Now that we‘re familiar with our language and the AST, let’s see how MLIR can help to compile Toy.
Other compilers, like LLVM (see the Kaleidoscope tutorial), offer a fixed set of predefined types and (usually low-level / RISC-like) instructions. It is up to the frontend for a given language to perform any language-specific type-checking, analysis, or transformation before emitting LLVM IR. For example, Clang will use its AST to perform not only static analysis but also transformations, such as C++ template instantiation through AST cloning and rewrite. Finally, languages with construction at a higher-level than C/C++ may require non-trivial lowering from their AST to generate LLVM IR.
As a consequence, multiple frontends end up reimplementing significant pieces of infrastructure to support the need for these analyses and transformation. MLIR addresses this issue by being designed for extensibility. As such, there are few pre-defined instructions (operations in MLIR terminology) or types.
MLIR is designed to be a completely extensible infrastructure; there is no closed set of attributes (think: constant metadata), operations, or types. MLIR supports this extensibility with the concept of Dialects. Dialects provide a grouping mechanism for abstraction under a unique namespace.
In MLIR, Operations are the core unit of abstraction and computation, similar in many ways to LLVM instructions. Operations can have application-specific semantics and can be used to represent all of the core IR structures in LLVM: instructions, globals (like functions), modules, etc.
Here is the MLIR assembly for the Toy transpose operations:
%t_tensor = "toy.transpose"(%tensor) {inplace = true} : (tensor<2x3xf64>) -> tensor<3x2xf64> loc("example/file/path":12:1)
Let's break down the anatomy of this MLIR operation:
%t_tensor
"toy.transpose"
.”. This can be read as the transpose operation in the toy dialect.(%tensor)
{ inplace = true }
(tensor<2x3xf64>) -> tensor<3x2xf64>
loc("example/file/path":12:1)
Shown here is the general form of an operation. As described above, the set of operations in MLIR is extensible. Operations are modeled using a small set of concepts, enabling operations to be reasoned about and manipulated generically. These concepts are:
In MLIR, every operation has a mandatory source location associated with it. Contrary to LLVM, where debug info locations are metadata and can be dropped, in MLIR, the location is a core requirement, and APIs depend on and manipulate it. Dropping a location is thus an explicit choice which cannot happen by mistake.
To provide an illustration: If a transformation replaces an operation by another, that new operation must still have a location attached. This makes it possible to track where that operation came from.
It's worth noting that the mlir-opt tool - a tool for testing compiler passes - does not include locations in the output by default. The -mlir-print-debuginfo flag specifies to include locations. (Run mlir-opt --help for more options.)
MLIR is designed to allow all IR elements, such as attributes, operations, and types, to be customized. At the same time, IR elements can always be reduced to the above fundamental concepts. This allows MLIR to parse, represent, and round-trip IR for any operation. For example, we could place our Toy operation from above into an .mlir file and round-trip through mlir-opt without registering any dialect:
func @toy_func(%tensor: tensor<2x3xf64>) -> tensor<3x2xf64> { %t_tensor = "toy.transpose"(%tensor) { inplace = true } : (tensor<2x3xf64>) -> tensor<3x2xf64> return %t_tensor : tensor<3x2xf64> }
In the cases of unregistered attributes, operations, and types, MLIR will enforce some structural constraints (e.g. dominance, etc.), but otherwise they are completely opaque. For instance, MLIR has little information about whether an unregistered operation can operate on particular data types, how many operands it can take, or how many results it produces. This flexibility can be useful for bootstrapping purposes, but it is generally advised against in mature systems. Unregistered operations must be treated conservatively by transformations and analyses, and they are much harder to construct and manipulate.
This handling can be observed by crafting what should be an invalid IR for Toy and seeing it round-trip without tripping the verifier:
func @main() { %0 = "toy.print"() : () -> tensor<2x3xf64> }
There are multiple problems here: the toy.print operation is not a terminator; it should take an operand; and it shouldn't return any values. In the next section, we will register our dialect and operations with MLIR, plug into the verifier, and add nicer APIs to manipulate our operations.
To effectively interface with MLIR, we will define a new Toy dialect. This dialect will model the structure of the Toy language, as well as provide an easy avenue for high-level analysis and transformation.
