diff --git a/mindspore/python/mindspore/ops/_vmap/vmap_array_ops.py b/mindspore/python/mindspore/ops/_vmap/vmap_array_ops.py
index 330c3a456a2..1f0e10bd115 100644
--- a/mindspore/python/mindspore/ops/_vmap/vmap_array_ops.py
+++ b/mindspore/python/mindspore/ops/_vmap/vmap_array_ops.py
@@ -30,6 +30,7 @@ from mindspore.ops.operations import _grad_ops as G
 from mindspore.ops.operations.array_ops import Fills, UniqueConsecutive, Col2Im, NonZero, IndexFill, \
     TensorScatterElements
 from mindspore.ops.operations.random_ops import RandomPoisson
+from mindspore.ops.operations._inner_ops import DynamicBroadcastTo
 from mindspore.ops.primitive import Primitive
 from mindspore.ops._vmap.vmap_base import vmap_rules_getters, vmap_general_preprocess, _bdim_at_front, \
     _raise_value_error, _vmap_clone_prim, _handle_broadcasting, get_unsupported_dynamic_vmap_rule, _broadcast_by_axis, \
@@ -841,6 +842,11 @@ def get_fill_vmap_rule(prim, axis_size):
     return vmap_rule
 
 
+@constexpr
+def to_tensor_with_type(x, type):
+    return Tensor(x, type)
+
+
 @vmap_rules_getters.register(P.FillV2)
 def get_fill_v2_vmap_rule(prim, axis_size):
     """VmapRule for `FillV2` operation."""
@@ -848,19 +854,16 @@ def get_fill_v2_vmap_rule(prim, axis_size):
         prim = Primitive(prim)
 
     def vmap_rule(shape_bdim, value_bdim):
-        is_all_none, result = vmap_general_preprocess(prim, shape_bdim,
-                                                      value_bdim)
+        is_all_none, result = vmap_general_preprocess(prim, shape_bdim, value_bdim)
         if is_all_none:
             return result
+
         value_shape, shape_dim = shape_bdim
-        if isinstance(value_shape, (Tensor_, Tensor)):
-            _raise_value_error(
-                "For `P.FillV2`, when the `shape` is a tensor, VMAP is not supported!"
-            )
         if shape_dim is not None:
             _raise_value_error(
                 "The source axis of `shape` in `P.FillV2` must be None, but got {}."
                 .format(shape_dim))
+
         value, vdim = value_bdim
         value_rank = F.rank(value)
         if value_rank != 1 or vdim != 0:
@@ -869,7 +872,26 @@ def get_fill_v2_vmap_rule(prim, axis_size):
                 "can be rank: 1 with source axis: 0 in vmap scope, but got value rank: "
                 "{} with source axis: {}.".format(value_rank, vdim))
         value = F.reshape(value, (axis_size,) + (1,) * len(value_shape))
-        out = P.BroadcastTo((axis_size,) + value_shape)(value)
+
+        out = None
+        if isinstance(value_shape, (Tensor_, Tensor)):
+            value_shape_rank = F.rank(value_shape)
+            if value_shape_rank != 1:
+                _raise_value_error(
+                    "The `shape` in `P.FillV2` must be 1-D tensor, thus the shape only "
+                    "can be rank: 1, but got shape rank: "
+                    "{}.".format(value_shape_rank))
+            axis_size_tensor = to_tensor_with_type((axis_size,),
+                                                   F.dtype(value_shape))
+            broad_cast_shape = F.concat((axis_size_tensor, value_shape))
+            out = DynamicBroadcastTo()(value, broad_cast_shape)
+        elif isinstance(value_shape, tuple):
+            out = P.BroadcastTo((axis_size,) + value_shape)(value)
+        else:
+            _raise_value_error(
+                f"For `P.FillV2`, the input `shape` should be Tuple or Tensor, but got `shape`: {value_shape}."
+            )
+
         return out, 0
 
     return vmap_rule
diff --git a/tests/st/ops/gpu/test_fill_v2_op.py b/tests/st/ops/gpu/test_fill_v2_op.py
index a4e9c2d8ce5..be955ce78fc 100644
--- a/tests/st/ops/gpu/test_fill_v2_op.py
+++ b/tests/st/ops/gpu/test_fill_v2_op.py
@@ -44,11 +44,11 @@ def dyn_case():
     assert out.asnumpy().shape == (2, 3)
 
 
-def vmap_case():
+def cla_fillv2(shape, value):
+    return P.FillV2()(shape, value)
 
-    def cla_fillv2(shape, value):
-        return P.FillV2()(shape, value)
 
+def vmap_tuple_case():
     shape = (2, 2)
     value = Tensor([1, 2], ms.float32)
     outputs = vmap(cla_fillv2, in_axes=(None, 0), out_axes=0)(shape, value)
@@ -57,6 +57,15 @@ def vmap_case():
     assert np.allclose(expect, outputs.asnumpy(), 1.e-4, 1.e-7)
 
 
+def vmap_tensor_case():
+    shape = Tensor((2, 2), ms.int32)
+    value = Tensor([1, 2], ms.float32)
+    outputs = vmap(cla_fillv2, in_axes=(None, 0), out_axes=0)(shape, value)
+
+    expect = np.array([[[1, 1], [1, 1]], [[2, 2], [2, 2]]]).astype(np.float32)
+    assert np.allclose(expect, outputs.asnumpy(), 1.e-4, 1.e-7)
+
+
 @pytest.mark.level0
 @pytest.mark.platform_x86_gpu
 @pytest.mark.env_onecard
@@ -68,7 +77,22 @@ def test_fill_v2_dyn():
     """
     context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
     dyn_case()
-    vmap_case()
     context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
     dyn_case()
-    vmap_case()
+
+
+@pytest.mark.level2
+@pytest.mark.platform_x86_gpu
+@pytest.mark.env_onecard
+def test_fill_v2_vmap():
+    """
+    Feature: test FillV2 vmap in gpu.
+    Description: inputs is static shape.
+    Expectation: expect correct out result.
+    """
+    context.set_context(mode=context.GRAPH_MODE, device_target='GPU')
+    vmap_tuple_case()
+    vmap_tensor_case()
+    context.set_context(mode=context.PYNATIVE_MODE, device_target='GPU')
+    vmap_tuple_case()
+    vmap_tensor_case()