Exporting Lux Models to Jax (via EnzymeJAX & Reactant)

Experimental

This feature is experimental and is subject to change without notice. Additionally, this feature currently requires some manual setup for interacting with Jax, which we are working on improving.

In this manual, we will go over how to export Lux models to StableHLO and use EnzymeJAX to run integrate Lux models with JAX. We assume that users are familiar with Reactant compilation of Lux models.

using Lux, Reactant, Random

const dev = reactant_device()

(::ReactantDevice{Missing, Missing}) (generic function with 1 method)

We simply define a Lux model and generate the stablehlo code using Reactant.@code_hlo.

model = Chain(
    Conv((5, 5), 1 => 6, relu),
    MaxPool((2, 2)),
    Conv((5, 5), 6 => 16, relu),
    MaxPool((2, 2)),
    FlattenLayer(3),
    Chain(
        Dense(256 => 128, relu),
        Dense(128 => 84, relu),
        Dense(84 => 10)
    )
)
ps, st = Lux.setup(Random.default_rng(), model) |> dev;

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0.0848756, 0.028985621, 0.0062304526, 0.08194776, 0.050536916, 0.01826127, -0.049772162, -0.0077092485, -0.002726585, 0.077142365, -0.06827348, -0.07511751, 0.044920534, -0.021493733, 0.0633891, -0.08228292, 0.058538716, -0.010758226, -0.07438879, -0.059717976, -0.08748786, 0.07617699, -0.037819527, -0.042031262, -0.020525968, -0.012325814, -0.046931073, -0.07960973, 0.042943552, -0.025457097, -0.0626142, -0.059606433, -0.063212916, 0.0721617, -0.07385581, 0.056094304, 0.05060687, 0.08194381, 0.043534946, -0.06417349, -0.049018756, 0.016516486, 0.06209356, 0.060682412, 0.053369742, 0.02956377, 0.007116095, -0.032974504, -0.011478747, -0.076226935, -0.020922981, -0.046454374, -0.0177742, -0.07904704, -0.056093924, -0.053680133, 0.02524553, 0.05246582, 0.04573325, 0.0438195, -0.07540421, 0.040488664, -0.07901101, -0.02310959, -0.035341546, -0.04592391, -0.06380603, 0.0037230735, 0.044462502, -0.022949, 0.06225748, 0.039102066, 0.049960107, 0.025559144, -0.038053177, -0.013179257, 0.008008301, 0.008930232])), layer_3 = (weight = Reactant.ConcreteRArray{Float32, 2}(Float32[0.18139583 0.058699332 0.06238915 -0.142154 -0.12538669 0.0036138995 0.03129849 -0.0011347574 0.0087575475 -0.1696142 -0.003534419 0.113438 -0.11244189 0.051808357 0.058087885 -0.1366208 -0.13614163 0.067496374 0.15376908 0.15215856 -0.061169844 -0.17558944 -0.14813639 -0.16713841 0.13279991 0.14761469 -0.078490496 0.040060427 -0.016812343 -0.15911607 -0.11261275 -0.097074226 0.156033 0.15773329 -0.017299812 0.030000648 -0.17853288 -0.18599497 0.1794136 0.087890714 0.021647306 0.012255448 0.1123119 0.11883769 -0.061405584 -0.08752933 -0.039814465 0.09862288 -0.013293716 0.14063697 -0.083316855 0.13788679 0.06980258 0.09576846 -0.098249584 -0.12507169 -0.1810046 -0.05321307 -0.14838128 -0.16001512 -0.068157986 -0.085303955 -0.07926996 0.14083607 0.18485896 0.17653018 0.108514935 -0.07128773 0.005869424 0.1119439 0.038738593 -0.10609723 0.052562293 0.1717784 -0.17568572 -0.0597 0.108876966 0.025707606 0.1108466 -0.0833912 0.1386984 0.07274901 0.01378015 -0.054409962; -0.044637576 0.101744756 0.060652163 -0.13070677 -0.1436292 -0.10131189 -0.15396808 0.10217014 0.1262764 0.1434843 -0.082346596 -0.16059393 -0.1101136 -0.13307787 0.14612485 0.086140975 0.03427821 0.1202304 -0.023290327 -0.11802108 0.05955352 0.1058557 -0.023989813 0.18666185 -0.14559515 0.05999787 0.13950837 0.0006066458 0.11993455 -0.13984111 0.11376992 0.077043496 0.087204404 -0.049027804 -0.078656465 0.11403233 0.06393363 0.100236475 -0.07347983 0.06984762 -0.18175347 -0.17046517 -0.07770054 -0.010171522 -0.08328984 -0.17485157 0.020921258 -0.017145876 0.03144632 -0.049342506 0.10825279 -0.15960914 -0.16877551 0.042281147 -0.15713914 0.04260801 -0.044484023 0.052892625 -0.14203785 -0.02871353 -0.022067867 -0.017114246 0.06096506 0.055151034 -0.102872126 -0.1139068 0.18659258 0.0067800237 -0.16964056 -0.024655595 -0.022899572 0.1859324 0.027683914 0.15950501 -0.114970684 -0.08956158 0.15015231 0.037961725 -0.045452766 0.062042598 -0.1468016 -0.17653437 -0.09924946 -0.07245997; 0.15299636 -0.1432842 -0.06626923 0.02378237 0.11815059 0.103134714 -0.16803214 -0.018827781 0.032837767 -0.019424086 0.15929511 -0.041655395 0.04438359 0.17075291 -0.08052878 0.1323113 0.059404943 -0.0072024316 -0.10618986 0.12279506 -0.071867764 -0.1225752 -0.14487286 -0.11184563 -0.10444357 0.05728387 0.106582716 -0.11372344 0.04677895 0.030959187 0.021999672 0.12503098 -0.17723857 -0.09805615 -0.04845146 -0.044491593 -0.1267445 0.1804459 0.14161815 0.002100574 0.03766331 -0.15322465 -0.11590068 0.013631778 -0.17737712 0.16082628 0.056152716 0.060266994 -0.1340805 -0.10237607 -0.16131085 0.044389132 0.026582858 -0.10392215 0.16987048 0.013220814 0.013132728 -0.17759056 0.08751152 -0.15045178 0.16561252 0.13052507 0.016879521 -0.15989119 0.066387974 -0.09091407 0.11496703 -0.13856868 -0.09714163 -0.1727698 0.02437996 0.13078734 -0.04740736 0.15631717 -0.1011341 0.15833716 -0.101816304 0.019252915 -0.040748514 -0.15542169 -0.0037705395 0.11572027 -0.012092522 -0.1346992; -0.08435929 0.1563739 -0.064259104 0.072548375 0.0818573 0.1794093 -0.00020428788 0.16023228 -0.065672584 0.114924885 0.04941516 0.02613542 -0.12657622 -0.0812997 0.04618118 0.12885047 -0.08227052 0.10414142 0.112652235 -0.012397399 0.034754623 -0.1363198 -0.085232854 -0.15640111 0.17949072 -0.1556106 -0.1262053 -0.04275564 -0.044072043 -0.061910942 0.07038215 -0.17347999 -0.16414763 -0.01651544 0.035716247 0.051313654 -0.040705957 0.13966273 0.16973493 0.1390836 0.08780317 -0.16351733 0.06594538 -0.07438047 -0.110497706 -0.12295514 -0.0799762 -0.056721922 -0.15398969 -0.060868934 -0.078305855 0.019305136 -0.09460606 -0.046439286 0.040766582 0.086347856 0.05554071 0.022367967 -0.16641779 0.11855113 -0.06584609 0.0036072985 0.05041452 0.031204566 -0.0087493695 0.04195942 0.025166608 -0.1772373 -0.1882248 -0.13836369 0.1663603 0.009927021 -0.081045784 -0.17177044 0.020930946 -0.07236905 -0.049462155 -0.15455432 -0.0051356503 -0.0781097 -0.084411085 -0.051816083 0.024991201 0.08981946; 0.00975175 -0.023217764 0.056022346 0.029073423 0.00701513 -0.032937005 -0.03756964 -0.074477255 -0.014269445 -0.18338227 -0.16941117 0.03228778 -0.051211733 -0.18354815 0.06315728 -0.0255012 -0.058384474 0.16349903 0.15043806 -0.00063244085 0.17223604 0.16115335 -0.10335198 0.14167394 0.051344045 0.18619986 -0.17533782 -0.17957321 -0.032570716 -0.063423544 0.036299463 0.1673134 0.04464963 0.147232 0.17121527 -0.10121009 0.110553175 0.07673222 0.14115834 0.093491055 0.06974369 0.084548846 0.03603951 -0.14041926 0.02626764 0.06323676 -0.06990266 -0.18788064 0.18744062 -0.09194973 0.070562065 0.16676219 0.026912764 0.1251003 -0.06562541 0.020363364 -0.120858245 0.07670482 0.02513766 0.15111797 0.099085726 0.16022414 0.09756796 0.09884614 -0.18110292 -0.069895245 0.10167271 0.12918617 0.12812325 0.066994146 0.15670982 0.12109567 0.15256064 -0.008205398 -0.034782827 -0.114309296 0.12997594 0.15364514 0.11971815 0.08163767 0.023742879 0.040562022 -0.097376265 0.120072946; 0.14642121 0.10897404 0.13718413 -0.029877845 -0.17628588 -0.12868612 0.061283097 -0.047704328 -0.18801078 0.1506263 0.103209056 0.04568337 -0.0628073 -0.06947608 0.06410361 -0.13988885 -0.04240136 -0.1281379 -0.091825865 0.1324029 -0.16521543 0.08627835 0.01936344 0.056629058 0.0812432 -0.056171462 0.18527903 -0.02748217 0.12135175 -0.1807543 0.02209783 -0.044440184 0.07853384 0.17476623 0.15794697 0.008195215 -0.117077634 0.06750912 -0.052935228 0.08142359 0.060376957 -0.01763988 0.15237512 -0.17393988 -0.026589166 0.09586927 -0.04098362 -0.12412782 -0.03776532 0.0840135 0.15492429 -0.14497858 0.024086054 0.08907201 -0.048723828 -0.063136056 0.1032337 0.16275622 -0.079007275 -0.15592414 -0.050675917 -0.016025176 -0.14753838 -0.13475405 -0.1841253 0.08412817 0.16995582 0.17227545 0.074757785 0.046366476 0.1394476 -0.14707135 -0.059349976 0.0031140384 -0.09825562 0.03411853 0.117600404 -0.027408322 0.1156332 0.054958776 -0.101796545 0.0098635135 -0.04354891 -0.14847396; 0.17040624 -0.05091999 0.098057166 -0.10401684 0.0140001625 0.16025169 0.083310254 -0.107358165 0.18036261 0.009419185 0.16327147 -0.017185885 -0.16640767 0.17925236 0.16383414 -0.12950493 -0.044454373 -0.07221029 0.077760756 0.106456175 0.059764072 -0.114546336 -0.02203146 0.14650244 0.1424281 -0.17155838 0.17959012 -0.18083313 0.14374512 0.057864044 0.0026579276 0.013620288 -0.17683779 -0.16401745 0.13467596 -0.16051097 0.13466406 -0.12724757 -0.1743732 -0.092099585 0.07358268 -0.06481754 0.1615175 0.06378905 -0.0101106055 -0.09951318 -0.062798515 -0.09616588 -0.025634186 0.05701249 0.0686694 -0.080462456 -0.117424145 -0.0044997176 0.15929101 -0.12200943 0.015267928 -0.13237812 -0.006761843 0.12622781 0.043437418 -0.07369498 0.04986102 -0.15255076 0.17745987 -0.1642958 0.11921561 0.11810437 0.08455092 -0.16346934 0.046882942 -0.083473876 0.08203357 -0.16434595 -0.05392247 0.05518564 0.07700236 -0.11897205 0.18385954 0.16521932 0.17168298 0.1133485 -0.0947351 -0.09315173; -0.13685592 0.06237095 0.022642724 0.10695349 -0.17051621 -0.15081982 0.084993266 -0.16582362 -0.09313443 0.16114815 0.08835908 0.101697065 -0.16811818 -0.02395611 0.008350909 -0.034065522 0.12029003 -0.17350115 -0.10122725 -0.04185795 -0.014884088 0.02124132 0.16210054 -0.08381419 0.07422843 0.01252883 -0.13043776 -0.104603164 -0.02244132 0.12291831 -0.058905263 0.14297576 0.0522229 -0.18871084 -0.0046985084 -0.12200954 0.1488839 0.0134781785 -0.14733389 0.06250033 0.026752025 -0.033430308 -0.06700156 0.16858247 -0.07459463 -0.10870541 -0.08909894 0.06274974 0.16495082 0.08451381 -0.12421611 -0.17474362 0.15548407 -0.07390339 0.15710996 0.062349323 -0.16302113 0.020020979 -0.02401448 -0.054485973 0.14264737 -0.047142312 0.14918694 -0.12238073 -0.01775072 -0.104605526 0.09271243 0.044172633 0.101783775 0.16460666 -0.04406051 -0.006497607 0.09114634 -0.03847945 0.14777751 0.1734129 0.17048953 0.09226672 0.04196753 0.008915404 0.019804569 0.1888071 -0.18798539 0.16065854; -0.13262649 -0.026803501 -0.08342562 -0.124582104 0.15020558 0.00391168 0.061267797 -0.14529331 0.12842847 -0.07870348 -0.057966255 -0.075757936 0.10453281 0.08812988 0.10587347 0.15102246 0.004828858 -0.036485728 0.02188293 -0.0006495174 -0.10289918 -0.047150332 0.05199719 0.15323336 0.07150932 -0.1462494 -0.13343449 -0.08848181 0.03651977 -0.10965404 0.08478192 -0.025842732 0.16306928 -0.15499517 0.079983234 0.060846608 -0.18582442 0.12848064 -0.10404563 0.07495702 0.087381296 -0.1256936 0.18696961 0.016390836 0.16464053 0.14058259 -0.14076199 0.049695708 0.17192341 -0.07709619 -0.06540145 -0.06736927 0.074324675 0.17206588 0.17168866 -0.04251337 -0.16979007 0.09186074 -0.035171174 -0.18797731 0.10568164 -0.123153105 -0.06286088 0.021246795 0.0067944867 0.04394424 -0.086508006 0.16117637 -0.037366997 0.06855699 0.003494341 0.12794945 0.17360586 -0.16108195 -0.015237447 -0.029251419 -0.17450061 -0.0060678995 0.06073998 -0.18881248 -0.0056865155 0.040752478 0.021063684 0.08187794; -0.15774088 -0.041667562 0.09009562 0.107785106 -0.092989236 0.09712352 0.051578768 0.12112338 -0.033375632 -0.04116378 -0.0041254302 0.030759428 0.043171313 0.009842968 -0.020628411 -0.03740595 0.082969174 -0.05275552 -0.141585 0.17979771 0.07748794 -0.071410485 -0.1419414 -0.12643331 0.05161353 -0.06494257 0.12405327 -0.009731429 -0.021228164 -0.0966757 0.0110953 0.0582721 0.1449704 0.12917158 0.17496225 -0.11871721 0.016435577 -0.083302975 0.039418437 0.0052418043 0.004968196 0.14697267 0.048424583 -0.057875983 0.010862943 -0.016167983 -0.11115049 0.058034696 -0.10126713 0.026402315 0.065198354 0.07924094 0.16884752 -0.08724203 0.16726622 -0.12207482 0.11358329 -0.17516099 -0.14418781 -0.12712301 -0.050196197 0.118509226 -0.1554253 0.095424354 -0.086282276 -0.023988979 0.117152676 0.06391689 0.007342491 -0.027615495 -0.025217792 0.09567318 0.014504979 0.101449095 0.025196707 0.053185903 -0.15119147 -0.07784271 0.12504388 -0.058369335 0.07120685 0.039521076 -0.12132327 0.13264379]), bias = Reactant.ConcreteRArray{Float32, 1}(Float32[0.079922244, -0.010632915, -0.0010757144, -0.04828227, -0.08182068, -0.04824383, -0.0437603, -0.106896296, -0.061070126, 0.043993264])))), (layer_1 = NamedTuple(), layer_2 = NamedTuple(), layer_3 = NamedTuple(), layer_4 = NamedTuple(), layer_5 = NamedTuple(), layer_6 = (layer_1 = NamedTuple(), layer_2 = NamedTuple(), layer_3 = NamedTuple())))

