[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. With `std::acummulate`, 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...
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@llvm/pr-subscribers-mlir-gpu @llvm/pr-subscribers-mlir-llvm Author: Jakub Kuderski (kuhar) ChangesUse wrappers around With 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... Patch is 30.53 KiB, truncated to 20.00 KiB below, full version: https://github.com/llvm/llvm-project/pull/162957.diff 32 Files Affected:
diff --git a/mlir/examples/toy/Ch2/mlir/MLIRGen.cpp b/mlir/examples/toy/Ch2/mlir/MLIRGen.cpp
index 39ae6a016eb41..a9592bcd7814a 100644
--- a/mlir/examples/toy/Ch2/mlir/MLIRGen.cpp
+++ b/mlir/examples/toy/Ch2/mlir/MLIRGen.cpp
@@ -264,8 +264,7 @@ class MLIRGenImpl {
// 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
diff --git a/mlir/examples/toy/Ch3/mlir/MLIRGen.cpp b/mlir/examples/toy/Ch3/mlir/MLIRGen.cpp
index 0573af699c1f4..8c21951948496 100644
--- a/mlir/examples/toy/Ch3/mlir/MLIRGen.cpp
+++ b/mlir/examples/toy/Ch3/mlir/MLIRGen.cpp
@@ -264,8 +264,7 @@ class MLIRGenImpl {
// 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
diff --git a/mlir/examples/toy/Ch4/mlir/MLIRGen.cpp b/mlir/examples/toy/Ch4/mlir/MLIRGen.cpp
index 7d676f1b39200..6b7ab40299be5 100644
--- a/mlir/examples/toy/Ch4/mlir/MLIRGen.cpp
+++ b/mlir/examples/toy/Ch4/mlir/MLIRGen.cpp
@@ -268,8 +268,7 @@ class MLIRGenImpl {
// 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
diff --git a/mlir/examples/toy/Ch5/mlir/MLIRGen.cpp b/mlir/examples/toy/Ch5/mlir/MLIRGen.cpp
index 7d676f1b39200..6b7ab40299be5 100644
--- a/mlir/examples/toy/Ch5/mlir/MLIRGen.cpp
+++ b/mlir/examples/toy/Ch5/mlir/MLIRGen.cpp
@@ -268,8 +268,7 @@ class MLIRGenImpl {
// 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
diff --git a/mlir/examples/toy/Ch6/mlir/MLIRGen.cpp b/mlir/examples/toy/Ch6/mlir/MLIRGen.cpp
index 7d676f1b39200..6b7ab40299be5 100644
--- a/mlir/examples/toy/Ch6/mlir/MLIRGen.cpp
+++ b/mlir/examples/toy/Ch6/mlir/MLIRGen.cpp
@@ -268,8 +268,7 @@ class MLIRGenImpl {
// 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
diff --git a/mlir/examples/toy/Ch7/mlir/MLIRGen.cpp b/mlir/examples/toy/Ch7/mlir/MLIRGen.cpp
index 75dbc9104a992..73133247335dc 100644
--- a/mlir/examples/toy/Ch7/mlir/MLIRGen.cpp
+++ b/mlir/examples/toy/Ch7/mlir/MLIRGen.cpp
@@ -405,8 +405,7 @@ class MLIRGenImpl {
// 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
diff --git a/mlir/lib/Conversion/MemRefToEmitC/MemRefToEmitC.cpp b/mlir/lib/Conversion/MemRefToEmitC/MemRefToEmitC.cpp
index 2b7bdc9a7b7f8..11f866c103639 100644
--- a/mlir/lib/Conversion/MemRefToEmitC/MemRefToEmitC.cpp
+++ b/mlir/lib/Conversion/MemRefToEmitC/MemRefToEmitC.cpp
@@ -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));
diff --git a/mlir/lib/Conversion/TosaToTensor/TosaToTensor.cpp b/mlir/lib/Conversion/TosaToTensor/TosaToTensor.cpp
index 802691c1f7d76..9bf9ca3ae7a89 100644
--- a/mlir/lib/Conversion/TosaToTensor/TosaToTensor.cpp
+++ b/mlir/lib/Conversion/TosaToTensor/TosaToTensor.cpp
@@ -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.
