手把手教你transformer

好的，关于 Transformer 的手把手教程，我可以给你一些简单的步骤：

1. 首先，你需要了解 Transformer 的基本结构和原理，包括 self-attention 机制、多头注意力机制等等。

2. 接着，你需要准备好数据集，并对数据进行预处理，包括分词、编码等等。

3. 然后，你需要搭建 Transformer 模型，包括输入层、编码器、解码器和输出层等等。

4. 在搭建模型的过程中，你需要注意一些细节问题，比如超参数的选择、模型的初始化、正则化等等。

5. 最后，你需要训练模型，并对模型进行评估和调优。

以上是一个简单的手把手教程，当然具体实现还需要根据具体情况进行调整和优化。如果你需要更详细的教程，可以参考一些开源的 Transformer 实现，比如 Google 的 Tensor2Tensor 和 OpenNMT 等等。

相关问题

手把手教你用python代码transformer

好的，我可以为您提供一些关于使用Python代码实现Transformer的指导。

首先，您需要安装PyTorch和torchtext库。然后，您可以按照以下步骤实现Transformer模型：

1. 定义输入和输出的词汇表，并使用torchtext库加载数据集。
2. 定义Transformer模型的编码器和解码器部分。
3. 实现自注意力机制（self-attention）和前向神经网络（feed-forward network）。
4. 实现残差连接（residual connection）和层归一化（layer normalization）。
5. 定义Transformer模型的训练和评估过程。

下面是一个简单的示例代码，用于实现一个基本的Transformer模型：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torchtext.datasets import TranslationDataset, Multi30k
from torchtext.data import Field, BucketIterator

# 定义输入和输出的词汇表
SRC = Field(tokenize='spacy', tokenizer_language='de', init_token='<sos>', eos_token='<eos>', lower=True)
TRG = Field(tokenize='spacy', tokenizer_language='en', init_token='<sos>', eos_token='<eos>', lower=True)

# 加载数据集
train_data, valid_data, test_data = Multi30k.splits(exts=('.de', '.en'), fields=(SRC, TRG))
SRC.build_vocab(train_data, min_freq=2)
TRG.build_vocab(train_data, min_freq=2)

# 定义Transformer模型的编码器和解码器部分
class Encoder(nn.Module):
    def __init__(self, input_dim, hid_dim, n_layers, n_heads, pf_dim, dropout, device):
        super().__init__()
        self.device = device
        self.tok_embedding = nn.Embedding(input_dim, hid_dim)
        self.pos_embedding = nn.Embedding(1000, hid_dim)
        self.layers = nn.ModuleList([EncoderLayer(hid_dim, n_heads, pf_dim, dropout, device) for _ in range(n_layers)])
        self.dropout = nn.Dropout(dropout)
        self.scale = torch.sqrt(torch.FloatTensor([hid_dim])).to(device)

    def forward(self, src, src_mask):
        # src: [batch_size, src_len]
        # src_mask: [batch_size, 1, 1, src_len]
        batch_size = src.shape[0]
        src_len = src.shape[1]
        pos = torch.arange(0, src_len).unsqueeze(0).repeat(batch_size, 1).to(self.device)
        # pos: [batch_size, src_len]
        src = self.dropout((self.tok_embedding(src) * self.scale) + self.pos_embedding(pos))
        for layer in self.layers:
            src = layer(src, src_mask)
        return src

class EncoderLayer(nn.Module):
    def __init__(self, hid_dim, n_heads, pf_dim, dropout, device):
        super().__init__()
        self.self_attn_layer_norm = nn.LayerNorm(hid_dim)
        self.ff_layer_norm = nn.LayerNorm(hid_dim)
        self.self_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout, device)
        self.positionwise_feedforward = PositionwiseFeedforwardLayer(hid_dim, pf_dim, dropout)
        self.dropout = nn.Dropout(dropout)

    def forward(self, src, src_mask):
        

