RT，直接上代码，可以跑通：

#encoding:utf-8

import torch

import torch.nn as nn

import numpy as np

import math

 

class Config(object):

    def __init__(self):

        self.vocab_size = 6

        self.d_model = 512

        self.n_heads = 4

        assert self.d_model % self.n_heads == 0

        self.dim_k = self.d_model // self.n_heads

        self.dim_v = self.d_model // self.n_heads

        self.padding_size = 30

        self.UNK = 5

        self.PAD = 4

        self.N = 6

        self.p = 0.1

config = Config()

 

class Embedding(nn.Module):

    def __init__(self, vocab_size):

        super(Embedding, self).__init__()

        self.embedding = nn.Embedding(vocab_size, config.d_model, padding_idx=config.PAD)

 

    def forward(self, x):

        # print("type(x):", type(x))

        # for i in range(len(x)):

        #     if len(x[i]) < config.padding_size:

        #         x[i].extend([config.UNK] * (config.padding_size - len(x[i])))

        #     else:

        #         tmp = x[i][:config.padding_size]

        #         print("tmp.shape:", tmp.shape)

        #         print("type(x[i]):", type(x[i]))

        #         print("x[i].shape:", x[i].shape)

        #         x[i,:] = x[i][:config.padding_size]

        x = self.embedding(torch.tensor(x))

        return x

   

class Positional_Encoding(nn.Module):

    def __init__(self, d_model):

        super(Positional_Encoding, self).__init__()

        self.d_model = d_model

    def forward(self, seq_len, embedding_dim):

        positional_encoding = np.zeros((seq_len, embedding_dim))

        for pos in range(positional_encoding.shape[0]):

            for i in range(positional_encoding.shape[1]):

                positional_encoding[pos][i] = math.sin(pos / (10000**(2*i/self.d_model))) if i%2==0 else math.cos(pos/(10000**(2*i/self.d_model)))

        return torch.from_numpy(positional_encoding)

   

class Multihead_Attention(nn.Module):

    def __init__(self, d_model, dim_k, dim_v, n_heads):

        super(Multihead_Attention, self).__init__()

        self.dim_v = dim_v

        self.dim_k = dim_k

        self.n_heads = n_heads

        self.q = nn.Linear(d_model, dim_k)

        self.k = nn.Linear(d_model, dim_k)

        self.v = nn.Linear(d_model, dim_v)

        self.o = nn.Linear(dim_v, d_model)

        self.norm_fact = 1 / math.sqrt(d_model)

    def generate_mask(self, dim):

        matrix = np.ones((dim, dim))

        mask = torch.BoolTensor(np.tril(matrix).astype(np.bool_))

        return mask

 

    def forward(self, x, y, requires_mask=False):

        Q = self.q(x).reshape(-1, x.shape[0], x.shape[1], self.dim_k//self.n_heads)

        K = self.k(x).reshape(-1, x.shape[0], x.shape[1], self.dim_k//self.n_heads)

        V = self.v(y).reshape(-1, x.shape[0], x.shape[1], self.dim_v//self.n_heads)

        attention_score = torch.matmul(Q, K.permute(0,1,3,2)) * self.norm_fact

        if requires_mask:

            mask = self.generate_mask(x.shape[1])

            attention_score.masked_fill(mask, value=float("-inf"))

        output = torch.matmul(attention_score, V).reshape(y.shape[0], y.shape[1], -1)

        output = self.o(output)

        return output

   

class Feed_Forward(nn.Module):

    def __init__(self, input_dim, hidden_dim=2048):

        super(Feed_Forward, self).__init__()

        self.L1 = nn.Linear(input_dim, hidden_dim)

        self.L2 = nn.Linear(hidden_dim, input_dim)

    def forward(self, x):

        output = nn.ReLU()(self.L1(x))

        output = self.L2(output)

        return output

   

class Add_Norm(nn.Module):

    def __init__(self):

        super(Add_Norm, self).__init__()

        self.dropout = nn.Dropout(config.p)

    def forward(self, x, sub_layer, **kwargs):

        sub_output = sub_layer(x, **kwargs)

        x = self.dropout(x+sub_output)

        layer_norm = nn.LayerNorm(x.size()[1:])

        out = layer_norm(x)

        return out

   

class Encoder(nn.Module):

    def __init__(self):

        super(Encoder, self).__init__()

        self.positional_encoding = Positional_Encoding(config.d_model)

        self.muti_atten = Multihead_Attention(config.d_model, config.dim_k, config.dim_v, config.n_heads)

        self.feed_forward = Feed_Forward(config.d_model)

        self.add_norm = Add_Norm()

 

    def forward(self, x):

        x += self.positional_encoding(x.shape[1], config.d_model)

        output = self.add_norm(x, self.muti_atten, y=x)

        output = self.add_norm(output, self.feed_forward)

        return output

   

class Decoder(nn.Module):

    def __init__(self):

        super(Decoder, self).__init__()

        self.positional_encoding = Positional_Encoding(config.d_model)

        self.muti_atten = Multihead_Attention(config.d_model, config.dim_k, config.dim_v, config.n_heads)

        self.feed_forward = Feed_Forward(config.d_model)

        self.add_norm = Add_Norm()

    def forward(self, x, encoder_output):

        x += self.positional_encoding(x.shape[1], config.d_model)

        output = self.add_norm(x, self.muti_atten, y=x, requires_mask=True)

        output = self.add_norm(output, self.muti_atten, y=encoder_output, requires_mask=True)

        output = self.add_norm(output, self.feed_forward)

        return output

   

class Transformer_layer(nn.Module):

    def __init__(self):

        super(Transformer_layer, self).__init__()

        self.encoder = Encoder()

        self.decoder = Decoder()

    def forward(self, x):

        x_input, x_output = x

        encoder_output = self.encoder(x_input)

        decoder_output = self.decoder(x_output, encoder_output)

        return (encoder_output, decoder_output)

   

class Transformer(nn.Module):

    def __init__(self, N, vocab_size, output_dim):

        super(Transformer, self).__init__()

        self.embedding_input = Embedding(vocab_size=vocab_size)

        self.embedding_output = Embedding(vocab_size=vocab_size)

        self.output_dim = output_dim

        self.linear = nn.Linear(config.d_model, output_dim)

        self.softmax = nn.Softmax(dim=-1)

        self.model = nn.Sequential(*[Transformer_layer() for _ in range(N)])

    def forward(self, x):

        x_input, x_output = x, x

        x_input = self.embedding_input(x_input)

        x_output = self.embedding_output(x_output)

        _, output = self.model((x_input, x_output))

        output = self.linear(output)

        output = self.softmax(output)

        return output

   

def main():

    transformer = Transformer(4, 1024, 512)

    input = (np.random.rand(8, 512) * 512).astype(np.int64)

    output = transformer(input)

    print("output.shape:", output.shape)

 

if __name__ == '__main__':

    main()

---------------------

输出：

output.shape: torch.Size([8, 512, 512])