serene/src/serene/passes/slir_lowering.cpp

94 lines
3.8 KiB
C++

/* -*- C++ -*-
* Serene programming language.
*
* Copyright (c) 2019-2021 Sameer Rahmani <lxsameer@gnu.org>
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
#include "serene/passes/slir_lowering.h"
#include <mlir/IR/BuiltinOps.h>
namespace serene::passes {
mlir::LogicalResult
ValueOpLowering::matchAndRewrite(serene::slir::ValueOp op,
mlir::PatternRewriter &rewriter) const {
auto value = op.value();
mlir::Location loc = op.getLoc();
// auto std_const =
rewriter.create<mlir::ConstantIntOp>(loc, (int64_t)value,
rewriter.getI64Type());
// rewriter.replaceOpWithNewOp<typename OpTy>(Operation *op, Args &&args...)
// Replace this operation with the generated alloc.
// rewriter.replaceOp(op, alloc);
rewriter.eraseOp(op);
return mlir::success();
}
void SLIRToAffinePass::getDependentDialects(
mlir::DialectRegistry &registry) const {
registry.insert<mlir::AffineDialect, mlir::memref::MemRefDialect,
mlir::StandardOpsDialect>();
};
/// Return the current function being transformed.
mlir::ModuleOp SLIRToAffinePass::getModule() { return this->getOperation(); }
void SLIRToAffinePass::runOnOperation() { runOnModule(); }
void SLIRToAffinePass::runOnModule() {
auto module = getModule();
// The first thing to define is the conversion target. This will define the
// final target for this lowering.
mlir::ConversionTarget target(getContext());
// We define the specific operations, or dialects, that are legal targets for
// this lowering. In our case, we are lowering to a combination of the
// `Affine`, `MemRef` and `Standard` dialects.
target.addLegalDialect<mlir::AffineDialect, mlir::memref::MemRefDialect,
mlir::StandardOpsDialect>();
// We also define the Toy dialect as Illegal so that the conversion will fail
// if any of these operations are *not* converted. Given that we actually want
// a partial lowering, we explicitly mark the Toy operations that don't want
// to lower, `toy.print`, as `legal`.
target.addIllegalDialect<serene::slir::SereneDialect>();
// target.addLegalOp<serene::slir::PrintOp>();
// Now that the conversion target has been defined, we just need to provide
// the set of patterns that will lower the Toy operations.
mlir::RewritePatternSet patterns(&getContext());
patterns.add<ValueOpLowering>(&getContext());
// With the target and rewrite patterns defined, we can now attempt the
// conversion. The conversion will signal failure if any of our `illegal`
// operations were not converted successfully.
if (failed(applyPartialConversion(module, target, std::move(patterns))))
signalPassFailure();
};
std::unique_ptr<mlir::Pass> createSLIRLowerToAffinePass() {
return std::make_unique<SLIRToAffinePass>();
};
} // namespace serene::passes