diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py
index 0f542bfab1f5a3daa6f83495e470d57b42431ae4..d2ac562dfd0bb16ed58a3eb4dbf98bbf6f69409f 100644
--- a/python/paddle/fluid/layers/nn.py
+++ b/python/paddle/fluid/layers/nn.py
@@ -69,7 +69,6 @@ __all__ = [
     'crf_decoding',
     'cos_sim',
     'conv2d',
-    'conv3d',
     'softmax',
     'pool2d',
     'pool3d',
@@ -1683,314 +1682,6 @@ def conv2d(
     return helper.append_activation(pre_act)
 
 
-def conv3d(
-    input,
-    num_filters,
-    filter_size,
-    stride=1,
-    padding=0,
-    dilation=1,
-    groups=None,
-    param_attr=None,
-    bias_attr=None,
-    use_cudnn=True,
-    act=None,
-    name=None,
-    data_format="NCDHW",
-):
-    r"""
-    :api_attr: Static Graph
-
-    The convolution3D layer calculates the output based on the input, filter
-    and strides, paddings, dilations, groups parameters. Input(Input) and
-    Output(Output) are in NCDHW or NDHWC format. Where N is batch size C is the number of
-    channels, D is the depth of the feature, H is the height of the feature,
-    and W is the width of the feature. Convlution3D is similar with Convlution2D
-    but adds one dimension(depth). If bias attribution and activation type are
-    provided, bias is added to the output of the convolution, and the
-    corresponding activation function is applied to the final result.
-
-    For each input :math:`X`, the equation is:
-
-    .. math::
-
-        Out = \sigma (W \\ast X + b)
-
-    In the above equation:
-
-    * :math:`X`: Input value, a tensor with NCDHW or NDHWC format.
-    * :math:`W`: Filter value, a tensor with MCDHW format.
-    * :math:`\\ast`: Convolution operation.
-    * :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
-    * :math:`\\sigma`: Activation function.
-    * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
-
-    Example:
-
-        - Input:
-
-          Input shape: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`
-
-          Filter shape: :math:`(C_{out}, C_{in}, D_f, H_f, W_f)`
-
-        - Output:
-          Output shape: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`
-
-        Where
-
-        .. math::
-
-            D_{out}&= \\frac{(D_{in} + 2 * paddings[0] - (dilations[0] * (D_f - 1) + 1))}{strides[0]} + 1 \\\\
-            H_{out}&= \\frac{(H_{in} + 2 * paddings[1] - (dilations[1] * (H_f - 1) + 1))}{strides[1]} + 1 \\\\
-            W_{out}&= \\frac{(W_{in} + 2 * paddings[2] - (dilations[2] * (W_f - 1) + 1))}{strides[2]} + 1
-
-    Args:
-        input (Tensor): The input is 5-D Tensor with shape [N, C, D, H, W], the data
-            type of input is float16 or float32 or float64.
-        num_filters(int): The number of filter. It is as same as the output
-            image channel.
-        filter_size (int|tuple): The filter size. If filter_size is a tuple,
-            it must contain three integers, (filter_size_depth, filter_size_height,
-            filter_size_width). Otherwise, filter_size_depth = filter_size_height = \
-            filter_size_width = filter_size.
-        stride (int|tuple): The stride size. It means the stride in convolution. If stride is a
-            tuple, it must contain three integers, (stride_depth, stride_height, stride_width).
-            Otherwise, stride_depth = stride_height = stride_width = stride. Default: stride = 1.
-        padding (string|int|list|tuple): The padding size. It means the number of zero-paddings
-            on both sides for each dimension. If `padding` is a string, either 'VALID' or
-            'SAME' which is the padding algorithm. If padding size is a tuple or list,
-            it could be in three forms: `[pad_depth, pad_height, pad_width]` or
-            `[pad_depth_front, pad_depth_back, pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`,
-            and when `data_format` is `"NCDHW"`, `pool_padding` can be in the form
-            `[[0,0], [0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]`.
-            when `data_format` is `"NDHWC"`, `pool_padding` can be in the form
-            `[[0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
-            Default: padding = 0.
-        dilation (int|tuple): The dilation size. It means the spacing between the kernel points.
-            If dilation is a tuple, it must contain three integers, (dilation_depth, dilation_height,
-            dilation_width). Otherwise, dilation_depth = dilation_height = dilation_width = dilation.
-            Default: dilation = 1.
-        groups (int): The groups number of the Conv3d Layer. According to grouped
-            convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
-            the first half of the filters is only connected to the first half
-            of the input channels, while the second half of the filters is only
-            connected to the second half of the input channels. Default: groups=1
-        param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights
-            of conv3d. If it is set to None or one attribute of ParamAttr, conv3d
-            will create ParamAttr as param_attr. If it is set to None, the parameter
-            is initialized with :math:`Normal(0.0, std)`, and the :math:`std` is
-            :math:`(\\frac{2.0 }{filter\_elem\_num})^{0.5}`. Default: None.
-        bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of conv3d.
-            If it is set to False, no bias will be added to the output units.
-            If it is set to None or one attribute of ParamAttr, conv3d
-            will create ParamAttr as bias_attr. If the Initializer of the bias_attr
-            is not set, the bias is initialized zero. Default: None.
-        use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
-            library is installed. Default: True
-        act (str): Activation type, if it is set to None, activation is not appended.
-            Default: None.
-        name(str|None): For detailed information, please refer
-           to :ref:`api_guide_Name`. Usually name is no need to set and
-           None by default.
-        data_format (str, optional): Specify the data format of the input, and the data format of the output
-            will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`.
-            The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
-            `[batch_size, input_channels, input_height, input_width]`.
-
-    Returns:
-        A Variable holding Tensor representing the conv3d, whose data type is
-        the same with input. If act is None, the tensor variable storing the
-        convolution result, and if act is not None, the tensor variable storing
-        convolution and non-linearity activation result.
-
-    Raises:
-        ValueError: If the type of `use_cudnn` is not bool.
-        ValueError: If `data_format` is not "NCDHW" or "NDHWC".
-        ValueError: If the channel dimmention of the input is less than or equal to zero.
-        ValueError: If `padding` is a string, but not "SAME" or "VALID".
-        ValueError: If `padding` is a tuple, but the element corresponding to the input's batch size is not 0
-            or the element corresponding to the input's channel is not 0.
-        ShapeError: If the input is not 5-D Tensor.
-        ShapeError: If the input's dimension size and filter's dimension size not equal.
-        ShapeError: If the dimension size of input minus the size of `stride` is not 2.
-        ShapeError: If the number of input channels is not equal to filter's channels * groups.
-        ShapeError: If the number of output channels is not be divided by groups.
-
-    Examples:
-        .. code-block:: python
-
-          import paddle
-          import numpy as np
-
-          paddle.enable_static()
-          data = paddle.static.data(name='data', shape=[None, 3, 12, 32, 32], dtype='float32')
-          param_attr = paddle.framework.ParamAttr(name='conv3d.weight', initializer=paddle.nn.initializer.XavierNormal(), learning_rate=0.001)
-          res = paddle.static.nn.conv3d(input=data, num_filters=2, filter_size=3, act="relu", param_attr=param_attr)
-          place = paddle.CPUPlace()
