17.整体代码讲解

代码

import collections

import math
import torch
from torch import nn
import os
import time
import numpy as np
from matplotlib import pyplot as plt
from matplotlib_inline import backend_inline
import hashlib
import os
import tarfile
import zipfile
import requests
from IPython import display
from torch.utils import data

DATA_HUB = dict()
DATA_URL = "http://d2l-data.s3-accelerate.amazonaws.com/"
DATA_HUB["fra-eng"] = (
    DATA_URL + "fra-eng.zip",
    "94646ad1522d915e7b0f9296181140edcf86a4f5",
)


def try_gpu(i=0):
    """如果存在，则返回gpu(i)，否则返回cpu()"""
    if torch.cuda.device_count() >= i + 1:
        return torch.device(f"cuda:{i}")
    return torch.device("cpu")


def bleu(pred_seq, label_seq, k):
    """计算BLEU"""
    pred_tokens, label_tokens = pred_seq.split(" "), label_seq.split(" ")
    len_pred, len_label = len(pred_tokens), len(label_tokens)
    score = math.exp(min(0, 1 - len_label / len_pred))
    for n in range(1, k + 1):
        num_matches, label_subs = 0, collections.defaultdict(int)
        for i in range(len_label - n + 1):
            label_subs[" ".join(label_tokens[i : i + n])] += 1
        for i in range(len_pred - n + 1):
            if label_subs[" ".join(pred_tokens[i : i + n])] > 0:
                num_matches += 1
                label_subs[" ".join(pred_tokens[i : i + n])] -= 1
        score *= math.pow(num_matches / (len_pred - n + 1), math.pow(0.5, n))
    return score


def count_corpus(tokens):  # @save
    """统计词元的频率"""
    # 这里的tokens是1D列表或2D列表
    # tokens:["大","哥","大","嫂"] 已经是词元
    # tokens:[["大","哥","大","嫂"]["过","年","好"]]
    if len(tokens) == 0 or isinstance(tokens[0], list):
        # 将空的/二维词元列表展平成一个列表
        tokens = [token for line in tokens for token in line]
    return collections.Counter(tokens) # Couter类统计频率


def download(name, cache_dir=os.path.join(".", "./data")):
    """下载一个DATA_HUB中的文件，返回本地文件名"""
    assert name in DATA_HUB, f"{name} 不存在于{DATA_HUB}"
    url, sha1_hash = DATA_HUB[name]
    os.makedirs(cache_dir, exist_ok=True)
    fname = os.path.join(cache_dir, url.split("/")[-1])
    if os.path.exists(fname):
        sha1 = hashlib.sha1()
        with open(fname, "rb") as f:
            while True:
                data = f.read(1048576)
                if not data:
                    break
                sha1.update(data)
        if sha1.hexdigest() == sha1_hash:
            return fname  # 命中缓存
    print(f"正在从{url}下载{fname}...")
    r = requests.get(url, stream=True, verify=True)
    with open(fname, "wb") as f:
        f.write(r.content)
    return fname


def download_extract(name, folder=None):  # @save
    """下载并解压zip/tar文件"""
    fname = download(name)
    base_dir = os.path.dirname(fname)
    data_dir, ext = os.path.splitext(fname)
    if ext == ".zip":
        fp = zipfile.ZipFile(fname, "r")
    elif ext in (".tar", ".gz"):
        fp = tarfile.open(fname, "r")
    else:
        assert False, "只有zip/tar文件可以被解压缩"
    fp.extractall(base_dir)
    return os.path.join(base_dir, folder) if folder else data_dir


def read_data_nmt():
    """载入“英语－法语”数据集"""
    data_dir = download_extract("fra-eng")
    with open(os.path.join(data_dir, "fra.txt"), "r", encoding="utf-8") as f:
        return f.read()


def masked_softmax(X, valid_lens):
    """通过在最后一个轴上掩蔽元素来执行softmax操作"""
    # X:3D张量，valid_lens:1D或2D张量
    if valid_lens is None:
        return nn.functional.softmax(X, dim=-1)
    else:
        shape = X.shape
        if valid_lens.dim() == 1:
            valid_lens = torch.repeat_interleave(valid_lens, shape[1])
        else:
            valid_lens = valid_lens.reshape(-1)
        # 最后一轴上被掩蔽的元素使用一个非常大的负值替换，从而其softmax输出为0
        X = sequence_mask(X.reshape(-1, shape[-1]), valid_lens, value=-1e6)
        return nn.functional.softmax(X.reshape(shape), dim=-1)


def sequence_mask(X, valid_len, value=0):
    """在序列中屏蔽不相关的项"""
    maxlen = X.size(1)
    mask = (
        torch.arange((maxlen), dtype=torch.float32, device=X.device)[None, :]
        < valid_len[:, None]
    )
    X[~mask] = value
    return X


def preprocess_nmt(text):
    """预处理“英语－法语”数据集"""

    def no_space(char, prev_char):
        return char in set(",.!?") and prev_char != " "

