mlir/unittests/IR/AttributeTest.cpp - llvm-project.git - Git at Google

 //===- AttributeTest.cpp - Attribute unit tests ---------------------------===//
 //
 // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
 // See https://llvm.org/LICENSE.txt for license information.
 // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 //
 //===----------------------------------------------------------------------===//

 #include "mlir/IR/AsmState.h"
 #include "mlir/IR/Builders.h"
 #include "mlir/IR/BuiltinAttributes.h"
 #include "mlir/IR/BuiltinTypes.h"
 #include "gtest/gtest.h"
 #include <optional>

 #include "../../test/lib/Dialect/Test/TestDialect.h"

 using namespace mlir;
 using namespace mlir::detail;

 //===----------------------------------------------------------------------===//
 // DenseElementsAttr
 //===----------------------------------------------------------------------===//

 template <typename EltTy>
 static void testSplat(Type eltType, const EltTy &splatElt) {
   RankedTensorType shape = RankedTensorType::get({2, 1}, eltType);

   // Check that the generated splat is the same for 1 element and N elements.
   DenseElementsAttr splat = DenseElementsAttr::get(shape, splatElt);
   EXPECT_TRUE(splat.isSplat());

   auto detectedSplat =
       DenseElementsAttr::get(shape, llvm::ArrayRef({splatElt, splatElt}));
   EXPECT_EQ(detectedSplat, splat);

   for (auto newValue : detectedSplat.template getValues<EltTy>())
     EXPECT_TRUE(newValue == splatElt);
 }

 namespace {
 TEST(DenseSplatTest, BoolSplat) {
   MLIRContext context;
   IntegerType boolTy = IntegerType::get(&context, 1);
   RankedTensorType shape = RankedTensorType::get({2, 2}, boolTy);

   // Check that splat is automatically detected for boolean values.
   /// True.
   DenseElementsAttr trueSplat = DenseElementsAttr::get(shape, true);
   EXPECT_TRUE(trueSplat.isSplat());
   /// False.
   DenseElementsAttr falseSplat = DenseElementsAttr::get(shape, false);
   EXPECT_TRUE(falseSplat.isSplat());
   EXPECT_NE(falseSplat, trueSplat);

   /// Detect and handle splat within 8 elements (bool values are bit-packed).
   /// True.
   auto detectedSplat = DenseElementsAttr::get(shape, {true, true, true, true});
   EXPECT_EQ(detectedSplat, trueSplat);
   /// False.
   detectedSplat = DenseElementsAttr::get(shape, {false, false, false, false});
   EXPECT_EQ(detectedSplat, falseSplat);
 }
 TEST(DenseSplatTest, BoolSplatRawRoundtrip) {
   MLIRContext context;
   IntegerType boolTy = IntegerType::get(&context, 1);
   RankedTensorType shape = RankedTensorType::get({2, 2}, boolTy);

   // Check that splat booleans properly round trip via the raw API.
   DenseElementsAttr trueSplat = DenseElementsAttr::get(shape, true);
   EXPECT_TRUE(trueSplat.isSplat());
   DenseElementsAttr trueSplatFromRaw =
       DenseElementsAttr::getFromRawBuffer(shape, trueSplat.getRawData());
   EXPECT_TRUE(trueSplatFromRaw.isSplat());

   EXPECT_EQ(trueSplat, trueSplatFromRaw);
 }

 TEST(DenseSplatTest, BoolSplatSmall) {
   MLIRContext context;
   Builder builder(&context);

   // Check that splats that don't fill entire byte are handled properly.
   auto tensorType = RankedTensorType::get({4}, builder.getI1Type());
   std::vector<char> data{0b00001111};
   auto trueSplatFromRaw =
       DenseIntOrFPElementsAttr::getFromRawBuffer(tensorType, data);
   EXPECT_TRUE(trueSplatFromRaw.isSplat());
   DenseElementsAttr trueSplat = DenseElementsAttr::get(tensorType, true);
   EXPECT_EQ(trueSplat, trueSplatFromRaw);
 }

 TEST(DenseSplatTest, LargeBoolSplat) {
   constexpr int64_t boolCount = 56;

   MLIRContext context;
   IntegerType boolTy = IntegerType::get(&context, 1);
   RankedTensorType shape = RankedTensorType::get({boolCount}, boolTy);

   // Check that splat is automatically detected for boolean values.
   /// True.
   DenseElementsAttr trueSplat = DenseElementsAttr::get(shape, true);
   DenseElementsAttr falseSplat = DenseElementsAttr::get(shape, false);
   EXPECT_TRUE(trueSplat.isSplat());
   EXPECT_TRUE(falseSplat.isSplat());

   /// Detect that the large boolean arrays are properly splatted.
   /// True.
   SmallVector<bool, 64> trueValues(boolCount, true);
   auto detectedSplat = DenseElementsAttr::get(shape, trueValues);
   EXPECT_EQ(detectedSplat, trueSplat);
   /// False.
   SmallVector<bool, 64> falseValues(boolCount, false);
   detectedSplat = DenseElementsAttr::get(shape, falseValues);
   EXPECT_EQ(detectedSplat, falseSplat);
 }

