秋长网站建设,门户网站区别,wordpress 手机 重定向 子目录,莒县网站设计课程链接如下#xff1a;
2.1认识Transformer架构-part1_哔哩哔哩_bilibili
因为网上可以找到源代码#xff0c;但是呢#xff0c;代码似乎有点小错误#xff0c;我自己改正后#xff0c;放到了GPU上运行#xff0c;
代码如下#xff1a;
# 来自https://www.bilibil…课程链接如下
2.1认识Transformer架构-part1_哔哩哔哩_bilibili
因为网上可以找到源代码但是呢代码似乎有点小错误我自己改正后放到了GPU上运行
代码如下
# 来自https://www.bilibili.com/video/BV188411H71g?p3vd_source3083729582baecf3ad2c3c52876b23aa
# 我已经使用GPU修改了代码,加了几处.cuda()就行了import copy
import math
import timeimport numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variablefrom pyitcast.transformer_utils import Batch
from pyitcast.transformer_utils import get_std_opt
from pyitcast.transformer_utils import LabelSmoothing
from pyitcast.transformer_utils import SimpleLossCompute
from pyitcast.transformer_utils import run_epoch
from pyitcast.transformer_utils import greedy_decode# 文本嵌入层
class Embeddings(nn.Module):def __init__(self, d_model, vocab):super(Embeddings, self).__init__()self.lut nn.Embedding(vocab, d_model)self.d_model d_modeldef forward(self, x):return self.lut(x) * math.sqrt(self.d_model)# 定义位置编码器即也是一个层
class PositionalEncoding(nn.Module):def __init__(self, d_model, dropout, max_len5000):super(PositionalEncoding, self).__init__()self.dropout nn.Dropout(pdropout)pe torch.zeros(max_len, d_model)position torch.arange(0, max_len).unsqueeze(1)div_term torch.exp(torch.arange(0, d_model, 2) * (-(math.log(10000.0) / d_model)))# 这意味着每个位置的频率随着位置的增加而减小。这使得模型能够学习序列中每个位置的重要性。pe[:, 0::2] torch.sin(position * div_term)pe[:, 1::2] torch.cos(position * div_term)pe pe.unsqueeze(0)self.register_buffer(pe, pe)def forward(self, x):x x self.pe[:, :x.size(1)]return self.dropout(x)# 构建掩码张量
def subsequent_maxk(size):attn_shape (1, size, size)subsequent_maxk np.triu(np.ones(attn_shape), k1).astype(uint8)return torch.from_numpy(1 - subsequent_maxk)# # 2.3.2注意力机制# 为下面函数重写了注意力机制否则代码会报错
# 注意力机制代码实现
def attention(query, key, value, maskNone, dropoutNone):d_k query.size(-1)scores torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)if mask is not None:# mask torch.zeros(1, 1, 1, 1).cuda()# print(mask.shape:, mask.shape)scores scores.masked_fill(mask 0, -1e9)p_attn F.softmax(scores, dim-1)if dropout is not None:p_attn dropout(p_attn)return torch.matmul(p_attn, value), p_attn# # 2.3.3多头注意力机制# 实现克隆函数
def clones(module, N):return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])# 实现多头注意力机制
class MultiHeadAttention(nn.Module):def __init__(self, head, embedding_dim, dropout0.1):super(MultiHeadAttention, self).__init__()assert embedding_dim % head 0self.d_k embedding_dim // headself.head headself.embedding_dim embedding_dimself.linears clones(nn.Linear(embedding_dim, embedding_dim), 4)self.attn Noneself.dropout nn.Dropout(pdropout)def forward(self, query, key, value, maskNone):if mask is not None:mask mask.unsqueeze(0)batch_size query.size(0)# 三个张量分别是三个输入分别用三个线性层进行处理并重塑维度query, key, value \[model(x).view(batch_size, -1, self.head, self.d_k).transpose(1, 2)for model, x in zip(self.linears, (query, key, value))]x, self.attn attention(query, key, value, maskmask,dropoutself.dropout)x (x.transpose(1, 2).contiguous().view(batch_size, -1, self.head * self.d_k))# 拷贝的四个层还有一个就是这个对输入进行线性变换得到输出return self.linears[-1](x)# # 2.3.4前馈全连接层# 构建前馈全连接网络类
class PositionWiseFeedForward(nn.Module):def __init__(self, d_model, d_ff, dropout0.1):super(PositionWiseFeedForward, self).__init__()self.w1 nn.Linear(d_model, d_ff)self.w2 nn.Linear(d_ff, d_model)self.dropout nn.Dropout(pdropout)def forward(self, x):return self.w2(self.dropout(F.dropout(F.relu(self.w1(x)))))# # 2.3.5规范化层class LayerNorm(nn.Module):def __init__(self, features, eps1e-6):super(LayerNorm, self).__init__()self.a2 nn.Parameter(torch.ones(features))self.b2 nn.Parameter(torch.zeros(features))self.eps epsdef forward(self, x):mean x.mean(-1, keepdimTrue)std x.std(-1, keepdimTrue)return self.a2 * (x - mean) / (std self.eps) self.b2# # 2.3.6子层连接结构# 构建子层连接结构
class SublayerConnection(nn.Module):def __init__(self, size, dropout0.1):super(SublayerConnection, self).__init__()self.norm LayerNorm(size)self.dropout nn.Dropout(pdropout)self.size sizedef forward(self, x, sublayer):return x self.dropout(sublayer(self.norm(x)))# # 2.3.7编码器层# 编码器层
class EncoderLayer(nn.Module):def __init__(self, size, self_attn, feed_forward, dropout):super(EncoderLayer, self).__init__()self.self_attn self_attnself.feed_forward feed_forwardself.size sizeself.sublayer clones(SublayerConnection(size, dropout), 2)def forward(self, x, mask):x self.sublayer[0](x, lambda x: self.self_attn(x, x, x, mask))return self.sublayer[1](x, self.feed_forward)# # 2.3.8编码器# 构建编码器类
class Encoder(nn.Module):def __init__(self, layer, N):super(Encoder, self).__init__()self.layers clones(layer, N)self.norm LayerNorm(layer.size)def forward(self, x, mask):for layer in self.layers:x layer(x, mask)return self.norm(x)# # 2.4解码器# # 2.4.1解码器层# 构建解码器层类
class DecoderLayer(nn.Module):def __init__(self, size, self_attn, src_attn, feed_forward, dropout):super(DecoderLayer, self).__init__()self.size sizeself.self_attn self_attnself.src_attn src_attnself.feed_forward feed_forwardself.dropout dropoutself.sublayer clones(SublayerConnection(size, dropout), 3)def forward(self, x, memory, source_mask, target_mask):m memoryx self.sublayer[0](x, lambda x: self.self_attn(x, x, x, target_mask))x self.sublayer[1](x, lambda x: self.src_attn(x, m, m, source_mask))return self.sublayer[2](x, self.feed_forward)# # 2.4.2 解码器# 构建解码器类
class Decoder(nn.Module):def __init__(self, layer, N):super(Decoder, self).__init__()self.layers clones(layer, N)self.norm LayerNorm(layer.size)def forward(self, x, memory, source_mask, target_mask):for layer in self.layers:x layer(x, memory, source_mask, target_mask)return self.norm(x)# # 2.5输出部分实现m nn.Linear(20, 30)
input torch.randn(128, 20)
output m(input)
print(output.shape)# 构建Generator类
import torch.nn.functional as Fclass Generator(nn.Module):def __init__(self, d_model, vocal_size):super(Generator, self).__init__()self.project nn.Linear(d_model, vocal_size)def forward(self, x):return F.log_softmax(self.project(x), dim1)# # 2.6 Transformer模型构建# 实现编码解码结构
class EncoderDecoder(nn.Module):def __init__(self, encoder, decoder, source_embed, target_embed, generator):super(EncoderDecoder, self).__init__()self.encoder encoderself.decoder decoderself.src_embed source_embedself.tgt_embed target_embedself.generator generatordef forward(self, source, target, source_mask, target_mask):return self.decode(self.encode(source, source_mask), source_mask,target, target_mask)def encode(self, source, source_mask):return self.encoder(self.src_embed(source), source_mask)def decode(self, memory, source_mask, target, target_mask):return self.decoder(self.tgt_embed(target), memory, source_mask,target_mask)# Transformer模型构建过程的代码分析
def make_model(source_vocab, target_vocab, N6, d_model512, d_ff2048, head8,dropout0.1):c copy.deepcopyattn MultiHeadAttention(head, d_model)ff PositionWiseFeedForward(d_model, d_ff, dropout)position PositionalEncoding(d_model, dropout)model EncoderDecoder(Encoder(EncoderLayer(d_model, c(attn), c(ff), dropout), N),Decoder(DecoderLayer(d_model, c(attn), c(attn), c(ff), dropout), N),nn.Sequential(Embeddings(d_model, source_vocab), c(position)),nn.Sequential(Embeddings(d_model, target_vocab), c(position)),Generator(d_model, target_vocab))for p in model.parameters():if p.dim() 1:nn.init.xavier_uniform_(p)return model# # 2.7模型基本测试运行# 构建数据集生成器
def data_generator(V, batch_size, num_batch):for i in range(num_batch):data torch.from_numpy(np.random.randint(1, V, size(batch_size, 10)))data[:, 0] 1# source Variable(data, requires_gradFalse).long()# target Variable(data, requires_gradFalse).long()source Variable(data, requires_gradFalse).long().cuda()target Variable(data, requires_gradFalse).long().cuda()yield Batch(source, target)V 11
batch_size 20
num_batch 30# 获得Transformer模型及其优化器和损失函数
model make_model(V, V, N2)# 将模型移动到GPU上
model.cuda()model_optimizer get_std_opt(model)criterion LabelSmoothing(sizeV, padding_idx0, smoothing0.0)
loss SimpleLossCompute(model.generator, criterion, model_optimizer)# 运行模型进行训练和评估
def run(model, loss, epochs10):for epoch in range(epochs):model.train()run_epoch(data_generator(V, 8, 20), model, loss)model.eval()run_epoch(data_generator(V, 8, 5), model, loss)start time.time()run(model, loss)end time.time()# 总时间
total_time end - start
print(fTotal time: {total_time:.3f}s)# 使用模型进行贪婪解码
def run(model, loss, epochs10):for epoch in range(epochs):model.train()run_epoch(data_generator(V, 8, 20), model, loss)model.eval()run_epoch(data_generator(V, 8, 5), model, loss)model.eval()source torch.LongTensor([[1, 3, 2, 5, 4, 6, 7, 8, 9, 10]]).cuda()source_mask torch.ones(1, 1, 10).cuda()result greedy_decode(model, source, source_mask, max_len10,start_symbol1)print(result)start time.time()run(model, loss)end time.time()# 总时间
total_time end - start
print(fTotal time: {total_time:.3f}s)然后来自这个人的源代码讲解也非常好和视频一样我也修改了可以放在GPU运行代码如下
【精选】PytorchTransformer(Encoder编码器-Decoder解码器、多头注意力机制、多头自注意力机制、掩码张量、前馈全连接层、规范化层、子层连接结构、pyitcast) part1_あずにゃん的博客-CSDN博客 import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import math
import matplotlib.pyplot as plt
import numpy as np
import copy# embedding nn.Embedding(10, 3)
# input1 torch.LongTensor([[1, 2, 4, 5], [4, 3, 2, 9]])
# print(embedding(input1))# embedding nn.Embedding(10, 3, padding_idx0)
# input1 torch.LongTensor([[0, 2, 0, 5]])
# print(embedding(input1))# 构建Embedding类来实现文本嵌入层
class Embeddings(nn.Module):def __init__(self, d_model, vocab):# d_model: 词嵌入的维度# vocab: 词表的大小super(Embeddings, self).__init__()# 定义Embedding层self.lut nn.Embedding(vocab, d_model)# 将参数传入类中self.d_model d_modeldef forward(self, x):# x: 代表输入进模型的文本通过词汇映射后的数字张量return self.lut(x) * math.sqrt(self.d_model)d_model 512
vocab 1000# x Variable(torch.LongTensor([[100, 2, 421, 508], [491, 998, 1, 221]]))
