Learning to Infer Program Sketches Maxwell Nye, Luke Hewitt, Josh - - PowerPoint PPT Presentation

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Learning to Infer Program Sketches Maxwell Nye, Luke Hewitt, Josh - - PowerPoint PPT Presentation

Dec 17, 2023 884 likes •1.13k views

Learning to Infer Program Sketches Maxwell Nye, Luke Hewitt, Josh Tenenbaum, Armando Solar-Lezama Goal: We want to automatically write code from the kinds of specifications humans can easily provide, such as examples or natural language

  • Learning to Infer Program Sketches Maxwell Nye, Luke Hewitt, Josh Tenenbaum, Armando Solar-Lezama Goal: We want to automatically write code from the kinds of specifications humans can easily provide, such as examples or natural language instruction. List Processing from IO: Text Editing from IO: Natural language + IO → code [1, 2, 3, 4, 5] → [2, 4] Max Nye → Nye, M. “Consider an array of numbers, [7, 8, 0, 9] → [8, 0] Luke Hewitt → Hewitt, L. find elements in the given array not divisible by two” [1, 2, 3, 4, 5] → [1, 3, 5] [7, 8, 0, 9] → [7, 9]

  • Learning to Infer Program Sketches Maxwell Nye, Luke Hewitt, Josh Tenenbaum, Armando Solar-Lezama Given: Goal: Write a program [1, 2, 3, 4, 5] → [2, 4] which maps inputs to [0, 6, 2, 7] → [0, 6, 2] outputs [5, 10, 5, 1, 8] → [10, 8] How might people solve problems like this?

  • Learning to Infer Program Sketches Maxwell Nye, Luke Hewitt, Josh Tenenbaum, Armando Solar-Lezama Given: Goal: Write a program [1, 2, 3, 4, 5] → [2, 4] which maps inputs to [0, 6, 2, 7] → [0, 6, 2] outputs [5, 10, 5, 1, 8] → [10, 8] How might people solve problems like this? People use a flexible trade-off between pattern recognition and reasoning

  • Learning to Infer Program Sketches Maxwell Nye, Luke Hewitt, Josh Tenenbaum, Armando Solar-Lezama Easy problem: Spec: [1, 2, 3, 4, 5] → [2, 4] [0, 6, 2, 7] → [0, 6, 2] [5, 10, 5, 1, 8] → [10, 8] Solution:

  • Learning to Infer Program Sketches Maxwell Nye, Luke Hewitt, Josh Tenenbaum, Armando Solar-Lezama Easy problem: Spec: [1, 2, 3, 4, 5] → [2, 4] [0, 6, 2, 7] → [0, 6, 2] [5, 10, 5, 1, 8] → [10, 8] Solution: filter(lambda x: x%2==0, input)

  • Learning to Infer Program Sketches Maxwell Nye, Luke Hewitt, Josh Tenenbaum, Armando Solar-Lezama Easy problem: Spec: [1, 2, 3, 4, 5] → [2, 4] [0, 6, 2, 7] → [0, 6, 2] [5, 10, 5, 1, 8] → [10, 8] Solution: filter(lambda x: x%2==0, input) Fast, using pattern recognition

  • Learning to Infer Program Sketches Maxwell Nye, Luke Hewitt, Josh Tenenbaum, Armando Solar-Lezama More difficult problem: Spec: [3, 4, 5, 6, 7] → [4, 7] [10, 8, 7, 3, 2, 1] → [10, 7, 1] [5, 1, 2, 13, 4] → [1, 13, 4] Solution:

  • Learning to Infer Program Sketches Maxwell Nye, Luke Hewitt, Josh Tenenbaum, Armando Solar-Lezama More difficult problem: Spec: [3, 4, 5, 6, 7] → [4, 7] [10, 8, 7, 3, 2, 1] → [10, 7, 1] [5, 1, 2, 13, 4] → [1, 13, 4] Solution: filter(<SOMETHING>, input) (Fast, using pattern recognition)

  • Learning to Infer Program Sketches Maxwell Nye, Luke Hewitt, Josh Tenenbaum, Armando Solar-Lezama More difficult problem: Spec: [3, 4, 5, 6, 7] → [4, 7] [10, 8, 7, 3, 2, 1] → [10, 7, 1] [5, 1, 2, 13, 4] → [1, 13, 4] Solution: filter(<SOMETHING>, input) filter(lambda x: x%3==1, Symbolic reasoning input) (Fast, using pattern recognition)

  • Learning to Infer Program Sketches Maxwell Nye, Luke Hewitt, Josh Tenenbaum, Armando Solar-Lezama More difficult problem: Spec: [3, 4, 5, 6, 7] → [4, 7] [10, 8, 7, 3, 2, 1] → [10, 7, 1] [5, 1, 2, 13, 4] → [1, 13, 4] Solution: filter(<SOMETHING>, input) filter(lambda x: x%3==1, Symbolic reasoning input) (Fast, using pattern recognition) (Slow)

  • Learning to Infer Program Sketches Maxwell Nye, Luke Hewitt, Josh Tenenbaum, Armando Solar-Lezama Very difficult problem: Spec: [2, 5, 0, 16, 12] → 0 [4, 23, 11, 9, 25] → 25 [3, 29, 30, 14, 16] → 14

  • Learning to Infer Program Sketches Maxwell Nye, Luke Hewitt, Josh Tenenbaum, Armando Solar-Lezama Very difficult problem: Spec: [2, 5, 0, 16, 12] → 0 [4, 23, 11, 9, 25] → 25 [3, 29, 30, 14, 16] → 14 [1, 7, 6, 9, 5] → 7 [5, 5, 1, 8, 8, 12, 4] → 12 [0, 4, 8, 5, 1] → 0 [3, 7, 2, 9, 1] → 9 [1, 0, 3, 7, 3, 8] → 0

