//===- SCFToGPU.cpp - Convert an affine loop nest to a GPU kernel -------===//
//
// 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 implements a straightforward conversion of an loop nest into a GPU
// kernel.  The caller is expected to guarantee that the conversion is correct
// or to further transform the kernel to ensure correctness.
//
//===----------------------------------------------------------------------===//

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

#include "mlir/Conversion/AffineToStandard/AffineToStandard.h"
#include "mlir/Dialect/Affine/IR/AffineOps.h"
#include "mlir/Dialect/GPU/GPUDialect.h"
#include "mlir/Dialect/GPU/ParallelLoopMapper.h"
#include "mlir/Dialect/SCF/SCF.h"
#include "mlir/Dialect/StandardOps/IR/Ops.h"
#include "mlir/IR/AffineExpr.h"
#include "mlir/IR/BlockAndValueMapping.h"
#include "mlir/IR/Builders.h"
#include "mlir/Pass/Pass.h"
#include "mlir/Transforms/DialectConversion.h"
#include "mlir/Transforms/LoopUtils.h"
#include "mlir/Transforms/Passes.h"
#include "mlir/Transforms/RegionUtils.h"
#include "llvm/ADT/Sequence.h"
#include "llvm/Support/Debug.h"

#define DEBUG_TYPE "loops-to-gpu"

using namespace mlir;
using namespace mlir::scf;

using llvm::seq;

// Extract an indexed value from KernelDim3.
static Value getDim3Value(const gpu::KernelDim3 &dim3, unsigned pos) {
  switch (pos) {
  case 0:
    return dim3.x;
  case 1:
    return dim3.y;
  case 2:
    return dim3.z;
  default:
    llvm_unreachable("dim3 position out of bounds");
  }
  return nullptr;
}

// Get the lower bound-related operands of a loop operation.
static Operation::operand_range getLowerBoundOperands(AffineForOp forOp) {
  return forOp.getLowerBoundOperands();
}
static SmallVector<Value, 1> getLowerBoundOperands(ForOp forOp) {
  SmallVector<Value, 1> bounds(1, forOp.lowerBound());
  return bounds;
}

// Get the upper bound-related operands of a loop operation.
static Operation::operand_range getUpperBoundOperands(AffineForOp forOp) {
  return forOp.getUpperBoundOperands();
}
static SmallVector<Value, 1> getUpperBoundOperands(ForOp forOp) {
  SmallVector<Value, 1> bounds(1, forOp.upperBound());
  return bounds;
}

// Get a Value that corresponds to the loop step.  If the step is an attribute,
// materialize a corresponding constant using builder.
static Value getOrCreateStep(AffineForOp forOp, OpBuilder &builder) {
  return builder.create<ConstantIndexOp>(forOp.getLoc(), forOp.getStep());
}
static Value getOrCreateStep(ForOp forOp, OpBuilder &) { return forOp.step(); }

// Get a Value for the loop lower bound.  If the value requires computation,
// materialize the instructions using builder.
static Value getOrEmitLowerBound(AffineForOp forOp, OpBuilder &builder) {
  return lowerAffineLowerBound(forOp, builder);
}
static Value getOrEmitLowerBound(ForOp forOp, OpBuilder &) {
  return forOp.lowerBound();
}

// Get a Value for the loop upper bound.  If the value requires computation,
// materialize the instructions using builder.
static Value getOrEmitUpperBound(AffineForOp forOp, OpBuilder &builder) {
  return lowerAffineUpperBound(forOp, builder);
}
static Value getOrEmitUpperBound(ForOp forOp, OpBuilder &) {
  return forOp.upperBound();
}

// Check the structure of the loop nest:
//   - there are enough loops to map to numDims;
//   - the loops are perfectly nested;
//   - the loop bounds can be computed above the outermost loop.
// This roughly corresponds to the "matcher" part of the pattern-based
// rewriting infrastructure.
template <typename OpTy>
static LogicalResult checkLoopNestMappableImpl(OpTy forOp, unsigned numDims) {
  Region &limit = forOp.region();
  for (unsigned i = 0, e = numDims; i < e; ++i) {
    Operation *nested = &forOp.getBody()->front();
    if (!areValuesDefinedAbove(getLowerBoundOperands(forOp), limit) ||
        !areValuesDefinedAbove(getUpperBoundOperands(forOp), limit))
      return forOp.emitError(
          "loops with bounds depending on other mapped loops "
          "are not supported");