# emb Embeddings(d_model, vocab)
# embr emb(x)
# print(embr:, embr)
# print(embr.shape)# m nn.Dropout(p0.2)
# input1 torch.randn(4, 5)
# output m(input1)
# print(output)# x torch.tensor([1, 2, 3, 4])
# y torch.unsqueeze(x, 0)
# print(y)
# z torch.unsqueeze(x, 1)
# print(z)# 构建位置编码器的类
class PositionalEncoding(nn.Module):def __init__(self, d_model, dropout, max_len5000):# d_model: 代表词嵌入的维度# dropout: 代表Dropout层的置零比率# max_len: 代表每隔句子的最大长度super(PositionalEncoding, self).__init__()# 实例化Dropout层self.dropout nn.Dropout(pdropout)# 初始化一个位置编码矩阵, 大小是max_len * d_modelpe torch.zeros(max_len, d_model)# 初始化一个绝对位置矩阵, max_len * 1position torch.arange(0., max_len).unsqueeze(1)# 定义一个变化矩阵div_term, 跳跃式的初始化div_term torch.exp(torch.arange(0., d_model, 2) * -(math.log(10000.0) / d_model))# 将前面定义的变化矩阵进行奇数, 偶数的分别赋值pe[:, 0::2] torch.sin(position * div_term)pe[:, 1::2] torch.cos(position * div_term)# 将二维张量扩充成三维张量pe pe.unsqueeze(0)# 将位置编码矩阵注册成模型的buffer, 这个buffer不是模型中的参数, 不跟随优化器同步更新# 注册成buffer后我们就可以在模型保存后重新加载的时候, 将这个位置编码器和模型参数一同加载进来self.register_buffer(pe, pe)def forward(self, x):# x: 代表文本序列的词嵌入表示# 首先明确pe的编码太长了, 将第二个维度, 也就是max_len对应的那个维度缩小成x的句子长度同等的长度x x Variable(self.pe[:, :x.size(1)], requires_gradFalse)return self.dropout(x)d_model 512
dropout 0.1
max_len 60# x embr
# pe PositionalEncoding(d_model, dropout, max_len)
# pe_result pe(x)
# print(pe_result)
# print(pe_result.shape)# 第一步设置一个画布
# plt.figure(figsize(15, 5))# 实例化PositionalEncoding类对象, 词嵌入维度给20, 置零比率设置为0
# pe PositionalEncoding(20, 0)# 向pe中传入一个全零初始化的x, 相当于展示pe
# y pe(Variable(torch.zeros(1, 100, 20)))# plt.plot(np.arange(100), y[0, :, 4:8].data.numpy())# plt.legend([dim %d%p for p in [4, 5, 6, 7]])# print(np.triu([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]], k-1))
# print(np.triu([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]], k0))
# print(np.triu([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]], k1))# 构建掩码张量的函数
def subsequent_mask(size):# size: 代表掩码张量后两个维度, 形成一个方阵attn_shape (1, size, size)# 使用np.ones()先构建一个全1的张量, 然后利用np.triu()形成上三角矩阵subsequent_mask np.triu(np.ones(attn_shape), k1).astype(uint8)# 使得这个三角矩阵反转return torch.from_numpy(1 - subsequent_mask)size 5# sm subsequent_mask(size)
# print(sm:, sm)# plt.figure(figsize(5, 5))
# plt.imshow(subsequent_mask(20)[0])# x Variable(torch.randn(5, 5))
# print(x)# mask Variable(torch.zeros(5, 5))
# print(mask)# y x.masked_fill(mask 0, -1e9)
# print(y)def attention(query, key, value, maskNone, dropoutNone):# query, key, value: 代表注意力的三个输入张量# mask: 掩码张量# dropout: 传入的Dropout实例化对象# 首先将query的最后一个维度提取出来, 代表的是词嵌入的维度d_k query.size(-1)# 按照注意力计算公式, 将query和key的转置进行矩阵乘法, 然后除以缩放稀疏scores torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_k)# 判断是否使用掩码张量if mask is not None:# 利用masked_fill方法, 将掩码张量和0进行位置的意义比较, 如果等于0, 替换成一个非常小的数值scores scores.masked_fill(mask 0, -1e9)# 对scores的最后一个维度上进行softmax操作p_attn F.softmax(scores, dim-1)# 判断是否使用dropoutif dropout is not None:p_attn dropout(p_attn)# 最后一步完成p_attn和value张量的乘法, 并返回query注意力表示return torch.matmul(p_attn, value), p_attn# query key value pe_result
# mask Variable(torch.zeros(2, 4, 4))
# attn, p_attn attention(query, key, value, maskmask)
# print(attn:, attn)
# print(attn.shape)
# print(p_attn:, p_attn)
# print(p_attn.shape)# x torch.randn(4, 4)
# print(x.size())
# y x.view(16)
# print(y.size())
# z x.view(-1, 8)
# print(z.size())# a torch.randn(1, 2, 3, 4)
# print(a.size())
# print(a)# b a.transpose(1, 2)
# print(b.size())
# print(b)# c a.view(1, 3, 2, 4)
# print(c.size())
# print(c)# 实现克隆函数, 因为在多头注意力机制下, 要用到多个结构相同的线性层
# 需要使用clone函数将他们一同初始化到一个网络层列表对象中
def clones(module, N):# module: 代表要克隆的目标网络层# N: 将module克隆几个return nn.ModuleList([copy.deepcopy(module) for _ in range(N)])# 实现多头注意力机制的类
