LLM 大模型必学的 6 项核心技术

本文探讨火爆的大模型背后，有哪些的核心技术。

一、Transformer

Transformer 是大模型的底层模型。在深度学习的早期阶段，循环神经网络（RNN）是处理序列数据的常用方法。尽管 RNN 及其变体在某些任务上表现良好，但它们在处理长序列时容易遇到梯度消失和模型退化问题。为了解决这些问题，Transformer 模型被提出。

import torch import torch.nn as nn import torch.optim as optim # 该示例仅用于说明 Transformer 的基本结构和原理。实际的 Transformer 模型（如 GPT 或 BERT）要复杂得多，并且需要更多的预处理步骤，如分词、填充、掩码等。 class Transformer(nn.Module): def __init__(self, d_model, nhead, num_encoder_layers, num_decoder_layers, dim_feedforward=2048): super(Transformer, self).__init__() self.model_type = 'Transformer' # encoder layers self.src_mask = None self.pos_encoder = PositionalEncoding(d_model, max_len=5000) encoder_layers = nn.TransformerEncoderLayer(d_model, nhead, dim_feedforward) self.transformer_encoder = nn.TransformerEncoder(encoder_layers, num_encoder_layers) # decoder layers decoder_layers = nn.TransformerDecoderLayer(d_model, nhead, dim_feedforward) self.transformer_decoder = nn.TransformerDecoder(decoder_layers, num_decoder_layers) # decoder self.decoder = nn.Linear(d_model, d_model) self.init_weights() def init_weights(self): initrange = 0.1 self.decoder.weight.data.uniform_(-initrange, initrange) def forward(self, src, tgt, teacher_forcing_ratio=0.5): batch_size = tgt.size(0) tgt_len = tgt.size(1) tgt_vocab_size = self.decoder.out_features # forward pass through encoder src = self.pos_encoder(src) output = self.transformer_encoder(src) # prepare decoder input with teacher forcing target_input = tgt[:, :-1].contiguous() target_input = target_input.view(batch_size * tgt_len, -1) target_input = torch.autograd.Variable(target_input) # forward pass through decoder output2 = self.transformer_decoder(target_input, output) output2 = output2.view(batch_size, tgt_len, -1) # generate predictions prediction = self.decoder(output2) prediction = prediction.view(batch_size * tgt_len, tgt_vocab_size) return prediction[:, -1], prediction class PositionalEncoding(nn.Module): def __init__(self, d_model, max_len=5000): super(PositionalEncoding, self).__init__() # Compute the positional encodings once in log space. pe = torch.zeros(max_len, d_model) position = torch.arange(0, max_len).unsqueeze(1).float() div_term = torch.exp(torch.arange(0, d_model, 2).float() * -(torch.log(torch.tensor(10000.0)) / d_model)) pe[:, 0::2] = torch.sin(position * div_term) pe[:, 1::2] = torch.cos(position * div_term) pe = pe.unsqueeze(0) self.register_buffer('pe', pe) def forward(self, x): x = x + self.pe[:, :x.size(1)] return x # 超参数 d_model = 512 nhead = 8 num_encoder_layers = 6 num_decoder_layers = 6 dim_feedforward = 2048 # 实例化模型 model = Transformer(d_model, nhead, num_encoder_layers, num_decoder_layers, dim_feedforward) # 随机生成数据 src = torch.randn(10, 32, 512) tgt = torch.randn(10, 32, 512) # 前向传播 prediction, predictions = model(src, tgt) print(prediction)

LLM 大模型必学的 6 项核心技术