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740c67b | 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 | import torch
from torch.optim import Optimizer
import math
"""
AMP対応完了(202507) p.data -> p 修正済み
"""
class EmoFact(Optimizer):
# クラス定義＆初期化
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999),
eps=1e-8, weight_decay=0.01):
defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
super().__init__(params, defaults)
self._init_lr = lr
self.should_stop = False # 停止フラグの初期化
# 感情EMA更新(緊張と安静)
def _update_ema(self, state, loss_val):
ema = state.setdefault('ema', {})
ema['short'] = 0.3 * loss_val + 0.7 * ema.get('short', loss_val)
ema['long'] = 0.01 * loss_val + 0.99 * ema.get('long', loss_val)
return ema
# 感情スカラー値生成(EMA差分、滑らかな非線形スカラー、tanh 5 * diff で鋭敏さ強調)
def _compute_scalar(self, ema):
diff = ema['short'] - ema['long']
return math.tanh(5 * diff)
# Shadow混合比率(> 0.6：70〜90%、 < -0.6：10%、 abs> 0.3：30%、 平時：0%)
def _decide_ratio(self, scalar):
if scalar > 0.6:
return 0.7 + 0.2 * scalar
elif scalar < -0.6:
return 0.1
elif abs(scalar) > 0.3:
return 0.3
return 0.0
# 損失取得(損失値 loss_val を数値化、感情判定に使用、存在しないパラメータ(更新不要)はスキップ)
@torch.no_grad()
def step(self, closure=None):
loss = closure() if closure is not None else None
loss_val = loss.item() if loss is not None else 0.0
for group in self.param_groups:
for p in group['params']:
if p.grad is None:
continue
grad = p.grad
state = self.state[p]
# 感情EMA更新・スカラー生成 (既存ロジックを維持)
ema = self._update_ema(state, loss_val)
scalar = self._compute_scalar(ema)
ratio = self._decide_ratio(scalar)
# shadow_param：必要時のみ更新 (既存ロジックを維持)
if ratio > 0:
if 'shadow' not in state:
state['shadow'] = p.clone()
else:
p.mul_(1 - ratio).add_(state['shadow'], alpha=ratio)
state['shadow'].lerp_(p, 0.05)
# --- 勾配補正ロジック ---
# 行列の形状が2次元以上の場合、分散情報ベースのAB近似を使用
if grad.dim() >= 2:
# 行と列の2乗平均を計算 (分散の軽量な近似)
r_sq = torch.mean(grad * grad, dim=tuple(range(1, grad.dim())), keepdim=True).add_(group['eps'])
c_sq = torch.mean(grad * grad, dim=0, keepdim=True).add_(group['eps'])
# 分散情報から勾配の近似行列を生成
# AB行列として見立てたものを直接生成し更新項を計算する
# A = sqrt(r_sq), B = sqrt(c_sq) とすることでAB行列の近似を再現
# これをEMAで平滑化する
beta1, beta2 = group['betas']
state.setdefault('exp_avg_r', torch.zeros_like(r_sq)).mul_(beta1).add_(torch.sqrt(r_sq), alpha=1 - beta1)
state.setdefault('exp_avg_c', torch.zeros_like(c_sq)).mul_(beta1).add_(torch.sqrt(c_sq), alpha=1 - beta1)
# 再構築した近似勾配の平方根の積で正規化
# これにより2次モーメントのような役割を果たす
denom = torch.sqrt(state['exp_avg_r'] * state['exp_avg_c']).add_(group['eps'])
# 最終的な更新項を計算
update_term = grad / denom
# 1次元(ベクトル)の勾配補正(decoupled weight decay 構造に近い)
else:
exp_avg = state.setdefault('exp_avg', torch.zeros_like(p))
exp_avg_sq = state.setdefault('exp_avg_sq', torch.zeros_like(p))
beta1, beta2 = group['betas']
exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
denom = exp_avg_sq.sqrt().add_(group['eps'])
update_term = exp_avg / denom
# 最終的なパラメータ更新 (decoupled weight decayも適用)
p.add_(p, alpha=-group['weight_decay'] * group['lr'])
p.add_(update_term, alpha=-group['lr'])
# --- Early Stop ロジック (既存ロジックを維持) ---
hist = self.state.setdefault('scalar_hist', [])
hist.append(scalar)
if len(hist) >= 33:
hist.pop(0)
# Early Stop判断
if len(self.state['scalar_hist']) >= 32:
buf = self.state['scalar_hist']
avg_abs = sum(abs(s) for s in buf) / len(buf)
std = sum((s - sum(buf)/len(buf))**2 for s in buf) / len(buf)
if avg_abs < 0.05 and std < 0.005:
self.should_stop = True
return loss
"""
https://github.com/muooon/EmoNavi
Fact is inspired by Adafactor,
and its VRAM-friendly design is something everyone loves.
""" |