From 8d3f444ab1e1df80eef8279eba635646fa5ae757 Mon Sep 17 00:00:00 2001 From: Tab Zhang Date: Sat, 10 Oct 2020 09:46:00 +0800 Subject: [PATCH] [tuner] Regularized Evolution (#2802) --- docs/en_US/NAS/ClassicNas.md | 19 +- docs/img/EvoNasTuner.png | Bin 0 -> 346174 bytes src/sdk/pynni/nni/constants.py | 5 + .../regularized_evolution_tuner/__init__.py | 1 + .../regularized_evolution_tuner.py | 172 ++++++++++++++++++ .../assets/classic_nas_search_space.json | 26 +++ src/sdk/pynni/tests/test_builtin_tuners.py | 41 ++++- test/config/integration_tests.yml | 4 + .../tuners/regularized_evolution_tuner.yml | 20 ++ .../tuners/seach_space_classic_nas.json | 26 +++ 10 files changed, 309 insertions(+), 5 deletions(-) create mode 100644 docs/img/EvoNasTuner.png create mode 100644 src/sdk/pynni/nni/regularized_evolution_tuner/__init__.py create mode 100644 src/sdk/pynni/nni/regularized_evolution_tuner/regularized_evolution_tuner.py create mode 100644 src/sdk/pynni/tests/assets/classic_nas_search_space.json create mode 100644 test/config/tuners/regularized_evolution_tuner.yml create mode 100644 test/config/tuners/seach_space_classic_nas.json diff --git a/docs/en_US/NAS/ClassicNas.md b/docs/en_US/NAS/ClassicNas.md index 6185d48d32..3c6bd2b416 100644 --- a/docs/en_US/NAS/ClassicNas.md +++ b/docs/en_US/NAS/ClassicNas.md @@ -24,10 +24,25 @@ At this point, trial code is ready. Then, we can prepare an NNI experiment, i.e. A file named `nni_auto_gen_search_space.json` is generated by this command. Then put the path of the generated search space in the field `searchSpacePath` of the experiment config file. The other fields of the config file can be filled by referring [this tutorial](../Tutorial/QuickStart.md). -Currently, we only support [PPO Tuner](../Tuner/BuiltinTuner.md) and [random tuner](https://github.com/microsoft/nni/tree/master/examples/tuners/random_nas_tuner) for classic NAS. More classic NAS algorithms will be supported soon. +Currently, we only support [PPO Tuner](../Tuner/BuiltinTuner.md), [Regularized Evolution Tuner](#regulaized-evolution-tuner) and [Random Tuner](https://github.com/microsoft/nni/tree/master/examples/tuners/random_nas_tuner) for classic NAS. More classic NAS algorithms will be supported soon. The complete examples can be found [here](https://github.com/microsoft/nni/tree/master/examples/nas/classic_nas) for PyTorch and [here](https://github.com/microsoft/nni/tree/master/examples/nas/classic_nas-tf) for TensorFlow. ## Standalone mode for easy debugging -We support a standalone mode for easy debugging, where you can directly run the trial command without launching an NNI experiment. This is for checking whether your trial code can correctly run. The first candidate(s) are chosen for `LayerChoice` and `InputChoice` in this standalone mode. \ No newline at end of file +We support a standalone mode for easy debugging, where you can directly run the trial command without launching an NNI experiment. This is for checking whether your trial code can correctly run. The first candidate(s) are chosen for `LayerChoice` and `InputChoice` in this standalone mode. + + + +## Regularized Evolution Tuner + +This is a tuner geared for NNI’s Neural Architecture Search (NAS) interface. It uses the [evolution algorithm](https://arxiv.org/pdf/1802.01548.pdf). + +The tuner first randomly initializes the number of `population` models and evaluates them. After that, every time to produce a new architecture, the