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//===- SparseTensorUtils.cpp - Sparse Tensor Utils for MLIR execution -----===//
//
// 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
//
//===----------------------------------------------------------------------===//
//
// This file implements a light-weight runtime support library that is useful
// for sparse tensor manipulations. The functionality provided in this library
// is meant to simplify benchmarking, testing, and debugging MLIR code that
// operates on sparse tensors. The provided functionality is **not** part
// of core MLIR, however.
//
//===----------------------------------------------------------------------===//
#include "mlir/ExecutionEngine/SparseTensorUtils.h"
#ifdef MLIR_CRUNNERUTILS_DEFINE_FUNCTIONS
#include <algorithm>
#include <cassert>
#include <cctype>
#include <cstdio>
#include <cstdlib>
#include <cstring>
#include <fstream>
#include <functional>
#include <iostream>
#include <limits>
#include <numeric>
//===----------------------------------------------------------------------===//
//
// Internal support for storing and reading sparse tensors.
//
// The following memory-resident sparse storage schemes are supported:
//
// (a) A coordinate scheme for temporarily storing and lexicographically
// sorting a sparse tensor by index (SparseTensorCOO).
//
// (b) A "one-size-fits-all" sparse tensor storage scheme defined by
// per-dimension sparse/dense annnotations together with a dimension
// ordering used by MLIR compiler-generated code (SparseTensorStorage).
//
// The following external formats are supported:
//
// (1) Matrix Market Exchange (MME): *.mtx
// https://math.nist.gov/MatrixMarket/formats.html
//
// (2) Formidable Repository of Open Sparse Tensors and Tools (FROSTT): *.tns
// http://frostt.io/tensors/file-formats.html
//
// Two public APIs are supported:
//
// (I) Methods operating on MLIR buffers (memrefs) to interact with sparse
// tensors. These methods should be used exclusively by MLIR
// compiler-generated code.
//
// (II) Methods that accept C-style data structures to interact with sparse
// tensors. These methods can be used by any external runtime that wants
// to interact with MLIR compiler-generated code.
//
// In both cases (I) and (II), the SparseTensorStorage format is externally
// only visible as an opaque pointer.
//
//===----------------------------------------------------------------------===//
namespace {
static constexpr int kColWidth = 1025;
/// A version of `operator*` on `uint64_t` which checks for overflows.
static inline uint64_t checkedMul(uint64_t lhs, uint64_t rhs) {
assert((lhs == 0 || rhs <= std::numeric_limits<uint64_t>::max() / lhs) &&
"Integer overflow");
return lhs * rhs;
}
// This macro helps minimize repetition of this idiom, as well as ensuring
// we have some additional output indicating where the error is coming from.
// (Since `fprintf` doesn't provide a stacktrace, this helps make it easier
// to track down whether an error is coming from our code vs somewhere else
// in MLIR.)
#define FATAL(...) \
do { \
fprintf(stderr, "SparseTensorUtils: " __VA_ARGS__); \
exit(1); \
} while (0)
// TODO: try to unify this with `SparseTensorFile::assertMatchesShape`
// which is used by `openSparseTensorCOO`. It's easy enough to resolve
// the `std::vector` vs pointer mismatch for `dimSizes`; but it's trickier
// to resolve the presence/absence of `perm` (without introducing extra
// overhead), so perhaps the code duplication is unavoidable.
//
/// Asserts that the `dimSizes` (in target-order) under the `perm` (mapping
/// semantic-order to target-order) are a refinement of the desired `shape`
/// (in semantic-order).
///
/// Precondition: `perm` and `shape` must be valid for `rank`.
static inline void
assertPermutedSizesMatchShape(const std::vector<uint64_t> &dimSizes,
uint64_t rank, const uint64_t *perm,
const uint64_t *shape) {
assert(perm && shape);
assert(rank == dimSizes.size() && "Rank mismatch");
for (uint64_t r = 0; r < rank; r++)
assert((shape[r] == 0 || shape[r] == dimSizes[perm[r]]) &&
"Dimension size mismatch");
}
/// A sparse tensor element in coordinate scheme (value and indices).
/// For example, a rank-1 vector element would look like
/// ({i}, a[i])
/// and a rank-5 tensor element like
/// ({i,j,k,l,m}, a[i,j,k,l,m])
/// We use pointer to a shared index pool rather than e.g. a direct
/// vector since that (1) reduces the per-element memory footprint, and
/// (2) centralizes the memory reservation and (re)allocation to one place.
template <typename V>
struct Element final {
Element(uint64_t *ind, V val) : indices(ind), value(val){};
uint64_t *indices; // pointer into shared index pool
V value;
};
/// The type of callback functions which receive an element. We avoid
/// packaging the coordinates and value together as an `Element` object
/// because this helps keep code somewhat cleaner.
template <typename V>
using ElementConsumer =
const std::function<void(const std::vector<uint64_t> &, V)> &;
/// A memory-resident sparse tensor in coordinate scheme (collection of
/// elements). This data structure is used to read a sparse tensor from
/// any external format into memory and sort the elements lexicographically
/// by indices before passing it back to the client (most packed storage
/// formats require the elements to appear in lexicographic index order).
template <typename V>
struct SparseTensorCOO final {
public:
SparseTensorCOO(const std::vector<uint64_t> &dimSizes, uint64_t capacity)
: dimSizes(dimSizes) {
if (capacity) {
elements.reserve(capacity);
indices.reserve(capacity * getRank());
}
}
/// Adds element as indices and value.
void add(const std::vector<uint64_t> &ind, V val) {
assert(!iteratorLocked && "Attempt to add() after startIterator()");
uint64_t *base = indices.data();
uint64_t size = indices.size();
uint64_t rank = getRank();
assert(ind.size() == rank && "Element rank mismatch");
for (uint64_t r = 0; r < rank; r++) {
assert(ind[r] < dimSizes[r] && "Index is too large for the dimension");
indices.push_back(ind[r]);
}
// This base only changes if indices were reallocated. In that case, we
// need to correct all previous pointers into the vector. Note that this
// only happens if we did not set the initial capacity right, and then only
// for every internal vector reallocation (which with the doubling rule
// should only incur an amortized linear overhead).
uint64_t *newBase = indices.data();
if (newBase != base) {
for (uint64_t i = 0, n = elements.size(); i < n; i++)
elements[i].indices = newBase + (elements[i].indices - base);
base = newBase;
}
// Add element as (pointer into shared index pool, value) pair.
elements.emplace_back(base + size, val);
}
/// Sorts elements lexicographically by index.
void sort() {
assert(!iteratorLocked && "Attempt to sort() after startIterator()");
// TODO: we may want to cache an `isSorted` bit, to avoid
// unnecessary/redundant sorting.
uint64_t rank = getRank();
std::sort(elements.begin(), elements.end(),
[rank](const Element<V> &e1, const Element<V> &e2) {
for (uint64_t r = 0; r < rank; r++) {
if (e1.indices[r] == e2.indices[r])
continue;
return e1.indices[r] < e2.indices[r];
}
return false;
});
}
/// Get the rank of the tensor.
uint64_t getRank() const { return dimSizes.size(); }
/// Getter for the dimension-sizes array.
const std::vector<uint64_t> &getDimSizes() const { return dimSizes; }
/// Getter for the elements array.
const std::vector<Element<V>> &getElements() const { return elements; }
/// Switch into iterator mode.
void startIterator() {
iteratorLocked = true;
iteratorPos = 0;
}
/// Get the next element.
const Element<V> *getNext() {
assert(iteratorLocked && "Attempt to getNext() before startIterator()");
if (iteratorPos < elements.size())
return &(elements[iteratorPos++]);
iteratorLocked = false;
return nullptr;
}
/// Factory method. Permutes the original dimensions according to
/// the given ordering and expects subsequent add() calls to honor
/// that same ordering for the given indices. The result is a
/// fully permuted coordinate scheme.
///
/// Precondition: `dimSizes` and `perm` must be valid for `rank`.
static SparseTensorCOO<V> *newSparseTensorCOO(uint64_t rank,
const uint64_t *dimSizes,
const uint64_t *perm,
uint64_t capacity = 0) {
std::vector<uint64_t> permsz(rank);
for (uint64_t r = 0; r < rank; r++) {
assert(dimSizes[r] > 0 && "Dimension size zero has trivial storage");
permsz[perm[r]] = dimSizes[r];
}
return new SparseTensorCOO<V>(permsz, capacity);
}
private:
const std::vector<uint64_t> dimSizes; // per-dimension sizes
std::vector<Element<V>> elements; // all COO elements
std::vector<uint64_t> indices; // shared index pool
bool iteratorLocked = false;
unsigned iteratorPos = 0;
};
// Forward.
template <typename V>
class SparseTensorEnumeratorBase;
// Helper macro for generating error messages when some
// `SparseTensorStorage<P,I,V>` is cast to `SparseTensorStorageBase`
// and then the wrong "partial method specialization" is called.
#define FATAL_PIV(NAME) FATAL("<P,I,V> type mismatch for: " #NAME);
/// Abstract base class for `SparseTensorStorage<P,I,V>`. This class
/// takes responsibility for all the `<P,I,V>`-independent aspects
/// of the tensor (e.g., shape, sparsity, permutation). In addition,
/// we use function overloading to implement "partial" method
/// specialization, which the C-API relies on to catch type errors
/// arising from our use of opaque pointers.
class SparseTensorStorageBase {
public:
/// Constructs a new storage object. The `perm` maps the tensor's
/// semantic-ordering of dimensions to this object's storage-order.
/// The `dimSizes` and `sparsity` arrays are already in storage-order.
///
/// Precondition: `perm` and `sparsity` must be valid for `dimSizes.size()`.
SparseTensorStorageBase(const std::vector<uint64_t> &dimSizes,
const uint64_t *perm, const DimLevelType *sparsity)
: dimSizes(dimSizes), rev(getRank()),
dimTypes(sparsity, sparsity + getRank()) {
assert(perm && sparsity);
const uint64_t rank = getRank();
// Validate parameters.
assert(rank > 0 && "Trivial shape is unsupported");
for (uint64_t r = 0; r < rank; r++) {
assert(dimSizes[r] > 0 && "Dimension size zero has trivial storage");
assert((dimTypes[r] == DimLevelType::kDense ||
dimTypes[r] == DimLevelType::kCompressed) &&
"Unsupported DimLevelType");
}
// Construct the "reverse" (i.e., inverse) permutation.
for (uint64_t r = 0; r < rank; r++)
rev[perm[r]] = r;
}
virtual ~SparseTensorStorageBase() = default;
/// Get the rank of the tensor.
uint64_t getRank() const { return dimSizes.size(); }
/// Getter for the dimension-sizes array, in storage-order.
const std::vector<uint64_t> &getDimSizes() const { return dimSizes; }
/// Safely lookup the size of the given (storage-order) dimension.
uint64_t getDimSize(uint64_t d) const {
assert(d < getRank());
return dimSizes[d];
}
/// Getter for the "reverse" permutation, which maps this object's
/// storage-order to the tensor's semantic-order.
const std::vector<uint64_t> &getRev() const { return rev; }
/// Getter for the dimension-types array, in storage-order.
const std::vector<DimLevelType> &getDimTypes() const { return dimTypes; }
/// Safely check if the (storage-order) dimension uses compressed storage.
bool isCompressedDim(uint64_t d) const {
assert(d < getRank());
return (dimTypes[d] == DimLevelType::kCompressed);
}
/// Allocate a new enumerator.
#define DECL_NEWENUMERATOR(VNAME, V) \
virtual void newEnumerator(SparseTensorEnumeratorBase<V> **, uint64_t, \
const uint64_t *) const { \
FATAL_PIV("newEnumerator" #VNAME); \
}
FOREVERY_V(DECL_NEWENUMERATOR)
#undef DECL_NEWENUMERATOR
/// Overhead storage.
#define DECL_GETPOINTERS(PNAME, P) \
virtual void getPointers(std::vector<P> **, uint64_t) { \
FATAL_PIV("getPointers" #PNAME); \
}
FOREVERY_FIXED_O(DECL_GETPOINTERS)
#undef DECL_GETPOINTERS
#define DECL_GETINDICES(INAME, I) \
virtual void getIndices(std::vector<I> **, uint64_t) { \
FATAL_PIV("getIndices" #INAME); \
}
FOREVERY_FIXED_O(DECL_GETINDICES)
#undef DECL_GETINDICES
/// Primary storage.
#define DECL_GETVALUES(VNAME, V) \
virtual void getValues(std::vector<V> **) { FATAL_PIV("getValues" #VNAME); }
FOREVERY_V(DECL_GETVALUES)
#undef DECL_GETVALUES
/// Element-wise insertion in lexicographic index order.
#define DECL_LEXINSERT(VNAME, V) \
virtual void lexInsert(const uint64_t *, V) { FATAL_PIV("lexInsert" #VNAME); }
FOREVERY_V(DECL_LEXINSERT)
#undef DECL_LEXINSERT
/// Expanded insertion.
#define DECL_EXPINSERT(VNAME, V) \
virtual void expInsert(uint64_t *, V *, bool *, uint64_t *, uint64_t) { \
FATAL_PIV("expInsert" #VNAME); \
}
FOREVERY_V(DECL_EXPINSERT)
#undef DECL_EXPINSERT
/// Finishes insertion.
virtual void endInsert() = 0;
protected:
// Since this class is virtual, we must disallow public copying in
// order to avoid "slicing". Since this class has data members,
// that means making copying protected.
// <https://github.com/isocpp/CppCoreGuidelines/blob/master/CppCoreGuidelines.md#Rc-copy-virtual>
SparseTensorStorageBase(const SparseTensorStorageBase &) = default;
// Copy-assignment would be implicitly deleted (because `dimSizes`
// is const), so we explicitly delete it for clarity.
SparseTensorStorageBase &operator=(const SparseTensorStorageBase &) = delete;
private:
const std::vector<uint64_t> dimSizes;
std::vector<uint64_t> rev;
const std::vector<DimLevelType> dimTypes;
};
#undef FATAL_PIV
// Forward.
template <typename P, typename I, typename V>
class SparseTensorEnumerator;
/// A memory-resident sparse tensor using a storage scheme based on
/// per-dimension sparse/dense annotations. This data structure provides a
/// bufferized form of a sparse tensor type. In contrast to generating setup
/// methods for each differently annotated sparse tensor, this method provides
/// a convenient "one-size-fits-all" solution that simply takes an input tensor
/// and annotations to implement all required setup in a general manner.
template <typename P, typename I, typename V>
class SparseTensorStorage final : public SparseTensorStorageBase {
/// Private constructor to share code between the other constructors.
/// Beware that the object is not necessarily guaranteed to be in a
/// valid state after this constructor alone; e.g., `isCompressedDim(d)`
/// doesn't entail `!(pointers[d].empty())`.
///
/// Precondition: `perm` and `sparsity` must be valid for `dimSizes.size()`.
SparseTensorStorage(const std::vector<uint64_t> &dimSizes,
const uint64_t *perm, const DimLevelType *sparsity)
: SparseTensorStorageBase(dimSizes, perm, sparsity), pointers(getRank()),
indices(getRank()), idx(getRank()) {}
public:
/// Constructs a sparse tensor storage scheme with the given dimensions,
/// permutation, and per-dimension dense/sparse annotations, using
/// the coordinate scheme tensor for the initial contents if provided.
///
/// Precondition: `perm` and `sparsity` must be valid for `dimSizes.size()`.
SparseTensorStorage(const std::vector<uint64_t> &dimSizes,
const uint64_t *perm, const DimLevelType *sparsity,
SparseTensorCOO<V> *coo)
: SparseTensorStorage(dimSizes, perm, sparsity) {
// Provide hints on capacity of pointers and indices.
// TODO: needs much fine-tuning based on actual sparsity; currently
// we reserve pointer/index space based on all previous dense
// dimensions, which works well up to first sparse dim; but
// we should really use nnz and dense/sparse distribution.
bool allDense = true;
uint64_t sz = 1;
for (uint64_t r = 0, rank = getRank(); r < rank; r++) {
if (isCompressedDim(r)) {
// TODO: Take a parameter between 1 and `dimSizes[r]`, and multiply
// `sz` by that before reserving. (For now we just use 1.)
pointers[r].reserve(sz + 1);
pointers[r].push_back(0);
indices[r].reserve(sz);
sz = 1;
allDense = false;
} else { // Dense dimension.
sz = checkedMul(sz, getDimSizes()[r]);
}
}
// Then assign contents from coordinate scheme tensor if provided.
if (coo) {
// Ensure both preconditions of `fromCOO`.
assert(coo->getDimSizes() == getDimSizes() && "Tensor size mismatch");
coo->sort();
// Now actually insert the `elements`.
const std::vector<Element<V>> &elements = coo->getElements();
uint64_t nnz = elements.size();
values.reserve(nnz);
fromCOO(elements, 0, nnz, 0);
} else if (allDense) {
values.resize(sz, 0);
}
}
/// Constructs a sparse tensor storage scheme with the given dimensions,
/// permutation, and per-dimension dense/sparse annotations, using
/// the given sparse tensor for the initial contents.
///
/// Preconditions:
/// * `perm` and `sparsity` must be valid for `dimSizes.size()`.
/// * The `tensor` must have the same value type `V`.
SparseTensorStorage(const std::vector<uint64_t> &dimSizes,
const uint64_t *perm, const DimLevelType *sparsity,
const SparseTensorStorageBase &tensor);
~SparseTensorStorage() final = default;
/// Partially specialize these getter methods based on template types.
void getPointers(std::vector<P> **out, uint64_t d) final {
assert(d < getRank());
*out = &pointers[d];
}
void getIndices(std::vector<I> **out, uint64_t d) final {
assert(d < getRank());
*out = &indices[d];
}
void getValues(std::vector<V> **out) final { *out = &values; }
/// Partially specialize lexicographical insertions based on template types.
void lexInsert(const uint64_t *cursor, V val) final {
// First, wrap up pending insertion path.
uint64_t diff = 0;
uint64_t top = 0;
if (!values.empty()) {
diff = lexDiff(cursor);
endPath(diff + 1);
top = idx[diff] + 1;
}
// Then continue with insertion path.
insPath(cursor, diff, top, val);
}
/// Partially specialize expanded insertions based on template types.
/// Note that this method resets the values/filled-switch array back
/// to all-zero/false while only iterating over the nonzero elements.
void expInsert(uint64_t *cursor, V *values, bool *filled, uint64_t *added,
uint64_t count) final {
if (count == 0)
return;
// Sort.
std::sort(added, added + count);
// Restore insertion path for first insert.
const uint64_t lastDim = getRank() - 1;
uint64_t index = added[0];
cursor[lastDim] = index;
lexInsert(cursor, values[index]);
assert(filled[index]);
values[index] = 0;
filled[index] = false;
// Subsequent insertions are quick.
for (uint64_t i = 1; i < count; i++) {
assert(index < added[i] && "non-lexicographic insertion");
index = added[i];
cursor[lastDim] = index;
insPath(cursor, lastDim, added[i - 1] + 1, values[index]);
assert(filled[index]);
values[index] = 0;
filled[index] = false;
}
}
/// Finalizes lexicographic insertions.
void endInsert() final {
if (values.empty())
finalizeSegment(0);
else
endPath(0);
}
void newEnumerator(SparseTensorEnumeratorBase<V> **out, uint64_t rank,
const uint64_t *perm) const final {
*out = new SparseTensorEnumerator<P, I, V>(*this, rank, perm);
}
/// Returns this sparse tensor storage scheme as a new memory-resident
/// sparse tensor in coordinate scheme with the given dimension order.
///
/// Precondition: `perm` must be valid for `getRank()`.
SparseTensorCOO<V> *toCOO(const uint64_t *perm) const {
SparseTensorEnumeratorBase<V> *enumerator;
newEnumerator(&enumerator, getRank(), perm);
SparseTensorCOO<V> *coo =
new SparseTensorCOO<V>(enumerator->permutedSizes(), values.size());
enumerator->forallElements([&coo](const std::vector<uint64_t> &ind, V val) {
coo->add(ind, val);
});
// TODO: This assertion assumes there are no stored zeros,
// or if there are then that we don't filter them out.
// Cf., <https://github.com/llvm/llvm-project/issues/54179>
assert(coo->getElements().size() == values.size());
delete enumerator;
return coo;
}
/// Factory method. Constructs a sparse tensor storage scheme with the given
/// dimensions, permutation, and per-dimension dense/sparse annotations,
/// using the coordinate scheme tensor for the initial contents if provided.
/// In the latter case, the coordinate scheme must respect the same
/// permutation as is desired for the new sparse tensor storage.
///
/// Precondition: `shape`, `perm`, and `sparsity` must be valid for `rank`.
static SparseTensorStorage<P, I, V> *
newSparseTensor(uint64_t rank, const uint64_t *shape, const uint64_t *perm,
const DimLevelType *sparsity, SparseTensorCOO<V> *coo) {
SparseTensorStorage<P, I, V> *n = nullptr;
if (coo) {
const auto &coosz = coo->getDimSizes();
assertPermutedSizesMatchShape(coosz, rank, perm, shape);
n = new SparseTensorStorage<P, I, V>(coosz, perm, sparsity, coo);
} else {
std::vector<uint64_t> permsz(rank);
for (uint64_t r = 0; r < rank; r++) {
assert(shape[r] > 0 && "Dimension size zero has trivial storage");
permsz[perm[r]] = shape[r];
}
// We pass the null `coo` to ensure we select the intended constructor.
n = new SparseTensorStorage<P, I, V>(permsz, perm, sparsity, coo);
}
return n;
}
/// Factory method. Constructs a sparse tensor storage scheme with
/// the given dimensions, permutation, and per-dimension dense/sparse
/// annotations, using the sparse tensor for the initial contents.
///
/// Preconditions:
/// * `shape`, `perm`, and `sparsity` must be valid for `rank`.
/// * The `tensor` must have the same value type `V`.
static SparseTensorStorage<P, I, V> *
newSparseTensor(uint64_t rank, const uint64_t *shape, const uint64_t *perm,
const DimLevelType *sparsity,
const SparseTensorStorageBase *source) {
assert(source && "Got nullptr for source");
SparseTensorEnumeratorBase<V> *enumerator;
source->newEnumerator(&enumerator, rank, perm);
const auto &permsz = enumerator->permutedSizes();
assertPermutedSizesMatchShape(permsz, rank, perm, shape);
auto *tensor =
new SparseTensorStorage<P, I, V>(permsz, perm, sparsity, *source);
delete enumerator;
return tensor;
}
private:
/// Appends an arbitrary new position to `pointers[d]`. This method
/// checks that `pos` is representable in the `P` type; however, it
/// does not check that `pos` is semantically valid (i.e., larger than
/// the previous position and smaller than `indices[d].capacity()`).
void appendPointer(uint64_t d, uint64_t pos, uint64_t count = 1) {
assert(isCompressedDim(d));
assert(pos <= std::numeric_limits<P>::max() &&
"Pointer value is too large for the P-type");
pointers[d].insert(pointers[d].end(), count, static_cast<P>(pos));
}
/// Appends index `i` to dimension `d`, in the semantically general
/// sense. For non-dense dimensions, that means appending to the
/// `indices[d]` array, checking that `i` is representable in the `I`
/// type; however, we do not verify other semantic requirements (e.g.,
/// that `i` is in bounds for `dimSizes[d]`, and not previously occurring
/// in the same segment). For dense dimensions, this method instead
/// appends the appropriate number of zeros to the `values` array,
/// where `full` is the number of "entries" already written to `values`
/// for this segment (aka one after the highest index previously appended).
void appendIndex(uint64_t d, uint64_t full, uint64_t i) {
if (isCompressedDim(d)) {
assert(i <= std::numeric_limits<I>::max() &&
"Index value is too large for the I-type");
indices[d].push_back(static_cast<I>(i));
} else { // Dense dimension.
assert(i >= full && "Index was already filled");
if (i == full)
return; // Short-circuit, since it'll be a nop.
if (d + 1 == getRank())
values.insert(values.end(), i - full, 0);
else
finalizeSegment(d + 1, 0, i - full);
}
}
/// Writes the given coordinate to `indices[d][pos]`. This method
/// checks that `i` is representable in the `I` type; however, it
/// does not check that `i` is semantically valid (i.e., in bounds
/// for `dimSizes[d]` and not elsewhere occurring in the same segment).
void writeIndex(uint64_t d, uint64_t pos, uint64_t i) {
assert(isCompressedDim(d));
// Subscript assignment to `std::vector` requires that the `pos`-th
// entry has been initialized; thus we must be sure to check `size()`
// here, instead of `capacity()` as would be ideal.
assert(pos < indices[d].size() && "Index position is out of bounds");
assert(i <= std::numeric_limits<I>::max() &&
"Index value is too large for the I-type");
indices[d][pos] = static_cast<I>(i);
}
/// Computes the assembled-size associated with the `d`-th dimension,
/// given the assembled-size associated with the `(d-1)`-th dimension.
/// "Assembled-sizes" correspond to the (nominal) sizes of overhead
/// storage, as opposed to "dimension-sizes" which are the cardinality
/// of coordinates for that dimension.
///
/// Precondition: the `pointers[d]` array must be fully initialized
/// before calling this method.
uint64_t assembledSize(uint64_t parentSz, uint64_t d) const {
if (isCompressedDim(d))
return pointers[d][parentSz];
// else if dense:
return parentSz * getDimSizes()[d];
}
/// Initializes sparse tensor storage scheme from a memory-resident sparse
/// tensor in coordinate scheme. This method prepares the pointers and
/// indices arrays under the given per-dimension dense/sparse annotations.
///
/// Preconditions:
/// (1) the `elements` must be lexicographically sorted.
/// (2) the indices of every element are valid for `dimSizes` (equal rank
/// and pointwise less-than).
void fromCOO(const std::vector<Element<V>> &elements, uint64_t lo,
uint64_t hi, uint64_t d) {
uint64_t rank = getRank();
assert(d <= rank && hi <= elements.size());
// Once dimensions are exhausted, insert the numerical values.
if (d == rank) {
assert(lo < hi);
values.push_back(elements[lo].value);
return;
}
// Visit all elements in this interval.
uint64_t full = 0;
while (lo < hi) { // If `hi` is unchanged, then `lo < elements.size()`.
// Find segment in interval with same index elements in this dimension.
uint64_t i = elements[lo].indices[d];
uint64_t seg = lo + 1;
while (seg < hi && elements[seg].indices[d] == i)
seg++;
// Handle segment in interval for sparse or dense dimension.
appendIndex(d, full, i);
full = i + 1;
fromCOO(elements, lo, seg, d + 1);
// And move on to next segment in interval.
lo = seg;
}
// Finalize the sparse pointer structure at this dimension.
finalizeSegment(d, full);
}
/// Finalize the sparse pointer structure at this dimension.
void finalizeSegment(uint64_t d, uint64_t full = 0, uint64_t count = 1) {
if (count == 0)
return; // Short-circuit, since it'll be a nop.
if (isCompressedDim(d)) {
appendPointer(d, indices[d].size(), count);
} else { // Dense dimension.
const uint64_t sz = getDimSizes()[d];
assert(sz >= full && "Segment is overfull");
count = checkedMul(count, sz - full);
// For dense storage we must enumerate all the remaining coordinates
// in this dimension (i.e., coordinates after the last non-zero
// element), and either fill in their zero values or else recurse
// to finalize some deeper dimension.
if (d + 1 == getRank())
values.insert(values.end(), count, 0);
else
finalizeSegment(d + 1, 0, count);
}
}
/// Wraps up a single insertion path, inner to outer.
void endPath(uint64_t diff) {
uint64_t rank = getRank();
assert(diff <= rank);
for (uint64_t i = 0; i < rank - diff; i++) {
const uint64_t d = rank - i - 1;
finalizeSegment(d, idx[d] + 1);
}
}
/// Continues a single insertion path, outer to inner.
void insPath(const uint64_t *cursor, uint64_t diff, uint64_t top, V val) {
uint64_t rank = getRank();
assert(diff < rank);
for (uint64_t d = diff; d < rank; d++) {
uint64_t i = cursor[d];
appendIndex(d, top, i);
top = 0;
idx[d] = i;
}
values.push_back(val);
}
/// Finds the lexicographic differing dimension.
uint64_t lexDiff(const uint64_t *cursor) const {
for (uint64_t r = 0, rank = getRank(); r < rank; r++)
if (cursor[r] > idx[r])
return r;
else
assert(cursor[r] == idx[r] && "non-lexicographic insertion");
assert(0 && "duplication insertion");
return -1u;
}
// Allow `SparseTensorEnumerator` to access the data-members (to avoid
// the cost of virtual-function dispatch in inner loops), without
// making them public to other client code.
friend class SparseTensorEnumerator<P, I, V>;
std::vector<std::vector<P>> pointers;
std::vector<std::vector<I>> indices;
std::vector<V> values;
std::vector<uint64_t> idx; // index cursor for lexicographic insertion.
};
/// A (higher-order) function object for enumerating the elements of some
/// `SparseTensorStorage` under a permutation. That is, the `forallElements`
/// method encapsulates the loop-nest for enumerating the elements of
/// the source tensor (in whatever order is best for the source tensor),
/// and applies a permutation to the coordinates/indices before handing
/// each element to the callback. A single enumerator object can be
/// freely reused for several calls to `forallElements`, just so long
/// as each call is sequential with respect to one another.
///
/// N.B., this class stores a reference to the `SparseTensorStorageBase`
/// passed to the constructor; thus, objects of this class must not
/// outlive the sparse tensor they depend on.
///
/// Design Note: The reason we define this class instead of simply using
/// `SparseTensorEnumerator<P,I,V>` is because we need to hide/generalize
/// the `<P,I>` template parameters from MLIR client code (to simplify the
/// type parameters used for direct sparse-to-sparse conversion). And the
/// reason we define the `SparseTensorEnumerator<P,I,V>` subclasses rather
/// than simply using this class, is to avoid the cost of virtual-method
/// dispatch within the loop-nest.
template <typename V>
class SparseTensorEnumeratorBase {
public:
/// Constructs an enumerator with the given permutation for mapping
/// the semantic-ordering of dimensions to the desired target-ordering.
///
/// Preconditions:
/// * the `tensor` must have the same `V` value type.
/// * `perm` must be valid for `rank`.
SparseTensorEnumeratorBase(const SparseTensorStorageBase &tensor,
uint64_t rank, const uint64_t *perm)
: src(tensor), permsz(src.getRev().size()), reord(getRank()),
cursor(getRank()) {
assert(perm && "Received nullptr for permutation");
assert(rank == getRank() && "Permutation rank mismatch");
const auto &rev = src.getRev(); // source-order -> semantic-order
const auto &dimSizes = src.getDimSizes(); // in source storage-order
for (uint64_t s = 0; s < rank; s++) { // `s` source storage-order
uint64_t t = perm[rev[s]]; // `t` target-order
reord[s] = t;
permsz[t] = dimSizes[s];
}
}
virtual ~SparseTensorEnumeratorBase() = default;
// We disallow copying to help avoid leaking the `src` reference.
// (In addition to avoiding the problem of slicing.)
SparseTensorEnumeratorBase(const SparseTensorEnumeratorBase &) = delete;
SparseTensorEnumeratorBase &
operator=(const SparseTensorEnumeratorBase &) = delete;
/// Returns the source/target tensor's rank. (The source-rank and
/// target-rank are always equal since we only support permutations.
/// Though once we add support for other dimension mappings, this
/// method will have to be split in two.)
uint64_t getRank() const { return permsz.size(); }
/// Returns the target tensor's dimension sizes.
const std::vector<uint64_t> &permutedSizes() const { return permsz; }
/// Enumerates all elements of the source tensor, permutes their
/// indices, and passes the permuted element to the callback.
/// The callback must not store the cursor reference directly,
/// since this function reuses the storage. Instead, the callback
/// must copy it if they want to keep it.
virtual void forallElements(ElementConsumer<V> yield) = 0;
protected:
const SparseTensorStorageBase &src;
std::vector<uint64_t> permsz; // in target order.
std::vector<uint64_t> reord; // source storage-order -> target order.
std::vector<uint64_t> cursor; // in target order.
};
template <typename P, typename I, typename V>
class SparseTensorEnumerator final : public SparseTensorEnumeratorBase<V> {
using Base = SparseTensorEnumeratorBase<V>;
public:
/// Constructs an enumerator with the given permutation for mapping
/// the semantic-ordering of dimensions to the desired target-ordering.
///
/// Precondition: `perm` must be valid for `rank`.
SparseTensorEnumerator(const SparseTensorStorage<P, I, V> &tensor,
uint64_t rank, const uint64_t *perm)
: Base(tensor, rank, perm) {}
~SparseTensorEnumerator() final = default;
void forallElements(ElementConsumer<V> yield) final {
forallElements(yield, 0, 0);
}
private:
/// The recursive component of the public `forallElements`.
void forallElements(ElementConsumer<V> yield, uint64_t parentPos,
uint64_t d) {
// Recover the `<P,I,V>` type parameters of `src`.
const auto &src =
static_cast<const SparseTensorStorage<P, I, V> &>(this->src);
if (d == Base::getRank()) {
assert(parentPos < src.values.size() &&
"Value position is out of bounds");
// TODO: <https://github.com/llvm/llvm-project/issues/54179>
yield(this->cursor, src.values[parentPos]);
} else if (src.isCompressedDim(d)) {
// Look up the bounds of the `d`-level segment determined by the
// `d-1`-level position `parentPos`.
const std::vector<P> &pointersD = src.pointers[d];
assert(parentPos + 1 < pointersD.size() &&
"Parent pointer position is out of bounds");
const uint64_t pstart = static_cast<uint64_t>(pointersD[parentPos]);
const uint64_t pstop = static_cast<uint64_t>(pointersD[parentPos + 1]);
// Loop-invariant code for looking up the `d`-level coordinates/indices.
const std::vector<I> &indicesD = src.indices[d];
assert(pstop <= indicesD.size() && "Index position is out of bounds");
uint64_t &cursorReordD = this->cursor[this->reord[d]];
for (uint64_t pos = pstart; pos < pstop; pos++) {
cursorReordD = static_cast<uint64_t>(indicesD[pos]);
forallElements(yield, pos, d + 1);
}
} else { // Dense dimension.
const uint64_t sz = src.getDimSizes()[d];
const uint64_t pstart = parentPos * sz;
uint64_t &cursorReordD = this->cursor[this->reord[d]];
for (uint64_t i = 0; i < sz; i++) {
cursorReordD = i;
forallElements(yield, pstart + i, d + 1);
}
}
}
};
/// Statistics regarding the number of nonzero subtensors in
/// a source tensor, for direct sparse=>sparse conversion a la
/// <https://arxiv.org/abs/2001.02609>.
///
/// N.B., this class stores references to the parameters passed to
/// the constructor; thus, objects of this class must not outlive
/// those parameters.
class SparseTensorNNZ final {
public:
/// Allocate the statistics structure for the desired sizes and
/// sparsity (in the target tensor's storage-order). This constructor
/// does not actually populate the statistics, however; for that see
/// `initialize`.
///
/// Precondition: `dimSizes` must not contain zeros.
SparseTensorNNZ(const std::vector<uint64_t> &dimSizes,
const std::vector<DimLevelType> &sparsity)
: dimSizes(dimSizes), dimTypes(sparsity), nnz(getRank()) {
assert(dimSizes.size() == dimTypes.size() && "Rank mismatch");
bool uncompressed = true;
(void)uncompressed;
uint64_t sz = 1; // the product of all `dimSizes` strictly less than `r`.
for (uint64_t rank = getRank(), r = 0; r < rank; r++) {
switch (dimTypes[r]) {
case DimLevelType::kCompressed:
assert(uncompressed &&
"Multiple compressed layers not currently supported");
uncompressed = false;
nnz[r].resize(sz, 0); // Both allocate and zero-initialize.
break;
case DimLevelType::kDense:
assert(uncompressed &&
"Dense after compressed not currently supported");
break;
case DimLevelType::kSingleton:
// Singleton after Compressed causes no problems for allocating
// `nnz` nor for the yieldPos loop. This remains true even
// when adding support for multiple compressed dimensions or
// for dense-after-compressed.
break;
}
sz = checkedMul(sz, dimSizes[r]);
}
}
// We disallow copying to help avoid leaking the stored references.
SparseTensorNNZ(const SparseTensorNNZ &) = delete;
SparseTensorNNZ &operator=(const SparseTensorNNZ &) = delete;
/// Returns the rank of the target tensor.
uint64_t getRank() const { return dimSizes.size(); }
/// Enumerate the source tensor to fill in the statistics. The
/// enumerator should already incorporate the permutation (from
/// semantic-order to the target storage-order).
template <typename V>
void initialize(SparseTensorEnumeratorBase<V> &enumerator) {
assert(enumerator.getRank() == getRank() && "Tensor rank mismatch");
assert(enumerator.permutedSizes() == dimSizes && "Tensor size mismatch");
enumerator.forallElements(
[this](const std::vector<uint64_t> &ind, V) { add(ind); });
}
/// The type of callback functions which receive an nnz-statistic.
using NNZConsumer = const std::function<void(uint64_t)> &;
/// Lexicographically enumerates all indicies for dimensions strictly
/// less than `stopDim`, and passes their nnz statistic to the callback.
