######################################################################
-def baseline(X, V):
+def baseline1(X, V):
Y = X.new(X.size())
W = V.new(V.size())
+
for t in range(X.size(1)):
if t == 0:
Y[:, t] = X[:, t]
W[:, t] = V[:, t]
else:
- m = (V[:, t] >= W[:, t - 1] - 1).long()
- Y[:, t] = m * X[:, t] + (1 - m) * Y[:, t - 1]
- W[:, t] = m * V[:, t] + (1 - m) * (W[:, t - 1] - 1)
+ m = (W[:, t - 1] - 1 >= V[:, t]).long()
+ W[:, t] = m * (W[:, t - 1] - 1) + (1 - m) * V[:, t]
+ Y[:, t] = m * Y[:, t - 1] + (1 - m) * (
+ X[:, t] * (1 + dv) + Y[:, t - 1] * dv0
+ )
+
+ return Y, W
+
+
+######################################################################
+
+
+def hs(x):
+ return x.sigmoid() # (x >= 0).float() + (x - x.detach()) * (x < 0).float()
+
+
+def baseline(X, V):
+ for t in range(X.size(1)):
+ if t == 0:
+ Y = X[:, t]
+ W = V[:, t]
+ else:
+ m = (W - 1 - V[:, t]).sigmoid()
+ # m = hs(W - 1 - V[:, t])
+ W = m * (W - 1) + (1 - m) * V[:, t]
+ Y = m * Y + (1 - m) * X[:, t]
return Y, W
def pscan(X, V, s=1):
if X.size(1) == 1:
- return X, V
+ return
T = 2 * (X.size(1) // 2)
Xf = X[:, :T].view(X.size(0), X.size(1) // 2, 2, X.size(2))
Vf = V[:, :T].view(V.size(0), V.size(1) // 2, 2)
+ # [:, :, 0] < [:, :, 1]
m = (Vf[:, :, 0] - s >= Vf[:, :, 1]).long()
Vf[:, :, 1] = m * (Vf[:, :, 0] - s) + (1 - m) * Vf[:, :, 1]
m = m[:, :, None]
pscan(Xf[:, :, 1], Vf[:, :, 1], s * 2)
- m = (Vf[:, 1:, 0] >= Vf[:, :-1, 1] - s).long()
- Vf[:, 1:, 0] = m * Vf[:, 1:, 0] + (1 - m) * (Vf[:, :-1, 1] - s)
+ # [:, :-1, 1] < [:, 1:, 0]
+ m = (Vf[:, :-1, 1] - s >= Vf[:, 1:, 0]).long()
+ Vf[:, 1:, 0] = m * (Vf[:, :-1, 1] - s) + (1 - m) * Vf[:, 1:, 0]
m = m[:, :, None]
- Xf[:, 1:, 0] = m * Xf[:, 1:, 0] + (1 - m) * Xf[:, :-1, 1]
+ Xf[:, 1:, 0] = m * Xf[:, :-1, 1] + (1 - m) * Xf[:, 1:, 0]
if T < X.size(1):
+ # [:, -2] < [:, -1]
m = (V[:, -2] - s >= V[:, -1]).long()
V[:, -1] = m * (V[:, -2] - s) + (1 - m) * V[:, -1]
m = m[:, None]
X[:, -1] = m * X[:, -2] + (1 - m) * X[:, -1]
+######################################################################
+
+
+def pscan_diff(X, V, s=1):
+ if X.size(1) == 1:
+ return X, V
+
+ T = 2 * (X.size(1) // 2)
+
+ Xf = X[:, :T].view(X.size(0), X.size(1) // 2, 2, X.size(2))
+ Vf = V[:, :T].view(V.size(0), V.size(1) // 2, 2)
+
+ Xr = X.new(X.size())
+ Vr = V.new(V.size())
+ Xrf = Xr[:, :T].view(Xr.size(0), Xr.size(1) // 2, 2, Xr.size(2))
+ Vrf = Vr[:, :T].view(Vr.size(0), Vr.size(1) // 2, 2)
+
+ # [:, :, 0] < [:, :, 1]
+ dv0 = (Vf[:, :, 0] - Vf[:, :, 0].detach())[:, :, None]
+ dv = (Vf[:, :, 1] - Vf[:, :, 1].detach())[:, :, None]
+ m = (Vf[:, :, 0] - s >= Vf[:, :, 1]).long()
+ Vv = m * (Vf[:, :, 0] - s) + (1 - m) * Vf[:, :, 1]
+ m = m[:, :, None]
+ Xx = m * Xf[:, :, 0] + (1 - m) * (Xf[:, :, 1] * (1 + dv) + Xf[:, :, 0] * dv0)
+
+ Xrf[:, :, 1], Vrf[:, :, 1] = pscan_diff(Xx, Vv, s * 2)
