1e2d5f7943
This removes mentions of `target` from the generic `loop` rewrite pass since there is not need for it anyway. It is enough to detect `loop`'s nesting within `teams` or `parallel` directives.
302 lines
11 KiB
C++
302 lines
11 KiB
C++
//===- GenericLoopConversion.cpp ------------------------------------------===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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#include "flang/Common/OpenMP-utils.h"
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#include "mlir/Dialect/Func/IR/FuncOps.h"
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#include "mlir/Dialect/OpenMP/OpenMPDialect.h"
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#include "mlir/IR/IRMapping.h"
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#include "mlir/Pass/Pass.h"
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#include "mlir/Transforms/DialectConversion.h"
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#include <memory>
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namespace flangomp {
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#define GEN_PASS_DEF_GENERICLOOPCONVERSIONPASS
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#include "flang/Optimizer/OpenMP/Passes.h.inc"
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} // namespace flangomp
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namespace {
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/// A conversion pattern to handle various combined forms of `omp.loop`. For how
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/// combined/composite directive are handled see:
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/// https://discourse.llvm.org/t/rfc-representing-combined-composite-constructs-in-the-openmp-dialect/76986.
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class GenericLoopConversionPattern
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: public mlir::OpConversionPattern<mlir::omp::LoopOp> {
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public:
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enum class GenericLoopCombinedInfo { Standalone, TeamsLoop, ParallelLoop };
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using mlir::OpConversionPattern<mlir::omp::LoopOp>::OpConversionPattern;
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explicit GenericLoopConversionPattern(mlir::MLIRContext *ctx)
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: mlir::OpConversionPattern<mlir::omp::LoopOp>{ctx} {
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// Enable rewrite recursion to make sure nested `loop` directives are
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// handled.
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this->setHasBoundedRewriteRecursion(true);
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}
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mlir::LogicalResult
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matchAndRewrite(mlir::omp::LoopOp loopOp, OpAdaptor adaptor,
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mlir::ConversionPatternRewriter &rewriter) const override {
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assert(mlir::succeeded(checkLoopConversionSupportStatus(loopOp)));
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GenericLoopCombinedInfo combinedInfo = findGenericLoopCombineInfo(loopOp);
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switch (combinedInfo) {
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case GenericLoopCombinedInfo::Standalone:
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rewriteStandaloneLoop(loopOp, rewriter);
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break;
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case GenericLoopCombinedInfo::ParallelLoop:
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llvm_unreachable(
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"not yet implemented: Combined `parallel loop` directive");
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break;
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case GenericLoopCombinedInfo::TeamsLoop:
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rewriteToDistributeParallelDo(loopOp, rewriter);
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break;
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}
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rewriter.eraseOp(loopOp);
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return mlir::success();
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}
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static mlir::LogicalResult
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checkLoopConversionSupportStatus(mlir::omp::LoopOp loopOp) {
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GenericLoopCombinedInfo combinedInfo = findGenericLoopCombineInfo(loopOp);
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switch (combinedInfo) {
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case GenericLoopCombinedInfo::Standalone:
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break;
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case GenericLoopCombinedInfo::ParallelLoop:
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return loopOp.emitError(
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"not yet implemented: Combined `parallel loop` directive");
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case GenericLoopCombinedInfo::TeamsLoop:
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break;
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}
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auto todo = [&loopOp](mlir::StringRef clauseName) {
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return loopOp.emitError()
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<< "not yet implemented: Unhandled clause " << clauseName << " in "
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<< loopOp->getName() << " operation";
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};
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// For standalone directives, `bind` is already supported. Other combined
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// forms will be supported in a follow-up PR.
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if (combinedInfo != GenericLoopCombinedInfo::Standalone &&
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loopOp.getBindKind())
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return todo("bind");
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if (loopOp.getOrder())
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return todo("order");
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if (!loopOp.getReductionVars().empty())
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return todo("reduction");
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// TODO For `teams loop`, check similar constrains to what is checked
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// by `TeamsLoopChecker` in SemaOpenMP.cpp.
