# Operation Definition Specification (ODS) In addition to specializing the `mlir::Op` C++ template, MLIR also supports defining operations and data types in a table-driven manner. This is achieved via [TableGen][TableGen], which is both a generic language and its tooling to maintain records of domain-specific information. Facts regarding an operation are specified concisely into a TableGen record, which will be expanded into an equivalent `mlir::Op` C++ template specialization at compiler build time. This manual explains in detail all the available mechanisms for defining operations in such a table-driven manner. It aims to be a specification instead of a tutorial. Please refer to [Quickstart tutorial to adding MLIR graph rewrite](Tutorials/QuickstartRewrites.md) for the latter. In addition to detailing each mechanism, this manual also tries to capture best practices. They are rendered as quoted bullet points. ## Motivation MLIR allows pluggable dialects, and dialects contain, among others, a list of operations. This open and extensible ecosystem leads to the "stringly" type IR problem, e.g., repetitive string comparisons during optimization and analysis passes, unintuitive accessor methods (e.g., generic/error prone `getOperand(3)` vs self-documenting `getStride()`) with more generic return types, verbose and generic constructors without default arguments, verbose textual IR dump, and so on. Furthermore, operation verification is: 1. best case: a central string-to-verification-function map, 1. middle case: duplication of verification across the code base, or 1. worst case: no verification functions. The fix is to support defining ops in a table-driven manner. Then for each dialect, we can have a central place that contains everything you need to know about each op, including its constraints, custom assembly form, etc. This description is also used to generate helper functions and classes to allow building, verification, parsing, printing, analysis, and many more. ## Benefits Compared to the C++ template, this table-driven approach has several benefits including but not limited to: * **Single source of truth**: We strive to encode all facts regarding an operation into the record, so that readers don't need to jump among code snippets to fully understand an operation. * **Removing boilerplate**: We can automatically generate operand/attribute/result getter methods, operation build methods, operation verify methods, and many more utilities from the record. This greatly reduces the boilerplate needed for defining a new op. * **Facilitating auto-generation**: The usage of these operation information records are by no means limited to op definition itself. We can use them to drive the auto-generation of many other components, like computation graph serialization. ## TableGen Syntax We use TableGen as the language for specifying operation information. TableGen itself just provides syntax for writing records; the syntax and constructs allowed in a TableGen file (typically with filename suffix `.td`) can be found [here][TableGenProgRef]. * TableGen `class` is similar to C++ class; it can be templated and subclassed. * TableGen `def` is similar to C++ object; it can be declared by specializing a TableGen `class` (e.g., `def MyDef : MyClass<...>;`) or completely independently (e.g., `def MyDef;`). It cannot be further templated or subclassed. * TableGen `dag` is a dedicated type for directed acyclic graph of elements. A `dag` has one operator and zero or more arguments. Its syntax is `(operator arg0, arg1, argN)`. The operator can be any TableGen `def`; an argument can be anything, including `dag` itself. We can have names attached to both the operator and the arguments like `(MyOp:$op_name MyArg:$arg_name)`. Please see the [language reference][TableGenProgRef] to learn about all the types and expressions supported by TableGen. ## Operation Definition MLIR defines several common constructs to help operation definition and provide their semantics via a special [TableGen backend][TableGenBackend]: [`OpDefinitionsGen`][OpDefinitionsGen]. These constructs are defined in [`OpBase.td`][OpBase]. The main ones are * The `Op` class: It is the main construct for defining operations. All facts regarding the operation are specified when specializing this class, with the help of the following constructs. * The `Dialect` class: Operations belonging to one logical group are placed in the same dialect. The `Dialect` class contains dialect-level information. * The `OpTrait` class hierarchy: They are used to specify special properties and constraints of the operation, including whether the operation has side effect or whether its output has the same shape as the input. * The `ins`/`outs` marker: These are two special makers builtin to the `OpDefinitionsGen` backend. They lead the definitions of operands/attributes and results respectively. * The `TypeConstraint` class hierarchy: They are used to specify the constraints over operands or results. A notable subclass hierarchy is `Type`, which stands for constraints for common C++ types. * The `AttrConstraint` class hierarchy: They are used to specify the constraints over attributes. A notable subclass hierarchy is `Attr`, which stands for constraints for attributes whose values are of common types. An operation is defined by specializing the `Op` class with concrete contents for all the fields it requires. For example, `tf.AvgPool` is defined as ```tablegen def TF_AvgPoolOp : TF_Op<"AvgPool", [NoSideEffect]> { let summary = "Performs average pooling on the input."; let description = [{ Each entry in `output` is the mean of the corresponding size `ksize` window in `value`. }]; let arguments = (ins TF_FpTensor:$value, Confined]>:$ksize, Confined]>:$strides, TF_AnyStrAttrOf<["SAME", "VALID"]>:$padding, DefaultValuedAttr:$data_format ); let results = (outs TF_FpTensor:$output ); TF_DerivedOperandTypeAttr T = TF_DerivedOperandTypeAttr<0>; } ``` In the following we describe all the fields needed. Please see the definition of the `Op` class for the complete list of fields supported. ### Operation name The operation name is a unique identifier of the operation within MLIR, e.g., `tf.Add` for addition operation in the TensorFlow dialect. This is the equivalent of the mnemonic in assembly language. It is used for parsing and printing in the textual format. It is also used for pattern matching in graph rewrites. The full operation name is composed of the dialect name and the op name, with the former provided via the dialect and the latter provided as the second template parameter to the `Op` class. ### Operation documentation This includes both a one-line `summary` and a longer human-readable `description`. They will be used to drive automatic generation of dialect documentation. They need to be provided in the operation's definition body: ```tablegen let summary = "..."; let description = [{ ... }]; ``` `description` should be written in Markdown syntax. Placing the documentation at the beginning is recommended since it helps in understanding the operation. > * Place documentation at the beginning of the operation definition > * The summary should be short and concise. It should be a one-liner without > trailing punctuation. Put expanded explanation in description. ### Operation arguments There are two kinds of arguments: operands and attributes. Operands are runtime values produced by other ops; while attributes are compile-time known constant values, including two categories: 1. Natural attributes: these attributes affect the behavior of the operations (e.g., padding for convolution); 1. Derived attributes: these attributes are not needed to define the operation but are instead derived from information of the operation. E.g., the output shape of type. This is mostly used for convenience interface generation or interaction with other frameworks/translation. All derived attributes should be materializable as an Attribute. That is, even though they are not materialized, it should be possible to store as an attribute. Both operands and attributes are specified inside the `dag`-typed `arguments`, led by `ins`: ```tablegen let arguments = (ins :$, ... :$, ... ); ``` Here `` is a TableGen `def` from the `TypeConstraint` class hierarchy. Similarly, `` is a TableGen `def` from the `AttrConstraint` class hierarchy. See [Constraints](#constraints) for more information. There is no requirements on the relative order of operands and attributes; they can mix freely. The relative order of operands themselves matters. From each named argument a named getter will be generated that returns the argument with the return type (in the case of attributes the return type will be constructed from the storage type, while for operands it will be `Value`). Each attribute's raw value (e.g., as stored) can also be accessed via generated `Attr` getters for use in transformation passes where the more user friendly return type is less suitable. All the arguments should be named to 1) provide documentation, 2) drive auto-generation of getter methods, 3) provide a handle to reference for other places like constraints. #### Variadic operands To declare a variadic operand, wrap the `TypeConstraint` for the operand with `Variadic<...>`. Normally operations have no variadic operands or just one variadic operand. For the latter case, it is easy to deduce which dynamic operands are for the static variadic operand definition. Though, if an operation has more than one variable length operands (either optional or variadic), it would be impossible to attribute dynamic operands to the corresponding static variadic operand definitions without further information from the operation. Therefore, either the `SameVariadicOperandSize` or `AttrSizedOperandSegments` trait is needed to indicate that all variable length operands have the same number of dynamic values. #### Optional operands To declare an optional operand, wrap the `TypeConstraint` for the operand with `Optional<...>`. Normally operations have no optional operands or just one optional operand. For the latter case, it is easy to deduce which dynamic operands are for the static operand definition. Though, if an operation has more than one variable length operands (either optional or variadic), it would be impossible to attribute dynamic operands to the corresponding static variadic operand definitions without further information from the operation. Therefore, either the `SameVariadicOperandSize` or `AttrSizedOperandSegments` trait is needed to indicate that all variable length operands have the same number of dynamic values. #### Optional attributes To declare an optional attribute, wrap the `AttrConstraint` for the attribute with `OptionalAttr<...>`. #### Attributes with default values To declare an attribute with a default value, wrap the `AttrConstraint` for the attribute with `DefaultValuedAttr<..., "...">`. The second parameter to `DefaultValuedAttr` should be a string containing the C++ default value. For example, a float default value should be specified as like `"0.5f"`, and an integer array default value should be specified as like `"{1, 2, 3}"`. #### Confining attributes `Confined` is provided as a general mechanism to help modelling further constraints on attributes beyond the ones brought by value types. You can use `Confined` to compose complex constraints out of more primitive ones. For example, a 32-bit integer attribute whose minimum value must be 10 can be expressed as `Confined]>`. Right now, the following primitive constraints are supported: * `IntMinValue`: Specifying an integer attribute to be greater than or equal to `N` * `IntMaxValue`: Specifying an integer attribute to be less than or equal to `N` * `ArrayMinCount`: Specifying an array attribute to have at least `N` elements * `IntArrayNthElemEq`: Specifying an integer array attribute's `I`-th element to be equal to `N` * `IntArrayNthElemMinValue`: Specifying an integer array attribute's `I`-th element to be greater than or equal to `N` TODO: Design and implement more primitive constraints ### Operation regions The regions of an operation are specified inside of the `dag`-typed `regions`, led by `region`: ```tablegen let regions = (region :$, ... ); ``` #### Variadic regions Similar to the `Variadic` class used for variadic operands and results, `VariadicRegion<...>` can be used for regions. Variadic regions can currently only be specified as the last region in the regions list. ### Operation results Similar to operands, results are specified inside the `dag`-typed `results`, led by `outs`: ```tablegen let results = (outs :$, ... ); ``` #### Variadic results Similar to variadic operands, `Variadic<...>` can also be used for results. And similarly, `SameVariadicResultSize` for multiple variadic results in the same operation. ### Operation successors For terminator operations, the successors are specified inside of the `dag`-typed `successors`, led by `successor`: ```tablegen let successors = (successor :$, ... ); ``` #### Variadic successors Similar to the `Variadic` class used for variadic operands and results, `VariadicSuccessor<...>` can be used for successors. Variadic successors can currently only be specified as the last successor in the successor list. ### Operation traits and constraints Traits are operation properties that affect syntax or semantics. MLIR C++ models various traits in the `mlir::OpTrait` namespace. Both operation traits, [interfaces](#operation-interfaces), and constraints involving multiple operands/attributes/results are provided as the second template parameter to the `Op` class. They should be deriving from the `OpTrait` class. See [Constraints](#constraints) for more information. ### Interfaces [Interfaces](Interfaces.md#attribute-operation-type-interfaces) allow for attributes, operations, and types to expose method calls without the caller needing to know the derived type. Operation interfaces defined in C++ can be accessed in the ODS framework via the `OpInterfaceTrait` class. Aside from using pre-existing interfaces in the C++ API, the ODS framework also provides a simplified mechanism for defining such interfaces which removes much of the boilerplate necessary. Providing a definition of the `AttrInterface`, `OpInterface`, or `TypeInterface` class will auto-generate the C++ classes for the interface. An interface includes a name, for the C++ class, a description, and a list of interface methods. ```tablegen def MyInterface : OpInterface<"MyInterface"> { let description = ...; let methods = [...]; } ``` There are two types of methods that can be used with an interface, `InterfaceMethod` and `StaticInterfaceMethod`. They are both comprised of the same core components, with the distinction that `StaticInterfaceMethod` models a static method on the derived operation. An `InterfaceMethod` is comprised of the following components: * Description - A string description of what this method does and its invariants. * ReturnType - A string corresponding to the C++ return type of the method. * MethodName - A string corresponding to the desired name of the method. * Arguments (Optional) - A dag of strings that correspond to a C++ type and variable name respectively. * MethodBody (Optional) - An optional explicit implementation of the interface method. - `ConcreteOp` is an implicitly defined typename that can be used to refer to the type of the derived operation currently being operated on. - In non-static methods, a variable 'ConcreteOp op' is defined and may be used to refer to an instance of the derived operation. * DefaultImplementation (Optional) - An optional explicit default implementation of the interface method. - This method is placed within the `Trait` class that is attached to the operation. As such, this method has the same characteristics as any other [`Trait`](Traits.md) method. - `ConcreteOp` is an implicitly defined typename that can be used to refer to the type of the derived operation currently being operated on. ODS also allows generating the declarations for the `InterfaceMethod` of the op if one specifies the interface with `DeclareOpInterfaceMethods` (see example below). Examples: ```tablegen def MyInterface : OpInterface<"MyInterface"> { let description = [{ My interface is very interesting. ... }]; let methods = [ // A simple non-static method with no inputs. InterfaceMethod<"'foo' is a non-static method with no inputs.", "unsigned", "foo" >, // A new non-static method accepting an input argument. InterfaceMethod<"/*insert doc here*/", "Value ", "bar", (ins "unsigned":$i) >, // Query a static property of the derived operation. StaticInterfaceMethod<"'fooStatic' is a static method with no inputs.", "unsigned", "fooStatic" >, // Provide the definition of a static interface method. // Note: `ConcreteOp` corresponds to the derived operation typename. StaticInterfaceMethod<"/*insert doc here*/", "Operation *", "create", (ins "OpBuilder &":$builder, "Location":$loc), [{ return builder.create(loc); }]>, // Provide a definition of the non-static method. // Note: `op` corresponds to the derived operation variable. InterfaceMethod<"/*insert doc here*/", "unsigned", "getNumInputsAndOutputs", (ins), [{ return op.getNumInputs() + op.getNumOutputs(); }]>, // Provide only a default definition of the method. // Note: `ConcreteOp` corresponds to the derived operation typename. InterfaceMethod<"/*insert doc here*/", "unsigned", "getNumWithDefault", (ins), /*methodBody=*/[{}], [{ ConcreteOp op = cast(this->getOperation()); return op.getNumInputs() + op.getNumOutputs(); }]>, ]; } // Operation interfaces can optionally be wrapped inside // DeclareOpInterfaceMethods. This would result in autogenerating declarations // for members `foo`, `bar` and `fooStatic`. Methods with bodies are not // declared inside the op declaration but instead handled by the op interface // trait directly. def OpWithInferTypeInterfaceOp : Op<... [DeclareOpInterfaceMethods]> { ... } // Methods that have a default implementation do not have declarations // generated. If an operation wishes to override the default behavior, it can // explicitly specify the method that it wishes to override. This will force // the generation of a declaration for those methods. def OpWithOverrideInferTypeInterfaceOp : Op<... [DeclareOpInterfaceMethods]> { ... } ``` Operation interfaces may also provide a verification method on `OpInterface` by setting `verify`. Setting `verify` results in the generated trait having a `verifyTrait` method that is applied to all operations implementing the trait. ### Builder methods For each operation, there are a few builders automatically generated based on the arguments and returns types. For example, given the following op definition: ```tablegen def MyOp : ... { let arguments = (ins I32:$i32_operand, F32:$f32_operand, ..., I32Attr:$i32_attr, F32Attr:$f32_attr, ... ); let results = (outs I32:$i32_result, F32:$f32_result, ... ); } ``` The following builders are generated: ```c++ // All result-types/operands/attributes have one aggregate parameter. static void build(OpBuilder &odsBuilder, OperationState &odsState, ArrayRef resultTypes, ValueRange operands, ArrayRef attributes); // Each result-type/operand/attribute has a separate parameter. The parameters // for attributes are of mlir::Attribute types. static void build(OpBuilder &odsBuilder, OperationState &odsState, Type i32_result, Type f32_result, ..., Value i32_operand, Value f32_operand, ..., IntegerAttr i32_attr, FloatAttr f32_attr, ...); // Each result-type/operand/attribute has a separate parameter. The parameters // for attributes are raw values unwrapped with mlir::Attribute instances. // (Note that this builder will not always be generated. See the following // explanation for more details.) static void build(OpBuilder &odsBuilder, OperationState &odsState, Type i32_result, Type f32_result, ..., Value i32_operand, Value f32_operand, ..., APInt i32_attr, StringRef f32_attr, ...); // Each operand/attribute has a separate parameter but result type is aggregate. static void build(OpBuilder &odsBuilder, OperationState &odsState, ArrayRef resultTypes, Value i32_operand, Value f32_operand, ..., IntegerAttr i32_attr, FloatAttr f32_attr, ...); // All operands/attributes have aggregate parameters. // Generated if return type can be inferred. static void build(OpBuilder &odsBuilder, OperationState &odsState, ValueRange operands, ArrayRef attributes); // (And manually specified builders depending on the specific op.) ``` The first form provides basic uniformity so that we can create ops using the same form regardless of the exact op. This is particularly useful for implementing declarative pattern rewrites. The second and third forms are good for use in manually written code given that they provide better guarantee via signatures. The third form will be generated if any of the op's attribute has different `Attr.returnType` from `Attr.storageType` and we know how to build an attribute from an unwrapped value (i.e., `Attr.constBuilderCall` is defined.) Additionally, for the third form, if an attribute appearing later in the `arguments` list has a default value, the default value will be supplied in the declaration. This works for `BoolAttr`, `StrAttr`, `EnumAttr` for now and the list can grow in the future. So if possible, default valued attribute should be placed at the end of the `arguments` list to leverage this feature. (This behavior is essentially due to C++ function parameter default value placement restrictions.) Otherwise, the builder of the third form will still be generated but default values for the attributes not at the end of the `arguments` list will not be supplied in the builder's signature. ODS will generate a builder that doesn't require return type specified if * Op implements InferTypeOpInterface interface; * All return types are either buildable types or are the same as a given operand (e.g., `AllTypesMatch` constraint between operand and result); And there may potentially exist other builders depending on the specific op; please refer to the [generated C++ file](#run-mlir-tblgen-to-see-the-generated-content) for the complete list. #### Custom builder methods However, if the above cases cannot satisfy all needs, you can define additional convenience build methods in the `builders` field as follows. ```tablegen def MyOp : Op<"my_op", []> { let arguments = (ins F32Attr:$attr); let builders = [ OpBuilderDAG<(ins "float":$val)> ]; } ``` The `builders` field is a list of custom builders that are added to the Op class. In this example, we provide a convenience builder that takes a floating point value instead of an attribute. The `ins` prefix is common to many function declarations in ODS, which use a TableGen [`dag`](#tablegen-syntax). What follows is a comma-separated list of types (quoted string) and names prefixed with the `$` sign. This will generate the declaration of a builder method that looks like: ```c++ class MyOp : /*...*/ { /*...*/ static void build(::mlir::OpBuilder &builder, ::mlir::OperationState &state, float val); }; ``` Note that the method has two additional leading arguments. These arguments are useful to construct the operation. In particular, the method must populate `state` with attributes, operands, regions and result types of the operation to be constructed. `builder` can be used to construct any IR objects that belong to the Op, such as types or nested operations. Since the type and name are generated as is in the C++ code, they should be valid C++ constructs for a type (in the namespace of the Op) and an identifier (e.g., `class` is not a valid identifier). Implementations of the builder can be provided directly in ODS, using TableGen code block as follows. ```tablegen def MyOp : Op<"my_op", []> { let arguments = (ins F32Attr:$attr); let builders = [ OpBuilderDAG<(ins "float":$val), [{ $_state.addAttribute("attr", $_builder.getF32FloatAttr(val)); }]> ]; } ``` The equivalents of `builder` and `state` arguments are available as `$_builder` and `$_state` special variables. The named arguments listed in the `ins` part are available directly, e.g. `val`. The body of the builder will be generated by substituting special variables and should otherwise be valid C++. While there is no limitation on the code size, we encourage one to define only short builders inline in ODS and put definitions of longer builders in C++ files. Finally, if some arguments need a default value, they can be defined using `CArg` to wrap the type and this value as follows. ```tablegen def MyOp : Op<"my_op", []> { let arguments = (ins F32Attr:$attr); let builders = [ OpBuilderDAG<(ins CArg<"float", "0.5f">:$val), [{ $_state.addAttribute("attr", $_builder.getF32FloatAttr(val)); }]> ]; } ``` The generated code will use default value in the declaration, but not in the definition, as required by C++. ```c++ /// Header file. class MyOp : /*...