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[mlir] Use llvm accumulate wrappers. NFCI. (#162957)

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Use wrappers around `std::accumulate` to make the code more concise and
less bug-prone: https://github.com/llvm/llvm-project/pull/162129.

With `std::accumulate`, it's the initial value that determines the
accumulator type. `llvm::sum_of` and `llvm::product_of` pick the right
accumulator type based on the range element type.

Found some funny bugs like a local accumulate helper that calculated a
sum with initial value of 1 -- we didn't hit the bug because the code
was actually dead...
This commit is contained in:
Jakub Kuderski
2025-10-11 11:33:18 -04:00
committed by GitHub
parent 7eee672023
commit 0820266651
32 changed files with 60 additions and 114 deletions
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3
mlir/examples/toy/Ch2/mlir/MLIRGen.cpp
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@@ -264,8 +264,7 @@ private:
// The attribute is a vector with a floating point value per element
// (number) in the array, see `collectData()` below for more details.
std::vector<double> data;
data.reserve(std::accumulate(lit.getDims().begin(), lit.getDims().end(), 1,
std::multiplies<int>()));
data.reserve(llvm::product_of(lit.getDims()));
collectData(lit, data);
// The type of this attribute is tensor of 64-bit floating-point with the

3
mlir/examples/toy/Ch3/mlir/MLIRGen.cpp
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@@ -264,8 +264,7 @@ private:
// The attribute is a vector with a floating point value per element
// (number) in the array, see `collectData()` below for more details.
std::vector<double> data;
data.reserve(std::accumulate(lit.getDims().begin(), lit.getDims().end(), 1,
std::multiplies<int>()));
data.reserve(llvm::product_of(lit.getDims()));
collectData(lit, data);
// The type of this attribute is tensor of 64-bit floating-point with the

3
mlir/examples/toy/Ch4/mlir/MLIRGen.cpp
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@@ -268,8 +268,7 @@ private:
// The attribute is a vector with a floating point value per element
// (number) in the array, see `collectData()` below for more details.
std::vector<double> data;
data.reserve(std::accumulate(lit.getDims().begin(), lit.getDims().end(), 1,
std::multiplies<int>()));
data.reserve(llvm::product_of(lit.getDims()));
collectData(lit, data);
// The type of this attribute is tensor of 64-bit floating-point with the

3
mlir/examples/toy/Ch5/mlir/MLIRGen.cpp
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@@ -268,8 +268,7 @@ private:
// The attribute is a vector with a floating point value per element
// (number) in the array, see `collectData()` below for more details.
std::vector<double> data;
data.reserve(std::accumulate(lit.getDims().begin(), lit.getDims().end(), 1,
std::multiplies<int>()));
data.reserve(llvm::product_of(lit.getDims()));
collectData(lit, data);
// The type of this attribute is tensor of 64-bit floating-point with the

3
mlir/examples/toy/Ch6/mlir/MLIRGen.cpp
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@@ -268,8 +268,7 @@ private:
// The attribute is a vector with a floating point value per element
// (number) in the array, see `collectData()` below for more details.
std::vector<double> data;
data.reserve(std::accumulate(lit.getDims().begin(), lit.getDims().end(), 1,
std::multiplies<int>()));
data.reserve(llvm::product_of(lit.getDims()));
collectData(lit, data);
// The type of this attribute is tensor of 64-bit floating-point with the

3
mlir/examples/toy/Ch7/mlir/MLIRGen.cpp
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@@ -405,8 +405,7 @@ private:
// The attribute is a vector with a floating point value per element
// (number) in the array, see `collectData()` below for more details.
std::vector<double> data;
data.reserve(std::accumulate(lit.getDims().begin(), lit.getDims().end(), 1,
std::multiplies<int>()));
data.reserve(llvm::product_of(lit.getDims()));
collectData(lit, data);
// The type of this attribute is tensor of 64-bit floating-point with the

