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llvm-project/mlir/lib/ExecutionEngine/SparseTensorRuntime.cpp
wren romano d9affadd00 [mlir][sparse] rename the values of the runtime DimLevelType 
This change is to make way for reusing the DimLevelType enum in lieu of the SparseTensorEncodingAttr::DimLevelType enum, but broken out to make it quick and easy to review

Reviewed By: aartbik

Differential Revision: https://reviews.llvm.org/D135995
2022-10-18 12:08:19 -07:00

683 lines
32 KiB
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//===- SparseTensorRuntime.cpp - SparseTensor runtime support lib ---------===//
//
// 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 for
// manipulating sparse tensors from MLIR. More specifically, it provides
// C-API wrappers so that MLIR-generated code can call into the C++ runtime
// support library. The functionality provided in this library is meant
// to simplify benchmarking, testing, and debugging of MLIR code operating
// on sparse tensors. However, the provided functionality is **not**
// part of core MLIR itself.
//
// 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.
//
//===----------------------------------------------------------------------===//
#include "mlir/ExecutionEngine/SparseTensorRuntime.h"
#ifdef MLIR_CRUNNERUTILS_DEFINE_FUNCTIONS
#include "mlir/ExecutionEngine/SparseTensor/COO.h"
#include "mlir/ExecutionEngine/SparseTensor/ErrorHandling.h"
#include "mlir/ExecutionEngine/SparseTensor/File.h"
#include "mlir/ExecutionEngine/SparseTensor/Storage.h"
#include <numeric>
using namespace mlir::sparse_tensor;
//===----------------------------------------------------------------------===//
//
// Implementation details for public functions, which don't have a good
// place to live in the C++ library this file is wrapping.
//
//===----------------------------------------------------------------------===//
namespace {
/// Wrapper class to avoid memory leakage issues. The `SparseTensorCOO<V>`
/// class provides a standard C++ iterator interface, where the iterator
/// is implemented as per `std::vector`'s iterator. However, for MLIR's
/// usage we need to have an iterator which also holds onto the underlying
/// `SparseTensorCOO<V>` so that it can be freed whenever the iterator
/// is freed.
//
// We name this `SparseTensorIterator` rather than `SparseTensorCOOIterator`
// for future-proofing, since the use of `SparseTensorCOO` is an
// implementation detail that we eventually want to change (e.g., to
// use `SparseTensorEnumerator` directly, rather than constructing the
// intermediate `SparseTensorCOO` at all).
template <typename V>
class SparseTensorIterator final {
public:
/// This ctor requires `coo` to be a non-null pointer to a dynamically
/// allocated object, and takes ownership of that object. Therefore,
/// callers must not free the underlying COO object, since the iterator's
/// dtor will do so.
explicit SparseTensorIterator(const SparseTensorCOO<V> *coo)
: coo(coo), it(coo->begin()), end(coo->end()) {}
~SparseTensorIterator() { delete coo; }
// Disable copy-ctor and copy-assignment, to prevent double-free.
SparseTensorIterator(const SparseTensorIterator<V> &) = delete;
SparseTensorIterator<V> &operator=(const SparseTensorIterator<V> &) = delete;
/// Gets the next element. If there are no remaining elements, then
/// returns nullptr.
const Element<V> *getNext() { return it < end ? &*it++ : nullptr; }
private:
const SparseTensorCOO<V> *const coo; // Owning pointer.
typename SparseTensorCOO<V>::const_iterator it;
const typename SparseTensorCOO<V>::const_iterator end;
};
/// Initializes sparse tensor from an external COO-flavored format.
/// Used by `IMPL_CONVERTTOMLIRSPARSETENSOR`.
// TODO: generalize beyond 64-bit indices.
template <typename V>
static SparseTensorStorage<uint64_t, uint64_t, V> *
toMLIRSparseTensor(uint64_t rank, uint64_t nse, const uint64_t *shape,
const V *values, const uint64_t *indices,
const uint64_t *perm, const DimLevelType *sparsity) {
#ifndef NDEBUG
// Verify that perm is a permutation of 0..(rank-1).
std::vector<bool> seen(rank, false);
for (uint64_t i = 0; i < rank; ++i) {
const uint64_t j = perm[i];
if (j >= rank || seen[j])
MLIR_SPARSETENSOR_FATAL("Not a permutation of 0..%" PRIu64 "\n", rank);
seen[j] = true;
}
// Verify that the sparsity values are supported.
