class DumbRec(nn.Module):
def __init__(
self,
- dim_in,
+ dim_model,
dim_qk,
dim_v,
nb_heads,
self.k_star = randw(nb_lines, dim_qk)
- self.w_qw = randw(nb_heads, dim_qk, dim_in)
- self.w_qr = 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.w_o = randw(dim_v * nb_heads, dim_in)
+ self.w_qw = randw(nb_heads, dim_qk, dim_model)
+ self.w_qr = randw(nb_heads, dim_qk, dim_model)
+ # self.w_k = randw(nb_heads, dim_qk, dim_model)
+ self.w_v = randw(nb_heads, dim_v, dim_model)
+ self.w_o = randw(dim_v * nb_heads, dim_model)
def reset_inner_loss(self):
self.acc_attention = 0
class KVRec(nn.Module):
def __init__(
self,
- dim_in,
+ dim_model,
dim_qk,
dim_v,
nb_heads,
self.k_star = randw(nb_lines, dim_qk)
- self.w_qw = randw(nb_heads, dim_qk, dim_in)
- self.w_qr = 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.w_o = randw(dim_v * nb_heads, dim_in)
+ self.w_qw = randw(nb_heads, dim_qk, dim_model)
+ self.w_qr = randw(nb_heads, dim_qk, dim_model)
+ self.w_k = randw(nb_heads, dim_qk, dim_model)
+ self.w_v = randw(nb_heads, dim_v, dim_model)
+ self.w_o = randw(dim_v * nb_heads, dim_model)
def reset_inner_loss(self):
self.acc_attention = 0
##############################
+# Returns a tensor with an additional index at rank win_dim, that move
+# along the same dimension as dim, on a domain {0...win_size-1}, and
+# dim is restricted on a domain reduced by win_size-1 values.
+
+
def moving_window(x, dim, win_dim, win_size):
size, stride = x.size(), x.stride()
size = size[:dim] + (size[dim] - win_size + 1,) + size[dim + 1 :]
class Caterpillar(nn.Module):
def __init__(
self,
- dim_in,
+ dim_model,
dim_qk,
dim_v,
nb_heads,
self.caterpillar_height = caterpillar_height
self.attention_dropout = attention_dropout
- self.w_G = randw(nb_heads, caterpillar_height, dim_in)
+ self.w_G = randw(nb_heads, caterpillar_height, dim_model)
self.b_G = nn.Parameter(
torch.full(
(nb_heads, caterpillar_height), -math.log(caterpillar_height - 1)
)
)
- self.w_K = randw(nb_heads, dim_qk, dim_in)
- self.w_V = randw(nb_heads, dim_v, dim_in)
- self.w_Q = randw(nb_heads, dim_qk, dim_in)
- self.w_O = randw(dim_v * nb_heads, dim_in)
+ self.w_K = randw(nb_heads, dim_qk, dim_model)
+ self.w_V = randw(nb_heads, dim_v, dim_model)
+ self.w_Q = randw(nb_heads, dim_qk, dim_model)
+ self.w_O = randw(dim_v * nb_heads, dim_model)
self.init_K_rec = randw(caterpillar_height, caterpillar_length, dim_qk)
self.init_V_rec = randw(caterpillar_height, caterpillar_length, dim_v)
######################################################################
# Compute the recurrent state
- # This is the Gating sequence that modulates if they key and
- # values should be stored in one of the CH pairs of the
- # current stack. The CH gating values are independent, which
- # means that the same thing could be stored multiple times or
- # not at all
+ # This is the Gating sequence that modulates the storing of
+ # the new key and value in the CH pairs of the current
+ # stack. The CH gating values are independent, which means
+ # that the current K/V could be stored in all the pairs of the
+ # recurrent state, or not at all.
G = (
torch.einsum("ntc,hec->nhet", X, self.w_G) + self.b_G[None, :, :, None]
).sigmoid()
+ G = F.dropout(G, self.attention_dropout, self.training)
+
V = torch.einsum("ntc,hdc->nhtd", X, self.w_V)
K = torch.einsum("ntc,hdc->nhtd", X, self.w_K)
init_rec_V = self.rec_V[:, :, t0 - CL : t0]
init_rec_K = self.rec_K[:, :, t0 - CL : t0]
- # Here there is a trick: The parallel scan operates with a
- # period of L, so we split the sequence indexing in two axes,
- # the second of size CL, and run the parallel scan using the
- # other alone as the sequence index.
+ # Here there is a trick: Since the stack at time t is computed
+ # by updating that at time t-L, the parallel scan operates
+ # with a period of L. To do so we split the time indexing in
+ # two axes, the second of size CL, and run the parallel scan
+ # using the other as the sequence index.
A = A.unflatten(2, (-1, CL))
gated_V = gated_V.unflatten(2, (-1, CL))
self.rec_K[:, :, t0 - CL + 1 : t1], dim=2, win_dim=3, win_size=CL
)
- # We have an attention score for each of the CHxCL value
+ # We have an attention score for each of the CHxCL values
ar = torch.einsum(
"nhtd,nftld->nhtfl",
class QKVAttention(nn.Module):
def __init__(
self,
- dim_in,
+ dim_model,
dim_qk,
dim_v,
nb_heads=1,
self.attention_dropout = attention_dropout
self.record_attention = False
- 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.w_o = randw(dim_v * nb_heads, dim_in)
+ self.w_q = randw(nb_heads, dim_qk, dim_model)
+ self.w_k = randw(nb_heads, dim_qk, dim_model)
+ self.w_v = randw(nb_heads, dim_v, dim_model)
+ self.w_o = randw(dim_v * nb_heads, dim_model)
def forward(self, bs):
x_q = bs.x
def attlayer():
if attention_layer == "mha":
return QKVAttention(
- dim_in=dim_model,
+ dim_model=dim_model,
dim_qk=dim_keys,
dim_v=dim_model // nb_heads,
nb_heads=nb_heads,
)
elif attention_layer == "dumbrec":
return DumbRec(
- dim_in=dim_model,
+ dim_model=dim_model,
dim_qk=dim_keys,
dim_v=dim_rec_v,
nb_heads=nb_heads,
)
elif attention_layer == "kvrec":
return KVRec(
- dim_in=dim_model,
+ dim_model=dim_model,
dim_qk=dim_keys,
dim_v=dim_rec_v,
nb_heads=nb_heads,
)
elif attention_layer == "caterpillar":
return Caterpillar(
- dim_in=dim_model,
+ dim_model=dim_model,
dim_qk=dim_keys,
dim_v=dim_rec_v,
nb_heads=nb_heads,
print("Basic check.")
m = Caterpillar(
- dim_in=4,
+ dim_model=4,
dim_qk=3,
dim_v=7,
nb_heads=1,