Source code for parakeet.modules.transformer
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from paddle import nn
from parakeet.modules import attention as attn
from paddle.nn import functional as F
__all__ = [
"PositionwiseFFN",
"TransformerEncoderLayer",
"TransformerDecoderLayer",
]
[docs]class PositionwiseFFN(nn.Layer):
"""A faithful implementation of Position-wise Feed-Forward Network
in `Attention is All You Need <https://arxiv.org/abs/1706.03762>`_.
It is basically a 2-layer MLP, with relu actication and dropout in between.
Parameters
----------
input_size: int
The feature size of the intput. It is also the feature size of the
output.
hidden_size: int
The hidden size.
dropout: float
The probability of the Dropout applied to the output of the first
layer, by default 0.
"""
def __init__(self, input_size: int, hidden_size: int, dropout=0.0):
super(PositionwiseFFN, self).__init__()
self.linear1 = nn.Linear(input_size, hidden_size)
self.linear2 = nn.Linear(hidden_size, input_size)
self.dropout = nn.Dropout(dropout)
self.input_size = input_size
self.hidden_szie = hidden_size
[docs] def forward(self, x):
r"""Forward pass of positionwise feed forward network.
Parameters
----------
x : Tensor [shape=(\*, input_size)]
The input tensor, where ``\*`` means arbitary shape.
Returns
-------
Tensor [shape=(\*, input_size)]
The output tensor.
"""
l1 = self.dropout(F.relu(self.linear1(x)))
l2 = self.linear2(l1)
return l2
[docs]class TransformerEncoderLayer(nn.Layer):
"""A faithful implementation of Transformer encoder layer in
`Attention is All You Need <https://arxiv.org/abs/1706.03762>`_.
Parameters
----------
d_model :int
The feature size of the input. It is also the feature size of the
output.
n_heads : int
The number of heads of self attention (a ``MultiheadAttention``
layer).
d_ffn : int
The hidden size of the positional feed forward network (a
``PositionwiseFFN`` layer).
dropout : float, optional
The probability of the dropout in MultiHeadAttention and
PositionwiseFFN, by default 0.
Notes
------
It uses the PostLN (post layer norm) scheme.
"""
def __init__(self, d_model, n_heads, d_ffn, dropout=0.):
super(TransformerEncoderLayer, self).__init__()
self.self_mha = attn.MultiheadAttention(d_model, n_heads, dropout)
self.layer_norm1 = nn.LayerNorm([d_model], epsilon=1e-6)
self.ffn = PositionwiseFFN(d_model, d_ffn, dropout)
self.layer_norm2 = nn.LayerNorm([d_model], epsilon=1e-6)
self.dropout = dropout
[docs] def forward(self, x, mask):
"""Forward pass of TransformerEncoderLayer.
Parameters
----------
x : Tensor [shape=(batch_size, time_steps, d_model)]
The input.
mask : Tensor
The padding mask. The shape is (batch_size, time_steps,
time_steps) or broadcastable shape.
Returns
-------
x :Tensor [shape=(batch_size, time_steps, d_model)]
The encoded output.
attn_weights : Tensor [shape=(batch_size, n_heads, time_steps, time_steps)]
The attention weights of the self attention.
"""
context_vector, attn_weights = self.self_mha(x, x, x, mask)
x = self.layer_norm1(
F.dropout(x + context_vector, self.dropout, training=self.training))
x = self.layer_norm2(
F.dropout(x + self.ffn(x), self.dropout, training=self.training))
return x, attn_weights
[docs]class TransformerDecoderLayer(nn.Layer):
"""A faithful implementation of Transformer decoder layer in
`Attention is All You Need <https://arxiv.org/abs/1706.03762>`_.
Parameters
----------
d_model :int
The feature size of the input. It is also the feature size of the
output.
n_heads : int
The number of heads of attentions (``MultiheadAttention``
layers).
d_ffn : int
The hidden size of the positional feed forward network (a
``PositionwiseFFN`` layer).
dropout : float, optional
The probability of the dropout in MultiHeadAttention and
PositionwiseFFN, by default 0.
Notes
------
It uses the PostLN (post layer norm) scheme.
"""
def __init__(self, d_model, n_heads, d_ffn, dropout=0.):
super(TransformerDecoderLayer, self).__init__()
self.self_mha = attn.MultiheadAttention(d_model, n_heads, dropout)
self.layer_norm1 = nn.LayerNorm([d_model], epsilon=1e-6)
self.cross_mha = attn.MultiheadAttention(d_model, n_heads, dropout)
self.layer_norm2 = nn.LayerNorm([d_model], epsilon=1e-6)
self.ffn = PositionwiseFFN(d_model, d_ffn, dropout)
self.layer_norm3 = nn.LayerNorm([d_model], epsilon=1e-6)
self.dropout = dropout
[docs] def forward(self, q, k, v, encoder_mask, decoder_mask):
"""Forward pass of TransformerEncoderLayer.
Parameters
----------
q : Tensor [shape=(batch_size, time_steps_q, d_model)]
The decoder input.
k : Tensor [shape=(batch_size, time_steps_k, d_model)]
The keys.
v : Tensor [shape=(batch_size, time_steps_k, d_model)]
The values
encoder_mask : Tensor
Encoder padding mask, shape is ``(batch_size, time_steps_k,
time_steps_k)`` or broadcastable shape.
decoder_mask : Tensor
Decoder mask, shape is ``(batch_size, time_steps_q, time_steps_k)``
or broadcastable shape.
Returns
--------
q : Tensor [shape=(batch_size, time_steps_q, d_model)]
The decoder output.
self_attn_weights : Tensor [shape=(batch_size, n_heads, time_steps_q, time_steps_q)]
Decoder self attention.
cross_attn_weights : Tensor [shape=(batch_size, n_heads, time_steps_q, time_steps_k)]
Decoder-encoder cross attention.
"""
context_vector, self_attn_weights = self.self_mha(q, q, q, decoder_mask)
q = self.layer_norm1(
F.dropout(q + context_vector, self.dropout, training=self.training))
context_vector, cross_attn_weights = self.cross_mha(q, k, v,
encoder_mask)
q = self.layer_norm2(
F.dropout(q + context_vector, self.dropout, training=self.training))
q = self.layer_norm3(
F.dropout(q + self.ffn(q), self.dropout, training=self.training))
return q, self_attn_weights, cross_attn_weights