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# Pytorch编写完整的Transformer
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本文涉及的jupter notebook在[Pytorch编写完整的Transformer](https://github.com/datawhalechina/learn-nlp-with-transformers/blob/main/docs/%E7%AF%87%E7%AB%A02-Transformer%E7%9B%B8%E5%85%B3%E5%8E%9F%E7%90%86/2.2.1-Pytorch%E7%BC%96%E5%86%99%E5%AE%8C%E6%95%B4%E7%9A%84Transformer.ipynb)
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在阅读完[2.2-图解transformer](./篇章2-Transformer相关原理/2.2-图解transformer.md)之后，希望大家能对transformer各个模块的设计和计算有一个形象的认识，本小节我们基于pytorch来实现一个Transformer，帮助大家进一步学习这个复杂的模型。
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**章节**
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- [词嵌入](#embed)
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- [位置编码](#pos)
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- [多头注意力](#multihead)
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- [搭建Transformer](#build)
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图：Transformer结构图
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## **<div id='embed'>词嵌入</div>**
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如上图所示，Transformer图里左边的是Encoder，右边是Decoder部分。Encoder输入源语言序列，Decoder里面输入需要被翻译的语言文本（在训练时）。一个文本常有许多序列组成，常见操作为将序列进行一些预处理（如词切分等）变成列表，一个序列的列表的元素通常为词表中不可切分的最小词，整个文本就是一个大列表，元素为一个一个由序列组成的列表。如一个序列经过切分后变为["am", "##ro", "##zi", "meets", "his", "father"]，接下来按照它们在词表中对应的索引进行转换，假设结果如[23, 94, 13, 41, 27, 96]。假如整个文本一共100个句子，那么就有100个列表为它的元素，因为每个序列的长度不一，需要设定最大长度，这里不妨设为128，那么将整个文本转换为数组之后，形状即为100 x 128，这就对应着batch_size和seq_length。
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输入之后，紧接着进行词嵌入处理，词嵌入就是将每一个词用预先训练好的向量进行映射。
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词嵌入在torch里基于`torch.nn.Embedding`实现，实例化时需要设置的参数为词表的大小和被映射的向量的维度比如`embed = nn.Embedding(10,8)`。向量的维度通俗来说就是向量里面有多少个数。注意，第一个参数是词表的大小，如果你目前最多有8个词，通常填写10（多一个位置留给unk和pad），你后面万一进入与这8个词不同的词就映射到unk上，序列padding的部分就映射到pad上。
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假如我们打算映射到8维（num_features或者embed_dim），那么，整个文本的形状变为100 x 128 x 8。接下来举个小例子解释一下：假设我们词表一共有10个词(算上unk和pad)，文本里有2个句子，每个句子有4个词，我们想要把每个词映射到8维的向量。于是2，4，8对应于batch_size, seq_length, embed_dim（如果batch在第一维的话）。
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另外，一般深度学习任务只改变num_features，所以讲维度一般是针对最后特征所在的维度。
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开始编程：
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所有需要的包的导入：
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```python
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import torch
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import torch.nn as nn
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from torch.nn.parameter import Parameter
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from torch.nn.init import xavier_uniform_
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from torch.nn.init import constant_
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from torch.nn.init import xavier_normal_
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import torch.nn.functional as F
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from typing import Optional, Tuple, Any
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from typing import List, Optional, Tuple
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import math
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import warnings
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```
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```python
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X = torch.zeros((2,4),dtype=torch.long)
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embed = nn.Embedding(10,8)
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print(embed(X).shape)
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```
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## **<div id='pos'>位置编码</div>**
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词嵌入之后紧接着就是位置编码，位置编码用以区分不同词以及同词不同特征之间的关系。代码中需要注意：X_只是初始化的矩阵，并不是输入进来的；完成位置编码之后会加一个dropout。另外，位置编码是最后加上去的，因此输入输出形状不变。
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```python
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Tensor = torch.Tensor
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def positional_encoding(X, num_features, dropout_p=0.1, max_len=512) -> Tensor:
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r'''
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给输入加入位置编码
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参数：
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- num_features: 输入进来的维度
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- dropout_p: dropout的概率，当其为非零时执行dropout
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- max_len: 句子的最大长度，默认512
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形状：
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- 输入： [batch_size, seq_length, num_features]
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- 输出： [batch_size, seq_length, num_features]
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例子：
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>>> X = torch.randn((2,4,10))
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>>> X = positional_encoding(X, 10)
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>>> print(X.shape)
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>>> torch.Size([2, 4, 10])
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'''
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dropout = nn.Dropout(dropout_p)
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P = torch.zeros((1,max_len,num_features))
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X_ = torch.arange(max_len,dtype=torch.float32).reshape(-1,1) / torch.pow(
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10000,
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torch.arange(0,num_features,2,dtype=torch.float32) /num_features)
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P[:,:,0::2] = torch.sin(X_)
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P[:,:,1::2] = torch.cos(X_)
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X = X + P[:,:X.shape[1],:].to(X.device)
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return dropout(X)
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```
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```python
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# 位置编码例子
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X = torch.randn((2,4,10))
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X = positional_encoding(X, 10)
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print(X.shape)
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```
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## **<div id='multihead'>多头注意力机制</div>**
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### 拆开看多头注意力机制
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**完整版本可运行的多头注意里机制的class在后面，[完整的多头注意力机制-MultiheadAttentionion](#mha) 再回来依次看下面的解释。**
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多头注意力类主要成分是：参数初始化、multi_head_attention_forward
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#### 初始化参数
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```python
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if self._qkv_same_embed_dim is False:
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# 初始化前后形状维持不变
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# (seq_length x embed_dim) x (embed_dim x embed_dim) ==> (seq_length x embed_dim)
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self.q_proj_weight = Parameter(torch.empty((embed_dim, embed_dim)))
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self.k_proj_weight = Parameter(torch.empty((embed_dim, self.kdim)))
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self.v_proj_weight = Parameter(torch.empty((embed_dim, self.vdim)))
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self.register_parameter('in_proj_weight', None)
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else:
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self.in_proj_weight = Parameter(torch.empty((3 * embed_dim, embed_dim)))
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self.register_parameter('q_proj_weight', None)
