mirror of
https://github.com/huggingface/pytorch-image-models.git
synced 2025-06-03 15:01:08 +08:00
Update Adan with newer impl (from original source) that includes multi-tensor fn
This commit is contained in:
Ross Wightman
parent
b59058bd88
commit
c5690c044e
@ -5,52 +5,94 @@ Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models[J].
|
||||
|
||||
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, Tuple
|
||||
|
||||
import torch
|
||||
from torch import Tensor
|
||||
from torch.optim.optimizer import Optimizer
|
||||
|
||||
from torch.optim 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[J]. arXiv preprint arXiv:2208.06677, 2022.
|
||||
""" 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): iterable of parameters to optimize or dicts defining parameter groups.
|
||||
lr (float, optional): learning rate. (default: 1e-3)
|
||||
betas (Tuple[float, float, flot], optional): coefficients used for computing
|
||||
running averages of gradient and its norm. (default: (0.98, 0.92, 0.99))
|
||||
eps (float, optional): term added to the denominator to improve
|
||||
numerical stability. (default: 1e-8)
|
||||
weight_decay (float, optional): decoupled weight decay (L2 penalty) (default: 0)
|
||||
no_prox (bool): how to perform the decoupled weight decay (default: False)
|
||||
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
|
||||
foreach: If True would use torch._foreach implementation. Faster but uses slightly more memory.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
def __init__(self,
|
||||
params,
|
||||
lr=1e-3,
|
||||
betas=(0.98, 0.92, 0.99),
|
||||
eps=1e-8,
|
||||
weight_decay=0.0,
|
||||
no_prox=False,
|
||||
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,
|
||||
foreach: bool = True,
|
||||
):
|
||||
if not 0.0 <= lr:
|
||||
raise ValueError("Invalid learning rate: {}".format(lr))
|
||||
raise ValueError('Invalid learning rate: {}'.format(lr))
|
||||
if not 0.0 <= eps:
|
||||
raise ValueError("Invalid epsilon value: {}".format(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]))
|
||||
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]))
|
||||
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)
|
||||
super(Adan, self).__init__(params, defaults)
|
||||
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,
|
||||
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)
|
||||
|
||||
@torch.no_grad()
|
||||
def restart_opt(self):
|
||||
@ -70,17 +112,23 @@ class Adan(Optimizer):
|
||||
|
||||
@torch.no_grad()
|
||||
def step(self, closure=None):
|
||||
""" Performs a single optimization step.
|
||||
"""
|
||||
"""Performs a single optimization step."""
|
||||
loss = None
|
||||
if closure is not None:
|
||||
with torch.enable_grad():
|
||||
loss = closure()
|
||||
|
||||
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 support by making it tensor, or pass list into kernel
|
||||
# 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:
|
||||
@ -93,32 +141,155 @@ class Adan(Optimizer):
|
||||
for p in group['params']:
|
||||
if p.grad is None:
|
||||
continue
|
||||
grad = p.grad
|
||||
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_diff'] = torch.zeros_like(p)
|
||||
state['exp_avg_sq'] = torch.zeros_like(p)
|
||||
state['pre_grad'] = grad.clone()
|
||||
state['exp_avg_diff'] = torch.zeros_like(p)
|
||||
|
||||
exp_avg, exp_avg_sq, exp_avg_diff = state['exp_avg'], state['exp_avg_diff'], state['exp_avg_sq']
|
||||
grad_diff = grad - state['pre_grad']
|
||||
if 'neg_pre_grad' not in state or group['step'] == 1:
|
||||
state['neg_pre_grad'] = -p.grad.clone()
|
||||
|
||||
exp_avg.lerp_(grad, 1. - beta1) # m_t
|
||||
exp_avg_diff.lerp_(grad_diff, 1. - beta2) # diff_t (v)
|
||||
update = grad + beta2 * grad_diff
|
||||
exp_avg_sq.mul_(beta3).addcmul_(update, update, value=1. - beta3) # n_t
|
||||
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'])
|
||||
|
||||
denom = (exp_avg_sq.sqrt() / math.sqrt(bias_correction3)).add_(group['eps'])
|
||||
update = (exp_avg / bias_correction1 + beta2 * exp_avg_diff / bias_correction2).div_(denom)
|
||||
if group['no_prox']:
|
||||
p.data.mul_(1 - group['lr'] * group['weight_decay'])
|
||||
p.add_(update, alpha=-group['lr'])
|
||||
if not params_with_grad:
|
||||
continue
|
||||
|
||||
kwargs = dict(
|
||||
params=params_with_grad,
|
||||
grads=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'],
|
||||
)
|
||||
|
||||
if group['foreach']:
|
||||
_multi_tensor_adan(**kwargs)
|
||||
else:
|
||||
p.add_(update, alpha=-group['lr'])
|
||||
p.data.div_(1 + group['lr'] * group['weight_decay'])
|
||||
|
||||
state['pre_grad'].copy_(grad)
|
||||
_single_tensor_adan(**kwargs)
|
||||
|
||||
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,
|
||||
):
|
||||
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 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,
|
||||
):
|
||||
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 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)
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user