Fix #36: MergeBatch 合并搬移效率收益建模 (move_eff) + t_cmd_ns 置 0

用户澄清: MergeBatch vs IterBatch 的本质区别不只是 DMA 命令数 —— 合并 b0 个
batch 的左/右矩阵一起搬入 L1, 使单块 tile = nValue*dValue*dt 放大 b0 倍 (堆叠
方向视转置: A ND 非转置沿 M(nValue), B ND 非转置沿 N(dValue)), 搬移效率更高,
即便 T_cmd=0 也有效益。

- models.move_eff: 单命令搬移效率 eff = min(1, tile/min_TileSize) (16KB 饱和,
  与进入条件4效率下限语义同源); gm_move_time 按 A/B 两侧字节加权
  t = (V_A/eff_A + V_B/eff_B)/BW_gm; 只影响时间列, GM 字节量仍 = V_in
- IterBatch: l1_form 补驻留侧返回; move_tiles 分侧口径 (a/b 双侧整K, c 驻留侧
  整K+对侧k_l1, d 双侧k_l1), evaluate 接入效率加权
- MergeBatch: 合并 tile 放大 b0 倍接入效率加权; beats_iterbatch 净收益 =
  命令节省(cmds差×T_cmd) + 效率节省(t_data差) − drain惩罚, K截断且效率打平且
  T_cmd>0 时严格退化为 v1.1 §4.5 闭式; 退役 T_cmd<=0 策略特判
- router: 退役 "T_cmd<=0 策略优先 MergeBatch" 覆盖, 时延模型统一终审
- hardware: t_cmd_ns 50 -> 0 (未标定按 0; 合并收益不再依赖 T_cmd 估计值)
- 作用域: 仅切B 两分支接入 (逐命令 tile 小、效率差显著); ASW/StreamK 单命令
  tile 通常已饱和, 极端小 tile 走 issue#34 效率降级标注通道
- 用户 case 家族 B=128,M=1~16,N=128,K=512: m=1~8 -> MergeBatch (效率节省
  ~0.61us > drain), m=16 -> IterBatch (iter A tile 恰达 16KB 饱和, 效率打平,
  drain 决定); 分界与时延全家族一致
- demo: merge_demo_k_trunc 形状 (2048,32,32,256)->(2048,16,64,128) (原形状
  两侧 tile 均已 16KB 饱和, t_cmd=0 下无收益转 IterBatch; 新形状 iter A tile
  4KB eff=0.25 vs 合并 16KB eff=1.0, 保持 MergeBatch 胜出演示且仍 K截断)
- 测试: 74/74 (新增 TestIssue36 5 例: 效率曲线/字节不变/效率差胜出/家族;
  TestArbitration/TestZeroCmdHandling 按 t_cmd=0+效率语义重写; TestIssue35
  家族期望更新)
- 文档: 01_MergeBatch §4/§5 效率模型+泛化净收益; 02_IterBatch 口径注;
  00_总纲胜出条件; 01_软件架构 T_cmd 标定说明; 05 时间列效率口径注; README 要点
- 验证: examples 重生成可复现 0 diff; 压力 10000 例 0 崩溃/0 NaN/0 违规/
  0 GM<V_in, 七分支覆盖 (MergeBatch 386 例)

