Fix

6 files changed, 177 insertions, 90 deletions

diff --git a/models/clip/embeddings.py b/models/clip/embeddings.py
index d02ccc3..8aaea8f 100644
--- a/models/clip/embeddings.py
+++ b/models/clip/embeddings.py
@@ -10,23 +10,21 @@ from transformers import CLIPTextModel
 from transformers.models.clip import CLIPTextConfig
 from transformers.models.clip.modeling_clip import CLIPTextEmbeddings
 
-from models.lora import LoraEmbedding
+from models.sparse import SparseEmbedding
 
 
 class ManagedCLIPTextEmbeddings(CLIPTextEmbeddings):
-    def __init__(self, config: CLIPTextConfig, embeddings: CLIPTextEmbeddings, r: int = 8, lora_alpha: int = 8, lora_dropout: float = 0.0):
+    def __init__(self, config: CLIPTextConfig, embeddings: CLIPTextEmbeddings, alpha: int = 8, dropout: float = 0.0):
         super().__init__(config)
 
         self.position_embedding = embeddings.position_embedding
         self.initializer_factor = config.initializer_factor
-        self.token_embedding = LoraEmbedding(
+        self.token_embedding = SparseEmbedding(
             self.token_embedding.num_embeddings,
             self.token_embedding.embedding_dim,
-            r,
-            lora_alpha,
-            lora_dropout,
+            alpha,
+            dropout,
         )
-
         self.token_embedding.weight = embeddings.token_embedding.weight
 
     def resize(self, size: int):
@@ -82,38 +80,17 @@ class ManagedCLIPTextEmbeddings(CLIPTextEmbeddings):
 
         return self.token_embedding(input_ids)
 
-    def forward(
-        self,
-        input_ids: Optional[torch.LongTensor] = None,
-        position_ids: Optional[torch.LongTensor] = None,
-        inputs_embeds: Optional[torch.FloatTensor] = None,
-    ) -> torch.Tensor:
-        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:
-            inputs_embeds = self.get_embed(input_ids)
-
-        position_embeddings = self.position_embedding(position_ids)
-        embeddings = inputs_embeds + position_embeddings
-
-        return embeddings
-
 
 def patch_managed_embeddings(
     text_encoder: CLIPTextModel,
-    r: int = 8,
-    lora_alpha: int = 8,
-    lora_dropout: float = 0.0
+    alpha: int = 8,
+    dropout: float = 0.0
 ) -> ManagedCLIPTextEmbeddings:
     text_embeddings = ManagedCLIPTextEmbeddings(
         text_encoder.config,
         text_encoder.text_model.embeddings,
-        r,
-        lora_alpha,
-        lora_dropout
+        alpha,
+        dropout
     )
     text_encoder.text_model.embeddings = text_embeddings
     return text_embeddings
diff --git a/models/lora.py b/models/lora.py
index 01a540b..e506cff 100644
--- a/models/lora.py
+++ b/models/lora.py
@@ -1,8 +1,8 @@
 from typing import Optional
+import math
 
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
 
 
 class LoraLayer():
@@ -42,14 +42,12 @@ class LoraEmbedding(nn.Embedding, LoraLayer):
             self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, merge_weights=merge_weights
         )
 
-        self.register_buffer('trainable_ids', self.weight.new_zeros(num_embeddings, dtype=torch.long))
-        self.trainable_ids -= 1
+        self.register_buffer('trainable_ids', self.weight.new_zeros(num_embeddings, dtype=torch.long) - 1)
 
-        if r > 0:
-            self.lora_A = nn.ParameterList()
-            self.lora_B = nn.Linear(r, embedding_dim, bias=False)
-            self.scaling = self.lora_alpha / self.r
-            self.weight.requires_grad = False
+        self.lora_A = nn.ParameterList()
+        self.lora_B = nn.Linear(r, embedding_dim, bias=False)
+        self.scaling = self.lora_alpha / self.r
+        self.weight.requires_grad = False
 
         self.reset_parameters()
 
