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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)
46 lines
2.8 KiB
CSV
46 lines
2.8 KiB
CSV
case_id,batch_a,batch_b,m,n,k,dtype_a,dtype_b,dtype_c,trans_a,trans_b,has_bias,out_nd,deterministic_level
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to_matmul_demo,1,1,2048,2048,2048,bf16,bf16,bf16,0,0,0,,0
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special_k0_demo,128,128,256,256,0,bf16,bf16,bf16,0,0,0,,0
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special_k1_demo,128,128,256,256,1,bf16,bf16,bf16,0,0,0,,0
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merge_demo_k_trunc,2048,2048,16,64,128,bf16,bf16,bf16,0,0,0,,0
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merge_iter_arbitrate,128,128,64,64,512,bf16,bf16,bf16,0,0,0,,0
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iter_demo_form_b,128,128,64,64,256,bf16,bf16,bf16,0,0,0,,0
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iter_demo_form_d,64,64,64,64,8192,bf16,bf16,bf16,0,0,0,,0
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streamk_demo,4,4,128,128,10240,bf16,bf16,bf16,0,0,0,,0
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asw_demo_full,2,2,8192,8192,1024,bf16,bf16,bf16,0,0,0,,0
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asw_demo_reduce_core,16,16,256,256,128,bf16,bf16,bf16,0,0,0,,0
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b4_m1_n128_k256,4,4,1,128,256,bf16,bf16,bf16,0,0,0,1,0
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b8_m2_n192_k128,8,8,2,192,128,bf16,bf16,bf16,0,0,0,1,0
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b16_m4_n256_k192,16,16,4,256,192,bf16,bf16,bf16,0,0,0,1,0
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b32_m8_n128_k256,32,32,8,128,256,bf16,bf16,bf16,0,0,0,1,0
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b64_m16_n256_k512,64,64,16,256,512,bf16,bf16,bf16,0,0,0,1,0
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b128_m8_n192_k256,128,128,8,192,256,bf16,bf16,bf16,0,0,0,1,0
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b32_m16_n8192_k7168,32,32,16,8192,7168,bf16,bf16,bf16,0,0,0,1,0
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b4_m1_n8192_k8192,4,4,1,8192,8192,bf16,bf16,bf16,0,0,0,1,0
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b16_m2_n4096_k7168,16,16,2,4096,7168,bf16,bf16,bf16,0,0,0,1,0
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b32_m64_n64_k7168,32,32,64,64,7168,bf16,bf16,bf16,0,0,0,1,0
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b64_m1024_n1024_k7168,64,64,1024,1024,7168,bf16,bf16,bf16,0,0,0,1,0
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b128_m2048_n2048_k1536,128,128,2048,2048,1536,bf16,bf16,bf16,0,0,0,1,0
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b64_m1024_n8192_k2048,64,64,1024,8192,2048,bf16,bf16,bf16,0,0,0,1,0
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b32_m1024_n1024_k512,32,32,1024,1024,512,bf16,bf16,bf16,0,0,0,1,0
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b128_m4096_n4096_k8192,128,128,4096,4096,8192,bf16,bf16,bf16,0,0,0,1,0
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b32_m8192_n4096_k7168,32,32,8192,4096,7168,bf16,bf16,bf16,0,0,0,1,0
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b32_m2048_n2048_k8192,32,32,2048,2048,8192,bf16,bf16,bf16,0,0,0,1,0
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b64_m4096_n2048_k128,64,64,4096,2048,128,bf16,bf16,bf16,0,0,0,1,0
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b16_m1024_n1024_k8192,16,16,1024,1024,8192,bf16,bf16,bf16,0,0,0,1,0
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b4_m32768_n128_k128,4,4,32768,128,128,bf16,bf16,bf16,0,0,0,1,0
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b8_m32768_n2048_k512,8,8,32768,2048,512,bf16,bf16,bf16,0,0,0,1,0
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b4_m131072_n128_k128,4,4,131072,128,128,bf16,bf16,bf16,0,0,0,1,0
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b8_m131072_n1024_k256,8,8,131072,1024,256,bf16,bf16,bf16,0,0,0,1,0
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b16_m32768_n8192_k7168,16,16,32768,8192,7168,bf16,bf16,bf16,0,0,0,1,0
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b4_m32768_n128_k8192,4,4,32768,128,8192,bf16,bf16,bf16,0,0,0,1,0
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b32_m131072_n8192_k128,32,32,131072,8192,128,bf16,bf16,bf16,0,0,0,1,0
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b64_m32768_n8192_k1536,64,64,32768,8192,1536,bf16,bf16,bf16,0,0,0,1,0
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b8_m131072_n8192_k8192,8,8,131072,8192,8192,bf16,bf16,bf16,0,0,0,1,0
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b8_m16_n7168_k1536,8,8,16,7168,1536,bf16,bf16,bf16,0,0,0,1,0
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b64_m1024_n7168_k7168,64,64,1024,7168,7168,bf16,bf16,bf16,0,0,0,1,0
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b32_m8192_n8192_k7168,32,32,8192,8192,7168,bf16,bf16,bf16,0,0,0,1,0
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b8_m4096_n4096_k128,8,8,4096,4096,128,bf16,bf16,bf16,0,0,0,1,0
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b128_m8192_n8192_k7168,128,128,8192,8192,7168,bf16,bf16,bf16,0,0,0,1,0
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special_k1_b64,64,64,8192,512,1,bf16,bf16,bf16,0,0,0,1,0
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