VectorDropLeadUnitDim.cpp

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//===- VectorDropLeadUnitDim.cpp - Conversion within the Vector dialect ---===//
//
// 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
//
//===----------------------------------------------------------------------===//
 
#include "mlir/Dialect/Utils/StructuredOpsUtils.h"
#include "mlir/Dialect/Vector/IR/VectorOps.h"
#include "mlir/Dialect/Vector/Transforms/VectorRewritePatterns.h"
#include "mlir/Dialect/Vector/Utils/VectorUtils.h"
#include "mlir/IR/Builders.h"
#include "mlir/IR/ImplicitLocOpBuilder.h"
#include "mlir/IR/TypeUtilities.h"
 
#define DEBUG_TYPE "vector-drop-unit-dim"
 
using namespace mlir;
using namespace mlir::vector;
 
// Trims leading one dimensions from `oldType` and returns the result type.
// Returns `vector<1xT>` if `oldType` only has one element.
static VectorType trimLeadingOneDims(VectorType oldType) {
  ArrayRef<int64_t> oldShape = oldType.getShape();
  ArrayRef<int64_t> newShape =
      oldShape.drop_while([](int64_t dim) { return dim == 1; });
  // Make sure we have at least 1 dimension per vector type requirements.
  if (newShape.empty())
    newShape = oldShape.take_back();
  return VectorType::get(newShape, oldType.getElementType());
}
 
/// Return a smallVector of size `rank` containing all zeros.
static SmallVector<int64_t> splatZero(int64_t rank) {
  return SmallVector<int64_t>(rank, 0);
}
namespace {
 
// Casts away leading one dimensions in vector.extract_strided_slice's vector
// input by inserting vector.broadcast.
struct CastAwayExtractStridedSliceLeadingOneDim
    : public OpRewritePattern<vector::ExtractStridedSliceOp> {
  using OpRewritePattern::OpRewritePattern;
 
  LogicalResult matchAndRewrite(vector::ExtractStridedSliceOp extractOp,
                                PatternRewriter &rewriter) const override {
    // vector.extract_strided_slice requires the input and output vector to have
    // the same rank. Here we drop leading one dimensions from the input vector
    // type to make sure we don't cause mismatch.
    VectorType oldSrcType = extractOp.getSourceVectorType();
    VectorType newSrcType = trimLeadingOneDims(oldSrcType);
 
    if (newSrcType.getRank() == oldSrcType.getRank())
      return failure();
 
    int64_t dropCount = oldSrcType.getRank() - newSrcType.getRank();
 
    VectorType oldDstType = extractOp.getType();
    VectorType newDstType =
        VectorType::get(oldDstType.getShape().drop_front(dropCount),
                        oldDstType.getElementType());
 
    Location loc = extractOp.getLoc();
 
    Value newSrcVector = rewriter.create<vector::ExtractOp>(
        loc, extractOp.getVector(), splatZero(dropCount));
 
    // The offsets/sizes/strides attribute can have a less number of elements
    // than the input vector's rank: it is meant for the leading dimensions.
    auto newOffsets = rewriter.getArrayAttr(
        extractOp.getOffsets().getValue().drop_front(dropCount));
    auto newSizes = rewriter.getArrayAttr(
        extractOp.getSizes().getValue().drop_front(dropCount));
    auto newStrides = rewriter.getArrayAttr(
        extractOp.getStrides().getValue().drop_front(dropCount));
 
    auto newExtractOp = rewriter.create<vector::ExtractStridedSliceOp>(
        loc, newDstType, newSrcVector, newOffsets, newSizes, newStrides);
 
    rewriter.replaceOpWithNewOp<vector::BroadcastOp>(extractOp, oldDstType,
                                                     newExtractOp);
 
    return success();
  }
};
 
