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0202015 | 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 | from collections.abc import Generator, Iterable
from dataclasses import dataclass
from enum import StrEnum
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from transformers import (
AutoConfig,
AutoModel,
ModernBertModel,
PreTrainedConfig,
PreTrainedModel,
PreTrainedTokenizer,
)
from transformers.modeling_outputs import TokenClassifierOutput
BATCH_SIZE = 16
class ModelURI(StrEnum):
BASE = "answerdotai/ModernBERT-base"
LARGE = "answerdotai/ModernBERT-large"
@dataclass(slots=True, frozen=True)
class LexicalExample:
concept: str
definition: str
@dataclass(slots=True, frozen=True)
class PaddedBatch:
input_ids: torch.Tensor
attention_mask: torch.Tensor
class DisamBertSingleSense(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")
self.config.vocab_size += 2
self.BaseModel.resize_token_embeddings(self.config.vocab_size)
self.classifier_head = nn.UninitializedParameter()
self.bias = nn.UninitializedParameter()
self.__entities = None
else:
self.BaseModel = ModernBertModel(config)
self.classifier_head = nn.Parameter(
torch.empty((config.ontology_size, config.hidden_size))
)
self.bias = nn.Parameter(torch.empty((1,config.ontology_size)))
self.__entities = pd.Series(config.entities)
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 init_classifier(
self, entities: Generator[LexicalExample], tokenizer: PreTrainedTokenizer
) -> None:
entity_ids = []
vectors = []
batch = []
n = 0
with self.BaseModel.device:
torch.cuda.empty_cache()
for entity in entities:
entity_ids.append(entity.concept)
batch.append(entity.definition)
n += 1
if n == BATCH_SIZE:
tokens = tokenizer(batch, padding=True, return_tensors="pt")
encoding = self.BaseModel(tokens["input_ids"], tokens["attention_mask"])
vectors.append(encoding.last_hidden_state.detach()[:, 0])
n = 0
batch = []
if n > 0:
tokens = tokenizer(batch, padding=True, return_tensors="pt")
encoding = self.BaseModel(tokens["input_ids"], tokens["attention_mask"])
vectors.append(encoding.last_hidden_state.detach()[:, 0])
self.__entities = pd.Series(entity_ids)
self.config.entities = entity_ids
self.config.ontology_size = len(entity_ids)
self.classifier_head = nn.Parameter(torch.cat(vectors, dim=0))
self.bias = nn.Parameter(
torch.nn.init.normal_(
torch.empty((1,self.config.ontology_size)),
std=self.classifier_head.std().item() * np.sqrt(self.config.hidden_size),
)
)
@property
def entities(self) -> pd.Series:
if self.__entities is None and hasattr(self.config, "entities"):
self.__entities = pd.Series(self.config.entities)
return self.__entities
def forward(
self,
input_ids: torch.Tensor,
attention_mask: torch.Tensor,
labels: Iterable[int] | None = None,
output_hidden_states: bool = False,
output_attentions: bool = False,
) -> TokenClassifierOutput:
assert not nn.parameter.is_lazy(self.classifier_head), (
"Run init_classifier to initialise weights"
)
base_model_output = self.BaseModel(
input_ids,
attention_mask,
output_hidden_states=output_hidden_states,
output_attentions=output_attentions,
)
token_vectors = base_model_output.last_hidden_state[:, 0]
logits = torch.einsum("ij,kj->ik", token_vectors, self.classifier_head) + self.bias
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,
)
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