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[flang] Optimize assignments of multidimensional arrays #146408

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c9a350f
[flang] Optimize assignments of multidimensional arrays
luporl Jun 9, 2025
cc08a23
Use hlfir.designate and hlfir.assign
luporl Jul 2, 2025
2eb7b7e
Remoeve isAllocatableArray check
luporl Jul 3, 2025
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71 changes: 64 additions & 7 deletions flang/lib/Optimizer/HLFIR/Transforms/OptimizedBufferization.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -21,6 +21,7 @@
#include "flang/Optimizer/HLFIR/HLFIROps.h"
#include "flang/Optimizer/HLFIR/Passes.h"
#include "flang/Optimizer/OpenMP/Passes.h"
#include "flang/Optimizer/Support/Utils.h"
#include "flang/Optimizer/Transforms/Utils.h"
#include "mlir/Dialect/Func/IR/FuncOps.h"
#include "mlir/IR/Dominance.h"
Expand Down Expand Up @@ -758,6 +759,16 @@ class BroadcastAssignBufferization
mlir::PatternRewriter &rewriter) const override;
};

static bool isAllocatableArray(mlir::Type ty) {
auto boxTy = mlir::dyn_cast<fir::BoxType>(ty);
if (!boxTy)
return false;
auto heapTy = mlir::dyn_cast<fir::HeapType>(boxTy.getElementType());
if (!heapTy)
return false;
return mlir::isa<fir::SequenceType>(heapTy.getElementType());
}

llvm::LogicalResult BroadcastAssignBufferization::matchAndRewrite(
hlfir::AssignOp assign, mlir::PatternRewriter &rewriter) const {
// Since RHS is a scalar and LHS is an array, LHS must be allocated
Expand Down Expand Up @@ -786,13 +797,59 @@ llvm::LogicalResult BroadcastAssignBufferization::matchAndRewrite(
mlir::Value shape = hlfir::genShape(loc, builder, lhs);
llvm::SmallVector<mlir::Value> extents =
hlfir::getIndexExtents(loc, builder, shape);
hlfir::LoopNest loopNest =
hlfir::genLoopNest(loc, builder, extents, /*isUnordered=*/true,
flangomp::shouldUseWorkshareLowering(assign));
builder.setInsertionPointToStart(loopNest.body);
auto arrayElement =
hlfir::getElementAt(loc, builder, lhs, loopNest.oneBasedIndices);
builder.create<hlfir::AssignOp>(loc, rhs, arrayElement);

bool isArrayRef =
mlir::isa<fir::SequenceType>(fir::unwrapRefType(lhs.getType()));
if (lhs.isSimplyContiguous() && extents.size() > 1 &&
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@vzakhari vzakhari Jul 2, 2025

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Isn't lhs.isSimplyContiguous() always true for the allocatable arrays? What is the reason for adding isAllocatableArray method?

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@luporl luporl Jul 2, 2025

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Yes, lhs.isSimplyContiguous() is always true for allocatable arrays.

I've added isAllocatableArray because the code now handles only !fir.ref<!fir.array> and !fir.box<!fir.heap<!fir.array>> types.

I guess it should also handle !fir.box<!fir.array>, when those are contiguous. They may appear when using non-default lower bounds.

By handling these cases, do you think it would be safe to drop the isAllocatableArray check? Or are there other array types that also pass the isSimplyContiguous check?

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@vzakhari vzakhari Jul 2, 2025

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I believe isSimplyContiguous may also return true for !fir.box<!fir.ptr<.... I do not think you need to explicitly handle all these different cases. There is hlfir::derefPointersAndAllocatables call above. If lhs is a box, you may use the box_addr operation to get the base address of the array, otherwise, lhs is the base address of the array already.

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@luporl luporl Jul 3, 2025

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Done. Removing the isAllocatableArray check also made it easier to support !fir.box<!fir.array>.
Now all boxed arrays are converted to !fir.box<!fir.array<?x type>>

(isArrayRef || isAllocatableArray(lhs.getType()))) {
// Flatten the array to use a single assign loop, that can be better
// optimized.
mlir::Value n = extents[0];
for (size_t i = 1; i < extents.size(); ++i)
n = builder.create<mlir::arith::MulIOp>(loc, n, extents[i]);
llvm::SmallVector<mlir::Value> flatExtents = {n};

mlir::Type flatArrayType;
mlir::Value flatArray = lhs.getBase();
if (isArrayRef) {
// Array references must have fixed shape, when used in assignments.
int64_t flatExtent = 1;
for (const mlir::Value &extent : extents) {
mlir::Operation *op = extent.getDefiningOp();
assert(op && "no defining operation for constant array extent");
flatExtent *= fir::toInt(mlir::cast<mlir::arith::ConstantOp>(*op));
}