// TODO: update this check to match what we actually support.
for (uint64_t i = 0; i < rank; ++i)
if (sparsity[i] != DimLevelType::Dense &&
sparsity[i] != DimLevelType::Compressed)
MLIR_SPARSETENSOR_FATAL("unsupported dimension level type: %d\n",
static_cast<uint8_t>(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.
/// Used by `IMPL_CONVERTFROMMLIRSPARSETENSOR`.
//
// 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
template <typename V>
static void
fromMLIRSparseTensor(const SparseTensorStorage<uint64_t, uint64_t, V> *tensor,
uint64_t *pRank, uint64_t *pNse, uint64_t **pShape,
V **pValues, uint64_t **pIndices) {
assert(tensor && "Received nullptr for tensor");
uint64_t rank = tensor->getRank();
std::vector<uint64_t> perm(rank);
std::iota(perm.begin(), perm.end(), 0);
SparseTensorCOO<V> *coo = tensor->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) { \
return new SparseTensorIterator<V>(coo); \
} 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!
MLIR_SPARSETENSOR_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; \
}
MLIR_SPARSETENSOR_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)
MLIR_SPARSETENSOR_FOREVERY_O(IMPL_SPARSEPOINTERS)
#undef IMPL_SPARSEPOINTERS
#define IMPL_SPARSEINDICES(INAME, I) \
IMPL_GETOVERHEAD(sparseIndices##INAME, I, getIndices)
MLIR_SPARSETENSOR_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; \
}
MLIR_SPARSETENSOR_FOREVERY_V(IMPL_ADDELT)
#undef IMPL_ADDELT
#define IMPL_GETNEXT(VNAME, V) \
bool _mlir_ciface_getNext##VNAME(void *iter, \
StridedMemRefType<index_type, 1> *iref, \
StridedMemRefType<V, 0> *vref) { \
assert(iter &&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<SparseTensorIterator<V> *>(iter)->getNext(); \
if (elem == nullptr) \
return false; \
for (uint64_t r = 0; r < isize; r++) \
indx[r] = elem->indices[r]; \
*value = elem->value; \
return true; \
}
MLIR_SPARSETENSOR_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); \
}
MLIR_SPARSETENSOR_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); \
}
MLIR_SPARSETENSOR_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) { \
assert(coo && "Got nullptr for COO object"); \
auto &coo_ = *static_cast<SparseTensorCOO<V> *>(coo); \
if (sort) \
coo_.sort(); \
return writeExtFROSTT(coo_, static_cast<char *>(dest)); \
}
MLIR_SPARSETENSOR_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); \
}
MLIR_SPARSETENSOR_FOREVERY_V(IMPL_DELCOO)
#undef IMPL_DELCOO
#define IMPL_DELITER(VNAME, V) \
void delSparseTensorIterator##VNAME(void *iter) { \
delete static_cast<SparseTensorIterator<V> *>(iter); \
}
MLIR_SPARSETENSOR_FOREVERY_V(IMPL_DELITER)
#undef IMPL_DELITER
char *getTensorFilename(index_type id) {
char var[80];
sprintf(var, "TENSOR%" PRIu64, id);
char *env = getenv(var);
if (!env)
MLIR_SPARSETENSOR_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");
SparseTensorReader 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);
}
// We can't use `static_cast` here because `DimLevelType` is an enum-class.