5
mlir/lib/Conversion/MemRefToEmitC/MemRefToEmitC.cpp
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@@ -22,6 +22,7 @@
#include "mlir/IR/TypeRange.h"
#include "mlir/IR/Value.h"
#include "mlir/Transforms/DialectConversion.h"
#include "llvm/ADT/STLExtras.h"
#include <cstdint>
#include <numeric>
@@ -110,9 +111,7 @@ static Value calculateMemrefTotalSizeBytes(Location loc, MemRefType memrefType,
{TypeAttr::get(memrefType.getElementType())}));
IndexType indexType = builder.getIndexType();
int64_t numElements = std::accumulate(memrefType.getShape().begin(),
memrefType.getShape().end(), int64_t{1},
std::multiplies<int64_t>());
int64_t numElements = llvm::product_of(memrefType.getShape());
emitc::ConstantOp numElementsValue = emitc::ConstantOp::create(
builder, loc, indexType, builder.getIndexAttr(numElements));

4
mlir/lib/Conversion/TosaToTensor/TosaToTensor.cpp
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@@ -18,6 +18,7 @@
#include "mlir/Dialect/Tosa/Utils/ConversionUtils.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/Transforms/DialectConversion.h"
#include "llvm/ADT/STLExtras.h"
#include <numeric>
@@ -70,8 +71,7 @@ TensorType inferReshapeExpandedType(TensorType inputType,
// Calculate the product of all elements in 'newShape' except for the -1
// placeholder, which we discard by negating the result.
int64_t totalSizeNoPlaceholder = -std::accumulate(
newShape.begin(), newShape.end(), 1, std::multiplies<int64_t>());
int64_t totalSizeNoPlaceholder = -llvm::product_of(newShape);
// If there is a 0 component in 'newShape', resolve the placeholder as
// 0.

7
mlir/lib/Conversion/VectorToAMX/VectorToAMX.cpp
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@@ -20,6 +20,7 @@
#include "mlir/Pass/Pass.h"
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
#include "llvm/ADT/STLExtras.h"
#include "llvm/Support/DebugLog.h"
#include <numeric>
@@ -265,8 +266,7 @@ loadStoreFromTransfer(PatternRewriter &rewriter,
if (isPacked)
src = collapseLastDim(rewriter, src);
int64_t rows = vecShape[0];
int64_t cols = std::accumulate(vecShape.begin() + 1, vecShape.end(), 1,
std::multiplies<int64_t>());
int64_t cols = llvm::product_of(vecShape.drop_front());
auto tileType = amx::TileType::get({rows, cols}, vecTy.getElementType());
Value zeroIndex = rewriter.createOrFold<arith::ConstantIndexOp>(loc, 0);
@@ -336,8 +336,7 @@ static TypedValue<amx::TileType> loadTile(PatternRewriter &rewriter,
ArrayRef<int64_t> shape = vecTy.getShape();
int64_t rows = shape[0];
int64_t cols = std::accumulate(shape.begin() + 1, shape.end(), 1,
std::multiplies<int64_t>());
int64_t cols = llvm::product_of(shape.drop_front());
auto tileType = amx::TileType::get({rows, cols}, vecTy.getElementType());
return amx::TileLoadOp::create(rewriter, loc, tileType, buf,

4
mlir/lib/Conversion/VectorToSCF/VectorToSCF.cpp
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@@ -26,6 +26,7 @@
#include "mlir/IR/Builders.h"
#include "mlir/Pass/Pass.h"
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
#include "llvm/ADT/STLExtras.h"
namespace mlir {
#define GEN_PASS_DEF_CONVERTVECTORTOSCF
@@ -760,8 +761,7 @@ struct DecomposePrintOpConversion : public VectorToSCFPattern<vector::PrintOp> {
if (vectorType.getRank() != 1) {
// Flatten n-D vectors to 1D. This is done to allow indexing with a
// non-constant value.
auto flatLength = std::accumulate(shape.begin(), shape.end(), 1,
std::multiplies<int64_t>());
int64_t flatLength = llvm::product_of(shape);
auto flatVectorType =
VectorType::get({flatLength}, vectorType.getElementType());
value = vector::ShapeCastOp::create(rewriter, loc, flatVectorType, value);

