llvm-external-projects/iree-dialects/lib/Dialect/LinalgExt/Transforms/Tiling.cpp

// Copyright 2021 The IREE Authors
//
// Licensed 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 "iree-dialects/Dialect/LinalgExt/IR/LinalgExtOps.h"
#include "iree-dialects/Dialect/LinalgExt/Transforms/Transforms.h"
#include "iree-dialects/Dialect/LinalgExt/Transforms/Utils.h"
#include "mlir/Dialect/Arith/Utils/Utils.h"
#include "mlir/Dialect/Linalg/Transforms/Transforms.h"
#include "mlir/Dialect/Linalg/Utils/Utils.h"
#include "mlir/Dialect/Tensor/IR/Tensor.h"
#include "mlir/IR/Operation.h"
#include "mlir/IR/OperationSupport.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/Transforms/GreedyPatternRewriteDriver.h"

using namespace mlir;
using namespace mlir::iree_compiler::IREE::LinalgExt;

// TODO: connect these patterns to PDL. Either via the transform dialect or via
// PDLL.

static bool isZero(Value v) {
  if (auto cst = v.getDefiningOp<arith::ConstantIndexOp>())
    return cst.value() == 0;
  return false;
}

SmallVector<Value> tileToSCF(PatternRewriter &rewriter, TilingInterface op,
                             TilingInterface clonedOp, ValueRange tileSizes) {
  // Compute lower and upper bounds of the loop nest.
  SmallVector<Range> ranges = clonedOp.getIterationDomain(rewriter);
  assert(tileSizes.size() <= ranges.size() &&
         "expected tile sizes to match the number of loops");

  // Fill the tile sizes with zeros for the untiled dimensions.
  Location loc = op->getLoc();
  SmallVector<OpFoldResult> tileSizesVec = getAsOpFoldResult(tileSizes);
  if (ranges.size() != tileSizes.size()) {
    tileSizesVec.resize(ranges.size(), rewriter.getIndexAttr(0));
  }

  SmallVector<Value> lbs, dims, steps;
  SmallVector<OpFoldResult> allDims;
  for (auto it : llvm::enumerate(ranges)) {
    allDims.push_back(it.value().size);
    if (!isConstantIntValue(tileSizesVec[it.index()], 0)) {
      lbs.push_back(
          getValueOrCreateConstantIndexOp(rewriter, loc, it.value().offset));
      dims.push_back(
          getValueOrCreateConstantIndexOp(rewriter, loc, it.value().size));
      steps.push_back(getValueOrCreateConstantIndexOp(
          rewriter, loc, tileSizesVec[it.index()]));
    }
  }

  // Generate loop nest: One loop per dimension.
  llvm::SmallPtrSet<Operation *, 1> preservedUses;
  SmallVector<Value> destOperand;

  if (failed(tensor::getOrCreateDestinations(rewriter, loc, clonedOp,
                                             destOperand)))
    clonedOp->emitOpError("failed to get destOperand");

  auto loopNest = mlir::scf::buildLoopNest(
      rewriter, loc, lbs, /*ubs=*/dims, steps, ValueRange(destOperand),
      [&](OpBuilder &b, Location loc, ValueRange localIvs,
          ValueRange iterArgs) -> scf::ValueVector {
        // Compute offsets and sizes of ExtractSliceOp.
        SmallVector<OpFoldResult> offsets = linalg::computeTileOffsets(
            b, loc, getAsOpFoldResult(localIvs), tileSizesVec);
        SmallVector<OpFoldResult> sizes =
            linalg::computeTileSizes(b, loc, tileSizesVec, allDims);
        // Create ExtractSliceOp: Extract a tile from the PadOp.
        // Note: The PadOp is located outside of the loop nest. It is
        // later moved inside by ExtractSliceOfPadTensorSwapPattern.
        auto map =
            AffineMap::getMultiDimIdentityMap(ranges.size(), b.getContext());
        assert(clonedOp->getNumResults() == 1 && "expected single result op");
        Value tiledOutput = linalg::makeTiledShape(
            b, loc, clonedOp->getResult(0), tileSizesVec, map, offsets, allDims,
            sizes, /*omitPartialTileCheck=*/false);
        auto sliceOp = tiledOutput.getDefiningOp<tensor::ExtractSliceOp>();
        preservedUses.insert(sliceOp);
        assert(sliceOp && "expected ExtractSliceOp");
        // Insert the tile into the output tensor.
        Value yieldValue =
            createMatchingSubsetInsertOp(b, loc, sliceOp, sliceOp, iterArgs[0]);
        return scf::ValueVector({yieldValue});
      });
  return loopNest.results;
}

