`athena.layers.transformer`¶

the transformer model

Module Contents¶

Classes¶

`Transformer`	A transformer model. User is able to modify the attributes as needed. The architecture
`TransformerEncoder`	TransformerEncoder is a stack of N encoder layers
`TransformerDecoder`	TransformerDecoder is a stack of N decoder layers
`TransformerEncoderLayer`	TransformerEncoderLayer is made up of self-attn and feedforward network.
`TransformerDecoderLayer`	TransformerDecoderLayer is made up of self-attn, multi-head-attn and feedforward network.

class athena.layers.transformer.Transformer(d_model=512, nhead=8, num_encoder_layers=6, num_decoder_layers=6, dim_feedforward=2048, dropout=0.1, activation='gelu', unidirectional=False, look_ahead=0, custom_encoder=None, custom_decoder=None)¶

Bases: tensorflow.keras.layers.Layer

A transformer model. User is able to modify the attributes as needed. The architecture is based on the paper “Attention Is All You Need”. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems, pages 6000-6010. Users can build the BERT(https://arxiv.org/abs/1810.04805) model with corresponding parameters.

Parameters:

d_model – the number of expected features in the encoder/decoder inputs (default=512).
nhead – the number of heads in the multiheadattention models (default=8).
num_encoder_layers – the number of sub-encoder-layers in the encoder (default=6).
num_decoder_layers – the number of sub-decoder-layers in the decoder (default=6).
dim_feedforward – the dimension of the feedforward network model (default=2048).
dropout – the dropout value (default=0.1).
activation – the activation function of encoder/decoder intermediate layer, relu or gelu (default=relu).
custom_encoder – custom encoder (default=None).
custom_decoder – custom decoder (default=None).

Examples::

>>> transformer_model = Transformer(nhead=16, num_encoder_layers=12)
>>> src = tf.random.normal((10, 32, 512))
>>> tgt = tf.random.normal((20, 32, 512))
>>> out = transformer_model(src, tgt)

Note: A full example to apply nn.Transformer module for the word language model is available in https://github.com/pytorch/examples/tree/master/word_language_model

call(self, src, tgt, src_mask=None, tgt_mask=None, memory_mask=None, return_encoder_output=False, return_attention_weights=False, training=None)¶

Take in and process masked source/target sequences.

Parameters:

src – the sequence to the encoder (required).
tgt – the sequence to the decoder (required).
src_mask – the additive mask for the src sequence (optional).
tgt_mask – the additive mask for the tgt sequence (optional).
memory_mask – the additive mask for the encoder output (optional).
src_key_padding_mask – the ByteTensor mask for src keys per batch (optional).
tgt_key_padding_mask – the ByteTensor mask for tgt keys per batch (optional).
memory_key_padding_mask – the ByteTensor mask for memory keys per batch (optional).

Shape:

src: \((N, S, E)\).
tgt: \((N, T, E)\).
src_mask: \((N, S)\).
tgt_mask: \((N, T)\).
memory_mask: \((N, S)\).

Note: [src/tgt/memory]_mask should be a ByteTensor where True values are positions that should be masked with float(‘-inf’) and False values will be unchanged. This mask ensures that no information will be taken from position i if it is masked, and has a separate mask for each sequence in a batch.

output: \((N, T, E)\).

Note: Due to the multi-head attention architecture in the transformer model, the output sequence length of a transformer is same as the input sequence (i.e. target) length of the decode.

where S is the source sequence length, T is the target sequence length, N is the batch size, E is the feature number

Examples

>>> output = transformer_model(src, tgt, src_mask=src_mask, tgt_mask=tgt_mask)

class athena.layers.transformer.TransformerEncoder(encoder_layers)¶

Bases: tensorflow.keras.layers.Layer

TransformerEncoder is a stack of N encoder layers

Parameters:	encoder_layer – an instance of the TransformerEncoderLayer() class (required). num_layers – the number of sub-encoder-layers in the encoder (required). norm – the layer normalization component (optional).

Examples::

>>> encoder_layer = [TransformerEncoderLayer(d_model=512, nhead=8)
>>>                    for _ in range(num_layers)]
>>> transformer_encoder = TransformerEncoder(encoder_layer)
>>> src = torch.rand(10, 32, 512)
>>> out = transformer_encoder(src)

call(self, src, src_mask=None, training=None)¶

Pass the input through the endocder layers in turn.

