test_nn_bceloss/0000755000175400017540000000000014732212054014136 5ustar jenkinsjenkinstest_nn_bceloss/mint_nn_bceloss_binary_case3.py0000755000175400017540000000234014732212054022310 0ustar jenkinsjenkinsimport torch from torch.nn.parameter import Parameter from common.utils import auto_generate_data @auto_generate_data() def mint_bceloss_benchmark(): """ benchmark: torch.nn.BCELoss inputs: logits: ((2, 3, 4), float32) labels: ((2, 3, 4), float32) weight: ((2, 3, 4), float32) reduction: 'none' outputs: output: ((2, 3, 4), float32) logits_grad: ((2, 3, 4), float32) logits_grad: ((2, 3, 4), float32) """ logits = torch.rand((2, 3, 4), dtype=torch.float32) logits.requires_grad = True labels = torch.rand((2, 3, 4), dtype=torch.float32) labels.requires_grad = True weight = torch.randn((2, 3, 4), dtype=torch.float32) reduction = 'none' net = torch.nn.BCELoss(weight=weight, reduction=reduction) output = net(logits, labels) grads = torch.ones_like(output, dtype=torch.float32) output.backward(gradient=grads) logits_grad = logits.grad labels_grad = labels.grad return [logits.detach().numpy(), labels.detach().numpy(), weight.numpy()], \ [output.detach().numpy(), logits_grad.detach().numpy(), labels_grad.detach().numpy()] if __name__ == "__main__": mint_bceloss_benchmark() test_nn_bceloss/mint_nn_bceloss_binary_case1_input0.npy0000755000175400017540000000023014770223760023767 0ustar jenkinsjenkinsNUMPYv{'descr': '(?H&>OQg?<0>3\?test_nn_bceloss/mint_nn_bceloss_binary_case5_output0.npy0000755000175400017540000000020414770223764024201 0ustar jenkinsjenkinsNUMPYv{'descr': 'վ]> >_?u?Z?7?H泿=T?rp>ʾУZ=?(@ >zVH"_?oZ@6_Y@ɷ +?6rVLg?h1kK{?>V?ڹ? >FiIʿI6?Lu @<˟L?>nw?/?X䂾$;>(W?@ӍJܿ [.k?6&Ķ?Q?C4H@x?dbЍn?#@ns: Ǿ4m>,X@Y<.> !龇?T?Qr@ l~<>?>D>??81? ?>6>I@<?@?Ƿ3_-X8;0*s?0HѾ;62ZZgj??ho?$$Nÿ8V@۲ @@YHօ?<0?u'?m@r B%n>S9*?nL?ش5?!?(xܾϓ?KEQ@?4&Wլ?˾j?J쯿R?U"?h?2a?~L??i@bջ|$r<^^o?:c?aT?s?|.Gg?G#CQM@j>]?P%==@51NF@{k?Dd?S?js?>>JemI?X>"jwf>8>m&B@]/@ c@?88?((=Z>> ?ڽ?2>@fצ?& P@ϲd@P c@쿮>M\X?aH@a?ѭz]Wm?ko1 @~7brx J2(jAa??0?@ >c><@?@?$0h_ݕ?c?z=@#5~@x\>. >0?T|?<9&>i:?MLj>.>?NW?e?H0?f?5*k?|X= ba?v>*?cf@`@>t[B|ܷ??NZ?N F>+8?7hP(]? ?z0;@U~?0ר?[@ʁc[ D{`n=}N?\gi1c?O>ģt?@4>J>Un?۲ @@ǾBR?Lk>? 8bο6U+Oh ?>A:Rs?U>b\@I濐܄??M?E̿T\ $@C[ s,ѡR?G<@:?2̷! ?˿\4|@s@nf>:>w?;f?2?Up>e=͠5@BB@eE%k*?" %??