Clean Fluid APIs in padde.fluid.layers.nn (#48908)

* Fix bug of reduce_sum op. When input.numel() > INT32_MAX, its result
is wrong.

* Remove climits.

* Clean Fluid APIs in python/paddle/fluid/layers/nn.py,
migrate spectral_norm and row_conv APIs, and remove ont_hot.
Including following files:
1. python/paddle/fluid/layers/nn.py
2. python/paddle/fluid/tests/unittests/collective/fleet/parallel_dygraph_transformer.py
3. python/paddle/fluid/tests/unittests/dist_transformer.py
4. python/paddle/fluid/tests/unittests/dygraph_to_static/transformer_dygraph_model.py
5. python/paddle/fluid/tests/unittests/ipu/test_one_hot_op_ipu.py
6. python/paddle/fluid/tests/unittests/test_imperative_auto_prune.py
7. python/paddle/fluid/tests/unittests/test_imperative_load_static_param.py
8. python/paddle/fluid/tests/unittests/test_layers.py
9. python/paddle/fluid/tests/unittests/test_one_hot_op.py
10. python/paddle/fluid/tests/unittests/test_row_conv_op.py
11. python/paddle/fluid/tests/unittests/test_runtime_and_compiletime_exception.py
12. python/paddle/fluid/tests/unittests/test_spectral_norm_op.py
13. python/paddle/static/nn/__init__.py
14. python/paddle/static/nn/common.py

* Polish code.

* Fix some bugs.

* Remove useless unittest.

* Fix some bug.

* Polish example code.

* Fix some bugs.

* Fix some bugs.

python/paddle/fluid/layers/nn.py

@@ -65,9 +65,6 @@ from collections.abc import Iterable
 __all__ = [
     'fc',
     'embedding',
-    'row_conv',
-    'spectral_norm',
-    'one_hot',
     'autoincreased_step_counter',
     'clip',
     'clip_by_norm',
@@ -741,130 +738,6 @@ def _pull_box_sparse(
     return outs
 
 
-@templatedoc()
-def spectral_norm(weight, dim=0, power_iters=1, eps=1e-12, name=None):
-    r"""
-    :api_attr: Static Graph
-
-    **Spectral Normalization Layer**
-
-    This operation calculates the spectral normalization value of weight parameters of
-    fc, conv1d, conv2d, conv3d layers which should be 2-D, 3-D, 4-D, 5-D
-    Parameters. Output tensor will be in same shape with input tensor.
-    Calculations are showed as follows.
-
-    Step 1:
-    Generate vector U in shape of [H], and V in shape of [W].
-    While H is the :attr:`dim` th dimension of the input weights,
-    and W is the product result of remaining dimensions.
-
-    Step 2:
-    :attr:`power_iters` should be a positive integer, do following
-    calculations with U and V for :attr:`power_iters` rounds. Calculations
-    as follows:
-
-    .. math::
-
-        \mathbf{v} := \\frac{\mathbf{W}^{T} \mathbf{u}}{\|\mathbf{W}^{T} \mathbf{u}\|_2}
-
-        \mathbf{u} := \\frac{\mathbf{W}^{T} \mathbf{v}}{\|\mathbf{W}^{T} \mathbf{v}\|_2}
-
-    Step 3:
-    Calculate :math:`\sigma(\mathbf{W})` and normalize weight values.
-
-    .. math::
-
-        \sigma(\mathbf{W}) = \mathbf{u}^{T} \mathbf{W} \mathbf{v}
-
-        \mathbf{W} = \\frac{\mathbf{W}}{\sigma(\mathbf{W})}
-
-
-    Refer to `Spectral Normalization <https://arxiv.org/abs/1802.05957>`_ .
-
-    Args:
-        weight(Tensor): ${weight_comment}
-        dim(int): ${dim_comment}
-        power_iters(int): ${power_iters_comment}
-        eps(float): ${eps_comment}
-        name(str, optional): For detailed information, please refer
-                             to :ref:`api_guide_Name`. Usually name is no need to set and
-                             None by default.
-
-    Returns:
-        Tensor: A tensor of weight parameters after spectral normalization.
-                  The data type and shape is same as input tensor.
-
-    Examples:
-       .. code-block:: python
-
-            import paddle
-
-            paddle.enable_static()
-            weight = paddle.static.data(name='weight', shape=[2, 8, 32, 32], dtype='float32')
-            x = paddle.static.nn.spectral_norm(weight=weight, dim=1, power_iters=2)
-            print(x.shape) # [2, 8, 32, 32]
-    """
-    helper = LayerHelper('spectral_norm', **locals())
-    check_variable_and_dtype(
-        weight, 'weight', ['float32', 'float64'], 'spectral_norm'
-    )
-    check_type(dim, 'dim', int, 'spectral_norm')
-    check_type(power_iters, 'power_iters', int, 'spectral_norm')
-    check_type(eps, 'eps', float, 'spectral_norm')
-    dtype = weight.dtype
-
-    # create intput and parameters
-    input_shape = weight.shape
-    assert weight.numel() > 0, "Any dimension of input cannot be equal to 0."
-    assert dim < len(input_shape), (
-        "The input `dim` should be less than the "
-        "rank of `weight`, but received dim="
-        "{}".format(dim)
-    )
-    h = input_shape[dim]
-    w = np.prod(input_shape) // h
-
-    u = helper.create_parameter(
-        attr=ParamAttr(),
-        shape=[h],
-        dtype=dtype,
-        default_initializer=Normal(0.0, 1.0),
-    )
-    u.stop_gradient = True
-    v = helper.create_parameter(
-        attr=ParamAttr(),
-        shape=[w],
-        dtype=dtype,
-        default_initializer=Normal(0.0, 1.0),
-    )
-    v.stop_gradient = True
-
-    if in_dygraph_mode():
-        return _C_ops.spectral_norm(weight, u, v, dim, power_iters, eps)
-
-    inputs = {'Weight': weight}
-    inputs['U'] = u
-    inputs['V'] = v
-
-    # create output
-    out = helper.create_variable(dtype=dtype)
-
-    helper.append_op(
-        type="spectral_norm",
-        inputs=inputs,
-        outputs={
-            "Out": out,
-        },
-        attrs={
-            "dim": dim,
-            "power_iters": power_iters,
-            "eps": eps,
-        },
-    )
-
-    return out
-
-
 def reduce_sum(input, dim=None, keep_dim=False, name=None):
     """
 
