[remove fluid] fluid dygraph Embedding (#48806)

* [remove fluid] fluid dygraph Embedding

* [remove fluid] fluid dygraph Embedding

* [remove fluid] fluid dygraph Embedding

* [remove fluid] fluid dygraph Embedding

* [remove fluid] fluid dygraph Embedding

* [remove fluid] fluid dygraph Embedding

python/paddle/fluid/dygraph/checkpoint.py

@@ -83,9 +83,10 @@ def save_dygraph(state_dict, model_path):
         .. code-block:: python
 
             import paddle.fluid as fluid
+            import paddle
 
             with fluid.dygraph.guard():
-                emb = fluid.dygraph.Embedding([10, 10])
+                emb = paddle.nn.Embedding(10, 10)
 
                 state_dict = emb.state_dict()
                 fluid.save_dygraph( state_dict, "paddle_dy")

python/paddle/fluid/dygraph/learning_rate_scheduler.py

@@ -170,10 +170,11 @@ class PiecewiseDecay(LearningRateDecay):
         .. code-block:: python
 
           import paddle.fluid as fluid
+          import paddle
           boundaries = [10000, 20000]
           values = [1.0, 0.5, 0.1]
           with fluid.dygraph.guard():
-              emb = fluid.dygraph.Embedding( [10, 10] )
+              emb = paddle.nn.Embedding(10, 10)
               optimizer = fluid.optimizer.SGD(
                  learning_rate=fluid.dygraph.PiecewiseDecay(boundaries, values, 0),
                  parameter_list = emb.parameters() )
@@ -240,9 +241,10 @@ class NaturalExpDecay(LearningRateDecay):
         .. code-block:: python
 
             import paddle.fluid as fluid
+            import paddle
             base_lr = 0.1
             with fluid.dygraph.guard():
-                emb = fluid.dygraph.Embedding([10, 10])
+                emb = paddle.nn.Embedding(10, 10)
                 sgd_optimizer = fluid.optimizer.SGD(
                         learning_rate=fluid.dygraph.NaturalExpDecay(
                             learning_rate=base_lr,
@@ -403,9 +405,10 @@ class InverseTimeDecay(LearningRateDecay):
         .. code-block:: python
 
           import paddle.fluid as fluid
+          import paddle
           base_lr = 0.1
           with fluid.dygraph.guard():
-              emb = fluid.dygraph.Embedding([10, 10])
+              emb = paddle.nn.Embedding(10, 10)
               sgd_optimizer = fluid.optimizer.SGD(
                   learning_rate=fluid.dygraph.InverseTimeDecay(
                         learning_rate=base_lr,
@@ -487,11 +490,12 @@ class PolynomialDecay(LearningRateDecay):
         .. code-block:: python
 
           import paddle.fluid as fluid
+          import paddle
           start_lr = 0.01
           total_step = 5000
           end_lr = 0
           with fluid.dygraph.guard():
-              emb = fluid.dygraph.Embedding( [10, 10])
+              emb = paddle.nn.Embedding(10, 10)
               optimizer  = fluid.optimizer.SGD(
                   learning_rate = fluid.dygraph.PolynomialDecay(
                   start_lr, total_step, end_lr, power=1.0),
@@ -639,10 +643,11 @@ class NoamDecay(LearningRateDecay):
         .. code-block:: python
 
           import paddle.fluid as fluid
+          import paddle
           warmup_steps = 100
           learning_rate = 0.01
           with fluid.dygraph.guard():
-              emb = fluid.dygraph.Embedding([10, 10])
+              emb = paddle.nn.Embedding(10, 10)
               optimizer  = fluid.optimizer.SGD(
                   learning_rate = fluid.dygraph.NoamDecay(
                          1/(warmup_steps *(learning_rate ** 2)),

python/paddle/fluid/dygraph/nn.py

@@ -51,7 +51,6 @@ from paddle import _C_ops, _legacy_C_ops
 
 __all__ = [
     'BatchNorm',
-    'Embedding',
 ]
 
 
@@ -360,187 +359,6 @@ class BatchNorm(layers.Layer):
         return self._helper.append_activation(batch_norm_out, self._act)
 