-          exe = paddle.static.Executor(place)
-          exe.run(paddle.static.default_startup_program())
-          x = np.random.rand(1, 3, 12, 32, 32).astype("float32")
-          output = exe.run(feed={"data": x}, fetch_list=[res])
-          print(output)
-    """
-
-    l_type = 'conv3d'
-    assert param_attr is not False, "param_attr should not be False here."
-    helper = LayerHelper(l_type, **locals())
-    dtype = helper.input_dtype()
-
-    if not isinstance(use_cudnn, bool):
-        raise ValueError(
-            "Attr(use_cudnn) should be True or False. Received "
-            "Attr(use_cudnn): %s. " % str(use_cudnn)
-        )
-
-    if data_format not in ["NCDHW", "NDHWC"]:
-        raise ValueError(
-            "Attr(data_format) should be 'NCDHW' or 'NDHWC'. Received "
-            "Attr(data_format): %s." % str(data_format)
-        )
-
-    channel_last = data_format == "NDHWC"
-    if len(input.shape) != 5:
-        raise ValueError(
-            "Input should be 5D tensor, but received input with the shape of {}".format(
-                input.shape
-            )
-        )
-    num_channels = input.shape[4] if channel_last else input.shape[1]
-    if num_channels < 0:
-        raise ValueError(
-            "The channel dimmention of the input(%s) should be defined. "
-            "Received: %s." % (str(input.shape), str(num_channels))
-        )
-
-    if groups is None:
-        num_filter_channels = num_channels
-    elif groups <= 0:
-        raise ValueError(
-            "the groups of conv3d should be greater than 0. Received groups: {}".format(
-                groups
-            )
-        )
-    else:
-        if num_channels % groups != 0:
-            raise ValueError(
-                "The number of input channels must be divisible by Attr(groups). "
-                "Received: number of channels(%s), groups(%s)."
-                % (str(num_channels), str(groups))
-            )
-        num_filter_channels = num_channels // groups
-
-    filter_size = utils.convert_to_list(filter_size, 3, 'filter_size')
-    stride = utils.convert_to_list(stride, 3, 'stride')
-    dilation = utils.convert_to_list(dilation, 3, 'dilation')
-
-    def _update_padding(padding, data_format):
-        def is_list_or_tuple(ele):
-            if isinstance(ele, list) or isinstance(ele, tuple):
-                return True
-            return False
-
-        if is_list_or_tuple(padding) and len(padding) == 5:
-            if is_list_or_tuple(padding[0]) and (data_format == "NCDHW"):
-                if not (padding[0] == [0, 0] and padding[1] == [0, 0]):
-                    raise ValueError(
-                        "Non-zero padding(%s) in the batch or channel dimensions "
-                        "is not supported." % str(padding)
-                    )
-                padding = padding[2:5]
-                padding = [ele for a_list in padding for ele in a_list]
-            elif is_list_or_tuple(padding[0]) and (data_format == "NDHWC"):
-                if not (padding[0] == [0, 0] and padding[4] == [0, 0]):
-                    raise ValueError(
-                        "Non-zero padding(%s) in the batch or channel dimensions "
-                        "is not supported." % str(padding)
-                    )
-                padding = padding[1:4]
-                padding = [ele for a_list in padding for ele in a_list]
-            padding = utils.convert_to_list(padding, 6, 'padding')
-            if utils._is_symmetric_padding(padding, 3):
-                padding = [padding[0], padding[2], padding[4]]
-        elif is_list_or_tuple(padding) and len(padding) == 6:
-            padding = utils.convert_to_list(padding, 6, 'padding')
-            if utils._is_symmetric_padding(padding, 3):
-                padding = [padding[0], padding[2], padding[4]]
-        else:
-            padding = utils.convert_to_list(padding, 3, 'padding')
-
-        return padding
-
-    padding_algorithm = "EXPLICIT"
-    if isinstance(padding, str):
-        padding = padding.upper()
-        if padding not in ["SAME", "VALID"]:
-            raise ValueError(
-                "Unknown padding: '%s'. It can only be 'SAME' or 'VALID'."
-                % str(padding)
-            )
-        if padding == "VALID":
-            padding_algorithm = "VALID"
-            padding = [0, 0, 0]
-        elif padding == "SAME":
-            padding_algorithm = "SAME"
-            padding = [0, 0, 0]
-
-    padding = _update_padding(padding, data_format)
-
-    input_shape = input.shape
-    filter_shape = [num_filters, num_filter_channels] + filter_size
-
-    def _get_default_param_initializer():
-        filter_elem_num = (
-            filter_size[0] * filter_size[1] * filter_size[2] * num_channels
-        )
-        if filter_elem_num <= 0:
-            raise ValueError(
-                "Invalid filter number, excepted number is larger than 0, but"
-                " received {}, please check the input shape and "
-                "filter size.".format(filter_elem_num)
-            )
-
-        std = (2.0 / filter_elem_num) ** 0.5
-        return Normal(0.0, std, 0)
-
-    filter_param = helper.create_parameter(
-        attr=helper.param_attr,
-        shape=filter_shape,
-        dtype=dtype,
-        default_initializer=_get_default_param_initializer(),
-    )
-
-    pre_bias = helper.create_variable_for_type_inference(dtype)
-
-    helper.append_op(
-        type=l_type,
-        inputs={
-            'Input': input,
-            'Filter': filter_param,
-        },
-        outputs={"Output": pre_bias},
-        attrs={
-            'strides': stride,
-            'paddings': padding,
-            'dilations': dilation,
-            'groups': groups,
-            'use_cudnn': use_cudnn,
-            'use_mkldnn': False,
-            "padding_algorithm": padding_algorithm,
-            "data_format": data_format,
-        },
-    )
-
-    if data_format == 'NCDHW':
-        pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
-    else:
-        pre_act = helper.append_bias_op(pre_bias, dim_start=4, dim_end=5)
-
-    return helper.append_activation(pre_act)
-
-
 @templatedoc()
 def pool2d(
     input,
diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_conv3d_op.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_conv3d_op.py
index 70e62f9eaf6c7643c7cb1b32b32639fd59875502..1142e6370345a3ed60ce4167588ac43b23be24ed 100644
--- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_conv3d_op.py
+++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_conv3d_op.py
@@ -15,6 +15,7 @@
 import unittest
 import numpy as np
 from inference_pass_test import InferencePassTest
+import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 from paddle.fluid.core import PassVersionChecker
@@ -29,7 +30,7 @@ class TensorRTSubgraphPassConv3dTest(InferencePassTest):
             data = fluid.data(
                 name="data", shape=[-1, 3, 6, 32, 32], dtype="float32"
             )
-            conv_out = fluid.layers.conv3d(
+            conv_out = paddle.static.nn.conv3d(
                 input=data,
                 num_filters=self.conv_num_filters,
                 filter_size=self.conv_filter_size,
@@ -113,7 +114,7 @@ class DynamicShapeTensorRTSubgraphPassConv3dTest(InferencePassTest):
             data = fluid.data(
                 name="data", shape=[-1, 6, -1, -1, -1], dtype="float32"
             )
-            conv_out = fluid.layers.conv3d(
+            conv_out = paddle.static.nn.conv3d(
                 input=data,
                 num_filters=self.conv_num_filters,
                 filter_size=self.conv_filter_size,
diff --git a/python/paddle/fluid/tests/unittests/npu/test_conv3d_op_npu.py b/python/paddle/fluid/tests/unittests/npu/test_conv3d_op_npu.py
index 81af05b156c86aba47d0a497707e2d36edcde32d..65ec9a489f4a532b4d79755aed10e688b9eee51f 100644
--- a/python/paddle/fluid/tests/unittests/npu/test_conv3d_op_npu.py
+++ b/python/paddle/fluid/tests/unittests/npu/test_conv3d_op_npu.py
@@ -364,7 +364,7 @@ class TestConv3DAPI(unittest.TestCase):
             dtype="float32",
         )
 
