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//===- ShapedTypeTest.cpp - ShapedType 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/AffineMap.h"
#include "mlir/IR/BuiltinAttributes.h"
#include "mlir/IR/BuiltinTypes.h"
#include "mlir/IR/Dialect.h"
#include "mlir/IR/DialectInterface.h"
#include "mlir/Support/LLVM.h"
#include "llvm/ADT/SmallVector.h"
#include "gtest/gtest.h"
#include <cstdint>
using namespace mlir;
using namespace mlir::detail;
namespace {
TEST(ShapedTypeTest, CloneMemref) {
MLIRContext context;
Type i32 = IntegerType::get(&context, 32);
Type f32 = FloatType::getF32(&context);
Attribute memSpace = IntegerAttr::get(IntegerType::get(&context, 64), 7);
Type memrefOriginalType = i32;
llvm::SmallVector<int64_t> memrefOriginalShape({10, 20});
AffineMap map = makeStridedLinearLayoutMap({2, 3}, 5, &context);
ShapedType memrefType =
(ShapedType)MemRefType::Builder(memrefOriginalShape, memrefOriginalType)
.setMemorySpace(memSpace)
.setLayout(AffineMapAttr::get(map));
// Update shape.
llvm::SmallVector<int64_t> memrefNewShape({30, 40});
ASSERT_NE(memrefOriginalShape, memrefNewShape);
ASSERT_EQ(memrefType.clone(memrefNewShape),
(ShapedType)MemRefType::Builder(memrefNewShape, memrefOriginalType)
.setMemorySpace(memSpace)
.setLayout(AffineMapAttr::get(map)));
// Update type.
Type memrefNewType = f32;
ASSERT_NE(memrefOriginalType, memrefNewType);
ASSERT_EQ(memrefType.clone(memrefNewType),
(MemRefType)MemRefType::Builder(memrefOriginalShape, memrefNewType)
.setMemorySpace(memSpace)
.setLayout(AffineMapAttr::get(map)));
// Update both.
ASSERT_EQ(memrefType.clone(memrefNewShape, memrefNewType),
(MemRefType)MemRefType::Builder(memrefNewShape, memrefNewType)
.setMemorySpace(memSpace)
.setLayout(AffineMapAttr::get(map)));
// Test unranked memref cloning.
ShapedType unrankedTensorType =
UnrankedMemRefType::get(memrefOriginalType, memSpace);
ASSERT_EQ(unrankedTensorType.clone(memrefNewShape),
(MemRefType)MemRefType::Builder(memrefNewShape, memrefOriginalType)
.setMemorySpace(memSpace));
ASSERT_EQ(unrankedTensorType.clone(memrefNewType),
UnrankedMemRefType::get(memrefNewType, memSpace));
ASSERT_EQ(unrankedTensorType.clone(memrefNewShape, memrefNewType),
(MemRefType)MemRefType::Builder(memrefNewShape, memrefNewType)
.setMemorySpace(memSpace));
}
TEST(ShapedTypeTest, CloneTensor) {
MLIRContext context;
Type i32 = IntegerType::get(&context, 32);
Type f32 = FloatType::getF32(&context);
Type tensorOriginalType = i32;
llvm::SmallVector<int64_t> tensorOriginalShape({10, 20});
// Test ranked tensor cloning.
ShapedType tensorType =
RankedTensorType::get(tensorOriginalShape, tensorOriginalType);
// Update shape.
llvm::SmallVector<int64_t> tensorNewShape({30, 40});
ASSERT_NE(tensorOriginalShape, tensorNewShape);
ASSERT_EQ(
tensorType.clone(tensorNewShape),
(ShapedType)RankedTensorType::get(tensorNewShape, tensorOriginalType));
// Update type.
Type tensorNewType = f32;
ASSERT_NE(tensorOriginalType, tensorNewType);
ASSERT_EQ(
tensorType.clone(tensorNewType),
(ShapedType)RankedTensorType::get(tensorOriginalShape, tensorNewType));
// Update both.
ASSERT_EQ(tensorType.clone(tensorNewShape, tensorNewType),
(ShapedType)RankedTensorType::get(tensorNewShape, tensorNewType));
// Test unranked tensor cloning.