手把手教你用pytorch代码实现Transformer模型

以下是手把手教你用PyTorch代码实现Transformer模型的步骤：

1.导入必要的库和模块

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F

2.定义Transformer模型的编码器和解码器

class Encoder(nn.Module):
    def __init__(self, input_dim, hid_dim, n_layers, n_heads, pf_dim, dropout, device):
        super().__init__()

        self.device = device

        self.tok_embedding = nn.Embedding(input_dim, hid_dim)
        self.pos_embedding = nn.Embedding(1000, hid_dim)
        self.layers = nn.ModuleList([EncoderLayer(hid_dim, n_heads, pf_dim, dropout, device) for _ in range(n_layers)])

        self.dropout = nn.Dropout(dropout)

        self.scale = torch.sqrt(torch.FloatTensor([hid_dim])).to(device)

    def forward(self, src, src_mask):
        # src = [batch size, src len]
        # src_mask = [batch size, 1, 1, src len]

        batch_size = src.shape[0]
        src_len = src.shape[1]

        pos = torch.arange(0, src_len).unsqueeze(0).repeat(batch_size, 1).to(self.device)

        src = self.dropout((self.tok_embedding(src) * self.scale) + self.pos_embedding(pos))

        for layer in self.layers:
            src = layer(src, src_mask)

        return src


class EncoderLayer(nn.Module):
    def __init__(self, hid_dim, n_heads, pf_dim, dropout, device):
        super().__init__()

        self.self_attn_layer_norm = nn.LayerNorm(hid_dim)
        self.ff_layer_norm = nn.LayerNorm(hid_dim)
        self.self_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout, device)
        self.positionwise_feedforward = PositionwiseFeedforwardLayer(hid_dim, pf_dim, dropout)
        self.dropout = nn.Dropout(dropout)

    def forward(self, src, src_mask):
        # src = [batch size, src len, hid dim]
        # src_mask = [batch size, 1, 1, src len]

        # self attention
        _src, _ = self.self_attention(src, src, src, src_mask)

        # dropout, residual connection and layer norm
        src = self.self_attn_layer_norm(src + self.dropout(_src))

        # positionwise feedforward
        _src = self.positionwise_feedforward(src)

        # dropout, residual and layer norm
        src = self.ff_layer_norm(src + self.dropout(_src))

        return src


class MultiHeadAttentionLayer(nn.Module):
    def __init__(self, hid_dim, n_heads, dropout, device):
        super().__init__()

        assert hid_dim % n_heads == 0

        self.hid_dim = hid_dim
        self.n_heads = n_heads
        self.head_dim = hid_dim // n_heads

        self.fc_q = nn.Linear(hid_dim, hid_dim)
        self.fc_k = nn.Linear(hid_dim, hid_dim)
        self.fc_v = nn.Linear(hid_dim, hid_dim)

        self.fc_o = nn.Linear(hid_dim, hid_dim)

        self.dropout = nn.Dropout(dropout)

        self.scale = torch.sqrt(torch.FloatTensor([self.head_dim])).to(device)

    def forward(self, query, key, value, mask=None):
        batch_size = query.shape[0]

        Q = self.fc_q(query)
        K = self.fc_k(key)
        V = self.fc_v(value)

        # Q = [batch size, query len, hid dim]
        # K = [batch size, key len, hid dim]
        # V = [batch size, value len, hid dim]

        Q = Q.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)
        K = K.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)
        V = V.view(batch_size, -1, self.n_heads, self.head_dim).permute(0, 2, 1, 3)

        # Q = [batch size, n heads, query len, head dim]
        # K = [batch size, n heads, key len, head dim]
        # V = [batch size, n heads, value len, head dim]

        energy = torch.matmul(Q, K.permute(0, 1, 3, 2)) / self.scale

        # energy = [batch size, n heads, query len, key len]

        if mask is not None:
            energy = energy.masked_fill(mask == 0, -1e10)

        attention = torch.softmax(energy, dim=-1)