flatArrayType =
fir::ReferenceType::get(fir::SequenceType::get({flatExtent}, eleTy));
flatArray = builder.createConvert(loc, flatArrayType, flatArray);
} else {
shape = builder.genShape(loc, flatExtents);
flatArrayType = fir::BoxType::get(
fir::HeapType::get(fir::SequenceType::get(eleTy, 1)));
flatArray = builder.create<fir::ReboxOp>(loc, flatArrayType, flatArray,
shape, /*slice=*/mlir::Value{});
}

hlfir::LoopNest loopNest =
hlfir::genLoopNest(loc, builder, flatExtents, /*isUnordered=*/true,
flangomp::shouldUseWorkshareLowering(assign));
builder.setInsertionPointToStart(loopNest.body);

mlir::Value arrayElement =
builder.create<hlfir::DesignateOp>(loc, fir::ReferenceType::get(eleTy),
flatArray, loopNest.oneBasedIndices);
builder.create<hlfir::AssignOp>(loc, rhs, arrayElement);
} else {
hlfir::LoopNest loopNest =
hlfir::genLoopNest(loc, builder, extents, /*isUnordered=*/true,
flangomp::shouldUseWorkshareLowering(assign));
builder.setInsertionPointToStart(loopNest.body);
auto arrayElement =
hlfir::getElementAt(loc, builder, lhs, loopNest.oneBasedIndices);
builder.create<hlfir::AssignOp>(loc, rhs, arrayElement);
}

rewriter.eraseOp(assign);
return mlir::success();
}
Expand Down
50 changes: 38 additions & 12 deletions flang/test/HLFIR/opt-scalar-assign.fir
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Original file line number Diff line number Diff line change
Expand Up @@ -12,18 +12,18 @@ func.func @_QPtest1() {
return
}
// CHECK-LABEL: func.func @_QPtest1() {
// CHECK: %[[VAL_0:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_1:.*]] = arith.constant 0.000000e+00 : f32
// CHECK: %[[VAL_2:.*]] = arith.constant 11 : index
// CHECK: %[[VAL_3:.*]] = arith.constant 13 : index
// CHECK: %[[VAL_4:.*]] = fir.alloca !fir.array<11x13xf32> {bindc_name = "x", uniq_name = "_QFtest1Ex"}
// CHECK: %[[VAL_5:.*]] = fir.shape %[[VAL_2]], %[[VAL_3]] : (index, index) -> !fir.shape<2>
// CHECK: %[[VAL_6:.*]]:2 = hlfir.declare %[[VAL_4]](%[[VAL_5]]) {uniq_name = "_QFtest1Ex"} : (!fir.ref<!fir.array<11x13xf32>>, !fir.shape<2>) -> (!fir.ref<!fir.array<11x13xf32>>, !fir.ref<!fir.array<11x13xf32>>)
// CHECK: fir.do_loop %[[VAL_7:.*]] = %[[VAL_0]] to %[[VAL_3]] step %[[VAL_0]] unordered {
// CHECK: fir.do_loop %[[VAL_8:.*]] = %[[VAL_0]] to %[[VAL_2]] step %[[VAL_0]] unordered {
// CHECK: %[[VAL_9:.*]] = hlfir.designate %[[VAL_6]]#0 (%[[VAL_8]], %[[VAL_7]]) : (!fir.ref<!fir.array<11x13xf32>>, index, index) -> !fir.ref<f32>
// CHECK: hlfir.assign %[[VAL_1]] to %[[VAL_9]] : f32, !fir.ref<f32>
// CHECK: }
// CHECK: %[[VAL_0:.*]] = arith.constant 143 : index
// CHECK: %[[VAL_1:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_2:.*]] = arith.constant 0.000000e+00 : f32
// CHECK: %[[VAL_3:.*]] = arith.constant 11 : index
// CHECK: %[[VAL_4:.*]] = arith.constant 13 : index
// CHECK: %[[VAL_5:.*]] = fir.alloca !fir.array<11x13xf32> {bindc_name = "x", uniq_name = "_QFtest1Ex"}
// CHECK: %[[VAL_6:.*]] = fir.shape %[[VAL_3]], %[[VAL_4]] : (index, index) -> !fir.shape<2>
// CHECK: %[[VAL_7:.*]]:2 = hlfir.declare %[[VAL_5]](%[[VAL_6]]) {uniq_name = "_QFtest1Ex"} : (!fir.ref<!fir.array<11x13xf32>>, !fir.shape<2>) -> (!fir.ref<!fir.array<11x13xf32>>, !fir.ref<!fir.array<11x13xf32>>)
// CHECK: %[[VAL_8:.*]] = fir.convert %[[VAL_7]]#0 : (!fir.ref<!fir.array<11x13xf32>>) -> !fir.ref<!fir.array<143xf32>>
// CHECK: fir.do_loop %[[VAL_9:.*]] = %[[VAL_1]] to %[[VAL_0]] step %[[VAL_1]] unordered {
// CHECK: %[[VAL_10:.*]] = hlfir.designate %[[VAL_8]] (%[[VAL_9]]) : (!fir.ref<!fir.array<143xf32>>, index) -> !fir.ref<f32>
// CHECK: hlfir.assign %[[VAL_2]] to %[[VAL_10]] : f32, !fir.ref<f32>
// CHECK: }
// CHECK: return
// CHECK: }
Expand Down Expand Up @@ -129,3 +129,29 @@ func.func @_QPtest5(%arg0: !fir.ref<!fir.array<77xcomplex<f32>>> {fir.bindc_name
// CHECK: }
// CHECK: return
// CHECK: }

