llvm-project/mlir/test/CAPI/ir.c

844 lines
32 KiB
C

/*===- ir.c - Simple test of C APIs ---------------------------------------===*\
|* *|
|* 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 *|
|* *|
\*===----------------------------------------------------------------------===*/
/* RUN: mlir-capi-ir-test 2>&1 | FileCheck %s
*/
#include "mlir-c/AffineMap.h"
#include "mlir-c/IR.h"
#include "mlir-c/Registration.h"
#include "mlir-c/StandardAttributes.h"
#include "mlir-c/StandardTypes.h"
#include <assert.h>
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
void populateLoopBody(MlirContext ctx, MlirBlock loopBody,
MlirLocation location, MlirBlock funcBody) {
MlirValue iv = mlirBlockGetArgument(loopBody, 0);
MlirValue funcArg0 = mlirBlockGetArgument(funcBody, 0);
MlirValue funcArg1 = mlirBlockGetArgument(funcBody, 1);
MlirType f32Type = mlirTypeParseGet(ctx, "f32");
MlirOperationState loadLHSState = mlirOperationStateGet("std.load", location);
MlirValue loadLHSOperands[] = {funcArg0, iv};
mlirOperationStateAddOperands(&loadLHSState, 2, loadLHSOperands);
mlirOperationStateAddResults(&loadLHSState, 1, &f32Type);
MlirOperation loadLHS = mlirOperationCreate(&loadLHSState);
mlirBlockAppendOwnedOperation(loopBody, loadLHS);
MlirOperationState loadRHSState = mlirOperationStateGet("std.load", location);
MlirValue loadRHSOperands[] = {funcArg1, iv};
mlirOperationStateAddOperands(&loadRHSState, 2, loadRHSOperands);
mlirOperationStateAddResults(&loadRHSState, 1, &f32Type);
MlirOperation loadRHS = mlirOperationCreate(&loadRHSState);
mlirBlockAppendOwnedOperation(loopBody, loadRHS);
MlirOperationState addState = mlirOperationStateGet("std.addf", location);
MlirValue addOperands[] = {mlirOperationGetResult(loadLHS, 0),
mlirOperationGetResult(loadRHS, 0)};
mlirOperationStateAddOperands(&addState, 2, addOperands);
mlirOperationStateAddResults(&addState, 1, &f32Type);
MlirOperation add = mlirOperationCreate(&addState);
mlirBlockAppendOwnedOperation(loopBody, add);
MlirOperationState storeState = mlirOperationStateGet("std.store", location);
MlirValue storeOperands[] = {mlirOperationGetResult(add, 0), funcArg0, iv};
mlirOperationStateAddOperands(&storeState, 3, storeOperands);
MlirOperation store = mlirOperationCreate(&storeState);
mlirBlockAppendOwnedOperation(loopBody, store);
MlirOperationState yieldState = mlirOperationStateGet("scf.yield", location);
MlirOperation yield = mlirOperationCreate(&yieldState);
mlirBlockAppendOwnedOperation(loopBody, yield);
}
MlirModule makeAdd(MlirContext ctx, MlirLocation location) {
MlirModule moduleOp = mlirModuleCreateEmpty(location);
MlirOperation module = mlirModuleGetOperation(moduleOp);
MlirRegion moduleBodyRegion = mlirOperationGetRegion(module, 0);
MlirBlock moduleBody = mlirRegionGetFirstBlock(moduleBodyRegion);
MlirType memrefType = mlirTypeParseGet(ctx, "memref<?xf32>");
MlirType funcBodyArgTypes[] = {memrefType, memrefType};
MlirRegion funcBodyRegion = mlirRegionCreate();
MlirBlock funcBody = mlirBlockCreate(
sizeof(funcBodyArgTypes) / sizeof(MlirType), funcBodyArgTypes);
mlirRegionAppendOwnedBlock(funcBodyRegion, funcBody);
MlirAttribute funcTypeAttr =
mlirAttributeParseGet(ctx, "(memref<?xf32>, memref<?xf32>) -> ()");
MlirAttribute funcNameAttr = mlirAttributeParseGet(ctx, "\"add\"");
MlirNamedAttribute funcAttrs[] = {
mlirNamedAttributeGet("type", funcTypeAttr),
mlirNamedAttributeGet("sym_name", funcNameAttr)};
MlirOperationState funcState = mlirOperationStateGet("func", location);
mlirOperationStateAddAttributes(&funcState, 2, funcAttrs);
mlirOperationStateAddOwnedRegions(&funcState, 1, &funcBodyRegion);
MlirOperation func = mlirOperationCreate(&funcState);
mlirBlockInsertOwnedOperation(moduleBody, 0, func);
MlirType indexType = mlirTypeParseGet(ctx, "index");
MlirAttribute indexZeroLiteral = mlirAttributeParseGet(ctx, "0 : index");
MlirNamedAttribute indexZeroValueAttr =
mlirNamedAttributeGet("value", indexZeroLiteral);
MlirOperationState constZeroState =
mlirOperationStateGet("std.constant", location);
mlirOperationStateAddResults(&constZeroState, 1, &indexType);
mlirOperationStateAddAttributes(&constZeroState, 1, &indexZeroValueAttr);