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self.register_parameter('k_proj_weight', None)
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self.register_parameter('v_proj_weight', None)
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if bias:
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self.in_proj_bias = Parameter(torch.empty(3 * embed_dim))
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else:
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self.register_parameter('in_proj_bias', None)
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# 后期会将所有头的注意力拼接在一起然后乘上权重矩阵输出
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# out_proj是为了后期准备的
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self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
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self._reset_parameters()
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```
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torch.empty是按照所给的形状形成对应的tensor，特点是填充的值还未初始化，类比torch.randn（标准正态分布），这就是一种初始化的方式。在PyTorch中，变量类型是tensor的话是无法修改值的，而Parameter()函数可以看作为一种类型转变函数，将不可改值的tensor转换为可训练可修改的模型参数，即与model.parameters绑定在一起，register_parameter的意思是是否将这个参数放到model.parameters，None的意思是没有这个参数。
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这里有个if判断，用以判断q,k,v的最后一维是否一致，若一致，则一个大的权重矩阵全部乘然后分割出来，若不是，则各初始化各的，其实初始化是不会改变原来的形状的（如，见注释）。
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可以发现最后有一个_reset_parameters()函数，这个是用来初始化参数数值的。xavier_uniform意思是从[连续型均匀分布](https://zh.wikipedia.org/wiki/%E9%80%A3%E7%BA%8C%E5%9E%8B%E5%9D%87%E5%8B%BB%E5%88%86%E5%B8%83)里面随机取样出值来作为初始化的值，xavier_normal_取样的分布是正态分布。正因为初始化值在训练神经网络的时候很重要，所以才需要这两个函数。
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constant_意思是用所给值来填充输入的向量。
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另外，在PyTorch的源码里，似乎projection代表是一种线性变换的意思，in_proj_bias的意思就是一开始的线性变换的偏置
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```python
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def _reset_parameters(self):
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if self._qkv_same_embed_dim:
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xavier_uniform_(self.in_proj_weight)
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else:
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xavier_uniform_(self.q_proj_weight)
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xavier_uniform_(self.k_proj_weight)
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xavier_uniform_(self.v_proj_weight)
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if self.in_proj_bias is not None:
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constant_(self.in_proj_bias, 0.)
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constant_(self.out_proj.bias, 0.)
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```
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#### multi_head_attention_forward
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这个函数如下代码所示，主要分成3个部分：
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- query, key, value通过_in_projection_packed变换得到q,k,v
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- 遮挡机制
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- 点积注意力
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```python
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import torch
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Tensor = torch.Tensor
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def multi_head_attention_forward(
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query: Tensor,
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key: Tensor,
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value: Tensor,
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num_heads: int,
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in_proj_weight: Tensor,
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in_proj_bias: Optional[Tensor],
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dropout_p: float,
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out_proj_weight: Tensor,
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out_proj_bias: Optional[Tensor],
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training: bool = True,
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key_padding_mask: Optional[Tensor] = None,
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need_weights: bool = True,
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attn_mask: Optional[Tensor] = None,
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use_seperate_proj_weight = None,
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q_proj_weight: Optional[Tensor] = None,
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k_proj_weight: Optional[Tensor] = None,
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v_proj_weight: Optional[Tensor] = None,
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) -> Tuple[Tensor, Optional[Tensor]]:
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r'''
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形状：
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输入：
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- query：`(L, N, E)`
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- key: `(S, N, E)`
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- value: `(S, N, E)`
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- key_padding_mask: `(N, S)`
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- attn_mask: `(L, S)` or `(N * num_heads, L, S)`
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输出：
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- attn_output:`(L, N, E)`
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- attn_output_weights:`(N, L, S)`
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'''
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tgt_len, bsz, embed_dim = query.shape
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src_len, _, _ = key.shape
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head_dim = embed_dim // num_heads
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q, k, v = _in_projection_packed(query, key, value, in_proj_weight, in_proj_bias)
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if attn_mask is not None:
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if attn_mask.dtype == torch.uint8:
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warnings.warn("Byte tensor for attn_mask in nn.MultiheadAttention is deprecated. Use bool tensor instead.")
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attn_mask = attn_mask.to(torch.bool)
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else:
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assert attn_mask.is_floating_point() or attn_mask.dtype == torch.bool, \
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f"Only float, byte, and bool types are supported for attn_mask, not {attn_mask.dtype}"
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if attn_mask.dim() == 2:
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correct_2d_size = (tgt_len, src_len)
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if attn_mask.shape != correct_2d_size:
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raise RuntimeError(f"The shape of the 2D attn_mask is {attn_mask.shape}, but should be {correct_2d_size}.")
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attn_mask = attn_mask.unsqueeze(0)
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elif attn_mask.dim() == 3:
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correct_3d_size = (bsz * num_heads, tgt_len, src_len)
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if attn_mask.shape != correct_3d_size:
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raise RuntimeError(f"The shape of the 3D attn_mask is {attn_mask.shape}, but should be {correct_3d_size}.")
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else:
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raise RuntimeError(f"attn_mask's dimension {attn_mask.dim()} is not supported")
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if key_padding_mask is not None and key_padding_mask.dtype == torch.uint8:
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warnings.warn("Byte tensor for key_padding_mask in nn.MultiheadAttention is deprecated. Use bool tensor instead.")
