e3f5073a96
Add bufferizations for extract_element and tensor_from_elements. Differential Revision: https://reviews.llvm.org/D89594
102 lines
3.6 KiB
C++
102 lines
3.6 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/StandardOps/IR/Ops.h"
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#include "mlir/Dialect/StandardOps/Transforms/Passes.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 BufferizeExtractElementOp : public OpConversionPattern<ExtractElementOp> {
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public:
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using OpConversionPattern::OpConversionPattern;
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LogicalResult
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matchAndRewrite(ExtractElementOp op, ArrayRef<Value> operands,
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ConversionPatternRewriter &rewriter) const override {
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ExtractElementOp::Adaptor adaptor(operands);
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rewriter.replaceOpWithNewOp<LoadOp>(op, adaptor.aggregate(),
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adaptor.indices());
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return success();
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}
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};
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} // namespace
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namespace {
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class BufferizeTensorCastOp : public OpConversionPattern<TensorCastOp> {
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public:
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using OpConversionPattern::OpConversionPattern;
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LogicalResult
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matchAndRewrite(TensorCastOp op, ArrayRef<Value> operands,
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ConversionPatternRewriter &rewriter) const override {
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auto resultType = getTypeConverter()->convertType(op.getType());
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rewriter.replaceOpWithNewOp<MemRefCastOp>(op, resultType, operands[0]);
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return success();
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}
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};
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} // namespace
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namespace {
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class BufferizeTensorFromElementsOp
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: public OpConversionPattern<TensorFromElementsOp> {
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public:
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using OpConversionPattern::OpConversionPattern;
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LogicalResult
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matchAndRewrite(TensorFromElementsOp op, ArrayRef<Value> operands,
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ConversionPatternRewriter &rewriter) const override {
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int numberOfElements = op.elements().size();
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auto resultType = MemRefType::get(
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{numberOfElements}, op.getType().cast<TensorType>().getElementType());
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Value result = rewriter.create<AllocOp>(op.getLoc(), resultType);
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for (auto element : llvm::enumerate(op.elements())) {
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Value index =
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rewriter.create<ConstantIndexOp>(op.getLoc(), element.index());
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rewriter.create<StoreOp>(op.getLoc(), element.value(), result, index);
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}
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rewriter.replaceOp(op, {result});
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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(MLIRContext *context,
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BufferizeTypeConverter &typeConverter,
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OwningRewritePatternList &patterns) {
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patterns.insert<BufferizeExtractElementOp, BufferizeTensorCastOp,
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BufferizeTensorFromElementsOp>(typeConverter, context);
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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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OwningRewritePatternList patterns;
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ConversionTarget target(*context);
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target.addLegalDialect<StandardOpsDialect>();
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populateStdBufferizePatterns(context, typeConverter, patterns);
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target.addIllegalOp<ExtractElementOp, TensorCastOp, TensorFromElementsOp>();
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if (failed(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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std::unique_ptr<Pass> mlir::createStdBufferizePass() {
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return std::make_unique<StdBufferizePass>();
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}
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