self.caterpillar_height = caterpillar_height
self.attention_dropout = attention_dropout
- warnings.warn("flash back", RuntimeWarning)
- self.proba_flashback = 1e-2
+ self.proba_flashback = 0.0
+ self.proba_gate_dropout = 0.0
self.w_G = randw(nb_heads, caterpillar_height, dim_model)
self.b_G = nn.Parameter(
torch.einsum("ntc,hec->nhet", X, self.w_G) + self.b_G[None, :, :, None]
).sigmoid()
- # That bas a bad idea
+ if self.training and self.proba_gate_dropout > 0.0:
+ warnings.warn("gate droupout", RuntimeWarning)
+ epsilon = 0.5
+
+ # That was a bad idea
# G = F.dropout(G, self.attention_dropout, self.training)
V = torch.einsum("ntc,hdc->nhtd", X, self.w_V)
# We prepare the arguments for the parallel scan
+ # Clip the gating
+ warnings.warn("gating clipping", RuntimeWarning)
+ G = G / G.sum(1, keepdim=True).clamp(min=1)
+
A = 1 - G.sum(1)
gated_V = torch.einsum("nhet,nhtd->netd", G, V)
gated_K = torch.einsum("nhet,nhtd->netd", G, K)
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