diff --git a/mindspore/python/mindspore/common/tensor.py b/mindspore/python/mindspore/common/tensor.py
index 6c075b1c3d2..e758f798f93 100644
--- a/mindspore/python/mindspore/common/tensor.py
+++ b/mindspore/python/mindspore/common/tensor.py
@@ -2699,6 +2699,8 @@ class Tensor(Tensor_):
             [0. 1. 0. 0.]
         """
         self._init_check()
+        if isinstance(value, (float, int)):
+            value = tensor_operator_registry.get("scalar_to_tensor")(value, self.dtype)
         if not isinstance(mask, Tensor):
             raise TypeError("For 'Tensor.masked_fill', the type of the argument 'mask' must be Tensor, but "
                             "got {}.".format(type(mask)))
diff --git a/mindspore/python/mindspore/nn/layer/pooling.py b/mindspore/python/mindspore/nn/layer/pooling.py
index 51fb44b9012..4ad9b66f570 100644
--- a/mindspore/python/mindspore/nn/layer/pooling.py
+++ b/mindspore/python/mindspore/nn/layer/pooling.py
@@ -753,7 +753,6 @@ class AdaptiveMaxPool3d(Cell):
             `ouput_size` can be a tuple with 3 elements, or a single D for :math:`(D, D, D)`. :math:`D`,
             :math:`H` and :math:`W` can be int or None which means the output size is the same as that of
             the input.
-
         return_indices (bool): If `return_indices` is True, the indices of max value would be output.
             Default: False.
 
diff --git a/mindspore/python/mindspore/ops/function/array_func.py b/mindspore/python/mindspore/ops/function/array_func.py
index d26608e9108..63395f67ec6 100644
--- a/mindspore/python/mindspore/ops/function/array_func.py
+++ b/mindspore/python/mindspore/ops/function/array_func.py
@@ -71,7 +71,6 @@ tensor_scatter_max_ = P.TensorScatterMax()
 scalar_to_array_ = P.ScalarToArray()
 scalar_to_tensor_ = P.ScalarToTensor()
 tuple_to_array_ = P.TupleToArray()
-masked_fill_ = P.MaskedFill()
 masked_select_ = P.MaskedSelect()
 matrix_band_part_ = P.array_ops.MatrixBandPart()
 ger_ = P.Ger()
@@ -3494,6 +3493,9 @@ def masked_fill(input_x, mask, value):
         >>> print(output)
         [0.5 0.5 3.  0.5]
     """
+    if isinstance(value, (float, int)) and isinstance(input_x, Tensor):
+        value = scalar_to_tensor_(value, input_x.dtype)
+    masked_fill_ = _get_cache_prim(P.MaskedFill)()
     return masked_fill_(input_x, mask, value)
 
 
diff --git a/mindspore/python/mindspore/ops/functional.py b/mindspore/python/mindspore/ops/functional.py
index 4d13097af47..9c39b882aca 100644
--- a/mindspore/python/mindspore/ops/functional.py
+++ b/mindspore/python/mindspore/ops/functional.py
@@ -834,6 +834,7 @@ tensor_operator_registry.register('sum', P.ReduceSum)
 tensor_operator_registry.register('split', P.Split)
 tensor_operator_registry.register('select', P.Select)
 tensor_operator_registry.register('zeros_like', P.ZerosLike)
+tensor_operator_registry.register('scalar_to_tensor', scalar_to_tensor)
 tensor_operator_registry.register('masked_fill', masked_fill)
 tensor_operator_registry.register('masked_select', masked_select)
 tensor_operator_registry.register('nonzero', nonzero)
diff --git a/tests/st/ops/gpu/test_maskedfill_op.py b/tests/st/ops/gpu/test_maskedfill_op.py
index 27da1042408..64055fd853a 100644
--- a/tests/st/ops/gpu/test_maskedfill_op.py
+++ b/tests/st/ops/gpu/test_maskedfill_op.py
@@ -20,6 +20,7 @@ import mindspore.context as context
 import mindspore.nn as nn
 from mindspore import Tensor
 from mindspore.ops import operations as P
+from mindspore.ops import functional as F
 
 context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
 
@@ -131,6 +132,40 @@ def test_maskedfill_float_value():
     maskedfill_value(0.5)
 
 
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_func_masked_fill_float():
+    """
+    Feature: Test func masked_fill.
+    Description: Test func masked_fill api with float value.
+    Expectation: The result match to expect.
+    """
+    inputs = Tensor(np.array([[1, 2, 3, 4], [5, 6, 7, 8]]).astype(np.float16))
+    mask = Tensor(np.array([[True, True, False, True], [False, False, True, False]]).astype(np.bool))
+    value = 22
+    expect = np.array([[22, 22, 3, 22], [5, 6, 22, 8]]).astype(np.float16)
+    output = F.masked_fill(inputs, mask, value)
+    assert (output.asnumpy() == expect).all()
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.env_onecard
+def test_tensor_masked_fill_float():
+    """
+    Feature: Test Tensor masked_fill.
+    Description: Test Tensor masked_fill api with float value.
+    Expectation: The result match to expect.
+    """
+    inputs = Tensor(np.array([[1, 2, 3, 4], [5, 6, 7, 8]]).astype(np.float16))
+    mask = Tensor(np.array([[True, True, False, True], [False, False, True, False]]).astype(np.bool))
+    value = 22
+    output = inputs.masked_fill(mask, value)
+    expect = np.array([[22, 22, 3, 22], [5, 6, 22, 8]]).astype(np.float16)
+    assert (output.asnumpy() == expect).all()
+
+
 @pytest.mark.level0
 @pytest.mark.platform_x86_gpu_training
 @pytest.mark.env_onecard