题目描述一：【选择】

Attention 机制中，输入QKV的shape为[N,L,D],请问 输出的shape和attention score 的shape分别是多少?
A. 输出:[N,L,D] attention score: [N,L,L,D]
B.输出:[N,L,D] attention score: [N,L,L]
C.输出:[N,L,L] attention score: [N,L,L,D]
D.输出:[N,L,L] attention score: [N,L,L]

正确答案是：B

题目描述二：【简答】

Transformer的核心思想是什么?它较之前的方法有什么优势(举例说明)?他和CNN有什么联系(开放问题)?

答：
Transformer的核心思想是使用自注意力机制（Self-Attention Mechanism）来建模序列数据中的依赖关系，取代传统的递归神经网络（RNN）和卷积神经网络（CNN）在处理序列任务时的局限性。具体来说，Transformer的架构主要由编码器（Encoder）和解码器（Decoder）组成，每个编码器和解码器模块由多头自注意力机制（Multi-Head Self-Attention Mechanism）和前馈神经网络（Feed-Forward Neural Network）组成。


总结：
Transformer通过自注意力机制实现了高效的序列建模，克服了RNN在长距离依赖和并行处理上的局限性。它在自然语言处理（如机器翻译、文本生成）和其他序列任务中表现出色。虽然与CNN在结构上不同，但两者都能高效地进行特征提取和处理，并利用并行计算加速训练和推理过程。

题目描述三：【代码】

请使用torch 或者numpy 编写attention机制。QKV都是三维张量。

• 实现一个attention 类
• 需要说明函数入参含义、输入输出shape
• 完成简单的scaled dot-product attention即可
• 有把握的，可以实现multi-head attentlon

Scaled Dot-Product Attention：下面是用PyTorch实现的一个Attention机制的代码。这个实现包括一个简单的Scaled Dot-Product Attention机制和一个Multi-Head Attention机制。

首先，我们实现一个简单的Scaled Dot-Product Attention机制。

import torch
import torch.nn.functional as F

class ScaledDotProductAttention:
    def __init__(self, d_k):
        """
        初始化函数
        :param d_k: int, 每个attention头的维度
        """
        self.d_k = d_k

    def __call__(self, Q, K, V):
        """
        执行Scaled Dot-Product Attention
        :param Q: torch.Tensor, 形状为[N, L, D]
        :param K: torch.Tensor, 形状为[N, L, D]
        :param V: torch.Tensor, 形状为[N, L, D]
        :return: 输出张量的形状为[N, L, D], attention score的形状为[N, L, L]
        """
        # 计算注意力分数
        scores = torch.matmul(Q, K.transpose(-2, -1)) / torch.sqrt(torch.tensor(self.d_k, dtype=torch.float32))
        attention_scores = F.softmax(scores, dim=-1)
        
        # 计算注意力输出
        attention_output = torch.matmul(attention_scores, V)
        
        return attention_output, attention_scores

# 使用示例
N, L, D = 2, 5, 64
d_k = D

Q = torch.rand(N, L, D)
K = torch.rand(N, L, D)
V = torch.rand(N, L, D)

attention = ScaledDotProductAttention(d_k)
output, scores = attention(Q, K, V)

print(f"Output shape: {output.shape}")
print(f"Attention scores shape: {scores.shape}")

Multi-Head Attention：

接下来，我们实现一个Multi-Head Attention机制，它是基于多个Scaled Dot-Product Attention头的。

class MultiHeadAttention:
    def __init__(self, num_heads, d_model):
        """
        初始化函数
        :param num_heads: int, 注意力头的数量
        :param d_model: int, 输入的特征维度
        """
        assert d_model % num_heads == 0, "d_model必须是num_heads的整数倍"
        
        self.num_heads = num_heads
        self.d_model = d_model
        self.d_k = d_model // num_heads
        
        self.W_q = torch.nn.Linear(d_model, d_model)
        self.W_k = torch.nn.Linear(d_model, d_model)
        self.W_v = torch.nn.Linear(d_model, d_model)
        self.W_o = torch.nn.Linear(d_model, d_model)
        
        self.attention = ScaledDotProductAttention(self.d_k)

