# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from paddle import nn from parakeet.modules import attention as attn from paddle.nn import functional as F __all__ = [ "PositionwiseFFN", "TransformerEncoderLayer", "TransformerDecoderLayer", ] class PositionwiseFFN(nn.Layer): """A faithful implementation of Position-wise Feed-Forward Network in `Attention is All You Need `_. 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 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 class TransformerEncoderLayer(nn.Layer): """A faithful implementation of Transformer encoder layer in `Attention is All You Need `_. 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 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 class TransformerDecoderLayer(nn.Layer): """A faithful implementation of Transformer decoder layer in `Attention is All You Need `_. 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 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