95 lines
3.5 KiB
C++
95 lines
3.5 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 std ops.
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//
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//===----------------------------------------------------------------------===//
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#include "mlir/Transforms/Bufferize.h"
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#include "PassDetail.h"
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#include "mlir/Dialect/MemRef/IR/MemRef.h"
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#include "mlir/Dialect/SCF/SCF.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/Dialect/Tensor/IR/Tensor.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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namespace {
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class BufferizeIndexCastOp : public OpConversionPattern<IndexCastOp> {
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public:
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using OpConversionPattern::OpConversionPattern;
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LogicalResult
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matchAndRewrite(IndexCastOp op, ArrayRef<Value> operands,
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ConversionPatternRewriter &rewriter) const override {
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IndexCastOp::Adaptor adaptor(operands);
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auto tensorType = op.getType().cast<RankedTensorType>();
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rewriter.replaceOpWithNewOp<IndexCastOp>(
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op, adaptor.in(),
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MemRefType::get(tensorType.getShape(), tensorType.getElementType()));
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return success();
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}
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};
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class BufferizeSelectOp : public OpConversionPattern<SelectOp> {
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public:
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using OpConversionPattern::OpConversionPattern;
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LogicalResult
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matchAndRewrite(SelectOp op, ArrayRef<Value> operands,
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ConversionPatternRewriter &rewriter) const override {
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if (!op.condition().getType().isa<IntegerType>())
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return rewriter.notifyMatchFailure(op, "requires scalar condition");
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SelectOp::Adaptor adaptor(operands);
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rewriter.replaceOpWithNewOp<SelectOp>(
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op, adaptor.condition(), adaptor.true_value(), adaptor.false_value());
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return success();
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}
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};
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} // namespace
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void mlir::populateStdBufferizePatterns(BufferizeTypeConverter &typeConverter,
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RewritePatternSet &patterns) {
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patterns.add<BufferizeSelectOp, BufferizeIndexCastOp>(typeConverter,
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patterns.getContext());
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}
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namespace {
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struct StdBufferizePass : public StdBufferizeBase<StdBufferizePass> {
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void runOnFunction() override {
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auto *context = &getContext();
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BufferizeTypeConverter typeConverter;
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RewritePatternSet patterns(context);
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ConversionTarget target(*context);
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target.addLegalDialect<scf::SCFDialect, StandardOpsDialect,
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memref::MemRefDialect>();
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populateStdBufferizePatterns(typeConverter, patterns);
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// We only bufferize the case of tensor selected type and scalar condition,
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// as that boils down to a select over memref descriptors (don't need to
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// touch the data).
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target.addDynamicallyLegalOp<IndexCastOp>(
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[&](IndexCastOp op) { return typeConverter.isLegal(op.getType()); });
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target.addDynamicallyLegalOp<SelectOp>([&](SelectOp op) {
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return typeConverter.isLegal(op.getType()) ||
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!op.condition().getType().isa<IntegerType>();
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});
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if (failed(
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applyPartialConversion(getFunction(), 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> mlir::createStdBufferizePass() {
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return std::make_unique<StdBufferizePass>();
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}
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