/// Since our use-case only requires the statistic not the coordinates
/// themselves, we do not bother to construct those coordinates.
void forallIndices(uint64_t stopDim, NNZConsumer yield) const {
assert(stopDim < getRank() && "Stopping-dimension is out of bounds");
assert(dimTypes[stopDim] == DimLevelType::kCompressed &&
"Cannot look up non-compressed dimensions");
forallIndices(yield, stopDim, 0, 0);
}
private:
/// Adds a new element (i.e., increment its statistics). We use
/// a method rather than inlining into the lambda in `initialize`,
/// to avoid spurious templating over `V`. And this method is private
/// to avoid needing to re-assert validity of `ind` (which is guaranteed
/// by `forallElements`).
void add(const std::vector<uint64_t> &ind) {
uint64_t parentPos = 0;
for (uint64_t rank = getRank(), r = 0; r < rank; r++) {
if (dimTypes[r] == DimLevelType::kCompressed)
nnz[r][parentPos]++;
parentPos = parentPos * dimSizes[r] + ind[r];
}
}
/// Recursive component of the public `forallIndices`.
void forallIndices(NNZConsumer yield, uint64_t stopDim, uint64_t parentPos,
uint64_t d) const {
assert(d <= stopDim);
if (d == stopDim) {
assert(parentPos < nnz[d].size() && "Cursor is out of range");
yield(nnz[d][parentPos]);
} else {
const uint64_t sz = dimSizes[d];
const uint64_t pstart = parentPos * sz;
for (uint64_t i = 0; i < sz; i++)
forallIndices(yield, stopDim, pstart + i, d + 1);
}
}
// All of these are in the target storage-order.
const std::vector<uint64_t> &dimSizes;
const std::vector<DimLevelType> &dimTypes;
std::vector<std::vector<uint64_t>> nnz;
};
template <typename P, typename I, typename V>
SparseTensorStorage<P, I, V>::SparseTensorStorage(
const std::vector<uint64_t> &dimSizes, const uint64_t *perm,
const DimLevelType *sparsity, const SparseTensorStorageBase &tensor)
: SparseTensorStorage(dimSizes, perm, sparsity) {
SparseTensorEnumeratorBase<V> *enumerator;
tensor.newEnumerator(&enumerator, getRank(), perm);
{
// Initialize the statistics structure.
SparseTensorNNZ nnz(getDimSizes(), getDimTypes());
nnz.initialize(*enumerator);
// Initialize "pointers" overhead (and allocate "indices", "values").
uint64_t parentSz = 1; // assembled-size (not dimension-size) of `r-1`.
for (uint64_t rank = getRank(), r = 0; r < rank; r++) {
if (isCompressedDim(r)) {
pointers[r].reserve(parentSz + 1);
pointers[r].push_back(0);
uint64_t currentPos = 0;
nnz.forallIndices(r, [this, &currentPos, r](uint64_t n) {
currentPos += n;
appendPointer(r, currentPos);
});
assert(pointers[r].size() == parentSz + 1 &&
"Final pointers size doesn't match allocated size");
// That assertion entails `assembledSize(parentSz, r)`
// is now in a valid state. That is, `pointers[r][parentSz]`
// equals the present value of `currentPos`, which is the
// correct assembled-size for `indices[r]`.
}
// Update assembled-size for the next iteration.
parentSz = assembledSize(parentSz, r);
// Ideally we need only `indices[r].reserve(parentSz)`, however
// the `std::vector` implementation forces us to initialize it too.
// That is, in the yieldPos loop we need random-access assignment
// to `indices[r]`; however, `std::vector`'s subscript-assignment
// only allows assigning to already-initialized positions.
if (isCompressedDim(r))
indices[r].resize(parentSz, 0);
}
values.resize(parentSz, 0); // Both allocate and zero-initialize.
}
// The yieldPos loop
enumerator->forallElements([this](const std::vector<uint64_t> &ind, V val) {
uint64_t parentSz = 1, parentPos = 0;
for (uint64_t rank = getRank(), r = 0; r < rank; r++) {
if (isCompressedDim(r)) {
// If `parentPos == parentSz` then it's valid as an array-lookup;
// however, it's semantically invalid here since that entry
// does not represent a segment of `indices[r]`. Moreover, that
// entry must be immutable for `assembledSize` to remain valid.
assert(parentPos < parentSz && "Pointers position is out of bounds");
const uint64_t currentPos = pointers[r][parentPos];
// This increment won't overflow the `P` type, since it can't
// exceed the original value of `pointers[r][parentPos+1]`
// which was already verified to be within bounds for `P`
// when it was written to the array.
pointers[r][parentPos]++;
writeIndex(r, currentPos, ind[r]);
parentPos = currentPos;
} else { // Dense dimension.
parentPos = parentPos * getDimSizes()[r] + ind[r];
}
parentSz = assembledSize(parentSz, r);
}
assert(parentPos < values.size() && "Value position is out of bounds");
values[parentPos] = val;
});
// No longer need the enumerator, so we'll delete it ASAP.
delete enumerator;
// The finalizeYieldPos loop
for (uint64_t parentSz = 1, rank = getRank(), r = 0; r < rank; r++) {
if (isCompressedDim(r)) {
assert(parentSz == pointers[r].size() - 1 &&
"Actual pointers size doesn't match the expected size");
// Can't check all of them, but at least we can check the last one.
assert(pointers[r][parentSz - 1] == pointers[r][parentSz] &&
"Pointers got corrupted");
// TODO: optimize this by using `memmove` or similar.
for (uint64_t n = 0; n < parentSz; n++) {
const uint64_t parentPos = parentSz - n;
pointers[r][parentPos] = pointers[r][parentPos - 1];
}
pointers[r][0] = 0;
}
parentSz = assembledSize(parentSz, r);
}
}
/// Helper to convert string to lower case.
static char *toLower(char *token) {
for (char *c = token; *c; c++)
*c = tolower(*c);
return token;
}
/// This class abstracts over the information stored in file headers,
/// as well as providing the buffers and methods for parsing those headers.
class SparseTensorFile final {
public:
enum class ValueKind {
kInvalid = 0,
kPattern = 1,
kReal = 2,
kInteger = 3,
kComplex = 4,
kUndefined = 5
};
explicit SparseTensorFile(char *filename) : filename(filename) {
assert(filename && "Received nullptr for filename");
}
// Disallows copying, to avoid duplicating the `file` pointer.
SparseTensorFile(const SparseTensorFile &) = delete;
SparseTensorFile &operator=(const SparseTensorFile &) = delete;
// This dtor tries to avoid leaking the `file`. (Though it's better
// to call `closeFile` explicitly when possible, since there are
// circumstances where dtors are not called reliably.)
~SparseTensorFile() { closeFile(); }
/// Opens the file for reading.
void openFile() {
if (file)
FATAL("Already opened file %s\n", filename);
file = fopen(filename, "r");
if (!file)
FATAL("Cannot find file %s\n", filename);
}
/// Closes the file.
void closeFile() {
if (file) {
fclose(file);
file = nullptr;
}
}
// TODO(wrengr/bixia): figure out how to reorganize the element-parsing
// loop of `openSparseTensorCOO` into methods of this class, so we can
// avoid leaking access to the `line` pointer (both for general hygiene
// and because we can't mark it const due to the second argument of
// `strtoul`/`strtoud` being `char * *restrict` rather than
// `char const* *restrict`).
//
/// Attempts to read a line from the file.
char *readLine() {
if (fgets(line, kColWidth, file))
return line;
FATAL("Cannot read next line of %s\n", filename);
}
/// Reads and parses the file's header.
void readHeader() {
assert(file && "Attempt to readHeader() before openFile()");
if (strstr(filename, ".mtx"))
readMMEHeader();
else if (strstr(filename, ".tns"))
readExtFROSTTHeader();
else
FATAL("Unknown format %s\n", filename);
assert(isValid() && "Failed to read the header");
}
ValueKind getValueKind() const { return valueKind_; }
bool isValid() const { return valueKind_ != ValueKind::kInvalid; }
/// Gets the MME "pattern" property setting. Is only valid after
/// parsing the header.
bool isPattern() const {
assert(isValid() && "Attempt to isPattern() before readHeader()");
return valueKind_ == ValueKind::kPattern;
}
/// Gets the MME "symmetric" property setting. Is only valid after
/// parsing the header.
bool isSymmetric() const {
assert(isValid() && "Attempt to isSymmetric() before readHeader()");
return isSymmetric_;
}
/// Gets the rank of the tensor. Is only valid after parsing the header.
uint64_t getRank() const {
assert(isValid() && "Attempt to getRank() before readHeader()");
return idata[0];
}
/// Gets the number of non-zeros. Is only valid after parsing the header.
uint64_t getNNZ() const {
assert(isValid() && "Attempt to getNNZ() before readHeader()");
return idata[1];
}
/// Gets the dimension-sizes array. The pointer itself is always
/// valid; however, the values stored therein are only valid after
/// parsing the header.
const uint64_t *getDimSizes() const { return idata + 2; }
/// Safely gets the size of the given dimension. Is only valid
/// after parsing the header.
uint64_t getDimSize(uint64_t d) const {
assert(d < getRank());
return idata[2 + d];
}
/// Asserts the shape subsumes the actual dimension sizes. Is only
/// valid after parsing the header.
void assertMatchesShape(uint64_t rank, const uint64_t *shape) const {
assert(rank == getRank() && "Rank mismatch");
for (uint64_t r = 0; r < rank; r++)
assert((shape[r] == 0 || shape[r] == idata[2 + r]) &&
"Dimension size mismatch");
}
private:
void readMMEHeader();
void readExtFROSTTHeader();
const char *filename;
FILE *file = nullptr;
ValueKind valueKind_ = ValueKind::kInvalid;
bool isSymmetric_ = false;
uint64_t idata[512];
char line[kColWidth];
};
/// Read the MME header of a general sparse matrix of type real.
void SparseTensorFile::readMMEHeader() {
char header[64];
char object[64];
char format[64];
char field[64];
char symmetry[64];
// Read header line.
if (fscanf(file, "%63s %63s %63s %63s %63s\n", header, object, format, field,
symmetry) != 5)
FATAL("Corrupt header in %s\n", filename);
// Process `field`, which specify pattern or the data type of the values.
if (strcmp(toLower(field), "pattern") == 0)
valueKind_ = ValueKind::kPattern;
else if (strcmp(toLower(field), "real") == 0)
valueKind_ = ValueKind::kReal;
else if (strcmp(toLower(field), "integer") == 0)
valueKind_ = ValueKind::kInteger;
else if (strcmp(toLower(field), "complex") == 0)
valueKind_ = ValueKind::kComplex;
else
FATAL("Unexpected header field value in %s\n", filename);
// Set properties.