@@ -943,171 +816,6 @@ def reduce_sum(input, dim=None, keep_dim=False, name=None):
     return out
 
 
-@templatedoc()
-def row_conv(input, future_context_size, param_attr=None, act=None):
-    """
-    :api_attr: Static Graph
-
-    ${comment}
-
-    Args:
-        input (${x_type}): ${x_comment}.
-        future_context_size (int): Future context size. Please note, the shape
-            of convolution kernel is [future_context_size + 1, D].
-        param_attr (ParamAttr): Attributes of parameters, including
-            name, initializer etc.
-        act (str): Non-linear activation to be applied to output variable.
-
-    Returns:
-        ${out_comment}.
-
-    Examples:
-
-      .. code-block:: python
-
-        # for LodTensor inputs
-        import paddle
-        paddle.enable_static()
-        x = paddle.static.data(name='x', shape=[9, 16],
-                               dtype='float32', lod_level=1)
-        out = paddle.static.nn.row_conv(input=x, future_context_size=2)
-        # for Tensor inputs
-        x = paddle.static.data(name='x', shape=[9, 4, 16], dtype='float32')
-        out = paddle.static.nn.row_conv(input=x, future_context_size=2)
-    """
-    helper = LayerHelper('row_conv', **locals())
-    check_variable_and_dtype(input, 'input', ['float32'], 'row_conv')
-    dtype = helper.input_dtype()
-    filter_shape = [future_context_size + 1, input.shape[-1]]
-    filter_param = helper.create_parameter(
-        attr=helper.param_attr, shape=filter_shape, dtype=dtype
-    )
-    out = helper.create_variable_for_type_inference(dtype)
-    helper.append_op(
-        type='row_conv',
-        inputs={'X': [input], 'Filter': [filter_param]},
-        outputs={'Out': [out]},
-    )
-    return helper.append_activation(out)
-
-
-@deprecated(since='2.0.0', update_to='paddle.nn.functional.one_hot')
-def one_hot(input, depth, allow_out_of_range=False):
-    """
-
-    **WARING:** This OP requires the last dimension of Tensor shape must be equal to 1.
-    This OP will be deprecated in a future release. It is recommended to use fluid. :ref:`api_fluid_one_hot` .
-
-    The operator converts each id in the input to an one-hot vector with a
-    :attr:`depth` length. The value in the vector dimension corresponding to the id
-    is 1, and the value in the remaining dimension is 0.
-
-    The shape of output Tensor or LoDTensor is generated by adding :attr:`depth` dimension
-    behind the last dimension of the input shape.
-
-    .. code-block:: text
-
-        Example 1 (allow_out_of_range=False):
-
-        input:
-            X.shape = [4, 1]
-            X.data = [[1], [1], [3], [0]]
-            depth = 4
-
-        output:
-            Out.shape = [4, 4]
-            Out.data = [[0., 1., 0., 0.],
-                        [0., 1., 0., 0.],
-                        [0., 0., 0., 1.],
-                        [1., 0., 0., 0.]]
-
-        Example 2 (allow_out_of_range=True):
-
-        input:
-            X.shape = [4, 1]
-            X.data = [[1], [1], [5], [0]]
-            depth = 4
-            allow_out_of_range = True
-
-        output:
-            Out.shape = [4, 4]
-            Out.data = [[0., 1., 0., 0.],
-                        [0., 1., 0., 0.],
-                        [0., 0., 0., 0.], # This id is 5, which goes beyond depth, so set it all-zeros data.
-                        [1., 0., 0., 0.]]
-
-        Example 3 (allow_out_of_range=False):
-
-        input:
-            X.shape = [4, 1]
-            X.data = [[1], [1], [5], [0]]
-            depth = 4
-            allow_out_of_range = False
-
-        output: Throw an exception for Illegal value
-            The second dimension in X is 5, which is greater than depth.
-            Allow_out_of_range =False means that does not allow the word id to exceed depth,
