From 4d7fe9e5dcbd3fd9ec8b04bf168d92f121e6ffcb Mon Sep 17 00:00:00 2001 From: xiemoyuan Date: Tue, 29 Sep 2020 12:48:15 +0000 Subject: [PATCH] Modify the docs for Transformer's APIs. test=document_fix --- python/paddle/nn/layer/transformer.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/python/paddle/nn/layer/transformer.py b/python/paddle/nn/layer/transformer.py index e6df5366d216c..ea4f6970bc686 100644 --- a/python/paddle/nn/layer/transformer.py +++ b/python/paddle/nn/layer/transformer.py @@ -644,7 +644,7 @@ class TransformerDecoderLayer(Layer): `weight_attr` to create parameters. Default: None, which means the default weight parameter property is used. See usage for details in :ref:`api_fluid_ParamAttr` . - bias_attr (ParamAttr|tuple, optional): To specify the bias parameter property. + bias_attr (ParamAttr|tuple|bool, optional): To specify the bias parameter property. If it is a tuple, `bias_attr[0]` would be used as `bias_attr` for self attention, `bias_attr[1]` would be used as `bias_attr` for cross attention, and `bias_attr[2]` would be used as `bias_attr` @@ -982,12 +982,12 @@ class Transformer(Layer): applies another layer normalization on the output of last encoder/decoder layer. Parameters: - d_model (int): The expected feature size in the encoder/decoder input - and output. - nhead (int): The number of heads in multi-head attention(MHA). - num_encoder_layers (int): The number of layers in encoder. - num_encoder_layers (int): The number of layers in decoder. - dim_feedforward (int): The hidden layer size in the feedforward network(FFN). + d_model (int, optional): The expected feature size in the encoder/decoder input + and output. Default 512 + nhead (int, optional): The number of heads in multi-head attention(MHA). Default 8 + num_encoder_layers (int, optional): The number of layers in encoder. Default 6 + num_decoder_layers (int, optional): The number of layers in decoder. Default 6 + dim_feedforward (int, optional): The hidden layer size in the feedforward network(FFN). Default 2048 dropout (float, optional): The dropout probability used in pre-process and post-precess of MHA and FFN sub-layer. Default 0.1 activation (str, optional): The activation function in the feedforward @@ -1015,7 +1015,7 @@ class Transformer(Layer): Default: None, which means the default weight parameter property is used. See usage for details in :code:`ParamAttr` . - bias_attr (ParamAttr|tuple, optional): To specify the bias parameter property. + bias_attr (ParamAttr|tuple|bool, optional): To specify the bias parameter property. If it is a tuple, the length of `bias_attr` could be 1, 2 or 3. If it is 3, `bias_attr[0]` would be used as `bias_attr` for self attention, `bias_attr[1]` would be used as `bias_attr` for cross attention of `TransformerDecoder`, @@ -1028,9 +1028,9 @@ class Transformer(Layer): The `False` value means the corresponding layer would not have trainable bias parameter. See usage for details in :code:`ParamAttr` . Default: None,which means the default bias parameter property is used. - custom_encoder (Layer): If custom encoder is provided, use it as the encoder. + custom_encoder (Layer, optional): If custom encoder is provided, use it as the encoder. Default None - custom_decoder (Layer): If custom decoder is provided, use it as the decoder. + custom_decoder (Layer, optional): If custom decoder is provided, use it as the decoder. Default None Examples: