.)$ has consistent variance, we rectify the variance with $r$
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\begin{align}
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r = \sqrt{\frac{C_{\text{var}}}{Var\big[\psi(.)\big]}}
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\end{align}
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### Approximating $Var[\psi(.)]$
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They estimate $Var[\psi(.)] \approx \frac{Var[\psi^2(.)]}{4 \mathbb{E}[\psi^2(.)}$
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based on first order expansion of $\sqrt{\psi^2(.)}$
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🤪 I didn't get how it was derived.
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From $\text{Scale-inv} \mathcal{X}^2$ distribution we have,
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\begin{align}
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\mathbb{E}\big[\psi^2(.)\big] &= \frac{\rho / \sigma^2}{\rho-2} \\
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Var\big[\psi^2(.)\big] &= \frac{2 \rho / \sigma^4}{(\rho-2)^2 (\rho - 2)}
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\end{align}
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which gives,
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$$
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Var[\psi(.)] \approx \frac{\rho}{2(\rho-2)(\rho-4)\sigma^2}
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$$
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### Rectification term
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We have
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\begin{align}
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r &= \sqrt{\frac{C_{\text{var}}}{Var\big[\psi(.)\big]}} \\
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Var[\psi(.)] &\approx \frac{\rho}{2(\rho-2)(\rho-4)\sigma^2}
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\end{align}
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where $C_{\text{var}}$ is $Var\big[\psi(.)\big]$ for $\rho_\infty$.
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Lt $\rho$ and step $t$ be $\rho_t$, and $r_t$ be the rectification term
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at step $t$.
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\begin{align}
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C_{\text{var}} &\approx \frac{\rho_\infty}{2(\rho_\infty-2)(\rho_\infty-4)\sigma^2} \\
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Var[\psi(g_1,...,g_t)] &\approx \frac{\rho_t}{2(\rho_t-2)(\rho_t-4)\sigma^2}
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\end{align}
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This gives,
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\begin{align}
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r_t &= \sqrt{\frac{(\rho_t-2)(\rho_t-4)\rho_\infty}{(\rho_\infty-2)(\rho_\infty-4)\rho_t}}
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\end{align}
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"""
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import math
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from typing import Dict, Optional
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import torch
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from labml_nn.optimizers import WeightDecay
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from labml_nn.optimizers.amsgrad import AMSGrad
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class RAdam(AMSGrad):
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"""
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## Rectified Adam Optimizer
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This class extends from AMSAdam optimizer defined in [`amsadam.py`](amsadam.html).
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"""
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def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
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weight_decay: WeightDecay = WeightDecay(),
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optimized_update: bool = True,
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amsgrad=False,
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degenerated_to_sgd=True, defaults=None):
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"""
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### Initialize the optimizer
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* `params` is the list of parameters
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* `lr` is the learning rate $\alpha$
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* `betas` is a tuple of ($\beta_1$, $\beta_2$)
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* `eps` is $\hat{\epsilon}$ or $\epsilon$ based on `optimized_update`
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* `weight_decay` is an instance of class `WeightDecay` defined in [`__init__.py`](index.html)
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* 'optimized_update' is a flag whether to optimize the bias correction of the second moment
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by doing it after adding $\epsilon$
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* `amsgrad` is a flag indicating whether to use AMSGrad or fallback to plain Adam
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* `degenerate_to_sgd` whether to use sgd when the rectification term $r_t is intractable.
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* `defaults` is a dictionary of default for group values.
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This is useful when you want to extend the class `RAdam`.
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"""
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self.degenerated_to_sgd = degenerated_to_sgd
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super().__init__(params, lr, betas, eps, weight_decay, optimized_update, amsgrad, defaults)
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def step_param(self, state: Dict[str, any], group: Dict[str, any], grad: torch.Tensor, param: torch.nn.Parameter):
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"""
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### Take an update step for a given parameter tensor
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* `state` is the optimizer state of the parameter (tensor)
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* `group` stores optimizer attributes of the parameter group
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* `grad` is the current gradient tensor $g_t$ for the parameter $\theta_{t-1}$
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* `param` is the parameter tensor $\theta_{t-1}$
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"""
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# Calculate weight decay
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grad = self.weight_decay(param, grad, group)
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# Get $m_t$ and $v_t$; i.e. $\sigma(.)$ and $\psi(.)$ without bias correction
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m, v = self.get_mv(state, group, grad)
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# Calculate $t$ the number of optimizer steps
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state['step'] += 1
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# Perform *RAdam* update
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self.r_adam_update(state, group, param, m, v)
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@staticmethod
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def calc_rectification_term(beta2: float, step: int) -> Optional[float]:
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"""
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### Calculate rectification term $r_t$
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"""
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# $\beta_2^t$
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beta2_t = beta2 ** step
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# $$\rho_\infty = \frac{2}{1 - \beta_2} - 1$$
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rho_inf = 2 / (1 - beta2) - 1
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# $$\rho_t = \frac{2}{1-\beta_2} - 1 - \frac{2 t \beta_2^t}{1-\beta_2^t}$$
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rho = rho_inf - 2 * step * beta2_t / (1 - beta2_t)
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# $r_t$ is tractable when $\rho_t >= 4$.