 
-class Embedding(layers.Layer):
-    r"""
-    :alias_main: paddle.nn.Embedding
-        :alias: paddle.nn.Embedding,paddle.nn.layer.Embedding,paddle.nn.layer.common.Embedding
-        :old_api: paddle.fluid.dygraph.Embedding
-
-    **Embedding Layer**
-
-    This interface is used to construct a callable object of the ``Embedding`` class.
-    For specific usage, refer to code examples. It implements the function of the Embedding Layer.
-    This layer is used to lookup embeddings vector of ids provided by :attr:`input` .
-    It automatically constructs a 2D embedding matrix based on the
-    input :attr:`size` (vocab_size, emb_size) and :attr:`dtype` .
-
-    The shape of output Tensor is generated by appending an emb_size dimension to the
-    last dimension of the input Tensor shape.
-
-    **Note:** The id in :attr:`input` must satisfy :math:`0 =< id < size[0]` ,
-    otherwise the program will throw an exception and exit.
-
-    .. code-block:: text
-
-        Case 1:
-
-        input is a Tensor. padding_idx = -1
-            input.data = [[1, 3], [2, 4], [4, 127]
-            input.shape = [3, 2]
-        Given size = [128, 16]
-        output is a Tensor:
-            out.shape = [3, 2, 16]
-            out.data = [[[0.129435295, 0.244512452, ..., 0.436322452],
-                        [0.345421456, 0.524563927, ..., 0.144534654]],
-
-                        [[0.345249859, 0.124939536, ..., 0.194353745],
-                        [0.945345345, 0.435394634, ..., 0.435345365]],
-
-                        [[0.945345345, 0.435394634, ..., 0.435345365],
-                        [0.0,         0.0,         ..., 0.0        ]]]  # padding data
-        The input padding_idx is less than 0, it is automatically converted to padding_idx = -1 + 128 = 127
-        It will pad all-zero data when ids is 127.
-
-    Parameters:
-        size(tuple|list): The shape of the look up table parameter. It should have two elements which indicate the size
-            of the dictionary of embeddings and the size of each embedding vector respectively.
-        is_sparse(bool): The flag indicating whether to use sparse update. This parameter only
-            affects the performance of the backwards gradient update. It is recommended to set
-            True because sparse update is faster. But some optimizer does not support sparse update,
-            such as :ref:`api_fluid_optimizer_AdadeltaOptimizer` , :ref:`api_fluid_optimizer_AdamaxOptimizer` ,
-            :ref:`api_fluid_optimizer_DecayedAdagradOptimizer` , :ref:`api_fluid_optimizer_FtrlOptimizer` ,
-            :ref:`api_fluid_optimizer_LambOptimizer` and :ref:`api_fluid_optimizer_LarsMomentumOptimizer` .
-            In these case, is_sparse must be False. Default: False.
-        is_distributed(bool): Whether to store the embedding matrix in a distributed manner. Only used
-            in multi-machine distributed CPU training. Default: False.
-        padding_idx(int|long|None): padding_idx needs to be in the interval [-vocab_size, vocab_size).
-            If :math:`padding\_idx < 0`, the :math:`padding\_idx` will automatically be converted
-            to :math:`vocab\_size + padding\_idx` . It will output all-zero padding data whenever lookup
-            encounters :math:`padding\_idx` in id. And the padding data will not be updated while training.
-            If set None, it makes no effect to output. Default: None.
-        param_attr(ParamAttr): To specify the weight parameter property. Default: None, which means the
-            default weight parameter property is used. See usage for details in :ref:`api_fluid_ParamAttr` . In addition,
-            user-defined or pre-trained word vectors can be loaded with the :attr:`param_attr` parameter.
-            The local word vector needs to be transformed into numpy format, and the shape of local word
-            vector should be consistent with :attr:`size` . Then :ref:`api_fluid_initializer_NumpyArrayInitializer`
-            is used to load custom or pre-trained word vectors. See code example 2 for details.
-        dtype(np.dtype|core.VarDesc.VarType|str): It refers to the data type of output Tensor.
-            It must be "float32" or "float64". Default: "float32".
-
-    Attribute:
-        **weight** (Parameter): the learnable weights of this layer.
-
-    Returns:
-        Variable: Embedding Tensor or LoDTensor mapped by input. The data type is the same as :attr:`dtype` .
-
-    Examples:
-
-        .. code-block:: python
-
-          import paddle.fluid as fluid
-          import paddle.fluid.dygraph.base as base
-          import numpy as np
-
-          # example 1
-          inp_word = np.array([[2, 3, 5], [4, 2, 1]]).astype('int64')
-          inp_word.shape  # [2, 3]
-          dict_size = 20
-          with fluid.dygraph.guard():
-              emb = fluid.dygraph.Embedding(
-                  size=[dict_size, 32],
-                  param_attr='emb.w',
-                  is_sparse=False)
-              static_rlt3 = emb(base.to_variable(inp_word))
-              static_rlt3.shape  # [2, 3, 32]
-
-          # example 2: load custom or pre-trained word vectors
-          weight_data = np.random.random(size=(128, 100))  # word vectors with numpy format
-          w_param_attrs = fluid.ParamAttr(
-              name="emb_weight",
-              learning_rate=0.5,
-              initializer=fluid.initializer.NumpyArrayInitializer(weight_data),
-              trainable=True)
-          with fluid.dygraph.guard():
-              emb = fluid.dygraph.Embedding(
-                  size=[128, 100],
-                  param_attr= w_param_attrs,
-                  is_sparse=False)
-              static_rlt3 = emb(base.to_variable(inp_word))
-    """
-
-    def __init__(
-        self,
-        size,
-        is_sparse=False,
-        is_distributed=False,
-        padding_idx=None,
-        param_attr=None,
-        dtype='float32',
-    ):
-        super().__init__()
-        self._size = size
-        self._is_sparse = is_sparse
-        self._is_distributed = is_distributed
-        self._padding_idx = (
-            -1
-            if padding_idx is None
-            else padding_idx
-            if padding_idx >= 0
-            else (size[0] + padding_idx)
-        )
-
-        self._param_attr = param_attr
-        self._dtype = dtype
-        self._remote_prefetch = self._is_sparse and (not self._is_distributed)
-        if self._remote_prefetch:
-            assert self._is_sparse is True and self._is_distributed is False
-
-        self.weight = self.create_parameter(
-            attr=self._param_attr,
-            shape=self._size,
-            dtype=self._dtype,
-            is_bias=False,
-        )
-
-    def forward(self, input):
-        if _non_static_mode():
-            return _legacy_C_ops.lookup_table_v2(
-                self.weight,
-                input,
-                'is_sparse',
-                self._is_sparse,
-                'is_distributed',
-                self._is_distributed,
-                'remote_prefetch',
-                self._remote_prefetch,
-                'padding_idx',
-                self._padding_idx,
-            )
-
-        check_variable_and_dtype(
-            input,
-            'input',
-            ['uint8', 'int8', 'int16', 'int32', 'int64'],
-            'Embedding',
-        )
-        attrs = {
-            'is_sparse': self._is_sparse,
-            'is_distributed': self._is_distributed,
-            'remote_prefetch': self._remote_prefetch,
-            'padding_idx': self._padding_idx,
-        }
-
-        out = self._helper.create_variable_for_type_inference(self._dtype)
-        self._helper.append_op(
-            type='lookup_table_v2',
-            inputs={'Ids': input, 'W': self.weight},
-            outputs={'Out': out},
-            attrs=attrs,
-        )
-
-        return out
-
-
 class RowConv(layers.Layer):
     """
     ***Row-convolution operator***

python/paddle/fluid/dygraph/parallel.py

@@ -723,10 +723,6 @@ class DataParallel(layers.Layer):
         def check_layer_sparse(sublayer):
             if isinstance(sublayer, paddle.nn.layer.common.Embedding):
                 return sublayer._sparse
-            # NOTE(shenliang03):This is for compatibility. If paddle.fluid.dygraph.Embedding
-            # is removed in the future, the check will also be removed here.
-            if isinstance(sublayer, paddle.fluid.dygraph.Embedding):
-                return sublayer._is_sparse
             return False
 
         is_sparse_gradient = [
@@ -875,8 +871,8 @@ class DataParallel(layers.Layer):
 
                 dist.init_parallel_env()
 
-                emb = fluid.dygraph.Embedding([10, 10])
-                emb = fluid.dygraph.DataParallel(emb)
+                emb = paddle.nn.Embedding(10, 10)
+                emb = paddle.fluid.dygraph.DataParallel(emb)
 
                 state_dict = emb.state_dict()
                 paddle.save(state_dict, "paddle_dy.pdparams")
@@ -910,7 +906,7 @@ class DataParallel(layers.Layer):
                 dist.init_parallel_env()
 
                 emb = paddle.nn.Embedding(10, 10)
-                emb = fluid.dygraph.DataParallel(emb)
+                emb = paddle.fluid.dygraph.DataParallel(emb)
 
                 state_dict = emb.state_dict()
                 paddle.save(state_dict, "paddle_dy.pdparams")

python/paddle/fluid/framework.py

@@ -1660,10 +1660,11 @@ class Variable(metaclass=VariableMetaClass):
 
