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901446a fcbc792 901446a fcbc792 901446a fcbc792 901446a fcbc792 901446a fcbc792 901446a fcbc792 901446a fcbc792 901446a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 | from collections.abc import Generator, Iterable
from dataclasses import dataclass
from enum import StrEnum
from nltk.tokenize import TreebankWordDetokenizer
import torch
import torch.nn as nn
from transformers import (
AutoConfig,
AutoModel,
BatchEncoding,
DebertaV2Model,
PreTrainedConfig,
PreTrainedModel,
PreTrainedTokenizer,
)
from transformers.modeling_outputs import TokenClassifierOutput
class ModelURI(StrEnum):
BASE = "microsoft/deberta-v3-base"
LARGE = "microsoft/deberta-v3-large"
class ConSec(PreTrainedModel):
def __init__(self, config: PreTrainedConfig):
super().__init__(config)
if config.init_basemodel:
self.BaseModel = AutoModel.from_pretrained(config.name_or_path,
device_map="auto",
dtype=torch.bfloat16)
self.config.vocab_size += 2
self.BaseModel.resize_token_embeddings(self.config.vocab_size)
else:
self.BaseModel = DebertaV2Model(config)
config.init_basemodel = False
self.loss = nn.CrossEntropyLoss()
self.post_init()
@classmethod
def from_base(cls, base_id: ModelURI):
config = AutoConfig.from_pretrained(base_id)
config.init_basemodel = True
return cls(config)
def add_special_tokens(self, start: int, end: int, gloss: int):
self.config.start_token = start
self.config.end_token = end
self.config.gloss_token = gloss
def forward(self,
input_ids: torch.Tensor | None = None,
attention_mask: torch.Tensor | None = None,
token_type_ids: torch.Tensor | None = None,
position_ids: torch.Tensor | None = None,
inputs_embeds: torch.Tensor | None = None,
labels: torch.Tensor | None = None,
output_attentions: bool | None = None,
output_hidden_states: bool | None = None,
return_dict: bool | None = None,
**kwargs)->TokenClassifierOutput:
base_model_output = self.BaseModel(input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
**kwargs)
token_vectors = base_model_output.last_hidden_state
selection = torch.zeros_like(input_ids, dtype=token_vectors.dtype)
starts = (input_ids == self.config.start_token).nonzero()
ends = (input_ids == self.config.end_token).nonzero()
for startpos, endpos in zip(starts, ends, strict=True):
selection[startpos[0], startpos[1] : endpos[1] + 1] = 1.0
entity_vectors = torch.einsum("ijk,ij->ik", token_vectors, selection)
gloss_vectors = self.gloss_vectors(
input_ids, starts, position_ids, token_vectors
)
logits = torch.einsum("ij,ikj->ik", entity_vectors, gloss_vectors)
return TokenClassifierOutput(
logits=logits,
loss=self.loss(logits, labels) if labels is not None else None,
hidden_states=base_model_output.hidden_states if output_hidden_states else None,
attentions=base_model_output.attentions if output_attentions else None,
)
def gloss_vectors(self,input_ids: torch.Tensor,
starts: torch.Tensor,
position_ids: torch.Tensor,
token_vectors: torch.Tensor)->torch.Tensor:
with self.device:
vectors = [token_vectors[i,((position_ids[i]==position_ids[i,j])&(input_ids[i]==self.config.gloss_token))]
for (i,j) in starts]
maxlen = max(vector.shape[0] for vector in vectors)
return torch.stack([torch.cat([vector,torch.zeros((maxlen-vector.shape[0],vector.shape[1]),
dtype=torch.bfloat16)])
for vector in vectors])
def json_sequencer(sentence:list[dict])->Generator[tuple[list[str], list[str], int]]:
for site in sorted([{"span":i,
"n_candidates":len(chunk["candidates"])}
for (i,chunk) in enumerate(sentence)
if "candidates" in chunk],
key = lambda x: x["n_candidates"]):
words = [word for chunk in sentence[:site["span"]]
for word in chunk["words"]]
words.append("[START]")
words.extend(sentence[site["span"]]["words"])
words.append("[END]")
words.extend([word for chunk in sentence[site["span"]+1:]
for word in chunk["words"]])
yield (words,
sentence[site["span"]]["candidates"],
site["span"])
def json_labeller(sentence,tags):
for tag in tags:
sentence[tag["index"]]["label"]=tag["label"]
return sentence
class ConSecTagger:
def __init__(self,model,
tokenizer,
ontology,
sequencer=json_sequencer,
labeller=json_labeller):
self.model = model
self.tokenizer = tokenizer
special_tokens = self.tokenizer.get_added_vocab()
self.start_token = special_tokens["[START]"]
self.gloss_token = special_tokens["[GLOSS]"]
self.sequencer = sequencer
self.detokenizer = TreebankWordDetokenizer()
self.glosses = {synset.concept:synset.definition
for synset in ontology}
self.label=labeller
def __call__(self,sentence):
already_tagged = []
for (words,candidates,index) in self.sequencer(sentence):
text = self.detokenizer.detokenize(words)
glosses = ['']
glosses.extend([self.glosses[candidate] for candidate in candidates])
glosses.extend([self.glosses[previous["label"]] for previous in already_tagged])
with self.model.device:
tokens = self.tokenizer(text,"[GLOSS] ".join(glosses),
return_tensors="pt")
length = tokens.input_ids.shape[1]
positions = torch.arange(length)
place = (tokens.input_ids==self.start_token).nonzero(as_tuple=True)[1].item()
wordpos = tokens.token_to_word(place)
gloss_positions = [index.item()
for index in (tokens.input_ids==self.gloss_token).nonzero(as_tuple=True)[1]]
gloss_positions.append(length)
n_candidates = len(candidates)
for (i,position) in enumerate(gloss_positions[:-1]):
if i<n_candidates:
end = (place + gloss_positions[i+1]-position)
positions[position:gloss_positions[i+1]] = torch.arange(place,end)
else:
known = already_tagged[i-n_candidates]
start = tokens.word_to_tokens(known["place"]).start
end = (start + gloss_positions[i+1] - position)
positions[position:gloss_positions[i+1]] = torch.arange(start,end)
prediction = self.model(input_ids=tokens.input_ids,
attention_mask=tokens.attention_mask,
token_type_ids=tokens.token_type_ids,
position_ids=positions.reshape((1,length)))
try:
label = candidates[prediction.logits.argmax()]
except IndexError:
print(text)
print(gloss_positions)
print([positions[pos].item() for pos in gloss_positions[:-1]])
print(already_tagged)
print(candidates)
print(prediction.logits)
print(prediction.logits.argmax())
raise
already_tagged.append({"label":label,
"place":wordpos,
"index":index})
return(self.label(sentence,already_tagged))
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