Internals of SMT Solvers. Leonardo de Moura Microsoft Research
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1 Internals of SMT Solvers Leonardo de Moura Microsoft Research
2 Acknowledgements Dejan Jovanovic (SRI International, NYU) Grant Passmore (Univ. Edinburgh)
3 Herbrand Award 2013 Greg Nelson
4 What is a SMT Solver?
5 Multiple Approaches is a portfolio of solvers
6 Preprocessing F Simplify Modular Architecture is a must have Variable elimination if-then-else elimination Solver
7 Equivalence Preserving Simplifications F Simplify F Examples: x + y + 1 x 2 y 1 p true p p
8 Preprocessor API F and F may be only equisatisfiable F Preprocessor F Model Converter Proof Converter
9 Example Variable Elimination Proof builder Model builder
10 Example Variable Elimination M, M(a) = M(b) + 1 Proof builder Model builder M
11 Example Variable Elimination b 5, a 6 Proof builder Model builder b 5
12 Model Converters Extension Filter M, M(a) = M(b) + 1 Model builder M
13 Model Converter: Filter p (q h) Tseitin CNF converter M k p k, k q, k h, k q h Model builder M
14 Model Converter: Filter p (q h) Tseitin CNF converter p t, q f, h t p k, k q, k h, k q h Model builder p t, k f, q f, h t
![Model Converter: Extension + Filter x: bitvec 4, y, z: bitvec[2] x =](/docs-images/90/104450247/images/15-3.jpg "concat(y, z) Bit-blaster M x 3 y 1, x 2 y 0, x 1 z 1, x 0 z 0 Model")
15 Model Converter: Extension + Filter x: bitvec 4, y, z: bitvec[2] x = concat(y, z) Bit-blaster M x 3 y 1, x 2 y 0, x 1 z 1, x 0 z 0 Model builder M
16 Preprocessors 1. Produce Equivalent Formula 2. Produce Equisatisfiable Formula 3. Assume closed world (non-incremental) Example: symmetry reduction
17 Simple QF_BV (bit-vector) solver F Simplify Variable elimination Bit-blasting Tseitin CNF converter SAT Solver
18 Under/Over-Approximations Under-approximation unsat answers cannot be trusted Over-approximation sat answers cannot be trusted
19 Under/Over-Approximations Under-approximation model finders Over-approximation proof finders
20 Under/Over-Approximations Under-approximation S S S Over-approximation S S \ S
21 Under/Over-Approximations Under-approximation Example: QF_NIA model finders add bounds to unbounded variables (and blast) Over-approximation Example: Boolean abstraction
22 Under/Over-Approximations Combining under and over is bad! sat and unsat answers cannot be trusted.
23 Tracking: under/overapproximations Proof and Model converters can check if the resultant models and proofs are valid.
24 CEGAR is your friend Counter-Example Guided Abstract Refinement Using over-approximation procedure Solver(F) Model F p := Abstract(F) loop (R, M) := Solve(F p ) if R = UNSAT then return UNSAT R := Check(F, M) if R = SAT then return SAT F p := Refine(F, F p, M)
