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authorVolpeon <git@volpeon.ink>2023-04-03 18:52:30 +0200
committerVolpeon <git@volpeon.ink>2023-04-03 18:52:30 +0200
commite68cb3542e08c9f22ce8a94fd88bebe0c121ca17 (patch)
tree87fbb9d92233aa1bb7342e31aec64d6d375f41e1 /models
parentTI: No tag dropout by default (diff)
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TI: Delta learning
Diffstat (limited to 'models')
-rw-r--r--models/clip/embeddings.py50
1 files changed, 33 insertions, 17 deletions
diff --git a/models/clip/embeddings.py b/models/clip/embeddings.py
index 1e21965..d8343a0 100644
--- a/models/clip/embeddings.py
+++ b/models/clip/embeddings.py
@@ -12,7 +12,7 @@ from transformers.models.clip import CLIPTextConfig
12from transformers.models.clip.modeling_clip import CLIPTextEmbeddings 12from transformers.models.clip.modeling_clip import CLIPTextEmbeddings
13 13
14 14
15def resize_embedding(old_embedding: nn.Embedding, new_num_embeddings: int, initializer_factor: float = 1.0) -> nn.Embedding: 15def 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 16 old_num_embeddings, old_embedding_dim = old_embedding.weight.shape
17 17
18 if old_num_embeddings == new_num_embeddings: 18 if old_num_embeddings == new_num_embeddings:
@@ -26,13 +26,16 @@ def resize_embedding(old_embedding: nn.Embedding, new_num_embeddings: int, initi
26 device=old_embedding.weight.device, 26 device=old_embedding.weight.device,
27 dtype=old_embedding.weight.dtype 27 dtype=old_embedding.weight.dtype
28 ) 28 )
29 new_embedding.weight.data.normal_(mean=0.0, std=initializer_factor * 0.02) 29 if initializer_factor is not None:
30 new_embedding.weight.data.normal_(mean=0.0, std=initializer_factor * 0.02)
31 else:
32 nn.init.zeros_(new_embedding.weight.data)
30 new_embedding.weight.data[:n, :] = old_embedding.weight.data[:n, :] 33 new_embedding.weight.data[:n, :] = old_embedding.weight.data[:n, :]
31 return new_embedding 34 return new_embedding
32 35
33 36
34class ManagedCLIPTextEmbeddings(CLIPTextEmbeddings): 37class ManagedCLIPTextEmbeddings(CLIPTextEmbeddings):
35 def __init__(self, config: CLIPTextConfig, embeddings: CLIPTextEmbeddings, alpha: float = 1.0, rank: int = 4): 38 def __init__(self, config: CLIPTextConfig, embeddings: CLIPTextEmbeddings, alpha: float = 1.0):
36 super().__init__(config) 39 super().__init__(config)
37 40
38 self.token_embedding = embeddings.token_embedding 41 self.token_embedding = embeddings.token_embedding
@@ -40,17 +43,16 @@ class ManagedCLIPTextEmbeddings(CLIPTextEmbeddings):
40 self.initializer_factor = config.initializer_factor 43 self.initializer_factor = config.initializer_factor
41 self.alpha = alpha 44 self.alpha = alpha
42 45
43 self.temp_token_embedding = nn.Embedding( 46 self.temp_token_embedding = nn.ParameterList()
44 self.token_embedding.num_embeddings,
45 self.token_embedding.embedding_dim,
46 device=self.token_embedding.weight.device,
47 dtype=self.token_embedding.weight.dtype
48 )
49 self.temp_token_embedding.weight.data = self.token_embedding.weight.data.clone().detach()
50 self.temp_token_ids = torch.tensor([], dtype=torch.long) 47 self.temp_token_ids = torch.tensor([], dtype=torch.long)
51 48
52 def resize(self, size: int): 49 def resize(self, size: int):
53 self.temp_token_embedding = resize_embedding(self.temp_token_embedding, size, self.initializer_factor) 50 for _ in range(len(self.temp_token_embedding), 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 ))
54 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)
55 57
56 def add_embed( 58 def add_embed(
@@ -85,7 +87,6 @@ class ManagedCLIPTextEmbeddings(CLIPTextEmbeddings):
85 token_ids = torch.tensor(token_ids, dtype=torch.long) 87 token_ids = torch.tensor(token_ids, dtype=torch.long)
86 88
87 self.temp_token_ids = torch.cat([self.temp_token_ids, token_ids]) 89 self.temp_token_ids = torch.cat([self.temp_token_ids, token_ids])
88 self.temp_token_embedding.weight.data[token_ids] = initializer
89 self.token_embedding.weight.data[token_ids] = initializer 90 self.token_embedding.weight.data[token_ids] = initializer
90 91
91 def load_embed(self, input_ids: list[int], filename: Path): 92 def load_embed(self, input_ids: list[int], filename: Path):
@@ -96,16 +97,31 @@ class ManagedCLIPTextEmbeddings(CLIPTextEmbeddings):
96 save_file({"embed": self.get_embed(input_ids)}, filename) 97 save_file({"embed": self.get_embed(input_ids)}, filename)
97 98
98 def persist(self): 99 def persist(self):
99 self.token_embedding.weight.data[self.temp_token_ids] = self.temp_token_embedding.weight.data[self.temp_token_ids] 100 for id, emb in zip(self.temp_token_ids, self.temp_token_embedding):
101 self.token_embedding.weight.data[id] += self.alpha * emb
102 nn.init.zeros_(emb)
100 self.temp_token_ids = torch.tensor([], dtype=torch.long) 103 self.temp_token_ids = torch.tensor([], dtype=torch.long)
101 104
102 def get_embed(self, input_ids: Union[list[int], torch.LongTensor]): 105 def get_embed(self, input_ids: Union[list[int], torch.LongTensor]):
103 if isinstance(input_ids, list): 106 if isinstance(input_ids, list):
104 input_ids = torch.tensor(input_ids, device=self.token_embedding.weight.device, dtype=torch.long) 107 input_ids = torch.tensor(input_ids, device=self.token_embedding.weight.device, dtype=torch.long)
105 108
109 all_temp_token_ids = self.temp_token_ids.to(input_ids.device)
110
106 embeds = self.token_embedding(input_ids) 111 embeds = self.token_embedding(input_ids)
107 mask = torch.isin(input_ids, self.temp_token_ids.to(input_ids.device)) 112 mask = torch.isin(input_ids, all_temp_token_ids)
108 embeds[mask] = self.temp_token_embedding(input_ids[mask]) 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
109 125
110 return embeds 126 return embeds
111 127
@@ -129,7 +145,7 @@ class ManagedCLIPTextEmbeddings(CLIPTextEmbeddings):
129 return embeddings 145 return embeddings
130 146
131 147
132def patch_managed_embeddings(text_encoder: CLIPTextModel) -> ManagedCLIPTextEmbeddings: 148def patch_managed_embeddings(text_encoder: CLIPTextModel, alpha: float = 1.0) -> ManagedCLIPTextEmbeddings:
133 text_embeddings = ManagedCLIPTextEmbeddings(text_encoder.config, text_encoder.text_model.embeddings) 149 text_embeddings = ManagedCLIPTextEmbeddings(text_encoder.config, text_encoder.text_model.embeddings, alpha)
134 text_encoder.text_model.embeddings = text_embeddings 150 text_encoder.text_model.embeddings = text_embeddings
135 return text_embeddings 151 return text_embeddings