503 lines
21 KiB
C++
503 lines
21 KiB
C++
//===- SimplifyHLFIRIntrinsics.cpp - Simplify HLFIR Intrinsics ------------===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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// Normally transformational intrinsics are lowered to calls to runtime
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// functions. However, some cases of the intrinsics are faster when inlined
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// into the calling function.
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//===----------------------------------------------------------------------===//
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#include "flang/Optimizer/Builder/Complex.h"
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#include "flang/Optimizer/Builder/FIRBuilder.h"
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#include "flang/Optimizer/Builder/HLFIRTools.h"
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#include "flang/Optimizer/Builder/IntrinsicCall.h"
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#include "flang/Optimizer/Dialect/FIRDialect.h"
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#include "flang/Optimizer/HLFIR/HLFIRDialect.h"
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#include "flang/Optimizer/HLFIR/HLFIROps.h"
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#include "flang/Optimizer/HLFIR/Passes.h"
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#include "mlir/Dialect/Arith/IR/Arith.h"
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#include "mlir/IR/Location.h"
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#include "mlir/Pass/Pass.h"
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#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
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namespace hlfir {
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#define GEN_PASS_DEF_SIMPLIFYHLFIRINTRINSICS
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#include "flang/Optimizer/HLFIR/Passes.h.inc"
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} // namespace hlfir
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static llvm::cl::opt<bool>
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simplifySum("flang-simplify-hlfir-sum",
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llvm::cl::desc("Expand hlfir.sum into an inline sequence"),
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llvm::cl::init(true));
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namespace {
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class TransposeAsElementalConversion
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: public mlir::OpRewritePattern<hlfir::TransposeOp> {
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public:
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using mlir::OpRewritePattern<hlfir::TransposeOp>::OpRewritePattern;
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llvm::LogicalResult
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matchAndRewrite(hlfir::TransposeOp transpose,
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mlir::PatternRewriter &rewriter) const override {
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hlfir::ExprType expr = transpose.getType();
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// TODO: hlfir.elemental supports polymorphic data types now,
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// so this can be supported.
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if (expr.isPolymorphic())
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return rewriter.notifyMatchFailure(transpose,
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"TRANSPOSE of polymorphic type");
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mlir::Location loc = transpose.getLoc();
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fir::FirOpBuilder builder{rewriter, transpose.getOperation()};
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mlir::Type elementType = expr.getElementType();
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hlfir::Entity array = hlfir::Entity{transpose.getArray()};
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mlir::Value resultShape = genResultShape(loc, builder, array);
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llvm::SmallVector<mlir::Value, 1> typeParams;
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hlfir::genLengthParameters(loc, builder, array, typeParams);
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auto genKernel = [&array](mlir::Location loc, fir::FirOpBuilder &builder,
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mlir::ValueRange inputIndices) -> hlfir::Entity {
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assert(inputIndices.size() == 2 && "checked in TransposeOp::validate");
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const std::initializer_list<mlir::Value> initList = {inputIndices[1],
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inputIndices[0]};
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mlir::ValueRange transposedIndices(initList);
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hlfir::Entity element =
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hlfir::getElementAt(loc, builder, array, transposedIndices);
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hlfir::Entity val = hlfir::loadTrivialScalar(loc, builder, element);
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return val;
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};
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hlfir::ElementalOp elementalOp = hlfir::genElementalOp(
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loc, builder, elementType, resultShape, typeParams, genKernel,
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/*isUnordered=*/true, /*polymorphicMold=*/nullptr,
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transpose.getResult().getType());
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// it wouldn't be safe to replace block arguments with a different
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// hlfir.expr type. Types can differ due to differing amounts of shape
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// information
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assert(elementalOp.getResult().getType() ==
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transpose.getResult().getType());
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rewriter.replaceOp(transpose, elementalOp);
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return mlir::success();
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}
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private:
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static mlir::Value genResultShape(mlir::Location loc,
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fir::FirOpBuilder &builder,
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hlfir::Entity array) {
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mlir::Value inShape = hlfir::genShape(loc, builder, array);
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llvm::SmallVector<mlir::Value> inExtents =
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hlfir::getExplicitExtentsFromShape(inShape, builder);
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if (inShape.getUses().empty())
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inShape.getDefiningOp()->erase();
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// transpose indices
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assert(inExtents.size() == 2 && "checked in TransposeOp::validate");
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return builder.create<fir::ShapeOp>(
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loc, mlir::ValueRange{inExtents[1], inExtents[0]});
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}
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};
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// Expand the SUM(DIM=CONSTANT) operation into .
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class SumAsElementalConversion : public mlir::OpRewritePattern<hlfir::SumOp> {
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public:
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using mlir::OpRewritePattern<hlfir::SumOp>::OpRewritePattern;
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llvm::LogicalResult
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matchAndRewrite(hlfir::SumOp sum,
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mlir::PatternRewriter &rewriter) const override {
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if (!simplifySum)
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return rewriter.notifyMatchFailure(sum, "SUM simplification is disabled");
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hlfir::Entity array = hlfir::Entity{sum.getArray()};
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bool isTotalReduction = hlfir::Entity{sum}.getRank() == 0;
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mlir::Value dim = sum.getDim();
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int64_t dimVal = 0;
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if (!isTotalReduction) {
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// In case of partial reduction we should ignore the operations
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// with invalid DIM values. They may appear in dead code
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// after constant propagation.
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auto constDim = fir::getIntIfConstant(dim);
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if (!constDim)
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return rewriter.notifyMatchFailure(sum, "Nonconstant DIM for SUM");
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dimVal = *constDim;
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if ((dimVal <= 0 || dimVal > array.getRank()))
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return rewriter.notifyMatchFailure(
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sum, "Invalid DIM for partial SUM reduction");
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}
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mlir::Location loc = sum.getLoc();
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fir::FirOpBuilder builder{rewriter, sum.getOperation()};
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mlir::Type elementType = hlfir::getFortranElementType(sum.getType());
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mlir::Value mask = sum.getMask();
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mlir::Value resultShape, dimExtent;
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llvm::SmallVector<mlir::Value> arrayExtents;
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if (isTotalReduction)
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arrayExtents = genArrayExtents(loc, builder, array);
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else
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std::tie(resultShape, dimExtent) =
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genResultShapeForPartialReduction(loc, builder, array, dimVal);
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// If the mask is present and is a scalar, then we'd better load its value
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// outside of the reduction loop making the loop unswitching easier.
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mlir::Value isPresentPred, maskValue;
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if (mask) {
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if (mlir::isa<fir::BaseBoxType>(mask.getType())) {
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// MASK represented by a box might be dynamically optional,
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// so we have to check for its presence before accessing it.
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isPresentPred =
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builder.create<fir::IsPresentOp>(loc, builder.getI1Type(), mask);
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}
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if (hlfir::Entity{mask}.isScalar())
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maskValue = genMaskValue(loc, builder, mask, isPresentPred, {});
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}
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auto genKernel = [&](mlir::Location loc, fir::FirOpBuilder &builder,
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mlir::ValueRange inputIndices) -> hlfir::Entity {
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// Loop over all indices in the DIM dimension, and reduce all values.
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// If DIM is not present, do total reduction.
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// Initial value for the reduction.
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mlir::Value reductionInitValue = genInitValue(loc, builder, elementType);
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// The reduction loop may be unordered if FastMathFlags::reassoc
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// transformations are allowed. The integer reduction is always
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// unordered.
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bool isUnordered = mlir::isa<mlir::IntegerType>(elementType) ||
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static_cast<bool>(sum.getFastmath() &
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mlir::arith::FastMathFlags::reassoc);
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llvm::SmallVector<mlir::Value> extents;
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if (isTotalReduction)
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extents = arrayExtents;
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else
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extents.push_back(
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builder.createConvert(loc, builder.getIndexType(), dimExtent));
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auto genBody = [&](mlir::Location loc, fir::FirOpBuilder &builder,
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mlir::ValueRange oneBasedIndices,
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mlir::ValueRange reductionArgs)
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-> llvm::SmallVector<mlir::Value, 1> {
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// Generate the reduction loop-nest body.
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// The initial reduction value in the innermost loop
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// is passed via reductionArgs[0].
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llvm::SmallVector<mlir::Value> indices;
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if (isTotalReduction) {
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indices = oneBasedIndices;
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} else {
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indices = inputIndices;
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indices.insert(indices.begin() + dimVal - 1, oneBasedIndices[0]);
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}
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mlir::Value reductionValue = reductionArgs[0];
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fir::IfOp ifOp;
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if (mask) {
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// Make the reduction value update conditional on the value
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// of the mask.
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if (!maskValue) {
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// If the mask is an array, use the elemental and the loop indices
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// to address the proper mask element.
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maskValue =
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genMaskValue(loc, builder, mask, isPresentPred, indices);
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}
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mlir::Value isUnmasked = builder.create<fir::ConvertOp>(
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loc, builder.getI1Type(), maskValue);
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ifOp = builder.create<fir::IfOp>(loc, elementType, isUnmasked,
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/*withElseRegion=*/true);
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// In the 'else' block return the current reduction value.