-            so it throws an exception.
-
-    Args:
-        input(Variable): Tensor or LoDTensor with shape :math:`[N_1, N_2, ..., N_k, 1]` ,
-            which contains at least one dimension and the last dimension must be 1.
-            The data type is int32 or int64.
-        depth(scalar): An integer defining the :attr:`depth` of the one hot dimension. If input
-            is word id, depth is generally the dictionary size.
-        allow_out_of_range(bool): A bool value indicating whether the input
-            indices could be out of range :math:`[0, depth)` . When input indices are
-            out of range, exceptions :code:`Illegal value` is raised if :attr:`allow_out_of_range`
-            is False, or zero-filling representations is created if it is set True.
-            Default: False.
-
-    Returns:
-        Variable: The one-hot representations of input. A Tensor or LoDTensor with type float32.
-
-    Examples:
-        .. code-block:: python
-
-            import paddle
-            import paddle.fluid as fluid
-            paddle.enable_static()
-
-            # Correspond to the first example above, where label.shape is [4, 1] and one_hot_label.shape is [4, 4].
-            label = fluid.data(name="label", shape=[4, 1], dtype="int64")
-            one_hot_label = fluid.layers.one_hot(input=label, depth=4)
-    """
-    if _non_static_mode():
-        if isinstance(depth, Variable):
-            depth = depth.numpy()
-            assert depth.shape == (
-                1,
-            ), "depth of type Variable should have shape [1]"
-            depth = depth.item(0)
-        out = _legacy_C_ops.one_hot(
-            input, 'depth', depth, 'allow_out_of_range', allow_out_of_range
-        )
-        out.stop_gradient = True
-        return out
-
-    helper = LayerHelper("one_hot", **locals())
-    check_variable_and_dtype(input, 'input', ['int32', 'int64'], 'one_hot')
-    check_type(depth, 'depth', (int, Variable), 'one_hot')
-    one_hot_out = helper.create_variable_for_type_inference(dtype='float32')
-
-    if not isinstance(depth, Variable):
-        # user attribute
-        inputs = {'X': input}
-        attrs = {'depth': depth, 'allow_out_of_range': allow_out_of_range}
-    else:
-        depth.stop_gradient = True
-        inputs = {'X': input, 'depth_tensor': depth}
-        attrs = {'allow_out_of_range': allow_out_of_range}
-    helper.append_op(
-        type="one_hot", inputs=inputs, attrs=attrs, outputs={'Out': one_hot_out}
-    )
-    one_hot_out.stop_gradient = True
-    return one_hot_out
-
-
 def autoincreased_step_counter(counter_name=None, begin=1, step=1):
     """
     :api_attr: Static Graph

python/paddle/fluid/tests/unittests/collective/fleet/parallel_dygraph_transformer.py

@@ -932,8 +932,8 @@ class TransFormer(Layer):
         predict = self._wrap_decoder_layer(dec_inputs, enc_output)
         if self._label_smooth_eps:
             label_out = F.label_smooth(
-                label=fluid.layers.one_hot(
-                    input=label, depth=self._trg_vocab_size
+                label=paddle.squeeze(
+                    paddle.nn.functional.one_hot(label, self._trg_vocab_size)
                 ),
                 epsilon=self._label_smooth_eps,
             )

python/paddle/fluid/tests/unittests/dist_transformer.py

@@ -1575,7 +1575,7 @@ def transformer(
     label, weights = make_all_inputs(label_data_input_fields)
     if label_smooth_eps:
         label = F.label_smooth(
-            label=layers.one_hot(input=label, depth=trg_vocab_size),
+            label=paddle.nn.functional.one_hot(label, trg_vocab_size),
             epsilon=label_smooth_eps,
         )
 

python/paddle/fluid/tests/unittests/dygraph_to_static/transformer_dygraph_model.py

