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()
标签：hiddens,Transformer,self,torch,num,valid,size From： https://www.cnblogs.com/o-Sakurajimamai-o/p/17755399.html