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builder.setInsertionPointToStart(&ifOp.getElseRegion().front());
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builder.create<fir::ResultOp>(loc, reductionValue);
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// In the 'then' block do the actual addition.
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builder.setInsertionPointToStart(&ifOp.getThenRegion().front());
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}
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hlfir::Entity element =
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hlfir::getElementAt(loc, builder, array, indices);
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hlfir::Entity elementValue =
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hlfir::loadTrivialScalar(loc, builder, element);
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// NOTE: we can use "Kahan summation" same way as the runtime
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// (e.g. when fast-math is not allowed), but let's start with
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// the simple version.
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reductionValue =
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genScalarAdd(loc, builder, reductionValue, elementValue);
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if (ifOp) {
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builder.create<fir::ResultOp>(loc, reductionValue);
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builder.setInsertionPointAfter(ifOp);
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reductionValue = ifOp.getResult(0);
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}
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return {reductionValue};
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};
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llvm::SmallVector<mlir::Value, 1> reductionFinalValues =
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hlfir::genLoopNestWithReductions(loc, builder, extents,
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{reductionInitValue}, genBody,
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isUnordered);
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return hlfir::Entity{reductionFinalValues[0]};
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};
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if (isTotalReduction) {
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hlfir::Entity result = genKernel(loc, builder, mlir::ValueRange{});
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rewriter.replaceOp(sum, result);
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return mlir::success();
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}
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hlfir::ElementalOp elementalOp = hlfir::genElementalOp(
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loc, builder, elementType, resultShape, {}, genKernel,
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/*isUnordered=*/true, /*polymorphicMold=*/nullptr,
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sum.getResult().getType());
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// it wouldn't be safe to replace block arguments with a different
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// hlfir.expr type. Types can differ due to differing amounts of shape
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// information
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assert(elementalOp.getResult().getType() == sum.getResult().getType());
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rewriter.replaceOp(sum, elementalOp);
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return mlir::success();
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}
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private:
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static llvm::SmallVector<mlir::Value>
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genArrayExtents(mlir::Location loc, fir::FirOpBuilder &builder,
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hlfir::Entity array) {
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mlir::Value inShape = hlfir::genShape(loc, builder, array);
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llvm::SmallVector<mlir::Value> inExtents =
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hlfir::getExplicitExtentsFromShape(inShape, builder);
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if (inShape.getUses().empty())
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inShape.getDefiningOp()->erase();
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return inExtents;
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}
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// Return fir.shape specifying the shape of the result
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// of a SUM reduction with DIM=dimVal. The second return value
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// is the extent of the DIM dimension.
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static std::tuple<mlir::Value, mlir::Value>
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genResultShapeForPartialReduction(mlir::Location loc,
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fir::FirOpBuilder &builder,
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hlfir::Entity array, int64_t dimVal) {
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llvm::SmallVector<mlir::Value> inExtents =
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genArrayExtents(loc, builder, array);
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assert(dimVal > 0 && dimVal <= static_cast<int64_t>(inExtents.size()) &&
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"DIM must be present and a positive constant not exceeding "
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"the array's rank");
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mlir::Value dimExtent = inExtents[dimVal - 1];
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inExtents.erase(inExtents.begin() + dimVal - 1);
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return {builder.create<fir::ShapeOp>(loc, inExtents), dimExtent};
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}
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// Generate the initial value for a SUM reduction with the given
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// data type.
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static mlir::Value genInitValue(mlir::Location loc,
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fir::FirOpBuilder &builder,
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mlir::Type elementType) {
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if (auto ty = mlir::dyn_cast<mlir::FloatType>(elementType)) {
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const llvm::fltSemantics &sem = ty.getFloatSemantics();
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return builder.createRealConstant(loc, elementType,
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llvm::APFloat::getZero(sem));
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} else if (auto ty = mlir::dyn_cast<mlir::ComplexType>(elementType)) {
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mlir::Value initValue = genInitValue(loc, builder, ty.getElementType());
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return fir::factory::Complex{builder, loc}.createComplex(ty, initValue,
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initValue);
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} else if (mlir::isa<mlir::IntegerType>(elementType)) {
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return builder.createIntegerConstant(loc, elementType, 0);
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}
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llvm_unreachable("unsupported SUM reduction type");
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}
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// Generate scalar addition of the two values (of the same data type).