tuner randomly chooses the number of `sample` architectures from `population`, then mutates the best model in `sample`, the parent model, to produce the child model. The mutation includes the hidden mutation and the op mutation. The hidden state mutation consists of replacing a hidden state with another hidden state from within the cell, subject to the constraint that no loops are formed. The op mutation behaves like the hidden state mutation as far as replacing one op with another op from the op set. Note that keeping the child model the same as its parent is not allowed. After evaluating the child model, it is added to the tail of the `population`, then pops the front one. + +Note that **trial concurrency should be less than the population of the model**, otherwise NO_MORE_TRIAL exception will be raised. + +The whole procedure is summarized by the pseudocode below. + +![](../../img/EvoNasTuner.png) + diff --git a/docs/img/EvoNasTuner.png b/docs/img/EvoNasTuner.png new file mode 100644 index 0000000000000000000000000000000000000000..36d5483c8bbf03ff9c1fc91c22e6dd55ac34d7e0 GIT binary patch literal 346174 zcmd?QRahKdx9>|rf|EewPUG&bp>cP&;1Hy7Xx!c12@VPF4#67t;1b*+xWndszqR%{ z=jL3Tn|-nCd8(>w*6dO9sX1!OfBa^+l7b{E5_Kfq|)sMSe2+_;!tGFQx4S1B1c)&;Bm9N8=p~%xjUf zn24IY!EyFScPuD=NVq2g@Ym|wR$=K73i97PH7LiV!DKvr+lWaKffRkK6GOa^v*U2& zIgqZ$o_9`5hw1I{{`k}S_^9wfoD>BhT#PJUiXve`3<>c+Ki^U=7Nvl|x77dK6e6Id z`0spxAq(>0pnw1J&&_+A!npr2k{gW)_%C~a@a?*I5a=J%|E73xsYv~|y|;%+{NMPn zOfX0)C@2UG6I1N#*Y7B7gQS7~b$GG>iHUvCso)wvd~Up>3KCnaHDhIG@9paogTph8 z6Zx;h3%k*uZvS@3CDniDJKyY|nxCiEZ*~5ESR4GCi|GA$)8F4u`Cs|^c1BWpU5{}k zV~Lah+t{1gkXJePtNnO8$V}59H;^L9#pM5G#Sod5mDK_JtO@#d^M4$m3;F-U9bVW$ zWPF1dv0pfsz;5Y_hLS{m{BFKvN!P9pz6pH=Ksr$HE`dQP&42vj0=(G;q^R#aK2S}1 zBAWk8C)N**y$TyJ+ER43S`f#~VM@ z1eOZj+ay{V*booht~W>-5LZalUAnL+ukYf~)>uBdyE^ldchO6(g*dfrVbD?Zr&4YY z;QOrIhxz^8Z|?HaulK>xUZ~*0`r%@{aqkxN4StcI>SGolP}II zA~4TEAL_=-z|vScP|oi!<`YmW%DH?mGDUZO2$5m_N^CjCyZN-f`1V zaJ)?Lb{cco(r@<+^oY&Y2uUQDIL>kj!Jdt%%*)o1_dc&1HQd=Kl5 zfrPRE&GakvJwQpqK9i(gtnm^!8L^o8Eqc7ck8Mh)lu)}vuT*2)->l^CXeOh=;j6)> zH9#z6W#A6unf)C01kYB2+CElX25Y6vh0~Z)jEjeFym*C^;~w;6YewYwS8{l}8vEKt z?nQ{-{eTE)flv9?3|v%g3X@XgLmI1vZgxZVi`a_?6-)j`-z72kvWm4al%#j?hMb26 zq;Z(vXU2gQrAoo94L^u>;m$)dapMYZh1P;gRq%+Hx@F}mvoqa&a|;m*E$ zj$fti_X|Qx`J9M-WI-U&@4XHhF?N#udU(|=tN;nE@@AEQ4NWvaM(T)FGBqq!$W6V@ z{fFGHylRdrH@0d=uJRL^%U)Ob_hhdfSeOOBzlUpJ*xT^z)M*)$9*PLdO3n#6kdu-n z3=VDs$;eQIL&@c=4g8`$de*E?rH1)#dPhwGDm2h{yyNmfAGGoIjn+&NV_zlSTwt+x zQU@~l)iA+_fMk>Crf6^a-%sh@+UpTScF31Vw`7cOAx2<&Hr&{>ky|eyebkZDn9jfw z!Oqd84HUst)NG{m^}x8AIWUULqe~q3K*P=^Bgf~y`OKt9<)j*cDjcm@%6AGPM1S1R zhVEu!0q}2OM{(KwbLWZwT-6vJs0fIH+V1#3WB~*-*72({=rs>oYMBFO6KFu&E;d$t zwRoeT5b^wo7p|LLF=0cH6Rfq735$#u4vkW|_r5oye2#`51EepDj_EO7`ztC=%WT_^%I9 ze-yPAs;=*=5D?>2_tEt12u(+d^|O3WW!>p0u|~7|8ELo0jr$w-Tn0qHZqrr(y%}*X)i#@hTnn-s;RU3%Xz9#{C#hG%ixaGHv=sAQ2oFEIY6U+t9cv;yYgQdeC3-fSywVda#0y2u(Y zg%Kt^CllD2&cXEh5jP3fv3VTQ61D$3W*7BPsYFC|$(d-d(9q4{{ef_5FSBTvmuLFe2zDLm(DUUUv+0qGAzAX1^>cG)pj?xZTdPR%B{MTukyuO&G?W`K4?iA@u88w{ zfwkKerNS<*T)m@Jy*#qk_JbhDPR28je9|7-dftc_WWqO1zXaBJ;tpG;O$sffa?X6W zjw?#q0aiXoI3mswePSL$+rxsIAhPxKQZ*mu79Zo!nRAs!&nezn-un_#!!MK{>DDdu zns2iFSj@DOQzp5acpA4p&)M>Ib4tE0yomlg^85T36*=6Jc>JgQn#PE`y#w2C+c9SX z-YwLf(G%`JSp()mF-SB%v#&S#p1Ryl5s^1*42L^Kqk-s4A^tp7v4vD3=Fh>UA)sIHnEQfaj^Ev^S}W1 zZ2BwiD*~fVY&J@za!7k)v2;)nF%_PZYNEQ&a=6`85QLCDl7#b2SOsaX}r-< zSlOf%YYZ|W*nfIY)CEi9#&p6&D8c9Rj6?HwAKikDuJ=%0tLQ0um=(V3bi$3cU&O>& za*lU3Q5(WxTwTf0@LeHPT2(+4#d(--9jG54PkSgyU#wjz&Q3{8o{xd`KQ-dy4s0!J?