9a14cb53cb
Part of the refactor discussed in: https://llvm.discourse.group/t/what-is-the-strategy-for-tensor-memref-conversion-bufferization/1938/17 Differential Revision: https://reviews.llvm.org/D89271
90 lines
3.2 KiB
C++
90 lines
3.2 KiB
C++
//====----- Bufferize.cpp - Bufferization of shape ops ---------*- C++-*--===//
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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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#include "mlir/Transforms/Bufferize.h"
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#include "PassDetail.h"
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#include "mlir/Dialect/Shape/IR/Shape.h"
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#include "mlir/Dialect/Shape/Transforms/Passes.h"
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#include "mlir/Dialect/StandardOps/IR/Ops.h"
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#include "mlir/IR/Operation.h"
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#include "mlir/IR/StandardTypes.h"
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#include "mlir/Pass/Pass.h"
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using namespace mlir;
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using namespace mlir::shape;
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namespace {
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// Propagate tensor to memref conversions through shape.assuming ops.
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class TypeConversionAssumingOpConverter
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: public BufferizeOpConversionPattern<shape::AssumingOp> {
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public:
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using BufferizeOpConversionPattern<
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shape::AssumingOp>::BufferizeOpConversionPattern;
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LogicalResult
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matchAndRewrite(shape::AssumingOp assumingOp, ArrayRef<Value> operands,
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ConversionPatternRewriter &rewriter) const final {
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SmallVector<Type, 2> newResultTypes;
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newResultTypes.reserve(assumingOp.getNumResults());
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for (auto result : assumingOp.getResults()) {
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auto originalType = result.getType();
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Type convertedType = converter.convertType(originalType);
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newResultTypes.push_back(convertedType);
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}
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auto newAssumingOp = rewriter.create<shape::AssumingOp>(
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assumingOp.getLoc(), newResultTypes, assumingOp.witness());
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rewriter.replaceOp(assumingOp, newAssumingOp.getResults());
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rewriter.inlineRegionBefore(assumingOp.doRegion(), newAssumingOp.doRegion(),
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newAssumingOp.doRegion().end());
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return success();
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}
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};
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struct ShapeBufferizePass : public ShapeBufferizeBase<ShapeBufferizePass> {
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void runOnFunction() override {
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MLIRContext &ctx = getContext();
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OwningRewritePatternList patterns;
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BufferizeTypeConverter converter;
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populateShapeTypeConversionPatterns(&ctx, converter, patterns);
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ConversionTarget target(getContext());
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auto isMemRefType = [](Type type) { return type.isa<BaseMemRefType>(); };
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target.addDynamicallyLegalOp<AssumingOp>([&](shape::AssumingOp op) {
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return std::all_of(op.result_type_begin(), op.result_type_end(),
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isMemRefType);
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});
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if (failed(mlir::applyPartialConversion(getFunction(), target, patterns)))
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signalPassFailure();
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}
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};
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} // namespace
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/// Populates `patterns` with the conversion patterns of tensor->memref.
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//
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// TODO: Change this to work generally with any type conversions.
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void mlir::populateShapeTypeConversionPatterns(
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MLIRContext *context, BufferizeTypeConverter &converter,
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OwningRewritePatternList &patterns) {
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patterns.insert<TypeConversionAssumingOpConverter>(context, converter);
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}
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//===----------------------------------------------------------------------===//
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// ShapeBufferizePass construction
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//===----------------------------------------------------------------------===//
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std::unique_ptr<FunctionPass> mlir::createShapeBufferizePass() {
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return std::make_unique<ShapeBufferizePass>();
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}
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