Failed to legalize operation onnx.Conv.

[vinayakdsci]

The ONNX operation onnx.Conv fails with failed to legalize operation 'onnx.Conv' during lowering, as reported in #812.

Duplicate of iree-org/iree#17944.

More details will be added later.

[kumardeepakamd]

llvm/torch-mlir#3670 seems to be in flight : Do we need more fixes?

[vivekkhandelwal1]

llvm/torch-mlir#3670 seems to be in flight : Do we need more fixes?

Once that is landed, we can test it again and update.

[vivekkhandelwal1]

@pdhirajkumarprasad This PR llvm/torch-mlir#3670 is merged. Can you please test the fix?

[pdhirajkumarprasad]

Complete IR:

module {
  func.func @CNTKGraph(%arg0: !torch.vtensor<[1,1,28,28],f32>) -> !torch.vtensor<[1,10],f32> attributes {torch.onnx_meta.ir_version = 3 : si64, torch.onnx_meta.opset_version = 17 : si64, torch.onnx_meta.producer_name = "CNTK", torch.onnx_meta.producer_version = "2.5.1"} {
    %0 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<"0x05AABB3D63B3F83D52CCAE3D4E19FB3D863FCABD86A1E5BD9A5C12BD1225C5BDC66F17BE3FC80E3DDD6F46BD952D0C3E5D8F153E9D49F5BD9408553EA29568BE7CBFD83E8257703DDD542BBE39DDBDBE5DDA44BEEA2AC13D7DE1BF3DEAF92ABC64206C3E7CA8E7BEB1E4233B56BD4BBCD829023D4FCC9BBE33AB933CF8E9C63CEF1F933E20182F3E315C98BD7BBB903CBEC5D7BDB88CA7BDFB43753DB33EF83936311DBE740CF73DCA7CDC3DE573C13DC10D8FBDF465D2BC2880DF3DEF58AABD351FA93DC3EC33BE31342CBE9253D73D457F32BE9683A8BD9DF3ED3E0CE5F73CBA2923BE695F4C3E78EFCEBD7234FDBEE6BFA83E22C5353ECEA0D23E6C70FC3DFD2F8DBE1ABEEEBEF117BEBEDFFCABBCAD1203BE22E7463DBC69C33ECD8B6DBE1CD6D03EED9DB93C22769F3D32912BBFD1937ABDFE8E3F3D13EA06BD6AA9113EEEA303BE28C8D43C17C412BC8DB2373EB60D09BD4B7BDEBC676C053E36563ABD04DF5B3D28FEB23C6A7CC3BBEC6F4BBD4BBA22BE3AE9DE3DD5252EBE32FBB23E497C32BE4C73293E2FBD0FBE4CE731BEB1EB15BDA043243D7990443EED252E3DD6C127BE7B42E23E86A09D3DB312E0BE82A94A3DAB6D71BEAD3AC9BDF699CFBEC0FCBEBEB989793E4D8705BFA01F113F0C230E3F2BDFD5BD3059383E91FBE8BD0E6822BD53ABE5BD39571BBBA50B4EBB1778D2BD32FB9B3CF5BE93BDBDEC80BDC6FFB13DE4D1733D14B7383EA7EFDC3D69B848BE8157033CA3A3813D4385723C803C93BD31E2DA3D8BED7ABE00E4793C029DABBE984C06BDAAE322BE0E90153FD08B96BD7907043FA602253EA8384DBEDE5AB03DE39188BEF50CF7BE31B9253D390AD4BC5659993E45DE5EBE4039A63D997A883E509294BE957F883EC0EC4FBEC78FFEBD9179C33B5DBF653E6AD10B3EA799E5BC96D48FBE54E7BA3CD657853DEA8B15BE4526D13D268B0CBED3CB653DE620A6BD540CE93BFFA553BEAAD5E73D607538BE3A3AB83E63546BBEFBC4FE3BE0C159BED2EFD1BDC090BA3DA1EE8A3E578614BE60A7B23D96CB8FBD3C180B3EC1CAF03DDC49C7BBC4001EBDD214B03DCC32423DF31316BDFC3EA73D147AAC3D5AFA093E041681BD30B8B53B524B9E3DFEC9C4BCD84297BD7F7C783E0A174D3EC29502BE226CC0BE5EC183BE93542E3EF4ED94BD26B8DFB90E9F0FBDF52B813ECDB5973E3929353E7071D0BE708C37BEFE47F9BE0A043F3F1E1F32BDE8D526BDBEDB673D862291BE1D528A3D9AAF8CBE2F3497BE718FA33E2B55CCBDBAC1B73EAE650CBEA2B178BD84A8B63D0ABFFDBD237BF6BE80C7F7BD6D959ABDAFCE013F2096253E0F0E093D303254BC1245E13DDC8739BEEF1656BE4ACC2E3E8E75553E6EDFD43DA4E6283E55C054BDFC7B983EF262F5BD258B35BE720612BEEF443EBB23B320BEFA987ABDC6163C3E0D258C3E2E87B8BDF771F2