    # 使用空格替换不间断空格
    # 使用小写字母替换大写字母
    text = text.replace("\u202f", " ").replace("\xa0", " ").lower()
    # 在单词和标点符号之间插入空格
    out = [
        " " + char if i > 0 and no_space(char, text[i - 1]) else char
        for i, char in enumerate(text)
    ]
    return "".join(out)


def tokenize_nmt(text, num_examples=None):
    """词元化“英语－法语”数据数据集"""
    source, target = [], []
    for i, line in enumerate(text.split("\n")):
        if num_examples and i > num_examples:
            break
        parts = line.split("\t")
        if len(parts) == 2:
            source.append(parts[0].split(" "))
            target.append(parts[1].split(" "))
    return source, target


def grad_clipping(net, theta):  # @save
    """裁剪梯度"""
    if isinstance(net, nn.Module): # 如果模型继承于nn.Module
        params = [p for p in net.parameters() if p.requires_grad] # 拿出所有参数,如果参数有梯度,就放进一个列表
    else:
        params = net.params
    norm = torch.sqrt(sum(torch.sum((p.grad**2)) for p in params)) # 对梯度平方求和，求和两次之后就变成一个标量了
    if norm > theta: # 和1比较
        for param in params:
            param.grad[:] *= theta / norm #/n 缩放模型大小,就是梯度裁剪


def truncate_pad(line, num_steps, padding_token):
    """截断或填充文本序列"""
    if len(line) > num_steps:
        return line[:num_steps]  # 截断
    return line + [padding_token] * (num_steps - len(line))  # 填充


def build_array_nmt(lines, vocab, num_steps):
    """将机器翻译的文本序列转换成小批量"""
    lines = [vocab[l] for l in lines]
    lines = [l + [vocab["<eos>"]] for l in lines]
    array = torch.tensor([truncate_pad(l, num_steps, vocab["<pad>"]) for l in lines])
    valid_len = (array != vocab["<pad>"]).type(torch.int32).sum(1)
    return array, valid_len


def load_array(data_arrays, batch_size, is_train=True):  # @save
    """构造一个PyTorch数据迭代器"""
    dataset = data.TensorDataset(*data_arrays)
    return data.DataLoader(dataset, batch_size, shuffle=is_train)


def load_data_nmt(batch_size, num_steps, num_examples=600):
    """返回翻译数据集的迭代器和词表"""
    text = preprocess_nmt(read_data_nmt())
    source, target = tokenize_nmt(text, num_examples)
    src_vocab = Vocab(source, min_freq=2, reserved_tokens=["<pad>", "<bos>", "<eos>"])
    tgt_vocab = Vocab(target, min_freq=2, reserved_tokens=["<pad>", "<bos>", "<eos>"])
    src_array, src_valid_len = build_array_nmt(source, src_vocab, num_steps)
    tgt_array, tgt_valid_len = build_array_nmt(target, tgt_vocab, num_steps)
    data_arrays = (src_array, src_valid_len, tgt_array, tgt_valid_len)
    data_iter = load_array(data_arrays, batch_size)
    return data_iter, src_vocab, tgt_vocab


def sequence_mask(X, valid_len, value=0):
    # """在序列中屏蔽不相关的项"""
    maxlen = X.size(1)
    mask = (
        torch.arange((maxlen), dtype=torch.float32, device=X.device)[None, :]
        < valid_len[:, None]
    )
    X[~mask] = value
    return X


def transpose_qkv(X, num_heads):
    # """为了多注意力头的并行计算而变换形状"""
    # 输入X的形状:(batch_size，查询或者“键－值”对的个数，num_hiddens)
    # 输出X的形状:(batch_size，查询或者“键－值”对的个数，num_heads，
    # num_hiddens/num_heads)
    X = X.reshape(X.shape[0], X.shape[1], num_heads, -1)
    # 输出X的形状:(batch_size，num_heads，查询或者“键－值”对的个数,
    # num_hiddens/num_heads)
    X = X.permute(0, 2, 1, 3)
    # 最终输出的形状:(batch_size*num_heads,查询或者“键－值”对的个数,
    # num_hiddens/num_heads)
    return X.reshape(-1, X.shape[2], X.shape[3])


