Delete emolynx.py

emolynx.py

@@ -1,129 +0,0 @@
-import torch
-from torch.optim import Optimizer
-import math
-from typing import Tuple, Callable, Union
-
-# Helper function (Lynx)
-def exists(val):
-    return val is not None
-
-class EmoLynx(Optimizer):
-    # クラス定義＆初期化
-    def __init__(self, params: Union[list, torch.nn.Module], lr=1e-3, betas=(0.9, 0.99), 
-    # lynx用ベータ･互換性の追加(lynx用beta1･beta2)
-                 eps=1e-8, weight_decay=0.01, decoupled_weight_decay: bool = False): 
-
-        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
-        super().__init__(params, defaults)
-        
-        # lynxに応じてウェイト減衰のため保存
-        self._init_lr = lr
-        self.decoupled_wd = decoupled_weight_decay
-        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%、 > 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: Callable | None = None): # クロージャの型ヒントを追加
-        loss = None
-        if exists(closure): # 一貫性のためにexistsヘルパーを使う
-            with torch.enable_grad():
-                loss = closure()
-        loss_val = loss.item() if loss is not None else 0.0
-
-        for group in self.param_groups:
-            # リンクス共通パラメータ抽出
-            lr, wd, beta1, beta2 = group['lr'], group['weight_decay'], *group['betas']
-            
-            # ウェイト減衰の処理を分離 (from lynx)
-            _wd_actual = wd
-            if self.decoupled_wd:
-                _wd_actual /= self._init_lr # 非連結時ウェイト減衰調整
-
-            for p in filter(lambda p: exists(p.grad), group['params']): # PGチェックにフィルタ
-
-                grad = p.grad # PG直接使用(計算に".data"不要)
-                state = self.state[p]
-
-                # EMA更新・スカラー生成(EMA差分からスカラーを生成しスパイク比率を決定)
-                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(スパイク部分に現在値を5%ずつ追従させる動的履歴)
-                if ratio > 0:
-                    if 'shadow' not in state:
-                        state['shadow'] = p.data.clone()
-                    else:
-                        p.data.mul_(1 - ratio).add_(state['shadow'], alpha=ratio) 
-                        state['shadow'].lerp_(p.data, 0.05) 
-                        # lynx更新前 p.data で shadow 更新(現在値を5%ずつ追従)
-                        # p.data.mul_(1 - ratio).add_(state['shadow'], alpha=ratio) 
-                        # EmoNavi: p.data = p.data * (1-ratio) + shadow * ratio
-
-                # --- Start Lynx Gradient Update Logic ---
-                
-                # lynx初期化(exp_avg_sq)
-                if 'exp_avg' not in state:
-                    state['exp_avg'] = torch.zeros_like(p)
-                exp_avg = state['exp_avg']
-
-                # Stepweight decay (from lynx): p.data = p.data * (1 - lr * wd)
-                # decoupled_wd 考慮 _wd_actual 使用(EmoNaviのwdは最後に適用)
-                p.data.mul_(1. - lr * _wd_actual)
-
-                # 勾配ブレンド
-                # m_t = beta1 * exp_avg_prev + (1 - beta1) * grad
-                blended_grad = grad.mul(1. - beta1).add_(exp_avg, alpha=beta1)
-                
-                # p: p.data = p.data - lr * sign(blended_grad)
-                p.data.add_(blended_grad.sign_(), alpha = -lr)
-
-                # exp_avg = beta2 * exp_avg + (1 - beta2) * grad
-                exp_avg.mul_(beta2).add_(grad, alpha = 1. - beta2)
-
-                # --- End Lynx Gradient Update Logic ---
-
-                # Early Stop用 scalar記録(バッファ共通で管理/最大32件保持/動静評価)
-                # この部分は p.state ではなく self.state ���アクセスする
-                hist = self.state.setdefault('scalar_hist', [])
-                hist.append(scalar)
-                if len(hist) > 32:
-                    hist.pop(0)
-
-        # Early Stop判断(静けさの合図) - This part is outside the inner loop
-        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 # 💡 外部からこれを見て判断可
-
-        return loss
-
-"""
-Lynx was developed with inspiration from Lion and Tiger, 
-which we deeply respect for their lightweight and intelligent design.  
-Lynx also integrates EmoNAVI to enhance its capabilities.
-"""