@@ -593,7 +593,9 @@ class CrossEntropyCriterion:
     def __call__(self, predict, label, weights):
         if self.label_smooth_eps:
             label_out = F.label_smooth(
-                label=layers.one_hot(input=label, depth=predict.shape[-1]),
+                label=paddle.squeeze(
+                    paddle.nn.functional.one_hot(label, predict.shape[-1])
+                ),
                 epsilon=self.label_smooth_eps,
             )
 

python/paddle/fluid/tests/unittests/ipu/test_one_hot_op_ipu.py

@@ -46,7 +46,7 @@ class TestBase(IPUOpTest):
         x = paddle.static.data(
             name=self.feed_list[0], shape=self.feed_shape[0], dtype='int32'
         )
-        out = paddle.fluid.layers.one_hot(x, **self.attrs)
+        out = paddle.nn.functional.one_hot(x, **self.attrs)
         self.fetch_list = [out.name]
 
     def run_model(self, exec_mode):

python/paddle/fluid/tests/unittests/test_imperative_auto_prune.py

@@ -418,7 +418,7 @@ class TestImperativeAutoPrune(unittest.TestCase):
             label = linear(label)
             label = fluid.layers.cast(label, dtype="float32")
             label = fluid.layers.cast(label, dtype='int64')
-            out = fluid.layers.one_hot(input=label, depth=100)
+            out = paddle.nn.functional.one_hot(label, 100)
             loss = paddle.mean(out)
             loss.backward()
             self.assertIsNone(linear.weight._grad_ivar())

python/paddle/fluid/tests/unittests/test_imperative_load_static_param.py

@@ -23,6 +23,8 @@ import paddle.fluid as fluid
 import paddle.fluid.framework as framework
 from paddle.nn import BatchNorm, Linear
 
+paddle.enable_static()
+
 
 class TestDygraphLoadStatic(unittest.TestCase):
     def testLoadStaticModel(self):
@@ -128,8 +130,8 @@ class TestDygraphLoadStatic(unittest.TestCase):
         )
         '''
         spec_norm = fluid.data(name='spec_norm', shape=[2, 8, 32, 32], dtype='float32')
-        spe_norm_out_1 = fluid.layers.spectral_norm(weight=spec_norm, dim=1, power_iters=2)
-        spe_norm_out_2 = fluid.layers.spectral_norm(weight=spec_norm, dim=1, power_iters=2)
+        spe_norm_out_1 = paddle.static.nn.spectral_norm(weight=spec_norm, dim=1, power_iters=2)
+        spe_norm_out_2 = paddle.static.nn.spectral_norm(weight=spec_norm, dim=1, power_iters=2)
         '''
 
         nodes_vector = fluid.data(

python/paddle/fluid/tests/unittests/test_imperative_transformer_sorted_gradient.py

@@ -1085,8 +1085,8 @@ class TransFormer(Layer):
         predict = self._wrap_decoder_layer(dec_inputs, enc_output)
         if self._label_smooth_eps:
             label_out = F.label_smooth(
-                label=fluid.layers.one_hot(
-                    input=label, depth=self._trg_vocab_size
+                label=paddle.squeeze(
+                    paddle.nn.functional.one_hot(label, self._trg_vocab_size)
                 ),
                 epsilon=self._label_smooth_eps,
             )

python/paddle/fluid/tests/unittests/test_layers.py

@@ -657,9 +657,9 @@ class TestLayer(LayerTest):
     def test_one_hot(self):
         with self.dynamic_graph():
             label = fluid.dygraph.to_variable(np.array([[1], [1], [3], [0]]))
-            one_hot_label1 = fluid.layers.one_hot(input=label, depth=4)
-            one_hot_label2 = fluid.layers.one_hot(
-                input=label, depth=fluid.dygraph.to_variable(np.array([4]))
+            one_hot_label1 = paddle.nn.functional.one_hot(label, 4)
+            one_hot_label2 = paddle.nn.functional.one_hot(
+                label, fluid.dygraph.to_variable(np.array([4]))
             )
             np.testing.assert_array_equal(
                 one_hot_label1.numpy(), one_hot_label2.numpy()
@@ -921,7 +921,9 @@ class TestLayer(LayerTest):
                 lod_level=1,
                 append_batch_size=False,
             )
-            ret = layers.spectral_norm(weight=Weight, dim=1, power_iters=2)
+            ret = paddle.static.nn.spectral_norm(
+                weight=Weight, dim=1, power_iters=2
+            )
             static_ret = self.get_static_graph_result(
                 feed={
                     'Weight': fluid.create_lod_tensor(
@@ -1791,7 +1793,7 @@ class TestBook(LayerTest):
     def make_one_hot(self):
         with fluid.framework._dygraph_place_guard(place=fluid.CPUPlace()):
             label = self._get_data(name="label", shape=[1], dtype="int32")
-            one_hot_label = layers.one_hot(input=label, depth=10)
+            one_hot_label = paddle.nn.functional.one_hot(label, 10)
             return one_hot_label
 
     def make_label_smooth(self):
@@ -1799,7 +1801,7 @@ class TestBook(LayerTest):
         self._force_to_use_cpu = True
         with fluid.framework._dygraph_place_guard(place=fluid.CPUPlace()):
             label = self._get_data(name="label", shape=[1], dtype="int32")
-            one_hot_label = layers.one_hot(input=label, depth=10)
+            one_hot_label = paddle.nn.functional.one_hot(label, 10)
             smooth_label = F.label_smooth(label=one_hot_label, epsilon=0.1)
             return smooth_label
 