def train_seq2seq(net, data_iter, lr, num_epochs, tgt_vocab, device):
    # """训练序列到序列模型"""

    def xavier_init_weights(m): # 初始化权重
        if type(m) == nn.Linear:
            nn.init.xavier_uniform_(m.weight) # 线性层的初始化方式
        if type(m) == nn.GRU:
            for param in m._flat_weights_names:
                if "weight" in param:
                    nn.init.xavier_uniform_(m._parameters[param])

    net.apply(xavier_init_weights) # 给模型应用函数
    net.to(device)
    optimizer = torch.optim.Adam(net.parameters(), lr=lr) # 优化器
    loss = MaskedSoftmaxCELoss() # 损失函数
    net.train()
    animator = Animator(xlabel="epoch", ylabel="loss", xlim=[10, num_epochs])
    for epoch in range(num_epochs): # 执行批量循环
        timer = Timer()
        metric = Accumulator(2)  # 训练损失总和，词元数量
        for batch in data_iter:
            optimizer.zero_grad() # 梯度置零
            X, X_valid_len, Y, Y_valid_len = [x.to(device) for x in batch] # 取出XY和它们的有效长度
            bos = torch.tensor(
                [tgt_vocab["<bos>"]] * Y.shape[0], device=device # 对Y添加bos
            ).reshape(-1, 1)
            dec_input = torch.cat([bos, Y[:, :-1]], 1)  # 强制教学
            Y_hat, _ = net(X, dec_input, X_valid_len)
            l = loss(Y_hat, Y, Y_valid_len)
            l.sum().backward()  # 损失函数的标量进行“反向传播”
            grad_clipping(net, 1) # 梯度裁剪
            num_tokens = Y_valid_len.sum() # 统计一下计算了多少token
            optimizer.step() # 梯度反传
            with torch.no_grad():
                metric.add(l.sum(), num_tokens)
        if (epoch + 1) % 10 == 0:
            animator.add(epoch + 1, (metric[0] / metric[1],))
    print(
        f"loss {metric[0] / metric[1]:.3f}, {metric[1] / timer.stop():.1f} "
        f"tokens/sec on {str(device)}"
    )


def predict_seq2seq(
    net,
    src_sentence,
    src_vocab,
    tgt_vocab,
    num_steps,
    device,
    save_attention_weights=False,
):
    # """序列到序列模型的预测"""
    # 在预测时将net设置为评估模式
    net.to(device)
    net.eval()
    src_tokens = src_vocab[src_sentence.lower().split(" ")] + [src_vocab["<eos>"]]
    enc_valid_len = torch.tensor([len(src_tokens)], device=device)
    src_tokens = truncate_pad(src_tokens, num_steps, src_vocab["<pad>"])
    # 添加批量轴
    enc_X = torch.unsqueeze(
        torch.tensor(src_tokens, dtype=torch.long, device=device), dim=0
    )
    enc_outputs = net.encoder(enc_X, enc_valid_len) # 编码器只执行次
    dec_state = net.decoder.init_state(enc_outputs, enc_valid_len) # 把编码器输出和有效长度都放进state里面
    # 添加批量轴
    dec_X = torch.unsqueeze(
        torch.tensor([tgt_vocab["<bos>"]], dtype=torch.long, device=device), dim=0
    )
    output_seq, attention_weight_seq = [], []
    for _ in range(num_steps):
        # 只使用解码器块进行了n次预测
        Y, dec_state = net.decoder(dec_X, dec_state) # Y:[b,n,vs]vs词表大小 预测时一句话b=1
        # 我们使用具有预测最高可能性的词元，作为解码器在下一时间步的输入
        dec_X = Y.argmax(dim=2) # 求维度里面最大值的下标,得到下标索引
        pred = dec_X.squeeze(dim=0).type(torch.int32).item() # 根据下标索引转化成整形，就是预测值,[1,n]
        # 保存注意力权重（稍后讨论）
        if save_attention_weights:
            attention_weight_seq.append(net.decoder.attention_weights)
        # 一旦序列结束词元被预测，输出序列的生成就完成了
        if pred == tgt_vocab["<eos>"]:
            break
        output_seq.append(pred) # 把值添加进output
    return " ".join(tgt_vocab.to_tokens(output_seq)), attention_weight_seq # 根据词表大小把这些值转换成对应的词元,用join连接起来


def transpose_output(X, num_heads):
    # """逆转transpose_qkv函数的操作"""
    X = X.reshape(-1, num_heads, X.shape[1], X.shape[2])
    X = X.permute(0, 2, 1, 3)
    return X.reshape(X.shape[0], X.shape[1], -1)


