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stellaraccident/tt_d6r4.mlir

Created September 17, 2021 23:58
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tt_d6r4.mlir
module {
func @main(%arg0: tensor<1x50x1024xf32>) -> tensor<1x50x1024xf32> attributes {tf.entry_function = {inputs = "Placeholder", outputs = "ffn/projection/transpose_6"}} {
%0 = "tosa.const"() {value = dense<[[-0.606424093, -0.257068604, -0.861039459, 0.599411249], [-1.40011382, -0.714802205, -0.834389805, -0.70947653], [0.200624198, -0.309171468, 0.1557163, 0.0122365123], [0.974201858, -0.0861519798, -1.01052082, 0.793641865], [0.46980384, 0.977718114, -1.16083705, 1.39096498], [0.0763229355, 0.101014435, 0.729743123, 0.239240512], [-0.188968942, -1.7925849, -1.52064347, -0.491924822], [0.16679813, -1.3849684, -0.231052741, 0.388421297], [0.162655845, 0.174614832, -0.278692663, 0.444319904], [-0.271921933, 0.744492352, 0.488966912, 0.248902649], [0.126721114, 0.763294458, -0.459857017, -1.4220233], [0.39206174, -1.0173862, -1.52627277, -0.120210558], [0.267897397, 0.127397686, -1.79463243, 0.0738144368], [1.07101059, 0.714992702, 0.119871214, 0.461607963], [0.734783053, -1.70889008, -0.237300709, -0.255349785], [-0.170076206, -1.38190603, -1.92018163, -0.0975050926]]> : tensor<16x4xf32>} : () -> tensor<16x4xf32>
%1 = "tosa.const"() {value = dense<[2, 0, 1]> : tensor<3xi32>} : () -> tensor<3xi32>
%2 = "tosa.const"() {value = dense<"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tensor<16x16xf32>} : () -> tensor<16x16xf32>
%3 = "tosa.const"() {value = dense<"0x7294593F40B4E2BDBB91DB3EE797C43C660BF33E4CB7FC3D429987BE6055363F3AA32EBD87B5B5BF34EA14BFEF500A3F206C24BF19AEEC3FEE7B68BFCC37483F380C503F0059FD3F8BA4783F932E8ABFA9B5BC3E52B6693FB42EB3BF3B8865BF05FB033FDCE2E8BF3590933FE9D2AD3DAA3D05BFEF43953E203ACA3FEE001A3FAC8EFDBE704785BFE442E5BF9981DA3E9C8D0BBF3DFDA1BFC7279DBE383B9F3E2086FD3FF4E640BF3B9013BFC3524E3F5EB4BFBE60DA86BEEEDE4D3D39A8143ED5EE8ABE3CF3E1BECF988BBF8CDB6DBE6606BA3FC17CB3BE65DCB33FA57A0EBF5F1EF43E76A01A3F22FF16BF68B6D7BF5626363D177057BE84FAA4BF1AF6673FDF99C8BE2254313F4CFE9A3F0801863E57C9BB3C5109C23FF953953FA4860BBF18F6613FAD10023F0EBF233E7CAD53BE5D5BEC3F84BB013F41BFC6BE077BC6BE748F233F0741BC3D2E6201BF4EBBD03FEF634B3F34FE533FFAD8C6BF88542F3EF4D480BEC67F3EBF885FF5BD1C81A33F48DFD33EAA77E23EABF681BFBBE44BBF732615BF69B1F0BE01218CBF642626BBA8219EBF32E656BC109B39BFACB39FBFB8F3543E90212F3F7A9D38BE58C4473FE53E153FD375793F31B05F3F39DC60BFE81576BF110EA33F3124D9BE1B906CBEA86AF93E140F8BBDDD271B3F5527953F91BD1EBFF27846BFC6C3833FE24CCC3FF25736BE74316FBF71BD6BBF21E8C23D700879BE4D97A0BFC98575BE803BD7BFB0D0473F4C19673FABB936BF465BC63E1582863D8F8BE8BE664E373F8F38DFBE56F3BB3F1B6C67BF89B920BFAE2FFEBC2B5F2CBE18A48B3F6338D43E4B5C2A3F84D2B5BE128C16BF1B8C9E3F667255BF66B3953FC6DC9C3E0CBD1FBFF96980BE891999BDD85D36BED8D0C5BDBB11CABE313153BFF641673FF62ACD3FCC65AE3E7306593FA3806CBF62BC61BF4A0ABC3E106E5ABED7E5ADBFCAF2013E5939ED3E4E668BBEF897AFBEAE7629BF7C95E13E9FD803BFB26901BE59F01BBFBDE5F83ECD00473F80400A3E8D5097BECD491DBEEED89C3EAA2BD33FA642D83F580FF43F5ECDA9BCC468BE3E8B787B3FF44189BEBFD85CBF72B79DBFBD308EBE3B24563E79FDC43FB4C93DBF2E4EA83FA34A63BF3AA78FBE6E1077BEE91D3EBD7FDC8D3F0C2549BF6F9285BFB2CC6E3F95561FBFCC1A27BFDBB41B3E5AD3C83FAEB49BBEAC214EBF2198033FF95647BF183DB6BF2A0770BEC70DD73FB69BF2BFDDA010BE0C19FCBE3A4582BEA782EA3E37B1D33F47AD15BFC15CB0BFAC3167BEF905FF3F7B162CBE81AC7C3F190318BF92E944BF9B5D07BFF34BA13ECAB578BFA7AAE83E0AD5B3BEF288C63FC3FD2BBFC9BF2B3F1DA4ACBE127242BB4B67A23FCA735CBFD2793E3FB1A38EBEDB48843F638BF43E5FBD333FEF25BDBFCF83B83FC26AD0BD6BE7563F2A63383EE17E46BF0A68ABBF"> : tensor<16x16xf32>} : () -> tensor<16x16xf32>
%4 = "tosa.const"() {value = dense<"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tensor<16x16xf32>} : () -> tensor<16x16xf32>
%5 = "tosa.const"() {value = dense<"0x00449C3F37AE10BE62041FBFE387563E0D499C3EF7C3253F737616BF434E1A3FCA4B2C3F14693ABF52C489BFE8D9043DB69AEB3F4EFC9DBF353DE13E098FB5BFD2E3EF3E95664BBFE51C41BF4450AABFEF0C1FBEAB53B93FE8AD3E3ED285143F4497E63FAFAA50BF16AF333F83D4AB3FEE002EBFDD08CBBFB57726BEB78941BF3DB355BE445E693F6522A53E108CA93F6984123FCA95E63ED2EABE3FBE5E39BF87E52EBF3C7500BF3938123E046C7EBFCC94673F049B59BF5F78823EDBC7EC3F980D97BFFFB49E3E82A2F23E814D733EC1BFF73F1890183F44E08ABF9D02AB3F6AE0323FA974723E6901203E2D599C3F887C243DAE183F3F1C369EBF81928CBFF098523F7078273EE154993E459ABE3C8C3DA03F8026DEBE128DEB3FD0B410BF5082DABFFE7A963F4A4171BF2721B7BEB2F9E4BEDB59DABE0A32D2BE84D657BEDB53983FF678AC3F0811DDBF2C3C66BF33FD693F45F9353F5F2F143FEBD1EE3E9589DBBD59C2973DA5A9B7BFDC6FBA3FCCE0D0BFB5CBCFBE25C99B3FB5092D3FF36CF0BFCDC6233F75EC323F9C08363E9022663FCA51F1BEBE7FBFBFF2E829BF8EB6A93F40E12E3E805CAF3F41EAE33EA48480BDE55BAA3F203B683E3B2F1C3E276B463FC15CDCBF89314DBF8D69D8BE3BA0E5BE8C1871BFCB743F3DFDF990BE772282BD4E8A833F2B4D1CBF034F0D3D6B06CB3E0A2A4FBFF0F3813E22309BBF"> : tensor<16x8xf32>} : () -> tensor<16x8xf32>