@@ -1992,7 +1994,7 @@ class TestBook(LayerTest):
                 dtype="float32",
                 append_batch_size=False,
             )
-            out = layers.spectral_norm(weight, dim=1, power_iters=1)
+            out = paddle.static.nn.spectral_norm(weight, dim=1, power_iters=1)
             return out
 
     def make_kldiv_loss(self):
@@ -2226,7 +2228,7 @@ class TestBook(LayerTest):
         # TODO(minqiyang): dygraph do not support lod now
         with self.static_graph():
             x = layers.data(name='x', shape=[16], dtype='float32', lod_level=1)
-            out = layers.row_conv(input=x, future_context_size=2)
+            out = paddle.static.nn.row_conv(input=x, future_context_size=2)
             return out
 
     def test_simple_conv2d(self):

python/paddle/fluid/tests/unittests/test_one_hot_op.py

-#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import unittest
-
-import numpy as np
-from op_test import OpTest
-
-import paddle
-import paddle.fluid as fluid
-import paddle.fluid.core as core
-from paddle.fluid.framework import Program, program_guard
-
-
-class TestOneHotOp(OpTest):
-    def setUp(self):
-        self.op_type = 'one_hot'
-        depth = 10
-        depth_np = np.array(10).astype('int32')
-        dimension = 12
-        x_lod = [[4, 1, 3, 3]]
-        x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
-        x = np.array(x).astype('int32').reshape([sum(x_lod[0]), 1])
-
-        out = np.zeros(shape=(np.product(x.shape[:-1]), depth)).astype(
-            'float32'
-        )
-
-        for i in range(np.product(x.shape)):
-            out[i, x[i]] = 1.0
-
-        self.inputs = {'X': (x, x_lod), 'depth_tensor': depth_np}
-        self.attrs = {'dtype': int(core.VarDesc.VarType.FP32)}
-        self.outputs = {'Out': (out, x_lod)}
-
-    def test_check_output(self):
-        self.check_output(check_dygraph=False)
-
-
-class TestOneHotOp_attr(OpTest):
-    def setUp(self):
-        self.op_type = 'one_hot'
-        depth = 10
-        dimension = 12
-        x_lod = [[4, 1, 3, 3]]
-        x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
-        x = np.array(x).astype('int32').reshape([sum(x_lod[0]), 1])
-
-        out = np.zeros(shape=(np.product(x.shape[:-1]), depth)).astype(
-            'float32'
-        )
-
-        for i in range(np.product(x.shape)):
-            out[i, x[i]] = 1.0
-
-        self.inputs = {'X': (x, x_lod)}
-        self.attrs = {'dtype': int(core.VarDesc.VarType.FP32), 'depth': depth}
-        self.outputs = {'Out': (out, x_lod)}
-
-    def test_check_output(self):
-        self.check_output(check_dygraph=False)
-
-
-class TestOneHotOp_default_dtype(OpTest):
-    def setUp(self):
-        self.op_type = 'one_hot'
-        depth = 10
-        depth_np = np.array(10).astype('int32')
-        dimension = 12
-        x_lod = [[4, 1, 3, 3]]
-        x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
-        x = np.array(x).astype('int32').reshape([sum(x_lod[0]), 1])
-
-        out = np.zeros(shape=(np.product(x.shape[:-1]), depth)).astype(
-            'float32'
-        )
-
-        for i in range(np.product(x.shape)):
-            out[i, x[i]] = 1.0
-
-        self.inputs = {'X': (x, x_lod), 'depth_tensor': depth_np}
-        self.attrs = {}
-        self.outputs = {'Out': (out, x_lod)}
-
-    def test_check_output(self):
-        self.check_output(check_dygraph=False)
-
-
-class TestOneHotOp_default_dtype_attr(OpTest):
-    def setUp(self):
-        self.op_type = 'one_hot'
-        depth = 10
-        dimension = 12
-        x_lod = [[4, 1, 3, 3]]
-        x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
-        x = np.array(x).astype('int32').reshape([sum(x_lod[0]), 1])
-
-        out = np.zeros(shape=(np.product(x.shape[:-1]), depth)).astype(
-            'float32'
-        )
-
-        for i in range(np.product(x.shape)):
-            out[i, x[i]] = 1.0
-
-        self.inputs = {'X': (x, x_lod)}
-        self.attrs = {'depth': depth}
-        self.outputs = {'Out': (out, x_lod)}
-
