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optimizers
This commit is contained in:
Varuna Jayasiri
0
labml_nn/optimizers/__init__.py
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0
labml_nn/optimizers/__init__.py
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175
labml_nn/optimizers/ada_belief/__init__.py
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labml_nn/optimizers/ada_belief/__init__.py
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"""
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This is forked from AdaBelief official implementation
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https://github.com/juntang-zhuang/Adabelief-Optimizer
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"""
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import math
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import torch
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from torch.optim.optimizer import Optimizer
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class AdaBelief(Optimizer):
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r"""Implements AdaBelief algorithm. Modified from Adam in PyTorch
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Arguments:
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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-16)
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weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
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amsgrad (boolean, optional): whether to use the AMSGrad variant of this
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algorithm from the paper `On the Convergence of Adam and Beyond`_
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(default: False)
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weight_decouple (boolean, optional): ( default: True) If set as True, then
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the optimizer uses decoupled weight decay as in AdamW
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fixed_decay (boolean, optional): (default: False) This is used when weight_decouple
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is set as True.
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When fixed_decay == True, the weight decay is performed as
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$W_{new} = W_{old} - W_{old} \times decay$.
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When fixed_decay == False, the weight decay is performed as
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$W_{new} = W_{old} - W_{old} \times decay \times lr$. Note that in this case, the
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weight decay ratio decreases with learning rate (lr).
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rectify (boolean, optional): (default: True) If set as True, then perform the rectified
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update similar to RAdam
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degenerated_to_sgd (boolean, optional) (default:True) If set as True, then perform SGD update
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when variance of gradient is high
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reference: AdaBelief Optimizer, adapting stepsizes by the belief in observed gradients, NeurIPS 2020
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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=0, amsgrad=False, weight_decouple=True, fixed_decay=False, rectify=True,
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degenerated_to_sgd=True):
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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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if isinstance(params, (list, tuple)) and len(params) > 0 and isinstance(params[0], dict):
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for param in params:
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if 'betas' in param and (param['betas'][0] != betas[0] or param['betas'][1] != betas[1]):
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param['buffer'] = [[None, None, None] for _ in range(10)]
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defaults = dict(lr=lr, betas=betas, eps=eps,
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weight_decay=weight_decay, amsgrad=amsgrad,
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buffer=[[None, None, None] for _ in range(10)])
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super().__init__(params, defaults)
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self.degenerated_to_sgd = degenerated_to_sgd
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self.weight_decouple = weight_decouple
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self.rectify = rectify
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self.fixed_decay = fixed_decay
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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('amsgrad', False)
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@torch.no_grad()
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def step(self, closure=None):
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"""Performs a single optimization step.
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Arguments:
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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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for p in group['params']:
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if p.grad is None:
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continue
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grad = p.grad.data
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if grad.is_sparse:
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raise RuntimeError('AdaBelief does not support sparse gradients,'
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' please consider SparseAdam instead')
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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'] = 0
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# Exponential moving average of gradient values
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state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
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# Exponential moving average of squared gradient values
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state['exp_avg_var'] = torch.zeros_like(p, memory_format=torch.preserve_format)
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if group['amsgrad']:
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# Maintains max of all exp. moving avg. of sq. grad. values
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state['max_exp_avg_var'] = torch.zeros_like(p, memory_format=torch.preserve_format)
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beta1, beta2 = group['betas']
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# get current state variable
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exp_avg, exp_avg_var = state['exp_avg'], state['exp_avg_var']
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state['step'] += 1
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bias_correction1 = 1 - beta1 ** state['step']
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bias_correction2 = 1 - beta2 ** state['step']
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# Update first and second moment running average
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exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
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grad_residual = grad - exp_avg
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exp_avg_var.mul_(beta2).addcmul_(grad_residual, grad_residual, value=1 - beta2)