BD0745DF3CA52CAB3EC6C413BEA5E293BE0A9182BEEA38C7BDC6E8133FF82395BE5C2F3E3E64C86EBEBC063A3E46F129BCF2451CBD4297B6BD8586253F816C2B3E67D8013E108EC8BD284E5B3EC670873EE5A45C3D886F42BF76C64FBEE259B23EB7C40CBE57DF923E46EF5B3E78CAC93DB63076BEE95E233EA93668BE2D958C3C24C43A3CF864333E037E4C3D4D959E3D64903BBD2D5EC83CC6B652BD103DFABEB5C486BD6E4ECE3D4764893EC81D23BD0290C83DDF2A22BDDFD19BBDE7A9DAB862E2053DB2B69FBE906DB63D8D5794BE2FC8A1BD194C7E3EC9ED273F604694BE8053B13E08EFDABE491380BD2E92293D1B33C2BDF41512BE5A56153D5DEE06BE7B1C393C2AD02BBD59B7CFBC028A35BEBF85473DCF4BE93B08FA0FBDCD18F7BD99EB1C3D366C49BE64DE1B3EFF48883D7743DA3C929B3ABEC08F4DBEF2937D3E7326123EBAEA4EBC0A0B013EA6B494BCD9B23DBC9DF2E83D20B1AABEA43D06BEA01F14BECD01603EFF2C943E090B803EAA24323E0ED6D03D762583BEAC73E5BDA83372BD8935103DD73627BDCC0BB5BDD55A8FBCAC7E0F3D8DD4D3BD232E8E3D9663903E494C883D847F2ABE7E88873DCB7C28BE2328D13ED132803D894882BD585A91BE42873B3E072BFD3E68EF973ED7198BBE3DF1FABB4F4AFEBEE3BCAF3EEABBC23D37DC8DBE4DBA453E51F578BE7D3F643E29F5383E6E4EAEBE9F80C93C2C0FF6BDCFC7AA3E60B573BEAB6CFB3C32D1AA3C8B186CBDF5574BBC3EEA81BE4B99ACBEE4A0FE3EA1F0CE3E4388B83CBD1765BE1D5A023B5F53A3BE1BB2F43C4994333EF2915B3E28C2883EFD4BE03D42F090BE2AC5A73E63128D3EF112C1BEAC982CBF0C35D9BE59E133BE8868633EF29E063F293EFF3E6FF53BBF676794BDA62102BD55361D3FAFF48C3EA7ED39BE1ED2303C53408E3E4048473DF38C443ECCAA11BE7BD3C03D03A011BED49E43BDCB800D3E8602E93D28A411BB6AF3463C421703BEC5E7573E5AE2C93DDFF1F0BBD023BBBDE78E3EBEC4D9BF3D3F25613EDECD613CD9DF973CA43FCD3D788A6B3D8DFFB23D28B584BE0E9F513D2D84013D82DB9A3D01B05CBE65703DBE7BD1893ECFDD6DBE98DF753E86DF5A3E158208BF084BA1BDCEDB583E75A52E3EA848093F654D4CBE0B56B9BB5FE947BE2100FEBD8A668A3DAFA2F9BD226E5F3D81219ABE13E80FBAA09E2F3EF32E453F9EB38ABE154E713D09F312BE9C4EC1BC1F46B6BC42AB6FBEED308DBE2D53CE3DD02B08BE10F10ABEC51F1A3E56290B3E9D4FABBD15CA123E3534DF3DC969E0BDADF9973BF130A83DD76269BD138705BD32FB2A3E17CFEEBD2478053EA462ED3D2817AC3D73330FBE482F8EBB69B39ABDC8339A3D12B6153E83D595BD1EE428BE995145BD5EF8E93D1B9629BECE68CBBDF64B31BE14D5C83D506E563D4FEB123D673787399A06E2BD07B4A23D558369BCA44B373D0CABC4BD0E7085BD40DE983CBEA6D83DFFE0E53DB761313E354786BD55EC033D4F7D873DC2EFD2BD35DF3ABDBBEAB9BD7416AF3D38F18CBDD6D4A83EEE560DBE13F780BDCD27303E4EC46BBD4DB60EBE0E898EBEDE9A6EBBEA8A2CBEAA40CD3E3399D73D2339F6BCB3CBA0BEBFEDD6BB60CD9ABCBB37BDBC09F054BEC262EE3DC6453CBD50BCF83D3BB0F13D51E5B63E9C0941BE05E507BE24345D3E03C991BDCDE676BDC66E6ABC97E6C83D9055DDBD76B1FD3C5FE7C7BD97C4CC3CAECD76BEA10DE03C9B0F0A3B304FF43DD186C63D0055A33E6F302ABE54D9273E25FC7EBE5FA8043D22A2C23DCE7E823D9751C93EC96ACCBE563842BE4E5179BECDCBBE3ED8EAB4BEC065AE3BDB1838BD138A6F3DB43F86BEDA3B72BD0844B6BED0EE533E165B8A3E99A85C3E0600B7BEF01312BC8F4DCCBE31B6603C44E4D83C1098113E6D614FBE98A98E3E0221DBB90E1E45BE5ABF223EBA4CBDBC2795E63CDCA7EE3DF39A92BC376867BD50E7A3BD511B97BDD027D4BDBAC72E3D04EBB03E5B02B6BE1B31843DB511BEBD0310EE3D24935B3E7F5E933ECA53B8BE26EBA0BEA236B43D7AADCEBEFFDF493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D7795073DF9CF87BD7936BD3E82EFB03D019D5EBE5BB7A7BE7442503E8A8F2A3DC4D418BD1C47833E82A885BE0356983E70BE07BE0E89B23E78D5CCBE656BC3BC258D1C3EF082B3BE0591CD3E263AB9BB9EFB82BE14F5FBBC3984F8BDEBD410BEB414723D7099B2BD408FB93D35D5143C2F0DD53B8C89C23BBCDC08BCDC2236BE9066E63EB9FD1EBEE7C957BE6760883D753F263EE64285BD55E3B63AF7463DBEF2FCE6BD3DF7CDBD5804C93D6A214DBE271EB63B334EBA3EA76FE63DB95CEDBE279C7B3D2755BFBE7FD3043FFAE0033E7714F2BD2C580EBE9FCE543E5F4D8BBE812E8A3EA82C63BE82BE703E13C315BD9EE7F93D"> : tensor<16x4x4x10xf32>} : () -> !torch.vtensor<[16,4,4,10],f32> 
    %1 = torch.operator "onnx.Constant"() {torch.onnx.value = 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DF07C953C0799703ED436DD3D8C760CBDAE7455BD57928DBD00FD0A3E87AB283E827646BE48A3F13D7EDDB3BDC97E3F3DEE711EBEDD9BF7BD7EEAA13CD49C28BE6920033DB48A92BEA40983BE62CFA63C560FEBBD7BD253BD354955BECED34CBD8F3789BD303B86BEBB93243D650F37BEBA03C4BD81E9C6BD37CA84BD2E01503DB09CA53DADA110BE7A3E17BE0A3DD93D47EBEDBDAFC3C2BD8E868ABEA8D0EFBC7D26303E59F3B6BDE7F3EEBDDE7116BE6A177DBCBADABE3EA34BD9BD1CBE95BDB2CD5DBC0547C33C5E8F0E3E4361B63D3B7B57BBE3999EBE49B74EBEE25601BE7366EA3D518CF23CFC355ABD660C8EBEA56D48BDF925B4BD1C9FD7BDDBDF95BD81A556BEC0A219BE49CD3F3CA13207BEA9BB0FBEDFF20FBEFE39ED3D11B1F8BCF4B138BE7F1FD1BDFFA3833DB6323E3E324950BE13FD63BE8CD9B4BE612535BED0117A3E44E8C6BD5AED5BBCFAF255BDF18481BD3D41953EE4F61BBD681A233E3A402A3CDB1A903D222FA43E7000D13D16DA943E0A8E153EA81A233EC3F2BB3E1898403E192E903DE1B8C73D4C70ACBD8ECF913CD897C23D090C64BDBD78BA3DD4BD083D9E6B333D1B4D43BD7A0B5E3EED7F7BBC3E4513BE4F33993C4789F83DD450323D176B84BB8C7180BBB6F93FBED58A9C3D244F5FBE3616FFBD9B71333EDD1A38BE138105BD95B6A5BEC14959BE38322ABE0707863B4291783D8528A03DDE0717BD54A067BC8216DDBD6B64DC3D19F7193E91BE22BEA633C4BDDB7E8FBD690548BDD81D02BE722CCA3CA8C6DE3C5D7EF7BCADA757BEDBB0D83C14CB093E45C4EC3DD81547BD48CF183DDD25893E3541D03DEC1C1E3EA98AAA3E6A269C3CDF948DBD5DD2B6BD579C0DBE54AC4BBDDFBD8A3D4D531ABEFA5AA7BBE4E4813C8A5134BD4864D43D92E96CBE7FF3C7BDB71D98BD9EB7723D9D39383E2652A7BC9C7E553D65049D3D80069BBDD442FB3DCF5B3C3D82C2E53D9CA5283EE2764EBBF653AFBD478E2FBE61E639BEC2C14CBED2A5E1BCA01AD6BD7E2F21BEEA79A0BD80005CBD9F38903C2A6A38BDF6B9D0BC2C6F7BBD8C6DAE3CAC7E863D5C2103BE8B2F9E3D3D6E293E19F3143D5AAF6BBEF152113E9AAC693EC22DA33E2D903FBECA41C8BE89C798B9A3104DBD4929EBBD89F7F8BD49E5FD3D01E59D3B630622BD96E32EBDAA3A903DC17633BCB53C6F3D02F2C5BC951EB33D412FEE3C03C8BF3D09FBA6BD9446C63CADB15BBD600891BDFF2F73BD428C77BE682F0DBEADE581BEE293D4BD8462B93D082CE0BBD5FD3FBE5C3262BE41FEBBBDFCE06EBB910D4DBEE32D04BEFDDBC1BDBDB5273BEA191D3CCB04493D031E853E407E003DCFA08ABDCD2A6BBD6279423E375C513C9F3509BED8F34DBE8995E1BDE95623BE0E144DBDA1817DBEDA3327BEDD3E723DC73844BDC53A9FBC06732DBE417AB93BB1ED9ABD63252CBE6480B4BDFD072ABE7BE4E1BD6E291BBD20422C3E27FF583E9A1F6D3D58650EBD43A7D2BD22D7DA3D65FB3F3D490744BD97B843BE9E5DF0BD0FF343BEF93305BEEB4325BE87688CBE1776E9BDB5B716BBC5C809BDC8832CBC025D25BE78E609BD5D9E493C5680023D0999E73D985D98BD8D2510BDACC6E2BCCE040C3EEBA182BBB3AFF5BDEAD14E3B1D174B3D5D3C0CBD307983BD5AFCCBBDA63DA2BD927DCCBD3699BABCFA8D063DD4519DBD599A813