fixed model laoading for aiiamoe and aiiasmoe with shared weights

src/aiia/model/Model.py

@@ -1,8 +1,9 @@
 from .config import AIIAConfig
 from torch import nn
 from transformers import PreTrainedModel
+from safetensors.torch import load_file
 import torch
-import copy
+import os
 
 
 class AIIABase(PreTrainedModel):
@@ -132,16 +133,33 @@ class AIIAmoe(PreTrainedModel):
     config_class = AIIAConfig
     base_model_prefix = "AIIA"
     
-    def __init__(self, config: AIIAConfig, base_class=AIIABase):
+    def __init__(self, config: AIIAConfig, base_class=None):
         super().__init__(config=config)
         self.config = config
 
-        # Get num_experts directly from config instead of parameter
-        num_experts = getattr(config, "num_experts", 3)  # default to 3 if not in config
+        # Get num_experts directly from config or set default to 3 if not present
+        if not hasattr(config, 'num_experts'):
+            config.num_experts = 3
+
+        # Get base_class_name as string (JSON serializable)
+        if not hasattr(config, 'base_class_name'):
+            config.base_class_name = 'AIIABase'
+
+        num_experts = config.num_experts
+        
+        # Simple mapping from string to class
+        if base_class is not None:
+            resolved_base_class = base_class
+        else:
+            if config.base_class_name == 'AIIABase':
+                resolved_base_class = AIIABase
+            elif config.base_class_name == 'AIIABaseShared':  # Replace with your second base class
+                resolved_base_class = AIIABaseShared
+            else:
+                raise ValueError(f"Unknown base_class_name: {config.base_class_name}")
         
-        # Initialize multiple experts from the chosen base class
         self.experts = nn.ModuleList([
-            AIIAExpert(self.config, base_class=base_class)
+            AIIAExpert(self.config, base_class=resolved_base_class)
             for _ in range(num_experts)
         ])
 
@@ -152,6 +170,36 @@ class AIIAmoe(PreTrainedModel):
             nn.Linear(gate_in_features, num_experts),
             nn.Softmax(dim=1)
         )
+            
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, base_class=None, base_class_name=None, **kwargs):        
+        # Load the config
+        config = cls.config_class.from_pretrained(pretrained_model_name_or_path, **kwargs)
+        
+        # Set base_class_name if provided
+        if base_class_name is not None:
+            config.base_class_name = base_class_name
+        elif not hasattr(config, 'base_class_name'):
+            config.base_class_name = 'AIIABase'
+        
+        # Create the model with the correct architecture
+        model = cls(config, base_class=base_class)
+        
+        # Try to load weights - check for both formats
+        safetensors_path = os.path.join(pretrained_model_name_or_path, "model.safetensors")
+        pytorch_path = os.path.join(pretrained_model_name_or_path, "pytorch_model.bin")
+        
+        if os.path.exists(safetensors_path):
+            state_dict = load_file(safetensors_path)
+            model.load_state_dict(state_dict, strict=False)
+        elif os.path.exists(pytorch_path):
+            state_dict = torch.load(pytorch_path, map_location="cpu")
+            model.load_state_dict(state_dict, strict=False)
+        else:
+            print("No weight files found - model initialized with random weights")
+        return model
+
+
         
     def forward(self, x: torch.Tensor) -> torch.Tensor:
         """