1 files changed, 0 insertions, 48 deletions

diff --git a/training/ti.py b/training/ti.py
deleted file mode 100644
index 031fe48..0000000
--- a/training/ti.py
+++ /dev/null
@@ -1,48 +0,0 @@
-from typing import Optional
-
-import torch
-import torch.nn as nn
-
-from transformers.models.clip import CLIPTextModel, CLIPTextConfig
-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, text_encoder.text_model.embeddings, new_ids)
-    text_encoder.text_model.embeddings = text_embeddings
-
-
-class TrainableEmbeddings(CLIPTextEmbeddings):
-    def __init__(self, config: CLIPTextConfig, embeddings: CLIPTextEmbeddings, new_ids: list[int]):
-        super().__init__(config)
-
-        self.token_embedding = embeddings.token_embedding
-        self.position_embedding = embeddings.position_embedding
-
-        self.train_indices = torch.tensor(new_ids)
-
-        self.trainable_embedding = nn.Embedding(self.token_embedding.num_embeddings, self.token_embedding.embedding_dim)
-        self.trainable_embedding.weight.data.zero_()
-        self.trainable_embedding.weight.data[self.train_indices] = self.token_embedding.weight.data[self.train_indices]
-
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-    ) -> torch.Tensor:
-        device = input_ids.device
-        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:
-            mask = torch.isin(input_ids, self.train_indices.to(device))
-            inputs_embeds = self.token_embedding(input_ids)
-            inputs_embeds[mask] = self.trainable_embedding(input_ids)[mask]
-
-        position_embeddings = self.position_embedding(position_ids)
-        embeddings = inputs_embeds + position_embeddings
-
-        return embeddings