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return mlir::success();
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}
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private:
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static GenericLoopCombinedInfo
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findGenericLoopCombineInfo(mlir::omp::LoopOp loopOp) {
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mlir::Operation *parentOp = loopOp->getParentOp();
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GenericLoopCombinedInfo result = GenericLoopCombinedInfo::Standalone;
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if (auto teamsOp = mlir::dyn_cast_if_present<mlir::omp::TeamsOp>(parentOp))
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result = GenericLoopCombinedInfo::TeamsLoop;
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if (auto parallelOp =
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mlir::dyn_cast_if_present<mlir::omp::ParallelOp>(parentOp))
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result = GenericLoopCombinedInfo::ParallelLoop;
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return result;
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}
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void rewriteStandaloneLoop(mlir::omp::LoopOp loopOp,
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mlir::ConversionPatternRewriter &rewriter) const {
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using namespace mlir::omp;
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std::optional<ClauseBindKind> bindKind = loopOp.getBindKind();
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if (!bindKind.has_value())
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return rewriteToSimdLoop(loopOp, rewriter);
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switch (*loopOp.getBindKind()) {
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case ClauseBindKind::Parallel:
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return rewriteToWsloop(loopOp, rewriter);
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case ClauseBindKind::Teams:
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return rewriteToDistrbute(loopOp, rewriter);
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case ClauseBindKind::Thread:
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return rewriteToSimdLoop(loopOp, rewriter);
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}
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}
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/// Rewrites standalone `loop` (without `bind` clause or with
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/// `bind(parallel)`) directives to equivalent `simd` constructs.
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///
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/// The reasoning behind this decision is that according to the spec (version
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/// 5.2, section 11.7.1):
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///
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/// "If the bind clause is not specified on a construct for which it may be
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/// specified and the construct is closely nested inside a teams or parallel
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/// construct, the effect is as if binding is teams or parallel. If none of
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/// those conditions hold, the binding region is not defined."
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///
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/// which means that standalone `loop` directives have undefined binding
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/// region. Moreover, the spec says (in the next paragraph):
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///
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/// "The specified binding region determines the binding thread set.
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/// Specifically, if the binding region is a teams region, then the binding
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/// thread set is the set of initial threads that are executing that region
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/// while if the binding region is a parallel region, then the binding thread
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/// set is the team of threads that are executing that region. If the binding
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/// region is not defined, then the binding thread set is the encountering
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/// thread."
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///
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/// which means that the binding thread set for a standalone `loop` directive
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/// is only the encountering thread.
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///
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/// Since the encountering thread is the binding thread (set) for a
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/// standalone `loop` directive, the best we can do in such case is to "simd"
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/// the directive.
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void rewriteToSimdLoop(mlir::omp::LoopOp loopOp,
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mlir::ConversionPatternRewriter &rewriter) const {
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loopOp.emitWarning(
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"Detected standalone OpenMP `loop` directive with thread binding, "
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"the associated loop will be rewritten to `simd`.");
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rewriteToSingleWrapperOp<mlir::omp::SimdOp, mlir::omp::SimdOperands>(
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loopOp, rewriter);
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}
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void rewriteToDistrbute(mlir::omp::LoopOp loopOp,
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mlir::ConversionPatternRewriter &rewriter) const {
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rewriteToSingleWrapperOp<mlir::omp::DistributeOp,
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mlir::omp::DistributeOperands>(loopOp, rewriter);
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}
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void rewriteToWsloop(mlir::omp::LoopOp loopOp,
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mlir::ConversionPatternRewriter &rewriter) const {
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rewriteToSingleWrapperOp<mlir::omp::WsloopOp, mlir::omp::WsloopOperands>(
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loopOp, rewriter);
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}
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// TODO Suggestion by Sergio: tag auto-generated operations for constructs
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// that weren't part of the original program, that would be useful
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// information for debugging purposes later on. This new attribute could be
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// used for `omp.loop`, but also for `do concurrent` transformations,
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// `workshare`, `workdistribute`, etc. The tag could be used for all kinds of
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// auto-generated operations using a dialect attribute (named something like
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// `omp.origin` or `omp.derived`) and perhaps hold the name of the operation
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// it was derived from, the reason it was transformed or something like that
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// we could use when emitting any messages related to it later on.