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key_padding_mask = key_padding_mask.to(torch.bool)
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# reshape q,k,v将Batch放在第一维以适合点积注意力
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# 同时为多头机制，将不同的头拼在一起组成一层
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q = q.contiguous().view(tgt_len, bsz * num_heads, head_dim).transpose(0, 1)
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k = k.contiguous().view(-1, bsz * num_heads, head_dim).transpose(0, 1)
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v = v.contiguous().view(-1, bsz * num_heads, head_dim).transpose(0, 1)
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if key_padding_mask is not None:
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assert key_padding_mask.shape == (bsz, src_len), \
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f"expecting key_padding_mask shape of {(bsz, src_len)}, but got {key_padding_mask.shape}"
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key_padding_mask = key_padding_mask.view(bsz, 1, 1, src_len). \
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expand(-1, num_heads, -1, -1).reshape(bsz * num_heads, 1, src_len)
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if attn_mask is None:
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attn_mask = key_padding_mask
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elif attn_mask.dtype == torch.bool:
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attn_mask = attn_mask.logical_or(key_padding_mask)
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else:
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attn_mask = attn_mask.masked_fill(key_padding_mask, float("-inf"))
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# 若attn_mask值是布尔值，则将mask转换为float
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if attn_mask is not None and attn_mask.dtype == torch.bool:
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new_attn_mask = torch.zeros_like(attn_mask, dtype=torch.float)
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new_attn_mask.masked_fill_(attn_mask, float("-inf"))
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attn_mask = new_attn_mask
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# 若training为True时才应用dropout
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if not training:
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dropout_p = 0.0
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attn_output, attn_output_weights = _scaled_dot_product_attention(q, k, v, attn_mask, dropout_p)
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attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len, bsz, embed_dim)
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attn_output = nn.functional.linear(attn_output, out_proj_weight, out_proj_bias)
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if need_weights:
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# average attention weights over heads
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attn_output_weights = attn_output_weights.view(bsz, num_heads, tgt_len, src_len)
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return attn_output, attn_output_weights.sum(dim=1) / num_heads
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else:
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return attn_output, None
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```
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##### query, key, value通过_in_projection_packed变换得到q,k,v
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```
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q, k, v = _in_projection_packed(query, key, value, in_proj_weight, in_proj_bias)
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```
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对于`nn.functional.linear`函数，其实就是一个线性变换，与`nn.Linear`不同的是，前者可以提供权重矩阵和偏置，执行，而后者是可以自由决定输出的维度。
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```python
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def _in_projection_packed(
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q: Tensor,
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k: Tensor,
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v: Tensor,
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w: Tensor,
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b: Optional[Tensor] = None,
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) -> List[Tensor]:
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r"""
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用一个大的权重参数矩阵进行线性变换
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参数:
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q, k, v: 对自注意来说，三者都是src；对于seq2seq模型，k和v是一致的tensor。
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但它们的最后一维(num_features或者叫做embed_dim)都必须保持一致。
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w: 用以线性变换的大矩阵，按照q,k,v的顺序压在一个tensor里面。
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b: 用以线性变换的偏置，按照q,k,v的顺序压在一个tensor里面。
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形状:
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输入:
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- q: shape:`(..., E)`，E是词嵌入的维度（下面出现的E均为此意）。
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- k: shape:`(..., E)`
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- v: shape:`(..., E)`
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- w: shape:`(E * 3, E)`
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- b: shape:`E * 3`
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输出:
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- 输出列表 :`[q', k', v']`，q,k,v经过线性变换前后的形状都一致。
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"""
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E = q.size(-1)
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# 若为自注意，则q = k = v = src，因此它们的引用变量都是src
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# 即k is v和q is k结果均为True
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# 若为seq2seq，k = v，因而k is v的结果是True
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if k is v:
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if q is k:
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return F.linear(q, w, b).chunk(3, dim=-1)
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else:
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# seq2seq模型
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w_q, w_kv = w.split([E, E * 2])
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if b is None:
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b_q = b_kv = None
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else:
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b_q, b_kv = b.split([E, E * 2])
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return (F.linear(q, w_q, b_q),) + F.linear(k, w_kv, b_kv).chunk(2, dim=-1)
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else:
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w_q, w_k, w_v = w.chunk(3)
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if b is None:
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b_q = b_k = b_v = None
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else:
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b_q, b_k, b_v = b.chunk(3)
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return F.linear(q, w_q, b_q), F.linear(k, w_k, b_k), F.linear(v, w_v, b_v)
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# q, k, v = _in_projection_packed(query, key, value, in_proj_weight, in_proj_bias)
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```
|
||||
|
||||
***
|
||||
|
||||
##### 遮挡机制
|
||||
|
||||
对于attn_mask来说，若为2D，形状如`(L, S)`，L和S分别代表着目标语言和源语言序列长度，若为3D,形状如`(N * num_heads, L, S)`，N代表着batch_size，num_heads代表注意力头的数目。若为attn_mask的dtype为ByteTensor，非0的位置会被忽略不做注意力；若为BoolTensor，True对应的位置会被忽略；若为数值，则会直接加到attn_weights。
|
||||
|
||||
因为在decoder解码的时候，只能看该位置和它之前的，如果看后面就犯规了，所以需要attn_mask遮挡住。
|
||||
|
||||
下面函数直接复制PyTorch的，意思是确保不同维度的mask形状正确以及不同类型的转换
|
||||
|
||||
|
||||
```python
|
||||
if attn_mask is not None:
|
||||
if attn_mask.dtype == torch.uint8:
|
||||
warnings.warn("Byte tensor for attn_mask in nn.MultiheadAttention is deprecated. Use bool tensor instead.")
|
||||
attn_mask = attn_mask.to(torch.bool)
|
||||
else:
|
||||
assert attn_mask.is_floating_point() or attn_mask.dtype == torch.bool, \
|
||||
f"Only float, byte, and bool types are supported for attn_mask, not {attn_mask.dtype}"
|
||||
# 对不同维度的形状判定
|
||||
if attn_mask.dim() == 2:
|
||||
correct_2d_size = (tgt_len, src_len)
|
||||
if attn_mask.shape != correct_2d_size:
|
||||
raise RuntimeError(f"The shape of the 2D attn_mask is {attn_mask.shape}, but should be {correct_2d_size}.")
|
||||
attn_mask = attn_mask.unsqueeze(0)
|
||||
elif attn_mask.dim() == 3:
|
||||
correct_3d_size = (bsz * num_heads, tgt_len, src_len)
|
||||
if attn_mask.shape != correct_3d_size:
|
||||
raise RuntimeError(f"The shape of the 3D attn_mask is {attn_mask.shape}, but should be {correct_3d_size}.")
