[AutoParallel] Add param name for dist_tensor parameter

python/paddle/base/framework.py

@@ -7598,10 +7598,12 @@ def from_tensor(cls, tensor, **kwargs):
         mesh = kwargs.get("process_mesh", None)
         placements = kwargs.get("placements", None)
         src_tensor = tensor
+
         if mesh is not None and placements is not None:
             src_tensor = core.eager.Tensor(
                 tensor, process_mesh=mesh, placements=placements
             )
+            param.name = tensor.name + ".dist"
 
         # 3. set param data
         param._set_impl(src_tensor)

python/paddle/distributed/auto_parallel/api.py

@@ -169,15 +169,16 @@ def shard_tensor(
     place = paddle.framework._get_paddle_place(place)
 
     # 1. create dense tensor
-    # `paddle.to_tensor` supports both dynamic and static mode
     if stop_gradient is None:
         stop_gradient = getattr(data, "stop_gradient", True)
+
     if isinstance(data, EagerParamBase) and not data._is_initialized():
         assert (
             data._init_func is not None
         ), "Get an uninitialized param with an unregistered init_func."
         tensor = data
     else:
+        # `paddle.to_tensor` supports both dynamic and static mode
         tensor = paddle.to_tensor(
             data, dtype=dtype, place=place, stop_gradient=stop_gradient
         )

test/auto_parallel/test_dist_tensor.py

@@ -52,6 +52,17 @@ def test_dist_tensor_creation(self):
         self.assertEqual(dist_tensor_with_numpy.placements, placements)
         self.assertEqual(dist_tensor_with_tensor.placements, placements)
 
+    def test_dist_parameter(self):
+        mesh = dist.ProcessMesh([[0, 1], [2, 3]], dim_names=["x", "y"])
+        placements = [Replicate(), Replicate()]
+
+        dense_param = paddle.create_parameter(
+            [10, 5], name="linear_1.weight", dtype='float32'
+        )
+        dist_param = dist.shard_tensor(dense_param, mesh, placements)
+
+        self.assertEqual(dense_param.name + ".dist", dist_param.name)
+
 
 class TestDistTensorFromFn(unittest.TestCase):
     def run_dtensor_from_fn(self):