class MultiHeadedAttention(nn.Module):def __init__(self, head, embedding_dim, dropout0.1):# head: 代表几个头的参数# embedding_dim: 代表词嵌入的维度# dropout: 进行Dropout操作时, 置零的比率super(MultiHeadedAttention, self).__init__()# 要确认一个事实: 多头的数量head需要整除词嵌入的维度embedding_dimassert embedding_dim % head 0# 得到每个头获得的词向量的维度self.d_k embedding_dim // headself.head headself.embedding_dim embedding_dim# 获得线性层, 要获得4个, 分别是Q,K,V以及最终的输出线性层self.linears clones(nn.Linear(embedding_dim, embedding_dim), 4)# 初始化注意力张量self.attn None# 初始化dropout对象self.dropout nn.Dropout(pdropout)def forward(self, query, key, value, maskNone):# query, key, value是注意力机制的三个输入张量, mask代表掩码张量# 首先判断是否使用掩码张量if mask is not None:# 使用unsqueeze将掩码张量进行维度扩充, 代表多头中的第n个头mask mask.unsqueeze(0)# 得到batch_sizebatch_size query.size(0)# 首先使用zip将网络层和输入数据连接在一起, 模型的输出利用view和transpose进行维度和形状的改变query, key, value \[model(x).view(batch_size, -1, self.head, self.d_k).transpose(1, 2)for model, x in zip(self.linears, (query, key, value))]# 将每个头的输出传入到注意力层x, self.attn attention(query, key, value, maskmask,dropoutself.dropout)# 得到每个头的计算结果是4维张量 需要进行形状的转换# 前面已经将1,2两个维度进行过转置, 在这里要重新转置回来# 注意: 经历了transpose()方法后, 必须要使用contiguous方法, 不然无法使用view()方法x x.transpose(1, 2).contiguous().view(batch_size, -1,self.head * self.d_k)# 最后将x输入线性层列表中的最后一个线性层中进行处理, 得到最终的多头注意力结构输出return self.linears[-1](x)# 实例化若干参数
head 8
embedding_dim 512
dropout 0.2# 若干输入参数的初始化
# query key value pe_result# mask Variable(torch.zeros(8, 4, 4))
# mha MultiHeadedAttention(head, embedding_dim, dropout)
# mha_result mha(query, key, value, mask)
# print(mha_result)
# print(mha_result.shape)# 构建前馈全连接网络类
class PositionwiseFeedForward(nn.Module):def __init__(self, d_model, d_ff, dropout0.1):# d_model: 代表词嵌入的维度, 同时也是两个线性层的输入维度和输出维度# d_ff: 代表第一个线性层的输出维度, 和第二个线性层的输入维度# dropout: 经过Dropout层处理时, 随机置零的比率super(PositionwiseFeedForward, self).__init__()# 定义两层全连接的线性层self.w1 nn.Linear(d_model, d_ff)self.w2 nn.Linear(d_ff, d_model)self.dropout nn.Dropout(pdropout)def forward(self, x):# x: 代表来自上一层的输出# 首先将x送入第一个线性层网络, 然后经历relu函数的激活, 再经历dropout层的处理# 最后送入第二个线性层return self.w2(self.dropout(F.relu(self.w1(x))))d_model 512
d_ff 64
dropout 0.2# x mha_result
# ff PositionwiseFeedForward(d_model, d_ff, dropout)
# ff_result ff(x)
# print(ff_result)
# print(ff_result.shape)# 构建规范化层的类
class LayerNorm(nn.Module):def __init__(self, features, eps1e-6):# features: 代表词嵌入的维度# eps: 一个足够小的正数, 用来在规范化计算公式的分母中, 防止除零操作super(LayerNorm, self).__init__()# 初始化两个参数张量a2, b2用于对结果做规范化操作计算# 将其用nn.Parameter进行封装, 代表他们也是模型中的参数self.a2 nn.Parameter(torch.ones(features))self.b2 nn.Parameter(torch.zeros(features))self.eps epsdef forward(self, x):# x: 代表上一层网络的输出# 首先对x进行最后一个维度上的求均值操作, 同时操持输出维度和输入维度一致mean x.mean(-1, keepdimTrue)# 接着对x进行字后一个维度上的求标准差的操作, 同时保持输出维度和输入维度一致std x.std(-1, keepdimTrue)# 按照规范化公式进行计算并返回return self.a2 * (x - mean) / (std self.eps) self.b2features d_model 512
eps 1e-6# x ff_result
# ln LayerNorm(features, eps)
# ln_result ln(x)
# print(ln_result)
# print(ln_result.shape)# 构建子层连接结构的类
class SublayerConnection(nn.Module):def __init__(self, size, dropout0.1):# size: 代表词嵌入的维度# dropout: 进行Dropout操作的置零比率super(SublayerConnection, self).__init__()# 实例化一个规范化层的对象self.norm LayerNorm(size)# 实例化一个dropout对象self.dropout nn.Dropout(pdropout)self.size sizedef forward(self, x, sublayer):# x: 代表上一层传入的张量# sublayer: 该子层连接中子层函数# 首先将x进行规范化, 然后送入子层函数中处理, 处理结果进入dropout层, 最后进行残差连接return x self.dropout(sublayer(self.norm(x)))size d_model 512