%6 = "tosa.const"() {value = dense<[[-1.22533154, 0.0746435821, 0.245186344, 0.118204482, 0.145576254, 1.10671771, -1.0414201, -0.464519471], [0.63179332, -0.31885916, -1.58991766, 1.28338099, -0.0285358131, -1.05405092, 0.412418932, -0.218622893], [-0.995368242, -0.448986739, -0.530866206, 0.462183148, 1.11698079, -0.691098988, 0.682296038, -1.31756854], [-0.187567741, 1.84321558, 0.314768672, -1.36764145, 0.759284317, -0.44244957, 1.42912459, 0.741238594]]> : tensor<4x8xf32>} : () -> tensor<4x8xf32>
%7 = "tosa.const"() {value = dense<[1, 2, 0]> : tensor<3xi32>} : () -> tensor<3xi32>
%8 = "tosa.const"() {value = dense<3.000000e+00> : tensor<f32>} : () -> tensor<f32>
%9 = "tosa.const"() {value = dense<4.471500e-02> : tensor<f32>} : () -> tensor<f32>
%10 = "tosa.const"() {value = dense<0.797884583> : tensor<f32>} : () -> tensor<f32>
%11 = "tosa.const"() {value = dense<1.000000e+00> : tensor<f32>} : () -> tensor<f32>
%12 = "tosa.const"() {value = dense<5.000000e-01> : tensor<f32>} : () -> tensor<f32>
%13 = "tosa.const"() {value = dense<[[1.0563544, -0.585759163, 1.52206767, 0.187397271], [1.81309879, -1.41881061, 0.458066404, 0.360637158], [-0.459458262, 0.902881681, 1.69229615, -0.137260884], [0.163390487, -0.452035576, 1.11027718, -1.36587167], [-8.036020e-01, 0.500859439, 1.51520991, 0.90689683], [0.248870611, -0.107561983, -1.52660823, -0.130355224], [-0.0653242916, -8.500080e-02, -1.50732124, 1.15429902], [1.0629611, -1.31330836, -0.625915825, 0.749254465], [1.69833684, 0.621885657, -1.42636383, 0.52667731], [0.0617649369, -0.0646568686, 0.0951418057, 1.49256814], [1.4192363, 1.54444838, -0.131583586, 1.07556891], [-0.818317353, -0.734247386, -1.01248229, -0.417290598], [-0.489185631, 0.649596631, -0.622175455, 0.439739645], [-0.489781111, -0.323369414, -0.843932092, 1.91294312], [-0.111886039, 0.853901684, 0.14976424, -1.17752886], [0.373102456, 1.82996058, 0.132075042, 1.14540327]]> : tensor<16x4xf32>} : () -> tensor<16x4xf32>
%14 = "tosa.const"() {value = dense<"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tensor<16x16xf32>} : () -> tensor<16x16xf32>
%15 = "tosa.const"() {value = dense<"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tensor<16x16xf32>} : () -> tensor<16x16xf32>
%16 = "tosa.const"() {value = dense<"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tensor<16x16xf32>} : () -> tensor<16x16xf32>
%17 = "tosa.const"() {value = dense<"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tensor<8x16xf32>} : () -> tensor<8x16xf32>