-        fluid.layers.conv3d(
+        paddle.static.nn.conv3d(
             input=input_NDHWC,
             num_filters=3,
             filter_size=[3, 3, 3],
@@ -375,7 +375,7 @@ class TestConv3DAPI(unittest.TestCase):
             data_format="NCDHW",
         )
 
-        fluid.layers.conv3d(
+        paddle.static.nn.conv3d(
             input=input_NCDHW,
             num_filters=3,
             filter_size=[3, 3, 3],
@@ -386,7 +386,7 @@ class TestConv3DAPI(unittest.TestCase):
             data_format="NCDHW",
         )
 
-        fluid.layers.conv3d(
+        paddle.static.nn.conv3d(
             input=input_NCDHW,
             num_filters=3,
             filter_size=[3, 3, 3],
@@ -397,7 +397,7 @@ class TestConv3DAPI(unittest.TestCase):
             data_format="NCDHW",
         )
 
-        fluid.layers.conv3d(
+        paddle.static.nn.conv3d(
             input=input_NDHWC,
             num_filters=3,
             filter_size=[3, 3, 3],
@@ -408,7 +408,7 @@ class TestConv3DAPI(unittest.TestCase):
             data_format="NDHWC",
         )
 
-        fluid.layers.conv3d(
+        paddle.static.nn.conv3d(
             input=input_NCDHW,
             num_filters=3,
             filter_size=[3, 3, 3],
@@ -419,7 +419,7 @@ class TestConv3DAPI(unittest.TestCase):
             data_format="NCDHW",
         )
 
-        fluid.layers.conv3d(
+        paddle.static.nn.conv3d(
             input=input_NCDHW,
             num_filters=3,
             filter_size=[3, 3, 3],
@@ -442,7 +442,7 @@ class TestConv3DAPI_Error(unittest.TestCase):
 
         # ValueError: cudnn
         def run_1():
-            fluid.layers.conv3d(
+            paddle.static.nn.conv3d(
                 input=input,
                 num_filters=3,
                 filter_size=3,
@@ -458,7 +458,7 @@ class TestConv3DAPI_Error(unittest.TestCase):
 
         # ValueError: data_format
         def run_2():
-            fluid.layers.conv3d(
+            paddle.static.nn.conv3d(
                 input=input,
                 num_filters=3,
                 filter_size=[3, 3, 3],
@@ -474,7 +474,7 @@ class TestConv3DAPI_Error(unittest.TestCase):
 
         # ValueError: padding
         def run_3():
-            fluid.layers.conv3d(
+            paddle.static.nn.conv3d(
                 input=input,
                 num_filters=3,
                 filter_size=3,
@@ -489,7 +489,7 @@ class TestConv3DAPI_Error(unittest.TestCase):
         self.assertRaises(ValueError, run_3)
 
         def run_4():
-            fluid.layers.conv3d(
+            paddle.static.nn.conv3d(
                 input=input,
                 num_filters=3,
                 filter_size=3,
@@ -504,7 +504,7 @@ class TestConv3DAPI_Error(unittest.TestCase):
         self.assertRaises(ValueError, run_4)
 
         def run_5():
-            fluid.layers.conv3d(
+            paddle.static.nn.conv3d(
                 input=input,
                 num_filters=3,
                 filter_size=0,
@@ -527,7 +527,7 @@ class TestConv3DAPI_Error(unittest.TestCase):
         )
 
         def run_6():
-            fluid.layers.conv3d(
+            paddle.static.nn.conv3d(
                 input=x,
                 num_filters=3,
                 filter_size=3,
@@ -543,7 +543,7 @@ class TestConv3DAPI_Error(unittest.TestCase):
 
         # ValueError: groups
         def run_7():
-            fluid.layers.conv3d(
+            paddle.static.nn.conv3d(
                 input=input,
                 num_filters=3,
                 filter_size=3,
@@ -559,7 +559,7 @@ class TestConv3DAPI_Error(unittest.TestCase):
 
