//===- AffineStructuresTest.cpp - Tests for AffineStructures ----*- C++ -*-===// // // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. // See https://llvm.org/LICENSE.txt for license information. // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception // //===----------------------------------------------------------------------===// #include "mlir/Analysis/AffineStructures.h" #include "mlir/IR/IntegerSet.h" #include "mlir/IR/MLIRContext.h" #include #include #include namespace mlir { using testing::ElementsAre; enum class TestFunction { Sample, Empty }; /// If fn is TestFunction::Sample (default): /// If hasSample is true, check that findIntegerSample returns a valid sample /// for the FlatAffineConstraints fac. /// If hasSample is false, check that findIntegerSample returns None. /// /// If fn is TestFunction::Empty, check that isIntegerEmpty returns the /// opposite of hasSample. static void checkSample(bool hasSample, const FlatAffineConstraints &fac, TestFunction fn = TestFunction::Sample) { Optional> maybeSample; switch (fn) { case TestFunction::Sample: maybeSample = fac.findIntegerSample(); if (!hasSample) { EXPECT_FALSE(maybeSample.hasValue()); if (maybeSample.hasValue()) { for (auto x : *maybeSample) llvm::errs() << x << ' '; llvm::errs() << '\n'; } } else { ASSERT_TRUE(maybeSample.hasValue()); EXPECT_TRUE(fac.containsPoint(*maybeSample)); } break; case TestFunction::Empty: EXPECT_EQ(!hasSample, fac.isIntegerEmpty()); break; } } /// Construct a FlatAffineConstraints from a set of inequality and /// equality constraints. static FlatAffineConstraints makeFACFromConstraints(unsigned ids, ArrayRef> ineqs, ArrayRef> eqs, unsigned syms = 0) { FlatAffineConstraints fac(ineqs.size(), eqs.size(), ids + 1, ids - syms, syms, /*numLocals=*/0); for (const auto &eq : eqs) fac.addEquality(eq); for (const auto &ineq : ineqs) fac.addInequality(ineq); return fac; } /// Check sampling for all the permutations of the dimensions for the given /// constraint set. Since the GBR algorithm progresses dimension-wise, different /// orderings may cause the algorithm to proceed differently. At least some of ///.these permutations should make it past the heuristics and test the /// implementation of the GBR algorithm itself. /// Use TestFunction fn to test. static void checkPermutationsSample(bool hasSample, unsigned nDim, ArrayRef> ineqs, ArrayRef> eqs, TestFunction fn = TestFunction::Sample) { SmallVector perm(nDim); std::iota(perm.begin(), perm.end(), 0); auto permute = [&perm](ArrayRef coeffs) { SmallVector permuted; for (unsigned id : perm) permuted.push_back(coeffs[id]); permuted.push_back(coeffs.back()); return permuted; }; do { SmallVector, 4> permutedIneqs, permutedEqs; for (const auto &ineq : ineqs) permutedIneqs.push_back(permute(ineq)); for (const auto &eq : eqs) permutedEqs.push_back(permute(eq)); checkSample(hasSample, makeFACFromConstraints(nDim, permutedIneqs, permutedEqs), fn); } while (std::next_permutation(perm.begin(), perm.end())); } TEST(FlatAffineConstraintsTest, FindSampleTest) { // Bounded