AssertionError: Elements to be reduced can only beeither Tensors or tuples containing Tensors.

I have been trying to use captum to interpret my Low-Resource Neural Machine Translation model (specifically, XLM).

I am getting the following error when trying to run IntegratedGradients.attribute function:

---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
<ipython-input-42-3042e38801c6> in <module>
----> 1 interpret_sentence(input_text, ground_truth)

<ipython-input-40-6d6b70739f3f> in interpret_sentence(src, trg)
     35         print(langs.shape)
     36         print(langs)
---> 37         attribution_ig, delta =  ig.attribute(src_embedding, baselines=202, additional_forward_args=(langs, idx, False), target=max_idx, n_steps=50, return_convergence_delta=True)
     38         attribution_igs.append(attribution_ig)
     39 

~/anaconda3/lib/python3.7/site-packages/captum/attr/_core/integrated_gradients.py in attribute(self, inputs, baselines, target, additional_forward_args, n_steps, method, internal_batch_size, return_convergence_delta)
    282             internal_batch_size=internal_batch_size,
    283             forward_fn=self.forward_func,
--> 284             target_ind=expanded_target,
    285         )
    286 

~/anaconda3/lib/python3.7/site-packages/captum/attr/_utils/batching.py in _batched_operator(operator, inputs, additional_forward_args, target_ind, internal_batch_size, **kwargs)
    166     ]
--> 167     return _reduce_list(all_outputs)

~/anaconda3/lib/python3.7/site-packages/captum/attr/_utils/batching.py in _reduce_list(val_list, red_func)
     65         for i in range(len(val_list[0])):
     66             final_out.append(
---> 67                 _reduce_list([val_elem[i] for val_elem in val_list], red_func)
     68             )
     69     else:

~/anaconda3/lib/python3.7/site-packages/captum/attr/_utils/batching.py in _reduce_list(val_list, red_func)
     69     else:
     70         raise AssertionError(
---> 71             "Elements to be reduced can only be"
     72             "either Tensors or tuples containing Tensors."
     73         )

AssertionError: Elements to be reduced can only beeither Tensors or tuples containing Tensors.

I am using the following arguments:

ig.attribute(src_embedding, baselines=1, additional_forward_args=(langs, idx, False), target=max_idx, n_steps=50, return_convergence_delta=True)

I tried printing all_outputs it shows [(None,)]

All my prediction and forward functions are working well and I thoroughly tested them seperately.

Please help.

[vivekmig]

Hi @RachitBansal , I looked into your code, it seems like the issue might be in the decoder, particularly in this line: tensor = tensor[-1, :, :].data.type_as(src_enc) # (bs, dim) Accessing the data directly no longer maintains the autograd dependency, which is likely causing the error. Removing the .data attribute and accessing the tensor directly should maintain the autograd compute graph appropriately. It seems like this method may be intended primarily for inference and not back-propagation, so alternatively, you may be able to use the same decoder method used in training (seems like it may use forward directly rather than the generate method) to fix the issue.

[RachitBansal]

zip notebook

Can you please try now?

Also, the problem might be in the fwd function inside the TransformerModel class in the translation.XLM.XLM.src.model.transformerIn python file.