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[mlir][sparse] introduce complex type to sparse tensor support 

This is the first implementation of complex (f64 and f32) support
in the sparse compiler, with complex add/mul as first operations.
Note that various features are still TBD, such as other ops, and
reading in complex values from file. Also, note that the
std::complex<float> had a bit of an ABI issue when passed as
single argument. It is still TBD if better solutions are possible.

Reviewed By: bixia

Differential Revision: https://reviews.llvm.org/D125596
This commit is contained in:
Aart Bik 2022-05-13 17:54:32 -07:00
parent 7dce9eb6e5
commit 736c1b66ef
11 changed files with 353 additions and 7 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
mlir/include/mlir/Dialect/SparseTensor/Utils/Merger.h
View File

@ -55,11 +55,13 @@ enum Kind {
kUnary, // semiring unary op
// Binary operations.
kMulF,
kMulC,
kMulI,
kDivF,
kDivS, // signed
kDivU, // unsigned
kAddF,
kAddC,
kAddI,
kSubF,
kSubI,

4
mlir/include/mlir/ExecutionEngine/SparseTensorUtils.h
View File

@ -42,7 +42,9 @@ enum class PrimaryType : uint32_t {
kI64 = 3,
kI32 = 4,
kI16 = 5,
kI8 = 6
kI8 = 6,
kC64 = 7,
kC32 = 8
};
/// The actions performed by @newSparseTensor.

2
mlir/lib/Dialect/SparseTensor/Pipelines/CMakeLists.txt
View File

@ -8,6 +8,8 @@ add_mlir_dialect_library(MLIRSparseTensorPipelines
MLIRArithmeticTransforms
MLIRAffineToStandard
MLIRBufferizationTransforms
MLIRComplexToLLVM
MLIRComplexToStandard
MLIRFuncTransforms
MLIRLinalgTransforms
MLIRMathToLibm

2
mlir/lib/Dialect/SparseTensor/Pipelines/SparseTensorPipelines.cpp
View File

@ -48,7 +48,9 @@ void mlir::sparse_tensor::buildSparseCompiler(
pm.addPass(createLowerAffinePass());
pm.addPass(createConvertVectorToLLVMPass(options.lowerVectorToLLVMOptions()));
pm.addPass(createMemRefToLLVMPass());
pm.addNestedPass<func::FuncOp>(createConvertComplexToStandardPass());
pm.addNestedPass<func::FuncOp>(createConvertMathToLLVMPass());
pm.addPass(createConvertComplexToLLVMPass());
pm.addPass(createConvertMathToLibmPass());
pm.addPass(createConvertFuncToLLVMPass());
pm.addPass(createReconcileUnrealizedCastsPass());

11
mlir/lib/Dialect/SparseTensor/Transforms/CodegenUtils.cpp
View File

@ -111,6 +111,13 @@ PrimaryType mlir::sparse_tensor::primaryTypeEncoding(Type elemTp) {
return PrimaryType::kI16;
if (elemTp.isInteger(8))
return PrimaryType::kI8;
if (auto complexTp = elemTp.dyn_cast<ComplexType>()) {
auto complexEltTp = complexTp.getElementType();
if (complexEltTp.isF64())
return PrimaryType::kC64;
if (complexEltTp.isF32())
return PrimaryType::kC32;
}
llvm_unreachable("Unknown primary type");
}
@ -128,6 +135,10 @@ StringRef mlir::sparse_tensor::primaryTypeFunctionSuffix(PrimaryType pt) {
return "I16";
case PrimaryType::kI8:
return "I8";
case PrimaryType::kC64:
return "C64";
case PrimaryType::kC32:
return "C32";
}
llvm_unreachable("Unknown PrimaryType");
}

1
mlir/lib/Dialect/SparseTensor/Utils/CMakeLists.txt
View File

@ -6,6 +6,7 @@ add_mlir_dialect_library(MLIRSparseTensorUtils
LINK_LIBS PUBLIC
MLIRArithmetic
MLIRComplex
MLIRIR
MLIRLinalg
)