    def split_heads(self, x, batch_size):
        """
        将最后一维分裂成(num_heads, d_k)形状，并将张量重塑为(batch_size * num_heads, seq_length, d_k)
        :param x: torch.Tensor, 形状为(batch_size, seq_length, d_model)
        :param batch_size: int, batch大小
        :return: torch.Tensor, 形状为(batch_size * num_heads, seq_length, d_k)
        """
        x = x.view(batch_size, -1, self.num_heads, self.d_k)
        return x.transpose(1, 2).contiguous().view(batch_size * self.num_heads, -1, self.d_k)

    def __call__(self, Q, K, V):
        """
        执行Multi-Head Attention
        :param Q: torch.Tensor, 形状为[N, L, D]
        :param K: torch.Tensor, 形状为[N, L, D]
        :param V: torch.Tensor, 形状为[N, L, D]
        :return: 输出张量的形状为[N, L, D], attention score的形状为[N * num_heads, L, L]
        """
        batch_size = Q.size(0)
        
        Q = self.split_heads(self.W_q(Q), batch_size)
        K = self.split_heads(self.W_k(K), batch_size)
        V = self.split_heads(self.W_v(V), batch_size)
        
        attention_output, attention_scores = self.attention(Q, K, V)
        
        attention_output = attention_output.view(batch_size, self.num_heads, -1, self.d_k)
        attention_output = attention_output.transpose(1, 2).contiguous().view(batch_size, -1, self.d_model)
        
        return self.W_o(attention_output), attention_scores.view(batch_size, self.num_heads, -1, attention_scores.size(-1))

# 使用示例
N, L, D = 2, 5, 64
num_heads = 8
d_model = D

Q = torch.rand(N, L, D)
K = torch.rand(N, L, D)
V = torch.rand(N, L, D)

multi_head_attention = MultiHeadAttention(num_heads, d_model)
output, scores = multi_head_attention(Q, K, V)

print(f"Output shape: {output.shape}")
print(f"Attention scores shape: {scores.shape}")

以上实现了简单的Scaled Dot-Product Attention和Multi-Head Attention机制，并包含示例代码用于测试。

无注释背诵版：

1. __init__是初始化d_k是每个向量的维度
2. torch.matmul，转置K.transpose(-2, -1)，/根号sqrt(d_k)
3. F.softmax(scores, dim=-1)得注意力分数
4. torch.matmul与V乘得输出

import torch
import torch.nn.functional as F

class Attention:
    def __init__(self, d_k): 
        self.d_k = d_k
    def __call__(self, Q, K, V):
        scores = torch.matmul(Q, K.transpose(-2, -1)) / torch.sqrt(torch.tensor(self.d_k, dtype=torch.float32))
        attention_scores = F.softmax(scores, dim=-1)
        attention_output = torch.matmul(attention_scores, V)
        return attention_output, attention_scores
    
class MultiHeadAttention:
    def __init__(self, num_heads, d_model):
        assert d_model % num_heads == 0
        self.nums_heads = num_heads
        self.d_model = d_model
        self.d_k = d_model // num_heads
        self.W_q = torch.nn.Linear(d_model, d_model)
        self.W_k = torch.nn.Linear(d_model, d_model)
        self.W_v = torch.nn.Linear(d_model, d_model)
        self.W_o = torch.nn.Linear(d_model, d_model)
        self.attention = Attention(self.d_k)

    def split_heads(self, x, batch_size):
        x = x.view(batch_size, -1, self.nums_heads, self.d_k)
        return x.transpose(1, 2).contiguous().view(batch_size * self.nums_heads, -1, self.d_k)
    
    def __call__(self, Q, K, V):
        batch_size = Q.size(0)
        Q = self.split_heads(self.W_q(Q), batch_size)
        K = self.split_heads(self.W_k(K), batch_size)
        V = self.split_heads(self.W_v(V), batch_size)
        attention_output, attention_score = self.attention(Q, K, V)
        attention_output = attention_output.view(batch_size, self.nums_heads, -1, self.d_k)
        attention_output = attention_output.transpose(1, 2).contiguous().view(batch_size, -1, self.d_model)
        return self.W_o(attention_output), attention_score.view(batch_size, self.nums_heads, -1, attention_score.size(-1))

使用示例

N, L, D = 2, 5, 64
num_heads = 8
d_model = D

Q = torch.rand(N, L, D)
K = torch.rand(N, L, D)
V = torch.rand(N, L, D)

multi_head_attention = MultiHeadAttention(num_heads, d_model)
output, scores = multi_head_attention(Q, K, V)

print(f"Output shape: {output.shape}")
print(f"Attention scores shape: {scores.shape}")

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