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import torch
from torch import nn
class AddNorm(nn.Module):
"""残差连接后进行层归一化"""
def __init__(self, normalized, dropout):
super(AddNorm, self).__init__()
self.dropout = nn.Dropout(dropout)
self.ln = nn.LayerNorm(normalized)
def forward(self, x, y):
return self.ln(x + self.dropout(y))
class PositionWiseFFN(nn.Module):
"""基于位置的前馈⽹络"""
def __init__(self, ffn_input, ffn_hiddens,mlp_bias=True):
super(PositionWiseFFN, self).__init__()
self.ffn = nn.Sequential(
nn.Linear(ffn_input, ffn_hiddens, bias=mlp_bias),
nn.ReLU(),
nn.Linear(ffn_hiddens, ffn_input, bias=mlp_bias),
)
def forward(self, x):
return self.ffn(x)
from torch.autograd import Variable
class PositionalEncoding1(nn.Module):
"Implement the PE function."
def __init__(self, d_model, dropout, max_len=5000):
super(PositionalEncoding1, self).__init__()
self.dropout = nn.Dropout(p=dropout)
# Compute the positional encodings once in log space.
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))
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 + Variable(self.pe[:, :x.size(1)],
requires_grad=False)
return self.dropout(x)
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)
# 创建⼀个⾜够⻓的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 AttentionEncode(nn.Module):
def __init__(self, dropout, embedding_size, num_heads,seq_len: int=40,ffn=False):
super(AttentionEncode, self).__init__()
self.dropout = dropout
self.embedding_size = embedding_size
self.num_heads = num_heads
self.seq_len = seq_len
self.is_ffn = ffn
self.att = nn.MultiheadAttention(embed_dim=self.embedding_size,
num_heads=num_heads,
dropout=0.6
)
self.addNorm = AddNorm(normalized=[self.seq_len, self.embedding_size], dropout=self.dropout)
self.FFN = PositionWiseFFN(ffn_input=self.embedding_size, ffn_hiddens=self.embedding_size*2)
def forward(self, x):
bs,_,_ = x.size()
MHAtt, _ = self.att(x, x, x)
MHAtt_encode = self.addNorm(x, MHAtt)
if self.is_ffn:
ffn_in = MHAtt_encode # bs,seq_len,feat_dims
ffn_out = self.FFN(ffn_in)
MHAtt_encode = self.addNorm(ffn_in,ffn_out)
return MHAtt_encode
class FAN_encode(nn.Module):
def __init__(self, dropout, shape):
super(FAN_encode, self).__init__()
self.dropout = dropout
self.addNorm = AddNorm(normalized=[1, shape], dropout=self.dropout)
self.FFN = PositionWiseFFN(ffn_input=shape, ffn_hiddens=(2*shape))
self.ln = nn.LayerNorm(shape)
def forward(self, x):
#x = self.ln(x)
ffn_out = self.FFN(x)
encode_output = self.addNorm(x, ffn_out)
return encode_output
class ffn_norm(nn.Module):
# 可接受二维输入和一维输入
def __init__(self,input_dims:int,hidden_dims:int,dropout:float,bias:bool=True):
super(ffn_norm,self).__init__()
self.inps_dims = input_dims
self.hidden_dims = hidden_dims
self.dropout = nn.Dropout(dropout)
self.ffn_bias = bias
self.ffn = nn.Sequential(
nn.Linear(self.inps_dims, self.hidden_dims, bias=self.ffn_bias),
nn.LeakyReLU(),
nn.Linear(self.hidden_dims, self.inps_dims, bias=self.ffn_bias),
)
self.ln = nn.LayerNorm(self.inps_dims)
def forward(self,x):
# x:[B,S,H] OR [B,shape],shape:S*H
ffn_out = self.ffn(x)
norm_out = self.ln(x + self.dropout(ffn_out))
return norm_out
def sequence_mask(X, valid_len, value=0.):
"""在序列中屏蔽不相关的项"""
valid_len = valid_len.float()
MaxLen = X.size(1)
mask = torch.arange(MaxLen, dtype=torch.float32, device=X.device)[None, :] < valid_len[:, None].to(X.device)
X[~mask] = value
return X
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)
# class AdditiveAttention(nn.Module):
# """加性注意⼒"""
#
# def __init__(self, key_size, query_size, num_hiddens, dropout):
# super(AdditiveAttention, self).__init__()
# self.W_k = nn.Linear(key_size, num_hiddens, bias=False)
# self.W_q = nn.Linear(query_size, num_hiddens, bias=False)
# self.w_v = nn.Linear(num_hiddens, 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)
# # 在维度扩展后，
# # queries的形状：(batch_size，查询的个数，1，num_hidden)
# # key的形状：(batch_size，1，“键－值”对的个数，num_hiddens)
# # 使⽤⼴播⽅式进⾏求和
# features = queries.unsqueeze(2) + keys.unsqueeze(1)
# features = torch.tanh(features)
# # self.w_v仅有⼀个输出，因此从形状中移除最后那个维度。
# # scores的形状：(batch_size，查询的个数，“键-值”对的个数)
# scores = self.w_v(features).squeeze(-1)
# attention_weights = masked_softmax(scores, valid_lens)
# # values的形状：(batch_size，“键－值”对的个数，值的维度)
# return torch.bmm(self.dropout(attention_weights), values)
class AdditiveAttention(nn.Module):
