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[flang] Inline hlfir.dot_product. #123143

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merged 3 commits into from
Jan 16, 2025
Merged

[flang] Inline hlfir.dot_product. #123143

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bfc3d7d
[NFC] Clean-up product code generation.
vzakhari Jan 15, 2025
2986f4a
[flang] Inline hlfir.dot_product.
vzakhari Jan 15, 2025
13b479a
[NFC] Extracted extents creation.
vzakhari Jan 16, 2025
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249 changes: 162 additions & 87 deletions flang/lib/Optimizer/HLFIR/Transforms/SimplifyHLFIRIntrinsics.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -37,6 +37,79 @@ static llvm::cl::opt<bool> forceMatmulAsElemental(

namespace {

// Helper class to generate operations related to computing
// product of values.
class ProductFactory {
public:
ProductFactory(mlir::Location loc, fir::FirOpBuilder &builder)
: loc(loc), builder(builder) {}

// Generate an update of the inner product value:
// acc += v1 * v2, OR
// acc += CONJ(v1) * v2, OR
// acc ||= v1 && v2
//
// CONJ parameter specifies whether the first complex product argument
// needs to be conjugated.
template <bool CONJ = false>
mlir::Value genAccumulateProduct(mlir::Value acc, mlir::Value v1,
mlir::Value v2) {
mlir::Type resultType = acc.getType();
acc = castToProductType(acc, resultType);
v1 = castToProductType(v1, resultType);
v2 = castToProductType(v2, resultType);
mlir::Value result;
if (mlir::isa<mlir::FloatType>(resultType)) {
result = builder.create<mlir::arith::AddFOp>(
loc, acc, builder.create<mlir::arith::MulFOp>(loc, v1, v2));
} else if (mlir::isa<mlir::ComplexType>(resultType)) {
if constexpr (CONJ)
result = fir::IntrinsicLibrary{builder, loc}.genConjg(resultType, v1);
else
result = v1;

result = builder.create<fir::AddcOp>(
loc, acc, builder.create<fir::MulcOp>(loc, result, v2));
} else if (mlir::isa<mlir::IntegerType>(resultType)) {
result = builder.create<mlir::arith::AddIOp>(
loc, acc, builder.create<mlir::arith::MulIOp>(loc, v1, v2));
} else if (mlir::isa<fir::LogicalType>(resultType)) {
result = builder.create<mlir::arith::OrIOp>(
loc, acc, builder.create<mlir::arith::AndIOp>(loc, v1, v2));
} else {
llvm_unreachable("unsupported type");
}

return builder.createConvert(loc, resultType, result);
}

private:
mlir::Location loc;
fir::FirOpBuilder &builder;

mlir::Value castToProductType(mlir::Value value, mlir::Type type) {
if (mlir::isa<fir::LogicalType>(type))
return builder.createConvert(loc, builder.getIntegerType(1), value);

// TODO: the multiplications/additions by/of zero resulting from
// complex * real are optimized by LLVM under -fno-signed-zeros
// -fno-honor-nans.
// We can make them disappear by default if we:
// * either expand the complex multiplication into real
// operations, OR
// * set nnan nsz fast-math flags to the complex operations.
if (fir::isa_complex(type) && !fir::isa_complex(value.getType())) {
mlir::Value zeroCmplx = fir::factory::createZeroValue(builder, loc, type);
fir::factory::Complex helper(builder, loc);
mlir::Type partType = helper.getComplexPartType(type);
return helper.insertComplexPart(zeroCmplx,
castToProductType(value, partType),
/*isImagPart=*/false);
}
return builder.createConvert(loc, type, value);
}
};

class TransposeAsElementalConversion
: public mlir::OpRewritePattern<hlfir::TransposeOp> {
public:
Expand Down Expand Up @@ -163,7 +236,8 @@ class SumAsElementalConversion : public mlir::OpRewritePattern<hlfir::SumOp> {
// If DIM is not present, do total reduction.

