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https://github.com/zama-ai/concrete.git
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c8c969773e
This commit rebases the compiler onto commit 465ee9bfb26d from
llvm-project with locally maintained patches on top, i.e.:
* 5d8669d669ee: Fix the element alignment (size) for memrefCopy
* 4239163ea337: fix: Do not fold the memref.subview if the offset are
!= 0 and strides != 1
* 72c5decfcc21: remove github stuff from llvm
* 8d0ce8f9eca1: Support arbitrary element types in named operations
via attributes
* 94f64805c38c: Copy attributes of scf.for on bufferization and make
it an allocation hoisting barrier
Main upstream changes from llvm-project that required modification of
concretecompiler:
* Switch to C++17
* Various changes in the interfaces for linalg named operations
* Transition from `llvm::Optional` to `std::optional`
* Use of enums instead of string values for iterator types in linalg
* Changed default naming convention of getter methods in
ODS-generated operation classes from `some_value()` to
`getSomeValue()`
* Renaming of Arithmetic dialect to Arith
* Refactoring of side effect interfaces (i.e., renaming from
`NoSideEffect` to `Pure`)
* Re-design of the data flow analysis framework
* Refactoring of build targets for Python bindings
* Refactoring of array attributes with integer values
* Renaming of `linalg.init_tensor` to `tensor.empty`
* Emission of `linalg.map` operations in bufferization of the Tensor
dialect requiring another linalg conversion pass and registration
of the bufferization op interfaces for linalg operations
* Refactoring of the one-shot bufferizer
* Necessity to run the expand-strided-metadata, affine-to-std and
finalize-memref-to-llvm passes before converson to the LLVM
dialect
* Renaming of `BlockAndValueMapping` to `IRMapping`
* Changes in the build function of `LLVM::CallOp`
* Refactoring of the construction of `llvm::ArrayRef` and
`llvm::MutableArrayRef` (direct invocation of constructor instead
of builder functions for some cases)
* New naming conventions for generated SSA values requiring rewrite
of some check tests
* Refactoring of `mlir::LLVM::lookupOrCreateMallocFn()`
* Interface changes in generated type parsers
* New dependencies for to mlir_float16_utils and
MLIRSparseTensorRuntime for the runtime
* Overhaul of MLIR-c deleting `mlir-c/Registration.h`
* Deletion of library MLIRLinalgToSPIRV
* Deletion of library MLIRLinalgAnalysis
* Deletion of library MLIRMemRefUtils
* Deletion of library MLIRQuantTransforms
* Deletion of library MLIRVectorToROCDL
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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: ::mlir::OpRewritePattern<mlir::linalg::GenericOp>(context, benefit),
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parallelizeLoops(parallelizeLoops) {}
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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.value()[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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