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self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) 这个是用来干嘛的

输出层，词汇投影层，将模型输出的隐藏状态向量映射回词表空间，用于预测下一个token

# 预测 logits，未经过 softmax

lm_logits = self.lm_head(hidden_states)  # shape: [B, L, vocab_size]

之后再通过 softmax 转换为每个 token 的概率。

class Qwen2Model(Qwen2PreTrainedModel):

    def __init__(self, config):

        super().__init__(config)

        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)

        self.layers = nn.ModuleList([

            Qwen2DecoderLayer(config) for _ in range(config.num_hidden_layers)

        ])    #这里由config文件决定的有很多很多层

        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

每一层结构

class Qwen2DecoderLayer(nn.Module):

    def __init__(self, config):

        ...

        self.self_attn = Qwen2Attention(config)

        self.mlp = Qwen2MLP(config)

        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

        self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)

    def forward(self, hidden_states, ...):

        residual = hidden_states

        hidden_states = self.input_layernorm(hidden_states)

        hidden_states, _ = self.self_attn(hidden_states, ...)

        hidden_states = residual + hidden_states

        residual = hidden_states

        hidden_states = self.post_attention_layernorm(hidden_states)

        hidden_states = self.mlp(hidden_states)

        hidden_states = residual + hidden_states

        return hidden_states, ...

self.mlp = Qwen2MLP(config) 这个是什么

标准Transformer FFN

class TransformerFFN(nn.Module):

    def __init__(self, embed_dim, hidden_dim):

        super().__init__()

        self.linear1 = nn.Linear(embed_dim, hidden_dim)

        self.relu = nn.ReLU()

        self.linear2 = nn.Linear(hidden_dim, embed_dim)

    def forward(self, x):

        return self.linear2(self.relu(self.linear1(x)))