5
mlir/lib/Conversion/XeGPUToXeVM/XeGPUToXeVM.cpp
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@@ -23,6 +23,7 @@
#include "mlir/Dialect/XeGPU/IR/XeGPU.h"
#include "mlir/Pass/Pass.h"
#include "mlir/Support/LLVM.h"
#include "llvm/ADT/STLExtras.h"
#include "llvm/Support/FormatVariadic.h"
#include "mlir/IR/BuiltinTypes.h"
@@ -774,9 +775,7 @@ struct ConvertXeGPUToXeVMPass
if (rank < 1 || type.getNumElements() == 1)
return elemType;
// Otherwise, convert the vector to a flat vector type.
int64_t sum =
std::accumulate(type.getShape().begin(), type.getShape().end(),
int64_t{1}, std::multiplies<int64_t>());
int64_t sum = llvm::product_of(type.getShape());
return VectorType::get(sum, elemType);
});
typeConverter.addConversion([&](xegpu::TensorDescType type) -> Type {

6
mlir/lib/Dialect/Arith/Utils/Utils.cpp
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@@ -351,9 +351,9 @@ Value createProduct(OpBuilder &builder, Location loc, ArrayRef<Value> values,
Value one = ConstantOp::create(builder, loc, resultType,
builder.getOneAttr(resultType));
ArithBuilder arithBuilder(builder, loc);
return std::accumulate(
values.begin(), values.end(), one,
[&arithBuilder](Value acc, Value v) { return arithBuilder.mul(acc, v); });
return llvm::accumulate(values, one, [&arithBuilder](Value acc, Value v) {
return arithBuilder.mul(acc, v);
});
}
/// Map strings to float types.

3
mlir/lib/Dialect/GPU/IR/GPUDialect.cpp
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@@ -2460,8 +2460,7 @@ static LogicalResult verifyDistributedType(Type expanded, Type distributed,
<< dDim << ")";
scales[i] = eDim / dDim;
}
if (std::accumulate(scales.begin(), scales.end(), 1,
std::multiplies<int64_t>()) != warpSize)
if (llvm::product_of(scales) != warpSize)
return op->emitOpError()
<< "incompatible distribution dimensions from " << expandedVecType
<< " to " << distributedVecType << " with warp size = " << warpSize;

4
mlir/lib/Dialect/GPU/Utils/DistributionUtils.cpp
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@@ -15,6 +15,7 @@
#include "mlir/Dialect/Arith/IR/Arith.h"
#include "mlir/IR/Value.h"
#include "llvm/ADT/DenseMap.h"
#include "llvm/ADT/STLExtras.h"
#include <numeric>
@@ -118,8 +119,7 @@ bool WarpDistributionPattern::delinearizeLaneId(
return false;
sizes.push_back(large / small);
}
if (std::accumulate(sizes.begin(), sizes.end(), 1,
std::multiplies<int64_t>()) != warpSize)
if (llvm::product_of(sizes) != warpSize)
return false;
AffineExpr s0, s1;

5
mlir/lib/Dialect/Linalg/Transforms/ShardingInterfaceImpl.cpp
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@@ -266,9 +266,8 @@ struct StructuredOpShardingInterface
LinalgOp linalgOp = llvm::cast<LinalgOp>(op);
SmallVector<utils::IteratorType> iteratorTypes =
linalgOp.getIteratorTypesArray();
unsigned reductionItersCount = std::accumulate(
iteratorTypes.begin(), iteratorTypes.end(), 0,
[](unsigned count, utils::IteratorType iter) {
unsigned reductionItersCount = llvm::accumulate(
iteratorTypes, 0u, [](unsigned count, utils::IteratorType iter) {
return count + (iter == utils::IteratorType::reduction);
});
shard::ReductionKind reductionKind = getReductionKindOfLinalgOp(linalgOp);

