#!/usr/bin/env python
# -*- coding: utf-8 -*-
import math
from typing import List
import torch
from torch import Tensor
from torch.optim.optimizer import Optimizer
[docs]
class Adan(Optimizer):
"""Implements a pytorch variant of Adan.
Adan was proposed in
Adan : Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models.
https://arxiv.org/abs/2208.06677
"""
def __init__(self,
params,
lr=1e-3,
betas=(0.98, 0.92, 0.99),
eps=1e-8,
weight_decay=0.0,
max_grad_norm=0.0,
no_prox=False,
foreach: bool = True):
if not 0.0 <= max_grad_norm:
raise ValueError('Invalid Max grad norm: {}'.format(max_grad_norm))
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]))
if not 0.0 <= betas[2] < 1.0:
raise ValueError('Invalid beta parameter at index 2: {}'.format(
betas[2]))
defaults = dict(
lr=lr,
betas=betas,
eps=eps,
weight_decay=weight_decay,
max_grad_norm=max_grad_norm,
no_prox=no_prox,
foreach=foreach)
super().__init__(params, defaults)
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def __setstate__(self, state):
super(Adan, self).__setstate__(state)
for group in self.param_groups:
group.setdefault('no_prox', False)
@torch.no_grad()
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def restart_opt(self):
for group in self.param_groups:
group['step'] = 0
for p in group['params']:
if p.requires_grad:
state = self.state[p]
# State initialization
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(p)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(p)
# Exponential moving average of gradient difference
state['exp_avg_diff'] = torch.zeros_like(p)
@torch.no_grad()
[docs]
def step(self):
"""Performs a single optimization step."""
if self.defaults['max_grad_norm'] > 0:
device = self.param_groups[0]['params'][0].device
global_grad_norm = torch.zeros(1, device=device)
max_grad_norm = torch.tensor(
self.defaults['max_grad_norm'], device=device)
for group in self.param_groups:
for p in group['params']:
if p.grad is not None:
grad = p.grad
global_grad_norm.add_(grad.pow(2).sum())
global_grad_norm = torch.sqrt(global_grad_norm) + group['eps']
clip_global_grad_norm = \
torch.clamp(max_grad_norm / global_grad_norm, max=1.0)
else:
clip_global_grad_norm = 1.0
for group in self.param_groups:
params_with_grad = []
grads = []
exp_avgs = []
exp_avg_sqs = []
exp_avg_diffs = []
pre_grads = []
beta1, beta2, beta3 = group['betas']
# assume same step across group now to simplify things
# per parameter step can be easily support
# by making it tensor, or pass list into kernel
if 'step' in group:
group['step'] += 1
else:
group['step'] = 1
bias_correction1 = 1.0 - beta1**group['step']
bias_correction2 = 1.0 - beta2**group['step']
bias_correction3 = 1.0 - beta3**group['step']
for p in group['params']:
if p.grad is None:
continue
params_with_grad.append(p)
grads.append(p.grad)
state = self.state[p]
if len(state) == 0:
state['exp_avg'] = torch.zeros_like(p)
state['exp_avg_sq'] = torch.zeros_like(p)
state['exp_avg_diff'] = torch.zeros_like(p)
if 'pre_grad' not in state or group['step'] == 1:
# at first step grad wouldn't be clipped
# by `clip_global_grad_norm`
# this is only to simplify implementation
state['pre_grad'] = p.grad
exp_avgs.append(state['exp_avg'])
exp_avg_sqs.append(state['exp_avg_sq'])
exp_avg_diffs.append(state['exp_avg_diff'])
pre_grads.append(state['pre_grad'])
kwargs = dict(
params=params_with_grad,
grads=grads,
exp_avgs=exp_avgs,
exp_avg_sqs=exp_avg_sqs,
exp_avg_diffs=exp_avg_diffs,
pre_grads=pre_grads,
beta1=beta1,
beta2=beta2,
beta3=beta3,
bias_correction1=bias_correction1,
bias_correction2=bias_correction2,
bias_correction3_sqrt=math.sqrt(bias_correction3),
