https://github.com/ergawy updated
https://github.com/llvm/llvm-project/pull/127634
>From 090ea42681a2e0bfbb73853eb75f8e31d3adf120 Mon Sep 17 00:00:00 2001
From: ergawy <kareem.erg...@amd.com>
Date: Tue, 18 Feb 2025 06:17:17 -0600
Subject: [PATCH] [flang][OpenMP] Extend `do concurrent` mapping to multi-range
loops
Adds support for converting mulit-range loops to OpenMP (on the host
only for now). The changes here "prepare" a loop nest for collapsing by
sinking iteration variables to the innermost `fir.do_loop` op in the
nest.
---
flang/docs/DoConcurrentConversionToOpenMP.md | 29 ++++
.../OpenMP/DoConcurrentConversion.cpp | 139 +++++++++++++++++-
.../multiple_iteration_ranges.f90 | 72 +++++++++
3 files changed, 239 insertions(+), 1 deletion(-)
create mode 100644
flang/test/Transforms/DoConcurrent/multiple_iteration_ranges.f90
diff --git a/flang/docs/DoConcurrentConversionToOpenMP.md
b/flang/docs/DoConcurrentConversionToOpenMP.md
index 19611615ee9d6..ecb4428d7d3ba 100644
--- a/flang/docs/DoConcurrentConversionToOpenMP.md
+++ b/flang/docs/DoConcurrentConversionToOpenMP.md
@@ -173,6 +173,35 @@ omp.parallel {
<!-- TODO -->
+### Multi-range loops
+
+The pass currently supports multi-range loops as well. Given the following
+example:
+
+```fortran
+ do concurrent(i=1:n, j=1:m)
+ a(i,j) = i * j
+ end do
+```
+
+The generated `omp.loop_nest` operation look like:
+
+```
+omp.loop_nest (%arg0, %arg1)
+ : index = (%17, %19) to (%18, %20)
+ inclusive step (%c1_2, %c1_4) {
+ fir.store %arg0 to %private_i#1 : !fir.ref<i32>
+ fir.store %arg1 to %private_j#1 : !fir.ref<i32>
+ ...
+ omp.yield
+}
+```
+
+It is worth noting that we have privatized versions for both iteration
+variables: `i` and `j`. These are locally allocated inside the parallel/target
+OpenMP region similar to what the single-range example in previous section
+shows.
+
<!--
More details about current status will be added along with relevant parts of
the
implementation in later upstreaming patches.
diff --git a/flang/lib/Optimizer/OpenMP/DoConcurrentConversion.cpp
b/flang/lib/Optimizer/OpenMP/DoConcurrentConversion.cpp
index 7ad2229df31ce..182820047b0c3 100644
--- a/flang/lib/Optimizer/OpenMP/DoConcurrentConversion.cpp
+++ b/flang/lib/Optimizer/OpenMP/DoConcurrentConversion.cpp
@@ -30,6 +30,9 @@ namespace looputils {
struct InductionVariableInfo {
/// the operation allocating memory for iteration variable,
mlir::Operation *iterVarMemDef;
+ /// the operation(s) updating the iteration variable with the current
+ /// iteration number.
+ llvm::SetVector<mlir::Operation *> indVarUpdateOps;
};
using LoopNestToIndVarMap =
@@ -70,6 +73,47 @@ mlir::Operation *findLoopIterationVarMemDecl(fir::DoLoopOp
doLoop) {
return result.getDefiningOp();
}
+/// Collects the op(s) responsible for updating a loop's iteration variable
with
+/// the current iteration number. For example, for the input IR:
+/// ```
+/// %i = fir.alloca i32 {bindc_name = "i"}
+/// %i_decl:2 = hlfir.declare %i ...
+/// ...
+/// fir.do_loop %i_iv = %lb to %ub step %step unordered {
+/// %1 = fir.convert %i_iv : (index) -> i32
+/// fir.store %1 to %i_decl#1 : !fir.ref<i32>
+/// ...