氽jPʔzFu?И?\?= ~J@?oО)?Ő-?BY??0^?*Xo4`5@V޾+>uqpixɃ?k>>'?CXtr&to>l)? e13?? /t?%OL?=?ȿH??/G? `2?0ҽ?H}@(PL^?X>ͭh?D.>\s[?e'=X?"K#@v1Q Ap?@H"% ޿U_W@%vJL?k?B@=}? hƾ2"vO~$?0@?'H?G؍@ {?k<`?_x <>?2?J><ھ ?K2K=J)}c@" rM?=(u<|?=xҼt?V??_J@?ᕿt>pӷ=h_].@)u&c>>>O-$:z@XczAK5H@+??t]ri?HPu-kH@KD?^ءBdl @9@b>VS(@?;=?\ſ)?UX>п#L@?|$>/xvCa"?2S8@Ĥ?CF%:=JJ )k`@?M?0l?|@ ״?"3?28a>"{|?T?3N?XTt3>/V G>n}ob>˛zM-?)W$%kƝ>Q*Ȕ>:V?9>{=D?糿O[?}&@k6?.@?@*?)1 ?rFh@4ۃ@L?ľu?"i ;3==R@jBX!?tNY[k@["|test_nn_bceloss/mint_nn_bceloss_binary_case6_output0.npy0000755000175400017540000000570014770223765024211 0ustar jenkinsjenkinsNUMPYv{'descr': 'I?B5:?>,A??^?;9?'$?9?T?Vn?˜??]S?w>.1?0,?=A?Q[-??r9?ޫC?8>;(@<~??։?3?e5@v@b1?v3?j@>‹?6'Y?5,?SI??$?tߥ?%?ZT? ?Z?P?d5?J@ѕ?لA?>0?r?QC?'pu?A:?8?'?\1?R@>txl?i>5>0)? >\T?Q?{? ~>y?pG>&?FI?;?l$?=>? >L@?Ě ??*j @A? 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(?y?u?m @&?7>G?z?<4?Ź?=^;?[O? 2?W?M>or>F?y?@We%?W?}?=? *G?4?*>\{?׆?F?1?:?Q)?>v?(A9@?ݒ? v?>''? ?A#?9??v?\6?a?tl>9?T>Yr?aw@5>*??m&?$”?;?=?h?@*@h?(?)?j@?j?b.?M?k&?s?(V>?zZ?+S? ?$Q?D5e?N1?"؏?ԛ?/>??@&?j?,?F+?$?< ?@?)?:-?@T?qh?Xm4??>+?b???2?2@`?T@v?i?1?_?e?G>]@>ĸ?|?É?? )?>̬>Ҳc?fG??4ǀ?5+@P>dz:?}>?[Z@?.G?6?T0?E;?1`????6??E??h?f>>L_&?B/?U?6_?.?&?Ă?S-??,D?P'?'m1?1}/?)*?݅??~??Yل>*??{9?g6? 0@)? /?7?-BO@ˆ?Hb?v1? Ɓ?&+?\(@^?4?hr?% ?G3?l'?1??v=(@????@?tT?"I??3>Ot??2?.? ?LϪ>Ԍ>@?;,?S?6_3?y?6?2??7?3?9?kwE?s?"<+ĥ?o?.g>>d?z>o6?Г>?e ??D\<@?R}*??V@(X?T?e? ?lX??yl?G?&`>P?@b??E?`"?N->;??D)?>VO@?aV>.S?5??.?'?f(??$?DC?8+??f9?Eb?yYo??H@FZ??bkQ?7?F >֩?u?f@?[_???*%??vA?t?˃?!W9?ҥb= @f?>0h??U? A,?"?"E?test_nn_bceloss/mint_nn_bceloss_binary_case6.py0000755000175400017540000000230314732212054022312 0ustar jenkinsjenkinsimport torch from torch.nn.parameter import Parameter from common.utils import auto_generate_data @auto_generate_data() def mint_bceloss_benchmark(): """ benchmark: torch.nn.BCELoss inputs: logits: ((2, 3, 4, 5, 6), float32) labels: ((2, 3, 4, 5, 6), float32) weight: Nnoe reduction: 'none' outputs: output: ((2, 3, 4, 5, 6), float32) logits_grad: ((2, 3, 4, 5, 6), float32) logits_grad: ((2, 3, 4, 5, 6), float32) """ logits = torch.rand((2, 3, 4, 5, 6), dtype=torch.float32) logits.requires_grad = True labels = torch.rand((2, 3, 4, 5, 6), dtype=torch.float32) labels.requires_grad = True weight = None reduction = 'none' net = torch.nn.BCELoss(weight=weight, reduction=reduction) output = net(logits, labels) grads = torch.ones_like(output, dtype=torch.float32) output.backward(gradient=grads) logits_grad = logits.grad labels_grad = labels.grad return [logits.detach().numpy(), labels.detach().numpy()], \ [output.detach().numpy(), logits_grad.detach().numpy(), labels_grad.detach().numpy()] if __name__ == "__main__": mint_bceloss_benchmark() test_nn_bceloss/mint_nn_bceloss_binary_case4_output0.npy0000755000175400017540000000020414770223763024177 0ustar jenkinsjenkinsNUMPYv{'descr': 'cm?wZm?5"?>H?>üүiy>u{4?M^<test_nn_bceloss/mint_nn_bceloss_binary_case5_output1.npy0000755000175400017540000000130014770223764024200 0ustar jenkinsjenkinsNUMPYv{'descr': 'w?.,@;Ϳ%ݿi|?>q{-@,@Xn ca??P ?q.%꿁M۾@>:AY}?gߘCAEbL(Bm M($?)0!mC?h+@"Bv@0D ;zp?:?Oa#A_?@y;to?$r/@nj9>ޘD%?v?Q@8$bޙ*?9YH@rF@2Ǘ?a~wb?W0@j+@qĨ/>A>, h> @Q#Sr@vAȤB??YAV5?iؿg?]@$M?IIb@?'`?ihŇ[)`>.ӿD?Ž2??PoC ?@j%@ U?6q?K@jӿ%ik{&?OW?Os+y}[@| r?D@m}>)if@7?S'ݿuA!ʿ  $test_nn_bceloss/mint_nn_bceloss_binary_case1_output2.npy0000755000175400017540000000023014770223760024172 0ustar jenkinsjenkinsNUMPYv{'descr': '>qM?P>tߧ>test_nn_bceloss/mint_nn_bceloss_binary_case6_output1.npy0000755000175400017540000000570014770223765024212 0ustar jenkinsjenkinsNUMPYv{'descr': '2JC-@ g`ƾX?T@]5n׵?Z?T׮?RPJ?pAF@KE?>x? @W8t#8#Q n-RAs>?pS<{@s rD>Έe?lk$Qn,&£Nl4?W#ҿ`JEL&A/{@ir?A?}?B=aѾ$Eӿ./=U?5y@@?+?0?-v=%Sn$̿`?ӻ?b㵡p+CnE.?Z@, QD@:@/@%>?*XcAH3/?w3?:*+C@S{?0gƱM!@=/Sᙿo @5>w77Z!A2>;ӸC%O?@*@ ?B?OE?2?:X@7!@:@р?c5Ag!g?bϾY @7*@?5 J>?`xn?w?H;צpR0{5?x? =@NA_SX_?$<ܿӎMF@ɾk>m促?@/x{?SBQӋ C?ZVs>$A|n9k(?m6ABN>?)?NBܿ8ߑ?.b0gHտu?-qˋ??ƾSrj@d:?g?pXTn?,L(??U?qo>_TZC#@({ . =B Q@$?^]@9>E?p@m!?);@Q&?Տ=eLQJa@r2hV>L7C/b>m\+Cf@'i$AR> > O?:gb)Ap0)ǣP?ZvCA<0?a7N),@D4A>&?3@BM?V02?mly{F/Aꦺ=?-%%v>r=a>e>&mӾ t_5A& ?tC[?=A!>4T"i>㝽yu?kFg?->+Ey@AuGpg&@Q+$lRBdts>O#7Bl^Qz@rؿAƱ@0@PT\??!?v?U@Quw@7 :B[|EAo)dOӿQ9@? ӓ?硰?:e?2 \@=AQ%߿f" B^>?x>??d@ 迵Qx)?xz?-3>BΔ@?r0@q@Ah?q=s?N츠 9AtKl?1Z@2@f Tˊ@)@6??~c@TAdv?±>2@|>ek@UH@ 8<@@bj%{rz?0*?se>Rw@Yt`I?EipGA\mh_>M22@l?>zB>Zv@_A<:0@WH^y׿ᄏk7IAM@7<Ē ?/zh_>5! 0&@)?8o?{wT -~C֖>3@sَc&:? l??ZABxJG]?.Y@X^@\O:X>S3@?Jo%?[A!?۱i|m? 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E?,?>>0;@>p?ٿa?A(>h=>k;>Q?K|?F>)O?