22
mlir/lib/Dialect/SparseTensor/Utils/Merger.cpp
View File

@ -8,6 +8,7 @@
#include "mlir/Dialect/SparseTensor/Utils/Merger.h"
#include "mlir/Dialect/Arithmetic/IR/Arithmetic.h"
#include "mlir/Dialect/Complex/IR/Complex.h"
#include "mlir/Dialect/Math/IR/Math.h"
#include "mlir/Dialect/SparseTensor/IR/SparseTensor.h"
@ -303,6 +304,7 @@ bool Merger::isSingleCondition(unsigned t, unsigned e) const {
assert(isInvariant(tensorExps[e].children.e1));
return isSingleCondition(t, tensorExps[e].children.e0);
case kMulF:
case kMulC:
case kMulI:
case kAndI:
if (isSingleCondition(t, tensorExps[e].children.e0))
@ -312,6 +314,7 @@ bool Merger::isSingleCondition(unsigned t, unsigned e) const {
return isInvariant(tensorExps[e].children.e0);
return false;
case kAddF:
case kAddC:
case kAddI:
return isSingleCondition(t, tensorExps[e].children.e0) &&
isSingleCondition(t, tensorExps[e].children.e1);
@ -371,21 +374,18 @@ static const char *kindToOpSymbol(Kind kind) {
case kUnary:
return "unary";
case kMulF:
return "*";
case kMulC:
case kMulI:
return "*";
case kDivF:
return "/";
case kDivS:
return "/";
case kDivU:
return "/";
case kAddF:
return "+";
case kAddC:
case kAddI:
return "+";
case kSubF:
return "-";
case kSubI:
return "-";
case kAndI:
@ -581,6 +581,7 @@ unsigned Merger::buildLattices(unsigned e, unsigned i) {
return takeDisj(kind, child0, buildLattices(rhs, i), unop);
}
case kMulF:
case kMulC:
case kMulI:
case kAndI:
// A multiplicative operation only needs to be performed
@ -590,6 +591,8 @@ unsigned Merger::buildLattices(unsigned e, unsigned i) {
// ---+---+---+
// !x | 0 | 0 |
// x | 0 |x*y|
//
// Note even here, 0*NaN=NaN and 0*Inf=NaN, but that is ignored.
return takeConj(kind, // take binary conjunction
buildLattices(tensorExps[e].children.e0, i),
buildLattices(tensorExps[e].children.e1, i));
@ -614,6 +617,7 @@ unsigned Merger::buildLattices(unsigned e, unsigned i) {
buildLattices(tensorExps[e].children.e0, i),
buildLattices(tensorExps[e].children.e1, i));
case kAddF:
case kAddC:
case kAddI:
case kSubF:
case kSubI:
@ -789,6 +793,8 @@ Optional<unsigned> Merger::buildTensorExp(linalg::GenericOp op, Value v) {
unsigned e1 = y.getValue();
if (isa<arith::MulFOp>(def))
return addExp(kMulF, e0, e1);
if (isa<complex::MulOp>(def))
return addExp(kMulC, e0, e1);
if (isa<arith::MulIOp>(def))
return addExp(kMulI, e0, e1);
if (isa<arith::DivFOp>(def) && !maybeZero(e1))
@ -799,6 +805,8 @@ Optional<unsigned> Merger::buildTensorExp(linalg::GenericOp op, Value v) {
return addExp(kDivU, e0, e1);
if (isa<arith::AddFOp>(def))
return addExp(kAddF, e0, e1);
if (isa<complex::AddOp>(def))
return addExp(kAddC, e0, e1);
if (isa<arith::AddIOp>(def))
return addExp(kAddI, e0, e1);
if (isa<arith::SubFOp>(def))
@ -927,6 +935,8 @@ Value Merger::buildExp(RewriterBase &rewriter, Location loc, unsigned e,
// Binary ops.
case kMulF:
return rewriter.create<arith::MulFOp>(loc, v0, v1);
case kMulC:
return rewriter.create<complex::MulOp>(loc, v0, v1);
case kMulI:
return rewriter.create<arith::MulIOp>(loc, v0, v1);
case kDivF:
@ -937,6 +947,8 @@ Value Merger::buildExp(RewriterBase &rewriter, Location loc, unsigned e,
return rewriter.create<arith::DivUIOp>(loc, v0, v1);
case kAddF:
return rewriter.create<arith::AddFOp>(loc, v0, v1);
case kAddC:
return rewriter.create<complex::AddOp>(loc, v0, v1);
case kAddI:
return rewriter.create<arith::AddIOp>(loc, v0, v1);
case kSubF:

81
mlir/lib/ExecutionEngine/SparseTensorUtils.cpp
View File

@ -21,6 +21,7 @@
#include <algorithm>
#include <cassert>
#include <complex>
#include <cctype>
#include <cinttypes>
#include <cstdio>
@ -33,6 +34,9 @@
#include <numeric>
#include <vector>
using complex64 = std::complex<double>;
using complex32 = std::complex<float>;
//===----------------------------------------------------------------------===//
//
// Internal support for storing and reading sparse tensors.
@ -287,6 +291,8 @@ public:
virtual void getValues(std::vector<int32_t> **) { fatal("vali32"); }
virtual void getValues(std::vector<int16_t> **) { fatal("vali16"); }
virtual void getValues(std::vector<int8_t> **) { fatal("vali8"); }
virtual void getValues(std::vector<complex64> **) { fatal("valc64"); }
virtual void getValues(std::vector<complex32> **) { fatal("valc32"); }
/// Element-wise insertion in lexicographic index order.
virtual void lexInsert(const uint64_t *, double) { fatal("insf64"); }
@ -295,6 +301,8 @@ public:
virtual void lexInsert(const uint64_t *, int32_t) { fatal("insi32"); }
virtual void lexInsert(const uint64_t *, int16_t) { fatal("ins16"); }
virtual void lexInsert(const uint64_t *, int8_t) { fatal("insi8"); }
virtual void lexInsert(const uint64_t *, complex64) { fatal("insc64"); }
virtual void lexInsert(const uint64_t *, complex32) { fatal("insc32"); }
/// Expanded insertion.
virtual void expInsert(uint64_t *, double *, bool *, uint64_t *, uint64_t) {
@ -315,6 +323,14 @@ public:
virtual void expInsert(uint64_t *, int8_t *, bool *, uint64_t *, uint64_t) {
fatal("expi8");
}
virtual void expInsert(uint64_t *, complex64 *, bool *, uint64_t *,
uint64_t) {
fatal("expc64");
}
virtual void expInsert(uint64_t *, complex32 *, bool *, uint64_t *,
uint64_t) {
fatal("expc32");
}
/// Finishes insertion.
virtual void endInsert() = 0;
@ -1006,6 +1022,7 @@ 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();
@ -1293,6 +1310,10 @@ _mlir_ciface_newSparseTensor(StridedMemRefType<DimLevelType, 1> *aref, // NOLINT
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).
fputs("unsupported combination of types\n", stderr);
exit(1);
@ -1319,6 +1340,8 @@ IMPL_SPARSEVALUES(sparseValuesI64, int64_t, getValues)
IMPL_SPARSEVALUES(sparseValuesI32, int32_t, getValues)
IMPL_SPARSEVALUES(sparseValuesI16, int16_t, getValues)
IMPL_SPARSEVALUES(sparseValuesI8, int8_t, getValues)
IMPL_SPARSEVALUES(sparseValuesC64, complex64, getValues)
IMPL_SPARSEVALUES(sparseValuesC32, complex32, getValues)
/// Helper to add value to coordinate scheme, one per value type.
IMPL_ADDELT(addEltF64, double)
@ -1327,6 +1350,17 @@ IMPL_ADDELT(addEltI64, int64_t)
IMPL_ADDELT(addEltI32, int32_t)
IMPL_ADDELT(addEltI16, int16_t)
IMPL_ADDELT(addEltI8, int8_t)
IMPL_ADDELT(addEltC64, complex64)
IMPL_ADDELT(addEltC32ABI, complex32)
// Make prototype explicit to accept the !llvm.struct<(f32, f32)> without
// any padding (which seem to happen for complex32 when passed as scalar;
// all other cases, e.g. pointer to array, work as expected).
// TODO: cleaner way to avoid ABI padding problem?
void *_mlir_ciface_addEltC32(void *tensor, float r, float i,
StridedMemRefType<index_type, 1> *iref,
StridedMemRefType<index_type, 1> *pref) {
return _mlir_ciface_addEltC32ABI(tensor, complex32(r, i), iref, pref);
}
/// Helper to enumerate elements of coordinate scheme, one per value type.
IMPL_GETNEXT(getNextF64, double)
@ -1335,6 +1369,8 @@ IMPL_GETNEXT(getNextI64, int64_t)
IMPL_GETNEXT(getNextI32, int32_t)
IMPL_GETNEXT(getNextI16, int16_t)
IMPL_GETNEXT(getNextI8, int8_t)