                 # example2: return tuple of ndarray
                 with fluid.dygraph.guard():
-                    embedding = fluid.dygraph.Embedding(
-                        size=[20, 32],
-                        param_attr='emb.w',
-                        is_sparse=True)
+                    embedding = paddle.nn.Embedding(
+                        20,
+                        32,
+                        weight_attr='emb.w',
+                        sparse=True)
                     x_data = np.arange(12).reshape(4, 3).astype('int64')
                     x_data = x_data.reshape((-1, 3, 1))
                     x = fluid.dygraph.base.to_variable(x_data)

python/paddle/fluid/optimizer.py

@@ -214,9 +214,10 @@ class Optimizer:
             .. code-block:: python
 
                 import paddle.fluid as fluid
+                import paddle
 
                 with fluid.dygraph.guard():
-                    emb = fluid.dygraph.Embedding([10, 10])
+                    emb = paddle.nn.Embedding(10, 10)
 
                     adam = fluid.optimizer.Adam(0.001, parameter_list=emb.parameters())
                     state_dict = adam.state_dict()
@@ -582,7 +583,7 @@ class Optimizer:
 
                 # example1: LearningRateDecay is not used, return value is all the same
                 with fluid.dygraph.guard():
-                    emb = fluid.dygraph.Embedding([10, 10])
+                    emb = paddle.nn.Embedding(10, 10)
                     adam = fluid.optimizer.Adam(0.001, parameter_list = emb.parameters())
                     lr = adam.current_step_lr()
                     print(lr) # 0.001

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

@@ -39,7 +39,6 @@ from paddle.distributed.fleet.meta_parallel.parallel_layers.pp_layers import (
 from paddle.distributed.sharding.group_sharded import group_sharded_parallel
 from paddle.distributed.utils.log_utils import get_logger
 from paddle.fluid.dataloader.dataset import IterableDataset
-from paddle.fluid.dygraph.nn import Embedding
 from paddle.incubate.distributed.utils.io import save_for_auto_inference
 from paddle.nn import Linear
 
@@ -131,7 +130,7 @@ class MLP(fluid.Layer):
         bias_attr=None,
     ):
         super(MLP, self).__init__()
-        self.embedding = Embedding((embedding_size, linear_size))
+        self.embedding = paddle.nn.Embedding(embedding_size, linear_size)
         self._linear1 = Linear(linear_size, linear_size)
         self._linear2 = Linear(linear_size, linear_size)
         self._linear3 = Linear(linear_size, 10)

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

@@ -18,7 +18,6 @@ from test_dist_base import TestParallelDyGraphRunnerBase, runtime_main
 import paddle
 import paddle.fluid as fluid
 import paddle.nn.functional as F
-from paddle.fluid.dygraph.nn import Embedding
 
 paddle.seed(123)
 np.random.seed(2021)
@@ -29,10 +28,10 @@ class SimpleNet(fluid.Layer):
         super().__init__()
         self.hidden_size = hidden_size
         self.vocab_size = vocab_size
-        self.embedding = Embedding(
-            size=[self.vocab_size, self.hidden_size],
-            dtype='float32',
-            is_sparse=is_sparse,
+        self.embedding = paddle.nn.Embedding(
+            self.vocab_size,
+            self.hidden_size,
+            sparse=is_sparse,
         )
 
         self.lin_a = paddle.nn.Linear(self.hidden_size, self.vocab_size)

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

@@ -18,7 +18,7 @@ from test_dist_base import TestParallelDyGraphRunnerBase, runtime_main
 import paddle
 import paddle.fluid as fluid
 import paddle.nn.functional as F
-from paddle.fluid.dygraph import Embedding, Layer, to_variable
+from paddle.fluid.dygraph import Layer, to_variable
 from paddle.optimizer.lr import NoamDecay
 
 """
@@ -513,11 +513,11 @@ class PrepareEncoderDecoderLayer(Layer):
         self._src_emb_dim = src_emb_dim
         self._src_vocab_size = src_vocab_size
         self._dropout_rate = dropout_rate
-        self._input_emb = Embedding(
-            size=[src_vocab_size, src_emb_dim],
-            is_sparse=is_sparse,
-            padding_idx=0,
-            param_attr=fluid.ParamAttr(
+        self._input_emb = paddle.nn.Embedding(
+            src_vocab_size,
+            src_emb_dim,
+            sparse=is_sparse,
+            weight_attr=fluid.ParamAttr(
                 name=word_emb_param_name,
                 initializer=fluid.initializer.Normal(0.0, src_emb_dim**-0.5),
             ),
@@ -527,10 +527,11 @@ class PrepareEncoderDecoderLayer(Layer):
             pos_inp = pos_inp1
         else:
             pos_inp = pos_inp2
-        self._pos_emb = Embedding(
-            size=[self._src_max_len, src_emb_dim],
-            is_sparse=is_sparse,
-            param_attr=fluid.ParamAttr(
+        self._pos_emb = paddle.nn.Embedding(
+            self._src_max_len,
+            src_emb_dim,
+            sparse=is_sparse,
+            weight_attr=fluid.ParamAttr(
                 name=pos_enc_param_name,
                 initializer=fluid.initializer.NumpyArrayInitializer(pos_inp),
                 trainable=False,

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

@@ -16,7 +16,7 @@ from transformer_dygraph_model import MultiHeadAttention, PrePostProcessLayer
 
 import paddle
 import paddle.fluid as fluid
-from paddle.fluid.dygraph import Embedding, Layer
+from paddle.fluid.dygraph import Layer
 from paddle.jit.api import declarative
 from paddle.nn import Linear
 
@@ -208,29 +208,29 @@ class BertModelLayer(Layer):
         self._param_initializer = fluid.initializer.TruncatedNormal(
             scale=config['initializer_range']
         )
-
-        self._src_emb = Embedding(
-            size=[self._voc_size, self._emb_size],
-            param_attr=fluid.ParamAttr(
+        paddle.set_default_dtype(self._dtype)
+        self._src_emb = paddle.nn.Embedding(
+            self._voc_size,
+            self._emb_size,
+            weight_attr=fluid.ParamAttr(
                 name=self._word_emb_name, initializer=self._param_initializer
             ),
-            dtype=self._dtype,
         )
 