@@ -70,8 +68,9 @@ class LoraEmbedding(nn.Embedding, LoraLayer):
         else:
             nn.init.zeros_(new_emb.weight.data)
         new_emb.weight.data[:n, :] = self.weight.data[:n, :]
-        new_emb.lora_A = self.lora_A
-        new_emb.lora_B = self.lora_B
+        for param in self.lora_A:
+            new_emb.lora_A.append(param)
+        new_emb.lora_B.weight[:].data = self.lora_B.weight[:].data
         new_emb.trainable_ids[:n] = self.trainable_ids[:n]
 
         return new_emb
@@ -87,60 +86,60 @@ class LoraEmbedding(nn.Embedding, LoraLayer):
         n2 = n1 + new_ids.shape[0]
         self.trainable_ids[new_ids] = torch.arange(n1, n2)
         for _ in new_ids:
-            self.lora_A.append(nn.Parameter(self.weight.new_zeros(self.r)))
+            w = self.weight.new_zeros(self.r)
+            self.lora_A.append(w)
+
+        if len(self.lora_A) > 1:
+            elems = torch.stack([param for param in self.lora_A])
+            nn.init.kaiming_uniform_(elems, a=math.sqrt(5))
 
     def get_weights(self, input_ids: torch.Tensor):
         if len(input_ids.shape) != 1:
             return torch.stack([self.get_weights(batch) for batch in input_ids])
 
-        trainable_ids = self.trainable_ids[input_ids]
-        mask = ~(trainable_ids == -1)
-        trainable_ids = trainable_ids[mask]
-
         weights = self.weight.new_zeros((input_ids.shape[0], self.embedding_dim))
-        elems = [self.lora_A[id] for id in trainable_ids]
 
-        if len(elems) != 0:
-            w = self.lora_B(self.lora_dropout(torch.stack(elems))) * self.scaling
-            weights[mask] = w.to(dtype=weights.dtype)
+        if not self.merged:
+            trainable_ids = self.trainable_ids[input_ids]
+            mask = ~(trainable_ids == -1)
+            elems = [self.lora_A[id] for id in trainable_ids[mask]]
+
+            if len(elems) != 0:
+                w = self.lora_B(self.lora_dropout(torch.stack(elems))) * self.scaling
+                weights[mask] = w.to(dtype=weights.dtype)
 
         return weights
 
     def persist(self):
-        if self.r > 0:
-            weights = self.get_weights(torch.arange(self.trainable_ids.shape[0]))
-            self.weight.data += weights
-            self.trainable_ids[:] = -1
-            self.lora_A = nn.ParameterList()
+        self.weight.data += self.get_weights(torch.arange(self.trainable_ids.shape[0]))
+        self.trainable_ids[:] = -1
+        self.lora_A = nn.ParameterList()
+        nn.init.zeros_(self.lora_B.weight)
 
     def reset_parameters(self):
         nn.Embedding.reset_parameters(self)
-        if hasattr(self, 'lora_A'):
+        if hasattr(self, "lora_A"):
             self.trainable_ids[:] = -1
             self.lora_A = nn.ParameterList()
             nn.init.zeros_(self.lora_B.weight)
 
     def train(self, mode: bool = True):
         nn.Embedding.train(self, mode)
-        if self.merge_weights and self.merged:
-            if self.r > 0:
-                weights = self.get_weights(torch.arange(self.trainable_ids.shape[0]))
-                self.weight.data -= weights
+        self.lora_A.train(mode)
+        self.lora_B.train(mode)
+        if not mode and self.merge_weights and not self.merged:
+            self.weight.data += self.get_weights(torch.arange(self.trainable_ids.shape[0]))
+            self.merged = True
+        elif self.merge_weights and self.merged:
+            self.weight.data -= self.get_weights(torch.arange(self.trainable_ids.shape[0]))
             self.merged = False
 
     def eval(self):
         nn.Embedding.eval(self)
-        if self.merge_weights and not self.merged:
-            if self.r > 0:
-                weights = self.get_weights(torch.arange(self.trainable_ids.shape[0]))
-                self.weight.data += weights
-            self.merged = True
+        self.lora_A.eval()
+        self.lora_B.eval()
 
