Transformer 用python 实现的简单代码

答：Transformer 可以使用 Python 语言实现，这里有一段简单的示例代码：import torch
from transformers import BertTokenizer, BertModel # 初始化tokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')# 输入文本
text = "Transformer 可以使用 Python 语言实现"# 标记化文本
tokenized_text = tokenizer.tokenize(text)# 将标记化的文本转换为id
indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)# 将id转换为张量
tokens_tensor = torch.tensor([indexed_tokens])# 加载预训练模型
model = BertModel.from_pretrained('bert-base-uncased')# 将张量输入模型
outputs = model(tokens_tensor)

相关问题

transformer代码python

以下是一个简单的 Transformer 模型的代码示例（使用 PyTorch）：

import torch
import torch.nn as nn
import torch.optim as optim

class TransformerModel(nn.Module):
    def __init__(self, vocab_size, embedding_dim, hidden_dim, num_layers):
        super(TransformerModel, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.transformer = nn.Transformer(
            d_model=embedding_dim,
            nhead=2,
            num_encoder_layers=num_layers,
            num_decoder_layers=num_layers,
            dim_feedforward=hidden_dim,
        )
        self.fc = nn.Linear(embedding_dim, vocab_size)
    
    def forward(self, src, tgt):
        src_embed = self.embedding(src)
        tgt_embed = self.embedding(tgt)
        
        src_pad_mask = self._generate_padding_mask(src)
        tgt_pad_mask = self._generate_padding_mask(tgt)
        tgt_subseq_mask = self._generate_subsequent_mask(tgt)
        tgt_mask = tgt_pad_mask & tgt_subseq_mask
        
        src_encoded = self.transformer.encoder(src_embed)
        tgt_decoded = self.transformer.decoder(tgt_embed, src_encoded, tgt_mask=tgt_mask, src_key_padding_mask=src_pad_mask, tgt_key_padding_mask=tgt_pad_mask)
        
        output = self.fc(tgt_decoded)
        return output
    
    def _generate_padding_mask(self, seq):
        mask = (seq == 0)  # Pad token index is assumed to be 0
        return mask
    
    def _generate_subsequent_mask(self, seq):
        seq_len = seq.shape[1]
        mask = torch.triu(torch.ones(seq_len, seq_len), diagonal=1).bool()  # Upper triangular matrix
        return mask

# 示例用法
vocab_size = 10000
embedding_dim = 256
hidden_dim = 512
num_layers = 4

model = TransformerModel(vocab_size, embedding_dim, hidden_dim, num_layers)
src = torch.tensor([[1, 2, 3, 4, 5]])
tgt = torch.tensor([[6, 7, 8, 9, 10]])
output = model(src, tgt)
print(output.shape)  # 输出: torch.Size([1, 5, 10000])

# 损失函数和优化器的定义和训练过程略过

这是一个简单的 Transformer 模型，用于序列到序列的任务。它使用了 Embedding 层、Transformer 层和全连接层来实现。你可以根据自己的需求进行修改和扩展。

Transformer的python代码

以下是一个简单的Transformer模型的Python代码示例：

import torch
import torch.nn as nn
import torch.optim as optim

class Transformer(nn.Module):
    def __init__(self, input_dim, output_dim, hidden_dim, num_layers, num_heads):
        super(Transformer, self).__init__()
        
        self.embedding = nn.Embedding(input_dim, hidden_dim)
        self.positional_encoding = PositionalEncoding(hidden_dim)
        self.encoder = Encoder(hidden_dim, num_layers, num_heads)
        self.decoder = Decoder(hidden_dim, num_layers, num_heads)
        self.fc = nn.Linear(hidden_dim, output_dim)
        
    def forward(self, src, trg):
        src_embedding = self.embedding(src)
        src_embedding = self.positional_encoding(src_embedding)
        
        trg_embedding = self.embedding(trg)
        trg_embedding = self.positional_encoding(trg_embedding)
        
        encoder_output = self.encoder(src_embedding)
        decoder_output = self.decoder(trg_embedding, encoder_output)
        
        output = self.fc(decoder_output)
        return output

class PositionalEncoding(nn.Module):
    def __init__(self, hidden_dim, max_len=1000):
        super(PositionalEncoding, self).__init__()
        
