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-rw-r--r--models/clip/embeddings.py52
1 files changed, 21 insertions, 31 deletions
diff --git a/models/clip/embeddings.py b/models/clip/embeddings.py
index d8343a0..a356434 100644
--- a/models/clip/embeddings.py
+++ b/models/clip/embeddings.py
@@ -11,6 +11,8 @@ from transformers import CLIPTextModel
11from transformers.models.clip import CLIPTextConfig 11from transformers.models.clip import CLIPTextConfig
12from transformers.models.clip.modeling_clip import CLIPTextEmbeddings 12from transformers.models.clip.modeling_clip import CLIPTextEmbeddings
13 13
14from models.sparse import PseudoSparseEmbedding
15
14 16
15def resize_embedding(old_embedding: nn.Embedding, new_num_embeddings: int, initializer_factor: Optional[float] = None) -> nn.Embedding: 17def resize_embedding(old_embedding: nn.Embedding, new_num_embeddings: int, initializer_factor: Optional[float] = None) -> nn.Embedding:
16 old_num_embeddings, old_embedding_dim = old_embedding.weight.shape 18 old_num_embeddings, old_embedding_dim = old_embedding.weight.shape
@@ -41,18 +43,16 @@ class ManagedCLIPTextEmbeddings(CLIPTextEmbeddings):
41 self.token_embedding = embeddings.token_embedding 43 self.token_embedding = embeddings.token_embedding
42 self.position_embedding = embeddings.position_embedding 44 self.position_embedding = embeddings.position_embedding
43 self.initializer_factor = config.initializer_factor 45 self.initializer_factor = config.initializer_factor
44 self.alpha = alpha
45 46
46 self.temp_token_embedding = nn.ParameterList() 47 self.token_override_embedding = PseudoSparseEmbedding(
47 self.temp_token_ids = torch.tensor([], dtype=torch.long) 48 self.token_embedding.embedding_dim,
49 device=self.token_embedding.weight.device,
50 dtype=self.token_embedding.weight.dtype,
51 )
52 self.alpha = alpha
48 53
49 def resize(self, size: int): 54 def resize(self, size: int):
50 for _ in range(len(self.temp_token_embedding), size): 55 self.token_override_embedding.resize(size)
51 self.temp_token_embedding.append(torch.zeros(
52 self.token_embedding.embedding_dim,
53 device=self.token_embedding.weight.device,
54 dtype=self.token_embedding.weight.dtype,
55 ))
56 self.token_embedding = resize_embedding(self.token_embedding, size, self.initializer_factor) 56 self.token_embedding = resize_embedding(self.token_embedding, size, self.initializer_factor)
57 57
58 def add_embed( 58 def add_embed(
@@ -86,8 +86,8 @@ class ManagedCLIPTextEmbeddings(CLIPTextEmbeddings):
86 86
87 token_ids = torch.tensor(token_ids, dtype=torch.long) 87 token_ids = torch.tensor(token_ids, dtype=torch.long)
88 88
89 self.temp_token_ids = torch.cat([self.temp_token_ids, token_ids])
90 self.token_embedding.weight.data[token_ids] = initializer 89 self.token_embedding.weight.data[token_ids] = initializer
90 self.token_override_embedding.set(token_ids)
91 91
92 def load_embed(self, input_ids: list[int], filename: Path): 92 def load_embed(self, input_ids: list[int], filename: Path):
93 with safe_open(filename, framework="pt", device="cpu") as file: 93 with safe_open(filename, framework="pt", device="cpu") as file:
@@ -97,33 +97,23 @@ class ManagedCLIPTextEmbeddings(CLIPTextEmbeddings):
97 save_file({"embed": self.get_embed(input_ids)}, filename) 97 save_file({"embed": self.get_embed(input_ids)}, filename)
98 98
99 def persist(self): 99 def persist(self):
100 for id, emb in zip(self.temp_token_ids, self.temp_token_embedding): 100 input_ids = torch.arange(self.token_embedding.num_embeddings)
101 self.token_embedding.weight.data[id] += self.alpha * emb 101 embs, mask = self.token_override_embedding(input_ids)
102 nn.init.zeros_(emb) 102 if embs is not None:
103 self.temp_token_ids = torch.tensor([], dtype=torch.long) 103 input_ids = input_ids[mask]
104 self.token_embedding.weight.data[input_ids] += self.alpha * embs
105 self.token_override_embedding.unset(input_ids)
104 106
105 def get_embed(self, input_ids: Union[list[int], torch.LongTensor]): 107 def get_embed(self, input_ids: Union[list[int], torch.LongTensor]):
106 if isinstance(input_ids, list): 108 if isinstance(input_ids, list):
107 input_ids = torch.tensor(input_ids, device=self.token_embedding.weight.device, dtype=torch.long) 109 input_ids = torch.tensor(input_ids, device=self.token_embedding.weight.device, dtype=torch.long)
108 110
109 all_temp_token_ids = self.temp_token_ids.to(input_ids.device) 111 embs = self.token_embedding(input_ids)
110 112 embs_override, mask = self.token_override_embedding(input_ids)
111 embeds = self.token_embedding(input_ids) 113 if embs_override is not None:
112 mask = torch.isin(input_ids, all_temp_token_ids) 114 embs[mask] += self.alpha * embs_override
113 temp_token_ids = input_ids[mask]
114
115 temp_token_ids = temp_token_ids.unsqueeze(1)
116 all_temp_token_ids = all_temp_token_ids.unsqueeze(0)
117 temp_token_ids = torch.nonzero(temp_token_ids == all_temp_token_ids)[:, 1].squeeze()
118
119 if len(temp_token_ids):
120 embeds_override = torch.stack([
121 self.temp_token_embedding[id]
122 for id in temp_token_ids
123 ])
124 embeds[mask] += self.alpha * embeds_override
125 115
126 return embeds 116 return embs
127 117
128 def forward( 118 def forward(
129 self, 119 self,