diff --git a/mlir/lib/Conversion/VectorToAMX/VectorToAMX.cpp b/mlir/lib/Conversion/VectorToAMX/VectorToAMX.cpp
index 79c2f23c8e7f3..245a3efe98ecc 100644
--- a/mlir/lib/Conversion/VectorToAMX/VectorToAMX.cpp
+++ b/mlir/lib/Conversion/VectorToAMX/VectorToAMX.cpp
@@ -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,
diff --git a/mlir/lib/Conversion/VectorToSCF/VectorToSCF.cpp b/mlir/lib/Conversion/VectorToSCF/VectorToSCF.cpp
index c45c45e4712f3..c9eba6962e6a4 100644
--- a/mlir/lib/Conversion/VectorToSCF/VectorToSCF.cpp
+++ b/mlir/lib/Conversion/VectorToSCF/VectorToSCF.cpp
@@ -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);
diff --git a/mlir/lib/Conversion/XeGPUToXeVM/XeGPUToXeVM.cpp b/mlir/lib/Conversion/XeGPUToXeVM/XeGPUToXeVM.cpp
index 9ead1d89069d6..71687b1479a7d 100644
--- a/mlir/lib/Conversion/XeGPUToXeVM/XeGPUToXeVM.cpp
+++ b/mlir/lib/Conversion/XeGPUToXeVM/XeGPUToXeVM.cpp
@@ -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 {
diff --git a/mlir/lib/Dialect/Arith/Utils/Utils.cpp b/mlir/lib/Dialect/Arith/Utils/Utils.cpp
index b1fc9aa57c3ba..f54bafff58322 100644
--- a/mlir/lib/Dialect/Arith/Utils/Utils.cpp
+++ b/mlir/lib/Dialect/Arith/Utils/Utils.cpp
@@ -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.
diff --git a/mlir/lib/Dialect/GPU/IR/GPUDialect.cpp b/mlir/lib/Dialect/GPU/IR/GPUDialect.cpp
index 19eba6beacd86..b5f8ddaadacdf 100644
--- a/mlir/lib/Dialect/GPU/IR/GPUDialect.cpp
+++ b/mlir/lib/Dialect/GPU/IR/GPUDialect.cpp
@@ -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;
diff --git a/mlir/lib/Dialect/GPU/Utils/DistributionUtils.cpp b/mlir/lib/Dialect/GPU/Utils/DistributionUtils.cpp
index 88f531f394765..572b746358379 100644
--- a/mlir/lib/Dialect/GPU/Utils/DistributionUtils.cpp
+++ b/mlir/lib/Dialect/GPU/Utils/DistributionUtils.cpp
@@ -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;
diff --git a/mlir/lib/Dialect/Linalg/Transforms/ShardingInterfaceImpl.cpp b/mlir/lib/Dialect/Linalg/Transforms/ShardingInterfaceImpl.cpp
index f277c5f5be5fc..0ae2a9cc0318c 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/ShardingInterfaceImpl.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/ShardingInterfaceImpl.cpp
@@ -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);
diff --git a/mlir/lib/Dialect/Quant/Utils/UniformSupport.cpp b/mlir/lib/Dialect/Quant/Utils/UniformSupport.cpp
index b66390819103e..8c4f80f13e2f6 100644
--- a/mlir/lib/Dialect/Quant/Utils/UniformSupport.cpp
+++ b/mlir/lib/Dialect/Quant/Utils/UniformSupport.cpp
@@ -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;
diff --git a/mlir/lib/Dialect/SPIRV/IR/SPIRVOps.cpp b/mlir/lib/Dialect/SPIRV/IR/SPIRVOps.cpp
index 55119984d9cc1..fe50865bb7c49 100644
--- a/mlir/lib/Dialect/SPIRV/IR/SPIRVOps.cpp