        position = torch.arange(0, max_len).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, hidden_dim, 2) * (-math.log(10000.0) / hidden_dim))
        pe = torch.zeros(max_len, hidden_dim)
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0).transpose(0, 1)
        
        self.register_buffer('pe', pe)
        
    def forward(self, x):
        x = x + self.pe[:x.size(0), :]
        return x

class Encoder(nn.Module):
    def __init__(self, hidden_dim, num_layers, num_heads):
        super(Encoder, self).__init__()
        
        self.layers = nn.ModuleList([EncoderLayer(hidden_dim, num_heads) for _ in range(num_layers)])
        
    def forward(self, x):
        for layer in self.layers:
            x = layer(x)
        return x

class EncoderLayer(nn.Module):
    def __init__(self, hidden_dim, num_heads):
        super(EncoderLayer, self).__init__()
        
        self.multihead_attention = MultiheadAttention(hidden_dim, num_heads)
        self.feed_forward = FeedForward(hidden_dim)
        self.layer_norm = nn.LayerNorm(hidden_dim)
        
    def forward(self, x):
        attention_output = self.multihead_attention(x)
        x = x + attention_output
        x = self.layer_norm(x)
        
        feed_forward_output = self.feed_forward(x)
        x = x + feed_forward_output
        x = self.layer_norm(x)
        
        return x

class Decoder(nn.Module):
    def __init__(self, hidden_dim, num_layers, num_heads):
        super(Decoder, self).__init__()
        
        self.layers = nn.ModuleList([DecoderLayer(hidden_dim, num_heads) for _ in range(num_layers)])
        
    def forward(self, x, encoder_output):
        for layer in self.layers:
            x = layer(x, encoder_output)
        return x

class DecoderLayer(nn.Module):
    def __init__(self, hidden_dim, num_heads):
        super(DecoderLayer, self).__init__()
        
        self.masked_multihead_attention = MultiheadAttention(hidden_dim, num_heads)
        self.multihead_attention = MultiheadAttention(hidden_dim, num_heads)
        self.feed_forward = FeedForward(hidden_dim)
        self.layer_norm = nn.LayerNorm(hidden_dim)
        
    def forward(self, x, encoder_output):
        masked_attention_output = self.masked_multihead_attention(x, x, x)
        x = x + masked_attention_output
        x = self.layer_norm(x)
        
        attention_output = self.multihead_attention(x, encoder_output, encoder_output)
        x = x + attention_output
        x = self.layer_norm(x)
        
        feed_forward_output = self.feed_forward(x)
        x = x + feed_forward_output
        x = self.layer_norm(x)
        
        return x

class MultiheadAttention(nn.Module):
    def __init__(self, hidden_dim, num_heads):
        super(MultiheadAttention, self).__init__()
        
        self.hidden_dim = hidden_dim
        self.num_heads = num_heads
        
        self.head_dim = hidden_dim // num_heads
        
        self.query_linear = nn.Linear(hidden_dim, hidden_dim)
        self.key_linear = nn.Linear(hidden_dim, hidden_dim)
        self.value_linear = nn.Linear(hidden_dim, hidden_dim)
        
        self.fc = nn.Linear(hidden_dim, hidden_dim)
        
    def forward(self, query, key, value):
        batch_size = query.shape[0]
        
        query = self.query_linear(query)
        key = self.key_linear(key)
        value = self.value_linear(value)
        
        query = query.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
        key = key.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, self.num_heads, self.head_dim).transpose(1, 2)
        
        attention_scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(self.head_dim)
        
        attention_weights = nn.functional.softmax(attention_scores, dim=-1)
        
        output = torch.matmul(attention_weights, value)
        output = output.transpose(1, 2).contiguous().view(batch_size, -1, self.hidden_dim)
        
        output = self.fc(output)
        
        return output

class FeedForward(nn.Module):
    def __init__(self, hidden_dim, feed_forward_dim=2048):
        super(FeedForward, self).__init__()
        
        self.fc1 = nn.Linear(hidden_dim, feed_forward_dim)
        self.fc2 = nn.Linear(feed_forward_dim, hidden_dim)
        
    def forward(self, x):
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return x

以上代码实现了一个基本的Transformer模型，包括Encoder、Decoder、MultiheadAttention等组件。你可以根据自己的需求进行修改和扩展。