|
||||
else:
|
||||
raise RuntimeError(f"attn_mask's dimension {attn_mask.dim()} is not supported")
|
||||
|
||||
```
|
||||
与`attn_mask`不同的是，`key_padding_mask`是用来遮挡住key里面的值，详细来说应该是`<PAD>`，被忽略的情况与attn_mask一致。
|
||||
|
||||
```python
|
||||
# 将key_padding_mask值改为布尔值
|
||||
if key_padding_mask is not None and key_padding_mask.dtype == torch.uint8:
|
||||
warnings.warn("Byte tensor for key_padding_mask in nn.MultiheadAttention is deprecated. Use bool tensor instead.")
|
||||
key_padding_mask = key_padding_mask.to(torch.bool)
|
||||
```
|
||||
|
||||
先介绍两个小函数，`logical_or`，输入两个tensor，并对这两个tensor里的值做`逻辑或`运算，只有当两个值均为0的时候才为`False`，其他时候均为`True`，另一个是`masked_fill`，输入是一个mask，和用以填充的值。mask由1，0组成，0的位置值维持不变，1的位置用新值填充。
|
||||
```python
|
||||
a = torch.tensor([0,1,10,0],dtype=torch.int8)
|
||||
b = torch.tensor([4,0,1,0],dtype=torch.int8)
|
||||
print(torch.logical_or(a,b))
|
||||
# tensor([ True, True, True, False])
|
||||
```
|
||||
|
||||
```python
|
||||
r = torch.tensor([[0,0,0,0],[0,0,0,0]])
|
||||
mask = torch.tensor([[1,1,1,1],[0,0,0,0]])
|
||||
print(r.masked_fill(mask,1))
|
||||
# tensor([[1, 1, 1, 1],
|
||||
# [0, 0, 0, 0]])
|
||||
```
|
||||
其实attn_mask和key_padding_mask有些时候对象是一致的，所以有时候可以合起来看。`-inf`做softmax之后值为0，即被忽略。
|
||||
```python
|
||||
if key_padding_mask is not None:
|
||||
assert key_padding_mask.shape == (bsz, src_len), \
|
||||
f"expecting key_padding_mask shape of {(bsz, src_len)}, but got {key_padding_mask.shape}"
|
||||
key_padding_mask = key_padding_mask.view(bsz, 1, 1, src_len). \
|
||||
expand(-1, num_heads, -1, -1).reshape(bsz * num_heads, 1, src_len)
|
||||
# 若attn_mask为空，直接用key_padding_mask
|
||||
if attn_mask is None:
|
||||
attn_mask = key_padding_mask
|
||||
elif attn_mask.dtype == torch.bool:
|
||||
attn_mask = attn_mask.logical_or(key_padding_mask)
|
||||
else:
|
||||
attn_mask = attn_mask.masked_fill(key_padding_mask, float("-inf"))
|
||||
|
||||
# 若attn_mask值是布尔值，则将mask转换为float
|
||||
if attn_mask is not None and attn_mask.dtype == torch.bool:
|
||||
new_attn_mask = torch.zeros_like(attn_mask, dtype=torch.float)
|
||||
new_attn_mask.masked_fill_(attn_mask, float("-inf"))
|
||||
attn_mask = new_attn_mask
|
||||
|
||||
```
|
||||
|
||||
***
|
||||
##### 点积注意力
|
||||
|
||||
|
||||
```python
|
||||
from typing import Optional, Tuple, Any
|
||||
def _scaled_dot_product_attention(
|
||||
q: Tensor,
|
||||
k: Tensor,
|
||||
v: Tensor,
|
||||
attn_mask: Optional[Tensor] = None,
|
||||
dropout_p: float = 0.0,
|
||||
) -> Tuple[Tensor, Tensor]:
|
||||
r'''
|
||||
在query, key, value上计算点积注意力，若有注意力遮盖则使用，并且应用一个概率为dropout_p的dropout
|
||||
|
||||
参数：
|
||||
- q: shape:`(B, Nt, E)` B代表batch size， Nt是目标语言序列长度，E是嵌入后的特征维度
|
||||
- key: shape:`(B, Ns, E)` Ns是源语言序列长度
|
||||
- value: shape:`(B, Ns, E)`与key形状一样
|
||||
- attn_mask: 要么是3D的tensor，形状为:`(B, Nt, Ns)`或者2D的tensor，形状如:`(Nt, Ns)`
|
||||
|
||||
- Output: attention values: shape:`(B, Nt, E)`，与q的形状一致;attention weights: shape:`(B, Nt, Ns)`
|
||||
|
||||
例子：
|
||||
>>> q = torch.randn((2,3,6))
|
||||
>>> k = torch.randn((2,4,6))
|
||||
>>> v = torch.randn((2,4,6))
|
||||
>>> out = scaled_dot_product_attention(q, k, v)
|
||||
>>> out[0].shape, out[1].shape
|
||||
>>> torch.Size([2, 3, 6]) torch.Size([2, 3, 4])
|
||||
'''
|
||||
B, Nt, E = q.shape
|
||||
q = q / math.sqrt(E)
|
||||
# (B, Nt, E) x (B, E, Ns) -> (B, Nt, Ns)
|
||||
attn = torch.bmm(q, k.transpose(-2,-1))
|
||||
if attn_mask is not None:
|
||||
attn += attn_mask
|
||||
# attn意味着目标序列的每个词对源语言序列做注意力
|
||||
attn = F.softmax(attn, dim=-1)
|
||||
if dropout_p:
|
||||
attn = F.dropout(attn, p=dropout_p)
|
||||
# (B, Nt, Ns) x (B, Ns, E) -> (B, Nt, E)
|
||||
output = torch.bmm(attn, v)
|
||||
return output, attn
|
||||
|
||||
```
|
||||
### **<div id='mha'>完整的多头注意力机制-MultiheadAttention</div>**
|
||||
[点击快速回到：拆开看多头注意力机制](#拆开看多头注意力机制)
|
||||
|
||||
```python
|
||||
class MultiheadAttention(nn.Module):
|
||||
r'''
|
||||
参数：
|
||||
embed_dim: 词嵌入的维度
|
||||
num_heads: 平行头的数量
|
||||
batch_first: 若`True`，则为(batch, seq, feture)，若为`False`，则为(seq, batch, feature)
|
||||
|
||||
例子：
|
||||
>>> multihead_attn = MultiheadAttention(embed_dim, num_heads)
|
||||
>>> attn_output, attn_output_weights = multihead_attn(query, key, value)
|
||||
'''
|
||||
def __init__(self, embed_dim, num_heads, dropout=0., bias=True,
|
||||
kdim=None, vdim=None, batch_first=False) -> None:
|
||||
# factory_kwargs = {'device': device, 'dtype': dtype}
|
||||
