26b1f61c14
`shard.allgather` concatenates along a specified gather-axis. However, `mpi.allgather` always concatenates along the first dimension and there is no MPI operation that allows gathering along an arbitrary axis. Hence, if gather-axis!=0, we need to create a temporary buffer where we gather along the first dimension and then copy from that buffer to the final output along the specified gather-axis. This is not ideal by far. Along the way also - fixing computation of memref size in mpitollvm - adding a simple canonicalization pattern for comm_size for easier debugging - adding more tests
99 lines
3.4 KiB
C++
99 lines
3.4 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/MPI/IR/Utils.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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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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struct FoldRank final : public mlir::OpRewritePattern<mlir::mpi::CommRankOp> {
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using mlir::OpRewritePattern<mlir::mpi::CommRankOp>::OpRewritePattern;
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LogicalResult matchAndRewrite(mlir::mpi::CommRankOp op,
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mlir::PatternRewriter &b) const override {
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return FoldToDLTIConst(op, "MPI:comm_world_rank", b);
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}
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};
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struct FoldSize final : public mlir::OpRewritePattern<mlir::mpi::CommSizeOp> {
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using mlir::OpRewritePattern<mlir::mpi::CommSizeOp>::OpRewritePattern;
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LogicalResult matchAndRewrite(mlir::mpi::CommSizeOp op,
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mlir::PatternRewriter &b) const override {
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return FoldToDLTIConst(op, "MPI:comm_world_size", b);
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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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void mlir::mpi::CommRankOp::getCanonicalizationPatterns(
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mlir::RewritePatternSet &results, mlir::MLIRContext *context) {
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results.add<FoldRank>(context);
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}
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void mlir::mpi::CommSizeOp::getCanonicalizationPatterns(
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mlir::RewritePatternSet &results, mlir::MLIRContext *context) {
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results.add<FoldSize>(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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