5
mlir/lib/Dialect/Quant/Utils/UniformSupport.cpp
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@@ -8,6 +8,7 @@
#include "mlir/Dialect/Quant/Utils/UniformSupport.h"
#include "mlir/IR/BuiltinTypes.h"
#include "llvm/ADT/STLExtras.h"
#include <numeric>
using namespace mlir;
@@ -76,9 +77,7 @@ UniformQuantizedPerAxisValueConverter::convert(DenseFPElementsAttr attr) {
// using the right quantization parameters.
int64_t flattenIndex = 0;
auto shape = type.getShape();
int64_t chunkSize =
std::accumulate(std::next(shape.begin(), quantizationDim + 1),
shape.end(), 1, std::multiplies<int64_t>());
int64_t chunkSize = llvm::product_of(shape.drop_front(quantizationDim + 1));
Type newElementType = IntegerType::get(attr.getContext(), storageBitWidth);
return attr.mapValues(newElementType, [&](const APFloat &old) {
int chunkIndex = (flattenIndex++) / chunkSize;

2
mlir/lib/Dialect/SPIRV/IR/SPIRVOps.cpp
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@@ -400,7 +400,7 @@ LogicalResult spirv::CompositeConstructOp::verify() {
return emitOpError("operand element type mismatch: expected to be ")
<< resultType.getElementType() << ", but provided " << elementType;
}
unsigned totalCount = std::accumulate(sizes.begin(), sizes.end(), 0);
unsigned totalCount = llvm::sum_of(sizes);
if (totalCount != cType.getNumElements())
return emitOpError("has incorrect number of operands: expected ")
<< cType.getNumElements() << ", but provided " << totalCount;

12
mlir/lib/Dialect/Shard/IR/ShardOps.cpp
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@@ -1010,18 +1010,6 @@ static LogicalResult verifyInGroupDevice(Location loc, StringRef deviceName,
return success();
}
template <typename It>
static auto product(It begin, It end) {
using ElementType = std::decay_t<decltype(*begin)>;
return std::accumulate(begin, end, static_cast<ElementType>(1),
std::multiplies<ElementType>());
}
template <typename R>
static auto product(R &&range) {
return product(adl_begin(range), adl_end(range));
}
static LogicalResult verifyDimensionCompatibility(Location loc,
int64_t expectedDimSize,
int64_t resultDimSize,

7
mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
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@@ -2368,9 +2368,10 @@ llvm::LogicalResult tosa::ReshapeOp::verify() {
}
}
int64_t newShapeElementsNum = std::accumulate(
shapeValues.begin(), shapeValues.end(), 1LL,
[](int64_t acc, int64_t dim) { return (dim > 0) ? acc * dim : acc; });
int64_t newShapeElementsNum =
llvm::accumulate(shapeValues, int64_t(1), [](int64_t acc, int64_t dim) {
return (dim > 0) ? acc * dim : acc;
});
bool isStaticNewShape =
llvm::all_of(shapeValues, [](int64_t s) { return s > 0; });
if ((isStaticNewShape && inputElementsNum != newShapeElementsNum) ||

7
mlir/lib/Dialect/Tosa/Transforms/TosaFolders.cpp
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@@ -20,6 +20,7 @@
#include "mlir/IR/BuiltinTypes.h"
#include "mlir/IR/DialectResourceBlobManager.h"
#include "mlir/IR/Matchers.h"
#include "llvm/ADT/STLExtras.h"
#include "llvm/ADT/SmallVector.h"
using namespace mlir;
@@ -375,8 +376,7 @@ llvm::APInt calculateReducedValue(const mlir::ElementsAttr &oldTensorAttr,
for (int64_t reductionAxisVal = 1; reductionAxisVal < oldShape[reductionAxis];
++reductionAxisVal) {
int64_t stride = std::accumulate(oldShape.begin() + reductionAxis + 1,
oldShape.end(), 1, std::multiplies<int>());
int64_t stride = llvm::product_of(oldShape.drop_front(reductionAxis + 1));
int64_t index = indexAtOldTensor + stride * reductionAxisVal;
reducedValue =
OperationType::calcOneElement(reducedValue, oldTensor[index]);
@@ -424,8 +424,7 @@ struct ReduceConstantOptimization : public OpRewritePattern<OperationType> {
auto oldShape = shapedOldElementsValues.getShape();
auto newShape = resultType.getShape();
auto newNumOfElements = std::accumulate(newShape.begin(), newShape.end(), 1,
std::multiplies<int>());
int64_t newNumOfElements = llvm::product_of(newShape);
llvm::SmallVector<APInt> newReducedTensor(newNumOfElements);
for (int64_t reductionIndex = 0; reductionIndex < newNumOfElements;

