From 3c5ce93138700c33a055f83ac1a46efb2975e28a Mon Sep 17 00:00:00 2001 From: =?utf8?q?Fran=C3=A7ois=20Fleuret?= Date: Sun, 7 Jan 2024 16:21:02 +0100 Subject: [PATCH] Update. --- mygpt.py | 13 ++----------- 1 file changed, 2 insertions(+), 11 deletions(-) diff --git a/mygpt.py b/mygpt.py index 6e13ff8..5ea927e 100755 --- a/mygpt.py +++ b/mygpt.py @@ -656,23 +656,14 @@ class Caterpillar(nn.Module): self.rec_K[:, :, t0:t1] = next_K.flatten(2, 3) 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,) + # 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 - # 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, device=X.device)[None, None, None, :] -- 2.39.5