Conversion/VectorToSCF/VectorToSCF.cpp

//===- VectorToSCF.cpp - Conversion from Vector to mix of SCF and Std -----===//
//
// 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 target-dependent lowering of vector transfer operations.
//
//===----------------------------------------------------------------------===//

#include <type_traits>

#include "mlir/Conversion/VectorToSCF/VectorToSCF.h"

#include "../PassDetail.h"
#include "mlir/Dialect/Affine/EDSC/Intrinsics.h"
#include "mlir/Dialect/SCF/EDSC/Builders.h"
#include "mlir/Dialect/SCF/EDSC/Intrinsics.h"
#include "mlir/Dialect/StandardOps/EDSC/Intrinsics.h"
#include "mlir/Dialect/Vector/EDSC/Intrinsics.h"
#include "mlir/Dialect/Vector/VectorOps.h"
#include "mlir/Dialect/Vector/VectorUtils.h"
#include "mlir/IR/AffineExpr.h"
#include "mlir/IR/AffineMap.h"
#include "mlir/IR/Attributes.h"
#include "mlir/IR/Builders.h"
#include "mlir/IR/Location.h"
#include "mlir/IR/Matchers.h"
#include "mlir/IR/OperationSupport.h"
#include "mlir/IR/PatternMatch.h"
#include "mlir/IR/Types.h"
#include "mlir/Pass/Pass.h"
#include "mlir/Transforms/Passes.h"

using namespace mlir;
using namespace mlir::edsc;
using namespace mlir::edsc::intrinsics;
using vector::TransferReadOp;
using vector::TransferWriteOp;

namespace {
/// Helper class captures the common information needed to lower N>1-D vector
/// transfer operations (read and write).
/// On construction, this class opens an edsc::ScopedContext for simpler IR
/// manipulation.
/// In pseudo-IR, for an n-D vector_transfer_read such as:
///
/// ```
///   vector_transfer_read(%m, %offsets, identity_map, %fill) :
///     memref<(leading_dims) x (major_dims) x (minor_dims) x type>,
///     vector<(major_dims) x (minor_dims) x type>
/// ```
///
/// where rank(minor_dims) is the lower-level vector rank (e.g. 1 for LLVM or
/// higher).
///
/// This is the entry point to emitting pseudo-IR resembling:
///
/// ```
///   %tmp = alloc(): memref<(major_dims) x vector<minor_dim x type>>
///   for (%ivs_major, {0}, {vector_shape}, {1}) { // (N-1)-D loop nest
///     if (any_of(%ivs_major + %offsets, <, major_dims)) {
///       %v = vector_transfer_read(
///         {%offsets_leading, %ivs_major + %offsets_major, %offsets_minor},
///          %ivs_minor):
///         memref<(leading_dims) x (major_dims) x (minor_dims) x type>,
///         vector<(minor_dims) x type>;
///       store(%v, %tmp);
///     } else {
///       %v = splat(vector<(minor_dims) x type>, %fill)
///       store(%v, %tmp, %ivs_major);
///     }
///   }
///   %res = load(%tmp, %0): memref<(major_dims) x vector<minor_dim x type>>):
//      vector<(major_dims) x (minor_dims) x type>
/// ```
///
template <typename ConcreteOp>
class NDTransferOpHelper {
public:
  NDTransferOpHelper(PatternRewriter &rewriter, ConcreteOp xferOp,
                     const VectorTransferToSCFOptions &options)
      : rewriter(rewriter), options(options), loc(xferOp.getLoc()),
        scope(std::make_unique<ScopedContext>(rewriter, loc)), xferOp(xferOp),
        op(xferOp.getOperation()) {
    vectorType = xferOp.getVectorType();
    // TODO: when we go to k > 1-D vectors adapt minorRank.
    minorRank = 1;
    majorRank = vectorType.getRank() - minorRank;
    leadingRank = xferOp.getLeadingMemRefRank();
    majorVectorType =
        VectorType::get(vectorType.getShape().take_front(majorRank),
                        vectorType.getElementType());
    minorVectorType =
        VectorType::get(vectorType.getShape().take_back(minorRank),
                        vectorType.getElementType());
    /// Memref of minor vector type is used for individual transfers.
    memRefMinorVectorType =
        MemRefType::get(majorVectorType.getShape(), minorVectorType, {},
                        xferOp.getMemRefType().getMemorySpace());
  }

  LogicalResult doReplace();

private:
  /// Creates the loop nest on the "major" dimensions and calls the
  /// `loopBodyBuilder` lambda in the context of the loop nest.
  template <typename Lambda>
  void emitLoops(Lambda loopBodyBuilder);

  /// Operate within the body of `emitLoops` to:
  ///   1. Compute the indexings `majorIvs + majorOffsets` and save them in
  ///      `majorIvsPlusOffsets`.
  ///   2. Return a boolean that determines whether the first `majorIvs.rank()`
  ///      dimensions `majorIvs + majorOffsets` are all within `memrefBounds`.
  Value emitInBoundsCondition(ValueRange majorIvs, ValueRange majorOffsets,
                              MemRefBoundsCapture &memrefBounds,
                              SmallVectorImpl<Value> &majorIvsPlusOffsets);

  /// Common state to lower vector transfer ops.
  PatternRewriter &rewriter;
  const VectorTransferToSCFOptions &options;
  Location loc;
  std::unique_ptr<ScopedContext> scope;
  ConcreteOp xferOp;
  Operation *op;
  // A vector transfer copies data between:
  //   - memref<(leading_dims) x (major_dims) x (minor_dims) x type>
  //   - vector<(major_dims) x (minor_dims) x type>
  unsigned minorRank;         // for now always 1
  unsigned majorRank;         // vector rank - minorRank
  unsigned leadingRank;       // memref rank - vector rank
  VectorType vectorType;      // vector<(major_dims) x (minor_dims) x type>
  VectorType majorVectorType; // vector<(major_dims) x type>
  VectorType minorVectorType; // vector<(minor_dims) x type>
  MemRefType memRefMinorVectorType; // memref<vector<(minor_dims) x type>>
};

template <typename ConcreteOp>
template <typename Lambda>
void NDTransferOpHelper<ConcreteOp>::emitLoops(Lambda loopBodyBuilder) {
  /// Loop nest operates on the major dimensions
  MemRefBoundsCapture memrefBoundsCapture(xferOp.memref());

  if (options.unroll) {
    auto shape = majorVectorType.getShape();
    auto strides = computeStrides(shape);
    unsigned numUnrolledInstances = computeMaxLinearIndex(shape);
    ValueRange indices(xferOp.indices());
    for (unsigned idx = 0; idx < numUnrolledInstances; ++idx) {
      SmallVector<int64_t, 4> offsets = delinearize(strides, idx);
      SmallVector<Value, 4> offsetValues =
          llvm::to_vector<4>(llvm::map_range(offsets, [](int64_t off) -> Value {
            return std_constant_index(off);
          }));
      loopBodyBuilder(offsetValues, indices.take_front(leadingRank),
                      indices.drop_front(leadingRank).take_front(majorRank),
                      indices.take_back(minorRank), memrefBoundsCapture);
    }
  } else {
    VectorBoundsCapture vectorBoundsCapture(majorVectorType);
    auto majorLbs = vectorBoundsCapture.getLbs();
    auto majorUbs = vectorBoundsCapture.getUbs();
    auto majorSteps = vectorBoundsCapture.getSteps();
    affineLoopNestBuilder(
        majorLbs, majorUbs, majorSteps, [&](ValueRange majorIvs) {
          ValueRange indices(xferOp.indices());
          loopBodyBuilder(majorIvs, indices.take_front(leadingRank),
                          indices.drop_front(leadingRank).take_front(majorRank),
                          indices.take_back(minorRank), memrefBoundsCapture);
        });
  }
}

static Optional<int64_t> extractConstantIndex(Value v) {
  if (auto cstOp = v.getDefiningOp<ConstantIndexOp>())
    return cstOp.getValue();
  if (auto affineApplyOp = v.getDefiningOp<AffineApplyOp>())
    if (affineApplyOp.getAffineMap().isSingleConstant())
      return affineApplyOp.getAffineMap().getSingleConstantResult();
  return None;
}