25
mlir/lib/Dialect/Utils/IndexingUtils.cpp
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@@ -81,21 +81,10 @@ SmallVector<int64_t> mlir::computeElementwiseMul(ArrayRef<int64_t> v1,
return computeElementwiseMulImpl(v1, v2);
}
int64_t mlir::computeSum(ArrayRef<int64_t> basis) {
assert(llvm::all_of(basis, [](int64_t s) { return s > 0; }) &&
"basis must be nonnegative");
if (basis.empty())
return 0;
return std::accumulate(basis.begin(), basis.end(), 1, std::plus<int64_t>());
}
int64_t mlir::computeProduct(ArrayRef<int64_t> basis) {
assert(llvm::all_of(basis, [](int64_t s) { return s > 0; }) &&
"basis must be nonnegative");
if (basis.empty())
return 1;
return std::accumulate(basis.begin(), basis.end(), 1,
std::multiplies<int64_t>());
return llvm::product_of(basis);
}
int64_t mlir::linearize(ArrayRef<int64_t> offsets, ArrayRef<int64_t> basis) {
@@ -158,19 +147,11 @@ SmallVector<AffineExpr> mlir::computeElementwiseMul(ArrayRef<AffineExpr> v1,
}
AffineExpr mlir::computeSum(MLIRContext *ctx, ArrayRef<AffineExpr> basis) {
if (basis.empty())
return getAffineConstantExpr(0, ctx);
return std::accumulate(basis.begin(), basis.end(),
getAffineConstantExpr(0, ctx),
std::plus<AffineExpr>());
return llvm::sum_of(basis, getAffineConstantExpr(0, ctx));
}
AffineExpr mlir::computeProduct(MLIRContext *ctx, ArrayRef<AffineExpr> basis) {
if (basis.empty())
return getAffineConstantExpr(1, ctx);
return std::accumulate(basis.begin(), basis.end(),
getAffineConstantExpr(1, ctx),
std::multiplies<AffineExpr>());
return llvm::product_of(basis, getAffineConstantExpr(1, ctx));
}
AffineExpr mlir::linearize(MLIRContext *ctx, ArrayRef<AffineExpr> offsets,

10
mlir/lib/Dialect/Utils/ReshapeOpsUtils.cpp
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@@ -374,11 +374,11 @@ mlir::composeReassociationIndices(
if (consumerReassociations.empty())
return composedIndices;
size_t consumerDims = std::accumulate(
consumerReassociations.begin(), consumerReassociations.end(), 0,
[](size_t all, ReassociationIndicesRef indices) {
return all + indices.size();
});
size_t consumerDims =
llvm::accumulate(consumerReassociations, size_t(0),
[](size_t all, ReassociationIndicesRef indices) {
return all + indices.size();
});
if (producerReassociations.size() != consumerDims)
return std::nullopt;

3
mlir/lib/Dialect/Vector/IR/VectorOps.cpp
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@@ -2496,8 +2496,7 @@ struct ToElementsOfBroadcast final : OpRewritePattern<ToElementsOp> {
auto srcElems = vector::ToElementsOp::create(
rewriter, toElementsOp.getLoc(), bcastOp.getSource());
int64_t dstCount = std::accumulate(dstShape.begin(), dstShape.end(), 1,
std::multiplies<int64_t>());
int64_t dstCount = llvm::product_of(dstShape);
SmallVector<Value> replacements;
replacements.reserve(dstCount);