-        self._pos_emb = Embedding(
-            size=[self._max_position_seq_len, self._emb_size],
-            param_attr=fluid.ParamAttr(
+        self._pos_emb = paddle.nn.Embedding(
+            self._max_position_seq_len,
+            self._emb_size,
+            weight_attr=fluid.ParamAttr(
                 name=self._pos_emb_name, initializer=self._param_initializer
             ),
-            dtype=self._dtype,
         )
 
-        self._sent_emb = Embedding(
-            size=[self._sent_types, self._emb_size],
-            param_attr=fluid.ParamAttr(
+        self._sent_emb = paddle.nn.Embedding(
+            self._sent_types,
+            self._emb_size,
+            weight_attr=fluid.ParamAttr(
                 name=self._sent_emb_name, initializer=self._param_initializer
             ),
-            dtype=self._dtype,
         )
 
         self.pooled_fc = Linear(

python/paddle/fluid/tests/unittests/dygraph_to_static/seq2seq_dygraph_model.py

@@ -21,8 +21,8 @@ import paddle.fluid as fluid
 from paddle.fluid import ParamAttr, layers
 from paddle.fluid.dygraph import Layer
 from paddle.fluid.dygraph.base import to_variable
-from paddle.fluid.dygraph.nn import Embedding
 from paddle.jit.api import declarative
+from paddle.nn import Embedding
 
 INF = 1.0 * 1e5
 alpha = 0.6
@@ -122,16 +122,18 @@ class BaseModel(fluid.dygraph.Layer):
         forget_bias = 1.0
 
         self.src_embeder = Embedding(
-            size=[self.src_vocab_size, self.hidden_size],
-            param_attr=fluid.ParamAttr(
+            self.src_vocab_size,
+            self.hidden_size,
+            weight_attr=fluid.ParamAttr(
                 initializer=uniform_initializer(init_scale)
             ),
         )
 
         self.tar_embeder = Embedding(
-            size=[self.tar_vocab_size, self.hidden_size],
-            is_sparse=False,
-            param_attr=fluid.ParamAttr(
+            self.tar_vocab_size,
+            self.hidden_size,
+            sparse=False,
+            weight_attr=fluid.ParamAttr(
                 initializer=uniform_initializer(init_scale)
             ),
         )
@@ -545,17 +547,19 @@ class AttentionModel(fluid.dygraph.Layer):
         forget_bias = 1.0
 
         self.src_embeder = Embedding(
-            size=[self.src_vocab_size, self.hidden_size],
-            param_attr=fluid.ParamAttr(
+            self.src_vocab_size,
+            self.hidden_size,
+            weight_attr=fluid.ParamAttr(
                 name='source_embedding',
                 initializer=uniform_initializer(init_scale),
             ),
         )
 
         self.tar_embeder = Embedding(
-            size=[self.tar_vocab_size, self.hidden_size],
-            is_sparse=False,
-            param_attr=fluid.ParamAttr(
+            self.tar_vocab_size,
+            self.hidden_size,
+            sparse=False,
+            weight_attr=fluid.ParamAttr(
                 name='target_embedding',
                 initializer=uniform_initializer(init_scale),
             ),

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

@@ -17,7 +17,7 @@ from functools import reduce
 import paddle
 import paddle.fluid as fluid
 import paddle.fluid.param_attr as attr
-from paddle.fluid.dygraph import Embedding, Layer
+from paddle.fluid.dygraph import Layer
 from paddle.jit.api import declarative
 from paddle.static import Variable
 
@@ -42,11 +42,12 @@ class EmbeddingLayer:
         """
         # TODO(huihuangzheng): The original code set the is_sparse=True, but it
         # causes crush in dy2stat. Set it to True after fixing it.
-        emb = Embedding(
-            size=[self.dict_size, self.emb_dim],
-            is_sparse=True,
+        emb = paddle.nn.Embedding(
+            self.dict_size,
+            self.emb_dim,
+            sparse=True,
             padding_idx=self.padding_idx,
-            param_attr=attr.ParamAttr(
+            weight_attr=attr.ParamAttr(
                 name=self.name, initializer=fluid.initializer.Xavier()
             ),
         )

python/paddle/fluid/tests/unittests/dygraph_to_static/simnet_dygraph_model_v2.py

@@ -38,11 +38,12 @@ class EmbeddingLayer:
         """
         # TODO(huihuangzheng): The original code set the is_sparse=True, but it
         # causes crush in dy2stat. Set it to True after fixing it.
-        emb = paddle.fluid.dygraph.Embedding(
-            size=[self.dict_size, self.emb_dim],
-            is_sparse=True,
+        emb = paddle.nn.Embedding(
+            self.dict_size,
+            self.emb_dim,
+            sparse=True,
             padding_idx=self.padding_idx,
-            param_attr=paddle.ParamAttr(
+            weight_attr=paddle.ParamAttr(
                 name=self.name,
                 initializer=paddle.nn.initializer.XavierUniform(),
             ),

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

@@ -25,7 +25,7 @@ os.environ["CUDA_VISIBLE_DEVICES"] = "2"
 import paddle
 import paddle.fluid as fluid
 from paddle import _legacy_C_ops
-from paddle.fluid.dygraph import Embedding, to_variable
+from paddle.fluid.dygraph import to_variable
 from paddle.fluid.dygraph.io import INFER_MODEL_SUFFIX, INFER_PARAMS_SUFFIX
 from paddle.fluid.framework import _non_static_mode
 from paddle.jit import ProgramTranslator
@@ -371,10 +371,10 @@ class LexNet(fluid.dygraph.Layer):
         self.bigru_num = args.bigru_num
         self.init_bound = 0.1
 
-        self.word_embedding = Embedding(
-            size=[self.vocab_size, self.word_emb_dim],
-            dtype='float32',
-            param_attr=fluid.ParamAttr(
+        self.word_embedding = paddle.nn.Embedding(
+            self.vocab_size,
+            self.word_emb_dim,
+            weight_attr=fluid.ParamAttr(
                 learning_rate=self.emb_lr,
                 name="word_emb",
                 initializer=fluid.initializer.Uniform(

