[mlir][scf] Considering affine.apply when fusing scf::ParallelOp

When checking the load indices of the second loop coincide with the
store indices of the first loop, it only considers the index values are
the same or not. However, there are some cases the index values come
from affine.apply operator. In these cases, it will treat them as
different even the affine map is the same and the affine operands are
the same.

We already check if the iteration space is the same in isFusionLegal().
When checking affine.apply, we only need to consider the operands come
from the same induction variables. If so, we know the results of
affine.apply are the same.

Author: Hsiangkai

mlir/lib/Dialect/SCF/Transforms/ParallelLoopFusion.cpp

@@ -10,6 +10,7 @@
 //
 //===----------------------------------------------------------------------===//
 
+#include "mlir/Dialect/Affine/IR/AffineOps.h"
 #include "mlir/Dialect/SCF/Transforms/Passes.h"
 
 #include "mlir/Analysis/AliasAnalysis.h"
@@ -27,6 +28,7 @@ namespace mlir {
 } // namespace mlir
 
 using namespace mlir;
+using namespace mlir::affine;
 using namespace mlir::scf;
 
 /// Verify there are no nested ParallelOps.
@@ -54,6 +56,16 @@ static bool equalIterationSpaces(ParallelOp firstPloop,
          matchOperands(firstPloop.getStep(), secondPloop.getStep());
 }
 
+static int getInductionVarIndex(Value operand, ParallelOp loop) {
+  auto indVars = loop.getInductionVars();
+  auto it = std::find(indVars.begin(), indVars.end(), operand);
+
+  if (it != indVars.end())
+    return static_cast<int>(std::distance(indVars.begin(), it));
+
+  return -1;
+}
+
 /// Checks if the parallel loops have mixed access to the same buffers. Returns
 /// `true` if the first parallel loop writes to the same indices that the second
 /// loop reads.
@@ -102,8 +114,25 @@ static bool haveNoReadsAfterWriteExceptSameIndex(
       return WalkResult::interrupt();
     for (int i = 0, e = storeIndices.size(); i < e; ++i) {
       if (firstToSecondPloopIndices.lookupOrDefault(storeIndices[i]) !=
-          loadIndices[i])
-        return WalkResult::interrupt();
+          loadIndices[i]) {
+        auto storeIndexDef = storeIndices[i].getDefiningOp<AffineApplyOp>();
+        auto loadIndexDef = loadIndices[i].getDefiningOp<AffineApplyOp>();
+        if (storeIndexDef && loadIndexDef) {
+          // When two indices come from affine.apply, we check the results of
+          // these two affine.apply are the same or not.
+          if (storeIndexDef.getAffineMap() != loadIndexDef.getAffineMap())
+            return WalkResult::interrupt();
+          if (storeIndexDef.getNumOperands() != loadIndexDef.getNumOperands())
+            return WalkResult::interrupt();
+          for (unsigned i = 0; i < storeIndexDef.getNumOperands(); ++i) {
+            if (getInductionVarIndex(storeIndexDef.getOperand(i), firstPloop) !=
+                getInductionVarIndex(loadIndexDef.getOperand(i), secondPloop))
+              return WalkResult::interrupt();
+          }
+        } else {
+          return WalkResult::interrupt();
+        }
+      }
     }
     return WalkResult::advance();
   });

mlir/test/Dialect/SCF/parallel-loop-fusion.mlir

@@ -480,3 +480,49 @@ func.func @do_not_fuse_multiple_stores_on_diff_indices(
 // CHECK:        scf.reduce
 // CHECK:      }
 // CHECK:      memref.dealloc [[SUM]]
+
+// -----
+
+func.func @fuse_same_indices_by_affine_apply(
+  %A: memref<2x2xf32>, %B: memref<2x2xf32>) {
+  %c0 = arith.constant 0 : index
+  %c1 = arith.constant 1 : index
+  %c2 = arith.constant 2 : index
+  %sum = memref.alloc()  : memref<2x3xf32>
+  scf.parallel (%i, %j) = (%c0, %c0) to (%c2, %c2) step (%c1, %c1) {
+    %B_elem = memref.load %B[%i, %j] : memref<2x2xf32>
+    %1 = affine.apply affine_map<(d0, d1) -> (d0 + d1)>(%i, %j)
+    memref.store %B_elem, %sum[%i, %1] : memref<2x3xf32>
+    scf.reduce
+  }
+  scf.parallel (%i, %j) = (%c0, %c0) to (%c2, %c2) step (%c1, %c1) {
+    %1 = affine.apply affine_map<(d0, d1) -> (d0 + d1)>(%i, %j)
+    %sum_elem = memref.load %sum[%i, %1] : memref<2x3xf32>
+    %A_elem = memref.load %A[%i, %j] : memref<2x2xf32>
+    %product = arith.mulf %sum_elem, %A_elem : f32
+    memref.store %product, %B[%i, %j] : memref<2x2xf32>
+    scf.reduce
+  }
+  memref.dealloc %sum : memref<2x3xf32>
+  return
+}
+// CHECK:      #[[$MAP:.+]] = affine_map<(d0, d1) -> (d0 + d1)>
+// CHECK-LABEL: fuse_same_indices_by_affine_apply
+// CHECK-SAME:  (%[[ARG0:.+]]: memref<2x2xf32>, %[[ARG1:.+]]: memref<2x2xf32>) {
+// CHECK-DAG:   %[[C0:.+]] = arith.constant 0 : index
+// CHECK-DAG:   %[[C1:.+]] = arith.constant 1 : index
+// CHECK-DAG:   %[[C2:.+]] = arith.constant 2 : index
+// CHECK:       %[[ALLOC:.+]] = memref.alloc() : memref<2x3xf32>
+// CHECK-NEXT:  scf.parallel (%[[ARG2:.+]], %[[ARG3:.+]]) = (%[[C0]], %[[C0]]) to (%[[C2]], %[[C2]]) step (%[[C1]], %[[C1]]) {
+// CHECK-NEXT:    %[[S0:.+]] = memref.load %[[ARG1]][%[[ARG2]], %[[ARG3]]] : memref<2x2xf32>
+// CHECK-NEXT:    %[[S1:.+]] = affine.apply #[[$MAP]](%[[ARG2]], %[[ARG3]])
+// CHECK-NEXT:    memref.store %[[S0]], %[[ALLOC]][%[[ARG2]], %[[S1]]] : memref<2x3xf32>
+// CHECK-NEXT:    %[[S2:.+]] = affine.apply #[[$MAP]](%[[ARG2]], %[[ARG3]])
+// CHECK-NEXT:    %[[S3:.+]] = memref.load %[[ALLOC]][%[[ARG2]], %[[S2]]] : memref<2x3xf32>
+// CHECK-NEXT:    %[[S4:.+]] = memref.load %[[ARG0]][%[[ARG2]], %[[ARG3]]] : memref<2x2xf32>
+// CHECK-NEXT:    %[[S5:.+]] = arith.mulf %[[S3]], %[[S4]] : f32
+// CHECK-NEXT:    memref.store %[[S5]], %[[ARG1]][%[[ARG2]], %[[ARG3]]] : memref<2x2xf32>
+// CHECK-NEXT:    scf.reduce
+// CHECK-NEXT:  }
+// CHECK-NEXT:  memref.dealloc %[[ALLOC]] : memref<2x3xf32>
+// CHECK-NEXT:  return