[autoparallel] fix parameters sharding bug (#2716)

colossalai/auto_parallel/passes/runtime_preparation_pass.py

@@ -426,8 +426,9 @@ def module_params_sharding_pass(gm: torch.fx.GraphModule, device_mesh: DeviceMes
             # we could use .data here, because all the operations just happen before the real training
             # loop, so we don't need to track these operations in the autograd graph.
             param = torch.nn.Parameter(
-                        shape_consistency_manager.apply_for_autoparallel_runtime(param.data, param.sharding_spec,
-                                                                                 target_sharding_spec).detach().clone())
+                shape_consistency_manager.apply_for_autoparallel_runtime(param.data, param.sharding_spec,
+                                                                         target_sharding_spec).detach().clone())
+        return param
 
     for node in nodes:
         if node.op == 'call_module':
@@ -438,7 +439,7 @@ def module_params_sharding_pass(gm: torch.fx.GraphModule, device_mesh: DeviceMes
             setattr(target_module, 'processed', True)
             for name, param in target_module.named_parameters():
                 target_sharding_spec = node.best_strategy.get_sharding_spec_by_name(name)
-                _shard_param(param, target_sharding_spec)
+                param = _shard_param(param, target_sharding_spec)
 
                 setattr(target_module, name, param)
                 _add_hook_for_grad_communication(node, param)
@@ -469,7 +470,7 @@ def module_params_sharding_pass(gm: torch.fx.GraphModule, device_mesh: DeviceMes
                 target = getattr(target_module, atoms[-1])
 
             target_sharding_spec = node.sharding_spec
-            _shard_param(target, target_sharding_spec)
+            target = _shard_param(target, target_sharding_spec)
 
             assert hasattr(target_module, atoms[-1])
             setattr(target_module, atoms[-1], target)