BMM/BMM_Theory/examples/plans.csv

@@ -2,7 +2,7 @@
 to_matmul_demo,转Matmul,Ascend950PR,batch_mat_mul_v3,32,1,0,0,1,"折叠为 Matmul [2048,2048]x[2048,2048], 复用 Matmul 切分体系",0,1,0,0,2048,0,1,,0,0,0,,,0,0,转Matmul后由 Matmul 体系决定,1,1,1,0,0,0,0,True,2,"BatchB=1免费折叠: 左矩阵 [1,2048,2048] 视图折叠为 [2048,2048], 零重排零 split"
 special_k0_demo,特殊分支,Ascend950PR,batch_mat_mul_v3,64,1,1,1,1,AIV 核间按行均分 (无 Cube tile 概念),0,1,0,0,0,0,1,UB驻留(AIV),0,0,0,allocate,direct_gm,0,0,不涉及(AIV逐元素),1,1,1,0,0,0,0,False,2,"K=0纯写值: 无任何计算, C=bias 或 0, 纯 AIV 写值; 按行均分到 AIV 核"
 special_k1_demo,特殊分支,Ascend950PR,batch_mat_mul_v3,64,1,1,1,1,AIV 核间按行均分 (无 Cube tile 概念),0,1,0,0,1,0,1,UB驻留(AIV) UB乒乓,0,0,0,allocate,direct_gm,0,0,不涉及(AIV逐元素),1,1,1,0,0,0,0,False,2,"K=1逐元素乘: 退化为 C=A⊙B 无累加深度, Cube 16x16x16 粒度浪费 15/16; 走 AIV 通路 GM->UB->Mul->GM, UB乒乓 (B>=2*AIV 双batch乒乓流水)"
-merge_demo_k_trunc,MergeBatch,Ascend950PR,batch_mat_mul_v3,32,32,1,1,1,切B均分(核间零重复读零依赖),64,4,128,128,256,256,8,合并驻留,128,128,128,allocate(GM->L1随路驻留L2),"resident(整case输入+输出<=L2: 输出驻留L2异步回写, GM写=0)",0,0,不涉及(核内不切M/N),1,1,1,0,0,0,0,True,2,"b0=4 (L0C上限5.7/算存比上限19.0/b_core=64); K截断; 合并后单次DMA搬入 A'[128,256]+B'[256,128]; 输出落点: L2驻留 (整case V_in+V_out=64.0MB vs L2=128MB)"
+merge_demo_k_trunc,MergeBatch,Ascend950PR,batch_mat_mul_v3,32,32,1,1,1,切B均分(核间零重复读零依赖),64,4,64,256,128,128,12,合并驻留,64,256,64,allocate(GM->L1随路驻留L2),"resident(整case输入+输出<=L2: 输出驻留L2异步回写, GM写=0)",0,0,不涉及(核内不切M/N),1,1,1,0,0,0,0,True,2,"b0=4 (L0C上限5.7/算存比上限23.7/b_core=64); K截断; 合并后单次DMA搬入 A'[64,128]+B'[128,256]; 输出落点: L2驻留 (整case V_in+V_out=40.0MB vs L2=128MB)"
 merge_iter_arbitrate,IterBatch,Ascend950PR,batch_mat_mul_v3,32,32,1,1,1,切B轮转分配(核间零重复读零依赖),4,1,64,64,512,512,2,b_双batch乒乓,64,64,256,allocate(GM->L1随路驻留L2),"resident(整case输入+输出<=L2: 输出驻留L2异步回写, GM写=0)",0,0,不涉及(核内不切M/N),1,1,1,0,0,0,0,True,2,双batch乒乓: 2*(MK+KN)*dtype=256KB <= L1; 输出落点: L2驻留
 iter_demo_form_b,IterBatch,Ascend950PR,batch_mat_mul_v3,32,32,1,1,1,切B轮转分配(核间零重复读零依赖),4,1,64,64,256,256,2,b_双batch乒乓,64,64,256,allocate(GM->L1随路驻留L2),"resident(整case输入+输出<=L2: 输出驻留L2异步回写, GM写=0)",0,0,不涉及(核内不切M/N),1,1,1,0,0,0,0,True,2,双batch乒乓: 2*(MK+KN)*dtype=128KB <= L1; 输出落点: L2驻留
 iter_demo_form_d,IterBatch,Ascend950PR,batch_mat_mul_v3,32,32,1,1,1,切B轮转分配(核间零重复读零依赖),2,1,64,64,8192,1024,1,d_两侧都切K,64,64,256,allocate(GM->L1随路驻留L2),"direct_gm(整case超L2: 输入优先驻留L2, 输出直写GM不占L2)",0,0,不涉及(核内不切M/N),1,1,1,0,0,0,0,True,2,"两侧都切K: k_L1=1024, K段成对流水, batch边界天然无缝; dValueA=2048B/dValueB=128B; 输出落点: 直写GM"