+/// }
+/// ```
+/// this function would return the first 2 ops in the `fir.do_loop`'s region.
+llvm::SetVector<mlir::Operation *>
+extractIndVarUpdateOps(fir::DoLoopOp doLoop) {
+ mlir::Value indVar = doLoop.getInductionVar();
+ llvm::SetVector<mlir::Operation *> indVarUpdateOps;
+
+ llvm::SmallVector<mlir::Value> toProcess;
+ toProcess.push_back(indVar);
+
+ llvm::DenseSet<mlir::Value> done;
+
+ while (!toProcess.empty()) {
+ mlir::Value val = toProcess.back();
+ toProcess.pop_back();
+
+ if (!done.insert(val).second)
+ continue;
+
+ for (mlir::Operation *user : val.getUsers()) {
+ indVarUpdateOps.insert(user);
+
+ for (mlir::Value result : user->getResults())
+ toProcess.push_back(result);
+ }
+ }
+
+ return std::move(indVarUpdateOps);
+}
+
/// Loop \p innerLoop is considered perfectly-nested inside \p outerLoop iff
/// there are no operations in \p outerloop's body other than:
///
@@ -166,7 +210,9 @@ mlir::LogicalResult collectLoopNest(fir::DoLoopOp
currentLoop,
while (true) {
loopNest.insert(
{currentLoop,
- InductionVariableInfo{findLoopIterationVarMemDecl(currentLoop)}});
+ InductionVariableInfo{
+ findLoopIterationVarMemDecl(currentLoop),
+ std::move(looputils::extractIndVarUpdateOps(currentLoop))}});
llvm::SmallVector<fir::DoLoopOp> unorderedLoops;
@@ -193,6 +239,96 @@ mlir::LogicalResult collectLoopNest(fir::DoLoopOp
currentLoop,
return mlir::success();
}
+
+/// Prepares the `fir.do_loop` nest to be easily mapped to OpenMP. In
+/// particular, this function would take this input IR:
+/// ```
+/// fir.do_loop %i_iv = %i_lb to %i_ub step %i_step unordered {
+/// fir.store %i_iv to %i#1 : !fir.ref<i32>
+/// %j_lb = arith.constant 1 : i32
+/// %j_ub = arith.constant 10 : i32
+/// %j_step = arith.constant 1 : index
+///
+/// fir.do_loop %j_iv = %j_lb to %j_ub step %j_step unordered {
+/// fir.store %j_iv to %j#1 : !fir.ref<i32>
+/// ...
+/// }
+/// }
+/// ```
+///
+/// into the following form (using generic op form since the result is
+/// technically an invalid `fir.do_loop` op:
+///
+/// ```
+/// "fir.do_loop"(%i_lb, %i_ub, %i_step) <{unordered}> ({
+/// ^bb0(%i_iv: index):
+/// %j_lb = "arith.constant"() <{value = 1 : i32}> : () -> i32
+/// %j_ub = "arith.constant"() <{value = 10 : i32}> : () -> i32
+/// %j_step = "arith.constant"() <{value = 1 : index}> : () -> index
+///
+/// "fir.do_loop"(%j_lb, %j_ub, %j_step) <{unordered}> ({
+/// ^bb0(%new_i_iv: index, %new_j_iv: index):
+/// "fir.store"(%new_i_iv, %i#1) : (i32, !fir.ref<i32>) -> ()
+/// "fir.store"(%new_j_iv, %j#1) : (i32, !fir.ref<i32>) -> ()
+/// ...
+/// })
+/// ```
+///
+/// What happened to the loop nest is the following:
+///
+/// * the innermost loop's entry block was updated from having one operand to
+/// having `n` operands where `n` is the number of loops in the nest,
+///
+/// * the outer loop(s)' ops that update the IVs were sank inside the innermost
+/// loop (see the `"fir.store"(%new_i_iv, %i#1)` op above),
+///
+/// * the innermost loop's entry block's arguments were mapped in order from
the
+/// outermost to the innermost IV.