 TEST(DenseSplatTest, BoolNonSplat) {
   MLIRContext context;
   IntegerType boolTy = IntegerType::get(&context, 1);
   RankedTensorType shape = RankedTensorType::get({6}, boolTy);

   // Check that we properly handle non-splat values.
   DenseElementsAttr nonSplat =
       DenseElementsAttr::get(shape, {false, false, true, false, false, true});
   EXPECT_FALSE(nonSplat.isSplat());
 }

 TEST(DenseSplatTest, OddIntSplat) {
   // Test detecting a splat with an odd(non 8-bit) integer bitwidth.
   MLIRContext context;
   constexpr size_t intWidth = 19;
   IntegerType intTy = IntegerType::get(&context, intWidth);
   APInt value(intWidth, 10);

   testSplat(intTy, value);
 }

 TEST(DenseSplatTest, Int32Splat) {
   MLIRContext context;
   IntegerType intTy = IntegerType::get(&context, 32);
   int value = 64;

   testSplat(intTy, value);
 }

 TEST(DenseSplatTest, IntAttrSplat) {
   MLIRContext context;
   IntegerType intTy = IntegerType::get(&context, 85);
   Attribute value = IntegerAttr::get(intTy, 109);

   testSplat(intTy, value);
 }

 TEST(DenseSplatTest, F32Splat) {
   MLIRContext context;
   FloatType floatTy = FloatType::getF32(&context);
   float value = 10.0;

   testSplat(floatTy, value);
 }

 TEST(DenseSplatTest, F64Splat) {
   MLIRContext context;
   FloatType floatTy = FloatType::getF64(&context);
   double value = 10.0;

   testSplat(floatTy, APFloat(value));
 }

 TEST(DenseSplatTest, FloatAttrSplat) {
   MLIRContext context;
   FloatType floatTy = FloatType::getF32(&context);
   Attribute value = FloatAttr::get(floatTy, 10.0);

   testSplat(floatTy, value);
 }

 TEST(DenseSplatTest, BF16Splat) {
   MLIRContext context;
   FloatType floatTy = FloatType::getBF16(&context);
   Attribute value = FloatAttr::get(floatTy, 10.0);

   testSplat(floatTy, value);
 }

 TEST(DenseSplatTest, StringSplat) {
   MLIRContext context;
   context.allowUnregisteredDialects();
   Type stringType =
       OpaqueType::get(StringAttr::get(&context, "test"), "string");
   StringRef value = "test-string";
   testSplat(stringType, value);
 }

 TEST(DenseSplatTest, StringAttrSplat) {
   MLIRContext context;
   context.allowUnregisteredDialects();
   Type stringType =
       OpaqueType::get(StringAttr::get(&context, "test"), "string");
   Attribute stringAttr = StringAttr::get("test-string", stringType);
   testSplat(stringType, stringAttr);
 }

 TEST(DenseComplexTest, ComplexFloatSplat) {
   MLIRContext context;
   ComplexType complexType = ComplexType::get(FloatType::getF32(&context));
   std::complex<float> value(10.0, 15.0);
   testSplat(complexType, value);
 }

 TEST(DenseComplexTest, ComplexIntSplat) {
   MLIRContext context;
   ComplexType complexType = ComplexType::get(IntegerType::get(&context, 64));
   std::complex<int64_t> value(10, 15);
   testSplat(complexType, value);
 }

 TEST(DenseComplexTest, ComplexAPFloatSplat) {
   MLIRContext context;
   ComplexType complexType = ComplexType::get(FloatType::getF32(&context));
   std::complex<APFloat> value(APFloat(10.0f), APFloat(15.0f));
   testSplat(complexType, value);
 }

 TEST(DenseComplexTest, ComplexAPIntSplat) {
   MLIRContext context;
   ComplexType complexType = ComplexType::get(IntegerType::get(&context, 64));
   std::complex<APInt> value(APInt(64, 10), APInt(64, 15));
   testSplat(complexType, value);
 }

 TEST(DenseScalarTest, ExtractZeroRankElement) {
   MLIRContext context;
   const int elementValue = 12;
   IntegerType intTy = IntegerType::get(&context, 32);
   Attribute value = IntegerAttr::get(intTy, elementValue);
   RankedTensorType shape = RankedTensorType::get({}, intTy);

   auto attr = DenseElementsAttr::get(shape, llvm::ArrayRef({elementValue}));
   EXPECT_TRUE(attr.getValues<Attribute>()[0] == value);
 }

 TEST(DenseSplatMapValuesTest, I32ToTrue) {
   MLIRContext context;
   const int elementValue = 12;
   IntegerType boolTy = IntegerType::get(&context, 1);
   IntegerType intTy = IntegerType::get(&context, 32);
   RankedTensorType shape = RankedTensorType::get({4}, intTy);

   auto attr =
       DenseElementsAttr::get(shape, llvm::ArrayRef({elementValue}))
           .mapValues(boolTy, [](const APInt &x) {
             return x.isZero() ? APInt::getZero(1) : APInt::getAllOnes(1);
           });
   EXPECT_EQ(attr.getNumElements(), 4);
   EXPECT_TRUE(attr.isSplat());
   EXPECT_TRUE(attr.getSplatValue<BoolAttr>().getValue());
 }

