diff options
Diffstat (limited to 'mlir')
38 files changed, 128 insertions, 223 deletions
diff --git a/mlir/examples/toy/Ch2/mlir/Dialect.cpp b/mlir/examples/toy/Ch2/mlir/Dialect.cpp index 0603943270b..d0746bb3c79 100644 --- a/mlir/examples/toy/Ch2/mlir/Dialect.cpp +++ b/mlir/examples/toy/Ch2/mlir/Dialect.cpp @@ -50,7 +50,7 @@ ToyDialect::ToyDialect(mlir::MLIRContext *ctx) : mlir::Dialect("toy", ctx) { /// expected to fill in order to build the operation. static void buildConstantOp(mlir::Builder *builder, mlir::OperationState &result, double value) { - auto dataType = builder->getTensorType({}, builder->getF64Type()); + auto dataType = RankedTensorType::get({}, builder->getF64Type()); auto dataAttribute = DenseElementsAttr::get(dataType, value); ConstantOp::build(builder, result, dataType, dataAttribute); } @@ -88,7 +88,7 @@ static mlir::LogicalResult verify(ConstantOp op) { static void buildAddOp(mlir::Builder *builder, mlir::OperationState &result, mlir::Value *lhs, mlir::Value *rhs) { - result.addTypes(builder->getTensorType(builder->getF64Type())); + result.addTypes(UnrankedTensorType::get(builder->getF64Type())); result.addOperands({lhs, rhs}); } @@ -96,14 +96,14 @@ static void buildGenericCallOp(mlir::Builder *builder, mlir::OperationState &result, StringRef callee, ArrayRef<mlir::Value *> arguments) { // Generic call always returns an unranked Tensor initially. - result.addTypes(builder->getTensorType(builder->getF64Type())); + result.addTypes(UnrankedTensorType::get(builder->getF64Type())); result.addOperands(arguments); result.addAttribute("callee", builder->getSymbolRefAttr(callee)); } static void buildMulOp(mlir::Builder *builder, mlir::OperationState &result, mlir::Value *lhs, mlir::Value *rhs) { - result.addTypes(builder->getTensorType(builder->getF64Type())); + result.addTypes(UnrankedTensorType::get(builder->getF64Type())); result.addOperands({lhs, rhs}); } @@ -144,7 +144,7 @@ static mlir::LogicalResult verify(ReturnOp op) { static void buildTransposeOp(mlir::Builder *builder, mlir::OperationState &result, mlir::Value *value) { - result.addTypes(builder->getTensorType(builder->getF64Type())); + result.addTypes(UnrankedTensorType::get(builder->getF64Type())); result.addOperands(value); } diff --git a/mlir/examples/toy/Ch2/mlir/MLIRGen.cpp b/mlir/examples/toy/Ch2/mlir/MLIRGen.cpp index 5f12d0a8798..55391d72245 100644 --- a/mlir/examples/toy/Ch2/mlir/MLIRGen.cpp +++ b/mlir/examples/toy/Ch2/mlir/MLIRGen.cpp @@ -282,7 +282,7 @@ private: // The type of this attribute is tensor of 64-bit floating-point with the // shape of the literal. mlir::Type elementType = builder.getF64Type(); - auto dataType = builder.getTensorType(lit.getDims(), elementType); + auto dataType = mlir::RankedTensorType::get(lit.getDims(), elementType); // This is the actual attribute that holds the list of values for this // tensor literal. @@ -443,10 +443,10 @@ private: mlir::Type getType(ArrayRef<int64_t> shape) { // If the shape is empty, then this type is unranked. if (shape.empty()) - return builder.getTensorType(builder.getF64Type()); + return mlir::UnrankedTensorType::get(builder.getF64Type()); // Otherwise, we use the given shape. - return builder.getTensorType(shape, builder.getF64Type()); + return mlir::RankedTensorType::get(shape, builder.getF64Type()); } /// Build an MLIR type from a Toy AST variable type (forward to the generic diff --git a/mlir/examples/toy/Ch3/mlir/Dialect.cpp b/mlir/examples/toy/Ch3/mlir/Dialect.cpp index 5ca50d961bf..37292a2d98a 100644 --- a/mlir/examples/toy/Ch3/mlir/Dialect.cpp +++ b/mlir/examples/toy/Ch3/mlir/Dialect.cpp @@ -50,7 +50,7 @@ ToyDialect::ToyDialect(mlir::MLIRContext *ctx) : mlir::Dialect("toy", ctx) { /// expected to fill in order to build the operation. static void buildConstantOp(mlir::Builder *builder, mlir::OperationState &state, double value) { - auto dataType = builder->getTensorType({}, builder->getF64Type()); + auto dataType = RankedTensorType::get({}, builder->getF64Type()); auto dataAttribute = DenseElementsAttr::get(dataType, value); ConstantOp::build(builder, state, dataType, dataAttribute); } @@ -88,7 +88,7 @@ static mlir::LogicalResult verify(ConstantOp op) { static void buildAddOp(mlir::Builder *builder, mlir::OperationState &state, mlir::Value *lhs, mlir::Value *rhs) { - state.addTypes(builder->getTensorType(builder->getF64Type())); + state.addTypes(UnrankedTensorType::get(builder->getF64Type())); state.addOperands({lhs, rhs}); } @@ -96,14 +96,14 @@ static void buildGenericCallOp(mlir::Builder *builder, mlir::OperationState &state, StringRef callee, ArrayRef<mlir::Value *> arguments) { // Generic call always returns an unranked Tensor initially. - state.addTypes(builder->getTensorType(builder->getF64Type())); + state.addTypes(UnrankedTensorType::get(builder->getF64Type())); state.addOperands(arguments); state.addAttribute("callee", builder->getSymbolRefAttr(callee)); } static void buildMulOp(mlir::Builder *builder, mlir::OperationState &state, mlir::Value *lhs, mlir::Value *rhs) { - state.addTypes(builder->getTensorType(builder->getF64Type())); + state.addTypes(UnrankedTensorType::get(builder->getF64Type())); state.addOperands({lhs, rhs}); } @@ -144,7 +144,7 @@ static mlir::LogicalResult verify(ReturnOp op) { static void buildTransposeOp(mlir::Builder *builder, mlir::OperationState &state, mlir::Value *value) { - state.addTypes(builder->getTensorType(builder->getF64Type())); + state.addTypes(UnrankedTensorType::get(builder->getF64Type())); state.addOperands(value); } diff --git a/mlir/examples/toy/Ch3/mlir/MLIRGen.cpp b/mlir/examples/toy/Ch3/mlir/MLIRGen.cpp index 5f12d0a8798..55391d72245 100644 --- a/mlir/examples/toy/Ch3/mlir/MLIRGen.cpp +++ b/mlir/examples/toy/Ch3/mlir/MLIRGen.cpp @@ -282,7 +282,7 @@ private: // The type of this attribute is tensor of 64-bit floating-point with the // shape of the literal. mlir::Type elementType = builder.getF64Type(); - auto dataType = builder.getTensorType(lit.getDims(), elementType); + auto dataType = mlir::RankedTensorType::get(lit.getDims(), elementType); // This is the actual attribute that holds the list of values for this // tensor literal. @@ -443,10 +443,10 @@ private: mlir::Type getType(ArrayRef<int64_t> shape) { // If the shape is empty, then this type is unranked. if (shape.empty()) - return builder.getTensorType(builder.getF64Type()); + return mlir::UnrankedTensorType::get(builder.getF64Type()); // Otherwise, we use the given shape. - return builder.getTensorType(shape, builder.getF64Type()); + return mlir::RankedTensorType::get(shape, builder.getF64Type()); } /// Build an MLIR type from a Toy AST variable type (forward to the generic diff --git a/mlir/examples/toy/Ch4/mlir/Dialect.cpp b/mlir/examples/toy/Ch4/mlir/Dialect.cpp index e31cb917d89..254f92f08fd 100644 --- a/mlir/examples/toy/Ch4/mlir/Dialect.cpp +++ b/mlir/examples/toy/Ch4/mlir/Dialect.cpp @@ -100,7 +100,7 @@ ToyDialect::ToyDialect(mlir::MLIRContext *ctx) : mlir::Dialect("toy", ctx) { /// expected to fill in order to build the operation. static void buildConstantOp(mlir::Builder *builder, mlir::OperationState &state, double value) { - auto dataType = builder->getTensorType({}, builder->getF64Type()); + auto dataType = RankedTensorType::get({}, builder->getF64Type()); auto