探索和构建 LLaMA 3 架构：深入探讨组件、编码和推理技术（八）编码器块
Transformer块
由于 只关注模型的推理，因此 只会研究transformer块
python
class EncoderBlock(nn.Module):
def __init__(self, args: ModelArgs):
super().__init__()
self.n_heads = args.n_heads
self.dim = args.dim
self.head_dim = args.dim // args.n_heads
self.attention = SelfAttention(args)
self.feed_forward = FeedForward(args)
# normalize BEFORE the self attention
self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps)
# Normalization BEFORE the feed forward
self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps)
def forward(self, x: torch.Tensor, start_pos: int, freqs_complex: torch.Tensor):
# (B, seq_len, dim) + (B, seq_len, dim) -> (B, seq_len, dim)
h = x + self.attention.forward(self.attention_norm(x), start_pos, freqs_complex)
out = h + self.feed_forward.forward(self.ffn_norm(h))
return out系列博客
探索和构建 LLaMA 3 架构：深入探讨组件、编码和推理技术（一）Llama3 模型 架构
https://duanzhihua.blog.csdn.net/article/details/138208650
探索和构建 LLaMA 3 架构：深入探讨组件、编码和推理技术（二）RoPE位置编码
https://duanzhihua.blog.csdn.net/article/details/138212328
探索和构建 LLaMA 3 架构：深入探讨组件、编码和推理技术（三）KV缓存
https://duanzhihua.blog.csdn.net/article/details/138213306
探索和构建 LLaMA 3 架构：深入探讨组件、编码和推理技术（四）分组多查询注意力
https://duanzhihua.blog.csdn.net/article/details/138216050
探索和构建 LLaMA 3 架构：深入探讨组件、编码和推理技术（五）RMS 均方根归一化
https://duanzhihua.blog.csdn.net/article/details/138216630
探索和构建 LLaMA 3 架构：深入探讨组件、编码和推理技术（六）SwiGLU 激活函数
https://duanzhihua.blog.csdn.net/article/details/138217261
探索和构建 LLaMA 3 架构：深入探讨组件、编码和推理技术（七）前馈神经网络
https://duanzhihua.blog.csdn.net/article/details/138218095