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from typing import Union, Optional
from pathlib import Path
import torch
from safetensors import safe_open
from safetensors.torch import save_file
from transformers import CLIPTextModel
from transformers.models.clip import CLIPTextConfig
from transformers.models.clip.modeling_clip import CLIPTextEmbeddings
from models.lora import LoraEmbedding
class ManagedCLIPTextEmbeddings(CLIPTextEmbeddings):
def __init__(self, config: CLIPTextConfig, embeddings: CLIPTextEmbeddings, r: int = 8, lora_alpha: int = 8, lora_dropout: float = 0.0):
super().__init__(config)
self.position_embedding = embeddings.position_embedding
self.initializer_factor = config.initializer_factor
self.token_embedding = LoraEmbedding(
self.token_embedding.num_embeddings,
self.token_embedding.embedding_dim,
r,
lora_alpha,
lora_dropout,
)
self.token_embedding.weight = embeddings.token_embedding.weight
def resize(self, size: int):
self.token_embedding = self.token_embedding.new_resized(size, self.initializer_factor)
def add_embed(
self,
token_ids: Union[int, list[int]],
initializer: Optional[Union[int, list[int], torch.FloatTensor]] = None,
initializer_noise: float = 0.0,
):
if isinstance(token_ids, int):
token_ids = [token_ids]
if initializer is None:
initializer = token_ids
if isinstance(initializer, int):
initializer = [initializer]
if isinstance(initializer, list):
initializer = (initializer * len(token_ids))[:len(token_ids)]
with torch.no_grad():
initializer = self.get_embed(initializer)
initializer = initializer.to(
device=self.token_embedding.weight.device,
dtype=self.token_embedding.weight.dtype,
)
if initializer_noise != 0:
initializer += torch.randn_like(initializer) * initializer_noise
token_ids = torch.tensor(token_ids, dtype=torch.long)
self.token_embedding.mark_trainable(token_ids)
self.token_embedding.weight[token_ids].data = initializer
def load_embed(self, input_ids: list[int], filename: Path):
with safe_open(filename, framework="pt", device="cpu") as file:
self.add_embed(input_ids, file.get_tensor("embed"))
def save_embed(self, input_ids: list[int], filename: Path):
save_file({"embed": self.get_embed(input_ids)}, filename)
def persist(self):
self.token_embedding.persist()
def get_embed(self, input_ids: Union[list[int], torch.LongTensor]):
if isinstance(input_ids, list):
input_ids = torch.tensor(input_ids, device=self.token_embedding.weight.device, dtype=torch.long)
return self.token_embedding(input_ids)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
) -> torch.Tensor:
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:
inputs_embeds = self.get_embed(input_ids)
position_embeddings = self.position_embedding(position_ids)
embeddings = inputs_embeds + position_embeddings
return embeddings
def patch_managed_embeddings(
text_encoder: CLIPTextModel,
r: int = 8,
lora_alpha: int = 8,
lora_dropout: float = 0.0
) -> ManagedCLIPTextEmbeddings:
text_embeddings = ManagedCLIPTextEmbeddings(
text_encoder.config,
text_encoder.text_model.embeddings,
r,
lora_alpha,
lora_dropout
)
text_encoder.text_model.embeddings = text_embeddings
return text_embeddings
|
