关于这个“这是B站目前讲的最好的【Transformer实战】教程！“视频的目前可以运行的源代码GPU版本

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?p=3&vd_source=3083729582baecf3ad2c3c52876b23aa
# 我已经使用GPU修改了代码,加了几处.cuda()就行了

import copy
import math
import time

import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable

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 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_model

    def 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_len=5000):
        super(PositionalEncoding, self).__init__()
        self.dropout = nn.Dropout(p=dropout)
        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), k=1).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, mask=None, dropout=None):
    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, dropout=0.1):
        super(MultiHeadAttention, self).__init__()
        assert embedding_dim % head == 0
        self.d_k = embedding_dim // head
        self.head = head
        self.embedding_dim = embedding_dim
        self.linears = clones(nn.Linear(embedding_dim, embedding_dim), 4)
        self.attn = None
        self.dropout = nn.Dropout(p=dropout)

    def forward(self, query, key, value, mask=None):
        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, mask=mask,
                                 dropout=self.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, dropout=0.1):
        super(PositionWiseFeedForward, self).__init__()

        self.w1 = nn.Linear(d_model, d_ff)
        self.w2 = nn.Linear(d_ff, d_model)
        self.dropout = nn.Dropout(p=dropout)

    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, eps=1e-6):
        super(LayerNorm, self).__init__()
        self.a2 = nn.Parameter(torch.ones(features))
        self.b2 = nn.Parameter(torch.zeros(features))
        self.eps = eps

    def forward(self, x):
        mean = x.mean(-1, keepdim=True)
        std = x.std(-1, keepdim=True)
        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, dropout=0.1):
        super(SublayerConnection, self).__init__()
        self.norm = LayerNorm(size)
        self.dropout = nn.Dropout(p=dropout)
        self.size = size

    def 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_attn
        self.feed_forward = feed_forward
        self.size = size

        self.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 = size
        self.self_attn = self_attn
        self.src_attn = src_attn
        self.feed_forward = feed_forward
        self.dropout = dropout

        self.sublayer = clones(SublayerConnection(size, dropout), 3)

    def forward(self, x, memory, source_mask, target_mask):
        m = memory
        x = 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 F


class 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), dim=1)


# # 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 = encoder
        self.decoder = decoder
        self.src_embed = source_embed
        self.tgt_embed = target_embed
        self.generator = generator

    def 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, N=6, d_model=512, d_ff=2048, head=8,
               dropout=0.1):
    c = copy.deepcopy
    attn = 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_grad=False).long()
        # target = Variable(data, requires_grad=False).long()

        source = Variable(data, requires_grad=False).long().cuda()
        target = Variable(data, requires_grad=False).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, N=2)

# 将模型移动到GPU上
model.cuda()

model_optimizer = get_std_opt(model)

criterion = LabelSmoothing(size=V, padding_idx=0, smoothing=0.0)
loss = SimpleLossCompute(model.generator, criterion, model_optimizer)


# 运行模型进行训练和评估
def run(model, loss, epochs=10):
    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(f"Total time: {total_time:.3f}s")


# 使用模型进行贪婪解码
def run(model, loss, epochs=10):
    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_len=10,
                           start_symbol=1)
    print(result)


start = time.time()

run(model, loss)

end = time.time()

# 总时间
total_time = end - start
print(f"Total 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】Pytorch：Transformer(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_idx=0)
# 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_model

    def 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(p=0.2)
# 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_len=5000):
        # 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(p=dropout)

        # 初始化一个位置编码矩阵, 大小是max_len * d_model
        pe = torch.zeros(max_len, d_model)

        # 初始化一个绝对位置矩阵, max_len * 1
        position = 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_grad=False)
        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]], k=0))
# print(np.triu([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]], k=1))


# 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), k=1).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, mask=None, dropout=None):
    # 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dropout
    if 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, mask=mask)
# 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, dropout=0.1):
        # 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_dim
        assert embedding_dim % head == 0

        # 得到每个头获得的词向量的维度
        self.d_k = embedding_dim // head

        self.head = head
        self.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(p=dropout)

    def forward(self, query, key, value, mask=None):
        # 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_size
        batch_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, mask=mask,
                                 dropout=self.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, dropout=0.1):
        # 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(p=dropout)

    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, eps=1e-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 = eps

    def 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, keepdim=True)
        # 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, keepdim=True)
        # 按照规范化公式进行计算并返回
        return self.a2 * (x - mean) / (std + self.eps) + self.b2


features = 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, dropout=0.1):
        # 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(p=dropout)
        self.size = size

    def 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_attn
        self.feed_forward = feed_forward
        self.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layer
        super(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 = size
        self.self_attn = self_attn
        self.src_attn = src_attn
        self.feed_forward = feed_forward
        self.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!=K=V
        # 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layer
        self.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 F


class 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 = encoder
        self.decoder = decoder
        self.src_embed = source_embed
        self.tgt_embed = target_embed
        self.generator = generator

    def 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, N=6, d_model=512, d_ff=2048, head=8,
               dropout=0.1):
    # 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 model


source_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 SimpleLossCompute

from pyitcast.transformer_utils import run_epoch
from pyitcast.transformer_utils import greedy_decode


def 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_grad=False, 样本的参数不需要参与梯度的计算
        source = Variable(data, requires_grad=False).long().cuda()
        target = Variable(data, requires_grad=False).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, N=2)
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(size=V, padding_idx=0, smoothing=0.0)

# 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(size=5, padding_idx=0, smoothing=0.5)

# 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, epochs=10):
# 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, epochs=10):
    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1
    result = greedy_decode(model, source, source_mask, max_len=10,
                           start_symbol=1)
    print(result)


import time

if __name__ == '__main__':
    start = time.time()

    run(model, loss)
    end = time.time()

    # 总时间
    total_time = end - start
    print(f"Total time: {total_time:.3f}s")