IMPL_GETNEXT(getNextC64, complex64)
IMPL_GETNEXT(getNextC32, complex32)
/// Insert elements in lexicographical index order, one per value type.
IMPL_LEXINSERT(lexInsertF64, double)
@ -1343,6 +1379,17 @@ IMPL_LEXINSERT(lexInsertI64, int64_t)
IMPL_LEXINSERT(lexInsertI32, int32_t)
IMPL_LEXINSERT(lexInsertI16, int16_t)
IMPL_LEXINSERT(lexInsertI8, int8_t)
IMPL_LEXINSERT(lexInsertC64, complex64)
IMPL_LEXINSERT(lexInsertC32ABI, complex32)
// Make prototype explicit to accept the !llvm.struct<(f32, f32)> without
// any padding (which seem to happen for complex32 when passed as scalar;
// all other cases, e.g. pointer to array, work as expected).
// TODO: cleaner way to avoid ABI padding problem?
void _mlir_ciface_lexInsertC32(void *tensor,
StridedMemRefType<index_type, 1> *cref, float r,
float i) {
_mlir_ciface_lexInsertC32ABI(tensor, cref, complex32(r, i));
}
/// Insert using expansion, one per value type.
IMPL_EXPINSERT(expInsertF64, double)
@ -1351,6 +1398,8 @@ IMPL_EXPINSERT(expInsertI64, int64_t)
IMPL_EXPINSERT(expInsertI32, int32_t)
IMPL_EXPINSERT(expInsertI16, int16_t)
IMPL_EXPINSERT(expInsertI8, int8_t)
IMPL_EXPINSERT(expInsertC64, complex64)
IMPL_EXPINSERT(expInsertC32, complex32)
#undef CASE
#undef IMPL_SPARSEVALUES
@ -1379,6 +1428,12 @@ void outSparseTensorI16(void *tensor, void *dest, bool sort) {
void outSparseTensorI8(void *tensor, void *dest, bool sort) {
return outSparseTensor<int8_t>(tensor, dest, sort);
}
void outSparseTensorC64(void *tensor, void *dest, bool sort) {
return outSparseTensor<complex64>(tensor, dest, sort);
}
void outSparseTensorC32(void *tensor, void *dest, bool sort) {
return outSparseTensor<complex32>(tensor, dest, sort);
}
//===----------------------------------------------------------------------===//
//
@ -1428,6 +1483,8 @@ IMPL_DELCOO(I64, int64_t)
IMPL_DELCOO(I32, int32_t)
IMPL_DELCOO(I16, int16_t)
IMPL_DELCOO(I8, int8_t)
IMPL_DELCOO(C64, complex64)
IMPL_DELCOO(C32, complex32)
#undef IMPL_DELCOO
/// Initializes sparse tensor from a COO-flavored format expressed using C-style
@ -1489,6 +1546,18 @@ void *convertToMLIRSparseTensorI8(uint64_t rank, uint64_t nse, uint64_t *shape,
return toMLIRSparseTensor<int8_t>(rank, nse, shape, values, indices, perm,
sparse);
}
void *convertToMLIRSparseTensorC64(uint64_t rank, uint64_t nse, uint64_t *shape,
complex64 *values, uint64_t *indices,
uint64_t *perm, uint8_t *sparse) {
return toMLIRSparseTensor<complex64>(rank, nse, shape, values, indices, perm,
sparse);
}
void *convertToMLIRSparseTensorC32(uint64_t rank, uint64_t nse, uint64_t *shape,
complex32 *values, uint64_t *indices,
uint64_t *perm, uint8_t *sparse) {
return toMLIRSparseTensor<complex32>(rank, nse, shape, values, indices, perm,
sparse);
}
/// Converts a sparse tensor to COO-flavored format expressed using C-style
/// data structures. The expected output parameters are pointers for these
@ -1540,6 +1609,18 @@ void convertFromMLIRSparseTensorI8(void *tensor, uint64_t *pRank,
int8_t **pValues, uint64_t **pIndices) {
fromMLIRSparseTensor<int8_t>(tensor, pRank, pNse, pShape, pValues, pIndices);
}
void convertFromMLIRSparseTensorC64(void *tensor, uint64_t *pRank,
uint64_t *pNse, uint64_t **pShape,
complex64 **pValues, uint64_t **pIndices) {
fromMLIRSparseTensor<complex64>(tensor, pRank, pNse, pShape, pValues,
pIndices);
}
void convertFromMLIRSparseTensorC32(void *tensor, uint64_t *pRank,
uint64_t *pNse, uint64_t **pShape,
complex32 **pValues, uint64_t **pIndices) {
fromMLIRSparseTensor<complex32>(tensor, pRank, pNse, pShape, pValues,
pIndices);
}
} // extern "C"

