compute_loss.py

import torch.distributions as dist
from typing import List, Dict
import itertools

start_token = "<|startoftext|>"
end_token = "<|endoftext|>"


def _get_outside_indices(subtree_indices, attn_map_idx_to_wp):
    flattened_subtree_indices = _flatten_indices(subtree_indices)
    outside_indices = [
        map_idx
        for map_idx in attn_map_idx_to_wp.keys() if (map_idx not in flattened_subtree_indices)
    ]
    return outside_indices


def _flatten_indices(related_indices):
    flattened_related_indices = []
    for item in related_indices:
        if isinstance(item, list):
            flattened_related_indices.extend(item)
        else:
            flattened_related_indices.append(item)
    return flattened_related_indices


def split_indices(related_indices: List[int]):
    noun = [related_indices[-1]]  # assumes noun is always last in the list
    modifier = related_indices[:-1]
    if isinstance(modifier, int):
        modifier = [modifier]
    return noun, modifier


def _symmetric_kl(attention_map1, attention_map2):
    # Convert map into a single distribution: 16x16 -> 256
    if len(attention_map1.shape) > 1:
        attention_map1 = attention_map1.reshape(-1)
    if len(attention_map2.shape) > 1:
        attention_map2 = attention_map2.reshape(-1)

    p = dist.Categorical(probs=attention_map1)
    q = dist.Categorical(probs=attention_map2)

    kl_divergence_pq = dist.kl_divergence(p, q)
    kl_divergence_qp = dist.kl_divergence(q, p)

    avg_kl_divergence = (kl_divergence_pq + kl_divergence_qp) / 2
    return avg_kl_divergence


def calculate_positive_loss(attention_maps, modifier, noun):
    src_indices = modifier
    dest_indices = noun

    if isinstance(src_indices, list) and isinstance(dest_indices, list):
        wp_pos_loss = [
            _symmetric_kl(attention_maps[s], attention_maps[d])
            for (s, d) in itertools.product(src_indices, dest_indices)
        ]
        positive_loss = max(wp_pos_loss)
    elif isinstance(dest_indices, list):
        wp_pos_loss = [
            _symmetric_kl(attention_maps[src_indices], attention_maps[d])
            for d in dest_indices
        ]
        positive_loss = max(wp_pos_loss)
    elif isinstance(src_indices, list):
        wp_pos_loss = [
            _symmetric_kl(attention_maps[s], attention_maps[dest_indices])
            for s in src_indices
        ]
        positive_loss = max(wp_pos_loss)
    else:
        positive_loss = _symmetric_kl(
            attention_maps[src_indices], attention_maps[dest_indices]
        )

    return positive_loss


def _calculate_outside_loss(attention_maps, src_indices, outside_loss):
    negative_loss = []
    computed_pairs = set()
    pair_counter = 0

    for outside_idx in outside_loss:
        if isinstance(src_indices, list):
            wp_neg_loss = []
            for t in src_indices:
                pair_key = (t, outside_idx)
                if pair_key not in computed_pairs:
                    wp_neg_loss.append(
                        _symmetric_kl(
                            attention_maps[t], attention_maps[outside_idx]
                        )
                    )
                    computed_pairs.add(pair_key)
            negative_loss.append(max(wp_neg_loss) if wp_neg_loss else 0)
            pair_counter += 1

        else:
            pair_key = (src_indices, outside_idx)
            if pair_key not in computed_pairs:
                negative_loss.append(
                    _symmetric_kl(
                        attention_maps[src_indices], attention_maps[outside_idx]
                    )
                )
                computed_pairs.add(pair_key)
                pair_counter += 1

    return negative_loss, pair_counter


def align_wordpieces_indices(
        wordpieces2indices, start_idx, target_word
):
    """
    Aligns a `target_word` that contains more than one wordpiece (the first wordpiece is `start_idx`)
    """

    wp_indices = [start_idx]
    wp = wordpieces2indices[start_idx].replace("</w>", "")