def use_svg_display():  # @save
    """使用svg格式在Jupyter中显示绘图"""
    backend_inline.set_matplotlib_formats("svg")


def set_axes(axes, xlabel, ylabel, xlim, ylim, xscale, yscale, legend):
    """设置matplotlib的轴"""
    axes.set_xlabel(xlabel)
    axes.set_ylabel(ylabel)
    axes.set_xscale(xscale)
    axes.set_yscale(yscale)
    axes.set_xlim(xlim)
    axes.set_ylim(ylim)
    if legend:
        axes.legend(legend)
    axes.grid()


def set_figsize(figsize=(3.5, 2.5)):  # @save
    """设置matplotlib的图表大小"""
    use_svg_display()
    plt.rcParams["figure.figsize"] = figsize


def dropout_layer(X, dropout):
    assert 0 <= dropout <= 1
    # 在本情况中，所有元素都被丢弃
    if dropout == 1:
        return torch.zeros_like(X)
    # 在本情况中，所有元素都被保留
    if dropout == 0:
        return X
    mask = (torch.rand(X.shape) > dropout).float()
    return mask * X / (1.0 - dropout)


class Accumulator:  # @save
    """在n个变量上累加"""

    def __init__(self, n):
        self.data = [0.0] * n

    def add(self, *args):
        self.data = [a + float(b) for a, b in zip(self.data, args)]

    def reset(self):
        self.data = [0.0] * len(self.data)

    def __getitem__(self, idx):
        return self.data[idx]


class Timer:  # @save
    """记录多次运行时间"""

    def __init__(self):
        self.times = []
        self.start()

    def start(self):
        """启动计时器"""
        self.tik = time.time()

    def stop(self):
        """停止计时器并将时间记录在列表中"""
        self.times.append(time.time() - self.tik)
        return self.times[-1]

    def avg(self):
        """返回平均时间"""
        return sum(self.times) / len(self.times)

    def sum(self):
        """返回时间总和"""
        return sum(self.times)

    def cumsum(self):
        """返回累计时间"""
        return np.array(self.times).cumsum().tolist()


class Animator:
    """在动画中绘制数据"""

    def __init__(
        self,
        xlabel=None,
        ylabel=None,
        legend=None,
        xlim=None,
        ylim=None,
        xscale="linear",
        yscale="linear",
        fmts=("-", "m--", "g-.", "r:"),
        nrows=1,
        ncols=1,
        figsize=(3.5, 2.5),
    ):
        # 增量地绘制多条线
        if legend is None:
            legend = []
        use_svg_display()
        self.fig, self.axes = plt.subplots(nrows, ncols, figsize=figsize)
        if nrows * ncols == 1:
            self.axes = [
                self.axes,
            ]
        # 使用lambda函数捕获参数
        self.config_axes = lambda: set_axes(
            self.axes[0], xlabel, ylabel, xlim, ylim, xscale, yscale, legend
        )
        self.X, self.Y, self.fmts = None, None, fmts

    def add(self, x, y):
        # 向图表中添加多个数据点
        if not hasattr(y, "__len__"):
            y = [y]
        n = len(y)
        if not hasattr(x, "__len__"):
            x = [x] * n
        if not self.X:
            self.X = [[] for _ in range(n)]
        if not self.Y:
            self.Y = [[] for _ in range(n)]
        for i, (a, b) in enumerate(zip(x, y)):
            if a is not None and b is not None:
                self.X[i].append(a)
                self.Y[i].append(b)
        self.axes[0].cla()
        for x, y, fmt in zip(self.X, self.Y, self.fmts):
            self.axes[0].plot(x, y, fmt)
        self.config_axes()
        display.display(self.fig)
        plt.draw()
        plt.pause(0.001)
        # display.clear_output(wait=True)