super(MultiheadAttention, self).__init__()
|
||||
self.embed_dim = embed_dim
|
||||
self.kdim = kdim if kdim is not None else embed_dim
|
||||
self.vdim = vdim if vdim is not None else embed_dim
|
||||
self._qkv_same_embed_dim = self.kdim == embed_dim and self.vdim == embed_dim
|
||||
|
||||
self.num_heads = num_heads
|
||||
self.dropout = dropout
|
||||
self.batch_first = batch_first
|
||||
self.head_dim = embed_dim // num_heads
|
||||
assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
|
||||
|
||||
if self._qkv_same_embed_dim is False:
|
||||
self.q_proj_weight = Parameter(torch.empty((embed_dim, embed_dim)))
|
||||
self.k_proj_weight = Parameter(torch.empty((embed_dim, self.kdim)))
|
||||
self.v_proj_weight = Parameter(torch.empty((embed_dim, self.vdim)))
|
||||
self.register_parameter('in_proj_weight', None)
|
||||
else:
|
||||
self.in_proj_weight = Parameter(torch.empty((3 * embed_dim, embed_dim)))
|
||||
self.register_parameter('q_proj_weight', None)
|
||||
self.register_parameter('k_proj_weight', None)
|
||||
self.register_parameter('v_proj_weight', None)
|
||||
|
||||
if bias:
|
||||
self.in_proj_bias = Parameter(torch.empty(3 * embed_dim))
|
||||
else:
|
||||
self.register_parameter('in_proj_bias', None)
|
||||
self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias)
|
||||
|
||||
self._reset_parameters()
|
||||
|
||||
def _reset_parameters(self):
|
||||
if self._qkv_same_embed_dim:
|
||||
xavier_uniform_(self.in_proj_weight)
|
||||
else:
|
||||
xavier_uniform_(self.q_proj_weight)
|
||||
xavier_uniform_(self.k_proj_weight)
|
||||
xavier_uniform_(self.v_proj_weight)
|
||||
|
||||
if self.in_proj_bias is not None:
|
||||
constant_(self.in_proj_bias, 0.)
|
||||
constant_(self.out_proj.bias, 0.)
|
||||
|
||||
|
||||
|
||||
def forward(self, query: Tensor, key: Tensor, value: Tensor, key_padding_mask: Optional[Tensor] = None,
|
||||
need_weights: bool = True, attn_mask: Optional[Tensor] = None) -> Tuple[Tensor, Optional[Tensor]]:
|
||||
if self.batch_first:
|
||||
query, key, value = [x.transpose(1, 0) for x in (query, key, value)]
|
||||
|
||||
if not self._qkv_same_embed_dim:
|
||||
attn_output, attn_output_weights = multi_head_attention_forward(
|
||||
query, key, value, self.num_heads,
|
||||
self.in_proj_weight, self.in_proj_bias,
|
||||
self.dropout, self.out_proj.weight, self.out_proj.bias,
|
||||
training=self.training,
|
||||
key_padding_mask=key_padding_mask, need_weights=need_weights,
|
||||
attn_mask=attn_mask, use_separate_proj_weight=True,
|
||||
q_proj_weight=self.q_proj_weight, k_proj_weight=self.k_proj_weight,
|
||||
v_proj_weight=self.v_proj_weight)
|
||||
else:
|
||||
attn_output, attn_output_weights = multi_head_attention_forward(
|
||||
query, key, value, self.num_heads,
|
||||
self.in_proj_weight, self.in_proj_bias,
|
||||
self.dropout, self.out_proj.weight, self.out_proj.bias,
|
||||
training=self.training,
|
||||
key_padding_mask=key_padding_mask, need_weights=need_weights,
|
||||
attn_mask=attn_mask)
|
||||
if self.batch_first:
|
||||
return attn_output.transpose(1, 0), attn_output_weights
|
||||
else:
|
||||
return attn_output, attn_output_weights
|
||||
```
|
||||
|
||||
|
||||
接下来可以实践一下，并且把位置编码加起来，可以发现加入位置编码和进行多头注意力的前后形状都是不会变的
|
||||
|
||||
|
||||
```python
|
||||
# 因为batch_first为False,所以src的shape：`(seq, batch, embed_dim)`
|
||||
src = torch.randn((2,4,100))
|
||||
src = positional_encoding(src,100,0.1)
|
||||
print(src.shape)
|
||||
multihead_attn = MultiheadAttention(100, 4, 0.1)
|
||||
attn_output, attn_output_weights = multihead_attn(src,src,src)
|
||||
print(attn_output.shape, attn_output_weights.shape)
|
||||
|
||||
# torch.Size([2, 4, 100])
|
||||
# torch.Size([2, 4, 100]) torch.Size([4, 2, 2])
|
||||
```
|
||||
|
||||
torch.Size([2, 4, 100])
|
||||
torch.Size([2, 4, 100]) torch.Size([4, 2, 2])
|
||||
|
||||
|
||||
***
|
||||
## **<div id='build'>TransformerEncoderLayer</div>**
|
||||
- Encoder Layer
|
||||
|
||||
|
||||
|
||||
|
||||
```python
|
||||
class TransformerEncoderLayer(nn.Module):
|
||||
r'''
|
||||
参数：
|
||||
d_model: 词嵌入的维度（必备）
|
||||
nhead: 多头注意力中平行头的数目（必备）
|
||||
dim_feedforward: 全连接层的神经元的数目，又称经过此层输入的维度（Default = 2048）
|
||||
dropout: dropout的概率（Default = 0.1）
|
||||
activation: 两个线性层中间的激活函数，默认relu或gelu
|
||||
lay_norm_eps: layer normalization中的微小量，防止分母为0（Default = 1e-5）
|
||||
batch_first: 若`True`，则为(batch, seq, feture)，若为`False`，则为(seq, batch, feature)（Default：False）