116
mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_complex32.mlir Normal file
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@ -0,0 +1,116 @@
// RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#trait_op = {
indexing_maps = [
affine_map<(i) -> (i)>, // a (in)
affine_map<(i) -> (i)>, // b (in)
affine_map<(i) -> (i)> // x (out)
],
iterator_types = ["parallel"],
doc = "x(i) = a(i) OP b(i)"
}
module {
func.func @cadd(%arga: tensor<?xcomplex<f32>, #SparseVector>,
%argb: tensor<?xcomplex<f32>, #SparseVector>)
-> tensor<?xcomplex<f32>, #SparseVector> {
%c = arith.constant 0 : index
%d = tensor.dim %arga, %c : tensor<?xcomplex<f32>, #SparseVector>
%xv = sparse_tensor.init [%d] : tensor<?xcomplex<f32>, #SparseVector>
%0 = linalg.generic #trait_op
ins(%arga, %argb: tensor<?xcomplex<f32>, #SparseVector>,
tensor<?xcomplex<f32>, #SparseVector>)
outs(%xv: tensor<?xcomplex<f32>, #SparseVector>) {
^bb(%a: complex<f32>, %b: complex<f32>, %x: complex<f32>):
%1 = complex.add %a, %b : complex<f32>
linalg.yield %1 : complex<f32>
} -> tensor<?xcomplex<f32>, #SparseVector>
return %0 : tensor<?xcomplex<f32>, #SparseVector>
}
func.func @cmul(%arga: tensor<?xcomplex<f32>, #SparseVector>,
%argb: tensor<?xcomplex<f32>, #SparseVector>)
-> tensor<?xcomplex<f32>, #SparseVector> {
%c = arith.constant 0 : index
%d = tensor.dim %arga, %c : tensor<?xcomplex<f32>, #SparseVector>
%xv = sparse_tensor.init [%d] : tensor<?xcomplex<f32>, #SparseVector>
%0 = linalg.generic #trait_op
ins(%arga, %argb: tensor<?xcomplex<f32>, #SparseVector>,
tensor<?xcomplex<f32>, #SparseVector>)
outs(%xv: tensor<?xcomplex<f32>, #SparseVector>) {
^bb(%a: complex<f32>, %b: complex<f32>, %x: complex<f32>):
%1 = complex.mul %a, %b : complex<f32>
linalg.yield %1 : complex<f32>
} -> tensor<?xcomplex<f32>, #SparseVector>
return %0 : tensor<?xcomplex<f32>, #SparseVector>
}
func.func @dump(%arg0: tensor<?xcomplex<f32>, #SparseVector>, %d: index) {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%mem = sparse_tensor.values %arg0 : tensor<?xcomplex<f32>, #SparseVector> to memref<?xcomplex<f32>>
scf.for %i = %c0 to %d step %c1 {
%v = memref.load %mem[%i] : memref<?xcomplex<f32>>
%real = complex.re %v : complex<f32>
%imag = complex.im %v : complex<f32>
vector.print %real : f32
vector.print %imag : f32
}
return
}
// Driver method to call and verify complex kernels.
func.func @entry() {
// Setup sparse vectors.
%v1 = arith.constant sparse<
[ [0], [28], [31] ],
[ (511.13, 2.0), (3.0, 4.0), (5.0, 6.0) ] > : tensor<32xcomplex<f32>>
%v2 = arith.constant sparse<
[ [1], [28], [31] ],
[ (1.0, 0.0), (2.0, 0.0), (3.0, 0.0) ] > : tensor<32xcomplex<f32>>
%sv1 = sparse_tensor.convert %v1 : tensor<32xcomplex<f32>> to tensor<?xcomplex<f32>, #SparseVector>
%sv2 = sparse_tensor.convert %v2 : tensor<32xcomplex<f32>> to tensor<?xcomplex<f32>, #SparseVector>
// Call sparse vector kernels.
%0 = call @cadd(%sv1, %sv2)
: (tensor<?xcomplex<f32>, #SparseVector>,
tensor<?xcomplex<f32>, #SparseVector>) -> tensor<?xcomplex<f32>, #SparseVector>
%1 = call @cmul(%sv1, %sv2)
: (tensor<?xcomplex<f32>, #SparseVector>,
tensor<?xcomplex<f32>, #SparseVector>) -> tensor<?xcomplex<f32>, #SparseVector>
//
// Verify the results.
//
// CHECK: 511.13
// CHECK-NEXT: 2
// CHECK-NEXT: 1
// CHECK-NEXT: 0
// CHECK-NEXT: 5
// CHECK-NEXT: 4
// CHECK-NEXT: 8
// CHECK-NEXT: 6
// CHECK-NEXT: 6
// CHECK-NEXT: 8
// CHECK-NEXT: 15
// CHECK-NEXT: 18
//
%d1 = arith.constant 4 : index
%d2 = arith.constant 2 : index
call @dump(%0, %d1) : (tensor<?xcomplex<f32>, #SparseVector>, index) -> ()
call @dump(%1, %d2) : (tensor<?xcomplex<f32>, #SparseVector>, index) -> ()
// Release the resources.
sparse_tensor.release %sv1 : tensor<?xcomplex<f32>, #SparseVector>
sparse_tensor.release %sv2 : tensor<?xcomplex<f32>, #SparseVector>
sparse_tensor.release %0 : tensor<?xcomplex<f32>, #SparseVector>
sparse_tensor.release %1 : tensor<?xcomplex<f32>, #SparseVector>
return
}
}