head 8
dropout 0.2# x pe_result
# mask Variable(torch.zeros(8, 4, 4))
# self_attn MultiHeadedAttention(head, d_model)# sublayer lambda x: self_attn(x, x, x, mask)# sc SublayerConnection(size, dropout)
# sc_result sc(x, sublayer)
# print(sc_result)
# print(sc_result.shape)# 构建编码器层的类
class EncoderLayer(nn.Module):def __init__(self, size, self_attn, feed_forward, dropout):# size: 代表词嵌入的维度# self_attn: 代表传入的多头自注意力子层的实例化对象# feed_forward: 代表前馈全连接层实例化对象# dropout: 进行dropout操作时的置零比率super(EncoderLayer, self).__init__()# 将两个实例化对象和参数传入类中self.self_attn self_attnself.feed_forward feed_forwardself.size size# 编码器层中有2个子层连接结构, 使用clones函数进行操作self.sublayer clones(SublayerConnection(size, dropout), 2)def forward(self, x, mask):# x: 代表上一层的传入张量# mask: 代表掩码张量# 首先让x经过第一个子层连接结构,内部包含多头自注意力机制子层# 再让张量经过第二个子层连接结构, 其中包含前馈全连接网络x self.sublayer[0](x, lambda x: self.self_attn(x, x, x, mask))return self.sublayer[1](x, self.feed_forward)# size d_model 512
# head 8
# d_ff 64
# x pe_result
# dropout 0.2# self_attn MultiHeadedAttention(head, d_model)
# ff PositionwiseFeedForward(d_model, d_ff, dropout)
# mask Variable(torch.zeros(8, 4, 4))# el EncoderLayer(size, self_attn, ff, dropout)
# el_result el(x, mask)
# print(el_result)
# print(el_result.shape)# 构建编码器类Encoder
class Encoder(nn.Module):def __init__(self, layer, N):# layer: 代表编码器层# N: 代表编码器中有几个layersuper(Encoder, self).__init__()# 首先使用clones函数克隆N个编码器层放置在self.layers中self.layers clones(layer, N)# 初始化一个规范化层, 作用在编码器的最后面self.norm LayerNorm(layer.size)def forward(self, x, mask):# x: 代表上一层输出的张量# mask: 代表掩码张量# 让x依次经历N个编码器层的处理, 最后再经过规范化层就可以输出了for layer in self.layers:x layer(x, mask)return self.norm(x)# size d_model 512
# d_ff 64
# head 8
# c copy.deepcopy
# attn MultiHeadedAttention(head, d_model)
# ff PositionwiseFeedForward(d_model, d_ff, dropout)
# dropout 0.2
# layer EncoderLayer(size, c(attn), c(ff), dropout)
# N 8
# mask Variable(torch.zeros(8, 4, 4))# en Encoder(layer, N)
# en_result en(x, mask)
# print(en_result)
# print(en_result.shape)# 构建解码器层类
class DecoderLayer(nn.Module):def __init__(self, size, self_attn, src_attn, feed_forward, dropout):# size: 代表词嵌入的维度# self_attn: 代表多头自注意力机制的对象# src_attn: 代表常规的注意力机制的对象# feed_forward: 代表前馈全连接层的对象# dropout: 代表Dropout的置零比率super(DecoderLayer, self).__init__()# 将参数传入类中self.size sizeself.self_attn self_attnself.src_attn src_attnself.feed_forward feed_forwardself.dropout dropout# 按照解码器层的结构图, 使用clones函数克隆3个子层连接对象self.sublayer clones(SublayerConnection(size, dropout), 3)def forward(self, x, memory, source_mask, target_mask):# x: 代表上一层输入的张量# memory: 代表编码器的语义存储张量# source_mask: 源数据的掩码张量# target_mask: 目标数据的掩码张量m memory# 第一步让x经历第一个子层, 多头自注意力机制的子层# 采用target_mask, 为了将解码时未来的信息进行遮掩, 比如模型解码第二个字符, 只能看见第一个字符信息x self.sublayer[0](x, lambda x: self.self_attn(x, x, x, target_mask))# 第二步让x经历第二个子层, 常规的注意力机制的子层, Q!KV# 采用source_mask, 为了遮掩掉对结果信息无用的数据x self.sublayer[1](x, lambda x: self.src_attn(x, m, m, source_mask))# 第三步让x经历第三个子层, 前馈全连接层return self.sublayer[2](x, self.feed_forward)# size d_model 512
