Numpy int32 error

[ACE07-Sev]

Hi I ran the Quantum_trainer, and been getting this and can't seem to find where the issue is.

Traceback (most recent call last):
File "C:\Users\elmm\Desktop\CQM\QNLP Depression.py", line 103, in
trainer.fit(train_dataset, val_dataset, logging_step=12)
File "C:\Users\elmm\AppData\Local\Programs\Python\Python39\lib\site-packages\lambeq\training\trainer.py", line 365, in fit
y_hat, loss = self.training_step(batch)
File "C:\Users\elmm\AppData\Local\Programs\Python\Python39\lib\site-packages\lambeq\training\quantum_trainer.py", line 149, in training_step
y_hat, loss = self.optimizer.backward(batch)
File "C:\Users\elmm\AppData\Local\Programs\Python\Python39\lib\site-packages\lambeq\training\spsa_optimizer.py", line 125, in backward
y0 = self.model(diagrams)
File "C:\Users\elmm\AppData\Local\Programs\Python\Python39\lib\site-packages\lambeq\training\model.py", line 59, in call
return self.forward(*args, **kwds)
File "C:\Users\elmm\AppData\Local\Programs\Python\Python39\lib\site-packages\lambeq\training\tket_model.py", line 131, in forward
return self.get_diagram_output(x)
File "C:\Users\elmm\AppData\Local\Programs\Python\Python39\lib\site-packages\lambeq\training\tket_model.py", line 103, in get_diagram_output
seed=self._randint()
File "C:\Users\elmm\AppData\Local\Programs\Python\Python39\lib\site-packages\lambeq\training\tket_model.py", line 71, in _randint
return np.random.randint(low, high)
File "mtrand.pyx", line 746, in numpy.random.mtrand.RandomState.randint
File "_bounded_integers.pyx", line 1334, in numpy.random._bounded_integers._rand_int32
ValueError: low is out of bounds for int32

[Thommy257]

Sorry, we are aware of the issue and it will be fixed in the next minor release. In the meantime, you can change the _randint() (line71-72) method in "C:\Users\elmm\AppData\Local\Programs\Python\Python39\lib\site-packages\lambeq\training\tket_model.py" to the following:

def _randint(self, low=-1 << 63, high=(1 << 63)-1):
    return np.random.randint(low, high, dtype=np.int64)

[ACE07-Sev]

Thank you so much. Right now it is running (it's a bit slow so it's on epoch 120, and has to reach 1000), but I think this fixed the issue.

[Thommy257]

This is to be expected for the TketModel. To debug your architecture we suggest to use the NumpyModel with jit support enabled (faster by an order of magnitude).

[dimkart]

Sorry, we are aware of the issue and it will be fixed in the next minor release.

@ACE07-Sev Note that the new release is now out, including a fix for this.