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45577fb79e
This commit rebases the compiler onto commit f69328049e9e from llvm-project. Changes: * Use of the one-shot bufferizer for improved memory management * A new pass `OneShotBufferizeDPSWrapper` that converts functions returning tensors to destination-passing-style as required by the one-shot bufferizer * A new pass `LinalgGenericOpWithTensorsToLoopsPass` that converts `linalg.generic` operations with value semantics to loop nests * Rebase onto a fork of llvm-project at f69328049e9e with local modifications to enable bufferization of `linalg.generic` operations with value semantics * Workaround for the absence of type propagation after type conversion via extra patterns in all dialect conversion passes * Printer, parser and verifier definitions moved from inline declarations in ODS to the respective source files as required by upstream changes * New tests for functions with a large number of inputs * Increase the number of allowed task inputs as required by new tests * Use upstream function `mlir_configure_python_dev_packages()` to locate Python development files for compatibility with various CMake versions Co-authored-by: Quentin Bourgerie <quentin.bourgerie@zama.ai> Co-authored-by: Ayoub Benaissa <ayoub.benaissa@zama.ai> Co-authored-by: Antoniu Pop <antoniu.pop@zama.ai>
73 lines
2.4 KiB
C++
73 lines
2.4 KiB
C++
// Part of the Concrete Compiler Project, under the BSD3 License with Zama
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// Exceptions. See
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// https://github.com/zama-ai/concrete-compiler-internal/blob/main/LICENSE.txt
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// for license information.
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#include <concretelang/Conversion/Passes.h>
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#include <concretelang/Support/LinalgExtras.h>
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#include <mlir/Dialect/Linalg/IR/Linalg.h>
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#include <mlir/Transforms/GreedyPatternRewriteDriver.h>
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namespace {
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struct LinalgGenericOpWithTensorsToLoopsPass
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: public LinalgGenericOpWithTensorsToLoopsBase<
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LinalgGenericOpWithTensorsToLoopsPass> {
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LinalgGenericOpWithTensorsToLoopsPass() = delete;
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LinalgGenericOpWithTensorsToLoopsPass(bool parallelizeLoops)
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: parallelizeLoops(parallelizeLoops){};
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void runOnOperation() final;
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private:
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bool parallelizeLoops;
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};
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} // namespace
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template <typename LoopType>
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class LinalgRewritePattern
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: public mlir::OpRewritePattern<mlir::linalg::GenericOp> {
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public:
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using OpRewritePattern<mlir::linalg::GenericOp>::OpRewritePattern;
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LinalgRewritePattern(::mlir::MLIRContext *context, bool parallelizeLoops,
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mlir::PatternBenefit benefit = 0)
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: parallelizeLoops(parallelizeLoops),
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::mlir::OpRewritePattern<mlir::linalg::GenericOp>(context, benefit) {}
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mlir::LogicalResult
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matchAndRewrite(mlir::linalg::GenericOp linalgOp,
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mlir::PatternRewriter &rewriter) const override {
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mlir::FailureOr<mlir::linalg::LinalgLoops> loops =
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mlir::concretelang::linalgextras::linalgTensorOpToLoopsImpl<LoopType>(
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rewriter, linalgOp, parallelizeLoops);
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if (((mlir::LogicalResult)loops).failed() || loops->size() == 0)
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return mlir::failure();
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rewriter.replaceOp(linalgOp, loops.getValue()[0]->getResult(0));
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return mlir::success();
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};
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private:
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bool parallelizeLoops;
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};
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void LinalgGenericOpWithTensorsToLoopsPass::runOnOperation() {
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auto op = this->getOperation();
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mlir::RewritePatternSet patterns(&getContext());
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patterns.insert<LinalgRewritePattern<mlir::scf::ForOp>>(&getContext(),
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parallelizeLoops);
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(void)applyPatternsAndFoldGreedily(op, std::move(patterns));
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}
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namespace mlir {
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namespace concretelang {
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std::unique_ptr<OperationPass<ModuleOp>>
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createLinalgGenericOpWithTensorsToLoopsPass(bool parallelizeLoops) {
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return std::make_unique<LinalgGenericOpWithTensorsToLoopsPass>(
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parallelizeLoops);
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}
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} // namespace concretelang
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} // namespace mlir
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