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if group['amsgrad']:
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max_exp_avg_var = state['max_exp_avg_var']
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# Maintains the maximum of all 2nd moment running avg. till now
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torch.max(max_exp_avg_var, exp_avg_var, out=max_exp_avg_var)
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# Use the max. for normalizing running avg. of gradient
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denom = ((max_exp_avg_var + group['eps']).sqrt_() / math.sqrt(bias_correction2)).add_(group['eps'])
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else:
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denom = (exp_avg_var.add_(group['eps']).sqrt() / math.sqrt(bias_correction2)).add_(group['eps'])
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# perform weight decay, check if decoupled weight decay
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if self.weight_decouple:
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if not self.fixed_decay:
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p.data.mul_(1.0 - group['lr'] * group['weight_decay'])
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else:
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p.data.mul_(1.0 - group['weight_decay'])
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else:
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if group['weight_decay'] != 0:
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grad.add_(p.data, alpha=group['weight_decay'])
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# update
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if not self.rectify:
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# Default update
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step_size = group['lr'] / bias_correction1
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p.data.addcdiv_(exp_avg, denom, value=-step_size)
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else: # Rectified update, forked from RAdam
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buffered = group['buffer'][int(state['step'] % 10)]
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if state['step'] == buffered[0]:
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N_sma, step_size = buffered[1], buffered[2]
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else:
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buffered[0] = state['step']
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beta2_t = beta2 ** state['step']
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N_sma_max = 2 / (1 - beta2) - 1
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N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
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buffered[1] = N_sma
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# more conservative since it's an approximated value
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if N_sma >= 5:
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step_size = math.sqrt(
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(1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (
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N_sma_max - 2)) / (1 - beta1 ** state['step'])
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elif self.degenerated_to_sgd:
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step_size = 1.0 / (1 - beta1 ** state['step'])
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else:
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step_size = -1
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buffered[2] = step_size
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if N_sma >= 5:
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denom = exp_avg_var.sqrt().add_(group['eps'])
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p.data.addcdiv_(exp_avg, denom, value=-step_size * group['lr'])
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elif step_size > 0:
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p.data.add_(exp_avg, alpha=-step_size * group['lr'])
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return loss
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111
labml_nn/optimizers/ada_belief/mnist.py
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labml_nn/optimizers/ada_belief/mnist.py
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.utils.data
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from labml import experiment, tracker
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from labml.configs import option
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from labml_helpers.datasets.mnist import MNISTConfigs
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from labml_helpers.device import DeviceConfigs
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from labml_helpers.metrics.accuracy import Accuracy
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from labml_helpers.module import Module
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from labml_helpers.optimizer import OptimizerConfigs
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from labml_helpers.seed import SeedConfigs
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from labml_helpers.train_valid import TrainValidConfigs, BatchIndex, hook_model_outputs
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class Net(Module):
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def __init__(self):
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super().__init__()
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self.conv1 = nn.Conv2d(1, 20, 5, 1)
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self.conv2 = nn.Conv2d(20, 50, 5, 1)
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self.fc1 = nn.Linear(4 * 4 * 50, 500)
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self.fc2 = nn.Linear(500, 10)
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def __call__(self, x: torch.Tensor):
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x = F.relu(self.conv1(x))
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x = F.max_pool2d(x, 2, 2)
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x = F.relu(self.conv2(x))
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x = F.max_pool2d(x, 2, 2)
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x = x.view(-1, 4 * 4 * 50)
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x = F.relu(self.fc1(x))
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return self.fc2(x)
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class Configs(MNISTConfigs, TrainValidConfigs):
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optimizer: torch.optim.Adam
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model: nn.Module
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set_seed = SeedConfigs()
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device: torch.device = DeviceConfigs()
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epochs: int = 10
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is_save_models = True
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model: nn.Module
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inner_iterations = 10
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accuracy_func = Accuracy()
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loss_func = nn.CrossEntropyLoss()
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def init(self):
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tracker.set_queue("loss.*", 20, True)
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tracker.set_scalar("accuracy.*", True)
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hook_model_outputs(self.mode, self.model, 'model')
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self.state_modules = [self.accuracy_func]
