[flang][OpenMP][DoConcurrent] Simplify loop-nest detection logic

ergawy · ergawy · commit d0e4fa24e0b3 · 2024-11-06T03:51:43.000-06:00
With llvm#114020, do-concurrent loop-nests are more conforment to the spec and easier to detect. All we need to do is to check that the only operations inside `loop A` which perfectly wraps `loop B` are: * the operations needed to update `loop A`'s iteration variable and * `loop B` itself. This PR simlifies the pass a bit using the above logic and replaces #127.
diff --git a/flang/lib/Optimizer/OpenMP/DoConcurrentConversion.cpp b/flang/lib/Optimizer/OpenMP/DoConcurrentConversion.cpp
@@ -36,7 +36,8 @@ namespace flangomp {
 #include "flang/Optimizer/OpenMP/Passes.h.inc"
 } // namespace flangomp
 
-#define DEBUG_TYPE "fopenmp-do-concurrent-conversion"
+#define DEBUG_TYPE "do-concurrent-conversion"
+#define DBGS() (llvm::dbgs() << "[" DEBUG_TYPE << "]: ")
 
 namespace Fortran {
 namespace lower {
@@ -45,14 +46,12 @@ namespace internal {
 // TODO The following 2 functions are copied from "flang/Lower/OpenMP/Utils.h".
 // This duplication is temporary until we find a solution for a shared location
 // for these utils that does not introduce circular CMake deps.
-mlir::omp::MapInfoOp
-createMapInfoOp(mlir::OpBuilder &builder, mlir::Location loc,
-                mlir::Value baseAddr, mlir::Value varPtrPtr, std::string name,
-                llvm::ArrayRef<mlir::Value> bounds,
-                llvm::ArrayRef<mlir::Value> members,
-                mlir::ArrayAttr membersIndex, uint64_t mapType,
-                mlir::omp::VariableCaptureKind mapCaptureType, mlir::Type retTy,
-                bool partialMap = false) {
+mlir::omp::MapInfoOp createMapInfoOp(
+    mlir::OpBuilder &builder, mlir::Location loc, mlir::Value baseAddr,
+    mlir::Value varPtrPtr, std::string name, llvm::ArrayRef<mlir::Value> bounds,
+    llvm::ArrayRef<mlir::Value> members, mlir::ArrayAttr membersIndex,
+    uint64_t mapType, mlir::omp::VariableCaptureKind mapCaptureType,
+    mlir::Type retTy, bool partialMap = false) {
   if (auto boxTy = llvm::dyn_cast<fir::BaseBoxType>(baseAddr.getType())) {
     baseAddr = builder.create<fir::BoxAddrOp>(loc, baseAddr);
     retTy = baseAddr.getType();
@@ -255,9 +254,24 @@ bool isIndVarUltimateOperand(mlir::Operation *op, fir::DoLoopOp doLoop) {
   return false;
 }
 
+/// For the \p doLoop parameter, find the operations that declares its induction
+/// variable or allocates memory for it.
+mlir::Operation *findLoopIndVarMemDecl(fir::DoLoopOp doLoop) {
+  mlir::Value result = nullptr;
+  mlir::visitUsedValuesDefinedAbove(
+      doLoop.getRegion(), [&](mlir::OpOperand *operand) {
+        if (isIndVarUltimateOperand(operand->getOwner(), doLoop)) {
+          assert(result == nullptr &&
+                 "loop can have only one induction variable");
+          result = operand->get();
+        }
+      });
+
+  assert(result != nullptr && result.getDefiningOp() != nullptr);
+  return result.getDefiningOp();
+}
+
 /// Collect the list of values used inside the loop but defined outside of it.
-/// The first item in the returned list is always the loop's induction
-/// variable.
 void collectLoopLiveIns(fir::DoLoopOp doLoop,
                         llvm::SmallVectorImpl<mlir::Value> &liveIns) {
   llvm::SmallDenseSet<mlir::Value> seenValues;
@@ -274,9 +288,6 @@ void collectLoopLiveIns(fir::DoLoopOp doLoop,
           return;
 
         liveIns.push_back(operand->get());
-
-        if (isIndVarUltimateOperand(operand->getOwner(), doLoop))
-          std::swap(*liveIns.begin(), *liveIns.rbegin());
       });
 }
 
@@ -366,24 +377,78 @@ void collectIndirectConstOpChain(mlir::Operation *link,
   opChain.insert(link);
 }
 