// Casts away leading one dimensions in vector.insert_strided_slice's vector
// inputs by inserting vector.broadcast.
struct CastAwayInsertStridedSliceLeadingOneDim
    : public OpRewritePattern<vector::InsertStridedSliceOp> {
  using OpRewritePattern::OpRewritePattern;
 
  LogicalResult matchAndRewrite(vector::InsertStridedSliceOp insertOp,
                                PatternRewriter &rewriter) const override {
    VectorType oldSrcType = insertOp.getSourceVectorType();
    VectorType newSrcType = trimLeadingOneDims(oldSrcType);
    VectorType oldDstType = insertOp.getDestVectorType();
    VectorType newDstType = trimLeadingOneDims(oldDstType);
 
    int64_t srcDropCount = oldSrcType.getRank() - newSrcType.getRank();
    int64_t dstDropCount = oldDstType.getRank() - newDstType.getRank();
    if (srcDropCount == 0 && dstDropCount == 0)
      return failure();
 
    // Trim leading one dimensions from both operands.
    Location loc = insertOp.getLoc();
 
    Value newSrcVector = rewriter.create<vector::ExtractOp>(
        loc, insertOp.getSource(), splatZero(srcDropCount));
    Value newDstVector = rewriter.create<vector::ExtractOp>(
        loc, insertOp.getDest(), splatZero(dstDropCount));
 
    auto newOffsets = rewriter.getArrayAttr(
        insertOp.getOffsets().getValue().take_back(newDstType.getRank()));
    auto newStrides = rewriter.getArrayAttr(
        insertOp.getStrides().getValue().take_back(newSrcType.getRank()));
 
    auto newInsertOp = rewriter.create<vector::InsertStridedSliceOp>(
        loc, newDstType, newSrcVector, newDstVector, newOffsets, newStrides);
 
    rewriter.replaceOpWithNewOp<vector::BroadcastOp>(insertOp, oldDstType,
                                                     newInsertOp);
 
    return success();
  }
};
 
// Casts away leading one dimensions in vector.insert's vector inputs by
// inserting vector.broadcast.
struct CastAwayInsertLeadingOneDim : public OpRewritePattern<vector::InsertOp> {
  using OpRewritePattern::OpRewritePattern;
 
  LogicalResult matchAndRewrite(vector::InsertOp insertOp,
                                PatternRewriter &rewriter) const override {
    Type oldSrcType = insertOp.getSourceType();
    Type newSrcType = oldSrcType;
    int64_t oldSrcRank = 0, newSrcRank = 0;
    if (auto type = oldSrcType.dyn_cast<VectorType>()) {
      newSrcType = trimLeadingOneDims(type);
      oldSrcRank = type.getRank();
      newSrcRank = newSrcType.cast<VectorType>().getRank();
    }
 
    VectorType oldDstType = insertOp.getDestVectorType();
    VectorType newDstType = trimLeadingOneDims(oldDstType);
 
    int64_t srcDropCount = oldSrcRank - newSrcRank;
    int64_t dstDropCount = oldDstType.getRank() - newDstType.getRank();
    if (srcDropCount == 0 && dstDropCount == 0)
      return failure();
 
    // Trim leading one dimensions from both operands.
    Location loc = insertOp.getLoc();
 
    Value newSrcVector = insertOp.getSource();
    if (oldSrcRank != 0) {
      newSrcVector = rewriter.create<vector::ExtractOp>(
          loc, insertOp.getSource(), splatZero(srcDropCount));
    }
    Value newDstVector = rewriter.create<vector::ExtractOp>(
        loc, insertOp.getDest(), splatZero(dstDropCount));
 
    unsigned oldPosRank = insertOp.getPosition().getValue().size();
    unsigned newPosRank = newDstType.getRank() - newSrcRank;
    SmallVector<Attribute> newPositions = llvm::to_vector(
        insertOp.getPosition().getValue().take_back(newPosRank));
    if (newPosRank > oldPosRank) {
      auto zeroAttr = rewriter.getZeroAttr(rewriter.getI64Type());
      newPositions.resize(newPosRank, zeroAttr);
    }
 
    auto newInsertOp = rewriter.create<vector::InsertOp>(
        loc, newDstType, newSrcVector, newDstVector,
        rewriter.getArrayAttr(newPositions));
 
    rewriter.replaceOpWithNewOp<vector::BroadcastOp>(insertOp, oldDstType,
                                                     newInsertOp);
 
    return success();
  }
};
 