        # attention = [batch size, n heads, query len, key len]

        x = torch.matmul(self.dropout(attention), V)

        # x = [batch size, n heads, query len, head dim]

        x = x.permute(0, 2, 1, 3).contiguous()

        # x = [batch size, query len, n heads, head dim]

        x = x.view(batch_size, -1, self.hid_dim)

        # x = [batch size, query len, hid dim]

        x = self.fc_o(x)

        # x = [batch size, query len, hid dim]

        return x, attention


class PositionwiseFeedforwardLayer(nn.Module):
    def __init__(self, hid_dim, pf_dim, dropout):
        super().__init__()

        self.fc_1 = nn.Linear(hid_dim, pf_dim)
        self.fc_2 = nn.Linear(pf_dim, hid_dim)

        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        # x = [batch size, seq len, hid dim]

        x = self.dropout(torch.relu(self.fc_1(x)))

        # x = [batch size, seq len, pf dim]

        x = self.fc_2(x)

        # x = [batch size, seq len, hid dim]

        return x


class Decoder(nn.Module):
    def __init__(self, output_dim, hid_dim, n_layers, n_heads, pf_dim, dropout, device):
        super().__init__()

        self.device = device

        self.tok_embedding = nn.Embedding(output_dim, hid_dim)
        self.pos_embedding = nn.Embedding(1000, hid_dim)

        self.layers = nn.ModuleList([DecoderLayer(hid_dim, n_heads, pf_dim, dropout, device) for _ in range(n_layers)])

        self.fc_out = nn.Linear(hid_dim, output_dim)

        self.dropout = nn.Dropout(dropout)

        self.scale = torch.sqrt(torch.FloatTensor([hid_dim])).to(device)

    def forward(self, trg, enc_src, trg_mask, src_mask):
        # trg = [batch size, trg len]
        # enc_src = [batch size, src len, hid dim]
        # trg_mask = [batch size, 1, trg len, trg len]
        # src_mask = [batch size, 1, 1, src len]

        batch_size = trg.shape[0]
        trg_len = trg.shape[1]

        pos = torch.arange(0, trg_len).unsqueeze(0).repeat(batch_size, 1).to(self.device)

        trg = self.dropout((self.tok_embedding(trg) * self.scale) + self.pos_embedding(pos))

        for layer in self.layers:
            trg, attention = layer(trg, enc_src, trg_mask, src_mask)

        output = self.fc_out(trg)

        return output, attention


class DecoderLayer(nn.Module):
    def __init__(self, hid_dim, n_heads, pf_dim, dropout, device):
        super().__init__()

        self.self_attn_layer_norm = nn.LayerNorm(hid_dim)
        self.enc_attn_layer_norm = nn.LayerNorm(hid_dim)
        self.ff_layer_norm = nn.LayerNorm(hid_dim)
        self.self_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout, device)
        self.encoder_attention = MultiHeadAttentionLayer(hid_dim, n_heads, dropout, device)
        self.positionwise_feedforward = PositionwiseFeedforwardLayer(hid_dim, pf_dim, dropout)
        self.dropout = nn.Dropout(dropout)

    def forward(self, trg, enc_src, trg_mask, src_mask):
        # trg = [batch size, trg len, hid dim]
        # enc_src = [batch size, src len, hid dim]
        # trg_mask = [batch size, 1, trg len, trg len]
        # src_mask = [batch size, 1, 1, src len]