// Missing foldings of scf.if make it necessary to perform poor man's folding
// eagerly, especially in the case of unrolling. In the future, this should go
// away once scf.if folds properly.
static Value onTheFlyFoldSLT(Value v, Value ub) {
  using namespace mlir::edsc::op;
  auto maybeCstV = extractConstantIndex(v);
  auto maybeCstUb = extractConstantIndex(ub);
  if (maybeCstV && maybeCstUb && *maybeCstV < *maybeCstUb)
    return Value();
  return slt(v, ub);
}

template <typename ConcreteOp>
Value NDTransferOpHelper<ConcreteOp>::emitInBoundsCondition(
    ValueRange majorIvs, ValueRange majorOffsets,
    MemRefBoundsCapture &memrefBounds,
    SmallVectorImpl<Value> &majorIvsPlusOffsets) {
  Value inBoundsCondition;
  majorIvsPlusOffsets.reserve(majorIvs.size());
  unsigned idx = 0;
  for (auto it : llvm::zip(majorIvs, majorOffsets, memrefBounds.getUbs())) {
    Value iv = std::get<0>(it), off = std::get<1>(it), ub = std::get<2>(it);
    using namespace mlir::edsc::op;
    majorIvsPlusOffsets.push_back(iv + off);
    if (xferOp.isMaskedDim(leadingRank + idx)) {
      Value inBoundsCond = onTheFlyFoldSLT(majorIvsPlusOffsets.back(), ub);
      if (inBoundsCond)
        inBoundsCondition = (inBoundsCondition)
                                ? (inBoundsCondition && inBoundsCond)
                                : inBoundsCond;
    }
    ++idx;
  }
  return inBoundsCondition;
}

// TODO: Parallelism and threadlocal considerations.
static Value setAllocAtFunctionEntry(MemRefType memRefMinorVectorType,
                                     Operation *op) {
  auto &b = ScopedContext::getBuilderRef();
  OpBuilder::InsertionGuard guard(b);
  b.setInsertionPointToStart(&op->getParentOfType<FuncOp>().front());
  Value res =
      std_alloca(memRefMinorVectorType, ValueRange{}, b.getI64IntegerAttr(128));
  return res;
}

template <>
LogicalResult NDTransferOpHelper<TransferReadOp>::doReplace() {
  Value alloc, result;
  if (options.unroll)
    result = std_splat(vectorType, xferOp.padding());
  else
    alloc = setAllocAtFunctionEntry(memRefMinorVectorType, op);

  emitLoops([&](ValueRange majorIvs, ValueRange leadingOffsets,
                ValueRange majorOffsets, ValueRange minorOffsets,
                MemRefBoundsCapture &memrefBounds) {
    /// Lambda to load 1-D vector in the current loop ivs + offset context.
    auto load1DVector = [&](ValueRange majorIvsPlusOffsets) -> Value {
      SmallVector<Value, 8> indexing;
      indexing.reserve(leadingRank + majorRank + minorRank);
      indexing.append(leadingOffsets.begin(), leadingOffsets.end());
      indexing.append(majorIvsPlusOffsets.begin(), majorIvsPlusOffsets.end());
      indexing.append(minorOffsets.begin(), minorOffsets.end());
      Value memref = xferOp.memref();
      auto map =
          getTransferMinorIdentityMap(xferOp.getMemRefType(), minorVectorType);
      ArrayAttr masked;
      if (!xferOp.isMaskedDim(xferOp.getVectorType().getRank() - 1)) {
        OpBuilder &b = ScopedContext::getBuilderRef();
        masked = b.getBoolArrayAttr({false});
      }
      return vector_transfer_read(minorVectorType, memref, indexing,
                                  AffineMapAttr::get(map), xferOp.padding(),
                                  masked);
    };

    // 1. Compute the inBoundsCondition in the current loops ivs + offset
    // context.
    SmallVector<Value, 4> majorIvsPlusOffsets;
    Value inBoundsCondition = emitInBoundsCondition(
        majorIvs, majorOffsets, memrefBounds, majorIvsPlusOffsets);

    if (inBoundsCondition) {
      // 2. If the condition is not null, we need an IfOp, which may yield
      // if `options.unroll` is true.
      SmallVector<Type, 1> resultType;
      if (options.unroll)
        resultType.push_back(vectorType);