116
mlir/test/Integration/Dialect/SparseTensor/CPU/sparse_complex64.mlir Normal file
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@ -0,0 +1,116 @@
// RUN: mlir-opt %s --sparse-compiler | \
// RUN: mlir-cpu-runner \
// RUN: -e entry -entry-point-result=void \
// RUN: -shared-libs=%mlir_integration_test_dir/libmlir_c_runner_utils%shlibext | \
// RUN: FileCheck %s
#SparseVector = #sparse_tensor.encoding<{dimLevelType = ["compressed"]}>
#trait_op = {
indexing_maps = [
affine_map<(i) -> (i)>, // a (in)
affine_map<(i) -> (i)>, // b (in)
affine_map<(i) -> (i)> // x (out)
],
iterator_types = ["parallel"],
doc = "x(i) = a(i) OP b(i)"
}
module {
func.func @cadd(%arga: tensor<?xcomplex<f64>, #SparseVector>,
%argb: tensor<?xcomplex<f64>, #SparseVector>)
-> tensor<?xcomplex<f64>, #SparseVector> {
%c = arith.constant 0 : index
%d = tensor.dim %arga, %c : tensor<?xcomplex<f64>, #SparseVector>
%xv = sparse_tensor.init [%d] : tensor<?xcomplex<f64>, #SparseVector>
%0 = linalg.generic #trait_op
ins(%arga, %argb: tensor<?xcomplex<f64>, #SparseVector>,
tensor<?xcomplex<f64>, #SparseVector>)
outs(%xv: tensor<?xcomplex<f64>, #SparseVector>) {
^bb(%a: complex<f64>, %b: complex<f64>, %x: complex<f64>):
%1 = complex.add %a, %b : complex<f64>
linalg.yield %1 : complex<f64>
} -> tensor<?xcomplex<f64>, #SparseVector>
return %0 : tensor<?xcomplex<f64>, #SparseVector>
}
func.func @cmul(%arga: tensor<?xcomplex<f64>, #SparseVector>,
%argb: tensor<?xcomplex<f64>, #SparseVector>)
-> tensor<?xcomplex<f64>, #SparseVector> {
%c = arith.constant 0 : index
%d = tensor.dim %arga, %c : tensor<?xcomplex<f64>, #SparseVector>
%xv = sparse_tensor.init [%d] : tensor<?xcomplex<f64>, #SparseVector>
%0 = linalg.generic #trait_op
ins(%arga, %argb: tensor<?xcomplex<f64>, #SparseVector>,
tensor<?xcomplex<f64>, #SparseVector>)
outs(%xv: tensor<?xcomplex<f64>, #SparseVector>) {
^bb(%a: complex<f64>, %b: complex<f64>, %x: complex<f64>):
%1 = complex.mul %a, %b : complex<f64>
linalg.yield %1 : complex<f64>
} -> tensor<?xcomplex<f64>, #SparseVector>
return %0 : tensor<?xcomplex<f64>, #SparseVector>
}
func.func @dump(%arg0: tensor<?xcomplex<f64>, #SparseVector>, %d: index) {
%c0 = arith.constant 0 : index