# head 8
# d_ff 64
# dropout 0.2# self_attn src_attn MultiHeadedAttention(head, d_model, dropout)# ff PositionwiseFeedForward(d_model, d_ff, dropout)# x pe_result# memory en_result# mask Variable(torch.zeros(8, 4, 4))
# source_mask target_mask mask# dl DecoderLayer(size, self_attn, src_attn, ff, dropout)
# dl_result dl(x, memory, source_mask, target_mask)
# print(dl_result)
# print(dl_result.shape)# 构建解码器类
class Decoder(nn.Module):def __init__(self, layer, N):# layer: 代表解码器层的对象# N: 代表将layer进行几层的拷贝super(Decoder, self).__init__()# 利用clones函数克隆N个layerself.layers clones(layer, N)# 实例化一个规范化层self.norm LayerNorm(layer.size)def forward(self, x, memory, source_mask, target_mask):# x: 代表目标数据的嵌入表示,# memory: 代表编码器的输出张量# source_mask: 源数据的掩码张量# target_mask: 目标数据的掩码张量# 要将x依次经历所有的编码器层处理, 最后通过规范化层for layer in self.layers:x layer(x, memory, source_mask, target_mask)return self.norm(x)# size d_model 512
# head 8
# d_ff 64
# dropout 0.2
# c copy.deepcopy
# attn MultiHeadedAttention(head, d_model)
# ff PositionwiseFeedForward(d_model, d_ff, dropout)
# layer DecoderLayer(d_model, c(attn), c(attn), c(ff), dropout)# N 8
# x pe_result
# memory en_result
# mask Variable(torch.zeros(8, 4, 4))
# source_mask target_mask mask# de Decoder(layer, N)
# de_result de(x, memory, source_mask, target_mask)
# print(de_result)
# print(de_result.shape)# 构建Generator类
import torch.nn.functional as Fclass Generator(nn.Module):def __init__(self, d_model, vocab_size):# d_model: 代表词嵌入的维度# vocab_size: 代表词表的总大小super(Generator, self).__init__()# 定义一个线性层, 作用是完成网络输出维度的变换self.project nn.Linear(d_model, vocab_size)def forward(self, x):# x: 代表上一层的输出张量# 首先将x送入线性层中, 让其经历softmax的处理return F.log_softmax(self.project(x), dim-1)# d_model 512
# vocab_size 1000
# x de_result# gen Generator(d_model, vocab_size)
# gen_result gen(x)
# print(gen_result)
# print(gen_result.shape)# 构建编码器-解码器结构类
class EncoderDecoder(nn.Module):def __init__(self, encoder, decoder, source_embed, target_embed, generator):# encoder: 代表编码器对象# decoder: 代表解码器对象# source_embed: 代表源数据的嵌入函数# target_embed: 代表目标数据的嵌入函数# generator: 代表输出部分类别生成器对象super(EncoderDecoder, self).__init__()self.encoder encoderself.decoder decoderself.src_embed source_embedself.tgt_embed target_embedself.generator generatordef forward(self, source, target, source_mask, target_mask):# source: 代表源数据# target: 代表目标数据# source_mask: 代表源数据的掩码张量# target_mask: 代表目标数据的掩码张量return self.decode(self.encode(source, source_mask), source_mask,target, target_mask)def encode(self, source, source_mask):return self.encoder(self.src_embed(source), source_mask)def decode(self, memory, source_mask, target, target_mask):# memory: 代表经历编码器编码后的输出张量return self.decoder(self.tgt_embed(target), memory, source_mask,target_mask)# vocab_size 1000
# d_model 512
# encoder en
# decoder de
# source_embed nn.Embedding(vocab_size, d_model)
# target_embed nn.Embedding(vocab_size, d_model)
# generator gen
#
# source target Variable(torch.LongTensor([[100, 2, 421, 508], [491, 998, 1, 221]]))
#
# source_mask target_mask Variable(torch.zeros(8, 4, 4))
#
# ed EncoderDecoder(encoder, decoder, source_embed, target_embed, generator)
# ed_result ed(source, target, source_mask, target_mask)
# print(ed_result)
# print(ed_result.shape)def make_model(source_vocab, target_vocab, N6, d_model512, d_ff2048, head8,dropout0.1):# source_vocab: 代表源数据的词汇总数# target_vocab: 代表目标数据的词汇总数# N: 代表编码器和解码器堆叠的层数# d_model: 代表词嵌入的维度# d_ff: 代表前馈全连接层中变换矩阵的维度# head: 多头注意力机制中的头数# dropout: 指置零的比率c copy.deepcopy# 实例化一个多头注意力的类attn MultiHeadedAttention(head, d_model)# 实例化一个前馈全连接层的网络对象ff PositionwiseFeedForward(d_model, d_ff, dropout)# 实例化一个位置编码器position PositionalEncoding(d_model, dropout)# 