cc @tobiasgrosser @wsmoses this PR adds some new ops and types to the MLIR MPI dialect. the goal is to get the minimum required ops here to get a project of us working, and if everything works well, continue adding ops to the mpi dialect on subsequent PRs until we achieve some level of compliance with the MPI standard. --- Things left to do in subsequent PRs: - Add back the `mpi.comm` type and add as optional argument of current implemented ops that should support it (i.e. `send`, `recv`, `isend`, `irecv`, `allreduce`, `barrier`). - Support defining custom `MPI_Op`s (the MPI operations, not the tablegen `MPI_Op`) as regions. - Add more ops.
72 lines
2.4 KiB
C++
72 lines
2.4 KiB
C++
//===- MPIOps.cpp - MPI dialect ops implementation ------------------------===//
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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 "mlir/Dialect/MPI/IR/MPI.h"
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#include "mlir/Dialect/MemRef/IR/MemRef.h"
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#include "mlir/IR/Builders.h"
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#include "mlir/IR/BuiltinAttributes.h"
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#include "mlir/IR/PatternMatch.h"
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using namespace mlir;
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using namespace mlir::mpi;
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namespace {
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// If input memref has dynamic shape and is a cast and if the cast's input has
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// static shape, fold the cast's static input into the given operation.
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template <typename OpT>
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struct FoldCast final : public mlir::OpRewritePattern<OpT> {
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using mlir::OpRewritePattern<OpT>::OpRewritePattern;
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LogicalResult matchAndRewrite(OpT op,
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mlir::PatternRewriter &b) const override {
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auto mRef = op.getRef();
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if (mRef.getType().hasStaticShape()) {
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return mlir::failure();
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}
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auto defOp = mRef.getDefiningOp();
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if (!defOp || !mlir::isa<mlir::memref::CastOp>(defOp)) {
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return mlir::failure();
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}
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auto src = mlir::cast<mlir::memref::CastOp>(defOp).getSource();
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if (!src.getType().hasStaticShape()) {
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return mlir::failure();
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}
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op.getRefMutable().assign(src);
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return mlir::success();
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}
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};
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} // namespace
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void mlir::mpi::SendOp::getCanonicalizationPatterns(
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mlir::RewritePatternSet &results, mlir::MLIRContext *context) {
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results.add<FoldCast<mlir::mpi::SendOp>>(context);
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}
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void mlir::mpi::RecvOp::getCanonicalizationPatterns(
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mlir::RewritePatternSet &results, mlir::MLIRContext *context) {
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results.add<FoldCast<mlir::mpi::RecvOp>>(context);
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}
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void mlir::mpi::ISendOp::getCanonicalizationPatterns(
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mlir::RewritePatternSet &results, mlir::MLIRContext *context) {
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results.add<FoldCast<mlir::mpi::ISendOp>>(context);
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}
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void mlir::mpi::IRecvOp::getCanonicalizationPatterns(
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mlir::RewritePatternSet &results, mlir::MLIRContext *context) {
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results.add<FoldCast<mlir::mpi::IRecvOp>>(context);
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}
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//===----------------------------------------------------------------------===//
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// TableGen'd op method definitions
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//===----------------------------------------------------------------------===//
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#define GET_OP_CLASSES
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#include "mlir/Dialect/MPI/IR/MPIOps.cpp.inc"
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