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from typing import List, NamedTuple, Optional, Dict, Tuple, Union
import torch
from tokenizers import AddedToken
from transformers import AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast
from src.utils.collections import flatten
class TokenizationOutput(NamedTuple):
input_ids: torch.Tensor
attention_mask: torch.Tensor
token_type_ids: Optional[torch.Tensor]
definitions_offsets: Dict[str, Tuple[int, int]]
relative_positions: Optional[torch.Tensor] = None
# utilities to extract the gold definition indices in the input sequence
def extract_gold_indices(
definition: str, definitions_sequence: str, offsets_mapping: List[Tuple[int, int]]
) -> Tuple[int, int]:
gold_start_offset = definitions_sequence.index(definition)
gold_end_offset = gold_start_offset + len(definition)
offset_start2bpe_idx = {}
offset_end2bpe_idx = {}
for i, (off_start, off_end) in enumerate(offsets_mapping):
if off_start == off_end:
continue # specials
offset_start2bpe_idx[off_start] = i
offset_end2bpe_idx[off_end] = i
start_bpe_idx = offset_start2bpe_idx[gold_start_offset]
end_bpe_idx = offset_end2bpe_idx[gold_end_offset]
assert start_bpe_idx < end_bpe_idx
return start_bpe_idx, end_bpe_idx
class ConsecTokenizer:
def __init__(
self,
transformer_model: Union[str, PreTrainedTokenizer, PreTrainedTokenizerFast],
target_marker: Tuple[str, str],
context_definitions_token: str,
context_markers: Dict,
add_prefix_space: bool,
):
if type(transformer_model) == str:
self.tokenizer = AutoTokenizer.from_pretrained(transformer_model)
else:
self.tokenizer = transformer_model
self.target_marker = target_marker
self.context_definitions_token = context_definitions_token
self.context_markers = [
(context_markers["pattern"][0].replace("#I#", f"{i}"), context_markers["pattern"][1].replace("#I#", f"{i}"))
for i in range(context_markers["number"])
]
assert (
len(set(self.context_markers)) == context_markers["number"]
), f"Error in given pattern: number of unique created patterns != specified number"
additional_special_tokens = [
*[AddedToken(t, single_word=True, lstrip=True) for t in self.target_marker],
*[AddedToken(t, single_word=True, lstrip=True) for p in self.context_markers for t in p],
AddedToken(context_definitions_token, single_word=True, lstrip=True),
]
self.tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
self.n_added_special_tokens = len(additional_special_tokens)
self.add_prefix_space = add_prefix_space
def mark_token(self, token: str, marker: Tuple[str, str]) -> str:
bom, eom = marker
if self.add_prefix_space:
return f"{bom} {token} {eom}"
else:
return f"{bom}{token}{eom}"
def tokenize(
self,
sentence: List[str], # tokens can be multiword
instance_idx: int,
instance_possible_definitions: List[str],
context_definitions2positions: List[Tuple[str, int]],
) -> TokenizationOutput:
raise NotImplementedError
@property
def pad_token_id(self) -> int:
return self.tokenizer.pad_token_id
@property
def model_max_length(self) -> int:
return self.tokenizer.model_max_length
class DeBERTaTokenizer(ConsecTokenizer):
def __init__(
self,
transformer_model: str,
target_marker: Tuple[str, str],
context_definitions_token: str,
context_markers: Dict,
add_prefix_space: bool,
optimize_relative_positions: bool = True,
enforce_symmetry: bool = True,
):
super().__init__(transformer_model, target_marker, context_definitions_token, context_markers, add_prefix_space)
self.optimize_relative_positions = optimize_relative_positions
self.enforce_symmetry = enforce_symmetry
def tokenize(
self,
sentence: List[str], # tokens can be multiword
instance_idx: int,
instance_possible_definitions: List[str],
context_definitions2positions: List[Tuple[str, int]],
**kwargs,
) -> TokenizationOutput:
if self.optimize_relative_positions:
return self.power_tokenize(
sentence, instance_idx, instance_possible_definitions, context_definitions2positions
)
else:
return self.plain_tokenize(
" ".join(sentence),
instance_possible_definitions,
[x[0] for x in context_definitions2positions],
**kwargs,
)
def deberta_tokenize(self, text: str) -> List[int]:
return self.tokenizer(text, return_attention_mask=False, return_token_type_ids=False, add_special_tokens=True,)[
