[flang][fir] Support promoting `fir.do_loop` with results to `affine.for`. #137790

NexMing · 2025-04-29T11:41:31Z

No description provided.

llvmbot · 2025-04-29T11:42:05Z

@llvm/pr-subscribers-flang-fir-hlfir

Author: MingYan (NexMing)

Changes

Full diff: https://github.com/llvm/llvm-project/pull/137790.diff

2 Files Affected:

(modified) flang/lib/Optimizer/Transforms/AffinePromotion.cpp (+34-5)
(modified) flang/test/Fir/affine-promotion.fir (+65)

diff --git a/flang/lib/Optimizer/Transforms/AffinePromotion.cpp b/flang/lib/Optimizer/Transforms/AffinePromotion.cpp
index 43fccf52dc8ab..ef82e400bea14 100644
--- a/flang/lib/Optimizer/Transforms/AffinePromotion.cpp
+++ b/flang/lib/Optimizer/Transforms/AffinePromotion.cpp
@@ -49,8 +49,9 @@ struct AffineIfAnalysis;
 /// second when doing rewrite.
 struct AffineFunctionAnalysis {
   explicit AffineFunctionAnalysis(mlir::func::FuncOp funcOp) {
-    for (fir::DoLoopOp op : funcOp.getOps<fir::DoLoopOp>())
-      loopAnalysisMap.try_emplace(op, op, *this);
+    funcOp->walk([&](fir::DoLoopOp doloop) {
+      loopAnalysisMap.try_emplace(doloop, doloop, *this);
+    });
   }
 
   AffineLoopAnalysis getChildLoopAnalysis(fir::DoLoopOp op) const;
@@ -102,10 +103,23 @@ struct AffineLoopAnalysis {
     return true;
   }
 
+  bool analysisResults(fir::DoLoopOp loopOperation) {
+    if (loopOperation.getFinalValue() &&
+        !loopOperation.getResult(0).use_empty()) {
+      LLVM_DEBUG(
+          llvm::dbgs()
+              << "AffineLoopAnalysis: cannot promote loop final value\n";);
+      return false;
+    }
+
+    return true;
+  }
+
   bool analyzeLoop(fir::DoLoopOp loopOperation,
                    AffineFunctionAnalysis &functionAnalysis) {
     LLVM_DEBUG(llvm::dbgs() << "AffineLoopAnalysis: \n"; loopOperation.dump(););
     return analyzeMemoryAccess(loopOperation) &&
+           analysisResults(loopOperation) &&
            analyzeBody(loopOperation, functionAnalysis);
   }
 
@@ -461,14 +475,28 @@ class AffineLoopConversion : public mlir::OpRewritePattern<fir::DoLoopOp> {
     LLVM_ATTRIBUTE_UNUSED auto loopAnalysis =
         functionAnalysis.getChildLoopAnalysis(loop);
     auto &loopOps = loop.getBody()->getOperations();
+    auto resultOp = cast<fir::ResultOp>(loop.getBody()->getTerminator());
+    auto results = resultOp.getOperands();
+    auto loopResults = loop->getResults();
     auto loopAndIndex = createAffineFor(loop, rewriter);
     auto affineFor = loopAndIndex.first;
     auto inductionVar = loopAndIndex.second;
 
+    if (loop.getFinalValue()) {
+      results = results.drop_front();
+      loopResults = loopResults.drop_front();
+    }
+
     rewriter.startOpModification(affineFor.getOperation());
     affineFor.getBody()->getOperations().splice(
         std::prev(affineFor.getBody()->end()), loopOps, loopOps.begin(),
         std::prev(loopOps.end()));
+    rewriter.replaceAllUsesWith(loop.getRegionIterArgs(),
+                                affineFor.getRegionIterArgs());
+    if (!results.empty()) {
+      rewriter.setInsertionPointToEnd(affineFor.getBody());
+      rewriter.create<affine::AffineYieldOp>(resultOp->getLoc(), results);
+    }
     rewriter.finalizeOpModification(affineFor.getOperation());
 
     rewriter.startOpModification(loop.getOperation());
@@ -479,7 +507,8 @@ class AffineLoopConversion : public mlir::OpRewritePattern<fir::DoLoopOp> {
 
