| //===- 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/Arithmetic/IR/Arithmetic.h" |
| #include "mlir/Dialect/GPU/GPUDialect.h" |
| #include "mlir/Dialect/GPU/ParallelLoopMapper.h" |
| #include "mlir/Dialect/MemRef/IR/MemRef.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; |
| |
| // Name of internal attribute to mark visited operations during conversion. |
| // |
| // NOTE: The conversion originally used the following legality criteria: |
| // `!parallelOp->hasAttr(gpu::getMappingAttrName())` |
| // But the provided pattern might reject some cases based on more detailed |
| // analysis of the `mapping` attribute. |
| // To avoid dialect conversion failure due to non-converted illegal operation |
| // we use this extra Unit attribute as a marker, that the operation was checked |
| // by the pattern and is should be considered as legal in the following legality |
| // checks. The `finalizeParallelLoopToGPUConversion` function performs clean up |
| // of this extra attributes ans is supposed to be called after the dialect |
| // conversion. |
| // |
| // TODO: Implement a cleaner solution, factoring out the "matching" logic |
| // from the pattern and its callees into a separate function that can be called |
| // from both the pattern and the op legality check. |
| static constexpr StringLiteral kVisitedAttrName = "SCFToGPU_visited"; |
| |
| // 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(); |
| } |
| |
| // Get the upper bound-related operands of a loop operation. |
| static Operation::operand_range getUpperBoundOperands(AffineForOp forOp) { |
| return forOp.getUpperBoundOperands(); |
| } |
| |
| // 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<arith::ConstantIndexOp>(forOp.getLoc(), |
| forOp.getStep()); |
| } |
| |
| // 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); |
| } |
| |
| // 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); |
| } |
| |
| // 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. |
| static LogicalResult checkAffineLoopNestMappableImpl(AffineForOp 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<AffineForOp>(nested))) |
| return nested->emitError("expected a nested loop"); |
| } |
| return success(); |
| } |
| |
| static LogicalResult checkAffineLoopNestMappable(AffineForOp 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 checkAffineLoopNestMappableImpl(forOp, numBlockDims + numThreadDims); |
| } |
| |
| namespace { |
| // Helper structure that holds common state of the loop to GPU kernel |
| // conversion. |
| struct AffineLoopToGpuConverter { |
| Optional<AffineForOp> collectBounds(AffineForOp forOp, unsigned numLoops); |
| |
| void createLaunch(AffineForOp rootForOp, AffineForOp 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<arith::ConstantIndexOp>()) |
| return def.value() == 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. |
| Optional<AffineForOp> |
| AffineLoopToGpuConverter::collectBounds(AffineForOp forOp, unsigned numLoops) { |
| OpBuilder builder(forOp.getOperation()); |
| dims.reserve(numLoops); |
| lbs.reserve(numLoops); |
| ivs.reserve(numLoops); |
| steps.reserve(numLoops); |
| AffineForOp 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<arith::SubIOp>(currentLoop.getLoc(), |
| upperBound, lowerBound); |
| Value step = getOrCreateStep(currentLoop, builder); |
| if (!isConstantOne(step)) |
| range = builder.create<arith::DivSIOp>(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<AffineForOp>(¤tLoop.getBody()->front()); |
| } |
| return currentLoop; |
| } |
| |
| // 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". |
| void AffineLoopToGpuConverter::createLaunch(AffineForOp rootForOp, |
| AffineForOp 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<arith::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<arith::MulIOp>(rootForOp.getLoc(), step, id); |
| |
| Value ivReplacement = |
| builder.create<arith::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. |
| static LogicalResult convertAffineLoopNestToGPULaunch(AffineForOp forOp, |
| unsigned numBlockDims, |
| unsigned numThreadDims) { |
| if (failed(checkAffineLoopNestMappable(forOp, numBlockDims, numThreadDims))) |
| return failure(); |
| |
| AffineLoopToGpuConverter converter; |
| auto maybeInnerLoop = |
