nb_lines,
attention_dropout=0.0,
len_max=1e5,
+ logger=print,
+ **kwargs,
):
super().__init__()
nb_lines,
attention_dropout=0.0,
len_max=1e5,
+ logger=print,
+ **kwargs,
):
super().__init__()
caterpillar_height,
attention_dropout=0.0,
len_max=1e5,
+ logger=print,
+ **kwargs,
):
super().__init__()
self.proba_gate_dropout = 0.0
+ default_bg = kwargs.get("default_bg")
+ if default_bg is None:
+ default_bg = -math.log(caterpillar_height - 1)
+ else:
+ default_bg = float(default_bg)
+
+ logger(f"default_bg {default_bg}")
+
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.b_G = nn.Parameter(torch.full((nb_heads, caterpillar_height), default_bg))
self.w_K = randw(nb_heads, dim_qk, dim_model)
self.w_V = randw(nb_heads, dim_v, dim_model)
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()
+ # Clip the gating to avoid values greater than 1 when several
+ # heads hit the same row
+
+ G = G / G.sum(1, keepdim=True).clamp(min=1)
+
+ ######################################################################
+ # Roll the gating indexes
+
+ # warnings.warn("rotating barrel", RuntimeWarning)
+
+ # 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
+ # G = G.gather(dim=2, index=r_barrel.expand_as(G))
+
######################################################################
# The "flashbacks"
G = (
G
- # + dropout_head * (1 - epsilon - G.detach())
+ + dropout_head * (1 - epsilon - G.detach())
- dropout_tail * G.detach()
)
# We prepare the arguments for the parallel scan
- # Clip the gating to avoid values greater than 1 when several
- # heads hit the same row
+ A = 1 - G.sum(1)
- G = G / G.sum(1, keepdim=True).clamp(min=1)
+ # warnings.warn("harmonic recurrence", RuntimeWarning)
+ # har = torch.arange(t0, t1, device = G.device).float() + 1
+ # A = har / (har + 1)
+ # G = G / har
- A = 1 - G.sum(1)
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",
nb_heads=1,
causal=False,
attention_dropout=0.0,
+ logger=print,
+ **kwargs,
):
super().__init__()
dropout=0.0,
len_max=1e5,
attention_layer="kvrec",
+ logger=print,
+ **kwargs,
):
super().__init__()
nb_heads=nb_heads,
causal=causal,
attention_dropout=dropout,
+ logger=logger,
+ **kwargs,
)
elif attention_layer == "dumbrec":
return DumbRec(
nb_heads=nb_heads,
nb_lines=nb_lines,
attention_dropout=dropout,
+ logger=logger,
+ **kwargs,
)
elif attention_layer == "kvrec":
return KVRec(
nb_heads=nb_heads,
nb_lines=nb_lines,
attention_dropout=dropout,
+ logger=logger,
+ **kwargs,
)
elif attention_layer == "caterpillar":
return Caterpillar(
caterpillar_length=self.caterpillar_length,
caterpillar_height=self.caterpillar_height,
attention_dropout=dropout,
+ logger=logger,
+ **kwargs,
)
else:
raise ValueError(f"Unknown attention type {attention_layer}.")
projects / mygptrnn.git / blobdiff