add Chinese annotations to all source files for learning purposes

Annotated 16 source files covering the full architecture:
engine (scheduler, block manager, model runner), layers (attention,
linear, sampler, etc.), model (qwen3), and utils.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

nanovllm/layers/layernorm.py

@@ -3,6 +3,16 @@ from torch import nn
 
 
 class RMSNorm(nn.Module):
+    """Root Mean Square Layer Normalization（RMSNorm）。
+
+    与 LayerNorm 相比，RMSNorm 不计算均值，只计算均方根，计算量更小。
+    公式: output = x / sqrt(mean(x^2) + eps) * weight
+
+    提供两个前向路径:
+    - rms_forward: 标准 RMSNorm。
+    - add_rms_forward: 将残差加法融合到 RMSNorm 中（x + residual → RMSNorm），
+      减少一次显存读写，是 vLLM 等推理框架的常见优化。
+    """
 
     def __init__(
         self,
@@ -31,6 +41,11 @@ class RMSNorm(nn.Module):
         x: torch.Tensor,
         residual: torch.Tensor,
     ) -> tuple[torch.Tensor, torch.Tensor]:
+        """融合残差加法和 RMSNorm。
+
+        Returns:
+            (normalized_output, updated_residual): 归一化后的输出和更新后的残差（= x + residual）。
+        """
         orig_dtype = x.dtype
         x = x.float().add_(residual.float())
         residual = x.to(orig_dtype)