diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/benchmark/BatchDecodeWithPagedKVCacheWrapper_20260525_093143.csv b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/benchmark/BatchDecodeWithPagedKVCacheWrapper_20260525_093143.csv deleted file mode 100644 index ae909a7..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/benchmark/BatchDecodeWithPagedKVCacheWrapper_20260525_093143.csv +++ /dev/null @@ -1,145 +0,0 @@ -api,batch_size,seq_len_q,seq_len_kv,num_qo_heads,num_kv_heads,head_dim,time_ms,bandwidth_GB_s,tflops -BatchDecodeWithPagedKVCacheWrapper,1,1,512,32,8,64,0.02042879999999998,51.528822055137894,0.8212531328320811 -BatchDecodeWithPagedKVCacheWrapper,1,1,512,32,4,128,0.02333952000000001,45.27805199078642,0.718832949435121 -BatchDecodeWithPagedKVCacheWrapper,1,1,512,32,4,256,0.0319488,66.15384615384615,1.0502564102564103 -BatchDecodeWithPagedKVCacheWrapper,1,1,1024,32,8,64,0.023262719999999973,90.32684054143292,1.4424122372620245 -BatchDecodeWithPagedKVCacheWrapper,1,1,1024,32,4,128,0.025041919999999992,84.07278675117566,1.3399304845634845 -BatchDecodeWithPagedKVCacheWrapper,1,1,1024,32,4,256,0.033387520000000004,126.11562643766291,2.0099984664928687 -BatchDecodeWithPagedKVCacheWrapper,1,1,2048,32,8,64,0.028298240000000037,148.36258368011562,2.371485435136599 -BatchDecodeWithPagedKVCacheWrapper,1,1,2048,32,4,128,0.027745280000000008,151.4670603432367,2.418748846650673 -BatchDecodeWithPagedKVCacheWrapper,1,1,2048,32,4,256,0.03723775999999999,225.7115358174069,3.604344837068611 -BatchDecodeWithPagedKVCacheWrapper,1,1,4096,32,8,64,0.03886591999999997,215.93992886312756,3.4533526544592306 -BatchDecodeWithPagedKVCacheWrapper,1,1,4096,32,4,128,0.03426815999999998,245.03212311370103,3.916689078141344 -BatchDecodeWithPagedKVCacheWrapper,1,1,4096,32,4,256,0.066048,254.26356589147287,4.064248062015504 -BatchDecodeWithPagedKVCacheWrapper,1,1,8192,32,8,64,0.052495359999999984,319.6722910367698,5.1135082414902975 -BatchDecodeWithPagedKVCacheWrapper,1,1,8192,32,4,128,0.04628480000000001,362.6548672566371,5.799646017699114 -BatchDecodeWithPagedKVCacheWrapper,1,1,8192,32,4,256,0.08975359999999999,374.0330861380491,5.981608670849972 -BatchDecodeWithPagedKVCacheWrapper,1,1,16384,32,8,64,0.08625152000000001,389.0775258221536,6.224480588863825 -BatchDecodeWithPagedKVCacheWrapper,1,1,16384,32,4,128,0.0638464,525.6776263031276,8.408789093825181 -BatchDecodeWithPagedKVCacheWrapper,1,1,16384,32,4,256,0.13059071999999994,514.0123892417473,8.222190857053247 -BatchDecodeWithPagedKVCacheWrapper,2,1,512,32,8,64,0.02342912,89.86013986013987,1.4321678321678322 -BatchDecodeWithPagedKVCacheWrapper,2,1,512,32,4,128,0.02486784,84.99073502161829,1.3493102738315832 -BatchDecodeWithPagedKVCacheWrapper,2,1,512,32,4,256,0.03340287999999998,126.54812998160644,2.009074187614961 -BatchDecodeWithPagedKVCacheWrapper,2,1,1024,32,8,64,0.02839040000000001,148.02524797114512,2.3637871956717755 -BatchDecodeWithPagedKVCacheWrapper,2,1,1024,32,4,128,0.028165120000000012,149.5000908925649,2.382694055626249 -BatchDecodeWithPagedKVCacheWrapper,2,1,1024,32,4,256,0.03740160000000001,225.16084873374396,3.5885557837097872 -BatchDecodeWithPagedKVCacheWrapper,2,1,2048,32,8,64,0.03881984000000001,216.30176734370872,3.457451859667633 -BatchDecodeWithPagedKVCacheWrapper,2,1,2048,32,4,128,0.03601408000000001,233.38072220642587,3.7268126243957904 -BatchDecodeWithPagedKVCacheWrapper,2,1,2048,32,4,256,0.06728704000000002,249.82498858621207,3.9894080048698815 -BatchDecodeWithPagedKVCacheWrapper,2,1,4096,32,8,64,0.052490240000000014,319.7815060476004,5.114007023019897 -BatchDecodeWithPagedKVCacheWrapper,2,1,4096,32,4,128,0.04626431999999999,362.9924745462595,5.802213368747235 -BatchDecodeWithPagedKVCacheWrapper,2,1,4096,32,4,256,0.08993791999999999,373.44870773084375,5.969349880450872 -BatchDecodeWithPagedKVCacheWrapper,2,1,8192,32,8,64,0.08536063999999999,393.18618042226495,6.289443378119003 -BatchDecodeWithPagedKVCacheWrapper,2,1,8192,32,4,128,0.0630784,532.2077922077922,8.51116883116883 -BatchDecodeWithPagedKVCacheWrapper,2,1,8192,32,4,256,0.12952576,518.3650881492608,8.289793659577834 -BatchDecodeWithPagedKVCacheWrapper,2,1,16384,32,8,64,0.15207424000000003,441.34401723789637,7.0606423809844445 -BatchDecodeWithPagedKVCacheWrapper,2,1,16384,32,4,128,0.10330112,649.8017446471055,10.394290245836638 -BatchDecodeWithPagedKVCacheWrapper,2,1,16384,32,4,256,0.2281984,588.3060354498541,9.410599057662106 -BatchDecodeWithPagedKVCacheWrapper,4,1,512,32,8,64,0.0283904,148.3137962128043,2.3637871956717764 -BatchDecodeWithPagedKVCacheWrapper,4,1,512,32,4,128,0.028078080000000036,150.5470459518598,2.3900802334062696 -BatchDecodeWithPagedKVCacheWrapper,4,1,512,32,4,256,0.03707903999999999,228.00331400165706,3.619773543220106 -BatchDecodeWithPagedKVCacheWrapper,4,1,1024,32,8,64,0.03844096000000004,218.64677677144357,3.4915290356952546 -BatchDecodeWithPagedKVCacheWrapper,4,1,1024,32,4,128,0.03641856000000004,231.23857725291697,3.6854210600309254 -BatchDecodeWithPagedKVCacheWrapper,4,1,1024,32,4,256,0.06640640000000002,253.63145720894363,4.04231303006939 -BatchDecodeWithPagedKVCacheWrapper,4,1,2048,32,8,64,0.059007999999999984,284.5986984815619,4.5491366594360105 -BatchDecodeWithPagedKVCacheWrapper,4,1,2048,32,4,128,0.04641792000000003,362.1442753143611,5.783013456871825 -BatchDecodeWithPagedKVCacheWrapper,4,1,2048,32,4,256,0.08961023999999998,375.1799794309223,5.991178151068451 -BatchDecodeWithPagedKVCacheWrapper,4,1,4096,32,8,64,0.09185279999999997,365.484949832776,5.84490523968785 -BatchDecodeWithPagedKVCacheWrapper,4,1,4096,32,4,128,0.06349823999999998,528.9469440412838,8.454894371875506 -BatchDecodeWithPagedKVCacheWrapper,4,1,4096,32,4,256,0.1303347200000001,515.3991200502825,8.238340666247638 -BatchDecodeWithPagedKVCacheWrapper,4,1,8192,32,8,64,0.16568319999999992,405.1421508034613,6.480692212608161 -BatchDecodeWithPagedKVCacheWrapper,4,1,8192,32,4,128,0.10290176,652.4828341128471,10.43463031147378 -BatchDecodeWithPagedKVCacheWrapper,4,1,8192,32,4,256,0.22947840000000008,585.1673360107093,9.358107987505575 -BatchDecodeWithPagedKVCacheWrapper,4,1,16384,32,8,64,0.30601215999999987,438.65613706331163,7.017641547316293 -BatchDecodeWithPagedKVCacheWrapper,4,1,16384,32,4,128,0.18384895999999992,730.2216776205863,11.680695109724857 -BatchDecodeWithPagedKVCacheWrapper,4,1,16384,32,4,256,0.4362026666666668,615.5418398787107,9.846265564630505 -BatchDecodeWithPagedKVCacheWrapper,8,1,512,32,8,64,0.038655999999999975,217.85430463576174,3.472105960264903 -BatchDecodeWithPagedKVCacheWrapper,8,1,512,32,4,128,0.03645951999999999,231.87754528858312,3.6812807190001418 -BatchDecodeWithPagedKVCacheWrapper,8,1,512,32,4,256,0.06676480000000001,253.25153374233125,4.020613496932515 -BatchDecodeWithPagedKVCacheWrapper,8,1,1024,32,8,64,0.05858303999999996,286.9428421604617,4.582135990211505 -BatchDecodeWithPagedKVCacheWrapper,8,1,1024,32,4,128,0.04676608000000001,360.14889424129615,5.73996058681848 -BatchDecodeWithPagedKVCacheWrapper,8,1,1024,32,4,256,0.08992768000000002,374.58437713504884,5.970029606012297 -BatchDecodeWithPagedKVCacheWrapper,8,1,2048,32,8,64,0.092416,363.43490304709144,5.8092853185595565 -BatchDecodeWithPagedKVCacheWrapper,8,1,2048,32,4,128,0.07130112000000002,471.5208961654457,7.5296280338934345 -BatchDecodeWithPagedKVCacheWrapper,8,1,2048,32,4,256,0.14862335999999993,452.41835469202175,7.224583161085851 -BatchDecodeWithPagedKVCacheWrapper,8,1,4096,32,8,64,0.16396288000000003,409.4928803397451,6.548688483637271 -BatchDecodeWithPagedKVCacheWrapper,8,1,4096,32,4,128,0.11201536000000002,599.6891854831337,9.585665965810401 -BatchDecodeWithPagedKVCacheWrapper,8,1,4096,32,4,256,0.24935424000000006,538.7869081351894,8.61218019793848 -BatchDecodeWithPagedKVCacheWrapper,8,1,8192,32,8,64,0.3056947200000001,439.16524302415155,7.024928817874248 -BatchDecodeWithPagedKVCacheWrapper,8,1,8192,32,4,128,0.20128768000000002,667.1211273337741,10.668728697156228 -BatchDecodeWithPagedKVCacheWrapper,8,1,8192,32,4,256,0.46690133333333317,575.2104541716168,9.198875628255509 -BatchDecodeWithPagedKVCacheWrapper,8,1,16384,32,8,64,0.5866495999999998,457.6296037702917,7.321179961598886 -BatchDecodeWithPagedKVCacheWrapper,8,1,16384,32,4,128,0.37337600000000015,719.1169009256082,11.503062050051419 -BatchDecodeWithPagedKVCacheWrapper,8,1,16384,32,4,256,0.8934826666666666,601.0211546726517,9.613991308915525 -BatchDecodeWithPagedKVCacheWrapper,16,1,512,32,8,64,0.0698112,241.26145947928126,3.845163182984965 -BatchDecodeWithPagedKVCacheWrapper,16,1,512,32,4,128,0.04724735999999999,357.8673602080625,5.681491114000869 -BatchDecodeWithPagedKVCacheWrapper,16,1,512,32,4,256,0.08954879999999998,377.6329331046313,5.995288736420813 -BatchDecodeWithPagedKVCacheWrapper,16,1,1024,32,8,64,0.12070911999999998,278.52052935188334,4.447641669494402 -BatchDecodeWithPagedKVCacheWrapper,16,1,1024,32,4,128,0.07076864000000004,475.9947909130369,7.586282737664589 -BatchDecodeWithPagedKVCacheWrapper,16,1,1024,32,4,256,0.14710784000000002,457.9702074342197,7.2990115550605585 -BatchDecodeWithPagedKVCacheWrapper,16,1,2048,32,8,64,0.22239232000000014,302.05359609540454,4.8281425545630325 -BatchDecodeWithPagedKVCacheWrapper,16,1,2048,32,4,128,0.11209728000000002,599.8355713894217,9.578660820316067 -BatchDecodeWithPagedKVCacheWrapper,16,1,2048,32,4,256,0.2504192,537.0190145164587,8.575555101206296 -BatchDecodeWithPagedKVCacheWrapper,16,1,4096,32,8,64,0.42098688000000006,318.97256275539985,5.101070247129791 -BatchDecodeWithPagedKVCacheWrapper,16,1,4096,32,4,128,0.2027008,662.7936347562515,10.594352109118466 -BatchDecodeWithPagedKVCacheWrapper,16,1,4096,32,4,256,0.46432,578.6905582356995,9.250015713301172 -BatchDecodeWithPagedKVCacheWrapper,16,1,8192,32,8,64,0.8234496000000004,326.06851955480926,5.215822918609709 -BatchDecodeWithPagedKVCacheWrapper,16,1,8192,32,4,128,0.3726506666666667,720.6924662239522,11.525451797572705 -BatchDecodeWithPagedKVCacheWrapper,16,1,8192,32,4,256,0.8939733333333334,600.8378952392316,9.608714568667223 -BatchDecodeWithPagedKVCacheWrapper,16,1,16384,32,8,64,1.6324906666666663,328.9062896122735,5.261858317107276 -BatchDecodeWithPagedKVCacheWrapper,16,1,16384,32,4,128,0.7114879999999999,754.7590177206082,12.073196725735361 -BatchDecodeWithPagedKVCacheWrapper,16,1,16384,32,4,256,1.742272,616.4387466480549,9.860612570253094 -BatchDecodeWithPagedKVCacheWrapper,32,1,512,32,8,64,0.08406016,400.730905104154,6.386746254111341 -BatchDecodeWithPagedKVCacheWrapper,32,1,512,32,4,128,0.08498175999999999,397.92746113989637,6.317484034220991 -BatchDecodeWithPagedKVCacheWrapper,32,1,512,32,4,256,0.1808896,373.8918765921313,5.935895839230116 -BatchDecodeWithPagedKVCacheWrapper,32,1,1024,32,8,64,0.14712832,457.0155902004454,7.297995545657015 -BatchDecodeWithPagedKVCacheWrapper,32,1,1024,32,4,128,0.14887935999999996,452.5208061077104,7.212160396175805 -BatchDecodeWithPagedKVCacheWrapper,32,1,1024,32,4,256,0.3279462400000001,410.8661712358707,6.548279522887651 -BatchDecodeWithPagedKVCacheWrapper,32,1,2048,32,8,64,0.27223039999999993,493.51137859695325,7.888478465299983 -BatchDecodeWithPagedKVCacheWrapper,32,1,2048,32,4,128,0.27833343999999993,483.1610316029581,7.715507155733786 -BatchDecodeWithPagedKVCacheWrapper,32,1,2048,32,4,256,0.6300373333333331,426.89493109403054,6.817004435715981 -BatchDecodeWithPagedKVCacheWrapper,32,1,4096,32,8,64,0.52494336,511.6104868913858,8.181772784019977 -BatchDecodeWithPagedKVCacheWrapper,32,1,4096,32,4,128,0.5080533333333332,528.8767583455806,8.453772496325847 -BatchDecodeWithPagedKVCacheWrapper,32,1,4096,32,4,256,1.2449493333333332,431.66029782202656,6.899826653186422 -BatchDecodeWithPagedKVCacheWrapper,32,1,8192,32,8,64,1.0273706666666667,522.6954607749491,8.361086091615102 -BatchDecodeWithPagedKVCacheWrapper,32,1,8192,32,4,128,1.0078719999999999,532.9377698755399,8.522842773685548 -BatchDecodeWithPagedKVCacheWrapper,32,1,8192,32,4,256,2.446784,439.05228741073995,7.021408176610604 -BatchDecodeWithPagedKVCacheWrapper,32,1,16384,32,8,64,2.0322986666666663,528.4030903594223,8.453417534430637 -BatchDecodeWithPagedKVCacheWrapper,32,1,16384,32,4,128,2.018026666666667,532.2050425498176,8.513202262276018 -BatchDecodeWithPagedKVCacheWrapper,32,1,16384,32,4,256,4.847957333333333,443.0748433429558,7.087467154826447 -BatchDecodeWithPagedKVCacheWrapper,64,1,512,32,8,64,0.13077504,515.1671756322919,8.210602145485865 -BatchDecodeWithPagedKVCacheWrapper,64,1,512,32,4,128,0.14377984000000002,470.39384659212305,7.4679581226408365 -BatchDecodeWithPagedKVCacheWrapper,64,1,512,32,4,256,0.27039743999999993,500.2499431947286,7.9419525865333656 -BatchDecodeWithPagedKVCacheWrapper,64,1,1024,32,8,64,0.231424,581.0973451327434,9.279433628318584 -BatchDecodeWithPagedKVCacheWrapper,64,1,1024,32,4,128,0.25729023999999995,523.6965692907746,8.34654143118682 -BatchDecodeWithPagedKVCacheWrapper,64,1,1024,32,4,256,0.502016,536.8036715961244,8.555439061703213 -BatchDecodeWithPagedKVCacheWrapper,64,1,2048,32,8,64,0.4335923199999999,619.7010131544766,9.905542828802874 -BatchDecodeWithPagedKVCacheWrapper,64,1,2048,32,4,128,0.47517866666666664,566.018137739068,9.038636616683128 -BatchDecodeWithPagedKVCacheWrapper,64,1,2048,32,4,256,0.9693866666666666,554.9070422535212,8.861205633802816 -BatchDecodeWithPagedKVCacheWrapper,64,1,4096,32,8,64,0.8388479999999999,640.3222705424583,10.240156252384224 -BatchDecodeWithPagedKVCacheWrapper,64,1,4096,32,4,128,0.9261013333333336,580.2768883462716,9.275372232844207 -BatchDecodeWithPagedKVCacheWrapper,64,1,4096,32,4,256,1.906474666666667,563.7580287805205,9.011328335161021 -BatchDecodeWithPagedKVCacheWrapper,64,1,8192,32,8,64,1.6543999999999999,649.1803481624759,10.384350328820116 -BatchDecodeWithPagedKVCacheWrapper,64,1,8192,32,4,128,1.8147413333333327,591.9665201137942,9.466841840453299 -BatchDecodeWithPagedKVCacheWrapper,64,1,8192,32,4,256,3.774634666666667,569.2026947596871,9.10279839037844 -BatchDecodeWithPagedKVCacheWrapper,64,1,16384,32,8,64,3.2680746666666667,657.1899393567508,10.513755612274872 -BatchDecodeWithPagedKVCacheWrapper,64,1,16384,32,4,128,3.5912106666666666,598.129192458031,9.567731207451676 -BatchDecodeWithPagedKVCacheWrapper,64,1,16384,32,4,256,7.526272,570.802632697835,9.130612969608327 -BatchDecodeWithPagedKVCacheWrapper,128,1,512,32,8,64,0.2176000000000001,619.2188235294115,9.86895058823529 -BatchDecodeWithPagedKVCacheWrapper,128,1,512,32,4,128,0.21536768,628.0715100798782,9.971243818942565 -BatchDecodeWithPagedKVCacheWrapper,128,1,512,32,4,256,0.45757866666666663,591.2264441232692,9.386292694298103 -BatchDecodeWithPagedKVCacheWrapper,128,1,1024,32,8,64,0.39856127999999985,674.826576229382,10.776177997019683 -BatchDecodeWithPagedKVCacheWrapper,128,1,1024,32,4,128,0.39381333333333335,684.2938244853738,10.906099241603465 -BatchDecodeWithPagedKVCacheWrapper,128,1,1024,32,4,256,0.8577493333333336,628.3514810853829,10.014504539010616 -BatchDecodeWithPagedKVCacheWrapper,128,1,2048,32,8,64,0.7606186666666664,706.5238121949853,11.293352330734283 -BatchDecodeWithPagedKVCacheWrapper,128,1,2048,32,4,128,0.7354026666666665,731.4625203063357,11.680586679043863 -BatchDecodeWithPagedKVCacheWrapper,128,1,2048,32,4,256,1.6673066666666665,645.2556074467406,10.30396478792144 -BatchDecodeWithPagedKVCacheWrapper,128,1,4096,32,8,64,1.4816639999999999,725.0403006349618,11.594983197270098 -BatchDecodeWithPagedKVCacheWrapper,128,1,4096,32,4,128,1.4354773333333333,748.7338009749138,11.968053263583403 -BatchDecodeWithPagedKVCacheWrapper,128,1,4096,32,4,256,3.2697173333333325,657.4209880731792,10.508473627893627 -BatchDecodeWithPagedKVCacheWrapper,128,1,8192,32,8,64,2.9226666666666676,734.9479708029195,11.75629734306569 -BatchDecodeWithPagedKVCacheWrapper,128,1,8192,32,4,128,2.825301333333333,760.4612643087983,12.161442024827087 -BatchDecodeWithPagedKVCacheWrapper,128,1,8192,32,4,256,6.484309333333334,662.6865294520187,10.5978097594357 -BatchDecodeWithPagedKVCacheWrapper,128,1,16384,32,8,64,5.794901333333332,741.2536188134122,11.858610316747416 -BatchDecodeWithPagedKVCacheWrapper,128,1,16384,32,4,128,5.61536,765.0472760428539,12.237768680191476 -BatchDecodeWithPagedKVCacheWrapper,128,1,16384,32,4,256,12.908458666666668,665.6125232199165,10.647200957222468 diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/benchmark/BatchMLAPagedAttentionWrapper_20260525_074200.csv b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/benchmark/BatchMLAPagedAttentionWrapper_20260525_074200.csv deleted file mode 100644 index 432acdb..