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

@@ -21,7 +21,6 @@ import numpy as np
 import paddle
 import paddle.fluid as fluid
 from paddle.fluid.dygraph.base import to_variable
-from paddle.fluid.dygraph.nn import Embedding
 from paddle.fluid.optimizer import SGDOptimizer
 from paddle.jit import ProgramTranslator
 from paddle.jit.api import declarative
@@ -156,11 +155,11 @@ class PtbModel(fluid.Layer):
             init_scale=init_scale,
             dropout=dropout,
         )
-        self.embedding = Embedding(
-            size=[vocab_size, hidden_size],
-            dtype='float32',
-            is_sparse=False,
-            param_attr=fluid.ParamAttr(
+        self.embedding = paddle.nn.Embedding(
+            vocab_size,
+            hidden_size,
+            sparse=False,
+            weight_attr=fluid.ParamAttr(
                 name='embedding_para',
                 initializer=fluid.initializer.UniformInitializer(
                     low=-init_scale, high=init_scale

python/paddle/fluid/tests/unittests/dygraph_to_static/test_ptb_lm_v2.py

@@ -150,11 +150,11 @@ class PtbModel(paddle.nn.Layer):
             init_scale=init_scale,
             dropout=dropout,
         )
-        self.embedding = paddle.fluid.dygraph.nn.Embedding(
-            size=[vocab_size, hidden_size],
-            dtype='float32',
-            is_sparse=False,
-            param_attr=paddle.ParamAttr(
+        self.embedding = paddle.nn.Embedding(
+            vocab_size,
+            hidden_size,
+            sparse=False,
+            weight_attr=paddle.ParamAttr(
                 name='embedding_para',
                 initializer=paddle.nn.initializer.Uniform(
                     low=-init_scale, high=init_scale

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

@@ -20,10 +20,9 @@ from test_lac import DynamicGRU
 import paddle
 import paddle.fluid as fluid
 from paddle.fluid.dygraph import to_variable
-from paddle.fluid.dygraph.nn import Embedding
 from paddle.jit import ProgramTranslator
 from paddle.jit.api import declarative
-from paddle.nn import Linear
+from paddle.nn import Embedding, Linear
 
 SEED = 2020
 program_translator = ProgramTranslator()
@@ -73,9 +72,9 @@ class CNN(fluid.dygraph.Layer):
         self.batch_size = batch_size
         self.seq_len = seq_len
         self.embedding = Embedding(
-            size=[self.dict_dim + 1, self.emb_dim],
-            dtype='float32',
-            is_sparse=False,
+            self.dict_dim + 1,
+            self.emb_dim,
+            sparse=False,
         )
         self._simple_conv_pool_1 = SimpleConvPool(
             self.channels,
@@ -124,9 +123,9 @@ class BOW(fluid.dygraph.Layer):
         self.batch_size = batch_size
         self.seq_len = seq_len
         self.embedding = Embedding(
-            size=[self.dict_dim + 1, self.emb_dim],
-            dtype='float32',
-            is_sparse=False,
+            self.dict_dim + 1,
+            self.emb_dim,
+            sparse=False,
         )
         self._fc1 = Linear(self.hid_dim, self.hid_dim)
         self._fc2 = Linear(self.hid_dim, self.fc_hid_dim)
@@ -167,10 +166,10 @@ class GRU(fluid.dygraph.Layer):
         self.batch_size = batch_size
         self.seq_len = seq_len
         self.embedding = Embedding(
-            size=[self.dict_dim + 1, self.emb_dim],
-            dtype='float32',
-            param_attr=fluid.ParamAttr(learning_rate=30),
-            is_sparse=False,
+            self.dict_dim + 1,
+            self.emb_dim,
+            weight_attr=fluid.ParamAttr(learning_rate=30),
+            sparse=False,
         )
         h_0 = np.zeros((self.batch_size, self.hid_dim), dtype="float32")
         h_0 = to_variable(h_0)
@@ -213,10 +212,10 @@ class BiGRU(fluid.dygraph.Layer):
         self.batch_size = batch_size
         self.seq_len = seq_len
         self.embedding = Embedding(
-            size=[self.dict_dim + 1, self.emb_dim],
-            dtype='float32',
-            param_attr=fluid.ParamAttr(learning_rate=30),
-            is_sparse=False,
+            self.dict_dim + 1,
+            self.emb_dim,
+            weight_attr=fluid.ParamAttr(learning_rate=30),
+            sparse=False,
         )
         h_0 = np.zeros((self.batch_size, self.hid_dim), dtype="float32")
         h_0 = to_variable(h_0)

python/paddle/fluid/tests/unittests/dygraph_to_static/test_word2vec.py

@@ -20,9 +20,9 @@ import numpy as np
 
 import paddle
 import paddle.fluid as fluid
-from paddle.fluid.dygraph.nn import Embedding
 from paddle.jit import ProgramTranslator
 from paddle.jit.api import declarative
+from paddle.nn import Embedding
 
 
 def fake_text():
@@ -227,9 +227,9 @@ class SkipGram(fluid.dygraph.Layer):
         self.embedding_size = embedding_size
 
         self.embedding = Embedding(
-            size=[self.vocab_size, self.embedding_size],
-            dtype='float32',
-            param_attr=fluid.ParamAttr(
+            self.vocab_size,
+            self.embedding_size,
+            weight_attr=fluid.ParamAttr(
                 name='embedding_para',
                 initializer=fluid.initializer.UniformInitializer(
                     low=-0.5 / self.embedding_size,
@@ -239,9 +239,9 @@ class SkipGram(fluid.dygraph.Layer):
         )
 
         self.embedding_out = Embedding(
-            size=[self.vocab_size, self.embedding_size],
-            dtype='float32',
-            param_attr=fluid.ParamAttr(
+            self.vocab_size,
+            self.embedding_size,
+            weight_attr=fluid.ParamAttr(
                 name='embedding_out_para',
                 initializer=fluid.initializer.UniformInitializer(
                     low=-0.5 / self.embedding_size,