sets with only inequalities. // 0 <= 7x <= 5 checkSample(true, makeFACFromConstraints(1, {{7, 0}, {-7, 5}}, {})); // 1 <= 5x and 5x <= 4 (no solution). checkSample(false, makeFACFromConstraints(1, {{5, -1}, {-5, 4}}, {})); // 1 <= 5x and 5x <= 9 (solution: x = 1). checkSample(true, makeFACFromConstraints(1, {{5, -1}, {-5, 9}}, {})); // Bounded sets with equalities. // x >= 8 and 40 >= y and x = y. checkSample( true, makeFACFromConstraints(2, {{1, 0, -8}, {0, -1, 40}}, {{1, -1, 0}})); // x <= 10 and y <= 10 and 10 <= z and x + 2y = 3z. // solution: x = y = z = 10. checkSample(true, makeFACFromConstraints( 3, {{-1, 0, 0, 10}, {0, -1, 0, 10}, {0, 0, 1, -10}}, {{1, 2, -3, 0}})); // x <= 10 and y <= 10 and 11 <= z and x + 2y = 3z. // This implies x + 2y >= 33 and x + 2y <= 30, which has no solution. checkSample(false, makeFACFromConstraints( 3, {{-1, 0, 0, 10}, {0, -1, 0, 10}, {0, 0, 1, -11}}, {{1, 2, -3, 0}})); // 0 <= r and r <= 3 and 4q + r = 7. // Solution: q = 1, r = 3. checkSample(true, makeFACFromConstraints(2, {{0, 1, 0}, {0, -1, 3}}, {{4, 1, -7}})); // 4q + r = 7 and r = 0. // Solution: q = 1, r = 3. checkSample(false, makeFACFromConstraints(2, {}, {{4, 1, -7}, {0, 1, 0}})); // The next two sets are large sets that should take a long time to sample // with a naive branch and bound algorithm but can be sampled efficiently with // the GBR algorithm. // // This is a triangle with vertices at (1/3, 0), (2/3, 0) and (10000, 10000). checkSample( true, makeFACFromConstraints( 2, {{0, 1, 0}, {300000, -299999, -100000}, {-300000, 299998, 200000}}, {})); // This is a tetrahedron with vertices at // (1/3, 0, 0), (2/3, 0, 0), (2/3, 0, 10000), and (10000, 10000, 10000). // The first three points form a triangular base on the xz plane with the // apex at the fourth point, which is the only integer point. checkPermutationsSample( true, 3, { {0, 1, 0, 0}, // y >= 0 {0, -1, 1, 0}, // z >= y {300000, -299998, -1, -100000}, // -300000x + 299998y + 100000 + z <= 0. {-150000, 149999, 0, 100000}, // -150000x + 149999y + 100000 >= 0. }, {}); // Same thing with some spurious extra dimensions equated to constants. checkSample(true, makeFACFromConstraints( 5, { {0, 1, 0, 1, -1, 0}, {0, -1, 1, -1, 1, 0}, {300000, -299998, -1, -9, 21, -112000}, {-150000, 149999, 0, -15, 47, 68000}, }, {{0, 0, 0, 1, -1, 0}, // p = q. {0, 0, 0, 1, 1, -2000}})); // p + q = 20000 => p = q = 10000. // This is a tetrahedron with vertices at // (1/3, 0, 0), (2/3, 0, 0), (2/3, 0, 100), (100, 100 - 1/3, 100). checkPermutationsSample(false, 3, { {0, 1, 0, 0}, {0, -300, 299, 0}, {300 * 299, -89400, -299, -100 * 299}, {-897, 894, 0, 598}, }, {}); // Two tests involving equalities that are integer empty but not rational // empty. // This is a line segment from (0, 1/3) to (100, 100 + 1/3). checkSample(false, makeFACFromConstraints( 2, { {1, 0, 0}, // x >= 0. {-1, 0, 100} // -x + 100 >= 0, i.e., x <= 100. }, { {3, -3, 1} // 3x - 3y + 1 = 0, i.e., y = x + 1/3. })); // A thin parallelogram. 0 <= x <= 100 and x + 1/3 <= y <= x + 2/3. checkSample(false, makeFACFromConstraints(2, { {1, 0, 0}, // x >= 0. {-1, 0, 100}, // x <= 100. {3, -3, 2}, // 3x - 3y >= -2. {-3, 3, -1}, // 3x - 3y <= -1. }, {})); checkSample(true, makeFACFromConstraints(2, { {2, 0, 0}, // 2x >= 1. {-2, 0, 99}, // 2x <= 99. {0, 2, 0}, // 2y >= 0. {0, -2, 99}, // 2y <= 99. }, {})); // 2D cone with apex at (10000, 10000) and // edges passing through (1/3, 0) and (2/3, 0). checkSample( true, makeFACFromConstraints( 2, {{300000, -299999, -100000}, {-300000, 299998, 200000}}, {})); // Cartesian product of a tetrahedron and a 2D cone. // The tetrahedron has vertices at // (1/3, 0, 0), (2/3, 0, 0), (2/3, 0, 10000), and (10000, 10000, 10000). // The first three points form a triangular base on the xz plane with the // apex at the fourth point, which is the only integer point. // The cone has apex at (10000, 10000) and // edges passing through (1/3, 0) and (2/3, 0). checkPermutationsSample( true /* not empty */, 5, { // Tetrahedron contraints: {0, 1, 0, 0, 0, 0}, // y >= 0 {0, -1, 1, 0, 0, 0}, // z >= y // -300000x + 299998y + 100000 + z <= 0. {300000, -299998, -1, 0, 0, -100000}, // -150000x + 149999y + 100000 >= 0. {-150000, 149999, 0, 0, 0, 100000}, // Triangle constraints: // 300000p - 299999q >= 100000 {0, 0, 0, 300000, -299999, -100000}, // -300000p + 299998q + 200000 >= 0 {0, 0, 0, -300000, 299998, 200000}, }, {}); // Cartesian product of same tetrahedron as above and {(p, q) : 1/3 <= p <= // 2/3}. Since the second set is empty, the whole set is too. checkPermutationsSample( false /* empty */, 5, { // Tetrahedron contraints: {0, 1, 0, 0, 0, 0}, // y >= 0 {0, -1, 1, 0, 0, 0}, // z >= y // -300000x + 299998y + 100000 + z <= 0. {300000, -299998, -1, 0, 0, -100000}, // -150000x + 149999y + 100000 >= 0. {-150000, 149999, 0, 0, 0, 100000}, // Second set constraints: // 3p >= 1 {0, 0, 0, 3, 0, -1}, // 3p <= 2 {0, 0, 0, -3, 0, 2}, }, {}); // Cartesian product of same tetrahedron as above and // {(p, q, r) : 1 <= p <= 2 and p = 3q + 3r}. // Since the second set is empty, the whole set is too. checkPermutationsSample( false /* empty */, 5, { // Tetrahedron contraints: {0, 1, 0, 0, 0, 0, 0}, // y >= 0 {0, -1, 1, 0, 0, 0, 0}, // z >= y // -300000x + 299998y + 100000 + z <= 0. {300000, -299998, -1, 0, 0, 0, -100000}, // -150000x + 149999y + 100000 >= 0. {-150000, 149999, 0, 0, 0, 0, 100000}, // Second set constraints: // p >= 1 {0, 0, 0, 1, 0, 0, -1}, // p <= 2 {0, 0, 0, -1, 0, 0, 2}, }, { {0, 0, 0, 1, -3, -3, 0}, // p = 3q + 3r }); // Cartesian product of a tetrahedron and a 2D cone. // The tetrahedron is empty and has vertices at // (1/3, 0, 0), (2/3, 0, 0), (2/3, 0, 100), and (100, 100 - 1/3, 100). // The cone has apex at (10000, 10000) and // edges passing through (1/3, 0) and (2/3, 0). // Since the tetrahedron is empty, the Cartesian product is too. checkPermutationsSample(false /* empty */, 5, { // Tetrahedron contraints: {0, 1, 0, 0, 0, 0}, {0, -300, 299, 0, 0, 0}, {300 * 299, -89400, -299, 0, 0, -100 * 299}, {-897, 894, 0, 0, 0, 598}, // Triangle constraints: // 300000p - 299999q >= 100000 {0, 0, 0, 300000, -299999, -100000}, // -300000p + 299998q + 200000 >= 0 {0, 0, 0, -300000, 299998, 200000}, }, {}); // Cartesian product of same tetrahedron as above and // {(p, q) : 1/3 <= p <= 2/3}. checkPermutationsSample(false /* empty */, 5, { // Tetrahedron contraints: {0, 1, 0, 0, 0, 0}, {0, -300, 299, 0, 0, 0}, {300 * 299, -89400, -299, 0, 0, -100 * 299}, {-897, 894, 0, 0, 0, 598}, // Second set constraints: // 3p >= 1 {0, 0, 0, 3, 0, -1}, // 3p <= 2 {0, 0, 0, -3, 0, 2}, }, {}); checkSample(true, makeFACFromConstraints(3, { {2, 0, 0, -1}, // 2x >= 1 }, {{ {1, -1, 0, -1}, // y = x - 1 {0, 1, -1, 0}, // z = y }})); } TEST(FlatAffineConstraintsTest, IsIntegerEmptyTest) { // 1 <= 5x and 5x <= 4 (no solution). EXPECT_TRUE( makeFACFromConstraints(1, {{5, -1}, {-5, 4}}, {}).isIntegerEmpty()); // 1 <= 5x and 5x <= 9 (solution: x = 1). EXPECT_FALSE( makeFACFromConstraints(1, {{5, -1}, {-5, 9}}, {}).isIntegerEmpty()); // Unbounded sets. EXPECT_TRUE(makeFACFromConstraints(3, { {0, 2, 0, -1}, // 2y >= 1 {0, -2, 0, 1}, // 2y <= 1 {0, 0, 2, -1}, // 2z >= 1 }, {{2, 0, 0, -1}} // 2x = 1 ) .isIntegerEmpty()); EXPECT_FALSE(makeFACFromConstraints(3, { {2, 0, 0, -1}, // 2x >= 1 {-3, 0, 0, 3}, // 3x <= 3 {0, 0, 5, -6}, // 5z >= 6 {0, 0, -7, 17}, // 7z <= 17 {0, 3, 0, -2}, // 3y >= 2 }, {}) .isIntegerEmpty()); EXPECT_FALSE(makeFACFromConstraints(3, { {2, 0, 0, -1}, // 2x >= 1 }, {{ {1, -1, 0, -1}, // y = x - 1 {0, 1, -1, 0}, // z = y }}) .isIntegerEmpty()); // FlatAffineConstraints::isEmpty() does not detect the following sets to be // empty. // 3x + 7y = 1 and 0 <= x, y <= 10. // Since x and y are non-negative, 3x + 7y can never be 1. EXPECT_TRUE( makeFACFromConstraints( 2, {{1, 0, 0}, {-1, 0, 10}, {0, 1, 0}, {0, -1, 10}}, {{3, 7, -1}}) .isIntegerEmpty()); // 2x = 3y and y = x - 1 and x + y = 6z + 2 and 0 <= x, y <= 100. // Substituting y = x - 1 in 3y = 2x, we obtain x = 3 and hence y = 2. // Since x + y = 5 cannot be equal to 6z + 2 for any z, the set is empty. EXPECT_TRUE( makeFACFromConstraints(3, { {1, 0, 0, 0}, {-1, 0, 0, 100}, {0, 1, 0, 0}, {0, -1, 0, 100}, }, {{2, -3, 0, 0}, {1, -1, 0, -1}, {1, 1, -6, -2}}) .isIntegerEmpty()); // 2x = 3y and y = x - 1 + 6z and x + y = 6q + 2 and 0 <= x, y <= 100. // 2x = 3y implies x is a multiple of 3 and y is even. // Now y = x - 1 + 6z implies y = 2 mod 3. In fact, since y is even, we have // y = 2 mod 6. Then since x = y + 1 + 6z, we have x = 3 mod 6, implying // x + y = 5 mod 6, which contradicts x + y = 6q + 2, so the set is empty. EXPECT_TRUE(makeFACFromConstraints( 4, { {1, 0, 0, 0, 0}, {-1, 0, 0, 0, 100}, {0, 1, 0, 0, 0}, {0, -1, 0, 0, 100}, }, {{2, -3, 0, 0, 0}, {1, -1, 6, 0, -1}, {1, 1, 0, -6, -2}}) .isIntegerEmpty()); // Set with symbols. FlatAffineConstraints fac6 = makeFACFromConstraints(2, { {1, 1, 0}, }, { {1, -1, 0}, }, 1); EXPECT_FALSE(fac6.isIntegerEmpty()); } TEST(FlatAffineConstraintsTest, removeRedundantConstraintsTest) { FlatAffineConstraints fac = makeFACFromConstraints(1, { {1, -2}, // x >= 2. {-1, 2} // x <= 