+/// Loop \p innerLoop is considered perfectly-nested inside \p outerLoop iff
+/// there are no operations in \p outerloop's other than:
+///
+/// 1. the operations needed to assing/update \p outerLoop's induction variable.
+/// 2. \p innerLoop itself.
+///
+/// \p return true if \p innerLoop is perfectly nested inside \p outerLoop
+/// according to the above definition.
+bool isPerfectlyNested(fir::DoLoopOp outerLoop, fir::DoLoopOp innerLoop) {
+  mlir::BackwardSliceOptions backwardSliceOptions;
+  backwardSliceOptions.inclusive = true;
+  // We will collect the backward slices for innerLoop's LB, UB, and step.
+  // However, we want to limit the scope of these slices to the scope of
+  // outerLoop's region.
+  backwardSliceOptions.filter = [&](mlir::Operation *op) {
+    return !mlir::areValuesDefinedAbove(op->getResults(),
+                                        outerLoop.getRegion());
+  };
+
+  mlir::ForwardSliceOptions forwardSliceOptions;
+  forwardSliceOptions.inclusive = true;
+  // We don't care about the outer-loop's induction variable's uses within the
+  // inner-loop, so we filter out these uses.
+  forwardSliceOptions.filter = [&](mlir::Operation *op) {
+    return mlir::areValuesDefinedAbove(op->getResults(), innerLoop.getRegion());
+  };
+
+  llvm::SetVector<mlir::Operation *> indVarSlice;
+  mlir::getForwardSlice(outerLoop.getInductionVar(), &indVarSlice,
+                        forwardSliceOptions);
+  llvm::DenseSet<mlir::Operation *> innerLoopSetupOpsSet(indVarSlice.begin(),
+                                                         indVarSlice.end());
+
+  llvm::DenseSet<mlir::Operation *> loopBodySet;
+  outerLoop.walk<mlir::WalkOrder::PreOrder>([&](mlir::Operation *op) {
+    if (op == outerLoop)
+      return mlir::WalkResult::advance();
+
+    if (op == innerLoop)
+      return mlir::WalkResult::skip();
+
+    if (mlir::isa<fir::ResultOp>(op))
+      return mlir::WalkResult::advance();
+
+    loopBodySet.insert(op);
+    return mlir::WalkResult::advance();
+  });
+
+  bool result = (loopBodySet == innerLoopSetupOpsSet);
+  mlir::Location loc = outerLoop.getLoc();
+  LLVM_DEBUG(DBGS() << "Loop pair starting at location " << loc << " is"
+                    << (result ? "" : " not") << " perfectly nested\n");
+
+  return result;
+}
+
 /// Starting with `outerLoop` collect a perfectly nested loop nest, if any. This
 /// function collects as much as possible loops in the nest; it case it fails to
 /// recognize a certain nested loop as part of the nest it just returns the
 /// parent loops it discovered before.
-mlir::LogicalResult collectLoopNest(fir::DoLoopOp outerLoop,
+mlir::LogicalResult collectLoopNest(fir::DoLoopOp currentLoop,
                                     LoopNestToIndVarMap &loopNest) {
-  assert(outerLoop.getUnordered());
-  llvm::SmallVector<mlir::Value> outerLoopLiveIns;
-  collectLoopLiveIns(outerLoop, outerLoopLiveIns);
+  assert(currentLoop.getUnordered());
 
   while (true) {
     loopNest.try_emplace(
-        outerLoop,
+        currentLoop,
         InductionVariableInfo{
-            outerLoopLiveIns.front().getDefiningOp(),
-            std::move(looputils::extractIndVarUpdateOps(outerLoop))});
+            findLoopIndVarMemDecl(currentLoop),
+            std::move(looputils::extractIndVarUpdateOps(currentLoop))});
 
-    auto directlyNestedLoops = outerLoop.getRegion().getOps<fir::DoLoopOp>();
+    auto directlyNestedLoops = currentLoop.getRegion().getOps<fir::DoLoopOp>();
     llvm::SmallVector<fir::DoLoopOp> unorderedLoops;
 
     for (auto nestedLoop : directlyNestedLoops)
@@ -398,69 +463,10 @@ mlir::LogicalResult collectLoopNest(fir::DoLoopOp outerLoop,
 
     fir::DoLoopOp nestedUnorderedLoop = unorderedLoops.front();
 
-    if ((nestedUnorderedLoop.getLowerBound().getDefiningOp() == nullptr) ||
-        (nestedUnorderedLoop.getUpperBound().getDefiningOp() == nullptr) ||
-        (nestedUnorderedLoop.getStep().getDefiningOp() == nullptr))
+    if (!isPerfectlyNested(currentLoop, nestedUnorderedLoop))
       return mlir::failure();
 