+++ b/mlir/lib/Dialect/SPIRV/IR/SPIRVOps.cpp
@@ -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;
diff --git a/mlir/lib/Dialect/Shard/IR/ShardOps.cpp b/mlir/lib/Dialect/Shard/IR/ShardOps.cpp
index 08fccfa25c0c7..135c03311ce0e 100644
--- a/mlir/lib/Dialect/Shard/IR/ShardOps.cpp
+++ b/mlir/lib/Dialect/Shard/IR/ShardOps.cpp
@@ -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,
diff --git a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
index c51b5e9cbfc78..00f84bc43f444 100644
--- a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
+++ b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
@@ -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) ||
diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaFolders.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaFolders.cpp
index d33ebe397cd35..5786f53b8133d 100644
--- a/mlir/lib/Dialect/Tosa/Transforms/TosaFolders.cpp
+++ b/mlir/lib/Dialect/Tosa/Transforms/TosaFolders.cpp
@@ -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;
diff --git a/mlir/lib/Dialect/Utils/IndexingUtils.cpp b/mlir/lib/Dialect/Utils/IndexingUtils.cpp
index e1648ab99ff25..305b06eb38fdf 100644
--- a/mlir/lib/Dialect/Utils/IndexingUtils.cpp
+++ b/mlir/lib/Dialect/Utils/IndexingUtils.cpp
@@ -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.em...
[truncated]
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@llvm/pr-subscribers-mlir-spirv Author: Jakub Kuderski (kuhar) ChangesUse wrappers around With 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... Patch is 30.53 KiB, truncated to 20.00 KiB below, full version: https://github.com/llvm/llvm-project/pull/162957.diff 32 Files Affected:
diff --git a/mlir/examples/toy/Ch2/mlir/MLIRGen.cpp b/mlir/examples/toy/Ch2/mlir/MLIRGen.cpp
index 39ae6a016eb41..a9592bcd7814a 100644
--- a/mlir/examples/toy/Ch2/mlir/MLIRGen.cpp
+++ b/mlir/examples/toy/Ch2/mlir/MLIRGen.cpp
@@ -264,8 +264,7 @@ class MLIRGenImpl {
// 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
diff --git a/mlir/examples/toy/Ch3/mlir/MLIRGen.cpp b/mlir/examples/toy/Ch3/mlir/MLIRGen.cpp
index 0573af699c1f4..8c21951948496 100644
--- a/mlir/examples/toy/Ch3/mlir/MLIRGen.cpp
+++ b/mlir/examples/toy/Ch3/mlir/MLIRGen.cpp
@@ -264,8 +264,7 @@ class MLIRGenImpl {
// 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
diff --git a/mlir/examples/toy/Ch4/mlir/MLIRGen.cpp b/mlir/examples/toy/Ch4/mlir/MLIRGen.cpp
index 7d676f1b39200..6b7ab40299be5 100644
--- a/mlir/examples/toy/Ch4/mlir/MLIRGen.cpp
+++ b/mlir/examples/toy/Ch4/mlir/MLIRGen.cpp
@@ -268,8 +268,7 @@ class MLIRGenImpl {
// 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
diff --git a/mlir/examples/toy/Ch5/mlir/MLIRGen.cpp b/mlir/examples/toy/Ch5/mlir/MLIRGen.cpp
index 7d676f1b39200..6b7ab40299be5 100644