dataAttribute = DenseElementsAttr::get(dataType, value); ConstantOp::build(builder, state, dataType, dataAttribute); } @@ -142,7 +142,7 @@ static mlir::LogicalResult verify(ConstantOp op) { static void buildAddOp(mlir::Builder *builder, mlir::OperationState &state, mlir::Value *lhs, mlir::Value *rhs) { - state.addTypes(builder->getTensorType(builder->getF64Type())); + state.addTypes(UnrankedTensorType::get(builder->getF64Type())); state.addOperands({lhs, rhs}); } @@ -154,7 +154,7 @@ static void buildGenericCallOp(mlir::Builder *builder, mlir::OperationState &state, StringRef callee, ArrayRef<mlir::Value *> arguments) { // Generic call always returns an unranked Tensor initially. - state.addTypes(builder->getTensorType(builder->getF64Type())); + state.addTypes(UnrankedTensorType::get(builder->getF64Type())); state.addOperands(arguments); state.addAttribute("callee", builder->getSymbolRefAttr(callee)); } @@ -171,7 +171,7 @@ Operation::operand_range GenericCallOp::getArgOperands() { return inputs(); } static void buildMulOp(mlir::Builder *builder, mlir::OperationState &state, mlir::Value *lhs, mlir::Value *rhs) { - state.addTypes(builder->getTensorType(builder->getF64Type())); + state.addTypes(UnrankedTensorType::get(builder->getF64Type())); state.addOperands({lhs, rhs}); } @@ -235,7 +235,7 @@ static mlir::LogicalResult verify(ReturnOp op) { static void buildTransposeOp(mlir::Builder *builder, mlir::OperationState &state, mlir::Value *value) { - state.addTypes(builder->getTensorType(builder->getF64Type())); + state.addTypes(UnrankedTensorType::get(builder->getF64Type())); state.addOperands(value); } diff --git a/mlir/examples/toy/Ch4/mlir/MLIRGen.cpp b/mlir/examples/toy/Ch4/mlir/MLIRGen.cpp index 5f12d0a8798..55391d72245 100644 --- a/mlir/examples/toy/Ch4/mlir/MLIRGen.cpp +++ b/mlir/examples/toy/Ch4/mlir/MLIRGen.cpp @@ -282,7 +282,7 @@ private: // The type of this attribute is tensor of 64-bit floating-point with the // shape of the literal. mlir::Type elementType = builder.getF64Type(); - auto dataType = builder.getTensorType(lit.getDims(), elementType); + auto dataType = mlir::RankedTensorType::get(lit.getDims(), elementType); // This is the actual attribute that holds the list of values for this // tensor literal. @@ -443,10 +443,10 @@ private: mlir::Type getType(ArrayRef<int64_t> shape) { // If the shape is empty, then this type is unranked. if (shape.empty()) - return builder.getTensorType(builder.getF64Type()); + return mlir::UnrankedTensorType::get(builder.getF64Type()); // Otherwise, we use the given shape. - return builder.getTensorType(shape, builder.getF64Type()); + return mlir::RankedTensorType::get(shape, builder.getF64Type()); } /// Build an MLIR type from a Toy AST variable type (forward to the generic diff --git a/mlir/examples/toy/Ch5/mlir/Dialect.cpp b/mlir/examples/toy/Ch5/mlir/Dialect.cpp index e31cb917d89..254f92f08fd 100644 --- a/mlir/examples/toy/Ch5/mlir/Dialect.cpp +++ b/mlir/examples/toy/Ch5/mlir/Dialect.cpp @@ -100,7 +100,7 @@ ToyDialect::ToyDialect(mlir::MLIRContext *ctx) : mlir::Dialect("toy", ctx) { /// expected to fill in order to build the operation. static void buildConstantOp(mlir::Builder *builder, mlir::OperationState &state, double value) { - auto dataType = builder->getTensorType({}, builder->getF64Type()); + auto dataType = RankedTensorType::get({}, builder->getF64Type()); auto dataAttribute = DenseElementsAttr::get(dataType, value); ConstantOp::build(builder, state, dataType, dataAttribute); } @@ -142,7 +142,7 @@ static mlir::LogicalResult verify(ConstantOp op) { static void buildAddOp(mlir::Builder *builder, mlir::OperationState &state, mlir::Value *lhs, mlir::Value *rhs) { - state.addTypes(builder->getTensorType(builder->getF64Type())); + state.addTypes(UnrankedTensorType::get(builder->getF64Type())); state.addOperands({lhs, rhs}); } @@ -154,7 +154,7 @@ static void buildGenericCallOp(mlir::Builder *builder, mlir::OperationState &state, StringRef callee, ArrayRef<mlir::Value *> arguments) { // Generic call always returns an unranked Tensor initially. - state.addTypes(builder->getTensorType(builder->getF64Type())); + state.addTypes(UnrankedTensorType::get(builder->getF64Type())); state.addOperands(arguments); state.addAttribute("callee", builder->getSymbolRefAttr(callee)); } @@ -171,7 +171,7 @@ Operation::operand_range GenericCallOp::getArgOperands() { return inputs(); } static void buildMulOp(mlir::Builder *builder, mlir::OperationState &state, mlir::Value *lhs, mlir::Value *rhs) { - state.addTypes(builder->getTensorType(builder->getF64Type())); + state.addTypes(UnrankedTensorType::get(builder->getF64Type())); state.addOperands({lhs, rhs}); } @@ -235,7 +235,7 @@ static mlir::LogicalResult verify(ReturnOp op) { static void buildTransposeOp(mlir::Builder *builder, mlir::OperationState &state, mlir::Value *value) { - state.addTypes(builder->getTensorType(builder->getF64Type())); + state.addTypes(UnrankedTensorType::get(builder->getF64Type())); state.addOperands(value); } diff --git a/mlir/examples/toy/Ch5/mlir/MLIRGen.cpp b/mlir/examples/toy/Ch5/mlir/MLIRGen.cpp index 5f12d0a8798..55391d72245 100644 --- a/mlir/examples/toy/Ch5/mlir/MLIRGen.cpp +++ b/mlir/examples/toy/Ch5/mlir/MLIRGen.cpp @@ -282,7 +282,7 @@ private: // The type of this attribute is tensor of 64-bit floating-point with the // shape of the literal. mlir::Type elementType = builder.getF64Type(); - auto dataType = builder.getTensorType(lit.getDims(), elementType); + auto dataType = mlir::RankedTensorType::get(lit.getDims(), elementType); // This is the actual attribute that holds the list of values for this // tensor literal. @@ -443,10 +443,10 @@ private: mlir::Type getType(ArrayRef<int64_t> shape) { // If the shape is empty, then this type is unranked. if (shape.empty()) - return builder.getTensorType(builder.getF64Type()); + return mlir::UnrankedTensorType::get(builder.getF64Type()); // Otherwise, we use the given shape. - return builder.getTensorType(shape, builder.getF64Type()); + return mlir::RankedTensorType::get(shape, builder.getF64Type()); } /// Build an MLIR type from a Toy AST variable type (forward to the generic diff --git a/mlir/examples/toy/Ch6/mlir/Dialect.cpp b/mlir/examples/toy/Ch6/mlir/Dialect.cpp index e31cb917d89..254f92f08fd 100644 --- a/mlir/examples/toy/Ch6/mlir/Dialect.cpp +++ b/mlir/examples/toy/Ch6/mlir/Dialect.cpp @@ -100,7 +100,7 @@ ToyDialect::ToyDialect(mlir::MLIRContext *ctx) : mlir::Dialect("toy", ctx) { /// expected to fill in order to build the operation. static void buildConstantOp(mlir::Builder *builder, mlir::OperationState &state, double value) { - auto dataType = builder->getTensorType({}, builder->getF64Type()); + auto dataType = RankedTensorType::get({}, builder->getF64Type()); auto dataAttribute = DenseElementsAttr::get(dataType, value); ConstantOp::build(builder, state, dataType, dataAttribute); } @@ -142,7 +142,7 @@ static mlir::LogicalResult verify(ConstantOp op) { static void buildAddOp(mlir::Builder *builder, mlir::OperationState &state, mlir::Value *lhs, mlir::Value *rhs) { - state.addTypes(builder->getTensorType(builder->getF64Type())); + state.addTypes(UnrankedTensorType::get(builder->getF64Type())); state.addOperands({lhs, rhs}); } @@ -154,7 +154,7 @@ static void buildGenericCallOp(mlir::Builder *builder, mlir::OperationState &state, StringRef callee, ArrayRef<mlir::Value *> arguments) { // Generic call always returns an unranked Tensor initially. - state.addTypes(builder->getTensorType(builder->getF64Type())); + state.addTypes(UnrankedTensorType::get(builder->getF64Type())); state.addOperands(arguments); state.addAttribute("callee", builder->getSymbolRefAttr(callee)); } @@ -171,7 +171,7 @@ Operation::operand_range GenericCallOp::getArgOperands() { return inputs(); } static void buildMulOp(mlir::Builder *builder, mlir::OperationState &state, mlir::Value *lhs, mlir::Value *rhs) { - state.addTypes(builder->getTensorType(builder->getF64Type())); + state.addTypes(UnrankedTensorType::get(builder->getF64Type())); state.addOperands({lhs, rhs}); } @@ -235,7 +235,7 @@ static mlir::LogicalResult verify(ReturnOp op) { static void buildTransposeOp(mlir::Builder *builder, mlir::OperationState &state, mlir::Value *value) { - state.addTypes(builder->getTensorType(builder->getF64Type())); + state.addTypes(UnrankedTensorType::get(builder->getF64Type())); state.addOperands(value); } diff --git a/mlir/examples/toy/Ch6/mlir/MLIRGen.cpp b/mlir/examples/toy/Ch6/mlir/MLIRGen.cpp index 5f12d0a8798..55391d72245 100644 --- a/mlir/examples/toy/Ch6/mlir/MLIRGen.cpp +++ b/mlir/examples/toy/Ch6/mlir/MLIRGen.cpp @@ -282,7 +282,7 @@ private: // The type of this attribute is tensor of 64-bit floating-point with the // shape of the literal. mlir::Type elementType = builder.getF64Type(); - auto dataType = builder.getTensorType(lit.getDims(), elementType); + auto dataType = mlir::RankedTensorType::get(lit.getDims(), elementType); // This is the actual attribute that holds the list of values for this // tensor literal. @@ -443,10 +443,10 @@ private: mlir::Type getType(ArrayRef<int64_t> shape) { // If the shape is empty, then this type is unranked. if (shape.empty()) - return builder.getTensorType(builder.getF64Type()); + return mlir::UnrankedTensorType::get(builder.getF64Type()); // Otherwise, we use the given shape. - return builder.getTensorType(shape, builder.getF64Type()); + return mlir::RankedTensorType::get(shape, builder.getF64Type()); } /// Build an MLIR type from a Toy AST variable type (forward to the generic diff --git a/mlir/include/mlir/Conversion/StandardToSPIRV/ConvertStandardToSPIRV.h b/mlir/include/mlir/Conversion/StandardToSPIRV/ConvertStandardToSPIRV.h index e92ad03d776..63e63cfebb9 100644 --- a/mlir/include/mlir/Conversion/StandardToSPIRV/ConvertStandardToSPIRV.h +++ b/mlir/include/mlir/Conversion/StandardToSPIRV/ConvertStandardToSPIRV.h @@ -135,11 +135,10 @@ private: case spirv::BuiltIn::LocalInvocationId: case spirv::BuiltIn::GlobalInvocationId: { auto ptrType = spirv::PointerType::get( - builder.getVectorType({3}, builder.getIntegerType(32)), + VectorType::get({3}, builder.getIntegerType(32)), spirv::StorageClass::Input); newVarOp = builder.create<spirv::GlobalVariableOp>( - loc, builder.getTypeAttr(ptrType), builder.getStringAttr(name), - nullptr); + loc, TypeAttr::get(ptrType), builder.getStringAttr(name), nullptr); newVarOp.setAttr( convertToSnakeCase(stringifyDecoration(spirv::Decoration::BuiltIn)), builder.getStringAttr(stringifyBuiltIn(builtin))); diff --git a/mlir/include/mlir/Dialect/AffineOps/AffineOpsBase.td b/mlir/include/mlir/Dialect/AffineOps/AffineOpsBase.td index 2ac1d379c12..fb4439a43ac 100644 --- a/mlir/include/mlir/Dialect/AffineOps/AffineOpsBase.td +++ b/mlir/include/mlir/Dialect/AffineOps/AffineOpsBase.td @@ -33,7 +33,7 @@ def AffineMapAttr : Attr< CPred<"$_self.isa<AffineMapAttr>()">, "AffineMap attribute"> { let storageType = [{ AffineMapAttr }]; let returnType = [{ AffineMap }]; - let constBuilderCall = "$_builder.getAffineMapAttr($0)"; + let constBuilderCall = "AffineMapAttr::get($0)"; } def AffineMapArrayAttr : TypedArrayAttrBase<AffineMapAttr, diff --git a/mlir/include/mlir/Dialect/StandardOps/Ops.td b/mlir/include/mlir/Dialect/StandardOps/Ops.td index 120928a4cff..ccacd1c16d1 100644 --- a/mlir/include/mlir/Dialect/StandardOps/Ops.td +++ b/mlir/include/mlir/Dialect/StandardOps/Ops.td @@ -1067,8 +1067,8 @@ def TensorLoadOp : Std_Op<"tensor_load", let builders = [OpBuilder< "Builder *builder, OperationState &result, Value *memref", [{ auto memrefType = memref->getType().cast<MemRefType>(); - auto resultType = builder->getTensorType(memrefType.getShape(), - memrefType.getElementType()); + auto resultType = RankedTensorType::get(memrefType.getShape(), + memrefType.getElementType()); result.addOperands(memref); result.addTypes(resultType); }]>]; diff --git a/mlir/include/mlir/IR/Builders.h b/mlir/include/mlir/IR/Builders.h index dcc5280f49e..0005a395e70 100644 --- a/mlir/include/mlir/IR/Builders.h +++ b/mlir/include/mlir/IR/Builders.h @@ -80,12 +80,6 @@ public: IntegerType getI1Type(); IntegerType getIntegerType(unsigned width); FunctionType getFunctionType(ArrayRef<Type> inputs, ArrayRef<Type> results); - MemRefType getMemRefType(ArrayRef<int64_t> shape, Type elementType, - ArrayRef<AffineMap> affineMapComposition = {}, - unsigned memorySpace = 0); - VectorType getVectorType(ArrayRef<int64_t> shape, Type elementType); - RankedTensorType getTensorType(ArrayRef<int64_t> shape, Type elementType); - UnrankedTensorType getTensorType(Type elementType); TupleType getTupleType(ArrayRef<Type> elementTypes); NoneType getNoneType(); @@ -105,22 +99,10 @@ public: FloatAttr getFloatAttr(Type type, double value); FloatAttr getFloatAttr(Type type, const APFloat &value); StringAttr getStringAttr(StringRef bytes); - StringAttr getStringAttr(StringRef bytes, Type type); ArrayAttr getArrayAttr(ArrayRef<Attribute> value); - AffineMapAttr getAffineMapAttr(AffineMap map); - IntegerSetAttr getIntegerSetAttr(IntegerSet set); - TypeAttr getTypeAttr(Type type); SymbolRefAttr getSymbolRefAttr(Operation *value); SymbolRefAttr getSymbolRefAttr(StringRef value); - ElementsAttr getDenseElementsAttr(ShapedType type, - ArrayRef<Attribute> values); - ElementsAttr getDenseIntElementsAttr(ShapedType type, - ArrayRef<int64_t> values); - ElementsAttr getSparseElementsAttr(ShapedType type, - DenseIntElementsAttr indices, - DenseElementsAttr values); - ElementsAttr getOpaqueElementsAttr(Dialect *dialect, ShapedType type, - StringRef bytes); + // Returns a 0-valued attribute of the given `type`. This function only // supports boolean, integer, and 16-/32-/64-bit float types, and vector or // ranked tensor of them. Returns null attribute otherwise. @@ -149,9 +131,6 @@ public: AffineExpr getAffineSymbolExpr(unsigned position); AffineExpr getAffineConstantExpr(int64_t constant); - AffineMap getAffineMap(unsigned dimCount, unsigned symbolCount, - ArrayRef<AffineExpr> results); - // Special cases of affine maps and integer sets /// Returns a zero result affine map with no dimensions or symbols: () -> (). AffineMap getEmptyAffineMap(); @@ -175,11 +154,6 @@ public: /// returns: (d0, d1)[s0] -> (d0 + 2, d1 + s0 + 2) AffineMap getShiftedAffineMap(AffineMap map, int64_t shift); - // Integer set. - IntegerSet getIntegerSet(unsigned dimCount, unsigned symbolCount, - ArrayRef<AffineExpr> constraints, - ArrayRef<bool> isEq); - // TODO: Helpers for affine map/exprs, etc. protected: MLIRContext *context; }; diff --git a/mlir/include/mlir/IR/OpBase.td b/mlir/include/mlir/IR/OpBase.td index dbb1e7f0a73..c1bd04fea4e 100644 --- a/mlir/include/mlir/IR/OpBase.td +++ b/mlir/include/mlir/IR/OpBase.td @@ -970,7 +970,7 @@ class IntElementsAttr<int width> : ElementsAttrBase< // Note that this is only constructing scalar elements attribute. let constBuilderCall = "DenseElementsAttr::get(" - "$_builder.getTensorType({}, $_builder.getIntegerType(" # width # ")), " + "RankedTensorType::get({}, $_builder.getIntegerType(" # width # ")), " "llvm::makeArrayRef($0)).cast<DenseIntElementsAttr>()"; let convertFromStorage = "$_self"; } @@ -989,7 +989,7 @@ class FloatElementsAttr<int width> : ElementsAttrBase< // Note that this is only constructing scalar elements attribute. let constBuilderCall = "DenseElementsAttr::get(" - "$_builder.getTensorType({}, $_builder.getF" # width # "Type())," + "RankedTensorType::get({}, $_builder.getF" # width # "Type())," "llvm::makeArrayRef($0))"; let convertFromStorage = "$_self"; } @@ -1013,7 +1013,7 @@ class RankedFloatElementsAttr<int width, list<int> dims> : ElementsAttrBase< let returnType = [{ DenseFPElementsAttr }]; let constBuilderCall = "DenseElementsAttr::get(" - "$_builder.getTensorType({" # StrJoinInt<dims>.result # + "RankedTensorType::get({" # StrJoinInt<dims>.result # "}, $_builder.getF" # width # "Type()), " "llvm::makeArrayRef($0)).cast<DenseFPElementsAttr>()"; let convertFromStorage = "$_self"; diff --git a/mlir/lib/Analysis/LoopAnalysis.cpp b/mlir/lib/Analysis/LoopAnalysis.cpp index b1895d308ad..55a0ac6df84 100644 --- a/mlir/lib/Analysis/LoopAnalysis.cpp +++ b/mlir/lib/Analysis/LoopAnalysis.cpp @@ -77,7 +77,7 @@ void mlir::buildTripCountMapAndOperands( SmallVector<AffineExpr, 4> lbSplatExpr(ubValueMap.getNumResults(), lbMap.getResult(0)); auto lbMapSplat = - b.getAffineMap(lbMap.getNumDims(), lbMap.getNumSymbols(), lbSplatExpr); + AffineMap::get(lbMap.getNumDims(), lbMap.getNumSymbols(), lbSplatExpr); AffineValueMap lbSplatValueMap(lbMapSplat, lbOperands); AffineValueMap tripCountValueMap; diff --git a/mlir/lib/Conversion/StandardToSPIRV/ConvertStandardToSPIRV.cpp b/mlir/lib/Conversion/StandardToSPIRV/ConvertStandardToSPIRV.cpp index a0906b75950..718f8077981 100644 --- a/mlir/lib/Conversion/StandardToSPIRV/ConvertStandardToSPIRV.cpp +++ b/mlir/lib/Conversion/StandardToSPIRV/ConvertStandardToSPIRV.cpp @@ -132,7 +132,7 @@ static Value *createAndLoadGlobalVarForEntryFnArg(PatternRewriter &rewriter, funcOp.getName().str() + "_arg_" + std::to_string(origArgNum); var = rewriter.create<spirv::GlobalVariableOp>( funcOp.getLoc(), - rewriter.getTypeAttr(getGlobalVarTypeForEntryFnArg(origArg->getType())), + TypeAttr::get(getGlobalVarTypeForEntryFnArg(origArg->getType())), rewriter.getStringAttr(varName), nullptr); var.setAttr( spirv::SPIRVDialect::getAttributeName(spirv::Decoration::DescriptorSet), diff --git a/mlir/lib/Dialect/AffineOps/AffineOps.cpp b/mlir/lib/Dialect/AffineOps/AffineOps.cpp index 38d4edb60f4..9980497640a 100644 --- a/mlir/lib/Dialect/AffineOps/AffineOps.cpp +++ b/mlir/lib/Dialect/AffineOps/AffineOps.cpp @@ -198,7 +198,7 @@ void AffineApplyOp::build(Builder *builder, OperationState &result, AffineMap map, ArrayRef<Value *> operands) { result.addOperands(operands); result.types.append(map.getNumResults(), builder->getIndexType()); - result.addAttribute("map", builder->getAffineMapAttr(map)); + result.addAttribute("map", AffineMapAttr::get(map)); } ParseResult AffineApplyOp::parse(OpAsmParser &parser, OperationState &result) { @@ -817,13 +817,13 @@ void AffineDmaStartOp::build(Builder *builder, OperationState &result, ArrayRef<Value *> tagIndices, Value *numElements, Value *stride, Value *elementsPerStride) { result.addOperands(srcMemRef); - result.addAttribute(getSrcMapAttrName(), builder->getAffineMapAttr(srcMap)); + result.addAttribute(getSrcMapAttrName(), AffineMapAttr::get(srcMap)); result.addOperands(srcIndices); result.addOperands(destMemRef); - result.addAttribute(getDstMapAttrName(), builder->getAffineMapAttr(dstMap)); + result.addAttribute(getDstMapAttrName(), AffineMapAttr::get(dstMap)); result.addOperands(destIndices); result.addOperands(tagMemRef); - result.addAttribute(getTagMapAttrName(), builder->getAffineMapAttr(tagMap)); + result.addAttribute(getTagMapAttrName(), AffineMapAttr::get(tagMap)); result.addOperands(tagIndices); result.addOperands(numElements); if (stride) { @@ -985,7 +985,7 @@ void AffineDmaWaitOp::build(Builder *builder, OperationState &result, Value *tagMemRef, AffineMap tagMap, ArrayRef<Value *> tagIndices, Value *numElements) { result.addOperands(tagMemRef); - result.addAttribute(getTagMapAttrName(), builder->getAffineMapAttr(tagMap)); + result.addAttribute(getTagMapAttrName(), AffineMapAttr::get(tagMap)); result.addOperands(tagIndices); result.addOperands(numElements); } @@ -1073,13 +1073,11 @@ void AffineForOp::build(Builder *builder, OperationState &result, builder->getIntegerAttr(builder->getIndexType(), step)); // Add the lower bound. - result.addAttribute(getLowerBoundAttrName(), - builder->getAffineMapAttr(lbMap)); + result.addAttribute(getLowerBoundAttrName(), AffineMapAttr::get(lbMap)); result.addOperands(lbOperands); // Add the upper bound. - result.addAttribute(getUpperBoundAttrName(), - builder->getAffineMapAttr(ubMap)); + result.addAttribute(getUpperBoundAttrName(), AffineMapAttr::get(ubMap)); result.addOperands(ubOperands); // Create a region and a block for the body. The argument of the region is @@ -1164,7 +1162,7 @@ static ParseResult parseBound(bool isLower, OperationState &result, // for storage. Analysis passes may expand it into a multi-dimensional map // if desired. AffineMap map = builder.getSymbolIdentityMap(); - result.addAttribute(boundAttrName, builder.getAffineMapAttr(map)); + result.addAttribute(boundAttrName, AffineMapAttr::get(map)); return success(); } @@ -1213,8 +1211,8 @@ static ParseResult parseBound(bool isLower, OperationState &result, if (auto integerAttr = boundAttr.dyn_cast<IntegerAttr>()) { result.attributes.pop_back(); result.addAttribute( - boundAttrName, builder.getAffineMapAttr( - builder.getConstantAffineMap(integerAttr.getInt()))); + boundAttrName, + AffineMapAttr::get(builder.getConstantAffineMap(integerAttr.getInt()))); return success(); } @@ -1752,7 +1750,7 @@ void AffineLoadOp::build(Builder *builder, OperationState &result, assert(operands.size() == 1 + map.getNumInputs() && "inconsistent operands"); result.addOperands(operands); if (map) - result.addAttribute(getMapAttrName(), builder->getAffineMapAttr(map)); + result.addAttribute(getMapAttrName(), AffineMapAttr::get(map)); auto memrefType = operands[0]->getType().cast<MemRefType>(); result.types.push_back(memrefType.getElementType()); } @@ -1764,7 +1762,7 @@ void AffineLoadOp::build(Builder *builder, OperationState &result, result.addOperands(memref); result.addOperands(mapOperands); auto memrefType = memref->getType().cast<MemRefType>(); - result.addAttribute(getMapAttrName(), builder->getAffineMapAttr(map)); + result.addAttribute(getMapAttrName(), AffineMapAttr::get(map)); result.types.push_back(memrefType.getElementType()); } @@ -1855,7 +1853,7 @@ void AffineStoreOp::build(Builder *builder, OperationState &result, result.addOperands(valueToStore); result.addOperands(memref); result.addOperands(mapOperands); - result.addAttribute(getMapAttrName(), builder->getAffineMapAttr(map)); + result.addAttribute(getMapAttrName(), AffineMapAttr::get(map)); } // Use identity map. diff --git a/mlir/lib/Dialect/LLVMIR/IR/LLVMDialect.cpp b/mlir/lib/Dialect/LLVMIR/IR/LLVMDialect.cpp index 5862efe71b5..23e3889c049 100644 --- a/mlir/lib/Dialect/LLVMIR/IR/LLVMDialect.cpp +++ b/mlir/lib/Dialect/LLVMIR/IR/LLVMDialect.cpp @@ -869,7 +869,7 @@ void GlobalOp::build(Builder *builder, OperationState &result, LLVMType type, ArrayRef<NamedAttribute> attrs) { result.addAttribute(SymbolTable::getSymbolAttrName(), builder->getStringAttr(name)); - result.addAttribute("type", builder->getTypeAttr(type)); + result.addAttribute("type", TypeAttr::get(type)); if (isConstant) result.addAttribute("constant", builder->getUnitAttr()); if (value) @@ -939,7 +939,7 @@ static ParseResult parseGlobalOp(OpAsmParser &parser, OperationState &result) { } } - result.addAttribute("type", parser.getBuilder().getTypeAttr(types[0])); + result.addAttribute("type", TypeAttr::get(types[0])); return success(); } @@ -1026,7 +1026,7 @@ void LLVMFuncOp::build(Builder *builder, OperationState &result, StringRef name, result.addRegion(); result.addAttribute(SymbolTable::getSymbolAttrName(), builder->getStringAttr(name)); - result.addAttribute("type", builder->getTypeAttr(type)); + result.addAttribute("type", TypeAttr::get(type)); result.attributes.append(attrs.begin(), attrs.end()); if (argAttrs.empty()) return; diff --git a/mlir/lib/Dialect/SPIRV/SPIRVOps.cpp b/mlir/lib/Dialect/SPIRV/SPIRVOps.cpp index 4b1c4e4089d..85d106ed33e 100644 --- a/mlir/lib/Dialect/SPIRV/SPIRVOps.cpp +++ b/mlir/lib/Dialect/SPIRV/SPIRVOps.cpp @@ -1244,7 +1244,7 @@ static ParseResult parseGlobalVariableOp(OpAsmParser &parser, if (!type.isa<spirv::PointerType>()) { return parser.emitError(loc, "expected spv.ptr type"); } - state.addAttribute(kTypeAttrName, parser.getBuilder().getTypeAttr(type)); + state.addAttribute(kTypeAttrName, TypeAttr::get(type)); return success(); } diff --git a/mlir/lib/Dialect/SPIRV/Serialization/Deserializer.cpp b/mlir/lib/Dialect/SPIRV/Serialization/Deserializer.cpp index accee7c2214..6ba18d1f1d0 100644 --- a/mlir/lib/Dialect/SPIRV/Serialization/Deserializer.cpp +++ b/mlir/lib/Dialect/SPIRV/Serialization/Deserializer.cpp @@ -954,8 +954,8 @@ LogicalResult Deserializer::processGlobalVariable(ArrayRef<uint32_t> operands) { << wordIndex << " of " << operands.size() << " processed"; } auto varOp = opBuilder.create<spirv::GlobalVariableOp>( - unknownLoc, opBuilder.getTypeAttr(type), - opBuilder.getStringAttr(variableName), initializer); + unknownLoc, TypeAttr::get(type), opBuilder.getStringAttr(variableName), + initializer); // Decorations. if (decorations.count(variableID)) { @@ -1065,7 +1065,7 @@ LogicalResult Deserializer::processType(spirv::Opcode opcode, return emitError(unknownLoc, "OpTypeVector references undefined <id> ") << operands[1]; } - typeMap[operands[0]] = opBuilder.getVectorType({operands[2]}, elementTy); + typeMap[operands[0]] = VectorType::get({operands[2]}, elementTy); } break; case spirv::Opcode::OpTypePointer: { if (operands.size() != 3) { @@ -1391,7 +1391,7 @@ Deserializer::processConstantComposite(ArrayRef<uint32_t> operands) { auto resultID = operands[1]; if (auto vectorType = resultType.dyn_cast<VectorType>()) { - auto attr = opBuilder.getDenseElementsAttr(vectorType, elements); + auto attr = DenseElementsAttr::get(vectorType, elements); // For normal constants, we just record the attribute (and its type) for // later materialization at use sites. constantMap.try_emplace(resultID, attr, resultType); diff --git a/mlir/lib/Dialect/SPIRV/Transforms/DecorateSPIRVCompositeTypeLayoutPass.cpp b/mlir/lib/Dialect/SPIRV/Transforms/DecorateSPIRVCompositeTypeLayoutPass.cpp index a854a1d511c..1fd6274b16e 100644 --- a/mlir/lib/Dialect/SPIRV/Transforms/DecorateSPIRVCompositeTypeLayoutPass.cpp +++ b/mlir/lib/Dialect/SPIRV/Transforms/DecorateSPIRVCompositeTypeLayoutPass.cpp @@ -58,7 +58,7 @@ public: } rewriter.replaceOpWithNewOp<spirv::GlobalVariableOp>( - op, rewriter.getTypeAttr(decoratedType), globalVarAttrs); + op, TypeAttr::get(decoratedType), globalVarAttrs); return matchSuccess(); } }; diff --git a/mlir/lib/Dialect/StandardOps/Ops.cpp b/mlir/lib/Dialect/StandardOps/Ops.cpp index 7177cfe7dff..739def41f8e 100644 --- a/mlir/lib/Dialect/StandardOps/Ops.cpp +++ b/mlir/lib/Dialect/StandardOps/Ops.cpp @@ -653,11 +653,11 @@ static Type getCheckedI1SameShape(Builder *build, Type type) { if (type.isIntOrIndexOrFloat()) return i1Type; if (auto tensorType = type.dyn_cast<RankedTensorType>()) - return build->getTensorType(tensorType.getShape(), i1Type); + return RankedTensorType::get(tensorType.getShape(), i1Type); if (type.isa<UnrankedTensorType>()) - return build->getTensorType(i1Type); + return UnrankedTensorType::get(i1Type); if (auto vectorType = type.dyn_cast<VectorType>()) - return build->getVectorType(vectorType.getShape(), i1Type); + return VectorType::get(vectorType.getShape(), i1Type); return Type(); } @@ -2241,7 +2241,7 @@ OpFoldResult TensorCastOp::fold(ArrayRef<Attribute> operands) { static Type getTensorTypeFromMemRefType(Builder &b, Type type) { if (auto memref = type.dyn_cast<MemRefType>()) - return b.getTensorType(memref.getShape(), memref.getElementType()); + return RankedTensorType::get(memref.getShape(), memref.getElementType()); return b.getNoneType(); } diff --git a/mlir/lib/Dialect/VectorOps/VectorOps.cpp b/mlir/lib/Dialect/VectorOps/VectorOps.cpp index 22f25683087..a7006f0f1a9 100644 --- a/mlir/lib/Dialect/VectorOps/VectorOps.cpp +++ b/mlir/lib/Dialect/VectorOps/VectorOps.cpp @@ -206,7 +206,7 @@ void VectorTransferReadOp::build(Builder *builder, OperationState &result, result.addOperands({*paddingValue}); } result.addAttribute(getPermutationMapAttrName(), - builder->getAffineMapAttr(permutationMap)); + AffineMapAttr::get(permutationMap)); result.addTypes(vectorType); } @@ -383,7 +383,7 @@ void VectorTransferWriteOp::build(Builder *builder, OperationState &result, result.addOperands({srcVector, dstMemRef}); result.addOperands(dstIndices); result.addAttribute(getPermutationMapAttrName(), - builder->getAffineMapAttr(permutationMap)); + AffineMapAttr::get(permutationMap)); } auto VectorTransferWriteOp::getIndices() -> operand_range { diff --git a/mlir/lib/IR/Builders.cpp b/mlir/lib/IR/Builders.cpp index 0a9389768b9..7ec5c3b6a26 100644 --- a/mlir/lib/IR/Builders.cpp +++ b/mlir/lib/IR/Builders.cpp @@ -72,25 +72,6 @@ FunctionType Builder::getFunctionType(ArrayRef<Type> inputs, return FunctionType::get(inputs, results, context); } -MemRefType Builder::getMemRefType(ArrayRef<int64_t> shape, Type elementType, - ArrayRef<AffineMap> affineMapComposition, - unsigned memorySpace) { - return MemRefType::get(shape, elementType, affineMapComposition, memorySpace); -} - -VectorType Builder::getVectorType(ArrayRef<int64_t> shape, Type elementType) { - return VectorType::get(shape, elementType); -} - -RankedTensorType Builder::getTensorType(ArrayRef<int64_t> shape, - Type elementType) { - return RankedTensorType::get(shape, elementType); -} - -UnrankedTensorType Builder::getTensorType(Type elementType) { - return