Description：
After Adding the ToANormalForm optimization, the follow-up script will crash. This crash happened in the intrp.evaluate() API for inference.
Following the crash message, I add opt_level = 2,3 to recompile the computational graph, however, this crash is unrelated with opt_level, I only related with this specific graph and the pass ToANormalForm!
How weird the crash message it is! Wish your comments !.
Crash message:
Traceback (most recent call last):
File "output2-sim.py", line 81, in <module>
res = intrp.evaluate()(input_23, input_10, input_0, input_48, input_21, input_6, input_3, input_13, input_4, input_37, input_17, input_31, )
File "/workplace/software/tvm/tvm-new/python/tvm/relay/backend/interpreter.py", line 171, in evaluate
return self._make_executor()
File "/workplace/software/tvm/tvm-new/python/tvm/relay/backend/vm.py", line 302, in _make_executor
self.executable = compile(self.mod, self.target)
File "/workplace/software/tvm/tvm-new/python/tvm/relay/backend/vm.py", line 79, in compile
compiler.lower(mod, target)
File "/workplace/software/tvm/tvm-new/python/tvm/relay/backend/vm.py", line 155, in lower
self._lower(mod, target, target_host)
File "/workplace/software/tvm/tvm-new/python/tvm/_ffi/_ctypes/packed_func.py", line 237, in __call__
raise get_last_ffi_error()
tvm._ffi.base.TVMError: Traceback (most recent call last):
........
Check failed: (!value.as<FunctionNode>()) is false: unexpected function:
fn (%p0: Tensor[(8, 8, 5), float64], Primitive=1, hash="5cb03226d13c4212") -> Tensor[(8, 8, 5), float64] {
tan(%p0, Tensor[(8, 8, 5), float64]) /* ty=Tensor[(8, 8, 5), float64] */
}
bound to var 'x_91'. Did you set opt_level = 2?
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 = ()) # shape=()
const_1 = relay.const([[[[636,298,550,378,248,831],[685,262,767,831,282,711],[860,848,678,49,724,709],[941,372,725,847,984,319],[939,203,69,906,42,566],[23,677,863,924,406,110],[754,90,372,872,273,5],[935,484,853,964,533,928],[25,825,651,749,671,986]],[[420,962,189,840,867,582],[406,241,610,268,164,16],[730,270,457,453,493,81],[809,427,565,13,391,449],[940,415,273,942,515,295],[928,934,256,468,774,475],[401,531,715,362,150,231],[377,879,500,186,683,992],[266,844,771,830,856,513]],[[630,796,927,254,89,793],[549,368,727,156,835,852],[630,587,382,697,949,531],[279,677,762,778,862,796],[121,480,639,243,661,495],[755,291,642,681,544,82],[474,444,450,552,600,636],[755,581,223,488,629,171],[370,907,199,483,684,61]],[[279,157,892,269,399,552],[115,154,842,756,186,386],[838,11,181,639,914,780],[274,668,713,496,507,341],[18,877,248,217,359,283],[629,989,791,520,258,190],[423,372,695,265,480,232],[2,669,243,182,307,156],[314,580,176,26,76,682]],[[366,445,910,965,13,269],[248,641,609,38,512,866],[579,935,238,625,551,69],[857,904,737,99,85,43],[606,750,622,133,775,49],[167,493,494,428,457,506],[696,56,499,657,446,362],[522,376,648,111,1,198],[179,857,101,915,307,538]],[[957,264,287,931,749,414],[979,267,906,824,694,714],[330,742,770,180,398,567],[893,919,942,893,382,942],[442,560,150,543,827,808],[432,135,424,70,65,172],[483,44,790,740,867,483],[454,548,576,575,79,973],[141,324,244,82,216,625]],[[376,657,536,877,551,362],[37,982,497,460,404,913],[983,886,308,772,978,175],[606,431,74,182,5,505],[154,497,828,749,578,43],[373,305,699,261,534,602],[974,570,583,470,29,338],[735,363,576,42,134,553],[568,739,983,642,272,339]],[[498,778,835,325,526,764],[719,251,69,417,511,954],[370,484,523,305,306,903],[642,40,265,217,81,750],[769,649,840,103,642,112],[441,139,889,275,815,414],[39,533,664,459,301,174],[412,671,10,286,975,315],[188,616,354,804,185,786]]]], dtype = "int64") # shape=(1, 8, 9, 6)