D02ADBDBDBA3F6FBEC01134BE5272F8BDD70FF23D345FCE380F332DBECEA012BE4942B6BDD357BFBD2462B9BD26C099BDD897A4BD8BEAC4BCF07EE7BC772E9F3CD06C433E852E513ECD64963A8877EFBDAD31713E3356623EDC62B33D7FD081BE9141A3BDAC5E75BE72FD0E3EAA5F6A3EAC7335BE045783BDF46530BE01C246BD05B900BE101E9DBD0FFB89BDAD33F9BDD2A777BD676F993D065AABBD300A24BE873CC63D5241D63CFC28EDBCFD8BE63D8B34AD3C4B72713EC5870D3E814030BE5C4D833EF0E9193E37B71DBEC315A8BB66D8D6BD95BF013DE95403BE569D833C60F9CCBDDD46273DFBE7AE3DA66781BEF56E9A3C9E1C70BBEA99A9BC53181ABD9FBCB73DA81DF6BD74F88BBDCE8E9BBDF005DA3BC05AFF3DCF5101BD55958EBED4F1B43DCD00BF3D2494533DB9C5513E98E2923E5E91283D7BA72DBE59C4B53CEFC20DBDE05C9F3D952FF63C48F3F0BD791C05BEB1F50F3D1C987E3D84F2003E675BD4BDD99473BE21FF063C0F93743D783D493ED852743CA2C252BDF1F6073C7FA9C43C83201DBEECD268BEE1D932BEEE4BB2BD96D5A7BD81CDCD3C23CC4BBE2D109FBED95E86BE7F3855BE369701BE7FFAC0BC94921FBEC0BB16BEDAB8D1BDB07ED03D6D2F233DD2FF283E339062BD225D21BDA67BE33DD8656F3DE12CCA3E6D11153D2082D03C1253063E61C6D13E5BE4F03E67F8F5BBF815DF3CC159C63CA521DBBCB37F02BE9BC3DD3B47A3C33D91F6783DC2D4B2BDB113E53C6119DFBD9B5FA53D01FD5B3C4DA41A3DD0017C3E7FA7BFB736E9ACBDEEF759BD406D7EBDB438BA3D88EA183CC6902D3D8C6924BEC16F2CBC363756BDF2AE313EAD794D3D7E13CEBD4F1D4CBC0B34493D27E51EBEAE64633D3874C93D142007BE8123D63C8032A83C79FC963D7B32FC3DE2EE26BD552BFC3DD1A5903C2348C0BDED0F303DE5681A3D5246DABDD8B770BC09A7EA3CFB969EBA32920BBE209DBDBEC2560F3E0E6E92BDB63A443E525C583E329DF3BB7029B33D5ADA0F3D4A148C3C5998683C394F283D6FA872BD079A5DBCB501CCBDF4DA09BEBD3B5BBDAACC573DAD650CBE8293F9BDF42102BE78F701BE46E0DE3D74B5B93DE81770BD249D99BD784BD5BDA505593E04CE583EE2AA8C3D231982BEC8EC24BEE43B8D3C88FD2F3DEAB7DD3D0FCF26BEF76E7FBE0E79A43A92EBBBBD6D6091BDA281CFBDF9BB59BECB4A6B3D31B8633CC1F257BAAAD606BE9BA05DBE9952DA3CA06D23BD487056BEA38AFBBDFF06D03CDC4D1B3D5EB1F03D3FB3F5BD53252EBED27CC7BDC7FD503E4FEC413E2C432ABD8FCE3F3DC46560BD9E0D9ABDA009A3BDFD6CDEBD909D05BEBA419CBC5A7B55BEE40A01BECC4154BD473D09BE3B1083BEC6FC41BDFA881E3D8DA125BE8FED41BE146673BE470DC03D5384013D6F85303EA9EB1A3ECEB9883D3B2F8E3C25707EBE0B8F8D3DCED9A93D16C3153EBD00183E65B92FBE825EC1BD6ACF4D3B9EDC403D2974183D6C7BB6BDB316063D4B7C31BE96F8FFBD2FE7F53D3D6B13BCA2B2BD3B54A853BEAEA8903DFE5A96BB5F7CACBDBE23443D1F70593C39F0283D69119FBD85C4353D5AE29D3DD48681BBD964373C38626C3E3B5F103D440B12BE469CC9BB8277E23D03A2233E5F37B3BDE4024BBE6F414BBD725EC5BAE6791DBD34E820BDA964903C974A553B4F103FBEA7CD843E5662F63B936073BE28BC53BEAEA2493D9754E93D240819BEAE0193BEC0463DBEF08ED7BC0A5266BE54420EBE722A53BEB4600EBE8B64A3BD184342BEB5DF09BED60B15BEA16967BDDEA216BEF760443DCF7E6E3DACABACBC29A0833C7E7A3E3ED2395C3D27408A3C6362DCBD06C51B3B99472ABE2506E3BC806493BDBDC315BE38B24BBEF291213C6649FFBC52D4ACBD474269BD5523F93C10E116BE5D5C7ABC59F9313DA1FBCABD00A9893D2B730CBEB547A0BD2375793D04F0F23C5A95A53D49D3603EAA50FF3B1FF534BE09061BBC9B365BBC0A0F6EBD9E7A5FBEAA9B35BD8E1B0BBDAD7135BE8940883B71EE81BDDB2FC33D220C74BD14862FBD93EA03BD548B8FBC7C1E5F3C2E6F423DE35B37BD0D9609BE9D0F2FBD43D207BDB87128BD6157F03CE591763E83654EBE6E7184BBF0D51D3E639E