^waa zMCKZ8Y^HUrP_fzQ@G{KCy>W!w$sTedP6UYWVmo@$3J+?5)>kH_qBWna{F zZt;eav_XDIdCiu~r~)n$`_Ox5AbQW##u#qHI^KE^r@T?SKueT=!-X;P8YMfsQ#lfc z`9i*^04iok(pC?>zFteGHRAKPZLA%JX;bwilAF#R&D1g_LWSpF39PoX zspRAuyXIQS_ya%-;i)D|hedAx8<)t$#U8EpM2|Fcx-Uc1J2Zx!Ncui4{+@AY6e8n# zt1W5h`NzoSjYVIp7O?WFHj)UBp$akro^UrIbE2aPi0+tu6HZvP@r zeWuu@o8AIabB}M8#BkCk^oO9J?m0Z}z{ZT7$@S|sYJ~}n_4Q$1%3IbDw_TvR=;1<2KdB)G{;1)R?u+c5cgr#R; 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z1z0?vpxxG*?O$B40_JZ9FXNx~8B-)x8o7n0PCOsBR$Dad_TWe(B|}OW>;EMH{?7v? zkq_{{At_oD&k>4u_xIZ{7%Q{rh?2Tz%J-~Y24>O6%96Z18XdMDT!)XF+erORx7u)E zcjSAzfJ4fE1pH_0Rc(3#wPZq9SJ!_=3b}2JcLZFfiTTz1|B;hfqB0*oeEv65djG$k nlllLq_1})<{O=oz@hohE51*4tAm#Tz$V*91UA9Kr{LB9WOJbsL literal 0 HcmV?d00001 diff --git a/src/sdk/pynni/nni/constants.py b/src/sdk/pynni/nni/constants.py index 412577571a..3e98f6eb6f 100644 --- a/src/sdk/pynni/nni/constants.py +++ b/src/sdk/pynni/nni/constants.py @@ -62,6 +62,11 @@ 'name': 'PBTTuner', 'class_name': 'nni.pbt_tuner.pbt_tuner.PBTTuner', 'class_args_validator': 'nni.pbt_tuner.pbt_tuner.PBTClassArgsValidator' + }, + { + 'name': 'RegularizedEvolutionTuner', + 'class_name': 'nni.regularized_evolution_tuner.regularized_evolution_tuner.RegularizedEvolutionTuner', + 'class_args_validator': 'nni.regularized_evolution_tuner.regularized_evolution_tuner.EvolutionClassArgsValidator' } ], 'assessors': [ diff --git a/src/sdk/pynni/nni/regularized_evolution_tuner/__init__.py b/src/sdk/pynni/nni/regularized_evolution_tuner/__init__.py new file mode 100644 index 0000000000..befa4e446b --- /dev/null +++ b/src/sdk/pynni/nni/regularized_evolution_tuner/__init__.py @@ -0,0 +1 @@ +from .regularized_evolution_tuner import RegularizedEvolutionTuner diff --git a/src/sdk/pynni/nni/regularized_evolution_tuner/regularized_evolution_tuner.py b/src/sdk/pynni/nni/regularized_evolution_tuner/regularized_evolution_tuner.py new file mode 100644 index 0000000000..a466df195b --- /dev/null +++ b/src/sdk/pynni/nni/regularized_evolution_tuner/regularized_evolution_tuner.py @@ -0,0 +1,172 @@ +import copy +import logging +import random +from collections import deque + +import nni +from schema import Schema, Optional +from nni.tuner import Tuner +from nni import ClassArgsValidator +from nni.utils import OptimizeMode, extract_scalar_reward + +logger = logging.getLogger(__name__) + + +class FinishedIndividual: + def __init__(self, parameter_id, parameters, result): + """ + Parameters + ---------- + parameter_id: int + the index of the parameter + parameters : dict + chosen architecture and parameters + result : float + final metric of the chosen one + """ + self.parameter_id = parameter_id + self.parameters = parameters + self.result = result + + +class EvolutionClassArgsValidator(ClassArgsValidator): + def validate_class_args(self, **kwargs): + Schema({ + 'optimize_mode': self.choices('optimize_mode', 'maximize', 'minimize'), + Optional('population_size'): self.range('population_size', int, 0, 99999), + Optional('sample_size'): self.range('sample_size', int, 0, 9999), + }).validate(kwargs) + + +class RegularizedEvolutionTuner(Tuner): + """ + RegularizedEvolutionTuner is tuner using Evolution NAS Tuner. + See ``Regularized Evolution for Image Classifier Architecture Search`` for details. + + Parameters + --- + optimize_mode: str + whether to maximize metric or not. default: 'maximize' + population_size: int + the maximum number of kept models + sample_size: int + the number of models chosen from population each time when evolution + """ + def __init__(self, optimize_mode="maximize", population_size=100, sample_size=25): + super(RegularizedEvolutionTuner, self).