From 148e7f62305e40306a96ffdae0a2553b07dd80a9 Mon Sep 17 00:00:00 2001 From: windweller Date: Wed, 30 Oct 2024 18:16:39 -0700 Subject: [PATCH] add backprop images --- README.md | 107 ++++++++++++++++++++++++++++++++++++--- docs/images/backprop.png | Bin 0 -> 275248 bytes 2 files changed, 101 insertions(+), 6 deletions(-) create mode 100644 docs/images/backprop.png diff --git a/README.md b/README.md index 0aeb9cfd..2f155b51 100644 --- a/README.md +++ b/README.md @@ -28,6 +28,107 @@ Or for development, clone the repo and run the following. The library requires Python >= 3.9. The installation script will git clone [AutoGen](https://github.com/microsoft/autogen). You may require [Git Large File Storage](https://git-lfs.com/) if git is unable to clone the repository otherwise. +## Updates + + - **2024.10.21** New [paper](https://arxiv.org/abs/2410.15625) by Nvidia, Stanford, Visa, & Intel applies Trace to optimize for mapper code of parallel programming. Trace (OptoPrime) learns code achieving 1.3X speed up under 10 minutes, compared with the code optimized by domain expert. + - **2024.9.25** [Trace Paper](https://arxiv.org/abs/2406.16218) is accepted to NeurIPS 2024. + - **2024.9.14** TextGrad is available as an optimizer in Trace. + + +## QuickStart + +Trace has two primitives: `node` and `bundle`. `node` is a primitive to define a node in the computation graph. `bundle` is a primitive to define a function that can be optimized. + +```python +from opto.trace import node +x = node(1, trainable=True) +y = node(3) +z = x / y +z2 = x / 3 # the int 3 would be converted to a node automatically + +list_of_nodes = [x, node(2), node(3)] +node_of_list = node([1, 2, 3]) + +node_of_list.append(3) +``` + +Once a node is declared, all the following operations on the node object will be automatically traced. +We built many magic functions to make a node object act like a normal Python object. By marking `trainable=True`, we tell our optimizer that this node's content +can be changed by the optimizer. + +For functions, Trace uses decorators like @bundle to wrap over Python functions. A bundled function behaves like any other Python functions. + +```python +from opto.trace import node, bundle + +@bundle(trainable=True) +def strange_sort_list(lst): + ''' + Given list of integers, return list in strange order. + Strange sorting, is when you start with the minimum value, + then maximum of the remaining integers, then minimum and so on. + ''' + lst = sorted(lst) + return lst + +test_input = [1, 2, 3, 4] +test_output = strange_sort_list(test_input) +print(test_output) +``` + +Now, after declaring what is trainable and what isn't, and use `node` and `bundle` to define the computation graph, we can use the optimizer to optimize the computation graph. + +```python +import autogen +from opto.optimizers import OptoPrime + +# we first declare a feedback function +# think of this as the reward function (or loss function) +def get_feedback(predict, target): + if predict == target: + return "test case passed!" + else: + return "test case failed!" + +test_ground_truth = [1, 4, 2, 3] +test_input = [1, 2, 3, 4] + +epoch = 2 + +optimizer = OptoPrime(strange_sort_list.parameters(), + config_list=autogen.config_list_from_json("OAI_CONFIG_LIST")) + +for i in range(epoch): + print(f"Training Epoch {i}") + test_output = strange_sort_list(test_input) + correctness = test_output.eq(test_ground_truth) + feedback = get_feedback(test_output, test_ground_truth) + + if correctness: + break + + optimizer.zero_feedback() + optimizer.backward(correctness, feedback) + optimizer.step() +``` + +Then, we can use the familiar PyTorch-like syntax to conduct the optimization. + +## Supported Optimizers + +Currently, we support three optimizers: +- OPRO: [Large Language Models as Optimizers](https://arxiv.org/abs/2309.03409) +- TextGrad: [TextGrad: Automatic "Differentiation" via Text](https://arxiv.org/abs/2406.07496) +- OptoPrime: Our proposed algorithm -- using the entire computational graph to perform parameter update. It is 2-3x faster than TextGrad. + +Using our framework, you can seamlessly switch between different optimizers: +```python +optimizer1 = OptoPrime(strange_sort_list.parameters()) +optimizer2 = OPRO(strange_sort_list.parameters()) +optimizer3 = TextGrad(strange_sort_list.parameters()) +``` + +And you can easily implement your own optimizer that works directly with `TraceGraph` (more tutorials on how to work with TraceGraph coming soon). ## Citation If you use this code in your research please cite the following [publication](https://arxiv.org/abs/2406.16218): @@ -39,12 +140,6 @@ If you use this code in your research please cite the following [publication](ht year={2024} } ``` -## Updates - - - **2024.10.21** New [paper](https://arxiv.org/abs/2410.15625) by Nvidia, Stanford, Visa, & Intel applies Trace to optimize for mapper code of parallel programming. Trace (OptoPrime) learns code achieving 1.3X speed up under 10 minutes, compared with the code optimized by domain expert. - - **2024.9.25** [Trace Paper](https://arxiv.org/abs/2406.16218) is accepted to NeurIPS 2024. - - **2024.9.14** TextGrad is available as a Trace optimizer. - ## Evaluation diff --git a/docs/images/backprop.png b/docs/images/backprop.png new file mode 100644 index 0000000000000000000000000000000000000000..afab579cb337c7861c6ba868b6c3dec776e6fd60 GIT binary patch literal 275248 zcmeFZcT`i`x;~7EiXs9cpmfDTk)rh81Ox=6gwTs@LAvxBVnjtjz(y~jL+D6v0YyN1 z?>sFgsS9~Rcq-`i?iA+E>(Dt|pkboFc{8}y`HhmC zAp?bo-sFiFh1JfJe%Q|T7Tny-e&%>3p}msijqtF7<@MaNa*=XZg3qp}Hr}|}`#6w< z;x5T4PPxDi8j4FaG?pZ9ZTGxzkE!Hnx5_&1D)e2%fROS z!tP%^Mgj`l35`^HMmzqzFqYhw+H!;}Q*&+av}WQTLIJn0OT2qkc#g#Td>pUZi6?)A z?bwRP>s_Vdx@hTlEQ()FjKLC$IgdP!6C!i(Ex1$a-chwp`F8iq6^K05?T?DHH}57g$#nRy{;_{GQ#H^k zBZkBJx~5>%3LCdsV7;s^bb{9?bBaIzqYjwv0hXYd@zF- zVpUI+mF1Oiignv5a?3P~r=gReZTPUK)O**5ASM3>b#Forw`@37*WPB+(xuj$5;$92 zY_p3#eNj-naczCFLW=MtCiVsJM3vWoczLCm&Jt_1p+~5}V(|h$Nea@TZ|M7s`x=cXpi5$7 zX({nsY`#;cj=z*Uopnqj>D22d;)Tb;>gl{m&8bNgA#$`U}z|0nYPkPu?Zz-6c^?p6!;%y2uS{O+L8K>wZDvbV5+Rf^@fH76aqc zNdi6f5xogNegDAdfJicUdn(b-ackB<39lo1^=ATkI zpXkVGYoFv~y1>$S=XvURZ7xn4yKC9xpy2E~!bzj|6SBrQFWFt(ycDXW+bvW=IZDg& zI!xZJTfLj7hpA_Fp3z-!_sXtF6@&CseAs0xcLDv2RA(t@!