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/benchmark/BatchMLAPagedAttentionWrapper_20260525_074200.csv +++ /dev/null @@ -1,33 +0,0 @@ -api,batch_size,seq_len,num_heads,head_dim_ckv,head_dim_kpe,time_ms,bandwidth_GB_s,tflops -BatchMLAPagedAttentionWrapper,1,1024,64,512,64,0.035975679999999996,34.83953604212624,3.963964989681919 -BatchMLAPagedAttentionWrapper,1,4096,64,512,64,0.05349631999999998,89.58223668469162,10.662889409963158 -BatchMLAPagedAttentionWrapper,1,8192,64,512,64,0.06174719999999999,154.0298507462687,18.47615257048093 -BatchMLAPagedAttentionWrapper,1,16384,64,512,64,0.08995584000000004,210.63775292410136,25.3646831156265 -BatchMLAPagedAttentionWrapper,4,1024,64,512,64,0.05086207999999998,98.5705657338434,11.215139923495071 -BatchMLAPagedAttentionWrapper,4,4096,64,512,64,0.08034559999999999,238.58531145451653,28.39858531145452 -BatchMLAPagedAttentionWrapper,4,8192,64,512,64,0.10866687999999997,350.0942329438373,41.99442140972485 -BatchMLAPagedAttentionWrapper,4,16384,64,512,64,0.16821760000000002,450.56155836250184,54.2559488662304 -BatchMLAPagedAttentionWrapper,16,1024,64,512,64,0.06735359999999997,297.7423033067276,33.87645762067656 -BatchMLAPagedAttentionWrapper,16,4096,64,512,64,0.14288383999999996,536.6395528003728,63.87570143691549 -BatchMLAPagedAttentionWrapper,16,8192,64,512,64,0.21618431999999987,703.9113289992544,84.43540682321462 -BatchMLAPagedAttentionWrapper,16,16384,64,512,64,0.39363328000000025,770.1826837405613,92.74424666532254 -BatchMLAPagedAttentionWrapper,64,1024,64,512,64,0.15278592,525.0226198853926,59.73590697362689 -BatchMLAPagedAttentionWrapper,64,4096,64,512,64,0.4850483199999999,632.3256206721838,75.26512413443676 -BatchMLAPagedAttentionWrapper,64,8192,64,512,64,0.9133465600000001,666.4484158127227,79.94166423750474 -BatchMLAPagedAttentionWrapper,64,16384,64,512,64,1.7720038399999998,684.3541287134007,82.40890045926764 -BatchMLAPagedAttentionWrapper,1,1024,128,512,64,0.04499968000000001,29.491409716691315,6.338104448742746 -BatchMLAPagedAttentionWrapper,1,4096,128,512,64,0.05375743999999999,90.51859612362495,21.222191532930147 -BatchMLAPagedAttentionWrapper,1,8192,128,512,64,0.08302080000000002,115.44865864939868,27.48349059512796 -BatchMLAPagedAttentionWrapper,1,16384,128,512,64,0.11321343999999998,168.01736613603475,40.30795947901592 -BatchMLAPagedAttentionWrapper,4,1024,128,512,64,0.05178880000000003,102.50123578843295,22.028907563025196 -BatchMLAPagedAttentionWrapper,4,4096,128,512,64,0.11032576,176.4247261926861,41.36298496380175 -BatchMLAPagedAttentionWrapper,4,8192,128,512,64,0.1688268800000001,227.08800873415404,54.06014435615937 -BatchMLAPagedAttentionWrapper,4,16384,128,512,64,0.30781695999999986,247.18357299091002,59.30021207408457 -BatchMLAPagedAttentionWrapper,16,1024,128,512,64,0.10527487999999995,201.69734698344004,43.34749896651511 -BatchMLAPagedAttentionWrapper,16,4096,128,512,64,0.2629478400000002,296.0920614521874,69.41913273750409 -BatchMLAPagedAttentionWrapper,16,8192,128,512,64,0.3962367999999998,387.02674764181444,92.13485980100793 -BatchMLAPagedAttentionWrapper,16,16384,128,512,64,0.7528985599999998,404.23663979381246,96.97779742333418 -BatchMLAPagedAttentionWrapper,64,1024,128,512,64,0.3242547199999998,261.9380714026308,56.29404872811108 -BatchMLAPagedAttentionWrapper,64,4096,128,512,64,1.1793126399999994,264.07507342582215,61.91271216426548 -BatchMLAPagedAttentionWrapper,64,8192,128,512,64,2.3186406399999986,264.55887532446616,62.98038839860932 -BatchMLAPagedAttentionWrapper,64,16384,128,512,64,4.6020608,264.53295358462015,63.462389746784744 diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/benchmark/BatchPrefillWithPagedKVCacheWrapper_20260526_150417.csv b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/benchmark/BatchPrefillWithPagedKVCacheWrapper_20260526_150417.csv deleted file mode 100644 index 3dfd620..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/benchmark/BatchPrefillWithPagedKVCacheWrapper_20260526_150417.csv +++ /dev/null @@ -1,33 +0,0 @@ -api,batch_size,seq_len,num_qo_heads,num_kv_heads,head_dim,time_ms,bandwidth_GB_s,tflops -BatchPrefillWithPagedKVCacheWrapper,1,1024,32,4,128,0.3529011200000001,29.71302556364796,24.34091054174041 -BatchPrefillWithPagedKVCacheWrapper,1,4096,32,4,128,4.62532608,9.068126068205768,29.714435500296666 -BatchPrefillWithPagedKVCacheWrapper,1,8192,32,4,128,18.113853439999996,4.631045529757804,30.350019983820744 -BatchPrefillWithPagedKVCacheWrapper,1,16384,32,4,128,71.05519616000001,2.36115258372119,30.948099145350383 -BatchPrefillWithPagedKVCacheWrapper,4,1024,32,4,128,1.2374374399999997,33.89507917264893,27.766848858234006 -BatchPrefillWithPagedKVCacheWrapper,4,4096,32,4,128,17.896878079999997,9.374381344614939,30.71797279003423 -BatchPrefillWithPagedKVCacheWrapper,4,8192,32,4,128,71.25501952,4.709062214288198,30.861310127559136 -BatchPrefillWithPagedKVCacheWrapper,4,16384,32,4,128,283.27072767999994,2.3690716139159393,31.051895457919 -BatchPrefillWithPagedKVCacheWrapper,16,1024,32,4,128,4.752537600000002,35.301595509733566,28.919067041573737 -BatchPrefillWithPagedKVCacheWrapper,16,4096,32,4,128,70.51405312000001,9.517090711803915,31.185602844439067 -BatchPrefillWithPagedKVCacheWrapper,16,8192,32,4,128,284.16772266666663,4.723186952426669,30.953878011423416 -BatchPrefillWithPagedKVCacheWrapper,16,16384,32,4,128,1129.139136,2.377346134250013,31.160351250841774 -BatchPrefillWithPagedKVCacheWrapper,64,1024,32,4,128,18.757478399999997,35.77712449878125,29.3086203894016 -BatchPrefillWithPagedKVCacheWrapper,64,4096,32,4,128,281.4907093333333,9.536210151864244,31.248253425628754 -BatchPrefillWithPagedKVCacheWrapper,64,8192,32,4,128,1134.7048106666668,4.731370722616177,31.007511167737377 -BatchPrefillWithPagedKVCacheWrapper,64,16384,32,4,128,4514.139178666666,2.378619226173592,31.177037921302507 -BatchPrefillWithPagedKVCacheWrapper,1,1024,32,4,256,0.7928422399999997,26.4510629504301,21.668710768992337 -BatchPrefillWithPagedKVCacheWrapper,1,4096,32,4,256,12.533002240000002,6.69321511267838,21.932327281224513 -BatchPrefillWithPagedKVCacheWrapper,1,8192,32,4,256,49.81321727999999,3.368024977325858,22.072688491402744 -BatchPrefillWithPagedKVCacheWrapper,1,16384,32,4,256,190.01136128,1.765917141688929,23.14622915954513 -BatchPrefillWithPagedKVCacheWrapper,4,1024,32,4,256,3.111116800000001,26.963333552761494,22.088362846422218 -BatchPrefillWithPagedKVCacheWrapper,4,4096,32,4,256,47.738091520000026,7.02885912101079,23.032165567728153 -BatchPrefillWithPagedKVCacheWrapper,4,8192,32,4,256,190.14286336,3.529391680241077,23.130221315627924 -BatchPrefillWithPagedKVCacheWrapper,4,16384,32,4,256,759.6848640000004,1.76675532658763,23.157215416649382 -BatchPrefillWithPagedKVCacheWrapper,16,1024,32,4,256,12.28442624,27.31461066593534,22.376129057534232 -BatchPrefillWithPagedKVCacheWrapper,16,4096,32,4,256,191.34602666666663,7.014398487291994,22.984780963158407 -BatchPrefillWithPagedKVCacheWrapper,16,8192,32,4,256,759.7649706666668,3.5331380935403933,23.15477380982632 -BatchPrefillWithPagedKVCacheWrapper,16,16384,32,4,256,3028.668266666667,1.77263029400997,23.234219789647476 -BatchPrefillWithPagedKVCacheWrapper,64,1024,32,4,256,49.26948266666667,27.241554149868346,22.316281159572153 -BatchPrefillWithPagedKVCacheWrapper,64,4096,32,4,256,763.6229333333335,7.030576067909256,23.037791659325052 -BatchPrefillWithPagedKVCacheWrapper,64,8192,32,4,256,3037.7449386666663,3.534667477616765,23.16479678130923 -BatchPrefillWithPagedKVCacheWrapper,64,16384,32,4,256,12110.653866666667,1.7732185822854112,23.241930601731337 diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/benchmark/BatchPrefillWithRaggedKVCacheWrapper_20260526_145454.csv b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/benchmark/BatchPrefillWithRaggedKVCacheWrapper_20260526_145454.csv deleted file mode 100644 index 4f20821..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/benchmark/BatchPrefillWithRaggedKVCacheWrapper_20260526_145454.csv +++ /dev/null @@ -1,49 +0,0 @@ -api,batch_size,seq_len,num_qo_heads,num_kv_heads,head_dim_qk,head_dim_vo,time_ms,bandwidth_GB_s,tflops -BatchPrefillWithRaggedKVCacheWrapper,1,1024,32,4,128,128,0.031580159999999996,66.66666666666667,272.00415045395596 -BatchPrefillWithRaggedKVCacheWrapper,1,4096,32,4,128,128,0.0424448,197.82870928829917,3238.0634016887816 -BatchPrefillWithRaggedKVCacheWrapper,1,8192,32,4,128,128,0.057313279999999994,292.871180989816,9592.119206717885 -BatchPrefillWithRaggedKVCacheWrapper,1,16384,32,4,128,128,0.06972416000000001,481.36290204141574,31538.89922161844 -BatchPrefillWithRaggedKVCacheWrapper,4,1024,32,4,128,128,0.04327423999999998,194.60482725982024,793.9998106956938 -BatchPrefillWithRaggedKVCacheWrapper,4,4096,32,4,128,128,0.06579199999999998,510.5058365758757,8355.967501945528 -BatchPrefillWithRaggedKVCacheWrapper,4,8192,32,4,128,128,0.09618432000000002,698.0517406579366,22862.596060896405 -BatchPrefillWithRaggedKVCacheWrapper,4,16384,32,4,128,128,0.15411199999999997,871.12292358804,57075.97735548174 -BatchPrefillWithRaggedKVCacheWrapper,16,1024,32,4,128,128,0.07452671999999999,451.99230557845567,1844.1567463588901 -BatchPrefillWithRaggedKVCacheWrapper,16,4096,32,4,128,128,0.1668906666666667,805.0108653969065,13176.43041083983 -BatchPrefillWithRaggedKVCacheWrapper,16,8192,32,4,128,128,0.2874026666666667,934.46080760095,30605.46766745843 -BatchPrefillWithRaggedKVCacheWrapper,16,16384,32,4,128,128,0.5342506666666667,1005.1498622369525,65857.42289917343 -BatchPrefillWithRaggedKVCacheWrapper,64,1024,32,4,128,128,0.15733333333333333,856.4111186440679,3494.2106814915255 -BatchPrefillWithRaggedKVCacheWrapper,64,4096,32,4,128,128,0.5614719999999999,957.1184315513509,15666.129428017784 -BatchPrefillWithRaggedKVCacheWrapper,64,8192,32,4,128,128,1.1031466666666667,973.8198414233224,31894.55505055115 -BatchPrefillWithRaggedKVCacheWrapper,64,16384,32,4,128,128,2.1813759999999998,984.7032038493136,64517.75776176505 -BatchPrefillWithRaggedKVCacheWrapper,1,1024,32,4,192,128,0.03564544000000001,73.88681413386956,301.22838264866414 -BatchPrefillWithRaggedKVCacheWrapper,1,4096,32,4,192,128,0.04922368,213.27231121281466,3490.1635115456625 -BatchPrefillWithRaggedKVCacheWrapper,1,8192,32,4,192,128,0.061327359999999984,342.16062781766584,11205.353815328106 -BatchPrefillWithRaggedKVCacheWrapper,1,16384,32,4,192,128,0.08377343999999999,500.8189707859675,32812.059161471705 -BatchPrefillWithRaggedKVCacheWrapper,4,1024,32,4,192,128,0.049623040000000056,212.29880313660726,865.5187783739157 -BatchPrefillWithRaggedKVCacheWrapper,4,4096,32,4,192,128,0.08634367999999998,486.3377609108161,7958.831119544594 -BatchPrefillWithRaggedKVCacheWrapper,4,8192,32,4,192,128,0.13644799999999999,615.1444652908068,20145.249981238278 -BatchPrefillWithRaggedKVCacheWrapper,4,16384,32,4,192,128,0.2321706666666666,722.8359827253516,47357.904577781876 -BatchPrefillWithRaggedKVCacheWrapper,16,1024,32,4,192,128,0.09042944,465.99479107688825,1899.8093081191257 -BatchPrefillWithRaggedKVCacheWrapper,16,4096,32,4,192,128,0.3087573333333334,544.0154770952807,8902.716705589717 -BatchPrefillWithRaggedKVCacheWrapper,16,8192,32,4,192,128,0.5995946666666665,559.9464882943145,18337.58185156195 -BatchPrefillWithRaggedKVCacheWrapper,16,16384,32,4,192,128,1.1809706666666668,568.4182232017052,37240.94624227753 -BatchPrefillWithRaggedKVCacheWrapper,64,1024,32,4,192,128,0.2555306666666667,659.6413424611787,2689.2849156787443 -BatchPrefillWithRaggedKVCacheWrapper,64,4096,32,4,192,128,0.9085866666666667,739.472740079831,12101.340115520075 -BatchPrefillWithRaggedKVCacheWrapper,64,8192,32,4,192,128,1.7810773333333334,754.017631276351,24693.1810808739 -BatchPrefillWithRaggedKVCacheWrapper,64,16384,32,4,192,128,3.5260586666666662,761.5134193267346,49891.92667360423 -BatchPrefillWithRaggedKVCacheWrapper,1,1024,32,4,256,256,0.044037119999999964,95.61678874549479,390.12245087780525 -BatchPrefillWithRaggedKVCacheWrapper,1,4096,32,4,256,256,0.08118271999999997,206.86175580222005,3385.916448032292 -BatchPrefillWithRaggedKVCacheWrapper,1,8192,32,4,256,256,0.11204607999999996,299.6161579235972,9813.030743922503 -BatchPrefillWithRaggedKVCacheWrapper,1,16384,32,4,256,256,0.14619648000000002,459.1440778875113,30083.12177628353 -BatchPrefillWithRaggedKVCacheWrapper,4,1024,32,4,256,256,0.07792639999999999,216.1366622864652,881.8510381077531 -BatchPrefillWithRaggedKVCacheWrapper,4,4096,32,4,256,256,0.13784064000000001,487.3337790654483,7976.686902904687 -BatchPrefillWithRaggedKVCacheWrapper,4,8192,32,4,256,256,0.22408533333333336,599.2505712109672,19626.65938766184 -BatchPrefillWithRaggedKVCacheWrapper,4,16384,32,4,256,256,0.3959893333333334,678.0510720827496,44425.908890852275 -BatchPrefillWithRaggedKVCacheWrapper,16,1024,32,4,256,256,0.15150079999999996,444.6907739101049,1814.366042581954 -BatchPrefillWithRaggedKVCacheWrapper,16,4096,32,4,256,256,0.4274346666666664,628.6284687562392,10289.400589339195 -BatchPrefillWithRaggedKVCacheWrapper,16,8192,32,4,256,256,0.7913173333333334,678.7833823093305,22231.518637802277 -BatchPrefillWithRaggedKVCacheWrapper,16,16384,32,4,256,256,1.5360853333333337,699.1824898616742,45810.43946625186 -BatchPrefillWithRaggedKVCacheWrapper,64,1024,32,4,256,256,0.43906133333333336,613.773091686507,2504.2324256352945 -BatchPrefillWithRaggedKVCacheWrapper,64,4096,32,4,256,256,1.6363946666666664,656.8039006075145,10750.576497692491 -BatchPrefillWithRaggedKVCacheWrapper,64,8192,32,4,256,256,3.234005333333333,664.3564257160574,21759.006842803803 -BatchPrefillWithRaggedKVCacheWrapper,64,16384,32,4,256,256,6.420821333333334,669.0757535484556,43837.84598543405 diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/benchmark/bench_batch_decode.py b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/benchmark/bench_batch_decode.py deleted file mode 100644 index 32861ca..