// Initial value for the reduction.
mlir::Value reductionInitValue = genInitValue(loc, builder, elementType);
mlir::Value reductionInitValue =
fir::factory::createZeroValue(builder, loc, elementType);

// The reduction loop may be unordered if FastMathFlags::reassoc
// transformations are allowed. The integer reduction is always
Expand Down Expand Up @@ -293,26 +367,6 @@ class SumAsElementalConversion : public mlir::OpRewritePattern<hlfir::SumOp> {
return {builder.create<fir::ShapeOp>(loc, inExtents), dimExtent};
}

// Generate the initial value for a SUM reduction with the given
// data type.
static mlir::Value genInitValue(mlir::Location loc,
fir::FirOpBuilder &builder,
mlir::Type elementType) {
if (auto ty = mlir::dyn_cast<mlir::FloatType>(elementType)) {
const llvm::fltSemantics &sem = ty.getFloatSemantics();
return builder.createRealConstant(loc, elementType,
llvm::APFloat::getZero(sem));
} else if (auto ty = mlir::dyn_cast<mlir::ComplexType>(elementType)) {
mlir::Value initValue = genInitValue(loc, builder, ty.getElementType());
return fir::factory::Complex{builder, loc}.createComplex(ty, initValue,
initValue);
} else if (mlir::isa<mlir::IntegerType>(elementType)) {
return builder.createIntegerConstant(loc, elementType, 0);
}

llvm_unreachable("unsupported SUM reduction type");
}

// Generate scalar addition of the two values (of the same data type).
static mlir::Value genScalarAdd(mlir::Location loc,
fir::FirOpBuilder &builder,
Expand Down Expand Up @@ -627,60 +681,6 @@ class MatmulConversion : public mlir::OpRewritePattern<Op> {
innerProductExtent[0]};
}

static mlir::Value castToProductType(mlir::Location loc,
fir::FirOpBuilder &builder,
mlir::Value value, mlir::Type type) {
if (mlir::isa<fir::LogicalType>(type))
return builder.createConvert(loc, builder.getIntegerType(1), value);

// TODO: the multiplications/additions by/of zero resulting from
// complex * real are optimized by LLVM under -fno-signed-zeros
// -fno-honor-nans.
// We can make them disappear by default if we:
// * either expand the complex multiplication into real
// operations, OR
// * set nnan nsz fast-math flags to the complex operations.
if (fir::isa_complex(type) && !fir::isa_complex(value.getType())) {
mlir::Value zeroCmplx = fir::factory::createZeroValue(builder, loc, type);
fir::factory::Complex helper(builder, loc);
mlir::Type partType = helper.getComplexPartType(type);
return helper.insertComplexPart(
zeroCmplx, castToProductType(loc, builder, value, partType),
/*isImagPart=*/false);
}
return builder.createConvert(loc, type, value);
}

// Generate an update of the inner product value:
// acc += v1 * v2, OR
// acc ||= v1 && v2
static mlir::Value genAccumulateProduct(mlir::Location loc,
fir::FirOpBuilder &builder,
mlir::Type resultType,
mlir::Value acc, mlir::Value v1,
mlir::Value v2) {
acc = castToProductType(loc, builder, acc, resultType);
v1 = castToProductType(loc, builder, v1, resultType);
v2 = castToProductType(loc, builder, v2, resultType);
mlir::Value result;
if (mlir::isa<mlir::FloatType>(resultType))
result = builder.create<mlir::arith::AddFOp>(
loc, acc, builder.create<mlir::arith::MulFOp>(loc, v1, v2));
else if (mlir::isa<mlir::ComplexType>(resultType))
result = builder.create<fir::AddcOp>(
loc, acc, builder.create<fir::MulcOp>(loc, v1, v2));
else if (mlir::isa<mlir::IntegerType>(resultType))
result = builder.create<mlir::arith::AddIOp>(
loc, acc, builder.create<mlir::arith::MulIOp>(loc, v1, v2));
else if (mlir::isa<fir::LogicalType>(resultType))
result = builder.create<mlir::arith::OrIOp>(
loc, acc, builder.create<mlir::arith::AndIOp>(loc, v1, v2));
else
llvm_unreachable("unsupported type");