 TEST(DenseSplatMapValuesTest, I32ToFalse) {
   MLIRContext context;
   const int elementValue = 0;
   IntegerType boolTy = IntegerType::get(&context, 1);
   IntegerType intTy = IntegerType::get(&context, 32);
   RankedTensorType shape = RankedTensorType::get({4}, intTy);

   auto attr =
       DenseElementsAttr::get(shape, llvm::ArrayRef({elementValue}))
           .mapValues(boolTy, [](const APInt &x) {
             return x.isZero() ? APInt::getZero(1) : APInt::getAllOnes(1);
           });
   EXPECT_EQ(attr.getNumElements(), 4);
   EXPECT_TRUE(attr.isSplat());
   EXPECT_FALSE(attr.getSplatValue<BoolAttr>().getValue());
 }
 } // namespace

 //===----------------------------------------------------------------------===//
 // DenseResourceElementsAttr
 //===----------------------------------------------------------------------===//

 template <typename AttrT, typename T>
 static void checkNativeAccess(MLIRContext *ctx, ArrayRef<T> data,
                               Type elementType) {
   auto type = RankedTensorType::get(data.size(), elementType);
   auto attr = AttrT::get(type, "resource",
                          UnmanagedAsmResourceBlob::allocateInferAlign(data));

   // Check that we can access and iterate the data properly.
   std::optional<ArrayRef<T>> attrData = attr.tryGetAsArrayRef();
   EXPECT_TRUE(attrData.has_value());
   EXPECT_EQ(*attrData, data);

   // Check that we cast to this attribute when possible.
   Attribute genericAttr = attr;
   EXPECT_TRUE(isa<AttrT>(genericAttr));
 }
 template <typename AttrT, typename T>
 static void checkNativeIntAccess(Builder &builder, size_t intWidth) {
   T data[] = {0, 1, 2};
   checkNativeAccess<AttrT, T>(builder.getContext(), llvm::ArrayRef(data),
                               builder.getIntegerType(intWidth));
 }

 namespace {
 TEST(DenseResourceElementsAttrTest, CheckNativeAccess) {
   MLIRContext context;
   Builder builder(&context);

   // Bool
   bool boolData[] = {true, false, true};
   checkNativeAccess<DenseBoolResourceElementsAttr>(
       &context, llvm::ArrayRef(boolData), builder.getI1Type());

   // Unsigned integers
   checkNativeIntAccess<DenseUI8ResourceElementsAttr, uint8_t>(builder, 8);
   checkNativeIntAccess<DenseUI16ResourceElementsAttr, uint16_t>(builder, 16);
   checkNativeIntAccess<DenseUI32ResourceElementsAttr, uint32_t>(builder, 32);
   checkNativeIntAccess<DenseUI64ResourceElementsAttr, uint64_t>(builder, 64);

   // Signed integers
   checkNativeIntAccess<DenseI8ResourceElementsAttr, int8_t>(builder, 8);
   checkNativeIntAccess<DenseI16ResourceElementsAttr, int16_t>(builder, 16);
   checkNativeIntAccess<DenseI32ResourceElementsAttr, int32_t>(builder, 32);
   checkNativeIntAccess<DenseI64ResourceElementsAttr, int64_t>(builder, 64);

   // Float
   float floatData[] = {0, 1, 2};
   checkNativeAccess<DenseF32ResourceElementsAttr>(
       &context, llvm::ArrayRef(floatData), builder.getF32Type());

   // Double
   double doubleData[] = {0, 1, 2};
   checkNativeAccess<DenseF64ResourceElementsAttr>(
       &context, llvm::ArrayRef(doubleData), builder.getF64Type());
 }

 TEST(DenseResourceElementsAttrTest, CheckNoCast) {
   MLIRContext context;
   Builder builder(&context);

   // Create a i32 attribute.
   ArrayRef<uint32_t> data;
   auto type = RankedTensorType::get(data.size(), builder.getI32Type());
   Attribute i32ResourceAttr = DenseI32ResourceElementsAttr::get(
       type, "resource", UnmanagedAsmResourceBlob::allocateInferAlign(data));

   EXPECT_TRUE(isa<DenseI32ResourceElementsAttr>(i32ResourceAttr));
   EXPECT_FALSE(isa<DenseF32ResourceElementsAttr>(i32ResourceAttr));
   EXPECT_FALSE(isa<DenseBoolResourceElementsAttr>(i32ResourceAttr));
 }

 TEST(DenseResourceElementsAttrTest, CheckInvalidData) {
   MLIRContext context;
   Builder builder(&context);

   // Create a bool attribute with data of the incorrect type.
   ArrayRef<uint32_t> data;
   auto type = RankedTensorType::get(data.size(), builder.getI32Type());
   EXPECT_DEBUG_DEATH(
       {
         DenseBoolResourceElementsAttr::get(
             type, "resource",
             UnmanagedAsmResourceBlob::allocateInferAlign(data));
       },
       "alignment mismatch between expected alignment and blob alignment");
 }