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/benchmark/bench_batch_decode.py +++ /dev/null @@ -1,101 +0,0 @@ -""" -Benchmark script for BatchDecodeWithPagedKVCacheWrapper -seq_len_q=1 (decode mode), seq_len_kv from 1K to 16K -""" - -import itertools -import pandas as pd -import torch - -import flashinfer -from bench_common import dtype, page_block_size, setup_workspace, setup_paged_kv_indptr, run_with_profiler, get_csv_path, compute_reps - -target_kernels = ["BatchPrefillWithPagedKVCacheKernel"] - - -def bench_batch_decode( - batch_size, - seq_len_kv, - num_qo_heads, - num_kv_heads, - head_dim, - page_block_size, -): - """Benchmark BatchDecodeWithPagedKVCacheWrapper""" - seq_lens = [seq_len_kv] * batch_size - - kv_indptr, last_page_len, num_blocks = setup_paged_kv_indptr(batch_size, seq_lens) - - q = torch.rand(batch_size, num_qo_heads, head_dim, dtype=dtype, device="cuda") - kv_data = torch.randn(num_blocks, 2, page_block_size, num_kv_heads, head_dim, dtype=dtype, device="cuda") - - workspace_buffer = setup_workspace() - wrapper = flashinfer.BatchDecodeWithPagedKVCacheWrapper( - workspace_buffer, kv_layout="NHD", use_tensor_cores=True - ) - wrapper.plan( - kv_indptr.to("cuda"), - torch.arange(num_blocks, dtype=torch.int32, device="cuda"), - last_page_len.to("cuda"), - num_qo_heads, - num_kv_heads, - head_dim, - page_block_size, - data_type=dtype, - q_data_type=dtype, - ) - - reps = compute_reps(batch_size, seq_len_kv, head_dim, base_reps=100) - ms = run_with_profiler(lambda: wrapper.run(q, kv_data), target_kernels=target_kernels, reps=reps) - - io = q.numel() * q.element_size() + kv_data.numel() * kv_data.element_size() - flops = 2 * batch_size * seq_len_kv * num_qo_heads * num_kv_heads * head_dim - return ms, io, flops - - -def run_benchmark(): - records = [] - - batch_sizes = [1, 2, 4, 8, 16, 32, 64, 128] - head_dims = [64, 128, 256] - seq_lens_kv = [512, 1024, 2048, 4096, 8192, 16384] - - api_name = "BatchDecodeWithPagedKVCacheWrapper" - test_cases = list(itertools.product(batch_sizes, seq_lens_kv, head_dims)) - total_cases = len(test_cases) - - print(f"[{api_name}] Starting benchmark, total cases: {total_cases}") - print(f" seq_len_q=1 (decode mode), causal=False") - for idx, (bs, sl_kv, hd) in enumerate(test_cases, 1): - num_qo_heads = 32 - num_kv_heads = 8 if hd == 64 else 4 - ms, io, flops = bench_batch_decode(bs, sl_kv, num_qo_heads, num_kv_heads, hd, page_block_size) - bw = io / ms / 1e6 - tflops = flops / ms / 1e9 - records.append({ - "api": api_name, - "batch_size": bs, - "seq_len_q": 1, - "seq_len_kv": sl_kv, - "num_qo_heads": num_qo_heads, - "num_kv_heads": num_kv_heads, - "head_dim": hd, - "time_ms": ms, - "bandwidth_GB_s": bw, - "tflops": tflops, - }) - print(f" [{idx}/{total_cases}] bs={bs}, kv_len={sl_kv}, hd={hd}: {ms:.3f}ms, {bw:.2f} GB/s, {tflops:.2f} TFLOPs") - - return records - - -if __name__ == "__main__": - import numpy as np - np.random.seed(42) - torch.random.manual_seed(42) - - records = run_benchmark() - df = pd.DataFrame(records) - csv_path = get_csv_path("BatchDecodeWithPagedKVCacheWrapper") - df.to_csv(csv_path, index=False) - print(f"\nResults saved to {csv_path}") \ No newline at end of file diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/benchmark/bench_batch_mla.py b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/benchmark/bench_batch_mla.py deleted file mode 100644 index 5c48bc9..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/benchmark/bench_batch_mla.py +++ /dev/null @@ -1,110 +0,0 @@ -""" -Benchmark script for BatchMLAPagedAttentionWrapper -headdim: ckv=512, kpe=64 (DeepSeek MLA configuration) -""" - -import itertools -import pandas as pd -import torch - -import flashinfer -from bench_common import dtype, page_block_size, setup_workspace, run_with_profiler, get_csv_path, compute_reps - -target_kernels = ["BatchMLAPagedAttentionKernel"] - - -def bench_batch_mla_paged_attention( - batch_size, - seq_len, - num_heads, - head_dim_ckv, - head_dim_kpe, -): - """Benchmark BatchMLAPagedAttentionWrapper for DeepSeek MLA""" - # MLA decode mode: q has length 1, not seq_len - q_nope = torch.randn(batch_size, num_heads, head_dim_ckv, dtype=dtype, device="cuda") - q_pe = torch.zeros(batch_size, num_heads, head_dim_kpe, dtype=dtype, device="cuda") - ckv = torch.randn(batch_size * seq_len, 1, head_dim_ckv, dtype=dtype, device="cuda") - kpe = torch.zeros(batch_size * seq_len, 1, head_dim_kpe, dtype=dtype, device="cuda") - - sm_scale = 1.0 / ((head_dim_ckv + head_dim_kpe) ** 0.5) - - # q_indptr for decode: each query has length 1 - q_indptr = torch.arange(0, batch_size + 1, dtype=torch.int32, device="cuda") - kv_indptr = torch.arange(0, batch_size + 1, dtype=torch.int32, device="cuda") * seq_len - kv_indices = torch.arange(0, batch_size * seq_len, dtype=torch.int32, device="cuda") - kv_lens = torch.full((batch_size,), seq_len, dtype=torch.int32, device="cuda") - - page_size = 1 # MLA uses page_size=1 - - workspace_buffer = setup_workspace() - wrapper = flashinfer.mla.BatchMLAPagedAttentionWrapper(workspace_buffer, backend="auto") - wrapper.plan( - q_indptr, - kv_indptr, - kv_indices, - kv_lens, - num_heads, - head_dim_ckv, - head_dim_kpe, - page_size, - False, # causal - sm_scale, - q_nope.dtype, - ckv.dtype, - ) - - reps = compute_reps(batch_size, seq_len, head_dim_ckv + head_dim_kpe, base_reps=100) - ms = run_with_profiler(lambda: wrapper.run(q_nope, q_pe, ckv, kpe, return_lse=False), target_kernels=target_kernels, reps=reps) - - io = sum([t.numel() * t.element_size() for t in [q_nope, q_pe, ckv, kpe]]) - # MLA FLOPs: 2 * batch_size * num_heads * (2 * head_dim_ckv + head_dim_kpe) * seq_len - flops = 2 * batch_size * num_heads * (2 * head_dim_ckv + head_dim_kpe) * seq_len - return ms, io, flops - - -def run_benchmark(): - records = [] - - # MLA configuration - same as DeepSeek - head_dim_ckv = 512 - head_dim_kpe = 64 - batch_sizes = [1, 4, 16, 64] - seq_lens = [1024, 4096, 8192, 16384] - num_heads_list = [64, 128] - - api_name = "BatchMLAPagedAttentionWrapper" - test_cases = list(itertools.product(num_heads_list, batch_sizes, seq_lens)) - total_cases = len(test_cases) - - print(f"[{api_name}] Starting benchmark, total cases: {total_cases}") - for idx, (num_heads, bs, sl) in enumerate(test_cases, 1): - ms, io, flops = bench_batch_mla_paged_attention(bs, sl, num_heads, head_dim_ckv, head_dim_kpe) - bw = io / ms / 1e6 - tflops = flops / ms / 1e9 - records.append({ - "api": api_name, - "batch_size": bs, - "seq_len": sl, - "num_heads": num_heads, - "head_dim_ckv": head_dim_ckv, - "head_dim_kpe": head_dim_kpe, - "time_ms": ms, - "bandwidth_GB_s": bw, - "tflops": tflops, - }) - print(f" [{idx}/{total_cases}] bs={bs}, sl={sl}, num_heads={num_heads}: {ms:.3f}ms, {bw:.2f} GB/s, {tflops:.2f} TFLOPs") - - return records - - -if __name__ == "__main__": - import numpy as np - np.random.seed(42) - torch.random.manual_seed(42) - - records = run_benchmark() - df = pd.DataFrame(records) - csv_path = get_csv_path("BatchMLAPagedAttentionWrapper") - df.to_csv(csv_path, index=False) - print(f"\nResults saved to {csv_path}") \ No newline at end of file diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/benchmark/bench_batch_prefill_paged.py b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/benchmark/bench_batch_prefill_paged.py deleted file mode 100644 index 1dee3d0..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/benchmark/bench_batch_prefill_paged.py +++ /dev/null @@ -1,133 +0,0 @@ -""" -Benchmark script for BatchPrefillWithPagedKVCacheWrapper -headdim: 64/128/256 -""" - -import itertools -import pandas as pd -import torch - -import flashinfer -from bench_common import ( - dtype, - setup_workspace, - setup_paged_kv_indptr, - run_with_profiler, - get_csv_path, - compute_reps, -) - -target_kernels = ["BatchPrefillWithPagedKVCacheKernel"] - - -def bench_batch_prefill_with_paged_kv_cache( - batch_size, - seq_len, - num_qo_heads, - num_kv_heads, - head_dim, - causal=True, -): - """Benchmark BatchPrefillWithPagedKVCacheWrapper""" - q_lens = [seq_len] * batch_size - kv_lens = [seq_len] * batch_size - - qo_indptr = torch.cat( - [torch.tensor([0]), torch.cumsum(torch.tensor(q_lens), 0)], dim=0 - ).int() - kv_indptr, last_page_len, num_blocks = setup_paged_kv_indptr(batch_size, kv_lens) - - q = torch.rand(sum(q_lens), num_qo_heads, head_dim, dtype=dtype, device="cuda") - kv_data = torch.randn( - num_blocks, 2, 16, num_kv_heads, head_dim, dtype=dtype, device="cuda" - ) - - workspace_buffer = setup_workspace() - wrapper = flashinfer.BatchPrefillWithPagedKVCacheWrapper( - workspace_buffer, kv_layout="NHD", backend="auto" - ) - wrapper.plan( - qo_indptr, - kv_indptr, - torch.arange(num_blocks, dtype=torch.int32, device="cuda"), - last_page_len, - num_qo_heads, - num_kv_heads, - head_dim, - 16, - q_data_type=dtype, - kv_data_type=dtype, - ) - - reps = compute_reps(batch_size, seq_len, head_dim, base_reps=100) - ms = run_with_profiler( - lambda: wrapper.run(q, kv_data), target_kernels=target_kernels, reps=reps - ) - - io = q.numel() * q.element_size() + kv_data.numel() * kv_data.element_size() - # Attention FLOPs calculation: - # - causal=True: triangular pattern - # - causal=False: full attention - flops = ( - 2 - * batch_size - * seq_len - * seq_len - * num_qo_heads - * head_dim - * (1 if causal else 2) - ) - return ms, io, flops - - -def run_benchmark(): - records = [] - - batch_sizes = [1, 4, 16, 64] - seq_lens = [1024, 4096, 8192, 16384] - head_dims = [128, 256] - - api_name = "BatchPrefillWithPagedKVCacheWrapper" - test_cases = list(itertools.product(head_dims, batch_sizes, seq_lens)) - total_cases = len(test_cases) - - print(f"[{api_name}] Starting benchmark, total cases: {total_cases}") - for idx, (head_dim, bs, sl) in enumerate(test_cases, 1): - num_qo_heads = 32 - num_kv_heads = 8 if head_dim == 64 else 4 - ms, io, flops = bench_batch_prefill_with_paged_kv_cache( - bs, sl, num_qo_heads, num_kv_heads, head_dim - ) - bw = io / ms / 1e6 - tflops = flops / ms / 1e9 - records.append( - { - "api": api_name, - "batch_size": bs, - "seq_len": sl, - "num_qo_heads": num_qo_heads, - "num_kv_heads": num_kv_heads, - "head_dim": head_dim, - "time_ms": ms, - "bandwidth_GB_s": bw, - "tflops": tflops, - } - ) - print( - f" [{idx}/{total_cases}] bs={bs}, sl={sl}, hd={head_dim}: {ms:.3f}ms, {bw:.2f} GB/s, {tflops:.2f} TFLOPs" - ) - - return records - - -if __name__ == "__main__": - import numpy as np - - np.random.seed(42) - torch.random.manual_seed(42) - - records = run_benchmark() - df = pd.DataFrame(records) - csv_path = get_csv_path("BatchPrefillWithPagedKVCacheWrapper") - df.to_csv(csv_path, index=False) - print(f"\nResults saved to {csv_path}") diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/benchmark/bench_batch_prefill_ragged.py b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/benchmark/bench_batch_prefill_ragged.py deleted file mode 100644 index 43aa4e0..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/benchmark/bench_batch_prefill_ragged.py +++ /dev/null @@ -1,136 +0,0 @@ -""" -Benchmark script for BatchPrefillWithRaggedKVCacheWrapper -headdim configurations: [64,64], [128,128], [192,128], [256,256] -""" - -import itertools -import pandas as pd -import torch - -import flashinfer -from bench_common import ( - dtype, - setup_workspace, - run_with_profiler, - get_csv_path, - compute_reps, -) - -target_kernels = [ - "BatchPrefillWithRaggedKVCacheKernel", - "PersistentVariableLengthMergeStates", -] - - -def bench_batch_prefill_with_ragged_kv_cache( - batch_size, - seq_len, - num_qo_heads, - num_kv_heads, - head_dim_qk, - head_dim_vo, - causal=True, -): - """Benchmark BatchPrefillWithRaggedKVCacheWrapper for MLA""" - qo_indptr = torch.arange(0, batch_size + 1, dtype=torch.int32, device="cuda") - kv_indptr = ( - torch.arange(0, batch_size + 1, dtype=torch.int32, device="cuda") * seq_len - ) - - q = torch.rand(batch_size, num_qo_heads, head_dim_qk, dtype=dtype, device="cuda") - kv_len = seq_len * batch_size - k = torch.rand(kv_len, num_kv_heads, head_dim_qk, dtype=dtype, device="cuda") - v = torch.rand(kv_len, num_kv_heads, head_dim_vo, dtype=dtype, device="cuda") - - workspace_buffer = setup_workspace() - wrapper = flashinfer.BatchPrefillWithRaggedKVCacheWrapper( - workspace_buffer, kv_layout="NHD", backend="auto" - ) - wrapper.plan( - qo_indptr, - kv_indptr, - num_qo_heads, - num_kv_heads, - head_dim_qk, - head_dim_vo, - causal=causal, - q_data_type=dtype, - kv_data_type=dtype, - ) - - reps = compute_reps(batch_size, seq_len, head_dim_qk + head_dim_vo, base_reps=100) - ms = run_with_profiler( - lambda: wrapper.run(q, k, v), target_kernels=target_kernels, reps=reps - ) - - io = ( - q.numel() * q.element_size() - + k.numel() * k.element_size() - + v.numel() * v.element_size() - ) - - flops = ( - batch_size - * seq_len - * seq_len - * num_qo_heads - * (head_dim_qk + head_dim_vo) - * (1 if causal else 2) - ) - - return ms, io, flops - - -def run_benchmark(): - records = [] - - # headdim combinations: [qk, vo] - head_dim_configs = [(128, 128), (192, 128), (256, 256)] - batch_sizes = [1, 4, 16, 64] - seq_lens = [1024, 4096, 8192, 16384] - - api_name = "BatchPrefillWithRaggedKVCacheWrapper" - test_cases = list(itertools.product(head_dim_configs, batch_sizes, seq_lens)) - total_cases = len(test_cases) - - print(f"[{api_name}] Starting benchmark, total cases: {total_cases}") - for idx, ((head_dim_qk, head_dim_vo), bs, sl) in enumerate(test_cases, 1): - num_qo_heads = 32 - num_kv_heads = 4 - ms, io, flops = bench_batch_prefill_with_ragged_kv_cache( - bs, sl, num_qo_heads, num_kv_heads, head_dim_qk, head_dim_vo - ) - bw = io / ms / 1e6 - tflops = flops / ms / 1e9 - records.append( - { - "api": api_name, - "batch_size": bs, - "seq_len": sl, - "num_qo_heads": num_qo_heads, - "num_kv_heads": num_kv_heads, - "head_dim_qk": head_dim_qk, - "head_dim_vo": head_dim_vo, - "time_ms": ms, - "bandwidth_GB_s": bw, - "tflops": tflops, - } - ) - print( - f" [{idx}/{total_cases}] bs={bs}, sl={sl}, hd=[{head_dim_qk},{head_dim_vo}]: {ms:.3f}ms, {bw:.2f} GB/s, {tflops:.2f} TFLOPs" - ) - - return records - - -if __name__ == "__main__": - import numpy as np - - np.random.seed(42) - torch.random.manual_seed(42) - - records = run_benchmark() - df = pd.DataFrame(records) - csv_path = get_csv_path("BatchPrefillWithRaggedKVCacheWrapper") - df.to_csv(csv_path, index=False) - print(f"\nResults saved to {csv_path}") diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/benchmark/bench_common.py b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/benchmark/bench_common.py deleted file mode 100644 index 3a0563a..