clang-p2996/mlir/lib/Dialect/Affine/Transforms/AffineExpandIndexOps.cpp

//===- AffineExpandIndexOps.cpp - Affine expand index ops pass ------------===//
//
// 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
//
//===----------------------------------------------------------------------===//
//
// This file implements a pass to expand affine index ops into one or more more
// fundamental operations.
//===----------------------------------------------------------------------===//

#include "mlir/Dialect/Affine/LoopUtils.h"
#include "mlir/Dialect/Affine/Passes.h"

#include "mlir/Dialect/Affine/IR/AffineOps.h"
#include "mlir/Dialect/Affine/Transforms/Transforms.h"
#include "mlir/Dialect/Affine/Utils.h"
#include "mlir/Dialect/Arith/Utils/Utils.h"
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"

namespace mlir {
namespace affine {
#define GEN_PASS_DEF_AFFINEEXPANDINDEXOPS
#include "mlir/Dialect/Affine/Passes.h.inc"
} // namespace affine
} // namespace mlir

using namespace mlir;
using namespace mlir::affine;

/// Given a basis (in static and dynamic components), return the sequence of
/// suffix products of the basis, including the product of the entire basis,
/// which must **not** contain an outer bound.
///
/// If excess dynamic values are provided, the values at the beginning
/// will be ignored. This allows for dropping the outer bound without
/// needing to manipulate the dynamic value array.
static SmallVector<Value> computeStrides(Location loc, RewriterBase &rewriter,
                                         ValueRange dynamicBasis,
                                         ArrayRef<int64_t> staticBasis) {
  if (staticBasis.empty())
    return {};

  SmallVector<Value> result;
  result.reserve(staticBasis.size());
  size_t dynamicIndex = dynamicBasis.size();
  Value dynamicPart = nullptr;
  int64_t staticPart = 1;
  for (int64_t elem : llvm::reverse(staticBasis)) {
    if (ShapedType::isDynamic(elem)) {
      if (dynamicPart)
        dynamicPart = rewriter.create<arith::MulIOp>(
            loc, dynamicPart, dynamicBasis[dynamicIndex - 1]);
      else
        dynamicPart = dynamicBasis[dynamicIndex - 1];
      --dynamicIndex;
    } else {
      staticPart *= elem;
    }

    if (dynamicPart && staticPart == 1) {
      result.push_back(dynamicPart);
    } else {
      Value stride =
          rewriter.createOrFold<arith::ConstantIndexOp>(loc, staticPart);
      if (dynamicPart)
        stride = rewriter.create<arith::MulIOp>(loc, dynamicPart, stride);
      result.push_back(stride);
    }
  }
  std::reverse(result.begin(), result.end());
  return result;
}

namespace {
/// Lowers `affine.delinearize_index` into a sequence of division and remainder
/// operations.
struct LowerDelinearizeIndexOps
    : public OpRewritePattern<AffineDelinearizeIndexOp> {
  using OpRewritePattern<AffineDelinearizeIndexOp>::OpRewritePattern;
  LogicalResult matchAndRewrite(AffineDelinearizeIndexOp op,
                                PatternRewriter &rewriter) const override {
    Location loc = op.getLoc();
    Value linearIdx = op.getLinearIndex();
    unsigned numResults = op.getNumResults();
    ArrayRef<int64_t> staticBasis = op.getStaticBasis();
    if (numResults == staticBasis.size())
      staticBasis = staticBasis.drop_front();

    if (numResults == 1) {
      rewriter.replaceOp(op, linearIdx);
      return success();
    }

    SmallVector<Value> results;
    results.reserve(numResults);
    SmallVector<Value> strides =
        computeStrides(loc, rewriter, op.getDynamicBasis(), staticBasis);

    Value zero = rewriter.createOrFold<arith::ConstantIndexOp>(loc, 0);

    Value initialPart =
        rewriter.create<arith::FloorDivSIOp>(loc, linearIdx, strides.front());
    results.push_back(initialPart);

    auto emitModTerm = [&](Value stride) -> Value {
      Value remainder = rewriter.create<arith::RemSIOp>(loc, linearIdx, stride);
      Value remainderNegative = rewriter.create<arith::CmpIOp>(
          loc, arith::CmpIPredicate::slt, remainder, zero);
      Value corrected = rewriter.create<arith::AddIOp>(loc, remainder, stride);
      Value mod = rewriter.create<arith::SelectOp>(loc, remainderNegative,
                                                   corrected, remainder);
      return mod;
    };

