Description：
Hi all, For the same model and input, ‘debug’ executor produced different predicted results from the ‘graph’ / ‘vm’ executor, How strange it is?
What causes this? Thanks for any comments in advance!
The reproducible script:
import tvm
from tvm import relay
from tvm.ir.transform import Sequential
from tvm.contrib import graph_runtime
import numpy as np
var_0 = relay.var("var_0", dtype = "int64", shape = (5, 7, 9, 5)) # shape=(5, 7, 9, 5)
var_1 = relay.ones_like(var_0) # shape=(5, 7, 9, 5)
var_2 = relay.round(var_1) # shape=(5, 7, 9, 5)
const_3 = relay.const([[[[[699,894,823,381,897],[873,263,659,756,83],[321,639,937,145,940],[570,255,627,939,43],[287,564,26,732,670],[753,225,228,374,509],[73,72,402,247,453],[651,119,67,309,874],[501,981,864,437,477]]]]], dtype = "int64") # shape=(1, 1, 1, 9, 5)
var_4 = relay.left_shift(var_2, const_3) # shape=(1, 5, 7, 9, 5)
tuple = relay.Tuple([var_4])
F = relay.Function([var_0], tuple)
mod = tvm.IRModule()
mod['main'] = F
mod = relay.transform.InferType()(mod)
print(mod.astext(show_meta_data=False))
intrp1 = relay.build_module.create_executor('debug', mod, tvm.device('llvm',0),'llvm')
intrp2 = relay.build_module.create_executor('graph', mod, tvm.device('llvm',0),'llvm')
input_0= np.array([[[[197,900,595,369,502],[225,178,41,364,348],[585,489,771,301,933],[148,493,300,610,439],[702,893,815,433,495],[486,853,162,478,490],[635,26,742,581,394],[243,805,923,635,168],[622,572,8,392,872]],[[292,540,716,592,501],[507,645,393,673,77],[239,158,930,753,635],[771,387,12,512,319],[757,107,475,680,741],[994,653,312,1,45],[536,292,936,603,235],[436,109,879,180,781],[956,419,938,237,523]],[[924,7,261,287,871],[579,44,977,405,75],[717,398,727,381,750],[123,916,41,58,518],[276,845,979,506,25],[759,813,795,697,49],[317,972,408,929,259],[278,507,302,606,263],[728,322,660,454,54]],[[761,577,321,801,986],[191,428,831,169,934],[207,927,746,1,975],[147,669,947,906,597],[557,535,455,210,140],[717,937,813,728,390],[867,488,318,187,640],[304,377,420,134,897],[705,340,824,802,692]],[[150,948,712,448,205],[660,4,739,466,213],[230,534,501,43,261],[891,261,100,208,799],[91,863,528,510,996],[424,566,687,599,368],[378,749,315,441,548],[872,100,552,610,565],[116,192,98,617,586]],[[710,859,846,161,418],[996,252,281,523,113],[628,299,679,315,897],[46,44,997,712,485],[545,935,584,448,897],[501,915,88,950,883],[673,660,741,870,172],[511,865,775,143,740],[888,770,390,918,84]],[[286,315,480,635,378],[964,531,313,899,978],[209,399,244,296,349],[127,320,360,219,189],[531,81,405,306,223],[496,545,993,885,462],[428,523,776,907,157],[153,222,687,817,121],[16,25,519,259,672]]],[[[219,737,991,578,308],[531,461,388,936,766],[611,431,310,955,316],[123,382,838,898,641],[346,402,862,32,219],[982,399,595,853,9],[267,71,746,257,1],[53,788,461,792,75],[578,402,505,887,356]],[[172,361,90,361,610],[82,58,363,943,89],[581,277,839,528,129],[848,794,551,945,50],[551,997,189,363,140],[263,940,542,768,178],[249,291,538,690,4],[147,771,61,510,714],[502,442,990,340,969]],[[470,539,762,20,483],[164,923,831,352,285],[971,615,225,864,734],[754,464,376,292,154],[379,790,924,440,651],[989,293,93,330,632],[413,151,523,527,171],[5,690,93,836,393],[729,158,7,305,21]],[[92,59,484,468,702],[989,198,491,265,989],[142,253,281,586,935],[265,998,85,787,524],[607,791,565,51,978],[958,780,135,964,84],[507,408,494,343,875],[195,331,72,686,595],[61,179,200,693,764]],[[134,957,113,570,743],[637,529,886,201,579],[215,510,710,702,474],[146,560,233,639,902],[459,834,233,530,519],[827,942,697,378,987],[812,863,943,924,433],[38,912,313,275,113],[243,841,974,953,542]],[[447,98,102,31,736],[3,489,569,587,371],[439,766,664,487,143],[650,298,6,945,574],[790,982,485,102,608],[597,344,448,571,648],[990,369,97,443,752],[833,797,240,753,384],[610,544,501,274,30]],[[643,275,680,1000,219],[253,789,552,737,242],[159,686,938,607,608],[585,948,328,682,742],[79,866,538,671,970],[273,280,513,773,905],[895,416,180,926,415],[398,178,556,950,266],[797,460,303,86,66]]],[[[910,23,365,590,704],[106,20,921,644,690],[242,916,322,755,41],[226,1,456,757,926],[222,507,455,777,808],[72,926,267,375,11],[685,636,33,49,577],[88