      // 3. If in-bounds, progressively lower to a 1-D transfer read, otherwise
      // splat a 1-D vector.
      ValueRange ifResults = conditionBuilder(
          resultType, inBoundsCondition,
          [&]() -> scf::ValueVector {
            Value vector = load1DVector(majorIvsPlusOffsets);
            // 3.a. If `options.unroll` is true, insert the 1-D vector in the
            // aggregate. We must yield and merge with the `else` branch.
            if (options.unroll) {
              vector = vector_insert(vector, result, majorIvs);
              return {vector};
            }
            // 3.b. Otherwise, just go through the temporary `alloc`.
            std_store(vector, alloc, majorIvs);
            return {};
          },
          [&]() -> scf::ValueVector {
            Value vector = std_splat(minorVectorType, xferOp.padding());
            // 3.c. If `options.unroll` is true, insert the 1-D vector in the
            // aggregate. We must yield and merge with the `then` branch.
            if (options.unroll) {
              vector = vector_insert(vector, result, majorIvs);
              return {vector};
            }
            // 3.d. Otherwise, just go through the temporary `alloc`.
            std_store(vector, alloc, majorIvs);
            return {};
          });

      if (!resultType.empty())
        result = *ifResults.begin();
    } else {
      // 4. Guaranteed in-bounds, progressively lower to a 1-D transfer read.
      Value loaded1D = load1DVector(majorIvsPlusOffsets);
      // 5.a. If `options.unroll` is true, insert the 1-D vector in the
      // aggregate.
      if (options.unroll)
        result = vector_insert(loaded1D, result, majorIvs);
      // 5.b. Otherwise, just go through the temporary `alloc`.
      else
        std_store(loaded1D, alloc, majorIvs);
    }
  });

  assert((!options.unroll ^ (bool)result) &&
         "Expected resulting Value iff unroll");
  if (!result)
    result = std_load(vector_type_cast(MemRefType::get({}, vectorType), alloc));
  rewriter.replaceOp(op, result);

  return success();
}

template <>
LogicalResult NDTransferOpHelper<TransferWriteOp>::doReplace() {
  Value alloc;
  if (!options.unroll) {
    alloc = setAllocAtFunctionEntry(memRefMinorVectorType, op);
    std_store(xferOp.vector(),
              vector_type_cast(MemRefType::get({}, vectorType), alloc));
  }

  emitLoops([&](ValueRange majorIvs, ValueRange leadingOffsets,
                ValueRange majorOffsets, ValueRange minorOffsets,
                MemRefBoundsCapture &memrefBounds) {
    // Lower to 1-D vector_transfer_write and let recursion handle it.
    auto emitTransferWrite = [&](ValueRange majorIvsPlusOffsets) {
      SmallVector<Value, 8> indexing;
      indexing.reserve(leadingRank + majorRank + minorRank);
      indexing.append(leadingOffsets.begin(), leadingOffsets.end());
      indexing.append(majorIvsPlusOffsets.begin(), majorIvsPlusOffsets.end());
      indexing.append(minorOffsets.begin(), minorOffsets.end());
      Value result;
      // If `options.unroll` is true, extract the 1-D vector from the
      // aggregate.
      if (options.unroll)
        result = vector_extract(xferOp.vector(), majorIvs);
      else
        result = std_load(alloc, majorIvs);
      auto map =
          getTransferMinorIdentityMap(xferOp.getMemRefType(), minorVectorType);
      ArrayAttr masked;
      if (!xferOp.isMaskedDim(xferOp.getVectorType().getRank() - 1)) {
        OpBuilder &b = ScopedContext::getBuilderRef();
        masked = b.getBoolArrayAttr({false});
      }
      vector_transfer_write(result, xferOp.memref(), indexing,
                            AffineMapAttr::get(map), masked);
    };

    // 1. Compute the inBoundsCondition in the current loops ivs + offset
    // context.
    SmallVector<Value, 4> majorIvsPlusOffsets;
    Value inBoundsCondition = emitInBoundsCondition(
        majorIvs, majorOffsets, memrefBounds, majorIvsPlusOffsets);

    if (inBoundsCondition) {
      // 2.a. If the condition is not null, we need an IfOp, to write
      // conditionally. Progressively lower to a 1-D transfer write.
      conditionBuilder(inBoundsCondition,
                       [&] { emitTransferWrite(majorIvsPlusOffsets); });
    } else {
      // 2.b. Guaranteed in-bounds. Progressively lower to a 1-D transfer write.
      emitTransferWrite(majorIvsPlusOffsets);
    }
  });

  rewriter.eraseOp(op);

