781 lines
33 KiB
C++
781 lines
33 KiB
C++
#include "mlir/Dialect/SparseTensor/Utils/Merger.h"
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#include "llvm/Support/Compiler.h"
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#include "gmock/gmock.h"
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#include "gtest/gtest.h"
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#include <memory>
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using namespace mlir;
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using namespace mlir::sparse_tensor;
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// Silence 'warning C4002: 'too many arguments for function-liked macro
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// invocation'
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// as MSVC handles ##__VA_ARGS__ differently as gcc/clang
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#if defined(_MSC_VER) && !defined(__clang__)
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#pragma warning(push)
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#pragma warning(disable : 4002)
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#endif
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namespace {
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///
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/// Defines macros to iterate binary and the combination of binary operations.
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///
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#define FOREVERY_BINOP(DO) \
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DO(mulf, Kind::kMulF) \
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DO(mulc, Kind::kMulC) \
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DO(muli, Kind::kMulI) \
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DO(addf, Kind::kAddF) \
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DO(addc, Kind::kAddC) \
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DO(addi, Kind::kAddI) \
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DO(subf, Kind::kSubF) \
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DO(subc, Kind::kSubC) \
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DO(subi, Kind::kSubI) \
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DO(andi, Kind::kAndI) \
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DO(xori, Kind::kXorI) \
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DO(ori, Kind::kOrI)
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// TODO: Disjunctive binary operations that need special handling are not
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// included, e.g., Division are not tested (for now) as it need a constant
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// non-zero dividend.
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// ##__VA_ARGS__ handles cases when __VA_ARGS__ is empty.
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#define FOREVERY_COMMON_DISJ_BINOP(TEST, ...) \
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TEST(addf, ##__VA_ARGS__) \
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TEST(addc, ##__VA_ARGS__) \
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TEST(addi, ##__VA_ARGS__) \
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TEST(xori, ##__VA_ARGS__) \
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TEST(ori, ##__VA_ARGS__)
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// TODO: Conjunctive binary operations that need special handling are not
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// included, e.g., substraction yields a different pattern as it is mapped to
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// negate operation.
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#define FOREVERY_COMMON_CONJ_BINOP(TEST, ...) \
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TEST(mulf, ##__VA_ARGS__) \
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TEST(mulc, ##__VA_ARGS__) \
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TEST(muli, ##__VA_ARGS__) \
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TEST(andi, ##__VA_ARGS__)
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#define FOREVERY_PAIR_OF_COMMON_CONJ_DISJ_BINOP(TEST) \
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FOREVERY_COMMON_CONJ_BINOP(TEST, addf) \
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FOREVERY_COMMON_CONJ_BINOP(TEST, addc) \
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FOREVERY_COMMON_CONJ_BINOP(TEST, addi) \
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FOREVERY_COMMON_CONJ_BINOP(TEST, xori) \
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FOREVERY_COMMON_CONJ_BINOP(TEST, ori)
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#define FOREVERY_PAIR_OF_COMMON_CONJ_CONJ_BINOP(TEST) \
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FOREVERY_COMMON_CONJ_BINOP(TEST, mulf) \
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FOREVERY_COMMON_CONJ_BINOP(TEST, mulc) \
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FOREVERY_COMMON_CONJ_BINOP(TEST, muli) \
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FOREVERY_COMMON_CONJ_BINOP(TEST, andi)
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#define FOREVERY_PAIR_OF_COMMON_DISJ_DISJ_BINOP(TEST) \
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FOREVERY_COMMON_DISJ_BINOP(TEST, addf) \
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FOREVERY_COMMON_DISJ_BINOP(TEST, addc) \
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FOREVERY_COMMON_DISJ_BINOP(TEST, addi) \
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FOREVERY_COMMON_DISJ_BINOP(TEST, ori) \
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FOREVERY_COMMON_DISJ_BINOP(TEST, xori)
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///
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/// Helper classes/functions for testing Merger.
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///
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/// Simple recursive data structure used to match expressions in Mergers.
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struct Pattern {
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Kind kind;
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/// Expressions representing tensors simply have a tensor number.
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unsigned tensorNum;
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/// Tensor operations point to their children.
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std::shared_ptr<Pattern> e0;
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std::shared_ptr<Pattern> e1;
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/// Constructors.
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/// Rather than using these, please use the readable helper constructor
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/// functions below to make tests more readable.
