From: François Fleuret Date: Wed, 10 Jan 2024 07:46:29 +0000 (+0100) Subject: Update. X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=3e4af6d54fb3d7bd6794035cb79e30ecdcadeb6f;p=mygptrnn.git Update. --- diff --git a/fridge b/fridge index bb6f46e..dcaac19 100644 --- a/fridge +++ b/fridge @@ -125,3 +125,44 @@ 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] + ) + diff --git a/mygpt.py b/mygpt.py index ed4b2a7..d8fd227 100755 --- a/mygpt.py +++ b/mygpt.py @@ -483,7 +483,6 @@ class Caterpillar(nn.Module): self.caterpillar_height = caterpillar_height self.attention_dropout = attention_dropout - self.proba_flashback = 0.0 self.proba_gate_dropout = 0.0 self.w_G = randw(nb_heads, caterpillar_height, dim_model) @@ -572,6 +571,8 @@ class Caterpillar(nn.Module): init_rec_V = self.rec_V[:, :, t0 - CL : t0] init_rec_K = self.rec_K[:, :, t0 - CL : t0] + ###################################################################### + if self.training and self.proba_gate_dropout > 0.0: warnings.warn("gate dropout", RuntimeWarning) epsilon = 0.5 @@ -595,42 +596,6 @@ class Caterpillar(nn.Module): self.rec_V[:, :, t0:t1] = next_V.flatten(2, 3) self.rec_K[:, :, t0:t1] = next_K.flatten(2, 3) - ################################################################# - - 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] - ) - ###################################################################### # compute the readout