MlirOperation constZero = mlirOperationCreate(&constZeroState);
mlirBlockAppendOwnedOperation(funcBody, constZero);
MlirValue funcArg0 = mlirBlockGetArgument(funcBody, 0);
MlirValue constZeroValue = mlirOperationGetResult(constZero, 0);
MlirValue dimOperands[] = {funcArg0, constZeroValue};
MlirOperationState dimState = mlirOperationStateGet("std.dim", location);
mlirOperationStateAddOperands(&dimState, 2, dimOperands);
mlirOperationStateAddResults(&dimState, 1, &indexType);
MlirOperation dim = mlirOperationCreate(&dimState);
mlirBlockAppendOwnedOperation(funcBody, dim);
MlirRegion loopBodyRegion = mlirRegionCreate();
MlirBlock loopBody = mlirBlockCreate(/*nArgs=*/1, &indexType);
mlirRegionAppendOwnedBlock(loopBodyRegion, loopBody);
MlirAttribute indexOneLiteral = mlirAttributeParseGet(ctx, "1 : index");
MlirNamedAttribute indexOneValueAttr =
mlirNamedAttributeGet("value", indexOneLiteral);
MlirOperationState constOneState =
mlirOperationStateGet("std.constant", location);
mlirOperationStateAddResults(&constOneState, 1, &indexType);
mlirOperationStateAddAttributes(&constOneState, 1, &indexOneValueAttr);
MlirOperation constOne = mlirOperationCreate(&constOneState);
mlirBlockAppendOwnedOperation(funcBody, constOne);
MlirValue dimValue = mlirOperationGetResult(dim, 0);
MlirValue constOneValue = mlirOperationGetResult(constOne, 0);
MlirValue loopOperands[] = {constZeroValue, dimValue, constOneValue};
MlirOperationState loopState = mlirOperationStateGet("scf.for", location);
mlirOperationStateAddOperands(&loopState, 3, loopOperands);
mlirOperationStateAddOwnedRegions(&loopState, 1, &loopBodyRegion);
MlirOperation loop = mlirOperationCreate(&loopState);
mlirBlockAppendOwnedOperation(funcBody, loop);
populateLoopBody(ctx, loopBody, location, funcBody);
MlirOperationState retState = mlirOperationStateGet("std.return", location);
MlirOperation ret = mlirOperationCreate(&retState);
mlirBlockAppendOwnedOperation(funcBody, ret);
return moduleOp;
}
struct OpListNode {
MlirOperation op;
struct OpListNode *next;
};
typedef struct OpListNode OpListNode;
struct ModuleStats {
unsigned numOperations;
unsigned numAttributes;
unsigned numBlocks;
unsigned numRegions;
unsigned numValues;
};
typedef struct ModuleStats ModuleStats;
void collectStatsSingle(OpListNode *head, ModuleStats *stats) {
MlirOperation operation = head->op;
stats->numOperations += 1;
stats->numValues += mlirOperationGetNumResults(operation);
stats->numAttributes += mlirOperationGetNumAttributes(operation);
unsigned numRegions = mlirOperationGetNumRegions(operation);
stats->numRegions += numRegions;
for (unsigned i = 0; i < numRegions; ++i) {
MlirRegion region = mlirOperationGetRegion(operation, i);
for (MlirBlock block = mlirRegionGetFirstBlock(region);
!mlirBlockIsNull(block); block = mlirBlockGetNextInRegion(block)) {
++stats->numBlocks;
stats->numValues += mlirBlockGetNumArguments(block);
for (MlirOperation child = mlirBlockGetFirstOperation(block);
!mlirOperationIsNull(child);
child = mlirOperationGetNextInBlock(child)) {
OpListNode *node = malloc(sizeof(OpListNode));
node->op = child;
node->next = head->next;
head->next = node;
}
}
}
}
void collectStats(MlirOperation operation) {
OpListNode *head = malloc(sizeof(OpListNode));
head->op = operation;
head->next = NULL;
ModuleStats stats;
stats.numOperations = 0;
stats.numAttributes = 0;
stats.numBlocks = 0;
stats.numRegions = 0;
stats.numValues = 0;
do {
collectStatsSingle(head, &stats);
OpListNode *next = head->next;
free(head);
head = next;
} while (head);
fprintf(stderr, "Number of operations: %u\n", stats.numOperations);
fprintf(stderr, "Number of attributes: %u\n", stats.numAttributes);
fprintf(stderr, "Number of blocks: %u\n", stats.numBlocks);
fprintf(stderr, "Number of regions: %u\n", stats.numRegions);
fprintf(stderr, "Number of values: %u\n", stats.numValues);
}
static void printToStderr(const char *str, intptr_t len, void *userData) {
(void)userData;
fwrite(str, 1, len, stderr);
}
static void printFirstOfEach(MlirOperation operation) {
// Assuming we are given a module, go to the first operation of the first
// function.