Generate an example input.

x = randn(Random.default_rng(), Float32, 28, 28, 1, 4) |> dev;

28×28×1×4 Reactant.ConcreteRArray{Float32, 4}:
[:, :, 1, 1] =
  0.0693325  -0.423616    1.07872   …   0.284702   -1.74668     1.24608
 -0.388372   -0.336906   -0.024543      0.763607    0.461403    0.524358
  2.13345    -1.09728    -2.02289       1.86508     0.280902   -0.225321
  0.600317    0.718761    0.864797     -0.461089   -0.106058   -0.602584
  2.38351    -0.292997   -0.102362      0.445425    1.81862     0.562875
 -0.763559   -0.432734   -1.94579   …   0.492834    1.10095     0.307779
 -0.0928091   0.500158   -0.656345      0.809652   -1.30183     1.04674
 -0.674203    0.56623     0.959849      0.330619    0.46461    -0.545123
  0.608586   -1.63048    -0.682961      0.159214   -0.950532   -1.38903
  0.0932572   0.663445    0.554253     -0.737437   -1.08684     0.896531
  ⋮                                 ⋱   ⋮                      
 -2.25725     0.876298    1.91024      -0.665405   -0.0777341  -0.531626
 -1.32794    -0.133021   -0.378703  …   1.16236    -0.649646    1.47762
 -0.696063    0.221808   -0.740343      0.286608    1.03794     0.469701
  0.0292798  -0.0510331   0.579802      0.0766193   0.261117    0.136539
  0.463691   -0.221081    1.55066      -0.226806    0.0409733   0.699204
 -0.725311   -0.406319    0.532943     -1.11171     0.468755    1.67395
  0.697855    0.48289    -1.09882   …  -2.77168     0.771705   -0.354841
 -0.765199    1.01969    -0.555739     -0.449902    0.127574   -0.205248
 -0.637825   -1.76086    -0.770079     -1.40613    -1.78155    -0.699864

[:, :, 1, 2] =
  0.351659   -0.703038   0.950472   …   1.91414    -1.43984    -0.510335
  0.569031   -0.55088    1.57225       -0.803675   -0.535108    0.412621
  1.08        1.09319   -0.702379      -0.914326   -0.0880074  -0.780679
 -0.154779    0.637349  -0.881809       0.763193   -0.731912   -0.764204
  0.645386    0.364434  -1.3722        -0.75569    -1.0643     -0.577052
 -2.89011    -0.496328   0.339538   …  -1.74834     0.228083   -0.794029
 -1.1766      1.10019   -0.490318       1.2032     -0.110351   -1.00609
 -1.89651    -1.96315   -0.754487       0.964351    1.87818    -0.5309
  0.79848     1.03886   -0.410096      -0.0761639   0.621745    0.847835
 -0.0365749   1.0078    -0.463132       0.751452   -1.05789     0.318569
  ⋮                                 ⋱   ⋮                      
 -1.1203      1.26625   -1.15811        0.294276    1.62326    -0.776538
 -0.177798    0.361185   0.295686   …  -0.177081   -0.780918    0.824192
  0.513576   -0.935364   0.0814671      1.17417    -1.92886     1.06819
 -0.0129912  -1.43166   -1.5504         0.0196996  -0.895906   -1.0299
  1.71888     0.453427   1.61204       -0.855893    0.445279    0.888125
 -0.599821    0.378618   0.565978       0.467776   -0.444482    0.176865
  0.431459   -1.03185    0.740449   …  -0.501875   -0.798734   -1.90323
  2.01706    -1.18083    1.23864       -1.29229    -0.179664   -0.743148
 -1.6797      1.72966   -0.711895       1.35777     1.86796     0.775137

[:, :, 1, 3] =
  0.44954     1.45479    0.791855   …   0.595445   -0.156437  -0.649262
  0.0455653   1.24115    0.713157       0.971481   -1.3061    -0.697567
 -0.511792   -0.42688    1.41848       -2.59061     0.259143   1.94259
 -1.60927     1.47028    0.0283367     -0.565658   -0.236979  -0.248763
 -1.87087    -0.435666   0.18871       -0.330288    1.8399    -1.34421
 -0.65931     0.778272   2.51446    …   1.04722    -1.36473   -1.41361
  1.01411     2.17853    1.41086        1.15259     1.06624    2.11729
  0.83099    -0.5665    -0.39345        0.948687   -0.368952  -0.280315
 -1.23403    -0.211008   0.769623      -0.382225   -1.18393    1.13788
 -0.282996    0.246495  -0.453514       0.261971   -1.78962    0.0473894
  ⋮                                 ⋱   ⋮                     
 -0.279684   -0.506223  -0.126196       2.00406    -1.81122    0.583252
 -0.587942    0.17722   -0.806907   …   0.388054   -1.66424   -1.2271
  2.04241    -0.811282  -1.82934        2.25989     1.69709    1.55675
 -0.785761    0.482697  -1.10743        0.0176254  -1.7621    -0.745583
 -0.647252    0.166588  -0.528163       0.760599   -1.98595   -1.38049
 -1.68016    -0.668969  -0.234502       0.155715    0.498571   0.686325
  2.19125    -0.550269   1.97988    …   0.309272    2.44185   -0.106064
  0.368998    1.69696   -0.452838      -0.561957   -0.583303   0.408157
  1.66073     0.730257  -0.283591       0.753012    0.796029   2.56915

