self.caterpillar_height = caterpillar_height
self.attention_dropout = attention_dropout
- self.proba_gate_dropout = 0.25
+ self.proba_gate_dropout = 0.0
- self.w_G = randw(nb_heads, caterpillar_height, dim_model, amplitude=1e-5)
+ 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_O = randw(dim_v * nb_heads, dim_model)
self.init_K_rec = randw(
- caterpillar_height, caterpillar_length, dim_qk, amplitude=1e-5
+ caterpillar_height,
+ caterpillar_length,
+ dim_qk,
)
self.init_V_rec = randw(
- caterpillar_height, caterpillar_length, dim_v, amplitude=1e-5
+ caterpillar_height,
+ caterpillar_length,
+ dim_v,
)
def reset_inner_loss(self):
DV = self.w_V.size(1)
DK = self.w_K.size(1)
DM = self.w_O.size(1)
- CH = self.caterpillar_height
- CL = self.caterpillar_length
+ R = self.caterpillar_height
+ L = self.caterpillar_length
assert (
- t0 >= CL and (t1 - t0) % CL == 0
+ t0 >= L and (t1 - t0) % L == 0
), f"bs.first should be greater than caterpillar_length, and bs.nb should be a multiple of caterpillar_length"
# We cache values to deal efficiently with auto-regression
if bs.init_cache:
- self.rec_V = X.new_zeros(N, CH, T, DV)
- self.rec_K = X.new_zeros(N, CH, T, DK)
+ self.rec_V = X.new_zeros(N, R, T, DV)
+ self.rec_K = X.new_zeros(N, R, T, DK)
# We start the recurrent sequences with optimizable
# initial values. No idea if it helps.
- self.rec_V[:, :, t0 - CL : t0] = self.init_V_rec[None, :, :, :]
- self.rec_K[:, :, t0 - CL : t0] = self.init_K_rec[None, :, :, :]
+ self.rec_V[:, :, t0 - L : t0] = self.init_V_rec[None, :, :, :]
+ self.rec_K[:, :, t0 - L : t0] = self.init_K_rec[None, :, :, :]
self.cache_Y = X.new_zeros(N, T, DM)
# Compute the recurrent state
# This is the Gating sequence that modulates the storing of
- # the new key and value in the CH pairs of the current
- # stack. There are CH independent gating values, which means
+ # the new key and value in the R pairs of the current
+ # stack. There are R independent gating values, which means
# that the current K/V may be stored in multiple pairs of the
# recurrent state, or not at all.
torch.einsum("ntc,hrc->nhrt", X, self.w_G) + self.b_G[None, :, :, None]
).sigmoid()
+ ######################################################################
+ # Roll the gating indexes
+
+ warnings.warn("rotating barrel", RuntimeWarning)
+
+ # print(f"SANITY2 {N=} {H=} {R=} {t0=} {t1=} {G.size()=}")
+
+ n_barrel = torch.arange(N, device=G.device)[:, None, None, None]
+ h_barrel = torch.arange(H, device=G.device)[None, :, None, None]
+ r_barrel = torch.arange(R, device=G.device)[None, None, :, None]
+ t_barrel = torch.arange(t1 - t0, device=G.device)[None, None, None, :]
+ r_barrel = (r_barrel + (t_barrel + t0) // L) % R
+
+ # GG = G.gather(dim=2,index=r_barrel)
+ G = G[n_barrel, h_barrel, r_barrel, t_barrel]
+
+ # print("SANITY", (GG-G).abs())
+ # exit(0)
+
######################################################################
# The "flashbacks"
epsilon = 0.5
dropout_head = (
- (
- torch.rand(G.size(), device=G.device)
- .flatten(2, 3)
- .sort(dim=2)
- .indices
- == 0
- )
- .unflatten(2, (CH, t1 - t0))
+ (torch.rand(N, H, 1, t1 - t0, device=G.device).sort(dim=3).indices == 0)
+ .expand_as(G)
.float()
)
G = (
G
- # + dropout_head * (1 - epsilon - G.detach())
+ + dropout_head * (1 - epsilon - G.detach())
- dropout_tail * G.detach()
)
G = G / G.sum(1, keepdim=True).clamp(min=1)
A = 1 - G.sum(1)
+
+ # warnings.warn("harmonic recurrence", RuntimeWarning)
+ # har = torch.arange(t0, t1, device = G.device).float() + 1
+ # A = har / (har + 1)
+ # G = G / har
+
gated_V = torch.einsum("nhrt,nhtd->nrtd", G, V)
gated_K = torch.einsum("nhrt,nhtd->nrtd", G, K)
# We start from cached values, which matters in inference
- init_rec_V = self.rec_V[:, :, t0 - CL : t0]
- init_rec_K = self.rec_K[:, :, t0 - CL : t0]
+ init_rec_V = self.rec_V[:, :, t0 - L : t0]
+ init_rec_K = self.rec_K[:, :, t0 - L : t0]
#################################################################
# Associative scan
# Here there is a trick: Since the stack at position t is
- # computed by updating that at position t-CL, the parallel
- # scan operates with a period of CL. To do so we split the
- # sequence indexing in two axes, the second of size CL, and
+ # computed by updating that at position t-L, the parallel
+ # scan operates with a period of L. To do so we split the
+ # sequence indexing in two axes, the second of size L, and
# run the parallel scan using the first as the sequence index.
- A = A.unflatten(2, (-1, CL))
- gated_V = gated_V.unflatten(2, (-1, CL))
- gated_K = gated_K.unflatten(2, (-1, CL))
+ A = A.unflatten(2, (-1, L))
+ gated_V = gated_V.unflatten(2, (-1, L))
+ gated_K = gated_K.unflatten(2, (-1, L))
next_V = pscan_dim(A, gated_V, init_rec_V, dim=2)
next_K = pscan_dim(A, gated_K, init_rec_K, dim=2)
# the column in the caterpillar
windowed_V = moving_window(
- self.rec_V[:, :, t0 - CL + 1 : t1], dim=2, win_dim=3, win_size=CL
+ self.rec_V[:, :, t0 - L + 1 : t1], dim=2, win_dim=3, win_size=L
)
windowed_K = moving_window(
- self.rec_K[:, :, t0 - CL + 1 : t1], dim=2, win_dim=3, win_size=CL
+ self.rec_K[:, :, t0 - L + 1 : t1], dim=2, win_dim=3, win_size=L
)
- # We have an attention score for each of the CHxCL values
+ # We have an attention score for each of the RxL values
ar = torch.einsum(
"nhtd,nftld->nhtfl",
projects / mygptrnn.git / blobdiff