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

@@ -18,7 +18,7 @@ import paddle
 import paddle.fluid as fluid
 import paddle.fluid.layers as layers
 import paddle.nn.functional as F
-from paddle.fluid.dygraph import Embedding, Layer, to_variable
+from paddle.fluid.dygraph import Layer, to_variable
 from paddle.fluid.layers.utils import map_structure
 from paddle.jit.api import dygraph_to_static_func
 from paddle.nn import Linear
@@ -276,10 +276,10 @@ class Encoder(Layer):
 class Embedder(Layer):
     def __init__(self, vocab_size, emb_dim, bos_idx=0):
         super().__init__()
-        self.word_embedder = Embedding(
-            size=[vocab_size, emb_dim],
-            padding_idx=bos_idx,
-            param_attr=fluid.ParamAttr(
+        self.word_embedder = paddle.nn.Embedding(
+            vocab_size,
+            emb_dim,
+            weight_attr=fluid.ParamAttr(
                 initializer=fluid.initializer.Normal(0.0, emb_dim**-0.5)
             ),
         )
@@ -311,9 +311,10 @@ class WrapEncoder(Layer):
         self.emb_dropout = prepostprocess_dropout
         self.emb_dim = d_model
         self.word_embedder = word_embedder
-        self.pos_encoder = Embedding(
-            size=[max_length, self.emb_dim],
-            param_attr=fluid.ParamAttr(
+        self.pos_encoder = paddle.nn.Embedding(
+            max_length,
+            self.emb_dim,
+            weight_attr=fluid.ParamAttr(
                 initializer=fluid.initializer.NumpyArrayInitializer(
                     position_encoding_init(max_length, self.emb_dim)
                 ),
@@ -499,9 +500,10 @@ class WrapDecoder(Layer):
         self.emb_dropout = prepostprocess_dropout
         self.emb_dim = d_model
         self.word_embedder = word_embedder
-        self.pos_encoder = Embedding(
-            size=[max_length, self.emb_dim],
-            param_attr=fluid.ParamAttr(
+        self.pos_encoder = paddle.nn.Embedding(
+            max_length,
+            self.emb_dim,
+            weight_attr=fluid.ParamAttr(
                 initializer=fluid.initializer.NumpyArrayInitializer(
                     position_encoding_init(max_length, self.emb_dim)
                 ),

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

@@ -18,7 +18,7 @@ from test_dist_base import TestParallelDyGraphRunnerBase, runtime_main
 import paddle
 import paddle.fluid as fluid
 from paddle.fluid.dygraph.base import to_variable
-from paddle.fluid.dygraph.nn import Embedding
+from paddle.nn import Embedding
 
 
 class SimpleNet(fluid.Layer):
@@ -37,10 +37,10 @@ class SimpleNet(fluid.Layer):
         self.init_scale = init_scale
         self.num_steps = num_steps
         self.embedding = Embedding(
-            size=[self.vocab_size, self.hidden_size],
-            dtype=dtype,
-            is_sparse=is_sparse,
-            param_attr=fluid.ParamAttr(
+            self.vocab_size,
+            self.hidden_size,
+            sparse=is_sparse,
+            weight_attr=fluid.ParamAttr(
                 initializer=fluid.initializer.UniformInitializer(
                     low=-init_scale, high=init_scale
                 )

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

@@ -19,6 +19,7 @@ import numpy as np
 import paddle
 import paddle.fluid as fluid
 from paddle.fluid.framework import _test_eager_guard
+from paddle.nn import Embedding
 from paddle.tensor import random
 
 
@@ -122,8 +123,8 @@ class AutoPruneLayer3(fluid.Layer):
 class MyLayer(fluid.Layer):
     def __init__(self, input_size, vocab_size, size, dtype="float32"):
         super().__init__(dtype=dtype)
-        self.embed0 = fluid.Embedding(size=(vocab_size, size))
-        self.embed1 = fluid.Embedding(size=(vocab_size, size))
+        self.embed0 = Embedding(vocab_size, size)
+        self.embed1 = Embedding(vocab_size, size)
         self.linear_0 = paddle.nn.Linear(input_size, size)
         self.linear_1 = paddle.nn.Linear(input_size, size)
 
@@ -144,8 +145,8 @@ class MyLayer(fluid.Layer):
 class MyLayer2(fluid.Layer):
     def __init__(self, input_size, vocab_size, size, dtype="float32"):
         super().__init__(dtype=dtype)
-        self.embed0 = fluid.Embedding(size=(vocab_size, size))
-        self.embed1 = fluid.Embedding(size=(vocab_size, size))
+        self.embed0 = Embedding(vocab_size, size)
+        self.embed1 = Embedding(vocab_size, size)
         self.linear_0 = paddle.nn.Linear(input_size, size)
         self.linear_1 = paddle.nn.Linear(input_size, size)
 

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

@@ -21,7 +21,7 @@ import numpy as np
 import paddle
 import paddle.fluid as fluid
 import paddle.fluid.framework as framework
-from paddle.fluid.dygraph.nn import BatchNorm, Embedding
+from paddle.fluid.dygraph.nn import BatchNorm
 from paddle.nn import Linear
 
 
@@ -206,8 +206,8 @@ class TestDygraphLoadStatic(unittest.TestCase):
                     self.batch_norm_1 = BatchNorm(10)
                     self.batch_norm_2 = BatchNorm(10)
 
-                    self.emb1 = Embedding([1000, 100])
-                    self.emb2 = Embedding([2000, 200])
+                    self.emb1 = paddle.nn.Embedding(1000, 100)
+                    self.emb2 = paddle.nn.Embedding(2000, 200)
 
                     self.layer_norm_1 = paddle.nn.LayerNorm([10])
                     self.layer_norm_2 = paddle.nn.LayerNorm(10)

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

@@ -22,7 +22,6 @@ import paddle
 import paddle.fluid as fluid
 import paddle.fluid.core as core
 from paddle.fluid.dygraph.base import to_variable
-from paddle.fluid.dygraph.nn import Embedding
 from paddle.fluid.framework import _test_eager_guard
 from paddle.fluid.optimizer import SGDOptimizer
 
@@ -42,11 +41,12 @@ class SimpleNet(fluid.Layer):
         self.vocab_size = vocab_size
         self.init_scale = init_scale
         self.num_steps = num_steps
-        self.embedding = Embedding(
-            size=[vocab_size, hidden_size],
-            dtype=dtype,
-            is_sparse=is_sparse,
-            param_attr=fluid.ParamAttr(
+        paddle.set_default_dtype(dtype)
+        self.embedding = paddle.nn.Embedding(
+            vocab_size,
+            hidden_size,
+            sparse=is_sparse,
+            weight_attr=fluid.ParamAttr(
                 name='embedding_para',
                 initializer=fluid.initializer.UniformInitializer(
                     low=-init_scale, high=init_scale

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

@@ -101,7 +101,7 @@ class TestImperativeNamedParameters(unittest.TestCase):
                     self.linear1 = paddle.nn.Linear(10, 10)
                     self.linear2 = paddle.nn.Linear(5, 5)
                     self.conv2d = paddle.nn.Conv2D(3, 2, 3)
-                    self.embedding = fluid.dygraph.Embedding(size=[128, 16])
+                    self.embedding = paddle.nn.Embedding(128, 16)
                     self.h_0 = fluid.dygraph.to_variable(
                         np.zeros([10, 10]).astype('float32')
                     )