namespace {

/// The tiling here works by two steps. The first step is to create a loop based
/// on the loop bounds of the operation obtained from `TilingInterface`.
///
/// ```mlir
///   %1 = <tiling interface op> ins(...) outs(%0 : ...)
///   ... <use_op> ... %1 ...
/// ```
///
/// is rewritten using a "noop" subtensor extract/insert pair
///
/// ```mlir
///   %1 = <tiling interface op> ins(...) outs(%0 : ...)
///   %2 = scf.for %iv0 = ... iter_args(%arg0 = %0) {
///     %3 = scf.for %iv1 = ... iter_args(%arg1 = %arg0) {
///       ...
///       %4 = tensor.extract_slice %1[%iv0, %iv1]....
///       %5 = tensor.insert_slice %4 into %arg1[%iv0, %iv1]...
///       scf.yield %5
///     }
///     scf.yield %3
///   }
///   ... <use_op> ... %2 ...
/// ```
///
/// Following this the `TilingInterface` -> `tensor::ExtractSliceOp` pattern is
/// replaced with
///
/// /// ```mlir
///   %2 = scf.for %iv0 = ... iter_args(%arg0 = %0) {
///     %3 = scf.for %iv1 = ... iter_args(%arg1 = %arg0) {
///       ...
///       %4 = tensor.extract_slice %0[%iv0, %iv1]
///       %5 = <tiling interface op> ins(...) outs(%4 : ...)
///       %6 = tensor.insert_slice %5 into %arg1[%iv0, %iv1]...
///       scf.yield %6
///     }
///     scf.yield %3
///   }
///   ... <use_op> ... %2 ...
/// ```
///
/// TODO(ravishankarm): The current approach seems to work for only tiling the
/// parallel loops of the operation. Specifically,
/// 1) the `%0` in the third snippet needs to be `%arg1`, for cases where the
///    tiled loop is a reduction.
/// 2) Current implementation is using the `getIterationDomain` method to get
/// the
///    initial loop structure as described in the second snippet. If any of
///    those loops are reductions, then that IR snippet itself is wrong (replace
///    this with the case of `linalg.matmul` and the error becomes apparent).

/// First pattern to introduce the loop nests.
struct OpTilingPattern : public OpInterfaceRewritePattern<TilingInterface> {
  OpTilingPattern(MLIRContext *context, linalg::LinalgTilingOptions opt,
                  LinalgTransformationFilter filt)
      : OpInterfaceRewritePattern<TilingInterface>(context), options(opt),
        filter(filt) {}

  LogicalResult matchAndRewrite(TilingInterface op,
                                PatternRewriter &rewriter) const override {
    if (failed(filter.checkAndNotify(rewriter, op)))
      return failure();

    /// Currently only handle single result operations.
    if (op->getNumResults() != 1)
      return failure();

    Location loc = op->getLoc();
    // Get rank and tile sizes.
    SmallVector<Value> tileSizes =
        options.tileSizeComputationFunction(rewriter, op);
    auto iteratorTypes = op.getLoopIteratorTypes();
    Value zero = rewriter.create<arith::ConstantIndexOp>(loc, 0);
    tileSizes.resize(iteratorTypes.size(), zero);

    /// Currently only handle operations with all parallel iterator types.
    for (auto iteratorType : enumerate(iteratorTypes)) {
      if (iteratorType.value() != utils::IteratorType::parallel &&
          !isZero(tileSizes[iteratorType.index()])) {
        return rewriter.notifyMatchFailure(
            op, "unhandled tiling of non-parallel iterator");
      }
    }

    auto clonedOp = cast<TilingInterface>(rewriter.clone(*op.getOperation()));
    SmallVector<Value> results = tileToSCF(rewriter, op, clonedOp, tileSizes);

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

private:
  linalg::LinalgTilingOptions options;
  LinalgTransformationFilter filter;
};
} // namespace