  return success();
}

} // namespace

/// Analyzes the `transfer` to find an access dimension along the fastest remote
/// MemRef dimension. If such a dimension with coalescing properties is found,
/// `pivs` and `vectorBoundsCapture` are swapped so that the invocation of
/// LoopNestBuilder captures it in the innermost loop.
template <typename TransferOpTy>
static int computeCoalescedIndex(TransferOpTy transfer) {
  // rank of the remote memory access, coalescing behavior occurs on the
  // innermost memory dimension.
  auto remoteRank = transfer.getMemRefType().getRank();
  // Iterate over the results expressions of the permutation map to determine
  // the loop order for creating pointwise copies between remote and local
  // memories.
  int coalescedIdx = -1;
  auto exprs = transfer.permutation_map().getResults();
  for (auto en : llvm::enumerate(exprs)) {
    auto dim = en.value().template dyn_cast<AffineDimExpr>();
    if (!dim) {
      continue;
    }
    auto memRefDim = dim.getPosition();
    if (memRefDim == remoteRank - 1) {
      // memRefDim has coalescing properties, it should be swapped in the last
      // position.
      assert(coalescedIdx == -1 && "Unexpected > 1 coalesced indices");
      coalescedIdx = en.index();
    }
  }
  return coalescedIdx;
}

/// Emits remote memory accesses that are clipped to the boundaries of the
/// MemRef.
template <typename TransferOpTy>
static SmallVector<Value, 8>
clip(TransferOpTy transfer, MemRefBoundsCapture &bounds, ArrayRef<Value> ivs) {
  using namespace mlir::edsc;

  Value zero(std_constant_index(0)), one(std_constant_index(1));
  SmallVector<Value, 8> memRefAccess(transfer.indices());
  SmallVector<Value, 8> clippedScalarAccessExprs(memRefAccess.size());
  // Indices accessing to remote memory are clipped and their expressions are
  // returned in clippedScalarAccessExprs.
  for (unsigned memRefDim = 0; memRefDim < clippedScalarAccessExprs.size();
       ++memRefDim) {
    // Linear search on a small number of entries.
    int loopIndex = -1;
    auto exprs = transfer.permutation_map().getResults();
    for (auto en : llvm::enumerate(exprs)) {
      auto expr = en.value();
      auto dim = expr.template dyn_cast<AffineDimExpr>();
      // Sanity check.
      assert(
          (dim || expr.template cast<AffineConstantExpr>().getValue() == 0) &&
          "Expected dim or 0 in permutationMap");
      if (dim && memRefDim == dim.getPosition()) {
        loopIndex = en.index();
        break;
      }
    }

    // We cannot distinguish atm between unrolled dimensions that implement
    // the "always full" tile abstraction and need clipping from the other
    // ones. So we conservatively clip everything.
    using namespace edsc::op;
    auto N = bounds.ub(memRefDim);
    auto i = memRefAccess[memRefDim];
    if (loopIndex < 0) {
      auto N_minus_1 = N - one;
      auto select_1 = std_select(slt(i, N), i, N_minus_1);
      clippedScalarAccessExprs[memRefDim] =
          std_select(slt(i, zero), zero, select_1);
    } else {
      auto ii = ivs[loopIndex];
      auto i_plus_ii = i + ii;
      auto N_minus_1 = N - one;
      auto select_1 = std_select(slt(i_plus_ii, N), i_plus_ii, N_minus_1);
      clippedScalarAccessExprs[memRefDim] =
          std_select(slt(i_plus_ii, zero), zero, select_1);
    }
  }

  return clippedScalarAccessExprs;
}

namespace mlir {

template <typename TransferOpTy>
VectorTransferRewriter<TransferOpTy>::VectorTransferRewriter(
    VectorTransferToSCFOptions options, MLIRContext *context)
    : RewritePattern(TransferOpTy::getOperationName(), 1, context),
      options(options) {}

/// Used for staging the transfer in a local buffer.
template <typename TransferOpTy>
MemRefType VectorTransferRewriter<TransferOpTy>::tmpMemRefType(
    TransferOpTy transfer) const {
  auto vectorType = transfer.getVectorType();
  return MemRefType::get(vectorType.getShape(), vectorType.getElementType(), {},
                         0);
}