*/ { /*...*/ static void build(::mlir::OpBuilder &builder, ::mlir::OperationState &state, float val = 0.5f); }; /// Source file. MyOp::build(::mlir::OpBuilder &builder, ::mlir::OperationState &state, float val) { state.addAttribute("attr", builder.getF32FloatAttr(val)); } ``` **Deprecated:** `OpBuilder` class allows one to specify the custom builder signature as a raw string, without separating parameters into different `dag` arguments. It also supports leading parameters of `OpBuilder &` and `OperationState &` types, which will be used instead of the autogenerated ones if present. ### Custom parser and printer methods Functions to parse and print the operation's custom assembly form. ### Custom verifier code Verification code will be automatically generated for [constraints](#constraints) specified on various entities of the op. To perform _additional_ verification, you can use ```tablegen let verifier = [{ ... }]; ``` Code placed in `verifier` will be called after the auto-generated verification code. The order of trait verification excluding those of `verifier` should not be relied upon. ### Declarative Assembly Format The custom assembly form of the operation may be specified in a declarative string that matches the operations operands, attributes, etc. With the ability to express additional information that needs to be parsed to build the operation: ```tablegen def CallOp : Std_Op<"call", ...> { let arguments = (ins FlatSymbolRefAttr:$callee, Variadic:$args); let results = (outs Variadic); let assemblyFormat = [{ $callee `(` $args `)` attr-dict `:` functional-type($args, results) }]; } ``` The format is comprised of three components: #### Directives A directive is a type of builtin function, with an optional set of arguments. The available directives are as follows: * `attr-dict` - Represents the attribute dictionary of the operation. * `attr-dict-with-keyword` - Represents the attribute dictionary of the operation, but prefixes the dictionary with an `attributes` keyword. * `custom` < UserDirective > ( Params ) - Represents a custom directive implemented by the user in C++. - See the [Custom Directives](#custom-directives) section below for more details. * `functional-type` ( inputs , results ) - Formats the `inputs` and `results` arguments as a [function type](LangRef.md#function-type). - The constraints on `inputs` and `results` are the same as the `input` of the `type` directive. * `operands` - Represents all of the operands of an operation. * `regions` - Represents all of the regions of an operation. * `results` - Represents all of the results of an operation. * `successors` - Represents all of the successors of an operation. * `type` ( input ) - Represents the type of the given input. - `input` must be either an operand or result [variable](#variables), the `operands` directive, or the `results` directive. * `type_ref` ( input ) - Represents a reference to the type of the given input that must have already been resolved. - `input` must be either an operand or result [variable](#variables), the `operands` directive, or the `results` directive. - Used to pass previously parsed types to custom directives. #### Literals A literal is either a keyword or punctuation surrounded by \`\`. The following are the set of valid punctuation: `:`, `,`, `=`, `<`, `>`, `(`, `)`, `{`, `}`, `[`, `]`, `->`, `?`, `+`, `*` #### Variables A variable is an entity that has been registered on the operation itself, i.e. an argument(attribute or operand), region, result, successor, etc. In the `CallOp` example above, the variables would be `$callee` and `$args`. Attribute variables are printed with their respective value type, unless that value type is buildable. In those cases, the type of the attribute is elided. #### Custom Directives The declarative assembly format specification allows for handling a large majority of the common cases when formatting an operation. For the operations that require or desire specifying parts of the operation in a form not supported by the declarative syntax, custom directives may be specified. A custom directive essentially allows for users to use C++ for printing and parsing subsections of an otherwise declaratively specified format. Looking at the specification of a custom directive above: ``` custom-directive ::= `custom` `<` UserDirective `>` `(` Params `)` ``` A custom directive has two main parts: The `UserDirective` and the `Params`. A custom directive is transformed into a call to a `print*` and a `parse*` method when generating the C++ code for the format. The `UserDirective` is an identifier used as a suffix to these two calls, i.e., `custom(...)` would result in calls to `parseMyDirective` and `printMyDirective` within the parser and printer respectively. `Params` may be any combination of variables (i.e. Attribute, Operand, Successor, etc.), type directives, and `attr-dict`. The type directives must refer to a variable, but that variable need not also be a parameter to the custom directive. The arguments to the `parse` method are firstly a reference to the `OpAsmParser`(`OpAsmParser &`), and secondly a set of output parameters corresponding to the parameters specified in the format. The mapping of declarative parameter to `parse` method argument is detailed below: * Attribute Variables - Single: `(e.g. Attribute) &` - Optional: `(e.g. Attribute) &` * Operand Variables - Single: `OpAsmParser::OperandType &` - Optional: `Optional &` - Variadic: `SmallVectorImpl &` * Region Variables - Single: `Region &` - Variadic: `SmallVectorImpl> &` * Successor Variables - Single: `Block *&` - Variadic: `SmallVectorImpl &` * Type Directives - Single: `Type &` - Optional: `Type &` - Variadic: `SmallVectorImpl &` * TypeRef Directives - Single: `Type` - Optional: `Type` - Variadic: `const SmallVectorImpl &` * `attr-dict` Directive: `NamedAttrList &` When a variable is optional, the value should only be specified if the variable is present. Otherwise, the value should remain `None` or null. The arguments to the `print` method is firstly a reference to the `OpAsmPrinter`(`OpAsmPrinter &`), second the op (e.g. `FooOp op` which can be `Operation *op` alternatively), and finally a set of output parameters corresponding to the parameters specified in the format. The mapping of declarative parameter to `print` method argument is detailed below: * Attribute Variables - Single: `(e.g. Attribute)` - Optional: `(e.g. Attribute)` * Operand Variables - Single: `Value` - Optional: `Value` - Variadic: `OperandRange` * Region Variables - Single: `Region &` - Variadic: `MutableArrayRef` * Successor Variables - Single: `Block *` - Variadic: `SuccessorRange` * Type Directives - Single: `Type` - Optional: `Type` - Variadic: `TypeRange` * TypeRef Directives - Single: `Type` - Optional: `Type` - Variadic: `TypeRange` * `attr-dict` Directive: `const MutableDictionaryAttr&` When a variable is optional, the provided value may be null. #### Optional Groups In certain situations operations may have "optional" information, e.g. attributes or an empty set of variadic operands. In these situations a section of the assembly format can be marked as `optional` based on the presence of this information. An optional group is defined by wrapping a set of elements within `()` followed by a `?