 TEST(DenseResourceElementsAttrTest, CheckInvalidType) {
   MLIRContext context;
   Builder builder(&context);

   // Create a bool attribute with incorrect type.
   ArrayRef<bool> data;
   auto type = RankedTensorType::get(data.size(), builder.getI32Type());
   EXPECT_DEBUG_DEATH(
       {
         DenseBoolResourceElementsAttr::get(
             type, "resource",
             UnmanagedAsmResourceBlob::allocateInferAlign(data));
       },
       "invalid shape element type for provided type `T`");
 }
 } // namespace

 //===----------------------------------------------------------------------===//
 // SparseElementsAttr
 //===----------------------------------------------------------------------===//

 namespace {
 TEST(SparseElementsAttrTest, GetZero) {
   MLIRContext context;
   context.allowUnregisteredDialects();

   IntegerType intTy = IntegerType::get(&context, 32);
   FloatType floatTy = FloatType::getF32(&context);
   Type stringTy = OpaqueType::get(StringAttr::get(&context, "test"), "string");

   ShapedType tensorI32 = RankedTensorType::get({2, 2}, intTy);
   ShapedType tensorF32 = RankedTensorType::get({2, 2}, floatTy);
   ShapedType tensorString = RankedTensorType::get({2, 2}, stringTy);

   auto indicesType =
       RankedTensorType::get({1, 2}, IntegerType::get(&context, 64));
   auto indices =
       DenseIntElementsAttr::get(indicesType, {APInt(64, 0), APInt(64, 0)});

   RankedTensorType intValueTy = RankedTensorType::get({1}, intTy);
   auto intValue = DenseIntElementsAttr::get(intValueTy, {1});

   RankedTensorType floatValueTy = RankedTensorType::get({1}, floatTy);
   auto floatValue = DenseFPElementsAttr::get(floatValueTy, {1.0f});

   RankedTensorType stringValueTy = RankedTensorType::get({1}, stringTy);
   auto stringValue = DenseElementsAttr::get(stringValueTy, {StringRef("foo")});

   auto sparseInt = SparseElementsAttr::get(tensorI32, indices, intValue);
   auto sparseFloat = SparseElementsAttr::get(tensorF32, indices, floatValue);
   auto sparseString =
       SparseElementsAttr::get(tensorString, indices, stringValue);

   // Only index (0, 0) contains an element, others are supposed to return
   // the zero/empty value.
   auto zeroIntValue =
       cast<IntegerAttr>(sparseInt.getValues<Attribute>()[{1, 1}]);
   EXPECT_EQ(zeroIntValue.getInt(), 0);
   EXPECT_TRUE(zeroIntValue.getType() == intTy);

   auto zeroFloatValue =
       cast<FloatAttr>(sparseFloat.getValues<Attribute>()[{1, 1}]);
   EXPECT_EQ(zeroFloatValue.getValueAsDouble(), 0.0f);
   EXPECT_TRUE(zeroFloatValue.getType() == floatTy);

   auto zeroStringValue =
       cast<StringAttr>(sparseString.getValues<Attribute>()[{1, 1}]);
   EXPECT_TRUE(zeroStringValue.empty());
   EXPECT_TRUE(zeroStringValue.getType() == stringTy);
 }

 //===----------------------------------------------------------------------===//
 // SubElements
 //===----------------------------------------------------------------------===//

 TEST(SubElementTest, Nested) {
   MLIRContext context;
   Builder builder(&context);

   BoolAttr trueAttr = builder.getBoolAttr(true);
   BoolAttr falseAttr = builder.getBoolAttr(false);
   ArrayAttr boolArrayAttr =
       builder.getArrayAttr({trueAttr, falseAttr, trueAttr});
   StringAttr strAttr = builder.getStringAttr("array");
   DictionaryAttr dictAttr =
       builder.getDictionaryAttr(builder.getNamedAttr(strAttr, boolArrayAttr));

   SmallVector<Attribute> subAttrs;
   dictAttr.walk([&](Attribute attr) { subAttrs.push_back(attr); });
   // Note that trueAttr appears only once, identical subattributes are skipped.
   EXPECT_EQ(llvm::ArrayRef(subAttrs),
             ArrayRef<Attribute>(
                 {strAttr, trueAttr, falseAttr, boolArrayAttr, dictAttr}));
 }

 // Test how many times we call copy-ctor when building an attribute.
 TEST(CopyCountAttr, CopyCount) {
   MLIRContext context;
   context.loadDialect<test::TestDialect>();

   test::CopyCount::counter = 0;
   test::CopyCount copyCount("hello");
   test::TestCopyCountAttr::get(&context, std::move(copyCount));
   int counter1 = test::CopyCount::counter;
   test::CopyCount::counter = 0;
   test::TestCopyCountAttr::get(&context, std::move(copyCount));
 #ifndef NDEBUG
   // One verification enabled only in assert-mode requires a copy.
   EXPECT_EQ(counter1, 1);
   EXPECT_EQ(test::CopyCount::counter, 1);
 #else
   EXPECT_EQ(counter1, 0);
   EXPECT_EQ(test::CopyCount::counter, 0);
 #endif
 }

 } // namespace
	//===- AttributeTest.cpp - Attribute unit tests ---------------------------===//
	//
	// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
	// See https://llvm.org/LICENSE.txt for license information.
	// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
	//
	//===----------------------------------------------------------------------===//

	#include "mlir/IR/AsmState.h"
	#include "mlir/IR/Builders.h"
	#include "mlir/IR/BuiltinAttributes.h"
	#include "mlir/IR/BuiltinTypes.h"
	#include "gtest/gtest.h"
	#include <optional>

	#include "../../test/lib/Dialect/Test/TestDialect.h"

	using namespace mlir;
	using namespace mlir::detail;

	//===----------------------------------------------------------------------===//
	// DenseElementsAttr
	//===----------------------------------------------------------------------===//

	template <typename EltTy>
	static void testSplat(Type eltType, const EltTy &splatElt) {
	RankedTensorType shape = RankedTensorType::get({2, 1}, eltType);

	// Check that the generated splat is the same for 1 element and N elements.
	DenseElementsAttr splat = DenseElementsAttr::get(shape, splatElt);
	EXPECT_TRUE(splat.isSplat());

	auto detectedSplat =
	DenseElementsAttr::get(shape, llvm::ArrayRef({splatElt, splatElt}));
	EXPECT_EQ(detectedSplat, splat);

	for (auto newValue : detectedSplat.template getValues<EltTy>())
	EXPECT_TRUE(newValue == splatElt);
	}

	namespace {
	TEST(DenseSplatTest, BoolSplat) {
	MLIRContext context;
	IntegerType boolTy = IntegerType::get(&context, 1);
	RankedTensorType shape = RankedTensorType::get({2, 2}, boolTy);

	// Check that splat is automatically detected for boolean values.
	/// True.
	DenseElementsAttr trueSplat = DenseElementsAttr::get(shape, true);
	EXPECT_TRUE(trueSplat.isSplat());
	/// False.
	DenseElementsAttr falseSplat = DenseElementsAttr::get(shape, false);
	EXPECT_TRUE(falseSplat.isSplat());
	EXPECT_NE(falseSplat, trueSplat);

	/// Detect and handle splat within 8 elements (bool values are bit-packed).
	/// True.
	auto detectedSplat = DenseElementsAttr::get(shape, {true, true, true, true});
	EXPECT_EQ(detectedSplat, trueSplat);
	/// False.
	detectedSplat = DenseElementsAttr::get(shape, {false, false, false, false});
	EXPECT_EQ(detectedSplat, falseSplat);
	}
	TEST(DenseSplatTest, BoolSplatRawRoundtrip) {
	MLIRContext context;
	IntegerType boolTy = IntegerType::get(&context, 1);
	RankedTensorType shape = RankedTensorType::get({2, 2}, boolTy);

	// Check that splat booleans properly round trip via the raw API.
	DenseElementsAttr trueSplat = DenseElementsAttr::get(shape, true);
	EXPECT_TRUE(trueSplat.isSplat());
	DenseElementsAttr trueSplatFromRaw =
	DenseElementsAttr::getFromRawBuffer(shape, trueSplat.getRawData());
	EXPECT_TRUE(trueSplatFromRaw.isSplat());

	EXPECT_EQ(trueSplat, trueSplatFromRaw);
	}

	TEST(DenseSplatTest, BoolSplatSmall) {
	MLIRContext context;
	Builder builder(&context);

	// Check that splats that don't fill entire byte are handled properly.
	auto tensorType = RankedTensorType::get({4}, builder.getI1Type());
	std::vector<char> data{0b00001111};
	auto trueSplatFromRaw =
	DenseIntOrFPElementsAttr::getFromRawBuffer(tensorType, data);
	EXPECT_TRUE(trueSplatFromRaw.isSplat());
	DenseElementsAttr trueSplat = DenseElementsAttr::get(tensorType, true);
	EXPECT_EQ(trueSplat, trueSplatFromRaw);
	}

	TEST(DenseSplatTest, LargeBoolSplat) {
	constexpr int64_t boolCount = 56;

	MLIRContext context;
	IntegerType boolTy = IntegerType::get(&context, 1);
	RankedTensorType shape = RankedTensorType::get({boolCount}, boolTy);

	// Check that splat is automatically detected for boolean values.
	/// True.
	DenseElementsAttr trueSplat = DenseElementsAttr::get(shape, true);
	DenseElementsAttr falseSplat = DenseElementsAttr::get(shape, false);
	EXPECT_TRUE(trueSplat.isSplat());
	EXPECT_TRUE(falseSplat.isSplat());

	/// Detect that the large boolean arrays are properly splatted.
	/// True.
	SmallVector<bool, 64> trueValues(boolCount, true);
	auto detectedSplat = DenseElementsAttr::get(shape, trueValues);
	EXPECT_EQ(detectedSplat, trueSplat);
	/// False.
	SmallVector<bool, 64> falseValues(boolCount, false);
	detectedSplat = DenseElementsAttr::get(shape, falseValues);
	EXPECT_EQ(detectedSplat, falseSplat);
	}

