refactor : 基于声明式 JSON 配置的预处理管线重构

- 用工厂注册的 MaskBuilder（chat/instruction/text）替换硬编码的 _transform_* 方法
- mask 规则以 role-to-action 映射声明在配置中，与 chat_template 完全解耦
- 单次编码 + role-span 追踪替代两次编码 + 长度差计算 mask 的方式
- 支持多轮对话训练：所有 assistant 轮次参与训练，而非仅最后一轮
- 新建 astrai.preprocessing 包（builder.py + pipeline.py），删除 astrai/preprocess.py
- CLI 精简为 --config 参数，所有参数通过 PipelineConfig JSON 配置
- 新增 PipelineConfig、InputConfig、ProcessingConfig、OutputConfig dataclass
- 文档：assets/docs/preprocessing.md
- 27 个测试覆盖 mask builder、pipeline、配置序列化、工厂注册

assets/docs/training.md

@@ -1,38 +1,5 @@
 # Training
 
-## Model Architecture
-
-The model uses a decoder-only Transformer with **GQA** (Grouped Query Attention) and optional **MLA** (Multi-head Latent Attention). 1.0 billion parameters, Chinese–English bilingual.
-
-```mermaid
-flowchart TB
-    subgraph Layers["Transformer Layers"]
-        direction TB
-        A[Input Embedding] --> B[Transformer Block\nLayer 1]
-        B --> C[Transformer Block\nLayer ...]
-        C --> D[Transformer Block\nLayer ...]
-        D --> E[RMSNorm]
-        E --> F[Linear]
-        F --> G[SoftMax]
-    end
-
-    subgraph TransformerBlock["Transformer Block"]
-        direction TB
-        H[x] --> I[RMSNorm]
-        I --> J[Linear → Q/K/V]
-        J --> K[Q]; J --> L[K]; J --> M[V]
-        K --> N[RoPE]; L --> O[RoPE]
-        N --> P["Q @ K^T / sqrt(d)"]; O --> P
-        P --> Q[Masked SoftMax]; Q --> R[S @ V]; M --> R
-        R --> S[Linear]; S --> T[+]; H --> T
-        T --> U[RMSNorm]
-        U --> V["Linear (gate)"]; U --> W["Linear (up)"]
-        V --> X[SiLU]; X --> Y[×]; W --> Y
-        Y --> Z["Linear (down)"]; Z --> AA[+]; T --> AA
-        AA --> BB[x']
-    end
-```
-
 ### Autoregression
 
 Given a token sequence, the model predicts the probability of the next token. Each generated token is appended to the input and fed back, repeating until an end-of-sequence token or max length.