+///
+/// With this IR change, we can directly inline the innermost loop's region
into
+/// the newly generated `omp.loop_nest` op.
+///
+/// Note that this function has a pre-condition that \p loopNest consists of
+/// perfectly nested loops; i.e. there are no in-between ops between 2 nested
+/// loops except for the ops to setup the inner loop's LB, UB, and step. These
+/// ops are handled/cloned by `genLoopNestClauseOps(..)`.
+void sinkLoopIVArgs(mlir::ConversionPatternRewriter &rewriter,
+ looputils::LoopNestToIndVarMap &loopNest) {
+ if (loopNest.size() <= 1)
+ return;
+
+ fir::DoLoopOp innermostLoop = loopNest.back().first;
+ mlir::Operation &innermostFirstOp =
innermostLoop.getRegion().front().front();
+
+ llvm::SmallVector<mlir::Type> argTypes;
+ llvm::SmallVector<mlir::Location> argLocs;
+
+ for (auto &[doLoop, indVarInfo] : llvm::drop_end(loopNest)) {
+ // Sink the IV update ops to the innermost loop. We need to do for all
loops
+ // except for the innermost one, hence the `drop_end` usage above.
+ for (mlir::Operation *op : indVarInfo.indVarUpdateOps)
+ op->moveBefore(&innermostFirstOp);
+
+ argTypes.push_back(doLoop.getInductionVar().getType());
+ argLocs.push_back(doLoop.getInductionVar().getLoc());
+ }
+
+ mlir::Region &innermmostRegion = innermostLoop.getRegion();
+ // Extend the innermost entry block with arguments to represent the outer
IVs.
+ innermmostRegion.addArguments(argTypes, argLocs);
+
+ unsigned idx = 1;
+ // In reverse, remap the IVs of the loop nest from the old values to the new
+ // ones. We do that in reverse since the first argument before this loop is
+ // the old IV for the innermost loop. Therefore, we want to replace it first
+ // before the old value (1st argument in the block) is remapped to be the IV
+ // of the outermost loop in the nest.
+ for (auto &[doLoop, _] : llvm::reverse(loopNest)) {
+ doLoop.getInductionVar().replaceAllUsesWith(
+ innermmostRegion.getArgument(innermmostRegion.getNumArguments() -
idx));
+ ++idx;
+ }
+}
} // namespace looputils
class DoConcurrentConversion : public mlir::OpConversionPattern<fir::DoLoopOp>
{
@@ -219,6 +355,7 @@ class DoConcurrentConversion : public
mlir::OpConversionPattern<fir::DoLoopOp> {
"Some `do concurent` loops are not perfectly-nested. "
"These will be serialized.");
+ looputils::sinkLoopIVArgs(rewriter, loopNest);
mlir::IRMapping mapper;
genParallelOp(doLoop.getLoc(), rewriter, loopNest, mapper);
mlir::omp::LoopNestOperands loopNestClauseOps;
diff --git a/flang/test/Transforms/DoConcurrent/multiple_iteration_ranges.f90
b/flang/test/Transforms/DoConcurrent/multiple_iteration_ranges.f90
new file mode 100644
index 0000000000000..232420fb07a75
--- /dev/null
+++ b/flang/test/Transforms/DoConcurrent/multiple_iteration_ranges.f90
@@ -0,0 +1,72 @@
+! Tests mapping of a `do concurrent` loop with multiple iteration ranges.