Parameters:	src – the sequnce to the encoder (required). mask – the mask for the src sequence (optional).

Shape:: see the docs in Transformer class.

set_unidirectional(self, uni=False)¶: whether to apply trianglar masks to make transformer unidirectional

class athena.layers.transformer.TransformerDecoder(decoder_layers)¶

Bases: tensorflow.keras.layers.Layer

TransformerDecoder is a stack of N decoder layers

Parameters:	decoder_layer – an instance of the TransformerDecoderLayer() class (required). num_layers – the number of sub-decoder-layers in the decoder (required). norm – the layer normalization component (optional).

Examples::

>>> decoder_layer = [TransformerDecoderLayer(d_model=512, nhead=8)
>>>                     for _ in range(num_layers)]
>>> transformer_decoder = TransformerDecoder(decoder_layer)
>>> memory = torch.rand(10, 32, 512)
>>> tgt = torch.rand(20, 32, 512)
>>> out = transformer_decoder(tgt, memory)

call(self, tgt, memory, tgt_mask=None, memory_mask=None, return_attention_weights=False, training=None)¶

Pass the inputs (and mask) through the decoder layer in turn.

Parameters:	tgt – the sequence to the decoder (required). memory – the sequnce from the last layer of the encoder (required). tgt_mask – the mask for the tgt sequence (optional). memory_mask – the mask for the memory sequence (optional).

Shape:: see the docs in Transformer class.

class athena.layers.transformer.TransformerEncoderLayer(d_model, nhead, dim_feedforward=2048, dropout=0.1, activation='gelu', unidirectional=False, look_ahead=0, ffn=None)¶

Bases: tensorflow.keras.layers.Layer

TransformerEncoderLayer is made up of self-attn and feedforward network. This standard encoder layer is based on the paper “Attention Is All You Need”. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems, pages 6000-6010. Users may modify or implement in a different way during application.

Parameters:	d_model – the number of expected features in the input (required). nhead – the number of heads in the multiheadattention models (required). dim_feedforward – the dimension of the feedforward network model (default=2048). dropout – the dropout value (default=0.1). activation – the activation function of intermediate layer, relu or gelu (default=relu).

Examples::

>>> encoder_layer = TransformerEncoderLayer(d_model=512, nhead=8)
>>> src = tf.random(10, 32, 512)
>>> out = encoder_layer(src)

call(self, src, src_mask=None, training=None)¶

Pass the input through the endocder layer.

Parameters:	src – the sequnce to the encoder layer (required). mask – the mask for the src sequence (optional).

Shape:: see the docs in Transformer class.

set_unidirectional(self, uni=False)¶: whether to apply trianglar masks to make transformer unidirectional

class athena.layers.transformer.TransformerDecoderLayer(d_model, nhead, dim_feedforward=2048, dropout=0.1, activation='gelu')¶

Bases: tensorflow.keras.layers.Layer

TransformerDecoderLayer is made up of self-attn, multi-head-attn and feedforward network. This standard decoder layer is based on the paper “Attention Is All You Need”. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems, pages 6000-6010. Users may modify or implement in a different way during application.

Parameters:	d_model – the number of expected features in the input (required). nhead – the number of heads in the multiheadattention models (required). dim_feedforward – the dimension of the feedforward network model (default=2048). dropout – the dropout value (default=0.1). activation – the activation function of intermediate layer, relu or gelu (default=relu).

Examples::

>>> decoder_layer = TransformerDecoderLayer(d_model=512, nhead=8)
>>> memory = tf.random(10, 32, 512)
>>> tgt = tf.random(20, 32, 512)
>>> out = decoder_layer(tgt, memory)

call(self, tgt, memory, tgt_mask=None, memory_mask=None, training=None)¶

Pass the inputs (and mask) through the decoder layer.

Parameters:	tgt – the sequence to the decoder layer (required). memory – the sequnce from the last layer of the encoder (required). tgt_mask – the mask for the tgt sequence (optional). memory_mask – the mask for the memory sequence (optional).

Shape:: see the docs in Transformer class.

athena.layers.transformer¶

Module Contents¶

Classes¶

`athena.layers.transformer`¶