FusePadOpWithLinalgProducer.cpp

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//===- FusePadOpWithLinalgProducer.cpp ---- Fuse pad with linalg producer -===//
//
// 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 patterns that fuses a linalg.generic -> tensor.pad op
// chain into a tensor.extract_slice -> linalg.generic -> tensor.insert_slice
// op chain.
//
//===----------------------------------------------------------------------===//
 
#include "mlir/Dialect/Linalg/Transforms/Transforms.h"
 
#include "mlir/Dialect/Linalg/IR/Linalg.h"
 
using namespace mlir;
 
namespace {
/// A sequence of operations
///
/// ```mlir
/// %0 = linalg. ...
/// %1 = tensor.pad %0 ...
/// ```
///
/// can be replaced with
///
/// ```mlir
/// %0 = linalg.fill
/// %1 = tensor.extract_slice %0 ...
/// %2 = linalg. .... outs(..., %1, ....) ....
/// %3 = tensor.insert_slice %2 into %1 ...
/// ```
///
/// if the `linalg.generic` has all parallel iterator types.
struct FusePadOp : OpRewritePattern<tensor::PadOp> {
  using OpRewritePattern<tensor::PadOp>::OpRewritePattern;
 
  LogicalResult matchAndRewrite(tensor::PadOp padOp,
                                PatternRewriter &rewriter) const override {
    // Only works on padding op that sets the padded value to a constant.
    Value padValue = padOp.getConstantPaddingValue();
    if (!padValue)
      return rewriter.notifyMatchFailure(padOp, "non constant padding");
 
    // This pattern could work for any Linalg op. For now restrict it to generic
    // ops.
    Value source = padOp.getSource();
    auto linalgOp = source.getDefiningOp<linalg::GenericOp>();
    if (!linalgOp) {
      return rewriter.notifyMatchFailure(
          padOp, "expected source to be linalg.generic op");
    }
    // All iterator types need to be parallel.
    if (linalgOp.getNumLoops() != linalgOp.getNumParallelLoops()) {
      return rewriter.notifyMatchFailure(
          padOp, "only supported for ops with all parallel iterator types");
    }
    ReifiedRankedShapedTypeDims resultShape;
    if (failed(reifyResultShapes(rewriter, padOp, resultShape)) ||
        resultShape.size() != 1) {
      return rewriter.notifyMatchFailure(
          padOp, "failed to get shape of pad op result");
    }
 
    Location loc = padOp.getLoc();
 
    // Create the tensor of same size as output of the pad op.
    RankedTensorType padResultType = padOp.getResultType();
    auto resultSizes = resultShape[0];
    auto emptyTensor = tensor::EmptyOp::create(rewriter, loc, resultSizes,
                                               padResultType.getElementType());
 
    // Fill the tensor with the pad value.
    // TODO: There is an option to fill only the boundaries. For now just
    // filling the whole tensor.
    // Move the padding value defined inside the PadOp block to outside.
    if (padValue.getParentBlock() == &padOp.getRegion().front())
      rewriter.moveOpBefore(padValue.getDefiningOp(), padOp);
    auto fillTensor = linalg::FillOp::create(rewriter, loc, padValue,
                                             emptyTensor.getResult());
 
    // Construct a slice of the fill result that is to be replaced with the
    // result of the generic op. The low pad values are the offsets, the size of
    // the source is the size of the slice.
    // TODO: This insert/extract could be potentially made a utility method.
    unsigned resultNumber = cast<OpResult>(source).getResultNumber();
    SmallVector<OpFoldResult> offsets = padOp.getMixedLowPad();
    SmallVector<OpFoldResult> sizes;
    sizes.reserve(offsets.size());
    for (const auto &shape :
         llvm::enumerate(cast<RankedTensorType>(source.getType()).getShape())) {
      if (ShapedType::isDynamic(shape.value())) {
        sizes.push_back(
            tensor::DimOp::create(rewriter, loc, source, shape.index())
                .getResult());
      } else {
        sizes.push_back(rewriter.getIndexAttr(shape.value()));
      }
    }
    SmallVector<OpFoldResult> strides(offsets.size(), rewriter.getIndexAttr(1));
    auto slice = tensor::ExtractSliceOp::create(
        rewriter, loc, fillTensor.getResult(0), offsets, sizes, strides);
 
    // Clone the generic op.
    auto clonedOp =
        cast<linalg::GenericOp>(rewriter.clone(*linalgOp.getOperation()));
    clonedOp.setDpsInitOperand(resultNumber, slice.getResult());
 
    // Insert it back into the result of the fill.
    rewriter.replaceOpWithNewOp<tensor::InsertSliceOp>(
        padOp, clonedOp.getResult(resultNumber), fillTensor.getResult(0),
        offsets, sizes, strides);
    return success();
  }
};
} // namespace
 
void mlir::linalg::populateFuseTensorPadWithProducerLinalgOpPatterns(
    RewritePatternSet &patterns) {
  patterns.add<FusePadOp>(patterns.getContext());
}