// Turns vector.transfer_read on vector with leading 1 dimensions into
// vector.shape_cast followed by vector.transfer_read on vector without leading
// 1 dimensions.
struct CastAwayTransferReadLeadingOneDim
    : public OpRewritePattern<vector::TransferReadOp> {
  using OpRewritePattern::OpRewritePattern;
 
  LogicalResult matchAndRewrite(vector::TransferReadOp read,
                                PatternRewriter &rewriter) const override {
    // TODO: support 0-d corner case.
    if (read.getTransferRank() == 0)
      return failure();
 
    if (read.getMask())
      return failure();
 
    auto shapedType = read.getSource().getType().cast<ShapedType>();
    if (shapedType.getElementType() != read.getVectorType().getElementType())
      return failure();
 
    VectorType oldType = read.getVectorType();
    VectorType newType = trimLeadingOneDims(oldType);
 
    if (newType == oldType)
      return failure();
 
    AffineMap oldMap = read.getPermutationMap();
    ArrayRef<AffineExpr> newResults =
        oldMap.getResults().take_back(newType.getRank());
    AffineMap newMap =
        AffineMap::get(oldMap.getNumDims(), oldMap.getNumSymbols(), newResults,
                       rewriter.getContext());
 
    ArrayAttr inBoundsAttr;
    if (read.getInBounds())
      inBoundsAttr = rewriter.getArrayAttr(
          read.getInBoundsAttr().getValue().take_back(newType.getRank()));
 
    auto newRead = rewriter.create<vector::TransferReadOp>(
        read.getLoc(), newType, read.getSource(), read.getIndices(),
        AffineMapAttr::get(newMap), read.getPadding(), /*mask=*/Value(),
        inBoundsAttr);
    rewriter.replaceOpWithNewOp<vector::BroadcastOp>(read, oldType, newRead);
 
    return success();
  }
};
 
// Turns vector.transfer_write on vector with leading 1 dimensions into
// vector.shape_cast followed by vector.transfer_write on vector without leading
// 1 dimensions.
struct CastAwayTransferWriteLeadingOneDim
    : public OpRewritePattern<vector::TransferWriteOp> {
  using OpRewritePattern::OpRewritePattern;
 
  LogicalResult matchAndRewrite(vector::TransferWriteOp write,
                                PatternRewriter &rewriter) const override {
    // TODO: support 0-d corner case.
    if (write.getTransferRank() == 0)
      return failure();
 
    if (write.getMask())
      return failure();
 
    auto shapedType = write.getSource().getType().dyn_cast<ShapedType>();
    if (shapedType.getElementType() != write.getVectorType().getElementType())
      return failure();
 
    VectorType oldType = write.getVectorType();
    VectorType newType = trimLeadingOneDims(oldType);
    if (newType == oldType)
      return failure();
    int64_t dropDim = oldType.getRank() - newType.getRank();
 
    AffineMap oldMap = write.getPermutationMap();
    ArrayRef<AffineExpr> newResults =
        oldMap.getResults().take_back(newType.getRank());
    AffineMap newMap =
        AffineMap::get(oldMap.getNumDims(), oldMap.getNumSymbols(), newResults,
                       rewriter.getContext());
 