"input_ids"
][1:-1]
def plain_tokenize(
self,
sentence: Union[str, List[int]],
instance_possible_definitions: Union[List[str], List[Tuple[str, List[int]]]],
context_definitions: Union[List[str], List[Tuple[str, List[int]]]],
use_specials: bool = True,
) -> TokenizationOutput:
if type(sentence) != list:
sentence = f" {sentence}"
sentence_input_ids = self.deberta_tokenize(sentence)
else:
sentence_input_ids = sentence
final_input_ids = [self.tokenizer.cls_token_id] + sentence_input_ids + [self.tokenizer.sep_token_id]
token_type_ids = [0] * len(final_input_ids)
definitions_offsets = dict()
for definition in instance_possible_definitions:
if type(definition) == tuple:
definition, definition_ids = definition
else:
definition_ids = self.deberta_tokenize(f" {definition}")
definitions_offsets[definition] = len(final_input_ids), len(final_input_ids) + len(definition_ids)
final_input_ids += definition_ids
token_type_ids += [1] * len(definition_ids)
# "context" definitions token
if self.context_definitions_token is not None and use_specials:
final_input_ids += self.deberta_tokenize(self.context_definitions_token)
token_type_ids.append(1)
else:
if use_specials:
assert len(context_definitions) == 0
for definition in context_definitions:
if type(definition) == tuple:
definition, definition_ids = definition
else:
definition_ids = self.deberta_tokenize(f" {definition}")
final_input_ids += definition_ids
token_type_ids += [1] * len(definition_ids)
# last [SEP] token
final_input_ids.append(self.tokenizer.sep_token_id)
token_type_ids.append(1)
final_input_ids = torch.tensor(final_input_ids, dtype=torch.long)
attention_mask = torch.ones_like(final_input_ids)
token_type_ids = torch.tensor(token_type_ids, dtype=torch.long)
return TokenizationOutput(final_input_ids, attention_mask, token_type_ids, definitions_offsets)
@lru_cache(maxsize=10_000)
def split_tokenize(self, word: str):
return self.deberta_tokenize(f" {word.strip()}")
@lru_cache(maxsize=10_000)
def _tokenize_sentence_list(self, sentence: str) -> Tuple[List[int], List[Tuple[int, int]]]:
sentence = sentence.split("\t")
sentence_input_ids = [self.tokenizer.cls_token_id]
token_offsets = []
for token in sentence:
token_ids = self.split_tokenize(f" {token.strip()}")
token_offsets.append((len(sentence_input_ids), len(sentence_input_ids) + len(token_ids)))
sentence_input_ids += token_ids
sentence_input_ids.append(self.tokenizer.sep_token_id)
return sentence_input_ids, token_offsets
@staticmethod
def mirror_arange(size: int, zero_pos: int):
return torch.cat([torch.flip(torch.arange(0, zero_pos + 1), dims=(0,)), -torch.arange(1, size - zero_pos)])
def power_tokenize(
self,
sentence: List[str], # tokens can be multiword
instance_idx: int,
instance_possible_definitions: List[str],
context_definitions2positions: List[Tuple[str, int]],
) -> TokenizationOutput:
sentence_input_ids, token_offsets = self._tokenize_sentence_list("\t".join(sentence))
instance_possible_definitions_ids = [self.deberta_tokenize(f" {ipd}") for ipd in instance_possible_definitions]
context_definitions_ids = [self.deberta_tokenize(f" {cd}") for cd, _ in context_definitions2positions]
total_input_ids = (
len(sentence_input_ids)
+ len(flatten(instance_possible_definitions_ids))
+ len(flatten(context_definitions_ids))
)
relative_positions = torch.zeros((total_input_ids + 1, total_input_ids + 1), dtype=torch.long)
# filling the sentences relative positions
for id_idx in range(len(sentence_input_ids)):
relative_positions[id_idx, :-1] = torch.cat(
[self.mirror_arange(len(sentence_input_ids), id_idx)]
+ [
-torch.arange(len(sentence_input_ids) - id_idx, len(sentence_input_ids) + len(def_ids) - id_idx)
for def_ids in instance_possible_definitions_ids + context_definitions_ids
]
)
# flipping the matrix
relative_positions.T[: len(sentence_input_ids)] = -relative_positions[: len(sentence_input_ids)]
curr_offset = len(sentence_input_ids)
# computing the relative positions for the possible definitions
definitions2positions = [(instance_idx, ipdid) for ipdid in instance_possible_definitions_ids]
definitions2positions += [