&Mt^HePt3b_*1Jz5QCd z={?!G}5+hAkXOMOdoOR?}3U8ha@nwDM`oQpH6q{(9#b^mB^lBA4?fmz@|^ zsaJ2V(&w{npDuVZ)GRr5eC?I@u>fA=1@j=;z`Eyb_2D!hPG2XZQV@fwf8}tv&V03m z#)*3OwACHDluJD!7d7tP$iEy(!}K!hrPfQ#g$ozX(v8#2as8mv=lQ_JtX+}Po%16m zP3_~=TdEXyb$YMs2rn=%TwY*_xhHaM?;UN+`St6ryf{+aCEWWfN8l6f7T-rKhR+}z3I8d!ZEwWWP zLLwrEJ1|=eq&q+Y%D00p9vh&@hFrMN`0{hwW6mYcQ(RG;2X+R+g2ItTRYpalmZeUm zG#(%in@xjFlg%@meT%obgYDEFY6~Lb5Y1WJS?1OYoe$clIz|O@EC=%p*@R1k{e`_5 zl^8=Ay@ZjEdWDV)2?~|ll|7OfP_Q?&>*zDeE(I^W8!Rk~Ez1;9slK}xy~V*|(5!Pq z$0nxpSOQ;ywyE?4yJ_+&-vmWfgRh>nd98cxim%Paiw&;b$zA`0m0g=n^WJ{jvqsnR zUVFY3d5~m9?Z7TP?A+}rVExGYqE*FUVb2c)P0vfG4-ZNvvKk(JF+h2Tt+`J7PODre zqpebnZP9$M`93_uI>RMnNyGTwsScrb18bc1rnOdk%bd)ddOJ@C#=wGip8gWO-%Tf@ z&yjqQwvlv^qPZ44U>@WEYYwaZ>(c7d`l{YtBxTDB%lB5g&VBakrIf{{_G1tBMmvZ3 zdYp1hGbgj;4@#7X*Lak;&xq-ZO}bo>JRysPUU;j)P#yrim^{gkSZZ4HcN`Rr9TI zS!qWpL8&FFQ#NrOa~(2b)m?4fAuFXQ^*If!<~n}Fc+Tou!xm!0XVreDZ8LA3#lk?K z_fpWsf`jFQI{_L2Y%#3ubnR|NCEEE3`|3-XlPAbO!Hdvti8uPQ_ z&*jf$Lj^(%&b!#LdYXNy3HIT?!g3`_#Zy^3gG@<|JQ$(*7n{9$A2b{T04jB1POT?g91tEZ9AUikqA!kDZf8K94^KGuZOB znz;KG^*-2|npTZYt4OO#D_DN)dCgBKCZa~t=UKzd@=VRtu4}nlkhfXQ`ANB8u$pSC zMK*Y`tUp-@Uea4)Df8aE6Z)Pt9?5@TrR=Tj(d2DyVr@k)!^5?dCI@Vsl0qcMU(eMb3>(D z_UeO3bB6N#Jh^fU4|x=oylg)MH+}FKOu@EE9Y$p%e4njaRLpU>^t&HT`MgmP#Km@P zC^SBFD8O|Gv7EW4!4+2}TJ1DheWF^aaCTB3o+o^b`x@>VWNgmCeo}MHtmNPztWh#Q zQX*<~Om&=Vf{TZO$A02rjeT2w!L0497tcO7qu0{%WZ~X4>uRF0A7oo(xo-kogK6v9 zhqEnHjiO9!jSPKB{Mga;A%R7wB%#R!9piKm$Ye9m=ss`GtK3S1C4FP#Ox^NI*8HeJ zDi)*1^K+JjhCE6U$~`7TXroF!&BEI#mG@zjlJvg0(_#nS4X5AJR>yH_D0SR-GsE6h zHwwuWPA*0N@wFvPq^m4Lw=6SzjB=DmGY3BD|H3=tTl>z)esL=L8Tv(6W+G*y zVgI=#1KM;MdBJy~b+*2ZLE=(^O@X(mZQY{F=8W)eY2;G=P+YZ1RcwF$bG-NEnO46t0@oNp3rJ)kgbKt@2m!`_)4Pb_B5B<5a}Mn^+41 zp8Du)=;3S^1QTo`>J0Y>Io%=>RyQ##zO0-3E^L{Y0n8MtZi}IYy^fQmn=OmQo!Noy z409FE72k#je+FAMJcnlO7wwhemS~~7_z~grvb_Z54eR}tCa+jbHcGM%b20QHWb5)$ z!*FwoRxI33rcc%@;MLx3fwB#rRr?Xq5NAh}Vx8=zE=D$nk4noeAd=^t@$X3M$LvVH zo+i2fhUKXk)UF`$!lOlFi+A)k{a_wJyzet^`r@5m6KAto~QUAs6!U~1O zjT@DknELm7yU#4%@<*Kvw_m!~kZ3sc6x!O)M~FfaY++JMsO35mEAg}AUkxWEfCSdz zp@FKkh6V{Ya7|7^c8rna1aNf>_%Cye>F;ahW1J+%|GZC1LK0+4LiRuJXaawK{(A%b z|M{7J{UuEz{p$^il%(T-U7v{m`D*vAq4U6>(~s{Ox{;931KBhEwPUI}SAUR@$dRbt zk=OG+wmjigl8T0~?VPG#dFVTR){lWJ_=CY!?OMZHas8UND;FM4m%XyO;W8y=j8_%8 zVWXw_vARWda8m5Xs}K=B9S7C2Dl6^K3tZY4u5D>*7I>|W#~5$pY85R=+Ij0&X0h>s z$9_!Z!M7VJ&aMLY(`n5KJ9@q3)a-IkNRE+`{XbvGg^j=!|y|E%t) zE&rd@{iCBwI(`f`OUs$A*Sl?rtKoh1AAC~1GEKCaEjLPMT<8e77UDouiBk08xS84; zrzRbhrZcqlL83fe2R-OtCya8|$Q}*^hj86iiqmO2kj_n;@6N+%Q-I(3; zmhru+kpSUJZz-ZPQw+I5?AezT<)|T$<>rSI!8+TWdW&@;5x)Fg-?0!}nP^a_+cy&q z;`jfujsF2(e}>{hV{wYX;MpY_*v5zB3%VTq83@6yv=%|Fxo^8ImzqSPkD!fK1!lZ{ zsiBMFgQVNH{a|LPy6dQX%~_=VvrZPKBjXc)HrEiQGZf|zLbD)B7C?<6dh2!CN)Wr9 z7Hm25JvgI}3{|{I@z|qA;)qx8N=}}_AGB^{teh?yamwj#`X}%loB28~n4-~zhiyPD8A7qebq7$ApM0p3yhk`t%E6yoF!(j32 zCoDK^JyH_8GQbt#H#l-~l=+CvkGP$Z>-QHcuQuk@U`@F~snT0$w=-L&x95Tvv}P{d zD;1%&Ix?3B-+hU-&G0?*bqQ39ptBiQP?vN2Cv5fliZB;w*P3;J=ReX(J^YPS*Q&X< ze{HWbs8=O%>>Hy9oQQFesweEbC#6W$YNOI!k!dDeP6=ZoNl!HXa|ToYnJl9d8GU>K z#rJeUH&umRL{OKOm?dFO_Ls<$UZeit%6_kh+!)_3+#(twgc^wsBLeWNe6mS(r)}4V zMf+wxbzOsPa0mMlaXL&+%EDyHA-XFPxm%zSAPNgu$WO7pjOG!^3HxMn>>v

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