From 208e48134e324e934ad964bdc61880cc923f4c0d Mon Sep 17 00:00:00 2001
From: Volpeon <git@volpeon.ink>
Date: Sat, 1 Apr 2023 22:13:55 +0200
Subject: Revert

---
 models/clip/embeddings.py | 42 ++++--------------------------------------
 1 file changed, 4 insertions(+), 38 deletions(-)

(limited to 'models/clip')

diff --git a/models/clip/embeddings.py b/models/clip/embeddings.py
index c9c788c..1e21965 100644
--- a/models/clip/embeddings.py
+++ b/models/clip/embeddings.py
@@ -31,41 +31,15 @@ def resize_embedding(old_embedding: nn.Embedding, new_num_embeddings: int, initi
     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):
+    def __init__(self, config: CLIPTextConfig, embeddings: CLIPTextEmbeddings, alpha: float = 1.0, rank: int = 4):
         super().__init__(config)
 
         self.token_embedding = embeddings.token_embedding
         self.position_embedding = embeddings.position_embedding
         self.initializer_factor = config.initializer_factor
+        self.alpha = alpha
 
-        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,
@@ -75,9 +49,6 @@ class ManagedCLIPTextEmbeddings(CLIPTextEmbeddings):
         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)
@@ -125,9 +96,7 @@ class ManagedCLIPTextEmbeddings(CLIPTextEmbeddings):
         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.token_embedding.weight.data[self.temp_token_ids] = self.temp_token_embedding.weight.data[self.temp_token_ids]
         self.temp_token_ids = torch.tensor([], dtype=torch.long)
 
     def get_embed(self, input_ids: Union[list[int], torch.LongTensor]):
@@ -135,11 +104,8 @@ class ManagedCLIPTextEmbeddings(CLIPTextEmbeddings):
             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)
+        embeds[mask] = self.temp_token_embedding(input_ids[mask])
 
         return embeds
 
-- 
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