mlir/lib/Dialect/SCF/Transforms/LoopSpecialization.cpp

//===- LoopSpecialization.cpp - scf.parallel/SCR.for specialization -------===//
//
// 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
//
//===----------------------------------------------------------------------===//
//
// Specializes parallel loops and for loops for easier unrolling and
// vectorization.
//
//===----------------------------------------------------------------------===//

#include "PassDetail.h"
#include "mlir/Dialect/Affine/IR/AffineOps.h"
#include "mlir/Dialect/SCF/Passes.h"
#include "mlir/Dialect/SCF/SCF.h"
#include "mlir/Dialect/StandardOps/IR/Ops.h"
#include "mlir/IR/AffineExpr.h"
#include "mlir/IR/BlockAndValueMapping.h"

using namespace mlir;
using scf::ForOp;
using scf::ParallelOp;

/// Rewrite a parallel loop with bounds defined by an affine.min with a constant
/// into 2 loops after checking if the bounds are equal to that constant. This
/// is beneficial if the loop will almost always have the constant bound and
/// that version can be fully unrolled and vectorized.
static void specializeParallelLoopForUnrolling(ParallelOp op) {
  SmallVector<int64_t, 2> constantIndices;
  constantIndices.reserve(op.upperBound().size());
  for (auto bound : op.upperBound()) {
    auto minOp = bound.getDefiningOp<AffineMinOp>();
    if (!minOp)
      return;
    int64_t minConstant = std::numeric_limits<int64_t>::max();
    for (AffineExpr expr : minOp.map().getResults()) {
      if (auto constantIndex = expr.dyn_cast<AffineConstantExpr>())
        minConstant = std::min(minConstant, constantIndex.getValue());
    }
    if (minConstant == std::numeric_limits<int64_t>::max())
      return;
    constantIndices.push_back(minConstant);
  }

  OpBuilder b(op);
  BlockAndValueMapping map;
  Value cond;
  for (auto bound : llvm::zip(op.upperBound(), constantIndices)) {
    Value constant = b.create<ConstantIndexOp>(op.getLoc(), std::get<1>(bound));
    Value cmp = b.create<CmpIOp>(op.getLoc(), CmpIPredicate::eq,
                                 std::get<0>(bound), constant);
    cond = cond ? b.create<AndOp>(op.getLoc(), cond, cmp) : cmp;
    map.map(std::get<0>(bound), constant);
  }
  auto ifOp = b.create<scf::IfOp>(op.getLoc(), cond, /*withElseRegion=*/true);
  ifOp.getThenBodyBuilder().clone(*op.getOperation(), map);
  ifOp.getElseBodyBuilder().clone(*op.getOperation());
  op.erase();
}

/// Rewrite a for loop with bounds defined by an affine.min with a constant into
/// 2 loops after checking if the bounds are equal to that constant. This is
/// beneficial if the loop will almost always have the constant bound and that
/// version can be fully unrolled and vectorized.
static void specializeForLoopForUnrolling(ForOp op) {
  auto bound = op.upperBound();
  auto minOp = bound.getDefiningOp<AffineMinOp>();
  if (!minOp)
    return;
  int64_t minConstant = std::numeric_limits<int64_t>::max();
  for (AffineExpr expr : minOp.map().getResults()) {
    if (auto constantIndex = expr.dyn_cast<AffineConstantExpr>())
      minConstant = std::min(minConstant, constantIndex.getValue());
  }
  if (minConstant == std::numeric_limits<int64_t>::max())
    return;

  OpBuilder b(op);
  BlockAndValueMapping map;
  Value constant = b.create<ConstantIndexOp>(op.getLoc(), minConstant);
  Value cond =
      b.create<CmpIOp>(op.getLoc(), CmpIPredicate::eq, bound, constant);
  map.map(bound, constant);
  auto ifOp = b.create<scf::IfOp>(op.getLoc(), cond, /*withElseRegion=*/true);
  ifOp.getThenBodyBuilder().clone(*op.getOperation(), map);
  ifOp.getElseBodyBuilder().clone(*op.getOperation());
  op.erase();
}

namespace {
struct ParallelLoopSpecialization
    : public SCFParallelLoopSpecializationBase<ParallelLoopSpecialization> {
  void runOnFunction() override {
    getFunction().walk(
        [](ParallelOp op) { specializeParallelLoopForUnrolling(op); });
  }
};

struct ForLoopSpecialization
    : public SCFForLoopSpecializationBase<ForLoopSpecialization> {
  void runOnFunction() override {
    getFunction().walk([](ForOp op) { specializeForLoopForUnrolling(op); });
  }
};
} // namespace

std::unique_ptr<Pass> mlir::createParallelLoopSpecializationPass() {
  return std::make_unique<ParallelLoopSpecialization>();
}

std::unique_ptr<Pass> mlir::createForLoopSpecializationPass() {
  return std::make_unique<ForLoopSpecialization>();
}