|
||||
|
||||
例子：
|
||||
>>> encoder_layer = TransformerEncoderLayer(d_model=512, nhead=8)
|
||||
>>> src = torch.randn((32, 10, 512))
|
||||
>>> out = encoder_layer(src)
|
||||
'''
|
||||
|
||||
def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation=F.relu,
|
||||
layer_norm_eps=1e-5, batch_first=False) -> None:
|
||||
super(TransformerEncoderLayer, self).__init__()
|
||||
self.self_attn = MultiheadAttention(d_model, nhead, dropout=dropout, batch_first=batch_first)
|
||||
self.linear1 = nn.Linear(d_model, dim_feedforward)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
self.linear2 = nn.Linear(dim_feedforward, d_model)
|
||||
|
||||
self.norm1 = nn.LayerNorm(d_model, eps=layer_norm_eps)
|
||||
self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps)
|
||||
self.dropout1 = nn.Dropout(dropout)
|
||||
self.dropout2 = nn.Dropout(dropout)
|
||||
self.activation = activation
|
||||
|
||||
|
||||
def forward(self, src: Tensor, src_mask: Optional[Tensor] = None, src_key_padding_mask: Optional[Tensor] = None) -> Tensor:
|
||||
src = positional_encoding(src, src.shape[-1])
|
||||
src2 = self.self_attn(src, src, src, attn_mask=src_mask,
|
||||
key_padding_mask=src_key_padding_mask)[0]
|
||||
src = src + self.dropout1(src2)
|
||||
src = self.norm1(src)
|
||||
src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
|
||||
src = src + self.dropout(src2)
|
||||
src = self.norm2(src)
|
||||
return src
|
||||
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
# 用小例子看一下
|
||||
encoder_layer = TransformerEncoderLayer(d_model=512, nhead=8)
|
||||
src = torch.randn((32, 10, 512))
|
||||
out = encoder_layer(src)
|
||||
print(out.shape)
|
||||
# torch.Size([32, 10, 512])
|
||||
```
|
||||
|
||||
|
||||
## Transformer layer组成Encoder
|
||||
|
||||
```python
|
||||
class TransformerEncoder(nn.Module):
|
||||
r'''
|
||||
参数：
|
||||
encoder_layer（必备）
|
||||
num_layers： encoder_layer的层数（必备）
|
||||
norm: 归一化的选择（可选）
|
||||
|
||||
例子：
|
||||
>>> encoder_layer = TransformerEncoderLayer(d_model=512, nhead=8)
|
||||
>>> transformer_encoder = TransformerEncoder(encoder_layer, num_layers=6)
|
||||
>>> src = torch.randn((10, 32, 512))
|
||||
>>> out = transformer_encoder(src)
|
||||
'''
|
||||
|
||||
def __init__(self, encoder_layer, num_layers, norm=None):
|
||||
super(TransformerEncoder, self).__init__()
|
||||
self.layer = encoder_layer
|
||||
self.num_layers = num_layers
|
||||
self.norm = norm
|
||||
|
||||
def forward(self, src: Tensor, mask: Optional[Tensor] = None, src_key_padding_mask: Optional[Tensor] = None) -> Tensor:
|
||||
output = positional_encoding(src, src.shape[-1])
|
||||
for _ in range(self.num_layers):
|
||||
output = self.layer(output, src_mask=mask, src_key_padding_mask=src_key_padding_mask)
|
||||
|
||||
if self.norm is not None:
|
||||
output = self.norm(output)
|
||||
|
||||
return output
|
||||
```
|
||||
|
||||
```python
|
||||
# 例子
|
||||
encoder_layer = TransformerEncoderLayer(d_model=512, nhead=8)
|
||||
transformer_encoder = TransformerEncoder(encoder_layer, num_layers=6)
|
||||
src = torch.randn((10, 32, 512))
|
||||
out = transformer_encoder(src)
|
||||
print(out.shape)
|
||||
# torch.Size([10, 32, 512])
|
||||
```
|
||||
|
||||
***
|
||||
## Decoder Layer:
|
||||
|
||||
|
||||
```python
|
||||
class TransformerDecoderLayer(nn.Module):
|
||||
r'''
|
||||
参数：
|
||||
d_model: 词嵌入的维度（必备）
|
||||
nhead: 多头注意力中平行头的数目（必备）
|
||||
dim_feedforward: 全连接层的神经元的数目，又称经过此层输入的维度（Default = 2048）
|
||||
dropout: dropout的概率（Default = 0.1）
|
||||
activation: 两个线性层中间的激活函数，默认relu或gelu
|
||||
lay_norm_eps: layer normalization中的微小量，防止分母为0（Default = 1e-5）
|
||||
batch_first: 若`True`，则为(batch, seq, feture)，若为`False`，则为(seq, batch, feature)（Default：False）
|
||||
|
||||
例子：
|
||||
>>> decoder_layer = TransformerDecoderLayer(d_model=512, nhead=8)
|
||||
>>> memory = torch.randn((10, 32, 512))
|
||||
>>> tgt = torch.randn((20, 32, 512))
|
||||
>>> out = decoder_layer(tgt, memory)
|
||||
'''
|
||||
def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation=F.relu,
|
||||
layer_norm_eps=1e-5, batch_first=False) -> None:
|
||||
super(TransformerDecoderLayer, self).__init__()
|
||||
self.self_attn = MultiheadAttention(d_model, nhead, dropout=dropout, batch_first=batch_first)
|
||||
self.multihead_attn = MultiheadAttention(d_model, nhead, dropout=dropout, batch_first=batch_first)
|
||||
|
||||
self.linear1 = nn.Linear(d_model, dim_feedforward)
|
||||
self.dropout = nn.Dropout(dropout)
|
||||
self.linear2 = nn.Linear(dim_feedforward, d_model)
|
||||
|
||||
self.norm1 = nn.LayerNorm(d_model, eps=layer_norm_eps)
|
||||
self.norm2 = nn.LayerNorm(d_model, eps=layer_norm_eps)
|
||||
self.norm3 = nn.LayerNorm(d_model, eps=layer_norm_eps)
|
||||
self.dropout1 = nn.Dropout(dropout)
|
||||
self.dropout2 = nn.Dropout(dropout)
|
||||
self.dropout3 = nn.Dropout(dropout)
|
||||
|
||||
self.activation = activation
|
||||
|
||||
def forward(self, tgt: Tensor, memory: Tensor, tgt_mask: Optional[Tensor] = None,
|
||||
memory_mask: Optional[Tensor] = None,tgt_key_padding_mask: Optional[Tensor] = None, memory_key_padding_mask: Optional[Tensor] = None) -> Tensor:
|
||||
r'''
|
||||
参数：
|
||||
tgt: 目标语言序列（必备）
|
||||
memory: 从最后一个encoder_layer跑出的句子（必备）
|
||||
tgt_mask: 目标语言序列的mask（可选）
|
||||
memory_mask（可选）
|
||||
tgt_key_padding_mask（可选）
|
||||
memory_key_padding_mask（可选）
|
||||
'''
|
||||
tgt2 = self.self_attn(tgt, tgt, tgt, attn_mask=tgt_mask,
|
||||
key_padding_mask=tgt_key_padding_mask)[0]
|
||||
tgt = tgt + self.dropout1(tgt2)
|
||||
tgt = self.norm1(tgt)
|
||||
tgt2 = self.multihead_attn(tgt, memory, memory, attn_mask=memory_mask,
|
||||
key_padding_mask=memory_key_padding_mask)[0]
|
||||
tgt = tgt + self.dropout2(tgt2)
|
||||
tgt = self.norm2(tgt)
|
||||
tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
|
||||
tgt = tgt + self.dropout3(tgt2)
|
||||
tgt = self.norm3(tgt)
|
||||
return tgt
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
# 可爱的小例子
|
||||
decoder_layer = nn.TransformerDecoderLayer(d_model=512, nhead=8)
|
||||
memory = torch.randn((10, 32, 512))
|
||||
tgt = torch.randn((20, 32, 512))
|
||||
out = decoder_layer(tgt, memory)
|
||||
print(out.shape)
|
||||
# torch.Size([20, 32, 512])
|
||||
```
|
||||
|
||||
```python
|
||||
## Decoder
|
||||
```
|
||||
|
||||
|
||||
```python
|
||||
class TransformerDecoder(nn.Module):
|
||||
r'''
|
||||
参数：
|
||||
decoder_layer（必备）
|
||||
num_layers: decoder_layer的层数（必备）
|
||||
norm: 归一化选择
|
||||
|
||||
例子：
|
||||
>>> decoder_layer =TransformerDecoderLayer(d_model=512, nhead=8)
|
||||
>>> transformer_decoder = TransformerDecoder(decoder_layer, num_layers=6)
|
||||
>>> memory = torch.rand(10, 32, 512)
|
||||
>>> tgt = torch.rand(20, 32, 512)
|
||||
>>> out = transformer_decoder(tgt, memory)
|
||||
'''
|
||||
def __init__(self, decoder_layer, num_layers, norm=None):
|
||||
super(TransformerDecoder, self).__init__()
|
||||
self.layer = decoder_layer
|
||||
self.num_layers = num_layers
|
||||
self.norm = norm
|
||||
|
||||
def forward(self, tgt: Tensor, memory: Tensor, tgt_mask: Optional[Tensor] = None,
|
||||
memory_mask: Optional[Tensor] = None, tgt_key_padding_mask: Optional[Tensor] = None,
|
||||
memory_key_padding_mask: Optional[Tensor] = None) -> Tensor:
|
||||
output = tgt
|
||||
for _ in range(self.num_layers):
|
||||
output = self.layer(output, memory, tgt_mask=tgt_mask,
|
||||
memory_mask=memory_mask,
|
||||
tgt_key_padding_mask=tgt_key_padding_mask,
|
||||
memory_key_padding_mask=memory_key_padding_mask)
|
||||
if self.norm is not None:
|
||||
output = self.norm(output)
|
||||
|
||||
return output
|
||||
```
|
||||
|
||||
```python
|
||||
# 可爱的小例子
|
||||
decoder_layer =TransformerDecoderLayer(d_model=512, nhead=8)
|
||||
transformer_decoder = TransformerDecoder(decoder_layer, num_layers=6)
|
||||
memory = torch.rand(10, 32, 512)
|
||||
tgt = torch.rand(20, 32, 512)
|
||||
out = transformer_decoder(tgt, memory)
|
||||
print(out.shape)
|
||||
# torch.Size([20, 32, 512])
|
||||
```
|
||||
|
||||
|
||||
总结一下，其实经过位置编码，多头注意力，Encoder Layer和Decoder Layer形状不会变的，而Encoder和Decoder分别与src和tgt形状一致
|
||||
|
||||
## Transformer
|
||||
|
||||
|
||||
```python
|
||||
class Transformer(nn.Module):
|
||||
r'''
|
||||
参数：
|
||||
d_model: 词嵌入的维度（必备）（Default=512）
|
||||
nhead: 多头注意力中平行头的数目（必备）（Default=8）
|
||||
num_encoder_layers:编码层层数（Default=8）
|
||||
num_decoder_layers:解码层层数（Default=8）
|
||||
dim_feedforward: 全连接层的神经元的数目，又称经过此层输入的维度（Default = 2048）
|
||||
dropout: dropout的概率（Default = 0.1）
|
||||
activation: 两个线性层中间的激活函数，默认relu或gelu
|
||||
custom_encoder: 自定义encoder（Default=None）
|
||||
custom_decoder: 自定义decoder（Default=None）
|
||||
lay_norm_eps: layer normalization中的微小量，防止分母为0（Default = 1e-5）
|
||||
batch_first: 若`True`，则为(batch, seq, feture)，若为`False`，则为(seq, batch, feature)（Default：False）
|
||||
|
||||
例子：