  • Learning to Infer Program Sketches Maxwell Nye, Luke Hewitt, Josh Tenenbaum, Armando Solar-Lezama Very difficult problem: Spec: [2, 5, 0, 16, 12] → 0 [4, 23, 11, 9, 25] → 25 [3, 29, 30, 14, 16] → 14 Solution: <SOMETHING> input[input[0]] Symbolic reasoning (Slow)

  • Q: How do we model this? A: Program sketches filter( [3, 4, 5, 6, 7] Pattern filter(<HOLE>, Symbolic → [4, 7] lambda x: recognition reasoning [10, 8, 7, 3, 2, 1] input) x%3==1, → [10, 7, 1] (e.g., neural network) (e.g., guess and check) input) Program sketch (program specification) Full program Flexible trade-off between pattern recognition and reasoning Solar-Lezama et al, 2008, Murali et al, 2017

  • Learning to Infer Program Sketches Maxwell Nye, Luke Hewitt, Josh Tenenbaum, Armando Solar-Lezama Our system: SketchAdapt Learned neural network ... .25 .05 .02 .03 .25 .30 Neural Production probabilities recognizer Neural [3, 4, 5, 6, 7] → [4, 7] Symbolic sketch filter(lambda x: [10, 8, 7, 1] → [10, 7, 1] filter(<HOLE>, input) enumerator generator [5, 1, 13, 4] → [1, 13, 4] x%3==1, input) Program specification Program sketch Full program

  • Learning to Infer Program Sketches Maxwell Nye, Luke Hewitt, Josh Tenenbaum, Armando Solar-Lezama Our system: SketchAdapt Learned neural network ... .25 .05 .02 .03 .25 .30 Neural Production probabilities recognizer Neural [3, 4, 5, 6, 7] → [4, 7] Symbolic sketch filter(lambda x: [10, 8, 7, 1] → [10, 7, 1] filter(<HOLE>, input) enumerator generator [5, 1, 13, 4] → [1, 13, 4] x%3==1, input) Program specification Program sketch Full program Sketch generator: RNN that proposes program sketches (c.f. RobustFill) Devlin et al, 2017 Balog et al, 2016

  • Learning to Infer Program Sketches Maxwell Nye, Luke Hewitt, Josh Tenenbaum, Armando Solar-Lezama Our system: SketchAdapt Learned neural network ... .25 .05 .02 .03 .25 .30 Neural Production probabilities recognizer Neural [3, 4, 5, 6, 7] → [4, 7] Symbolic sketch filter(lambda x: [10, 8, 7, 1] → [10, 7, 1] filter(<HOLE>, input) enumerator generator [5, 1, 13, 4] → [1, 13, 4] x%3==1, input) Program specification Program sketch Full program Sketch generator: Symbolic synthesizer: RNN that proposes enumerator that fills in program sketches sketches, guided by (c.f. RobustFill) neural recognizer Devlin et al, 2017 (c.f DeepCoder) Balog et al, 2016

  • Ours Pattern recognition only (neural network) Results: list processing Reasoning only (symbolic enumeration) SketchAdapt can recognize familiar problems and generalize to unfamiliar problems Trained on length 3 programs SketchAdapt Length 3 test programs: List Processing: length 3 test programs 100 % of problems solved 80 60 40 20 SketchAdapt (ours) Synthesizer only (Deepcoder) Generator only (RobustFill) 0 10 1 10 2 10 3 10 4 10 5 Number of candidates evaluated per problem

  • Ours Pattern recognition only (neural network) Results: list processing Reasoning only (symbolic enumeration) SketchAdapt can recognize familiar problems and generalize to unfamiliar problems Trained on length 3 programs SketchAdapt Length 3 test programs: Length 4 test programs: List Processing: length 3 test programs List Processing: length 4 test programs 100 SketchAdapt (ours) Synthesizer only (Deepcoder) 50 Generator only (RobustFill) % of problems solved % of problems solved 80 SketchAdapt 40 60 30 40 20 20 10 SketchAdapt (ours) Synthesizer only (Deepcoder) Generator only (RobustFill) 0 0 10 1 10 2 10 3 10 4 10 5 10 1 10 2 10 3 10 4 10 5 Number of candidates evaluated per problem Number of candidates evaluated per problem

  • Natural language + IO examples → Code

  • Natural language + IO examples → Code Requires less data than pure neural approaches: AOgROisS 100 2ur mRGeO % Rf test SrRgrDms sROveG GenerDtRr RnOy (RRbust)iOO) 80 6ynthesizer RnOy (DeeSFRGer) 60 40 SketchAdapt 20 0 2000 4000 6000 8000 79214 (fuOO GDtDset) 1umber Rf trDining SrRgrDms useG

  • Natural language + IO examples → Code Requires less data than pure neural approaches: Generalizes to unseen concepts: AOgROisS 100 2ur mRGeO % Rf test SrRgrDms sROveG GenerDtRr RnOy (RRbust)iOO) 80 6ynthesizer RnOy (DeeSFRGer) 60 40 SketchAdapt 20 0 2000 4000 6000 8000 79214 (fuOO GDtDset) 1umber Rf trDining SrRgrDms useG

  • Learning to Infer Program Sketches Maxwell Nye, Luke Hewitt, Josh Tenenbaum, Armando Solar-Lezama Come see our poster: Today (Thurs) 06:30 - 09:00 PM @ Pacific Ballroom #182 input head tail sum +1 -1 .25 .03 .02 .06 .40 .05 ... [1, 3, -4, 3]-> 3 [-3, 0, 2, -1]-> 2 Count >0 (Map (HOLE) ) Count >0 (Map +1 input) [7,-4,-5, 2]-> 2