前言 

        视频链接：20、Transformer模型Decoder原理精讲及其PyTorch逐行实现_哔哩哔哩_bilibili

        文章链接：Transformer模型：WordEmbedding实现-CSDN博客 

                          Transformer模型：Postion Embedding实现-CSDN博客 

                          Transformer模型：Encoder的self-attention mask实现-CSDN博客

                          Transformer模型：intra-attention mask实现-CSDN博客

                          Transformer模型：Decoder的self-attention mask实现-CSDN博客 

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正文

        首先看Attention的计算公式：

# Part6:构造scaled self-attention mask
def scaled_dot_product_attention(Q, K, V, attn_mask):
    # shape of Q,K,V:(batch size*num head, seg len, model dim/num head)
    score = torch.bmm(Q, K.transpose(-2,-1))/torch.sqrt(model_dim)
    masked_score = score.masked_fill(attn_mask,-1e9)
    prob = F.softmax(masked_score, -1)
    context = torch.bmm(prob,V)
    return context

        这里其实就是公式计算。

        至此，难点集合就学习完了。

代码

import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F

# 句子数
batch_size = 2

# 单词表大小
max_num_src_words = 10
max_num_tgt_words = 10

# 序列的最大长度
max_src_seg_len = 12
max_tgt_seg_len = 12
max_position_len = 12

# 模型的维度
model_dim = 8

# 生成固定长度的序列
src_len = torch.Tensor([11, 9]).to(torch.int32)
tgt_len = torch.Tensor([10, 11]).to(torch.int32)

# 单词索引构成的句子
src_seq = torch.cat(
    [torch.unsqueeze(F.pad(torch.randint(1, max_num_src_words, (L,)), (0, max_src_seg_len - L)), 0) for L in src_len])
tgt_seq = torch.cat(
    [torch.unsqueeze(F.pad(torch.randint(1, max_num_tgt_words, (L,)), (0, max_tgt_seg_len - L)), 0) for L in tgt_len])

# Part1:构造Word Embedding
src_embedding_table = nn.Embedding(max_num_src_words + 1, model_dim)
tgt_embedding_table = nn.Embedding(max_num_tgt_words + 1, model_dim)
src_embedding = src_embedding_table(src_seq)
tgt_embedding = tgt_embedding_table(tgt_seq)

# 构造Pos序列跟i序列
pos_mat = torch.arange(max_position_len).reshape((-1, 1))
i_mat = torch.pow(10000, torch.arange(0, 8, 2) / model_dim)

# Part2:构造Position Embedding
pe_embedding_table = torch.zeros(max_position_len, model_dim)
pe_embedding_table[:, 0::2] = torch.sin(pos_mat / i_mat)
pe_embedding_table[:, 1::2] = torch.cos(pos_mat / i_mat)

pe_embedding = nn.Embedding(max_position_len, model_dim)
pe_embedding.weight = nn.Parameter(pe_embedding_table, requires_grad=False)

# 构建位置索引
src_pos = torch.cat([torch.unsqueeze(torch.arange(max_position_len), 0) for _ in src_len]).to(torch.int32)
tgt_pos = torch.cat([torch.unsqueeze(torch.arange(max_position_len), 0) for _ in tgt_len]).to(torch.int32)

src_pe_embedding = pe_embedding(src_pos)
tgt_pe_embedding = pe_embedding(tgt_pos)

# Part3:构造encoder self-attention mask
valid_encoder_pos = torch.unsqueeze(
    torch.cat([torch.unsqueeze(F.pad(torch.ones(L), (0, max_src_seg_len - L)), 0) for L in src_len]), 2)
valid_encoder_pos_matrix = torch.bmm(valid_encoder_pos, valid_encoder_pos.transpose(1, 2))
invalid_encoder_pos_matrix = 1 - torch.bmm(valid_encoder_pos, valid_encoder_pos.transpose(1, 2))
mask_encoder_self_attention = invalid_encoder_pos_matrix.to(torch.bool)
score = torch.randn(batch_size, max_src_seg_len, max_src_seg_len)
mask_score1 = score.masked_fill(mask_encoder_self_attention, -1e9)
prob1 = F.softmax(mask_score1, -1)

# Part4:构造intra-attention mask
valid_encoder_pos = torch.unsqueeze(
    torch.cat([torch.unsqueeze(F.pad(torch.ones(L), (0, max_src_seg_len - L)), 0) for L in src_len]), 2)
valid_decoder_pos = torch.unsqueeze(
    torch.cat([torch.unsqueeze(F.pad(torch.ones(L), (0, max_tgt_seg_len - L)), 0) for L in tgt_len]), 2)

valid_cross_pos_matrix = torch.bmm(valid_decoder_pos, valid_encoder_pos.transpose(1, 2))
invalid_cross_pos_matrix = 1 - valid_cross_pos_matrix
mask_cross_attention = invalid_cross_pos_matrix.to(torch.bool)
mask_score2 = score.masked_fill(mask_cross_attention, -1e9)
prob2 = F.softmax(mask_score2, -1)

# Part5:构造Decoder self-attention mask
valid_decoder_tri_matrix = torch.cat(
    [torch.unsqueeze(F.pad(torch.tril(torch.ones(L, L)), (0, max_tgt_seg_len - L, 0, max_tgt_seg_len - L)), 0) for L in
     tgt_len])
invalid_decoder_tri_matrix = 1 - valid_decoder_tri_matrix
mask_decoder_self_attention = invalid_decoder_tri_matrix.to(torch.bool)
score2 = torch.randn(batch_size, max_tgt_seg_len, max_tgt_seg_len)
mask_score3 = score2.masked_fill(mask_decoder_self_attention, -1e9)
prob3 = F.softmax(mask_score3, -1)

# Part6:构造scaled self-attention mask
def scaled_dot_product_attention(Q, K, V, attn_mask):
    # shape of Q,K,V:(batch size*num head, seg len, model dim/num head)
    score = torch.bmm(Q, K.transpose(-2,-1))/torch.sqrt(model_dim)
    masked_score = score.masked_fill(attn_mask,-1e9)
    prob = F.softmax(masked_score, -1)
    context = torch.bmm(prob,V)
    return context