var_2 = relay.add(var_0, const_1) # shape=(1, 8, 9, 6)
var_3 = relay.var("var_3", dtype = "uint64", shape = (9, 5, 8, 7)) # shape=(9, 5, 8, 7)
var_4 = relay.var("var_4", dtype = "uint64", shape = (1, 1, 1, 1, 1, 1)) # shape=(1, 1, 1, 1, 1, 1)
var_5 = relay.right_shift(var_3, var_4) # shape=(1, 1, 9, 5, 8, 7)
var_6 = relay.var("var_6", dtype = "float64", shape = (8, 8, 5)) # shape=(8, 8, 5)
var_7 = relay.rsqrt(var_6) # shape=(8, 8, 5)
var_8 = relay.tanh(var_6) # shape=(8, 8, 5)
var_9 = relay.sign(var_8) # shape=(8, 8, 5)
var_10 = relay.var("var_10", dtype = "bool", shape = (7, 3, 2, 10)) # shape=(7, 3, 2, 10)
var_11 = relay.isnan(var_10) # shape=(7, 3, 2, 10)
var_12 = relay.acosh(var_9) # shape=(8, 8, 5)
var_13 = relay.var("var_13", dtype = "int64", shape = (1, 6, 8, 1, 1)) # shape=(1, 6, 8, 1, 1)
var_14 = relay.subtract(var_2, var_13) # shape=(1, 6, 8, 9, 6)
const_15 = relay.const([[[[[[385],[30],[421],[216],[543],[247],[794],[237]]]]]], dtype = "uint64") # shape=(1, 1, 1, 1, 8, 1)
var_16 = relay.left_shift(var_3, const_15) # shape=(1, 1, 9, 5, 8, 7)
var_17 = relay.var("var_17", dtype = "float64", shape = (1, 1, 8, 5)) # shape=(1, 1, 8, 5)
var_18 = relay.floor_divide(var_6, var_17) # shape=(1, 8, 8, 5)
var_19 = relay.sqrt(var_9) # shape=(8, 8, 5)
var_20 = relay.log(var_7) # shape=(8, 8, 5)
var_21 = relay.var("var_21", dtype = "float64", shape = (1, 1, 1, 1, 8, 5)) # shape=(1, 1, 1, 1, 8, 5)
var_22 = relay.equal(var_19, var_21) # shape=(1, 1, 1, 8, 8, 5)
var_23 = relay.var("var_23", dtype = "bool", shape = (1, 1, 1, 1, 1, 1, 1, 1, 1)) # shape=(1, 1, 1, 1, 1, 1, 1, 1, 1)
var_24 = relay.logical_and(var_11, var_23) # shape=(1, 1, 1, 1, 1, 7, 3, 2, 10)
const_25 = relay.const([[[[[362.168074],[388.704631],[881.889718],[150.323137],[531.965733],[609.129755],[768.978949],[206.949825]]]]], dtype = "float64") # shape=(1, 1, 1, 8, 1)
var_26 = relay.not_equal(var_19, const_25) # shape=(1, 1, 8, 8, 5)
var_27 = relay.isfinite(var_19) # shape=(8, 8, 5)
var_28 = relay.ceil(var_4) # shape=(1, 1, 1, 1, 1, 1)
var_29 = relay.divide(var_12, var_17) # shape=(1, 8, 8, 5)
var_30 = relay.tan(var_9) # shape=(8, 8, 5)
var_31 = relay.var("var_31", dtype = "float64", shape = (8, 8, 5)) # shape=(8, 8, 5)
var_32 = relay.floor_mod(var_29, var_31) # shape=(1, 8, 8, 5)
var_33 = relay.cosh(var_7) # shape=(8, 8, 5)
var_34 = relay.subtract(var_4, var_3) # shape=(1, 1, 9, 5, 8, 7)
var_35 = relay.mod(var_19, var_21) # shape=(1, 1, 1, 8, 8, 5)
var_36 = relay.log(var_9) # shape=(8, 8, 5)
var_37 = relay.var("var_37", dtype = "uint64", shape = (1, 1, 1, 1, 2, 2, 3, 5)) # shape=(1, 1, 1, 1, 2, 2, 3, 5)
var_38 = relay.greater(var_28, var_37) # shape=(1, 1, 1, 1, 2, 2, 3, 5)
var_39 = relay.less(var_0, const_1) # shape=(1, 8, 9, 6)
var_40 = relay.cosh(var_12) # shape=(8, 8, 5)
var_41 = relay.maximum(var_28, var_37) # shape=(1, 1, 1, 1, 2, 2, 3, 5)
const_42 = relay.const([[[[342],[991],[936],[101],[406],[636],[414],[825]],[[586],[388],[92],[9],[298],[845],[175],[64]],[[654],[325],[712],[278],[514],[598],[258],[100]],[[328],[71],[572],[861],[254],[144],[374],[947]],[[134],[309],[400],[892],[296],[813],[716],[233]]],[[[553],[159],[241],[202],[3],[415],[265],[656]],[[92],[976],[285],[605],[573],[542],[56],[252]],[[612],[627],[112],[218],[122],[485],[516],[256]],[[145],[267],[499],[440],[80],[214],[24],[632]],[[725],[265],[833],[727],[31],[449],[735],[122]]],[[[424],[19],[78],[348],[913],[133],[599],[876]],[[112],[62],[93],[233],[547],[961],[840],[691]],[[227],[338],[483],[658],[904],[506],[641],[628]],[[122],[825],[706],[153],[273],[440],[626],[696]],[[811],[704],[395],[723],[836],[993],[950],[299]]],[[[55],[43],[532],[953],[355],[723],[995],[581]],[[61],[829],[239],[964],[687],[231],[943],[808]],[[56],[648],[312],[328],[440],[938],[376],[602]],[[641],[770],[676],[828],[115],[625],[127],[521]],[[19],[10],[473],[373],[732],[819],[306],[144]]],[[[648],[896],[459],[686],[126],[401],[493],[181]],[[401],[805],[861],[192],[742],[236],[793],[734]],[[5],[468],[561],[471],[92],[39],[991],[111]],[[48],[815],[835],[132],[986],[140],[275],[633]],[[387],[86],[318],[513],[838],[162],[45],[238]]],[[[318],[905],[429],[59],[140],[573],[792],[497]],[[40],[705],[319],[484],[743],[662],[946],[791]],[[476],[780],[274],[813],[272],[548],[445],[10]],[[985],[114],[874],[823],[628],[919],[60],[945]],[[823],[841],[4],[315],[413],[147],[163],[453]]],[[[203],[481],[288],[946],[142],[233],[88],[970]],[[364],[361],[782],[987],[260],[579],[997],[245]],[[44],[870],[67],[671],[140],[478],[616],[315]],[[318],[971],[629],[83],[469],[143],[887],[672]],[[623],[174],[969],[117],[758],[56],[438],[121]]],[[[416],[219],[460],[27],[149],[808],[271],[193]],[[29],[689],[215],[169],[519],[182],[483],[836]],[[504],[463],[918],[325],[605],[804],[996],[579]],[[329],[964],[695],[86],[371],[132],[559],[138]],[[703],[18],[164],[851],[177],[787],[395],[205]]],[[[475],[962],[373],[345],[143],[855],[181],[999]],[[669],[450],[323],[273],[606],[670],[852],[934]],[[985],[898],[372],[355],[30],[930],[492],[732]],[[299],[655],[934],[475],[441],[681],[679],[268]],[[642],[404],[612],[136],[610],[144],[134],[279]]]], dtype = "uint64") # shape=(9, 5, 8, 1)
var_43 = relay.add(var_16, const_42) # shape=(1, 1, 9, 5, 8, 7)
var_44 = relay.cosh(var_19) # shape=(8, 8, 5)
var_45 = relay.divide(var_7, var_21) # shape=(1, 1, 1, 8, 8, 5)
var_46 = relay.acos(var_20) # shape=(8, 8, 5)
var_48 = relay.var("var_48", dtype = "float64", shape = (8, 1, 1)) # shape=(8, 1, 1)
var_49 = relay.greater(var_32, var_48) # shape=(1, 8, 8, 5)
tuple = relay.Tuple([var_40,var_49,var_26,var_46,var_22,var_14,var_35,var_39,var_5,var_18,var_24,var_43,var_33,var_27, var_30,])
F = relay.Function([var_23,var_10,var_0,var_48,var_21,var_6,var_3,var_13,var_4,var_37,var_17,var_31,], tuple)
mod = tvm.IRModule()
mod['main'] = F
mod = relay.transform.InferType()(mod)
#print(mod.astext(show_meta_data=False))
seq = Sequential([
relay.transform.ToANormalForm(),
])
mod = seq(mod)
input_23= np.array([[[[[[[[[True]]]]]]]]], dtype='bool')