class Vocab:
    """文本词表"""
    # 初始化类
    # tokens:list ["go","some","play","run"]
    def __init__(self, tokens=None, min_freq=0, reserved_tokens=None):
        if tokens is None:
            tokens = []
        if reserved_tokens is None: # 特殊字符
            reserved_tokens = []
        # 按出现频率排序
        counter = count_corpus(tokens) # 统计频率
        # 排序，item拿到类似字典的键值对 x[1]频率 [(文字,频率),(文字,频率)]
        self._token_freqs = sorted(counter.items(), key=lambda x: x[1], reverse=True)
        # 未知词元的索引为0
        # 保存所有的词元
        self.idx_to_token = ["<unk>"] + reserved_tokens
        # 字典，转化为键值对方便查找
        self.token_to_idx = {token: idx for idx, token in enumerate(self.idx_to_token)}
        # 将未舍弃的所有词元添加到(_token_freqs)添加到idx_to_token和token_to_idx
        for token, freq in self._token_freqs:
            if freq < min_freq: # 截断频率，默认为0，每个词都不舍弃
                break
            if token not in self.token_to_idx:
                self.idx_to_token.append(token)
                self.token_to_idx[token] = len(self.idx_to_token) - 1 # 把索引加到这个字典里
    # 返回词表的长度，list方便计算
    def __len__(self):
        return len(self.idx_to_token)
    # 实现词元转为对应的数字
    # tokens：list,tuple
    def __getitem__(self, tokens):
        if not isinstance(tokens, (list, tuple)): # 如果是一个单独的词元
            return self.token_to_idx.get(tokens, self.unk) # 在字典里用get方法找到它
        return [self.__getitem__(token) for token in tokens] # 一个一个拿出来
    # 将数字转化为词元
    def to_tokens(self, indices):
        if not isinstance(indices, (list, tuple)):
            return self.idx_to_token[indices] # 单独索引直接返回
        return [self.idx_to_token[index] for index in indices] # 遍历按照list返回

    @property # 装饰器
    def unk(self):  # 未知词元的索引为0
        return 0

    @property # 装饰器
    def token_freqs(self):
        return self._token_freqs # 返回原始的未经舍弃的list


class MaskedSoftmaxCELoss(nn.CrossEntropyLoss):
    # """带遮蔽的softmax交叉熵损失函数"""

    # pred的形状：(batch_size,num_steps,vocab_size)
    # label的形状：(batch_size,num_steps)
    # valid_len的形状：(batch_size,)
    def forward(self, pred, label, valid_len):
        weights = torch.ones_like(label)
        weights = sequence_mask(weights, valid_len)
        self.reduction = "none"
        unweighted_loss = super(MaskedSoftmaxCELoss, self).forward(
            pred.permute(0, 2, 1), label
        )
        weighted_loss = (unweighted_loss * weights).mean(dim=1)
        return weighted_loss


class MultiHeadAttention(nn.Module):
    # """多头注意力"""

    def __init__(
        self,
        key_size,
        query_size,
        value_size,
        num_hiddens,
        num_heads,
        dropout,
        bias=False,
        **kwargs,
    ):
        super(MultiHeadAttention, self).__init__(**kwargs)
        self.num_heads = num_heads
        self.attention = DotProductAttention(dropout)
        self.W_q = nn.Linear(query_size, num_hiddens, bias=bias)
        self.W_k = nn.Linear(key_size, num_hiddens, bias=bias)
        self.W_v = nn.Linear(value_size, num_hiddens, bias=bias)
        self.W_o = nn.Linear(num_hiddens, num_hiddens, bias=bias)

    def forward(self, queries, keys, values, valid_lens):
        # queries，keys，values的形状:
        # (batch_size，查询或者“键－值”对的个数，num_hiddens)
        # valid_lens　的形状:
        # (batch_size，)或(batch_size，查询的个数)
        # 经过变换后，输出的queries，keys，values　的形状:
        # (batch_size*num_heads，查询或者“键－值”对的个数，
        # num_hiddens/num_heads)
        queries = transpose_qkv(self.W_q(queries), self.num_heads)
        keys = transpose_qkv(self.W_k(keys), self.num_heads)
        values = transpose_qkv(self.W_v(values), self.num_heads)
        if valid_lens is not None:
            # 在轴0，将第一项（标量或者矢量）复制num_heads次，
            # 然后如此复制第二项，然后诸如此类。
            valid_lens = torch.repeat_interleave(
                valid_lens, repeats=self.num_heads, dim=0
            )
        # output的形状:(batch_size*num_heads，查询的个数，
        # num_hiddens/num_heads)
        output = self.attention(queries, keys, values, valid_lens)
        # output_concat的形状:(batch_size，查询的个数，num_hiddens)
        output_concat = transpose_output(output, self.num_heads)
        return self.W_o(output_concat)


class PositionalEncoding(nn.Module):
    # """位置编码"""

    def __init__(self, num_hiddens, dropout, max_len=1000):
        super(PositionalEncoding, self).__init__()
        self.dropout = nn.Dropout(dropout)
        # 创建一个足够长的P
        self.P = torch.zeros((1, max_len, num_hiddens))
        X = torch.arange(max_len, dtype=torch.float32).reshape(-1, 1) / torch.pow(
            10000, torch.arange(0, num_hiddens, 2, dtype=torch.float32) / num_hiddens
        )
        self.P[:, :, 0::2] = torch.sin(X)
        self.P[:, :, 1::2] = torch.cos(X)