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

@@ -21,7 +21,7 @@ import paddle
 import paddle.fluid as fluid
 from paddle.fluid import core
 from paddle.fluid.dygraph.base import to_variable
-from paddle.fluid.dygraph.nn import BatchNorm, Embedding
+from paddle.fluid.dygraph.nn import BatchNorm
 from paddle.fluid.framework import _test_eager_guard
 from paddle.nn import Linear
 
@@ -371,8 +371,8 @@ class OCRAttention(fluid.dygraph.Layer):
             Config.decoder_size,
             bias_attr=False,
         )
-        self.embedding = Embedding(
-            [Config.num_classes + 2, Config.word_vector_dim], dtype='float32'
+        self.embedding = paddle.nn.Embedding(
+            Config.num_classes + 2, Config.word_vector_dim
         )
         self.gru_decoder_with_attention = GRUDecoderWithAttention(
             Config.decoder_size, Config.num_classes

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

@@ -23,9 +23,9 @@ import paddle.fluid as fluid
 import paddle.fluid.core as core
 import paddle.fluid.framework as framework
 from paddle.fluid.dygraph.base import to_variable
-from paddle.fluid.dygraph.nn import Embedding
 from paddle.fluid.framework import _test_eager_guard
 from paddle.fluid.optimizer import SGDOptimizer
+from paddle.nn import Embedding
 
 
 class SimpleLSTMRNN(fluid.Layer):
@@ -172,10 +172,10 @@ class PtbModel(fluid.Layer):
             dropout=dropout,
         )
         self.embedding = Embedding(
-            size=[vocab_size, hidden_size],
-            dtype='float32',
-            is_sparse=is_sparse,
-            param_attr=fluid.ParamAttr(
+            vocab_size,
+            hidden_size,
+            sparse=is_sparse,
+            weight_attr=fluid.ParamAttr(
                 name='embedding_para',
                 initializer=fluid.initializer.UniformInitializer(
                     low=-init_scale, high=init_scale

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

@@ -22,9 +22,9 @@ import paddle.fluid as fluid
 import paddle.fluid.core as core
 from paddle.fluid.dygraph.base import to_variable
 from paddle.fluid.dygraph.learning_rate_scheduler import LearningRateDecay
-from paddle.fluid.dygraph.nn import Embedding
 from paddle.fluid.framework import _test_eager_guard
 from paddle.fluid.optimizer import Adam
+from paddle.nn import Embedding
 
 
 class SimpleLSTMRNN(fluid.Layer):
@@ -167,10 +167,10 @@ class PtbModel(fluid.Layer):
             dropout=dropout,
         )
         self.embedding = Embedding(
-            size=[vocab_size, hidden_size],
-            dtype='float32',
-            is_sparse=False,
-            param_attr=fluid.ParamAttr(
+            vocab_size,
+            hidden_size,
+            sparse=False,
+            weight_attr=fluid.ParamAttr(
                 name='embedding_para',
                 initializer=fluid.initializer.UniformInitializer(
                     low=-init_scale, high=init_scale
@@ -991,7 +991,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
 
     def func_testOnlyLoadParams(self):
         with fluid.dygraph.guard():
-            emb = fluid.dygraph.Embedding([10, 10])
+            emb = paddle.nn.Embedding(10, 10)
             state_dict = emb.state_dict()
             fluid.save_dygraph(state_dict, os.path.join('saved_dy', 'emb_dy'))
 
@@ -1011,7 +1011,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
 
     def func_test_load_compatible_with_keep_name_table(self):
         with fluid.dygraph.guard():
-            emb = fluid.dygraph.Embedding([10, 10])
+            emb = paddle.nn.Embedding(10, 10)
             state_dict = emb.state_dict()
             fluid.save_dygraph(state_dict, os.path.join('saved_dy', 'emb_dy'))
 

python/paddle/fluid/tests/unittests/test_imperative_save_load_v2.py

@@ -23,8 +23,8 @@ import paddle.fluid as fluid
 import paddle.fluid.core as core
 from paddle.fluid.dygraph.base import to_variable
 from paddle.fluid.dygraph.learning_rate_scheduler import LearningRateDecay
-from paddle.fluid.dygraph.nn import Embedding
 from paddle.fluid.framework import _test_eager_guard
+from paddle.nn import Embedding
 from paddle.optimizer import Adam
 
 
@@ -168,10 +168,10 @@ class PtbModel(fluid.Layer):
             dropout=dropout,
         )
         self.embedding = Embedding(
-            size=[vocab_size, hidden_size],
-            dtype='float32',
-            is_sparse=False,
-            param_attr=fluid.ParamAttr(
+            vocab_size,
+            hidden_size,
+            sparse=False,
+            weight_attr=fluid.ParamAttr(
                 name='embedding_para',
                 initializer=fluid.initializer.UniformInitializer(
                     low=-init_scale, high=init_scale
@@ -1015,7 +1015,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
 
     def func_testOnlyLoadParams(self):
         with fluid.dygraph.guard():
-            emb = fluid.dygraph.Embedding([10, 10])
+            emb = paddle.nn.Embedding(10, 10)
             state_dict = emb.state_dict()
             paddle.save(
                 state_dict,
@@ -1028,7 +1028,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
 
     def func_test_no_state_in_input_dict(self):
         with fluid.dygraph.guard():
-            emb = fluid.dygraph.Embedding([10, 10])
+            emb = paddle.nn.Embedding(10, 10)
             state_dict = emb.state_dict()
             paddle.save(
                 state_dict,
@@ -1044,7 +1044,7 @@ class TestDygraphPtbRnn(unittest.TestCase):
 
     def func_test_state_shape_mismatch(self):
         with fluid.dygraph.guard():
-            emb = fluid.dygraph.Embedding([10, 10])
+            emb = paddle.nn.Embedding(10, 10)
             state_dict = emb.state_dict()
             paddle.save(
                 state_dict,

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

@@ -27,11 +27,11 @@ from paddle.fluid.optimizer import SGDOptimizer
 class SimpleNet(paddle.nn.Layer):
     def __init__(self, vocab_size, hidden_size, dtype):
         super().__init__()
-        self.emb = fluid.dygraph.Embedding(
-            size=[vocab_size, hidden_size],
-            dtype=dtype,
-            param_attr='emb.w',
-            is_sparse=True,
+        self.emb = paddle.nn.Embedding(
+            vocab_size,
+            hidden_size,
+            weight_attr='emb.w',
+            sparse=True,
         )
 
     def forward(self, input):