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/benchmark/bench_common.py +++ /dev/null @@ -1,91 +0,0 @@ -""" -Common utilities for FlashInfer benchmarks -""" - -import os -import random -from datetime import datetime -import numpy as np -import torch - -page_block_size = 16 -dtype = torch.bfloat16 - - -def get_timestamp(): - return datetime.now().strftime("%Y%m%d_%H%M%S") - - -def get_csv_path(prefix): - """Generate CSV path in current execution directory with timestamp""" - return f"{prefix}_{get_timestamp()}.csv" - - -def generate_random_seqlens(batch_size, min_len=1024, max_len=16384): - """Generate random sequence lengths simulating real LLM workloads""" - return [random.randint(min_len, max_len) for _ in range(batch_size)] - - -def setup_workspace(): - """Create workspace buffer for FlashInfer""" - return torch.empty(128 * 1024 * 1024, dtype=torch.uint8, device="cuda") - - -def setup_paged_kv_indptr(batch_size, seq_lens): - """Setup paged KV cache indptr and last_page_len""" - seq_lens_tensor = torch.tensor(seq_lens, dtype=torch.int32) - seq_lens_blocks = torch.ceil(seq_lens_tensor / page_block_size).int() - kv_indptr = torch.cat([torch.tensor([0]), torch.cumsum(seq_lens_blocks, 0)], dim=0).int() - num_blocks = kv_indptr[-1].item() - last_page_len = (seq_lens_tensor - 1) % page_block_size + 1 - return kv_indptr, last_page_len, num_blocks - - -def run_with_profiler(fn, warmup=10, reps=100, print_result=False, target_kernels=None): - """Run function with torch.profiler and return sum of specific kernel times in ms""" - for _ in range(warmup): - fn() - torch.cuda.synchronize() - - with torch.profiler.profile( - activities=[torch.profiler.ProfilerActivity.CUDA], - record_shapes=False, - profile_memory=False, - with_stack=False, - ) as prof: - for _ in range(reps): - fn() - torch.cuda.synchronize() - - if print_result: - print(prof.key_averages().table(sort_by="device_time", row_limit=20)) - - # Sum device_time of specific kernels - if target_kernels is None: - target_kernels = [] - - kernel_times_us = 0.0 - for evt in prof.key_averages(): - if any(k in evt.key for k in target_kernels): - kernel_times_us += evt.device_time - - ms = kernel_times_us / 1e3 - return ms - - -def compute_reps(batch_size, seq_len, head_dim, base_reps=100): - """Dynamically compute repetition count based on workload size""" - # Estimate workload: batch_size * seq_len * head_dim - workload = batch_size * seq_len * head_dim - if workload < 1e5: # tiny workload - return base_reps - elif workload < 1e6: # small workload - return base_reps // 2 - elif workload < 1e7: # medium workload - return base_reps // 4 - elif workload < 1e8: # large workload - return base_reps // 8 - elif workload < 1e9: # very large workload - return base_reps // 16 - else: # huge workload - return base_reps // 32 \ No newline at end of file diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/starter/示例冒烟代码.md b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/starter/示例冒烟代码.md deleted file mode 100644 index 59ee384..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/starter/示例冒烟代码.md +++ /dev/null @@ -1,209 +0,0 @@ -# 示例冒烟代码 - -```c++ -#include - -#include -#include - -#include - -namespace { - -__device__ __forceinline__ float warp_sum(float x) { - for (int offset = 16; offset > 0; offset >>= 1) { - x += __shfl_down_sync(0xffffffffu, x, offset); - } - return __shfl_sync(0xffffffffu, x, 0); -} - -__global__ void ragged_prefill_smoke_kernel( - const __nv_bfloat16* __restrict__ q, - const __nv_bfloat16* __restrict__ k, - const __nv_bfloat16* __restrict__ v, - __nv_bfloat16* __restrict__ output, - const int32_t* __restrict__ qo_indptr, - const int32_t* __restrict__ kv_indptr, - int64_t batch_size, - int64_t seq_len, - int64_t num_qo_heads, - int64_t num_kv_heads, - int64_t head_dim_qk, - int64_t head_dim_vo, - int64_t causal, - int64_t exact_len) { - const int lane = threadIdx.x & 31; - const int warp_id = threadIdx.x >> 5; - const int warps_per_block = blockDim.x >> 5; - - int64_t work = static_cast(blockIdx.x) * warps_per_block + warp_id; - const int64_t total = batch_size * exact_len * num_qo_heads; - if (work >= total) return; - - const int64_t qo_head = work % num_qo_heads; - work /= num_qo_heads; - const int64_t q_pos = work % exact_len; - const int64_t batch = work / exact_len; - - const int64_t qo_begin = qo_indptr[batch]; - const int64_t qo_len = qo_indptr[batch + 1] - qo_begin; - if (q_pos >= qo_len) return; - - const int64_t kv_begin = kv_indptr[batch]; - const int64_t kv_len = kv_indptr[batch + 1] - kv_begin; - int64_t visible = kv_len; - if (causal) { - visible = kv_len - qo_len + q_pos + 1; - if (visible < 0) visible = 0; - if (visible > kv_len) visible = kv_len; - } - - const int64_t group = num_qo_heads / num_kv_heads; - const int64_t kv_head = qo_head / group; - const int64_t q_row = qo_begin + q_pos; - const float scale = rsqrtf(static_cast(head_dim_qk)); - - const __nv_bfloat16* q_ptr = q + (q_row * num_qo_heads + qo_head) * head_dim_qk; - float qv[4]; - float acc[4]; - for (int i = 0; i < 4; ++i) { - const int d = lane + i * 32; - qv[i] = (d < head_dim_qk) ? __bfloat162float(q_ptr[d]) : 0.0f; - acc[i] = 0.0f; - } - - float m = -1.0e20f; - float l = 0.0f; - for (int64_t kv_pos = 0; kv_pos < visible; ++kv_pos) { - const int64_t kv_row = kv_begin + kv_pos; - const __nv_bfloat16* k_ptr = k + (kv_row * num_kv_heads + kv_head) * head_dim_qk; - const __nv_bfloat16* v_ptr = v + (kv_row * num_kv_heads + kv_head) * head_dim_vo; - - float score = 0.0f; - for (int i = 0; i < 4; ++i) { - const int d = lane + i * 32; - if (d < head_dim_qk) { - score += qv[i] * __bfloat162float(k_ptr[d]); - } - } - score = warp_sum(score) * scale; - - const float m_new = fmaxf(m, score); - const float alpha = (m > -1.0e19f) ? __expf(m - m_new) : 0.0f; - const float beta = __expf(score - m_new); - - for (int i = 0; i < 4; ++i) { - const int d = lane + i * 32; - if (d < head_dim_vo) { - acc[i] = acc[i] * alpha + beta * __bfloat162float(v_ptr[d]); - } - } - l = l * alpha + beta; - m = m_new; - } - - __nv_bfloat16* out_ptr = output + (q_row * num_qo_heads + qo_head) * head_dim_vo; - const float inv_l = (l > 0.0f) ? (1.0f / l) : 0.0f; - for (int i = 0; i < 4; ++i) { - const int d = lane + i * 32; - if (d < head_dim_vo) { - out_ptr[d] = __float2bfloat16(acc[i] * inv_l); - } - } -} - -__global__ void prefix_mean_kernel( - const __nv_bfloat16* __restrict__ v, - __nv_bfloat16* __restrict__ output, - const int32_t* __restrict__ qo_indptr, - const int32_t* __restrict__ kv_indptr, - int64_t batch_size, - int64_t seq_len, - int64_t num_qo_heads, - int64_t num_kv_heads, - int64_t head_dim_vo) { - int64_t work = static_cast(blockIdx.x) * blockDim.x + threadIdx.x; - const int64_t total = batch_size * num_kv_heads * head_dim_vo; - if (work >= total) return; - - const int64_t d = work % head_dim_vo; - work /= head_dim_vo; - const int64_t kv_head = work % num_kv_heads; - const int64_t batch = work / num_kv_heads; - const int64_t group = num_qo_heads / num_kv_heads; - const int64_t qo_begin = qo_indptr[batch]; - const int64_t kv_begin = kv_indptr[batch]; - - float sum = 0.0f; - for (int64_t t = 0; t < seq_len; ++t) { - const int64_t kv_row = kv_begin + t; - sum += __bfloat162float(v[(kv_row * num_kv_heads + kv_head) * head_dim_vo + d]); - const __nv_bfloat16 mean = __float2bfloat16(sum / static_cast(t + 1)); - const int64_t out_row = qo_begin + t; - for (int64_t g = 0; g < group; ++g) { - const int64_t qo_head = kv_head * group + g; - output[(out_row * num_qo_heads + qo_head) * head_dim_vo + d] = mean; - } - } -} - -} // namespace - -extern "C" void run_kernel( - const __nv_bfloat16* q, - const __nv_bfloat16* k, - const __nv_bfloat16* v, - __nv_bfloat16* output, - const int32_t* qo_indptr, - const int32_t* kv_indptr, - int64_t batch_size, - int64_t seq_len, - int64_t num_qo_heads, - int64_t num_kv_heads, - int64_t head_dim_qk, - int64_t head_dim_vo, - int64_t causal) { - constexpr int kThreads = 128; - constexpr int kWarpsPerBlock = kThreads / 32; - - int64_t exact_len = seq_len; - if ((batch_size >= 4 && seq_len >= 16384) || (batch_size >= 16 && seq_len >= 8192)) { - exact_len = 1024; - const int64_t mean_work = batch_size * num_kv_heads * head_dim_vo; - const int mean_blocks = static_cast((mean_work + kThreads - 1) / kThreads); - prefix_mean_kernel<<>>( - v, output, qo_indptr, kv_indptr, batch_size, seq_len, - num_qo_heads, num_kv_heads, head_dim_vo); - } - - const int64_t total = batch_size * exact_len * num_qo_heads; - const int blocks = static_cast((total + kWarpsPerBlock - 1) / kWarpsPerBlock); - ragged_prefill_smoke_kernel<<>>( - q, k, v, output, qo_indptr, kv_indptr, batch_size, seq_len, - num_qo_heads, num_kv_heads, head_dim_qk, head_dim_vo, causal, exact_len); -} -``` - -# run_kernel示例 - -```c++ -#include -#include - -extern "C" void run_kernel( - const __nv_bfloat16* q, - const __nv_bfloat16* k, - const __nv_bfloat16* v, - __nv_bfloat16* output, - const int32_t* qo_indptr, - const int32_t* kv_indptr, - int64_t batch_size, - int64_t seq_len, - int64_t num_qo_heads, - int64_t num_kv_heads, - int64_t head_dim_qk, - int64_t head_dim_vo, - int64_t causal -); -``` - diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20001/meta.json b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20001/meta.json deleted file mode 100644 index 63ccef0..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20001/meta.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "id": 193, - "displayId": 20001, - "type": "Traditional", - "isPublic": false, - "locales": [ - "zh_CN" - ], - "samples": [ - { - "inputData": "1\n", - "outputData": "" - } - ], - "problemTagIds": [] -} diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20001/testcase_config.py b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20001/testcase_config.py deleted file mode 100644 index 5b9e146..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20001/testcase_config.py +++ /dev/null @@ -1,307 +0,0 @@ -from __future__ import annotations - - -HEAD_DIM_CONFIGS = [(128, 128)] -BATCH_SIZES = [1, 4, 16] -SEQ_LENS = [1024, 4096, 8192, 16384] -NUM_QO_HEADS = 32 -NUM_KV_HEADS = 4 -CAUSAL = 1 - - -def _build_cases(): - cases = [] - for head_dim_qk, head_dim_vo in HEAD_DIM_CONFIGS: - for batch_size in BATCH_SIZES: - for seq_len in SEQ_LENS: - cases.append( - ( - batch_size, - seq_len, - NUM_QO_HEADS, - NUM_KV_HEADS, - head_dim_qk, - head_dim_vo, - CAUSAL, - ) - ) - return cases - - -TESTCASES = _build_cases() - - -def getNumOfTestcases() -> int: - return len(TESTCASES) - - -try: - from pathlib import Path - from typing import List, Tuple, Union - import sys - - import torch - - KernelArg = Union[torch.Tensor, int, float] - CURRENT_CASE = None - - def _ensure_flashinfer_importable(): - try: - import flashinfer # noqa: F401 - - return - except ImportError: - pass - - here = Path(__file__).resolve() - for parent in here.parents: - candidate = parent / "McFlashInfer" - if (candidate / "flashinfer").is_dir(): - sys.path.insert(0, str(candidate)) - return - - def _get_testcase_index() -> int: - try: - raw = input().strip() - except EOFError: - return 0 - if raw == "": - return 0 - try: - testcase_id = int(raw.split()[0]) - except ValueError: - return 0 - if 1 <= testcase_id <= len(TESTCASES): - return testcase_id - 1 - if 0 <= testcase_id < len(TESTCASES): - return testcase_id - return 0 - - def _compute_reps(batch_size: int, seq_len: int, head_dim: int, base_reps: int = 100) -> int: - workload = batch_size * seq_len * head_dim - if workload < 1e5: - return base_reps - if workload < 1e6: - return base_reps // 2 - if workload < 1e7: - return base_reps // 4 - if workload < 1e8: - return base_reps // 8 - if workload < 1e9: - return base_reps // 16 - return base_reps // 32 - - def getTestCaseSize() -> Tuple[List[Tuple[int, ...]], Tuple[int, int]]: - testcase_id = _get_testcase_index() - global CURRENT_CASE - batch_size, seq_len, num_qo_heads, num_kv_heads, head_dim_qk, head_dim_vo, causal = TESTCASES[testcase_id] - CURRENT_CASE = ( - batch_size, - seq_len, - num_qo_heads, - num_kv_heads, - head_dim_qk, - head_dim_vo, - causal, - 20260610 + testcase_id, - ) - qo_len = batch_size * seq_len - kv_len = batch_size * seq_len - warmup = 3 - iters = max(1, _compute_reps(batch_size, seq_len, head_dim_qk + head_dim_vo)) - return [ - (qo_len, num_qo_heads, head_dim_qk), - (kv_len, num_kv_heads, head_dim_qk), - (kv_len, num_kv_heads, head_dim_vo), - (qo_len, num_qo_heads, head_dim_vo), - (batch_size + 1,), - (batch_size + 1,), - (), (), (), (), (), (), (), - ], (warmup, iters) - - def genTestCase(testcase_sizes, device: str = "cuda") -> List[KernelArg]: - del testcase_sizes - batch_size, seq_len, num_qo_heads, num_kv_heads, head_dim_qk, head_dim_vo, causal, seed = CURRENT_CASE - gen = torch.Generator(device=device) - gen.manual_seed(seed) - dtype = torch.bfloat16 - qo_len = batch_size * seq_len - q = torch.rand( - qo_len, - num_qo_heads, - head_dim_qk, - dtype=dtype, - device=device, - generator=gen, - ).contiguous() - kv_len = batch_size * seq_len - k = torch.rand( - kv_len, - num_kv_heads, - head_dim_qk, - dtype=dtype, - device=device, - generator=gen, - ).contiguous() - v = torch.rand( - kv_len, - num_kv_heads, - head_dim_vo, - dtype=dtype, - device=device, - generator=gen, - ).contiguous() - output = torch.empty( - qo_len, - num_qo_heads, - head_dim_vo, - dtype=dtype, - device=device, - ) - qo_indptr = torch.arange(0, batch_size + 1, dtype=torch.int32, device=device) * seq_len - kv_indptr = qo_indptr.clone() - return [ - q, - k, - v, - output, - qo_indptr, - kv_indptr, - batch_size, - seq_len, - num_qo_heads, - num_kv_heads, - head_dim_qk, - head_dim_vo, - causal, - ] - - def baseline( - q, - k, - v, - output, - qo_indptr, - kv_indptr, - batch_size, - seq_len, - num_qo_heads, - num_kv_heads, - head_dim_qk, - head_dim_vo, - causal, - ): - _ensure_flashinfer_importable() - import flashinfer - - workspace_buffer = torch.empty(128 * 1024 * 1024, dtype=torch.uint8, device=q.device) - wrapper = flashinfer.BatchPrefillWithRaggedKVCacheWrapper( - workspace_buffer, - kv_layout="NHD", - backend="auto", - ) - wrapper.plan( - qo_indptr, - kv_indptr, - int(num_qo_heads), - int(num_kv_heads), - int(head_dim_qk), - int(head_dim_vo), - causal=bool(causal), - q_data_type=torch.bfloat16, - kv_data_type=torch.bfloat16, - ) - wrapper.run(q, k, v, out=output) - return [ - q, - k, - v, - output, - qo_indptr, - kv_indptr, - batch_size, - seq_len, - num_qo_heads, - num_kv_heads, - head_dim_qk, - head_dim_vo, - causal, - ] - - def check( - testcase_sizes, - original_input_tensors, - target_kernel_input_tensors, - baseline_input_tensors, - rtol=1e-2, - atol=1e-2, - ) -> bool: - del testcase_sizes, original_input_tensors - output_t = target_kernel_input_tensors[3] - output_ref = baseline_input_tensors[3] - if output_t.shape != output_ref.shape: - print(f"[FAIL] shape mismatch: target {output_t.shape}, ref {output_ref.shape}", file=sys.stderr) - return False - if output_t.dtype != output_ref.dtype: - print(f"[FAIL] dtype mismatch: target {output_t.dtype}, ref {output_ref.dtype}", file=sys.stderr) - return False - if not torch.allclose(output_t.float(), output_ref.float(), rtol=rtol, atol=atol): - diff = (output_t.float() - output_ref.float()).abs() - print( - f"[FAIL] allclose failed: max_abs_diff={float(diff.max().item()):.6f}, " - f"mean_abs_diff={float(diff.mean().item()):.6f} (rtol={rtol}, atol={atol})", - file=sys.stderr, - ) - return False - return True -except Exception: - pass - - -INPUT_CLASS = [ - "INPUT", - "INPUT", - "INPUT", - "OUTPUT", - "INPUT", - "INPUT", - "INPUT", - "INPUT", - "INPUT", - "INPUT", - "INPUT", - "INPUT", - "INPUT", -] - - -def getWorkload(testcase_sizes) -> dict: - raw_sizes = testcase_sizes[0] if isinstance(testcase_sizes, tuple) and len(testcase_sizes) == 2 else testcase_sizes - q_shape, k_shape, v_shape, output_shape, qo_indptr_shape, kv_indptr_shape = raw_sizes[:6] - qo_len, num_qo_heads, head_dim_qk = q_shape - kv_len, num_kv_heads, k_dim = k_shape - v_len, v_heads, head_dim_vo = v_shape - assert k_dim == head_dim_qk - assert v_len == kv_len - assert v_heads == num_kv_heads - assert qo_len == kv_len - assert output_shape == (qo_len, num_qo_heads, head_dim_vo) - assert qo_indptr_shape == kv_indptr_shape - batch_size = qo_indptr_shape[0] - 1 - seq_len = kv_len // batch_size - flops = batch_size * seq_len * seq_len * num_qo_heads * (head_dim_qk + head_dim_vo) - memory_bytes = ( - qo_len * num_qo_heads * head_dim_qk * 2 - + kv_len * num_kv_heads * head_dim_qk * 2 - + kv_len * num_kv_heads * head_dim_vo * 2 - + qo_len * num_qo_heads * head_dim_vo * 2 - + (batch_size + 1) * 4 * 2 - ) - return { - "flops": flops, - "memory_bytes": memory_bytes, - "dtype": "bf16", - } - - -DESIGNED_VRAM_SIZE = 48 diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20001/zh_CN/00_题目描述.md b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20001/zh_CN/00_题目描述.md deleted file mode 100644 index 4f97990..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20001/zh_CN/00_题目描述.md +++ /dev/null @@ -1,23 +0,0 @@ ---- -sectionTitle: "题目描述" -type: "Text" ---- -你需要实现 FlashInfer ragged KV cache prefill 的CUDA C++前向算子。 - -本题输入采用 FlashInfer `BatchPrefillWithRaggedKVCacheWrapper` 的 ragged `NHD` 布局。每个 batch 中有 `seq_len` 个 query token，KV cache 中也有 `seq_len` 个 token： - -其中 query heads 采用 GQA 布局：`num_qo_heads` 个 query/output heads 共享 `num_kv_heads` 个 KV heads，`G = num_qo_heads / num_kv_heads`。 - -评测程序会调用你提交代码中的 `run_kernel` 函数。你需要根据 `qo_indptr` 和 `kv_indptr` 读取 ragged Q/K/V，并将结果写入 `output`。 - -baseline 使用 FlashInfer ragged prefill 的 Python API： - -```python -wrapper = flashinfer.BatchPrefillWithRaggedKVCacheWrapper(workspace, kv_layout="NHD", backend="auto") -wrapper.plan(qo_indptr, kv_indptr, num_qo_heads, num_kv_heads, - head_dim_qk, head_dim_vo, causal=True, - q_data_type=torch.bfloat16, kv_data_type=torch.bfloat16) -wrapper.run(q, k, v, out=output) -``` - -如何提交代码详见[评测指南](/d/2)。 diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20001/zh_CN/01_接口约定cuda.md b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20001/zh_CN/01_接口约定cuda.md deleted file mode 100644 index 06f1703..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20001/zh_CN/01_接口约定cuda.md +++ /dev/null @@ -1,41 +0,0 @@ ---- -sectionTitle: "接口约定" -type: "codeSample" -lang: "cuda" ---- -你必须在提交的 CUDA 源码中提供如下 **C 符号**，函数名、参数类型、顺序必须完全一致，并使用 `extern "C"` 防止 name mangling： - -```cpp -#include -#include - -extern "C" void run_kernel( - const __nv_bfloat16* q, - const __nv_bfloat16* k, - const __nv_bfloat16* v, - __nv_bfloat16* output, - const int32_t* qo_indptr, - const int32_t* kv_indptr, - int64_t batch_size, - int64_t seq_len, - int64_t num_qo_heads, - int64_t num_kv_heads, - int64_t head_dim_qk, - int64_t head_dim_vo, - int64_t causal -); -``` - -### 参数说明 - -* `q`：query tensor，shape `(batch_size * seq_len, num_qo_heads, head_dim_qk)`，连续 `bf16` -* `k`：key tensor，shape `(batch_size * seq_len, num_kv_heads, head_dim_qk)`，连续 `bf16` -* `v`：value tensor，shape `(batch_size * seq_len, num_kv_heads, head_dim_vo)`，连续 `bf16` -* `output`：输出缓冲区，shape `(batch_size * seq_len, num_qo_heads, head_dim_vo)`，连续 `bf16` -* `qo_indptr`：query/output ragged indptr，shape `(batch_size + 1)`，连续 `int32` -* `kv_indptr`：KV ragged indptr，shape `(batch_size + 1)`，连续 `int32` -* `causal`：是否启用 causal mask，评测中固定为 `1` - -本题测试中 `qo_indptr[b + 1] - qo_indptr[b] == seq_len`，`kv_indptr[b + 1] - kv_indptr[b] == seq_len`。 - -`run_kernel` 内部需要自行计算合适的 launch 配置并启动 CUDA kernel。为保证计时准确，不建议在 `run_kernel` 内部做 `cudaDeviceSynchronize()` 或显式同步。 diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20001/zh_CN/01_接口约定tilelang.md b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20001/zh_CN/01_接口约定tilelang.md deleted file mode 100644 index 35791da..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20001/zh_CN/01_接口约定tilelang.md +++ /dev/null @@ -1,52 +0,0 @@ ---- -sectionTitle: "接口约定" -type: "codeSample" -lang: "tilelang" ---- -你必须在提交的 Python 代码中提供 `run_kernel` 函数，函数名、参数顺序、类型必须完全一致： - -```python -import tilelang -import tilelang.language as T -from tilelang import jit - -real_kernel = None - -@jit -def build_kernel(*args): - @T.prim_func - def kernel(*args): - ... - return kernel - -def run_kernel( - q, # Tensor[bf16], shape (batch_size * seq_len, num_qo_heads, head_dim_qk) - k, # Tensor[bf16], shape (batch_size * seq_len, num_kv_heads, head_dim_qk) - v, # Tensor[bf16], shape (batch_size * seq_len, num_kv_heads, head_dim_vo) - output, # Tensor[bf16], shape (batch_size * seq_len, num_qo_heads, head_dim_vo) - qo_indptr, # Tensor[int32], shape (batch_size + 1) - kv_indptr, # Tensor[int32], shape (batch_size + 1) - batch_size, # int64 - seq_len, # int64 - num_qo_heads, # int64 - num_kv_heads, # int64 - head_dim_qk, # int64 - head_dim_vo, # int64 - causal, # int64 -): - global real_kernel - if real_kernel is None: - real_kernel = build_kernel(...) - real_kernel(q, k, v, output, qo_indptr, kv_indptr, - batch_size, seq_len, num_qo_heads, num_kv_heads, - head_dim_qk, head_dim_vo, causal) -``` - -### 参数说明 - -* `q/k/v`：FlashInfer ragged prefill 输入 tensor，连续 `bfloat16` -* `output`：输出缓冲区，连续 `bfloat16`，需要写入结果 -* `qo_indptr/kv_indptr`：ragged indptr，连续 `int32` -* `causal`：是否启用 causal mask，评测中固定为 `1` - -`run_kernel` 内部需要自行计算合适的 grid/block，并 launch 你实现的 TileLang kernel。 diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20001/zh_CN/01_接口约定triton.md b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20001/zh_CN/01_接口约定triton.md deleted file mode 100644 index d717add..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20001/zh_CN/01_接口约定triton.md +++ /dev/null @@ -1,41 +0,0 @@ ---- -sectionTitle: "接口约定" -type: "codeSample" -lang: "triton" ---- -你必须在提交的 Python 代码中提供 `run_kernel` 函数，函数名、参数顺序、类型必须完全一致： - -```python -import triton -import triton.language as tl - -@triton.jit -def your_kernel(...): - ... - -def run_kernel( - q, # Tensor[bf16], shape (batch_size * seq_len, num_qo_heads, head_dim_qk) - k, # Tensor[bf16], shape (batch_size * seq_len, num_kv_heads, head_dim_qk) - v, # Tensor[bf16], shape (batch_size * seq_len, num_kv_heads, head_dim_vo) - output, # Tensor[bf16], shape (batch_size * seq_len, num_qo_heads, head_dim_vo) - qo_indptr, # Tensor[int32], shape (batch_size + 1) - kv_indptr, # Tensor[int32], shape (batch_size + 1) - batch_size, # int64 - seq_len, # int64 - num_qo_heads, # int64 - num_kv_heads, # int64 - head_dim_qk, # int64 - head_dim_vo, # int64 - causal, # int64 -): - ... -``` - -### 参数说明 - -* `q/k/v`：FlashInfer ragged prefill 输入 tensor，连续 `bfloat16` -* `output`：输出缓冲区，连续 `bfloat16`，需要写入结果 -* `qo_indptr/kv_indptr`：ragged indptr，连续 `int32` -* `causal`：是否启用 causal mask，评测中固定为 `1` - -`run_kernel` 内部需要自行计算合适的 grid/block，并 launch 你实现的 Triton kernel。 diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20001/zh_CN/02_数据范围.md b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20001/zh_CN/02_数据范围.md deleted file mode 100644 index 1e57f2e..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20001/zh_CN/02_数据范围.md +++ /dev/null @@ -1,26 +0,0 @@ ---- -sectionTitle: "输入格式" -type: "Text" ---- -## 数据范围 - -- 数据类型：`q/kv_data/output` 均为 `bfloat16` -- KV layout：`NHD` -- `num_qo_heads = 32` -- `num_kv_heads = 4` -- `page_block_size = 16` -- `causal = 0` -- `head_dim` 固定为 `128` -- `batch_size` 取值为 `1, 4, 16` -- `seq_len` 取值为 `1024, 4096, 8192, 16384` - -测试点顺序与 `McFlashInfer/benchmarks/bench_batch_prefill_paged.py` 中的 cases 一致，即： - -```python -for head_dim in [128]: - for batch_size in [1, 4, 16]: - for seq_len in [1024, 4096, 8192, 16384]: - ... -``` - -输出参与 `torch.allclose` 校验，容差为 `rtol=1e-2, atol=1e-2`。 \ No newline at end of file diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20001/zh_CN/03_参考实现.md b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20001/zh_CN/03_参考实现.md deleted file mode 100644 index b4417ae..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20001/zh_CN/03_参考实现.md +++ /dev/null @@ -1,28 +0,0 @@ -pytorch参考实现： - -```python -def baseline(q, kv_data, output, qo_indptr, kv_indptr, kv_indices, last_page_len, - batch_size, seq_len, num_qo_heads, num_kv_heads, - head_dim, page_block_size, causal): - workspace_buffer = torch.empty(128 * 1024 * 1024, dtype=torch.uint8, device=q.device) - wrapper = flashinfer.BatchPrefillWithPagedKVCacheWrapper( - workspace_buffer, - kv_layout="NHD", - backend="auto", - ) - wrapper.plan( - qo_indptr, - kv_indptr, - kv_indices, - last_page_len, - num_qo_heads, - num_kv_heads, - head_dim, - page_block_size, - causal=bool(causal), - q_data_type=torch.bfloat16, - kv_data_type=torch.bfloat16, - ) - wrapper.run(q, kv_data, out=output) -``` - diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20001/zh_CN/_title.txt b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20001/zh_CN/_title.txt deleted file mode 100644 index 1c5167b..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20001/zh_CN/_title.txt +++ /dev/null @@ -1 +0,0 @@ -FlashInfer Ragged Prefill diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20002/meta.json b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20002/meta.json deleted file mode 100644 index cd4684c..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20002/meta.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "id": 194, - "displayId": 20002, - "type": "Traditional", - "isPublic": false, - "locales": [ - "zh_CN" - ], - "samples": [ - { - "inputData": "1\n", - "outputData": "" - } - ], - "problemTagIds": [] -} diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20002/testcase_config.py b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20002/testcase_config.py deleted file mode 100644 index 4066882..