return builder.createConvert(loc, resultType, result);
}

static mlir::LogicalResult
genContiguousMatmul(mlir::Location loc, fir::FirOpBuilder &builder,
hlfir::Entity result, mlir::Value resultShape,
Expand Down Expand Up @@ -748,9 +748,9 @@ class MatmulConversion : public mlir::OpRewritePattern<Op> {
hlfir::loadElementAt(loc, builder, lhs, {I, K});
hlfir::Entity rhsElementValue =
hlfir::loadElementAt(loc, builder, rhs, {K, J});
mlir::Value productValue = genAccumulateProduct(
loc, builder, resultElementType, resultElementValue,
lhsElementValue, rhsElementValue);
mlir::Value productValue =
ProductFactory{loc, builder}.genAccumulateProduct(
resultElementValue, lhsElementValue, rhsElementValue);
builder.create<hlfir::AssignOp>(loc, productValue, resultElement);
return {};
};
Expand Down Expand Up @@ -785,9 +785,9 @@ class MatmulConversion : public mlir::OpRewritePattern<Op> {
hlfir::loadElementAt(loc, builder, lhs, {J, K});
hlfir::Entity rhsElementValue =
hlfir::loadElementAt(loc, builder, rhs, {K});
mlir::Value productValue = genAccumulateProduct(
loc, builder, resultElementType, resultElementValue,
lhsElementValue, rhsElementValue);
mlir::Value productValue =
ProductFactory{loc, builder}.genAccumulateProduct(
resultElementValue, lhsElementValue, rhsElementValue);
builder.create<hlfir::AssignOp>(loc, productValue, resultElement);
return {};
};
Expand Down Expand Up @@ -817,9 +817,9 @@ class MatmulConversion : public mlir::OpRewritePattern<Op> {
hlfir::loadElementAt(loc, builder, lhs, {K});
hlfir::Entity rhsElementValue =
hlfir::loadElementAt(loc, builder, rhs, {K, J});
mlir::Value productValue = genAccumulateProduct(
loc, builder, resultElementType, resultElementValue,
lhsElementValue, rhsElementValue);
mlir::Value productValue =
ProductFactory{loc, builder}.genAccumulateProduct(
resultElementValue, lhsElementValue, rhsElementValue);
builder.create<hlfir::AssignOp>(loc, productValue, resultElement);
return {};
};
Expand Down Expand Up @@ -885,9 +885,9 @@ class MatmulConversion : public mlir::OpRewritePattern<Op> {
hlfir::loadElementAt(loc, builder, lhs, lhsIndices);
hlfir::Entity rhsElementValue =
hlfir::loadElementAt(loc, builder, rhs, rhsIndices);
mlir::Value productValue = genAccumulateProduct(
loc, builder, resultElementType, reductionArgs[0], lhsElementValue,
rhsElementValue);
mlir::Value productValue =
ProductFactory{loc, builder}.genAccumulateProduct(
reductionArgs[0], lhsElementValue, rhsElementValue);
return {productValue};
};
llvm::SmallVector<mlir::Value, 1> innerProductValue =
Expand All @@ -904,6 +904,79 @@ class MatmulConversion : public mlir::OpRewritePattern<Op> {
}
};

class DotProductConversion
: public mlir::OpRewritePattern<hlfir::DotProductOp> {
public:
using mlir::OpRewritePattern<hlfir::DotProductOp>::OpRewritePattern;

llvm::LogicalResult
matchAndRewrite(hlfir::DotProductOp product,
mlir::PatternRewriter &rewriter) const override {
hlfir::Entity op = hlfir::Entity{product};
if (!op.isScalar())
return rewriter.notifyMatchFailure(product, "produces non-scalar result");

