[flang][OpenMP] Upstream do concurrent loop-nest detection.

flang/docs/DoConcurrentConversionToOpenMP.md

@@ -53,6 +53,79 @@ that:
 * It has been tested in a very limited way so far.
 * It has been tested mostly on simple synthetic inputs.
 
+### Loop nest detection
+
+On the `FIR` dialect level, the following loop:
+```fortran
+  do concurrent(i=1:n, j=1:m, k=1:o)
+    a(i,j,k) = i + j + k
+  end do
+```
+is modelled as a nest of `fir.do_loop` ops such that an outer loop's region
+contains **only** the following:
+  1. The operations needed to assign/update the outer loop's induction variable.
+  1. The inner loop itself.
+
+So the MLIR structure for the above example looks similar to the following:
+```
+  fir.do_loop %i_idx = %34 to %36 step %c1 unordered {
+    %i_idx_2 = fir.convert %i_idx : (index) -> i32
+    fir.store %i_idx_2 to %i_iv#1 : !fir.ref<i32>
+
+    fir.do_loop %j_idx = %37 to %39 step %c1_3 unordered {
+      %j_idx_2 = fir.convert %j_idx : (index) -> i32
+      fir.store %j_idx_2 to %j_iv#1 : !fir.ref<i32>
+
+      fir.do_loop %k_idx = %40 to %42 step %c1_5 unordered {
+        %k_idx_2 = fir.convert %k_idx : (index) -> i32
+        fir.store %k_idx_2 to %k_iv#1 : !fir.ref<i32>
+
+        ... loop nest body goes here ...
+      }
+    }
+  }
+```
+This applies to multi-range loops in general; they are represented in the IR as
+a nest of `fir.do_loop` ops with the above nesting structure.
+
+Therefore, the pass detects such "perfectly" nested loop ops to identify multi-range
+loops and map them as "collapsed" loops in OpenMP.
+
+#### Further info regarding loop nest detection
+
+Loop nest detection is currently limited to the scenario described in the previous
+section. However, this is quite limited and can be extended in the future to cover
+more cases. At the moment, for the following loop nest, even though both loops are
+perfectly nested, only the outer loop is parallelized:
+```fortran
+do concurrent(i=1:n)
+  do concurrent(j=1:m)
+    a(i,j) = i * j
+  end do
+end do
+```
+
+Similarly, for the following loop nest, even though the intervening statement `x = 41`
+does not have any memory effects that would affect parallelization, this nest is
+not parallelized either (only the outer loop is).
+
+```fortran
+do concurrent(i=1:n)
+  x = 41
+  do concurrent(j=1:m)
+    a(i,j) = i * j
+  end do
+end do
+```
+
+The above also has the consequence that the `j` variable will **not** be
+privatized in the OpenMP parallel/target region. In other words, it will be
+treated as if it was a `shared` variable. For more details about privatization,
+see the "Data environment" section below.
+
+See `flang/test/Transforms/DoConcurrent/loop_nest_test.f90` for more examples
+of what is and is not detected as a perfect loop nest.
+
 <!--
 More details about current status will be added along with relevant parts of the
 implementation in later upstreaming patches.
@@ -63,6 +136,17 @@ implementation in later upstreaming patches.
 This section describes some of the open questions/issues that are not tackled yet
 even in the downstream implementation.
 
+### Separate MLIR op for `do concurrent`
+
+At the moment, both increment and concurrent loops are represented by one MLIR
+op: `fir.do_loop`; where we differentiate concurrent loops with the `unordered`
+attribute. This is not ideal since the `fir.do_loop` op support only single
+iteration ranges. Consequently, to model multi-range `do concurrent` loops, flang
+emits a nest of `fir.do_loop` ops which we have to detect in the OpenMP conversion
+pass to handle multi-range loops. Instead, it would better to model multi-range
+concurrent loops using a separate op which the IR more representative of the input
+Fortran code and also easier to detect and transform.
+
 ### Delayed privatization
 
