• 经典transformer的学习

• 文章转自微信公众号【机器学习炼丹术】

• 作者：陈亦新（已授权）

• 联系方式: 微信cyx645016617

• 欢迎交流，共同进步

• 代码细讲

  • transformer

  • Embedding

  • Encoder_MultipleLayers

  • Encoder

• 完整代码

代码细讲

transformer

class transformer(nn.Sequential):def __init__(self, encoding, **config):super(transformer, self).__init__()if encoding == 'drug':self.emb = Embeddings(config['input_dim_drug'], config['transformer_emb_size_drug'], 50, config['transformer_dropout_rate'])self.encoder = Encoder_MultipleLayers(config['transformer_n_layer_drug'], config['transformer_emb_size_drug'], config['transformer_intermediate_size_drug'], config['transformer_num_attention_heads_drug'],config['transformer_attention_probs_dropout'],config['transformer_hidden_dropout_rate'])elif encoding == 'protein':self.emb = Embeddings(config['input_dim_protein'], config['transformer_emb_size_target'], 545, config['transformer_dropout_rate'])self.encoder = Encoder_MultipleLayers(config['transformer_n_layer_target'], config['transformer_emb_size_target'], config['transformer_intermediate_size_target'], config['transformer_num_attention_heads_target'],config['transformer_attention_probs_dropout'],config['transformer_hidden_dropout_rate'])### parameter v (tuple of length 2) is from utils.drug2emb_encoder def forward(self, v):e = v[0].long().to(device)e_mask = v[1].long().to(device)print(e.shape,e_mask.shape)ex_e_mask = e_mask.unsqueeze(1).unsqueeze(2)ex_e_mask = (1.0 - ex_e_mask) * -10000.0emb = self.emb(e)encoded_layers = self.encoder(emb.float(), ex_e_mask.float())return encoded_layers[:,0]

• 只要有两个组件，一个是Embedding层，一个是Encoder_MultipleLayers模块；

• forward的输入v是一个元组，包含两个元素：第一个是数据，第二个是mask。对应有效数据的位置。

Embedding

class Embeddings(nn.Module):"""Construct the embeddings from protein/target, position embeddings."""def __init__(self, vocab_size, hidden_size, max_position_size, dropout_rate):super(Embeddings, self).__init__()self.word_embeddings = nn.Embedding(vocab_size, hidden_size)self.position_embeddings = nn.Embedding(max_position_size, hidden_size)self.LayerNorm = nn.LayerNorm(hidden_size)self.dropout = nn.Dropout(dropout_rate)def forward(self, input_ids):seq_length = input_ids.size(1)position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)position_ids = position_ids.unsqueeze(0).expand_as(input_ids)words_embeddings = self.word_embeddings(input_ids)position_embeddings = self.position_embeddings(position_ids)embeddings = words_embeddings + position_embeddingsembeddings = self.LayerNorm(embeddings)embeddings = self.dropout(embeddings)return embeddings

• 包含三个组件，一个是Embedding，其他是LayerNorm和Dropout层。

torch.nn.Embedding(num_embeddings, embedding_dim, padding_idx=None,max_norm=None,  norm_type=2.0,   scale_grad_by_freq=False, sparse=False,  _weight=None)

其为一个简单的存储固定大小的词典的嵌入向量的查找表，意思就是说，给一个编号，嵌入层就能返回这个编号对应的嵌入向量，嵌入向量反映了各个编号代表的符号之间的语义关系。

输入为一个编号列表，输出为对应的符号嵌入向量列表。

• num_embeddings (python:int) – 词典的大小尺寸，比如总共出现5000个词，那就输入5000。此时index为（0-4999）

• embedding_dim (python:int) – 嵌入向量的维度，即用多少维来表示一个符号。

• padding_idx (python:int, optional) – 填充id，比如，输入长度为100，但是每次的句子长度并不一样，后面就需要用统一的数字填充，而这里就是指定这个数字，这样，网络在遇到填充id时，就不会计算其与其它符号的相关性。（初始化为0）

• max_norm (python:float, optional) – 最大范数，如果嵌入向量的范数超过了这个界限，就要进行再归一化。

• norm_type (python:float, optional) – 指定利用什么范数计算，并用于对比max_norm，默认为2范数。

• scale_grad_by_freq (boolean, optional) – 根据单词在mini-batch中出现的频率，对梯度进行放缩。默认为False.