-    llvm::SmallVector<mlir::Value> nestedLiveIns;
-    collectLoopLiveIns(nestedUnorderedLoop, nestedLiveIns);
-
-    llvm::DenseSet<mlir::Value> outerLiveInsSet;
-    llvm::DenseSet<mlir::Value> nestedLiveInsSet;
-
-    // Returns a "unified" view of an mlir::Value. This utility checks if the
-    // value is defined by an op, and if so, return the first value defined by
-    // that op (if there are many), otherwise just returns the value.
-    //
-    // This serves the purpose that if, for example, `%op_res#0` is used in the
-    // outer loop and `%op_res#1` is used in the nested loop (or vice versa),
-    // that we detect both as the same value. If we did not do so, we might
-    // falesely detect that the 2 loops are not perfectly nested since they use
-    // "different" sets of values.
-    auto getUnifiedLiveInView = [](mlir::Value liveIn) {
-      return liveIn.getDefiningOp() != nullptr
-                 ? liveIn.getDefiningOp()->getResult(0)
-                 : liveIn;
-    };
-
-    // Re-package both lists of live-ins into sets so that we can use set
-    // equality to compare the values used in the outerloop vs. the nestd one.
-
-    for (auto liveIn : nestedLiveIns)
-      nestedLiveInsSet.insert(getUnifiedLiveInView(liveIn));
-
-    mlir::Value outerLoopIV;
-    for (auto liveIn : outerLoopLiveIns) {
-      outerLiveInsSet.insert(getUnifiedLiveInView(liveIn));
-
-      // Keep track of the IV of the outerloop. See `isPerfectlyNested` for more
-      // info on the reason.
-      if (outerLoopIV == nullptr)
-        outerLoopIV = getUnifiedLiveInView(liveIn);
-    }
-
-    // For the 2 loops to be perfectly nested, either:
-    // * both would have exactly the same set of live-in values or,
-    // * the outer loop would have exactly 1 extra live-in value: the outer
-    //   loop's induction variable; this happens when the outer loop's IV is
-    //   *not* referenced in the nested loop.
-    bool isPerfectlyNested = [&]() {
-      if (outerLiveInsSet == nestedLiveInsSet)
-        return true;
-
-      if ((outerLiveInsSet.size() == nestedLiveIns.size() + 1) &&
-          !nestedLiveInsSet.contains(outerLoopIV))
-        return true;
-
-      return false;
-    }();
-
-    if (!isPerfectlyNested)
-      return mlir::failure();
-
-    outerLoop = nestedUnorderedLoop;
-    outerLoopLiveIns = std::move(nestedLiveIns);
+    currentLoop = nestedUnorderedLoop;
   }
 
   return mlir::success();
@@ -634,10 +640,6 @@ class DoConcurrentConversion : public mlir::OpConversionPattern<fir::DoLoopOp> {
                   "defining operation.");
     }
 
-    llvm::SmallVector<mlir::Value> outermostLoopLiveIns;
-    looputils::collectLoopLiveIns(doLoop, outermostLoopLiveIns);
-    assert(!outermostLoopLiveIns.empty());
-
     looputils::LoopNestToIndVarMap loopNest;
     bool hasRemainingNestedLoops =
         failed(looputils::collectLoopNest(doLoop, loopNest));
@@ -646,15 +648,19 @@ class DoConcurrentConversion : public mlir::OpConversionPattern<fir::DoLoopOp> {
                         "Some `do concurent` loops are not perfectly-nested. "
                         "These will be serialzied.");
 
+    llvm::SmallVector<mlir::Value> loopNestLiveIns;
+    looputils::collectLoopLiveIns(loopNest.back().first, loopNestLiveIns);
+    assert(!loopNestLiveIns.empty());
+
     llvm::SetVector<mlir::Value> locals;
     looputils::collectLoopLocalValues(loopNest.back().first, locals);
     // We do not want to map "loop-local" values to the device through
     // `omp.map.info` ops. Therefore, we remove them from the list of live-ins.
-    outermostLoopLiveIns.erase(llvm::remove_if(outermostLoopLiveIns,
-                                               [&](mlir::Value liveIn) {
-                                                 return locals.contains(liveIn);
-                                               }),
-                               outermostLoopLiveIns.end());
+    loopNestLiveIns.erase(llvm::remove_if(loopNestLiveIns,
+                                          [&](mlir::Value liveIn) {
+                                            return locals.contains(liveIn);
+                                          }),
+                          loopNestLiveIns.end());
 
     looputils::sinkLoopIVArgs(rewriter, loopNest);
 
@@ -669,12 +675,12 @@ class DoConcurrentConversion : public mlir::OpConversionPattern<fir::DoLoopOp> {
       // The outermost loop will contain all the live-in values in all nested
       // loops since live-in values are collected recursively for all nested
       // ops.
-      for (mlir::Value liveIn : outermostLoopLiveIns)
+      for (mlir::Value liveIn : loopNestLiveIns)
         targetClauseOps.mapVars.push_back(
             genMapInfoOpForLiveIn(rewriter, liveIn));
 
