Trying to fix assert errors

model/brain_state.py

@@ -6,8 +6,7 @@ from model.tokenizer import Tokenizer
 DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 tokenizer = Tokenizer()
-VOCAB_SIZE = len(tokenizer.vocab) + 10  # Slight buffer
+VOCAB_SIZE = len(tokenizer.vocab) + 10  # with a small buffer
 
 model = TinyTransformer(vocab_size=VOCAB_SIZE).to(DEVICE)
-optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
 loss_fn = nn.CrossEntropyLoss()

model/dynamic_expand.py

@@ -1,41 +1,36 @@
 import torch
 from model.brain_architecture import TinyTransformer
-from model.brain_state import model, tokenizer, DEVICE, optimizer
+from model.brain_state import model, tokenizer, DEVICE
-import copy
+
+optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
+
+
+def get_optimizer():
+    global optimizer
+    return optimizer
 
 
 def expand_model_if_needed():
-    current_vocab_size = len(tokenizer.vocab) + 10  # Tiny buffer
+    global model, optimizer
+
+    current_vocab_size = len(tokenizer.vocab) + 10  # Buffer
     old_vocab_size = model.head.out_features
 
     if current_vocab_size <= old_vocab_size:
-        return  # No expansion needed
+        return
 
     print(f"Expanding model from {old_vocab_size} -> {current_vocab_size}")
 
-    # Save old model
+    old_state = model.state_dict()
-    old_model = copy.deepcopy(model).to('cpu')
-
-    # Create new model
     new_model = TinyTransformer(vocab_size=current_vocab_size).to(DEVICE)
-    new_optimizer = torch.optim.Adam(new_model.parameters(), lr=1e-4)
 
-    # Copy parameters
+    # Transfer matching parameters
     with torch.no_grad():
-        for name, param in old_model.named_parameters():
+        for name, param in new_model.named_parameters():
-            if name in dict(new_model.named_parameters()):
+            if name in old_state and old_state[name].shape == param.shape:
-                try:
+                param.copy_(old_state[name])
-                    new_param = dict(new_model.named_parameters())[name]
-                    if param.shape == new_param.shape:
-                        new_param.copy_(param)
-                    else:
-                        print(f"Skipping mismatched param: {name}")
-                except Exception as e:
-                    print(f"Error copying param: {name} — {e}")
 
-    # Replace global references
+    model = new_model
-    globals()["model"] = new_model
+    opt =  get_optimizer()
-    globals()["optimizer"] = new_optimizer
-
-    print("Expansion complete.")
 
+    print("Model expanded and optimizer rebuilt.")

model/trainer.py

@@ -1,8 +1,8 @@
 import torch
 import time
-from model.brain_state import model, tokenizer, DEVICE, optimizer, loss_fn
+from model.brain_state import model, tokenizer, DEVICE,loss_fn
 from context.context import add_to_context, get_recent_context
-from model.dynamic_expand import expand_model_if_needed
+from model.dynamic_expand import expand_model_if_needed, get_optimizer
 from model.brainmap import update_brainmap
 
 LOSS_FILE = "data/logs/loss.log"
@@ -31,9 +31,10 @@ def train_on_message(text: str):
     output = model(input_tensor)
     loss = loss_fn(output.view(-1, output.size(-1)), target_tensor.view(-1))
 
-    optimizer.zero_grad()
+    opt = get_optimizer()
+    opt.zero_grad()
     loss.backward()
-    optimizer.step()
+    opt.step()
 
     log_loss(loss.item())
     add_to_context(text)