2. }, {{1, -2}}); // x == 2. fac.removeRedundantConstraints(); // Both inequalities are redundant given the equality. Both have been removed. EXPECT_EQ(fac.getNumInequalities(), 0u); EXPECT_EQ(fac.getNumEqualities(), 1u); FlatAffineConstraints fac2 = makeFACFromConstraints(2, { {1, 0, -3}, // x >= 3. {0, 1, -2} // y >= 2 (redundant). }, {{1, -1, 0}}); // x == y. fac2.removeRedundantConstraints(); // The second inequality is redundant and should have been removed. The // remaining inequality should be the first one. EXPECT_EQ(fac2.getNumInequalities(), 1u); EXPECT_THAT(fac2.getInequality(0), ElementsAre(1, 0, -3)); EXPECT_EQ(fac2.getNumEqualities(), 1u); FlatAffineConstraints fac3 = makeFACFromConstraints(3, {}, {{1, -1, 0, 0}, // x == y. {1, 0, -1, 0}, // x == z. {0, 1, -1, 0}}); // y == z. fac3.removeRedundantConstraints(); // One of the three equalities can be removed. EXPECT_EQ(fac3.getNumInequalities(), 0u); EXPECT_EQ(fac3.getNumEqualities(), 2u); FlatAffineConstraints fac4 = makeFACFromConstraints( 17, {{0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1}, {0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 500}, {0, 0, 0, -16, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1}, {0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 998}, {0, 0, 0, 16, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 15}, {0, 0, 0, 0, -16, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1}, {0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 998}, {0, 0, 0, 0, 16, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 15}, {0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, -1}, {0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 500}, {0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 16, 0, 0, 0, 0, 0, 15}, {0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, -16, 0, 0, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -16, 0, 1, 0, 0, 0}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, -1}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 998}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 16, 0, -1, 0, 0, 15}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 1}, {0, 0, 0, 0, 0, 0, -1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 8, 8}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, -1, 8, 8}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, -8, -1}, {0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, -8, -1}, {0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, -10}, {0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 10}, {0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, -13}, {0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 13}, {0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -10}, {0, 0, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10}, {1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -13}, {-1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 13}}, {}); // The above is a large set of constraints without any redundant constraints, // as verified by the Fourier-Motzkin based