         # ValueError: filter num
         def run_8():
-            fluid.layers.conv3d(
+            paddle.static.nn.conv3d(
                 input=input,
                 num_filters=0,
                 filter_size=0,
diff --git a/python/paddle/fluid/tests/unittests/test_conv3d_layer.py b/python/paddle/fluid/tests/unittests/test_conv3d_layer.py
index a8ab4d65961f106107dd97820b36246c27a82c1d..fd7dc6bb630291304dd6e5fd8ee3862d75d91fc5 100644
--- a/python/paddle/fluid/tests/unittests/test_conv3d_layer.py
+++ b/python/paddle/fluid/tests/unittests/test_conv3d_layer.py
@@ -101,7 +101,7 @@ class Conv3DTestCase(unittest.TestCase):
                     bias_attr = False
                 else:
                     bias_attr = I.NumpyArrayInitializer(self.bias)
-                y_var = fluid.layers.conv3d(
+                y_var = paddle.static.nn.conv3d(
                     x_var,
                     self.num_filters,
                     self.filter_size,
diff --git a/python/paddle/fluid/tests/unittests/test_conv3d_op.py b/python/paddle/fluid/tests/unittests/test_conv3d_op.py
index 54a3621e0ba72e1611558b30339b9df279b1b0e3..d9cd69e4d550ce57f195df9da2a0410937fc56b8 100644
--- a/python/paddle/fluid/tests/unittests/test_conv3d_op.py
+++ b/python/paddle/fluid/tests/unittests/test_conv3d_op.py
@@ -878,7 +878,7 @@ class TestConv3DAPI(unittest.TestCase):
             dtype="float32",
         )
 
-        fluid.layers.conv3d(
+        paddle.static.nn.conv3d(
             input=input_NDHWC,
             num_filters=3,
             filter_size=[3, 3, 3],
@@ -889,7 +889,7 @@ class TestConv3DAPI(unittest.TestCase):
             data_format="NCDHW",
         )
 
-        fluid.layers.conv3d(
+        paddle.static.nn.conv3d(
             input=input_NCDHW,
             num_filters=3,
             filter_size=[3, 3, 3],
@@ -900,7 +900,7 @@ class TestConv3DAPI(unittest.TestCase):
             data_format="NCDHW",
         )
 
-        fluid.layers.conv3d(
+        paddle.static.nn.conv3d(
             input=input_NCDHW,
             num_filters=3,
             filter_size=[3, 3, 3],
@@ -911,7 +911,7 @@ class TestConv3DAPI(unittest.TestCase):
             data_format="NCDHW",
         )
 
-        fluid.layers.conv3d(
+        paddle.static.nn.conv3d(
             input=input_NDHWC,
             num_filters=3,
             filter_size=[3, 3, 3],
@@ -922,7 +922,7 @@ class TestConv3DAPI(unittest.TestCase):
             data_format="NDHWC",
         )
 
-        fluid.layers.conv3d(
+        paddle.static.nn.conv3d(
             input=input_NCDHW,
             num_filters=3,
             filter_size=[3, 3, 3],
@@ -933,7 +933,7 @@ class TestConv3DAPI(unittest.TestCase):
             data_format="NCDHW",
         )
 
-        fluid.layers.conv3d(
+        paddle.static.nn.conv3d(
             input=input_NCDHW,
             num_filters=3,
             filter_size=[3, 3, 3],
@@ -956,7 +956,7 @@ class TestConv3DAPI_Error(unittest.TestCase):
 
         # ValueError: cudnn
         def run_1():
-            fluid.layers.conv3d(
+            paddle.static.nn.conv3d(
                 input=input,
                 num_filters=3,
                 filter_size=3,
@@ -972,7 +972,7 @@ class TestConv3DAPI_Error(unittest.TestCase):
 
         # ValueError: data_format
         def run_2():
-            fluid.layers.conv3d(
+            paddle.static.nn.conv3d(
                 input=input,
                 num_filters=3,
                 filter_size=[3, 3, 3],
@@ -988,7 +988,7 @@ class TestConv3DAPI_Error(unittest.TestCase):
 
         # ValueError: padding
         def run_3():
-            fluid.layers.conv3d(
+            paddle.static.nn.conv3d(
                 input=input,
                 num_filters=3,
                 filter_size=3,
@@ -1003,7 +1003,7 @@ class TestConv3DAPI_Error(unittest.TestCase):
         self.assertRaises(ValueError, run_3)
 
         def run_4():
-            fluid.layers.conv3d(
+            paddle.static.nn.conv3d(
                 input=input,
                 num_filters=3,
                 filter_size=3,
@@ -1018,7 +1018,7 @@ class TestConv3DAPI_Error(unittest.TestCase):
         self.assertRaises(ValueError, run_4)
 
         def run_5():
-            fluid.layers.conv3d(
+            paddle.static.nn.conv3d(
                 input=input,
                 num_filters=3,
                 filter_size=0,
@@ -1041,7 +1041,7 @@ class TestConv3DAPI_Error(unittest.TestCase):
         )
 
         def run_6():
-            fluid.layers.conv3d(
+            paddle.static.nn.conv3d(
                 input=x,
                 num_filters=3,
                 filter_size=3,
@@ -1057,7 +1057,7 @@ class TestConv3DAPI_Error(unittest.TestCase):
 
         # ValueError: groups
         def run_7():
-            fluid.layers.conv3d(
+            paddle.static.nn.conv3d(
                 input=input,
                 num_filters=3,
                 filter_size=3,
@@ -1073,7 +1073,7 @@ class TestConv3DAPI_Error(unittest.TestCase):
 