/// This is the definition of the Toy dialect. A dialect inherits from /// mlir::Dialect and registers custom attributes, operations, and types. It can /// also override virtual methods to change some general behavior, which will be /// demonstrated in later chapters of the tutorial. class ToyDialect : public mlir::Dialect { public: explicit ToyDialect(mlir::MLIRContext *ctx); /// Provide a utility accessor to the dialect namespace. static llvm::StringRef getDialectNamespace() { return "toy"; } /// An initializer called from the constructor of ToyDialect that is used to /// register attributes, operations, types, and more within the Toy dialect. void initialize(); };
This is the C++ definition of a dialect, but MLIR also supports defining dialects declaratively via tablegen. Using the declarative specification is much cleaner as it removes the need for a large portion of the boilerplate when defining a new dialect. It also enables easy generation of dialect documentation, which can be described directly alongside the dialect. In this declarative format, the toy dialect would be specified as:
// Provide a definition of the 'toy' dialect in the ODS framework so that we // can define our operations. def Toy_Dialect : Dialect { // The namespace of our dialect, this corresponds 1-1 with the string we // provided in `ToyDialect::getDialectNamespace`. let name = "toy"; // A short one-line summary of our dialect. let summary = "A high-level dialect for analyzing and optimizing the " "Toy language"; // A much longer description of our dialect. let description = [{ The Toy language is a tensor-based language that allows you to define functions, perform some math computation, and print results. This dialect provides a representation of the language that is amenable to analysis and optimization. }]; // The C++ namespace that the dialect class definition resides in. let cppNamespace = "toy"; }
To see what this generates, we can run the mlir-tblgen command with the gen-dialect-decls action like so:
${build_root}/bin/mlir-tblgen -gen-dialect-decls ${mlir_src_root}/examples/toy/Ch2/include/toy/Ops.td -I ${mlir_src_root}/include/
After the dialect has been defined, it can now be loaded into an MLIRContext:
context.loadDialect<ToyDialect>();
By default, an MLIRContext only loads the Builtin Dialect, which provides a few core IR components, meaning that other dialects, such as our Toy dialect, must be explicitly loaded.
Now that we have a Toy dialect, we can start defining the operations. This will allow for providing semantic information that the rest of the system can hook into. As an example, let's walk through the creation of a toy.constant operation. This operation will represent a constant value in the Toy language.
%4 = "toy.constant"() {value = dense<1.0> : tensor<2x3xf64>} : () -> tensor<2x3xf64>
This operation takes zero operands, a dense elements attribute named value to represent the constant value, and returns a single result of RankedTensorType. An operation class inherits from the CRTP mlir::Op class which also takes some optional traits to customize its behavior. Traits are a mechanism with which we can inject additional behavior into an Operation, such as additional accessors, verification, and more. Let's look below at a possible definition for the constant operation that we have described above:
class ConstantOp : public mlir::Op< /// `mlir::Op` is a CRTP class, meaning that we provide the /// derived class as a template parameter. ConstantOp, /// The ConstantOp takes zero input operands. mlir::OpTrait::ZeroOperands, /// The ConstantOp returns a single result. mlir::OpTrait::OneResult, /// We also provide a utility `getType` accessor that /// returns the TensorType of the single result. mlir::OpTraits::OneTypedResult<TensorType>::Impl> { public: /// Inherit the constructors from the base Op class. using Op::Op; /// Provide the unique name for this operation. MLIR will use this to register /// the operation and uniquely identify it throughout the system. The name /// provided here must be prefixed by the parent dialect namespace followed /// by a `.`. static llvm::StringRef getOperationName() { return "toy.constant"; } /// Return the value of the constant by fetching it from the attribute. mlir::DenseElementsAttr getValue(); /// Operations may provide additional verification beyond what the attached /// traits provide. Here we will ensure that the specific invariants of the /// constant operation are upheld, for example the result type must be /// of TensorType and matches the type of the constant `value`. LogicalResult verify(); /// Provide an interface to build this operation from a set of input values. /// This interface is used by the `builder` classes to allow for easily /// generating instances of this operation: /// mlir::OpBuilder::create<ConstantOp>(...) /// This method populates the given `state` that MLIR uses to create /// operations. This state is a collection of all of the discrete elements /// that an operation may contain. /// Build a constant with the given return type and `value` attribute. static void build(mlir::OpBuilder &builder, mlir::OperationState &state, mlir::Type result, mlir::DenseElementsAttr value); /// Build a constant and reuse the type from the given 'value'. static void build(mlir::OpBuilder &builder, mlir::OperationState &state, mlir::DenseElementsAttr value); /// Build a constant by broadcasting the given 'value'. static void build(mlir::OpBuilder &builder, mlir::OperationState &state, double value); };
