NiccoloN
113 lines
4.0 KiB
C++
113 lines
4.0 KiB
C++
#pragma once
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#include "mlir/IR/BuiltinTypes.h"
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#include "mlir/IR/OpDefinition.h"
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#include "mlir/IR/PatternMatch.h"
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#include "mlir/IR/Value.h"
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#include "llvm/ADT/ArrayRef.h"
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#include "llvm/ADT/DenseMap.h"
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#include "llvm/ADT/SmallVector.h"
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#include <cstddef>
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#include <optional>
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#include <type_traits>
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#include <utility>
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namespace onnx_mlir {
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using HSliceId = size_t;
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using CoreId = size_t;
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llvm::SmallVector<int64_t> computeRowMajorStrides(llvm::ArrayRef<int64_t> shape);
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llvm::SmallVector<int64_t>
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delinearizeIndex(int64_t linearIndex, llvm::ArrayRef<int64_t> shape, llvm::ArrayRef<int64_t> strides);
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int64_t linearizeIndex(llvm::ArrayRef<int64_t> indices, llvm::ArrayRef<int64_t> strides);
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int64_t getNumElements(llvm::ArrayRef<int64_t> shape);
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bool hasByteSizedElementType(mlir::Type elementType);
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size_t getElementTypeSizeInBytes(mlir::Type elementType);
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size_t getShapedTypeSizeInBytes(mlir::ShapedType shapedType);
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bool isMemoryContiguous(llvm::ArrayRef<int64_t> srcShape,
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llvm::ArrayRef<int64_t> offsets,
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llvm::ArrayRef<int64_t> sizes,
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llvm::ArrayRef<int64_t> strides);
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bool isContiguousSubviewWithDynamicOffsets(llvm::ArrayRef<int64_t> sourceShape,
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llvm::ArrayRef<mlir::OpFoldResult> mixedOffsets,
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llvm::ArrayRef<int64_t> staticSizes,
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llvm::ArrayRef<int64_t> staticStrides);
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template <class A, class B, class C = std::common_type_t<A, B>>
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constexpr C ceilIntegerDivide(A a, B b) {
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static_assert(std::is_integral_v<A>, "A must be an integer type");
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static_assert(std::is_integral_v<B>, "B must be an integer type");
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C ac = static_cast<C>(a);
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C bc = static_cast<C>(b);
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return 1 + (ac - 1) / bc;
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}
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template <class A, class B, class C = std::common_type_t<A, B>>
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constexpr std::pair<C, C> ceilIntegerDivideWithRemainder(A a, B b) {
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static_assert(std::is_integral_v<A>, "A must be an integer type");
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static_assert(std::is_integral_v<B>, "B must be an integer type");
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C ac = static_cast<C>(a);
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C bc = static_cast<C>(b);
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return {ceilIntegerDivide(ac, bc), ac % bc};
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}
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template <class T>
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bool isVectorShape(mlir::ArrayRef<T> shape) {
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return shape.size() == 2 && (shape[0] == 1 || shape[1] == 1);
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}
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template <class T>
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bool isMatrixShape(mlir::ArrayRef<T> shape) {
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return shape.size() == 2;
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}
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template <class T>
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bool isHVectorShape(mlir::ArrayRef<T> shape) {
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return shape.size() == 2 && shape[0] == 1;
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}
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inline auto getTensorShape(mlir::Value tensor) {
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return mlir::cast<mlir::RankedTensorType>(tensor.getType()).getShape();
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}
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inline bool haveSameStaticShape(mlir::Value lhs, mlir::Value rhs) {
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auto lhsType = mlir::dyn_cast<mlir::RankedTensorType>(lhs.getType());
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auto rhsType = mlir::dyn_cast<mlir::RankedTensorType>(rhs.getType());
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return lhsType && rhsType && lhsType.hasStaticShape() && rhsType.hasStaticShape()
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&& lhsType.getShape() == rhsType.getShape();
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}
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bool hasStaticPositiveShape(mlir::ArrayRef<int64_t> shape);
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bool hasStaticPositiveShape(mlir::RankedTensorType type);
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int64_t getStaticShapeElementCount(mlir::ArrayRef<int64_t> shape);
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llvm::SmallVector<int64_t> permuteShape(mlir::ArrayRef<int64_t> shape, mlir::ArrayRef<int64_t> permutation);
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llvm::SmallVector<int64_t> invertPermutation(mlir::ArrayRef<int64_t> permutation);
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mlir::FailureOr<llvm::SmallVector<int64_t>> getTransposePermutationChecked(std::optional<mlir::ArrayAttr> permAttr,
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int64_t rank);
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llvm::SmallVector<mlir::OpFoldResult> getStaticIndexAttrs(mlir::Builder& builder, llvm::ArrayRef<int64_t> values);
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llvm::SmallVector<mlir::OpFoldResult> getUnitStrides(mlir::PatternRewriter& rewriter, int64_t rank);
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llvm::SmallVector<mlir::OpFoldResult> getZeroOffsets(mlir::PatternRewriter& rewriter, int64_t rank);
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llvm::SmallVector<mlir::OpFoldResult> getStaticSizes(mlir::PatternRewriter& rewriter, llvm::ArrayRef<int64_t> shape);
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} // namespace onnx_mlir
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