    def forward(self, X):
        X = X + self.P[:, : X.shape[1], :].to(X.device)
        return self.dropout(X)


class PositionWiseFFN(nn.Module):
    # """基于位置的前馈网络"""

    def __init__(self, ffn_num_input, ffn_num_hiddens, ffn_num_outputs, **kwargs):
        super(PositionWiseFFN, self).__init__(**kwargs)
        self.dense1 = nn.Linear(ffn_num_input, ffn_num_hiddens)
        self.relu = nn.ReLU()
        self.dense2 = nn.Linear(ffn_num_hiddens, ffn_num_outputs)

    def forward(self, X):
        return self.dense2(self.relu(self.dense1(X)))


class AddNorm(nn.Module):
    # """残差连接后进行层规范化"""

    def __init__(self, normalized_shape, dropout, **kwargs):
        super(AddNorm, self).__init__(**kwargs)
        self.dropout = nn.Dropout(dropout)
        self.ln = nn.LayerNorm(normalized_shape)
        nn.Softmax()

    def forward(self, X, Y):
        return self.ln(self.dropout(Y) + X)


class Encoder(nn.Module):
    # """编码器-解码器架构的基本编码器接口"""

    def __init__(self, **kwargs):
        super(Encoder, self).__init__(**kwargs)

    def forward(self, X, *args):
        raise NotImplementedError


class Decoder(nn.Module):
    # """编码器-解码器架构的基本解码器接口"""

    def __init__(self, **kwargs):
        super(Decoder, self).__init__(**kwargs)

    def init_state(self, enc_outputs, *args):
        raise NotImplementedError

    def forward(self, X, state):
        raise NotImplementedError


class EncoderDecoder(nn.Module):
    # """编码器-解码器架构的基类"""

    def __init__(self, encoder, decoder, **kwargs):
        super(EncoderDecoder, self).__init__(**kwargs)
        self.encoder = encoder
        self.decoder = decoder

    def forward(self, enc_X, dec_X, *args):
        enc_outputs = self.encoder(enc_X, *args)
        dec_state = self.decoder.init_state(enc_outputs, *args)
        return self.decoder(dec_X, dec_state)


class DotProductAttention(nn.Module):
    # """缩放点积注意力"""

    def __init__(self, dropout, **kwargs):
        super(DotProductAttention, self).__init__(**kwargs)
        self.dropout = nn.Dropout(dropout)
        # queries的形状：(batch_size，查询的个数，d)
        # keys的形状：(batch_size，“键－值”对的个数，d)
        # values的形状：(batch_size，“键－值”对的个数，值的维度)
        # valid_lens的形状:(batch_size，)或者(batch_size，查询的个数)

    def forward(self, queries, keys, values, valid_lens=None):
        d = queries.shape[-1]
        # 设置transpose_b=True为了交换keys的最后两个维度
        scores = torch.bmm(queries, keys.transpose(1, 2)) / math.sqrt(d)
        self.attention_weights = masked_softmax(scores, valid_lens)
        return torch.bmm(self.dropout(self.attention_weights), values)


class AttentionDecoder(Decoder):
    # """带有注意力机制解码器的基本接口"""

    def __init__(self, **kwargs):
        super(AttentionDecoder, self).__init__(**kwargs)

    @property
    def attention_weights(self):
        raise NotImplementedError


class EncoderBlock(nn.Module):
    # """Transformer编码器块"""

    def __init__(
        self,
        key_size,
        query_size,
        value_size,
        num_hiddens,
        norm_shape,
        ffn_num_input,
        ffn_num_hiddens,
        num_heads,
        dropout,
        use_bias=False,
        **kwargs,
    ):
        super(EncoderBlock, self).__init__(**kwargs)
        self.attention = MultiHeadAttention(
            key_size, query_size, value_size, num_hiddens, num_heads, dropout, use_bias
        )
        self.addnorm1 = AddNorm(norm_shape, dropout)
        self.ffn = PositionWiseFFN(ffn_num_input, ffn_num_hiddens, num_hiddens)
        self.addnorm2 = AddNorm(norm_shape, dropout)

    def forward(self, X, valid_lens):
        Y = self.addnorm1(X, self.attention(X, X, X, valid_lens))
        return self.addnorm2(Y, self.ffn(Y))


