utils/formatting.py

# formatting util module providing formatting functions for the model input and output

# external imports
import re
import torch
import numpy as np
from numpy import ndarray


# globally defined tokens that are removed from the output
SPECIAL_TOKENS = [
    "[CLS]",
    "[SEP]",
    "[PAD]",
    "[UNK]",
    "[MASK]",
    "▁",
    "Ġ",
    "</w>",
    "<0x0A>",
    "<0x0D>",
    "<0x09>",
    "<s>",
    "</s>",
]


# function to format the model repose nicely
# takes a list of strings and returning a combined string
def format_output_text(output: list):

    # remove special tokens from list using other function
    formatted_output = format_tokens(output)

    # start string with first list item if it is not empty
    if formatted_output[0] != "":
        output_str = formatted_output[0]
    else:
        # alternatively start with second list item
        output_str = formatted_output[1]

    # add all other list items with a space in between
    for txt in formatted_output[1:]:
        # check if the token is a punctuation mark or other special character
        if txt in [
            ".",
            ",",
            "!",
            "?",
            ":",
            ";",
            ")",
            "]",
            "}",
            "'",
            '"',
            "[",
            "{",
            "(",
            "<",
        ]:
            # add punctuation mark without space
            output_str += txt
        # add token with space if not empty
        elif txt != "":
            output_str += " " + txt

    # return the combined string with multiple spaces removed
    return re.sub(r"\s+", " ", output_str)


# format the tokens by removing special tokens and special characters
def format_tokens(tokens: list):

    # initialize empty list
    updated_tokens = []

    # loop through tokens
    for t in tokens:
        # remove special token from start of token if found
        if t.startswith("▁"):
            t = t.lstrip("▁")

        # loop through special tokens list and remove from current token if matched
        for s in SPECIAL_TOKENS:
            t = t.replace(s, "")

        # add token to list
        updated_tokens.append(t)

    # return the list of tokens
    return updated_tokens


# function to flatten shap values into a 2d list by summing them up
def flatten_attribution(values: ndarray, axis: int = 0):
    return np.sum(values, axis=axis)


# function to flatten values into a 2d list by averaging the attention values
def flatten_attention(values: ndarray, axis: int = 0):
    return np.mean(values, axis=axis)


# function to get averaged decoder attention from attention values
def avg_attention(attention_values, model: str):

    # check if model is godel
    if model == "godel":
        # get attention values for the input and output vectors
        attention = attention_values.encoder_attentions[0][0].detach().numpy()
        return np.mean(attention, axis=1)

    # extracting attention values for mistral
    attention = attention_values.to(torch.device("cpu")).detach().numpy()

    # removing the last dimension and transposing to get the correct shape
    attention = attention[:, :, :, 0]

    # return the averaged attention values
    return np.mean(attention, axis=1)