• sparse (bool, optional) – 若为True,则与权重矩阵相关的梯度转变为稀疏张量。

举一个例子：

如果你的整数最大超过了设置的字典的容量，那么就会出错误：

• Embedding其中有可学习参数！是一个num_embedding * embedding_dim的矩阵。

Encoder_MultipleLayers

class Encoder_MultipleLayers(nn.Module):def __init__(self, n_layer, hidden_size, intermediate_size, num_attention_heads, attention_probs_dropout_prob, hidden_dropout_prob):super(Encoder_MultipleLayers, self).__init__()layer = Encoder(hidden_size, intermediate_size, num_attention_heads, attention_probs_dropout_prob, hidden_dropout_prob)self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(n_layer)])    def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True):all_encoder_layers = []for layer_module in self.layer:hidden_states = layer_module(hidden_states, attention_mask)return hidden_states

• transformer中的embedding，目的是将数据转换成对应的向量。这个Encoder-multilayer则是提取特征的关键。

• 结构很简单，就是由==n_layer==个Encoder堆叠而成。

Encoder

class Encoder(nn.Module):def __init__(self, hidden_size, intermediate_size, num_attention_heads, attention_probs_dropout_prob, hidden_dropout_prob):super(Encoder, self).__init__()self.attention = Attention(hidden_size, num_attention_heads, attention_probs_dropout_prob, hidden_dropout_prob)self.intermediate = Intermediate(hidden_size, intermediate_size)self.output = Output(intermediate_size, hidden_size, hidden_dropout_prob)def forward(self, hidden_states, attention_mask):attention_output = self.attention(hidden_states, attention_mask)intermediate_output = self.intermediate(attention_output)layer_output = self.output(intermediate_output, attention_output)return layer_output

• 其中包含了Attention部分，Intermediate和Output。

class Attention(nn.Module):def __init__(self, hidden_size, num_attention_heads, attention_probs_dropout_prob, hidden_dropout_prob):super(Attention, self).__init__()self.self = SelfAttention(hidden_size, num_attention_heads, attention_probs_dropout_prob)self.output = SelfOutput(hidden_size, hidden_dropout_prob)def forward(self, input_tensor, attention_mask):self_output = self.self(input_tensor, attention_mask)attention_output = self.output(self_output, input_tensor)return attention_output

class SelfAttention(nn.Module):def __init__(self, hidden_size, num_attention_heads, attention_probs_dropout_prob):super(SelfAttention, self).__init__()if hidden_size % num_attention_heads != 0:raise ValueError("The hidden size (%d) is not a multiple of the number of attention ""heads (%d)" % (hidden_size, num_attention_heads))self.num_attention_heads = num_attention_headsself.attention_head_size = int(hidden_size / num_attention_heads)self.all_head_size = self.num_attention_heads * self.attention_head_sizeself.query = nn.Linear(hidden_size, self.all_head_size)self.key = nn.Linear(hidden_size, self.all_head_size)self.value = nn.Linear(hidden_size, self.all_head_size)self.dropout = nn.Dropout(attention_probs_dropout_prob)def transpose_for_scores(self, x):# num_attention_heads = 8, attention_head_size = 128 / 8 = 16new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)x = x.view(*new_x_shape)return x.permute(0, 2, 1, 3)def forward(self, hidden_states, attention_mask):# hidden_states.shape = [batch,50,128]mixed_query_layer = self.query(hidden_states)mixed_key_layer = self.key(hidden_states)mixed_value_layer = self.value(hidden_states)query_layer = self.transpose_for_scores(mixed_query_layer)key_layer = self.transpose_for_scores(mixed_key_layer)value_layer = self.transpose_for_scores(mixed_value_layer)# query_layer.shape = [batch,8,50,16]# Take the dot product between "query" and "key" to get the raw attention scores.attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))# attention_score.shape = [batch,8,50,50]attention_scores = attention_scores / math.sqrt(self.attention_head_size)attention_scores = attention_scores + attention_mask# Normalize the attention scores to probabilities.attention_probs = nn.Softmax(dim=-1)(attention_scores)# This is actually dropping out entire tokens to attend to, which might# seem a bit unusual, but is taken from the original Transformer paper.attention_probs = self.dropout(attention_probs)context_layer = torch.matmul(attention_probs, value_layer)context_layer = context_layer.permute(0, 2, 1, 3).contiguous()new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)context_layer = context_layer.view(*new_context_layer_shape)return context_layer