isSymmetric_ = (strcmp(toLower(symmetry), "symmetric") == 0);
// Make sure this is a general sparse matrix.
if (strcmp(toLower(header), "%%matrixmarket") ||
strcmp(toLower(object), "matrix") ||
strcmp(toLower(format), "coordinate") ||
(strcmp(toLower(symmetry), "general") && !isSymmetric_))
FATAL("Cannot find a general sparse matrix in %s\n", filename);
// Skip comments.
while (true) {
readLine();
if (line[0] != '%')
break;
}
// Next line contains M N NNZ.
idata[0] = 2; // rank
if (sscanf(line, "%" PRIu64 "%" PRIu64 "%" PRIu64 "\n", idata + 2, idata + 3,
idata + 1) != 3)
FATAL("Cannot find size in %s\n", filename);
}
/// Read the "extended" FROSTT header. Although not part of the documented
/// format, we assume that the file starts with optional comments followed
/// by two lines that define the rank, the number of nonzeros, and the
/// dimensions sizes (one per rank) of the sparse tensor.
void SparseTensorFile::readExtFROSTTHeader() {
// Skip comments.
while (true) {
readLine();
if (line[0] != '#')
break;
}
// Next line contains RANK and NNZ.
if (sscanf(line, "%" PRIu64 "%" PRIu64 "\n", idata, idata + 1) != 2)
FATAL("Cannot find metadata in %s\n", filename);
// Followed by a line with the dimension sizes (one per rank).
for (uint64_t r = 0; r < idata[0]; r++)
if (fscanf(file, "%" PRIu64, idata + 2 + r) != 1)
FATAL("Cannot find dimension size %s\n", filename);
readLine(); // end of line
// The FROSTT format does not define the data type of the nonzero elements.
valueKind_ = ValueKind::kUndefined;
}
// Adds a value to a tensor in coordinate scheme. If is_symmetric_value is true,
// also adds the value to its symmetric location.
template <typename T, typename V>
static inline void addValue(T *coo, V value,
const std::vector<uint64_t> indices,
bool is_symmetric_value) {
// TODO: <https://github.com/llvm/llvm-project/issues/54179>
coo->add(indices, value);
// We currently chose to deal with symmetric matrices by fully constructing
// them. In the future, we may want to make symmetry implicit for storage
// reasons.
if (is_symmetric_value)
coo->add({indices[1], indices[0]}, value);
}
// Reads an element of a complex type for the current indices in coordinate
// scheme.
template <typename V>
static inline void readCOOValue(SparseTensorCOO<std::complex<V>> *coo,
const std::vector<uint64_t> indices,
char **linePtr, bool is_pattern,
bool add_symmetric_value) {
// Read two values to make a complex. The external formats always store
// numerical values with the type double, but we cast these values to the
// sparse tensor object type. For a pattern tensor, we arbitrarily pick the
// value 1 for all entries.
V re = is_pattern ? 1.0 : strtod(*linePtr, linePtr);
V im = is_pattern ? 1.0 : strtod(*linePtr, linePtr);
std::complex<V> value = {re, im};
addValue(coo, value, indices, add_symmetric_value);
}
// Reads an element of a non-complex type for the current indices in coordinate
// scheme.
template <typename V,
typename std::enable_if<
!std::is_same<std::complex<float>, V>::value &&
!std::is_same<std::complex<double>, V>::value>::type * = nullptr>
static void inline readCOOValue(SparseTensorCOO<V> *coo,
const std::vector<uint64_t> indices,
char **linePtr, bool is_pattern,
bool is_symmetric_value) {
// The external formats always store these numerical values with the type
// double, but we cast these values to the sparse tensor object type.
// For a pattern tensor, we arbitrarily pick the value 1 for all entries.
double value = is_pattern ? 1.0 : strtod(*linePtr, linePtr);
addValue(coo, value, indices, is_symmetric_value);
}
/// Reads a sparse tensor with the given filename into a memory-resident
/// sparse tensor in coordinate scheme.
template <typename V>
static SparseTensorCOO<V> *
openSparseTensorCOO(char *filename, uint64_t rank, const uint64_t *shape,
const uint64_t *perm, PrimaryType valTp) {
SparseTensorFile stfile(filename);
stfile.openFile();
stfile.readHeader();
// Check tensor element type against the value type in the input file.
SparseTensorFile::ValueKind valueKind = stfile.getValueKind();
bool tensorIsInteger =
(valTp >= PrimaryType::kI64 && valTp <= PrimaryType::kI8);
bool tensorIsReal = (valTp >= PrimaryType::kF64 && valTp <= PrimaryType::kI8);
if ((valueKind == SparseTensorFile::ValueKind::kReal && tensorIsInteger) ||
(valueKind == SparseTensorFile::ValueKind::kComplex && tensorIsReal)) {
FATAL("Tensor element type %d not compatible with values in file %s\n",
static_cast<int>(valTp), filename);
}
stfile.assertMatchesShape(rank, shape);
// Prepare sparse tensor object with per-dimension sizes
// and the number of nonzeros as initial capacity.
uint64_t nnz = stfile.getNNZ();
auto *coo = SparseTensorCOO<V>::newSparseTensorCOO(rank, stfile.getDimSizes(),
perm, nnz);
// Read all nonzero elements.
std::vector<uint64_t> indices(rank);
for (uint64_t k = 0; k < nnz; k++) {
char *linePtr = stfile.readLine();
for (uint64_t r = 0; r < rank; r++) {
uint64_t idx = strtoul(linePtr, &linePtr, 10);
// Add 0-based index.
indices[perm[r]] = idx - 1;
}
readCOOValue(coo, indices, &linePtr, stfile.isPattern(),
stfile.isSymmetric() && indices[0] != indices[1]);
}
// Close the file and return tensor.
stfile.closeFile();
return coo;
}
/// Writes the sparse tensor to `dest` in extended FROSTT format.
template <typename V>
static void outSparseTensor(void *tensor, void *dest, bool sort) {
assert(tensor && dest);
auto coo = static_cast<SparseTensorCOO<V> *>(tensor);
if (sort)
coo->sort();
char *filename = static_cast<char *>(dest);
auto &dimSizes = coo->getDimSizes();
auto &elements = coo->getElements();
uint64_t rank = coo->getRank();
uint64_t nnz = elements.size();
std::fstream file;
file.open(filename, std::ios_base::out | std::ios_base::trunc);
assert(file.is_open());
file << "; extended FROSTT format\n" << rank << " " << nnz << std::endl;
for (uint64_t r = 0; r < rank - 1; r++)
file << dimSizes[r] << " ";
file << dimSizes[rank - 1] << std::endl;
for (uint64_t i = 0; i < nnz; i++) {
auto &idx = elements[i].indices;
for (uint64_t r = 0; r < rank; r++)
file << (idx[r] + 1) << " ";
file << elements[i].value << std::endl;
}
file.flush();
file.close();
assert(file.good());
}
/// Initializes sparse tensor from an external COO-flavored format.
template <typename V>
static SparseTensorStorage<uint64_t, uint64_t, V> *
toMLIRSparseTensor(uint64_t rank, uint64_t nse, uint64_t *shape, V *values,
uint64_t *indices, uint64_t *perm, uint8_t *sparse) {
const DimLevelType *sparsity = (DimLevelType *)(sparse);
#ifndef NDEBUG
// Verify that perm is a permutation of 0..(rank-1).
std::vector<uint64_t> order(perm, perm + rank);
std::sort(order.begin(), order.end());
for (uint64_t i = 0; i < rank; ++i)
if (i != order[i])
FATAL("Not a permutation of 0..%" PRIu64 "\n", rank);
// Verify that the sparsity values are supported.
for (uint64_t i = 0; i < rank; ++i)
if (sparsity[i] != DimLevelType::kDense &&
sparsity[i] != DimLevelType::kCompressed)
FATAL("Unsupported sparsity value %d\n", static_cast<int>(sparsity[i]));
#endif
// Convert external format to internal COO.
auto *coo = SparseTensorCOO<V>::newSparseTensorCOO(rank, shape, perm, nse);
std::vector<uint64_t> idx(rank);
for (uint64_t i = 0, base = 0; i < nse; i++) {
for (uint64_t r = 0; r < rank; r++)
idx[perm[r]] = indices[base + r];
coo->add(idx, values[i]);
base += rank;
}
// Return sparse tensor storage format as opaque pointer.
auto *tensor = SparseTensorStorage<uint64_t, uint64_t, V>::newSparseTensor(
rank, shape, perm, sparsity, coo);
delete coo;
return tensor;
}
/// Converts a sparse tensor to an external COO-flavored format.
template <typename V>
static void fromMLIRSparseTensor(void *tensor, uint64_t *pRank, uint64_t *pNse,
uint64_t **pShape, V **pValues,
uint64_t **pIndices) {
assert(tensor);
auto sparseTensor =
static_cast<SparseTensorStorage<uint64_t, uint64_t, V> *>(tensor);
uint64_t rank = sparseTensor->getRank();
std::vector<uint64_t> perm(rank);
std::iota(perm.begin(), perm.end(), 0);
SparseTensorCOO<V> *coo = sparseTensor->toCOO(perm.data());
const std::vector<Element<V>> &elements = coo->getElements();
uint64_t nse = elements.size();
uint64_t *shape = new uint64_t[rank];
for (uint64_t i = 0; i < rank; i++)
shape[i] = coo->getDimSizes()[i];
V *values = new V[nse];
uint64_t *indices = new uint64_t[rank * nse];
for (uint64_t i = 0, base = 0; i < nse; i++) {
values[i] = elements[i].value;
for (uint64_t j = 0; j < rank; j++)
indices[base + j] = elements[i].indices[j];
base += rank;
}
delete coo;
*pRank = rank;
*pNse = nse;
*pShape = shape;
*pValues = values;
*pIndices = indices;
}
} // anonymous namespace
extern "C" {
//===----------------------------------------------------------------------===//
//
// Public functions which operate on MLIR buffers (memrefs) to interact
// with sparse tensors (which are only visible as opaque pointers externally).