36BEAA1340BB112B75BC8F66463E39960E3D7F2186BD3625563DD3E9803EEF941F3E0620E4BD3A518DBDD1BF03BEAAAB8ABD89F362BEEE5998BDBF11FA3C37F24FBE4AEF63BEB79CECBDF1B9DFBD7136373E9116463E01E9963D3372663E9F5A84BDE767D03CB014A63DEE6D843ECC870A3EC38202BC5799483C0678CF3D0C39703E3B3FC7BDDADD86BE409594BD58737C3D205CC3BD2CFA40BE589EEDBDC1661BBEC2C49DBD5962B7BD9593943C24DCC8BCFEB3CEBD44AD56BE8011BF3D6E421FBD5A6754BE402A48BE618734BE332AF0BD06ED253EE0AB37BEF82563BE57666A3DEEBE92BDE3DBD43B1503513E9191103F6F524C3E878807BE4F94943E29EE893E57A9A03E4FB4EE3DC0FBD43D08BDA13D793A853E785E253EB5B348BDA1DFBBBD232767BD3C77FEBD296D86BEC7C50FBE0C2C083DD3797ABD0390213CBF2B723E88624BBEB157893DC977033DDCFCA53EA70D1A3EF46890BC07754C3E16E333BE1469203E17E1EDBDBE0F2FBEAC295F3B0964E8BDD32A013DEA6C08BEF5DD56BE9A9E053CB30A96BC559E14BDABC416BE21B365BEC30AF93DD86CC03D1EBF1B3D76A293BDF4139BBEC121C03D4368043EA31CF4BD2EF150BE8D35CDBD3250AA3D6F8A95BE8734A6BEE0E30CBE698C9E3DC672683DDC7A453D70F62ABEBF592CBEB9A883BE7642B43C58B3B83D9697C63D8107B93D0A5B04BE15B18A3BDD1614BD2A7F8A3C3B1D2FBE151803BE0B57213B690303BE24E687BEFE0A69BEFC724EBD6B58473DA6A148BD73D2083E196AA73D877893BDD3D0BD3D2DDD583D58CED13DE9ED313C8CF3EFBD8EE2513C35EF093E2474EC3D3DC43ABEF54199BDEAFC133EB76A443DE9E3B3BDD61A25BEF00F11BD99BAE03B22B051BE5E2FADBD7E61C1BC2080BFBD68D18DBD05280FBE7210D6BD284AEE3DD423AD3DC256733C741408BEB78428BD09B7093EC80DD93CA9252B3C8594F93D7248F93D03AE8ABDFC53463DE463EA3DDBCE963D16AD493D563AAABD397FEABD56F450BCBAF7F9BDBE55B4BD43E3293EF72801BE47201D3ECC91D0BD41E091BEE4F049BE3A9E143E5E92EFBD405522BE3F7A71BE3B4612BECDD5A83D422DCB3B12E9FD3C923056BEE3BA47BE9A6315BD758F21BE2C414ABED6AAA4BE430373BE130DFA3CF8DF9FBE4A2AAABE1561B6BE85E0953DAFCE503EA3272CBE74817DBE5AC45DBE63F0943ED51FCC3E1EFAF8BD8A14333DF4B1A73E1A33B93E181C81BEA9DBDA3D1A2AE0BCAC7149BEE21C4EBE0A4882BE442AC73DDAAF6C3E2C97933D04BAFD3D5CA1F33D3343213E770BF73CB5A4B4BDDADA2E3ECC7E223E938601BEBCDC54BE28644DBDFE49D43D0E3110BED74F153EE3BCA1BDAD26BEBD60D3FDBC36E8B1BDA49A593EDB0F763D77EAA23D144C46BDD59FECBCF4111EBD9585CABC9D02ECBDDE42233DBED902BDAEF773BEAFA40DBDF416C7BD26E0883D48248BBC872DCE3D89F2D6BD3B250EBE8B8411BEF9BB8DBEF8A3A03E46592BBD8EAE5ABE6ABA28BD11E304BE118E76BD8D14453D0B44B63DB7CDDA3DB9856E3EE72F27BEC3166CBE9F1A1EBE619D9CBD614399BBB001623D893C99BDDE5CCDBC91472D3E0919AB3D308D363E3AF65B3D8503F83C660E133E1C4BCB3D4956873E1BA1093D35470A3C64568E3E04145E3E5E0F9ABD45F2093E4CC9A23DF86C573ED24C613E3DF8C8BDFE3A81BDF4BB22BC810C39BEB89548BEBC243CBE5770E0BD42ED9B3C0DF6113EEF432A3E678E13BD4A94D1BD494B73BD3EBD35BDD202BD3B8A7600BDBC190EBEBE4656BEEF468BBE141D3ABECD06C5BD2B332A3D5D778E394D09B9BDB9C6B6BDC2A6AD3DE402B6BC983EC6BDFD3AF03B47C52F3D349210BED38A37BED7E941BE4DFFA0BD9373F83D1585B13CC3E79CBDDE478FBE3658C5BD8BC7793EE2303B3E45CE99BCF52280BE47CD6EBC54BFF03DE1DB96BE800293BC3C8B41BD7A1928BE447B88BD4786953D7F863EBDF834DFBD921C823BEFA7AE3DE37E2C3D0A21403E2FBF4CBE611118BE3851C9BC94C857BED644B63D5C6A5F3D21B5E3BD53EB343EF8889ABD49E1453DB4DE5BBEEA2BCABDCBD13D3E01033DBEC935A9BDD1ABDABCCBCFDCBDE4FBCEBD973E593D26C721BE24ED