__init__() + self.optimize_mode = OptimizeMode(optimize_mode) + self.population_size = population_size + self.sample_size = sample_size + self.initial_population = deque() + self.population = deque() + self.history = {} + self.search_space = None + self._from_initial = {} # whether the parameter is from initial population + + def generate_parameters(self, parameter_id, **kwargs): + """ + This function will returns a dict of trial (hyper-)parameters, as a serializable object. + + Parameters + --- + parameter_id: int + the index of current set of parameters + """ + if self.initial_population: + arch = self.initial_population.popleft() + self.history[parameter_id] = arch + self._from_initial[parameter_id] = True + return arch + elif self.population: + sample = [] + while len(sample) < self.sample_size: + sample.append(random.choice(list(self.population))) + + candidate = max(sample, key=lambda x: x.result) + arch = self._mutate_model(candidate) + self.history[parameter_id] = arch + self._from_initial[parameter_id] = False + return arch + else: + raise nni.NoMoreTrialError + + def receive_trial_result(self, parameter_id, parameters, value, **kwargs): + """ + Record the result from a trial + + Parameters + ---------- + parameter_id : int + parameters : dict + value : dict/float + if value is dict, it should have "default" key. + value is final metrics of the trial. + """ + reward = extract_scalar_reward(value) + if parameter_id not in self.history: + raise RuntimeError('Received parameter_id not in total_data.') + params = self.history[parameter_id] + + if self.optimize_mode == OptimizeMode.Minimize: + reward = -reward + + self.population.append(FinishedIndividual(parameter_id, params, reward)) + if len(self.population) > self.population_size: + self.population.popleft() + + def update_search_space(self, search_space): + """ + Update search space. + Search_space contains the information that user pre-defined. + + Parameters + ---------- + search_space : dict + """ + logger.info('update search space %s', search_space) + assert self.search_space is None + self.search_space = search_space + + for _, val in search_space.items(): + if val['_type'] != 'layer_choice' and val['_type'] != 'input_choice': + raise ValueError('Unsupported search space type: %s' % (val['_type'])) + + self._generate_initial_population() + + def trial_end(self, parameter_id, success, **kwargs): + if not success: + del self.history[parameter_id] + if self._from_initial[parameter_id]: + self.initial_population.append(self._random_model()) + del self._from_initial[parameter_id] + + def _mutate(self, key, individual): + mutate_val = self.search_space[key] + if mutate_val['_type'] == 'layer_choice': + idx = random.randint(0, len(mutate_val['_value']) - 1) + individual[key] = {'_value': mutate_val['_value'][idx], '_idx': idx} + elif mutate_val['_type'] == 'input_choice': + candidates = mutate_val['_value']['candidates'] + n_chosen = mutate_val['_value']['n_chosen'] + idxs = [random.randint(0, len(candidates) - 1) for _ in range(n_chosen)] + vals = [candidates[k] for k in idxs] + individual[key] = {'_value': vals, '_idx': idxs} + else: + raise KeyError + + def _random_model(self): + individual = {} + for key in self.search_space.keys(): + self._mutate(key, individual) + return