UnrankedTensorType::get(elementType); -} - TupleType Builder::getTupleType(ArrayRef<Type> elementTypes) { return TupleType::get(elementTypes, context); } @@ -165,24 +146,10 @@ StringAttr Builder::getStringAttr(StringRef bytes) { return StringAttr::get(bytes, context); } -StringAttr Builder::getStringAttr(StringRef bytes, Type type) { - return StringAttr::get(bytes, type); -} - ArrayAttr Builder::getArrayAttr(ArrayRef<Attribute> value) { return ArrayAttr::get(value, context); } -AffineMapAttr Builder::getAffineMapAttr(AffineMap map) { - return AffineMapAttr::get(map); -} - -IntegerSetAttr Builder::getIntegerSetAttr(IntegerSet set) { - return IntegerSetAttr::get(set); -} - -TypeAttr Builder::getTypeAttr(Type type) { return TypeAttr::get(type); } - SymbolRefAttr Builder::getSymbolRefAttr(Operation *value) { auto symName = value->getAttrOfType<StringAttr>(SymbolTable::getSymbolAttrName()); @@ -193,27 +160,6 @@ SymbolRefAttr Builder::getSymbolRefAttr(StringRef value) { return SymbolRefAttr::get(value, getContext()); } -ElementsAttr Builder::getDenseElementsAttr(ShapedType type, - ArrayRef<Attribute> values) { - return DenseElementsAttr::get(type, values); -} - -ElementsAttr Builder::getDenseIntElementsAttr(ShapedType type, - ArrayRef<int64_t> values) { - return DenseIntElementsAttr::get(type, values); -} - -ElementsAttr Builder::getSparseElementsAttr(ShapedType type, - DenseIntElementsAttr indices, - DenseElementsAttr values) { - return SparseElementsAttr::get(type, indices, values); -} - -ElementsAttr Builder::getOpaqueElementsAttr(Dialect *dialect, ShapedType type, - StringRef bytes) { - return OpaqueElementsAttr::get(dialect, type, bytes); -} - ArrayAttr Builder::getI32ArrayAttr(ArrayRef<int32_t> values) { auto attrs = functional::map( [this](int32_t v) -> Attribute { return getI32IntegerAttr(v); }, values); @@ -255,7 +201,7 @@ ArrayAttr Builder::getStrArrayAttr(ArrayRef<StringRef> values) { ArrayAttr Builder::getAffineMapArrayAttr(ArrayRef<AffineMap> values) { auto attrs = functional::map( - [this](AffineMap v) -> Attribute { return getAffineMapAttr(v); }, values); + [](AffineMap v) -> Attribute { return AffineMapAttr::get(v); }, values); return getArrayAttr(attrs); } @@ -278,7 +224,7 @@ Attribute Builder::getZeroAttr(Type type) { auto element = getZeroAttr(vtType.getElementType()); if (!element) return {}; - return getDenseElementsAttr(vtType, element); + return DenseElementsAttr::get(vtType, element); } default: break; @@ -290,11 +236,6 @@ Attribute Builder::getZeroAttr(Type type) { // Affine Expressions, Affine Maps, and Integet Sets. //===----------------------------------------------------------------------===// -AffineMap Builder::getAffineMap(unsigned dimCount, unsigned symbolCount, - ArrayRef<AffineExpr> results) { - return AffineMap::get(dimCount, symbolCount, results); -} - AffineExpr Builder::getAffineDimExpr(unsigned position) { return mlir::getAffineDimExpr(position, context); } @@ -307,12 +248,6 @@ AffineExpr Builder::getAffineConstantExpr(int64_t constant) { return mlir::getAffineConstantExpr(constant, context); } -IntegerSet Builder::getIntegerSet(unsigned dimCount, unsigned symbolCount, - ArrayRef<AffineExpr> constraints, - ArrayRef<bool> isEq) { - return IntegerSet::get(dimCount, symbolCount, constraints, isEq); -} - AffineMap Builder::getEmptyAffineMap() { return AffineMap::get(context); } AffineMap Builder::getConstantAffineMap(int64_t val) { @@ -347,9 +282,8 @@ AffineMap Builder::getSingleDimShiftAffineMap(int64_t shift) { AffineMap Builder::getShiftedAffineMap(AffineMap map, int64_t shift) { SmallVector<AffineExpr, 4> shiftedResults; shiftedResults.reserve(map.getNumResults()); - for (auto resultExpr : map.getResults()) { + for (auto resultExpr : map.getResults()) shiftedResults.push_back(resultExpr + shift); - } return AffineMap::get(map.getNumDims(), map.getNumSymbols(), shiftedResults); } diff --git a/mlir/lib/IR/Function.cpp b/mlir/lib/IR/Function.cpp index 474dd1a5934..4f5a4737698 100644 --- a/mlir/lib/IR/Function.cpp +++ b/mlir/lib/IR/Function.cpp @@ -57,7 +57,7 @@ void FuncOp::build(Builder *builder, OperationState &result, StringRef name, FunctionType type, ArrayRef<NamedAttribute> attrs) { result.addAttribute(SymbolTable::getSymbolAttrName(), builder->getStringAttr(name)); - result.addAttribute(getTypeAttrName(), builder->getTypeAttr(type)); + result.addAttribute(getTypeAttrName(), TypeAttr::get(type)); result.attributes.append(attrs.begin(), attrs.end()); result.addRegion(); } diff --git a/mlir/lib/IR/FunctionSupport.cpp b/mlir/lib/IR/FunctionSupport.cpp index b40eebb04b2..468301e9431 100644 --- a/mlir/lib/IR/FunctionSupport.cpp +++ b/mlir/lib/IR/FunctionSupport.cpp @@ -133,7 +133,7 @@ mlir::impl::parseFunctionLikeOp(OpAsmParser &parser, OperationState &result, std::string errorMessage; if (auto type = funcTypeBuilder(builder, argTypes, results, impl::VariadicFlag(isVariadic), errorMessage)) - result.addAttribute(getTypeAttrName(), builder.getTypeAttr(type)); + result.addAttribute(getTypeAttrName(), TypeAttr::get(type)); else return parser.emitError(signatureLocation) << "failed to construct function type" diff --git a/mlir/lib/Parser/Parser.cpp b/mlir/lib/Parser/Parser.cpp index 80d34ab76e5..873476ebffb 100644 --- a/mlir/lib/Parser/Parser.cpp +++ b/mlir/lib/Parser/Parser.cpp @@ -1063,9 +1063,9 @@ Attribute Parser::parseAttribute(Type type) { if (parseAffineMapOrIntegerSetReference(map, set)) return nullptr; if (map) - return builder.getAffineMapAttr(map); + return AffineMapAttr::get(map); assert(set); - return builder.getIntegerSetAttr(set); + return IntegerSetAttr::get(set); } // Parse an array attribute. @@ -1164,7 +1164,7 @@ Attribute Parser::parseAttribute(Type type) { default: // Parse a type attribute. if (Type type = parseType()) - return builder.getTypeAttr(type); + return TypeAttr::get(type); return nullptr; } } @@ -1381,7 +1381,7 @@ Attribute Parser::parseOpaqueElementsAttr() { if (!type) return nullptr; - return builder.getOpaqueElementsAttr(dialect, type, llvm::fromHex(val)); + return OpaqueElementsAttr::get(dialect, type, llvm::fromHex(val)); } namespace { @@ -2496,8 +2496,8 @@ ParseResult AffineParser::parseAffineMapOfSSAIds(AffineMap &map) { if (exprs.empty()) map = AffineMap(); else - map = builder.getAffineMap(numDimOperands, - dimsAndSymbols.size() - numDimOperands, exprs); + map = AffineMap::get(numDimOperands, dimsAndSymbols.size() - numDimOperands, + exprs); return success(); } @@ -2525,7 +2525,7 @@ AffineMap AffineParser::parseAffineMapRange(unsigned numDims, return AffineMap(); // Parsed a valid affine map. - return builder.getAffineMap(numDims, numSymbols, exprs); + return AffineMap::get(numDims, numSymbols, exprs); } /// Parse an affine constraint. @@ -2600,11 +2600,11 @@ IntegerSet AffineParser::parseIntegerSetConstraints(unsigned numDims, if (constraints.empty()) { /* 0 == 0 */ auto zero = getAffineConstantExpr(0, getContext()); - return builder.getIntegerSet(numDims, numSymbols, zero, true); + return IntegerSet::get(numDims, numSymbols, zero, true); } // Parsed a valid integer set. - return builder.getIntegerSet(numDims, numSymbols, constraints, isEqs); + return IntegerSet::get(numDims, numSymbols, constraints, isEqs); } /// Parse an ambiguous