     def forward(self, input_ids: torch.LongTensor):
         result = nn.Embedding.forward(self, input_ids)
-
-        if self.r > 0 and not self.merged:
-            weights = self.get_weights(input_ids)
-            result += weights
-
+        result += self.get_weights(input_ids)
         return result
diff --git a/models/sparse.py b/models/sparse.py
new file mode 100644
index 0000000..bd45696
--- /dev/null
+++ b/models/sparse.py
@@ -0,0 +1,110 @@
+from typing import Optional
+
+import torch
+import torch.nn as nn
+
+
+class SparseEmbedding(nn.Embedding):
+    def __init__(
+        self,
+        num_embeddings: int,
+        embedding_dim: int,
+        alpha: int = 1,
+        dropout: float = 0.0,
+        **kwargs
+    ):
+        nn.Embedding.__init__(self, num_embeddings, embedding_dim, **kwargs)
+
+        self.register_buffer('trainable_ids', self.weight.new_zeros(num_embeddings, dtype=torch.long) - 1)
+
+        self.trainable = nn.ParameterList()
+        self.scaling = alpha
+        self.dropout_p = dropout
+        self.weight.requires_grad = False
+
+        if dropout > 0.:
+            self.dropout = nn.Dropout(p=dropout)
+        else:
+            self.dropout = nn.Identity()
+
+        self.reset_parameters()
+
+    def new_resized(self, new_num_embeddings: int, initializer_factor: Optional[float] = None):
+        n = min(self.num_embeddings, new_num_embeddings)
+
+        new_emb = SparseEmbedding(
+            new_num_embeddings,
+            self.embedding_dim,
+            self.scaling,
+            self.dropout_p,
+            device=self.weight.device,
+            dtype=self.weight.dtype
+        )
+        if initializer_factor is not None:
+            new_emb.weight.data.normal_(mean=0.0, std=initializer_factor * 0.02)
+        else:
+            nn.init.zeros_(new_emb.weight.data)
+        new_emb.weight.data[:n, :] = self.weight.data[:n, :]
+        for param in self.trainable:
+            new_emb.trainable.append(param)
+        new_emb.trainable_ids[:n] = self.trainable_ids[:n]
+
+        return new_emb
+
+    def mark_trainable(self, input_ids: torch.LongTensor):
+        trainable_ids = self.trainable_ids[input_ids]
+        new_ids = input_ids[trainable_ids == -1]
+
+        if new_ids.shape[0] == 0:
+            return
+
+        n1 = len(self.trainable)
+        n2 = n1 + new_ids.shape[0]
+        self.trainable_ids[new_ids] = torch.arange(n1, n2)
+        for _ in new_ids:
+            self.trainable.append(self.weight.new_zeros(self.embedding_dim))
+
+    def get_weights(self, input_ids: torch.Tensor):
+        original_shape = input_ids.shape
+
+        if len(input_ids.shape) != 1:
+            input_ids = input_ids.view(input_ids.shape[0] * input_ids.shape[1])
+
+        weights = self.weight.new_zeros((input_ids.shape[0], self.embedding_dim))
+
+        trainable_ids = self.trainable_ids[input_ids]
+        mask = ~(trainable_ids == -1)
+        elems = [self.trainable[id] for id in trainable_ids[mask]]
+
+        if len(elems) != 0:
+            w = self.dropout(torch.stack(elems)) * self.scaling
+            weights[mask] = w.to(dtype=weights.dtype)
+
+        if len(original_shape) != 1:
+            weights = weights.view(original_shape[0], original_shape[1], -1)
+
+        return weights
+
+    def persist(self):
+        self.weight.data += self.get_weights(torch.arange(self.trainable_ids.shape[0]))
+        self.trainable_ids[:] = -1
+        self.trainable = nn.ParameterList()
+
+    def reset_parameters(self):
+        nn.Embedding.reset_parameters(self)
+        if hasattr(self, "trainable"):
+            self.trainable_ids[:] = -1
+            self.trainable = nn.ParameterList()
+
+    def train(self, mode: bool = True):
+        nn.Embedding.train(self, mode)
+        self.trainable.train(mode)
+
+    def eval(self):
+        nn.Embedding.eval(self)
+        self.trainable.eval()
+
+    def forward(self, input_ids: torch.LongTensor):
+        result = nn.Embedding.forward(self, input_ids)
+        result += self.get_weights(input_ids)
+        return result
diff --git a/train_lora.py b/train_lora.py
index d5dde02..5c78664 100644
--- a/train_lora.py
+++ b/train_lora.py
@@ -507,6 +507,12 @@ def parse_args():
         help="The weight of prior preservation loss."
     )
     parser.add_argument(
+        "--emb_alpha",
+        type=float,
+        default=1.0,
+        help="Embedding alpha"
+    )
+    parser.add_argument(
         "--emb_dropout",
         type=float,
         default=0,
@@ -660,7 +666,10 @@ def main():
     save_args(output_dir, args)
 
