+ dropout_head * (1 - epsilon - G.detach())
- dropout_tail * G.detach()
)
+
+######################################################################
+
+2024 Jan 18 07:39:29 (from mygpt.py)
+
+class Calibrator:
+ def __init__(self, w=None, b=None):
+ self.w = w
+ self.b = b
+ self.s, self.s_sq, self.n = 0, 0, 0
+ self.mean, self.std = 0, 0
+
+ def update(self, X):
+ X = X.detach()
+ self.s += X.sum(dim=0)
+ self.s_sq += X.pow(2).sum(dim=0)
+ self.n += X.size(0)
+
+ def moments(self):
+ mean = self.s / self.n
+ std = (self.s_sq / self.n - mean * mean).sqrt()
+ return mean, std
+
+ def normalize(self):
+ mean, std = self.moments()
+ if self.b is not None:
+ self.b.sub_(mean)
+ if self.w is not None:
+ self.w.div_(std)
+ result = mean - self.mean, std - self.std
+ self.mean, self.std = mean, std
+ self.s, self.s_sq, self.n = 0, 0, 0
+ return result
+
+
+
+######################################################################
+
+2024 Jan 18 07:39:34 (from mygpt.py)
+
+ # self.calibrator_G = Calibrator()
+ # self.calibrator_rec_V = Calibrator()
+ # self.calibrator_rec_K = Calibrator()
+
+
+######################################################################
+
+2024 Jan 18 07:39:37 (from mygpt.py)
+
+ # self.calibrator_G.update(G.reshape(-1, G.size(-1)))
+
+
+######################################################################
+
+2024 Jan 18 07:39:42 (from mygpt.py)
+
+ # self.calibrator_rec_V.update(
+ # next_V.permute(0, 1, 3, 2).reshape(-1, next_V.size(2))
+ # )
+ # self.calibrator_rec_K.update(
+ # next_K.permute(0, 1, 3, 2).reshape(-1, next_K.size(2))
+ # )
+
+
+######################################################################
+
+2024 Jan 18 07:47:12 (from mygpt.py)
+
+ ######################################################################
+ # Roll the gating indexes
+
+ # warnings.warn("rotating barrel", RuntimeWarning)
+
+ # r_barrel = torch.arange(R, device=G.device)[None, None, :, None]
+ # t_barrel = torch.arange(t1 - t0, device=G.device)[None, None, None, :]
+ # r_barrel = (r_barrel + (t_barrel + t0) // L) % R
+ # G = G.gather(dim=2, index=r_barrel.expand_as(G))
+
+
+######################################################################
+
+2024 Jan 18 07:47:25 (from mygpt.py)
+
+ # warnings.warn("harmonic recurrence", RuntimeWarning)
+ # har = torch.arange(t0, t1, device = G.device).float() + 1
+ # A = har / (har + 1)
+ # G = G / har
+