` and has the following requirements: * The first element of the group must either be a attribute, literal, operand, or region. - This is because the first element must be optionally parsable. * Exactly one argument variable within the group must be marked as the anchor of the group. - The anchor is the element whose presence controls whether the group should be printed/parsed. - An element is marked as the anchor by adding a trailing `^`. - The first element is *not* required to be the anchor of the group. - When a non-variadic region anchors a group, the detector for printing the group is if the region is empty. * Literals, variables, custom directives, and type directives are the only valid elements within the group. - Any attribute variable may be used, but only optional attributes can be marked as the anchor. - Only variadic or optional operand arguments can be used. - All region variables can be used. When a non-variable length region is used, if the group is not present the region is empty. - The operands to a type directive must be defined within the optional group. An example of an operation with an optional group is `std.return`, which has a variadic number of operands. ```tablegen def ReturnOp : ... { let arguments = (ins Variadic:$operands); // We only print the operands and types if there are a non-zero number // of operands. let assemblyFormat = "attr-dict ($operands^ `:` type($operands))?"; } ``` ##### Unit Attributes In MLIR, the [`unit` Attribute](LangRef.md#unit-attribute) is special in that it only has one possible value, i.e. it derives meaning from its existence. When a unit attribute is used to anchor an optional group and is not the first element of the group, the presence of the unit attribute can be directly correlated with the presence of the optional group itself. As such, in these situations the unit attribute will not be printed or present in the output and will be automatically inferred when parsing by the presence of the optional group itself. For example, the following operation: ```tablegen def FooOp : ... { let arguments = (ins UnitAttr:$is_read_only); let assemblyFormat = "attr-dict (`is_read_only` $is_read_only^)?"; } ``` would be formatted as such: ```mlir // When the unit attribute is present: foo.op is_read_only // When the unit attribute is not present: foo.op ``` #### Requirements The format specification has a certain set of requirements that must be adhered to: 1. The output and operation name are never shown as they are fixed and cannot be altered. 1. All operands within the operation must appear within the format, either individually or with the `operands` directive. 1. All regions within the operation must appear within the format, either individually or with the `regions` directive. 1. All successors within the operation must appear within the format, either individually or with the `successors` directive. 1. All operand and result types must appear within the format using the various `type` directives, either individually or with the `operands` or `results` directives. 1. The `attr-dict` directive must always be present. 1. Must not contain overlapping information; e.g. multiple instances of 'attr-dict', types, operands, etc. - Note that `attr-dict` does not overlap with individual attributes. These attributes will simply be elided when printing the attribute dictionary. ##### Type Inference One requirement of the format is that the types of operands and results must always be present. In certain instances, the type of a variable may be deduced via type constraints or other information available. In these cases, the type of that variable may be elided from the format. * Buildable Types Some type constraints may only have one representation, allowing for them to be directly buildable; for example the `I32` or `Index` types. Types in `ODS` may mark themselves as buildable by setting the `builderCall` field or inheriting from the `BuildableType` class. * Trait Equality Constraints There are many operations that have known type equality constraints registered as traits on the operation; for example the true, false, and result values of a `select` operation often have the same type. The assembly format may inspect these equal constraints to discern the types of missing variables. The currently supported traits are: `AllTypesMatch`, `TypesMatchWith`, `SameTypeOperands`, and `SameOperandsAndResultType`. ### `hasCanonicalizer` This boolean field indicate whether canonicalization patterns have been defined for this operation. If it is `1`, then `::getCanonicalizationPatterns()` should be defined. ### `hasFolder` This boolean field indicate whether general folding rules have been defined for this operation. If it is `1`, then `::fold()` should be defined. ### Extra declarations One of the goals of table-driven op definition is to auto-generate as much logic and methods needed for each op as possible. With that said, there will always be long-tail cases that won't be covered. For such cases, you can use `extraClassDeclaration`. Code in `extraClassDeclaration` will be copied literally to the generated C++ op class. Note that `extraClassDeclaration` is a mechanism intended for long-tail cases by power users; for not-yet-implemented widely-applicable cases, improving the infrastructure is preferable. ### Generated C++ code [OpDefinitionsGen][OpDefinitionsGen] processes the op definition spec file and generates two files containing the corresponding C++ code: one for declarations, the other for definitions. The former is generated via the `-gen-op-decls` command-line option, while the latter is via the `-gen-op-defs` option. The definition file contains all the op method definitions, which can be included and enabled by defining `GET_OP_CLASSES`. For each operation, OpDefinitionsGen generates an operation class and an [operand adaptor](#operand-adaptors) class. Besides, it also contains a comma-separated list of all defined ops, which can be included and enabled by defining `GET_OP_LIST`. #### Class name and namespaces For each operation, its generated C++ class name is the symbol `def`ed with TableGen with dialect prefix removed. The first `_` serves as the delimiter. For example, for `def TF_AddOp`, the C++ class name would be `AddOp`. We remove the `TF` prefix because it is for scoping ops; other dialects may as well define their own `AddOp`s. The namespaces of the generated C++ class will come from the dialect's `cppNamespace` field. For example, if a dialect's `cppNamespace` is `A::B`, then an op of that dialect will be placed in `namespace A { namespace B { ... } }`. If a dialect does not specify a `cppNamespace`, we then use the dialect's name as the namespace. This means the qualified name of the generated C++ class does not necessarily match exactly with the operation name as explained in [Operation name](#operation-name). This is to allow flexible naming to satisfy coding style requirements. #### Operand adaptors For each operation, we automatically generate an _operand adaptor_. This class solves the problem of accessing operands provided as a list of `Value`s without using "magic" constants. The operand adaptor takes a reference to an array of `Value` and provides methods with the same names as those in the operation class to access them. For example, for a binary arithmetic operation, it may provide `.lhs()` to access the first operand and `.rhs()` to access the second operand. The operand adaptor class lives in the same namespace as the operation class, and has the name of the operation followed by `Adaptor` as well as an alias `Adaptor` inside the op class. Operand adaptors can be used in function templates that also process operations: ```c++ template std::pair zip(BinaryOpTy &&op) { return std::make_pair(op.lhs(), op.rhs());; } void process(AddOp op, ArrayRef newOperands) { zip(op); zip(Adaptor(newOperands)); /*...