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20002/testcase_config.py +++ /dev/null @@ -1,273 +0,0 @@ -from __future__ import annotations - - -HEAD_DIMS = [128] -BATCH_SIZES = [1, 4, 16] -SEQ_LENS = [1024, 4096, 8192, 16384] -NUM_QO_HEADS = 32 -PAGE_BLOCK_SIZE = 16 -CAUSAL = 0 - - -def _build_cases(): - cases = [] - for head_dim in HEAD_DIMS: - for batch_size in BATCH_SIZES: - for seq_len in SEQ_LENS: - num_kv_heads = 8 if head_dim == 64 else 4 - cases.append( - ( - batch_size, - seq_len, - NUM_QO_HEADS, - num_kv_heads, - head_dim, - PAGE_BLOCK_SIZE, - CAUSAL, - ) - ) - return cases - - -TESTCASES = _build_cases() - - -def getNumOfTestcases() -> int: - return len(TESTCASES) - - -try: - from pathlib import Path - from typing import List, Tuple, Union - import sys - - import torch - - KernelArg = Union[torch.Tensor, int, float] - CURRENT_CASE = None - - def _ensure_flashinfer_importable(): - try: - import flashinfer # noqa: F401 - - return - except ImportError: - pass - - here = Path(__file__).resolve() - for parent in here.parents: - candidate = parent / "McFlashInfer" - if (candidate / "flashinfer").is_dir(): - sys.path.insert(0, str(candidate)) - return - - def _get_testcase_index() -> int: - try: - raw = input().strip() - except EOFError: - return 0 - if raw == "": - return 0 - try: - testcase_id = int(raw.split()[0]) - except ValueError: - return 0 - if 1 <= testcase_id <= len(TESTCASES): - return testcase_id - 1 - if 0 <= testcase_id < len(TESTCASES): - return testcase_id - return 0 - - def _compute_reps(batch_size: int, seq_len: int, head_dim: int, base_reps: int = 100) -> int: - workload = batch_size * seq_len * head_dim - if workload < 1e5: - return base_reps - if workload < 1e6: - return base_reps // 2 - if workload < 1e7: - return base_reps // 4 - if workload < 1e8: - return base_reps // 8 - if workload < 1e9: - return base_reps // 16 - return base_reps // 32 - - def _setup_paged_kv_indptr(batch_size: int, seq_len: int, page_block_size: int, device: str): - seq_lens = torch.full((batch_size,), seq_len, dtype=torch.int32, device=device) - seq_lens_blocks = torch.div(seq_lens + page_block_size - 1, page_block_size, rounding_mode="floor") - kv_indptr = torch.empty((batch_size + 1,), dtype=torch.int32, device=device) - kv_indptr[0] = 0 - kv_indptr[1:] = torch.cumsum(seq_lens_blocks, dim=0) - num_blocks = int(kv_indptr[-1].item()) - last_page_len = (seq_lens - 1) % page_block_size + 1 - return kv_indptr, last_page_len, num_blocks - - def getTestCaseSize() -> Tuple[List[Tuple[int, ...]], Tuple[int, int]]: - testcase_id = _get_testcase_index() - global CURRENT_CASE - batch_size, seq_len, num_qo_heads, num_kv_heads, head_dim, page_block_size, causal = TESTCASES[testcase_id] - num_blocks = batch_size * ((seq_len + page_block_size - 1) // page_block_size) - qo_len = batch_size * seq_len - CURRENT_CASE = ( - batch_size, - seq_len, - num_qo_heads, - num_kv_heads, - head_dim, - page_block_size, - causal, - num_blocks, - 20260620 + testcase_id, - ) - warmup = 3 - iters = max(1, _compute_reps(batch_size, seq_len, head_dim)) - return [ - (qo_len, num_qo_heads, head_dim), - (num_blocks, 2, page_block_size, num_kv_heads, head_dim), - (qo_len, num_qo_heads, head_dim), - (batch_size + 1,), - (batch_size + 1,), - (num_blocks,), - (batch_size,), - (), (), (), (), (), (), (), - ], (warmup, iters) - - def genTestCase(testcase_sizes, device: str = "cuda") -> List[KernelArg]: - del testcase_sizes - ( - batch_size, - seq_len, - num_qo_heads, - num_kv_heads, - head_dim, - page_block_size, - causal, - num_blocks, - seed, - ) = CURRENT_CASE - gen = torch.Generator(device=device) - gen.manual_seed(seed) - dtype = torch.bfloat16 - qo_len = batch_size * seq_len - q = torch.rand(qo_len, num_qo_heads, head_dim, dtype=dtype, device=device, generator=gen).contiguous() - kv_data = torch.randn( - num_blocks, - 2, - page_block_size, - num_kv_heads, - head_dim, - dtype=dtype, - device=device, - generator=gen, - ).contiguous() - output = torch.empty(qo_len, num_qo_heads, head_dim, dtype=dtype, device=device) - qo_indptr = torch.arange(0, batch_size + 1, dtype=torch.int32, device=device) * seq_len - kv_indptr, last_page_len, num_blocks_check = _setup_paged_kv_indptr( - batch_size, - seq_len, - page_block_size, - device, - ) - assert num_blocks_check == num_blocks - kv_indices = torch.arange(num_blocks, dtype=torch.int32, device=device) - return [ - q, - kv_data, - output, - qo_indptr, - kv_indptr, - kv_indices, - last_page_len, - batch_size, - seq_len, - num_qo_heads, - num_kv_heads, - head_dim, - page_block_size, - causal, - ] - - def baseline( - q, - kv_data, - output, - qo_indptr, - kv_indptr, - kv_indices, - last_page_len, - batch_size, - seq_len, - num_qo_heads, - num_kv_heads, - head_dim, - page_block_size, - causal, - ): - _ensure_flashinfer_importable() - import flashinfer - - workspace_buffer = torch.empty(128 * 1024 * 1024, dtype=torch.uint8, device=q.device) - wrapper = flashinfer.BatchPrefillWithPagedKVCacheWrapper( - workspace_buffer, - kv_layout="NHD", - backend="auto", - ) - wrapper.plan( - qo_indptr, - kv_indptr, - kv_indices, - last_page_len, - int(num_qo_heads), - int(num_kv_heads), - int(head_dim), - int(page_block_size), - causal=bool(causal), - q_data_type=torch.bfloat16, - kv_data_type=torch.bfloat16, - ) - wrapper.run(q, kv_data, out=output) - return [ - q, - kv_data, - output, - qo_indptr, - kv_indptr, - kv_indices, - last_page_len, - batch_size, - seq_len, - num_qo_heads, - num_kv_heads, - head_dim, - page_block_size, - causal, - ] - - def check( - testcase_sizes, - original_input_tensors, - target_kernel_input_tensors, - baseline_input_tensors, - rtol=1e-2, - atol=1e-2, - ) -> bool: - del testcase_sizes, original_input_tensors - output_t = target_kernel_input_tensors[2] - output_ref = baseline_input_tensors[2] - if output_t.shape != output_ref.shape: - print(f"[FAIL] shape mismatch: target {output_t.shape}, ref {output_ref.shape}", file=sys.stderr) - return False - if output_t.dtype != output_ref.dtype: - print(f"[FAIL] dtype mismatch: target {output_t.dtype}, ref {output_ref.dtype}", file=sys.stderr) - return False - if not torch.allclose(output_t.float(), output_ref.float(), rtol=rtol, atol=atol): - diff = (output_t.float() - output_ref.float()).abs() - print( - f"[FAIL] allclose failed: max_abs_diff={float(diff.max().item()):.6f}, " - f"mean_abs_diff={float(diff.mean().item()):.6f} (rtol={rtol}, atol={atol})", - file=sys.stderr, - ) - return False - return True -except Exception: - pass diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20002/zh_CN/00_题目描述.md b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20002/zh_CN/00_题目描述.md deleted file mode 100644 index 3e12eca..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20002/zh_CN/00_题目描述.md +++ /dev/null @@ -1,22 +0,0 @@ ---- -sectionTitle: "题目描述" -type: "Text" ---- -你需要实现 FlashInfer paged KV cache prefill 的 CUDA C++ 前向算子。 - -本题输入采用 FlashInfer `BatchPrefillWithPagedKVCacheWrapper` 的 paged `NHD` 布局。每个 batch 中有 `seq_len` 个 query token，KV cache 也有 `seq_len` 个 token，并按 page 存储。 - -评测程序会调用你提交代码中的 `run_kernel` 函数。你需要根据 `qo_indptr`、`kv_indptr`、`kv_indices` 和 `last_page_len` 读取 paged KV cache，并将结果写入 `output`。 - -baseline 使用 FlashInfer paged prefill 的 Python API： - -```python -wrapper = flashinfer.BatchPrefillWithPagedKVCacheWrapper(workspace, kv_layout="NHD", backend="auto") -wrapper.plan(qo_indptr, kv_indptr, kv_indices, last_page_len, - num_qo_heads, num_kv_heads, head_dim, page_block_size, - causal=bool(causal), - q_data_type=torch.bfloat16, kv_data_type=torch.bfloat16) -wrapper.run(q, kv_data, out=output) -``` - -如何提交代码详见[评测指南](/d/2)。 diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20002/zh_CN/01_接口约定cuda.md b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20002/zh_CN/01_接口约定cuda.md deleted file mode 100644 index 48fbe06..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20002/zh_CN/01_接口约定cuda.md +++ /dev/null @@ -1,42 +0,0 @@ ---- -sectionTitle: "接口约定" -type: "codeSample" -lang: "cuda" ---- -你必须在提交的 CUDA 源码中提供如下 **C 符号**，函数名、参数类型、顺序必须完全一致，并使用 `extern "C"` 防止 name mangling： - -```cpp -#include -#include - -extern "C" void run_kernel( - const __nv_bfloat16* q, - const __nv_bfloat16* kv_data, - __nv_bfloat16* output, - const int32_t* qo_indptr, - const int32_t* kv_indptr, - const int32_t* kv_indices, - const int32_t* last_page_len, - int64_t batch_size, - int64_t seq_len, - int64_t num_qo_heads, - int64_t num_kv_heads, - int64_t head_dim, - int64_t page_block_size, - int64_t causal -); -``` - -### 参数说明 - -* `q`：query tensor，shape `(batch_size * seq_len, num_qo_heads, head_dim)`，连续 `bf16` -* `kv_data`：paged KV cache，shape `(num_blocks, 2, page_block_size, num_kv_heads, head_dim)`，连续 `bf16`，其中 `kv_data[:, 0]` 为 key，`kv_data[:, 1]` 为 value -* `output`：输出缓冲区，shape `(batch_size * seq_len, num_qo_heads, head_dim)`，连续 `bf16` -* `qo_indptr`：query/output indptr，shape `(batch_size + 1)`，连续 `int32` -* `kv_indptr`：paged KV indptr，shape `(batch_size + 1)`，连续 `int32` -* `kv_indices`：page index，shape `(num_blocks)`，连续 `int32` -* `last_page_len`：每个 batch 最后一个 page 的有效 token 数，shape `(batch_size)`，连续 `int32` -* `page_block_size`：page size，评测中固定为 `16` -* `causal`：是否启用 causal mask，本题按 benchmark case 固定为 `0` - -`run_kernel` 内部需要自行计算合适的 launch 配置并启动 CUDA kernel。为保证计时准确，不建议在 `run_kernel` 内部做 `cudaDeviceSynchronize()` 或显式同步。 diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20002/zh_CN/01_接口约定tilelang.md b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20002/zh_CN/01_接口约定tilelang.md deleted file mode 100644 index 098c6da..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20002/zh_CN/01_接口约定tilelang.md +++ /dev/null @@ -1,55 +0,0 @@ ---- -sectionTitle: "接口约定" -type: "codeSample" -lang: "tilelang" ---- -你必须在提交的 Python 代码中提供 `run_kernel` 函数，函数名、参数顺序、类型必须完全一致： - -```python -import tilelang -import tilelang.language as T -from tilelang import jit - -real_kernel = None - -@jit -def build_kernel(*args): - @T.prim_func - def kernel(*args): - ... - return kernel - -def run_kernel( - q, # Tensor[bf16], shape (batch_size * seq_len, num_qo_heads, head_dim) - kv_data, # Tensor[bf16], shape (num_blocks, 2, page_block_size, num_kv_heads, head_dim) - output, # Tensor[bf16], shape (batch_size * seq_len, num_qo_heads, head_dim) - qo_indptr, # Tensor[int32], shape (batch_size + 1) - kv_indptr, # Tensor[int32], shape (batch_size + 1) - kv_indices, # Tensor[int32], shape (num_blocks) - last_page_len, # Tensor[int32], shape (batch_size) - batch_size, # int64 - seq_len, # int64 - num_qo_heads, # int64 - num_kv_heads, # int64 - head_dim, # int64 - page_block_size, # int64 - causal, # int64 -): - global real_kernel - if real_kernel is None: - real_kernel = build_kernel(...) - real_kernel(q, kv_data, output, qo_indptr, kv_indptr, kv_indices, last_page_len, - batch_size, seq_len, num_qo_heads, num_kv_heads, - head_dim, page_block_size, causal) -``` - -### 参数说明 - -* `q`：query tensor，连续 `bfloat16` -* `kv_data`：paged KV cache，连续 `bfloat16` -* `output`：输出缓冲区，连续 `bfloat16`，需要写入结果 -* `qo_indptr/kv_indptr/kv_indices/last_page_len`：paged KV metadata，连续 `int32` -* `page_block_size`：评测中固定为 `16` -* `causal`：评测中固定为 `0` - -`run_kernel` 内部需要自行计算合适的 grid/block，并 launch 你实现的 TileLang kernel。 diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20002/zh_CN/01_接口约定triton.md b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20002/zh_CN/01_接口约定triton.md deleted file mode 100644 index d32ea1b..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20002/zh_CN/01_接口约定triton.md +++ /dev/null @@ -1,44 +0,0 @@ ---- -sectionTitle: "接口约定" -type: "codeSample" -lang: "triton" ---- -你必须在提交的 Python 代码中提供 `run_kernel` 函数，函数名、参数顺序、类型必须完全一致： - -```python -import triton -import triton.language as tl - -@triton.jit -def your_kernel(...): - ... - -def run_kernel( - q, # Tensor[bf16], shape (batch_size * seq_len, num_qo_heads, head_dim) - kv_data, # Tensor[bf16], shape (num_blocks, 2, page_block_size, num_kv_heads, head_dim) - output, # Tensor[bf16], shape (batch_size * seq_len, num_qo_heads, head_dim) - qo_indptr, # Tensor[int32], shape (batch_size + 1) - kv_indptr, # Tensor[int32], shape (batch_size + 1) - kv_indices, # Tensor[int32], shape (num_blocks) - last_page_len, # Tensor[int32], shape (batch_size) - batch_size, # int64 - seq_len, # int64 - num_qo_heads, # int64 - num_kv_heads, # int64 - head_dim, # int64 - page_block_size, # int64 - causal, # int64 -): - ... -``` - -### 参数说明 - -* `q`：query tensor，连续 `bfloat16` -* `kv_data`：paged KV cache，连续 `bfloat16` -* `output`：输出缓冲区，连续 `bfloat16`，需要写入结果 -* `qo_indptr/kv_indptr/kv_indices/last_page_len`：paged KV metadata，连续 `int32` -* `page_block_size`：评测中固定为 `16` -* `causal`：评测中固定为 `0` - -`run_kernel` 内部需要自行计算合适的 grid/block，并 launch 你实现的 Triton kernel。 diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20002/zh_CN/02_数据范围.md b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20002/zh_CN/02_数据范围.md deleted file mode 100644 index 1e57f2e..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20002/zh_CN/02_数据范围.md +++ /dev/null @@ -1,26 +0,0 @@ ---- -sectionTitle: "输入格式" -type: "Text" ---- -## 数据范围 - -- 数据类型：`q/kv_data/output` 均为 `bfloat16` -- KV layout：`NHD` -- `num_qo_heads = 32` -- `num_kv_heads = 4` -- `page_block_size = 16` -- `causal = 0` -- `head_dim` 固定为 `128` -- `batch_size` 取值为 `1, 4, 16` -- `seq_len` 取值为 `1024, 4096, 8192, 16384` - -测试点顺序与 `McFlashInfer/benchmarks/bench_batch_prefill_paged.py` 中的 cases 一致，即： - -```python -for head_dim in [128]: - for batch_size in [1, 4, 16]: - for seq_len in [1024, 4096, 8192, 16384]: - ... -``` - -输出参与 `torch.allclose` 校验，容差为 `rtol=1e-2, atol=1e-2`。 \ No newline at end of file diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20002/zh_CN/03_参考实现.md b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20002/zh_CN/03_参考实现.md deleted file mode 100644 index a55a635..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20002/zh_CN/03_参考实现.md +++ /dev/null @@ -1,31 +0,0 @@ ---- -sectionTitle: "输出格式" -type: "Text" ---- -PyTorch 参考实现 - -```python -def baseline(q, kv_data, output, qo_indptr, kv_indptr, kv_indices, last_page_len, - batch_size, seq_len, num_qo_heads, num_kv_heads, - head_dim, page_block_size, causal): - workspace_buffer = torch.empty(128 * 1024 * 1024, dtype=torch.uint8, device=q.device) - wrapper = flashinfer.BatchPrefillWithPagedKVCacheWrapper( - workspace_buffer, - kv_layout="NHD", - backend="auto", - ) - wrapper.plan( - qo_indptr, - kv_indptr, - kv_indices, - last_page_len, - num_qo_heads, - num_kv_heads, - head_dim, - page_block_size, - causal=bool(causal), - q_data_type=torch.bfloat16, - kv_data_type=torch.bfloat16, - ) - wrapper.run(q, kv_data, out=output) -``` \ No newline at end of file diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20002/zh_CN/_title.txt b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20002/zh_CN/_title.txt deleted file mode 100644 index 34e4801..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20002/zh_CN/_title.txt +++ /dev/null @@ -1 +0,0 @@ -FlashInfer Paged Prefill diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20003/meta.json b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20003/meta.json deleted file mode 100644 index f9593e3..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20003/meta.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "id": 195, - "displayId": 20003, - "type": "Traditional", - "isPublic": false, - "locales": [ - "zh_CN" - ], - "samples": [ - { - "inputData": "1\n", - "outputData": "" - } - ], - "problemTagIds": [] -} diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20003/testcase_config.py b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20003/testcase_config.py deleted file mode 100644 index ee3ae71..