ٿR?)!?0><^>f:>h4P?h=72?>}7?R)>>ds?k?>NK>`Z? >K?o?֤=J?^ ?P>N$`?|?J?d>-?>z?Ї3=%h>Gm?|K>H=Mk8?>K>he?K~?A?0v?>->=?a?>M?5$??7?>E]?$?lsE??7>D?zm? .?Mx?SY?YT?'s ?L?0:7<j?PP*=G?=z?*s?3 >H?B > ?6OO?F?>Z]>$c>=>%>L>Ԋ>E>ZZ? !>Ms=9=B~?=)E?>>Ƙ>>Z(?Վf?^)>OJ@?>x=?L?\Ƶ>L>d> z?;?=:8>?@h<\W??s}?I><>?_<"?,w>5#?>??Hu>\H>0Q>?w?>>v4?>з;@h>\? &:+"?е>9yO?9? n?H>? ?h?h?m?,>k0?B.?>[ ?}aC?Bt>>Aځ>v=<?`/i>Q>?q?b|?-C/?>?j?C?D?W ?T? ?>v?;S?h?A?x>\f7> h?n?>{3E?,}?p-?;'>LQK?G?!Z?צm?,@?{?)c?<?c=t??87&?L?_?>'2?@<#4?(? a?c>N?B'?O>p?J?n5>_>0>S>cc?%? ?-?@ V<5@?>8>A=8?>'>ZN?0s>vE?*;B>|>k? '=`>+(?x}a?`<:=test_nn_bceloss/mint_nn_bceloss_binary_case2_output0.npy0000755000175400017540000000020414770223761024173 0ustar jenkinsjenkinsNUMPYv{'descr': 'A-2㢾x>Iľ?1R<test_nn_bceloss/mint_nn_bceloss_binary_case3_input0.npy0000755000175400017540000000034014770223762023775 0ustar jenkinsjenkinsNUMPYv{'descr': '(?H&>OQg?<0>3\?I?ؾ>>qM?P>tߧ>c?l.M?M?N>Y> ? H?L+>(>D=&Q>/?test_nn_bceloss/mint_nn_bceloss_binary_case5.py0000755000175400017540000000224214732212054022313 0ustar jenkinsjenkinsimport torch from torch.nn.parameter import Parameter from common.utils import auto_generate_data @auto_generate_data() def mint_bceloss_benchmark(): """ benchmark: torch.nn.BCELoss inputs: logits: ((2, 3, 4, 6), float32) labels: ((2, 3, 4, 6), float32) weight: Nnoe reduction: 'sum' outputs: output: ((), float32) logits_grad: ((2, 3, 4, 6), float32) logits_grad: ((2, 3, 4, 6), float32) """ logits = torch.rand((2, 3, 4, 6), dtype=torch.float32) logits.requires_grad = True labels = torch.rand((2, 3, 4, 6), dtype=torch.float32) labels.requires_grad = True weight = None reduction = 'sum' net = torch.nn.BCELoss(weight=weight, reduction=reduction) output = net(logits, labels) grads = torch.ones_like(output, dtype=torch.float32) output.backward(gradient=grads) logits_grad = logits.grad labels_grad = labels.grad return [logits.detach().numpy(), labels.detach().numpy()], \ [output.detach().numpy(), logits_grad.detach().numpy(), labels_grad.detach().numpy()] if __name__ == "__main__": mint_bceloss_benchmark() test_nn_bceloss/mint_nn_bceloss_binary_case1_output0.npy0000755000175400017540000000020414770223760024171 0ustar jenkinsjenkinsNUMPYv{'descr': 'test_nn_bceloss/mint_nn_bceloss_binary_case3_input2.npy0000755000175400017540000000034014770223762023777 0ustar jenkinsjenkinsNUMPYv{'descr': '?