NiccoloN
121 lines
4.6 KiB
C++
121 lines
4.6 KiB
C++
#pragma once
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#include "mlir/Dialect/Tensor/IR/Tensor.h"
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#include "mlir/IR/BuiltinTypes.h"
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#include "mlir/IR/Value.h"
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#include "mlir/IR/ValueRange.h"
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#include "mlir/Transforms/DialectConversion.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 <cassert>
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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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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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mlir::Value transposeMaybeInCompute(mlir::Value value,
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mlir::RankedTensorType resultType,
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mlir::ArrayRef<int64_t> permutation,
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mlir::PatternRewriter& rewriter,
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mlir::Location loc);
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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, mlir::ArrayRef<int64_t> shape);
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/// Slices a statically shaped tensor along one axis into contiguous pieces of
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/// at most `sliceSize` elements.
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llvm::SmallVector<mlir::Value> sliceTensor(const mlir::Value& tensorToSlice,
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size_t axis,
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int64_t sliceSize,
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mlir::ConversionPatternRewriter& rewriter,
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mlir::Location loc);
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llvm::SmallVector<mlir::Value> sliceVector(const mlir::Value& vectorToSlice,
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int64_t sliceSize,
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mlir::ConversionPatternRewriter& rewriter,
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mlir::Location loc);
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/// Partitions one logical vector into per-core crossbar-sized slices using the
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/// current PIM target geometry.
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llvm::DenseMap<CoreId, llvm::SmallVector<mlir::Value>> sliceVectorPerCrossbarPerCore(
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const mlir::Value& vectorToSlice, mlir::ConversionPatternRewriter& rewriter, mlir::Location loc);
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mlir::Value extractAxisSlice(
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mlir::PatternRewriter& rewriter, mlir::Location loc, mlir::Value source, int64_t axis, int64_t offset, int64_t size);
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mlir::Value insertStaticSlice(mlir::PatternRewriter& rewriter,
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mlir::Location loc,
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mlir::Value source,
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mlir::Value dest,
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llvm::ArrayRef<mlir::OpFoldResult> offsets);
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} // namespace onnx_mlir
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