#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, \
reinterpret_cast<DimLevelType *>(sparse)); \
}
MLIR_SPARSETENSOR_FOREVERY_V(IMPL_CONVERTTOMLIRSPARSETENSOR)
#undef IMPL_CONVERTTOMLIRSPARSETENSOR
#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>( \
static_cast<SparseTensorStorage<uint64_t, uint64_t, V> *>(tensor), \
pRank, pNse, pShape, pValues, pIndices); \
}
MLIR_SPARSETENSOR_FOREVERY_V(IMPL_CONVERTFROMMLIRSPARSETENSOR)
#undef IMPL_CONVERTFROMMLIRSPARSETENSOR
void *createSparseTensorReader(char *filename) {
SparseTensorReader *stfile = new SparseTensorReader(filename);
stfile->openFile();
stfile->readHeader();
return static_cast<void *>(stfile);
}
index_type getSparseTensorReaderRank(void *p) {
return static_cast<SparseTensorReader *>(p)->getRank();
}
bool getSparseTensorReaderIsSymmetric(void *p) {
return static_cast<SparseTensorReader *>(p)->isSymmetric();
}
index_type getSparseTensorReaderNNZ(void *p) {
return static_cast<SparseTensorReader *>(p)->getNNZ();
}
index_type getSparseTensorReaderDimSize(void *p, index_type d) {
return static_cast<SparseTensorReader *>(p)->getDimSize(d);
}
void _mlir_ciface_getSparseTensorReaderDimSizes(
void *p, StridedMemRefType<index_type, 1> *dref) {
assert(p && dref);
assert(dref->strides[0] == 1);
index_type *dimSizes = dref->data + dref->offset;
SparseTensorReader &file = *static_cast<SparseTensorReader *>(p);
const index_type *sizes = file.getDimSizes();
index_type rank = file.getRank();
for (uint64_t r = 0; r < rank; ++r)
dimSizes[r] = sizes[r];
}
void delSparseTensorReader(void *p) {
delete static_cast<SparseTensorReader *>(p);
}
#define IMPL_GETNEXT(VNAME, V) \
void _mlir_ciface_getSparseTensorReaderNext##VNAME( \
void *p, StridedMemRefType<index_type, 1> *iref, \
StridedMemRefType<V, 0> *vref) { \
assert(p &&iref &&vref); \
assert(iref->strides[0] == 1); \
index_type *indices = iref->data + iref->offset; \
SparseTensorReader *stfile = static_cast<SparseTensorReader *>(p); \
index_type rank = stfile->getRank(); \
V *value = vref->data + vref->offset; \
*value = stfile->readCOOElement<V>(rank, indices); \
}
MLIR_SPARSETENSOR_FOREVERY_V(IMPL_GETNEXT)
#undef IMPL_GETNEXT
void *createSparseTensorWriter(char *filename) {
SparseTensorWriter *file =
(filename[0] == 0) ? &std::cout : new std::ofstream(filename);
*file << "# extended FROSTT format\n";
return static_cast<void *>(file);
}
void delSparseTensorWriter(void *p) {
SparseTensorWriter *file = static_cast<SparseTensorWriter *>(p);
file->flush();
assert(file->good());
if (file != &std::cout)
delete file;
}
void _mlir_ciface_outSparseTensorWriterMetaData(
void *p, index_type rank, index_type nnz,
StridedMemRefType<index_type, 1> *dref) {
assert(p && dref);
assert(dref->strides[0] == 1);
assert(rank != 0);
index_type *dimSizes = dref->data + dref->offset;
SparseTensorWriter &file = *static_cast<SparseTensorWriter *>(p);
file << rank << " " << nnz << std::endl;
for (index_type r = 0; r < rank - 1; ++r)
file << dimSizes[r] << " ";
file << dimSizes[rank - 1] << std::endl;
}
#define IMPL_OUTNEXT(VNAME, V) \
void _mlir_ciface_outSparseTensorWriterNext##VNAME( \
void *p, index_type rank, StridedMemRefType<index_type, 1> *iref, \
StridedMemRefType<V, 0> *vref) { \
assert(p &&iref &&vref); \
assert(iref->strides[0] == 1); \
index_type *indices = iref->data + iref->offset; \
SparseTensorWriter &file = *static_cast<SparseTensorWriter *>(p); \
for (uint64_t r = 0; r < rank; ++r) \
file << (indices[r] + 1) << " "; \
V *value = vref->data + vref->offset; \
file << *value << std::endl; \
}
MLIR_SPARSETENSOR_FOREVERY_V(IMPL_OUTNEXT)
#undef IMPL_OUTNEXT
} // extern "C"
#endif // MLIR_CRUNNERUTILS_DEFINE_FUNCTIONS
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