82BDFCF4C93D49F3F43CD922C73A4A75853E1326663DCB1B6EBEBA719E3B25FF32BE9FF6493E326A8B3D9A69853A0B547F3DF34AFDBD30A113BE24B018BED76A3CBE18B6153ED5653FBE5E8F32BE0761873BE8B70EBE8C335EBD005DBB3CC2C65FBD8F89343C793BFFBD28BF25BD8B165A3E2D49C93D60F0AB3DCD0F80BD43A7D7BDD9FD3A3A14E5943E5969BF3DCFFF3CBD1146AC3C5EC0A33D3B8DBF3E092B563D32642CBE2481A73DA9EFE83DCDA41C3E71DB703E93933F3E6C900C3E433DEDBD32ED01BE54EF5DBE174A27BE7A806ABE13CF43BE763DC8BD92152ABD93E652BD7B18EEBC6706CABD7A88B9BD0EBF4ABEC79687BDB30AECBD98C1A73DE1D81A3D26D579BE91F51FBEB4D411BD0FEF87BE4819D5BC2B2F083D83CF15BE11B01F3D6EDDA2BE45B9103E79CD5E3E8E7DBFBCDF794ABE1A0705BE6A76303E67E9313EDFA8DABC17B574BDA8711A3E6D5A353EC84B67BD132847BE6C8BE6BD1DE7D03D2057833DBE71B73C185647BDF49F89BDDF3F24BE3802993E3E73C03DD78754BD640A52BE0D0B8ABDA884C03E3986913D7C50B8BDF0B8B5BD1638E13DDB469F3E781B23BD19F430BE91A9B4BD2647A83DC24A353EFD4292BC9AFD85BE8B5E26BD39310E3E5AE680BD118B6FBE12322BBE201D86BD4CADA9BD991D533C1E68FB3CA72738BE48007F3CC5364D3D79C4DDBDC5B73ABEAB3C4ABD0DA4F43DBBE27ABE3A8B35BEA7D183BE390602BE9D11E03DD76449BEDB043BBE48E5FABD2812FABDDBE443BEABA09CBEA86EBBBD76556CBD2373053C0F2CF4BD91CFA8BEF876413DF562813DBF7E513D04BA09BEE740AABDD23CF73D42D3D03DC2AE74BE28E2C8BDED4001BD3E8A9B3E488AA33D278375BEA74E36BEB22691BCF478813E91387D3A23528FBEF282ABBDA696963EAB8E723C8CABD9BDD52A73BEAF3985BCAB16B2BE9AC6D5BEF7730CBD1F1FF6BCC96A25BE370B90BEAF0317BEF6947A3DBA88EC3BDABD59BE321058BDE82E073DA1F0503D1859B6BD59BA9CBDD18712BE104B373D2E5A223D39B503BE307B9EBCD656B4BD8533053D2EA3E5BD9383613C8F363DBE591C6ABEF61EA1BE306BA2BE5829C9BD95DD443CE153B0BDB6D97FBE2B37D3BCE4BF21BE4B12273CF49A803C953F323D0DAA6BBC4FC0D33C8E950F3E1C0D32BEA2580FBC1265E4BD7BBEE83DD8F88BBD833B81BE788F86BD5419E5BD10DECFBDE19A12BEACB1F6BDF39A69BE103A77BEA19DB3BD63AB09BE34B6C23B6B8E9DBE1C545ABCB5D5053DF4A2C1BC7A25C73DF7DE51BEC03D2F3D352D123E850EBC3D62433FBEAA4E163EB4872A3E61BD063E1CB9213C75AA25BEB962BA3EE983503E3A14C33D7E3C64BB0E3116BE01BAF63B2615853D8166743DD2658C3D6BF19DBE779F7EBEF2DA6E3CD5F9073EE6EF493ECE5FDDBE431A8E3DEC6E85BDB27110BEC7F8C0BD2E12D1BDA749C2BB028DE5BAC2EC55BE3FC2C2BD7B9255BE68F2523D9E1C10BE92BD5F3C1841E9BD"> : tensor<16x8x5x5xf32>} : () -> !torch.vtensor<[16,8,5,5],f32> 
    %2 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<"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tensor<8x1x5x5xf32>} : () -> !torch.vtensor<[8,1,5,5],f32> 
    %3 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<[[[-0.161539719]], [[-0.433835655]], [[0.091641359]], [[-0.0168522168]], [[-0.0650264397]], [[-0.131737873]], [[0.0204175506]], [[-0.121110231]]]> : tensor<8x1x1xf32>} : () -> !torch.vtensor<[8,1,1],f32> 
    %4 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<[[[-0.0822488219]], [[-0.108868778]], [[-0.141039595]], [[-0.204869166]], [[-0.17913565]], [[-0.215438381]], [[-0.133805066]], [[-0.195724562]], [[-0.268250644]], [[-0.258212209]], [[-0.0761560649]], [[0.0132841459]], [[-0.00444464432]], [[-0.414740831]], [[-0.17879115]], [[-0.0386558883]]]> : tensor<16x1x1xf32>} : () -> !torch.vtensor<[16,1,1],f32> 
