110 lines
4.2 KiB
C++
110 lines
4.2 KiB
C++
//===- TestLinalgFusionTransforms.cpp - Test Linalg fusion patterns -------===//
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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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//
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// This file implements logic for testing Linalg fusion patterns.
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//
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//===----------------------------------------------------------------------===//
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#include "mlir/Dialect/Affine/IR/AffineOps.h"
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#include "mlir/Dialect/Func/IR/FuncOps.h"
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#include "mlir/Dialect/Linalg/Transforms/Transforms.h"
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#include "mlir/Dialect/SCF/Transforms/Patterns.h"
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#include "mlir/Pass/Pass.h"
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#include "mlir/Pass/PassManager.h"
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#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
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#include "mlir/Transforms/Passes.h"
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using namespace mlir;
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using namespace mlir::linalg;
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static LogicalResult fuseLinalgOpsGreedily(func::FuncOp f) {
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OpBuilder b(f);
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DenseSet<Operation *> eraseSet;
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// Save original Linalg ops, we only want to make a pass over those.
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SmallVector<LinalgOp, 8> linalgOps;
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f.walk([&](LinalgOp op) {
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// TODO: support multi-results.
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if (op->getNumResults() <= 1)
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linalgOps.push_back(op);
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});
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// Tile and Fuse for tensors inputs (TODO: all tensor operands).
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bool changed = false;
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for (LinalgOp linalgOp : llvm::reverse(linalgOps)) {
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for (OpOperand &opOperand : linalgOp->getOpOperands()) {
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if (isa<MemRefType>(opOperand.get().getType()))
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continue;
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if (isa<RankedTensorType>(opOperand.get().getType())) {
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// Tile and Fuse tensor input.
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if (opOperand.getOperandNumber() >= linalgOp.getNumDpsInputs())
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continue;
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auto info = fuseProducerOfTensor(b, opOperand);
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if (failed(info))
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continue;
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auto *originalOp = info->originalProducer.getOperation();
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auto *originalOpInLinalgOpsVector =
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std::find(linalgOps.begin(), linalgOps.end(), originalOp);
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*originalOpInLinalgOpsVector = info->fusedProducer;
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// Don't mark for erasure in the tensor case, let DCE handle this.
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changed = true;
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}
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}
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}
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// The `fuseProducerOfBuffer` function performs structural checks and in
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// particular that no covering read or write exist between the consumer and
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// the producer. As a consequence, the only fusions that may occur preserve
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// subsequent dependences and are guaranteed by construction to produce the
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// whole view. We may thus erase the producer once it is fused.
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for (auto *e : eraseSet)
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e->erase();
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return changed ? success() : failure();
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}
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namespace {
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struct TestLinalgGreedyFusion
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: public PassWrapper<TestLinalgGreedyFusion, OperationPass<func::FuncOp>> {
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MLIR_DEFINE_EXPLICIT_INTERNAL_INLINE_TYPE_ID(TestLinalgGreedyFusion)
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void getDependentDialects(DialectRegistry ®istry) const override {
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registry.insert<affine::AffineDialect, linalg::LinalgDialect,
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memref::MemRefDialect, scf::SCFDialect>();
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}
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StringRef getArgument() const final { return "test-linalg-greedy-fusion"; }
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StringRef getDescription() const final {
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return "Test Linalg fusion by applying a greedy test transformation.";
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}
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void runOnOperation() override {
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MLIRContext *context = &getContext();
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RewritePatternSet patterns =
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linalg::getLinalgTilingCanonicalizationPatterns(context);
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patterns.add<ExtractSliceOfPadTensorSwapPattern>(context);
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scf::populateSCFForLoopCanonicalizationPatterns(patterns);
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FrozenRewritePatternSet frozenPatterns(std::move(patterns));
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OpPassManager pm(func::FuncOp::getOperationName());
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pm.addPass(createLoopInvariantCodeMotionPass());
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pm.addPass(createCanonicalizerPass());
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pm.addPass(createCSEPass());
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do {
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(void)applyPatternsAndFoldGreedily(getOperation(), frozenPatterns);
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if (failed(runPipeline(pm, getOperation())))
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this->signalPassFailure();
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} while (succeeded(fuseLinalgOpsGreedily(getOperation())));
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}
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};
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} // namespace
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namespace mlir {
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namespace test {
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void registerTestLinalgGreedyFusion() {
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PassRegistration<TestLinalgGreedyFusion>();
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}
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} // namespace test
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} // namespace mlir
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