X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=fridge;h=143092cd4fb00e6e311d1276a9ead442e08992a1;hb=8012a611e9920816fe6ba382b69305242136bc2a;hp=bb6f46e4746f393f0916a565e858fcf6b335632b;hpb=de0831357e74b1d1a61b1a41890e20ed1a2c9b96;p=mygptrnn.git diff --git a/fridge b/fridge index bb6f46e..143092c 100644 --- a/fridge +++ b/fridge @@ -125,3 +125,213 @@ def insert_flash_back(rec_V, V, rec_K, K, t0, t1, CL, proba): # That was a bad idea # G = F.dropout(G, self.attention_dropout, self.training) + +###################################################################### + +2024 Jan 10 08:46:13 (from mygpt.py) + + ################################################################# + # Flashbacks. This version sucks, about to replace it + if self.training and self.proba_flashback > 0.0: + warnings.warn("flash back", RuntimeWarning) + # This piece of code makes the assumption that there is + # nothing informative before t0, otherwise we'd have to + # implement a cache for V and K too. This should not be + # too much of a problem since this is used only during + # train, where full sequence are available + + n = torch.arange(N, device=X.device)[:, None, None, None] + t = torch.arange(t0, t1, device=X.device)[None, None, :, None] + dv = torch.arange(DV, device=X.device)[None, None, None, :] + dk = torch.arange(DK, device=X.device)[None, None, None, :] + + u = ( + torch.rand(N, CH, t1 - t0, 1, device=X.device).mul(t).long() // CL + ) * CL + + src_time = t - u - t0 + src_head = torch.randint(H, (N, CH, t1 - t0, 1), device=X.device) + + mask = ( + torch.rand(N, CH, t1 - t0, DV, device=X.device) <= self.proba_flashback + ).long() + + self.rec_V[:, :, t0:t1] = ( + mask * V[n, src_head, src_time, dv] + + (1 - mask) * self.rec_V[:, :, t0:t1] + ) + + self.rec_K[:, :, t0:t1] = ( + mask * K[n, src_head, src_time, dk] + + (1 - mask) * self.rec_K[:, :, t0:t1] + ) + + +###################################################################### + +2024 Jan 13 13:38:31 (from mygpt.py) + + g= F.sigmoid(self.b_G) + a=1-g + + print(f"\n\nSANITY {a**T}\n") + exit(0) + + +###################################################################### + +2024 Jan 14 13:39:37 (from mygpt.py) + + epsilon = 0.5 + + dropout_head = ( + (torch.rand(N, H, 1, t1 - t0, device=G.device).sort(dim=3).indices == 0) + .expand_as(G) + .float() + ) + + dropout_tail = dropout_head.cumsum(dim=3) - dropout_head + + dropout_active = ( + torch.rand(N, 1, 1, 1, device=G.device) < self.proba_gate_dropout + ).long() + + dropout_head *= dropout_active + dropout_tail *= dropout_active + + G = ( + G + + 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] +