    %5 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<[1, 256]> : tensor<2xsi64>} : () -> !torch.vtensor<[2],si64> 
    %6 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<[256, 10]> : tensor<2xsi64>} : () -> !torch.vtensor<[2],si64> 
    %7 = torch.operator "onnx.Constant"() {torch.onnx.value = dense<[[-0.0448560268, 0.00779166119, 0.0681008175, 0.0299937408, -0.126409635, 0.14021875, -0.0552849025, -0.0493838154, 0.0843220502, -0.0545404144]]> : tensor<1x10xf32>} : () -> !torch.vtensor<[1,10],f32> 
    %none = torch.constant.none
    %8 = torch.operator "onnx.Reshape"(%0, %6) : (!torch.vtensor<[16,4,4,10],f32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[256,10],f32> 
    %9 = torch.operator "onnx.Conv"(%arg0, %2) {torch.onnx.auto_pad = "SAME_UPPER", torch.onnx.dilations = [1 : si64, 1 : si64], torch.onnx.group = 1 : si64, torch.onnx.kernel_shape = [5 : si64, 5 : si64], torch.onnx.strides = [1 : si64, 1 : si64]} : (!torch.vtensor<[1,1,28,28],f32>, !torch.vtensor<[8,1,5,5],f32>) -> !torch.vtensor<[1,8,28,28],f32> 
    %10 = torch.operator "onnx.Add"(%9, %3) : (!torch.vtensor<[1,8,28,28],f32>, !torch.vtensor<[8,1,1],f32>) -> !torch.vtensor<[1,8,28,28],f32> 
    %11 = torch.operator "onnx.Relu"(%10) : (!torch.vtensor<[1,8,28,28],f32>) -> !torch.vtensor<[1,8,28,28],f32> 
    %12 = torch.operator "onnx.MaxPool"(%11) {torch.onnx.auto_pad = "NOTSET", torch.onnx.kernel_shape = [2 : si64, 2 : si64], torch.onnx.pads = [0 : si64, 0 : si64, 0 : si64, 0 : si64], torch.onnx.strides = [2 : si64, 2 : si64]} : (!torch.vtensor<[1,8,28,28],f32>) -> !torch.vtensor<[1,8,14,14],f32> 
    %13 = torch.operator "onnx.Conv"(%12, %1) {torch.onnx.auto_pad = "SAME_UPPER", torch.onnx.dilations = [1 : si64, 1 : si64], torch.onnx.group = 1 : si64, torch.onnx.kernel_shape = [5 : si64, 5 : si64], torch.onnx.strides = [1 : si64, 1 : si64]} : (!torch.vtensor<[1,8,14,14],f32>, !torch.vtensor<[16,8,5,5],f32>) -> !torch.vtensor<[1,16,14,14],f32> 
    %14 = torch.operator "onnx.Add"(%13, %4) : (!torch.vtensor<[1,16,14,14],f32>, !torch.vtensor<[16,1,1],f32>) -> !torch.vtensor<[1,16,14,14],f32> 
    %15 = torch.operator "onnx.Relu"(%14) : (!torch.vtensor<[1,16,14,14],f32>) -> !torch.vtensor<[1,16,14,14],f32> 
    %16 = torch.operator "onnx.MaxPool"(%15) {torch.onnx.auto_pad = "NOTSET", torch.onnx.kernel_shape = [3 : si64, 3 : si64], torch.onnx.pads = [0 : si64, 0 : si64, 0 : si64, 0 : si64], torch.onnx.strides = [3 : si64, 3 : si64]} : (!torch.vtensor<[1,16,14,14],f32>) -> !torch.vtensor<[1,16,4,4],f32> 
    %17 = torch.operator "onnx.Reshape"(%16, %5) : (!torch.vtensor<[1,16,4,4],f32>, !torch.vtensor<[2],si64>) -> !torch.vtensor<[1,256],f32> 
    %18 = torch.operator "onnx.MatMul"(%17, %8) : (!torch.vtensor<[1,256],f32>, !torch.vtensor<[256,10],f32>) -> !torch.vtensor<[1,10],f32> 
    %19 = torch.operator "onnx.Add"(%18, %7) : (!torch.vtensor<[1,10],f32>, !torch.vtensor<[1,10],f32>) -> !torch.vtensor<[1,10],f32> 
    return %19 : !torch.vtensor<[1,10],f32>
  }
}