| converter.collectBounds(forOp, numBlockDims + numThreadDims); |
| if (!maybeInnerLoop) |
| return failure(); |
| converter.createLaunch(forOp, *maybeInnerLoop, numBlockDims, numThreadDims); |
| |
| return success(); |
| } |
| |
| LogicalResult mlir::convertAffineLoopNestToGPULaunch(AffineForOp forOp, |
| unsigned numBlockDims, |
| unsigned numThreadDims) { |
| return ::convertAffineLoopNestToGPULaunch(forOp, numBlockDims, numThreadDims); |
| } |
| |
| 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<arith::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<arith::ConstantIndexOp>(minOp.getLoc(), |
| constExpr.getValue()); |
| } |
| } |
| } |
| |
| if (auto multiplyOp = upperBound.getDefiningOp<arith::MulIOp>()) { |
| if (auto lhs = dyn_cast_or_null<arith::ConstantIndexOp>( |
| deriveStaticUpperBound(multiplyOp.getOperand(0), rewriter) |
| .getDefiningOp())) |
| if (auto rhs = dyn_cast_or_null<arith::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.value() < 0 || rhs.value() < 0) |
| return {}; |
| |
| return rewriter.create<arith::ConstantIndexOp>( |
| multiplyOp.getLoc(), lhs.value() * rhs.value()); |
| } |
| } |
| |
| 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: 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: 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 (auto constOp = val.getDefiningOp<arith::ConstantOp>()) |
| return rewriter.create<arith::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().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: 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<arith::ConstantOp>(lowerBound.getDefiningOp())) |
| return failure(); |
| // The step must also be constant or defined outside of the loop nest. |
| if (!launchIndependent(step) && |
| !isa_and_nonnull<arith::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<arith::ConstantOp>(upperBound.getDefiningOp()); |
| { |
| PatternRewriter::InsertionGuard guard(rewriter); |
| rewriter.setInsertionPoint(launchOp); |
| if (!boundIsPrecise) { |
| upperBound = deriveStaticUpperBound(upperBound, rewriter); |
| if (!upperBound) { |
| return rewriter.notifyMatchFailure( |
| parallelOp, |
| "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( |
| 1, 2, |
| ((rewriter.getAffineDimExpr(0) - rewriter.getAffineSymbolExpr(0)) |
| .ceilDiv(rewriter.getAffineSymbolExpr(1)))); |
| 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 rewriter.notifyMatchFailure( |
| parallelOp, "cannot redefine the bound for processor " + |
| Twine(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); |
| arith::CmpIOp pred = rewriter.create<arith::CmpIOp>( |
| loc, arith::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); |
| } |
| |
| // Propagate custom user defined optional attributes, that can be used at |
| // later stage, such as extension data for GPU kernel dispatch |
| for (const auto &namedAttr : parallelOp->getAttrs()) { |
| if (namedAttr.getName() == gpu::getMappingAttrName() || |
| namedAttr.getName() == ParallelOp::getOperandSegmentSizeAttr()) |
| continue; |
| launchOp->setAttr(namedAttr.getName(), namedAttr.getValue()); |
| } |
| |
| 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 { |
| // Mark the operation as visited for recursive legality check. |
| parallelOp->setAttr(kVisitedAttrName, rewriter.getUnitAttr()); |
| |
| // We can only transform starting at the outer-most loop. Launches inside of |
| // parallel loops are not supported. |
| if (auto parentLoop = parallelOp->getParentOfType<ParallelOp>()) |
| return failure(); |
| // 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<arith::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(RewritePatternSet &patterns) { |
| patterns.add<ParallelToGpuLaunchLowering>(patterns.getContext()); |
| } |
| |
| void mlir::configureParallelLoopToGPULegality(ConversionTarget &target) { |
| target.addLegalDialect<memref::MemRefDialect>(); |
| target.addDynamicallyLegalOp<scf::ParallelOp>([](scf::ParallelOp parallelOp) { |
| return !parallelOp->hasAttr(gpu::getMappingAttrName()) || |
| parallelOp->hasAttr(kVisitedAttrName); |
| }); |
| } |
| |
| void mlir::finalizeParallelLoopToGPUConversion(Operation *op) { |
| op->walk([](scf::ParallelOp parallelOp) { |
| parallelOp->removeAttr(kVisitedAttrName); |
| }); |
| } |
