tokenization_utils.py

import torch
from .colbert_configuration import ColBERTConfig
from transformers import AutoTokenizer

DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")

def _split_into_batches(ids, mask, bsize):
    batches = []
    for offset in range(0, ids.size(0), bsize):
        batches.append((ids[offset:offset+bsize], mask[offset:offset+bsize]))

    return batches

def _sort_by_length(ids, mask, bsize):
    if ids.size(0) <= bsize:
        return ids, mask, torch.arange(ids.size(0))

    indices = mask.sum(-1).sort().indices
    reverse_indices = indices.sort().indices

    return ids[indices], mask[indices], reverse_indices

class QueryTokenizer():
    def __init__(self, config: ColBERTConfig, verbose: int = 3):
        self.tok = AutoTokenizer.from_pretrained(config.checkpoint)
        self.tok.base = config.checkpoint
        self.verbose = verbose

        self.config = config
        self.query_maxlen = config.query_maxlen
        self.background_maxlen = 512 - self.query_maxlen + 1  # FIXME: Make this configurable

        self.Q_marker_token, self.Q_marker_token_id = config.query_token, self.tok.convert_tokens_to_ids(config.query_token_id)
        self.cls_token, self.cls_token_id = self.tok.cls_token, self.tok.cls_token_id
        self.sep_token, self.sep_token_id = self.tok.sep_token, self.tok.sep_token_id
        self.mask_token, self.mask_token_id = self.tok.mask_token, self.tok.mask_token_id
        self.pad_token,self.pad_token_id = self.tok.pad_token,self.tok.pad_token_id
        self.used = False

    def tokenize(self, batch_text, add_special_tokens=False):
        assert type(batch_text) in [list, tuple], (type(batch_text))

        tokens = [self.tok.tokenize(x, add_special_tokens=False) for x in batch_text]

        if not add_special_tokens:
            return tokens

        prefix, suffix = [self.cls_token, self.Q_marker_token], [self.sep_token]
        tokens = [prefix + lst + suffix + [self.mask_token] * (self.query_maxlen - (len(lst)+3)) for lst in tokens]

        return tokens

    def encode(self, batch_text, add_special_tokens=False):
        assert type(batch_text) in [list, tuple], (type(batch_text))

        ids = self.tok(batch_text, add_special_tokens=False).to(DEVICE)['input_ids']

        if not add_special_tokens:
            return ids

        prefix, suffix = [self.cls_token_id, self.Q_marker_token_id], [self.sep_token_id]
        ids = [prefix + lst + suffix + [self.mask_token_id] * (self.query_maxlen - (len(lst)+3)) for lst in ids]

        return ids

    def tensorize(self, batch_text, bsize=None, context=None, full_length_search=False):
        assert type(batch_text) in [list, tuple], (type(batch_text))

        # add placehold for the [Q] marker
        batch_text = ['. ' + x for x in batch_text]

        # Full length search is only available for single inference (for now)
        # Batched full length search requires far deeper changes to the code base
        assert(full_length_search == False or (type(batch_text) == list and len(batch_text) == 1))

        if full_length_search:
            # Tokenize each string in the batch
            un_truncated_ids = self.tok(batch_text, add_special_tokens=False).to(DEVICE)['input_ids']
            # Get the longest length in the batch
            max_length_in_batch = max(len(x) for x in un_truncated_ids)
            # Set the max length
            max_length = self.max_len(max_length_in_batch)
        else:
            # Max length is the default max length from the config
            max_length = self.query_maxlen

        obj = self.tok(batch_text, padding='max_length', truncation=True,
                       return_tensors='pt', max_length=max_length).to(DEVICE)

        ids, mask = obj['input_ids'], obj['attention_mask']

        # postprocess for the [Q] marker and the [MASK] augmentation
        ids[:, 1] = self.Q_marker_token_id
        ids[ids == self.pad_token_id] = self.mask_token_id

        if context is not None:
            assert len(context) == len(batch_text), (len(context), len(batch_text))

            obj_2 = self.tok(context, padding='longest', truncation=True,
                            return_tensors='pt', max_length=self.background_maxlen).to(DEVICE)

            ids_2, mask_2 = obj_2['input_ids'][:, 1:], obj_2['attention_mask'][:, 1:]  # Skip the first [SEP]

            ids = torch.cat((ids, ids_2), dim=-1)
            mask = torch.cat((mask, mask_2), dim=-1)

        if self.config.attend_to_mask_tokens:
            mask[ids == self.mask_token_id] = 1
            assert mask.sum().item() == mask.size(0) * mask.size(1), mask

        if bsize:
            batches = _split_into_batches(ids, mask, bsize)
            return batches
        
        if self.used is False:
            self.used = True

            firstbg = (context is None) or context[0]
            if self.verbose > 1:
                print()
                print("#> QueryTokenizer.tensorize(batch_text[0], batch_background[0], bsize) ==")
                print(f"#> Input: {batch_text[0]}, \t\t {firstbg}, \t\t {bsize}")
                print(f"#> Output IDs: {ids[0].size()}, {ids[0]}")
                print(f"#> Output Mask: {mask[0].size()}, {mask[0]}")
                print()

        return ids, mask

    # Ensure that query_maxlen <= length <= 500 tokens
    def max_len(self, length):
        return min(500, max(self.query_maxlen, length))


class DocTokenizer():
    def __init__(self, config: ColBERTConfig):
        self.tok = AutoTokenizer.from_pretrained(config.checkpoint)
        self.tok.base = config.checkpoint

        self.config = config
        self.doc_maxlen = config.doc_maxlen

        self.D_marker_token, self.D_marker_token_id = self.config.doc_token, self.tok.convert_tokens_to_ids(self.config.doc_token_id)
        self.cls_token, self.cls_token_id = self.tok.cls_token, self.tok.cls_token_id
        self.sep_token, self.sep_token_id = self.tok.sep_token, self.tok.sep_token_id

    def tokenize(self, batch_text, add_special_tokens=False):
        assert type(batch_text) in [list, tuple], (type(batch_text))

        tokens = [self.tok.tokenize(x, add_special_tokens=False).to(DEVICE) for x in batch_text]

        if not add_special_tokens:
            return tokens

        prefix, suffix = [self.cls_token, self.D_marker_token], [self.sep_token]
        tokens = [prefix + lst + suffix for lst in tokens]

        return tokens

    def encode(self, batch_text, add_special_tokens=False):
        assert type(batch_text) in [list, tuple], (type(batch_text))

        ids = self.tok(batch_text, add_special_tokens=False).to(DEVICE)['input_ids']

        if not add_special_tokens:
            return ids

        prefix, suffix = [self.cls_token_id, self.D_marker_token_id], [self.sep_token_id]
        ids = [prefix + lst + suffix for lst in ids]

        return ids

    def tensorize(self, batch_text, bsize=None):
        assert type(batch_text) in [list, tuple], (type(batch_text))

        # add placehold for the [D] marker
        batch_text = ['. ' + x for x in batch_text]

        obj = self.tok(batch_text, padding='max_length', truncation='longest_first',
                       return_tensors='pt', max_length=self.doc_maxlen).to(DEVICE)

        ids, mask = obj['input_ids'], obj['attention_mask']

        # postprocess for the [D] marker
        ids[:, 1] = self.D_marker_token_id

        if bsize:
            ids, mask, reverse_indices = _sort_by_length(ids, mask, bsize)
            batches = _split_into_batches(ids, mask, bsize)
            return batches, reverse_indices

        return ids, mask