0e9a4a3b65
Transforms/ should only contain dialect-independent transformations, and these files are a much better fit for the bufferization dialect anyways. Differential Revision: https://reviews.llvm.org/D117839
93 lines
3.3 KiB
C++
93 lines
3.3 KiB
C++
//===- Bufferize.cpp - Bufferization for std ops --------------------------===//
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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 bufferization of tensor-valued arith.constant ops.
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//
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//===----------------------------------------------------------------------===//
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#include "PassDetail.h"
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#include "mlir/Dialect/Bufferization/IR/Bufferization.h"
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#include "mlir/Dialect/Bufferization/Transforms/BufferUtils.h"
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#include "mlir/Dialect/Bufferization/Transforms/Bufferize.h"
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#include "mlir/Dialect/MemRef/IR/MemRef.h"
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#include "mlir/Dialect/StandardOps/IR/Ops.h"
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#include "mlir/Dialect/StandardOps/Transforms/Passes.h"
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#include "mlir/IR/BlockAndValueMapping.h"
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#include "mlir/Transforms/DialectConversion.h"
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using namespace mlir;
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using namespace mlir::bufferization;
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namespace {
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class BufferizeTensorConstantOp
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: public OpConversionPattern<arith::ConstantOp> {
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public:
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BufferizeTensorConstantOp(GlobalCreator &globals,
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TypeConverter &typeConverter, MLIRContext *context)
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: OpConversionPattern<arith::ConstantOp>(typeConverter, context,
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/*benefit=*/1),
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globals(globals) {}
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LogicalResult
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matchAndRewrite(arith::ConstantOp op, OpAdaptor adaptor,
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ConversionPatternRewriter &rewriter) const override {
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auto type = op.getType().dyn_cast<RankedTensorType>();
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if (!type)
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return failure();
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auto globalMemref = globals.getGlobalFor(op);
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rewriter.replaceOpWithNewOp<memref::GetGlobalOp>(op, globalMemref.type(),
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globalMemref.getName());
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return success();
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}
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GlobalCreator &globals;
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};
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} // namespace
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void mlir::populateTensorConstantBufferizePatterns(
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GlobalCreator &globalCreator,
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bufferization::BufferizeTypeConverter &typeConverter,
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RewritePatternSet &patterns) {
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patterns.add<BufferizeTensorConstantOp>(globalCreator, typeConverter,
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patterns.getContext());
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}
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namespace {
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class TensorConstantBufferizePass
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: public TensorConstantBufferizeBase<TensorConstantBufferizePass> {
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public:
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explicit TensorConstantBufferizePass(unsigned alignment) {
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if (alignment)
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this->alignment = alignment;
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}
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void runOnOperation() override {
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auto module = getOperation();
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GlobalCreator globals(module, alignment);
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auto *context = &getContext();
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bufferization::BufferizeTypeConverter typeConverter;
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RewritePatternSet patterns(context);
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ConversionTarget target(*context);
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target.addLegalDialect<memref::MemRefDialect>();
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populateTensorConstantBufferizePatterns(globals, typeConverter, patterns);
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target.addDynamicallyLegalOp<arith::ConstantOp>([&](arith::ConstantOp op) {
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return typeConverter.isLegal(op.getType());
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});
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if (failed(applyPartialConversion(module, target, std::move(patterns))))
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signalPassFailure();
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}
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};
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} // namespace
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std::unique_ptr<Pass>
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mlir::createTensorConstantBufferizePass(unsigned alignment) {
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return std::make_unique<TensorConstantBufferizePass>(alignment);
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}
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