MlirRegion region = mlirOperationGetRegion(operation, 0);
MlirBlock block = mlirRegionGetFirstBlock(region);
operation = mlirBlockGetFirstOperation(block);
region = mlirOperationGetRegion(operation, 0);
block = mlirRegionGetFirstBlock(region);
operation = mlirBlockGetFirstOperation(block);
// In the module we created, the first operation of the first function is an
// "std.dim", which has an attribute an a single result that we can use to
// test the printing mechanism.
mlirBlockPrint(block, printToStderr, NULL);
fprintf(stderr, "\n");
mlirOperationPrint(operation, printToStderr, NULL);
fprintf(stderr, "\n");
MlirNamedAttribute namedAttr = mlirOperationGetAttribute(operation, 0);
mlirAttributePrint(namedAttr.attribute, printToStderr, NULL);
fprintf(stderr, "\n");
MlirValue value = mlirOperationGetResult(operation, 0);
mlirValuePrint(value, printToStderr, NULL);
fprintf(stderr, "\n");
MlirType type = mlirValueGetType(value);
mlirTypePrint(type, printToStderr, NULL);
fprintf(stderr, "\n");
}
/// Dumps instances of all standard types to check that C API works correctly.
/// Additionally, performs simple identity checks that a standard type
/// constructed with C API can be inspected and has the expected type. The
/// latter achieves full coverage of C API for standard types. Returns 0 on
/// success and a non-zero error code on failure.
static int printStandardTypes(MlirContext ctx) {
// Integer types.
MlirType i32 = mlirIntegerTypeGet(ctx, 32);
MlirType si32 = mlirIntegerTypeSignedGet(ctx, 32);
MlirType ui32 = mlirIntegerTypeUnsignedGet(ctx, 32);
if (!mlirTypeIsAInteger(i32) || mlirTypeIsAF32(i32))
return 1;
if (!mlirTypeIsAInteger(si32) || !mlirIntegerTypeIsSigned(si32))
return 2;
if (!mlirTypeIsAInteger(ui32) || !mlirIntegerTypeIsUnsigned(ui32))
return 3;
if (mlirTypeEqual(i32, ui32) || mlirTypeEqual(i32, si32))
return 4;
if (mlirIntegerTypeGetWidth(i32) != mlirIntegerTypeGetWidth(si32))
return 5;
mlirTypeDump(i32);
fprintf(stderr, "\n");
mlirTypeDump(si32);
fprintf(stderr, "\n");
mlirTypeDump(ui32);
fprintf(stderr, "\n");
// Index type.
MlirType index = mlirIndexTypeGet(ctx);
if (!mlirTypeIsAIndex(index))
return 6;
mlirTypeDump(index);
fprintf(stderr, "\n");
// Floating-point types.
MlirType bf16 = mlirBF16TypeGet(ctx);
MlirType f16 = mlirF16TypeGet(ctx);
MlirType f32 = mlirF32TypeGet(ctx);
MlirType f64 = mlirF64TypeGet(ctx);
if (!mlirTypeIsABF16(bf16))
return 7;
if (!mlirTypeIsAF16(f16))
return 9;
if (!mlirTypeIsAF32(f32))
return 10;
if (!mlirTypeIsAF64(f64))
return 11;
mlirTypeDump(bf16);
fprintf(stderr, "\n");
mlirTypeDump(f16);
fprintf(stderr, "\n");
mlirTypeDump(f32);
fprintf(stderr, "\n");
mlirTypeDump(f64);
fprintf(stderr, "\n");
// None type.
MlirType none = mlirNoneTypeGet(ctx);
if (!mlirTypeIsANone(none))
return 12;
mlirTypeDump(none);
fprintf(stderr, "\n");
// Complex type.
MlirType cplx = mlirComplexTypeGet(f32);
if (!mlirTypeIsAComplex(cplx) ||
!mlirTypeEqual(mlirComplexTypeGetElementType(cplx), f32))
return 13;
mlirTypeDump(cplx);
fprintf(stderr, "\n");
// Vector (and Shaped) type. ShapedType is a common base class for vectors,
// memrefs and tensors, one cannot create instances of this class so it is
// tested on an instance of vector type.
int64_t shape[] = {2, 3};
MlirType vector =
mlirVectorTypeGet(sizeof(shape) / sizeof(int64_t), shape, f32);
if (!mlirTypeIsAVector(vector) || !mlirTypeIsAShaped(vector))
return 14;
if (!mlirTypeEqual(mlirShapedTypeGetElementType(vector), f32) ||
!mlirShapedTypeHasRank(vector) || mlirShapedTypeGetRank(vector) != 2 ||
mlirShapedTypeGetDimSize(vector, 0) != 2 ||
mlirShapedTypeIsDynamicDim(vector, 0) ||
mlirShapedTypeGetDimSize(vector, 1) != 3 ||
!mlirShapedTypeHasStaticShape(vector))
return 15;
mlirTypeDump(vector);
fprintf(stderr, "\n");
// Ranked tensor type.
MlirType rankedTensor =
mlirRankedTensorTypeGet(sizeof(shape) / sizeof(int64_t), shape, f32);
if (!mlirTypeIsATensor(rankedTensor) ||
!mlirTypeIsARankedTensor(rankedTensor))
return 16;
mlirTypeDump(rankedTensor);
fprintf(stderr, "\n");
// Unranked tensor type.
MlirType unrankedTensor = mlirUnrankedTensorTypeGet(f32);
if (!mlirTypeIsATensor(unrankedTensor) ||
!mlirTypeIsAUnrankedTensor(unrankedTensor) ||
mlirShapedTypeHasRank(unrankedTensor))
return 17;
mlirTypeDump(unrankedTensor);
fprintf(stderr, "\n");
// MemRef type.
MlirType memRef = mlirMemRefTypeContiguousGet(
f32, sizeof(shape) / sizeof(int64_t), shape, 2);
if (!mlirTypeIsAMemRef(memRef) ||
mlirMemRefTypeGetNumAffineMaps(memRef) != 0 ||
mlirMemRefTypeGetMemorySpace(memRef) != 2)
return 18;
mlirTypeDump(memRef);
fprintf(stderr, "\n");
// Unranked MemRef type.
MlirType unrankedMemRef = mlirUnrankedMemRefTypeGet(f32, 4);
if (!mlirTypeIsAUnrankedMemRef(unrankedMemRef) ||
mlirTypeIsAMemRef(unrankedMemRef) ||
mlirUnrankedMemrefGetMemorySpace(unrankedMemRef) != 4)
return 19;
mlirTypeDump(unrankedMemRef);
fprintf(stderr, "\n");
// Tuple type.
MlirType types[] = {unrankedMemRef, f32};
MlirType tuple = mlirTupleTypeGet(ctx, 2, types);
if (!mlirTypeIsATuple(tuple) || mlirTupleTypeGetNumTypes(tuple) != 2 ||
!mlirTypeEqual(mlirTupleTypeGetType(tuple, 0), unrankedMemRef) ||
!mlirTypeEqual(mlirTupleTypeGetType(tuple, 1), f32))
return 20;
mlirTypeDump(tuple);
fprintf(stderr, "\n");
return 0;
}
void callbackSetFixedLengthString(const char *data, intptr_t len,
void *userData) {
strncpy(userData, data, len);
}
int printStandardAttributes(MlirContext ctx) {
MlirAttribute floating =
mlirFloatAttrDoubleGet(ctx, mlirF64TypeGet(ctx), 2.0);
if (!mlirAttributeIsAFloat(floating) ||
fabs(mlirFloatAttrGetValueDouble(floating) - 2.0) > 1E-6)
return 1;
mlirAttributeDump(floating);
MlirAttribute integer = mlirIntegerAttrGet(mlirIntegerTypeGet(ctx, 32), 42);
if (!mlirAttributeIsAInteger(integer) ||
mlirIntegerAttrGetValueInt(integer) != 42)
return 2;
mlirAttributeDump(integer);
MlirAttribute boolean = mlirBoolAttrGet(ctx, 1);
if (!mlirAttributeIsABool(boolean) || !mlirBoolAttrGetValue(boolean))
return 3;
mlirAttributeDump(boolean);
const char data[] = "abcdefghijklmnopqestuvwxyz";
MlirAttribute opaque =
mlirOpaqueAttrGet(ctx, "std", 3, data, mlirNoneTypeGet(ctx));
if (!mlirAttributeIsAOpaque(opaque) ||
strcmp("std", mlirOpaqueAttrGetDialectNamespace(opaque)))
return 4;
MlirStringRef opaqueData = mlirOpaqueAttrGetData(opaque);
if (opaqueData.length != 3 ||
strncmp(data, opaqueData.data, opaqueData.length))
return 5;
mlirAttributeDump(opaque);
MlirAttribute string = mlirStringAttrGet(ctx, 2, data + 3);
if (!mlirAttributeIsAString(string))
return 6;
MlirStringRef stringValue = mlirStringAttrGetValue(string);
if (stringValue.length != 2 ||
strncmp(data + 3, stringValue.data, stringValue.length))
return 7;
mlirAttributeDump(string);
MlirAttribute flatSymbolRef = mlirFlatSymbolRefAttrGet(ctx, 3, data + 5);
if (!mlirAttributeIsAFlatSymbolRef(flatSymbolRef))
return 8;
MlirStringRef flatSymbolRefValue =
mlirFlatSymbolRefAttrGetValue(flatSymbolRef);
if (flatSymbolRefValue.length != 3 ||
strncmp(data + 5, flatSymbolRefValue.data, flatSymbolRefValue.length))
return 9;
mlirAttributeDump(flatSymbolRef);
MlirAttribute symbols[] = {flatSymbolRef, flatSymbolRef};
MlirAttribute symbolRef = mlirSymbolRefAttrGet(ctx, 2, data + 8, 2, symbols);
if (!mlirAttributeIsASymbolRef(symbolRef) ||
mlirSymbolRefAttrGetNumNestedReferences(symbolRef) != 2 ||
!mlirAttributeEqual(mlirSymbolRefAttrGetNestedReference(symbolRef, 0),
flatSymbolRef) ||
!mlirAttributeEqual(mlirSymbolRefAttrGetNestedReference(symbolRef, 1),
flatSymbolRef))
return 10;
MlirStringRef symbolRefLeaf = mlirSymbolRefAttrGetLeafReference(symbolRef);