     LLVM_DEBUG(llvm::dbgs() << "AffineLoopConversion: loop rewriten to:\n";
                affineFor.dump(););
-    rewriter.replaceOp(loop, affineFor.getOperation()->getResults());
+    rewriter.replaceAllUsesWith(loopResults, affineFor->getResults());
+    rewriter.eraseOp(loop);
     return success();
   }
 
@@ -503,7 +532,7 @@ class AffineLoopConversion : public mlir::OpRewritePattern<fir::DoLoopOp> {
         ValueRange(op.getUpperBound()),
         mlir::AffineMap::get(0, 1,
                              1 + mlir::getAffineSymbolExpr(0, op.getContext())),
-        step);
+        step, op.getIterOperands());
     return std::make_pair(affineFor, affineFor.getInductionVar());
   }
 
@@ -528,7 +557,7 @@ class AffineLoopConversion : public mlir::OpRewritePattern<fir::DoLoopOp> {
         genericUpperBound.getResult(),
         mlir::AffineMap::get(0, 1,
                              1 + mlir::getAffineSymbolExpr(0, op.getContext())),
-        1);
+        1, op.getIterOperands());
     rewriter.setInsertionPointToStart(affineFor.getBody());
     auto actualIndex = rewriter.create<affine::AffineApplyOp>(
         op.getLoc(), actualIndexMap,
diff --git a/flang/test/Fir/affine-promotion.fir b/flang/test/Fir/affine-promotion.fir
index aae35c6ef5659..f50f851a89eae 100644
--- a/flang/test/Fir/affine-promotion.fir
+++ b/flang/test/Fir/affine-promotion.fir
@@ -131,3 +131,68 @@ func.func @loop_with_if(%a: !arr_d1, %v: f32) {
 // CHECK:   }
 // CHECK:   return
 // CHECK: }
+
+func.func @loop_with_result(%arg0: !fir.ref<!fir.array<100xf32>>, %arg1: !fir.ref<!fir.array<100x100xf32>>) -> f32 {
+  %c1 = arith.constant 1 : index
+  %cst = arith.constant 0.000000e+00 : f32
+  %c100 = arith.constant 100 : index
+  %0 = fir.shape %c100 : (index) -> !fir.shape<1>
+  %1 = fir.shape %c100, %c100 : (index, index) -> !fir.shape<2>
+  %2 = fir.alloca i32
+  %3:2 = fir.do_loop %arg2 = %c1 to %c100 step %c1 iter_args(%arg3 = %cst) -> (index, f32) {
+    %6 = fir.array_coor %arg0(%0) %arg2 : (!fir.ref<!fir.array<100xf32>>, !fir.shape<1>, index) -> !fir.ref<f32>
+    %7 = fir.load %6 : !fir.ref<f32>
+    %8 = arith.addf %arg3, %7 fastmath<contract> : f32
+    %9 = arith.addi %arg2, %c1 overflow<nsw> : index
+    fir.result %9, %8 : index, f32
+  }
+  %4:2 = fir.do_loop %arg2 = %c1 to %c100 step %c1 iter_args(%arg3 = %3#1) -> (index, f32) {
+    %6 = fir.array_coor %arg1(%1) %c1, %arg2 : (!fir.ref<!fir.array<100x100xf32>>, !fir.shape<2>, index, index) -> !fir.ref<f32>
+    %7 = fir.convert %6 : (!fir.ref<f32>) -> !fir.ref<!fir.array<100xf32>>
+    %8 = fir.do_loop %arg4 = %c1 to %c100 step %c1 iter_args(%arg5 = %arg3) -> (f32) {
+      %10 = fir.array_coor %7(%0) %arg4 : (!fir.ref<!fir.array<100xf32>>, !fir.shape<1>, index) -> !fir.ref<f32>
+      %11 = fir.load %10 : !fir.ref<f32>
+      %12 = arith.addf %arg5, %11 fastmath<contract> : f32
+      fir.result %12 : f32
+    }
+    %9 = arith.addi %arg2, %c1 overflow<nsw> : index
+    fir.result %9, %8 : index, f32