-    def test_check_output(self):
-        self.check_output(check_dygraph=False)
-
-
-class TestOneHotOp_out_of_range(OpTest):
-    def setUp(self):
-        self.op_type = 'one_hot'
-        depth = 10
-        x_lod = [[4, 1, 3, 3]]
-        x = [np.random.choice([-1, depth]) for i in range(sum(x_lod[0]))]
-        x = np.array(x).astype('int32').reshape([sum(x_lod[0]), 1])
-
-        out = np.zeros(shape=(np.product(x.shape[:-1]), depth)).astype(
-            'float32'
-        )
-
-        self.inputs = {'X': (x, x_lod)}
-        self.attrs = {'depth': depth, 'allow_out_of_range': True}
-        self.outputs = {'Out': (out, x_lod)}
-
-    def test_check_output(self):
-        self.check_output(check_dygraph=False)
-
-
-class TestOneHotOp_exception(unittest.TestCase):
-    def setUp(self):
-        self.op_type = 'one_hot'
-        self.depth = 10
-        self.place = core.CPUPlace()
-        self.dimension = 12
-        self.x = core.LoDTensor()
-        x_lod = [[4, 1, 3, 3]]
-        data = [np.random.randint(11, 20) for i in range(sum(x_lod[0]))]
-        data = np.array(data).astype('int').reshape([sum(x_lod[0]), 1])
-        self.x.set(data, self.place)
-        self.x.set_recursive_sequence_lengths(x_lod)
-
-    def test_check_output(self):
-        program = Program()
-        with program_guard(program):
-            x = fluid.layers.data(
-                name='x', shape=[self.dimension], dtype='float32', lod_level=1
-            )
-            block = program.current_block()
-            one_hot_out = block.create_var(
-                name="one_hot_out",
-                type=core.VarDesc.VarType.LOD_TENSOR,
-                dtype='float32',
-            )
-            block.append_op(
-                type='one_hot',
-                inputs={'X': x},
-                attrs={'depth': self.depth},
-                outputs={'Out': one_hot_out},
-            )
-            exe = fluid.Executor(self.place)
-
-            def run():
-                exe.run(
-                    feed={'x': self.x},
-                    fetch_list=[one_hot_out],
-                    return_numpy=False,
-                )
-
-            self.assertRaises(ValueError, run)
-
-
-class TestOneHotOpError(unittest.TestCase):
-    def test_errors(self):
-        with program_guard(Program(), Program()):
-            # the input must be Variable
-            in_w = np.random.random((4, 1)).astype("int32")
-            self.assertRaises(TypeError, fluid.layers.one_hot, in_w)
-            # the input must be int32 or int 64
-            in_w2 = fluid.layers.data(
-                name="in_w2",
-                shape=[4, 1],
-                append_batch_size=False,
-                dtype="float32",
-            )
-            self.assertRaises(TypeError, fluid.layers.one_hot, in_w2)
-            # the depth must be int, long or Variable
-            in_r = fluid.layers.data(
-                name="in_r",
-                shape=[4, 1],
-                append_batch_size=False,
-                dtype="int32",
-            )
-            depth_w = np.array([4])
-            self.assertRaises(TypeError, fluid.layers.one_hot, in_r, 4.1)
-            self.assertRaises(TypeError, fluid.layers.one_hot, in_r, depth_w)
-
-
-if __name__ == '__main__':
-    paddle.enable_static()
-    unittest.main()

python/paddle/fluid/tests/unittests/test_row_conv_op.py

@@ -17,8 +17,11 @@ import unittest
 import numpy as np
 from op_test import OpTest
 
+import paddle
 from paddle import fluid
 
+paddle.enable_static()
+
 
 def row_conv_forward(x, lod, wt):
     out = np.zeros_like(x)
@@ -191,7 +194,7 @@ class TestRowConvLayer(unittest.TestCase):
         with fluid.unique_name.guard():
             with fluid.program_guard(main, start):
                 x = fluid.data("x", (-1, -1, self.C), "float32")
-                out = fluid.layers.row_conv(
+                out = paddle.static.nn.row_conv(
                     x,
                     self.context_length,
                     param_attr=fluid.initializer.NumpyArrayInitializer(self.w),

