##############################
class QKVAttention(nn.Module):
- def __init__(self, dim_in, dim_qk, dim_v, nb_heads = 1, causal = False, attention_dropout = 0.0):
+ def __init__(
+ self,
+ dim_in, dim_qk, dim_v,
+ nb_heads = 1, causal = False, attention_dropout = 0.0
+ ):
super().__init__()
def randw(*d):
- return nn.Parameter(torch.empty(*d).normal_(0, 1 / math.sqrt(d[-1])))
+ return nn.Parameter(torch.randn(*d) / math.sqrt(d[-1]))
+
+ self.causal = causal
+ self.attention_dropout = attention_dropout
self.w_q = randw(nb_heads, dim_qk, dim_in)
self.w_k = randw(nb_heads, dim_qk, dim_in)
self.w_v = randw(nb_heads, dim_v, dim_in)
- self.causal = causal
- self.attention_dropout = attention_dropout
+ self.w_o = randw(dim_in, dim_v * nb_heads)
+
+ def forward(self, x_q, x_kv = None):
+ if x_kv is None: x_kv = x_q
+
+ q = torch.einsum('ntc,hdc->nhtd', x_q, self.w_q)
+ k = torch.einsum('ntc,hdc->nhtd', x_kv, self.w_k)
+ v = torch.einsum('ntc,hdc->nhtd', x_kv, self.w_v)
+
+ a = torch.einsum('nhtd,nhsd->nhts', q, k) / math.sqrt(q.size(3))
- def forward(self, x):
- q = torch.einsum('ntc,hdc->nhtd', x, self.w_q)
- k = torch.einsum('ntc,hdc->nhtd', x, self.w_k)
- v = torch.einsum('ntc,hdc->nhtd', x, self.w_v)
- r = math.sqrt(q.size(3))
- a = torch.einsum('nhtd,nhsd->nhts', q, k).div(r)
if self.causal:
- mask = torch.tril(q.new_ones(a.size(2), a.size(3)))[None, None, :, :] == 0
+ mask = torch.arange(a.size(2), device = q.device)[None, None, :, None] \
+ < torch.arange(a.size(3), device = q.device)[None, None, None, :]
a = a.masked_fill(mask, float('-inf'))
+
a = a.softmax(dim = 3)
a = F.dropout(a, self.attention_dropout, self.training)
- y = torch.einsum('nhts,nhsd->nhtd', a, v)
- return y.permute(0, 2, 1, 3).flatten(2) # nhtd -> nt(hd)
+ y = torch.einsum('nhts,nhsd->nthd', a, v).flatten(2)
+
+ y = y @ self.w_o
+
+ return y
##############################
nb_heads = nb_heads,
causal = True, attention_dropout = dropout
),
- nn.Linear(in_features = dim_model, out_features = dim_model),
),
Residual(
nn.LayerNorm(dim_model),
self.readout = nn.Linear(in_features = dim_model, out_features = vocabulary_size)
def forward(self, x):
+ x = F.pad(x, (1, 0))
x = self.embedding(x)
x = self.trunk(x)
x = self.readout(x)
- return x
+ return x[:, :-1]
######################################################################
if __name__ == '__main__':
+ print('Basic check.')
+
vocabulary_size = 10
x = torch.randint(vocabulary_size, (25, 100))