Transformer

import math
import torch
from torch import nn
import matplotlib.pyplot as plt
from d2l import torch as d2l


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


def masked_softmax(x, valid_lens):
    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)


x = torch.ones(2, 3, 4)
print(masked_softmax(torch.rand(2, 2, 4), torch.tensor([2, 3])))


# 加性注意力:

class AdditiveAttention(nn.Module):
    """加性注意力"""

    def __init__(self, key_size, query_size, num_hidden, dropout, **kwargs):
        super(AdditiveAttention, self).__init__(**kwargs)
        self.w_k = nn.Linear(key_size, num_hidden, bias=False)
        self.w_q = nn.Linear(query_size, num_hidden, bias=False)
        self.w_v = nn.Linear(num_hidden, 1, bias=False)
        self.dropout = nn.Dropout(dropout)

    def forward(self, queries, keys, values, valid_lens):
        queries, keys = self.w_q(queries), self.w_k(keys)
        features = queries.unsqueeze(2) + keys.unsqueeze(1)
        features = torch.tanh(features)
        scores = self.w_v(features).squeeze(-1)

        self.attention_weights = masked_softmax(scores, valid_lens)
        return torch.bmm(self.dropout(self.attention_weights), values)


queries, keys = torch.normal(0, 1, (2, 1, 20)), torch.ones((2, 10, 2))

print("queries:")
print(queries)
print("keys:")
print(keys)

values = torch.arange(40, dtype=torch.float32).reshape(1, 10, 4).repeat(2, 1, 1)
print("values:")
print(values)

valid_lens = torch.tensor([2, 6])

attention = AdditiveAttention(key_size=2, query_size=20, num_hidden=8,
                              dropout=0.1)

attention.eval()
print(attention(queries, keys, values, valid_lens))

d2l.show_heatmaps(attention.attention_weights.reshape((1, 1, 2, 10)),
                  xlabel='Keys', ylabel='Queries')
plt.show()


# 点积模型:

class DotProductAttention(nn.Module):
    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]
        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)


queries = torch.normal(0, 1, (2, 1, 2))
attention = DotProductAttention(dropout=0.5)
attention.eval()
print(attention(queries, keys, values, valid_lens))

d2l.show_heatmaps(attention.attention_weights.reshape((1, 1, 2, 10)),
                  xlabel='Keys', ylabel='Queries')
plt.show()


import torch
from torch import nn
from d2l import torch as d2l
import matplotlib.pyplot as plt


# @save
class AttentionDecoder(d2l.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 Seq2SeqAttentionDecoder(AttentionDecoder):
    def __init__(self, vocab_size, embed_size, num_hiddens, num_layers,
                 dropout=0, **kwargs):
        super(Seq2SeqAttentionDecoder, self).__init__(**kwargs)
        self.attention = d2l.AdditiveAttention(num_hiddens, num_hiddens, num_hiddens, dropout)
        self.embedding = nn.Embedding(vocab_size, embed_size)
        self.rnn = nn.GRU(embed_size + num_hiddens, num_hiddens, num_layers,
                          dropout=dropout)
        self.dense = nn.Linear(num_hiddens, vocab_size)

    def init_state(self, enc_outputs, enc_valid_lens, *args):
        # outputs的形状为(batch_size，num_steps，num_hidden).
        # hidden_state的形状为(num_layers，batch_size，num_hidden)
        outputs, hidden_state = enc_outputs
        return (outputs.permute(1, 0, 2), hidden_state, enc_valid_lens)

    def forward(self, x, state):
        # enc_outputs的形状为(batch_size,num_steps,num_hidden).
        # hidden_state的形状为(num_layers,batch_size,
        # num_hidden)
        enc_outputs, hidden_state, enc_valid_lens = state
        # 输出X的形状为(num_steps,batch_size,embed_size)
        x = self.embedding(x).permute(1, 0, 2)
        outputs, self._attention_weights = [], []
        for x_ in x:
            query = torch.unsqueeze(hidden_state[-1], dim=1)  # query的形状为(batch_size,1,num_hidden)
            context = self.attention(query, enc_outputs, enc_outputs, enc_valid_lens)
            # context的形状为(batch_size,1,num_hidden)
            x_ = torch.cat((context, torch.unsqueeze(x_, dim=1)), dim=-1)
            # 将x变形为(1,batch_size,embed_size+num_hidden)
            out, hidden_state = self.rnn(x_.permute(1, 0, 2), hidden_state)
            outputs.append(out)
            self._attention_weights.append(self.attention.attention_weights)
        # 全连接层变换后，outputs的形状为
        # (num_steps,batch_size,vocab_size)
        outputs = self.dense(torch.cat(outputs, dim=0))
        return outputs.permute(1, 0, 2), [enc_outputs, hidden_state, enc_valid_lens]