    // Generate all the intermediate parts
    for (size_t i = 0, e = strides.size() - 1; i < e; ++i) {
      Value thisStride = strides[i];
      Value nextStride = strides[i + 1];
      Value modulus = emitModTerm(thisStride);
      // We know both inputs are positive, so floorDiv == div.
      // This could potentially be a divui, but it's not clear if that would
      // cause issues.
      Value divided = rewriter.create<arith::DivSIOp>(loc, modulus, nextStride);
      results.push_back(divided);
    }

    results.push_back(emitModTerm(strides.back()));

    rewriter.replaceOp(op, results);
    return success();
  }
};

/// Lowers `affine.linearize_index` into a sequence of multiplications and
/// additions. Make a best effort to sort the input indices so that
/// the most loop-invariant terms are at the left of the additions
/// to enable loop-invariant code motion.
struct LowerLinearizeIndexOps final : OpRewritePattern<AffineLinearizeIndexOp> {
  using OpRewritePattern::OpRewritePattern;
  LogicalResult matchAndRewrite(AffineLinearizeIndexOp op,
                                PatternRewriter &rewriter) const override {
    // Should be folded away, included here for safety.
    if (op.getMultiIndex().empty()) {
      rewriter.replaceOpWithNewOp<arith::ConstantIndexOp>(op, 0);
      return success();
    }

    Location loc = op.getLoc();
    ValueRange multiIndex = op.getMultiIndex();
    size_t numIndexes = multiIndex.size();
    ArrayRef<int64_t> staticBasis = op.getStaticBasis();
    if (numIndexes == staticBasis.size())
      staticBasis = staticBasis.drop_front();

    SmallVector<Value> strides =
        computeStrides(loc, rewriter, op.getDynamicBasis(), staticBasis);
    SmallVector<std::pair<Value, int64_t>> scaledValues;
    scaledValues.reserve(numIndexes);

    // Note: strides doesn't contain a value for the final element (stride 1)
    // and everything else lines up. We use the "mutable" accessor so we can get
    // our hands on an `OpOperand&` for the loop invariant counting function.
    for (auto [stride, idxOp] :
         llvm::zip_equal(strides, llvm::drop_end(op.getMultiIndexMutable()))) {
      Value scaledIdx =
          rewriter.create<arith::MulIOp>(loc, idxOp.get(), stride);
      int64_t numHoistableLoops = numEnclosingInvariantLoops(idxOp);
      scaledValues.emplace_back(scaledIdx, numHoistableLoops);
    }
    scaledValues.emplace_back(
        multiIndex.back(),
        numEnclosingInvariantLoops(op.getMultiIndexMutable()[numIndexes - 1]));

    // Sort by how many enclosing loops there are, ties implicitly broken by
    // size of the stride.
    llvm::stable_sort(scaledValues,
                      [&](auto l, auto r) { return l.second > r.second; });

    Value result = scaledValues.front().first;
    for (auto [scaledValue, numHoistableLoops] :
         llvm::drop_begin(scaledValues)) {
      std::ignore = numHoistableLoops;
      result = rewriter.create<arith::AddIOp>(loc, result, scaledValue);
    }
    rewriter.replaceOp(op, result);
    return success();
  }
};

class ExpandAffineIndexOpsPass
    : public affine::impl::AffineExpandIndexOpsBase<ExpandAffineIndexOpsPass> {
public:
  ExpandAffineIndexOpsPass() = default;

  void runOnOperation() override {
    MLIRContext *context = &getContext();
    RewritePatternSet patterns(context);
    populateAffineExpandIndexOpsPatterns(patterns);
    if (failed(
            applyPatternsAndFoldGreedily(getOperation(), std::move(patterns))))
      return signalPassFailure();
  }
};

} // namespace

void mlir::affine::populateAffineExpandIndexOpsPatterns(
    RewritePatternSet &patterns) {
  patterns.insert<LowerDelinearizeIndexOps, LowerLinearizeIndexOps>(
      patterns.getContext());
}

std::unique_ptr<Pass> mlir::affine::createAffineExpandIndexOpsPass() {
  return std::make_unique<ExpandAffineIndexOpsPass>();
}