25 CEGAR is your friend Counter-Example Guided Abstract Refinement Using under-approximation procedure Solver(F) Proof F p := Abstract(F) loop (R, Pr) := Solve(F p ) if R = SAT then return SAT R := Check(F, Pr) if R = UNSAT then return UNSAT F p := Refine(F, F p, M)
26 CEGAR is your friend Counter-Example Guided Abstract Refinement Refinements: Incremental Solver Run over and under-approximation is parallel
27 Uninterpreted Functions by CEGAR Suppose we have a Solver that does not support uninterpreted functions (example: QF_BV solver) Congruence Rule: x 1 = y 1,, x n = y n f(x 1,, x n ) = f(y 1,, y n )
28 Uninterpreted Functions by CEGAR Congruence Rule: x 1 = y 1,, xn = yn f(x 1,, xn) Abstract: replace each f-application with a fresh variable (over-approximation) a = b + 1, f(a 1) = c, f(b) c a = b + 1, k 1 = c, k 2 c k 1 f a 1, k 2 f(b)
29 Uninterpreted Functions by CEGAR Congruence Rule: x 1 = y 1,, xn = yn f(x 1,, xn) Check: check if congruence rule is satisfied a = b + 1, k 1 = c, k 2 c k 1 f a 1, k 2 f(b) a 1, b 0, c 0, k 1 0, k 2 1
30 Uninterpreted Functions by CEGAR Congruence Rule: x 1 = y 1,, xn = yn f(x 1,, xn) Refine: expand congruence axiom a 1 = b k 1 = k 2 a = b + 1, k 1 = c, k 2 c k 1 f a 1, k 2 f(b) a 1, b 0, c 0, k 1 0, k 2 1
31 Uninterpreted Functions by CEGAR Congruence Rule: x 1 = y 1,, xn = yn f(x 1,, xn) Refine: expand congruence axiom a 1 = b k 1 = k 2 a = b + 1, k 1 = c, k 2 c, (a 1 = b k 1 = k 2 ) unsat a 1 b k 1 = k 2
32 Simple QF_UFBV Solver UF by CEGAR QF_BV solver
33 Simple QF_AUFBV Solver arrays on top of UF AUF by CEGAR QF_BV solver Lemmas on Demand For Theory of Arrays [Brummayer-Biere 2009]
34 Simple UFBV Solver model-based quantifier instantiation MBQI UF by CEGAR QF_BV solver Efficiently solving quantified bit-vector formulas [Wintersteiger at al 2010]
35 Simple QF_NIA solver by CEGAR nonlinear integer arithmetic Hilbert s 10 th Problem DPRM theorem: QF_NIA is undecidable Idea: use (under-approximation) CEGAR 1. Add lower/upper bounds to all variables, and convert into QF_BV 2. If SAT done 3. Otherwise, refine: increase lower/upper bounds
36 Lazy SMT as CEGAR Suppose we have a Solver that can only process a conjunction of literals. Examples: Congurence Closure (UF), Simplex (Linear Real Arithmetic)
37 Lazy SMT as CEGAR: 1. Abstract Basic Idea x 0, y = x + 1, (y > 2 y < 1) p 1, p 2, (p 3 p 4 ) p 1 (x 0), p 2 (y = x + 1), p 3 (y > 2), p 4 (y < 1) [Audemard et al ], [Barrett et al ], [de Moura et al ] [Flanagan et al ],
38 Lazy SMT as CEGAR: 2. Solve Basic Idea x 0, y = x + 1, (y > 2 y < 1) p 1, p 2, (p 3 p 4 ) p 1 (x 0), p 2 (y = x + 1), p 3 (y > 2), p 4 (y < 1) SAT Solver
39 Lazy SMT as CEGAR: 2. Solve Basic Idea x 0, y = x + 1, (y > 2 y < 1) p 1, p 2, (p 3 p 4 ) p 1 (x 0), p 2 (y = x + 1), p 3 (y > 2), p 4 (y < 1) SAT Solver Assignment p 1, p 2, p 3, p 4
40 Lazy SMT as CEGAR: 3. Check Basic Idea x 0, y = x + 1, (y > 2 y < 1) p 1, p 2, (p 3 p 4 ) p 1 (x 0), p 2 (y = x + 1), p 3 (y > 2), p 4 (y < 1) SAT Solver Assignment p 1, p 2, p 3, p 4 x 0, y = x + 1, (y > 2), y < 1
41 Lazy SMT as CEGAR: 3. Check Basic Idea x 0, y = x + 1, (y > 2 y < 1) p 1, p 2, (p 3 p 4 ) p 1 (x 0), p 2 (y = x + 1), p 3 (y > 2), p 4 (y < 1) SAT Solver Assignment p 1, p 2, p 3, p 4 x 0, y = x + 1, (y > 2), y < 1 Unsatisfiable x 0, y = x + 1, y < 1 Theory Solver
42 Lazy SMT as CEGAR: 4. Refine Basic Idea x 0, y = x + 1, (y > 2 y < 1) p 1, p 2, (p 3 p 4 ) p 1 (x 0), p 2 (y = x + 1), p 3 (y > 2), p 4 (y < 1) SAT Solver Assignment p 1, p 2, p 3, p 4 x 0, y = x + 1, (y > 2), y < 1 New Lemma p 1 p 2 p 4 Unsatisfiable x 0, y = x + 1, y < 1 Theory Solver
43 Lazy SMT as CEGAR: 4. Refine Basic Idea New Lemma p 1 p 2 p 4 Unsatisfiable x 0, y = x + 1, y < 1 Theory Solver AKA Theory conflict
44 Lazy SMT as CEGAR: refinements Many refinements: Incrementality Efficient Backtracking Efficient Lemma Generation Theory propagation - DPLL(T) [Ganzinger et all 2004] Many SMT solvers are based on DPLL(T)
45 Proofs DPLL(T) weakness Theories are second-class citizens. DPLL(T) is not model-driven (key property of CDCL). Models
46 CDCL: Conflict Driven Clause Learning DPLL Resolution Model Proof
47 DPLL(T) weakness DPLL(T) works well only for easy theories. Examples: Uninterpreted functions Difference logic (x y c) Linear real arithmetic Hard theories : Linear integer arithmetic Arrays Nonlinear real arithmetic
48 Example: Nonlinear Real Arithmetic x 2 4x + y 2 y + 8 < 1 xy 2x 2y + 4 > 1 PSPACE PSPACE membership Canny 1988, Grigor ev 1988 QF_NRA NP NP-hardness x is Boolean x (x-1) = 0 x or y or z x + y + z > 0
49 The RISE of Model-Driven Techniques in SMT
50 Proofs Saturation x Search Proof-finding Model-finding Models
51 Two procedures Resolution Proof-finder Saturation DPLL Model-finder Search CDCL is model-driven proof search
52 Linear Arithmetic Fourier-Motzkin Proof-finder Saturation Simplex Model-finder Search
53 Fourier-Motzkin t 1 ax, bx t 2 bt 1 abx, abx at 2 bt 1 at 2 Very similar to Resolution Exponential time and space
54 Polynomial Constraints AKA Existential Theory of the Reals R x 2 4x + y 2 y + 8 < 1 xy 2x 2y + 4 > 1
55 CAD Big Picture 1. Project/Saturate set of polynomials 2. Lift/Search: Incrementally build assignment v: x k α k Isolate roots of polynomials f i (α, x) Select a feasible cell C, and assign x k some α k C If there is no feasible cell, then backtrack