    // The innermost loop can have an arbitrary body, skip the perfect nesting
    // check for it.
    if (i == e - 1)
      break;

    auto begin = forOp.getBody()->begin(), end = forOp.getBody()->end();
    if (forOp.getBody()->empty() || std::next(begin, 2) != end)
      return forOp.emitError("expected perfectly nested loops in the body");

    if (!(forOp = dyn_cast<OpTy>(nested)))
      return nested->emitError("expected a nested loop");
  }
  return success();
}

template <typename OpTy>
static LogicalResult checkLoopNestMappable(OpTy forOp, unsigned numBlockDims,
                                           unsigned numThreadDims) {
  if (numBlockDims < 1 || numThreadDims < 1) {
    LLVM_DEBUG(llvm::dbgs() << "nothing to map");
    return success();
  }

  if (numBlockDims > 3) {
    return forOp.emitError("cannot map to more than 3 block dimensions");
  }
  if (numThreadDims > 3) {
    return forOp.emitError("cannot map to more than 3 thread dimensions");
  }
  return checkLoopNestMappableImpl(forOp, numBlockDims + numThreadDims);
}

template <typename OpTy>
static LogicalResult checkLoopOpMappable(OpTy forOp, unsigned numBlockDims,
                                         unsigned numThreadDims) {
  if (numBlockDims < 1 || numThreadDims < 1) {
    LLVM_DEBUG(llvm::dbgs() << "nothing to map");
    return success();
  }

  if (numBlockDims > 3) {
    return forOp.emitError("cannot map to more than 3 block dimensions");
  }
  if (numThreadDims > 3) {
    return forOp.emitError("cannot map to more than 3 thread dimensions");
  }
  if (numBlockDims != numThreadDims) {
    // TODO(ravishankarm) : This can probably be relaxed by having a one-trip
    // loop for the missing dimension, but there is not reason to handle this
    // case for now.
    return forOp.emitError(
        "mismatch in block dimensions and thread dimensions");
  }

  // Check that the forOp contains perfectly nested loops for numBlockDims
  if (failed(checkLoopNestMappableImpl(forOp, numBlockDims))) {
    return failure();
  }

  // Get to the innermost loop.
  for (auto i : seq<unsigned>(0, numBlockDims - 1)) {
    forOp = cast<OpTy>(&forOp.getBody()->front());
    (void)i;
  }

  // The forOp now points to the body of the innermost loop mapped to blocks.
  for (Operation &op : *forOp.getBody()) {
    // If the operation is a loop, check that it is mappable to workItems.
    if (auto innerLoop = dyn_cast<OpTy>(&op)) {
      if (failed(checkLoopNestMappableImpl(innerLoop, numThreadDims))) {
        return failure();
      }
      continue;
    }
    // TODO(ravishankarm) : If it is not a loop op, it is assumed that the
    // statement is executed by all threads. It might be a collective operation,
    // or some non-side effect instruction. Have to decide on "allowable"
    // statements and check for those here.
  }
  return success();
}

namespace {
// Helper structure that holds common state of the loop to GPU kernel
// conversion.
struct LoopToGpuConverter {
  template <typename OpTy>
  Optional<OpTy> collectBounds(OpTy forOp, unsigned numLoops);

  template <typename OpTy>
  void createLaunch(OpTy rootForOp, OpTy innermostForOp, unsigned numBlockDims,
                    unsigned numThreadDims);

  // Ranges of the loops mapped to blocks or threads.
  SmallVector<Value, 6> dims;
  // Lower bounds of the loops mapped to blocks or threads.
  SmallVector<Value, 6> lbs;
  // Induction variables of the loops mapped to blocks or threads.
  SmallVector<Value, 6> ivs;
  // Steps of the loops mapped to blocks or threads.
  SmallVector<Value, 6> steps;
};
} // namespace

// Return true if the value is obviously a constant "one".
static bool isConstantOne(Value value) {
  if (auto def = value.getDefiningOp<ConstantIndexOp>())
    return def.getValue() == 1;
  return false;
}

// Collect ranges, bounds, steps and induction variables in preparation for
// mapping a loop nest of depth "numLoops" rooted at "forOp" to a GPU kernel.
// This may fail if the IR for computing loop bounds cannot be constructed, for
// example if an affine loop uses semi-affine maps. Return the last loop to be
// mapped on success, llvm::None on failure.
template <typename OpTy>
Optional<OpTy> LoopToGpuConverter::collectBounds(OpTy forOp,
                                                 unsigned numLoops) {
  OpBuilder builder(forOp.getOperation());
  dims.reserve(numLoops);
  lbs.reserve(numLoops);
  ivs.reserve(numLoops);
  steps.reserve(numLoops);
  OpTy currentLoop = forOp;
  for (unsigned i = 0; i < numLoops; ++i) {
    Value lowerBound = getOrEmitLowerBound(currentLoop, builder);
    Value upperBound = getOrEmitUpperBound(currentLoop, builder);
    if (!lowerBound || !upperBound) {
      return llvm::None;
    }

