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@@ -1 +1,56 @@
-#
+一、RMSNorm操作
+代码实现几乎一样，但在返回值的操作上略有不同：
+OSS: 
+    return (self.weight * hidden_states).to(input_dtype) 
+Qwen3:
+    return self.weight * hidden_states.to(input_dtype) 
+更推荐OSS的方式
+
+二、MoE层
+1.专家网络权重形式不一样
+OSS采用大矩阵存储所有的MLP,且有bias，gate_up_proj形状为num_experts*hidden_size*(2*expert_dim),合并了两个上采样的权重；
+down_proj的权重形状为：num_experts*expert_dim*hidden_size
+
+Qwen3采用存储的粒度更细，每个专家的权重独立存储，且门控和上采样权重分开，不使用bias,形状均为hidden_size*expert_dim;
+下采样权重为：expert_dim*hiddent_size
+所有专家的权重定义为：nn.ModuleList(
+            [Qwen3MoeMLP(config, intermediate_size=config.moe_intermediate_size) for _ in range(self.num_experts)]
+        )
+
+2.专家的前向计算方式不一样
+Qwen3采用：Loop over all available experts
+OSS可以有两种方式：
+ When training is is more efficient to just loop over the experts and compute the output for each expert
+For inference we can sacrifice some memory and compute the output for all experts at once. 
+
+3.激活函数
+OSS使用：
+    glu = gate * torch.sigmoid(gate * self.alpha) 
+Qwen3使用：
+    silu
+4.MLP结构不一样，OSS including clamping and a residual connection
+OSS使用：
+                gate = gate.clamp(min=None, max=self.limit)
+                up = up.clamp(min=-self.limit, max=self.limit)
+                glu = gate * torch.sigmoid(gate * self.alpha)
+                gated_output = (up + 1) * glu
+                out = gated_output @ self.down_proj[expert_idx] + self.down_proj_bias[expert_idx]
+
+Qwen使用：
+        down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+三、RoPE代码一样
+四、 eager_attention的计算
+    OSS使用了GQA和sink attention
+            combined_logits = torch.cat([attn_weights, sinks], dim=-1)
+            combined_logits = combined_logits - combined_logits.max(dim=-1, keepdim=True).values
+            probs = F.softmax(combined_logits, dim=-1, dtype=combined_logits.dtype)
+            scores = probs[..., :-1]
+    Qwen3使用了GQA，未使用sink attention
+            attn_weights = torch.matmul(query, key_states.transpose(2, 3)) * scaling
+            attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
+五、 Attention类型不一样
+    OSS交替使用sliding_attention,full_attention
+    Qwen3支持sliding_window
+六、负载均衡
+    均使用Switch Transformer