PyTorch: import math
import torch
from torch import nn
from d2l import torch as d2l
class MultiHeadAttention(nn.Module):
    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)
    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:
            valid_lens = torch.repeat_interleave(valid_lens, repeats=self.num_heads, dim=0)
        output = self.attention(queries, keys, values, valid_lens)
        output_concat = transpose_output(output, self.num_heads)
        return self.W_o(output_concat)
def transpose_qkv(X, num_heads):
    X = X.reshape(X.shape[0], X.shape[1], num_heads, -1)
    X = X.permute(0, 2, 1, 3)
    return X.reshape(-1, X.shape[2], X.shape[3])
def transpose_output(X, num_heads):
    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)
num_hiddens, num_heads = 100, 5
attention = MultiHeadAttention(num_hiddens, num_hiddens, num_hiddens, num_hiddens, num_heads, 0.5)
attention.eval()


Paddle: import warnings
from d2l import paddle as d2l
warnings.filterwarnings("ignore")
import math
import paddle
from paddle import nn
class MultiHeadAttention(nn.Layer):
    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_attr=bias)
        self.W_k = nn.Linear(key_size, num_hiddens, bias_attr=bias)
        self.W_v = nn.Linear(value_size, num_hiddens, bias_attr=bias)
        self.W_o = nn.Linear(num_hiddens, num_hiddens, bias_attr=bias)
    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:
            valid_lens = paddle.repeat_interleave(valid_lens, repeats=self.num_heads, axis=0)
        output = self.attention(queries, keys, values, valid_lens)
        output_concat = transpose_output(output, self.num_heads)
        return self.W_o(output_concat)
def transpose_qkv(X, num_heads):
    X = X.reshape((X.shape[0], X.shape[1], num_heads, -1))
    X = X.transpose((0, 2, 1, 3))
    return X.reshape((-1, X.shape[2], X.shape[3]))
def transpose_output(X, num_heads):
    X = X.reshape((-1, num_heads, X.shape[1], X.shape[2]))
    X = X.transpose((0, 2, 1, 3))
    return X.reshape((X.shape[0], X.shape[1], -1))
num_hiddens, num_heads = 100, 5
attention = MultiHeadAttention(num_hiddens, num_hiddens, num_hiddens, num_hiddens, num_heads, 0.5)
attention.eval()
batch_size, num_queries = 2, 4
num_kvpairs, valid_lens =  6, paddle.to_tensor([3, 2])
X = paddle.ones((batch_size, num_queries, num_hiddens))
Y = paddle.ones((batch_size, num_kvpairs, num_hiddens))
attention(X, Y, Y, valid_lens).shape