removeRedundantInequalities. unsigned nIneq = fac4.getNumInequalities(); unsigned nEq = fac4.getNumEqualities(); fac4.removeRedundantInequalities(); ASSERT_EQ(fac4.getNumInequalities(), nIneq); ASSERT_EQ(fac4.getNumEqualities(), nEq); // Now we test that removeRedundantConstraints does not find any constraints // to be redundant either. fac4.removeRedundantConstraints(); EXPECT_EQ(fac4.getNumInequalities(), nIneq); EXPECT_EQ(fac4.getNumEqualities(), nEq); FlatAffineConstraints fac5 = makeFACFromConstraints(2, { {128, 0, 127}, // [0]: 128x >= -127. {-1, 0, 7}, // [1]: x <= 7. {-128, 1, 0}, // [2]: y >= 128x. {0, 1, 0} // [3]: y >= 0. }, {}); // [0] implies that 128x >= 0, since x has to be an integer. (This should be // caught by GCDTightenInqualities().) // So [2] and [0] imply [3] since we have y >= 128x >= 0. fac5.removeRedundantConstraints(); EXPECT_EQ(fac5.getNumInequalities(), 3u); SmallVector redundantConstraint = {0, 1, 0}; for (unsigned i = 0; i < 3; ++i) { // Ensure that the removed constraint was the redundant constraint [3]. EXPECT_NE(fac5.getInequality(i), ArrayRef(redundantConstraint)); } } TEST(FlatAffineConstraintsTest, addConstantUpperBound) { FlatAffineConstraints fac = makeFACFromConstraints(2, {}, {}); fac.addBound(FlatAffineConstraints::UB, 0, 1); EXPECT_EQ(fac.atIneq(0, 0), -1); EXPECT_EQ(fac.atIneq(0, 1), 0); EXPECT_EQ(fac.atIneq(0, 2), 1); fac.addBound(FlatAffineConstraints::UB, {1, 2, 3}, 1); EXPECT_EQ(fac.atIneq(1, 0), -1); EXPECT_EQ(fac.atIneq(1, 1), -2); EXPECT_EQ(fac.atIneq(1, 2), -2); } TEST(FlatAffineConstraintsTest, addConstantLowerBound) { FlatAffineConstraints fac = makeFACFromConstraints(2, {}, {}); fac.addBound(FlatAffineConstraints::LB, 0, 1); EXPECT_EQ(fac.atIneq(0, 0), 1); EXPECT_EQ(fac.atIneq(0, 1), 0); EXPECT_EQ(fac.atIneq(0, 2), -1); fac.addBound(FlatAffineConstraints::LB, {1, 2, 3}, 1); EXPECT_EQ(fac.atIneq(1, 0), 1); EXPECT_EQ(fac.atIneq(1, 1), 2); EXPECT_EQ(fac.atIneq(1, 2), 2); } TEST(FlatAffineConstraintsTest, removeInequality) { FlatAffineConstraints fac = makeFACFromConstraints(1, {{0, 0}, {1, 1}, {2, 2}, {3, 3}, {4, 4}}, {}); fac.removeInequalityRange(0, 0); EXPECT_EQ(fac.getNumInequalities(), 5u); fac.removeInequalityRange(1, 3); EXPECT_EQ(fac.getNumInequalities(), 3u); EXPECT_THAT(fac.getInequality(0), ElementsAre(0, 0)); EXPECT_THAT(fac.getInequality(1), ElementsAre(3, 3)); EXPECT_THAT(fac.getInequality(2), ElementsAre(4, 4)); fac.removeInequality(1); EXPECT_EQ(fac.getNumInequalities(), 2u); EXPECT_THAT(fac.getInequality(0), ElementsAre(0, 0)); EXPECT_THAT(fac.getInequality(1), ElementsAre(4, 4)); } TEST(FlatAffineConstraintsTest, removeEquality) { FlatAffineConstraints fac = makeFACFromConstraints(1, {}, {{0, 0}, {1, 1}, {2, 2}, {3, 3}, {4, 4}}); fac.removeEqualityRange(0, 0); EXPECT_EQ(fac.getNumEqualities(), 5u); fac.removeEqualityRange(1, 3); EXPECT_EQ(fac.getNumEqualities(), 3u); EXPECT_THAT(fac.getEquality(0), ElementsAre(0, 0)); EXPECT_THAT(fac.getEquality(1), ElementsAre(3, 3)); EXPECT_THAT(fac.getEquality(2), ElementsAre(4, 