%18 = "tosa.const"() {value = dense<[[-1.02178693, -0.774996101, -1.52258968, -0.178453282, -0.643984317, -1.68353987, 0.491179764, -0.513923526, 1.46934521, -0.242064282, 1.1112653, 1.12746799, 0.359243423, 0.763190627, 0.677628457, -0.0771146566], [0.358723283, 1.56100762, -1.00107813, 1.06848967, 0.191538572, 1.26359141, 1.3128494, -0.745550752, 0.0958536267, 0.272410125, -0.569127619, -0.122098833, 0.63338536, -1.23098612, -0.495527387, 1.5940038]]> : tensor<2x16xf32>} : () -> tensor<2x16xf32>
%19 = "tosa.const"() {value = dense<0.000000e+00> : tensor<16xf32>} : () -> tensor<16xf32>
%20 = "tosa.const"() {value = dense<0.000000e+00> : tensor<4xf32>} : () -> tensor<4xf32>
%21 = "tosa.const"() {value = dense<0.000000e+00> : tensor<8xf32>} : () -> tensor<8xf32>
%22 = "tosa.const"() {value = dense<0.000000e+00> : tensor<2xf32>} : () -> tensor<2xf32>
%23 = "tosa.reshape"(%arg0) {new_shape = [12800, 4]} : (tensor<1x50x1024xf32>) -> tensor<12800x4xf32>
%24 = "tosa.fully_connected"(%23, %0, %19) : (tensor<12800x4xf32>, tensor<16x4xf32>, tensor<16xf32>) -> tensor<12800x16xf32>
%25 = "tosa.reshape"(%24) {new_shape = [12800, 4, 4]} : (tensor<12800x16xf32>) -> tensor<12800x4x4xf32>
%26 = "tosa.transpose"(%25, %1) : (tensor<12800x4x4xf32>, tensor<3xi32>) -> tensor<4x12800x4xf32>
%27 = "tosa.reshape"(%26) {new_shape = [12800, 16]} : (tensor<4x12800x4xf32>) -> tensor<12800x16xf32>
%28 = "tosa.fully_connected"(%27, %2, %19) : (tensor<12800x16xf32>, tensor<16x16xf32>, tensor<16xf32>) -> tensor<12800x16xf32>
%29 = "tosa.reshape"(%28) {new_shape = [12800, 4, 4]} : (tensor<12800x16xf32>) -> tensor<12800x4x4xf32>
%30 = "tosa.transpose"(%29, %1) : (tensor<12800x4x4xf32>, tensor<3xi32>) -> tensor<4x12800x4xf32>
%31 = "tosa.reshape"(%30) {new_shape = [12800, 16]} : (tensor<4x12800x4xf32>) -> tensor<12800x16xf32>
%32 = "tosa.fully_connected"(%31, %3, %19) : (tensor<12800x16xf32>, tensor<16x16xf32>, tensor<16xf32>) -> tensor<12800x16xf32>
%33 = "tosa.reshape"(%32) {new_shape = [12800, 4, 4]} : (tensor<12800x16xf32>) -> tensor<12800x4x4xf32>
%34 = "tosa.transpose"(%33, %1) : (tensor<12800x4x4xf32>, tensor<3xi32>) -> tensor<4x12800x4xf32>
%35 = "tosa.reshape"(%34) {new_shape = [12800, 16]} : (tensor<4x12800x4xf32>) -> tensor<12800x16xf32>
%36 = "tosa.fully_connected"(%35, %4, %19) : (tensor<12800x16xf32>, tensor<16x16xf32>, tensor<16xf32>) -> tensor<12800x16xf32>
%37 = "tosa.reshape"(%36) {new_shape = [12800, 4, 4]} : (tensor<12800x16xf32>) -> tensor<12800x4x4xf32>
%38 = "tosa.transpose"(%37, %1) : (tensor<12800x4x4xf32>, tensor<3xi32>) -> tensor<4x12800x4xf32>
%39 = "tosa.reshape"(%38) {new_shape = [25600, 8]} : (tensor<4x12800x4xf32>) -> tensor<25600x8xf32>