MlirStringRef symbolRefRoot = mlirSymbolRefAttrGetRootReference(symbolRef);
if (symbolRefLeaf.length != 3 ||
strncmp(data + 5, symbolRefLeaf.data, symbolRefLeaf.length) ||
symbolRefRoot.length != 2 ||
strncmp(data + 8, symbolRefRoot.data, symbolRefRoot.length))
return 11;
mlirAttributeDump(symbolRef);
MlirAttribute type = mlirTypeAttrGet(mlirF32TypeGet(ctx));
if (!mlirAttributeIsAType(type) ||
!mlirTypeEqual(mlirF32TypeGet(ctx), mlirTypeAttrGetValue(type)))
return 12;
mlirAttributeDump(type);
MlirAttribute unit = mlirUnitAttrGet(ctx);
if (!mlirAttributeIsAUnit(unit))
return 13;
mlirAttributeDump(unit);
int64_t shape[] = {1, 2};
int bools[] = {0, 1};
uint32_t uints32[] = {0u, 1u};
int32_t ints32[] = {0, 1};
uint64_t uints64[] = {0u, 1u};
int64_t ints64[] = {0, 1};
float floats[] = {0.0f, 1.0f};
double doubles[] = {0.0, 1.0};
MlirAttribute boolElements = mlirDenseElementsAttrBoolGet(
mlirRankedTensorTypeGet(2, shape, mlirIntegerTypeGet(ctx, 1)), 2, bools);
MlirAttribute uint32Elements = mlirDenseElementsAttrUInt32Get(
mlirRankedTensorTypeGet(2, shape, mlirIntegerTypeUnsignedGet(ctx, 32)), 2,
uints32);
MlirAttribute int32Elements = mlirDenseElementsAttrInt32Get(
mlirRankedTensorTypeGet(2, shape, mlirIntegerTypeGet(ctx, 32)), 2,
ints32);
MlirAttribute uint64Elements = mlirDenseElementsAttrUInt64Get(
mlirRankedTensorTypeGet(2, shape, mlirIntegerTypeUnsignedGet(ctx, 64)), 2,
uints64);
MlirAttribute int64Elements = mlirDenseElementsAttrInt64Get(
mlirRankedTensorTypeGet(2, shape, mlirIntegerTypeGet(ctx, 64)), 2,
ints64);
MlirAttribute floatElements = mlirDenseElementsAttrFloatGet(
mlirRankedTensorTypeGet(2, shape, mlirF32TypeGet(ctx)), 2, floats);
MlirAttribute doubleElements = mlirDenseElementsAttrDoubleGet(
mlirRankedTensorTypeGet(2, shape, mlirF64TypeGet(ctx)), 2, doubles);
if (!mlirAttributeIsADenseElements(boolElements) ||
!mlirAttributeIsADenseElements(uint32Elements) ||
!mlirAttributeIsADenseElements(int32Elements) ||
!mlirAttributeIsADenseElements(uint64Elements) ||
!mlirAttributeIsADenseElements(int64Elements) ||
!mlirAttributeIsADenseElements(floatElements) ||
!mlirAttributeIsADenseElements(doubleElements))
return 14;
if (mlirDenseElementsAttrGetBoolValue(boolElements, 1) != 1 ||
mlirDenseElementsAttrGetUInt32Value(uint32Elements, 1) != 1 ||
mlirDenseElementsAttrGetInt32Value(int32Elements, 1) != 1 ||
mlirDenseElementsAttrGetUInt64Value(uint64Elements, 1) != 1 ||
mlirDenseElementsAttrGetInt64Value(int64Elements, 1) != 1 ||
fabsf(mlirDenseElementsAttrGetFloatValue(floatElements, 1) - 1.0f) >
1E-6f ||
fabs(mlirDenseElementsAttrGetDoubleValue(doubleElements, 1) - 1.0) > 1E-6)
return 15;
mlirAttributeDump(boolElements);
mlirAttributeDump(uint32Elements);
mlirAttributeDump(int32Elements);
mlirAttributeDump(uint64Elements);
mlirAttributeDump(int64Elements);
mlirAttributeDump(floatElements);
mlirAttributeDump(doubleElements);
MlirAttribute splatBool = mlirDenseElementsAttrBoolSplatGet(
mlirRankedTensorTypeGet(2, shape, mlirIntegerTypeGet(ctx, 1)), 1);
MlirAttribute splatUInt32 = mlirDenseElementsAttrUInt32SplatGet(
mlirRankedTensorTypeGet(2, shape, mlirIntegerTypeGet(ctx, 32)), 1);
MlirAttribute splatInt32 = mlirDenseElementsAttrInt32SplatGet(
mlirRankedTensorTypeGet(2, shape, mlirIntegerTypeGet(ctx, 32)), 1);
MlirAttribute splatUInt64 = mlirDenseElementsAttrUInt64SplatGet(
mlirRankedTensorTypeGet(2, shape, mlirIntegerTypeGet(ctx, 64)), 1);
MlirAttribute splatInt64 = mlirDenseElementsAttrInt64SplatGet(
mlirRankedTensorTypeGet(2, shape, mlirIntegerTypeGet(ctx, 64)), 1);