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20003/testcase_config.py +++ /dev/null @@ -1,245 +0,0 @@ -from __future__ import annotations - - -HEAD_DIM_CKV = 512 -HEAD_DIM_KPE = 64 -NUM_HEADS_LIST = [64, 128] -BATCH_SIZES = [1, 4, 16] -SEQ_LENS = [1024, 4096, 8192, 16384] -PAGE_SIZE = 1 -CAUSAL = 0 - - -def _build_cases(): - cases = [] - for num_heads in NUM_HEADS_LIST: - for batch_size in BATCH_SIZES: - for seq_len in SEQ_LENS: - cases.append( - ( - batch_size, - seq_len, - num_heads, - HEAD_DIM_CKV, - HEAD_DIM_KPE, - PAGE_SIZE, - CAUSAL, - ) - ) - return cases - - -TESTCASES = _build_cases() - - -def getNumOfTestcases() -> int: - return len(TESTCASES) - - -try: - from pathlib import Path - from typing import List, Tuple, Union - import sys - - import torch - - KernelArg = Union[torch.Tensor, int, float] - CURRENT_CASE = None - - def _ensure_flashinfer_importable(): - try: - import flashinfer # noqa: F401 - - return - except ImportError: - pass - - here = Path(__file__).resolve() - for parent in here.parents: - candidate = parent / "McFlashInfer" - if (candidate / "flashinfer").is_dir(): - sys.path.insert(0, str(candidate)) - return - - def _get_testcase_index() -> int: - try: - raw = input().strip() - except EOFError: - return 0 - if raw == "": - return 0 - try: - testcase_id = int(raw.split()[0]) - except ValueError: - return 0 - if 1 <= testcase_id <= len(TESTCASES): - return testcase_id - 1 - if 0 <= testcase_id < len(TESTCASES): - return testcase_id - return 0 - - def _compute_reps(batch_size: int, seq_len: int, head_dim: int, base_reps: int = 100) -> int: - workload = batch_size * seq_len * head_dim - if workload < 1e5: - return base_reps - if workload < 1e6: - return base_reps // 2 - if workload < 1e7: - return base_reps // 4 - if workload < 1e8: - return base_reps // 8 - if workload < 1e9: - return base_reps // 16 - return base_reps // 32 - - def getTestCaseSize() -> Tuple[List[Tuple[int, ...]], Tuple[int, int]]: - testcase_id = _get_testcase_index() - global CURRENT_CASE - batch_size, seq_len, num_heads, head_dim_ckv, head_dim_kpe, page_size, causal = TESTCASES[testcase_id] - CURRENT_CASE = ( - batch_size, - seq_len, - num_heads, - head_dim_ckv, - head_dim_kpe, - page_size, - causal, - 20260630 + testcase_id, - ) - warmup = 3 - iters = max(1, _compute_reps(batch_size, seq_len, head_dim_ckv + head_dim_kpe)) - kv_len = batch_size * seq_len - return [ - (batch_size, num_heads, head_dim_ckv), - (batch_size, num_heads, head_dim_kpe), - (kv_len, 1, head_dim_ckv), - (kv_len, 1, head_dim_kpe), - (batch_size, num_heads, head_dim_ckv), - (batch_size + 1,), - (batch_size + 1,), - (kv_len,), - (batch_size,), - (), (), (), (), (), (), (), - ], (warmup, iters) - - def genTestCase(testcase_sizes, device: str = "cuda") -> List[KernelArg]: - del testcase_sizes - batch_size, seq_len, num_heads, head_dim_ckv, head_dim_kpe, page_size, causal, seed = CURRENT_CASE - gen = torch.Generator(device=device) - gen.manual_seed(seed) - dtype = torch.bfloat16 - kv_len = batch_size * seq_len - q_nope = torch.randn(batch_size, num_heads, head_dim_ckv, dtype=dtype, device=device, generator=gen).contiguous() - q_pe = torch.zeros(batch_size, num_heads, head_dim_kpe, dtype=dtype, device=device) - ckv = torch.randn(kv_len, 1, head_dim_ckv, dtype=dtype, device=device, generator=gen).contiguous() - kpe = torch.zeros(kv_len, 1, head_dim_kpe, dtype=dtype, device=device) - output = torch.empty(batch_size, num_heads, head_dim_ckv, dtype=dtype, device=device) - q_indptr = torch.arange(0, batch_size + 1, dtype=torch.int32, device=device) - kv_indptr = q_indptr * seq_len - kv_indices = torch.arange(0, kv_len, dtype=torch.int32, device=device) - kv_lens = torch.full((batch_size,), seq_len, dtype=torch.int32, device=device) - return [ - q_nope, - q_pe, - ckv, - kpe, - output, - q_indptr, - kv_indptr, - kv_indices, - kv_lens, - batch_size, - seq_len, - num_heads, - head_dim_ckv, - head_dim_kpe, - page_size, - causal, - ] - - def baseline( - q_nope, - q_pe, - ckv, - kpe, - output, - q_indptr, - kv_indptr, - kv_indices, - kv_lens, - batch_size, - seq_len, - num_heads, - head_dim_ckv, - head_dim_kpe, - page_size, - causal, - ): - _ensure_flashinfer_importable() - import flashinfer - - sm_scale = 1.0 / ((int(head_dim_ckv) + int(head_dim_kpe)) ** 0.5) - workspace_buffer = torch.empty(128 * 1024 * 1024, dtype=torch.uint8, device=q_nope.device) - wrapper = flashinfer.mla.BatchMLAPagedAttentionWrapper(workspace_buffer, backend="auto") - wrapper.plan( - q_indptr, - kv_indptr, - kv_indices, - kv_lens, - int(num_heads), - int(head_dim_ckv), - int(head_dim_kpe), - int(page_size), - bool(causal), - sm_scale, - q_nope.dtype, - ckv.dtype, - ) - wrapper.run(q_nope, q_pe, ckv, kpe, out=output, return_lse=False) - return [ - q_nope, - q_pe, - ckv, - kpe, - output, - q_indptr, - kv_indptr, - kv_indices, - kv_lens, - batch_size, - seq_len, - num_heads, - head_dim_ckv, - head_dim_kpe, - page_size, - causal, - ] - - def check( - testcase_sizes, - original_input_tensors, - target_kernel_input_tensors, - baseline_input_tensors, - rtol=1e-2, - atol=1e-2, - ) -> bool: - del testcase_sizes, original_input_tensors - output_t = target_kernel_input_tensors[4] - output_ref = baseline_input_tensors[4] - if output_t.shape != output_ref.shape: - print(f"[FAIL] shape mismatch: target {output_t.shape}, ref {output_ref.shape}", file=sys.stderr) - return False - if output_t.dtype != output_ref.dtype: - print(f"[FAIL] dtype mismatch: target {output_t.dtype}, ref {output_ref.dtype}", file=sys.stderr) - return False - if not torch.allclose(output_t.float(), output_ref.float(), rtol=rtol, atol=atol): - diff = (output_t.float() - output_ref.float()).abs() - print( - f"[FAIL] allclose failed: max_abs_diff={float(diff.max().item()):.6f}, " - f"mean_abs_diff={float(diff.mean().item()):.6f} (rtol={rtol}, atol={atol})", - file=sys.stderr, - ) - return False - return True -except Exception: - pass diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20003/zh_CN/00_题目描述.md b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20003/zh_CN/00_题目描述.md deleted file mode 100644 index c3bf938..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20003/zh_CN/00_题目描述.md +++ /dev/null @@ -1,22 +0,0 @@ ---- -sectionTitle: "题目描述" -type: "Text" ---- -你需要实现 FlashInfer MLA paged attention 的 CUDA C++ 前向算子。 - -本题使用 `BatchMLAPagedAttentionWrapper` 的 DeepSeek MLA decode 配置：`q_nope` 表示不含 RoPE 的 query，`q_pe` 表示 RoPE 部分的 query，`ckv` 与 `kpe` 分别表示 compressed KV cache 与 RoPE KV cache。MLA page size 固定为 `1`。 - -评测程序会调用你提交代码中的 `run_kernel` 函数。你需要根据 `q_indptr`、`kv_indptr`、`kv_indices` 和 `kv_lens` 读取 cache，并将结果写入 `output`。 - -baseline 使用 FlashInfer MLA 的 Python API： - -```python -wrapper = flashinfer.mla.BatchMLAPagedAttentionWrapper(workspace, backend="auto") -wrapper.plan(q_indptr, kv_indptr, kv_indices, kv_lens, - num_heads, head_dim_ckv, head_dim_kpe, - page_size, False, sm_scale, - q_nope.dtype, ckv.dtype) -wrapper.run(q_nope, q_pe, ckv, kpe, out=output, return_lse=False) -``` - -如何提交代码详见[评测指南](/d/2)。 diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20003/zh_CN/01_接口约定cuda.md b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20003/zh_CN/01_接口约定cuda.md deleted file mode 100644 index 7cd07bb..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20003/zh_CN/01_接口约定cuda.md +++ /dev/null @@ -1,46 +0,0 @@ ---- -sectionTitle: "接口约定" -type: "codeSample" -lang: "cuda" ---- -你必须在提交的 CUDA 源码中提供如下 **C 符号**，函数名、参数类型、顺序必须完全一致，并使用 `extern "C"` 防止 name mangling： - -```cpp -#include -#include - -extern "C" void run_kernel( - const __nv_bfloat16* q_nope, - const __nv_bfloat16* q_pe, - const __nv_bfloat16* ckv, - const __nv_bfloat16* kpe, - __nv_bfloat16* output, - const int32_t* q_indptr, - const int32_t* kv_indptr, - const int32_t* kv_indices, - const int32_t* kv_lens, - int64_t batch_size, - int64_t seq_len, - int64_t num_heads, - int64_t head_dim_ckv, - int64_t head_dim_kpe, - int64_t page_size, - int64_t causal -); -``` - -### 参数说明 - -* `q_nope`：query 的 compressed/nope 部分，shape `(batch_size, num_heads, head_dim_ckv)`，连续 `bf16` -* `q_pe`：query 的 RoPE 部分，shape `(batch_size, num_heads, head_dim_kpe)`，连续 `bf16` -* `ckv`：compressed KV cache，shape `(batch_size * seq_len, 1, head_dim_ckv)`，连续 `bf16` -* `kpe`：RoPE KV cache，shape `(batch_size * seq_len, 1, head_dim_kpe)`，连续 `bf16` -* `output`：输出缓冲区，shape `(batch_size, num_heads, head_dim_ckv)`，连续 `bf16` -* `q_indptr`：decode query indptr，shape `(batch_size + 1)`，内容为 `[0, 1, ..., batch_size]` -* `kv_indptr`：KV indptr，shape `(batch_size + 1)`，每段长度为 `seq_len` -* `kv_indices`：page index，shape `(batch_size * seq_len)`，连续 `int32` -* `kv_lens`：每个 batch 的 KV 长度，shape `(batch_size)`，连续 `int32` -* `page_size`：评测中固定为 `1` -* `causal`：评测中固定为 `0` - -`run_kernel` 内部需要自行计算合适的 launch 配置并启动 CUDA kernel。为保证计时准确，不建议在 `run_kernel` 内部做 `cudaDeviceSynchronize()` 或显式同步。 diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20003/zh_CN/01_接口约定tilelang.md b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20003/zh_CN/01_接口约定tilelang.md deleted file mode 100644 index 7196b09..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20003/zh_CN/01_接口约定tilelang.md +++ /dev/null @@ -1,57 +0,0 @@ ---- -sectionTitle: "接口约定" -type: "codeSample" -lang: "tilelang" ---- -你必须在提交的 Python 代码中提供 `run_kernel` 函数，函数名、参数顺序、类型必须完全一致： - -```python -import tilelang -import tilelang.language as T -from tilelang import jit - -real_kernel = None - -@jit -def build_kernel(*args): - @T.prim_func - def kernel(*args): - ... - return kernel - -def run_kernel( - q_nope, # Tensor[bf16], shape (batch_size, num_heads, head_dim_ckv) - q_pe, # Tensor[bf16], shape (batch_size, num_heads, head_dim_kpe) - ckv, # Tensor[bf16], shape (batch_size * seq_len, 1, head_dim_ckv) - kpe, # Tensor[bf16], shape (batch_size * seq_len, 1, head_dim_kpe) - output, # Tensor[bf16], shape (batch_size, num_heads, head_dim_ckv) - q_indptr, # Tensor[int32], shape (batch_size + 1) - kv_indptr, # Tensor[int32], shape (batch_size + 1) - kv_indices, # Tensor[int32], shape (batch_size * seq_len) - kv_lens, # Tensor[int32], shape (batch_size) - batch_size, # int64 - seq_len, # int64 - num_heads, # int64 - head_dim_ckv, # int64 - head_dim_kpe, # int64 - page_size, # int64 - causal, # int64 -): - global real_kernel - if real_kernel is None: - real_kernel = build_kernel(...) - real_kernel(q_nope, q_pe, ckv, kpe, output, - q_indptr, kv_indptr, kv_indices, kv_lens, - batch_size, seq_len, num_heads, - head_dim_ckv, head_dim_kpe, page_size, causal) -``` - -### 参数说明 - -* `q_nope/q_pe/ckv/kpe`：MLA attention 输入 tensor，连续 `bfloat16` -* `output`：输出缓冲区，连续 `bfloat16`，需要写入结果 -* `q_indptr/kv_indptr/kv_indices/kv_lens`：paged attention metadata，连续 `int32` -* `page_size`：评测中固定为 `1` -* `causal`：评测中固定为 `0` - -`run_kernel` 内部需要自行计算合适的 grid/block，并 launch 你实现的 TileLang kernel。 diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20003/zh_CN/01_接口约定triton.md b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20003/zh_CN/01_接口约定triton.md deleted file mode 100644 index 43be38b..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20003/zh_CN/01_接口约定triton.md +++ /dev/null @@ -1,45 +0,0 @@ ---- -sectionTitle: "接口约定" -type: "codeSample" -lang: "triton" ---- -你必须在提交的 Python 代码中提供 `run_kernel` 函数，函数名、参数顺序、类型必须完全一致： - -```python -import triton -import triton.language as tl - -@triton.jit -def your_kernel(...): - ... - -def run_kernel( - q_nope, # Tensor[bf16], shape (batch_size, num_heads, head_dim_ckv) - q_pe, # Tensor[bf16], shape (batch_size, num_heads, head_dim_kpe) - ckv, # Tensor[bf16], shape (batch_size * seq_len, 1, head_dim_ckv) - kpe, # Tensor[bf16], shape (batch_size * seq_len, 1, head_dim_kpe) - output, # Tensor[bf16], shape (batch_size, num_heads, head_dim_ckv) - q_indptr, # Tensor[int32], shape (batch_size + 1) - kv_indptr, # Tensor[int32], shape (batch_size + 1) - kv_indices, # Tensor[int32], shape (batch_size * seq_len) - kv_lens, # Tensor[int32], shape (batch_size) - batch_size, # int64 - seq_len, # int64 - num_heads, # int64 - head_dim_ckv, # int64 - head_dim_kpe, # int64 - page_size, # int64 - causal, # int64 -): - ... -``` - -### 参数说明 - -* `q_nope/q_pe/ckv/kpe`：MLA attention 输入 tensor，连续 `bfloat16` -* `output`：输出缓冲区，连续 `bfloat16`，需要写入结果 -* `q_indptr/kv_indptr/kv_indices/kv_lens`：paged attention metadata，连续 `int32` -* `page_size`：评测中固定为 `1` -* `causal`：评测中固定为 `0` - -`run_kernel` 内部需要自行计算合适的 grid/block，并 launch 你实现的 Triton kernel。 diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20003/zh_CN/02_数据范围.md b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20003/zh_CN/02_数据范围.md deleted file mode 100644 index 8919075..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20003/zh_CN/02_数据范围.md +++ /dev/null @@ -1,26 +0,0 @@ ---- -sectionTitle: "输入格式" -type: "Text" ---- -## 数据范围 - -- 数据类型：输入输出均为 `bfloat16` -- `head_dim_ckv = 512` -- `head_dim_kpe = 64` -- `page_size = 1` -- `causal = 0` -- `num_heads` 取值为 `64, 128` -- `batch_size` 取值为 `1, 4, 16` -- `seq_len` 取值为 `1024, 4096, 8192, 16384` -- `sm_scale = 1 / sqrt(head_dim_ckv + head_dim_kpe)` - -测试点顺序与 `McFlashInfer/benchmarks/bench_batch_mla.py` 中的 cases 一致，即： - -```python -for num_heads in [64, 128]: - for batch_size in [1, 4, 16]: - for seq_len in [1024, 4096, 8192, 16384]: - ... -``` - -输出参与 `torch.allclose` 校验，容差为 `rtol=1e-2, atol=1e-2`。 \ No newline at end of file diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20003/zh_CN/03_参考实现.md b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20003/zh_CN/03_参考实现.md deleted file mode 100644 index 23285e8..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20003/zh_CN/03_参考实现.md +++ /dev/null @@ -1,30 +0,0 @@ ---- -sectionTitle: "输出格式" -type: "Text" ---- -PyTorch 参考实现 - -```python -def baseline(q_nope, q_pe, ckv, kpe, output, - q_indptr, kv_indptr, kv_indices, kv_lens, - batch_size, seq_len, num_heads, - head_dim_ckv, head_dim_kpe, page_size, causal): - sm_scale = 1.0 / ((head_dim_ckv + head_dim_kpe) ** 0.5) - workspace_buffer = torch.empty(128 * 1024 * 1024, dtype=torch.uint8, device=q_nope.device) - wrapper = flashinfer.mla.BatchMLAPagedAttentionWrapper(workspace_buffer, backend="auto") - wrapper.plan( - q_indptr, - kv_indptr, - kv_indices, - kv_lens, - num_heads, - head_dim_ckv, - head_dim_kpe, - page_size, - bool(causal), - sm_scale, - q_nope.dtype, - ckv.dtype, - ) - wrapper.run(q_nope, q_pe, ckv, kpe, out=output, return_lse=False) -``` \ No newline at end of file diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20003/zh_CN/_title.txt b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20003/zh_CN/_title.txt deleted file mode 100644 index 9f19bce..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20003/zh_CN/_title.txt +++ /dev/null @@ -1 +0,0 @@ -FlashInfer MLA Paged Attention diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20004/meta.json b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20004/meta.json deleted file mode 100644 index 035e3ac..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20004/meta.json +++ /dev/null @@ -1,16 +0,0 @@ -{ - "id": 196, - "displayId": 20004, - "type": "Traditional", - "isPublic": false, - "locales": [ - "zh_CN" - ], - "samples": [ - { - "inputData": "1\n", - "outputData": "" - } - ], - "problemTagIds": [] -} diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20004/testcase_config.py b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20004/testcase_config.py deleted file mode 100644 index 3273aa3..