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


encoder = d2l.Seq2SeqEncoder(vocab_size=10, embed_size=8, num_hiddens=16,
                             num_layers=2)
encoder.eval()

decoder = Seq2SeqAttentionDecoder(vocab_size=10, embed_size=8, num_hiddens=16,
                                  num_layers=2)
decoder.eval()

x = torch.zeros((4, 7), dtype=torch.long)
state = decoder.init_state(encoder(x), None)
output, state = decoder(x, state)
print(output.shape, len(state), state[0].shape, len(state[1]), state[1][0].shape)

embed_size, num_hiddens, num_layers, dropout = 32, 32, 2, 0.1
batch_size, num_steps = 64, 10
lr, num_epochs, device = 0.005, 250, d2l.try_gpu()
tion_weight_seq = d2l.predict_seq2seq(
        net, eng, src_vocab
# train_iter, src_vocab, tgt_vocab = d2l.load_data_nmt(batch_size, num_steps)
# encoder = d2l.Seq2SeqEncoder(
#     len(src_vocab), embed_size, num_hiddens, num_layers, dropout)
# decoder = Seq2SeqAttentionDecoder(
#     len(tgt_vocab), embed_size, num_hiddens, num_layers, dropout)
# net = d2l.EncoderDecoder(encoder, decoder)
# print(d2l.train_seq2seq(net, train_iter, lr, num_epochs, tgt_vocab, device))
#
# plt.show()
# 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_atten, tgt_vocab, num_steps, device, True)
    print(f'{eng} => {translation}, ',
          f'bleu {d2l.bleu(translation, fra, k=2):.3f}')

Transformer

多头注意力

import math
import torch
from torch import nn
from d2l import torch as d2l


# @save
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])


# @save
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)


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 = d2l.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)

    # 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)
    def forward(self, queries, keys, values, valid_lens):
        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)

num_hiddens, num_heads = 100, 5
attention = MultiHeadAttention(num_hiddens, num_hiddens, num_hiddens,
                               num_hiddens, num_heads, 0.5)
print(attention.eval())

batch_size, num_queries = 2, 4
num_kvpairs, valid_lens =  6, torch.tensor([3, 2])
X = torch.ones((batch_size, num_queries, num_hiddens))
Y = torch.ones((batch_size, num_kvpairs, num_hiddens))
print(attention(X, Y, Y, valid_lens).shape)

import math
import pandas as pd
import torch
from torch import nn
from d2l import torch as d2l

class PositionWiseFFN(nn.Module):
    def __init__(self,ffn_num_input,ffn_num_hiddens,ffn_num_output,**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_output)

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

ffn = PositionWiseFFN(4, 4, 8)
ffn.eval()
print(ffn(torch.ones((2, 3, 4)))[0])

ln = nn.LayerNorm(2)
bn = nn.BatchNorm1d(2)
X = torch.tensor([[1, 2], [2, 3]], dtype=torch.float32)
# 在训练模式下计算X的均值和方差
print('layer norm:', ln(X), '\nbatch norm:', bn(X))

# 用残差连接和层归一化

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

    def forward(self,x,y):
        return self.ln(self.dropout(y)+x) #输入和输出相加然后进入下一层

# 实现编码器中的一层

#@save
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 = d2l.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))

X = torch.ones((2, 100, 24))
valid_lens = torch.tensor([3, 2])
encoder_blk = EncoderBlock(24, 24, 24, 24, [100, 24], 24, 48, 8, 0.5)
encoder_blk.eval()
print(encoder_blk(X, valid_lens).shape)

#@save
class TransformerEncoder(d2l.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.pos_encoding = d2l.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

encoder = TransformerEncoder(
    200, 24, 24, 24, 24, [100, 24], 24, 48, 8, 2, 0.5)
encoder.eval()