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static mlir::Value genScalarAdd(mlir::Location loc,
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fir::FirOpBuilder &builder,
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mlir::Value value1, mlir::Value value2) {
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mlir::Type ty = value1.getType();
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assert(ty == value2.getType() && "reduction values' types do not match");
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if (mlir::isa<mlir::FloatType>(ty))
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return builder.create<mlir::arith::AddFOp>(loc, value1, value2);
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else if (mlir::isa<mlir::ComplexType>(ty))
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return builder.create<fir::AddcOp>(loc, value1, value2);
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else if (mlir::isa<mlir::IntegerType>(ty))
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return builder.create<mlir::arith::AddIOp>(loc, value1, value2);
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llvm_unreachable("unsupported SUM reduction type");
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}
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static mlir::Value genMaskValue(mlir::Location loc,
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fir::FirOpBuilder &builder, mlir::Value mask,
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mlir::Value isPresentPred,
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mlir::ValueRange indices) {
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mlir::OpBuilder::InsertionGuard guard(builder);
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fir::IfOp ifOp;
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mlir::Type maskType =
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hlfir::getFortranElementType(fir::unwrapPassByRefType(mask.getType()));
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if (isPresentPred) {
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ifOp = builder.create<fir::IfOp>(loc, maskType, isPresentPred,
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/*withElseRegion=*/true);
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// Use 'true', if the mask is not present.
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builder.setInsertionPointToStart(&ifOp.getElseRegion().front());
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mlir::Value trueValue = builder.createBool(loc, true);
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trueValue = builder.createConvert(loc, maskType, trueValue);
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builder.create<fir::ResultOp>(loc, trueValue);
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// Load the mask value, if the mask is present.
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builder.setInsertionPointToStart(&ifOp.getThenRegion().front());
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}
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hlfir::Entity maskVar{mask};
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if (maskVar.isScalar()) {
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if (mlir::isa<fir::BaseBoxType>(mask.getType())) {
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// MASK may be a boxed scalar.
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mlir::Value addr = hlfir::genVariableRawAddress(loc, builder, maskVar);
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mask = builder.create<fir::LoadOp>(loc, hlfir::Entity{addr});
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} else {
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mask = hlfir::loadTrivialScalar(loc, builder, maskVar);
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}
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} else {
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// Load from the mask array.
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assert(!indices.empty() && "no indices for addressing the mask array");
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maskVar = hlfir::getElementAt(loc, builder, maskVar, indices);
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mask = hlfir::loadTrivialScalar(loc, builder, maskVar);
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}
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if (!isPresentPred)
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return mask;
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builder.create<fir::ResultOp>(loc, mask);
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return ifOp.getResult(0);
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}
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};
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class CShiftAsElementalConversion
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: public mlir::OpRewritePattern<hlfir::CShiftOp> {
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public:
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using mlir::OpRewritePattern<hlfir::CShiftOp>::OpRewritePattern;
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llvm::LogicalResult
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matchAndRewrite(hlfir::CShiftOp cshift,
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mlir::PatternRewriter &rewriter) const override {
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using Fortran::common::maxRank;
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hlfir::ExprType expr = mlir::dyn_cast<hlfir::ExprType>(cshift.getType());
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assert(expr &&
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"expected an expression type for the result of hlfir.cshift");
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unsigned arrayRank = expr.getRank();
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// When it is a 1D CSHIFT, we may assume that the DIM argument
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// (whether it is present or absent) is equal to 1, otherwise,
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// the program is illegal.
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int64_t dimVal = 1;
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if (arrayRank != 1)
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if (mlir::Value dim = cshift.getDim()) {
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auto constDim = fir::getIntIfConstant(dim);
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if (!constDim)
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return rewriter.notifyMatchFailure(cshift,
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"Nonconstant DIM for CSHIFT");
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dimVal = *constDim;
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}
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if (dimVal <= 0 || dimVal > arrayRank)
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return rewriter.notifyMatchFailure(cshift, "Invalid DIM for CSHIFT");
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mlir::Location loc = cshift.getLoc();
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fir::FirOpBuilder builder{rewriter, cshift.getOperation()};
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mlir::Type elementType = expr.getElementType();
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hlfir::Entity array = hlfir::Entity{cshift.getArray()};
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mlir::Value arrayShape = hlfir::genShape(loc, builder, array);
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llvm::SmallVector<mlir::Value> arrayExtents =
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hlfir::getExplicitExtentsFromShape(arrayShape, builder);
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llvm::SmallVector<mlir::Value, 1> typeParams;
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hlfir::genLengthParameters(loc, builder, array, typeParams);
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hlfir::Entity shift = hlfir::Entity{cshift.getShift()};
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// The new index computation involves MODULO, which is not implemented
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// for IndexType, so use I64 instead.