4)); fac.removeEquality(1); EXPECT_EQ(fac.getNumEqualities(), 2u); EXPECT_THAT(fac.getEquality(0), ElementsAre(0, 0)); EXPECT_THAT(fac.getEquality(1), ElementsAre(4, 4)); } TEST(FlatAffineConstraintsTest, clearConstraints) { FlatAffineConstraints fac = makeFACFromConstraints(1, {}, {}); fac.addInequality({1, 0}); EXPECT_EQ(fac.atIneq(0, 0), 1); EXPECT_EQ(fac.atIneq(0, 1), 0); fac.clearConstraints(); fac.addInequality({1, 0}); EXPECT_EQ(fac.atIneq(0, 0), 1); EXPECT_EQ(fac.atIneq(0, 1), 0); } /// Check if the expected division representation of local variables matches the /// computed representation. The expected division representation is given as /// a vector of expressions set in `divisions` and the corressponding /// denominator in `denoms`. If expected denominator for a variable is /// non-positive, the local variable is expected to not have a computed /// representation. static void checkDivisionRepresentation( FlatAffineConstraints &fac, const std::vector> &divisions, const SmallVector &denoms) { assert(divisions.size() == fac.getNumLocalIds() && "Size of expected divisions does not match number of local variables"); assert( denoms.size() == fac.getNumLocalIds() && "Size of expected denominators does not match number of local variables"); std::vector>> res( fac.getNumLocalIds(), llvm::None); fac.getLocalReprLbUbPairs(res); // Check if all expected divisions are computed. for (unsigned i = 0, e = fac.getNumLocalIds(); i < e; ++i) if (denoms[i] > 0) EXPECT_TRUE(res[i].hasValue()); else EXPECT_FALSE(res[i].hasValue()); unsigned divOffset = fac.getNumDimAndSymbolIds(); for (unsigned i = 0, e = fac.getNumLocalIds(); i < e; ++i) { if (!res[i]) continue; // Check if the bounds are of the form: // 0 <= expr - divisor * id <= divisor - 1 // Rearranging, we have: // divisor * id - expr + (divisor - 1) >= 0 <-- Lower bound for 'id' // -divisor * id + expr >= 0 <-- Upper bound for 'id' // where `id = expr floordiv divisor`. unsigned ubPos = res[i]->first, lbPos = res[i]->second; const SmallVector &expr = divisions[i]; // Check if lower bound is of the correct form. int64_t computedDivisorLb = fac.atIneq(lbPos, i + divOffset); EXPECT_EQ(computedDivisorLb, denoms[i]); for (unsigned c = 0, f = fac.getNumLocalIds(); c < f; ++c) { if (c == i + divOffset) continue; EXPECT_EQ(fac.atIneq(lbPos, c), -expr[c]); } // Check if constant term of lower bound matches expected constant term. EXPECT_EQ(fac.atIneq(lbPos, fac.getNumCols() - 1), -expr.back() + (denoms[i] - 1)); // Check if upper bound is of the correct form. int64_t computedDivisorUb = fac.atIneq(ubPos, i + divOffset); EXPECT_EQ(computedDivisorUb, -denoms[i]); for (unsigned c = 0, f = fac.getNumLocalIds(); c < f; ++c) { if (c == i + divOffset) continue; EXPECT_EQ(fac.atIneq(ubPos, c), expr[c]); } // Check if constant term of upper bound matches expected constant term. EXPECT_EQ(fac.atIneq(ubPos, fac.getNumCols() - 1), expr.back()); } } TEST(FlatAffineConstraintsTest, computeLocalReprSimple) { FlatAffineConstraints fac = makeFACFromConstraints(1, {}, {}); fac.addLocalFloorDiv({1, 