	TEST(DenseSplatTest, BoolNonSplat) {
	MLIRContext context;
	IntegerType boolTy = IntegerType::get(&context, 1);
	RankedTensorType shape = RankedTensorType::get({6}, boolTy);

	// Check that we properly handle non-splat values.
	DenseElementsAttr nonSplat =
	DenseElementsAttr::get(shape, {false, false, true, false, false, true});
	EXPECT_FALSE(nonSplat.isSplat());
	}

	TEST(DenseSplatTest, OddIntSplat) {
	// Test detecting a splat with an odd(non 8-bit) integer bitwidth.
	MLIRContext context;
	constexpr size_t intWidth = 19;
	IntegerType intTy = IntegerType::get(&context, intWidth);
	APInt value(intWidth, 10);

	testSplat(intTy, value);
	}

	TEST(DenseSplatTest, Int32Splat) {
	MLIRContext context;
	IntegerType intTy = IntegerType::get(&context, 32);
	int value = 64;

	testSplat(intTy, value);
	}

	TEST(DenseSplatTest, IntAttrSplat) {
	MLIRContext context;
	IntegerType intTy = IntegerType::get(&context, 85);
	Attribute value = IntegerAttr::get(intTy, 109);

	testSplat(intTy, value);
	}

	TEST(DenseSplatTest, F32Splat) {
	MLIRContext context;
	FloatType floatTy = FloatType::getF32(&context);
	float value = 10.0;

	testSplat(floatTy, value);
	}

	TEST(DenseSplatTest, F64Splat) {
	MLIRContext context;
	FloatType floatTy = FloatType::getF64(&context);
	double value = 10.0;

	testSplat(floatTy, APFloat(value));
	}

	TEST(DenseSplatTest, FloatAttrSplat) {
	MLIRContext context;
	FloatType floatTy = FloatType::getF32(&context);
	Attribute value = FloatAttr::get(floatTy, 10.0);

	testSplat(floatTy, value);
	}

	TEST(DenseSplatTest, BF16Splat) {
	MLIRContext context;
	FloatType floatTy = FloatType::getBF16(&context);
	Attribute value = FloatAttr::get(floatTy, 10.0);

	testSplat(floatTy, value);
	}

	TEST(DenseSplatTest, StringSplat) {
	MLIRContext context;
	context.allowUnregisteredDialects();
	Type stringType =
	OpaqueType::get(StringAttr::get(&context, "test"), "string");
	StringRef value = "test-string";
	testSplat(stringType, value);
	}

	TEST(DenseSplatTest, StringAttrSplat) {
	MLIRContext context;
	context.allowUnregisteredDialects();
	Type stringType =
	OpaqueType::get(StringAttr::get(&context, "test"), "string");
	Attribute stringAttr = StringAttr::get("test-string", stringType);
	testSplat(stringType, stringAttr);
	}

	TEST(DenseComplexTest, ComplexFloatSplat) {
	MLIRContext context;
	ComplexType complexType = ComplexType::get(FloatType::getF32(&context));
	std::complex<float> value(10.0, 15.0);
	testSplat(complexType, value);
	}

	TEST(DenseComplexTest, ComplexIntSplat) {
	MLIRContext context;
	ComplexType complexType = ComplexType::get(IntegerType::get(&context, 64));
	std::complex<int64_t> value(10, 15);
	testSplat(complexType, value);
	}

	TEST(DenseComplexTest, ComplexAPFloatSplat) {
	MLIRContext context;
	ComplexType complexType = ComplexType::get(FloatType::getF32(&context));
	std::complex<APFloat> value(APFloat(10.0f), APFloat(15.0f));
	testSplat(complexType, value);
	}

	TEST(DenseComplexTest, ComplexAPIntSplat) {
	MLIRContext context;
	ComplexType complexType = ComplexType::get(IntegerType::get(&context, 64));
	std::complex<APInt> value(APInt(64, 10), APInt(64, 15));
	testSplat(complexType, value);
	}

	TEST(DenseScalarTest, ExtractZeroRankElement) {
	MLIRContext context;
	const int elementValue = 12;
	IntegerType intTy = IntegerType::get(&context, 32);
	Attribute value = IntegerAttr::get(intTy, elementValue);
	RankedTensorType shape = RankedTensorType::get({}, intTy);

	auto attr = DenseElementsAttr::get(shape, llvm::ArrayRef({elementValue}));
	EXPECT_TRUE(attr.getValues<Attribute>()[0] == value);
	}

	TEST(DenseSplatMapValuesTest, I32ToTrue) {
	MLIRContext context;
	const int elementValue = 12;
	IntegerType boolTy = IntegerType::get(&context, 1);
	IntegerType intTy = IntegerType::get(&context, 32);
	RankedTensorType shape = RankedTensorType::get({4}, intTy);

	auto attr =
	DenseElementsAttr::get(shape, llvm::ArrayRef({elementValue}))
	.mapValues(boolTy, [](const APInt &x) {
	return x.isZero() ? APInt::getZero(1) : APInt::getAllOnes(1);
	});
	EXPECT_EQ(attr.getNumElements(), 4);
	EXPECT_TRUE(attr.isSplat());
	EXPECT_TRUE(attr.getSplatValue<BoolAttr>().getValue());
	}