/// Lowers TransferReadOp into a combination of:
///   1. local memory allocation;
///   2. perfect loop nest over:
///      a. scalar load from local buffers (viewed as a scalar memref);
///      a. scalar store to original memref (with clipping).
///   3. vector_load from local buffer (viewed as a memref<1 x vector>);
///   4. local memory deallocation.
///
/// Lowers the data transfer part of a TransferReadOp while ensuring no
/// out-of-bounds accesses are possible. Out-of-bounds behavior is handled by
/// clipping. This means that a given value in memory can be read multiple
/// times and concurrently.
///
/// Important notes about clipping and "full-tiles only" abstraction:
/// =================================================================
/// When using clipping for dealing with boundary conditions, the same edge
/// value will appear multiple times (a.k.a edge padding). This is fine if the
/// subsequent vector operations are all data-parallel but **is generally
/// incorrect** in the presence of reductions or extract operations.
///
/// More generally, clipping is a scalar abstraction that is expected to work
/// fine as a baseline for CPUs and GPUs but not for vector_load and DMAs.
/// To deal with real vector_load and DMAs, a "padded allocation + view"
/// abstraction with the ability to read out-of-memref-bounds (but still within
/// the allocated region) is necessary.
///
/// Whether using scalar loops or vector_load/DMAs to perform the transfer,
/// junk values will be materialized in the vectors and generally need to be
/// filtered out and replaced by the "neutral element". This neutral element is
/// op-dependent so, in the future, we expect to create a vector filter and
/// apply it to a splatted constant vector with the proper neutral element at
/// each ssa-use. This filtering is not necessary for pure data-parallel
/// operations.
///
/// In the case of vector_store/DMAs, Read-Modify-Write will be required, which
/// also have concurrency implications. Note that by using clipped scalar stores
/// in the presence of data-parallel only operations, we generate code that
/// writes the same value multiple time on the edge locations.
///
/// TODO: implement alternatives to clipping.
/// TODO: support non-data-parallel operations.

/// Performs the rewrite.
template <>
LogicalResult VectorTransferRewriter<TransferReadOp>::matchAndRewrite(
    Operation *op, PatternRewriter &rewriter) const {
  using namespace mlir::edsc::op;

  TransferReadOp transfer = cast<TransferReadOp>(op);
  if (transfer.permutation_map().isMinorIdentity()) {
    // If > 1D, emit a bunch of loops around 1-D vector transfers.
    if (transfer.getVectorType().getRank() > 1)
      return NDTransferOpHelper<TransferReadOp>(rewriter, transfer, options)
          .doReplace();
    // If 1-D this is now handled by the target-specific lowering.
    if (transfer.getVectorType().getRank() == 1)
      return failure();
  }

  // Conservative lowering to scalar load / stores.
  // 1. Setup all the captures.
  ScopedContext scope(rewriter, transfer.getLoc());
  StdIndexedValue remote(transfer.memref());
  MemRefBoundsCapture memRefBoundsCapture(transfer.memref());
  VectorBoundsCapture vectorBoundsCapture(transfer.vector());
  int coalescedIdx = computeCoalescedIndex(transfer);
  // Swap the vectorBoundsCapture which will reorder loop bounds.
  if (coalescedIdx >= 0)
    vectorBoundsCapture.swapRanges(vectorBoundsCapture.rank() - 1,
                                   coalescedIdx);

  auto lbs = vectorBoundsCapture.getLbs();
  auto ubs = vectorBoundsCapture.getUbs();
  SmallVector<Value, 8> steps;
  steps.reserve(vectorBoundsCapture.getSteps().size());
  for (auto step : vectorBoundsCapture.getSteps())
    steps.push_back(std_constant_index(step));

  // 2. Emit alloc-copy-load-dealloc.
  Value tmp = std_alloc(tmpMemRefType(transfer));
  StdIndexedValue local(tmp);
  Value vec = vector_type_cast(tmp);
  loopNestBuilder(lbs, ubs, steps, [&](ValueRange loopIvs) {
    auto ivs = llvm::to_vector<8>(loopIvs);
    // Swap the ivs which will reorder memory accesses.
    if (coalescedIdx >= 0)
      std::swap(ivs.back(), ivs[coalescedIdx]);
    // Computes clippedScalarAccessExprs in the loop nest scope (ivs exist).
    local(ivs) = remote(clip(transfer, memRefBoundsCapture, ivs));
  });
  Value vectorValue = std_load(vec);
  (std_dealloc(tmp)); // vexing parse