56 CAD Big Picture x 2 + y 2 1 < 0 x y 1 > 0 1. Saturate x 4 x x 2 1 x 2. Search (, 1) 1 ( 1, 0) 0 (0, 1) 1 (1, ) x 4 x x x
57 CAD Big Picture x 2 + y 2 1 < 0 x y 1 > 0 1. Saturate x 4 x x 2 1 x (, 1 2 ) 1 2 ( 1 2, ) 4 + y y x 2 2. Search (, 1) 1 ( 1, 0) 0 (0, 1) 1 (1, ) x 4 x x x
58 CAD Big Picture x 2 + y 2 1 < 0 x y 1 > 0 1. Saturate x 4 x x 2 1 x (, 1 2 ) 1 2 ( 1 2, ) 4 + y y CONFLICT x 2 2. Search (, 1) 1 ( 1, 0) 0 (0, 1) 1 (1, ) x 4 x x x
59 Proofs NLSat: Model-Driven Search Static x Dynamic Optimistic approach Key ideas Models Start the Search before Saturate/Project We saturate on demand Model guides the saturation
60 Experimental Results (1) OUR NEW ENGINE
61 Experimental Results (2) OUR NEW ENGINE
62 Other examples Delayed Theory Combination [Bruttomesso et al 2006] X Model-Based Theory Combination
63 Other examples Array Theory by Axiom Instantiation X Lemmas on Demand For Theory of Array [Brummayer-Biere 2009] a, i, v: a i v i = v a, i, j, v: i = j a i v j = a[j]
64 Other examples (for linear arithmetic) Fourier-Motzkin X Generalizing DPLL to richer logics [McMillan et al 2009] Conflict Resolution [Korovin et al 2009]
65 Saturation: successful instances Polynomial time procedures Gaussian Elimination Congruence Closure
66 MCSat Model-Driven SMT Lift ideas from CDCL to SMT Generalize ideas found in model-driven approaches Easier to implement Model construction is explicit
67 MCSat x 2, x 1 y 1, (x 2 + y 2 1 xy > 1)
68 MCSat x 2, x 1 y 1, (x 2 + y 2 1 xy > 1) x 2 Propagations
69 MCSat x 2, x 1 y 1, (x 2 + y 2 1 xy > 1) x 2 x 1 Propagations
70 MCSat x 2, x 1 y 1, (x 2 + y 2 1 xy > 1) x 2 x 1 y 1 Propagations
71 MCSat x 2, x 1 y 1, (x 2 + y 2 1 xy > 1) x 2 x 1 y 1 x 2 + y 2 1 Boolean Decisions
72 MCSat x 2, x 1 y 1, (x 2 + y 2 1 xy > 1) x 2 x 1 y 1 x 2 + y 2 1 x 2 Semantic Decisions
73 MCSat x 2, x 1 y 1, (x 2 + y 2 1 xy > 1) x 2 x 1 y 1 x 2 + y 2 1 x 2 Conflict We can t find a value for y s.t. 4 + y 2 1
74 MCSat x 2, x 1 y 1, (x 2 + y 2 1 xy > 1) x 2 x 1 y 1 x 2 + y 2 1 x 2 Conflict We can t find a value for y s.t. 4 + y 2 1 Learning that x 2 + y 2 1 (x= 2) is not productive
75 MCSat x 2, x 1 y 1, (x 2 + y 2 1 xy > 1) x 2 x 1 y 1 x 2 + y 2 1 (x = 2) x 2 + y 2 1 (x = 2) Learning that x 2 + y 2 1 (x= 2) is not productive
76 MCSat x 2, x 1 y 1, (x 2 + y 2 1 xy > 1) x 2 x 1 y 1 x 2 + y 2 1 (x = 2) x 3 x 2 + y 2 1 (x = 2) Learning that x 2 + y 2 1 (x= 2) is not productive
77 MCSat x 2, x 1 y 1, (x 2 + y 2 1 xy > 1) x 2 x 1 y 1 x 2 + y 2 1 (x = 2) x 3 Same Conflict x 2 + y 2 1 (x = 2) We can t find a value for y s.t. 9 + y 2 1 Learning that x 2 + y 2 1 (x= 2) is not productive