[pdhirajkumarprasad]

We still have issue with conv and can be reproduce with below ir

model.mlir 
module {
  func.func @main_graph(%arg0: !torch.vtensor<[1,128],si64>, %arg1: !torch.vtensor<[1,128],f32>, %arg3: !torch.vtensor<[?,768,128],f32>, %arg4: !torch.vtensor<[768,1,9],f32>) -> !torch.vtensor<[?,768,128],f32>   attributes {torch.onnx_meta.ir_version = 8 : si64, torch.onnx_meta.opset_version = 17 : si64, torch.onnx_meta.producer_name = "pytorch", torch.onnx_meta.producer_version = "2.1.0"} {
    %323 = torch.operator "onnx.Conv"(%arg3, %arg4) {torch.onnx.dilations = [1 : si64], torch.onnx.group = 768 : si64, torch.onnx.kernel_shape = [9 : si64], torch.onnx.pads = [4 : si64, 4 : si64], torch.onnx.strides = [1 : si64]} : (!torch.vtensor<[?,768,128],f32>, !torch.vtensor<[768,1,9],f32>) -> !torch.vtensor<[?,768,128],f32> 
    return %323 : !torch.vtensor<[?,768,128],f32> 
  }
}

[vivekkhandelwal1]

Hi @pdhirajkumarprasad, can you please tell me how many models are impacted through this issue? And the list of the failing ones too? The issue is that the Torch-MLIR does not have the support for 2d group convolution, and hence resulting in this failure.

[zjgarvey]

This is specifically a depthwise conv op. There isn't a linalg op for grouped 1-d conv, but there are depthwise 1-d conv ops in linalg dialect.

[pdhirajkumarprasad]

Hi @pdhirajkumarprasad, can you please tell me how many models are impacted through this issue? And the list of the failing ones too? The issue is that the Torch-MLIR does not have the support for 2d group convolution, and hence resulting in this failure.

we have 52 models failing due to this

[vivekkhandelwal1]

Fix added in llvm/torch-mlir#3770.

CC: @pdhirajkumarprasad

[vivekkhandelwal1]

Closing this since the PR is merged.