input_10= np.array([[[[True,True,True,True,False,True,True,True,False,False],[False,True,True,False,False,True,True,True,True,False]],[[False,True,True,False,True,True,False,False,False,False],[False,True,True,True,False,True,False,True,False,False]],[[True,True,True,True,True,True,False,False,False,True],[False,True,True,False,True,False,True,False,False,True]]],[[[False,False,True,True,True,True,True,False,True,True],[False,False,False,False,True,False,True,False,False,False]],[[True,True,True,False,True,False,False,False,False,True],[False,False,True,True,False,True,False,True,True,True]],[[False,True,False,True,True,True,True,True,True,True],[True,True,False,False,True,True,False,False,False,True]]],[[[True,False,True,False,True,True,True,True,False,False],[True,True,False,True,False,True,False,True,False,False]],[[False,True,True,True,True,False,False,False,False,False],[True,True,False,False,False,True,False,True,True,False]],[[False,False,True,False,True,True,True,True,False,False],[True,True,True,False,False,True,True,False,True,True]]],[[[True,False,True,True,False,True,True,False,False,False],[True,False,False,False,False,True,False,False,False,False]],[[False,False,True,True,False,True,False,True,False,True],[True,True,True,False,False,False,True,True,False,True]],[[True,True,False,True,False,False,False,False,False,True],[False,False,True,False,False,True,True,False,True,True]]],[[[False,False,False,True,False,True,True,True,False,False],[False,False,True,False,True,True,True,False,True,True]],[[True,False,False,False,False,False,True,True,False,False],[True,False,False,True,False,False,False,True,True,True]],[[True,False,True,True,False,False,False,True,False,False],[True,True,False,True,True,False,True,True,True,True]]],[[[True,False,False,False,False,False,False,False,True,False],[True,True,False,False,False,False,True,False,False,True]],[[False,True,True,False,False,False,False,True,True,False],[False,True,False,False,True,False,False,True,True,False]],[[True,False,True,True,True,True,False,False,True,False],[True,False,True,False,False,True,True,True,False,False]]],[[[True,False,True,True,True,False,False,True,False,True],[True,True,True,False,True,False,True,True,False,True]],[[True,False,True,False,False,True,True,True,False,True],[False,True,True,True,True,False,False,True,False,False]],[[False,True,True,True,False,False,False,True,True,False],[False,False,False,True,False,True,True,True,False,True]]]], dtype='bool')
input_0= np.array(747, dtype='int64')
input_48= np.array([[[122.133409]],[[873.012666]],[[112.799182]],[[549.954889]],[[285.162184]],[[446.894630]],[[364.299159]],[[941.380909]]], dtype='float64')
input_21= np.array([[[[[[765.870284,58.095633,430.083893,987.593363,824.593750],[598.857312,856.143432,123.217221,321.804117,418.989648],[410.367349,821.701565,475.345331,744.487422,6.735596],[125.700092,208.542364,823.651075,953.812884,97.931715],[195.114441,56.851886,877.827199,776.421219,529.933517],[75.998831,221.848961,781.055934,37.509250,38.846547],[695.374436,983.481984,771.250841,950.327402,196.737472],[754.132921,818.674846,956.033588,271.847098,103.728574]]]]]], dtype='float64')
input_6= np.array([[[510.219826,216.744337,766.320575,572.017013,878.873983],[257.205352,232.441755,593.093385,863.898428,458.404530],[59.305483,445.236434,213.753589,306.514717,661.636709],[768.612768,47.195815,903.361749,91.297727,339.271942],[512.919396,298.250483,779.732598,711.623454,258.741261],[886.710296,358.872520,184.456037,278.200253,476.575139],[665.137052,697.714087,98.322953,24.148325,892.919898],[512.470807,382.310476,540.652687,444.112240,685.196575]],[[412.926675,774.988885,650.266417,2.602441,634.449074],[936.053889,528.529028,63.052991,142.094836,