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mlir::Type calcType = builder.getI64Type();
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mlir::Value one = builder.createIntegerConstant(loc, calcType, 1);
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mlir::Value shiftVal;
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if (shift.isScalar()) {
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shiftVal = hlfir::loadTrivialScalar(loc, builder, shift);
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shiftVal = builder.createConvert(loc, calcType, shiftVal);
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}
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auto genKernel = [&](mlir::Location loc, fir::FirOpBuilder &builder,
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mlir::ValueRange inputIndices) -> hlfir::Entity {
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llvm::SmallVector<mlir::Value, maxRank> indices{inputIndices};
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if (!shift.isScalar()) {
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// When the array is not a vector, section
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// (s(1), s(2), ..., s(dim-1), :, s(dim+1), ..., s(n)
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// of the result has a value equal to:
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// CSHIFT(ARRAY(s(1), s(2), ..., s(dim-1), :, s(dim+1), ..., s(n)),
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// SH, 1),
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// where SH is either SHIFT (if scalar) or
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// SHIFT(s(1), s(2), ..., s(dim-1), s(dim+1), ..., s(n)).
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llvm::SmallVector<mlir::Value, maxRank> shiftIndices{indices};
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shiftIndices.erase(shiftIndices.begin() + dimVal - 1);
|
|
hlfir::Entity shiftElement =
|
|
hlfir::getElementAt(loc, builder, shift, shiftIndices);
|
|
shiftVal = hlfir::loadTrivialScalar(loc, builder, shiftElement);
|
|
shiftVal = builder.createConvert(loc, calcType, shiftVal);
|
|
}
|
|
|
|
// Element i of the result (1-based) is element
|
|
// 'MODULO(i + SH - 1, SIZE(ARRAY)) + 1' (1-based) of the original
|
|
// ARRAY (or its section, when ARRAY is not a vector).
|
|
mlir::Value index =
|
|
builder.createConvert(loc, calcType, inputIndices[dimVal - 1]);
|
|
mlir::Value extent = arrayExtents[dimVal - 1];
|
|
mlir::Value newIndex =
|
|
builder.create<mlir::arith::AddIOp>(loc, index, shiftVal);
|
|
newIndex = builder.create<mlir::arith::SubIOp>(loc, newIndex, one);
|
|
newIndex = fir::IntrinsicLibrary{builder, loc}.genModulo(
|
|
calcType, {newIndex, builder.createConvert(loc, calcType, extent)});
|
|
newIndex = builder.create<mlir::arith::AddIOp>(loc, newIndex, one);
|
|
newIndex = builder.createConvert(loc, builder.getIndexType(), newIndex);
|
|
|
|
indices[dimVal - 1] = newIndex;
|
|
hlfir::Entity element = hlfir::getElementAt(loc, builder, array, indices);
|
|
return hlfir::loadTrivialScalar(loc, builder, element);
|
|
};
|
|
|
|
hlfir::ElementalOp elementalOp = hlfir::genElementalOp(
|
|
loc, builder, elementType, arrayShape, typeParams, genKernel,
|
|
/*isUnordered=*/true,
|
|
array.isPolymorphic() ? static_cast<mlir::Value>(array) : nullptr,
|
|
cshift.getResult().getType());
|
|
rewriter.replaceOp(cshift, elementalOp);
|
|
return mlir::success();
|
|
}
|
|
};
|
|
|
|
class SimplifyHLFIRIntrinsics
|
|
: public hlfir::impl::SimplifyHLFIRIntrinsicsBase<SimplifyHLFIRIntrinsics> {
|
|
public:
|
|
void runOnOperation() override {
|
|
mlir::MLIRContext *context = &getContext();
|
|
|
|
mlir::GreedyRewriteConfig config;
|
|
// Prevent the pattern driver from merging blocks
|
|
config.enableRegionSimplification =
|
|
mlir::GreedySimplifyRegionLevel::Disabled;
|
|
|
|
mlir::RewritePatternSet patterns(context);
|
|
patterns.insert<TransposeAsElementalConversion>(context);
|
|
patterns.insert<SumAsElementalConversion>(context);
|
|
patterns.insert<CShiftAsElementalConversion>(context);
|
|
|
|
if (mlir::failed(mlir::applyPatternsAndFoldGreedily(
|
|
getOperation(), std::move(patterns), config))) {
|
|
mlir::emitError(getOperation()->getLoc(),
|
|
"failure in HLFIR intrinsic simplification");
|
|
signalPassFailure();
|
|
}
|
|
}
|
|
};
|
|
} // namespace
|