[Doc] Fix embedding doc; test=document_fix

python/paddle/nn/functional/input.py

@@ -132,7 +132,21 @@ def embedding_renorm_(
     x: Tensor, weight: Tensor, max_norm: float, norm_type: float = 2.0
 ) -> Tensor:
     r"""
-    This operator is used to update the embedding weight by renorm.
+    Renorm the weight of embedding with respect to the provided :attr:`max_norm` and :attr:`norm_type` .
+
+    Note:
+        In the dynamic graph mode, the input weight will be updated in-place, and the return value will be the changed weight.
+
+    Args:
+        x(Tensor): A Tensor with type int32/int64, which contains the id information. The value of the input id should
+            satisfy :math:`0<= id < weight.shape[0]` .
+        weight (Tensor): The weight. A Tensor with shape of lookup table parameter. It should have two elements which
+            indicates the size of the dictionary of embeddings and the size of each embedding vector respectively.
+        max_norm(float): The maximum norm for each embedding vector.
+        norm_type(float, optional): The p of the p-norm to compute for the max_norm option. Default: 2.0.
+
+    Returns:
+        Tensor, The updated weight. The data type is the same as :attr:`weight`.
     """
     with paddle.set_grad_enabled(False):
         unique_x = paddle.unique(x)
@@ -201,7 +215,7 @@ def embedding(
             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.
         max_norm(float, optional): If provided, will renormalize the embedding vectors to have a norm larger than
-            :attr:`max\_norm` . It will inplace update the input embedding weight. Default: None.
+            :attr:`max\_norm` . It will inplace update the input embedding weight in dynamic graph mode. Default: None.
         norm_type(float, optional): The p of the p-norm to compute for the max_norm option. Default: 2.0.
         name(str|None, optional): For detailed information, please refer
            to :ref:`api_guide_Name`. Usually name is no need to set and

python/paddle/nn/layer/common.py

@@ -1701,6 +1701,9 @@ class Embedding(Layer):
             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.
+        max_norm(float, optional): If provided, will renormalize the embedding vectors to have a norm larger than
+            :attr:`max\_norm` . It will inplace update the input embedding weight in dynamic graph mode. Default: None.
+        norm_type(float, optional): The p of the p-norm to compute for the max_norm option. Default: 2.0.
         sparse(bool, optional): 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,