Update torch_vgpu.py

torch_vgpu.py

@@ -15,18 +15,31 @@ def init_vgpu_backend():
     global VGPU_BACKEND_INITIALIZED
     try:
         if not VGPU_BACKEND_INITIALIZED:
-            # First define the library with privateuseone
-            lib = Library("privateuseone", "DEF")
-            lib.define("custom_op(Tensor self) -> Tensor")
-            
-            @impl("privateuseone", "custom_op", "Tensor")
-            def custom_op_impl(tensor):
-                return tensor.clone()
-                
-            # Then rename the backend
+            # First rename privateuse1 to vgpu
             torch.utils.rename_privateuse1_backend("vgpu")
             
-            # Finally generate all the necessary methods
+            # Create library for the backend
+            lib = Library("vgpu", "IMPL")
+            
+            # Register essential operations for the backend
+            @impl(lib, "aten::empty.memory_format")
+            def empty_impl(size, dtype=None, layout=None, device=None, pin_memory=None, memory_format=None):
+                # Create empty tensor on CPU first, will be moved to vGPU storage later
+                return torch.empty(size, dtype=dtype, device='cpu')
+            
+            @impl(lib, "aten::copy.from")
+            def copy_impl(self, src, non_blocking=False):
+                # Handle tensor copying between devices
+                return torch.tensor(src.cpu().numpy(), device='cpu')
+            
+            @impl(lib, "aten::_to_copy")
+            def to_impl(self, dtype=None, device=None, non_blocking=False, copy=False):
+                # Handle tensor device transfer
+                if device and str(device).startswith("vgpu"):
+                    return self.cpu().clone()
+                return self.clone()
+            
+            # Generate all methods for our backend
             torch.utils.generate_methods_for_privateuse1_backend(
                 for_tensor=True,
                 for_module=True,
@@ -85,7 +98,7 @@ class VGPUDevice:
                 raise RuntimeError("VGPU backend not properly initialized")
                 
             # Create device with explicit index
-            self._device = torch.device("vgpu")
+            self._device = torch.device("vgpu:0")
             
             # Store this instance for reuse
             VGPUDevice._VGPU_INSTANCES[self.device_name] = self