ShapedType unrankedTensorType = UnrankedTensorType::get(tensorOriginalType);
ASSERT_EQ(
unrankedTensorType.clone(tensorNewShape),
(ShapedType)RankedTensorType::get(tensorNewShape, tensorOriginalType));
ASSERT_EQ(unrankedTensorType.clone(tensorNewType),
(ShapedType)UnrankedTensorType::get(tensorNewType));
ASSERT_EQ(
unrankedTensorType.clone(tensorNewShape),
(ShapedType)RankedTensorType::get(tensorNewShape, tensorOriginalType));
}
TEST(ShapedTypeTest, CloneVector) {
MLIRContext context;
Type i32 = IntegerType::get(&context, 32);
Type f32 = FloatType::getF32(&context);
Type vectorOriginalType = i32;
llvm::SmallVector<int64_t> vectorOriginalShape({10, 20});
ShapedType vectorType =
VectorType::get(vectorOriginalShape, vectorOriginalType);
// Update shape.
llvm::SmallVector<int64_t> vectorNewShape({30, 40});
ASSERT_NE(vectorOriginalShape, vectorNewShape);
ASSERT_EQ(vectorType.clone(vectorNewShape),
VectorType::get(vectorNewShape, vectorOriginalType));
// Update type.
Type vectorNewType = f32;
ASSERT_NE(vectorOriginalType, vectorNewType);
ASSERT_EQ(vectorType.clone(vectorNewType),
VectorType::get(vectorOriginalShape, vectorNewType));
// Update both.
ASSERT_EQ(vectorType.clone(vectorNewShape, vectorNewType),
VectorType::get(vectorNewShape, vectorNewType));
}
TEST(ShapedTypeTest, VectorTypeBuilder) {
MLIRContext context;
Type f32 = FloatType::getF32(&context);
SmallVector<int64_t> shape{2, 4, 8, 9, 1};
SmallVector<bool> scalableDims{true, false, true, false, false};
VectorType vectorType = VectorType::get(shape, f32, scalableDims);
{
// Drop some dims.
VectorType dropFrontTwoDims =
VectorType::Builder(vectorType).dropDim(0).dropDim(0);
ASSERT_EQ(vectorType.getElementType(), dropFrontTwoDims.getElementType());
ASSERT_EQ(vectorType.getShape().drop_front(2), dropFrontTwoDims.getShape());
ASSERT_EQ(vectorType.getScalableDims().drop_front(2),
dropFrontTwoDims.getScalableDims());
}
{
// Set some dims.
VectorType setTwoDims =
VectorType::Builder(vectorType).setDim(0, 10).setDim(3, 12);
ASSERT_EQ(setTwoDims.getShape(), ArrayRef<int64_t>({10, 4, 8, 12, 1}));
ASSERT_EQ(vectorType.getElementType(), setTwoDims.getElementType());
ASSERT_EQ(vectorType.getScalableDims(), setTwoDims.getScalableDims());
}
{
// Test for bug from:
// https://github.com/llvm/llvm-project/commit/b44b3494f60296db6aca38a14cab061d9b747a0a
// Constructs a temporary builder, modifies it, copies it to `builder`.
// This used to lead to a use-after-free. Running under sanitizers will
// catch any issues.
VectorType::Builder builder = VectorType::Builder(vectorType).setDim(0, 16);
VectorType newVectorType = VectorType(builder);
ASSERT_EQ(newVectorType.getDimSize(0), 16);
}
{
// Make builder from scratch (without scalable dims) -- this use to lead to
// a use-after-free see: https://github.com/llvm/llvm-project/pull/68969.
// Running under sanitizers will catch any issues.
SmallVector<int64_t> shape{1, 2, 3, 4};
VectorType::Builder builder(shape, f32);
ASSERT_EQ(VectorType(builder).getShape(), ArrayRef(shape));
}
{
// Set vector shape (without scalable dims) -- this use to lead to
// a use-after-free see: https://github.com/llvm/llvm-project/pull/68969.
// Running under sanitizers will catch any issues.
