chore: import upstream snapshot with attribution
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wehub-resource-sync
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"""
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---
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title: Adam Optimizer
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summary: A simple PyTorch implementation/tutorial of Adam optimizer
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---
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# Adam Optimizer
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This is a [PyTorch](https://pytorch.org) implementation of popular optimizer *Adam* from paper
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[Adam: A Method for Stochastic Optimization](https://arxiv.org/abs/1412.6980).
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*Adam* update is,
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\begin{align}
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m_t &\leftarrow \beta_1 m_{t-1} + (1 - \beta_1) \cdot g_t \\
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v_t &\leftarrow \beta_2 v_{t-1} + (1 - \beta_2) \cdot g_t^2 \\
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\hat{m}_t &\leftarrow \frac{m_t}{1-\beta_1^t} \\
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\hat{v}_t &\leftarrow \frac{v_t}{1-\beta_2^t} \\
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\theta_t &\leftarrow \theta_{t-1} - \alpha \cdot \frac{\hat{m}_t}{\sqrt{\hat{v}_t} + \epsilon}
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\end{align}
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where $\alpha$, $\beta_1$, $\beta_2$ and $\epsilon$ are scalar hyper parameters.
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$m_t$ and $v_t$ are first and second order moments.
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$\hat{m}_t$ and $\hat{v}_t$ are biased corrected moments.
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$\epsilon$ is used as a fix for division by zero error, but also acts as a form of a hyper-parameter
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that acts against variance in gradients.
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Effective step taken assuming $\epsilon = 0$ is,
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$$\Delta t = \alpha \cdot \frac{\hat{m}_t}{\hat{v}_t}$$
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This is bounded by,
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$$\vert \Delta t \vert \le \alpha \cdot \frac{1 - \beta_1}{\sqrt{1-\beta_2}}$$
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when $1-\beta_1 \gt \sqrt{1-\beta_2}$
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and
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$$\vert \Delta t\vert \le \alpha$$
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otherwise.
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And in most common scenarios,
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$$\vert \Delta t \vert \approx \alpha$$
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"""
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import math
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from typing import Dict, Any, Tuple, Optional
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import torch
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from labml import tracker
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from torch import nn
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from labml_nn.optimizers import GenericAdaptiveOptimizer, WeightDecay
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class Adam(GenericAdaptiveOptimizer):
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"""
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## Adam Optimizer
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We extend the class `GenericAdaptiveOptimizer` defined in [`__init__.py`](index.html)
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to implement the Adam optimizer.
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"""
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def __init__(self, params,
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lr: float = 1e-3, betas: Tuple[float, float] = (0.9, 0.999), eps: float = 1e-16,
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weight_decay: WeightDecay = WeightDecay(),
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optimized_update: bool = True,
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defaults: Optional[Dict[str, Any]] = 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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* `defaults` is a dictionary of default for group values.
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This is useful when you want to extend the class `Adam`.
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"""
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defaults = {} if defaults is None else defaults
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defaults.update(weight_decay.defaults())
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super().__init__(params, defaults, lr, betas, eps)
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self.weight_decay = weight_decay
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self.optimized_update = optimized_update
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def init_state(self, state: Dict[str, any], group: Dict[str, any], param: nn.Parameter):
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"""
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### Initialize a parameter state
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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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"""
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# This is the number of optimizer steps taken on the parameter, $t$
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state['step'] = 0
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# Exponential moving average of gradients, $m_t$
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state['exp_avg'] = torch.zeros_like(param, memory_format=torch.preserve_format)
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# Exponential moving average of squared gradient values, $v_t$
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state['exp_avg_sq'] = torch.zeros_like(param, memory_format=torch.preserve_format)
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def get_mv(self, state: Dict[str, Any], group: Dict[str, Any], grad: torch.Tensor):
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"""
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### Calculate $m_t$ and and $v_t$
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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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"""
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# Get $\beta_1$ and $\beta_2$
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beta1, beta2 = group['betas']
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# Get $m_{t-1}$ and $v_{t-1}$
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m, v = state['exp_avg'], state['exp_avg_sq']
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# In-place calculation of $m_t$
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# $$m_t \leftarrow \beta_1 m_{t-1} + (1 - \beta_1) \cdot g_t$$
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m.mul_(beta1).add_(grad, alpha=1 - beta1)
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# In-place calculation of $v_t$
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# $$v_t \leftarrow \beta_2 v_{t-1} + (1 - \beta_2) \cdot g_t^2$$
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v.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
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return m, v
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def get_lr(self, state: Dict[str, any], group: Dict[str, any]):
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"""
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### Get learning-rate
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This returns the modified learning rate based on the state.
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For *Adam* this is just the specified learning rate for the parameter group,
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$\alpha$.
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"""
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return group['lr']
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def 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 *Adam* 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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This computes the following
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\begin{align}
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\theta_t &\leftarrow \theta_{t-1} - \alpha \cdot \frac{\hat{m}_t}{\sqrt{\hat{v}_t} + \epsilon}
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\end{align}
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Since $\alpha$, $\beta_1$, $\beta_2$ and $\epsilon$ are scalars and others are tensors
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we modify this calculation to optimize the computation.
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\begin{align}
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\theta_t &\leftarrow \theta_{t-1} - \alpha \cdot \frac{\hat{m}_t}{\sqrt{\hat{v}_t} + \epsilon} \\
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\theta_t &\leftarrow \theta_{t-1} - \alpha \cdot
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\frac{m_t / (1-\beta_1^t)}{\sqrt{v_t/(1-\beta_2^t)} + \epsilon} \\
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\theta_t &\leftarrow \theta_{t-1} - \alpha \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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\end{align}
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where
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$$\hat{\epsilon} = (1-\beta_2^t) \epsilon$$
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is what we should specify as the hyper-parameter.
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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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# Get learning rate
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lr = self.get_lr(state, group)
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# Whether to optimize the computation
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if self.optimized_update:
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# $\sqrt{v_t} + \hat{\epsilon}$
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denominator = v.sqrt().add_(group['eps'])
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# $\alpha \frac{\sqrt{1-\beta_2^t}}{1-\beta_1^t}$
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step_size = lr * math.sqrt(bias_correction2) / bias_correction1
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# $\theta_t \leftarrow \theta_{t-1} - \alpha \frac{\sqrt{1-\beta_2^t}}{1-\beta_1^t} \cdot
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# \frac{m_t}{\sqrt{v_t} + \hat{\epsilon}}$
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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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# $\frac{\sqrt{v_t}}{\sqrt{1-\beta_2^t}} + \epsilon$
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denominator = (v.sqrt() / math.sqrt(bias_correction2)).add_(group['eps'])
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# $\frac{\alpha}{1-\beta_1^t}$
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step_size = lr / bias_correction1
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# $\theta_t \leftarrow \theta_{t-1} - \alpha \cdot
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# \frac{\hat{m}_t}{\sqrt{\hat{v}_t} + \epsilon}$
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param.data.addcdiv_(m, denominator, value=-step_size)
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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 $v_t$
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m, v = self.get_mv(state, group, grad)
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# Increment $t$ the number of optimizer steps
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state['step'] += 1
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# Perform *Adam* update
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self.adam_update(state, group, param, m, v)
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