def slice(self):
return self.x[:, self.first : self.first + self.nb]
+ def complete(self):
+ return self.first == 0 and self.nb == x.size(1)
+
######################################################################
def randw(*d):
return nn.Parameter(torch.randn(*d) / math.sqrt(d[-1]))
- assert causal, "TODO: Switch off the cache when non-causal!!!"
self.causal = causal
self.attention_dropout = attention_dropout
def forward(self, bs_q):
x_q = bs_q.x
+ assert (
+ self.causal or bs_q.complete()
+ ), "Partial evaluation is only possible for causal models"
+
if bs_q.first == 0:
self.cache_k = x_q.new_zeros(
x_q.size(0), self.w_k.size(0), x_q.size(1), self.w_k.size(1)