import pscan
-
# X is /.../xTxD A is /.../xT Y_init is /.../xD
return Y
+def pscan_rgrad(grad_Y, A, X, Y_init, dim=-2, eps=1e-2):
+ with torch.no_grad():
+ s_A, s_X = 0, 0
+ for t in range(X.size(dim) - 1, 0, -1):
+ delta = (grad_Y[t] - s_A) / A[t].grad
+ s_A += A[t].grad * delta
+ A[t].grad = delta
+ delta = (grad_Y[t] - s_X) / X[t].grad
+ s_X += X[t].grad * delta
+ X[t].grad = delta
+
+
def pscan_shape(A, X, Y_init):
s = X.size()
A = A.reshape(-1, s[-2])
attention_dropout=0.0,
len_max=1e5,
logger=print,
- **kwargs,
+ args=None,
):
super().__init__()
attention_dropout=0.0,
len_max=1e5,
logger=print,
- **kwargs,
+ args=None,
):
super().__init__()
##############################
-class Calibrator:
- def __init__(self, w=None, b=None):
- self.w = w
- self.b = b
- self.s, self.s_sq, self.n = 0, 0, 0
- self.mean, self.std = 0, 0
-
- def update(self, X):
- X = X.detach()
- self.s += X.sum(dim=0)
- self.s_sq += X.pow(2).sum(dim=0)
- self.n += X.size(0)
-
- def moments(self):
- mean = self.s / self.n
- std = (self.s_sq / self.n - mean * mean).sqrt()
- return mean, std
-
- def normalize(self):
- mean, std = self.moments()
- if self.b is not None:
- self.b.sub_(mean)
- if self.w is not None:
- self.w.div_(std)
- result = mean - self.mean, std - self.std
- self.mean, self.std = mean, std
- self.s, self.s_sq, self.n = 0, 0, 0
- return result
-
-
class Caterpillar(nn.Module):
def __init__(
self,
attention_dropout=0.0,
len_max=1e5,
logger=print,
- **kwargs,
+ args=None,
):
super().__init__()
self.caterpillar_height = caterpillar_height
self.attention_dropout = attention_dropout
- ######################################################################
- # sup_args
-
- x = kwargs.get("gate_dropout")
- if x is None:
- self.proba_gate_dropout = 0.0
- else:
- self.proba_gate_dropout = float(x)
-
- logger(f"self.proba_gate_dropout {self.proba_gate_dropout}")
-
- x = kwargs.get("default_bg")
- if x is None:
- default_bg = -math.log(caterpillar_height - 1)
- else:
- default_bg = float(x)
-
- logger(f"default_bg {default_bg}")
+ self.gate_dropout_proba = args.gate_dropout_proba
+ self.gate_dropout_sync = args.gate_dropout_sync
######################################################################
+ default_bg = -math.log(caterpillar_height - 1)
self.w_G = randw(nb_heads, caterpillar_height, dim_model)
self.b_G = nn.Parameter(torch.full((nb_heads, caterpillar_height), default_bg))
dim_v,
)
- self.calibrator_G = Calibrator()
- self.calibrator_rec_V = Calibrator()
- self.calibrator_rec_K = Calibrator()
-
def reset_inner_loss(self):
self.acc_attention = 0
self.acc_nb = 0
torch.einsum("ntc,hrc->nhrt", X, self.w_G) + self.b_G[None, :, :, None]
).sigmoid()
- self.calibrator_G.update(G.reshape(-1, G.size(-1)))
-
- # warnings.warn("softmax gating", RuntimeWarning)
+ # Clip the gating to avoid values greater than 1 when several
+ # heads hit the same row
- # G = (
- # torch.einsum("ntc,hrc->nhrt", X, self.w_G) + self.b_G[None, :, :, None]
- # ).softmax(dim=2)
+ G = G / G.sum(1, keepdim=True).clamp(min=1)
######################################################################
- # The "flashbacks"
- if self.training and self.proba_gate_dropout > 0.0:
- # This is a better implementation of "flashbacks".
+ def recurrence(G, V, K):
+ # We prepare the arguments for the parallel scan
- # G is NxHxExT where e is the caterpillar's row.
+ A = 1 - G.sum(1)
- warnings.warn("gate dropout", RuntimeWarning)
+ gated_V = torch.einsum("nhrt,nhtd->nrtd", G, V)
+ gated_K = torch.einsum("nhrt,nhtd->nrtd", G, K)
- kill = (
- torch.rand(G.size(), device=G.device) <= self.proba_gate_dropout
- ).float()
+ # We start from cached values, which matters in inference
- alpha = G / (1 - self.proba_gate_dropout)
+ init_rec_V = self.rec_V[:, :, t0 - L : t0]
+ init_rec_K = self.rec_K[:, :, t0 - L : t0]
- G = alpha * (1 - kill)
+ # Here there is a trick: Since the stack at position t is
+ # 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.