lr=group['lr'],
weight_decay=group['weight_decay'],
eps=group['eps'],
no_prox=group['no_prox'],
clip_global_grad_norm=clip_global_grad_norm,
)
if group['foreach']:
copy_grads = _multi_tensor_adan(**kwargs)
else:
copy_grads = _single_tensor_adan(**kwargs)
for p, copy_grad in zip(params_with_grad, copy_grads):
self.state[p]['pre_grad'] = copy_grad
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def _single_tensor_adan(
params: List[Tensor],
grads: List[Tensor],
exp_avgs: List[Tensor],
exp_avg_sqs: List[Tensor],
exp_avg_diffs: List[Tensor],
pre_grads: List[Tensor],
*,
beta1: float,
beta2: float,
beta3: float,
bias_correction1: float,
bias_correction2: float,
bias_correction3_sqrt: float,
lr: float,
weight_decay: float,
eps: float,
no_prox: bool,
clip_global_grad_norm: Tensor,
):
copy_grads = []
for i, param in enumerate(params):
grad = grads[i]
exp_avg = exp_avgs[i]
exp_avg_sq = exp_avg_sqs[i]
exp_avg_diff = exp_avg_diffs[i]
pre_grad = pre_grads[i]
grad = grad.mul_(clip_global_grad_norm)
copy_grads.append(grad.clone())
diff = grad - pre_grad
update = grad + beta2 * diff
exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) # m_t
exp_avg_diff.mul_(beta2).add_(diff, alpha=1 - beta2) # diff_t
exp_avg_sq.mul_(beta3).addcmul_(update, update, value=1 - beta3) # n_t
denom = (exp_avg_sq.sqrt() / bias_correction3_sqrt).add_(eps)
update = exp_avg / bias_correction1
update.add_(beta2 * exp_avg_diff / bias_correction2).div_(denom)
if no_prox:
param.mul_(1 - lr * weight_decay)
param.add_(update, alpha=-lr)
else:
param.add_(update, alpha=-lr)
param.div_(1 + lr * weight_decay)
return copy_grads
[docs]
def _multi_tensor_adan(
params: List[Tensor],
grads: List[Tensor],
exp_avgs: List[Tensor],
exp_avg_sqs: List[Tensor],
exp_avg_diffs: List[Tensor],
pre_grads: List[Tensor],
*,
beta1: float,
beta2: float,
beta3: float,
bias_correction1: float,
bias_correction2: float,
bias_correction3_sqrt: float,
lr: float,
weight_decay: float,
eps: float,
no_prox: bool,
clip_global_grad_norm: Tensor,
):
if clip_global_grad_norm < 1.0:
torch._foreach_mul_(grads, clip_global_grad_norm.item())
copy_grads = [g.clone() for g in grads]
diff = torch._foreach_sub(grads, pre_grads)
# NOTE: line below while looking identical gives different result,
# due to float precision errors.
# using mul+add produces identical results to single-tensor,
# using add+alpha doesn't
# update = torch._foreach_add(grads, torch._foreach_mul(diff, beta2))
update = torch._foreach_add(grads, diff, alpha=beta2)
torch._foreach_mul_(exp_avgs, beta1)
torch._foreach_add_(exp_avgs, grads, alpha=1 - beta1) # m_t
torch._foreach_mul_(exp_avg_diffs, beta2)
torch._foreach_add_(exp_avg_diffs, diff, alpha=1 - beta2) # diff_t
torch._foreach_mul_(exp_avg_sqs, beta3)
torch._foreach_addcmul_(
exp_avg_sqs, update, update, value=1 - beta3) # n_t
denom = torch._foreach_sqrt(exp_avg_sqs)
torch._foreach_div_(denom, bias_correction3_sqrt)
torch._foreach_add_(denom, eps)
update = torch._foreach_div(exp_avgs, bias_correction1)
# NOTE: same issue as above.
# beta2 * diff / bias_correction2 != diff * (beta2 / bias_correction2) # noqa
# using faster version by default. uncomment for tests to pass
# torch._foreach_add_(update, torch._foreach_div(torch._foreach_mul(exp_avg_diffs, beta2), bias_correction2)) # noqa
torch._foreach_add_(
update, torch._foreach_mul(exp_avg_diffs, beta2 / bias_correction2))
torch._foreach_div_(update, denom)
if no_prox:
torch._foreach_mul_(params, 1 - lr * weight_decay)
else:
torch._foreach_add_(params, update, alpha=-lr)
torch._foreach_div_(params, 1 + lr * weight_decay)
return copy_grads