-      targetOp = genTargetOp(doLoop.getLoc(), rewriter, mapper,
-                             outermostLoopLiveIns, targetClauseOps);
+      targetOp = genTargetOp(doLoop.getLoc(), rewriter, mapper, loopNestLiveIns,
+                             targetClauseOps);
       genTeamsOp(doLoop.getLoc(), rewriter);
     }
 
@@ -1062,10 +1068,11 @@ class DoConcurrentConversionPass
         context, mapTo == flangomp::DoConcurrentMappingKind::DCMK_Device,
         concurrentLoopsToSkip);
     mlir::ConversionTarget target(*context);
-    target.addLegalDialect<
-        fir::FIROpsDialect, hlfir::hlfirDialect, mlir::arith::ArithDialect,
-        mlir::func::FuncDialect, mlir::omp::OpenMPDialect,
-        mlir::cf::ControlFlowDialect, mlir::math::MathDialect>();
+    target
+        .addLegalDialect<fir::FIROpsDialect, hlfir::hlfirDialect,
+                         mlir::arith::ArithDialect, mlir::func::FuncDialect,
+                         mlir::omp::OpenMPDialect, mlir::cf::ControlFlowDialect,
+                         mlir::math::MathDialect, mlir::LLVM::LLVMDialect>();
 
     target.addDynamicallyLegalOp<fir::DoLoopOp>([&](fir::DoLoopOp op) {
       return !op.getUnordered() || concurrentLoopsToSkip.contains(op);
diff --git a/flang/test/Transforms/DoConcurrent/loop_nest_test.f90 b/flang/test/Transforms/DoConcurrent/loop_nest_test.f90
@@ -0,0 +1,87 @@
+! Tests loop-nest detection algorithm for do-concurrent mapping.
+
+! REQUIRES: asserts
+
+! RUN: %flang_fc1 -emit-hlfir  -fopenmp -fdo-concurrent-parallel=host \
+! RUN:   -mmlir -debug %s -o - 2> %t.log || true
+
+! RUN: FileCheck %s < %t.log
+
+program main
+  implicit none
+
+contains
+
+subroutine foo(n)
+  implicit none
+  integer :: n, m
+  integer :: i, j, k
+  integer :: x
+  integer, dimension(n) :: a
+  integer, dimension(n, n, n) :: b
+
+  ! CHECK: Loop pair starting at location
+  ! CHECK: loc("{{.*}}":[[# @LINE + 1]]:{{.*}}) is perfectly nested
+  do concurrent(i=1:n, j=1:bar(n*m, n/m))
+    a(i) = n
+  end do
+
+  ! CHECK: Loop pair starting at location
+  ! CHECK: loc("{{.*}}":[[# @LINE + 1]]:{{.*}}) is perfectly nested
+  do concurrent(i=bar(n, x):n, j=1:bar(n*m, n/m))
+    a(i) = n
+  end do
+
+  ! CHECK: Loop pair starting at location
+  ! CHECK: loc("{{.*}}":[[# @LINE + 1]]:{{.*}}) is not perfectly nested
+  do concurrent(i=bar(n, x):n)
+    do concurrent(j=1:bar(n*m, n/m))
+      a(i) = n
+    end do
+  end do
+
+  ! CHECK: Loop pair starting at location
+  ! CHECK: loc("{{.*}}":[[# @LINE + 1]]:{{.*}}) is not perfectly nested
+  do concurrent(i=1:n)
+    x = 10
+    do concurrent(j=1:m)
+      b(i,j,k) = i * j + k
+    end do
+  end do
+
+  ! CHECK: Loop pair starting at location
+  ! CHECK: loc("{{.*}}":[[# @LINE + 1]]:{{.*}}) is not perfectly nested
+  do concurrent(i=1:n)
+    do concurrent(j=1:m)
+      b(i,j,k) = i * j + k
+    end do
+    x = 10
+  end do
+
+  ! CHECK: Loop pair starting at location
+  ! CHECK: loc("{{.*}}":[[# @LINE + 1]]:{{.*}}) is not perfectly nested
+  do concurrent(i=1:n)
+    do concurrent(j=1:m)
+      b(i,j,k) = i * j + k
+      x = 10
+    end do
+  end do
+
+  ! CHECK: Loop pair starting at location
+  ! CHECK: loc("{{.*}}":[[# @LINE + 1]]:{{.*}}) is perfectly nested
+  do concurrent(i=bar(n, x):n, j=1:bar(n*m, n/m), k=1:bar(n*m, bar(n*m, n/m)))
+    a(i) = n
+  end do
+
+
+end subroutine
+
+pure function bar(n, m)
+    implicit none
+    integer, intent(in) :: n, m
+    integer :: bar
+
+    bar = n + m
+end function
+
+end program main
diff --git a/flang/test/Transforms/DoConcurrent/multiple_iteration_ranges.f90 b/flang/test/Transforms/DoConcurrent/multiple_iteration_ranges.f90