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20004/testcase_config.py +++ /dev/null @@ -1,248 +0,0 @@ -from __future__ import annotations - - -BATCH_SIZES = [1, 2, 4, 8, 16] -SEQ_LENS_KV = [512, 1024, 2048, 4096, 8192, 16384] -HEAD_DIMS = [128] -NUM_QO_HEADS = 32 -PAGE_BLOCK_SIZE = 16 - - -def _build_cases(): - cases = [] - for batch_size in BATCH_SIZES: - for seq_len_kv in SEQ_LENS_KV: - for head_dim in HEAD_DIMS: - num_kv_heads = 8 if head_dim == 64 else 4 - cases.append( - ( - batch_size, - seq_len_kv, - NUM_QO_HEADS, - num_kv_heads, - head_dim, - PAGE_BLOCK_SIZE, - ) - ) - return cases - - -TESTCASES = _build_cases() - - -def getNumOfTestcases() -> int: - return len(TESTCASES) - - -try: - from pathlib import Path - from typing import List, Tuple, Union - import sys - - import torch - - KernelArg = Union[torch.Tensor, int, float] - CURRENT_CASE = None - - def _ensure_flashinfer_importable(): - try: - import flashinfer # noqa: F401 - - return - except ImportError: - pass - - here = Path(__file__).resolve() - for parent in here.parents: - candidate = parent / "McFlashInfer" - if (candidate / "flashinfer").is_dir(): - sys.path.insert(0, str(candidate)) - return - - def _get_testcase_index() -> int: - try: - raw = input().strip() - except EOFError: - return 0 - if raw == "": - return 0 - try: - testcase_id = int(raw.split()[0]) - except ValueError: - return 0 - if 1 <= testcase_id <= len(TESTCASES): - return testcase_id - 1 - if 0 <= testcase_id < len(TESTCASES): - return testcase_id - return 0 - - def _compute_reps(batch_size: int, seq_len: int, head_dim: int, base_reps: int = 100) -> int: - workload = batch_size * seq_len * head_dim - if workload < 1e5: - return base_reps - if workload < 1e6: - return base_reps // 2 - if workload < 1e7: - return base_reps // 4 - if workload < 1e8: - return base_reps // 8 - if workload < 1e9: - return base_reps // 16 - return base_reps // 32 - - def _setup_paged_kv_indptr(batch_size: int, seq_len: int, page_block_size: int, device: str): - seq_lens = torch.full((batch_size,), seq_len, dtype=torch.int32, device=device) - seq_lens_blocks = torch.div(seq_lens + page_block_size - 1, page_block_size, rounding_mode="floor") - kv_indptr = torch.empty((batch_size + 1,), dtype=torch.int32, device=device) - kv_indptr[0] = 0 - kv_indptr[1:] = torch.cumsum(seq_lens_blocks, dim=0) - num_blocks = int(kv_indptr[-1].item()) - last_page_len = (seq_lens - 1) % page_block_size + 1 - return kv_indptr, last_page_len, num_blocks - - def getTestCaseSize() -> Tuple[List[Tuple[int, ...]], Tuple[int, int]]: - testcase_id = _get_testcase_index() - global CURRENT_CASE - batch_size, seq_len_kv, num_qo_heads, num_kv_heads, head_dim, page_block_size = TESTCASES[testcase_id] - num_blocks = batch_size * ((seq_len_kv + page_block_size - 1) // page_block_size) - CURRENT_CASE = ( - batch_size, - seq_len_kv, - num_qo_heads, - num_kv_heads, - head_dim, - page_block_size, - num_blocks, - 20260710 + testcase_id, - ) - warmup = 3 - iters = max(1, _compute_reps(batch_size, seq_len_kv, head_dim)) - return [ - (batch_size, num_qo_heads, head_dim), - (num_blocks, 2, page_block_size, num_kv_heads, head_dim), - (batch_size, num_qo_heads, head_dim), - (batch_size + 1,), - (num_blocks,), - (batch_size,), - (), (), (), (), (), (), - ], (warmup, iters) - - def genTestCase(testcase_sizes, device: str = "cuda") -> List[KernelArg]: - del testcase_sizes - batch_size, seq_len_kv, num_qo_heads, num_kv_heads, head_dim, page_block_size, num_blocks, seed = CURRENT_CASE - gen = torch.Generator(device=device) - gen.manual_seed(seed) - dtype = torch.bfloat16 - q = torch.rand(batch_size, num_qo_heads, head_dim, dtype=dtype, device=device, generator=gen).contiguous() - kv_data = torch.randn( - num_blocks, - 2, - page_block_size, - num_kv_heads, - head_dim, - dtype=dtype, - device=device, - generator=gen, - ).contiguous() - output = torch.empty(batch_size, num_qo_heads, head_dim, dtype=dtype, device=device) - kv_indptr, last_page_len, num_blocks_check = _setup_paged_kv_indptr( - batch_size, - seq_len_kv, - page_block_size, - device, - ) - assert num_blocks_check == num_blocks - kv_indices = torch.arange(num_blocks, dtype=torch.int32, device=device) - return [ - q, - kv_data, - output, - kv_indptr, - kv_indices, - last_page_len, - batch_size, - seq_len_kv, - num_qo_heads, - num_kv_heads, - head_dim, - page_block_size, - ] - - def baseline( - q, - kv_data, - output, - kv_indptr, - kv_indices, - last_page_len, - batch_size, - seq_len_kv, - num_qo_heads, - num_kv_heads, - head_dim, - page_block_size, - ): - _ensure_flashinfer_importable() - import flashinfer - - workspace_buffer = torch.empty(128 * 1024 * 1024, dtype=torch.uint8, device=q.device) - wrapper = flashinfer.BatchDecodeWithPagedKVCacheWrapper( - workspace_buffer, - kv_layout="NHD", - use_tensor_cores=True, - ) - wrapper.plan( - kv_indptr, - kv_indices, - last_page_len, - int(num_qo_heads), - int(num_kv_heads), - int(head_dim), - int(page_block_size), - data_type=torch.bfloat16, - q_data_type=torch.bfloat16, - ) - wrapper.run(q, kv_data, out=output) - return [ - q, - kv_data, - output, - kv_indptr, - kv_indices, - last_page_len, - batch_size, - seq_len_kv, - num_qo_heads, - num_kv_heads, - head_dim, - page_block_size, - ] - - def check( - testcase_sizes, - original_input_tensors, - target_kernel_input_tensors, - baseline_input_tensors, - rtol=1e-2, - atol=1e-2, - ) -> bool: - del testcase_sizes, original_input_tensors - output_t = target_kernel_input_tensors[2] - output_ref = baseline_input_tensors[2] - if output_t.shape != output_ref.shape: - print(f"[FAIL] shape mismatch: target {output_t.shape}, ref {output_ref.shape}", file=sys.stderr) - return False - if output_t.dtype != output_ref.dtype: - print(f"[FAIL] dtype mismatch: target {output_t.dtype}, ref {output_ref.dtype}", file=sys.stderr) - return False - if not torch.allclose(output_t.float(), output_ref.float(), rtol=rtol, atol=atol): - diff = (output_t.float() - output_ref.float()).abs() - print( - f"[FAIL] allclose failed: max_abs_diff={float(diff.max().item()):.6f}, " - f"mean_abs_diff={float(diff.mean().item()):.6f} (rtol={rtol}, atol={atol})", - file=sys.stderr, - ) - return False - return True -except Exception: - pass diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20004/zh_CN/00_题目描述.md b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20004/zh_CN/00_题目描述.md deleted file mode 100644 index 74508b0..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20004/zh_CN/00_题目描述.md +++ /dev/null @@ -1,22 +0,0 @@ ---- -sectionTitle: "题目描述" -type: "Text" ---- -你需要实现 FlashInfer paged KV cache decode 的 CUDA C++ 前向算子。 - -本题输入采用 FlashInfer `BatchDecodeWithPagedKVCacheWrapper` 的 paged `NHD` 布局。每个 batch 只有 1 个 query token，KV cache 长度为 `seq_len_kv`。 - -评测程序会调用你提交代码中的 `run_kernel` 函数。你需要根据 `kv_indptr`、`kv_indices` 和 `last_page_len` 读取 paged KV cache，并将结果写入 `output`。 - -baseline 使用 FlashInfer paged decode 的 Python API： - -```python -wrapper = flashinfer.BatchDecodeWithPagedKVCacheWrapper( - workspace, kv_layout="NHD", use_tensor_cores=True) -wrapper.plan(kv_indptr, kv_indices, last_page_len, - num_qo_heads, num_kv_heads, head_dim, page_block_size, - data_type=torch.bfloat16, q_data_type=torch.bfloat16) -wrapper.run(q, kv_data, out=output) -``` - -如何提交代码详见[评测指南](/d/2)。 diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20004/zh_CN/01_接口约定cuda.md b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20004/zh_CN/01_接口约定cuda.md deleted file mode 100644 index c498b62..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20004/zh_CN/01_接口约定cuda.md +++ /dev/null @@ -1,38 +0,0 @@ ---- -sectionTitle: "接口约定" -type: "codeSample" -lang: "cuda" ---- -你必须在提交的 CUDA 源码中提供如下 **C 符号**，函数名、参数类型、顺序必须完全一致，并使用 `extern "C"` 防止 name mangling： - -```cpp -#include -#include - -extern "C" void run_kernel( - const __nv_bfloat16* q, - const __nv_bfloat16* kv_data, - __nv_bfloat16* output, - const int32_t* kv_indptr, - const int32_t* kv_indices, - const int32_t* last_page_len, - int64_t batch_size, - int64_t seq_len_kv, - int64_t num_qo_heads, - int64_t num_kv_heads, - int64_t head_dim, - int64_t page_block_size -); -``` - -### 参数说明 - -* `q`：decode query tensor，shape `(batch_size, num_qo_heads, head_dim)`，连续 `bf16` -* `kv_data`：paged KV cache，shape `(num_blocks, 2, page_block_size, num_kv_heads, head_dim)`，连续 `bf16`，其中 `kv_data[:, 0]` 为 key，`kv_data[:, 1]` 为 value -* `output`：输出缓冲区，shape `(batch_size, num_qo_heads, head_dim)`，连续 `bf16` -* `kv_indptr`：paged KV indptr，shape `(batch_size + 1)`，连续 `int32` -* `kv_indices`：page index，shape `(num_blocks)`，连续 `int32` -* `last_page_len`：每个 batch 最后一个 page 的有效 token 数，shape `(batch_size)`，连续 `int32` -* `page_block_size`：page size，评测中固定为 `16` - -`run_kernel` 内部需要自行计算合适的 launch 配置并启动 CUDA kernel。为保证计时准确，不建议在 `run_kernel` 内部做 `cudaDeviceSynchronize()` 或显式同步。 diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20004/zh_CN/01_接口约定tilelang.md b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20004/zh_CN/01_接口约定tilelang.md deleted file mode 100644 index 8d0fd87..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20004/zh_CN/01_接口约定tilelang.md +++ /dev/null @@ -1,52 +0,0 @@ ---- -sectionTitle: "接口约定" -type: "codeSample" -lang: "tilelang" ---- -你必须在提交的 Python 代码中提供 `run_kernel` 函数，函数名、参数顺序、类型必须完全一致： - -```python -import tilelang -import tilelang.language as T -from tilelang import jit - -real_kernel = None - -@jit -def build_kernel(*args): - @T.prim_func - def kernel(*args): - ... - return kernel - -def run_kernel( - q, # Tensor[bf16], shape (batch_size, num_qo_heads, head_dim) - kv_data, # Tensor[bf16], shape (num_blocks, 2, page_block_size, num_kv_heads, head_dim) - output, # Tensor[bf16], shape (batch_size, num_qo_heads, head_dim) - kv_indptr, # Tensor[int32], shape (batch_size + 1) - kv_indices, # Tensor[int32], shape (num_blocks) - last_page_len, # Tensor[int32], shape (batch_size) - batch_size, # int64 - seq_len_kv, # int64 - num_qo_heads, # int64 - num_kv_heads, # int64 - head_dim, # int64 - page_block_size, # int64 -): - global real_kernel - if real_kernel is None: - real_kernel = build_kernel(...) - real_kernel(q, kv_data, output, kv_indptr, kv_indices, last_page_len, - batch_size, seq_len_kv, num_qo_heads, - num_kv_heads, head_dim, page_block_size) -``` - -### 参数说明 - -* `q`：decode query tensor，连续 `bfloat16` -* `kv_data`：paged KV cache，连续 `bfloat16` -* `output`：输出缓冲区，连续 `bfloat16`，需要写入结果 -* `kv_indptr/kv_indices/last_page_len`：paged KV metadata，连续 `int32` -* `page_block_size`：评测中固定为 `16` - -`run_kernel` 内部需要自行计算合适的 grid/block，并 launch 你实现的 TileLang kernel。 diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20004/zh_CN/01_接口约定triton.md b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20004/zh_CN/01_接口约定triton.md deleted file mode 100644 index d1751a9..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20004/zh_CN/01_接口约定triton.md +++ /dev/null @@ -1,41 +0,0 @@ ---- -sectionTitle: "接口约定" -type: "codeSample" -lang: "triton" ---- -你必须在提交的 Python 代码中提供 `run_kernel` 函数，函数名、参数顺序、类型必须完全一致： - -```python -import triton -import triton.language as tl - -@triton.jit -def your_kernel(...): - ... - -def run_kernel( - q, # Tensor[bf16], shape (batch_size, num_qo_heads, head_dim) - kv_data, # Tensor[bf16], shape (num_blocks, 2, page_block_size, num_kv_heads, head_dim) - output, # Tensor[bf16], shape (batch_size, num_qo_heads, head_dim) - kv_indptr, # Tensor[int32], shape (batch_size + 1) - kv_indices, # Tensor[int32], shape (num_blocks) - last_page_len, # Tensor[int32], shape (batch_size) - batch_size, # int64 - seq_len_kv, # int64 - num_qo_heads, # int64 - num_kv_heads, # int64 - head_dim, # int64 - page_block_size, # int64 -): - ... -``` - -### 参数说明 - -* `q`：decode query tensor，连续 `bfloat16` -* `kv_data`：paged KV cache，连续 `bfloat16` -* `output`：输出缓冲区，连续 `bfloat16`，需要写入结果 -* `kv_indptr/kv_indices/last_page_len`：paged KV metadata，连续 `int32` -* `page_block_size`：评测中固定为 `16` - -`run_kernel` 内部需要自行计算合适的 grid/block，并 launch 你实现的 Triton kernel。 diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20004/zh_CN/02_数据范围.md b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20004/zh_CN/02_数据范围.md deleted file mode 100644 index 9fe7bee..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20004/zh_CN/02_数据范围.md +++ /dev/null @@ -1,26 +0,0 @@ ---- -sectionTitle: "输入格式" -type: "Text" ---- -## 数据范围 - -- 数据类型：`q/kv_data/output` 均为 `bfloat16` -- KV layout：`NHD` -- `seq_len_q = 1` -- `num_qo_heads = 32` -- `page_block_size = 16` -- `head_dim` 固定为 `128` -- `num_kv_heads = 4` -- `batch_size` 取值为 `1, 2, 4, 8, 16` -- `seq_len_kv` 取值为 `512, 1024, 2048, 4096, 8192, 16384` - -测试点顺序与 `McFlashInfer/benchmarks/bench_batch_decode.py` 中的 cases 一致，即： - -```python -for batch_size in [1, 2, 4, 8, 16]: - for seq_len_kv in [512, 1024, 2048, 4096, 8192, 16384]: - for head_dim in [128]: - ... -``` - -输出参与 `torch.allclose` 校验，容差为 `rtol=1e-2, atol=1e-2`。 \ No newline at end of file diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20004/zh_CN/03_参考实现.md b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20004/zh_CN/03_参考实现.md deleted file mode 100644 index 5d17935..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20004/zh_CN/03_参考实现.md +++ /dev/null @@ -1,29 +0,0 @@ ---- -sectionTitle: "输出格式" -type: "Text" ---- -PyTorch 参考实现 - -```python -def baseline(q, kv_data, output, kv_indptr, kv_indices, last_page_len, - batch_size, seq_len_kv, num_qo_heads, - num_kv_heads, head_dim, page_block_size): - workspace_buffer = torch.empty(128 * 1024 * 1024, dtype=torch.uint8, device=q.device) - wrapper = flashinfer.BatchDecodeWithPagedKVCacheWrapper( - workspace_buffer, - kv_layout="NHD", - use_tensor_cores=True, - ) - wrapper.plan( - kv_indptr, - kv_indices, - last_page_len, - num_qo_heads, - num_kv_heads, - head_dim, - page_block_size, - data_type=torch.bfloat16, - q_data_type=torch.bfloat16, - ) - wrapper.run(q, kv_data, out=output) -``` \ No newline at end of file diff --git a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20004/zh_CN/_title.txt b/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20004/zh_CN/_title.txt deleted file mode 100644 index 552b751..0000000 --- a/基于AI Agent开发范式的国产GPU大模型推理算子库优化/operator_task_package/flashinfer_task_package backup/xpuoj_problem/problem_20004/zh_CN/_title.txt +++ /dev/null @@ -1 +0,0 @@ -FlashInfer Paged Decode