        # self attention
        _trg, _ = self.self_attention(trg, trg, trg, trg_mask)

        # dropout, residual connection and layer norm
        trg = self.self_attn_layer_norm(trg + self.dropout(_trg))

        # encoder attention
        _trg, attention = self.encoder_attention(trg, enc_src, enc_src, src_mask)

        # dropout, residual connection and layer norm
        trg = self.enc_attn_layer_norm(trg + self.dropout(_trg))

        # positionwise feedforward
        _trg = self.positionwise_feedforward(trg)

        # dropout, residual and layer norm
        trg = self.ff_layer_norm(trg + self.dropout(_trg))

        return trg, attention

3.定义完模型后，我们需要定义一些辅助函数，如下所示：

def get_pad_mask(seq, pad_idx):
    return (seq != pad_idx).unsqueeze(-2)


def get_subsequent_mask(seq):
    sz_b, len_s = seq.size()
    subsequent_mask = (1 - torch.triu(torch.ones((1, len_s, len_s), device=seq.device), diagonal=1)).bool()
    return subsequent_mask


def get_clones(module, N):
    return nn.ModuleList([copy.deepcopy(module) for i in range(N)])


def greedy_decode(model, src, src_mask, max_len, start_symbol):
    memory = model.encode(src, src_mask)
    ys = torch.ones(1, 1).fill_(start_symbol).type_as(src.data)
    for i in range(max_len - 1):
        out = model.decode(memory, src_mask, ys, subsequent_mask)
        prob = model.generator(out[:, -1])
        _, next_word = torch.max(prob, dim=1)
        next_word = next_word.item()
        ys = torch.cat([ys, torch.ones(1, 1).type_as(src.data).fill_(next_word)], dim=1)
        if next_word == 2:
            break
    return ys

4.定义完辅助函数后，我们需要定义完整的Transformer模型，如下所示：

class Transformer(nn.Module):
    def __init__(self, src_vocab, trg_vocab, hid_dim, n_layers, n_heads, pf_dim, dropout, device, max_length=100):
        super().__init__()

        self.device = device
        self.src_vocab = src_vocab
        self.trg_vocab = trg_vocab
        self.hid_dim = hid_dim
        self.n_layers = n_layers
        self.n_heads = n_heads
        self.pf_dim = pf_dim
        self.dropout = dropout
        self.max_length = max_length

        self.encoder = Encoder(src_vocab, hid_dim, n_layers, n_heads, pf_dim, dropout, device)
        self.decoder = Decoder(trg_vocab, hid_dim, n_layers, n_heads, pf_dim, dropout, device)
        self.src_pad_idx = src_vocab.stoi['<pad>']
        self.trg_pad_idx = trg_vocab.stoi['<pad>']

        self.device = device

    def make_src_mask(self, src):
        # src = [batch size, src len]

        src_mask = (src != self.src_pad_idx).unsqueeze(1).unsqueeze(2)

        # src_mask = [batch size, 1, 1, src len]

        return src_mask

    def make_trg_mask(self, trg):
        # trg = [batch size, trg len]

        trg_pad_mask = (trg != self.trg_pad_idx).unsqueeze(1).unsqueeze(2)

        # trg_pad_mask = [batch size, 1, 1, trg len]

        trg_len = trg.shape[1]

        trg_sub_mask = torch.tril(torch.ones((trg_len, trg_len), device=self.device)).bool()

        # trg_sub_mask = [trg len, trg len]

        trg_mask = trg_pad_mask & trg_sub_mask

        # trg_mask = [batch size, 1, trg len, trg len]

        return trg_mask

    def forward(self, src, trg):
        # src = [batch size, src len]
        # trg = [batch size, trg len]

        src_mask = self.make_src_mask(src)
        trg_mask = self.make_trg_mask(trg)

        enc_src = self.encoder(src, src_mask)

        output, attention = self.decoder(trg, enc_src, trg_mask, src_mask)

        return output, attention

    def encode(self, src, src_mask):
        # src = [batch size, src len]
        # src_mask = [batch size, 1, 1, src len]

        enc_src = self.encoder(src, src_mask)

        return enc_src

    def decode(self, memory, src_mask, trg, trg_mask):
        # memory = [batch size, src len, hid dim]
        # src_mask = [batch size, 1, 1, src len]
        # trg = [batch size, trg