    Value range =
        builder.create<SubIOp>(currentLoop.getLoc(), upperBound, lowerBound);
    Value step = getOrCreateStep(currentLoop, builder);
    if (!isConstantOne(step))
      range = builder.create<SignedDivIOp>(currentLoop.getLoc(), range, step);
    dims.push_back(range);

    lbs.push_back(lowerBound);
    ivs.push_back(currentLoop.getInductionVar());
    steps.push_back(step);

    if (i != numLoops - 1)
      currentLoop = cast<OpTy>(&currentLoop.getBody()->front());
  }
  return currentLoop;
}

/// Given `nDims` perfectly nested loops rooted as `rootForOp`, convert them o
/// be partitioned across workgroups or workitems. The values for the
/// workgroup/workitem id along each dimension is passed in with `ids`. The
/// number of workgroups/workitems along each dimension are passed in with
/// `nids`. The innermost loop is mapped to the x-dimension, followed by the
/// next innermost loop to y-dimension, followed by z-dimension.
template <typename OpTy>
static OpTy createGPULaunchLoops(OpTy rootForOp, ArrayRef<Value> ids,
                                 ArrayRef<Value> nids) {
  auto nDims = ids.size();
  assert(nDims == nids.size());
  for (auto dim : llvm::seq<unsigned>(0, nDims)) {
    // TODO(ravishankarm): Don't always need to generate a loop here. If nids >=
    // number of iterations of the original loop, this becomes a if
    // condition. Though that does rely on how the workgroup/workitem sizes are
    // specified to begin with.
    mapLoopToProcessorIds(rootForOp, ids[dim], nids[dim]);
    if (dim != nDims - 1) {
      rootForOp = cast<OpTy>(rootForOp.getBody()->front());
    }
  }
  return rootForOp;
}

/// Utility method to convert the gpu::KernelDim3 object for representing id of
/// each workgroup/workitem and number of workgroup/workitems along a dimension
/// of the launch into a container.
static void packIdAndNumId(gpu::KernelDim3 kernelIds,
                           gpu::KernelDim3 kernelNids, unsigned nDims,
                           SmallVectorImpl<Value> &ids,
                           SmallVectorImpl<Value> &nids) {
  assert(nDims <= 3 && "invalid number of launch dimensions");
  std::array<Value, 3> allIds = {kernelIds.z, kernelIds.y, kernelIds.x};
  std::array<Value, 3> allNids = {kernelNids.z, kernelNids.y, kernelNids.x};
  ids.clear();
  ids.append(std::next(allIds.begin(), allIds.size() - nDims), allIds.end());
  nids.clear();
  nids.append(std::next(allNids.begin(), allNids.size() - nDims),
              allNids.end());
}

/// Generate the body of the launch operation.
template <typename OpTy>
static LogicalResult
createLaunchBody(OpBuilder &builder, OpTy rootForOp, gpu::LaunchOp launchOp,
                 unsigned numBlockDims, unsigned numThreadDims) {
  OpBuilder::InsertionGuard bodyInsertionGuard(builder);
  builder.setInsertionPointToEnd(&launchOp.body().front());
  auto terminatorOp = builder.create<gpu::TerminatorOp>(launchOp.getLoc());

  rootForOp.getOperation()->moveBefore(terminatorOp);
  SmallVector<Value, 3> workgroupID, numWorkGroups;
  packIdAndNumId(launchOp.getBlockIds(), launchOp.getGridSize(), numBlockDims,
                 workgroupID, numWorkGroups);

  // Partition the loop for mapping to workgroups.
  auto loopOp = createGPULaunchLoops(rootForOp, workgroupID, numWorkGroups);

  // Iterate over the body of the loopOp and get the loops to partition for
  // thread blocks.
  SmallVector<OpTy, 1> threadRootForOps;
  for (Operation &op : *loopOp.getBody()) {
    if (auto threadRootForOp = dyn_cast<OpTy>(&op)) {
      threadRootForOps.push_back(threadRootForOp);
    }
  }

  SmallVector<Value, 3> workItemID, workGroupSize;
  packIdAndNumId(launchOp.getThreadIds(), launchOp.getBlockSize(),
                 numThreadDims, workItemID, workGroupSize);
  for (auto &loopOp : threadRootForOps) {
    builder.setInsertionPoint(loopOp);
    createGPULaunchLoops(loopOp, workItemID, workGroupSize);
  }
  return success();
}

