NiccoloN
50 lines
1.7 KiB
C++
50 lines
1.7 KiB
C++
#pragma once
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#include "mlir/Dialect/Tensor/IR/Tensor.h"
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#include "src/Accelerators/PIM/Common/PimCommon.hpp"
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namespace onnx_mlir {
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/**
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* \brief Get the offset of the ExtractSliceOp based on its static offsets and
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* its static tensor input.
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*
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* The static offsets represent the starting position of the slice in each
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* dimension, while the static tensor input gives its dimension size.
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*
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* \param sliceOp The ExtractSliceOp for which the actual offset needs to be
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* calculated.
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* \param inputShape The ShapedType of the ExtractSliceOp's input tensor
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* \return The actual offset of the ExtractSliceOp.
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*/
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size_t getSliceActualOffset(mlir::tensor::ExtractSliceOp& sliceOp, mlir::ShapedType& inputShape);
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mlir::IntegerAttr getTensorSizeInBytesAttr(mlir::Builder& builder, mlir::Value value);
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template <class T>
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size_t rangeLength(const mlir::iterator_range<T> range) {
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return std::distance(range.begin(), range.end());
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}
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/**
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* Retrieves the earliest operation that uses the given value within the value's
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* block.
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*
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* @param value The value for which to find the earliest user operation.
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* @return The earliest user operation that uses the given value within the
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* current block.
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*/
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mlir::Operation* getEarliestUserWithinBlock(mlir::Value value);
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mlir::SmallVector<mlir::Value> getOpOperandsSortedByUses(mlir::Operation* operation);
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mlir::Value getBestOutputTensorFromOperandsOrAllocate(mlir::RewriterBase& rewriter, mlir::Operation* operation);
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inline mlir::tensor::EmptyOp
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createEmptyTensorFromShaped(mlir::IRRewriter& rewriter, mlir::Location loc, mlir::ShapedType shapedType) {
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return mlir::tensor::EmptyOp::create(rewriter, loc, shapedType.getShape(), shapedType.getElementType());
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}
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} // namespace onnx_mlir
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