and we can register this operation in the ToyDialect initializer:
void ToyDialect::initialize() { addOperations<ConstantOp>(); }
Now that we have defined an operation, we will want to access and transform it. In MLIR, there are two main classes related to operations: Operation and Op. The Operation class is used to generically model all operations. It is ‘opaque’, in the sense that it does not describe the properties of particular operations or types of operations. Instead, the Operation class provides a general API into an operation instance. On the other hand, each specific type of operation is represented by an Op derived class. For instance ConstantOp represents a operation with zero inputs, and one output, which is always set to the same value. Op derived classes act as smart pointer wrapper around a Operation*, provide operation-specific accessor methods, and type-safe properties of operations. This means that when we define our Toy operations, we are simply defining a clean, semantically useful interface for building and interfacing with the Operation class. This is why our ConstantOp defines no class fields; all of the data for this operation is stored in the referenced Operation. A side effect of this design is that we always pass around Op derived classes “by-value”, instead of by reference or pointer (passing by value is a common idiom in MLIR and applies similarly to attributes, types, etc). Given a generic Operation* instance, we can always get a specific Op instance using LLVM's casting infrastructure:
void processConstantOp(mlir::Operation *operation) { ConstantOp op = llvm::dyn_cast<ConstantOp>(operation); // This operation is not an instance of `ConstantOp`. if (!op) return; // Get the internal operation instance wrapped by the smart pointer. mlir::Operation *internalOperation = op.getOperation(); assert(internalOperation == operation && "these operation instances are the same"); }
In addition to specializing the mlir::Op C++ template, MLIR also supports defining operations in a declarative manner. This is achieved via the Operation Definition Specification framework. Facts regarding an operation are specified concisely into a TableGen record, which will be expanded into an equivalent mlir::Op C++ template specialization at compile time. Using the ODS framework is the desired way for defining operations in MLIR given the simplicity, conciseness, and general stability in the face of C++ API changes.
Lets see how to define the ODS equivalent of our ConstantOp:
Operations in ODS are defined by inheriting from the Op class. To simplify our operation definitions, we will define a base class for operations in the Toy dialect.
// Base class for toy dialect operations. This operation inherits from the base // `Op` class in OpBase.td, and provides: // * The parent dialect of the operation. // * The mnemonic for the operation, or the name without the dialect prefix. // * A list of traits for the operation. class Toy_Op<string mnemonic, list<OpTrait> traits = []> : Op<Toy_Dialect, mnemonic, traits>;
With all of the preliminary pieces defined, we can begin to define the constant operation.
We define a toy operation by inheriting from our base ‘Toy_Op’ class above. Here we provide the mnemonic and a list of traits for the operation. The mnemonic here matches the one given in ConstantOp::getOperationName without the dialect prefix; toy.. Missing here from our C++ definition are the ZeroOperands and OneResult traits; these will be automatically inferred based upon the arguments and results fields we define later.
def ConstantOp : Toy_Op<"constant"> { }
At this point you probably might want to know what the C++ code generated by TableGen looks like. Simply run the mlir-tblgen command with the gen-op-decls or the gen-op-defs action like so:
${build_root}/bin/mlir-tblgen -gen-op-defs ${mlir_src_root}/examples/toy/Ch2/include/toy/Ops.td -I ${mlir_src_root}/include/
Depending on the selected action, this will print either the ConstantOp class declaration or its implementation. Comparing this output to the hand-crafted implementation is incredibly useful when getting started with TableGen.