  // 3. Propagate.
  rewriter.replaceOp(op, vectorValue);
  return success();
}

/// Lowers TransferWriteOp into a combination of:
///   1. local memory allocation;
///   2. vector_store to local buffer (viewed as a memref<1 x vector>);
///   3. perfect loop nest over:
///      a. scalar load from local buffers (viewed as a scalar memref);
///      a. scalar store to original memref (with clipping).
///   4. local memory deallocation.
///
/// More specifically, lowers the data transfer part while ensuring no
/// out-of-bounds accesses are possible. Out-of-bounds behavior is handled by
/// clipping. This means that a given value in memory can be written to multiple
/// times and concurrently.
///
/// See `Important notes about clipping and full-tiles only abstraction` in the
/// description of `readClipped` above.
///
/// TODO: implement alternatives to clipping.
/// TODO: support non-data-parallel operations.
template <>
LogicalResult VectorTransferRewriter<TransferWriteOp>::matchAndRewrite(
    Operation *op, PatternRewriter &rewriter) const {
  using namespace edsc::op;

  TransferWriteOp transfer = cast<TransferWriteOp>(op);
  if (transfer.permutation_map().isMinorIdentity()) {
    // If > 1D, emit a bunch of loops around 1-D vector transfers.
    if (transfer.getVectorType().getRank() > 1)
      return NDTransferOpHelper<TransferWriteOp>(rewriter, transfer, options)
          .doReplace();
    // If 1-D this is now handled by the target-specific lowering.
    if (transfer.getVectorType().getRank() == 1)
      return failure();
  }

  // 1. Setup all the captures.
  ScopedContext scope(rewriter, transfer.getLoc());
  StdIndexedValue remote(transfer.memref());
  MemRefBoundsCapture memRefBoundsCapture(transfer.memref());
  Value vectorValue(transfer.vector());
  VectorBoundsCapture vectorBoundsCapture(transfer.vector());
  int coalescedIdx = computeCoalescedIndex(transfer);
  // Swap the vectorBoundsCapture which will reorder loop bounds.
  if (coalescedIdx >= 0)
    vectorBoundsCapture.swapRanges(vectorBoundsCapture.rank() - 1,
                                   coalescedIdx);

  auto lbs = vectorBoundsCapture.getLbs();
  auto ubs = vectorBoundsCapture.getUbs();
  SmallVector<Value, 8> steps;
  steps.reserve(vectorBoundsCapture.getSteps().size());
  for (auto step : vectorBoundsCapture.getSteps())
    steps.push_back(std_constant_index(step));

  // 2. Emit alloc-store-copy-dealloc.
  Value tmp = std_alloc(tmpMemRefType(transfer));
  StdIndexedValue local(tmp);
  Value vec = vector_type_cast(tmp);
  std_store(vectorValue, vec);
  loopNestBuilder(lbs, ubs, steps, [&](ValueRange loopIvs) {
    auto ivs = llvm::to_vector<8>(loopIvs);
    // Swap the ivs which will reorder memory accesses.
    if (coalescedIdx >= 0)
      std::swap(ivs.back(), ivs[coalescedIdx]);
    // Computes clippedScalarAccessExprs in the loop nest scope (ivs exist).
    remote(clip(transfer, memRefBoundsCapture, ivs)) = local(ivs);
  });
  (std_dealloc(tmp)); // vexing parse...

  rewriter.eraseOp(op);
  return success();
}

void populateVectorToSCFConversionPatterns(
    OwningRewritePatternList &patterns, MLIRContext *context,
    const VectorTransferToSCFOptions &options) {
  patterns.insert<VectorTransferRewriter<vector::TransferReadOp>,
                  VectorTransferRewriter<vector::TransferWriteOp>>(options,
                                                                   context);
}

} // namespace mlir

namespace {

struct ConvertVectorToSCFPass
    : public ConvertVectorToSCFBase<ConvertVectorToSCFPass> {
  ConvertVectorToSCFPass() = default;
  ConvertVectorToSCFPass(const VectorTransferToSCFOptions &options) {
    this->fullUnroll = options.unroll;
  }

  void runOnFunction() override {
    OwningRewritePatternList patterns;
    auto *context = getFunction().getContext();
    populateVectorToSCFConversionPatterns(
        patterns, context, VectorTransferToSCFOptions().setUnroll(fullUnroll));
    applyPatternsAndFoldGreedily(getFunction(), patterns);
  }
};

} // namespace

std::unique_ptr<Pass>
mlir::createConvertVectorToSCFPass(const VectorTransferToSCFOptions &options) {
  return std::make_unique<ConvertVectorToSCFPass>(options);
}