	TEST(DenseSplatMapValuesTest, I32ToFalse) {
	MLIRContext context;
	const int elementValue = 0;
	IntegerType boolTy = IntegerType::get(&context, 1);
	IntegerType intTy = IntegerType::get(&context, 32);
	RankedTensorType shape = RankedTensorType::get({4}, intTy);

	auto attr =
	DenseElementsAttr::get(shape, llvm::ArrayRef({elementValue}))
	.mapValues(boolTy, [](const APInt &x) {
	return x.isZero() ? APInt::getZero(1) : APInt::getAllOnes(1);
	});
	EXPECT_EQ(attr.getNumElements(), 4);
	EXPECT_TRUE(attr.isSplat());
	EXPECT_FALSE(attr.getSplatValue<BoolAttr>().getValue());
	}
	} // namespace

	//===----------------------------------------------------------------------===//
	// DenseResourceElementsAttr
	//===----------------------------------------------------------------------===//

	template <typename AttrT, typename T>
	static void checkNativeAccess(MLIRContext *ctx, ArrayRef<T> data,
	Type elementType) {
	auto type = RankedTensorType::get(data.size(), elementType);
	auto attr = AttrT::get(type, "resource",
	UnmanagedAsmResourceBlob::allocateInferAlign(data));

	// Check that we can access and iterate the data properly.
	std::optional<ArrayRef<T>> attrData = attr.tryGetAsArrayRef();
	EXPECT_TRUE(attrData.has_value());
	EXPECT_EQ(*attrData, data);

	// Check that we cast to this attribute when possible.
	Attribute genericAttr = attr;
	EXPECT_TRUE(isa<AttrT>(genericAttr));
	}
	template <typename AttrT, typename T>
	static void checkNativeIntAccess(Builder &builder, size_t intWidth) {
	T data[] = {0, 1, 2};
	checkNativeAccess<AttrT, T>(builder.getContext(), llvm::ArrayRef(data),
	builder.getIntegerType(intWidth));
	}

	namespace {
	TEST(DenseResourceElementsAttrTest, CheckNativeAccess) {
	MLIRContext context;
	Builder builder(&context);

	// Bool
	bool boolData[] = {true, false, true};
	checkNativeAccess<DenseBoolResourceElementsAttr>(
	&context, llvm::ArrayRef(boolData), builder.getI1Type());

	// Unsigned integers
	checkNativeIntAccess<DenseUI8ResourceElementsAttr, uint8_t>(builder, 8);
	checkNativeIntAccess<DenseUI16ResourceElementsAttr, uint16_t>(builder, 16);
	checkNativeIntAccess<DenseUI32ResourceElementsAttr, uint32_t>(builder, 32);
	checkNativeIntAccess<DenseUI64ResourceElementsAttr, uint64_t>(builder, 64);

	// Signed integers
	checkNativeIntAccess<DenseI8ResourceElementsAttr, int8_t>(builder, 8);
	checkNativeIntAccess<DenseI16ResourceElementsAttr, int16_t>(builder, 16);
	checkNativeIntAccess<DenseI32ResourceElementsAttr, int32_t>(builder, 32);
	checkNativeIntAccess<DenseI64ResourceElementsAttr, int64_t>(builder, 64);

	// Float
	float floatData[] = {0, 1, 2};
	checkNativeAccess<DenseF32ResourceElementsAttr>(
	&context, llvm::ArrayRef(floatData), builder.getF32Type());

	// Double
	double doubleData[] = {0, 1, 2};
	checkNativeAccess<DenseF64ResourceElementsAttr>(
	&context, llvm::ArrayRef(doubleData), builder.getF64Type());
	}

	TEST(DenseResourceElementsAttrTest, CheckNoCast) {
	MLIRContext context;
	Builder builder(&context);

	// Create a i32 attribute.
	ArrayRef<uint32_t> data;
	auto type = RankedTensorType::get(data.size(), builder.getI32Type());
	Attribute i32ResourceAttr = DenseI32ResourceElementsAttr::get(
	type, "resource", UnmanagedAsmResourceBlob::allocateInferAlign(data));

	EXPECT_TRUE(isa<DenseI32ResourceElementsAttr>(i32ResourceAttr));
	EXPECT_FALSE(isa<DenseF32ResourceElementsAttr>(i32ResourceAttr));
	EXPECT_FALSE(isa<DenseBoolResourceElementsAttr>(i32ResourceAttr));
	}

	TEST(DenseResourceElementsAttrTest, CheckInvalidData) {
	MLIRContext context;
	Builder builder(&context);

	// Create a bool attribute with data of the incorrect type.
	ArrayRef<uint32_t> data;
	auto type = RankedTensorType::get(data.size(), builder.getI32Type());
	EXPECT_DEBUG_DEATH(
	{
	DenseBoolResourceElementsAttr::get(
	type, "resource",
	UnmanagedAsmResourceBlob::allocateInferAlign(data));
	},
	"alignment mismatch between expected alignment and blob alignment");
	}