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# We are being a little more conservative since it's an approximated value
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if rho >= 5:
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# $$r_t = \sqrt{\frac{(\rho_t-2)(\rho_t-4)\rho_\infty}{(\rho_\infty-2)(\rho_\infty-4)\rho_t}}$$
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r2 = (rho - 4) / (rho_inf - 4) * (rho - 2) / rho * rho_inf / (rho_inf - 2)
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return math.sqrt(r2)
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else:
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return None
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def r_adam_update(self, state: Dict[str, any], group: Dict[str, any], param: torch.nn.Parameter,
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m: torch.Tensor, v: torch.Tensor):
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"""
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### Do the *RAdam* parameter update
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* `state` is the optimizer state of the parameter (tensor)
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* `group` stores optimizer attributes of the parameter group
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* `param` is the parameter tensor $\theta_{t-1}$
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* `m` and `v` are the uncorrected first and second moments $m_t$ and $v_t$;
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i.e. $\sigma(.)$ and $\psi(.)$ without bias correction
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"""
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# Get $\beta_1$ and $\beta_2$
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beta1, beta2 = group['betas']
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# Bias correction term for $\hat{m}_t$, $1 - \beta_1^t$
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bias_correction1 = 1 - beta1 ** state['step']
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# Bias correction term for $\hat{v}_t$, $1 - \beta_2^t$
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bias_correction2 = 1 - beta2 ** state['step']
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r = self.calc_rectification_term(beta2, state['step'])
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# If $r_t$ is intractable
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if r is not None:
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# Whether to optimize the computation by combining scalar computations
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if self.optimized_update:
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# Denominator $\sqrt{v_t} + \hat{\epsilon}$
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denominator = v.sqrt().add_(group['eps'])
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# Step size $\alpha \sqrt{r_t} * \frac{\sqrt{1-\beta_2^t}}{1-\beta_1^t}$
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step_size = self.get_lr(state, group) * math.sqrt(bias_correction2) * r / bias_correction1
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# Update parameters $\theta_t \leftarrow \theta_{t-1} - \alpha \sqrt{r_t} \frac{\sqrt{1-\beta_2^t}}{1-\beta_1^t} \cdot
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# \frac{m_t}{\sqrt{v_t} + \hat{\epsilon}}$
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param.data.addcdiv_(m, denominator, value=-step_size)
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# Computation without optimization
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else:
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# Denominator $\frac{\sqrt{v_t}}{\sqrt{1-\beta_2^t}} + \epsilon$
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denominator = (v.sqrt() / math.sqrt(bias_correction2)).add_(group['eps'])
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# Step size $\frac{\alpha \sqrt{r_t}}{1-\beta_1^t}$
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step_size = self.get_lr(state, group) * r / bias_correction1
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# Update parameters $\theta_t \leftarrow \theta_{t-1} - \alpha \sqrt{r_t} \cdot
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# \frac{\hat{m}_t}{\sqrt{\hat{v}_t} + \epsilon}$
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param.data.addcdiv_(m, denominator, value=-step_size)
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# If $r_t$ is intractable do a SGD with momentum
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elif self.degenerated_to_sgd:
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# Step size $\frac{\alpha}{1-\beta_1^t}$
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step_size = self.get_lr(state, group) / bias_correction1
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# Update parameters
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# $\theta_t \leftarrow \theta_{t-1} - \alpha \cdot \hat{m}_t$
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param.data.add_(m, alpha=-step_size)
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def _test_rectification_term():
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"""
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### Plot $r_t$ against $t$ for various $\beta_2$
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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beta2 = [0.9999, 0.999, 0.99, 0.9, 0.8, 0.6, 0.5]
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plt.plot(np.arange(1, 5_000), [[RAdam.calc_rectification_term(b, i) for b in beta2] for i in range(1, 5_000)])
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plt.legend(beta2)
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plt.title("Optimizer")
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plt.show()
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if __name__ == '__main__':
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_test_rectification_term()
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