From 2e654c017780d37f3304436e2feb84b619f1c023 Mon Sep 17 00:00:00 2001
From: Volpeon <git@volpeon.ink>
Date: Mon, 3 Apr 2023 22:25:20 +0200
Subject: Improved sparse embeddings

---
 models/clip/embeddings.py | 52 +++++++++++++++++++----------------------------
 1 file changed, 21 insertions(+), 31 deletions(-)

(limited to 'models/clip')

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
 from transformers.models.clip import CLIPTextConfig
 from transformers.models.clip.modeling_clip import CLIPTextEmbeddings
 
+from models.sparse import PseudoSparseEmbedding
+
 
 def resize_embedding(old_embedding: nn.Embedding, new_num_embeddings: int, initializer_factor: Optional[float] = None) -> nn.Embedding:
     old_num_embeddings, old_embedding_dim = old_embedding.weight.shape
@@ -41,18 +43,16 @@ class ManagedCLIPTextEmbeddings(CLIPTextEmbeddings):
         self.token_embedding = embeddings.token_embedding
         self.position_embedding = embeddings.position_embedding
         self.initializer_factor = config.initializer_factor
-        self.alpha = alpha
 
-        self.temp_token_embedding = nn.ParameterList()
-        self.temp_token_ids = torch.tensor([], dtype=torch.long)
+        self.token_override_embedding = PseudoSparseEmbedding(
+            self.token_embedding.embedding_dim,
+            device=self.token_embedding.weight.device,
+            dtype=self.token_embedding.weight.dtype,
+        )
+        self.alpha = alpha
 
     def resize(self, size: int):
-        for _ in range(len(self.temp_token_embedding), size):
-            self.temp_token_embedding.append(torch.zeros(
-                self.token_embedding.embedding_dim,
-                device=self.token_embedding.weight.device,
-                dtype=self.token_embedding.weight.dtype,
-            ))
+        self.token_override_embedding.resize(size)
         self.token_embedding = resize_embedding(self.token_embedding, size, self.initializer_factor)
 
     def add_embed(
@@ -86,8 +86,8 @@ class ManagedCLIPTextEmbeddings(CLIPTextEmbeddings):
 
         token_ids = torch.tensor(token_ids, dtype=torch.long)
 
-        self.temp_token_ids = torch.cat([self.temp_token_ids, token_ids])
         self.token_embedding.weight.data[token_ids] = initializer
+        self.token_override_embedding.set(token_ids)
 
     def load_embed(self, input_ids: list[int], filename: Path):
         with safe_open(filename, framework="pt", device="cpu") as file:
@@ -97,33 +97,23 @@ class ManagedCLIPTextEmbeddings(CLIPTextEmbeddings):
         save_file({"embed": self.get_embed(input_ids)}, filename)
 
     def persist(self):
-        for id, emb in zip(self.temp_token_ids, self.temp_token_embedding):
-            self.token_embedding.weight.data[id] += self.alpha * emb
-            nn.init.zeros_(emb)
-        self.temp_token_ids = torch.tensor([], dtype=torch.long)
+        input_ids = torch.arange(self.token_embedding.num_embeddings)
+        embs, mask = self.token_override_embedding(input_ids)
+        if embs is not None:
+            input_ids = input_ids[mask]
+            self.token_embedding.weight.data[input_ids] += self.alpha * embs
+        self.token_override_embedding.unset(input_ids)
 
     def get_embed(self, input_ids: Union[list[int], torch.LongTensor]):
         if isinstance(input_ids, list):
             input_ids = torch.tensor(input_ids, device=self.token_embedding.weight.device, dtype=torch.long)
 
-        all_temp_token_ids = self.temp_token_ids.to(input_ids.device)
-
-        embeds = self.token_embedding(input_ids)
-        mask = torch.isin(input_ids, all_temp_token_ids)
-        temp_token_ids = input_ids[mask]
-
-        temp_token_ids = temp_token_ids.unsqueeze(1)
-        all_temp_token_ids = all_temp_token_ids.unsqueeze(0)
-        temp_token_ids = torch.nonzero(temp_token_ids == all_temp_token_ids)[:, 1].squeeze()
-
-        if len(temp_token_ids):
-            embeds_override = torch.stack([
-                self.temp_token_embedding[id]
-                for id in temp_token_ids
-            ])
-            embeds[mask] += self.alpha * embeds_override
+        embs = self.token_embedding(input_ids)
+        embs_override, mask = self.token_override_embedding(input_ids)
+        if embs_override is not None:
+            embs[mask] += self.alpha * embs_override
 
-        return embeds
+        return embs
 
     def forward(
         self,
-- 
cgit v1.2.3-54-g00ecf