func.func @_QPtest6(%arg0: !fir.ref<!fir.box<!fir.heap<!fir.array<?x?xi32>>>> {fir.bindc_name = "x"}) {
%c0_i32 = arith.constant 0 : i32
%0:2 = hlfir.declare %arg0 {fortran_attrs = #fir.var_attrs<allocatable>, uniq_name = "_QFtest6Ex"} : (!fir.ref<!fir.box<!fir.heap<!fir.array<?x?xi32>>>>) -> (!fir.ref<!fir.box<!fir.heap<!fir.array<?x?xi32>>>>, !fir.ref<!fir.box<!fir.heap<!fir.array<?x?xi32>>>>)
hlfir.assign %c0_i32 to %0#0 realloc : i32, !fir.ref<!fir.box<!fir.heap<!fir.array<?x?xi32>>>>
return
}

// CHECK-LABEL: func.func @_QPtest6(
// CHECK-SAME: %[[VAL_0:.*]]: !fir.ref<!fir.box<!fir.heap<!fir.array<?x?xi32>>>> {fir.bindc_name = "x"}) {
// CHECK: %[[VAL_1:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_3:.*]] = arith.constant 0 : i32
// CHECK: %[[VAL_4:.*]]:2 = hlfir.declare %[[VAL_0]] {fortran_attrs = #fir.var_attrs<allocatable>, uniq_name = "_QFtest6Ex"} : (!fir.ref<!fir.box<!fir.heap<!fir.array<?x?xi32>>>>) -> (!fir.ref<!fir.box<!fir.heap<!fir.array<?x?xi32>>>>, !fir.ref<!fir.box<!fir.heap<!fir.array<?x?xi32>>>>)
// CHECK: %[[VAL_5:.*]] = fir.load %[[VAL_4]]#0 : !fir.ref<!fir.box<!fir.heap<!fir.array<?x?xi32>>>>
// CHECK: %[[VAL_6:.*]]:3 = fir.box_dims %[[VAL_5]], %[[VAL_2]] : (!fir.box<!fir.heap<!fir.array<?x?xi32>>>, index) -> (index, index, index)
// CHECK: %[[VAL_7:.*]]:3 = fir.box_dims %[[VAL_5]], %[[VAL_1]] : (!fir.box<!fir.heap<!fir.array<?x?xi32>>>, index) -> (index, index, index)
// CHECK: %[[VAL_8:.*]] = arith.muli %[[VAL_6]]#1, %[[VAL_7]]#1 : index
// CHECK: %[[VAL_9:.*]] = fir.shape %[[VAL_8]] : (index) -> !fir.shape<1>
// CHECK: %[[VAL_10:.*]] = fir.rebox %[[VAL_5]](%[[VAL_9]]) : (!fir.box<!fir.heap<!fir.array<?x?xi32>>>, !fir.shape<1>) -> !fir.box<!fir.heap<!fir.array<?xi32>>>
// CHECK: fir.do_loop %[[VAL_11:.*]] = %[[VAL_1]] to %[[VAL_8]] step %[[VAL_1]] unordered {
// CHECK: %[[VAL_12:.*]] = hlfir.designate %[[VAL_10]] (%[[VAL_11]]) : (!fir.box<!fir.heap<!fir.array<?xi32>>>, index) -> !fir.ref<i32>
// CHECK: hlfir.assign %[[VAL_3]] to %[[VAL_12]] : i32, !fir.ref<i32>
// CHECK: }
// CHECK: return
// CHECK: }
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