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//===- IndexingUtils.cpp - Helpers related to index computations ----------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
#include "mlir/Dialect/Utils/IndexingUtils.h"
#include "mlir/IR/AffineExpr.h"
#include "mlir/IR/Builders.h"
#include "mlir/IR/BuiltinAttributes.h"
#include <numeric>
#include <optional>
using namespace mlir;
SmallVector<int64_t> mlir::computeStrides(ArrayRef<int64_t> sizes) {
SmallVector<int64_t> strides(sizes.size(), 1);
for (int64_t r = strides.size() - 2; r >= 0; --r)
strides[r] = strides[r + 1] * sizes[r + 1];
return strides;
}
SmallVector<int64_t> mlir::computeElementwiseMul(ArrayRef<int64_t> v1,
ArrayRef<int64_t> v2) {
SmallVector<int64_t> result;
for (auto it : llvm::zip(v1, v2))
result.push_back(std::get<0>(it) * std::get<1>(it));
return result;
}
std::optional<SmallVector<int64_t>>
mlir::computeShapeRatio(ArrayRef<int64_t> shape, ArrayRef<int64_t> subShape) {
if (shape.size() < subShape.size())
return std::nullopt;
assert(llvm::all_of(shape, [](int64_t s) { return s > 0; }) &&
"shape must be nonnegative");
assert(llvm::all_of(subShape, [](int64_t s) { return s > 0; }) &&
"subShape must be nonnegative");
// Starting from the end, compute the integer divisors.
std::vector<int64_t> result;
result.reserve(shape.size());
for (auto [size, subSize] :
llvm::zip(llvm::reverse(shape), llvm::reverse(subShape))) {
// If integral division does not occur, return and let the caller decide.
if (size % subSize != 0)
return std::nullopt;
result.push_back(size / subSize);
}
// At this point we computed the ratio (in reverse) for the common size.
// Fill with the remaining entries from the shape (still in reverse).
int commonSize = subShape.size();
std::copy(shape.rbegin() + commonSize, shape.rend(),
std::back_inserter(result));
// Reverse again to get it back in the proper order and return.
return SmallVector<int64_t>{result.rbegin(), result.rend()};
}
int64_t mlir::linearize(ArrayRef<int64_t> offsets, ArrayRef<int64_t> basis) {
assert(offsets.size() == basis.size());
int64_t linearIndex = 0;
for (unsigned idx = 0, e = basis.size(); idx < e; ++idx)
linearIndex += offsets[idx] * basis[idx];
return linearIndex;
}
llvm::SmallVector<int64_t> mlir::delinearize(ArrayRef<int64_t> sliceStrides,
int64_t index) {
int64_t rank = sliceStrides.size();
SmallVector<int64_t> vectorOffsets(rank);
for (int64_t r = 0; r < rank; ++r) {
assert(sliceStrides[r] > 0);
vectorOffsets[r] = index / sliceStrides[r];
index %= sliceStrides[r];
}
return vectorOffsets;
}
int64_t mlir::computeMaxLinearIndex(ArrayRef<int64_t> basis) {
if (basis.empty())
return 0;
return std::accumulate(basis.begin(), basis.end(), 1,
std::multiplies<int64_t>());
}
llvm::SmallVector<int64_t>
mlir::invertPermutationVector(ArrayRef<int64_t> permutation) {
SmallVector<int64_t> inversion(permutation.size());
for (const auto &pos : llvm::enumerate(permutation)) {
inversion[pos.value()] = pos.index();
}
return inversion;
}
bool mlir::isPermutationVector(ArrayRef<int64_t> interchange) {
llvm::SmallDenseSet<int64_t, 4> seenVals;
for (auto val : interchange) {
if (seenVals.count(val))
return false;
seenVals.insert(val);
}
return seenVals.size() == interchange.size();
}
llvm::SmallVector<int64_t> mlir::getI64SubArray(ArrayAttr arrayAttr,
unsigned dropFront,
unsigned dropBack) {
assert(arrayAttr.size() > dropFront + dropBack && "Out of bounds");
auto range = arrayAttr.getAsRange<IntegerAttr>();
SmallVector<int64_t> res;
res.reserve(arrayAttr.size() - dropFront - dropBack);
for (auto it = range.begin() + dropFront, eit = range.end() - dropBack;
it != eit; ++it)
res.push_back((*it).getValue().getSExtValue());
return res;
}
mlir::AffineExpr mlir::getLinearAffineExpr(ArrayRef<int64_t> basis,
mlir::Builder &b) {
AffineExpr resultExpr = b.getAffineDimExpr(0);
resultExpr = resultExpr * basis[0];
for (unsigned i = 1; i < basis.size(); i++)
resultExpr = resultExpr + b.getAffineDimExpr(i) * basis[i];
return resultExpr;
}
llvm::SmallVector<mlir::AffineExpr>
mlir::getDelinearizedAffineExpr(mlir::ArrayRef<int64_t> strides, Builder &b) {
AffineExpr resultExpr = b.getAffineDimExpr(0);
int64_t rank = strides.size();
SmallVector<AffineExpr> vectorOffsets(rank);
vectorOffsets[0] = resultExpr.floorDiv(strides[0]);
resultExpr = resultExpr % strides[0];
for (unsigned i = 1; i < rank; i++) {
vectorOffsets[i] = resultExpr.floorDiv(strides[i]);
resultExpr = resultExpr % strides[i];
}
return vectorOffsets;
}
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