 So far, we emit the privatization logic for IVs inline in the parallel/target
@@ -150,6 +234,7 @@ targeting OpenMP.
 - [x] Command line options for `flang` and `bbc`.
 - [x] Conversion pass skeleton (no transormations happen yet).
 - [x] Status description and tracking document (this document).
+- [x] Loop nest detection to identify multi-range loops.
 - [ ] Basic host/CPU mapping support.
 - [ ] Basic device/GPU mapping support.
 - [ ] More advanced host and device support (expaned to multiple items as needed).

flang/lib/Optimizer/OpenMP/DoConcurrentConversion.cpp

@@ -9,8 +9,10 @@
 #include "flang/Optimizer/Dialect/FIROps.h"
 #include "flang/Optimizer/OpenMP/Passes.h"
 #include "flang/Optimizer/OpenMP/Utils.h"
+#include "mlir/Analysis/SliceAnalysis.h"
 #include "mlir/Dialect/OpenMP/OpenMPDialect.h"
 #include "mlir/Transforms/DialectConversion.h"
+#include "mlir/Transforms/RegionUtils.h"
 
 namespace flangomp {
 #define GEN_PASS_DEF_DOCONCURRENTCONVERSIONPASS
@@ -21,6 +23,131 @@ namespace flangomp {
 #define DBGS() (llvm::dbgs() << "[" DEBUG_TYPE << "]: ")
 