	TEST(DenseResourceElementsAttrTest, CheckInvalidType) {
	MLIRContext context;
	Builder builder(&context);

	// Create a bool attribute with incorrect type.
	ArrayRef<bool> data;
	auto type = RankedTensorType::get(data.size(), builder.getI32Type());
	EXPECT_DEBUG_DEATH(
	{
	DenseBoolResourceElementsAttr::get(
	type, "resource",
	UnmanagedAsmResourceBlob::allocateInferAlign(data));
	},
	"invalid shape element type for provided type `T`");
	}
	} // namespace

	//===----------------------------------------------------------------------===//
	// SparseElementsAttr
	//===----------------------------------------------------------------------===//

	namespace {
	TEST(SparseElementsAttrTest, GetZero) {
	MLIRContext context;
	context.allowUnregisteredDialects();

	IntegerType intTy = IntegerType::get(&context, 32);
	FloatType floatTy = FloatType::getF32(&context);
	Type stringTy = OpaqueType::get(StringAttr::get(&context, "test"), "string");

	ShapedType tensorI32 = RankedTensorType::get({2, 2}, intTy);
	ShapedType tensorF32 = RankedTensorType::get({2, 2}, floatTy);
	ShapedType tensorString = RankedTensorType::get({2, 2}, stringTy);

	auto indicesType =
	RankedTensorType::get({1, 2}, IntegerType::get(&context, 64));
	auto indices =
	DenseIntElementsAttr::get(indicesType, {APInt(64, 0), APInt(64, 0)});

	RankedTensorType intValueTy = RankedTensorType::get({1}, intTy);
	auto intValue = DenseIntElementsAttr::get(intValueTy, {1});

	RankedTensorType floatValueTy = RankedTensorType::get({1}, floatTy);
	auto floatValue = DenseFPElementsAttr::get(floatValueTy, {1.0f});

	RankedTensorType stringValueTy = RankedTensorType::get({1}, stringTy);
	auto stringValue = DenseElementsAttr::get(stringValueTy, {StringRef("foo")});

	auto sparseInt = SparseElementsAttr::get(tensorI32, indices, intValue);
	auto sparseFloat = SparseElementsAttr::get(tensorF32, indices, floatValue);
	auto sparseString =
	SparseElementsAttr::get(tensorString, indices, stringValue);

	// Only index (0, 0) contains an element, others are supposed to return
	// the zero/empty value.
	auto zeroIntValue =
	cast<IntegerAttr>(sparseInt.getValues<Attribute>()[{1, 1}]);
	EXPECT_EQ(zeroIntValue.getInt(), 0);
	EXPECT_TRUE(zeroIntValue.getType() == intTy);

	auto zeroFloatValue =
	cast<FloatAttr>(sparseFloat.getValues<Attribute>()[{1, 1}]);
	EXPECT_EQ(zeroFloatValue.getValueAsDouble(), 0.0f);
	EXPECT_TRUE(zeroFloatValue.getType() == floatTy);

	auto zeroStringValue =
	cast<StringAttr>(sparseString.getValues<Attribute>()[{1, 1}]);
	EXPECT_TRUE(zeroStringValue.empty());
	EXPECT_TRUE(zeroStringValue.getType() == stringTy);
	}

	//===----------------------------------------------------------------------===//
	// SubElements
	//===----------------------------------------------------------------------===//

	TEST(SubElementTest, Nested) {
	MLIRContext context;
	Builder builder(&context);

	BoolAttr trueAttr = builder.getBoolAttr(true);
	BoolAttr falseAttr = builder.getBoolAttr(false);
	ArrayAttr boolArrayAttr =
	builder.getArrayAttr({trueAttr, falseAttr, trueAttr});
	StringAttr strAttr = builder.getStringAttr("array");
	DictionaryAttr dictAttr =
	builder.getDictionaryAttr(builder.getNamedAttr(strAttr, boolArrayAttr));

	SmallVector<Attribute> subAttrs;
	dictAttr.walk([&](Attribute attr) { subAttrs.push_back(attr); });
	// Note that trueAttr appears only once, identical subattributes are skipped.
	EXPECT_EQ(llvm::ArrayRef(subAttrs),
	ArrayRef<Attribute>(
	{strAttr, trueAttr, falseAttr, boolArrayAttr, dictAttr}));
	}

	// Test how many times we call copy-ctor when building an attribute.
	TEST(CopyCountAttr, CopyCount) {
	MLIRContext context;
	context.loadDialect<test::TestDialect>();

	test::CopyCount::counter = 0;
	test::CopyCount copyCount("hello");
	test::TestCopyCountAttr::get(&context, std::move(copyCount));
	int counter1 = test::CopyCount::counter;
	test::CopyCount::counter = 0;
	test::TestCopyCountAttr::get(&context, std::move(copyCount));
	#ifndef NDEBUG
	// One verification enabled only in assert-mode requires a copy.
	EXPECT_EQ(counter1, 1);
	EXPECT_EQ(test::CopyCount::counter, 1);
	#else
	EXPECT_EQ(counter1, 0);
	EXPECT_EQ(test::CopyCount::counter, 0);
	#endif
	}

	} // namespace