[:, :, 1, 4] =
 -1.30556    -1.76606    -0.102552   …  -0.757897     0.844432   -0.230305
 -1.87644    -0.612699   -0.409143      -0.306025     1.13655     0.570291
  1.35736    -1.37773    -0.514924      -0.0327042   -1.05062     0.0277007
 -1.71801    -0.464733   -0.640684       0.598324    -0.0237537   0.77599
 -0.271766   -0.838042    0.444151      -0.940421    -0.717739    0.152535
  1.05935    -0.565167   -1.27049    …  -0.460655     1.17721     0.428739
 -0.282804   -1.49593    -2.54169        0.25486      1.09307     1.75219
 -1.25632    -0.362553    0.305122       2.13352     -1.44251     0.501179
 -0.621103   -1.11994     1.65988       -1.28509     -0.928562   -1.13418
  0.493673   -1.4562      1.70243        1.01968     -0.964679   -1.17621
  ⋮                                  ⋱   ⋮                       
 -1.22057     0.531929   -0.0841045     -1.01232      1.89455    -0.486553
  0.916459    1.43045    -0.830426   …   1.10855      0.829399   -0.070775
  1.59552    -0.779778   -0.326881      -0.152029    -0.101989    1.85498
 -1.05128     0.491878    1.17028       -0.814344    -0.767232   -0.104464
  0.243358   -0.0288306  -0.140886      -0.00362951  -1.42057     1.26966
 -1.44502     0.654612   -0.267343      -0.686056    -0.588395    0.085004
  0.670296    0.889431    0.564166   …   1.48393      0.38317     0.778055
 -0.979213   -0.0132893   1.06811        0.0480311   -0.861362    1.11876
 -0.0377959  -2.00042    -0.734298       2.18866      0.176042    0.0990701

Now instead of compiling the model, we will use Reactant.@code_hlo to generate the StableHLO code.

hlo_code = @code_hlo model(x, ps, st)