python/paddle/fluid/tests/unittests/test_runtime_and_compiletime_exception.py

-#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import unittest
-
-import numpy as np
-
-import paddle
-import paddle.fluid as fluid
-
-
-class TestRunTimeException(unittest.TestCase):
-    def test_run_time_exception(self):
-        place = fluid.CPUPlace()
-        exe = fluid.Executor(place)
-
-        train_program = fluid.Program()
-        startup_program = fluid.Program()
-        with fluid.program_guard(train_program, startup_program):
-            label = fluid.layers.data(name="label", shape=[1], dtype="int64")
-            fluid.layers.one_hot(input=label, depth=100)
-
-        def _run_program():
-            x = np.random.random(size=(10)).astype('int64')
-            exe.run(train_program, feed={"label": x})
-
-        self.assertRaises(ValueError, _run_program)
-
-
-class TestCompileTimeException(unittest.TestCase):
-    def test_compile_time_exception(self):
-        self.assertRaises(ValueError, self.build_model)
-
-    def build_model(self):
-        train_program = fluid.Program()
-        startup_program = fluid.Program()
-        with fluid.program_guard(train_program, startup_program):
-            label = fluid.layers.data(
-                name="label", shape=[1], dtype="int64", append_batch_size=False
-            )
-            fluid.layers.one_hot(input=label, depth=100)
-
-
-if __name__ == '__main__':
-    paddle.enable_static()
-    unittest.main()

python/paddle/fluid/tests/unittests/test_spectral_norm_op.py

@@ -18,9 +18,10 @@ import numpy as np
 from op_test import OpTest, skip_check_grad_ci
 
 import paddle
-import paddle.fluid as fluid
 from paddle.fluid.framework import Program, program_guard
 
+paddle.enable_static()
+
 
 def spectral_norm(weight, u, v, dim, power_iters, eps):
     shape = weight.shape
@@ -136,14 +137,14 @@ class TestSpectralNormOpError(unittest.TestCase):
 
             def test_Variable():
                 weight_1 = np.random.random((2, 4)).astype("float32")
-                fluid.layers.spectral_norm(weight_1, dim=1, power_iters=2)
+                paddle.static.nn.spectral_norm(weight_1, dim=1, power_iters=2)
 
             # the weight type of spectral_norm must be Variable
             self.assertRaises(TypeError, test_Variable)
 
             def test_weight_dtype():
                 weight_2 = np.random.random((2, 4)).astype("int32")
-                fluid.layers.spectral_norm(weight_2, dim=1, power_iters=2)
+                paddle.static.nn.spectral_norm(weight_2, dim=1, power_iters=2)
 
             # the data type of type must be float32 or float64
             self.assertRaises(TypeError, test_weight_dtype)

python/paddle/static/nn/__init__.py

@@ -30,12 +30,13 @@ from .control_flow import (
 )
 from .common import bilinear_tensor_product  # noqa: F401
 from .common import py_func  # noqa: F401
+from .common import row_conv  # noqa: F401
+from .common import spectral_norm  # noqa: F401
 from ...tensor.creation import create_parameter  # noqa: F401
 from .loss import nce  # noqa: F401
 from .common import prelu  # noqa: F401
 from .common import layer_norm  # noqa: F401
-from ...fluid.layers import row_conv  # noqa: F401
-from ...fluid.layers import spectral_norm  # noqa: F401
+
 
 from ...fluid.input import embedding  # noqa: F401
 from ...fluid.contrib.layers import sparse_embedding  # noqa: F401

python/paddle/static/nn/common.py

@@ -3233,6 +3233,178 @@ def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None):
     return out
 