609.998753],[167.859361,191.520660,387.184552,465.530402,933.687086],[169.775430,91.177467,692.412354,153.989590,234.595365],[962.198311,981.289984,139.389934,894.055072,344.692764],[261.889024,721.456256,990.335531,919.058738,473.097243],[276.151821,132.689100,321.904760,442.224574,214.614787],[190.620782,789.267804,278.928045,155.515605,289.823085]],[[537.896738,424.303511,637.298413,542.165925,638.862583],[762.451263,400.538711,693.624593,663.669560,802.568586],[280.355056,261.356877,18.899880,368.547894,267.170718],[852.737367,773.031213,49.059960,931.787327,948.245146],[125.187898,644.491300,242.703815,535.486592,841.483795],[389.141353,797.435565,169.357395,997.998922,907.977849],[585.007469,907.935321,391.395068,441.705457,448.522343],[551.796847,159.586369,217.853784,287.888594,231.560689]],[[770.845503,170.191706,514.027331,926.939449,832.508278],[972.792089,38.541705,638.373755,488.793875,824.965918],[223.119525,248.192787,634.099559,819.973775,171.886070],[122.757899,768.659796,96.124873,222.004999,659.454502],[888.840235,432.279760,118.571709,126.496058,823.016032],[678.025293,280.376193,560.320199,601.474682,742.820609],[68.740507,781.875002,485.324994,587.772811,164.527965],[444.564698,105.992222,524.503806,146.786270,906.412192]],[[424.474905,998.172677,809.439845,944.018139,635.855360],[914.507871,112.208643,229.676226,765.758019,999.195732],[939.029681,264.613550,870.630789,312.850411,562.049759],[75.886562,584.707543,629.476530,594.048389,165.774840],[640.527925,885.534567,622.177922,330.983138,276.012282],[657.430377,295.117800,987.435176,572.857217,370.927679],[491.200276,198.728347,208.243368,124.278860,568.894875],[651.986529,748.935000,545.692469,742.554037,911.253139]],[[220.550315,131.634562,135.178260,145.015436,57.561365],[315.240660,86.062678,481.218197,931.381506,533.752432],[628.818224,685.585985,615.168200,791.073650,559.549166],[669.887925,157.114102,955.469375,195.658742,384.972646],[125.765164,365.400165,447.378517,471.324063,93.616788],[206.054135,91.089069,184.544784,628.020160,213.365709],[668.846137,7.075895,585.580562,354.421524,808.219391],[601.504701,980.474843,566.429749,817.590973,413.685035]],[[683.213013,230.237738,887.318617,123.301046,856.732279],[367.531544,401.653742,904.225716,959.788856,191.472530],[266.122213,220.002448,473.288979,582.094352,333.517711],[365.266703,914.852020,491.063816,144.882378,240.099277],[799.893013,3.644151,324.035114,481.847504,106.170020],[795.071224,115.944970,557.395601,23.566124,185.046875],[526.057338,776.849258,74.614642,279.486799,825.472914],[193.881215,693.755144,581.181795,367.878301,76.146004]],[[708.155072,977.571555,53.587714,855.678363,370.730562],[838.962648,734.081009,548.222921,406.783086,515.866014],[763.751855,596.895447,955.480735,296.636771,10.788027],[312.742971,632.307813,16.355010,675.196970,604.191326],[108.218783,129.290951,145.075938,236.320325,457.064281],[567.374407,234.280086,955.731958,820.835365,554.168552],[172.042756,96.216218,674.531561,354.413509,775.479306],[984.497869,323.198927,776.793726,246.300116,523.963304]]], dtype='float64')