    # Run over the next wordpieces in the sequence (which is why we use +1)
    for wp_idx in range(start_idx + 1, len(wordpieces2indices)):
        if wp == target_word:
            break

        wp2 = wordpieces2indices[wp_idx].replace("</w>", "")
        if target_word.startswith(wp + wp2) and wp2 != target_word:
            wp += wordpieces2indices[wp_idx].replace("</w>", "")
            wp_indices.append(wp_idx)
        else:
            wp_indices = (
                []
            )  # if there's no match, you want to clear the list and finish
            break

    return wp_indices


def extract_attribution_indices(doc):
    # doc = parser(prompt)
    subtrees = []
    modifiers = ["amod", "nmod", "compound", "npadvmod", "advmod", "acomp"]

    for w in doc:
        if w.pos_ not in ["NOUN", "PROPN"] or w.dep_ in modifiers:
            continue
        subtree = []
        stack = []
        for child in w.children:
            if child.dep_ in modifiers:
                subtree.append(child)
                stack.extend(child.children)

        while stack:
            node = stack.pop()
            if node.dep_ in modifiers or node.dep_ == "conj":
                subtree.append(node)
                stack.extend(node.children)
        if subtree:
            subtree.append(w)
            subtrees.append(subtree)
    return subtrees

def extract_attribution_indices_with_verbs(doc):
    '''This function specifically addresses cases where a verb is between
       a noun and its modifier. For instance: "a dog that is red"
       here, the aux is between 'dog' and 'red'. '''

    subtrees = []
    modifiers = ["amod", "nmod", "compound", "npadvmod", "advmod", "acomp",
                 'relcl']
    for w in doc:
        if w.pos_ not in ["NOUN", "PROPN"] or w.dep_ in modifiers:
            continue
        subtree = []
        stack = []
        for child in w.children:
            if child.dep_ in modifiers:
                if child.pos_ not in ['AUX', 'VERB']:
                    subtree.append(child)
                stack.extend(child.children)

        while stack:
            node = stack.pop()
            if node.dep_ in modifiers or node.dep_ == "conj":
                # we don't want to add 'is' or other verbs to the loss, we want their children
                if node.pos_ not in ['AUX', 'VERB']:
                    subtree.append(node)
                stack.extend(node.children)
        if subtree:
            subtree.append(w)
            subtrees.append(subtree)
        return subtrees

def extract_attribution_indices_with_verb_root(doc):
    '''This function specifically addresses cases where a verb is between
       a noun and its modifier. For instance: "a dog that is red"
       here, the aux is between 'dog' and 'red'. '''

    subtrees = []
    modifiers = ["amod", "nmod", "compound", "npadvmod", "advmod", "acomp"]
    for w in doc:
        subtree = []
        stack = []

        # if w is a verb/aux and has a noun child and a modifier child, add them to the stack
        if w.pos_ != 'AUX' or w.dep_ in modifiers:
            continue

        for child in w.children:
            if child.dep_ in modifiers or child.pos_ in ['NOUN', 'PROPN']:
                if child.pos_ not in ['AUX', 'VERB']:
                    subtree.append(child)
                stack.extend(child.children)
        # did not find a pair of noun and modifier
        if len(subtree) < 2:
            continue

        while stack:
            node = stack.pop()
            if node.dep_ in modifiers or node.dep_ == "conj":
                # we don't want to add 'is' or other verbs to the loss, we want their children
                if node.pos_ not in ['AUX']:
                    subtree.append(node)
                stack.extend(node.children)

        if subtree:
            if w.pos_ not in ['AUX']:
                subtree.append(w)
            subtrees.append(subtree)
    return subtrees

def calculate_negative_loss(
        attention_maps, modifier, noun, subtree_indices, attn_map_idx_to_wp
):
    outside_indices = _get_outside_indices(subtree_indices, attn_map_idx_to_wp)
    negative_modifier_loss, num_modifier_pairs = _calculate_outside_loss(
        attention_maps, modifier, outside_indices
    )
    negative_noun_loss, num_noun_pairs = _calculate_outside_loss(
        attention_maps, noun, outside_indices
    )

    negative_modifier_loss = -sum(negative_modifier_loss) / len(outside_indices)
    negative_noun_loss = -sum(negative_noun_loss) / len(outside_indices)

    negative_loss = (negative_modifier_loss + negative_noun_loss) / 2

    return negative_loss

def get_indices(tokenizer, prompt: str) -> Dict[str, int]:
    """Utility function to list the indices of the tokens you wish to alter"""
    ids = tokenizer(prompt).input_ids
    indices = {
        i: tok
        for tok, i in zip(
            tokenizer.convert_ids_to_tokens(ids), range(len(ids))
        )
    }
    return indices

def get_attention_map_index_to_wordpiece(tokenizer, prompt):
    attn_map_idx_to_wp = {}

    wordpieces2indices = get_indices(tokenizer, prompt)

    # Ignore `start_token` and `end_token`
    for i in list(wordpieces2indices.keys())[1:-1]:
        wordpiece = wordpieces2indices[i]
        wordpiece = wordpiece.replace("</w>", "")
        attn_map_idx_to_wp[i] = wordpiece

    return attn_map_idx_to_wp