Integrated gradients using with pack_padded_sequence returns error

Captum
1

Hi all,

I am using integrated gradient (IG) package from Captum package, which I apply one LSTM on varying length sequences and then I try to get IG from the trained model using the following line of code:

attr, delta = ig.attribute((data, seq_lengths), target=1, return_convergence_delta=True)

but I am getting the following error:

RuntimeError: lengths array must be sorted in decreasing order when enforce_sorted is True. You can pass enforce_sorted=False to pack_padded_sequence and/or pack_sequence to sidestep this requirement if you do not need ONNX exportability.

however, I have sorted the lengths of the array in each batch in decreasing order.
please note that If I use this IG without using pack_padded_sequence it works perfectly.

regarding the previous error, I set enforce_sorted=False in pack_padded_sequence but I am getting another error:

RuntimeError: Length of all samples has to be greater than 0, but found an element in ‘lengths’ that is <= 0

Here is the length of all the samples which none of them are less than zero:

tensor([23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23,
23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23,
23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23,
23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23,
23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23,
23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23,
22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 21, 21, 21, 20,
14, 10])

any help would be much appreciated.

2

Thank you for the question @Mostafa_Alishahi!
Replied on github: https://github.com/pytorch/captum/issues/275

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