From: François Fleuret Date: Sun, 7 Jan 2024 15:00:37 +0000 (+0100) Subject: Update. X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=10c1ad582d28ec19465485d709c26ba9669d6369;p=mygptrnn.git Update. --- diff --git a/mygpt.py b/mygpt.py index 7f0fb9b..5754d55 100755 --- a/mygpt.py +++ b/mygpt.py @@ -562,6 +562,9 @@ class Caterpillar(nn.Module): self.caterpillar_height = caterpillar_height self.attention_dropout = attention_dropout + warnings.warn("flash back", RuntimeWarning) + self.proba_flashback = 0.1 + self.w_G = randw(nb_heads, caterpillar_height, dim_model) self.b_G = nn.Parameter( torch.full( @@ -593,6 +596,7 @@ class Caterpillar(nn.Module): N = bs.x.size(0) T = bs.x.size(1) + H = self.w_V.size(0) DV = self.w_V.size(1) DK = self.w_K.size(1) DM = self.w_O.size(1) @@ -661,9 +665,37 @@ class Caterpillar(nn.Module): self.rec_V[:, :, t0:t1] = next_V.flatten(2, 3) self.rec_K[:, :, t0:t1] = next_K.flatten(2, 3) - warnings.warn("flash back", RuntimeWarning) - if self.training: - insert_flash_back(self.rec_V, V, self.rec_K, K, t0, t1, CL, proba=1e-2 / CL) + if self.training and self.proba_flashback: + insert_flash_back( + self.rec_V, + V, + self.rec_K, + K, + t0, + t1, + CL, + proba=self.proba_flashback / CL, + ) + + # 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)[None, None, None, :] + # dk = torch.arange(DK)[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) + + # mk = ( + # torch.rand(self.rec_V[:, :, t0:t1].size()) <= self.proba_flashback + # ).long() + # self.rec_V[:, :, t0:t1] = V[n, src_head, src_time, dv] + # self.rec_K[:, :, t0:t1] = K[n, src_head, src_time, dk] + + exit(0) ###################################################################### # compute the readout