<(N?}*XlW?H>5J*uN?reo?C@ֿ ,_a>d; ?RƼ؅g?g?eZtest_nn_bceloss/mint_nn_bceloss_binary_case4_output1.npy0000755000175400017540000000114014770223763024200 0ustar jenkinsjenkinsNUMPYv{'descr': 'O@;◽e=q ?;?<\/޼-ŰC;<ae[p3cw;S8;{΃ԑ9% aA?>Ξ>>Ǩ>]? 1'??#?>t?(+=X>ޙD?a@?R>t ?>OL?!>V>\@?o>4h>þ>.Օ>0S>9>= P?G ?-?Ba?>N?lT>> >h ?T D>^?͹&?=k>'F?>mp`?{?> <`^?00=ƪ?>dߔ>h?Ld>L>F?n>y?^U>>_N?P>?v>Nn?p=jx=`I P?p;v>d?p>>>s?c?t>$>h>x;3?l|6>=OP?x=6*=س#>{?rw?>X7?>a>J?dR>??d{>V=ov'?jY<>9>@=#zZ?(>m!?w>Y~?`={,?V??&9?u?*?V?P?J?ߕ>0>PD=>'>,z?Z=7g?Ks?$=n>m>N>(?H&>OQg?<0>3\?I?ؾ>>qM?P>tߧ>c?l.M?M?N>Y> ? H?L+>(>D=&Q>/?g?Ԗ>z?N;Y ?@+=Mg?"?hn>(6?8?2? >U ?iA?J?6>X>t@B>5?>?T/?*?'?fu? =>F?F֞>0> =>p[>E?h>kw?С=3s@?# 0?OY??b?g>&(? j>>f+?0$?9U?d>I5?̫?c?>@=Zk?r??i>w?0=DL>`?>?? L>>B?脣=u!?XC?,>ٹ>6>w>T >T>|=Z>>r>L>)=Vy>d"?=.?"?q=`?k$?3&?F!?,? )?> p?'?,?4*? a?(D?ߵ>@>7P?Eg>`W ?FR? =y==1;?]?$4_>`?p?P@>0>1<"?EF]?l(?L> aA?>Ξ>>Ǩ>]? 1'??#?>t?(+=X>ޙD?a@?R>t ?>OL?!>V>\@?o>4h>þ>.Օ>0S>9>= P?G ?-?Ba?>N?lT>> >h ?T D>^?͹&?=k>'F?>mp`?{?> <`^?00=ƪ?>dߔ>h?Ld>L>F?n>y?^U>>_N?P>?v>Nn?p=jx=`I P?p;v>d?p>>>s?c?t>$>h>x;3?l|6>=OP?x=6*=س#>{?rw?>X7?>a>J?dR>??d{>V=ov'?jY<>9>@=#zZ?(>m!?w>Y~?`={,?V??&9?u?*?V?P?J?ߕ>0>PD=>'>,z?Z=7g?Ks?$=n>m>N>'?p??#?br>p?lH>ڊ>?=ƚ>,d>>F%>r?b> ??,>e0?b{?^?R>\>{zI? 2?t>?>VS>p=d>>0;o?L?4m>h?`>c,i?T>p>{>Ƴ;<>L9?{?D>i@?4>H"g?8>*_?oF"?:@>??\}(>M?8=?r%?2 G?z>j>P=~?>I?A >w=F#?e?ҩ?؈>>M?&;?k?f|k?`>#?>A?2>?>PC?9O?:p>>xs?=^=?FB?, >@>oK? U?M)?$J?>r> ,?^8?)>|>8i?=>K?_[?>=>>gT?]?cl?ܐ=t?}bz?F)??>xo ?piK=og?E>pJ?N?]^?l>PT?ܒq?<? ?ϢC?>8>">>>(8?K>(>w??=jY?:P?&?K8+?Z= k?t?GF?>>?b=;=;t?No?S>|/>>V?g1?:?>%n>??=Y-??&=8.>8~??>p?"a>s>>#Zl?#N?)3?q? }?b=l?>>d?j[?=?|>L>T>3>N?8?#?j>>p^?>\w#>d>p?>~S?B0>/? [>2c?%? I?]>?>Zh>>3k?<>:8?m?HE\>2k(?4>f>{?6=?~;P9?`-9t>=8?>s^?Y?C?=3z?$A>n>gd?P=n>d>L?~?19?'? $>u=F>(?T>jd?p ?`>s?h.>9?>>g$?r>??6?0a? > ? >n?<)m?1_8? >j>-?Q>Fa>??)>O?ғ>O>(%0?x~=hC?>„>d4?{=9?>|-?dv? p? '?B?(o=^?$t?+=7>h>[??@>v]?Rp?O+=>Y?AB?2==:<>msG?-[?{=@>X>i?>lR?T\?5j?i>`:?w?/>= V?И=2t>G[?vt>#?KM??"??L? >k?1?.?H>2t?n?0?M?`?<>B>>:H}H>d>J4&?>?>C?NA>oQ?vp>i?>c?|?L?/>i?7?>:?f>L?92??>W ?UY#?w?'>͚>b~>(>pA>2M?(c>h=>`U{=|}.>l%>a?q?82>ay?D=}s+?8F?=6?n,C?80>=^t?V>`?X3?