With the shell of the operation defined, we can now provide the inputs and outputs to our operation. The inputs, or arguments, to an operation may be attributes or types for SSA operand values. The results correspond to a set of types for the values produced by the operation:
def ConstantOp : Toy_Op<"constant"> { // The constant operation takes an attribute as the only input. // `F64ElementsAttr` corresponds to a 64-bit floating-point ElementsAttr. let arguments = (ins F64ElementsAttr:$value); // The constant operation returns a single value of TensorType. // F64Tensor corresponds to a 64-bit floating-point TensorType. let results = (outs F64Tensor); }
By providing a name to the arguments or results, e.g. $value, ODS will automatically generate a matching accessor: DenseElementsAttr ConstantOp::value().
The next step after defining the operation is to document it. Operations may provide summary and description fields to describe the semantics of the operation. This information is useful for users of the dialect and can even be used to auto-generate Markdown documents.
def ConstantOp : Toy_Op<"constant"> { // Provide a summary and description for this operation. This can be used to // auto-generate documentation of the operations within our dialect. let summary = "constant operation"; let description = [{ Constant operation turns a literal into an SSA value. The data is attached to the operation as an attribute. For example: %0 = "toy.constant"() { value = dense<[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]> : tensor<2x3xf64> } : () -> tensor<2x3xf64> }]; // The constant operation takes an attribute as the only input. // `F64ElementsAttr` corresponds to a 64-bit floating-point ElementsAttr. let arguments = (ins F64ElementsAttr:$value); // The generic call operation returns a single value of TensorType. // F64Tensor corresponds to a 64-bit floating-point TensorType. let results = (outs F64Tensor); }
At this point we‘ve already covered a majority of the original C++ operation definition. The next piece to define is the verifier. Luckily, much like the named accessor, the ODS framework will automatically generate a lot of the necessary verification logic based upon the constraints we have given. This means that we don’t need to verify the structure of the return type, or even the input attribute value. In many cases, additional verification is not even necessary for ODS operations. To add additional verification logic, an operation can override the verifier field. The verifier field allows for defining a C++ code blob that will be run as part of ConstantOp::verify. This blob can assume that all of the other invariants of the operation have already been verified:
def ConstantOp : Toy_Op<"constant"> { // Provide a summary and description for this operation. This can be used to // auto-generate documentation of the operations within our dialect. let summary = "constant operation"; let description = [{ Constant operation turns a literal into an SSA value. The data is attached to the operation as an attribute. For example: %0 = "toy.constant"() { value = dense<[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]> : tensor<2x3xf64> } : () -> tensor<2x3xf64> }]; // The constant operation takes an attribute as the only input. // `F64ElementsAttr` corresponds to a 64-bit floating-point ElementsAttr. let arguments = (ins F64ElementsAttr:$value); // The generic call operation returns a single value of TensorType. // F64Tensor corresponds to a 64-bit floating-point TensorType. let results = (outs F64Tensor); // Add additional verification logic to the constant operation. Here we invoke // a static `verify` method in a C++ source file. This codeblock is executed // inside of ConstantOp::verify, so we can use `this` to refer to the current // operation instance. let verifier = [{ return ::verify(*this); }]; }
build MethodsThe final missing component here from our original C++ example are the build methods. ODS can generate some simple build methods automatically, and in this case it will generate our first build method for us. For the rest, we define the builders field. This field takes a list of OpBuilder objects that take a string corresponding to a list of C++ parameters, as well as an optional code block that can be used to specify the implementation inline.
