Spaces:
Running
Running
File size: 8,910 Bytes
6a662e6 |
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 |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import logging
from typing import Any
import torch
import torch.optim
import torch.distributed as dist
logger = logging.getLogger(__name__)
_params_t = Any
def to_real(x):
if torch.is_complex(x):
return x.real
else:
return x
class DAdaptAdam(torch.optim.Optimizer):
"""Adam with D-Adaptation automatic step-sizes.
Leave LR set to 1 unless you encounter instability.
Args:
params (iterable):
Iterable of parameters to optimize or dicts defining parameter groups.
lr (float):
Learning rate adjustment parameter. Increases or decreases the D-adapted learning rate.
betas (tuple[float, float], optional): coefficients used for computing
running averages of gradient and its square (default: (0.9, 0.999))
momentum (float):
Momentum value in the range [0,1) (default: 0.9).
eps (float):
Term added to the denominator outside of the root operation to improve numerical stability. (default: 1e-8).
weight_decay (float):
Weight decay, i.e. a L2 penalty (default: 0).
log_every (int):
Log using print every k steps, default 0 (no logging).
decouple (boolean):
Use AdamW style decoupled weight decay
d0 (float):
Initial D estimate for D-adaptation (default 1e-6). Rarely needs changing.
growth_rate (float):
prevent the D estimate from growing faster than this multiplicative rate.
Default is inf, for unrestricted. Values like 1.02 give a kind of learning
rate warmup effect.
fsdp_in_use (bool):
If you're using sharded parameters, this should be set to True. The optimizer
will attempt to auto-detect this, but if you're using an implementation other
than PyTorch's builtin version, the auto-detection won't work.
"""
def __init__(self, params, lr=1.0,
betas=(0.9, 0.999),
eps=1e-8,
weight_decay=0,
log_every=0,
decouple=True,
d0=1e-6,
growth_rate=float('inf')):
if not 0.0 < d0:
raise ValueError("Invalid d0 value: {}".format(d0))
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 decouple:
logger.info("Using decoupled weight decay")
from .fsdp import is_fsdp_used
fsdp_in_use = is_fsdp_used()
defaults = dict(lr=lr, betas=betas, eps=eps,
weight_decay=weight_decay,
d=d0,
k=0,
gsq_weighted=0.0,
log_every=log_every,
decouple=decouple,
growth_rate=growth_rate,
fsdp_in_use=fsdp_in_use)
super().__init__(params, defaults)
@property
def supports_memory_efficient_fp16(self):
return False
@property
def supports_flat_params(self):
return True
def step(self, closure=None):
"""Performs a single optimization step.
Args:
closure (callable, optional): A closure that reevaluates the model
and returns the loss.
"""
loss = None
if closure is not None:
loss = closure()
g_sq = 0.0
sksq_weighted = 0.0
sk_l1 = 0.0
lr = max(group['lr'] for group in self.param_groups)
group = self.param_groups[0]
gsq_weighted = group['gsq_weighted']
d = group['d']
dlr = d*lr
growth_rate = group['growth_rate']
decouple = group['decouple']
fsdp_in_use = group['fsdp_in_use']
log_every = group['log_every']
beta1, beta2 = group['betas']
for group in self.param_groups:
group_lr = group['lr']
decay = group['weight_decay']
k = group['k']
eps = group['eps']
if group_lr not in [lr, 0.0]:
raise RuntimeError("Setting different lr values in different parameter "
"groups is only supported for values of 0")
for p in group['params']:
if p.grad is None:
continue
if hasattr(p, "_fsdp_flattened"):
fsdp_in_use = True
grad = p.grad.data
# Apply weight decay (coupled variant)
if decay != 0 and not decouple:
grad.add_(p.data, alpha=decay)
state = self.state[p]
# State initialization
if 'step' not in state:
state['step'] = 0
state['s'] = torch.zeros_like(p.data, memory_format=torch.preserve_format).detach()
# Exponential moving average of gradient values
state['exp_avg'] = torch.zeros_like(p.data, memory_format=torch.preserve_format).detach()
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(
to_real(p.data), memory_format=torch.preserve_format).detach()
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
grad_grad = to_real(grad * grad.conj())
# Adam EMA updates
if group_lr > 0:
exp_avg.mul_(beta1).add_(grad, alpha=dlr*(1-beta1))
exp_avg_sq.mul_(beta2).add_(grad_grad, alpha=1-beta2)
denom = exp_avg_sq.sqrt().add_(eps)
g_sq += grad_grad.div_(denom).sum().item()
s = state['s']
s.mul_(beta2).add_(grad, alpha=dlr*(1-beta2))
sksq_weighted += to_real(s * s.conj()).div_(denom).sum().item()
sk_l1 += s.abs().sum().item()
######
gsq_weighted = beta2*gsq_weighted + g_sq*(dlr**2)*(1-beta2)
d_hat = d
# if we have not done any progres, return
# if we have any gradients available, will have sk_l1 > 0 (unless \|g\|=0)
if sk_l1 == 0:
return loss
if lr > 0.0:
if fsdp_in_use:
dist_tensor = torch.zeros(3, device='cuda')
dist_tensor[0] = sksq_weighted
dist_tensor[1] = gsq_weighted
dist_tensor[2] = sk_l1
dist.all_reduce(dist_tensor, op=dist.ReduceOp.SUM)
global_sksq_weighted = dist_tensor[0]
global_gsq_weighted = dist_tensor[1]
global_sk_l1 = dist_tensor[2]
else:
global_sksq_weighted = sksq_weighted
global_gsq_weighted = gsq_weighted
global_sk_l1 = sk_l1
d_hat = (global_sksq_weighted/(1-beta2) - global_gsq_weighted)/global_sk_l1
d = max(d, min(d_hat, d*growth_rate))
if log_every > 0 and k % log_every == 0:
logger.info(
f"(k={k}) dlr: {dlr:1.1e} d_hat: {d_hat:1.1e}, d: {d:1.8}. "
f"sksq_weighted={global_sksq_weighted:1.1e} gsq_weighted={global_gsq_weighted:1.1e} "
f"sk_l1={global_sk_l1:1.1e}{' (FSDP)' if fsdp_in_use else ''}")
for group in self.param_groups:
group['gsq_weighted'] = gsq_weighted
group['d'] = d
group_lr = group['lr']
decay = group['weight_decay']
k = group['k']
eps = group['eps']
for p in group['params']:
if p.grad is None:
continue
grad = p.grad.data
state = self.state[p]
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
state['step'] += 1
denom = exp_avg_sq.sqrt().add_(eps)
denom = denom.type(p.type())
# Apply weight decay (decoupled variant)
if decay != 0 and decouple and group_lr > 0:
p.data.add_(p.data, alpha=-decay * dlr)
# Take step
p.data.addcdiv_(exp_avg, denom, value=-1)
group['k'] = k + 1
return loss
|