`4<.X ?M ?H?test_nn_bceloss/mint_nn_bceloss_binary_case2_input0.npy0000755000175400017540000000034014770223761023773 0ustar jenkinsjenkinsNUMPYv{'descr': '(?H&>OQg?<0>3\?I?ؾ>>qM?P>tߧ>c?l.M?M?N>Y> ? H?L+>(>D=&Q>/?test_nn_bceloss/mint_nn_bceloss_binary_case5_input0.npy0000755000175400017540000000130014770223764023776 0ustar jenkinsjenkinsNUMPYv{'descr': '(?H&>OQg?<0>3\?I?ؾ>>qM?P>tߧ>c?l.M?M?N>Y> ? H?L+>(>D=&Q>/?g?Ԗ>z?N;Y ?@+=Mg?"?hn>(6?8?2? >U ?iA?J?6>X>t@B>5?>?T/?*?'?fu? =>F?F֞>0> =>p[>E?h>kw?С=3s@?# 0?OY??b?g>&(? j>>f+?0$?9U?d>I5?̫?c?>@=Zk?r??i>w?0=DL>`?>?? L>>B?脣=u!?XC?,>ٹ>6>w>T >T>|=Z>>r>L>)=Vy>d"?=.?"?q=`?k$?3&?F!?,? )?> p?'?,?4*? a?(D?ߵ>@>7P?Eg>`W ?FR? =y==1;?]?$4_>`?p?P@>0>1<"?test_nn_bceloss/mint_nn_bceloss_binary_case4_output2.npy0000755000175400017540000000114014770223763024201 0ustar jenkinsjenkinsNUMPYv{'descr': 'K?c;w@;|W0D79 ]z?N;Y ?@+=Mg?"?hn>(6?8?2? >U ?iA?J?6>X>t@B>5?>?T/?*?'?test_nn_bceloss/mint_nn_bceloss_binary_case3_output1.npy0000755000175400017540000000034014770223762024177 0ustar jenkinsjenkinsNUMPYv{'descr': 'cm?wZm?5"?>H?>üүiy>u{4?M^<test_nn_bceloss/mint_nn_bceloss_binary_case2_output2.npy0000755000175400017540000000034014770223761024176 0ustar jenkinsjenkinsNUMPYv{'descr': '*XlW?H>5J*uN?reo?C@ֿ ,_a>d; ?RƼ؅g?g?eZtest_nn_bceloss/mint_nn_bceloss_binary_case5_output2.npy0000755000175400017540000000130014770223764024201 0ustar jenkinsjenkinsNUMPYv{'descr': 'վ]> >_?u?Z?7?H泿=T?rp>ʾУZ=?(@ >zVH"_?oZ@6_Y@ɷ +?6rVLg?h1kK{?>V?ڹ? >FiIʿI6?Lu @<˟L?>nw?/?X䂾$;>(W?@ӍJܿ [.k?6&Ķ?Q?C4H@x?dbЍn?#@ns: Ǿ4m>,X@Y<.> !龇?T?Qr@ l~<>?>D>??81? ?>6>I@<?@?Ƿ3_-X8;0*s?0HѾ;62ZZgj??ho?$$Nÿ8V@۲ @@YHօ?<0?u'?m@rtest_nn_bceloss/mint_nn_bceloss_binary_case4.py0000755000175400017540000000224414732212054022314 0ustar jenkinsjenkinsimport torch from torch.nn.parameter import Parameter from common.utils import auto_generate_data @auto_generate_data() def mint_bceloss_benchmark(): """ benchmark: torch.nn.BCELoss inputs: logits: ((2, 3, 4, 5), float32) labels: ((2, 3, 4, 5), float32) weight: Nnoe reduction: 'mean' outputs: output: ((), float32) logits_grad: ((2, 3, 4, 5), float32) logits_grad: ((2, 3, 4, 5), float32) """ logits = torch.rand((2, 3, 4, 5), dtype=torch.float32) logits.requires_grad = True labels = torch.rand((2, 3, 4, 5), dtype=torch.float32) labels.requires_grad = True weight = None reduction = 'mean' net = torch.nn.BCELoss(weight=weight, reduction=reduction) output = net(logits, labels) grads = torch.ones_like(output, dtype=torch.float32) output.backward(gradient=grads) logits_grad = logits.grad labels_grad = labels.grad return [logits.detach().numpy(), labels.detach().numpy()], \ [output.detach().numpy(), logits_grad.detach().numpy(), labels_grad.detach().numpy()] if __name__ == "__main__": mint_bceloss_benchmark() test_nn_bceloss/mint_nn_bceloss_binary_case2_input1.npy0000755000175400017540000000034014770223761023774 0ustar jenkinsjenkinsNUMPYv{'descr': 'z?N;Y ?@+=Mg?"?hn>(6?8?2? >U ?iA?J?6>X>t@B>5?>?T/?*?'?test_nn_bceloss/mint_nn_bceloss_binary_case4_input0.npy0000755000175400017540000000114014770223763023776 0ustar jenkinsjenkinsNUMPYv{'descr': '(?H&>OQg?<0>3\?I?ؾ>>qM?P>tߧ>c?l.M?M?N>Y> ? H?L+>(>D=&Q>/?g?Ԗ>z?N;Y ?@+=Mg?"?hn>(6?8?2? >U ?iA?J?6>X>t@B>5?>?T/?*?'?fu? =>F?F֞>0> =>p[>E?h>kw?С=3s@?# 0?OY??b?g>&(? j>>f+?0$?9U?d>I5?̫?c?>@=Zk?r??i>w?0=DL>`?>?? L>>B?脣=u!?XC?,>ٹ>6>w>T >T>|=Z>>r>L>)=Vy>d"?=.?"?q=`?k$?3&?F!?,? )?>test_nn_bceloss/mint_nn_bceloss_binary_case1.py0000755000175400017540000000230114732212054022303 0ustar jenkinsjenkinsimport torch from torch.nn.parameter import Parameter from common.utils import auto_generate_data @auto_generate_data() def mint_bceloss_benchmark(): """ benchmark: torch.nn.BCELoss inputs: logits: ((2, 3), float32) labels: ((2, 3), float32) weight: ((2, 3), float32) reduction: 'mean' outputs: output: ((), float32) logits_grad: ((2, 3), float32) logits_grad: ((2, 3), float32) """ logits = torch.rand((2, 3), dtype=torch.float32) logits.requires_grad = True labels = torch.rand((2, 3), dtype=torch.float32) labels.requires_grad = True weight = torch.randn((2, 3), dtype=torch.float32) reduction = 'mean' net = torch.nn.BCELoss(weight=weight, reduction=reduction) output = net(logits, labels) grads = torch.ones_like(output, dtype=torch.float32) output.backward(gradient=grads) logits_grad = logits.grad labels_grad = labels.grad return [logits.detach().numpy(), labels.detach().numpy(), weight.numpy()], \ [output.detach().numpy(), logits_grad.detach().numpy(), labels_grad.detach().numpy()] if __name__ == "__main__": mint_bceloss_benchmark()