244 lines
10 KiB
Python
244 lines
10 KiB
Python
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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=False):
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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, 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((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'])
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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=False):
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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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defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
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super(PlainRAdam, self).__init__(params, defaults)
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def __setstate__(self, state):
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super(PlainRAdam, 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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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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# 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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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'])
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denom = exp_avg_sq.sqrt().add_(group['eps'])
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p_data_fp32.addcdiv_(-step_size, exp_avg, denom)
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p.data.copy_(p_data_fp32)
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elif self.degenerated_to_sgd:
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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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step_size = group['lr'] / (1 - beta1 ** state['step'])
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p_data_fp32.add_(-step_size, exp_avg)
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p.data.copy_(p_data_fp32)
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return loss
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class AdamW(Optimizer):
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def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, warmup = 0):
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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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defaults = dict(lr=lr, betas=betas, eps=eps,
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weight_decay=weight_decay, warmup = warmup)
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super(AdamW, self).__init__(params, defaults)
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def __setstate__(self, state):
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super(AdamW, 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('Adam does not support sparse gradients, please consider SparseAdam instead')
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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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state['step'] += 1
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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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denom = exp_avg_sq.sqrt().add_(group['eps'])
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bias_correction1 = 1 - beta1 ** state['step']
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bias_correction2 = 1 - beta2 ** state['step']
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if group['warmup'] > state['step']:
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scheduled_lr = 1e-8 + state['step'] * group['lr'] / group['warmup']
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else:
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scheduled_lr = group['lr']
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step_size = scheduled_lr * math.sqrt(bias_correction2) / bias_correction1
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if group['weight_decay'] != 0:
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p_data_fp32.add_(-group['weight_decay'] * scheduled_lr, p_data_fp32)
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p_data_fp32.addcdiv_(-step_size, exp_avg, denom)
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p.data.copy_(p_data_fp32)
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return loss
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