// Convert the computation rooted at the `rootForOp`, into a GPU kernel with the
// given workgroup size and number of workgroups.
template <typename OpTy>
static LogicalResult createLaunchFromOp(OpTy rootForOp,
                                        ArrayRef<Value> numWorkGroups,
                                        ArrayRef<Value> workGroupSizes) {
  OpBuilder builder(rootForOp.getOperation());
  if (numWorkGroups.size() > 3) {
    return rootForOp.emitError("invalid ")
           << numWorkGroups.size() << "-D workgroup specification";
  }
  auto loc = rootForOp.getLoc();
  Value one = builder.create<ConstantOp>(
      loc, builder.getIntegerAttr(builder.getIndexType(), 1));
  SmallVector<Value, 3> numWorkGroups3D(3, one), workGroupSize3D(3, one);
  for (auto numWorkGroup : enumerate(numWorkGroups)) {
    numWorkGroups3D[numWorkGroup.index()] = numWorkGroup.value();
  }
  for (auto workGroupSize : enumerate(workGroupSizes)) {
    workGroupSize3D[workGroupSize.index()] = workGroupSize.value();
  }

  auto launchOp = builder.create<gpu::LaunchOp>(
      rootForOp.getLoc(), numWorkGroups3D[0], numWorkGroups3D[1],
      numWorkGroups3D[2], workGroupSize3D[0], workGroupSize3D[1],
      workGroupSize3D[2]);
  if (failed(createLaunchBody(builder, rootForOp, launchOp,
                              numWorkGroups.size(), workGroupSizes.size()))) {
    return failure();
  }

  return success();
}

// Replace the rooted at "rootForOp" with a GPU launch operation.  This expects
// "innermostForOp" to point to the last loop to be transformed to the kernel,
// and to have (numBlockDims + numThreadDims) perfectly nested loops between
// "rootForOp" and "innermostForOp".
// TODO(ravishankarm) : This method can be modified to use the
// createLaunchFromOp method, since that is a strict generalization of this
// method.
template <typename OpTy>
void LoopToGpuConverter::createLaunch(OpTy rootForOp, OpTy innermostForOp,
                                      unsigned numBlockDims,
                                      unsigned numThreadDims) {
  OpBuilder builder(rootForOp.getOperation());
  // Prepare the grid and block sizes for the launch operation.  If there is
  // no loop mapped to a specific dimension, use constant "1" as its size.
  Value constOne = (numBlockDims < 3 || numThreadDims < 3)
                       ? builder.create<ConstantIndexOp>(rootForOp.getLoc(), 1)
                       : nullptr;
  Value gridSizeX = numBlockDims > 0 ? dims[0] : constOne;
  Value gridSizeY = numBlockDims > 1 ? dims[1] : constOne;
  Value gridSizeZ = numBlockDims > 2 ? dims[2] : constOne;
  Value blockSizeX = numThreadDims > 0 ? dims[numBlockDims] : constOne;
  Value blockSizeY = numThreadDims > 1 ? dims[numBlockDims + 1] : constOne;
  Value blockSizeZ = numThreadDims > 2 ? dims[numBlockDims + 2] : constOne;

  // Create a launch op and move the body region of the innermost loop to the
  // launch op.
  auto launchOp = builder.create<gpu::LaunchOp>(
      rootForOp.getLoc(), gridSizeX, gridSizeY, gridSizeZ, blockSizeX,
      blockSizeY, blockSizeZ);

  // Replace the loop terminator (loops contain only a single block) with the
  // gpu terminator and move the operations from the loop body block to the gpu
  // launch body block.  Do not move the entire block because of the difference
  // in block arguments.
  Operation &terminator = innermostForOp.getBody()->back();
  Location terminatorLoc = terminator.getLoc();
  terminator.erase();
  builder.setInsertionPointToEnd(innermostForOp.getBody());
  builder.create<gpu::TerminatorOp>(terminatorLoc, llvm::None);
  launchOp.body().front().getOperations().splice(
      launchOp.body().front().begin(),
      innermostForOp.getBody()->getOperations());

  // Remap the loop iterators to use block/thread identifiers instead.  Loops
  // may iterate from LB with step S whereas GPU thread/block ids always iterate
  // from 0 to N with step 1.  Therefore, loop induction variables are replaced
  // with (gpu-thread/block-id * S) + LB.
  builder.setInsertionPointToStart(&launchOp.body().front());
  auto lbArgumentIt = lbs.begin();
  auto stepArgumentIt = steps.begin();
  for (auto en : llvm::enumerate(ivs)) {
    Value id =
        en.index() < numBlockDims
            ? getDim3Value(launchOp.getBlockIds(), en.index())
            : getDim3Value(launchOp.getThreadIds(), en.index() - numBlockDims);
    Value step = steps[en.index()];
    if (!isConstantOne(step))
      id = builder.create<MulIOp>(rootForOp.getLoc(), step, id);