+
+! RUN: split-file %s %t
+
+! RUN: %flang_fc1 -emit-hlfir -fopenmp -fdo-concurrent-to-openmp=host
%t/multi_range.f90 -o - \
+! RUN: | FileCheck %s
+
+!--- multi_range.f90
+program main
+ integer, parameter :: n = 20
+ integer, parameter :: m = 40
+ integer, parameter :: l = 60
+ integer :: a(n, m, l)
+
+ do concurrent(i=3:n, j=5:m, k=7:l)
+ a(i,j,k) = i * j + k
+ end do
+end
+
+! CHECK: func.func @_QQmain
+
+! CHECK: %[[C3:.*]] = arith.constant 3 : i32
+! CHECK: %[[LB_I:.*]] = fir.convert %[[C3]] : (i32) -> index
+! CHECK: %[[C20:.*]] = arith.constant 20 : i32
+! CHECK: %[[UB_I:.*]] = fir.convert %[[C20]] : (i32) -> index
+! CHECK: %[[STEP_I:.*]] = arith.constant 1 : index
+
+! CHECK: %[[C5:.*]] = arith.constant 5 : i32
+! CHECK: %[[LB_J:.*]] = fir.convert %[[C5]] : (i32) -> index
+! CHECK: %[[C40:.*]] = arith.constant 40 : i32
+! CHECK: %[[UB_J:.*]] = fir.convert %[[C40]] : (i32) -> index
+! CHECK: %[[STEP_J:.*]] = arith.constant 1 : index
+
+! CHECK: %[[C7:.*]] = arith.constant 7 : i32
+! CHECK: %[[LB_K:.*]] = fir.convert %[[C7]] : (i32) -> index
+! CHECK: %[[C60:.*]] = arith.constant 60 : i32
+! CHECK: %[[UB_K:.*]] = fir.convert %[[C60]] : (i32) -> index
+! CHECK: %[[STEP_K:.*]] = arith.constant 1 : index
+
+! CHECK: omp.parallel {
+
+! CHECK-NEXT: %[[ITER_VAR_I:.*]] = fir.alloca i32 {bindc_name = "i"}
+! CHECK-NEXT: %[[BINDING_I:.*]]:2 = hlfir.declare %[[ITER_VAR_I]] {uniq_name =
"_QFEi"}
+
+! CHECK-NEXT: %[[ITER_VAR_J:.*]] = fir.alloca i32 {bindc_name = "j"}
+! CHECK-NEXT: %[[BINDING_J:.*]]:2 = hlfir.declare %[[ITER_VAR_J]] {uniq_name =
"_QFEj"}
+
+! CHECK-NEXT: %[[ITER_VAR_K:.*]] = fir.alloca i32 {bindc_name = "k"}
+! CHECK-NEXT: %[[BINDING_K:.*]]:2 = hlfir.declare %[[ITER_VAR_K]] {uniq_name =
"_QFEk"}
+
+! CHECK: omp.wsloop {
+! CHECK-NEXT: omp.loop_nest
+! CHECK-SAME: (%[[ARG0:[^[:space:]]+]], %[[ARG1:[^[:space:]]+]],
%[[ARG2:[^[:space:]]+]])
+! CHECK-SAME: : index = (%[[LB_I]], %[[LB_J]], %[[LB_K]])
+! CHECK-SAME: to (%[[UB_I]], %[[UB_J]], %[[UB_K]]) inclusive
+! CHECK-SAME: step (%[[STEP_I]], %[[STEP_J]], %[[STEP_K]]) {
+
+! CHECK-NEXT: %[[IV_IDX_I:.*]] = fir.convert %[[ARG0]]
+! CHECK-NEXT: fir.store %[[IV_IDX_I]] to %[[BINDING_I]]#1
+
+! CHECK-NEXT: %[[IV_IDX_J:.*]] = fir.convert %[[ARG1]]
+! CHECK-NEXT: fir.store %[[IV_IDX_J]] to %[[BINDING_J]]#1
+
+! CHECK-NEXT: %[[IV_IDX_K:.*]] = fir.convert %[[ARG2]]
+! CHECK-NEXT: fir.store %[[IV_IDX_K]] to %[[BINDING_K]]#1
+
+! CHECK: omp.yield
+! CHECK-NEXT: }
+! CHECK-NEXT: }
+
+! CHECK-NEXT: omp.terminator
+! CHECK-NEXT: }
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