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])
##############################
-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,
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)
# G = (
G = alpha * (1 - kill)
- ######################################################################
- # Clip the gating to avoid values greater than 1 when several
- # heads hit the same row
+ def recurrence(G, V, K):
+ # 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)
+ G = G / G.sum(1, keepdim=True).clamp(min=1)
- ######################################################################
- # Roll the gating indexes
-
- # warnings.warn("rotating barrel", RuntimeWarning)
+ # We prepare the arguments for the parallel scan
- # 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))
+ A = 1 - G.sum(1)
- # We prepare the arguments for the parallel scan
+ gated_V = torch.einsum("nhrt,nhtd->nrtd", G, V)
+ gated_K = torch.einsum("nhrt,nhtd->nrtd", G, K)
- A = 1 - G.sum(1)
+ # We start from cached values, which matters in inference
- # warnings.warn("harmonic recurrence", RuntimeWarning)
- # har = torch.arange(t0, t1, device = G.device).float() + 1
- # A = har / (har + 1)
- # G = G / har
+ init_rec_V = self.rec_V[:, :, t0 - L : t0]
+ init_rec_K = self.rec_K[:, :, t0 - L : t0]
- gated_V = torch.einsum("nhrt,nhtd->nrtd", G, V)
- gated_K = torch.einsum("nhrt,nhtd->nrtd", G, K)
+ # Associative scan
- # We start from cached values, which matters in inference
+ # 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.
- init_rec_V = self.rec_V[:, :, t0 - L : t0]
- init_rec_K = self.rec_K[:, :, t0 - L : t0]
-
- #################################################################
- # Associative scan
+ A = A.unflatten(2, (-1, L))
+ gated_V = gated_V.unflatten(2, (-1, L))
+ gated_K = gated_K.unflatten(2, (-1, L))
- # 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.
+ next_V = pscan_dim(A, gated_V, init_rec_V, dim=2)
+ next_K = pscan_dim(A, gated_K, init_rec_K, dim=2)
- A = A.unflatten(2, (-1, L))
- gated_V = gated_V.unflatten(2, (-1, L))
- gated_K = gated_K.unflatten(2, (-1, L))
+ next_V = next_V.flatten(2, 3)
+ next_K = next_K.flatten(2, 3)
- next_V = pscan_dim(A, gated_V, init_rec_V, dim=2)
- next_K = pscan_dim(A, gated_K, init_rec_K, dim=2)
+ return next_V, next_K
- next_V = next_V.flatten(2, 3)
- next_K = next_K.flatten(2, 3)
+ #################################################################
- 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_K = recurrence(G, V, K)
self.rec_V[:, :, t0:t1] = next_V
self.rec_K[:, :, t0:t1] = next_K
projects / mygptrnn.git / blobdiff