6
mlir/lib/Dialect/Vector/Transforms/LowerVectorShapeCast.cpp
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@@ -21,6 +21,7 @@
#include "mlir/IR/Location.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/IR/TypeUtilities.h"
#include "llvm/ADT/STLExtras.h"
#include <numeric>
#define DEBUG_TYPE "vector-shape-cast-lowering"
@@ -166,10 +167,7 @@ class ShapeCastOpRewritePattern : public OpRewritePattern<vector::ShapeCastOp> {
const VectorType resultType = shapeCast.getResultVectorType();
const ArrayRef<int64_t> resultShape = resultType.getShape();
const int64_t nSlices =
std::accumulate(sourceShape.begin(), sourceShape.begin() + sourceDim, 1,
std::multiplies<int64_t>());
const int64_t nSlices = llvm::product_of(sourceShape.take_front(sourceDim));
SmallVector<int64_t> extractIndex(sourceDim, 0);
SmallVector<int64_t> insertIndex(resultDim, 0);
Value result = ub::PoisonOp::create(rewriter, loc, resultType);

4
mlir/lib/Dialect/Vector/Transforms/VectorDropLeadUnitDim.cpp
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@@ -15,6 +15,7 @@
#include "mlir/Dialect/Vector/Utils/VectorUtils.h"
#include "mlir/IR/Builders.h"
#include "mlir/IR/TypeUtilities.h"
#include "llvm/ADT/STLExtras.h"
#define DEBUG_TYPE "vector-drop-unit-dim"
@@ -557,8 +558,7 @@ struct CastAwayConstantMaskLeadingOneDim
// If any of the dropped unit dims has a size of `0`, the entire mask is a
// zero mask, else the unit dim has no effect on the mask.
int64_t flatLeadingSize =
std::accumulate(dimSizes.begin(), dimSizes.begin() + dropDim + 1,
static_cast<int64_t>(1), std::multiplies<int64_t>());
llvm::product_of(dimSizes.take_front(dropDim + 1));
SmallVector<int64_t> newDimSizes = {flatLeadingSize};
newDimSizes.append(dimSizes.begin() + dropDim + 1, dimSizes.end());

3
mlir/lib/Dialect/XeGPU/Utils/XeGPUUtils.cpp
View File

@@ -52,8 +52,7 @@ mlir::xegpu::getDistributedVectorType(xegpu::TensorDescType tdescTy) {
// compute sgSize by multiply elements of laneLayout
// e.g. for 2D layout, sgSize = laneLayout[0] * laneLayout[1]
// e.g. for 1D layout, sgSize = laneLayout[0]
auto sgSize = std::accumulate(laneLayout.begin(), laneLayout.end(), 1,
std::multiplies<int64_t>());
int64_t sgSize = llvm::product_of(laneLayout);
// Case 1: regular loads/stores
auto scatterAttr = tdescTy.getEncodingOfType<ScatterTensorDescAttr>();

6
mlir/lib/IR/Operation.cpp
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@@ -18,6 +18,7 @@
#include "mlir/IR/PatternMatch.h"
#include "mlir/IR/TypeUtilities.h"
#include "mlir/Interfaces/FoldInterfaces.h"
#include "llvm/ADT/STLExtras.h"
#include "llvm/ADT/SmallVector.h"
#include "llvm/Support/ErrorHandling.h"
#include <numeric>
@@ -1274,10 +1275,7 @@ LogicalResult OpTrait::impl::verifyValueSizeAttr(Operation *op,
return op->emitOpError("'")
<< attrName << "' attribute cannot have negative elements";
size_t totalCount =
std::accumulate(sizes.begin(), sizes.end(), 0,
[](unsigned all, int32_t one) { return all + one; });
size_t totalCount = llvm::sum_of(sizes, size_t(0));
if (totalCount != expectedCount)
return op->emitOpError()
<< valueGroupName << " count (" << expectedCount