    Value ivReplacement =
        builder.create<AddIOp>(rootForOp.getLoc(), *lbArgumentIt, id);
    en.value().replaceAllUsesWith(ivReplacement);
    std::advance(lbArgumentIt, 1);
    std::advance(stepArgumentIt, 1);
  }

  // We are done and can erase the original outermost loop.
  rootForOp.erase();
}

// Generic loop to GPU kernel conversion function.
template <typename OpTy>
static LogicalResult convertLoopNestToGPULaunch(OpTy forOp,
                                                unsigned numBlockDims,
                                                unsigned numThreadDims) {
  if (failed(checkLoopNestMappable(forOp, numBlockDims, numThreadDims)))
    return failure();

  LoopToGpuConverter converter;
  auto maybeInnerLoop =
      converter.collectBounds(forOp, numBlockDims + numThreadDims);
  if (!maybeInnerLoop)
    return failure();
  converter.createLaunch(forOp, *maybeInnerLoop, numBlockDims, numThreadDims);

  return success();
}

// Generic loop to GPU kernel conversion function when loop is imperfectly
// nested. The workgroup size and num workgroups is provided as input
template <typename OpTy>
static LogicalResult convertLoopToGPULaunch(OpTy forOp,
                                            ArrayRef<Value> numWorkGroups,
                                            ArrayRef<Value> workGroupSize) {
  if (failed(checkLoopOpMappable(forOp, numWorkGroups.size(),
                                 workGroupSize.size()))) {
    return failure();
  }
  return createLaunchFromOp(forOp, numWorkGroups, workGroupSize);
}

LogicalResult mlir::convertAffineLoopNestToGPULaunch(AffineForOp forOp,
                                                     unsigned numBlockDims,
                                                     unsigned numThreadDims) {
  return ::convertLoopNestToGPULaunch(forOp, numBlockDims, numThreadDims);
}

LogicalResult mlir::convertLoopNestToGPULaunch(ForOp forOp,
                                               unsigned numBlockDims,
                                               unsigned numThreadDims) {
  return ::convertLoopNestToGPULaunch(forOp, numBlockDims, numThreadDims);
}

LogicalResult mlir::convertLoopToGPULaunch(scf::ForOp forOp,
                                           ArrayRef<Value> numWorkGroups,
                                           ArrayRef<Value> workGroupSizes) {
  return ::convertLoopToGPULaunch(forOp, numWorkGroups, workGroupSizes);
}

namespace {
struct ParallelToGpuLaunchLowering : public OpRewritePattern<ParallelOp> {
  using OpRewritePattern<ParallelOp>::OpRewritePattern;

  LogicalResult matchAndRewrite(ParallelOp parallelOp,
                                PatternRewriter &rewriter) const override;
};
} // namespace

/// Tries to derive a static upper bound from the defining operation of
/// `upperBound`.
static Value deriveStaticUpperBound(Value upperBound,
                                    PatternRewriter &rewriter) {
  if (auto op = upperBound.getDefiningOp<ConstantIndexOp>()) {
    return op;
  }

  if (auto minOp = upperBound.getDefiningOp<AffineMinOp>()) {
    for (const AffineExpr &result : minOp.map().getResults()) {
      if (auto constExpr = result.dyn_cast<AffineConstantExpr>()) {
        return rewriter.create<ConstantIndexOp>(minOp.getLoc(),
                                                constExpr.getValue());
      }
    }
  }

  if (auto multiplyOp = upperBound.getDefiningOp<MulIOp>()) {
    if (auto lhs = dyn_cast_or_null<ConstantIndexOp>(
            deriveStaticUpperBound(multiplyOp.getOperand(0), rewriter)
                .getDefiningOp()))
      if (auto rhs = dyn_cast_or_null<ConstantIndexOp>(
              deriveStaticUpperBound(multiplyOp.getOperand(1), rewriter)
                  .getDefiningOp())) {
        // Assumptions about the upper bound of minimum computations no longer
        // work if multiplied by a negative value, so abort in this case.
        if (lhs.getValue() < 0 || rhs.getValue() < 0)
          return {};

        return rewriter.create<ConstantIndexOp>(
            multiplyOp.getLoc(), lhs.getValue() * rhs.getValue());
      }
  }

  return {};
}

static bool isMappedToProcessor(gpu::Processor processor) {
  return processor != gpu::Processor::Sequential;
}

static unsigned getLaunchOpArgumentNum(gpu::Processor processor) {
  switch (processor) {
  case gpu::Processor::BlockX:
    return 0;
  case gpu::Processor::BlockY:
    return 1;
  case gpu::Processor::BlockZ:
    return 2;
  case gpu::Processor::ThreadX:
    return 3;
  case gpu::Processor::ThreadY:
    return 4;
  case gpu::Processor::ThreadZ:
    return 5;
  default:;
  }
  llvm_unreachable(
      "invalid processor type while retrieving launch op argument number");
}

/// Modifies the current transformation state to capture the effect of the given
/// `scf.parallel` operation on index substitutions and the operations to be
/// inserted.
/// Specifically, if a dimension of a parallel loop is mapped to a hardware id,
/// this function will
/// - compute the loop index based on the hardware id and affine map from the
///   mapping and update `cloningMap` to substitute all uses.
/// - derive a new upper bound for the hardware id and augment the provided
///   `gpu.launch operation` accordingly.
/// - if the upper bound is imprecise, insert a conditional in the `gpu.launch`
///   and update the rewriter to insert into the conditional's body.
/// If the dimension is mapped to sequential,
/// - insert a for loop into the body and update the rewriter to insert into
///   the for loop's body.
/// - update the `cloningMap` to replace uses of the index with the index of
///   the new for loop.
/// In either case,
/// - append the instructions from the loops body to worklist, in reverse order.
/// To note the end of the current scope in case a loop or conditional was
/// inserted, a sentinel (the `gpu.launch` operation) is inserted into the
/// worklist. This signals the processor of the worklist to pop the rewriter
/// one scope-level up.
static LogicalResult processParallelLoop(
    ParallelOp parallelOp, gpu::LaunchOp launchOp,
    BlockAndValueMapping &cloningMap, SmallVectorImpl<Operation *> &worklist,
    DenseMap<gpu::Processor, Value> &bounds, PatternRewriter &rewriter) {
  // TODO(herhut): Verify that this is a valid GPU mapping.
  // processor ids: 0-2 block [x/y/z], 3-5 -> thread [x/y/z], 6-> sequential
  ArrayAttr mapping =
      parallelOp.getAttrOfType<ArrayAttr>(gpu::getMappingAttrName());

  // TODO(herhut): Support reductions.
  if (!mapping || parallelOp.getNumResults() != 0)
    return failure();

  Location loc = parallelOp.getLoc();

  auto launchIndependent = [&launchOp](Value val) {
    return val.getParentRegion()->isAncestor(launchOp.getParentRegion());
  };

  auto ensureLaunchIndependent = [&rewriter,
                                  launchIndependent](Value val) -> Value {
    if (launchIndependent(val))
      return val;
    if (ConstantOp constOp = val.getDefiningOp<ConstantOp>())
      return rewriter.create<ConstantOp>(constOp.getLoc(), constOp.getValue());
    return {};
  };

  for (auto config : llvm::zip(mapping, parallelOp.getInductionVars(),
                               parallelOp.lowerBound(), parallelOp.upperBound(),
                               parallelOp.step())) {
    Attribute mappingAttribute;
    Value iv, lowerBound, upperBound, step;
    std::tie(mappingAttribute, iv, lowerBound, upperBound, step) = config;
    auto annotation = mappingAttribute.dyn_cast<gpu::ParallelLoopDimMapping>();
    if (!annotation)
      return parallelOp.emitOpError()
             << "expected mapping attribute for lowering to GPU";
    Value newIndex;
    gpu::Processor processor = gpu::getProcessor(annotation);

    if (isMappedToProcessor(processor)) {
      // Use the corresponding thread/grid index as replacement for the loop iv.
      Value operand = launchOp.body().front().getArgument(
          getLaunchOpArgumentNum(processor));
      // Take the indexmap and add the lower bound and step computations in.
      // This computes operand * step + lowerBound.
      // Use an affine map here so that it composes nicely with the provided
      // annotation.
      AffineMap lowerAndStep = AffineMap::get(
          1, 2,
          rewriter.getAffineDimExpr(0) * rewriter.getAffineSymbolExpr(0) +
              rewriter.getAffineSymbolExpr(1));
      newIndex = rewriter.create<AffineApplyOp>(
          loc, annotation.map().getValue().compose(lowerAndStep),
          ValueRange{operand, step, lowerBound});
      // If there was also a bound, insert that, too.
      // TODO(herhut): Check that we do not assign bounds twice.
      if (annotation.bound().getValue()) {
        // We pass as the single operand to the bound-map the number of
        // iterations, which is (upperBound - lowerBound) ceilDiv step. To
        // support inner loops with dynamic upper bounds (as generated by e.g.
        // tiling), try to derive a max for the bounds. If the used bound for
        // the hardware id is imprecise, wrap the contained code into a
        // conditional. If the lower-bound is constant or defined before the
        // launch, we can use it in the launch bounds. Otherwise fail.
        if (!launchIndependent(lowerBound) &&
            !isa_and_nonnull<ConstantOp>(lowerBound.getDefiningOp()))
          return failure();
        // The step must also be constant or defined outside of the loop nest.
        if (!launchIndependent(step) &&
            !isa_and_nonnull<ConstantOp>(step.getDefiningOp()))
          return failure();
        // If the upper-bound is constant or defined before the launch, we can
        // use it in the launch bounds directly. Otherwise try derive a bound.
        bool boundIsPrecise =
            launchIndependent(upperBound) ||
            isa_and_nonnull<ConstantOp>(upperBound.getDefiningOp());
        {
          PatternRewriter::InsertionGuard guard(rewriter);
          rewriter.setInsertionPoint(launchOp);
          if (!boundIsPrecise) {
            upperBound = deriveStaticUpperBound(upperBound, rewriter);
            if (!upperBound) {
              return parallelOp.emitOpError()
                     << "cannot derive loop-invariant upper bound for number "
                        "of iterations";
            }
          }
          // Compute the number of iterations needed. We compute this as an
          // affine expression ceilDiv (upperBound - lowerBound) step. We use
          // affine.apply here so that it composes nicely with the provided map.
          AffineMap stepMap =
              AffineMap::get(0, 3,
                             ((rewriter.getAffineSymbolExpr(0) -
                               rewriter.getAffineSymbolExpr(1))
                                  .ceilDiv(rewriter.getAffineSymbolExpr(2))));
          Value launchBound = rewriter.create<AffineApplyOp>(
              loc, annotation.bound().getValue().compose(stepMap),
              ValueRange{
                  ensureLaunchIndependent(
                      cloningMap.lookupOrDefault(upperBound)),
                  ensureLaunchIndependent(
                      cloningMap.lookupOrDefault(lowerBound)),
                  ensureLaunchIndependent(cloningMap.lookupOrDefault(step))});
          // todo(herhut,ravishankarm): Update the behavior of setMappingAttr
          // when this condition is relaxed.
          if (bounds.find(processor) != bounds.end()) {
            return parallelOp.emitOpError()
                   << "cannot redefine the bound for processor "
                   << static_cast<int64_t>(processor);
          }
          bounds[processor] = launchBound;
        }
        if (!boundIsPrecise) {
          // We are using an approximation, create a surrounding conditional.
          Value originalBound = std::get<3>(config);
          CmpIOp pred = rewriter.create<CmpIOp>(
              loc, CmpIPredicate::slt, newIndex,
              cloningMap.lookupOrDefault(originalBound));
          scf::IfOp ifOp = rewriter.create<scf::IfOp>(loc, pred, false);
          rewriter.setInsertionPointToStart(&ifOp.thenRegion().front());
          // Put a sentinel into the worklist so we know when to pop out of the
          // if body again. We use the launchOp here, as that cannot be part of
          // the bodies instruction.
          worklist.push_back(launchOp.getOperation());
        }
      }
    } else {
      // Create a sequential for loop.
      auto loopOp = rewriter.create<scf::ForOp>(
          loc, cloningMap.lookupOrDefault(lowerBound),
          cloningMap.lookupOrDefault(upperBound),
          cloningMap.lookupOrDefault(step));
      newIndex = loopOp.getInductionVar();
      rewriter.setInsertionPointToStart(loopOp.getBody());
      // Put a sentinel into the worklist so we know when to pop out of the loop
      // body again. We use the launchOp here, as that cannot be part of the
      // bodies instruction.
      worklist.push_back(launchOp.getOperation());
    }
    cloningMap.map(iv, newIndex);
  }
  Block *body = parallelOp.getBody();
  worklist.reserve(worklist.size() + body->getOperations().size());
  for (Operation &op : llvm::reverse(body->without_terminator()))
    worklist.push_back(&op);
  return success();
}

/// Lower a `scf.parallel` operation into a corresponding `gpu.launch`
/// operation.
///
/// This essentially transforms a loop nest into a corresponding SIMT function.
/// The conversion is driven by mapping annotations on the `scf.parallel`
/// operations. The mapping is provided via a `DictionaryAttribute` named
/// `mapping`, which has three entries:
///  - processor: the hardware id to map to. 0-2 are block dimensions, 3-5 are
///               thread dimensions and 6 is sequential.
///  - map : An affine map that is used to pre-process hardware ids before
///          substitution.
///  - bound : An affine map that is used to compute the bound of the hardware
///            id based on an upper bound of the number of iterations.
/// If the `scf.parallel` contains nested `scf.parallel` operations, those
/// need to be annotated, as well. Structurally, the transformation works by
/// splicing all operations from nested `scf.parallel` operations into a single
/// sequence. Indices mapped to hardware ids are substituted with those ids,
/// wheras sequential mappings result in a sequential for-loop. To have more
/// flexibility when mapping code to hardware ids, the transform supports two
/// affine maps. The first `map` is used to compute the actual index for
/// substitution from the hardware id. The second `bound` is used to compute the
/// launch dimension for the hardware id from the number of iterations the
/// mapped loop is performing. Note that the number of iterations might be
/// imprecise if the corresponding loop-bounds are loop-dependent. In such case,
/// the hardware id might iterate over additional indices. The transformation
/// caters for this by predicating the created sequence of instructions on
/// the actual loop bound. This only works if an static upper bound for the
/// dynamic loop bound can be derived, currently via analyzing `affine.min`
/// operations.
LogicalResult
ParallelToGpuLaunchLowering::matchAndRewrite(ParallelOp parallelOp,
                                             PatternRewriter &rewriter) const {
  // Create a launch operation. We start with bound one for all grid/block
  // sizes. Those will be refined later as we discover them from mappings.
  Location loc = parallelOp.getLoc();
  Value constantOne = rewriter.create<ConstantIndexOp>(parallelOp.getLoc(), 1);
  gpu::LaunchOp launchOp = rewriter.create<gpu::LaunchOp>(
      parallelOp.getLoc(), constantOne, constantOne, constantOne, constantOne,
      constantOne, constantOne);
  rewriter.setInsertionPointToEnd(&launchOp.body().front());
  rewriter.create<gpu::TerminatorOp>(loc);
  rewriter.setInsertionPointToStart(&launchOp.body().front());

  BlockAndValueMapping cloningMap;
  llvm::DenseMap<gpu::Processor, Value> launchBounds;
  SmallVector<Operation *, 16> worklist;
  if (failed(processParallelLoop(parallelOp, launchOp, cloningMap, worklist,
                                 launchBounds, rewriter)))
    return failure();

  // Whether we have seen any side-effects. Reset when leaving an inner scope.
  bool seenSideeffects = false;
  // Whether we have left a nesting scope (and hence are no longer innermost).
  bool leftNestingScope = false;
  while (!worklist.empty()) {
    Operation *op = worklist.pop_back_val();
    // Now walk over the body and clone it.
    // TODO: This is only correct if there either is no further scf.parallel
    //       nested or this code is side-effect free. Otherwise we might need
    //       predication. We are overly conservative for now and only allow
    //       side-effects in the innermost scope.
    if (auto nestedParallel = dyn_cast<ParallelOp>(op)) {
      // Before entering a nested scope, make sure there have been no
      // sideeffects until now.
      if (seenSideeffects)
        return failure();
      // A nested scf.parallel needs insertion of code to compute indices.
      // Insert that now. This will also update the worklist with the loops
      // body.
      if (failed(processParallelLoop(nestedParallel, launchOp, cloningMap,
                                     worklist, launchBounds, rewriter)))
        return failure();
    } else if (op == launchOp.getOperation()) {
      // Found our sentinel value. We have finished the operations from one
      // nesting level, pop one level back up.
      auto parent = rewriter.getInsertionPoint()->getParentOp();
      rewriter.setInsertionPointAfter(parent);
      leftNestingScope = true;
      seenSideeffects = false;
    } else {
      // Otherwise we copy it over.
      Operation *clone = rewriter.clone(*op, cloningMap);
      cloningMap.map(op->getResults(), clone->getResults());
      // Check for side effects.
      // TODO: Handle region side effects properly.
      seenSideeffects |= !MemoryEffectOpInterface::hasNoEffect(clone) ||
                         clone->getNumRegions() != 0;
      // If we are no longer in the innermost scope, sideeffects are disallowed.
      if (seenSideeffects && leftNestingScope)
        return failure();
    }
  }

  // Now that we succeeded creating the launch operation, also update the
  // bounds.
  for (auto bound : launchBounds)
    launchOp.setOperand(getLaunchOpArgumentNum(std::get<0>(bound)),
                        std::get<1>(bound));

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

void mlir::populateParallelLoopToGPUPatterns(OwningRewritePatternList &patterns,
                                             MLIRContext *ctx) {
  patterns.insert<ParallelToGpuLaunchLowering>(ctx);
}