input_3= np.array([[[[29,582,617,87,75,437,3],[486,905,401,730,859,116,421],[665,595,721,512,412,604,209],[547,710,822,627,549,977,7],[324,999,753,704,580,721,142],[654,158,144,491,414,896,220],[272,12,640,288,958,713,152],[721,316,360,267,377,533,894]],[[278,510,900,953,508,4,656],[439,725,150,92,234,293,582],[647,541,153,271,552,792,558],[861,856,709,581,171,420,200],[900,953,93,177,814,344,129],[673,699,136,463,775,285,554],[8,930,487,7,822,991,277],[725,134,186,585,990,895,517]],[[512,666,716,411,970,160,939],[783,503,67,807,554,555,269],[328,191,174,688,472,12,46],[293,2,674,17,136,859,953],[477,105,470,988,771,185,399],[740,345,689,875,199,756,33],[104,662,654,432,204,179,471],[676,191,868,320,544,541,337]],[[31,751,289,507,856,110,495],[978,295,245,69,991,933,295],[189,40,680,293,53,333,76],[257,511,898,284,53,765,603],[597,657,291,627,407,580,134],[614,689,980,943,335,576,364],[325,860,658,866,900,689,510],[304,21,937,912,884,186,195]],[[936,302,798,884,958,88,511],[716,19,996,330,60,327,624],[746,902,987,423,761,645,288],[12,685,149,316,58,437,579],[941,974,774,228,275,923,464],[232,10,326,947,381,321,628],[440,999,252,537,900,238,959],[12,234,246,376,919,746,691]]],[[[976,534,269,268,507,394,847],[781,316,310,364,678,635,663],[58,307,290,849,305,893,385],[556,131,344,568,716,941,943],[986,39,985,961,572,605,580],[431,351,427,211,666,736,927],[343,723,589,752,29,230,952],[686,123,689,241,253,384,160]],[[320,676,102,306,714,438,618],[638,395,198,68,745,624,630],[410,711,556,105,433,496,208],[462,726,160,147,848,200,739],[452,583,899,771,258,352,428],[972,142,46,961,536,243,380],[280,866,361,41,928,269,497],[361,764,705,174,489,216,672]],[[688,415,410,139,997,660,262],[606,12,689,929,153,734,889],[40,328,268,671,193,629,711],[473,249,208,833,12,264,6],[853,831,29,540,245,438,679],[593,98,292,550,461,980,479],[965,66,367,4,393,987,674],[938,967,736,410,215,295,594]],[[578,910,951,430,740,979,970],[336,768,1000,928,217,291,478],[677,622,308,993,39,674,996],[784,12,21,73,978,109,834],[544,403,427,122,313,377,551],[52,355,872,388,122,871,667],[339,513,496,367,487,803,360],[877,829,707,660,840,80,84]],[[170,188,917,65,942,343,538],[254,71,89,658,777,312,397],[251,535,415,941,399,263,307],[885,65,18,114,245,77,125],[437,508,209,958,47,125,22],[988,820,560,594,890,1000,251],[19,311,999,269,197,413,561],[596,675,219,832,92,237,945]]],[[[688,665,70,124,172,278,81],[218,754,103,557,925,14,150],[167,13,752,185,675,750,805],[872,515,365,467,541,583,298],[984,171,595,672,835,664,795],[358,293,876,927,398,330,484],[323,695,985,489,59,737,25],[85,838,829,956,704,193,774]],[[245,127,424,228,298,370,251],[484,33,46,842,677,273,120],[74,954,603,748,1000,588,236],[58,676,612,142,513,440,450],[217,984,575,813,111,350,392],[408,719,643,891,103,40,84],[779,664,204,205,617,158,952],[616,97,540,25,772,151,518]],[[637,943,967,853,926,894,17],[388,243,408,147,314,402,390],[416,441,473,547,456,28,751],[72,186,54,39,282,945,63],[54,448,581,690,742,899,894],[19,144,262,407,387,21,553],[52,423,942,467,215,767,13],[671,794,115,94,979,521,485]],[[613,817,547,18,264,479,59],[5,730,952,24,873,565,782],[611,585,334,662,7,628,481],[574,394,845,596,539,312,689],[870,832,173,482,648,72,851],[264,902,909,620,983,212,643],[208,776,776,170,360,462,832],[719,89,664,644,834,860,239]],[[372,171,279,593,2,804,74],[2,227,276,265,480,184,884],[463,395,879,670,170,654,839],[882,115,22,952,555,37,595],[388,897,833,112,67,463,56],[421,266,482,422,492,757,38],[972,941,273,786,687,151,807],[209,157,645,90,623,19,41]]],[[[178,407,635,917,303,819,380],[722,281,436,142,899,917