(rel_idx, cdid) for cdid, (_, rel_idx) in zip(context_definitions_ids, context_definitions2positions)
]
for def_num in range(len(definitions2positions)):
rel_token_idx, def_ids = definitions2positions[def_num]
possible_def_token_pos = list(range(len(def_ids)))
instance_start_pos, instance_end_pos = token_offsets[rel_token_idx]
for token_pos in possible_def_token_pos:
for off_idx, inst_token_pos in enumerate(range(instance_start_pos, instance_end_pos)):
relative_positions[curr_offset + token_pos, inst_token_pos] = (
token_pos + (instance_end_pos - instance_start_pos) - off_idx
)
if self.enforce_symmetry or rel_token_idx != instance_idx:
relative_positions[inst_token_pos, curr_offset + token_pos] = -relative_positions[
curr_offset + token_pos, inst_token_pos
]
relative_positions[curr_offset + token_pos, len(sentence_input_ids) : -1] = self.mirror_arange(
total_input_ids - len(sentence_input_ids), curr_offset + token_pos - len(sentence_input_ids)
)
curr_offset += len(possible_def_token_pos)
last_token_positions = torch.min(relative_positions, dim=-1)[0] - 1
relative_positions[-1] = -last_token_positions
relative_positions.T[-1] = last_token_positions
relative_positions[-1, -1] = 0
relative_positions[relative_positions == -0] = 0
tokenization_output = self.plain_tokenize(
sentence_input_ids[1:-1],
[(x, y) for x, y in zip(instance_possible_definitions, instance_possible_definitions_ids)],
[(x, y) for x, y in zip([x[0] for x in context_definitions2positions], context_definitions_ids)],
use_specials=False,
)
assert tokenization_output.input_ids.shape[0] == relative_positions.shape[0]
return TokenizationOutput(
tokenization_output.input_ids,
tokenization_output.attention_mask,
tokenization_output.token_type_ids,
tokenization_output.definitions_offsets,
relative_positions,
)
@property
def model_max_length(self) -> int:
return 24_528 # from paper: deberta-large
class MBartTokenizer(ConsecTokenizer):
def __init__(
self,
transformer_model: str,
target_marker: Tuple[str, str],
context_definitions_token: str,
context_markers: Dict,
add_prefix_space: bool,
source_language: str = "en_XX",
target_language: str = "en_EN",
):
tokenizer = AutoTokenizer.from_pretrained(transformer_model, src_lang=source_language, tgt_lang=target_language)
self.source_language = source_language
super().__init__(tokenizer, target_marker, context_definitions_token, context_markers, add_prefix_space)
def mbart_tokenize(self, text: str) -> List[int]:
tokenization_out = self.tokenizer(
text,
return_attention_mask=False,
return_token_type_ids=False,
add_special_tokens=True,
)["input_ids"]
return tokenization_out[:-2]
def tokenize(
self,
sentence: Union[str, List[int]],
instance_idx: int,
instance_possible_definitions: List[str],
context_definitions2positions: List[Tuple[str, int]],
) -> TokenizationOutput:
use_specials = False
context_definitions = [x[0] for x in context_definitions2positions]
sentence = f" {' '.join(sentence)}"
sentence_input_ids = self.mbart_tokenize(sentence)
final_input_ids = sentence_input_ids
definitions_offsets = dict()
for definition in instance_possible_definitions:
if type(definition) == tuple:
definition, definition_ids = definition
else:
definition_ids = self.mbart_tokenize(f" {definition}")
definitions_offsets[definition] = len(final_input_ids), len(final_input_ids) + len(definition_ids)
final_input_ids += definition_ids
# "context" definitions token
if self.context_definitions_token is not None and use_specials:
final_input_ids += self.mbart_tokenize(self.context_definitions_token)
else:
if use_specials:
assert len(context_definitions) == 0
for definition in context_definitions:
if type(definition) == tuple:
definition, definition_ids = definition
else:
definition_ids = self.mbart_tokenize(f" {definition}")
final_input_ids += definition_ids
# last [SEP] token
final_input_ids.append(self.tokenizer.sep_token_id)
# lang id
final_input_ids += self.mbart_tokenize(self.source_language)
final_input_ids = torch.tensor(final_input_ids, dtype=torch.long)
attention_mask = torch.ones_like(final_input_ids)
return TokenizationOutput(final_input_ids, attention_mask, None, definitions_offsets)
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