def ConstantOp : Toy_Op<"constant"> { ... // Add custom build methods for the constant operation. These methods populate // the `state` that MLIR uses to create operations, i.e. these are used when // using `builder.create<ConstantOp>(...)`. let builders = [ // Build a constant with a given constant tensor value. OpBuilder<(ins "DenseElementsAttr":$value), [{ // Call into an autogenerated `build` method. build(builder, result, value.getType(), value); }]>, // Build a constant with a given constant floating-point value. This builder // creates a declaration for `ConstantOp::build` with the given parameters. OpBuilder<(ins "double":$value)> ]; }
At this point we can generate our “Toy IR”. For example, the following:
# User defined generic function that operates on unknown shaped arguments. def multiply_transpose(a, b) { return transpose(a) * transpose(b); } def main() { var a<2, 3> = [[1, 2, 3], [4, 5, 6]]; var b<2, 3> = [1, 2, 3, 4, 5, 6]; var c = multiply_transpose(a, b); var d = multiply_transpose(b, a); print(d); }
Results in the following IR:
module { func @multiply_transpose(%arg0: tensor<*xf64>, %arg1: tensor<*xf64>) -> tensor<*xf64> { %0 = "toy.transpose"(%arg0) : (tensor<*xf64>) -> tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":5:10) %1 = "toy.transpose"(%arg1) : (tensor<*xf64>) -> tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":5:25) %2 = "toy.mul"(%0, %1) : (tensor<*xf64>, tensor<*xf64>) -> tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":5:25) "toy.return"(%2) : (tensor<*xf64>) -> () loc("test/Examples/Toy/Ch2/codegen.toy":5:3) } loc("test/Examples/Toy/Ch2/codegen.toy":4:1) func @main() { %0 = "toy.constant"() {value = dense<[[1.000000e+00, 2.000000e+00, 3.000000e+00], [4.000000e+00, 5.000000e+00, 6.000000e+00]]> : tensor<2x3xf64>} : () -> tensor<2x3xf64> loc("test/Examples/Toy/Ch2/codegen.toy":9:17) %1 = "toy.reshape"(%0) : (tensor<2x3xf64>) -> tensor<2x3xf64> loc("test/Examples/Toy/Ch2/codegen.toy":9:3) %2 = "toy.constant"() {value = dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00, 5.000000e+00, 6.000000e+00]> : tensor<6xf64>} : () -> tensor<6xf64> loc("test/Examples/Toy/Ch2/codegen.toy":10:17) %3 = "toy.reshape"(%2) : (tensor<6xf64>) -> tensor<2x3xf64> loc("test/Examples/Toy/Ch2/codegen.toy":10:3) %4 = "toy.generic_call"(%1, %3) {callee = @multiply_transpose} : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":11:11) %5 = "toy.generic_call"(%3, %1) {callee = @multiply_transpose} : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":12:11) "toy.print"(%5) : (tensor<*xf64>) -> () loc("test/Examples/Toy/Ch2/codegen.toy":13:3) "toy.return"() : () -> () loc("test/Examples/Toy/Ch2/codegen.toy":8:1) } loc("test/Examples/Toy/Ch2/codegen.toy":8:1) } loc(unknown)
One thing to notice here is that all of our Toy operations are printed using the generic assembly format. This format is the one shown when breaking down toy.transpose at the beginning of this chapter. MLIR allows for operations to define their own custom assembly format, either declaratively or imperatively via C++. Defining a custom assembly format allows for tailoring the generated IR into something a bit more readable by removing a lot of the fluff that is required by the generic format. Let's walk through an example of an operation format that we would like to simplify.
toy.printThe current form of toy.print is a little verbose. There are a lot of additional characters that we would like to strip away. Let's begin by thinking of what a good format of toy.print would be, and see how we can implement it. Looking at the basics of toy.print we get:
toy.print %5 : tensor<*xf64> loc(...)
Here we have stripped much of the format down to the bare essentials, and it has become much more readable. To provide a custom assembly format, an operation can either override the parser and printer fields for a C++ format, or the assemblyFormat field for the declarative format. Let's look at the C++ variant first, as this is what the declarative format maps to internally.