    ArrayAttr inBoundsAttr;
    if (write.getInBounds())
      inBoundsAttr = rewriter.getArrayAttr(
          write.getInBoundsAttr().getValue().take_back(newType.getRank()));
 
    auto newVector = rewriter.create<vector::ExtractOp>(
        write.getLoc(), write.getVector(), splatZero(dropDim));
    rewriter.replaceOpWithNewOp<vector::TransferWriteOp>(
        write, newVector, write.getSource(), write.getIndices(),
        AffineMapAttr::get(newMap), inBoundsAttr);
 
    return success();
  }
};
 
/// Turns vector.contract on vector with leading 1 dimensions into
/// vector.extract followed by vector.contract on vector without leading
/// 1 dimensions. Also performs tranpose of lhs and rhs operands if required
/// prior to extract.
struct CastAwayContractionLeadingOneDim
    : public OpRewritePattern<vector::ContractionOp> {
  using OpRewritePattern::OpRewritePattern;
 
  LogicalResult matchAndRewrite(vector::ContractionOp contractOp,
                                PatternRewriter &rewriter) const override {
    VectorType oldAccType = contractOp.getAccType().dyn_cast<VectorType>();
    if (oldAccType == nullptr)
      return failure();
    if (oldAccType.getRank() < 2)
      return failure();
    // TODO: implement masks.
    if (!contractOp.getMasks().empty())
      return failure();
    if (oldAccType.getShape()[0] != 1)
      return failure();
    // currently we support only dropping one dim but the pattern can be applied
    // greedily to drop more.
    int64_t dropDim = 1;
 
    auto oldIndexingMaps = contractOp.getIndexingMapsArray();
    SmallVector<AffineMap> newIndexingMaps;
 
    auto oldIteratorTypes = contractOp.getIteratorTypes();
    SmallVector<Attribute> newIteratorTypes;
 
    int64_t dimToDrop = oldIndexingMaps[2].getDimPosition(0);
 
    if (!isParallelIterator(oldIteratorTypes[dimToDrop]))
      // only parallel type iterators can be dropped.
      return failure();
 
    for (const auto &it : llvm::enumerate(oldIteratorTypes)) {
      int64_t currDim = it.index();
      if (currDim == dimToDrop)
        continue;
      newIteratorTypes.push_back(it.value());
    }
 
    SmallVector<Value> operands = {contractOp.getLhs(), contractOp.getRhs(),
                                   contractOp.getAcc()};
    SmallVector<Value> newOperands;
 
    for (const auto &it : llvm::enumerate(oldIndexingMaps)) {
      // Check if the dim to be dropped exists as a leading dim in the operand
      // if it does then we use vector.extract to drop it.
      bool validExtract = false;
      SmallVector<AffineExpr> results;
      auto map = it.value();
      int64_t orginalZeroDim = it.value().getDimPosition(0);
      if (orginalZeroDim != dimToDrop) {
        // There are two reasons to be in this path, 1. We need to
        // tranpose the operand to make the dim to be dropped
        // leading. 2. The dim to be dropped does not exist and in
        // that case we dont want to add a unit tranpose but we must
        // check all the indices to make sure this is the case.
        bool tranposeNeeded = false;
        SmallVector<int64_t> perm;
        SmallVector<AffineExpr> transposeResults;
 
        for (int64_t i = 0, e = map.getNumResults(); i < e; ++i) {
          int64_t currDim = map.getDimPosition(i);
          if (currDim == dimToDrop) {
            tranposeNeeded = true;
            perm.insert(perm.begin(), i);
            auto targetExpr = rewriter.getAffineDimExpr(currDim);
            transposeResults.insert(transposeResults.begin(), targetExpr);
          } else {
            perm.push_back(i);
            auto targetExpr = rewriter.getAffineDimExpr(currDim);
            transposeResults.push_back(targetExpr);
          }
        }
        // Do the tranpose now if needed so that we can drop the
        // correct dim using extract later.
        if (tranposeNeeded) {
          map = AffineMap::get(map.getNumDims(), 0, transposeResults,
                               contractOp.getContext());
          operands[it.index()] = rewriter.create<vector::TransposeOp>(
              contractOp.getLoc(), operands[it.index()], perm);
        }
      }
      // We have taken care to have the dim to be dropped be
      // the leading dim. If its still not leading that means it
      // does not exist in this operand and hence we do not need
      // an extract.
      if (map.getDimPosition(0) == dimToDrop)
        validExtract = true;
 