 
+@templatedoc()
+def row_conv(input, future_context_size, param_attr=None, act=None):
+    """
+    :api_attr: Static Graph
+
+    ${comment}
+
+    Args:
+        input (${x_type}): ${x_comment}.
+        future_context_size (int): Future context size. Please note, the shape
+            of convolution kernel is [future_context_size + 1, D].
+        param_attr (ParamAttr): Attributes of parameters, including
+            name, initializer etc.
+        act (str): Non-linear activation to be applied to output variable.
+
+    Returns:
+        ${out_comment}.
+
+    Examples:
+
+      .. code-block:: python
+
+        # for LodTensor inputs
+        import paddle
+        paddle.enable_static()
+        x = paddle.static.data(name='x', shape=[9, 16],
+                               dtype='float32', lod_level=1)
+        out_x = paddle.static.nn.row_conv(input=x, future_context_size=2)
+        # for Tensor inputs
+        y = paddle.static.data(name='y', shape=[9, 4, 16], dtype='float32')
+        out_y = paddle.static.nn.row_conv(input=y, future_context_size=2)
+    """
+    helper = LayerHelper('row_conv', **locals())
+    check_variable_and_dtype(input, 'input', ['float32'], 'row_conv')
+    dtype = helper.input_dtype()
+    filter_shape = [future_context_size + 1, input.shape[-1]]
+    filter_param = helper.create_parameter(
+        attr=helper.param_attr, shape=filter_shape, dtype=dtype
+    )
+    out = helper.create_variable_for_type_inference(dtype)
+    helper.append_op(
+        type='row_conv',
+        inputs={'X': [input], 'Filter': [filter_param]},
+        outputs={'Out': [out]},
+    )
+    return helper.append_activation(out)
+
+
+@templatedoc()
+def spectral_norm(weight, dim=0, power_iters=1, eps=1e-12, name=None):
+    r"""
+    :api_attr: Static Graph
+
+    **Spectral Normalization Layer**
+
+    This operation calculates the spectral normalization value of weight parameters of
+    fc, conv1d, conv2d, conv3d layers which should be 2-D, 3-D, 4-D, 5-D
+    Parameters. Output tensor will be in same shape with input tensor.
+    Calculations are showed as follows.
+
+    Step 1:
+    Generate vector U in shape of [H], and V in shape of [W].
+    While H is the :attr:`dim` th dimension of the input weights,
+    and W is the product result of remaining dimensions.
+
+    Step 2:
+    :attr:`power_iters` should be a positive integer, do following
+    calculations with U and V for :attr:`power_iters` rounds. Calculations
+    as follows:
+
+    .. math::
+
+        \mathbf{v} := \\frac{\mathbf{W}^{T} \mathbf{u}}{\|\mathbf{W}^{T} \mathbf{u}\|_2}
+
+        \mathbf{u} := \\frac{\mathbf{W}^{T} \mathbf{v}}{\|\mathbf{W}^{T} \mathbf{v}\|_2}
+
+    Step 3:
+    Calculate :math:`\sigma(\mathbf{W})` and normalize weight values.
+
+    .. math::
+
+        \sigma(\mathbf{W}) = \mathbf{u}^{T} \mathbf{W} \mathbf{v}
+
+        \mathbf{W} = \\frac{\mathbf{W}}{\sigma(\mathbf{W})}
+
+
+    Refer to `Spectral Normalization <https://arxiv.org/abs/1802.05957>`_ .
+
+    Args:
+        weight(Tensor): ${weight_comment}
+        dim(int): ${dim_comment}
+        power_iters(int): ${power_iters_comment}
+        eps(float): ${eps_comment}
+        name(str, optional): For detailed information, please refer
+                             to :ref:`api_guide_Name`. Usually name is no need to set and
+                             None by default.
+
+    Returns:
+        Tensor: A tensor of weight parameters after spectral normalization.
+                  The data type and shape is same as input tensor.
+
+    Examples:
+       .. code-block:: python
+
+            import paddle
+
+            paddle.enable_static()
+            weight = paddle.static.data(name='weight', shape=[2, 8, 32, 32], dtype='float32')
+            x = paddle.static.nn.spectral_norm(weight=weight, dim=1, power_iters=2)
+            print(x.shape) # [2, 8, 32, 32]
+    """
+    helper = LayerHelper('spectral_norm', **locals())
+    check_variable_and_dtype(
+        weight, 'weight', ['float32', 'float64'], 'spectral_norm'
+    )
+    check_type(dim, 'dim', int, 'spectral_norm')
+    check_type(power_iters, 'power_iters', int, 'spectral_norm')
+    check_type(eps, 'eps', float, 'spectral_norm')
+    dtype = weight.dtype
+
+    # create intput and parameters
+    input_shape = weight.shape
+    assert weight.numel() > 0, "Any dimension of input cannot be equal to 0."
+    assert dim < len(input_shape), (
+        "The input `dim` should be less than the "
+        "rank of `weight`, but received dim="
+        "{}".format(dim)
+    )
+    h = input_shape[dim]
+    w = np.prod(input_shape) // h
+
+    u = helper.create_parameter(
+        attr=ParamAttr(),
+        shape=[h],
+        dtype=dtype,
+        default_initializer=Normal(0.0, 1.0),
+    )
+    u.stop_gradient = True
+    v = helper.create_parameter(
+        attr=ParamAttr(),
+        shape=[w],
+        dtype=dtype,
+        default_initializer=Normal(0.0, 1.0),
+    )
+    v.stop_gradient = True
+
+    if paddle.framework.in_dygraph_mode():
+        return paddle._C_ops.spectral_norm(weight, u, v, dim, power_iters, eps)
+
+    inputs = {'Weight': weight}
+    inputs['U'] = u
+    inputs['V'] = v
+
+    # create output
+    out = helper.create_variable(dtype=dtype)
+
+    helper.append_op(
+        type="spectral_norm",
+        inputs=inputs,
+        outputs={
+            "Out": out,
+        },
+        attrs={
+            "dim": dim,
+            "power_iters": power_iters,
+            "eps": eps,
+        },
+    )
+
+    return out
+
+
 # For debug usage
 py_func.registered_func = PyFuncRegistry.registered_func
 py_func.registered_func_num = PyFuncRegistry.registered_func_num