class DecoderBlock(nn.Module):
    # """解码器中第i个块"""

    def __init__(
        self,
        key_size,
        query_size,
        value_size,
        num_hiddens,
        norm_shape,
        ffn_num_input,
        ffn_num_hiddens,
        num_heads,
        dropout,
        i,
        **kwargs,
    ):
        super(DecoderBlock, self).__init__(**kwargs)
        self.i = i # 表示这是第i个块
        self.attention1 = MultiHeadAttention(
            key_size, query_size, value_size, num_hiddens, num_heads, dropout
        )
        self.addnorm1 = AddNorm(norm_shape, dropout) # dropout在addnorm里面
        self.attention2 = MultiHeadAttention(
            key_size, query_size, value_size, num_hiddens, num_heads, dropout
        )
        self.addnorm2 = AddNorm(norm_shape, dropout)
        self.ffn = PositionWiseFFN(ffn_num_input, ffn_num_hiddens, num_hiddens)
        self.addnorm3 = AddNorm(norm_shape, dropout)

    def forward(self, X, state): # 输入的output 推理阶段大小[1,1]state存放3个量,1个编码器输出,1个用来产生编码器mask,1个用来连接推理结果
        enc_outputs, enc_valid_lens = state[0], state[1]
        # 训练阶段，输出序列的所有词元都在同一时间处理，
        # 因此state[2][self.i]初始化为None。
        # 预测阶段，输出序列是通过词元一个接着一个解码的，
        # 因此state[2][self.i]包含着直到当前时间步第i个块解码的输出表示 [bos] he is
        if state[2][self.i] is None:
            key_values = X
        else:
            key_values = torch.cat((state[2][self.i], X), axis=1)
        state[2][self.i] = key_values
        if self.training:
            batch_size, num_steps, _ = X.shape
            # dec_valid_lens的开头:(batch_size,num_steps),
            # 其中每一行是[1,2,...,num_steps]
            dec_valid_lens = torch.arange(1, num_steps + 1, device=X.device).repeat(
                batch_size, 1
            )
        else:
            dec_valid_lens = None
        # 自注意力
        X2 = self.attention1(X, key_values, key_values, dec_valid_lens)
        Y = self.addnorm1(X, X2) # dropout加在addnorm里面
        # 编码器－解码器注意力。
        # enc_outputs的开头:(batch_size,num_steps,num_hiddens)
        Y2 = self.attention2(Y, enc_outputs, enc_outputs, enc_valid_lens) # Q来自addnorm,解码器输出做K,V
        Z = self.addnorm2(Y, Y2)
        return self.addnorm3(Z, self.ffn(Z)), state


class TransformerEncoder(Encoder):
    # """Transformer编码器"""

    def __init__(
        self,
        vocab_size,
        key_size,
        query_size,
        value_size,
        num_hiddens,
        norm_shape,
        ffn_num_input,
        ffn_num_hiddens,
        num_heads,
        num_layers,
        dropout,
        use_bias=False,
        **kwargs,
    ):
        super(TransformerEncoder, self).__init__(**kwargs)
        self.num_hiddens = num_hiddens
        self.embedding = nn.Embedding(vocab_size, num_hiddens)
        # self.embedding = nn.Embedding(vocab_size, num_hiddens, device=try_gpu())
        self.pos_encoding = PositionalEncoding(num_hiddens, dropout)
        self.blks = nn.Sequential()
        for i in range(num_layers):
            self.blks.add_module(
                "block" + str(i),
                EncoderBlock(
                    key_size,
                    query_size,
                    value_size,
                    num_hiddens,
                    norm_shape,
                    ffn_num_input,
                    ffn_num_hiddens,
                    num_heads,
                    dropout,
                    use_bias,
                ),
            )

    def forward(self, X, valid_lens, *args):
        # 因为位置编码值在-1和1之间，
        # 因此嵌入值乘以嵌入维度的平方根进行缩放，
        # 然后再与位置编码相加。
        X = self.pos_encoding(self.embedding(X) * math.sqrt(self.num_hiddens))
        self.attention_weights = [None] * len(self.blks)
        for i, blk in enumerate(self.blks):
            X = blk(X, valid_lens)
            self.attention_weights[i] = blk.attention.attention.attention_weights
        return X


