Files
clang-p2996/mlir/lib/Conversion/MathToLibm/MathToLibm.cpp
River Riddle 7ceffae18c [mlir] Convert OpTrait::FunctionLike to FunctionOpInterface
This commit refactors the FunctionLike trait into an interface (FunctionOpInterface).
FunctionLike as it is today is already a pseudo-interface, with many users checking the
presence of the trait and then manually into functionality implemented in the
function_like_impl namespace. By transitioning to an interface, these accesses are much
cleaner (ideally with no direct calls to the impl namespace outside of the implementation
of the derived function operations, e.g. for parsing/printing utilities).

I've tried to maintain as much compatability with the current state as possible, while
also trying to clean up as much of the cruft as possible. The general migration plan for
current users of FunctionLike is as follows:

* function_like_impl -> function_interface_impl
Realistically most user calls should remove references to functions within this namespace
outside of a vary narrow set (e.g. parsing/printing utilities). Calls to the attribute name
accessors should be migrated to the `FunctionOpInterface::` equivalent, most everything
else should be updated to be driven through an instance of the interface.

* OpTrait::FunctionLike -> FunctionOpInterface
`hasTrait` checks will need to be moved to isa, along with the other various Trait vs
Interface API differences.

* populateFunctionLikeTypeConversionPattern -> populateFunctionOpInterfaceTypeConversionPattern

Fixes #52917

Differential Revision: https://reviews.llvm.org/D117272
2022-01-18 20:56:53 -08:00

153 lines
5.8 KiB
C++

//===-- MathToLibm.cpp - conversion from Math to libm calls ---------------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
#include "mlir/Conversion/MathToLibm/MathToLibm.h"
#include "../PassDetail.h"
#include "mlir/Dialect/Arithmetic/IR/Arithmetic.h"
#include "mlir/Dialect/Math/IR/Math.h"
#include "mlir/Dialect/StandardOps/IR/Ops.h"
#include "mlir/Dialect/Vector/VectorOps.h"
#include "mlir/Dialect/Vector/VectorUtils.h"
#include "mlir/IR/BuiltinDialect.h"
#include "mlir/IR/PatternMatch.h"
using namespace mlir;
namespace {
// Pattern to convert vector operations to scalar operations. This is needed as
// libm calls require scalars.
template <typename Op>
struct VecOpToScalarOp : public OpRewritePattern<Op> {
public:
using OpRewritePattern<Op>::OpRewritePattern;
LogicalResult matchAndRewrite(Op op, PatternRewriter &rewriter) const final;
};
// Pattern to convert scalar math operations to calls to libm functions.
// Additionally the libm function signatures are declared.
template <typename Op>
struct ScalarOpToLibmCall : public OpRewritePattern<Op> {
public:
using OpRewritePattern<Op>::OpRewritePattern;
ScalarOpToLibmCall<Op>(MLIRContext *context, StringRef floatFunc,
StringRef doubleFunc, PatternBenefit benefit)
: OpRewritePattern<Op>(context, benefit), floatFunc(floatFunc),
doubleFunc(doubleFunc){};
LogicalResult matchAndRewrite(Op op, PatternRewriter &rewriter) const final;
private:
std::string floatFunc, doubleFunc;
};
} // namespace
template <typename Op>
LogicalResult
VecOpToScalarOp<Op>::matchAndRewrite(Op op, PatternRewriter &rewriter) const {
auto opType = op.getType();
auto loc = op.getLoc();
auto vecType = opType.template dyn_cast<VectorType>();
if (!vecType)
return failure();
if (!vecType.hasRank())
return failure();
auto shape = vecType.getShape();
int64_t numElements = vecType.getNumElements();
Value result = rewriter.create<arith::ConstantOp>(
loc, DenseElementsAttr::get(
vecType, FloatAttr::get(vecType.getElementType(), 0.0)));
SmallVector<int64_t> ones(shape.size(), 1);
SmallVector<int64_t> strides = computeStrides(shape, ones);
for (auto linearIndex = 0; linearIndex < numElements; ++linearIndex) {
SmallVector<int64_t> positions = delinearize(strides, linearIndex);
SmallVector<Value> operands;
for (auto input : op->getOperands())
operands.push_back(
rewriter.create<vector::ExtractOp>(loc, input, positions));
Value scalarOp =
rewriter.create<Op>(loc, vecType.getElementType(), operands);
result =
rewriter.create<vector::InsertOp>(loc, scalarOp, result, positions);
}
rewriter.replaceOp(op, {result});
return success();
}
template <typename Op>
LogicalResult
ScalarOpToLibmCall<Op>::matchAndRewrite(Op op,
PatternRewriter &rewriter) const {
auto module = SymbolTable::getNearestSymbolTable(op);
auto type = op.getType();
// TODO: Support Float16 by upcasting to Float32
if (!type.template isa<Float32Type, Float64Type>())
return failure();
auto name = type.getIntOrFloatBitWidth() == 64 ? doubleFunc : floatFunc;
auto opFunc = dyn_cast_or_null<SymbolOpInterface>(
SymbolTable::lookupSymbolIn(module, name));
// Forward declare function if it hasn't already been
if (!opFunc) {
OpBuilder::InsertionGuard guard(rewriter);
rewriter.setInsertionPointToStart(&module->getRegion(0).front());
auto opFunctionTy = FunctionType::get(
rewriter.getContext(), op->getOperandTypes(), op->getResultTypes());
opFunc =
rewriter.create<FuncOp>(rewriter.getUnknownLoc(), name, opFunctionTy);
opFunc.setPrivate();
}
assert(isa<FunctionOpInterface>(SymbolTable::lookupSymbolIn(module, name)));
rewriter.replaceOpWithNewOp<CallOp>(op, name, op.getType(),
op->getOperands());
return success();
}
void mlir::populateMathToLibmConversionPatterns(RewritePatternSet &patterns,
PatternBenefit benefit) {
patterns.add<VecOpToScalarOp<math::Atan2Op>, VecOpToScalarOp<math::ExpM1Op>,
VecOpToScalarOp<math::TanhOp>>(patterns.getContext(), benefit);
patterns.add<ScalarOpToLibmCall<math::Atan2Op>>(patterns.getContext(),
"atan2f", "atan2", benefit);
patterns.add<ScalarOpToLibmCall<math::ErfOp>>(patterns.getContext(), "erff",
"erf", benefit);
patterns.add<ScalarOpToLibmCall<math::ExpM1Op>>(patterns.getContext(),
"expm1f", "expm1", benefit);
patterns.add<ScalarOpToLibmCall<math::TanhOp>>(patterns.getContext(), "tanhf",
"tanh", benefit);
}
namespace {
struct ConvertMathToLibmPass
: public ConvertMathToLibmBase<ConvertMathToLibmPass> {
void runOnOperation() override;
};
} // namespace
void ConvertMathToLibmPass::runOnOperation() {
auto module = getOperation();
RewritePatternSet patterns(&getContext());
populateMathToLibmConversionPatterns(patterns, /*benefit=*/1);
ConversionTarget target(getContext());
target.addLegalDialect<arith::ArithmeticDialect, BuiltinDialect,
StandardOpsDialect, vector::VectorDialect>();
target.addIllegalDialect<math::MathDialect>();
if (failed(applyPartialConversion(module, target, std::move(patterns))))
signalPassFailure();
}
std::unique_ptr<OperationPass<ModuleOp>> mlir::createConvertMathToLibmPass() {
return std::make_unique<ConvertMathToLibmPass>();
}