The MLIR classes Type/Attribute/Operation/Op/Value support cast/dyn_cast/isa/dyn_cast_or_null functionality through llvm's doCast functionality in addition to defining methods with the same name. This change begins the migration of uses of the method to the corresponding function call as has been decided as more consistent. Note that there still exist classes that only define methods directly, such as AffineExpr, and this does not include work currently to support a functional cast/isa call. Caveats include: - This clang-tidy script probably has more problems. - This only touches C++ code, so nothing that is being generated. Context: - https://mlir.llvm.org/deprecation/ at "Use the free function variants for dyn_cast/cast/isa/…" - Original discussion at https://discourse.llvm.org/t/preferred-casting-style-going-forward/68443 Implementation: This first patch was created with the following steps. The intention is to only do automated changes at first, so I waste less time if it's reverted, and so the first mass change is more clear as an example to other teams that will need to follow similar steps. Steps are described per line, as comments are removed by git: 0. Retrieve the change from the following to build clang-tidy with an additional check: https://github.com/llvm/llvm-project/compare/main...tpopp:llvm-project:tidy-cast-check 1. Build clang-tidy 2. Run clang-tidy over your entire codebase while disabling all checks and enabling the one relevant one. Run on all header files also. 3. Delete .inc files that were also modified, so the next build rebuilds them to a pure state. 4. Some changes have been deleted for the following reasons: - Some files had a variable also named cast - Some files had not included a header file that defines the cast functions - Some files are definitions of the classes that have the casting methods, so the code still refers to the method instead of the function without adding a prefix or removing the method declaration at the same time. ``` ninja -C $BUILD_DIR clang-tidy run-clang-tidy -clang-tidy-binary=$BUILD_DIR/bin/clang-tidy -checks='-*,misc-cast-functions'\ -header-filter=mlir/ mlir/* -fix rm -rf $BUILD_DIR/tools/mlir/**/*.inc git restore mlir/lib/IR mlir/lib/Dialect/DLTI/DLTI.cpp\ mlir/lib/Dialect/Complex/IR/ComplexDialect.cpp\ mlir/lib/**/IR/\ mlir/lib/Dialect/SparseTensor/Transforms/SparseVectorization.cpp\ mlir/lib/Dialect/Vector/Transforms/LowerVectorMultiReduction.cpp\ mlir/test/lib/Dialect/Test/TestTypes.cpp\ mlir/test/lib/Dialect/Transform/TestTransformDialectExtension.cpp\ mlir/test/lib/Dialect/Test/TestAttributes.cpp\ mlir/unittests/TableGen/EnumsGenTest.cpp\ mlir/test/python/lib/PythonTestCAPI.cpp\ mlir/include/mlir/IR/ ``` Differential Revision: https://reviews.llvm.org/D150123
191 lines
7.8 KiB
C++
191 lines
7.8 KiB
C++
//===- RuntimeOpVerification.cpp - Op Verification ------------------------===//
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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/Dialect/MemRef/Transforms/RuntimeOpVerification.h"
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#include "mlir/Dialect/Arith/IR/Arith.h"
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#include "mlir/Dialect/ControlFlow/IR/ControlFlow.h"
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#include "mlir/Dialect/ControlFlow/IR/ControlFlowOps.h"
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#include "mlir/Dialect/MemRef/IR/MemRef.h"
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#include "mlir/Interfaces/RuntimeVerifiableOpInterface.h"
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using namespace mlir;
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/// Generate an error message string for the given op and the specified error.
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static std::string generateErrorMessage(Operation *op, const std::string &msg) {
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std::string buffer;
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llvm::raw_string_ostream stream(buffer);
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OpPrintingFlags flags;
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stream << "ERROR: Runtime op verification failed\n";
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op->print(stream, flags);
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stream << "\n^ " << msg;
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stream << "\nLocation: ";
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op->getLoc().print(stream);
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return stream.str();
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}
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namespace mlir {
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namespace memref {
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namespace {
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struct CastOpInterface
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: public RuntimeVerifiableOpInterface::ExternalModel<CastOpInterface,
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CastOp> {
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void generateRuntimeVerification(Operation *op, OpBuilder &builder,
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Location loc) const {
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auto castOp = cast<CastOp>(op);
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auto srcType = cast<BaseMemRefType>(castOp.getSource().getType());
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// Nothing to check if the result is an unranked memref.
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auto resultType = dyn_cast<MemRefType>(castOp.getType());
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if (!resultType)
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return;
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if (isa<UnrankedMemRefType>(srcType)) {
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// Check rank.
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Value srcRank = builder.create<RankOp>(loc, castOp.getSource());
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Value resultRank =
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builder.create<arith::ConstantIndexOp>(loc, resultType.getRank());
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Value isSameRank = builder.create<arith::CmpIOp>(
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loc, arith::CmpIPredicate::eq, srcRank, resultRank);
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builder.create<cf::AssertOp>(loc, isSameRank,
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generateErrorMessage(op, "rank mismatch"));
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}
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// Get source offset and strides. We do not have an op to get offsets and
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// strides from unranked memrefs, so cast the source to a type with fully
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// dynamic layout, from which we can then extract the offset and strides.
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// (Rank was already verified.)
