File size: 11,747 Bytes
53a37bd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 |
""" Adan Optimizer
Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models[J]. arXiv preprint arXiv:2208.06677, 2022.
https://arxiv.org/abs/2208.06677
Implementation adapted from https://github.com/sail-sg/Adan
"""
# Copyright 2022 Garena Online Private Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
from typing import List, Optional, Tuple
import torch
from torch import Tensor
from torch.optim.optimizer import Optimizer
class MultiTensorApply(object):
available = False
warned = False
def __init__(self, chunk_size):
try:
MultiTensorApply.available = True
self.chunk_size = chunk_size
except ImportError as err:
MultiTensorApply.available = False
MultiTensorApply.import_err = err
def __call__(self, op, noop_flag_buffer, tensor_lists, *args):
return op(self.chunk_size, noop_flag_buffer, tensor_lists, *args)
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
Arguments:
params: Iterable of parameters to optimize or dicts defining parameter groups.
lr: Learning rate.
betas: Coefficients used for first- and second-order moments.
eps: Term added to the denominator to improve numerical stability.
weight_decay: Decoupled weight decay (L2 penalty)
no_prox: How to perform the weight decay
caution: Enable caution from 'Cautious Optimizers'
foreach: If True would use torch._foreach implementation. Faster but uses slightly more memory.
"""
def __init__(self,
params,
lr: float = 1e-3,
betas: Tuple[float, float, float] = (0.98, 0.92, 0.99),
eps: float = 1e-8,
weight_decay: float = 0.0,
no_prox: bool = False,
caution: bool = False,
foreach: Optional[bool] = None,
):
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,
no_prox=no_prox,
caution=caution,
foreach=foreach,
)
super().__init__(params, defaults)
def __setstate__(self, state):
super(Adan, self).__setstate__(state)
for group in self.param_groups:
group.setdefault('no_prox', False)
group.setdefault('caution', False)
@torch.no_grad()
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()
def step(self, closure=None):
"""Performs a single optimization step."""
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
try:
has_scalar_maximum = 'Scalar' in torch.ops.aten._foreach_maximum_.overloads()
except:
has_scalar_maximum = False
for group in self.param_groups:
params_with_grad = []
grads = []
exp_avgs = []
exp_avg_sqs = []
exp_avg_diffs = []
neg_pre_grads = []
beta1, beta2, beta3 = group['betas']
# assume same step across group now to simplify things
# per parameter step can be easily supported by making it a 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 'neg_pre_grad' not in state or group['step'] == 1:
state['neg_pre_grad'] = -p.grad.clone()
exp_avgs.append(state['exp_avg'])
exp_avg_sqs.append(state['exp_avg_sq'])
exp_avg_diffs.append(state['exp_avg_diff'])
neg_pre_grads.append(state['neg_pre_grad'])
if not params_with_grad:
continue
if group['foreach'] is None:
use_foreach = not group['caution'] or has_scalar_maximum
else:
use_foreach = group['foreach']
if use_foreach:
func = _multi_tensor_adan
else:
func = _single_tensor_adan
func(
params_with_grad,
grads,
exp_avgs=exp_avgs,
exp_avg_sqs=exp_avg_sqs,
exp_avg_diffs=exp_avg_diffs,
neg_pre_grads=neg_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'],
caution=group['caution'],
)
return loss
def _single_tensor_adan(
params: List[Tensor],
grads: List[Tensor],
exp_avgs: List[Tensor],
exp_avg_sqs: List[Tensor],
exp_avg_diffs: List[Tensor],
neg_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,
caution: bool,
):
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]
neg_grad_or_diff = neg_pre_grads[i]
# for memory saving, we use `neg_grad_or_diff` to get some temp variable in an inplace way
neg_grad_or_diff.add_(grad)
exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) # m_t
exp_avg_diff.mul_(beta2).add_(neg_grad_or_diff, alpha=1 - beta2) # diff_t
neg_grad_or_diff.mul_(beta2).add_(grad)
exp_avg_sq.mul_(beta3).addcmul_(neg_grad_or_diff, neg_grad_or_diff, value=1 - beta3) # n_t
denom = (exp_avg_sq.sqrt() / bias_correction3_sqrt).add_(eps)
step_size_diff = lr * beta2 / bias_correction2
step_size = lr / bias_correction1
if caution:
# Apply caution as per 'Cautious Optimizers' - https://arxiv.org/abs/2411.16085
mask = (exp_avg * grad > 0).to(grad.dtype)
mask.div_(mask.mean().clamp_(min=1e-3))
exp_avg = exp_avg * mask
if no_prox:
param.mul_(1 - lr * weight_decay)
param.addcdiv_(exp_avg, denom, value=-step_size)
param.addcdiv_(exp_avg_diff, denom, value=-step_size_diff)
else:
param.addcdiv_(exp_avg, denom, value=-step_size)
param.addcdiv_(exp_avg_diff, denom, value=-step_size_diff)
param.div_(1 + lr * weight_decay)
neg_grad_or_diff.zero_().add_(grad, alpha=-1.0)
def _multi_tensor_adan(
params: List[Tensor],
grads: List[Tensor],
exp_avgs: List[Tensor],
exp_avg_sqs: List[Tensor],
exp_avg_diffs: List[Tensor],
neg_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,
caution: bool,
):
if len(params) == 0:
return
# for memory saving, we use `neg_pre_grads` to get some temp variable in a inplace way
torch._foreach_add_(neg_pre_grads, grads)
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, neg_pre_grads, alpha=1 - beta2) # diff_t
torch._foreach_mul_(neg_pre_grads, beta2)
torch._foreach_add_(neg_pre_grads, grads)
torch._foreach_mul_(exp_avg_sqs, beta3)
torch._foreach_addcmul_(exp_avg_sqs, neg_pre_grads, neg_pre_grads, value=1 - beta3) # n_t
denom = torch._foreach_sqrt(exp_avg_sqs)
torch._foreach_div_(denom, bias_correction3_sqrt)
torch._foreach_add_(denom, eps)
step_size_diff = lr * beta2 / bias_correction2
step_size = lr / bias_correction1
if caution:
# Apply caution as per 'Cautious Optimizers' - https://arxiv.org/abs/2411.16085
masks = torch._foreach_mul(exp_avgs, grads)
masks = [(m > 0).to(g.dtype) for m, g in zip(masks, grads)]
mask_scale = [m.mean() for m in masks]
torch._foreach_maximum_(mask_scale, 1e-3)
torch._foreach_div_(masks, mask_scale)
exp_avgs = torch._foreach_mul(exp_avgs, masks)
if no_prox:
torch._foreach_mul_(params, 1 - lr * weight_decay)
torch._foreach_addcdiv_(params, exp_avgs, denom, value=-step_size)
torch._foreach_addcdiv_(params, exp_avg_diffs, denom, value=-step_size_diff)
else:
torch._foreach_addcdiv_(params, exp_avgs, denom, value=-step_size)
torch._foreach_addcdiv_(params, exp_avg_diffs, denom, value=-step_size_diff)
torch._foreach_div_(params, 1 + lr * weight_decay)
torch._foreach_zero_(neg_pre_grads)
torch._foreach_add_(neg_pre_grads, grads, alpha=-1.0)
|