         # ValueError: filter num
         def run_8():
-            fluid.layers.conv3d(
+            paddle.static.nn.conv3d(
                 input=input,
                 num_filters=0,
                 filter_size=0,
diff --git a/python/paddle/fluid/tests/unittests/test_conv_nn_grad.py b/python/paddle/fluid/tests/unittests/test_conv_nn_grad.py
index b09a86f5bfa0b7abc0ab1c045d21d5285dbc10c1..19b9d6fdee97c47d794314ad8d903f3159f6c262 100644
--- a/python/paddle/fluid/tests/unittests/test_conv_nn_grad.py
+++ b/python/paddle/fluid/tests/unittests/test_conv_nn_grad.py
@@ -110,7 +110,7 @@ class TestConv3DDoubleGradCheck(unittest.TestCase):
         eps = 0.005
         dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64
         x = layers.data('x', shape, False, dtype)
-        y = layers.conv3d(x, 2, 1, bias_attr=False)
+        y = paddle.static.nn.conv3d(x, 2, 1, bias_attr=False)
         x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
 
         w = fluid.default_main_program().global_block().all_parameters()
@@ -137,7 +137,7 @@ class TestConv3DDoubleGradCheckTest1(unittest.TestCase):
         eps = 0.005
         dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64
         x = layers.data('x', shape, False, dtype)
-        y = layers.conv3d(x, 2, 1, padding=1, bias_attr=False)
+        y = paddle.static.nn.conv3d(x, 2, 1, padding=1, bias_attr=False)
         x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
 
         w = fluid.default_main_program().global_block().all_parameters()
@@ -332,7 +332,7 @@ class TestConv3DDoubleGradCheck_AsyPadding(unittest.TestCase):
         eps = 0.005
         dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64
         x = layers.data('x', shape, False, dtype)
-        y = layers.conv3d(
+        y = paddle.static.nn.conv3d(
             input=x,
             num_filters=2,
             filter_size=1,
@@ -365,7 +365,7 @@ class TestConv3DoubleGradCheck_PaddingSAME(unittest.TestCase):
         eps = 0.005
         dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64
         x = layers.data('x', shape, False, dtype)
-        y = layers.conv3d(
+        y = paddle.static.nn.conv3d(
             input=x,
             num_filters=2,
             filter_size=1,
@@ -399,7 +399,7 @@ class TestConv3DoubleGradCheck_PaddingVALID(unittest.TestCase):
         eps = 0.005
         dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64
         x = layers.data('x', shape, False, dtype)
-        y = layers.conv3d(
+        y = paddle.static.nn.conv3d(
             input=x,
             num_filters=2,
             filter_size=1,
@@ -432,7 +432,7 @@ class TestConv3DDoubleGradCheck_ChannelLast(unittest.TestCase):
         eps = 0.005
         dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64
         x = layers.data('x', shape, False, dtype)
-        y = layers.conv3d(
+        y = paddle.static.nn.conv3d(
             input=x,
             num_filters=2,
             filter_size=1,
@@ -467,7 +467,7 @@ class TestConv3DDoubleGradCheck_ChannelLast_AsyPadding(unittest.TestCase):
         eps = 0.005
         dtype = np.float32 if fluid.core.is_compiled_with_rocm() else np.float64
         x = layers.data('x', shape, False, dtype)
-        y = layers.conv3d(
+        y = paddle.static.nn.conv3d(
             input=x,
             num_filters=2,
             filter_size=1,
diff --git a/python/paddle/fluid/tests/unittests/test_functional_conv3d.py b/python/paddle/fluid/tests/unittests/test_functional_conv3d.py
index d2d7074a4b1a3343e5c44aaf0566c24f71757ef2..71123cb51e74b8e1d7fd9fe1a9e1d39d395940ad 100644
--- a/python/paddle/fluid/tests/unittests/test_functional_conv3d.py
+++ b/python/paddle/fluid/tests/unittests/test_functional_conv3d.py
@@ -87,7 +87,7 @@ class TestFunctionalConv3D(TestCase):
                         (-1, self.in_channels, -1, -1, -1),
                         dtype=self.dtype,
                     )
-                y = fluid.layers.conv3d(
+                y = paddle.static.nn.conv3d(
                     x,
                     self.out_channels,
                     self.filter_shape,
@@ -480,7 +480,7 @@ class TestFunctionalConv3DErrorCase11(TestCase):
         with fluid.unique_name.guard():
             with fluid.program_guard(main, start):
                 x = fluid.data("input", self.input.shape, dtype=paddle.float32)
-                y = fluid.layers.conv3d(
+                y = paddle.static.nn.conv3d(
                     x,
                     self.num_filters,
                     self.filter_size,
diff --git a/python/paddle/fluid/tests/unittests/test_imperative_load_static_param.py b/python/paddle/fluid/tests/unittests/test_imperative_load_static_param.py
index c96fe97fac87fe954ba7f6094b210cad38eab940..7147e924a191a3d5b090ee6bf82381568e15afa3 100644
--- a/python/paddle/fluid/tests/unittests/test_imperative_load_static_param.py
+++ b/python/paddle/fluid/tests/unittests/test_imperative_load_static_param.py
@@ -51,10 +51,10 @@ class TestDygraphLoadStatic(unittest.TestCase):
         conv3d_in = fluid.data(
             name='conv3d_in', shape=[None, 3, 12, 32, 32], dtype='float32'
         )
-        conv3d_out_1 = fluid.layers.conv3d(
+        conv3d_out_1 = paddle.static.nn.conv3d(
             input=conv3d_in, num_filters=2, filter_size=3, act="relu"
         )
-        conv3d_out_2 = fluid.layers.conv3d(
+        conv3d_out_2 = paddle.static.nn.conv3d(
             input=conv3d_in, num_filters=2, filter_size=3, act="relu"
         )
 
diff --git a/python/paddle/fluid/tests/unittests/test_layers.py b/python/paddle/fluid/tests/unittests/test_layers.py
index 937c0272220016d3bcbf09df702d9a7fbc502233..8301a02a2e21dede401ba4b515880572f146d565 100644
--- a/python/paddle/fluid/tests/unittests/test_layers.py
+++ b/python/paddle/fluid/tests/unittests/test_layers.py
@@ -1688,7 +1688,9 @@ class TestLayer(LayerTest):
             images = layers.data(
                 name='pixel', shape=[3, 6, 6, 6], dtype='float32'
             )
-            ret = layers.conv3d(input=images, num_filters=3, filter_size=2)
+            ret = paddle.static.nn.conv3d(
+                input=images, num_filters=3, filter_size=2
+            )
             static_ret = self.get_static_graph_result(
                 feed={'pixel': np.ones([2, 3, 6, 6, 6], dtype='float32')},
                 fetch_list=[ret],
diff --git a/python/paddle/fluid/tests/unittests/xpu/test_conv3d_op_xpu.py b/python/paddle/fluid/tests/unittests/xpu/test_conv3d_op_xpu.py
index f949d7eeef87bc4aa0a005e381d204014b5d8e3b..3b6b1a4363f16d0f7821c403549abd7fc64371fa 100644
--- a/python/paddle/fluid/tests/unittests/xpu/test_conv3d_op_xpu.py
+++ b/python/paddle/fluid/tests/unittests/xpu/test_conv3d_op_xpu.py
@@ -525,7 +525,7 @@ class TestConv3DAPI(unittest.TestCase):
             dtype="float32",
         )
 
