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[mlir] Add unit test for RankedTensorType wrapper class example. #99789

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Jul 22, 2024
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[mlir] Add unit test for RankedTensorType wrapper class example. #99789

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58 changes: 58 additions & 0 deletions mlir/unittests/IR/ShapedTypeTest.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,7 @@
#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>
Expand Down Expand Up @@ -226,4 +227,61 @@ TEST(ShapedTypeTest, RankedTensorTypeBuilder) {
}
}

/// 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();
Comment on lines +243 to +244
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If we're going to be adding these encoding wrappers, should we consider a templatized get encoding that does the cast/dyn_cast to the wrapped type?

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That's a good idea yes. Like "getEncodingAs" ?

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