78 x 2, x 1 y 1, (x 2 + y 2 1 xy > 1) x 2 x 1 y 1 x 2 + y 2 1 x 2 y Conflict x 2 + y 2 1 x 2 x 1 x, x 1 (x 2 + y 2 1) x 1
79 MCSat x 2, x 1 y 1, (x 2 + y 2 1 xy > 1) x 2 x 1 y 1 x 2 + y 2 1 x 1 (x 2 + y 2 1) x 1
80 MCSat x 2, x 1 y 1, (x 2 + y 2 1 xy > 1) x 2 x 1 y 1 x 2 + y 2 1 x 1 (x 2 + y 2 1) x 1 Conflict x 2 (x 1)
81 MCSat x 2, x 1 y 1, (x 2 + y 2 1 xy > 1) x 2 x 1 y 1 x 2 + y 2 1 (x 2 + y 2 1) x 1 Learned by resolution x 2 (x 2 + y 2 1)
82 MCSat x 2, x 1 y 1, (x 2 + y 2 1 xy > 1) x 2 x 1 y 1 (x 2 + y 2 1) x 2 (x 2 + y 2 1) (x 2 + y 2 1) x 1
83 MCSat: FM Example x + z z 0 x y 0 y 0 x + z + 1 0, x y 0 z 0, y 0 z + 1 x, x y 1 x, x 0 We can t find a value of x
84 MCSat: FM Example x + z z 0 x y 0 y 0 x + z + 1 0, x y 0 z 0, y 0 x: x + z x y 0 z + 1 y 0 Fourier-Motzkin x + z x y 0 z + 1 y 0
85 MCSat: FM Example x + z z 0 x y 0 z + 1 y 0 x + z x y 0 z + 1 y 0
86 MCSat: FM Example x + z z 0 x y 0 z + 1 y 0 y 1 x + z x y 0 z + 1 y 0 x + z + 1 0, x y 0 z 0, y 1 z + 1 x, x y 1 x, x 1
87 MCSat: FM Example x + z z 0 x y 0 z + 1 y 0 y 1 x 1 x + z x y 0 z + 1 y 0 x + z + 1 0, x y 0 z 0, y 1 z + 1 x, x y 1 x, x 1
88 MCSat: Another Example 4xy 4x + y > 1, x 2 + y 2 < 1, x 3 + 2x 2 + 3y 2 5 < 0
89 MCSat: Another Example 4xy 4x + y > 1, x 2 + y 2 < 1, x 3 + 2x 2 + 3y 2 5 < 0 Feasible Region x 3 + 2x 2 + 3y 2 5 < 0 4xy 4x + y > 1 What is the core? x 2 + y 2 < 1 Starting search Partial solution: x 0.5 Can we extend it to y?
90 MCSat: Another Example 4xy 4x + y > 1, x 2 + y 2 < 1, x 3 + 2x 2 + 3y 2 5 < 0 Feasible Region x 3 + 2x 2 + 3y 2 5 < 0 4xy 4x + y > 1 What is the core? x 2 + y 2 < 1 Starting search Partial solution: x 0.5 Can we extend it to y?
91 MCSat Finite Basis Every theory that admits quantifier elimination has a finite basis (given a fixed assignment order) F[x, y 1,, y m ] y 1 α 1,, y m α m x: F[x, y 1,, y m ] C 1 [y 1,, y m ] C k [y 1,, y m ] F x, y 1,, y m C k [y 1,, y m ]
92 MCSat Finite Basis F n [x 1, x 2,, x n 1, x n ] F n 1 [x 1, x 2,, x n 1 ] F 2 [x 1, x 2 ] F 1 [x 1 ]
93 MCSat Finite Basis F n [x 1, x 2,, x n 1, x n ] F n 1 [x 1, x 2,, x n 1 ] F 2 [x 1, x 2 ] F 1 [x 1 ]
94 MCSat Finite Basis F n [x 1, x 2,, x n 1, x n ] F n 1 [x 1, x 2,, x n 1 ] F 2 [x 1, x 2 ] F 1 [x 1 ]
95 MCSat Finite Basis F n [x 1, x 2,, x n 1, x n ] F n 1 [x 1, x 2,, x n 1 ] F 2 [x 1, x 2 ] F 1 [x 1 ]
96 MCSat Finite Basis Every finite theory has a finite basis Example: Fixed size Bit-vectors F[x, y 1,, y m ] y 1 α 1,, y m α m F x, y 1,, y m (y 1 = α 1 ) (y m = α m )
97 MCSat Finite Basis Theory of uninterpreted functions has a finite basis Theory of arrays has a finite basis [Brummayer- Biere 2009] In both cases the Finite Basis is essentially composed of equalities between existing terms.
