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UMAP可视化

Qwen2.5VL输入的input ids的token embedding的UMAP可视化：

其中type 0代表img token的占位符，由于都是同样的token，所以在空间距离上接近

type1代表system prompt的token

type2代表text token

Qwen2.5VL输入的input ids的token embedding+将img token的占位符换成经过ViT的实际img token的UMAP可视化：

可以看出此时img token和text token还是分为了明显不同的簇

在经过LLM之后得到的最后一层的hidden embedding中，UMAP显示此时的img token基本上和text token融合到了一起。其中type4代表img token之间的<|vision_start|>和<|vision_end|> token

直接将LLM得到的最后一层的hidden embedding经过mlp head并按照最大概率转换成ids，并用detokenizer解码得到text，发现视觉token基本上转化为了图像里面包含的物体的名字，这进一步说明其实最后visual token已经对齐到了text语意空间。

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对LLM的attention进行可视化

对attention进行可视化发现确实出现了attetnion sink的情况：

Qwen2.5VL 3B一共有36个layer

feature dim是2048，head数是16，每个head管128维的子向量

选择可视化Qwen2.5VL的第一个head的前100个token的attention map：

由于是自回归，存在causal attention的attention mask，因此非零的attention只会出现在对角线的左下角

可以观察到在某些维度确实出现了attention sink的情况。

但是最后由于token的数量过多（1k+以上），导致全局的attention map基本看不出东西

疑问：当token序列过长时会不会出现attention值过度平滑的情况？（attention的总和必须为1）

答案：其实在绝大多数情况下，由于softmax的存在，很多attention对应的值都是非常稀疏的，对于大多数token的attention基本都是0

第100个token对前100个token的causal attention的分布图（比较特殊）：

绝大多数情况都是大部分attention的值都接近于0：