208 lines
7.2 KiB
Python
208 lines
7.2 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from paddle import nn
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from parakeet.modules import attention as attn
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from paddle.nn import functional as F
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__all__ = [
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"PositionwiseFFN",
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"TransformerEncoderLayer",
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"TransformerDecoderLayer",
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]
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class PositionwiseFFN(nn.Layer):
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"""A faithful implementation of Position-wise Feed-Forward Network
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in `Attention is All You Need <https://arxiv.org/abs/1706.03762>`_.
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It is basically a 2-layer MLP, with relu actication and dropout in between.
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Parameters
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----------
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input_size: int
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The feature size of the intput. It is also the feature size of the
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output.
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hidden_size: int
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The hidden size.
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dropout: float
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The probability of the Dropout applied to the output of the first
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layer, by default 0.
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"""
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def __init__(self, input_size: int, hidden_size: int, dropout=0.0):
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super(PositionwiseFFN, self).__init__()
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self.linear1 = nn.Linear(input_size, hidden_size)
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self.linear2 = nn.Linear(hidden_size, input_size)
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self.dropout = nn.Dropout(dropout)
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self.input_size = input_size
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self.hidden_szie = hidden_size
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def forward(self, x):
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r"""Forward pass of positionwise feed forward network.
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Parameters
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----------
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x : Tensor [shape=(\*, input_size)]
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The input tensor, where ``\*`` means arbitary shape.
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Returns
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-------
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Tensor [shape=(\*, input_size)]
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The output tensor.
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"""
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l1 = self.dropout(F.relu(self.linear1(x)))
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l2 = self.linear2(l1)
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return l2
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class TransformerEncoderLayer(nn.Layer):
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"""A faithful implementation of Transformer encoder layer in
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`Attention is All You Need <https://arxiv.org/abs/1706.03762>`_.
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Parameters
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----------
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d_model :int
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The feature size of the input. It is also the feature size of the
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output.
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n_heads : int
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The number of heads of self attention (a ``MultiheadAttention``
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layer).
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d_ffn : int
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The hidden size of the positional feed forward network (a
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``PositionwiseFFN`` layer).
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dropout : float, optional
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The probability of the dropout in MultiHeadAttention and
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PositionwiseFFN, by default 0.
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Notes
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------
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It uses the PostLN (post layer norm) scheme.
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"""
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def __init__(self, d_model, n_heads, d_ffn, dropout=0.):
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super(TransformerEncoderLayer, self).__init__()
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self.self_mha = attn.MultiheadAttention(d_model, n_heads, dropout)
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self.layer_norm1 = nn.LayerNorm([d_model], epsilon=1e-6)
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self.ffn = PositionwiseFFN(d_model, d_ffn, dropout)
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self.layer_norm2 = nn.LayerNorm([d_model], epsilon=1e-6)
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self.dropout = dropout
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def forward(self, x, mask):
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"""Forward pass of TransformerEncoderLayer.
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Parameters
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----------
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x : Tensor [shape=(batch_size, time_steps, d_model)]
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The input.
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mask : Tensor
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The padding mask. The shape is (batch_size, time_steps,
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time_steps) or broadcastable shape.
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Returns
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-------
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x :Tensor [shape=(batch_size, time_steps, d_model)]
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The encoded output.
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attn_weights : Tensor [shape=(batch_size, n_heads, time_steps, time_steps)]
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The attention weights of the self attention.
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"""
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context_vector, attn_weights = self.self_mha(x, x, x, mask)
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x = self.layer_norm1(
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F.dropout(x + context_vector, self.dropout, training=self.training))
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x = self.layer_norm2(
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F.dropout(x + self.ffn(x), self.dropout, training=self.training))
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return x, attn_weights
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class TransformerDecoderLayer(nn.Layer):
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"""A faithful implementation of Transformer decoder layer in
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`Attention is All You Need <https://arxiv.org/abs/1706.03762>`_.
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Parameters
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----------
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d_model :int
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The feature size of the input. It is also the feature size of the
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output.
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n_heads : int
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The number of heads of attentions (``MultiheadAttention``
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layers).
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d_ffn : int
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The hidden size of the positional feed forward network (a
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``PositionwiseFFN`` layer).
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dropout : float, optional
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The probability of the dropout in MultiHeadAttention and
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PositionwiseFFN, by default 0.
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Notes
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------
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It uses the PostLN (post layer norm) scheme.
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"""
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def __init__(self, d_model, n_heads, d_ffn, dropout=0.):
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super(TransformerDecoderLayer, self).__init__()
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self.self_mha = attn.MultiheadAttention(d_model, n_heads, dropout)
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self.layer_norm1 = nn.LayerNorm([d_model], epsilon=1e-6)
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self.cross_mha = attn.MultiheadAttention(d_model, n_heads, dropout)
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self.layer_norm2 = nn.LayerNorm([d_model], epsilon=1e-6)
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self.ffn = PositionwiseFFN(d_model, d_ffn, dropout)
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self.layer_norm3 = nn.LayerNorm([d_model], epsilon=1e-6)
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self.dropout = dropout
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def forward(self, q, k, v, encoder_mask, decoder_mask):
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"""Forward pass of TransformerEncoderLayer.
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Parameters
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----------
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q : Tensor [shape=(batch_size, time_steps_q, d_model)]
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The decoder input.
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k : Tensor [shape=(batch_size, time_steps_k, d_model)]
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The keys.
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v : Tensor [shape=(batch_size, time_steps_k, d_model)]
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The values
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encoder_mask : Tensor
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Encoder padding mask, shape is ``(batch_size, time_steps_k,
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time_steps_k)`` or broadcastable shape.
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decoder_mask : Tensor
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Decoder mask, shape is ``(batch_size, time_steps_q, time_steps_k)``
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or broadcastable shape.
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Returns
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--------
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q : Tensor [shape=(batch_size, time_steps_q, d_model)]
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The decoder output.
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self_attn_weights : Tensor [shape=(batch_size, n_heads, time_steps_q, time_steps_q)]
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Decoder self attention.
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cross_attn_weights : Tensor [shape=(batch_size, n_heads, time_steps_q, time_steps_k)]
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Decoder-encoder cross attention.
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"""
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context_vector, self_attn_weights = self.self_mha(q, q, q, decoder_mask)
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q = self.layer_norm1(
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F.dropout(q + context_vector, self.dropout, training=self.training))
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context_vector, cross_attn_weights = self.cross_mha(q, k, v,
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encoder_mask)
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q = self.layer_norm2(
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F.dropout(q + context_vector, self.dropout, training=self.training))
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q = self.layer_norm3(
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F.dropout(q + self.ffn(q), self.dropout, training=self.training))
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return q, self_attn_weights, cross_attn_weights
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