98 MCSat: Uninterpreted Functions a = b + 1, f a 1 < c, f b > a a = b + 1, f k < c, f b > a, k = a 1 a = b + 1, f k < c, f b > a, k = a 1 Treat f(k) and f(b) as variables Generalized variables
99 MCSat: Uninterpreted Functions a = b + 1, f k < c, f b > a, k = a 1 k 0 b 0 f(k) 0 f(b) 2 Conflict: f k and f b must be equal k = b f k = f(b)
100 MCSat: Uninterpreted Functions a = b + 1, f k < c, f b > a, k = a 1 k 0 b 0 f(k) 0 k = b (Semantic) Propagation k = b f k = f(b)
101 MCSat: Uninterpreted Functions a = b + 1, f k < c, f b > a, k = a 1 k 0 b 0 f(k) 0 k = b f k = f(b) k = b f k = f(b)
102 MCSat: Uninterpreted Functions a = b + 1, f k < c, f b > a, k = a 1 k 0 b 0 f(k) 0 k = b f k = f(b) f(b) 0 k = b f k = f(b)
103 MCSat Finite Basis We can also use literals from the finite basis in decisions. Application: simulate branch&bound for bounded linear integer arithmetic LP solution: x 1 = 0.8 x 2 = 2.4 x x 1 0 x x 1 = 0 x 2 = 3 x 1 = 1 x 2 = x 1
104 MCSat: Termination Propagations Boolean Decisions Semantic Decisions
105 MCSat Propagations Boolean Decisions Semantic Decisions
106 MCSat Propagations Boolean Decisions Semantic Decisions
107 MCSat Maximal Elements FiniteBasis
108 x 2, x 1 y 1, (x 2 + y 2 1 xy > 1) x 2 x 1 y 1 x 2 + y 2 1 x 1 Conflict x 2 (x 1) (x 2 + y 2 1) x 1
109 x 2, x 1 y 1, (x 2 + y 2 1 xy > 1) x 2 x 1 y 1 x 2 + y 2 1 x 1 Conflict x 2 (x 1) (x 2 + y 2 1) x 1 x 2, x 1 y 1, (x 2 + y 2 1 xy > 1) x 2 x 1 y 1 (x 2 + y 2 1) x 2 (x 2 + y 2 1) (x 2 + y 2 1) x 1
110 x 2, x 1 y 1, (x 2 + y 2 1 xy > 1) x 2 x 1 y 1 x 2 + y 2 1 x 1 Conflict x 2 (x 1) (x 2 + y 2 1) x 1 x 2, x 1 y 1, (x 2 + y 2 1 xy > 1) x 2 x 1 y 1 (x 2 + y 2 1) x 2 (x 2 + y 2 1) (x 2 + y 2 1) x 1
111 MCSat x < 1 p, p x = 2 x 1
112 MCSat x < 1 p, p x = 2 x 1 p
113 MCSat x < 1 p, p x = 2 x 1 p Conflict (evaluates to false)
114 MCSat x < 1 p, p x = 2 x 1 p New clause x < 1 x = 2
115 MCSat x < 1 p, p x = 2 x 1 p New clause x < 1 x = 2 x < 1
116 MCSat x < 1 p, p x = 2 x 1 p New clause x < 1 x = 2 x < 1
117 MCSat: Architecture Arithmetic Arrays Boolean Lists
118 MCSat: development
119 MCSat prototype: 7k lines of code Deduction Rules Boolean Resolution Fourier-Motzkin Equality Split Normalization Ackermann expansion aka Congruence
120 MCSat: preliminary results prototype: 7k lines of code QF_LRA
121 MCSat: preliminary results prototype: 7k lines of code QF_UFLRA and QF_UFLIA
122 Conclusion Mode-driven techniques are very promising Preprocessing CEGAR MCSat: new framework for developing SMT solvers MCSat generalizes NLSat Modular architecture
123 Resources: Papers The Strategy Challenge in SMT Solving, L. de Moura and G. Passmore. Solving non-linear arithmetic, D. Jovanovic and L. de Moura A Model Constructing Satisfiability Calculus, L. de Moura and D. Jovanonic The Design and Implementation of the Model Constructing Satisfiability Calculus, D. Jovanovic, C. Barrett, L. de Moura
124 Resources: Source Code nlsat mcsat tactic/preprocessors
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