%40 = "tosa.fully_connected"(%39, %5, %19) : (tensor<25600x8xf32>, tensor<16x8xf32>, tensor<16xf32>) -> tensor<25600x16xf32>
%41 = "tosa.reshape"(%40) {new_shape = [25600, 4, 4]} : (tensor<25600x16xf32>) -> tensor<25600x4x4xf32>
%42 = "tosa.transpose"(%41, %1) : (tensor<25600x4x4xf32>, tensor<3xi32>) -> tensor<4x25600x4xf32>
%43 = "tosa.reshape"(%42) {new_shape = [51200, 8]} : (tensor<4x25600x4xf32>) -> tensor<51200x8xf32>
%44 = "tosa.fully_connected"(%43, %6, %20) : (tensor<51200x8xf32>, tensor<4x8xf32>, tensor<4xf32>) -> tensor<51200x4xf32>
%45 = "tosa.reshape"(%44) {new_shape = [51200, 1, 4]} : (tensor<51200x4xf32>) -> tensor<51200x1x4xf32>
%46 = "tosa.transpose"(%45, %1) : (tensor<51200x1x4xf32>, tensor<3xi32>) -> tensor<4x51200x1xf32>
%47 = "tosa.reshape"(%46) {new_shape = [4096, 1, 50]} : (tensor<4x51200x1xf32>) -> tensor<4096x1x50xf32>
%48 = "tosa.transpose"(%47, %7) : (tensor<4096x1x50xf32>, tensor<3xi32>) -> tensor<1x50x4096xf32>
%49 = "tosa.pow"(%48, %8) : (tensor<1x50x4096xf32>, tensor<f32>) -> tensor<1x50x4096xf32>
%50 = "tosa.reshape"(%9) {new_shape = [1, 1, 1]} : (tensor<f32>) -> tensor<1x1x1xf32>
%51 = "tosa.mul"(%49, %50) {shift = 0 : i32} : (tensor<1x50x4096xf32>, tensor<1x1x1xf32>) -> tensor<1x50x4096xf32>
%52 = "tosa.add"(%48, %51) : (tensor<1x50x4096xf32>, tensor<1x50x4096xf32>) -> tensor<1x50x4096xf32>
%53 = "tosa.reshape"(%10) {new_shape = [1, 1, 1]} : (tensor<f32>) -> tensor<1x1x1xf32>
%54 = "tosa.mul"(%52, %53) {shift = 0 : i32} : (tensor<1x50x4096xf32>, tensor<1x1x1xf32>) -> tensor<1x50x4096xf32>
%55 = "tosa.tanh"(%54) : (tensor<1x50x4096xf32>) -> tensor<1x50x4096xf32>
%56 = "tosa.reshape"(%11) {new_shape = [1, 1, 1]} : (tensor<f32>) -> tensor<1x1x1xf32>
%57 = "tosa.add"(%55, %56) : (tensor<1x50x4096xf32>, tensor<1x1x1xf32>) -> tensor<1x50x4096xf32>
%58 = "tosa.reshape"(%12) {new_shape = [1, 1, 1]} : (tensor<f32>) -> tensor<1x1x1xf32>
%59 = "tosa.mul"(%57, %58) {shift = 0 : i32} : (tensor<1x50x4096xf32>, tensor<1x1x1xf32>) -> tensor<1x50x4096xf32>
%60 = "tosa.mul"(%48, %59) {shift = 0 : i32} : (tensor<1x50x4096xf32>, tensor<1x50x4096xf32>) -> tensor<1x50x4096xf32>
%61 = "tosa.reshape"(%60) {new_shape = [51200, 4]} : (tensor<1x50x4096xf32>) -> tensor<51200x4xf32>
%62 = "tosa.fully_connected"(%61, %13, %19) : (tensor<51200x4xf32>, tensor<16x4xf32>, tensor<16xf32>) -> tensor<51200x16xf32>
%63 = "tosa.reshape"(%62) {new_shape = [51200, 4, 4]} : (tensor<51200x16xf32>) -> tensor<51200x4x4xf32>
