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from dataclasses import dataclass
from enum import StrEnum
from itertools import chain
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from transformers import (
AutoConfig,
AutoModel,
AutoTokenizer,
ModernBertModel,
PreTrainedConfig,
PreTrainedModel,
)
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 DisamBert(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.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((config.ontology_size, 1)))
self.__entities = pd.Series(config.entities)
config.init_basemodel = False
self.tokenizer = AutoTokenizer.from_pretrained(config.tokenizer_path)
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
config.tokenizer_path = base_id
return cls(config)
def init_classifier(self, entities: Generator[LexicalExample]) -> 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 = self.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 = self.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((self.config.ontology_size, 1)),
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,
lengths: list[list[int]],
candidates: list[list[list[int]]],
labels: Iterable[list[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
span_vectors = torch.cat(
[
torch.vstack(
[
torch.sum(chunk, dim=0)
for chunk in self.split(token_vectors[i], sentence_indices)
]
)
for (i, sentence_indices) in enumerate(lengths)
]
)
logits = torch.einsum("ij,kj->ki", span_vectors, self.classifier_head) + self.bias
logits1 = logits - logits.min()
mask = torch.zeros_like(logits)
for i, concepts in enumerate(chain.from_iterable(candidates)):
mask[concepts, i] = torch.tensor(1.0)
logits2 = logits1 * mask
sentence_lengths = [len(sentence_indices) for sentence_indices in lengths]
maxlen = max(sentence_lengths)
split_logits = torch.split(logits2, sentence_lengths, dim=1)
logits3 = torch.stack(
[
self.extend_to_max_length(sentence, length, maxlen)
for (sentence, length) in zip(split_logits, sentence_lengths, strict=True)
]
)
return TokenClassifierOutput(
logits=logits3,
loss=self.loss(logits3, 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 split(self, vectors: torch.Tensor, lengths: list[int]) -> tuple[torch.Tensor, ...]:
maxlen = vectors.shape[0]
total_length = sum(lengths)
is_padded = total_length < maxlen
chunks = vectors.split((lengths + [maxlen - total_length]) if is_padded else lengths)
return chunks[:-1] if is_padded else chunks
def pad(self, tokens: Iterable[list[int]]) -> PaddedBatch:
lengths = [len(sentence) for sentence in tokens]
maxlen = max(lengths)
input_ids = torch.tensor(
[
sentence + [self.config.pad_token_id] * (maxlen - length)
for (sentence, length) in zip(tokens, lengths, strict=True)
]
)
attention_mask = torch.vstack(
[torch.cat((torch.ones(length), torch.zeros(maxlen - length))) for length in lengths]
)
return PaddedBatch(input_ids, attention_mask)
def extend_to_max_length(
self, sentence: torch.Tensor, length: int, maxlength: int
) -> torch.Tensor:
with self.BaseModel.device:
return (
torch.cat(
[
sentence,
torch.zeros((self.config.ontology_size, maxlength - length)),
],
dim=1,
)
if length < maxlength
else sentence
)
def pad_labels(self, labels: list[list[int]]) -> torch.Tensor:
unk = len(self.config.entities) - 1
lengths = [len(seq) for seq in labels]
maxlen = max(lengths)
with self.BaseModel.device:
return torch.tensor(
[
seq + [unk] * (maxlen - length)
for (seq, length) in zip(labels, lengths, strict=True)
]
)
def tokenize(
self, batch: list[dict[str, str | list[int]]]
) -> dict[str, torch.Tensor | list[list[int]]]:
all_indices = []
all_tokens = []
with self.BaseModel.device:
for example in batch:
text = example["text"]
span_indices = example["indices"]
indices = []
tokens = []
last_span = len(span_indices) - 2
for i, position in enumerate(span_indices[:-1]):
span = text[position : span_indices[i + 1]]
span_tokens = self.tokenizer([span], padding=False)["input_ids"][0]
if i > 0:
span_tokens = span_tokens[1:]
if i < last_span:
span_tokens = span_tokens[:-1]
indices.append(len(span_tokens))
tokens.extend(span_tokens)
all_indices.append(indices)
all_tokens.append(tokens)
padded = self.pad(all_tokens)
result = {
"input_ids": padded.input_ids,
"attention_mask": padded.attention_mask,
"lengths": all_indices,
"candidates": [example["candidates"] for example in batch],
}
if "labels" in batch[0]:
result["labels"] = self.pad_labels([example["labels"] for example in batch])
return result
|