%c1 = arith.constant 1 : index
%mem = sparse_tensor.values %arg0 : tensor<?xcomplex<f64>, #SparseVector> to memref<?xcomplex<f64>>
scf.for %i = %c0 to %d step %c1 {
%v = memref.load %mem[%i] : memref<?xcomplex<f64>>
%real = complex.re %v : complex<f64>
%imag = complex.im %v : complex<f64>
vector.print %real : f64
vector.print %imag : f64
}
return
}
// Driver method to call and verify complex kernels.
func.func @entry() {
// Setup sparse vectors.
%v1 = arith.constant sparse<
[ [0], [28], [31] ],
[ (511.13, 2.0), (3.0, 4.0), (5.0, 6.0) ] > : tensor<32xcomplex<f64>>
%v2 = arith.constant sparse<
[ [1], [28], [31] ],
[ (1.0, 0.0), (2.0, 0.0), (3.0, 0.0) ] > : tensor<32xcomplex<f64>>
%sv1 = sparse_tensor.convert %v1 : tensor<32xcomplex<f64>> to tensor<?xcomplex<f64>, #SparseVector>
%sv2 = sparse_tensor.convert %v2 : tensor<32xcomplex<f64>> to tensor<?xcomplex<f64>, #SparseVector>
// Call sparse vector kernels.
%0 = call @cadd(%sv1, %sv2)
: (tensor<?xcomplex<f64>, #SparseVector>,
tensor<?xcomplex<f64>, #SparseVector>) -> tensor<?xcomplex<f64>, #SparseVector>
%1 = call @cmul(%sv1, %sv2)
: (tensor<?xcomplex<f64>, #SparseVector>,
tensor<?xcomplex<f64>, #SparseVector>) -> tensor<?xcomplex<f64>, #SparseVector>
//
// Verify the results.
//
// CHECK: 511.13
// CHECK-NEXT: 2
// CHECK-NEXT: 1
// CHECK-NEXT: 0
// CHECK-NEXT: 5
// CHECK-NEXT: 4
// CHECK-NEXT: 8
// CHECK-NEXT: 6
// CHECK-NEXT: 6
// CHECK-NEXT: 8
// CHECK-NEXT: 15
// CHECK-NEXT: 18
//
%d1 = arith.constant 4 : index
%d2 = arith.constant 2 : index
call @dump(%0, %d1) : (tensor<?xcomplex<f64>, #SparseVector>, index) -> ()
call @dump(%1, %d2) : (tensor<?xcomplex<f64>, #SparseVector>, index) -> ()
// Release the resources.
sparse_tensor.release %sv1 : tensor<?xcomplex<f64>, #SparseVector>
sparse_tensor.release %sv2 : tensor<?xcomplex<f64>, #SparseVector>
sparse_tensor.release %0 : tensor<?xcomplex<f64>, #SparseVector>
sparse_tensor.release %1 : tensor<?xcomplex<f64>, #SparseVector>
return
}
}

1
utils/bazel/llvm-project-overlay/mlir/BUILD.bazel
View File

@ -2009,6 +2009,7 @@ cc_library(
includes = ["include"],
deps = [
":ArithmeticDialect",
":ComplexDialect",
":IR",
":LinalgOps",
":MathDialect",