From bbede84054a8799e6ebd275c0139ce1ec33e21b4 Mon Sep 17 00:00:00 2001 From: Logan Zou <74288839+logan-zou@users.noreply.github.com> Date: Tue, 3 Jun 2025 14:10:23 +0800 Subject: [PATCH] fix config bug --- docs/chapter2/第二章 Transformer架构.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/chapter2/第二章 Transformer架构.md b/docs/chapter2/第二章 Transformer架构.md index 6812428..91c4a59 100644 --- a/docs/chapter2/第二章 Transformer架构.md +++ b/docs/chapter2/第二章 Transformer架构.md @@ -472,7 +472,7 @@ out = h + self.feed_forward.forward(self.fnn_norm(h)) ```python class EncoderLayer(nn.Module): '''Encoder层''' - def __init__(self, config): + def __init__(self, args): super().__init__() # 一个 Layer 中有两个 LayerNorm，分别在 Attention 之前和 MLP 之前 self.attention_norm = LayerNorm(args.n_embd) @@ -529,7 +529,7 @@ class DecoderLayer(nn.Module): self.attention = MultiHeadAttention(args, is_causal=False) self.ffn_norm = LayerNorm(args.n_embd) # 第三个部分是 MLP - self.feed_forward = MLP(config) + self.feed_forward = MLP(args) def forward(self, x, enc_out): # Layer Norm @@ -830,7 +830,7 @@ class Transformer(nn.Module): # 输入为 idx，维度为 (batch size, sequence length, 1)；targets 为目标序列，用于计算 loss device = idx.device b, t = idx.size() - assert t <= self.config.block_size, f"不能计算该序列，该序列长度为 {t}, 最大序列长度只有 {self.config.block_size}" + assert t <= self.args.block_size, f"不能计算该序列，该序列长度为 {t}, 最大序列长度只有 {self.args.block_size}" # 通过 self.transformer # 首先将输入 idx 通过 Embedding 层，得到维度为 (batch size, sequence length, n_embd)