VectorType::Builder builder(vectorType);
SmallVector<int64_t> newShape{2, 2};
builder.setShape(newShape);
ASSERT_EQ(VectorType(builder).getShape(), ArrayRef(newShape));
}
}
TEST(ShapedTypeTest, RankedTensorTypeBuilder) {
MLIRContext context;
Type f32 = FloatType::getF32(&context);
SmallVector<int64_t> shape{2, 4, 8, 16, 32};
RankedTensorType tensorType = RankedTensorType::get(shape, f32);
{
// Drop some dims.
RankedTensorType dropFrontTwoDims =
RankedTensorType::Builder(tensorType).dropDim(0).dropDim(1).dropDim(0);
ASSERT_EQ(tensorType.getElementType(), dropFrontTwoDims.getElementType());
ASSERT_EQ(dropFrontTwoDims.getShape(), ArrayRef<int64_t>({16, 32}));
}
{
// Insert some dims.
RankedTensorType insertTwoDims =
RankedTensorType::Builder(tensorType).insertDim(7, 2).insertDim(9, 3);
ASSERT_EQ(tensorType.getElementType(), insertTwoDims.getElementType());
ASSERT_EQ(insertTwoDims.getShape(),
ArrayRef<int64_t>({2, 4, 7, 9, 8, 16, 32}));
}
{
// Test for bug from:
// https://github.com/llvm/llvm-project/commit/b44b3494f60296db6aca38a14cab061d9b747a0a
// Constructs a temporary builder, modifies it, copies it to `builder`.
// This used to lead to a use-after-free. Running under sanitizers will
// catch any issues.
RankedTensorType::Builder builder =
RankedTensorType::Builder(tensorType).dropDim(0);
RankedTensorType newTensorType = RankedTensorType(builder);
ASSERT_EQ(tensorType.getShape().drop_front(), newTensorType.getShape());
}
}
/// Simple wrapper class to enable "isa querying" and simple accessing of
/// encoding.
class TensorWithString : public RankedTensorType {
public:
using RankedTensorType::RankedTensorType;
static TensorWithString get(ArrayRef<int64_t> shape, Type elementType,
StringRef name) {
return mlir::cast<TensorWithString>(RankedTensorType::get(
shape, elementType, StringAttr::get(elementType.getContext(), name)));
}
StringRef getName() const {
if (Attribute enc = getEncoding())
return mlir::cast<StringAttr>(enc).getValue();
return {};
}
static bool classof(Type type) {
if (auto rt = mlir::dyn_cast_or_null<RankedTensorType>(type))
return mlir::isa_and_present<StringAttr>(rt.getEncoding());
return false;
}
};
TEST(ShapedTypeTest, RankedTensorTypeView) {
MLIRContext context;
Type f32 = FloatType::getF32(&context);
Type noEncodingRankedTensorType = RankedTensorType::get({10, 20}, f32);
UnitAttr unitAttr = UnitAttr::get(&context);
Type unitEncodingRankedTensorType =
RankedTensorType::get({10, 20}, f32, unitAttr);
StringAttr stringAttr = StringAttr::get(&context, "app");
Type stringEncodingRankedTensorType =
RankedTensorType::get({10, 20}, f32, stringAttr);
EXPECT_FALSE(mlir::isa<TensorWithString>(noEncodingRankedTensorType));
EXPECT_FALSE(mlir::isa<TensorWithString>(unitEncodingRankedTensorType));
ASSERT_TRUE(mlir::isa<TensorWithString>(stringEncodingRankedTensorType));
// Cast to TensorWithString view.
auto view = mlir::cast<TensorWithString>(stringEncodingRankedTensorType);
ASSERT_TRUE(mlir::isa<TensorWithString>(view));
EXPECT_EQ(view.getName(), "app");
// Verify one could cast view type back to base type.
ASSERT_TRUE(mlir::isa<RankedTensorType>(view));
Type viewCreated = TensorWithString::get({10, 20}, f32, "bob");
ASSERT_TRUE(mlir::isa<TensorWithString>(viewCreated));
ASSERT_TRUE(mlir::isa<RankedTensorType>(viewCreated));
view = mlir::cast<TensorWithString>(viewCreated);
EXPECT_EQ(view.getName(), "bob");
}
} // namespace
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