*/ } ``` ## Constraints Constraint is a core concept in table-driven operation definition: operation verification and graph operation matching are all based on satisfying constraints. So both the operation definition and rewrite rules specification significantly involve writing constraints. We have the `Constraint` class in [`OpBase.td`][OpBase] has the common base class for all constraints. An operation's constraint can cover different range; it may * Only concern a single attribute (e.g. being a 32-bit integer greater than 5), * Multiple operands and results (e.g., the 1st result's shape must be the same as the 1st operand), or * Intrinsic to the operation itself (e.g., having no side effect). We call them as single-entity constraint, multi-entity constraint, and traits, respectively. ### Single-entity constraint Constraints scoped to a single operand, attribute, or result are specified at the entity's declaration place as described in [Operation arguments](#operation-arguments) and [Operation results](#operation-results). To help modelling constraints of common types, a set of `TypeConstraint`s are created; they are the `Type` subclass hierarchy. It includes `F32` for the constraints of being a float, `TensorOf<[F32]>` for the constraints of being a float tensor, and so on. Similarly, a set of `AttrConstraint`s are created for helping modelling constraints of common attribute kinds. They are the `Attr` subclass hierarchy. It includes `F32Attr` for the constraints of being a float attribute, `F32ArrayAttr` for the constraints of being a float array attribute, and so on. ### Multi-entity constraint Constraints involving more than one operand/attribute/result are quite common on operations, like the element type and shape relation between operands and results. These constraints should be specified as the `Op` class template parameter as described in [Operation traits and constraints](#operation-traits-and-constraints). Multi-entity constraints are modeled as `PredOpTrait` (a subclass of `OpTrait`) in [`OpBase.td`][OpBase].A bunch of constraint primitives are provided to help specification. See [`OpBase.td`][OpBase] for the complete list. ### Trait Traits are intrinsic properties of the operation like having side effect or not, commutative or not, whether is a terminator, etc. These constraints should be specified as the `Op` class template parameter as described in [Operation traits and constraints](#operation-traits-and-constraints). Traits are modeled as `NativeOpTrait` (a subclass of `OpTrait`) in [`OpBase.td`][OpBase]. They are backed and will be translated into the corresponding C++ `mlir::OpTrait` classes. ### How to specify new constraint To write a constraint, you need to provide its predicates and give it a descriptive name. Predicates, modeled with the `Pred` class, are the workhorse for composing constraints. The predicate for a constraint is typically built up in a nested manner, using the two categories of predicates: 1. `CPred`: the primitive leaf predicate. 2. Compound predicate: a predicate composed from child predicates using predicate combiners (conjunction: `And`, disjunction: `Or`, negation: `Neg`, substitution: `SubstLeaves`, concatenation: `Concat`). `CPred` is the basis for composing more complex predicates. It is the "atom" predicate from the perspective of TableGen and the "interface" between TableGen and C++. What is inside is already C++ code, which will be treated as opaque strings with special placeholders to be substituted. You can put any C++ code that returns a boolean value inside a `CPred`, including evaluating expressions, calling functions, calling class methods, and so on. To help interaction with the C++ environment, there are a few special placeholders provided to refer to entities in the context where this predicate is used. They serve as "hooks" to the enclosing environment. This includes `$_builder`, `$_op`, and `$_self`: * `$_builder` will be replaced by a `mlir::Builder` instance so that you can access common build methods. * `$_op` will be replaced by the current operation so that you can access information of the current operation. * `$_self` will be replaced with the entity this predicate is attached to. E.g., `BoolAttr` is an attribute constraint that wraps a `CPred<"$_self.isa()">`. Then for `F32:$attr`,`$_self` will be replaced by `$attr`. For type constraints, it's a little bit special since we want the constraints on each type definition reads naturally and we want to attach type constraints directly to an operand/result, `$_self` will be replaced by the operand/result's type. E.g., for `F32` in `F32:$operand`, its `$_self` will be expanded as `getOperand(...).getType()`. TODO: Reconsider the leading symbol for special placeholders. Eventually we want to allow referencing operand/result $-names; such $-names can start with underscore. For example, to write an attribute `attr` is an `IntegerAttr`, in C++ you can just call `attr.isa()`. The code can be wrapped in a `CPred` as `$_self.isa()`, with `$_self` as the special placeholder to be replaced by the current attribute `attr` at expansion time. For more complicated predicates, you can wrap it in a single `CPred`, or you can use predicate combiners to combine them. For example, to write the constraint that an attribute `attr` is a 32-bit or 64-bit integer, you can write it as ```tablegen And<[ CPred<"$_self.isa()">, Or<[ CPred<"$_self.cast().getType().isInteger(32)">, CPred<"$_self.cast().getType().isInteger(64)"> ]> ]> ``` (Note that the above is just to show with a familiar example how you can use `CPred` and predicate combiners to write complicated predicates. For integer attributes specifically, [`OpBase.td`][OpBase] already defines `I32Attr` and `I64Attr`. So you can actually reuse them to write it as `Or<[I32Attr.predicate, I64Attr.predicate]>`.) TODO: Build up a library of reusable primitive constraints If the predicate is very complex to write with `CPred` together with predicate combiners, you can also write it as a normal C++ function and use the `CPred` as a way to "invoke" the function. For example, to verify an attribute `attr` has some property, you can write a C++ function like ```cpp bool HasSomeProperty(Attribute attr) { ... } ``` and then define the op as: ```tablegen def HasSomeProperty : AttrConstraint, "has some property">; def MyOp : Op<...> { let arguments = (ins ... HasSomeProperty:$attr ); } ``` As to whether we should define the predicate using a single `CPred` wrapping the whole expression, multiple `CPred`s with predicate combiners, or a single `CPred` "invoking" a function, there are no clear-cut criteria. Defining using `CPred` and predicate combiners is preferable since it exposes more information (instead hiding all the logic behind a C++ function) into the op definition spec so that it can potentially drive more auto-generation cases. But it will require a nice library of common predicates as the building blocks to avoid the duplication, which is being worked on right now. ## Attribute Definition An attribute is a compile-time known constant of an operation. ODS provides attribute wrappers over C++ attribute classes. There are a few common C++ [attribute classes][AttrClasses] defined in MLIR's core IR library and one is free to define dialect-specific attribute classes. ODS allows one to use these attributes in TableGen to define operations, potentially with more fine-grained constraints. For example, `StrAttr` directly maps to `StringAttr`; `F32Attr`/`F64Attr` requires the `FloatAttr` to additionally be of a certain bitwidth. ODS attributes are defined as having a storage type (corresponding to a backing `mlir::Attribute` that _stores_ the attribute), a return type (corresponding to the C++ _return_ type of the generated of the helper getters) as well as method to convert between the internal storage and the helper method. ### Attribute decorators There are a few important attribute adapters/decorators/modifiers that can be applied to ODS attributes to specify common additional properties like optionality, default values, etc.: * `DefaultValuedAttr`: specifies the [default value](#attributes-with-default-values) for an attribute. * `OptionalAttr`: specifies an attribute as [optional](#optional-attributes). * `Confined`: adapts an attribute with [further constraints](#confining-attributes). ### Enum attributes Some attributes can only take values from a predefined enum, e.g., the comparison kind of a comparison op. To define such attributes, ODS provides several mechanisms: `StrEnumAttr`, `IntEnumAttr`, and `BitEnumAttr`. * `StrEnumAttr`: each enum case is a string, the attribute is stored as a [`StringAttr`][StringAttr] in the op. * `IntEnumAttr`: each enum case is an integer, the attribute is stored as a [`IntegerAttr`][IntegerAttr] in the op. * `BitEnumAttr`: each enum case is a bit, the attribute is