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c1fa60b4cd
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. 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 follows a previous patch that updated calls `op.cast<T>()-> cast<T>(op)`. However some cases could not handle an unprefixed `cast` call due to occurrences of variables named cast, or occurring inside of class definitions which would resolve to the method. All C++ files that did not work automatically with `cast<T>()` are updated here to `llvm::cast` and similar with the intention that they can be easily updated after the methods are removed through a find-replace. See https://github.com/llvm/llvm-project/compare/main...tpopp:llvm-project:tidy-cast-check for the clang-tidy check that is used and then update printed occurrences of the function to include `llvm::` before. One can then run the following: ``` ninja -C $BUILD_DIR clang-tidy run-clang-tidy -clang-tidy-binary=$BUILD_DIR/bin/clang-tidy -checks='-*,misc-cast-functions'\ -export-fixes /tmp/cast/casts.yaml mlir/*\ -header-filter=mlir/ -fix rm -rf $BUILD_DIR/tools/mlir/**/*.inc ``` Differential Revision: https://reviews.llvm.org/D150348
86 lines
3.1 KiB
C++
86 lines
3.1 KiB
C++
//===- BuiltinAttributeInterfaces.cpp -------------------------------------===//
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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/IR/BuiltinAttributeInterfaces.h"
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#include "mlir/IR/BuiltinTypes.h"
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#include "mlir/IR/Diagnostics.h"
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#include "llvm/ADT/Sequence.h"
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using namespace mlir;
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using namespace mlir::detail;
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//===----------------------------------------------------------------------===//
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/// Tablegen Interface Definitions
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//===----------------------------------------------------------------------===//
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#include "mlir/IR/BuiltinAttributeInterfaces.cpp.inc"
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//===----------------------------------------------------------------------===//
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// ElementsAttr
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//===----------------------------------------------------------------------===//
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Type ElementsAttr::getElementType(ElementsAttr elementsAttr) {
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return elementsAttr.getShapedType().getElementType();
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}
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int64_t ElementsAttr::getNumElements(ElementsAttr elementsAttr) {
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return elementsAttr.getShapedType().getNumElements();
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}
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bool ElementsAttr::isValidIndex(ShapedType type, ArrayRef<uint64_t> index) {
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// Verify that the rank of the indices matches the held type.
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int64_t rank = type.getRank();
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if (rank == 0 && index.size() == 1 && index[0] == 0)
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return true;
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if (rank != static_cast<int64_t>(index.size()))
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return false;
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// Verify that all of the indices are within the shape dimensions.
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ArrayRef<int64_t> shape = type.getShape();
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return llvm::all_of(llvm::seq<int>(0, rank), [&](int i) {
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int64_t dim = static_cast<int64_t>(index[i]);
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return 0 <= dim && dim < shape[i];
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});
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}
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bool ElementsAttr::isValidIndex(ElementsAttr elementsAttr,
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ArrayRef<uint64_t> index) {
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return isValidIndex(elementsAttr.getShapedType(), index);
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}
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uint64_t ElementsAttr::getFlattenedIndex(Type type, ArrayRef<uint64_t> index) {
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ShapedType shapeType = llvm::cast<ShapedType>(type);
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assert(isValidIndex(shapeType, index) &&
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"expected valid multi-dimensional index");
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// Reduce the provided multidimensional index into a flattended 1D row-major
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// index.
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auto rank = shapeType.getRank();
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ArrayRef<int64_t> shape = shapeType.getShape();
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uint64_t valueIndex = 0;
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uint64_t dimMultiplier = 1;
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for (int i = rank - 1; i >= 0; --i) {
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valueIndex += index[i] * dimMultiplier;
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dimMultiplier *= shape[i];
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}
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return valueIndex;
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}
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//===----------------------------------------------------------------------===//
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// MemRefLayoutAttrInterface
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//===----------------------------------------------------------------------===//
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LogicalResult mlir::detail::verifyAffineMapAsLayout(
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AffineMap m, ArrayRef<int64_t> shape,
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function_ref<InFlightDiagnostic()> emitError) {
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if (m.getNumDims() != shape.size())
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return emitError() << "memref layout mismatch between rank and affine map: "
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<< shape.size() << " != " << m.getNumDims();
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return success();
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}
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