,155,597,8,150],[638,602,417,959,356]],[[457,537,708,264,645],[985,486,151,439,614],[310,510,539,577,236],[550,261,872,934,661],[448,22,167,396,381],[668,34,982,85,344],[689,893,880,396,509],[525,380,994,27,170],[959,337,32,498,265]],[[267,399,525,490,332],[537,290,353,56,685],[86,723,70,67,159],[414,756,404,645,503],[912,521,235,257,548],[404,215,884,435,712],[148,54,462,24,895],[794,912,184,498,967],[221,583,42,290,650]],[[200,55,757,603,700],[259,866,220,493,122],[767,897,337,2,683],[400,501,88,862,876],[983,655,140,166,152],[106,386,735,147,28],[736,699,82,492,653],[781,750,519,353,595],[640,119,843,976,473]],[[525,728,325,965,589],[553,947,595,692,112],[746,149,850,832,648],[229,567,346,310,410],[998,443,512,868,795],[106,508,265,948,835],[89,824,562,414,140],[502,318,86,96,9],[550,194,157,751,377]],[[804,979,296,501,640],[705,851,82,216,718],[228,673,577,845,972],[764,933,148,677,698],[287,179,15,373,626],[23,274,819,532,24],[548,335,2,843,836],[641,899,38,75,467],[755,302,139,684,146]],[[463,447,431,610,123],[128,248,301,495,972],[927,869,245,97,400],[620,644,87,621,486],[274,614,737,311,688],[203,417,341,341,100],[839,155,546,269,764],[669,748,364,969,242],[335,247,111,932,696]]],[[[510,551,339,948,172],[177,221,137,913,531],[176,115,948,516,807],[47,706,962,593,974],[77,613,722,440,933],[315,127,532,425,58],[227,287,608,917,234],[131,445,807,267,357],[337,442,823,636,310]],[[982,35,15,295,979],[341,371,591,414,163],[523,728,289,54,505],[346,632,143,953,549],[728,84,993,534,702],[702,223,496,876,858],[157,209,244,171,503],[222,863,226,812,276],[740,687,356,28,92]],[[860,725,76,354,29],[624,81,464,968,967],[166,669,189,661,897],[398,817,105,642,339],[960,215,202,185,27],[829,924,65,184,303],[508,395,27,583,748],[55,558,829,871,526],[795,36,546,335,48]],[[442,84,216,899,77],[906,858,292,459,42],[670,288,317,734,823],[619,593,218,997,176],[965,403,733,145,273],[258,939,660,156,625],[707,597,709,274,495],[785,180,352,428,638],[745,97,277,61,182]],[[100,31,775,317,27],[302,633,430,34,778],[54,644,68,714,151],[693,420,747,753,46],[594,889,225,297,317],[214,42,765,843,454],[947,942,485,73,610],[863,374,242,644,759],[371,698,754,439,411]],[[904,483,182,3,235],[579,596,475,803,892],[143,369,285,908,211],[739,206,504,575,278],[465,437,3,58,433],[761,429,130,867,219],[892,770,701,425,772],[287,4,367,113,158],[611,256,878,247,515]],[[440,337,72,295,911],[701,759,700,703,169],[132,463,949,613,329],[167,856,451,219,632],[222,505,987,941,969],[145,903,224,374,149],[90,814,486,161,108],[396,861,219,447,563],[739,930,378,687,894]]],[[[58,205,101,860,423],[733,82,279,719,374],[247,215,276,823,589],[776,912,754,261,425],[861,9,637,431,807],[552,521,737,929,207],[982,338,763,83,198],[537,815,631,167,885],[356,766,100,983,588]],[[40,758,851,793,19],[627,5,379,264,436],[185,815,956,273,95],[515,255,432,629,337],[981,166,151,963,332],[35,670,97,486,652],[36,525,410,887,317],[780,513,322,158,128],[109,694,942,416,967]],[[36,282,221,820,911],[557,152,76,59,467],[759,445,136,208,931],[788,595,455,549,833],[772,328,698,445,837],[825,553,530,767,968],[848,154,250,68,973],[160,976,125,587,34],[591,697,479,78,256]],[[409,217,851,863,765],[683,986,444,732,430],[632,557,982,514,323],[302,361,476,903,781],[801,414,756,277,1000],[790,219,48,620,296],[656,380,513,506,594],[629,540,580,73,272],[361,704,180,695,569]],[[854,996,930,681,898],[710,481,311,465,757],[662,606,975,61,577],[623,716,956,135,573],[902,763,465,833,187],[88,193,243,267,887],[811,120,234,740,800],[483,801,633,145,618],[741,806,575,716,219]],[[152,690,934,459,824],[859,360,938,323,192],[125,410,737,367,676],[623,177,147,209,269],[298,691,69,282,836],[686,23,993,261,90],[563,412,779,497,222],[954,707,582,891,29],[125,15,438,861,733]],[[465,836,910,963,44],[178,612,86,246,894],[273,284,916,266,544],[357,180,307,135,28],[528,440,734,461,330],[762,938,697,551,798],[429,367,985,338,329],[28,515,941,466,113],[186,738,396,453,355]]]], dtype='int64')
res1 = intrp1.evaluate()(input_0)[0].asnumpy()
res2 = intrp2.evaluate()(input_0)[0].asnumpy()
np.testing.assert_allclose(res1,res2, atol=1e-3, rtol=1e-3)
The result of the script:
