moe(wip): gpt-oss-20b groundwork — config fields, arch doc, MXFP4 tools

Phase 19 start. config.rs: explicit head_dim (gpt-oss=64) + MoE fields
(num_local_experts, num_experts_per_tok, swiglu_limit, sliding_window,
layer_types) with accessors; Qwen3/GPT-2 paths unchanged (fall back to
hidden/num_heads when head_dim absent).

docs/19-moe-gpt-oss.md: architecture + exact HF reference math (router
softmax-after-topk, interleaved clamped (up+1)*glu experts, attention
sinks, alternating sliding window, rotate_half RoPE theta=150000,
head_dim 64), verified tensor layout, MXFP4 dequant plan.
docs/MOE_PROGRESS.md: resume/handoff snapshot.

tools/mxfp4_probe.py: inspect safetensors + validate MXFP4 decode (done).
tools/gptoss_dequant.py: MXFP4 experts -> plain BF16 safetensors dir so
the existing loader reads it (no MXFP4 in Rust for the first pass).

Verified: llama.cpp (dash5, LLM_ARCH_OPENAI_MOE) runs the gpt-oss-20b
MXFP4 GGUF correctly (17*24 -> 408) = the correctness oracle. MXFP4 decode
validated in numpy. Model + GGUF staged on dash5.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

docs/19-moe-gpt-oss.md

@@ -59,6 +59,41 @@ o = (scores @ v) -> merge heads -> @Wo + bo
 与 Qwen3 的新增点：MoE FFN、MXFP4 反量化、attention sinks（softmax 多一列再丢）、
 交替 sliding window、q/k/v/o bias、head_dim=64、clamped `(up+1)*glu`、rope_theta=150000。
 
+### 实测张量布局（layer 0，已用 `tools/mxfp4_probe.py` 核对）
+```
+self_attn.q_proj.weight [4096,2880] +bias[4096]   # 64 heads*64
+self_attn.k_proj.weight [512,2880]  +bias[512]    # 8 kv*64
+self_attn.v_proj.weight [512,2880]  +bias[512]
+self_attn.o_proj.weight [2880,4096] +bias[2880]
+self_attn.sinks [64]                               # 每 q-head 一个标量（BF16）
+input_layernorm.weight [2880]; post_attention_layernorm.weight [2880]
+mlp.router.weight [32,2880] +bias[32]
+mlp.experts.gate_up_proj_blocks [32,5760,90,16] U8 + _scales [32,5760,90] U8 + _bias[32,5760] BF16
+mlp.experts.down_proj_blocks    [32,2880,90,16] U8 + _scales [32,2880,90] U8 + _bias[32,2880] BF16
+# 全局: model.embed_tokens.weight, model.norm.weight, lm_head.weight (BF16)
+```
+MXFP4 打包：`[..., nblk=90, 16]` U8，每 16 字节 = 32 个 FP4 码（低 nibble=偶 idx，高 nibble=奇 idx），
+每 block 一个 E8M0 scale；`90*32 = 2880 = 输入(hidden)维`。即 gate_up 每 expert 权重逻辑 shape
+`[5760 out, 2880 in]`（**已转置存储**：行=out，列=in，与 HF `nn.Linear` 一致 `y=x·Wᵀ`）。
+
+### RoPE（**rotate_half，非 interleave**）
+```
+dim = head_dim = 64; base = rope_theta = 150000
+inv_freq = 1 / base^(arange(0,64,2)/64)        # 32 项
+freqs = pos ⊗ inv_freq                          # [S, 32]；cos/sin = cos(freqs)/sin(freqs)  (不 doubling)
+# 应用: x=[.., 64], first=x[:32], second=x[32:]
+#   out_first  = first*cos - second*sin
+#   out_second = second*cos + first*sin
+```
+> ⚠️ 与 Qwen3 的 RoPE kernel（interleave）不同 —— gptoss 走 rotate_half。需单独处理。
+
+### Decoder layer（pre-norm 残差，结构同 Qwen3）
+```
+h = x + attn(input_norm(x))            # attn 含 sinks/bias/滑窗
+out = h + moe(post_norm(h))            # moe = router + top4 experts 加权和
+```
+最终：`logits = lm_head(norm(h_last))`。无 q_norm/k_norm（与 Qwen3 不同，gptoss 没有）。
+
 ## 3. MXFP4 反量化（expert 权重）
 
 expert 张量名：`model.layers.{i}.mlp.experts.gate_up_proj_blocks/_scales`、