mirror of
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
93 lines
3.3 KiB
C++
93 lines
3.3 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/Transforms/Passes.h"
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#include "mlir/Dialect/Bufferization/Transforms/Bufferize.h"
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#include "mlir/Dialect/SCF/IR/SCF.h"
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#include "mlir/IR/IRMapping.h"
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#include "mlir/IR/Operation.h"
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#include "mlir/Transforms/DialectConversion.h"
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#include "mlir/Transforms/Passes.h"
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#include <mlir/Transforms/GreedyPatternRewriteDriver.h>
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namespace {
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class ForOpPattern : public mlir::OpRewritePattern<mlir::scf::ForOp> {
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public:
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ForOpPattern(::mlir::MLIRContext *context, mlir::PatternBenefit benefit = 1)
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: ::mlir::OpRewritePattern<mlir::scf::ForOp>(context, benefit) {}
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mlir::LogicalResult
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matchAndRewrite(mlir::scf::ForOp forOp,
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mlir::PatternRewriter &rewriter) const override {
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auto attr = forOp->getAttrOfType<mlir::BoolAttr>("parallel");
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if (attr == nullptr) {
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return mlir::failure();
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}
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assert(forOp.getRegionIterArgs().size() == 0 &&
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"unexpecting iter args when loops are bufferized");
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if (attr.getValue()) {
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rewriter.replaceOpWithNewOp<mlir::scf::ParallelOp>(
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forOp, mlir::ValueRange{forOp.getLowerBound()},
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mlir::ValueRange{forOp.getUpperBound()}, forOp.getStep(),
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std::nullopt,
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[&](mlir::OpBuilder &builder, mlir::Location location,
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mlir::ValueRange indVar, mlir::ValueRange iterArgs) {
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mlir::IRMapping map;
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map.map(forOp.getInductionVar(), indVar.front());
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for (auto &op : forOp.getRegion().front()) {
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auto newOp = builder.clone(op, map);
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map.map(op.getResults(), newOp->getResults());
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}
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});
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} else {
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rewriter.replaceOpWithNewOp<mlir::scf::ForOp>(
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forOp, forOp.getLowerBound(), forOp.getUpperBound(), forOp.getStep(),
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std::nullopt,
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[&](mlir::OpBuilder &builder, mlir::Location location,
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mlir::Value indVar, mlir::ValueRange iterArgs) {
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mlir::IRMapping map;
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map.map(forOp.getInductionVar(), indVar);
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for (auto &op : forOp.getRegion().front()) {
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auto newOp = builder.clone(op, map);
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map.map(op.getResults(), newOp->getResults());
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}
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});
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}
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return mlir::success();
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}
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};
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} // namespace
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namespace {
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struct ForLoopToParallelPass
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: public ForLoopToParallelBase<ForLoopToParallelPass> {
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void runOnOperation() override {
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auto func = getOperation();
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auto *context = &getContext();
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mlir::RewritePatternSet patterns(context);
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mlir::ConversionTarget target(*context);
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patterns.add<ForOpPattern>(context);
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target.addDynamicallyLegalOp<mlir::scf::ForOp>([&](mlir::scf::ForOp op) {
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auto r = op->getAttrOfType<mlir::BoolAttr>("parallel") == nullptr;
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return r;
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});
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target.markUnknownOpDynamicallyLegal(
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[&](mlir::Operation *op) { return true; });
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if (mlir::applyPatternsAndFoldGreedily(func, std::move(patterns))
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.failed()) {
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this->signalPassFailure();
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};
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}
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};
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} // namespace
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std::unique_ptr<mlir::OperationPass<mlir::ModuleOp>>
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mlir::concretelang::createForLoopToParallel() {
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return std::make_unique<ForLoopToParallelPass>();
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}
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