reference to either and affine map or an integer set. @@ -3715,7 +3715,7 @@ public: return failure(); // Add AffineMap attribute. if (map) { - mapAttr = parser.builder.getAffineMapAttr(map); + mapAttr = AffineMapAttr::get(map); attrs.push_back(parser.builder.getNamedAttr(attrName, mapAttr)); } diff --git a/mlir/lib/Quantizer/Transforms/AddDefaultStatsTestPass.cpp b/mlir/lib/Quantizer/Transforms/AddDefaultStatsTestPass.cpp index a82a288caf3..a32bb2c9b3c 100644 --- a/mlir/lib/Quantizer/Transforms/AddDefaultStatsTestPass.cpp +++ b/mlir/lib/Quantizer/Transforms/AddDefaultStatsTestPass.cpp @@ -81,7 +81,7 @@ void AddDefaultStatsPass::runWithConfig(SolverContext &solverContext, APFloat minValue(-1.0f); APFloat maxValue(1.0f); ElementsAttr layerStats = DenseFPElementsAttr::get( - b.getTensorType({2}, b.getF32Type()), {minValue, maxValue}); + RankedTensorType::get({2}, b.getF32Type()), {minValue, maxValue}); auto statsOp = b.create<StatisticsOp>(func.getLoc(), arg, layerStats, nullptr, nullptr); arg->replaceAllUsesWith(statsOp); @@ -107,7 +107,7 @@ void AddDefaultStatsPass::runWithConfig(SolverContext &solverContext, APFloat minValue(-1.0f); APFloat maxValue(1.0f); ElementsAttr layerStats = DenseFPElementsAttr::get( - b.getTensorType({2}, b.getF32Type()), {minValue, maxValue}); + RankedTensorType::get({2}, b.getF32Type()), {minValue, maxValue}); auto statsOp = b.create<StatisticsOp>(op->getLoc(), op->getResult(0), layerStats, nullptr, nullptr); originalResult->replaceAllUsesWith(statsOp); diff --git a/mlir/lib/Transforms/LoopFusion.cpp b/mlir/lib/Transforms/LoopFusion.cpp index 15dc36c9c13..7e08c6bbc57 100644 --- a/mlir/lib/Transforms/LoopFusion.cpp +++ b/mlir/lib/Transforms/LoopFusion.cpp @@ -953,8 +953,8 @@ static Value *createPrivateMemRef(AffineForOp forOp, Operation *srcStoreOpInst, } else { newMemSpace = oldMemRefType.getMemorySpace(); } - auto newMemRefType = top.getMemRefType( - newShape, oldMemRefType.getElementType(), {}, newMemSpace); + auto newMemRefType = MemRefType::get(newShape, oldMemRefType.getElementType(), + {}, newMemSpace); // Gather alloc operands for the dynamic dimensions of the memref. SmallVector<Value *, 4> allocOperands; unsigned dynamicDimCount = 0; @@ -988,7 +988,7 @@ static Value *createPrivateMemRef(AffineForOp forOp, Operation *srcStoreOpInst, } auto indexRemap = zeroOffsetCount == rank ? AffineMap() - : b.getAffineMap(outerIVs.size() + rank, 0, remapExprs); + : AffineMap::get(outerIVs.size() + rank, 0, remapExprs); // Replace all users of 'oldMemRef' with 'newMemRef'. LogicalResult res = replaceAllMemRefUsesWith(oldMemRef, newMemRef, {}, indexRemap, diff --git a/mlir/lib/Transforms/LoopTiling.cpp b/mlir/lib/Transforms/LoopTiling.cpp index af1ecd06ee6..4ee7197f2df 100644 --- a/mlir/lib/Transforms/LoopTiling.cpp +++ b/mlir/lib/Transforms/LoopTiling.cpp @@ -168,13 +168,13 @@ constructTiledIndexSetHyperRect(MutableArrayRef<AffineForOp> origLoops, boundExprs.push_back(dim + tileSizes[i]); boundExprs.append(origUbMap.getResults().begin(), origUbMap.getResults().end()); - auto ubMap = b.getAffineMap(origUbMap.getNumDims() + 1, + auto ubMap = AffineMap::get(origUbMap.getNumDims() + 1, origUbMap.getNumSymbols(), boundExprs); newLoops[width + i].setUpperBound(/*operands=*/ubOperands, ubMap); } else { // No need of the min expression. auto dim = b.getAffineDimExpr(0); - auto ubMap = b.getAffineMap(1, 0, dim + tileSizes[i]); + auto ubMap = AffineMap::get(1, 0, dim + tileSizes[i]); newLoops[width + i].setUpperBound(newLoops[i].getInductionVar(), ubMap); } } diff --git a/mlir/lib/Transforms/LoopUnrollAndJam.cpp b/mlir/lib/Transforms/LoopUnrollAndJam.cpp index 2b7330f4175..230869abcd5 100644 --- a/mlir/lib/Transforms/LoopUnrollAndJam.cpp +++ b/mlir/lib/Transforms/LoopUnrollAndJam.cpp @@ -223,7 +223,7 @@ LogicalResult mlir::loopUnrollJamByFactor(AffineForOp forOp, if (!forOpIV->use_empty()) { // iv' = iv + i, i = 1 to unrollJamFactor-1. auto d0 = builder.getAffineDimExpr(0); - auto bumpMap = builder.getAffineMap(1, 0, {d0 + i * step}); + auto bumpMap = AffineMap::get(1, 0, {d0 + i * step}); auto ivUnroll = builder.create<AffineApplyOp>(forInst->getLoc(), bumpMap, forOpIV); operandMapping.map(forOpIV, ivUnroll); diff --git a/mlir/lib/Transforms/PipelineDataTransfer.cpp b/mlir/lib/Transforms/PipelineDataTransfer.cpp index bb9c39baf55..7e175fb22d2 100644 --- a/mlir/lib/Transforms/PipelineDataTransfer.cpp +++ b/mlir/lib/Transforms/PipelineDataTransfer.cpp @@ -82,8 +82,8 @@ static bool doubleBuffer(Value *oldMemRef, AffineForOp forOp) { newShape[0] = 2; std::copy(oldShape.begin(), oldShape.end(), newShape.begin() + 1); auto newMemRefType = - bInner.getMemRefType(newShape, oldMemRefType.getElementType(), {}, - oldMemRefType.getMemorySpace()); + MemRefType::get(newShape, oldMemRefType.getElementType(), {}, + oldMemRefType.getMemorySpace()); return newMemRefType; }; @@ -109,8 +109,8 @@ static bool doubleBuffer(Value *oldMemRef, AffineForOp forOp) { // Create 'iv mod 2' value to index the leading dimension. auto d0 = bInner.getAffineDimExpr(0); int64_t step = forOp.getStep(); - auto modTwoMap = bInner.getAffineMap(/*dimCount=*/1, /*symbolCount=*/0, - {d0.floorDiv(step) % 2}); + auto modTwoMap = AffineMap::get(/*dimCount=*/1, /*symbolCount=*/0, + {d0.floorDiv(step) % 2}); auto ivModTwoOp = bInner.create<AffineApplyOp>(forOp.getLoc(), modTwoMap, forOp.getInductionVar()); diff --git a/mlir/lib/Transforms/Utils/LoopUtils.cpp b/mlir/lib/Transforms/Utils/LoopUtils.cpp index 1872044b0fb..fb96772f053 100644 --- a/mlir/lib/Transforms/Utils/LoopUtils.cpp +++ b/mlir/lib/Transforms/Utils/LoopUtils.cpp @@ -87,7 +87,7 @@ void mlir::getCleanupLoopLowerBound(AffineForOp forOp, unsigned unrollFactor, for (unsigned i = 0, e = tripCountMap.getNumResults(); i < e; i++) { auto tripCountExpr = tripCountMap.getResult(i); bumpExprs[i] = (tripCountExpr - tripCountExpr % unrollFactor) * step; - auto bumpMap = b.getAffineMap(tripCountMap.getNumDims(), + auto bumpMap = AffineMap::get(tripCountMap.getNumDims(), tripCountMap.getNumSymbols(), bumpExprs[i]); bumpValues[i] = b.create<AffineApplyOp>(forOp.getLoc(), bumpMap, tripCountOperands); @@ -100,7 +100,7 @@ void mlir::getCleanupLoopLowerBound(AffineForOp forOp, unsigned unrollFactor, operands->clear(); operands->push_back(lb); operands->append(bumpValues.begin(), bumpValues.end()); - *map = b.getAffineMap(1 + tripCountMap.getNumResults(), 0, newUbExprs); + *map = AffineMap::get(1 + tripCountMap.getNumResults(), 0, newUbExprs); // Simplify the map + operands. fullyComposeAffineMapAndOperands(map, operands); *map = simplifyAffineMap(*map); @@ -487,7 +487,7 @@ LogicalResult mlir::loopUnrollByFactor(AffineForOp forOp, if (!forOpIV->use_empty()) { // iv' = iv + 1/2/3...unrollFactor-1; auto d0 = builder.getAffineDimExpr(0); - auto bumpMap = builder.getAffineMap(1, 0, {d0 + i * step}); + auto bumpMap = AffineMap::get(1, 0, {d0 + i * step}); auto ivUnroll = builder.create<AffineApplyOp>(forOp.getLoc(), bumpMap, forOpIV); operandMap.map(forOpIV, ivUnroll); @@ -676,7 +676,7 @@ static void augmentMapAndBounds(OpBuilder &b, Value *iv, AffineMap *map, auto bounds = llvm::to_vector<4>(map->getResults()); bounds.push_back(b.getAffineDimExpr(map->getNumDims()) + offset); operands->insert(operands->begin() + map->getNumDims(), iv); - *map = b.getAffineMap(map->getNumDims() + 1, map->getNumSymbols(), bounds); + *map = AffineMap::get(map->getNumDims() + 1, map->getNumSymbols(), bounds); canonicalizeMapAndOperands(map, operands); } @@ -1229,7 +1229,7 @@ static AffineForOp generatePointWiseCopy(Location loc, Value *memref, ? memIndicesStart[d] : b.create<AffineApplyOp>( loc, - b.getAffineMap(memAffineMap.getNumDims(), + AffineMap::get(memAffineMap.getNumDims(), memAffineMap.getNumSymbols(), memAffineMap.getResult(d)), memIndicesStart); @@ -1238,7 +1238,7 @@ static AffineForOp generatePointWiseCopy(Location loc, Value *memref, SmallVector<Value *, 2> operands = {memBase, forOp.getInductionVar()}; auto memIndex = b.create<AffineApplyOp>( loc, - b.getAffineMap(2, 0, b.getAffineDimExpr(0) + b.getAffineDimExpr(1)), + AffineMap::get(2, 0, b.getAffineDimExpr(0) + b.getAffineDimExpr(1)), operands); memIndices.push_back(memIndex); } @@ -1381,7 +1381,7 @@ static LogicalResult generateCopy( } else { // The coordinate for the start location is just the lower bound along the // corresponding dimension on the memory region (stored in 'offset'). - auto map = top.getAffineMap( + auto map = AffineMap::get( cst->getNumDimIds() + cst->getNumSymbolIds() - rank, 0, offset); memIndices.push_back(b.create<AffineApplyOp>(loc, map, regionSymbols)); } @@ -1401,8 +1401,8 @@ static LogicalResult generateCopy( if (!existingBuf) { AffineMap fastBufferLayout = b.getMultiDimIdentityMap(rank); auto fastMemRefType = - top.getMemRefType(fastBufferShape, memRefType.getElementType(), - fastBufferLayout, copyOptions.fastMemorySpace); + MemRefType::get(fastBufferShape, memRefType.getElementType(), + fastBufferLayout, copyOptions.fastMemorySpace); // Create the fast memory space buffer just before the 'affine.for' // operation. @@ -1470,8 +1470,8 @@ static LogicalResult generateCopy( } else { // DMA generation. // Create a tag (single element 1-d memref) for the DMA. - auto tagMemRefType = top.getMemRefType({1}, top.getIntegerType(32), {}, - copyOptions.tagMemorySpace); + auto tagMemRefType = MemRefType::get({1}, top.getIntegerType(32), {}, + copyOptions.tagMemorySpace); auto tagMemRef = prologue.create<AllocOp>(loc, tagMemRefType); SmallVector<Value *, 4> tagIndices({zeroIndex}); @@ -1532,7 +1532,7 @@ static LogicalResult generateCopy( auto dimExpr = b.getAffineDimExpr(regionSymbols.size() + i); remapExprs.push_back(dimExpr - offsets[i]); } - auto indexRemap = b.getAffineMap(regionSymbols.size() + rank, 0, remapExprs); + auto indexRemap = AffineMap::get(regionSymbols.size() + rank, 0, remapExprs); // Record the begin since it may be invalidated by memref replacement. Block::iterator prevOfBegin; diff --git a/mlir/lib/Transforms/Utils/Utils.cpp b/mlir/lib/Transforms/Utils/Utils.cpp index d6400ac50ed..35a5273a28f 100644 --- a/mlir/lib/Transforms/Utils/Utils.cpp +++ b/mlir/lib/Transforms/Utils/Utils.cpp @@ -119,8 +119,8 @@ LogicalResult mlir::replaceAllMemRefUsesWith(Value *oldMemRef, Value *newMemRef, oldMemRefOperands.reserve(oldMemRefRank); if (oldMap != builder.getMultiDimIdentityMap(oldMap.getNumDims())) { for (auto resultExpr : oldMap.getResults()) { - auto singleResMap = builder.getAffineMap( - oldMap.getNumDims(), oldMap.getNumSymbols(), resultExpr); + auto singleResMap = AffineMap::get(oldMap.getNumDims(), + oldMap.getNumSymbols(), resultExpr); auto afOp = builder.create<AffineApplyOp>(op->getLoc(), singleResMap, oldMapOperands); oldMemRefOperands.push_back(afOp); @@ -147,7 +147,7 @@ LogicalResult mlir::replaceAllMemRefUsesWith(Value *oldMemRef, Value *newMemRef, indexRemap != builder.getMultiDimIdentityMap(indexRemap.getNumDims())) { // Remapped indices. for (auto resultExpr : indexRemap.getResults()) { - auto singleResMap = builder.getAffineMap( + auto singleResMap = AffineMap::get( indexRemap.getNumDims(), indexRemap.getNumSymbols(), resultExpr); auto afOp = builder.create<AffineApplyOp>(op->getLoc(), singleResMap, remapOperands); @@ -210,7 +210,7 @@ LogicalResult mlir::replaceAllMemRefUsesWith(Value *oldMemRef, Value *newMemRef, state.types.push_back(result->getType()); // Add attribute for 'newMap', other Attributes do not change. - auto newMapAttr = builder.getAffineMapAttr(newMap); + auto newMapAttr = AffineMapAttr::get(newMap); for (auto namedAttr : op->getAttrs()) { if (namedAttr.first == oldMapAttrPair.first) { state.attributes.push_back({namedAttr.first, newMapAttr}); @@ -371,8 +371,8 @@ void mlir::createAffineComputationSlice( // Create an affine.apply for each of the map results. sliceOps->reserve(composedMap.getNumResults()); for (auto resultExpr : composedMap.getResults()) { - auto singleResMap = builder.getAffineMap( - composedMap.getNumDims(), composedMap.getNumSymbols(), resultExpr); + auto singleResMap = AffineMap::get(composedMap.getNumDims(), + composedMap.getNumSymbols(), resultExpr); sliceOps->push_back(builder.create<AffineApplyOp>( opInst->getLoc(), singleResMap, composedOpOperands)); } @@ -457,7 +457,7 @@ LogicalResult mlir::normalizeMemRef(AllocOp allocOp) { auto *oldMemRef = allocOp.getResult(); SmallVector<Value *, 4> symbolOperands(allocOp.getSymbolicOperands()); - auto newMemRefType = b.getMemRefType(newShape, memrefType.getElementType(), + auto newMemRefType = MemRefType::get(newShape, memrefType.getElementType(), b.getMultiDimIdentityMap(newRank)); auto newAlloc = b.create<AllocOp>(allocOp.getLoc(), newMemRefType); diff --git a/mlir/lib/Transforms/Vectorize.cpp b/mlir/lib/Transforms/Vectorize.cpp index a54b05e980a..1e10f372b5f 100644 --- a/mlir/lib/Transforms/Vectorize.cpp +++ b/mlir/lib/Transforms/Vectorize.cpp @@ -772,7 +772,7 @@ static void computeMemoryOpIndices(Operation *op, AffineMap map, OpBuilder builder(op); for (auto resultExpr : map.getResults()) { auto singleResMap = - builder.getAffineMap(map.getNumDims(), map.getNumSymbols(), resultExpr); + AffineMap::get(map.getNumDims(), map.getNumSymbols(), resultExpr); auto afOp = builder.create<AffineApplyOp>(op->getLoc(), singleResMap, mapOperands); results.push_back(afOp); diff --git a/mlir/test/EDSC/builder-api-test.cpp b/mlir/test/EDSC/builder-api-test.cpp index 73b2141970a..73a7366b5e8 100644 --- a/mlir/test/EDSC/builder-api-test.cpp +++ b/mlir/test/EDSC/builder-api-test.cpp @@ -772,7 +772,7 @@ TEST_FUNC(affine_if_op) { builder.getAffineSymbolExpr(0), // s0 >= 0 builder.getAffineSymbolExpr(1) // s1 >= 0 }; - auto intSet = builder.getIntegerSet(2, 2, affineExprs, isEq); + auto intSet = IntegerSet::get(2, 2, affineExprs, isEq); SmallVector<Value *, 4> affineIfArgs = {zero, zero, ten, ten}; intrinsics::affine_if(intSet, affineIfArgs, /*withElseRegion=*/false); diff --git a/mlir/unittests/Dialect/SPIRV/SerializationTest.cpp b/mlir/unittests/Dialect/SPIRV/SerializationTest.cpp index ad9d6b21c95..ecf37139ac3 100644 --- a/mlir/unittests/Dialect/SPIRV/SerializationTest.cpp +++ b/mlir/unittests/Dialect/SPIRV/SerializationTest.cpp @@ -71,7 +71,7 @@ protected: OpBuilder opBuilder(module.body()); auto ptrType = spirv::PointerType::get(type, spirv::StorageClass::Uniform); opBuilder.create<spirv::GlobalVariableOp>( - UnknownLoc::get(&context), opBuilder.getTypeAttr(ptrType), + UnknownLoc::get(&context), TypeAttr::get(ptrType), opBuilder.getStringAttr(name), nullptr); } |
