1 files changed, 12 insertions, 12 deletions

diff --git a/training/ti.py b/training/ti.py
index 2efd2f2..2e5139a 100644
--- a/training/ti.py
+++ b/training/ti.py
@@ -9,9 +9,15 @@ 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.weight = text_encoder.text_model.embeddings.token_embedding.weight
-    text_embeddings.position_embedding.weight = text_encoder.text_model.embeddings.position_embedding.weight
+
+    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
 
 
@@ -19,15 +25,12 @@ class TrainableEmbeddings(CLIPTextEmbeddings):
     def __init__(self, config: CLIPTextConfig, new_ids: list[int]):
         super().__init__(config)
 
-        self.token_embedding.weight.requires_grad = False
-        self.position_embedding.weight.requires_grad = False
-
         self.id_mapping = {new_ids[i]: i for i in range(len(new_ids))}
 
-        indices = torch.arange(self.token_embedding.num_embeddings)
-        self.train_indices = indices[torch.isin(indices, torch.tensor(new_ids))]
+        self.train_indices = torch.tensor(new_ids)
 
-        self.trainable_embedding = nn.Embedding.from_pretrained(self.token_embedding.weight[self.train_indices])
+        self.trainable_embedding = nn.Embedding(len(new_ids), self.token_embedding.embedding_dim)
+        self.trainable_embedding.weight.data = self.token_embedding.weight.data[self.train_indices]
         self.trainable_embedding.weight.requires_grad = True
 
     def forward(
@@ -42,10 +45,7 @@ class TrainableEmbeddings(CLIPTextEmbeddings):
             position_ids = self.position_ids[:, :seq_length]
 
         if inputs_embeds is None:
-            mask = torch.isin(
-                input_ids,
-                self.train_indices.to(input_ids.device)
-            ).unsqueeze(-1).expand(-1, -1, self.token_embedding.embedding_dim)
+            mask = torch.isin(input_ids, self.train_indices.to(input_ids.device))[:, :, None]
 
             trainable_input_ids = torch.tensor([
                 [