Merge pull request #1912 from borglab/profiling

cmake/FindGooglePerfTools.cmake

@@ -1,42 +1,38 @@
 # -*- cmake -*-
 
-# - Find Google perftools
-# Find the Google perftools includes and libraries
-# This module defines
-#  GOOGLE_PERFTOOLS_INCLUDE_DIR, where to find heap-profiler.h, etc.
-#  GOOGLE_PERFTOOLS_FOUND, If false, do not try to use Google perftools.
-# also defined for general use are
-#  TCMALLOC_LIBRARY, where to find the tcmalloc library.
-
-FIND_PATH(GOOGLE_PERFTOOLS_INCLUDE_DIR google/heap-profiler.h
-/usr/local/include
-/usr/include
-)
+# - Find GPerfTools (formerly Google perftools)
+# Find the GPerfTools libraries
+# If false, do not try to use Google perftools.
+# Also defined for general use are
+# - GPERFTOOLS_TCMALLOC: where to find the tcmalloc library
+# - GPERFTOOLS_PROFILER: where to find the profiler library
 
 SET(TCMALLOC_NAMES ${TCMALLOC_NAMES} tcmalloc)
-FIND_LIBRARY(TCMALLOC_LIBRARY
+find_library(GPERFTOOLS_TCMALLOC
   NAMES ${TCMALLOC_NAMES}
   PATHS /usr/lib /usr/local/lib
-  )
+)
+find_library(GPERFTOOLS_PROFILER
+   NAMES profiler
+   PATHS /usr/lib /usr/local/lib
+)
 
-IF (TCMALLOC_LIBRARY AND GOOGLE_PERFTOOLS_INCLUDE_DIR)
-    SET(TCMALLOC_LIBRARIES ${TCMALLOC_LIBRARY})
-    SET(GOOGLE_PERFTOOLS_FOUND "YES")
-ELSE (TCMALLOC_LIBRARY AND GOOGLE_PERFTOOLS_INCLUDE_DIR)
-  SET(GOOGLE_PERFTOOLS_FOUND "NO")
-ENDIF (TCMALLOC_LIBRARY AND GOOGLE_PERFTOOLS_INCLUDE_DIR)
+IF (GPERFTOOLS_TCMALLOC AND GPERFTOOLS_PROFILER)
+    SET(TCMALLOC_LIBRARIES ${GPERFTOOLS_TCMALLOC})
+    SET(GPERFTOOLS_FOUND "YES")
+ELSE (GPERFTOOLS_TCMALLOC AND GPERFTOOLS_PROFILER)
+  SET(GPERFTOOLS_FOUND "NO")
+ENDIF (GPERFTOOLS_TCMALLOC AND GPERFTOOLS_PROFILER)
 
-IF (GOOGLE_PERFTOOLS_FOUND)
-   IF (NOT GOOGLE_PERFTOOLS_FIND_QUIETLY)
-      MESSAGE(STATUS "Found Google perftools: ${GOOGLE_PERFTOOLS_LIBRARIES}")
-   ENDIF (NOT GOOGLE_PERFTOOLS_FIND_QUIETLY)
-ELSE (GOOGLE_PERFTOOLS_FOUND)
+IF (GPERFTOOLS_FOUND)
+   MESSAGE(STATUS "Found Gperftools: ${GPERFTOOLS_PROFILER}")
+ELSE (GPERFTOOLS_FOUND)
    IF (GOOGLE_PERFTOOLS_FIND_REQUIRED)
       MESSAGE(FATAL_ERROR "Could not find Google perftools library")
    ENDIF (GOOGLE_PERFTOOLS_FIND_REQUIRED)
-ENDIF (GOOGLE_PERFTOOLS_FOUND)
+ENDIF (GPERFTOOLS_FOUND)
 
 MARK_AS_ADVANCED(
-  TCMALLOC_LIBRARY
-  GOOGLE_PERFTOOLS_INCLUDE_DIR
-  )
+  GPERFTOOLS_TCMALLOC
+  GPERFTOOLS_PROFILER
+)

cmake/HandleAllocators.cmake

@@ -7,7 +7,7 @@ else()
     list(APPEND possible_allocators BoostPool STL)
     set(preferred_allocator STL)
 endif()
-if(GOOGLE_PERFTOOLS_FOUND)
+if(GPERFTOOLS_FOUND)
     list(APPEND possible_allocators tcmalloc)
 endif()
 

cmake/HandlePerfTools.cmake

@@ -1,4 +1,4 @@
 
 ###############################################################################
 # Find Google perftools
-find_package(GooglePerfTools)
+find_package(GooglePerfTools)

gtsam/CMakeLists.txt

@@ -147,6 +147,10 @@ if (GTSAM_USE_EIGEN_MKL)
   target_include_directories(gtsam PUBLIC ${MKL_INCLUDE_DIR})
 endif()
 
+if (GPERFTOOLS_FOUND)
+  target_link_libraries(gtsam PRIVATE ${GPERFTOOLS_TCMALLOC} ${GPERFTOOLS_PROFILER})
+endif()
+
 # Add includes for source directories 'BEFORE' boost and any system include
 # paths so that the compiler uses GTSAM headers in our source directory instead
 # of any previously installed GTSAM headers.

gtsam/discrete/DecisionTreeFactor.cpp

@@ -407,11 +407,9 @@ namespace gtsam {
   };
 
   /* ************************************************************************ */
-  DecisionTreeFactor DecisionTreeFactor::prune(size_t maxNrAssignments) const {
-    const size_t N = maxNrAssignments;
-
+  double DecisionTreeFactor::computeThreshold(const size_t N) const {
     // Set of all keys
-    std::set<Key> allKeys(keys().begin(), keys().end());
+    std::set<Key> allKeys = this->labels();
     MinHeap min_heap;
 
     auto op = [&](const Assignment<Key>& a, double p) {
@@ -433,26 +431,40 @@ namespace gtsam {
         nrAssignments *= cardinalities_.at(k);
       }
 
+      // If min-heap is empty, fill it initially.
+      // This is because there is nothing at the top.
       if (min_heap.empty()) {
         min_heap.push(p, std::min(nrAssignments, N));
 
       } else {
-        // If p is larger than the smallest element,
-        // then we insert into the max heap.
-        if (p > min_heap.top()) {
-          for (size_t i = 0; i < std::min(nrAssignments, N); ++i) {
+        for (size_t i = 0; i < std::min(nrAssignments, N); ++i) {
+          // If p is larger than the smallest element,
+          // then we insert into the min heap.
+          // We check against the top each time because the
+          // heap maintains the smallest element at the top.
+          if (p > min_heap.top()) {
             if (min_heap.size() == N) {
               min_heap.pop();
             }
             min_heap.push(p);
+          } else {
+            // p is <= min value so move to the next one
+            break;
           }
         }
       }
       return p;
     };
     this->visitWith(op);
 
-    double threshold = min_heap.top();
+    return min_heap.top();
+  }
+
+  /* ************************************************************************ */
+  DecisionTreeFactor DecisionTreeFactor::prune(size_t maxNrAssignments) const {
+    const size_t N = maxNrAssignments;
+
+    double threshold = computeThreshold(N);
 
     // Now threshold the decision tree
     size_t total = 0;

gtsam/discrete/DecisionTreeFactor.h

@@ -224,6 +224,17 @@ namespace gtsam {
     /// Get all the probabilities in order of assignment values
     std::vector<double> probabilities() const;
 
+    /**
+     * @brief Compute the probability value which is the threshold above which
+     * only `N` leaves are present.
+     *
+     * This is used for pruning out the smaller probabilities.
+     *
+     * @param N The number of leaves to keep post pruning.
+     * @return double
+     */
+    double computeThreshold(const size_t N) const;
+
     /**
      * @brief Prune the decision tree of discrete variables.
      *

gtsam/discrete/tests/testDecisionTreeFactor.cpp

@@ -140,11 +140,46 @@ TEST(DecisionTreeFactor, enumerate) {
   EXPECT(actual == expected);
 }
 
+namespace pruning_fixture {
+
+DiscreteKey A(1, 2), B(2, 2), C(3, 2);
+DecisionTreeFactor f(A& B& C, "1 5 3 7 2 6 4 8");
+
+DiscreteKey D(4, 2);
+DecisionTreeFactor factor(
+    D& C & B & A,
+    "0.0 0.0 0.0 0.60658897 0.61241912 0.61241969 0.61247685 0.61247742 0.0 "
+    "0.0 0.0 0.99995287 1.0 1.0 1.0 1.0");
+
+}  // namespace pruning_fixture
+
+/* ************************************************************************* */
+// Check if computing the correct threshold works.
+TEST(DecisionTreeFactor, ComputeThreshold) {
+  using namespace pruning_fixture;
+
+  // Only keep the leaves with the top 5 values.
+  double threshold = f.computeThreshold(5);
+  EXPECT_DOUBLES_EQUAL(4.0, threshold, 1e-9);
+
+  // Check for more extreme pruning where we only keep the top 2 leaves
+  threshold = f.computeThreshold(2);
+  EXPECT_DOUBLES_EQUAL(7.0, threshold, 1e-9);
+
+  threshold = factor.computeThreshold(5);
+  EXPECT_DOUBLES_EQUAL(0.99995287, threshold, 1e-9);
+
+  threshold = factor.computeThreshold(3);
+  EXPECT_DOUBLES_EQUAL(1.0, threshold, 1e-9);
+
+  threshold = factor.computeThreshold(6);
+  EXPECT_DOUBLES_EQUAL(0.61247742, threshold, 1e-9);
+}
+
 /* ************************************************************************* */
 // Check pruning of the decision tree works as expected.
 TEST(DecisionTreeFactor, Prune) {
-  DiscreteKey A(1, 2), B(2, 2), C(3, 2);
-  DecisionTreeFactor f(A & B & C, "1 5 3 7 2 6 4 8");
+  using namespace pruning_fixture;
 
   // Only keep the leaves with the top 5 values.
   size_t maxNrAssignments = 5;
@@ -160,12 +195,6 @@ TEST(DecisionTreeFactor, Prune) {
   DecisionTreeFactor expected2(A & B & C, "0 0 0 7 0 0 0 8");
   EXPECT(assert_equal(expected2, pruned2));
 
-  DiscreteKey D(4, 2);
-  DecisionTreeFactor factor(
-      D & C & B & A,
-      "0.0 0.0 0.0 0.60658897 0.61241912 0.61241969 0.61247685 0.61247742 0.0 "
-      "0.0 0.0 0.99995287 1.0 1.0 1.0 1.0");
-
   DecisionTreeFactor expected3(D & C & B & A,
                                "0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 "
                                "0.999952870000 1.0 1.0 1.0 1.0");

gtsam/geometry/geometry.i

@@ -360,6 +360,7 @@ class Rot3 {
   // Standard Interface
   static gtsam::Rot3 Expmap(gtsam::Vector v);
   static gtsam::Vector Logmap(const gtsam::Rot3& p);
+  gtsam::Rot3 expmap(const gtsam::Vector& v);
   gtsam::Vector logmap(const gtsam::Rot3& p);
   gtsam::Matrix matrix() const;
   gtsam::Matrix transpose() const;

gtsam/hybrid/HybridBayesTree.cpp

@@ -210,9 +210,11 @@ void HybridBayesTree::prune(const size_t maxNrLeaves) {
       if (conditional->isHybrid()) {
         auto hybridGaussianCond = conditional->asHybrid();
 
-        // Imperative
-        clique->conditional() = std::make_shared<HybridConditional>(
-            hybridGaussianCond->prune(parentData.prunedDiscreteProbs));
+        if (!hybridGaussianCond->pruned()) {
+          // Imperative
+          clique->conditional() = std::make_shared<HybridConditional>(
+              hybridGaussianCond->prune(parentData.prunedDiscreteProbs));
+        }
       }
       return parentData;
     }

gtsam/hybrid/HybridGaussianConditional.cpp

@@ -120,7 +120,7 @@ struct HybridGaussianConditional::Helper {
 
 /* *******************************************************************************/
 HybridGaussianConditional::HybridGaussianConditional(
-    const DiscreteKeys &discreteParents, Helper &&helper)
+    const DiscreteKeys &discreteParents, Helper &&helper, bool pruned)
     : BaseFactor(discreteParents,
                  FactorValuePairs(
                      [&](const GaussianFactorValuePair
@@ -130,7 +130,8 @@ HybridGaussianConditional::HybridGaussianConditional(
                      },
                      std::move(helper.pairs))),
       BaseConditional(*helper.nrFrontals),
-      negLogConstant_(helper.minNegLogConstant) {}
+      negLogConstant_(helper.minNegLogConstant),
+      pruned_(pruned) {}
 
 HybridGaussianConditional::HybridGaussianConditional(
     const DiscreteKey &discreteParent,
@@ -166,8 +167,9 @@ HybridGaussianConditional::HybridGaussianConditional(
     : HybridGaussianConditional(discreteParents, Helper(conditionals)) {}
 
 HybridGaussianConditional::HybridGaussianConditional(
-    const DiscreteKeys &discreteParents, const FactorValuePairs &pairs)
-    : HybridGaussianConditional(discreteParents, Helper(pairs)) {}
+    const DiscreteKeys &discreteParents, const FactorValuePairs &pairs,
+    bool pruned)
+    : HybridGaussianConditional(discreteParents, Helper(pairs), pruned) {}
 
 /* *******************************************************************************/
 const HybridGaussianConditional::Conditionals
@@ -331,7 +333,7 @@ HybridGaussianConditional::shared_ptr HybridGaussianConditional::prune(
 
   FactorValuePairs prunedConditionals = factors().apply(pruner);
   return std::make_shared<HybridGaussianConditional>(discreteKeys(),
-                                                     prunedConditionals);
+                                                     prunedConditionals, true);
 }
 
 /* *******************************************************************************/

gtsam/hybrid/HybridGaussianConditional.h

@@ -68,6 +68,9 @@ class GTSAM_EXPORT HybridGaussianConditional
   ///< Take advantage of the neg-log space so everything is a minimization
   double negLogConstant_;
 
+  /// Flag to indicate if the conditional has been pruned.
+  bool pruned_ = false;
+
  public:
   /// @name Constructors
   /// @{
@@ -150,9 +153,10 @@ class GTSAM_EXPORT HybridGaussianConditional
    *
    * @param discreteParents the discrete parents. Will be placed last.
    * @param conditionalPairs Decision tree of GaussianFactor/scalar pairs.
+   * @param pruned Flag indicating if conditional has been pruned.
    */
   HybridGaussianConditional(const DiscreteKeys &discreteParents,
-                            const FactorValuePairs &pairs);
+                            const FactorValuePairs &pairs, bool pruned = false);
 
   /// @}
   /// @name Testable
@@ -233,6 +237,9 @@ class GTSAM_EXPORT HybridGaussianConditional
   HybridGaussianConditional::shared_ptr prune(
       const DecisionTreeFactor &discreteProbs) const;
 
+  /// Return true if the conditional has already been pruned.
+  bool pruned() const { return pruned_; }
+
   /// @}
 
  private:
@@ -241,7 +248,7 @@ class GTSAM_EXPORT HybridGaussianConditional
 
   /// Private constructor that uses helper struct above.
   HybridGaussianConditional(const DiscreteKeys &discreteParents,
-                            Helper &&helper);
+                            Helper &&helper, bool pruned = false);
 
   /// Check whether `given` has values for all frontal keys.
   bool allFrontalsGiven(const VectorValues &given) const;