- if self.training and self.proba_flashback > 0.0:
- # 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
-
- 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_V = (
- torch.rand(N, CH, t1 - t0, DV, device=X.device) <= self.proba_flashback
- ).long()
- self.rec_V[:, :, t0:t1] = (
- mask_V * V[n, src_head, src_time, dv]
- + (1 - mask_V) * self.rec_V[:, :, t0:t1]
- )
-
- mask_K = (
- torch.rand(N, CH, t1 - t0, DK, device=X.device) <= self.proba_flashback
- ).long()
- self.rec_K[:, :, t0:t1] = (
- mask_K * K[n, src_head, src_time, dk]
- + (1 - mask_K) * self.rec_K[:, :, t0:t1]
- )
-