mlir::Location loc = product.getLoc();
fir::FirOpBuilder builder{rewriter, product.getOperation()};
hlfir::Entity lhs = hlfir::Entity{product.getLhs()};
hlfir::Entity rhs = hlfir::Entity{product.getRhs()};
mlir::Type resultElementType = product.getType();
bool isUnordered = mlir::isa<mlir::IntegerType>(resultElementType) ||
mlir::isa<fir::LogicalType>(resultElementType) ||
static_cast<bool>(builder.getFastMathFlags() &
mlir::arith::FastMathFlags::reassoc);

mlir::Value extent = genProductExtent(loc, builder, lhs, rhs);

auto genBody = [&](mlir::Location loc, fir::FirOpBuilder &builder,
mlir::ValueRange oneBasedIndices,
mlir::ValueRange reductionArgs)
-> llvm::SmallVector<mlir::Value, 1> {
hlfir::Entity lhsElementValue =
hlfir::loadElementAt(loc, builder, lhs, oneBasedIndices);
hlfir::Entity rhsElementValue =
hlfir::loadElementAt(loc, builder, rhs, oneBasedIndices);
mlir::Value productValue =
ProductFactory{loc, builder}.genAccumulateProduct</*CONJ=*/true>(
reductionArgs[0], lhsElementValue, rhsElementValue);
return {productValue};
};

mlir::Value initValue =
fir::factory::createZeroValue(builder, loc, resultElementType);

llvm::SmallVector<mlir::Value, 1> result = hlfir::genLoopNestWithReductions(
loc, builder, {extent},
/*reductionInits=*/{initValue}, genBody, isUnordered);

rewriter.replaceOp(product, result[0]);
return mlir::success();
}

private:
static mlir::Value genProductExtent(mlir::Location loc,
fir::FirOpBuilder &builder,
hlfir::Entity input1,
hlfir::Entity input2) {
mlir::Value input1Shape = hlfir::genShape(loc, builder, input1);
llvm::SmallVector<mlir::Value, 1> input1Extents =
hlfir::getExplicitExtentsFromShape(input1Shape, builder);
if (input1Shape.getUses().empty())
input1Shape.getDefiningOp()->erase();
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@jeanPerier jeanPerier Jan 16, 2025

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Maybe this pattern should be placed in an hlfir helper (the same will be needed for many intrinsics, and the op erasure is a bit distracting).

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@vzakhari vzakhari Jan 16, 2025

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Thanks! I will move it into HLFIRTools.

mlir::Value input2Shape = hlfir::genShape(loc, builder, input2);
llvm::SmallVector<mlir::Value, 1> input2Extents =
hlfir::getExplicitExtentsFromShape(input2Shape, builder);
if (input2Shape.getUses().empty())
input2Shape.getDefiningOp()->erase();

assert(input1Extents.size() == 1 && input2Extents.size() == 1 &&
"hlfir.dot_product arguments must be vectors");
llvm::SmallVector<mlir::Value, 1> extent =
fir::factory::deduceOptimalExtents(input1Extents, input2Extents);
return extent[0];
}
};

class SimplifyHLFIRIntrinsics
: public hlfir::impl::SimplifyHLFIRIntrinsicsBase<SimplifyHLFIRIntrinsics> {
public:
Expand Down Expand Up @@ -939,6 +1012,8 @@ class SimplifyHLFIRIntrinsics
if (forceMatmulAsElemental || this->allowNewSideEffects)
patterns.insert<MatmulConversion<hlfir::MatmulOp>>(context);

patterns.insert<DotProductConversion>(context);

if (mlir::failed(mlir::applyPatternsGreedily(
getOperation(), std::move(patterns), config))) {
mlir::emitError(getOperation()->getLoc(),
Expand Down
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