//
//===----------------------------------------------------------------------===//
#define CASE(p, i, v, P, I, V) \
if (ptrTp == (p) && indTp == (i) && valTp == (v)) { \
SparseTensorCOO<V> *coo = nullptr; \
if (action <= Action::kFromCOO) { \
if (action == Action::kFromFile) { \
char *filename = static_cast<char *>(ptr); \
coo = openSparseTensorCOO<V>(filename, rank, shape, perm, v); \
} else if (action == Action::kFromCOO) { \
coo = static_cast<SparseTensorCOO<V> *>(ptr); \
} else { \
assert(action == Action::kEmpty); \
} \
auto *tensor = SparseTensorStorage<P, I, V>::newSparseTensor( \
rank, shape, perm, sparsity, coo); \
if (action == Action::kFromFile) \
delete coo; \
return tensor; \
} \
if (action == Action::kSparseToSparse) { \
auto *tensor = static_cast<SparseTensorStorageBase *>(ptr); \
return SparseTensorStorage<P, I, V>::newSparseTensor(rank, shape, perm, \
sparsity, tensor); \
} \
if (action == Action::kEmptyCOO) \
return SparseTensorCOO<V>::newSparseTensorCOO(rank, shape, perm); \
coo = static_cast<SparseTensorStorage<P, I, V> *>(ptr)->toCOO(perm); \
if (action == Action::kToIterator) { \
coo->startIterator(); \
} else { \
assert(action == Action::kToCOO); \
} \
return coo; \
}
#define CASE_SECSAME(p, v, P, V) CASE(p, p, v, P, P, V)
// Assume index_type is in fact uint64_t, so that _mlir_ciface_newSparseTensor
// can safely rewrite kIndex to kU64. We make this assertion to guarantee
// that this file cannot get out of sync with its header.
static_assert(std::is_same<index_type, uint64_t>::value,
"Expected index_type == uint64_t");
void *
_mlir_ciface_newSparseTensor(StridedMemRefType<DimLevelType, 1> *aref, // NOLINT
StridedMemRefType<index_type, 1> *sref,
StridedMemRefType<index_type, 1> *pref,
OverheadType ptrTp, OverheadType indTp,
PrimaryType valTp, Action action, void *ptr) {
assert(aref && sref && pref);
assert(aref->strides[0] == 1 && sref->strides[0] == 1 &&
pref->strides[0] == 1);
assert(aref->sizes[0] == sref->sizes[0] && sref->sizes[0] == pref->sizes[0]);
const DimLevelType *sparsity = aref->data + aref->offset;
const index_type *shape = sref->data + sref->offset;
const index_type *perm = pref->data + pref->offset;
uint64_t rank = aref->sizes[0];
// Rewrite kIndex to kU64, to avoid introducing a bunch of new cases.
// This is safe because of the static_assert above.
if (ptrTp == OverheadType::kIndex)
ptrTp = OverheadType::kU64;
if (indTp == OverheadType::kIndex)
indTp = OverheadType::kU64;
// Double matrices with all combinations of overhead storage.
CASE(OverheadType::kU64, OverheadType::kU64, PrimaryType::kF64, uint64_t,
uint64_t, double);
CASE(OverheadType::kU64, OverheadType::kU32, PrimaryType::kF64, uint64_t,
uint32_t, double);
CASE(OverheadType::kU64, OverheadType::kU16, PrimaryType::kF64, uint64_t,
uint16_t, double);
CASE(OverheadType::kU64, OverheadType::kU8, PrimaryType::kF64, uint64_t,
uint8_t, double);
CASE(OverheadType::kU32, OverheadType::kU64, PrimaryType::kF64, uint32_t,
uint64_t, double);
CASE(OverheadType::kU32, OverheadType::kU32, PrimaryType::kF64, uint32_t,
uint32_t, double);
CASE(OverheadType::kU32, OverheadType::kU16, PrimaryType::kF64, uint32_t,
uint16_t, double);
CASE(OverheadType::kU32, OverheadType::kU8, PrimaryType::kF64, uint32_t,
uint8_t, double);
CASE(OverheadType::kU16, OverheadType::kU64, PrimaryType::kF64, uint16_t,
uint64_t, double);
CASE(OverheadType::kU16, OverheadType::kU32, PrimaryType::kF64, uint16_t,
uint32_t, double);
CASE(OverheadType::kU16, OverheadType::kU16, PrimaryType::kF64, uint16_t,
uint16_t, double);
CASE(OverheadType::kU16, OverheadType::kU8, PrimaryType::kF64, uint16_t,
uint8_t, double);
CASE(OverheadType::kU8, OverheadType::kU64, PrimaryType::kF64, uint8_t,
uint64_t, double);
CASE(OverheadType::kU8, OverheadType::kU32, PrimaryType::kF64, uint8_t,
uint32_t, double);
CASE(OverheadType::kU8, OverheadType::kU16, PrimaryType::kF64, uint8_t,
uint16_t, double);
CASE(OverheadType::kU8, OverheadType::kU8, PrimaryType::kF64, uint8_t,
uint8_t, double);
// Float matrices with all combinations of overhead storage.
CASE(OverheadType::kU64, OverheadType::kU64, PrimaryType::kF32, uint64_t,
uint64_t, float);
CASE(OverheadType::kU64, OverheadType::kU32, PrimaryType::kF32, uint64_t,
uint32_t, float);
CASE(OverheadType::kU64, OverheadType::kU16, PrimaryType::kF32, uint64_t,
uint16_t, float);
CASE(OverheadType::kU64, OverheadType::kU8, PrimaryType::kF32, uint64_t,
uint8_t, float);
CASE(OverheadType::kU32, OverheadType::kU64, PrimaryType::kF32, uint32_t,
uint64_t, float);
CASE(OverheadType::kU32, OverheadType::kU32, PrimaryType::kF32, uint32_t,
uint32_t, float);
CASE(OverheadType::kU32, OverheadType::kU16, PrimaryType::kF32, uint32_t,
uint16_t, float);
CASE(OverheadType::kU32, OverheadType::kU8, PrimaryType::kF32, uint32_t,
uint8_t, float);
CASE(OverheadType::kU16, OverheadType::kU64, PrimaryType::kF32, uint16_t,
uint64_t, float);
CASE(OverheadType::kU16, OverheadType::kU32, PrimaryType::kF32, uint16_t,
uint32_t, float);
CASE(OverheadType::kU16, OverheadType::kU16, PrimaryType::kF32, uint16_t,
uint16_t, float);
CASE(OverheadType::kU16, OverheadType::kU8, PrimaryType::kF32, uint16_t,
uint8_t, float);
CASE(OverheadType::kU8, OverheadType::kU64, PrimaryType::kF32, uint8_t,
uint64_t, float);
CASE(OverheadType::kU8, OverheadType::kU32, PrimaryType::kF32, uint8_t,
uint32_t, float);
CASE(OverheadType::kU8, OverheadType::kU16, PrimaryType::kF32, uint8_t,
uint16_t, float);
CASE(OverheadType::kU8, OverheadType::kU8, PrimaryType::kF32, uint8_t,
uint8_t, float);
// Two-byte floats with both overheads of the same type.
CASE_SECSAME(OverheadType::kU64, PrimaryType::kF16, uint64_t, f16);
CASE_SECSAME(OverheadType::kU64, PrimaryType::kBF16, uint64_t, bf16);
CASE_SECSAME(OverheadType::kU32, PrimaryType::kF16, uint32_t, f16);
CASE_SECSAME(OverheadType::kU32, PrimaryType::kBF16, uint32_t, bf16);
CASE_SECSAME(OverheadType::kU16, PrimaryType::kF16, uint16_t, f16);
CASE_SECSAME(OverheadType::kU16, PrimaryType::kBF16, uint16_t, bf16);
CASE_SECSAME(OverheadType::kU8, PrimaryType::kF16, uint8_t, f16);
CASE_SECSAME(OverheadType::kU8, PrimaryType::kBF16, uint8_t, bf16);
// Integral matrices with both overheads of the same type.
CASE_SECSAME(OverheadType::kU64, PrimaryType::kI64, uint64_t, int64_t);
CASE_SECSAME(OverheadType::kU64, PrimaryType::kI32, uint64_t, int32_t);
CASE_SECSAME(OverheadType::kU64, PrimaryType::kI16, uint64_t, int16_t);
CASE_SECSAME(OverheadType::kU64, PrimaryType::kI8, uint64_t, int8_t);
CASE_SECSAME(OverheadType::kU32, PrimaryType::kI64, uint32_t, int64_t);
CASE_SECSAME(OverheadType::kU32, PrimaryType::kI32, uint32_t, int32_t);
CASE_SECSAME(OverheadType::kU32, PrimaryType::kI16, uint32_t, int16_t);
CASE_SECSAME(OverheadType::kU32, PrimaryType::kI8, uint32_t, int8_t);
CASE_SECSAME(OverheadType::kU16, PrimaryType::kI64, uint16_t, int64_t);
CASE_SECSAME(OverheadType::kU16, PrimaryType::kI32, uint16_t, int32_t);
CASE_SECSAME(OverheadType::kU16, PrimaryType::kI16, uint16_t, int16_t);
CASE_SECSAME(OverheadType::kU16, PrimaryType::kI8, uint16_t, int8_t);
CASE_SECSAME(OverheadType::kU8, PrimaryType::kI64, uint8_t, int64_t);
CASE_SECSAME(OverheadType::kU8, PrimaryType::kI32, uint8_t, int32_t);
CASE_SECSAME(OverheadType::kU8, PrimaryType::kI16, uint8_t, int16_t);
CASE_SECSAME(OverheadType::kU8, PrimaryType::kI8, uint8_t, int8_t);
// Complex matrices with wide overhead.