class TransformerDecoder(AttentionDecoder):
    def __init__(
        self,
        vocab_size,
        key_size,
        query_size,
        value_size,
        num_hiddens,
        norm_shape,
        ffn_num_input,
        ffn_num_hiddens,
        num_heads,
        num_layers,
        dropout,
        **kwargs,
    ):
        super(TransformerDecoder, self).__init__(**kwargs)
        self.num_hiddens = num_hiddens
        self.num_layers = num_layers
        self.embedding = nn.Embedding(vocab_size, num_hiddens)
        self.pos_encoding = PositionalEncoding(num_hiddens, dropout) # dropout在里面
        self.blks = nn.Sequential()
        for i in range(num_layers): # n个block块
            self.blks.add_module(
                "block" + str(i),
                DecoderBlock(
                    key_size,
                    query_size,
                    value_size,
                    num_hiddens,
                    norm_shape,
                    ffn_num_input,
                    ffn_num_hiddens,
                    num_heads,
                    dropout,
                    i,
                ),
            )
        self.dense = nn.Linear(num_hiddens, vocab_size) # 线性层,不执行softmax不影响下标

    def init_state(self, enc_outputs, enc_valid_lens, *args):
        return [enc_outputs, enc_valid_lens, [None] * self.num_layers]
        # state 第一个有效数字是编码器输出,第二个有效数字是编码器的有效长度,用来产生mask,第三个是用来保存KV

    def forward(self, X, state):
        X = self.pos_encoding(self.embedding(X) * math.sqrt(self.num_hiddens)) # *根号d,位置编码
        self._attention_weights = [[None] * len(self.blks) for _ in range(2)]
        for i, blk in enumerate(self.blks): # block块
            X, state = blk(X, state)
            # 解码器自注意力权重
            self._attention_weights[0][i] = blk.attention1.attention.attention_weights
            # “编码器－解码器”自注意力权重
            self._attention_weights[1][i] = blk.attention2.attention.attention_weights
        return self.dense(X), state

    @property
    def attention_weights(self):
        return self._attention_weights


if __name__ == "__main__":
    num_hiddens, num_layers, dropout, batch_size, num_steps = 32, 2, 0.1, 64, 10
    lr, num_epochs, device = 0.005, 200, try_gpu()
    ffn_num_input, ffn_num_hiddens, num_heads = 32, 64, 4
    key_size, query_size, value_size = 32, 32, 32
    norm_shape = [32]
    train_iter, src_vocab, tgt_vocab = load_data_nmt(batch_size, num_steps)
    encoder = TransformerEncoder(
        len(src_vocab),
        key_size,
        query_size,
        value_size,
        num_hiddens,
        norm_shape,
        ffn_num_input,
        ffn_num_hiddens,
        num_heads,
        num_layers,
        dropout,
    )
    decoder = TransformerDecoder(
        len(tgt_vocab),
        key_size,
        query_size,
        value_size,
        num_hiddens,
        norm_shape,
        ffn_num_input,
        ffn_num_hiddens,
        num_heads,
        num_layers,
        dropout,
    )
    net = EncoderDecoder(encoder, decoder)
    train_seq2seq(net, train_iter, lr, num_epochs, tgt_vocab, device) # 训练

    engs = ["go .", "i lost .", "he's calm .", "i'm home ."]
    fras = ["va !", "j'ai perdu .", "il est calme .", "je suis chez moi ."]
    for eng, fra in zip(engs, fras):
        translation, dec_attention_weight_seq = predict_seq2seq( # 预测
            net, eng, src_vocab, tgt_vocab, num_steps, device, True
        )
        print(f"{eng} => {translation}, ", f"bleu {bleu(translation, fra, k=2):.3f}")

输出结果
```python
<Figure size 350x250 with 1 Axes>
<Figure size 350x250 with 1 Axes>
<Figure size 350x250 with 1 Axes>
<Figure size 350x250 with 1 Axes>
<Figure size 350x250 with 1 Axes>
<Figure size 350x250 with 1 Axes>
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<Figure size 350x250 with 1 Axes>
<Figure size 350x250 with 1 Axes>
<Figure size 350x250 with 1 Axes>
<Figure size 350x250 with 1 Axes>
<Figure size 350x250 with 1 Axes>
<Figure size 350x250 with 1 Axes>
<Figure size 350x250 with 1 Axes>
<Figure size 350x250 with 1 Axes>
<Figure size 350x250 with 1 Axes>
<Figure size 350x250 with 1 Axes>
<Figure size 350x250 with 1 Axes>
<Figure size 350x250 with 1 Axes>
<Figure size 350x250 with 1 Axes>
loss 0.034, 10150.2 tokens/sec on cpu
go . => va !,  bleu 1.000
i lost . => je vous en <unk> .,  bleu 0.000
he's calm . => il est calme .,  bleu 1.000
i'm home . => je suis chez moi .,  bleu 1.000