fb4cedcc1e
This patch prepares MLIR code base to change the value of kDynamicSize. https://discourse.llvm.org/t/rfc-unify-kdynamicsize-and-kdynamicstrideoroffset/64534/4 Differential Revision: https://reviews.llvm.org/D136327
68 lines
2.4 KiB
C++
68 lines
2.4 KiB
C++
//===- TosaToTensor.cpp - Lowering Tosa to Tensor Dialect -------------===//
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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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// These rewriters lower from the Tosa to the Tensor dialect.
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//
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//===----------------------------------------------------------------------===//
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#include "mlir/Conversion/TosaToTensor/TosaToTensor.h"
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#include "mlir/Dialect/Arith/IR/Arith.h"
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#include "mlir/Dialect/Tensor/IR/Tensor.h"
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#include "mlir/Dialect/Tosa/IR/TosaOps.h"
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#include "mlir/IR/PatternMatch.h"
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#include "mlir/Transforms/GreedyPatternRewriteDriver.h"
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using namespace mlir;
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using namespace tosa;
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namespace {
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class SliceOpConverter : public OpRewritePattern<tosa::SliceOp> {
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public:
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using OpRewritePattern<tosa::SliceOp>::OpRewritePattern;
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LogicalResult matchAndRewrite(tosa::SliceOp sliceOp,
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PatternRewriter &rewriter) const final {
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Location loc = sliceOp.getLoc();
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Value input = sliceOp.getInput();
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SmallVector<int64_t> strides, sizes;
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auto starts = sliceOp.getStart();
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strides.resize(sliceOp.getType().template cast<ShapedType>().getRank(), 1);
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SmallVector<Value> dynSizes;
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for (const auto &i : llvm::enumerate(sliceOp.getSize())) {
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int64_t size = i.value().cast<IntegerAttr>().getInt();
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size_t index = i.index();
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sizes.push_back(size == -1 ? ShapedType::kDynamicSize : size);
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if (!ShapedType::isDynamic(sizes.back()))
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continue;
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auto dim = rewriter.create<tensor::DimOp>(loc, input, index);
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auto offset = rewriter.create<arith::ConstantOp>(
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loc,
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rewriter.getIndexAttr(starts[index].cast<IntegerAttr>().getInt()));
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dynSizes.push_back(rewriter.create<arith::SubIOp>(loc, dim, offset));
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}
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auto newSliceOp = rewriter.create<tensor::ExtractSliceOp>(
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sliceOp.getLoc(), sliceOp.getType(), input, ValueRange({}), dynSizes,
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ValueRange({}), starts, rewriter.getI64ArrayAttr(sizes),
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rewriter.getI64ArrayAttr(strides));
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rewriter.replaceOp(sliceOp, newSliceOp.getResult());
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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::tosa::populateTosaToTensorConversionPatterns(
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RewritePatternSet *patterns) {
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patterns->add<SliceOpConverter>(patterns->getContext());
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}
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