CASE_SECSAME(OverheadType::kU64, PrimaryType::kC64, uint64_t, complex64);
CASE_SECSAME(OverheadType::kU64, PrimaryType::kC32, uint64_t, complex32);
// Unsupported case (add above if needed).
// TODO: better pretty-printing of enum values!
FATAL("unsupported combination of types: <P=%d, I=%d, V=%d>\n",
static_cast<int>(ptrTp), static_cast<int>(indTp),
static_cast<int>(valTp));
}
#undef CASE
#undef CASE_SECSAME
#define IMPL_SPARSEVALUES(VNAME, V) \
void _mlir_ciface_sparseValues##VNAME(StridedMemRefType<V, 1> *ref, \
void *tensor) { \
assert(ref &&tensor); \
std::vector<V> *v; \
static_cast<SparseTensorStorageBase *>(tensor)->getValues(&v); \
ref->basePtr = ref->data = v->data(); \
ref->offset = 0; \
ref->sizes[0] = v->size(); \
ref->strides[0] = 1; \
}
FOREVERY_V(IMPL_SPARSEVALUES)
#undef IMPL_SPARSEVALUES
#define IMPL_GETOVERHEAD(NAME, TYPE, LIB) \
void _mlir_ciface_##NAME(StridedMemRefType<TYPE, 1> *ref, void *tensor, \
index_type d) { \
assert(ref &&tensor); \
std::vector<TYPE> *v; \
static_cast<SparseTensorStorageBase *>(tensor)->LIB(&v, d); \
ref->basePtr = ref->data = v->data(); \
ref->offset = 0; \
ref->sizes[0] = v->size(); \
ref->strides[0] = 1; \
}
#define IMPL_SPARSEPOINTERS(PNAME, P) \
IMPL_GETOVERHEAD(sparsePointers##PNAME, P, getPointers)
FOREVERY_O(IMPL_SPARSEPOINTERS)
#undef IMPL_SPARSEPOINTERS
#define IMPL_SPARSEINDICES(INAME, I) \
IMPL_GETOVERHEAD(sparseIndices##INAME, I, getIndices)
FOREVERY_O(IMPL_SPARSEINDICES)
#undef IMPL_SPARSEINDICES
#undef IMPL_GETOVERHEAD
#define IMPL_ADDELT(VNAME, V) \
void *_mlir_ciface_addElt##VNAME(void *coo, StridedMemRefType<V, 0> *vref, \
StridedMemRefType<index_type, 1> *iref, \
StridedMemRefType<index_type, 1> *pref) { \
assert(coo &&vref &&iref &&pref); \
assert(iref->strides[0] == 1 && pref->strides[0] == 1); \
assert(iref->sizes[0] == pref->sizes[0]); \
const index_type *indx = iref->data + iref->offset; \
const index_type *perm = pref->data + pref->offset; \
uint64_t isize = iref->sizes[0]; \
std::vector<index_type> indices(isize); \
for (uint64_t r = 0; r < isize; r++) \
indices[perm[r]] = indx[r]; \
V *value = vref->data + vref->offset; \
static_cast<SparseTensorCOO<V> *>(coo)->add(indices, *value); \
return coo; \
}
FOREVERY_V(IMPL_ADDELT)
#undef IMPL_ADDELT
#define IMPL_GETNEXT(VNAME, V) \
bool _mlir_ciface_getNext##VNAME(void *coo, \
StridedMemRefType<index_type, 1> *iref, \
StridedMemRefType<V, 0> *vref) { \
assert(coo &&iref &&vref); \
assert(iref->strides[0] == 1); \
index_type *indx = iref->data + iref->offset; \
V *value = vref->data + vref->offset; \
const uint64_t isize = iref->sizes[0]; \
const Element<V> *elem = \
static_cast<SparseTensorCOO<V> *>(coo)->getNext(); \
if (elem == nullptr) \
return false; \
for (uint64_t r = 0; r < isize; r++) \
indx[r] = elem->indices[r]; \
*value = elem->value; \
return true; \
}
FOREVERY_V(IMPL_GETNEXT)
#undef IMPL_GETNEXT
#define IMPL_LEXINSERT(VNAME, V) \
void _mlir_ciface_lexInsert##VNAME(void *tensor, \
StridedMemRefType<index_type, 1> *cref, \
StridedMemRefType<V, 0> *vref) { \
assert(tensor &&cref &&vref); \
assert(cref->strides[0] == 1); \
index_type *cursor = cref->data + cref->offset; \
assert(cursor); \
V *value = vref->data + vref->offset; \
static_cast<SparseTensorStorageBase *>(tensor)->lexInsert(cursor, *value); \
}
FOREVERY_V(IMPL_LEXINSERT)
#undef IMPL_LEXINSERT
#define IMPL_EXPINSERT(VNAME, V) \
void _mlir_ciface_expInsert##VNAME( \
void *tensor, StridedMemRefType<index_type, 1> *cref, \
StridedMemRefType<V, 1> *vref, StridedMemRefType<bool, 1> *fref, \
StridedMemRefType<index_type, 1> *aref, index_type count) { \
assert(tensor &&cref &&vref &&fref &&aref); \
assert(cref->strides[0] == 1); \
assert(vref->strides[0] == 1); \
assert(fref->strides[0] == 1); \
assert(aref->strides[0] == 1); \
assert(vref->sizes[0] == fref->sizes[0]); \
index_type *cursor = cref->data + cref->offset; \
V *values = vref->data + vref->offset; \
bool *filled = fref->data + fref->offset; \
index_type *added = aref->data + aref->offset; \
static_cast<SparseTensorStorageBase *>(tensor)->expInsert( \
cursor, values, filled, added, count); \
}
FOREVERY_V(IMPL_EXPINSERT)
#undef IMPL_EXPINSERT
//===----------------------------------------------------------------------===//
//
// Public functions which accept only C-style data structures to interact
// with sparse tensors (which are only visible as opaque pointers externally).
//
//===----------------------------------------------------------------------===//
index_type sparseDimSize(void *tensor, index_type d) {
return static_cast<SparseTensorStorageBase *>(tensor)->getDimSize(d);
}
void endInsert(void *tensor) {
return static_cast<SparseTensorStorageBase *>(tensor)->endInsert();
}
#define IMPL_OUTSPARSETENSOR(VNAME, V) \
void outSparseTensor##VNAME(void *coo, void *dest, bool sort) { \
return outSparseTensor<V>(coo, dest, sort); \
}
FOREVERY_V(IMPL_OUTSPARSETENSOR)
#undef IMPL_OUTSPARSETENSOR
void delSparseTensor(void *tensor) {
delete static_cast<SparseTensorStorageBase *>(tensor);
}
#define IMPL_DELCOO(VNAME, V) \
void delSparseTensorCOO##VNAME(void *coo) { \
delete static_cast<SparseTensorCOO<V> *>(coo); \
}
FOREVERY_V(IMPL_DELCOO)
#undef IMPL_DELCOO
char *getTensorFilename(index_type id) {
char var[80];
sprintf(var, "TENSOR%" PRIu64, id);
char *env = getenv(var);
if (!env)
FATAL("Environment variable %s is not set\n", var);
return env;
}
void readSparseTensorShape(char *filename, std::vector<uint64_t> *out) {
assert(out && "Received nullptr for out-parameter");
SparseTensorFile stfile(filename);
stfile.openFile();
stfile.readHeader();
stfile.closeFile();
const uint64_t rank = stfile.getRank();
const uint64_t *dimSizes = stfile.getDimSizes();
out->reserve(rank);
out->assign(dimSizes, dimSizes + rank);
}
// TODO: generalize beyond 64-bit indices.
#define IMPL_CONVERTTOMLIRSPARSETENSOR(VNAME, V) \
void *convertToMLIRSparseTensor##VNAME( \
uint64_t rank, uint64_t nse, uint64_t *shape, V *values, \
uint64_t *indices, uint64_t *perm, uint8_t *sparse) { \
return toMLIRSparseTensor<V>(rank, nse, shape, values, indices, perm, \
sparse); \
}
FOREVERY_V(IMPL_CONVERTTOMLIRSPARSETENSOR)
#undef IMPL_CONVERTTOMLIRSPARSETENSOR
// TODO: Currently, values are copied from SparseTensorStorage to
// SparseTensorCOO, then to the output. We may want to reduce the number
// of copies.
//
// TODO: generalize beyond 64-bit indices, no dim ordering, all dimensions
// compressed
#define IMPL_CONVERTFROMMLIRSPARSETENSOR(VNAME, V) \
void convertFromMLIRSparseTensor##VNAME(void *tensor, uint64_t *pRank, \
uint64_t *pNse, uint64_t **pShape, \
V **pValues, uint64_t **pIndices) { \
fromMLIRSparseTensor<V>(tensor, pRank, pNse, pShape, pValues, pIndices); \
}
FOREVERY_V(IMPL_CONVERTFROMMLIRSPARSETENSOR)
#undef IMPL_CONVERTFROMMLIRSPARSETENSOR
} // extern "C"
#endif // MLIR_CRUNNERUTILS_DEFINE_FUNCTIONS