individual + + def _mutate_model(self, model): + new_individual = copy.deepcopy(model.parameters) + mutate_key = random.choice(list(new_individual.keys())) + self._mutate(mutate_key, new_individual) + return new_individual + + def _generate_initial_population(self): + while len(self.initial_population) < self.population_size: + self.initial_population.append(self._random_model()) + logger.info('init population done.') diff --git a/src/sdk/pynni/tests/assets/classic_nas_search_space.json b/src/sdk/pynni/tests/assets/classic_nas_search_space.json new file mode 100644 index 0000000000..bac470b72f --- /dev/null +++ b/src/sdk/pynni/tests/assets/classic_nas_search_space.json @@ -0,0 +1,26 @@ +{ + "first_conv": { + "_type": "layer_choice", + "_value": [ + "conv5x5", + "conv3x3" + ] + }, + "mid_conv": { + "_type": "layer_choice", + "_value": [ + "0", + "1" + ] + }, + "skip": { + "_type": "input_choice", + "_value": { + "candidates": [ + "", + "" + ], + "n_chosen": 1 + } + } +} diff --git a/src/sdk/pynni/tests/test_builtin_tuners.py b/src/sdk/pynni/tests/test_builtin_tuners.py index acd0012531..0930d27771 100644 --- a/src/sdk/pynni/tests/test_builtin_tuners.py +++ b/src/sdk/pynni/tests/test_builtin_tuners.py @@ -19,6 +19,7 @@ from nni.metis_tuner.metis_tuner import MetisTuner from nni.msg_dispatcher import _pack_parameter, MsgDispatcher from nni.pbt_tuner.pbt_tuner import PBTTuner +from nni.regularized_evolution_tuner.regularized_evolution_tuner import RegularizedEvolutionTuner try: from nni.smac_tuner.smac_tuner import SMACTuner @@ -57,7 +58,8 @@ def send_trial_result(self, tuner, parameter_id, parameters, metrics): tuner.receive_trial_result(parameter_id, parameters, metrics) tuner.trial_end(parameter_id, True) - def search_space_test_one(self, tuner_factory, search_space): + def search_space_test_one(self, tuner_factory, search_space, nas=False): + # nas: whether the test checks classic nas tuner tuner = tuner_factory() self.assertIsInstance(tuner, Tuner) tuner.update_search_space(search_space) @@ -68,12 +70,14 @@ def search_space_test_one(self, tuner_factory, search_space): (i + 1) * self.params_each_round)), st_callback=self.send_trial_callback(queue)) logger.debug(parameters) - self.check_range(parameters, search_space) + check_range = lambda parameters, search_space: self.nas_check_range(parameters, search_space) \ + if nas else self.check_range(parameters, search_space) + check_range(parameters, search_space) for k in range(min(len(parameters), self.params_each_round)): self.send_trial_result(tuner, self.params_each_round * i + k, parameters[k], random.uniform(-100, 100)) while queue: id_, params = queue.popleft() - self.check_range([params], search_space) + check_range([params], search_space) self.send_trial_result(tuner, id_, params, random.uniform(-100, 100)) if not parameters and not self.exhaustive: raise ValueError("No parameters generated") @@ -123,6 +127,19 @@ def check_range(self, generated_params, search_space): for layer_name in item["_value"].keys(): self.assertIn(v[layer_name]["chosen_layer"], item["layer_choice"]) + def nas_check_range(self, generated_params, search_space): + for params in generated_params: + for k in params: + v = params[k] + items = search_space[k] + if items['_type'] == 'layer_choice': + self.assertIn(v['_value'], items['_value']) + elif items['_type'] == 'input_choice': + for choice in v['_value']: + self.assertIn(choice, items['_value']['candidates']) + else: + raise KeyError + def search_space_test_all(self, tuner_factory, supported_types=None, ignore_types=None, fail_types=None): # Three types: 1. supported; 2. ignore; 3. fail. # NOTE(yuge): ignore types @@ -163,6 +180,20 @@ def search_space_test_all(self, tuner_factory, supported_types=None, ignore_type logger.info("Full supported search space: %s", full_supported_search_space) self.search_space_test_one(tuner_factory, full_supported_search_space) + def nas_search_space_test_all(self, tuner_factory): + # Since classic tuner should support only LayerChoice and InputChoice, + # ignore type and fail type are dismissed here. + with open(os.path.join(os.path.dirname(__file__), "assets/classic_nas_search_space.json"), "r") as fp: + search_space_all = json.load(fp) + full_supported_search_space = dict() + for single in search_space_all: + space = search_space_all[single] + single_search_space = {single: space} + self.search_space_test_one(tuner_factory, single_search_space, nas=True) + full_supported_search_space.update(single_search_space) + logger.info("Full supported search space: %s", full_supported_search_space) + self.search_space_test_one(tuner_factory, full_supported_search_space, nas=True) + def import_data_test_for_pbt(self): """ test1: import data with complete epoch @@ -368,6 +399,10 @@ def tearDown(self): else: os.remove(file) + def test_regularized_evolution_tuner(self): + tuner_fn = lambda: RegularizedEvolutionTuner() + self.nas_search_space_test_all(tuner_fn) + if __name__ == '__main__': main() diff --git a/test/config/integration_tests.yml b/test/config/integration_tests.yml index fa17fd26c3..a4c50788ca 100644 --- a/test/config/integration_tests.yml +++ b/test/config/integration_tests.yml @@ -250,8 +250,12 @@ testCases: - name: tuner-metis configFile: test/config/tuners/metis.yml +- name: tuner-regularized_evolution + configFile: test/config/tuners/regularized_evolution_tuner.yml + ######################################################################### # nni customized-tuners test ######################################################################### - name: customized-tuners-demotuner configFile: test/config/customized_tuners/demotuner-sklearn-classification.yml + diff --git a/test/config/tuners/regularized_evolution_tuner.yml b/test/config/tuners/regularized_evolution_tuner.yml new file mode 100644 index 0000000000..0c67d082a6 --- /dev/null +++ b/test/config/tuners/regularized_evolution_tuner.yml @@ -0,0 +1,20 @@ +authorName: nni +experimentName: default_test +maxExecDuration: 10m +maxTrialNum: 1 +trialConcurrency: 1 +searchSpacePath: seach_space_classic_nas.json +tuner: + builtinTunerName: RegularizedEvolutionTuner + classArgs: + optimize_mode: maximize +trial: + codeDir: ../../../examples/nas/classic_nas + command: python3 mnist.py --epochs 1 + gpuNum: 0 + +useAnnotation: false +multiPhase: false +multiThread: false + +trainingServicePlatform: local diff --git a/test/config/tuners/seach_space_classic_nas.json b/test/config/tuners/seach_space_classic_nas.json new file mode 100644 index 0000000000..bac470b72f --- /dev/null +++ b/test/config/tuners/seach_space_classic_nas.json @@ -0,0 +1,26 @@ +{ + "first_conv": { + "_type": "layer_choice", + "_value": [ + "conv5x5", + "conv3x3" + ] + }, + "mid_conv": { + "_type": "layer_choice", + "_value": [ + "0", + "1" + ] + }, + "skip": { + "_type": "input_choice", + "_value": { + "candidates": [ + "", + "" + ], + "n_chosen": 1 + } + } +}