这一段和一般的vit处理的流程类似。虽然transformer是从NLP到CV的，但从CV的vit再回看NLP的transformer也是有一种乐趣。里面要注意的点是multihead的概念。本来hidden-size是128，如果设置multihead的数量为8，那么其实好比卷积里面的通道数量。会把128的token看成8个16个token，然后分别做自注意力。但是把multihead比作卷积的概念感觉说的过去，比作分组卷积的概念好像也OK：

• 比作卷积。如果固定了每一个head的size数量为16，那么head就好比通道数，那么增加head的数量，其实就是增加了卷积核通道数的感觉；

• 比作分组卷积。如果固定了hidden-size的数量为128，那么head的数量就是分组的数量，那么增加head的数量就好比卷积分组变多，降低了计算量。

-其他部分的代码都是FC + LayerNorm +Dropout，不再赘述。

完整代码

import torch.nn as nn
import torch.nn.functional as F
import copy,math
class Embeddings(nn.Module):"""Construct the embeddings from protein/target, position embeddings."""def __init__(self, vocab_size, hidden_size, max_position_size, dropout_rate):super(Embeddings, self).__init__()self.word_embeddings = nn.Embedding(vocab_size, hidden_size)self.position_embeddings = nn.Embedding(max_position_size, hidden_size)self.LayerNorm = nn.LayerNorm(hidden_size)self.dropout = nn.Dropout(dropout_rate)def forward(self, input_ids):seq_length = input_ids.size(1)position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)position_ids = position_ids.unsqueeze(0).expand_as(input_ids)words_embeddings = self.word_embeddings(input_ids)position_embeddings = self.position_embeddings(position_ids)embeddings = words_embeddings + position_embeddingsembeddings = self.LayerNorm(embeddings)embeddings = self.dropout(embeddings)return embeddings
class Encoder_MultipleLayers(nn.Module):def __init__(self, n_layer, hidden_size, intermediate_size, num_attention_heads, attention_probs_dropout_prob, hidden_dropout_prob):super(Encoder_MultipleLayers, self).__init__()layer = Encoder(hidden_size, intermediate_size, num_attention_heads, attention_probs_dropout_prob, hidden_dropout_prob)self.layer = nn.ModuleList([copy.deepcopy(layer) for _ in range(n_layer)])    def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True):all_encoder_layers = []for layer_module in self.layer:hidden_states = layer_module(hidden_states, attention_mask)#if output_all_encoded_layers:#    all_encoder_layers.append(hidden_states)#if not output_all_encoded_layers:#    all_encoder_layers.append(hidden_states)return hidden_states
class Encoder(nn.Module):def __init__(self, hidden_size, intermediate_size, num_attention_heads, attention_probs_dropout_prob, hidden_dropout_prob):super(Encoder, self).__init__()self.attention = Attention(hidden_size, num_attention_heads, attention_probs_dropout_prob, hidden_dropout_prob)self.intermediate = Intermediate(hidden_size, intermediate_size)self.output = Output(intermediate_size, hidden_size, hidden_dropout_prob)def forward(self, hidden_states, attention_mask):attention_output = self.attention(hidden_states, attention_mask)intermediate_output = self.intermediate(attention_output)layer_output = self.output(intermediate_output, attention_output)return layer_output
class Attention(nn.Module):def __init__(self, hidden_size, num_attention_heads, attention_probs_dropout_prob, hidden_dropout_prob):super(Attention, self).__init__()self.self = SelfAttention(hidden_size, num_attention_heads, attention_probs_dropout_prob)self.output = SelfOutput(hidden_size, hidden_dropout_prob)def forward(self, input_tensor, attention_mask):self_output = self.self(input_tensor, attention_mask)attention_output = self.output(self_output, input_tensor)return attention_output  class SelfAttention(nn.Module):def __init__(self, hidden_size, num_attention_heads, attention_probs_dropout_prob):super(SelfAttention, self).