"""注意⼒机制"""
def __init__(self, input_size, value_size, num_hiddens, dropout):
super(AdditiveAttention, self).__init__()
self.W_k = nn.Linear(input_size, num_hiddens, bias=False)
self.W_q = nn.Linear(input_size, num_hiddens, bias=False)
self.w_v = nn.Linear(input_size, num_hiddens, bias=False)
self.w_o = nn.Linear(50, value_size, bias=False)
self.dropout = nn.Dropout(dropout)
def forward(self, queries, keys, values, valid_lens=None):
queries, keys = self.W_q(queries), self.W_k(keys)
d = queries.shape[-1]
# 在维度扩展后，
# queries的形状：(batch_size，查询的个数，1，num_hidden)
# key的形状：(batch_size，1，“键－值”对的个数，num_hiddens)
# 使⽤⼴播⽅式进⾏求和
# features = queries + keys
# features = torch.tanh(features)
# self.w_v仅有⼀个输出，因此从形状中移除最后那个维度。
# scores的形状：(batch_size，查询的个数，“键-值”对的个数)
scores = torch.bmm(queries, keys.transpose(1, 2)) / math.sqrt(d)
scores = self.w_o(scores).permute(0, 2, 1)
attention_weights = masked_softmax(scores, valid_lens)
# attention_weights = nn.Softmax(dim=1)(scores)
values = self.w_v(values)
# values = torch.transpose(values, 1, 2)
# values的形状：(batch_size，“键－值”对的个数，值的维度)
return torch.bmm(self.dropout(attention_weights), values), attention_weights
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):
super(MultiHeadAttention, self).__init__()
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=None):
# 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)
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 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 DotProductAttention(nn.Module):
"""缩放点积注意力"""
def __init__(self, dropout):
super(DotProductAttention, self).__init__()
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)
attention_weights = masked_softmax(scores, valid_lens)
return torch.bmm(self.dropout(attention_weights), values)
class MASK_AttentionEncode(nn.Module):
def __init__(self, dropout, embedding_size, num_heads):
super(MASK_AttentionEncode, self).__init__()
self.dropout = dropout
self.embedding_size = embedding_size
self.num_heads = num_heads
self.at1 = MultiHeadAttention(key_size=self.embedding_size,
query_size=self.embedding_size,
value_size=self.embedding_size,
num_hiddens=self.embedding_size,
num_heads=self.num_heads,
dropout=self.dropout)
self.addNorm = AddNorm(normalized=[50, self.embedding_size], dropout=self.dropout)
self.FFN = PositionWiseFFN(ffn_num_input=64, ffn_num_hiddens=192, ffn_num_outputs=64)
def forward(self, x, y=None):
# Multi, _ = self.at1(x, x, x)
Multi = self.at1(x, x, x, y)
Multi_encode = self.addNorm(x, Multi)
# encode_output = self.addNorm(Multi_encode, self.FFN(Multi_encode))
return Multi_encode
class transformer_encode(nn.Module):
def __init__(self, dropout, embedding, num_heads):
super(transformer_encode, self).__init__()
self.dropout = dropout
self.embedding_size = embedding
self.num_heads = num_heads
self.attention = nn.MultiheadAttention(embed_dim=192,
num_heads=8,
dropout=0.6
)
self.at1 = MultiHeadAttention(key_size=self.embedding_size,
query_size=self.embedding_size,
value_size=self.embedding_size,
num_hiddens=self.embedding_size,
num_heads=self.num_heads,
dropout=self.dropout)
self.addNorm = AddNorm(normalized=[50, self.embedding_size], dropout=self.dropout)
self.ffn = PositionWiseFFN(ffn_num_input=self.embedding_size, ffn_num_hiddens=2*self.embedding_size,
ffn_num_outputs=self.embedding_size)
def forward(self, x, valid=None):
# Multi, _ = self.attention(x, x, x)
Multi = self.at1(x, x, x, valid)
Multi_encode = self.addNorm(x, Multi)
encode_output = self.addNorm(Multi_encode, self.ffn(Multi_encode))
return encode_output
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