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
|
from typing import Union, Optional
from pathlib import Path
import torch
import torch.nn as nn
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
def resize_embedding(old_embedding: nn.Embedding, new_num_embeddings: int, initializer_factor: float = 1.0) -> nn.Embedding:
old_num_embeddings, old_embedding_dim = old_embedding.weight.shape
if old_num_embeddings == new_num_embeddings:
return old_embedding
n = min(old_num_embeddings, new_num_embeddings)
new_embedding = nn.Embedding(
new_num_embeddings,
old_embedding_dim,
device=old_embedding.weight.device,
dtype=old_embedding.weight.dtype
)
new_embedding.weight.data.normal_(mean=0.0, std=initializer_factor * 0.02)
new_embedding.weight.data[:n, :] = old_embedding.weight.data[:n, :]
return new_embedding
class OverlayLinear(nn.Module):
def __init__(self, in_features, out_features, rank=4):
super().__init__()
if rank > min(in_features, out_features):
raise ValueError(f"Rank {rank} must be less or equal than {min(in_features, out_features)}")
self.rank = rank
self.down = nn.Linear(in_features, rank, bias=False)
self.up = nn.Linear(rank, out_features, bias=False)
self.reset()
def reset(self):
nn.init.normal_(self.down.weight, std=1 / self.rank)
nn.init.zeros_(self.up.weight)
def forward(self, hidden_states):
orig_dtype = hidden_states.dtype
dtype = self.down.weight.dtype
down_hidden_states = self.down(hidden_states.to(dtype))
up_hidden_states = self.up(down_hidden_states)
return up_hidden_states.to(orig_dtype)
class ManagedCLIPTextEmbeddings(CLIPTextEmbeddings):
def __init__(self, config: CLIPTextConfig, embeddings: CLIPTextEmbeddings, rank: int = 128):
super().__init__(config)
self.token_embedding = embeddings.token_embedding
self.position_embedding = embeddings.position_embedding
self.initializer_factor = config.initializer_factor
self.overlay = OverlayLinear(self.token_embedding.embedding_dim, self.token_embedding.embedding_dim, rank)
self.temp_token_embedding = nn.Embedding(
self.token_embedding.num_embeddings,
self.token_embedding.embedding_dim,
device=self.token_embedding.weight.device,
dtype=self.token_embedding.weight.dtype
)
self.temp_token_embedding.weight.data = self.token_embedding.weight.data.clone().detach()
self.temp_token_ids = torch.tensor([], dtype=torch.long)
def reset_overlay(self):
self.overlay.reset()
def resize(self, size: int):
self.temp_token_embedding = resize_embedding(self.temp_token_embedding, size, self.initializer_factor)
self.token_embedding = resize_embedding(self.token_embedding, 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.temp_token_ids = torch.cat([self.temp_token_ids, token_ids])
self.temp_token_embedding.weight.data[token_ids] = initializer
self.token_embedding.weight.data[token_ids] = 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):
embeds = self.temp_token_embedding.weight.data[self.temp_token_ids]
self.token_embedding.weight.data[self.temp_token_ids] = embeds + self.overlay(embeds)
self.overlay.reset()
self.temp_token_ids = torch.tensor([], dtype=torch.long)
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)
embeds = self.token_embedding(input_ids)
mask = torch.isin(input_ids, self.temp_token_ids.to(input_ids.device))
temp_embeds = self.temp_token_embedding(input_ids[mask])
embeds[mask] = temp_embeds + self.overlay(temp_embeds)
return embeds
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) -> ManagedCLIPTextEmbeddings:
text_embeddings = ManagedCLIPTextEmbeddings(text_encoder.config, text_encoder.text_model.embeddings)
text_encoder.text_model.embeddings = text_embeddings
return text_embeddings
|