stored as a [`IntegerAttr`][IntegerAttr] in the op. All these `*EnumAttr` attributes require fully specifying all of the allowed cases via their corresponding `*EnumAttrCase`. With this, ODS is able to generate additional verification to only accept allowed cases. To facilitate the interaction between `*EnumAttr`s and their C++ consumers, the [`EnumsGen`][EnumsGen] TableGen backend can generate a few common utilities: a C++ enum class, `llvm::DenseMapInfo` for the enum class, conversion functions from/to strings. This is controlled via the `-gen-enum-decls` and `-gen-enum-defs` command-line options of `mlir-tblgen`. For example, given the following `EnumAttr`: ```tablegen def Case15: I32EnumAttrCase<"Case15", 15>; def Case20: I32EnumAttrCase<"Case20", 20>; def MyIntEnum: I32EnumAttr<"MyIntEnum", "An example int enum", [Case15, Case20]> { let cppNamespace = "Outer::Inner"; let stringToSymbolFnName = "ConvertToEnum"; let symbolToStringFnName = "ConvertToString"; } ``` The following will be generated via `mlir-tblgen -gen-enum-decls`: ```c++ namespace Outer { namespace Inner { // An example int enum enum class MyIntEnum : uint32_t { Case15 = 15, Case20 = 20, }; llvm::Optional symbolizeMyIntEnum(uint32_t); llvm::StringRef ConvertToString(MyIntEnum); llvm::Optional ConvertToEnum(llvm::StringRef); inline constexpr unsigned getMaxEnumValForMyIntEnum() { return 20; } } // namespace Inner } // namespace Outer namespace llvm { template<> struct DenseMapInfo { using StorageInfo = llvm::DenseMapInfo; static inline Outer::Inner::MyIntEnum getEmptyKey() { return static_cast(StorageInfo::getEmptyKey()); } static inline Outer::Inner::MyIntEnum getTombstoneKey() { return static_cast(StorageInfo::getTombstoneKey()); } static unsigned getHashValue(const Outer::Inner::MyIntEnum &val) { return StorageInfo::getHashValue(static_cast(val)); } static bool isEqual(const Outer::Inner::MyIntEnum &lhs, const Outer::Inner::MyIntEnum &rhs) { return lhs == rhs; } }; } ``` The following will be generated via `mlir-tblgen -gen-enum-defs`: ```c++ namespace Outer { namespace Inner { llvm::StringRef ConvertToString(MyIntEnum val) { switch (val) { case MyIntEnum::Case15: return "Case15"; case MyIntEnum::Case20: return "Case20"; } return ""; } llvm::Optional ConvertToEnum(llvm::StringRef str) { return llvm::StringSwitch>(str) .Case("Case15", MyIntEnum::Case15) .Case("Case20", MyIntEnum::Case20) .Default(llvm::None); } llvm::Optional symbolizeMyIntEnum(uint32_t value) { switch (value) { case 15: return MyIntEnum::Case15; case 20: return MyIntEnum::Case20; default: return llvm::None; } } } // namespace Inner } // namespace Outer ``` Similarly for the following `BitEnumAttr` definition: ```tablegen def None: BitEnumAttrCase<"None", 0x0000>; def Bit1: BitEnumAttrCase<"Bit1", 0x0001>; def Bit2: BitEnumAttrCase<"Bit2", 0x0002>; def Bit3: BitEnumAttrCase<"Bit3", 0x0004>; def MyBitEnum: BitEnumAttr<"MyBitEnum", "An example bit enum", [None, Bit1, Bit2, Bit3]>; ``` We can have: ```c++ // An example bit enum enum class MyBitEnum : uint32_t { None = 0, Bit1 = 1, Bit2 = 2, Bit3 = 4, }; llvm::Optional symbolizeMyBitEnum(uint32_t); std::string stringifyMyBitEnum(MyBitEnum); llvm::Optional symbolizeMyBitEnum(llvm::StringRef); inline MyBitEnum operator|(MyBitEnum lhs, MyBitEnum rhs) { return static_cast(static_cast(lhs) | static_cast(rhs)); } inline MyBitEnum operator&(MyBitEnum lhs, MyBitEnum rhs) { return static_cast(static_cast(lhs) & static_cast(rhs)); } inline bool bitEnumContains(MyBitEnum bits, MyBitEnum bit) { return (static_cast(bits) & static_cast(bit)) != 0; } namespace llvm { template<> struct DenseMapInfo<::MyBitEnum> { using StorageInfo = llvm::DenseMapInfo; static inline ::MyBitEnum getEmptyKey() { return static_cast<::MyBitEnum>(StorageInfo::getEmptyKey()); } static inline ::MyBitEnum getTombstoneKey() { return static_cast<::MyBitEnum>(StorageInfo::getTombstoneKey()); } static unsigned getHashValue(const ::MyBitEnum &val) { return StorageInfo::getHashValue(static_cast(val)); } static bool isEqual(const ::MyBitEnum &lhs, const ::MyBitEnum &rhs) { return lhs == rhs; } }; ``` ```c++ std::string stringifyMyBitEnum(MyBitEnum symbol) { auto val = static_cast(symbol); // Special case for all bits unset. if (val == 0) return "None"; llvm::SmallVector strs; if (1u & val) { strs.push_back("Bit1"); val &= ~1u; } if (2u & val) { strs.push_back("Bit2"); val &= ~2u; } if (4u & val) { strs.push_back("Bit3"); val &= ~4u; } if (val) return ""; return llvm::join(strs, "|"); } llvm::Optional symbolizeMyBitEnum(llvm::StringRef str) { // Special case for all bits unset. if (str == "None") return MyBitEnum::None; llvm::SmallVector symbols; str.split(symbols, "|"); uint32_t val = 0; for (auto symbol : symbols) { auto bit = llvm::StringSwitch>(symbol) .Case("Bit1", 1) .Case("Bit2", 2) .Case("Bit3", 4) .Default(llvm::None); if (bit) { val |= *bit; } else { return llvm::None; } } return static_cast(val); } llvm::Optional symbolizeMyBitEnum(uint32_t value) { // Special case for all bits unset. if (value == 0) return MyBitEnum::None; if (value & ~(1u | 2u | 4u)) return llvm::None; return static_cast(value); } ``` ## Type Definitions MLIR defines the TypeDef class hierarchy to enable generation of data types from their specifications. A type is defined by specializing the TypeDef class with concrete contents for all the fields it requires. For example, an integer type could be defined as: ```tablegen // All of the types will extend this class. class Test_Type : TypeDef { } // An alternate int type. def IntegerType : Test_Type<"TestInteger"> { let mnemonic = "int"; let summary = "An integer type with special semantics"; let description = [{ An alternate integer type. This type differentiates itself from the standard integer type by not having a SignednessSemantics parameter, just a width. }]; let parameters = (ins "unsigned":$width); // We define the printer inline. let printer = [{ $_printer << "int<" << getImpl()->width << ">"; }]; // The parser is defined here also. let parser = [{ if (parser.parseLess()) return Type(); int width; if ($_parser.parseInteger(width)) return Type(); if ($_parser.parseGreater()) return Type(); return get(ctxt, width); }]; ``` ### Type name The name of the C++ class which gets generated defaults to `Type` (e.g. `TestIntegerType` in the above example). This can be overridden via the `cppClassName` field. The field `mnemonic` is to specify the asm name for parsing. It is optional and not specifying it will imply that no parser or printer methods are attached to this class. ### Type documentation The `summary` and `description` fields exist and are to be used the same way as in Operations. Namely, the summary should be a one-liner and `description` should be a longer explanation. ### Type parameters The `parameters` field is a list of the types parameters. If no parameters are specified (the default), this type is considered a singleton type. Parameters are in the `"c++Type":$paramName` format. To use C++ types as parameters which need allocation in the storage constructor, there are two options: - Set `hasCustomStorageConstructor` to generate the TypeStorage class with a constructor which is just declared -- no definition -- so you can write it yourself. - Use the `TypeParameter` tablegen class instead of the "c++Type" string. ### TypeParameter tablegen class This is used to further specify attributes about each of the types parameters. It includes documentation (`description` and `syntax`), the C++ type to use, and a custom allocator to use in the storage constructor method. ```tablegen // DO NOT DO THIS! let parameters = (ins "ArrayRef":$dims); ``` The default storage constructor blindly copies fields by value. It does not know anything about the types. In this case, the ArrayRef requires allocation with `dims = allocator.copyInto(dims)`. You can specify the necessary constructor by specializing the `TypeParameter` tblgen class: ```tablegen class ArrayRefIntParam : TypeParameter<"::llvm::ArrayRef", "Array of ints"> { let allocator = [{$_dst = $_allocator.copyInto($_self);}]; } ... let parameters = (ins ArrayRefIntParam:$dims); ``` The `allocator` code block has the