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Pattern(unsigned tensorNum) : kind(Kind::kTensor), tensorNum(tensorNum) {}
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Pattern(Kind kind, const std::shared_ptr<Pattern> &e0,
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const std::shared_ptr<Pattern> &e1)
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: kind(kind), e0(e0), e1(e1) {
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assert(kind >= Kind::kMulF);
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assert(e0 && e1);
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}
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};
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///
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/// Readable Pattern builder functions.
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/// These should be preferred over the actual constructors.
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///
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static std::shared_ptr<Pattern> tensorPattern(unsigned tensorNum) {
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return std::make_shared<Pattern>(tensorNum);
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}
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#define IMPL_BINOP_PATTERN(OP, KIND) \
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LLVM_ATTRIBUTE_UNUSED static std::shared_ptr<Pattern> OP##Pattern( \
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const std::shared_ptr<Pattern> &e0, \
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const std::shared_ptr<Pattern> &e1) { \
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return std::make_shared<Pattern>(KIND, e0, e1); \
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}
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FOREVERY_BINOP(IMPL_BINOP_PATTERN)
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#undef IMPL_BINOP_PATTERN
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class MergerTestBase : public ::testing::Test {
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protected:
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MergerTestBase(unsigned numTensors, unsigned numLoops)
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: numTensors(numTensors), numLoops(numLoops),
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merger(numTensors, numLoops, /*numFilterLoops=*/0) {}
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///
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/// Expression construction helpers.
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///
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unsigned tensor(unsigned tensor) {
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return merger.addExp(Kind::kTensor, tensor);
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}
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#define IMPL_BINOP_EXPR(OP, KIND) \
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LLVM_ATTRIBUTE_UNUSED unsigned OP##Expr(unsigned e0, unsigned e1) { \
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return merger.addExp(KIND, e0, e1); \
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}
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FOREVERY_BINOP(IMPL_BINOP_EXPR)
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#undef IMPL_BINOP_EXPR
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///
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/// Comparison helpers.
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///
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/// For readability of tests.
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unsigned lat(unsigned lat) { return lat; }
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/// Returns true if a lattice point with an expression matching the given
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/// pattern and bits matching the given bits is present in lattice points
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/// [p, p+n) of lattice set s. This is useful for testing partial ordering
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/// constraints between lattice points. We generally know how contiguous
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/// groups of lattice points should be ordered with respect to other groups,
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/// but there is no required ordering within groups.
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/// If simple is true, then compare the lat.simple field instead to test the
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/// result after optimization
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bool latPointWithinRange(unsigned s, unsigned p, unsigned n,
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const std::shared_ptr<Pattern> &pattern,
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const BitVector &bits, bool simple) {
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for (unsigned i = p; i < p + n; ++i) {
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if (compareExpression(merger.lat(merger.set(s)[i]).exp, pattern) &&
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compareBits(s, i, bits, simple))
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return true;
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}
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return false;
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}
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/// Wrapper over latPointWithinRange for readability of tests.
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void expectLatPointWithinRange(unsigned s, unsigned p, unsigned n,
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const std::shared_ptr<Pattern> &pattern,
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const BitVector &bits, bool simple = false) {
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EXPECT_TRUE(latPointWithinRange(s, p, n, pattern, bits, simple));
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}
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/// Wrapper over expectLatPointWithinRange for a single lat point.
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void expectLatPoint(unsigned s, unsigned p,
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const std::shared_ptr<Pattern> &pattern,
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const BitVector &bits, bool simple = false) {
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EXPECT_TRUE(latPointWithinRange(s, p, 1, pattern, bits, simple));
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}
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/// Converts a vector of (loop, tensor) pairs to a bitvector with the
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/// corresponding bits set.
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BitVector
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loopsToBits(const std::vector<std::pair<unsigned, unsigned>> &loops) {
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BitVector testBits = BitVector(numTensors + 1, false);
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for (auto l : loops) {
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auto loop = std::get<0>(l);
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auto tensor = std::get<1>(l);
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testBits.set(numTensors * loop + tensor);
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}
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return testBits;
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}
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/// Returns true if the bits of lattice point p in set s match the given bits.
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/// If simple is true, then compare the lat.simple field instead to test the
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/// result after optimization
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bool compareBits(unsigned s, unsigned p, const BitVector &bits, bool simple) {
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if (simple)
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return merger.lat(merger.set(s)[p]).simple == bits;
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return merger.lat(merger.set(s)[p]).bits == bits;
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}
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/// Check that there are n lattice points in set s.
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void expectNumLatPoints(unsigned s, unsigned n) {
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EXPECT_THAT(merger.set(s).size(), n);
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}
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/// Compares expressions for equality. Equality is defined recursively as:
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/// - Operations are equal if they have the same kind and children.
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/// - Leaf tensors are equal if they refer to the same tensor.
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bool compareExpression(unsigned e, const std::shared_ptr<Pattern> &pattern) {
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auto tensorExp = merger.exp(e);
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if (tensorExp.kind != pattern->kind)
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return false;
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switch (tensorExp.kind) {
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// Leaf.
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case kTensor:
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return tensorExp.tensor == pattern->tensorNum;
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case kInvariant:
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case kIndex:
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llvm_unreachable("invariant not handled yet");
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// Unary operations.
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case kAbsF:
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case kAbsC:
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case kAbsI:
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case kCeilF:
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case kFloorF:
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case kSqrtF:
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case kSqrtC:
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case kExpm1F:
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case kExpm1C:
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case kLog1pF:
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case kLog1pC:
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case kSinF:
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case kSinC:
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case kTanhF:
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case kTanhC:
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case kNegF:
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case kNegC:
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case kNegI:
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case kTruncF:
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case kExtF:
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case kCastFS:
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case kCastFU:
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case kCastSF:
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case kCastUF:
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case kCastS:
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case kCastU:
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case kCastIdx:
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case kTruncI:
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case kCIm:
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case kCRe:
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case kBitCast:
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case kSelect:
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case kBinaryBranch:
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case kUnary:
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return compareExpression(tensorExp.children.e0, pattern->e0);
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// Binary operations.
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case kMulF:
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case kMulC:
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case kMulI:
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case kDivF:
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case kDivC:
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case kDivS:
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case kDivU:
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case kAddF:
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case kAddC:
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case kAddI:
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case kSubF:
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case kSubC:
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case kSubI:
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case kAndI:
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case kOrI:
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case kXorI:
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case kShrS:
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case kShrU:
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case kShlI:
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case kBinary:
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case kReduce:
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return compareExpression(tensorExp.children.e0, pattern->e0) &&
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compareExpression(tensorExp.children.e1, pattern->e1);
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}
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llvm_unreachable("unexpected kind");
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}
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unsigned numTensors;
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unsigned numLoops;
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Merger merger;
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};
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///
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/// Tests with all sparse inputs.
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///
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class MergerTest3T1L : public MergerTestBase {
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protected:
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// Our three tensors (two inputs, one output).
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const unsigned t0 = 0, t1 = 1, t2 = 2;
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// Our single loop.
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const unsigned l0 = 0;
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MergerTest3T1L() : MergerTestBase(3, 1) {
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EXPECT_TRUE(merger.getOutTensorID() == t2);
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// Tensor 0: sparse input vector.
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merger.addExp(Kind::kTensor, t0, -1u);
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merger.setDimAndDimLevelType(t0, l0, 0, DimLevelType::Compressed);
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// Tensor 1: sparse input vector.
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merger.addExp(Kind::kTensor, t1, -1u);
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merger.setDimAndDimLevelType(t1, l0, 0, DimLevelType::Compressed);
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// Tensor 2: dense output vector.
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merger.addExp(Kind::kTensor, t2, -1u);
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merger.setDimAndDimLevelType(t2, l0, 0, DimLevelType::Dense);
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}
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};
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class MergerTest4T1L : public MergerTestBase {
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protected:
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// Our four tensors (three inputs, one output).
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const unsigned t0 = 0, t1 = 1, t2 = 2, t3 = 3;
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// Our single loop.
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const unsigned l0 = 0;
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MergerTest4T1L() : MergerTestBase(4, 1) {
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EXPECT_TRUE(merger.getOutTensorID() == t3);
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// Tensor 0: sparse input vector.
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merger.addExp(Kind::kTensor, t0, -1u);
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merger.setDimAndDimLevelType(t0, l0, 0, DimLevelType::Compressed);
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// Tensor 1: sparse input vector.
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merger.addExp(Kind::kTensor, t1, -1u);
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merger.setDimAndDimLevelType(t1, l0, 0, DimLevelType::Compressed);
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// Tensor 2: sparse input vector
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merger.addExp(Kind::kTensor, t2, -1u);
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merger.setDimAndDimLevelType(t2, l0, 0, DimLevelType::Compressed);
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// Tensor 3: dense output vector
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merger.addExp(Kind::kTensor, t3, -1u);
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merger.setDimAndDimLevelType(t3, l0, 0, DimLevelType::Dense);
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}
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};
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///
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/// Tests with both sparse and dense input.
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///
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class MergerTest3T1LD : public MergerTestBase {
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protected:
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// Our three tensors (two inputs, one output).
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const unsigned t0 = 0, t1 = 1, t2 = 2;
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// Our single loop.
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const unsigned l0 = 0;
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MergerTest3T1LD() : MergerTestBase(3, 1) {
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EXPECT_TRUE(merger.getOutTensorID() == t2);
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// Tensor 0: sparse input vector.
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merger.addExp(Kind::kTensor, t0, -1u);
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merger.setDimAndDimLevelType(t0, l0, 0, DimLevelType::Compressed);
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// Tensor 1: dense input vector.
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merger.addExp(Kind::kTensor, t1, -1u);
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merger.setDimAndDimLevelType(t1, l0, 0, DimLevelType::Dense);
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// Tensor 2: dense output vector.
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merger.addExp(Kind::kTensor, t2, -1u);
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merger.setDimAndDimLevelType(t2, l0, 0, DimLevelType::Dense);
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}
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};
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///
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/// Tests with both undef and dense input.
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///
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class MergerTest4T1LU : public MergerTestBase {
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protected:
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// Our three tensors (three inputs, one output).
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const unsigned t0 = 0, t1 = 1, t2 = 2, t3 = 3;
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// Our single loop.
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const unsigned l0 = 0;
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MergerTest4T1LU() : MergerTestBase(4, 1) {
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EXPECT_TRUE(merger.getOutTensorID() == t3);
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// Tensor 0: undef input vector.
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merger.addExp(Kind::kTensor, t0, -1u);
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merger.setDimAndDimLevelType(t0, l0, 0, DimLevelType::Undef);
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// Tensor 1: dense input vector.
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merger.addExp(Kind::kTensor, t1, -1u);
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merger.setDimAndDimLevelType(t1, l0, 0, DimLevelType::Dense);
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// Tensor 2: undef input vector.
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merger.addExp(Kind::kTensor, t2, -1u);
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merger.setDimAndDimLevelType(t2, l0, 0, DimLevelType::Undef);
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// Tensor 3: dense output vector.
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merger.addExp(Kind::kTensor, t3, -1u);
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merger.setDimAndDimLevelType(t3, l0, 0, DimLevelType::Dense);
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}
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};
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///
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/// Tests with operation on sparse output.
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///
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class MergerTest3T1LSo : public MergerTestBase {
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protected:
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// Our three tensors (two inputs, one output, one synthetic).
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const unsigned t0 = 0, t1 = 1, t2 = 2, t3 = 3;
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// Our single loop.
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const unsigned l0 = 0;
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MergerTest3T1LSo() : MergerTestBase(3, 1) {
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EXPECT_TRUE(merger.getOutTensorID() == t2);
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EXPECT_TRUE(merger.getSynTensorID() == t3);
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merger.setHasSparseOut(true);
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// Tensor 0: undef input vector.
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merger.addExp(Kind::kTensor, t0, -1u);
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merger.setDimAndDimLevelType(t0, l0, 0, DimLevelType::Undef);
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// Tensor 1: undef input vector.
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merger.addExp(Kind::kTensor, t1, -1u);
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merger.setDimAndDimLevelType(t1, l0, 0, DimLevelType::Undef);
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// Tensor 2: sparse output vector.
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merger.addExp(Kind::kTensor, t2, -1u);
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merger.setDimAndDimLevelType(t2, l0, 0, DimLevelType::Compressed);
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}
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};
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} // namespace
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/// Vector multiplication (conjunction) of 3 vectors, i.e.;
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/// a(i) = b(i) * c(i) * d(i)
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/// which should form the single lattice point
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/// {
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/// lat( i_00_U i_01_D i_02_U / (tensor_0 * tensor_1 * tensor2) )
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/// }
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/// after optimization, the dense dimesion should be kept, despite it appears
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/// in the middle
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/// {
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/// lat( i_01_D / (tensor_0 * tensor_1 * tensor2) )
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/// }
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#define IMPL_MERGER_TEST_CONJ_CONJ_UNDEF(CONJ1, CONJ2) \
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TEST_F(MergerTest4T1LU, vector_##CONJ1##_##CONJ2) { \
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auto em = CONJ1##Expr(t0, t1); \
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auto e = CONJ2##Expr(em, t2); \
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auto p0 = tensorPattern(t0); \
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auto p1 = tensorPattern(t1); \
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auto p2 = tensorPattern(t2); \
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auto s = merger.buildLattices(e, l0); \
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expectNumLatPoints(s, 1); \
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expectLatPoint(s, lat(0), CONJ2##Pattern(CONJ1##Pattern(p0, p1), p2), \
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loopsToBits({{l0, t0}, {l0, t1}, {l0, t2}})); \
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s = merger.optimizeSet(s); \
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expectNumLatPoints(s, 1); \
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expectLatPoint(s, lat(0), CONJ2##Pattern(CONJ1##Pattern(p0, p1), p2), \
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loopsToBits({{l0, t1}}), true); \
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}
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FOREVERY_PAIR_OF_COMMON_CONJ_CONJ_BINOP(IMPL_MERGER_TEST_CONJ_CONJ_UNDEF)
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#undef IMPL_MERGER_TEST_CONJ_CONJ_UNDEF
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/// Vector multiplication (conjunction) of 2 vectors, i.e.;
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/// o(i) = b(i) * c(i) * o(i)
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/// which should form the single lattice point (note how a synthetic tensor
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/// i_03_U is created for the sparse output)
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/// {
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/// lat( i_00_U i_01_U i_03_U / (tensor_0 * tensor_1 * output_tensor_2) )
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/// }
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/// after optimization, the synthetic tensor should be preserved.
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/// {
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/// lat( i_03_U / (tensor_0 * tensor_1 * output_tensor2) )
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/// }
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#define IMPL_MERGER_TEST_CONJ_CONJ_SPARSE_OUT(CONJ1, CONJ2) \
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TEST_F(MergerTest3T1LSo, vector_##CONJ1##_##CONJ2) { \
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auto em = CONJ1##Expr(t0, t1); \
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auto e = CONJ2##Expr(em, t2); \
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auto p0 = tensorPattern(t0); \
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auto p1 = tensorPattern(t1); \
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auto p2 = tensorPattern(t2); \
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auto s = merger.buildLattices(e, l0); \
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expectNumLatPoints(s, 1); \
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expectLatPoint(s, lat(0), CONJ2##Pattern(CONJ1##Pattern(p0, p1), p2), \
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loopsToBits({{l0, t0}, {l0, t1}, {l0, t3}})); \
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s = merger.optimizeSet(s); \
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expectNumLatPoints(s, 1); \
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expectLatPoint(s, lat(0), CONJ2##Pattern(CONJ1##Pattern(p0, p1), p2), \
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loopsToBits({{l0, t3}}), true); \
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}
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FOREVERY_PAIR_OF_COMMON_CONJ_CONJ_BINOP(IMPL_MERGER_TEST_CONJ_CONJ_SPARSE_OUT)
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#undef IMPL_MERGER_TEST_CONJ_CONJ_SPARSE_OUT
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/// Vector addition (disjunction) of 2 vectors. i.e.;
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/// a(i) = b(i) + c(i)
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/// which should form the 3 lattice points
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/// {
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/// lat( i_00 i_01 / (tensor_0 + tensor_1) )
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/// lat( i_00 / tensor_0 )
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/// lat( i_01 / tensor_1 )
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/// }
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/// and after optimization, the lattice points do not change (as there is no
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/// duplicated point and all input vectors are sparse vector).
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/// {
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/// lat( i_00 i_01 / (tensor_0 + tensor_1) )
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/// lat( i_00 / tensor_0 )
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/// lat( i_01 / tensor_1 )
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/// }
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#define IMPL_MERGER_TEST_DISJ(OP) \
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TEST_F(MergerTest3T1L, vector_##OP) { \
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auto e = OP##Expr(tensor(t0), tensor(t1)); \
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auto p0 = tensorPattern(t0); \
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auto p1 = tensorPattern(t1); \
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auto s = merger.buildLattices(e, l0); \
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\
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expectNumLatPoints(s, 3); \
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expectLatPoint(s, lat(0), OP##Pattern(p0, p1), \
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loopsToBits({{l0, t0}, {l0, t1}})); \
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expectLatPointWithinRange(s, lat(1), 2, p0, loopsToBits({{l0, t0}})); \
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expectLatPointWithinRange(s, lat(1), 2, p1, loopsToBits({{l0, t1}})); \
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\
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s = merger.optimizeSet(s); \
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expectNumLatPoints(s, 3); \
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expectLatPoint(s, lat(0), OP##Pattern(p0, p1), \
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loopsToBits({{l0, t0}, {l0, t1}}), true); \
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expectLatPointWithinRange(s, lat(1), 2, p0, loopsToBits({{l0, t0}}), \
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true); \
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expectLatPointWithinRange(s, lat(1), 2, p1, loopsToBits({{l0, t1}}), \
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true); \
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}
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FOREVERY_COMMON_DISJ_BINOP(IMPL_MERGER_TEST_DISJ)
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#undef IMPL_MERGER_TEST_DISJ
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/// Vector multiplication (conjunction) of 2 vectors, i.e.;
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/// a(i) = b(i) * c(i)
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/// which should form the single lattice point
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/// {
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/// lat( i_00 i_01 / (tensor_0 * tensor_1) )
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/// }
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#define IMPL_MERGER_TEST_CONJ(OP) \
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TEST_F(MergerTest3T1L, vector_##OP) { \
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auto e = OP##Expr(t0, t1); \
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auto p0 = tensorPattern(t0); \
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auto p1 = tensorPattern(t1); \
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auto s = merger.buildLattices(e, l0); \
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\
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expectNumLatPoints(s, 1); \
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expectLatPoint(s, lat(0), OP##Pattern(p0, p1), \
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loopsToBits({{l0, t0}, {l0, t1}})); \
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\
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s = merger.optimizeSet(s); \
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expectNumLatPoints(s, 1); \
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expectLatPoint(s, lat(0), OP##Pattern(p0, p1), \
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loopsToBits({{l0, t0}, {l0, t1}}), true); \
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}
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FOREVERY_COMMON_CONJ_BINOP(IMPL_MERGER_TEST_CONJ)
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#undef IMPL_MERGER_TEST_CONJ
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/// Vector multiplication (conjunction) then addition (disjunction), i.e.;
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/// a(i) = b(i) * c(i) + d(i);
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/// which should form
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/// {
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/// lat( i_00 i_01 i_02 / (tensor_0 * tensor_1) + tensor_2 )
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/// lat( i_00 i_01 / tensor_0 * tensor_1
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/// lat( i_02 / tensor_2 )
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/// }
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#define IMPL_MERGER_TEST_CONJ_DISJ(CONJ, DISJ) \
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TEST_F(MergerTest4T1L, vector_##CONJ##_##DISJ) { \
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auto em = CONJ##Expr(t0, t1); \
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auto e = DISJ##Expr(em, t2); \
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auto p0 = tensorPattern(t0); \
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auto p1 = tensorPattern(t1); \
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auto p2 = tensorPattern(t2); \
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auto s = merger.buildLattices(e, l0); \
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\
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expectNumLatPoints(s, 3); \
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expectLatPoint(s, lat(0), DISJ##Pattern(CONJ##Pattern(p0, p1), p2), \
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loopsToBits({{l0, t0}, {l0, t1}, {l0, t2}})); \
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expectLatPointWithinRange(s, lat(1), 2, CONJ##Pattern(p0, p1), \
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loopsToBits({{l0, t0}, {l0, t1}})); \
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expectLatPointWithinRange(s, lat(1), 2, p2, loopsToBits({{l0, t2}})); \
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\
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s = merger.optimizeSet(s); \
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expectNumLatPoints(s, 3); \
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expectLatPoint(s, lat(0), DISJ##Pattern(CONJ##Pattern(p0, p1), p2), \
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loopsToBits({{l0, t0}, {l0, t1}, {l0, t2}})); \
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expectLatPointWithinRange(s, lat(1), 2, CONJ##Pattern(p0, p1), \
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loopsToBits({{l0, t0}, {l0, t1}})); \
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expectLatPointWithinRange(s, lat(1), 2, p2, loopsToBits({{l0, t2}})); \
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}
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FOREVERY_PAIR_OF_COMMON_CONJ_DISJ_BINOP(IMPL_MERGER_TEST_CONJ_DISJ)
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|
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#undef IMPL_MERGER_TEST_CONJ_DISJ
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|
|
|
/// Vector addition (disjunction) then addition (disjunction), i.e.;
|
|
/// a(i) = b(i) + c(i) + d(i)
|
|
/// which should form
|
|
/// {
|
|
/// lat( i_00 i_01 i_02 / (tensor_0 + tensor_1) + tensor_2 )
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|
/// lat( i_02 i_01 / tensor_2 + tensor_1 )
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|
/// lat( i_02 i_00 / tensor_2 + tensor_0 )
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|
/// lat( i_01 i_00 / tensor_1 + tensor_0 )
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|
/// lat( i_02 / tensor_2 )
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|
/// lat( i_01 / tensor_1 )
|
|
/// lat( i_00 / tensor_0 )
|
|
/// }
|
|
#define IMPL_MERGER_TEST_DISJ_DISJ(DISJ1, DISJ2) \
|
|
TEST_F(MergerTest4T1L, Vector_##DISJ1##_##DISJ2) { \
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auto em = DISJ1##Expr(t0, t1); \
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auto e = DISJ2##Expr(em, t2); \
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auto p0 = tensorPattern(t0); \
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auto p1 = tensorPattern(t1); \
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|
auto p2 = tensorPattern(t2); \
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auto s = merger.buildLattices(e, l0); \
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|
\
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expectNumLatPoints(s, 7); \
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|
expectLatPoint(s, lat(0), DISJ2##Pattern(DISJ1##Pattern(p0, p1), p2), \
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|
loopsToBits({{l0, t0}, {l0, t1}, {l0, t2}})); \
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|
expectLatPointWithinRange(s, lat(1), 6, DISJ2##Pattern(p1, p2), \
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|
loopsToBits({{l0, t1}, {l0, t2}})); \
|
|
expectLatPointWithinRange(s, lat(1), 6, DISJ2##Pattern(p0, p2), \
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|
loopsToBits({{l0, t0}, {l0, t2}})); \
|
|
expectLatPointWithinRange(s, lat(1), 6, DISJ1##Pattern(p0, p1), \
|
|
loopsToBits({{l0, t0}, {l0, t1}})); \
|
|
expectLatPointWithinRange(s, lat(1), 6, p2, loopsToBits({{l0, t2}})); \
|
|
expectLatPointWithinRange(s, lat(1), 6, p1, loopsToBits({{l0, t1}})); \
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|
expectLatPointWithinRange(s, lat(1), 6, p0, loopsToBits({{l0, t0}})); \
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|
\
|
|
s = merger.optimizeSet(s); \
|
|
expectNumLatPoints(s, 7); \
|
|
expectLatPoint(s, lat(0), DISJ2##Pattern(DISJ1##Pattern(p0, p1), p2), \
|
|
loopsToBits({{l0, t0}, {l0, t1}, {l0, t2}})); \
|
|
expectLatPointWithinRange(s, lat(1), 6, DISJ2##Pattern(p1, p2), \
|
|
loopsToBits({{l0, t1}, {l0, t2}})); \
|
|
expectLatPointWithinRange(s, lat(1), 6, DISJ2##Pattern(p0, p2), \
|
|
loopsToBits({{l0, t0}, {l0, t2}})); \
|
|
expectLatPointWithinRange(s, lat(1), 6, DISJ1##Pattern(p0, p1), \
|
|
loopsToBits({{l0, t0}, {l0, t1}})); \
|
|
expectLatPointWithinRange(s, lat(1), 6, p2, loopsToBits({{l0, t2}})); \
|
|
expectLatPointWithinRange(s, lat(1), 6, p1, loopsToBits({{l0, t1}})); \
|
|
expectLatPointWithinRange(s, lat(1), 6, p0, loopsToBits({{l0, t0}})); \
|
|
}
|
|
|
|
FOREVERY_PAIR_OF_COMMON_DISJ_DISJ_BINOP(IMPL_MERGER_TEST_DISJ_DISJ)
|
|
|
|
#undef IMPL_MERGER_TEST_DISJ_DISJ
|
|
|
|
/// Vector multiplication (conjunction) then multiplication (conjunction), i.e.;
|
|
/// a(i) = b(i) * c(i) * d(i);
|
|
/// which should form
|
|
/// {
|
|
/// lat( i_00 i_01 i_02 / tensor_0 * tensor_1 * tensor_2 )
|
|
/// }
|
|
#define IMPL_MERGER_TEST_CONJ_CONJ(CONJ1, CONJ2) \
|
|
TEST_F(MergerTest4T1L, vector_##CONJ1##_##CONJ2) { \
|
|
auto em = CONJ1##Expr(t0, t1); \
|
|
auto e = CONJ2##Expr(em, t2); \
|
|
auto p0 = tensorPattern(t0); \
|
|
auto p1 = tensorPattern(t1); \
|
|
auto p2 = tensorPattern(t2); \
|
|
auto s = merger.buildLattices(e, l0); \
|
|
expectNumLatPoints(s, 1); \
|
|
expectLatPoint(s, lat(0), CONJ2##Pattern(CONJ1##Pattern(p0, p1), p2), \
|
|
loopsToBits({{l0, t0}, {l0, t1}, {l0, t2}})); \
|
|
s = merger.optimizeSet(s); \
|
|
expectNumLatPoints(s, 1); \
|
|
expectLatPoint(s, lat(0), CONJ2##Pattern(CONJ1##Pattern(p0, p1), p2), \
|
|
loopsToBits({{l0, t0}, {l0, t1}, {l0, t2}}), true); \
|
|
}
|
|
|
|
FOREVERY_PAIR_OF_COMMON_CONJ_CONJ_BINOP(IMPL_MERGER_TEST_CONJ_CONJ)
|
|
|
|
#undef IMPL_MERGER_TEST_CONJ_CONJ
|
|
|
|
/// Vector addition (disjunction) of 2 vectors, i.e.;
|
|
/// a(i) = b(i) + c(i)
|
|
/// which should form the 3 lattice points
|
|
/// {
|
|
/// lat( i_00 i_01 / (sparse_tensor_0 + dense_tensor_1) )
|
|
/// lat( i_00 / sparse_tensor_0 )
|
|
/// lat( i_01 / dense_tensor_1 )
|
|
/// }
|
|
/// which should be optimized to
|
|
/// {
|
|
/// lat( i_00 i_01 / (sparse_tensor_0 + dense_tensor_1) ) (not singleton)
|
|
/// lat( i_01 / dense_tensor_0 ) (no sparse dimension)
|
|
/// }
|
|
///
|
|
/// lat( i_00 / sparse_tensor_0 ) should be opted out as it only has dense diff
|
|
/// with lat( i_00 i_01 / (sparse_tensor_0 + dense_tensor_1) ).
|
|
#define IMPL_MERGER_TEST_OPTIMIZED_DISJ(OP) \
|
|
TEST_F(MergerTest3T1LD, vector_opted_##OP) { \
|
|
auto e = OP##Expr(tensor(t0), tensor(t1)); \
|
|
auto p0 = tensorPattern(t0); \
|
|
auto p1 = tensorPattern(t1); \
|
|
auto s = merger.buildLattices(e, l0); \
|
|
\
|
|
expectNumLatPoints(s, 3); \
|
|
expectLatPoint(s, lat(0), OP##Pattern(p0, p1), \
|
|
loopsToBits({{l0, t0}, {l0, t1}})); \
|
|
expectLatPointWithinRange(s, lat(1), 2, p0, loopsToBits({{l0, t0}})); \
|
|
expectLatPointWithinRange(s, lat(1), 2, p1, loopsToBits({{l0, t1}})); \
|
|
\
|
|
s = merger.optimizeSet(s); \
|
|
expectNumLatPoints(s, 2); \
|
|
expectLatPoint(s, lat(0), OP##Pattern(p0, p1), \
|
|
loopsToBits({{l0, t0}, {l0, t1}}), true); \
|
|
expectLatPoint(s, lat(1), p1, loopsToBits({{l0, t1}}), true); \
|
|
}
|
|
|
|
FOREVERY_COMMON_DISJ_BINOP(IMPL_MERGER_TEST_OPTIMIZED_DISJ)
|
|
|
|
#undef IMPL_MERGER_TEST_OPTIMIZED_CONJ
|
|
|
|
/// Vector multiplication (conjunction) of 2 vectors, i.e.:
|
|
/// a(i) = b(i) * c(i)
|
|
/// which should form the single lattice point
|
|
/// {
|
|
/// lat( i_00 i_01 / (sparse_tensor_0 * dense_tensor_1) )
|
|
/// }
|
|
/// it should be optimized to
|
|
/// {
|
|
/// lat( i_00 / (sparse_tensor_0 * dense_tensor_1) )
|
|
/// }
|
|
/// since i_01 is a dense dimension.
|
|
#define IMPL_MERGER_TEST_OPTIMIZED_CONJ(OP) \
|
|
TEST_F(MergerTest3T1LD, vector_opted_##OP) { \
|
|
auto e = OP##Expr(t0, t1); \
|
|
auto p0 = tensorPattern(t0); \
|
|
auto p1 = tensorPattern(t1); \
|
|
auto s = merger.buildLattices(e, l0); \
|
|
\
|
|
expectNumLatPoints(s, 1); \
|
|
expectLatPoint(s, lat(0), OP##Pattern(p0, p1), \
|
|
loopsToBits({{l0, t0}, {l0, t1}})); \
|
|
\
|
|
s = merger.optimizeSet(s); \
|
|
expectNumLatPoints(s, 1); \
|
|
expectLatPoint(s, lat(0), OP##Pattern(p0, p1), loopsToBits({{l0, t0}}), \
|
|
true); \
|
|
}
|
|
|
|
FOREVERY_COMMON_CONJ_BINOP(IMPL_MERGER_TEST_OPTIMIZED_CONJ)
|
|
|
|
#undef IMPL_MERGER_TEST_OPTIMIZED_CONJ
|
|
|
|
// TODO: mult-dim tests
|
|
|
|
// restore warning status
|
|
#if defined(_MSC_VER) && !defined(__clang__)
|
|
#pragma warning(pop)
|
|
#endif
|