MlirAttribute splatFloat = mlirDenseElementsAttrFloatSplatGet(
mlirRankedTensorTypeGet(2, shape, mlirF32TypeGet(ctx)), 1.0f);
MlirAttribute splatDouble = mlirDenseElementsAttrDoubleSplatGet(
mlirRankedTensorTypeGet(2, shape, mlirF64TypeGet(ctx)), 1.0);
if (!mlirAttributeIsADenseElements(splatBool) ||
!mlirDenseElementsAttrIsSplat(splatBool) ||
!mlirAttributeIsADenseElements(splatUInt32) ||
!mlirDenseElementsAttrIsSplat(splatUInt32) ||
!mlirAttributeIsADenseElements(splatInt32) ||
!mlirDenseElementsAttrIsSplat(splatInt32) ||
!mlirAttributeIsADenseElements(splatUInt64) ||
!mlirDenseElementsAttrIsSplat(splatUInt64) ||
!mlirAttributeIsADenseElements(splatInt64) ||
!mlirDenseElementsAttrIsSplat(splatInt64) ||
!mlirAttributeIsADenseElements(splatFloat) ||
!mlirDenseElementsAttrIsSplat(splatFloat) ||
!mlirAttributeIsADenseElements(splatDouble) ||
!mlirDenseElementsAttrIsSplat(splatDouble))
return 16;
if (mlirDenseElementsAttrGetBoolSplatValue(splatBool) != 1 ||
mlirDenseElementsAttrGetUInt32SplatValue(splatUInt32) != 1 ||
mlirDenseElementsAttrGetInt32SplatValue(splatInt32) != 1 ||
mlirDenseElementsAttrGetUInt64SplatValue(splatUInt64) != 1 ||
mlirDenseElementsAttrGetInt64SplatValue(splatInt64) != 1 ||
fabsf(mlirDenseElementsAttrGetFloatSplatValue(splatFloat) - 1.0f) >
1E-6f ||
fabs(mlirDenseElementsAttrGetDoubleSplatValue(splatDouble) - 1.0) > 1E-6)
return 17;
mlirAttributeDump(splatBool);
mlirAttributeDump(splatUInt32);
mlirAttributeDump(splatInt32);
mlirAttributeDump(splatUInt64);
mlirAttributeDump(splatInt64);
mlirAttributeDump(splatFloat);
mlirAttributeDump(splatDouble);
mlirAttributeDump(mlirElementsAttrGetValue(floatElements, 2, uints64));
mlirAttributeDump(mlirElementsAttrGetValue(doubleElements, 2, uints64));
int64_t indices[] = {4, 7};
int64_t two = 2;
MlirAttribute indicesAttr = mlirDenseElementsAttrInt64Get(
mlirRankedTensorTypeGet(1, &two, mlirIntegerTypeGet(ctx, 64)), 2,
indices);
MlirAttribute valuesAttr = mlirDenseElementsAttrFloatGet(
mlirRankedTensorTypeGet(1, &two, mlirF32TypeGet(ctx)), 2, floats);
MlirAttribute sparseAttr = mlirSparseElementsAttribute(
mlirRankedTensorTypeGet(2, shape, mlirF32TypeGet(ctx)), indicesAttr,
valuesAttr);
mlirAttributeDump(sparseAttr);
return 0;
}
int printAffineMap(MlirContext ctx) {
MlirAffineMap emptyAffineMap = mlirAffineMapEmptyGet(ctx);
MlirAffineMap affineMap = mlirAffineMapGet(ctx, 3, 2);
MlirAffineMap constAffineMap = mlirAffineMapConstantGet(ctx, 2);
MlirAffineMap multiDimIdentityAffineMap =
mlirAffineMapMultiDimIdentityGet(ctx, 3);
MlirAffineMap minorIdentityAffineMap =
mlirAffineMapMinorIdentityGet(ctx, 3, 2);
unsigned permutation[] = {1, 2, 0};
MlirAffineMap permutationAffineMap = mlirAffineMapPermutationGet(
ctx, sizeof(permutation) / sizeof(unsigned), permutation);
mlirAffineMapDump(emptyAffineMap);
mlirAffineMapDump(affineMap);
mlirAffineMapDump(constAffineMap);
mlirAffineMapDump(multiDimIdentityAffineMap);
mlirAffineMapDump(minorIdentityAffineMap);
mlirAffineMapDump(permutationAffineMap);
if (!mlirAffineMapIsIdentity(emptyAffineMap) ||
mlirAffineMapIsIdentity(affineMap) ||
mlirAffineMapIsIdentity(constAffineMap) ||
!mlirAffineMapIsIdentity(multiDimIdentityAffineMap) ||
mlirAffineMapIsIdentity(minorIdentityAffineMap) ||
mlirAffineMapIsIdentity(permutationAffineMap))
return 1;
if (!mlirAffineMapIsMinorIdentity(emptyAffineMap) ||
mlirAffineMapIsMinorIdentity(affineMap) ||
!mlirAffineMapIsMinorIdentity(multiDimIdentityAffineMap) ||
!mlirAffineMapIsMinorIdentity(minorIdentityAffineMap) ||
