tensor compression seems to not work

[LukGross]

The straight forward TT-decomposition of a full tensor does not work properly for me.

Minimal example:

import tntorch as tn
import torch
import numpy as np

X, Y, Z = np.meshgrid(range(128), range(128), range(128))
full = torch.Tensor(
    np.sqrt(np.sqrt(X) * (Y + Z) + Y * Z**2) * (X + np.sin(Y) * np.cos(Z))
)  # Some analytical 3D function
print(full.shape)

t = tn.Tensor(full, ranks_tt=3, requires_grad=True)  # You can also pass a list of ranks


def metrics():
    print(t)
    print(
        "Compression ratio: {}/{} = {:g}".format(
            full.numel(), t.numel(), full.numel() / t.numel()
        )
    )
    print("Relative error:", tn.relative_error(full, t))
    print("RMSE:", tn.rmse(full, t))
    print("R^2:", tn.r_squared(full, t))


metrics()

Output:

torch.Size([128, 128, 128])
3D TT tensor:

 128 128 128
  |   |   |
 (0) (1) (2)
 / \ / \ / \
1   3   3   1

Compression ratio: 2097152/2097152.0 = 1
Relative error: tensor(0.0005, grad_fn=<DivBackward0>)
RMSE: tensor(22.0728, grad_fn=<DivBackward0>)
R^2: tensor(1.0000, grad_fn=<RsubBackward1>)

The expected output would be the one given in the tutorial. Especially, compression ratio should be $>0$.

I experience this behavior both with python 3.9.6 and 3.12.2 on an M1 MacBook under macOS Sonoma 14.4.1