+  }
+  %5 = fir.convert %4#0 : (index) -> i32
+  fir.store %5 to %2 : !fir.ref<i32>
+  return %4#1 : f32
+}
+
+// CHECK-LABEL:   func.func @loop_with_result(
+// CHECK-SAME:      %[[ARG0:.*]]: !fir.ref<!fir.array<100xf32>>,
+// CHECK-SAME:      %[[ARG1:.*]]: !fir.ref<!fir.array<100x100xf32>>) -> f32 {
+// CHECK:           %[[VAL_0:.*]] = arith.constant 1 : index
+// CHECK:           %[[VAL_1:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK:           %[[VAL_2:.*]] = arith.constant 100 : index
+// CHECK:           %[[VAL_3:.*]] = fir.shape %[[VAL_2]] : (index) -> !fir.shape<1>
+// CHECK:           %[[VAL_4:.*]] = fir.shape %[[VAL_2]], %[[VAL_2]] : (index, index) -> !fir.shape<2>
+// CHECK:           %[[VAL_5:.*]] = fir.alloca i32
+// CHECK:           %[[VAL_6:.*]] = fir.convert %[[ARG0]] : (!fir.ref<!fir.array<100xf32>>) -> memref<?xf32>
+// CHECK:           %[[VAL_7:.*]] = affine.for %[[VAL_8:.*]] = %[[VAL_0]] to #{{.*}}(){{\[}}%[[VAL_2]]] iter_args(%[[VAL_9:.*]] = %[[VAL_1]]) -> (f32) {
+// CHECK:             %[[VAL_10:.*]] = affine.apply #{{.*}}(%[[VAL_8]]){{\[}}%[[VAL_0]], %[[VAL_2]], %[[VAL_0]]]
+// CHECK:             %[[VAL_11:.*]] = affine.load %[[VAL_6]]{{\[}}%[[VAL_10]]] : memref<?xf32>
+// CHECK:             %[[VAL_12:.*]] = arith.addf %[[VAL_9]], %[[VAL_11]] fastmath<contract> : f32
+// CHECK:             affine.yield %[[VAL_12]] : f32
+// CHECK:           }
+// CHECK:           %[[VAL_13:.*]]:2 = fir.do_loop %[[VAL_14:.*]] = %[[VAL_0]] to %[[VAL_2]] step %[[VAL_0]] iter_args(%[[VAL_15:.*]] = %[[VAL_7]]) -> (index, f32) {
+// CHECK:             %[[VAL_16:.*]] = fir.array_coor %[[ARG1]](%[[VAL_4]]) %[[VAL_0]], %[[VAL_14]] : (!fir.ref<!fir.array<100x100xf32>>, !fir.shape<2>, index, index) -> !fir.ref<f32>
+// CHECK:             %[[VAL_17:.*]] = fir.convert %[[VAL_16]] : (!fir.ref<f32>) -> !fir.ref<!fir.array<100xf32>>
+// CHECK:             %[[VAL_18:.*]] = fir.convert %[[VAL_17]] : (!fir.ref<!fir.array<100xf32>>) -> memref<?xf32>
+// CHECK:             %[[VAL_19:.*]] = affine.for %[[VAL_20:.*]] = %[[VAL_0]] to #{{.*}}(){{\[}}%[[VAL_2]]] iter_args(%[[VAL_21:.*]] = %[[VAL_15]]) -> (f32) {
+// CHECK:               %[[VAL_22:.*]] = affine.apply #{{.*}}(%[[VAL_20]]){{\[}}%[[VAL_0]], %[[VAL_2]], %[[VAL_0]]]
+// CHECK:               %[[VAL_23:.*]] = affine.load %[[VAL_18]]{{\[}}%[[VAL_22]]] : memref<?xf32>
+// CHECK:               %[[VAL_24:.*]] = arith.addf %[[VAL_21]], %[[VAL_23]] fastmath<contract> : f32
+// CHECK:               affine.yield %[[VAL_24]] : f32
+// CHECK:             }
+// CHECK:             %[[VAL_25:.*]] = arith.addi %[[VAL_14]], %[[VAL_0]] overflow<nsw> : index
+// CHECK:             fir.result %[[VAL_25]], %[[VAL_19]] : index, f32
+// CHECK:           }
+// CHECK:           %[[VAL_26:.*]] = fir.convert %[[VAL_27:.*]]#0 : (index) -> i32
+// CHECK:           fir.store %[[VAL_26]] to %[[VAL_5]] : !fir.ref<i32>
+// CHECK:           return %[[VAL_27]]#1 : f32
+// CHECK:         }