,915],[390,673,304,713,965,576,498],[652,79,304,212,587,301,301],[209,671,693,738,77,679,7],[732,849,738,805,129,525,298],[379,793,564,769,466,867,833],[782,794,331,785,872,986,996]],[[810,286,648,19,308,340,108],[737,370,466,468,218,204,624],[699,728,921,77,521,484,197],[338,350,30,471,495,360,256],[719,345,251,528,983,899,898],[290,590,6,378,960,471,845],[529,26,820,227,754,740,304],[626,223,500,315,924,881,785]],[[419,240,392,137,937,643,16],[271,893,914,912,834,919,290],[793,741,486,322,767,306,900],[872,45,555,849,620,407,163],[895,287,299,665,527,691,153],[815,333,169,85,577,82,348],[410,1000,637,555,92,123,228],[858,428,127,81,824,682,929]],[[443,88,443,338,374,742,2],[252,432,155,418,116,323,854],[44,404,202,453,755,838,7],[198,312,586,408,739,713,488],[563,394,769,357,833,211,694],[558,304,48,162,735,202,579],[202,876,433,245,631,986,50],[737,175,408,934,487,994,693]],[[225,706,533,139,451,301,496],[635,863,189,192,167,588,353],[253,141,284,807,368,68,51],[998,53,452,86,227,860,372],[713,853,64,290,910,596,428],[712,248,275,346,111,816,889],[629,403,242,881,544,877,39],[911,944,442,261,996,893,346]]],[[[574,752,69,639,956,133,928],[217,80,707,928,328,982,273],[790,797,162,418,551,755,650],[94,631,689,357,926,130,617],[273,22,314,846,126,383,484],[433,867,763,650,298,470,577],[625,803,202,414,599,715,183],[149,821,833,595,803,873,951]],[[728,2,919,1000,375,584,197],[852,318,681,285,1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dtype='uint64')
input_13= np.array([[[[[652]],[[764]],[[774]],[[398]],[[521]],[[381]],[[657]],[[99]]],[[[229]],[[550]],[[641]],[[230]],[[390]],[[266]],[[571]],[[925]]],[[[953]],[[153]],[[922]],[[726]],[[883]],[[841]],[[118]],[[767]]],[[[693]],[[408]],[[157]],[[120]],[[943]],[[570]],[[475]],[[947]]],[[[685]],[[249]],[[344]],[[205]],[[981]],[[352]],[[303]],[[210]]],[[[901]],[[295]],[[439]],[[643]],[[560]],[[361]],[[567]],[[512]]]]], dtype='int64')
input_4= np.array([[[[[[514]]]]]], dtype='uint64')
input_37= np.array([[[[[[[[841,589,748,681,706],[514,373,465,22,844],[408,943,319,354,627]],[[567,49,831,899,401],[133,108,653,428,898],[295,339,611,214,203]]],[[[124,54,791,871,734],[849,736,459,665,757],[654,72,700,972,777]],[[326,890,178,157,789],[930,641,248,934,420],[146,229,759,756,794]]]]]]]], dtype='uint64')
input_17= np.array([[[[303.855300,151.333439,670.191648,532.802340,551.356850],[661.895324,431.599569,577.713769,781.414682,271.389381],[380.115591,645.117225,247.362507,153.331866,136.887278],[467.668713,962.911096,386.113607,275.925683,382.136493],[108.028168,738.965384,397.343143,994.747053,268.276147],[438.264079,178.444893,74.818383,632.219189,484.096031],[899.048138,425.474561,517.167302,341.753171,601.176228],[395.642785,240.153572,917.228680,972.283818,72.220457]]]], dtype='float64')
input_31= np.array([[[478.114419,420.131411,842.919182,592.083074,935.622621],[674.829410,940.799209,686.256571,541.215966,860.264473],[27.823276,523.117792,915.185881,447.939195,914.471078],[338.075654,112.972631,955.853925,959.561728,997.235400],[232.676321,696.704462,596.429548,332.358352,946.388354],[851.171693,627.719608,173.751780,172.040803,864.560907],[19.486851,950.252226,371.805529,63.230040,666.512839],[318.677305,654.733874,958.979028,142.689704,441.161455]],[[556.118762,879.852079,736.823447,752.684240,407.383712],[553.299079,93.119131,670.294628