__init__()if hidden_size % num_attention_heads != 0:raise ValueError("The hidden size (%d) is not a multiple of the number of attention ""heads (%d)" % (hidden_size, num_attention_heads))self.num_attention_heads = num_attention_headsself.attention_head_size = int(hidden_size / num_attention_heads)self.all_head_size = self.num_attention_heads * self.attention_head_sizeself.query = nn.Linear(hidden_size, self.all_head_size)self.key = nn.Linear(hidden_size, self.all_head_size)self.value = nn.Linear(hidden_size, self.all_head_size)self.dropout = nn.Dropout(attention_probs_dropout_prob)def transpose_for_scores(self, x):new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)x = x.view(*new_x_shape)return x.permute(0, 2, 1, 3)def forward(self, hidden_states, attention_mask):mixed_query_layer = self.query(hidden_states)mixed_key_layer = self.key(hidden_states)mixed_value_layer = self.value(hidden_states)query_layer = self.transpose_for_scores(mixed_query_layer)key_layer = self.transpose_for_scores(mixed_key_layer)value_layer = self.transpose_for_scores(mixed_value_layer)# Take the dot product between "query" and "key" to get the raw attention scores.attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))attention_scores = attention_scores / math.sqrt(self.attention_head_size)attention_scores = attention_scores + attention_mask# Normalize the attention scores to probabilities.attention_probs = nn.Softmax(dim=-1)(attention_scores)# This is actually dropping out entire tokens to attend to, which might# seem a bit unusual, but is taken from the original Transformer paper.attention_probs = self.dropout(attention_probs)context_layer = torch.matmul(attention_probs, value_layer)context_layer = context_layer.permute(0, 2, 1, 3).contiguous()new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)context_layer = context_layer.view(*new_context_layer_shape)return context_layerclass SelfOutput(nn.Module):def __init__(self, hidden_size, hidden_dropout_prob):super(SelfOutput, self).__init__()self.dense = nn.Linear(hidden_size, hidden_size)self.LayerNorm = nn.LayerNorm(hidden_size)self.dropout = nn.Dropout(hidden_dropout_prob)def forward(self, hidden_states, input_tensor):hidden_states = self.dense(hidden_states)hidden_states = self.dropout(hidden_states)hidden_states = self.LayerNorm(hidden_states + input_tensor)return hidden_states   class Intermediate(nn.Module):def __init__(self, hidden_size, intermediate_size):super(Intermediate, self).__init__()self.dense = nn.Linear(hidden_size, intermediate_size)def forward(self, hidden_states):hidden_states = self.dense(hidden_states)hidden_states = F.relu(hidden_states)return hidden_statesclass Output(nn.Module):def __init__(self, intermediate_size, hidden_size, hidden_dropout_prob):super(Output, self).__init__()self.dense = nn.Linear(intermediate_size, hidden_size)self.LayerNorm = nn.LayerNorm(hidden_size)self.dropout = nn.Dropout(hidden_dropout_prob)def forward(self, hidden_states, input_tensor):hidden_states = self.dense(hidden_states)hidden_states = self.dropout(hidden_states)hidden_states = self.LayerNorm(hidden_states + input_tensor)return hidden_states
class transformer(nn.Sequential):def __init__(self, encoding, **config):super(transformer, self).__init__()if encoding == 'drug':self.emb = Embeddings(config['input_dim_drug'], config['transformer_emb_size_drug'], 50, config['transformer_dropout_rate'])self.encoder = Encoder_MultipleLayers(config['transformer_n_layer_drug'], config['transformer_emb_size_drug'], config['transformer_intermediate_size_drug'], config['transformer_num_attention_heads_drug'],config['transformer_attention_probs_dropout'],config['transformer_hidden_dropout_rate'])elif encoding == 'protein':self.emb = Embeddings(config['input_dim_protein'], config['transformer_emb_size_target'], 545, config['transformer_dropout_rate'])self.encoder = Encoder_MultipleLayers(config['transformer_n_layer_target'], config['transformer_emb_size_target'], config['transformer_intermediate_size_target'], config['transformer_num_attention_heads_target'],config['transformer_attention_probs_dropout'],config['transformer_hidden_dropout_rate'])### parameter v (tuple of length 2) is from utils.drug2emb_encoder def forward(self, v):e = v[0].long().to(device)e_mask = v[1].long().to(device)print(e.shape,e_mask.shape)ex_e_mask = e_mask.unsqueeze(1).unsqueeze(2)ex_e_mask = (1.0 - ex_e_mask) * -10000.0emb = self.emb(e)encoded_layers = self.encoder(emb.float(), ex_e_mask.float())return encoded_layers[:,0]

往期精彩回顾适合初学者入门人工智能的路线及资料下载中国大学慕课《机器学习》（黄海广主讲）机器学习及深度学习笔记等资料打印机器学习在线手册深度学习笔记专辑《统计学习方法》的代码复现专辑
AI基础下载本站qq群955171419，加入微信群请扫码：