9
mlir/lib/IR/OperationSupport.cpp
View File

@@ -406,15 +406,13 @@ OperandRangeRange::OperandRangeRange(OperandRange operands,
OperandRange OperandRangeRange::join() const {
const OwnerT &owner = getBase();
ArrayRef<int32_t> sizeData = llvm::cast<DenseI32ArrayAttr>(owner.second);
return OperandRange(owner.first,
std::accumulate(sizeData.begin(), sizeData.end(), 0));
return OperandRange(owner.first, llvm::sum_of(sizeData));
}
OperandRange OperandRangeRange::dereference(const OwnerT &object,
ptrdiff_t index) {
ArrayRef<int32_t> sizeData = llvm::cast<DenseI32ArrayAttr>(object.second);
uint32_t startIndex =
std::accumulate(sizeData.begin(), sizeData.begin() + index, 0);
uint32_t startIndex = llvm::sum_of(sizeData.take_front(index));
return OperandRange(object.first + startIndex, *(sizeData.begin() + index));
}
@@ -565,8 +563,7 @@ MutableOperandRange MutableOperandRangeRange::dereference(const OwnerT &object,
ptrdiff_t index) {
ArrayRef<int32_t> sizeData =
llvm::cast<DenseI32ArrayAttr>(object.second.getValue());
uint32_t startIndex =
std::accumulate(sizeData.begin(), sizeData.begin() + index, 0);
uint32_t startIndex = llvm::sum_of(sizeData.take_front(index));
return object.first.slice(
startIndex, *(sizeData.begin() + index),
MutableOperandRange::OperandSegment(index, object.second));

4
mlir/lib/IR/TypeUtilities.cpp
View File

@@ -104,8 +104,8 @@ LogicalResult mlir::verifyCompatibleShapes(TypeRange types1, TypeRange types2) {
LogicalResult mlir::verifyCompatibleDims(ArrayRef<int64_t> dims) {
if (dims.empty())
return success();
auto staticDim = std::accumulate(
dims.begin(), dims.end(), dims.front(), [](auto fold, auto dim) {
auto staticDim =
llvm::accumulate(dims, dims.front(), [](auto fold, auto dim) {
return ShapedType::isDynamic(dim) ? fold : dim;
});
return success(llvm::all_of(dims, [&](auto dim) {

3
mlir/lib/Rewrite/ByteCode.cpp
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@@ -1835,8 +1835,7 @@ executeGetOperandsResults(RangeT values, Operation *op, unsigned index,
return nullptr;
ArrayRef<int32_t> segments = segmentAttr;
unsigned startIndex =
std::accumulate(segments.begin(), segments.begin() + index, 0);
unsigned startIndex = llvm::sum_of(segments.take_front(index));
values = values.slice(startIndex, *std::next(segments.begin(), index));
LDBG() << " * Extracting range[" << startIndex << ", "

3
mlir/lib/Target/LLVMIR/ModuleTranslation.cpp
View File

@@ -922,8 +922,7 @@ llvm::CallInst *mlir::LLVM::detail::createIntrinsicCall(
assert(opBundleSizes.size() == opBundleTagsAttr.size() &&
"operand bundles and tags do not match");
numOpBundleOperands =
std::accumulate(opBundleSizes.begin(), opBundleSizes.end(), size_t(0));
numOpBundleOperands = llvm::sum_of(opBundleSizes);
assert(numOpBundleOperands <= intrOp->getNumOperands() &&
"operand bundle operands is more than the number of operands");

4
mlir/tools/mlir-tblgen/OpDefinitionsGen.cpp
View File

@@ -3513,9 +3513,9 @@ void OpEmitter::genCodeForAddingArgAndRegionForBuilder(
body << "(" << operandName << " ? 1 : 0)";
} else if (operand.isVariadicOfVariadic()) {
body << llvm::formatv(
"static_cast<int32_t>(std::accumulate({0}.begin(), {0}.end(), 0, "
"llvm::accumulate({0}, int32_t(0), "
"[](int32_t curSum, ::mlir::ValueRange range) {{ return curSum + "
"static_cast<int32_t>(range.size()); }))",
"static_cast<int32_t>(range.size()); })",
operandName);
} else {
body << "static_cast<int32_t>(" << getArgumentName(op, i) << ".size())";