X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=fridge;h=a4d860b73ac2f720363299030a75611f33110871;hb=HEAD;hp=f87c1df50ed1bb6ba34e7ae3ded2bcf10c1a597a;hpb=3dd98b99909b2bca323673263874e2abb39ac10c;p=mygptrnn.git diff --git a/fridge b/fridge index f87c1df..a4d860b 100644 --- a/fridge +++ b/fridge @@ -204,3 +204,204 @@ def insert_flash_back(rec_V, V, rec_K, K, t0, t1, CL, proba): + 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 + + +###################################################################### + +2024 Jan 18 08:46:18 (from mygpt.py) + + # warnings.warn("softmax gating", RuntimeWarning) + + # G = ( + # torch.einsum("ntc,hrc->nhrt", X, self.w_G) + self.b_G[None, :, :, None] + # ).softmax(dim=2) + +###################################################################### + +2024 Jan 21 16:55:24 (from main.py) + + with open("test.dat", "a") as f: + for m filter(lambda m: isinstance(m,mygpt.Catenn.Linear),model.modules()): + for p in m.parameters() ] + + + for m in model.modules(): + if isinstance(m, mygpt.Caterpillar): + + + +###################################################################### + +2024 Feb 13 22:53:52 (from mygpt.py) + + ###################################################################### + # Prepare the keys + + k_star = self.k_star[:, None, :].expand(-1, t1 - t0, -1) + + warnings.warn("rotating key barrel", RuntimeWarning) + k_star = self.k_star[:, None, :].expand(-1, x_q.size(1), -1) + t_barrel = torch.arange(t0, t1, device=k_star.device) + t_barrel = t_barrel[None, :].expand(k_star.size(0), t1 - t0) + l_barrel = ( + torch.arange(k_star.size(0), device=k_star.device)[:, None] + t_barrel + ) % k_star.size(0) + k_star = k_star[l_barrel, t_barrel] + + +###################################################################### + +2024 Feb 15 23:10:50 (from main.py) + + +def add_memex_v4(batches, memex_proba, marker_token): + for input in batches: + if torch.rand(1).item() < memex_proba: + t = ( + torch.arange(2 * input.size(1), device=input.device)[None, :] + .expand(input.size(0), -1) + .clone() + ) + + u = torch.rand(t.size(), device=t.device) + u[:, : input.size(1)] = 1.0 + memex_v3_proba_fragment = 1 / 20 + u = (u < memex_v3_proba_fragment).long() + v = u * torch.randint(input.size(1), u.size()) + u[:, input.size(1) + 1 :] = v[:, input.size(1) + 1 :] - u[ + :, : input.size(1) - 1 + ] * input.size(1) + u = u.cumsum().clamp(min=0) + + u0 = torch.randint(input.size(1), (input.size(0), 1), device=input.device) + caterpillar_length = args.nb_lines // args.caterpillar_height + u1 = ( + u0 + + torch.randint( + caterpillar_length, (input.size(0), 1), device=input.device + ) + + 1 + ) + + m0 = (t < u0).long() + m1 = (t >= u1).long() * (t < u1 + input.size(1)).long() + + t = t * m0 + ((-1) * (1 - m0) * (1 - m1)) + (t - u1) * m1 + m = (t < 0).long() + n = torch.arange(input.size(0), device=input.device)[:, None].expand( + -1, t.size(1) + ) + + new_input = input[n, t.clamp(min=0)] + new_input = (1 - m) * new_input + m * (marker_token) + + yield new_input + + yield input + + + +###################################################################### + +2024 Feb 16 17:07:48 (from main.py) + + # ||gn + lambda * gm|| = max(||gn||,||gm||) + # ||gn||^2 + lambda + lambda^2||gm||^2 = max(||gn||^2,||gm||^2) + # A = ||gm||^2 B = C = ||gn||^2 - max(||gn||^2, ||gm||^2) + +###################################################################### + +2024 Feb 16 17:07:51 (from main.py) + + # A,B,C = gmgm, gngm, gngn - max(gngn,gmgm) + # Delta = B*B - 4*A*C + # if(delta >= 0): + # l = ( -B - sqrt(Delta))/(2*A) + # ||gn||+l*rho*||gm|| = max(||gn||,rho*||gm||)