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205 lines
7.8 KiB
Python
205 lines
7.8 KiB
Python
"""
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---
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title: AMSGrad Optimizer
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summary: A simple PyTorch implementation/tutorial of AMSGrad optimizer.
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---
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# AMSGrad
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This is a [PyTorch](https://pytorch.org) implementation of the paper
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[On the Convergence of Adam and Beyond](https://arxiv.org/abs/1904.09237).
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We implement this as an extension to our [Adam optimizer implementation](adam.html).
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The implementation it self is really small since it's very similar to Adam.
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We also have an implementation of the synthetic example described in the paper where Adam fails to converge.
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"""
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from typing import Dict
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import torch
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from torch import nn
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from labml_nn.optimizers import WeightDecay
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from labml_nn.optimizers.adam import Adam
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class AMSGrad(Adam):
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"""
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## AMSGrad Optimizer
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This class extends from Adam optimizer defined in [`adam.py`](adam.html).
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Adam optimizer is extending the class `GenericAdaptiveOptimizer`
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defined in [`__init__.py`](index.html).
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"""
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def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-16,
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weight_decay: WeightDecay = WeightDecay(),
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optimized_update: bool = True,
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amsgrad=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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* `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(dict(amsgrad=amsgrad))
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super().__init__(params, lr, betas, eps, weight_decay, optimized_update, defaults)
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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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# Call `init_state` of Adam optimizer which we are extending
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super().init_state(state, group, param)
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# If `amsgrad` flag is `True` for this parameter group, we maintain the maximum of
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# exponential moving average of squared gradient
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if group['amsgrad']:
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state['max_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 $v_t$ or $\max(v_1, v_2, ..., v_{t-1}, v_t)$
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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 $m_t$ and $v_t$ from *Adam*
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m, v = super().get_mv(state, group, grad)
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# If this parameter group is using `amsgrad`
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if group['amsgrad']:
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# Get $\max(v_1, v_2, ..., v_{t-1})$.
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#
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# 🗒 The paper uses the notation $\hat{v}_t$ for this, which we don't use
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# that here because it confuses with the Adam's usage of the same notation
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# for bias corrected exponential moving average.
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v_max = state['max_exp_avg_sq']
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# Calculate $\max(v_1, v_2, ..., v_{t-1}, v_t)$.
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#
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# 🤔 I feel you should be taking / maintaining the max of the bias corrected
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# second exponential average of squared gradient.
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# But this is how it's
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# [implemented in PyTorch also](https://github.com/pytorch/pytorch/blob/19f4c5110e8bcad5e7e75375194262fca0a6293a/torch/optim/functional.py#L90).
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# I guess it doesn't really matter since bias correction only increases the value
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# and it only makes an actual difference during the early few steps of the training.
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torch.maximum(v_max, v, out=v_max)
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return m, v_max
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else:
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# Fall back to *Adam* if the parameter group is not using `amsgrad`
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return m, v
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def _synthetic_experiment(is_adam: bool):
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"""
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## Synthetic Experiment
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This is the synthetic experiment described in the paper,
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that shows a scenario where *Adam* fails.
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The paper (and Adam) formulates the problem of optimizing as
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minimizing the expected value of a function, $\mathbb{E}[f(\theta)]$
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with respect to the parameters $\theta$.
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In the stochastic training setting we do not get hold of the function $f$
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it self; that is,
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when you are optimizing a NN $f$ would be the function on entire
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batch of data.
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What we actually evaluate is a mini-batch so the actual function is
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realization of the stochastic $f$.
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This is why we are talking about an expected value.
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So let the function realizations be $f_1, f_2, ..., f_T$ for each time step
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of training.
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We measure the performance of the optimizer as the regret,
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$$R(T) = \sum_{t=1}^T \big[ f_t(\theta_t) - f_t(\theta^*) \big]$$
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where $theta_t$ is the parameters at time step $t$, and $\theta^*$ is the
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optimal parameters that minimize $\mathbb{E}[f(\theta)]$.
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Now lets define the synthetic problem,
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\begin{align}
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f_t(x) =
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\begin{cases}
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1010 x, & \text{for $t \mod 101 = 1$} \\
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-10 x, & \text{otherwise}
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\end{cases}
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\end{align}
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where $-1 \le x \le +1$.
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The optimal solution is $x = -1$.
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This code will try running *Adam* and *AMSGrad* on this problem.
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"""
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# Define $x$ parameter
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x = nn.Parameter(torch.tensor([.0]))
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# Optimal, $x^* = -1$
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x_star = nn.Parameter(torch.tensor([-1]), requires_grad=False)
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def func(t: int, x_: nn.Parameter):
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"""
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### $f_t(x)$
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"""
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if t % 101 == 1:
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return (1010 * x_).sum()
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else:
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return (-10 * x_).sum()
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# Initialize the relevant optimizer
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if is_adam:
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optimizer = Adam([x], lr=1e-2, betas=(0.9, 0.99))
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else:
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optimizer = AMSGrad([x], lr=1e-2, betas=(0.9, 0.99))
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# $R(T)$
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total_regret = 0
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from labml import monit, tracker, experiment
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# Create experiment to record results
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with experiment.record(name='synthetic', comment='Adam' if is_adam else 'AMSGrad'):
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# Run for $10^7$ steps
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for step in monit.loop(10_000_000):
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# $f_t(\theta_t) - f_t(\theta^*)$
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regret = func(step, x) - func(step, x_star)
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# $R(T) = \sum_{t=1}^T \big[ f_t(\theta_t) - f_t(\theta^*) \big]$
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total_regret += regret.item()
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# Track results every 1,000 steps
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if (step + 1) % 1000 == 0:
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tracker.save(loss=regret, x=x, regret=total_regret / (step + 1))
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# Calculate gradients
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regret.backward()
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# Optimize
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optimizer.step()
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# Clear gradients
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optimizer.zero_grad()
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# Make sure $-1 \le x \le +1$
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x.data.clamp_(-1., +1.)
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if __name__ == '__main__':
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# Run the synthetic experiment is *Adam*.
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# [Here are the results](https://app.labml.ai/run/61ebfdaa384411eb94d8acde48001122).
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# You can see that Adam converges at $x = +1$
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_synthetic_experiment(True)
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# Run the synthetic experiment is *AMSGrad*
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# [Here are the results](https://app.labml.ai/run/uuid=bc06405c384411eb8b82acde48001122).
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# You can see that AMSGrad converges to true optimal $x = -1$
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_synthetic_experiment(False)
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