- ######################################################################
- # Clip the gating to avoid values greater than 1 when several
- # heads hit the same row
+ A = A.unflatten(2, (-1, L))
+ gated_V = gated_V.unflatten(2, (-1, L))
+ gated_K = gated_K.unflatten(2, (-1, L))
- G = G / G.sum(1, keepdim=True).clamp(min=1)
+ next_V = pscan_dim(A, gated_V, init_rec_V, dim=2)
+ next_K = pscan_dim(A, gated_K, init_rec_K, dim=2)
- ######################################################################
- # Roll the gating indexes
+ next_V = next_V.flatten(2, 3)
+ next_K = next_K.flatten(2, 3)
- # warnings.warn("rotating barrel", RuntimeWarning)
+ return next_V, next_K
- # 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))
-
- # We prepare the arguments for the parallel scan
-
- 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)
+ next_V, next_K = recurrence(G, V, K)
- # We start from cached values, which matters in inference
+ if self.training and self.gate_dropout_proba > 0.0:
+ # G is NxHxRxT where r is the caterpillar's row.
- init_rec_V = self.rec_V[:, :, t0 - L : t0]
- init_rec_K = self.rec_K[:, :, t0 - L : t0]
+ warnings.warn("gate dropout", RuntimeWarning)
- #################################################################
- # Associative scan
+ if self.gate_dropout_sync:
+ shape_kill = (N, 1, 1)
+ else:
+ shape_kill = (N, H, R)
- # Here there is a trick: Since the stack at position t is
- # 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.
+ # Pick a point in each of the NxHxR timeline and set this
+ # entry and the following to 1
+ kill = (
+ torch.rand(*shape_kill, t1 - t0, device=G.device).sort(dim=3).indices
+ == 0
+ ).cumsum(dim=3)
- A = A.unflatten(2, (-1, L))
- gated_V = gated_V.unflatten(2, (-1, L))
- gated_K = gated_K.unflatten(2, (-1, L))
+ # Keep these mask for only some of the NxHxR
+ kill = kill * (
+ torch.rand(*shape_kill, 1, device=G.device) <= self.gate_dropout_proba
+ )
- 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 coefficient to keep are the complementary
+ mask = 1 - kill
- next_V = next_V.flatten(2, 3)
- next_K = next_K.flatten(2, 3)
+ masked_next_V, masked_next_K = recurrence(G * mask, V, K)
- self.calibrator_rec_V.update(
- next_V.permute(0, 1, 3, 2).reshape(-1, next_V.size(2))
- )
- self.calibrator_rec_K.update(
- next_K.permute(0, 1, 3, 2).reshape(-1, next_K.size(2))
- )
+ next_V = next_V.detach() + (masked_next_V - masked_next_V.detach()) / (
+ 1 - self.gate_dropout_proba
+ )
+ next_K = next_K.detach() + (masked_next_K - masked_next_K.detach()) / (
+ 1 - self.gate_dropout_proba
+ )
self.rec_V[:, :, t0:t1] = next_V
self.rec_K[:, :, t0:t1] = next_K
Q = torch.einsum("ntc,hdc->nhtd", X, self.w_Q)
- # We build tensors NxHxTxFxL where N is the sample index, H
- # the head, T the time, F the row in the caterpillar, and L
+ # We build tensors NxHxTxRxL where N is the sample index, H
+ # the head, T the time, R the row in the caterpillar, and L
# the column in the caterpillar
windowed_V = moving_window(
# We have an attention score for each of the RxL values
ar = torch.einsum(
- "nhtd,nftld->nhtfl",
+ "nhtd,nrtld->nhtrl",
Q,
windowed_K,
) / math.sqrt(DK)
causal=False,
attention_dropout=0.0,
logger=print,
- **kwargs,
+ args=None,
):
super().__init__()
len_max=1e5,
attention_layer="kvrec",
logger=print,
- **kwargs,
+ args=None,
):
super().__init__()
causal=causal,
attention_dropout=dropout,
logger=logger,
- **kwargs,
+ args=args,
)
elif attention_layer == "dumbrec":
return DumbRec(
nb_lines=nb_lines,
attention_dropout=dropout,
logger=logger,
- **kwargs,
+ args=args,
)
elif attention_layer == "kvrec":
return KVRec(
nb_lines=nb_lines,
attention_dropout=dropout,
logger=logger,
- **kwargs,
+ args=args,
)
elif attention_layer == "caterpillar":
return Caterpillar(
caterpillar_height=self.caterpillar_height,
attention_dropout=dropout,
logger=logger,
- **kwargs,
+ args=args,
)
else:
raise ValueError(f"Unknown attention type {attention_layer}.")
projects / mygptrnn.git / blobdiff