 namespace {
+namespace looputils {
+using LoopNest = llvm::SetVector<fir::DoLoopOp>;
+
+/// Loop \p innerLoop is considered perfectly-nested inside \p outerLoop iff
+/// there are no operations in \p outerloop's body other than:
+///
+/// 1. the operations needed to assign/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::ForwardSliceOptions forwardSliceOptions;
+  forwardSliceOptions.inclusive = true;
+  // The following will be used as an example to clarify the internals of this
+  // function:
+  // ```
+  // 1. fir.do_loop %i_idx = %34 to %36 step %c1 unordered {
+  // 2.   %i_idx_2 = fir.convert %i_idx : (index) -> i32
+  // 3.   fir.store %i_idx_2 to %i_iv#1 : !fir.ref<i32>
+  //
+  // 4.   fir.do_loop %j_idx = %37 to %39 step %c1_3 unordered {
+  // 5.     %j_idx_2 = fir.convert %j_idx : (index) -> i32
+  // 6.     fir.store %j_idx_2 to %j_iv#1 : !fir.ref<i32>
+  //        ... loop nest body, possible uses %i_idx ...
+  //      }
+  //    }
+  // ```
+  // In this example, the `j` loop is perfectly nested inside the `i` loop and
+  // below is how we find that.
+
+  // We don't care about the outer-loop's induction variable's uses within the
+  // inner-loop, so we filter out these uses.
+  //
+  // This filter tells `getForwardSlice` (below) to only collect operations
+  // which produce results defined above (i.e. outside) the inner-loop's body.
+  //
+  // Since `outerLoop.getInductionVar()` is a block argument (to the
+  // outer-loop's body), the filter effectively collects uses of
+  // `outerLoop.getInductionVar()` inside the outer-loop but outside the
+  // inner-loop.
+  forwardSliceOptions.filter = [&](mlir::Operation *op) {
+    return mlir::areValuesDefinedAbove(op->getResults(), innerLoop.getRegion());
+  };
+
+  llvm::SetVector<mlir::Operation *> indVarSlice;
+  // The forward slice of the `i` loop's IV will be the 2 ops in line 1 & 2
+  // above. Uses of `%i_idx` inside the `j` loop are not collected because of
+  // the filter.
+  mlir::getForwardSlice(outerLoop.getInductionVar(), &indVarSlice,
+                        forwardSliceOptions);
+  llvm::DenseSet<mlir::Operation *> indVarSet(indVarSlice.begin(),
+                                              indVarSlice.end());
+
+  llvm::DenseSet<mlir::Operation *> outerLoopBodySet;
+  // The following walk collects ops inside `outerLoop` that are **not**:
+  // * the outer-loop itself,
+  // * or the inner-loop,
+  // * or the `fir.result` op (the outer-loop's terminator).
+  //
+  // For the above example, this will also populate `outerLoopBodySet` with ops
+  // in line 1 & 2 since we skip the `i` loop, the `j` loop, and the terminator.
+  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();
+
+    outerLoopBodySet.insert(op);
+    return mlir::WalkResult::advance();
+  });
+
+  // If `outerLoopBodySet` ends up having the same ops as `indVarSet`, then
+  // `outerLoop` only contains ops that setup its induction variable +
+  // `innerLoop` + the `fir.result` terminator. In other words, `innerLoop` is
+  // perfectly nested inside `outerLoop`.
+  bool result = (outerLoopBodySet == indVarSet);
+  mlir::Location loc = outerLoop.getLoc();
+  LLVM_DEBUG(DBGS() << "Loop pair starting at location " << loc << " is"
+                    << (result ? "" : " not") << " perfectly nested\n");
+
+  return result;
+}
+
+/// Starting with `currentLoop` 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 currentLoop,
+                                    LoopNest &loopNest) {
+  assert(currentLoop.getUnordered());
+
+  while (true) {
+    loopNest.insert(currentLoop);
+    llvm::SmallVector<fir::DoLoopOp> unorderedLoops;
+
+    for (auto nestedLoop : currentLoop.getRegion().getOps<fir::DoLoopOp>())
+      if (nestedLoop.getUnordered())
+        unorderedLoops.push_back(nestedLoop);
+
+    if (unorderedLoops.empty())
+      break;
+
+    // Having more than one unordered loop means that we are not dealing with a
+    // perfect loop nest (i.e. a mulit-range `do concurrent` loop); which is the
+    // case we are after here.
+    if (unorderedLoops.size() > 1)
+      return mlir::failure();
+
+    fir::DoLoopOp nestedUnorderedLoop = unorderedLoops.front();
+
+    if (!isPerfectlyNested(currentLoop, nestedUnorderedLoop))
+      return mlir::failure();
+
+    currentLoop = nestedUnorderedLoop;
+  }
+
+  return mlir::success();
+}
+} // namespace looputils
+
 class DoConcurrentConversion : public mlir::OpConversionPattern<fir::DoLoopOp> {
 public:
   using mlir::OpConversionPattern<fir::DoLoopOp>::OpConversionPattern;
@@ -31,6 +158,14 @@ class DoConcurrentConversion : public mlir::OpConversionPattern<fir::DoLoopOp> {
   mlir::LogicalResult
   matchAndRewrite(fir::DoLoopOp doLoop, OpAdaptor adaptor,
                   mlir::ConversionPatternRewriter &rewriter) const override {
+    looputils::LoopNest loopNest;
+    bool hasRemainingNestedLoops =
+        failed(looputils::collectLoopNest(doLoop, loopNest));
+    if (hasRemainingNestedLoops)
+      mlir::emitWarning(doLoop.getLoc(),
+                        "Some `do concurent` loops are not perfectly-nested. "
+                        "These will be serialized.");
+
     // TODO This will be filled in with the next PRs that upstreams the rest of
     // the ROCm implementaion.
     return mlir::success();

flang/test/Transforms/DoConcurrent/loop_nest_test.f90

@@ -0,0 +1,89 @@
+! Tests loop-nest detection algorithm for do-concurrent mapping.
+
+! REQUIRES: asserts
+
+! RUN: %flang_fc1 -emit-hlfir  -fopenmp -fdo-concurrent-to-openmp=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
+
+  ! Verify the (i,j) and (j,k) pairs of loops are detected as perfectly nested.
+  !
+  ! CHECK: Loop pair starting at location
+  ! CHECK: loc("{{.*}}":[[# @LINE + 3]]:{{.*}}) is perfectly nested
+  ! 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