实例化模型model,利用的是EncoderDecoder类# 编码器的结构里面有2个子层, attention层和前馈全连接层# 解码器的结构中有3个子层, 两个attention层和前馈全连接层model EncoderDecoder(Encoder(EncoderLayer(d_model, c(attn), c(ff), dropout), N),Decoder(DecoderLayer(d_model, c(attn), c(attn), c(ff), dropout), N),nn.Sequential(Embeddings(d_model, source_vocab), c(position)),nn.Sequential(Embeddings(d_model, target_vocab), c(position)),Generator(d_model, target_vocab))# 初始化整个模型中的参数, 判断参数的维度大于1, 将矩阵初始化成一个服从均匀分布的矩阵for p in model.parameters():if p.dim() 1:nn.init.xavier_uniform_(p)return modelsource_vocab 11
target_vocab 11
N 6# if __name__ __main__:
# res make_model(source_vocab, target_vocab, N)
# print(res)# ------------------------------------------------------from pyitcast.transformer_utils import Batch
from pyitcast.transformer_utils import get_std_opt
from pyitcast.transformer_utils import LabelSmoothing
from pyitcast.transformer_utils import SimpleLossComputefrom pyitcast.transformer_utils import run_epoch
from pyitcast.transformer_utils import greedy_decodedef data_generator(V, batch_size, num_batch):# V: 随机生成数据的最大值1# batch_size: 每次输送给模型的样本数量, 经历这些样本训练后进行一次参数的更新# num_batch: 一共输送模型多少轮数据for i in range(num_batch):# 使用numpy中的random.randint()来随机生成[1, V)# 分布的形状(batch, 10)data torch.from_numpy(np.random.randint(1, V, size(batch_size, 10)))# 将数据的第一列全部设置为1, 作为起始标志data[:, 0] 1# 因为是copy任务, 所以源数据和目标数据完全一致# 设置参数requires_gradFalse, 样本的参数不需要参与梯度的计算source Variable(data, requires_gradFalse).long().cuda()target Variable(data, requires_gradFalse).long().cuda()yield Batch(source, target)V 11
batch_size 20
num_batch 30# if __name__ __main__:
# res data_generator(V, batch_size, num_batch)
# print(res)# 使用make_model()函数获得模型的实例化对象
model make_model(V, V, N2)
model.cuda()# 使用工具包get_std_opt获得模型的优化器
model_optimizer get_std_opt(model)# 使用工具包LabelSmoothing获得标签平滑对象
criterion LabelSmoothing(sizeV, padding_idx0, smoothing0.0)# 使用工具包SimpleLossCompute获得利用标签平滑的结果得到的损失计算方法
loss SimpleLossCompute(model.generator, criterion, model_optimizer)# crit LabelSmoothing(size5, padding_idx0, smoothing0.5)# predict Variable(torch.FloatTensor([[0, 0.2, 0.7, 0.1, 0],
# [0, 0.2, 0.7, 0.1, 0],
# [0, 0.2, 0.7, 0.1, 0]]))# target Variable(torch.LongTensor([2, 1, 0]))# crit(predict, target)# plt.imshow(crit.true_dist)# def run(model, loss, epochs10):
# model: 代表将要训练的模型
# loss: 代表使用的损失计算方法
# epochs: 代表模型训练的轮次数
# for epoch in range(epochs):
# 首先进入训练模式, 所有的参数将会被更新
# model.train()
# 训练时, 传入的batch_size是20
# run_epoch(data_generator(V, 8, 20), model, loss)# 训练结束后, 进入评估模式, 所有的参数固定不变
# model.eval()
# 评估时, 传入的batch_size是5
# run_epoch(data_generator(V, 8, 5), model, loss)# if __name__ __main__:
# run(model, loss)def run(model, loss, epochs10):for epoch in range(epochs):# 首先进入训练模式, 所有的参数将会被更新model.train()run_epoch(data_generator(V, 8, 20), model, loss)# 训练结束后, 进入评估模式, 所有的参数固定不变model.eval()run_epoch(data_generator(V, 8, 5), model, loss)# 跳出for循环后, 代表模型训练结束, 进入评估模式model.eval()# run_epoch(data_generator(V, 8, 5), model, loss)# 初始化一个输入张量source torch.LongTensor([[1, 3, 2, 5, 4, 6, 7, 8, 9, 10]]).cuda()# 初始化一个输入张量的掩码张量, 全1代表没有任何的遮掩source_mask torch.ones(1, 1, 10).cuda()# 设定解码的最大长度max_len等于10, 起始数字的标志默认等于1result greedy_decode(model, source, source_mask, max_len10,start_symbol1)print(result)import timeif __name__ __main__:start time.time()run(model, loss)end time.time()# 总时间total_time end - startprint(fTotal time: {total_time:.3f}s)