tblah · 2025-04-29T16:46:31Z

Thank you for contributing this. After this patch, do you know of any remaining limitations of the affine promotion pass?

And if you don't mind me asking, what are you using this for?

NexMing · 2025-05-01T06:00:10Z

Thank you for contributing this. After this patch, do you know of any remaining limitations of the affine promotion pass?

And if you don't mind me asking, what are you using this for?

I haven't done detailed testing, so I'm not sure how many features are still missing.
Currently, I'm planning to promote to Affine for some advanced optimizations. I've already successfully tried one optimization path: FIR → Affine (Optimization) → SCF → OMP → LLVM, to automatically apply OpenMP parallelization on loops.

tblah

Thank you for upstreaming this. Just a minor comment from me.

This pass was always experimental so I don't think this needs to work in every case before it can be merged.

If you plan to upstream your whole optimization pipeline (very welcome!) then please create an RFC at https://discourse.llvm.org/c/subprojects/flang/33

flang/lib/Optimizer/Transforms/AffinePromotion.cpp

tblah

LGTM, thanks!

llvmbot added flang Flang issues not falling into any other category flang:fir-hlfir labels Apr 29, 2025

tblah reviewed May 1, 2025

View reviewed changes

flang/lib/Optimizer/Transforms/AffinePromotion.cpp Show resolved Hide resolved

NexMing requested a review from tblah May 7, 2025 05:28

[fir] Support promoting fir.do_loop with results to affine.for.

17dfda2

NexMing force-pushed the fir-to-affine branch from 87e12b8 to 17dfda2 Compare May 8, 2025 05:50

Add a test that loop with multiple results.

98746e8

NexMing force-pushed the fir-to-affine branch from 2b6bcf2 to 98746e8 Compare May 8, 2025 07:52

NexMing changed the title ~~[fir] Support promoting fir.do_loop with results to affine.for.~~ [flang][fir] Support promoting fir.do_loop with results to affine.for. May 8, 2025

tblah approved these changes May 8, 2025

View reviewed changes

NexMing merged commit db2d576 into llvm:main May 9, 2025
11 checks passed

NexMing deleted the fir-to-affine branch May 9, 2025 02:55

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[flang][fir] Support promoting `fir.do_loop` with results to `affine.for`. #137790

[flang][fir] Support promoting `fir.do_loop` with results to `affine.for`. #137790

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[flang][fir] Support promoting fir.do_loop with results to affine.for. #137790

[flang][fir] Support promoting fir.do_loop with results to affine.for. #137790

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[flang][fir] Support promoting `fir.do_loop` with results to `affine.for`. #137790

[flang][fir] Support promoting `fir.do_loop` with results to `affine.for`. #137790

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