4}, 10); fac.addLocalFloorDiv({1, 0, 100}, 10); std::vector> divisions = {{1, 0, 0, 4}, {1, 0, 0, 100}}; SmallVector denoms = {10, 10}; // Check if floordivs can be computed when no other inequalities exist // and floor divs do not depend on each other. checkDivisionRepresentation(fac, divisions, denoms); } TEST(FlatAffineConstraintsTest, computeLocalReprConstantFloorDiv) { FlatAffineConstraints fac = makeFACFromConstraints(4, {}, {}); fac.addInequality({1, 0, 3, 1, 2}); fac.addInequality({1, 2, -8, 1, 10}); fac.addEquality({1, 2, -4, 1, 10}); fac.addLocalFloorDiv({0, 0, 0, 0, 10}, 30); fac.addLocalFloorDiv({0, 0, 0, 0, 0, 99}, 101); std::vector> divisions = {{0, 0, 0, 0, 0, 0, 10}, {0, 0, 0, 0, 0, 0, 99}}; SmallVector denoms = {30, 101}; // Check if floordivs with constant numerator can be computed. checkDivisionRepresentation(fac, divisions, denoms); } TEST(FlatAffineConstraintsTest, computeLocalReprRecursive) { FlatAffineConstraints fac = makeFACFromConstraints(4, {}, {}); fac.addInequality({1, 0, 3, 1, 2}); fac.addInequality({1, 2, -8, 1, 10}); fac.addEquality({1, 2, -4, 1, 10}); fac.addLocalFloorDiv({0, -2, 7, 2, 10}, 3); fac.addLocalFloorDiv({3, 0, 9, 2, 2, 10}, 5); fac.addLocalFloorDiv({0, 1, -123, 2, 0, -4, 10}, 3); fac.addInequality({1, 2, -2, 1, -5, 0, 6, 100}); fac.addInequality({1, 2, -8, 1, 3, 7, 0, -9}); std::vector> divisions = {{0, -2, 7, 2, 0, 0, 0, 10}, {3, 0, 9, 2, 2, 0, 0, 10}, {0, 1, -123, 2, 0, -4, 10}}; SmallVector denoms = {3, 5, 3}; // Check if floordivs which may depend on other floordivs can be computed. checkDivisionRepresentation(fac, divisions, denoms); } TEST(FlatAffineConstraintsTest, removeIdRange) { FlatAffineConstraints fac(3, 2, 1); fac.addInequality({10, 11, 12, 20, 21, 30, 40}); fac.removeId(FlatAffineConstraints::IdKind::Symbol, 1); EXPECT_THAT(fac.getInequality(0), testing::ElementsAre(10, 11, 12, 20, 30, 40)); fac.removeIdRange(FlatAffineConstraints::IdKind::Dimension, 0, 2); EXPECT_THAT(fac.getInequality(0), testing::ElementsAre(12, 20, 30, 40)); fac.removeIdRange(FlatAffineConstraints::IdKind::Local, 1, 1); EXPECT_THAT(fac.getInequality(0), testing::ElementsAre(12, 20, 30, 40)); fac.removeIdRange(FlatAffineConstraints::IdKind::Local, 0, 1); EXPECT_THAT(fac.getInequality(0), testing::ElementsAre(12, 20, 40)); } TEST(FlatAffineConstraintsTest, simplifyLocalsTest) { // (x) : (exists y: 2x + y = 1 and y = 2). FlatAffineConstraints fac(1, 0, 1); fac.addEquality({2, 1, -1}); fac.addEquality({0, 1, -2}); EXPECT_TRUE(fac.isEmpty()); // (x) : (exists y, z, w: 3x + y = 1 and 2y = z and 3y = w and z = w). FlatAffineConstraints fac2(1, 0, 3); fac2.addEquality({3, 1, 0, 0, -1}); fac2.addEquality({0, 2, -1, 0, 0}); fac2.addEquality({0, 3, 0, -1, 0}); fac2.addEquality({0, 0, 1, -1, 0}); EXPECT_TRUE(fac2.isEmpty()); // (x) : (exists y: x >= y + 1 and 2x + y = 0 and y >= -1). FlatAffineConstraints fac3(1, 0, 1); fac3.addInequality({1, -1, -1}); fac3.addInequality({0, 1, 1}); fac3.addEquality({2, 1, 0}); EXPECT_TRUE(fac3.isEmpty()); } } // namespace mlir