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from typing import Optional
import torch
import torch.nn as nn
from transformers.models.clip import CLIPTextModel, CLIPTextConfig
from transformers.models.clip.modeling_clip import CLIPTextEmbeddings
def patch_trainable_embeddings(text_encoder: CLIPTextModel, new_ids: list[int]):
text_embeddings = TrainableEmbeddings(text_encoder.config, new_ids)
text_embeddings.token_embedding = text_encoder.text_model.embeddings.token_embedding
text_embeddings.token_embedding.weight.requires_grad = False
text_embeddings.position_embedding = text_encoder.text_model.embeddings.position_embedding
text_embeddings.position_embedding.weight.requires_grad = False
text_encoder.text_model.embeddings = text_embeddings
return text_embeddings
class TrainableEmbeddings(CLIPTextEmbeddings):
def __init__(self, config: CLIPTextConfig, new_ids: list[int]):
super().__init__(config)
self.train_indices = torch.tensor(new_ids)
self.trainable_embedding = nn.Embedding(self.token_embedding.num_embeddings, self.token_embedding.embedding_dim)
self.trainable_embedding.weight.data = self.token_embedding.weight.data.clone()
self.trainable_embedding.weight.requires_grad = True
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
) -> torch.Tensor:
device = input_ids.device
seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2]
if position_ids is None:
position_ids = self.position_ids[:, :seq_length]
if inputs_embeds is None:
mask = torch.isin(input_ids, self.train_indices.to(device))
inputs_embeds = self.token_embedding(input_ids)
inputs_embeds[mask] = self.trainable_embedding(input_ids)[mask]
position_embeddings = self.position_embedding(position_ids)
embeddings = inputs_embeds + position_embeddings
return embeddings
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