     tokenizer, text_encoder, vae, unet, noise_scheduler, sample_scheduler, embeddings = get_models(
-        args.pretrained_model_name_or_path, args.emb_dropout)
+        args.pretrained_model_name_or_path,
+        args.emb_alpha,
+        args.emb_dropout
+    )
 
     unet_config = LoraConfig(
         r=args.lora_r,
diff --git a/train_ti.py b/train_ti.py
index 7f5fb49..45e730a 100644
--- a/train_ti.py
+++ b/train_ti.py
@@ -484,19 +484,13 @@ def parse_args():
         help="The weight of prior preservation loss."
     )
     parser.add_argument(
-        "--lora_r",
-        type=int,
-        default=8,
-        help="Lora rank, only used if use_lora is True"
-    )
-    parser.add_argument(
-        "--lora_alpha",
-        type=int,
-        default=32,
-        help="Lora alpha, only used if use_lora is True"
+        "--emb_alpha",
+        type=float,
+        default=1.0,
+        help="Embedding alpha"
     )
     parser.add_argument(
-        "--lora_dropout",
+        "--emb_dropout",
         type=float,
         default=0,
         help="Embedding dropout probability.",
@@ -669,9 +663,8 @@ def main():
 
     tokenizer, text_encoder, vae, unet, noise_scheduler, sample_scheduler, embeddings = get_models(
         args.pretrained_model_name_or_path,
-        args.lora_r,
-        args.lora_alpha,
-        args.lora_dropout
+        args.emb_alpha,
+        args.emb_dropout
     )
 
     tokenizer.set_use_vector_shuffle(args.vector_shuffle)
diff --git a/training/functional.py b/training/functional.py
index 1fdfdc8..2da0f69 100644
--- a/training/functional.py
+++ b/training/functional.py
@@ -68,9 +68,8 @@ class TrainingStrategy():
 
 def get_models(
     pretrained_model_name_or_path: str,
-    emb_r: int = 8,
-    emb_lora_alpha: int = 8,
-    emb_lora_dropout: float = 0.0
+    emb_alpha: int = 8,
+    emb_dropout: float = 0.0
 ):
     tokenizer = MultiCLIPTokenizer.from_pretrained(pretrained_model_name_or_path, subfolder='tokenizer')
     text_encoder = CLIPTextModel.from_pretrained(pretrained_model_name_or_path, subfolder='text_encoder')
@@ -80,7 +79,7 @@ def get_models(
     sample_scheduler = UniPCMultistepScheduler.from_pretrained(
         pretrained_model_name_or_path, subfolder='scheduler')
 
-    embeddings = patch_managed_embeddings(text_encoder, emb_r, emb_lora_alpha, emb_lora_dropout)
+    embeddings = patch_managed_embeddings(text_encoder, emb_alpha, emb_dropout)
 
     return tokenizer, text_encoder, vae, unet, noise_scheduler, sample_scheduler, embeddings