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

@@ -22,9 +22,9 @@ import paddle.fluid as fluid
 import paddle.fluid.core as core
 import paddle.fluid.framework as framework
 from paddle.fluid.dygraph.base import to_variable
-from paddle.fluid.dygraph.nn import Embedding
 from paddle.fluid.framework import _test_eager_guard
 from paddle.fluid.optimizer import SGDOptimizer
+from paddle.nn import Embedding
 
 
 class SimpleNet(fluid.Layer):
@@ -42,11 +42,12 @@ class SimpleNet(fluid.Layer):
         self.vocab_size = vocab_size
         self.init_scale = init_scale
         self.num_steps = num_steps
+        paddle.set_default_dtype(dtype)
         self.embedding = Embedding(
-            size=[vocab_size, hidden_size],
-            dtype=dtype,
-            is_sparse=is_sparse,
-            param_attr=fluid.ParamAttr(
+            vocab_size,
+            hidden_size,
+            sparse=is_sparse,
+            weight_attr=fluid.ParamAttr(
                 name='embedding_para',
                 initializer=fluid.initializer.UniformInitializer(
                     low=-init_scale, high=init_scale

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

@@ -20,7 +20,7 @@ from test_imperative_base import new_program_scope
 import paddle
 import paddle.fluid as fluid
 import paddle.nn.functional as F
-from paddle.fluid import Embedding, Layer, core
+from paddle.fluid import Layer, core
 from paddle.fluid.dygraph import guard, to_variable
 from paddle.fluid.framework import _in_legacy_dygraph, _test_eager_guard
 from paddle.nn import Linear
@@ -664,11 +664,11 @@ class PrepareEncoderDecoderLayer(Layer):
         self._src_emb_dim = src_emb_dim
         self._src_vocab_size = src_vocab_size
         self._dropout_rate = dropout_rate
-        self._input_emb = Embedding(
-            size=[src_vocab_size, src_emb_dim],
-            is_sparse=is_sparse,
-            padding_idx=0,
-            param_attr=fluid.ParamAttr(
+        self._input_emb = paddle.nn.Embedding(
+            src_vocab_size,
+            src_emb_dim,
+            sparse=is_sparse,
+            weight_attr=fluid.ParamAttr(
                 name=word_emb_param_name,
                 initializer=fluid.initializer.Normal(0.0, src_emb_dim**-0.5),
             ),
@@ -678,10 +678,11 @@ class PrepareEncoderDecoderLayer(Layer):
             pos_inp = pos_inp1
         else:
             pos_inp = pos_inp2
-        self._pos_emb = Embedding(
-            size=[self._src_max_len, src_emb_dim],
-            is_sparse=is_sparse,
-            param_attr=fluid.ParamAttr(
+        self._pos_emb = paddle.nn.Embedding(
+            self._src_max_len,
+            src_emb_dim,
+            sparse=is_sparse,
+            weight_attr=fluid.ParamAttr(
                 name=pos_enc_param_name,
                 initializer=fluid.initializer.NumpyArrayInitializer(pos_inp),
                 trainable=False,

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

@@ -26,7 +26,7 @@ import paddle.fluid.layers as layers
 import paddle.fluid.nets as nets
 import paddle.nn.functional as F
 from paddle.fluid import core
-from paddle.fluid.dygraph import base, nn, to_variable
+from paddle.fluid.dygraph import base, to_variable
 from paddle.fluid.framework import (
     Program,
     _test_eager_guard,
@@ -732,8 +732,8 @@ class TestLayer(LayerTest):
             )[0]
         with self.static_graph():
             data_t = layers.data(name='word', shape=[1], dtype='int64')
-            emb2 = nn.Embedding(
-                size=[dict_size, 32], param_attr='emb.w', is_sparse=False
+            emb2 = paddle.nn.Embedding(
+                dict_size, 32, weight_attr='emb.w', sparse=False
             )
             emb_rlt = emb2(data_t)
             static_rlt2 = self.get_static_graph_result(
@@ -741,16 +741,17 @@ class TestLayer(LayerTest):
             )[0]
         with self.dynamic_graph():
             with _test_eager_guard():
-                emb2 = nn.Embedding(
-                    size=[dict_size, 32],
-                    param_attr='eager_emb.w',
-                    is_sparse=False,
+                emb2 = paddle.nn.Embedding(
+                    dict_size,
+                    32,
+                    weight_attr='eager_emb.w',
+                    sparse=False,
                 )
                 dy_eager_rlt = emb2(base.to_variable(inp_word))
                 dy_eager_rlt_value = dy_eager_rlt.numpy()
 
-            emb2 = nn.Embedding(
-                size=[dict_size, 32], param_attr='emb.w', is_sparse=False
+            emb2 = paddle.nn.Embedding(
+                dict_size, 32, weight_attr='emb.w', sparse=False
             )
             dy_rlt = emb2(base.to_variable(inp_word))
             dy_rlt_value = dy_rlt.numpy()
@@ -767,11 +768,12 @@ class TestLayer(LayerTest):
                         custom_weight
                     )
                 )
-                emb1 = nn.Embedding(size=[dict_size, 32], is_sparse=False)
-                emb2 = nn.Embedding(
-                    size=[dict_size, 32],
-                    param_attr=weight_attr,
-                    is_sparse=False,
+                emb1 = paddle.nn.Embedding(dict_size, 32, sparse=False)
+                emb2 = paddle.nn.Embedding(
+                    dict_size,
+                    32,
+                    weight_attr=weight_attr,
+                    sparse=False,
                 )
                 rep1 = emb1(base.to_variable(inp_word))
                 rep2 = emb2(base.to_variable(inp_word))
@@ -797,9 +799,9 @@ class TestLayer(LayerTest):
                     custom_weight
                 )
             )
-            emb1 = nn.Embedding(size=[dict_size, 32], is_sparse=False)
-            emb2 = nn.Embedding(
-                size=[dict_size, 32], param_attr=weight_attr, is_sparse=False
+            emb1 = paddle.nn.Embedding(dict_size, 32, sparse=False)
+            emb2 = paddle.nn.Embedding(
+                dict_size, 32, weight_attr=weight_attr, sparse=False
             )
             rep1 = emb1(base.to_variable(inp_word))
             rep2 = emb2(base.to_variable(inp_word))

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

@@ -689,9 +689,7 @@ class TestBeamSearch(ModuleApiTest):
         beam_size=4,
         max_step_num=20,
     ):
-        embedder = paddle.fluid.dygraph.Embedding(
-            size=[vocab_size, embed_dim], dtype="float64"
-        )
+        embedder = paddle.nn.Embedding(vocab_size, embed_dim)
         output_layer = nn.Linear(hidden_size, vocab_size)
         cell = nn.LSTMCell(embed_dim, hidden_size)
         self.max_step_num = max_step_num