attribute speed and memory optimizations

[gsarti]

Description

Small speed and memory optimizations to the main attribute loop, addressing issues #243 #240

• Move tensors to CPU right away in the forward pass to avoid OOM when cloning
• Fix remap_from_filtered behavior on sequence_scores tensors.
• Use torch-native padding when converting lists of FeatureAttributionStepOutput to FeatureAttributionSequenceOutput in get_sequences_from_batched_steps.
• Bump ruff version + update deps

[gsarti]

Description of the problem of optimizing the get_sequences_from_batched_steps function:

You have a list of Pytorch tensors representing the results of computations performed at every step of a batched generation process involving one or more sequences. Every tensor in the list has a shape of [batch_size, sequence_length, ...], corresponding to scores for all previously generated steps for all sequences. Importantly, the sequences are not guaranteed to have the same length, so a decrease in batch_size between two consecutive tensors would denote the end of one or more sequences. Write a Python function to convert the list described above to a list of tensors of shape [max_sequence_length, num_sequence_steps, ...] representing the individual sequences, where max_sequence_length is the maximal length of the specific sequence, and num_sequence_steps is the number of steps for which scores are produced for the sequence.