-        fluid.layers.conv3d(
+        paddle.static.nn.conv3d(
             input=input_NDHWC,
             num_filters=3,
             filter_size=[3, 3, 3],
@@ -536,7 +536,7 @@ class TestConv3DAPI(unittest.TestCase):
             data_format="NCDHW",
         )
 
-        fluid.layers.conv3d(
+        paddle.static.nn.conv3d(
             input=input_NCDHW,
             num_filters=3,
             filter_size=[3, 3, 3],
@@ -547,7 +547,7 @@ class TestConv3DAPI(unittest.TestCase):
             data_format="NCDHW",
         )
 
-        fluid.layers.conv3d(
+        paddle.static.nn.conv3d(
             input=input_NCDHW,
             num_filters=3,
             filter_size=[3, 3, 3],
@@ -558,7 +558,7 @@ class TestConv3DAPI(unittest.TestCase):
             data_format="NCDHW",
         )
 
-        fluid.layers.conv3d(
+        paddle.static.nn.conv3d(
             input=input_NDHWC,
             num_filters=3,
             filter_size=[3, 3, 3],
@@ -569,7 +569,7 @@ class TestConv3DAPI(unittest.TestCase):
             data_format="NDHWC",
         )
 
-        fluid.layers.conv3d(
+        paddle.static.nn.conv3d(
             input=input_NCDHW,
             num_filters=3,
             filter_size=[3, 3, 3],
@@ -580,7 +580,7 @@ class TestConv3DAPI(unittest.TestCase):
             data_format="NCDHW",
         )
 
-        fluid.layers.conv3d(
+        paddle.static.nn.conv3d(
             input=input_NCDHW,
             num_filters=3,
             filter_size=[3, 3, 3],
@@ -603,7 +603,7 @@ class TestConv3DAPI_Error(unittest.TestCase):
 
         # ValueError: cudnn
         def run_1():
-            fluid.layers.conv3d(
+            paddle.static.nn.conv3d(
                 input=input,
                 num_filters=3,
                 filter_size=3,
@@ -619,7 +619,7 @@ class TestConv3DAPI_Error(unittest.TestCase):
 
         # ValueError: data_format
         def run_2():
-            fluid.layers.conv3d(
+            paddle.static.nn.conv3d(
                 input=input,
                 num_filters=3,
                 filter_size=[3, 3, 3],
@@ -635,7 +635,7 @@ class TestConv3DAPI_Error(unittest.TestCase):
 
         # ValueError: padding
         def run_3():
-            fluid.layers.conv3d(
+            paddle.static.nn.conv3d(
                 input=input,
                 num_filters=3,
                 filter_size=3,
@@ -650,7 +650,7 @@ class TestConv3DAPI_Error(unittest.TestCase):
         self.assertRaises(ValueError, run_3)
 
         def run_4():
-            fluid.layers.conv3d(
+            paddle.static.nn.conv3d(
                 input=input,
                 num_filters=3,
                 filter_size=3,
@@ -665,7 +665,7 @@ class TestConv3DAPI_Error(unittest.TestCase):
         self.assertRaises(ValueError, run_4)
 
         def run_5():
-            fluid.layers.conv3d(
+            paddle.static.nn.conv3d(
                 input=input,
                 num_filters=3,
                 filter_size=0,
@@ -688,7 +688,7 @@ class TestConv3DAPI_Error(unittest.TestCase):
         )
 
         def run_6():
-            fluid.layers.conv3d(
+            paddle.static.nn.conv3d(
                 input=x,
                 num_filters=3,
                 filter_size=3,
@@ -704,7 +704,7 @@ class TestConv3DAPI_Error(unittest.TestCase):
 
         # ValueError: groups
         def run_7():
-            fluid.layers.conv3d(
+            paddle.static.nn.conv3d(
                 input=input,
                 num_filters=3,
                 filter_size=3,
@@ -720,7 +720,7 @@ class TestConv3DAPI_Error(unittest.TestCase):
 
         # ValueError: filter num
         def run_8():
-            fluid.layers.conv3d(
+            paddle.static.nn.conv3d(
                 input=input,
                 num_filters=0,
                 filter_size=0,
diff --git a/python/paddle/static/nn/__init__.py b/python/paddle/static/nn/__init__.py
index 7f695a48219d45821b6648e4906e909da1f955e8..ef966ecd98ce4a3c8fccf0f7bf8b237faac73916 100755
--- a/python/paddle/static/nn/__init__.py
+++ b/python/paddle/static/nn/__init__.py
@@ -14,6 +14,7 @@
 
 from .common import fc  # noqa: F401
 from .common import deform_conv2d  # noqa: F401
+from .common import conv3d  # noqa: F401
 from .common import conv2d_transpose  # noqa: F401
 from .common import conv3d_transpose  # noqa: F401
 
@@ -22,7 +23,6 @@ from ...fluid.layers import bilinear_tensor_product  # noqa: F401
 from ...fluid.layers import case  # noqa: F401
 from ...fluid.layers import cond  # noqa: F401
 from ...fluid.layers import conv2d  # noqa: F401
-from ...fluid.layers import conv3d  # noqa: F401
 from ...fluid.layers import create_parameter  # noqa: F401
 from ...fluid.layers import crf_decoding  # noqa: F401
 from ...fluid.layers import data_norm  # noqa: F401
diff --git a/python/paddle/static/nn/common.py b/python/paddle/static/nn/common.py
index aee2009edd28f10736e42623b379aba2fb19086b..a7470f2fb2e03f9ee980b9d219fc36f8e396a0a2 100755
--- a/python/paddle/static/nn/common.py
+++ b/python/paddle/static/nn/common.py
@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import paddle
+from paddle.fluid.initializer import Normal
 from paddle.fluid.framework import static_only, Variable, _non_static_mode
 
 from paddle.fluid.data_feeder import check_dtype
@@ -176,6 +177,314 @@ def fc(
     )
 