|
||||
>>> transformer_model = Transformer(nhead=16, num_encoder_layers=12)
|
||||
>>> src = torch.rand((10, 32, 512))
|
||||
>>> tgt = torch.rand((20, 32, 512))
|
||||
>>> out = transformer_model(src, tgt)
|
||||
'''
|
||||
def __init__(self, d_model: int = 512, nhead: int = 8, num_encoder_layers: int = 6,
|
||||
num_decoder_layers: int = 6, dim_feedforward: int = 2048, dropout: float = 0.1,
|
||||
activation = F.relu, custom_encoder: Optional[Any] = None, custom_decoder: Optional[Any] = None,
|
||||
layer_norm_eps: float = 1e-5, batch_first: bool = False) -> None:
|
||||
super(Transformer, self).__init__()
|
||||
if custom_encoder is not None:
|
||||
self.encoder = custom_encoder
|
||||
else:
|
||||
encoder_layer = TransformerEncoderLayer(d_model, nhead, dim_feedforward, dropout,
|
||||
activation, layer_norm_eps, batch_first)
|
||||
encoder_norm = nn.LayerNorm(d_model, eps=layer_norm_eps)
|
||||
self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers)
|
||||
|
||||
if custom_decoder is not None:
|
||||
self.decoder = custom_decoder
|
||||
else:
|
||||
decoder_layer = TransformerDecoderLayer(d_model, nhead, dim_feedforward, dropout,
|
||||
activation, layer_norm_eps, batch_first)
|
||||
decoder_norm = nn.LayerNorm(d_model, eps=layer_norm_eps)
|
||||
self.decoder = TransformerDecoder(decoder_layer, num_decoder_layers, decoder_norm)
|
||||
|
||||
self._reset_parameters()
|
||||
|
||||
self.d_model = d_model
|
||||
self.nhead = nhead
|
||||
|
||||
self.batch_first = batch_first
|
||||
|
||||
def forward(self, src: Tensor, tgt: Tensor, src_mask: Optional[Tensor] = None, tgt_mask: Optional[Tensor] = None,
|
||||
memory_mask: Optional[Tensor] = None, src_key_padding_mask: Optional[Tensor] = None,
|
||||
tgt_key_padding_mask: Optional[Tensor] = None, memory_key_padding_mask: Optional[Tensor] = None) -> Tensor:
|
||||
r'''
|
||||
参数：
|
||||
src: 源语言序列（送入Encoder）（必备）
|
||||
tgt: 目标语言序列（送入Decoder）（必备）
|
||||
src_mask: （可选)
|
||||
tgt_mask: （可选）
|
||||
memory_mask: （可选）
|
||||
src_key_padding_mask: （可选）
|
||||
tgt_key_padding_mask: （可选）
|
||||
memory_key_padding_mask: （可选）
|
||||
|
||||
形状：
|
||||
- src: shape:`(S, N, E)`, `(N, S, E)` if batch_first.
|
||||
- tgt: shape:`(T, N, E)`, `(N, T, E)` if batch_first.
|
||||
- src_mask: shape:`(S, S)`.
|
||||
- tgt_mask: shape:`(T, T)`.
|
||||
- memory_mask: shape:`(T, S)`.
|
||||
- src_key_padding_mask: shape:`(N, S)`.
|
||||
- tgt_key_padding_mask: shape:`(N, T)`.
|
||||
- memory_key_padding_mask: shape:`(N, S)`.
|
||||
|
||||
[src/tgt/memory]_mask确保有些位置不被看到，如做decode的时候，只能看该位置及其以前的，而不能看后面的。
|
||||
若为ByteTensor，非0的位置会被忽略不做注意力；若为BoolTensor，True对应的位置会被忽略；
|
||||
若为数值，则会直接加到attn_weights
|
||||
|
||||
[src/tgt/memory]_key_padding_mask 使得key里面的某些元素不参与attention计算，三种情况同上
|
||||
|
||||
- output: shape:`(T, N, E)`, `(N, T, E)` if batch_first.
|
||||
|
||||
注意：
|
||||
src和tgt的最后一维需要等于d_model，batch的那一维需要相等
|
||||
|
||||
例子:
|
||||
>>> output = transformer_model(src, tgt, src_mask=src_mask, tgt_mask=tgt_mask)
|
||||
'''
|
||||
memory = self.encoder(src, mask=src_mask, src_key_padding_mask=src_key_padding_mask)
|
||||
output = self.decoder(tgt, memory, tgt_mask=tgt_mask, memory_mask=memory_mask,
|
||||
tgt_key_padding_mask=tgt_key_padding_mask,
|
||||
memory_key_padding_mask=memory_key_padding_mask)
|
||||
return output
|
||||
|
||||
def generate_square_subsequent_mask(self, sz: int) -> Tensor:
|
||||
r'''产生关于序列的mask，被遮住的区域赋值`-inf`，未被遮住的区域赋值为`0`'''
|
||||
mask = (torch.triu(torch.ones(sz, sz)) == 1).transpose(0, 1)
|
||||
mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill(mask == 1, float(0.0))
|
||||
return mask
|
||||
|
||||
def _reset_parameters(self):
|
||||
r'''用正态分布初始化参数'''
|
||||
for p in self.parameters():
|
||||
if p.dim() > 1:
|
||||
xavier_uniform_(p)
|
||||
```
|
||||
|
||||
```python
|
||||
# 小例子
|
||||
transformer_model = Transformer(nhead=16, num_encoder_layers=12)
|
||||
src = torch.rand((10, 32, 512))
|
||||
tgt = torch.rand((20, 32, 512))
|
||||
out = transformer_model(src, tgt)
|
||||
print(out.shape)
|
||||
# torch.Size([20, 32, 512])
|
||||
```
|
||||
|
||||
到此为止，PyTorch的Transformer库我们已经全部实现，相比于官方的版本，手写的这个少了较多的判定语句。
|
||||
## 致谢
|
||||
本文由台运鹏撰写，本项目成员重新组织和整理。最后，期待您的阅读反馈和star，谢谢。
|
||||
|
|
@ -4,3 +4,7 @@ notebook
|
|||
torch==1.9.0
|
||||
sklearn
|
||||
scipy
|
||||
matplotlib
|
||||
torchtext
|
||||
seaborn
|
||||
spacy
|
||||
|
|
|
|||
Loading…
Reference in New Issue