      for (int64_t i = 0, e = map.getNumResults(); i < e; ++i) {
        int64_t currDim = map.getDimPosition(i);
        if (currDim == dimToDrop)
          // This is the dim we are dropping.
          continue;
        auto targetExpr = rewriter.getAffineDimExpr(
            currDim < dimToDrop ? currDim : currDim - 1);
        results.push_back(targetExpr);
      }
      newIndexingMaps.push_back(AffineMap::get(map.getNumDims() - 1, 0, results,
                                               contractOp.getContext()));
      // Extract if its a valid extraction, otherwise use the operand
      // without extraction.
      newOperands.push_back(validExtract
                                ? rewriter.create<vector::ExtractOp>(
                                      contractOp.getLoc(), operands[it.index()],
                                      splatZero(dropDim))
                                : operands[it.index()]);
    }
    auto newContractOp = rewriter.create<vector::ContractionOp>(
        contractOp.getLoc(), newOperands[0], newOperands[1], newOperands[2],
        rewriter.getAffineMapArrayAttr(newIndexingMaps),
        rewriter.getArrayAttr(newIteratorTypes), contractOp.getKind());
    rewriter.replaceOpWithNewOp<vector::BroadcastOp>(
        contractOp, contractOp->getResultTypes()[0], newContractOp);
    return success();
  }
};
 
class CastAwayElementwiseLeadingOneDim : public RewritePattern {
public:
  CastAwayElementwiseLeadingOneDim(MLIRContext *context,
                                   PatternBenefit benefit = 1)
      : RewritePattern(MatchAnyOpTypeTag(), benefit, context) {}
 
  LogicalResult matchAndRewrite(Operation *op,
                                PatternRewriter &rewriter) const override {
    if (!OpTrait::hasElementwiseMappableTraits(op) || op->getNumResults() != 1)
      return failure();
    auto vecType = op->getResultTypes()[0].dyn_cast<VectorType>();
    if (!vecType)
      return failure();
    VectorType newVecType = trimLeadingOneDims(vecType);
    if (newVecType == vecType)
      return failure();
    int64_t dropDim = vecType.getRank() - newVecType.getRank();
    SmallVector<Value, 4> newOperands;
    for (Value operand : op->getOperands()) {
      if (auto opVecType = operand.getType().dyn_cast<VectorType>()) {
        newOperands.push_back(rewriter.create<vector::ExtractOp>(
            op->getLoc(), operand, splatZero(dropDim)));
      } else {
        newOperands.push_back(operand);
      }
    }
    Operation *newOp =
        rewriter.create(op->getLoc(), op->getName().getIdentifier(),
                        newOperands, newVecType, op->getAttrs());
    rewriter.replaceOpWithNewOp<vector::BroadcastOp>(op, vecType,
                                                     newOp->getResult(0));
    return success();
  }
};
 
} // namespace
 
void mlir::vector::populateCastAwayVectorLeadingOneDimPatterns(
    RewritePatternSet &patterns, PatternBenefit benefit) {
  patterns
      .add<CastAwayExtractStridedSliceLeadingOneDim,
           CastAwayInsertStridedSliceLeadingOneDim, CastAwayInsertLeadingOneDim,
           CastAwayTransferReadLeadingOneDim,
           CastAwayTransferWriteLeadingOneDim, CastAwayElementwiseLeadingOneDim,
           CastAwayContractionLeadingOneDim>(patterns.getContext(), benefit);
  populateShapeCastFoldingPatterns(patterns, benefit);
}