following substitutions: - `$_allocator` is the TypeStorageAllocator in which to allocate objects. - `$_dst` is the variable in which to place the allocated data. MLIR includes several specialized classes for common situations: - `StringRefParameter` for StringRefs. - `ArrayRefParameter` for ArrayRefs of value types - `SelfAllocationParameter` for C++ classes which contain a method called `allocateInto(StorageAllocator &allocator)` to allocate itself into `allocator`. - `ArrayRefOfSelfAllocationParameter` for arrays of objects which self-allocate as per the last specialization. If we were to use one of these included specializations: ```tablegen let parameters = (ins ArrayRefParameter<"int", "The dimensions">:$dims ); ``` ### Parsing and printing If a mnemonic is specified, the `printer` and `parser` code fields are active. The rules for both are: - If null, generate just the declaration. - If non-null and non-empty, use the code in the definition. The `$_printer` or `$_parser` substitutions are valid and should be used. - It is an error to have an empty code block. For each dialect, two "dispatch" functions will be created: one for parsing and one for printing. You should add calls to these in your `Dialect::printType` and `Dialect::parseType` methods. They are created in the dialect's namespace and their function signatures are: ```c++ Type generatedTypeParser(MLIRContext* ctxt, DialectAsmParser& parser, StringRef mnemonic); LogicalResult generatedTypePrinter(Type type, DialectAsmPrinter& printer); ``` The mnemonic, parser, and printer fields are optional. If they're not defined, the generated code will not include any parsing or printing code and omit the type from the dispatch functions above. In this case, the dialect author is responsible for parsing/printing the types in `Dialect::printType` and `Dialect::parseType`. ### Other fields - If the `genStorageClass` field is set to 1 (the default) a storage class is generated with member variables corresponding to each of the specified `parameters`. - If the `genAccessors` field is 1 (the default) accessor methods will be generated on the Type class (e.g. `int getWidth() const` in the example above). - If the `genVerifyInvariantsDecl` field is set, a declaration for a method `static LogicalResult verifyConstructionInvariants(Location, parameters...)` is added to the class as well as a `getChecked(Location, parameters...)` method which gets the result of `verifyConstructionInvariants` before calling `get`. - The `storageClass` field can be used to set the name of the storage class. - The `storageNamespace` field is used to set the namespace where the storage class should sit. Defaults to "detail". - The `extraClassDeclaration` field is used to include extra code in the class declaration. ## Debugging Tips ### Run `mlir-tblgen` to see the generated content TableGen syntax sometimes can be obscure; reading the generated content can be a very helpful way to understand and debug issues. To build `mlir-tblgen`, run `cmake --build . --target mlir-tblgen` in your build directory and find the `mlir-tblgen` binary in the `bin/` subdirectory. All the supported generators can be found via `mlir-tblgen --help`. For example, `--gen-op-decls` and `--gen-op-defs` as explained in [Generated C++ code](#generated-c++-code). To see the generated code, invoke `mlir-tblgen` with a specific generator by providing include paths via `-I`. For example, ```sh # To see op C++ class declaration mlir-tblgen --gen-op-decls -I /path/to/mlir/include /path/to/input/td/file # To see op C++ class definition mlir-tblgen --gen-op-defs -I /path/to/mlir/include /path/to/input/td/file # To see op documentation mlir-tblgen --gen-dialect-doc -I /path/to/mlir/include /path/to/input/td/file # To see op interface C++ class declaration mlir-tblgen --gen-op-interface-decls -I /path/to/mlir/include /path/to/input/td/file # To see op interface C++ class definition mlir-tblgen --gen-op-interface-defs -I /path/to/mlir/include /path/to/input/td/file # To see op interface documentation mlir-tblgen --gen-op-interface-doc -I /path/to/mlir/include /path/to/input/td/file ``` ## Appendix ### Requirements and existing mechanisms analysis The op description should as declarative as possible to allow a wide range of tools to work with them and query methods generated from them. In particular this means specifying traits, constraints and shape inference information in a way that is easily analyzable (e.g., avoid opaque calls to C++ functions where possible). We considered the approaches of several contemporary systems and focused on requirements that were desirable: * Ops registered using a registry separate from C++ code. * Unknown ops are allowed in MLIR, so ops need not be registered. The ability of the compiler to optimize those ops or graphs containing those ops is constrained but correct. * The current proposal does not include a runtime op description, but it does not preclude such description, it can be added later. * The op registry is essential for generating C++ classes that make manipulating ops, verifying correct construction etc. in C++ easier by providing a typed representation and accessors. * The op registry will be defined in [TableGen](https://llvm.org/docs/TableGen/index.html) and be used to generate C++ classes and utility functions (builder/verifier/parser/printer). * TableGen is a modelling specification language used by LLVM's backends and fits in well with trait-based modelling. This is an implementation decision and there are alternative ways of doing this. But the specification language is good for the requirements of modelling the traits (as seen from usage in LLVM processor backend modelling) and easy to extend, so a practical choice. If another good option comes up, we will consider it. * MLIR allows both defined and undefined ops. * Defined ops should have fixed semantics and could have a corresponding reference implementation defined using, for example, EDSC. * Dialects are under full control of the dialect owner and normally live with the framework of the dialect. * The op's traits (e.g., commutative) are modelled along with the op in the registry. * The op's operand/return type constraints are modelled along with the op in the registry (see [Shape inference](ShapeInference.md) discussion below), this allows (e.g.) optimized concise syntax in textual dumps. * Behavior of the op is documented along with the op with a summary and a description. The description is written in markdown and extracted for inclusion in the generated LangRef section of the dialect. * The generic assembly form of printing and parsing is available as normal, but a custom parser and printer can either be specified or automatically generated from an optional string representation showing the mapping of the "assembly" string to operands/type. * Parser-level remappings (e.g., `eq` to enum) will be supported as part of the parser generation. * Matching patterns are specified separately from the op description. * Contrasted with LLVM there is no "base" set of ops that every backend needs to be aware of. Instead there are many different dialects and the transformations/legalizations between these dialects form a graph of transformations. * Reference implementation may be provided along with the op definition. * The reference implementation may be in terms of either standard ops or other reference implementations. TODO: document expectation if the dependent op's definition changes. [TableGen]: https://llvm.org/docs/TableGen/index.html [TableGenProgRef]: https://llvm.org/docs/TableGen/ProgRef.html [TableGenBackend]: https://llvm.org/docs/TableGen/BackEnds.html#introduction [OpBase]: https://github.com/llvm/llvm-project/blob/master/mlir/include/mlir/IR/OpBase.td [OpDefinitionsGen]: https://github.com/llvm/llvm-project/blob/master/mlir/tools/mlir-tblgen/OpDefinitionsGen.cpp [EnumsGen]: https://github.com/llvm/llvm-project/blob/master/mlir/tools/mlir-tblgen/EnumsGen.cpp [StringAttr]: LangRef.md#string-attribute [IntegerAttr]: LangRef.md#integer-attribute [AttrClasses]: https://github.com/llvm/llvm-project/blob/master/mlir/include/mlir/IR/Attributes.h