,405.775039,323.550115],[680.835501,528.836311,500.220223,775.471836,174.028407],[574.798047,533.229285,734.512666,325.475597,179.796380],[151.312877,491.526276,672.566009,419.911239,477.616626],[714.248678,418.822372,133.116567,399.082442,509.266295],[732.693163,301.229567,379.141509,336.558188,50.294117],[931.663077,266.839453,19.893865,846.573675,472.997734]],[[83.445171,760.557755,562.690669,70.385132,100.480965],[559.415439,940.896090,576.301985,180.393743,162.063625],[918.045678,648.418119,895.362362,993.868416,272.517971],[155.067847,409.669552,939.617989,626.858230,804.824836],[338.768911,180.981336,405.114951,10.913907,76.645669],[701.342664,849.987904,926.395809,951.478576,338.504486],[835.298105,100.684326,37.278617,544.163049,508.721474],[965.064537,222.662826,446.534555,426.870949,694.296388]],[[808.459657,520.633868,494.834996,41.517185,574.503812],[131.179154,553.623637,694.038602,776.324571,235.824722],[98.849880,50.499941,81.242592,419.283081,177.567996],[265.754145,870.516295,104.349826,703.267985,772.117226],[613.592250,836.795211,551.097572,441.882948,175.270984],[248.393880,886.245238,662.123349,634.036618,302.101773],[668.058233,870.622665,486.312982,814.251714,889.303226],[323.231576,193.931674,909.656645,448.691902,521.944266]],[[87.359203,833.412334,438.818654,855.136984,827.466530],[947.455240,194.436543,496.676899,821.924531,891.441493],[904.882970,80.702317,682.163469,718.776549,615.614537],[273.456228,284.284977,906.943462,421.163362,948.335165],[583.324230,102.789216,464.782737,526.285037,822.662316],[75.589879,336.577637,430.631452,916.369813,947.313369],[186.048845,280.875188,726.944025,280.786630,105.833163],[749.943818,108.023849,860.266831,679.783222,36.885537]],[[886.573775,426.415142,255.198302,98.830785,912.688642],[980.834884,100.630250,15.018004,201.901898,621.971757],[547.813875,695.933527,753.112925,544.186785,229.867462],[155.951493,462.106096,539.351298,522.193816,294.109603],[203.625457,934.909643,542.190114,185.479283,97.994413],[228.671640,90.394207,314.140466,722.565934,434.191999],[591.387260,879.750746,110.231723,174.367705,460.406875],[233.298784,470.858084,349.309882,77.044461,497.453456]],[[195.285015,719.267575,408.359937,70.196037,175.504197],[49.573201,401.702006,909.045015,659.217293,996.560367],[707.837643,261.272104,515.252249,632.153806,292.248621],[200.594403,475.663062,323.305450,970.821097,546.397632],[577.005809,228.456796,53.810840,112.538297,779.364534],[53.988150,933.074440,331.640383,37.174725,839.909241],[914.890142,464.991605,243.685396,49.934156,824.439018],[228.440840,427.603604,241.507765,557.155591,385.182053]],[[742.662731,289.487341,633.677278,707.558490,688.579079],[756.104397,389.345784,291.875079,386.539390,102.324703],[567.995267,942.374549,992.301127,610.521750,196.866673],[126.019380,748.959835,869.673375,100.684640,141.509478],[838.305631,801.536865,705.604482,379.009868,232.906858],[994.521237,146.781096,568.739879,381.205825,513.364250],[989.147103,395.880853,318.499386,465.830435,240.012742],[153.497576,261.458023,460.747041,940.608579,480.776561]]], dtype='float64')
intrp = relay.build_module.create_executor('vm', mod, tvm.device('llvm',0),'llvm')
res = intrp.evaluate()(input_23, input_10, input_0, input_48, input_21, input_6, input_3, input_13, input_4, input_37, input_17, input_31, ) # Crash Here!!