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template <typename OpTy, typename OpOperandsTy>
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void
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rewriteToSingleWrapperOp(mlir::omp::LoopOp loopOp,
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mlir::ConversionPatternRewriter &rewriter) const {
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OpOperandsTy clauseOps;
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clauseOps.privateVars = loopOp.getPrivateVars();
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auto privateSyms = loopOp.getPrivateSyms();
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if (privateSyms)
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clauseOps.privateSyms.assign(privateSyms->begin(), privateSyms->end());
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Fortran::common::openmp::EntryBlockArgs args;
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args.priv.vars = clauseOps.privateVars;
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auto wrapperOp = rewriter.create<OpTy>(loopOp.getLoc(), clauseOps);
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mlir::Block *opBlock = genEntryBlock(rewriter, args, wrapperOp.getRegion());
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mlir::IRMapping mapper;
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mlir::Block &loopBlock = *loopOp.getRegion().begin();
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for (auto [loopOpArg, opArg] :
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llvm::zip_equal(loopBlock.getArguments(), opBlock->getArguments()))
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mapper.map(loopOpArg, opArg);
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rewriter.clone(*loopOp.begin(), mapper);
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}
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void rewriteToDistributeParallelDo(
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mlir::omp::LoopOp loopOp,
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mlir::ConversionPatternRewriter &rewriter) const {
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mlir::omp::ParallelOperands parallelClauseOps;
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parallelClauseOps.privateVars = loopOp.getPrivateVars();
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auto privateSyms = loopOp.getPrivateSyms();
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if (privateSyms)
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parallelClauseOps.privateSyms.assign(privateSyms->begin(),
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privateSyms->end());
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Fortran::common::openmp::EntryBlockArgs parallelArgs;
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parallelArgs.priv.vars = parallelClauseOps.privateVars;
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auto parallelOp = rewriter.create<mlir::omp::ParallelOp>(loopOp.getLoc(),
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parallelClauseOps);
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mlir::Block *parallelBlock =
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genEntryBlock(rewriter, parallelArgs, parallelOp.getRegion());
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parallelOp.setComposite(true);
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rewriter.setInsertionPoint(
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rewriter.create<mlir::omp::TerminatorOp>(loopOp.getLoc()));
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mlir::omp::DistributeOperands distributeClauseOps;
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auto distributeOp = rewriter.create<mlir::omp::DistributeOp>(
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loopOp.getLoc(), distributeClauseOps);
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distributeOp.setComposite(true);
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rewriter.createBlock(&distributeOp.getRegion());
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mlir::omp::WsloopOperands wsloopClauseOps;
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auto wsloopOp =
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rewriter.create<mlir::omp::WsloopOp>(loopOp.getLoc(), wsloopClauseOps);
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wsloopOp.setComposite(true);
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rewriter.createBlock(&wsloopOp.getRegion());
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mlir::IRMapping mapper;
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mlir::Block &loopBlock = *loopOp.getRegion().begin();
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for (auto [loopOpArg, parallelOpArg] : llvm::zip_equal(
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loopBlock.getArguments(), parallelBlock->getArguments()))
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mapper.map(loopOpArg, parallelOpArg);
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rewriter.clone(*loopOp.begin(), mapper);
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}
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};
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class GenericLoopConversionPass
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: public flangomp::impl::GenericLoopConversionPassBase<
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GenericLoopConversionPass> {
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public:
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GenericLoopConversionPass() = default;
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void runOnOperation() override {
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mlir::func::FuncOp func = getOperation();
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if (func.isDeclaration())
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return;
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mlir::MLIRContext *context = &getContext();
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mlir::RewritePatternSet patterns(context);
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patterns.insert<GenericLoopConversionPattern>(context);
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mlir::ConversionTarget target(*context);
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target.markUnknownOpDynamicallyLegal(
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[](mlir::Operation *) { return true; });
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target.addDynamicallyLegalOp<mlir::omp::LoopOp>(
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[](mlir::omp::LoopOp loopOp) {
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return mlir::failed(
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GenericLoopConversionPattern::checkLoopConversionSupportStatus(
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loopOp));
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});
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if (mlir::failed(mlir::applyFullConversion(getOperation(), target,
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std::move(patterns)))) {
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mlir::emitError(func.getLoc(), "error in converting `omp.loop` op");
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signalPassFailure();
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}
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}
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};
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} // namespace
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