--- a/mlir/examples/toy/Ch5/mlir/MLIRGen.cpp
+++ b/mlir/examples/toy/Ch5/mlir/MLIRGen.cpp
@@ -268,8 +268,7 @@ class MLIRGenImpl {
// 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
diff --git a/mlir/examples/toy/Ch6/mlir/MLIRGen.cpp b/mlir/examples/toy/Ch6/mlir/MLIRGen.cpp
index 7d676f1b39200..6b7ab40299be5 100644
--- a/mlir/examples/toy/Ch6/mlir/MLIRGen.cpp
+++ b/mlir/examples/toy/Ch6/mlir/MLIRGen.cpp
@@ -268,8 +268,7 @@ class MLIRGenImpl {
// 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
diff --git a/mlir/examples/toy/Ch7/mlir/MLIRGen.cpp b/mlir/examples/toy/Ch7/mlir/MLIRGen.cpp
index 75dbc9104a992..73133247335dc 100644
--- a/mlir/examples/toy/Ch7/mlir/MLIRGen.cpp
+++ b/mlir/examples/toy/Ch7/mlir/MLIRGen.cpp
@@ -405,8 +405,7 @@ class MLIRGenImpl {
// 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
diff --git a/mlir/lib/Conversion/MemRefToEmitC/MemRefToEmitC.cpp b/mlir/lib/Conversion/MemRefToEmitC/MemRefToEmitC.cpp
index 2b7bdc9a7b7f8..11f866c103639 100644
--- a/mlir/lib/Conversion/MemRefToEmitC/MemRefToEmitC.cpp
+++ b/mlir/lib/Conversion/MemRefToEmitC/MemRefToEmitC.cpp
@@ -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));
diff --git a/mlir/lib/Conversion/TosaToTensor/TosaToTensor.cpp b/mlir/lib/Conversion/TosaToTensor/TosaToTensor.cpp
index 802691c1f7d76..9bf9ca3ae7a89 100644
--- a/mlir/lib/Conversion/TosaToTensor/TosaToTensor.cpp
+++ b/mlir/lib/Conversion/TosaToTensor/TosaToTensor.cpp
@@ -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.
diff --git a/mlir/lib/Conversion/VectorToAMX/VectorToAMX.cpp b/mlir/lib/Conversion/VectorToAMX/VectorToAMX.cpp
index 79c2f23c8e7f3..245a3efe98ecc 100644
--- a/mlir/lib/Conversion/VectorToAMX/VectorToAMX.cpp
+++ b/mlir/lib/Conversion/VectorToAMX/VectorToAMX.cpp
@@ -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,
diff --git a/mlir/lib/Conversion/VectorToSCF/VectorToSCF.cpp b/mlir/lib/Conversion/VectorToSCF/VectorToSCF.cpp
index c45c45e4712f3..c9eba6962e6a4 100644
--- a/mlir/lib/Conversion/VectorToSCF/VectorToSCF.cpp
+++ b/mlir/lib/Conversion/VectorToSCF/VectorToSCF.cpp
@@ -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);
diff --git a/mlir/lib/Conversion/XeGPUToXeVM/XeGPUToXeVM.cpp b/mlir/lib/Conversion/XeGPUToXeVM/XeGPUToXeVM.cpp
index 9ead1d89069d6..71687b1479a7d 100644
--- a/mlir/lib/Conversion/XeGPUToXeVM/XeGPUToXeVM.cpp
+++ b/mlir/lib/Conversion/XeGPUToXeVM/XeGPUToXeVM.cpp
@@ -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 {
diff --git a/mlir/lib/Dialect/Arith/Utils/Utils.cpp b/mlir/lib/Dialect/Arith/Utils/Utils.cpp
index b1fc9aa57c3ba..f54bafff58322 100644
--- a/mlir/lib/Dialect/Arith/Utils/Utils.cpp
+++ b/mlir/lib/Dialect/Arith/Utils/Utils.cpp
@@ -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.
