quick fix

Signed-off-by: Che Ruan <[email protected]>

Author: mercykid

vllm_ascend/eplb/adaptor/vllm_adaptor.py

@@ -194,20 +194,34 @@ def _export_tensor_to_file(self, expert_maps, expert_map_record_path: str):
                 json.dump(record, f, indent=4)
 
     def do_update_expert_map(self, layer_id, updated_expert_map):
-        self.expert_map_per_layer[layer_id] = updated_expert_map.clone()
-        self.expert_map_per_layer_cpu[layer_id] = updated_expert_map.clone()
+        pad_len = self.expert_map_per_layer[layer_id].shape[0] - updated_expert_map.shape[0]
+        updated_expert_map_padded = torch.nn.functional.pad(
+                                    updated_expert_map,
+                                    pad=(0,pad_len),
+                                    mode='constant',
+                                    value=-1
+                                    )
+        self.expert_map_per_layer[layer_id].copy_(updated_expert_map_padded)
+        self.expert_map_per_layer_cpu[layer_id].copy_(updated_expert_map)
 
     def do_update_expert_weight(self, layer_id, local_expert_to_replace,
                                 buffer_tensor_id):
         for expert_tensor, buffer_tensor in zip(
                 self.expert_param_per_layer[layer_id][local_expert_to_replace],
                 self.buffer_tensor_list[buffer_tensor_id]):
-            expert_tensor = buffer_tensor.clone()
+            expert_tensor.copy_(buffer_tensor)
             logger.debug(f"Expert tensor shape is :{expert_tensor.shape}")
 
     def do_update_log2phy_map(self, layer_id, updated_log2phy_map):
         if self.log2phy_map_per_layer[layer_id] is not None:
-            self.log2phy_map_per_layer[layer_id].copy_(updated_log2phy_map)
+            pad_len = self.log2phy_map_per_layer[layer_id].shape[0] - updated_log2phy_map.shape[0]
+            updated_log2phy_map_padded = torch.nn.functional.pad(
+                                        updated_log2phy_map,
+                                        pad=(0,pad_len),
+                                        mode='constant',
+                                        value=-1
+                                        )
+            self.log2phy_map_per_layer[layer_id].copy_(updated_log2phy_map_padded)
 
     def global2local(self, placement: torch.Tensor,
                      E_local: int) -> torch.Tensor: