Added a separate class (PBQPBuilder) for PBQP Problem construction. This class can be extended to support custom constraints.

For now the allocator still uses the old (internal) construction mechanism by default. This will be phased out soon assuming 
no issues with the builder system come up.

To invoke the new construction mechanism just pass '-regalloc=pbqp -pbqp-builder' to llc. To provide custom constraints a
Target just needs to extend PBQPBuilder and pass an instance of their derived builder to the RegAllocPBQP constructor.



git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@114272 91177308-0d34-0410-b5e6-96231b3b80d8

1 files changed, 616 insertions, 0 deletions

diff --git a/include/llvm/CodeGen/PBQP/HeuristicSolver.h b/include/llvm/CodeGen/PBQP/HeuristicSolver.h
new file mode 100644
index 0000000000..35514f9674
--- /dev/null
+++ b/include/llvm/CodeGen/PBQP/HeuristicSolver.h
@@ -0,0 +1,616 @@
+//===-- HeuristicSolver.h - Heuristic PBQP Solver --------------*- C++ -*-===//
+//
+//                     The LLVM Compiler Infrastructure
+//
+// This file is distributed under the University of Illinois Open Source
+// License. See LICENSE.TXT for details.
+//
+//===----------------------------------------------------------------------===//
+//
+// Heuristic PBQP solver. This solver is able to perform optimal reductions for
+// nodes of degree 0, 1 or 2. For nodes of degree >2 a plugable heuristic is
+// used to select a node for reduction. 
+//
+//===----------------------------------------------------------------------===//
+
+#ifndef LLVM_CODEGEN_PBQP_HEURISTICSOLVER_H
+#define LLVM_CODEGEN_PBQP_HEURISTICSOLVER_H
+
+#include "Graph.h"
+#include "Solution.h"
+#include <vector>
+#include <limits>
+
+namespace PBQP {
+
+  /// \brief Heuristic PBQP solver implementation.
+  ///
+  /// This class should usually be created (and destroyed) indirectly via a call
+  /// to HeuristicSolver<HImpl>::solve(Graph&).
+  /// See the comments for HeuristicSolver.
+  ///
+  /// HeuristicSolverImpl provides the R0, R1 and R2 reduction rules,
+  /// backpropagation phase, and maintains the internal copy of the graph on
+  /// which the reduction is carried out (the original being kept to facilitate
+  /// backpropagation).
+  template <typename HImpl>
+  class HeuristicSolverImpl {
+  private:
+
+    typedef typename HImpl::NodeData HeuristicNodeData;
+    typedef typename HImpl::EdgeData HeuristicEdgeData;
+
+    typedef std::list<Graph::EdgeItr> SolverEdges;
+
+  public:
+  
+    /// \brief Iterator type for edges in the solver graph.
+    typedef SolverEdges::iterator SolverEdgeItr;
+
+  private:
+
+    class NodeData {
+    public:
+      NodeData() : solverDegree(0) {}
+
+      HeuristicNodeData& getHeuristicData() { return hData; }
+
+      SolverEdgeItr addSolverEdge(Graph::EdgeItr eItr) {
+        ++solverDegree;
+        return solverEdges.insert(solverEdges.end(), eItr);
+      }
+
+      void removeSolverEdge(SolverEdgeItr seItr) {
+        --solverDegree;
+        solverEdges.erase(seItr);
+      }
+
+      SolverEdgeItr solverEdgesBegin() { return solverEdges.begin(); }
+      SolverEdgeItr solverEdgesEnd() { return solverEdges.end(); }
+      unsigned getSolverDegree() const { return solverDegree; }
+      void clearSolverEdges() {
+        solverDegree = 0;
+        solverEdges.clear(); 
+      }
+      
+    private:
+      HeuristicNodeData hData;
+      unsigned solverDegree;
+      SolverEdges solverEdges;
+    };
+ 
+    class EdgeData {
+    public:
+      HeuristicEdgeData& getHeuristicData() { return hData; }
+
+      void setN1SolverEdgeItr(SolverEdgeItr n1SolverEdgeItr) {
+        this->n1SolverEdgeItr = n1SolverEdgeItr;
+      }
+
+      SolverEdgeItr getN1SolverEdgeItr() { return n1SolverEdgeItr; }
+
+      void setN2SolverEdgeItr(SolverEdgeItr n2SolverEdgeItr){
+        this->n2SolverEdgeItr = n2SolverEdgeItr;
+      }
+
+      SolverEdgeItr getN2SolverEdgeItr() { return n2SolverEdgeItr; }
+
+    private:
+
+      HeuristicEdgeData hData;
+      SolverEdgeItr n1SolverEdgeItr, n2SolverEdgeItr;
+    };
+
+    Graph &g;
+    HImpl h;
+    Solution s;
+    std::vector<Graph::NodeItr> stack;
+
+    typedef std::list<NodeData> NodeDataList;
+    NodeDataList nodeDataList;
+
+    typedef std::list<EdgeData> EdgeDataList;
+    EdgeDataList edgeDataList;
+
+  public:
+
+    /// \brief Construct a heuristic solver implementation to solve the given
+    ///        graph.
+    /// @param g The graph representing the problem instance to be solved.
+    HeuristicSolverImpl(Graph &g) : g(g), h(*this) {}  
+
+    /// \brief Get the graph being solved by this solver.
+    /// @return The graph representing the problem instance being solved by this
+    ///         solver.
+    Graph& getGraph() { return g; }
+
+    /// \brief Get the heuristic data attached to the given node.
+    /// @param nItr Node iterator.
+    /// @return The heuristic data attached to the given node.
+    HeuristicNodeData& getHeuristicNodeData(Graph::NodeItr nItr) {
+      return getSolverNodeData(nItr).getHeuristicData();
+    }
+
+    /// \brief Get the heuristic data attached to the given edge.
+    /// @param eItr Edge iterator.
+    /// @return The heuristic data attached to the given node.
+    HeuristicEdgeData& getHeuristicEdgeData(Graph::EdgeItr eItr) {
+      return getSolverEdgeData(eItr).getHeuristicData();
+    }
+
+    /// \brief Begin iterator for the set of edges adjacent to the given node in
+    ///        the solver graph.
+    /// @param nItr Node iterator.
+    /// @return Begin iterator for the set of edges adjacent to the given node
+    ///         in the solver graph. 
+    SolverEdgeItr solverEdgesBegin(Graph::NodeItr nItr) {
+      return getSolverNodeData(nItr).solverEdgesBegin();
+    }
+
+    /// \brief End iterator for the set of edges adjacent to the given node in
+    ///        the solver graph.
+    /// @param nItr Node iterator.
+    /// @return End iterator for the set of edges adjacent to the given node in
+    ///         the solver graph. 
+    SolverEdgeItr solverEdgesEnd(Graph::NodeItr nItr) {
+      return getSolverNodeData(nItr).solverEdgesEnd();
+    }
+
+    /// \brief Remove a node from the solver graph.
+    /// @param eItr Edge iterator for edge to be removed.
+    ///
+    /// Does <i>not</i> notify the heuristic of the removal. That should be
+    /// done manually if necessary.
+    void removeSolverEdge(Graph::EdgeItr eItr) {
+      EdgeData &eData = getSolverEdgeData(eItr);
+      NodeData &n1Data = getSolverNodeData(g.getEdgeNode1(eItr)),
+               &n2Data = getSolverNodeData(g.getEdgeNode2(eItr));
+
+      n1Data.removeSolverEdge(eData.getN1SolverEdgeItr());
+      n2Data.removeSolverEdge(eData.getN2SolverEdgeItr());
+    }
+
+    /// \brief Compute a solution to the PBQP problem instance with which this
+    ///        heuristic solver was constructed.
+    /// @return A solution to the PBQP problem.
+    ///
+    /// Performs the full PBQP heuristic solver algorithm, including setup,
+    /// calls to the heuristic (which will call back to the reduction rules in
+    /// this class), and cleanup.
+    Solution computeSolution() {
+      setup();
+      h.setup();
+      h.reduce();
+      backpropagate();
+      h.cleanup();
+      cleanup();
+      return s;
+    }
+
+    /// \brief Add to the end of the stack.
+    /// @param nItr Node iterator to add to the reduction stack.
+    void pushToStack(Graph::NodeItr nItr) {
+      getSolverNodeData(nItr).clearSolverEdges();
+      stack.push_back(nItr);
+    }
+
+    /// \brief Returns the solver degree of the given node.
+    /// @param nItr Node iterator for which degree is requested.
+    /// @return Node degree in the <i>solver</i> graph (not the original graph).
+    unsigned getSolverDegree(Graph::NodeItr nItr) {
+      return  getSolverNodeData(nItr).getSolverDegree();
+    }
+
+    /// \brief Set the solution of the given node.
+    /// @param nItr Node iterator to set solution for.
+    /// @param selection Selection for node.
+    void setSolution(const Graph::NodeItr &nItr, unsigned selection) {
+      s.setSelection(nItr, selection);
+
+      for (Graph::AdjEdgeItr aeItr = g.adjEdgesBegin(nItr),
+                             aeEnd = g.adjEdgesEnd(nItr);
+           aeItr != aeEnd; ++aeItr) {
+        Graph::EdgeItr eItr(*aeItr);
+        Graph::NodeItr anItr(g.getEdgeOtherNode(eItr, nItr));
+        getSolverNodeData(anItr).addSolverEdge(eItr);
+      }
+    }
+
+    /// \brief Apply rule R0.
+    /// @param nItr Node iterator for node to apply R0 to.
+    ///
+    /// Node will be automatically pushed to the solver stack.
+    void applyR0(Graph::NodeItr nItr) {
+      assert(getSolverNodeData(nItr).getSolverDegree() == 0 &&
+             "R0 applied to node with degree != 0.");
+
+      // Nothing to do. Just push the node onto the reduction stack.
+      pushToStack(nItr);
+
+      s.recordR0();
+    }
+
+    /// \brief Apply rule R1.
+    /// @param xnItr Node iterator for node to apply R1 to.
+    ///
+    /// Node will be automatically pushed to the solver stack.
+    void applyR1(Graph::NodeItr xnItr) {
+      NodeData &nd = getSolverNodeData(xnItr);
+      assert(nd.getSolverDegree() == 1 &&
+             "R1 applied to node with degree != 1.");
+
+      Graph::EdgeItr eItr = *nd.solverEdgesBegin();
+
+      const Matrix &eCosts = g.getEdgeCosts(eItr);
+      const Vector &xCosts = g.getNodeCosts(xnItr);
+      
+      // Duplicate a little to avoid transposing matrices.
+      if (xnItr == g.getEdgeNode1(eItr)) {
+        Graph::NodeItr ynItr = g.getEdgeNode2(eItr);
+        Vector &yCosts = g.getNodeCosts(ynItr);
+        for (unsigned j = 0; j < yCosts.getLength(); ++j) {
+          PBQPNum min = eCosts[0][j] + xCosts[0];
+          for (unsigned i = 1; i < xCosts.getLength(); ++i) {
+            PBQPNum c = eCosts[i][j] + xCosts[i];
+            if (c < min)
+              min = c;
+          }
+          yCosts[j] += min;
+        }
+        h.handleRemoveEdge(eItr, ynItr);
+     } else {
+        Graph::NodeItr ynItr = g.getEdgeNode1(eItr);
+        Vector &yCosts = g.getNodeCosts(ynItr);
+        for (unsigned i = 0; i < yCosts.getLength(); ++i) {
+          PBQPNum min = eCosts[i][0] + xCosts[0];
+          for (unsigned j = 1; j < xCosts.getLength(); ++j) {
+            PBQPNum c = eCosts[i][j] + xCosts[j];
+            if (c < min)
+              min = c;
+          }
+          yCosts[i] += min;
+        }
+        h.handleRemoveEdge(eItr, ynItr);
+      }
+      removeSolverEdge(eItr);
+      assert(nd.getSolverDegree() == 0 &&
+             "Degree 1 with edge removed should be 0.");
+      pushToStack(xnItr);
+      s.recordR1();
+    }
+
+    /// \brief Apply rule R2.
+    /// @param xnItr Node iterator for node to apply R2 to.
+    ///
+    /// Node will be automatically pushed to the solver stack.
+    void applyR2(Graph::NodeItr xnItr) {
+      assert(getSolverNodeData(xnItr).getSolverDegree() == 2 &&
+             "R2 applied to node with degree != 2.");
+
+      NodeData &nd = getSolverNodeData(xnItr);
+      const Vector &xCosts = g.getNodeCosts(xnItr);
+
+      SolverEdgeItr aeItr = nd.solverEdgesBegin();
+      Graph::EdgeItr yxeItr = *aeItr,
+                     zxeItr = *(++aeItr);
+
+      Graph::NodeItr ynItr = g.getEdgeOtherNode(yxeItr, xnItr),
+                     znItr = g.getEdgeOtherNode(zxeItr, xnItr);
+
+      bool flipEdge1 = (g.getEdgeNode1(yxeItr) == xnItr),
+           flipEdge2 = (g.getEdgeNode1(zxeItr) == xnItr);
+
+      const Matrix *yxeCosts = flipEdge1 ?
+        new Matrix(g.getEdgeCosts(yxeItr).transpose()) :
+        &g.getEdgeCosts(yxeItr);
+
+      const Matrix *zxeCosts = flipEdge2 ?
+        new Matrix(g.getEdgeCosts(zxeItr).transpose()) :
+        &g.getEdgeCosts(zxeItr);
+
+      unsigned xLen = xCosts.getLength(),
+               yLen = yxeCosts->getRows(),
+               zLen = zxeCosts->getRows();
+               
+      Matrix delta(yLen, zLen);
+
+      for (unsigned i = 0; i < yLen; ++i) {
+        for (unsigned j = 0; j < zLen; ++j) {
+          PBQPNum min = (*yxeCosts)[i][0] + (*zxeCosts)[j][0] + xCosts[0];
+          for (unsigned k = 1; k < xLen; ++k) {
+            PBQPNum c = (*yxeCosts)[i][k] + (*zxeCosts)[j][k] + xCosts[k];
+            if (c < min) {
+              min = c;
+            }
+          }
+          delta[i][j] = min;
+        }
+      }
+
+      if (flipEdge1)
+        delete yxeCosts;
+
+      if (flipEdge2)
+        delete zxeCosts;
+
+      Graph::EdgeItr yzeItr = g.findEdge(ynItr, znItr);
+      bool addedEdge = false;
+
+      if (yzeItr == g.edgesEnd()) {
+        yzeItr = g.addEdge(ynItr, znItr, delta);
+        addedEdge = true;
+      } else {
+        Matrix &yzeCosts = g.getEdgeCosts(yzeItr);
+        h.preUpdateEdgeCosts(yzeItr);
+        if (ynItr == g.getEdgeNode1(yzeItr)) {
+          yzeCosts += delta;
+        } else {
+          yzeCosts += delta.transpose();
+        }
+      }
+
+      bool nullCostEdge = tryNormaliseEdgeMatrix(yzeItr);
+
+      if (!addedEdge) {
+        // If we modified the edge costs let the heuristic know.
+        h.postUpdateEdgeCosts(yzeItr);
+      }
+ 
+      if (nullCostEdge) {
+        // If this edge ended up null remove it.
+        if (!addedEdge) {
+          // We didn't just add it, so we need to notify the heuristic
+          // and remove it from the solver.
+          h.handleRemoveEdge(yzeItr, ynItr);
+          h.handleRemoveEdge(yzeItr, znItr);
+          removeSolverEdge(yzeItr);
+        }
+        g.removeEdge(yzeItr);
+      } else if (addedEdge) {
+        // If the edge was added, and non-null, finish setting it up, add it to
+        // the solver & notify heuristic.
+        edgeDataList.push_back(EdgeData());
+        g.setEdgeData(yzeItr, &edgeDataList.back());
+        addSolverEdge(yzeItr);
+        h.handleAddEdge(yzeItr);
+      }
+
+      h.handleRemoveEdge(yxeItr, ynItr);
+      removeSolverEdge(yxeItr);
+      h.handleRemoveEdge(zxeItr, znItr);
+      removeSolverEdge(zxeItr);
+
+      pushToStack(xnItr);
+      s.recordR2();
+    }
+
+    /// \brief Record an application of the RN rule.
+    ///
+    /// For use by the HeuristicBase.
+    void recordRN() { s.recordRN(); } 
+
+  private:
+
+    NodeData& getSolverNodeData(Graph::NodeItr nItr) {
+      return *static_cast<NodeData*>(g.getNodeData(nItr));
+    }
+
+    EdgeData& getSolverEdgeData(Graph::EdgeItr eItr) {
+      return *static_cast<EdgeData*>(g.getEdgeData(eItr));
+    }
+
+    void addSolverEdge(Graph::EdgeItr eItr) {
+      EdgeData &eData = getSolverEdgeData(eItr);
+      NodeData &n1Data = getSolverNodeData(g.getEdgeNode1(eItr)),
+               &n2Data = getSolverNodeData(g.getEdgeNode2(eItr));
+
+      eData.setN1SolverEdgeItr(n1Data.addSolverEdge(eItr));
+      eData.setN2SolverEdgeItr(n2Data.addSolverEdge(eItr));
+    }
+
+    void setup() {
+      if (h.solverRunSimplify()) {
+        simplify();
+      }
+
+      // Create node data objects.
+      for (Graph::NodeItr nItr = g.nodesBegin(), nEnd = g.nodesEnd();
+           nItr != nEnd; ++nItr) {
+        nodeDataList.push_back(NodeData());
+        g.setNodeData(nItr, &nodeDataList.back());
+      }
+
+      // Create edge data objects.
+      for (Graph::EdgeItr eItr = g.edgesBegin(), eEnd = g.edgesEnd();
+           eItr != eEnd; ++eItr) {
+        edgeDataList.push_back(EdgeData());
+        g.setEdgeData(eItr, &edgeDataList.back());
+        addSolverEdge(eItr);
+      }
+    }
+
+    void simplify() {
+      disconnectTrivialNodes();
+      eliminateIndependentEdges();
+    }
+
+    // Eliminate trivial nodes.
+    void disconnectTrivialNodes() {
+      unsigned numDisconnected = 0;
+
+      for (Graph::NodeItr nItr = g.nodesBegin(), nEnd = g.nodesEnd();
+           nItr != nEnd; ++nItr) {
+
+        if (g.getNodeCosts(nItr).getLength() == 1) {
+
+          std::vector<Graph::EdgeItr> edgesToRemove;
+
+          for (Graph::AdjEdgeItr aeItr = g.adjEdgesBegin(nItr),
+                                 aeEnd = g.adjEdgesEnd(nItr);
+               aeItr != aeEnd; ++aeItr) {
+
+            Graph::EdgeItr eItr = *aeItr;
+
+            if (g.getEdgeNode1(eItr) == nItr) {
+              Graph::NodeItr otherNodeItr = g.getEdgeNode2(eItr);
+              g.getNodeCosts(otherNodeItr) +=
+                g.getEdgeCosts(eItr).getRowAsVector(0);
+            }
+            else {
+              Graph::NodeItr otherNodeItr = g.getEdgeNode1(eItr);
+              g.getNodeCosts(otherNodeItr) +=
+                g.getEdgeCosts(eItr).getColAsVector(0);
+            }
+
+            edgesToRemove.push_back(eItr);
+          }
+
+          if (!edgesToRemove.empty())
+            ++numDisconnected;
+
+          while (!edgesToRemove.empty()) {
+            g.removeEdge(edgesToRemove.back());
+            edgesToRemove.pop_back();
+          }
+        }
+      }
+    }
+
+    void eliminateIndependentEdges() {
+      std::vector<Graph::EdgeItr> edgesToProcess;
+      unsigned numEliminated = 0;
+
+      for (Graph::EdgeItr eItr = g.edgesBegin(), eEnd = g.edgesEnd();
+           eItr != eEnd; ++eItr) {
+        edgesToProcess.push_back(eItr);
+      }
+
+      while (!edgesToProcess.empty()) {
+        if (tryToEliminateEdge(edgesToProcess.back()))
+          ++numEliminated;
+        edgesToProcess.pop_back();
+      }
+    }
+
+    bool tryToEliminateEdge(Graph::EdgeItr eItr) {
+      if (tryNormaliseEdgeMatrix(eItr)) {
+        g.removeEdge(eItr);
+        return true; 
+      }
+      return false;
+    }
+
+    bool tryNormaliseEdgeMatrix(Graph::EdgeItr &eItr) {
+
+      const PBQPNum infinity = std::numeric_limits<PBQPNum>::infinity();
+
+      Matrix &edgeCosts = g.getEdgeCosts(eItr);
+      Vector &uCosts = g.getNodeCosts(g.getEdgeNode1(eItr)),
+             &vCosts = g.getNodeCosts(g.getEdgeNode2(eItr));
+
+      for (unsigned r = 0; r < edgeCosts.getRows(); ++r) {
+        PBQPNum rowMin = infinity;
+
+        for (unsigned c = 0; c < edgeCosts.getCols(); ++c) {
+          if (vCosts[c] != infinity && edgeCosts[r][c] < rowMin)
+            rowMin = edgeCosts[r][c];
+        }
+
+        uCosts[r] += rowMin;
+
+        if (rowMin != infinity) {
+          edgeCosts.subFromRow(r, rowMin);
+        }
+        else {
+          edgeCosts.setRow(r, 0);
+        }
+      }
+
+      for (unsigned c = 0; c < edgeCosts.getCols(); ++c) {
+        PBQPNum colMin = infinity;
+
+        for (unsigned r = 0; r < edgeCosts.getRows(); ++r) {
+          if (uCosts[r] != infinity && edgeCosts[r][c] < colMin)
+            colMin = edgeCosts[r][c];
+        }
+
+        vCosts[c] += colMin;
+
+        if (colMin != infinity) {
+          edgeCosts.subFromCol(c, colMin);
+        }
+        else {
+          edgeCosts.setCol(c, 0);
+        }
+      }
+
+      return edgeCosts.isZero();
+    }
+
+    void backpropagate() {
+      while (!stack.empty()) {
+        computeSolution(stack.back());
+        stack.pop_back();
+      }
+    }
+
+    void computeSolution(Graph::NodeItr nItr) {
+
+      NodeData &nodeData = getSolverNodeData(nItr);
+
+      Vector v(g.getNodeCosts(nItr));
+
+      // Solve based on existing solved edges.
+      for (SolverEdgeItr solvedEdgeItr = nodeData.solverEdgesBegin(),
+                         solvedEdgeEnd = nodeData.solverEdgesEnd();
+           solvedEdgeItr != solvedEdgeEnd; ++solvedEdgeItr) {
+
+        Graph::EdgeItr eItr(*solvedEdgeItr);
+        Matrix &edgeCosts = g.getEdgeCosts(eItr);
+
+        if (nItr == g.getEdgeNode1(eItr)) {
+          Graph::NodeItr adjNode(g.getEdgeNode2(eItr));
+          unsigned adjSolution = s.getSelection(adjNode);
+          v += edgeCosts.getColAsVector(adjSolution);
+        }
+        else {
+          Graph::NodeItr adjNode(g.getEdgeNode1(eItr));
+          unsigned adjSolution = s.getSelection(adjNode);
+          v += edgeCosts.getRowAsVector(adjSolution);
+        }
+
+      }
+
+      setSolution(nItr, v.minIndex());
+    }
+
+    void cleanup() {
+      h.cleanup();
+      nodeDataList.clear();
+      edgeDataList.clear();
+    }
+  };
+
+  /// \brief PBQP heuristic solver class.
+  ///
+  /// Given a PBQP Graph g representing a PBQP problem, you can find a solution
+  /// by calling
+  /// <tt>Solution s = HeuristicSolver<H>::solve(g);</tt>
+  ///
+  /// The choice of heuristic for the H parameter will affect both the solver
+  /// speed and solution quality. The heuristic should be chosen based on the
+  /// nature of the problem being solved.
+  /// Currently the only solver included with LLVM is the Briggs heuristic for
+  /// register allocation.
+  template <typename HImpl>
+  class HeuristicSolver {
+  public:
+    static Solution solve(Graph &g) {
+      HeuristicSolverImpl<HImpl> hs(g);
+      return hs.computeSolution();
+    }
+  };
+
+}
+
+#endif // LLVM_CODEGEN_PBQP_HEURISTICSOLVER_H