POT sinkhorn_stabilized and sinkhorn_epsilon_scaling methods for sinkhorn getting stuck

[annabel-ma]

Describe the bug

I am running sinkhorn between two datasets of 100 images (each 28x28 -- this is just 2 subsets of fashionmnist), but for some reason, I am finding that the sinkhorn_epsilon_scaling method is getting stuck at a certain value (I have attached the log), no matter if regularization is 0.0001, 0.001, 0.01, 0.1, 1. It is the same for different datasets of the same size -- why is this?

It. |Err

0|8.288824e-25|

10|1.999996e-03|
20|1.999996e-03|
30|1.999996e-03|
40|1.999996e-03|
50|1.999996e-03|
60|1.999996e-03|
70|1.999996e-03|
80|1.999996e-03|
90|1.999996e-03|
It. |Err

100|1.999996e-03|
110|1.999996e-03|
120|1.999996e-03|
130|1.999996e-03|
140|1.999996e-03|
150|1.999996e-03|
160|1.999996e-03|
170|1.999996e-03|
180|1.999996e-03|
190|1.999996e-03|

Environment (please complete the following information):

• OS (e.g. MacOS, Windows, Linux):
• Python version:
• How was POT installed (source, pip, conda):
• Build command you used (if compiling from source):
• Only for GPU related bugs:
  • CUDA version:
  • GPU models and configuration:
  • Any other relevant information:

Output of the following code snippet:

import platform; print(platform.platform())
import sys; print("Python", sys.version)
import numpy; print("NumPy", numpy.__version__)
import scipy; print("SciPy", scipy.__version__)
import ot; print("POT", ot.__version__)

Additional context

[annabel-ma]

I am using torch float64 tensors

[annabel-ma]

N = len(data1['ys'])
    X = torch.stack(data1[feature]).reshape((N, -1)).to(torch.float64)
    Y = torch.stack(data2[feature]).reshape((N, -1)).to(torch.float64)
    M = ot.dist(X, Y)
    Mmax = M.max()
    M_normalized = M / Mmax if float(Mmax) != 0.0 else M

    a = torch.full((N,), 1.0 / N, dtype=torch.float64)
    b = torch.full((N,), 1.0 / N, dtype=torch.float64)

   W, log = ot.sinkhorn(
                a, b, M_normalized,
                reg=reg,
                method=method,
                numItermax=numItermax,
                verbose=is_verbose,
                log=True,
                warn = True
            )