mlirAffineMapIsMinorIdentity(permutationAffineMap))
return 2;
if (!mlirAffineMapIsEmpty(emptyAffineMap) ||
mlirAffineMapIsEmpty(affineMap) ||
mlirAffineMapIsEmpty(constAffineMap) ||
mlirAffineMapIsEmpty(multiDimIdentityAffineMap) ||
mlirAffineMapIsEmpty(minorIdentityAffineMap) ||
mlirAffineMapIsEmpty(permutationAffineMap))
return 3;
if (mlirAffineMapIsSingleConstant(emptyAffineMap) ||
mlirAffineMapIsSingleConstant(affineMap) ||
!mlirAffineMapIsSingleConstant(constAffineMap) ||
mlirAffineMapIsSingleConstant(multiDimIdentityAffineMap) ||
mlirAffineMapIsSingleConstant(minorIdentityAffineMap) ||
mlirAffineMapIsSingleConstant(permutationAffineMap))
return 4;
if (mlirAffineMapGetSingleConstantResult(constAffineMap) != 2)
return 5;
if (mlirAffineMapGetNumDims(emptyAffineMap) != 0 ||
mlirAffineMapGetNumDims(affineMap) != 3 ||
mlirAffineMapGetNumDims(constAffineMap) != 0 ||
mlirAffineMapGetNumDims(multiDimIdentityAffineMap) != 3 ||
mlirAffineMapGetNumDims(minorIdentityAffineMap) != 3 ||
mlirAffineMapGetNumDims(permutationAffineMap) != 3)
return 6;
if (mlirAffineMapGetNumSymbols(emptyAffineMap) != 0 ||
mlirAffineMapGetNumSymbols(affineMap) != 2 ||
mlirAffineMapGetNumSymbols(constAffineMap) != 0 ||
mlirAffineMapGetNumSymbols(multiDimIdentityAffineMap) != 0 ||
mlirAffineMapGetNumSymbols(minorIdentityAffineMap) != 0 ||
mlirAffineMapGetNumSymbols(permutationAffineMap) != 0)
return 7;
if (mlirAffineMapGetNumResults(emptyAffineMap) != 0 ||
mlirAffineMapGetNumResults(affineMap) != 0 ||
mlirAffineMapGetNumResults(constAffineMap) != 1 ||
mlirAffineMapGetNumResults(multiDimIdentityAffineMap) != 3 ||
mlirAffineMapGetNumResults(minorIdentityAffineMap) != 2 ||
mlirAffineMapGetNumResults(permutationAffineMap) != 3)
return 8;
if (mlirAffineMapGetNumInputs(emptyAffineMap) != 0 ||
mlirAffineMapGetNumInputs(affineMap) != 5 ||
mlirAffineMapGetNumInputs(constAffineMap) != 0 ||
mlirAffineMapGetNumInputs(multiDimIdentityAffineMap) != 3 ||
mlirAffineMapGetNumInputs(minorIdentityAffineMap) != 3 ||
mlirAffineMapGetNumInputs(permutationAffineMap) != 3)
return 9;
if (!mlirAffineMapIsProjectedPermutation(emptyAffineMap) ||
!mlirAffineMapIsPermutation(emptyAffineMap) ||
mlirAffineMapIsProjectedPermutation(affineMap) ||
mlirAffineMapIsPermutation(affineMap) ||
mlirAffineMapIsProjectedPermutation(constAffineMap) ||
mlirAffineMapIsPermutation(constAffineMap) ||
!mlirAffineMapIsProjectedPermutation(multiDimIdentityAffineMap) ||
!mlirAffineMapIsPermutation(multiDimIdentityAffineMap) ||
!mlirAffineMapIsProjectedPermutation(minorIdentityAffineMap) ||
mlirAffineMapIsPermutation(minorIdentityAffineMap) ||
!mlirAffineMapIsProjectedPermutation(permutationAffineMap) ||
!mlirAffineMapIsPermutation(permutationAffineMap))
return 10;
intptr_t sub[] = {1};
MlirAffineMap subMap = mlirAffineMapGetSubMap(
multiDimIdentityAffineMap, sizeof(sub) / sizeof(intptr_t), sub);
MlirAffineMap majorSubMap =
mlirAffineMapGetMajorSubMap(multiDimIdentityAffineMap, 1);
MlirAffineMap minorSubMap =
mlirAffineMapGetMinorSubMap(multiDimIdentityAffineMap, 1);
mlirAffineMapDump(subMap);
mlirAffineMapDump(majorSubMap);
mlirAffineMapDump(minorSubMap);
return 0;
}
int main() {
MlirContext ctx = mlirContextCreate();
mlirRegisterAllDialects(ctx);
MlirLocation location = mlirLocationUnknownGet(ctx);
MlirModule moduleOp = makeAdd(ctx, location);
MlirOperation module = mlirModuleGetOperation(moduleOp);
mlirOperationDump(module);
// clang-format off
// CHECK: module {
// CHECK: func @add(%[[ARG0:.*]]: memref<?xf32>, %[[ARG1:.*]]: memref<?xf32>) {