 
+def conv3d(
+    input,
+    num_filters,
+    filter_size,
+    stride=1,
+    padding=0,
+    dilation=1,
+    groups=None,
+    param_attr=None,
+    bias_attr=None,
+    use_cudnn=True,
+    act=None,
+    name=None,
+    data_format="NCDHW",
+):
+    r"""
+    :api_attr: Static Graph
+
+    The convolution3D layer calculates the output based on the input, filter
+    and strides, paddings, dilations, groups parameters. Input(Input) and
+    Output(Output) are in NCDHW or NDHWC format. Where N is batch size C is the number of
+    channels, D is the depth of the feature, H is the height of the feature,
+    and W is the width of the feature. Convlution3D is similar with Convlution2D
+    but adds one dimension(depth). If bias attribution and activation type are
+    provided, bias is added to the output of the convolution, and the
+    corresponding activation function is applied to the final result.
+
+    For each input :math:`X`, the equation is:
+
+    .. math::
+
+        Out = \sigma (W \\ast X + b)
+
+    In the above equation:
+
+    * :math:`X`: Input value, a tensor with NCDHW or NDHWC format.
+    * :math:`W`: Filter value, a tensor with MCDHW format.
+    * :math:`\\ast`: Convolution operation.
+    * :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
+    * :math:`\\sigma`: Activation function.
+    * :math:`Out`: Output value, the shape of :math:`Out` and :math:`X` may be different.
+
+    Example:
+
+        - Input:
+
+          Input shape: :math:`(N, C_{in}, D_{in}, H_{in}, W_{in})`
+
+          Filter shape: :math:`(C_{out}, C_{in}, D_f, H_f, W_f)`
+
+        - Output:
+          Output shape: :math:`(N, C_{out}, D_{out}, H_{out}, W_{out})`
+
+        Where
+
+        .. math::
+
+            D_{out}&= \\frac{(D_{in} + 2 * paddings[0] - (dilations[0] * (D_f - 1) + 1))}{strides[0]} + 1 \\\\
+            H_{out}&= \\frac{(H_{in} + 2 * paddings[1] - (dilations[1] * (H_f - 1) + 1))}{strides[1]} + 1 \\\\
+            W_{out}&= \\frac{(W_{in} + 2 * paddings[2] - (dilations[2] * (W_f - 1) + 1))}{strides[2]} + 1
+
+    Args:
+        input (Tensor): The input is 5-D Tensor with shape [N, C, D, H, W], the data
+            type of input is float16 or float32 or float64.
+        num_filters(int): The number of filter. It is as same as the output
+            image channel.
+        filter_size (int|tuple): The filter size. If filter_size is a tuple,
+            it must contain three integers, (filter_size_depth, filter_size_height,
+            filter_size_width). Otherwise, filter_size_depth = filter_size_height = \
+            filter_size_width = filter_size.
+        stride (int|tuple): The stride size. It means the stride in convolution. If stride is a
+            tuple, it must contain three integers, (stride_depth, stride_height, stride_width).
+            Otherwise, stride_depth = stride_height = stride_width = stride. Default: stride = 1.
+        padding (string|int|list|tuple): The padding size. It means the number of zero-paddings
+            on both sides for each dimension. If `padding` is a string, either 'VALID' or
+            'SAME' which is the padding algorithm. If padding size is a tuple or list,
+            it could be in three forms: `[pad_depth, pad_height, pad_width]` or
+            `[pad_depth_front, pad_depth_back, pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`,
+            and when `data_format` is `"NCDHW"`, `pool_padding` can be in the form
+            `[[0,0], [0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]`.
+            when `data_format` is `"NDHWC"`, `pool_padding` can be in the form
+            `[[0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
+            Default: padding = 0.
+        dilation (int|tuple): The dilation size. It means the spacing between the kernel points.
+            If dilation is a tuple, it must contain three integers, (dilation_depth, dilation_height,
+            dilation_width). Otherwise, dilation_depth = dilation_height = dilation_width = dilation.
+            Default: dilation = 1.
+        groups (int): The groups number of the Conv3d Layer. According to grouped
+            convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
+            the first half of the filters is only connected to the first half
+            of the input channels, while the second half of the filters is only
+            connected to the second half of the input channels. Default: groups=1
+        param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights
+            of conv3d. If it is set to None or one attribute of ParamAttr, conv3d
+            will create ParamAttr as param_attr. If it is set to None, the parameter
+            is initialized with :math:`Normal(0.0, std)`, and the :math:`std` is
+            :math:`(\\frac{2.0 }{filter\_elem\_num})^{0.5}`. Default: None.
+        bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of conv3d.
+            If it is set to False, no bias will be added to the output units.
+            If it is set to None or one attribute of ParamAttr, conv3d
+            will create ParamAttr as bias_attr. If the Initializer of the bias_attr
+            is not set, the bias is initialized zero. Default: None.
+        use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
+            library is installed. Default: True
+        act (str): Activation type, if it is set to None, activation is not appended.
+            Default: None.
+        name(str|None): For detailed information, please refer
+           to :ref:`api_guide_Name`. Usually name is no need to set and
+           None by default.
+        data_format (str, optional): Specify the data format of the input, and the data format of the output
+            will be consistent with that of the input. An optional string from: `"NCHW"`, `"NHWC"`.
+            The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
+            `[batch_size, input_channels, input_height, input_width]`.
+
+    Returns:
+        A Variable holding Tensor representing the conv3d, whose data type is
+        the same with input. If act is None, the tensor variable storing the
+        convolution result, and if act is not None, the tensor variable storing
+        convolution and non-linearity activation result.
+
+    Raises:
+        ValueError: If the type of `use_cudnn` is not bool.
+        ValueError: If `data_format` is not "NCDHW" or "NDHWC".
+        ValueError: If the channel dimmention of the input is less than or equal to zero.
+        ValueError: If `padding` is a string, but not "SAME" or "VALID".
+        ValueError: If `padding` is a tuple, but the element corresponding to the input's batch size is not 0
+            or the element corresponding to the input's channel is not 0.
+        ShapeError: If the input is not 5-D Tensor.
+        ShapeError: If the input's dimension size and filter's dimension size not equal.
+        ShapeError: If the dimension size of input minus the size of `stride` is not 2.