+
+ # [:, :-1, 1] < [:, 1:, 0]
+ dv0 = (Vrf[:, :-1, 1] - Vrf[:, :-1, 1].detach())[:, :, None]
+ dv = (Vf[:, 1:, 0] - Vf[:, 1:, 0].detach())[:, :, None]
+ m = (Vrf[:, :-1, 1] - s >= Vf[:, 1:, 0]).long()
+ Vrf[:, 1:, 0] = m * (Vrf[:, :-1, 1] - s) + (1 - m) * Vf[:, 1:, 0]
+ m = m[:, :, None]
+ Xrf[:, 1:, 0] = m * Xrf[:, :-1, 1] + (1 - m) * (
+ Xf[:, 1:, 0] * (1 + dv) + Xrf[:, :-1, 1] * dv0
+ )
+
+ Xr[:, 0] = X[:, 0]
+ Vr[:, 0] = V[:, 0]
+
+ if T < X.size(1):
+ # [:, -2] < [:, -1]
+ dx = X[:, -2] - X[:, -2].detach()
+ dv = (V[:, -1] - V[:, -1].detach())[:, None]
+ m = (V[:, -2] - s >= V[:, -1]).long()
+ Vr[:, -1] = m * (Vr[:, -2] - s) + (1 - m) * V[:, -1]
+ m = m[:, None]
+ Xr[:, -1] = m * Xr[:, -2] + (1 - m) * (X[:, -1] * (1 + dv) + dx)
+
+ return Xr, Vr
+
+
######################################################################
if __name__ == "__main__":
N = 1
- T = 513
- D = 2
+ T = 64
+ D = 128
+
+ torch.autograd.set_detect_anomaly(True)
- # X = torch.randn(N, T, D, dtype=torch.float64).requires_grad_()
- # V = torch.rand(N, T, dtype=torch.float64) * 50
+ for k in range(0):
+ X = torch.randn(N, T, D, dtype=torch.float64).requires_grad_()
+ V = torch.rand(N, T, dtype=torch.float64)
- # X0, V0 = baseline(X, V)
+ X0, V0 = baseline(X, V)
- # print("########### X0 V0 ###########################################")
- # print(V0)
- # print(X0)
+ # print("########### X0 V0 ###########################################")
+ # print(V0)
+ # print(X0)
- # X1, V1 = X.clone(), V.clone()
- # pscan(X1, V1)
+ X1, V1 = pscan_diff(X, V)
- # print("########### X V ############################################")
- # print(V)
- # print(X)
+ # print("########### X V ############################################")
+ # print(V)
+ # print(X)
- # print("ERROR", ((X0 - X1).abs().max() + (V0 - V1).abs().max()).item())
+ error = ((X0 - X1).abs().max() + (V0 - V1).abs().max()).item()
+ if error > 0:
+ print("ERROR", error)
+ print(X0)
+ print(X1)
+ exit(0)
+
+ # exit(0)
# s = X1.sum()
# print(torch.autograd.grad(s, X))
# f.write(f"{V1[0,t].item()}\n")
Y = torch.randn(1, 1, D)
- X = torch.randn(
- N, T, D
- ) # * 0.1 + (torch.rand(N,T,1).sort(dim=1).indices==0).float() * Y
- V = torch.rand(N, T).requires_grad_()
+ X = torch.randn(N, T, D) * 0.1
+
+ m = (torch.rand(N, T, 1).sort(dim=1).indices == 0).float()
+ X = (1 - m) * X + m * Y
+ V = torch.rand(N, T) # + 100* m.squeeze(dim=-1)
+ V = V.requires_grad_()
- optimizer = torch.optim.SGD([V], lr=1e-2)
+ optimizer = torch.optim.SGD([V], lr=1e-1)
for k in range(1000):
- X1, V1 = X.clone(), V.clone()
- pscan(X1, V1)
- # X1=X1*(1+V1-V1.detach())[:,:,None]
- loss = (X1[:, -1:] - Y).pow(2).mean()
+ X1, V1 = baseline(X, V)
+ loss = (X1 - Y).pow(2).mean()
print(k, loss.item())
optimizer.zero_grad()
loss.backward()
projects / mygptrnn.git / blobdiff