Module:
module {
  func.func @main(%arg0: tensor<4x1x28x28xf32>, %arg1: tensor<6x1x5x5xf32>, %arg2: tensor<6xf32>, %arg3: tensor<16x6x5x5xf32>, %arg4: tensor<16xf32>, %arg5: tensor<256x128xf32>, %arg6: tensor<128xf32>, %arg7: tensor<128x84xf32>, %arg8: tensor<84xf32>, %arg9: tensor<84x10xf32>, %arg10: tensor<10xf32>) -> tensor<4x10xf32> {
    %cst = stablehlo.constant dense<0.000000e+00> : tensor<84x4xf32>
    %cst_0 = stablehlo.constant dense<0.000000e+00> : tensor<128x4xf32>
    %cst_1 = stablehlo.constant dense<0.000000e+00> : tensor<8x8x16x4xf32>
    %cst_2 = stablehlo.constant dense<0.000000e+00> : tensor<24x24x6x4xf32>
    %cst_3 = stablehlo.constant dense<0xFF800000> : tensor<f32>
    %0 = stablehlo.transpose %arg0, dims = [3, 2, 1, 0] : (tensor<4x1x28x28xf32>) -> tensor<28x28x1x4xf32>
    %1 = stablehlo.transpose %arg1, dims = [3, 2, 1, 0] : (tensor<6x1x5x5xf32>) -> tensor<5x5x1x6xf32>
    %2 = stablehlo.transpose %arg3, dims = [3, 2, 1, 0] : (tensor<16x6x5x5xf32>) -> tensor<5x5x6x16xf32>
    %3 = stablehlo.reverse %1, dims = [0, 1] : tensor<5x5x1x6xf32>
    %4 = stablehlo.convolution(%0, %3) dim_numbers = [0, 1, f, b]x[0, 1, i, o]->[0, 1, f, b], window = {stride = [1, 1], pad = [[0, 0], [0, 0]], rhs_dilate = [1, 1]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<28x28x1x4xf32>, tensor<5x5x1x6xf32>) -> tensor<24x24x6x4xf32>
    %5 = stablehlo.broadcast_in_dim %arg2, dims = [2] : (tensor<6xf32>) -> tensor<24x24x6x4xf32>
    %6 = stablehlo.add %4, %5 : tensor<24x24x6x4xf32>
    %7 = stablehlo.compare  LT, %6, %cst_2 : (tensor<24x24x6x4xf32>, tensor<24x24x6x4xf32>) -> tensor<24x24x6x4xi1>
    %8 = stablehlo.select %7, %cst_2, %6 : tensor<24x24x6x4xi1>, tensor<24x24x6x4xf32>
    %9 = "stablehlo.reduce_window"(%8, %cst_3) <{padding = dense<0> : tensor<4x2xi64>, window_dilations = array<i64: 1, 1, 1, 1>, window_dimensions = array<i64: 2, 2, 1, 1>, window_strides = array<i64: 2, 2, 1, 1>}> ({
    ^bb0(%arg11: tensor<f32>, %arg12: tensor<f32>):
      %33 = stablehlo.maximum %arg11, %arg12 : tensor<f32>
      stablehlo.return %33 : tensor<f32>
    }) : (tensor<24x24x6x4xf32>, tensor<f32>) -> tensor<12x12x6x4xf32>
    %10 = stablehlo.reverse %2, dims = [0, 1] : tensor<5x5x6x16xf32>
    %11 = stablehlo.convolution(%9, %10) dim_numbers = [0, 1, f, b]x[0, 1, i, o]->[0, 1, f, b], window = {stride = [1, 1], pad = [[0, 0], [0, 0]], rhs_dilate = [1, 1]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<12x12x6x4xf32>, tensor<5x5x6x16xf32>) -> tensor<8x8x16x4xf32>
    %12 = stablehlo.broadcast_in_dim %arg4, dims = [2] : (tensor<16xf32>) -> tensor<8x8x16x4xf32>
    %13 = stablehlo.add %11, %12 : tensor<8x8x16x4xf32>
    %14 = stablehlo.compare  LT, %13, %cst_1 : (tensor<8x8x16x4xf32>, tensor<8x8x16x4xf32>) -> tensor<8x8x16x4xi1>
    %15 = stablehlo.select %14, %cst_1, %13 : tensor<8x8x16x4xi1>, tensor<8x8x16x4xf32>
    %16 = "stablehlo.reduce_window"(%15, %cst_3) <{padding = dense<0> : tensor<4x2xi64>, window_dilations = array<i64: 1, 1, 1, 1>, window_dimensions = array<i64: 2, 2, 1, 1>, window_strides = array<i64: 2, 2, 1, 1>}> ({
    ^bb0(%arg11: tensor<f32>, %arg12: tensor<f32>):
      %33 = stablehlo.maximum %arg11, %arg12 : tensor<f32>
      stablehlo.return %33 : tensor<f32>
    }) : (tensor<8x8x16x4xf32>, tensor<f32>) -> tensor<4x4x16x4xf32>
    %17 = stablehlo.transpose %16, dims = [3, 2, 1, 0] : (tensor<4x4x16x4xf32>) -> tensor<4x16x4x4xf32>
    %18 = stablehlo.reshape %17 : (tensor<4x16x4x4xf32>) -> tensor<4x256xf32>
    %19 = stablehlo.dot_general %arg5, %18, contracting_dims = [0] x [1], precision = [DEFAULT, DEFAULT] : (tensor<256x128xf32>, tensor<4x256xf32>) -> tensor<128x4xf32>
    %20 = stablehlo.broadcast_in_dim %arg6, dims = [0] : (tensor<128xf32>) -> tensor<128x4xf32>
    %21 = stablehlo.add %19, %20 : tensor<128x4xf32>
    %22 = stablehlo.compare  LT, %21, %cst_0 : (tensor<128x4xf32>, tensor<128x4xf32>) -> tensor<128x4xi1>
    %23 = stablehlo.select %22, %cst_0, %21 : tensor<128x4xi1>, tensor<128x4xf32>
    %24 = stablehlo.dot_general %arg7, %23, contracting_dims = [0] x [0], precision = [DEFAULT, DEFAULT] : (tensor<128x84xf32>, tensor<128x4xf32>) -> tensor<84x4xf32>
    %25 = stablehlo.broadcast_in_dim %arg8, dims = [0] : (tensor<84xf32>) -> tensor<84x4xf32>
    %26 = stablehlo.add %24, %25 : tensor<84x4xf32>
    %27 = stablehlo.compare  LT, %26, %cst : (tensor<84x4xf32>, tensor<84x4xf32>) -> tensor<84x4xi1>
    %28 = stablehlo.select %27, %cst, %26 : tensor<84x4xi1>, tensor<84x4xf32>
    %29 = stablehlo.dot_general %arg9, %28, contracting_dims = [0] x [0], precision = [DEFAULT, DEFAULT] : (tensor<84x10xf32>, tensor<84x4xf32>) -> tensor<10x4xf32>
    %30 = stablehlo.broadcast_in_dim %arg10, dims = [0] : (tensor<10xf32>) -> tensor<10x4xf32>
    %31 = stablehlo.add %29, %30 : tensor<10x4xf32>
    %32 = stablehlo.transpose %31, dims = [1, 0] : (tensor<10x4xf32>) -> tensor<4x10xf32>
    return %32 : tensor<4x10xf32>
  }
}