+        ShapeError: If the number of input channels is not equal to filter's channels * groups.
+        ShapeError: If the number of output channels is not be divided by groups.
+
+    Examples:
+        .. code-block:: python
+
+          import paddle
+          import numpy as np
+
+          paddle.enable_static()
+          data = paddle.static.data(name='data', shape=[None, 3, 12, 32, 32], dtype='float32')
+          param_attr = paddle.framework.ParamAttr(name='conv3d.weight', initializer=paddle.nn.initializer.XavierNormal(), learning_rate=0.001)
+          res = paddle.static.nn.conv3d(input=data, num_filters=2, filter_size=3, act="relu", param_attr=param_attr)
+          place = paddle.CPUPlace()
+          exe = paddle.static.Executor(place)
+          exe.run(paddle.static.default_startup_program())
+          x = np.random.rand(1, 3, 12, 32, 32).astype("float32")
+          output = exe.run(feed={"data": x}, fetch_list=[res])
+          print(output)
+    """
+
+    l_type = 'conv3d'
+    assert param_attr is not False, "param_attr should not be False here."
+    helper = LayerHelper(l_type, **locals())
+    dtype = helper.input_dtype()
+
+    if not isinstance(use_cudnn, bool):
+        raise ValueError(
+            "Attr(use_cudnn) should be True or False. Received "
+            "Attr(use_cudnn): %s. " % str(use_cudnn)
+        )
+
+    if data_format not in ["NCDHW", "NDHWC"]:
+        raise ValueError(
+            "Attr(data_format) should be 'NCDHW' or 'NDHWC'. Received "
+            "Attr(data_format): %s." % str(data_format)
+        )
+
+    channel_last = data_format == "NDHWC"
+    if len(input.shape) != 5:
+        raise ValueError(
+            "Input should be 5D tensor, but received input with the shape of {}".format(
+                input.shape
+            )
+        )
+    num_channels = input.shape[4] if channel_last else input.shape[1]
+    if num_channels < 0:
+        raise ValueError(
+            "The channel dimmention of the input(%s) should be defined. "
+            "Received: %s." % (str(input.shape), str(num_channels))
+        )
+
+    if groups is None:
+        num_filter_channels = num_channels
+    elif groups <= 0:
+        raise ValueError(
+            "the groups of conv3d should be greater than 0. Received groups: {}".format(
+                groups
+            )
+        )
+    else:
+        if num_channels % groups != 0:
+            raise ValueError(
+                "The number of input channels must be divisible by Attr(groups). "
+                "Received: number of channels(%s), groups(%s)."
+                % (str(num_channels), str(groups))
+            )
+        num_filter_channels = num_channels // groups
+
+    filter_size = utils.convert_to_list(filter_size, 3, 'filter_size')
+    stride = utils.convert_to_list(stride, 3, 'stride')
+    dilation = utils.convert_to_list(dilation, 3, 'dilation')
+
+    def _update_padding(padding, data_format):
+        def is_list_or_tuple(ele):
+            if isinstance(ele, list) or isinstance(ele, tuple):
+                return True
+            return False
+
+        if is_list_or_tuple(padding) and len(padding) == 5:
+            if is_list_or_tuple(padding[0]) and (data_format == "NCDHW"):
+                if not (padding[0] == [0, 0] and padding[1] == [0, 0]):
+                    raise ValueError(
+                        "Non-zero padding(%s) in the batch or channel dimensions "
+                        "is not supported." % str(padding)
+                    )
+                padding = padding[2:5]
+                padding = [ele for a_list in padding for ele in a_list]
+            elif is_list_or_tuple(padding[0]) and (data_format == "NDHWC"):
+                if not (padding[0] == [0, 0] and padding[4] == [0, 0]):
+                    raise ValueError(
+                        "Non-zero padding(%s) in the batch or channel dimensions "
+                        "is not supported." % str(padding)
+                    )
+                padding = padding[1:4]
+                padding = [ele for a_list in padding for ele in a_list]
+            padding = utils.convert_to_list(padding, 6, 'padding')
+            if utils._is_symmetric_padding(padding, 3):
+                padding = [padding[0], padding[2], padding[4]]
+        elif is_list_or_tuple(padding) and len(padding) == 6:
+            padding = utils.convert_to_list(padding, 6, 'padding')
+            if utils._is_symmetric_padding(padding, 3):
+                padding = [padding[0], padding[2], padding[4]]
+        else:
+            padding = utils.convert_to_list(padding, 3, 'padding')
+
+        return padding
+
+    padding_algorithm = "EXPLICIT"
+    if isinstance(padding, str):
+        padding = padding.upper()
+        if padding not in ["SAME", "VALID"]:
+            raise ValueError(
+                "Unknown padding: '%s'. It can only be 'SAME' or 'VALID'."
+                % str(padding)
+            )
+        if padding == "VALID":
+            padding_algorithm = "VALID"
+            padding = [0, 0, 0]
+        elif padding == "SAME":
+            padding_algorithm = "SAME"
+            padding = [0, 0, 0]
+
+    padding = _update_padding(padding, data_format)
+
+    input_shape = input.shape
+    filter_shape = [num_filters, num_filter_channels] + filter_size
+
+    def _get_default_param_initializer():
+        filter_elem_num = (
+            filter_size[0] * filter_size[1] * filter_size[2] * num_channels
+        )
+        if filter_elem_num <= 0:
+            raise ValueError(
+                "Invalid filter number, excepted number is larger than 0, but"
+                " received {}, please check the input shape and "
+                "filter size.".format(filter_elem_num)
+            )
+
+        std = (2.0 / filter_elem_num) ** 0.5
+        return Normal(0.0, std, 0)
+
+    filter_param = helper.create_parameter(
+        attr=helper.param_attr,
+        shape=filter_shape,
+        dtype=dtype,
+        default_initializer=_get_default_param_initializer(),
+    )
+
+    pre_bias = helper.create_variable_for_type_inference(dtype)
+
+    helper.append_op(
+        type=l_type,
+        inputs={
+            'Input': input,
+            'Filter': filter_param,
+        },
+        outputs={"Output": pre_bias},
+        attrs={
+            'strides': stride,
+            'paddings': padding,
+            'dilations': dilation,
+            'groups': groups,
+            'use_cudnn': use_cudnn,
+            'use_mkldnn': False,
+            "padding_algorithm": padding_algorithm,
+            "data_format": data_format,
+        },
+    )
+
+    if data_format == 'NCDHW':
+        pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
+    else:
+        pre_act = helper.append_bias_op(pre_bias, dim_start=4, dim_end=5)
+
+    return helper.append_activation(pre_act)
+
+
 def conv2d_transpose(
     input,
     num_filters,