%64 = "tosa.transpose"(%63, %1) : (tensor<51200x4x4xf32>, tensor<3xi32>) -> tensor<4x51200x4xf32>
%65 = "tosa.reshape"(%64) {new_shape = [51200, 16]} : (tensor<4x51200x4xf32>) -> tensor<51200x16xf32>
%66 = "tosa.fully_connected"(%65, %14, %19) : (tensor<51200x16xf32>, tensor<16x16xf32>, tensor<16xf32>) -> tensor<51200x16xf32>
%67 = "tosa.reshape"(%66) {new_shape = [51200, 4, 4]} : (tensor<51200x16xf32>) -> tensor<51200x4x4xf32>
%68 = "tosa.transpose"(%67, %1) : (tensor<51200x4x4xf32>, tensor<3xi32>) -> tensor<4x51200x4xf32>
%69 = "tosa.reshape"(%68) {new_shape = [51200, 16]} : (tensor<4x51200x4xf32>) -> tensor<51200x16xf32>
%70 = "tosa.fully_connected"(%69, %15, %19) : (tensor<51200x16xf32>, tensor<16x16xf32>, tensor<16xf32>) -> tensor<51200x16xf32>
%71 = "tosa.reshape"(%70) {new_shape = [51200, 4, 4]} : (tensor<51200x16xf32>) -> tensor<51200x4x4xf32>
%72 = "tosa.transpose"(%71, %1) : (tensor<51200x4x4xf32>, tensor<3xi32>) -> tensor<4x51200x4xf32>
%73 = "tosa.reshape"(%72) {new_shape = [51200, 16]} : (tensor<4x51200x4xf32>) -> tensor<51200x16xf32>
%74 = "tosa.fully_connected"(%73, %16, %19) : (tensor<51200x16xf32>, tensor<16x16xf32>, tensor<16xf32>) -> tensor<51200x16xf32>
%75 = "tosa.reshape"(%74) {new_shape = [51200, 4, 4]} : (tensor<51200x16xf32>) -> tensor<51200x4x4xf32>
%76 = "tosa.transpose"(%75, %1) : (tensor<51200x4x4xf32>, tensor<3xi32>) -> tensor<4x51200x4xf32>
%77 = "tosa.reshape"(%76) {new_shape = [51200, 16]} : (tensor<4x51200x4xf32>) -> tensor<51200x16xf32>
%78 = "tosa.fully_connected"(%77, %17, %21) : (tensor<51200x16xf32>, tensor<8x16xf32>, tensor<8xf32>) -> tensor<51200x8xf32>
%79 = "tosa.reshape"(%78) {new_shape = [51200, 4, 2]} : (tensor<51200x8xf32>) -> tensor<51200x4x2xf32>
%80 = "tosa.transpose"(%79, %1) : (tensor<51200x4x2xf32>, tensor<3xi32>) -> tensor<2x51200x4xf32>
%81 = "tosa.reshape"(%80) {new_shape = [25600, 16]} : (tensor<2x51200x4xf32>) -> tensor<25600x16xf32>
%82 = "tosa.fully_connected"(%81, %18, %22) : (tensor<25600x16xf32>, tensor<2x16xf32>, tensor<2xf32>) -> tensor<25600x2xf32>
%83 = "tosa.reshape"(%82) {new_shape = [25600, 1, 2]} : (tensor<25600x2xf32>) -> tensor<25600x1x2xf32>
%84 = "tosa.transpose"(%83, %1) : (tensor<25600x1x2xf32>, tensor<3xi32>) -> tensor<2x25600x1xf32>
%85 = "tosa.reshape"(%84) {new_shape = [1024, 1, 50]} : (tensor<2x25600x1xf32>) -> tensor<1024x1x50xf32>
%86 = "tosa.transpose"(%85, %7) : (tensor<1024x1x50xf32>, tensor<3xi32>) -> tensor<1x50x1024xf32>
return %86 : tensor<1x50x1024xf32>
}
}
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