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int64_t dynamicOffset = ShapedType::kDynamic;
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SmallVector<int64_t> dynamicShape(resultType.getRank(),
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ShapedType::kDynamic);
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auto stridedLayout = StridedLayoutAttr::get(builder.getContext(),
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dynamicOffset, dynamicShape);
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auto dynStridesType =
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MemRefType::get(dynamicShape, resultType.getElementType(),
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stridedLayout, resultType.getMemorySpace());
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Value helperCast =
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builder.create<CastOp>(loc, dynStridesType, castOp.getSource());
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auto metadataOp = builder.create<ExtractStridedMetadataOp>(loc, helperCast);
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// Check dimension sizes.
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for (const auto &it : llvm::enumerate(resultType.getShape())) {
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// Static dim size -> static/dynamic dim size does not need verification.
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if (auto rankedSrcType = dyn_cast<MemRefType>(srcType))
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if (!rankedSrcType.isDynamicDim(it.index()))
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continue;
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// Static/dynamic dim size -> dynamic dim size does not need verification.
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if (resultType.isDynamicDim(it.index()))
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continue;
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Value srcDimSz =
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builder.create<DimOp>(loc, castOp.getSource(), it.index());
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Value resultDimSz =
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builder.create<arith::ConstantIndexOp>(loc, it.value());
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Value isSameSz = builder.create<arith::CmpIOp>(
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loc, arith::CmpIPredicate::eq, srcDimSz, resultDimSz);
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builder.create<cf::AssertOp>(
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loc, isSameSz,
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generateErrorMessage(op, "size mismatch of dim " +
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std::to_string(it.index())));
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}
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// Get result offset and strides.
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int64_t resultOffset;
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SmallVector<int64_t> resultStrides;
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if (failed(getStridesAndOffset(resultType, resultStrides, resultOffset)))
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return;
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// Check offset.
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if (resultOffset != ShapedType::kDynamic) {
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// Static/dynamic offset -> dynamic offset does not need verification.
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Value srcOffset = metadataOp.getResult(1);
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Value resultOffsetVal =
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builder.create<arith::ConstantIndexOp>(loc, resultOffset);
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Value isSameOffset = builder.create<arith::CmpIOp>(
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loc, arith::CmpIPredicate::eq, srcOffset, resultOffsetVal);
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builder.create<cf::AssertOp>(loc, isSameOffset,
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generateErrorMessage(op, "offset mismatch"));
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}
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// Check strides.
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for (const auto &it : llvm::enumerate(resultStrides)) {
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// Static/dynamic stride -> dynamic stride does not need verification.
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if (it.value() == ShapedType::kDynamic)
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continue;
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Value srcStride =
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metadataOp.getResult(2 + resultType.getRank() + it.index());
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Value resultStrideVal =
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builder.create<arith::ConstantIndexOp>(loc, it.value());
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Value isSameStride = builder.create<arith::CmpIOp>(
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loc, arith::CmpIPredicate::eq, srcStride, resultStrideVal);
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builder.create<cf::AssertOp>(
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loc, isSameStride,
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generateErrorMessage(op, "stride mismatch of dim " +
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std::to_string(it.index())));
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}
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}
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};
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struct ExpandShapeOpInterface
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: public RuntimeVerifiableOpInterface::ExternalModel<ExpandShapeOpInterface,
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ExpandShapeOp> {
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void generateRuntimeVerification(Operation *op, OpBuilder &builder,
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Location loc) const {
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auto expandShapeOp = cast<ExpandShapeOp>(op);
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// Verify that the expanded dim sizes are a product of the collapsed dim
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// size.
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for (const auto &it :
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llvm::enumerate(expandShapeOp.getReassociationIndices())) {
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Value srcDimSz =
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builder.create<DimOp>(loc, expandShapeOp.getSrc(), it.index());
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int64_t groupSz = 1;
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bool foundDynamicDim = false;
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for (int64_t resultDim : it.value()) {
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if (expandShapeOp.getResultType().isDynamicDim(resultDim)) {
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// Keep this assert here in case the op is extended in the future.
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assert(!foundDynamicDim &&
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"more than one dynamic dim found in reassoc group");
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(void)foundDynamicDim;
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foundDynamicDim = true;
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continue;
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}
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groupSz *= expandShapeOp.getResultType().getDimSize(resultDim);
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}
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Value staticResultDimSz =
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builder.create<arith::ConstantIndexOp>(loc, groupSz);
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// staticResultDimSz must divide srcDimSz evenly.
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Value mod =
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builder.create<arith::RemSIOp>(loc, srcDimSz, staticResultDimSz);
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Value isModZero = builder.create<arith::CmpIOp>(
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loc, arith::CmpIPredicate::eq, mod,
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builder.create<arith::ConstantIndexOp>(loc, 0));
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builder.create<cf::AssertOp>(
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loc, isModZero,
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generateErrorMessage(op, "static result dims in reassoc group do not "
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"divide src dim evenly"));
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}
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}
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};
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} // namespace
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} // namespace memref
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} // namespace mlir
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void mlir::memref::registerRuntimeVerifiableOpInterfaceExternalModels(
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DialectRegistry ®istry) {
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registry.addExtension(+[](MLIRContext *ctx, memref::MemRefDialect *dialect) {
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CastOp::attachInterface<CastOpInterface>(*ctx);
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ExpandShapeOp::attachInterface<ExpandShapeOpInterface>(*ctx);
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// Load additional dialects of which ops may get created.
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ctx->loadDialect<arith::ArithDialect, cf::ControlFlowDialect>();
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});
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}
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