/// Consider a stripped definition of `toy.print` here. def PrintOp : Toy_Op<"print"> { let arguments = (ins F64Tensor:$input); // Divert the printer and parser to static functions in our .cpp // file that correspond to 'print' and 'printPrintOp'. 'printer' and 'parser' // here correspond to an instance of a 'OpAsmParser' and 'OpAsmPrinter'. More // details on these classes is shown below. let printer = [{ return ::print(printer, *this); }]; let parser = [{ return ::parse$cppClass(parser, result); }]; }
A C++ implementation for the printer and parser is shown below:
/// The 'OpAsmPrinter' class is a stream that will allows for formatting /// strings, attributes, operands, types, etc. static void print(mlir::OpAsmPrinter &printer, PrintOp op) { printer << "toy.print " << op.input(); printer.printOptionalAttrDict(op.getAttrs()); printer << " : " << op.input().getType(); } /// The 'OpAsmParser' class provides a collection of methods for parsing /// various punctuation, as well as attributes, operands, types, etc. Each of /// these methods returns a `ParseResult`. This class is a wrapper around /// `LogicalResult` that can be converted to a boolean `true` value on failure, /// or `false` on success. This allows for easily chaining together a set of /// parser rules. These rules are used to populate an `mlir::OperationState` /// similarly to the `build` methods described above. static mlir::ParseResult parsePrintOp(mlir::OpAsmParser &parser, mlir::OperationState &result) { // Parse the input operand, the attribute dictionary, and the type of the // input. mlir::OpAsmParser::OperandType inputOperand; mlir::Type inputType; if (parser.parseOperand(inputOperand) || parser.parseOptionalAttrDict(result.attributes) || parser.parseColon() || parser.parseType(inputType)) return mlir::failure(); // Resolve the input operand to the type we parsed in. if (parser.resolveOperand(inputOperand, inputType, result.operands)) return mlir::failure(); return mlir::success(); }
With the C++ implementation defined, let's see how this can be mapped to the declarative format. The declarative format is largely composed of three different components:
PrintOp example above, a variable would be $input.A direct mapping of our C++ format looks something like:
/// Consider a stripped definition of `toy.print` here. def PrintOp : Toy_Op<"print"> { let arguments = (ins F64Tensor:$input); // In the following format we have two directives, `attr-dict` and `type`. // These correspond to the attribute dictionary and the type of a given // variable represectively. let assemblyFormat = "$input attr-dict `:` type($input)"; }
The declarative format has many more interesting features, so be sure to check it out before implementing a custom format in C++. After beautifying the format of a few of our operations we now get a much more readable:
module { func @multiply_transpose(%arg0: tensor<*xf64>, %arg1: tensor<*xf64>) -> tensor<*xf64> { %0 = toy.transpose(%arg0 : tensor<*xf64>) to tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":5:10) %1 = toy.transpose(%arg1 : tensor<*xf64>) to tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":5:25) %2 = toy.mul %0, %1 : tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":5:25) toy.return %2 : tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":5:3) } loc("test/Examples/Toy/Ch2/codegen.toy":4:1) func @main() { %0 = toy.constant dense<[[1.000000e+00, 2.000000e+00, 3.000000e+00], [4.000000e+00, 5.000000e+00, 6.000000e+00]]> : tensor<2x3xf64> loc("test/Examples/Toy/Ch2/codegen.toy":9:17) %1 = toy.reshape(%0 : tensor<2x3xf64>) to tensor<2x3xf64> loc("test/Examples/Toy/Ch2/codegen.toy":9:3) %2 = toy.constant dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00, 5.000000e+00, 6.000000e+00]> : tensor<6xf64> loc("test/Examples/Toy/Ch2/codegen.toy":10:17) %3 = toy.reshape(%2 : tensor<6xf64>) to tensor<2x3xf64> loc("test/Examples/Toy/Ch2/codegen.toy":10:3) %4 = toy.generic_call @multiply_transpose(%1, %3) : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":11:11) %5 = toy.generic_call @multiply_transpose(%3, %1) : (tensor<2x3xf64>, tensor<2x3xf64>) -> tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":12:11) toy.print %5 : tensor<*xf64> loc("test/Examples/Toy/Ch2/codegen.toy":13:3) toy.return loc("test/Examples/Toy/Ch2/codegen.toy":8:1) } loc("test/Examples/Toy/Ch2/codegen.toy":8:1) } loc(unknown)
Above we introduce several of the concepts for defining operations in the ODS framework, but there are many more that we haven't had a chance to: regions, variadic operands, etc. Check out the full specification for more details.
We can now generate our “Toy IR”. You can build toyc-ch2 and try yourself on the above example: toyc-ch2 test/Examples/Toy/Ch2/codegen.toy -emit=mlir -mlir-print-debuginfo. We can also check our RoundTrip: toyc-ch2 test/Examples/Toy/Ch2/codegen.toy -emit=mlir -mlir-print-debuginfo 2> codegen.mlir followed by toyc-ch2 codegen.mlir -emit=mlir. You should also use mlir-tblgen on the final definition file and study the generated C++ code.
At this point, MLIR knows about our Toy dialect and operations. In the next chapter, we will leverage our new dialect to implement some high-level language-specific analyses and transformations for the Toy language.