diff --git a/mlir/lib/Dialect/GPU/IR/GPUDialect.cpp b/mlir/lib/Dialect/GPU/IR/GPUDialect.cpp
index 19eba6beacd86..b5f8ddaadacdf 100644
--- a/mlir/lib/Dialect/GPU/IR/GPUDialect.cpp
+++ b/mlir/lib/Dialect/GPU/IR/GPUDialect.cpp
@@ -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;
diff --git a/mlir/lib/Dialect/GPU/Utils/DistributionUtils.cpp b/mlir/lib/Dialect/GPU/Utils/DistributionUtils.cpp
index 88f531f394765..572b746358379 100644
--- a/mlir/lib/Dialect/GPU/Utils/DistributionUtils.cpp
+++ b/mlir/lib/Dialect/GPU/Utils/DistributionUtils.cpp
@@ -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;
diff --git a/mlir/lib/Dialect/Linalg/Transforms/ShardingInterfaceImpl.cpp b/mlir/lib/Dialect/Linalg/Transforms/ShardingInterfaceImpl.cpp
index f277c5f5be5fc..0ae2a9cc0318c 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/ShardingInterfaceImpl.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/ShardingInterfaceImpl.cpp
@@ -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);
diff --git a/mlir/lib/Dialect/Quant/Utils/UniformSupport.cpp b/mlir/lib/Dialect/Quant/Utils/UniformSupport.cpp
index b66390819103e..8c4f80f13e2f6 100644
--- a/mlir/lib/Dialect/Quant/Utils/UniformSupport.cpp
+++ b/mlir/lib/Dialect/Quant/Utils/UniformSupport.cpp
@@ -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;
diff --git a/mlir/lib/Dialect/SPIRV/IR/SPIRVOps.cpp b/mlir/lib/Dialect/SPIRV/IR/SPIRVOps.cpp
index 55119984d9cc1..fe50865bb7c49 100644
--- a/mlir/lib/Dialect/SPIRV/IR/SPIRVOps.cpp
+++ b/mlir/lib/Dialect/SPIRV/IR/SPIRVOps.cpp
@@ -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;
diff --git a/mlir/lib/Dialect/Shard/IR/ShardOps.cpp b/mlir/lib/Dialect/Shard/IR/ShardOps.cpp
index 08fccfa25c0c7..135c03311ce0e 100644
--- a/mlir/lib/Dialect/Shard/IR/ShardOps.cpp
+++ b/mlir/lib/Dialect/Shard/IR/ShardOps.cpp
@@ -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,
diff --git a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
index c51b5e9cbfc78..00f84bc43f444 100644
--- a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
+++ b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
@@ -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) ||
diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaFolders.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaFolders.cpp
index d33ebe397cd35..5786f53b8133d 100644
--- a/mlir/lib/Dialect/Tosa/Transforms/TosaFolders.cpp
+++ b/mlir/lib/Dialect/Tosa/Transforms/TosaFolders.cpp
@@ -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;
diff --git a/mlir/lib/Dialect/Utils/IndexingUtils.cpp b/mlir/lib/Dialect/Utils/IndexingUtils.cpp
index e1648ab99ff25..305b06eb38fdf 100644
--- a/mlir/lib/Dialect/Utils/IndexingUtils.cpp
+++ b/mlir/lib/Dialect/Utils/IndexingUtils.cpp
@@ -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.em...
[truncated]
|
CoTinker
approved these changes
Oct 11, 2025
|
LLVM Buildbot has detected a new failure on builder Full details are available at: https://lab.llvm.org/buildbot/#/builders/169/builds/15875 Here is the relevant piece of the build log for the reference |
DharuniRAcharya
pushed a commit
to DharuniRAcharya/llvm-project
that referenced
this pull request
Oct 13, 2025
Use wrappers around `std::accumulate` to make the code more concise and less bug-prone: llvm#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...
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Use wrappers around
std::accumulateto make the code more concise and less bug-prone: #162129.With
std::accumulate, it's the initial value that determines the accumulator type.llvm::sum_ofandllvm::product_ofpick 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...