Now we just save this into an mlir file.

open("exported_lux_model.mlir", "w") do io
    write(io, string(hlo_code))
end

4964

Now we define a python script to run the model using EnzymeJAX.

from enzyme_ad.jax import primitives

import jax
import jax.numpy as jnp

with open("exported_lux_model.mlir", "r") as file:
    code = file.read()


def run_lux_model(
    x,
    weight1,
    bias1,
    weight3,
    bias3,
    weight6_1,
    bias6_1,
    weight6_2,
    bias6_2,
    weight6_3,
    bias6_3,
):
    return primitives.ffi_call(
        x,
        weight1,
        bias1,
        weight3,
        bias3,
        weight6_1,
        bias6_1,
        weight6_2,
        bias6_2,
        weight6_3,
        bias6_3,
        out_shapes=[
            jax.core.ShapedArray([4, 10], jnp.float32),
        ],
        fn="main",
        source=code,
        lang=primitives.LANG_MHLO,
        pipeline_options=primitives.JaXPipeline(""),
    )


# Note that all the inputs must be transposed, i.e. if the julia function has an input of
# shape (28, 28, 1, 4), then the input to the exported function called from python must be
# of shape (4, 1, 28, 28). This is because multi-dimensional arrays in Julia are stored in
# column-major order, while in JAX/Python they are stored in row-major order.

# Input as defined in our exported Lux model
x = jax.random.normal(jax.random.PRNGKey(0), (4, 1, 28, 28))

# Weights and biases corresponding to `ps` and `st` in our exported Lux model
weight1 = jax.random.normal(jax.random.PRNGKey(0), (6, 1, 5, 5))
bias1 = jax.random.normal(jax.random.PRNGKey(0), (6,))
weight3 = jax.random.normal(jax.random.PRNGKey(0), (16, 6, 5, 5))
bias3 = jax.random.normal(jax.random.PRNGKey(0), (16,))
weight6_1 = jax.random.normal(jax.random.PRNGKey(0), (256, 128))
bias6_1 = jax.random.normal(jax.random.PRNGKey(0), (128,))
weight6_2 = jax.random.normal(jax.random.PRNGKey(0), (128, 84))
bias6_2 = jax.random.normal(jax.random.PRNGKey(0), (84,))
weight6_3 = jax.random.normal(jax.random.PRNGKey(0), (84, 10))
bias6_3 = jax.random.normal(jax.random.PRNGKey(0), (10,))

# Run the exported Lux model
print(
    jax.jit(run_lux_model)(
        x,
        weight1,
        bias1,
        weight3,
        bias3,
        weight6_1,
        bias6_1,
        weight6_2,
        bias6_2,
        weight6_3,
        bias6_3,
    )
)