release code
This commit is contained in:
miunangel
332
Dassl.ProGrad.pytorch/dassl/optim/radam.py
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332
Dassl.ProGrad.pytorch/dassl/optim/radam.py
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"""
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Imported from: https://github.com/LiyuanLucasLiu/RAdam
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https://arxiv.org/abs/1908.03265
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@article{liu2019radam,
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title={On the Variance of the Adaptive Learning Rate and Beyond},
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author={Liu, Liyuan and Jiang, Haoming and He, Pengcheng and Chen, Weizhu and Liu, Xiaodong and Gao, Jianfeng and Han, Jiawei},
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journal={arXiv preprint arXiv:1908.03265},
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year={2019}
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}
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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__(
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self,
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params,
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lr=1e-3,
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betas=(0.9, 0.999),
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eps=1e-8,
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weight_decay=0,
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degenerated_to_sgd=True,
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):
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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(
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"Invalid beta parameter at index 0: {}".format(betas[0])
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)
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if not 0.0 <= betas[1] < 1.0:
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raise ValueError(
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"Invalid beta parameter at index 1: {}".format(betas[1])
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)
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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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self.buffer = [[None, None, None] for ind 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(
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"RAdam does not support sparse gradients"
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)
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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(
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p_data_fp32
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)
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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 = self.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"
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] * 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) *
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(N_sma-2) / N_sma * N_sma_max / (N_sma_max-2)
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) / (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_(
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-group["weight_decay"] * group["lr"], p_data_fp32
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)
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denom = exp_avg_sq.sqrt().add_(group["eps"])
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p_data_fp32.addcdiv_(
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-step_size * group["lr"], exp_avg, denom
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)
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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_(
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-group["weight_decay"] * group["lr"], p_data_fp32
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)
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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__(
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self,
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params,
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lr=1e-3,
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betas=(0.9, 0.999),
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eps=1e-8,
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weight_decay=0,
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degenerated_to_sgd=True,
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):
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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(
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"Invalid beta parameter at index 0: {}".format(betas[0])
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)
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if not 0.0 <= betas[1] < 1.0:
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raise ValueError(
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"Invalid beta parameter at index 1: {}".format(betas[1])
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)
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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(
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"RAdam does not support sparse gradients"
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)
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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(
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p_data_fp32
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)
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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_(
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-group["weight_decay"] * group["lr"], p_data_fp32
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)
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step_size = (
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group["lr"] * math.sqrt(
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(1-beta2_t) * (N_sma-4) / (N_sma_max-4) *
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(N_sma-2) / N_sma * N_sma_max / (N_sma_max-2)
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) / (1 - beta1**state["step"])
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)
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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_(
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-group["weight_decay"] * group["lr"], p_data_fp32
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)
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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__(
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self,
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params,
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lr=1e-3,
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betas=(0.9, 0.999),
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eps=1e-8,
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weight_decay=0,
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warmup=0
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):
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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(
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"Invalid beta parameter at index 0: {}".format(betas[0])
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)
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if not 0.0 <= betas[1] < 1.0:
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raise ValueError(
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"Invalid beta parameter at index 1: {}".format(betas[1])
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)
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defaults = dict(
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lr=lr,
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betas=betas,
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eps=eps,
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weight_decay=weight_decay,
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warmup=warmup
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)
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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(
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"Adam does not support sparse gradients, please consider SparseAdam instead"
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)
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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(
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p_data_fp32
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)
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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[
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"warmup"]
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else:
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scheduled_lr = group["lr"]
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step_size = (
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scheduled_lr * math.sqrt(bias_correction2) /
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bias_correction1
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)
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if group["weight_decay"] != 0:
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p_data_fp32.add_(
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-group["weight_decay"] * scheduled_lr, p_data_fp32
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)
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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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