// CHECK: %[[C0:.*]] = constant 0 : index
// CHECK: %[[DIM:.*]] = dim %[[ARG0]], %[[C0]] : memref<?xf32>
// CHECK: %[[C1:.*]] = constant 1 : index
// CHECK: scf.for %[[I:.*]] = %[[C0]] to %[[DIM]] step %[[C1]] {
// CHECK: %[[LHS:.*]] = load %[[ARG0]][%[[I]]] : memref<?xf32>
// CHECK: %[[RHS:.*]] = load %[[ARG1]][%[[I]]] : memref<?xf32>
// CHECK: %[[SUM:.*]] = addf %[[LHS]], %[[RHS]] : f32
// CHECK: store %[[SUM]], %[[ARG0]][%[[I]]] : memref<?xf32>
// CHECK: }
// CHECK: return
// CHECK: }
// CHECK: }
// clang-format on
collectStats(module);
// clang-format off
// CHECK: Number of operations: 13
// CHECK: Number of attributes: 4
// CHECK: Number of blocks: 3
// CHECK: Number of regions: 3
// CHECK: Number of values: 9
// clang-format on
printFirstOfEach(module);
// clang-format off
// CHECK: %[[C0:.*]] = constant 0 : index
// CHECK: %[[DIM:.*]] = dim %{{.*}}, %[[C0]] : memref<?xf32>
// CHECK: %[[C1:.*]] = constant 1 : index
// CHECK: scf.for %[[I:.*]] = %[[C0]] to %[[DIM]] step %[[C1]] {
// CHECK: %[[LHS:.*]] = load %{{.*}}[%[[I]]] : memref<?xf32>
// CHECK: %[[RHS:.*]] = load %{{.*}}[%[[I]]] : memref<?xf32>
// CHECK: %[[SUM:.*]] = addf %[[LHS]], %[[RHS]] : f32
// CHECK: store %[[SUM]], %{{.*}}[%[[I]]] : memref<?xf32>
// CHECK: }
// CHECK: return
// CHECK: constant 0 : index
// CHECK: 0 : index
// CHECK: constant 0 : index
// CHECK: index
// clang-format on
mlirModuleDestroy(moduleOp);
// clang-format off
// CHECK-LABEL: @types
// CHECK: i32
// CHECK: si32
// CHECK: ui32
// CHECK: index
// CHECK: bf16
// CHECK: f16
// CHECK: f32
// CHECK: f64
// CHECK: none
// CHECK: complex<f32>
// CHECK: vector<2x3xf32>
// CHECK: tensor<2x3xf32>
// CHECK: tensor<*xf32>
// CHECK: memref<2x3xf32, 2>
// CHECK: memref<*xf32, 4>
// CHECK: tuple<memref<*xf32, 4>, f32>
// CHECK: 0
// clang-format on
fprintf(stderr, "@types\n");
int errcode = printStandardTypes(ctx);
fprintf(stderr, "%d\n", errcode);
// clang-format off
// CHECK-LABEL: @attrs
// CHECK: 2.000000e+00 : f64
// CHECK: 42 : i32
// CHECK: true
// CHECK: #std.abc
// CHECK: "de"
// CHECK: @fgh
// CHECK: @ij::@fgh::@fgh
// CHECK: f32
// CHECK: unit
// CHECK: dense<{{\[}}[false, true]]> : tensor<1x2xi1>
// CHECK: dense<{{\[}}[0, 1]]> : tensor<1x2xui32>
// CHECK: dense<{{\[}}[0, 1]]> : tensor<1x2xi32>
// CHECK: dense<{{\[}}[0, 1]]> : tensor<1x2xui64>
// CHECK: dense<{{\[}}[0, 1]]> : tensor<1x2xi64>
// CHECK: dense<{{\[}}[0.000000e+00, 1.000000e+00]]> : tensor<1x2xf32>
// CHECK: dense<{{\[}}[0.000000e+00, 1.000000e+00]]> : tensor<1x2xf64>
// CHECK: dense<true> : tensor<1x2xi1>
// CHECK: dense<1> : tensor<1x2xi32>
// CHECK: dense<1> : tensor<1x2xi32>
// CHECK: dense<1> : tensor<1x2xi64>
// CHECK: dense<1> : tensor<1x2xi64>
// CHECK: dense<1.000000e+00> : tensor<1x2xf32>
// CHECK: dense<1.000000e+00> : tensor<1x2xf64>
// CHECK: 1.000000e+00 : f32
// CHECK: 1.000000e+00 : f64
// CHECK: sparse<[4, 7], [0.000000e+00, 1.000000e+00]> : tensor<1x2xf32>
// clang-format on
fprintf(stderr, "@attrs\n");
errcode = printStandardAttributes(ctx);
fprintf(stderr, "%d\n", errcode);
// clang-format off
// CHECK-LABEL: @affineMap
// CHECK: () -> ()
// CHECK: (d0, d1, d2)[s0, s1] -> ()
// CHECK: () -> (2)
// CHECK: (d0, d1, d2) -> (d0, d1, d2)
// CHECK: (d0, d1, d2) -> (d1, d2)
// CHECK: (d0, d1, d2) -> (d1, d2, d0)
// CHECK: (d0, d1, d2) -> (d1)
// CHECK: (d0, d1, d2) -> (d0)
// CHECK: (d0, d1, d2) -> (d2)
// CHECK: 0
// clang-format on
fprintf(stderr, "@affineMap\n");
errcode = printAffineMap(ctx);
fprintf(stderr, "%d\n", errcode);
mlirContextDestroy(ctx);
return 0;
}