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def step(self, batch: any, batch_idx: BatchIndex):
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data, target = batch[0].to(self.device), batch[1].to(self.device)
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if self.mode.is_train:
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tracker.add_global_step(len(data))
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with self.mode.update(is_log_activations=batch_idx.is_last):
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output = self.model(data)
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loss = self.loss_func(output, target)
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self.accuracy_func(output, target)
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tracker.add("loss.", loss)
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if self.mode.is_train:
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loss.backward()
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self.optimizer.step()
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if batch_idx.is_last:
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tracker.add('model', self.model)
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tracker.add('optimizer', (self.optimizer, {'model': self.model}))
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self.optimizer.zero_grad()
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tracker.save()
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@option(Configs.model)
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def model(c: Configs):
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return Net().to(c.device)
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@option(OptimizerConfigs.optimizer, 'AdaBelief')
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def ada_belief(c: OptimizerConfigs):
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from labml_nn.optimizers.ada_belief import AdaBelief
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return AdaBelief(c.parameters, lr=c.learning_rate, betas=c.betas, eps=c.eps)
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@option(Configs.optimizer)
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def _optimizer(c: Configs):
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opt_conf = OptimizerConfigs()
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opt_conf.parameters = c.model.parameters()
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return opt_conf
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def main():
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conf = Configs()
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conf.inner_iterations = 10
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experiment.create(name='mnist_ada_belief')
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experiment.configs(conf, {'inner_iterations': 10,
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'optimizer.optimizer': 'AdaBelief',
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'optimizer.learning_rate': 1.5e-4})
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conf.set_seed.set()
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experiment.add_pytorch_models(dict(model=conf.model))
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with experiment.start():
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conf.run()
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if __name__ == '__main__':
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main()
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254
labml_nn/optimizers/radam/__init__.py
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254
labml_nn/optimizers/radam/__init__.py
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"""
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Forked from https://github.com/LiyuanLucasLiu/RAdam
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"""
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import math
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import torch
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from torch.optim.optimizer import Optimizer
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class RAdam(Optimizer):
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def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, degenerated_to_sgd=True):
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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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self.degenerated_to_sgd = degenerated_to_sgd
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if isinstance(params, (list, tuple)) and len(params) > 0 and isinstance(params[0], dict):
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for param in params:
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if 'betas' in param and (param['betas'][0] != betas[0] or param['betas'][1] != betas[1]):
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param['buffer'] = [[None, None, None] for _ in range(10)]
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defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay,
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buffer=[[None, None, None] for _ in range(10)])
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super(RAdam, self).__init__(params, defaults)
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def __setstate__(self, state):
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super(RAdam, self).__setstate__(state)
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def step(self, closure=None):
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loss = None
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if closure is not None:
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loss = closure()
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for group in self.param_groups:
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for p in group['params']:
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if p.grad is None:
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continue
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grad = p.grad.data.float()
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if grad.is_sparse:
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raise RuntimeError('RAdam does not support sparse gradients')
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p_data_fp32 = p.data.float()
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state = self.state[p]
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if len(state) == 0:
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state['step'] = 0
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state['exp_avg'] = torch.zeros_like(p_data_fp32)
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state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
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else:
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state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
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state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)
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exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
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beta1, beta2 = group['betas']
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exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
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exp_avg.mul_(beta1).add_(1 - beta1, grad)
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state['step'] += 1
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buffered = group['buffer'][int(state['step'] % 10)]
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if state['step'] == buffered[0]:
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N_sma, step_size = buffered[1], buffered[2]
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else:
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buffered[0] = state['step']
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beta2_t = beta2 ** state['step']
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N_sma_max = 2 / (1 - beta2) - 1
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N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
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buffered[1] = N_sma
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# more conservative since it's an approximated value
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if N_sma >= 5:
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step_size = math.sqrt(
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(1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (
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N_sma_max - 2)) / (1 - beta1 ** state['step'])
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elif self.degenerated_to_sgd:
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step_size = 1.0 / (1 - beta1 ** state['step'])
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else:
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step_size = -1
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buffered[2] = step_size
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# more conservative since it's an approximated value
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if N_sma >= 5:
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if group['weight_decay'] != 0:
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p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
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denom = exp_avg_sq.sqrt().add_(group['eps'])
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p_data_fp32.addcdiv_(-step_size * group['lr'], exp_avg, denom)
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p.data.copy_(p_data_fp32)
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elif step_size > 0:
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if group['weight_decay'] != 0:
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p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
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p_data_fp32.add_(-step_size * group['lr'], exp_avg)
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p.data.copy_(p_data_fp32)
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return loss
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class PlainRAdam(Optimizer):
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def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, degenerated_to_sgd=True):
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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:
|
||||
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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self.degenerated_to_sgd = degenerated_to_sgd
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defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
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||||
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super(PlainRAdam, self).__init__(params, defaults)
|
||||
|
||||
def __setstate__(self, state):
|
||||
super(PlainRAdam, self).__setstate__(state)
|
||||
|
||||
def step(self, closure=None):
|
||||
|
||||
loss = None
|
||||
if closure is not None:
|
||||
loss = closure()
|
||||
|
||||
for group in self.param_groups:
|
||||
|
||||
for p in group['params']:
|
||||
if p.grad is None:
|
||||
continue
|
||||
grad = p.grad.data.float()
|
||||
if grad.is_sparse:
|
||||
raise RuntimeError('RAdam does not support sparse gradients')
|
||||
|
||||
p_data_fp32 = p.data.float()
|
||||
|
||||
state = self.state[p]
|
||||
|
||||
if len(state) == 0:
|
||||
state['step'] = 0
|
||||
state['exp_avg'] = torch.zeros_like(p_data_fp32)
|
||||
state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
|
||||
else:
|
||||
state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
|
||||
state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)
|
||||
|
||||
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
|
||||
beta1, beta2 = group['betas']
|
||||
|
||||
exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
|
||||
exp_avg.mul_(beta1).add_(1 - beta1, grad)
|
||||
|
||||
state['step'] += 1
|
||||
beta2_t = beta2 ** state['step']
|
||||
N_sma_max = 2 / (1 - beta2) - 1
|
||||
N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)
|
||||
|
||||
# more conservative since it's an approximated value
|
||||
if N_sma >= 5:
|
||||
if group['weight_decay'] != 0:
|
||||
p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
|
||||
step_size = group['lr'] * math.sqrt(
|
||||
(1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (
|
||||
N_sma_max - 2)) / (1 - beta1 ** state['step'])
|
||||
denom = exp_avg_sq.sqrt().add_(group['eps'])
|
||||
p_data_fp32.addcdiv_(-step_size, exp_avg, denom)
|
||||
p.data.copy_(p_data_fp32)
|
||||
elif self.degenerated_to_sgd:
|
||||
if group['weight_decay'] != 0:
|
||||
p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)
|
||||
step_size = group['lr'] / (1 - beta1 ** state['step'])
|
||||
p_data_fp32.add_(-step_size, exp_avg)
|
||||
p.data.copy_(p_data_fp32)
|
||||
|
||||
return loss
|
||||
|
||||
|
||||
class AdamW(Optimizer):
|
||||
|
||||
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, warmup=0):
|
||||
if not 0.0 <= lr:
|
||||
raise ValueError("Invalid learning rate: {}".format(lr))
|
||||
if not 0.0 <= eps:
|
||||
raise ValueError("Invalid epsilon value: {}".format(eps))
|
||||
if not 0.0 <= betas[0] < 1.0:
|
||||
raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
|
||||
if not 0.0 <= betas[1] < 1.0:
|
||||
raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
|
||||
|
||||
defaults = dict(lr=lr, betas=betas, eps=eps,
|
||||
weight_decay=weight_decay, warmup=warmup)
|
||||
super(AdamW, self).__init__(params, defaults)
|
||||
|
||||
def __setstate__(self, state):
|
||||
super(AdamW, self).__setstate__(state)
|
||||
|
||||
def step(self, closure=None):
|
||||
loss = None
|
||||
if closure is not None:
|
||||
loss = closure()
|
||||
|
||||
for group in self.param_groups:
|
||||
|
||||
for p in group['params']:
|
||||
if p.grad is None:
|
||||
continue
|
||||
grad = p.grad.data.float()
|
||||
if grad.is_sparse:
|
||||
raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead')
|
||||
|
||||
p_data_fp32 = p.data.float()
|
||||
|
||||
state = self.state[p]
|
||||
|
||||
if len(state) == 0:
|
||||
state['step'] = 0
|
||||
state['exp_avg'] = torch.zeros_like(p_data_fp32)
|
||||
state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)
|
||||
else:
|
||||
state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)
|
||||
state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)
|
||||
|
||||
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
|
||||
beta1, beta2 = group['betas']
|
||||
|
||||
state['step'] += 1
|
||||
|
||||
exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
|
||||
exp_avg.mul_(beta1).add_(1 - beta1, grad)
|
||||
|
||||
denom = exp_avg_sq.sqrt().add_(group['eps'])
|
||||
bias_correction1 = 1 - beta1 ** state['step']
|
||||
bias_correction2 = 1 - beta2 ** state['step']
|
||||
|
||||
if group['warmup'] > state['step']:
|
||||
scheduled_lr = 1e-8 + state['step'] * group['lr'] / group['warmup']
|
||||
else:
|
||||
scheduled_lr = group['lr']
|
||||
|
||||
step_size = scheduled_lr * math.sqrt(bias_correction2) / bias_correction1
|
||||
|
||||
if group['weight_decay'] != 0:
|
||||
p_data_fp32.add_(-group['weight_decay'] * scheduled_lr, p_data_fp32)
|
||||
|
||||
p_data_fp32.addcdiv_(-step_size, exp_avg, denom)
|
||||
|
||||
p.data.copy_(p_data_fp32)
|
||||
|
||||
return loss
|
||||
Reference in New Issue
Block a user